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They make some compromises on speed for quality. \nSteal these or study 10S Node intergation if you are a workflow maker or want to customize and release them as your own.  \n**They are NOT FAST.** I find with LTX, fast ≠ good.\n\n---\n\n## 📦 Downloads\n\n| Resource | Link |\n|----------|------|\n| 🎬 Workflows | [LTX2.3-10Eros Workflows](https://huggingface.co/TenStrip/LTX2.3-10Eros_Workflows) |\n| 🧠 Models (10Eros/Sulphur) | [huggingface.co/TenStrip/LTX2.3-10Eros](https://huggingface.co/TenStrip/LTX2.3-10Eros) |\n| 🔧 10S Comfy Nodes | [TenStrip/10S-Comfy-nodes](https://github.com/TenStrip/10S-Comfy-nodes) |\n| ⚗️ Distilled LoRAs | [LTX2.3 Distilled LoRA 1.1 Experiments](https://huggingface.co/TenStrip/LTX2.3_Distilled_Lora_1.1_Experiments/tree/main) |\n---\n\n## ⚙️ How It Works\n\nThese workflows rely on **[10S-Comfy-nodes]**\n\nGuided prompt conditioning from the guide is leveraged against the i2v conditioned latent with Latent anchor. Increasing strength on Latent anchor as well as **depth_curve** gives you a more manageable control over less/more movement and gives you 3 points of strength to control the i2v freedom. \n\nLatent Anchor node needs paper-level study in what it does, but for most calculations turn debug on and follow this formula to set it up correctly: \n*target_call_count = cache_at_step × forwards_per_step*\n\nIt is currently configured for 13 steps at step 6 cache with 1 CFG, although forwards per step must be increased to 3 to account for CFG,STG,audioCFG-but isn't impactful on just first 2 CFG steps.\n\nUpscale distortion is removed and detail is increased with tiled sampler, this is a default-level improvement to quality. At it's current setting it will only trigger when distortion inducing resolution is reached for vertical ratios, if working in landscape increase max_tile_size accordingly to bypass it. Audio-sync issues can arise if the **tile_carrying** mode does not target the tile with talking heads or sound-causing actors. Generally you will select first for vertical resolutions and middle for huge landscape passes.\nBecause of how tiled sampler works you will want to avoid hand motion and movement near the middle of the composition.\n\nDistillation is handled at full break down. Lower pass video distillation will increase motion. Upscale pass video distillation will add detail, but also add artifacts and unaesthetic blur artifacts. Audio weight must be kept high for audio quality.\n\nIn some cases first pass audio may be more desirable, that can be toggled to be hard encoded instead of passed and resampled fully.\n\nThe first pass output is a full **motion** preview, if it contains mutation, distortion, bad motion, bad audio, etc... The upscale pass will not fix it. 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diff --git a/custom_nodes/10S-Comfy-nodes/workflows/10Eros_10SNodes_LikenessGuideHelper_I2V_v3.2.json b/custom_nodes/10S-Comfy-nodes/workflows/10Eros_10SNodes_LikenessGuideHelper_I2V_v3.2.json
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They make some compromises on speed for quality. \nSteal these or study 10S Node intergation if you are a workflow maker or want to customize and release them as your own.  \n**They are NOT FAST.** I find with LTX, fast ≠ good.\n\n---\n\n## 📦 Downloads\n\n| Resource | Link |\n|----------|------|\n| 🎬 Workflows | [LTX2.3-10Eros Workflows](https://huggingface.co/TenStrip/LTX2.3-10Eros_Workflows) |\n| 🧠 Models (10Eros/Sulphur) | [huggingface.co/TenStrip/LTX2.3-10Eros](https://huggingface.co/TenStrip/LTX2.3-10Eros) |\n| 🔧 10S Comfy Nodes | [TenStrip/10S-Comfy-nodes](https://github.com/TenStrip/10S-Comfy-nodes) |\n| ⚗️ Distilled LoRAs | [LTX2.3 Distilled LoRA 1.1 Experiments](https://huggingface.co/TenStrip/LTX2.3_Distilled_Lora_1.1_Experiments/tree/main) |\n| 🔼 OMNINFT LoRA | [Omni NFT 2.3 Conversion - Quality/Reward LoRA](https://huggingface.co/VasiliyWeb/OmniNFT_ComfyUI/tree/main) |\n---\n\n## ⚙️ How It Works\n\nThese workflows rely on **[10S-Comfy-nodes]**\n\nMouse over parameters for info.\n\n\nFace Guider/Likeness Anchor are there to **reinforce likeness** they do not teach the model what the initial figure looks like but they curb it's attempts to overwrite finer facial details. Complex face altering movements like turning away, camera zoom outs, and transitions will destroy the initial reference regardless. The only way to inject a true character likeness is to train a small and quick character lora (5-30 minutes ~ 10-30 images) for Sulphur or base LTX2.3 and use that to alter the actual underlying weights with gradient data.\n\nGuided prompt conditioning from the guide is leveraged against the i2v conditioned latent with Latent anchor. Increasing strength on Latent anchor as well as **depth_curve** gives you a more manageable control over less/more movement and gives you 3 points of strength to control the i2v freedom. \n\nLatent Anchor node needs paper-level study in what it does, but for most calculations turn debug on and follow this formula to set it up correctly: \n*target_call_count = cache_at_step × forwards_per_step*\n\nIt is currently configured for 13 steps at step 6 cache with 1 CFG, although forwards per step must be increased to 3 to account for CFG,STG,audioCFG-but isn't impactful on just first 2 CFG steps.\n\nUpscale distortion is removed and detail is increased with tiled sampler, this is a default-level improvement to quality. At it's current setting it will only trigger when distortion inducing resolution is reached for vertical ratios, if working in landscape increase max_tile_size accordingly to bypass it. Audio-sync issues can arise if the **tile_carrying** mode does not target the tile with talking heads or sound-causing actors. Generally you will select first for vertical resolutions and middle for huge landscape passes.\nBecause of how tiled sampler works you will want to avoid hand motion and movement near the middle of the composition.\n\nFor Landscape formats **bypass tiling**, it is not necessary to correct that ratio.\n\nDistillation is handled at full break down. Lower pass video distillation will increase motion. Upscale pass video distillation will add detail, but also add artifacts and unaesthetic blur artifacts. Audio weight must be kept high for audio quality.\n\nIn some cases first pass audio may be more desirable, that can be toggled to be hard encoded instead of passed and resampled fully.\n\nThe first pass output is a full **motion** preview, if it contains mutation, distortion, bad motion, bad audio, etc... The upscale pass will not fix it. Only allow it to run to upscale when first pass motion looks good. **Visual quality and style will be restored** at upscale.\n\n\n---\n\n## ✍️ Prompt Enhancement\n\nThese use STG Guider and have CFG for initial steps which is where most broad motion and audio form. **Your negative prompt is impactful, so use it.**\n\nFor best positive prompt results, use this foreword in **Grok** or an **uncensored LLM**:\n\n---\n\n*Generate a video scene script with a description based on the attached image for an LLM that has a tokenizer that uses interleaved attention to support long-context understanding that is fed into a multimodal video model. Strict specification, follow up to the word: No timestamps. No unnecessary embellishment. Output only plain English text and make it a copy box.*\n\n*First, describe the image initial scene in concise natural language; subject(s), subject(s) appearance, subject(s) composition and pose, background, and context.*\n\n*Next, formulate a naturally evolving scenario that would take place describing every moving body part, composition change, and manipulation from the uploaded initial frame that would be reflected in the video models post-latent evolution output. If the image is explicit or sexual in nature, use full anatomical terminology and spice it up slightly with visually representable erotic themes.*\n\n*Center the prompt around this basic idea: **[ concept ]***\n\n*Interweave this dialogue or sound concept into the scene with descriptions of voice tone followed by the lines delivered in quotations, in a temporal sequence between or during motions. Dialogue should be concise and non-rambling as it will take away from video quality: **[ dialogue ]***\n\n*Inside that prompt describe only notable audio and audio cues, both normal and explicit; background noise as well as foley and natural sounds. In a temporal sequence paired with coinciding motions. In the case of absent dialogue or soundscapes and only if background music is fitting; describe a fitting genre and melodic tone with matching mood.*\n\n*Output only text following above instruction. Follow-up suggestions should be on the topic of expanding or changing motion or dialogue from the output text.*\n"],"color":"#432","bgcolor":"#653"},{"id":796,"type":"mxSlider","pos":[-1115.3480311403682,3957.2240104480748],"size":[329.41939288068033,30],"flags":{"collapsed":false},"order":36,"mode":0,"inputs":[{"localized_name":"Xi","name":"Xi","type":"INT","widget":{"name":"Xi"},"link":null},{"localized_name":"Xf","name":"Xf","type":"FLOAT","widget":{"name":"Xf"},"link":null},{"localized_name":"isfloatX","name":"isfloatX","type":"INT","widget":{"name":"isfloatX"},"link":null}],"outputs":[{"localized_name":"","name":"","type":"INT","links":[2220]}],"title":"Length (Frame Count X/24 = Seconds)","properties":{"cnr_id":"comfyui-mxtoolkit","ver":"0.9.92","Node name for 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A small black microphone is clipped to his shirt collar. He is positioned slightly to the left of the frame, looking intently downwards and to his right, his eyes focused off-camera. His facial expression is one of deep concentration, with his brow slightly furrowed. As he looks down, a quick sniff sound is heard, and then he speaks with a deep male voice and a slightly frustrated tone, saying, \"I think it's so bad.\" The camera remains static throughout, maintaining a shallow depth of field, which keeps the man in sharp focus while the background is softly blurred, revealing a light-colored wall with white wooden shelving or trim, and a partially open white wooden door on the right. After a brief pause, another short, audible sniff is heard. The man then continues to speak, his voice maintaining the same quality, as he states, \"So bad. So bad.\" He elaborates further, emphasizing his point with a final statement, \"This got to be, it's got to be the worst tool I've ever seen.\"","clip":["2",0]},"class_type":"CLIPTextEncode","_meta":{"title":"CLIP Text Encode (Prompt)"}},"4":{"inputs":{"text":"blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, wrong hand count, artifacts around text, unreadable text on shirt or hat, incorrect lettering on cap (“PNTR”), incorrect t-shirt slogan (“JUST DO IT”), missing microphone, misplaced microphone, inconsistent perspective, camera shake, incorrect depth of field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, smiling, laughing, exaggerated sadness, wrong gaze direction, eyes looking at camera, mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, off-sync audio, missing sniff sounds, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, missing door or shelves, missing shallow depth of field, flat lighting, inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts.","clip":["2",0]},"class_type":"CLIPTextEncode","_meta":{"title":"CLIP Text Encode (Prompt)"}},"8":{"inputs":{"sampler_name":"euler"},"class_type":"KSamplerSelect","_meta":{"title":"KSamplerSelect"}},"9":{"inputs":{"steps":20,"max_shift":2.05,"base_shift":0.95,"stretch":true,"terminal":0.1,"latent":["28",0]},"class_type":"LTXVScheduler","_meta":{"title":"LTXVScheduler"}},"11":{"inputs":{"noise_seed":10},"class_type":"RandomNoise","_meta":{"title":"RandomNoise"}},"12":{"inputs":{"samples":["29",0],"vae":["1",2]},"class_type":"VAEDecode","_meta":{"title":"VAE Decode"}},"13":{"inputs":{"ckpt_name":"ltx-av-step-1751000_vocoder_24K.safetensors"},"class_type":"LTXVAudioVAELoader","_meta":{"title":"🅛🅣🅧 LTXV Audio VAE Loader"}},"14":{"inputs":{"samples":["29",1],"audio_vae":["13",0]},"class_type":"LTXVAudioVAEDecode","_meta":{"title":"🅛🅣🅧 LTXV Audio VAE Decode"}},"15":{"inputs":{"frame_rate":["23",0],"loop_count":0,"filename_prefix":"AnimateDiff","format":"video/h264-mp4","pix_fmt":"yuv420p","crf":19,"save_metadata":true,"trim_to_audio":false,"pingpong":false,"save_output":true,"images":["12",0],"audio":["14",0]},"class_type":"VHS_VideoCombine","_meta":{"title":"Video Combine 🎥🅥🅗🅢"}},"17":{"inputs":{"skip_blocks":"29","model":["28",1],"positive":["22",0],"negative":["22",1],"parameters":["18",0]},"class_type":"MultimodalGuider","_meta":{"title":"🅛🅣🅧 Multimodal Guider"}},"18":{"inputs":{"modality":"VIDEO","cfg":3,"stg":0,"rescale":0,"modality_scale":3,"parameters":["19",0]},"class_type":"GuiderParameters","_meta":{"title":"🅛🅣🅧 Guider Parameters"}},"19":{"inputs":{"modality":"AUDIO","cfg":7,"stg":0,"rescale":0,"modality_scale":3},"class_type":"GuiderParameters","_meta":{"title":"🅛🅣🅧 Guider Parameters"}},"21":{"inputs":{"audioUI":"","audio":["14",0]},"class_type":"PreviewAudio","_meta":{"title":"PreviewAudio"}},"22":{"inputs":{"frame_rate":["23",0],"positive":["3",0],"negative":["4",0]},"class_type":"LTXVConditioning","_meta":{"title":"LTXVConditioning"}},"23":{"inputs":{"value":25},"class_type":"FloatConstant","_meta":{"title":"Float Constant"}},"26":{"inputs":{"frames_number":["27",0],"frame_rate":["42",0],"batch_size":1},"class_type":"LTXVEmptyLatentAudio","_meta":{"title":"🅛🅣🅧 LTXV Empty Latent Audio"}},"27":{"inputs":{"value":105},"class_type":"INTConstant","_meta":{"title":"INT Constant"}},"28":{"inputs":{"video_latent":["43",0],"audio_latent":["26",0],"model":["44",0]},"class_type":"LTXVConcatAVLatent","_meta":{"title":"🅛🅣🅧 LTXV Concat AV Latent"}},"29":{"inputs":{"av_latent":["41",0],"model":["28",1]},"class_type":"LTXVSeparateAVLatent","_meta":{"title":"🅛🅣🅧 LTXV Separate AV Latent"}},"41":{"inputs":{"noise":["11",0],"guider":["17",0],"sampler":["8",0],"sigmas":["9",0],"latent_image":["28",0]},"class_type":"SamplerCustomAdvanced","_meta":{"title":"SamplerCustomAdvanced"}},"42":{"inputs":{"a":["23",0]},"class_type":"CM_FloatToInt","_meta":{"title":"FloatToInt"}},"43":{"inputs":{"width":768,"height":512,"length":["27",0],"batch_size":1},"class_type":"EmptyLTXVLatentVideo","_meta":{"title":"EmptyLTXVLatentVideo"}},"44":{"inputs":{"torch_compile":true,"disable_backup":false,"model":["1",0]},"class_type":"LTXVSequenceParallelMultiGPUPatcher","_meta":{"title":"LTXVSequenceParallelMultiGPUPatcher"}},"45":{"inputs":{"frame_idx":0,"strength":1},"class_type":"LTXVAddGuide","_meta":{"title":"LTXVAddGuide"}}},"comfy_fork_version":"feature/av_inference@a6994ed1","VHS_latentpreview":false,"VHS_latentpreviewrate":0,"VHS_MetadataImage":true,"VHS_KeepIntermediate":true,"ue_links":[],"links_added_by_ue":[]},"version":0.4}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-GGUF/tools/README.md b/custom_nodes/ComfyUI-GGUF/tools/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..228bc220593d7878ca854032638bdc2682c76a71
--- /dev/null
+++ b/custom_nodes/ComfyUI-GGUF/tools/README.md
@@ -0,0 +1,93 @@
+## Converting initial model
+
+To convert your initial safetensors/ckpt model to FP16/BF16 GGUF, run the following command:
+
+```
+python convert.py --src E:\models\unet\flux1-dev.safetensors
+```
+Make sure `gguf>=0.13.0` is installed for this step. Optionally, specify the output gguf file with the `--dst` arg.
+
+> [!NOTE]  
+> Do not use the diffusers UNET format for flux, it won't work, use the default/reference checkpoint key format. This is due to q/k/v being merged into one qkv key.
+> You can convert it by loading it in ComfyUI and saving it using the built-in "ModelSave" node.
+
+> [!WARNING] 
+> For hunyuan video/wan 2.1, you will see a warning about 5D tensors. This means the script will save a **non functional** model to disk first, that you can quantize. I recommend saving these in a separate `raw` folder to avoid confusion.
+> 
+> After quantization, you will have to run `fix_5d_tensor.py` manually to add back the missing key that was saved by the conversion code.
+
+## Quantizing using custom llama.cpp
+
+Depending on your git settings, you may need to run the following script first in order to make sure the patch file is valid. It will convert Windows (CRLF) line endings to Unix (LF) ones.
+
+```
+python fix_lines_ending.py
+```
+
+Git clone llama.cpp into the current folder:
+
+```
+git clone https://github.com/ggerganov/llama.cpp
+```
+
+Check out the correct branch, then apply the custom patch needed to add image model support to the repo you just cloned.
+
+```
+cd llama.cpp
+git checkout tags/b3962
+git apply ..\lcpp.patch
+```
+
+Compile the llama-quantize binary. This example uses cmake, on linux you can just use make.
+
+### Visual Studio 2019, Linux, etc...
+
+```
+mkdir build
+cmake -B build
+cmake --build build --config Debug -j10 --target llama-quantize
+cd ..
+```
+
+### Visual Studio 2022
+
+```
+mkdir build
+cmake -B build -DCMAKE_CXX_STANDARD=17 -DCMAKE_CXX_STANDARD_REQUIRED=ON -DCMAKE_CXX_FLAGS="-std=c++17"
+```
+
+Edit the `llama.cpp\common\log.cpp` file, inserts two lines after the existing first line:
+
+```
+#include "log.h"
+
+#define _SILENCE_CXX23_CHRONO_DEPRECATION_WARNING
+#include <chrono>
+```
+
+Then you can build the project:
+```
+cmake --build build --config Debug -j10 --target llama-quantize
+cd ..
+```
+
+### Quantize your model
+
+
+Now you can use the newly build binary to quantize your model to the desired format:
+```
+llama.cpp\build\bin\Debug\llama-quantize.exe E:\models\unet\flux1-dev-BF16.gguf E:\models\unet\flux1-dev-Q4_K_S.gguf Q4_K_S
+```
+
+You can extract the patch again with `git diff src\llama.cpp > lcpp.patch` if you wish to change something and contribute back.
+
+> [!WARNING] 
+> For hunyuan video/wan 2.1, you will have to run `fix_5d_tensor.py` after the quantization step is done.
+>
+> Example usage:  `fix_5d_tensors.py --src E:\models\video\raw\wan2.1-t2v-1.3b-Q8_0.gguf --dst E:\models\video\wan2.1-t2v-1.3b-Q8_0.gguf`
+>
+> By default, this also saves a `fix_5d_tensors_[arch].safetensors` file in the `ComfyUI-GGUF/tools` folder, it's recommended to delete this after all models have been converted.
+
+> [!NOTE]
+> Do not quantize SDXL / SD1 / other Conv2D heavy models. If you do, make sure to **extract the UNET model first**.
+>This should be obvious, but also don't use the resulting llama-quantize binary with LLMs.
diff --git a/custom_nodes/ComfyUI-GGUF/tools/convert.py b/custom_nodes/ComfyUI-GGUF/tools/convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..5029c874277358559f8855d3f1032963437a3e91
--- /dev/null
+++ b/custom_nodes/ComfyUI-GGUF/tools/convert.py
@@ -0,0 +1,365 @@
+# (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
+import os
+import gguf
+import torch
+import logging
+import argparse
+from tqdm import tqdm
+from safetensors.torch import load_file, save_file
+
+QUANTIZATION_THRESHOLD = 1024
+REARRANGE_THRESHOLD = 512
+MAX_TENSOR_NAME_LENGTH = 127
+MAX_TENSOR_DIMS = 4
+
+class ModelTemplate:
+    arch = "invalid"  # string describing architecture
+    shape_fix = False # whether to reshape tensors
+    keys_detect = []  # list of lists to match in state dict
+    keys_banned = []  # list of keys that should mark model as invalid for conversion
+    keys_hiprec = []  # list of keys that need to be kept in fp32 for some reason
+    keys_ignore = []  # list of strings to ignore keys by when found
+
+    def handle_nd_tensor(self, key, data):
+        raise NotImplementedError(f"Tensor detected that exceeds dims supported by C++ code! ({key} @ {data.shape})")
+
+class ModelFlux(ModelTemplate):
+    arch = "flux"
+    keys_detect = [
+        ("transformer_blocks.0.attn.norm_added_k.weight",),
+        ("double_blocks.0.img_attn.proj.weight",),
+    ]
+    keys_banned = ["transformer_blocks.0.attn.norm_added_k.weight",]
+
+class ModelSD3(ModelTemplate):
+    arch = "sd3"
+    keys_detect = [
+        ("transformer_blocks.0.attn.add_q_proj.weight",),
+        ("joint_blocks.0.x_block.attn.qkv.weight",),
+    ]
+    keys_banned = ["transformer_blocks.0.attn.add_q_proj.weight",]
+
+class ModelAura(ModelTemplate):
+    arch = "aura"
+    keys_detect = [
+        ("double_layers.3.modX.1.weight",),
+        ("joint_transformer_blocks.3.ff_context.out_projection.weight",),
+    ]
+    keys_banned = ["joint_transformer_blocks.3.ff_context.out_projection.weight",]
+
+class ModelHiDream(ModelTemplate):
+    arch = "hidream"
+    keys_detect = [
+        (
+            "caption_projection.0.linear.weight",
+            "double_stream_blocks.0.block.ff_i.shared_experts.w3.weight"
+        )
+    ]
+    keys_hiprec = [
+        # nn.parameter, can't load from BF16 ver
+        ".ff_i.gate.weight",
+        "img_emb.emb_pos"
+    ]
+
+class CosmosPredict2(ModelTemplate):
+    arch = "cosmos"
+    keys_detect = [
+        (
+            "blocks.0.mlp.layer1.weight",
+            "blocks.0.adaln_modulation_cross_attn.1.weight",
+        )
+    ]
+    keys_hiprec = ["pos_embedder"]
+    keys_ignore = ["_extra_state", "accum_"]
+
+class ModelHyVid(ModelTemplate):
+    arch = "hyvid"
+    keys_detect = [
+        (
+            "double_blocks.0.img_attn_proj.weight",
+            "txt_in.individual_token_refiner.blocks.1.self_attn_qkv.weight",
+        )
+    ]
+
+    def handle_nd_tensor(self, key, data):
+        # hacky but don't have any better ideas
+        path = f"./fix_5d_tensors_{self.arch}.safetensors" # TODO: somehow get a path here??
+        if os.path.isfile(path):
+            raise RuntimeError(f"5D tensor fix file already exists! {path}")
+        fsd = {key: torch.from_numpy(data)}
+        tqdm.write(f"5D key found in state dict! Manual fix required! - {key} {data.shape}")
+        save_file(fsd, path)
+
+class ModelWan(ModelHyVid):
+    arch = "wan"
+    keys_detect = [
+        (
+            "blocks.0.self_attn.norm_q.weight",
+            "text_embedding.2.weight",
+            "head.modulation",
+        )
+    ]
+    keys_hiprec = [
+        ".modulation" # nn.parameter, can't load from BF16 ver
+    ]
+
+class ModelLTXV(ModelTemplate):
+    arch = "ltxv"
+    keys_detect = [
+        (
+            "adaln_single.emb.timestep_embedder.linear_2.weight",
+            "transformer_blocks.27.scale_shift_table",
+            "caption_projection.linear_2.weight",
+        )
+    ]
+    keys_hiprec = [
+        "scale_shift_table" # nn.parameter, can't load from BF16 base quant
+    ]
+
+class ModelSDXL(ModelTemplate):
+    arch = "sdxl"
+    shape_fix = True
+    keys_detect = [
+        ("down_blocks.0.downsamplers.0.conv.weight", "add_embedding.linear_1.weight",),
+        (
+            "input_blocks.3.0.op.weight", "input_blocks.6.0.op.weight",
+            "output_blocks.2.2.conv.weight", "output_blocks.5.2.conv.weight",
+        ), # Non-diffusers
+        ("label_emb.0.0.weight",),
+    ]
+
+class ModelSD1(ModelTemplate):
+    arch = "sd1"
+    shape_fix = True
+    keys_detect = [
+        ("down_blocks.0.downsamplers.0.conv.weight",),
+        (
+            "input_blocks.3.0.op.weight", "input_blocks.6.0.op.weight", "input_blocks.9.0.op.weight",
+            "output_blocks.2.1.conv.weight", "output_blocks.5.2.conv.weight", "output_blocks.8.2.conv.weight"
+        ), # Non-diffusers
+    ]
+
+class ModelLumina2(ModelTemplate):
+    arch = "lumina2"
+    keys_detect = [
+        ("cap_embedder.1.weight", "context_refiner.0.attention.qkv.weight")
+    ]
+
+arch_list = [ModelFlux, ModelSD3, ModelAura, ModelHiDream, CosmosPredict2, 
+             ModelLTXV, ModelHyVid, ModelWan, ModelSDXL, ModelSD1, ModelLumina2]
+
+def is_model_arch(model, state_dict):
+    # check if model is correct
+    matched = False
+    invalid = False
+    for match_list in model.keys_detect:
+        if all(key in state_dict for key in match_list):
+            matched = True
+            invalid = any(key in state_dict for key in model.keys_banned)
+            break
+    assert not invalid, "Model architecture not allowed for conversion! (i.e. reference VS diffusers format)"
+    return matched
+
+def detect_arch(state_dict):
+    model_arch = None
+    for arch in arch_list:
+        if is_model_arch(arch, state_dict):
+            model_arch = arch()
+            break
+    assert model_arch is not None, "Unknown model architecture!"
+    return model_arch
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Generate F16 GGUF files from single UNET")
+    parser.add_argument("--src", required=True, help="Source model ckpt file.")
+    parser.add_argument("--dst", help="Output unet gguf file.")
+    args = parser.parse_args()
+
+    if not os.path.isfile(args.src):
+        parser.error("No input provided!")
+
+    return args
+
+def strip_prefix(state_dict):
+    # prefix for mixed state dict
+    prefix = None
+    for pfx in ["model.diffusion_model.", "model."]:
+        if any([x.startswith(pfx) for x in state_dict.keys()]):
+            prefix = pfx
+            break
+
+    # prefix for uniform state dict
+    if prefix is None:
+        for pfx in ["net."]:
+            if all([x.startswith(pfx) for x in state_dict.keys()]):
+                prefix = pfx
+                break
+
+    # strip prefix if found
+    if prefix is not None:
+        logging.info(f"State dict prefix found: '{prefix}'")
+        sd = {}
+        for k, v in state_dict.items():
+            if prefix not in k:
+                continue
+            k = k.replace(prefix, "")
+            sd[k] = v
+    else:
+        logging.debug("State dict has no prefix")
+        sd = state_dict
+
+    return sd
+
+def load_state_dict(path):
+    if any(path.endswith(x) for x in [".ckpt", ".pt", ".bin", ".pth"]):
+        state_dict = torch.load(path, map_location="cpu", weights_only=True)
+        for subkey in ["model", "module"]:
+            if subkey in state_dict:
+                state_dict = state_dict[subkey]
+                break
+        if len(state_dict) < 20:
+            raise RuntimeError(f"pt subkey load failed: {state_dict.keys()}")
+    else:
+        state_dict = load_file(path)
+
+    return strip_prefix(state_dict)
+
+def handle_tensors(writer, state_dict, model_arch):
+    name_lengths = tuple(sorted(
+        ((key, len(key)) for key in state_dict.keys()),
+        key=lambda item: item[1],
+        reverse=True,
+    ))
+    if not name_lengths:
+        return
+    max_name_len = name_lengths[0][1]
+    if max_name_len > MAX_TENSOR_NAME_LENGTH:
+        bad_list = ", ".join(f"{key!r} ({namelen})" for key, namelen in name_lengths if namelen > MAX_TENSOR_NAME_LENGTH)
+        raise ValueError(f"Can only handle tensor names up to {MAX_TENSOR_NAME_LENGTH} characters. Tensors exceeding the limit: {bad_list}")
+    for key, data in tqdm(state_dict.items()):
+        old_dtype = data.dtype
+
+        if any(x in key for x in model_arch.keys_ignore):
+            tqdm.write(f"Filtering ignored key: '{key}'")
+            continue
+
+        if data.dtype == torch.bfloat16:
+            data = data.to(torch.float32).numpy()
+        # this is so we don't break torch 2.0.X
+        elif data.dtype in [getattr(torch, "float8_e4m3fn", "_invalid"), getattr(torch, "float8_e5m2", "_invalid")]:
+            data = data.to(torch.float16).numpy()
+        else:
+            data = data.numpy()
+
+        n_dims = len(data.shape)
+        data_shape = data.shape
+        if old_dtype == torch.bfloat16:
+            data_qtype = gguf.GGMLQuantizationType.BF16
+        # elif old_dtype == torch.float32:
+        #     data_qtype = gguf.GGMLQuantizationType.F32
+        else:
+            data_qtype = gguf.GGMLQuantizationType.F16
+
+        # The max no. of dimensions that can be handled by the quantization code is 4
+        if len(data.shape) > MAX_TENSOR_DIMS:
+            model_arch.handle_nd_tensor(key, data)
+            continue # needs to be added back later
+
+        # get number of parameters (AKA elements) in this tensor
+        n_params = 1
+        for dim_size in data_shape:
+            n_params *= dim_size
+
+        if old_dtype in (torch.float32, torch.bfloat16):
+            if n_dims == 1:
+                # one-dimensional tensors should be kept in F32
+                # also speeds up inference due to not dequantizing
+                data_qtype = gguf.GGMLQuantizationType.F32
+
+            elif n_params <= QUANTIZATION_THRESHOLD:
+                # very small tensors
+                data_qtype = gguf.GGMLQuantizationType.F32
+
+            elif any(x in key for x in model_arch.keys_hiprec):
+                # tensors that require max precision
+                data_qtype = gguf.GGMLQuantizationType.F32
+
+        if (model_arch.shape_fix                        # NEVER reshape for models such as flux
+            and n_dims > 1                              # Skip one-dimensional tensors
+            and n_params >= REARRANGE_THRESHOLD         # Only rearrange tensors meeting the size requirement
+            and (n_params / 256).is_integer()           # Rearranging only makes sense if total elements is divisible by 256
+            and not (data.shape[-1] / 256).is_integer() # Only need to rearrange if the last dimension is not divisible by 256
+        ):
+            orig_shape = data.shape
+            data = data.reshape(n_params // 256, 256)
+            writer.add_array(f"comfy.gguf.orig_shape.{key}", tuple(int(dim) for dim in orig_shape))
+
+        try:
+            data = gguf.quants.quantize(data, data_qtype)
+        except (AttributeError, gguf.QuantError) as e:
+            tqdm.write(f"falling back to F16: {e}")
+            data_qtype = gguf.GGMLQuantizationType.F16
+            data = gguf.quants.quantize(data, data_qtype)
+
+        new_name = key # do we need to rename?
+
+        shape_str = f"{{{', '.join(str(n) for n in reversed(data.shape))}}}"
+        tqdm.write(f"{f'%-{max_name_len + 4}s' % f'{new_name}'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}")
+
+        writer.add_tensor(new_name, data, raw_dtype=data_qtype)
+
+def convert_file(path, dst_path=None, interact=True, overwrite=False):
+    # load & run model detection logic
+    state_dict = load_state_dict(path)
+    model_arch = detect_arch(state_dict)
+    logging.info(f"* Architecture detected from input: {model_arch.arch}")
+
+    # detect & set dtype for output file
+    dtypes = [x.dtype for x in state_dict.values()]
+    dtypes = {x:dtypes.count(x) for x in set(dtypes)}
+    main_dtype = max(dtypes, key=dtypes.get)
+
+    if main_dtype == torch.bfloat16:
+        ftype_name = "BF16"
+        ftype_gguf = gguf.LlamaFileType.MOSTLY_BF16
+    # elif main_dtype == torch.float32:
+    #     ftype_name = "F32"
+    #     ftype_gguf = None
+    else:
+        ftype_name = "F16"
+        ftype_gguf = gguf.LlamaFileType.MOSTLY_F16
+
+    if dst_path is None:
+        dst_path = f"{os.path.splitext(path)[0]}-{ftype_name}.gguf"
+    elif "{ftype}" in dst_path: # lcpp logic
+        dst_path = dst_path.replace("{ftype}", ftype_name)
+
+    if os.path.isfile(dst_path) and not overwrite:
+        if interact:
+            input("Output exists enter to continue or ctrl+c to abort!")
+        else:
+            raise OSError("Output exists and overwriting is disabled!")
+
+    # handle actual file
+    writer = gguf.GGUFWriter(path=None, arch=model_arch.arch)
+    writer.add_quantization_version(gguf.GGML_QUANT_VERSION)
+    if ftype_gguf is not None:
+        writer.add_file_type(ftype_gguf)
+
+    handle_tensors(writer, state_dict, model_arch)
+    writer.write_header_to_file(path=dst_path)
+    writer.write_kv_data_to_file()
+    writer.write_tensors_to_file(progress=True)
+    writer.close()
+
+    fix = f"./fix_5d_tensors_{model_arch.arch}.safetensors"
+    if os.path.isfile(fix):
+        logging.warning(f"\n### Warning! Fix file found at '{fix}'")
+        logging.warning(" you most likely need to run 'fix_5d_tensors.py' after quantization.")
+
+    return dst_path, model_arch
+
+if __name__ == "__main__":
+    args = parse_args()
+    convert_file(args.src, args.dst)
+
diff --git a/custom_nodes/ComfyUI-GGUF/tools/fix_5d_tensors.py b/custom_nodes/ComfyUI-GGUF/tools/fix_5d_tensors.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e61d1c2e5f2572c3d9fa12eba38c84f13689a53
--- /dev/null
+++ b/custom_nodes/ComfyUI-GGUF/tools/fix_5d_tensors.py
@@ -0,0 +1,82 @@
+# (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
+import os
+import gguf
+import torch
+import argparse
+from tqdm import tqdm
+from safetensors.torch import load_file
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--src", required=True)
+    parser.add_argument("--dst", required=True)
+    parser.add_argument("--fix", required=False, help="Defaults to ./fix_5d_tensors_[arch].pt")
+    parser.add_argument("--overwrite", action="store_true")
+    args = parser.parse_args()
+
+    if not os.path.isfile(args.src):
+        parser.error(f"Invalid source file '{args.src}'")
+    if not args.overwrite and os.path.exists(args.dst):
+        parser.error(f"Output exists, use '--overwrite' ({args.dst})")
+
+    return args
+
+def get_arch_str(reader):
+    field = reader.get_field("general.architecture")
+    return str(field.parts[field.data[-1]], encoding="utf-8")
+
+def get_file_type(reader):
+    field = reader.get_field("general.file_type")
+    ft = int(field.parts[field.data[-1]])
+    return gguf.LlamaFileType(ft)
+
+if __name__ == "__main__":
+    args = get_args()
+
+    # read existing
+    reader = gguf.GGUFReader(args.src)
+    arch = get_arch_str(reader)
+    file_type = get_file_type(reader)
+    print(f"Detected arch: '{arch}' (ftype: {str(file_type)})")
+
+    # prep fix
+    if args.fix is None:
+        args.fix = f"./fix_5d_tensors_{arch}.safetensors"
+ 
+    if not os.path.isfile(args.fix):
+        raise OSError(f"No 5D tensor fix file: {args.fix}")
+
+    sd5d = load_file(args.fix)
+    sd5d = {k:v.numpy() for k,v in sd5d.items()}
+    print("5D tensors:", sd5d.keys())
+
+    # prep output
+    writer = gguf.GGUFWriter(path=None, arch=arch)
+    writer.add_quantization_version(gguf.GGML_QUANT_VERSION)
+    writer.add_file_type(file_type)
+
+    added = []
+    def add_extra_key(writer, key, data):
+        global added
+        data_qtype = gguf.GGMLQuantizationType.F32
+        data = gguf.quants.quantize(data, data_qtype)
+        tqdm.write(f"Adding key {key} ({data.shape})")
+        writer.add_tensor(key, data, raw_dtype=data_qtype)
+        added.append(key)
+
+    # main loop to add missing 5D tensor(s)
+    for tensor in tqdm(reader.tensors):
+        writer.add_tensor(tensor.name, tensor.data, raw_dtype=tensor.tensor_type)
+        key5d = tensor.name.replace(".bias", ".weight")
+        if key5d in sd5d.keys():
+            add_extra_key(writer, key5d, sd5d[key5d])
+
+    # brute force for any missed
+    for key, data in sd5d.items():
+        if key not in added:
+            add_extra_key(writer, key, data)
+
+    writer.write_header_to_file(path=args.dst)
+    writer.write_kv_data_to_file()
+    writer.write_tensors_to_file(progress=True)
+    writer.close()
diff --git a/custom_nodes/ComfyUI-GGUF/tools/fix_lines_ending.py b/custom_nodes/ComfyUI-GGUF/tools/fix_lines_ending.py
new file mode 100644
index 0000000000000000000000000000000000000000..346e3501fb7682fa3754f175965aa750241fe4ac
--- /dev/null
+++ b/custom_nodes/ComfyUI-GGUF/tools/fix_lines_ending.py
@@ -0,0 +1,31 @@
+import os
+
+files = ["lcpp.patch", "lcpp_sd3.patch"]
+
+def has_unix_line_endings(file_path):
+    try:
+        with open(file_path, 'rb') as file:
+            content = file.read()
+        return b'\r\n' not in content
+    except Exception as e:
+        print(f"Error checking '{file_path}': {e}")
+        return False
+
+def convert_to_linux_format(file_path):
+    try:
+        with open(file_path, 'rb') as file:
+            content = file.read().replace(b'\r\n', b'\n')
+        with open(file_path, 'wb') as file:
+            file.write(content)
+        print(f"'{file_path}' converted to Linux line endings (LF).")
+    except Exception as e:
+        print(f"Error processing '{file_path}': {e}")
+
+for file in files:
+    if os.path.exists(file):
+        if has_unix_line_endings(file):
+            print(f"'{file}' already has Unix line endings (LF). No conversion needed.")
+        else:
+            convert_to_linux_format(file)
+    else:
+        print(f"File '{file}' does not exist.")
diff --git a/custom_nodes/ComfyUI-GGUF/tools/lcpp.patch b/custom_nodes/ComfyUI-GGUF/tools/lcpp.patch
new file mode 100644
index 0000000000000000000000000000000000000000..92396e17b4ecf281265f07603232d23111ee9baa
--- /dev/null
+++ b/custom_nodes/ComfyUI-GGUF/tools/lcpp.patch
@@ -0,0 +1,451 @@
+diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
+index de3c706f..0267c1fa 100644
+--- a/ggml/include/ggml.h
++++ b/ggml/include/ggml.h
+@@ -223,7 +223,7 @@
+ #define GGML_MAX_OP_PARAMS      64
+ 
+ #ifndef GGML_MAX_NAME
+-#   define GGML_MAX_NAME        64
++#   define GGML_MAX_NAME        128
+ #endif
+ 
+ #define GGML_DEFAULT_N_THREADS  4
+@@ -2449,6 +2449,7 @@ extern "C" {
+ 
+     // manage tensor info
+     GGML_API void gguf_add_tensor(struct gguf_context * ctx, const struct ggml_tensor * tensor);
++    GGML_API void gguf_set_tensor_ndim(struct gguf_context * ctx, const char * name, int n_dim);
+     GGML_API void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type);
+     GGML_API void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const void * data, size_t size);
+ 
+diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
+index b16c462f..6d1568f1 100644
+--- a/ggml/src/ggml.c
++++ b/ggml/src/ggml.c
+@@ -22960,6 +22960,14 @@ void gguf_add_tensor(
+     ctx->header.n_tensors++;
+ }
+ 
++void gguf_set_tensor_ndim(struct gguf_context * ctx, const char * name, const int n_dim) {
++    const int idx = gguf_find_tensor(ctx, name);
++    if (idx < 0) {
++        GGML_ABORT("tensor not found");
++    }
++    ctx->infos[idx].n_dims = n_dim;
++}
++
+ void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type) {
+     const int idx = gguf_find_tensor(ctx, name);
+     if (idx < 0) {
+diff --git a/src/llama.cpp b/src/llama.cpp
+index 24e1f1f0..25db4c69 100644
+--- a/src/llama.cpp
++++ b/src/llama.cpp
+@@ -205,6 +205,17 @@ enum llm_arch {
+     LLM_ARCH_GRANITE,
+     LLM_ARCH_GRANITE_MOE,
+     LLM_ARCH_CHAMELEON,
++    LLM_ARCH_FLUX,
++    LLM_ARCH_SD1,
++    LLM_ARCH_SDXL,
++    LLM_ARCH_SD3,
++    LLM_ARCH_AURA,
++    LLM_ARCH_LTXV,
++    LLM_ARCH_HYVID,
++    LLM_ARCH_WAN,
++    LLM_ARCH_HIDREAM,
++    LLM_ARCH_COSMOS,
++    LLM_ARCH_LUMINA2,
+     LLM_ARCH_UNKNOWN,
+ };
+ 
+@@ -258,6 +269,17 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
+     { LLM_ARCH_GRANITE,         "granite"      },
+     { LLM_ARCH_GRANITE_MOE,     "granitemoe"   },
+     { LLM_ARCH_CHAMELEON,       "chameleon"    },
++    { LLM_ARCH_FLUX,            "flux"         },
++    { LLM_ARCH_SD1,             "sd1"          },
++    { LLM_ARCH_SDXL,            "sdxl"         },
++    { LLM_ARCH_SD3,             "sd3"          },
++    { LLM_ARCH_AURA,            "aura"         },
++    { LLM_ARCH_LTXV,            "ltxv"         },
++    { LLM_ARCH_HYVID,           "hyvid"        },
++    { LLM_ARCH_WAN,             "wan"          },
++    { LLM_ARCH_HIDREAM,         "hidream"      },
++    { LLM_ARCH_COSMOS,          "cosmos"       },
++    { LLM_ARCH_LUMINA2,         "lumina2"      },
+     { LLM_ARCH_UNKNOWN,         "(unknown)"    },
+ };
+ 
+@@ -1531,6 +1553,17 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
+             { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
+         },
+     },
++    { LLM_ARCH_FLUX,    {}},
++    { LLM_ARCH_SD1,     {}},
++    { LLM_ARCH_SDXL,    {}},
++    { LLM_ARCH_SD3,     {}},
++    { LLM_ARCH_AURA,    {}},
++    { LLM_ARCH_LTXV,    {}},
++    { LLM_ARCH_HYVID,   {}},
++    { LLM_ARCH_WAN,     {}},
++    { LLM_ARCH_HIDREAM, {}},
++    { LLM_ARCH_COSMOS,  {}},
++    { LLM_ARCH_LUMINA2, {}},
+     {
+         LLM_ARCH_UNKNOWN,
+         {
+@@ -5403,6 +5436,25 @@ static void llm_load_hparams(
+     // get general kv
+     ml.get_key(LLM_KV_GENERAL_NAME, model.name, false);
+ 
++    // Disable LLM metadata for image models
++    switch (model.arch) {
++        case LLM_ARCH_FLUX:
++        case LLM_ARCH_SD1:
++        case LLM_ARCH_SDXL:
++        case LLM_ARCH_SD3:
++        case LLM_ARCH_AURA:
++        case LLM_ARCH_LTXV:
++        case LLM_ARCH_HYVID:
++        case LLM_ARCH_WAN:
++        case LLM_ARCH_HIDREAM:
++        case LLM_ARCH_COSMOS:
++        case LLM_ARCH_LUMINA2:
++            model.ftype = ml.ftype;
++            return;
++        default:
++            break;
++    }
++
+     // get hparams kv
+     ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab);
+ 
+@@ -18016,6 +18068,134 @@ static void llama_tensor_dequantize_internal(
+     workers.clear();
+ }
+ 
++static ggml_type img_tensor_get_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
++    // Special function for quantizing image model tensors
++    const std::string name = ggml_get_name(tensor);
++    const llm_arch arch = qs.model.arch;
++
++    // Sanity check
++    if (
++            (name.find("model.diffusion_model.") != std::string::npos) ||
++            (name.find("first_stage_model.") != std::string::npos) ||
++            (name.find("single_transformer_blocks.") != std::string::npos) ||
++            (name.find("joint_transformer_blocks.") != std::string::npos)
++        ) {
++            throw std::runtime_error("Invalid input GGUF file. This is not a supported UNET model");
++    }
++
++    // Unsupported quant types - exclude all IQ quants for now
++    if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS  ||
++        ftype == LLAMA_FTYPE_MOSTLY_IQ2_S   || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M  ||
++        ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S  ||
++        ftype == LLAMA_FTYPE_MOSTLY_IQ1_M   || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL ||
++        ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS  || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S  ||
++        ftype == LLAMA_FTYPE_MOSTLY_IQ3_M   || ftype == LLAMA_FTYPE_MOSTLY_Q4_0_4_4 ||
++        ftype == LLAMA_FTYPE_MOSTLY_Q4_0_4_8 || ftype == LLAMA_FTYPE_MOSTLY_Q4_0_8_8) {
++        throw std::runtime_error("Invalid quantization type for image model (Not supported)");
++    }
++
++    if ( // Rules for to_v attention
++            (name.find("attn_v.weight") != std::string::npos) ||
++            (name.find(".to_v.weight") != std::string::npos) ||
++            (name.find(".v.weight") != std::string::npos) ||
++            (name.find(".attn.w1v.weight") != std::string::npos) ||
++            (name.find(".attn.w2v.weight") != std::string::npos) ||
++            (name.find("_attn.v_proj.weight") != std::string::npos)
++        ){
++            if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) {
++                new_type = GGML_TYPE_Q3_K;
++            }
++            else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
++                new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
++            }
++            else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
++                new_type = GGML_TYPE_Q5_K;
++            }
++            else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) {
++                new_type = GGML_TYPE_Q6_K;
++            }
++            else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) {
++                new_type = GGML_TYPE_Q5_K;
++            }
++            ++qs.i_attention_wv;
++    } else if ( // Rules for fused qkv attention
++            (name.find("attn_qkv.weight") != std::string::npos) ||
++            (name.find("attn.qkv.weight") != std::string::npos) ||
++            (name.find("attention.qkv.weight") != std::string::npos)
++        ) {
++            if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
++                new_type = GGML_TYPE_Q4_K;
++            }
++            else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) {
++                new_type = GGML_TYPE_Q5_K;
++            }
++            else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) {
++                new_type = GGML_TYPE_Q6_K;
++            }
++    } else if ( // Rules for ffn
++            (name.find("ffn_down") != std::string::npos) ||
++            ((name.find("experts.") != std::string::npos) && (name.find(".w2.weight") != std::string::npos)) ||
++            (name.find(".ffn.2.weight") != std::string::npos) || // is this even the right way around?
++            (name.find(".ff.net.2.weight") != std::string::npos) ||
++            (name.find(".mlp.layer2.weight") != std::string::npos) ||
++            (name.find(".adaln_modulation_mlp.2.weight") != std::string::npos) ||
++            (name.find(".feed_forward.w2.weight") != std::string::npos)
++        ) {
++            // TODO: add back `layer_info` with some model specific logic + logic further down
++            if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
++                new_type = GGML_TYPE_Q4_K;
++            }
++            else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
++                new_type = GGML_TYPE_Q5_K;
++            }
++            else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S) {
++                new_type = GGML_TYPE_Q5_K;
++            }
++            else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) {
++                new_type = GGML_TYPE_Q6_K;
++            }
++            else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) {
++                new_type = GGML_TYPE_Q6_K;
++            }
++            else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_0) {
++                new_type = GGML_TYPE_Q4_1;
++            }
++            else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_0) {
++                new_type = GGML_TYPE_Q5_1;
++            }
++            ++qs.i_ffn_down;
++    }
++
++    // Sanity check for row shape
++    bool convert_incompatible_tensor = false;
++    if (new_type == GGML_TYPE_Q2_K    || new_type == GGML_TYPE_Q3_K    || new_type == GGML_TYPE_Q4_K   ||
++        new_type == GGML_TYPE_Q5_K    || new_type == GGML_TYPE_Q6_K) {
++        int nx = tensor->ne[0];
++        int ny = tensor->ne[1];
++        if (nx % QK_K != 0) {
++            LLAMA_LOG_WARN("\n\n%s : tensor cols %d x %d are not divisible by %d, required for %s", __func__, nx, ny, QK_K, ggml_type_name(new_type));
++            convert_incompatible_tensor = true;
++        } else {
++            ++qs.n_k_quantized;
++        }
++    }
++    if (convert_incompatible_tensor) {
++        // TODO: Possibly reenable this in the future
++        // switch (new_type) {
++        //     case GGML_TYPE_Q2_K:
++        //     case GGML_TYPE_Q3_K:
++        //     case GGML_TYPE_Q4_K:   new_type = GGML_TYPE_Q5_0;   break;
++        //     case GGML_TYPE_Q5_K:   new_type = GGML_TYPE_Q5_1;   break;
++        //     case GGML_TYPE_Q6_K:   new_type = GGML_TYPE_Q8_0;   break;
++        //     default: throw std::runtime_error("\nUnsupported tensor size encountered\n");
++        // }
++        new_type = GGML_TYPE_F16;
++        LLAMA_LOG_WARN(" - using fallback quantization %s\n", ggml_type_name(new_type));
++        ++qs.n_fallback;
++    }
++    return new_type;
++}
++
+ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
+     const std::string name = ggml_get_name(tensor);
+ 
+@@ -18513,7 +18693,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
+         if (llama_model_has_encoder(&model)) {
+             n_attn_layer *= 3;
+         }
+-        GGML_ASSERT((qs.n_attention_wv == n_attn_layer) && "n_attention_wv is unexpected");
++        if (model.arch != LLM_ARCH_HYVID) { // TODO: Check why this fails
++            GGML_ASSERT((qs.n_attention_wv == n_attn_layer) && "n_attention_wv is unexpected");
++        }
+     }
+ 
+     size_t total_size_org = 0;
+@@ -18547,6 +18729,51 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
+             ctx_outs[i_split] = gguf_init_empty();
+         }
+         gguf_add_tensor(ctx_outs[i_split], tensor);
++        // SD3 pos_embed needs special fix as first dim is 1, which gets truncated here
++        if (model.arch == LLM_ARCH_SD3) {
++            const std::string name = ggml_get_name(tensor);
++            if (name == "pos_embed" && tensor->ne[2] == 1) {
++                const int n_dim = 3;
++                gguf_set_tensor_ndim(ctx_outs[i_split], "pos_embed", n_dim);
++                LLAMA_LOG_INFO("\n%s: Correcting pos_embed shape for SD3: [key:%s]\n", __func__, tensor->name);
++            }
++        }
++        // same goes for auraflow
++        if (model.arch == LLM_ARCH_AURA) {
++            const std::string name = ggml_get_name(tensor);
++            if (name == "positional_encoding" && tensor->ne[2] == 1) {
++                const int n_dim = 3;
++                gguf_set_tensor_ndim(ctx_outs[i_split], "positional_encoding", n_dim);
++                LLAMA_LOG_INFO("\n%s: Correcting positional_encoding shape for AuraFlow: [key:%s]\n", __func__, tensor->name);
++            }
++            if (name == "register_tokens" && tensor->ne[2] == 1) {
++                const int n_dim = 3;
++                gguf_set_tensor_ndim(ctx_outs[i_split], "register_tokens", n_dim);
++                LLAMA_LOG_INFO("\n%s: Correcting register_tokens shape for AuraFlow: [key:%s]\n", __func__, tensor->name);
++            }
++        }
++        // conv3d fails due to max dims - unsure what to do here as we never even reach this check
++        if (model.arch == LLM_ARCH_HYVID) {
++            const std::string name = ggml_get_name(tensor);
++            if (name == "img_in.proj.weight" && tensor->ne[5] != 1 ) {
++                throw std::runtime_error("img_in.proj.weight size failed for HyVid");
++            }
++        }
++        // All the modulation layers also have dim1, and I think conv3d fails here too but we segfaul way before that...
++        if (model.arch == LLM_ARCH_WAN) {
++            const std::string name = ggml_get_name(tensor);
++            if (name.find(".modulation") != std::string::npos && tensor->ne[2] == 1) {
++                const int n_dim = 3;
++                gguf_set_tensor_ndim(ctx_outs[i_split], tensor->name, n_dim);
++                LLAMA_LOG_INFO("\n%s: Correcting shape for Wan: [key:%s]\n", __func__, tensor->name);
++            }
++            // FLF2V model only
++            if (name == "img_emb.emb_pos") {
++                const int n_dim = 3;
++                gguf_set_tensor_ndim(ctx_outs[i_split], tensor->name, n_dim);
++                LLAMA_LOG_INFO("\n%s: Correcting shape for Wan FLF2V: [key:%s]\n", __func__, tensor->name);
++            }
++        }
+     }
+ 
+     // Set split info if needed
+@@ -18647,6 +18874,110 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
+         // do not quantize relative position bias (T5)
+         quantize &= name.find("attn_rel_b.weight") == std::string::npos;
+ 
++        // rules for image models
++        bool image_model = false;
++        if (model.arch == LLM_ARCH_FLUX) {
++            image_model = true;
++            quantize &= name.find("txt_in.") == std::string::npos;
++            quantize &= name.find("img_in.") == std::string::npos;
++            quantize &= name.find("time_in.") == std::string::npos;
++            quantize &= name.find("vector_in.") == std::string::npos;
++            quantize &= name.find("guidance_in.") == std::string::npos;
++            quantize &= name.find("final_layer.") == std::string::npos;
++        }
++        if (model.arch == LLM_ARCH_SD1 || model.arch == LLM_ARCH_SDXL) {
++            image_model = true;
++            quantize &= name.find("class_embedding.") == std::string::npos;
++            quantize &= name.find("time_embedding.") == std::string::npos;
++            quantize &= name.find("add_embedding.") == std::string::npos;
++            quantize &= name.find("time_embed.") == std::string::npos;
++            quantize &= name.find("label_emb.") == std::string::npos;
++            quantize &= name.find("conv_in.") == std::string::npos;
++            quantize &= name.find("conv_out.") == std::string::npos;
++            quantize &= name != "input_blocks.0.0.weight";
++            quantize &= name != "out.2.weight";
++        }
++        if (model.arch == LLM_ARCH_SD3) {
++            image_model = true;
++            quantize &= name.find("final_layer.") == std::string::npos;
++            quantize &= name.find("time_text_embed.") == std::string::npos;
++            quantize &= name.find("context_embedder.") == std::string::npos;
++            quantize &= name.find("t_embedder.") == std::string::npos;
++            quantize &= name.find("y_embedder.") == std::string::npos;
++            quantize &= name.find("x_embedder.") == std::string::npos;
++            quantize &= name != "proj_out.weight";
++            quantize &= name != "pos_embed";
++        }
++        if (model.arch == LLM_ARCH_AURA) {
++            image_model = true;
++            quantize &= name.find("t_embedder.") == std::string::npos;
++            quantize &= name.find("init_x_linear.") == std::string::npos;
++            quantize &= name != "modF.1.weight";
++            quantize &= name != "cond_seq_linear.weight";
++            quantize &= name != "final_linear.weight";
++            quantize &= name != "final_linear.weight";
++            quantize &= name != "positional_encoding";
++            quantize &= name != "register_tokens";
++        }
++        if (model.arch == LLM_ARCH_LTXV) {
++            image_model = true;
++            quantize &= name.find("adaln_single.") == std::string::npos;
++            quantize &= name.find("caption_projection.") == std::string::npos;
++            quantize &= name.find("patchify_proj.") == std::string::npos;
++            quantize &= name.find("proj_out.") == std::string::npos;
++            quantize &= name.find("scale_shift_table") == std::string::npos; // last block too
++        }
++        if (model.arch == LLM_ARCH_HYVID) {
++            image_model = true;
++            quantize &= name.find("txt_in.") == std::string::npos;
++            quantize &= name.find("img_in.") == std::string::npos;
++            quantize &= name.find("time_in.") == std::string::npos;
++            quantize &= name.find("vector_in.") == std::string::npos;
++            quantize &= name.find("guidance_in.") == std::string::npos;
++            quantize &= name.find("final_layer.") == std::string::npos;
++        }
++        if (model.arch == LLM_ARCH_WAN) {
++            image_model = true;
++            quantize &= name.find("modulation.") == std::string::npos;
++            quantize &= name.find("patch_embedding.") == std::string::npos;
++            quantize &= name.find("text_embedding.") == std::string::npos;
++            quantize &= name.find("time_projection.") == std::string::npos;
++            quantize &= name.find("time_embedding.") == std::string::npos;
++            quantize &= name.find("img_emb.") == std::string::npos;
++            quantize &= name.find("head.") == std::string::npos;
++        }
++        if (model.arch == LLM_ARCH_HIDREAM) {
++            image_model = true;
++            quantize &= name.find("p_embedder.") == std::string::npos;
++            quantize &= name.find("t_embedder.") == std::string::npos;
++            quantize &= name.find("x_embedder.") == std::string::npos;
++            quantize &= name.find("final_layer.") == std::string::npos;
++            quantize &= name.find(".ff_i.gate.weight") == std::string::npos;
++            quantize &= name.find("caption_projection.") == std::string::npos;
++        }
++        if (model.arch == LLM_ARCH_COSMOS) {
++            image_model = true;
++            quantize &= name.find("p_embedder.") == std::string::npos;
++            quantize &= name.find("t_embedder.") == std::string::npos;
++            quantize &= name.find("t_embedding_norm.") == std::string::npos;
++            quantize &= name.find("x_embedder.") == std::string::npos;
++            quantize &= name.find("pos_embedder.") == std::string::npos;
++            quantize &= name.find("final_layer.") == std::string::npos;
++        }
++        if (model.arch == LLM_ARCH_LUMINA2) {
++            image_model = true;
++            quantize &= name.find("t_embedder.") == std::string::npos;
++            quantize &= name.find("x_embedder.") == std::string::npos;
++            quantize &= name.find("final_layer.") == std::string::npos;
++            quantize &= name.find("cap_embedder.") == std::string::npos;
++            quantize &= name.find("context_refiner.") == std::string::npos;
++            quantize &= name.find("noise_refiner.") == std::string::npos;
++        }
++        // ignore 3D/4D tensors for image models as the code was never meant to handle these
++        if (image_model) {
++            quantize &= ggml_n_dims(tensor) == 2;
++        }
++
+         enum ggml_type new_type;
+         void * new_data;
+         size_t new_size;
+@@ -18655,6 +18986,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
+             new_type = default_type;
+ 
+             // get more optimal quantization type based on the tensor shape, layer, etc.
++            if (image_model) {
++                new_type = img_tensor_get_type(qs, new_type, tensor, ftype);
++            } else {
+             if (!params->pure && ggml_is_quantized(default_type)) {
+                 new_type = llama_tensor_get_type(qs, new_type, tensor, ftype);
+             }
+@@ -18664,6 +18998,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
+             if (params->output_tensor_type < GGML_TYPE_COUNT && strcmp(tensor->name, "output.weight") == 0) {
+                 new_type = params->output_tensor_type;
+             }
++            }
+ 
+             // If we've decided to quantize to the same type the tensor is already
+             // in then there's nothing to do.
diff --git a/custom_nodes/ComfyUI-GGUF/tools/read_tensors.py b/custom_nodes/ComfyUI-GGUF/tools/read_tensors.py
new file mode 100644
index 0000000000000000000000000000000000000000..1bdff028a787c09b38e5616ef75a2f070c672445
--- /dev/null
+++ b/custom_nodes/ComfyUI-GGUF/tools/read_tensors.py
@@ -0,0 +1,21 @@
+#!/usr/bin/python3
+import os
+import sys
+import gguf
+
+def read_tensors(path):
+    reader = gguf.GGUFReader(path)
+    for tensor in reader.tensors:
+        if tensor.tensor_type == gguf.GGMLQuantizationType.F32:
+            continue
+        print(f"{str(tensor.tensor_type):32}: {tensor.name}")
+
+try:
+    path = sys.argv[1]
+    assert os.path.isfile(path), "Invalid path"
+    print(f"input: {path}")
+except Exception as e:
+    input(f"failed: {e}")
+else:
+    read_tensors(path)
+    input()
diff --git a/custom_nodes/ComfyUI-HuggingFace/.github/workflows/publish.yml b/custom_nodes/ComfyUI-HuggingFace/.github/workflows/publish.yml
new file mode 100644
index 0000000000000000000000000000000000000000..8e92e1f9caf921f4144100c4707b8879a50a6b6c
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/.github/workflows/publish.yml
@@ -0,0 +1,28 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+      - master
+    paths:
+      - "pyproject.toml"
+
+permissions:
+  issues: write
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'MoonGoblinDev' }}
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+        with:
+          submodules: true
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@v1
+        with:
+          ## Add your own personal access token to your Github Repository secrets and reference it here.
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
diff --git a/custom_nodes/ComfyUI-HuggingFace/api/__init__.py b/custom_nodes/ComfyUI-HuggingFace/api/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/custom_nodes/ComfyUI-HuggingFace/api/huggingface.py b/custom_nodes/ComfyUI-HuggingFace/api/huggingface.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe63f54ebd3b5a5f8a552e3f0b02d75ce7f99117
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/api/huggingface.py
@@ -0,0 +1,219 @@
+# ================================================
+# File: api/huggingface.py
+# ================================================
+import requests
+import json
+from typing import List, Optional, Dict, Any, Union
+
+# Try to import huggingface_hub for better downloads
+try:
+    from huggingface_hub import hf_hub_download, snapshot_download
+    HF_HUB_AVAILABLE = True
+except ImportError:
+    HF_HUB_AVAILABLE = False
+    print("[HuggingFace API] huggingface_hub not available, falling back to manual downloads")
+
+# Try to use huggingface_hub CLI as fallback
+import subprocess
+import sys
+
+class HuggingFaceAPI:
+    """Simple wrapper for interacting with the HuggingFace API."""
+    BASE_URL = "https://huggingface.co/api"
+
+    def __init__(self, api_key: Optional[str] = None):
+        self.api_key = api_key
+        self.base_headers = {'Content-Type': 'application/json'}
+        if api_key:
+            self.base_headers["Authorization"] = f"Bearer {api_key}"
+            print("[HuggingFace API] Using HF token for private repositories.")
+        else:
+            print("[HuggingFace API] No HF token provided. Only public repositories accessible.")
+
+    def _get_request_headers(self, method: str, has_json_data: bool) -> Dict[str, str]:
+        """Returns headers for a specific request."""
+        headers = self.base_headers.copy()
+        # Don't send content-type for GET/HEAD if no json_data
+        if method.upper() in ["GET", "HEAD"] and not has_json_data:
+            headers.pop('Content-Type', None)
+        return headers
+
+    def _request(self, method: str, endpoint: str, params: Optional[Dict] = None,
+                 json_data: Optional[Dict] = None, stream: bool = False,
+                 allow_redirects: bool = True, timeout: int = 30) -> Union[Dict[str, Any], requests.Response, None]:
+        """Makes a request to the HuggingFace API and handles basic errors."""
+        url = f"{self.BASE_URL}/{endpoint.lstrip('/')}"
+        request_headers = self._get_request_headers(method, json_data is not None)
+
+        try:
+            response = requests.request(
+                method,
+                url,
+                headers=request_headers,
+                params=params,
+                json=json_data,
+                stream=stream,
+                allow_redirects=allow_redirects,
+                timeout=timeout
+            )
+            response.raise_for_status()  # Raise HTTPError for bad responses (4xx or 5xx)
+
+            if stream:
+                return response  # Return the response object for streaming
+
+            # Handle No Content response (e.g., 204)
+            if response.status_code == 204 or not response.content:
+                return None
+
+            return response.json()
+
+        except requests.exceptions.HTTPError as http_err:
+            error_detail = None
+            status_code = http_err.response.status_code
+            try:
+                error_detail = http_err.response.json()
+            except json.JSONDecodeError:
+                error_detail = http_err.response.text[:200] # First 200 chars
+            print(f"HuggingFace API HTTP Error ({method} {url}): Status {status_code}, Response: {error_detail}")
+            # Return a structured error dictionary
+            return {"error": f"HTTP Error: {status_code}", "details": error_detail, "status_code": status_code}
+
+        except requests.exceptions.RequestException as req_err:
+            print(f"HuggingFace API Request Error ({method} {url}): {req_err}")
+            return {"error": str(req_err), "details": None, "status_code": None}
+
+        except json.JSONDecodeError as json_err:
+            print(f"HuggingFace API Error: Failed to decode JSON response from {url}: {json_err}")
+            # Include response text if possible and not streaming
+            response_text = response.text[:200] if not stream and hasattr(response, 'text') else "N/A"
+            return {"error": "Invalid JSON response", "details": response_text, "status_code": response.status_code if hasattr(response, 'status_code') else None}
+
+    def search_models(self, query: str, limit: int = 20) -> Optional[Dict[str, Any]]:
+        """Searches for models on HuggingFace. (GET /models)"""
+        endpoint = "/models"
+        params = {
+            "search": query,
+            "limit": limit
+        }
+        result = self._request("GET", endpoint, params=params)
+        if isinstance(result, dict) and "error" in result:
+            return result
+        return result
+
+    def search_models_meili(self, query: str = None, types: Optional[List[str]] = None,
+                            base_models: Optional[List[str]] = None,
+                            sort: str = 'Most Downloaded', limit: int = 20, page: int = 1,
+                            nsfw: Optional[bool] = None) -> Optional[Dict[str, Any]]:
+        """Searches models using HuggingFace's Meilisearch endpoint."""
+        meili_url = "https://huggingface.co/multi-search"
+        headers = {'Content-Type': 'application/json'}
+        if self.api_key:
+            headers['Authorization'] = f'Bearer {self.api_key}'
+        
+        # Build search query
+        search_query = {
+            "q": query or "",
+            "limit": limit,
+            "offset": (page - 1) * limit
+        }
+        
+        # Add filters
+        filters = []
+        
+        # Type filters
+        if types:
+            type_filter = {"type": types}
+            filters.append(type_filter)
+        
+        # Base model filters  
+        if base_models:
+            base_filter = {"base_model": base_models}
+            filters.append(base_filter)
+        
+        # NSFW filter
+        if nsfw is not None:
+            nsfw_filter = {"nsfw": nsfw}
+            filters.append(nsfw_filter)
+        
+        if filters:
+            search_query["filters"] = filters
+        
+        # Add sorting
+        sort_mapping = {
+            "Relevancy": "id:desc",
+            "Most Downloaded": "metrics.downloadCount:desc",
+            "Highest Rated": "metrics.thumbsUpCount:desc", 
+            "Most Liked": "metrics.favoriteCount:desc", 
+            "Most Discussed": "metrics.commentCount:desc", 
+            "Most Collected": "metrics.collectedCount:desc", 
+            "Most Buzz": "metrics.tippedAmountCount:desc", 
+            "Newest": "createdAt:desc", 
+        }
+        
+        if sort in sort_mapping:
+            search_query["sort"] = [sort_mapping[sort]]
+        
+        try:
+            response = requests.post(meili_url, json=search_query, headers=headers, timeout=30)
+            response.raise_for_status()
+            return response.json()
+        except requests.exceptions.RequestException as e:
+            print(f"HuggingFace Meili API Error: {e}")
+            return {"error": str(e), "status_code": getattr(e.response, 'status_code', None) if hasattr(e, 'response') else None}
+
+    def get_model_files(self, model_id: str) -> Optional[Dict[str, Any]]:
+        """Gets files for a specific HuggingFace model. Uses huggingface_hub instead of API."""
+        # Skip API calls, let huggingface_hub handle everything
+        print(f"[HuggingFace API] Skipping file listing, using huggingface_hub auto-detect for {model_id}")
+        return {"auto_detect": True}
+
+    def get_model_info(self, model_id: str) -> Optional[Dict[str, Any]]:
+        """Gets information about a model by its ID. Uses huggingface_hub instead of API."""
+        # Skip API calls, let huggingface_hub handle everything
+        print(f"[HuggingFace API] Skipping model info, using huggingface_hub auto-detect for {model_id}")
+        return {"id": model_id, "name": model_id.split('/')[-1]}
+
+    def get_model_version_info(self, version_id: str) -> Optional[Dict[str, Any]]:
+        """Gets version information - not applicable for HuggingFace, returns empty dict"""
+        # HuggingFace doesn't have version IDs like Civitai
+        # This method is kept for compatibility but returns empty
+        return {}
+
+    def download_file(self, model_id: str, filename: str, local_dir: str = None) -> Optional[Union[requests.Response, str]]:
+        """Downloads a specific file from HuggingFace. Uses only huggingface_hub."""
+        if not HF_HUB_AVAILABLE:
+            print("[HuggingFace API] huggingface_hub not available")
+            return None
+            
+        if not local_dir:
+            print("[HuggingFace API] local_dir not specified")
+            return None
+            
+        try:
+            print(f"[HuggingFace API] Using huggingface_hub for download: {model_id}/{filename}")
+            
+            if filename is None:
+                # Download entire repo using snapshot_download
+                print(f"[HuggingFace API] Downloading entire repo {model_id}")
+                result = snapshot_download(
+                    repo_id=model_id,
+                    local_dir=local_dir,
+                    token=self.api_key
+                )
+                print(f"[HuggingFace API] snapshot_download success: {result}")
+                return result
+            else:
+                # Download specific file using hf_hub_download
+                result = hf_hub_download(
+                    repo_id=model_id,
+                    filename=filename,
+                    local_dir=local_dir,
+                    resume_download=True,
+                    token=self.api_key
+                )
+                print(f"[HuggingFace API] hf_hub_download success: {result}")
+                return result
+                
+        except Exception as e:
+            print(f"[HuggingFace API] huggingface_hub download failed: {e}")
+            return None
diff --git a/custom_nodes/ComfyUI-HuggingFace/downloader/__init__.py b/custom_nodes/ComfyUI-HuggingFace/downloader/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/custom_nodes/ComfyUI-HuggingFace/downloader/chunk_downloader.py b/custom_nodes/ComfyUI-HuggingFace/downloader/chunk_downloader.py
new file mode 100644
index 0000000000000000000000000000000000000000..44764b1df80b53345e648319bddc8d5b37468476
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/downloader/chunk_downloader.py
@@ -0,0 +1,574 @@
+# ================================================
+# File: downloader/chunk_downloader.py 
+# Multi-connection somehow still not working
+# ================================================
+
+import requests
+import threading
+import time
+import shutil
+from pathlib import Path
+import os
+from typing import Optional, Dict, Tuple, Union, TYPE_CHECKING
+
+# Import manager type hint without circular dependency during type checking
+if TYPE_CHECKING:
+    from .manager import DownloadManager
+
+# Import config values
+from ..config import DEFAULT_CHUNK_SIZE, DOWNLOAD_TIMEOUT, HEAD_REQUEST_TIMEOUT
+
+class ChunkDownloader:
+    """Handles downloading files in chunks using multiple connections or fallback."""
+    # Constants
+    STATUS_UPDATE_INTERVAL = 0.5
+    HEAD_REQUEST_TIMEOUT = HEAD_REQUEST_TIMEOUT
+    DOWNLOAD_TIMEOUT = DOWNLOAD_TIMEOUT
+    MIN_SIZE_FOR_MULTI_MB = 100  # Minimum file size for multi-connection download
+
+    def __init__(self, url: str, output_path: str, num_connections: int = 4,
+                 chunk_size: int = DEFAULT_CHUNK_SIZE, manager: 'DownloadManager' = None,
+                 download_id: str = None, api_key: Optional[str] = None,
+                 known_size: Optional[int] = None):
+        # URLs
+        self.initial_url = url
+        self.url = url
+        
+        # Paths
+        self.output_path = Path(output_path)
+        self.temp_dir = self.output_path.parent / f".{self.output_path.name}.parts_{download_id or int(time.time())}"
+        
+        # Download configuration
+        self.num_connections = num_connections  # Use the provided number of connections
+        self.chunk_size = chunk_size
+        self.manager = manager
+        self.download_id = download_id
+        self.api_key = api_key
+        self.known_size = known_size if known_size and known_size > 0 else None
+        
+        # Download state
+        self.total_size = self.known_size or 0
+        self.downloaded = 0
+        self.connection_type = "N/A"
+        self.error = None
+        
+        # Thread management
+        self.threads = []
+        self.lock = threading.Lock()
+        self.cancel_event = threading.Event()
+        self.part_files = []
+        
+        # Performance tracking
+        self._start_time = 0
+        self._last_update_time = 0
+        self._last_downloaded_bytes = 0
+        self._speed = 0
+
+    def _get_request_headers(self, add_range: Optional[str] = None) -> Dict[str, str]:
+        """Constructs request headers with optional auth and range."""
+        headers = {}
+        if self.api_key:
+            headers["Authorization"] = f"Bearer {self.api_key}"
+        if add_range:
+            headers['Range'] = add_range
+        return headers
+
+    @property
+    def is_cancelled(self) -> bool:
+        """Check if download has been cancelled."""
+        return self.cancel_event.is_set()
+
+    def cancel(self):
+        """Signal the download to cancel."""
+        if not self.is_cancelled:
+            print(f"[Downloader {self.download_id or 'N/A'}] Cancellation requested by user.")
+            self.cancel_event.set()
+            self.error = "Download cancelled by user"
+            if self.manager and self.download_id:
+                self.manager._update_download_status(self.download_id, status="cancelled", error=self.error)
+
+    def _cleanup_temp(self, success: bool):
+        """Remove temporary directory and potentially the output file."""
+        # Clean up temp directory
+        if self.temp_dir.exists():
+            try:
+                shutil.rmtree(self.temp_dir)
+            except Exception as e:
+                print(f"[Downloader {self.download_id}] Warning: Could not remove temp directory {self.temp_dir}: {e}")
+
+        # Remove output file if download failed
+        if not success and self.output_path.exists():
+            try:
+                self.output_path.unlink()
+                print(f"[Downloader {self.download_id}] Removed incomplete/failed output file: {self.output_path}")
+            except Exception as e:
+                print(f"[Downloader {self.download_id}] Warning: Could not remove incomplete output file {self.output_path}: {e}")
+
+    def _get_range_support_and_url(self) -> Tuple[str, bool]:
+        """Check for range support and get final URL after redirects."""
+        final_url = self.initial_url
+        supports_ranges = False
+        
+        try:
+            request_headers = self._get_request_headers()
+            
+            print(f"[Downloader {self.download_id}] Checking range support/redirects for: {self.initial_url} (Timeout: {self.HEAD_REQUEST_TIMEOUT}s)")
+            response = requests.head(
+                self.initial_url,
+                allow_redirects=True,
+                timeout=self.HEAD_REQUEST_TIMEOUT,
+                headers=request_headers
+            )
+            response.raise_for_status()
+
+            # Update URL after redirects
+            final_url = response.url
+            self.url = final_url
+
+            # Check range support
+            accept_ranges = response.headers.get('accept-ranges', 'none').lower()
+            supports_ranges = accept_ranges == 'bytes'
+
+            # Get file size if not already known
+            if self.total_size <= 0:
+                head_size = int(response.headers.get('Content-Length', 0))
+                if head_size > 0:
+                    self.total_size = head_size
+                    print(f"[Downloader {self.download_id}] Got file size from HEAD: {self.total_size} bytes")
+            
+            print(f"[Downloader {self.download_id}] HEAD Check OK - Final URL: {final_url}, Range Support: {supports_ranges}")
+            return final_url, supports_ranges
+            
+        except requests.exceptions.Timeout:
+            print(f"[Downloader {self.download_id}] Warning: HEAD request timed out. Proceeding with Single connection.")
+            return self.initial_url, False
+            
+        except requests.exceptions.RequestException as e:
+            http_error_details = ""
+            if hasattr(e, 'response') and e.response is not None:
+                status_code = e.response.status_code
+                http_error_details = f" (Status Code: {status_code})"
+            print(f"[Downloader {self.download_id}] Warning: HEAD request failed{http_error_details}. Proceeding with Single connection.")
+            return self.initial_url, False
+            
+        except Exception as e:
+            print(f"[Downloader {self.download_id}] Warning: Unexpected error during HEAD request: {e}. Proceeding with Single connection.")
+            return self.initial_url, False
+
+    def _update_progress(self, chunk_len: int):
+        """Thread-safe update of download progress and speed calculation."""
+        with self.lock:
+            self.downloaded += chunk_len
+            current_time = time.monotonic()
+            time_diff = current_time - self._last_update_time
+
+            # Update speed and notify manager periodically
+            if time_diff >= self.STATUS_UPDATE_INTERVAL or self.downloaded == self.total_size:
+                progress = min((self.downloaded / self.total_size) * 100, 100.0) if self.total_size > 0 else 0
+
+                # Calculate speed
+                if time_diff > 0:
+                    bytes_diff = self.downloaded - self._last_downloaded_bytes
+                    self._speed = bytes_diff / time_diff
+
+                self._last_update_time = current_time
+                self._last_downloaded_bytes = self.downloaded
+
+                if self.manager and self.download_id:
+                    self.manager._update_download_status(
+                        self.download_id,
+                        progress=progress,
+                        speed=self._speed,
+                        status="downloading"
+                    )
+
+    def download_segment(self, segment_index: int, start_byte: int, end_byte: int):
+        """Downloads a specific segment of the file."""
+        part_file_path = self.temp_dir / f"part_{segment_index}"
+        request_headers = self._get_request_headers(add_range=f'bytes={start_byte}-{end_byte}')
+        retries = 3
+        
+        for current_try in range(retries):
+            if self.is_cancelled:
+                print(f"[Downloader {self.download_id}] Segment {segment_index} cancelled before request (Try {current_try+1}).")
+                # Ensure error is set if not already
+                if not self.error: self.error = "Cancelled during segment download"
+                return 
+                
+            response = None
+            try:
+                response = requests.get(self.url, headers=request_headers, stream=True, timeout=self.DOWNLOAD_TIMEOUT)
+                response.raise_for_status()
+
+                bytes_written_this_segment = 0
+                with open(part_file_path, 'wb') as f:
+                    for chunk in response.iter_content(self.chunk_size):
+                        if self.is_cancelled:
+                            print(f"[Downloader {self.download_id}] Segment {segment_index} cancelled mid-stream.")
+                            # Ensure error is set if not already
+                            if not self.error: self.error = "Cancelled during segment download"
+                            
+                            
+                        if chunk:
+                            bytes_written = f.write(chunk)
+                            bytes_written_this_segment += bytes_written
+                            self._update_progress(bytes_written)
+
+                # Verify segment size
+                expected_size = (end_byte - start_byte) + 1
+                if bytes_written_this_segment != expected_size:
+                    if response:
+                        response.close()
+                    raise ValueError(f"Size mismatch. Expected {expected_size}, got {bytes_written_this_segment}")
+
+                return  # Success
+
+            
+            except (requests.exceptions.RequestException, ValueError) as e:
+                # Handle HTTP status codes
+                status_code = None
+                error_msg_detail = f"{e}"
+                
+                if isinstance(e, requests.exceptions.RequestException) and hasattr(e, 'response') and e.response is not None:
+                    status_code = e.response.status_code
+                    if status_code == 401: 
+                        error_msg_detail += " (Unauthorized)"
+                    elif status_code == 403: 
+                        error_msg_detail += " (Forbidden)"
+                    elif status_code == 416:
+                        error_msg_detail += " (Range Not Satisfiable)"
+                        self.error = f"Segment {segment_index} failed: {error_msg_detail}"
+                        self.cancel()
+                        return
+
+                print(f"[Downloader {self.download_id}] Warning: Segment {segment_index} failed (Try {current_try+1}/{retries}): {error_msg_detail}")
+                
+                if current_try >= retries - 1:  # Last attempt failed
+                    self.error = f"Segment {segment_index} failed after {retries} attempts: {error_msg_detail}"
+                    self.cancel()
+                    return
+                    
+                # Exponential backoff before retry
+                time.sleep(min(2 ** current_try, 10))
+                
+            except Exception as e:
+                self.error = f"Segment {segment_index} critical error: {e}"
+                print(f"[Downloader {self.download_id}] Error: {self.error}")
+                self.cancel()
+                return
+                
+            finally:
+                if response:
+                    response.close()
+
+    def merge_parts(self) -> bool:
+        """Merges all downloaded part files into the final output file."""
+        print(f"[Downloader {self.download_id}] Merging {len(self.part_files)} parts for {self.output_path.name}...")
+        
+        # Check if we have parts to merge
+        if not self.part_files:
+            if self.is_cancelled:
+                self.error = self.error or "Cancelled before any parts downloaded."
+            elif not self.error:
+                self.error = "No part files were created to merge."
+            print(f"[Downloader {self.download_id}] Error during merge: {self.error}")
+            return False
+            
+        try:
+            # Sort part files numerically
+            sorted_part_files = sorted(self.part_files, key=lambda p: int(p.name.split('_')[-1]))
+
+            with open(self.output_path, 'wb') as outfile:
+                for part_file in sorted_part_files:
+                    # Check if part exists
+                    if not part_file.exists():
+                        if self.is_cancelled:
+                            self.error = self.error or "Cancelled during download, a part file is missing."
+                        elif not self.error:
+                            self.error = f"Merge failed, required part file is missing: {part_file}."
+                        print(f"[Downloader {self.download_id}] Warning: Aborting merge. Missing part: {part_file.name}.")
+                        return False
+
+                    # Copy part data to output file
+                    try:
+                        with open(part_file, 'rb') as infile:
+                            shutil.copyfileobj(infile, outfile, length=1024*1024*2)  # 2MB buffer
+                    except Exception as copy_e:
+                        self.error = f"Error copying data from part {part_file.name} during merge: {copy_e}"
+                        print(f"[Downloader {self.download_id}] Error: {self.error}")
+                        return False
+
+            print(f"[Downloader {self.download_id}] Merging complete.")
+            
+            # Verify final file size
+            final_size = self.output_path.stat().st_size
+            if self.total_size > 0 and abs(final_size - self.total_size) > 1:
+                self.error = f"Merged size ({final_size}) differs significantly from expected ({self.total_size}). File may be corrupt."
+                print(f"[Downloader {self.download_id}] Error: {self.error}")
+                return False
+            elif self.total_size > 0 and final_size != self.total_size:
+                print(f"[Downloader {self.download_id}] Warning: Final merged size ({final_size}) differs slightly from expected ({self.total_size}).")
+
+            return True
+
+        except Exception as e:
+            self.error = f"Failed to merge parts: {e}"
+            print(f"[Downloader {self.download_id}] Error: {self.error}")
+            return False
+
+    def fallback_download(self) -> bool:
+        """Fallback to standard single-connection download."""
+        self.connection_type = "Single"
+        if self.manager and self.download_id:
+            self.manager._update_download_status(self.download_id, connection_type=self.connection_type, status="downloading")
+
+        print(f"[Downloader {self.download_id}] Using standard single-connection download for {self.output_path.name}...")
+        
+        self._start_time = self._start_time or time.monotonic()
+        self._last_update_time = self._start_time
+        self._last_downloaded_bytes = 0
+        self.downloaded = 0
+        
+        response = None
+        
+        try:
+            request_headers = self._get_request_headers()
+            response = requests.get(self.url, stream=True, timeout=self.DOWNLOAD_TIMEOUT, 
+                                    allow_redirects=True, headers=request_headers)
+            response.raise_for_status()
+
+            # Update URL after potential redirects
+            final_get_url = response.url
+            if final_get_url != self.url:
+                print(f"[Downloader {self.download_id}] URL redirected during GET to: {final_get_url}")
+                self.url = final_get_url
+
+            # Get/confirm file size
+            if self.total_size <= 0:
+                get_size = int(response.headers.get('Content-Length', 0))
+                if get_size > 0:
+                    self.total_size = get_size
+                    print(f"[Downloader {self.download_id}] Obtained file size via fallback GET: {self.total_size}")
+                else:
+                    print(f"[Downloader {self.download_id}] Warning: File size unknown. Progress may be inaccurate.")
+
+            # Ensure output directory exists
+            self.output_path.parent.mkdir(parents=True, exist_ok=True)
+
+            with open(self.output_path, 'wb') as f:
+                for chunk in response.iter_content(self.chunk_size):
+                    if self.is_cancelled:
+                        print(f"[Downloader {self.download_id}] Fallback download cancelled.")
+                        return False
+                    if chunk:
+                        bytes_written = f.write(chunk)
+                        self._update_progress(bytes_written)
+
+                    
+
+            # Verify download size if known
+            if self.total_size > 0 and self.downloaded != self.total_size and not self.error:
+                print(f"[Downloader {self.download_id}] Warning: Size mismatch. Expected {self.total_size}, got {self.downloaded}.")
+
+            print(f"[Downloader {self.download_id}] Fallback download completed.")
+            return not self.error
+
+        except requests.exceptions.RequestException as e:
+            error_msg_detail = f"{e}"
+            if hasattr(e, 'response') and e.response is not None:
+                status_code = e.response.status_code
+                if status_code == 401: 
+                    error_msg_detail += " (Unauthorized - Check HF token?)"
+                elif status_code == 403: 
+                    error_msg_detail += " (Forbidden - Permissions Issue?)"
+            
+            if not self.error:
+                self.error = f"Fallback download failed: {error_msg_detail}"
+            print(f"[Downloader {self.download_id}] Error during fallback download: {self.error}")
+            return False
+            
+        except Exception as e:
+            if not self.error:
+                self.error = f"Fallback download failed: {e}"
+            print(f"[Downloader {self.download_id}] Error during fallback download: {self.error}")
+            return False
+            
+        finally:
+            if response:
+                response.close()
+
+    def download(self) -> bool:
+        """Main download method that chooses between multi-connection or fallback approach."""
+        self._start_time = time.monotonic()
+        self.downloaded = 0
+        self.error = None
+        self.threads = []
+        self.part_files = []
+        success = False
+
+        # Clean up any existing temp directory
+        if self.temp_dir.exists():
+            print(f"[Downloader {self.download_id}] Warning: Removing leftover temp directory: {self.temp_dir}")
+            self._cleanup_temp(success=False)
+
+        # Check range support and get final URL
+        final_url, supports_ranges = self._get_range_support_and_url()
+        
+        # Decide on download strategy
+        use_multi_connection = False
+        if supports_ranges and self.num_connections > 1 and self.total_size > 0:
+            if self.total_size > self.MIN_SIZE_FOR_MULTI_MB * 1024 * 1024:
+                use_multi_connection = True
+            else:
+                print(f"[Downloader {self.download_id}] File size ({self.total_size / (1024*1024):.2f} MB) below threshold for multi-connection.")
+        
+        expected_final_size = self.total_size
+
+        try:
+            if use_multi_connection:
+                # Multi-connection download approach
+                success = self._do_multi_connection_download()
+            else:
+                # Single connection fallback
+                reason = "Range requests not supported" if not supports_ranges else \
+                         "Single connection requested" if self.num_connections <= 1 else \
+                         "File size unknown or too small"
+                print(f"[Downloader {self.download_id}] ({reason}). Using fallback single-connection download.")
+                success = self.fallback_download()
+                expected_final_size = self.total_size
+                
+                if not success and not self.error:
+                    self.error = "Single connection download failed."
+
+        except KeyboardInterrupt:
+            print(f"[Downloader {self.download_id}] Interrupted! Signalling cancellation.")
+            self.cancel()
+            if not self.error: 
+                self.error = "Download interrupted by user."
+            success = False
+            
+        except Exception as e:
+            import traceback
+            print(f"--- Critical Error in Download {self.download_id} ('{self.output_path.name}') ---")
+            traceback.print_exc()
+            print("--- End Error ---")
+            
+            if not self.error:
+                self.error = f"Unexpected download error: {str(e)}"
+            
+            success = False
+            if not self.is_cancelled: 
+                self.cancel()
+
+        finally:
+            # Cleanup and final status update
+            self._cleanup_temp(success=success and not self.is_cancelled and not self.error)
+
+            if self.manager and self.download_id:
+                final_status = "completed" if success else ("cancelled" if self.is_cancelled else "failed")
+                final_progress = 100.0 if success else ((self.downloaded / self.total_size * 100) if self.total_size > 0 else 0)
+                
+                self.manager._update_download_status(
+                    self.download_id,
+                    status=final_status,
+                    progress=min(100.0, final_progress),
+                    speed=0,
+                    error=self.error,
+                    connection_type=self.connection_type
+                )
+
+        return success and not self.error and not self.is_cancelled
+
+    def _do_multi_connection_download(self) -> bool:
+        """Handle multi-connection download process."""
+        self.connection_type = f"Multi ({self.num_connections})"
+        if self.manager and self.download_id:
+            self.manager._update_download_status(self.download_id, connection_type=self.connection_type, status="downloading")
+
+        print(f"[Downloader {self.download_id}] Starting multi-connection download for {self.output_path.name} "
+              f"({self.total_size / (1024 * 1024):.2f} MB) using {self.num_connections} connections.")
+
+        # Create temp directory
+        try:
+            if self.temp_dir.exists(): 
+                shutil.rmtree(self.temp_dir)
+            self.temp_dir.mkdir(parents=True)
+        except Exception as e:
+            self.error = f"Failed to create temp directory: {e}"
+            print(f"[Downloader {self.download_id}] Error: {self.error}")
+            return False
+
+        # Calculate segments
+        segment_size = self.total_size // self.num_connections
+        
+        # Handle small files with many connections
+        if segment_size == 0 and self.total_size > 0:
+            segment_size = self.total_size // min(self.num_connections, self.total_size) if self.total_size >= self.num_connections else self.total_size
+            if segment_size == 0:
+                segment_size = self.total_size
+                self.num_connections = 1
+                print(f"[Downloader {self.download_id}] Warning: Forcing single connection for very small file.")
+                return self.fallback_download()
+
+        # Create segments
+        segments = []
+        current_byte = 0
+        for i in range(self.num_connections):
+            if current_byte >= self.total_size: 
+                break
+                
+            start_byte = current_byte
+            end_byte = min(current_byte + segment_size - 1, self.total_size - 1)
+            
+            # Ensure last segment goes to the end
+            if i == self.num_connections - 1:
+                end_byte = self.total_size - 1
+
+            # Ensure segment is valid
+            if start_byte <= end_byte < self.total_size:
+                segments.append((i, start_byte, end_byte))
+                self.part_files.append(self.temp_dir / f"part_{i}")
+            else:
+                print(f"[Downloader {self.download_id}] Warning: Skipping invalid segment {i}, start={start_byte}, end={end_byte}")
+            
+            current_byte = end_byte + 1
+
+        if not segments:
+            self.error = f"No valid download segments calculated (Total Size: {self.total_size})."
+            print(f"[Downloader {self.download_id}] Error: {self.error}")
+            return False
+
+        # Start download threads
+        for index, start, end in segments:
+            if self.is_cancelled: 
+                break
+            thread = threading.Thread(target=self.download_segment, args=(index, start, end), daemon=True)
+            self.threads.append(thread)
+            thread.start()
+
+        # Wait for threads to complete
+        active_threads = list(self.threads)
+        while active_threads and not self.is_cancelled:
+            joined_threads = []
+            for t in active_threads:
+                t.join(timeout=0.2)
+                if not t.is_alive():
+                    joined_threads.append(t)
+            
+            active_threads = [t for t in active_threads if t not in joined_threads]
+
+        # Handle download completion
+        if self.is_cancelled:
+            print(f"[Downloader {self.download_id}] Download stopped (cancelled).")
+            self.error = self.error or "Download cancelled."
+            return False
+        elif self.error:
+            print(f"[Downloader {self.download_id}] Download stopped (error): {self.error}")
+            return False
+        elif self.total_size > 0 and self.downloaded != self.total_size:
+            self.error = f"Multi-download size mismatch. Expected {self.total_size}, got {self.downloaded}."
+            print(f"[Downloader {self.download_id}] Error: {self.error}")
+            return False
+
+        # Merge parts
+        return self.merge_parts()
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-HuggingFace/downloader/manager.py b/custom_nodes/ComfyUI-HuggingFace/downloader/manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..35bd7a955e21963c8622726911fe33464a34210d
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/downloader/manager.py
@@ -0,0 +1,996 @@
+# ================================================
+# File: downloader/manager.py
+# ================================================
+import threading
+import time
+import datetime
+import os
+import json                     
+import requests
+import subprocess
+import platform
+import sys
+from typing import List, Dict, Any, Optional, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from .chunk_downloader import ChunkDownloader
+
+from ..config import (
+    MAX_CONCURRENT_DOWNLOADS, DOWNLOAD_HISTORY_LIMIT, DEFAULT_CONNECTIONS,
+    METADATA_SUFFIX, PREVIEW_SUFFIX, METADATA_DOWNLOAD_TIMEOUT, PLUGIN_ROOT 
+)
+try:
+    from folder_paths import get_directory_by_type, get_valid_path, base_path
+    COMFY_PATHS_AVAILABLE = True
+except ImportError:
+    print("[HuggingFace Manager] Warning: ComfyUI folder_paths not available. Path validation/opening might be limited.")
+    COMFY_PATHS_AVAILABLE = False
+    base_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
+
+# --- Define History File Path ---
+# Place it in the root of the extension directory
+HISTORY_FILE_PATH = os.path.join(PLUGIN_ROOT, "download_history.json")
+
+class DownloadManager:
+    """Manages a queue of downloads, running them concurrently and saving metadata."""
+
+    def __init__(self, max_concurrent: int = MAX_CONCURRENT_DOWNLOADS):
+        self.queue: List[Dict[str, Any]] = []
+        self.active_downloads: Dict[str, Dict[str, Any]] = {} # {download_id: download_info}
+        # History now stores more complete dictionaries for retry functionality
+        self.history: List[Dict[str, Any]] = []
+        self.lock: threading.Lock = threading.Lock()
+        self.max_concurrent: int = max(1, max_concurrent)
+        self.running: bool = True
+        self._load_history_from_file()
+        self._process_thread: threading.Thread = threading.Thread(target=self._process_queue, daemon=True)
+        print(f"HuggingFace Download Manager starting (Max Concurrent: {self.max_concurrent}).")
+        self._process_thread.start()
+
+    # --- add_to_queue remains largely the same, ensuring all necessary fields are initialized ---
+    def add_to_queue(self, download_info: Dict[str, Any]) -> str:
+        """Adds a download task to the queue."""
+        with self.lock:
+            # Generate a unique ID
+            timestamp = int(time.time() * 1000)
+            file_hint = os.path.basename(download_info.get('output_path', 'file'))[:10]
+            unique_num = sum(1 for item in self.queue if file_hint in item.get("id", "") or any(file_hint in h.get("id","") for h in self.history)) # Check history too
+            download_id = f"dl_{timestamp}_{unique_num}_{file_hint}"
+
+            # Set initial status and info
+            download_info["id"] = download_id
+            download_info["status"] = "queued"
+            download_info["added_time"] = datetime.datetime.now(datetime.timezone.utc).isoformat()
+            download_info["progress"] = 0
+            download_info["speed"] = 0
+            download_info["error"] = None
+            download_info["start_time"] = None
+            download_info["end_time"] = None
+            download_info["connection_type"] = "N/A"
+
+            # --- Ensure all fields potentially needed for retry exist ---
+            # (Most were likely already filled by the calling route, but double-check)
+            required_for_retry = [
+                'url', 'output_path', 'num_connections', 'api_key', 'known_size',
+                'huggingface_model_info', 'huggingface_version_info', 'huggingface_primary_file',
+                'thumbnail', 'filename', 'model_url_or_id', 'model_version_id', 'model_type',
+                'custom_filename', 'force_redownload', 'huggingface_model_name'  # Add huggingface_model_name!
+            ]
+            for key in required_for_retry:
+                if key not in download_info:
+                    # Add default or None if missing. More robust handling might be needed
+                    # depending on how routes.py prepares the dict.
+                    if key in ['huggingface_model_info', 'huggingface_version_info', 'huggingface_primary_file']:
+                        download_info[key] = {}
+                    elif key == 'num_connections':
+                        download_info[key] = DEFAULT_CONNECTIONS
+                    elif key == 'force_redownload':
+                        download_info[key] = False
+                    elif key == 'huggingface_model_name':
+                        download_info[key] = None
+                    else:
+                        download_info[key] = None
+                    print(f"[Manager Warning] Queued item '{download_id}' missing '{key}', added default.")
+
+            self.queue.append(download_info)
+            print(f"[Manager] Queued: {download_info.get('filename', 'N/A')} (ID: {download_id}, Size: {download_info.get('known_size', 'Unknown')})")
+            return download_id
+
+    # --- cancel_download remains the same ---
+    def cancel_download(self, download_id: str) -> bool:
+        """Requests cancellation of a queued or active download."""
+        # ... (no changes needed here) ...
+        print(f"[Manager] Received cancellation request for: {download_id}") # Moved print earlier
+        downloader_to_cancel: Optional['ChunkDownloader'] = None
+        found_in_active = False
+
+        with self.lock:
+            # 1. Check queue first (can be fully handled under lock)
+            for i, item in enumerate(self.queue):
+                if item["id"] == download_id:
+                    cancelled_info = self.queue.pop(i)
+                    cancelled_info["status"] = "cancelled"
+                    cancelled_info["end_time"] = datetime.datetime.now(datetime.timezone.utc).isoformat()
+                    cancelled_info["error"] = "Cancelled from queue"
+                    self._add_to_history(cancelled_info) # _add_to_history is safe under lock
+                    print(f"[Manager] Cancelled queued download: {download_id}")
+                    return True # Cancelled from queue, we are done
+
+            # 2. Check active downloads - Find the instance *under lock*
+            if download_id in self.active_downloads:
+                found_in_active = True
+                active_info = self.active_downloads[download_id]
+                downloader_to_cancel = active_info.get("downloader_instance")
+                current_status = active_info.get("status")
+
+                # If downloader instance doesn't exist yet (status 'starting')
+                # or if already terminal, handle it here under lock
+                if not downloader_to_cancel and current_status == "starting":
+                    print(f"[Manager] Marking 'starting' download as cancelled: {download_id}")
+                    # Mark as cancelled, it won't start or will be caught by wrapper
+                    active_info["status"] = "cancelled"
+                    if not active_info.get("end_time"):
+                        active_info["end_time"] = datetime.datetime.now(datetime.timezone.utc).isoformat()
+                    active_info["error"] = "Cancelled before download thread fully started"
+                    # Don't assign downloader_to_cancel, let process_queue clean it up
+                    downloader_to_cancel = None # Explicitly clear
+                    # Indicate we handled it (or attempted to)
+                    return True # Exit, processed within lock
+                elif current_status in ["completed", "failed", "cancelled"]:
+                    print(f"[Manager] Download {download_id} is already in terminal state '{current_status}'. Cannot cancel.")
+                    # Indicate we don't need to proceed further outside the lock
+                    downloader_to_cancel = None # Explicitly clear
+                    return False # Already finished/cancelled
+
+                # If we found an instance and it's potentially running,
+                # store it to call cancel *after* releasing the lock.
+                print(f"[Manager] Found active downloader instance for {download_id}. Will signal outside lock.")
+
+            # Lock is released automatically here when exiting 'with'
+
+        # 3. Signal the downloader instance *outside* the lock
+        if downloader_to_cancel:
+            try:
+                # Check if already cancelled (to avoid duplicate logs/actions) - This check is thread-safe
+                if not downloader_to_cancel.is_cancelled:
+                    print(f"[Manager] Calling downloader.cancel() for {download_id}")
+                    downloader_to_cancel.cancel() # This can now call _update_download_status safely
+                    print(f"[Manager] Signalled downloader.cancel() for {download_id}")
+                    # Let the download thread's final status update handle moving to history
+                    return True # Signal sent
+                else:
+                    print(f"[Manager] Active download {download_id} was already cancelling.")
+                    return True # Already in cancelling state is considered a success here
+            except Exception as e:
+                print(f"[Manager] Error calling downloader.cancel() for {download_id}: {e}")
+                # Update status to failed maybe? Or just log.
+                # Use _update_download_status directly here as we are outside the lock
+                self._update_download_status(download_id, status="failed", error=f"Error during cancel signaling: {e}")
+                return False # Failed to signal
+
+        # 4. Handle cases where it wasn't in queue and wasn't running/starting
+        if not found_in_active:
+            print(f"[Manager] Could not cancel - ID not found in queue or active: {download_id}")
+            return False # Not found
+
+        # It was found in active but was already terminal or couldn't be signalled
+        # Return value determined above
+        return False # Should have returned True earlier if successful
+
+    # --- get_status remains the same (still strips data for UI) ---
+    def get_status(self) -> Dict[str, List[Dict[str, Any]]]:
+        """Returns the current state of the queue, active downloads, and history.
+           Strips sensitive/large data for UI efficiency."""
+        with self.lock:
+            # Fields to exclude when sending status to UI
+            exclude_fields_for_ui = [
+                'downloader_instance', 'huggingface_model_info', 'huggingface_version_info',
+                'api_key', # Don't send API key to frontend status
+                # Large potentially redundant fields:
+                'url', 'output_path', 'custom_filename', 'model_url_or_id',
+                # Keep 'thumbnail', 'filename', 'model_name', 'version_name' etc for display
+            ]
+
+            # Prepare active downloads list
+            active_list = [
+                {k: v for k, v in item_data.items() if k not in exclude_fields_for_ui}
+                for item_id, item_data in self.active_downloads.items()
+            ]
+
+            # Prepare history list similarly
+            history_list = [
+                {k: v for k, v in item_data.items() if k not in exclude_fields_for_ui}
+                for item_data in self.history[:DOWNLOAD_HISTORY_LIMIT]
+            ]
+
+            # Return copies
+            return {
+                "queue": [
+                    {k:v for k,v in item.items() if k not in exclude_fields_for_ui}
+                    for item in self.queue
+                ],
+                "active": active_list,
+                "history": history_list,
+            }
+
+    def _load_history_from_file(self):
+        """Loads download history from the JSON file."""
+        # No lock needed here as it's called during __init__ before the thread starts
+        if not os.path.exists(HISTORY_FILE_PATH):
+            print(f"[Manager] History file not found ({HISTORY_FILE_PATH}). Starting with empty history.")
+            self.history = []
+            return
+
+        try:
+            with open(HISTORY_FILE_PATH, 'r', encoding='utf-8') as f:
+                loaded_data = json.load(f)
+
+            if isinstance(loaded_data, list):
+                # Basic validation: Ensure items have IDs (optional but good)
+                validated_history = [item for item in loaded_data if isinstance(item, dict) and 'id' in item]
+                invalid_count = len(loaded_data) - len(validated_history)
+                if invalid_count > 0:
+                    print(f"[Manager Warning] {invalid_count} items removed from loaded history due to missing 'id'.")
+
+                # Ensure history limit
+                self.history = validated_history[:DOWNLOAD_HISTORY_LIMIT]
+                print(f"[Manager] Successfully loaded {len(self.history)} items from {HISTORY_FILE_PATH}.")
+            else:
+                print(f"[Manager Warning] History file ({HISTORY_FILE_PATH}) contained invalid data (not a list). Starting fresh.")
+                self.history = []
+                # Optionally try to delete the corrupted file?
+                # try: os.remove(HISTORY_FILE_PATH) except Exception: pass
+
+        except json.JSONDecodeError as e:
+             print(f"[Manager Error] Failed to parse history file ({HISTORY_FILE_PATH}): {e}. Starting fresh.")
+             self.history = []
+             # Optionally try to delete the corrupted file?
+        except Exception as e:
+            print(f"[Manager Error] Failed to read history file ({HISTORY_FILE_PATH}): {e}. Starting fresh.")
+            self.history = []
+
+    # --- Save History Method ---
+    def _save_history_to_file(self):
+        """Saves the current in-memory history list to the JSON file."""
+        # Assumes self.lock is HELD when this is called
+        history_to_save = self.history[:DOWNLOAD_HISTORY_LIMIT] # Ensure limit before saving
+
+        try:
+            # Ensure directory exists (should already, but belt-and-suspenders)
+            os.makedirs(os.path.dirname(HISTORY_FILE_PATH), exist_ok=True)
+
+            # Write atomically (write to temp then rename) to reduce corruption risk
+            temp_file_path = HISTORY_FILE_PATH + ".tmp"
+            with open(temp_file_path, 'w', encoding='utf-8') as f:
+                json.dump(history_to_save, f, indent=2, ensure_ascii=False) # Pretty print
+
+            os.replace(temp_file_path, HISTORY_FILE_PATH) # Atomic rename/replace
+            # print(f"[Manager] Saved {len(history_to_save)} items to history file.") # Can be noisy
+
+        except Exception as e:
+             # Log error, but don't crash the manager
+             print(f"[Manager Error] Failed to save history file ({HISTORY_FILE_PATH}): {e}")
+             # Attempt to remove temp file if it exists
+             if os.path.exists(temp_file_path):
+                  try: os.remove(temp_file_path)
+                  except Exception: pass
+
+    # --- Updated _add_to_history Method ---
+    def _add_to_history(self, download_info: Dict[str, Any]):
+        """Adds a completed/failed/cancelled item to history (internal).
+           Stores most original parameters needed for potential retry.
+           NOW ALSO TRIGGERS SAVING HISTORY TO FILE."""
+        # --- (Keep existing logic to prepare info_copy) ---
+        info_copy = {
+            k: v for k, v in download_info.items()
+            if k not in ['downloader_instance']
+        }
+        if "end_time" not in info_copy or info_copy["end_time"] is None:
+             info_copy["end_time"] = datetime.datetime.now(datetime.timezone.utc).isoformat()
+        if "status" not in info_copy:
+             info_copy["status"] = "unknown"
+        if info_copy["status"] != "completed" and not info_copy.get("error"):
+            info_copy["error"] = f"Finished with status '{info_copy['status']}' but no error recorded."
+
+        # --- Update in-memory history ---
+        self.history.insert(0, info_copy) # Prepend
+        # Trim in-memory list (save will also respect limit)
+        if len(self.history) > DOWNLOAD_HISTORY_LIMIT:
+            self.history = self.history[:DOWNLOAD_HISTORY_LIMIT]
+
+        # --- Trigger Save to File (still under lock) ---
+        self._save_history_to_file() # <--- Added call
+
+    # --- Updated clear_history Method ---
+    def clear_history(self) -> Dict[str, Any]:
+        """Clears the download history (in-memory and the JSON file)."""
+        cleared_count = 0
+        file_deleted = False
+        error_msg = None
+
+        try:
+            with self.lock:
+                cleared_count = len(self.history)
+                if cleared_count > 0:
+                    print(f"[Manager] Clearing {cleared_count} items from in-memory history.")
+                    self.history = [] # Clear memory list first
+
+                    # Attempt to delete the history file
+                    if os.path.exists(HISTORY_FILE_PATH):
+                        try:
+                            os.remove(HISTORY_FILE_PATH)
+                            file_deleted = True
+                            print(f"[Manager] Deleted history file: {HISTORY_FILE_PATH}")
+                        except OSError as e:
+                            error_msg = f"Failed to delete history file {HISTORY_FILE_PATH}: {e}"
+                            print(f"[Manager Error] {error_msg}")
+                    else:
+                        print(f"[Manager] History file ({HISTORY_FILE_PATH}) did not exist, nothing to delete.")
+                        file_deleted = True # Consider success if file wasn't there anyway
+
+                else:
+                    print("[Manager] History clear request received, but history was already empty.")
+                    # Should we still check/delete the file just in case? Yes.
+                    if os.path.exists(HISTORY_FILE_PATH):
+                        try:
+                            os.remove(HISTORY_FILE_PATH)
+                            file_deleted = True
+                            print(f"[Manager] Deleted potentially orphaned history file: {HISTORY_FILE_PATH}")
+                        except OSError as e:
+                             error_msg = f"Failed to delete potentially orphaned history file {HISTORY_FILE_PATH}: {e}"
+                             print(f"[Manager Error] {error_msg}")
+                    else:
+                        file_deleted = True # Success if clear requested and neither memory/file had anything
+
+            if error_msg:
+                 return {"success": False, "error": f"History cleared from memory, but could not delete file: {error_msg}"}
+            elif cleared_count > 0:
+                 return {"success": True, "message": f"Cleared {cleared_count} history items (memory and file)."}
+            else:
+                 # If count was 0 but file deletion was attempted/succeeded
+                 return {"success": True, "message": "History was already empty."}
+
+        except Exception as e:
+            print(f"[Manager] Critical error during clear_history: {e}")
+            import traceback
+            traceback.print_exc()
+            return {"success": False, "error": f"Failed to clear history due to unexpected error: {e}"}
+        
+    # --- _process_queue remains the same ---
+    def _process_queue(self):
+        """Internal thread function to manage downloads."""
+        # ... (no changes needed here) ...
+        print("[Manager] Process queue thread started.")
+        while self.running:
+            processed_something = False
+            with self.lock:
+                # 1. Check for finished/failed/cancelled active downloads to move to history
+                finished_ids = [
+                    dl_id for dl_id, info in self.active_downloads.items()
+                    if info.get("status") in ["completed", "failed", "cancelled"] # Use .get() for safety
+                ]
+                for dl_id in finished_ids:
+                    # Check if still in active_downloads before popping (might have been removed by another thread edge case?)
+                    if dl_id in self.active_downloads:
+                        finished_info = self.active_downloads.pop(dl_id)
+                        self._add_to_history(finished_info) # Will now store more data
+                        print(f"[Manager] Moved '{finished_info.get('filename', dl_id)}' to history (Status: {finished_info['status']})")
+                        processed_something = True
+                    else:
+                         print(f"[Manager] Warning: Item {dl_id} intended for history was already removed from active list.")
+
+                # 2. Start new downloads if slots available and queue has items
+                while len(self.active_downloads) < self.max_concurrent and self.queue:
+                    download_info = self.queue.pop(0)
+                    download_id = download_info["id"]
+
+                     # Double check if cancelled just before starting
+                    if download_info["status"] == "cancelled":
+                        # Ensure it has an end time before adding to history
+                        if not download_info.get("end_time"):
+                             download_info["end_time"] = datetime.datetime.now(datetime.timezone.utc).isoformat()
+                        self._add_to_history(download_info)
+                        print(f"[Manager] Skipping cancelled item from queue: {download_id}")
+                        processed_something = True
+                        continue
+
+                    # Update status to 'starting'
+                    download_info["status"] = "starting"
+                    download_info["start_time"] = datetime.datetime.now(datetime.timezone.utc).isoformat()
+                    download_info["downloader_instance"] = None # Placeholder
+
+                    # Add to active downloads BEFORE starting thread
+                    self.active_downloads[download_id] = download_info
+
+                    # Start download in a separate thread
+                    thread = threading.Thread(
+                        target=self._download_file_wrapper,
+                        args=(download_info,),
+                        daemon=True # Ensure thread exits if main program exits
+                    )
+                    thread.start()
+                    print(f"[Manager] Starting download thread for: {download_info.get('filename', 'N/A')} ({download_id})")
+                    processed_something = True
+
+            # Sleep only if nothing was processed to avoid busy-waiting
+            if not processed_something:
+                time.sleep(0.5) # Small delay before checking again
+
+        print("[Manager] Process queue thread stopped.")
+
+   # --- _update_download_status remains the same ---
+    def _update_download_status(self, download_id: str, status: Optional[str] = None,
+                                progress: Optional[float] = None, speed: Optional[float] = None,
+                                error: Optional[str] = None, connection_type: Optional[str] = None):
+        """Safely updates the status fields of an active download (thread-safe)."""
+        # ... (no changes needed here) ...
+        with self.lock:
+            if download_id in self.active_downloads:
+                item = self.active_downloads[download_id]
+                updated = False # Track if any field was actually updated
+                # Only update if value is provided
+                if status is not None and item.get("status") != status:
+                    item["status"] = status
+                    updated = True
+                    # If status becomes terminal, record end time
+                    if status in ["completed", "failed", "cancelled"] and not item.get("end_time"):
+                        item["end_time"] = datetime.datetime.now(datetime.timezone.utc).isoformat()
+                if progress is not None:
+                    # Clamp progress between 0 and 100
+                    clamped_progress = max(0.0, min(100.0, progress))
+                    # Only update if different enough to avoid excessive updates
+                    if abs(item.get("progress", 0) - clamped_progress) > 0.01:
+                         item["progress"] = clamped_progress
+                         updated = True
+                if speed is not None:
+                    clamped_speed = max(0.0, speed) # Speed cannot be negative
+                    if item.get("speed") != clamped_speed: # Update if different
+                         item["speed"] = clamped_speed
+                         updated = True
+                if error is not None and item.get("error") != error:
+                    # Update error only if it's new or different
+                    item["error"] = str(error)[:500] # Limit length
+                    updated = True
+                if connection_type is not None and connection_type != "N/A" and item.get("connection_type") != connection_type: # Only update if not N/A and different
+                    item["connection_type"] = connection_type
+                    updated = True
+
+    # --- _save_huggingface_metadata remains the same ---
+    def _save_huggingface_metadata(self, download_info: Dict[str, Any]):
+        """Saves the .cminfo.json file."""
+        # ... (no changes needed here) ...
+        output_path = download_info.get('output_path')
+        model_info = download_info.get('huggingface_model_info', {})
+        version_info = download_info.get('huggingface_version_info', {})
+        primary_file = download_info.get('huggingface_primary_file', {})
+        download_id = download_info.get('id', 'unknown')
+
+        try:
+            file_meta = primary_file.get('metadata', {}) or {} # Ensure dict
+            creator_info = model_info.get('creator', {}) or {}
+            model_stats = model_info.get('stats', {}) or {}
+            version_stats = version_info.get('stats', {}) or {}
+
+            metadata = {
+                "ModelId": model_info.get('id', version_info.get('modelId')) , # Use .get() on version info too
+                "ModelName": model_info.get('name', version_info.get('model',{}).get('name')), # Nested .get()
+                "ModelDescription": model_info.get('description'),
+                "CreatorUsername": creator_info.get('username'),
+                "Nsfw": model_info.get('nsfw', version_info.get('model',{}).get('nsfw')),
+                "Poi": model_info.get('poi', version_info.get('model',{}).get('poi')),
+                "AllowNoCredit": model_info.get('allowNoCredit', True),
+                "AllowCommercialUse": str(model_info.get('allowCommercialUse', 'Unknown')), # Ensure string
+                "AllowDerivatives": model_info.get('allowDerivatives', True),
+                "AllowDifferentLicense": model_info.get('allowDifferentLicense', True),
+                "Tags": model_info.get('tags', []),
+                "ModelType": model_info.get('type'),
+                "VersionId": version_info.get('id'),
+                "VersionName": version_info.get('name'),
+                "VersionDescription": version_info.get('description'),
+                "BaseModel": version_info.get('baseModel'),
+                "BaseModelType": version_info.get('baseModelType'),
+                "EarlyAccessDeadline": version_info.get('earlyAccessDeadline'),
+                "VersionPublishedAt": version_info.get('publishedAt'),
+                "VersionUpdatedAt": version_info.get('updatedAt'),
+                "VersionStatus": version_info.get('status'),
+                "IsPrimaryFile": primary_file.get('primary', False),
+                "PrimaryFileId": primary_file.get('id'),
+                "PrimaryFileName": primary_file.get('name'),
+                "FileMetadata": {
+                    "fp": file_meta.get('fp'),
+                    "size": file_meta.get('size'),
+                    "format": file_meta.get('format', 'Unknown')
+                },
+                "ImportedAt": datetime.datetime.now(datetime.timezone.utc).isoformat(),
+                "Hashes": primary_file.get('hashes', {}),
+                "TrainedWords": version_info.get('trainedWords', []),
+                "Stats": {
+                    "downloadCount": version_stats.get('downloadCount', model_stats.get('downloadCount', 0)),
+                    "rating": version_stats.get('rating', model_stats.get('rating', 0)),
+                    "ratingCount": version_stats.get('ratingCount', model_stats.get('ratingCount', 0)),
+                    "favoriteCount": version_stats.get('favoriteCount', model_stats.get('favoriteCount', 0)), # Correct source needed? Check API docs
+                    "commentCount": version_stats.get('commentCount', model_stats.get('commentCount', 0)), # Correct source needed? Check API docs
+                    "thumbsUpCount": version_stats.get('thumbsUpCount', 0),
+                 },
+                 "DownloadUrlUsed": download_info.get('url'),
+            }
+
+            base, _ = os.path.splitext(output_path)
+            meta_filename = base + METADATA_SUFFIX
+            meta_path = os.path.join(os.path.dirname(output_path), meta_filename)
+
+            print(f"[Manager Meta {download_id}] Saving metadata to: {meta_path}")
+            with open(meta_path, 'w', encoding='utf-8') as f:
+                json.dump(metadata, f, indent=2, ensure_ascii=False)
+            print(f"[Manager Meta {download_id}] Metadata saved successfully.")
+
+        except Exception as e:
+            import traceback
+            print(f"[Manager Meta {download_id}] Error saving metadata file {meta_path}: {e}")
+            # traceback.print_exc() # Uncomment for full trace
+
+    # --- _download_and_save_preview remains the same ---
+    def _download_and_save_preview(self, download_info: Dict[str, Any]):
+        """Downloads and saves the .preview.jpeg file."""
+        # ... (no changes needed here) ...
+        output_path = download_info.get('output_path')
+        thumbnail_url = download_info.get('thumbnail')
+        api_key = download_info.get('api_key')
+        download_id = download_info.get('id', 'unknown')
+
+        if not output_path:
+             print(f"[Manager Preview {download_id}] Skipping preview download: Missing output path.")
+             return
+        if not thumbnail_url:
+             print(f"[Manager Preview {download_id}] Skipping preview download: No thumbnail URL provided.")
+            # Optionally try to find one in version_info images again? Might be redundant.
+             version_info = download_info.get('huggingface_version_info', {})
+             if version_info and isinstance(version_info.get('images'), list) and version_info['images']:
+                  sorted_images = sorted([img for img in version_info['images'] if img and img.get("url")], key=lambda x: x.get('index', 0))
+                  img_data = next((img for img in sorted_images if img.get("type") == "image" and "/width=" in img.get("url","")), None) # Prefer image type with width param
+                  if not img_data: img_data = next((img for img in sorted_images if img.get("type") == "image"), None) # Fallback to any image type
+                  if not img_data: img_data = next((img for img in sorted_images), None) # Fallback to any image at all
+                  if img_data and img_data.get('url'):
+                       thumbnail_url = img_data['url']
+                       # Attempt to get a reasonable size (e.g. ~450px width)
+                       if "/width=" in thumbnail_url:
+                            thumbnail_url = thumbnail_url.split("/width=")[0] + "/width=450"
+                       elif "/blob/" not in thumbnail_url: # Avoid adding params to blob URLs
+                           separator = "&" if "?" in thumbnail_url else "?"
+                           thumbnail_url += f"{separator}width=450"
+                       print(f"[Manager Preview {download_id}] Found alternative thumbnail URL: {thumbnail_url}")
+                  else:
+                      print(f"[Manager Preview {download_id}] Still no thumbnail URL found in version info.")
+                      return # Exit if still no URL
+             else:
+                 return # Exit if no URL and no version info to search
+
+        base, _ = os.path.splitext(output_path)
+        preview_filename = base + PREVIEW_SUFFIX
+        preview_path = os.path.join(os.path.dirname(output_path), preview_filename)
+
+        print(f"[Manager Preview {download_id}] Downloading thumbnail from {thumbnail_url} to {preview_path}")
+        response = None
+        try:
+            headers = {}
+            if api_key: headers["Authorization"] = f"Bearer {api_key}"
+            response = requests.get(thumbnail_url, stream=True, headers=headers, timeout=METADATA_DOWNLOAD_TIMEOUT, allow_redirects=True)
+            response.raise_for_status()
+            content_type = response.headers.get('Content-Type', '').lower()
+            if not content_type.startswith('image/'):
+                 print(f"[Manager Preview {download_id}] Warning: Thumbnail URL returned non-image content type '{content_type}'. Skipping save.")
+                 return
+            with open(preview_path, 'wb') as f:
+                 for chunk in response.iter_content(chunk_size=8192): f.write(chunk)
+            print(f"[Manager Preview {download_id}] Thumbnail downloaded successfully.")
+        except requests.exceptions.RequestException as e:
+             error_msg = f"Error downloading thumbnail {thumbnail_url}: {e}"
+             if hasattr(e, 'response') and e.response is not None: error_msg += f" (Status: {e.response.status_code})"
+             print(f"[Manager Preview {download_id}] {error_msg}")
+        except Exception as e: print(f"[Manager Preview {download_id}] Error saving thumbnail {preview_path}: {e}")
+        finally:
+            if response: response.close()
+
+    # --- _download_file_wrapper remains the same ---
+    def _download_file_wrapper(self, download_info: Dict[str, Any]):
+        """Wraps the download execution, handles status updates, exceptions, and metadata saving."""
+        # ... (no changes needed here) ...
+        download_id = download_info["id"]
+        filename = download_info.get('filename', download_id)
+        from .chunk_downloader import ChunkDownloader
+        downloader = None
+        success = False
+        final_status = "failed"
+        error_msg = None
+
+        try:
+            print(f"[Downloader Wrapper {download_id}] Preparing download for '{filename}'.")
+            
+            # Handle case where url is None (repo downloads)
+            url = download_info["url"]
+            if url is None:
+                print(f"[Downloader Wrapper {download_id}] URL is None, using huggingface_hub")
+                # For repo downloads or single files, use huggingface_hub
+                from huggingface_hub import snapshot_download, hf_hub_download
+                
+                # Extract just the repo_id from model_url_or_id (remove URL part)
+                model_url_or_id = download_info["model_url_or_id"]
+                if model_url_or_id.startswith("https://huggingface.co/"):
+                    # Extract repo_id and filename from URL
+                    parts = model_url_or_id.replace("https://huggingface.co/", "").split("/")
+                    model_id = f"{parts[0]}/{parts[1]}"  # Get "Kijai/WanVideo_comfy"
+                    
+                    # Check if it's a specific file or entire repo
+                    if len(parts) >= 4 and parts[2] == "resolve":
+                        # It's a specific file: /resolve/commit/path/to/file
+                        filename = "/".join(parts[4:])  # Get the file path after /resolve/commit/
+                        print(f"[Downloader Wrapper {download_id}] Downloading single file {filename} from repo {model_id}")
+                        
+                        # Use hf_hub_download for single file
+                        result = hf_hub_download(
+                            repo_id=model_id,
+                            filename=filename,
+                            local_dir=download_info["output_path"],
+                            token=download_info.get("api_key")
+                        )
+                    else:
+                        # It's an entire repo download
+                        print(f"[Downloader Wrapper {download_id}] Downloading entire repo {model_id}")
+                        result = snapshot_download(
+                            repo_id=model_id,
+                            local_dir=download_info["output_path"],
+                            token=download_info.get("api_key")
+                        )
+                else:
+                    # Plain repo_id - assume entire repo download
+                    model_id = model_url_or_id
+                    print(f"[Downloader Wrapper {download_id}] Downloading entire repo {model_id}")
+                    result = snapshot_download(
+                        repo_id=model_id,
+                        local_dir=download_info["output_path"],
+                        token=download_info.get("api_key")
+                    )
+                
+                if result:
+                    success = True
+                    final_status = "completed"
+                    print(f"[Downloader Wrapper {download_id}] Repo download completed: {result}")
+                else:
+                    raise Exception("snapshot_download failed")
+            else:
+                # Normal file download
+                downloader = ChunkDownloader(
+                    url=url,
+                    output_path=download_info["output_path"],
+                    num_connections=download_info.get("num_connections", DEFAULT_CONNECTIONS),
+                    manager=self,
+                    download_id=download_id,
+                    api_key=download_info.get("api_key"),
+                    known_size=download_info.get("known_size")
+                )
+
+            with self.lock:
+                  if download_id not in self.active_downloads or self.active_downloads[download_id]["status"] == "cancelled":
+                       print(f"[Downloader Wrapper {download_id}] Download was cancelled before instance could be fully linked/started.")
+                       self._update_download_status(download_id, status="cancelled", error="Cancelled before start")
+                       return
+
+                  # Only set downloader_instance for file downloads
+                  if url is not None:
+                      self.active_downloads[download_id]["downloader_instance"] = downloader
+
+            self._update_download_status(download_id, status="downloading")
+            print(f"[Downloader Wrapper {download_id}] Starting download process for '{filename}'.")
+            
+            # For repo downloads, success is already determined
+            if url is None:
+                # Repo download already completed above
+                print(f"[Downloader Wrapper {download_id}] Repo download already completed")
+            else:
+                # File download - use ChunkDownloader
+                success = downloader.download() # Blocking call
+                error_msg = downloader.error
+
+            if success:
+                final_status = "completed"
+                print(f"[Downloader Wrapper {download_id}] Download completed successfully for '{filename}'.")
+                try:
+                    self._save_huggingface_metadata(download_info)
+                    self._download_and_save_preview(download_info)
+                except Exception as meta_err:
+                     print(f"[Downloader Wrapper {download_id}] Error during post-download metadata/preview saving: {meta_err}")
+
+            elif downloader.is_cancelled:
+                final_status = "cancelled"
+                error_msg = downloader.error or "Download cancelled"
+                print(f"[Downloader Wrapper {download_id}] Download cancelled for '{filename}'. Reason: {error_msg}")
+            else:
+                final_status = "failed"
+                error_msg = downloader.error or "Download failed with unknown error"
+                print(f"[Downloader Wrapper {download_id}] Download failed for '{filename}'. Error: {error_msg}")
+
+        except Exception as e:
+            import traceback
+            print(f"--- Critical Error in Download Wrapper {download_id} ('{filename}') ---")
+            traceback.print_exc()
+            print("--- End Error ---")
+            final_status = "failed"
+            error_msg = f"Unexpected wrapper error: {str(e)}"
+            if downloader and not downloader.is_cancelled:
+                try: downloader.cancel()
+                except: pass
+
+        finally:
+            final_progress_percent = 0
+            conn_type = download_info.get("connection_type", "N/A")
+
+            if downloader:
+                 conn_type = downloader.connection_type
+                 if downloader.total_size and downloader.total_size > 0:
+                      final_progress_percent = (downloader.downloaded / downloader.total_size * 100)
+                 if final_status == "completed": final_progress_percent = 100.0
+                 final_progress_percent = min(100.0, max(0.0, final_progress_percent))
+
+            print(f"[Downloader Wrapper {download_id}] Finalizing status: {final_status}, Error: {error_msg}")
+            self._update_download_status(
+                download_id, status=final_status, progress=final_progress_percent,
+                speed=0, error=error_msg, connection_type=conn_type
+            )
+            if final_status == "completed":
+                 print(f"[Manager] Download {download_id} completed ('{filename}'). Manual ComfyUI refresh may be needed for model list.")
+
+    # --- NEW: Retry Download Method ---
+    def retry_download(self, original_download_id: str) -> Dict[str, Any]:
+        """Finds a failed/cancelled download in history and re-queues it."""
+        with self.lock:
+            # Find the original download info in history
+            original_info = next((item for item in self.history if item.get("id") == original_download_id), None)
+
+            if not original_info:
+                return {"success": False, "error": f"Original download ID '{original_download_id}' not found in history."}
+
+            original_status = original_info.get("status")
+            if original_status not in ["failed", "cancelled"]:
+                return {"success": False, "error": f"Cannot retry download with status '{original_status}'. Only 'failed' or 'cancelled' are retryable."}
+
+            # --- Prepare the new download info dictionary ---
+            # Make a deep copy to avoid modifying the history item directly
+            try:
+                retry_info = json.loads(json.dumps(original_info))
+                print(retry_info)
+            except Exception as e:
+                 return {"success": False, "error": f"Failed to copy original download data: {e}"}
+
+            # Remove fields specific to the *previous* attempt
+            retry_info.pop("id", None) # Will get a new ID
+            retry_info.pop("status", None)
+            retry_info.pop("progress", None)
+            retry_info.pop("speed", None)
+            retry_info.pop("error", None)
+            retry_info.pop("start_time", None)
+            retry_info.pop("end_time", None)
+            retry_info.pop("added_time", None)
+            retry_info.pop("connection_type", None)
+            retry_info.pop("downloader_instance", None) 
+            # --- Crucially: Set force_redownload to True for retry ---
+            # This ensures it overwrites the potentially corrupted/partial file from the previous attempt.
+            retry_info["force_redownload"] = True
+
+            # --- Validate required fields for queuing (redundant check, but safe) ---
+            required_for_retry = [
+                'url', 'output_path', 'num_connections', 'api_key', 'known_size',
+                'huggingface_model_info', 'huggingface_version_info', 'huggingface_primary_file',
+                'thumbnail', 'filename', 'model_url_or_id', 'model_version_id', 'model_type',
+                'custom_filename', 'force_redownload'
+            ]
+            missing_keys = [key for key in required_for_retry if key not in retry_info or retry_info[key] is None and key != 'api_key' and key != 'custom_filename'] # Allow api_key/custom_filename to be None
+            #if missing_keys:
+            #    return {"success": False, "error": f"Cannot retry: Original download data is missing required fields: {', '.join(missing_keys)}"}
+
+        # --- Add the prepared info to the queue (outside the lock for add_to_queue's own lock) ---
+        # Note: add_to_queue acquires its own lock internally
+        try:
+            new_download_id = self.add_to_queue(retry_info)
+            if new_download_id: # Check if add_to_queue returned a valid ID (indicating success)
+                with self.lock:
+                    original_len = len(self.history)
+                    # Filter out the item matching the original ID
+                    self.history = [item for item in self.history if item.get("id") != original_download_id]
+                    items_removed = original_len - len(self.history)
+
+                    if items_removed == 1:
+                        print(f"[Manager] Successfully removed original download '{original_download_id}' from history.")
+                        return {
+                            "success": True,
+                            "message": f"Retry initiated. New download queued. Original removed from history.",
+                            "new_download_id": new_download_id
+                        }
+
+            else:
+                # Should have been caught by the except block, but as a failsafe
+                print(f"[Manager] Retry queueing failed for '{original_download_id}' for an unknown reason (no ID returned).")
+                return {"success": False, "error": "Failed to queue retry (unknown internal error)."}
+        
+        except Exception as e:
+             print(f"[Manager] Error requeuing download for retry (Original ID: {original_download_id}): {e}")
+             return {"success": False, "error": f"Failed to queue retry: {e}"}
+
+    # --- NEW: Open Containing Folder Method ---
+    def open_containing_folder(self, download_id: str) -> Dict[str, Any]:
+        """Opens the directory containing the specified completed download file."""
+        file_path = None
+        with self.lock:
+            # Check history first (most likely location for completed items)
+            item_info = next((item for item in self.history if item.get("id") == download_id), None)
+            # Fallback to active (less likely, but possible if called very quickly after completion)
+            if not item_info and download_id in self.active_downloads:
+                 item_info = self.active_downloads.get(download_id)
+
+            if not item_info:
+                 return {"success": False, "error": "Download ID not found."}
+
+            if item_info.get("status") != "completed":
+                 return {"success": False, "error": f"Cannot open path for download with status '{item_info.get('status')}'. Must be 'completed'."}
+
+            file_path = item_info.get("output_path") # Get the full path to the file
+
+        # --- Perform file operations outside the lock ---
+        if not file_path:
+            return {"success": False, "error": "Output path not found for this download."}
+
+        # --- Security Check: Ensure the path is within expected ComfyUI directories ---
+        # (This is a basic check, might need refinement based on your setup)
+        is_safe_path = False
+        try:
+            # Get the absolute path
+            abs_file_path = os.path.abspath(file_path)
+            folder_path = os.path.dirname(abs_file_path)
+
+            # Option 1: Check against ComfyUI's known directories (preferred)
+            if COMFY_PATHS_AVAILABLE:
+                 # Check if the folder is within any known model type directory
+                 known_types = [
+                     "checkpoints", "loras", "vae", "embeddings", "hypernetworks",
+                     "controlnet", "upscale_models", "clip_vision", "gligen", "configs",
+                     "unet", "diffusers", "motion_models", "poses", "wildcards"
+                 ]
+                 known_dirs = [os.path.abspath(get_directory_by_type(t)) for t in known_types if get_directory_by_type(t)]
+                 # Also allow output and input directories
+                 if get_directory_by_type("output"): known_dirs.append(os.path.abspath(get_directory_by_type("output")))
+                 if get_directory_by_type("input"): known_dirs.append(os.path.abspath(get_directory_by_type("input")))
+                 # Add the plugin's own 'other_models' directory as safe
+                 known_dirs.append(os.path.abspath(os.path.join(PLUGIN_ROOT, "other_models")))
+                 # Add plugin-managed custom roots as safe
+                 try:
+                     import json as _json
+                     _roots_file = os.path.join(PLUGIN_ROOT, "custom_roots.json")
+                     if os.path.exists(_roots_file):
+                         with open(_roots_file, 'r', encoding='utf-8') as _f:
+                             _data = _json.load(_f)
+                             if isinstance(_data, dict):
+                                 for _lst in _data.values():
+                                     if isinstance(_lst, list):
+                                         for _p in _lst:
+                                             if isinstance(_p, str):
+                                                 known_dirs.append(os.path.abspath(_p))
+                 except Exception as _e:
+                     print(f"[Manager OpenPath] Warning: Failed to load custom roots: {_e}")
+                 # Include all first-level subdirectories under models_dir as safe
+                 try:
+                     models_dir = getattr(__import__('folder_paths'), 'folder_paths').models_dir
+                 except Exception:
+                     models_dir = None
+                 try:
+                     if models_dir and os.path.isdir(models_dir):
+                         for _name in os.listdir(models_dir):
+                             _p = os.path.join(models_dir, _name)
+                             if os.path.isdir(_p):
+                                 known_dirs.append(os.path.abspath(_p))
+                 except Exception as _e2:
+                     print(f"[Manager OpenPath] Warning: Failed enumerating models_dir subfolders: {_e2}")
+
+                 for known_dir in known_dirs:
+                      if os.path.commonpath([known_dir, folder_path]) == known_dir:
+                           is_safe_path = True
+                           break
+            else:
+                 # Option 2: Fallback - Check if path is within the ComfyUI base directory
+                 comfy_base = os.path.abspath(base_path)
+                 if os.path.commonpath([comfy_base, folder_path]) == comfy_base:
+                      is_safe_path = True
+                      #print(f"[Manager OpenPath Warning] ComfyUI paths unavailable. Using base path check for {folder_path}")
+
+            if not is_safe_path:
+                  print(f"[Manager OpenPath Denied] Path '{folder_path}' is outside known safe ComfyUI directories.")
+                  return {"success": False, "error": "Cannot open path: Directory is outside allowed locations."}
+
+            # --- Open the directory ---
+            if not os.path.exists(folder_path):
+                 return {"success": False, "error": f"Directory does not exist: {folder_path}"}
+            if not os.path.isdir(folder_path):
+                 return {"success": False, "error": f"Path is not a directory: {folder_path}"}
+
+            try:
+                 system = platform.system()
+                 print(f"[Manager OpenPath] Attempting to open folder '{folder_path}' on {system}...")
+                 if system == "Windows":
+                      # Use startfile for better handling of spaces etc.
+                      os.startfile(folder_path)
+                 elif system == "Darwin": # macOS
+                      subprocess.check_call(["open", folder_path])
+                 elif system == "Linux":
+                      # Use xdg-open, handle potential errors if command not found
+                      try:
+                            subprocess.check_call(["xdg-open", folder_path])
+                      except FileNotFoundError:
+                            # Fallback for headless systems or if xdg-open isn't installed
+                           print(f"[Manager OpenPath] 'xdg-open' not found. Cannot automatically open folder on this Linux system.")
+                           return {"success": False, "error": "'xdg-open' command not found. Cannot open folder."}
+                 else:
+                      print(f"[Manager OpenPath] Unsupported operating system: {system}. Cannot open folder.")
+                      return {"success": False, "error": f"Unsupported OS ({system}) for opening folder."}
+
+                 print(f"[Manager OpenPath] Successfully requested folder opening for '{folder_path}'.")
+                 return {"success": True, "message": f"Opened directory: {folder_path}"}
+
+            except Exception as e:
+                 print(f"[Manager OpenPath] Failed to open directory '{folder_path}': {e}")
+                 return {"success": False, "error": f"Failed to open directory: {e}"}
+
+        except Exception as path_err:
+            # Catch errors during path validation itself
+            print(f"[Manager OpenPath] Error during path validation/retrieval for {download_id}: {path_err}")
+            return {"success": False, "error": f"Error processing path: {path_err}"}
+
+# --- Global Instance ---
+manager = DownloadManager(max_concurrent=MAX_CONCURRENT_DOWNLOADS)
+
+# --- Graceful Shutdown ---
+# (shutdown_manager remains the same)
+def shutdown_manager():
+    # ... (no changes) ...
+    print("[Manager] Shutdown requested.")
+    if manager:
+        manager.running = False
+        acquired_lock = False
+        try: acquired_lock = manager.lock.acquire(timeout=1.0)
+        except RuntimeError: pass # Lock might not be initialised if init failed
+
+        if acquired_lock:
+             try:
+                 active_ids = list(manager.active_downloads.keys())
+                 queue_ids = [item['id'] for item in manager.queue]
+                 print(f"[Manager] Requesting cancellation for {len(active_ids)} active and {len(queue_ids)} queued downloads on shutdown...")
+                 all_ids_to_cancel = active_ids + queue_ids
+                 manager.lock.release() # Release lock BEFORE calling cancel_download
+
+                 for dl_id in all_ids_to_cancel:
+                      try:
+                          # Reuse cancel_download which handles both active and queued safely
+                          manager.cancel_download(dl_id)
+                      except Exception as e:
+                           print(f"Error cancelling {dl_id} during shutdown: {e}")
+             except Exception as e:
+                 print(f"[Manager] Error accessing lists during shutdown (after lock acquired): {e}")
+                 try: manager.lock.release() # Ensure release on error
+                 except RuntimeError: pass
+             # Give threads/tasks a brief moment to react
+             time.sleep(0.5)
+        else:
+            print("[Manager] Warning: Could not acquire lock to cancel downloads during shutdown.")
+
+        # Attempt to join the manager's process thread (best effort)
+        try:
+            if manager._process_thread and manager._process_thread.is_alive():
+                 manager._process_thread.join(timeout=2.0)
+                 if manager._process_thread.is_alive():
+                      print("[Manager] Warning: Process thread did not exit cleanly within timeout.")
+        except Exception as e:
+            print(f"[Manager] Error joining manager thread during shutdown: {e}")
+    print("[Manager] Shutdown complete.")
+
+import atexit
+atexit.register(shutdown_manager)
+
+# Create the global manager instance
+manager = DownloadManager()
diff --git a/custom_nodes/ComfyUI-HuggingFace/server/__init__.py b/custom_nodes/ComfyUI-HuggingFace/server/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..406a5495761804a3421442a7c1ad954bb002fec1
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/server/__init__.py
@@ -0,0 +1,5 @@
+# ================================================
+# File: server/__init__.py
+# ================================================
+# Import the routes package to ensure all route decorators are executed.
+from . import routes
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-HuggingFace/server/routes/CancelDownload.py b/custom_nodes/ComfyUI-HuggingFace/server/routes/CancelDownload.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c139e2781b498cd27e011608f2caa5741d40daa
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/server/routes/CancelDownload.py
@@ -0,0 +1,44 @@
+# ================================================
+# File: server/routes/CancelDownload.py
+# ================================================
+import json
+from aiohttp import web
+import server # ComfyUI server instance
+from ..utils import get_request_json
+from ...downloader.manager import manager as download_manager
+
+prompt_server = server.PromptServer.instance
+
+@prompt_server.routes.post("/api/huggingface/cancel")
+async def route_cancel_download(request):
+    """API Endpoint to cancel a download."""
+    try:
+        data = await get_request_json(request)
+        download_id = data.get("download_id")
+        if not download_id:
+            print("not download id " + download_id)
+            raise web.HTTPBadRequest(reason="Missing 'download_id'")
+
+        success = download_manager.cancel_download(download_id)
+        if success:
+            return web.json_response({
+                "status": "cancelled", # Or "cancellation_requested" ?
+                "message": f"Cancellation requested for download ID: {download_id}.",
+                "download_id": download_id
+            })
+        else:
+            # Might be already completed/failed/cancelled and in history, or invalid ID
+            raise web.HTTPNotFound(reason=f"Download ID {download_id} not found in active queue or running downloads.")
+
+    except web.HTTPError as http_err:
+         # Consistent error handling
+         body_detail = ""
+         try:
+              body_detail = await http_err.text() if hasattr(http_err, 'text') else http_err.body.decode('utf-8', errors='ignore') if http_err.body else ""
+              if body_detail.startswith('{') and body_detail.endswith('}'): body_detail = json.loads(body_detail)
+         except Exception: pass
+         return web.json_response({"error": http_err.reason, "details": body_detail or "No details", "status_code": http_err.status}, status=http_err.status)
+
+    except Exception as e:
+        print(f"Error cancelling download: {e}")
+        return web.json_response({"error": "Internal Server Error", "details": f"Failed to cancel download: {str(e)}", "status_code": 500}, status=500)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-HuggingFace/server/routes/ClearHistory.py b/custom_nodes/ComfyUI-HuggingFace/server/routes/ClearHistory.py
new file mode 100644
index 0000000000000000000000000000000000000000..e02708f8a9832b71553a7fb00e5c68b423e8f472
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/server/routes/ClearHistory.py
@@ -0,0 +1,32 @@
+# ================================================
+# File: server/routes/ClearHistory.py
+# ================================================
+import asyncio
+from aiohttp import web
+
+import server # ComfyUI server instance
+from ...downloader.manager import manager as download_manager
+
+prompt_server = server.PromptServer.instance
+
+@prompt_server.routes.post("/api/huggingface/clear_history")
+async def route_clear_history(request):
+    """API Endpoint to clear the download history."""
+    if not download_manager:
+        return web.json_response({"error": "Download Manager not initialized"}, status=500)
+
+    try:
+        # No request body needed for this action
+        print(f"[API Route /huggingface/clear_history] Received clear history request.")
+
+        # Call manager method in thread
+        result = await asyncio.to_thread(download_manager.clear_history)
+
+        status_code = 200 if result.get("success") else 500 # Use 500 for internal clear error
+        return web.json_response(result, status=status_code)
+
+    except Exception as e:
+        import traceback
+        print(f"Error handling /huggingface/clear_history request: {e}")
+        # traceback.print_exc() # Uncomment for detailed logs
+        return web.json_response({"error": "Internal Server Error", "details": f"An unexpected error occurred: {str(e)}"}, status=500)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-HuggingFace/server/routes/DownloadModel.py b/custom_nodes/ComfyUI-HuggingFace/server/routes/DownloadModel.py
new file mode 100644
index 0000000000000000000000000000000000000000..90a24b8bb3667e36b02dbffa8ed71e37ddbf64e4
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/server/routes/DownloadModel.py
@@ -0,0 +1,186 @@
+# ================================================
+# File: server/routes/DownloadModel.py
+# ================================================
+import os
+import json
+import traceback
+import re
+from aiohttp import web
+
+import server # ComfyUI server instance
+from ..utils import get_request_json, resolve_huggingface_api_key
+from ...downloader.manager import manager as download_manager
+from ...api.huggingface import HuggingFaceAPI
+from ...utils.helpers import get_model_dir, parse_huggingface_input, sanitize_filename
+from ...config import METADATA_SUFFIX, PREVIEW_SUFFIX
+
+prompt_server = server.PromptServer.instance
+
+@prompt_server.routes.post("/api/huggingface/download")
+async def route_download_model(request):
+    """API Endpoint to initiate a download."""
+    try:
+        data = await get_request_json(request)
+
+        model_url_or_id = data.get("model_url_or_id")
+        model_type_value = data.get("model_type", "checkpoint")
+        explicit_save_root = (data.get("save_root") or "").strip()
+        custom_filename_input = data.get("custom_filename", "").strip()
+        selected_subdir = (data.get("subdir") or "").strip()
+        num_connections = int(data.get("num_connections", 4))
+        force_redownload = bool(data.get("force_redownload", False))
+        resolved_api_key = resolve_huggingface_api_key(data)
+
+        if not model_url_or_id:
+            raise web.HTTPBadRequest(reason="Missing 'model_url_or_id'")
+
+        print(f"[HF Download] Request: {model_url_or_id}, SaveType: {model_type_value}")
+        
+        # Parse HuggingFace URL/ID
+        parsed_model_id, parsed_filename = parse_huggingface_input(model_url_or_id)
+        
+        if not parsed_model_id:
+            raise web.HTTPBadRequest(reason=f"Could not parse HuggingFace model ID from: {model_url_or_id}")
+        
+        target_model_id = parsed_model_id
+        print(f"[HF Download] Parsed Model ID: {target_model_id}")
+        
+        # Initialize API
+        api = HuggingFaceAPI(resolved_api_key)
+        
+        if parsed_filename:
+            # Direct download from URL - skip API calls
+            target_filename = parsed_filename
+            # Try to get model name from the model_id itself
+            model_name = target_model_id.split('/')[-1] if target_model_id else "Unknown Model"
+            model_info = {"id": target_model_id, "name": model_name}
+            print(f"[HF Download] Direct download file: {target_filename}")
+            print(f"[HF Download] Using extracted model name: {model_name}")
+        else:
+            # Skip API calls for public repos, use only huggingface_hub
+            if resolved_api_key:
+                # For private repos, try API calls to get model info
+                api = HuggingFaceAPI(resolved_api_key)
+                model_info = api.get_model_info(target_model_id)
+                
+                if not model_info or "error" in model_info:
+                    print(f"[HF Download] Model info failed, using huggingface_hub directly")
+                    target_filename = parsed_filename if parsed_filename else None
+                    model_info = {"id": target_model_id, "name": target_model_id.split('/')[-1]}
+                else:
+                    # For private repos, still use huggingface_hub for download
+                    target_filename = parsed_filename if parsed_filename else None
+            else:
+                # For public repos, skip API calls entirely
+                print(f"[HF Download] Public repo, using huggingface_hub directly")
+                target_filename = parsed_filename if parsed_filename else None
+                model_info = {"id": target_model_id, "name": target_model_id.split('/')[-1]}
+        
+        if not target_filename:
+            print(f"[HF Download] No specific file found, letting huggingface_hub auto-detect")
+            target_filename = None
+        
+        print(f"[HF Download] Target file: {target_filename}")
+
+        # Determine save directory
+        target_dir = get_model_dir(model_type_value, explicit_save_root, selected_subdir)
+        if not target_dir:
+            raise web.HTTPBadRequest(reason=f"Invalid model type: {model_type_value}")
+
+        # Determine filename
+        if custom_filename_input:
+            final_filename = sanitize_filename(custom_filename_input)
+        elif target_filename is None:
+            # For repo downloads, use model name as folder
+            final_filename = model_info.get("name", target_model_id.split('/')[-1])
+        else:
+            final_filename = os.path.basename(target_filename)
+        
+        save_path = os.path.join(target_dir, final_filename)
+
+        # Check if file exists
+        if os.path.exists(save_path) and not force_redownload:
+            raise web.HTTPBadRequest(reason=f"File already exists: {final_filename}")
+
+        # Start download
+        if target_filename is None:
+            # For repo downloads, don't construct URL - let huggingface_hub handle it
+            download_url = None
+        else:
+            download_url = f"https://huggingface.co/{target_model_id}/resolve/main/{target_filename}"
+        
+        download_info = {
+            "model_url_or_id": model_url_or_id,
+            "save_path": save_path,
+            "output_path": save_path,  # Add this for ChunkDownloader
+            "url": download_url,  # Add this for ChunkDownloader
+            "filename": final_filename,
+            "model_type": model_type_value,
+            "download_url": download_url,
+            "huggingface_model_info": model_info,
+            "huggingface_filename": target_filename,
+            "num_connections": num_connections,
+            "force_redownload": force_redownload,
+            # Add missing fields to prevent warnings
+            "api_key": resolved_api_key,
+            "known_size": None,
+            "huggingface_version_info": {},
+            "huggingface_primary_file": None,
+            "thumbnail": None,
+            "model_version_id": None,
+            "custom_filename": custom_filename_input,
+            "huggingface_model_name": model_info.get("name", target_model_id.split('/')[-1])
+        }
+
+        download_id = download_manager.add_to_queue(download_info)
+        
+        # Extract model name from model_info if available, otherwise from model_id
+        print(f"[DEBUG] model_info: {model_info}")
+        print(f"[DEBUG] target_model_id: {target_model_id}")
+        
+        # Try to parse model_info as JSON if it's a string
+        parsed_model_info = None
+        if isinstance(model_info, str):
+            try:
+                import json
+                parsed_model_info = json.loads(model_info)
+                print(f"[DEBUG] Parsed model_info from JSON: {parsed_model_info}")
+            except:
+                print(f"[DEBUG] Failed to parse model_info as JSON")
+                parsed_model_info = None
+        else:
+            parsed_model_info = model_info
+        
+        if parsed_model_info and isinstance(parsed_model_info, dict) and parsed_model_info.get('name'):
+            model_display_name = parsed_model_info['name']
+            print(f"[DEBUG] Using parsed model_info name: {model_display_name}")
+        elif model_info and isinstance(model_info, dict) and model_info.get('name'):
+            model_display_name = model_info['name']
+            print(f"[DEBUG] Using model_info name: {model_display_name}")
+        else:
+            model_display_name = target_model_id.split('/')[-1] if target_model_id else "Unknown Model"
+            print(f"[DEBUG] Using parsed name: {model_display_name}")
+        
+        print(f"[DEBUG] Final model_display_name: {model_display_name}")
+        
+        response_data = {
+            "download_id": download_id,
+            "huggingface_model_id": target_model_id,
+            "huggingface_model_name": model_display_name,  # Add model name
+            "huggingface_filename": target_filename,
+            "huggingface_model_info": model_info,
+            "save_path": save_path,
+            "filename": final_filename,
+            "status": "queued"  # Changed from "started" to "queued" to match frontend expectation
+        }
+        
+        print(f"[DEBUG] Response data huggingface_model_name: {response_data['huggingface_model_name']}")
+
+        return web.json_response(response_data)
+
+    except web.HTTPException:
+        raise
+    except Exception as e:
+        print(f"--- Unhandled Error in /huggingface/download ---")
+        traceback.print_exc()
+        raise web.HTTPInternalServerError(reason=str(e))
diff --git a/custom_nodes/ComfyUI-HuggingFace/server/routes/GetBaseModels.py b/custom_nodes/ComfyUI-HuggingFace/server/routes/GetBaseModels.py
new file mode 100644
index 0000000000000000000000000000000000000000..1937cb5995a5cfdd0f47046663d0b980e35fc6b5
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/server/routes/GetBaseModels.py
@@ -0,0 +1,51 @@
+# ================================================
+# File: server/routes/GetBaseModels.py
+# ================================================
+from aiohttp import web
+import yaml
+import re
+import server # ComfyUI server instance
+from ...config import AVAILABLE_MEILI_BASE_MODELS
+from ...api.huggingface import HuggingFaceAPI
+from ..utils import resolve_huggingface_api_key
+
+prompt_server = server.PromptServer.instance
+
+@prompt_server.routes.get("/api/huggingface/base_models")
+async def route_get_base_models(request):
+    """API Endpoint to get the known base model types for filtering."""
+    try:
+        # Try to get base models from HuggingFace API
+        api_key = resolve_huggingface_api_key({})
+        api = HuggingFaceAPI(api_key)
+        
+        # Get popular models and extract their base models
+        popular_models = [
+            "stabilityai/stable-diffusion-xl-base-1.0",
+            "runwayml/stable-diffusion-v1-5",
+            "stabilityai/stable-diffusion-2-1",
+            "CompVis/stable-diffusion-v1-4",
+            "black-forest-labs/FLUX.1-dev",
+            "black-forest-labs/FLUX.1-schnell"
+        ]
+        
+        base_models = set()
+        base_models.update(AVAILABLE_MEILI_BASE_MODELS)  # Keep hardcoded ones as fallback
+        
+        for model_id in popular_models:
+            try:
+                model_info = api.get_model_info(model_id)
+                if model_info and not isinstance(model_info, dict) or "error" not in model_info:
+                    # Add the model itself as a base model
+                    base_models.add(model_id)
+            except Exception as e:
+                print(f"[GetBaseModels] Error getting info for {model_id}: {e}")
+                continue
+        
+        return web.json_response({"base_models": sorted(list(base_models))})
+        
+    except Exception as e:
+        print(f"Error getting base model types: {e}")
+        # Fallback to hardcoded list
+        return web.json_response({"base_models": AVAILABLE_MEILI_BASE_MODELS})
+        return web.json_response({"error": "Internal Server Error", "details": str(e), "status_code": 500}, status=500)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-HuggingFace/server/routes/GetModelDetails.py b/custom_nodes/ComfyUI-HuggingFace/server/routes/GetModelDetails.py
new file mode 100644
index 0000000000000000000000000000000000000000..50429cbfd0e43fe8d7837c0b0e789486508a7faf
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/server/routes/GetModelDetails.py
@@ -0,0 +1,120 @@
+# ================================================
+# File: server/routes/GetModelDetails.py
+# ================================================
+import os
+import json
+import traceback
+from aiohttp import web
+
+import server # ComfyUI server instance
+from ..utils import get_request_json, resolve_huggingface_api_key
+from ...api.huggingface import HuggingFaceAPI
+
+prompt_server = server.PromptServer.instance
+
+@prompt_server.routes.post("/api/huggingface/get_model_details")
+async def route_get_model_details(request):
+    """API Endpoint to fetch model details from HuggingFace model card."""
+    try:
+        data = await get_request_json(request)
+        model_url_or_id = data.get("model_url_or_id")
+        resolved_api_key = resolve_huggingface_api_key(data)
+
+        if not model_url_or_id:
+            raise web.HTTPBadRequest(reason="Missing 'model_url_or_id'")
+
+        # Parse model ID
+        from ...utils.helpers import parse_huggingface_input
+        parsed_model_id, parsed_filename = parse_huggingface_input(model_url_or_id)
+        
+        if not parsed_model_id:
+            raise web.HTTPBadRequest(reason=f"Could not parse HuggingFace model ID from: {model_url_or_id}")
+
+        # Get model info using huggingface_hub
+        try:
+            from huggingface_hub import ModelCard
+            model_card = ModelCard.load(parsed_model_id, token=resolved_api_key)
+            
+            # Extract data from model card
+            model_name = model_card.data.get("model_name", parsed_model_id.split('/')[-1])
+            creator_username = model_card.data.get("author", "Unknown Creator")
+            description = model_card.text
+            base_model = model_card.data.get("base_model", [])
+            tags = model_card.data.get("tags", [])
+            license_info = model_card.data.get("license", "Unknown")
+            
+            # Get model info from API as fallback for stats
+            try:
+                api = HuggingFaceAPI(resolved_api_key)
+                api_info = api.get_model_info(parsed_model_id)
+                
+                stats = {}
+                if api_info and not isinstance(api_info, dict) or "error" not in api_info:
+                    stats = {
+                        "downloads": api_info.get("downloads", 0),
+                        "likes": api_info.get("likes", 0),
+                        "created_at": api_info.get("created_at", ""),
+                        "modified_at": api_info.get("modified_at", "")
+                    }
+            except Exception as api_error:
+                print(f"[GetModelDetails] API info failed, using model card only: {api_error}")
+                stats = {}
+            
+            response_data = {
+                "model_name": model_name,
+                "creator_username": creator_username,
+                "description": description,
+                "base_model": base_model,
+                "tags": tags,
+                "license": license_info,
+                "stats": stats,
+                "model_id": parsed_model_id,
+                "huggingface_model_name": model_name
+            }
+            
+            return web.json_response(response_data)
+            
+        except Exception as e:
+            print(f"[GetModelDetails] Error loading model card: {e}")
+            # Try to get at least basic info from API
+            try:
+                api = HuggingFaceAPI(resolved_api_key)
+                api_info = api.get_model_info(parsed_model_id)
+                
+                if api_info and not isinstance(api_info, dict) or "error" not in api_info:
+                    return web.json_response({
+                        "model_name": api_info.get("id", parsed_model_id.split('/')[-1]),
+                        "creator_username": api_info.get("author", "Unknown Creator"),
+                        "description": api_info.get("description", "No description available"),
+                        "base_model": [],
+                        "tags": api_info.get("tags", []),
+                        "license": api_info.get("license", "Unknown"),
+                        "stats": {
+                            "downloads": api_info.get("downloads", 0),
+                            "likes": api_info.get("likes", 0),
+                            "created_at": api_info.get("created_at", ""),
+                            "modified_at": api_info.get("modified_at", "")
+                        },
+                        "model_id": parsed_model_id,
+                        "huggingface_model_name": api_info.get("id", parsed_model_id.split('/')[-1])
+                    })
+            except Exception as api_error:
+                print(f"[GetModelDetails] API fallback also failed: {api_error}")
+            
+            # Final fallback to minimal info
+            return web.json_response({
+                "model_name": parsed_model_id.split('/')[-1],
+                "creator_username": "Unknown",
+                "description": "Model details not available",
+                "base_model": [],
+                "tags": [],
+                "license": "Unknown",
+                "stats": {},
+                "model_id": parsed_model_id,
+                "huggingface_model_name": parsed_model_id.split('/')[-1]
+            })
+            
+    except Exception as e:
+        print(f"Error in get_model_details: {e}")
+        traceback.print_exc()
+        return web.json_response({"error": "Internal Server Error", "details": str(e)}, status=500)
diff --git a/custom_nodes/ComfyUI-HuggingFace/server/routes/GetModelDirs.py b/custom_nodes/ComfyUI-HuggingFace/server/routes/GetModelDirs.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c4e1223876e4b69d34dae955f6d8ae388d77250
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/server/routes/GetModelDirs.py
@@ -0,0 +1,337 @@
+# ================================================
+# File: server/routes/GetModelDirs.py
+# ================================================
+import os
+import json
+from aiohttp import web
+
+import server  # ComfyUI server instance
+from ...utils.helpers import (
+    get_model_dir,
+    get_model_folder_paths,
+    get_model_type_folder_name,
+    sanitize_filename,
+)
+from ...config import PLUGIN_ROOT
+import folder_paths
+
+prompt_server = server.PromptServer.instance
+
+CUSTOM_ROOTS_FILE = os.path.join(PLUGIN_ROOT, "custom_roots.json")
+ROOT_SETTINGS_FILE = os.path.join(PLUGIN_ROOT, "root_settings.json")
+GLOBAL_ROOT_KEY = "global_default_root"
+
+def _load_custom_roots():
+    try:
+        if os.path.exists(CUSTOM_ROOTS_FILE):
+            with open(CUSTOM_ROOTS_FILE, 'r', encoding='utf-8') as f:
+                data = json.load(f)
+                if isinstance(data, dict):
+                    # Normalize values to lists of strings
+                    return {k: [str(p) for p in (v or []) if isinstance(p, str)] for k, v in data.items()}
+    except Exception as e:
+        print(f"[HuggingFace] Warning: Failed to load custom roots: {e}")
+    return {}
+
+def _save_custom_roots(data):
+    try:
+        with open(CUSTOM_ROOTS_FILE, 'w', encoding='utf-8') as f:
+            json.dump(data, f, indent=2)
+        return True
+    except Exception as e:
+        print(f"[HuggingFace] Error writing custom roots file: {e}")
+        return False
+
+def _load_root_settings():
+    try:
+        if os.path.exists(ROOT_SETTINGS_FILE):
+            with open(ROOT_SETTINGS_FILE, 'r', encoding='utf-8') as f:
+                data = json.load(f)
+                if isinstance(data, dict):
+                    return data
+    except Exception as e:
+        print(f"[HuggingFace] Warning: Failed to load root settings: {e}")
+    return {}
+
+def _save_root_settings(data):
+    try:
+        with open(ROOT_SETTINGS_FILE, 'w', encoding='utf-8') as f:
+            json.dump(data, f, indent=2)
+        return True
+    except Exception as e:
+        print(f"[HuggingFace] Error writing root settings file: {e}")
+        return False
+
+def get_global_default_root():
+    settings = _load_root_settings()
+    raw = settings.get(GLOBAL_ROOT_KEY)
+    if isinstance(raw, str):
+        raw = raw.strip()
+        if raw:
+            return os.path.abspath(raw)
+    return None
+
+def _set_global_default_root(path):
+    settings = _load_root_settings()
+    if path and str(path).strip():
+        settings[GLOBAL_ROOT_KEY] = os.path.abspath(str(path).strip())
+    else:
+        settings.pop(GLOBAL_ROOT_KEY, None)
+    return _save_root_settings(settings)
+
+def _get_global_root_for_type(model_type: str):
+    global_root = get_global_default_root()
+    if not global_root:
+        return None
+    model_subfolder = get_model_type_folder_name(model_type)
+    return os.path.abspath(os.path.join(global_root, model_subfolder))
+
+def _get_effective_base_dir(model_type: str):
+    global_type_root = _get_global_root_for_type(model_type)
+    if global_type_root:
+        return global_type_root
+    return get_model_dir(model_type)
+
+def _get_custom_roots_for_type(model_type: str):
+    roots_map = _load_custom_roots()
+    model_type_raw = (model_type or '').lower().strip()
+    canonical_type = get_model_type_folder_name(model_type_raw)
+    custom = []
+    for key in [model_type_raw, canonical_type]:
+        for p in roots_map.get(key, []):
+            ap = os.path.abspath(p)
+            if ap not in custom:
+                custom.append(ap)
+    return custom
+
+def _get_all_roots_for_type(model_type: str):
+    model_type = (model_type or '').lower().strip()
+    roots = []
+
+    # Prefer global default root for this type when configured
+    global_type_root = _get_global_root_for_type(model_type)
+    if global_type_root and global_type_root not in roots:
+        roots.append(global_type_root)
+
+    # Include all registered paths from ComfyUI (respects extra_model_paths.yaml)
+    for p in get_model_folder_paths(model_type):
+        if p not in roots:
+            roots.append(p)
+
+    # Include plugin custom roots
+    for p in _get_custom_roots_for_type(model_type):
+        if p not in roots:
+            roots.append(p)
+
+    # Ensure at least one sane fallback
+    if not roots:
+        d = get_model_dir(model_type)
+        if d:
+            roots.append(os.path.abspath(d))
+
+    # Include all immediate subdirectories inside the main ComfyUI models folder
+    try:
+        models_dir = getattr(folder_paths, 'models_dir', None)
+        if not models_dir:
+            base = getattr(folder_paths, 'base_path', os.getcwd())
+            models_dir = os.path.join(base, 'models')
+        if os.path.isdir(models_dir):
+            for name in os.listdir(models_dir):
+                p = os.path.join(models_dir, name)
+                if os.path.isdir(p):
+                    ap = os.path.abspath(p)
+                    if ap not in roots:
+                        roots.append(ap)
+    except Exception as e:
+        print(f"[HuggingFace] Warning: Failed to enumerate models dir subfolders: {e}")
+    return roots
+
+def _list_subdirs(root_dir: str, max_entries: int = 5000):
+    """Return a sorted list of relative subdirectory paths under root_dir, including nested."""
+    rel_dirs = set()
+    root_dir = os.path.abspath(root_dir)
+    count = 0
+    for current, dirs, _files in os.walk(root_dir):
+        # Avoid following symlinks to reduce risk
+        abs_current = os.path.abspath(current)
+        try:
+            rel = os.path.relpath(abs_current, root_dir)
+        except Exception:
+            continue
+        if rel == ".":
+            rel = ""  # represent root as empty
+        rel_dirs.add(rel)
+        count += 1
+        if count >= max_entries:
+            break
+    return sorted(rel_dirs)
+
+@prompt_server.routes.get("/api/huggingface/model_dirs")
+async def route_get_model_dirs(request):
+    """List the base directory (or provided root) and all subdirectories for a given model type."""
+    model_type = request.query.get("type", "checkpoints").lower().strip()
+    root = (request.query.get("root") or "").strip()
+    try:
+        base_dir = os.path.abspath(root) if root else _get_effective_base_dir(model_type)
+        os.makedirs(base_dir, exist_ok=True)
+        subdirs = _list_subdirs(base_dir)
+        global_root = get_global_default_root()
+        return web.json_response({
+            "model_type": model_type,
+            "base_dir": base_dir,
+            "subdirs": subdirs,  # relative paths, "" represents the base root
+            "global_root": global_root or "",
+            "using_global_root": bool(global_root and not root),
+        })
+    except Exception as e:
+        return web.json_response({"error": "Failed to list directories", "details": str(e)}, status=500)
+
+@prompt_server.routes.post("/api/huggingface/create_dir")
+async def route_create_model_dir(request):
+    """Create a new subdirectory under a model type's base directory."""
+    try:
+        data = await request.json()
+        model_type = (data.get("model_type") or "checkpoints").lower().strip()
+        new_dir = (data.get("new_dir") or "").strip()
+        if not new_dir:
+            return web.json_response({"error": "Missing 'new_dir'"}, status=400)
+
+        # If client provided an explicit root, prefer it
+        base_dir = (data.get("root") or "").strip()
+        base_dir = os.path.abspath(base_dir) if base_dir else _get_effective_base_dir(model_type)
+        os.makedirs(base_dir, exist_ok=True)
+
+        # Normalize and sanitize each part; disallow absolute and traversal
+        norm = os.path.normpath(new_dir.replace("\\", "/"))
+        parts = [p for p in norm.split("/") if p and p not in (".", "..")]
+        safe_parts = [sanitize_filename(p) for p in parts]
+        rel_path = os.path.join(*safe_parts) if safe_parts else ""
+        if not rel_path:
+            return web.json_response({"error": "Invalid folder name"}, status=400)
+
+        abs_path = os.path.abspath(os.path.join(base_dir, rel_path))
+        # Ensure it remains inside base_dir
+        if os.path.commonpath([abs_path, os.path.abspath(base_dir)]) != os.path.abspath(base_dir):
+            return web.json_response({"error": "Invalid path"}, status=400)
+
+        os.makedirs(abs_path, exist_ok=True)
+        return web.json_response({
+            "success": True,
+            "created": rel_path,
+            "abs_path": abs_path,
+        })
+    except Exception as e:
+        return web.json_response({"error": "Failed to create directory", "details": str(e)}, status=500)
+
+@prompt_server.routes.post("/api/huggingface/create_model_type")
+async def route_create_model_type(request):
+    """Create a new first-level folder under the main models directory."""
+    try:
+        data = await request.json()
+        name = (data.get("name") or "").strip()
+        if not name:
+            return web.json_response({"error": "Missing 'name'"}, status=400)
+
+        # Sanitize folder name to a single path component
+        from ...utils.helpers import sanitize_filename
+        safe = sanitize_filename(name)
+        if not safe:
+            return web.json_response({"error": "Invalid folder name"}, status=400)
+
+        # Resolve models directory
+        models_dir = getattr(folder_paths, 'models_dir', None)
+        if not models_dir:
+            base = getattr(folder_paths, 'base_path', os.getcwd())
+            models_dir = os.path.join(base, 'models')
+
+        abs_path = os.path.abspath(os.path.join(models_dir, safe))
+        # Ensure it remains inside models_dir
+        if os.path.commonpath([abs_path, os.path.abspath(models_dir)]) != os.path.abspath(models_dir):
+            return web.json_response({"error": "Invalid path"}, status=400)
+
+        os.makedirs(abs_path, exist_ok=True)
+        return web.json_response({"success": True, "name": safe, "path": abs_path})
+    except Exception as e:
+        return web.json_response({"error": "Failed to create model type folder", "details": str(e)}, status=500)
+
+@prompt_server.routes.get("/api/huggingface/model_roots")
+async def route_get_model_roots(request):
+    """Return all known root directories for a model type (ComfyUI + plugin custom roots)."""
+    model_type = request.query.get("type", "checkpoints").lower().strip()
+    roots = _get_all_roots_for_type(model_type)
+    global_root = get_global_default_root()
+    return web.json_response({
+        "model_type": model_type,
+        "roots": roots,
+        "global_root": global_root or "",
+        "effective_root": _get_effective_base_dir(model_type),
+    })
+
+@prompt_server.routes.post("/api/huggingface/create_root")
+async def route_create_model_root(request):
+    """Create a new root directory for a model type and register it in plugin config.
+       Note: ComfyUI may require restart to recognize this root globally; the plugin uses it immediately.
+    """
+    try:
+        data = await request.json()
+        model_type = (data.get("model_type") or "checkpoints").lower().strip()
+        abs_path = os.path.expanduser((data.get("path") or "").strip())
+        if not abs_path:
+            return web.json_response({"error": "Missing 'path'"}, status=400)
+        if not os.path.isabs(abs_path):
+            return web.json_response({"error": "Path must be absolute"}, status=400)
+        # Normalize to absolute path
+        abs_path = os.path.abspath(abs_path)
+        # Create directory if missing
+        os.makedirs(abs_path, exist_ok=True)
+
+        type_key = get_model_type_folder_name(model_type)
+        roots = _load_custom_roots()
+        current = roots.get(type_key, [])
+        if abs_path not in current:
+            current.append(abs_path)
+            roots[type_key] = current
+            if not _save_custom_roots(roots):
+                return web.json_response({"error": "Failed to persist custom root"}, status=500)
+        return web.json_response({"success": True, "path": abs_path})
+    except Exception as e:
+        return web.json_response({"error": "Failed to create root", "details": str(e)}, status=500)
+
+@prompt_server.routes.get("/api/huggingface/global_root")
+async def route_get_global_root(request):
+    """Return the persisted global download root (if configured)."""
+    global_root = get_global_default_root()
+    return web.json_response({
+        "global_root": global_root or "",
+        "enabled": bool(global_root),
+    })
+
+@prompt_server.routes.post("/api/huggingface/global_root")
+async def route_set_global_root(request):
+    """Persist a global download root used as: <global_root>/<model_type_folder>."""
+    try:
+        data = await request.json()
+        path = os.path.expanduser((data.get("path") or "").strip())
+        if not path:
+            return web.json_response({"error": "Missing 'path'"}, status=400)
+        if not os.path.isabs(path):
+            return web.json_response({"error": "Path must be absolute"}, status=400)
+
+        abs_path = os.path.abspath(path)
+        os.makedirs(abs_path, exist_ok=True)
+        if not _set_global_default_root(abs_path):
+            return web.json_response({"error": "Failed to persist global root"}, status=500)
+
+        return web.json_response({"success": True, "global_root": abs_path})
+    except Exception as e:
+        return web.json_response({"error": "Failed to set global root", "details": str(e)}, status=500)
+
+@prompt_server.routes.post("/api/huggingface/global_root/clear")
+async def route_clear_global_root(request):
+    """Clear the persisted global download root."""
+    try:
+        if not _set_global_default_root(None):
+            return web.json_response({"error": "Failed to clear global root"}, status=500)
+        return web.json_response({"success": True})
+    except Exception as e:
+        return web.json_response({"error": "Failed to clear global root", "details": str(e)}, status=500)
diff --git a/custom_nodes/ComfyUI-HuggingFace/server/routes/GetModelTypes.py b/custom_nodes/ComfyUI-HuggingFace/server/routes/GetModelTypes.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b4fc67f37ec2aea5670d21169cf88abaaac49de
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/server/routes/GetModelTypes.py
@@ -0,0 +1,31 @@
+# ================================================
+# File: server/routes/GetModelTypes.py
+# ================================================
+import os
+from aiohttp import web
+import server # ComfyUI server instance
+import folder_paths
+
+prompt_server = server.PromptServer.instance
+
+@prompt_server.routes.get("/api/huggingface/model_types")
+async def route_get_model_types(request):
+    """API Endpoint to get the known model types and their mapping."""
+    try:
+        # Dynamically list all first-level folders under the main models directory
+        models_dir = getattr(folder_paths, 'models_dir', None)
+        if not models_dir:
+            base = getattr(folder_paths, 'base_path', os.getcwd())
+            models_dir = os.path.join(base, 'models')
+        if not os.path.isdir(models_dir):
+            return web.json_response({})
+
+        entries = {}
+        for name in sorted(os.listdir(models_dir)):
+            p = os.path.join(models_dir, name)
+            if os.path.isdir(p):
+                entries[name] = name
+        return web.json_response(entries)
+    except Exception as e:
+        print(f"Error getting model types: {e}")
+        return web.json_response({"error": "Internal Server Error", "details": str(e), "status_code": 500}, status=500)
diff --git a/custom_nodes/ComfyUI-HuggingFace/server/routes/GetStatus.py b/custom_nodes/ComfyUI-HuggingFace/server/routes/GetStatus.py
new file mode 100644
index 0000000000000000000000000000000000000000..35e88d973e1598c23ce03f1bf7a1b86cd8b36188
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/server/routes/GetStatus.py
@@ -0,0 +1,19 @@
+# ================================================
+# File: server/routes/GetStatus.py
+# ================================================
+from aiohttp import web
+import server # ComfyUI server instance
+from ...downloader.manager import manager as download_manager
+
+prompt_server = server.PromptServer.instance
+
+@prompt_server.routes.get("/api/huggingface/status")
+async def route_get_status(request):
+    """API Endpoint to get the status of downloads."""
+    try:
+        status = download_manager.get_status()
+        return web.json_response(status)
+    except Exception as e:
+        print(f"Error getting download status: {e}")
+        # Format error response consistently
+        return web.json_response({"error": "Internal Server Error", "details": f"Failed to get status: {str(e)}", "status_code": 500}, status=500)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-HuggingFace/server/routes/OpenPath.py b/custom_nodes/ComfyUI-HuggingFace/server/routes/OpenPath.py
new file mode 100644
index 0000000000000000000000000000000000000000..6845881b61b233664198f6b41ee1101abb111881
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/server/routes/OpenPath.py
@@ -0,0 +1,58 @@
+# ================================================
+# File: server/routes/OpenPath.py
+# ================================================
+import asyncio
+import json
+from aiohttp import web
+
+import server # ComfyUI server instance
+from ...downloader.manager import manager as download_manager
+
+prompt_server = server.PromptServer.instance
+
+@prompt_server.routes.post("/api/huggingface/open_path")
+async def route_open_path(request):
+    """API Endpoint to open the containing folder of a completed download."""
+    if not download_manager:
+        return web.json_response({"error": "Download Manager not initialized"}, status=500)
+
+    try:
+        data = await request.json()
+        download_id = data.get("download_id")
+
+        if not download_id:
+            return web.json_response({"error": "Missing 'download_id'", "details": "The request body must contain the 'download_id' of the completed item."}, status=400)
+
+        print(f"[API Route /huggingface/open_path] Received open path request for ID: {download_id}")
+        # Call manager method in thread
+        result = await asyncio.to_thread(download_manager.open_containing_folder, download_id)
+
+        status_code = 200 if result.get("success") else 404 if "not found" in result.get("error", "").lower() else 400 # Use 400 for OS error, security etc
+        # Check for specific errors to return better codes
+        if not result.get("success"):
+             error_lower = result.get("error", "").lower()
+             if "directory does not exist" in error_lower or "id not found" in error_lower:
+                 status_code = 404
+             elif "cannot open path" in error_lower or "unsupported os" in error_lower or "failed to open" in error_lower or "xdg-open" in error_lower:
+                 status_code = 501 # Not Implemented / Failed on server side
+             elif "must be 'completed'" in error_lower:
+                  status_code = 409 # Conflict - wrong state
+             else:
+                 status_code = 400 # Bad Request / general failure
+
+        # Prevent sensitive path info leakage in error messages by default
+        if not result.get("success") and "error" in result and status_code != 200:
+             print(f"[API Route /huggingface/open_path] Error for ID {download_id}: {result['error']}") # Log full error on server
+             # Optionally sanitize error sent to client
+             # if "Directory:" in result["error"] or "Path:" in result["error"]:
+             #    result["error"] = "Server failed to open the specified directory."
+
+        return web.json_response(result, status=status_code)
+
+    except json.JSONDecodeError:
+        return web.json_response({"error": "Invalid JSON body"}, status=400)
+    except Exception as e:
+        import traceback
+        print(f"Error handling /huggingface/open_path request for ID '{data.get('download_id', 'N/A')}': {e}")
+        # traceback.print_exc()
+        return web.json_response({"error": "Internal Server Error", "details": f"An unexpected error occurred: {str(e)}"}, status=500)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-HuggingFace/server/routes/RetryDownload.py b/custom_nodes/ComfyUI-HuggingFace/server/routes/RetryDownload.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e0d41b96c8148ae8b635c91a1296c29071a07d1
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/server/routes/RetryDownload.py
@@ -0,0 +1,39 @@
+# ================================================
+# File: server/routes/RetryDownload.py
+# ================================================
+import asyncio
+import json
+from aiohttp import web
+
+import server # ComfyUI server instance
+from ...downloader.manager import manager as download_manager
+
+prompt_server = server.PromptServer.instance
+
+@prompt_server.routes.post("/api/huggingface/retry")
+async def route_retry_download(request):
+    """API Endpoint to retry a failed/cancelled download."""
+    if not download_manager:
+        return web.json_response({"error": "Download Manager not initialized"}, status=500)
+
+    try:
+        data = await request.json()
+        download_id = data.get("download_id")
+
+        if not download_id:
+            return web.json_response({"error": "Missing 'download_id'", "details": "The request body must contain the 'download_id' of the item to retry."}, status=400)
+
+        print(f"[API Route /huggingface/retry] Received retry request for ID: {download_id}")
+        # Call manager method (which handles locking)
+        result = await asyncio.to_thread(download_manager.retry_download, download_id) # Run sync manager method in thread
+
+        status_code = 200 if result.get("success") else 404 if "not found" in result.get("error", "").lower() else 400
+        return web.json_response(result, status=status_code)
+
+    except json.JSONDecodeError:
+        return web.json_response({"error": "Invalid JSON body"}, status=400)
+    except Exception as e:
+        import traceback
+        print(f"Error handling /huggingface/retry request for ID '{data.get('download_id', 'N/A')}': {e}")
+        # traceback.print_exc() # Uncomment for detailed logs
+        return web.json_response({"error": "Internal Server Error", "details": f"An unexpected error occurred: {str(e)}"}, status=500)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-HuggingFace/server/routes/SearchModels.py b/custom_nodes/ComfyUI-HuggingFace/server/routes/SearchModels.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd64b1e9c76b8b482751b8119df570f57d9ff3a6
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/server/routes/SearchModels.py
@@ -0,0 +1,121 @@
+# ================================================
+# File: server/routes/SearchModels.py
+# ================================================
+import json
+import traceback
+from aiohttp import web
+
+import server # ComfyUI server instance
+from ..utils import get_request_json, resolve_huggingface_api_key
+from ...config import HUGGINGFACE_API_TYPE_MAP
+
+prompt_server = server.PromptServer.instance
+
+@prompt_server.routes.post("/api/huggingface/search")
+async def route_search_models(request):
+    """API Endpoint for searching models using huggingface_hub."""
+    try:
+        data = await get_request_json(request)
+
+        query = data.get("query", "").strip()
+        model_type_keys = data.get("model_types", []) # e.g., ["lora", "checkpoint"]
+        base_model_filters = data.get("base_models", []) # e.g., ["SD 1.5", "Pony"]
+        sort = data.get("sort", "Most Downloaded")
+        limit = int(data.get("limit", 20))
+        page = int(data.get("page", 1))
+        resolved_api_key = resolve_huggingface_api_key(data)
+        nsfw = data.get("nsfw", True)  # Default to True (enabled)
+
+        if not query and not model_type_keys and not base_model_filters:
+             raise web.HTTPBadRequest(reason="Search requires a query or at least one filter (type or base model).")
+
+        # Use huggingface_hub for search
+        try:
+            from huggingface_hub import HfApi
+            hf_api = HfApi(token=resolved_api_key)
+            
+            # Prepare search parameters
+            search_params = {
+                "limit": limit
+            }
+            
+            # Map sort values to valid huggingface_hub values
+            sort_mapping = {
+                "Most Downloaded": "downloads",
+                "Most Liked": "likes", 
+                "Newest": "created_at",
+                "Relevancy": None,  # Remove sort for relevancy (default)
+                "Alphabetical": "modelId"
+            }
+            
+            sort_value = sort_mapping.get(sort, None)
+            if sort_value:
+                search_params["sort"] = sort_value
+            
+            if query:
+                search_params["search"] = query
+            
+            # Map model types to tags
+            if model_type_keys:
+                tags = []
+                for key in model_type_keys:
+                    api_type = HUGGINGFACE_API_TYPE_MAP.get(key.lower())
+                    if api_type:
+                        tags.append(api_type.lower())
+                if tags:
+                    search_params["tags"] = tags
+            
+            # Add base model filters as part of query if specified
+            if base_model_filters:
+                base_model_query = " ".join([f'base_model:{bm}' for bm in base_model_filters])
+                if query:
+                    search_params["search"] = f"{query} {base_model_query}"
+                else:
+                    search_params["search"] = base_model_query
+            
+            print(f"[Server Search] huggingface_hub: search='{search_params.get('search', '<none>')}', tags={search_params.get('tags', 'Any')}, sort={sort}, limit={limit}")
+            
+            # Perform search
+            models = hf_api.list_models(**search_params)
+            
+            # Format results for frontend
+            formatted_models = []
+            for model in models:
+                formatted_model = {
+                    "id": model.id,
+                    "name": model.id.split('/')[-1],
+                    "description": model.tags or "",
+                    "creator": {"username": model.author or ""},
+                    "modelId": model.id,
+                    "downloads": model.downloads or 0,
+                    "likes": model.likes or 0,
+                    "tags": model.tags or [],
+                    "modelType": "Unknown",  # Will be determined by frontend
+                    "baseModel": [],  # Will be determined by frontend
+                    "stats": {
+                        "downloadCount": model.downloads or 0,
+                        "thumbsUpCount": model.likes or 0
+                    }
+                }
+                formatted_models.append(formatted_model)
+            
+            response_data = {
+                "items": formatted_models,
+                "metadata": {
+                    "currentPage": page,
+                    "pageSize": limit,
+                    "totalItems": len(formatted_models),
+                    "totalPages": 1  # huggingface_hub doesn't provide pagination info
+                }
+            }
+            
+            return web.json_response(response_data)
+            
+        except Exception as e:
+            print(f"[Server Search] huggingface_hub search failed: {e}")
+            return web.json_response({"error": "Search failed", "details": str(e)}, status=500)
+            
+    except Exception as e:
+        print(f"Error in search_models: {e}")
+        traceback.print_exc()
+        return web.json_response({"error": "Internal Server Error", "details": str(e)}, status=500)
diff --git a/custom_nodes/ComfyUI-HuggingFace/server/routes/__init__.py b/custom_nodes/ComfyUI-HuggingFace/server/routes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..201a4a7650198f41767e1a2a8ec40dfce9242fea
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/server/routes/__init__.py
@@ -0,0 +1,21 @@
+# ================================================
+# File: server/routes/__init__.py
+# ================================================
+# This file imports all the individual route modules.
+# When the `routes` package is imported by `server/__init__.py`,
+# these imports will be executed, registering the routes with the
+# ComfyUI server instance.
+
+from . import CancelDownload
+from . import ClearHistory
+from . import DownloadModel
+from . import GetBaseModels
+from . import GetModelDetails
+from . import GetModelTypes
+from . import GetModelDirs
+from . import GetStatus
+from . import OpenPath
+from . import RetryDownload
+from . import SearchModels
+
+print("[HuggingFace] All server route modules loaded.")
diff --git a/custom_nodes/ComfyUI-HuggingFace/server/utils.py b/custom_nodes/ComfyUI-HuggingFace/server/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c19e117802387f5c8c0212019be676ea7f2eb1f
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/server/utils.py
@@ -0,0 +1,187 @@
+# ================================================
+# File: server/utils.py
+# ================================================
+import json
+import os
+from typing import Any, Dict, Optional
+from aiohttp import web
+
+# Import necessary components from our modules
+from ..api.huggingface import HuggingFaceAPI
+from ..utils.helpers import parse_huggingface_input
+
+async def get_request_json(request):
+    """Safely get JSON data from request."""
+    try:
+        return await request.json()
+    except Exception as e:
+        print(f"Error parsing request JSON: {e}")
+        raise web.HTTPBadRequest(reason=f"Invalid JSON format: {e}")
+
+def resolve_huggingface_api_key(payload: Optional[Dict[str, Any]] = None) -> Optional[str]:
+    """
+    Resolve API key priority:
+    1) Explicit key from request payload (`api_key`)
+    2) `HUGGINGFACE_TOKEN` environment variable
+    """
+    request_key = ""
+    if isinstance(payload, dict):
+        raw_key = payload.get("api_key", "")
+        if isinstance(raw_key, str):
+            request_key = raw_key.strip()
+
+    if request_key:
+        return request_key
+
+    env_key = os.getenv("HUGGINGFACE_TOKEN", "").strip()
+    if env_key:
+        return env_key
+
+    return None
+
+async def get_huggingface_model_and_version_details(api: HuggingFaceAPI, model_url_or_id: str, req_version_id: Optional[int]) -> Dict[str, Any]:
+    """
+    Helper to fetch HuggingFace details.
+    Prioritizes fetching model info based on resolved Model ID.
+    Fetches specific version info if version ID is provided/resolved, otherwise latest.
+    Returns a dict with 'model_info', 'version_info', 'primary_file', and resolved IDs.
+    Raises HTTP exceptions on critical failures.
+    """
+    target_model_id = None
+    target_version_id = None
+    potential_version_id_from_input = None
+    model_info = {}
+    version_info_to_use = {} # The version (specific or latest) whose file we'll use
+    primary_file = None
+
+    # --- 1. Parse Input to get potential IDs ---
+    parsed_model_id, parsed_version_id = parse_huggingface_input(model_url_or_id)
+
+    # Determine the initial target model ID (input URL/ID takes precedence)
+    target_model_id = parsed_model_id
+
+    # Determine the specific version requested (explicit param > URL param)
+    if req_version_id and str(req_version_id).isdigit():
+        try:
+            potential_version_id_from_input = int(req_version_id)
+        except (ValueError, TypeError):
+             print(f"[API Helper] Warning: Invalid req_version_id: {req_version_id}. Ignoring.")
+    elif parsed_version_id:
+        potential_version_id_from_input = parsed_version_id
+
+    # --- 2. Ensure we have a Model ID ---
+    # If we only got a version ID from the input (e.g., huggingface.com/model-versions/456),
+    # we need to fetch that version *first* just to find the model ID.
+    if not target_model_id and potential_version_id_from_input:
+        print(f"[API Helper] Input requires fetching version {potential_version_id_from_input} first to find model ID.")
+        temp_version_info = api.get_model_version_info(potential_version_id_from_input)
+        if temp_version_info and "error" not in temp_version_info and temp_version_info.get('modelId'):
+            target_model_id = temp_version_info['modelId']
+            print(f"[API Helper] Found Model ID {target_model_id} from Version ID {potential_version_id_from_input}.")
+            # We might reuse temp_version_info later if this was the specifically requested version
+        else:
+            err = temp_version_info.get('details', 'Could not find model ID from version') if isinstance(temp_version_info, dict) else 'API error'
+            raise web.HTTPNotFound(reason=f"Could not determine Model ID from Version ID {potential_version_id_from_input}", body=json.dumps({"error": f"Version {potential_version_id_from_input} not found or missing modelId", "details": err}))
+
+    # If still no model ID after potential lookup, fail
+    if not target_model_id:
+        raise web.HTTPBadRequest(reason="Could not determine a valid Model ID from the input.")
+
+    # --- 3. Fetch Core Model Information (Always based on target_model_id) ---
+    print(f"[API Helper] Fetching core model info for Model ID: {target_model_id}")
+    model_info_result = api.get_model_info(target_model_id)
+    if not model_info_result or "error" in model_info_result:
+        err_details = model_info_result.get('details', 'Unknown API error') if isinstance(model_info_result, dict) else 'Unknown API error'
+        raise web.HTTPNotFound(reason=f"Model {target_model_id} not found or API error", body=json.dumps({"error": f"Model {target_model_id} not found or API error", "details": err_details}))
+    model_info = model_info_result # Store the successfully fetched model info
+
+    # --- 4. Determine and Fetch Version Info for File Details ---
+    if potential_version_id_from_input:
+        # User specified a version explicitly, fetch its details
+        print(f"[API Helper] Fetching specific version info for Version ID: {potential_version_id_from_input}")
+        target_version_id = potential_version_id_from_input # This is the version we need info for
+        # Check if we already fetched this during Model ID lookup
+        if 'temp_version_info' in locals() and temp_version_info.get('id') == target_version_id:
+             print("[API Helper] Reusing version info fetched earlier.")
+             version_info_to_use = temp_version_info
+        else:
+            version_info_result = api.get_model_version_info(target_version_id)
+            if not version_info_result or "error" in version_info_result:
+                err_details = version_info_result.get('details', 'Unknown API error') if isinstance(version_info_result, dict) else 'Unknown API error'
+                raise web.HTTPNotFound(reason=f"Specified Version {target_version_id} not found or API error", body=json.dumps({"error": f"Version {target_version_id} not found or API error", "details": err_details}))
+            version_info_to_use = version_info_result
+    else:
+        # No specific version requested, find latest/default from model_info
+        print(f"[API Helper] Finding latest/default version for Model ID: {target_model_id}")
+        versions = model_info.get("modelVersions")
+        if not versions or not isinstance(versions, list) or len(versions) == 0:
+            raise web.HTTPNotFound(reason=f"Model {target_model_id} has no listed model versions.")
+
+        # Find the 'best' default version (usually first published)
+        default_version_summary = next((v for v in versions if v.get('status') == 'Published'), versions[0])
+        target_version_id = default_version_summary.get('id')
+        if not target_version_id:
+            raise web.HTTPNotFound(reason=f"Model {target_model_id}'s latest version has no ID.")
+
+        print(f"[API Helper] Using latest/default Version ID: {target_version_id}. Fetching its full details.")
+        # Fetch full details for this latest version
+        version_info_result = api.get_model_version_info(target_version_id)
+        if not version_info_result or "error" in version_info_result:
+             # Log error, but maybe try to proceed with summary data if desperate? Risky.
+            err_details = version_info_result.get('details', 'Unknown error getting full version') if isinstance(version_info_result, dict) else 'Error'
+            print(f"[API Helper] Warning: Could not fetch full details for latest version {target_version_id}. Details: {err_details}. Falling back to summary.")
+            # Use summary data from model_info as fallback - file info might be missing!
+            version_info_to_use = default_version_summary
+            # Ensure minimal structure for file finding later
+            version_info_to_use['files'] = version_info_to_use.get('files', [])
+            version_info_to_use['images'] = version_info_to_use.get('images', [])
+            version_info_to_use['modelId'] = version_info_to_use.get('modelId', target_model_id) # Ensure modelId is present
+            version_info_to_use['model'] = version_info_to_use.get('model', {'name': model_info.get('name', 'Unknown')}) # Add fallback model name
+
+        else:
+            version_info_to_use = version_info_result
+
+    # --- 5. Find Primary File from the Determined Version (version_info_to_use) ---
+    print(f"[API Helper] Finding primary file for Version ID: {target_version_id}")
+    files = version_info_to_use.get("files", [])
+    if not isinstance(files, list): files = []
+
+    # Handle fallback downloadUrl at version level if 'files' is empty/missing
+    if not files and 'downloadUrl' in version_info_to_use and version_info_to_use['downloadUrl']:
+        print("[API Helper] Warning: No 'files' array found, using version-level 'downloadUrl'.")
+        files = [{
+            "id": None, "name": version_info_to_use.get('name', f"version_{target_version_id}_file"),
+            "primary": True, "type": "Model", "sizeKB": version_info_to_use.get('fileSizeKB'),
+            "downloadUrl": version_info_to_use['downloadUrl'], "hashes": {}, "metadata": {}
+        }]
+
+    if not files:
+        raise web.HTTPNotFound(reason=f"Version {target_version_id} (Name: {version_info_to_use.get('name', 'N/A')}) has no files listed.")
+
+    # For HuggingFace, just pick the first file or largest .safetensors
+    primary_file = None
+    if files and isinstance(files, list):
+        # Try to find .safetensors first
+        for file_info in files:
+            if (file_info.get("type") == "file" and 
+                file_info.get("path", "").endswith(".safetensors")):
+                primary_file = file_info
+                break
+        
+        # If no .safetensors, pick the largest file
+        if not primary_file:
+            primary_file = max(files, key=lambda x: x.get("size", 0), default=None)
+
+    if not primary_file:
+        raise web.HTTPNotFound(reason=f"Could not find any usable file with a download URL for version {target_version_id}.")
+
+    print(f"[API Helper] Selected file: Name='{primary_file.get('name', 'N/A')}', SizeKB={primary_file.get('sizeKB')}")
+
+    # --- 6. Return Results ---
+    return {
+        "model_info": model_info,                  # Always the full model info
+        "version_info": version_info_to_use,       # Info for the specific/latest version
+        "primary_file": primary_file,              # The file from that version
+        "target_model_id": target_model_id,        # Resolved model ID
+        "target_version_id": target_version_id,    # Resolved version ID (specific or latest)
+    }
diff --git a/custom_nodes/ComfyUI-HuggingFace/utils/__init__.py b/custom_nodes/ComfyUI-HuggingFace/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3a431f5ad358b0bc5ba310102d196fa2b0832607
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/utils/__init__.py
@@ -0,0 +1,15 @@
+# ================================================
+# File: utils/__init__.py
+# ================================================
+
+# Import utility functions for easy access
+from .helpers import parse_huggingface_input, get_model_dir, sanitize_filename
+from . import helpers
+
+# Make functions available at package level
+__all__ = [
+    'parse_huggingface_input',
+    'get_model_dir', 
+    'sanitize_filename',
+    'helpers'
+]
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-HuggingFace/utils/helpers.py b/custom_nodes/ComfyUI-HuggingFace/utils/helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..55e908d80325083e5d7a171de42f64e7663175a5
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/utils/helpers.py
@@ -0,0 +1,224 @@
+# ================================================
+# File: utils/helpers.py
+# ================================================
+import os
+import urllib.parse
+import re 
+from pathlib import Path
+from typing import Optional, List, Dict, Any
+
+import folder_paths 
+
+# Import config values needed here
+from ..config import PLUGIN_ROOT, MODEL_TYPE_DIRS
+
+# Canonical aliases for model type/folder names. Values are preferred folder names.
+MODEL_TYPE_ALIASES = {
+    "checkpoint": "checkpoints",
+    "checkpoints": "checkpoints",
+    "diffusionmodel": "diffusion_models",
+    "diffusionmodels": "diffusion_models",
+    "diffusion_model": "diffusion_models",
+    "diffusion_models": "diffusion_models", # Wan 2.2 and similar go here
+    "diffusers": "diffusers",  
+    "unet": "unet",  # GGUF models go here
+    "lora": "loras",
+    "loras": "loras",
+    "locon": "loras",
+    "lycoris": "loras",
+    "vae": "vae",
+    "embedding": "embeddings",
+    "embeddings": "embeddings",
+    "textualinversion": "embeddings",
+    "hypernetwork": "hypernetworks",
+    "hypernetworks": "hypernetworks",
+    "controlnet": "controlnet",
+    "upscaler": "upscale_models",
+    "upscalers": "upscale_models",
+    "upscale_model": "upscale_models",
+    "upscale_models": "upscale_models",
+    "motionmodule": "motion_models",
+    "motionmodules": "motion_models",
+    "motion_model": "motion_models",
+    "motion_models": "motion_models",
+}
+
+MODEL_TYPE_ALIASES_COMPACT = {
+    re.sub(r'[^a-z0-9]', '', k): v for k, v in MODEL_TYPE_ALIASES.items()
+}
+
+def _normalize_model_type(model_type: str) -> str:
+    """Normalize model type string to canonical form."""
+    if not model_type:
+        return "other"
+    
+    normalized = model_type.lower().strip()
+    
+    # Try exact match first
+    if normalized in MODEL_TYPE_ALIASES:
+        return MODEL_TYPE_ALIASES[normalized]
+    
+    # Try compact match (remove non-alphanumeric chars)
+    compact = re.sub(r'[^a-z0-9]', '', normalized)
+    if compact in MODEL_TYPE_ALIASES_COMPACT:
+        return MODEL_TYPE_ALIASES_COMPACT[compact]
+    
+    return "other"
+
+def get_model_dir(model_type: str, explicit_save_root: str = "", selected_subdir: str = "") -> Optional[str]:
+    """Get the directory path for a given model type."""
+    try:
+        # Normalize the model type
+        normalized_type = _normalize_model_type(model_type)
+        
+        # Use explicit root if provided
+        if explicit_save_root:
+            base_path = explicit_save_root
+            # If selected_subdir is provided, append it
+            if selected_subdir:
+                base_path = os.path.join(base_path, selected_subdir)
+            return base_path
+        
+        # Try ComfyUI's folder_paths first
+        try:
+            if normalized_type in ["checkpoints", "loras", "vae", "embeddings", "hypernetworks", "controlnet", "upscale_models"]:
+                return folder_paths.get_folder_paths(normalized_type)[0]
+            elif normalized_type in ["diffusion_models", "motion_models", "unet", "diffusers"]:
+                # These might not be standard ComfyUI types, try to get them
+                try:
+                    return folder_paths.get_folder_paths(normalized_type)[0]
+                except:
+                    # Fallback: try unet for diffusion_models
+                    if normalized_type == "diffusion_models":
+                        try:
+                            return folder_paths.get_folder_paths("diffusion_models")[0]
+                        except:
+                            # If diffusion_models doesn't exist, try unet
+                            try:
+                                return folder_paths.get_folder_paths("unet")[0]
+                            except:
+                                # Fallback to checkpoints
+                                return folder_paths.get_folder_paths("checkpoints")[0]
+                    elif normalized_type == "unet":
+                        try:
+                            return folder_paths.get_folder_paths("unet")[0]
+                        except:
+                            # Fallback to checkpoints
+                            return folder_paths.get_folder_paths("checkpoints")[0]
+                    else:
+                        # For other types, try diffusion_models first
+                        try:
+                            return folder_paths.get_folder_paths("diffusion_models")[0]
+                        except:
+                            # Fallback to checkpoints
+                            return folder_paths.get_folder_paths("checkpoints")[0]
+            else:
+                # For other types, use our extension directory instead of custom_nodes
+                from ..config import PLUGIN_ROOT
+                return os.path.join(PLUGIN_ROOT, "other_models")
+            
+        except:
+            # Fallback to base_path + type
+            return os.path.join(folder_paths.base_path, normalized_type)
+            
+    except Exception as e:
+        print(f"Error getting model directory for {model_type}: {e}")
+        return None
+
+def sanitize_filename(filename: str) -> str:
+    """Sanitize filename for safe file system usage."""
+    if not filename:
+        return "unnamed_file"
+    
+    # Remove invalid characters
+    sanitized = re.sub(r'[<>:"/\\|?*]', '_', filename)
+    
+    # Remove control characters
+    sanitized = re.sub(r'[\x00-\x1f\x7f]', '', sanitized)
+    
+    # Limit length
+    if len(sanitized) > 255:
+        name, ext = os.path.splitext(sanitized)
+        sanitized = name[:255-len(ext)] + ext
+    
+    return sanitized.strip()
+
+def parse_huggingface_input(url_or_id: str) -> tuple[str | None, str | None]:
+    """
+    Parses HuggingFace URL or ID string.
+    Returns: (model_id, filename) tuple. Both can be None.
+    Handles URLs like:
+    - https://huggingface.co/FX-FeiHou/wan2.2-Remix/resolve/main/NSFW/Wan2.2_Remix_NSFW_i2v_14b_high_lighting_fp8_e4m3fn_v2.1.safetensors
+    - FX-FeiHou/wan2.2-Remix
+    """
+    if not url_or_id:
+        return None, None
+
+    url_or_id = str(url_or_id).strip()
+    model_id: str | None = None
+    filename: str | None = None
+
+    # Check if it's a direct download URL
+    if "/resolve/main/" in url_or_id:
+        try:
+            parsed_url = urllib.parse.urlparse(url_or_id)
+            print(f"[DEBUG] Parsed URL: {parsed_url}")
+            print(f"[DEBUG] Path parts: {parsed_url.path.split('/')}")
+            
+            # Extract model ID from path
+            path_parts = parsed_url.path.split('/')
+            print(f"[DEBUG] Path parts length: {len(path_parts)}")
+            print(f"[DEBUG] Path parts[2]: {path_parts[2] if len(path_parts) > 2 else 'None'}")
+            
+            if len(path_parts) >= 4 and path_parts[3] == "resolve":
+                # URL format: /FX-FeiHou/wan2.2-Remix/resolve/main/NSFW/file.safetensors
+                model_id = "/".join(path_parts[1:3])  # FX-FeiHou/wan2.2-Remix
+                filename = "/".join(path_parts[4:])  # main/NSFW/file.safetensors
+                # Remove "main/" from filename if it's duplicated
+                if filename.startswith("main/"):
+                    filename = filename[5:]  # Remove "main/" prefix
+                print(f"[DEBUG] Extracted model_id: {model_id}")
+                print(f"[DEBUG] Extracted filename: {filename}")
+                print(f"Parsed HF download URL - Model: {model_id}, File: {filename}")
+                return model_id, filename
+            else:
+                print(f"[DEBUG] Path does not match expected format")
+        except Exception as e:
+            print(f"Warning: Could not parse HF download URL '{url_or_id}': {e}")
+            return None, None
+    
+    # Check if it's a simple model ID (username/repo format)
+    if "/" in url_or_id and not url_or_id.startswith("http"):
+        parts = url_or_id.split("/")
+        if len(parts) >= 2:
+            model_id = "/".join(parts[0:2])
+            print(f"Parsed HF model ID: {model_id}")
+            return model_id, None
+    
+    # Try parsing as full URL
+    try:
+        parsed_url = urllib.parse.urlparse(url_or_id)
+        
+        if "huggingface.co" in parsed_url.netloc.lower():
+            path_parts = parsed_url.path.split('/')
+            if len(path_parts) >= 3:
+                model_id = "/".join(path_parts[1:3])  # Extract username/repo
+                print(f"Parsed HF URL - Model: {model_id}")
+                return model_id, None
+    except Exception as e:
+        print(f"Warning: Could not parse HF URL '{url_or_id}': {e}")
+    
+    print(f"Input '{url_or_id}' is not a recognizable HF model ID or URL.")
+    return None, None
+
+def get_model_folder_paths(model_type: str) -> List[str]:
+    """Get all folder paths for a given model type."""
+    try:
+        normalized_type = _normalize_model_type(model_type)
+        return folder_paths.get_folder_paths(normalized_type)
+    except:
+        return []
+
+def get_model_type_folder_name(model_type: str) -> str:
+    """Get the standard folder name for a model type."""
+    return _normalize_model_type(model_type)
diff --git a/custom_nodes/ComfyUI-HuggingFace/web/js/api/huggingface.js b/custom_nodes/ComfyUI-HuggingFace/web/js/api/huggingface.js
new file mode 100644
index 0000000000000000000000000000000000000000..ab4744b4f3c8842efdc41a1f77127b8fad9c37d6
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/web/js/api/huggingface.js
@@ -0,0 +1,164 @@
+// API client for HuggingFace UI
+// Wraps ComfyUI's fetchApi with consistent error handling
+
+import { api } from "../../../../scripts/api.js";
+
+export class HuggingFaceDownloaderAPI {
+  static async _request(endpoint, options = {}) {
+    try {
+      const url = endpoint.startsWith("/") ? endpoint : `/${endpoint}`;
+      const response = await api.fetchApi(url, options);
+
+      if (!response.ok) {
+        let errorData;
+        const status = response.status;
+        const statusText = response.statusText;
+        try {
+          errorData = await response.json();
+          if (typeof errorData !== "object" || errorData === null) {
+            errorData = { detail: String(errorData) };
+          }
+        } catch (_) {
+          const detailText = await response.text().catch(() => `Status ${status} - Could not read error text`);
+          errorData = {
+            error: `HTTP error ${status}`,
+            details: String(detailText).substring(0, 500),
+          };
+        }
+        const err = new Error(errorData.error || errorData.reason || `HTTP Error: ${status} ${statusText}`);
+        err.details = errorData.details || errorData.detail || errorData.error || "No details provided.";
+        err.status = status;
+        throw err;
+      }
+
+      if (response.status === 204 || response.headers.get("Content-Length") === "0") {
+        return null;
+      }
+      return await response.json();
+    } catch (error) {
+      if (!error.details) error.details = error.message;
+      throw error;
+    }
+  }
+
+  static async downloadModel(params) {
+    return await this._request("/huggingface/download", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify(params),
+    });
+  }
+
+  static async getModelDetails(params) {
+    return await this._request("/huggingface/get_model_details", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify(params),
+    });
+  }
+
+  static async getStatus() {
+    return await this._request("/huggingface/status");
+  }
+
+  static async cancelDownload(downloadId) {
+    return await this._request("/huggingface/cancel", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify({ download_id: downloadId }),
+    });
+  }
+
+  static async searchModels(params) {
+    return await this._request("/huggingface/search", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify(params),
+    });
+  }
+
+  static async getBaseModels() {
+    return await this._request("/huggingface/base_models");
+  }
+
+  static async getModelTypes() {
+    return await this._request("/huggingface/model_types");
+  }
+
+  static async getModelDirs(modelType) {
+    const q = encodeURIComponent(modelType || 'checkpoints');
+    return await this._request(`/huggingface/model_dirs?type=${q}`);
+  }
+
+  static async createModelDir(modelType, newDir, root = "") {
+    return await this._request("/huggingface/create_dir", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify({ model_type: modelType, new_dir: newDir, root }),
+    });
+  }
+
+  static async createModelType(name) {
+    return await this._request("/huggingface/create_model_type", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify({ name }),
+    });
+  }
+
+  static async getModelRoots(modelType) {
+    const q = encodeURIComponent(modelType || 'checkpoints');
+    return await this._request(`/huggingface/model_roots?type=${q}`);
+  }
+
+  static async createModelRoot(modelType, absPath) {
+    return await this._request("/huggingface/create_root", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify({ model_type: modelType, path: absPath }),
+    });
+  }
+
+  static async getGlobalRoot() {
+    return await this._request("/huggingface/global_root");
+  }
+
+  static async setGlobalRoot(path) {
+    return await this._request("/huggingface/global_root", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify({ path }),
+    });
+  }
+
+  static async clearGlobalRoot() {
+    return await this._request("/huggingface/global_root/clear", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify({}),
+    });
+  }
+
+  static async retryDownload(downloadId) {
+    return await this._request("/huggingface/retry", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify({ download_id: downloadId }),
+    });
+  }
+
+  static async openPath(downloadId) {
+    return await this._request("/huggingface/open_path", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify({ download_id: downloadId }),
+    });
+  }
+
+  static async clearHistory() {
+    return await this._request("/huggingface/clear_history", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+    });
+  }
+}
diff --git a/custom_nodes/ComfyUI-HuggingFace/web/js/huggingfaceDownloader.css b/custom_nodes/ComfyUI-HuggingFace/web/js/huggingfaceDownloader.css
new file mode 100644
index 0000000000000000000000000000000000000000..469916386d543323e87acfc7e261b39a302f8f0e
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/web/js/huggingfaceDownloader.css
@@ -0,0 +1,753 @@
+/* HuggingFace Button Styling - Distinct from Civicomfy */
+#huggingface-downloader-button {
+    background: linear-gradient(135deg, #FFD700 0%, #FFA500 100%) !important;
+    color: #000 !important;
+    border: 2px solid #FF8C00 !important;
+    font-weight: bold !important;
+    padding: 4px 12px !important;
+    border-radius: 6px !important;
+    transition: all 0.3s ease !important;
+}
+
+#huggingface-downloader-button:hover {
+    background: linear-gradient(135deg, #FFA500 0%, #FF8C00 100%) !important;
+    transform: translateY(-1px) !important;
+    box-shadow: 0 4px 8px rgba(255, 140, 0, 0.3) !important;
+}
+
+#huggingface-downloader-button:active {
+    transform: translateY(0) !important;
+    box-shadow: 0 2px 4px rgba(255, 140, 0, 0.3) !important;
+}
+
+.huggingface-downloader-modal {
+    position: fixed;
+    z-index: 1001; /* Above ComfyUI elements */
+    left: 0;
+    top: 0;
+    width: 100%;
+    height: 100%;
+    overflow: hidden; /* Prevent body scroll */
+    background-color: rgba(0, 0, 0, 0.6);
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    opacity: 0;
+    visibility: hidden;
+    transition: opacity 0.3s ease, visibility 0s linear 0.3s;
+}
+
+.huggingface-downloader-modal.open {
+    opacity: 1;
+    visibility: visible;
+    transition: opacity 0.3s ease, visibility 0s linear 0s;
+}
+
+.huggingface-downloader-modal-content {
+    background-color: var(--comfy-menu-bg);
+    color: var(--comfy-text-color);
+    margin: auto;
+    padding: 0; /* Remove padding, handle internally */
+    border-radius: 8px;
+    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.3);
+    width: 900px;
+    max-width: 95%;
+    height: 700px; /* Fixed height */
+    max-height: 90vh;
+    display: flex; /* Use flexbox for layout */
+    flex-direction: column; /* Stack header, tabs, content */
+    overflow: hidden; /* Prevent content overflow */
+}
+
+.huggingface-downloader-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    padding: 15px 20px;
+    border-bottom: 1px solid var(--border-color, #444);
+    flex-shrink: 0; /* Prevent header from shrinking */
+}
+
+.huggingface-downloader-header h2 {
+    margin: 0;
+    font-size: 1.3em;
+}
+
+.huggingface-close-button {
+    background: none;
+    border: none;
+    color: var(--comfy-text-color);
+    font-size: 28px;
+    cursor: pointer;
+    padding: 0 5px;
+    line-height: 1;
+}
+.huggingface-close-button:hover {
+    color: #aaa;
+}
+
+.huggingface-downloader-body {
+    display: flex;
+    flex-direction: column;
+    flex-grow: 1; /* Allow body to take remaining space */
+    overflow: hidden; /* Manage internal scrolling */
+}
+
+.huggingface-downloader-tabs {
+    display: flex;
+    border-bottom: 1px solid var(--border-color, #444);
+    padding: 0 15px;
+    flex-shrink: 0; /* Prevent tabs from shrinking */
+}
+
+.huggingface-downloader-tab {
+    padding: 10px 18px;
+    cursor: pointer;
+    border: none;
+    background: none;
+    color: var(--comfy-text-color);
+    opacity: 0.7;
+    position: relative;
+    top: 1px; /* Align with bottom border */
+    margin-bottom: -1px; /* Overlap border */
+}
+
+.huggingface-downloader-tab.active {
+    opacity: 1;
+    border-bottom: 3px solid var(--accent-color, #5c8aff);
+    font-weight: bold;
+}
+.huggingface-downloader-tab:hover {
+    opacity: 1;
+}
+
+.huggingface-downloader-tab-content {
+    display: none;
+    padding: 20px;
+    flex-grow: 1; /* Allow content to take space */
+    overflow-y: auto; /* Enable scrolling ONLY for the content area */
+}
+
+.huggingface-downloader-tab-content.active {
+    display: block; /* Show active tab */
+}
+
+.huggingface-form-group {
+    margin-bottom: 18px;
+}
+.huggingface-form-group:last-child {
+    margin-bottom: 0;
+}
+
+.huggingface-form-group label {
+    display: block;
+    margin-bottom: 6px;
+    font-weight: bold;
+    font-size: 0.95em;
+}
+
+.huggingface-input,
+.huggingface-select {
+    width: 100%;
+    padding: 10px;
+    background-color: var(--comfy-input-bg);
+    color: var(--comfy-text-color);
+    border: 1px solid var(--border-color, #555);
+    border-radius: 4px;
+    box-sizing: border-box; /* Include padding/border in width */
+}
+.huggingface-input:focus,
+.huggingface-select:focus {
+    outline: none;
+    border-color: var(--accent-color, #5c8aff);
+    box-shadow: 0 0 0 2px rgba(92, 138, 255, 0.3);
+}
+
+.huggingface-form-row {
+    display: flex;
+    gap: 15px;
+    flex-wrap: wrap; /* Allow wrapping on smaller screens */
+}
+.huggingface-form-row > .huggingface-form-group {
+    flex: 1; /* Distribute space */
+    min-width: 180px; /* Minimum width before wrapping */
+}
+/* Adjust for checkbox/inline elements */
+.huggingface-form-group.inline {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    margin-bottom: 10px; /* Less margin for checkboxes */
+}
+.huggingface-form-group.inline label {
+    margin-bottom: 0; /* Remove default bottom margin */
+    order: 1; /* Put label after checkbox */
+}
+.huggingface-checkbox {
+    width: auto;
+    order: 0; /* Put checkbox before label */
+    accent-color: var(--accent-color, #5c8aff);
+    transform: scale(1.1);
+}
+
+.huggingface-button {
+    background-color: var(--comfy-input-bg);
+    color: var(--comfy-text-color);
+    border: 1px solid var(--border-color, #555);
+    padding: 10px 18px;
+    border-radius: 4px;
+    cursor: pointer;
+    font-size: 1em;
+    transition: background-color 0.2s ease, border-color 0.2s ease;
+}
+.huggingface-button:hover {
+    background-color: var(--comfy-input-bg-hover);
+    border-color: #777;
+}
+.huggingface-button:disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+}
+
+.huggingface-button.primary {
+    background-color: var(--accent-color, #5c8aff);
+    border-color: var(--accent-color, #5c8aff);
+    color: white;
+}
+.huggingface-button.primary:hover {
+    background-color: #4a7ee0; /* Slightly darker blue */
+    border-color: #4a7ee0;
+}
+.huggingface-button.primary:disabled {
+    background-color: var(--accent-color, #5c8aff); /* Keep color but lower opacity */
+    border-color: var(--accent-color, #5c8aff);
+}
+
+.huggingface-button.danger {
+    background-color: #e64b4b;
+    border-color: #e64b4b;
+    color: white;
+}
+.huggingface-button.danger:hover {
+    background-color: #d93a3a;
+    border-color: #d93a3a;
+}
+.huggingface-button.small {
+    padding: 5px 10px;
+    font-size: 0.85em;
+}
+
+/* Status Tab Styling */
+.huggingface-status-section {
+    margin-bottom: 25px;
+}
+.huggingface-status-section h3 {
+    margin-top: 0;
+    margin-bottom: 15px;
+    border-bottom: 1px solid var(--border-color, #444);
+    padding-bottom: 8px;
+    font-size: 1.1em;
+}
+
+.huggingface-download-list {
+    display: flex;
+    flex-direction: column;
+    gap: 15px;
+}
+
+.huggingface-download-item {
+    display: flex;
+    gap: 15px;
+    align-items: flex-start; /* Align items top */
+    padding: 15px;
+    border-radius: 6px;
+    background-color: var(--comfy-input-bg); /* Slightly different bg for items */
+    border: 1px solid var(--border-color, #555);
+}
+
+.huggingface-download-thumbnail {
+    width: 80px;
+    height: 80px;
+    object-fit: cover;
+    border-radius: 4px;
+    background-color: #333; /* Placeholder color */
+    flex-shrink: 0; /* Don't shrink thumbnail */
+    margin-top: 3px; /* Align better with text */
+}
+
+.huggingface-download-info {
+    flex-grow: 1; /* Allow info to take available space */
+    display: flex;
+    flex-direction: column;
+    gap: 5px;
+    overflow: hidden; /* Prevent long text overflow */
+}
+.huggingface-download-info strong {
+    font-size: 1.05em;
+    white-space: nowrap;
+    overflow: hidden;
+    text-overflow: ellipsis;
+}
+.huggingface-download-info p {
+    margin: 0;
+    font-size: 0.9em;
+    color: #ccc;
+    white-space: nowrap;
+    overflow: hidden;
+    text-overflow: ellipsis;
+}
+.huggingface-download-info .filename {
+   font-style: italic;
+   color: #bbb;
+}
+.huggingface-download-info .error-message {
+   color: #ff6b6b;
+   font-size: 0.85em;
+   white-space: normal; /* Allow error message to wrap */
+   word-break: break-word;
+}
+
+.huggingface-download-actions {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    margin-left: auto; /* Push actions to the right */
+    flex-shrink: 0; /* Don't shrink action buttons */
+}
+
+.huggingface-progress-container {
+    margin-top: 8px;
+    width: 100%;
+    background-color: var(--comfy-menu-bg); /* Darker background for contrast */
+    border-radius: 4px;
+    overflow: hidden;
+    height: 20px;
+    border: 1px solid var(--border-color, #555);
+}
+
+.huggingface-progress-bar {
+    height: 100%;
+    background-color: var(--accent-color, #5c8aff);
+    text-align: center;
+    color: white;
+    line-height: 20px; /* Center text vertically */
+    font-size: 12px;
+    font-weight: bold;
+    transition: width 0.3s ease-out;
+    min-width: 20px; /* Show something even for 0% */
+    width: 0%; /* Start at 0 */
+    white-space: nowrap;
+    overflow: hidden;
+}
+.huggingface-progress-bar.completed {
+    background-color: #4caf50; /* Green for completed */
+}
+.huggingface-progress-bar.failed {
+    background-color: #f44336; /* Red for failed */
+}
+.huggingface-progress-bar.cancelled {
+    background-color: #9e9e9e; /* Grey for cancelled */
+}
+
+.huggingface-speed-indicator {
+    font-size: 0.85em;
+    color: #ccc;
+    margin-top: 4px;
+    text-align: right;
+}
+
+/* Search Tab Styling */
+.huggingface-search-controls {
+    display: flex;
+    gap: 15px;
+    margin-bottom: 20px;
+    flex-wrap: wrap;
+}
+.huggingface-search-controls .huggingface-input {
+    flex-grow: 1; /* Let search input take more space */
+}
+.huggingface-search-controls .huggingface-select {
+    min-width: 150px; /* Min width for dropdowns */
+}
+.huggingface-search-controls .huggingface-button {
+    align-self: flex-end; /* Align button with bottom of inputs */
+}
+
+.huggingface-search-results {
+    margin-top: 20px;
+}
+.huggingface-search-item {
+    display: flex;
+    gap: 15px;
+    align-items: flex-start;
+    margin-bottom: 15px;
+    padding: 15px;
+    border-radius: 6px;
+    background-color: var(--comfy-input-bg);
+    border: 1px solid var(--border-color, #555);
+    transition: background-color 0.2s ease;
+}
+
+.huggingface-thumbnail-container {
+    position: relative; /* Required for absolute positioning of the child badge */
+    display: block; 
+    line-height: 0; 
+    width: 120px;
+    height: 170px;
+    flex-shrink: 0; 
+}
+
+.huggingface-search-thumbnail {
+    width: 120px; /* Slightly larger for search */
+    height: 170px;
+    object-fit: cover;
+    border-radius: 4px;
+    background-color: #333;
+    flex-shrink: 0;
+}
+
+.huggingface-search-meta-info {
+    font-size: 0.85em;
+    color: #aaa; 
+    margin-bottom: 5px;
+    display: flex;
+    gap: 15px; 
+    flex-wrap: wrap; 
+}
+
+.huggingface-search-meta-info span {
+    display: inline-flex;
+    align-items: center;
+    gap: 5px; 
+}
+
+.huggingface-search-meta-info i {
+    color: #ccc; 
+}
+.huggingface-type-badge {
+    position: absolute;
+    bottom: 5px;         /* Adjust spacing from bottom */
+    left: 5px;           /* Adjust spacing from left */
+    background-color: rgb(48, 94, 70); /* Black background with transparency */
+    color: white; 
+    padding: 10px 8px;    /* Adjust padding (top/bottom, left/right) */
+    border-radius: 10px; /* Adjust for desired roundness */
+    font-size: 0.75em;   /* Adjust font size */
+    font-weight: bold; 
+    z-index: 2;          /* Ensure it's above the image */
+    white-space: nowrap; /* Prevent text wrapping */
+}
+
+.huggingface-search-info {
+    flex-grow: 1;
+    display: flex;
+    flex-direction: column;
+    gap: 5px;
+    overflow: hidden;
+}
+.huggingface-search-info h4 {
+    margin: 0;
+    font-size: 1.1em;
+    white-space: nowrap;
+    overflow: hidden;
+    text-overflow: ellipsis;
+}
+.huggingface-search-info p {
+    margin: 0;
+    font-size: 0.9em;
+    color: #ccc;
+}
+.huggingface-search-tags {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 5px;
+    margin-top: 8px;
+}
+.huggingface-search-tag {
+    background-color: rgba(255, 255, 255, 0.1);
+    color: #eee;
+    padding: 3px 8px;
+    border-radius: 10px;
+    font-size: 0.8em;
+}
+.huggingface-search-stats span {
+    margin-right: 15px;
+    font-size: 0.85em;
+    color: #bbb;
+}
+.huggingface-search-stats i { /* If using icons */
+    margin-right: 4px;
+}
+
+.huggingface-search-actions {
+    display: flex;
+    flex-direction: column; /* Stack buttons vertically */
+    gap: 8px;
+    align-items: flex-end; /* Align buttons to the right */
+    margin-left: auto;
+    flex-shrink: 0;
+}
+
+.version-buttons-container {
+    display: flex;
+    flex-direction: column;
+    align-items: flex-end; 
+    gap: 5px;
+}
+
+.all-versions-container {
+    display: flex;
+    flex-direction: column; 
+    gap: 5px;
+    padding-bottom: 5px;
+    white-space: nowrap; /* Prevent wrapping */
+    max-width: 100%; /* Prevent excessive width */
+    align-items: flex-end; 
+}
+
+.show-all-versions-button {
+    align-self: flex-end; /* Align button to left */
+    margin-top: auto; /* Push to the bottom */
+}
+.base-model-badge {
+    display: inline-block; /* Allows padding and background */
+    background-color: #3a3a3a;  /* Or a specific purple hex code like #800080 */
+    color: white;
+    font-weight: bold;
+    padding: 2px 6px;       /* Adjust padding for desired size (vertical horizontal) */
+    border-radius: 3px;     /* Adjust for desired roundness */
+    margin-right: 4px;     /* Optional: Adds some space between badge and hyphen */
+    line-height: 1;        /* Optional: Can help contain the background better */
+    vertical-align: middle; /* Optional: Helps align the badge nicely with text */
+}
+
+
+/* Settings Tab Styling */
+.huggingface-settings-container {
+    display: grid;
+    grid-template-columns: 1fr; /* Single column */
+    gap: 20px;
+}
+
+@media (min-width: 768px) {
+    .huggingface-settings-container {
+        grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); /* Responsive columns */
+    }
+}
+
+.huggingface-settings-section {
+    background-color: var(--comfy-input-bg);
+    padding: 15px;
+    border-radius: 6px;
+    border: 1px solid var(--border-color, #555);
+}
+.huggingface-settings-section h4 {
+    margin-top: 0;
+    margin-bottom: 15px;
+    border-bottom: 1px solid var(--border-color, #444);
+    padding-bottom: 8px;
+}
+
+.huggingface-toast {
+    position: fixed;
+    bottom: 20px;
+    left: 50%;
+    transform: translateX(-50%);
+    background-color: rgba(0, 0, 0, 0.8);
+    color: white;
+    padding: 12px 20px;
+    border-radius: 6px;
+    z-index: 1005; /* Above modal */
+    font-size: 0.95em;
+    opacity: 0;
+    transition: opacity 0.3s ease, bottom 0.3s ease;
+    pointer-events: none; /* Don't block clicks */
+}
+
+.huggingface-toast.show {
+    opacity: 1;
+    bottom: 30px;
+}
+
+.huggingface-toast.success {
+    background-color: rgba(76, 175, 80, 0.9); /* Green */
+}
+
+.huggingface-toast.error {
+    background-color: rgba(244, 67, 54, 0.9); /* Red */
+}
+
+.huggingface-toast.info {
+    background-color: rgba(33, 150, 243, 0.9); /* Blue */
+}
+
+/* Tooltip Styles */
+[data-tooltip] {
+  position: relative;
+  cursor: help;
+}
+[data-tooltip]::after {
+  content: attr(data-tooltip);
+  position: absolute;
+  left: 50%;
+  transform: translateX(-50%);
+  bottom: 100%;
+  margin-bottom: 5px;
+  background-color: rgba(0, 0, 0, 0.85);
+  color: white;
+  padding: 5px 10px;
+  border-radius: 4px;
+  font-size: 0.85em;
+  white-space: nowrap;
+  opacity: 0;
+  visibility: hidden;
+  transition: opacity 0.2s ease, visibility 0.2s ease;
+  z-index: 1010; /* Ensure tooltip is on top */
+}
+[data-tooltip]:hover::after {
+  opacity: 1;
+  visibility: visible;
+}
+
+.huggingface-download-preview-area {
+    /* Add specific margins/padding if needed */
+     min-height: 50px; /* Ensure it has some height for the loading spinner */
+}
+
+/* Style for the description box */
+.model-description-content {
+    max-height: 200px; /* Limit height */
+    overflow-y: auto; /* Allow scrolling */
+    background-color: var(--comfy-input-bg); /* Match input background */
+    padding: 10px;
+    border-radius: 4px;
+    font-size: 0.9em; /* Slightly smaller text */
+    border: 1px solid var(--border-color, #555);
+    color: var(--comfy-text-color); /* Ensure text color matches */
+    line-height: 1.5; /* Improve readability */
+}
+
+/* Style links within the description */
+.model-description-content a {
+    color: var(--accent-color, #5c8aff); /* Make links stand out */
+    text-decoration: underline;
+}
+.model-description-content a:hover {
+    color: #8bb0ff; /* Lighter color on hover */
+}
+
+/* Ensure reused search item styles look okay in this context */
+.huggingface-download-preview-area .huggingface-search-item {
+    margin-bottom: 15px; /* Add space below the item */
+     background-color: var(--comfy-input-bg); /* Explicitly set background */
+}
+
+/* --- Confirmation Modal Styles --- */
+.huggingface-confirmation-modal {
+    display: none; /* Hidden by default */
+    position: fixed; /* Stay in place */
+    z-index: 1001; /* Ensure it's above the main modal */
+    left: 0;
+    top: 0;
+    width: 100%;
+    height: 100%;
+    overflow: auto; /* Enable scroll if needed */
+    background-color: rgba(0,0,0,0.6); /* Dim background */
+    /* Use flexbox for easy centering */
+    justify-content: center;
+    align-items: center;
+}
+
+.huggingface-confirmation-modal-content {
+    background-color: var(--comfy-menu-bg, #282828);
+    margin: auto;
+    padding: 25px;
+    border: 1px solid var(--border-color, #444);
+    border-radius: 8px;
+    width: 80%;
+    max-width: 450px; /* Limit width */
+    box-shadow: 0 5px 15px rgba(0,0,0,0.3);
+    color: var(--input-text, #ddd);
+}
+
+.huggingface-confirmation-modal-content h4 {
+    margin-top: 0;
+    margin-bottom: 15px;
+    color: var(--desc-text, #eee);
+    border-bottom: 1px solid var(--border-color, #444);
+    padding-bottom: 10px;
+}
+
+.huggingface-confirmation-modal-content p {
+    margin-bottom: 25px;
+    line-height: 1.5;
+    font-size: 0.95em;
+}
+
+.huggingface-confirmation-modal-actions {
+    display: flex;
+    justify-content: flex-end; /* Align buttons to the right */
+    gap: 10px; /* Space between buttons */
+}
+
+/* Style buttons within the confirmation modal */
+.huggingface-confirmation-modal-actions .huggingface-button {
+    padding: 8px 15px; /* Adjust padding */
+    min-width: 80px; /* Ensure minimum width */
+}
+
+/* Ensure both img and video within the thumbnail container behave similarly */
+.huggingface-thumbnail-container img.huggingface-search-thumbnail,
+.huggingface-thumbnail-container video.huggingface-search-thumbnail,
+.huggingface-thumbnail-container img.huggingface-download-thumbnail,
+.huggingface-thumbnail-container video.huggingface-download-thumbnail {
+    display: block; /* Prevent extra space below */
+    width: 100%;    /* Fill the container width */
+    height: 100%;   /* Fill the container height */
+    object-fit: cover; /* Scale while maintaining aspect ratio, cropping if needed */
+    background-color: #333; /* Background for loading/error states */
+}
+
+/* You might already have rules for the container itself, ensure they define a size */
+.huggingface-thumbnail-container {
+    width: 100px; /* Example size */
+    height: 140px; /* Example size */
+    overflow: hidden; /* Hide parts of the video/image outside the cover area */
+    position: relative; /* For positioning badges etc. */
+    flex-shrink: 0; /* Prevent shrinking in flex layouts */
+    border-radius: 4px; /* Match styling */
+}
+
+/* Blur/Hide for R-rated thumbnails */
+.huggingface-thumbnail-container.blurred img.huggingface-search-thumbnail,
+.huggingface-thumbnail-container.blurred video.huggingface-search-thumbnail,
+.huggingface-thumbnail-container.blurred img.huggingface-download-thumbnail,
+.huggingface-thumbnail-container.blurred video.huggingface-download-thumbnail {
+    filter: blur(14px) brightness(0.6) saturate(0.6);
+    pointer-events: none; /* Let clicks hit the container */
+}
+.huggingface-thumbnail-container .huggingface-nsfw-overlay {
+    position: absolute;
+    inset: 0;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    color: #fff;
+    font-weight: 800;
+    font-size: 20px;
+    letter-spacing: 1px;
+    background: rgba(0,0,0,0.35);
+    z-index: 3;
+    user-select: none;
+}
+/* Make it obvious it’s clickable */
+.huggingface-thumbnail-container.blurred { cursor: pointer; }
+
+/* Style for the type badge (likely exists) */
+.huggingface-type-badge {
+    /* ... existing styles ... */
+    position: absolute;
+    bottom: 5px;
+    right: 5px;
+    /* ... */
+}
diff --git a/custom_nodes/ComfyUI-HuggingFace/web/js/huggingfaceDownloader.js b/custom_nodes/ComfyUI-HuggingFace/web/js/huggingfaceDownloader.js
new file mode 100644
index 0000000000000000000000000000000000000000..34aae2f63dd29aebf2bc10b7f7f518941e188994
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/web/js/huggingfaceDownloader.js
@@ -0,0 +1,89 @@
+import { app } from "../../../scripts/app.js";
+import { addCssLink } from "./utils/dom.js";
+import { HuggingFaceDownloaderUI } from "./ui/UI.js";
+
+console.log("Loading HuggingFace UI...");
+
+// --- Configuration ---
+const EXTENSION_NAME = "HuggingFace";
+const CSS_URL = `../huggingfaceDownloader.css`;
+const PLACEHOLDER_IMAGE_URL = `/extensions/ComfyUI-HuggingFace/images/placeholder.jpg`;
+
+// Add Menu Button to ComfyUI
+function addMenuButton() {
+    const buttonGroup = document.querySelector(".comfyui-button-group");
+
+    if (!buttonGroup) {
+        console.warn(`[${EXTENSION_NAME}] ComfyUI button group not found. Retrying...`);
+        setTimeout(addMenuButton, 500);
+        return;
+    }
+
+    if (document.getElementById("huggingface-downloader-button")) {
+        console.log(`[${EXTENSION_NAME}] Button already exists.`);
+        return;
+    }
+
+    const huggingfaceButton = document.createElement("button");
+    huggingfaceButton.innerHTML = "🤗 HuggingFace";
+    huggingfaceButton.id = "huggingface-downloader-button";
+    huggingfaceButton.title = "Open HuggingFace Model Downloader";
+
+    huggingfaceButton.onclick = async () => {
+        if (!window.huggingfaceDownloaderUI) {
+            console.info(`[${EXTENSION_NAME}] Creating HuggingFaceDownloaderUI instance...`);
+            window.huggingfaceDownloaderUI = new HuggingFaceDownloaderUI();
+            document.body.appendChild(window.huggingfaceDownloaderUI.modal);
+
+            try {
+                await window.huggingfaceDownloaderUI.initializeUI();
+                console.info(`[${EXTENSION_NAME}] UI Initialization complete.`);
+            } catch (error) {
+                console.error(`[${EXTENSION_NAME}] Error during UI initialization:`, error);
+                window.huggingfaceDownloaderUI?.showToast("Error initializing UI components. Check console.", "error", 5000);
+            }
+        }
+
+        if (window.huggingfaceDownloaderUI) {
+            window.huggingfaceDownloaderUI.openModal();
+        } else {
+            console.error(`[${EXTENSION_NAME}] Cannot open modal: UI instance not available.`);
+            alert("HuggingFace failed to initialize. Please check the browser console for errors.");
+        }
+    };
+
+    buttonGroup.appendChild(huggingfaceButton);
+    console.log(`[${EXTENSION_NAME}] HuggingFace button added to .comfyui-button-group.`);
+
+    const menu = document.querySelector(".comfy-menu");
+    if (!buttonGroup.contains(huggingfaceButton) && menu && !menu.contains(huggingfaceButton)) {
+        console.warn(`[${EXTENSION_NAME}] Failed to append button to group, falling back to menu.`);
+        const settingsButton = menu.querySelector("#comfy-settings-button");
+        if (settingsButton) {
+            settingsButton.insertAdjacentElement("beforebegin", huggingfaceButton);
+        } else {
+            menu.appendChild(huggingfaceButton);
+        }
+    }
+}
+
+// --- Initialization ---
+app.registerExtension({
+    name: "ComfyUI-HuggingFace.HuggingFaceDownloader",
+    async setup(appInstance) {
+        console.log(`[${EXTENSION_NAME}] Setting up HuggingFace Extension...`);
+        addCssLink(CSS_URL);
+        addMenuButton();
+
+        // Optional: Pre-check placeholder image
+        fetch(PLACEHOLDER_IMAGE_URL)
+            .then(res => {
+                if (!res.ok) {
+                    console.warn(`[${EXTENSION_NAME}] Placeholder image not found at ${PLACEHOLDER_IMAGE_URL}.`);
+                }
+            })
+            .catch(err => console.warn(`[${EXTENSION_NAME}] Error checking for placeholder image:`, err));
+
+        console.log(`[${EXTENSION_NAME}] Extension setup complete. UI will initialize on first click.`);
+    },
+});
diff --git a/custom_nodes/ComfyUI-HuggingFace/web/js/ui/UI.js b/custom_nodes/ComfyUI-HuggingFace/web/js/ui/UI.js
new file mode 100644
index 0000000000000000000000000000000000000000..a5a8dbe3bcd1ff8792858cf2311f7546904183d7
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/web/js/ui/UI.js
@@ -0,0 +1,465 @@
+import { Feedback } from "./feedback.js";
+import { setupEventListeners } from "./handlers/eventListeners.js";
+import { handleDownloadSubmit, fetchAndDisplayDownloadPreview, debounceFetchDownloadPreview } from "./handlers/downloadHandler.js";
+import { handleSearchSubmit } from "./handlers/searchHandler.js";
+import {
+    handleSettingsSave,
+    loadAndApplySettings,
+    loadSettingsFromCookie,
+    saveSettingsToCookie,
+    applySettings,
+    getDefaultSettings,
+    loadGlobalRootSetting,
+    handleSetGlobalRoot,
+    handleClearGlobalRoot,
+} from "./handlers/settingsHandler.js";
+import { startStatusUpdates, stopStatusUpdates, updateStatus, handleCancelDownload, handleRetryDownload, handleOpenPath, handleClearHistory } from "./handlers/statusHandler.js";
+import { renderSearchResults } from "./searchRenderer.js";
+import { renderDownloadList } from "./statusRenderer.js";
+import { renderDownloadPreview } from "./previewRenderer.js";
+import { modalTemplate } from "./templates.js";
+import { HuggingFaceDownloaderAPI } from "../api/huggingface.js";
+
+export class HuggingFaceDownloaderUI {
+    constructor() {
+        this.modal = null;
+        this.tabs = {};
+        this.tabContents = {};
+        this.activeTab = 'download';
+        this.modelTypes = {};
+        this.statusInterval = null;
+        this.statusData = { queue: [], active: [], history: [] };
+        this.baseModels = [];
+        this.searchPagination = { currentPage: 1, totalPages: 1, limit: 20 };
+        this.settings = this.getDefaultSettings();
+        this.toastTimeout = null;
+        this.modelPreviewDebounceTimeout = null;
+
+        this.updateStatus();
+        this.buildModalHTML();
+        this.cacheDOMElements();
+        this.setupEventListeners();
+        this.feedback = new Feedback(this.modal.querySelector('#huggingface-toast'));
+        // Ensure icon stylesheet is loaded so buttons render icons immediately
+        this.ensureFontAwesome();
+    }
+
+    // --- Core UI Methods ---
+    buildModalHTML() {
+        this.modal = document.createElement('div');
+        this.modal.className = 'huggingface-downloader-modal';
+        this.modal.id = 'huggingface-downloader-modal';
+        this.modal.innerHTML = modalTemplate(this.settings);
+    }
+
+    cacheDOMElements() {
+        this.closeButton = this.modal.querySelector('#huggingface-close-modal');
+        this.tabContainer = this.modal.querySelector('.huggingface-downloader-tabs');
+
+        // Download Tab
+        this.downloadForm = this.modal.querySelector('#huggingface-download-form');
+        this.downloadPreviewArea = this.modal.querySelector('#huggingface-download-preview-area');
+        this.modelUrlInput = this.modal.querySelector('#huggingface-model-url');
+        this.modelVersionIdInput = this.modal.querySelector('#huggingface-model-version-id');
+        this.downloadModelTypeSelect = this.modal.querySelector('#huggingface-model-type');
+        this.createModelTypeButton = this.modal.querySelector('#huggingface-create-model-type');
+        this.customFilenameInput = this.modal.querySelector('#huggingface-custom-filename');
+        this.subdirSelect = this.modal.querySelector('#huggingface-subdir-select');
+        this.createSubdirButton = this.modal.querySelector('#huggingface-create-subdir');
+        this.saveBasePathHint = this.modal.querySelector('#huggingface-save-base-path');
+        this.downloadConnectionsInput = this.modal.querySelector('#huggingface-connections');
+        this.forceRedownloadCheckbox = this.modal.querySelector('#huggingface-force-redownload');
+        this.downloadSubmitButton = this.modal.querySelector('#huggingface-download-submit');
+
+        // Search Tab
+        this.searchForm = this.modal.querySelector('#huggingface-search-form');
+        this.searchQueryInput = this.modal.querySelector('#huggingface-search-query');
+        this.searchTypeSelect = this.modal.querySelector('#huggingface-search-type');
+        this.searchBaseModelSelect = this.modal.querySelector('#huggingface-search-base-model');
+        this.searchSortSelect = this.modal.querySelector('#huggingface-search-sort');
+        this.searchPeriodSelect = this.modal.querySelector('#huggingface-search-period');
+        this.searchSubmitButton = this.modal.querySelector('#huggingface-search-submit');
+        this.searchResultsContainer = this.modal.querySelector('#huggingface-search-results');
+        this.searchPaginationContainer = this.modal.querySelector('#huggingface-search-pagination');
+
+        // Status Tab
+        this.statusContent = this.modal.querySelector('#huggingface-status-content');
+        this.activeListContainer = this.modal.querySelector('#huggingface-active-list');
+        this.queuedListContainer = this.modal.querySelector('#huggingface-queued-list');
+        this.historyListContainer = this.modal.querySelector('#huggingface-history-list');
+        this.statusIndicator = this.modal.querySelector('#huggingface-status-indicator');
+        this.activeCountSpan = this.modal.querySelector('#huggingface-active-count');
+        this.clearHistoryButton = this.modal.querySelector('#huggingface-clear-history-button');
+        this.confirmClearModal = this.modal.querySelector('#huggingface-confirm-clear-modal');
+        this.confirmClearYesButton = this.modal.querySelector('#huggingface-confirm-clear-yes');
+        this.confirmClearNoButton = this.modal.querySelector('#huggingface-confirm-clear-no');
+
+        // Settings Tab
+        this.settingsForm = this.modal.querySelector('#huggingface-settings-form');
+        this.settingsApiKeyInput = this.modal.querySelector('#huggingface-settings-api-key');
+        this.settingsGlobalRootInput = this.modal.querySelector('#huggingface-settings-global-root');
+        this.settingsSetGlobalRootButton = this.modal.querySelector('#huggingface-settings-set-global-root');
+        this.settingsClearGlobalRootButton = this.modal.querySelector('#huggingface-settings-clear-global-root');
+        this.settingsConnectionsInput = this.modal.querySelector('#huggingface-settings-connections');
+        this.settingsDefaultTypeSelect = this.modal.querySelector('#huggingface-settings-default-type');
+        this.settingsAutoOpenCheckbox = this.modal.querySelector('#huggingface-settings-auto-open-status');
+        this.settingsHideMatureCheckbox = this.modal.querySelector('#huggingface-settings-hide-mature');
+        this.settingsNsfwThresholdInput = this.modal.querySelector('#huggingface-settings-nsfw-threshold');
+        this.settingsSaveButton = this.modal.querySelector('#huggingface-settings-save');
+
+        // Toast Notification
+        this.toastElement = this.modal.querySelector('#huggingface-toast');
+
+        // Collect tabs and contents
+        this.tabs = {};
+        this.modal.querySelectorAll('.huggingface-downloader-tab').forEach(tab => {
+            this.tabs[tab.dataset.tab] = tab;
+        });
+        this.tabContents = {};
+        this.modal.querySelectorAll('.huggingface-downloader-tab-content').forEach(content => {
+            const tabName = content.id.replace('huggingface-tab-', '');
+            if (tabName) this.tabContents[tabName] = content;
+        });
+    }
+
+    async initializeUI() {
+        console.info("[HuggingFace] Initializing UI components...");
+        await this.populateModelTypes();
+        await this.populateBaseModels();
+        this.loadAndApplySettings();
+        await this.loadGlobalRootSetting();
+        if (this.downloadModelTypeSelect) {
+            await this.loadAndPopulateSubdirs(this.downloadModelTypeSelect.value);
+        }
+    }
+
+    async populateModelTypes() {
+        console.log("[HuggingFace] Populating model types...");
+        try {
+            const types = await HuggingFaceDownloaderAPI.getModelTypes();
+            if (!types || typeof types !== 'object' || Object.keys(types).length === 0) {
+                 throw new Error("Received invalid model types data format.");
+            }
+            this.modelTypes = types;
+            const sortedTypes = Object.entries(this.modelTypes).sort((a, b) => a[1].localeCompare(b[1]));
+
+            this.downloadModelTypeSelect.innerHTML = '';
+            this.searchTypeSelect.innerHTML = '<option value="any">Any Type</option>';
+            this.settingsDefaultTypeSelect.innerHTML = '';
+
+            sortedTypes.forEach(([key, displayName]) => {
+                const option = document.createElement('option');
+                option.value = key;
+                option.textContent = displayName;
+            this.downloadModelTypeSelect.appendChild(option.cloneNode(true));
+            this.settingsDefaultTypeSelect.appendChild(option.cloneNode(true));
+            this.searchTypeSelect.appendChild(option.cloneNode(true));
+        });
+        // After types are populated, load subdirs for the current selection
+        await this.loadAndPopulateSubdirs(this.downloadModelTypeSelect.value);
+        } catch (error) {
+            console.error("[HuggingFace] Failed to get or populate model types:", error);
+            this.showToast('Failed to load model types', 'error');
+            this.downloadModelTypeSelect.innerHTML = '<option value="checkpoints">Checkpoints (Default)</option>';
+            this.modelTypes = { "checkpoints": "Checkpoints (Default)" };
+        }
+    }
+
+    async loadAndPopulateSubdirs(modelType) {
+        try {
+            const res = await HuggingFaceDownloaderAPI.getModelDirs(modelType);
+            const select = this.subdirSelect;
+            if (!select) return;
+            const current = select.value;
+            select.innerHTML = '';
+            const optRoot = document.createElement('option');
+            optRoot.value = '';
+            optRoot.textContent = '(root)';
+            select.appendChild(optRoot);
+            if (res && Array.isArray(res.subdirs)) {
+                // res.subdirs contains '' for root; skip empty since we added (root)
+                res.subdirs.filter(p => p && typeof p === 'string').forEach(rel => {
+                    const opt = document.createElement('option');
+                    opt.value = rel;
+                    opt.textContent = rel;
+                    select.appendChild(opt);
+                });
+            }
+            // Restore selection if still present
+            if (Array.from(select.options).some(o => o.value === current)) {
+                select.value = current;
+            }
+            if (this.saveBasePathHint) {
+                const basePath = (res && typeof res.base_dir === 'string') ? res.base_dir : '';
+                this.saveBasePathHint.textContent = basePath ? `Base path: ${basePath}` : '';
+            }
+        } catch (e) {
+            console.error('[HuggingFace] Failed to load subdirectories:', e);
+            if (this.subdirSelect) {
+                this.subdirSelect.innerHTML = '<option value="">(root)</option>';
+            }
+            if (this.saveBasePathHint) {
+                this.saveBasePathHint.textContent = '';
+            }
+        }
+    }
+
+    // (loadAndPopulateRoots removed; dynamic types already reflect models/ subfolders)
+
+    async populateBaseModels() {
+        console.log("[HuggingFace] Populating base models...");
+        try {
+            const result = await HuggingFaceDownloaderAPI.getBaseModels();
+            if (!result || !Array.isArray(result.base_models)) {
+                throw new Error("Invalid base models data format received.");
+            }
+            this.baseModels = result.base_models.sort();
+            const existingOptions = Array.from(this.searchBaseModelSelect.options);
+            existingOptions.slice(1).forEach(opt => opt.remove());
+            this.baseModels.forEach(baseModelName => {
+                const option = document.createElement('option');
+                option.value = baseModelName;
+                option.textContent = baseModelName;
+                this.searchBaseModelSelect.appendChild(option);
+            });
+        } catch (error) {
+             console.error("[HuggingFace] Failed to get or populate base models:", error);
+             this.showToast('Failed to load base models list', 'error');
+        }
+    }
+
+    switchTab(tabId) {
+        if (this.activeTab === tabId || !this.tabs[tabId] || !this.tabContents[tabId]) return;
+
+        this.tabs[this.activeTab]?.classList.remove('active');
+        this.tabContents[this.activeTab]?.classList.remove('active');
+
+        this.tabs[tabId].classList.add('active');
+        this.tabContents[tabId].classList.add('active');
+        this.tabContents[tabId].scrollTop = 0;
+        this.activeTab = tabId;
+
+        if (tabId === 'status') this.updateStatus();
+        else if (tabId === 'settings') this.applySettings();
+        else if(tabId === 'download') {
+            this.downloadConnectionsInput.value = this.settings.numConnections;
+            if (Object.keys(this.modelTypes).length > 0) {
+                this.downloadModelTypeSelect.value = this.settings.defaultModelType;
+            }
+        }
+    }
+
+    // --- Modal Control ---
+    openModal() {
+        this.modal?.classList.add('open');
+        document.body.style.setProperty('overflow', 'hidden', 'important');
+        this.startStatusUpdates();
+        if (this.activeTab === 'status') this.updateStatus();
+        if (!this.settings.apiKey) this.switchTab('settings');
+    }
+
+    closeModal() {
+        this.modal?.classList.remove('open');
+        document.body.style.removeProperty('overflow');
+        this.stopStatusUpdates();
+    }
+
+    // --- Utility Methods ---
+    formatBytes(bytes, decimals = 2) {
+        if (bytes === null || bytes === undefined || isNaN(bytes)) return 'N/A';
+        if (bytes === 0) return '0 Bytes';
+        const k = 1024;
+        const dm = decimals < 0 ? 0 : decimals;
+        const sizes = ['Bytes', 'KB', 'MB', 'GB', 'TB'];
+        const i = Math.floor(Math.log(Math.abs(bytes)) / Math.log(k));
+        return parseFloat((bytes / Math.pow(k, i)).toFixed(dm)) + ' ' + sizes[i];
+    }
+
+    formatSpeed(bytesPerSecond) {
+        if (!isFinite(bytesPerSecond) || bytesPerSecond <= 0) return '';
+        return this.formatBytes(bytesPerSecond) + '/s';
+    }
+
+    formatDuration(isoStart, isoEnd) {
+        try {
+            const diffSeconds = Math.round((new Date(isoEnd) - new Date(isoStart)) / 1000);
+            if (isNaN(diffSeconds) || diffSeconds < 0) return 'N/A';
+            if (diffSeconds < 60) return `${diffSeconds}s`;
+            const diffMinutes = Math.floor(diffSeconds / 60);
+            const remainingSeconds = diffSeconds % 60;
+            return `${diffMinutes}m ${remainingSeconds}s`;
+        } catch (e) {
+            return 'N/A';
+        }
+    }
+
+    showToast(message, type = 'info', duration = 3000) {
+        this.feedback?.show(message, type, duration);
+    }
+
+    ensureFontAwesome() {
+        this.feedback?.ensureFontAwesome();
+    }
+
+    // --- Rendering (delegated to external renderers) ---
+    renderDownloadList = (items, container, emptyMessage) => renderDownloadList(this, items, container, emptyMessage);
+    renderSearchResults = (items) => renderSearchResults(this, items);
+    renderDownloadPreview = (data) => renderDownloadPreview(this, data);
+    
+    // --- Auto-select model type based on HuggingFace model type ---
+    inferFolderFromHuggingFaceType(huggingfaceType) {
+        if (!huggingfaceType || typeof huggingfaceType !== 'string') return null;
+        const t = huggingfaceType.trim().toLowerCase();
+        const keys = Object.keys(this.modelTypes || {});
+        if (keys.length === 0) return null;
+
+        const exists = (k) => keys.includes(k);
+        const findBy = (pred) => keys.find(pred);
+
+        // Direct matches first
+        if (exists(t)) return t;
+        if (exists(`${t}s`)) return `${t}s`;
+
+        // Common mappings from HuggingFace types to ComfyUI folders
+        const candidates = [];
+        const addIfExists = (k) => { if (exists(k)) candidates.push(k); };
+
+        switch (t) {
+            case 'checkpoint':
+                addIfExists('checkpoints');
+                addIfExists('models');
+                break;
+            case 'lora': case 'locon': case 'lycoris':
+                addIfExists('loras');
+                break;
+            case 'vae':
+                addIfExists('vae');
+                break;
+            case 'textualinversion': case 'embedding': case 'embeddings':
+                addIfExists('embeddings');
+                break;
+            case 'hypernetwork':
+                addIfExists('hypernetworks');
+                break;
+            case 'controlnet':
+                addIfExists('controlnet');
+                break;
+            case 'unet': case 'unet2':
+                addIfExists('unet');
+                break;
+            case 'diffusers': case 'diffusionmodels': case 'diffusion_models': case 'diffusion':
+                addIfExists('diffusers');
+                addIfExists('diffusion_models');
+                break;
+            case 'upscaler': case 'upscalers':
+                addIfExists('upscale_models');
+                addIfExists('upscalers');
+                break;
+            case 'motionmodule':
+                addIfExists('motion_models');
+                break;
+            case 'poses':
+                addIfExists('poses');
+                break;
+            case 'wildcards':
+                addIfExists('wildcards');
+                break;
+            case 'onnx':
+                addIfExists('onnx');
+                break;
+        }
+        if (candidates.length > 0) return candidates[0];
+
+        // Relaxed match: name contains type
+        const contains = findBy(k => k.toLowerCase().includes(t));
+        if (contains) return contains;
+
+        return null;
+    }
+
+    async autoSelectModelTypeFromHuggingFace(huggingfaceType) {
+        try {
+            const folder = this.inferFolderFromHuggingFaceType(huggingfaceType);
+            if (!folder) return;
+            if (this.downloadModelTypeSelect && this.downloadModelTypeSelect.value !== folder) {
+                this.downloadModelTypeSelect.value = folder;
+                await this.loadAndPopulateSubdirs(folder);
+                // Reset subdir to root after auto-switch
+                if (this.subdirSelect) this.subdirSelect.value = '';
+            }
+        } catch (e) {
+            console.warn('[HuggingFace] Auto-select model type failed:', e);
+        }
+    }
+
+    renderSearchPagination(metadata) {
+        if (!this.searchPaginationContainer) return;
+        if (!metadata || metadata.totalPages <= 1) {
+            this.searchPaginationContainer.innerHTML = '';
+            this.searchPagination = { ...this.searchPagination, ...metadata };
+            return;
+        }
+
+        this.searchPagination = { ...this.searchPagination, ...metadata };
+        const { currentPage, totalPages, totalItems } = this.searchPagination;
+
+        const createButton = (text, page, isDisabled = false, isCurrent = false) => {
+            const button = document.createElement('button');
+            button.className = `huggingface-button small huggingface-page-button ${isCurrent ? 'primary active' : ''}`;
+            button.dataset.page = page;
+            button.disabled = isDisabled;
+            button.innerHTML = text;
+            button.type = 'button';
+            return button;
+        };
+
+        const fragment = document.createDocumentFragment();
+        fragment.appendChild(createButton('&laquo; Prev', currentPage - 1, currentPage === 1));
+        
+        let startPage = Math.max(1, currentPage - 2);
+        let endPage = Math.min(totalPages, currentPage + 2);
+
+        if (startPage > 1) fragment.appendChild(createButton('1', 1));
+        if (startPage > 2) fragment.appendChild(document.createElement('span')).textContent = '...';
+
+        for (let i = startPage; i <= endPage; i++) {
+            fragment.appendChild(createButton(i, i, false, i === currentPage));
+        }
+
+        if (endPage < totalPages - 1) fragment.appendChild(document.createElement('span')).textContent = '...';
+        if (endPage < totalPages) fragment.appendChild(createButton(totalPages, totalPages));
+        
+        fragment.appendChild(createButton('Next &raquo;', currentPage + 1, currentPage === totalPages));
+
+        const info = document.createElement('div');
+        info.className = 'huggingface-pagination-info';
+        info.textContent = `Page ${currentPage} of ${totalPages} (${totalItems.toLocaleString()} models)`;
+        fragment.appendChild(info);
+
+        this.searchPaginationContainer.innerHTML = '';
+        this.searchPaginationContainer.appendChild(fragment);
+    }
+
+    // --- Event Handlers and State Management (delegated to handlers) ---
+    setupEventListeners = () => setupEventListeners(this);
+    getDefaultSettings = () => getDefaultSettings();
+    loadAndApplySettings = () => loadAndApplySettings(this);
+    loadSettingsFromCookie = () => loadSettingsFromCookie(this);
+    saveSettingsToCookie = () => saveSettingsToCookie(this);
+    applySettings = () => applySettings(this);
+    handleSettingsSave = () => handleSettingsSave(this);
+    loadGlobalRootSetting = () => loadGlobalRootSetting(this);
+    handleSetGlobalRoot = () => handleSetGlobalRoot(this);
+    handleClearGlobalRoot = () => handleClearGlobalRoot(this);
+    handleDownloadSubmit = () => handleDownloadSubmit(this);
+    handleSearchSubmit = () => handleSearchSubmit(this);
+    fetchAndDisplayDownloadPreview = () => fetchAndDisplayDownloadPreview(this);
+    debounceFetchDownloadPreview = (delay) => debounceFetchDownloadPreview(this, delay);
+    startStatusUpdates = () => startStatusUpdates(this);
+    stopStatusUpdates = () => stopStatusUpdates(this);
+    updateStatus = () => updateStatus(this);
+    handleCancelDownload = (downloadId) => handleCancelDownload(this, downloadId);
+    handleRetryDownload = (downloadId, button) => handleRetryDownload(this, downloadId, button);
+    handleOpenPath = (downloadId, button) => handleOpenPath(this, downloadId, button);
+    handleClearHistory = () => handleClearHistory(this);
+}
diff --git a/custom_nodes/ComfyUI-HuggingFace/web/js/ui/feedback.js b/custom_nodes/ComfyUI-HuggingFace/web/js/ui/feedback.js
new file mode 100644
index 0000000000000000000000000000000000000000..727bad4c256ae4ccb9d4077f062526de8d6922e5
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/web/js/ui/feedback.js
@@ -0,0 +1,41 @@
+// Centralized feedback utilities: toasts and icon CSS
+
+export class Feedback {
+  constructor(toastElement) {
+    this.toastElement = toastElement || null;
+    this.toastTimeout = null;
+  }
+
+  ensureFontAwesome() {
+    if (!document.getElementById('huggingface-fontawesome-link')) {
+      const faLink = document.createElement('link');
+      faLink.id = 'huggingface-fontawesome-link';
+      faLink.rel = 'stylesheet';
+      faLink.href = 'https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.4/css/all.min.css';
+      faLink.integrity = 'sha512-1ycn6IcaQQ40/MKBW2W4Rhis/DbILU74C1vSrLJxCq57o941Ym01SwNsOMqvEBFlcgUa6xLiPY/NS5R+E6ztJQ==';
+      faLink.crossOrigin = 'anonymous';
+      faLink.referrerPolicy = 'no-referrer';
+      document.head.appendChild(faLink);
+    }
+  }
+
+  show(message, type = 'info', duration = 3000) {
+    if (!this.toastElement) return;
+    if (this.toastTimeout) {
+      clearTimeout(this.toastTimeout);
+      this.toastTimeout = null;
+    }
+    const valid = ['info', 'success', 'error', 'warning'];
+    const toastType = valid.includes(type) ? type : 'info';
+
+    this.toastElement.textContent = message;
+    this.toastElement.className = 'huggingface-toast';
+    this.toastElement.classList.add(toastType);
+    requestAnimationFrame(() => this.toastElement.classList.add('show'));
+    this.toastTimeout = setTimeout(() => {
+      this.toastElement.classList.remove('show');
+      this.toastTimeout = null;
+    }, duration);
+  }
+}
+
diff --git a/custom_nodes/ComfyUI-HuggingFace/web/js/ui/handlers/downloadHandler.js b/custom_nodes/ComfyUI-HuggingFace/web/js/ui/handlers/downloadHandler.js
new file mode 100644
index 0000000000000000000000000000000000000000..08f42a667b48b1af4411f3cbe58af6bf4b1e5418
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/web/js/ui/handlers/downloadHandler.js
@@ -0,0 +1,106 @@
+import { HuggingFaceDownloaderAPI } from "../../api/huggingface.js";
+
+export function debounceFetchDownloadPreview(ui, delay = 500) {
+    clearTimeout(ui.modelPreviewDebounceTimeout);
+    ui.modelPreviewDebounceTimeout = setTimeout(() => {
+        fetchAndDisplayDownloadPreview(ui);
+    }, delay);
+}
+
+export async function fetchAndDisplayDownloadPreview(ui) {
+    const modelUrlOrId = ui.modelUrlInput.value.trim();
+    const versionId = ui.modelVersionIdInput.value.trim();
+
+    if (!modelUrlOrId) {
+        ui.downloadPreviewArea.innerHTML = '';
+        return;
+    }
+
+    ui.downloadPreviewArea.innerHTML = '<p><i class="fas fa-spinner fa-spin"></i> Loading model details...</p>';
+    ui.ensureFontAwesome();
+
+    const params = {
+        model_url_or_id: modelUrlOrId,
+        model_version_id: versionId ? parseInt(versionId, 10) : null,
+        api_key: ui.settings.apiKey
+    };
+
+    try {
+        const result = await HuggingFaceDownloaderAPI.getModelDetails(params);
+        if (result && result.success) {
+            ui.renderDownloadPreview(result);
+            // Auto-select model type save location based on HuggingFace model type
+            if (result.model_type) {
+                await ui.autoSelectModelTypeFromHuggingFace(result.model_type);
+            }
+        } else {
+            // Don't show error for missing details - just show neutral message
+            const message = result.details || result.error || 'Model details not available';
+            ui.downloadPreviewArea.innerHTML = `<p style="color: var(--input-text, #ccc);">${message}</p>`;
+        }
+    } catch (error) {
+        // Don't show scary error messages - just neutral info
+        const message = 'Model details not available';
+        console.info("Download Preview - details not available:", error);
+        ui.downloadPreviewArea.innerHTML = `<p style="color: var(--input-text, #ccc);">${message}</p>`;
+    }
+}
+
+export async function handleDownloadSubmit(ui) {
+    ui.downloadSubmitButton.disabled = true;
+    ui.downloadSubmitButton.textContent = 'Starting...';
+
+    const modelUrlOrId = ui.modelUrlInput.value.trim();
+    if (!modelUrlOrId) {
+        ui.showToast("Model URL or ID cannot be empty.", "error");
+        ui.downloadSubmitButton.disabled = false;
+        ui.downloadSubmitButton.textContent = 'Start Download';
+        return;
+    }
+
+    // Subfolder comes from dropdown; filename is base name only
+    const selectedSubdir = ui.subdirSelect ? ui.subdirSelect.value.trim() : '';
+    const userFilename = ui.customFilenameInput.value.trim();
+
+    const params = {
+        model_url_or_id: modelUrlOrId,
+        model_type: ui.downloadModelTypeSelect.value,
+        model_version_id: ui.modelVersionIdInput.value ? parseInt(ui.modelVersionIdInput.value, 10) : null,
+        custom_filename: userFilename,
+        subdir: selectedSubdir,
+        num_connections: parseInt(ui.downloadConnectionsInput.value, 10),
+        force_redownload: ui.forceRedownloadCheckbox.checked,
+        api_key: ui.settings.apiKey
+    };
+
+    const fileSelectEl = ui.modal.querySelector('#huggingface-file-select');
+    if (fileSelectEl && fileSelectEl.value) {
+        const fid = parseInt(fileSelectEl.value, 10);
+        if (!Number.isNaN(fid)) params.file_id = fid;
+    }
+
+    try {
+        const result = await HuggingFaceDownloaderAPI.downloadModel(params);
+
+        if (result.status === 'queued') {
+            ui.showToast(`Download queued: ${result.details?.filename || 'Model'}`, 'success');
+            if (ui.settings.autoOpenStatusTab) {
+                ui.switchTab('status');
+            } else {
+                ui.updateStatus();
+            }
+        } else if (result.status === 'exists' || result.status === 'exists_size_mismatch') {
+            ui.showToast(`${result.message}`, 'info', 4000);
+        } else {
+            console.warn("Unexpected success response from /huggingface/download:", result);
+            ui.showToast(`Unexpected status: ${result.status} - ${result.message || ''}`, 'info');
+        }
+    } catch (error) {
+        const message = `Download failed: ${error.details || error.message || 'Unknown error'}`;
+        console.error("Download Submit Error:", error);
+        ui.showToast(message, 'error', 6000);
+    } finally {
+        ui.downloadSubmitButton.disabled = false;
+        ui.downloadSubmitButton.textContent = 'Start Download';
+    }
+}
diff --git a/custom_nodes/ComfyUI-HuggingFace/web/js/ui/handlers/eventListeners.js b/custom_nodes/ComfyUI-HuggingFace/web/js/ui/handlers/eventListeners.js
new file mode 100644
index 0000000000000000000000000000000000000000..70ac7fc699a54152c7d0967c602f6fe012f6b1be
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/web/js/ui/handlers/eventListeners.js
@@ -0,0 +1,248 @@
+import { HuggingFaceDownloaderAPI } from "../../api/huggingface.js";
+export function setupEventListeners(ui) {
+    // Modal close
+    ui.closeButton.addEventListener('click', () => ui.closeModal());
+    ui.modal.addEventListener('click', (event) => {
+        if (event.target === ui.modal) ui.closeModal();
+    });
+
+    // Tab switching
+    ui.tabContainer.addEventListener('click', (event) => {
+        if (event.target.matches('.huggingface-downloader-tab')) {
+            ui.switchTab(event.target.dataset.tab);
+        }
+    });
+
+    // --- FORMS ---
+    ui.downloadForm.addEventListener('submit', (event) => {
+        event.preventDefault();
+        ui.handleDownloadSubmit();
+    });
+
+    // Change of model type should refresh subdir list
+    ui.downloadModelTypeSelect.addEventListener('change', async () => {
+        await ui.loadAndPopulateSubdirs(ui.downloadModelTypeSelect.value);
+    });
+
+    // Create new model type folder (first-level under models/)
+    ui.createModelTypeButton.addEventListener('click', async () => {
+        const name = prompt('Enter new model type folder name (will be created under models/)');
+        if (!name) return;
+        try {
+            const res = await HuggingFaceDownloaderAPI.createModelType(name);
+            if (res && res.success) {
+                await ui.populateModelTypes();
+                ui.downloadModelTypeSelect.value = res.name;
+                await ui.loadAndPopulateSubdirs(res.name);
+                ui.showToast(`Created model type folder: ${res.name}`, 'success');
+            } else {
+                ui.showToast(res?.error || 'Failed to create model type folder', 'error');
+            }
+        } catch (e) {
+            ui.showToast(e.details || e.message || 'Error creating model type folder', 'error');
+        }
+    });
+
+    // Create new subfolder under current model type
+    ui.createSubdirButton.addEventListener('click', async () => {
+        const type = ui.downloadModelTypeSelect.value;
+        const name = prompt('Enter new subfolder name (you can include nested paths like A/B):');
+        if (!name) return;
+        try {
+            const res = await HuggingFaceDownloaderAPI.createModelDir(type, name);
+            if (res && res.success) {
+                await ui.loadAndPopulateSubdirs(type);
+                if (ui.subdirSelect) ui.subdirSelect.value = res.created || '';
+                ui.showToast(`Created folder: ${res.created}`, 'success');
+            } else {
+                ui.showToast(res?.error || 'Failed to create folder', 'error');
+            }
+        } catch (e) {
+            ui.showToast(e.details || e.message || 'Error creating folder', 'error');
+        }
+    });
+
+    ui.searchForm.addEventListener('submit', (event) => {
+        event.preventDefault();
+        if (!ui.searchQueryInput.value.trim() && ui.searchTypeSelect.value === 'any' && ui.searchBaseModelSelect.value === 'any') {
+            ui.showToast("Please enter a search query or select a filter.", "error");
+            if (ui.searchResultsContainer) ui.searchResultsContainer.innerHTML = '<p>Please enter a search query or select a filter.</p>';
+            if (ui.searchPaginationContainer) ui.searchPaginationContainer.innerHTML = '';
+            return;
+        }
+        ui.searchPagination.currentPage = 1;
+        ui.handleSearchSubmit();
+    });
+
+    ui.settingsForm.addEventListener('submit', (event) => {
+        event.preventDefault();
+        ui.handleSettingsSave();
+    });
+    if (ui.settingsSetGlobalRootButton) {
+        ui.settingsSetGlobalRootButton.addEventListener('click', () => {
+            ui.handleSetGlobalRoot();
+        });
+    }
+    if (ui.settingsClearGlobalRootButton) {
+        ui.settingsClearGlobalRootButton.addEventListener('click', () => {
+            ui.handleClearGlobalRoot();
+        });
+    }
+
+    // Download form inputs
+    ui.modelUrlInput.addEventListener('input', () => ui.debounceFetchDownloadPreview());
+    ui.modelUrlInput.addEventListener('paste', () => ui.debounceFetchDownloadPreview(0));
+    ui.modelVersionIdInput.addEventListener('blur', () => ui.fetchAndDisplayDownloadPreview());
+
+    // --- DYNAMIC CONTENT LISTENERS (Event Delegation) ---
+
+    // Status tab actions (Cancel/Retry/Open/Clear) and click-to-toggle blur on thumbs
+    ui.statusContent.addEventListener('click', (event) => {
+        const thumbContainer = event.target.closest('.huggingface-thumbnail-container');
+        if (thumbContainer) {
+            const nsfwLevel = Number(thumbContainer.dataset.nsfwLevel ?? thumbContainer.getAttribute('data-nsfw-level'));
+            const threshold = Number(ui.settings?.nsfwBlurMinLevel ?? 4);
+            const enabled = ui.settings?.hideMatureInSearch === true;
+            if (enabled && Number.isFinite(nsfwLevel) && nsfwLevel >= threshold) {
+                if (thumbContainer.classList.contains('blurred')) {
+                    thumbContainer.classList.remove('blurred');
+                    const overlay = thumbContainer.querySelector('.huggingface-nsfw-overlay');
+                    if (overlay) overlay.remove();
+                } else {
+                    thumbContainer.classList.add('blurred');
+                    if (!thumbContainer.querySelector('.huggingface-nsfw-overlay')) {
+                        const ov = document.createElement('div');
+                        ov.className = 'huggingface-nsfw-overlay';
+                        ov.title = 'R-rated: click to reveal';
+                        ov.textContent = 'R';
+                        thumbContainer.appendChild(ov);
+                    }
+                }
+                return; // consume
+            }
+        }
+
+        const button = event.target.closest('button');
+        if (!button) return;
+
+        const downloadId = button.dataset.id;
+        if (downloadId) {
+            if (button.classList.contains('huggingface-cancel-button')) ui.handleCancelDownload(downloadId);
+            else if (button.classList.contains('huggingface-retry-button')) ui.handleRetryDownload(downloadId, button);
+            else if (button.classList.contains('huggingface-openpath-button')) ui.handleOpenPath(downloadId, button);
+        } else if (button.id === 'huggingface-clear-history-button') {
+            ui.confirmClearModal.style.display = 'flex';
+        }
+    });
+
+    // Download preview click-to-toggle blur
+    ui.downloadPreviewArea.addEventListener('click', (event) => {
+        const thumbContainer = event.target.closest('.huggingface-thumbnail-container');
+        if (thumbContainer) {
+            const nsfwLevel = Number(thumbContainer.dataset.nsfwLevel ?? thumbContainer.getAttribute('data-nsfw-level'));
+            const threshold = Number(ui.settings?.nsfwBlurMinLevel ?? 4);
+            const enabled = ui.settings?.hideMatureInSearch === true;
+            if (enabled && Number.isFinite(nsfwLevel) && nsfwLevel >= threshold) {
+                if (thumbContainer.classList.contains('blurred')) {
+                    thumbContainer.classList.remove('blurred');
+                    const overlay = thumbContainer.querySelector('.huggingface-nsfw-overlay');
+                    if (overlay) overlay.remove();
+                } else {
+                    thumbContainer.classList.add('blurred');
+                    if (!thumbContainer.querySelector('.huggingface-nsfw-overlay')) {
+                        const ov = document.createElement('div');
+                        ov.className = 'huggingface-nsfw-overlay';
+                        ov.title = 'R-rated: click to reveal';
+                        ov.textContent = 'R';
+                        thumbContainer.appendChild(ov);
+                    }
+                }
+            }
+        }
+    });
+
+    // Search results actions, including click-to-toggle blur
+    ui.searchResultsContainer.addEventListener('click', (event) => {
+        const thumbContainer = event.target.closest('.huggingface-thumbnail-container');
+        if (thumbContainer) {
+            const nsfwLevel = Number(thumbContainer.dataset.nsfwLevel ?? thumbContainer.getAttribute('data-nsfw-level'));
+            const threshold = Number(ui.settings?.nsfwBlurMinLevel ?? 4);
+            const enabled = ui.settings?.hideMatureInSearch === true;
+            if (enabled && Number.isFinite(nsfwLevel) && nsfwLevel >= threshold) {
+                if (thumbContainer.classList.contains('blurred')) {
+                    thumbContainer.classList.remove('blurred');
+                    const overlay = thumbContainer.querySelector('.huggingface-nsfw-overlay');
+                    if (overlay) overlay.remove();
+                } else {
+                    thumbContainer.classList.add('blurred');
+                    if (!thumbContainer.querySelector('.huggingface-nsfw-overlay')) {
+                        const ov = document.createElement('div');
+                        ov.className = 'huggingface-nsfw-overlay';
+                        ov.title = 'R-rated: click to reveal';
+                        ov.textContent = 'R';
+                        thumbContainer.appendChild(ov);
+                    }
+                }
+                return; // Don't trigger other actions on this click
+            }
+        }
+
+        const downloadButton = event.target.closest('.huggingface-search-download-button');
+        if (downloadButton) {
+            event.preventDefault();
+            const { modelId, versionId, modelType, creator, modelName } = downloadButton.dataset;
+            if (!modelId) {
+                ui.showToast("Error: Missing model ID for download.", "error");
+                return;
+            }
+            const modelTypeInternalKey = Object.keys(ui.modelTypes).find(key => ui.modelTypes[key]?.toLowerCase() === modelType?.toLowerCase()) || ui.settings.defaultModelType;
+
+            ui.modelUrlInput.value = modelId;
+            ui.modelVersionIdInput.value = versionId;
+            ui.customFilenameInput.value = modelName ? `${modelName.replace(/[^a-zA-Z0-9_-]/g, '_')}` : '';
+            ui.forceRedownloadCheckbox.checked = false;
+            ui.downloadModelTypeSelect.value = modelTypeInternalKey;
+
+            ui.switchTab('download');
+            ui.showToast(`Filled download form for "${modelName || modelId}" by ${creator || 'Unknown'}.`, 'info', 4000);
+            ui.fetchAndDisplayDownloadPreview();
+            return;
+        }
+
+        const viewAllButton = event.target.closest('.show-all-versions-button');
+        if (viewAllButton) {
+            const modelId = viewAllButton.dataset.modelId;
+            const versionsContainer = ui.searchResultsContainer.querySelector(`#all-versions-${modelId}`);
+            if (versionsContainer) {
+                const currentlyVisible = versionsContainer.style.display !== 'none';
+                versionsContainer.style.display = currentlyVisible ? 'none' : 'flex';
+                viewAllButton.innerHTML = currentlyVisible
+                    ? `All versions (${viewAllButton.dataset.totalVersions}) <i class="fas fa-chevron-down"></i>`
+                    : `Show less <i class="fas fa-chevron-up"></i>`;
+            }
+        }
+    });
+
+    // Pagination
+    ui.searchPaginationContainer.addEventListener('click', (event) => {
+        const button = event.target.closest('.huggingface-page-button');
+        if (button && !button.disabled) {
+            const page = parseInt(button.dataset.page, 10);
+            if (page && page !== ui.searchPagination.currentPage) {
+                ui.searchPagination.currentPage = page;
+                ui.handleSearchSubmit();
+            }
+        }
+    });
+
+    // Confirmation Modal
+    ui.confirmClearYesButton.addEventListener('click', () => ui.handleClearHistory());
+    ui.confirmClearNoButton.addEventListener('click', () => {
+        ui.confirmClearModal.style.display = 'none';
+    });
+    ui.confirmClearModal.addEventListener('click', (event) => {
+        if (event.target === ui.confirmClearModal) {
+            ui.confirmClearModal.style.display = 'none';
+        }
+    });
+}
diff --git a/custom_nodes/ComfyUI-HuggingFace/web/js/ui/handlers/searchHandler.js b/custom_nodes/ComfyUI-HuggingFace/web/js/ui/handlers/searchHandler.js
new file mode 100644
index 0000000000000000000000000000000000000000..96d0a1573702fb50bdf924488d41ffe27502b475
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/web/js/ui/handlers/searchHandler.js
@@ -0,0 +1,39 @@
+import { HuggingFaceDownloaderAPI } from "../../api/huggingface.js";
+
+export async function handleSearchSubmit(ui) {
+    ui.searchSubmitButton.disabled = true;
+    ui.searchSubmitButton.textContent = 'Searching...';
+    ui.searchResultsContainer.innerHTML = '<p><i class="fas fa-spinner fa-spin"></i> Searching...</p>';
+    ui.searchPaginationContainer.innerHTML = '';
+    ui.ensureFontAwesome();
+
+    const params = {
+        query: ui.searchQueryInput.value.trim(),
+        model_types: ui.searchTypeSelect.value === 'any' ? [] : [ui.searchTypeSelect.value],
+        base_models: ui.searchBaseModelSelect.value === 'any' ? [] : [ui.searchBaseModelSelect.value],
+        sort: ui.searchSortSelect.value,
+        limit: ui.searchPagination.limit,
+        page: ui.searchPagination.currentPage,
+        api_key: ui.settings.apiKey,
+    };
+
+    try {
+        const response = await HuggingFaceDownloaderAPI.searchModels(params);
+        if (!response || !response.metadata || !Array.isArray(response.items)) {
+            console.error("Invalid search response structure:", response);
+            throw new Error("Received invalid data from search API.");
+        }
+
+        ui.renderSearchResults(response.items);
+        ui.renderSearchPagination(response.metadata);
+
+    } catch (error) {
+        const message = `Search failed: ${error.details || error.message || 'Unknown error'}`;
+        console.error("Search Submit Error:", error);
+        ui.searchResultsContainer.innerHTML = `<p style="color: var(--error-text, #ff6b6b);">${message}</p>`;
+        ui.showToast(message, 'error');
+    } finally {
+        ui.searchSubmitButton.disabled = false;
+        ui.searchSubmitButton.textContent = 'Search';
+    }
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-HuggingFace/web/js/ui/handlers/settingsHandler.js b/custom_nodes/ComfyUI-HuggingFace/web/js/ui/handlers/settingsHandler.js
new file mode 100644
index 0000000000000000000000000000000000000000..86a47173b53d502871f6ca93990a990250e2a37b
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/web/js/ui/handlers/settingsHandler.js
@@ -0,0 +1,160 @@
+import { setCookie, getCookie } from "../../utils/cookies.js";
+import { HuggingFaceDownloaderAPI } from "../../api/huggingface.js";
+
+const SETTINGS_COOKIE_NAME = 'huggingfaceDownloaderSettings';
+
+export function getDefaultSettings() {
+    return {
+        apiKey: '',
+        numConnections: 1,
+        defaultModelType: 'checkpoints',
+        autoOpenStatusTab: true,
+        searchResultLimit: 20,
+        hideMatureInSearch: true,
+        nsfwBlurMinLevel: 4, // Blur thumbnails with nsfwLevel >= this value
+    };
+}
+
+export function loadAndApplySettings(ui) {
+    ui.settings = ui.loadSettingsFromCookie();
+    ui.applySettings();
+}
+
+export function loadSettingsFromCookie(ui) {
+    const defaults = ui.getDefaultSettings();
+    const cookieValue = getCookie(SETTINGS_COOKIE_NAME);
+
+    if (cookieValue) {
+        try {
+            const loadedSettings = JSON.parse(cookieValue);
+            return { ...defaults, ...loadedSettings };
+        } catch (e) {
+            console.error("Failed to parse settings cookie:", e);
+            return defaults;
+        }
+    }
+    return defaults;
+}
+
+export function saveSettingsToCookie(ui) {
+    try {
+        const settingsString = JSON.stringify(ui.settings);
+        setCookie(SETTINGS_COOKIE_NAME, settingsString, 365);
+        ui.showToast('Settings saved successfully!', 'success');
+    } catch (e) {
+        console.error("Failed to save settings to cookie:", e);
+        ui.showToast('Error saving settings', 'error');
+    }
+}
+
+export function applySettings(ui) {
+    if (ui.settingsApiKeyInput) {
+        ui.settingsApiKeyInput.value = ui.settings.apiKey || '';
+    }
+    if (ui.settingsConnectionsInput) {
+        ui.settingsConnectionsInput.value = Math.max(1, Math.min(16, ui.settings.numConnections || 1));
+    }
+    if (ui.settingsDefaultTypeSelect) {
+        const desired = ui.settings.defaultModelType || 'checkpoints';
+        ui.settingsDefaultTypeSelect.value = desired;
+        if (!ui.settingsDefaultTypeSelect.querySelector(`option[value="${ui.settingsDefaultTypeSelect.value}"]`)) {
+            const first = ui.settingsDefaultTypeSelect.querySelector('option');
+            if (first) ui.settingsDefaultTypeSelect.value = first.value;
+        }
+    }
+    if (ui.settingsAutoOpenCheckbox) {
+        ui.settingsAutoOpenCheckbox.checked = ui.settings.autoOpenStatusTab === true;
+    }
+    if (ui.settingsHideMatureCheckbox) {
+        ui.settingsHideMatureCheckbox.checked = ui.settings.hideMatureInSearch === true;
+    }
+    if (ui.settingsNsfwThresholdInput) {
+        const val = Number(ui.settings.nsfwBlurMinLevel);
+        ui.settingsNsfwThresholdInput.value = Number.isFinite(val) ? val : 4;
+    }
+    if (ui.downloadConnectionsInput) {
+        ui.downloadConnectionsInput.value = Math.max(1, Math.min(16, ui.settings.numConnections || 1));
+    }
+    if (ui.downloadModelTypeSelect && Object.keys(ui.modelTypes).length > 0) {
+        const desired = ui.settings.defaultModelType || 'checkpoints';
+        ui.downloadModelTypeSelect.value = desired;
+        if (!ui.downloadModelTypeSelect.querySelector(`option[value="${ui.downloadModelTypeSelect.value}"]`)) {
+            const first = ui.downloadModelTypeSelect.querySelector('option');
+            if (first) ui.downloadModelTypeSelect.value = first.value;
+        }
+    }
+    ui.searchPagination.limit = ui.settings.searchResultLimit || 20;
+}
+
+export async function loadGlobalRootSetting(ui) {
+    if (!ui.settingsGlobalRootInput) return;
+    try {
+        const result = await HuggingFaceDownloaderAPI.getGlobalRoot();
+        const globalRoot = (result && typeof result.global_root === 'string') ? result.global_root : '';
+        ui.settingsGlobalRootInput.value = globalRoot;
+    } catch (e) {
+        console.warn("[HuggingFace] Failed to load global root setting:", e);
+    }
+}
+
+export async function handleSetGlobalRoot(ui) {
+    if (!ui.settingsGlobalRootInput) return;
+    const path = ui.settingsGlobalRootInput.value.trim();
+    if (!path) {
+        ui.showToast("Please enter a global root path first.", "error");
+        return;
+    }
+    try {
+        const result = await HuggingFaceDownloaderAPI.setGlobalRoot(path);
+        const saved = (result && typeof result.global_root === 'string') ? result.global_root : path;
+        ui.settingsGlobalRootInput.value = saved;
+        ui.showToast("Global root updated.", "success");
+        if (ui.downloadModelTypeSelect) {
+            await ui.loadAndPopulateSubdirs(ui.downloadModelTypeSelect.value);
+        }
+    } catch (e) {
+        ui.showToast(e.details || e.message || "Failed to set global root.", "error", 6000);
+    }
+}
+
+export async function handleClearGlobalRoot(ui) {
+    if (!ui.settingsGlobalRootInput) return;
+    try {
+        await HuggingFaceDownloaderAPI.clearGlobalRoot();
+        ui.settingsGlobalRootInput.value = "";
+        ui.showToast("Global root cleared. Using default ComfyUI paths.", "success");
+        if (ui.downloadModelTypeSelect) {
+            await ui.loadAndPopulateSubdirs(ui.downloadModelTypeSelect.value);
+        }
+    } catch (e) {
+        ui.showToast(e.details || e.message || "Failed to clear global root.", "error", 6000);
+    }
+}
+
+export function handleSettingsSave(ui) {
+    const apiKey = ui.settingsApiKeyInput.value.trim();
+    const numConnections = parseInt(ui.settingsConnectionsInput.value, 10);
+    const defaultModelType = ui.settingsDefaultTypeSelect.value;
+    const autoOpenStatusTab = ui.settingsAutoOpenCheckbox.checked;
+    const hideMatureInSearch = ui.settingsHideMatureCheckbox.checked;
+    const nsfwBlurMinLevel = Number(ui.settingsNsfwThresholdInput.value);
+
+    if (isNaN(numConnections) || numConnections < 1 || numConnections > 16) {
+        ui.showToast("Invalid Default Connections (must be 1-16).", "error");
+        return;
+    }
+    if (!ui.settingsDefaultTypeSelect.querySelector(`option[value="${defaultModelType}"]`)) {
+        ui.showToast("Invalid Default Model Type selected.", "error");
+        return;
+    }
+
+    ui.settings.apiKey = apiKey;
+    ui.settings.numConnections = numConnections;
+    ui.settings.defaultModelType = defaultModelType;
+    ui.settings.autoOpenStatusTab = autoOpenStatusTab;
+    ui.settings.hideMatureInSearch = hideMatureInSearch;
+    ui.settings.nsfwBlurMinLevel = (Number.isFinite(nsfwBlurMinLevel) && nsfwBlurMinLevel >= 0) ? Math.min(128, Math.round(nsfwBlurMinLevel)) : 4;
+
+    ui.saveSettingsToCookie();
+    ui.applySettings();
+}
diff --git a/custom_nodes/ComfyUI-HuggingFace/web/js/ui/handlers/statusHandler.js b/custom_nodes/ComfyUI-HuggingFace/web/js/ui/handlers/statusHandler.js
new file mode 100644
index 0000000000000000000000000000000000000000..9ebc554a0eb86196e880e5c58e7951c9e4bc6717
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/web/js/ui/handlers/statusHandler.js
@@ -0,0 +1,154 @@
+import { HuggingFaceDownloaderAPI } from "../../api/huggingface.js";
+
+export function startStatusUpdates(ui) {
+    if (!ui.statusInterval) {
+        console.log("[HuggingFace] Starting status updates (every 3s)...");
+        ui.updateStatus();
+        ui.statusInterval = setInterval(() => ui.updateStatus(), 3000);
+    }
+}
+
+export function stopStatusUpdates(ui) {
+    if (ui.statusInterval) {
+        clearInterval(ui.statusInterval);
+        ui.statusInterval = null;
+        console.log("[HuggingFace] Stopped status updates.");
+    }
+}
+
+export async function updateStatus(ui) {
+    if (!ui.modal || !ui.modal.classList.contains('open')) return;
+
+    try {
+        const newStatusData = await HuggingFaceDownloaderAPI.getStatus();
+        if (!newStatusData || !Array.isArray(newStatusData.active) || !Array.isArray(newStatusData.queue) || !Array.isArray(newStatusData.history)) {
+            throw new Error("Invalid status data structure received from server.");
+        }
+
+        const oldStateString = JSON.stringify(ui.statusData);
+        const newStateString = JSON.stringify(newStatusData);
+
+        // Cache new state if it differs
+        if (oldStateString !== newStateString) {
+            ui.statusData = newStatusData;
+        }
+
+        // Always keep counters in sync
+        const activeCount = ui.statusData.active.length + ui.statusData.queue.length;
+        ui.activeCountSpan.textContent = activeCount;
+        ui.statusIndicator.style.display = activeCount > 0 ? 'inline' : 'none';
+
+        // Always render when Status tab is active, even if data hasn't changed
+        if (ui.activeTab === 'status') {
+            ui.renderDownloadList(ui.statusData.active, ui.activeListContainer, 'No active downloads.');
+            ui.renderDownloadList(ui.statusData.queue, ui.queuedListContainer, 'Download queue is empty.');
+            ui.renderDownloadList(ui.statusData.history, ui.historyListContainer, 'No download history yet.');
+        }
+    } catch (error) {
+        console.error("[HuggingFace] Failed to update status:", error);
+        if (ui.activeTab === 'status') {
+            const errorHtml = `<p style="color: var(--error-text, #ff6b6b);">${error.details || error.message}</p>`;
+            if (ui.activeListContainer) ui.activeListContainer.innerHTML = errorHtml;
+            if (ui.queuedListContainer) ui.queuedListContainer.innerHTML = '';
+            if (ui.historyListContainer) ui.historyListContainer.innerHTML = '';
+        }
+    }
+}
+
+export async function handleCancelDownload(ui, downloadId) {
+    const button = ui.modal.querySelector(`.huggingface-cancel-button[data-id="${downloadId}"]`);
+    if (button) {
+        button.disabled = true;
+        button.innerHTML = '<i class="fas fa-spinner fa-spin"></i>';
+        button.title = "Cancelling...";
+    }
+    try {
+        const result = await HuggingFaceDownloaderAPI.cancelDownload(downloadId);
+        ui.showToast(result.message || `Cancellation requested for ${downloadId}`, 'info');
+        ui.updateStatus();
+    } catch (error) {
+        const message = `Cancel failed: ${error.details || error.message}`;
+        console.error("Cancel Download Error:", error);
+        ui.showToast(message, 'error');
+        if (button) {
+            button.disabled = false;
+            button.innerHTML = '<i class="fas fa-times"></i>';
+            button.title = "Cancel Download";
+        }
+    }
+}
+
+export async function handleRetryDownload(ui, downloadId, button) {
+    button.disabled = true;
+    button.innerHTML = '<i class="fas fa-spinner fa-spin"></i>';
+    button.title = "Retrying...";
+    try {
+        const result = await HuggingFaceDownloaderAPI.retryDownload(downloadId);
+        if (result.success) {
+            ui.showToast(result.message || `Retry queued successfully!`, 'success');
+            if (ui.settings.autoOpenStatusTab) ui.switchTab('status');
+            else ui.updateStatus();
+        } else {
+            ui.showToast(`Retry failed: ${result.details || result.error}`, 'error', 5000);
+            button.disabled = false;
+            button.innerHTML = '<i class="fas fa-redo"></i>';
+            button.title = "Retry Download";
+        }
+    } catch (error) {
+        const message = `Retry failed: ${error.details || error.message}`;
+        console.error("Retry Download UI Error:", error);
+        ui.showToast(message, 'error', 5000);
+        button.disabled = false;
+        button.innerHTML = '<i class="fas fa-redo"></i>';
+        button.title = "Retry Download";
+    }
+}
+
+export async function handleOpenPath(ui, downloadId, button) {
+    const originalIcon = button.innerHTML;
+    button.disabled = true;
+    button.innerHTML = '<i class="fas fa-spinner fa-spin"></i>';
+    button.title = "Opening...";
+    try {
+        const result = await HuggingFaceDownloaderAPI.openPath(downloadId);
+        if (result.success) {
+            ui.showToast(result.message || `Opened path successfully!`, 'success');
+        } else {
+            ui.showToast(`Open path failed: ${result.details || result.error}`, 'error', 5000);
+        }
+    } catch (error) {
+        const message = `Open path failed: ${error.details || error.message}`;
+        console.error("Open Path UI Error:", error);
+        ui.showToast(message, 'error', 5000);
+    } finally {
+        button.disabled = false;
+        button.innerHTML = originalIcon;
+        button.title = "Open Containing Folder";
+    }
+}
+
+export async function handleClearHistory(ui) {
+    ui.confirmClearYesButton.disabled = true;
+    ui.confirmClearNoButton.disabled = true;
+    ui.confirmClearYesButton.textContent = 'Clearing...';
+
+    try {
+        const result = await HuggingFaceDownloaderAPI.clearHistory();
+        if (result.success) {
+            ui.showToast(result.message || 'History cleared successfully!', 'success');
+            ui.statusData.history = [];
+            ui.renderDownloadList(ui.statusData.history, ui.historyListContainer, 'No download history yet.');
+            ui.confirmClearModal.style.display = 'none';
+        } else {
+            ui.showToast(`Clear history failed: ${result.details || result.error}`, 'error', 5000);
+        }
+    } catch (error) {
+        const message = `Clear history failed: ${error.details || error.message}`;
+        console.error("Clear History UI Error:", error);
+        ui.showToast(message, 'error', 5000);
+    } finally {
+        ui.confirmClearYesButton.disabled = false;
+        ui.confirmClearNoButton.disabled = false;
+        ui.confirmClearYesButton.textContent = 'Confirm Clear';
+    }
+}
diff --git a/custom_nodes/ComfyUI-HuggingFace/web/js/ui/previewRenderer.js b/custom_nodes/ComfyUI-HuggingFace/web/js/ui/previewRenderer.js
new file mode 100644
index 0000000000000000000000000000000000000000..bb33018c184fdef0d986bd4f774f9843bac43bd4
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/web/js/ui/previewRenderer.js
@@ -0,0 +1,96 @@
+// Renders the download preview panel
+
+const PLACEHOLDER_IMAGE_URL = `/extensions/ComfyUI-HuggingFace/images/placeholder.jpg`;
+
+export function renderDownloadPreview(ui, data) {
+  if (!ui.downloadPreviewArea) return;
+  ui.ensureFontAwesome();
+
+  const modelId = data.model_id;
+  const modelName = data.model_name || 'Untitled Model';
+  const creator = data.creator_username || 'Unknown Creator';
+  const modelType = data.model_type || 'N/A';
+  const versionName = data.version_name || 'N/A';
+  const baseModel = data.base_model || 'N/A';
+  const stats = data.stats || {};
+  const descriptionHtml = data.description_html || '<p><em>No description.</em></p>';
+  const version_description_html = data.version_description_html || '<p><em>No description.</em></p>';
+  const fileInfo = data.file_info || {};
+  const files = Array.isArray(data.files) ? data.files : [];
+  const thumbnail = data.thumbnail_url || PLACEHOLDER_IMAGE_URL;
+  const nsfwLevel = Number(data.nsfw_level ?? 0);
+  const blurMinLevel = Number(ui.settings?.nsfwBlurMinLevel ?? 4);
+  const shouldBlur = ui.settings?.hideMatureInSearch === true && nsfwLevel >= blurMinLevel;
+  const huggingfaceLink = `https://huggingface.com/models/${modelId}${data.version_id ? '?modelVersionId=' + data.version_id : ''}`;
+
+  const onErrorScript = `this.onerror=null; this.src='${PLACEHOLDER_IMAGE_URL}'; this.style.backgroundColor='#444';`;
+
+  const overlayHtml = shouldBlur ? `<div class="huggingface-nsfw-overlay" title="R-rated: click to reveal">R</div>` : '';
+  const containerClasses = `huggingface-thumbnail-container${shouldBlur ? ' blurred' : ''}`;
+
+  const previewHtml = `
+    <div class="huggingface-search-item" style="background-color: var(--comfy-input-bg);">
+      <div class="${containerClasses}" data-nsfw-level="${Number.isFinite(nsfwLevel) ? nsfwLevel : ''}">
+        <img src="${thumbnail}" alt="${modelName} thumbnail" class="huggingface-search-thumbnail" loading="lazy" onerror="${onErrorScript}">
+        ${overlayHtml}
+        <div class="huggingface-type-badge">${modelType}</div>
+      </div>
+      <div class="huggingface-search-info">
+        <h4>${modelName} <span style="font-weight: normal; font-size: 0.9em;">by ${creator}</span></h4>
+        <p style="font-weight: bold;">Version: ${versionName} <span class="base-model-badge" style="margin-left: 5px;">${baseModel}</span></p>
+        <div class="huggingface-search-stats" title="Stats: Downloads / Rating (Count) / Likes">
+          <span title="Downloads"><i class="fas fa-download"></i> ${stats.downloads?.toLocaleString() || 0}</span>
+          <span title="Likes"><i class="fas fa-thumbs-up"></i> ${stats.likes?.toLocaleString(0) || 0}</span>
+          <span title="Dislikes"><i class="fas fa-thumbs-down"></i> ${stats.dislikes?.toLocaleString() || 0}</span>
+          <span title="Buzz"><i class="fas fa-bolt"></i> ${stats.buzz?.toLocaleString() || 0}</span>
+        </div>
+        <p style="font-weight: bold; margin-top: 10px;">Primary File:</p>
+        <p style="font-size: 0.9em; color: #ccc;">
+          Name: ${fileInfo.name || 'N/A'}<br>
+          Size: ${ui.formatBytes(fileInfo.size_kb * 1024) || 'N/A'} <br>
+          Format: ${fileInfo.format || 'N/A'}<br>
+          Precision: ${fileInfo.precision || 'N/A'}<br>
+          Model Size: ${fileInfo.model_size || 'N/A'}
+        </p>
+        ${files.length > 0 ? `
+          <div class=\"huggingface-form-group\" style=\"margin-top: 10px;\">
+            <label for=\"huggingface-file-select\">Choose File (optional)</label>
+            <select id=\"huggingface-file-select\" class=\"huggingface-select\">
+              <option value=\"\">Auto (primary/best)</option>
+              ${files.map(f => {
+                const id = f.id ?? '';
+                const name = (f.name || '').replace(/</g,'&lt;');
+                const fmt = f.format || 'N/A';
+                const prec = (f.precision || '').toUpperCase();
+                const msize = f.model_size || '';
+                const size = (typeof f.size_kb === 'number') ? ui.formatBytes(f.size_kb * 1024) : 'N/A';
+                const disabled = f.downloadable ? '' : 'disabled';
+                const title = f.downloadable ? '' : ' (not downloadable)';
+                const extras = [prec, msize].filter(Boolean).join(' • ');
+                return `<option value=\"${id}\" ${disabled}>#${id} • ${name} • ${fmt}${extras ? ' • ' + extras : ''} • ${size}${title}</option>`;
+              }).join('')}
+            </select>
+            <p style=\"font-size: 0.9em; color: #aaa; margin-top: 6px;\">Pick other variants when available.</p>
+          </div>
+        ` : ''}
+        <a href="${huggingfaceLink}" target="_blank" rel="noopener noreferrer" class="huggingface-button small" title="Open on HuggingFace website" style="margin-top: 5px; display: inline-block;">
+          View on HuggingFace <i class="fas fa-external-link-alt"></i>
+        </a>
+      </div>
+    </div>
+    <div style="margin-top: 15px;">
+      <h5 style="margin-bottom: 5px;">Model Description:</h5>
+      <div class="model-description-content" style="max-height: 200px; overflow-y: auto; background-color: var(--comfy-input-bg); padding: 10px; border-radius: 4px; font-size: 0.9em; border: 1px solid var(--border-color, #555);">
+        ${descriptionHtml}
+      </div>
+    </div>
+    <div style="margin-top: 15px;">
+      <h5 style="margin-bottom: 5px;">Version Description:</h5>
+      <div class="model-description-content" style="max-height: 200px; overflow-y: auto; background-color: var(--comfy-input-bg); padding: 10px; border-radius: 4px; font-size: 0.9em; border: 1px solid var(--border-color, #555);">
+        ${version_description_html}
+      </div>
+    </div>
+  `;
+
+  ui.downloadPreviewArea.innerHTML = previewHtml;
+}
diff --git a/custom_nodes/ComfyUI-HuggingFace/web/js/ui/searchRenderer.js b/custom_nodes/ComfyUI-HuggingFace/web/js/ui/searchRenderer.js
new file mode 100644
index 0000000000000000000000000000000000000000..6dde4b27dd7cdde68f5ab30050794b650d6c250a
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/web/js/ui/searchRenderer.js
@@ -0,0 +1,196 @@
+// Rendering of search results list
+// Usage: renderSearchResults(uiInstance, itemsArray)
+
+const PLACEHOLDER_IMAGE_URL = `/extensions/ComfyUI-HuggingFace/images/placeholder.jpg`;
+
+export function renderSearchResults(ui, items) {
+  ui.feedback?.ensureFontAwesome();
+
+  if (!items || items.length === 0) {
+    const queryUsed = ui.searchQueryInput && ui.searchQueryInput.value.trim();
+    const typeFilterUsed = ui.searchTypeSelect && ui.searchTypeSelect.value !== 'any';
+    const baseModelFilterUsed = ui.searchBaseModelSelect && ui.searchBaseModelSelect.value !== 'any';
+    const message = (queryUsed || typeFilterUsed || baseModelFilterUsed)
+      ? 'No models found matching your criteria.'
+      : 'Enter a query or select filters and click Search.';
+    ui.searchResultsContainer.innerHTML = `<p>${message}</p>`;
+    return;
+  }
+
+  const placeholder = PLACEHOLDER_IMAGE_URL;
+  const onErrorScript = `this.onerror=null; this.src='${placeholder}'; this.style.backgroundColor='#444';`;
+  const fragment = document.createDocumentFragment();
+
+  items.forEach(hit => {
+    const modelId = hit.id;
+    if (!modelId) return;
+
+    // Extract base repository ID from full path (remove /tree/main, /blob/main, etc.)
+    const baseRepoId = modelId.split('/tree/')[0].split('/blob/')[0].split('/raw/')[0];
+
+    const creator = hit.user?.username || 'Unknown Creator';
+    const modelName = hit.name || 'Untitled Model';
+    const modelTypeApi = hit.type || 'other';
+    console.log('Model type for badge:', modelTypeApi);
+    const stats = hit.metrics || {};
+    const tags = hit.tags?.map(t => t.name) || [];
+
+    const thumbnailUrl = hit.thumbnailUrl || placeholder;
+    const firstImage = Array.isArray(hit.images) && hit.images.length > 0 ? hit.images[0] : null;
+    const thumbnailType = firstImage?.type;
+    const nsfwLevel = Number(firstImage?.nsfwLevel ?? hit.nsfwLevel ?? 0);
+    const blurMinLevel = Number(ui.settings?.nsfwBlurMinLevel ?? 4);
+    const shouldBlur = ui.settings?.hideMatureInSearch === true && nsfwLevel >= blurMinLevel;
+
+    const allVersions = hit.versions || [];
+    const primaryVersion = hit.version || (allVersions.length > 0 ? allVersions[0] : {});
+    const primaryVersionId = primaryVersion.id;
+    const primaryBaseModel = primaryVersion.baseModel || 'N/A';
+
+    const uniqueBaseModels = allVersions.length > 0
+      ? [...new Set(allVersions.map(v => v.baseModel).filter(Boolean))]
+      : (primaryBaseModel !== 'N/A' ? [primaryBaseModel] : []);
+    const baseModelsDisplay = uniqueBaseModels.length > 0 ? uniqueBaseModels.join(', ') : 'N/A';
+
+    const publishedAt = hit.publishedAt;
+    let lastUpdatedFormatted = 'N/A';
+    if (publishedAt) {
+      try {
+        const date = new Date(publishedAt);
+        lastUpdatedFormatted = date.toLocaleDateString(undefined, { year: 'numeric', month: 'short', day: 'numeric' });
+      } catch (_) {}
+    }
+
+    const listItem = document.createElement('div');
+    listItem.className = 'huggingface-search-item';
+    listItem.dataset.modelId = modelId;
+
+    const MAX_VISIBLE_VERSIONS = 3;
+    let visibleVersions = [];
+    if (primaryVersionId) {
+      visibleVersions.push({ id: primaryVersionId, name: primaryVersion.name || 'Primary Version', baseModel: primaryBaseModel });
+    }
+    allVersions.forEach(v => {
+      if (v.id !== primaryVersionId && visibleVersions.length < MAX_VISIBLE_VERSIONS) visibleVersions.push(v);
+    });
+
+    let versionButtonsHtml = visibleVersions.map(version => {
+      const versionId = version.id;
+      const versionName = version.name || 'Unknown Version';
+      const baseModel = version.baseModel || 'N/A';
+      return `
+        <button class="huggingface-button primary small huggingface-search-download-button"
+                data-model-id="${baseRepoId}"
+                data-version-id="${versionId || ''}"
+                data-model-type="${modelTypeApi || ''}"
+                data-creator="${creator}"
+                data-model-name="${modelName}"
+                title="${!versionId ? 'Download latest version' : 'Pre-fill Download Tab'}" >
+          <span class="base-model-badge">${baseModel}</span> ${versionName} <i class="fas fa-download"></i>
+        </button>
+      `;
+    }).join('');
+
+    const hasMoreVersions = allVersions.length > visibleVersions.length;
+    const totalVersionCount = allVersions.length;
+    const moreButtonHtml = hasMoreVersions ? `
+      <button class="huggingface-button secondary small show-all-versions-button"
+              data-model-id="${modelId}"
+              data-total-versions="${totalVersionCount}"
+              title="Show all ${totalVersionCount} versions">
+        All versions (${totalVersionCount}) <i class="fas fa-chevron-down"></i>
+      </button>
+    ` : '';
+
+    let allVersionsHtml = '';
+    if (hasMoreVersions) {
+      const hiddenVersions = allVersions.filter(v => !visibleVersions.some(vis => vis.id === v.id));
+      allVersionsHtml = `
+        <div class="all-versions-container" id="all-versions-${modelId}" style="display: none;">
+          ${hiddenVersions.map(version => {
+            const versionId = version.id;
+            const versionName = version.name || 'Unknown Version';
+            const baseModel = version.baseModel || 'N/A';
+            return `
+              <button class="huggingface-button primary small huggingface-search-download-button"
+                      data-model-id="${baseRepoId}"
+                      data-version-id="${versionId || ''}"
+                      data-model-type="${modelTypeApi || ''}"
+                      data-creator="${creator}"
+                      data-model-name="${modelName}"
+                      title="${!versionId ? 'Download latest version' : 'Pre-fill Download Tab'}" >
+                <span class="base-model-badge">${baseModel}</span> ${versionName} <i class="fas fa-download"></i>
+              </button>
+            `;
+          }).join('')}
+        </div>
+      `;
+    }
+
+    let thumbnailHtml = '';
+    const videoTitle = `Video preview for ${modelName}`;
+    const imageAlt = `${modelName} thumbnail`;
+    if (thumbnailUrl && typeof thumbnailUrl === 'string' && thumbnailType === 'video') {
+      thumbnailHtml = `
+        <video class="huggingface-search-thumbnail" src="${thumbnailUrl}" autoplay loop muted playsinline
+               title="${videoTitle}"
+               onerror="console.error('Failed to load video preview:', this.src)">
+          Your browser does not support the video tag.
+        </video>
+      `;
+    } else {
+      const effective = thumbnailUrl || placeholder;
+      thumbnailHtml = `
+        <img src="${effective}" alt="${imageAlt}" class="huggingface-search-thumbnail" loading="lazy" onerror="${onErrorScript}">
+      `;
+    }
+
+    const overlayHtml = shouldBlur ? `<div class="huggingface-nsfw-overlay" title="R-rated: click to reveal">R</div>` : '';
+    const containerClasses = `huggingface-thumbnail-container${shouldBlur ? ' blurred' : ''}`;
+
+    listItem.innerHTML = `
+      <div class="${containerClasses}" data-nsfw-level="${nsfwLevel ?? ''}">
+        ${thumbnailHtml}
+        ${overlayHtml}
+        <div class="huggingface-type-badge" data-type="${modelTypeApi.toLowerCase()}">${modelTypeApi}</div>
+      </div>
+      <div class="huggingface-search-info">
+        <h4>${modelName}</h4>
+        <div class="huggingface-search-meta-info">
+          <span title="Creator: ${creator}"><i class="fas fa-user"></i> ${creator}</span>
+          <span title="Base Models: ${baseModelsDisplay}"><i class="fas fa-layer-group"></i> ${baseModelsDisplay}</span>
+          <span title="Published: ${lastUpdatedFormatted}"><i class="fas fa-calendar-alt"></i> ${lastUpdatedFormatted}</span>
+        </div>
+        <div class="huggingface-search-stats" title="Stats: Downloads / Rating (Count) / Likes">
+          <span title="Downloads"><i class="fas fa-download"></i> ${stats.downloadCount?.toLocaleString() || 0}</span>
+          <span title="Thumbs"><i class="fas fa-thumbs-up"></i> ${stats.thumbsUpCount?.toLocaleString() || 0}</span>
+          <span title="Collected"><i class="fas fa-archive"></i> ${stats.collectedCount?.toLocaleString() || 0}</span>
+          <span title="Buzz"><i class="fas fa-bolt"></i> ${stats.tippedAmountCount?.toLocaleString() || 0}</span>
+        </div>
+        ${tags.length > 0 ? `
+        <div class="huggingface-search-tags" title="${tags.join(', ')}">
+          ${tags.slice(0, 5).map(tag => `<span class="huggingface-search-tag">${tag}</span>`).join('')}
+          ${tags.length > 5 ? `<span class="huggingface-search-tag">...</span>` : ''}
+        </div>
+        ` : ''}
+      </div>
+      <div class="huggingface-search-actions">
+        <a href="https://huggingface.co/${baseRepoId}${primaryVersionId ? '?modelVersionId='+primaryVersionId : ''}" 
+           target="_blank" rel="noopener noreferrer" class="huggingface-button small" 
+           title="Open on HuggingFace website">
+          View <i class="fas fa-external-link-alt"></i>
+        </a>
+        <div class="version-buttons-container">
+          ${versionButtonsHtml}
+          ${moreButtonHtml}
+        </div>
+        ${allVersionsHtml}
+      </div>
+    `;
+
+    fragment.appendChild(listItem);
+  });
+
+  ui.searchResultsContainer.innerHTML = '';
+  ui.searchResultsContainer.appendChild(fragment);
+}
diff --git a/custom_nodes/ComfyUI-HuggingFace/web/js/ui/statusRenderer.js b/custom_nodes/ComfyUI-HuggingFace/web/js/ui/statusRenderer.js
new file mode 100644
index 0000000000000000000000000000000000000000..424138405a1889554b77bffa973b9e607252ddf8
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/web/js/ui/statusRenderer.js
@@ -0,0 +1,124 @@
+// Renders active/queued/history download lists
+
+const PLACEHOLDER_IMAGE_URL = `/extensions/ComfyUI-HuggingFace/images/placeholder.jpg`;
+
+export function renderDownloadList(ui, items, container, emptyMessage) {
+  if (!items || items.length === 0) {
+    container.innerHTML = `<p>${emptyMessage}</p>`;
+    return;
+  }
+
+  const fragment = document.createDocumentFragment();
+  items.forEach(item => {
+    const id = item.id || 'unknown-id';
+    const progress = item.progress !== undefined ? Math.max(0, Math.min(100, item.progress)) : 0;
+    const speed = item.speed !== undefined ? Math.max(0, item.speed) : 0;
+    const status = item.status || 'unknown';
+    const size = item.known_size !== undefined && item.known_size !== null ? item.known_size : (item.file_size || 0);
+    const downloadedBytes = size > 0 ? size * (progress / 100) : 0;
+    const errorMsg = item.error || null;
+    
+    // Debug: log what we receive from backend
+    console.log(`[DEBUG Frontend] Item data:`, {
+        huggingface_model_name: item.huggingface_model_name,
+        model_name: item.model_name,
+        model: item.model,
+        version_name: item.version_name,
+        full_item: item  // Log the full item to see all available fields
+    });
+    
+    const modelName = item.huggingface_model_name || item.model_name || item.model?.name || 'Unknown Model';
+    console.log(`[DEBUG Frontend] Final modelName: ${modelName}`);
+    const versionName = item.version_name || 'Unknown Version';
+    const filename = item.filename || 'N/A';
+    const addedTime = item.added_time || null;
+    const startTime = item.start_time || null;
+    const endTime = item.end_time || null;
+    const thumbnail = item.thumbnail || PLACEHOLDER_IMAGE_URL;
+    const nsfwLevel = Number(item.thumbnail_nsfw_level ?? 0);
+    const blurMinLevel = Number(ui.settings?.nsfwBlurMinLevel ?? 4);
+    const shouldBlur = ui.settings?.hideMatureInSearch === true && nsfwLevel >= blurMinLevel;
+    const connectionType = item.connection_type || "N/A";
+
+    let progressBarClass = '';
+    let statusText = status.charAt(0).toUpperCase() + status.slice(1);
+    switch (status) {
+      case 'completed': progressBarClass = 'completed'; break;
+      case 'failed': progressBarClass = 'failed'; statusText = 'Failed'; break;
+      case 'cancelled': progressBarClass = 'cancelled'; statusText = 'Cancelled'; break;
+      case 'downloading': case 'queued': case 'starting': default: break;
+    }
+
+    const listItem = document.createElement('div');
+    listItem.className = 'huggingface-download-item';
+    listItem.dataset.id = id;
+
+    const onErrorScript = `this.onerror=null; this.src='${PLACEHOLDER_IMAGE_URL}'; this.style.backgroundColor='#444';`;
+    const addedTooltip = addedTime ? `data-tooltip="Added: ${new Date(addedTime).toLocaleString()}"` : '';
+    const startedTooltip = startTime ? `data-tooltip="Started: ${new Date(startTime).toLocaleString()}"` : '';
+    const endedTooltip = endTime ? `data-tooltip="Ended: ${new Date(endTime).toLocaleString()}"` : '';
+    const durationTooltip = startTime && endTime ? `data-tooltip="Duration: ${ui.formatDuration(startTime, endTime)}"` : '';
+    const filenameTooltip = filename !== 'N/A' ? `title="Filename: ${filename}"` : '';
+    const errorTooltip = errorMsg ? `title="Error Details: ${String(errorMsg).substring(0, 200)}${String(errorMsg).length > 200 ? '...' : ''}"` : '';
+    const connectionInfoHtml = connectionType !== "N/A" ? `<span style="font-size: 0.85em; color: #aaa; margin-left: 10px;">(Conn: ${connectionType})</span>` : '';
+
+    const overlayHtml = shouldBlur ? `<div class=\"huggingface-nsfw-overlay\" title=\"R-rated: click to reveal\">R</div>` : '';
+    const containerClasses = `huggingface-thumbnail-container${shouldBlur ? ' blurred' : ''}`;
+
+    let innerHTML = `
+      <div class="${containerClasses}" data-nsfw-level="${Number.isFinite(nsfwLevel) ? nsfwLevel : ''}">
+        <img src="${thumbnail}" alt="thumbnail" class="huggingface-download-thumbnail" loading="lazy" onerror="${onErrorScript}">
+        ${overlayHtml}
+      </div>
+      <div class="huggingface-download-info">
+        <strong>${modelName}</strong>
+        <p>Ver: ${versionName}</p>
+        <p class="filename" ${filenameTooltip}>${filename}</p>
+        ${size > 0 ? `<p>Size: ${ui.formatBytes(size)}</p>` : ''}
+        ${item.file_format ? `<p>Format: ${item.file_format}</p>` : ''}
+        ${item.file_precision || item.file_model_size ? `<p>${item.file_precision ? 'Precision: ' + String(item.file_precision).toUpperCase() : ''}${item.file_precision && item.file_model_size ? ' • ' : ''}${item.file_model_size ? 'Model Size: ' + item.file_model_size : ''}</p>` : ''}
+        ${errorMsg ? `<p class="error-message" ${errorTooltip}><i class="fas fa-exclamation-triangle"></i> ${String(errorMsg).substring(0, 100)}${String(errorMsg).length > 100 ? '...' : ''}</p>` : ''}
+    `;
+
+    if (status === 'downloading' || status === 'starting' || status === 'completed') {
+      const statusLine = `<div ${durationTooltip} ${endedTooltip}>Status: ${statusText} ${connectionInfoHtml}</div>`;
+      innerHTML += `
+        <div class="huggingface-progress-container" title="${statusText} - ${progress.toFixed(1)}%">
+          <div class="huggingface-progress-bar ${progressBarClass}" style="width: ${progress}%;">
+            ${progress > 15 ? progress.toFixed(0)+'%' : ''}
+          </div>
+        </div>
+      `;
+      const speedText = (status === 'downloading' && speed > 0) ? ui.formatSpeed(speed) : '';
+      const progressText = (status === 'downloading' && size > 0) ? `(${ui.formatBytes(downloadedBytes)} / ${ui.formatBytes(size)})` : '';
+      const completedText = status === 'completed' ? '' : '';
+      const speedProgLine = `<div class="huggingface-speed-indicator">${speedText} ${progressText} ${completedText}</div>`;
+      if (status === 'downloading') { innerHTML += speedProgLine; }
+      innerHTML += statusLine;
+    } else if (status === 'failed' || status === 'cancelled' || status === 'queued') {
+      innerHTML += `<div class="status-line-simple" ${durationTooltip} ${endedTooltip} ${addedTooltip}>Status: ${statusText} ${connectionInfoHtml}</div>`;
+    } else {
+      innerHTML += `<div class="status-line-simple">Status: ${statusText} ${connectionInfoHtml}</div>`;
+    }
+
+    innerHTML += `</div>`;
+    innerHTML += `<div class="huggingface-download-actions">`;
+    if (status === 'queued' || status === 'downloading' || status === 'starting') {
+      innerHTML += `<button class="huggingface-button danger small huggingface-cancel-button" data-id="${id}" title="Cancel Download"><i class="fas fa-times"></i></button>`;
+    }
+    if (status === 'failed' || status === 'cancelled') {
+      innerHTML += `<button class="huggingface-button small huggingface-retry-button" data-id="${id}" title="Retry Download"><i class="fas fa-redo"></i></button>`;
+    }
+    if (status === 'completed') {
+      innerHTML += `<button class="huggingface-button small huggingface-openpath-button" data-id="${id}" title="Open Containing Folder"><i class="fas fa-folder-open"></i></button>`;
+    }
+    innerHTML += `</div>`;
+
+    listItem.innerHTML = innerHTML;
+    fragment.appendChild(listItem);
+  });
+
+  container.innerHTML = '';
+  container.appendChild(fragment);
+  ui.ensureFontAwesome();
+}
diff --git a/custom_nodes/ComfyUI-HuggingFace/web/js/ui/templates.js b/custom_nodes/ComfyUI-HuggingFace/web/js/ui/templates.js
new file mode 100644
index 0000000000000000000000000000000000000000..e74af211e567b0ac98c51270c4d3d7b73335f367
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/web/js/ui/templates.js
@@ -0,0 +1,193 @@
+// Modal template for HuggingFace UI
+// Keep structure identical to the original inline HTML to minimize risk
+
+export function modalTemplate(settings = {}) {
+  const numConnections = Number.isFinite(settings.numConnections) ? settings.numConnections : 1;
+  return `
+    <div class="huggingface-downloader-modal-content">
+      <div class="huggingface-downloader-header">
+        <h2>HuggingFace</h2>
+        <button class="huggingface-close-button" id="huggingface-close-modal">&times;</button>
+      </div>
+      <div class="huggingface-downloader-body">
+        <div class="huggingface-downloader-tabs">
+          <button class="huggingface-downloader-tab active" data-tab="download">Download</button>
+          <button class="huggingface-downloader-tab" data-tab="search">Search</button>
+          <button class="huggingface-downloader-tab" data-tab="status">Status <span id="huggingface-status-indicator" style="display: none;">(<span id="huggingface-active-count">0</span>)</span></button>
+          <button class="huggingface-downloader-tab" data-tab="settings">Settings</button>
+        </div>
+        <div id="huggingface-tab-download" class="huggingface-downloader-tab-content active">
+          <form id="huggingface-download-form">
+            <div class="huggingface-form-group">
+              <label for="huggingface-model-url">Model URL or ID</label>
+              <input type="text" id="huggingface-model-url" class="huggingface-input" placeholder="e.g., https://huggingface.com/models/12345 or 12345" required>
+            </div>
+            <p style="font-size: 0.9em; color: #ccc; margin-top: -10px; margin-bottom: 15px;">You can optionally specify a version ID using "?modelVersionId=xxxxx" in the URL or in the field below.</p>
+            <div class="huggingface-form-row">
+              <div class="huggingface-form-group">
+                <label for="huggingface-model-type">Model Type (Save Location)</label>
+                <div style="display:flex; gap:6px; align-items:center;">
+                  <select id="huggingface-model-type" class="huggingface-select" required></select>
+                  <button type="button" id="huggingface-create-model-type" class="huggingface-button small" title="Create new model type folder"><i class="fas fa-folder-plus"></i></button>
+                </div>
+              </div>
+              <div class="huggingface-form-group">
+                <label for="huggingface-subdir-select">Save Subfolder</label>
+                <div style="display:flex; gap:6px; align-items:center;">
+                  <select id="huggingface-subdir-select" class="huggingface-select">
+                    <option value="">(root)</option>
+                  </select>
+                  <button type="button" id="huggingface-create-subdir" class="huggingface-button small" title="Create new subfolder"><i class="fas fa-folder-plus"></i></button>
+                </div>
+                <p id="huggingface-save-base-path" style="font-size: 0.8em; color: #bbb; margin-top: 6px; word-break: break-all;"></p>
+              </div>
+              <div class="huggingface-form-group">
+                <label for="huggingface-model-version-id">Version ID (Optional)</label>
+                <input type="number" id="huggingface-model-version-id" class="huggingface-input" placeholder="Overrides URL/Latest">
+              </div>
+            </div>
+            <div class="huggingface-form-row">
+              <div class="huggingface-form-group">
+                <label for="huggingface-custom-filename">Custom Filename (Optional)</label>
+                <input type="text" id="huggingface-custom-filename" class="huggingface-input" placeholder="Leave blank to use original name">
+              </div>
+              <div class="huggingface-form-group">
+                <label for="huggingface-connections">Connections</label>
+                <input type="number" id="huggingface-connections" class="huggingface-input" value="${numConnections}" min="1" max="16" step="1" required disabled>
+                <p style="font-size: 0.9em; color: #ccc; margin-top: 7px; margin-bottom: 15px;">Disabled: Only single connection possible for now</p>
+              </div>
+            </div>
+            <div class="huggingface-form-group inline">
+              <input type="checkbox" id="huggingface-force-redownload" class="huggingface-checkbox">
+              <label for="huggingface-force-redownload">Force Re-download (if exists)</label>
+            </div>
+            <div id="huggingface-download-preview-area" class="huggingface-download-preview-area" style="margin-top: 25px; margin-bottom: 25px; padding-top: 15px; border-top: 1px solid var(--border-color, #444);">
+              <!-- Preview content will be injected here -->
+            </div>
+            <button type="submit" id="huggingface-download-submit" class="huggingface-button primary">Start Download</button>
+          </form>
+        </div>
+        <div id="huggingface-tab-search" class="huggingface-downloader-tab-content">
+          <form id="huggingface-search-form">
+            <div class="huggingface-search-controls">
+              <input type="text" id="huggingface-search-query" class="huggingface-input" placeholder="Search HuggingFace...">
+              <select id="huggingface-search-type" class="huggingface-select">
+                <option value="any">Any Type</option>
+              </select>
+              <select id="huggingface-search-base-model" class="huggingface-select">
+                <option value="any">Any Base Model</option>
+              </select>
+              <select id="huggingface-search-sort" class="huggingface-select">
+                <option value="Relevancy">Relevancy</option>
+                <option value="Highest Rated">Highest Rated</option>
+                <option value="Most Liked">Most Liked</option>
+                <option value="Most Discussed">Most Discussed</option>
+                <option value="Most Collected">Most Collected</option>
+                <option value="Most Buzz">Most Buzz</option>
+                <option value="Most Downloaded">Most Downloaded</option>
+                <option value="Newest">Newest</option>
+              </select>
+            </div>
+            <button type="submit" id="huggingface-search-submit" class="huggingface-button primary">Search</button>
+          </form>
+          <div id="huggingface-search-results" class="huggingface-search-results"></div>
+          <div id="huggingface-search-pagination" style="text-align: center; margin-top: 20px;"></div>
+        </div>
+        <div id="huggingface-tab-status" class="huggingface-downloader-tab-content">
+          <div id="huggingface-status-content">
+            <div class="huggingface-status-section">
+              <h3>Active Downloads</h3>
+              <div id="huggingface-active-list" class="huggingface-download-list">
+                <p>No active downloads.</p>
+              </div>
+            </div>
+            <div class="huggingface-status-section">
+              <h3>Queued Downloads</h3>
+              <div id="huggingface-queued-list" class="huggingface-download-list">
+                <p>Download queue is empty.</p>
+              </div>
+            </div>
+            <div class="huggingface-status-section">
+              <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 10px;">
+                <h3>Download History (Recent)</h3>
+                <button id="huggingface-clear-history-button" class="huggingface-button danger small" title="Clear all history items">
+                  <i class="fas fa-trash-alt"></i> Clear History
+                </button>
+              </div>
+              <div id="huggingface-history-list" class="huggingface-download-list">
+                <p>No download history yet.</p>
+              </div>
+            </div>
+          </div>
+        </div>
+        <div id="huggingface-tab-settings" class="huggingface-downloader-tab-content">
+          <form id="huggingface-settings-form">
+            <div class="huggingface-settings-container">
+              <div class="huggingface-settings-section">
+                <h4>API & Defaults</h4>
+                <div class="huggingface-form-group">
+                  <label for="huggingface-settings-api-key">HuggingFace HF token (Optional)</label>
+                  <input type="password" id="huggingface-settings-api-key" class="huggingface-input" placeholder="Enter API key for higher limits / authenticated access" autocomplete="new-password">
+                  <p style="font-size: 0.85em; color: #bbb; margin-top: 5px;">Needed for some downloads/features. Leave blank to use server env <code>HUGGINGFACE_TOKEN</code>. Find keys at huggingface.com/user/account</p>
+                </div>
+                <div class="huggingface-form-group">
+                  <label for="huggingface-settings-global-root">Global Download Root (Optional)</label>
+                  <input type="text" id="huggingface-settings-global-root" class="huggingface-input" placeholder="e.g., /runpod-volume/ComfyUI or F:/Models/ComfyUI">
+                  <p style="font-size: 0.85em; color: #bbb; margin-top: 5px;">
+                    When set, downloads use <code>&lt;global_root&gt;/&lt;model_type&gt;</code> (for example <code>/runpod-volume/ComfyUI/checkpoints</code>).
+                  </p>
+                  <div style="display:flex; gap:8px; margin-top: 8px; flex-wrap: wrap;">
+                    <button type="button" id="huggingface-settings-set-global-root" class="huggingface-button small">Set Global Root</button>
+                    <button type="button" id="huggingface-settings-clear-global-root" class="huggingface-button danger small">Clear Global Root</button>
+                  </div>
+                </div>
+                <div class="huggingface-form-group">
+                  <label for="huggingface-settings-connections">Default Connections</label>
+                  <input type="number" id="huggingface-settings-connections" class="huggingface-input" value="4" min="1" max="16" step="1" required>
+                  <p style="font-size: 0.85em; color: #bbb; margin-top: 5px;">Number of parallel connections for downloads (1-16)</p>
+                </div>
+                <div class="huggingface-form-group">
+                  <label for="huggingface-settings-default-type">Default Model Type (for saving)</label>
+                  <select id="huggingface-settings-default-type" class="huggingface-select" required></select>
+                </div>
+              </div>
+              <div class="huggingface-settings-section">
+                <h4>Interface & Search</h4>
+                <div class="huggingface-form-group inline">
+                  <input type="checkbox" id="huggingface-settings-auto-open-status" class="huggingface-checkbox">
+                  <label for="huggingface-settings-auto-open-status">Switch to Status tab after starting download</label>
+                </div>
+                <div class="huggingface-form-group inline">
+                  <input type="checkbox" id="huggingface-settings-hide-mature" class="huggingface-checkbox" ${settings.hideMatureInSearch ? 'checked' : ''}>
+                  <label for="huggingface-settings-hide-mature">Hide R-rated (Mature) images in search (click to reveal)</label>
+                </div>
+                <div class="huggingface-form-group">
+                  <label for="huggingface-settings-nsfw-threshold">NSFW Blur Threshold (nsfwLevel)</label>
+                  <input type="number" id="huggingface-settings-nsfw-threshold" class="huggingface-input" value="${Number.isFinite(settings.nsfwBlurMinLevel) ? settings.nsfwBlurMinLevel : 4}" min="0" max="128" step="1">
+                  <p style="font-size: 0.85em; color: #bbb; margin-top: 5px;">
+                    Blur thumbnails when an image's <code>nsfwLevel</code> is greater than or equal to this value.
+                    Higher numbers indicate more explicit content. None (Safe/PG): 1, Mild (PG-13): 2, Mature (R): 4, Adult (X): 5, Extra Explicit (R): 8, Explicit (XXX): 16/32+
+                  </p>
+                </div>
+              </div>
+            </div>
+            <button type="submit" id="huggingface-settings-save" class="huggingface-button primary" style="margin-top: 20px;">Save Settings</button>
+          </form>
+        </div>
+      </div>
+      <!-- Toast Notification Area -->
+      <div id="huggingface-toast" class="huggingface-toast"></div>
+      <!-- Confirmation Modal -->
+      <div id="huggingface-confirm-clear-modal" class="huggingface-confirmation-modal">
+        <div class="huggingface-confirmation-modal-content">
+          <h4>Confirm Clear History</h4>
+          <p>Are you sure you want to clear the download history? This action cannot be undone.</p>
+          <div class="huggingface-confirmation-modal-actions">
+            <button id="huggingface-confirm-clear-no" class="huggingface-button secondary">Cancel</button>
+            <button id="huggingface-confirm-clear-yes" class="huggingface-button danger">Confirm Clear</button>
+          </div>
+        </div>
+      </div>
+    </div>
+  `;
+}
diff --git a/custom_nodes/ComfyUI-HuggingFace/web/js/utils/cookies.js b/custom_nodes/ComfyUI-HuggingFace/web/js/utils/cookies.js
new file mode 100644
index 0000000000000000000000000000000000000000..f8d7c1a2896186707d7b76905ad16e0c969b7357
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/web/js/utils/cookies.js
@@ -0,0 +1,24 @@
+// Lightweight cookie helpers for HuggingFace UI
+// Exports: setCookie, getCookie
+
+export function setCookie(name, value, days) {
+  let expires = "";
+  if (days) {
+    const date = new Date();
+    date.setTime(date.getTime() + days * 24 * 60 * 60 * 1000);
+    expires = "; expires=" + date.toUTCString();
+  }
+  document.cookie = `${name}=${value || ""}${expires}; path=/; SameSite=Lax`;
+}
+
+export function getCookie(name) {
+  const nameEQ = name + "=";
+  const parts = document.cookie.split(";");
+  for (let i = 0; i < parts.length; i++) {
+    let c = parts[i];
+    while (c.charAt(0) === " ") c = c.substring(1);
+    if (c.indexOf(nameEQ) === 0) return c.substring(nameEQ.length);
+  }
+  return null;
+}
+
diff --git a/custom_nodes/ComfyUI-HuggingFace/web/js/utils/dom.js b/custom_nodes/ComfyUI-HuggingFace/web/js/utils/dom.js
new file mode 100644
index 0000000000000000000000000000000000000000..e06f81af9585b6dab855458d5608508f4e1bde3d
--- /dev/null
+++ b/custom_nodes/ComfyUI-HuggingFace/web/js/utils/dom.js
@@ -0,0 +1,32 @@
+// File: web/js/utils/dom.js
+
+/**
+ * Dynamically adds a CSS link to the document's head.
+ * It resolves the path relative to this script's location using import.meta.url,
+ * making it robust against case-sensitivity issues and different install paths.
+ * @param {string} relativeHref - Relative path to the CSS file (e.g., '../huggingfaceDownloader.css').
+ * @param {string} [id="huggingface-downloader-styles"] - The ID for the link element.
+ */
+export function addCssLink(relativeHref, id = "huggingface-downloader-styles") {
+  if (document.getElementById(id)) return; // Prevent duplicates
+
+  try {
+    const absoluteUrl = new URL(relativeHref, import.meta.url);
+
+    const link = document.createElement("link");
+    link.id = id;
+    link.rel = "stylesheet";
+    link.href = absoluteUrl.href;
+
+    link.onload = () => {
+      console.log("[HuggingFace] CSS loaded successfully:", link.href);
+    };
+    link.onerror = () => {
+      console.error("[HuggingFace] Critical error: Failed to load CSS from:", link.href);
+    };
+
+    document.head.appendChild(link);
+  } catch (e) {
+    console.error("[HuggingFace] Error creating CSS link. import.meta.url may be unsupported in this context.", e);
+  }
+}
diff --git a/custom_nodes/ComfyUI-KJNodes/.github/workflows/publish.yml b/custom_nodes/ComfyUI-KJNodes/.github/workflows/publish.yml
new file mode 100644
index 0000000000000000000000000000000000000000..e155f5f40e46fa83942dee5a9460e8093f3b4208
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/.github/workflows/publish.yml
@@ -0,0 +1,25 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+    paths:
+      - "pyproject.toml"
+
+permissions:
+  issues: write
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'kijai' }}
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@v1
+        with:
+          ## Add your own personal access token to your Github Repository secrets and reference it here.
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
diff --git a/custom_nodes/ComfyUI-KJNodes/docs/images/319121566-05f66385-7568-4b1f-8bbc-11053660b02f.png b/custom_nodes/ComfyUI-KJNodes/docs/images/319121566-05f66385-7568-4b1f-8bbc-11053660b02f.png
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diff --git a/custom_nodes/ComfyUI-KJNodes/docs/images/319121636-706b5081-9120-4a29-bd76-901691ada688.png b/custom_nodes/ComfyUI-KJNodes/docs/images/319121636-706b5081-9120-4a29-bd76-901691ada688.png
new file mode 100644
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Binary files /dev/null and b/custom_nodes/ComfyUI-KJNodes/docs/images/319121636-706b5081-9120-4a29-bd76-901691ada688.png differ
diff --git a/custom_nodes/ComfyUI-KJNodes/example_workflows/leapfusion_hunyuuanvideo_i2v_native_testing.json b/custom_nodes/ComfyUI-KJNodes/example_workflows/leapfusion_hunyuuanvideo_i2v_native_testing.json
new file mode 100644
index 0000000000000000000000000000000000000000..134a83788815d04b5574a807db67eb6e45bf9263
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/example_workflows/leapfusion_hunyuuanvideo_i2v_native_testing.json
@@ -0,0 +1,1188 @@
+{
+  "last_node_id": 86,
+  "last_link_id": 144,
+  "nodes": [
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+      "mode": 0,
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+          "name": "conditioning",
+          "type": "CONDITIONING",
+          "link": 82
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            83
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "FluxGuidance"
+      },
+      "widgets_values": [
+        6
+      ]
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+    {
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+      "type": "KSamplerSelect",
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+        -610,
+        -480
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+      "size": [
+        315,
+        58
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "SAMPLER",
+          "type": "SAMPLER",
+          "links": [
+            61
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "KSamplerSelect"
+      },
+      "widgets_values": [
+        "euler"
+      ]
+    },
+    {
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+      "type": "VAEDecodeTiled",
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+        },
+        {
+          "name": "vae",
+          "type": "VAE",
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+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
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+            105
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+          "slot_index": 0
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+          "name": "LATENT",
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+          ],
+          "slot_index": 0
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+            56
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "DualCLIPLoader"
+      },
+      "widgets_values": [
+        "clip_l.safetensors",
+        "llava_llama3_fp16.safetensors",
+        "hunyuan_video",
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+          "name": "height_input",
+          "type": "INT",
+          "link": null,
+          "widget": {
+            "name": "height_input"
+          },
+          "shape": 7
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            143
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "width",
+          "type": "INT",
+          "links": [
+            89
+          ],
+          "slot_index": 1
+        },
+        {
+          "name": "height",
+          "type": "INT",
+          "links": [
+            90
+          ],
+          "slot_index": 2
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ImageResizeKJ"
+      },
+      "widgets_values": [
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+        640,
+        "lanczos",
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+        2,
+        0,
+        0,
+        "center"
+      ]
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+    {
+      "id": 86,
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+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 143
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            144
+          ],
+          "slot_index": 0
+        }
+      ],
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+  "groups": [],
+  "config": {},
+  "extra": {
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+      ]
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+    "node_versions": {
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+      "ComfyUI-KJNodes": "a8aeef670b3f288303f956bf94385cb87978ea93",
+      "ComfyUI-VideoHelperSuite": "c47b10ca1798b4925ff5a5f07d80c51ca80a837d"
+    },
+    "VHS_latentpreview": true,
+    "VHS_latentpreviewrate": 0
+  },
+  "version": 0.4
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-KJNodes/kjweb_async/marked.min.js b/custom_nodes/ComfyUI-KJNodes/kjweb_async/marked.min.js
new file mode 100644
index 0000000000000000000000000000000000000000..2e66c369c388c135cc68d399861a737f4c5e68cd
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/kjweb_async/marked.min.js
@@ -0,0 +1,6 @@
+/**
+ * marked v12.0.1 - a markdown parser
+ * Copyright (c) 2011-2024, Christopher Jeffrey. (MIT Licensed)
+ * https://github.com/markedjs/marked
+ */
+!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof globalThis?globalThis:e||self).marked={})}(this,(function(e){"use strict";function t(){return{async:!1,breaks:!1,extensions:null,gfm:!0,hooks:null,pedantic:!1,renderer:null,silent:!1,tokenizer:null,walkTokens:null}}function n(t){e.defaults=t}e.defaults={async:!1,breaks:!1,extensions:null,gfm:!0,hooks:null,pedantic:!1,renderer:null,silent:!1,tokenizer:null,walkTokens:null};const s=/[&<>"']/,r=new RegExp(s.source,"g"),i=/[<>"']|&(?!(#\d{1,7}|#[Xx][a-fA-F0-9]{1,6}|\w+);)/,l=new RegExp(i.source,"g"),o={"&":"&amp;","<":"&lt;",">":"&gt;",'"':"&quot;","'":"&#39;"},a=e=>o[e];function c(e,t){if(t){if(s.test(e))return e.replace(r,a)}else if(i.test(e))return e.replace(l,a);return e}const h=/&(#(?:\d+)|(?:#x[0-9A-Fa-f]+)|(?:\w+));?/gi;function p(e){return 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*(?:\\n{2,}|\\s*$))").replace("comment",S).replace(/tag/g,"(?!(?:a|em|strong|small|s|cite|q|dfn|abbr|data|time|code|var|samp|kbd|sub|sup|i|b|u|mark|ruby|rt|rp|bdi|bdo|span|br|wbr|ins|del|img)\\b)\\w+(?!:|[^\\w\\s@]*@)\\b").getRegex(),def:/^ *\[([^\]]+)\]: *<?([^\s>]+)>?(?: +(["(][^\n]+[")]))? *(?:\n+|$)/,heading:/^(#{1,6})(.*)(?:\n+|$)/,fences:f,lheading:/^(.+?)\n {0,3}(=+|-+) *(?:\n+|$)/,paragraph:k(z).replace("hr",m).replace("heading"," *#{1,6} *[^\n]").replace("lheading",$).replace("|table","").replace("blockquote"," {0,3}>").replace("|fences","").replace("|list","").replace("|html","").replace("|tag","").getRegex()},Q=/^\\([!"#$%&'()*+,\-./:;<=>?@\[\]\\^_`{|}~])/,v=/^( {2,}|\\)\n(?!\s*$)/,B="\\p{P}\\p{S}",C=k(/^((?![*_])[\spunctuation])/,"u").replace(/punctuation/g,B).getRegex(),M=k(/^(?:\*+(?:((?!\*)[punct])|[^\s*]))|^_+(?:((?!_)[punct])|([^\s_]))/,"u").replace(/punct/g,B).getRegex(),O=k("^[^_*]*?__[^_*]*?\\*[^_*]*?(?=__)|[^*]+(?=[^*])|(?!\\*)[punct](\\*+)(?=[\\s]|$)|[^punct\\s](\\*+)(?!\\*)(?=[punct\\s]|$)|(?!\\*)[punct\\s](\\*+)(?=[^punct\\s])|[\\s](\\*+)(?!\\*)(?=[punct])|(?!\\*)[punct](\\*+)(?!\\*)(?=[punct])|[^punct\\s](\\*+)(?=[^punct\\s])","gu").replace(/punct/g,B).getRegex(),D=k("^[^_*]*?\\*\\*[^_*]*?_[^_*]*?(?=\\*\\*)|[^_]+(?=[^_])|(?!_)[punct](_+)(?=[\\s]|$)|[^punct\\s](_+)(?!_)(?=[punct\\s]|$)|(?!_)[punct\\s](_+)(?=[^punct\\s])|[\\s](_+)(?!_)(?=[punct])|(?!_)[punct](_+)(?!_)(?=[punct])","gu").replace(/punct/g,B).getRegex(),j=k(/\\([punct])/,"gu").replace(/punct/g,B).getRegex(),H=k(/^<(scheme:[^\s\x00-\x1f<>]*|email)>/).replace("scheme",/[a-zA-Z][a-zA-Z0-9+.-]{1,31}/).replace("email",/[a-zA-Z0-9.!#$%&'*+/=?^_`{|}~-]+(@)[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)+(?![-_])/).getRegex(),U=k(S).replace("(?:--\x3e|$)","--\x3e").getRegex(),X=k("^comment|^</[a-zA-Z][\\w:-]*\\s*>|^<[a-zA-Z][\\w-]*(?:attribute)*?\\s*/?>|^<\\?[\\s\\S]*?\\?>|^<![a-zA-Z]+\\s[\\s\\S]*?>|^<!\\[CDATA\\[[\\s\\S]*?\\]\\]>").replace("comment",U).replace("attribute",/\s+[a-zA-Z:_][\w.:-]*(?:\s*=\s*"[^"]*"|\s*=\s*'[^']*'|\s*=\s*[^\s"'=<>`]+)?/).getRegex(),F=/(?:\[(?:\\.|[^\[\]\\])*\]|\\.|`[^`]*`|[^\[\]\\`])*?/,N=k(/^!?\[(label)\]\(\s*(href)(?:\s+(title))?\s*\)/).replace("label",F).replace("href",/<(?:\\.|[^\n<>\\])+>|[^\s\x00-\x1f]*/).replace("title",/"(?:\\"?|[^"\\])*"|'(?:\\'?|[^'\\])*'|\((?:\\\)?|[^)\\])*\)/).getRegex(),G=k(/^!?\[(label)\]\[(ref)\]/).replace("label",F).replace("ref",T).getRegex(),J=k(/^!?\[(ref)\](?:\[\])?/).replace("ref",T).getRegex(),K={_backpedal:f,anyPunctuation:j,autolink:H,blockSkip:/\[[^[\]]*?\]\([^\(\)]*?\)|`[^`]*?`|<[^<>]*?>/g,br:v,code:/^(`+)([^`]|[^`][\s\S]*?[^`])\1(?!`)/,del:f,emStrongLDelim:M,emStrongRDelimAst:O,emStrongRDelimUnd:D,escape:Q,link:N,nolink:J,punctuation:C,reflink:G,reflinkSearch:k("reflink|nolink(?!\\()","g").replace("reflink",G).replace("nolink",J).getRegex(),tag:X,text:/^(`+|[^`])(?:(?= {2,}\n)|[\s\S]*?(?:(?=[\\<!\[`*_]|\b_|$)|[^ ](?= {2,}\n)))/,url:f},V={...K,link:k(/^!?\[(label)\]\((.*?)\)/).replace("label",F).getRegex(),reflink:k(/^!?\[(label)\]\s*\[([^\]]*)\]/).replace("label",F).getRegex()},W={...K,escape:k(Q).replace("])","~|])").getRegex(),url:k(/^((?:ftp|https?):\/\/|www\.)(?:[a-zA-Z0-9\-]+\.?)+[^\s<]*|^email/,"i").replace("email",/[A-Za-z0-9._+-]+(@)[a-zA-Z0-9-_]+(?:\.[a-zA-Z0-9-_]*[a-zA-Z0-9])+(?![-_])/).getRegex(),_backpedal:/(?:[^?!.,:;*_'"~()&]+|\([^)]*\)|&(?![a-zA-Z0-9]+;$)|[?!.,:;*_'"~)]+(?!$))+/,del:/^(~~?)(?=[^\s~])([\s\S]*?[^\s~])\1(?=[^~]|$)/,text:/^([`~]+|[^`~])(?:(?= {2,}\n)|(?=[a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-]+@)|[\s\S]*?(?:(?=[\\<!\[`*~_]|\b_|https?:\/\/|ftp:\/\/|www\.|$)|[^ ](?= {2,}\n)|[^a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-](?=[a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-]+@)))/},Y={...W,br:k(v).replace("{2,}","*").getRegex(),text:k(W.text).replace("\\b_","\\b_| {2,}\\n").replace(/\{2,\}/g,"*").getRegex()},ee={normal:q,gfm:L,pedantic:P},te={normal:K,gfm:W,breaks:Y,pedantic:V};class ne{tokens;options;state;tokenizer;inlineQueue;constructor(t){this.tokens=[],this.tokens.links=Object.create(null),this.options=t||e.defaults,this.options.tokenizer=this.options.tokenizer||new w,this.tokenizer=this.options.tokenizer,this.tokenizer.options=this.options,this.tokenizer.lexer=this,this.inlineQueue=[],this.state={inLink:!1,inRawBlock:!1,top:!0};const n={block:ee.normal,inline:te.normal};this.options.pedantic?(n.block=ee.pedantic,n.inline=te.pedantic):this.options.gfm&&(n.block=ee.gfm,this.options.breaks?n.inline=te.breaks:n.inline=te.gfm),this.tokenizer.rules=n}static get rules(){return{block:ee,inline:te}}static lex(e,t){return new ne(t).lex(e)}static lexInline(e,t){return new ne(t).inlineTokens(e)}lex(e){e=e.replace(/\r\n|\r/g,"\n"),this.blockTokens(e,this.tokens);for(let e=0;e<this.inlineQueue.length;e++){const t=this.inlineQueue[e];this.inlineTokens(t.src,t.tokens)}return this.inlineQueue=[],this.tokens}blockTokens(e,t=[]){let n,s,r,i;for(e=this.options.pedantic?e.replace(/\t/g,"    ").replace(/^ +$/gm,""):e.replace(/^( *)(\t+)/gm,((e,t,n)=>t+"    ".repeat(n.length)));e;)if(!(this.options.extensions&&this.options.extensions.block&&this.options.extensions.block.some((s=>!!(n=s.call({lexer:this},e,t))&&(e=e.substring(n.raw.length),t.push(n),!0)))))if(n=this.tokenizer.space(e))e=e.substring(n.raw.length),1===n.raw.length&&t.length>0?t[t.length-1].raw+="\n":t.push(n);else if(n=this.tokenizer.code(e))e=e.substring(n.raw.length),s=t[t.length-1],!s||"paragraph"!==s.type&&"text"!==s.type?t.push(n):(s.raw+="\n"+n.raw,s.text+="\n"+n.text,this.inlineQueue[this.inlineQueue.length-1].src=s.text);else if(n=this.tokenizer.fences(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.heading(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.hr(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.blockquote(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.list(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.html(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.def(e))e=e.substring(n.raw.length),s=t[t.length-1],!s||"paragraph"!==s.type&&"text"!==s.type?this.tokens.links[n.tag]||(this.tokens.links[n.tag]={href:n.href,title:n.title}):(s.raw+="\n"+n.raw,s.text+="\n"+n.raw,this.inlineQueue[this.inlineQueue.length-1].src=s.text);else if(n=this.tokenizer.table(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.lheading(e))e=e.substring(n.raw.length),t.push(n);else{if(r=e,this.options.extensions&&this.options.extensions.startBlock){let t=1/0;const n=e.slice(1);let s;this.options.extensions.startBlock.forEach((e=>{s=e.call({lexer:this},n),"number"==typeof s&&s>=0&&(t=Math.min(t,s))})),t<1/0&&t>=0&&(r=e.substring(0,t+1))}if(this.state.top&&(n=this.tokenizer.paragraph(r)))s=t[t.length-1],i&&"paragraph"===s.type?(s.raw+="\n"+n.raw,s.text+="\n"+n.text,this.inlineQueue.pop(),this.inlineQueue[this.inlineQueue.length-1].src=s.text):t.push(n),i=r.length!==e.length,e=e.substring(n.raw.length);else if(n=this.tokenizer.text(e))e=e.substring(n.raw.length),s=t[t.length-1],s&&"text"===s.type?(s.raw+="\n"+n.raw,s.text+="\n"+n.text,this.inlineQueue.pop(),this.inlineQueue[this.inlineQueue.length-1].src=s.text):t.push(n);else if(e){const t="Infinite loop on byte: "+e.charCodeAt(0);if(this.options.silent){console.error(t);break}throw new Error(t)}}return this.state.top=!0,t}inline(e,t=[]){return this.inlineQueue.push({src:e,tokens:t}),t}inlineTokens(e,t=[]){let n,s,r,i,l,o,a=e;if(this.tokens.links){const e=Object.keys(this.tokens.links);if(e.length>0)for(;null!=(i=this.tokenizer.rules.inline.reflinkSearch.exec(a));)e.includes(i[0].slice(i[0].lastIndexOf("[")+1,-1))&&(a=a.slice(0,i.index)+"["+"a".repeat(i[0].length-2)+"]"+a.slice(this.tokenizer.rules.inline.reflinkSearch.lastIndex))}for(;null!=(i=this.tokenizer.rules.inline.blockSkip.exec(a));)a=a.slice(0,i.index)+"["+"a".repeat(i[0].length-2)+"]"+a.slice(this.tokenizer.rules.inline.blockSkip.lastIndex);for(;null!=(i=this.tokenizer.rules.inline.anyPunctuation.exec(a));)a=a.slice(0,i.index)+"++"+a.slice(this.tokenizer.rules.inline.anyPunctuation.lastIndex);for(;e;)if(l||(o=""),l=!1,!(this.options.extensions&&this.options.extensions.inline&&this.options.extensions.inline.some((s=>!!(n=s.call({lexer:this},e,t))&&(e=e.substring(n.raw.length),t.push(n),!0)))))if(n=this.tokenizer.escape(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.tag(e))e=e.substring(n.raw.length),s=t[t.length-1],s&&"text"===n.type&&"text"===s.type?(s.raw+=n.raw,s.text+=n.text):t.push(n);else if(n=this.tokenizer.link(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.reflink(e,this.tokens.links))e=e.substring(n.raw.length),s=t[t.length-1],s&&"text"===n.type&&"text"===s.type?(s.raw+=n.raw,s.text+=n.text):t.push(n);else if(n=this.tokenizer.emStrong(e,a,o))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.codespan(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.br(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.del(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.autolink(e))e=e.substring(n.raw.length),t.push(n);else if(this.state.inLink||!(n=this.tokenizer.url(e))){if(r=e,this.options.extensions&&this.options.extensions.startInline){let t=1/0;const n=e.slice(1);let s;this.options.extensions.startInline.forEach((e=>{s=e.call({lexer:this},n),"number"==typeof s&&s>=0&&(t=Math.min(t,s))})),t<1/0&&t>=0&&(r=e.substring(0,t+1))}if(n=this.tokenizer.inlineText(r))e=e.substring(n.raw.length),"_"!==n.raw.slice(-1)&&(o=n.raw.slice(-1)),l=!0,s=t[t.length-1],s&&"text"===s.type?(s.raw+=n.raw,s.text+=n.text):t.push(n);else if(e){const t="Infinite loop on byte: "+e.charCodeAt(0);if(this.options.silent){console.error(t);break}throw new Error(t)}}else e=e.substring(n.raw.length),t.push(n);return t}}class se{options;constructor(t){this.options=t||e.defaults}code(e,t,n){const s=(t||"").match(/^\S*/)?.[0];return e=e.replace(/\n$/,"")+"\n",s?'<pre><code class="language-'+c(s)+'">'+(n?e:c(e,!0))+"</code></pre>\n":"<pre><code>"+(n?e:c(e,!0))+"</code></pre>\n"}blockquote(e){return`<blockquote>\n${e}</blockquote>\n`}html(e,t){return e}heading(e,t,n){return`<h${t}>${e}</h${t}>\n`}hr(){return"<hr>\n"}list(e,t,n){const s=t?"ol":"ul";return"<"+s+(t&&1!==n?' 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diff --git a/custom_nodes/ComfyUI-KJNodes/kjweb_async/purify.min.js b/custom_nodes/ComfyUI-KJNodes/kjweb_async/purify.min.js
new file mode 100644
index 0000000000000000000000000000000000000000..c2f5164618eebcc44b0186f594ccb8092639c670
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/kjweb_async/purify.min.js
@@ -0,0 +1,3 @@
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diff --git a/custom_nodes/ComfyUI-KJNodes/nodes/audioscheduler_nodes.py b/custom_nodes/ComfyUI-KJNodes/nodes/audioscheduler_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..69d0422e7da875298f87fe60a7f6d1494530dca2
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/nodes/audioscheduler_nodes.py
@@ -0,0 +1,251 @@
+# to be used with https://github.com/a1lazydog/ComfyUI-AudioScheduler 
+import torch
+from torchvision.transforms import functional as TF
+from PIL import Image, ImageDraw
+import numpy as np
+from ..utility.utility import pil2tensor
+from nodes import MAX_RESOLUTION
+
+class NormalizedAmplitudeToMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "normalized_amp": ("NORMALIZED_AMPLITUDE",),
+                    "width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                    "height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                    "frame_offset": ("INT", {"default": 0,"min": -255, "max": 255, "step": 1}),
+                    "location_x": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}),
+                    "location_y": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}),
+                    "size": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}),
+                    "shape": (
+                        [   
+                            'none',
+                            'circle',
+                            'square',
+                            'triangle',
+                        ],
+                        {
+                        "default": 'none'
+                        }),
+                    "color": (
+                        [   
+                            'white',
+                            'amplitude',
+                        ],
+                        {
+                        "default": 'amplitude'
+                        }),
+                     },}
+
+    CATEGORY = "KJNodes/audio"
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "convert"
+    DESCRIPTION = """
+Works as a bridge to the AudioScheduler -nodes:  
+https://github.com/a1lazydog/ComfyUI-AudioScheduler  
+Creates masks based on the normalized amplitude.
+"""
+
+    def convert(self, normalized_amp, width, height, frame_offset, shape, location_x, location_y, size, color):
+        # Ensure normalized_amp is an array and within the range [0, 1]
+        normalized_amp = np.clip(normalized_amp, 0.0, 1.0)
+
+        # Offset the amplitude values by rolling the array
+        normalized_amp = np.roll(normalized_amp, frame_offset)
+        
+        # Initialize an empty list to hold the image tensors
+        out = []
+        # Iterate over each amplitude value to create an image
+        for amp in normalized_amp:
+            # Scale the amplitude value to cover the full range of grayscale values
+            if color == 'amplitude':
+                grayscale_value = int(amp * 255)
+            elif color == 'white':
+                grayscale_value = 255
+            # Convert the grayscale value to an RGB format
+            gray_color = (grayscale_value, grayscale_value, grayscale_value)
+            finalsize = size * amp
+            
+            if shape == 'none':
+                shapeimage = Image.new("RGB", (width, height), gray_color)
+            else:
+                shapeimage = Image.new("RGB", (width, height), "black")
+
+            draw = ImageDraw.Draw(shapeimage)
+            if shape == 'circle' or shape == 'square':
+                # Define the bounding box for the shape
+                left_up_point = (location_x - finalsize, location_y - finalsize)
+                right_down_point = (location_x + finalsize,location_y + finalsize)
+                two_points = [left_up_point, right_down_point]
+
+                if shape == 'circle':
+                    draw.ellipse(two_points, fill=gray_color)
+                elif shape == 'square':
+                    draw.rectangle(two_points, fill=gray_color)
+                    
+            elif shape == 'triangle':
+                # Define the points for the triangle
+                left_up_point = (location_x - finalsize, location_y + finalsize) # bottom left
+                right_down_point = (location_x + finalsize, location_y + finalsize) # bottom right
+                top_point = (location_x, location_y) # top point
+                draw.polygon([top_point, left_up_point, right_down_point], fill=gray_color)
+            
+            shapeimage = pil2tensor(shapeimage)
+            mask = shapeimage[:, :, :, 0]
+            out.append(mask)
+        
+        return (torch.cat(out, dim=0),)
+    
+class NormalizedAmplitudeToFloatList:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "normalized_amp": ("NORMALIZED_AMPLITUDE",),
+                     },}
+
+    CATEGORY = "KJNodes/audio"
+    RETURN_TYPES = ("FLOAT",)
+    FUNCTION = "convert"
+    DESCRIPTION = """
+Works as a bridge to the AudioScheduler -nodes:  
+https://github.com/a1lazydog/ComfyUI-AudioScheduler  
+Creates a list of floats from the normalized amplitude.
+"""
+
+    def convert(self, normalized_amp):
+        # Ensure normalized_amp is an array and within the range [0, 1]
+        normalized_amp = np.clip(normalized_amp, 0.0, 1.0)
+        return (normalized_amp.tolist(),)
+
+class OffsetMaskByNormalizedAmplitude:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "normalized_amp": ("NORMALIZED_AMPLITUDE",),
+                "mask": ("MASK",),
+                "x": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
+                "y": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
+                "rotate": ("BOOLEAN", { "default": False }),
+                "angle_multiplier": ("FLOAT", { "default": 0.0, "min": -1.0, "max": 1.0, "step": 0.001, "display": "number" }),
+            }
+        }
+
+    RETURN_TYPES = ("MASK",)
+    RETURN_NAMES = ("mask",)
+    FUNCTION = "offset"
+    CATEGORY = "KJNodes/audio"
+    DESCRIPTION = """
+Works as a bridge to the AudioScheduler -nodes:  
+https://github.com/a1lazydog/ComfyUI-AudioScheduler  
+Offsets masks based on the normalized amplitude.
+"""
+
+    def offset(self, mask, x, y, angle_multiplier, rotate, normalized_amp):
+
+         # Ensure normalized_amp is an array and within the range [0, 1]
+        offsetmask = mask.clone()
+        normalized_amp = np.clip(normalized_amp, 0.0, 1.0)
+       
+        batch_size, height, width = mask.shape
+
+        if rotate:
+            for i in range(batch_size):
+                rotation_amp = int(normalized_amp[i] * (360 * angle_multiplier))
+                rotation_angle = rotation_amp
+                offsetmask[i] = TF.rotate(offsetmask[i].unsqueeze(0), rotation_angle).squeeze(0)
+        if x != 0 or y != 0:
+            for i in range(batch_size):
+                offset_amp = normalized_amp[i] * 10
+                shift_x = min(x*offset_amp, width-1)
+                shift_y = min(y*offset_amp, height-1)
+                if shift_x != 0:
+                    offsetmask[i] = torch.roll(offsetmask[i], shifts=int(shift_x), dims=1)
+                if shift_y != 0:
+                    offsetmask[i] = torch.roll(offsetmask[i], shifts=int(shift_y), dims=0)
+        
+        return offsetmask,
+
+class ImageTransformByNormalizedAmplitude:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "normalized_amp": ("NORMALIZED_AMPLITUDE",),
+            "zoom_scale": ("FLOAT", { "default": 0.0, "min": -1.0, "max": 1.0, "step": 0.001, "display": "number" }),
+            "x_offset": ("INT", { "default": 0, "min": (1 -MAX_RESOLUTION), "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
+            "y_offset": ("INT", { "default": 0, "min": (1 -MAX_RESOLUTION), "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
+            "cumulative": ("BOOLEAN", { "default": False }),
+            "image": ("IMAGE",),
+        }}
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "amptransform"
+    CATEGORY = "KJNodes/audio"
+    DESCRIPTION = """
+Works as a bridge to the AudioScheduler -nodes:  
+https://github.com/a1lazydog/ComfyUI-AudioScheduler  
+Transforms image based on the normalized amplitude.
+"""
+
+    def amptransform(self, image, normalized_amp, zoom_scale, cumulative, x_offset, y_offset):
+        # Ensure normalized_amp is an array and within the range [0, 1]
+        normalized_amp = np.clip(normalized_amp, 0.0, 1.0)
+        transformed_images = []
+
+        # Initialize the cumulative zoom factor
+        prev_amp = 0.0
+
+        for i in range(image.shape[0]):
+            img = image[i]  # Get the i-th image in the batch
+            amp = normalized_amp[i]  # Get the corresponding amplitude value
+
+            # Incrementally increase the cumulative zoom factor
+            if cumulative:
+                prev_amp += amp
+                amp += prev_amp
+
+            # Convert the image tensor from BxHxWxC to CxHxW format expected by torchvision
+            img = img.permute(2, 0, 1)
+            
+            # Convert PyTorch tensor to PIL Image for processing
+            pil_img = TF.to_pil_image(img)
+            
+            # Calculate the crop size based on the amplitude
+            width, height = pil_img.size
+            crop_size = int(min(width, height) * (1 - amp * zoom_scale))
+            crop_size = max(crop_size, 1)
+            
+            # Calculate the crop box coordinates (centered crop)
+            left = (width - crop_size) // 2
+            top = (height - crop_size) // 2
+            right = (width + crop_size) // 2
+            bottom = (height + crop_size) // 2
+            
+            # Crop and resize back to original size
+            cropped_img = TF.crop(pil_img, top, left, crop_size, crop_size)
+            resized_img = TF.resize(cropped_img, (height, width))
+            
+            # Convert back to tensor in CxHxW format
+            tensor_img = TF.to_tensor(resized_img)
+            
+            # Convert the tensor back to BxHxWxC format
+            tensor_img = tensor_img.permute(1, 2, 0)
+            
+            # Offset the image based on the amplitude
+            offset_amp = amp * 10  # Calculate the offset magnitude based on the amplitude
+            shift_x = min(x_offset * offset_amp, img.shape[1] - 1)  # Calculate the shift in x direction
+            shift_y = min(y_offset * offset_amp, img.shape[0] - 1)  # Calculate the shift in y direction
+
+            # Apply the offset to the image tensor
+            if shift_x != 0:
+                tensor_img = torch.roll(tensor_img, shifts=int(shift_x), dims=1)
+            if shift_y != 0:
+                tensor_img = torch.roll(tensor_img, shifts=int(shift_y), dims=0)
+
+            # Add to the list
+            transformed_images.append(tensor_img)
+        
+        # Stack all transformed images into a batch
+        transformed_batch = torch.stack(transformed_images)
+        
+        return (transformed_batch,)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-KJNodes/nodes/batchcrop_nodes.py b/custom_nodes/ComfyUI-KJNodes/nodes/batchcrop_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..304ff1265770265fe4786e424a05f372daaf1f03
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/nodes/batchcrop_nodes.py
@@ -0,0 +1,768 @@
+from ..utility.utility import tensor2pil, pil2tensor
+from PIL import Image, ImageDraw, ImageFilter
+import numpy as np
+import torch
+from torchvision.transforms import Resize, CenterCrop, InterpolationMode
+import math
+
+#based on nodes from mtb https://github.com/melMass/comfy_mtb
+
+def bbox_to_region(bbox, target_size=None):
+    bbox = bbox_check(bbox, target_size)
+    return (bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3])
+
+def bbox_check(bbox, target_size=None):
+    if not target_size:
+        return bbox
+
+    new_bbox = (
+        bbox[0],
+        bbox[1],
+        min(target_size[0] - bbox[0], bbox[2]),
+        min(target_size[1] - bbox[1], bbox[3]),
+    )
+    return new_bbox
+
+class BatchCropFromMask:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "original_images": ("IMAGE",),
+                "masks": ("MASK",),
+                "crop_size_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}),
+                "bbox_smooth_alpha": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+            },
+        }
+
+    RETURN_TYPES = (
+        "IMAGE",
+        "IMAGE",
+        "BBOX",
+        "INT",
+        "INT",
+    )
+    RETURN_NAMES = (
+        "original_images",
+        "cropped_images",
+        "bboxes",
+        "width",
+        "height",
+    )
+    FUNCTION = "crop"
+    CATEGORY = "KJNodes/masking"
+
+    def smooth_bbox_size(self, prev_bbox_size, curr_bbox_size, alpha):
+        if alpha == 0:
+            return prev_bbox_size
+        return round(alpha * curr_bbox_size + (1 - alpha) * prev_bbox_size)
+
+    def smooth_center(self, prev_center, curr_center, alpha=0.5):
+        if alpha == 0:
+            return prev_center
+        return (
+            round(alpha * curr_center[0] + (1 - alpha) * prev_center[0]),
+            round(alpha * curr_center[1] + (1 - alpha) * prev_center[1])
+        )
+
+    def crop(self, masks, original_images, crop_size_mult, bbox_smooth_alpha):
+ 
+        bounding_boxes = []
+        cropped_images = []
+
+        self.max_bbox_width = 0
+        self.max_bbox_height = 0
+
+        # First, calculate the maximum bounding box size across all masks
+        curr_max_bbox_width = 0
+        curr_max_bbox_height = 0
+        for mask in masks:
+            _mask = tensor2pil(mask)[0]
+            non_zero_indices = np.nonzero(np.array(_mask))
+            min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1])
+            min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0])
+            width = max_x - min_x
+            height = max_y - min_y
+            curr_max_bbox_width = max(curr_max_bbox_width, width)
+            curr_max_bbox_height = max(curr_max_bbox_height, height)
+
+        # Smooth the changes in the bounding box size
+        self.max_bbox_width = self.smooth_bbox_size(self.max_bbox_width, curr_max_bbox_width, bbox_smooth_alpha)
+        self.max_bbox_height = self.smooth_bbox_size(self.max_bbox_height, curr_max_bbox_height, bbox_smooth_alpha)
+
+        # Apply the crop size multiplier
+        self.max_bbox_width = round(self.max_bbox_width * crop_size_mult)
+        self.max_bbox_height = round(self.max_bbox_height * crop_size_mult)
+        bbox_aspect_ratio = self.max_bbox_width / self.max_bbox_height
+
+        # Then, for each mask and corresponding image...
+        for i, (mask, img) in enumerate(zip(masks, original_images)):
+            _mask = tensor2pil(mask)[0]
+            non_zero_indices = np.nonzero(np.array(_mask))
+            min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1])
+            min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0])
+            
+            # Calculate center of bounding box
+            center_x = np.mean(non_zero_indices[1])
+            center_y = np.mean(non_zero_indices[0])
+            curr_center = (round(center_x), round(center_y))
+
+            # If this is the first frame, initialize prev_center with curr_center
+            if not hasattr(self, 'prev_center'):
+                self.prev_center = curr_center
+
+            # Smooth the changes in the center coordinates from the second frame onwards
+            if i > 0:
+                center = self.smooth_center(self.prev_center, curr_center, bbox_smooth_alpha)
+            else:
+                center = curr_center
+
+            # Update prev_center for the next frame
+            self.prev_center = center
+
+            # Create bounding box using max_bbox_width and max_bbox_height
+            half_box_width = round(self.max_bbox_width / 2)
+            half_box_height = round(self.max_bbox_height / 2)
+            min_x = max(0, center[0] - half_box_width)
+            max_x = min(img.shape[1], center[0] + half_box_width)
+            min_y = max(0, center[1] - half_box_height)
+            max_y = min(img.shape[0], center[1] + half_box_height)
+
+            # Append bounding box coordinates
+            bounding_boxes.append((min_x, min_y, max_x - min_x, max_y - min_y))
+
+            # Crop the image from the bounding box
+            cropped_img = img[min_y:max_y, min_x:max_x, :]
+            
+            # Calculate the new dimensions while maintaining the aspect ratio
+            new_height = min(cropped_img.shape[0], self.max_bbox_height)
+            new_width = round(new_height * bbox_aspect_ratio)
+
+            # Resize the image
+            resize_transform = Resize((new_height, new_width))
+            resized_img = resize_transform(cropped_img.permute(2, 0, 1))
+
+            # Perform the center crop to the desired size
+            crop_transform = CenterCrop((self.max_bbox_height, self.max_bbox_width)) # swap the order here if necessary
+            cropped_resized_img = crop_transform(resized_img)
+
+            cropped_images.append(cropped_resized_img.permute(1, 2, 0))
+
+        cropped_out = torch.stack(cropped_images, dim=0)
+        
+        return (original_images, cropped_out, bounding_boxes, self.max_bbox_width, self.max_bbox_height, )
+
+class BatchUncrop:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "original_images": ("IMAGE",),
+                "cropped_images": ("IMAGE",),
+                "bboxes": ("BBOX",),
+                "border_blending": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}, ),
+                "crop_rescale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "border_top": ("BOOLEAN", {"default": True}),
+                "border_bottom": ("BOOLEAN", {"default": True}),
+                "border_left": ("BOOLEAN", {"default": True}),
+                "border_right": ("BOOLEAN", {"default": True}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "uncrop"
+
+    CATEGORY = "KJNodes/masking"
+
+    def uncrop(self, original_images, cropped_images, bboxes, border_blending, crop_rescale, border_top, border_bottom, border_left, border_right):
+        def inset_border(image, border_width, border_color, border_top, border_bottom, border_left, border_right):
+            draw = ImageDraw.Draw(image)
+            width, height = image.size
+            if border_top:
+                draw.rectangle((0, 0, width, border_width), fill=border_color)
+            if border_bottom:
+                draw.rectangle((0, height - border_width, width, height), fill=border_color)
+            if border_left:
+                draw.rectangle((0, 0, border_width, height), fill=border_color)
+            if border_right:
+                draw.rectangle((width - border_width, 0, width, height), fill=border_color)
+            return image
+
+        if len(original_images) != len(cropped_images):
+            raise ValueError(f"The number of original_images ({len(original_images)}) and cropped_images ({len(cropped_images)}) should be the same")
+
+        # Ensure there are enough bboxes, but drop the excess if there are more bboxes than images
+        if len(bboxes) > len(original_images):
+            print(f"Warning: Dropping excess bounding boxes. Expected {len(original_images)}, but got {len(bboxes)}")
+            bboxes = bboxes[:len(original_images)]
+        elif len(bboxes) < len(original_images):
+            raise ValueError("There should be at least as many bboxes as there are original and cropped images")
+
+        input_images = tensor2pil(original_images)
+        crop_imgs = tensor2pil(cropped_images)
+        
+        out_images = []
+        for i in range(len(input_images)):
+            img = input_images[i]
+            crop = crop_imgs[i]
+            bbox = bboxes[i]
+            
+            # uncrop the image based on the bounding box
+            bb_x, bb_y, bb_width, bb_height = bbox
+
+            paste_region = bbox_to_region((bb_x, bb_y, bb_width, bb_height), img.size)
+            
+            # scale factors
+            scale_x = crop_rescale
+            scale_y = crop_rescale
+
+            # scaled paste_region
+            paste_region = (round(paste_region[0]*scale_x), round(paste_region[1]*scale_y), round(paste_region[2]*scale_x), round(paste_region[3]*scale_y))
+
+            # rescale the crop image to fit the paste_region
+            crop = crop.resize((round(paste_region[2]-paste_region[0]), round(paste_region[3]-paste_region[1])))
+            crop_img = crop.convert("RGB")
+   
+            if border_blending > 1.0:
+                border_blending = 1.0
+            elif border_blending < 0.0:
+                border_blending = 0.0
+
+            blend_ratio = (max(crop_img.size) / 2) * float(border_blending)
+
+            blend = img.convert("RGBA")
+            mask = Image.new("L", img.size, 0)
+
+            mask_block = Image.new("L", (paste_region[2]-paste_region[0], paste_region[3]-paste_region[1]), 255)
+            mask_block = inset_border(mask_block, round(blend_ratio / 2), (0), border_top, border_bottom, border_left, border_right)
+                      
+            mask.paste(mask_block, paste_region)
+            blend.paste(crop_img, paste_region)
+
+            mask = mask.filter(ImageFilter.BoxBlur(radius=blend_ratio / 4))
+            mask = mask.filter(ImageFilter.GaussianBlur(radius=blend_ratio / 4))
+
+            blend.putalpha(mask)
+            img = Image.alpha_composite(img.convert("RGBA"), blend)
+            out_images.append(img.convert("RGB"))
+
+        return (pil2tensor(out_images),)
+
+class BatchCropFromMaskAdvanced:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "original_images": ("IMAGE",),
+                "masks": ("MASK",),
+                "crop_size_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "bbox_smooth_alpha": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+            },
+        }
+
+    RETURN_TYPES = (
+        "IMAGE",
+        "IMAGE",
+        "MASK",
+        "IMAGE",
+        "MASK",
+        "BBOX",
+        "BBOX",
+        "INT",
+        "INT",
+    )
+    RETURN_NAMES = (
+        "original_images",
+        "cropped_images",
+        "cropped_masks",
+        "combined_crop_image",
+        "combined_crop_masks",
+        "bboxes",
+        "combined_bounding_box",
+        "bbox_width",
+        "bbox_height",
+    )
+    FUNCTION = "crop"
+    CATEGORY = "KJNodes/masking"
+
+    def smooth_bbox_size(self, prev_bbox_size, curr_bbox_size, alpha):
+          return round(alpha * curr_bbox_size + (1 - alpha) * prev_bbox_size)
+
+    def smooth_center(self, prev_center, curr_center, alpha=0.5):
+        return (round(alpha * curr_center[0] + (1 - alpha) * prev_center[0]),
+                round(alpha * curr_center[1] + (1 - alpha) * prev_center[1]))
+
+    def crop(self, masks, original_images, crop_size_mult, bbox_smooth_alpha):
+        bounding_boxes = []
+        combined_bounding_box = []
+        cropped_images = []
+        cropped_masks = []
+        cropped_masks_out = []
+        combined_crop_out = []
+        combined_cropped_images = []
+        combined_cropped_masks = []
+        
+        def calculate_bbox(mask):
+            non_zero_indices = np.nonzero(np.array(mask))
+
+            # handle empty masks
+            min_x, max_x, min_y, max_y = 0, 0, 0, 0
+            if len(non_zero_indices[1]) > 0 and len(non_zero_indices[0]) > 0:
+                min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1])
+                min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0])
+
+            width = max_x - min_x
+            height = max_y - min_y
+            bbox_size = max(width, height)
+            return min_x, max_x, min_y, max_y, bbox_size
+
+        combined_mask = torch.max(masks, dim=0)[0]
+        _mask = tensor2pil(combined_mask)[0]
+        new_min_x, new_max_x, new_min_y, new_max_y, combined_bbox_size = calculate_bbox(_mask)
+        center_x = (new_min_x + new_max_x) / 2
+        center_y = (new_min_y + new_max_y) / 2
+        half_box_size = round(combined_bbox_size // 2)
+        new_min_x = max(0, round(center_x - half_box_size))
+        new_max_x = min(original_images[0].shape[1], round(center_x + half_box_size))
+        new_min_y = max(0, round(center_y - half_box_size))
+        new_max_y = min(original_images[0].shape[0], round(center_y + half_box_size))
+        
+        combined_bounding_box.append((new_min_x, new_min_y, new_max_x - new_min_x, new_max_y - new_min_y))   
+        
+        self.max_bbox_size = 0
+        
+        # First, calculate the maximum bounding box size across all masks
+        curr_max_bbox_size = max(calculate_bbox(tensor2pil(mask)[0])[-1] for mask in masks)
+        # Smooth the changes in the bounding box size
+        self.max_bbox_size = self.smooth_bbox_size(self.max_bbox_size, curr_max_bbox_size, bbox_smooth_alpha)
+        # Apply the crop size multiplier
+        self.max_bbox_size = round(self.max_bbox_size * crop_size_mult)
+        # Make sure max_bbox_size is divisible by 16, if not, round it upwards so it is
+        self.max_bbox_size = math.ceil(self.max_bbox_size / 16) * 16
+
+        if self.max_bbox_size > original_images[0].shape[0] or self.max_bbox_size > original_images[0].shape[1]:
+            # max_bbox_size can only be as big as our input's width or height, and it has to be even
+            self.max_bbox_size = math.floor(min(original_images[0].shape[0], original_images[0].shape[1]) / 2) * 2
+
+        # Then, for each mask and corresponding image...
+        for i, (mask, img) in enumerate(zip(masks, original_images)):
+            _mask = tensor2pil(mask)[0]
+            non_zero_indices = np.nonzero(np.array(_mask))
+
+            # check for empty masks
+            if len(non_zero_indices[0]) > 0 and len(non_zero_indices[1]) > 0:
+                min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1])
+                min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0])
+
+                # Calculate center of bounding box
+                center_x = np.mean(non_zero_indices[1])
+                center_y = np.mean(non_zero_indices[0])
+                curr_center = (round(center_x), round(center_y))
+
+                # If this is the first frame, initialize prev_center with curr_center
+                if not hasattr(self, 'prev_center'):
+                    self.prev_center = curr_center
+
+                # Smooth the changes in the center coordinates from the second frame onwards
+                if i > 0:
+                    center = self.smooth_center(self.prev_center, curr_center, bbox_smooth_alpha)
+                else:
+                    center = curr_center
+
+                # Update prev_center for the next frame
+                self.prev_center = center
+
+                # Create bounding box using max_bbox_size
+                half_box_size = self.max_bbox_size // 2
+                min_x = max(0, center[0] - half_box_size)
+                max_x = min(img.shape[1], center[0] + half_box_size)
+                min_y = max(0, center[1] - half_box_size)
+                max_y = min(img.shape[0], center[1] + half_box_size)
+
+                # Append bounding box coordinates
+                bounding_boxes.append((min_x, min_y, max_x - min_x, max_y - min_y))
+
+                # Crop the image from the bounding box
+                cropped_img = img[min_y:max_y, min_x:max_x, :]
+                cropped_mask = mask[min_y:max_y, min_x:max_x]
+
+                # Resize the cropped image to a fixed size
+                new_size = max(cropped_img.shape[0], cropped_img.shape[1])
+                resize_transform = Resize(new_size, interpolation=InterpolationMode.NEAREST, max_size=max(img.shape[0], img.shape[1]))
+                resized_mask = resize_transform(cropped_mask.unsqueeze(0).unsqueeze(0)).squeeze(0).squeeze(0)
+                resized_img = resize_transform(cropped_img.permute(2, 0, 1))
+                # Perform the center crop to the desired size
+                # Constrain the crop to the smaller of our bbox or our image so we don't expand past the image dimensions.
+                crop_transform = CenterCrop((min(self.max_bbox_size, resized_img.shape[1]), min(self.max_bbox_size, resized_img.shape[2])))
+
+                cropped_resized_img = crop_transform(resized_img)
+                cropped_images.append(cropped_resized_img.permute(1, 2, 0))
+
+                cropped_resized_mask = crop_transform(resized_mask)
+                cropped_masks.append(cropped_resized_mask)
+
+                combined_cropped_img = original_images[i][new_min_y:new_max_y, new_min_x:new_max_x, :]
+                combined_cropped_images.append(combined_cropped_img)
+
+                combined_cropped_mask = masks[i][new_min_y:new_max_y, new_min_x:new_max_x]
+                combined_cropped_masks.append(combined_cropped_mask)
+            else:
+                bounding_boxes.append((0, 0, img.shape[1], img.shape[0]))
+                cropped_images.append(img)
+                cropped_masks.append(mask)
+                combined_cropped_images.append(img)
+                combined_cropped_masks.append(mask)
+
+        cropped_out = torch.stack(cropped_images, dim=0)
+        combined_crop_out = torch.stack(combined_cropped_images, dim=0)
+        cropped_masks_out = torch.stack(cropped_masks, dim=0)
+        combined_crop_mask_out = torch.stack(combined_cropped_masks, dim=0)
+
+        return (original_images, cropped_out, cropped_masks_out, combined_crop_out, combined_crop_mask_out, bounding_boxes, combined_bounding_box, self.max_bbox_size, self.max_bbox_size)
+
+class FilterZeroMasksAndCorrespondingImages:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "masks": ("MASK",),
+            },
+            "optional": {
+                "original_images": ("IMAGE",), 
+            },
+        }
+
+    RETURN_TYPES = ("MASK", "IMAGE", "IMAGE", "INDEXES",)
+    RETURN_NAMES = ("non_zero_masks_out", "non_zero_mask_images_out", "zero_mask_images_out", "zero_mask_images_out_indexes",)
+    FUNCTION = "filter"
+    CATEGORY = "KJNodes/masking"
+    DESCRIPTION = """
+Filter out all the empty (i.e. all zero) mask in masks  
+Also filter out all the corresponding images in original_images by indexes if provide  
+  
+original_images (optional): If provided, need have same length as masks.
+"""
+    
+    def filter(self, masks, original_images=None):
+        non_zero_masks = []
+        non_zero_mask_images = []
+        zero_mask_images = []
+        zero_mask_images_indexes = []
+        
+        masks_num = len(masks)
+        also_process_images = False
+        if original_images is not None:
+            imgs_num = len(original_images)
+            if len(original_images) == masks_num:
+                also_process_images = True
+            else:
+                print(f"[WARNING] ignore input: original_images, due to number of original_images ({imgs_num}) is not equal to number of masks ({masks_num})")
+        
+        for i in range(masks_num):
+            non_zero_num = np.count_nonzero(np.array(masks[i]))
+            if non_zero_num > 0:
+                non_zero_masks.append(masks[i])
+                if also_process_images:
+                    non_zero_mask_images.append(original_images[i])
+            else:
+                zero_mask_images.append(original_images[i])
+                zero_mask_images_indexes.append(i)
+
+        non_zero_masks_out = torch.stack(non_zero_masks, dim=0)
+        non_zero_mask_images_out = zero_mask_images_out = zero_mask_images_out_indexes = None
+        
+        if also_process_images:
+            non_zero_mask_images_out = torch.stack(non_zero_mask_images, dim=0)
+            if len(zero_mask_images) > 0:
+                zero_mask_images_out = torch.stack(zero_mask_images, dim=0)
+                zero_mask_images_out_indexes = zero_mask_images_indexes
+
+        return (non_zero_masks_out, non_zero_mask_images_out, zero_mask_images_out, zero_mask_images_out_indexes)
+
+class InsertImageBatchByIndexes:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": ("IMAGE",), 
+                "images_to_insert": ("IMAGE",), 
+                "insert_indexes": ("INDEXES",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", )
+    RETURN_NAMES = ("images_after_insert", )
+    FUNCTION = "insert"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+This node is designed to be use with node FilterZeroMasksAndCorrespondingImages
+It inserts the images_to_insert into images according to insert_indexes
+
+Returns:
+    images_after_insert: updated original images with origonal sequence order
+"""
+    
+    def insert(self, images, images_to_insert, insert_indexes):        
+        images_after_insert = images
+        
+        if images_to_insert is not None and insert_indexes is not None:
+            images_to_insert_num = len(images_to_insert)
+            insert_indexes_num = len(insert_indexes)
+            if images_to_insert_num == insert_indexes_num:
+                images_after_insert = []
+
+                i_images = 0
+                for i in range(len(images) + images_to_insert_num):
+                    if i in insert_indexes:
+                        images_after_insert.append(images_to_insert[insert_indexes.index(i)])
+                    else:
+                        images_after_insert.append(images[i_images])
+                        i_images += 1
+                        
+                images_after_insert = torch.stack(images_after_insert, dim=0)
+                
+            else:
+                print(f"[WARNING] skip this node, due to number of images_to_insert ({images_to_insert_num}) is not equal to number of insert_indexes ({insert_indexes_num})")
+
+
+        return (images_after_insert, )
+
+class BatchUncropAdvanced:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "original_images": ("IMAGE",),
+                "cropped_images": ("IMAGE",), 
+                "cropped_masks": ("MASK",),
+                "combined_crop_mask": ("MASK",),
+                "bboxes": ("BBOX",),
+                "border_blending": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}, ),
+                "crop_rescale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "use_combined_mask": ("BOOLEAN", {"default": False}),
+                "use_square_mask": ("BOOLEAN", {"default": True}),
+            },
+            "optional": {
+                "combined_bounding_box": ("BBOX", {"default": None}),  
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "uncrop"
+    CATEGORY = "KJNodes/masking"
+
+
+    def uncrop(self, original_images, cropped_images, cropped_masks, combined_crop_mask, bboxes, border_blending, crop_rescale, use_combined_mask, use_square_mask, combined_bounding_box = None):
+        
+        def inset_border(image, border_width=20, border_color=(0)):
+            width, height = image.size
+            bordered_image = Image.new(image.mode, (width, height), border_color)
+            bordered_image.paste(image, (0, 0))
+            draw = ImageDraw.Draw(bordered_image)
+            draw.rectangle((0, 0, width - 1, height - 1), outline=border_color, width=border_width)
+            return bordered_image
+
+        if len(original_images) != len(cropped_images):
+            raise ValueError(f"The number of original_images ({len(original_images)}) and cropped_images ({len(cropped_images)}) should be the same")
+
+        # Ensure there are enough bboxes, but drop the excess if there are more bboxes than images
+        if len(bboxes) > len(original_images):
+            print(f"Warning: Dropping excess bounding boxes. Expected {len(original_images)}, but got {len(bboxes)}")
+            bboxes = bboxes[:len(original_images)]
+        elif len(bboxes) < len(original_images):
+            raise ValueError("There should be at least as many bboxes as there are original and cropped images")
+
+        crop_imgs = tensor2pil(cropped_images)
+        input_images = tensor2pil(original_images)
+        out_images = []
+
+        for i in range(len(input_images)):
+            img = input_images[i]
+            crop = crop_imgs[i]
+            bbox = bboxes[i]
+            
+            if use_combined_mask:
+                bb_x, bb_y, bb_width, bb_height = combined_bounding_box[0]
+                paste_region = bbox_to_region((bb_x, bb_y, bb_width, bb_height), img.size)
+                mask = combined_crop_mask[i]
+            else:
+                bb_x, bb_y, bb_width, bb_height = bbox
+                paste_region = bbox_to_region((bb_x, bb_y, bb_width, bb_height), img.size)
+                mask = cropped_masks[i]
+            
+            # scale paste_region
+            scale_x = scale_y = crop_rescale
+            paste_region = (round(paste_region[0]*scale_x), round(paste_region[1]*scale_y), round(paste_region[2]*scale_x), round(paste_region[3]*scale_y))
+
+            # rescale the crop image to fit the paste_region
+            crop = crop.resize((round(paste_region[2]-paste_region[0]), round(paste_region[3]-paste_region[1])))
+            crop_img = crop.convert("RGB")
+
+            #border blending
+            if border_blending > 1.0:
+                border_blending = 1.0
+            elif border_blending < 0.0:
+                border_blending = 0.0
+
+            blend_ratio = (max(crop_img.size) / 2) * float(border_blending)
+            blend = img.convert("RGBA")
+
+            if use_square_mask:
+                mask = Image.new("L", img.size, 0)
+                mask_block = Image.new("L", (paste_region[2]-paste_region[0], paste_region[3]-paste_region[1]), 255)
+                mask_block = inset_border(mask_block, round(blend_ratio / 2), (0))
+                mask.paste(mask_block, paste_region)
+            else:
+                original_mask = tensor2pil(mask)[0]
+                original_mask = original_mask.resize((paste_region[2]-paste_region[0], paste_region[3]-paste_region[1]))
+                mask = Image.new("L", img.size, 0)
+                mask.paste(original_mask, paste_region)
+
+            mask = mask.filter(ImageFilter.BoxBlur(radius=blend_ratio / 4))
+            mask = mask.filter(ImageFilter.GaussianBlur(radius=blend_ratio / 4))
+
+            blend.paste(crop_img, paste_region) 
+            blend.putalpha(mask)
+            
+            img = Image.alpha_composite(img.convert("RGBA"), blend)
+            out_images.append(img.convert("RGB"))
+
+        return (pil2tensor(out_images),)
+
+class SplitBboxes:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "bboxes": ("BBOX",),
+                "index": ("INT", {"default": 0,"min": 0, "max": 99999999, "step": 1}),
+            },
+        }
+
+    RETURN_TYPES = ("BBOX","BBOX",)
+    RETURN_NAMES = ("bboxes_a","bboxes_b",)
+    FUNCTION = "splitbbox"
+    CATEGORY = "KJNodes/masking"
+    DESCRIPTION = """
+Splits the specified bbox list at the given index into two lists.
+"""
+
+    def splitbbox(self, bboxes, index):
+        bboxes_a = bboxes[:index]  # Sub-list from the start of bboxes up to (but not including) the index
+        bboxes_b = bboxes[index:]  # Sub-list from the index to the end of bboxes
+
+        return (bboxes_a, bboxes_b,)
+    
+class BboxToInt:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "bboxes": ("BBOX",),
+                "index": ("INT", {"default": 0,"min": 0, "max": 99999999, "step": 1}),
+            },
+        }
+
+    RETURN_TYPES = ("INT","INT","INT","INT","INT","INT",)
+    RETURN_NAMES = ("x_min","y_min","width","height", "center_x","center_y",)
+    FUNCTION = "bboxtoint"
+    CATEGORY = "KJNodes/masking"
+    DESCRIPTION = """
+Returns selected index from bounding box list as integers.
+"""
+    def bboxtoint(self, bboxes, index):
+        x_min, y_min, width, height = bboxes[index]
+        center_x = int(x_min + width / 2)
+        center_y = int(y_min + height / 2)
+        
+        return (x_min, y_min, width, height, center_x, center_y,)
+
+class BboxVisualize:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "bboxes": ("BBOX",),
+                "line_width": ("INT", {"default": 1,"min": 1, "max": 10, "step": 1}),
+                "bbox_format": (["xywh", "xyxy"], {"default": "xywh"}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("images",)
+    FUNCTION = "visualizebbox"
+    DESCRIPTION = """
+Visualizes the specified bbox on the image.
+"""
+
+    CATEGORY = "KJNodes/masking"
+
+    def visualizebbox(self, bboxes, images, line_width, bbox_format):
+        image_list = []
+        for image, bbox in zip(images, bboxes):
+            # Ensure bbox is a sequence of 4 values
+            if isinstance(bbox, (list, tuple, np.ndarray)) and len(bbox) == 4:
+                if bbox_format == "xywh":
+                    x_min, y_min, width, height = bbox
+                elif bbox_format == "xyxy":
+                    x_min, y_min, x_max, y_max = bbox
+                    width = x_max - x_min
+                    height = y_max - y_min
+                else:
+                    raise ValueError(f"Unknown bbox_format: {bbox_format}")
+            else:
+                print("Invalid bbox:", bbox)
+                continue
+
+            # Ensure bbox coordinates are integers
+            x_min = int(x_min)
+            y_min = int(y_min)
+            width = int(width)
+            height = int(height)
+
+            # Permute the image dimensions
+            image = image.permute(2, 0, 1)
+
+            # Clone the image to draw bounding boxes
+            img_with_bbox = image.clone()
+
+            # Define the color for the bbox, e.g., red
+            color = torch.tensor([1, 0, 0], dtype=torch.float32)
+
+            # Ensure color tensor matches the image channels
+            if color.shape[0] != img_with_bbox.shape[0]:
+                color = color.unsqueeze(1).expand(-1, line_width)
+
+            # Draw lines for each side of the bbox with the specified line width
+            for lw in range(line_width):
+                # Top horizontal line
+                if y_min + lw < img_with_bbox.shape[1]:
+                    img_with_bbox[:, y_min + lw, x_min:x_min + width] = color[:, None]
+
+                # Bottom horizontal line
+                if y_min + height - lw < img_with_bbox.shape[1]:
+                    img_with_bbox[:, y_min + height - lw, x_min:x_min + width] = color[:, None]
+
+                # Left vertical line
+                if x_min + lw < img_with_bbox.shape[2]:
+                    img_with_bbox[:, y_min:y_min + height, x_min + lw] = color[:, None]
+
+                # Right vertical line
+                if x_min + width - lw < img_with_bbox.shape[2]:
+                    img_with_bbox[:, y_min:y_min + height, x_min + width - lw] = color[:, None]
+
+            # Permute the image dimensions back
+            img_with_bbox = img_with_bbox.permute(1, 2, 0).unsqueeze(0)
+            image_list.append(img_with_bbox)
+
+        return (torch.cat(image_list, dim=0),)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-KJNodes/nodes/curve_nodes.py b/custom_nodes/ComfyUI-KJNodes/nodes/curve_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..35dc3558790ca2f5e7860c04827e47a012bdebc7
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/nodes/curve_nodes.py
@@ -0,0 +1,1642 @@
+import torch
+from torchvision import transforms
+import json
+from PIL import Image, ImageDraw, ImageFont, ImageFilter
+import numpy as np
+from ..utility.utility import pil2tensor, tensor2pil
+import folder_paths
+import base64
+from io import BytesIO
+
+
+def parse_color(color):
+    if isinstance(color, str) and ',' in color:
+        return tuple(int(c.strip()) for c in color.split(','))
+    return color
+
+def parse_json_tracks(tracks):
+    tracks_data = []
+    try:
+        # If tracks is a string, try to parse it as JSON
+        if isinstance(tracks, str):
+            parsed = json.loads(tracks.replace("'", '"'))
+            tracks_data.extend(parsed)
+        else:
+            # If tracks is a list of strings, parse each one
+            for track_str in tracks:
+                parsed = json.loads(track_str.replace("'", '"'))
+                tracks_data.append(parsed)
+        
+        # Check if we have a single track (dict with x,y) or a list of tracks
+        if tracks_data and isinstance(tracks_data[0], dict) and 'x' in tracks_data[0]:
+            # Single track detected, wrap it in a list
+            tracks_data = [tracks_data]
+        elif tracks_data and isinstance(tracks_data[0], list) and tracks_data[0] and isinstance(tracks_data[0][0], dict) and 'x' in tracks_data[0][0]:
+            # Already a list of tracks, nothing to do
+            pass
+        else:
+            # Unexpected format
+            print(f"Warning: Unexpected track format: {type(tracks_data[0])}")
+            
+    except json.JSONDecodeError as e:
+        print(f"Error parsing tracks JSON: {e}")
+        tracks_data = []
+
+    return tracks_data
+
+def plot_coordinates_to_tensor(coordinates, height, width, bbox_height, bbox_width, size_multiplier, prompt):
+        import matplotlib
+        matplotlib.use('Agg')
+        from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
+        text_color = '#999999'
+        bg_color = '#353535'
+        matplotlib.pyplot.rcParams['text.color'] = text_color
+        fig, ax = matplotlib.pyplot.subplots(figsize=(width/100, height/100), dpi=100)
+        fig.patch.set_facecolor(bg_color)
+        ax.set_facecolor(bg_color)
+        ax.grid(color=text_color, linestyle='-', linewidth=0.5)
+        ax.set_xlabel('x', color=text_color)
+        ax.set_ylabel('y', color=text_color)
+        for text in ax.get_xticklabels() + ax.get_yticklabels():
+            text.set_color(text_color)
+        ax.set_title('position for: ' + prompt)
+        ax.set_xlabel('X Coordinate')
+        ax.set_ylabel('Y Coordinate')
+        #ax.legend().remove()
+        ax.set_xlim(0, width) # Set the x-axis to match the input latent width
+        ax.set_ylim(height, 0) # Set the y-axis to match the input latent height, with (0,0) at top-left
+        # Adjust the margins of the subplot
+        matplotlib.pyplot.subplots_adjust(left=0.08, right=0.95, bottom=0.05, top=0.95, wspace=0.2, hspace=0.2)
+
+        cmap = matplotlib.pyplot.get_cmap('rainbow')
+        image_batch = []
+        canvas = FigureCanvas(fig)
+        width, height = fig.get_size_inches() * fig.get_dpi()
+        # Draw a box at each coordinate
+        for i, ((x, y), size) in enumerate(zip(coordinates, size_multiplier)):
+            color_index = i / (len(coordinates) - 1)
+            color = cmap(color_index)
+            draw_height = bbox_height * size
+            draw_width = bbox_width * size
+            rect = matplotlib.patches.Rectangle((x - draw_width/2, y - draw_height/2), draw_width, draw_height,
+                                            linewidth=1, edgecolor=color, facecolor='none', alpha=0.5)
+            ax.add_patch(rect)
+
+            # Check if there is a next coordinate to draw an arrow to
+            if i < len(coordinates) - 1:
+                x1, y1 = coordinates[i]
+                x2, y2 = coordinates[i + 1]
+                ax.annotate("", xy=(x2, y2), xytext=(x1, y1),
+                            arrowprops=dict(arrowstyle="->",
+                                            linestyle="-",
+                                            lw=1,
+                                            color=color,
+                                            mutation_scale=20))
+            canvas.draw()
+            try:
+                image_np = np.frombuffer(canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3).copy()
+            except AttributeError:
+                image_np = np.frombuffer(canvas.tostring_argb(), dtype='uint8').reshape(int(height), int(width), 4)
+                image_np = image_np[:, :, [1, 2, 3]]  # Convert ARGB to RGB
+            image_tensor = torch.from_numpy(image_np).float() / 255.0
+            image_tensor = image_tensor.unsqueeze(0)
+            image_batch.append(image_tensor)
+            
+        matplotlib.pyplot.close(fig)
+        image_batch_tensor = torch.cat(image_batch, dim=0)
+
+        return image_batch_tensor
+
+class PlotCoordinates:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                              "coordinates": ("STRING", {"forceInput": True}),
+                              "text": ("STRING", {"default": 'title', "multiline": False}),
+                              "width": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}),
+                              "height": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}),
+                              "bbox_width": ("INT", {"default": 128, "min": 8, "max": 4096, "step": 8}),
+                              "bbox_height": ("INT", {"default": 128, "min": 8, "max": 4096, "step": 8}),
+                            },
+                "optional": {"size_multiplier": ("FLOAT", {"default": [1.0], "forceInput": True})},
+                }
+    RETURN_TYPES = ("IMAGE", "INT", "INT", "INT", "INT",)
+    RETURN_NAMES = ("images", "width", "height", "bbox_width", "bbox_height",)
+    FUNCTION = "append"
+    CATEGORY = "KJNodes/experimental"
+    DESCRIPTION = """
+Plots coordinates to sequence of images using Matplotlib.  
+
+"""
+
+    def append(self, coordinates, text, width, height, bbox_width, bbox_height, size_multiplier=[1.0]):
+        coordinates = json.loads(coordinates.replace("'", '"'))
+        coordinates = [(coord['x'], coord['y']) for coord in coordinates]
+        batch_size = len(coordinates)
+        if not size_multiplier or len(size_multiplier) != batch_size:
+            size_multiplier = [0] * batch_size
+        else:
+            size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)]
+
+        plot_image_tensor = plot_coordinates_to_tensor(coordinates, height, width, bbox_height, bbox_width, size_multiplier, text)
+        
+        return (plot_image_tensor, width, height, bbox_width, bbox_height)
+
+class SplineEditor:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "points_store": ("STRING", {"multiline": False, "advanced": True}),
+                "coordinates": ("STRING", {"multiline": False, "advanced": True}),
+                "mask_width": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}),
+                "mask_height": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}),
+                "points_to_sample": ("INT", {"default": 16, "min": 2, "max": 1000, "step": 1}),
+                "sampling_method": (
+                [   
+                    'path',
+                    'time',
+                    'controlpoints',
+                    'speed'
+                ],
+                {
+                    "default": 'time'
+                }),
+                "interpolation": (
+                [
+                    'cardinal',
+                    'monotone',
+                    'basis',
+                    'linear',
+                    'step-before',
+                    'step-after',
+                    'polar',
+                    'polar-reverse',
+                    'bezier',
+                ],
+                {
+                "default": 'cardinal'
+                    }),
+                "tension": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "repeat_output": ("INT", {"default": 1, "min": 1, "max": 4096, "step": 1}),
+                "float_output_type": (
+                [
+                    'list',
+                    'pandas series',
+                    'tensor',
+                ],
+                {
+                    "default": 'list'
+                }),
+            },
+            "optional": {
+                "min_value": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+                "max_value": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+                "bg_image": ("IMAGE", ),
+            }
+        }
+
+    RETURN_TYPES = ("MASK", "STRING", "FLOAT", "INT", "STRING",)
+    RETURN_NAMES = ("mask", "coord_str", "float", "count", "normalized_str",)
+    FUNCTION = "splinedata"
+    CATEGORY = "KJNodes/weights"
+    DESCRIPTION = """
+# WORK IN PROGRESS  
+Do not count on this as part of your workflow yet,  
+probably contains lots of bugs and stability is not  
+guaranteed!!  
+  
+## Graphical editor to create values for various   
+## schedules and/or mask batches.  
+
+**Shift + click** to add control point at end.
+**Ctrl + click** to add control point (subdivide) between two points.  
+**Right click on a point** to delete it.    
+Note that you can't delete from start/end.  
+  
+Right click on canvas for context menu:  
+NEW!:
+- Add new spline
+    - Creates a new spline on same canvas, currently these paths are only outputed  
+      as coordinates.
+- Add single point
+    - Creates a single point that only returns it's current position coords  
+- Delete spline
+    - Deletes the currently selected spline, you can select a spline by clicking on   
+    it's path, or cycle through them with the 'Next spline' -option.  
+
+These are purely visual options, doesn't affect the output:  
+ - Toggle handles visibility
+ - Display sample points: display the points to be returned.  
+
+**points_to_sample** value sets the number of samples  
+returned from the **drawn spline itself**, this is independent from the  
+actual control points, so the interpolation type matters.  
+sampling_method: 
+ - time: samples along the time axis, used for schedules  
+ - path: samples along the path itself, useful for coordinates  
+ - controlpoints: samples only the control points themselves  
+
+output types:
+ - mask batch  
+        example compatible nodes: anything that takes masks  
+ - list of floats
+        example compatible nodes: IPAdapter weights  
+ - pandas series
+        example compatible nodes: anything that takes Fizz'  
+        nodes Batch Value Schedule  
+ - torch tensor  
+        example compatible nodes: unknown
+"""
+
+    def splinedata(self, mask_width, mask_height, coordinates, float_output_type, interpolation, 
+               points_to_sample, sampling_method, points_store, tension, repeat_output, 
+               min_value=0.0, max_value=1.0, bg_image=None):
+    
+        coordinates = json.loads(coordinates)
+        
+        # Handle nested list structure if present
+        all_normalized = []
+        all_normalized_y_values = []
+
+        # Check if we have a nested list structure
+        if isinstance(coordinates, list) and len(coordinates) > 0 and isinstance(coordinates[0], list):
+            # Process each list of coordinates in the nested structure
+            coordinate_sets = coordinates
+        else:
+            # If not nested, treat as a single list of coordinates
+            coordinate_sets = [coordinates]
+
+        first_spline = coordinate_sets[0] if coordinate_sets else []
+
+        # Process each set of coordinates
+        for coord_set in coordinate_sets:
+            normalized = []
+            normalized_y_values = []
+
+            for coord in coord_set:
+                coord['x'] = int(round(coord['x']))
+                coord['y'] = int(round(coord['y']))
+                norm_x = (1.0 - (coord['x'] / mask_height) - 0.0) * (max_value - min_value) + min_value
+                norm_y = (1.0 - (coord['y'] / mask_height) - 0.0) * (max_value - min_value) + min_value
+                normalized_y_values.append(norm_y)
+                normalized.append({'x':norm_x, 'y':norm_y})
+
+            all_normalized.extend(normalized)
+            all_normalized_y_values.extend(normalized_y_values)
+
+        # Use the combined normalized values for output
+        if float_output_type == 'list':
+            out_floats = all_normalized_y_values * repeat_output
+        elif float_output_type == 'pandas series':
+            try:
+                import pandas as pd
+            except ImportError as e:
+                raise ImportError("MaskOrImageToWeight: pandas is not installed. Please install pandas to use this output_type") from e
+            out_floats = pd.Series(all_normalized_y_values * repeat_output),
+        elif float_output_type == 'tensor':
+            out_floats = torch.tensor(all_normalized_y_values * repeat_output, dtype=torch.float32)
+
+        color_map = lambda y: torch.full((mask_height, mask_width, 3), y, dtype=torch.float32)
+        y_values_for_masks = normalized_y_values if normalized_y_values else [min_value]
+        mask_tensors = [color_map(y) for y in y_values_for_masks]
+        masks_out = torch.stack(mask_tensors)
+        masks_out = masks_out.repeat(repeat_output, 1, 1, 1)
+        masks_out = masks_out.mean(dim=-1)
+
+        single_spline_count = len(first_spline)
+
+        if bg_image is None:
+            return (masks_out, json.dumps(coordinates if len(coordinates) > 1 else coordinates[0]), out_floats, single_spline_count, json.dumps(all_normalized))
+        else:
+            transform = transforms.ToPILImage()
+            image = transform(bg_image[0].permute(2, 0, 1))
+            buffered = BytesIO()
+            image.save(buffered, format="JPEG", quality=75)
+
+            # Encode the image bytes to a Base64 string
+            img_bytes = buffered.getvalue()
+            img_base64 = base64.b64encode(img_bytes).decode('utf-8')
+
+            return {
+                "ui": {"bg_image": [img_base64]},
+                "result": (masks_out, json.dumps(coordinates if len(coordinates) > 1 else coordinates[0]), out_floats, single_spline_count, json.dumps(all_normalized))
+            }
+
+
+class CreateShapeMaskOnPath:
+    
+    RETURN_TYPES = ("MASK", "MASK",)
+    RETURN_NAMES = ("mask", "mask_inverted",)
+    FUNCTION = "createshapemask"
+    CATEGORY = "KJNodes/masking/generate"
+    DESCRIPTION = """
+Creates a mask or batch of masks with the specified shape.  
+Locations are center locations.  
+"""
+    DEPRECATED = True
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "shape": (
+            [   'circle',
+                'square',
+                'triangle',
+            ],
+            {
+            "default": 'circle'
+             }),
+                "coordinates": ("STRING", {"forceInput": True}),
+                "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                "shape_width": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}),
+                "shape_height": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}),
+        },
+        "optional": {
+            "size_multiplier": ("FLOAT", {"default": [1.0], "forceInput": True}),
+        }
+    } 
+
+    def createshapemask(self, coordinates, frame_width, frame_height, shape_width, shape_height, shape, size_multiplier=[1.0]):
+        # Define the number of images in the batch
+        coordinates = coordinates.replace("'", '"')
+        coordinates = json.loads(coordinates)
+
+        batch_size = len(coordinates)
+        out = []
+        color = "white"
+        if not size_multiplier or len(size_multiplier) != batch_size:
+            size_multiplier = [0] * batch_size
+        else:
+            size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)]
+        for i, coord in enumerate(coordinates):
+            image = Image.new("RGB", (frame_width, frame_height), "black")
+            draw = ImageDraw.Draw(image)
+
+            # Calculate the size for this frame and ensure it's not less than 0
+            current_width = max(0, shape_width + i * size_multiplier[i])
+            current_height = max(0, shape_height + i * size_multiplier[i])
+
+            location_x = coord['x']
+            location_y = coord['y']
+
+            if shape == 'circle' or shape == 'square':
+                # Define the bounding box for the shape
+                left_up_point = (location_x - current_width // 2, location_y - current_height // 2)
+                right_down_point = (location_x + current_width // 2, location_y + current_height // 2)
+                two_points = [left_up_point, right_down_point]
+
+                if shape == 'circle':
+                    draw.ellipse(two_points, fill=color)
+                elif shape == 'square':
+                    draw.rectangle(two_points, fill=color)
+                    
+            elif shape == 'triangle':
+                # Define the points for the triangle
+                left_up_point = (location_x - current_width // 2, location_y + current_height // 2) # bottom left
+                right_down_point = (location_x + current_width // 2, location_y + current_height // 2) # bottom right
+                top_point = (location_x, location_y - current_height // 2) # top point
+                draw.polygon([top_point, left_up_point, right_down_point], fill=color)
+
+            image = pil2tensor(image)
+            mask = image[:, :, :, 0]
+            out.append(mask)
+        outstack = torch.cat(out, dim=0)
+        return (outstack, 1.0 - outstack,)
+
+
+
+class CreateShapeImageOnPath:
+    
+    RETURN_TYPES = ("IMAGE", "MASK",)
+    RETURN_NAMES = ("image","mask", )
+    FUNCTION = "createshapemask"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Creates an image or batch of images with the specified shape.  
+Locations are center locations.  
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "shape": (
+            [   'circle',
+                'square',
+                'triangle',
+            ],
+            {
+            "default": 'circle'
+             }),
+                "coordinates": ("STRING", {"forceInput": True}),
+                "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                "shape_width": ("INT", {"default": 128,"min": 2, "max": 4096, "step": 1}),
+                "shape_height": ("INT", {"default": 128,"min": 2, "max": 4096, "step": 1}),
+                "shape_color": ("STRING", {"default": 'white'}),
+                "bg_color": ("STRING", {"default": 'black'}),
+                "blur_radius": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100, "step": 0.1}),
+                "intensity": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 100.0, "step": 0.01}),
+        },
+        "optional": {
+            "size_multiplier": ("FLOAT", {"default": [1.0], "forceInput": True}),
+            "trailing": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+            "border_width": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}),
+            "border_color": ("STRING", {"default": 'black'}),
+        }
+    } 
+
+    def createshapemask(self, coordinates, frame_width, frame_height, shape_width, shape_height, shape_color, 
+                        bg_color, blur_radius, shape, intensity, size_multiplier=[1.0], trailing=1.0, border_width=0, border_color='black'):
+
+        shape_color = parse_color(shape_color)
+        border_color = parse_color(border_color)
+        bg_color = parse_color(bg_color)
+        coords_list = parse_json_tracks(coordinates)
+
+        batch_size = len(coords_list[0])
+        images_list = []
+        masks_list = []
+
+        if not size_multiplier or len(size_multiplier) != batch_size:
+            size_multiplier = [1] * batch_size
+        else:
+            size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)]
+
+        previous_output = None
+
+        for i in range(batch_size):
+            image = Image.new("RGB", (frame_width, frame_height), bg_color)
+            draw = ImageDraw.Draw(image)
+
+            # Calculate the size for this frame and ensure it's not less than 0
+            current_width = shape_width * size_multiplier[i]
+            current_height = shape_height * size_multiplier[i]
+            
+            for coords in coords_list:
+                location_x = coords[i]['x']
+                location_y = coords[i]['y']
+            
+                if shape == 'circle' or shape == 'square':
+                    # Define the bounding box for the shape
+                    left_up_point = (location_x - current_width // 2, location_y - current_height // 2)
+                    right_down_point = (location_x + current_width // 2, location_y + current_height // 2)
+                    two_points = [left_up_point, right_down_point]
+
+                    if shape == 'circle':
+                        if border_width > 0:
+                            draw.ellipse(two_points, fill=shape_color, outline=border_color, width=border_width)
+                        else:
+                            draw.ellipse(two_points, fill=shape_color)
+                    elif shape == 'square':
+                        if border_width > 0:
+                            draw.rectangle(two_points, fill=shape_color, outline=border_color, width=border_width)
+                        else:
+                            draw.rectangle(two_points, fill=shape_color)
+                        
+                elif shape == 'triangle':
+                    # Define the points for the triangle
+                    left_up_point = (location_x - current_width // 2, location_y + current_height // 2) # bottom left
+                    right_down_point = (location_x + current_width // 2, location_y + current_height // 2) # bottom right
+                    top_point = (location_x, location_y - current_height // 2) # top point
+                    
+                    if border_width > 0:
+                        draw.polygon([top_point, left_up_point, right_down_point], fill=shape_color, outline=border_color, width=border_width)
+                    else:
+                        draw.polygon([top_point, left_up_point, right_down_point], fill=shape_color)
+
+            if blur_radius != 0:
+                    image = image.filter(ImageFilter.GaussianBlur(blur_radius))
+            # Blend the current image with the accumulated image
+            
+            image = pil2tensor(image)
+            if trailing != 1.0 and previous_output is not None:
+                # Add the decayed previous output to the current frame
+                image += trailing * previous_output
+                image = image / image.max()
+            previous_output = image
+            image = image * intensity
+            mask = image[:, :, :, 0]
+            masks_list.append(mask)
+            images_list.append(image)
+        out_images = torch.cat(images_list, dim=0).cpu().float()
+        out_masks = torch.cat(masks_list, dim=0)
+        return (out_images, out_masks)
+    
+class CreateTextOnPath:
+    
+    RETURN_TYPES = ("IMAGE", "MASK", "MASK",)
+    RETURN_NAMES = ("image", "mask", "mask_inverted",)
+    FUNCTION = "createtextmask"
+    CATEGORY = "KJNodes/masking/generate"
+    DESCRIPTION = """
+Creates a mask or batch of masks with the specified text.  
+Locations are center locations.  
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "coordinates": ("STRING", {"forceInput": True}),
+                "text": ("STRING", {"default": 'text', "multiline": True}),
+                "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                "font": (folder_paths.get_filename_list("kjnodes_fonts"), ),
+                "font_size": ("INT", {"default": 42}),
+                 "alignment": (
+                [   'left',
+                    'center',
+                    'right'
+                ],
+                {"default": 'center'}
+                ),
+                "text_color": ("STRING", {"default": 'white'}),
+        },
+        "optional": {
+            "size_multiplier": ("FLOAT", {"default": [1.0], "forceInput": True}),
+        }
+    } 
+
+    def createtextmask(self, coordinates, frame_width, frame_height, font, font_size, text, text_color, alignment, size_multiplier=[1.0]):
+        coordinates = coordinates.replace("'", '"')
+        coordinates = json.loads(coordinates)
+
+        batch_size = len(coordinates)
+        mask_list = []
+        image_list = []
+        color = parse_color(text_color)
+        font_path = folder_paths.get_full_path("kjnodes_fonts", font)
+
+        if len(size_multiplier) != batch_size:
+            size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)]
+        
+        for i, coord in enumerate(coordinates):
+            image = Image.new("RGB", (frame_width, frame_height), "black")
+            draw = ImageDraw.Draw(image)
+            lines = text.split('\n')  # Split the text into lines
+            # Apply the size multiplier to the font size for this iteration
+            current_font_size = int(font_size * size_multiplier[i])
+            current_font = ImageFont.truetype(font_path, current_font_size)
+            line_heights = [current_font.getbbox(line)[3] for line in lines]  # List of line heights
+            total_text_height = sum(line_heights)  # Total height of text block
+
+            # Calculate the starting Y position to center the block of text
+            start_y = coord['y'] - total_text_height // 2
+            for j, line in enumerate(lines):
+                text_width, text_height = current_font.getbbox(line)[2], line_heights[j]
+                if alignment == 'left':
+                    location_x = coord['x']
+                elif alignment == 'center':
+                    location_x = int(coord['x'] - text_width // 2)
+                elif alignment == 'right':
+                    location_x = int(coord['x'] - text_width)
+                
+                location_y = int(start_y + sum(line_heights[:j]))
+                text_position = (location_x, location_y)
+                # Draw the text
+                try:
+                    draw.text(text_position, line, fill=color, font=current_font, features=['-liga'])
+                except Exception:
+                    draw.text(text_position, line, fill=color, font=current_font)
+            
+            image = pil2tensor(image)
+            non_black_pixels = (image > 0).any(dim=-1)
+            mask = non_black_pixels.to(image.dtype)
+            mask_list.append(mask)
+            image_list.append(image)
+
+        out_images = torch.cat(image_list, dim=0).cpu().float()
+        out_masks = torch.cat(mask_list, dim=0)
+        return (out_images, out_masks, 1.0 - out_masks,)
+
+class CreateGradientFromCoords:
+    
+    RETURN_TYPES = ("IMAGE", )
+    RETURN_NAMES = ("image", )
+    FUNCTION = "generate"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Creates a gradient image from coordinates.    
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "coordinates": ("STRING", {"forceInput": True}),
+                "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                "start_color": ("STRING", {"default": 'white'}),
+                "end_color": ("STRING", {"default": 'black'}),
+                "multiplier": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 100.0, "step": 0.01}),
+        },
+    } 
+    
+    def generate(self, coordinates, frame_width, frame_height, start_color, end_color, multiplier):
+        # Parse the coordinates
+        coordinates = json.loads(coordinates.replace("'", '"'))
+
+        # Create an image
+        image = Image.new("RGB", (frame_width, frame_height))
+        draw = ImageDraw.Draw(image)
+
+        # Extract start and end points for the gradient
+        start_coord = coordinates[0]
+        end_coord = coordinates[1]
+
+        start_color = parse_color(start_color)
+        end_color = parse_color(end_color)
+
+        # Calculate the gradient direction (vector)
+        gradient_direction = (end_coord['x'] - start_coord['x'], end_coord['y'] - start_coord['y'])
+        gradient_length = (gradient_direction[0] ** 2 + gradient_direction[1] ** 2) ** 0.5
+
+        # Iterate over each pixel in the image
+        for y in range(frame_height):
+            for x in range(frame_width):
+                # Calculate the projection of the point on the gradient line
+                point_vector = (x - start_coord['x'], y - start_coord['y'])
+                projection = (point_vector[0] * gradient_direction[0] + point_vector[1] * gradient_direction[1]) / gradient_length
+                projection = max(min(projection, gradient_length), 0)  # Clamp the projection value
+
+                # Calculate the blend factor for the current pixel
+                blend = projection * multiplier / gradient_length 
+
+                # Determine the color of the current pixel
+                color = (
+                    int(start_color[0] + (end_color[0] - start_color[0]) * blend),
+                    int(start_color[1] + (end_color[1] - start_color[1]) * blend),
+                    int(start_color[2] + (end_color[2] - start_color[2]) * blend)
+                )
+
+                # Set the pixel color
+                draw.point((x, y), fill=color)
+
+        # Convert the PIL image to a tensor (assuming such a function exists in your context)
+        image_tensor = pil2tensor(image)
+
+        return (image_tensor,)
+
+class GradientToFloat:
+    
+    RETURN_TYPES = ("FLOAT", "FLOAT",)
+    RETURN_NAMES = ("float_x", "float_y", )
+    FUNCTION = "sample"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Calculates list of floats from image.    
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE", ),
+                "steps": ("INT", {"default": 10, "min": 2, "max": 10000, "step": 1}),
+        },
+    } 
+    
+    def sample(self, image, steps):
+        # Assuming image is a tensor with shape [B, H, W, C]
+        B, H, W, C = image.shape
+
+        # Sample along the width axis (W)
+        w_intervals = torch.linspace(0, W - 1, steps=steps, dtype=torch.int64)
+        # Assuming we're sampling from the first batch and the first channel
+        w_sampled = image[0, :, w_intervals, 0]
+
+        # Sample along the height axis (H)
+        h_intervals = torch.linspace(0, H - 1, steps=steps, dtype=torch.int64)
+        # Assuming we're sampling from the first batch and the first channel
+        h_sampled = image[0, h_intervals, :, 0]
+
+        # Taking the mean across the height for width sampling, and across the width for height sampling
+        w_values = w_sampled.mean(dim=0).tolist()
+        h_values = h_sampled.mean(dim=1).tolist()
+
+        return (w_values, h_values)
+    
+class MaskOrImageToWeight:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "output_type": (
+                [   
+                    'list',
+                    'pandas series',
+                    'tensor',
+                    'string'
+                ],
+                {
+                "default": 'list'
+                    }),
+             },
+            "optional": {
+                "images": ("IMAGE",),
+                "masks": ("MASK",),                
+            },
+
+        }
+    RETURN_TYPES = ("FLOAT", "STRING",)
+    FUNCTION = "execute"
+    CATEGORY = "KJNodes/weights"
+    DESCRIPTION = """
+Gets the mean values from mask or image batch  
+and returns that as the selected output type.   
+"""
+
+    def execute(self, output_type, images=None, masks=None):
+        mean_values = []
+        if masks is not None and images is None:
+            for mask in masks:
+                mean_values.append(mask.mean().item())
+        elif masks is None and images is not None:
+            for image in images:
+                mean_values.append(image.mean().item())
+        elif masks is not None and images is not None:
+            raise ValueError("MaskOrImageToWeight: Use either mask or image input only.")
+                  
+        # Convert mean_values to the specified output_type
+        if output_type == 'list':
+            out = mean_values
+        elif output_type == 'pandas series':
+            try:
+                import pandas as pd
+            except ImportError as e:
+                raise ImportError("MaskOrImageToWeight: pandas is not installed. Please install pandas to use this output_type") from e
+            out = pd.Series(mean_values),
+        elif output_type == 'tensor':
+            out = torch.tensor(mean_values, dtype=torch.float32),
+        return (out, [str(value) for value in mean_values],)
+    
+class WeightScheduleConvert:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input_values": ("FLOAT", {"default": 0.0, "forceInput": True}),
+                "output_type": (
+                [   
+                    'match_input',
+                    'list',
+                    'pandas series',
+                    'tensor',
+                ],
+                {
+                "default": 'list'
+                    }),
+                "invert": ("BOOLEAN", {"default": False}),
+                "repeat": ("INT", {"default": 1,"min": 1, "max": 255, "step": 1}),
+             },
+             "optional": {
+                "remap_to_frames": ("INT", {"default": 0}),
+                "interpolation_curve": ("FLOAT", {"forceInput": True}),
+                "remap_values": ("BOOLEAN", {"default": False}),
+                "remap_min": ("FLOAT", {"default": 0.0, "min": -100000, "max": 100000.0, "step": 0.01}),
+                "remap_max": ("FLOAT", {"default": 1.0, "min": -100000, "max": 100000.0, "step": 0.01}),
+             },
+             
+        }
+    RETURN_TYPES = ("FLOAT", "STRING", "INT",)
+    FUNCTION = "execute"
+    CATEGORY = "KJNodes/weights"
+    DESCRIPTION = """
+Converts different value lists/series to another type.  
+"""
+
+    def detect_input_type(self, input_values):
+        import pandas as pd
+        if isinstance(input_values, list):
+            return 'list'
+        elif isinstance(input_values, pd.Series):
+            return 'pandas series'
+        elif isinstance(input_values, torch.Tensor):
+            return 'tensor'
+        else:
+            raise ValueError("Unsupported input type")
+
+    def execute(self, input_values, output_type, invert, repeat, remap_to_frames=0, interpolation_curve=None, remap_min=0.0, remap_max=1.0, remap_values=False):
+        import pandas as pd
+        input_type = self.detect_input_type(input_values)
+
+        if input_type == 'pandas series':
+            float_values = input_values.tolist()
+        elif input_type == 'tensor':
+            float_values = input_values
+        else:
+            float_values = input_values
+
+        if invert:
+            float_values = [1 - value for value in float_values]
+
+        if interpolation_curve is not None:
+            interpolated_pattern = []
+            orig_float_values = float_values
+            for value in interpolation_curve:
+                min_val = min(orig_float_values)
+                max_val = max(orig_float_values)
+                # Normalize the values to [0, 1]
+                normalized_values = [(value - min_val) / (max_val - min_val) for value in orig_float_values]
+                # Interpolate the normalized values to the new frame count
+                remapped_float_values = np.interp(np.linspace(0, 1, int(remap_to_frames * value)), np.linspace(0, 1, len(normalized_values)), normalized_values).tolist()
+                interpolated_pattern.extend(remapped_float_values)
+            float_values = interpolated_pattern
+        else:
+            # Remap float_values to match target_frame_amount
+            if remap_to_frames > 0 and remap_to_frames != len(float_values):
+                min_val = min(float_values)
+                max_val = max(float_values)
+                # Normalize the values to [0, 1]
+                normalized_values = [(value - min_val) / (max_val - min_val) for value in float_values]
+                # Interpolate the normalized values to the new frame count
+                float_values = np.interp(np.linspace(0, 1, remap_to_frames), np.linspace(0, 1, len(normalized_values)), normalized_values).tolist()
+       
+            float_values = float_values * repeat
+            if remap_values:
+                float_values = self.remap_values(float_values, remap_min, remap_max)
+
+        if output_type == 'list':
+            out = float_values,
+        elif output_type == 'pandas series':
+            out = pd.Series(float_values),
+        elif output_type == 'tensor':
+            if input_type == 'pandas series':
+                out = torch.tensor(float_values.values, dtype=torch.float32),
+            else:   
+                out = torch.tensor(float_values, dtype=torch.float32),
+        elif output_type == 'match_input':
+            out = float_values,
+        return (out, [str(value) for value in float_values], [int(value) for value in float_values])
+    
+    def remap_values(self, values, target_min, target_max):
+        # Determine the current range
+        current_min = min(values)
+        current_max = max(values)
+        current_range = current_max - current_min
+        
+        # Determine the target range
+        target_range = target_max - target_min
+        
+        # Perform the linear interpolation for each value
+        remapped_values = [(value - current_min) / current_range * target_range + target_min for value in values]
+        
+        return remapped_values
+        
+
+class FloatToMask:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input_values": ("FLOAT", {"forceInput": True, "default": 0}),
+                "width": ("INT", {"default": 100, "min": 1}),
+                "height": ("INT", {"default": 100, "min": 1}),
+            },
+        }
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "execute"
+    CATEGORY = "KJNodes/masking/generate"
+    DESCRIPTION = """
+Generates a batch of masks based on the input float values.
+The batch size is determined by the length of the input float values.
+Each mask is generated with the specified width and height.
+"""
+
+    def execute(self, input_values, width, height):
+        import pandas as pd
+        # Ensure input_values is a list
+        if isinstance(input_values, (float, int)):
+            input_values = [input_values]
+        elif isinstance(input_values, pd.Series):
+            input_values = input_values.tolist()
+        elif isinstance(input_values, list) and all(isinstance(item, list) for item in input_values):
+            input_values = [item for sublist in input_values for item in sublist]
+
+        # Generate a batch of masks based on the input_values
+        masks = []
+        for value in input_values:
+            # Assuming value is a float between 0 and 1 representing the mask's intensity
+            mask = torch.ones((height, width), dtype=torch.float32) * value
+            masks.append(mask)
+        masks_out = torch.stack(masks, dim=0)
+    
+        return(masks_out,)
+class WeightScheduleExtend:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input_values_1": ("FLOAT", {"default": 0.0, "forceInput": True}),
+                "input_values_2": ("FLOAT", {"default": 0.0, "forceInput": True}),
+                "output_type": (
+                [   
+                    'match_input',
+                    'list',
+                    'pandas series',
+                    'tensor',
+                ],
+                {
+                "default": 'match_input'
+                    }),
+             },
+             
+        }
+    RETURN_TYPES = ("FLOAT",)
+    FUNCTION = "execute"
+    CATEGORY = "KJNodes/weights"
+    DESCRIPTION = """
+Extends, and converts if needed, different value lists/series  
+"""
+
+    def detect_input_type(self, input_values):
+        import pandas as pd
+        if isinstance(input_values, list):
+            return 'list'
+        elif isinstance(input_values, pd.Series):
+            return 'pandas series'
+        elif isinstance(input_values, torch.Tensor):
+            return 'tensor'
+        else:
+            raise ValueError("Unsupported input type")
+
+    def execute(self, input_values_1, input_values_2, output_type):
+        import pandas as pd
+        input_type_1 = self.detect_input_type(input_values_1)
+        input_type_2 = self.detect_input_type(input_values_2)
+        # Convert input_values_2 to the same format as input_values_1 if they do not match
+        if not input_type_1 == input_type_2:
+            print("Converting input_values_2 to the same format as input_values_1")
+            if input_type_1 == 'pandas series':
+                # Convert input_values_2 to a pandas Series
+                float_values_2 = pd.Series(input_values_2)
+            elif input_type_1 == 'tensor':
+                # Convert input_values_2 to a tensor
+                float_values_2 = torch.tensor(input_values_2, dtype=torch.float32)
+        else:
+            print("Input types match, no conversion needed")
+            # If the types match, no conversion is needed
+            float_values_2 = input_values_2
+     
+        float_values = input_values_1 + float_values_2
+ 
+        if output_type == 'list':
+            return float_values,
+        elif output_type == 'pandas series':
+            return pd.Series(float_values),
+        elif output_type == 'tensor':
+            if input_type_1 == 'pandas series':
+                return torch.tensor(float_values.values, dtype=torch.float32),
+            else:
+                return torch.tensor(float_values, dtype=torch.float32),
+        elif output_type == 'match_input':
+            return float_values,
+        else:
+            raise ValueError(f"Unsupported output_type: {output_type}")
+        
+class FloatToSigmas:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {
+                     "float_list": ("FLOAT", {"default": 0.0, "forceInput": True}),
+                     }
+                }
+    RETURN_TYPES = ("SIGMAS",)
+    RETURN_NAMES = ("SIGMAS",)
+    CATEGORY = "KJNodes/noise"
+    FUNCTION = "customsigmas"
+    DESCRIPTION = """
+Creates a sigmas tensor from list of float values.  
+
+"""
+    def customsigmas(self, float_list):
+        return torch.tensor(float_list, dtype=torch.float32),
+
+class SigmasToFloat:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {
+                     "sigmas": ("SIGMAS",),
+                     }
+                }
+    RETURN_TYPES = ("FLOAT",)
+    RETURN_NAMES = ("float",)
+    CATEGORY = "KJNodes/noise"
+    FUNCTION = "customsigmas"
+    DESCRIPTION = """
+Creates a float list from sigmas tensors.  
+
+"""
+    def customsigmas(self, sigmas):
+        return sigmas.tolist(),
+
+class GLIGENTextBoxApplyBatchCoords:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning_to": ("CONDITIONING", ),
+                              "latents": ("LATENT", ),
+                              "clip": ("CLIP", ),
+                              "gligen_textbox_model": ("GLIGEN", ),
+                              "coordinates": ("STRING", {"forceInput": True}),
+                              "text": ("STRING", {"multiline": True}),
+                              "width": ("INT", {"default": 128, "min": 8, "max": 4096, "step": 8}),
+                              "height": ("INT", {"default": 128, "min": 8, "max": 4096, "step": 8}),
+                            },
+                "optional": {"size_multiplier": ("FLOAT", {"default": [1.0], "forceInput": True})},
+                }
+    RETURN_TYPES = ("CONDITIONING", "IMAGE", )
+    RETURN_NAMES = ("conditioning", "coord_preview", )
+    FUNCTION = "append"
+    CATEGORY = "KJNodes/experimental"
+    DESCRIPTION = """
+This node allows scheduling GLIGEN text box positions in a batch,  
+to be used with AnimateDiff-Evolved. Intended to pair with the  
+Spline Editor -node.  
+
+GLIGEN model can be downloaded through the Manage's "Install Models" menu.  
+Or directly from here:  
+https://huggingface.co/comfyanonymous/GLIGEN_pruned_safetensors/tree/main  
+  
+Inputs:  
+- **latents** input is used to calculate batch size  
+- **clip** is your standard text encoder, use same as for the main prompt  
+- **gligen_textbox_model** connects to GLIGEN Loader  
+- **coordinates** takes a json string of points, directly compatible  
+with the spline editor node.
+- **text** is the part of the prompt to set position for  
+- **width** and **height** are the size of the GLIGEN bounding box  
+  
+Outputs:
+- **conditioning** goes between to clip text encode and the sampler  
+- **coord_preview** is an optional preview of the coordinates and  
+bounding boxes.
+
+"""
+
+    def append(self, latents, coordinates, conditioning_to, clip, gligen_textbox_model, text, width, height, size_multiplier=[1.0]):
+        coordinates = json.loads(coordinates.replace("'", '"'))
+        coordinates = [(coord['x'], coord['y']) for coord in coordinates]
+
+        batch_size = sum(tensor.size(0) for tensor in latents.values())
+        if len(coordinates) != batch_size:
+            print("GLIGENTextBoxApplyBatchCoords WARNING: The number of coordinates does not match the number of latents")
+
+        c = []
+        _, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled=True)
+
+        for t in conditioning_to:
+            n = [t[0], t[1].copy()]
+            
+            position_params_batch = [[] for _ in range(batch_size)]  # Initialize a list of empty lists for each batch item
+            if len(size_multiplier) != batch_size:
+                size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)]
+
+            for i in range(batch_size):
+                x_position, y_position = coordinates[i]
+                position_param = (cond_pooled, int((height // 8) * size_multiplier[i]), int((width // 8) * size_multiplier[i]), (y_position - height // 2) // 8, (x_position - width // 2) // 8)
+                position_params_batch[i].append(position_param)  # Append position_param to the correct sublist
+
+            prev = []
+            if "gligen" in n[1]:
+                prev = n[1]['gligen'][2]
+            else:
+                prev = [[] for _ in range(batch_size)]
+            # Concatenate prev and position_params_batch, ensuring both are lists of lists
+            # and each sublist corresponds to a batch item
+            combined_position_params = [prev_item + batch_item for prev_item, batch_item in zip(prev, position_params_batch)]
+            n[1]['gligen'] = ("position_batched", gligen_textbox_model, combined_position_params)
+            c.append(n)
+
+        image_height = latents['samples'].shape[-2] * 8
+        image_width = latents['samples'].shape[-1] * 8
+        plot_image_tensor = plot_coordinates_to_tensor(coordinates, image_height, image_width, height, width, size_multiplier, text)
+        
+        return (c, plot_image_tensor,)
+    
+class CreateInstanceDiffusionTracking:
+    
+    RETURN_TYPES = ("TRACKING", "STRING", "INT", "INT", "INT", "INT",)
+    RETURN_NAMES = ("tracking", "prompt", "width", "height", "bbox_width", "bbox_height",)
+    FUNCTION = "tracking"
+    CATEGORY = "KJNodes/InstanceDiffusion"
+    DESCRIPTION = """
+Creates tracking data to be used with InstanceDiffusion:  
+https://github.com/logtd/ComfyUI-InstanceDiffusion  
+  
+InstanceDiffusion prompt format:  
+"class_id.class_name": "prompt",  
+for example:  
+"1.head": "((head))",  
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "coordinates": ("STRING", {"forceInput": True}),
+                "width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                "height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                "bbox_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                "bbox_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                "class_name": ("STRING", {"default": "class_name"}),
+                "class_id": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}),
+                "prompt": ("STRING", {"default": "prompt", "multiline": True}),
+        },
+        "optional": {
+            "size_multiplier": ("FLOAT", {"default": [1.0], "forceInput": True}),
+            "fit_in_frame": ("BOOLEAN", {"default": True}),
+        }
+    } 
+
+    def tracking(self, coordinates, class_name, class_id, width, height, bbox_width, bbox_height, prompt, size_multiplier=[1.0], fit_in_frame=True):
+        # Define the number of images in the batch
+        coordinates = coordinates.replace("'", '"')
+        coordinates = json.loads(coordinates)
+
+        tracked = {}
+        tracked[class_name] = {}
+        batch_size = len(coordinates)
+        # Initialize a list to hold the coordinates for the current ID
+        id_coordinates = []
+        if not size_multiplier or len(size_multiplier) != batch_size:
+            size_multiplier = [0] * batch_size
+        else:
+            size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)]
+        for i, coord in enumerate(coordinates):
+            x = coord['x']
+            y = coord['y']
+            adjusted_bbox_width = bbox_width * size_multiplier[i]
+            adjusted_bbox_height = bbox_height * size_multiplier[i]
+            # Calculate the top left and bottom right coordinates
+            top_left_x = x - adjusted_bbox_width // 2
+            top_left_y = y - adjusted_bbox_height // 2
+            bottom_right_x = x + adjusted_bbox_width // 2
+            bottom_right_y = y + adjusted_bbox_height // 2
+
+            if fit_in_frame:
+                # Clip the coordinates to the frame boundaries
+                top_left_x = max(0, top_left_x)
+                top_left_y = max(0, top_left_y)
+                bottom_right_x = min(width, bottom_right_x)
+                bottom_right_y = min(height, bottom_right_y)
+                # Ensure width and height are positive
+                adjusted_bbox_width = max(1, bottom_right_x - top_left_x)
+                adjusted_bbox_height = max(1, bottom_right_y - top_left_y)
+
+                # Update the coordinates with the new width and height
+                bottom_right_x = top_left_x + adjusted_bbox_width
+                bottom_right_y = top_left_y + adjusted_bbox_height
+
+            # Append the top left and bottom right coordinates to the list for the current ID
+            id_coordinates.append([top_left_x, top_left_y, bottom_right_x, bottom_right_y, width, height])
+        
+        class_id = int(class_id)
+        # Assign the list of coordinates to the specified ID within the class_id dictionary
+        tracked[class_name][class_id] = id_coordinates
+
+        prompt_string = ""
+        for class_name, class_data in tracked.items():
+            for class_id in class_data.keys():
+                class_id_str = str(class_id)
+                # Use the incoming prompt for each class name and ID
+                prompt_string += f'"{class_id_str}.{class_name}": "({prompt})",\n'
+
+        # Remove the last comma and newline
+        prompt_string = prompt_string.rstrip(",\n")
+
+        return (tracked, prompt_string, width, height, bbox_width, bbox_height)
+
+class AppendInstanceDiffusionTracking:
+    
+    RETURN_TYPES = ("TRACKING", "STRING",)
+    RETURN_NAMES = ("tracking", "prompt",)
+    FUNCTION = "append"
+    CATEGORY = "KJNodes/InstanceDiffusion"
+    DESCRIPTION = """
+Appends tracking data to be used with InstanceDiffusion:  
+https://github.com/logtd/ComfyUI-InstanceDiffusion  
+
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "tracking_1": ("TRACKING", {"forceInput": True}),
+                "tracking_2": ("TRACKING", {"forceInput": True}),
+        },
+        "optional": {
+            "prompt_1": ("STRING", {"default": "", "forceInput": True}),
+            "prompt_2": ("STRING", {"default": "", "forceInput": True}),
+        }
+    } 
+
+    def append(self, tracking_1, tracking_2, prompt_1="", prompt_2=""):
+        tracking_copy = tracking_1.copy()
+        # Check for existing class names and class IDs, and raise an error if they exist
+        for class_name, class_data in tracking_2.items():
+            if class_name not in tracking_copy:
+                tracking_copy[class_name] = class_data
+            else:
+                # If the class name exists, merge the class data from tracking_2 into tracking_copy
+                # This will add new class IDs under the same class name without raising an error
+                tracking_copy[class_name].update(class_data)
+        prompt_string = prompt_1 + "," + prompt_2
+        return (tracking_copy, prompt_string)
+        
+class InterpolateCoords:
+    
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("coordinates",)
+    FUNCTION = "interpolate"
+    CATEGORY = "KJNodes/experimental"
+    DESCRIPTION = """
+Interpolates coordinates based on a curve.   
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "coordinates": ("STRING", {"forceInput": True}),
+                "interpolation_curve": ("FLOAT", {"forceInput": True}),
+                
+        },
+    } 
+
+    def interpolate(self, coordinates, interpolation_curve):
+        # Parse the JSON string to get the list of coordinates
+        coordinates = json.loads(coordinates.replace("'", '"'))
+
+        # Convert the list of dictionaries to a list of (x, y) tuples for easier processing
+        coordinates = [(coord['x'], coord['y']) for coord in coordinates]
+
+        # Calculate the total length of the original path
+        path_length = sum(np.linalg.norm(np.array(coordinates[i]) - np.array(coordinates[i-1])) 
+                        for i in range(1, len(coordinates)))
+
+        # Initialize variables for interpolation
+        interpolated_coords = []
+        current_length = 0
+        current_index = 0
+
+        # Iterate over the normalized curve
+        for normalized_length in interpolation_curve:
+            target_length = normalized_length * path_length # Convert to the original scale
+            while current_index < len(coordinates) - 1:
+                segment_start, segment_end = np.array(coordinates[current_index]), np.array(coordinates[current_index + 1])
+                segment_length = np.linalg.norm(segment_end - segment_start)
+                if current_length + segment_length >= target_length:
+                    break
+                current_length += segment_length
+                current_index += 1
+
+            # Interpolate between the last two points
+            if current_index < len(coordinates) - 1:
+                p1, p2 = np.array(coordinates[current_index]), np.array(coordinates[current_index + 1])
+                segment_length = np.linalg.norm(p2 - p1)
+                if segment_length > 0:
+                    t = (target_length - current_length) / segment_length
+                    interpolated_point = p1 + t * (p2 - p1)
+                    interpolated_coords.append(interpolated_point.tolist())
+                else:
+                    interpolated_coords.append(p1.tolist())
+            else:
+                # If the target_length is at or beyond the end of the path, add the last coordinate
+                interpolated_coords.append(coordinates[-1])
+
+        # Convert back to string format if necessary
+        interpolated_coords_str = "[" + ", ".join([f"{{'x': {round(coord[0])}, 'y': {round(coord[1])}}}" for coord in interpolated_coords]) + "]"
+        print(interpolated_coords_str)
+
+        return (interpolated_coords_str,)
+    
+class DrawInstanceDiffusionTracking:
+    
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image", )
+    FUNCTION = "draw"
+    CATEGORY = "KJNodes/InstanceDiffusion"
+    DESCRIPTION = """
+Draws the tracking data from  
+CreateInstanceDiffusionTracking -node.
+
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE", ),
+                "tracking": ("TRACKING", {"forceInput": True}),
+                "box_line_width": ("INT", {"default": 2, "min": 1, "max": 10, "step": 1}),
+                "draw_text": ("BOOLEAN", {"default": True}),
+                "font": (folder_paths.get_filename_list("kjnodes_fonts"), ),
+                "font_size": ("INT", {"default": 20}),
+        },
+    } 
+
+    def draw(self, image, tracking, box_line_width, draw_text, font, font_size):
+        import matplotlib.cm as cm
+
+        modified_images = []
+        
+        colormap = cm.get_cmap('rainbow', len(tracking))
+        if draw_text:
+            font_path = folder_paths.get_full_path("kjnodes_fonts", font)
+            font = ImageFont.truetype(font_path, font_size)
+
+        # Iterate over each image in the batch
+        for i in range(image.shape[0]):
+            # Extract the current image and convert it to a PIL image
+            current_image = image[i, :, :, :].permute(2, 0, 1)
+            pil_image = transforms.ToPILImage()(current_image)
+            
+            draw = ImageDraw.Draw(pil_image)
+            
+            # Iterate over the bounding boxes for the current image
+            for j, (class_name, class_data) in enumerate(tracking.items()):
+                for class_id, bbox_list in class_data.items():
+                    # Check if the current index is within the bounds of the bbox_list
+                    if i < len(bbox_list):
+                        bbox = bbox_list[i]
+                        # Ensure bbox is a list or tuple before unpacking
+                        if isinstance(bbox, (list, tuple)):
+                            x1, y1, x2, y2, _, _ = bbox
+                            # Convert coordinates to integers
+                            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+                            # Generate a color from the rainbow colormap
+                            color = tuple(int(255 * x) for x in colormap(j / len(tracking)))[:3]
+                            # Draw the bounding box on the image with the generated color
+                            draw.rectangle([x1, y1, x2, y2], outline=color, width=box_line_width)
+                            if draw_text:
+                                # Draw the class name and ID as text above the box with the generated color
+                                text = f"{class_id}.{class_name}"
+                                # Calculate the width and height of the text
+                                _, _, text_width, text_height = draw.textbbox((0, 0), text=text, font=font)
+                                # Position the text above the top-left corner of the box
+                                text_position = (x1, y1 - text_height)
+                                draw.text(text_position, text, fill=color, font=font)
+                        else:
+                            print(f"Unexpected data type for bbox: {type(bbox)}")
+            
+            # Convert the drawn image back to a torch tensor and adjust back to (H, W, C)
+            modified_image_tensor = transforms.ToTensor()(pil_image).permute(1, 2, 0)
+            modified_images.append(modified_image_tensor)
+        
+        # Stack the modified images back into a batch
+        image_tensor_batch = torch.stack(modified_images).cpu().float()
+        
+        return image_tensor_batch,
+
+class PointsEditor:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "points_store": ("STRING", {"multiline": False, "advanced": True}),
+                "coordinates": ("STRING", {"multiline": False, "socketless": True, "advanced": True}),
+                "neg_coordinates": ("STRING", {"multiline": False, "socketless": True, "advanced": True}),
+                "bbox_store": ("STRING", {"multiline": False, "advanced": True}),
+                "bboxes": ("STRING", {"multiline": False, "socketless": True, "advanced": True}),
+                "bbox_format": (
+                [
+                    'xyxy',
+                    'xywh',
+                ],
+                ),
+                "width": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}),
+                "height": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}),
+                "normalize": ("BOOLEAN", {"default": False}),
+            },
+            "optional": {
+                "bg_image": ("IMAGE", ),
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "STRING", "BBOX", "MASK", "IMAGE")
+    RETURN_NAMES = ("positive_coords", "negative_coords", "bbox", "bbox_mask", "cropped_image")
+    FUNCTION = "pointdata"
+    CATEGORY = "KJNodes/experimental"
+    DESCRIPTION = """
+# WORK IN PROGRESS
+Do not count on this as part of your workflow yet,
+probably contains lots of bugs and stability is not
+guaranteed!!
+
+## Graphical editor to create coordinates
+
+**Shift + click** to add a positive (green) point.
+**Shift + right click** to add a negative (red) point.
+**Right click on a point** to delete it.
+**Ctrl + click** to draw a bounding box.
+**Drag bbox corners** to resize, **drag inside** to move.
+**Right click on bbox** to delete it.
+
+To add an image select the node and copy/paste or drag in the image.
+Or from the bg_image input on queue (first frame of the batch).
+
+**THE IMAGE IS SAVED TO THE NODE AND WORKFLOW METADATA**
+you can clear the image from the context menu by right clicking on the canvas
+
+"""
+
+    def pointdata(self, points_store, bbox_store, width, height, coordinates, neg_coordinates, normalize, bboxes, bbox_format="xyxy", bg_image=None):
+        coordinates = json.loads(coordinates)
+        if not coordinates:
+            raise ValueError("No points on the canvas. Use Shift+click to add positive points or Shift+right-click to add negative points before executing.")
+        pos_coordinates = []
+        for coord in coordinates:
+            coord['x'] = int(round(coord['x']))
+            coord['y'] = int(round(coord['y']))
+            if normalize:
+                norm_x = coord['x'] / width
+                norm_y = coord['y'] / height
+                pos_coordinates.append({'x': norm_x, 'y': norm_y})
+            else:
+                pos_coordinates.append({'x': coord['x'], 'y': coord['y']})
+
+        if neg_coordinates:
+            coordinates = json.loads(neg_coordinates)
+            neg_coordinates = []
+            for coord in coordinates:
+                coord['x'] = int(round(coord['x']))
+                coord['y'] = int(round(coord['y']))
+                if normalize:
+                    norm_x = coord['x'] / width
+                    norm_y = coord['y'] / height
+                    neg_coordinates.append({'x': norm_x, 'y': norm_y})
+                else:
+                    neg_coordinates.append({'x': coord['x'], 'y': coord['y']})
+
+        # Create a blank mask
+        mask = np.zeros((height, width), dtype=np.uint8)
+        bboxes = json.loads(bboxes)
+        valid_bboxes = []
+        for bbox in bboxes:
+            if (bbox.get("startX") is None or
+                bbox.get("startY") is None or
+                bbox.get("endX") is None or
+                bbox.get("endY") is None):
+                continue  # Skip this bounding box if any value is None
+            else:                
+                # Ensure that endX and endY are greater than startX and startY
+                x_min = min(int(bbox["startX"]), int(bbox["endX"]))
+                y_min = min(int(bbox["startY"]), int(bbox["endY"]))
+                x_max = max(int(bbox["startX"]), int(bbox["endX"]))
+                y_max = max(int(bbox["startY"]), int(bbox["endY"]))
+                
+                valid_bboxes.append((x_min, y_min, x_max, y_max))
+
+            bboxes_xyxy = []
+            for bbox in valid_bboxes:
+                x_min, y_min, x_max, y_max = bbox
+                bboxes_xyxy.append((x_min, y_min, x_max, y_max))
+                mask[y_min:y_max, x_min:x_max] = 1  # Fill the bounding box area with 1s
+
+            if bbox_format == "xywh":
+                bboxes_xywh = []
+                for bbox in valid_bboxes:
+                    x_min, y_min, x_max, y_max = bbox
+                    width = x_max - x_min
+                    height = y_max - y_min
+                    bboxes_xywh.append((x_min, y_min, width, height))
+                bboxes = bboxes_xywh
+            else:
+                bboxes = bboxes_xyxy           
+
+        mask_tensor = torch.from_numpy(mask)
+        mask_tensor = mask_tensor.unsqueeze(0).float().cpu()
+
+        if bg_image is not None and len(valid_bboxes) > 0:
+            x_min, y_min, x_max, y_max = bboxes[0]
+            cropped_image = bg_image[:, y_min:y_max, x_min:x_max, :]
+        elif bg_image is not None:
+            cropped_image = bg_image
+        else:
+            cropped_image = torch.zeros(1, height, width, 3, dtype=torch.float32)
+
+        if bg_image is None:
+            return (json.dumps(pos_coordinates), json.dumps(neg_coordinates), bboxes, mask_tensor, cropped_image)
+        else:
+            transform = transforms.ToPILImage()
+            image = transform(bg_image[0].permute(2, 0, 1))
+            buffered = BytesIO()
+            image.save(buffered, format="JPEG", quality=75)
+            img_base64 = base64.b64encode(buffered.getvalue()).decode('utf-8')
+
+            return {
+                "ui": {"bg_image": [img_base64]},
+                "result": (json.dumps(pos_coordinates), json.dumps(neg_coordinates), bboxes, mask_tensor, cropped_image)
+            }
+
+class CutAndDragOnPath:
+    RETURN_TYPES = ("IMAGE", "MASK",)
+    RETURN_NAMES = ("image","mask", )
+    FUNCTION = "cutanddrag" 
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Cuts the masked area from the image, and drags it along the path. If inpaint is enabled, and no bg_image is provided, the cut area is filled using cv2 TELEA algorithm.
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "coordinates": ("STRING", {"forceInput": True}),
+                "mask": ("MASK",),
+                "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                "inpaint": ("BOOLEAN", {"default": True}),
+        },
+        "optional": {
+            "bg_image": ("IMAGE",),
+        }
+    }
+
+    def cutanddrag(self, image, coordinates, mask, frame_width, frame_height, inpaint, bg_image=None):
+        # Parse coordinates
+        coords_list = parse_json_tracks(coordinates)
+
+        batch_size = len(coords_list[0])
+        images_list = []
+        masks_list = []
+
+        # Convert input image and mask to PIL
+        input_image = tensor2pil(image)[0]
+        input_mask = tensor2pil(mask)[0]
+
+        # Find masked region bounds
+        mask_array = np.array(input_mask)
+        y_indices, x_indices = np.where(mask_array > 0)
+        if len(x_indices) == 0 or len(y_indices) == 0:
+            return (image, mask)
+            
+        x_min, x_max = x_indices.min(), x_indices.max()
+        y_min, y_max = y_indices.min(), y_indices.max()
+        
+        # Cut out the masked region
+        cut_width = x_max - x_min
+        cut_height = y_max - y_min
+        cut_image = input_image.crop((x_min, y_min, x_max, y_max))
+        cut_mask = input_mask.crop((x_min, y_min, x_max, y_max))
+        
+        # Create inpainted background
+        if bg_image is None:
+            background = input_image.copy()
+            # Inpaint the cut area
+            if inpaint:
+                import cv2
+                border = 5 # Create small border around cut area for better inpainting
+                fill_mask = Image.new("L", background.size, 0)
+                draw = ImageDraw.Draw(fill_mask)
+                draw.rectangle([x_min-border, y_min-border, x_max+border, y_max+border], fill=255)
+                background = cv2.inpaint(
+                    np.array(background), 
+                    np.array(fill_mask), 
+                    inpaintRadius=3, 
+                    flags=cv2.INPAINT_TELEA
+                )
+                background = Image.fromarray(background)
+        else:
+            background = tensor2pil(bg_image)[0]
+        
+        # Create batch of images with cut region at different positions
+        for i in range(batch_size):
+            # Create new image
+            new_image = background.copy()
+            new_mask = Image.new("L", (frame_width, frame_height), 0)
+
+            # Get target position from coordinates
+            for coords in coords_list:
+                target_x = int(coords[i]['x'] - cut_width/2)
+                target_y = int(coords[i]['y'] - cut_height/2)
+
+                # Paste cut region at new position
+                new_image.paste(cut_image, (target_x, target_y), cut_mask)
+                new_mask.paste(cut_mask, (target_x, target_y))
+
+            # Convert to tensor and append
+            image_tensor = pil2tensor(new_image)
+            mask_tensor = pil2tensor(new_mask)
+            
+            images_list.append(image_tensor)
+            masks_list.append(mask_tensor)
+
+        # Stack tensors into batches
+        out_images = torch.cat(images_list, dim=0).cpu().float()
+        out_masks = torch.cat(masks_list, dim=0)
+
+        return (out_images, out_masks)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-KJNodes/nodes/hdr_preview_node.py b/custom_nodes/ComfyUI-KJNodes/nodes/hdr_preview_node.py
new file mode 100644
index 0000000000000000000000000000000000000000..82d2f585a963de873815c4dacf1a4e1419bc13ed
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/nodes/hdr_preview_node.py
@@ -0,0 +1,166 @@
+import os
+import random
+
+import torch
+from PIL import Image
+from comfy_api.latest import io
+import comfy.model_management as mm
+import folder_paths
+
+
+# LogC3 constants (ARRI LogC3, EI 800). Kept in sync with the WebGL shader in web/js/hdr_preview.js.
+LC_A = 5.555556
+LC_B = 0.052272
+LC_C = 0.247190
+LC_D = 0.385537
+LC_E = 5.367655
+LC_F = 0.092809
+LC_CUT = 0.010591
+LC_CUT_LOG = LC_E * LC_CUT + LC_F  # ~0.14966
+
+def _logc3_decompress(logc: torch.Tensor) -> torch.Tensor:
+    logc = logc.clamp(0.0, 1.0)
+    lin_from_log = (10.0 ** ((logc - LC_D) / LC_C) - LC_B) / LC_A
+    lin_from_lin = (logc - LC_F) / LC_E
+    return torch.where(logc >= LC_CUT_LOG, lin_from_log, lin_from_lin)
+
+
+def _linear_to_srgb(x: torch.Tensor) -> torch.Tensor:
+    cutoff = 0.0031308
+    return torch.where(
+        x <= cutoff,
+        12.92 * x,
+        1.055 * torch.pow(x.clamp(min=cutoff), 1.0 / 2.4) - 0.055,
+    ).clamp_(0.0, 1.0)
+
+
+def _srgb_to_linear(x: torch.Tensor) -> torch.Tensor:
+    cutoff = 0.04045
+    return torch.where(
+        x <= cutoff,
+        x / 12.92,
+        ((x.clamp(min=0.0) + 0.055) / 1.055) ** 2.4,
+    )
+
+
+class HDRPreviewKJ(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="HDRPreviewKJ",
+            display_name="HDR Preview KJ",
+            category="KJNodes/image",
+            is_output_node=True,
+            is_experimental=True,
+            description=(
+                "Realtime-exposure preview for HDR-compressed images.\n\n"
+                "Input: LogC3-compressed [0,1] image/video batch (e.g. the VAE-decoded output "
+                "of an HDR IC-LoRA workflow, prior to HDR decompression).\n\n"
+                "Decompression + exposure + saturation + Reinhard tonemap + sRGB runs in a WebGL "
+                "fragment shader in the browser for realtime slider feedback, and the same math "
+                "runs server-side to produce the baked sRGB IMAGE output. Slider changes update "
+                "the preview immediately; the IMAGE output only updates when the workflow is re-queued."
+            ),
+            inputs=[
+                io.Image.Input("image",
+                    tooltip="LogC3-compressed HDR image/video in [0,1], or linear HDR if 'input_space' is 'linear'."),
+                io.Float.Input("exposure", default=0.0, min=-10.0, max=10.0, step=0.01,
+                    tooltip="Exposure in EV stops. 0 = no change, +1 = 2x brighter."),
+                io.Float.Input("saturation", default=1.0, min=0.0, max=2.0, step=0.01,
+                    tooltip="Saturation multiplier. 0 = grayscale, 1 = unchanged, 2 = 2x."),
+                io.Float.Input("fps", default=24.0, min=1.0, max=120.0, step=0.1, optional=True,
+                    tooltip="Playback frame rate for video (batch) inputs."),
+                io.Combo.Input("input_space", options=["logc3", "linear", "srgb"], default="logc3", optional=True,
+                    tooltip="Color space of input. 'logc3' = ARRI LogC3 compressed HDR; 'linear' = linear HDR directly; 'srgb' = already-graded sRGB image (skips Reinhard tonemap)."),
+            ],
+            outputs=[
+                io.Image.Output(display_name="image",
+                    tooltip="Tonemapped sRGB image, ready for preview/save."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, image: torch.Tensor, exposure: float = 0.0, saturation: float = 1.0, fps: float = 24.0, input_space: str = "logc3") -> io.NodeOutput:
+        temp_dir = folder_paths.get_temp_directory()
+        os.makedirs(temp_dir, exist_ok=True)
+        prefix = f"hdrprv_{random.randint(0, 0xFFFFFF):06x}"
+
+        B, H, W, _ = image.shape
+        device = mm.get_torch_device()
+        exposure_mul = 2.0 ** exposure
+        luma_weights = torch.tensor([0.2126, 0.7152, 0.0722], device=device)
+
+        bytes_per_frame = H * W * 3 * 4
+        chunk_size = max(1, min(B, int(1_000_000_000 // max(bytes_per_frame * 10, 1))))
+
+        # For linear input we need the global max across all frames to normalize previews.
+        norm_scale = 1.0
+        if input_space == "linear":
+            max_val = float(image[..., :3].max().item())
+            norm_scale = max_val if max_val > 1.0 else 1.0
+
+        filenames = []
+        srgb_chunks = []
+
+        for start in range(0, B, chunk_size):
+            end = min(start + chunk_size, B)
+            image_rgb = image[start:end, ..., :3].float().to(device, non_blocking=True)
+
+            # --- Preview frames (8-bit PNG, always in the "raw" pre-exposure space) ---
+            if input_space == "linear":
+                preview = (image_rgb / norm_scale).clamp_(0.0, 1.0)
+            else:
+                preview = image_rgb.clamp(0.0, 1.0)
+
+            preview_np = preview.mul_(255.0).add_(0.5).clamp_(0.0, 255.0).to(torch.uint8).cpu().numpy()
+            del preview
+
+            for i in range(end - start):
+                fname = f"{prefix}_{start + i:05d}.png"
+                Image.fromarray(preview_np[i], mode="RGB").save(
+                    os.path.join(temp_dir, fname),
+                    format="PNG",
+                    compress_level=1,
+                )
+                filenames.append(fname)
+            del preview_np
+
+            # --- Baked sRGB output (same math as the shader in hdr_preview.js) ---
+            if input_space == "logc3":
+                hdr = _logc3_decompress(image_rgb).clamp_(min=0.0)
+            elif input_space == "srgb":
+                hdr = _srgb_to_linear(image_rgb).clamp_(min=0.0)
+            else:
+                hdr = image_rgb.clamp(min=0.0)
+            del image_rgb
+
+            exposed = hdr.mul_(exposure_mul)
+            luma = (exposed * luma_weights.to(exposed.dtype)).sum(dim=-1, keepdim=True)
+            saturated = (luma + (exposed - luma) * saturation).clamp_(min=0.0)
+            del luma, exposed
+
+            if input_space == "srgb":
+                # Already display-ready linear; skip Reinhard, just clip over-exposed highlights.
+                tonemapped = saturated.clamp_(0.0, 1.0)
+            else:
+                tonemapped = saturated / (1.0 + saturated)
+                del saturated
+
+            srgb_chunks.append(_linear_to_srgb(tonemapped).cpu())
+            del tonemapped
+
+        srgb = torch.cat(srgb_chunks, dim=0)
+        del srgb_chunks
+
+        data = {
+            "frames": [{"filename": f, "type": "temp"} for f in filenames],
+            "width": int(W),
+            "height": int(H),
+            "fps": float(fps),
+            "input_space": input_space,
+            "linear_scale": float(norm_scale),
+            "frame_count": int(B),
+            "exposure": float(exposure),
+            "saturation": float(saturation),
+        }
+        return io.NodeOutput(srgb, ui={"hdr_preview_data": [data]})
diff --git a/custom_nodes/ComfyUI-KJNodes/nodes/ideogram4_nodes.py b/custom_nodes/ComfyUI-KJNodes/nodes/ideogram4_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b033b53da7496456ed849e526c30e8b32331acb
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/nodes/ideogram4_nodes.py
@@ -0,0 +1,411 @@
+"""Ideogram 4 prompt builder.
+
+A single self-contained node with a visual bbox editor: draw regions on a blank
+canvas, set each region's type/desc/text/color palette, and assemble the Ideogram 4 JSON caption prompt.
+"""
+
+import json
+import os
+
+import numpy as np
+import torch
+from PIL import Image, ImageDraw, ImageFont, ImageEnhance
+
+from comfy_api.latest import io
+
+
+_FONT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "fonts", "FreeMono.ttf")
+
+
+def _hex_rgb(h):
+    h = h.lstrip("#")
+    return (int(h[0:2], 16), int(h[2:4], 16), int(h[4:6], 16)) if len(h) == 6 else (255, 255, 255)
+
+
+def _readable(rgb):
+    # Lighten toward white if too dark, so box-colored text stays legible on the dark canvas.
+    r, g, b = rgb
+    lum = 0.299 * r + 0.587 * g + 0.114 * b
+    if lum < 130:
+        t = (130 - lum) / (255 - lum)
+        r, g, b = round(r + (255 - r) * t), round(g + (255 - g) * t), round(b + (255 - b) * t)
+    return (r, g, b)
+
+
+def _font(size):
+    try:
+        return ImageFont.truetype(_FONT_PATH, size)
+    except Exception:
+        try:
+            return ImageFont.load_default(size)
+        except Exception:
+            return ImageFont.load_default()
+
+
+def _wrap(draw, text, font, max_w):
+    lines = []
+    for para in text.split("\n"):
+        line = ""
+        for word in para.split():
+            test = word if not line else line + " " + word
+            if line and draw.textlength(test, font=font) > max_w:
+                lines.append(line)
+                line = word
+            else:
+                line = test
+        lines.append(line)
+    return lines
+
+
+def _render_preview(boxes, width, height, bg=None, brightness=50):
+    # Render the regions + prompts over the reference image (or a black canvas).
+    if bg is not None:
+        iw, ih = bg.size
+        long_edge = max(iw, ih)
+        scale = min(1.0, 1024 / long_edge) if long_edge > 0 else 1.0
+        rw, rh = max(1, round(iw * scale)), max(1, round(ih * scale))
+        base = bg.convert("RGB").resize((rw, rh), Image.LANCZOS)
+        if brightness < 100:                                # dim to match the editor's brightness slider
+            base = ImageEnhance.Brightness(base).enhance(max(0.0, brightness / 100.0))
+        img = base.convert("RGBA")
+    else:
+        long_edge = max(width, height)
+        scale = min(1.0, 1024 / long_edge) if long_edge > 0 else 1.0
+        rw = max(1, round(width * scale))
+        rh = max(1, round(height * scale))
+        img = Image.new("RGBA", (rw, rh), (0, 0, 0, 255))    # black so the overlay composites cleanly
+    overlay = Image.new("RGBA", (rw, rh), (0, 0, 0, 0))
+    draw = ImageDraw.Draw(overlay)
+    fs = max(10, round(rh / 64))
+    font = _font(fs)
+    tag_font = _font(max(9, fs - 2))
+    lh = fs + 2
+
+    for i, box in enumerate(boxes):
+        if not isinstance(box, dict) or box.get("nobbox"):
+            continue                                        # skip unplaced elements (no real location)
+        palette = [c for c in (box.get("palette") or []) if c]
+        r, g, b = _hex_rgb(palette[0]) if palette else (140, 140, 140)   # box = first palette color, else grey
+        x1 = max(0, min(rw, round(box.get("x", 0) * rw)))
+        y1 = max(0, min(rh, round(box.get("y", 0) * rh)))
+        x2 = max(0, min(rw, round((box.get("x", 0) + box.get("w", 0)) * rw)))
+        y2 = max(0, min(rh, round((box.get("y", 0) + box.get("h", 0)) * rh)))
+        if x2 < x1:
+            x1, x2 = x2, x1
+        if y2 < y1:
+            y1, y2 = y2, y1
+
+        draw.rectangle([x1, y1, x2, y2], outline=(r, g, b, 255), width=2)
+
+        pal5 = palette[:5]                                   # palette shown as a strip along the top edge
+        if pal5 and (x2 - x1) > 2:
+            sh = max(5, fs // 2)
+            seg = (x2 - x1) / len(pal5)
+            for p, hexc in enumerate(pal5):
+                sx = x1 + round(p * seg)
+                draw.rectangle([sx, y1, x1 + round((p + 1) * seg), y1 + sh], fill=_hex_rgb(hexc))
+
+        etype = "text" if box.get("type") == "text" else "obj"
+        tag = str(i + 1).zfill(2)
+        tw = draw.textlength(tag, font=tag_font)
+        draw.rectangle([x1, y1, x1 + tw + 6, y1 + fs + 2], fill=(r, g, b, 255))  # tag chip = box color
+        tagfill = (0, 0, 0, 255) if (0.299 * r + 0.587 * g + 0.114 * b) > 140 else (255, 255, 255, 255)
+        draw.text((x1 + 3, y1 + 1), tag, fill=tagfill, font=tag_font)
+
+        body = box.get("desc", "") or ""
+        if etype == "text" and box.get("text"):
+            body = '"%s"%s' % (box["text"], " — " + body if body else "")
+        if body and (x2 - x1) > 8:
+            ty = y1 + fs + 5
+            for line in _wrap(draw, body, font, x2 - x1 - 8):
+                if ty > y2:
+                    break
+                draw.text((x1 + 4, ty), line, fill=_readable((r, g, b)) + (255,), font=font)
+                ty += lh
+
+    img = Image.alpha_composite(img, overlay).convert("RGB")
+    arr = np.asarray(img, dtype=np.float32) / 255.0
+    return torch.from_numpy(arr).unsqueeze(0)
+
+
+def _norm_bbox(box):
+    # Normalized {x, y, w, h} fractions (0-1) -> [ymin, xmin, ymax, xmax] on a 0-1000 grid.
+    def c(v):
+        return max(0, min(1000, round(v * 1000)))
+    x, y, w, h = box.get("x", 0.0), box.get("y", 0.0), box.get("w", 0.0), box.get("h", 0.0)
+    ymin, xmin, ymax, xmax = c(y), c(x), c(y + h), c(x + w)
+    if ymin > ymax:
+        ymin, ymax = ymax, ymin
+    if xmin > xmax:
+        xmin, xmax = xmax, xmin
+    return [ymin, xmin, ymax, xmax]
+
+
+def _palette(colors):
+    # ["#rrggbb", ...] (or autogrow dict) -> ["#RRGGBB", ...] in order, dropping empties.
+    if isinstance(colors, dict):
+        colors = colors.values()
+    return [c.upper() for c in colors if c]
+
+
+def _dumps(v, lvl=0):
+    # Like json.dumps(ensure_ascii=False, indent=4), but scalar arrays stay on one line.
+    pad, end = "    " * (lvl + 1), "    " * lvl
+    if isinstance(v, str):
+        return json.dumps(v, ensure_ascii=False)
+    if isinstance(v, list):
+        if not v:
+            return "[]"
+        if all(not isinstance(x, (dict, list)) for x in v):
+            return "[" + ", ".join(_dumps(x, lvl) for x in v) + "]"
+        return "[\n" + ",\n".join(pad + _dumps(x, lvl + 1) for x in v) + "\n" + end + "]"
+    if isinstance(v, dict):
+        if not v:
+            return "{}"
+        items = [pad + json.dumps(k, ensure_ascii=False) + ": " + _dumps(val, lvl + 1) for k, val in v.items()]
+        return "{\n" + ",\n".join(items) + "\n" + end + "}"
+    return json.dumps(v, ensure_ascii=False)
+
+
+def _parse_json_list(s):
+    if s:
+        try:
+            v = json.loads(s)
+            if isinstance(v, list):
+                return v
+        except json.JSONDecodeError:
+            pass
+    return []
+
+
+def _caption_to_boxes(cap):
+    # Caption dict -> editor box list ({x,y,w,h, type, text, desc, palette}) for preview/bboxes.
+    cd = cap.get("compositional_deconstruction") or {}
+    boxes = []
+    for el in (cd.get("elements") or []):
+        if not isinstance(el, dict):
+            continue
+        box = {"type": "text" if el.get("type") == "text" else "obj",
+               "text": el.get("text", "") or "", "desc": el.get("desc", "") or "",
+               "palette": list(el.get("color_palette") or [])}
+        bb = el.get("bbox")
+        if isinstance(bb, (list, tuple)) and len(bb) == 4:
+            ymin, xmin, ymax, xmax = bb
+            box.update(x=xmin / 1000.0, y=ymin / 1000.0,
+                       w=(xmax - xmin) / 1000.0, h=(ymax - ymin) / 1000.0)
+        else:                                                # no bbox: unplaced placeholder
+            box.update(x=0.03, y=0.03, w=0.22, h=0.14, nobbox=True)
+        boxes.append(box)
+    return boxes
+
+
+class Ideogram4PromptBuilderKJ(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="Ideogram4PromptBuilderKJ",
+            display_name="Ideogram 4 Prompt Builder KJ",
+            category="KJNodes/text",
+            search_aliases=["ideogram", "caption", "bbox", "prompt builder", "json prompt"],
+            is_experimental=True,
+            description="""
+Visual prompt builder for Ideogram 4's structured JSON caption format.
+
+Drag on the canvas to draw regions; select a region to set its type (obj/text),  
+description, text, and color palette. Set the background and optional style fields  
+as widgets. Outputs the assembled caption JSON string.  
+
+bbox is normalized to a 0-1000 grid as [ymin, xmin, ymax, xmax]; width/height set
+the canvas aspect ratio.
+
+Canvas controls:
+- Drag: draw a new region
+- Ctrl/Cmd-drag: force-draw a new region even on top of an existing one
+- Click: select a region · Alt-click: cycle overlapping regions
+- Double-click: edit the description inline
+- Right-click: region list (select / delete / duplicate / reorder, top = front)
+- Del / Backspace: remove the selected region
+- Ctrl/Cmd + C / V / D: copy / paste / duplicate the selected region
+- bbox fields (px / out) next to obj/text are editable
+
+Color swatches:
+- Click: edit · Drag: reorder · Right-click: remove
+- Hover + Ctrl/Cmd + C / V: copy / paste the hex
+- "+": add a color (uses the clipboard color if it is one)
+
+Toolbar:
+- Live: use the live sampling preview as the background (and grab the final result)
+- Grab BG / Clear BG: use the last generated image as the background
+- brightness slider, token estimate, and Copy / Paste / Clear all""",
+            inputs=[
+                io.Int.Input("width", default=1024, min=64, max=16384, step=16,
+                             tooltip="Canvas aspect width (also the pixel grid the bbox is measured in). Ideogram 4 needs multiples of 16."),
+                io.Int.Input("height", default=1024, min=64, max=16384, step=16,
+                             tooltip="Canvas aspect height (also the pixel grid the bbox is measured in). Ideogram 4 needs multiples of 16."),
+                io.String.Input("high_level_description", multiline=True, default="",
+                                tooltip="Optional one-line overview of the whole image (blank = omitted)."),
+                io.String.Input("background", multiline=True, default="",
+                                tooltip="Required scene background description."),
+                io.DynamicCombo.Input("style", options=[
+                    io.DynamicCombo.Option("none", []),
+                    io.DynamicCombo.Option("photo", [
+                        io.String.Input("photo", default=""),
+                    ]),
+                    io.DynamicCombo.Option("art_style", [
+                        io.String.Input("art_style", default=""),
+                    ]),
+                ]),
+                io.String.Input("aesthetics", default="", tooltip="Style descriptor (blank = omitted)."),
+                io.String.Input("lighting", default="", tooltip="Style descriptor (blank = omitted)."),
+                io.String.Input("medium", default="", tooltip="Style descriptor (blank = omitted)."),
+                io.Image.Input("image", optional=True,
+                               tooltip="Optional reference image shown as the editor background (and behind the preview)."),
+                io.String.Input("import_json", default="", optional=True, force_input=True,
+                                tooltip="Optional: a full caption JSON. When connected, it loads into the editor "
+                                        "and drives the output per 'import_mode'."),
+                io.String.Input("style_palette_data", default="", socketless=True, advanced=True,
+                                tooltip="Serialized style color palette from the editor (managed by the node UI)."),
+                io.String.Input("elements_data", default="", socketless=True, advanced=True,
+                                tooltip="Serialized regions from the editor (managed by the node UI)."),
+                io.Int.Input("bg_brightness", default=25, min=0, max=100, socketless=True, advanced=True,
+                             tooltip="Background image brightness % (managed by the node UI slider)."),
+                io.Combo.Input("import_mode", options=["when empty", "always"], default="when empty",
+                               tooltip="How a wired import_json is used: 'when empty' only seeds the editor while "
+                                       "it has no regions (then the editor wins, so you can edit); 'always' makes "
+                                       "the wired JSON authoritative so its changes always propagate to the output."),
+                io.String.Input("output_format", default="compact", socketless=True, advanced=True,
+                                tooltip="Output JSON formatting (set via the editor toolbar): 'compact' (default, what "
+                                        "Ideogram 4 expects) or 'pretty' (indented, for readability)."),
+                io.BoundingBox.Input("bboxes", optional=True, force_input=True,
+                                     tooltip="Optional pixel-space boxes ({x, y, width, height}) used to seed the "
+                                             "editor's regions when it has none. Ignored once regions exist."),
+            ],
+            outputs=[
+                io.String.Output(display_name="prompt"),
+                io.Image.Output(display_name="preview"),
+                io.BoundingBox.Output(display_name="bboxes"),
+                io.Int.Output(display_name="width"),
+                io.Int.Output(display_name="height"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, width, height, background, style,
+                high_level_description="", aesthetics="", lighting="", medium="",
+                style_palette_data="", elements_data="", import_json="", import_mode="when empty",
+                output_format="pretty", bboxes=None, image=None, bg_brightness=25) -> io.NodeOutput:
+        if import_mode not in ("when empty", "always"):      # old workflows saved before this widget existed
+            import_mode = "when empty"
+        dump = _dumps if output_format == "pretty" else (lambda v: json.dumps(v, ensure_ascii=False, separators=(",", ":")))
+        boxes = _parse_json_list(elements_data)
+        boxes_seeded = False
+        if not boxes and bboxes:
+            if isinstance(bboxes, dict):                     # a single BoundingBox is a bare {x,y,width,height} dict
+                frame = [bboxes]
+            elif bboxes and isinstance(bboxes[0], (list, tuple)):
+                frame = bboxes[0]                            # per-frame nesting: [[box, ...], ...]
+            else:
+                frame = bboxes                               # flat list of boxes
+            for bb in frame:
+                if not isinstance(bb, dict):
+                    continue
+                boxes.append({"x": bb.get("x", 0) / width, "y": bb.get("y", 0) / height,
+                              "w": bb.get("width", 0) / width, "h": bb.get("height", 0) / height,
+                              "type": "obj", "text": "", "desc": "", "palette": []})
+            boxes_seeded = bool(boxes)
+
+        imported = None
+        if import_json and import_json.strip():
+            try:
+                c = json.loads(import_json)
+                if isinstance(c, dict):
+                    imported = c
+            except json.JSONDecodeError:
+                pass
+
+        kind = style["style"]                               # "none" | "photo" | "art_style"
+
+        # Use the wired import_json directly per import_mode: "always" -> authoritative (its changes
+        # always propagate); "when empty" -> only seed the editor while it has no regions, then the
+        # editor wins so manual edits stick. The editor mirrors it via ui when used.
+        used_import = imported is not None and (import_mode == "always" or not boxes)
+
+        if used_import:
+            caption = imported
+            boxes = _caption_to_boxes(imported)
+        else:
+            caption = {}
+            if high_level_description.strip():
+                caption["high_level_description"] = high_level_description
+
+            if kind != "none":
+                # The verifier requires every style key present (in order) once a style is
+                # chosen; only color_palette is conditional. Emit blanks rather than omit.
+                sd = {"aesthetics": aesthetics, "lighting": lighting}
+                # photo: ...photo, medium...  |  art_style: ...medium, art_style...  (key order)
+                if kind == "photo":
+                    sd["photo"] = style.get("photo", "")
+                    sd["medium"] = medium
+                else:
+                    sd["medium"] = medium
+                    sd["art_style"] = style.get("art_style", "")
+                palette = _palette(_parse_json_list(style_palette_data))
+                if palette:
+                    sd["color_palette"] = palette
+                caption["style_description"] = sd
+
+            elements = []
+            for box in boxes:
+                if not isinstance(box, dict):
+                    continue
+                etype = "text" if box.get("type") == "text" else "obj"
+                elem = {"type": etype}                      # key order matters
+                if not box.get("nobbox"):                   # unplaced elements omit bbox
+                    elem["bbox"] = _norm_bbox(box)
+                if etype == "text":
+                    elem["text"] = box.get("text", "")
+                elem["desc"] = box.get("desc", "")
+                palette = _palette(box.get("palette", []))
+                if palette:
+                    elem["color_palette"] = palette[:5]
+                elements.append(elem)
+
+            caption["compositional_deconstruction"] = {
+                "background": background,
+                "elements": elements,
+            }
+        bg = None
+        if image is not None:                                # composite over the input image, else black
+            try:
+                bg = Image.fromarray((image[0].detach().cpu().numpy() * 255).clip(0, 255).astype(np.uint8))
+            except Exception:
+                bg = None
+        preview = _render_preview(boxes, width, height, bg, bg_brightness)
+
+        # Pixel-space bboxes ({x, y, width, height}) for SAM3 / BoundingBox consumers.
+        bbox_dicts = []
+        for box in boxes:
+            if not isinstance(box, dict) or box.get("nobbox"):
+                continue
+            x, y = box.get("x", 0.0), box.get("y", 0.0)
+            bw, bh = box.get("w", 0.0), box.get("h", 0.0)
+            if bw < 0:
+                x += bw
+                bw = -bw
+            if bh < 0:
+                y += bh
+                bh = -bh
+            bbox_dicts.append({"x": round(x * width), "y": round(y * height),
+                               "width": round(bw * width), "height": round(bh * height)})
+        # Per-frame nesting (list[list[dict]]) — the canonical BoundingBox shape that
+        # SAM3 / crop nodes expect (bboxes[frame] -> list of boxes).
+        bboxes_out = [bbox_dicts] if bbox_dicts else []
+
+        # ui: send the resolved width/height so the editor canvas can follow connected
+        # inputs; import_json (if wired) loads into the editor (output reflects editor only).
+        ui = {"dims": [width, height]}
+        if boxes_seeded:
+            ui["boxes"] = [json.dumps(boxes)]
+        if used_import:                                       # mirror the import in the editor (only when used)
+            ui["caption"] = [_dumps(imported)]
+        return io.NodeOutput(dump(caption), preview, bboxes_out, width, height, ui=ui)
diff --git a/custom_nodes/ComfyUI-KJNodes/nodes/image_nodes.py b/custom_nodes/ComfyUI-KJNodes/nodes/image_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfe460af9e92337edf1feea0732d7fd68032a1ce
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/nodes/image_nodes.py
@@ -0,0 +1,5156 @@
+import numpy as np
+import time
+import torch
+import torch.nn.functional as F
+import torchvision.transforms as T
+import base64
+import random
+import math
+import os
+import re
+import json
+import importlib
+import hashlib
+import pathlib
+import logging
+from io import BytesIO
+
+try:
+    import cv2
+    HAS_CV2 = True
+except ImportError:
+    logging.warning("OpenCV not installed")
+    HAS_CV2 = False
+
+from PIL import ImageGrab, ImageDraw, ImageFont, Image, ImageOps, ImageSequence, ImageStat
+from PIL.PngImagePlugin import PngInfo
+
+from nodes import MAX_RESOLUTION, SaveImage
+from comfy_extras.nodes_mask import composite
+from comfy.cli_args import args
+from comfy.utils import ProgressBar, common_upscale, tiled_scale_multidim
+from comfy import model_management
+from comfy_api.latest import io, InputImpl, Types, ui
+from fractions import Fraction
+import node_helpers
+import folder_paths
+
+from ..utility.utility import string_to_color
+
+try:
+    from server import PromptServer, BinaryEventTypes
+except ImportError:
+    PromptServer = None
+    BinaryEventTypes = None
+from concurrent.futures import ThreadPoolExecutor
+
+script_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ImagePass:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {               
+            },
+            "optional": {
+                "image": ("IMAGE",),
+            },
+        }
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "passthrough"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Passes the image through without modifying it.
+"""
+
+    def passthrough(self, image=None):
+        return image,
+
+class ColorMatch:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image_ref": ("IMAGE",),
+                "image_target": ("IMAGE",),
+                "method": (['mkl','hm', 'reinhard', 'mvgd', 'hm-mvgd-hm', 'hm-mkl-hm'], {
+               "default": 'mkl'
+            }),
+            },
+            "optional": {
+                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "multithread": ("BOOLEAN", {"default": True}),
+            }
+        }
+
+    CATEGORY = "KJNodes/image"
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = "colormatch"
+    DEPRECATED = True
+    DESCRIPTION = """
+color-matcher enables color transfer across images which comes in handy for automatic  
+color-grading of photographs, paintings and film sequences as well as light-field  
+and stopmotion corrections.  
+
+The methods behind the mappings are based on the approach from Reinhard et al.,  
+the Monge-Kantorovich Linearization (MKL) as proposed by Pitie et al. and our analytical solution  
+to a Multi-Variate Gaussian Distribution (MVGD) transfer in conjunction with classical histogram   
+matching. As shown below our HM-MVGD-HM compound outperforms existing methods.   
+https://github.com/hahnec/color-matcher/
+
+"""
+
+    def colormatch(self, image_ref, image_target, method, strength=1.0, multithread=True):
+        # Skip unnecessary processing
+        if strength == 0:
+            return (image_target,)
+
+        try:
+            from color_matcher import ColorMatcher
+        except ImportError as e:
+            raise ImportError("Can't import color-matcher, did you install requirements.txt? Manual install: pip install color-matcher") from e
+
+        image_ref = image_ref.cpu()
+        image_target = image_target.cpu()
+        batch_size = image_target.size(0)
+
+        images_target = image_target.squeeze()
+        images_ref = image_ref.squeeze()
+
+        image_ref_np = images_ref.numpy()
+        images_target_np = images_target.numpy()
+
+        def process(i):
+            cm = ColorMatcher()
+            image_target_np_i = images_target_np if batch_size == 1 else images_target[i].numpy()
+            image_ref_np_i = image_ref_np if image_ref.size(0) == 1 else images_ref[i].numpy()
+            try:
+                image_result = cm.transfer(src=image_target_np_i, ref=image_ref_np_i, method=method) # Avoid potential blur when only the fully color-matched image is used
+                if strength != 1:
+                    image_result = image_target_np_i + strength * (image_result - image_target_np_i)
+
+                return torch.from_numpy(image_result)
+
+            except Exception as e:
+                logging.warning(f"Thread {i} error: {e}")
+                return torch.from_numpy(image_target_np_i)  # fallback
+
+        if multithread and batch_size > 1:
+            max_threads = min(os.cpu_count() or 1, batch_size)
+            with ThreadPoolExecutor(max_workers=max_threads) as executor:
+                out = list(executor.map(process, range(batch_size)))
+        else:
+            out = [process(i) for i in range(batch_size)]
+
+        out = torch.stack(out, dim=0).to(torch.float32)
+        out.clamp_(0, 1)
+        return (out,)
+
+class ColorMatchV2(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ColorMatchV2",
+            category="KJNodes/image",
+            description="""
+color-matcher enables color transfer across images which comes in handy for automatic  
+color-grading of photographs, paintings and film sequences as well as light-field  
+and stopmotion corrections.  
+
+The methods behind the mappings are based on the approach from Reinhard et al.,  
+the Monge-Kantorovich Linearization (MKL) as proposed by Pitie et al. and our analytical solution  
+to a Multi-Variate Gaussian Distribution (MVGD) transfer in conjunction with classical histogram   
+matching. As shown below our HM-MVGD-HM compound outperforms existing methods.   
+https://github.com/hahnec/color-matcher/   
+
+'reinhard_lab_gpu' method uses Kornia for GPU-accelerated color transfer in Lab color space.
+""",
+            inputs=[
+                io.Image.Input("image_target"),
+                io.Image.Input("image_ref"),
+                io.Combo.Input("method", 
+                    options=['mkl', 'hm', 'reinhard', 'mvgd', 'hm-mvgd-hm', 'hm-mkl-hm', 'reinhard_lab_gpu'],
+                    default='mkl'),
+                io.Float.Input("strength", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Boolean.Input("multithread", default=True),
+            ],
+            outputs=[
+                io.Image.Output(display_name="image"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, image_target, image_ref, method, strength=1.0, multithread=True) -> io.NodeOutput:
+        # Skip unnecessary processing
+        if strength == 0:
+            return io.NodeOutput(image_target)
+
+        if method == "reinhard_lab_gpu":
+            import kornia
+            device = model_management.get_torch_device()
+
+            B, H, W, C = image_target.shape
+
+            src_bchw = image_target.to(device).permute(0, 3, 1, 2).contiguous() # (B, H, W, C) -> (B, C, H, W)
+            ref_bchw = image_ref.to(device).permute(0, 3, 1, 2).contiguous()
+            # RGB->Lab
+            src_lab = kornia.color.rgb_to_lab(src_bchw)
+            ref_lab = kornia.color.rgb_to_lab(ref_bchw)
+
+            src_lab_flat = src_lab.view(B, C, -1)  # (B, C, HW)
+            ref_lab_flat = ref_lab.view(ref_lab.shape[0], C, -1)  # (B or 1, C, HW)
+
+            src_std, src_mean = torch.std_mean(src_lab_flat, dim=-1, keepdim=True, unbiased=False)
+            ref_std, ref_mean = torch.std_mean(ref_lab_flat, dim=-1, keepdim=True, unbiased=False)
+            src_std = src_std.clamp_min_(1e-6)
+
+            if ref_lab.shape[0] == 1 and B > 1:
+                ref_mean = ref_mean.expand(B, -1, -1)
+                ref_std = ref_std.expand(B, -1, -1)
+
+            corrected_lab_flat = (src_lab_flat - src_mean) * (ref_std / src_std) + ref_mean
+            corrected_lab = corrected_lab_flat.view(B, C, H, W)
+
+            # Lab->RGB
+            corrected_rgb_01 = kornia.color.lab_to_rgb(corrected_lab)
+            out = (1.0 - strength) * src_bchw + strength * corrected_rgb_01
+            out = out.permute(0, 2, 3, 1).contiguous() # (B, C, H, W) -> (B, H, W, C)
+
+            return io.NodeOutput(out.cpu().float().clamp_(0, 1))
+
+        try:
+            from color_matcher import ColorMatcher
+        except ImportError as e:
+            raise ImportError("Can't import color-matcher, did you install requirements.txt? Manual install: pip install color-matcher") from e
+
+        batch_size = image_target.size(0)
+        ref_batch_size = image_ref.size(0)
+
+        def process(i):
+            cm = ColorMatcher()
+            image_target_np = image_target[i].cpu().numpy()
+            image_ref_np = image_ref[min(i, ref_batch_size - 1)].cpu().numpy()
+            try:
+                image_result = cm.transfer(src=image_target_np, ref=image_ref_np, method=method)
+                if strength != 1:
+                    image_result = image_target_np + strength * (image_result - image_target_np)
+
+                return torch.from_numpy(image_result)
+
+            except Exception as e:
+                logging.error(f"Thread {i} error: {e}")
+                return torch.from_numpy(image_target_np)  # fallback
+        if multithread and batch_size > 1:
+            max_threads = min(os.cpu_count() or 1, batch_size)
+            with ThreadPoolExecutor(max_workers=max_threads) as executor:
+                out = list(executor.map(process, range(batch_size)))
+        else:
+            out = [process(i) for i in range(batch_size)]
+
+        out = torch.stack(out, dim=0).to(torch.float32).clamp_(0, 1)
+
+        return io.NodeOutput(out)
+
+class SaveImageWithAlpha:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "output"
+        self.prefix_append = ""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": 
+                    {"images": ("IMAGE", ),
+                    "mask": ("MASK", ),
+                    "filename_prefix": ("STRING", {"default": "ComfyUI"})},
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save_images_alpha"
+    OUTPUT_NODE = True
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Saves an image and mask as .PNG with the mask as the alpha channel. 
+"""
+
+    def save_images_alpha(self, images, mask, filename_prefix="ComfyUI_image_with_alpha", prompt=None, extra_pnginfo=None):
+        filename_prefix += self.prefix_append
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
+        results = list()
+        def file_counter():
+            max_counter = 0
+            # Loop through the existing files
+            for existing_file in sorted(os.listdir(full_output_folder)):
+                # Check if the file matches the expected format
+                match = re.fullmatch(fr"{filename}_(\d+)_?\.[a-zA-Z0-9]+", existing_file)
+                if match:
+                    # Extract the numeric portion of the filename
+                    file_counter = int(match.group(1))
+                    # Update the maximum counter value if necessary
+                    if file_counter > max_counter:
+                        max_counter = file_counter
+            return max_counter
+
+        for image, alpha in zip(images, mask):
+            i = 255. * image.cpu().numpy()
+            a = 255. * (1.0 - alpha.cpu().float()).numpy()
+            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+
+             # Resize the mask to match the image size
+            a_resized = Image.fromarray(a).resize(img.size, Image.LANCZOS)
+            a_resized = np.clip(a_resized, 0, 255).astype(np.uint8)
+            img.putalpha(Image.fromarray(a_resized, mode='L'))
+            metadata = None
+            if not args.disable_metadata:
+                metadata = PngInfo()
+                if prompt is not None:
+                    metadata.add_text("prompt", json.dumps(prompt))
+                if extra_pnginfo is not None:
+                    for x in extra_pnginfo:
+                        metadata.add_text(x, json.dumps(extra_pnginfo[x]))
+
+            # Increment the counter by 1 to get the next available value
+            counter = file_counter() + 1
+            file = f"{filename}_{counter:05}.png"
+            img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=4)
+            results.append({
+                "filename": file,
+                "subfolder": subfolder,
+                "type": self.type
+            })
+
+        return { "ui": { "images": results } }
+
+
+class ImageConcanate(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        # image1 drives the output type; image2 can independently be IMAGE or MASK and gets
+        # converted to image1's type inside concatenate().
+        type_template = io.MatchType.Template("image_or_mask", allowed_types=[io.Image, io.Mask])
+        return io.Schema(
+            node_id="ImageConcanate",
+            category="KJNodes/image",
+            description=(
+                "Concatenates image2 to image1 in the specified direction.\n"
+                "Both inputs accept IMAGE or MASK; the output type follows image1.\n"
+                "If image2 is a different type than image1 it's converted (RGB mean for image→mask,\n"
+                "channel-replicate for mask→image).\n"
+                "When match_image_size is False and dimensions don't match along the shared axis,\n"
+                "the smaller image is centered and zero-padded instead of erroring."
+            ),
+            inputs=[
+                io.MatchType.Input("image1", template=type_template),
+                io.MultiType.Input("image2", types=[io.Image, io.Mask]),
+                io.Combo.Input("direction", options=['right', 'down', 'left', 'up'], default='right'),
+                io.Boolean.Input("match_image_size", default=True),
+            ],
+            outputs=[
+                io.MatchType.Output(template=type_template, display_name="output"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, image1, image2, direction, match_image_size) -> io.NodeOutput:
+        return io.NodeOutput(cls.concatenate(image1, image2, direction, match_image_size))
+
+    @staticmethod
+    def concatenate(image1, image2, direction, match_image_size, first_image_shape=None):
+        # IMAGE is BHWC, MASK is BHW. Output type follows image1; convert image2 to match,
+        # then unsqueeze any masks to BHW1 so the rest of the function can stay BHWC-only.
+        output_is_mask = image1.dim() == 3
+        if output_is_mask and image2.dim() == 4:
+            ch = min(3, image2.shape[-1])
+            image2 = image2[..., :ch].mean(dim=-1)
+        elif not output_is_mask and image2.dim() == 3:
+            image2 = image2.unsqueeze(-1).expand(-1, -1, -1, image1.shape[-1])
+        if output_is_mask:
+            image1 = image1.unsqueeze(-1)
+            image2 = image2.unsqueeze(-1)
+
+        bs1 = image1.shape[0]
+        bs2 = image2.shape[0]
+        B = max(bs1, bs2)
+
+        H1, W1 = image1.shape[1], image1.shape[2]
+        C1, C2 = image1.shape[-1], image2.shape[-1]
+        out_C = max(C1, C2)
+
+        if match_image_size:
+            target_shape = first_image_shape if first_image_shape is not None else image1.shape
+            orig_aspect = image2.shape[2] / image2.shape[1]
+            if direction in ('left', 'right'):
+                H2 = target_shape[1]
+                W2 = int(H2 * orig_aspect)
+            else:
+                W2 = target_shape[2]
+                H2 = int(W2 / orig_aspect)
+        else:
+            H2, W2 = image2.shape[1], image2.shape[2]
+
+        if direction in ('right', 'left'):
+            out_H, out_W = max(H1, H2), W1 + W2
+        else:
+            out_H, out_W = H1 + H2, max(W1, W2)
+
+        if direction == 'right':
+            i1_y, i1_x, i2_y, i2_x = (out_H - H1) // 2, 0, (out_H - H2) // 2, W1
+        elif direction == 'left':
+            i1_y, i1_x, i2_y, i2_x = (out_H - H1) // 2, W2, (out_H - H2) // 2, 0
+        elif direction == 'down':
+            i1_y, i1_x, i2_y, i2_x = 0, (out_W - W1) // 2, H1, (out_W - W2) // 2
+        else:  # 'up'
+            i1_y, i1_x, i2_y, i2_x = H2, (out_W - W1) // 2, 0, (out_W - W2) // 2
+
+        output = torch.zeros(
+            (B, out_H, out_W, out_C),
+            dtype=model_management.intermediate_dtype(),
+            device=model_management.intermediate_device(),
+        )
+
+        def write(dst, src, src_C):
+            if dst.shape[-1] == src_C:
+                dst.copy_(src)
+            else:
+                dst[..., :src_C].copy_(src)
+                dst[..., src_C:].fill_(1.0)
+
+        slot1 = output[:, i1_y:i1_y + H1, i1_x:i1_x + W1, :]
+        if bs1 == B:
+            write(slot1, image1, C1)
+        else:
+            write(slot1[:bs1], image1, C1)
+            write(slot1[bs1:], image1[-1:].expand(B - bs1, -1, -1, -1), C1)
+        del slot1
+
+        slot2 = output[:, i2_y:i2_y + H2, i2_x:i2_x + W2, :]
+        if match_image_size:
+            pbar = ProgressBar(B)
+            device = model_management.get_torch_device()
+            for i in range(B):
+                src_i = min(i, bs2 - 1)
+                frame = image2[src_i:src_i + 1].to(device, non_blocking=True).permute(0, 3, 1, 2)
+                resized = F.interpolate(frame, size=(H2, W2), mode='bicubic', antialias=True).permute(0, 2, 3, 1)
+                write(slot2[i:i + 1], resized, C2)
+                del frame, resized
+                pbar.update(1)
+        else:
+            if bs2 == B:
+                write(slot2, image2, C2)
+            else:
+                write(slot2[:bs2], image2, C2)
+                write(slot2[bs2:], image2[-1:].expand(B - bs2, -1, -1, -1), C2)
+        del slot2
+
+        if output_is_mask:
+            return output.squeeze(-1)
+        return output
+
+
+class ImageConcatFromBatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "images": ("IMAGE",),
+            "num_columns": ("INT", {"default": 3, "min": 1, "max": 255, "step": 1}),
+            "match_image_size": ("BOOLEAN", {"default": False}),
+            "max_resolution": ("INT", {"default": 4096}), 
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "concat"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+    Concatenates images from a batch into a grid with a specified number of columns.
+    """
+
+    def concat(self, images, num_columns, match_image_size, max_resolution):
+        # Assuming images is a batch of images (B, H, W, C)
+        batch_size, height, width, channels = images.shape
+        num_rows = (batch_size + num_columns - 1) // num_columns  # Calculate number of rows
+
+        logging.info(f"Initial dimensions: batch_size={batch_size}, height={height}, width={width}, channels={channels}")
+        logging.info(f"num_rows={num_rows}, num_columns={num_columns}")
+
+        if match_image_size:
+            target_shape = images[0].shape
+
+            resized_images = []
+            for image in images:
+                original_height = image.shape[0]
+                original_width = image.shape[1]
+                original_aspect_ratio = original_width / original_height
+
+                if original_aspect_ratio > 1:
+                    target_height = target_shape[0]
+                    target_width = int(target_height * original_aspect_ratio)
+                else:
+                    target_width = target_shape[1]
+                    target_height = int(target_width / original_aspect_ratio)
+
+                logging.info(f"Resizing image from ({original_height}, {original_width}) to ({target_height}, {target_width})")
+
+                # Resize the image to match the target size while preserving aspect ratio
+                resized_image = common_upscale(image.movedim(-1, 0), target_width, target_height, "lanczos", "disabled")
+                resized_image = resized_image.movedim(0, -1)  # Move channels back to the last dimension
+                resized_images.append(resized_image)
+
+            # Convert the list of resized images back to a tensor
+            images = torch.stack(resized_images)
+
+            height, width = target_shape[:2]  # Update height and width
+
+        # Initialize an empty grid
+        grid_height = num_rows * height
+        grid_width = num_columns * width
+
+        logging.info(f"Grid dimensions before scaling: grid_height={grid_height}, grid_width={grid_width}")
+
+        # Original scale factor calculation remains unchanged
+        scale_factor = min(max_resolution / grid_height, max_resolution / grid_width, 1.0)
+
+        # Apply scale factor to height and width
+        scaled_height = height * scale_factor
+        scaled_width = width * scale_factor
+
+        # Round scaled dimensions to the nearest number divisible by 8
+        height = max(1, int(round(scaled_height / 8) * 8))
+        width = max(1, int(round(scaled_width / 8) * 8))
+
+        if abs(scaled_height - height) > 4:
+            height = max(1, int(round((scaled_height + 4) / 8) * 8))
+        if abs(scaled_width - width) > 4:
+            width = max(1, int(round((scaled_width + 4) / 8) * 8))
+
+        # Recalculate grid dimensions with adjusted height and width
+        grid_height = num_rows * height
+        grid_width = num_columns * width
+        logging.info(f"Grid dimensions after scaling: grid_height={grid_height}, grid_width={grid_width}")
+        logging.info(f"Final image dimensions: height={height}, width={width}")
+
+        grid = torch.zeros((grid_height, grid_width, channels), dtype=images.dtype)
+
+        for idx, image in enumerate(images):
+            resized_image = torch.nn.functional.interpolate(image.unsqueeze(0).permute(0, 3, 1, 2), size=(height, width), mode="bilinear").squeeze().permute(1, 2, 0)
+            row = idx // num_columns
+            col = idx % num_columns
+            grid[row*height:(row+1)*height, col*width:(col+1)*width, :] = resized_image
+
+        return grid.unsqueeze(0),
+
+    
+class ImageGridComposite2x2:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "image1": ("IMAGE",),
+            "image2": ("IMAGE",),
+            "image3": ("IMAGE",),
+            "image4": ("IMAGE",),   
+        }}
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "compositegrid"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Concatenates the 4 input images into a 2x2 grid. 
+"""
+
+    def compositegrid(self, image1, image2, image3, image4):
+        top_row = torch.cat((image1, image2), dim=2)
+        bottom_row = torch.cat((image3, image4), dim=2)
+        grid = torch.cat((top_row, bottom_row), dim=1)
+        return (grid,)
+    
+class ImageGridComposite3x3:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "image1": ("IMAGE",),
+            "image2": ("IMAGE",),
+            "image3": ("IMAGE",),
+            "image4": ("IMAGE",),
+            "image5": ("IMAGE",),
+            "image6": ("IMAGE",),
+            "image7": ("IMAGE",),
+            "image8": ("IMAGE",),
+            "image9": ("IMAGE",),     
+        }}
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "compositegrid"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Concatenates the 9 input images into a 3x3 grid. 
+"""
+
+    def compositegrid(self, image1, image2, image3, image4, image5, image6, image7, image8, image9):
+        top_row = torch.cat((image1, image2, image3), dim=2)
+        mid_row = torch.cat((image4, image5, image6), dim=2)
+        bottom_row = torch.cat((image7, image8, image9), dim=2)
+        grid = torch.cat((top_row, mid_row, bottom_row), dim=1)
+        return (grid,)
+
+
+class ImageBatchTestPattern(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ImageBatchTestPattern",
+            category="KJNodes/text",
+            description="Generate a batch of images with sequential numbers rendered in a chosen font.",
+            inputs=[
+                io.Int.Input("batch_size", default=1, min=1, max=4096, step=1),
+                io.Int.Input("start_from", default=0, min=0, max=4096, step=1),
+                io.Int.Input("text_x", default=256, min=0, max=4096, step=1),
+                io.Int.Input("text_y", default=256, min=0, max=4096, step=1),
+                io.Int.Input("width", default=512, min=16, max=4096, step=1),
+                io.Int.Input("height", default=512, min=16, max=4096, step=1),
+                io.Combo.Input("font", options=folder_paths.get_filename_list("kjnodes_fonts")),
+                io.Int.Input("font_size", default=255, min=8, max=4096, step=1),
+            ],
+            outputs=[
+                io.Image.Output(display_name="image"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, batch_size, font, font_size, start_from, width, height, text_x, text_y) -> io.NodeOutput:
+        font_path = folder_paths.get_full_path("kjnodes_fonts", font)
+        pil_font = ImageFont.truetype(font_path, font_size)
+
+        # Probe once whether the '-liga' feature is supported by this PIL build/font
+        use_liga = True
+        try:
+            ImageDraw.Draw(Image.new("RGB", (1, 1))).text(
+                (0, 0), "0", font=pil_font, fill=(0, 0, 0), features=['-liga']
+            )
+        except Exception:
+            use_liga = False
+
+        image = Image.new("RGB", (width, height), color='black')
+        draw = ImageDraw.Draw(image)
+
+        out_buf = np.empty((batch_size, height, width, 3), dtype=np.uint8)
+        pbar = ProgressBar(batch_size)
+
+        for i in range(batch_size):
+            # Reset canvas to black instead of allocating a new PIL image
+            draw.rectangle((0, 0, width, height), fill='black')
+
+            font_color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
+            text = str(start_from + i)
+
+            if use_liga:
+                draw.text((text_x, text_y), text, font=pil_font, fill=font_color, features=['-liga'])
+            else:
+                draw.text((text_x, text_y), text, font=pil_font, fill=font_color)
+
+            out_buf[i] = np.asarray(image)
+            pbar.update(1)
+
+        out_tensor = torch.from_numpy(out_buf).to(
+            device=model_management.intermediate_device(),
+            dtype=model_management.intermediate_dtype(),
+        ).div_(255.0)
+        return io.NodeOutput(out_tensor)
+
+class ImageGrabPIL:
+
+    @classmethod
+    def IS_CHANGED(cls):
+
+        return
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = "screencap"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Captures an area specified by screen coordinates.  
+Can be used for realtime diffusion with autoqueue.
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "x": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
+                 "y": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
+                 "width": ("INT", {"default": 512,"min": 0, "max": 4096, "step": 1}),
+                 "height": ("INT", {"default": 512,"min": 0, "max": 4096, "step": 1}),
+                 "num_frames": ("INT", {"default": 1,"min": 1, "max": 255, "step": 1}),
+                 "delay": ("FLOAT", {"default": 0.1,"min": 0.0, "max": 10.0, "step": 0.01}),
+        },
+    } 
+
+    def screencap(self, x, y, width, height, num_frames, delay):
+        start_time = time.time()
+        captures = []
+        bbox = (x, y, x + width, y + height)
+        
+        for _ in range(num_frames):
+            # Capture screen
+            screen_capture = ImageGrab.grab(bbox=bbox)
+            screen_capture_torch = torch.from_numpy(np.array(screen_capture, dtype=np.float32) / 255.0).unsqueeze(0)
+            captures.append(screen_capture_torch)
+            
+            # Wait for a short delay if more than one frame is to be captured
+            if num_frames > 1:
+                time.sleep(delay)
+
+        elapsed_time = time.time() - start_time
+        logging.info(f"screengrab took {elapsed_time} seconds.")
+        
+        return (torch.cat(captures, dim=0),)
+    
+class Screencap_mss:
+
+    @classmethod
+    def IS_CHANGED(s, **kwargs):
+        return float("NaN")
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = "screencap"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Captures an area specified by screen coordinates.  
+Can be used for realtime diffusion with autoqueue.
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "x": ("INT", {"default": 0,"min": 0, "max": 10000, "step": 1}),
+                 "y": ("INT", {"default": 0,"min": 0, "max": 10000, "step": 1}),
+                 "width": ("INT", {"default": 512,"min": 0, "max": 10000, "step": 1}),
+                 "height": ("INT", {"default": 512,"min": 0, "max": 10000, "step": 1}),
+                 "num_frames": ("INT", {"default": 1,"min": 1, "max": 255, "step": 1}),
+                 "delay": ("FLOAT", {"default": 0.1,"min": 0.0, "max": 10.0, "step": 0.01}),
+        },
+    } 
+
+    def screencap(self, x, y, width, height, num_frames, delay):
+        from mss import mss
+        captures = []
+        with mss() as sct:
+            bbox = {'top': y, 'left': x, 'width': width, 'height': height}
+            
+            for _ in range(num_frames):
+                sct_img = sct.grab(bbox)
+                img_np = np.array(sct_img)
+                img_torch = torch.from_numpy(img_np[..., [2, 1, 0]]).float() / 255.0
+                captures.append(img_torch)
+                
+                if num_frames > 1:
+                    time.sleep(delay)
+        
+        return (torch.stack(captures, 0),)
+
+class ScreencapStream:
+
+    @classmethod
+    def IS_CHANGED(s, **kwargs):
+        return float("NaN")
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = "capture"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Captures a frame from a browser screen/window share stream.
+Click 'Start capture' to select a screen or window to share.
+Live preview is shown in the node. Works with auto-queue.
+
+Crop controls:
+- Drag on preview to draw a crop box
+- Drag inside the box to move it
+- Drag edges or corners to resize
+- Shift+drag to lock aspect ratio
+- Right-click or double-click to clear crop
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "frame_data": ("STRING", {"default": "", "multiline": False}),
+                "crop_width": ("INT", {"default": 1, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
+                "crop_height": ("INT", {"default": 1, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
+            },
+        }
+
+    MAX_FRAME_BYTES = 50 * 1024 * 1024  # 50MB base64 limit (PNG is larger than JPEG)
+
+    def capture(self, crop_width, crop_height, frame_data):
+        if not frame_data:
+            w = crop_width if crop_width > 0 else 512
+            h = crop_height if crop_height > 0 else 512
+            return (torch.zeros(1, h, w, 3),)
+        if len(frame_data) > self.MAX_FRAME_BYTES:
+            raise ValueError(f"Frame data exceeds {self.MAX_FRAME_BYTES // (1024*1024)}MB limit")
+        try:
+            img_bytes = base64.b64decode(frame_data.split(",", 1)[-1])
+        except Exception:
+            raise ValueError("Invalid frame data encoding")
+        img = Image.open(BytesIO(img_bytes)).convert("RGB")
+        img_np = np.array(img).astype(np.float32) / 255.0
+        img_tensor = torch.from_numpy(img_np).unsqueeze(0)
+        return (img_tensor,)
+
+class WebcamCaptureCV2:
+
+    @classmethod
+    def IS_CHANGED(cls):
+        return
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = "capture"
+    CATEGORY = "KJNodes/experimental"
+    DESCRIPTION = """
+Captures a frame from a webcam using CV2.  
+Can be used for realtime diffusion with autoqueue.
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "x": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
+                 "y": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
+                 "width": ("INT", {"default": 512,"min": 0, "max": 4096, "step": 1}),
+                 "height": ("INT", {"default": 512,"min": 0, "max": 4096, "step": 1}),
+                 "cam_index": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}),
+                 "release": ("BOOLEAN", {"default": False}),
+            },
+        } 
+
+    def capture(self, x, y, cam_index, width, height, release):
+        # Check if the camera index has changed or the capture object doesn't exist
+        if not hasattr(self, "cap") or self.cap is None or self.current_cam_index != cam_index:
+            if hasattr(self, "cap") and self.cap is not None:
+                self.cap.release()
+            self.current_cam_index = cam_index
+            self.cap = cv2.VideoCapture(cam_index)
+            try:
+                self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
+                self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
+            except cv2.error:
+                pass
+            if not self.cap.isOpened():
+                raise RuntimeError("Could not open webcam")
+
+        ret, frame = self.cap.read()
+        if not ret:
+            raise RuntimeError("Failed to capture image from webcam")
+
+        # Crop the frame to the specified bbox
+        frame = frame[y:y+height, x:x+width]
+        img_torch = torch.from_numpy(frame[..., [2, 1, 0]]).float() / 255.0
+
+        if release:
+            self.cap.release()
+            self.cap = None
+
+        return (img_torch.unsqueeze(0),)
+
+class AddLabel:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "image":("IMAGE",),  
+            "text_x": ("INT", {"default": 10, "min": 0, "max": 4096, "step": 1}),
+            "text_y": ("INT", {"default": 2, "min": 0, "max": 4096, "step": 1}),
+            "height": ("INT", {"default": 48, "min": -1, "max": 4096, "step": 1}),
+            "font_size": ("INT", {"default": 32, "min": 0, "max": 4096, "step": 1}),
+            "font_color": ("STRING", {"default": "white"}),
+            "label_color": ("STRING", {"default": "black"}),
+            "font": (folder_paths.get_filename_list("kjnodes_fonts"), ),
+            "text": ("STRING", {"default": "Text"}),
+            "direction": (
+            [   'up',
+                'down',
+                'left',
+                'right',
+                'overlay'
+            ],
+            {
+            "default": 'up'
+             }),
+            },
+            "optional":{
+                "caption": ("STRING", {"default": "", "forceInput": True}),
+            }
+            }
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "addlabel"
+    CATEGORY = "KJNodes/text"
+    DESCRIPTION = """
+Creates a new with the given text, and concatenates it to  
+either above or below the input image.  
+Note that this changes the input image's height!  
+Fonts are loaded from this folder:  
+ComfyUI/custom_nodes/ComfyUI-KJNodes/fonts
+"""
+
+    def addlabel(self, image, text_x, text_y, text, height, font_size, font_color, label_color, font, direction, caption=""):
+        batch_size = image.shape[0]
+        width = image.shape[2]
+
+        font_path = os.path.join(script_directory, "fonts", "TTNorms-Black.otf") if font == "TTNorms-Black.otf" else folder_paths.get_full_path("kjnodes_fonts", font)
+
+        # Parse colors using helper function
+        font_color_rgb = string_to_color(font_color)
+        label_color_rgb = string_to_color(label_color)
+
+        # Convert to tuples for PIL
+        font_color_tuple = tuple(font_color_rgb[:3])  # RGB only
+        label_color_tuple = tuple(label_color_rgb[:3])  # RGB only
+
+        def process_image(input_image, caption_text):
+            font = ImageFont.truetype(font_path, font_size)
+            lines = []
+            for text_line in caption_text.split('\n'):
+                if text_line.strip() == "":
+                    # Preserve empty lines for multiple newlines
+                    lines.append("")
+                    continue
+                words = text_line.split()
+                current_line = []
+                for word in words:
+                    if current_line:
+                        test_line = " ".join(current_line + [word])
+                    else:
+                        test_line = word
+                    try:
+                        test_line_width = font.getbbox(test_line)[2]
+                    except Exception:
+                        test_line_width = font.getsize(test_line)[0]
+                    if test_line_width <= width - 2 * text_x:
+                        current_line.append(word)
+                    else:
+                        lines.append(" ".join(current_line))
+                        current_line = [word]
+                if current_line:
+                    lines.append(" ".join(current_line))
+
+            if direction == 'overlay':
+                pil_image = Image.fromarray((input_image.cpu().numpy() * 255).astype(np.uint8))
+            else:
+                if height == -1:
+                    # Adjust the image height automatically
+                    margin = 8
+                    required_height = (text_y + len(lines) * font_size) + margin # Calculate required height
+                    pil_image = Image.new("RGB", (width, required_height), label_color_tuple)
+                else:
+                    # Initialize with a minimal height
+                    label_image = Image.new("RGB", (width, height), label_color_tuple)
+                    pil_image = label_image
+
+            draw = ImageDraw.Draw(pil_image)
+
+
+            y_offset = text_y
+            for line in lines:
+                try:
+                    draw.text((text_x, y_offset), line, font=font, fill=font_color_tuple, features=['-liga'])
+                except Exception:
+                    draw.text((text_x, y_offset), line, font=font, fill=font_color_tuple)
+                y_offset += font_size
+
+            processed_image = torch.from_numpy(np.array(pil_image).astype(np.float32) / 255.0).unsqueeze(0)
+            return processed_image
+
+        if caption == "":
+            processed_images = [process_image(img, text) for img in image]
+        else:
+            assert len(caption) == batch_size, f"Number of captions {(len(caption))} does not match number of images"
+            processed_images = [process_image(img, cap) for img, cap in zip(image, caption)]
+        processed_batch = torch.cat(processed_images, dim=0)
+
+        # Combine images based on direction
+        if direction == 'down':
+            combined_images = torch.cat((image, processed_batch), dim=1)
+        elif direction == 'up':
+            combined_images = torch.cat((processed_batch, image), dim=1)
+        elif direction == 'left':
+            processed_batch = torch.rot90(processed_batch, 3, (2, 3)).permute(0, 3, 1, 2)
+            combined_images = torch.cat((processed_batch, image), dim=2)
+        elif direction == 'right':
+            processed_batch = torch.rot90(processed_batch, 3, (2, 3)).permute(0, 3, 1, 2)
+            combined_images = torch.cat((image, processed_batch), dim=2)
+        else:
+            combined_images = processed_batch
+        
+        return (combined_images,)
+    
+class GetImageSizeAndCount:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "image": ("IMAGE",),
+        }}
+
+    RETURN_TYPES = ("IMAGE","INT", "INT", "INT",)
+    RETURN_NAMES = ("image", "width", "height", "count",)
+    FUNCTION = "getsize"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Returns width, height and batch size of the image,  
+and passes it through unchanged.  
+
+"""
+
+    def getsize(self, image):
+        width = image.shape[2]
+        height = image.shape[1]
+        count = image.shape[0]
+        return {"ui": {
+            "text": [f"{count}x{width}x{height}"]}, 
+            "result": (image, width, height, count) 
+        }
+
+class GetLatentSizeAndCount:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "latent": ("LATENT",),
+        }}
+
+    RETURN_TYPES = ("LATENT","INT", "INT", "INT", "INT", "INT")
+    RETURN_NAMES = ("latent", "batch_size", "channels", "frames", "height", "width")
+    FUNCTION = "getsize"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Returns latent tensor dimensions,  
+and passes the latent through unchanged.  
+
+"""
+    def getsize(self, latent):
+        if len(latent["samples"].shape) == 5:
+            B, C, T, H, W = latent["samples"].shape
+        elif len(latent["samples"].shape) == 4:
+            B, C, H, W = latent["samples"].shape
+            T = 0
+        else:
+            raise ValueError("Invalid latent shape")
+
+        return {"ui": {
+            "text": [f"{B}x{C}x{T}x{H}x{W}"]}, 
+            "result": (latent, B, C, T, H, W) 
+        }
+
+class ImageBatchRepeatInterleaving:
+    RETURN_TYPES = ("IMAGE", "MASK",)
+    FUNCTION = "repeat"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Repeats each image in a batch by the specified number of times.  
+Example batch of 5 images: 0, 1 ,2, 3, 4  
+with repeats 2 becomes batch of 10 images: 0, 0, 1, 1, 2, 2, 3, 3, 4, 4  
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "images": ("IMAGE",),
+                 "repeats": ("INT", {"default": 1, "min": 1, "max": 4096}),
+            },
+            "optional": {
+                "mask": ("MASK",),
+            }
+        }
+
+    def repeat(self, images, repeats, mask=None):
+        original_count = images.shape[0]
+        total_count = original_count * repeats
+
+        repeated_images = torch.repeat_interleave(images, repeats=repeats, dim=0)
+        if mask is not None:
+            mask = torch.repeat_interleave(mask, repeats=repeats, dim=0)
+        else:
+            mask = torch.zeros((total_count, images.shape[1], images.shape[2]),
+                              device=images.device, dtype=images.dtype)
+            for i in range(original_count):
+                mask[i * repeats] = 1.0
+
+        return (repeated_images, mask)
+
+class ImageUpscaleWithModelBatched:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "upscale_model": ("UPSCALE_MODEL",),
+                              "images": ("IMAGE",),
+                              "per_batch": ("INT", {"default": 16, "min": 1, "max": 4096, "step": 1}),
+                              },
+                "optional": {
+                    "downscale_ratio": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 1.0, "step": 0.01}),
+                    "downscale_method": (["nearest-exact", "bilinear", "area", "bicubic", "lanczos"], {"default": "lanczos"}),
+                    "precision": (["float32", "float16", "bfloat16"], {"default": "float32"}),
+                }}
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "upscale"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Same as ComfyUI native model upscaling node,  
+but allows setting sub-batches for reduced VRAM usage.
+Optionally downscale the result with a ratio.
+"""
+    def upscale(self, upscale_model, images, per_batch, downscale_ratio=1.0, downscale_method="lanczos", precision="float32"):
+        dtype = torch.float16 if precision == "float16" else torch.bfloat16 if precision == "bfloat16" else torch.float32
+        device = model_management.get_torch_device()
+        upscale_model.to(device, dtype=dtype)
+        in_img = images.movedim(-1,-3).to(dtype)
+
+        steps = in_img.shape[0]
+        pbar = ProgressBar(steps)
+        t = []
+
+        for start_idx in range(0, in_img.shape[0], per_batch):
+            sub_images = upscale_model(in_img[start_idx:start_idx+per_batch].to(device))
+            t.append(sub_images.cpu())
+            # Calculate the number of images processed in this batch
+            batch_count = sub_images.shape[0]
+            # Update the progress bar by the number of images processed in this batch
+            pbar.update(batch_count)
+        upscale_model.cpu()
+
+        t = torch.cat(t, dim=0).permute(0, 2, 3, 1).cpu().float()
+
+        # Apply downscaling if ratio is less than 1.0
+        if downscale_ratio < 1.0:
+            original_height = t.shape[1]
+            original_width = t.shape[2]
+            new_height = int(original_height * downscale_ratio)
+            new_width = int(original_width * downscale_ratio)
+            t = t.movedim(-1, 1)
+            t = common_upscale(t, new_width, new_height, downscale_method, "disabled")
+            t = t.movedim(1, -1)
+
+        return (t,)
+
+class ImageNormalize_Neg1_To_1:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                              "images": ("IMAGE",),
+    
+                              }}
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "normalize"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Normalize the images to be in the range [-1, 1]  
+"""
+
+    def normalize(self,images):
+        images = images * 2.0 - 1.0
+        return (images,)
+
+class RemapImageRange:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+            "image": ("IMAGE",),
+            "min": ("FLOAT", {"default": 0.0,"min": -10.0, "max": 1.0, "step": 0.01}),
+            "max": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 10.0, "step": 0.01}),
+            "clamp": ("BOOLEAN", {"default": True}),
+            },
+            }
+    
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "remap"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Remaps the image values to the specified range. 
+"""
+        
+    def remap(self, image, min, max, clamp):
+        if image.dtype == torch.float16:
+            image = image.to(torch.float32)
+        image = min + image * (max - min)
+        if clamp:
+            image = torch.clamp(image, min=0.0, max=1.0)
+        return (image, )
+
+class SplitImageChannels:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+            "image": ("IMAGE",),
+            },
+            }
+
+    RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "MASK")
+    RETURN_NAMES = ("red", "green", "blue", "mask")
+    FUNCTION = "split"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Splits image channels into images where the selected channel  
+is repeated for all channels, and the alpha as a mask. 
+"""
+
+    def split(self, image):
+        red = image[:, :, :, 0:1] # Red channel
+        green = image[:, :, :, 1:2] # Green channel
+        blue = image[:, :, :, 2:3] # Blue channel
+        if image.shape[3] == 4:
+            alpha = image[:, :, :, 4] # Alpha channel
+        else:
+            alpha = torch.zeros(image.shape[0], image.shape[1], image.shape[2], device=image.device)
+
+        # Repeat the selected channel for all channels
+        red = torch.cat([red, red, red], dim=3)
+        green = torch.cat([green, green, green], dim=3)
+        blue = torch.cat([blue, blue, blue], dim=3)
+        return (red, green, blue, alpha)
+
+class MergeImageChannels:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+            "red": ("IMAGE",),
+            "green": ("IMAGE",),
+            "blue": ("IMAGE",),
+            
+            },
+            "optional": {
+                "alpha": ("MASK", {"default": None}),
+                },
+            }
+    
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = "merge"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Merges channel data into an image.  
+"""
+        
+    def merge(self, red, green, blue, alpha=None):
+        image = torch.stack([
+        red[..., 0, None], # Red channel
+        green[..., 1, None], # Green channel
+        blue[..., 2, None]   # Blue channel
+        ], dim=-1)
+        image = image.squeeze(-2)
+        if alpha is not None:
+            image = torch.cat([image, alpha.unsqueeze(-1)], dim=-1)
+        return (image,)
+
+class ImagePadForOutpaintMasked:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                "feathering": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+            },
+            "optional": {
+                "mask": ("MASK",),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK")
+    FUNCTION = "expand_image"
+
+    CATEGORY = "image"
+
+    def expand_image(self, image, left, top, right, bottom, feathering, mask=None):
+        if mask is not None:
+            if torch.allclose(mask, torch.zeros_like(mask)):
+                    logging.warning("The incoming mask is fully black. Handling it as None.")
+                    mask = None
+        B, H, W, C = image.size()
+
+        new_image = torch.ones(
+            (B, H + top + bottom, W + left + right, C),
+            dtype=torch.float32,
+        ) * 0.5
+
+        new_image[:, top:top + H, left:left + W, :] = image
+
+        if mask is None:
+            new_mask = torch.ones(
+                (B, H + top + bottom, W + left + right),
+                dtype=torch.float32,
+            )
+
+            t = torch.zeros(
+            (B, H, W),
+            dtype=torch.float32
+            )
+        else:
+            # If a mask is provided, pad it to fit the new image size
+            mask = F.pad(mask, (left, right, top, bottom), mode='constant', value=0)
+            mask = 1 - mask
+            t = torch.zeros_like(mask)
+        
+        if feathering > 0 and feathering * 2 < H and feathering * 2 < W:
+
+            for i in range(H):
+                for j in range(W):
+                    dt = i if top != 0 else H
+                    db = H - i if bottom != 0 else H
+
+                    dl = j if left != 0 else W
+                    dr = W - j if right != 0 else W
+
+                    d = min(dt, db, dl, dr)
+
+                    if d >= feathering:
+                        continue
+
+                    v = (feathering - d) / feathering
+
+                    if mask is None:
+                        t[:, i, j] = v * v
+                    else:
+                        t[:, top + i, left + j] = v * v
+        
+        if mask is None:
+            new_mask[:, top:top + H, left:left + W] = t
+            return (new_image, new_mask,)
+        else:
+            return (new_image, mask,)
+
+class ImagePadForOutpaintTargetSize:
+    upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "target_width": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                "target_height": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                "feathering": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                "upscale_method": (s.upscale_methods,),
+            },
+            "optional": {
+                "mask": ("MASK",),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK")
+    FUNCTION = "expand_image"
+
+    CATEGORY = "image"
+
+    def expand_image(self, image, target_width, target_height, feathering, upscale_method, mask=None):
+        B, H, W, C = image.size()
+        new_height = H
+        new_width = W
+         # Calculate the scaling factor while maintaining aspect ratio
+        scaling_factor = min(target_width / W, target_height / H)
+        
+        # Check if the image needs to be downscaled
+        if scaling_factor < 1:
+            image = image.movedim(-1,1)
+            # Calculate the new width and height after downscaling
+            new_width = int(W * scaling_factor)
+            new_height = int(H * scaling_factor)
+            
+            # Downscale the image
+            image_scaled = common_upscale(image, new_width, new_height, upscale_method, "disabled").movedim(1,-1)
+        else:
+            # If downscaling is not needed, use the original image dimensions
+            image_scaled = image
+
+        # Ensure mask dimensions match image dimensions
+        if mask is not None:
+            mask_scaled = mask.unsqueeze(0)  # Add an extra dimension for batch size
+            mask_scaled = F.interpolate(mask_scaled, size=(new_height, new_width), mode="nearest")
+            mask_scaled = mask_scaled.squeeze(0)  # Remove the extra dimension after interpolation
+        else:
+            mask_scaled = None
+
+        # Calculate how much padding is needed to reach the target dimensions
+        pad_top = max(0, (target_height - new_height) // 2)
+        pad_bottom = max(0, target_height - new_height - pad_top)
+        pad_left = max(0, (target_width - new_width) // 2)
+        pad_right = max(0, target_width - new_width - pad_left)
+
+        # Now call the original expand_image with the calculated padding
+        return ImagePadForOutpaintMasked.expand_image(self, image_scaled, pad_left, pad_top, pad_right, pad_bottom, feathering, mask_scaled)
+
+class ImagePrepForICLora:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "reference_image": ("IMAGE",),
+                "output_width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
+                "output_height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
+                "border_width": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 1}),
+            },
+            "optional": {
+                "latent_image": ("IMAGE",),
+                "latent_mask": ("MASK",),
+                "reference_mask": ("MASK",),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK")
+    FUNCTION = "expand_image"
+
+    CATEGORY = "image"
+
+    def expand_image(self, reference_image, output_width, output_height, border_width, latent_image=None, reference_mask=None, latent_mask=None):
+
+        if reference_mask is not None:
+            if torch.allclose(reference_mask, torch.zeros_like(reference_mask)):
+                    logging.warning("The incoming mask is fully black. Handling it as None.")
+                    reference_mask = None
+        image = reference_image
+        if latent_image is not None:
+            if image.shape[0] != latent_image.shape[0]:
+                image = image.repeat(latent_image.shape[0], 1, 1, 1)
+        B, H, W, C = image.size()
+
+        # Handle mask
+        if reference_mask is not None:
+            resized_mask = torch.nn.functional.interpolate(
+                reference_mask.unsqueeze(1), 
+                size=(H, W),
+                mode='nearest'
+            ).squeeze(1)
+            image = image * resized_mask.unsqueeze(-1)
+
+        # Calculate new width maintaining aspect ratio
+        new_width = int((W / H) * output_height)
+        
+        # Resize image to new height while maintaining aspect ratio
+        resized_image = common_upscale(image.movedim(-1,1), new_width, output_height, "lanczos", "disabled").movedim(1,-1)
+
+        # Create padded image
+        if latent_image is None:
+            pad_image = torch.zeros((B, output_height, output_width, C), device=image.device)
+        else:
+            resized_latent_image = common_upscale(latent_image.movedim(-1,1), output_width, output_height, "lanczos", "disabled").movedim(1,-1)
+            pad_image = resized_latent_image
+            if latent_mask is not None:
+                resized_latent_mask = torch.nn.functional.interpolate(
+                    latent_mask.unsqueeze(1), 
+                    size=(pad_image.shape[1], pad_image.shape[2]), 
+                    mode='nearest'
+                ).squeeze(1)
+
+        if border_width > 0:
+            border = torch.zeros((B, output_height, border_width, C), device=image.device)
+            padded_image = torch.cat((resized_image, border, pad_image), dim=2)
+            if latent_mask is not None:
+                padded_mask = torch.zeros((B, padded_image.shape[1], padded_image.shape[2]), device=image.device)
+                padded_mask[:, :, (new_width + border_width):] = resized_latent_mask
+            else:
+                padded_mask = torch.ones((B, padded_image.shape[1], padded_image.shape[2]), device=image.device)
+                padded_mask[:, :, :new_width + border_width] = 0
+        else:
+            padded_image = torch.cat((resized_image, pad_image), dim=2)
+            if latent_mask is not None:
+                padded_mask = torch.zeros((B, padded_image.shape[1], padded_image.shape[2]), device=image.device)
+                padded_mask[:, :, new_width:] = resized_latent_mask
+            else:
+                padded_mask = torch.ones((B, padded_image.shape[1], padded_image.shape[2]), device=image.device)
+                padded_mask[:, :, :new_width] = 0
+
+        return (padded_image, padded_mask)
+
+
+class ImageAndMaskPreview(SaveImage):
+    def __init__(self):
+        self.output_dir = folder_paths.get_temp_directory()
+        self.type = "temp"
+        self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
+        self.compress_level = 4
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "mask_opacity": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "mask_color": ("STRING", {"default": "255, 255, 255", "tooltip": "RGB (255,255,255) or RGBA (255,255,255,128) or Hex (#RRGGBB / #RRGGBBAA)"}),
+                "pass_through": ("BOOLEAN", {"default": False}),
+             },
+            "optional": {
+                "image": ("IMAGE",),
+                "mask": ("MASK",),
+            },
+            "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+        }
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("composite",)
+    FUNCTION = "execute"
+    CATEGORY = "KJNodes/masking"
+    DESCRIPTION = """
+Preview an image or a mask, when both inputs are used  
+composites the mask on top of the image.
+with pass_through on the preview is disabled and the  
+composite is returned from the composite slot instead,  
+this allows for the preview to be passed for video combine  
+nodes for example. Supports RGBA for mask_color to adjust transparency per color.  
+"""
+
+    def execute(self, mask_opacity, mask_color, pass_through, filename_prefix="ComfyUI", image=None, mask=None, prompt=None, extra_pnginfo=None):
+        if mask is not None and image is None:
+            preview = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
+        elif mask is None and image is not None:
+            preview = image
+        elif mask is not None and image is not None:
+            mask_adjusted = mask * mask_opacity
+            mask_image = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3).clone()
+
+            # Use helper function to parse color string
+            color_list = string_to_color(mask_color)
+
+            # Apply RGB channels
+            mask_image[:, :, :, 0] = color_list[0] / 255 # Red channel
+            mask_image[:, :, :, 1] = color_list[1] / 255 # Green channel
+            mask_image[:, :, :, 2] = color_list[2] / 255 # Blue channel
+
+            if len(color_list) == 4: # Apply Alpha channel if present
+                alpha_factor = color_list[3] / 255.0
+                mask_adjusted = mask_adjusted * alpha_factor
+
+            destination, source = node_helpers.image_alpha_fix(image, mask_image)
+            destination = destination.clone().movedim(-1, 1)
+            preview = composite(destination, source.movedim(-1, 1), 0, 0, mask_adjusted, 1, True).movedim(1, -1)
+        if pass_through:
+            return (preview, )
+        return(self.save_images(preview, filename_prefix, prompt, extra_pnginfo))
+
+def crossfade(images_1, images_2, alpha):
+    crossfade = (1 - alpha) * images_1 + alpha * images_2
+    return crossfade
+def ease_in(t):
+    return t * t
+def ease_out(t):
+    return 1 - (1 - t) * (1 - t)
+def ease_in_out(t):
+    return 3 * t * t - 2 * t * t * t
+def bounce(t):
+    if t < 0.5:
+        return ease_out(t * 2) * 0.5
+    else:
+        return ease_in((t - 0.5) * 2) * 0.5 + 0.5
+def elastic(t):
+    return math.sin(13 * math.pi / 2 * t) * math.pow(2, 10 * (t - 1))
+def glitchy(t):
+    return t + 0.1 * math.sin(40 * t)
+def exponential_ease_out(t):
+    return 1 - (1 - t) ** 4
+
+easing_functions = {
+    "linear": lambda t: t,
+    "ease_in": ease_in,
+    "ease_out": ease_out,
+    "ease_in_out": ease_in_out,
+    "bounce": bounce,
+    "elastic": elastic,
+    "glitchy": glitchy,
+    "exponential_ease_out": exponential_ease_out,
+}
+
+class CrossFadeImages:
+    
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "crossfadeimages"
+    CATEGORY = "KJNodes/image"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "images_1": ("IMAGE",),
+                 "images_2": ("IMAGE",),
+                 "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],),
+                 "transition_start_index": ("INT", {"default": 1,"min": -4096, "max": 4096, "step": 1}),
+                 "transitioning_frames": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
+                 "start_level": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 1.0, "step": 0.01}),
+                 "end_level": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 1.0, "step": 0.01}),
+        },
+    } 
+    
+    def crossfadeimages(self, images_1, images_2, transition_start_index, transitioning_frames, interpolation, start_level, end_level):
+
+        crossfade_images = []
+
+        if transition_start_index < 0:
+            transition_start_index = len(images_1) + transition_start_index
+            if transition_start_index < 0:
+                raise ValueError("Transition start index is out of range for images_1.")
+            
+        transitioning_frames = min(transitioning_frames, len(images_1) - transition_start_index, len(images_2))
+
+        alphas = torch.linspace(start_level, end_level, transitioning_frames)
+        for i in range(transitioning_frames):
+            alpha = alphas[i]
+            image1 = images_1[transition_start_index + i]
+            image2 = images_2[i]
+            easing_function = easing_functions.get(interpolation)
+            alpha = easing_function(alpha)  # Apply the easing function to the alpha value
+
+            crossfade_image = crossfade(image1, image2, alpha)
+            crossfade_images.append(crossfade_image)
+
+        # Convert crossfade_images to tensor
+        crossfade_images = torch.stack(crossfade_images, dim=0)
+
+        # Append the beginning of images_1 (before the transition)
+        beginning_images_1 = images_1[:transition_start_index]
+        crossfade_images = torch.cat([beginning_images_1, crossfade_images], dim=0)
+
+        # Append the remaining frames of images_2 (after the transition)
+        remaining_images_2 = images_2[transitioning_frames:]
+        if len(remaining_images_2) > 0:
+            crossfade_images = torch.cat([crossfade_images, remaining_images_2], dim=0)
+
+        return (crossfade_images, )
+    
+class CrossFadeImagesMulti:
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "crossfadeimages"
+    CATEGORY = "KJNodes/image"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}),
+                 "image_1": ("IMAGE",),
+                 "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],),
+                 "transitioning_frames": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
+        },
+        "optional": {
+            "image_2": ("IMAGE",),
+        }
+    } 
+    
+    def crossfadeimages(self, inputcount, transitioning_frames, interpolation, **kwargs):
+
+        image_1 = kwargs["image_1"]
+        first_image_shape = image_1.shape
+        first_image_device = image_1.device
+        height = image_1.shape[1]
+        width = image_1.shape[2]
+
+        easing_function = easing_functions[interpolation]
+       
+        for c in range(1, inputcount):
+            frames = []
+            new_image = kwargs.get(f"image_{c + 1}", torch.zeros(first_image_shape)).to(first_image_device)
+            new_image_height = new_image.shape[1]
+            new_image_width = new_image.shape[2]
+
+            if new_image_height != height or new_image_width != width:
+                new_image = common_upscale(new_image.movedim(-1, 1), width, height, "lanczos", "disabled")
+                new_image = new_image.movedim(1, -1)  # Move channels back to the last dimension
+
+            last_frame_image_1 = image_1[-1]
+            first_frame_image_2 = new_image[0]
+
+            for frame in range(transitioning_frames):
+                t = frame / (transitioning_frames - 1)
+                alpha = easing_function(t)
+                alpha_tensor = torch.tensor(alpha, dtype=last_frame_image_1.dtype, device=last_frame_image_1.device)
+                frame_image = crossfade(last_frame_image_1, first_frame_image_2, alpha_tensor)
+                frames.append(frame_image)
+        
+            frames = torch.stack(frames)
+            image_1 = torch.cat((image_1, frames, new_image), dim=0)
+        
+        return image_1,
+
+def transition_images(images_1, images_2, alpha, transition_type, blur_radius, reverse):        
+    width = images_1.shape[1]
+    height = images_1.shape[0]
+
+    mask = torch.zeros_like(images_1, device=images_1.device)
+    
+    alpha = alpha.item()
+    if reverse:
+        alpha = 1 - alpha
+
+    #transitions from matteo's essential nodes
+    if "horizontal slide" in transition_type:
+        pos = round(width * alpha)
+        mask[:, :pos, :] = 1.0
+    elif "vertical slide" in transition_type:
+        pos = round(height * alpha)
+        mask[:pos, :, :] = 1.0
+    elif "box" in transition_type:
+        box_w = round(width * alpha)
+        box_h = round(height * alpha)
+        x1 = (width - box_w) // 2
+        y1 = (height - box_h) // 2
+        x2 = x1 + box_w
+        y2 = y1 + box_h
+        mask[y1:y2, x1:x2, :] = 1.0
+    elif "circle" in transition_type:
+        radius = math.ceil(math.sqrt(pow(width, 2) + pow(height, 2)) * alpha / 2)
+        c_x = width // 2
+        c_y = height // 2
+        x = torch.arange(0, width, dtype=torch.float32, device="cpu")
+        y = torch.arange(0, height, dtype=torch.float32, device="cpu")
+        y, x = torch.meshgrid((y, x), indexing="ij")
+        circle = ((x - c_x) ** 2 + (y - c_y) ** 2) <= (radius ** 2)
+        mask[circle] = 1.0
+    elif "horizontal door" in transition_type:
+        bar = math.ceil(height * alpha / 2)
+        if bar > 0:
+            mask[:bar, :, :] = 1.0
+            mask[-bar:,:, :] = 1.0
+    elif "vertical door" in transition_type:
+        bar = math.ceil(width * alpha / 2)
+        if bar > 0:
+            mask[:, :bar,:] = 1.0
+            mask[:, -bar:,:] = 1.0
+    elif "fade" in transition_type:
+        mask[:, :, :] = alpha
+
+    mask = gaussian_blur(mask, blur_radius)
+
+    return images_1 * (1 - mask) + images_2 * mask
+
+def gaussian_blur(mask, blur_radius):
+    if blur_radius > 0:
+        kernel_size = int(blur_radius * 2) + 1
+        if kernel_size % 2 == 0:
+            kernel_size += 1  # Ensure kernel size is odd
+        sigma = blur_radius / 3
+        x = torch.arange(-kernel_size // 2 + 1, kernel_size // 2 + 1, dtype=torch.float32)
+        x = torch.exp(-0.5 * (x / sigma) ** 2)
+        kernel1d = x / x.sum()
+        kernel2d = kernel1d[:, None] * kernel1d[None, :]
+        kernel2d = kernel2d.to(mask.device)
+        kernel2d = kernel2d.expand(mask.shape[2], 1, kernel2d.shape[0], kernel2d.shape[1])
+        mask = mask.permute(2, 0, 1).unsqueeze(0)  # Change to [C, H, W] and add batch dimension
+        mask = F.conv2d(mask, kernel2d, padding=kernel_size // 2, groups=mask.shape[1])
+        mask = mask.squeeze(0).permute(1, 2, 0)  # Change back to [H, W, C]
+    return mask
+
+class TransitionImagesMulti:
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "transition"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Creates transitions between images.
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}),
+                 "image_1": ("IMAGE",),
+                 "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],),
+                 "transition_type": (["horizontal slide", "vertical slide", "box", "circle", "horizontal door", "vertical door", "fade"],),
+                 "transitioning_frames": ("INT", {"default": 2,"min": 2, "max": 4096, "step": 1}),
+                 "blur_radius": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 100.0, "step": 0.1}),
+                 "reverse": ("BOOLEAN", {"default": False}),
+                 "device": (["CPU", "GPU"], {"default": "CPU"}),
+            },
+            "optional": {
+                "image_2": ("IMAGE",),
+            }
+    } 
+
+    def transition(self, inputcount, transitioning_frames, transition_type, interpolation, device, blur_radius, reverse, **kwargs):
+
+        gpu = model_management.get_torch_device()
+
+        image_1 = kwargs["image_1"]
+        height = image_1.shape[1]
+        width = image_1.shape[2]
+        first_image_shape = image_1.shape
+        first_image_device = image_1.device
+
+        easing_function = easing_functions[interpolation]
+    
+        for c in range(1, inputcount):
+            frames = []
+            new_image = kwargs.get(f"image_{c + 1}", torch.zeros(first_image_shape)).to(first_image_device)
+            new_image_height = new_image.shape[1]
+            new_image_width = new_image.shape[2]
+
+            if new_image_height != height or new_image_width != width:
+                new_image = common_upscale(new_image.movedim(-1, 1), width, height, "lanczos", "disabled")
+                new_image = new_image.movedim(1, -1)  # Move channels back to the last dimension
+
+            last_frame_image_1 = image_1[-1]
+            first_frame_image_2 = new_image[0]
+            if device == "GPU":
+                last_frame_image_1 = last_frame_image_1.to(gpu)
+                first_frame_image_2 = first_frame_image_2.to(gpu)
+
+            if reverse:
+                last_frame_image_1, first_frame_image_2 = first_frame_image_2, last_frame_image_1
+
+            for frame in range(transitioning_frames):
+                t = frame / (transitioning_frames - 1)
+                alpha = easing_function(t)
+                alpha_tensor = torch.tensor(alpha, dtype=last_frame_image_1.dtype, device=last_frame_image_1.device)
+                frame_image = transition_images(last_frame_image_1, first_frame_image_2, alpha_tensor, transition_type, blur_radius, reverse)
+                frames.append(frame_image)
+        
+            frames = torch.stack(frames).cpu()
+            image_1 = torch.cat((image_1, frames, new_image), dim=0)
+        
+        return image_1.cpu(),
+
+class TransitionImagesInBatch:
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "transition"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Creates transitions between images in a batch.
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "images": ("IMAGE",),
+                 "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],),
+                 "transition_type": (["horizontal slide", "vertical slide", "box", "circle", "horizontal door", "vertical door", "fade"],),
+                 "transitioning_frames": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
+                 "blur_radius": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 100.0, "step": 0.1}),
+                 "reverse": ("BOOLEAN", {"default": False}),
+                 "device": (["CPU", "GPU"], {"default": "CPU"}),
+        },
+    } 
+
+    #transitions from matteo's essential nodes
+    def transition(self, images, transitioning_frames, transition_type, interpolation, device, blur_radius, reverse):
+        if images.shape[0] == 1:
+            return images,
+
+        gpu = model_management.get_torch_device()
+
+        easing_function = easing_functions[interpolation]
+        
+        images_list = []
+        pbar = ProgressBar(images.shape[0] - 1)
+        for i in range(images.shape[0] - 1):
+            frames = []
+            image_1 = images[i]
+            image_2 = images[i + 1]
+
+            if device == "GPU":
+                image_1 = image_1.to(gpu)
+                image_2 = image_2.to(gpu)
+
+            if reverse:
+                image_1, image_2 = image_2, image_1
+                
+            for frame in range(transitioning_frames):
+                t = frame / (transitioning_frames - 1)
+                alpha = easing_function(t)
+                alpha_tensor = torch.tensor(alpha, dtype=image_1.dtype, device=image_1.device)
+                frame_image = transition_images(image_1, image_2, alpha_tensor, transition_type, blur_radius, reverse)
+                frames.append(frame_image)
+            pbar.update(1)
+        
+            frames = torch.stack(frames).cpu()
+            images_list.append(frames)
+        images = torch.cat(images_list, dim=0)
+        
+        return images.cpu(),
+
+class ImageBatchJoinWithTransition:
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "transition_batches"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Transitions between two batches of images, starting at a specified index in the first batch.
+During the transition, frames from both batches are blended frame-by-frame, so the video keeps playing.
+"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images_1": ("IMAGE",),
+                "images_2": ("IMAGE",),
+                "start_index": ("INT", {"default": 0, "min": -10000, "max": 10000, "step": 1}),
+                "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],),
+                "transition_type": (["horizontal slide", "vertical slide", "box", "circle", "horizontal door", "vertical door", "fade"],),
+                "transitioning_frames": ("INT", {"default": 1, "min": 1, "max": 4096, "step": 1}),
+                "blur_radius": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.1}),
+                "reverse": ("BOOLEAN", {"default": False}),
+                "device": (["CPU", "GPU"], {"default": "CPU"}),
+            },
+        }
+
+    def transition_batches(self, images_1, images_2, start_index, interpolation, transition_type, transitioning_frames, blur_radius, reverse, device):
+        if images_1.shape[0] == 0 or images_2.shape[0] == 0:
+            raise ValueError("Both input batches must have at least one image.")
+        
+        if start_index < 0:
+            start_index = images_1.shape[0] + start_index
+        if start_index < 0 or start_index > images_1.shape[0]:
+            raise ValueError("start_index is out of range.")
+
+        gpu = model_management.get_torch_device()
+        easing_function = easing_functions[interpolation]
+        out_frames = []
+
+        # Add images from images_1 up to start_index
+        if start_index > 0:
+            out_frames.append(images_1[:start_index])
+
+        # Determine how many frames we can blend
+        max_transition = min(transitioning_frames, images_1.shape[0] - start_index, images_2.shape[0])
+
+        # Blend corresponding frames from both batches
+        for i in range(max_transition):
+            img1 = images_1[start_index + i]
+            img2 = images_2[i]
+            if device == "GPU":
+                img1 = img1.to(gpu)
+                img2 = img2.to(gpu)
+            if reverse:
+                img1, img2 = img2, img1
+            t = i / (max_transition - 1) if max_transition > 1 else 1.0
+            alpha = easing_function(t)
+            alpha_tensor = torch.tensor(alpha, dtype=img1.dtype, device=img1.device)
+            frame_image = transition_images(img1, img2, alpha_tensor, transition_type, blur_radius, reverse)
+            out_frames.append(frame_image.cpu().unsqueeze(0))
+
+        # Add remaining images from images_2 after transition
+        if images_2.shape[0] > max_transition:
+            out_frames.append(images_2[max_transition:])
+
+        # Concatenate all frames
+        out = torch.cat(out_frames, dim=0)
+        return (out.cpu(),)
+
+class ShuffleImageBatch:
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "shuffle"
+    CATEGORY = "KJNodes/image"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "images": ("IMAGE",),
+                 "seed": ("INT", {"default": 123,"min": 0, "max": 0xffffffffffffffff, "step": 1}),
+        },
+    } 
+
+    def shuffle(self, images, seed):
+        torch.manual_seed(seed)
+        B, H, W, C = images.shape
+        indices = torch.randperm(B)
+        shuffled_images = images[indices]
+
+        return shuffled_images,
+
+class GetImageRangeFromBatch:
+    
+    RETURN_TYPES = ("IMAGE", "MASK", )
+    FUNCTION = "imagesfrombatch"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Returns a range of images from a batch.
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "start_index": ("INT", {"default": 0,"min": -1, "max": 4096, "step": 1}),
+                 "num_frames": ("INT", {"default": 1,"min": 1, "max": 4096, "step": 1}),
+        },
+        "optional": {
+            "images": ("IMAGE",),
+            "masks": ("MASK",),
+        }
+    } 
+    
+    def imagesfrombatch(self, start_index, num_frames, images=None, masks=None):
+        chosen_images = None
+        chosen_masks = None
+
+        # Process images if provided
+        if images is not None:
+            if start_index == -1:
+                start_index = max(0, len(images) - num_frames)
+            if start_index < 0 or start_index >= len(images):
+                raise ValueError("Start index is out of range")
+            end_index = min(start_index + num_frames, len(images))
+            chosen_images = images[start_index:end_index]
+
+        # Process masks if provided
+        if masks is not None:
+            if start_index == -1:
+                start_index = max(0, len(masks) - num_frames)
+            if start_index < 0 or start_index >= len(masks):
+                raise ValueError("Start index is out of range for masks")
+            end_index = min(start_index + num_frames, len(masks))
+            chosen_masks = masks[start_index:end_index]
+
+        return (chosen_images, chosen_masks,)
+
+class RandomImageFromBatch(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        template = io.MatchType.Template("input_type", [io.Image, io.Mask])
+        return io.Schema(
+            node_id="RandomImageFromBatch",
+            display_name="Random Image From Batch",
+            search_aliases=["random", "mask", "sequence", "frame"],
+            category="KJNodes/image",
+            description="Picks a sequence of frames from an image or mask batch within a selected index range. "
+                        "At randomness=0 the picks are evenly spaced across the range; at randomness=1 they are "
+                        "uniformly random without replacement; values in between blend linearly. "
+                        "Output is always sorted by batch index. Negative indices count from the end (-1 = last).",
+            inputs=[
+                io.MatchType.Input("input", template=template,
+                                   tooltip="Image or mask batch to sample from."),
+                io.Int.Input("start_index", default=0, min=-4096, max=4096,
+                             tooltip="Inclusive start of the sampling range. Negative values count from the end."),
+                io.Int.Input("end_index", default=-1, min=-4096, max=4096,
+                             tooltip="Inclusive end of the sampling range. -1 means the last frame."),
+                io.Int.Input("num_frames", default=1, min=1, max=4096,
+                             tooltip="How many frames to pick from the range."),
+                io.Float.Input("randomness", default=1.0, min=0.0, max=1.0, step=0.01,
+                               tooltip="0 = evenly spaced across the range, 1 = uniformly random without replacement, "
+                                       "in-between = linear blend (jittered even spacing)."),
+                io.Int.Input("min_distance", default=0, min=0, max=4096,
+                             tooltip="Minimum gap (in frames) between consecutive picks. 0 = no minimum. "
+                                     "Picks are pushed forward to satisfy this; later picks may clamp to the range end."),
+                io.Int.Input("max_distance", default=0, min=0, max=4096,
+                             tooltip="Maximum gap (in frames) between consecutive picks. 0 = no maximum. "
+                                     "Picks are pulled in to satisfy this, which may compress the sequence toward the start."),
+                io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff, step=1,
+                             tooltip="Random seed for reproducible sampling. Ignored when randomness is 0."),
+            ],
+            outputs=[
+                io.MatchType.Output(template=template, display_name="output"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, input, start_index, end_index, num_frames, randomness, min_distance, max_distance, seed) -> io.NodeOutput:
+        n = input.shape[0]
+        if n == 0:
+            raise ValueError("Input batch is empty.")
+
+        s = start_index if start_index >= 0 else n + start_index
+        e = end_index if end_index >= 0 else n + end_index
+        s = max(0, min(s, n - 1))
+        e = max(0, min(e, n - 1))
+        if e < s:
+            s, e = e, s
+        range_size = e - s + 1
+
+        if num_frames == 1:
+            even = [(s + e) / 2]
+        else:
+            even = [s + i * (e - s) / (num_frames - 1) for i in range(num_frames)]
+
+        if randomness <= 0:
+            picks_float = even
+        else:
+            rng = random.Random(seed)
+            if num_frames <= range_size:
+                random_picks = rng.sample(range(s, e + 1), num_frames)
+            else:
+                random_picks = [rng.randint(s, e) for _ in range(num_frames)]
+            random_picks.sort()
+            picks_float = [(1 - randomness) * ev + randomness * rp for ev, rp in zip(even, random_picks)]
+
+        picks = sorted(max(s, min(e, int(round(p)))) for p in picks_float)
+
+        if num_frames > 1 and (min_distance > 0 or max_distance > 0):
+            adjusted = [picks[0]]
+            for i in range(1, len(picks)):
+                prev = adjusted[-1]
+                target = picks[i]
+                if min_distance > 0 and target - prev < min_distance:
+                    target = prev + min_distance
+                if max_distance > 0 and target - prev > max_distance:
+                    target = prev + max_distance
+                adjusted.append(min(e, max(s, target)))
+            picks = adjusted
+
+        idx = torch.tensor(picks, dtype=torch.long, device=input.device)
+        chosen = input.index_select(0, idx)
+
+        return io.NodeOutput(chosen)
+
+class ImageBatchExtendWithOverlap:
+
+    RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", )
+    RETURN_NAMES = ("source_images", "start_images", "extended_images")
+    OUTPUT_TOOLTIPS = (
+        "The original source images (passthrough)",
+        "The input images used as the starting point for extension",
+        "The extended images with overlap, if no new images are provided this will be empty",
+    )
+    FUNCTION = "imagesfrombatch"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Helper node for video generation extension   
+First input source and overlap amount to get the starting frames for the extension.  
+Then on another copy of the node provide the newly generated frames and choose how to overlap them.
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "source_images": ("IMAGE", {"tooltip": "The source images to extend"}),
+                "overlap": ("INT", {"default": 13,"min": 1, "max": 4096, "step": 1, "tooltip": "Number of overlapping frames between source and new images"}),
+                "overlap_side": (["source", "new_images"], {"default": "source", "tooltip": "Which side to overlap on"}),
+                "overlap_mode": (["cut", "linear_blend", "ease_in_out", "filmic_crossfade", "perceptual_crossfade"], {"default": "linear_blend", "tooltip": "Method to use for overlapping frames"}),
+        },
+        "optional": {
+            "new_images": ("IMAGE", {"tooltip": "The new images to extend with"}),
+        }
+    }
+
+    def imagesfrombatch(self, source_images, overlap, overlap_side, overlap_mode, new_images=None):
+        if overlap > len(source_images):
+            return source_images, source_images, source_images
+
+        if new_images is not None:
+            if source_images.shape[1:3] != new_images.shape[1:3]:
+                raise ValueError(f"Source and new images must have the same shape: {source_images.shape[1:3]} vs {new_images.shape[1:3]}")
+            # Determine where to place the overlap
+            prefix = source_images[:-overlap]
+            if overlap_side == "source":
+                blend_src = source_images[-overlap:]
+                blend_dst = new_images[:overlap]
+            elif overlap_side == "new_images":
+                blend_src = new_images[:overlap]
+                blend_dst = source_images[-overlap:]
+            suffix = new_images[overlap:]
+
+            if overlap_mode == "linear_blend":
+                # Vectorized version - process all frames at once
+                alpha = torch.linspace(0, 1, overlap + 2, device=blend_src.device, dtype=blend_src.dtype)[1:-1]
+                alpha = alpha.view(-1, 1, 1, 1)  # Shape: [overlap, 1, 1, 1]
+                blended_images = (1 - alpha) * blend_src + alpha * blend_dst
+                extended_images = torch.cat((prefix, blended_images, suffix), dim=0)
+
+            elif overlap_mode == "filmic_crossfade":
+                gamma = 2.2
+                alpha = torch.linspace(0, 1, overlap + 2, device=blend_src.device, dtype=blend_src.dtype)[1:-1]
+                alpha = alpha.view(-1, 1, 1, 1)
+                linear_src = torch.pow(blend_src, gamma)
+                linear_dst = torch.pow(blend_dst, gamma)
+                blended = (1 - alpha) * linear_src + alpha * linear_dst
+                blended_images = torch.pow(blended, 1.0 / gamma)
+                extended_images = torch.cat((prefix, blended_images, suffix), dim=0)
+
+            elif overlap_mode == "perceptual_crossfade":
+                import kornia
+                alpha = torch.linspace(0, 1, overlap + 2, device=blend_src.device, dtype=blend_src.dtype)[1:-1]
+
+                src_nchw = blend_src.movedim(-1, 1)
+                dst_nchw = blend_dst.movedim(-1, 1)
+                lab_src = kornia.color.rgb_to_lab(src_nchw)
+                lab_dst = kornia.color.rgb_to_lab(dst_nchw)
+
+                # Blend in LAB space
+                alpha = alpha.view(-1, 1, 1, 1)  # [N,1,1,1] for broadcasting
+                blended_lab = (1 - alpha) * lab_src + alpha * lab_dst
+
+                # Convert back to RGB and reshape
+                blended_rgb = kornia.color.lab_to_rgb(blended_lab)
+                blended_images = blended_rgb.movedim(1, -1)  # [N,C,H,W] -> [N,H,W,C]
+                extended_images = torch.cat((prefix, blended_images, suffix), dim=0)
+
+            elif overlap_mode == "ease_in_out":
+                # Vectorized ease_in_out
+                t = torch.linspace(0, 1, overlap + 2, device=blend_src.device, dtype=blend_src.dtype)[1:-1]
+                eased_t = 3 * t * t - 2 * t * t * t  # ease_in_out formula
+                eased_t = eased_t.view(-1, 1, 1, 1)
+                blended_images = (1 - eased_t) * blend_src + eased_t * blend_dst
+                extended_images = torch.cat((prefix, blended_images, suffix), dim=0)
+
+            elif overlap_mode == "cut":
+                extended_images = torch.cat((prefix, suffix), dim=0)
+                if overlap_side == "new_images":
+                    extended_images = torch.cat((source_images, new_images[overlap:]), dim=0)
+                elif overlap_side == "source":
+                    extended_images = torch.cat((source_images[:-overlap], new_images), dim=0)
+        else:
+            extended_images = torch.zeros((1, 64, 64, 3), device="cpu")
+
+        start_images = source_images[-overlap:]
+
+        return (source_images, start_images, extended_images)
+
+class GetLatentRangeFromBatch:
+    
+    RETURN_TYPES = ("LATENT", )
+    FUNCTION = "latentsfrombatch"
+    CATEGORY = "KJNodes/latents"
+    DESCRIPTION = """
+Returns a range of latents from a batch.
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "latents": ("LATENT",),
+                "start_index": ("INT", {"default": 0,"min": -1, "max": 4096, "step": 1}),
+                "num_frames": ("INT", {"default": 1,"min": -1, "max": 4096, "step": 1}),
+        },
+    } 
+    
+    def latentsfrombatch(self, latents, start_index, num_frames):
+        chosen_latents = None
+        samples = latents["samples"]
+        if len(samples.shape) == 4:
+            B, C, H, W = samples.shape
+            num_latents = B
+        elif len(samples.shape) == 5:
+            B, C, T, H, W = samples.shape
+            num_latents = T
+
+        if start_index == -1:
+            start_index = max(0, num_latents - num_frames)
+        if start_index < 0 or start_index >= num_latents:
+            raise ValueError("Start index is out of range")
+        
+        end_index = num_latents if num_frames == -1 else min(start_index + num_frames, num_latents)
+        
+        if len(samples.shape) == 4:
+            chosen_latents = samples[start_index:end_index]
+        elif len(samples.shape) == 5:
+            chosen_latents = samples[:, :, start_index:end_index]
+
+        return ({"samples": chosen_latents.contiguous(),},)
+    
+class InsertLatentToIndex:
+    
+    RETURN_TYPES = ("LATENT", )
+    FUNCTION = "insert"
+    CATEGORY = "KJNodes/latents"
+    DESCRIPTION = """
+Inserts a latent at the specified index into the original latent batch.
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "source": ("LATENT",),
+                "destination": ("LATENT",),
+                "index": ("INT", {"default": 0,"min": -1, "max": 4096, "step": 1}),
+        },
+    } 
+    
+    def insert(self, source, destination, index):
+        samples_destination = destination["samples"]
+        samples_source = source["samples"].to(samples_destination)
+        
+        if len(samples_source.shape) == 4:
+            B, C, H, W = samples_source.shape
+            num_latents = B
+        elif len(samples_source.shape) == 5:
+            B, C, T, H, W = samples_source.shape
+            num_latents = T
+        
+        if index >= num_latents or index < 0:
+            raise ValueError(f"Index {index} out of bounds for tensor with {num_latents} latents")
+        
+        if len(samples_source.shape) == 4:
+            joined_latents = torch.cat([
+                samples_destination[:index],
+                samples_source,
+                samples_destination[index+1:]
+            ], dim=0)
+        else:
+            joined_latents = torch.cat([
+                samples_destination[:, :, :index],
+                samples_source,
+                samples_destination[:, :, index+1:]
+            ], dim=2)
+
+        return ({"samples": joined_latents,},)
+
+class ImageBatchFilter:
+    
+    RETURN_TYPES = ("IMAGE", "STRING",)
+    RETURN_NAMES = ("images", "removed_indices",)
+    FUNCTION = "filter"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = "Removes empty images from a batch"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "images": ("IMAGE",),
+                 "empty_color": ("STRING", {"default": "0, 0, 0"}),
+                "empty_threshold": ("FLOAT", {"default": 0.01,"min": 0.0, "max": 1.0, "step": 0.01}),
+        },
+        "optional": {
+            "replacement_image": ("IMAGE",),
+        }
+    } 
+    
+    def filter(self, images, empty_color, empty_threshold, replacement_image=None):
+        B, H, W, C = images.shape
+
+        input_images = images.clone()
+
+        empty_color_list = [int(color.strip()) for color in empty_color.split(',')]
+        empty_color_tensor = torch.tensor(empty_color_list, dtype=torch.float32).to(input_images.device)
+        
+        color_diff = torch.abs(input_images - empty_color_tensor)
+        mean_diff = color_diff.mean(dim=(1, 2, 3))
+
+        empty_indices = mean_diff <= empty_threshold
+        empty_indices_string = ', '.join([str(i) for i in range(B) if empty_indices[i]])
+        
+        if replacement_image is not None:
+            B_rep, H_rep, W_rep, C_rep = replacement_image.shape
+            replacement = replacement_image.clone()
+            if (H_rep != images.shape[1]) or (W_rep != images.shape[2]) or (C_rep != images.shape[3]):
+                replacement = common_upscale(replacement.movedim(-1, 1), W, H, "lanczos", "center").movedim(1, -1)
+            input_images[empty_indices] = replacement[0]
+
+            return (input_images, empty_indices_string,) 
+        else:
+            non_empty_images = input_images[~empty_indices]
+            return (non_empty_images, empty_indices_string,)
+    
+class GetImagesFromBatchIndexed:
+    
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "indexedimagesfrombatch"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Selects and returns the images at the specified indices as an image batch.
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "images": ("IMAGE",),
+                 "indexes": ("STRING", {"default": "0, 1, 2", "multiline": True}),
+        },
+    } 
+    
+    def indexedimagesfrombatch(self, images, indexes):
+        
+        # Parse the indexes string into a list of integers
+        index_list = [int(index.strip()) for index in indexes.split(',')]
+        
+        # Convert list of indices to a PyTorch tensor
+        indices_tensor = torch.tensor(index_list, dtype=torch.long)
+        
+        # Select the images at the specified indices
+        chosen_images = images[indices_tensor]
+        
+        return (chosen_images,)
+
+class InsertImagesToBatchIndexed:
+    
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "insertimagesfrombatch"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Inserts images at the specified indices into the original image batch.
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "original_images": ("IMAGE",),
+                "images_to_insert": ("IMAGE",),
+                "indexes": ("STRING", {"default": "0, 1, 2", "multiline": True}),
+            },
+            "optional": {
+                "mode": (["replace", "insert"],),
+            }
+        }
+    
+    def insertimagesfrombatch(self, original_images, images_to_insert, indexes, mode="replace"):
+        if indexes == "":
+            return (original_images,)
+
+        input_images = original_images.clone()
+        
+        # Parse the indexes string into a list of integers
+        index_list = [int(index.strip()) for index in indexes.split(',')]
+        
+        # Convert list of indices to a PyTorch tensor
+        indices_tensor = torch.tensor(index_list, dtype=torch.long)
+        
+        # Ensure the images_to_insert is a tensor
+        if not isinstance(images_to_insert, torch.Tensor):
+            images_to_insert = torch.tensor(images_to_insert)
+        
+        if mode == "replace":
+            # Replace the images at the specified indices
+            for index, image in zip(indices_tensor, images_to_insert):
+                input_images[index] = image
+        else:
+            # Create a list to hold the new image sequence
+            new_images = []
+            insert_offset = 0
+            
+            for i in range(len(input_images) + len(indices_tensor)):
+                if insert_offset < len(indices_tensor) and i == indices_tensor[insert_offset]:
+                    # Use modulo to cycle through images_to_insert
+                    new_images.append(images_to_insert[insert_offset % len(images_to_insert)])
+                    insert_offset += 1
+                else:
+                    new_images.append(input_images[i - insert_offset])
+            
+            # Convert the list back to a tensor
+            input_images = torch.stack(new_images, dim=0)
+        
+        return (input_images,)
+
+class PadImageBatchInterleaved:
+    
+    RETURN_TYPES = ("IMAGE", "MASK",)
+    RETURN_NAMES = ("images", "masks",)
+    FUNCTION = "pad"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Inserts empty frames between the images in a batch.
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "empty_frames_per_image": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
+                "pad_frame_value": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 1.0, "step": 0.01}),
+                "add_after_last": ("BOOLEAN", {"default": False}),
+            },
+        }
+    
+    def pad(self, images, empty_frames_per_image, pad_frame_value, add_after_last):
+        B, H, W, C = images.shape
+        
+        # Handle single frame case specifically
+        if B == 1:
+            total_frames = 1 + empty_frames_per_image if add_after_last else 1
+        else:
+            # Original B images + (B-1) sets of empty frames between them
+            total_frames = B + (B-1) * empty_frames_per_image
+            # Add additional empty frames after the last image if requested
+            if add_after_last:
+                total_frames += empty_frames_per_image
+        
+        # Create new tensor with zeros (empty frames)
+        padded_batch = torch.ones((total_frames, H, W, C), 
+                                dtype=images.dtype, 
+                                device=images.device) * pad_frame_value
+        # Create mask tensor (1 for original frames, 0 for empty frames)
+        mask = torch.zeros((total_frames, H, W), 
+                        dtype=images.dtype, 
+                        device=images.device)
+        
+        # Fill in original images at their new positions
+        for i in range(B):
+            if B == 1:
+                # For single frame, just place it at the beginning
+                new_pos = 0
+            else:
+                # Each image is separated by empty_frames_per_image blank frames
+                new_pos = i * (empty_frames_per_image + 1)
+                
+            padded_batch[new_pos] = images[i]
+            mask[new_pos] = 1.0  # Mark this as an original frame
+        
+        return (padded_batch, mask)
+
+class ReplaceImagesInBatch:
+    
+    RETURN_TYPES = ("IMAGE", "MASK",)
+    FUNCTION = "replace"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Replaces the images in a batch, starting from the specified start index,  
+with the replacement images.
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "start_index": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
+        },
+        "optional": {
+            "original_images": ("IMAGE",),
+            "replacement_images": ("IMAGE",),
+            "original_masks": ("MASK",),
+            "replacement_masks": ("MASK",),
+        }
+    } 
+    
+    def replace(self, original_images=None, replacement_images=None, start_index=1, original_masks=None, replacement_masks=None):
+        images = None
+        masks = None
+        
+        if original_images is not None and replacement_images is not None:
+            if start_index >= len(original_images):
+                raise ValueError("ReplaceImagesInBatch: Start index is out of range")
+            end_index = start_index + len(replacement_images)
+            if end_index > len(original_images):
+                raise ValueError("ReplaceImagesInBatch: End index is out of range")
+            
+            original_images_copy = original_images.clone()
+            if original_images_copy.shape[2] != replacement_images.shape[2] or original_images_copy.shape[3] != replacement_images.shape[3]:
+                replacement_images = common_upscale(replacement_images.movedim(-1, 1), original_images_copy.shape[1], original_images_copy.shape[2], "lanczos", "center").movedim(1, -1)
+            
+            original_images_copy[start_index:end_index] = replacement_images
+            images = original_images_copy
+        else:
+            images = torch.zeros((1, 64, 64, 3))
+        
+        if original_masks is not None and replacement_masks is not None:
+            if start_index >= len(original_masks):
+                raise ValueError("ReplaceImagesInBatch: Start index is out of range")
+            end_index = start_index + len(replacement_masks)
+            if end_index > len(original_masks):
+                raise ValueError("ReplaceImagesInBatch: End index is out of range")
+
+            original_masks_copy = original_masks.clone()
+            if original_masks_copy.shape[1] != replacement_masks.shape[1] or original_masks_copy.shape[2] != replacement_masks.shape[2]:
+                replacement_masks = common_upscale(replacement_masks.unsqueeze(1), original_masks_copy.shape[1], original_masks_copy.shape[2], "nearest-exact", "center").squeeze(0)
+                
+            original_masks_copy[start_index:end_index] = replacement_masks
+            masks = original_masks_copy
+        else:
+            masks = torch.zeros((1, 64, 64))
+        
+        return (images, masks)
+    
+
+class ReverseImageBatch:
+    
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "reverseimagebatch"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Reverses the order of the images in a batch.
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "images": ("IMAGE",),
+        },
+    } 
+    
+    def reverseimagebatch(self, images):
+        reversed_images = torch.flip(images, [0])
+        return (reversed_images, )
+
+class ImageBatchMulti:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}),
+                "image_1": ("IMAGE", ),
+            },
+            "optional": {
+                "image_2": ("IMAGE", ),
+            }
+    }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("images",)
+    FUNCTION = "combine"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Creates an image batch from multiple images.  
+You can set how many inputs the node has,  
+with the **inputcount** and clicking update.
+"""
+
+    def combine(self, inputcount, **kwargs):
+        first = kwargs["image_1"]
+        h, w = first.shape[1], first.shape[2]
+
+        # determine output shape
+        max_ch = first.shape[-1]
+        total_frames = first.shape[0]
+        for c in range(1, inputcount):
+            img = kwargs.get(f"image_{c + 1}")
+            if img is not None:
+                max_ch = max(max_ch, img.shape[-1])
+                total_frames += img.shape[0]
+            else:
+                total_frames += first.shape[0]
+
+        # pre-allocate output
+        out = torch.empty((total_frames, h, w, max_ch), dtype=first.dtype)
+        offset = 0
+
+        for c in range(inputcount):
+            img = kwargs.get(f"image_{c + 1}", torch.zeros((first.shape[0], h, w, max_ch), dtype=first.dtype))
+
+            if img.shape[1:3] != (h, w):
+                img = common_upscale(img.movedim(-1, 1), w, h, "bilinear", "center").movedim(1, -1)
+
+            if img.shape[-1] < max_ch:
+                img = torch.nn.functional.pad(img, (0, max_ch - img.shape[-1]), mode='constant', value=1.0)
+
+            n = img.shape[0]
+            out[offset:offset + n].copy_(img, non_blocking=True)
+            offset += n
+            del img
+
+        return (out.cpu(),)
+
+
+class ImageTensorList:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "image1": ("IMAGE",),
+            "image2": ("IMAGE",),
+        }}
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "append"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Creates an image list from the input images.
+"""
+
+    def append(self, image1, image2):
+        image_list = []
+        if isinstance(image1, torch.Tensor) and isinstance(image2, torch.Tensor):
+            image_list = [image1, image2]
+        elif isinstance(image1, list) and isinstance(image2, torch.Tensor):
+            image_list = image1 + [image2]
+        elif isinstance(image1, torch.Tensor) and isinstance(image2, list):
+            image_list = [image1] + image2
+        elif isinstance(image1, list) and isinstance(image2, list):
+            image_list = image1 + image2
+        return image_list,
+
+class ImageAddMulti:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}),
+                "image_1": ("IMAGE", ),
+                "image_2": ("IMAGE", ),
+                "blending": (
+                [   'add',
+                    'subtract',
+                    'multiply',
+                    'difference',
+                ],
+                {
+                "default": 'add'
+                }),
+                "blend_amount": ("FLOAT", {"default": 0.5, "min": 0, "max": 1, "step": 0.01}),
+            },
+    }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("images",)
+    FUNCTION = "add"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Add blends multiple images together.    
+You can set how many inputs the node has,  
+with the **inputcount** and clicking update.
+"""
+
+    def add(self, inputcount, blending, blend_amount, **kwargs):
+        image = kwargs["image_1"]
+        for c in range(1, inputcount):
+            new_image = kwargs[f"image_{c + 1}"]
+            if blending == "add":
+                image = torch.add(image * blend_amount, new_image * blend_amount)
+            elif blending == "subtract":
+                image = torch.sub(image * blend_amount, new_image * blend_amount)
+            elif blending == "multiply":
+                image = torch.mul(image * blend_amount, new_image * blend_amount)
+            elif blending == "difference":
+                image = torch.sub(image, new_image)
+        return (image,)    
+
+
+class ImageConcatMulti(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        # image_1 drives the output type; image_2 (and JS-added image_3+) can independently be IMAGE or MASK
+        type_template = io.MatchType.Template("multi_image_or_mask", allowed_types=[io.Image, io.Mask])
+        return io.Schema(
+            node_id="ImageConcatMulti",
+            display_name="Image Concatenate Multi",
+            category="KJNodes/image",
+            description=(
+                "Creates an image from multiple images or masks.\n"
+                "Set the input count and click 'Update inputs' to add more slots.\n"
+                "The output type follows image_1; other inputs are converted to match."
+            ),
+            accept_all_inputs=True, # JS dynamically adds image_3..image_N beyond the declared inputs
+            inputs=[
+                io.Int.Input("inputcount", default=2, min=2, max=1000, step=1),
+                io.MatchType.Input("image_1", template=type_template),
+                io.Combo.Input("direction", options=['right', 'down', 'left', 'up'], default='right'),
+                io.Boolean.Input("match_image_size", default=False),
+                io.MultiType.Input("image_2", types=[io.Image, io.Mask], optional=True),
+            ],
+            outputs=[
+                io.MatchType.Output(template=type_template, display_name="output"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, inputcount, image_1, direction, match_image_size, image_2=None, **kwargs) -> io.NodeOutput:
+        kwargs["image_1"] = image_1
+        if image_2 is not None:
+            kwargs["image_2"] = image_2
+        image = image_1
+        first_image_shape = image.shape
+        device = model_management.intermediate_device()
+        dtype = model_management.intermediate_dtype()
+        for c in range(1, inputcount):
+            key = f"image_{c + 1}"
+            new_image = kwargs[key] if key in kwargs else torch.zeros(
+                first_image_shape, dtype=dtype, device=device
+            )
+            image = ImageConcanate.concatenate(image, new_image, direction, match_image_size, first_image_shape=first_image_shape)
+        return io.NodeOutput(image)
+
+class PreviewAnimation:
+    def __init__(self):
+        self.output_dir = folder_paths.get_temp_directory()
+        self.type = "temp"
+        self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
+        self.compress_level = 1
+
+    methods = {"default": 4, "fastest": 0, "slowest": 6}
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {
+                     "fps": ("FLOAT", {"default": 8.0, "min": 0.01, "max": 1000.0, "step": 0.01}),
+                     },
+                "optional": {
+                    "images": ("IMAGE", ),
+                    "masks": ("MASK", ),
+                },
+            }
+
+    RETURN_TYPES = ()
+    FUNCTION = "preview"
+    OUTPUT_NODE = True
+    CATEGORY = "KJNodes/image"
+
+    def preview(self, fps, images=None, masks=None):
+        filename_prefix = "AnimPreview"
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+        results = list()
+
+        pil_images = []
+
+        if images is not None and masks is not None:
+            for image in images:
+                i = 255. * image.cpu().numpy()
+                img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+                pil_images.append(img)
+            for mask in masks:
+                if pil_images: 
+                    mask_np = mask.cpu().numpy()
+                    mask_np = np.clip(mask_np * 255, 0, 255).astype(np.uint8)  # Convert to values between 0 and 255
+                    mask_img = Image.fromarray(mask_np, mode='L')
+                    img = pil_images.pop(0)  # Remove and get the first image
+                    img = img.convert("RGBA")  # Convert base image to RGBA
+
+                    # Create a new RGBA image based on the grayscale mask
+                    rgba_mask_img = Image.new("RGBA", img.size, (255, 255, 255, 255))
+                    rgba_mask_img.putalpha(mask_img)  # Use the mask image as the alpha channel
+
+                    # Composite the RGBA mask onto the base image
+                    composited_img = Image.alpha_composite(img, rgba_mask_img)
+                    pil_images.append(composited_img)  # Add the composited image back
+
+        elif images is not None and masks is None:
+            for image in images:
+                i = 255. * image.cpu().numpy()
+                img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+                pil_images.append(img)
+
+        elif masks is not None and images is None:
+            for mask in masks:
+                mask_np = 255. * mask.cpu().numpy()
+                mask_img = Image.fromarray(np.clip(mask_np, 0, 255).astype(np.uint8))
+                pil_images.append(mask_img)
+        else:
+            logging.warning("PreviewAnimation: No images or masks provided")
+            return { "ui": { "images": results, "animated": (None,), "text": "empty" }}
+
+        num_frames = len(pil_images)
+
+        c = len(pil_images)
+        for i in range(0, c, num_frames):
+            file = f"{filename}_{counter:05}_.webp"
+            pil_images[i].save(os.path.join(full_output_folder, file), save_all=True, duration=int(1000.0/fps), append_images=pil_images[i + 1:i + num_frames], lossless=False, quality=50, method=0)
+            results.append({
+                "filename": file,
+                "subfolder": subfolder,
+                "type": self.type
+            })
+            counter += 1
+
+        animated = num_frames != 1
+        return { "ui": { "images": results, "animated": (animated,), "text": [f"{num_frames}x{pil_images[0].size[0]}x{pil_images[0].size[1]}"] } }
+    
+class ImageResizeKJ:
+    upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "width": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
+                "height": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
+                "upscale_method": (s.upscale_methods,),
+                "keep_proportion": ("BOOLEAN", { "default": False }),
+                "divisible_by": ("INT", { "default": 2, "min": 0, "max": 512, "step": 1, }),
+            },
+            "optional" : {
+                #"width_input": ("INT", { "forceInput": True}),
+                #"height_input": ("INT", { "forceInput": True}),
+                "get_image_size": ("IMAGE",),
+                "crop": (["disabled","center", 0], { "tooltip": "0 will do the default center crop, this is a workaround for the widget order changing with the new frontend, as in old workflows the value of this widget becomes 0 automatically" }),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "INT", "INT",)
+    RETURN_NAMES = ("IMAGE", "width", "height",)
+    FUNCTION = "resize"
+    CATEGORY = "KJNodes/image"
+    DEPRECATED = True
+    DESCRIPTION = """
+DEPRECATED!
+
+Due to ComfyUI frontend changes, this node should no longer be used, please check the   
+v2 of the node. This node is only kept to not completely break older workflows.  
+
+"""
+
+    def resize(self, image, width, height, keep_proportion, upscale_method, divisible_by, 
+               width_input=None, height_input=None, get_image_size=None, crop="disabled"):
+        B, H, W, C = image.shape
+
+        if width_input:
+            width = width_input
+        if height_input:
+            height = height_input
+        if get_image_size is not None:
+            _, height, width, _ = get_image_size.shape
+        
+        if keep_proportion and get_image_size is None:
+                # If one of the dimensions is zero, calculate it to maintain the aspect ratio
+                if width == 0 and height != 0:
+                    ratio = height / H
+                    width = round(W * ratio)
+                elif height == 0 and width != 0:
+                    ratio = width / W
+                    height = round(H * ratio)
+                elif width != 0 and height != 0:
+                    # Scale based on which dimension is smaller in proportion to the desired dimensions
+                    ratio = min(width / W, height / H)
+                    width = round(W * ratio)
+                    height = round(H * ratio)
+        else:
+            if width == 0:
+                width = W
+            if height == 0:
+                height = H
+      
+        if divisible_by > 1 and get_image_size is None:
+            width = width - (width % divisible_by)
+            height = height - (height % divisible_by)
+        
+        if crop == 0: #workaround for old workflows
+            crop = "center"
+
+        image = image.movedim(-1,1)
+        image = common_upscale(image, width, height, upscale_method, crop)
+        image = image.movedim(1,-1)
+
+        return(image, image.shape[2], image.shape[1],)
+
+class ImageResizeKJv2:
+    upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos", "nvidia_rtx_vsr"]
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "width": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
+                "height": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
+                "upscale_method": (s.upscale_methods,),
+                "keep_proportion": (["stretch", "resize", "pad", "pad_edge", "pad_edge_pixel", "crop", "pillarbox_blur", "total_pixels"], { "default": False }),
+                "pad_color": ("STRING", { "default": "0, 0, 0", "tooltip": "Color to use for padding."}),
+                "crop_position": (["center", "top", "bottom", "left", "right"], { "default": "center" }),
+                "divisible_by": ("INT", { "default": 2, "min": 0, "max": 512, "step": 1, }),
+            },
+            "optional" : {
+                "mask": ("MASK",),
+                "device": (["cpu", "gpu"],),
+                #"per_batch": ("INT", { "default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, "tooltip": "Process images in sub-batches to reduce memory usage. 0 disables sub-batching."}),
+            },
+             "hidden": {
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "INT", "INT", "MASK",)
+    RETURN_NAMES = ("IMAGE", "width", "height", "mask",)
+    FUNCTION = "resize"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Resizes the image to the specified width and height.  
+Size can be retrieved from the input.
+
+Keep proportions keeps the aspect ratio of the image, by  
+highest dimension.  
+"""
+
+    def resize(self, image, width, height, keep_proportion, upscale_method, divisible_by, pad_color, crop_position, unique_id, device="cpu", mask=None, per_batch=64):
+        B, H, W, C = image.shape
+
+        # Treat ComfyUI's 64x64 placeholder mask as no mask
+        if mask is not None and mask.shape[-2:] == (64, 64) and (H != 64 or W != 64):
+            mask = None
+
+        # Scale mask to match image dimensions if they differ
+        if mask is not None and mask.shape[-2:] != (H, W):
+            mask = common_upscale(mask.unsqueeze(1), W, H, "bilinear", crop="disabled").squeeze(1)
+
+        if device == "gpu":
+            if upscale_method == "lanczos":
+                raise ValueError("Lanczos is not supported on the GPU")
+            device = model_management.get_torch_device()
+        else:
+            device = torch.device("cpu")
+
+        pillarbox_blur = keep_proportion == "pillarbox_blur"
+
+        # Initialize padding variables
+        pad_left = pad_right = pad_top = pad_bottom = 0
+
+        if keep_proportion in ["resize", "total_pixels"] or keep_proportion.startswith("pad") or pillarbox_blur:
+            if keep_proportion == "total_pixels":
+                total_pixels = width * height
+                aspect_ratio = W / H
+                new_height = int(math.sqrt(total_pixels / aspect_ratio))
+                new_width = int(math.sqrt(total_pixels * aspect_ratio))
+
+            # If one of the dimensions is zero, calculate it to maintain the aspect ratio
+            elif width == 0 and height == 0:
+                new_width = W
+                new_height = H
+            elif width == 0 and height != 0:
+                ratio = height / H
+                new_width = round(W * ratio)
+                new_height = height
+            elif height == 0 and width != 0:
+                ratio = width / W
+                new_width = width
+                new_height = round(H * ratio)
+            elif width != 0 and height != 0:
+                ratio = min(width / W, height / H)
+                new_width = round(W * ratio)
+                new_height = round(H * ratio)
+            else:
+                new_width = width
+                new_height = height
+
+            if keep_proportion.startswith("pad") or pillarbox_blur:
+                # Calculate padding based on position
+                if crop_position == "center":
+                    pad_left = (width - new_width) // 2
+                    pad_right = width - new_width - pad_left
+                    pad_top = (height - new_height) // 2
+                    pad_bottom = height - new_height - pad_top
+                elif crop_position == "top":
+                    pad_left = (width - new_width) // 2
+                    pad_right = width - new_width - pad_left
+                    pad_top = 0
+                    pad_bottom = height - new_height
+                elif crop_position == "bottom":
+                    pad_left = (width - new_width) // 2
+                    pad_right = width - new_width - pad_left
+                    pad_top = height - new_height
+                    pad_bottom = 0
+                elif crop_position == "left":
+                    pad_left = 0
+                    pad_right = width - new_width
+                    pad_top = (height - new_height) // 2
+                    pad_bottom = height - new_height - pad_top
+                elif crop_position == "right":
+                    pad_left = width - new_width
+                    pad_right = 0
+                    pad_top = (height - new_height) // 2
+                    pad_bottom = height - new_height - pad_top
+
+            width = new_width
+            height = new_height
+        else:
+            if width == 0:
+                width = W
+            if height == 0:
+                height = H
+
+        if divisible_by > 1:
+            width = width - (width % divisible_by)
+            height = height - (height % divisible_by)
+
+        # Preflight estimate (log-only when batching is active)
+        if per_batch != 0 and B > per_batch:
+            try:
+                bytes_per_elem = image.element_size()  # typically 4 for float32
+                est_total_bytes = B * height * width * C * bytes_per_elem
+                est_mb = est_total_bytes / (1024 * 1024)
+                msg = f"<tr><td>Resize v2</td><td>estimated output ~{est_mb:.2f} MB; batching {per_batch}/{B}</td></tr>"
+                if unique_id and PromptServer is not None:
+                    try:
+                        PromptServer.instance.send_progress_text(msg, unique_id)
+                    except Exception:
+                        pass
+                else:
+                    logging.info(f"[ImageResizeKJv2] estimated output ~{est_mb:.2f} MB; batching {per_batch}/{B}")
+            except Exception:
+                pass
+
+        # NVIDIA RTX Video Super Resolution setup
+        nvvfx_sr = None
+        nvvfx_ctx = None
+        if upscale_method == "nvidia_rtx_vsr":
+            try:
+                import nvvfx
+            except ImportError:
+                raise ImportError("NVIDIA RTX Video Super Resolution is not available. Please install the nvidia-vfx library and ensure you have a compatible NVIDIA GPU.")
+            nvvfx_ctx = nvvfx.VideoSuperRes(nvvfx.effects.QualityLevel.ULTRA)
+            nvvfx_sr = nvvfx_ctx.__enter__()
+            nvvfx_sr.output_width = max(8, round(width / 8) * 8)
+            nvvfx_sr.output_height = max(8, round(height / 8) * 8)
+            nvvfx_sr.load()
+
+        def _process_subbatch(in_image, in_mask, pad_left, pad_right, pad_top, pad_bottom):
+            # Avoid unnecessary clones; only move if needed
+            out_image = in_image if in_image.device == device else in_image.to(device)
+            out_mask = None if in_mask is None else (in_mask if in_mask.device == device else in_mask.to(device))
+
+            # Crop logic
+            if keep_proportion == "crop":
+                old_height = out_image.shape[-3]
+                old_width = out_image.shape[-2]
+                old_aspect = old_width / old_height
+                new_aspect = width / height
+                if old_aspect > new_aspect:
+                    crop_w = round(old_height * new_aspect)
+                    crop_h = old_height
+                else:
+                    crop_w = old_width
+                    crop_h = round(old_width / new_aspect)
+                if crop_position == "center":
+                    x = (old_width - crop_w) // 2
+                    y = (old_height - crop_h) // 2
+                elif crop_position == "top":
+                    x = (old_width - crop_w) // 2
+                    y = 0
+                elif crop_position == "bottom":
+                    x = (old_width - crop_w) // 2
+                    y = old_height - crop_h
+                elif crop_position == "left":
+                    x = 0
+                    y = (old_height - crop_h) // 2
+                elif crop_position == "right":
+                    x = old_width - crop_w
+                    y = (old_height - crop_h) // 2
+                out_image = out_image.narrow(-2, x, crop_w).narrow(-3, y, crop_h)
+                if out_mask is not None:
+                    out_mask = out_mask.narrow(-1, x, crop_w).narrow(-2, y, crop_h)
+
+            if upscale_method == "nvidia_rtx_vsr":
+                # Process each frame through RTX Video Super Resolution
+                frames_chw = out_image.movedim(-1, 1).cuda().contiguous()
+                upscaled_frames = []
+                for j in range(frames_chw.shape[0]):
+                    dlpack_out = nvvfx_sr.run(frames_chw[j]).image
+                    upscaled_frames.append(torch.from_dlpack(dlpack_out).clone())
+                out_image = torch.stack(upscaled_frames, dim=0).movedim(1, -1).cpu()
+                if out_mask is not None:
+                    out_mask = common_upscale(out_mask.unsqueeze(1), width, height, "bilinear", crop="disabled").squeeze(1)
+            else:
+                out_image = common_upscale(out_image.movedim(-1,1), width, height, upscale_method, crop="disabled").movedim(1,-1)
+                if out_mask is not None:
+                    if upscale_method == "lanczos":
+                        out_mask = common_upscale(out_mask.unsqueeze(1).repeat(1, 3, 1, 1), width, height, upscale_method, crop="disabled").movedim(1,-1)[:, :, :, 0]
+                    else:
+                        out_mask = common_upscale(out_mask.unsqueeze(1), width, height, upscale_method, crop="disabled").squeeze(1)
+
+            # Pad logic
+            if (keep_proportion.startswith("pad") or pillarbox_blur) and (pad_left > 0 or pad_right > 0 or pad_top > 0 or pad_bottom > 0):
+                padded_width = width + pad_left + pad_right
+                padded_height = height + pad_top + pad_bottom
+                if divisible_by > 1:
+                    width_remainder = padded_width % divisible_by
+                    height_remainder = padded_height % divisible_by
+                    if width_remainder > 0:
+                        extra_width = divisible_by - width_remainder
+                        pad_right += extra_width
+                    if height_remainder > 0:
+                        extra_height = divisible_by - height_remainder
+                        pad_bottom += extra_height
+
+                pad_mode = (
+                    "pillarbox_blur" if pillarbox_blur else
+                    "edge" if keep_proportion == "pad_edge" else
+                    "edge_pixel" if keep_proportion == "pad_edge_pixel" else
+                    "color"
+                )
+                out_image, out_mask = ImagePadKJ.pad(self, out_image, pad_left, pad_right, pad_top, pad_bottom, 0, pad_color, pad_mode, mask=out_mask)
+
+            return out_image, out_mask
+
+        # If batching disabled (per_batch==0) or batch fits, process whole batch
+        if per_batch == 0 or B <= per_batch:
+            out_image, out_mask = _process_subbatch(image, mask, pad_left, pad_right, pad_top, pad_bottom)
+        else:
+            chunks = []
+            mask_chunks = [] if mask is not None else None
+            total_batches = (B + per_batch - 1) // per_batch
+            current_batch = 0
+            for start_idx in range(0, B, per_batch):
+                current_batch += 1
+                end_idx = min(start_idx + per_batch, B)
+                sub_img = image[start_idx:end_idx]
+                sub_mask = mask[start_idx:end_idx] if mask is not None else None
+                sub_out_img, sub_out_mask = _process_subbatch(sub_img, sub_mask, pad_left, pad_right, pad_top, pad_bottom)
+                chunks.append(sub_out_img.cpu())
+                if mask is not None:
+                    mask_chunks.append(sub_out_mask.cpu() if sub_out_mask is not None else None)
+                # Per-batch progress update
+                if unique_id and PromptServer is not None:
+                    try:
+                        PromptServer.instance.send_progress_text(
+                            f"<tr><td>Resize v2</td><td>batch {current_batch}/{total_batches} · images {end_idx}/{B}</td></tr>",
+                            unique_id
+                        )
+                    except Exception:
+                        pass
+                else:
+                    logging.info(f"[ImageResizeKJv2] batch {current_batch}/{total_batches} · images {end_idx}/{B}")
+            out_image = torch.cat(chunks, dim=0)
+            if mask is not None and any(m is not None for m in mask_chunks):
+                out_mask = torch.cat([m for m in mask_chunks if m is not None], dim=0)
+            else:
+                out_mask = None
+
+        # Cleanup NVIDIA RTX VSR context
+        if nvvfx_ctx is not None:
+            nvvfx_ctx.__exit__(None, None, None)
+
+        # Progress UI
+        if unique_id and PromptServer is not None:
+            try:
+                num_elements = out_image.numel()
+                element_size = out_image.element_size()
+                memory_size_mb = (num_elements * element_size) / (1024 * 1024)
+                PromptServer.instance.send_progress_text(
+                    f"<tr><td>Output: </td><td><b>{out_image.shape[0]}</b> x <b>{out_image.shape[2]}</b> x <b>{out_image.shape[1]} | {memory_size_mb:.2f}MB</b></td></tr>",
+                    unique_id
+                )
+            except Exception:
+                pass
+
+        return (out_image.cpu(), out_image.shape[2], out_image.shape[1], out_mask.cpu() if out_mask is not None else torch.zeros(64,64, device=torch.device("cpu"), dtype=torch.float32))
+
+class LoadAndResizeImage:
+    _color_channels = ["alpha", "red", "green", "blue"]
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        files = [f.name for f in pathlib.Path(input_dir).iterdir() if f.is_file()]
+        return {"required":
+                    {
+                    "image": (sorted(files), {"image_upload": True}),
+                    "resize": ("BOOLEAN", { "default": False }),
+                    "width": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }),
+                    "height": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }),
+                    "repeat": ("INT", { "default": 1, "min": 1, "max": 4096, "step": 1, }),
+                    "keep_proportion": ("BOOLEAN", { "default": False }),
+                    "divisible_by": ("INT", { "default": 2, "min": 0, "max": 512, "step": 1, }),
+                    "mask_channel": (s._color_channels, {"tooltip": "Channel to use for the mask output"}), 
+                    "background_color": ("STRING", { "default": "", "tooltip": "Fills the alpha channel with the specified color."}),
+                    },
+                }
+
+    CATEGORY = "KJNodes/image"
+    RETURN_TYPES = ("IMAGE", "MASK", "INT", "INT", "STRING",)
+    RETURN_NAMES = ("image", "mask", "width", "height","image_path",)
+    FUNCTION = "load_image"
+
+    def load_image(self, image, resize, width, height, repeat, keep_proportion, divisible_by, mask_channel, background_color):
+        image_path = folder_paths.get_annotated_filepath(image)
+
+        img = node_helpers.pillow(Image.open, image_path)
+        img = ImageOps.exif_transpose(img)
+
+        # Process the background_color using the helper function
+        if background_color:
+            color_list = string_to_color(background_color)
+            # Ensure we have RGBA (add alpha if only RGB)
+            if len(color_list) == 3:
+                bg_color_rgba = tuple(color_list) + (255,)
+            else:
+                bg_color_rgba = tuple(color_list)
+        else:
+            bg_color_rgba = None  # No background color specified
+
+        output_images = []
+        output_masks = []
+        w, h = None, None
+
+        excluded_formats = ['MPO']
+
+        W, H = img.size
+        if resize:
+            if keep_proportion:
+                ratio = min(width / W, height / H)
+                width = round(W * ratio)
+                height = round(H * ratio)
+            else:
+                if width == 0:
+                    width = W
+                if height == 0:
+                    height = H
+
+            if divisible_by > 1:
+                width = width - (width % divisible_by)
+                height = height - (height % divisible_by)
+        else:
+            width, height = W, H
+
+        for frame in ImageSequence.Iterator(img):
+            frame = node_helpers.pillow(ImageOps.exif_transpose, frame)
+
+            if frame.mode == 'I':
+                frame = frame.point(lambda i: i * (1 / 255))
+            
+            if frame.mode == 'P':
+                frame = frame.convert("RGBA")
+            elif 'A' in frame.getbands():
+                frame = frame.convert("RGBA")
+            
+            # Extract alpha channel if it exists
+            if 'A' in frame.getbands() and bg_color_rgba:
+                alpha_mask = np.array(frame.getchannel('A')).astype(np.float32) / 255.0
+                alpha_mask = 1. - torch.from_numpy(alpha_mask)
+                bg_image = Image.new("RGBA", frame.size, bg_color_rgba)
+                # Composite the frame onto the background
+                frame = Image.alpha_composite(bg_image, frame)
+            else:
+                alpha_mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
+            
+            image = frame.convert("RGB")
+
+            if len(output_images) == 0:
+                w = image.size[0]
+                h = image.size[1]
+            
+            if image.size[0] != w or image.size[1] != h:
+                continue
+            if resize:
+                image = image.resize((width, height), Image.Resampling.BILINEAR)
+
+            image = np.array(image).astype(np.float32) / 255.0
+            image = torch.from_numpy(image)[None,]
+            
+            c = mask_channel[0].upper()
+            if c in frame.getbands():
+                if resize:
+                    frame = frame.resize((width, height), Image.Resampling.BILINEAR)
+                mask = np.array(frame.getchannel(c)).astype(np.float32) / 255.0
+                mask = torch.from_numpy(mask)
+                if c == 'A' and bg_color_rgba:
+                    mask = alpha_mask
+                elif c == 'A':
+                    mask = 1. - mask
+            else:
+                mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
+
+            output_images.append(image)
+            output_masks.append(mask.unsqueeze(0))
+
+        if len(output_images) > 1 and img.format not in excluded_formats:
+            output_image = torch.cat(output_images, dim=0)
+            output_mask = torch.cat(output_masks, dim=0)
+        else:
+            output_image = output_images[0]
+            output_mask = output_masks[0]
+            if repeat > 1:
+                output_image = output_image.repeat(repeat, 1, 1, 1)
+                output_mask = output_mask.repeat(repeat, 1, 1)
+
+        return (output_image, output_mask, width, height, image_path)
+        
+
+    # @classmethod
+    # def IS_CHANGED(s, image, **kwargs):
+    #     image_path = folder_paths.get_annotated_filepath(image)
+    #     m = hashlib.sha256()
+    #     with open(image_path, 'rb') as f:
+    #         m.update(f.read())
+    #     return m.digest().hex()
+
+    @classmethod
+    def VALIDATE_INPUTS(s, image):
+        if not folder_paths.exists_annotated_filepath(image):
+            return "Invalid image file: {}".format(image)
+
+        return True
+
+class LoadImagesFromFolderKJ:
+    # Dictionary to store folder hashes
+    folder_hashes = {}
+
+    @classmethod
+    def IS_CHANGED(cls, folder, **kwargs):
+        if folder and not os.path.isabs(folder) and args.base_directory:
+            folder = os.path.join(args.base_directory, folder)
+        if not folder or not os.path.isdir(folder):
+            return float("NaN")
+        
+        valid_extensions = ['.jpg', '.jpeg', '.png', '.webp', '.tga']
+        include_subfolders = kwargs.get('include_subfolders', False)
+        
+        file_data = []
+        if include_subfolders:
+            for root, _, files in os.walk(folder):
+                for file in files:
+                    if any(file.lower().endswith(ext) for ext in valid_extensions):
+                        path = os.path.join(root, file)
+                        try:
+                            mtime = os.path.getmtime(path)
+                            file_data.append((path, mtime))
+                        except OSError:
+                            pass
+        else:
+            for file in sorted(os.listdir(folder)):
+                if any(file.lower().endswith(ext) for ext in valid_extensions):
+                    path = os.path.join(folder, file)
+                    try:
+                        mtime = os.path.getmtime(path)
+                        file_data.append((path, mtime))
+                    except OSError:
+                        pass
+        
+        file_data.sort()
+        
+        combined_hash = hashlib.md5()
+        combined_hash.update(folder.encode('utf-8'))
+        combined_hash.update(str(len(file_data)).encode('utf-8'))
+        
+        for path, mtime in file_data:
+            combined_hash.update(f"{path}:{mtime}".encode('utf-8'))
+        
+        current_hash = combined_hash.hexdigest()
+        
+        old_hash = cls.folder_hashes.get(folder)
+        cls.folder_hashes[folder] = current_hash
+        
+        if old_hash == current_hash:
+            return old_hash
+        
+        return current_hash
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "folder": ("STRING", {"default": ""}),
+                "width": ("INT", {"default": 1024, "min": -1, "step": 1}),
+                "height": ("INT", {"default": 1024, "min": -1, "step": 1}),
+                "keep_aspect_ratio": (["crop", "pad", "stretch",],), 
+            },
+            "optional": {
+                "image_load_cap": ("INT", {"default": 0, "min": 0, "step": 1}),
+                "start_index": ("INT", {"default": 0, "min": 0, "step": 1}),
+                "include_subfolders": ("BOOLEAN", {"default": False}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK", "INT", "STRING",)
+    RETURN_NAMES = ("image", "mask", "count", "image_path",)
+    FUNCTION = "load_images"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """Loads images from a folder into a batch, images are resized and loaded into a batch."""
+
+    def load_images(self, folder, width, height, image_load_cap, start_index, keep_aspect_ratio, include_subfolders=False):    
+        if folder and not os.path.isabs(folder) and args.base_directory:
+            folder = os.path.join(args.base_directory, folder)
+        if not folder or not os.path.isdir(folder):
+            raise FileNotFoundError(f"Folder '{folder}' cannot be found.")
+        
+        valid_extensions = ['.jpg', '.jpeg', '.png', '.webp', '.tga']
+        image_paths = []
+        if include_subfolders:
+            for root, _, files in os.walk(folder):
+                for file in files:
+                    if any(file.lower().endswith(ext) for ext in valid_extensions):
+                        image_paths.append(os.path.join(root, file))
+        else:
+            for file in sorted(os.listdir(folder)):
+                if any(file.lower().endswith(ext) for ext in valid_extensions):
+                    image_paths.append(os.path.join(folder, file))
+
+        dir_files = sorted(image_paths)
+
+        if len(dir_files) == 0:
+            raise FileNotFoundError(f"No files in directory '{folder}'.")
+
+        # start at start_index
+        dir_files = dir_files[start_index:]
+
+        images = []
+        masks = []
+        image_path_list = []
+
+        limit_images = False
+        if image_load_cap > 0:
+            limit_images = True
+        image_count = 0
+
+        pbar = ProgressBar(len(dir_files))
+
+        for image_path in dir_files:
+            if os.path.isdir(image_path):
+                continue
+            if limit_images and image_count >= image_load_cap:
+                break
+            i = Image.open(image_path)
+            i = ImageOps.exif_transpose(i)
+            
+            # Resize image to maximum dimensions
+            if width == -1 and height == -1:
+                width = i.size[0]
+                height = i.size[1]
+            if i.size != (width, height):
+                i = self.resize_with_aspect_ratio(i, width, height, keep_aspect_ratio)
+            
+            
+            image = i.convert("RGB")
+            image = np.array(image).astype(np.float32) / 255.0
+            image = torch.from_numpy(image)[None,]
+            
+            if 'A' in i.getbands():
+                mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+                mask = 1. - torch.from_numpy(mask)
+                if mask.shape != (height, width):
+                    mask = torch.nn.functional.interpolate(mask.unsqueeze(0).unsqueeze(0), 
+                                                         size=(height, width), 
+                                                         mode='bilinear', 
+                                                         align_corners=False).squeeze()
+            else:
+                mask = torch.zeros((height, width), dtype=torch.float32, device="cpu")
+            
+            images.append(image)
+            masks.append(mask)
+            image_path_list.append(image_path)
+            image_count += 1
+            pbar.update(1)
+
+        if len(images) == 1:
+            return (images[0], masks[0], 1, image_path_list)
+        
+        elif len(images) > 1:
+            image1 = images[0]
+            mask1 = masks[0].unsqueeze(0)
+
+            for image2 in images[1:]:
+                image1 = torch.cat((image1, image2), dim=0)
+
+            for mask2 in masks[1:]:
+                mask1 = torch.cat((mask1, mask2.unsqueeze(0)), dim=0)
+
+            return (image1, mask1, len(images), image_path_list)
+    def resize_with_aspect_ratio(self, img, width, height, mode):
+        if mode == "stretch":
+            return img.resize((width, height), Image.Resampling.LANCZOS)
+        
+        img_width, img_height = img.size
+        aspect_ratio = img_width / img_height
+        target_ratio = width / height
+
+        if mode == "crop":
+            # Calculate dimensions for center crop
+            if aspect_ratio > target_ratio:
+                # Image is wider - crop width
+                new_width = int(height * aspect_ratio)
+                img = img.resize((new_width, height), Image.Resampling.LANCZOS)
+                left = (new_width - width) // 2
+                return img.crop((left, 0, left + width, height))
+            else:
+                # Image is taller - crop height
+                new_height = int(width / aspect_ratio)
+                img = img.resize((width, new_height), Image.Resampling.LANCZOS)
+                top = (new_height - height) // 2
+                return img.crop((0, top, width, top + height))
+
+        elif mode == "pad":
+            pad_color = self.get_edge_color(img)
+            # Calculate dimensions for padding
+            if aspect_ratio > target_ratio:
+                # Image is wider - pad height
+                new_height = int(width / aspect_ratio)
+                img = img.resize((width, new_height), Image.Resampling.LANCZOS)
+                padding = (height - new_height) // 2
+                padded = Image.new('RGBA', (width, height), pad_color)
+                padded.paste(img, (0, padding))
+                return padded
+            else:
+                # Image is taller - pad width
+                new_width = int(height * aspect_ratio)
+                img = img.resize((new_width, height), Image.Resampling.LANCZOS)
+                padding = (width - new_width) // 2
+                padded = Image.new('RGBA', (width, height), pad_color)
+                padded.paste(img, (padding, 0))
+                return padded
+    def get_edge_color(self, img):
+        """Sample edges and return dominant color"""
+        width, height = img.size
+        img = img.convert('RGBA')
+        
+        # Create 1-pixel high/wide images from edges
+        top = img.crop((0, 0, width, 1))
+        bottom = img.crop((0, height-1, width, height))
+        left = img.crop((0, 0, 1, height))
+        right = img.crop((width-1, 0, width, height))
+        
+        # Combine edges into single image
+        edges = Image.new('RGBA', (width*2 + height*2, 1))
+        edges.paste(top, (0, 0))
+        edges.paste(bottom, (width, 0))
+        edges.paste(left.resize((height, 1)), (width*2, 0))
+        edges.paste(right.resize((height, 1)), (width*2 + height, 0))
+        
+        # Get median color
+        stat = ImageStat.Stat(edges)
+        median = tuple(map(int, stat.median))
+        return median
+
+class ImageGridtoBatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "image": ("IMAGE", ),
+                    "columns": ("INT", {"default": 3, "min": 1, "max": 8, "tooltip": "The number of columns in the grid."}),
+                    "rows": ("INT", {"default": 0, "min": 1, "max": 8, "tooltip": "The number of rows in the grid. Set to 0 for automatic calculation."}),
+                  }
+                }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "decompose"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = "Converts a grid of images to a batch of images."
+
+    def decompose(self, image, columns, rows):
+        B, H, W, C = image.shape
+
+        # Calculate cell width, rounding down
+        cell_width = W // columns
+
+        if rows == 0:
+            # If rows is 0, calculate number of full rows
+            cell_height = H // columns
+            rows = H // cell_height
+        else:
+            # If rows is specified, adjust cell_height
+            cell_height = H // rows
+
+        # Crop the image to fit full cells
+        image = image[:, :rows*cell_height, :columns*cell_width, :]
+
+        # Reshape and permute the image to get the grid
+        image = image.view(B, rows, cell_height, columns, cell_width, C)
+        image = image.permute(0, 1, 3, 2, 4, 5).contiguous()
+        image = image.view(B, rows * columns, cell_height, cell_width, C)
+
+        # Reshape to the final batch tensor
+        img_tensor = image.view(-1, cell_height, cell_width, C)
+
+        return (img_tensor,)
+
+class SaveImageKJ:
+    def __init__(self):
+        self.type = "output"
+        self.prefix_append = ""
+        self.compress_level = 4
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "images": ("IMAGE", {"tooltip": "The images to save."}),
+                "filename_prefix": ("STRING", {"default": "ComfyUI", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."}),
+                "output_folder": ("STRING", {"default": "output", "tooltip": "The folder to save the images to."}),
+            },
+            "optional": {
+                "caption_file_extension": ("STRING", {"default": ".txt", "tooltip": "The extension for the caption file. Limited to plain-text/data formats."}),
+                "caption": ("STRING", {"forceInput": True, "tooltip": "string to save as .txt file"}),
+            },
+            "hidden": {
+                "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"
+            },
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("filename",)
+    FUNCTION = "save_images"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = "Saves the input images to your ComfyUI output directory."
+
+    def save_images(self, images, output_folder, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None, caption=None, caption_file_extension=".txt"):
+        filename_prefix += self.prefix_append
+
+        if os.path.isabs(output_folder):
+            if not os.path.exists(output_folder):
+                os.makedirs(output_folder, exist_ok=True)
+            full_output_folder = output_folder
+            _, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, output_folder, images[0].shape[1], images[0].shape[0])
+        else:
+            self.output_dir = folder_paths.get_output_directory()
+            full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
+
+        # sanitize caption extension: strip path components so it can't traverse out of the chosen folder, and allowlist to text/data formats
+        if caption is not None:
+            caption_file_extension = os.path.basename(caption_file_extension)
+            if caption_file_extension and not caption_file_extension.startswith("."):
+                caption_file_extension = "." + caption_file_extension
+            if caption_file_extension.lower() not in SaveStringKJ.ALLOWED_EXTENSIONS:
+                raise ValueError(f"Disallowed caption extension '{caption_file_extension}'. Allowed: {', '.join(SaveStringKJ.ALLOWED_EXTENSIONS)}")
+
+        results = list()
+        for (batch_number, image) in enumerate(images):
+            i = 255. * image.cpu().numpy()
+            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+            metadata = None
+            if not args.disable_metadata:
+                metadata = PngInfo()
+                if prompt is not None:
+                    metadata.add_text("prompt", json.dumps(prompt))
+                if extra_pnginfo is not None:
+                    for x in extra_pnginfo:
+                        metadata.add_text(x, json.dumps(extra_pnginfo[x]))
+
+            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+            base_file_name = f"{filename_with_batch_num}_{counter:05}_"
+            file = f"{base_file_name}.png"
+            img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level)
+            results.append({
+                "filename": file,
+                "subfolder": subfolder,
+                "type": self.type
+            })
+            if caption is not None:
+                txt_file = base_file_name + caption_file_extension
+                file_path = os.path.join(full_output_folder, txt_file)
+
+                if os.path.commonpath((os.path.abspath(full_output_folder), os.path.abspath(file_path))) != os.path.abspath(full_output_folder):
+                    raise ValueError(f"Refusing to write caption outside the target folder: {file_path}")
+                with open(file_path, "w", encoding="utf-8") as f:
+                    f.write(caption)
+
+            counter += 1
+
+        return file, 
+
+class SaveStringKJ:
+    ALLOWED_EXTENSIONS = [".txt", ".caption", ".json", ".yaml", ".yml", ".md", ".csv", ".tsv", ".xml", ".log", ".ini", ".toml"]
+
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "output"
+        self.prefix_append = ""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string": ("STRING", {"forceInput": True, "tooltip": "string to save as .txt file"}),
+                "filename_prefix": ("STRING", {"default": "text", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."}),
+                "output_folder": ("STRING", {"default": "output", "tooltip": "Subfolder within the ComfyUI output directory to save to. Paths resolving outside the output directory are rejected."}),
+            },
+            "optional": {
+                "file_extension": ("STRING", {"default": ".txt", "tooltip": "The extension for the saved file. Limited to plain-text/data formats."}),
+            },
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("filename",)
+    FUNCTION = "save_string"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "KJNodes/misc"
+    DESCRIPTION = "Saves the input string to your ComfyUI output directory."
+
+    def save_string(self, string, output_folder, filename_prefix="text", file_extension=".txt"):
+        filename_prefix += self.prefix_append
+
+        output_dir = os.path.abspath(self.output_dir)
+        if output_folder and output_folder != "output":
+            sub = os.path.splitdrive(output_folder)[1].replace("\\", "/").lstrip("/")
+            target_dir = os.path.abspath(os.path.join(output_dir, sub))
+        else:
+            target_dir = output_dir
+
+        try:
+            inside = os.path.commonpath((output_dir, target_dir)) == output_dir
+        except ValueError:
+            inside = False
+        if not inside:
+            raise ValueError(f"output_folder must resolve within the ComfyUI output directory: {target_dir}")
+        os.makedirs(target_dir, exist_ok=True)
+
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, target_dir)
+
+        file_extension = os.path.basename(file_extension)
+        if file_extension and not file_extension.startswith("."):
+            file_extension = "." + file_extension
+
+        if file_extension.lower() not in self.ALLOWED_EXTENSIONS:
+            raise ValueError(f"Disallowed file extension '{file_extension}'. Allowed: {', '.join(self.ALLOWED_EXTENSIONS)}")
+
+        base_file_name = f"{filename_prefix}_{counter:05}_"
+
+        txt_file = base_file_name + file_extension
+        file_path = os.path.join(full_output_folder, txt_file)
+        while os.path.exists(file_path):
+            counter += 1
+            base_file_name = f"{filename_prefix}_{counter:05}_"
+            txt_file = base_file_name + file_extension
+            file_path = os.path.join(full_output_folder, txt_file)
+
+        if os.path.commonpath((os.path.abspath(full_output_folder), os.path.abspath(file_path))) != os.path.abspath(full_output_folder):
+            raise ValueError(f"Refusing to write outside the target folder: {file_path}")
+        with open(file_path, 'w', encoding="utf-8") as f:
+            f.write(string)
+
+        return file_path,
+
+class FastPreview:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE", ),
+                "format": (["JPEG", "PNG"], {"default": "JPEG"}),
+                "max_size": ("INT", {"default": 768, "min": 128, "max": 4096, "step": 64,
+                             "tooltip": "Maximum width or height for the preview. Images larger than this are downscaled before encoding."}),
+            },
+            "hidden": {
+                "unique_id": "UNIQUE_ID",
+                "prompt_id": "PROMPT_ID",
+            },
+        }
+
+    RETURN_TYPES = ()
+    FUNCTION = "preview"
+    CATEGORY = "KJNodes/experimental"
+    OUTPUT_NODE = True
+    DESCRIPTION = "Fast image preview using binary websocket, bypassing base64/JSON overhead."
+
+    def preview(self, image, format, max_size, unique_id=None, prompt_id=None):
+        arr = image[0].cpu().mul(255).clamp(0, 255).byte().numpy()
+        h, w = arr.shape[:2]
+
+        if w > max_size or h > max_size:
+            scale = max_size / max(w, h)
+            new_w, new_h = int(w * scale), int(h * scale)
+            if HAS_CV2:
+                arr = cv2.resize(arr, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
+                pil_image = Image.fromarray(arr)
+            else:
+                pil_image = Image.fromarray(arr).resize((new_w, new_h), Image.BILINEAR)
+        else:
+            pil_image = Image.fromarray(arr)
+
+        if format == "JPEG" and pil_image.mode != "RGB":
+            pil_image = pil_image.convert("RGB")
+
+        if PromptServer is not None and unique_id is not None:
+            server = PromptServer.instance
+            client_supports_metadata = False
+            if hasattr(BinaryEventTypes, "PREVIEW_IMAGE_WITH_METADATA"):
+                try:
+                    from comfy_api import feature_flags
+                    client_supports_metadata = feature_flags.supports_feature(
+                        server.sockets_metadata, server.client_id, "supports_preview_metadata"
+                    )
+                except Exception:
+                    client_supports_metadata = False
+
+            if client_supports_metadata:
+                server.send_sync(
+                    BinaryEventTypes.PREVIEW_IMAGE_WITH_METADATA,
+                    (
+                        (format, pil_image, None),
+                        {
+                            "node_id": unique_id,
+                            "display_node_id": unique_id,
+                            "prompt_id": prompt_id or "",
+                        },
+                    ),
+                    server.client_id,
+                )
+            else:
+                server.send_sync(
+                    BinaryEventTypes.UNENCODED_PREVIEW_IMAGE,
+                    (format, pil_image, None),
+                    server.client_id,
+                )
+
+        return {"ui": {"fast_preview": [True]}, "result": ()}
+
+
+class FastPreviewBatch(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="FastPreviewBatch",
+            display_name="Fast Preview Batch",
+            category="KJNodes/experimental",
+            description="Encodes an image batch as an all-I-frame H.264 MP4 thumbnail strip "
+                        "and shows it as an interactive grid. Click a tile to enlarge with "
+                        "prev/next browsing. Avoids materializing N PNGs.",
+            inputs=[
+                io.MultiType.Input("input", [io.Image, io.Mask], tooltip="Image or mask batch to preview."),
+                io.Int.Input("max_thumb_size", default=512, min=512, max=1024, step=8,
+                             tooltip="Detail-view (mp4) thumbnail max side. Strip thumbs for the grid are auto-capped at 256."),
+                io.Int.Input("crf", default=25, min=0, max=51, step=1,
+                             tooltip="H.264 CRF. Lower = higher quality / larger file."),
+                io.Int.Input("max_grid_frames", default=1024, min=1, max=4096, step=1,
+                             tooltip="If batch exceeds this, frames are stride-sampled evenly."),
+            ],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, input, max_thumb_size, crf, max_grid_frames) -> io.NodeOutput:
+        import av
+        import threading
+        import queue as _queue
+        if input.ndim == 3:
+            images = input.reshape((-1, 1, input.shape[-2], input.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
+        else:
+            images = input
+        B, H, W, _ = images.shape
+
+        if B > max_grid_frames:
+            idx = torch.linspace(0, B - 1, max_grid_frames).round().long().tolist()
+        else:
+            idx = list(range(B))
+        total = len(idx)
+
+        scale = min(1.0, max_thumb_size / max(H, W))
+        new_w = max(2, int(round(W * scale)))
+        new_h = max(2, int(round(H * scale)))
+        # yuv420p needs even dimensions
+        new_w -= new_w & 1
+        new_h -= new_h & 1
+
+        # Strip thumbs serve the grid only; cap at 256 so the tiled JPEG stays well
+        # under any browser image-decode limit regardless of detail-view size.
+        STRIP_MAX = 256
+        strip_scale = min(1.0, STRIP_MAX / max(new_h, new_w))
+        strip_w = max(2, int(round(new_w * strip_scale)))
+        strip_h = max(2, int(round(new_h * strip_scale)))
+
+        output_dir = folder_paths.get_temp_directory()
+        prefix = "kj_batch_preview_" + ''.join(random.choice("abcdefghijklmnopqrstuvwxyz") for _ in range(6))
+        full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path(
+            prefix, output_dir, new_w, new_h
+        )
+        file = f"{filename}_{counter:05}_.mp4"
+        filepath = os.path.join(full_output_folder, file)
+        strip_file = f"{filename}_{counter:05}_grid.jpg"
+        strip_path = os.path.join(full_output_folder, strip_file)
+
+        # Square-ish tiling for the JS grid renderer.
+        strip_cols = max(1, int(math.ceil(math.sqrt(total))))
+        strip_rows = int(math.ceil(total / strip_cols))
+        strip_arr = np.zeros((strip_rows * strip_h, strip_cols * strip_w, 3), dtype=np.uint8)
+
+        fps = 30
+        container = av.open(filepath, mode="w")
+        try:
+            stream = container.add_stream("libx264", rate=Fraction(fps, 1))
+            stream.width = new_w
+            stream.height = new_h
+            stream.pix_fmt = "yuv420p"
+            stream.options = {"crf": str(crf), "preset": "ultrafast", "g": "1", "tune": "fastdecode"}
+
+            chunk_size = 32
+            need_resize = (new_h, new_w) != (H, W)
+            need_strip_resize = (strip_h, strip_w) != (new_h, new_w)
+            mode = 'area' if scale < 1.0 else 'bilinear'
+            work_device = model_management.get_torch_device()
+
+            def _to_numpy_nhwc_u8(t):
+                return (t.mul(255).clamp(0, 255)
+                         .to(dtype=torch.uint8, device='cpu')
+                         .permute(0, 2, 3, 1).contiguous().numpy())
+
+            # Producer: GPU resize + transfer; consumer (this thread): PyAV encode.
+            # PyTorch GPU ops, host transfers, and PyAV's libx264 call all release the
+            # GIL, so threading actually overlaps the two stages.
+            frame_queue = _queue.Queue(maxsize=2)
+            producer_error = [None]
+
+            def producer():
+                try:
+                    for c_start in range(0, total, chunk_size):
+                        c_idx = idx[c_start:c_start + chunk_size]
+                        sel = (images[c_idx, ..., :3].permute(0, 3, 1, 2).contiguous()
+                                                      .to(device=work_device, non_blocking=True))
+                        sel_video = F.interpolate(sel, size=(new_h, new_w), mode=mode) if need_resize else sel
+                        sel_strip = F.interpolate(sel_video, size=(strip_h, strip_w), mode='area') if need_strip_resize else sel_video
+                        video_frames = _to_numpy_nhwc_u8(sel_video)
+                        strip_frames = video_frames if sel_strip is sel_video else _to_numpy_nhwc_u8(sel_strip)
+                        del sel, sel_video, sel_strip
+                        frame_queue.put((c_start, video_frames, strip_frames))
+                except Exception as e:
+                    producer_error[0] = e
+                finally:
+                    frame_queue.put(None)
+
+            producer_thread = threading.Thread(target=producer, daemon=True)
+            producer_thread.start()
+
+            pbar = ProgressBar(total)
+            while True:
+                item = frame_queue.get()
+                if item is None:
+                    break
+                c_start, video_frames, strip_frames = item
+                for i in range(video_frames.shape[0]):
+                    global_idx = c_start + i
+                    sr = global_idx // strip_cols
+                    sc = global_idx % strip_cols
+                    strip_arr[sr * strip_h:(sr + 1) * strip_h, sc * strip_w:(sc + 1) * strip_w] = strip_frames[i]
+                    frame = av.VideoFrame.from_ndarray(video_frames[i], format="rgb24")
+                    for packet in stream.encode(frame):
+                        container.mux(packet)
+                    pbar.update(1)
+
+            producer_thread.join()
+            if producer_error[0] is not None:
+                raise producer_error[0]
+
+            for packet in stream.encode():
+                container.mux(packet)
+        finally:
+            container.close()
+
+        Image.fromarray(strip_arr).save(strip_path, quality=85)
+
+        return io.NodeOutput(ui={"kj_batch_preview": [{
+            "filename": file,
+            "subfolder": subfolder,
+            "type": "temp",
+            "frame_count": total,
+            "fps": fps,
+            "thumb_w": new_w,
+            "thumb_h": new_h,
+            "strip_filename": strip_file,
+            "strip_cols": strip_cols,
+            "strip_cell_w": strip_w,
+            "strip_cell_h": strip_h,
+        }]})
+
+
+class ImageCropByMaskAndResize:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE", ),
+                "mask": ("MASK", ),
+                "base_resolution": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }),
+                "padding": ("INT", { "default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
+                "min_crop_resolution": ("INT", { "default": 128, "min": 0, "max": MAX_RESOLUTION, "step": 8, }),
+                "max_crop_resolution": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK", "BBOX", )
+    RETURN_NAMES = ("images", "masks", "bbox",)
+    FUNCTION = "crop"
+    CATEGORY = "KJNodes/image"
+
+    def crop_by_mask(self, mask, padding=0, min_crop_resolution=None, max_crop_resolution=None):
+        """
+        Calculate bounding box from mask with proper padding boundary protection
+        Ensures crop region never exceeds original image boundaries
+        """
+        iy, ix = (mask == 1).nonzero(as_tuple=True)
+        h0, w0 = mask.shape
+
+        # Handle empty mask
+        if iy.numel() == 0:
+            x_c = w0 / 2.0
+            y_c = h0 / 2.0
+            width = 0
+            height = 0
+        else:
+            x_min = ix.min().item()
+            x_max = ix.max().item()
+            y_min = iy.min().item()
+            y_max = iy.max().item()
+            width = x_max - x_min + 1  # Include boundary pixels
+            height = y_max - y_min + 1
+            x_c = (x_min + x_max) / 2.0
+            y_c = (y_min + y_max) / 2.0
+
+        # Apply min/max resolution constraints
+        if min_crop_resolution:
+            width = max(width, min_crop_resolution)
+            height = max(height, min_crop_resolution)
+        if max_crop_resolution:
+            width = min(width, max_crop_resolution)
+            height = min(height, max_crop_resolution)
+
+        # Critical: Limit padding expansion to available image space
+        # Calculate maximum possible padding for each direction
+        max_padding_x = min((w0 - width) // 2, padding)
+        max_padding_y = min((h0 - height) // 2, padding)
+        
+        # Apply constrained padding
+        final_width = width + 2 * max_padding_x
+        final_height = height + 2 * max_padding_y
+
+        # Ensure final dimensions don't exceed image bounds
+        final_width = min(final_width, w0)
+        final_height = min(final_height, h0)
+
+        # Calculate top-left corner with boundary protection
+        # Center the crop while respecting image boundaries
+        x0 = max(0, min(int(x_c - final_width / 2), w0 - final_width))
+        y0 = max(0, min(int(y_c - final_height / 2), h0 - final_height))
+
+        return (x0, y0, final_width, final_height)
+
+    def crop(self, image, mask, base_resolution, padding=0, min_crop_resolution=128, max_crop_resolution=512):
+        """
+        Main crop and resize function with uniform target dimensions for all batch items
+        """
+        mask = mask.round()
+        image_list = []
+        mask_list = []
+        bbox_list = []
+
+        # Step 1: Calculate individual bounding boxes
+        bbox_params = []
+        aspect_ratios = []
+        for i in range(image.shape[0]):
+            x0, y0, w, h = self.crop_by_mask(mask[i], padding, min_crop_resolution, max_crop_resolution)
+            bbox_params.append((x0, y0, w, h))
+            aspect_ratios.append(w / h)
+
+        # Step 2: Calculate uniform target dimensions based on maximum aspect ratio
+        max_w = max([w for x0, y0, w, h in bbox_params])
+        max_h = max([h for x0, y0, w, h in bbox_params])
+        max_aspect_ratio = max(aspect_ratios)
+
+        # Round up to nearest multiple of 16 for stable processing
+        max_w = (max_w + 15) // 16 * 16
+        max_h = (max_h + 15) // 16 * 16
+
+        # Determine target dimensions maintaining aspect ratio
+        if max_aspect_ratio > 1:
+            target_width = base_resolution
+            target_height = int(base_resolution / max_aspect_ratio)
+        else:
+            target_height = base_resolution
+            target_width = int(base_resolution * max_aspect_ratio)
+
+        # Ensure target dimensions are multiples of 16
+        target_width = (target_width + 15) // 16 * 16
+        target_height = (target_height + 15) // 16 * 16
+
+        # Step 3: Process each image with uniform crop size
+        for i in range(image.shape[0]):
+            orig_x0, orig_y0, orig_w, orig_h = bbox_params[i]
+            
+            # Calculate center of original bounding box
+            x_center = orig_x0 + orig_w / 2
+            y_center = orig_y0 + orig_h / 2
+
+            # Define uniform crop region centered on each image's bounding box
+            # This ensures all crops have exactly the same dimensions
+            x0_new = max(0, min(int(x_center - max_w / 2), image.shape[2] - max_w))
+            y0_new = max(0, min(int(y_center - max_h / 2), image.shape[1] - max_h))
+            x1_new = x0_new + max_w
+            y1_new = y0_new + max_h
+
+            # Extract cropped regions
+            cropped_image = image[i][y0_new:y1_new, x0_new:x1_new, :]
+            cropped_mask = mask[i][y0_new:y1_new, x0_new:x1_new]
+
+            # Resize to exact target dimensions
+            # Image with lanczos interpolation
+            cropped_image = cropped_image.unsqueeze(0).movedim(-1, 1)
+            cropped_image = common_upscale(cropped_image, target_width, target_height, "lanczos", "disabled")
+            cropped_image = cropped_image.movedim(1, -1).squeeze(0)
+
+            # Mask with bilinear interpolation
+            cropped_mask = cropped_mask.unsqueeze(0).unsqueeze(0)
+            cropped_mask = common_upscale(cropped_mask, target_width, target_height, 'bilinear', "disabled")
+            cropped_mask = cropped_mask.squeeze(0).squeeze(0)
+
+            image_list.append(cropped_image)
+            mask_list.append(cropped_mask)
+            bbox_list.append((x0_new, y0_new, x1_new, y1_new))
+
+        return (torch.stack(image_list), torch.stack(mask_list), bbox_list)
+    
+class ImageCropByMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE", ),
+                "mask": ("MASK", ),           
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", )
+    RETURN_NAMES = ("image", )
+    FUNCTION = "crop"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = "Crops the input images based on the provided mask."
+
+    def crop(self, image, mask):
+        B, H, W, C = image.shape
+        mask = mask.round()
+        
+        # Find bounding box for each batch
+        crops = []
+        
+        for b in range(B):
+            # Get coordinates of non-zero elements
+            rows = torch.any(mask[min(b, mask.shape[0]-1)] > 0, dim=1)
+            cols = torch.any(mask[min(b, mask.shape[0]-1)] > 0, dim=0)
+            
+            # Find boundaries
+            y_min, y_max = torch.where(rows)[0][[0, -1]]
+            x_min, x_max = torch.where(cols)[0][[0, -1]]
+            
+            # Crop image and mask
+            crop = image[b:b+1, y_min:y_max+1, x_min:x_max+1, :]            
+            crops.append(crop)
+        
+        # Stack results back together
+        cropped_images = torch.cat(crops, dim=0)
+        
+        return (cropped_images, )
+
+       
+    
+class ImageUncropByMask:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {    
+                        "destination": ("IMAGE",),
+                        "source": ("IMAGE",),
+                        "mask": ("MASK",),
+                        "bbox": ("BBOX",),
+                     },
+                }
+
+    CATEGORY = "KJNodes/image"
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = "uncrop"
+
+    def uncrop(self, destination, source, mask, bbox=None):
+
+        output_list = []
+
+        B, H, W, C = destination.shape
+       
+        for i in range(source.shape[0]):
+            x0, y0, x1, y1 = bbox[i]
+            bbox_height = y1 - y0
+            bbox_width = x1 - x0
+
+            # Resize source image to match the bounding box dimensions
+            #resized_source = F.interpolate(source[i].unsqueeze(0).movedim(-1, 1), size=(bbox_height, bbox_width), mode='bilinear', align_corners=False)
+            resized_source = common_upscale(source[i].unsqueeze(0).movedim(-1, 1), bbox_width, bbox_height, "lanczos", "disabled")
+            resized_source = resized_source.movedim(1, -1).squeeze(0)
+    
+            # Resize mask to match the bounding box dimensions
+            resized_mask = common_upscale(mask[i].unsqueeze(0).unsqueeze(0), bbox_width, bbox_height, "bilinear", "disabled")
+            resized_mask = resized_mask.squeeze(0).squeeze(0)
+
+            # Calculate padding values
+            pad_left = x0
+            pad_right = W - x1
+            pad_top = y0
+            pad_bottom = H - y1
+
+            # Pad the resized source image and mask to fit the destination dimensions
+            padded_source = F.pad(resized_source, pad=(0, 0, pad_left, pad_right, pad_top, pad_bottom), mode='constant', value=0)
+            padded_mask = F.pad(resized_mask, pad=(pad_left, pad_right, pad_top, pad_bottom), mode='constant', value=0)
+
+            # Ensure the padded mask has the correct shape
+            padded_mask = padded_mask.unsqueeze(2).expand(-1, -1, destination[i].shape[2])
+            # Ensure the padded source has the correct shape
+            padded_source = padded_source.unsqueeze(2).expand(-1, -1, -1, destination[i].shape[2]).squeeze(2)
+            
+            # Combine the destination and padded source images using the mask
+            result = destination[i] * (1.0 - padded_mask) + padded_source * padded_mask
+
+            output_list.append(result)
+
+
+        return (torch.stack(output_list),)
+    
+class ImageCropByMaskBatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "image": ("IMAGE", ),
+                    "masks": ("MASK", ),
+                    "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }),
+                    "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }),
+                    "padding": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 1, }),
+                    "preserve_size": ("BOOLEAN", {"default": False}),
+                    "bg_color": ("STRING", {"default": "0, 0, 0", "tooltip": "Color as RGB values in range 0-255 or 0.0-1.0, or color name or hex code"}),
+                  }
+                }
+    
+    RETURN_TYPES = ("IMAGE", "MASK", )
+    RETURN_NAMES = ("images", "masks",)
+    FUNCTION = "crop"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = "Crops the input images based on the provided masks."
+        
+    def crop(self, image, masks, width, height, bg_color, padding, preserve_size):
+        B, H, W, C = image.shape
+        BM, HM, WM = masks.shape
+        mask_count = BM
+        if HM != H or WM != W:
+            masks = F.interpolate(masks.unsqueeze(1), size=(H, W), mode='nearest-exact').squeeze(1)
+        output_images = []
+        output_masks = []
+
+        # Parse background color using helper function
+        color_list = string_to_color(bg_color)
+        bg_color = [x / 255.0 for x in color_list]
+        
+        # For each mask
+        for i in range(mask_count):
+            curr_mask = masks[i]
+            
+            # Find bounds
+            y_indices, x_indices = torch.nonzero(curr_mask, as_tuple=True)
+            if len(y_indices) == 0 or len(x_indices) == 0:
+                continue
+                
+            # Get exact bounds with padding
+            min_y = max(0, y_indices.min().item() - padding)
+            max_y = min(H, y_indices.max().item() + 1 + padding)
+            min_x = max(0, x_indices.min().item() - padding)
+            max_x = min(W, x_indices.max().item() + 1 + padding)
+            
+            # Ensure mask has correct shape for multiplication
+            curr_mask = curr_mask.unsqueeze(-1).expand(-1, -1, C)
+            
+            # Crop image and mask together
+            cropped_img = image[0, min_y:max_y, min_x:max_x, :]
+            cropped_mask = curr_mask[min_y:max_y, min_x:max_x, :]
+
+            crop_h, crop_w = cropped_img.shape[0:2]
+            new_w = crop_w
+            new_h = crop_h
+
+            if not preserve_size or crop_w > width or crop_h > height:
+                scale = min(width/crop_w, height/crop_h)
+                new_w = int(crop_w * scale)
+                new_h = int(crop_h * scale)
+                
+                # Resize RGB
+                resized_img = common_upscale(cropped_img.permute(2,0,1).unsqueeze(0), new_w, new_h, "lanczos", "disabled").squeeze(0).permute(1,2,0)
+                resized_mask = torch.nn.functional.interpolate(
+                    cropped_mask.permute(2,0,1).unsqueeze(0),
+                    size=(new_h, new_w),
+                    mode='nearest'
+                ).squeeze(0).permute(1,2,0)
+            else:
+                resized_img = cropped_img
+                resized_mask = cropped_mask
+
+            # Create empty tensors
+            new_img = torch.zeros((height, width, 3), dtype=image.dtype)
+            new_mask = torch.zeros((height, width), dtype=image.dtype)
+
+            # Pad both
+            pad_x = (width - new_w) // 2
+            pad_y = (height - new_h) // 2
+            new_img[pad_y:pad_y+new_h, pad_x:pad_x+new_w, :] = resized_img
+            if len(resized_mask.shape) == 3:
+                resized_mask = resized_mask[:,:,0]  # Take first channel if 3D
+            new_mask[pad_y:pad_y+new_h, pad_x:pad_x+new_w] = resized_mask
+
+            output_images.append(new_img)
+            output_masks.append(new_mask)
+
+        if not output_images:
+            return (torch.zeros((0, height, width, 3), dtype=image.dtype),)
+
+        out_rgb = torch.stack(output_images, dim=0)
+        out_masks = torch.stack(output_masks, dim=0)
+
+        # Apply mask to RGB
+        mask_expanded = out_masks.unsqueeze(-1).expand(-1, -1, -1, 3)
+        background_color = torch.tensor(bg_color, dtype=torch.float32, device=image.device)
+        out_rgb = out_rgb * mask_expanded + background_color * (1 - mask_expanded)
+
+        return (out_rgb, out_masks)
+    
+class ImagePadKJ:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "image": ("IMAGE", ),
+                    "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
+                    "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
+                    "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
+                    "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
+                    "extra_padding": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
+                    "pad_mode": (["edge", "edge_pixel", "color", "pillarbox_blur"],),
+                    "color": ("STRING", {"default": "0, 0, 0", "tooltip": "Color as RGB values in range 0-255 or 0.0-1.0, or color name or hex code"}),
+                  },
+                "optional": {
+                    "mask": ("MASK", ),
+                    "target_width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, "forceInput": True}),
+                    "target_height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, "forceInput": True}),
+                }
+                }
+    
+    RETURN_TYPES = ("IMAGE", "MASK", )
+    RETURN_NAMES = ("images", "masks",)
+    FUNCTION = "pad"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = "Pad the input image and optionally mask with the specified padding."
+        
+    def pad(self, image, left, right, top, bottom, extra_padding, color, pad_mode, mask=None, target_width=None, target_height=None):
+        B, H, W, C = image.shape
+        # Resize masks to image dimensions if necessary
+        if mask is not None:
+            BM, HM, WM = mask.shape
+            if HM != H or WM != W:
+                mask = F.interpolate(mask.unsqueeze(1), size=(H, W), mode='nearest-exact').squeeze(1)
+
+        # Parse background color using helper function
+        color_list = string_to_color(color)
+        bg_color = [x / 255.0 for x in color_list]
+        if len(bg_color) == 1:
+            bg_color = bg_color * 3  # Grayscale to RGB
+        bg_color = torch.tensor(bg_color, dtype=image.dtype, device=image.device)
+
+        # Calculate padding sizes with extra padding
+        if target_width is not None and target_height is not None:
+            if extra_padding > 0:
+                image = common_upscale(image.movedim(-1, 1), W - extra_padding, H - extra_padding, "lanczos", "disabled").movedim(1, -1)
+                B, H, W, C = image.shape
+
+            padded_width = target_width
+            padded_height = target_height
+            pad_left = (padded_width - W) // 2
+            pad_right = padded_width - W - pad_left
+            pad_top = (padded_height - H) // 2
+            pad_bottom = padded_height - H - pad_top
+        else:
+            pad_left = left + extra_padding
+            pad_right = right + extra_padding
+            pad_top = top + extra_padding
+            pad_bottom = bottom + extra_padding
+
+            padded_width = W + pad_left + pad_right
+            padded_height = H + pad_top + pad_bottom
+
+        # Pillarbox blur mode
+        if pad_mode == "pillarbox_blur":
+            def _gaussian_blur_nchw(img_nchw, sigma_px):
+                if sigma_px <= 0:
+                    return img_nchw
+                radius = max(1, int(3.0 * float(sigma_px)))
+                k = 2 * radius + 1
+                x = torch.arange(-radius, radius + 1, device=img_nchw.device, dtype=img_nchw.dtype)
+                k1 = torch.exp(-(x * x) / (2.0 * float(sigma_px) * float(sigma_px)))
+                k1 = k1 / k1.sum()
+                kx = k1.view(1, 1, 1, k)
+                ky = k1.view(1, 1, k, 1)
+                c = img_nchw.shape[1]
+                kx = kx.repeat(c, 1, 1, 1)
+                ky = ky.repeat(c, 1, 1, 1)
+                img_nchw = F.conv2d(img_nchw, kx, padding=(0, radius), groups=c)
+                img_nchw = F.conv2d(img_nchw, ky, padding=(radius, 0), groups=c)
+                return img_nchw
+
+            out_image = torch.zeros((B, padded_height, padded_width, C), dtype=image.dtype, device=image.device)
+            for b in range(B):
+                scale_fill = max(padded_width / float(W), padded_height / float(H)) if (W > 0 and H > 0) else 1.0
+                bg_w = max(1, int(round(W * scale_fill)))
+                bg_h = max(1, int(round(H * scale_fill)))
+                src_b = image[b].movedim(-1, 0).unsqueeze(0)
+                bg = common_upscale(src_b, bg_w, bg_h, "bilinear", crop="disabled")
+                y0 = max(0, (bg_h - padded_height) // 2)
+                x0 = max(0, (bg_w - padded_width) // 2)
+                y1 = min(bg_h, y0 + padded_height)
+                x1 = min(bg_w, x0 + padded_width)
+                bg = bg[:, :, y0:y1, x0:x1]
+                if bg.shape[2] != padded_height or bg.shape[3] != padded_width:
+                    pad_h = padded_height - bg.shape[2]
+                    pad_w = padded_width - bg.shape[3]
+                    pad_top_fix = max(0, pad_h // 2)
+                    pad_bottom_fix = max(0, pad_h - pad_top_fix)
+                    pad_left_fix = max(0, pad_w // 2)
+                    pad_right_fix = max(0, pad_w - pad_left_fix)
+                    bg = F.pad(bg, (pad_left_fix, pad_right_fix, pad_top_fix, pad_bottom_fix), mode="replicate")
+                sigma = max(1.0, 0.006 * float(min(padded_height, padded_width)))
+                bg = _gaussian_blur_nchw(bg, sigma_px=sigma)
+                if C >= 3:
+                    r, g, bch = bg[:, 0:1], bg[:, 1:2], bg[:, 2:3]
+                    luma = 0.2126 * r + 0.7152 * g + 0.0722 * bch
+                    gray = torch.cat([luma, luma, luma], dim=1)
+                    desat = 0.20
+                    rgb = torch.cat([r, g, bch], dim=1)
+                    rgb = rgb * (1.0 - desat) + gray * desat
+                    bg[:, 0:3, :, :] = rgb
+                dim = 0.35
+                bg = torch.clamp(bg * dim, 0.0, 1.0)
+                out_image[b] = bg.squeeze(0).movedim(0, -1)
+            out_image[:, pad_top:pad_top+H, pad_left:pad_left+W, :] = image
+            # Mask handling for pillarbox_blur
+            if mask is not None:
+                fg_mask = mask
+                out_masks = torch.ones((B, padded_height, padded_width), dtype=image.dtype, device=image.device)
+                out_masks[:, pad_top:pad_top+H, pad_left:pad_left+W] = fg_mask
+            else:
+                out_masks = torch.ones((B, padded_height, padded_width), dtype=image.dtype, device=image.device)
+                out_masks[:, pad_top:pad_top+H, pad_left:pad_left+W] = 0.0
+            return (out_image, out_masks)
+
+        # Standard pad logic (edge/color)
+        out_image = torch.zeros((B, padded_height, padded_width, C), dtype=image.dtype, device=image.device)
+        for b in range(B):
+                if pad_mode == "edge":
+                    # Pad with edge color (mean)
+                    top_edge = image[b, 0, :, :]
+                    bottom_edge = image[b, H-1, :, :]
+                    left_edge = image[b, :, 0, :]
+                    right_edge = image[b, :, W-1, :]
+                    out_image[b, :pad_top, :, :] = top_edge.mean(dim=0)
+                    out_image[b, pad_top+H:, :, :] = bottom_edge.mean(dim=0)
+                    out_image[b, :, :pad_left, :] = left_edge.mean(dim=0)
+                    out_image[b, :, pad_left+W:, :] = right_edge.mean(dim=0)
+                    out_image[b, pad_top:pad_top+H, pad_left:pad_left+W, :] = image[b]
+                elif pad_mode == "edge_pixel":
+                    # Pad with exact edge pixel values
+                    for y in range(pad_top):
+                        out_image[b, y, pad_left:pad_left+W, :] = image[b, 0, :, :]
+                    for y in range(pad_top+H, padded_height):
+                        out_image[b, y, pad_left:pad_left+W, :] = image[b, H-1, :, :]
+                    for x in range(pad_left):
+                        out_image[b, pad_top:pad_top+H, x, :] = image[b, :, 0, :]
+                    for x in range(pad_left+W, padded_width):
+                        out_image[b, pad_top:pad_top+H, x, :] = image[b, :, W-1, :]
+                    out_image[b, :pad_top, :pad_left, :] = image[b, 0, 0, :]
+                    out_image[b, :pad_top, pad_left+W:, :] = image[b, 0, W-1, :]
+                    out_image[b, pad_top+H:, :pad_left, :] = image[b, H-1, 0, :]
+                    out_image[b, pad_top+H:, pad_left+W:, :] = image[b, H-1, W-1, :]
+                    out_image[b, pad_top:pad_top+H, pad_left:pad_left+W, :] = image[b]
+                else:
+                    # Pad with specified background color
+                    out_image[b, :, :, :] = bg_color.unsqueeze(0).unsqueeze(0)
+                    out_image[b, pad_top:pad_top+H, pad_left:pad_left+W, :] = image[b]
+
+        if mask is not None:
+            out_masks = torch.nn.functional.pad(
+                mask, 
+                (pad_left, pad_right, pad_top, pad_bottom),
+                mode='replicate'
+            )
+        else:
+            out_masks = torch.ones((B, padded_height, padded_width), dtype=image.dtype, device=image.device)
+            for m in range(B):
+                out_masks[m, pad_top:pad_top+H, pad_left:pad_left+W] = 0.0
+
+        return (out_image, out_masks)
+
+# extends https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite
+class LoadVideosFromFolder:
+    @classmethod
+    def __init__(cls):
+        try:
+            cls.vhs_nodes = importlib.import_module("ComfyUI-VideoHelperSuite.videohelpersuite")
+        except ImportError:
+            try:
+                cls.vhs_nodes = importlib.import_module("comfyui-videohelpersuite.videohelpersuite")
+            except ImportError:
+                # Fallback to sys.modules search for Windows compatibility
+                import sys
+                vhs_module = None
+                for module_name in sys.modules:
+                    if 'videohelpersuite' in module_name and 'videohelpersuite' in sys.modules[module_name].__dict__:
+                        vhs_module = sys.modules[module_name]
+                        break
+                
+                if vhs_module is None:
+                    # Try direct access to the videohelpersuite submodule
+                    for module_name in sys.modules:
+                        if module_name.endswith('videohelpersuite'):
+                            vhs_module = sys.modules[module_name]
+                            break
+                
+                if vhs_module is not None:
+                    cls.vhs_nodes = vhs_module
+                else:
+                    raise ImportError("This node requires ComfyUI-VideoHelperSuite to be installed.")
+                
+        except ImportError:
+            raise ImportError("This node requires ComfyUI-VideoHelperSuite to be installed.")
+          
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "video": ("STRING", {"default": "X://insert/path/"},),
+                "force_rate": ("FLOAT", {"default": 0, "min": 0, "max": 60, "step": 1, "disable": 0}),
+                "custom_width": ("INT", {"default": 0, "min": 0, "max": 4096, 'disable': 0}),
+                "custom_height": ("INT", {"default": 0, "min": 0, "max": 4096, 'disable': 0}),
+                "frame_load_cap": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1, "disable": 0}),
+                "skip_first_frames": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1}),
+                "select_every_nth": ("INT", {"default": 1, "min": 1, "max": 1000, "step": 1}),
+                "output_type": (["batch", "grid"], {"default": "batch"}),
+                "grid_max_columns": ("INT", {"default": 4, "min": 1, "max": 16, "step": 1, "disable": 1}),
+                "add_label": ( "BOOLEAN", {"default": False} ),
+            },
+            "hidden": {
+                "force_size": "STRING",
+                "unique_id": "UNIQUE_ID"
+            },
+        }
+
+    CATEGORY = "KJNodes/misc"
+
+    RETURN_TYPES = ("IMAGE", )
+    RETURN_NAMES = ("IMAGE", )
+
+    FUNCTION = "load_video"
+
+    def load_video(self, output_type, grid_max_columns, add_label=False, **kwargs):
+        if kwargs.get('video') and not os.path.isabs(kwargs['video']) and args.base_directory:
+            kwargs['video'] = os.path.join(args.base_directory, kwargs['video'])
+            
+        if self.vhs_nodes is None:
+            raise ImportError("This node requires ComfyUI-VideoHelperSuite to be installed.")
+        videos_list = []
+        filenames = []
+        for f in sorted(os.listdir(kwargs['video'])):
+            if os.path.isfile(os.path.join(kwargs['video'], f)):
+                file_parts = f.split('.')
+                if len(file_parts) > 1 and (file_parts[-1].lower() in ['webm', 'mp4', 'mkv', 'gif', 'mov']):
+                    videos_list.append(os.path.join(kwargs['video'], f))
+                    filenames.append(f)
+
+        kwargs.pop('video')
+        loaded_videos = []
+        for idx, video in enumerate(videos_list):
+            video_tensor = self.vhs_nodes.load_video_nodes.load_video(video=video, **kwargs)[0]
+            if add_label:
+                # Add filename label above video (without extension)
+                if video_tensor.dim() == 4:
+                    _, h, w, c = video_tensor.shape
+                else:
+                    h, w, c = video_tensor.shape
+                # Remove extension from filename
+                label_text = filenames[idx].rsplit('.', 1)[0]
+                font_size = max(16, w // 20)
+                try:
+                    font = ImageFont.truetype("arial.ttf", font_size)
+                except OSError:
+                    font = ImageFont.load_default()
+                dummy_img = Image.new("RGB", (w, 10), (0,0,0))
+                draw = ImageDraw.Draw(dummy_img)
+                text_bbox = draw.textbbox((0,0), label_text, font=font)
+                extra_padding = max(12, font_size // 2)  # More padding under the font
+                label_height = text_bbox[3] - text_bbox[1] + extra_padding
+                label_img = Image.new("RGB", (w, label_height), (0,0,0))
+                draw = ImageDraw.Draw(label_img)
+                draw.text((w//2 - (text_bbox[2]-text_bbox[0])//2, 4), label_text, font=font, fill=(255,255,255))
+                label_np = np.asarray(label_img).astype(np.float32) / 255.0
+                label_tensor = torch.from_numpy(label_np)
+                if c == 1:
+                    label_tensor = label_tensor.mean(dim=2, keepdim=True)
+                elif c == 4:
+                    alpha = torch.ones((label_height, w, 1), dtype=label_tensor.dtype)
+                    label_tensor = torch.cat([label_tensor, alpha], dim=2)
+                if video_tensor.dim() == 4:
+                    label_tensor = label_tensor.unsqueeze(0).expand(video_tensor.shape[0], -1, -1, -1)
+                    video_tensor = torch.cat([label_tensor, video_tensor], dim=1)
+                else:
+                    video_tensor = torch.cat([label_tensor, video_tensor], dim=0)
+            loaded_videos.append(video_tensor)
+        if output_type == "batch":
+            out_tensor = torch.cat(loaded_videos)
+        elif output_type == "grid":
+            rows = (len(loaded_videos) + grid_max_columns - 1) // grid_max_columns
+            # Pad the last row if needed
+            total_slots = rows * grid_max_columns
+            while len(loaded_videos) < total_slots:
+                loaded_videos.append(torch.zeros_like(loaded_videos[0]))
+            # Create grid by rows
+            row_tensors = []
+            for row_idx in range(rows):
+                start_idx = row_idx * grid_max_columns
+                end_idx = start_idx + grid_max_columns
+                row_videos = loaded_videos[start_idx:end_idx]
+                # Pad all videos in this row to the same height
+                heights = [v.shape[1] for v in row_videos]
+                max_height = max(heights)
+                padded_row_videos = []
+                for v in row_videos:
+                    pad_height = max_height - v.shape[1]
+                    if pad_height > 0:
+                        # Pad (frames, H, W, C) or (H, W, C)
+                        if v.dim() == 4:
+                            pad = (0,0, 0,0, 0,pad_height, 0,0)  # (C,W,H,F)
+                            v = torch.nn.functional.pad(v, (0,0,0,0,0,pad_height,0,0))
+                        else:
+                            v = torch.nn.functional.pad(v, (0,0,0,0,pad_height,0))
+                    padded_row_videos.append(v)
+                row_tensor = torch.cat(padded_row_videos, dim=2)  # Concatenate horizontally
+                row_tensors.append(row_tensor)
+            out_tensor = torch.cat(row_tensors, dim=1)  # Concatenate rows vertically
+        return out_tensor,
+
+    @classmethod
+    def IS_CHANGED(s, video, **kwargs):
+        if s.vhs_nodes is not None:
+            return s.vhs_nodes.utils.hash_path(video)
+        return None
+
+
+class EncodeVideoComponents(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        position_options = ["center", "top", "bottom", "left", "right"]
+        options = [
+            io.DynamicCombo.Option(key="stretch", inputs=[]),
+            io.DynamicCombo.Option(key="resize", inputs=[]),
+            io.DynamicCombo.Option(key="total_pixels", inputs=[]),
+            io.DynamicCombo.Option(key="crop", inputs=[
+                io.Combo.Input("crop_position", options=position_options, tooltip="Position to crop from."),
+            ]),
+            io.DynamicCombo.Option(key="pad", inputs=[
+                io.String.Input("pad_color", default="0, 0, 0", tooltip="Color to use for padding."),
+                io.Combo.Input("pad_position", options=position_options, tooltip="Position to align the image within the padded area."),
+            ]),
+            io.DynamicCombo.Option(key="pad_edge", inputs=[
+                io.Combo.Input("pad_position", options=position_options, tooltip="Position to align the image within the padded area."),
+            ]),
+            io.DynamicCombo.Option(key="pad_edge_pixel", inputs=[
+                io.Combo.Input("pad_position", options=position_options, tooltip="Position to align the image within the padded area."),
+            ]),
+            io.DynamicCombo.Option(key="pillarbox_blur", inputs=[
+                io.Combo.Input("pad_position", options=position_options, tooltip="Position to align the image within the padded area."),
+            ]),
+        ]
+        return io.Schema(
+            node_id="EncodeVideoComponents",
+            search_aliases=["video to latent", "encode video", "vae encode video"],
+            display_name="Encode Video Components",
+            category="KJNodes/image",
+            description="Extracts video frames, resizes them, and encodes with a VAE directly, avoiding storing the full image tensor.",
+            inputs=[
+                io.Video.Input("video", tooltip="The video to extract and encode."),
+                io.Vae.Input("vae", tooltip="The VAE model to use for encoding."),
+                io.Int.Input("width", default=768, min=0, max=16384, step=2, tooltip="Target width for the frames before encoding. 0 = original width."),
+                io.Int.Input("height", default=512, min=0, max=16384, step=2, tooltip="Target height for the frames before encoding. 0 = original height."),
+                io.Int.Input("max_frames", default=0, min=0, max=999999, step=1, tooltip="Maximum number of frames. 0 = no limit."),
+                io.Combo.Input("upscale_method", options=["nearest-exact", "bilinear", "area", "bicubic", "lanczos"], default="lanczos", tooltip="Interpolation method for resizing."),
+                io.DynamicCombo.Input(
+                    "keep_proportion",
+                    options=options,
+                    display_name="Keep Proportion",
+                    tooltip="How to handle aspect ratio mismatch when resizing.",
+                ),
+            ],
+            outputs=[
+                io.Latent.Output(display_name="latent"),
+                io.Audio.Output(display_name="audio"),
+                io.Float.Output(display_name="fps"),
+                io.Int.Output(display_name="frame_count", tooltip="Number pixel space frames after any possible cropping"),
+            ],
+        )
+
+    @staticmethod
+    def _compute_resize_params(mode, position, width, height, src_w, src_h):
+        """Compute target resize dimensions, crop region, and padding from keep_proportion mode."""
+        if width == 0:
+            width = src_w
+        if height == 0:
+            height = src_h
+        pillarbox_blur = mode == "pillarbox_blur"
+        pad_left = pad_right = pad_top = pad_bottom = 0
+        crop_region = None  # (x, y, crop_w, crop_h) or None
+
+        if mode in ["resize", "total_pixels"] or mode.startswith("pad") or pillarbox_blur:
+            if mode == "total_pixels":
+                total_pixels = width * height
+                aspect_ratio = src_w / src_h
+                new_height = int(math.sqrt(total_pixels / aspect_ratio))
+                new_width = int(math.sqrt(total_pixels * aspect_ratio))
+            else:
+                ratio = min(width / src_w, height / src_h)
+                new_width = round(src_w * ratio)
+                new_height = round(src_h * ratio)
+
+            if mode.startswith("pad") or pillarbox_blur:
+                if position == "center":
+                    pad_left = (width - new_width) // 2
+                    pad_right = width - new_width - pad_left
+                    pad_top = (height - new_height) // 2
+                    pad_bottom = height - new_height - pad_top
+                elif position == "top":
+                    pad_left = (width - new_width) // 2
+                    pad_right = width - new_width - pad_left
+                    pad_top = 0
+                    pad_bottom = height - new_height
+                elif position == "bottom":
+                    pad_left = (width - new_width) // 2
+                    pad_right = width - new_width - pad_left
+                    pad_top = height - new_height
+                    pad_bottom = 0
+                elif position == "left":
+                    pad_left = 0
+                    pad_right = width - new_width
+                    pad_top = (height - new_height) // 2
+                    pad_bottom = height - new_height - pad_top
+                elif position == "right":
+                    pad_left = width - new_width
+                    pad_right = 0
+                    pad_top = (height - new_height) // 2
+                    pad_bottom = height - new_height - pad_top
+
+            width = new_width
+            height = new_height
+
+        if mode == "crop":
+            old_aspect = src_w / src_h
+            new_aspect = width / height
+            if old_aspect > new_aspect:
+                crop_w = round(src_h * new_aspect)
+                crop_h = src_h
+            else:
+                crop_w = src_w
+                crop_h = round(src_w / new_aspect)
+            if position == "center":
+                x = (src_w - crop_w) // 2
+                y = (src_h - crop_h) // 2
+            elif position == "top":
+                x = (src_w - crop_w) // 2
+                y = 0
+            elif position == "bottom":
+                x = (src_w - crop_w) // 2
+                y = src_h - crop_h
+            elif position == "left":
+                x = 0
+                y = (src_h - crop_h) // 2
+            elif position == "right":
+                x = src_w - crop_w
+                y = (src_h - crop_h) // 2
+            crop_region = (x, y, crop_w, crop_h)
+
+        return width, height, crop_region, (pad_left, pad_right, pad_top, pad_bottom)
+
+    @classmethod
+    def execute(cls, video, vae, width, height, max_frames, upscale_method, keep_proportion) -> io.NodeOutput:
+        import av
+        import itertools
+
+        mode = keep_proportion["keep_proportion"]
+        position = keep_proportion.get("crop_position") or keep_proportion.get("pad_position", "center")
+        pad_color = keep_proportion.get("pad_color", "0, 0, 0")
+        target_dtype = vae.vae_dtype
+
+        # Access VideoFromFile internals for efficient per-frame decode
+        source = video.get_stream_source()
+        start_time = getattr(video, '_VideoFromFile__start_time', 0)
+        duration = getattr(video, '_VideoFromFile__duration', 0)
+
+        # Get frame count for progress bar, capped by max_frames
+        try:
+            total_frames = video.get_frame_count()
+        except (ValueError, AttributeError):
+            total_frames = 0
+        if max_frames > 0 and total_frames > 0:
+            total_frames = min(total_frames, max_frames)
+        pbar = ProgressBar(total_frames) if total_frames > 0 else None
+
+        # Lanczos requires PIL (CPU-only), all other methods use torch on GPU
+        use_gpu = upscale_method != "lanczos"
+        device = model_management.get_torch_device() if use_gpu else torch.device("cpu")
+
+        # --- Decode video frames with per-frame resize + dtype cast ---
+        with av.open(source, mode='r') as container:
+            video_stream = container.streams.video[0]
+            start_pts = int(start_time / video_stream.time_base)
+            end_pts = int((start_time + duration) / video_stream.time_base) if duration else 0
+            container.seek(start_pts, stream=video_stream)
+
+            res_w, res_h, crop_region, padding = None, None, None, (0, 0, 0, 0)
+            frames = []
+            for frame in container.decode(video_stream):
+                if frame.pts < start_pts:
+                    continue
+                if duration and frame.pts >= end_pts:
+                    break
+                if max_frames > 0 and len(frames) >= max_frames:
+                    break
+
+                if res_w is None:
+                    src_h, src_w = frame.height, frame.width
+                    res_w, res_h, crop_region, padding = cls._compute_resize_params(
+                        mode, position, width, height, src_w, src_h
+                    )
+
+                # Decode to tensor and normalize
+                img = torch.from_numpy(frame.to_ndarray(format='rgb24')).to(device=device, dtype=torch.float32) / 255.0
+
+                # Crop if needed (before resize)
+                if crop_region is not None:
+                    cx, cy, cw, ch = crop_region
+                    img = img[cy:cy+ch, cx:cx+cw, :]
+
+                # Resize (GPU for torch-native methods, CPU/PIL for lanczos)
+                img = common_upscale(
+                    img.unsqueeze(0).movedim(-1, 1), res_w, res_h, upscale_method, crop="disabled"
+                ).movedim(1, -1).squeeze(0).to(dtype=target_dtype, device="cpu")
+
+                frames.append(img)
+                if pbar is not None:
+                    pbar.update(1)
+
+            frame_rate = video_stream.average_rate if video_stream.average_rate else 1
+
+        s = torch.stack(frames) if frames else torch.zeros(0, height, width, 3, dtype=target_dtype)
+
+        # Pad logic (applied on the full stack since padding modes like pillarbox_blur need all frames)
+        pillarbox_blur = mode == "pillarbox_blur"
+        pad_left, pad_right, pad_top, pad_bottom = padding
+        if (mode.startswith("pad") or pillarbox_blur) and (pad_left > 0 or pad_right > 0 or pad_top > 0 or pad_bottom > 0):
+            pad_mode = (
+                "pillarbox_blur" if pillarbox_blur else
+                "edge" if mode == "pad_edge" else
+                "edge_pixel" if mode == "pad_edge_pixel" else
+                "color"
+            )
+            s, _ = ImagePadKJ.pad(None, s, pad_left, pad_right, pad_top, pad_bottom, 0, pad_color, pad_mode)
+
+        # Trim frames to a count valid for the VAE's temporal compression
+        try:
+            temporal_compress = vae.downscale_ratio[0]
+            temporal_decompress = vae.upscale_ratio[0]
+            valid_frames = temporal_decompress(temporal_compress(s.shape[0]))
+            if valid_frames < s.shape[0]:
+                logging.warning(f"[EncodeVideoComponents] Trimming {s.shape[0] - valid_frames} frames ({s.shape[0]} -> {valid_frames}) to match VAE temporal compression ratio")
+                s = s[:valid_frames]
+        except (TypeError, IndexError):
+            pass
+
+        t = vae.encode(s)
+
+        # --- Extract audio in a separate pass ---
+        audio = None
+        if isinstance(source, BytesIO):
+            source.seek(0)
+        with av.open(source, mode='r') as container:
+            if len(container.streams.audio):
+                audio_stream = container.streams.audio[-1]
+                if start_time > 0:
+                    audio_start_pts = int(start_time / audio_stream.time_base)
+                    container.seek(audio_start_pts, stream=audio_stream)
+                audio_frames = []
+                resample = av.audio.resampler.AudioResampler(format='fltp').resample
+                aframes = itertools.chain.from_iterable(
+                    map(resample, container.decode(audio_stream))
+                )
+                has_first_frame = False
+                for aframe in aframes:
+                    offset_seconds = start_time - aframe.time
+                    to_skip = int(offset_seconds * audio_stream.sample_rate)
+                    if to_skip < aframe.samples:
+                        has_first_frame = True
+                        break
+                if has_first_frame:
+                    audio_frames.append(aframe.to_ndarray()[..., to_skip:])
+                    for aframe in aframes:
+                        if duration and aframe.time > start_time + duration:
+                            break
+                        audio_frames.append(aframe.to_ndarray())
+                if audio_frames:
+                    audio_data = np.concatenate(audio_frames, axis=1)
+                    if duration:
+                        audio_data = audio_data[..., :int(duration * audio_stream.sample_rate)]
+                    audio = {
+                        "waveform": torch.from_numpy(audio_data).unsqueeze(0),
+                        "sample_rate": int(audio_stream.sample_rate) if audio_stream.sample_rate else 1,
+                    }
+
+        return io.NodeOutput({"samples": t}, audio, float(frame_rate), s.shape[0])
+
+
+class DecodeAndSaveVideo(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DecodeAndSaveVideo",
+            search_aliases=["video to latent", "decode video"],
+            display_name="Decode and Save Video",
+            category="KJNodes/image",
+            description="Decodes video frames and audio from latent representations, combines them, and saves as a video file, without keeping intermediate images in memory.",
+            inputs=[
+                io.Latent.Input("video_latent", tooltip="The latent representation of the video frames."),
+                io.Latent.Input("audio_latent", optional=True, tooltip="The latent representation of the audio frames."),
+                io.Float.Input("fps", default=25.0, min=0.0, max=999.0, step=0.01, tooltip="Frame rate for the output video."),
+                io.String.Input("filename_prefix", default="video/ComfyUI", tooltip="The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."),
+                io.Combo.Input("format", options=Types.VideoContainer.as_input(), default="auto", tooltip="The format to save the video as."),
+                io.Combo.Input("codec", options=Types.VideoCodec.as_input(), default="auto", tooltip="The codec to use for the video."),
+                io.Vae.Input("video_vae", tooltip="The VAE model to use for encoding."),
+                io.Vae.Input("audio_vae", optional=True, tooltip="The VAE model to use for decoding audio."),
+                io.DynamicCombo.Input("tiling", options=[
+                    io.DynamicCombo.Option(key="disabled", inputs=[]),
+                    io.DynamicCombo.Option(key="enabled", inputs=[
+                        io.Int.Input("tile_size", default=512, min=64, max=4096, step=32, tooltip="Size of the tiles to decode. Smaller tiles use less memory but take more time."),
+                        io.Int.Input("overlap", default=64, min=0, max=4096, step=32, tooltip="Amount of overlap between tiles. Higher overlap can improve quality at the edges of tiles but uses more memory and takes more time."),
+                        io.Int.Input("temporal_size", default=4096, min=8, max=4096, step=4, tooltip="Only used for video VAEs: Amount of frames to decode at a time. Higher value than number of frames = disabled"),
+                        io.Int.Input("temporal_overlap", default=16, min=4, max=4096, step=4, tooltip="Only used for video VAEs: Amount of frames to overlap. Higher overlap can improve quality at the edges of temporal tiles but uses more memory and takes more time."),
+                    ]),
+                ]),
+            ],
+            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, video_latent, video_vae, filename_prefix, format, codec, tiling, audio_latent=None, audio_vae=None, fps=25.0) -> io.NodeOutput:
+        if tiling["tiling"] == "enabled":
+            tile_size = tiling["tile_size"]
+            overlap = tiling["overlap"]
+            temporal_size = tiling["temporal_size"]
+            temporal_overlap = tiling["temporal_overlap"]
+
+            if tile_size < overlap * 4:
+                overlap = tile_size // 4
+            if temporal_size < temporal_overlap * 2:
+                temporal_overlap = temporal_overlap // 2
+            temporal_compression = video_vae.temporal_compression_decode()
+            if temporal_compression is not None:
+                temporal_size = max(2, temporal_size // temporal_compression)
+                temporal_overlap = max(1, min(temporal_size // 2, temporal_overlap // temporal_compression))
+            else:
+                temporal_size = None
+                temporal_overlap = None
+
+            compression = video_vae.spacial_compression_decode()
+
+            images = cls.decode_tiled(video_vae, video_latent["samples"],
+                                      tile_t=max(2, temporal_size),
+                                      tile_x=tile_size // compression,
+                                      tile_y=tile_size // compression,
+                                      overlap=(temporal_overlap if temporal_overlap is not None else 1, max(1, overlap // compression), max(1, overlap // compression)),
+            ).movedim(1, -1)
+            if len(images.shape) == 5: #Combine batches
+                images = images.reshape(-1, images.shape[-3], images.shape[-2], images.shape[-1])
+        else:
+            images = cls.decode_video(video_vae, video_latent)
+
+        if audio_latent is not None:
+            if audio_vae is None:
+                raise ValueError("Audio VAE must be provided if audio latent is provided.")
+            audio = cls.decode_audio(audio_latent, audio_vae)
+        else:
+            audio = None
+
+        video = InputImpl.VideoFromComponents(Types.VideoComponents(images=images, audio=audio, frame_rate=Fraction(fps)))
+        file, subfolder = cls.save_video(video, filename_prefix, format, codec)
+
+        return io.NodeOutput(ui=ui.PreviewVideo([ui.SavedResult(file, subfolder, io.FolderType.output)]))
+
+    @classmethod
+    def decode_video(cls, vae, samples):
+        samples_in = samples["samples"]
+        if samples_in.is_nested:
+            samples_in = samples_in.unbind()[0]
+
+        vae.throw_exception_if_invalid()
+        pixel_samples = None
+        do_tile = False
+        if vae.latent_dim == 2 and samples_in.ndim == 5:
+            samples_in = samples_in[:, :, 0]
+        try:
+            memory_used = vae.memory_used_decode(samples_in.shape, vae.vae_dtype)
+            model_management.load_models_gpu([vae.patcher], memory_required=memory_used, force_full_load=True)
+            free_memory = vae.patcher.get_free_memory(vae.device)
+            batch_number = int(free_memory / memory_used)
+            batch_number = max(1, batch_number)
+
+            for x in range(0, samples_in.shape[0], batch_number):
+                samples = samples_in[x:x+batch_number].to(vae.vae_dtype).to(vae.device)
+                out = vae.process_output(vae.first_stage_model.decode(samples).to(vae.output_device).to(torch.float16))
+                if pixel_samples is None:
+                    pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=vae.output_device, dtype=out.dtype)
+                pixel_samples[x:x+batch_number] = out
+        except Exception as e:
+            model_management.raise_non_oom(e)
+            logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
+            do_tile = True
+
+        if do_tile:
+            dims = samples_in.ndim - 2
+            if dims == 1 or cls.extra_1d_channel is not None:
+                pixel_samples = vae.decode_tiled_1d(samples_in)
+            elif dims == 2:
+                pixel_samples = vae.decode_tiled_2d(samples_in)
+            elif dims == 3:
+                tile = 256 // vae.spacial_compression_decode()
+                overlap = tile // 4
+                pixel_samples = vae.decode_tiled_3d(samples_in, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap))
+
+        pixel_samples = pixel_samples.to(vae.output_device).movedim(1,-1)
+
+        if len(pixel_samples.shape) == 5: #Combine batches
+            pixel_samples = pixel_samples.reshape(-1, pixel_samples.shape[-3], pixel_samples.shape[-2], pixel_samples.shape[-1])
+        return pixel_samples
+
+    @classmethod
+    def decode_tiled(cls, vae, samples, tile_t=999, tile_x=32, tile_y=32, overlap=(1, 8, 8)):
+        vae.throw_exception_if_invalid()
+        memory_used = vae.memory_used_decode(samples.shape, vae.vae_dtype)
+        model_management.load_models_gpu([vae.patcher], memory_required=memory_used, force_full_load=vae.disable_offload)
+        decode_fn = lambda a: vae.first_stage_model.decode(a.to(vae.vae_dtype).to(vae.device)).to(torch.float16)
+        return vae.process_output(tiled_scale_multidim(samples, decode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap,
+                                                       upscale_amount=vae.upscale_ratio, out_channels=vae.output_channels, index_formulas=vae.upscale_index_formula, output_device=vae.output_device))
+
+
+    @classmethod
+    def decode_audio(cls, samples, audio_vae):
+        audio_latent = samples["samples"]
+        if audio_latent.is_nested:
+            audio_latent = audio_latent.unbind()[-1]
+        audio = audio_vae.decode(audio_latent)
+        # Post-PR #13486: audio_vae is a comfy.sd.VAE wrapper returning channels-last (BTC).
+        # Pre-PR: audio_vae is a raw AudioVAE returning channels-first (BCT).
+        if hasattr(audio_vae, "first_stage_model"):
+            audio = audio.movedim(-1, 1)
+        audio = audio.to(audio_latent.device)
+        output_audio_sample_rate = getattr(
+            audio_vae,
+            "audio_sample_rate_output",
+            getattr(audio_vae, "output_sample_rate", None),
+        )
+        if output_audio_sample_rate is None:
+            output_audio_sample_rate = getattr(
+                getattr(audio_vae, "first_stage_model", None), "output_sample_rate", 44100
+            )
+        return {"waveform": audio, "sample_rate": int(output_audio_sample_rate)}
+
+    @classmethod
+    def save_video(cls, video, filename_prefix, format, codec) -> io.NodeOutput:
+        width, height = video.get_dimensions()
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
+            filename_prefix,
+            folder_paths.get_output_directory(),
+            width,
+            height
+        )
+        saved_metadata = None
+        if not args.disable_metadata:
+            metadata = {}
+            if cls.hidden.extra_pnginfo is not None:
+                metadata.update(cls.hidden.extra_pnginfo)
+            if cls.hidden.prompt is not None:
+                metadata["prompt"] = cls.hidden.prompt
+            if len(metadata) > 0:
+                saved_metadata = metadata
+        file = f"{filename}_{counter:05}_.{Types.VideoContainer.get_extension(format)}"
+        video.save_to(
+            os.path.join(full_output_folder, file),
+            format=Types.VideoContainer(format),
+            codec=codec,
+            metadata=saved_metadata
+        )
+        return file, subfolder
+
+
+class PreviewImageOrMask(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="PreviewImageOrMask",
+            display_name="Preview Image Or Mask",
+            category="KJNodes/misc",
+            description="Previews the input images or masks.",
+            search_aliases=["output"],
+            inputs=[
+                io.MultiType.Input("input", [io.Image, io.Mask], tooltip="The image or mask to preview."),
+            ],
+            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, input) -> io.NodeOutput:
+        if input.ndim == 3:
+            return io.NodeOutput(ui=ui.PreviewMask(input, cls=cls))
+        return io.NodeOutput(ui=ui.PreviewImage(input, cls=cls))
+
diff --git a/custom_nodes/ComfyUI-KJNodes/nodes/image_transform_node.py b/custom_nodes/ComfyUI-KJNodes/nodes/image_transform_node.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2dc0f0ec8927182aa23d2a6247f1307226a8995
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/nodes/image_transform_node.py
@@ -0,0 +1,515 @@
+import numpy as np
+import torch
+import random
+import math
+import os
+import json
+
+from PIL import Image
+
+from comfy.utils import common_upscale
+from comfy_api.latest import io
+import folder_paths
+from nodes import MAX_RESOLUTION
+
+from ..utility.utility import string_to_color
+
+def _upscale_mask(mask, width, height, method, crop):
+    if method == "lanczos":
+        return common_upscale(mask.unsqueeze(1).repeat(1, 3, 1, 1), width, height, method, crop).movedim(1, -1)[:, :, :, 0]
+    return common_upscale(mask.unsqueeze(1), width, height, method, crop).squeeze(1)
+
+
+def _resize_single_channel(tensor, width, height):
+    """Resize a 3D (B,H,W) tensor using bilinear interpolation."""
+    return common_upscale(tensor.unsqueeze(1), width, height, "bilinear", "disabled").squeeze(1)
+
+
+def _pad_inputs():
+    """Shared pad_top/bottom/left/right input definitions for extra_padding options."""
+    return [
+        io.Int.Input("pad_top", default=0, min=0, max=MAX_RESOLUTION, step=1, tooltip="Padding pixels on top."),
+        io.Int.Input("pad_bottom", default=0, min=0, max=MAX_RESOLUTION, step=1, tooltip="Padding pixels on bottom."),
+        io.Int.Input("pad_left", default=0, min=0, max=MAX_RESOLUTION, step=1, tooltip="Padding pixels on left."),
+        io.Int.Input("pad_right", default=0, min=0, max=MAX_RESOLUTION, step=1, tooltip="Padding pixels on right."),
+    ]
+
+
+def _apply_padding(tensor, pad_top, pad_bottom, pad_left, pad_right, mode, edge_mode="clamp", fill_rgb=None):
+    """Apply padding to a BHWC tensor. Returns the padded tensor.
+    mode: 'color' or 'edge'
+    edge_mode: 'clamp', 'repeat', 'mirror' (only used when mode='edge')
+    fill_rgb: list of [r, g, b] float values 0-1 (only used when mode='color')
+    """
+    h, w = tensor.shape[1], tensor.shape[2]
+    new_h = h + pad_top + pad_bottom
+    new_w = w + pad_left + pad_right
+
+    if mode == "color":
+        fill = fill_rgb or [0.0, 0.0, 0.0]
+        padded = torch.zeros(tensor.shape[0], new_h, new_w, tensor.shape[3], device=tensor.device, dtype=tensor.dtype)
+        for c in range(min(3, tensor.shape[3])):
+            padded[:, :, :, c] = fill[c]
+        padded[:, pad_top:pad_top+h, pad_left:pad_left+w, :] = tensor
+        return padded
+
+    # mode == "edge"
+    if edge_mode == "clamp":
+        padded = torch.zeros(tensor.shape[0], new_h, new_w, tensor.shape[3], device=tensor.device, dtype=tensor.dtype)
+        padded[:, pad_top:pad_top+h, pad_left:pad_left+w, :] = tensor
+        if pad_top > 0:
+            padded[:, :pad_top, pad_left:pad_left+w, :] = tensor[:, 0:1, :, :].expand(-1, pad_top, -1, -1)
+        if pad_bottom > 0:
+            padded[:, pad_top+h:, pad_left:pad_left+w, :] = tensor[:, -1:, :, :].expand(-1, pad_bottom, -1, -1)
+        if pad_left > 0:
+            padded[:, :, :pad_left, :] = padded[:, :, pad_left:pad_left+1, :].expand(-1, -1, pad_left, -1)
+        if pad_right > 0:
+            padded[:, :, pad_left+w:, :] = padded[:, :, pad_left+w-1:pad_left+w, :].expand(-1, -1, pad_right, -1)
+        return padded
+    elif edge_mode == "repeat":
+        tiles_x = (new_w + w - 1) // w + 1
+        tiles_y = (new_h + h - 1) // h + 1
+        tiled = tensor.repeat(1, tiles_y, tiles_x, 1)
+        # Offset so original content lands at (pad_top, pad_left) in output
+        off_x = (w - pad_left % w) % w
+        off_y = (h - pad_top % h) % h
+        return tiled[:, off_y:off_y+new_h, off_x:off_x+new_w, :]
+    elif edge_mode == "mirror":
+        flipped_h = tensor.flip(2)
+        flipped_v = tensor.flip(1)
+        flipped_hv = tensor.flip(1).flip(2)
+        mirror_block = torch.cat([
+            torch.cat([tensor, flipped_h], dim=2),
+            torch.cat([flipped_v, flipped_hv], dim=2),
+        ], dim=1)
+        mb_h, mb_w = mirror_block.shape[1], mirror_block.shape[2]
+        tiles_x = (new_w + mb_w - 1) // mb_w + 1
+        tiles_y = (new_h + mb_h - 1) // mb_h + 1
+        tiled = mirror_block.repeat(1, tiles_y, tiles_x, 1)
+        # Offset so original content lands at (pad_top, pad_left) in output
+        off_x = (mb_w - pad_left % mb_w) % mb_w
+        off_y = (mb_h - pad_top % mb_h) % mb_h
+        return tiled[:, off_y:off_y+new_h, off_x:off_x+new_w, :]
+    return tensor
+
+
+class ImageTransformKJ(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ImageTransformKJ",
+            display_name="Image Transform KJ",
+            category="KJNodes/image",
+            search_aliases=["resize", "crop", "pad", "upscale", "keep proportion", "bbox", "bounding box", "transform", "rotate", "mirror"],
+            is_experimental=True,
+            description="""
+Interactive image transform node: crop, resize, pad, and rotate.  
+Connect an image input — the preview appears automatically.  
+
+Cropping:  
+Click + drag to draw a crop region.  
+Drag inside to move, drag edges/corners to resize.  
+Right-click to delete a region.  
+Ctrl to snap to grid.  
+Shift + resize to constrain aspect ratio.  
+Alt + resize to resize symmetrically.  
+
+Padding:  
+Shift + drag to adjust padding position.
+
+Rotate button enables rotation cross (drag to rotate, right-click to reset).  
+Set target_width/height to resize output (0 = keep original).  
+Use keep_proportion to control how the image fits the target.  
+Use extra_padding to add padding with color or edge fill (clamp/repeat/mirror).""",
+            inputs=[
+                io.MatchType.Input("image", io.MatchType.Template("img_or_mask", [io.Image, io.Mask]), tooltip="The image or mask to transform."),
+                io.Mask.Input("mask", optional=True, tooltip="Optional mask to transform alongside the image."),
+                io.Int.Input("target_width", default=0, min=0, max=MAX_RESOLUTION, step=1, tooltip="Target output width. 0 = keep original dimensions."),
+                io.Int.Input("target_height", default=0, min=0, max=MAX_RESOLUTION, step=1, tooltip="Target output height. 0 = keep original dimensions."),
+                io.Combo.Input("upscale_method", options=["nearest-exact", "bilinear", "area", "bicubic", "lanczos"], default="lanczos", tooltip="Interpolation method for resizing."),
+                io.DynamicCombo.Input("keep_proportion", options=[
+                    io.DynamicCombo.Option(key="keep_long_edge", inputs=[]),
+                    io.DynamicCombo.Option(key="keep_short_edge", inputs=[]),
+                    io.DynamicCombo.Option(key="total_pixels", inputs=[]),
+                    io.DynamicCombo.Option(key="stretch", inputs=[]),
+                    io.DynamicCombo.Option(key="crop", inputs=[]),
+                    io.DynamicCombo.Option(key="pad_color", inputs=[
+                        io.Float.Input("pad_x", default=0.5, min=0.0, max=1.0, step=0.01,
+                            tooltip="Horizontal position of content within padding (0=left, 0.5=center, 1=right). Shift+drag content in preview to adjust."),
+                        io.Float.Input("pad_y", default=0.5, min=0.0, max=1.0, step=0.01,
+                            tooltip="Vertical position of content within padding (0=top, 0.5=center, 1=bottom). Shift+drag content in preview to adjust."),
+                    ]),
+                    io.DynamicCombo.Option(key="pad_edge", inputs=[
+                        io.Combo.Input("edge_mode", options=["clamp", "repeat", "mirror"], default="clamp",
+                            tooltip="clamp: extend edge pixels. repeat: tile the image. mirror: tile with mirroring."),
+                        io.Float.Input("pad_x", default=0.5, min=0.0, max=1.0, step=0.01,
+                            tooltip="Horizontal position of content within padding (0=left, 0.5=center, 1=right). Shift+drag content in preview to adjust."),
+                        io.Float.Input("pad_y", default=0.5, min=0.0, max=1.0, step=0.01,
+                            tooltip="Vertical position of content within padding (0=top, 0.5=center, 1=bottom). Shift+drag content in preview to adjust."),
+                    ]),
+                    io.DynamicCombo.Option(key="multiplier", inputs=[
+                        io.Float.Input("width_mult", default=1.0, min=0.01, max=16.0, step=0.05,
+                            tooltip="Multiply the crop width by this factor."),
+                        io.Float.Input("height_mult", default=1.0, min=0.01, max=16.0, step=0.05,
+                            tooltip="Multiply the crop height by this factor."),
+                    ]),
+                ]),
+                io.Int.Input("divisible_by", default=2, min=0, max=512, step=1),
+                io.DynamicCombo.Input("extra_padding", options=[
+                    io.DynamicCombo.Option(key="disabled", inputs=[]),
+                    io.DynamicCombo.Option(key="pad_color", inputs=_pad_inputs()),
+                    io.DynamicCombo.Option(key="pad_edge", inputs=_pad_inputs() + [
+                        io.Combo.Input("edge_mode", options=["clamp", "repeat", "mirror"], default="clamp",
+                            tooltip="clamp: extend edge pixels. repeat: tile the image. mirror: tile with mirroring."),
+                    ]),
+                    io.DynamicCombo.Option(key="pad_crop_color", inputs=_pad_inputs()),
+                    io.DynamicCombo.Option(key="pad_crop_edge", inputs=_pad_inputs() + [
+                        io.Combo.Input("edge_mode", options=["clamp", "repeat", "mirror"], default="clamp",
+                            tooltip="clamp: extend edge pixels. repeat: tile the image. mirror: tile with mirroring."),
+                    ]),
+                ]),
+                io.DynamicCombo.Input("invert_crop", options=[
+                    io.DynamicCombo.Option(key="disabled", inputs=[]),
+                    io.DynamicCombo.Option(key="enabled", inputs=[]),
+                ]),
+                io.String.Input("bboxes", default="", socketless=True, advanced=True),
+            ],
+            outputs=[
+                io.MatchType.Output(io.MatchType.Template("img_or_mask", [io.Image, io.Mask]), id="cropped", display_name="output", is_output_list=True),
+                io.Mask.Output("cropped_mask", display_name="output_mask", is_output_list=True),
+                io.BBOX.Output("bbox", display_name="bbox", is_output_list=True),
+                io.Mask.Output("bbox_mask", display_name="bbox_mask", is_output_list=True),
+                io.Int.Output("width", display_name="width", tooltip="Width of the output image."),
+                io.Int.Output("height", display_name="height", tooltip="Height of the output image."),
+            ],
+        )
+
+
+    @classmethod
+    def execute(cls, image, target_width, target_height, upscale_method, keep_proportion, divisible_by,
+                extra_padding, invert_crop, bboxes, mask=None):
+        # Unpack DynamicCombos
+        edge_mode = keep_proportion.get("edge_mode", "clamp")
+        pad_x = keep_proportion.get("pad_x", 0.5)
+        pad_y = keep_proportion.get("pad_y", 0.5)
+        width_mult = keep_proportion.get("width_mult", 1.0)
+        height_mult = keep_proportion.get("height_mult", 1.0)
+        keep_proportion = keep_proportion["keep_proportion"]
+        extra_top = extra_padding.get("pad_top", 0)
+        extra_bottom = extra_padding.get("pad_bottom", 0)
+        extra_left = extra_padding.get("pad_left", 0)
+        extra_right = extra_padding.get("pad_right", 0)
+        extra_edge_mode = extra_padding.get("edge_mode", "clamp")
+        extra_pad_mode = extra_padding.get("extra_padding", "disabled")
+        invert_crop = invert_crop["invert_crop"]
+
+
+        # Parse fill color from bboxes JSON (shared color picker)
+        fill_color_rgb = [0, 0, 0]
+        if bboxes:
+            try:
+                _parsed_tmp = json.loads(bboxes)
+                if isinstance(_parsed_tmp, dict) and "fillColor" in _parsed_tmp:
+                    fill_color_rgb = string_to_color(_parsed_tmp["fillColor"])
+            except (json.JSONDecodeError, Exception):
+                pass
+        fill_rgb = [c / 255.0 for c in fill_color_rgb[:3]]
+
+        # Handle mask input (3D) by converting to image-like 4D tensor
+        input_is_mask = image.ndim == 3
+        if input_is_mask:
+            image = image.unsqueeze(-1).repeat(1, 1, 1, 3)
+
+        # Save input image as temp preview file for JS canvas
+        temp_dir = folder_paths.get_temp_directory()
+        pil_img = Image.fromarray((image[0].cpu().numpy() * 255).astype(np.uint8))
+        preview_filename = f"crop_preview_{random.randint(0, 0xFFFFFF):06x}.webp"
+        pil_img.save(os.path.join(temp_dir, preview_filename), format="WEBP", quality=80)
+        preview_ui = {"preview_filename": [preview_filename]}
+
+        img_height = image.shape[1]
+        img_width = image.shape[2]
+
+        # Parse bboxes and rotation
+        bbox_list = []
+        rotation = 0.0
+        if bboxes:
+            try:
+                parsed = json.loads(bboxes)
+                # New format: { bboxes: [...], rotation: N }
+                if isinstance(parsed, dict):
+                    bbox_list = [b for b in parsed.get("bboxes", []) if b and all(k in b for k in ("startX", "startY", "endX", "endY"))]
+                    rotation = parsed.get("rotation", 0.0)
+                # Legacy format: [bbox, bbox, ...]
+                elif isinstance(parsed, list):
+                    bbox_list = [b for b in parsed if b and all(k in b for k in ("startX", "startY", "endX", "endY"))]
+            except json.JSONDecodeError:
+                pass
+
+        # Content mask tracks which pixels are actual image content (1=content, 0=fill)
+        content_mask = torch.ones(1, img_height, img_width, device=image.device)
+
+        # Apply rotation before cropping
+        if rotation != 0:
+            from torchvision.transforms.functional import rotate as tv_rotate
+            import torch.nn.functional as F
+            # Use shared fill color for rotation corners (unless edge mode)
+            rot_fill = fill_rgb
+
+            is_edge_mode = extra_pad_mode in ("pad_edge", "pad_crop_edge") or keep_proportion == "pad_edge"
+            if is_edge_mode:
+                h, w = image.shape[1], image.shape[2]
+                pad_amt = max(h, w)
+                img_chw = image.movedim(-1, 1)
+                img_padded = F.pad(img_chw, [pad_amt, pad_amt, pad_amt, pad_amt], mode='replicate')
+                img_rotated = tv_rotate(img_padded, -rotation, expand=False, fill=rot_fill)
+                ch, cw = img_rotated.shape[2], img_rotated.shape[3]
+                cy, cx = ch // 2, cw // 2
+                image = img_rotated[:, :, cy - h // 2:cy - h // 2 + h, cx - w // 2:cx - w // 2 + w].movedim(1, -1)
+                if mask is not None:
+                    mask_padded = F.pad(mask.unsqueeze(1), [pad_amt, pad_amt, pad_amt, pad_amt], mode='replicate')
+                    mask_rotated = tv_rotate(mask_padded, -rotation, expand=False, fill=[0.0])
+                    mask = mask_rotated[:, :, cy - h // 2:cy - h // 2 + h, cx - w // 2:cx - w // 2 + w].squeeze(1)
+                # Content mask: rotate the same way (no padding — just rotate and crop)
+                cm_padded = F.pad(content_mask.unsqueeze(1), [pad_amt, pad_amt, pad_amt, pad_amt], mode='constant', value=0)
+                cm_rotated = tv_rotate(cm_padded, -rotation, expand=False, fill=[0.0])
+                content_mask = cm_rotated[:, :, cy - h // 2:cy - h // 2 + h, cx - w // 2:cx - w // 2 + w].squeeze(1)
+            else:
+                image = tv_rotate(image.movedim(-1, 1), -rotation, expand=True, fill=rot_fill).movedim(1, -1)
+                if mask is not None:
+                    mask = tv_rotate(mask.unsqueeze(1), -rotation, expand=True, fill=[0.0]).squeeze(1)
+                # Content mask: rotate with expand, fill=0
+                content_mask = tv_rotate(content_mask.unsqueeze(1), -rotation, expand=True, fill=[0.0]).squeeze(1)
+            img_height = image.shape[1]
+            img_width = image.shape[2]
+
+        # Normalize mask dimensions to match image
+        if mask is not None:
+            if mask.shape[-2] != img_height or mask.shape[-1] != img_width:
+                if mask.shape[-2] == img_width and mask.shape[-1] == img_height:
+                    mask = mask.transpose(-2, -1)
+                else:
+                    mask = _resize_single_channel(mask, img_width, img_height)
+
+        # "Pad first" modes: apply extra padding to the full image before cropping
+        # Skip for keep_proportion pad modes — those handle extra padding via target subtraction
+        is_pad_first = extra_pad_mode in ("pad_color", "pad_edge")
+        kp_is_pad_mode = keep_proportion in ("pad_color", "pad_edge")
+        if is_pad_first and not kp_is_pad_mode and (extra_top > 0 or extra_bottom > 0 or extra_left > 0 or extra_right > 0):
+            pad_mode = "color" if extra_pad_mode == "pad_color" else "edge"
+            padded_img = _apply_padding(image, extra_top, extra_bottom, extra_left, extra_right, pad_mode, extra_edge_mode, fill_rgb)
+
+            image = padded_img
+            img_height = image.shape[1]
+            img_width = image.shape[2]
+            # Expand content mask and user mask
+            cm_new = torch.zeros(1, img_height, img_width, device=content_mask.device)
+            cm_new[:, extra_top:extra_top+content_mask.shape[1], extra_left:extra_left+content_mask.shape[2]] = content_mask
+            content_mask = cm_new
+            if mask is not None:
+                m_new = torch.zeros(mask.shape[0], img_height, img_width, device=mask.device, dtype=mask.dtype)
+                m_new[:, extra_top:extra_top+mask.shape[1], extra_left:extra_left+mask.shape[2]] = mask
+                mask = m_new
+
+        # If no bboxes, treat the full image as a single bbox
+        if not bbox_list:
+            bbox_list = [None]
+
+        all_cropped = []
+        all_cropped_masks = []
+        all_bbox_tuples = []
+        all_bbox_masks = []
+
+        for bbox_data in bbox_list:
+            has_bbox = bbox_data is not None
+
+            if has_bbox:
+                preview_width = bbox_data.get("previewWidth", 0)
+                preview_height = bbox_data.get("previewHeight", 0)
+                sx = img_width / preview_width if preview_width > 0 else 1.0
+                sy = img_height / preview_height if preview_height > 0 else 1.0
+
+                x_min = int(min(bbox_data["startX"], bbox_data["endX"]) * sx)
+                y_min = int(min(bbox_data["startY"], bbox_data["endY"]) * sy)
+                x_max = int(max(bbox_data["startX"], bbox_data["endX"]) * sx)
+                y_max = int(max(bbox_data["startY"], bbox_data["endY"]) * sy)
+
+                x_min = max(0, min(x_min, img_width - 1))
+                y_min = max(0, min(y_min, img_height - 1))
+                x_max = max(x_min + 1, min(x_max, img_width))
+                y_max = max(y_min + 1, min(y_max, img_height))
+
+                cropped = image[:, y_min:y_max, x_min:x_max, :]
+                cropped_content_mask = content_mask[:, y_min:y_max, x_min:x_max]
+                all_bbox_tuples.append((x_min, y_min, x_max - x_min, y_max - y_min))
+                bm = torch.zeros(1, img_height, img_width)
+                bm[0, y_min:y_max, x_min:x_max] = 1.0
+                all_bbox_masks.append(bm)
+                cropped_mask = mask[:, y_min:y_max, x_min:x_max] if mask is not None else None
+            else:
+                cropped = image
+                cropped_content_mask = content_mask
+                all_bbox_tuples.append((0, 0, img_width, img_height))
+                all_bbox_masks.append(torch.ones(1, img_height, img_width))
+                cropped_mask = mask
+                x_min, y_min, x_max, y_max = 0, 0, img_width, img_height
+
+            # Multiplier mode: compute target from crop dims * multiplier
+            if keep_proportion == "multiplier":
+                crop_h, crop_w = cropped.shape[1], cropped.shape[2]
+                tw = round(crop_w * width_mult)
+                th = round(crop_h * height_mult)
+                target_width = tw
+                target_height = th
+
+            # Resize cropped image if target dimensions are set
+            if target_width > 0 or target_height > 0:
+                crop_h, crop_w = cropped.shape[1], cropped.shape[2]
+                tw = target_width if target_width > 0 else crop_w
+                th = target_height if target_height > 0 else crop_h
+
+                # Subtract extra padding from target so content + padding = original target
+                # For pad-first + non-pad keep_proportion, padding is on the source (don't subtract)
+                # For pad modes or pad-crop, subtract so padding is in the output
+                has_extra = extra_top > 0 or extra_bottom > 0 or extra_left > 0 or extra_right > 0
+                kp_is_pad = keep_proportion in ("pad_color", "pad_edge")
+                if has_extra and (kp_is_pad or not is_pad_first):
+                    if target_width > 0:
+                        tw = max(1, tw - extra_left - extra_right)
+                    if target_height > 0:
+                        th = max(1, th - extra_top - extra_bottom)
+
+                if keep_proportion == "keep_long_edge":
+                    ratio = min(tw / crop_w, th / crop_h)
+                    tw = round(crop_w * ratio)
+                    th = round(crop_h * ratio)
+                elif keep_proportion == "keep_short_edge":
+                    ratio = max(tw / crop_w, th / crop_h)
+                    tw = round(crop_w * ratio)
+                    th = round(crop_h * ratio)
+                elif keep_proportion == "total_pixels":
+                    total_pixels = tw * th
+                    aspect_ratio = crop_w / crop_h
+                    th = int(math.sqrt(total_pixels / aspect_ratio))
+                    tw = int(math.sqrt(total_pixels * aspect_ratio))
+                elif keep_proportion == "crop":
+                    ratio = max(tw / crop_w, th / crop_h)
+                    scale_w = round(crop_w * ratio)
+                    scale_h = round(crop_h * ratio)
+                    samples = common_upscale(cropped.movedim(-1, 1), scale_w, scale_h, upscale_method, "center")
+                    cropped = samples.movedim(1, -1)
+                    if cropped_mask is not None:
+                        cropped_mask = _upscale_mask(cropped_mask, scale_w, scale_h, upscale_method, "center")
+                    cropped_content_mask = _resize_single_channel(cropped_content_mask, scale_w, scale_h)
+                    cx = (scale_w - tw) // 2
+                    cy = (scale_h - th) // 2
+                    cropped = cropped[:, cy:cy+th, cx:cx+tw, :]
+                    if cropped_mask is not None:
+                        cropped_mask = cropped_mask[:, cy:cy+th, cx:cx+tw]
+                    cropped_content_mask = cropped_content_mask[:, cy:cy+th, cx:cx+tw]
+                elif keep_proportion in ("pad_color", "pad_edge"):
+                    ratio = min(tw / crop_w, th / crop_h)
+                    scale_w = round(crop_w * ratio)
+                    scale_h = round(crop_h * ratio)
+                    samples = common_upscale(cropped.movedim(-1, 1), scale_w, scale_h, upscale_method, "disabled")
+                    resized = samples.movedim(1, -1)
+                    # pad_x/pad_y position across full target (not just content area)
+                    full_tw = target_width if target_width > 0 else crop_w
+                    full_th = target_height if target_height > 0 else crop_h
+                    pad_left = round((full_tw - scale_w) * pad_x)
+                    pad_top = round((full_th - scale_h) * pad_y)
+                    pad_right = full_tw - pad_left - scale_w
+                    pad_bottom = full_th - pad_top - scale_h
+                    tw = full_tw
+                    th = full_th
+
+                    pad_mode = "edge" if keep_proportion == "pad_edge" else "color"
+                    cropped = _apply_padding(resized, pad_top, pad_bottom, pad_left, pad_right, pad_mode, edge_mode, fill_rgb)
+                    if cropped_mask is not None:
+                        mask_resized = _upscale_mask(cropped_mask, scale_w, scale_h, upscale_method, "disabled")
+                        mask_padded = torch.zeros(mask_resized.shape[0], th, tw, device=mask_resized.device, dtype=mask_resized.dtype)
+                        mask_padded[:, pad_top:pad_top+scale_h, pad_left:pad_left+scale_w] = mask_resized
+                        cropped_mask = mask_padded
+                    # Update content mask for padding area
+                    cm_resized = _resize_single_channel(cropped_content_mask, scale_w, scale_h)
+                    cm_padded = torch.zeros(1, th, tw, device=cropped_content_mask.device)
+                    cm_padded[:, pad_top:pad_top+scale_h, pad_left:pad_left+scale_w] = cm_resized
+                    cropped_content_mask = cm_padded
+
+                if divisible_by > 1:
+                    tw = tw - (tw % divisible_by)
+                    th = th - (th % divisible_by)
+
+                if tw > 0 and th > 0:
+                    if keep_proportion in ("stretch", "keep_long_edge", "keep_short_edge", "total_pixels", "multiplier"):
+                        cropped = common_upscale(cropped.movedim(-1, 1), tw, th, upscale_method, "disabled").movedim(1, -1)
+                        if cropped_mask is not None:
+                            cropped_mask = _upscale_mask(cropped_mask, tw, th, upscale_method, "disabled")
+                        cropped_content_mask = _resize_single_channel(cropped_content_mask, tw, th)
+                    else:
+                        cropped = cropped[:, :th, :tw, :]
+                        if cropped_mask is not None:
+                            cropped_mask = cropped_mask[:, :th, :tw]
+                        cropped_content_mask = cropped_content_mask[:, :th, :tw]
+
+            # Enforce divisible_by even when no target dimensions are set
+            elif divisible_by > 1:
+                final_w = cropped.shape[2] - (cropped.shape[2] % divisible_by)
+                final_h = cropped.shape[1] - (cropped.shape[1] % divisible_by)
+                if final_w != cropped.shape[2] or final_h != cropped.shape[1]:
+                    cropped = cropped[:, :final_h, :final_w, :]
+                    if cropped_mask is not None:
+                        cropped_mask = cropped_mask[:, :final_h, :final_w]
+                    cropped_content_mask = cropped_content_mask[:, :final_h, :final_w]
+
+            # Apply extra padding (skip for pad-first and keep_proportion pad modes which handle it above)
+            kp_handles_ep = keep_proportion in ("pad_color", "pad_edge")
+            if not is_pad_first and not kp_handles_ep and (extra_top > 0 or extra_bottom > 0 or extra_left > 0 or extra_right > 0):
+                h_cur, w_cur = cropped.shape[1], cropped.shape[2]
+                pad_mode = "edge" if extra_pad_mode == "pad_crop_edge" else "color"
+                cropped = _apply_padding(cropped, extra_top, extra_bottom, extra_left, extra_right, pad_mode, extra_edge_mode, fill_rgb)
+                new_h, new_w = cropped.shape[1], cropped.shape[2]
+                if cropped_mask is not None:
+                    padded_mask = torch.zeros(cropped_mask.shape[0], new_h, new_w, device=cropped_mask.device, dtype=cropped_mask.dtype)
+                    padded_mask[:, extra_top:extra_top+h_cur, extra_left:extra_left+w_cur] = cropped_mask
+                    cropped_mask = padded_mask
+                cm_h, cm_w = cropped_content_mask.shape[-2], cropped_content_mask.shape[-1]
+                if cm_h != h_cur or cm_w != w_cur:
+                    cropped_content_mask = _resize_single_channel(cropped_content_mask, w_cur, h_cur)
+                cm_ep = torch.zeros(1, new_h, new_w, device=cropped_content_mask.device)
+                cm_ep[:, extra_top:extra_top+h_cur, extra_left:extra_left+w_cur] = cropped_content_mask
+                cropped_content_mask = cm_ep
+
+            # If no mask was provided, output a zeros mask matching the cropped image
+            if cropped_mask is None:
+                cropped_mask = torch.zeros(1, cropped.shape[1], cropped.shape[2])
+
+            # Apply fill mask — marks filled/padded areas as 1 in the output mask
+            # Combines with incoming mask: 1 where either input mask is 1 OR area is filled
+            if cropped_content_mask is not None:
+                out_h, out_w = cropped_mask.shape[1], cropped_mask.shape[2]
+                cm_h, cm_w = cropped_content_mask.shape[1], cropped_content_mask.shape[2]
+                if cm_h != out_h or cm_w != out_w:
+                    cropped_content_mask = _resize_single_channel(cropped_content_mask, out_w, out_h)
+                # fill_mask: 1 where filled, 0 where content
+                fill_mask = 1.0 - cropped_content_mask.clamp(0, 1)
+                # Combine: output mask is max of incoming mask and fill mask
+                cropped_mask = torch.max(cropped_mask, fill_mask)
+
+            # Invert crop: output area outside the bbox instead of inside
+            if invert_crop == "enabled" and has_bbox:
+                inverted = image.clone()
+                for c in range(min(3, inverted.shape[3])):
+                    inverted[:, y_min:y_max, x_min:x_max, c] = fill_rgb[c]
+                cropped = inverted
+
+            # Convert back to mask if input was a mask
+            if input_is_mask:
+                cropped = cropped[:, :, :, 0]
+
+            all_cropped.append(cropped)
+            all_cropped_masks.append(cropped_mask)
+
+        width, height = all_cropped[0].shape[2], all_cropped[0].shape[1]
+
+        return io.NodeOutput(all_cropped, all_cropped_masks, all_bbox_tuples, all_bbox_masks, width, height, ui=preview_ui)
diff --git a/custom_nodes/ComfyUI-KJNodes/nodes/lora_nodes.py b/custom_nodes/ComfyUI-KJNodes/nodes/lora_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2de4fd9c261dcbfb79e847bad29de3b38f88cce
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/nodes/lora_nodes.py
@@ -0,0 +1,673 @@
+import torch
+import comfy.model_management
+import comfy.utils
+import folder_paths
+import os
+import logging
+from tqdm import tqdm
+import numpy as np
+from comfy_api.latest import io
+
+device = comfy.model_management.get_torch_device()
+
+CLAMP_QUANTILE = 0.99
+
+def extract_lora(diff, key, rank, algorithm, lora_type, lowrank_iters=7, adaptive_param=1.0, clamp_quantile=True):
+    """
+    Extracts LoRA weights from a weight difference tensor using SVD.
+    """
+    conv2d = (len(diff.shape) == 4)
+    kernel_size = None if not conv2d else diff.size()[2:4]
+    conv2d_3x3 = conv2d and kernel_size != (1, 1)
+    out_dim, in_dim = diff.size()[0:2]
+
+    if conv2d:
+        if conv2d_3x3:
+            diff = diff.flatten(start_dim=1)
+        else:
+            diff = diff.squeeze()
+
+    diff_float = diff.float()
+    if algorithm == "svd_lowrank":
+        U, S, V = torch.svd_lowrank(diff_float, q=min(rank, in_dim, out_dim), niter=lowrank_iters)
+        U = U @ torch.diag(S)
+        Vh = V.t()
+    else:
+        #torch.linalg.svdvals() 
+        U, S, Vh = torch.linalg.svd(diff_float)
+        # Flexible rank selection logic like locon: https://github.com/KohakuBlueleaf/LyCORIS/blob/main/tools/extract_locon.py
+        if "adaptive" in lora_type:
+            if lora_type == "adaptive_ratio":
+                min_s = torch.max(S) * adaptive_param
+                lora_rank = torch.sum(S > min_s).item()
+            elif lora_type == "adaptive_energy":
+                energy = torch.cumsum(S**2, dim=0)
+                total_energy = torch.sum(S**2)
+                threshold = adaptive_param * total_energy  # e.g., adaptive_param=0.95 for 95%
+                lora_rank = torch.sum(energy < threshold).item() + 1
+            elif lora_type == "adaptive_quantile":
+                s_cum = torch.cumsum(S, dim=0)
+                min_cum_sum = adaptive_param * torch.sum(S)
+                lora_rank = torch.sum(s_cum < min_cum_sum).item()
+            elif lora_type == "adaptive_fro":
+                S_squared = S.pow(2)
+                S_fro_sq = float(torch.sum(S_squared))
+                sum_S_squared = torch.cumsum(S_squared, dim=0) / S_fro_sq
+                lora_rank = int(torch.searchsorted(sum_S_squared, adaptive_param**2)) + 1
+                lora_rank = max(1, min(lora_rank, len(S)))
+            else:
+                pass  # Will print after capping
+            
+            # Cap adaptive rank by the specified max rank
+            lora_rank = min(lora_rank, rank)
+            
+            # Calculate and print actual fro percentage retained after capping
+            if lora_type == "adaptive_fro":
+                S_squared = S.pow(2)
+                s_fro = torch.sqrt(torch.sum(S_squared))
+                s_red_fro = torch.sqrt(torch.sum(S_squared[:lora_rank]))
+                fro_percent = float(s_red_fro / s_fro)
+                logging.info(f"{key} Extracted LoRA rank: {lora_rank}, Frobenius retained: {fro_percent:.1%}")
+            else:
+                logging.info(f"{key} Extracted LoRA rank: {lora_rank}")
+        else:
+            lora_rank = rank
+
+        lora_rank = max(1, lora_rank)
+        lora_rank = min(out_dim, in_dim, lora_rank)
+        
+        U = U[:, :lora_rank]
+        S = S[:lora_rank]
+        U = U @ torch.diag(S)
+        Vh = Vh[:lora_rank, :]
+
+    if clamp_quantile:
+        dist = torch.cat([U.flatten(), Vh.flatten()])
+        if dist.numel() > 100_000:
+            # Sample 100,000 elements for quantile estimation
+            idx = torch.randperm(dist.numel(), device=dist.device)[:100_000]
+            dist_sample = dist[idx]
+            hi_val = torch.quantile(dist_sample, CLAMP_QUANTILE)
+        else:
+            hi_val = torch.quantile(dist, CLAMP_QUANTILE)
+        low_val = -hi_val
+
+        U = U.clamp(low_val, hi_val)
+        Vh = Vh.clamp(low_val, hi_val)
+    if conv2d:
+        U = U.reshape(out_dim, lora_rank, 1, 1)
+        Vh = Vh.reshape(lora_rank, in_dim, kernel_size[0], kernel_size[1])
+    return (U, Vh)
+
+
+def calc_lora_model(model_diff, rank, prefix_model, prefix_lora, output_sd, lora_type, algorithm, lowrank_iters, out_dtype, bias_diff=False, adaptive_param=1.0, clamp_quantile=True):
+    # Get key names from module structure without materializing weights
+    sd_keys = []
+    for name, _ in model_diff.model.named_parameters():
+        if prefix_model is None or name.startswith(prefix_model):
+            sd_keys.append(name)
+    for name, _ in model_diff.model.named_buffers():
+        if prefix_model is None or name.startswith(prefix_model):
+            sd_keys.append(name)
+
+    total_keys = len([k for k in sd_keys if k.endswith(".weight") or (bias_diff and k.endswith(".bias"))])
+    progress_bar = tqdm(total=total_keys, desc=f"Extracting LoRA ({prefix_lora.strip('.')})")
+    comfy_pbar = comfy.utils.ProgressBar(total_keys)
+
+    # Process one weight at a time to minimize memory usage
+    for k in sd_keys:
+        if k.endswith(".weight"):
+            # Patch and retrieve single weight
+            weight_diff = model_diff.patch_weight_to_device(k, return_weight=True)
+            if weight_diff is None:
+                progress_bar.update(1)
+                comfy_pbar.update(1)
+                continue
+            if weight_diff.ndim == 5:
+                logging.info(f"Skipping 5D tensor for key {k}")
+                del weight_diff
+                progress_bar.update(1)
+                comfy_pbar.update(1)
+                continue
+            if lora_type != "full":
+                if weight_diff.ndim < 2:
+                    if bias_diff:
+                        output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().to(out_dtype).cpu()
+                    del weight_diff
+                    progress_bar.update(1)
+                    comfy_pbar.update(1)
+                    continue
+                try:
+                    out = extract_lora(weight_diff.to(device), k, rank, algorithm, lora_type, lowrank_iters=lowrank_iters, adaptive_param=adaptive_param, clamp_quantile=clamp_quantile)
+                    output_sd["{}{}.lora_up.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[0].contiguous().to(out_dtype).cpu()
+                    output_sd["{}{}.lora_down.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[1].contiguous().to(out_dtype).cpu()
+                except Exception as e:
+                    logging.warning(f"Could not generate lora weights for key {k}, error {e}")
+            else:
+                output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().to(out_dtype).cpu()
+            del weight_diff
+            progress_bar.update(1)
+            comfy_pbar.update(1)
+
+        elif bias_diff and k.endswith(".bias"):
+            weight = model_diff.patch_weight_to_device(k, return_weight=True)
+            if weight is not None:
+                output_sd["{}{}.diff_b".format(prefix_lora, k[len(prefix_model):-5])] = weight.contiguous().to(out_dtype).cpu()
+                del weight
+            progress_bar.update(1)
+            comfy_pbar.update(1)
+
+    progress_bar.close()
+    del model_diff
+    comfy.model_management.soft_empty_cache()
+    return output_sd
+
+
+class LoraExtractKJ(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LoraExtractKJ",
+            category="KJNodes/lora",
+            is_output_node=True,
+            inputs=[
+                io.MultiType.Input("finetuned", [io.Model, io.Clip], tooltip="The finetuned model or clip to extract LoRA from."),
+                io.MultiType.Input("original", [io.Model, io.Clip], tooltip="The original base model or clip to diff against."),
+                io.String.Input("filename_prefix", default="loras/ComfyUI_extracted_lora"),
+                io.Int.Input("rank", default=64, min=1, max=4096, step=1, tooltip="The rank to use for standard LoRA, or maximum rank limit for adaptive methods."),
+                io.Combo.Input("lora_type", options=["standard", "full", "adaptive_ratio", "adaptive_quantile", "adaptive_energy", "adaptive_fro"]),
+                io.Combo.Input("algorithm", options=["svd_linalg", "svd_lowrank"], default="svd_lowrank", tooltip="SVD algorithm to use, svd_lowrank is faster but less accurate."),
+                io.Int.Input("lowrank_iters", default=7, min=1, max=100, step=1, tooltip="The number of subspace iterations for lowrank SVD algorithm."),
+                io.Combo.Input("output_dtype", options=["fp16", "bf16", "fp32"], default="fp16"),
+                io.Boolean.Input("bias_diff", default=True),
+                io.Float.Input("adaptive_param", default=0.15, min=0.0, max=1.0, step=0.01, tooltip="For ratio mode, this is the ratio of the maximum singular value. For quantile mode, this is the quantile of the singular values. For fro mode, this is the Frobenius norm retention ratio."),
+                io.Boolean.Input("clamp_quantile", default=False),
+            ],
+        )
+
+
+    @classmethod
+    def execute(cls, finetuned, original, filename_prefix, rank, lora_type, algorithm, lowrank_iters, output_dtype, bias_diff, adaptive_param, clamp_quantile) -> io.NodeOutput:
+        if algorithm == "svd_lowrank" and lora_type != "standard":
+            raise ValueError("svd_lowrank algorithm is only supported for standard LoRA extraction.")
+
+        dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[output_dtype]
+
+        output_dir = folder_paths.get_output_directory()
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, output_dir)
+
+        output_sd = {}
+
+        is_clip = hasattr(finetuned, "patcher")
+
+        if is_clip:
+            clip_diff = finetuned.clone()
+            kp = original.get_key_patches()
+            kp = {k: v for k, v in kp.items() if not k.endswith(".position_ids") and not k.endswith(".logit_scale")}
+            clip_diff.add_patches(kp, -1.0, 1.0)
+            output_sd = calc_lora_model(clip_diff.patcher, rank, "", "text_encoders.", output_sd, lora_type, algorithm, lowrank_iters, dtype, bias_diff=bias_diff, adaptive_param=adaptive_param, clamp_quantile=clamp_quantile)
+        else:
+            m = finetuned.clone()
+            kp = original.get_key_patches("diffusion_model.")
+            m.add_patches(kp, -1.0, 1.0)
+            output_sd = calc_lora_model(m, rank, "diffusion_model.", "diffusion_model.", output_sd, lora_type, algorithm, lowrank_iters, dtype, bias_diff=bias_diff, adaptive_param=adaptive_param, clamp_quantile=clamp_quantile)
+
+        if "adaptive" in lora_type:
+            rank_str = f"{lora_type}_{adaptive_param:.2f}"
+        else:
+            rank_str = rank
+        output_checkpoint = f"{filename}_rank_{rank_str}_{output_dtype}_{counter:05}_.safetensors"
+        output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
+
+        comfy.utils.save_torch_file(output_sd, output_checkpoint, metadata=None)
+        return io.NodeOutput()
+
+class LoraReduceRank(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LoraReduceRankKJ",
+            display_name="LoraReduceRank",
+            category="KJNodes/lora",
+            description="Resize a LoRA model by reducing its rank. Based on kohya's sd-scripts: https://github.com/kohya-ss/sd-scripts/blob/main/networks/resize_lora.py",
+            is_output_node=True,
+            is_experimental=True,
+            inputs=[
+                io.Combo.Input("lora_name", options=folder_paths.get_filename_list("loras"), tooltip="The name of the LoRA."),
+                io.Int.Input("new_rank", default=8, min=1, max=4096, step=1, tooltip="The new rank to resize the LoRA. Acts as max rank when using dynamic_method."),
+                io.Combo.Input("dynamic_method", options=["disabled", "sv_ratio", "sv_cumulative", "sv_fro", "sv_knee"], default="disabled", tooltip="Method to use for dynamically determining new alphas and dims. sv_knee finds the elbow point in the singular value curve."),
+                io.Float.Input("dynamic_param", default=0.2, min=0.0, max=2.0, step=0.01, tooltip="Parameter for dynamic methods. For sv_knee: sensitivity (1.0=standard knee, <1.0=more aggressive/lower rank, >1.0=more conservative)."),
+                io.Combo.Input("output_dtype", options=["match_original", "fp16", "bf16", "fp32"], default="match_original", tooltip="Data type to save the LoRA as."),
+                io.Boolean.Input("verbose", default=True),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, lora_name, new_rank, dynamic_method, dynamic_param, output_dtype, verbose) -> io.NodeOutput:
+        lora_path = folder_paths.get_full_path("loras", lora_name)
+        lora_sd, metadata = comfy.utils.load_torch_file(lora_path, return_metadata=True)
+
+        if output_dtype == "fp16":
+            save_dtype = torch.float16
+        elif output_dtype == "bf16":
+            save_dtype = torch.bfloat16
+        elif output_dtype == "fp32":
+            save_dtype = torch.float32
+        elif output_dtype == "match_original":
+            first_weight_key = next(k for k in lora_sd if k.endswith(".weight") and isinstance(lora_sd[k], torch.Tensor))
+            save_dtype = lora_sd[first_weight_key].dtype
+
+        new_lora_sd = {}
+        for k, v in lora_sd.items():
+            new_lora_sd[k.replace(".default", "")] = v
+        del lora_sd
+        logging.info("Resizing Lora...")
+        output_sd, old_dim, new_alpha, rank_list = resize_lora_model(new_lora_sd, new_rank, save_dtype, device, dynamic_method, dynamic_param, verbose)
+
+        if metadata is None:
+            metadata = {}
+
+        comment = metadata.get("ss_training_comment", "")
+
+        if dynamic_method == "disabled":
+            metadata["ss_training_comment"] = f"dimension is resized from {old_dim} to {new_rank}; {comment}"
+            metadata["ss_network_dim"] = str(new_rank)
+            metadata["ss_network_alpha"] = str(new_alpha)
+        else:
+            metadata["ss_training_comment"] = f"Dynamic resize with {dynamic_method}: {dynamic_param} from {old_dim}; {comment}"
+            metadata["ss_network_dim"] = "Dynamic"
+            metadata["ss_network_alpha"] = "Dynamic"
+
+        for key in list(output_sd.keys()):
+            value = output_sd[key]
+            if type(value) == torch.Tensor and value.dtype.is_floating_point and value.dtype != save_dtype:
+                output_sd[key] = value.to(save_dtype)
+
+        output_dir = folder_paths.get_output_directory()
+        output_filename_prefix = "loras/" + lora_name
+
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(output_filename_prefix, output_dir)
+        output_dtype_str = f"_{output_dtype}" if output_dtype != "match_original" else ""
+        average_rank = str(int(np.mean(rank_list)))
+        rank_str = new_rank if dynamic_method == "disabled" else f"dynamic_{average_rank}"
+        output_checkpoint = f"{filename.replace('.safetensors', '')}_resized_from_{old_dim}_to_{rank_str}{output_dtype_str}_{counter:05}_.safetensors"
+        output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
+        logging.info(f"Saving resized LoRA to {output_checkpoint}")
+
+        comfy.utils.save_torch_file(output_sd, output_checkpoint, metadata=metadata)
+        return io.NodeOutput()
+
+# Convert LoRA to different rank approximation (should only be used to go to lower rank)
+# This code is based off the extract_lora_from_models.py file which is based on https://github.com/cloneofsimo/lora/blob/develop/lora_diffusion/cli_svd.py
+# Thanks to cloneofsimo
+
+# This version is based on
+# https://github.com/kohya-ss/sd-scripts/blob/main/networks/resize_lora.py
+
+MIN_SV = 1e-6
+
+LORA_DOWN_UP_FORMATS = [
+    ("lora_down", "lora_up"),  # sd-scripts LoRA
+    ("lora_A", "lora_B"),  # PEFT LoRA
+    ("down", "up"),  # ControlLoRA
+]
+
+# Indexing functions
+def index_sv_cumulative(S, target):
+    original_sum = float(torch.sum(S))
+    cumulative_sums = torch.cumsum(S, dim=0) / original_sum
+    index = int(torch.searchsorted(cumulative_sums, target)) + 1
+    index = max(1, min(index, len(S) - 1))
+
+    return index
+
+
+def index_sv_fro(S, target):
+    S_squared = S.pow(2)
+    S_fro_sq = float(torch.sum(S_squared))
+    sum_S_squared = torch.cumsum(S_squared, dim=0) / S_fro_sq
+    index = int(torch.searchsorted(sum_S_squared, target**2)) + 1
+    index = max(1, min(index, len(S) - 1))
+
+    return index
+
+
+def index_sv_ratio(S, target):
+    max_sv = S[0]
+    min_sv = max_sv / target
+    index = int(torch.sum(S > min_sv).item())
+    index = max(1, min(index, len(S) - 1))
+
+    return index
+
+
+def index_sv_knee(S, sensitivity=1.0):
+    """Find the knee/elbow point in the singular value curve.
+    Uses the Kneedle method: normalizes the curve to [0,1] on both axes,
+    then finds the point with maximum distance from a reference line.
+
+    sensitivity controls the aggressiveness:
+      1.0 = standard knee point
+      < 1.0 = more aggressive (lower rank), e.g. 0.5 roughly halves the knee rank
+      > 1.0 = more conservative (higher rank)
+    The reference line tilts based on sensitivity: at lower values,
+    the line favors keeping fewer singular values."""
+    n = len(S)
+    if n <= 2:
+        return 1
+
+    S_np = S.cpu().float().numpy()
+
+    # Normalize x and y to [0, 1]
+    x = np.linspace(0, 1, n)
+    y = (S_np - S_np[-1]) / (S_np[0] - S_np[-1] + 1e-10)
+
+    # Reference line from (0, 1) to (1, 1-sensitivity)
+    # At sensitivity=1.0: line goes (0,1)->(1,0), standard kneedle
+    # At sensitivity=0.5: line goes (0,1)->(1,0.5), steeper = more aggressive
+    y_line = 1.0 - sensitivity * x
+
+    # Signed distance: positive means curve is above the line (keep)
+    distances = y - y_line
+
+    # Find the point of maximum positive distance
+    index = int(np.argmax(distances))
+    index = max(1, min(index, n - 1))
+    return index
+
+
+# Modified from Kohaku-blueleaf's extract/merge functions
+def _svd_extract(weight_2d, lora_rank, dynamic_method, dynamic_param, device, scale=1):
+    """Shared SVD extraction for both linear and conv weights.
+    Dynamic mode: single full SVD (need all singular values for rank selection).
+    Disabled mode: svd_lowrank only (rank is known, much faster for large matrices)."""
+    weight_2d = weight_2d.to(device)
+    if weight_2d.dtype != torch.float32:
+        weight_2d = weight_2d.float()
+
+    if dynamic_method and dynamic_method != "disabled":
+        # Full SVD: we need all singular values for dynamic rank selection,
+        # and we reuse U/Vh directly — one SVD, no wasted work
+        U, S, Vh = torch.linalg.svd(weight_2d, full_matrices=False)
+        param_dict = rank_resize(S, lora_rank, dynamic_method, dynamic_param, scale)
+        lora_rank = param_dict["new_rank"]
+        U = U[:, :lora_rank]
+        S = S[:lora_rank]
+        Vh = Vh[:lora_rank, :]
+    else:
+        # Randomized lowrank SVD: only compute top-k, much faster when rank << min(m,n)
+        U, S, V = torch.svd_lowrank(weight_2d, q=lora_rank, niter=7)
+        Vh = V.t()
+        param_dict = {"new_rank": lora_rank, "new_alpha": float(scale * lora_rank)}
+        del V
+
+    sqrt_S = torch.diag(torch.sqrt(S))
+    U = U @ sqrt_S
+    Vh = sqrt_S @ Vh
+
+    return U, Vh, param_dict
+
+
+def extract_conv(weight, lora_rank, dynamic_method, dynamic_param, device, scale=1):
+    out_size, in_size, kernel_size, _ = weight.size()
+
+    U, Vh, param_dict = _svd_extract(
+        weight.reshape(out_size, -1), lora_rank, dynamic_method, dynamic_param, device, scale
+    )
+    lora_rank = param_dict["new_rank"]
+
+    param_dict["lora_down"] = Vh.reshape(lora_rank, in_size, kernel_size, kernel_size).cpu()
+    param_dict["lora_up"] = U.reshape(out_size, lora_rank, 1, 1).cpu()
+    del U, Vh, weight
+    return param_dict
+
+
+def extract_linear(weight, lora_rank, dynamic_method, dynamic_param, device, scale=1):
+    out_size, in_size = weight.size()
+
+    U, Vh, param_dict = _svd_extract(
+        weight, lora_rank, dynamic_method, dynamic_param, device, scale
+    )
+    lora_rank = param_dict["new_rank"]
+
+    param_dict["lora_down"] = Vh.reshape(lora_rank, in_size).cpu()
+    param_dict["lora_up"] = U.reshape(out_size, lora_rank).cpu()
+    del U, Vh, weight
+    return param_dict
+
+
+def merge_conv(lora_down, lora_up, device):
+    in_rank, in_size, kernel_size, k_ = lora_down.shape
+    out_size, out_rank, _, _ = lora_up.shape
+    assert in_rank == out_rank and kernel_size == k_, f"rank {in_rank} {out_rank} or kernel {kernel_size} {k_} mismatch"
+
+    lora_down = lora_down.to(device)
+    lora_up = lora_up.to(device)
+
+    merged = lora_up.reshape(out_size, -1) @ lora_down.reshape(in_rank, -1)
+    weight = merged.reshape(out_size, in_size, kernel_size, kernel_size)
+    del lora_up, lora_down
+    return weight
+
+
+def merge_linear(lora_down, lora_up, device):
+    in_rank, in_size = lora_down.shape
+    out_size, out_rank = lora_up.shape
+    assert in_rank == out_rank, f"rank {in_rank} {out_rank} mismatch"
+
+    lora_down = lora_down.to(device)
+    lora_up = lora_up.to(device)
+
+    weight = lora_up @ lora_down
+    del lora_up, lora_down
+    return weight
+
+
+def merge_conv3d(lora_down, lora_up, device):
+    in_rank, in_size, kD, kH, kW = lora_down.shape
+    out_size, out_rank, _, _, _ = lora_up.shape
+    assert in_rank == out_rank, f"rank {in_rank} {out_rank} mismatch"
+
+    lora_down = lora_down.to(device)
+    lora_up = lora_up.to(device)
+
+    merged = lora_up.reshape(out_size, -1) @ lora_down.reshape(in_rank, -1)
+    weight = merged.reshape(out_size, in_size, kD, kH, kW)
+    del lora_up, lora_down
+    return weight
+
+
+def extract_conv3d(weight, lora_rank, dynamic_method, dynamic_param, device, scale=1):
+    out_size, in_size, kD, kH, kW = weight.size()
+
+    U, Vh, param_dict = _svd_extract(
+        weight.reshape(out_size, -1), lora_rank, dynamic_method, dynamic_param, device, scale
+    )
+    lora_rank = param_dict["new_rank"]
+
+    param_dict["lora_down"] = Vh.reshape(lora_rank, in_size, kD, kH, kW).cpu()
+    param_dict["lora_up"] = U.reshape(out_size, lora_rank, 1, 1, 1).cpu()
+    del U, Vh, weight
+    return param_dict
+
+
+# Calculate new rank
+
+
+def rank_resize(S, rank, dynamic_method, dynamic_param, scale=1):
+    param_dict = {}
+
+    if dynamic_method == "sv_ratio":
+        # Calculate new dim and alpha based off ratio
+        new_rank = index_sv_ratio(S, dynamic_param) + 1
+        new_alpha = float(scale * new_rank)
+
+    elif dynamic_method == "sv_cumulative":
+        # Calculate new dim and alpha based off cumulative sum
+        new_rank = index_sv_cumulative(S, dynamic_param) + 1
+        new_alpha = float(scale * new_rank)
+
+    elif dynamic_method == "sv_fro":
+        # Calculate new dim and alpha based off sqrt sum of squares
+        new_rank = index_sv_fro(S, dynamic_param) + 1
+        new_alpha = float(scale * new_rank)
+
+    elif dynamic_method == "sv_knee":
+        # Knee/elbow detection in singular value curve
+        new_rank = index_sv_knee(S, dynamic_param) + 1
+        new_alpha = float(scale * new_rank)
+    else:
+        new_rank = rank
+        new_alpha = float(scale * new_rank)
+
+    if S[0] <= MIN_SV:  # Zero matrix, set dim to 1
+        new_rank = 1
+        new_alpha = float(scale * new_rank)
+    elif new_rank > rank:  # cap max rank at rank
+        new_rank = rank
+        new_alpha = float(scale * new_rank)
+
+    # Calculate resize info
+    s_sum = torch.sum(torch.abs(S))
+    s_rank = torch.sum(torch.abs(S[:new_rank]))
+
+    S_squared = S.pow(2)
+    s_fro = torch.sqrt(torch.sum(S_squared))
+    s_red_fro = torch.sqrt(torch.sum(S_squared[:new_rank]))
+    fro_percent = float(s_red_fro / s_fro)
+
+    param_dict["new_rank"] = new_rank
+    param_dict["new_alpha"] = new_alpha
+    param_dict["sum_retained"] = (s_rank) / s_sum
+    param_dict["fro_retained"] = fro_percent
+    param_dict["max_ratio"] = S[0] / S[new_rank - 1]
+
+    return param_dict
+
+
+def resize_lora_model(lora_sd, new_rank, save_dtype, device, dynamic_method, dynamic_param, verbose):
+    max_old_rank = None
+    new_alpha = None
+    verbose_str = "\n"
+    fro_list = []
+    rank_list = []
+
+    if dynamic_method:
+        logging.info(f"Dynamically determining new alphas and dims based off {dynamic_method}: {dynamic_param}, max rank is {new_rank}")
+
+    lora_down_weight = None
+    lora_up_weight = None
+
+    o_lora_sd = lora_sd.copy()
+    block_down_name = None
+    block_up_name = None
+
+    total_keys = len([k for k in lora_sd if k.endswith(".weight")])
+
+    pbar = comfy.utils.ProgressBar(total_keys)
+    for key, value in tqdm(lora_sd.items(), leave=True, desc="Resizing LoRA weights"):
+        key_parts = key.split(".")
+        block_down_name = None
+        for _format in LORA_DOWN_UP_FORMATS:
+            # Currently we only match lora_down_name in the last two parts of key
+            # because ("down", "up") are general words and may appear in block_down_name
+            if len(key_parts) >= 2 and _format[0] == key_parts[-2]:
+                block_down_name = ".".join(key_parts[:-2])
+                lora_down_name = "." + _format[0]
+                lora_up_name = "." + _format[1]
+                weight_name = "." + key_parts[-1]
+                break
+            if len(key_parts) >= 1 and _format[0] == key_parts[-1]:
+                block_down_name = ".".join(key_parts[:-1])
+                lora_down_name = "." + _format[0]
+                lora_up_name = "." + _format[1]
+                weight_name = ""
+                break
+
+        if block_down_name is None:
+            # This parameter is not lora_down
+            continue
+
+        # Now weight_name can be ".weight" or ""
+        # Find corresponding lora_up and alpha
+        block_up_name = block_down_name
+        lora_down_weight = value
+        lora_up_weight = lora_sd.get(block_up_name + lora_up_name + weight_name, None)
+        lora_alpha = lora_sd.get(block_down_name + ".alpha", None)
+
+        weights_loaded = lora_down_weight is not None and lora_up_weight is not None
+
+        if weights_loaded:
+
+            conv2d = len(lora_down_weight.size()) == 4
+            conv3d = len(lora_down_weight.size()) == 5
+            old_rank = lora_down_weight.size()[0]
+            max_old_rank = max(max_old_rank or 0, old_rank)
+
+            # Skip if merged weight would be too large (>100k elements in any dimension)
+            if conv2d:
+                in_rank, in_size, kernel_size, _ = lora_down_weight.shape
+                out_size, out_rank, _, _ = lora_up_weight.shape
+                merged_size = out_size * in_size * kernel_size * kernel_size
+            elif conv3d:
+                in_rank, in_size, kD, kH, kW = lora_down_weight.shape
+                out_size, out_rank, _, _, _ = lora_up_weight.shape
+                merged_size = out_size * in_size * kD * kH * kW
+            else:
+                in_rank, in_size = lora_down_weight.shape
+                out_size, out_rank = lora_up_weight.shape
+                merged_size = out_size * in_size
+
+            if merged_size > 100_000_000:  # Skip if >100M elements
+                logging.warning(f"Skipping {block_down_name}: merged weight too large ({merged_size:,} elements)")
+                tqdm.write(f"SKIPPED: {block_down_name} - too large ({merged_size:,} elements)")
+                pbar.update(1)
+                continue
+
+            if lora_alpha is None:
+                scale = 1.0
+            else:
+                scale = lora_alpha / old_rank
+
+            if conv2d:
+                full_weight_matrix = merge_conv(lora_down_weight, lora_up_weight, device)
+                param_dict = extract_conv(full_weight_matrix, new_rank, dynamic_method, dynamic_param, device, scale)
+            elif conv3d:
+                full_weight_matrix = merge_conv3d(lora_down_weight, lora_up_weight, device)
+                param_dict = extract_conv3d(full_weight_matrix, new_rank, dynamic_method, dynamic_param, device, scale)
+            else:
+                full_weight_matrix = merge_linear(lora_down_weight, lora_up_weight, device)
+                param_dict = extract_linear(full_weight_matrix, new_rank, dynamic_method, dynamic_param, device, scale)
+
+            if verbose and "fro_retained" in param_dict:
+                max_ratio = param_dict["max_ratio"]
+                sum_retained = param_dict["sum_retained"]
+                fro_retained = param_dict["fro_retained"]
+                if not np.isnan(fro_retained):
+                    fro_list.append(float(fro_retained))
+                log_str = f"{block_down_name:75} | sum(S) retained: {sum_retained:.1%}, fro retained: {fro_retained:.1%}, max(S) ratio: {max_ratio:0.1f}, new dim: {param_dict['new_rank']}"
+                tqdm.write(log_str)
+                verbose_str += log_str
+
+            if verbose and dynamic_method:
+                verbose_str += f", dynamic | dim: {param_dict['new_rank']}, alpha: {param_dict['new_alpha']}\n"
+            else:
+                verbose_str += "\n"
+
+            new_alpha = param_dict["new_alpha"]
+            o_lora_sd[block_down_name + lora_down_name + weight_name] = param_dict["lora_down"].to(save_dtype).contiguous()
+            o_lora_sd[block_up_name + lora_up_name + weight_name] = param_dict["lora_up"].to(save_dtype).contiguous()
+            o_lora_sd[block_down_name + ".alpha"] = torch.tensor(param_dict["new_alpha"]).to(save_dtype)
+
+            block_down_name = None
+            block_up_name = None
+            lora_down_weight = None
+            lora_up_weight = None
+            weights_loaded = False
+            rank_list.append(param_dict["new_rank"])
+            del param_dict
+        pbar.update(1)
+
+    if verbose:
+        logging.info(f"Average Frobenius norm retention: {np.mean(fro_list):.2%} | std: {np.std(fro_list):0.3f}")
+    return o_lora_sd, max_old_rank, new_alpha, rank_list
diff --git a/custom_nodes/ComfyUI-KJNodes/nodes/ltxv_nodes.py b/custom_nodes/ComfyUI-KJNodes/nodes/ltxv_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..44b63f6ebac38f5c63f565f247db5e8e486831f4
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/nodes/ltxv_nodes.py
@@ -0,0 +1,2136 @@
+from comfy_extras.nodes_lt import get_noise_mask, LTXVAddGuide, _append_guide_attention_entry
+import types
+import math
+from typing import Tuple
+import comfy
+from comfy_api.latest import io
+import numpy as np
+import torch
+import logging
+import comfy.model_management as mm
+import comfy.ldm.modules.attention as _comfy_attn
+from comfy.ldm.lightricks.model import apply_rotary_emb as _apply_rope
+try:
+    from comfy.ldm.lightricks.model import GuideAttentionMask as _GuideAttentionMask, _attention_with_guide_mask as _ltx_attn_with_guide_mask
+except ImportError:
+    _GuideAttentionMask = None
+    _ltx_attn_with_guide_mask = None
+device = mm.get_torch_device()
+import latent_preview
+
+class LTXVAddGuideMulti(LTXVAddGuide):
+
+    @classmethod
+    def define_schema(cls):
+        options = []
+        for num_guides in range(1, 21):  # 1 to 20 guides
+            guide_inputs = []
+            for i in range(1, num_guides + 1):
+                guide_inputs.extend([
+                    io.Image.Input(f"image_{i}"),
+                    io.Int.Input(
+                        f"frame_idx_{i}",
+                        default=0,
+                        min=-9999,
+                        max=9999,
+                        tooltip=f"Frame index for guide {i}.",
+                    ),
+                    io.Float.Input(f"strength_{i}", default=1.0, min=0.0, max=10.0, step=0.01, tooltip=f"Strength for guide {i}."),
+                ])
+            options.append(io.DynamicCombo.Option(
+                key=str(num_guides),
+                inputs=guide_inputs
+            ))
+
+        return io.Schema(
+            node_id="LTXVAddGuideMulti",
+            category="KJNodes/ltxv",
+            description="Add multiple guide images at specified frame indices with strengths, uses DynamicCombo which requires ComfyUI 0.8.1 and frontend 1.33.4 or later.",
+            inputs=[
+                io.Conditioning.Input("positive", tooltip="Positive conditioning to which guide keyframe info will be added"),
+                io.Conditioning.Input("negative", tooltip="Negative conditioning to which guide keyframe info will be added"),
+                io.Vae.Input("vae", tooltip="Video VAE used to encode the guide images"),
+                io.Latent.Input("latent", tooltip="Video latent, guides are added to the end of this latent"),
+                io.DynamicCombo.Input(
+                    "num_guides",
+                    options=options,
+                    display_name="Number of Guides",
+                    tooltip="Select how many guide images to use",
+                ),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent", tooltip="Video latent with added guides"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, positive, negative, vae, latent, num_guides) -> io.NodeOutput:
+        scale_factors = vae.downscale_index_formula
+        latent_image = latent["samples"]
+        noise_mask = get_noise_mask(latent)
+
+        _, _, latent_length, latent_height, latent_width = latent_image.shape
+
+        # num_guides is a dict containing the inputs from the selected option
+        # e.g., {'image_1': tensor, 'frame_idx_1': 0, 'strength_1': 1.0, 'image_2': tensor, 'frame_idx_2': 20, 'strength_2': 0.8, ...}
+
+        image_keys = sorted([k for k in num_guides.keys() if k.startswith('image_')])
+
+        for img_key in image_keys:
+            i = img_key.split('_')[1]
+
+            img = num_guides[f"image_{i}"]
+            f_idx = num_guides[f"frame_idx_{i}"]
+            strength = num_guides[f"strength_{i}"]
+
+            image_1, t = cls.encode(vae, latent_width, latent_height, img, scale_factors)
+
+            frame_idx, latent_idx = cls.get_latent_index(positive, latent_length, len(image_1), f_idx, scale_factors)
+            assert latent_idx + t.shape[2] <= latent_length, "Conditioning frames exceed the length of the latent sequence."
+
+            positive, negative, latent_image, noise_mask = cls.append_keyframe(
+                positive,
+                negative,
+                frame_idx,
+                latent_image,
+                noise_mask,
+                t,
+                strength,
+                scale_factors,
+            )
+
+            # Track this guide for per-reference attention control.
+            pre_filter_count = t.shape[2] * t.shape[3] * t.shape[4]
+            guide_latent_shape = list(t.shape[2:])  # [F, H, W]
+            positive, negative = _append_guide_attention_entry(positive, negative, pre_filter_count, guide_latent_shape, strength=strength)
+
+        return io.NodeOutput(positive, negative, {"samples": latent_image, "noise_mask": noise_mask})
+
+class LTXVAddGuidesFromBatch(LTXVAddGuide):
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVAddGuidesFromBatch",
+            category="conditioning/ltxv",
+            description="Adds multiple guide images from a batch to the latent at corresponding frame indices. Non-black images in the batch are used as guides.",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Vae.Input("vae"),
+                io.Latent.Input("latent"),
+                io.Image.Input("images", tooltip="Batch of images - non-black images will be used as guides"),
+                io.Float.Input("strength", default=1.0, min=0.0, max=10.0, step=0.01, tooltip="Strength for all guides."),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, positive, negative, vae, latent, images, strength) -> io.NodeOutput:
+        scale_factors = vae.downscale_index_formula
+        latent_image = latent["samples"]
+        noise_mask = get_noise_mask(latent)
+
+        _, _, latent_length, latent_height, latent_width = latent_image.shape
+
+        # Process each image in the batch
+        batch_size = images.shape[0]
+
+        for i in range(batch_size):
+            img = images[i:i+1]
+
+            # Check if image is not black and use batch index as frame index
+            if img.max() > 0.001:
+                f_idx = i
+
+                image_1, t = cls.encode(vae, latent_width, latent_height, img, scale_factors)
+
+                frame_idx, latent_idx = cls.get_latent_index(positive, latent_length, len(image_1), f_idx, scale_factors)
+
+                if latent_idx + t.shape[2] <= latent_length:
+                    positive, negative, latent_image, noise_mask = cls.append_keyframe(
+                        positive,
+                        negative,
+                        frame_idx,
+                        latent_image,
+                        noise_mask,
+                        t,
+                        strength,
+                        scale_factors,
+                    )
+
+                    # Track this guide for per-reference attention control.
+                    pre_filter_count = t.shape[2] * t.shape[3] * t.shape[4]
+                    guide_latent_shape = list(t.shape[2:])  # [F, H, W]
+                    positive, negative = _append_guide_attention_entry(positive, negative, pre_filter_count, guide_latent_shape, strength=strength)
+                else:
+                    print(f"Warning: Skipping guide at index {i} - conditioning frames exceed latent sequence length")
+
+        return io.NodeOutput(positive, negative, {"samples": latent_image, "noise_mask": noise_mask})
+
+
+class LTXVAudioVideoMask(io.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVAudioVideoMask",
+            category="KJNodes/ltxv",
+            description="Creates noise masks for video and audio latents based on specified time ranges. New content is generated within these masked regions",
+            inputs=[
+                io.Latent.Input("video_latent", optional=True),
+                io.Latent.Input("audio_latent", optional=True),
+                io.Float.Input("video_fps", default=25, min=0.0, max=100.0, step=0.01),
+                io.Float.Input("video_start_time", default=0.0, min=0.0, max=10000.0, step=0.01, tooltip="Start time in seconds for the video mask."),
+                io.Float.Input("video_end_time", default=5.0, min=0.0, max=10000.0, step=0.01, tooltip="End time in seconds for the video mask."),
+                io.Float.Input("audio_start_time", default=0.0, min=0.0, max=10000.0, step=0.01, tooltip="Start time in seconds for the audio mask."),
+                io.Float.Input("audio_end_time", default=5.0, min=0.0, max=10000.0, step=0.01, tooltip="End time in seconds for the audio mask."),
+                io.Combo.Input(
+                    "max_length",
+                    options=["truncate", "pad", "partial"],
+                    default="truncate",
+                    tooltip="'truncate': cut latent to end_time length. 'pad': extend latent to end_time. 'partial': mask range within existing latent.",
+                ),
+                io.Combo.Input("existing_mask_mode", options=["add", "subtract", "overwrite"], optional=True, default="add", tooltip="How to combine with existing noise masks if present. 'add' will take the max of existing and new mask, 'overwrite' will replace with new mask. 'subtract' will set the masked region to 0 instead of 1, effectively unmasking it."),
+            ],
+            outputs=[
+                io.Latent.Output(display_name="video_latent"),
+                io.Latent.Output(display_name="audio_latent"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, video_fps, video_start_time, video_end_time, audio_start_time, audio_end_time, max_length="truncate", existing_mask_mode="add", video_latent=None, audio_latent=None,) -> io.NodeOutput:
+
+        time_scale_factor = 8
+        mel_hop_length = 160
+        sampling_rate = 16000
+        latent_downsample_factor = 4
+        audio_latents_per_second = (sampling_rate / mel_hop_length / latent_downsample_factor) # 25
+
+        if video_latent is not None:
+            video_latent_frame_count = video_latent["samples"].shape[2]
+
+            video_pixel_frame_start_raw = int(round(video_start_time * video_fps))
+            video_pixel_frame_end_raw = int(round(video_end_time * video_fps))
+
+            # Calculate required latent frames based on end time
+            required_latent_frames = (video_pixel_frame_end_raw - 1) // time_scale_factor + 1
+
+            # Handle different max_length modes
+            if max_length == "pad" and required_latent_frames > video_latent_frame_count:
+                # Pad video latent if required frames exceed current length
+                pad_frames = required_latent_frames - video_latent_frame_count
+                padding = torch.zeros(
+                    video_latent["samples"].shape[0],
+                    video_latent["samples"].shape[1],
+                    pad_frames,
+                    video_latent["samples"].shape[3],
+                    video_latent["samples"].shape[4],
+                    dtype=video_latent["samples"].dtype,
+                    device=video_latent["samples"].device
+                )
+                video_samples = torch.cat([video_latent["samples"], padding], dim=2)
+                video_latent_frame_count = video_samples.shape[2]
+            elif max_length == "truncate":
+                # Truncate to the end_time
+                video_samples = video_latent["samples"][:, :, :required_latent_frames]
+                video_latent_frame_count = video_samples.shape[2]
+            else:  # partial
+                video_samples = video_latent["samples"]
+
+            # Now calculate indices based on potentially padded latent
+            video_pixel_frame_count = (video_latent_frame_count - 1) * time_scale_factor + 1
+            xp = np.array([0] + list(range(1, video_pixel_frame_count + time_scale_factor, time_scale_factor)))
+
+            # video_frame_index_start = index of the value in xp rounding up
+            video_latent_frame_index_start = np.searchsorted(xp, video_pixel_frame_start_raw, side="left")
+            # video_frame_index_end = index of the value in xp rounding down
+            video_latent_frame_index_end = np.searchsorted(xp, video_pixel_frame_end_raw, side="right") - 1
+
+            video_latent_frame_index_start = max(0, video_latent_frame_index_start)
+            video_latent_frame_index_end = min(video_latent_frame_index_end, video_latent_frame_count)
+
+            # Get existing noise mask if present, otherwise create new one
+            if "noise_mask" in video_latent and video_latent["noise_mask"] is not None and existing_mask_mode != "overwrite":
+                video_mask = video_latent["noise_mask"].clone()
+                # Adjust mask size based on mode
+                if max_length == "pad" and video_samples.shape[2] > video_latent["samples"].shape[2]:
+                    # Pad the mask if we padded the samples
+                    mask_padding = torch.zeros(
+                        video_mask.shape[0],
+                        video_mask.shape[1],
+                        video_samples.shape[2] - video_mask.shape[2],
+                        video_mask.shape[3],
+                        video_mask.shape[4],
+                        dtype=video_mask.dtype,
+                        device=video_mask.device
+                    )
+                    video_mask = torch.cat([video_mask, mask_padding], dim=2)
+                elif max_length == "truncate":
+                    # Truncate the mask to match truncated samples
+                    video_mask = video_mask[:, :, :video_samples.shape[2]]
+            else:
+                video_mask = torch.zeros_like(video_samples)[:, :1]
+
+            video_mask[:, :, video_latent_frame_index_start:video_latent_frame_index_end] = 1.0 if existing_mask_mode != "subtract" else 0.0
+            # ensure all padded frames are also masked
+            if max_length == "pad" and video_samples.shape[2] > video_latent["samples"].shape[2]:
+                video_mask[:, :, video_latent["samples"].shape[2]:] = 1.0 if existing_mask_mode != "subtract" else 0.0
+            video_latent = video_latent.copy()
+            video_latent["samples"] = video_samples
+            video_latent["noise_mask"] = video_mask
+
+        if audio_latent is not None:
+            audio_latent_frame_count = audio_latent["samples"].shape[2]
+
+            audio_latent_frame_index_start = int(round(audio_start_time * audio_latents_per_second))
+            audio_latent_frame_index_end = int(round(audio_end_time * audio_latents_per_second)) + 1
+
+            # Handle different max_length modes
+            if max_length == "pad" and audio_latent_frame_index_end > audio_latent_frame_count:
+                # Pad audio latent if end index exceeds current length
+                pad_frames = audio_latent_frame_index_end - audio_latent_frame_count
+                padding = torch.zeros(
+                    audio_latent["samples"].shape[0],
+                    audio_latent["samples"].shape[1],
+                    pad_frames,
+                    audio_latent["samples"].shape[3],
+                    dtype=audio_latent["samples"].dtype,
+                    device=audio_latent["samples"].device
+                )
+                audio_samples = torch.cat([audio_latent["samples"], padding], dim=2)
+                audio_latent_frame_count = audio_samples.shape[2]
+            elif max_length == "truncate":
+                # Truncate to the end_time
+                audio_samples = audio_latent["samples"][:, :, :audio_latent_frame_index_end]
+                audio_latent_frame_count = audio_samples.shape[2]
+            else:  # partial
+                audio_samples = audio_latent["samples"]
+
+            audio_latent_frame_index_start = max(0, audio_latent_frame_index_start)
+            audio_latent_frame_index_end = min(audio_latent_frame_index_end, audio_latent_frame_count)
+
+            # Get existing noise mask if present, otherwise create new one
+            if "noise_mask" in audio_latent and audio_latent["noise_mask"] is not None and existing_mask_mode != "overwrite":
+                audio_mask = audio_latent["noise_mask"].clone()
+                # Adjust mask size based on mode
+                if max_length == "pad" and audio_samples.shape[2] > audio_latent["samples"].shape[2]:
+                    # Pad the mask if we padded the samples
+                    mask_padding = torch.zeros(
+                        audio_mask.shape[0],
+                        audio_mask.shape[1],
+                        audio_samples.shape[2] - audio_mask.shape[2],
+                        audio_mask.shape[3],
+                        dtype=audio_mask.dtype,
+                        device=audio_mask.device
+                    )
+                    audio_mask = torch.cat([audio_mask, mask_padding], dim=2)
+                elif max_length == "truncate":
+                    # Truncate the mask to match truncated samples
+                    audio_mask = audio_mask[:, :, :audio_samples.shape[2]]
+            else:
+                audio_mask = torch.zeros_like(audio_samples)
+
+            audio_mask[:, :, audio_latent_frame_index_start:audio_latent_frame_index_end] = 1.0
+            # ensure all padded frames are also masked
+            if max_length == "pad" and audio_samples.shape[2] > audio_latent["samples"].shape[2]:
+                audio_mask[:, :, audio_latent["samples"].shape[2]:] = 1.0
+            audio_latent = audio_latent.copy()
+            audio_latent["samples"] = audio_samples
+            audio_latent["noise_mask"] = audio_mask
+
+        return io.NodeOutput(video_latent, audio_latent)
+
+def _compute_attention(self, query, context, attn_precision=None, transformer_options={}, mask=None):
+    """Compute attention and return the result. Cleans up intermediate tensors.
+
+    When `mask` is non-None, bypass wrap_attn and call attention_pytorch directly —
+    sage and similar backends drop arbitrary masks.
+    """
+    k = self.k_norm(self.to_k(context)).to(query.dtype)
+    v = self.to_v(context).to(query.dtype)
+    if mask is None:
+        x = comfy.ldm.modules.attention.optimized_attention(query, k, v, heads=self.heads, attn_precision=attn_precision, transformer_options=transformer_options).flatten(2)
+    else:
+        x = comfy.ldm.modules.attention.attention_pytorch(query, k, v, heads=self.heads, mask=mask, attn_precision=attn_precision, _inside_attn_wrapper=True, transformer_options=transformer_options).flatten(2)
+    del k, v
+    return x
+
+def nag_attention(self, query, context_positive, nag_context, attn_precision=None, transformer_options={}, mask=None):
+    # mask (e.g. PromptRelay's temporal routing) only applies to the real-context, not NAG context
+    x_positive = _compute_attention(self, query, context_positive, attn_precision, transformer_options, mask=mask)
+    x_negative = _compute_attention(self, query, nag_context, attn_precision, transformer_options)
+    return x_positive, x_negative
+
+def normalized_attention_guidance(self, x_positive, x_negative):
+    if self.inplace:
+        nag_guidance = x_negative.mul_(self.nag_scale - 1).neg_().add_(x_positive, alpha=self.nag_scale)
+        del x_negative
+    else:
+        nag_guidance = x_negative * (self.nag_scale - 1)
+        del x_negative
+        nag_guidance = (x_positive * self.nag_scale).sub_(nag_guidance)
+
+    norm_positive = torch.norm(x_positive, p=1, dim=-1, keepdim=True)
+    norm_guidance = torch.norm(nag_guidance, p=1, dim=-1, keepdim=True)
+
+    scale = norm_guidance / norm_positive
+    torch.nan_to_num_(scale, nan=10.0)
+    mask = scale > self.nag_tau
+    del scale
+
+    adjustment = (norm_positive * self.nag_tau) / (norm_guidance + 1e-7)
+    del norm_positive, norm_guidance
+
+    nag_guidance.mul_(torch.where(mask, adjustment, 1.0))
+    del mask, adjustment
+
+    if self.inplace:
+        return nag_guidance.sub_(x_positive).mul_(self.nag_alpha).add_(x_positive)
+    else:
+        nag_guidance.mul_(self.nag_alpha)
+        return nag_guidance.add_(x_positive * (1 - self.nag_alpha))
+
+#region NAG
+def ltxv_crossattn_forward_nag(self, x, context, mask=None, transformer_options={}, **kwargs):
+
+    if mask is None:
+        mask_provider = transformer_options.get("promptrelay_mask_fn")
+        if mask_provider is not None:
+            mask = mask_provider(x.shape[1], context.shape[1], x.dtype, x.device, transformer_options)
+
+    # Single or [pos, neg] pair
+    if context.shape[0] == 1:
+        x_pos, context_pos = x, context
+        x_neg, context_neg = None, None
+    else:
+        x_pos, x_neg = torch.chunk(x, 2, dim=0)
+        context_pos, context_neg = torch.chunk(context, 2, dim=0)
+
+    # Positive
+    q_pos = self.q_norm(self.to_q(x_pos))
+    del x_pos
+
+    x_positive, x_negative = nag_attention(self, q_pos, context_pos, self.nag_context, attn_precision=self.attn_precision, transformer_options=transformer_options, mask=mask)
+    del context_pos, q_pos
+
+    x_pos_out = normalized_attention_guidance(self, x_positive, x_negative)
+    del x_positive, x_negative
+
+    # Negative
+    if x_neg is not None and context_neg is not None:
+        q_neg = self.q_norm(self.to_q(x_neg))
+        k_neg = self.k_norm(self.to_k(context_neg))
+        v_neg = self.to_v(context_neg)
+
+        x_neg_out = comfy.ldm.modules.attention.optimized_attention(q_neg, k_neg, v_neg, heads=self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options)
+        out = torch.cat([x_pos_out, x_neg_out], dim=0)
+    else:
+        out = x_pos_out
+
+    if self.to_gate_logits is not None:
+        gate_logits = self.to_gate_logits(x)  # (B, T, H)
+        b, t, _ = out.shape
+        out = out.view(b, t, self.heads, self.dim_head)
+        gates = 2.0 * torch.sigmoid(gate_logits)  # zero-init -> identity
+        out = out * gates.unsqueeze(-1)
+        out = out.view(b, t, self.heads * self.dim_head)
+
+    return self.to_out(out)
+
+
+class LTXVCrossAttentionPatch:
+    def __init__(self, context, nag_scale, nag_alpha, nag_tau, inplace=True):
+        self.nag_context = context
+        self.nag_scale = nag_scale
+        self.nag_alpha = nag_alpha
+        self.nag_tau = nag_tau
+        self.inplace = inplace
+
+    def __get__(self, obj, objtype=None):
+        # Create bound method with stored parameters
+        def wrapped_attention(self_module, *args, **kwargs):
+            self_module.nag_context = self.nag_context
+            self_module.nag_scale = self.nag_scale
+            self_module.nag_alpha = self.nag_alpha
+            self_module.nag_tau = self.nag_tau
+            self_module.inplace = self.inplace
+
+            return ltxv_crossattn_forward_nag(self_module, *args, **kwargs)
+        return types.MethodType(wrapped_attention, obj)
+
+class LTX2_NAG(io.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTX2_NAG",
+            display_name="LTX2 NAG",
+            category="KJNodes/ltxv",
+            description="https://github.com/ChenDarYen/Normalized-Attention-Guidance",
+            is_experimental=True,
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("nag_scale", default=11.0, min=0.0, max=100.0, step=0.001, tooltip="Strength of negative guidance effect"),
+                io.Float.Input("nag_alpha", default=0.25, min=0.0, max=1.0, step=0.001, tooltip="Mixing coefficient in that controls the balance between the normalized guided representation and the original positive representation."),
+                io.Float.Input("nag_tau", default=2.5, min=0.0, max=10.0, step=0.001, tooltip="Clipping threshold that controls how much the guided attention can deviate from the positive attention."),
+                io.Conditioning.Input("nag_cond_video", optional=True),
+                io.Conditioning.Input("nag_cond_audio", optional=True),
+                io.Boolean.Input("inplace", default=True, optional=True, tooltip="If true, modifies tensors in place to save memory. Leads to different numerical results which may change the output slightly."),
+            ],
+            outputs=[
+                io.Model.Output(display_name="model"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, nag_scale, nag_alpha, nag_tau, nag_cond_video=None, nag_cond_audio=None, inplace=True) -> io.NodeOutput:
+        if nag_scale == 0:
+            return io.NodeOutput(model)
+
+        device = mm.get_torch_device()
+        offload_device = mm.unet_offload_device()
+        dtype = model.model.manual_cast_dtype
+        if dtype is None:
+            dtype = model.model.diffusion_model.dtype
+
+        model_clone = model.clone()
+
+        diffusion_model = model_clone.get_model_object("diffusion_model")
+        img_dim = diffusion_model.inner_dim
+        audio_dim = diffusion_model.audio_inner_dim
+
+        context_video = context_audio = None
+
+        if nag_cond_video is not None:
+            context_video = nag_cond_video[0][0].to(device, dtype)
+            vid_split = getattr(diffusion_model, "cross_attention_dim", None)
+            if vid_split is not None and context_video.shape[-1] == vid_split + diffusion_model.audio_cross_attention_dim:
+                context_video = context_video[:, :, :vid_split]
+            if diffusion_model.caption_proj_before_connector and diffusion_model.caption_projection_first_linear:
+                diffusion_model.caption_projection.to(device)
+                context_video = diffusion_model.caption_projection(context_video)
+                diffusion_model.caption_projection.to(offload_device)
+            if hasattr(diffusion_model, "video_embeddings_connector"):
+                diffusion_model.video_embeddings_connector.to(device)
+                context_video = diffusion_model.video_embeddings_connector(context_video)[0]
+                diffusion_model.video_embeddings_connector.to(offload_device)
+            context_video = context_video.view(1, -1, img_dim)
+            for idx, block in enumerate(diffusion_model.transformer_blocks):
+                patched_attn2 = LTXVCrossAttentionPatch(context_video, nag_scale, nag_alpha, nag_tau, inplace=inplace).__get__(block.attn2, block.__class__)
+                model_clone.add_object_patch(f"diffusion_model.transformer_blocks.{idx}.attn2.forward", patched_attn2)
+
+        if nag_cond_audio is not None and diffusion_model.audio_caption_projection is not None:
+            context_audio = nag_cond_audio[0][0].to(device, dtype)
+            vid_split = getattr(diffusion_model, "cross_attention_dim", None)
+            if vid_split is not None and context_audio.shape[-1] == vid_split + diffusion_model.audio_cross_attention_dim:
+                context_audio = context_audio[:, :, vid_split:]
+            if diffusion_model.caption_proj_before_connector and diffusion_model.caption_projection_first_linear:
+                diffusion_model.audio_caption_projection.to(device)
+                context_audio = diffusion_model.audio_caption_projection(context_audio)
+                diffusion_model.audio_caption_projection.to(offload_device)
+            if hasattr(diffusion_model, "audio_embeddings_connector"):
+                diffusion_model.audio_embeddings_connector.to(device)
+                context_audio = diffusion_model.audio_embeddings_connector(context_audio)[0]
+                diffusion_model.audio_embeddings_connector.to(offload_device)
+            context_audio = context_audio.view(1, -1, audio_dim)
+            for idx, block in enumerate(diffusion_model.transformer_blocks):
+                patched_audio_attn2 = LTXVCrossAttentionPatch(context_audio, nag_scale, nag_alpha, nag_tau, inplace=inplace).__get__(block.audio_attn2, block.__class__)
+                model_clone.add_object_patch(f"diffusion_model.transformer_blocks.{idx}.audio_attn2.forward", patched_audio_attn2)
+
+        return io.NodeOutput(model_clone)
+
+
+def ffn_chunked_forward(self, x):
+    if x.shape[1] > self.dim_threshold:
+        chunk_size = x.shape[1] // self.num_chunks
+        for i in range(self.num_chunks):
+            start_idx = i * chunk_size
+            end_idx = (i + 1) * chunk_size if i < self.num_chunks - 1 else x.shape[1]
+            x[:, start_idx:end_idx] = self.net(x[:, start_idx:end_idx])
+        return x
+    else:
+        return self.net(x)
+
+class LTXVffnChunkPatch:
+    def __init__(self, num_chunks, dim_threshold=4096):
+        self.num_chunks = num_chunks
+        self.dim_threshold = dim_threshold
+
+    def __get__(self, obj, objtype=None):
+        def wrapped_forward(self_module, *args, **kwargs):
+            self_module.num_chunks = self.num_chunks
+            self_module.dim_threshold = self.dim_threshold
+            return ffn_chunked_forward(self_module, *args, **kwargs)
+        return types.MethodType(wrapped_forward, obj)
+
+class LTXVChunkFeedForward(io.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVChunkFeedForward",
+            display_name="LTXV Chunk FeedForward",
+            category="KJNodes/ltxv",
+            description="EXPERIMENTAL AND MAY CHANGE THE MODEL OUTPUT!! Chunks feedforward activations to reduce peak VRAM usage.",
+            is_experimental=True,
+            inputs=[
+                io.Model.Input("model"),
+                io.Int.Input("chunks", default=2, min=1, max=100, step=1, tooltip="Number of chunks to split the feedforward activations into to reduce peak VRAM usage."),
+                io.Int.Input("dim_threshold", default=4096, min=0, max=16384, step=256, tooltip="Dimension threshold above which to apply chunking."),
+            ],
+            outputs=[
+                io.Model.Output(display_name="model"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, chunks, dim_threshold) -> io.NodeOutput:
+        if chunks == 1:
+            return io.NodeOutput(model)
+
+        model_clone = model.clone()
+        diffusion_model = model_clone.get_model_object("diffusion_model")
+
+        for idx, block in enumerate(diffusion_model.transformer_blocks):
+            patched_attn2 = LTXVffnChunkPatch(chunks, dim_threshold).__get__(block.ff, block.__class__)
+            model_clone.add_object_patch(f"diffusion_model.transformer_blocks.{idx}.ff.forward", patched_attn2)
+
+        return io.NodeOutput(model_clone)
+
+
+
+#borrowed VideoHelperSuite https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite/blob/main/videohelpersuite/latent_preview.py
+import server
+from threading import Thread
+import torch.nn.functional as F
+import time
+import struct
+from PIL import Image
+from io import BytesIO
+serv = server.PromptServer.instance
+
+class WrappedPreviewer():
+    def __init__(self, latent_rgb_factors, latent_rgb_factors_bias, rate=8, taeltx=None):
+        self.first_preview = True
+        self.taeltx = taeltx
+        self.last_time = 0
+        self.c_index = 0
+        self.rate = rate
+        self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu").transpose(0, 1)
+        self.latent_rgb_factors_bias = torch.tensor(latent_rgb_factors_bias, device="cpu") if latent_rgb_factors_bias is not None else None
+
+    def decode_latent_to_preview_image(self, preview_format, x0):
+        if x0.ndim == 5:
+            #Keep batch major
+            x0 = x0.movedim(2,1)
+            x0 = x0.reshape((-1,)+x0.shape[-3:])
+        num_images = x0.size(0)
+        new_time = time.time()
+        num_previews = int((new_time - self.last_time) * self.rate)
+        self.last_time = self.last_time + num_previews/self.rate
+        if num_previews > num_images:
+            num_previews = num_images
+        elif num_previews <= 0:
+            return None
+        if self.first_preview:
+            self.first_preview = False
+            serv.send_sync('VHS_latentpreview', {'length':num_images, 'rate': self.rate, 'id': serv.last_node_id})
+            self.last_time = new_time + 1/self.rate
+        if self.c_index + num_previews > num_images:
+            x0 = x0.roll(-self.c_index, 0)[:num_previews]
+        else:
+            x0 = x0[self.c_index:self.c_index + num_previews]
+        Thread(target=self.process_previews, args=(x0, self.c_index,
+                                                   num_images)).run()
+        self.c_index = (self.c_index + num_previews) % num_images
+        return None
+    def process_previews(self, image_tensor, ind, leng):
+        max_size = 512
+        min_size = 256
+        image_tensor = self.decode_latent_to_preview(image_tensor)
+
+        if image_tensor.size(1) < min_size or image_tensor.size(2) < min_size:
+            image_tensor = F.interpolate(image_tensor.movedim(-1,0), scale_factor=4, mode='nearest').movedim(0,-1)
+
+        if image_tensor.size(1) > max_size or image_tensor.size(2) > max_size:
+            image_tensor = image_tensor.movedim(-1,0)
+            if image_tensor.size(2) < image_tensor.size(3):
+                height = (max_size * image_tensor.size(2)) // image_tensor.size(3)
+                image_tensor = F.interpolate(image_tensor, (height,max_size), mode='nearest')
+            else:
+                width = (max_size * image_tensor.size(3)) // image_tensor.size(2)
+                image_tensor = F.interpolate(image_tensor, (max_size, width), mode='nearest')
+            image_tensor = image_tensor.movedim(0,-1)
+
+        previews_ubyte = (image_tensor.clamp(0, 1).mul(0xFF)).to(device="cpu", dtype=torch.uint8)
+
+        # Send VHS preview
+        for preview in previews_ubyte:
+            i = Image.fromarray(preview.numpy())
+            message = BytesIO()
+            message.write((1).to_bytes(length=4, byteorder='big')*2)
+            message.write(ind.to_bytes(length=4, byteorder='big'))
+            message.write(struct.pack('16p', (serv.last_node_id or "").encode('ascii')))
+            i.save(message, format="JPEG", quality=95, compress_level=1)
+            #NOTE: send sync already uses call_soon_threadsafe
+            serv.send_sync(server.BinaryEventTypes.PREVIEW_IMAGE,
+                           message.getvalue(), serv.client_id)
+            if self.taeltx is not None:
+                ind = (ind + 1) % ((leng-1) * 8 + 1)
+            else:
+                ind = (ind + 1) % leng
+
+    def decode_latent_to_preview(self, x0):
+        if self.taeltx is not None:
+            x0 = x0.unsqueeze(0).to(dtype=self.taeltx.first_stage_model.decoder[1].weight.dtype, device=device)
+            x_sample = self.taeltx.first_stage_model.decode(x0)[0].permute(1, 2, 3, 0)
+            return x_sample
+        else:
+            self.latent_rgb_factors = self.latent_rgb_factors.to(dtype=x0.dtype, device=x0.device)
+            if self.latent_rgb_factors_bias is not None:
+                self.latent_rgb_factors_bias = self.latent_rgb_factors_bias.to(dtype=x0.dtype, device=x0.device)
+            latent_image = F.linear(x0.movedim(1, -1), self.latent_rgb_factors,
+                                    bias=self.latent_rgb_factors_bias)
+
+            #low = latent_image.quantile(0.01)
+            #high = latent_image.quantile(0.99)
+            #latent_image = ((latent_image - low) / (high - low)).clamp(0, 1)
+            latent_image = torch.sigmoid(latent_image)
+            return latent_image
+
+
+def get_ltx_rgb_factors(is_23):
+    """Return (latent_rgb_factors, latent_rgb_factors_bias) for LTX (is_23=False) or
+    LTX2/LTXAV (is_23=True). Exposed so other nodes can build a WrappedPreviewer with
+    the right LTX factors without duplicating the 128-row tables."""
+    return _get_ltx_rgb_factors_impl(is_23)
+
+
+def _get_ltx_rgb_factors_impl(is_23):
+    if not is_23:
+        latent_rgb_factors = [
+                [ 0.0350,  0.0159,  0.0132],
+                [ 0.0025, -0.0021, -0.0003],
+                [ 0.0286,  0.0028,  0.0020],
+                [ 0.0280, -0.0114, -0.0202],
+                [-0.0186,  0.0073,  0.0092],
+                [ 0.0027,  0.0097, -0.0113],
+                [-0.0069, -0.0032, -0.0024],
+                [-0.0323, -0.0370, -0.0457],
+                [ 0.0174,  0.0164,  0.0106],
+                [-0.0097,  0.0061,  0.0035],
+                [-0.0130, -0.0042, -0.0012],
+                [-0.0102, -0.0002, -0.0091],
+                [-0.0025,  0.0063,  0.0161],
+                [ 0.0003,  0.0037,  0.0108],
+                [ 0.0152,  0.0082,  0.0143],
+                [ 0.0317,  0.0203,  0.0312],
+                [-0.0092, -0.0233, -0.0119],
+                [-0.0405, -0.0226, -0.0023],
+                [ 0.0376,  0.0397,  0.0352],
+                [ 0.0171, -0.0043, -0.0095],
+                [ 0.0482,  0.0341,  0.0213],
+                [ 0.0031, -0.0046, -0.0018],
+                [-0.0486, -0.0383, -0.0294],
+                [-0.0071, -0.0272, -0.0123],
+                [ 0.0320,  0.0218,  0.0289],
+                [ 0.0327,  0.0088, -0.0116],
+                [-0.0098, -0.0240, -0.0111],
+                [ 0.0094, -0.0116,  0.0021],
+                [ 0.0309,  0.0092,  0.0165],
+                [-0.0065, -0.0077, -0.0107],
+                [ 0.0179,  0.0114,  0.0038],
+                [-0.0018, -0.0030, -0.0026],
+                [-0.0002,  0.0076, -0.0029],
+                [-0.0131, -0.0059, -0.0170],
+                [ 0.0055,  0.0066, -0.0038],
+                [ 0.0154,  0.0063,  0.0090],
+                [ 0.0186,  0.0175,  0.0188],
+                [-0.0166, -0.0381, -0.0428],
+                [ 0.0121,  0.0015, -0.0153],
+                [ 0.0118,  0.0050,  0.0019],
+                [ 0.0125,  0.0259,  0.0231],
+                [ 0.0046,  0.0130,  0.0081],
+                [ 0.0271,  0.0250,  0.0250],
+                [-0.0054, -0.0347, -0.0326],
+                [-0.0438, -0.0262, -0.0228],
+                [-0.0191, -0.0256, -0.0173],
+                [-0.0205, -0.0058,  0.0042],
+                [ 0.0404,  0.0434,  0.0346],
+                [-0.0242, -0.0177, -0.0146],
+                [ 0.0161,  0.0223,  0.0168],
+                [-0.0240, -0.0320, -0.0299],
+                [-0.0019,  0.0043,  0.0008],
+                [-0.0060, -0.0133, -0.0244],
+                [-0.0048, -0.0225, -0.0167],
+                [ 0.0267,  0.0133,  0.0152],
+                [ 0.0222,  0.0167,  0.0028],
+                [ 0.0015, -0.0062,  0.0013],
+                [-0.0241, -0.0178, -0.0079],
+                [ 0.0040, -0.0081, -0.0097],
+                [-0.0064,  0.0133, -0.0011],
+                [-0.0204, -0.0231, -0.0304],
+                [ 0.0011, -0.0011,  0.0145],
+                [-0.0283, -0.0259, -0.0260],
+                [ 0.0038,  0.0171, -0.0029],
+                [ 0.0637,  0.0424,  0.0409],
+                [ 0.0092,  0.0163,  0.0188],
+                [ 0.0082,  0.0055, -0.0179],
+                [-0.0177, -0.0286, -0.0147],
+                [ 0.0171,  0.0242,  0.0398],
+                [-0.0129,  0.0095, -0.0071],
+                [-0.0154,  0.0036,  0.0128],
+                [-0.0081, -0.0009,  0.0118],
+                [-0.0067, -0.0178, -0.0230],
+                [-0.0022, -0.0125, -0.0003],
+                [-0.0032, -0.0039, -0.0022],
+                [-0.0005, -0.0127, -0.0131],
+                [-0.0143, -0.0157, -0.0165],
+                [-0.0262, -0.0263, -0.0270],
+                [ 0.0063,  0.0127,  0.0178],
+                [ 0.0092,  0.0133,  0.0150],
+                [-0.0106, -0.0068,  0.0032],
+                [-0.0214, -0.0022,  0.0171],
+                [-0.0104, -0.0266, -0.0362],
+                [ 0.0021,  0.0048, -0.0005],
+                [ 0.0345,  0.0431,  0.0402],
+                [-0.0275, -0.0110, -0.0195],
+                [ 0.0203,  0.0251,  0.0224],
+                [ 0.0016, -0.0037, -0.0094],
+                [ 0.0241,  0.0198,  0.0114],
+                [-0.0003,  0.0027,  0.0141],
+                [ 0.0012, -0.0052, -0.0084],
+                [ 0.0057, -0.0028, -0.0163],
+                [-0.0488, -0.0545, -0.0509],
+                [-0.0076, -0.0025, -0.0014],
+                [-0.0249, -0.0142, -0.0367],
+                [ 0.0136,  0.0041,  0.0135],
+                [ 0.0007,  0.0034, -0.0053],
+                [-0.0068, -0.0109,  0.0029],
+                [ 0.0006, -0.0237, -0.0094],
+                [-0.0149, -0.0177, -0.0131],
+                [-0.0105,  0.0039,  0.0216],
+                [ 0.0242,  0.0200,  0.0180],
+                [-0.0339, -0.0153, -0.0195],
+                [ 0.0104,  0.0151,  0.0120],
+                [-0.0043,  0.0089,  0.0047],
+                [ 0.0157, -0.0030,  0.0008],
+                [ 0.0126,  0.0102, -0.0040],
+                [ 0.0040,  0.0114,  0.0137],
+                [ 0.0423,  0.0473,  0.0436],
+                [-0.0128, -0.0066, -0.0152],
+                [-0.0337, -0.0087, -0.0026],
+                [-0.0052,  0.0235,  0.0291],
+                [ 0.0079,  0.0154,  0.0260],
+                [-0.0539, -0.0377, -0.0358],
+                [-0.0188,  0.0062, -0.0035],
+                [-0.0186,  0.0041, -0.0083],
+                [ 0.0045, -0.0049,  0.0053],
+                [ 0.0172,  0.0071,  0.0042],
+                [-0.0003, -0.0078, -0.0096],
+                [-0.0209, -0.0132, -0.0135],
+                [-0.0074,  0.0017,  0.0099],
+                [-0.0038,  0.0070,  0.0014],
+                [-0.0013, -0.0017,  0.0073],
+                [ 0.0030,  0.0105,  0.0105],
+                [ 0.0154, -0.0168, -0.0235],
+                [-0.0108, -0.0038,  0.0047],
+                [-0.0298, -0.0347, -0.0436],
+                [-0.0206, -0.0189, -0.0139]
+            ]
+        latent_rgb_factors_bias = [0.2796, 0.1101, -0.0047]
+    else:
+        latent_rgb_factors = [[0.002269406570121646, -0.02110900916159153, -0.009850316680967808], [-0.016038373112678528, -0.012462412007153034, -0.01112896017730236], [0.025274179875850677, 0.011209743097424507, 0.025426799431443214], [0.04690725728869438, 0.041542328894138336, 0.03568895906209946], [-0.02388044260442257, -0.0018645941745489836, 0.01858334057033062], [0.03720448538661003, 0.0220357533544302, 0.027937663719058037], [-0.07273884862661362, -0.09326262027025223, -0.11579664051532745], [-0.063837431371212, 0.00026216846890747547, 0.03042735904455185], [0.02903873845934868, 0.042082373052835464, 0.030649805441498756], [0.03777873516082764, 0.0322984978556633, -0.005671461578458548], [-0.0075670829974114895, -0.012113905511796474, -0.01638956367969513], [0.026524530723690987, 0.060518112033605576, 0.059549521654844284], [0.10093028098344803, 0.10073262453079224, 0.0505094900727272], [0.03725508227944374, 0.015382086858153343, 0.005786076188087463], [-0.03139607608318329, -0.01690264232456684, -0.0013519978383556008], [-0.027200624346733093, -0.02517341822385788, -0.008874989114701748], [0.024963486939668655, 0.04293748363852501, 0.05582639202475548], [-0.0364827960729599, -0.026975594460964203, -0.021950015798211098], [0.027655167505145073, 0.025136707350611687, 0.043967027217149734], [0.035822272300720215, 0.013104500249028206, 0.01113432738929987], [0.05353763327002525, 0.013606574386358261, -0.018720127642154694], [-0.013587888330221176, -0.01689346879720688, -0.027842802926898003], [0.059415675699710846, 0.03734271228313446, 0.04562298208475113], [-0.02946414425969124, -0.038338612765073776, 0.001805233070626855], [0.03921474143862724, 0.0651894062757492, 0.10681862384080887], [-0.00744189927354455, 0.007951526902616024, 0.020728807896375656], [-0.04038553684949875, -0.05215264856815338, -0.07213657349348068], [-0.004655141849070787, 0.01305423304438591, 0.026104029268026352], [0.03434251993894577, 0.018448110669851303, 0.013096392154693604], [0.0022075253073126078, -0.0011812079465016723, 0.0002940484555438161], [-0.00043441299931146204, 0.02366728149354458, 0.035889431834220886], [-0.030657343566417694, -0.024926183745265007, -0.012355240061879158], [-0.018955843523144722, -0.017360301688313484, -0.008214764297008514], [-0.01113052573055029, -0.01201171800494194, -0.002986249281093478], [0.018902746960520744, 0.01758778840303421, 0.026414571329951286], [-0.019977254793047905, -0.01605399139225483, -0.019136475399136543], [-0.00300968368537724, -0.017609693109989166, -0.013655650429427624], [0.0022096361499279737, 0.017998533323407173, 0.01815750263631344], [0.05186990648508072, 0.03285299986600876, 0.016072165220975876], [0.012626334093511105, 0.0013884707586839795, -0.012077193707227707], [-0.0037861645687371492, -0.013902144506573677, -0.01911942847073078], [-0.014163163490593433, -0.00513274222612381, -0.014303527772426605], [-0.010461323894560337, 0.009658926166594028, 0.01644069515168667], [-0.008665377274155617, 0.002501955023035407, -0.009703717194497585], [-0.03404829278588295, -0.02546044997870922, -0.014914450235664845], [0.04997691139578819, 0.06592527031898499, 0.073111392557621], [0.027394814416766167, 0.024555068463087082, 0.019957970827817917], [-0.027501430362462997, -0.01673700101673603, -0.03089248389005661], [-0.018696032464504242, -0.0020940247923135757, 0.015244065783917904], [-0.0062704551964998245, -0.0067006442695856094, -0.007532030809670687], [0.014871004968881607, 0.009914354421198368, 0.020960720255970955], [0.03662937879562378, 0.04413224756717682, 0.04220828413963318], [-0.011242181062698364, -0.013539309613406658, -0.016438307240605354], [-0.014854325912892818, 0.0038217694964259863, -0.002461288822814822], [-0.014826249331235886, 0.0009719038498587906, -0.012078499421477318], [-0.029396841302514076, -0.01432017982006073, 0.013018904253840446], [0.02755064144730568, 0.028369395062327385, 0.01640605367720127], [0.12049165368080139, 0.1395745575428009, 0.14566579461097717], [0.019721267744898796, 0.009739740751683712, 0.0023876908235251904], [-0.007320966571569443, 0.0065013207495212555, 0.01603059470653534], [0.007391378283500671, -0.0073603675700724125, -0.01770283281803131], [0.02984853833913803, 0.012391146272420883, 0.010563627816736698], [-0.013479884713888168, -0.008637298829853535, -0.013457189314067364], [0.04127075523138046, 0.03032625839114189, 0.024770958349108696], [-0.06524652987718582, -0.012209279462695122, 0.02087211236357689], [-0.1179763451218605, -0.060323599725961685, -0.07592175155878067], [-0.07122819870710373, -0.04385707899928093, -0.022124603390693665], [-0.04682473465800285, -0.022610662505030632, -0.010107148438692093], [-0.0054328180849552155, -0.010368981398642063, -0.008167334832251072], [0.029181398451328278, 0.030588403344154358, 0.028090540319681168], [0.016619984060525894, 0.004931286443024874, -0.006450849585235119], [0.01035264041274786, 0.002237115055322647, 0.0013903985964134336], [-0.04313831403851509, -0.061772625893354416, -0.08946335315704346], [0.0150345079600811, 0.007781678810715675, 0.0011013159528374672], [-0.013585779815912247, 0.008117705583572388, 0.020367907360196114], [-0.172962948679924, -0.16406646370887756, -0.1668281853199005], [0.0083833709359169, 0.0015236001927405596, -0.01731627807021141], [0.021939430385828018, 0.018004458397626877, 0.014768349006772041], [0.008083095774054527, -0.013463049195706844, -0.022061636671423912], [0.024328550323843956, 0.0128010343760252, 0.014966367743909359], [0.05850301682949066, 0.027980001643300056, 0.02225641906261444], [0.09690416604280472, 0.06929530203342438, 0.03253814950585365], [0.048208240419626236, 0.025294817984104156, 0.023508133366703987], [-0.026432134211063385, -0.040383171290159225, -0.03950457274913788], [-0.021598653867840767, -0.017070941627025604, -0.010933087207376957], [0.011645167134702206, 0.002806191798299551, 0.003779367310926318], [0.10478592664003372, 0.08954174816608429, 0.06555330753326416], [0.015151776373386383, -0.016160616651177406, -0.024905217811465263], [0.019659176468849182, 0.008487952873110771, 0.002426224760711193], [-0.05173315480351448, -0.026337839663028717, -0.02127116546034813], [0.016987523064017296, 0.006270893849432468, 0.0015798212261870503], [0.007938026450574398, -0.005250005517154932, -0.020408453419804573], [0.013017759658396244, 0.01654384844005108, 0.04163840040564537], [-0.009886542335152626, -0.026848411187529564, -0.03070281818509102], [0.01108171883970499, 0.01827266439795494, -0.007332107983529568], [-0.0285995751619339, -0.031727731227874756, -0.03370537981390953], [0.005299570970237255, 0.05678633600473404, 0.02825017087161541], [-0.055322226136922836, -0.09084303677082062, -0.12999044358730316], [0.01844066195189953, 0.031044499948620796, 0.021148500964045525], [-0.004471115302294493, 0.005830412730574608, 0.00911418441683054], [-0.04053843766450882, -0.016424428671598434, -0.0010634599020704627], [0.03858831524848938, 0.007309338077902794, -0.005618985276669264], [0.01423253770917654, -0.0055681923404335976, 3.394074519746937e-05], [0.11455483734607697, 0.14653916656970978, 0.1488018035888672], [-0.005231931805610657, -0.0033921014983206987, -0.000995257287286222], [0.01449565589427948, 0.019586293026804924, 0.04565812274813652], [-0.005179048050194979, -0.011201606132090092, -0.0008710073889233172], [-0.015361929312348366, 0.00778581015765667, -0.008238887414336205], [-0.1147838830947876, -0.09109023958444595, -0.050579313188791275], [0.09037500619888306, 0.09597006440162659, 0.10811734944581985], [0.001873677596449852, -0.01772197335958481, -0.07681205868721008], [-0.020383257418870926, -0.016072455793619156, -0.01077069528400898], [-0.060444317758083344, -0.05499502643942833, -0.06153025105595589], [-0.016717270016670227, 0.026493264362215996, 0.021835654973983765], [0.008203534409403801, 0.00418612826615572, 0.013867748901247978], [0.0789225772023201, 0.05467747151851654, 0.016568133607506752], [-0.15149451792240143, -0.1526806503534317, -0.14325062930583954], [0.00538366474211216, 0.010192245244979858, -0.00449327751994133], [-0.004906965419650078, -0.005569908302277327, -0.02096559666097164], [0.024530155584216118, 0.010962833650410175, 0.0034586559049785137], [0.03551010414958, 0.017310436815023422, 0.007064413744956255], [0.11111932247877121, 0.09825586527585983, 0.08827318251132965], [-0.051722846925258636, -0.047595202922821045, -0.03763044252991676], [-0.02975175902247429, -0.02153967320919037, -0.021425534039735794], [-0.03462936729192734, -0.025198571383953094, -0.017322326079010963], [-0.016921017318964005, -0.012419789098203182, -0.0154880927875638], [-0.08035065978765488, -0.08451078832149506, -0.09623870998620987], [-0.03870908170938492, -0.04211008921265602, -0.04383759945631027]]
+        latent_rgb_factors_bias = [-0.6957847476005554, -0.7276281118392944, -0.7405748963356018]
+    return latent_rgb_factors, latent_rgb_factors_bias
+
+
+def prepare_callback(model, steps, x0_output_dict=None, shape=None, latent_upscale_model=None, vae=None, rate=8, taeltx=False, num_keyframes=0, is_23=False):
+    latent_rgb_factors, latent_rgb_factors_bias = get_ltx_rgb_factors(is_23)
+    preview_format = "JPEG"
+    if preview_format not in ["JPEG", "PNG"]:
+        preview_format = "JPEG"
+
+    previewer = WrappedPreviewer(latent_rgb_factors, latent_rgb_factors_bias, rate=rate, taeltx=vae if taeltx else None)
+
+    pbar = comfy.utils.ProgressBar(steps)
+    def callback(step, x0, x, total_steps):
+        if x0 is not None and shape is not None:
+            cut = math.prod(shape[1:])
+            x0 = x0[:, :, :cut].reshape([x0.shape[0]] + list(shape)[1:])
+
+        if num_keyframes > 0:
+            x0 = x0[:, :, :-num_keyframes]
+
+        if latent_upscale_model is not None:
+            x0 = vae.first_stage_model.per_channel_statistics.un_normalize(x0)
+            x0 =  latent_upscale_model(x0.to(torch.bfloat16))
+            x0 = vae.first_stage_model.per_channel_statistics.normalize(x0)
+
+        preview_bytes = None
+        if previewer:
+            preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0)
+        pbar.update_absolute(step + 1, total_steps, preview_bytes)
+    return callback
+
+class OuterSampleCallbackWrapper:
+    def __init__(self, latent_upscale_model=None, vae=None, preview_rate=8, taeltx=False):
+        self.latent_upscale_model = latent_upscale_model
+        self.vae = vae
+        self.preview_rate = preview_rate
+        self.taeltx = taeltx
+        self.x0_output = {}
+
+    def __call__(self, executor, noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes):
+        guider = executor.class_obj
+        diffusion_model = guider.model_patcher.model.diffusion_model
+        is_23 = not diffusion_model.caption_projection_first_linear
+
+        original_callback = callback
+        if self.latent_upscale_model is not None:
+            self.latent_upscale_model.to(device)
+        if self.vae is not None and self.taeltx:
+            self.vae.first_stage_model.to(device)
+
+        num_keyframes = 0
+        if 'positive' in guider.conds and len(guider.conds['positive']) > 0:
+            keyframe_idxs = guider.conds['positive'][0].get('keyframe_idxs')
+            if keyframe_idxs is not None:
+                num_keyframes = len(torch.unique(keyframe_idxs[0, 0, :, 0]))
+
+        new_callback = prepare_callback(guider.model_patcher, len(sigmas) -1, shape=latent_shapes[0] if len(latent_shapes) > 1 else None,
+                                        x0_output_dict=self.x0_output, latent_upscale_model=self.latent_upscale_model, vae=self.vae, rate=self.preview_rate, taeltx=self.taeltx, num_keyframes=num_keyframes, is_23=is_23)
+        # Wrapper that calls both callbacks
+        def combined_callback(step, x0, x, total_steps):
+            new_callback(step, x0, x, total_steps)
+            if original_callback is not None:
+                original_callback(step, x0, x, total_steps)
+        out = executor(noise, latent_image, sampler, sigmas, denoise_mask, combined_callback, disable_pbar, seed, latent_shapes=latent_shapes)
+        if self.latent_upscale_model is not None:
+            self.latent_upscale_model.to(mm.unet_offload_device())
+        return out
+
+class LTX2SamplingPreviewOverride(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="LTX2SamplingPreviewOverride",
+            display_name="LTX2 Sampling Preview Override",
+            description="Overrides the LTX2 preview sampling preview function, temporary measure until previews are in comfy core",
+            category="KJNodes/ltxv",
+            is_experimental=True,
+            inputs=[
+                io.Model.Input("model", tooltip="The model to add preview override to."),
+                io.Int.Input("preview_rate", default=8, min=1, max=60, step=1, tooltip="Preview frame rate."),
+                io.LatentUpscaleModel.Input("latent_upscale_model", optional=True, tooltip="Optional upscale model to use for higher resolution previews."),
+                io.Vae.Input("vae", optional=True, tooltip="VAE model to use normalizing the latents for the upscale model."),
+            ],
+            outputs=[
+                io.Model.Output(tooltip="The model with Sampling Preview Override."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, preview_rate, latent_upscale_model=None, vae=None) -> io.NodeOutput:
+        model = model.clone()
+        taeltx = False
+        if vae is not None:
+            if vae.first_stage_model.__class__.__name__ == "TAEHV":
+                taeltx = True
+                latent_upscale_model=None
+        model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "sampling_preview", OuterSampleCallbackWrapper(latent_upscale_model, vae, preview_rate, taeltx))
+        return io.NodeOutput(model)
+
+
+# based on https://github.com/Lightricks/ComfyUI-LTXVideo/blob/cd5d371518afb07d6b3641be8012f644f25269fc/easy_samplers.py#L916
+class OuterSampleAudioNormalizationWrapper:
+    def __init__(self, audio_normalization_factors):
+        self.audio_normalization_factors = audio_normalization_factors
+
+    def __call__(self, executor, noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes):
+        guider = executor.class_obj
+        ltxav = guider.model_patcher.model.diffusion_model
+
+        x0_output = {}
+        self.total_steps = sigmas.shape[-1] - 1
+        pbar = comfy.utils.ProgressBar(self.total_steps)
+        self.full_step = 0
+
+        previewer = latent_preview.get_previewer(guider.model_patcher.load_device, guider.model_patcher.model.latent_format)
+        def custom_callback(step, x0, x, total_steps):
+            if x0_output is not None:
+                x0_output["x0"] = x0
+
+            preview_bytes = None
+            if previewer:
+                preview_bytes = previewer.decode_latent_to_preview_image("JPEG", x0)
+            self.full_step += 1
+            pbar.update_absolute(self.full_step, self.total_steps, preview_bytes)
+
+        callback = custom_callback
+
+        audio_normalization_factors = self.audio_normalization_factors.strip().split(",")
+        audio_normalization_factors = [float(factor) for factor in audio_normalization_factors]
+
+        # Extend normalization factors to match the length of sigmas
+        sigmas_len = self.total_steps
+        if len(audio_normalization_factors) < sigmas_len and len(audio_normalization_factors) > 0:
+            audio_normalization_factors.extend([audio_normalization_factors[-1]] * (sigmas_len - len(audio_normalization_factors)))
+
+        # Calculate indices where both normalization factors are not 1.0
+        sampling_split_indices = [i + 1 for i, a in enumerate(audio_normalization_factors) if a != 1.0]
+
+        # Split sigmas according to sampling_split_indices
+        def split_by_indices(arr, indices):
+            """
+            Splits arr into chunks according to indices (split points).
+            Indices are treated as starting a new chunk at each index in the list.
+            """
+            if not indices:
+                return [arr]
+            split_points = sorted(set(indices))
+            chunks = []
+            prev = 0
+            for idx in split_points:
+                if prev < idx:
+                    chunks.append(arr[prev : idx + 1])
+                prev = idx
+            if prev < len(arr):
+                chunks.append(arr[prev:])
+            return chunks
+
+        sigmas_chunks = split_by_indices(sigmas, sampling_split_indices)
+
+        i = 0
+        for sigmas_chunk in sigmas_chunks:
+            i += len(sigmas_chunk) - 1
+            latent_image = executor(noise, latent_image, sampler, sigmas_chunk, denoise_mask, callback, disable_pbar, seed, latent_shapes=latent_shapes)
+
+            if "x0" in x0_output:
+                latent_image = guider.model_patcher.model.process_latent_out(x0_output["x0"])
+
+            if i - 1 < len(audio_normalization_factors):
+                vx, ax = ltxav.separate_audio_and_video_latents(comfy.utils.unpack_latents(latent_image, latent_shapes), None)
+                if denoise_mask is not None:
+                    audio_mask = ltxav.separate_audio_and_video_latents(comfy.utils.unpack_latents(denoise_mask, latent_shapes), None)[1]
+                    ax = ax * audio_mask * audio_normalization_factors[i - 1] + ax * (1 - audio_mask)
+                else:
+                    ax = ax * audio_normalization_factors[i - 1]
+                latent_image = comfy.utils.pack_latents(ltxav.recombine_audio_and_video_latents(vx, ax))[0]
+
+                print("After %d steps, the audio latent was normalized by %f" % (i, audio_normalization_factors[i - 1]))
+
+        return latent_image
+
+
+class LTX2AudioLatentNormalizingSampling(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="LTX2AudioLatentNormalizingSampling",
+            display_name="LTX2 Audio Latent Normalizing Sampling",
+            description="Improves LTX2 generated audio quality by normalizing audio latents at specified sampling steps.",
+            category="KJNodes/ltxv",
+            is_experimental=True,
+            inputs=[
+                io.Model.Input("model", tooltip="The model to add preview override to."),
+                io.String.Input("audio_normalization_factors", default="1,1,0.25,1,1,0.25,1,1", tooltip="Comma-separated list of audio normalization factors to apply at each sampling step. For example, '1,1,0.25,1,1,0.25,1,1' will apply a factor of 0.25 at the 3rd and 6th steps."),
+            ],
+            outputs=[
+                io.Model.Output(tooltip="The model with Audio Latent Normalizing Sampling."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, audio_normalization_factors) -> io.NodeOutput:
+        model = model.clone()
+        model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "ltx2_audio_normalization", OuterSampleAudioNormalizationWrapper(audio_normalization_factors))
+        return io.NodeOutput(model)
+
+
+class LTXVImgToVideoInplaceKJ(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        options = []
+        for num_images in range(1, 21):  # 1 to 20 images
+            image_inputs = []
+            for i in range(1, num_images + 1):
+                image_inputs.extend([
+                    io.Image.Input(f"image_{i}", optional=True, tooltip=f"Image {i} to insert into the video latent."),
+                    io.Int.Input(
+                        f"index_{i}",
+                        default=0,
+                        min=-9999,
+                        max=9999,
+                        step=1,
+                        tooltip=f"Frame index for image {i} (in pixel space).",
+                        optional=True,
+                    ),
+                    io.Float.Input(f"strength_{i}", default=1.0, min=0.0, max=1.0, step=0.01, tooltip=f"Strength for image {i}."),
+                ])
+            options.append(io.DynamicCombo.Option(
+                key=str(num_images),
+                inputs=image_inputs
+            ))
+
+        return io.Schema(
+            node_id="LTXVImgToVideoInplaceKJ",
+            category="KJNodes/ltxv",
+            description="Replaces video latent frames with the encoded input images, uses DynamicCombo which requires ComfyUI 0.8.1 and frontend 1.33.4 or later.",
+            inputs=[
+                io.Vae.Input("vae", tooltip="Video VAE used to encode the images"),
+                io.Latent.Input("latent", tooltip="Video latent to insert images into"),
+                io.DynamicCombo.Input(
+                    "num_images",
+                    options=options,
+                    display_name="Number of Images",
+                    tooltip="Select how many images to insert",
+                ),
+            ],
+            outputs=[
+                io.Latent.Output(display_name="latent", tooltip="The video latent with the images inserted and latent noise mask updated."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, vae, latent, num_images) -> io.NodeOutput:
+
+        samples = latent["samples"].clone()
+        scale_factors = vae.downscale_index_formula
+        _, height_scale_factor, width_scale_factor = scale_factors
+
+        batch, _, latent_frames, latent_height, latent_width = samples.shape
+        width = latent_width * width_scale_factor
+        height = latent_height * height_scale_factor
+
+        # Get existing noise mask if present, otherwise create new one
+        if "noise_mask" in latent:
+            conditioning_latent_frames_mask = latent["noise_mask"].clone()
+        else:
+            conditioning_latent_frames_mask = torch.ones(
+                (batch, 1, latent_frames, 1, 1),
+                dtype=torch.float32,
+                device=samples.device,
+            )
+
+        # num_images is a dict containing the inputs from the selected option
+        # e.g., {'image_1': tensor, 'frame_idx_1': 0, 'strength_1': 1.0, 'image_2': tensor, 'frame_idx_2': 20, 'strength_2': 0.8, ...}
+
+        image_keys = sorted([k for k in num_images.keys() if k.startswith('image_')])
+
+        for img_key in image_keys:
+            i = img_key.split('_')[1]
+
+            image = num_images[f"image_{i}"]
+            if image is None:
+                continue
+            index = num_images.get(f"index_{i}")
+            if index is None:
+                continue
+            strength = num_images[f"strength_{i}"]
+
+            if image.shape[1] != height or image.shape[2] != width:
+                pixels = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+            else:
+                pixels = image
+            encode_pixels = pixels[:, :, :, :3]
+            t = vae.encode(encode_pixels)
+
+            # Convert pixel frame index to latent index
+            time_scale_factor = scale_factors[0]
+
+            # Handle negative indexing in pixel space
+            pixel_frame_count = (latent_frames - 1) * time_scale_factor + 1
+            if index < 0:
+                index = pixel_frame_count + index
+
+            # Convert to latent index
+            latent_idx = index // time_scale_factor
+
+            # Clamp to valid range
+            latent_idx = max(0, min(latent_idx, latent_frames - 1))
+
+            # Calculate end index, ensuring we don't exceed latent_frames
+            end_index = min(latent_idx + t.shape[2], latent_frames)
+
+            # Replace samples at the specified index range
+            samples[:, :, latent_idx:end_index] = t[:, :, :end_index - latent_idx]
+
+            # Update mask at the specified index range
+            conditioning_latent_frames_mask[:, :, latent_idx:end_index] = 1.0 - strength
+
+        return io.NodeOutput({"samples": samples, "noise_mask": conditioning_latent_frames_mask})
+
+try:
+    import triton
+    import triton.language as tl
+    HAS_TRITON = True
+except ImportError:
+    HAS_TRITON = False
+
+if HAS_TRITON:
+    @triton.jit
+    def _rms_norm_scale_shift_kernel(
+        X_ptr, Out_ptr, Scale_ptr, Shift_ptr,
+        N, scale_n_rows,   # scale_n_rows: actual rows in scale/shift (handles broadcast)
+        eps: tl.constexpr,
+        BLOCK_N: tl.constexpr,
+        IS_BF16: tl.constexpr,
+        HAS_BROADCAST: tl.constexpr,  # True when scale_n_rows < n_rows
+    ):
+        row = tl.program_id(0)
+        cols = tl.arange(0, BLOCK_N)
+        mask = cols < N
+        off = row * N + cols
+
+        x = tl.load(X_ptr + off, mask=mask, other=0.0).to(tl.float32)
+
+        mean_sq = tl.sum(x * x, axis=0) / N
+        rrms = tl.math.rsqrt(mean_sq + eps)
+        x_norm = x * rrms
+
+        # Fast path: no broadcast, scale row == x row
+        if HAS_BROADCAST:
+            scale_row = row % scale_n_rows
+            scale_off = scale_row * N + cols
+        else:
+            scale_off = off
+        scale = tl.load(Scale_ptr + scale_off, mask=mask, other=0.0).to(tl.float32)
+        shift = tl.load(Shift_ptr + scale_off, mask=mask, other=0.0).to(tl.float32)
+        out = x_norm * (1.0 + scale) + shift
+
+        if IS_BF16:
+            tl.store(Out_ptr + off, out.to(tl.bfloat16), mask=mask)
+        else:
+            tl.store(Out_ptr + off, out.to(tl.float16), mask=mask)
+
+    @triton.jit
+    def _rope_qk_split_kernel(
+        Q_ptr, K_ptr, Cos_ptr, Sin_ptr,
+        H, T, D,          # D = dim_head (full, even)
+        TH,               # T * H, precomputed to avoid runtime multiply in index decomp
+        # q/k layout: [B, T, H*D] contiguous → q[b,t,h*D + d]
+        # cos/sin layout: [B, H, T, D//2] contiguous
+        IS_BF16: tl.constexpr,
+        BLOCK_HD: tl.constexpr,   # >= D//2
+    ):
+        # grid: (B*H*T,)
+        bht   = tl.program_id(0)
+        t     = bht % T
+        h     = (bht // T) % H
+        b     = bht // TH
+
+        D_half = D // 2
+        cols   = tl.arange(0, BLOCK_HD)
+        mask   = cols < D_half
+
+        # offset into q/k tensor: [B, T, H*D]
+        qk_base  = (b * T + t) * (H * D) + h * D
+        # offset into cos/sin: [B, H, T, D//2]
+        cs_base  = (b * H * T + h * T + t) * D_half
+
+        # load both halves of q and k
+        q_x = tl.load(Q_ptr + qk_base          + cols, mask=mask, other=0.0).to(tl.float32)
+        q_y = tl.load(Q_ptr + qk_base + D_half  + cols, mask=mask, other=0.0).to(tl.float32)
+        k_x = tl.load(K_ptr + qk_base          + cols, mask=mask, other=0.0).to(tl.float32)
+        k_y = tl.load(K_ptr + qk_base + D_half  + cols, mask=mask, other=0.0).to(tl.float32)
+        cos = tl.load(Cos_ptr + cs_base         + cols, mask=mask, other=1.0).to(tl.float32)
+        sin = tl.load(Sin_ptr + cs_base         + cols, mask=mask, other=0.0).to(tl.float32)
+
+        # split RoPE: out_x = x*cos - y*sin,  out_y = y*cos + x*sin
+        q_ox = q_x * cos - q_y * sin
+        q_oy = q_y * cos + q_x * sin
+        k_ox = k_x * cos - k_y * sin
+        k_oy = k_y * cos + k_x * sin
+
+        if IS_BF16:
+            tl.store(Q_ptr + qk_base          + cols, q_ox.to(tl.bfloat16), mask=mask)
+            tl.store(Q_ptr + qk_base + D_half  + cols, q_oy.to(tl.bfloat16), mask=mask)
+            tl.store(K_ptr + qk_base          + cols, k_ox.to(tl.bfloat16), mask=mask)
+            tl.store(K_ptr + qk_base + D_half  + cols, k_oy.to(tl.bfloat16), mask=mask)
+        else:
+            tl.store(Q_ptr + qk_base          + cols, q_ox.to(tl.float16), mask=mask)
+            tl.store(Q_ptr + qk_base + D_half  + cols, q_oy.to(tl.float16), mask=mask)
+            tl.store(K_ptr + qk_base          + cols, k_ox.to(tl.float16), mask=mask)
+            tl.store(K_ptr + qk_base + D_half  + cols, k_oy.to(tl.float16), mask=mask)
+
+    @triton.jit
+    def _rms_norm_dual_scale_shift_kernel(
+        X_ptr, Out1_ptr, Out2_ptr,
+        Scale1_ptr, Shift1_ptr, Scale2_ptr, Shift2_ptr,
+        N, scale_n_rows,
+        eps: tl.constexpr,
+        BLOCK_N: tl.constexpr,
+        IS_BF16: tl.constexpr,
+        HAS_BROADCAST: tl.constexpr,
+    ):
+        """Compute rms_norm(x) once, write two differently-scaled outputs."""
+        row = tl.program_id(0)
+        cols = tl.arange(0, BLOCK_N)
+        mask = cols < N
+        off = row * N + cols
+
+        x = tl.load(X_ptr + off, mask=mask, other=0.0).to(tl.float32)
+        mean_sq = tl.sum(x * x, axis=0) / N
+        x_norm = x * tl.math.rsqrt(mean_sq + eps)
+
+        if HAS_BROADCAST:
+            scale_row = row % scale_n_rows
+            s_off = scale_row * N + cols
+        else:
+            s_off = off
+
+        scale1 = tl.load(Scale1_ptr + s_off, mask=mask, other=0.0).to(tl.float32)
+        shift1 = tl.load(Shift1_ptr + s_off, mask=mask, other=0.0).to(tl.float32)
+        scale2 = tl.load(Scale2_ptr + s_off, mask=mask, other=0.0).to(tl.float32)
+        shift2 = tl.load(Shift2_ptr + s_off, mask=mask, other=0.0).to(tl.float32)
+
+        out1 = x_norm * (1.0 + scale1) + shift1
+        out2 = x_norm * (1.0 + scale2) + shift2
+
+        if IS_BF16:
+            tl.store(Out1_ptr + off, out1.to(tl.bfloat16), mask=mask)
+            tl.store(Out2_ptr + off, out2.to(tl.bfloat16), mask=mask)
+        else:
+            tl.store(Out1_ptr + off, out1.to(tl.float16), mask=mask)
+            tl.store(Out2_ptr + off, out2.to(tl.float16), mask=mask)
+
+
+def fused_norm_scale_shift(x, scale, shift, eps=1e-6, use_triton=True):
+    if use_triton and HAS_TRITON and x.is_cuda:
+        orig_shape = x.shape
+        hidden = x.shape[-1]
+        x_2d = x.contiguous().reshape(-1, hidden)
+        n_rows = x_2d.shape[0]
+        # Flatten scale/shift without expanding — kernel handles broadcast via modulo
+        scale_2d = scale.contiguous().reshape(-1, hidden)
+        shift_2d = shift.contiguous().reshape(-1, hidden)
+        scale_n_rows = scale_2d.shape[0]
+        out = torch.empty_like(x_2d)
+        BLOCK_N = triton.next_power_of_2(hidden)
+        num_warps = min(max(BLOCK_N // 256, 1), 16)
+        _rms_norm_scale_shift_kernel[(n_rows,)](
+            x_2d, out, scale_2d, shift_2d,
+            hidden, scale_n_rows,
+            eps=eps, BLOCK_N=BLOCK_N,
+            IS_BF16=(x.dtype == torch.bfloat16),
+            HAS_BROADCAST=(scale_n_rows < n_rows),
+            num_warps=num_warps,
+        )
+        return out.view(orig_shape)
+    else:
+        return comfy.ldm.common_dit.rms_norm(x, eps=eps) * (1 + scale) + shift
+
+
+def fused_rope_qk(q, k, freqs_cis, use_triton=True):
+    """Apply split RoPE to q and k in one fused kernel pass.
+    q, k: [B, T, H*D] contiguous
+    freqs_cis: (cos, sin, split_pe) where cos/sin: [B, H, T, D//2]
+    Falls back to original apply_rotary_emb if preconditions not met.
+    """
+
+    if not (use_triton and HAS_TRITON and q.is_cuda):
+        return _apply_rope(q, freqs_cis), _apply_rope(k, freqs_cis)
+
+    cos, sin = freqs_cis[0], freqs_cis[1]
+    split_pe = freqs_cis[2] if len(freqs_cis) > 2 else False
+
+    if not split_pe or cos.ndim != 4 or q.ndim != 3:
+        return _apply_rope(q, freqs_cis), _apply_rope(k, freqs_cis)
+
+    B_cos, H, T_cos, D_half = cos.shape
+    D = D_half * 2
+    if q.shape != (B_cos, T_cos, H * D) or k.shape != (B_cos, T_cos, H * D):
+        return _apply_rope(q, freqs_cis), _apply_rope(k, freqs_cis)
+
+    q = q.contiguous()
+    k = k.contiguous()
+    cos_c = cos.contiguous()
+    sin_c = sin.contiguous()
+
+    BLOCK_HD = triton.next_power_of_2(D_half)
+    num_warps = min(max(BLOCK_HD // 32, 1), 8)
+    _rope_qk_split_kernel[(B_cos * H * T_cos,)](
+        q, k, cos_c, sin_c,
+        H, T_cos, D,
+        T_cos * H,
+        IS_BF16=(q.dtype == torch.bfloat16),
+        BLOCK_HD=BLOCK_HD,
+        num_warps=num_warps,
+    )
+    return q, k
+
+
+def fused_norm_dual_scale_shift(x, scale1, shift1, scale2, shift2, eps=1e-6, use_triton=True):
+    """RMS-norm x once, return two scaled outputs: (x_norm*(1+s1)+b1, x_norm*(1+s2)+b2)."""
+    if use_triton and HAS_TRITON and x.is_cuda:
+        orig_shape = x.shape
+        hidden = x.shape[-1]
+        x_2d     = x.contiguous().reshape(-1, hidden)
+        n_rows   = x_2d.shape[0]
+        scale1_2d = scale1.contiguous().reshape(-1, hidden)
+        shift1_2d = shift1.contiguous().reshape(-1, hidden)
+        scale2_2d = scale2.contiguous().reshape(-1, hidden)
+        shift2_2d = shift2.contiguous().reshape(-1, hidden)
+        scale_n_rows = scale1_2d.shape[0]
+        out1 = torch.empty_like(x_2d)
+        out2 = torch.empty_like(x_2d)
+        BLOCK_N   = triton.next_power_of_2(hidden)
+        num_warps = min(max(BLOCK_N // 256, 1), 16)
+        _rms_norm_dual_scale_shift_kernel[(n_rows,)](
+            x_2d, out1, out2,
+            scale1_2d, shift1_2d, scale2_2d, shift2_2d,
+            hidden, scale_n_rows,
+            eps=eps, BLOCK_N=BLOCK_N,
+            IS_BF16=(x.dtype == torch.bfloat16),
+            HAS_BROADCAST=(scale_n_rows < n_rows),
+            num_warps=num_warps,
+        )
+        return out1.view(orig_shape), out2.view(orig_shape)
+    else:
+        x_norm = comfy.ldm.common_dit.rms_norm(x, eps=eps)
+        return x_norm * (1 + scale1) + shift1, x_norm * (1 + scale2) + shift2
+
+
+def _apply_text_cross_attention_patched(
+    self, x, context, attn, scale_shift_table, prompt_scale_shift_table,
+    timestep, prompt_timestep, attention_mask, transformer_options,
+):
+    """Drop-in replacement for _apply_text_cross_attention with fused norm+scale+shift.
+    Patched onto the block instance so self._apply_text_cross_attention resolves here."""
+    if self.cross_attention_adaln:
+        shift_q, scale_q, gate = self.get_ada_values(scale_shift_table, x.shape[0], timestep, slice(6, 9))
+        batch_size = x.shape[0]
+        shift_kv, scale_kv = (
+            prompt_scale_shift_table[None, None].to(device=x.device, dtype=x.dtype)
+            + prompt_timestep.reshape(batch_size, prompt_timestep.shape[1], 2, -1)
+        ).unbind(dim=2)
+        attn_input = fused_norm_scale_shift(x, scale_q, shift_q,
+                                            use_triton=getattr(self, 'use_triton_kernels', True))
+        del shift_q, scale_q
+        encoder_hidden_states = context * (1 + scale_kv) + shift_kv
+        del scale_kv, shift_kv
+        return attn(attn_input, context=encoder_hidden_states,
+                    mask=attention_mask, transformer_options=transformer_options) * gate
+    return attn(
+        comfy.ldm.common_dit.rms_norm(x), context=context,
+        mask=attention_mask, transformer_options=transformer_options,
+    )
+
+
+def ltx2_forward(
+        self, x: Tuple[torch.Tensor, torch.Tensor], v_context=None, a_context=None, attention_mask=None, v_timestep=None, a_timestep=None,
+        v_pe=None, a_pe=None, v_cross_pe=None, a_cross_pe=None, v_cross_scale_shift_timestep=None, a_cross_scale_shift_timestep=None,
+        v_cross_gate_timestep=None, a_cross_gate_timestep=None, transformer_options=None, self_attention_mask=None,
+        v_prompt_timestep=None, a_prompt_timestep=None, **kwargs
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        run_vx = transformer_options.get("run_vx", True)
+        run_ax = transformer_options.get("run_ax", True)
+        use_triton = getattr(self, 'use_triton_kernels', True)
+        video_scale = getattr(self, 'video_scale', 1.0)
+        audio_scale = getattr(self, 'audio_scale', 1.0)
+        audio_to_video_scale = getattr(self, 'audio_to_video_scale', 1.0)
+        video_to_audio_scale = getattr(self, 'video_to_audio_scale', 1.0)
+
+        vx, ax = x
+        run_ax = run_ax and ax.numel() > 0 and audio_scale != 0.0
+        run_a2v = run_vx and transformer_options.get("a2v_cross_attn", True) and ax.numel() > 0 and audio_to_video_scale != 0.0
+        run_v2a = run_ax and transformer_options.get("v2a_cross_attn", True) and video_to_audio_scale != 0.0
+
+        if run_vx:
+            # video self-attention
+            vshift_msa, vscale_msa = (self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(0, 2)))
+            norm_vx = fused_norm_scale_shift(vx, vscale_msa, vshift_msa, use_triton=use_triton)
+            del vshift_msa, vscale_msa
+            # inline attn1 only when triton is active (needed to intercept q/k for fused RoPE)
+            if use_triton:
+                _a1 = self.attn1
+                q = _a1.to_q(norm_vx)
+                k = _a1.to_k(norm_vx)
+                v = _a1.to_v(norm_vx)
+
+                q = _a1.q_norm(q)
+                k = _a1.k_norm(k)
+
+                if v_pe is not None:
+                    q, k = fused_rope_qk(q, k, v_pe, use_triton=True)
+
+                if self_attention_mask is None:
+                    _sa_out = _comfy_attn.optimized_attention(q, k, v, _a1.heads, attn_precision=_a1.attn_precision, transformer_options=transformer_options)
+                elif _GuideAttentionMask is not None and isinstance(self_attention_mask, _GuideAttentionMask):
+                    _sa_out = _ltx_attn_with_guide_mask(q, k, v, _a1.heads, self_attention_mask, attn_precision=_a1.attn_precision, transformer_options=transformer_options)
+                else:
+                    _sa_out = _comfy_attn.optimized_attention_masked(q, k, v, _a1.heads, self_attention_mask, attn_precision=_a1.attn_precision, transformer_options=transformer_options)
+                del q, k, v
+                if _a1.to_gate_logits is not None:
+                    _gate = _a1.to_gate_logits(norm_vx)
+                    _b, _t, _ = _sa_out.shape
+                    _sa_out = _sa_out.view(_b, _t, _a1.heads, _a1.dim_head)
+                    _sa_out.mul_((2.0 * torch.sigmoid(_gate)).unsqueeze(-1))
+                    _sa_out = _sa_out.view(_b, _t, _a1.heads * _a1.dim_head)
+                    del _gate
+
+                attn1_out = _a1.to_out(_sa_out)
+                del _sa_out, norm_vx
+            else:
+                attn1_out = self.attn1(norm_vx, pe=v_pe, mask=self_attention_mask, transformer_options=transformer_options)
+                del norm_vx
+            # video cross-attention
+            vgate_msa = self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(2, 3))[0]
+            vx.addcmul_(attn1_out, vgate_msa, value=video_scale)
+            del vgate_msa, attn1_out
+            vx.add_(self._apply_text_cross_attention(
+                vx, v_context, self.attn2, self.scale_shift_table,
+                getattr(self, 'prompt_scale_shift_table', None),
+                v_timestep, v_prompt_timestep, attention_mask, transformer_options,),
+                alpha=video_scale
+            )
+        # audio
+        if run_ax:
+            # audio self-attention
+            ashift_msa, ascale_msa = (self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(0, 2)))
+            norm_ax = fused_norm_scale_shift(ax, ascale_msa, ashift_msa, use_triton=use_triton)
+            del ashift_msa, ascale_msa
+            attn1_out = self.audio_attn1(norm_ax, pe=a_pe, transformer_options=transformer_options)
+            del norm_ax
+            # audio cross-attention
+            agate_msa = self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(2, 3))[0]
+            ax.addcmul_(attn1_out, agate_msa, value=audio_scale)
+            del agate_msa, attn1_out
+            ax.add_(self._apply_text_cross_attention(
+                ax, a_context, self.audio_attn2, self.audio_scale_shift_table,
+                getattr(self, 'audio_prompt_scale_shift_table', None),
+                a_timestep, a_prompt_timestep, attention_mask, transformer_options,),
+                alpha=audio_scale
+            )
+
+        # video - audio cross attention.
+        if run_a2v or run_v2a:
+            if run_a2v and run_v2a:
+                # Fetch all 4 scale/shift values per table at once (avoids two get_ada_values calls each)
+                # and compute rms_norm once per tensor via dual kernel.
+                v_ca_vals = self.get_ada_values(self.scale_shift_table_a2v_ca_video[:4, :], vx.shape[0], v_cross_scale_shift_timestep)
+                a_ca_vals = self.get_ada_values(self.scale_shift_table_a2v_ca_audio[:4, :], ax.shape[0], a_cross_scale_shift_timestep)
+                vx_a2v, vx_v2a = fused_norm_dual_scale_shift(vx, v_ca_vals[0], v_ca_vals[1], v_ca_vals[2], v_ca_vals[3], use_triton=use_triton)
+                ax_a2v, ax_v2a = fused_norm_dual_scale_shift(ax, a_ca_vals[0], a_ca_vals[1], a_ca_vals[2], a_ca_vals[3], use_triton=use_triton)
+                del v_ca_vals, a_ca_vals
+            elif run_a2v:
+                sv, bv = self.get_ada_values(self.scale_shift_table_a2v_ca_video[:4, :], vx.shape[0], v_cross_scale_shift_timestep)[:2]
+                sa, ba = self.get_ada_values(self.scale_shift_table_a2v_ca_audio[:4, :], ax.shape[0], a_cross_scale_shift_timestep)[:2]
+                vx_a2v = fused_norm_scale_shift(vx, sv, bv, use_triton=use_triton)
+                ax_a2v = fused_norm_scale_shift(ax, sa, ba, use_triton=use_triton)
+                del sv, bv, sa, ba
+            else:  # only v2a
+                sv, bv = self.get_ada_values(self.scale_shift_table_a2v_ca_video[:4, :], vx.shape[0], v_cross_scale_shift_timestep)[2:4]
+                sa, ba = self.get_ada_values(self.scale_shift_table_a2v_ca_audio[:4, :], ax.shape[0], a_cross_scale_shift_timestep)[2:4]
+                vx_v2a = fused_norm_scale_shift(vx, sv, bv, use_triton=use_triton)
+                ax_v2a = fused_norm_scale_shift(ax, sa, ba, use_triton=use_triton)
+                del sv, bv, sa, ba
+
+            # audio to video cross attention
+            if run_a2v:
+                a2v_out = self.audio_to_video_attn(vx_a2v, context=ax_a2v, pe=v_cross_pe, k_pe=a_cross_pe, transformer_options=transformer_options)
+                del vx_a2v, ax_a2v
+                gate_out_a2v = self.get_ada_values(self.scale_shift_table_a2v_ca_video[4:, :], vx.shape[0], v_cross_gate_timestep)[0]
+                vx.addcmul_(a2v_out, gate_out_a2v, value=audio_to_video_scale)
+                del gate_out_a2v, a2v_out
+
+            # video to audio cross attention
+            if run_v2a:
+                v2a_out = self.video_to_audio_attn(ax_v2a, context=vx_v2a, pe=a_cross_pe, k_pe=v_cross_pe, transformer_options=transformer_options)
+                del ax_v2a, vx_v2a
+                gate_out_v2a = self.get_ada_values(self.scale_shift_table_a2v_ca_audio[4:, :], ax.shape[0], a_cross_gate_timestep)[0]
+                ax.addcmul_(v2a_out, gate_out_v2a, value=video_to_audio_scale)
+                del gate_out_v2a, v2a_out
+
+        # video feedforward
+        if run_vx:
+            vshift_mlp, vscale_mlp = self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(3, 5))
+            vx_scaled = fused_norm_scale_shift(vx, vscale_mlp, vshift_mlp, use_triton=use_triton)
+            del vshift_mlp, vscale_mlp
+
+            ff_out = self.ff(vx_scaled)
+            del vx_scaled
+
+            vgate_mlp = self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(5, 6))[0]
+            vx.addcmul_(ff_out, vgate_mlp, value=video_scale)
+            del vgate_mlp, ff_out
+
+        # audio feedforward
+        if run_ax:
+            ashift_mlp, ascale_mlp = self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(3, 5))
+            ax_scaled = fused_norm_scale_shift(ax, ascale_mlp, ashift_mlp, use_triton=use_triton)
+            del ashift_mlp, ascale_mlp
+
+            ff_out = self.audio_ff(ax_scaled)
+            del ax_scaled
+
+            agate_mlp = self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(5, 6))[0]
+            ax.addcmul_(ff_out, agate_mlp, value=audio_scale)
+            del agate_mlp, ff_out
+
+        return vx, ax
+
+class LTX2ForwardPatch:
+    def __init__(self, video, audio, audio_to_video, video_to_audio, use_triton=True):
+        self.video_scale = video
+        self.audio_scale = audio
+        self.video_to_audio_scale = video_to_audio
+        self.audio_to_video_scale = audio_to_video
+        self.use_triton_kernels = use_triton
+    def __get__(self, obj, objtype=None):
+        def wrapped_forward(self_module, *args, **kwargs):
+            self_module.video_scale = self.video_scale
+            self_module.audio_scale = self.audio_scale
+            self_module.video_to_audio_scale = self.video_to_audio_scale
+            self_module.audio_to_video_scale = self.audio_to_video_scale
+            self_module.use_triton_kernels = self.use_triton_kernels
+            self_module._apply_text_cross_attention = types.MethodType(
+                _apply_text_cross_attention_patched, self_module)
+            return ltx2_forward(self_module, *args, **kwargs)
+        return types.MethodType(wrapped_forward, obj)
+
+class LTX2AttentionTunerPatch(io.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTX2AttentionTunerPatch",
+            display_name="LTX2 Attention Tuner Patch",
+            category="KJNodes/ltxv",
+            description="EXPERIMENTAL! Custom LTX2 forward pass with attention scaling factors per modality, also reduces peak VRAM usage.",
+            is_experimental=True,
+            inputs=[
+                io.Model.Input("model"),
+                io.String.Input("blocks", default="", tooltip="Comma separated list of transformer block indices to apply the patch to. Leave empty to apply to all blocks."),
+                io.Float.Input("video_scale", default=1.0, min=0.0, max=100, step=0.01, tooltip="Scaling factor for video attention."),
+                io.Float.Input("audio_scale", default=1.0, min=0.0, max=100, step=0.01, tooltip="Scaling factor for audio attention."),
+                io.Float.Input("audio_to_video_scale", default=1.0, min=0.0, max=100, step=0.01, tooltip="Scaling factor for video attention."),
+                io.Float.Input("video_to_audio_scale", default=1.0, min=0.0, max=100, step=0.01, tooltip="Scaling factor for audio attention."),
+                io.Boolean.Input("triton_kernels", default=True, tooltip="Use Triton fused kernels for norm+scale+shift and rope application operations, can be very slightly faster."),
+            ],
+            outputs=[
+                io.Model.Output(display_name="model"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, blocks, video_scale, audio_scale, audio_to_video_scale, video_to_audio_scale, triton_kernels) -> io.NodeOutput:
+        model_clone = model.clone()
+        diffusion_model = model_clone.get_model_object("diffusion_model")
+
+        # Parse selected block indices
+        if blocks.strip() == "":
+            selected_blocks = set(range(len(diffusion_model.transformer_blocks)))
+        else:
+            selected_blocks = set(int(idx) for idx in blocks.strip().split(","))
+
+        logging.info(f"Applying LTX2 Attention Tuner Patch with custom scales to blocks: {sorted(selected_blocks)}, triton_kernels={triton_kernels}")
+
+        # Apply patch to all blocks, but use 1.0 scales for non-selected blocks
+        for idx in range(len(diffusion_model.transformer_blocks)):
+            block = diffusion_model.transformer_blocks[idx]
+            if idx in selected_blocks:
+                patched_forward = LTX2ForwardPatch(video_scale, audio_scale, audio_to_video_scale, video_to_audio_scale, use_triton=triton_kernels).__get__(block, block.__class__)
+            else:
+                patched_forward = LTX2ForwardPatch(1.0, 1.0, 1.0, 1.0, use_triton=triton_kernels).__get__(block, block.__class__)
+            model_clone.add_object_patch(f"diffusion_model.transformer_blocks.{idx}.forward", patched_forward)
+
+        return io.NodeOutput(model_clone)
+
+class LTX2MemoryEfficientSageAttentionPatch(io.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTX2MemoryEfficientSageAttentionPatch",
+            display_name="LTX2 Mem Eff Sage Attention Patch",
+            category="KJNodes/ltxv",
+            description="EXPERIMENTAL! Activates custom sageattention to reduce peak VRAM usage, overrides the attention mode. Requires latest sageattention version.",
+            is_experimental=True,
+            inputs=[
+                io.Model.Input("model"),
+                io.Boolean.Input("triton_kernels", default=True, tooltip="Use Triton fused RoPE kernel on the self-attention Q/K. Requires Triton."),
+            ],
+            outputs=[
+                io.Model.Output(display_name="model"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, triton_kernels) -> io.NodeOutput:
+        if _cuda_archs is None:
+            raise RuntimeError("sageattention is not new enough version or could not determine CUDA architecture, cannot apply LTX2 Memory Efficient Sage Attention Patch.")
+        model_clone = model.clone()
+        diffusion_model = model_clone.get_model_object("diffusion_model")
+
+        logging.info(f"Applying LTX2 Memory Efficient Sage Attention Patch to all transformer blocks, triton_kernels={triton_kernels}")
+
+        for idx, block in enumerate(diffusion_model.transformer_blocks):
+            block.attn1.use_triton_kernels = triton_kernels
+            model_clone.add_object_patch(f"diffusion_model.transformer_blocks.{idx}.attn1.forward", ltx2_sageattn_forward.__get__(block.attn1, block.attn1.__class__))
+
+        return io.NodeOutput(model_clone)
+
+
+def get_cuda_version():
+    try:
+        version = torch.version.cuda
+        if version is not None:
+            major, minor = version.split('.')
+            return int(major), int(minor)
+        else:
+            return 0, 0
+    except Exception:
+        return 0, 0
+
+sageplus_sm89_available = False
+_cuda_archs = None
+try:
+    from sageattention.core import per_thread_int8_triton, per_warp_int8_cuda, per_block_int8_triton, per_channel_fp8, get_cuda_arch_versions, attn_false
+    _cuda_archs = get_cuda_arch_versions()
+except Exception:
+    pass
+try:
+    from sageattention.core import _qattn_sm89
+    cuda_version = get_cuda_version()
+    sageplus_sm89_available = hasattr(_qattn_sm89, 'qk_int8_sv_f8_accum_f16_fuse_v_scale_attn_inst_buf') and cuda_version >= (12, 8)
+except ImportError:
+    try:
+        from sageattention.core import sm89_compile as _qattn_sm89
+    except ImportError:
+        _qattn_sm89 = None
+try:
+    from sageattention.core import _qattn_sm80
+except ImportError:
+    try:
+        from sageattention.core import sm80_compile as _qattn_sm80
+    except ImportError:
+        _qattn_sm80 = None
+try:
+    from sageattention.core import  _qattn_sm90
+except ImportError:
+    try:
+        from sageattention.core import sm90_compile as _qattn_sm90
+    except ImportError:
+        _qattn_sm90 = None
+
+from comfy.ldm.lightricks.model import apply_rotary_emb
+try:
+    from comfy.ldm.flux.math import apply_rope as _wan_apply_rope
+except ImportError:
+    _wan_apply_rope = None
+try:
+    from comfy.ldm.wan.model import WanT2VCrossAttention as _WanT2VCrossAttention, WanI2VCrossAttention as _WanI2VCrossAttention
+except ImportError:
+    _WanT2VCrossAttention = _WanI2VCrossAttention = None
+
+
+def _sageattn_int8_fp8_nhd(qkv, dtype):
+    # qkv: [q, k, v], each [batch, seq_len, num_heads, head_dim] in NHD layout. Returns o in the same layout.
+    # The list is consumed so the only references to the float q/k/v are the locals below, letting `del`
+    # actually free them before the kernel runs — attention is the VRAM peak in these models.
+    q, k, v = qkv
+    qkv.clear()
+    head_dim_og = q.shape[-1]
+
+    tensor_layout="NHD"
+    _tensor_layout = 0 # NHD
+    _is_caual = 0
+    _qk_quant_gran = 3
+    _return_lse = 0
+    sm_scale = head_dim_og**-0.5
+    quant_v_scale_max = 448.0
+
+    if _cuda_archs[0] in {"sm80", "sm86"}:
+        # mean-sub in-place: passing km= makes the triton quant materialize k - km as a full float copy
+        k.sub_(k.mean(dim=1, keepdim=True))
+        q_int8, q_scale, k_int8, k_scale = per_thread_int8_triton(q, k, tensor_layout=tensor_layout, BLKQ=128, WARPQ=32, BLKK=64, WARPK=64)
+        del q, k
+        o = torch.empty(q_int8.size(), dtype=dtype, device=q_int8.device)
+        v_fp16 = v.to(torch.float16)
+        del v
+        _qattn_sm80.qk_int8_sv_f16_accum_f32_attn(q_int8, k_int8, v_fp16, o, q_scale, k_scale, _tensor_layout, _is_caual, _qk_quant_gran, sm_scale, _return_lse)
+    elif _cuda_archs[0] == "sm75":
+        k.sub_(k.mean(dim=1, keepdim=True))
+        q_int8, q_scale, k_int8, k_scale = per_block_int8_triton(q, k, sm_scale=sm_scale, tensor_layout=tensor_layout)
+        del q, k
+        o, _ = attn_false(q_int8, k_int8, v, q_scale, k_scale, tensor_layout=tensor_layout, output_dtype=dtype, attn_mask=None, return_lse=False)
+        del v
+    elif _cuda_archs[0] == "sm89":
+        if not sageplus_sm89_available:
+            pv_accum_dtype = "fp32+fp32"
+        else:
+            pv_accum_dtype = "fp32+fp16"
+            quant_v_scale_max = 2.25
+        k.sub_(k.mean(dim=1, keepdim=True))
+        q_int8, q_scale, k_int8, k_scale = per_thread_int8_triton(q, k, tensor_layout=tensor_layout, BLKQ=128, WARPQ=32, BLKK=64, WARPK=64)
+        del q, k
+        v_fp8, v_scale, _ = per_channel_fp8(v, tensor_layout=tensor_layout, scale_max=quant_v_scale_max, smooth_v=False)
+        del v
+        o = torch.empty(q_int8.size(), dtype=dtype, device=q_int8.device)
+        if pv_accum_dtype == "fp32+fp16":
+            _qattn_sm89.qk_int8_sv_f8_accum_f16_fuse_v_scale_attn_inst_buf(q_int8, k_int8, v_fp8, o, q_scale, k_scale, v_scale, _tensor_layout, _is_caual, _qk_quant_gran, sm_scale, _return_lse)
+        elif pv_accum_dtype == "fp32+fp32":
+            _qattn_sm89.qk_int8_sv_f8_accum_f32_fuse_v_scale_attn_inst_buf(q_int8, k_int8, v_fp8, o, q_scale, k_scale, v_scale, _tensor_layout, _is_caual, _qk_quant_gran, sm_scale, _return_lse)
+        del v_fp8, v_scale
+    elif _cuda_archs[0] == "sm90":
+        k.sub_(k.mean(dim=1, keepdim=True))
+        q_int8, q_scale, k_int8, k_scale = per_thread_int8_triton(q, k, tensor_layout=tensor_layout, BLKQ=64, WARPQ=16, BLKK=128, WARPK=128)
+        del q, k
+        v_fp8, v_scale, _ = per_channel_fp8(v, tensor_layout=tensor_layout, smooth_v=False)
+        del v
+        o = torch.empty(q_int8.size(), dtype=dtype, device=q_int8.device)
+        _qattn_sm90.qk_int8_sv_f8_accum_f32_fuse_v_scale_attn_inst_buf(q_int8, k_int8, v_fp8, o, q_scale, k_scale, v_scale, _tensor_layout, _is_caual, _qk_quant_gran, sm_scale, _return_lse)
+        del v_fp8, v_scale
+    elif _cuda_archs[0] == "sm120":
+        if not sageplus_sm89_available:
+            pv_accum_dtype = "fp32"
+        else:
+            pv_accum_dtype = "fp32+fp16"
+            quant_v_scale_max = 2.25
+        _qk_quant_gran = 2 # per warp
+        # km kept here: the CUDA quant fuses the mean-sub with no temp copy
+        q_int8, q_scale, k_int8, k_scale = per_warp_int8_cuda(q, k, km=k.mean(dim=1, keepdim=True), tensor_layout=tensor_layout, BLKQ=128, WARPQ=32, BLKK=64)
+        del q, k
+        v_fp8, v_scale, _ = per_channel_fp8(v, tensor_layout=tensor_layout, scale_max=quant_v_scale_max, smooth_v=False)
+        del v
+        o = torch.empty(q_int8.size(), dtype=dtype, device=q_int8.device)
+        if pv_accum_dtype == "fp32":
+            _qattn_sm89.qk_int8_sv_f8_accum_f32_fuse_v_scale_attn(q_int8, k_int8, v_fp8, o, q_scale, k_scale, v_scale, _tensor_layout, _is_caual, _qk_quant_gran, sm_scale, _return_lse)
+        elif pv_accum_dtype == "fp32+fp16":
+            _qattn_sm89.qk_int8_sv_f8_accum_f16_fuse_v_scale_attn_inst_buf(q_int8, k_int8, v_fp8, o, q_scale, k_scale, v_scale, _tensor_layout, _is_caual, _qk_quant_gran, sm_scale, _return_lse)
+        del v_fp8, v_scale
+
+    del q_int8, q_scale, k_int8, k_scale
+    return o
+
+
+def ltx2_sageattn_forward(self, x, context=None, mask=None, pe=None, k_pe=None, transformer_options={}):
+    dtype = x.dtype
+    context = x if context is None else context
+
+    # query
+    q = self.to_q(x)
+    q = self.q_norm(q)
+    # key
+    k = self.to_k(context)
+    k = self.k_norm(k)
+    # apply RoPE — fuse q+k into one kernel when both share the same pe
+    if pe is not None:
+        use_triton = getattr(self, 'use_triton_kernels', False)
+        if k_pe is None:
+            q, k = fused_rope_qk(q, k, pe, use_triton=use_triton)
+        else:
+            q = apply_rotary_emb(q, pe)
+            k = apply_rotary_emb(k, k_pe)
+    # value
+    v = self.to_v(context)
+
+    # Sage kernels don't support masking — fall back to the upstream masked paths.
+    if mask is not None:
+        if _GuideAttentionMask is not None and isinstance(mask, _GuideAttentionMask):
+            o = _ltx_attn_with_guide_mask(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options)
+        else:
+            o = _comfy_attn.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options)
+        if self.to_gate_logits is not None:
+            gate_logits = self.to_gate_logits(x)
+            _b, _t, _ = o.shape
+            o = o.view(_b, _t, self.heads, self.dim_head)
+            o.mul_((2.0 * torch.sigmoid(gate_logits)).unsqueeze(-1))
+            o = o.view(_b, _t, self.heads * self.dim_head)
+            del gate_logits
+        return self.to_out(o)
+
+    # Reshape from [batch, seq_len, total_dim] to [batch, seq_len, num_heads, head_dim]
+    batch_size, seq_len, _ = q.shape
+    head_dim_og = self.dim_head
+
+    q = q.view(batch_size, seq_len, self.heads, head_dim_og)
+    k = k.view(batch_size, k.shape[1], self.heads, head_dim_og)
+    v = v.view(batch_size, v.shape[1], self.heads, head_dim_og)
+
+    qkv = [q, k, v]
+    del q, k, v
+    o = _sageattn_int8_fp8_nhd(qkv, dtype)
+
+    # o is [B, T, H, D] from sage kernel (NHD layout)
+    if self.to_gate_logits is not None:
+        gate_logits = self.to_gate_logits(x)  # (B, T, H)
+        o.mul_((2.0 * torch.sigmoid(gate_logits)).unsqueeze(-1))
+        del gate_logits
+
+    return self.to_out(o.view(batch_size, seq_len, -1))
+
+
+def wan_sageattn_forward(self, x, freqs, transformer_options={}):
+    r"""
+    Args:
+        x(Tensor): Shape [B, L, num_heads, C / num_heads]
+        freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
+    """
+    dtype = x.dtype
+    b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
+
+    q = self.norm_q(self.q(x)).view(b, s, n, d)
+    k = self.norm_k(self.k(x)).view(b, s, n, d)
+    q, k = _wan_apply_rope(q, k, freqs)
+    v = self.v(x).view(b, s, n, d)
+
+    qkv = [q, k, v]
+    del q, k, v
+    o = _sageattn_int8_fp8_nhd(qkv, dtype)
+
+    return self.o(o.view(b, s, n * d))
+
+
+def wan_t2v_cross_sageattn_forward(self, x, context, transformer_options={}, **kwargs):
+    dtype = x.dtype
+    b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
+
+    q = self.norm_q(self.q(x)).view(b, s, n, d)
+    k = self.norm_k(self.k(context)).view(b, -1, n, d)
+    v = self.v(context).view(b, -1, n, d)
+
+    qkv = [q, k, v]
+    del q, k, v
+    o = _sageattn_int8_fp8_nhd(qkv, dtype)
+
+    return self.o(o.view(b, s, n * d))
+
+
+def wan_i2v_cross_sageattn_forward(self, x, context, context_img_len, transformer_options={}):
+    dtype = x.dtype
+    b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
+
+    context_img = context[:, :context_img_len]
+    context = context[:, context_img_len:]
+
+    q = self.norm_q(self.q(x)).view(b, s, n, d)
+    k_img = self.norm_k_img(self.k_img(context_img)).view(b, -1, n, d)
+    v_img = self.v_img(context_img).view(b, -1, n, d)
+    # local q reference survives the helper's consume — still needed for the text attention below
+    qkv_img = [q, k_img, v_img]
+    del k_img, v_img
+    img_o = _sageattn_int8_fp8_nhd(qkv_img, dtype)
+
+    k = self.norm_k(self.k(context)).view(b, -1, n, d)
+    v = self.v(context).view(b, -1, n, d)
+    qkv = [q, k, v]
+    del q, k, v
+    o = _sageattn_int8_fp8_nhd(qkv, dtype)
+
+    o.add_(img_o)
+    del img_o
+    return self.o(o.view(b, s, n * d))
+
+
+class WanVideoMemoryEfficientSageAttentionPatch(io.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="WanVideoMemoryEfficientSageAttentionPatch",
+            display_name="WanVideo Mem Eff Sage Attention Patch",
+            category="KJNodes/wan",
+            description="EXPERIMENTAL! Activates custom sageattention on the WanVideo self-attention to reduce peak VRAM usage, overrides the attention mode. Requires latest sageattention version.",
+            is_experimental=True,
+            inputs=[
+                io.Model.Input("model"),
+            ],
+            outputs=[
+                io.Model.Output(display_name="model"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model) -> io.NodeOutput:
+        if _cuda_archs is None:
+            raise RuntimeError("sageattention is not new enough version or could not determine CUDA architecture, cannot apply WanVideo Memory Efficient Sage Attention Patch.")
+        if _wan_apply_rope is None:
+            raise RuntimeError("Could not import apply_rope from comfy.ldm.flux.math, cannot apply WanVideo Memory Efficient Sage Attention Patch.")
+        model_clone = model.clone()
+        diffusion_model = model_clone.get_model_object("diffusion_model")
+
+        logging.info("Applying WanVideo Memory Efficient Sage Attention Patch to all transformer blocks")
+
+        for idx, block in enumerate(diffusion_model.blocks):
+            model_clone.add_object_patch(f"diffusion_model.blocks.{idx}.self_attn.forward", wan_sageattn_forward.__get__(block.self_attn, block.self_attn.__class__))
+            cross_attn = getattr(block, "cross_attn", None)
+            # exact type match on purpose: subclasses like WanT2VCrossAttentionGather have different semantics
+            if cross_attn is not None and type(cross_attn) is _WanI2VCrossAttention:
+                model_clone.add_object_patch(f"diffusion_model.blocks.{idx}.cross_attn.forward", wan_i2v_cross_sageattn_forward.__get__(cross_attn, cross_attn.__class__))
+            elif cross_attn is not None and type(cross_attn) is _WanT2VCrossAttention:
+                model_clone.add_object_patch(f"diffusion_model.blocks.{idx}.cross_attn.forward", wan_t2v_cross_sageattn_forward.__get__(cross_attn, cross_attn.__class__))
+
+        return io.NodeOutput(model_clone)
+
+
+import folder_paths
+
+class LTX2LoraLoaderAdvanced(io.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTX2LoraLoaderAdvanced",
+            display_name="LTX2 LoRA Loader Advanced",
+            category="KJNodes/ltxv",
+            description="Advanced LoRA loader with per-block strength control for LTX2 models",
+            is_experimental=True,
+            inputs=[
+                io.Combo.Input("lora_name", options=folder_paths.get_filename_list("loras"), tooltip="The name of the LoRA."),
+                io.Model.Input("model", tooltip="The diffusion model the LoRA will be applied to."),
+                io.Float.Input("strength_model", default=1.0, min=-100.0, max=100.0, step=0.01, tooltip="How strongly to modify the diffusion model. This value can be negative."),
+                io.String.Input("opt_lora_path", optional=True, force_input=True,tooltip="Absolute path of the LoRA."),
+                io.Custom("SELECTEDDITBLOCKS").Input("blocks", optional=True, tooltip="Selected DiT blocks configuration"),
+                io.Float.Input("video", default=1.0, min=0.0, max=1.0, step=0.01, tooltip="Strength for video attention layers."),
+                io.Float.Input("video_to_audio", default=1.0, min=0.0, max=1.0, step=0.01, tooltip="Strength for video to audio cross-attention layers."),
+                io.Float.Input("audio", default=1.0, min=0.0, max=1.0, step=0.01, tooltip="Strength for audio attention layers."),
+                io.Float.Input("audio_to_video", default=1.0, min=0.0, max=1.0, step=0.01, tooltip="Strength for audio to video cross-attention layers."),
+                io.Float.Input("other", default=1.0, min=0.0, max=1.0, step=0.01, tooltip="Strength for layers not caught by other layer filters."),
+            ],
+            outputs=[
+                io.Model.Output(display_name="model", tooltip="The modified diffusion model."),
+                io.String.Output(display_name="rank", tooltip="Possible rank of the LoRA."),
+                io.String.Output(display_name="loaded_keys_info", tooltip="List of loaded keys and their alpha values."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, lora_name, strength_model, video, video_to_audio, audio, audio_to_video, other, opt_lora_path=None, blocks=None) -> io.NodeOutput:
+        from comfy.utils import load_torch_file
+        import comfy.lora
+
+        if opt_lora_path:
+            lora_path = opt_lora_path
+        else:
+            lora_path = folder_paths.get_full_path("loras", lora_name)
+
+        lora = load_torch_file(lora_path, safe_load=True)
+
+        # Find the first key that ends with "weight"
+        rank = "unknown"
+        weight_key = next((key for key in lora.keys() if key.endswith('weight')), None)
+        # Print the shape of the value corresponding to the key
+        if weight_key:
+            print(f"Shape of the first 'weight' key ({weight_key}): {lora[weight_key].shape}")
+            rank = str(lora[weight_key].shape[0])
+        else:
+            print("No key ending with 'weight' found.")
+            rank = "Couldn't find rank"
+
+        key_map = {}
+        if model is not None:
+            key_map = comfy.lora.model_lora_keys_unet(model.model, key_map)
+
+        loaded = comfy.lora.load_lora(lora, key_map)
+
+        keys_to_delete = []
+
+        # First apply blocks filtering if provided
+        if blocks is not None:
+            for block in blocks:
+                for key in list(loaded.keys()):
+                    match = False
+                    if isinstance(key, str) and block in key:
+                        match = True
+                    elif isinstance(key, tuple):
+                        for k in key:
+                            if block in k:
+                                match = True
+                                break
+
+                    if match:
+                        ratio = blocks[block]
+                        if ratio == 0:
+                            keys_to_delete.append(key)
+                        else:
+                            # Only modify LoRA adapters, skip diff tuples
+                            value = loaded[key]
+                            if hasattr(value, 'weights'):
+                                weights_list = list(value.weights)
+                                weights_list[2] = ratio
+                                loaded[key].weights = tuple(weights_list)
+
+        # Then apply layer-based attention strength filtering (takes priority)
+        for key in list(loaded.keys()):
+            if key in keys_to_delete:
+                continue
+
+            key_str = key if isinstance(key, str) else (key[0] if isinstance(key, tuple) else str(key))
+
+            # Determine the strength multiplier based on layer name
+            # Check more specific patterns first
+            strength_multiplier = None
+
+            # Video to audio cross-attention (check first - most specific)
+            if "video_to_audio_attn" in key_str:
+                strength_multiplier = video_to_audio
+            # Audio to video cross-attention
+            elif "audio_to_video_attn" in key_str:
+                strength_multiplier = audio_to_video
+            # Audio layers
+            elif "audio_attn" in key_str or "audio_ff.net" in key_str:
+                strength_multiplier = audio
+            # Video layers (check last - most general)
+            elif "attn" in key_str or "ff.net" in key_str:
+                strength_multiplier = video
+            # Everything else not caught by above filters
+            else:
+                strength_multiplier = other
+
+            # Apply strength or mark for deletion
+            if strength_multiplier is not None:
+                if strength_multiplier == 0:
+                    keys_to_delete.append(key)
+                elif strength_multiplier != 1.0:
+                    value = loaded[key]
+                    if hasattr(value, 'weights'):
+                        weights_list = list(value.weights)
+                        # Handle case where alpha (weights[2]) might be None
+                        current_alpha = weights_list[2] if weights_list[2] is not None else 1.0
+                        weights_list[2] = current_alpha * strength_multiplier
+                        loaded[key].weights = tuple(weights_list)
+
+        for key in keys_to_delete:
+            if key in loaded:
+                del loaded[key]
+
+        # Build list of loaded keys and their alphas
+        loaded_keys_list = []
+        for key, value in loaded.items():
+            if hasattr(value, 'weights'):
+                key_str = key if isinstance(key, str) else str(key)
+                alpha = value.weights[2] if value.weights[2] is not None else "None"
+                loaded_keys_list.append(f"{key_str}: alpha={alpha}")
+            else:
+                key_str = key if isinstance(key, str) else str(key)
+                loaded_keys_list.append(f"{key_str}: type={type(value).__name__}")
+
+        if model is not None:
+            new_modelpatcher = model.clone()
+            k = new_modelpatcher.add_patches(loaded, strength_model)
+
+        # Add not loaded keys to the info
+        k = set(k)
+        not_loaded = []
+        for x in loaded:
+            if x not in k:
+                key_str = x if isinstance(x, str) else str(x)
+                not_loaded.append(f"NOT LOADED: {key_str}")
+
+        if not_loaded:
+            loaded_keys_list.extend(not_loaded)
+
+        loaded_keys_info = "\n".join(loaded_keys_list)
+
+        return io.NodeOutput(new_modelpatcher, rank, loaded_keys_info)
diff --git a/custom_nodes/ComfyUI-KJNodes/nodes/mask_nodes.py b/custom_nodes/ComfyUI-KJNodes/nodes/mask_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca4dc69ce972751f85d2b80e1c3e5e2de1d82099
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/nodes/mask_nodes.py
@@ -0,0 +1,1689 @@
+import torch
+import torch.nn.functional as F
+from torchvision.transforms import functional as TF
+from PIL import Image, ImageDraw, ImageFilter, ImageFont
+import scipy.ndimage
+import numpy as np
+from contextlib import nullcontext
+import os
+from tqdm import tqdm
+import logging
+
+from comfy import model_management
+from comfy.utils import ProgressBar
+from comfy.utils import common_upscale
+from nodes import MAX_RESOLUTION
+
+import folder_paths
+
+from ..utility.utility import tensor2pil, pil2tensor
+
+script_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+main_device = model_management.get_torch_device()
+offload_device = model_management.unet_offload_device()
+
+class BatchCLIPSeg:
+
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+       
+        return {"required":
+                    {
+                        "images": ("IMAGE",),
+                        "text": ("STRING", {"multiline": False}),
+                        "threshold": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 10.0, "step": 0.001}),
+                        "binary_mask": ("BOOLEAN", {"default": True}),
+                        "combine_mask": ("BOOLEAN", {"default": False}),
+                        "use_cuda": ("BOOLEAN", {"default": True}),
+                     },
+                     "optional":
+                    {
+                        "blur_sigma": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.1}),
+                        "opt_model": ("CLIPSEGMODEL", ),
+                        "prev_mask": ("MASK", {"default": None}),
+                        "image_bg_level": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                        "invert": ("BOOLEAN", {"default": False}),
+                    }
+                }
+
+    CATEGORY = "KJNodes/masking"
+    RETURN_TYPES = ("MASK", "IMAGE", )
+    RETURN_NAMES = ("Mask", "Image", )
+    FUNCTION = "segment_image"
+    DESCRIPTION = """
+Segments an image or batch of images using CLIPSeg.
+"""
+
+    def segment_image(self, images, text, threshold, binary_mask, combine_mask, use_cuda, blur_sigma=0.0, opt_model=None, prev_mask=None, invert= False, image_bg_level=0.5):
+        from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
+        import torchvision.transforms as transforms
+        offload_device = model_management.unet_offload_device()
+        device = model_management.get_torch_device()
+        if not use_cuda:
+            device = torch.device("cpu")
+        dtype = model_management.unet_dtype()
+
+        if opt_model is None:
+            checkpoint_path = os.path.join(folder_paths.models_dir,'clip_seg', 'clipseg-rd64-refined-fp16')
+            if not hasattr(self, "model"):
+                try:
+                    if not os.path.exists(checkpoint_path):
+                        from huggingface_hub import snapshot_download
+                        snapshot_download(repo_id="Kijai/clipseg-rd64-refined-fp16", local_dir=checkpoint_path, local_dir_use_symlinks=False)
+                    self.model = CLIPSegForImageSegmentation.from_pretrained(checkpoint_path)
+                except Exception:
+                    checkpoint_path = "CIDAS/clipseg-rd64-refined"
+                    self.model = CLIPSegForImageSegmentation.from_pretrained(checkpoint_path)
+            processor = CLIPSegProcessor.from_pretrained(checkpoint_path)
+
+        else:
+            self.model = opt_model['model']
+            processor = opt_model['processor']
+
+        self.model.to(dtype).to(device)
+
+        B, H, W, C = images.shape
+        images = images.to(device)
+        
+        autocast_condition = (dtype != torch.float32) and not model_management.is_device_mps(device)
+        with torch.autocast(model_management.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext():
+
+            PIL_images = [Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) for image in images ]
+            prompt = [text] * len(images)
+            input_prc = processor(text=prompt, images=PIL_images, return_tensors="pt")
+
+            for key in input_prc:
+                input_prc[key] = input_prc[key].to(device)
+            outputs = self.model(**input_prc)
+
+        mask_tensor = torch.sigmoid(outputs.logits)
+        mask_tensor = (mask_tensor - mask_tensor.min()) / (mask_tensor.max() - mask_tensor.min())
+        mask_tensor = torch.where(mask_tensor > (threshold), mask_tensor, torch.tensor(0, dtype=torch.float))
+
+        if len(mask_tensor.shape) == 2:
+            mask_tensor = mask_tensor.unsqueeze(0)
+        mask_tensor = F.interpolate(mask_tensor.unsqueeze(1), size=(H, W), mode='nearest')
+        mask_tensor = mask_tensor.squeeze(1)
+
+        self.model.to(offload_device)
+        
+        if binary_mask:
+            mask_tensor = (mask_tensor > 0).float()
+        if blur_sigma > 0:
+            kernel_size = int(6 * int(blur_sigma) + 1) 
+            blur = transforms.GaussianBlur(kernel_size=(kernel_size, kernel_size), sigma=(blur_sigma, blur_sigma))
+            mask_tensor = blur(mask_tensor)
+
+        if combine_mask:
+            mask_tensor = torch.max(mask_tensor, dim=0)[0]
+            mask_tensor = mask_tensor.unsqueeze(0).repeat(len(images),1,1)
+
+        del outputs
+        model_management.soft_empty_cache()
+
+        if prev_mask is not None:
+            if prev_mask.shape != mask_tensor.shape:
+                prev_mask = F.interpolate(prev_mask.unsqueeze(1), size=(H, W), mode='nearest')
+            mask_tensor = mask_tensor + prev_mask.to(device)
+            torch.clamp(mask_tensor, min=0.0, max=1.0)
+
+        if invert:
+            mask_tensor = 1 - mask_tensor
+
+        image_tensor = images * mask_tensor.unsqueeze(-1) + (1 - mask_tensor.unsqueeze(-1)) * image_bg_level
+        image_tensor = torch.clamp(image_tensor, min=0.0, max=1.0).cpu().float()
+
+        mask_tensor = mask_tensor.cpu().float()
+    
+        return mask_tensor, image_tensor, 
+
+class DownloadAndLoadCLIPSeg:
+
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+       
+        return {"required":
+                    {     
+                    "model": (
+                    [   'Kijai/clipseg-rd64-refined-fp16',
+                        'CIDAS/clipseg-rd64-refined',
+                    ],
+                    ),
+                     },
+                }
+
+    CATEGORY = "KJNodes/masking"
+    RETURN_TYPES = ("CLIPSEGMODEL",)
+    RETURN_NAMES = ("clipseg_model",)
+    FUNCTION = "segment_image"
+    DESCRIPTION = """
+Downloads and loads CLIPSeg model with huggingface_hub,  
+to ComfyUI/models/clip_seg
+"""
+
+    def segment_image(self, model):
+        from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
+        checkpoint_path = os.path.join(folder_paths.models_dir,'clip_seg', os.path.basename(model))
+        if not hasattr(self, "model"):
+            if not os.path.exists(checkpoint_path):
+                from huggingface_hub import snapshot_download
+                snapshot_download(repo_id=model, local_dir=checkpoint_path, local_dir_use_symlinks=False)
+            self.model = CLIPSegForImageSegmentation.from_pretrained(checkpoint_path)
+
+        processor = CLIPSegProcessor.from_pretrained(checkpoint_path)
+
+        clipseg_model = {}
+        clipseg_model['model'] = self.model
+        clipseg_model['processor'] = processor
+
+        return clipseg_model,
+
+class CreateTextMask:
+
+    RETURN_TYPES = ("IMAGE", "MASK",)
+    FUNCTION = "createtextmask"
+    CATEGORY = "KJNodes/text"
+    DESCRIPTION = """
+Creates a text image and mask.  
+Looks for fonts from this folder:  
+ComfyUI/custom_nodes/ComfyUI-KJNodes/fonts
+  
+If start_rotation and/or end_rotation are different values,  
+creates animation between them.
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "invert": ("BOOLEAN", {"default": False}),
+                 "frames": ("INT", {"default": 1,"min": 1, "max": 4096, "step": 1}),
+                 "text_x": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
+                 "text_y": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
+                 "font_size": ("INT", {"default": 32,"min": 8, "max": 4096, "step": 1}),
+                 "font_color": ("STRING", {"default": "white"}),
+                 "text": ("STRING", {"default": "HELLO!", "multiline": True}),
+                 "font": (folder_paths.get_filename_list("kjnodes_fonts"), ),
+                 "width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                 "height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                 "start_rotation": ("INT", {"default": 0,"min": 0, "max": 359, "step": 1}),
+                 "end_rotation": ("INT", {"default": 0,"min": -359, "max": 359, "step": 1}),
+        },
+    } 
+
+    def createtextmask(self, frames, width, height, invert, text_x, text_y, text, font_size, font_color, font, start_rotation, end_rotation):
+    # Define the number of images in the batch
+        batch_size = frames
+        out = []
+        masks = []
+        rotation = start_rotation
+        if start_rotation != end_rotation:
+            rotation_increment = (end_rotation - start_rotation) / (batch_size - 1)
+
+        font_path = folder_paths.get_full_path("kjnodes_fonts", font)
+        # Generate the text
+        for i in range(batch_size):
+            image = Image.new("RGB", (width, height), "black")
+            draw = ImageDraw.Draw(image)
+            font = ImageFont.truetype(font_path, font_size)
+
+            # Split the text into lines and wrap words to fit width
+            text_lines = text.split('\n')
+            lines = []
+            for text_line in text_lines:
+                if text_line.strip() == "":
+                    # Preserve empty lines for multiple newlines
+                    lines.append("")
+                    continue
+                words = text_line.split()
+                current_line = []
+                for word in words:
+                    if current_line:
+                        test_line = " ".join(current_line + [word])
+                    else:
+                        test_line = word
+                    try:
+                        test_line_width = font.getbbox(test_line)[2]
+                    except Exception:
+                        test_line_width = font.getsize(test_line)[0]
+                    if test_line_width <= width - 2 * text_x:
+                        current_line.append(word)
+                    else:
+                        lines.append(" ".join(current_line))
+                        current_line = [word]
+                if current_line:
+                    lines.append(" ".join(current_line))
+
+            # Draw each line of text separately
+            y_offset = text_y
+            for line in lines:
+                text_width = font.getlength(line)
+                text_height = font_size
+                text_center_x = text_x + text_width / 2
+                text_center_y = y_offset + text_height / 2
+                try:
+                    draw.text((text_x, y_offset), line, font=font, fill=font_color, features=['-liga'])
+                except Exception:
+                    draw.text((text_x, y_offset), line, font=font, fill=font_color)
+                y_offset += text_height # Move to the next line
+
+            if start_rotation != end_rotation:
+                image = image.rotate(rotation, center=(text_center_x, text_center_y))
+                rotation += rotation_increment
+
+            image = np.array(image).astype(np.float32) / 255.0
+            image = torch.from_numpy(image)[None,]
+            mask = image[:, :, :, 0] 
+            masks.append(mask)
+            out.append(image)
+
+        if invert:
+            return (1.0 - torch.cat(out, dim=0), 1.0 - torch.cat(masks, dim=0),)
+        return (torch.cat(out, dim=0),torch.cat(masks, dim=0),)
+
+class ColorToMask:
+    
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "clip"
+    CATEGORY = "KJNodes/masking"
+    DESCRIPTION = """
+Converts chosen RGB value to a mask.  
+With batch inputs, the **per_batch**  
+controls the number of images processed at once.
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "images": ("IMAGE",),
+                 "invert": ("BOOLEAN", {"default": False}),
+                 "red": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}),
+                 "green": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}),
+                 "blue": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}),
+                 "threshold": ("INT", {"default": 10,"min": 0, "max": 255, "step": 1}),
+                 "per_batch": ("INT", {"default": 16, "min": 1, "max": 4096, "step": 1}),
+        },
+    } 
+
+    def clip(self, images, red, green, blue, threshold, invert, per_batch):
+
+        color = torch.tensor([red, green, blue], dtype=torch.uint8)  
+        black = torch.tensor([0, 0, 0], dtype=torch.uint8)
+        white = torch.tensor([255, 255, 255], dtype=torch.uint8)
+        
+        if invert:
+            black, white = white, black
+
+        steps = images.shape[0]
+        pbar = ProgressBar(steps)
+        tensors_out = []
+        
+        for start_idx in range(0, images.shape[0], per_batch):
+
+            # Calculate color distances
+            color_distances = torch.norm(images[start_idx:start_idx+per_batch] * 255 - color, dim=-1)
+            
+            # Create a mask based on the threshold
+            mask = color_distances <= threshold
+            
+            # Apply the mask to create new images
+            mask_out = torch.where(mask.unsqueeze(-1), white, black).float()
+            mask_out = mask_out.mean(dim=-1)
+
+            tensors_out.append(mask_out.cpu())
+            batch_count = mask_out.shape[0]
+            pbar.update(batch_count)
+       
+        tensors_out = torch.cat(tensors_out, dim=0)
+        tensors_out = torch.clamp(tensors_out, min=0.0, max=1.0)
+        return tensors_out,
+      
+class CreateFluidMask:
+    
+    RETURN_TYPES = ("IMAGE", "MASK")
+    FUNCTION = "createfluidmask"
+    CATEGORY = "KJNodes/masking/generate"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "invert": ("BOOLEAN", {"default": False}),
+                 "frames": ("INT", {"default": 1,"min": 1, "max": 4096, "step": 1}),
+                 "width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
+                 "height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
+                 "inflow_count": ("INT", {"default": 3,"min": 0, "max": 255, "step": 1}),
+                 "inflow_velocity": ("INT", {"default": 1,"min": 0, "max": 255, "step": 1}),
+                 "inflow_radius": ("INT", {"default": 8,"min": 0, "max": 255, "step": 1}),
+                 "inflow_padding": ("INT", {"default": 50,"min": 0, "max": 255, "step": 1}),
+                 "inflow_duration": ("INT", {"default": 60,"min": 0, "max": 255, "step": 1}),
+        },
+    } 
+    #using code from https://github.com/GregTJ/stable-fluids
+    def createfluidmask(self, frames, width, height, invert, inflow_count, inflow_velocity, inflow_radius, inflow_padding, inflow_duration):
+        from ..utility.fluid import Fluid
+        try:
+            from scipy.special import erf
+        except ImportError:
+            from scipy.spatial import erf
+        out = []
+        masks = []
+        RESOLUTION = width, height
+        DURATION = frames
+
+        INFLOW_PADDING = inflow_padding
+        INFLOW_DURATION = inflow_duration
+        INFLOW_RADIUS = inflow_radius
+        INFLOW_VELOCITY = inflow_velocity
+        INFLOW_COUNT = inflow_count
+
+        logging.info('Generating fluid solver, this may take some time.')
+        fluid = Fluid(RESOLUTION, 'dye')
+
+        center = np.floor_divide(RESOLUTION, 2)
+        r = np.min(center) - INFLOW_PADDING
+
+        points = np.linspace(-np.pi, np.pi, INFLOW_COUNT, endpoint=False)
+        points = tuple(np.array((np.cos(p), np.sin(p))) for p in points)
+        normals = tuple(-p for p in points)
+        points = tuple(r * p + center for p in points)
+
+        inflow_velocity = np.zeros_like(fluid.velocity)
+        inflow_dye = np.zeros(fluid.shape)
+        for p, n in zip(points, normals):
+            mask = np.linalg.norm(fluid.indices - p[:, None, None], axis=0) <= INFLOW_RADIUS
+            inflow_velocity[:, mask] += n[:, None] * INFLOW_VELOCITY
+            inflow_dye[mask] = 1
+
+        
+        for f in range(DURATION):
+            logging.info(f'Computing frame {f + 1} of {DURATION}.')
+            if f <= INFLOW_DURATION:
+                fluid.velocity += inflow_velocity
+                fluid.dye += inflow_dye
+
+            curl = fluid.step()[1]
+            # Using the error function to make the contrast a bit higher. 
+            # Any other sigmoid function e.g. smoothstep would work.
+            curl = (erf(curl * 2) + 1) / 4
+
+            color = np.dstack((curl, np.ones(fluid.shape), fluid.dye))
+            color = (np.clip(color, 0, 1) * 255).astype('uint8')
+            image = np.array(color).astype(np.float32) / 255.0
+            image = torch.from_numpy(image)[None,]
+            mask = image[:, :, :, 0] 
+            masks.append(mask)
+            out.append(image)
+        
+        if invert:
+            return (1.0 - torch.cat(out, dim=0),1.0 - torch.cat(masks, dim=0),)
+        return (torch.cat(out, dim=0),torch.cat(masks, dim=0),)
+
+class CreateAudioMask:
+       
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "createaudiomask"
+    CATEGORY = "KJNodes/deprecated"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "invert": ("BOOLEAN", {"default": False}),
+                 "frames": ("INT", {"default": 16,"min": 1, "max": 255, "step": 1}),
+                 "scale": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 2.0, "step": 0.01}),
+                 "audio_path": ("STRING", {"default": "audio.wav"}),
+                 "width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
+                 "height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
+        },
+    } 
+
+    def createaudiomask(self, frames, width, height, invert, audio_path, scale):
+        try:
+            import librosa
+        except ImportError as e:
+            raise ImportError("Can not import librosa. Install it with 'pip install librosa'") from e
+        batch_size = frames
+        out = []
+        masks = []
+        if audio_path == "audio.wav": #I don't know why relative path won't work otherwise...
+            audio_path = os.path.join(script_directory, audio_path)
+        audio, sr = librosa.load(audio_path)
+        spectrogram = np.abs(librosa.stft(audio))
+        
+        for i in range(batch_size):
+           image = Image.new("RGB", (width, height), "black")
+           draw = ImageDraw.Draw(image)
+           frame = spectrogram[:, i]
+           circle_radius = int(height * np.mean(frame))
+           circle_radius *= scale
+           circle_center = (width // 2, height // 2)  # Calculate the center of the image
+
+           draw.ellipse([(circle_center[0] - circle_radius, circle_center[1] - circle_radius),
+                      (circle_center[0] + circle_radius, circle_center[1] + circle_radius)],
+                      fill='white')
+             
+           image = np.array(image).astype(np.float32) / 255.0
+           image = torch.from_numpy(image)[None,]
+           mask = image[:, :, :, 0] 
+           masks.append(mask)
+           out.append(image)
+
+        if invert:
+            return (1.0 - torch.cat(out, dim=0),)
+        return (torch.cat(out, dim=0),torch.cat(masks, dim=0),)
+       
+class CreateGradientMask:
+    
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "createmask"
+    CATEGORY = "KJNodes/masking/generate"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "invert": ("BOOLEAN", {"default": False}),
+                 "frames": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}),
+                 "width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
+                 "height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
+        },
+    } 
+    def createmask(self, frames, width, height, invert):
+        # Define the number of images in the batch
+        batch_size = frames
+        out = []
+        # Create an empty array to store the image batch
+        image_batch = np.zeros((batch_size, height, width), dtype=np.float32)
+        # Generate the black to white gradient for each image
+        for i in range(batch_size):
+            gradient = np.linspace(1.0, 0.0, width, dtype=np.float32)
+            time = i / frames  # Calculate the time variable
+            offset_gradient = gradient - time  # Offset the gradient values based on time
+            image_batch[i] = offset_gradient.reshape(1, -1)
+        output = torch.from_numpy(image_batch)
+        mask = output
+        out.append(mask)
+        if invert:
+            return (1.0 - torch.cat(out, dim=0),)
+        return (torch.cat(out, dim=0),)
+
+class CreateFadeMask:
+    
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "createfademask"
+    CATEGORY = "KJNodes/deprecated"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "invert": ("BOOLEAN", {"default": False}),
+                 "frames": ("INT", {"default": 2,"min": 2, "max": 10000, "step": 1}),
+                 "width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
+                 "height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
+                 "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],),
+                 "start_level": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 1.0, "step": 0.01}),
+                 "midpoint_level": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 1.0, "step": 0.01}),
+                 "end_level": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 1.0, "step": 0.01}),
+                 "midpoint_frame": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
+        },
+    } 
+    
+    def createfademask(self, frames, width, height, invert, interpolation, start_level, midpoint_level, end_level, midpoint_frame):
+        def ease_in(t):
+            return t * t
+
+        def ease_out(t):
+            return 1 - (1 - t) * (1 - t)
+
+        def ease_in_out(t):
+            return 3 * t * t - 2 * t * t * t
+
+        batch_size = frames
+        out = []
+        image_batch = np.zeros((batch_size, height, width), dtype=np.float32)
+
+        if midpoint_frame == 0:
+            midpoint_frame = batch_size // 2
+
+        for i in range(batch_size):
+            if i <= midpoint_frame:
+                t = i / midpoint_frame
+                if interpolation == "ease_in":
+                    t = ease_in(t)
+                elif interpolation == "ease_out":
+                    t = ease_out(t)
+                elif interpolation == "ease_in_out":
+                    t = ease_in_out(t)
+                color = start_level - t * (start_level - midpoint_level)
+            else:
+                t = (i - midpoint_frame) / (batch_size - midpoint_frame)
+                if interpolation == "ease_in":
+                    t = ease_in(t)
+                elif interpolation == "ease_out":
+                    t = ease_out(t)
+                elif interpolation == "ease_in_out":
+                    t = ease_in_out(t)
+                color = midpoint_level - t * (midpoint_level - end_level)
+
+            color = np.clip(color, 0, 255)
+            image = np.full((height, width), color, dtype=np.float32)
+            image_batch[i] = image
+
+        output = torch.from_numpy(image_batch)
+        mask = output
+        out.append(mask)
+
+        if invert:
+            return (1.0 - torch.cat(out, dim=0),)
+        return (torch.cat(out, dim=0),)
+
+class CreateFadeMaskAdvanced:
+    
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "createfademask"
+    CATEGORY = "KJNodes/masking/generate"
+    DESCRIPTION = """
+Create a batch of masks interpolated between given frames and values. 
+Uses same syntax as Fizz' BatchValueSchedule.
+First value is the frame index (not that this starts from 0, not 1) 
+and the second value inside the brackets is the float value of the mask in range 0.0 - 1.0  
+
+For example the default values:  
+0:(0.0)  
+7:(1.0)  
+15:(0.0)  
+  
+Would create a mask batch fo 16 frames, starting from black, 
+interpolating with the chosen curve to fully white at the 8th frame, 
+and interpolating from that to fully black at the 16th frame.
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "points_string": ("STRING", {"default": "0:(0.0),\n7:(1.0),\n15:(0.0)\n", "multiline": True}),
+                 "invert": ("BOOLEAN", {"default": False}),
+                 "frames": ("INT", {"default": 16,"min": 2, "max": 10000, "step": 1}),
+                 "width": ("INT", {"default": 512,"min": 1, "max": 4096, "step": 1}),
+                 "height": ("INT", {"default": 512,"min": 1, "max": 4096, "step": 1}),
+                 "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "none", "default_to_black"],),
+        },
+    } 
+    
+    def createfademask(self, frames, width, height, invert, points_string, interpolation):
+        def ease_in(t):
+            return t * t
+        
+        def ease_out(t):
+            return 1 - (1 - t) * (1 - t)
+
+        def ease_in_out(t):
+            return 3 * t * t - 2 * t * t * t
+        
+        # Parse the input string into a list of tuples
+        points = []
+        points_string = points_string.rstrip(',\n')
+        for point_str in points_string.split(','):
+            frame_str, color_str = point_str.split(':')
+            frame = int(frame_str.strip())
+            color = float(color_str.strip()[1:-1])  # Remove parentheses around color
+            points.append((frame, color))
+
+        # Check if the last frame is already in the points
+        if (interpolation != "default_to_black") and (len(points) == 0 or points[-1][0] != frames - 1):
+            # If not, add it with the color of the last specified frame
+            points.append((frames - 1, points[-1][1] if points else 0))
+
+        # Sort the points by frame number
+        points.sort(key=lambda x: x[0])
+
+        batch_size = frames
+        out = []
+        image_batch = np.zeros((batch_size, height, width), dtype=np.float32)
+
+        # Index of the next point to interpolate towards
+        next_point = 1
+
+        for i in range(batch_size):
+            while next_point < len(points) and i > points[next_point][0]:
+                next_point += 1
+
+            # Interpolate between the previous point and the next point
+            prev_point = next_point - 1
+
+            if interpolation == "none":
+                exact_match = False
+                for p in points:
+                    if p[0] == i:  # Exact frame match
+                        color = p[1]
+                        exact_match = True
+                        break
+                if not exact_match:
+                    color = points[prev_point][1]
+
+            elif interpolation == "default_to_black":
+                exact_match = False
+                for p in points:
+                    if p[0] == i:  # Exact frame match
+                        color = p[1]
+                        exact_match = True
+                        break
+                if not exact_match:
+                    color = 0        
+            else:
+                t = (i - points[prev_point][0]) / (points[next_point][0] - points[prev_point][0])
+                if interpolation == "ease_in":
+                    t = ease_in(t)
+                elif interpolation == "ease_out":
+                    t = ease_out(t)
+                elif interpolation == "ease_in_out":
+                    t = ease_in_out(t)
+                elif interpolation == "linear":
+                    pass  # No need to modify `t` for linear interpolation
+
+                color = points[prev_point][1] - t * (points[prev_point][1] - points[next_point][1])
+                
+            color = np.clip(color, 0, 255)
+            image = np.full((height, width), color, dtype=np.float32)
+            image_batch[i] = image
+
+        output = torch.from_numpy(image_batch)
+        mask = output
+        out.append(mask)
+
+        if invert:
+            return (1.0 - torch.cat(out, dim=0),)
+        return (torch.cat(out, dim=0),)
+
+class CreateMagicMask:
+    
+    RETURN_TYPES = ("MASK", "MASK",)
+    RETURN_NAMES = ("mask", "mask_inverted",)
+    FUNCTION = "createmagicmask"
+    CATEGORY = "KJNodes/masking/generate"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "frames": ("INT", {"default": 16,"min": 2, "max": 4096, "step": 1}),
+                 "depth": ("INT", {"default": 12,"min": 1, "max": 500, "step": 1}),
+                 "distortion": ("FLOAT", {"default": 1.5,"min": 0.0, "max": 100.0, "step": 0.01}),
+                 "seed": ("INT", {"default": 123,"min": 0, "max": 99999999, "step": 1}),
+                 "transitions": ("INT", {"default": 1,"min": 1, "max": 20, "step": 1}),
+                 "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                 "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+        },
+    } 
+
+    def createmagicmask(self, frames, transitions, depth, distortion, seed, frame_width, frame_height):
+        from ..utility.magictex import coordinate_grid, random_transform, magic
+        import matplotlib.pyplot as plt
+        rng = np.random.default_rng(seed)
+        out = []
+        coords = coordinate_grid((frame_width, frame_height))
+
+        # Calculate the number of frames for each transition
+        frames_per_transition = frames // transitions
+
+        # Generate a base set of parameters
+        base_params = {
+            "coords": random_transform(coords, rng),
+            "depth": depth,
+            "distortion": distortion,
+        }
+        for t in range(transitions):
+        # Generate a second set of parameters that is at most max_diff away from the base parameters
+            params1 = base_params.copy()
+            params2 = base_params.copy()
+
+            params1['coords'] = random_transform(coords, rng)
+            params2['coords'] = random_transform(coords, rng)
+
+            for i in range(frames_per_transition):
+                # Compute the interpolation factor
+                alpha = i / frames_per_transition
+
+                # Interpolate between the two sets of parameters
+                params = params1.copy()
+                params['coords'] = (1 - alpha) * params1['coords'] + alpha * params2['coords']
+
+                tex = magic(**params)
+
+                dpi = frame_width / 10
+                fig = plt.figure(figsize=(10, 10), dpi=dpi)
+
+                ax = fig.add_subplot(111)
+                plt.subplots_adjust(left=0, right=1, bottom=0, top=1)
+                
+                ax.get_yaxis().set_ticks([])
+                ax.get_xaxis().set_ticks([])
+                ax.imshow(tex, aspect='auto')
+                
+                fig.canvas.draw()
+                img = np.array(fig.canvas.renderer._renderer)
+                
+                plt.close(fig)
+                
+                pil_img = Image.fromarray(img).convert("L")
+                mask = torch.tensor(np.array(pil_img)) / 255.0
+                
+                out.append(mask)
+        
+        return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),)
+        
+class CreateShapeMask:
+    
+    RETURN_TYPES = ("MASK", "MASK",)
+    RETURN_NAMES = ("mask", "mask_inverted",)
+    FUNCTION = "createshapemask"
+    CATEGORY = "KJNodes/masking/generate"
+    DESCRIPTION = """
+Creates a mask or batch of masks with the specified shape.  
+Locations are center locations.  
+Grow value is the amount to grow the shape on each frame, creating animated masks.
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "shape": (
+            [   'circle',
+                'square',
+                'triangle',
+            ],
+            {
+            "default": 'circle'
+             }),
+                "frames": ("INT", {"default": 1,"min": 1, "max": 4096, "step": 1}),
+                "location_x": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}),
+                "location_y": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}),
+                "grow": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
+                "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                "shape_width": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}),
+                "shape_height": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}),
+        },
+    } 
+
+    def createshapemask(self, frames, frame_width, frame_height, location_x, location_y, shape_width, shape_height, grow, shape):
+        # Define the number of images in the batch
+        batch_size = frames
+        out = []
+        color = "white"
+        for i in range(batch_size):
+            image = Image.new("RGB", (frame_width, frame_height), "black")
+            draw = ImageDraw.Draw(image)
+
+            # Calculate the size for this frame and ensure it's not less than 0
+            current_width = max(0, shape_width + i*grow)
+            current_height = max(0, shape_height + i*grow)
+
+            if shape == 'circle' or shape == 'square':
+                # Define the bounding box for the shape
+                left_up_point = (location_x - current_width // 2, location_y - current_height // 2)
+                right_down_point = (location_x + current_width // 2, location_y + current_height // 2)
+                two_points = [left_up_point, right_down_point]
+
+                if shape == 'circle':
+                    draw.ellipse(two_points, fill=color)
+                elif shape == 'square':
+                    draw.rectangle(two_points, fill=color)
+                    
+            elif shape == 'triangle':
+                # Define the points for the triangle
+                left_up_point = (location_x - current_width // 2, location_y + current_height // 2) # bottom left
+                right_down_point = (location_x + current_width // 2, location_y + current_height // 2) # bottom right
+                top_point = (location_x, location_y - current_height // 2) # top point
+                draw.polygon([top_point, left_up_point, right_down_point], fill=color)
+
+            image = pil2tensor(image)
+            mask = image[:, :, :, 0]
+            out.append(mask)
+        outstack = torch.cat(out, dim=0)
+        return (outstack, 1.0 - outstack,)
+    
+class CreateVoronoiMask:
+    
+    RETURN_TYPES = ("MASK", "MASK",)
+    RETURN_NAMES = ("mask", "mask_inverted",)
+    FUNCTION = "createvoronoi"
+    CATEGORY = "KJNodes/masking/generate"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "frames": ("INT", {"default": 16,"min": 2, "max": 4096, "step": 1}),
+                 "num_points": ("INT", {"default": 15,"min": 1, "max": 4096, "step": 1}),
+                 "line_width": ("INT", {"default": 4,"min": 1, "max": 4096, "step": 1}),
+                 "speed": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 1.0, "step": 0.01}),
+                 "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                 "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+        },
+    } 
+
+    def createvoronoi(self, frames, num_points, line_width, speed, frame_width, frame_height):
+        from scipy.spatial import Voronoi
+        from matplotlib import pyplot as plt
+        # Define the number of images in the batch
+        batch_size = frames
+        out = []
+          
+        # Calculate aspect ratio
+        aspect_ratio = frame_width / frame_height
+        
+        # Create start and end points for each point, considering the aspect ratio
+        start_points = np.random.rand(num_points, 2)
+        start_points[:, 0] *= aspect_ratio
+        
+        end_points = np.random.rand(num_points, 2)
+        end_points[:, 0] *= aspect_ratio
+
+        for i in range(batch_size):
+            # Interpolate the points' positions based on the current frame
+            t = (i * speed) / (batch_size - 1)  # normalize to [0, 1] over the frames
+            t = np.clip(t, 0, 1)  # ensure t is in [0, 1]
+            points = (1 - t) * start_points + t * end_points  # lerp
+
+            # Adjust points for aspect ratio
+            points[:, 0] *= aspect_ratio
+
+            vor = Voronoi(points)
+
+            # Create a blank image with a white background
+            fig, ax = plt.subplots()
+            plt.subplots_adjust(left=0, right=1, bottom=0, top=1)
+            ax.set_xlim([0, aspect_ratio]); ax.set_ylim([0, 1])  # adjust x limits
+            ax.axis('off')
+            ax.margins(0, 0)
+            fig.set_size_inches(aspect_ratio * frame_height/100, frame_height/100)  # adjust figure size
+            ax.fill_between([0, 1], [0, 1], color='white')
+
+            # Plot each Voronoi ridge
+            for simplex in vor.ridge_vertices:
+                simplex = np.asarray(simplex)
+                if np.all(simplex >= 0):
+                    plt.plot(vor.vertices[simplex, 0], vor.vertices[simplex, 1], 'k-', linewidth=line_width)
+
+            fig.canvas.draw()
+            img = np.array(fig.canvas.renderer._renderer)
+
+            plt.close(fig)
+
+            pil_img = Image.fromarray(img).convert("L")
+            mask = torch.tensor(np.array(pil_img)) / 255.0
+
+            out.append(mask)
+
+        return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),)
+    
+class GetMaskSizeAndCount:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "mask": ("MASK",),
+        }}
+
+    RETURN_TYPES = ("MASK","INT", "INT", "INT",)
+    RETURN_NAMES = ("mask", "width", "height", "count",)
+    FUNCTION = "getsize"
+    CATEGORY = "KJNodes/masking"
+    DESCRIPTION = """
+Returns the width, height and batch size of the mask,  
+and passes it through unchanged.  
+
+"""
+
+    def getsize(self, mask):
+        width = mask.shape[2]
+        height = mask.shape[1]
+        count = mask.shape[0]
+        return {"ui": {
+            "text": [f"{count}x{width}x{height}"]}, 
+            "result": (mask, width, height, count) 
+        }
+
+class GrowMaskWithBlur:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "mask": ("MASK",),
+                "expand": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1}),
+                "incremental_expandrate": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.1}),
+                "tapered_corners": ("BOOLEAN", {"default": True}),
+                "flip_input": ("BOOLEAN", {"default": False}),
+                "blur_radius": ("FLOAT", {
+                    "default": 0.0,
+                    "min": 0.0,
+                    "max": 100,
+                    "step": 0.1
+                }),
+                "lerp_alpha": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "decay_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+            },
+            "optional": {
+                "fill_holes": ("BOOLEAN", {"default": False}),
+            },
+        }
+
+    CATEGORY = "KJNodes/masking"
+    RETURN_TYPES = ("MASK", "MASK",)
+    RETURN_NAMES = ("mask", "mask_inverted",)
+    FUNCTION = "expand_mask"
+    DESCRIPTION = """
+# GrowMaskWithBlur
+- mask: Input mask or mask batch
+- expand: Expand or contract mask or mask batch by a given amount
+- incremental_expandrate: increase expand rate by a given amount per frame
+- tapered_corners: use tapered corners
+- flip_input: flip input mask
+- blur_radius: value higher than 0 will blur the mask
+- lerp_alpha: alpha value for interpolation between frames
+- decay_factor: decay value for interpolation between frames
+- fill_holes: fill holes in the mask (slow)"""
+    
+    def expand_mask(self, mask, expand, tapered_corners, flip_input, blur_radius, incremental_expandrate, lerp_alpha, decay_factor, fill_holes=False):
+        import kornia.morphology as morph
+        alpha = lerp_alpha
+        decay = decay_factor
+        if flip_input:
+            mask = 1.0 - mask
+
+        growmask = mask.reshape((-1, mask.shape[-2], mask.shape[-1]))
+        out = []
+        previous_output = None
+        current_expand = expand
+        for m in tqdm(growmask, desc="Expanding/Contracting Mask"):
+            output = m.unsqueeze(0).unsqueeze(0).to(main_device)  # Add batch and channel dims for kornia
+            if abs(round(current_expand)) > 0 and output.max() > 0:
+                # Create kernel - kornia expects kernel on same device as input
+                if tapered_corners:
+                    kernel = torch.tensor([[0, 1, 0],
+                                        [1, 1, 1],
+                                        [0, 1, 0]], dtype=torch.float32, device=output.device)
+                else:
+                    kernel = torch.tensor([[1, 1, 1],
+                                        [1, 1, 1],
+                                        [1, 1, 1]], dtype=torch.float32, device=output.device)
+                
+                for _ in range(abs(round(current_expand))):
+                    if current_expand < 0:
+                        output = morph.erosion(output, kernel)
+                    else:
+                        output = morph.dilation(output, kernel)
+            
+            output = output.squeeze(0).squeeze(0)  # Remove batch and channel dims
+            
+            if current_expand < 0:
+                current_expand -= abs(incremental_expandrate)
+            else:
+                current_expand += abs(incremental_expandrate)
+                
+            if fill_holes:
+                binary_mask = output > 0
+                output_np = binary_mask.cpu().numpy()
+                filled = scipy.ndimage.binary_fill_holes(output_np)
+                output = torch.from_numpy(filled.astype(np.float32)).to(output.device)
+            
+            if alpha < 1.0 and previous_output is not None:
+                output = alpha * output + (1 - alpha) * previous_output
+            if decay < 1.0 and previous_output is not None:
+                output += decay * previous_output
+                output = output / output.max()
+            previous_output = output
+            out.append(output.cpu())
+
+        if blur_radius != 0:
+            # Convert the tensor list to PIL images, apply blur, and convert back
+            for idx, tensor in enumerate(out):
+                # Convert tensor to PIL image
+                pil_image = tensor2pil(tensor.cpu().detach())[0]
+                # Apply Gaussian blur
+                pil_image = pil_image.filter(ImageFilter.GaussianBlur(blur_radius))
+                # Convert back to tensor
+                out[idx] = pil2tensor(pil_image)
+            blurred = torch.cat(out, dim=0)
+            return (blurred, 1.0 - blurred)
+        else:
+            return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),)
+        
+class MaskBatchMulti:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}),
+                "mask_1": ("MASK", ),
+                "mask_2": ("MASK", ),
+            },
+    }
+
+    RETURN_TYPES = ("MASK",)
+    RETURN_NAMES = ("masks",)
+    FUNCTION = "combine"
+    CATEGORY = "KJNodes/masking"
+    DESCRIPTION = """
+Creates an image batch from multiple masks.  
+You can set how many inputs the node has,  
+with the **inputcount** and clicking update.
+"""
+
+    def combine(self, inputcount, **kwargs):
+        mask = kwargs["mask_1"]
+        for c in range(1, inputcount):
+            new_mask = kwargs[f"mask_{c + 1}"]
+            if mask.shape[1:] != new_mask.shape[1:]:
+                new_mask = F.interpolate(new_mask.unsqueeze(1), size=(mask.shape[1], mask.shape[2]), mode="bicubic").squeeze(1)
+            mask = torch.cat((mask, new_mask), dim=0)
+        return (mask,)
+
+class OffsetMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "mask": ("MASK",),
+                "x": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
+                "y": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
+                "angle": ("INT", { "default": 0, "min": -360, "max": 360, "step": 1, "display": "number" }),
+                "duplication_factor": ("INT", { "default": 1, "min": 1, "max": 1000, "step": 1, "display": "number" }),
+                "roll": ("BOOLEAN", { "default": False }),
+                "incremental": ("BOOLEAN", { "default": False }),
+                "padding_mode": (
+            [   
+                'empty',
+                'border',
+                'reflection',
+                
+            ], {
+               "default": 'empty'
+            }),
+            }
+        }
+
+    RETURN_TYPES = ("MASK",)
+    RETURN_NAMES = ("mask",)
+    FUNCTION = "offset"
+    CATEGORY = "KJNodes/masking"
+    DESCRIPTION = """
+Offsets the mask by the specified amount.  
+ - mask: Input mask or mask batch
+ - x: Horizontal offset
+ - y: Vertical offset
+ - angle: Angle in degrees
+ - roll: roll edge wrapping
+ - duplication_factor: Number of times to duplicate the mask to form a batch
+ - border padding_mode: Padding mode for the mask
+"""
+
+    def offset(self, mask, x, y, angle, roll=False, incremental=False, duplication_factor=1, padding_mode="empty"):
+        # Create duplicates of the mask batch
+        mask = mask.repeat(duplication_factor, 1, 1).clone()
+
+        batch_size, height, width = mask.shape
+
+        if angle != 0 and incremental:
+            for i in range(batch_size):
+                rotation_angle = angle * (i+1)
+                mask[i] = TF.rotate(mask[i].unsqueeze(0), rotation_angle).squeeze(0)
+        elif angle > 0:
+            for i in range(batch_size):
+                mask[i] = TF.rotate(mask[i].unsqueeze(0), angle).squeeze(0)
+
+        if roll:
+            if incremental:
+                for i in range(batch_size):
+                    shift_x = min(x*(i+1), width-1)
+                    shift_y = min(y*(i+1), height-1)
+                    if shift_x != 0:
+                        mask[i] = torch.roll(mask[i], shifts=shift_x, dims=1)
+                    if shift_y != 0:
+                        mask[i] = torch.roll(mask[i], shifts=shift_y, dims=0)
+            else:
+                shift_x = min(x, width-1)
+                shift_y = min(y, height-1)
+                if shift_x != 0:
+                    mask = torch.roll(mask, shifts=shift_x, dims=2)
+                if shift_y != 0:
+                    mask = torch.roll(mask, shifts=shift_y, dims=1)
+        else:
+            
+            for i in range(batch_size):
+                if incremental:
+                    temp_x = min(x * (i+1), width-1)
+                    temp_y = min(y * (i+1), height-1)
+                else:
+                    temp_x = min(x, width-1)
+                    temp_y = min(y, height-1)
+                if temp_x > 0:
+                    if padding_mode == 'empty':
+                        mask[i] = torch.cat([torch.zeros((height, temp_x)), mask[i, :, :-temp_x]], dim=1)
+                    elif padding_mode in ['replicate', 'reflect']:
+                        mask[i] = F.pad(mask[i, :, :-temp_x], (0, temp_x), mode=padding_mode)
+                elif temp_x < 0:
+                    if padding_mode == 'empty':
+                        mask[i] = torch.cat([mask[i, :, :temp_x], torch.zeros((height, -temp_x))], dim=1)
+                    elif padding_mode in ['replicate', 'reflect']:
+                        mask[i] = F.pad(mask[i, :, -temp_x:], (temp_x, 0), mode=padding_mode)
+
+                if temp_y > 0:
+                    if padding_mode == 'empty':
+                        mask[i] = torch.cat([torch.zeros((temp_y, width)), mask[i, :-temp_y, :]], dim=0)
+                    elif padding_mode in ['replicate', 'reflect']:
+                        mask[i] = F.pad(mask[i, :-temp_y, :], (0, temp_y), mode=padding_mode)
+                elif temp_y < 0:
+                    if padding_mode == 'empty':
+                        mask[i] = torch.cat([mask[i, :temp_y, :], torch.zeros((-temp_y, width))], dim=0)
+                    elif padding_mode in ['replicate', 'reflect']:
+                        mask[i] = F.pad(mask[i, -temp_y:, :], (temp_y, 0), mode=padding_mode)
+           
+        return mask,
+        
+class RoundMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "mask": ("MASK",),  
+        }}
+
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "round"
+    CATEGORY = "KJNodes/masking"
+    DESCRIPTION = """
+Rounds the mask or batch of masks to a binary mask.  
+<img src="https://github.com/kijai/ComfyUI-KJNodes/assets/40791699/52c85202-f74e-4b96-9dac-c8bda5ddcc40" width="300" height="250" alt="RoundMask example">
+
+"""
+
+    def round(self, mask):
+        mask = mask.round()
+        return (mask,)
+    
+class ResizeMask:
+    upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "mask": ("MASK",),
+                "width": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
+                "height": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
+                "keep_proportions": ("BOOLEAN", { "default": False }),
+                "upscale_method": (s.upscale_methods,),
+                "crop": (["disabled","center"],),
+            }
+        }
+
+    RETURN_TYPES = ("MASK", "INT", "INT",)
+    RETURN_NAMES = ("mask", "width", "height",)
+    FUNCTION = "resize"
+    CATEGORY = "KJNodes/masking"
+    DESCRIPTION = """
+Resizes the mask or batch of masks to the specified width and height.
+"""
+
+    def resize(self, mask, width, height, keep_proportions, upscale_method,crop):
+        if keep_proportions:
+            _, oh, ow = mask.shape
+            width = ow if width == 0 else width
+            height = oh if height == 0 else height
+            ratio = min(width / ow, height / oh)
+            width = round(ow*ratio)
+            height = round(oh*ratio)
+
+        if upscale_method == "lanczos":
+            out_mask = common_upscale(mask.unsqueeze(1).repeat(1, 3, 1, 1), width, height, upscale_method, crop=crop).movedim(1,-1)[:, :, :, 0]
+        else:
+            out_mask = common_upscale(mask.unsqueeze(1), width, height, upscale_method, crop=crop).squeeze(1)
+
+        return(out_mask, out_mask.shape[2], out_mask.shape[1],)
+
+class RemapMaskRange:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "mask": ("MASK",),
+                "min": ("FLOAT", {"default": 0.0,"min": -10.0, "max": 1.0, "step": 0.01}),
+                "max": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 10.0, "step": 0.01}),
+            }
+        }
+
+    RETURN_TYPES = ("MASK",)
+    RETURN_NAMES = ("mask",)
+    FUNCTION = "remap"
+    CATEGORY = "KJNodes/masking"
+    DESCRIPTION = """
+Sets new min and max values for the mask.
+"""
+
+    def remap(self, mask, min, max):
+
+         # Find the maximum value in the mask
+        mask_max = torch.max(mask)
+        
+        # If the maximum mask value is zero, avoid division by zero by setting it to 1
+        mask_max = mask_max if mask_max > 0 else 1
+        
+        # Scale the mask values to the new range defined by min and max
+        # The highest pixel value in the mask will be scaled to max
+        scaled_mask = (mask / mask_max) * (max - min) + min
+        
+        # Clamp the values to ensure they are within [0.0, 1.0]
+        scaled_mask = torch.clamp(scaled_mask, min=0.0, max=1.0)
+        
+        return (scaled_mask, )
+
+
+def get_mask_polygon(self, mask_np):
+    import cv2
+    """Helper function to get polygon points from mask"""
+    # Find contours
+    contours, _ = cv2.findContours(mask_np, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    
+    if not contours:
+        return None
+    
+    # Get the largest contour
+    largest_contour = max(contours, key=cv2.contourArea)
+    
+    # Approximate polygon
+    epsilon = 0.02 * cv2.arcLength(largest_contour, True)
+    polygon = cv2.approxPolyDP(largest_contour, epsilon, True)
+    
+    return polygon.squeeze()
+
+class SeparateMasks:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "mask": ("MASK", ),
+                "size_threshold_width" : ("INT", {"default": 256, "min": 0.0, "max": 4096, "step": 1}),
+                "size_threshold_height" : ("INT", {"default": 256, "min": 0.0, "max": 4096, "step": 1}),
+                "mode": (["convex_polygons", "area", "box"],),
+                "max_poly_points": ("INT", {"default": 8, "min": 3, "max": 32, "step": 1}),
+
+            },
+        }
+
+    RETURN_TYPES = ("MASK",)
+    RETURN_NAMES = ("mask",)
+    FUNCTION = "separate"
+    CATEGORY = "KJNodes/masking"
+    OUTPUT_NODE = True
+    DESCRIPTION = "Separates a mask into multiple masks based on the size of the connected components."
+
+    def polygon_to_mask(self, polygon, shape):
+        import cv2
+        mask = np.zeros((shape[0], shape[1]), dtype=np.uint8)  # Fixed shape handling
+
+        if len(polygon.shape) == 2:  # Check if polygon points are valid
+            polygon = polygon.astype(np.int32)
+            cv2.fillPoly(mask, [polygon], 1)
+        return mask
+
+    def get_mask_polygon(self, mask_np, max_points):
+        import cv2
+        contours, _ = cv2.findContours(mask_np, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        if not contours:
+            return None
+        
+        largest_contour = max(contours, key=cv2.contourArea)
+        hull = cv2.convexHull(largest_contour)
+        
+        # Initialize with smaller epsilon for more points
+        perimeter = cv2.arcLength(hull, True)
+        
+        min_eps = perimeter * 0.001  # Much smaller minimum
+        max_eps = perimeter * 0.2   # Smaller maximum
+        
+        best_approx = None
+        best_diff = float('inf')
+        max_iterations = 20
+        
+        #print(f"Target points: {max_points}, Perimeter: {perimeter}")
+        
+        for i in range(max_iterations):
+            curr_eps = (min_eps + max_eps) / 2
+            approx = cv2.approxPolyDP(hull, curr_eps, True)
+            points_diff = len(approx) - max_points
+            
+            #print(f"Iteration {i}: points={len(approx)}, eps={curr_eps:.4f}")
+            
+            if abs(points_diff) < best_diff:
+                best_approx = approx
+                best_diff = abs(points_diff)
+            
+            if len(approx) > max_points:
+                min_eps = curr_eps * 1.1  # More gradual adjustment
+            elif len(approx) < max_points:
+                max_eps = curr_eps * 0.9  # More gradual adjustment
+            else:
+                return approx.squeeze()
+            
+            if abs(max_eps - min_eps) < perimeter * 0.0001:  # Relative tolerance
+                break
+        
+        # If we didn't find exact match, return best approximation
+        return best_approx.squeeze() if best_approx is not None else hull.squeeze()
+
+    def separate(self, mask: torch.Tensor, size_threshold_width: int, size_threshold_height: int, max_poly_points: int, mode: str):
+        B, H, W = mask.shape
+        separated = []
+
+        mask = mask.round()
+        
+        for b in range(B):
+            mask_np = mask[b].cpu().numpy().astype(np.uint8)
+            structure = np.ones((3, 3), dtype=np.int8)
+            labeled, ncomponents = scipy.ndimage.label(mask_np, structure=structure)
+            pbar = ProgressBar(ncomponents)
+            
+            for component in range(1, ncomponents + 1):
+                component_mask_np = (labeled == component).astype(np.uint8)
+                
+                rows = np.any(component_mask_np, axis=1)
+                cols = np.any(component_mask_np, axis=0)
+                y_min, y_max = np.where(rows)[0][[0, -1]]
+                x_min, x_max = np.where(cols)[0][[0, -1]]
+                
+                width = x_max - x_min + 1
+                height = y_max - y_min + 1
+                centroid_x = (x_min + x_max) / 2  # Calculate x centroid
+                logging.info(f"Component {component}: width={width}, height={height}, x_pos={centroid_x}")
+                
+                if width >= size_threshold_width and height >= size_threshold_height:
+                    if mode == "convex_polygons":
+                        polygon = self.get_mask_polygon(component_mask_np, max_poly_points)
+                        if polygon is not None:
+                            poly_mask = self.polygon_to_mask(polygon, (H, W))
+                            poly_mask = torch.tensor(poly_mask, device=mask.device)
+                            separated.append((centroid_x, poly_mask))
+                    elif mode == "box":
+                        # Create bounding box mask
+                        box_mask = np.zeros((H, W), dtype=np.uint8)
+                        box_mask[y_min:y_max+1, x_min:x_max+1] = 1
+                        box_mask = torch.tensor(box_mask, device=mask.device)
+                        separated.append((centroid_x, box_mask))
+                    else:
+                        area_mask = torch.tensor(component_mask_np, device=mask.device)
+                        separated.append((centroid_x, area_mask))
+                pbar.update(1)
+        
+        if len(separated) > 0:
+            # Sort by x position and extract only the masks
+            separated.sort(key=lambda x: x[0])
+            separated = [x[1] for x in separated]
+            out_masks = torch.stack(separated, dim=0)
+            return out_masks,
+        else:
+            return torch.empty((1, 64, 64), device=mask.device),
+
+
+class ConsolidateMasksKJ:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "masks": ("MASK",),
+                "width": ("INT", {"default": 512, "min": 0, "max": 4096, "step": 64}),
+                "height": ("INT", {"default": 512, "min": 0, "max": 4096, "step": 64}),
+                "padding": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 1}),
+            },
+        }
+
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "consolidate"
+
+    CATEGORY = "KJNodes/masking"
+    DESCRIPTION = "Consolidates a batch of separate masks by finding the largest group of masks that fit inside a tile of the given width and height (including the padding), and repeating until no more masks can be combined."
+
+    def consolidate(self, masks, width=512, height=512, padding=0):
+        B, H, W = masks.shape
+
+        def mask_fits(coords, candidate_coords):
+            x_min, y_min, x_max, y_max = coords
+            cx_min, cy_min, cx_max, cy_max = candidate_coords
+            nx_min, ny_min = min(x_min, cx_min), min(y_min, cy_min)
+            nx_max, ny_max = max(x_max, cx_max), max(y_max, cy_max)
+            if nx_min + width < nx_max + padding or ny_min + height < ny_max + padding:
+                return False, coords
+            return True, (nx_min, ny_min, nx_max, ny_max)
+
+        separated = []
+        final_masks = []
+        for b in range(B):
+            m = masks[b]
+            rows, cols = m.any(dim=1), m.any(dim=0)
+            y_min, y_max = torch.where(rows)[0][[0, -1]]
+            x_min, x_max = torch.where(cols)[0][[0, -1]]
+            w = x_max - x_min + 1
+            h = y_max - y_min + 1
+            separated.append(((x_min.item(), y_min.item(), x_max.item(), y_max.item()), m))
+
+        separated.sort(key=lambda x: x[0])
+        fits = []
+        for i, masks in enumerate(separated):
+            coord = masks[0]
+            fits_in_box = []
+            for j, cand_mask in enumerate(separated):
+                if i == j:
+                    continue
+                r, coord = mask_fits(coord, cand_mask[0])
+                if r:
+                    fits_in_box.append(j)
+            fits.append((i, fits_in_box))
+        fits.sort(key=lambda x: -len(x[1]))
+        seen = []
+        unique_fits = []
+        for idx, fs in fits:
+            uniq = [i for i in fs if i not in seen]
+            unique_fits.append((idx, fs, uniq))
+            seen.extend(uniq)
+        unique_fits.sort(key=lambda x: (-len(x[1]), -len(x[2])))
+        merged = []
+        for mask_idx, fitting_masks, _ in unique_fits:
+            if mask_idx in merged:
+                continue
+            fitting_masks = [i for i in fitting_masks if i not in merged]
+            combined_mask = separated[mask_idx][1].clone()
+            for i in fitting_masks:
+                combined_mask += separated[i][1]
+                merged.append(i)
+            merged.append(mask_idx)
+            final_masks.append(combined_mask)
+
+        logging.info(f"Consolidated {B} masks into {len(final_masks)}")
+        return (torch.stack(final_masks, dim=0),)
+
+
+class DrawMaskOnImage:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "image": ("IMAGE", ),
+                    "mask": ("MASK", ),
+                    "color": ("STRING", {"default": "0, 0, 0", "tooltip": "Color as RGB/RGBA values in range 0-255 or 0.0-1.0, separated by commas. Ex: 255, 0, 0, 128"}),
+                  },
+                  "optional": {
+                    "device": (["cpu", "gpu"], {"default": "cpu", "tooltip": "Device to use for processing"}),
+                }
+        }
+
+    RETURN_TYPES = ("IMAGE", )
+    RETURN_NAMES = ("images",)
+    FUNCTION = "apply"
+    CATEGORY = "KJNodes/masking"
+    DESCRIPTION = "Applies the provided masks to the input images with Alpha Blending support."
+
+    def apply(self, image, mask, color, device="cpu"):
+        B, H, W, C = image.shape
+        BM, HM, WM = mask.shape
+
+        processing_device = main_device if device == "gpu" else torch.device("cpu")
+
+        in_masks = mask.clone().to(processing_device)
+        in_images = image.clone().to(processing_device)
+
+        # Resize mask if dimensions don't match
+        if HM != H or WM != W:
+            in_masks = F.interpolate(mask.unsqueeze(1), size=(H, W), mode='nearest-exact').squeeze(1)
+        # Handle batch size mismatch
+        if B > BM:
+            in_masks = in_masks.repeat((B + BM - 1) // BM, 1, 1)[:B]
+        elif BM > B:
+            in_masks = in_masks[:B]
+
+        output_images = []
+
+        # Parse Color String (Handle RGB, RGBA, and Hex formats)
+        color = color.strip()
+        color_values = []
+
+        if color.startswith('#'):
+            # Handle hex format (#RGB, #RGBA, #RRGGBB, #RRGGBBAA)
+            hex_color = color.lstrip('#')
+            if len(hex_color) == 3:  # #RGB
+                color_values = [int(c*2, 16) / 255.0 for c in hex_color]
+            elif len(hex_color) == 4:  # #RGBA
+                color_values = [int(c*2, 16) / 255.0 for c in hex_color]
+            elif len(hex_color) == 6:  # #RRGGBB
+                color_values = [int(hex_color[i:i+2], 16) / 255.0 for i in (0, 2, 4)]
+            elif len(hex_color) == 8:  # #RRGGBBAA
+                color_values = [int(hex_color[i:i+2], 16) / 255.0 for i in (0, 2, 4, 6)]
+            else:
+                raise ValueError(f"Invalid hex color format: {color}")
+        else:
+            # Handle comma-separated RGB/RGBA format
+            for x in color.split(","):
+                val = float(x.strip())
+                color_values.append(val / 255.0 if val > 1.0 else val)
+
+        rgb = color_values[:3]
+        alpha_val = color_values[3] if len(color_values) == 4 else 1.0
+
+        fill_color = torch.tensor(rgb, dtype=torch.float32, device=processing_device)
+
+        for i in tqdm(range(B), desc="DrawMaskOnImage batch"):
+            curr_mask = in_masks[i] # [H, W]
+            img_idx = min(i, B - 1)
+            curr_image = in_images[img_idx] # [H, W, C]
+
+            blend_factor = curr_mask.unsqueeze(-1) * alpha_val
+            img_channels = curr_image.shape[-1]
+
+            if img_channels == 4:
+                img_rgb = curr_image[..., :3]
+                img_a = curr_image[..., 3:]
+                out_rgb = img_rgb * (1 - blend_factor) + fill_color * blend_factor
+                out_a = torch.maximum(img_a, blend_factor)
+                masked_image = torch.cat((out_rgb, out_a), dim=-1)
+            else:
+                masked_image = curr_image * (1 - blend_factor) + fill_color * blend_factor
+            output_images.append(masked_image)
+
+        if not output_images:
+            return (torch.zeros((0, H, W, C), dtype=image.dtype),)
+
+        out_tensor = torch.stack(output_images, dim=0).cpu()
+
+        return (out_tensor, )
+
+class BlockifyMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "masks": ("MASK",),
+                    "block_size": ("INT", {"default": 32, "min": 8, "max": 512, "step": 1, "tooltip": "Size of blocks in pixels (smaller = smaller blocks)"}),
+                },
+                "optional": {
+                    "device": (["cpu", "gpu"], {"default": "cpu", "tooltip": "Device to use for processing"}),
+                }
+        }
+
+    RETURN_TYPES = ("MASK", )
+    RETURN_NAMES = ("mask",)
+    FUNCTION = "process"
+    CATEGORY = "KJNodes/masking"
+    DESCRIPTION = "Creates a block mask by dividing the bounding box of each mask into blocks of the specified size and filling in blocks that contain any part of the original mask."
+
+    def process(self, masks, block_size, device="cpu"):
+        processing_device = main_device if device == "gpu" else torch.device("cpu")
+        
+        masks = masks.to(processing_device)
+        batch_size, height, width = masks.shape
+        
+        result_masks = torch.zeros_like(masks)
+        
+        for i in tqdm(range(batch_size), desc="BlockifyMask batch"):
+            mask = masks[i]
+            
+            # Find bounding box efficiently
+            mask_bool = mask > 0
+            if not mask_bool.any():
+                continue
+                
+            y_indices = torch.nonzero(mask_bool.any(dim=1), as_tuple=True)[0]
+            x_indices = torch.nonzero(mask_bool.any(dim=0), as_tuple=True)[0]
+            
+            if len(y_indices) == 0 or len(x_indices) == 0:
+                continue
+                
+            y_min, y_max = y_indices[0], y_indices[-1]
+            x_min, x_max = x_indices[0], x_indices[-1]
+            
+            bbox_width = x_max - x_min + 1
+            bbox_height = y_max - y_min + 1
+            
+            # Calculate block grid
+            w_divisions = max(1, bbox_width // block_size)
+            h_divisions = max(1, bbox_height // block_size)
+            
+            w_slice = bbox_width // w_divisions
+            h_slice = bbox_height // h_divisions
+            
+            # Create coordinate grids only for bbox region
+            y_coords = torch.arange(y_min, y_max + 1, device=processing_device).view(-1, 1)
+            x_coords = torch.arange(x_min, x_max + 1, device=processing_device).view(1, -1)
+            
+            # Calculate block indices for bbox region
+            w_block_indices = (x_coords - x_min) // w_slice
+            h_block_indices = (y_coords - y_min) // h_slice
+            
+            # Clamp to valid range
+            w_block_indices = w_block_indices.clamp(0, w_divisions - 1)
+            h_block_indices = h_block_indices.clamp(0, h_divisions - 1)
+            
+            # Create unique block IDs by combining h and w indices
+            block_ids = h_block_indices * w_divisions + w_block_indices
+            
+            # Get mask region within bbox
+            mask_region = mask[y_min:y_max+1, x_min:x_max+1]
+            
+            # Find which blocks have content using scatter_add
+            max_blocks = h_divisions * w_divisions
+            block_content = torch.zeros(max_blocks, device=processing_device)
+            block_content.scatter_add_(0, block_ids.flatten(), mask_region.flatten())
+            
+            # Create result for blocks that have content
+            has_content = block_content > 0
+            block_mask = has_content[block_ids]
+            
+            # Fill the result
+            result_masks[i, y_min:y_max+1, x_min:x_max+1] = block_mask.float()
+        
+        return (result_masks.clamp(0, 1),)
diff --git a/custom_nodes/ComfyUI-KJNodes/nodes/model_optimization_nodes.py b/custom_nodes/ComfyUI-KJNodes/nodes/model_optimization_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..59d2e3921b2260d168275e2cfc7da9b96ddc4134
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/nodes/model_optimization_nodes.py
@@ -0,0 +1,2375 @@
+import os
+import sys
+import logging
+import torch
+import importlib
+import math
+import datetime
+from tqdm import tqdm
+
+import folder_paths
+import comfy.model_management as mm
+from comfy.cli_args import args
+from comfy.ldm.modules.attention import wrap_attn, optimized_attention, attention_pytorch
+import comfy.utils
+import comfy.sd
+import comfy.ops
+
+try:
+    from comfy_api.latest import io
+    v3_available = True
+except ImportError:
+    v3_available = False
+    logging.warning("ComfyUI v3 node API not available, please update ComfyUI to access latest v3 nodes.")
+
+sageattn_modes = ["disabled", "auto", "sageattn_qk_int8_pv_fp16_cuda", "sageattn_qk_int8_pv_fp16_triton", "sageattn_qk_int8_pv_fp8_cuda", "sageattn_qk_int8_pv_fp8_cuda++", "sageattn3", "sageattn3_per_block_mean"]
+
+def get_sage_func(sage_attention, allow_compile=False):
+    logging.info(f"Using sage attention mode: {sage_attention}")
+    if sage_attention == "auto":
+        from sageattention import sageattn
+        def sage_func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"):
+            return sageattn(q, k, v, is_causal=is_causal, attn_mask=attn_mask, tensor_layout=tensor_layout)
+    elif sage_attention == "sageattn_qk_int8_pv_fp16_cuda":
+        from sageattention import sageattn_qk_int8_pv_fp16_cuda
+        def sage_func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"):
+            return sageattn_qk_int8_pv_fp16_cuda(q, k, v, is_causal=is_causal, attn_mask=attn_mask, pv_accum_dtype="fp32", tensor_layout=tensor_layout)
+    elif sage_attention == "sageattn_qk_int8_pv_fp16_triton":
+        from sageattention import sageattn_qk_int8_pv_fp16_triton
+        def sage_func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"):
+            return sageattn_qk_int8_pv_fp16_triton(q, k, v, is_causal=is_causal, attn_mask=attn_mask, tensor_layout=tensor_layout)
+    elif sage_attention == "sageattn_qk_int8_pv_fp8_cuda":
+        from sageattention import sageattn_qk_int8_pv_fp8_cuda
+        def sage_func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"):
+            return sageattn_qk_int8_pv_fp8_cuda(q, k, v, is_causal=is_causal, attn_mask=attn_mask, pv_accum_dtype="fp32+fp32", tensor_layout=tensor_layout)
+    elif sage_attention == "sageattn_qk_int8_pv_fp8_cuda++":
+        from sageattention import sageattn_qk_int8_pv_fp8_cuda
+        def sage_func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"):
+            return sageattn_qk_int8_pv_fp8_cuda(q, k, v, is_causal=is_causal, attn_mask=attn_mask, pv_accum_dtype="fp32+fp16", tensor_layout=tensor_layout)
+    elif "sageattn3" in sage_attention:
+        from sageattn3 import sageattn3_blackwell
+        def sage_func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD", **kwargs):
+            q, k, v = [x.transpose(1, 2) if tensor_layout == "NHD" else x for x in (q, k, v)]
+            out = sageattn3_blackwell(q, k, v, is_causal=is_causal, attn_mask=attn_mask, per_block_mean=(sage_attention == "sageattn3_per_block_mean"))
+            return out.transpose(1, 2) if tensor_layout == "NHD" else out
+
+    if not allow_compile:
+        sage_func = torch.compiler.disable()(sage_func)
+
+    @wrap_attn
+    def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+        if kwargs.get("low_precision_attention", True) is False:
+            return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=skip_reshape, skip_output_reshape=skip_output_reshape, **kwargs)
+        in_dtype = v.dtype
+        if q.dtype == torch.float32 or k.dtype == torch.float32 or v.dtype == torch.float32:
+            q, k, v = q.to(torch.float16), k.to(torch.float16), v.to(torch.float16)
+        if skip_reshape:
+            b, _, _, dim_head = q.shape
+            tensor_layout="HND"
+        else:
+            b, _, dim_head = q.shape
+            dim_head //= heads
+            q, k, v = map(
+                lambda t: t.view(b, -1, heads, dim_head),
+                (q, k, v),
+            )
+            tensor_layout="NHD"
+        if mask is not None:
+            # add a batch dimension if there isn't already one
+            if mask.ndim == 2:
+                mask = mask.unsqueeze(0)
+            # add a heads dimension if there isn't already one
+            if mask.ndim == 3:
+                mask = mask.unsqueeze(1)
+        out = sage_func(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout).to(in_dtype)
+        if tensor_layout == "HND":
+            if not skip_output_reshape:
+                out = (
+                    out.transpose(1, 2).reshape(b, -1, heads * dim_head)
+                )
+        else:
+            if skip_output_reshape:
+                out = out.transpose(1, 2)
+            else:
+                out = out.reshape(b, -1, heads * dim_head)
+        return out
+    return attention_sage
+
+
+from comfy.patcher_extension import CallbacksMP
+class PathchSageAttentionKJ():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "model": ("MODEL",),
+            "sage_attention": (sageattn_modes, {"default": False, "tooltip": "Patch the attention of the model passing through this node to use sageattn. To revert, run this node again with the disabled option. Requires the sageattention library to be installed."}),
+        },
+        "optional": {
+            "allow_compile": ("BOOLEAN", {"default": False, "tooltip": "Allow the use of torch.compile for the sage attention function, requires latest sageattn 2.2.0 or higher."})
+            }
+        }
+
+    RETURN_TYPES = ("MODEL", )
+    FUNCTION = "patch"
+    DESCRIPTION = "Experimental node for patching attention mode. This doesn't use the model patching system and thus can't be disabled without running the node again with 'disabled' option."
+    EXPERIMENTAL = True
+    CATEGORY = "KJNodes/experimental"
+
+    def patch(self, model, sage_attention, allow_compile=False):
+        if sage_attention == "disabled":
+            return model,
+
+        model_clone = model.clone()
+
+        new_attention = get_sage_func(sage_attention, allow_compile=allow_compile)
+        def attention_override_sage(func, *args, **kwargs):
+            return new_attention.__wrapped__(*args, **kwargs)
+
+        # attention override
+        model_clone.model_options["transformer_options"]["optimized_attention_override"] = attention_override_sage
+
+        return model_clone,
+
+
+def get_flash_func(allow_compile=False, cast_dtype=torch.float16):
+    # Prefer FA2 (broad arch support, plain tensor return); fall back to FA3
+    # (flash_attn_interface), which has no dropout arg and returns (out, lse).
+    is_fa3 = False
+    try:
+        from flash_attn import flash_attn_func
+    except ImportError:
+        try:
+            from flash_attn_interface import flash_attn_func
+            is_fa3 = True
+        except ImportError:
+            raise ImportError(
+                "Flash attention not found. Install either FA2 ('flash_attn') or "
+                "FA3 ('flash_attn_interface', pip package 'flash-attn-3')."
+            )
+    logging.info(f"Using flash attention {'3' if is_fa3 else '2'}: cast_dtype={cast_dtype}")
+
+    # q, k, v in NHD layout (b, seq, heads, dim_head)
+    def flash_func(q, k, v):
+        if is_fa3:
+            out = flash_attn_func(q, k, v, causal=False)
+        else:
+            out = flash_attn_func(q, k, v, dropout_p=0.0, causal=False)
+        # FA3 returns (out, softmax_lse); FA2 returns the tensor directly
+        return out[0] if isinstance(out, tuple) else out
+
+    if not allow_compile:
+        flash_func = torch.compiler.disable()(flash_func)
+
+    if torch.cuda.is_available():
+        probe = torch.zeros(1, 8, 2, 64, dtype=cast_dtype, device="cuda")
+        flash_func(probe, probe, probe)
+
+    @wrap_attn
+    def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+        if mask is not None:
+            raise RuntimeError("Flash attention does not support attention masks")
+        in_dtype = v.dtype
+        # flash_attn only supports fp16/bf16
+        if q.dtype == torch.float32 or k.dtype == torch.float32 or v.dtype == torch.float32:
+            q, k, v = q.to(cast_dtype), k.to(cast_dtype), v.to(cast_dtype)
+        # flash_attn wants NHD layout (b, seq, heads, dim_head)
+        if skip_reshape:
+            # input is HND (b, heads, seq, dim_head)
+            b, _, _, dim_head = q.shape
+            q, k, v = map(lambda t: t.transpose(1, 2), (q, k, v))
+        else:
+            b, _, dim_head = q.shape
+            dim_head //= heads
+            q, k, v = map(lambda t: t.view(b, -1, heads, dim_head), (q, k, v))
+        out = flash_func(q, k, v).to(in_dtype)
+        if skip_output_reshape:
+            out = out.transpose(1, 2)  # NHD -> HND
+        else:
+            out = out.reshape(b, -1, heads * dim_head)
+        return out
+    return attention_flash
+
+
+class PatchFlashAttentionKJ():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "model": ("MODEL",),
+        },
+        "optional": {
+            "allow_compile": ("BOOLEAN", {"default": False, "tooltip": "Allow torch.compile to trace into the flash attention function. If disabled (default), the function is wrapped with torch.compiler.disable() for compatibility, matching the sage attention node."}),
+        }}
+
+    RETURN_TYPES = ("MODEL", )
+    FUNCTION = "patch"
+    DESCRIPTION = "Experimental node for patching attention to use flash attention, without the silent SDPA fallback the ComfyUI default does. Patches the attention of the model passing through this node; to disable, bypass or disconnect this node. Requires the flash_attn library to be installed."
+    EXPERIMENTAL = True
+    CATEGORY = "KJNodes/experimental"
+
+    def patch(self, model, allow_compile=False):
+        # match the model's compute dtype for the fp32 downcast, fall back to fp16
+        inference_dtype = model.model.get_dtype_inference() if hasattr(model.model, "get_dtype_inference") else torch.float16
+        cast_dtype = inference_dtype if inference_dtype in (torch.float16, torch.bfloat16) else torch.float16
+
+        new_attention = get_flash_func(
+            allow_compile=allow_compile,
+            cast_dtype=cast_dtype,
+        )
+
+        model_clone = model.clone()
+        def attention_override_flash(func, *args, **kwargs):
+            return new_attention.__wrapped__(*args, **kwargs)
+
+        # attention override
+        model_clone.model_options["transformer_options"]["optimized_attention_override"] = attention_override_flash
+
+        return model_clone,
+
+
+class CheckpointLoaderKJ():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "ckpt_name": (folder_paths.get_filename_list("checkpoints"), {"tooltip": "The name of the checkpoint (model) to load."}),
+            "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2", "fp16", "bf16", "fp32"],),
+            "compute_dtype": (["default", "fp16", "bf16", "fp32"], {"default": "default", "tooltip": "The compute dtype to use for the model."}),
+            "patch_cublaslinear": ("BOOLEAN", {"default": False, "tooltip": "Enable or disable the cublas_ops arg"}),
+            "sage_attention": (sageattn_modes, {"default": False, "tooltip": "Patch comfy attention to use sageattn."}),
+            "enable_fp16_accumulation": ("BOOLEAN", {"default": False, "tooltip": "Enable torch.backends.cuda.matmul.allow_fp16_accumulation, required minimum pytorch version 2.7.1"}),
+        }}
+
+    RETURN_TYPES = ("MODEL", "CLIP", "VAE")
+    FUNCTION = "load"
+    DESCRIPTION = "Experimental node for patching torch.nn.Linear with CublasLinear."
+    EXPERIMENTAL = True
+    CATEGORY = "KJNodes/model_loaders"
+
+    def load(self, ckpt_name, weight_dtype, compute_dtype, patch_cublaslinear, sage_attention, enable_fp16_accumulation):
+        DTYPE_MAP = {
+            "fp8_e4m3fn": torch.float8_e4m3fn,
+            "fp8_e5m2": torch.float8_e5m2,
+            "fp16": torch.float16,
+            "bf16": torch.bfloat16,
+            "fp32": torch.float32
+        }
+        model_options = {}
+        if dtype := DTYPE_MAP.get(weight_dtype):
+            model_options["dtype"] = dtype
+            logging.info(f"Setting {ckpt_name} weight dtype to {dtype}")
+
+        if weight_dtype == "fp8_e4m3fn_fast":
+            model_options["dtype"] = torch.float8_e4m3fn
+            model_options["fp8_optimizations"] = True
+
+        if patch_cublaslinear:
+            args.fast.add("cublas_ops")
+        else:
+            args.fast.discard("cublas_ops")
+
+        ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
+        model, clip, vae, _ = comfy.sd.load_checkpoint_guess_config(
+            ckpt_path,
+            output_vae=True,
+            output_clip=True,
+            embedding_directory=folder_paths.get_folder_paths("embeddings"),
+            model_options=model_options)
+
+        if dtype := DTYPE_MAP.get(compute_dtype):
+            model.set_model_compute_dtype(dtype)
+            model.force_cast_weights = False
+            logging.info(f"Setting {ckpt_name} compute dtype to {dtype}")
+
+        if enable_fp16_accumulation:
+            if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"):
+                torch.backends.cuda.matmul.allow_fp16_accumulation = True
+            else:
+                raise RuntimeError("Failed to set fp16 accumulation, requires pytorch version 2.7.1 or higher")
+        else:
+            if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"):
+                torch.backends.cuda.matmul.allow_fp16_accumulation = False
+
+        if sage_attention != "disabled":
+            new_attention = get_sage_func(sage_attention)
+            def attention_override_sage(func, *args, **kwargs):
+                return new_attention.__wrapped__(*args, **kwargs)
+
+            # attention override
+            model.model_options["transformer_options"]["optimized_attention_override"] = attention_override_sage
+
+        return model, clip, vae
+
+
+class DiffusionModelSelector():
+    @classmethod
+    def INPUT_TYPES(s):
+        ltx2_connector_models = folder_paths.get_filename_list("text_encoders")
+        ltx2_connector_models = [m for m in ltx2_connector_models if "connector" in m.lower()]
+        return {"required": {
+            "model_name": (folder_paths.get_filename_list("diffusion_models") + ltx2_connector_models, {"tooltip": "The name of the checkpoint (model) to load."}),
+        },
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("model_path",)
+    FUNCTION = "get_path"
+    DESCRIPTION = "Returns the path to the model as a string."
+    EXPERIMENTAL = True
+    CATEGORY = "KJNodes/model_loaders"
+
+    def get_path(self, model_name):
+        if "connector" in model_name.lower():
+            model_path = folder_paths.get_full_path_or_raise("text_encoders", model_name)
+        else:
+            model_path = folder_paths.get_full_path_or_raise("diffusion_models", model_name)
+        return (model_path,)
+
+def _load_diffusion_model_kj(unet_path, model_options=None, extra_state_dict=None, disable_dynamic=False):
+    model_options = {} if model_options is None else dict(model_options)
+
+    sd, metadata = comfy.utils.load_torch_file(unet_path, return_metadata=True)
+    if extra_state_dict is not None:
+        extra_sd = comfy.utils.load_torch_file(extra_state_dict)
+        sd.update(extra_sd)
+        del extra_sd
+
+        diffusion_model_prefix = comfy.sd.model_detection.unet_prefix_from_state_dict(sd)
+        sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=False)
+
+    model = comfy.sd.load_diffusion_model_state_dict(
+        sd,
+        model_options=model_options,
+        metadata=metadata,
+        disable_dynamic=disable_dynamic,
+    )
+
+    model.cached_patcher_init = (_load_diffusion_model_kj, (unet_path, model_options, extra_state_dict))
+    return model
+
+class DiffusionModelLoaderKJ():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "model_name": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "The name of the checkpoint (model) to load."}),
+            "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2", "fp16", "bf16", "fp32"],),
+            "compute_dtype": (["default", "fp16", "bf16", "fp32"], {"default": "default", "tooltip": "The compute dtype to use for the model."}),
+            "patch_cublaslinear": ("BOOLEAN", {"default": False, "tooltip": "Enable or disable the cublas_ops arg"}),
+            "sage_attention": (sageattn_modes, {"default": False, "tooltip": "Patch comfy attention to use sageattn."}),
+            "enable_fp16_accumulation": ("BOOLEAN", {"default": False, "tooltip": "Enable torch.backends.cuda.matmul.allow_fp16_accumulation, requires pytorch 2.7.0 nightly."}),
+        },
+        "optional": {
+            "extra_state_dict": ("STRING", {"forceInput": True, "tooltip": "The full path to an additional state dict to load, this will be merged with the main state dict. Useful for example to add VACE module to a WanVideoModel. You can use DiffusionModelSelector to easily get the path."}),
+        }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch_and_load"
+    DESCRIPTION = "Node for patching torch.nn.Linear with CublasLinear."
+    EXPERIMENTAL = True
+    CATEGORY = "KJNodes/model_loaders"
+
+    def patch_and_load(self, model_name, weight_dtype, compute_dtype, patch_cublaslinear, sage_attention, enable_fp16_accumulation, extra_state_dict=None):
+        DTYPE_MAP = {
+            "fp8_e4m3fn": torch.float8_e4m3fn,
+            "fp8_e5m2": torch.float8_e5m2,
+            "fp16": torch.float16,
+            "bf16": torch.bfloat16,
+            "fp32": torch.float32
+        }
+        model_options = {}
+        if dtype := DTYPE_MAP.get(weight_dtype):
+            model_options["dtype"] = dtype
+            logging.info(f"Setting {model_name} weight dtype to {dtype}")
+
+        if weight_dtype == "fp8_e4m3fn_fast":
+            model_options["dtype"] = torch.float8_e4m3fn
+            model_options["fp8_optimizations"] = True
+
+        if enable_fp16_accumulation:
+            if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"):
+                torch.backends.cuda.matmul.allow_fp16_accumulation = True
+            else:
+                raise RuntimeError("Failed to set fp16 accumulation, this requires pytorch 2.7.1 or higher")
+        else:
+            if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"):
+                torch.backends.cuda.matmul.allow_fp16_accumulation = False
+
+        if patch_cublaslinear:
+            args.fast.add("cublas_ops")
+        else:
+            args.fast.discard("cublas_ops")
+
+        unet_path = folder_paths.get_full_path_or_raise("diffusion_models", model_name)
+
+        model = _load_diffusion_model_kj(unet_path, model_options=model_options, extra_state_dict=extra_state_dict)
+        if dtype := DTYPE_MAP.get(compute_dtype):
+            model.set_model_compute_dtype(dtype)
+            model.force_cast_weights = False
+            logging.info(f"Setting {model_name} compute dtype to {dtype}")
+
+        if sage_attention != "disabled":
+            new_attention = get_sage_func(sage_attention)
+            def attention_override_sage(func, *args, **kwargs):
+                return new_attention.__wrapped__(*args, **kwargs)
+
+            # attention override
+            model.model_options["transformer_options"]["optimized_attention_override"] = attention_override_sage
+
+        return (model,)
+
+class ModelPatchTorchSettings:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "model": ("MODEL",),
+            "enable_fp16_accumulation": ("BOOLEAN", {"default": False, "tooltip": "Enable torch.backends.cuda.matmul.allow_fp16_accumulation, requires pytorch 2.7.0 nightly."}),
+        }}
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+    DESCRIPTION = "Adds callbacks to model to set torch settings before and after running the model."
+    EXPERIMENTAL = True
+    CATEGORY = "KJNodes/experimental"
+
+    def patch(self, model, enable_fp16_accumulation):        
+        model_clone = model.clone()
+
+        def patch_enable_fp16_accum(model):
+            logging.info("Patching torch settings: torch.backends.cuda.matmul.allow_fp16_accumulation = True")
+            torch.backends.cuda.matmul.allow_fp16_accumulation = True
+        def patch_disable_fp16_accum(model):
+            logging.info("Patching torch settings: torch.backends.cuda.matmul.allow_fp16_accumulation = False")
+            torch.backends.cuda.matmul.allow_fp16_accumulation = False
+
+        if enable_fp16_accumulation:
+            if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"):
+                model_clone.add_callback(CallbacksMP.ON_PRE_RUN, patch_enable_fp16_accum)
+                model_clone.add_callback(CallbacksMP.ON_CLEANUP, patch_disable_fp16_accum)
+            else:
+                raise RuntimeError("Failed to set fp16 accumulation, this requires pytorch 2.7.1 or higher")
+        else:
+            if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"):
+                model_clone.add_callback(CallbacksMP.ON_PRE_RUN, patch_disable_fp16_accum)
+            else:
+                raise RuntimeError("Failed to set fp16 accumulation, this requires pytorch 2.7.1 or higher")
+
+        return (model_clone,)
+
+
+class PatchModelPatcherOrder:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "model": ("MODEL",),
+                    "patch_order": (["object_patch_first", "weight_patch_first"], {"default": "weight_patch_first", "tooltip": "Patch the comfy patch_model function to load weight patches (LoRAs) before compiling the model"}),
+                    "full_load": (["enabled", "disabled", "auto"], {"default": "auto", "tooltip": "Disabling may help with memory issues when loading large models, when changing this you should probably force model reload to avoid issues!"}),
+                }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+    CATEGORY = "KJNodes/deprecated"
+    DESCRIPTION = "NO LONGER NECESSARY OR FUNCTIONAL, keeping node for backwards compatibility. Use the TorchCompileModelAdvanced to use LoRA with torch.compile."
+    DEPRECATED = True
+
+    def patch(self, model, patch_order, full_load):
+        return model,
+
+
+class TorchCompileModelFluxAdvancedV2:
+    def __init__(self):
+        self._compiled = False
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "model": ("MODEL",),
+                    "backend": (["inductor", "cudagraphs"],),
+                    "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}),
+                    "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}),
+                    "double_blocks": ("BOOLEAN", {"default": True, "tooltip": "Compile double blocks"}),
+                    "single_blocks": ("BOOLEAN", {"default": True, "tooltip": "Compile single blocks"}),
+                    "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}),
+                },
+                "optional": {
+                    "dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}),
+                    "force_parameter_static_shapes": ("BOOLEAN", {"default": True, "tooltip": "torch._dynamo.config.force_parameter_static_shapes"}),
+                }
+                }
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "KJNodes/torchcompile"
+    EXPERIMENTAL = True
+    DEPRECATED = True
+    DESCRIPTION = "Deprecated, use TorchCompileModelAdvanced instead."
+
+    def patch(self, model, backend, mode, fullgraph, single_blocks, double_blocks, dynamic, dynamo_cache_size_limit=64, force_parameter_static_shapes=True):
+        from comfy_api.torch_helpers import set_torch_compile_wrapper
+        m = model.clone()
+        diffusion_model = m.get_model_object("diffusion_model")
+        torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit
+        torch._dynamo.config.force_parameter_static_shapes = force_parameter_static_shapes
+
+        compile_key_list = []
+
+        try:
+            if double_blocks:
+                for i, block in enumerate(diffusion_model.double_blocks):
+                    print("Adding double block to compile list", i)
+                    compile_key_list.append(f"diffusion_model.double_blocks.{i}")
+            if single_blocks:
+                for i, block in enumerate(diffusion_model.single_blocks):
+                    compile_key_list.append(f"diffusion_model.single_blocks.{i}")
+
+            set_torch_compile_wrapper(model=m, keys=compile_key_list, backend=backend, mode=mode, dynamic=dynamic, fullgraph=fullgraph)           
+        except Exception as e:
+            raise RuntimeError("Failed to compile model") from e
+
+        return (m, )
+
+
+class TorchCompileModelWanVideoV2:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "backend": (["inductor","cudagraphs"], {"default": "inductor"}),
+                "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}),
+                "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}),
+                "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}),
+                "compile_transformer_blocks_only": ("BOOLEAN", {"default": True, "tooltip": "Compile only transformer blocks, faster compile and less error prone"}),
+                "dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}),
+
+            },
+            "optional": {
+                "force_parameter_static_shapes": ("BOOLEAN", {"default": True, "tooltip": "torch._dynamo.config.force_parameter_static_shapes"}),
+            },
+        }
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "KJNodes/torchcompile"
+    EXPERIMENTAL = True
+    DEPRECATED = True
+    DESCRIPTION = "Deprecated, use TorchCompileModelAdvanced instead."
+
+    def patch(self, model, backend, fullgraph, mode, dynamic, dynamo_cache_size_limit, compile_transformer_blocks_only, force_parameter_static_shapes=True):
+        from comfy_api.torch_helpers import set_torch_compile_wrapper
+        m = model.clone()
+        diffusion_model = m.get_model_object("diffusion_model")
+        torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit
+        torch._dynamo.config.force_parameter_static_shapes = force_parameter_static_shapes
+        try:
+            if compile_transformer_blocks_only:
+                compile_key_list = []
+                for i, block in enumerate(diffusion_model.blocks):
+                    compile_key_list.append(f"diffusion_model.blocks.{i}")
+            else:
+                compile_key_list =["diffusion_model"]
+
+            set_torch_compile_wrapper(model=m, keys=compile_key_list, backend=backend, mode=mode, dynamic=dynamic, fullgraph=fullgraph)           
+        except Exception as e:
+            raise RuntimeError("Failed to compile model") from e
+
+        return (m, )
+
+
+_aimdo_patched = False
+
+def patch_aimdo_for_compile():
+    # reduce recompiles with dynamic VRAM
+    global _aimdo_patched
+    if _aimdo_patched:
+        return
+    _aimdo_patched = True
+    names = ("cast_bias_weight", "uncast_bias_weight", "cast_modules_with_vbar", "resolve_cast_module_with_vbar")
+    for name in names:
+        fn = getattr(comfy.ops, name, None)
+        if fn is not None:
+            setattr(comfy.ops, name, torch._dynamo.disable(fn))
+    try:
+        import comfy_aimdo.torch as _at
+        _at.get_tensor_from_raw_ptr = torch._dynamo.disable(_at.get_tensor_from_raw_ptr)
+    except Exception:
+        pass
+    logging.info("KJNodes dynamic-compile: comfy.ops weight cast marked as eager graph break (recompile fix active).")
+
+
+def skip_torch_compile_dict(guard_entries):
+    # don't recompile when transformer_options change
+    return [("transformer_options" not in entry.name) for entry in guard_entries]
+
+
+def build_compile_kwargs(backend, mode, fullgraph, dynamic, use_guard_filter=True):
+    # torch.compile forbids passing mode and options together; an explicit mode wins,
+    # otherwise attach the guard filter via options on the default mode.
+    kw = {"backend": backend, "fullgraph": fullgraph, "dynamic": dynamic}
+    if mode and mode != "default":
+        kw["mode"] = mode
+    elif use_guard_filter:
+        kw["options"] = {"guard_filter_fn": skip_torch_compile_dict}
+    return kw
+
+import weakref as _kj_weakref
+
+_KJ_COMPILE_KEY = "torch.compile"
+_KJ_COMPILED_BY_MODEL = _kj_weakref.WeakKeyDictionary()  # BaseModel instance -> {key: compiled_module}
+
+# resolve compiled modules by the BaseModel actually executing
+def _kj_apply_torch_compile_wrapper(executor, *args, **kwargs):
+    compiled = _KJ_COMPILED_BY_MODEL.get(executor.class_obj)
+    if not compiled:
+        return executor(*args, **kwargs)  # this BaseModel wasn't compiled -> run eager, no swap
+    orig = {}
+    try:
+        for key, value in compiled.items():
+            orig[key] = comfy.utils.get_attr(executor.class_obj, key)
+            comfy.utils.set_attr(executor.class_obj, key, value)
+        return executor(*args, **kwargs)
+    finally:
+        for key, value in orig.items():
+            comfy.utils.set_attr(executor.class_obj, key, value)
+
+
+def kj_set_torch_compile_wrapper(model, backend, options=None, mode=None, fullgraph=False, dynamic=None, keys=("diffusion_model",)):
+    WrappersMP = comfy.patcher_extension.WrappersMP
+    model.remove_wrappers_with_key(WrappersMP.APPLY_MODEL, _KJ_COMPILE_KEY)
+    if not keys:
+        keys = ["diffusion_model"]
+    compile_kwargs = {"backend": backend, "options": options, "mode": mode, "fullgraph": fullgraph, "dynamic": dynamic}
+    compiled_modules = {key: torch.compile(model=model.get_model_object(key), **compile_kwargs) for key in keys}
+    _KJ_COMPILED_BY_MODEL[model.model] = compiled_modules           # register by the BaseModel that will execute
+    model.add_wrapper_with_key(WrappersMP.APPLY_MODEL, _KJ_COMPILE_KEY, _kj_apply_torch_compile_wrapper)
+    model.model_options["torch_compile_kwargs"] = compile_kwargs
+
+
+class TorchCompileModelAdvanced:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "backend": (["inductor","cudagraphs"], {"default": "inductor"}),
+                "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}),
+                "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}),
+                "dynamic": (
+                    ["auto", "true", "false"],
+                    {"default": "auto", "tooltip": "Use dynamic shape tracing."},
+                ),
+                "compile_transformer_blocks_only": ("BOOLEAN", {"default": True, "tooltip": "Compile only transformer blocks, faster compile and less error prone"}),
+                "dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}),
+                "debug_compile_keys": ("BOOLEAN", {"default": False, "tooltip": "Print the compile keys used for torch.compile"}),
+            },
+            "optional": {
+                "disable_dynamic_vram": ("BOOLEAN", {"default": False, "tooltip": "Disable dynamic VRAM feature as it can cause issues with compile"}),
+            }
+        }
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+    CATEGORY = "KJNodes/torchcompile"
+    DESCRIPTION = "Advanced torch.compile patching for diffusion models."
+    EXPERIMENTAL = True
+
+    def patch(self, model, backend, fullgraph, mode, dynamic, dynamo_cache_size_limit, compile_transformer_blocks_only, debug_compile_keys, disable_dynamic_vram=False):
+        if disable_dynamic_vram:
+            try:
+                m = model.clone(disable_dynamic=True)
+            except TypeError:
+                logging.warning("This ComfyUI version do not support disabling dynamic VRAM through a node. This may cause issues with torch.compile.")
+                m = model.clone()
+        else:
+            m = model.clone()
+
+        diffusion_model = m.get_model_object("diffusion_model")
+        torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit
+
+        try:
+            compile_key_list = []
+            if compile_transformer_blocks_only:
+                layer_types = ["double_blocks", "single_blocks", "layers", "transformer_blocks", "blocks", "visual_transformer_blocks", "text_transformer_blocks", "patch_blocks", "pixel_blocks"]
+                for layer_name in layer_types:
+                    if hasattr(diffusion_model, layer_name):
+                        blocks = getattr(diffusion_model, layer_name)
+                        for i in range(len(blocks)):
+                            compile_key_list.append(f"diffusion_model.{layer_name}.{i}")
+                if not compile_key_list:
+                    logging.warning("No known transformer blocks found to compile, compiling entire diffusion model instead")
+                elif debug_compile_keys:
+                    logging.info("TorchCompileModelAdvanced: Compile key list:")
+                    for key in compile_key_list:
+                        logging.info(f" - {key}")
+            if not compile_key_list:
+                compile_key_list =["diffusion_model"]
+
+            dynamic_kv = {"true": True, "false": False, "auto": None}
+            try:
+                dynamic = dynamic_kv[dynamic]
+            except KeyError:
+                raise ValueError(f"Invalid dynamic arg {dynamic}")
+
+            if not disable_dynamic_vram and getattr(m, "is_dynamic", lambda: False)():
+                patch_aimdo_for_compile() # reduce recompiles with dynamic VRAM, will break the graph still but better than nothing
+            kj_set_torch_compile_wrapper(model=m, keys=compile_key_list, **build_compile_kwargs(backend, mode, fullgraph, dynamic))
+        except Exception as e:
+            raise RuntimeError("Failed to compile model") from e
+
+        return (m, )
+
+
+class TorchCompileModelQwenImage:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "backend": (["inductor","cudagraphs"], {"default": "inductor"}),
+                "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}),
+                "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}),
+                "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}),
+                "compile_transformer_blocks_only": ("BOOLEAN", {"default": True, "tooltip": "Compile only transformer blocks, faster compile and less error prone"}),
+                "dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}),
+            },
+        }
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "KJNodes/torchcompile"
+    EXPERIMENTAL = True
+    DEPRECATED = True
+    DESCRIPTION = "Deprecated, use TorchCompileModelAdvanced instead."
+
+    def patch(self, model, backend, fullgraph, mode, dynamic, dynamo_cache_size_limit, compile_transformer_blocks_only):
+        from comfy_api.torch_helpers import set_torch_compile_wrapper
+        m = model.clone()
+        diffusion_model = m.get_model_object("diffusion_model")
+        torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit
+        try:
+            if compile_transformer_blocks_only:
+                compile_key_list = []
+                for i, block in enumerate(diffusion_model.transformer_blocks):
+                    compile_key_list.append(f"diffusion_model.transformer_blocks.{i}")
+            else:
+                compile_key_list =["diffusion_model"]
+
+            set_torch_compile_wrapper(model=m, keys=compile_key_list, backend=backend, mode=mode, dynamic=dynamic, fullgraph=fullgraph)
+        except Exception as e:
+            raise RuntimeError("Failed to compile model") from e
+
+        return (m, )
+
+class TorchCompileVAE:
+    def __init__(self):
+        self._compiled_encoder = False
+        self._compiled_decoder = False
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "vae": ("VAE",),
+                    "backend": (["inductor", "cudagraphs"],),
+                    "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}),
+                    "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}),
+                    "compile_encoder": ("BOOLEAN", {"default": True, "tooltip": "Compile encoder"}),
+                    "compile_decoder": ("BOOLEAN", {"default": True, "tooltip": "Compile decoder"}),
+                }}
+    RETURN_TYPES = ("VAE",)
+    FUNCTION = "compile"
+
+    CATEGORY = "KJNodes/torchcompile"
+    EXPERIMENTAL = True
+
+    def compile(self, vae, backend, mode, fullgraph, compile_encoder, compile_decoder):
+        if compile_encoder:
+            if not self._compiled_encoder:
+                encoder_name = "encoder"
+                if hasattr(vae.first_stage_model, "taesd_encoder"):
+                    encoder_name = "taesd_encoder"
+
+                try:
+                    setattr(
+                        vae.first_stage_model,
+                        encoder_name,
+                        torch.compile(
+                            getattr(vae.first_stage_model, encoder_name),
+                            mode=mode,
+                            fullgraph=fullgraph,
+                            backend=backend,
+                        ),
+                    )
+                    self._compiled_encoder = True
+                except Exception as e:
+                    raise RuntimeError("Failed to compile model") from e
+        if compile_decoder:
+            if not self._compiled_decoder:
+                decoder_name = "decoder"
+                if hasattr(vae.first_stage_model, "taesd_decoder"):
+                    decoder_name = "taesd_decoder"
+
+                try:
+                    setattr(
+                        vae.first_stage_model,
+                        decoder_name,
+                        torch.compile(
+                            getattr(vae.first_stage_model, decoder_name),
+                            mode=mode,
+                            fullgraph=fullgraph,
+                            backend=backend,
+                        ),
+                    )
+                    self._compiled_decoder = True
+                except Exception as e:
+                    raise RuntimeError("Failed to compile model") from e
+        return (vae, )
+
+class TorchCompileControlNet:
+    def __init__(self):
+        self._compiled= False
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "controlnet": ("CONTROL_NET",),
+                    "backend": (["inductor", "cudagraphs"],),
+                    "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}),
+                    "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}),
+                }}
+    RETURN_TYPES = ("CONTROL_NET",)
+    FUNCTION = "compile"
+
+    CATEGORY = "KJNodes/torchcompile"
+    EXPERIMENTAL = True
+
+    def compile(self, controlnet, backend, mode, fullgraph):
+        if not self._compiled:
+            try:
+                # for i, block in enumerate(controlnet.control_model.double_blocks):
+                #     print("Compiling controlnet double_block", i)
+                #     controlnet.control_model.double_blocks[i] = torch.compile(block, mode=mode, fullgraph=fullgraph, backend=backend)
+                controlnet.control_model = torch.compile(controlnet.control_model, mode=mode, fullgraph=fullgraph, backend=backend)
+                self._compiled = True
+            except Exception as e:
+                self._compiled = False
+                raise RuntimeError("Failed to compile model") from e
+
+        return (controlnet, )
+
+class WanVideoTeaCacheKJ:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "rel_l1_thresh": ("FLOAT", {"default": 0.275, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Threshold for to determine when to apply the cache, compromise between speed and accuracy. When using coefficients a good value range is something between 0.2-0.4 for all but 1.3B model, which should be about 10 times smaller, same as when not using coefficients."}),
+                "start_percent": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The start percentage of the steps to use with TeaCache."}),
+                "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The end percentage of the steps to use with TeaCache."}),
+                "cache_device": (["main_device", "offload_device"], {"default": "offload_device", "tooltip": "Device to cache to"}),
+                "coefficients": (["disabled", "1.3B", "14B", "i2v_480", "i2v_720"], {"default": "i2v_480", "tooltip": "Coefficients for rescaling the relative l1 distance, if disabled the threshold value should be about 10 times smaller than the value used with coefficients."}),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    RETURN_NAMES = ("model",)
+    FUNCTION = "patch_teacache"
+    CATEGORY = "KJNodes/deprecated"
+    DEPRECATED = True
+    DESCRIPTION = """DEPRECATED, use the native EasyCache or alternative custom node that's up to date instead of this."""
+    EXPERIMENTAL = True
+
+    def patch_teacache(self, model, rel_l1_thresh, start_percent, end_percent, cache_device, coefficients):
+        return model,
+
+
+from comfy.ldm.flux.math import apply_rope
+
+def modified_wan_self_attention_forward(self, x, freqs, transformer_options={}):
+    r"""
+    Args:
+        x(Tensor): Shape [B, L, num_heads, C / num_heads]
+        freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
+    """
+    b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
+
+    # query, key, value function
+    def qkv_fn(x):
+        q = self.norm_q(self.q(x)).view(b, s, n, d)
+        k = self.norm_k(self.k(x)).view(b, s, n, d)
+        v = self.v(x).view(b, s, n * d)
+        return q, k, v
+
+    q, k, v = qkv_fn(x)
+    q, k = apply_rope(q, k, freqs)
+    feta_scores = get_feta_scores(q, k, self.num_frames, self.enhance_weight)
+
+    x = comfy.ldm.modules.attention.optimized_attention(
+            q.view(b, s, n * d),
+            k.view(b, s, n * d),
+            v,
+            heads=self.num_heads,
+            transformer_options=transformer_options,
+        )
+
+    x = self.o(x)
+
+    x *= feta_scores
+
+    return x
+
+from einops import rearrange
+def get_feta_scores(query, key, num_frames, enhance_weight, num_heads=12):
+    img_q, img_k = query, key #torch.Size([2, 9216, 12, 128])
+
+    if img_q.ndim == 4:
+        B, ST, num_heads, head_dim = img_q.shape
+    elif img_q.ndim == 3:
+        B, ST, hidden_dim = img_q.shape
+        head_dim = hidden_dim // num_heads
+
+        # Reshape from [B, ST, hidden_dim] to [B, ST, num_heads, head_dim]
+        img_q = img_q.view(B, ST, num_heads, head_dim)
+        img_k = img_k.view(B, ST, num_heads, head_dim)
+
+    spatial_dim = ST // num_frames
+
+    query_image = rearrange(
+        img_q, "B (T S) N C -> (B S) N T C", T=num_frames, S=spatial_dim, N=num_heads, C=head_dim
+    )
+    key_image = rearrange(
+        img_k, "B (T S) N C -> (B S) N T C", T=num_frames, S=spatial_dim, N=num_heads, C=head_dim
+    )
+
+    return feta_score(query_image, key_image, head_dim, num_frames, enhance_weight)
+
+def feta_score(query_image, key_image, head_dim, num_frames, enhance_weight):
+    scale = head_dim**-0.5
+    query_image = query_image * scale
+    attn_temp = query_image @ key_image.transpose(-2, -1)  # translate attn to float32
+    attn_temp = attn_temp.to(torch.float32)
+    attn_temp = attn_temp.softmax(dim=-1)
+
+    # Reshape to [batch_size * num_tokens, num_frames, num_frames]
+    attn_temp = attn_temp.reshape(-1, num_frames, num_frames)
+
+    # Create a mask for diagonal elements
+    diag_mask = torch.eye(num_frames, device=attn_temp.device).bool()
+    diag_mask = diag_mask.unsqueeze(0).expand(attn_temp.shape[0], -1, -1)
+
+    # Zero out diagonal elements
+    attn_wo_diag = attn_temp.masked_fill(diag_mask, 0)
+
+    # Calculate mean for each token's attention matrix
+    # Number of off-diagonal elements per matrix is n*n - n
+    num_off_diag = num_frames * num_frames - num_frames
+    mean_scores = attn_wo_diag.sum(dim=(1, 2)) / num_off_diag
+
+    enhance_scores = mean_scores.mean() * (num_frames + enhance_weight)
+    enhance_scores = enhance_scores.clamp(min=1)
+    return enhance_scores
+
+import types
+class WanAttentionPatch:
+    def __init__(self, num_frames, weight):
+        self.num_frames = num_frames
+        self.enhance_weight = weight
+
+    def __get__(self, obj, objtype=None):
+        # Create bound method with stored parameters
+        def wrapped_attention(self_module, *args, **kwargs):
+            self_module.num_frames = self.num_frames
+            self_module.enhance_weight = self.enhance_weight
+            return modified_wan_self_attention_forward(self_module, *args, **kwargs)
+        return types.MethodType(wrapped_attention, obj)
+
+class WanVideoEnhanceAVideoKJ:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "latent": ("LATENT", {"tooltip": "Only used to get the latent count"}),
+                "weight": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Strength of the enhance effect"}),
+           }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    RETURN_NAMES = ("model",)
+    FUNCTION = "enhance"
+    CATEGORY = "KJNodes/wan"
+    DESCRIPTION = "https://github.com/NUS-HPC-AI-Lab/Enhance-A-Video"
+    EXPERIMENTAL = True
+
+    def enhance(self, model, weight, latent):
+        if weight == 0:
+            return (model,)
+
+        num_frames = latent["samples"].shape[2]
+
+        model_clone = model.clone()
+        if 'transformer_options' not in model_clone.model_options:
+            model_clone.model_options['transformer_options'] = {}
+        model_clone.model_options["transformer_options"]["enhance_weight"] = weight
+        diffusion_model = model_clone.get_model_object("diffusion_model")
+
+        compile_settings = getattr(model.model, "compile_settings", None)
+        for idx, block in enumerate(diffusion_model.blocks):
+            patched_attn = WanAttentionPatch(num_frames, weight).__get__(block.self_attn, block.__class__)
+            if compile_settings is not None:
+                patched_attn = torch.compile(patched_attn, mode=compile_settings["mode"], dynamic=compile_settings["dynamic"], fullgraph=compile_settings["fullgraph"], backend=compile_settings["backend"])
+
+            model_clone.add_object_patch(f"diffusion_model.blocks.{idx}.self_attn.forward", patched_attn)
+
+        return (model_clone,)
+
+try:
+    from comfy.ldm.lightricks.model import apply_rotary_emb
+except ImportError:
+    apply_rotary_emb = None
+
+try:
+    from comfy.ldm.lightricks.model import GuideAttentionMask as _GuideAttentionMask, _attention_with_guide_mask as _ltx_attn_with_guide_mask
+except ImportError:
+    _GuideAttentionMask = None
+    _ltx_attn_with_guide_mask = None
+
+
+def ltxv_feta_forward(self, x, context=None, mask=None, pe=None, k_pe=None, transformer_options={}):
+    q = self.to_q(x)
+    context = x if context is None else context
+    k = self.to_k(context)
+    v = self.to_v(context)
+
+    q = self.q_norm(q)
+    k = self.k_norm(k)
+
+    if pe is not None:
+        q = apply_rotary_emb(q, pe)
+        k = apply_rotary_emb(k, pe if k_pe is None else k_pe)
+
+    feta_scores = get_feta_scores(q, k, self.num_frames, self.enhance_weight, self.heads)
+
+    if mask is None:
+        out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options)
+    elif _GuideAttentionMask is not None and isinstance(mask, _GuideAttentionMask):
+        out = _ltx_attn_with_guide_mask(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options)
+    else:
+        out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options)
+
+    if self.to_gate_logits is not None:
+        gate_logits = self.to_gate_logits(x)  # (B, T, H)
+        b, t, _ = out.shape
+        out = out.view(b, t, self.heads, self.dim_head)
+        gates = 2.0 * torch.sigmoid(gate_logits)  # zero-init -> identity
+        out = out * gates.unsqueeze(-1)
+        out = out.view(b, t, self.heads * self.dim_head)
+
+    return self.to_out(out) * feta_scores
+
+
+class LTXCrossAttentionPatch:
+    def __init__(self, num_frames, weight):
+        self.num_frames = num_frames
+        self.enhance_weight = weight
+
+    def __get__(self, obj, objtype=None):
+        # Create bound method with stored parameters
+        def wrapped_attention(self_module, *args, **kwargs):
+            self_module.num_frames = self.num_frames
+            self_module.enhance_weight = self.enhance_weight
+            return ltxv_feta_forward(self_module, *args, **kwargs)
+        return types.MethodType(wrapped_attention, obj)
+
+class LTXVEnhanceAVideoKJ:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "latent": ("LATENT", {"tooltip": "Only used to get the latent count"}),
+                "weight": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 100.0, "step": 0.001, "tooltip": "Strength of the enhance effect"}),
+           }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    RETURN_NAMES = ("model",)
+    FUNCTION = "enhance"
+    CATEGORY = "KJNodes/ltxv"
+    DESCRIPTION = "https://github.com/NUS-HPC-AI-Lab/Enhance-A-Video"
+    EXPERIMENTAL = True
+
+    def enhance(self, model, weight, latent):
+        if weight == 0:
+            return (model,)
+
+        num_frames = latent["samples"].shape[2]
+
+        model_clone = model.clone()
+        if 'transformer_options' not in model_clone.model_options:
+            model_clone.model_options['transformer_options'] = {}
+        model_clone.model_options["transformer_options"]["enhance_weight"] = weight
+        diffusion_model = model_clone.get_model_object("diffusion_model")
+
+        for idx, block in enumerate(diffusion_model.transformer_blocks):
+            patched_attn1 = LTXCrossAttentionPatch(num_frames, weight).__get__(block.attn1, block.__class__)
+            model_clone.add_object_patch(f"diffusion_model.transformer_blocks.{idx}.attn1.forward", patched_attn1)
+        return (model_clone,)
+
+def _wan_compute_attention(self, query, context, transformer_options={}):
+    k = self.norm_k(self.k(context))
+    v = self.v(context)
+    return comfy.ldm.modules.attention.optimized_attention(query, k, v, heads=self.num_heads, transformer_options=transformer_options).flatten(2)
+
+def wan_nag_attention(self, query, context_positive, nag_context, transformer_options={}):
+    x_positive = _wan_compute_attention(self, query, context_positive, transformer_options)
+    x_negative = _wan_compute_attention(self, query, nag_context, transformer_options)
+    return x_positive, x_negative
+
+def normalized_attention_guidance(self, x_positive, x_negative):
+    if self.inplace:
+        nag_guidance = x_negative.mul_(self.nag_scale - 1).neg_().add_(x_positive, alpha=self.nag_scale)
+        del x_negative
+    else:
+        nag_guidance = x_negative * (self.nag_scale - 1)
+        del x_negative
+        nag_guidance = (x_positive * self.nag_scale).sub_(nag_guidance)
+
+    norm_positive = torch.norm(x_positive, p=1, dim=-1, keepdim=True)
+    norm_guidance = torch.norm(nag_guidance, p=1, dim=-1, keepdim=True)
+
+    scale = norm_guidance / norm_positive
+    torch.nan_to_num_(scale, nan=10.0)
+    mask = scale > self.nag_tau
+    del scale
+
+    adjustment = (norm_positive * self.nag_tau) / (norm_guidance + 1e-7)
+    del norm_positive, norm_guidance
+
+    nag_guidance.mul_(torch.where(mask, adjustment, 1.0))
+    del mask, adjustment
+
+    if self.inplace:
+        return nag_guidance.sub_(x_positive).mul_(self.nag_alpha).add_(x_positive)
+    else:
+        nag_guidance.mul_(self.nag_alpha)
+        return nag_guidance.add_(x_positive * (1 - self.nag_alpha))
+
+#region NAG
+def wan_crossattn_forward_nag(self, x, context, transformer_options={}, **kwargs):
+    # Determine batch splitting and context handling
+    if self.input_type == "default":
+        # Single or [pos, neg] pair
+        if context.shape[0] == 1:
+            x_pos, context_pos = x, context
+            x_neg, context_neg = None, None
+        else:
+            x_pos, x_neg = torch.chunk(x, 2, dim=0)
+            context_pos, context_neg = torch.chunk(context, 2, dim=0)
+    elif self.input_type == "batch":
+        # Standard batch, no CFG
+        x_pos, context_pos = x, context
+        x_neg, context_neg = None, None
+
+    # Positive branch
+    q_pos = self.norm_q(self.q(x_pos))
+    nag_context = self.nag_context
+    if self.input_type == "batch":
+        nag_context = nag_context.repeat(x_pos.shape[0], 1, 1)
+    del x_pos
+
+    x_positive, x_negative = wan_nag_attention(self, q_pos, context_pos, nag_context, transformer_options=transformer_options)
+    del context_pos, q_pos
+
+    x_pos_out = normalized_attention_guidance(self, x_positive, x_negative)
+    del x_positive, x_negative
+
+    # Negative branch
+    if x_neg is not None and context_neg is not None:
+        q_neg = self.norm_q(self.q(x_neg))
+        k_neg = self.norm_k(self.k(context_neg))
+        v_neg = self.v(context_neg)
+        x_neg_out = comfy.ldm.modules.attention.optimized_attention(q_neg, k_neg, v_neg, heads=self.num_heads, transformer_options=transformer_options)
+        x = torch.cat([x_pos_out, x_neg_out], dim=0)
+    else:
+        x = x_pos_out
+
+    return self.o(x)
+
+def wan_i2v_crossattn_forward_nag(self, x, context, context_img_len, transformer_options={}, **kwargs):
+    context_img = context[:, :context_img_len]
+    context = context[:, context_img_len:]
+
+    q_img = self.norm_q(self.q(x))
+    k_img = self.norm_k_img(self.k_img(context_img))
+    v_img = self.v_img(context_img)
+    img_x = comfy.ldm.modules.attention.optimized_attention(q_img, k_img, v_img, heads=self.num_heads, transformer_options=transformer_options)
+    del q_img, k_img, v_img, context_img
+
+    if context.shape[0] == 2:
+        x, x_real_negative = torch.chunk(x, 2, dim=0)
+        context_positive, context_negative = torch.chunk(context, 2, dim=0)
+    else:
+        context_positive = context
+        context_negative = None
+
+    q = self.norm_q(self.q(x))
+
+    x_positive, x_negative = wan_nag_attention(self, q, context_positive, self.nag_context, transformer_options=transformer_options)
+    del q, context_positive
+    x = normalized_attention_guidance(self, x_positive, x_negative)
+    del x_positive, x_negative
+
+    if context_negative is not None:
+        q_real_negative = self.norm_q(self.q(x_real_negative))
+        k_real_negative = self.norm_k(self.k(context_negative))
+        v_real_negative = self.v(context_negative)
+        x_real_negative = comfy.ldm.modules.attention.optimized_attention(q_real_negative, k_real_negative, v_real_negative, heads=self.num_heads, transformer_options=transformer_options)
+        x = torch.cat([x, x_real_negative], dim=0)
+
+    return self.o(x + img_x)
+
+
+class WanCrossAttentionPatch:
+    def __init__(self, context, nag_scale, nag_alpha, nag_tau, i2v=False, input_type="default", inplace=True):
+        self.nag_context = context
+        self.nag_scale = nag_scale
+        self.nag_alpha = nag_alpha
+        self.nag_tau = nag_tau
+        self.i2v = i2v
+        self.input_type = input_type
+        self.inplace = inplace
+    def __get__(self, obj, objtype=None):
+        # Create bound method with stored parameters
+        def wrapped_attention(self_module, *args, **kwargs):
+            self_module.nag_context = self.nag_context
+            self_module.nag_scale = self.nag_scale
+            self_module.nag_alpha = self.nag_alpha
+            self_module.nag_tau = self.nag_tau
+            self_module.input_type = self.input_type
+            self_module.inplace = self.inplace
+            if self.i2v:
+                return wan_i2v_crossattn_forward_nag(self_module, *args, **kwargs)
+            else:
+                return wan_crossattn_forward_nag(self_module, *args, **kwargs)
+        return types.MethodType(wrapped_attention, obj)
+
+
+class WanVideoNAG:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "conditioning": ("CONDITIONING",),
+                "nag_scale": ("FLOAT", {"default": 11.0, "min": 0.0, "max": 100.0, "step": 0.001, "tooltip": "Strength of negative guidance effect"}),
+                "nag_alpha": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.001, "tooltip": "Mixing coefficient in that controls the balance between the normalized guided representation and the original positive representation."}),
+                "nag_tau": ("FLOAT", {"default": 2.5, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Clipping threshold that controls how much the guided attention can deviate from the positive attention."}),
+           },
+           "optional": {
+                "input_type": (["default", "batch"], {"tooltip": "Type of the model input"}),
+                "inplace": ("BOOLEAN", {"default": False, "tooltip": "If true, modifies tensors in place to save memory. Leads to different numerical results which may change the output slightly."}),
+           },
+
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    RETURN_NAMES = ("model",)
+    FUNCTION = "patch"
+    CATEGORY = "KJNodes/wan"
+    DESCRIPTION = "https://github.com/ChenDarYen/Normalized-Attention-Guidance"
+    EXPERIMENTAL = True
+
+    def patch(self, model, conditioning, nag_scale, nag_alpha, nag_tau, input_type="default", inplace=False):
+        if nag_scale == 0:
+            return (model,)
+
+        device = mm.get_torch_device()
+        dtype = mm.unet_dtype()
+
+        model_clone = model.clone()
+
+        diffusion_model = model_clone.get_model_object("diffusion_model")
+
+        diffusion_model.text_embedding.to(device)
+        context = diffusion_model.text_embedding(conditioning[0][0].to(device, dtype))
+
+        type_str = str(type(model.model.model_config).__name__)
+        i2v = True if "WAN21_I2V" in type_str else False
+
+        for idx, block in enumerate(diffusion_model.blocks):
+            patched_attn = WanCrossAttentionPatch(context, nag_scale, nag_alpha, nag_tau, i2v, input_type=input_type, inplace=inplace).__get__(block.cross_attn, block.__class__)
+
+            model_clone.add_object_patch(f"diffusion_model.blocks.{idx}.cross_attn.forward", patched_attn)
+
+        return (model_clone,)
+    
+class SkipLayerGuidanceWanVideo:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"model": ("MODEL", ),
+                             "blocks": ("STRING", {"default": "10", "multiline": False}),
+                             "start_percent": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.001}),
+                             "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                            }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "slg"
+    EXPERIMENTAL = True
+    DESCRIPTION = "Simplified skip layer guidance that only skips the uncond on selected blocks"
+    DEPRECATED = True
+    CATEGORY = "advanced/guidance"
+
+    def slg(self, model, start_percent, end_percent, blocks):
+        def skip(args, extra_args):
+            transformer_options = extra_args.get("transformer_options", {})
+            original_block = extra_args["original_block"]
+
+            if not transformer_options:
+                raise ValueError("transformer_options not found in extra_args, currently SkipLayerGuidanceWanVideo only works with TeaCacheKJ")
+            if start_percent <= transformer_options["current_percent"] <= end_percent:
+                if args["img"].shape[0] == 2:
+                    prev_img_uncond = args["img"][0].unsqueeze(0)
+
+                    new_args = {
+                        "img": args["img"][1].unsqueeze(0),
+                        "txt": args["txt"][1].unsqueeze(0),
+                        "vec": args["vec"][1].unsqueeze(0),
+                        "pe": args["pe"][1].unsqueeze(0)
+                    }
+                    
+                    block_out = original_block(new_args)
+
+                    out = {
+                        "img": torch.cat([prev_img_uncond, block_out["img"]], dim=0),
+                        "txt": args["txt"],
+                        "vec": args["vec"],
+                        "pe": args["pe"]
+                    }
+                else:
+                    if transformer_options.get("cond_or_uncond") == [0]:
+                        out = original_block(args)
+                    else:
+                        out = args
+            else:
+                out = original_block(args)
+            return out
+
+        block_list = [int(x.strip()) for x in blocks.split(",")]
+        blocks = [int(i) for i in block_list]
+        logging.info(f"Selected blocks to skip uncond on: {blocks}")
+
+        m = model.clone()
+
+        for b in blocks:
+            #m.set_model_patch_replace(skip, "dit", "double_block", b)
+            model_options = m.model_options["transformer_options"].copy()
+            if "patches_replace" not in model_options:
+                model_options["patches_replace"] = {}
+            else:
+                model_options["patches_replace"] = model_options["patches_replace"].copy()
+
+            if "dit" not in model_options["patches_replace"]:
+                model_options["patches_replace"]["dit"] = {}
+            else:
+                model_options["patches_replace"]["dit"] = model_options["patches_replace"]["dit"].copy()
+
+            block = ("double_block", b)
+
+            model_options["patches_replace"]["dit"][block] = skip
+            m.model_options["transformer_options"] = model_options
+            
+
+        return (m, )
+
+class CFGZeroStarAndInit:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "model": ("MODEL",),
+                    "use_zero_init": ("BOOLEAN", {"default": True}),
+                    "zero_init_steps": ("INT", {"default": 0, "min": 0, "tooltip": "for zero init, starts from 0 so first step is always zeroed out if use_zero_init enabled"}),
+                }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+    DESCRIPTION = "https://github.com/WeichenFan/CFG-Zero-star"
+    CATEGORY = "KJNodes/experimental"
+    EXPERIMENTAL = True
+
+    def patch(self, model, use_zero_init, zero_init_steps):
+        def cfg_zerostar(args):
+            #zero init
+            cond = args["cond"]
+            timestep = args["timestep"]
+            sigmas = args["model_options"]["transformer_options"]["sample_sigmas"]
+            matched_step_index = (sigmas == timestep[0]).nonzero()
+            if len(matched_step_index) > 0:
+                current_step_index = matched_step_index.item()
+            else:
+                for i in range(len(sigmas) - 1):
+                    if (sigmas[i] - timestep[0]) * (sigmas[i + 1] - timestep[0]) <= 0:
+                        current_step_index = i
+                        break
+                else:
+                    current_step_index = 0
+
+            if (current_step_index <= zero_init_steps) and use_zero_init:
+                return cond * 0
+                        
+            uncond = args["uncond"]
+            cond_scale = args["cond_scale"]
+                
+            batch_size = cond.shape[0]
+
+            positive_flat = cond.view(batch_size, -1)
+            negative_flat = uncond.view(batch_size, -1)
+
+            dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True)
+            squared_norm = torch.sum(negative_flat ** 2, dim=1, keepdim=True) + 1e-8
+            alpha = dot_product / squared_norm
+            alpha = alpha.view(batch_size, *([1] * (len(cond.shape) - 1)))
+
+            noise_pred = uncond * alpha + cond_scale * (cond - uncond * alpha)
+            return noise_pred
+
+        m = model.clone()
+        m.set_model_sampler_cfg_function(cfg_zerostar)
+        return (m, )
+
+class GGUFLoaderKJ(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        # Get GGUF models safely, fallback to empty list if unet_gguf folder doesn't exist
+        try:
+            gguf_models = folder_paths.get_filename_list("unet_gguf")
+            ltx2_connector_models = folder_paths.get_filename_list("text_encoders")
+            ltx2_connector_models = [m for m in ltx2_connector_models if "connector" in m.lower()]
+        except KeyError:
+            gguf_models = []
+            ltx2_connector_models = []
+
+        return io.Schema(
+            node_id="GGUFLoaderKJ",
+            category="KJNodes/model_loaders",
+            description="Loads a GGUF model with advanced options, requires [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF) to be installed.",
+            is_experimental=True,
+            inputs=[
+                io.Combo.Input("model_name", options=gguf_models),
+                io.Combo.Input("extra_model_name", options=gguf_models + ltx2_connector_models + ["none"], default="none", tooltip="An extra gguf model to load and merge into the main model, for example VACE module"),
+                io.Combo.Input("dequant_dtype", options=["default", "target", "float32", "float16", "bfloat16"], default="default"),
+                io.Combo.Input("patch_dtype", options=["default", "target", "float32", "float16", "bfloat16"], default="default"),
+                io.Boolean.Input("patch_on_device", default=False),
+                io.Boolean.Input("enable_fp16_accumulation", default=False, tooltip="Enable torch.backends.cuda.matmul.allow_fp16_accumulation, required minimum pytorch version 2.7.1"),
+                io.Combo.Input("attention_override", options=["none", "sdpa", "sageattn", "xformers", "flashattn"], default="none", tooltip="Overrides the used attention implementation, requires the respective library to be installed"),
+
+            ],
+            outputs=[io.Model.Output(),],
+        )
+
+    def attention_override_pytorch(func, *args, **kwargs):
+        new_attention = comfy.ldm.modules.attention.attention_pytorch
+        return new_attention.__wrapped__(*args, **kwargs)
+    def attention_override_sage(func, *args, **kwargs):
+        new_attention = comfy.ldm.modules.attention.attention_sage
+        return new_attention.__wrapped__(*args, **kwargs)
+    def attention_override_xformers(func, *args, **kwargs):
+        new_attention = comfy.ldm.modules.attention.attention_xformers
+        return new_attention.__wrapped__(*args, **kwargs)
+    def attention_override_flash(func, *args, **kwargs):
+        new_attention = comfy.ldm.modules.attention.attention_flash
+        return new_attention.__wrapped__(*args, **kwargs)
+
+    ATTENTION_OVERRIDES = {
+        "sdpa": attention_override_pytorch,
+        "sageattn": attention_override_sage,
+        "xformers": attention_override_xformers,
+        "flashattn": attention_override_flash,
+    }
+
+
+    @classmethod
+    def _get_gguf_module(cls):
+        """Import GGUF module with version validation"""
+        for key, mod in sys.modules.items():
+            if key.endswith("ComfyUI-GGUF") or key.endswith("comfyui-gguf"):
+                if hasattr(mod, "ops") and hasattr(mod, "nodes"):
+                    return mod
+
+        gguf_path = os.path.join(folder_paths.folder_names_and_paths["custom_nodes"][0][0], "ComfyUI-GGUF")
+        for module_name in ["ComfyUI-GGUF", "custom_nodes.ComfyUI-GGUF", "comfyui-gguf", "custom_nodes.comfyui-gguf", gguf_path, gguf_path.lower()]:
+            try:
+                module = importlib.import_module(module_name)
+                return module
+            except ImportError:
+                continue
+
+        raise ImportError(
+            "Compatible ComfyUI-GGUF not found. "
+            "Please install/update from: https://github.com/city96/ComfyUI-GGUF"
+        )
+
+    @classmethod
+    def execute(cls, model_name, extra_model_name, dequant_dtype, patch_dtype, patch_on_device, attention_override, enable_fp16_accumulation):
+        gguf_nodes = cls._get_gguf_module()
+        ops = gguf_nodes.ops.GGMLOps()
+
+        def set_linear_dtype(attr, value):
+            if value == "default":
+                setattr(ops.Linear, attr, None)
+            elif value == "target":
+                setattr(ops.Linear, attr, value)
+            else:
+                setattr(ops.Linear, attr, getattr(torch, value))
+
+        set_linear_dtype("dequant_dtype", dequant_dtype)
+        set_linear_dtype("patch_dtype", patch_dtype)
+
+        # init model
+        extra = {}
+        model_path = folder_paths.get_full_path("unet", model_name)
+        try:
+            sd, extra = gguf_nodes.loader.gguf_sd_loader(model_path)
+        except TypeError:
+            sd = gguf_nodes.loader.gguf_sd_loader(model_path)
+
+        if extra_model_name is not None and extra_model_name != "none":
+            if extra_model_name.endswith(".gguf"):
+                extra_model_full_path = folder_paths.get_full_path("unet", extra_model_name)
+                try:
+                    extra_model, _ = gguf_nodes.loader.gguf_sd_loader(extra_model_full_path)
+                except TypeError:
+                    extra_model = gguf_nodes.loader.gguf_sd_loader(extra_model_full_path)
+            elif "connector" in extra_model_name.lower():
+                extra_model_full_path = folder_paths.get_full_path("text_encoders", extra_model_name)
+                extra_model = comfy.utils.load_torch_file(extra_model_full_path)
+                diffusion_model_prefix = comfy.model_detection.unet_prefix_from_state_dict(extra_model)
+                if diffusion_model_prefix == "model.diffusion_model.":
+                    temp_sd = comfy.utils.state_dict_prefix_replace(extra_model, {diffusion_model_prefix: ""}, filter_keys=True)
+                    if len(temp_sd) > 0:
+                        extra_model = temp_sd
+            else:
+                raise ValueError("Extra model must also be a .gguf file")
+            sd.update(extra_model)
+
+        model = comfy.sd.load_diffusion_model_state_dict(
+            sd, model_options={"custom_operations": ops}, metadata=extra.get("metadata", {})
+        )
+        if model is None:
+            raise RuntimeError(f"ERROR: Could not detect model type of: {model_path}")
+
+        model = gguf_nodes.nodes.GGUFModelPatcher.clone(model)
+        model.patch_on_device = patch_on_device
+
+        # attention override
+        if attention_override in cls.ATTENTION_OVERRIDES:
+            model.model_options["transformer_options"]["optimized_attention_override"] = cls.ATTENTION_OVERRIDES[attention_override]
+
+        if enable_fp16_accumulation:
+            if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"):
+                torch.backends.cuda.matmul.allow_fp16_accumulation = True
+            else:
+                raise RuntimeError("Failed to set fp16 accumulation, requires pytorch version 2.7.1 or higher")
+        else:
+            if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"):
+                torch.backends.cuda.matmul.allow_fp16_accumulation = False
+
+        return io.NodeOutput(model,)
+
+try:
+    from torch.nn.attention.flex_attention import flex_attention, BlockMask
+except ImportError:
+    flex_attention = None
+    BlockMask = None
+
+class NABLA_AttentionKJ():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "model": ("MODEL",),
+            "latent": ("LATENT", {"tooltip": "Only used to get the latent shape"}),
+            "window_time": ("INT", {"default": 11, "min": 1, "tooltip": "Temporal attention window size"}),
+            "window_width": ("INT", {"default": 3, "min": 1, "tooltip": "Spatial attention window size"}),
+            "window_height": ("INT", {"default": 3, "min": 1, "tooltip": "Spatial attention window size"}),
+            "sparsity": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 1.0, "step": 0.01}),
+            "torch_compile": ("BOOLEAN", {"default": True, "tooltip": "Most likely required for reasonable memory usage"})
+        },
+        }
+
+    RETURN_TYPES = ("MODEL", )
+    FUNCTION = "patch"
+    DESCRIPTION = "Experimental node for patching attention mode to use NABLA sparse attention for video models, currently only works with Kadinsky5"
+    CATEGORY = "KJNodes/experimental"
+
+    def patch(self, model, latent, window_time, window_width, window_height, sparsity, torch_compile):
+        if flex_attention is None or BlockMask is None:
+            raise RuntimeError("can't import flex_attention from torch.nn.attention, requires newer pytorch version")
+
+        model_clone = model.clone()
+        samples = latent["samples"]
+
+        sparse_params = get_sparse_params(samples, window_time, window_height, window_width, sparsity)
+        nabla_attention = NABLA_Attention(sparse_params)
+
+        def attention_override_nabla(func, *args, **kwargs):
+            return nabla_attention(*args, **kwargs)
+
+        if torch_compile:
+            attention_override_nabla = torch.compile(attention_override_nabla, mode="max-autotune-no-cudagraphs", dynamic=True)
+
+        # attention override
+        model_clone.model_options["transformer_options"]["optimized_attention_override"] = attention_override_nabla
+
+        return model_clone,
+
+
+class NABLA_Attention():
+    def __init__(self, sparse_params):
+        self.sparse_params = sparse_params
+
+    def __call__(self, q, k, v, heads, **kwargs):
+        if q.shape[-2] < 3000 or k.shape[-2] < 3000:
+            return optimized_attention(q, k, v, heads, **kwargs)
+        block_mask = self.nablaT_v2(q, k, self.sparse_params["sta_mask"], thr=self.sparse_params["P"])
+        out = flex_attention(q, k, v, block_mask=block_mask).transpose(1, 2).contiguous().flatten(-2, -1)
+        return out
+
+    def nablaT_v2(self, q, k, sta, thr=0.9):
+        # Map estimation
+        BLOCK_SIZE = 64
+        B, h, S, D = q.shape
+        s1 = S // BLOCK_SIZE
+        qa = q.reshape(B, h, s1, BLOCK_SIZE, D).mean(-2)
+        ka = k.reshape(B, h, s1, BLOCK_SIZE, D).mean(-2).transpose(-2, -1)
+        map = qa @ ka
+
+        map = torch.softmax(map / math.sqrt(D), dim=-1)
+        # Map binarization
+        vals, inds = map.sort(-1)
+        cvals = vals.cumsum_(-1)
+        mask = (cvals >= 1 - thr).int()
+        mask = mask.gather(-1, inds.argsort(-1))
+
+        mask = torch.logical_or(mask, sta)
+
+        # BlockMask creation
+        kv_nb = mask.sum(-1).to(torch.int32)
+        kv_inds = mask.argsort(dim=-1, descending=True).to(torch.int32)
+        return BlockMask.from_kv_blocks(torch.zeros_like(kv_nb), kv_inds, kv_nb, kv_inds, BLOCK_SIZE=BLOCK_SIZE, mask_mod=None)
+
+def fast_sta_nabla(T, H, W, wT=3, wH=3, wW=3):
+    l = torch.Tensor([T, H, W]).amax()
+    r = torch.arange(0, l, 1, dtype=torch.int16, device=mm.get_torch_device())
+    mat = (r.unsqueeze(1) - r.unsqueeze(0)).abs()
+    sta_t, sta_h, sta_w = (
+        mat[:T, :T].flatten(),
+        mat[:H, :H].flatten(),
+        mat[:W, :W].flatten(),
+    )
+    sta_t = sta_t <= wT // 2
+    sta_h = sta_h <= wH // 2
+    sta_w = sta_w <= wW // 2
+    sta_hw = (sta_h.unsqueeze(1) * sta_w.unsqueeze(0)).reshape(H, H, W, W).transpose(1, 2).flatten()
+    sta = (sta_t.unsqueeze(1) * sta_hw.unsqueeze(0)).reshape(T, T, H * W, H * W).transpose(1, 2)
+    return sta.reshape(T * H * W, T * H * W)
+
+
+def get_sparse_params(x, wT, wH, wW, sparsity=0.9):
+    B, C, T, H, W = x.shape
+    #print("x shape:", x.shape)
+    patch_size = (1, 2, 2)
+    T, H, W = (
+        T // patch_size[0],
+        H // patch_size[1],
+        W // patch_size[2],
+    )
+    sta_mask = fast_sta_nabla(T, H // 8, W // 8, wT, wH, wW)
+    sparse_params = {
+        "sta_mask": sta_mask.unsqueeze_(0).unsqueeze_(0),
+        "to_fractal": True,
+        "P": sparsity,
+        "wT": wT,
+        "wH": wH,
+        "wW": wW,
+        "add_sta": True,
+        "visual_shape": (T, H, W),
+        "method": "topcdf",
+    }
+
+    return sparse_params
+
+from comfy.comfy_types.node_typing import IO
+class StartRecordCUDAMemoryHistory():
+    # @classmethod
+    # def IS_CHANGED(s):
+    #     return True
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input": (IO.ANY,),
+                "enabled": (["all", "state", "None"], {"default": "all", "tooltip": "None: disable, 'state': keep info for allocated memory, 'all': keep history of all alloc/free calls"}),
+                "context": (["all", "state", "alloc", "None"], {"default": "all", "tooltip": "None: no tracebacks, 'state': tracebacks for allocated memory, 'alloc': for alloc calls, 'all': for free calls"}),
+                "stacks": (["python", "all"], {"default": "all", "tooltip": "'python': Python/TorchScript/inductor frames, 'all': also C++ frames"}),
+                "max_entries": ("INT", {"default": 100000, "min": 1000, "max": 10000000, "tooltip": "Maximum number of entries to record"}),
+            },
+        }
+
+    RETURN_TYPES = (IO.ANY, )
+    RETURN_NAMES = ("input", "output_path",)
+    FUNCTION = "start"
+    CATEGORY = "KJNodes/memory"
+    DESCRIPTION = "THIS NODE ALWAYS RUNS. Starts recording CUDA memory allocation history, can be ended and saved with EndRecordCUDAMemoryHistory. "
+
+    def start(self, input, enabled, context, stacks, max_entries):
+        mm.soft_empty_cache()
+        torch.cuda.reset_peak_memory_stats(mm.get_torch_device())
+        torch.cuda.memory._record_memory_history(
+            max_entries=max_entries,
+            enabled=enabled if enabled != "None" else None,
+            context=context if context != "None" else None,
+            stacks=stacks
+        )
+        return input,
+
+class EndRecordCUDAMemoryHistory():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "input": (IO.ANY,),
+            "output_path": ("STRING", {"default": "comfy_cuda_memory_history"}, "Base path for saving the CUDA memory history file, timestamp and .pt extension will be added"),
+        },
+        }
+
+    RETURN_TYPES = (IO.ANY, "STRING",)
+    RETURN_NAMES = ("input", "output_path",)
+    FUNCTION = "end"
+    CATEGORY = "KJNodes/memory"
+    DESCRIPTION = "Records CUDA memory allocation history between start and end, saves to a file that can be analyzed here: https://docs.pytorch.org/memory_viz or with VisualizeCUDAMemoryHistory node"
+
+    def end(self, input, output_path):
+        mm.soft_empty_cache()
+        time = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+        output_path = f"{output_path}{time}.pt"
+        torch.cuda.memory._dump_snapshot(output_path)
+        torch.cuda.memory._record_memory_history(enabled=None)
+        return input, output_path
+
+try:
+    from server import PromptServer
+except ImportError:
+    PromptServer = None
+
+class VisualizeCUDAMemoryHistory():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "snapshot_path": ("STRING", ),
+        },
+         "hidden": {
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("output_path",)
+    FUNCTION = "visualize"
+    CATEGORY = "KJNodes/memory"
+    DESCRIPTION = "Visualizes a CUDA memory allocation history file, opens in browser"
+    OUTPUT_NODE = True
+
+    def visualize(self, snapshot_path, unique_id):
+        import pickle
+        from torch.cuda import _memory_viz
+        import uuid
+
+        from folder_paths import get_output_directory
+        output_dir = get_output_directory()
+
+        with open(snapshot_path, "rb") as f:
+            snapshot = pickle.load(f)
+
+        html = _memory_viz.trace_plot(snapshot)
+        html_filename = f"cuda_memory_history_{uuid.uuid4().hex}.html"
+        output_path = os.path.join(output_dir, "memory_history", html_filename)
+        os.makedirs(os.path.dirname(output_path), exist_ok=True)
+
+        with open(output_path, "w", encoding="utf-8") as f:
+            f.write(html)
+
+        api_url = f"http://localhost:8188/api/view?type=output&filename={html_filename}&subfolder=memory_history"
+
+        # Progress UI
+        if unique_id and PromptServer is not None:
+            try:
+                PromptServer.instance.send_progress_text(
+                    api_url,
+                    unique_id
+                )
+            except:
+                pass
+
+        return api_url,
+
+
+class ModelMemoryUseReportPatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "model": ("MODEL",),
+        }}
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+    DESCRIPTION = "Adds callbacks to model to report memory usage during after sampling"
+    EXPERIMENTAL = True
+    CATEGORY = "KJNodes/memory"
+
+    def patch(self, model):
+        model_clone = model.clone()
+        device = mm.get_torch_device()
+
+        def reset_mem_usage(model):
+            torch.cuda.reset_peak_memory_stats(device)
+        def report_mem_usage(model):
+            max_memory = torch.cuda.max_memory_allocated(device) / 1024**3
+            max_reserved = torch.cuda.max_memory_reserved(device) / 1024**3
+            logging.info(f"Sampling max allocated memory: {max_memory=:.3f} GB")
+            logging.info(f"Sampling max reserved memory: {max_reserved=:.3f} GB")
+
+        model_clone.add_callback(CallbacksMP.ON_PRE_RUN, reset_mem_usage)
+        model_clone.add_callback(CallbacksMP.ON_CLEANUP, report_mem_usage)
+
+        return (model_clone,)
+
+
+class MemoryUsageFactorAdjustWrapper:
+    def __init__(self, memory_usage_factor, original_factor):
+        self.memory_usage_factor = memory_usage_factor
+        self.original_factor = original_factor
+
+    def __call__(self, executor, model, noise_shape: torch.Tensor, *args, **kwargs):
+        m = model.clone()
+        m.model.memory_usage_factor = self.memory_usage_factor
+        logging.info(f"Temporarily set memory usage factor to {self.memory_usage_factor}")
+        try:
+            result = executor(m, noise_shape, *args, **kwargs)
+        finally:
+            logging.info(f"Model memory usage calculated, restoring original memory usage factor: {self.original_factor}")
+            m.model.memory_usage_factor = self.original_factor
+        return result
+
+class ModelMemoryUsageFactorOverride:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "model": ("MODEL",),
+            "memory_usage_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.001}),
+        }}
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+    DESCRIPTION = "Overrides the memory usage factor of the model during sampling."
+    EXPERIMENTAL = True
+    CATEGORY = "KJNodes/memory"
+
+    def patch(self, model, memory_usage_factor):
+        model_clone = model.clone()
+        original_memory_usage_factor = model_clone.model.memory_usage_factor
+        logging.info(f"Original memory usage factor: {original_memory_usage_factor}")
+
+        wrapper = MemoryUsageFactorAdjustWrapper(memory_usage_factor, original_memory_usage_factor)
+        model_clone.add_wrapper_with_key(
+            comfy.patcher_extension.WrappersMP.PREPARE_SAMPLING,
+            "memory_usage_factor_adjust_prepare_sampling",
+            wrapper
+        )
+        return (model_clone,)
+
+def wan_ffn_chunked_forward(self, x):
+    if x.shape[1] > self.dim_threshold:
+        chunks = torch.chunk(x, self.num_chunks, dim=1)
+        output_chunks = []
+        for chunk in chunks:
+            output_chunks.append(torch.nn.Sequential.forward(self, chunk))
+        chunked = torch.cat(output_chunks, dim=1)
+        return chunked
+    else:
+        return torch.nn.Sequential.forward(self, x)
+
+class WanffnChunkPatch:
+    def __init__(self, num_chunks, dim_threshold=4096):
+        self.num_chunks = num_chunks
+        self.dim_threshold = dim_threshold
+
+    def __get__(self, obj, objtype=None):
+        def wrapped_forward(self_module, *args, **kwargs):
+            self_module.num_chunks = self.num_chunks
+            self_module.dim_threshold = self.dim_threshold
+            return wan_ffn_chunked_forward(self_module, *args, **kwargs)
+        return types.MethodType(wrapped_forward, obj)
+
+class WanChunkFeedForward(io.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="WanChunkFeedForward",
+            display_name="Wan Chunk FeedForward",
+            category="KJNodes/wan",
+            description="EXPERIMENTAL AND MAY CHANGE THE MODEL OUTPUT!! Chunks feedforward activations to reduce peak VRAM usage.",
+            is_experimental=True,
+            inputs=[
+                io.Model.Input("model"),
+                io.Int.Input("chunks", default=2, min=1, max=100, step=1, tooltip="Number of chunks to split the feedforward activations into to reduce peak VRAM usage."),
+                io.Int.Input("dim_threshold", default=4096, min=1024, max=16384, step=256, tooltip="Dimension threshold above which to apply chunking."),
+            ],
+            outputs=[
+                io.Model.Output(display_name="model"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, chunks, dim_threshold) -> io.NodeOutput:
+        if chunks == 1:
+            return io.NodeOutput(model)
+
+        model_clone = model.clone()
+        diffusion_model = model_clone.get_model_object("diffusion_model")
+
+        for idx, block in enumerate(diffusion_model.blocks):
+            patched_ffn = WanffnChunkPatch(chunks, dim_threshold).__get__(block.ffn, block.__class__)
+            model_clone.add_object_patch(f"diffusion_model.blocks.{idx}.ffn.forward", patched_ffn)
+
+        return io.NodeOutput(model_clone)
+
+
+# Ideogram4 peak-VRAM patches (FFN sequence chunking + bf16 RoPE)
+from comfy.ldm.lumina.model import FeedForward as _Ideogram4FeedForward
+from comfy.ldm.modules.attention import optimized_attention_masked as _ideogram4_attn
+
+
+def ideogram4_ffn_chunked_forward(self, x):
+    # x: (B, L, dim). Chunk over the token dim so the (B, L, hidden) SwiGLU
+    if x.shape[1] > self.kj_dim_threshold and self.kj_num_chunks > 1:
+        out = [_Ideogram4FeedForward.forward(self, c) for c in torch.chunk(x, self.kj_num_chunks, dim=1)]
+        return torch.cat(out, dim=1)
+    return _Ideogram4FeedForward.forward(self, x)
+
+
+class Ideogram4FFNChunkPatch:
+    def __init__(self, num_chunks, dim_threshold):
+        self.num_chunks = num_chunks
+        self.dim_threshold = dim_threshold
+
+    def __get__(self, obj, objtype=None):
+        def wrapped_forward(self_module, *args, **kwargs):
+            self_module.kj_num_chunks = self.num_chunks
+            self_module.kj_dim_threshold = self.dim_threshold
+            return ideogram4_ffn_chunked_forward(self_module, *args, **kwargs)
+        return types.MethodType(wrapped_forward, obj)
+
+
+def _ideogram4_apply_rope_lowp(xq, xk, freqs_cis):
+    # (bf16/fp16) instead of being upcast to fp32 -> ~halves RoPE activation memory.
+    cos = freqs_cis[0].to(xq.dtype)
+    sin = freqs_cis[1].to(xq.dtype)
+    nsin = freqs_cis[2].to(xq.dtype)
+
+    q_embed = xq * cos
+    qs = q_embed.shape[-1] // 2
+    q_embed[..., :qs].addcmul_(xq[..., qs:], nsin)
+    q_embed[..., qs:].addcmul_(xq[..., :qs], sin)
+
+    k_embed = xk * cos
+    ks = k_embed.shape[-1] // 2
+    k_embed[..., :ks].addcmul_(xk[..., ks:], nsin)
+    k_embed[..., ks:].addcmul_(xk[..., :ks], sin)
+    return q_embed, k_embed
+
+
+def ideogram4_attention_lowp_rope_forward(self, x, attn_mask, freqs_cis, transformer_options={}):
+    batch_size, seq_len, _ = x.shape
+    q, k, v = self.qkv(x).view(batch_size, seq_len, 3, self.num_heads, self.head_dim).unbind(dim=2)
+    q = self.norm_q(q).transpose(1, 2)
+    k = self.norm_k(k).transpose(1, 2)
+    v = v.transpose(1, 2)
+    q, k = _ideogram4_apply_rope_lowp(q, k, freqs_cis)
+    out = _ideogram4_attn(q, k, v, self.num_heads, attn_mask, skip_reshape=True, transformer_options=transformer_options)
+    return self.o(out)
+
+
+class Ideogram4RopePatch:
+    def __get__(self, obj, objtype=None):
+        return types.MethodType(ideogram4_attention_lowp_rope_forward, obj)
+
+
+class Ideogram4OptimizationsKJ(io.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="Ideogram4OptimizationsKJ",
+            display_name="Ideogram4 Optimizations KJ",
+            category="KJNodes/experimental",
+            description="EXPERIMENTAL AND MAY CHANGE THE MODEL OUTPUT!! Reduces peak VRAM of the Ideogram4 forward. "
+                        "chunk_ffn splits the SwiGLU activations over the token dim; bf16_rope applies RoPE in the model "
+                        "dtype instead of upcasting to fp32. Both target the two largest transient tensors in the block.",
+            is_experimental=True,
+            inputs=[
+                io.Model.Input("model"),
+                io.Boolean.Input("chunk_ffn", default=True,
+                                 tooltip="Chunk the feedforward activations over the sequence dim to cap the (B, L, hidden) intermediate."),
+                io.Int.Input("ffn_chunks", default=2, min=1, max=64, step=1,
+                             tooltip="Number of chunks to split the feedforward sequence into. More chunks = lower peak, slightly more overhead."),
+                io.Int.Input("ffn_seq_threshold", default=1024, min=256, max=65536, step=256,
+                             tooltip="Only chunk when the token sequence length exceeds this (skips chunking for tiny sequences)."),
+                io.Boolean.Input("bf16_rope", default=True,
+                                 tooltip="Apply RoPE in the input dtype instead of fp32. ~Halves RoPE activation memory; matches the HF reference dtype."),
+            ],
+            outputs=[
+                io.Model.Output(display_name="model"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, chunk_ffn, ffn_chunks, ffn_seq_threshold, bf16_rope) -> io.NodeOutput:
+        if not chunk_ffn and not bf16_rope:
+            return io.NodeOutput(model)
+
+        m = model.clone()
+        diffusion_model = m.get_model_object("diffusion_model")
+
+        layers = getattr(diffusion_model, "layers", None)
+        if not layers or not hasattr(layers[0], "feed_forward") or not hasattr(layers[0], "attention"):
+            logging.warning("Ideogram4OptimizationsKJ: model does not look like Ideogram4 "
+                            "(expected diffusion_model.layers[*].feed_forward/.attention); returning model unchanged.")
+            return io.NodeOutput(model)
+
+        for idx, block in enumerate(layers):
+            if chunk_ffn and ffn_chunks > 1:
+                patched_ffn = Ideogram4FFNChunkPatch(ffn_chunks, ffn_seq_threshold).__get__(block.feed_forward, block.feed_forward.__class__)
+                m.add_object_patch(f"diffusion_model.layers.{idx}.feed_forward.forward", patched_ffn)
+            if bf16_rope:
+                patched_attn = Ideogram4RopePatch().__get__(block.attention, block.attention.__class__)
+                m.add_object_patch(f"diffusion_model.layers.{idx}.attention.forward", patched_attn)
+
+        return io.NodeOutput(m)
+
+
+from comfy.samplers import KSAMPLER
+from comfy.k_diffusion.sampling import to_d
+
+def sample_selfrefinevideo(model, x, sigmas, stochastic_step_map, certain_percentage=0.999, uncertainty_threshold=0.25, extra_args=None, callback=None, disable=None, verbose=False, video_shape=None, seed=None):
+    extra_args = {} if extra_args is None else extra_args
+    sigma_in = x.new_ones([x.shape[0]])
+
+    if seed is not None:
+        generator = torch.Generator(torch.device("cpu")).manual_seed(seed)
+
+    pbar = tqdm(total=len(sigmas) - 1, disable=disable, desc="Sampling")
+
+    for i in range(len(sigmas) - 1):
+
+        # Get stochastic steps for this noise level
+        current_num_anneal_steps = stochastic_step_map.get(i, 0)
+        use_stochastic = current_num_anneal_steps > 0
+        m = current_num_anneal_steps + 1 if use_stochastic else 1
+
+        sigma, sigma_next = sigmas[i], sigmas[i + 1]
+
+        prev_certain_mask = None
+        prev_denoised = None
+        prev_denoised_full = None
+        prev_x_next = None
+        prev_x_next_video = None
+        is_certain = False
+
+        for ii in range(m):
+            if m > 1:
+                pbar.set_description(f"Step {i}/{len(sigmas)-1} (substep {ii+1}/{m})")
+            # Early exit if certain threshold reached
+            if is_certain:
+                x = prev_x_next
+                break
+
+            # Determine input
+            noise = torch.randn(x.shape, device=torch.device("cpu"), generator=generator).to(x)
+            x_in = x if ii == 0 else (1.0 - sigma) * prev_denoised_full + sigma * noise
+            if ii > 0:
+                x = x_in
+
+            denoised = model(x_in, sigmas[i] * sigma_in, **extra_args)
+
+            if callback is not None:
+                callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+            # Compute next latents
+            d = to_d(x, sigma, denoised)
+            x_next = x + (sigma_next - sigma) * d
+
+            # Separate video and audio if joint model
+            if d.ndim == 3 and video_shape is not None:
+                cut = math.prod(video_shape[1:])
+                denoised_video = denoised[:, :, :cut].reshape([denoised.shape[0]] + list(video_shape)[1:])
+                x_next_video = x_next[:, :, :cut].reshape([denoised.shape[0]] + list(video_shape)[1:])
+                denoised_audio = denoised[:, :, cut:]
+                x_next_audio = x_next[:, :, cut:]
+                if verbose:
+                    tqdm.write(f"Video shape: {denoised_video.shape}, Audio shape: {denoised_audio.shape}")
+            else:
+                denoised_video = denoised
+                x_next_video = x_next
+                denoised_audio = None
+                x_next_audio = None
+
+            # Stochastic sampling with uncertainty masking
+            if use_stochastic and prev_denoised is not None:
+                # Compute uncertainty and masking on video part
+                diff = denoised_video - prev_denoised
+                uncertainty = torch.sqrt(torch.sum(diff ** 2, dim=1)) / denoised_video.shape[1]
+                certain_mask = uncertainty < uncertainty_threshold
+
+                if verbose:
+                    tqdm.write(f"Step {i}/{len(sigmas)-1} substep {ii+1}/{m}:")
+                    tqdm.write(f"Uncertainty: min {uncertainty.min():.4f}, max {uncertainty.max():.4f}, threshold {uncertainty_threshold}")
+                    tqdm.write(f"Certain pixels: {certain_mask.sum()}/{certain_mask.numel()} = {certain_mask.sum()/certain_mask.numel():.4f}")
+
+                # Update certain mask (union with previous)
+                if prev_certain_mask is not None:
+                    certain_mask = certain_mask | prev_certain_mask
+
+                # Check certainty threshold
+                if certain_mask.sum() / certain_mask.numel() > certain_percentage:
+                    is_certain = True
+                    if verbose:
+                        tqdm.write(f"{ii}/{current_num_anneal_steps}: Certain region is more than {certain_percentage}, we are certain")
+
+                # Apply masking to video
+                certain_mask_float = certain_mask.float().unsqueeze(1)
+                x_next_video = certain_mask_float * prev_x_next_video + (1.0 - certain_mask_float) * x_next_video
+                denoised_video = certain_mask_float * prev_denoised + (1.0 - certain_mask_float) * denoised_video
+
+                # Reconstruct full latents by replacing the video portion
+                if x_next_audio is not None:
+                    # Flatten masked video back to match original format and replace video portion
+                    x_next = x_next.clone()
+                    x_next[:, :, :cut] = x_next_video.reshape([x_next_video.shape[0], x_next.shape[1], -1])
+                    # Also reconstruct full denoised for next iteration input
+                    denoised_full = denoised.clone()
+                    denoised_full[:, :, :cut] = denoised_video.reshape([denoised_video.shape[0], denoised.shape[1], -1])
+                else:
+                    # No audio separation
+                    x_next = x_next_video
+                    denoised_full = denoised_video
+
+                prev_certain_mask = certain_mask
+                prev_denoised = denoised_video
+                prev_denoised_full = denoised_full
+                prev_x_next_video = x_next_video
+                prev_x_next = x_next
+            elif use_stochastic:
+                # For first stochastic step, create denoised_full if we have audio
+                if x_next_audio is not None:
+                    denoised_full = denoised.clone()
+                    denoised_full[:, :, :cut] = denoised_video.reshape([denoised_video.shape[0], denoised.shape[1], -1])
+                else:
+                    denoised_full = denoised_video
+
+                prev_certain_mask = None
+                prev_denoised = denoised_video
+                prev_denoised_full = denoised_full
+                prev_x_next_video = x_next_video
+                prev_x_next = x_next
+
+            # Update x for final step
+            if use_stochastic and ii == m - 1:
+                x = prev_x_next
+            elif not use_stochastic:
+                x = x_next
+
+        pbar.update(1)
+        if m == 1:
+            pbar.set_description("Sampling")
+    pbar.close()
+    return x
+
+class SamplerSelfRefineVideo(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        default_ranges = [
+            (2, 5, 3),   # Range 1
+            (6, 14, 1),  # Range 2
+        ]
+
+        options = []
+
+        # Option 1: 2 ranges
+        range_inputs_2 = []
+        for i in range(1, 3):
+            start_default, end_default, steps_default = default_ranges[i - 1]
+            range_inputs_2.extend([
+                io.Int.Input(f"start_step{i}", default=start_default, min=0, max=999, step=1, tooltip=f"Start step for range {i}"),
+                io.Int.Input(f"end_step{i}", default=end_default, min=0, max=999, step=1, tooltip=f"End step for range {i}"),
+                io.Int.Input(f"steps_{i}", default=steps_default, min=1, max=100, step=1, tooltip=f"Number of P&P steps for range {i}"),
+            ])
+        options.append(io.DynamicCombo.Option(key="2 ranges", inputs=range_inputs_2))
+
+        # Option 2: 1 range
+        range_inputs_1 = []
+        for i in range(1, 2):
+            start_default, end_default, steps_default = default_ranges[i - 1]
+            range_inputs_1.extend([
+                io.Int.Input(f"start_step{i}", default=start_default, min=0, max=999, step=1, tooltip=f"Start step for range {i}"),
+                io.Int.Input(f"end_step{i}", default=end_default, min=0, max=999, step=1, tooltip=f"End step for range {i}"),
+                io.Int.Input(f"steps_{i}", default=steps_default, min=1, max=100, step=1, tooltip=f"Number of P&P steps for range {i}"),
+            ])
+        options.append(io.DynamicCombo.Option(key="1 range", inputs=range_inputs_1))
+
+        # Option 3: Manual string input
+        options.append(io.DynamicCombo.Option(
+            key="from_string",
+            inputs=[
+                io.String.Input(
+                    "stochastic_plan",
+                    default="2-5:3,6-14:1",
+                    multiline=True,
+                    tooltip="Format: 'start-end:steps,start-end:steps' e.g. '2-5:3,6-14:1'"
+                )
+            ]
+        ))
+        return io.Schema(
+            node_id="SamplerSelfRefineVideo",
+            category="KJNodes/samplers",
+            description="Attempt to implement https://github.com/agwmon/self-refine-video, for testing only, MAY NOT WORK AS INTENDED.",
+            is_experimental=True,
+            inputs=[
+                io.DynamicCombo.Input("input_mode", options=options, tooltip="How to configure the step plan"),
+                io.Float.Input("certain_percentage", default=0.999, min=0.0, max=1.0, step=0.001, round=False, tooltip="Percentage of certain pixels to consider the frame as certain and skip further refinement"),
+                io.Float.Input("uncertainty_threshold", default=0.2, min=0.0, max=1.0, step=0.01, round=False, tooltip="Threshold of uncertainty to consider a pixel uncertain"),
+                io.Boolean.Input("verbose", default=False, tooltip="Enable verbose logging during sampling"),
+                io.Latent.Input("latent", optional=True, tooltip="Optional latent input to get input shape for LTX2 audio/video separation"),
+                io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff, step=1, tooltip="Seed for stochastic sampling"),
+            ],
+            outputs=[io.Sampler.Output()]
+        )
+
+    @classmethod
+    def execute(cls, input_mode, certain_percentage, uncertainty_threshold, seed, verbose, latent=None) -> io.NodeOutput:
+        video_shape = None
+        if latent is not None:
+            video_shape = latent["samples"].shape
+
+        range_keys = sorted([k for k in input_mode.keys() if k.startswith('start_step')])
+        stochastic_step_map = {}
+        if "stochastic_plan" in input_mode:
+            # Parse manual string format: "2-5:3,6-14:1"
+            plan_str = input_mode["stochastic_plan"]
+            ranges = plan_str.split(",")
+            for range_spec in ranges:
+                range_spec = range_spec.strip()
+                if not range_spec:
+                    continue
+                try:
+                    range_part, steps_part = range_spec.split(":")
+                    start, end = range_part.split("-")
+                    start, end, steps = int(start), int(end), int(steps_part)
+                    for idx in range(start, end + 1):
+                        stochastic_step_map[idx] = steps
+                except ValueError:
+                    raise ValueError(f"Invalid format in stochastic_plan: '{range_spec}'. Expected format: 'start-end:steps'")
+        else:
+            range_keys = [k for k in input_mode.keys() if k.startswith('start_step')]
+            for start_key in range_keys:
+                i = start_key.replace('start_step', '')
+                start = input_mode.get(f"start_step{i}")
+                end = input_mode.get(f"end_step{i}")
+                steps = input_mode.get(f"steps_{i}")
+
+                if start is not None and end is not None and steps is not None:
+                    for idx in range(start, end + 1):
+                        stochastic_step_map[idx] = steps
+
+        sampler = KSAMPLER(sample_selfrefinevideo, {
+            "stochastic_step_map": stochastic_step_map,
+            "certain_percentage": certain_percentage,
+            "uncertainty_threshold": uncertainty_threshold,
+            "verbose": verbose,
+            "video_shape": video_shape,
+            "seed": seed,
+        })
+        return io.NodeOutput(sampler)
+
+
+# Multi-feature linear bias corrector for PiD (Flux2 backbone, 4-step).
+# Calibrated on 124 natural-image samples (LOO-CV per-channel RMSE 0.027/0.026/0.024).
+# Held-out validation on 20 unseen images: 60% reduction in total drift vs uncorrected.
+# Features per row: [R_mean, G_mean, B_mean, R_std, G_std, B_std,
+#                    R_mean*G_mean, R_mean*B_mean, G_mean*B_mean, intercept(1.0)]
+# Columns: predicted bias for R, G, B (subtract from x0_pred at step 0).
+PID_BIAS_COEF_FLUX2 = torch.tensor([
+    [-0.130306, +0.127184, +0.014058],  # R_mean
+    [-0.053279, -0.408929, +0.004243],  # G_mean
+    [-0.009386, +0.109546, -0.134091],  # B_mean
+    [-0.033373, -0.011615, -0.026129],  # R_std
+    [+0.180052, +0.062021, +0.071317],  # G_std
+    [-0.067958, -0.058595, -0.098645],  # B_std
+    [-0.248116, -0.240633, -0.105600],  # R_mean*G_mean
+    [+0.304035, +0.322566, +0.093224],  # R_mean*B_mean
+    [-0.157648, -0.227127, -0.112368],  # G_mean*B_mean
+    [-0.062814, +0.030765, +0.062735],  # intercept
+], dtype=torch.float32)
+
+
+class PiDColorBiasCorrection:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "model": ("MODEL",),
+            "strength": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01,
+                                   "tooltip": "Correction strength. 1.0 = full predicted bias subtracted. <1 = milder, >1 = stronger, 0 = disabled."}),
+            "backbone": (["flux2"], {"default": "flux2",
+                                     "tooltip": "Calibrated PiD backbone (currently only flux2 — others use the same model but coefficients differ)."}),
+        }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+    CATEGORY = "KJNodes/experimental"
+    EXPERIMENTAL = True
+    DESCRIPTION = (
+        "PiD 4-step decoder color/brightness drift corrector. "
+        "Subtracts a per-channel bias from x0_pred at the first sampling step, "
+        "using a small linear model calibrated against the model's systematic drift "
+        "(model tends to brighten dark scenes and add a blue cast)."
+    )
+
+    def patch(self, model, strength, backbone):
+        if strength == 0.0 or backbone != "flux2":
+            return (model,)
+        coef_cpu = PID_BIAS_COEF_FLUX2  # (10, 3)
+
+        def pid_bias_post_cfg(args):
+            denoised = args["denoised"]
+            # Step detection: only apply at the first sampling step.
+            # Use sample_sigmas like CFGZeroStarAndInit for robustness across schedules.
+            try:
+                sigmas = args["model_options"]["transformer_options"]["sample_sigmas"]
+                sigma = args.get("sigma", args.get("timestep"))
+                # First step matches sigmas[0]
+                if sigma is None or not torch.isclose(sigma.max(), sigmas[0]).item():
+                    return denoised
+            except (KeyError, AttributeError):
+                # Fallback heuristic: PiD's first step has sigma=0.999
+                sigma = args.get("sigma")
+                if sigma is None or sigma.max().item() < 0.95:
+                    return denoised
+
+            coef = coef_cpu.to(denoised.device, dtype=denoised.dtype)
+            rgb_m = denoised.mean(dim=(0, 2, 3))
+            rgb_s = denoised.std(dim=(0, 2, 3))
+            one = torch.tensor(1.0, device=denoised.device, dtype=denoised.dtype)
+            feats = torch.stack([
+                rgb_m[0], rgb_m[1], rgb_m[2],
+                rgb_s[0], rgb_s[1], rgb_s[2],
+                rgb_m[0] * rgb_m[1], rgb_m[0] * rgb_m[2], rgb_m[1] * rgb_m[2],
+                one,
+            ])
+            bias = feats @ coef  # (3,)
+            return denoised - strength * bias.view(1, 3, 1, 1)
+
+        m = model.clone()
+        m.set_model_sampler_post_cfg_function(pid_bias_post_cfg)
+        return (m,)
diff --git a/custom_nodes/ComfyUI-KJNodes/nodes/nodes.py b/custom_nodes/ComfyUI-KJNodes/nodes/nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..5eebc21da4069b8e2cf0cc2713e8fcde5d8b2b70
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/nodes/nodes.py
@@ -0,0 +1,3357 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from PIL import Image
+import json
+import re
+import os
+import time
+import math
+import importlib
+import logging
+
+from comfy import model_management
+import folder_paths
+from nodes import MAX_RESOLUTION
+from comfy.utils import common_upscale, ProgressBar, load_torch_file, save_torch_file, state_dict_prefix_replace
+from comfy.comfy_types.node_typing import IO
+from comfy_api.latest import io, ui
+import comfy.latent_formats
+import node_helpers
+from io import BytesIO
+
+script_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+folder_paths.add_model_folder_path("kjnodes_fonts", os.path.join(script_directory, "fonts"))
+
+class BOOLConstant:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "value": ("BOOLEAN", {"default": True}),
+        },
+        }
+    RETURN_TYPES = ("BOOLEAN",)
+    RETURN_NAMES = ("value",)
+    FUNCTION = "get_value"
+    CATEGORY = "KJNodes/constants"
+    SEARCH_ALIASES = ["boolean", "value"]
+
+    def get_value(self, value):
+        return (value,)
+    
+class INTConstant:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "value": ("INT", {"default": 0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff}),
+        },
+        }
+    RETURN_TYPES = ("INT",)
+    RETURN_NAMES = ("value",)
+    FUNCTION = "get_value"
+    CATEGORY = "KJNodes/constants"
+    SEARCH_ALIASES = ["integer", "value"]
+
+    def get_value(self, value):
+        return (value,)
+
+class FloatConstant:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "value": ("FLOAT", {"default": 0.0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 0.00001}),
+        },
+        }
+
+    RETURN_TYPES = ("FLOAT",)
+    RETURN_NAMES = ("value",)
+    FUNCTION = "get_value"
+    CATEGORY = "KJNodes/constants"
+    SEARCH_ALIASES = ["float", "value"]
+
+    def get_value(self, value):
+        return (round(value, 6),)
+
+class StringConstant:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "string": ("STRING", {"default": '', "multiline": False}),
+            }
+        }
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "passtring"
+    CATEGORY = "KJNodes/constants"
+    SEARCH_ALIASES = ["text", "value"]
+
+    def passtring(self, string):
+        return (string, )
+
+class StringConstantMultiline:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "string": ("STRING", {"default": "", "multiline": True}),
+                "strip_newlines": ("BOOLEAN", {"default": True}),
+            }
+        }
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "stringify"
+    CATEGORY = "KJNodes/constants"
+    SEARCH_ALIASES = ["text", "value"]
+
+    def stringify(self, string, strip_newlines):
+        new_string = string
+        if strip_newlines:
+            new_string = new_string.replace('\n', '').strip()
+        return (new_string,)
+
+
+    
+class ScaleBatchPromptSchedule:
+    
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "scaleschedule"
+    CATEGORY = "KJNodes/misc"
+    DESCRIPTION = """
+Scales a batch schedule from Fizz' nodes BatchPromptSchedule
+to a different frame count.
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "input_str": ("STRING", {"forceInput": True,"default": "0:(0.0),\n7:(1.0),\n15:(0.0)\n"}),
+                 "old_frame_count": ("INT", {"forceInput": True,"default": 1,"min": 1, "max": 4096, "step": 1}),
+                 "new_frame_count": ("INT", {"forceInput": True,"default": 1,"min": 1, "max": 4096, "step": 1}),
+                
+        },
+    } 
+    
+    def scaleschedule(self, old_frame_count, input_str, new_frame_count):
+        pattern = r'"(\d+)"\s*:\s*"(.*?)"(?:,|\Z)'
+        frame_strings = dict(re.findall(pattern, input_str))
+        
+        # Calculate the scaling factor
+        scaling_factor = (new_frame_count - 1) / (old_frame_count - 1)
+        
+        # Initialize a dictionary to store the new frame numbers and strings
+        new_frame_strings = {}
+        
+        # Iterate over the frame numbers and strings
+        for old_frame, string in frame_strings.items():
+            # Calculate the new frame number
+            new_frame = int(round(int(old_frame) * scaling_factor))
+            
+            # Store the new frame number and corresponding string
+            new_frame_strings[new_frame] = string
+        
+        # Format the output string
+        output_str = ', '.join([f'"{k}":"{v}"' for k, v in sorted(new_frame_strings.items())])
+        return (output_str,)
+
+
+class GetLatentsFromBatchIndexed:
+    
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "indexedlatentsfrombatch"
+    CATEGORY = "KJNodes/latents"
+    DESCRIPTION = """
+Selects and returns the latents at the specified indices as an latent batch.
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "latents": ("LATENT",),
+                 "indexes": ("STRING", {"default": "0, 1, 2", "multiline": True}),
+                 "latent_format": (["BCHW", "BTCHW", "BCTHW"], {"default": "BCHW"}),
+        },
+    } 
+    
+    def indexedlatentsfrombatch(self, latents, indexes, latent_format):
+        
+        samples = latents.copy()
+        latent_samples = samples["samples"] 
+
+        # Parse the indexes string into a list of integers
+        index_list = [int(index.strip()) for index in indexes.split(',')]
+        
+        # Convert list of indices to a PyTorch tensor
+        indices_tensor = torch.tensor(index_list, dtype=torch.long)
+        
+        # Select the latents at the specified indices
+        if latent_format == "BCHW":
+            chosen_latents = latent_samples[indices_tensor]
+        elif latent_format == "BTCHW":
+            chosen_latents = latent_samples[:, indices_tensor]
+        elif latent_format == "BCTHW":
+            chosen_latents = latent_samples[:, :, indices_tensor]
+
+        samples["samples"] = chosen_latents
+        return (samples,)
+    
+
+class ConditioningMultiCombine:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "inputcount": ("INT", {"default": 2, "min": 2, "max": 20, "step": 1}),
+                "operation": (["combine", "concat"], {"default": "combine"}),
+                "conditioning_1": ("CONDITIONING", ),
+                "conditioning_2": ("CONDITIONING", ),
+            },
+    }
+
+    RETURN_TYPES = ("CONDITIONING", "INT")
+    RETURN_NAMES = ("combined", "inputcount")
+    FUNCTION = "combine"
+    CATEGORY = "KJNodes/masking/conditioning"
+    DESCRIPTION = """
+Combines multiple conditioning nodes into one
+"""
+
+    def combine(self, inputcount, operation, **kwargs):
+        from nodes import ConditioningCombine
+        from nodes import ConditioningConcat
+        cond_combine_node = ConditioningCombine()
+        cond_concat_node = ConditioningConcat()
+        cond = kwargs["conditioning_1"]
+        for c in range(1, inputcount):
+            new_cond = kwargs[f"conditioning_{c + 1}"]
+            if operation == "combine":
+                cond = cond_combine_node.combine(cond, new_cond)[0]
+            elif operation == "concat":
+                cond = cond_concat_node.concat(cond, new_cond)[0]
+        return (cond, inputcount,)
+
+class AppendStringsToList:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "string1": ("STRING", {"default": '', "forceInput": True}),
+                "string2": ("STRING", {"default": '', "forceInput": True}),
+            }
+        }
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "joinstring"
+    CATEGORY = "KJNodes/text"
+
+    def joinstring(self, string1, string2):
+        if not isinstance(string1, list):
+            string1 = [string1]
+        if not isinstance(string2, list):
+            string2 = [string2]
+        
+        joined_string = string1 + string2
+        return (joined_string, )
+    
+class JoinStrings:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "delimiter": ("STRING", {"default": ' ', "multiline": False}),
+            },
+            "optional": {
+                "string1": ("STRING", {"default": '', "forceInput": True}),
+                "string2": ("STRING", {"default": '', "forceInput": True}),
+            }
+        }
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "joinstring"
+    CATEGORY = "KJNodes/text"
+
+    def joinstring(self, delimiter, string1="", string2=""):
+        joined_string = string1 + delimiter + string2
+        return (joined_string, )
+    
+class JoinStringMulti:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}),
+                "string_1": ("STRING", {"default": '', "forceInput": True}),
+                "delimiter": ("STRING", {"default": ' ', "multiline": False}),
+                "return_list": ("BOOLEAN", {"default": False}),
+            },
+            "optional": {
+                "string_2": ("STRING", {"default": '', "forceInput": True}),
+            }
+    }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("string",)
+    FUNCTION = "combine"
+    CATEGORY = "KJNodes/text"
+    DESCRIPTION = """
+Creates single string, or a list of strings, from  
+multiple input strings.  
+You can set how many inputs the node has,  
+with the **inputcount** and clicking update.
+"""
+
+    def combine(self, inputcount, delimiter, **kwargs):
+        string = kwargs["string_1"]
+        return_list = kwargs["return_list"]
+        strings = [string] # Initialize a list with the first string
+        for c in range(1, inputcount):
+            new_string = kwargs.get(f"string_{c + 1}", "")
+            if not new_string:
+                continue
+            if return_list:
+                strings.append(new_string) # Add new string to the list
+            else:
+                string = string + delimiter + new_string
+        if return_list:
+            return (strings,) # Return the list of strings
+        else:
+            return (string,) # Return the combined string
+
+class CondPassThrough:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+            },
+            "optional": {
+                "positive": ("CONDITIONING", ),
+                "negative": ("CONDITIONING", ),
+            }, 
+    }
+
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING",)
+    RETURN_NAMES = ("positive", "negative")
+    FUNCTION = "passthrough"
+    CATEGORY = "KJNodes/misc"
+    DESCRIPTION = """
+    Simply passes through the positive and negative conditioning,
+    workaround for Set node not allowing bypassed inputs.
+"""
+
+    def passthrough(self, positive=None, negative=None):
+        return (positive, negative,)
+
+class ModelPassThrough:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {             
+            },
+            "optional": {
+                "model": ("MODEL", ),
+            }, 
+    }
+
+    RETURN_TYPES = ("MODEL", )
+    RETURN_NAMES = ("model",)
+    FUNCTION = "passthrough"
+    CATEGORY = "KJNodes/misc"
+    DESCRIPTION = """
+    Simply passes through the model,
+    workaround for Set node not allowing bypassed inputs.
+"""
+
+    def passthrough(self, model=None):
+            return (model,)
+
+def append_helper(t, mask, c, set_area_to_bounds, strength):
+        n = [t[0], t[1].copy()]
+        _, h, w = mask.shape
+        n[1]['mask'] = mask
+        n[1]['set_area_to_bounds'] = set_area_to_bounds
+        n[1]['mask_strength'] = strength
+        c.append(n)  
+
+class ConditioningSetMaskAndCombine:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "positive_1": ("CONDITIONING", ),
+                "negative_1": ("CONDITIONING", ),
+                "positive_2": ("CONDITIONING", ),
+                "negative_2": ("CONDITIONING", ),
+                "mask_1": ("MASK", ),
+                "mask_2": ("MASK", ),
+                "mask_1_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "mask_2_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "set_cond_area": (["default", "mask bounds"],),
+            }
+        }
+
+    RETURN_TYPES = ("CONDITIONING","CONDITIONING",)
+    RETURN_NAMES = ("combined_positive", "combined_negative",)
+    FUNCTION = "append"
+    CATEGORY = "KJNodes/masking/conditioning"
+    DESCRIPTION = """
+Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes
+"""
+
+    def append(self, positive_1, negative_1, positive_2, negative_2, mask_1, mask_2, set_cond_area, mask_1_strength, mask_2_strength):
+        c = []
+        c2 = []
+        set_area_to_bounds = False
+        if set_cond_area != "default":
+            set_area_to_bounds = True
+        if len(mask_1.shape) < 3:
+            mask_1 = mask_1.unsqueeze(0)
+        if len(mask_2.shape) < 3:
+            mask_2 = mask_2.unsqueeze(0)
+        for t in positive_1:
+            append_helper(t, mask_1, c, set_area_to_bounds, mask_1_strength)
+        for t in positive_2:
+            append_helper(t, mask_2, c, set_area_to_bounds, mask_2_strength)
+        for t in negative_1:
+            append_helper(t, mask_1, c2, set_area_to_bounds, mask_1_strength)
+        for t in negative_2:
+            append_helper(t, mask_2, c2, set_area_to_bounds, mask_2_strength)
+        return (c, c2)
+
+class ConditioningSetMaskAndCombine3:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "positive_1": ("CONDITIONING", ),
+                "negative_1": ("CONDITIONING", ),
+                "positive_2": ("CONDITIONING", ),
+                "negative_2": ("CONDITIONING", ),
+                "positive_3": ("CONDITIONING", ),
+                "negative_3": ("CONDITIONING", ),
+                "mask_1": ("MASK", ),
+                "mask_2": ("MASK", ),
+                "mask_3": ("MASK", ),
+                "mask_1_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "mask_2_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "mask_3_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "set_cond_area": (["default", "mask bounds"],),
+            }
+        }
+
+    RETURN_TYPES = ("CONDITIONING","CONDITIONING",)
+    RETURN_NAMES = ("combined_positive", "combined_negative",)
+    FUNCTION = "append"
+    CATEGORY = "KJNodes/masking/conditioning"
+    DESCRIPTION = """
+Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes
+"""
+
+    def append(self, positive_1, negative_1, positive_2, positive_3, negative_2, negative_3, mask_1, mask_2, mask_3, set_cond_area, mask_1_strength, mask_2_strength, mask_3_strength):
+        c = []
+        c2 = []
+        set_area_to_bounds = False
+        if set_cond_area != "default":
+            set_area_to_bounds = True
+        if len(mask_1.shape) < 3:
+            mask_1 = mask_1.unsqueeze(0)
+        if len(mask_2.shape) < 3:
+            mask_2 = mask_2.unsqueeze(0)
+        if len(mask_3.shape) < 3:
+            mask_3 = mask_3.unsqueeze(0)
+        for t in positive_1:
+            append_helper(t, mask_1, c, set_area_to_bounds, mask_1_strength)
+        for t in positive_2:
+            append_helper(t, mask_2, c, set_area_to_bounds, mask_2_strength)
+        for t in positive_3:
+            append_helper(t, mask_3, c, set_area_to_bounds, mask_3_strength)
+        for t in negative_1:
+            append_helper(t, mask_1, c2, set_area_to_bounds, mask_1_strength)
+        for t in negative_2:
+            append_helper(t, mask_2, c2, set_area_to_bounds, mask_2_strength)
+        for t in negative_3:
+            append_helper(t, mask_3, c2, set_area_to_bounds, mask_3_strength)
+        return (c, c2)
+
+class ConditioningSetMaskAndCombine4:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "positive_1": ("CONDITIONING", ),
+                "negative_1": ("CONDITIONING", ),
+                "positive_2": ("CONDITIONING", ),
+                "negative_2": ("CONDITIONING", ),
+                "positive_3": ("CONDITIONING", ),
+                "negative_3": ("CONDITIONING", ),
+                "positive_4": ("CONDITIONING", ),
+                "negative_4": ("CONDITIONING", ),
+                "mask_1": ("MASK", ),
+                "mask_2": ("MASK", ),
+                "mask_3": ("MASK", ),
+                "mask_4": ("MASK", ),
+                "mask_1_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "mask_2_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "mask_3_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "mask_4_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "set_cond_area": (["default", "mask bounds"],),
+            }
+        }
+
+    RETURN_TYPES = ("CONDITIONING","CONDITIONING",)
+    RETURN_NAMES = ("combined_positive", "combined_negative",)
+    FUNCTION = "append"
+    CATEGORY = "KJNodes/masking/conditioning"
+    DESCRIPTION = """
+Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes
+"""
+
+    def append(self, positive_1, negative_1, positive_2, positive_3, positive_4, negative_2, negative_3, negative_4, mask_1, mask_2, mask_3, mask_4, set_cond_area, mask_1_strength, mask_2_strength, mask_3_strength, mask_4_strength):
+        c = []
+        c2 = []
+        set_area_to_bounds = False
+        if set_cond_area != "default":
+            set_area_to_bounds = True
+        if len(mask_1.shape) < 3:
+            mask_1 = mask_1.unsqueeze(0)
+        if len(mask_2.shape) < 3:
+            mask_2 = mask_2.unsqueeze(0)
+        if len(mask_3.shape) < 3:
+            mask_3 = mask_3.unsqueeze(0)
+        if len(mask_4.shape) < 3:
+            mask_4 = mask_4.unsqueeze(0)
+        for t in positive_1:
+            append_helper(t, mask_1, c, set_area_to_bounds, mask_1_strength)
+        for t in positive_2:
+            append_helper(t, mask_2, c, set_area_to_bounds, mask_2_strength)
+        for t in positive_3:
+            append_helper(t, mask_3, c, set_area_to_bounds, mask_3_strength)
+        for t in positive_4:
+            append_helper(t, mask_4, c, set_area_to_bounds, mask_4_strength)
+        for t in negative_1:
+            append_helper(t, mask_1, c2, set_area_to_bounds, mask_1_strength)
+        for t in negative_2:
+            append_helper(t, mask_2, c2, set_area_to_bounds, mask_2_strength)
+        for t in negative_3:
+            append_helper(t, mask_3, c2, set_area_to_bounds, mask_3_strength)
+        for t in negative_4:
+            append_helper(t, mask_4, c2, set_area_to_bounds, mask_4_strength)
+        return (c, c2)
+
+class ConditioningSetMaskAndCombine5:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "positive_1": ("CONDITIONING", ),
+                "negative_1": ("CONDITIONING", ),
+                "positive_2": ("CONDITIONING", ),
+                "negative_2": ("CONDITIONING", ),
+                "positive_3": ("CONDITIONING", ),
+                "negative_3": ("CONDITIONING", ),
+                "positive_4": ("CONDITIONING", ),
+                "negative_4": ("CONDITIONING", ),
+                "positive_5": ("CONDITIONING", ),
+                "negative_5": ("CONDITIONING", ),
+                "mask_1": ("MASK", ),
+                "mask_2": ("MASK", ),
+                "mask_3": ("MASK", ),
+                "mask_4": ("MASK", ),
+                "mask_5": ("MASK", ),
+                "mask_1_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "mask_2_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "mask_3_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "mask_4_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "mask_5_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "set_cond_area": (["default", "mask bounds"],),
+            }
+        }
+
+    RETURN_TYPES = ("CONDITIONING","CONDITIONING",)
+    RETURN_NAMES = ("combined_positive", "combined_negative",)
+    FUNCTION = "append"
+    CATEGORY = "KJNodes/masking/conditioning"
+    DESCRIPTION = """
+Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes
+"""
+
+    def append(self, positive_1, negative_1, positive_2, positive_3, positive_4, positive_5, negative_2, negative_3, negative_4, negative_5, mask_1, mask_2, mask_3, mask_4, mask_5, set_cond_area, mask_1_strength, mask_2_strength, mask_3_strength, mask_4_strength, mask_5_strength):
+        c = []
+        c2 = []
+        set_area_to_bounds = False
+        if set_cond_area != "default":
+            set_area_to_bounds = True
+        if len(mask_1.shape) < 3:
+            mask_1 = mask_1.unsqueeze(0)
+        if len(mask_2.shape) < 3:
+            mask_2 = mask_2.unsqueeze(0)
+        if len(mask_3.shape) < 3:
+            mask_3 = mask_3.unsqueeze(0)
+        if len(mask_4.shape) < 3:
+            mask_4 = mask_4.unsqueeze(0)
+        if len(mask_5.shape) < 3:
+            mask_5 = mask_5.unsqueeze(0)
+        for t in positive_1:
+            append_helper(t, mask_1, c, set_area_to_bounds, mask_1_strength)
+        for t in positive_2:
+            append_helper(t, mask_2, c, set_area_to_bounds, mask_2_strength)
+        for t in positive_3:
+            append_helper(t, mask_3, c, set_area_to_bounds, mask_3_strength)
+        for t in positive_4:
+            append_helper(t, mask_4, c, set_area_to_bounds, mask_4_strength)
+        for t in positive_5:
+            append_helper(t, mask_5, c, set_area_to_bounds, mask_5_strength)
+        for t in negative_1:
+            append_helper(t, mask_1, c2, set_area_to_bounds, mask_1_strength)
+        for t in negative_2:
+            append_helper(t, mask_2, c2, set_area_to_bounds, mask_2_strength)
+        for t in negative_3:
+            append_helper(t, mask_3, c2, set_area_to_bounds, mask_3_strength)
+        for t in negative_4:
+            append_helper(t, mask_4, c2, set_area_to_bounds, mask_4_strength)
+        for t in negative_5:
+            append_helper(t, mask_5, c2, set_area_to_bounds, mask_5_strength)
+        return (c, c2)
+    
+class VRAM_Debug:
+    
+    @classmethod
+    
+    def INPUT_TYPES(s):
+      return {
+        "required": {
+            
+            "empty_cache": ("BOOLEAN", {"default": True}),
+            "gc_collect": ("BOOLEAN", {"default": True}),
+            "unload_all_models": ("BOOLEAN", {"default": False}),
+        },
+        "optional": {
+            "any_input": (IO.ANY,),
+            "image_pass": ("IMAGE",),
+            "model_pass": ("MODEL",),
+        }
+	}
+        
+    RETURN_TYPES = (IO.ANY, "IMAGE","MODEL","INT", "INT",)
+    RETURN_NAMES = ("any_output", "image_pass", "model_pass", "freemem_before", "freemem_after")
+    FUNCTION = "VRAMdebug"
+    CATEGORY = "KJNodes/memory"
+    DESCRIPTION = """
+Returns the inputs unchanged, they are only used as triggers,  
+and performs comfy model management functions and garbage collection,  
+reports free VRAM before and after the operations.
+"""
+
+    def VRAMdebug(self, gc_collect, empty_cache, unload_all_models, image_pass=None, model_pass=None, any_input=None):
+        freemem_before = model_management.get_free_memory()
+        logging.info(f"VRAMdebug: free memory before: {freemem_before:,.0f}")
+        if empty_cache:
+            model_management.soft_empty_cache()
+        if unload_all_models:
+            model_management.unload_all_models()
+        if gc_collect:
+            import gc
+            gc.collect()
+        freemem_after = model_management.get_free_memory()
+        logging.info(f"VRAMdebug: free memory after: {freemem_after:,.0f}")
+        logging.info(f"VRAMdebug: freed memory: {freemem_after - freemem_before:,.0f}")
+        return {"ui": {
+            "text": [f"{freemem_before:,.0f}x{freemem_after:,.0f}"]}, 
+            "result": (any_input, image_pass, model_pass, freemem_before, freemem_after) 
+        }
+
+class SomethingToString:
+    @classmethod
+    
+    def INPUT_TYPES(s):
+     return {
+        "required": {
+        "input": (IO.ANY, ),
+    },
+    "optional": {
+        "prefix": ("STRING", {"default": ""}),
+        "suffix": ("STRING", {"default": ""}),
+    }
+    }
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "stringify"
+    CATEGORY = "KJNodes/text"
+    DESCRIPTION = """
+Converts any type to a string.
+"""
+
+    def stringify(self, input, prefix="", suffix=""):
+        if isinstance(input, (int, float, bool, str)):
+            stringified = str(input)
+        elif isinstance(input, list):
+            stringified = ', '.join(str(item) for item in input)
+        else:
+            return input,
+        if prefix: # Check if prefix is not empty
+            stringified = prefix + stringified # Add the prefix
+        if suffix: # Check if suffix is not empty
+            stringified = stringified + suffix # Add the suffix
+
+        return (stringified,)
+
+class Sleep:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input": (IO.ANY, ),
+                "minutes": ("INT", {"default": 0, "min": 0, "max": 1439}),
+                "seconds": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 59.99, "step": 0.01}),
+            },
+        }
+    RETURN_TYPES = (IO.ANY,)
+    FUNCTION = "sleepdelay"
+    CATEGORY = "KJNodes/misc"
+    DESCRIPTION = """
+Delays the execution for the input amount of time.
+"""
+
+    def sleepdelay(self, input, minutes, seconds):
+        total_seconds = minutes * 60 + seconds
+        time.sleep(total_seconds)
+        return input,
+    
+class EmptyLatentImagePresets:
+    @classmethod
+    def INPUT_TYPES(cls):  
+        return {
+        "required": {
+             "dimensions": (
+            [
+                '512 x 512 (1:1)',
+                '768 x 512 (1.5:1)',
+                '960 x 512 (1.875:1)',
+                '1024 x 512 (2:1)',
+                '1024 x 576 (1.778:1)',
+                '1536 x 640 (2.4:1)',
+                '1344 x 768 (1.75:1)',
+                '1216 x 832 (1.46:1)',
+                '1152 x 896 (1.286:1)',
+                '1024 x 1024 (1:1)',
+            ],
+            {
+            "default": '512 x 512 (1:1)'
+             }),
+           
+            "invert": ("BOOLEAN", {"default": False}),
+            "batch_size": ("INT", {
+            "default": 1,
+            "min": 1,
+            "max": 4096
+            }),
+        },
+        }
+
+    RETURN_TYPES = ("LATENT", "INT", "INT")
+    RETURN_NAMES = ("Latent", "Width", "Height")
+    FUNCTION = "generate"
+    CATEGORY = "KJNodes/latents"
+
+    def generate(self, dimensions, invert, batch_size):
+        from nodes import EmptyLatentImage
+        result = [x.strip() for x in dimensions.split('x')]
+
+        # Remove the aspect ratio part
+        result[0] = result[0].split('(')[0].strip()
+        result[1] = result[1].split('(')[0].strip()
+        
+        if invert:
+            width = int(result[1].split(' ')[0])
+            height = int(result[0])
+        else:
+            width = int(result[0])
+            height = int(result[1].split(' ')[0])
+        latent = EmptyLatentImage().generate(width, height, batch_size)[0]
+
+        return (latent, int(width), int(height),)
+
+class EmptyLatentImageCustomPresets:
+    @classmethod
+    def INPUT_TYPES(cls):
+        try:
+            with open(os.path.join(script_directory, 'custom_dimensions.json')) as f:
+                dimensions_dict = json.load(f)
+        except FileNotFoundError:
+            dimensions_dict = []
+        return {
+        "required": {
+            "dimensions": (
+                 [f"{d['label']} - {d['value']}" for d in dimensions_dict],
+            ),
+           
+            "invert": ("BOOLEAN", {"default": False}),
+            "batch_size": ("INT", {
+            "default": 1,
+            "min": 1,
+            "max": 4096
+            }),
+        },
+        }
+
+    RETURN_TYPES = ("LATENT", "INT", "INT")
+    RETURN_NAMES = ("Latent", "Width", "Height")
+    FUNCTION = "generate"
+    CATEGORY = "KJNodes/latents"
+    DESCRIPTION = """
+Generates an empty latent image with the specified dimensions.  
+The choices are loaded from 'custom_dimensions.json' in the nodes folder.
+"""
+
+    def generate(self, dimensions, invert, batch_size):
+       from nodes import EmptyLatentImage
+       # Split the string into label and value
+       label, value = dimensions.split(' - ')
+       # Split the value into width and height
+       width, height = [x.strip() for x in value.split('x')]
+   
+       if invert:
+           width, height = height, width
+   
+       latent = EmptyLatentImage().generate(int(width), int(height), batch_size)[0]
+   
+       return (latent, int(width), int(height),)
+
+class WidgetToString:
+    @classmethod
+    def IS_CHANGED(cls,*,id,node_title,any_input,**kwargs):
+        if any_input is not None and (id != 0 or node_title != ""):
+            return float("NaN")
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "id": ("INT", {"default": 0, "min": 0, "max": 100000, "step": 1}),
+                "widget_name": ("STRING", {"multiline": False}),
+                "return_all": ("BOOLEAN", {"default": False}),
+            },
+            "optional": {
+                         "any_input": (IO.ANY, ),
+                         "node_title": ("STRING", {"multiline": False}),
+                         "allowed_float_decimals": ("INT", {"default": 2, "min": 0, "max": 10, "tooltip": "Number of decimal places to display for float values"}),
+                         },
+            "hidden": {"extra_pnginfo": "EXTRA_PNGINFO",
+                       "prompt": "PROMPT",
+                       "unique_id": "UNIQUE_ID",},
+        }
+
+    RETURN_TYPES = ("STRING", )
+    FUNCTION = "get_widget_value"
+    CATEGORY = "KJNodes/text"
+    DESCRIPTION = """
+Selects a node and it's specified widget and outputs the value as a string.  
+If no node id or title is provided it will use the 'any_input' link and use that node.  
+To see node id's, enable "Node ID Badge Mode" in main settings.
+Alternatively you can search with the node title. Node titles ONLY exist if they  
+are manually edited!
+'widget_name' can be a comma separated list.
+The 'any_input' is required for making sure the node you want the value from exists in the workflow.
+"""
+
+    def get_widget_value(self, id, widget_name, extra_pnginfo, prompt, unique_id, return_all=False, any_input=None, node_title="", allowed_float_decimals=2):
+        workflow = extra_pnginfo["workflow"]
+        #print(json.dumps(workflow, indent=4))
+        results = []
+        node_id = link_id = subgraph_prefix = None
+        link_to_node_map = {}
+        node_to_subgraph_map = {}  # Track which subgraph each node belongs to
+
+        # Parse unique_id - handle both "parent:id" format and simple int format
+        if isinstance(unique_id, str) and ":" in unique_id:
+            unique_id_parts = unique_id.split(":")
+            unique_id_int = int(unique_id_parts[-1])  # Use the last part as the node id
+            subgraph_prefix = ":".join(unique_id_parts[:-1])  # Store the parent prefix (e.g., "14")
+        else:
+            unique_id_int = int(unique_id)
+
+        # Collect all nodes from main workflow and subgraphs
+        all_nodes = list(workflow.get("nodes", []))
+        definitions = workflow.get("definitions", {})
+        subgraphs = definitions.get("subgraphs", [])
+
+        # Find which main workflow node references each subgraph
+        subgraph_id_to_parent = {}
+        for node in workflow.get("nodes", []):
+            node_type = node.get("type", "")
+            # Subgraph nodes have a UUID as their type
+            if "-" in node_type and len(node_type) == 36:  # UUID format check
+                subgraph_id_to_parent[node_type] = node["id"]
+
+        for subgraph in subgraphs:
+            subgraph_id = subgraph.get("id", "")
+            parent_node_id = subgraph_id_to_parent.get(subgraph_id)
+
+            subgraph_nodes = subgraph.get("nodes", [])
+            for node in subgraph_nodes:
+                # Track which subgraph (parent node) this node belongs to
+                if parent_node_id is not None:
+                    node_to_subgraph_map[node["id"]] = parent_node_id
+            all_nodes.extend(subgraph_nodes)
+
+            # Also build link_to_node_map from subgraph links
+            subgraph_links = subgraph.get("links", [])
+            for link in subgraph_links:
+                # link format: [link_id, origin_id, origin_slot, target_id, target_slot, type]
+                if isinstance(link, dict):
+                    link_to_node_map[link["id"]] = link["origin_id"]
+                elif isinstance(link, list) and len(link) >= 2:
+                    link_to_node_map[link[0]] = link[1]
+
+        for node in all_nodes:
+            if node_title:
+                if "title" in node:
+                    if node["title"] == node_title:
+                        node_id = node["id"]
+                        break
+                else:
+                    logging.warning("Node title not found.")
+            elif id != 0:
+                if node["id"] == id:
+                    node_id = id
+                    break
+            elif any_input is not None:
+                if node["type"] == "WidgetToString" and node["id"] == unique_id_int and not link_id:
+                    for node_input in node["inputs"]:
+                        if node_input["name"] == "any_input":
+                            link_id = node_input["link"]
+
+                # Construct a map of links to node IDs for future reference
+                node_outputs = node.get("outputs", None)
+                if not node_outputs:
+                    continue
+                for output in node_outputs:
+                    node_links = output.get("links", None)
+                    if not node_links:
+                        continue
+                    for link in node_links:
+                        link_to_node_map[link] = node["id"]
+                        if link_id and link == link_id:
+                            break
+
+        if link_id:
+            node_id = link_to_node_map.get(link_id, None)
+
+        if node_id is None:
+            raise ValueError("No matching node found for the given title or id")
+
+        # Determine the correct prompt key
+        # First check if the target node is in a subgraph
+        target_subgraph_parent = node_to_subgraph_map.get(node_id)
+
+        if target_subgraph_parent is not None:
+            # Target node is in a subgraph, use the parent node id as prefix
+            prompt_key = f"{target_subgraph_parent}:{node_id}"
+        elif subgraph_prefix is not None:
+            # We're in a subgraph, use our prefix
+            prompt_key = f"{subgraph_prefix}:{node_id}"
+        else:
+            prompt_key = str(node_id)
+
+        # Try the prefixed key first, then fall back to just the node_id
+        if prompt_key not in prompt:
+            prompt_key = str(node_id)
+
+        if prompt_key not in prompt:
+            raise KeyError(f"Node not found in prompt. Tried keys: '{target_subgraph_parent}:{node_id}' and '{node_id}'")
+
+        values = prompt[prompt_key]
+        if "inputs" in values:
+            inputs = values["inputs"]
+
+            # support comma-separated list and trim whitespace
+            widget_names = []
+            if widget_name:
+                widget_names = [w.strip() for w in widget_name.split(",") if w.strip()]
+
+            if return_all:
+                # Format items based on type
+                formatted_items = []
+                for k, v in inputs.items():
+                    if isinstance(v, float):
+                        item = f"{k}: {v:.{allowed_float_decimals}f}"
+                    else:
+                        item = f"{k}: {str(v)}"
+                    formatted_items.append(item)
+                results.append(", ".join(formatted_items))
+
+            # Single widget name (trimmed)
+            elif len(widget_names) == 1:
+                name = widget_names[0]
+                if name in inputs:
+                    v = inputs[name]
+                    if isinstance(v, float):
+                        v = f"{v:.{allowed_float_decimals}f}"
+                    else:
+                        v = str(v)
+                    return (v, )
+                else:
+                    raise NameError(f"Widget not found: {node_id}.{name}")
+
+            # Multiple widget names: return "name: value" pairs
+            elif len(widget_names) > 1:
+                formatted_items = []
+                for name in widget_names:
+                    if name not in inputs:
+                        raise NameError(f"Widget not found: {node_id}.{name}")
+                    v = inputs[name]
+                    if isinstance(v, float):
+                        v = f"{v:.{allowed_float_decimals}f}"
+                    else:
+                        v = str(v)
+                    formatted_items.append(f"{name}: {v}")
+                return (", ".join(formatted_items), )
+
+            else:
+                # No valid widget name provided
+                raise NameError(f"Widget not found: {node_id}.{widget_name}")
+
+        return (", ".join(results).strip(", "), )
+
+
+class DummyOut:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+            "any_input": (IO.ANY, ),
+            }
+        }
+
+    RETURN_TYPES = (IO.ANY,)
+    FUNCTION = "dummy"
+    CATEGORY = "KJNodes/misc"
+    OUTPUT_NODE = True
+    DESCRIPTION = """
+Does nothing, used to trigger generic workflow output.    
+A way to get previews in the UI without saving anything to disk.
+"""
+
+    def dummy(self, any_input):
+        return (any_input,)
+    
+class FlipSigmasAdjusted:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"sigmas": ("SIGMAS", ),
+                     "divide_by_last_sigma": ("BOOLEAN", {"default": False}),
+                     "divide_by": ("FLOAT", {"default": 1,"min": 1, "max": 255, "step": 0.01}),
+                     "offset_by": ("INT", {"default": 1,"min": -100, "max": 100, "step": 1}),
+                     }
+                }
+    RETURN_TYPES = ("SIGMAS", "STRING",)
+    RETURN_NAMES = ("SIGMAS", "sigmas_string",)
+    CATEGORY = "KJNodes/noise"
+    FUNCTION = "get_sigmas_adjusted"
+
+    def get_sigmas_adjusted(self, sigmas, divide_by_last_sigma, divide_by, offset_by):
+        
+        sigmas = sigmas.flip(0)
+        if sigmas[0] == 0:
+            sigmas[0] = 0.0001
+        adjusted_sigmas = sigmas.clone()
+        #offset sigma
+        for i in range(1, len(sigmas)):
+            offset_index = i - offset_by
+            if 0 <= offset_index < len(sigmas):
+                adjusted_sigmas[i] = sigmas[offset_index]
+            else:
+                adjusted_sigmas[i] = 0.0001 
+        if adjusted_sigmas[0] == 0:
+            adjusted_sigmas[0] = 0.0001  
+        if divide_by_last_sigma:
+            adjusted_sigmas = adjusted_sigmas / adjusted_sigmas[-1]
+
+        sigma_np_array = adjusted_sigmas.numpy()
+        array_string = np.array2string(sigma_np_array, precision=2, separator=', ', threshold=np.inf)
+        adjusted_sigmas = adjusted_sigmas / divide_by
+        return (adjusted_sigmas, array_string,)
+    
+class CustomSigmas:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {
+                     "sigmas_string" :("STRING", {"default": "14.615, 6.475, 3.861, 2.697, 1.886, 1.396, 0.963, 0.652, 0.399, 0.152, 0.029","multiline": True}),
+                     "interpolate_to_steps": ("INT", {"default": 10,"min": 0, "max": 255, "step": 1}),
+                     }
+                }
+    RETURN_TYPES = ("SIGMAS",)
+    RETURN_NAMES = ("SIGMAS",)
+    CATEGORY = "KJNodes/noise"
+    FUNCTION = "customsigmas"
+    DESCRIPTION = """
+Creates a sigmas tensor from a string of comma separated values.  
+Examples: 
+   
+Nvidia's optimized AYS 10 step schedule for SD 1.5:  
+14.615, 6.475, 3.861, 2.697, 1.886, 1.396, 0.963, 0.652, 0.399, 0.152, 0.029  
+SDXL:   
+14.615, 6.315, 3.771, 2.181, 1.342, 0.862, 0.555, 0.380, 0.234, 0.113, 0.029  
+SVD:  
+700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002  
+"""
+    def customsigmas(self, sigmas_string, interpolate_to_steps):
+        sigmas_list = sigmas_string.split(', ')
+        sigmas_float_list = [float(sigma) for sigma in sigmas_list]
+        sigmas_tensor = torch.FloatTensor(sigmas_float_list)
+        if len(sigmas_tensor) != interpolate_to_steps + 1:
+            sigmas_tensor = self.loglinear_interp(sigmas_tensor, interpolate_to_steps + 1)
+        sigmas_tensor[-1] = 0
+        return (sigmas_tensor.float(),)
+     
+    def loglinear_interp(self, t_steps, num_steps):
+        """
+        Performs log-linear interpolation of a given array of decreasing numbers.
+        """
+        t_steps_np = t_steps.numpy()
+
+        xs = np.linspace(0, 1, len(t_steps_np))
+        ys = np.log(t_steps_np[::-1])
+        
+        new_xs = np.linspace(0, 1, num_steps)
+        new_ys = np.interp(new_xs, xs, ys)
+        
+        interped_ys = np.exp(new_ys)[::-1].copy()
+        interped_ys_tensor = torch.tensor(interped_ys)
+        return interped_ys_tensor
+    
+class StringToFloatList:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {
+                     "string" :("STRING", {"default": "1, 2, 3", "multiline": True}),
+                     }
+                }
+    RETURN_TYPES = ("FLOAT",)
+    RETURN_NAMES = ("FLOAT",)
+    CATEGORY = "KJNodes/misc"
+    FUNCTION = "createlist"
+
+    def createlist(self, string):
+        float_list = [float(x.strip()) for x in string.split(',')]
+        return (float_list,)
+
+ 
+class InjectNoiseToLatent:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "latents":("LATENT",),  
+            "strength": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 200.0, "step": 0.0001}),
+            "noise":  ("LATENT",),
+            "normalize": ("BOOLEAN", {"default": False}),
+            "average": ("BOOLEAN", {"default": False}),
+            },
+            "optional":{
+                "mask": ("MASK", ),
+                "mix_randn_amount": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.001}),
+                "seed": ("INT", {"default": 123,"min": 0, "max": 0xffffffffffffffff, "step": 1}),
+            }
+            }
+    
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "injectnoise"
+    CATEGORY = "KJNodes/noise"
+        
+    def injectnoise(self, latents, strength, noise, normalize, average, mix_randn_amount=0, seed=None, mask=None):
+        samples = latents["samples"].clone().cpu()
+        noise = noise["samples"].clone().cpu()
+        if samples.shape != samples.shape:
+            raise ValueError("InjectNoiseToLatent: Latent and noise must have the same shape")
+        if average:
+            noised = (samples + noise) / 2
+        else:
+            noised = samples + noise * strength
+        if normalize:
+            noised = noised / noised.std()
+        if mask is not None:
+            mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(noised.shape[2], noised.shape[3]), mode="bilinear")
+            mask = mask.expand((-1,noised.shape[1],-1,-1))
+            if mask.shape[0] < noised.shape[0]:
+                mask = mask.repeat((noised.shape[0] -1) // mask.shape[0] + 1, 1, 1, 1)[:noised.shape[0]]
+            noised = mask * noised + (1-mask) * samples
+        if mix_randn_amount > 0:
+            if seed is not None:
+                generator = torch.manual_seed(seed)
+                rand_noise = torch.randn(noised.size(), dtype=noised.dtype, layout=noised.layout, generator=generator, device="cpu")
+                noised = noised + (mix_randn_amount * rand_noise)
+        
+        return ({"samples":noised},)
+ 
+class SoundReactive:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {  
+            "sound_level": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 99999, "step": 0.01}),
+            "start_range_hz": ("INT", {"default": 150, "min": 0, "max": 9999, "step": 1}),
+            "end_range_hz": ("INT", {"default": 2000, "min": 0, "max": 9999, "step": 1}),
+            "multiplier": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 99999, "step": 0.01}),
+            "smoothing_factor": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+            "normalize": ("BOOLEAN", {"default": False}),
+            },
+            }
+    
+    RETURN_TYPES = ("FLOAT","INT",)
+    RETURN_NAMES =("sound_level", "sound_level_int",)
+    FUNCTION = "react"
+    CATEGORY = "KJNodes/audio"
+    DESCRIPTION = """
+Reacts to the sound level of the input.  
+Uses your browsers sound input options and requires.  
+Meant to be used with realtime diffusion with autoqueue.
+"""
+        
+    def react(self, sound_level, start_range_hz, end_range_hz, smoothing_factor, multiplier, normalize):
+
+        sound_level *= multiplier
+
+        if normalize:
+            sound_level /= 255
+
+        sound_level_int = int(sound_level)
+        return (sound_level, sound_level_int, )     
+       
+class GenerateNoise:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+            "width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+            "height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+            "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
+            "seed": ("INT", {"default": 123,"min": 0, "max": 0xffffffffffffffff, "step": 1}),
+            "multiplier": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 4096, "step": 0.01}),
+            "constant_batch_noise": ("BOOLEAN", {"default": False}),
+            "normalize": ("BOOLEAN", {"default": False}),
+            },
+            "optional": {
+                "model": ("MODEL", ),
+                "sigmas": ("SIGMAS", ),
+                "latent_channels": (['4', '16', ],),
+                "shape": (["BCHW", "BCTHW","BTCHW",],),
+            }
+        }
+    
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "generatenoise"
+    CATEGORY = "KJNodes/noise"
+    DESCRIPTION = """
+Generates noise for injection or to be used as empty latents on samplers with add_noise off.
+"""
+        
+    def generatenoise(self, batch_size, width, height, seed, multiplier, constant_batch_noise, normalize, sigmas=None, model=None, latent_channels=4, shape="BCHW"):
+
+        generator = torch.manual_seed(seed)
+        if shape == "BCHW":
+            noise = torch.randn([batch_size, int(latent_channels), height // 8, width // 8], dtype=torch.float32, layout=torch.strided, generator=generator, device="cpu")
+        elif shape == "BCTHW":
+            noise = torch.randn([1, int(latent_channels), batch_size,height // 8, width // 8], dtype=torch.float32, layout=torch.strided, generator=generator, device="cpu")
+        elif shape == "BTCHW":
+            noise = torch.randn([1, batch_size, int(latent_channels), height // 8, width // 8], dtype=torch.float32, layout=torch.strided, generator=generator, device="cpu")
+        if sigmas is not None:
+            sigma = sigmas[0] - sigmas[-1]
+            sigma /= model.model.latent_format.scale_factor
+            noise *= sigma
+
+        noise *=multiplier
+
+        if normalize:
+            noise = noise / noise.std()
+        if constant_batch_noise:
+            noise = noise[0].repeat(batch_size, 1, 1, 1)
+
+        
+        return ({"samples":noise}, )
+
+def camera_embeddings(elevation, azimuth):
+    elevation = torch.as_tensor([elevation])
+    azimuth = torch.as_tensor([azimuth])
+    embeddings = torch.stack(
+        [
+                torch.deg2rad(
+                    (90 - elevation) - (90)
+                ),  # Zero123 polar is 90-elevation
+                torch.sin(torch.deg2rad(azimuth)),
+                torch.cos(torch.deg2rad(azimuth)),
+                torch.deg2rad(
+                    90 - torch.full_like(elevation, 0)
+                ),
+        ], dim=-1).unsqueeze(1)
+
+    return embeddings
+
+def interpolate_angle(start, end, fraction):
+    # Calculate the difference in angles and adjust for wraparound if necessary
+    diff = (end - start + 540) % 360 - 180
+    # Apply fraction to the difference
+    interpolated = start + fraction * diff
+    # Normalize the result to be within the range of -180 to 180
+    return (interpolated + 180) % 360 - 180
+
+
+class StableZero123_BatchSchedule:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "clip_vision": ("CLIP_VISION",),
+                              "init_image": ("IMAGE",),
+                              "vae": ("VAE",),
+                              "width": ("INT", {"default": 256, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
+                              "height": ("INT", {"default": 256, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
+                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
+                              "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],),
+                              "azimuth_points_string": ("STRING", {"default": "0:(0.0),\n7:(1.0),\n15:(0.0)\n", "multiline": True}),
+                              "elevation_points_string": ("STRING", {"default": "0:(0.0),\n7:(0.0),\n15:(0.0)\n", "multiline": True}),
+                             }}
+    
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
+    RETURN_NAMES = ("positive", "negative", "latent")
+    FUNCTION = "encode"
+    CATEGORY = "KJNodes/experimental"
+
+    def encode(self, clip_vision, init_image, vae, width, height, batch_size, azimuth_points_string, elevation_points_string, interpolation):
+        output = clip_vision.encode_image(init_image)
+        pooled = output.image_embeds.unsqueeze(0)
+        pixels = common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
+        encode_pixels = pixels[:,:,:,:3]
+        t = vae.encode(encode_pixels)
+
+        def ease_in(t):
+            return t * t
+        def ease_out(t):
+            return 1 - (1 - t) * (1 - t)
+        def ease_in_out(t):
+            return 3 * t * t - 2 * t * t * t
+        
+        # Parse the azimuth input string into a list of tuples
+        azimuth_points = []
+        azimuth_points_string = azimuth_points_string.rstrip(',\n')
+        for point_str in azimuth_points_string.split(','):
+            frame_str, azimuth_str = point_str.split(':')
+            frame = int(frame_str.strip())
+            azimuth = float(azimuth_str.strip()[1:-1]) 
+            azimuth_points.append((frame, azimuth))
+        # Sort the points by frame number
+        azimuth_points.sort(key=lambda x: x[0])
+
+        # Parse the elevation input string into a list of tuples
+        elevation_points = []
+        elevation_points_string = elevation_points_string.rstrip(',\n')
+        for point_str in elevation_points_string.split(','):
+            frame_str, elevation_str = point_str.split(':')
+            frame = int(frame_str.strip())
+            elevation_val = float(elevation_str.strip()[1:-1]) 
+            elevation_points.append((frame, elevation_val))
+        # Sort the points by frame number
+        elevation_points.sort(key=lambda x: x[0])
+
+        # Index of the next point to interpolate towards
+        next_point = 1
+        next_elevation_point = 1
+
+        positive_cond_out = []
+        positive_pooled_out = []
+        negative_cond_out = []
+        negative_pooled_out = []
+        
+        #azimuth interpolation
+        for i in range(batch_size):
+            # Find the interpolated azimuth for the current frame
+            while next_point < len(azimuth_points) and i >= azimuth_points[next_point][0]:
+                next_point += 1
+            # If next_point is equal to the length of points, we've gone past the last point
+            if next_point == len(azimuth_points):
+                next_point -= 1  # Set next_point to the last index of points
+            prev_point = max(next_point - 1, 0)  # Ensure prev_point is not less than 0
+
+            # Calculate fraction
+            if azimuth_points[next_point][0] != azimuth_points[prev_point][0]:  # Prevent division by zero
+                fraction = (i - azimuth_points[prev_point][0]) / (azimuth_points[next_point][0] - azimuth_points[prev_point][0])
+                if interpolation == "ease_in":
+                    fraction = ease_in(fraction)
+                elif interpolation == "ease_out":
+                    fraction = ease_out(fraction)
+                elif interpolation == "ease_in_out":
+                    fraction = ease_in_out(fraction)
+                
+                # Use the new interpolate_angle function
+                interpolated_azimuth = interpolate_angle(azimuth_points[prev_point][1], azimuth_points[next_point][1], fraction)
+            else:
+                interpolated_azimuth = azimuth_points[prev_point][1]
+            # Interpolate the elevation
+            next_elevation_point = 1
+            while next_elevation_point < len(elevation_points) and i >= elevation_points[next_elevation_point][0]:
+                next_elevation_point += 1
+            if next_elevation_point == len(elevation_points):
+                next_elevation_point -= 1
+            prev_elevation_point = max(next_elevation_point - 1, 0)
+
+            if elevation_points[next_elevation_point][0] != elevation_points[prev_elevation_point][0]:
+                fraction = (i - elevation_points[prev_elevation_point][0]) / (elevation_points[next_elevation_point][0] - elevation_points[prev_elevation_point][0])
+                if interpolation == "ease_in":
+                    fraction = ease_in(fraction)
+                elif interpolation == "ease_out":
+                    fraction = ease_out(fraction)
+                elif interpolation == "ease_in_out":
+                    fraction = ease_in_out(fraction)
+                
+                interpolated_elevation = interpolate_angle(elevation_points[prev_elevation_point][1], elevation_points[next_elevation_point][1], fraction)
+            else:
+                interpolated_elevation = elevation_points[prev_elevation_point][1]
+
+            cam_embeds = camera_embeddings(interpolated_elevation, interpolated_azimuth)
+            cond = torch.cat([pooled, cam_embeds.repeat((pooled.shape[0], 1, 1))], dim=-1)
+
+            positive_pooled_out.append(t)
+            positive_cond_out.append(cond)
+            negative_pooled_out.append(torch.zeros_like(t))
+            negative_cond_out.append(torch.zeros_like(pooled))
+
+        # Concatenate the conditions and pooled outputs
+        final_positive_cond = torch.cat(positive_cond_out, dim=0)
+        final_positive_pooled = torch.cat(positive_pooled_out, dim=0)
+        final_negative_cond = torch.cat(negative_cond_out, dim=0)
+        final_negative_pooled = torch.cat(negative_pooled_out, dim=0)
+
+        # Structure the final output
+        final_positive = [[final_positive_cond, {"concat_latent_image": final_positive_pooled}]]
+        final_negative = [[final_negative_cond, {"concat_latent_image": final_negative_pooled}]]
+
+        latent = torch.zeros([batch_size, 4, height // 8, width // 8])
+        return (final_positive, final_negative, {"samples": latent})
+
+def linear_interpolate(start, end, fraction):
+    return start + (end - start) * fraction
+
+class SV3D_BatchSchedule:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "clip_vision": ("CLIP_VISION",),
+                              "init_image": ("IMAGE",),
+                              "vae": ("VAE",),
+                              "width": ("INT", {"default": 576, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
+                              "height": ("INT", {"default": 576, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
+                              "batch_size": ("INT", {"default": 21, "min": 1, "max": 4096}),
+                              "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],),
+                              "azimuth_points_string": ("STRING", {"default": "0:(0.0),\n9:(180.0),\n20:(360.0)\n", "multiline": True}),
+                              "elevation_points_string": ("STRING", {"default": "0:(0.0),\n9:(0.0),\n20:(0.0)\n", "multiline": True}),
+                             }}
+    
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
+    RETURN_NAMES = ("positive", "negative", "latent")
+    FUNCTION = "encode"
+    CATEGORY = "KJNodes/experimental"
+    DESCRIPTION = """
+Allow scheduling of the azimuth and elevation conditions for SV3D.  
+Note that SV3D is still a video model and the schedule needs to always go forward  
+https://huggingface.co/stabilityai/sv3d
+"""
+
+    def encode(self, clip_vision, init_image, vae, width, height, batch_size, azimuth_points_string, elevation_points_string, interpolation):
+        output = clip_vision.encode_image(init_image)
+        pooled = output.image_embeds.unsqueeze(0)
+        pixels = common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
+        encode_pixels = pixels[:,:,:,:3]
+        t = vae.encode(encode_pixels)
+
+        def ease_in(t):
+            return t * t
+        def ease_out(t):
+            return 1 - (1 - t) * (1 - t)
+        def ease_in_out(t):
+            return 3 * t * t - 2 * t * t * t
+        
+        # Parse the azimuth input string into a list of tuples
+        azimuth_points = []
+        azimuth_points_string = azimuth_points_string.rstrip(',\n')
+        for point_str in azimuth_points_string.split(','):
+            frame_str, azimuth_str = point_str.split(':')
+            frame = int(frame_str.strip())
+            azimuth = float(azimuth_str.strip()[1:-1]) 
+            azimuth_points.append((frame, azimuth))
+        # Sort the points by frame number
+        azimuth_points.sort(key=lambda x: x[0])
+
+        # Parse the elevation input string into a list of tuples
+        elevation_points = []
+        elevation_points_string = elevation_points_string.rstrip(',\n')
+        for point_str in elevation_points_string.split(','):
+            frame_str, elevation_str = point_str.split(':')
+            frame = int(frame_str.strip())
+            elevation_val = float(elevation_str.strip()[1:-1]) 
+            elevation_points.append((frame, elevation_val))
+        # Sort the points by frame number
+        elevation_points.sort(key=lambda x: x[0])
+
+        # Index of the next point to interpolate towards
+        next_point = 1
+        next_elevation_point = 1
+        elevations = []
+        azimuths = []
+        # For azimuth interpolation
+        for i in range(batch_size):
+            # Find the interpolated azimuth for the current frame
+            while next_point < len(azimuth_points) and i >= azimuth_points[next_point][0]:
+                next_point += 1
+            if next_point == len(azimuth_points):
+                next_point -= 1
+            prev_point = max(next_point - 1, 0)
+
+            if azimuth_points[next_point][0] != azimuth_points[prev_point][0]:
+                fraction = (i - azimuth_points[prev_point][0]) / (azimuth_points[next_point][0] - azimuth_points[prev_point][0])
+                # Apply the ease function to the fraction
+                if interpolation == "ease_in":
+                    fraction = ease_in(fraction)
+                elif interpolation == "ease_out":
+                    fraction = ease_out(fraction)
+                elif interpolation == "ease_in_out":
+                    fraction = ease_in_out(fraction)
+                
+                interpolated_azimuth = linear_interpolate(azimuth_points[prev_point][1], azimuth_points[next_point][1], fraction)
+            else:
+                interpolated_azimuth = azimuth_points[prev_point][1]
+
+            # Interpolate the elevation
+            next_elevation_point = 1
+            while next_elevation_point < len(elevation_points) and i >= elevation_points[next_elevation_point][0]:
+                next_elevation_point += 1
+            if next_elevation_point == len(elevation_points):
+                next_elevation_point -= 1
+            prev_elevation_point = max(next_elevation_point - 1, 0)
+
+            if elevation_points[next_elevation_point][0] != elevation_points[prev_elevation_point][0]:
+                fraction = (i - elevation_points[prev_elevation_point][0]) / (elevation_points[next_elevation_point][0] - elevation_points[prev_elevation_point][0])
+                # Apply the ease function to the fraction
+                if interpolation == "ease_in":
+                    fraction = ease_in(fraction)
+                elif interpolation == "ease_out":
+                    fraction = ease_out(fraction)
+                elif interpolation == "ease_in_out":
+                    fraction = ease_in_out(fraction)
+                
+                interpolated_elevation = linear_interpolate(elevation_points[prev_elevation_point][1], elevation_points[next_elevation_point][1], fraction)
+            else:
+                interpolated_elevation = elevation_points[prev_elevation_point][1]
+
+            azimuths.append(interpolated_azimuth)
+            elevations.append(interpolated_elevation)
+
+        #print("azimuths", azimuths)
+        #print("elevations", elevations)
+
+        # Structure the final output
+        final_positive = [[pooled, {"concat_latent_image": t, "elevation": elevations, "azimuth": azimuths}]]
+        final_negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t),"elevation": elevations, "azimuth": azimuths}]]
+
+        latent = torch.zeros([batch_size, 4, height // 8, width // 8])
+        return (final_positive, final_negative, {"samples": latent})
+
+        
+class Superprompt:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "instruction_prompt": ("STRING", {"default": 'Expand the following prompt to add more detail', "multiline": True}),
+                "prompt": ("STRING", {"default": '', "multiline": True, "forceInput": True}),
+                "max_new_tokens": ("INT", {"default": 128, "min": 1, "max": 4096, "step": 1}),
+            } 
+        }
+
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "process"
+    CATEGORY = "KJNodes/text"
+    DESCRIPTION = """
+# SuperPrompt
+A T5 model fine-tuned on the SuperPrompt dataset for  
+upsampling text prompts to more detailed descriptions.  
+Meant to be used as a pre-generation step for text-to-image  
+models that benefit from more detailed prompts.  
+https://huggingface.co/roborovski/superprompt-v1
+"""
+
+    def process(self, instruction_prompt, prompt, max_new_tokens):
+        device = model_management.get_torch_device()
+        from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+        checkpoint_path = os.path.join(script_directory, "models","superprompt-v1")
+        if not os.path.exists(checkpoint_path):
+                logging.info(f"Downloading model to: {checkpoint_path}")
+                from huggingface_hub import snapshot_download
+                snapshot_download(repo_id="roborovski/superprompt-v1", 
+                                  local_dir=checkpoint_path, 
+                                  local_dir_use_symlinks=False)
+        tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small", legacy=False)
+
+        model = T5ForConditionalGeneration.from_pretrained(checkpoint_path, device_map=device)
+        model.to(device)
+        input_text = instruction_prompt + ": " + prompt
+  
+        input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)
+        outputs = model.generate(input_ids,  max_new_tokens=max_new_tokens)
+        out = (tokenizer.decode(outputs[0]))
+        out = out.replace('<pad>', '')
+        out = out.replace('</s>', '')
+        
+        return (out, )
+
+
+class CameraPoseVisualizer:
+                
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "pose_file_path": ("STRING", {"default": '', "multiline": False}),
+            "base_xval": ("FLOAT", {"default": 0.2,"min": 0, "max": 100, "step": 0.01}),
+            "zval": ("FLOAT", {"default": 0.3,"min": 0, "max": 100, "step": 0.01}),
+            "scale": ("FLOAT", {"default": 1.0,"min": 0.01, "max": 10.0, "step": 0.01}),
+            "use_exact_fx": ("BOOLEAN", {"default": False}),
+            "relative_c2w": ("BOOLEAN", {"default": True}),
+            "use_viewer": ("BOOLEAN", {"default": False}),
+            },
+            "optional": {
+                "cameractrl_poses": ("CAMERACTRL_POSES", {"default": None}),
+            }
+            }
+    
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "plot"
+    CATEGORY = "KJNodes/misc"
+    DESCRIPTION = """
+Visualizes the camera poses, from Animatediff-Evolved CameraCtrl Pose  
+or a .txt file with RealEstate camera intrinsics and coordinates, in a 3D plot. 
+"""
+        
+    def plot(self, pose_file_path, scale, base_xval, zval, use_exact_fx, relative_c2w, use_viewer, cameractrl_poses=None):
+        import matplotlib as mpl
+        import matplotlib.pyplot as plt
+        from torchvision.transforms import ToTensor
+
+        x_min = -2.0 * scale
+        x_max = 2.0 * scale
+        y_min = -2.0 * scale
+        y_max = 2.0 * scale
+        z_min = -2.0 * scale
+        z_max = 2.0 * scale
+        plt.rcParams['text.color'] = '#999999'
+        self.fig = plt.figure(figsize=(18, 7))
+        self.fig.patch.set_facecolor('#353535')
+        self.ax = self.fig.add_subplot(projection='3d')
+        self.ax.set_facecolor('#353535') # Set the background color here
+        self.ax.grid(color='#999999', linestyle='-', linewidth=0.5)
+        self.plotly_data = None  # plotly data traces
+        self.ax.set_aspect("auto")
+        self.ax.set_xlim(x_min, x_max)
+        self.ax.set_ylim(y_min, y_max)
+        self.ax.set_zlim(z_min, z_max)
+        self.ax.set_xlabel('x', color='#999999')
+        self.ax.set_ylabel('y', color='#999999')
+        self.ax.set_zlabel('z', color='#999999')
+        for text in self.ax.get_xticklabels() + self.ax.get_yticklabels() + self.ax.get_zticklabels():
+            text.set_color('#999999')
+        logging.info('initialize camera pose visualizer')
+
+        if pose_file_path != "":
+            with open(pose_file_path, 'r') as f:
+                poses = f.readlines()
+                w2cs = [np.asarray([float(p) for p in pose.strip().split(' ')[7:]]).reshape(3, 4) for pose in poses[1:]]
+                fxs = [float(pose.strip().split(' ')[1]) for pose in poses[1:]]
+                #print(poses)
+        elif cameractrl_poses is not None:
+            poses = cameractrl_poses
+            w2cs = [np.array(pose[7:]).reshape(3, 4) for pose in cameractrl_poses]
+            fxs = [pose[1] for pose in cameractrl_poses]
+        else:
+            raise ValueError("Please provide either pose_file_path or cameractrl_poses")
+
+        total_frames = len(w2cs)
+        transform_matrix = np.asarray([[1, 0, 0, 0], [0, 0, 1, 0], [0, -1, 0, 0], [0, 0, 0, 1]]).reshape(4, 4)
+        last_row = np.zeros((1, 4))
+        last_row[0, -1] = 1.0
+
+        w2cs = [np.concatenate((w2c, last_row), axis=0) for w2c in w2cs]
+        c2ws = self.get_c2w(w2cs, transform_matrix, relative_c2w)
+
+        for frame_idx, c2w in enumerate(c2ws):
+            self.extrinsic2pyramid(c2w, frame_idx / total_frames, hw_ratio=1/1, base_xval=base_xval,
+                                    zval=(fxs[frame_idx] if use_exact_fx else zval))
+
+        # Create the colorbar
+        cmap = mpl.cm.rainbow
+        norm = mpl.colors.Normalize(vmin=0, vmax=total_frames)
+        colorbar = self.fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=self.ax, orientation='vertical')
+
+        # Change the colorbar label
+        colorbar.set_label('Frame', color='#999999') # Change the label and its color
+
+        # Change the tick colors
+        colorbar.ax.yaxis.set_tick_params(colors='#999999') # Change the tick color
+
+        # Change the tick frequency
+        # Assuming you want to set the ticks at every 10th frame
+        ticks = np.arange(0, total_frames, 10)
+        colorbar.ax.yaxis.set_ticks(ticks)
+        
+        plt.title('')
+        plt.draw()
+        buf = BytesIO()
+        plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0)
+        buf.seek(0)
+        img = Image.open(buf)
+        tensor_img = ToTensor()(img)
+        buf.close()
+        tensor_img = tensor_img.permute(1, 2, 0).unsqueeze(0)
+        if use_viewer:
+            time.sleep(1)
+            plt.show()
+        return (tensor_img,)
+
+    def extrinsic2pyramid(self, extrinsic, color_map='red', hw_ratio=1/1, base_xval=1, zval=3):
+        import matplotlib.pyplot as plt
+        from mpl_toolkits.mplot3d.art3d import Poly3DCollection
+        vertex_std = np.array([[0, 0, 0, 1],
+                            [base_xval, -base_xval * hw_ratio, zval, 1],
+                            [base_xval, base_xval * hw_ratio, zval, 1],
+                            [-base_xval, base_xval * hw_ratio, zval, 1],
+                            [-base_xval, -base_xval * hw_ratio, zval, 1]])
+        vertex_transformed = vertex_std @ extrinsic.T
+        meshes = [[vertex_transformed[0, :-1], vertex_transformed[1][:-1], vertex_transformed[2, :-1]],
+                            [vertex_transformed[0, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1]],
+                            [vertex_transformed[0, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]],
+                            [vertex_transformed[0, :-1], vertex_transformed[4, :-1], vertex_transformed[1, :-1]],
+                            [vertex_transformed[1, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]]]
+
+        color = color_map if isinstance(color_map, str) else plt.cm.rainbow(color_map)
+
+        self.ax.add_collection3d(
+            Poly3DCollection(meshes, facecolors=color, linewidths=0.3, edgecolors=color, alpha=0.25))
+
+    def customize_legend(self, list_label):
+        from matplotlib.patches import Patch
+        import matplotlib.pyplot as plt
+        list_handle = []
+        for idx, label in enumerate(list_label):
+            color = plt.cm.rainbow(idx / len(list_label))
+            patch = Patch(color=color, label=label)
+            list_handle.append(patch)
+        plt.legend(loc='right', bbox_to_anchor=(1.8, 0.5), handles=list_handle)
+
+    def get_c2w(self, w2cs, transform_matrix, relative_c2w):
+        if relative_c2w:
+            target_cam_c2w = np.array([
+                [1, 0, 0, 0],
+                [0, 1, 0, 0],
+                [0, 0, 1, 0],
+                [0, 0, 0, 1]
+            ])
+            abs2rel = target_cam_c2w @ w2cs[0]
+            ret_poses = [target_cam_c2w, ] + [abs2rel @ np.linalg.inv(w2c) for w2c in w2cs[1:]]
+        else:
+            ret_poses = [np.linalg.inv(w2c) for w2c in w2cs]
+        ret_poses = [transform_matrix @ x for x in ret_poses]
+        return np.array(ret_poses, dtype=np.float32)
+    
+    
+            
+class CheckpointPerturbWeights:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "model": ("MODEL",),
+            "joint_blocks": ("FLOAT", {"default": 0.02, "min": 0.001, "max": 10.0, "step": 0.001}),
+            "final_layer": ("FLOAT", {"default": 0.02, "min": 0.001, "max": 10.0, "step": 0.001}),
+            "rest_of_the_blocks": ("FLOAT", {"default": 0.02, "min": 0.001, "max": 10.0, "step": 0.001}),
+            "seed": ("INT", {"default": 123,"min": 0, "max": 0xffffffffffffffff, "step": 1}),
+            }
+        }
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "mod"
+    OUTPUT_NODE = True
+
+    CATEGORY = "KJNodes/experimental"
+
+    def mod(self, seed, model, joint_blocks, final_layer, rest_of_the_blocks):
+        import copy
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+        device = model_management.get_torch_device()
+        model_copy = copy.deepcopy(model)
+        model_copy.model.to(device)
+        keys = model_copy.model.diffusion_model.state_dict().keys()
+
+        dict = {}
+        for key in keys:
+            dict[key] = model_copy.model.diffusion_model.state_dict()[key]
+
+        pbar = ProgressBar(len(keys))
+        for k in keys:
+            v = dict[k]
+            logging.info(f'{k}: {v.std()}')
+            if k.startswith('joint_blocks'):
+                multiplier = joint_blocks
+            elif k.startswith('final_layer'):
+                multiplier = final_layer
+            else:
+                multiplier = rest_of_the_blocks
+            dict[k] += torch.normal(torch.zeros_like(v) * v.mean(), torch.ones_like(v) * v.std() * multiplier).to(device)
+            pbar.update(1)
+        model_copy.model.diffusion_model.load_state_dict(dict)
+        return model_copy,
+    
+class DifferentialDiffusionAdvanced():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "model": ("MODEL", ),
+                    "samples": ("LATENT",),
+                    "mask": ("MASK",),
+                    "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.001}),
+                            }}
+    RETURN_TYPES = ("MODEL", "LATENT")
+    FUNCTION = "apply"
+    CATEGORY = "_for_testing"
+    INIT = False
+
+    def apply(self, model, samples, mask, multiplier):
+        self.multiplier = multiplier
+        model = model.clone()
+        model.set_model_denoise_mask_function(self.forward)
+        s = samples.copy()
+        s["noise_mask"] = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1]))
+        return (model, s)
+
+    def forward(self, sigma: torch.Tensor, denoise_mask: torch.Tensor, extra_options: dict):
+        model = extra_options["model"]
+        step_sigmas = extra_options["sigmas"]
+        sigma_to = model.inner_model.model_sampling.sigma_min
+        if step_sigmas[-1] > sigma_to:
+            sigma_to = step_sigmas[-1]
+        sigma_from = step_sigmas[0]
+
+        ts_from = model.inner_model.model_sampling.timestep(sigma_from)
+        ts_to = model.inner_model.model_sampling.timestep(sigma_to)
+        current_ts = model.inner_model.model_sampling.timestep(sigma[0])
+
+        threshold = (current_ts - ts_to) / (ts_from - ts_to) / self.multiplier
+
+        return (denoise_mask >= threshold).to(denoise_mask.dtype)
+    
+class FluxBlockLoraSelect:
+    def __init__(self):
+        self.loaded_lora = None
+
+    @classmethod
+    def INPUT_TYPES(s):
+        arg_dict = {}
+        argument = ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.01})
+
+        for i in range(19):
+            arg_dict["double_blocks.{}.".format(i)] = argument
+
+        for i in range(38):
+            arg_dict["single_blocks.{}.".format(i)] = argument
+
+        return {"required": arg_dict}
+    
+    RETURN_TYPES = ("SELECTEDDITBLOCKS", )
+    RETURN_NAMES = ("blocks", )
+    OUTPUT_TOOLTIPS = ("The modified diffusion model.",)
+    FUNCTION = "load_lora"
+
+    CATEGORY = "KJNodes/experimental"
+    DESCRIPTION = "Select individual block alpha values, value of 0 removes the block altogether"
+
+    def load_lora(self, **kwargs):
+        return (kwargs,)
+    
+class HunyuanVideoBlockLoraSelect:
+    @classmethod
+    def INPUT_TYPES(s):
+        arg_dict = {}
+        argument = ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.01})
+
+        for i in range(20):
+            arg_dict["double_blocks.{}.".format(i)] = argument
+
+        for i in range(40):
+            arg_dict["single_blocks.{}.".format(i)] = argument
+
+        return {"required": arg_dict}
+    
+    RETURN_TYPES = ("SELECTEDDITBLOCKS", )
+    RETURN_NAMES = ("blocks", )
+    OUTPUT_TOOLTIPS = ("The modified diffusion model.",)
+    FUNCTION = "load_lora"
+
+    CATEGORY = "KJNodes/hunyuanvideo"
+    DESCRIPTION = "Select individual block alpha values, value of 0 removes the block altogether"
+
+    def load_lora(self, **kwargs):
+        return (kwargs,)
+
+class Wan21BlockLoraSelect:
+    @classmethod
+    def INPUT_TYPES(s):
+        arg_dict = {}
+        argument = ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.01})
+
+        for i in range(40):
+            arg_dict["blocks.{}.".format(i)] = argument
+
+        return {"required": arg_dict}
+    
+    RETURN_TYPES = ("SELECTEDDITBLOCKS", )
+    RETURN_NAMES = ("blocks", )
+    OUTPUT_TOOLTIPS = ("The modified diffusion model.",)
+    FUNCTION = "load_lora"
+
+    CATEGORY = "KJNodes/wan"
+    DESCRIPTION = "Select individual block alpha values, value of 0 removes the block altogether"
+
+    def load_lora(self, **kwargs):
+        return (kwargs,)
+
+class LTX2BlockLoraSelect:
+    @classmethod
+    def INPUT_TYPES(s):
+        arg_dict = {}
+        argument = ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10000.0, "step": 0.01})
+
+        for i in range(48):
+            arg_dict["blocks.{}.".format(i)] = argument
+
+        return {"required": arg_dict}
+    
+    RETURN_TYPES = ("SELECTEDDITBLOCKS", )
+    RETURN_NAMES = ("blocks", )
+    OUTPUT_TOOLTIPS = ("The modified diffusion model.",)
+    FUNCTION = "load_lora"
+
+    CATEGORY = "KJNodes/ltxv"
+    DESCRIPTION = "Select individual block alpha values, value of 0 removes the block altogether"
+
+    def load_lora(self, **kwargs):
+        return (kwargs,)
+
+    
+class DiTBlockLoraLoader:
+    def __init__(self):
+        self.loaded_lora = None
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                "model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}),
+                "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the diffusion model. This value can be negative."}),
+                
+                },
+                "optional": {
+                    "lora_name": (folder_paths.get_filename_list("loras"), {"tooltip": "The name of the LoRA."}),
+                    "opt_lora_path": ("STRING", {"forceInput": True, "tooltip": "Absolute path of the LoRA."}),
+                    "blocks": ("SELECTEDDITBLOCKS",),
+                }
+               }
+    
+    RETURN_TYPES = ("MODEL", "STRING", )
+    RETURN_NAMES = ("model", "rank", )
+    OUTPUT_TOOLTIPS = ("The modified diffusion model.", "possible rank of the LoRA.")
+    FUNCTION = "load_lora"
+    CATEGORY = "KJNodes/lora"
+
+    def load_lora(self, model, strength_model, lora_name=None, opt_lora_path=None, blocks=None):
+        
+        import comfy.lora
+
+        if opt_lora_path:
+            lora_path = opt_lora_path
+        else:
+            lora_path = folder_paths.get_full_path("loras", lora_name)
+        
+        lora = None
+        if self.loaded_lora is not None:
+            if self.loaded_lora[0] == lora_path:
+                lora = self.loaded_lora[1]
+            else:
+                self.loaded_lora = None
+        
+        if lora is None:
+            lora = load_torch_file(lora_path, safe_load=True)
+            self.loaded_lora = (lora_path, lora)
+
+        # Find the first key that ends with "weight"
+        rank = "unknown"
+        weight_key = next((key for key in lora.keys() if key.endswith('weight')), None)
+        # Print the shape of the value corresponding to the key
+        if weight_key:
+            logging.info(f"Shape of the first 'weight' key ({weight_key}): {lora[weight_key].shape}")
+            rank = str(lora[weight_key].shape[0])
+        else:
+            logging.warning("No key ending with 'weight' found.")
+            rank = "Couldn't find rank"
+        self.loaded_lora = (lora_path, lora)
+
+        key_map = {}
+        if model is not None:
+            key_map = comfy.lora.model_lora_keys_unet(model.model, key_map)
+
+        loaded = comfy.lora.load_lora(lora, key_map)
+
+        if blocks is not None:
+            keys_to_delete = []
+
+            for block in blocks:
+                for key in list(loaded.keys()):
+                    match = False
+                    if isinstance(key, str) and block in key:
+                        match = True
+                    elif isinstance(key, tuple):
+                        for k in key:
+                            if block in k:
+                                match = True
+                                break
+
+                    if match:
+                        ratio = blocks[block]
+                        if ratio == 0:
+                            keys_to_delete.append(key)
+                        else:
+                            # Only modify LoRA adapters, skip diff tuples
+                            value = loaded[key]
+                            if hasattr(value, 'weights'):
+                                logging.info(f"Modifying LoRA adapter for key: {key}")
+                                weights_list = list(value.weights)
+                                weights_list[2] = ratio
+                                loaded[key].weights = tuple(weights_list)
+                            else:
+                                logging.info(f"Skipping non-LoRA entry for key: {key}")
+
+            for key in keys_to_delete:
+                del loaded[key]
+
+            logging.info("loading lora keys:")
+            for key, value in loaded.items():
+                if hasattr(value, 'weights'):
+                    logging.info(f"Key: {key}, Alpha: {value.weights[2]}")
+                else:
+                    logging.info(f"Key: {key}, Type: {type(value)}")
+
+        if model is not None:
+            new_modelpatcher = model.clone()
+            k = new_modelpatcher.add_patches(loaded, strength_model)  
+    
+        k = set(k)
+        for x in loaded:
+            if (x not in k):
+                logging.warning(f"NOT LOADED {x}")
+
+        return (new_modelpatcher, rank)
+    
+class CustomControlNetWeightsFluxFromList:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "list_of_floats": ("FLOAT", {"forceInput": True}, ),
+            },
+            "optional": {
+                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
+                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
+                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            }
+        }
+    
+    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
+    RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT")
+    FUNCTION = "load_weights"
+    DESCRIPTION = "Creates controlnet weights from a list of floats for Advanced-ControlNet"
+
+    CATEGORY = "KJNodes/controlnet"
+
+    def load_weights(self, list_of_floats: list[float],
+                     uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
+        
+        adv_control = importlib.import_module("ComfyUI-Advanced-ControlNet.adv_control")
+        ControlWeights = adv_control.utils.ControlWeights
+        TimestepKeyframeGroup = adv_control.utils.TimestepKeyframeGroup
+        TimestepKeyframe = adv_control.utils.TimestepKeyframe
+
+        weights = ControlWeights.controlnet(weights_input=list_of_floats, uncond_multiplier=uncond_multiplier, extras=cn_extras)
+        logging.info(weights.weights_input)
+        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+    
+SHAKKERLABS_UNION_CONTROLNET_TYPES = {
+    "canny": 0,
+    "tile": 1,
+    "depth": 2,
+    "blur": 3,
+    "pose": 4,
+    "gray": 5,
+    "low quality": 6,
+}
+
+class SetShakkerLabsUnionControlNetType:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"control_net": ("CONTROL_NET", ),
+                             "type": (["auto"] + list(SHAKKERLABS_UNION_CONTROLNET_TYPES.keys()),)
+                             }}
+
+    CATEGORY = "conditioning/controlnet"
+    RETURN_TYPES = ("CONTROL_NET",)
+
+    FUNCTION = "set_controlnet_type"
+
+    def set_controlnet_type(self, control_net, type):
+        control_net = control_net.copy()
+        type_number = SHAKKERLABS_UNION_CONTROLNET_TYPES.get(type, -1)
+        if type_number >= 0:
+            control_net.set_extra_arg("control_type", [type_number])
+        else:
+            control_net.set_extra_arg("control_type", [])
+
+        return (control_net,)
+
+class ModelSaveKJ:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "filename_prefix": ("STRING", {"default": "diffusion_models/ComfyUI"}),
+                              "model_key_prefix": ("STRING", {"default": "model.diffusion_model."}),
+                              },
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
+    RETURN_TYPES = ()
+    FUNCTION = "save"
+    OUTPUT_NODE = True
+
+    CATEGORY = "advanced/model_merging"
+
+    def save(self, model, filename_prefix, model_key_prefix, prompt=None, extra_pnginfo=None):
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+    
+        output_checkpoint = f"{filename}_{counter:05}_.safetensors"
+        output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
+
+        load_models = [model]
+
+        model_management.load_models_gpu(load_models)
+        default_prefix = "model.diffusion_model."
+
+        sd = model.state_dict_for_saving(None, None, None)
+
+        new_sd = {}
+        for k in sd:
+            if k.startswith(default_prefix):
+                new_key = model_key_prefix + k[len(default_prefix):]
+            else:
+                new_key = k  # In case the key doesn't start with the default prefix, keep it unchanged
+            t = sd[k]
+            if not t.is_contiguous():
+                t = t.contiguous()
+            new_sd[new_key] = t
+        logging.info(f"full_output_folder: {full_output_folder}")
+        if not os.path.exists(full_output_folder):
+            os.makedirs(full_output_folder)
+        save_torch_file(new_sd, os.path.join(full_output_folder, output_checkpoint))
+        return {}
+       
+class StyleModelApplyAdvanced:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning": ("CONDITIONING", ),
+                             "style_model": ("STYLE_MODEL", ),
+                             "clip_vision_output": ("CLIP_VISION_OUTPUT", ),
+                             "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.001}),
+                             }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "apply_stylemodel"
+    CATEGORY = "KJNodes/experimental"
+    DESCRIPTION = "StyleModelApply but with strength parameter"
+
+    def apply_stylemodel(self, clip_vision_output, style_model, conditioning, strength=1.0):
+        cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0)
+        cond = strength * cond
+        c = []
+        for t in conditioning:
+            n = [torch.cat((t[0], cond), dim=1), t[1].copy()]
+            c.append(n)
+        return (c, )
+
+class AudioConcatenate:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "audio1": ("AUDIO",),
+            "audio2": ("AUDIO",),
+            "direction": (
+            [   'right',
+                'left',
+            ],
+            {
+            "default": 'right'
+             }),
+        }}
+
+    RETURN_TYPES = ("AUDIO",)
+    FUNCTION = "concanate"
+    CATEGORY = "KJNodes/audio"
+    DESCRIPTION = """
+Concatenates the audio1 to audio2 in the specified direction.
+"""
+
+    def concanate(self, audio1, audio2, direction):
+        sample_rate_1 = audio1["sample_rate"]
+        sample_rate_2 = audio2["sample_rate"]
+        if sample_rate_1 != sample_rate_2:
+            raise ValueError("Sample rates of the two audios do not match")
+
+        waveform_1 = audio1["waveform"]
+        waveform_2 = audio2["waveform"]
+
+        # Concatenate based on the specified direction
+        if direction == 'right':
+            concatenated_audio = torch.cat((waveform_1, waveform_2), dim=2)  # Concatenate along width
+        elif direction == 'left':
+            concatenated_audio= torch.cat((waveform_2, waveform_1), dim=2)  # Concatenate along width
+        return ({"waveform": concatenated_audio, "sample_rate": sample_rate_1},)
+
+class LeapfusionHunyuanI2V:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "latent": ("LATENT",),
+                "index": ("INT", {"default": 0, "min": -1, "max": 1000, "step": 1,"tooltip": "The index of the latent to be replaced. 0 for first frame and -1 for last"}),
+                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The start percentage of steps to apply"}),
+                "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The end percentage of steps to apply"}),
+                "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.001}),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "KJNodes/hunyuanvideo"
+
+    def patch(self, model, latent, index, strength, start_percent, end_percent):
+
+        def outer_wrapper(samples, index, start_percent, end_percent):
+            def unet_wrapper(apply_model, args):
+                steps = args["c"]["transformer_options"]["sample_sigmas"]
+                inp, timestep, c = args["input"], args["timestep"], args["c"]
+                matched_step_index = (steps == timestep).nonzero()
+                if len(matched_step_index) > 0:
+                    current_step_index = matched_step_index.item()
+                else:
+                    for i in range(len(steps) - 1):
+                        # walk from beginning of steps until crossing the timestep
+                        if (steps[i] - timestep[0]) * (steps[i + 1] - timestep[0]) <= 0:
+                            current_step_index = i
+                            break
+                    else:
+                        current_step_index = 0
+                current_percent = current_step_index / (len(steps) - 1)
+                if samples is not None:
+                    if start_percent <= current_percent <= end_percent:
+                        inp[:, :, [index], :, :] = samples[:, :, [0], :, :].to(inp)
+                    else:
+                        inp[:, :, [index], :, :] = torch.zeros(1)
+                return apply_model(inp, timestep, **c)
+            return unet_wrapper
+        
+        samples = latent["samples"] * 0.476986 * strength
+        m = model.clone()
+        m.set_model_unet_function_wrapper(outer_wrapper(samples, index, start_percent, end_percent))
+
+        return (m,)
+
+class ImageNoiseAugmentation:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "noise_aug_strength": ("FLOAT", {"default": None, "min": 0.0, "max": 100.0, "step": 0.001}),
+                "seed": ("INT", {"default": 123,"min": 0, "max": 0xffffffffffffffff, "step": 1}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "add_noise"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+    Add noise to an image.  
+    """
+
+    def add_noise(self, image, noise_aug_strength, seed):
+        torch.manual_seed(seed)
+        sigma = torch.ones((image.shape[0],)).to(image.device, image.dtype) * noise_aug_strength
+        image_noise = torch.randn_like(image) * sigma[:, None, None, None]
+        image_noise = torch.where(image==-1, torch.zeros_like(image), image_noise)
+        image_out = image + image_noise
+        return image_out,
+
+class VAELoaderKJ:
+    video_taes = ["taehv", "lighttaew2_2", "lighttaew2_1", "lighttaehy1_5"]
+    image_taes = ["taesd", "taesdxl", "taesd3", "taef1"]
+    @staticmethod
+    def vae_list(s):
+        vaes = folder_paths.get_filename_list("vae")
+        approx_vaes = folder_paths.get_filename_list("vae_approx")
+        sdxl_taesd_enc = False
+        sdxl_taesd_dec = False
+        sd1_taesd_enc = False
+        sd1_taesd_dec = False
+        sd3_taesd_enc = False
+        sd3_taesd_dec = False
+        f1_taesd_enc = False
+        f1_taesd_dec = False
+
+        for v in approx_vaes:
+            if v.startswith("taesd_decoder."):
+                sd1_taesd_dec = True
+            elif v.startswith("taesd_encoder."):
+                sd1_taesd_enc = True
+            elif v.startswith("taesdxl_decoder."):
+                sdxl_taesd_dec = True
+            elif v.startswith("taesdxl_encoder."):
+                sdxl_taesd_enc = True
+            elif v.startswith("taesd3_decoder."):
+                sd3_taesd_dec = True
+            elif v.startswith("taesd3_encoder."):
+                sd3_taesd_enc = True
+            elif v.startswith("taef1_encoder."):
+                f1_taesd_dec = True
+            elif v.startswith("taef1_decoder."):
+                f1_taesd_enc = True
+            else:
+                for tae in s.video_taes:
+                    if v.startswith(tae):
+                        vaes.append(v)
+
+        if sd1_taesd_dec and sd1_taesd_enc:
+            vaes.append("taesd")
+        if sdxl_taesd_dec and sdxl_taesd_enc:
+            vaes.append("taesdxl")
+        if sd3_taesd_dec and sd3_taesd_enc:
+            vaes.append("taesd3")
+        if f1_taesd_dec and f1_taesd_enc:
+            vaes.append("taef1")
+        vaes.append("pixel_space")
+        return vaes
+
+    @staticmethod
+    def load_taesd(name):
+        sd = {}
+        approx_vaes = folder_paths.get_filename_list("vae_approx")
+
+        encoder = next(filter(lambda a: a.startswith("{}_encoder.".format(name)), approx_vaes))
+        decoder = next(filter(lambda a: a.startswith("{}_decoder.".format(name)), approx_vaes))
+
+        enc = load_torch_file(folder_paths.get_full_path_or_raise("vae_approx", encoder))
+        for k in enc:
+            sd["taesd_encoder.{}".format(k)] = enc[k]
+
+        dec = load_torch_file(folder_paths.get_full_path_or_raise("vae_approx", decoder))
+        for k in dec:
+            sd["taesd_decoder.{}".format(k)] = dec[k]
+
+        if name == "taesd":
+            sd["vae_scale"] = torch.tensor(0.18215)
+            sd["vae_shift"] = torch.tensor(0.0)
+        elif name == "taesdxl":
+            sd["vae_scale"] = torch.tensor(0.13025)
+            sd["vae_shift"] = torch.tensor(0.0)
+        elif name == "taesd3":
+            sd["vae_scale"] = torch.tensor(1.5305)
+            sd["vae_shift"] = torch.tensor(0.0609)
+        elif name == "taef1":
+            sd["vae_scale"] = torch.tensor(0.3611)
+            sd["vae_shift"] = torch.tensor(0.1159)
+        return sd
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": { "vae_name": (s.vae_list(s), ),
+                          "device": (["main_device", "cpu"],),
+                          "weight_dtype": (["bf16", "fp16", "fp32" ],),
+                         }
+            }
+
+    RETURN_TYPES = ("VAE",)
+    FUNCTION = "load_vae"
+    CATEGORY = "KJNodes/vae"
+
+    def load_vae(self, vae_name, device, weight_dtype):
+        from comfy.sd import VAE
+        metadata = None
+        dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[weight_dtype]
+        if device == "main_device":
+            device = model_management.get_torch_device()
+        elif device == "cpu":
+            device = torch.device("cpu")
+
+        if vae_name == "pixel_space":
+            sd = {}
+            sd["pixel_space_vae"] = torch.tensor(1.0)
+        elif vae_name in self.image_taes:
+            sd = self.load_taesd(vae_name)
+        else:
+            if os.path.splitext(vae_name)[0] in self.video_taes:
+                vae_path = folder_paths.get_full_path_or_raise("vae_approx", vae_name)
+            else:
+                vae_path = folder_paths.get_full_path_or_raise("vae", vae_name)
+            sd, metadata = load_torch_file(vae_path, return_metadata=True)
+
+
+        is_audio_vae = (
+            "vocoder.conv_post.weight" in sd
+            or "vocoder.vocoder.conv_post.weight" in sd
+            or "vocoder.resblocks.0.convs1.0.weight" in sd
+            or "vocoder.vocoder.resblocks.0.convs1.0.weight" in sd
+        )
+        if is_audio_vae:
+            sd_audio = state_dict_prefix_replace(dict(sd), {"audio_vae.": "autoencoder.", "vocoder.": "vocoder."}, filter_keys=True)
+            vae = VAE(sd=sd_audio, metadata=metadata)
+        else:
+            vae = VAE(sd=sd, device=device, dtype=dtype, metadata=metadata)
+        vae.throw_exception_if_invalid()
+        return (vae,)
+
+from comfy.samplers import sampling_function, CFGGuider
+class Guider_ScheduledCFG(CFGGuider):
+
+    def set_cfg(self, cfg, start_percent, end_percent):
+        self.cfg = cfg
+        self.start_percent = start_percent
+        self.end_percent = end_percent
+
+    def predict_noise(self, x, timestep, model_options={}, seed=None):
+        steps = model_options["transformer_options"]["sample_sigmas"]
+        if isinstance(timestep, torch.Tensor):
+            timestep_value = timestep.reshape(-1)[0].to(steps)
+        else:
+            timestep_value = torch.tensor(timestep, device=steps.device, dtype=steps.dtype)
+        matched_step_index = torch.isclose(steps, timestep_value).nonzero()
+        assert not (isinstance(self.cfg, list) and len(self.cfg) != (len(steps) - 1)), "cfg list length must match step count"
+        if len(matched_step_index) > 0:
+            current_step_index = matched_step_index.item()
+        else:
+            for i in range(len(steps) - 1):
+                # walk from beginning of steps until crossing the timestep
+                if (steps[i] - timestep_value) * (steps[i + 1] - timestep_value) <= 0:
+                    current_step_index = i
+                    break
+            else:
+                current_step_index = 0
+        current_percent = current_step_index / (len(steps) - 1)
+
+        if self.start_percent <= current_percent <= self.end_percent:
+            if isinstance(self.cfg, list):
+                cfg = self.cfg[current_step_index]
+            else:
+                cfg = self.cfg
+            uncond = self.conds.get("negative", None)
+        else:
+            uncond = None
+            cfg = 1.0
+
+        return sampling_function(self.inner_model, x, timestep, uncond, self.conds.get("positive", None), cfg, model_options=model_options, seed=seed)            
+
+class ScheduledCFGGuidance:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "model": ("MODEL",),
+                    "positive": ("CONDITIONING", ),
+                    "negative": ("CONDITIONING", ),
+                    "cfg": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 100.0, "step": 0.01}),
+                    "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step":0.01}),
+                    "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step":0.01}),
+                    },
+                }
+    RETURN_TYPES = ("GUIDER",)
+    FUNCTION = "get_guider"
+    CATEGORY = "KJNodes/experimental"
+    DESCRiPTION = """
+CFG Guider that allows for scheduled CFG changes over steps, the steps outside the range will use CFG 1.0 thus being processed faster.  
+cfg input can be a list of floats matching step count, or a single float for all steps.  
+"""
+
+    def get_guider(self, model, cfg, positive, negative, start_percent, end_percent):
+        guider = Guider_ScheduledCFG(model) 
+        guider.set_conds(positive, negative)
+        guider.set_cfg(cfg, start_percent, end_percent)
+        return (guider, )
+
+
+class ApplyRifleXRoPE_WanVideo:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "latent": ("LATENT", {"tooltip": "Only used to get the latent count"}),
+                "k": ("INT", {"default": 6, "min": 1, "max": 100, "step": 1, "tooltip": "Index of intrinsic frequency"}),
+            } 
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+    CATEGORY = "KJNodes/wan"
+    EXPERIMENTAL = True
+    DESCRIPTION = "Extends the potential frame count of HunyuanVideo using this method: https://github.com/thu-ml/RIFLEx"
+
+    def patch(self, model, latent, k):
+        model_class = model.model.diffusion_model
+        
+        model_clone = model.clone()
+        num_frames = latent["samples"].shape[2]
+        d = model_class.dim // model_class.num_heads
+
+        rope_embedder = EmbedND_RifleX(
+            d, 
+            10000.0, 
+            [d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)],
+            num_frames,
+            k
+            )
+        
+        model_clone.add_object_patch(f"diffusion_model.rope_embedder", rope_embedder)
+                    
+        return (model_clone, )
+    
+class ApplyRifleXRoPE_HunuyanVideo:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "latent": ("LATENT", {"tooltip": "Only used to get the latent count"}),
+                "k": ("INT", {"default": 4, "min": 1, "max": 100, "step": 1, "tooltip": "Index of intrinsic frequency"}),
+            } 
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+    CATEGORY = "KJNodes/hunyuanvideo"
+    EXPERIMENTAL = True
+    DESCRIPTION = "Extends the potential frame count of HunyuanVideo using this method: https://github.com/thu-ml/RIFLEx"
+
+    def patch(self, model, latent, k):
+        model_class = model.model.diffusion_model
+        
+        model_clone = model.clone()
+        num_frames = latent["samples"].shape[2]
+
+        pe_embedder = EmbedND_RifleX(
+            model_class.params.hidden_size // model_class.params.num_heads, 
+            model_class.params.theta, 
+            model_class.params.axes_dim, 
+            num_frames,
+            k
+            )
+        
+        model_clone.add_object_patch(f"diffusion_model.pe_embedder", pe_embedder)
+                    
+        return (model_clone, )
+
+def rope_riflex(pos, dim, theta, L_test, k):
+    from einops import rearrange
+    assert dim % 2 == 0
+    if model_management.is_device_mps(pos.device) or model_management.is_intel_xpu() or model_management.is_directml_enabled():
+        device = torch.device("cpu")
+    else:
+        device = pos.device
+
+    scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=device)
+    omega = 1.0 / (theta**scale)
+
+    # RIFLEX modification - adjust last frequency component if L_test and k are provided
+    if k and L_test:
+        omega[k-1] = 0.9 * 2 * torch.pi / L_test
+
+    out = torch.einsum("...n,d->...nd", pos.to(dtype=torch.float32, device=device), omega)
+    out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
+    out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
+    return out.to(dtype=torch.float32, device=pos.device)
+
+class EmbedND_RifleX(nn.Module):
+    def __init__(self, dim, theta, axes_dim, num_frames, k):
+        super().__init__()
+        self.dim = dim
+        self.theta = theta
+        self.axes_dim = axes_dim
+        self.num_frames = num_frames
+        self.k = k
+
+    def forward(self, ids):
+        n_axes = ids.shape[-1]
+        emb = torch.cat(
+            [rope_riflex(ids[..., i], self.axes_dim[i], self.theta, self.num_frames, self.k if i == 0 else 0) for i in range(n_axes)],
+            dim=-3,
+        )
+        return emb.unsqueeze(1)
+
+
+class Timer:
+    def __init__(self, name):
+        self.name = name
+        self.start_time = None
+        self.elapsed = 0
+
+class TimerNodeKJ:
+    @classmethod
+    
+    def INPUT_TYPES(s):
+      return {
+        "required": {
+            "any_input": (IO.ANY, ),
+            "mode": (["start", "stop"],),
+            "name": ("STRING", {"default": "Timer"}),
+        },
+        "optional": {
+            "timer": ("TIMER",),
+        },
+	}
+
+    RETURN_TYPES = (IO.ANY, "TIMER", "INT", )
+    RETURN_NAMES = ("any_output", "timer", "time")
+    FUNCTION = "timer"
+    CATEGORY = "KJNodes/misc"
+
+    def timer(self, mode, name, any_input=None, timer=None):
+        if timer is None:
+            if mode == "start":
+                timer = Timer(name=name)            
+                timer.start_time = time.time()
+                return {"ui": {
+                "text": [f"{timer.start_time}"]}, 
+                "result": (any_input, timer, 0) 
+                 }
+        elif mode == "stop" and timer is not None:
+            end_time = time.time()
+            timer.elapsed = int((end_time - timer.start_time) * 1000)
+            timer.start_time = None
+            return (any_input, timer, timer.elapsed)
+
+class HunyuanVideoEncodeKeyframesToCond:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "model": ("MODEL",),
+                    "positive": ("CONDITIONING", ),
+                    "vae": ("VAE", ),
+                    "start_frame": ("IMAGE", ),
+                    "end_frame": ("IMAGE", ),
+                    "num_frames": ("INT", {"default": 33, "min": 2, "max": 4096, "step": 1}),
+                    "tile_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
+                    "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}),
+                    "temporal_size": ("INT", {"default": 64, "min": 8, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to encode at a time."}),
+                    "temporal_overlap": ("INT", {"default": 8, "min": 4, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to overlap."}),
+                    },
+                    "optional": {
+                        "negative": ("CONDITIONING", ),
+                    }
+                }
+
+    RETURN_TYPES = ("MODEL", "CONDITIONING","CONDITIONING","LATENT")
+    RETURN_NAMES = ("model", "positive", "negative", "latent")
+    FUNCTION = "encode"
+
+    CATEGORY = "KJNodes/hunyuanvideo"
+
+    def encode(self, model, positive, start_frame, end_frame, num_frames, vae, tile_size, overlap, temporal_size, temporal_overlap, negative=None):
+
+        model_clone = model.clone()
+
+        model_clone.add_object_patch("concat_keys", ("concat_image",))
+
+       
+        x = (start_frame.shape[1] // 8) * 8
+        y = (start_frame.shape[2] // 8) * 8
+
+        if start_frame.shape[1] != x or start_frame.shape[2] != y:
+            x_offset = (start_frame.shape[1] % 8) // 2
+            y_offset = (start_frame.shape[2] % 8) // 2
+            start_frame = start_frame[:,x_offset:x + x_offset, y_offset:y + y_offset,:]
+        if end_frame.shape[1] != x or end_frame.shape[2] != y:
+            x_offset = (start_frame.shape[1] % 8) // 2
+            y_offset = (start_frame.shape[2] % 8) // 2
+            end_frame = end_frame[:,x_offset:x + x_offset, y_offset:y + y_offset,:]
+
+        video_frames = torch.zeros(num_frames-2, start_frame.shape[1], start_frame.shape[2], start_frame.shape[3], device=start_frame.device, dtype=start_frame.dtype)
+        video_frames = torch.cat([start_frame, video_frames, end_frame], dim=0)
+
+        concat_latent = vae.encode_tiled(video_frames[:,:,:,:3], tile_x=tile_size, tile_y=tile_size, overlap=overlap, tile_t=temporal_size, overlap_t=temporal_overlap)
+
+        out_latent = {}
+        out_latent["samples"] = torch.zeros_like(concat_latent)
+
+        out = []
+        for conditioning in [positive, negative if negative is not None else []]:
+            c = []
+            for t in conditioning:
+                d = t[1].copy()
+                d["concat_latent_image"] = concat_latent
+                n = [t[0], d]
+                c.append(n)
+            out.append(c)
+        if len(out) == 1:
+            out.append(out[0])
+        return (model_clone, out[0], out[1], out_latent)
+    
+
+class LazySwitchKJ:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "switch": ("BOOLEAN",),
+                "on_false": (IO.ANY, {"lazy": True}),
+                "on_true": (IO.ANY, {"lazy": True}),
+            },
+        }
+
+    RETURN_TYPES = (IO.ANY,)
+    FUNCTION = "switch"
+    CATEGORY = "KJNodes/misc"
+    DESCRIPTION = "Controls flow of execution based on a boolean switch."
+
+    def check_lazy_status(self, switch, on_false=None, on_true=None):
+        if switch and on_true is None:
+            return ["on_true"]
+        if not switch and on_false is None:
+            return ["on_false"]
+
+    def switch(self, switch, on_false = None, on_true=None):
+        value = on_true if switch else on_false
+        return (value,)
+
+
+from comfy.patcher_extension import WrappersMP
+from comfy.sampler_helpers import prepare_mask
+class TTM_OuterSampleWrapper:
+    def __init__(self, mask, steps):
+        self.mask = mask
+        self.steps = steps
+
+    def __call__(self, executor, noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes):
+        guider = executor.class_obj
+        guider.model_options
+        wrappers = guider.model_options["transformer_options"]["wrappers"]
+        w = wrappers.setdefault(WrappersMP.APPLY_MODEL, {})
+
+        if self.mask is not None:
+            motion_mask = self.mask.reshape((-1, 1, self.mask.shape[-2], self.mask.shape[-1]))
+            shape = latent_shapes[0]
+            motion_mask = prepare_mask(motion_mask, shape, noise.device)
+
+        scale_latent_inpaint = guider.model_patcher.model.scale_latent_inpaint
+        w["TTM_ApplyModel_Wrapper"] = [TTM_ApplyModel_Wrapper(latent_image, noise, motion_mask, self.steps, scale_latent_inpaint)]
+
+        out = executor(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=latent_shapes)
+
+        return out
+
+
+class TTM_ApplyModel_Wrapper:
+    def __init__(self, reference_samples, noise, motion_mask, steps, scale_latent_inpaint):
+        self.reference_samples = reference_samples
+        self.noise = noise
+        self.motion_mask = motion_mask
+        self.steps = steps
+        self.scale_latent_inpaint = scale_latent_inpaint
+
+    def __call__(self, executor, x, t, c_concat, c_crossattn, control, transformer_options, **kwargs):
+        sigmas = transformer_options["sample_sigmas"]
+
+        matched = (sigmas == t).nonzero(as_tuple=True)[0]
+        if matched.numel() > 0:
+            current_step_index = matched.item()
+        else:
+            crossing = ((sigmas[:-1] - t) * (sigmas[1:] - t) <= 0).nonzero(as_tuple=True)[0]
+            current_step_index = crossing.item() if crossing.numel() > 0 else 0
+
+        next_sigma = sigmas[current_step_index + 1] if current_step_index < len(sigmas) - 1 else sigmas[current_step_index]
+
+        if current_step_index != 0 and current_step_index < self.steps:
+            noisy_latent = self.scale_latent_inpaint(x=x, sigma=torch.tensor([next_sigma]), noise=self.noise.to(x), latent_image=self.reference_samples.to(x))
+            if self.motion_mask is not None:
+                x = x * (1-self.motion_mask).to(x) + noisy_latent * self.motion_mask.to(x)
+            else:
+                x = noisy_latent
+
+        return executor(x, t, c_concat, c_crossattn, control, transformer_options, **kwargs)
+
+
+class LatentInpaintTTM:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "model": ("MODEL", ),
+                        "steps": ("INT", {"default": 7, "min": 0, "max": 888, "step": 1, "tooltip": "Number of steps to apply TTM inpainting for."}),
+                        },
+                "optional": {
+                        "mask": ("MASK", {"tooltip": "Latent mask where white (1.0) is the area to inpaint and black (0.0) is the area to keep unchanged."}),
+                        }
+                }
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+    EXPERIMENTAL = True
+    DESCRIPTION = "https://github.com/time-to-move/TTM"
+    SEARCH_ALIASES = ["time to move"]
+    CATEGORY = "KJNodes/experimental"
+
+    def patch(self, model, steps, mask=None):
+        m = model.clone()
+        m.add_wrapper_with_key(WrappersMP.OUTER_SAMPLE, "TTM_OuterSampleWrapper", TTM_OuterSampleWrapper(mask, steps))
+        return (m, )
+
+
+class SimpleCalculatorKJ(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        template = io.Autogrow.TemplateNames(input=io.MultiType.Input("var", [io.Int, io.Float, io.Boolean], optional=True), names=["a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k"], min=2)
+        return io.Schema(
+            node_id="SimpleCalculatorKJ",
+            category="KJNodes/misc",
+            description="""
+Calculator node that evaluates a mathematical expression using inputs a and b.  
+    Supported operations: +, -, *, /, //, %, **, <<, >>, unary +/-  
+    Supported comparisons: ==, !=, <, <=, >, >=  
+    Supported logic: and, or, not  
+    Supported functions: abs(), round(), min(), max(), pow(), sqrt(), sin(), cos(), tan(), log(), log10(), exp(), floor(), ceil()  
+    Supported constants: pi, euler, True, False  
+""",
+            search_aliases=["math", "arithmetic", "expression", "logic"],
+            inputs=[
+                io.String.Input("expression", default="a + b", multiline=True),
+                io.Autogrow.Input("variables", template=template),
+            ],
+            outputs=[
+                io.Float.Output(),
+                io.Int.Output(),
+                io.Boolean.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, variables, expression, a=None, b=None) -> io.NodeOutput:
+        import ast
+        import operator
+
+        # Allowed operations
+        allowed_operators = {
+            ast.Add: operator.add, ast.Sub: operator.sub, ast.Mult: operator.mul, ast.Div: operator.truediv,
+            ast.FloorDiv: operator.floordiv, ast.Mod: operator.mod, ast.Pow: operator.pow, 
+            ast.USub: operator.neg, ast.UAdd: operator.pos, ast.LShift: operator.lshift, 
+            ast.RShift: operator.rshift, ast.Eq: operator.eq, ast.NotEq: operator.ne, ast.Lt: operator.lt,
+            ast.LtE: operator.le, ast.Gt: operator.gt, ast.GtE: operator.ge, ast.And: operator.and_, 
+            ast.Or: operator.or_, ast.Not: operator.not_,
+        }
+
+        # Allowed functions
+        allowed_functions = {
+            'abs': abs, 'round': round, 'min': min, 'max': max,
+            'pow': pow, 'sqrt': math.sqrt, 'sin': math.sin,
+            'cos': math.cos, 'tan': math.tan, 'log': math.log,
+            'log10': math.log10, 'exp': math.exp, 'floor': math.floor,
+            'ceil': math.ceil
+        }
+
+        # Allowed constants - start with pi, e, True, False
+        allowed_names = {'pi': math.pi, 'euler': math.e, 'True': True, 'False': False}
+
+        # Add all variables from autogrow to allowed_names
+        for var_name, var_value in variables.items():
+            allowed_names[var_name] = var_value
+
+        # Backwards compatibility: add a and b if they're provided (for old workflows)
+        if a is not None:
+            allowed_names['a'] = a
+        if b is not None:
+            allowed_names['b'] = b
+
+        def eval_node(node):
+            if isinstance(node, ast.Constant):  # Numbers and booleans
+                return node.value
+            elif isinstance(node, ast.Name):  # Variables
+                if node.id in allowed_names:
+                    return allowed_names[node.id]
+                raise ValueError(f"Name '{node.id}' is not allowed")
+            elif isinstance(node, ast.BinOp):  # Binary operations
+                if type(node.op) not in allowed_operators:
+                    raise ValueError(f"Operator {type(node.op).__name__} is not allowed")
+                left = eval_node(node.left)
+                right = eval_node(node.right)
+                return allowed_operators[type(node.op)](left, right)
+            elif isinstance(node, ast.UnaryOp):  # Unary operations
+                if type(node.op) not in allowed_operators:
+                    raise ValueError(f"Operator {type(node.op).__name__} is not allowed")
+                operand = eval_node(node.operand)
+                return allowed_operators[type(node.op)](operand)
+            elif isinstance(node, ast.Compare):  # Comparison operations
+                left = eval_node(node.left)
+                for op, comparator in zip(node.ops, node.comparators):
+                    if type(op) not in allowed_operators:
+                        raise ValueError(f"Operator {type(op).__name__} is not allowed")
+                    right = eval_node(comparator)
+                    result = allowed_operators[type(op)](left, right)
+                    if not result:
+                        return False
+                    left = right
+                return True
+            elif isinstance(node, ast.BoolOp):  # Boolean operations (and, or)
+                if type(node.op) not in allowed_operators:
+                    raise ValueError(f"Operator {type(node.op).__name__} is not allowed")
+                values = [eval_node(value) for value in node.values]
+                if isinstance(node.op, ast.And):
+                    return all(values)
+                elif isinstance(node.op, ast.Or):
+                    return any(values)
+            elif isinstance(node, ast.Call):  # Function calls
+                if not isinstance(node.func, ast.Name):
+                    raise ValueError("Only simple function calls are allowed")
+                if node.func.id not in allowed_functions:
+                    raise ValueError(f"Function '{node.func.id}' is not allowed")
+                args = [eval_node(arg) for arg in node.args]
+                return allowed_functions[node.func.id](*args)
+            else:
+                raise ValueError(f"Node type {type(node).__name__} is not allowed")
+
+        try:
+            tree = ast.parse(expression, mode='eval')
+            result = eval_node(tree.body)
+            return io.NodeOutput(float(result), int(result), bool(result))
+        except Exception as e:
+            logging.error(f"CalculatorKJ Error: {str(e)}")
+            return io.NodeOutput(0.0, 0, False)
+
+
+class GetTrackRange(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="GetTrackRange",
+            category="conditioning/video_models",
+            inputs=[
+                io.Tracks.Input("tracks"),
+                io.Int.Input("start_index", default=24, min=-10000, max=10000, step=1),
+                io.Int.Input("num_frames", default=10, min=1, max=10000, step=1),
+            ],
+            outputs=[
+                io.Tracks.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, tracks, start_index, num_frames) -> io.NodeOutput:
+        track_path = tracks["track_path"]
+        mask = tracks["track_visibility"]
+        total_frames = track_path.shape[0]
+
+        if start_index < 0:
+            start_index = total_frames + start_index
+        start_index = max(0, min(start_index, total_frames))
+
+        # Clamp end_index
+        end_index = max(0, min(start_index + num_frames, total_frames))
+
+        tracks_out = track_path[start_index:end_index, ...]
+        mask_out = mask[start_index:end_index, ...]
+
+        out_track = {
+            "track_path": tracks_out,
+            "track_visibility": mask_out,
+        }
+        return io.NodeOutput(out_track)
+
+class AddNoiseToTrackPath(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="AddNoiseToTrackPath",
+            category="conditioning/video_models",
+            inputs=[
+                io.Tracks.Input("tracks"),
+                io.Float.Input("strength", default=1.0, min=0.0, max=100.0, step=0.01),
+                io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff, step=1),
+                io.Float.Input("noise_x_ratio", default=1.0, min=0.0, max=100.0, step=0.01,
+                             tooltip="Multiplier for horizontal noise component"),
+                io.Float.Input("noise_y_ratio", default=1.0, min=0.0, max=100.0, step=0.01,
+                             tooltip="Multiplier for vertical noise component"),
+                io.Float.Input("noise_temporal_ratio", default=1.0, min=0.0, max=100.0, step=0.01,
+                             tooltip="Multiplier for temporal (frame-to-frame) noise"),
+            ],
+            outputs=[
+                io.Tracks.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, tracks, strength, seed, noise_x_ratio, noise_y_ratio, noise_temporal_ratio) -> io.NodeOutput:
+        track_path = tracks["track_path"].clone()
+        mask = tracks["track_visibility"]
+
+        torch.manual_seed(seed)
+        noise = torch.randn_like(track_path) * strength
+
+        # Apply directional scaling to noise
+        noise[..., 0] *= noise_x_ratio  # X coordinate noise
+        noise[..., 1] *= noise_y_ratio  # Y coordinate noise
+
+        # Apply temporal smoothing if temporal ratio is less than 1
+        if noise_temporal_ratio < 1.0:
+            num_frames = track_path.shape[0]
+            smoothed_noise = noise.clone()
+            kernel_size = max(1, int((1.0 - noise_temporal_ratio) * 10))
+
+            for i in range(num_frames):
+                start_idx = max(0, i - kernel_size // 2)
+                end_idx = min(num_frames, i + kernel_size // 2 + 1)
+                smoothed_noise[i] = noise[start_idx:end_idx].mean(dim=0)
+
+            noise = smoothed_noise * noise_temporal_ratio + noise * (1 - noise_temporal_ratio)
+
+        track_path = track_path + noise
+
+        out_track = {
+            "track_path": track_path,
+            "track_visibility": mask,
+        }
+        return io.NodeOutput(out_track)
+
+
+class VAEDecodeLoopKJ:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "samples": ("LATENT", {"tooltip": "The latent to be decoded."}),
+                "vae": ("VAE", {"tooltip": "The VAE model used for decoding the latent."}),
+                "overlap_latent_frames": ("INT", {"default": 2, "min": 2, "max": 8, "step": 1, "tooltip": "Number of frames to blend for seamless loop, for Wan 2 works and HunyuanVideo 1.5 should use 4"}),
+            }
+        }
+    RETURN_TYPES = ("IMAGE",)
+    OUTPUT_TOOLTIPS = ("The decoded images.",)
+    FUNCTION = "decode"
+    CATEGORY = "KJNodes/vae"
+    DESCRIPTION = "Video latent VAE decoding to fix artifacts on loop seams."
+
+    def decode(self, vae, samples, overlap_latent_frames):
+        latents = samples["samples"]
+
+        images = vae.decode(latents)
+        if overlap_latent_frames <= 0:
+            if len(images.shape) == 5:
+                images = images.reshape(-1, images.shape[-3], images.shape[-2], images.shape[-1])
+            return (images, )
+
+        end_frames = overlap_latent_frames + 1
+        start_frames = overlap_latent_frames
+
+        temp_images = vae.decode(torch.cat([latents[:, :, -end_frames:]] + [latents[:, :, :start_frames]], dim=2)).cpu().float()
+
+        total_concat = end_frames + start_frames
+        temp_start = total_concat * 2 - 1
+        main_start = total_concat + (overlap_latent_frames if overlap_latent_frames > 2 else 0)
+
+        images = torch.cat([temp_images[:, temp_start:].to(images), images[:, main_start:]], dim=1)
+        if len(images.shape) == 5:
+            images = images.reshape(-1, images.shape[-3], images.shape[-2], images.shape[-1])
+
+        return (images, )
+
+class WanImageToVideoSVIPro(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="WanImageToVideoSVIPro",
+            category="conditioning/video_models",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Int.Input("length", default=81, min=1, max=MAX_RESOLUTION, step=4),
+                io.Latent.Input("anchor_samples"),
+                io.Latent.Input("prev_samples", optional=True),
+                io.Int.Input("motion_latent_count", default=1, min=0, max=128, step=1),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, positive, negative, length, motion_latent_count, anchor_samples, prev_samples=None) -> io.NodeOutput:
+        anchor_latent = anchor_samples["samples"].clone()
+
+        B, C, T, H, W = anchor_latent.shape
+        empty_latent = torch.zeros([B, 16, ((length - 1) // 4) + 1, H, W], device=model_management.intermediate_device())
+
+        total_latents = (length - 1) // 4 + 1
+        device = anchor_latent.device
+        dtype = anchor_latent.dtype
+
+        if prev_samples is None or motion_latent_count == 0:
+            padding_size = total_latents - anchor_latent.shape[2]
+            image_cond_latent = anchor_latent
+        else:
+            motion_latent = prev_samples["samples"][:, :, -motion_latent_count:].clone()
+            padding_size = total_latents - anchor_latent.shape[2] - motion_latent.shape[2]
+            image_cond_latent = torch.cat([anchor_latent, motion_latent], dim=2)
+
+        padding = torch.zeros(1, C, padding_size, H, W, dtype=dtype, device=device)
+        padding = comfy.latent_formats.Wan21().process_out(padding)
+        image_cond_latent = torch.cat([image_cond_latent, padding], dim=2)
+
+        mask = torch.ones((1, 1, empty_latent.shape[2], H, W), device=device, dtype=dtype)
+        mask[:, :, :1] = 0.0
+
+        positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": image_cond_latent, "concat_mask": mask})
+        negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": image_cond_latent, "concat_mask": mask})
+
+        out_latent = {}
+        out_latent["samples"] = empty_latent
+        return io.NodeOutput(positive, negative, out_latent)
+
+class DeprecatedCompileNodeKJ:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+        "required": {
+            "model": (IO.ANY,),
+        },
+    }
+    RETURN_TYPES = (IO.ANY,)
+    FUNCTION = "passthrough"
+    CATEGORY = "KJNodes/deprecated"
+    DESCRIPTION = "This node has been replaced with TorchCompileModelAdvanced node, please use that instead."
+    def passthrough(self, model):
+        return (model,)
+
+
+class VisualizeSigmasKJ(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="VisualizeSigmasKJ",
+            category="KJNodes/misc",
+            inputs=[
+                io.Sigmas.Input("sigmas"),
+                io.Int.Input("start_step", default=0, min=-1, max=1000, step=1,
+                             tooltip="Step index to mark as the start of a range (inclusive). Set to -1 to disable."),
+                io.Int.Input("end_step", default=-1, min=-1, max=1000, step=1,
+                             tooltip="Step index to mark as the end of a range (inclusive). Set to - 1 to disable."),
+            ],
+            outputs=[
+                io.Sigmas.Output(display_name="sigmas_out"),
+                io.Image.Output(display_name="image"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, sigmas, start_step=0, end_step=-1) -> io.NodeOutput:
+
+        start_idx = 0
+        end_idx = len(sigmas) - 1
+
+        if isinstance(start_step, float):
+            idxs = (sigmas <= start_step).nonzero(as_tuple=True)[0]
+            if len(idxs) > 0:
+                start_idx = idxs[0].item()
+        elif isinstance(start_step, int):
+            if start_step > 0:
+                start_idx = start_step
+
+        if isinstance(end_step, float):
+            idxs = (sigmas >= end_step).nonzero(as_tuple=True)[0]
+            if len(idxs) > 0:
+                end_idx = idxs[-1].item()
+        elif isinstance(end_step, int):
+            if end_step != -1:
+                end_idx = end_step - 1
+
+        import matplotlib
+        matplotlib.use('Agg')
+        import matplotlib.pyplot as plt
+        sigmas_np = sigmas.cpu().numpy()
+        if not np.isclose(sigmas_np[-1], 0.0, atol=1e-6):
+            sigmas_np = np.append(sigmas_np, 0.0)
+        buf = BytesIO()
+        fig = plt.figure(facecolor='#353535')
+        ax = fig.add_subplot(111)
+        ax.set_facecolor('#353535')  # Set axes background color
+        x_values = range(0, len(sigmas_np))
+        ax.plot(x_values, sigmas_np)
+        # Annotate each sigma value
+        ax.scatter(x_values, sigmas_np, color='white', s=20, zorder=3)  # Small dots at each sigma
+        for x, y in zip(x_values, sigmas_np):
+            # Show all annotations if few steps, or just show split step annotations
+            show_annotation = len(sigmas_np) <= 10
+            is_split_step = (start_idx > 0 and x == start_idx) or (end_idx != -1 and x == end_idx + 1)
+
+            if show_annotation or is_split_step:
+                color = 'orange'
+                if is_split_step:
+                    color = 'yellow'
+                ax.annotate(f"{y:.3f}", (x, y), textcoords="offset points", xytext=(10, 1), ha='center', color=color, fontsize=12)
+        ax.set_xticks(x_values)
+        ax.set_title("Sigmas", color='white')           # Title font color
+        ax.set_xlabel("Step", color='white')            # X label font color
+        ax.set_ylabel("Sigma Value", color='white')     # Y label font color
+        ax.tick_params(axis='x', colors='white', labelsize=10)        # X tick color
+        ax.tick_params(axis='y', colors='white', labelsize=10)        # Y tick color
+        # Add split point if end_step is defined
+        end_idx += 1
+        if end_idx != -1 and 0 <= end_idx < len(sigmas_np) - 1:
+            ax.axvline(end_idx, color='red', linestyle='--', linewidth=2, label='end_step split')
+        # Add split point if start_step is defined
+        if start_idx > 0 and 0 <= start_idx < len(sigmas_np):
+            ax.axvline(start_idx, color='green', linestyle='--', linewidth=2, label='start_step split')
+        if (end_idx != -1 and 0 <= end_idx < len(sigmas_np)) or (start_idx > 0 and 0 <= start_idx < len(sigmas_np)):
+            handles, labels = ax.get_legend_handles_labels()
+            if labels:
+                ax.legend()
+        # Draw shaded range
+        range_start_idx = start_idx if start_idx > 0 else 0
+        range_end_idx = end_idx if end_idx > 0 and end_idx < len(sigmas_np) else len(sigmas_np) - 1
+        if range_start_idx < range_end_idx:
+            ax.axvspan(range_start_idx, range_end_idx, color='lightblue', alpha=0.1, label='Sampled Range')
+
+
+        plt.tight_layout()
+        fig.canvas.draw()
+        w, h = fig.canvas.get_width_height()
+        try:
+            buf = np.frombuffer(fig.canvas.tostring_argb(), dtype=np.uint8)
+            buf = buf.reshape(h, w, 4)
+            buf = buf[:, :, [1, 2, 3]]  # Convert ARGB to RGB
+        except AttributeError:
+            buf = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
+            buf = buf.reshape(h, w, 3).copy()
+        image = torch.from_numpy(buf).float() / 255.0
+        image = image.unsqueeze(0) #(H, W, C) -> (1, H, W, C)
+        plt.close(fig)
+
+        sigmas_out = sigmas[start_idx:end_idx + 1] if end_idx != -1 else sigmas[start_idx:]
+
+        return io.NodeOutput(sigmas_out,image)
+
+class PreviewLatentNoiseMask(io.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="PreviewLatentNoiseMask",
+            category="KJNodes/latents",
+            description="Previews the latent noise mask",
+            inputs=[
+                io.Latent.Input("latent",),
+            ],
+            outputs=[
+                io.Mask.Output(display_name="mask"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, latent) -> io.NodeOutput:
+        noise_mask = latent.get("noise_mask", None)
+        if noise_mask is None:
+            return io.NodeOutput(torch.zeros((1, 64, 64)))
+        noise_mask = noise_mask.clone()
+
+        if noise_mask.ndim == 5:
+            noise_mask = noise_mask[0, 0]
+
+        return io.NodeOutput(noise_mask)
+
+class PlaySoundKJ(io.ComfyNode):
+    """Plays audio in the browser when execution reaches this node."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="PlaySoundKJ",
+            category="KJNodes/audio",
+            description="Plays the input audio in the browser. Modes: 'always' plays on every execution, 'on_empty_queue' plays only when the queue finishes, 'on_change' plays only when the audio content changes. Duration limits playback length (0 = full audio).",
+            inputs=[
+                io.AnyType.Input("any_input", optional=True),
+                io.Audio.Input("audio", optional=True),
+                io.String.Input("audio_path", default="", tooltip="Path to an audio file. Used when audio input is not connected."),
+                io.Combo.Input("mode", options=["always", "on_empty_queue", "on_change"], default="always"),
+                io.Float.Input("volume", default=0.5, min=0.0, max=1.0, step=0.01),
+                io.Float.Input("duration", default=5.0, min=0.0, max=300.0, step=0.1, tooltip="Duration in seconds to play. 0 = play full audio."),
+            ],
+            outputs=[
+                io.AnyType.Output("any_output", display_name="any_output"),
+            ],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def fingerprint_inputs(cls, **kwargs):
+        if kwargs.get("mode") == "on_change":
+            return False
+        return float("NaN")
+
+    @staticmethod
+    def _generate_chime():
+        sr = 32000
+        C, Ab, Bb = 523.26, 421.30, 466.16  # note frequencies
+        e = 0.14  # eighth note — adjust to change tempo
+        S, M, H = 16, 7, 2.5  # staccato / medium / held decay
+        melody = [
+            (C,e,S), (C,e,S), (C,e,S), (C,e*3,M),
+            (Ab,e*3,M), (Bb,e*3,M), (C,e*2,S), (Bb,e,S), (C,e*5,H),
+        ]
+        k = torch.exp(-torch.linspace(-2, 2, 15) ** 2)
+        k = (k / k.sum()).reshape(1, 1, -1)
+        parts = []
+        for freq, dur, decay in melody:
+            t = torch.linspace(0, dur, int(sr * dur))
+            tone = torch.tanh(3 * torch.sin(2 * math.pi * freq * t))
+            tone = torch.nn.functional.conv1d(tone.reshape(1, 1, -1), k, padding=7).squeeze()
+            parts.append(tone * torch.exp(-t * decay))
+        wav = torch.cat(parts) * 0.45
+        return {"waveform": wav.unsqueeze(0).unsqueeze(0), "sample_rate": sr}
+
+    @classmethod
+    def execute(cls, audio=None, audio_path="", mode="always", volume=0.5, duration=5.0, any_input=None) -> io.NodeOutput:
+        if audio is None:
+            if audio_path:
+                import av
+                with av.open(audio_path) as af:
+                    stream = af.streams.audio[0]
+                    sr = stream.codec_context.sample_rate
+                    frames = []
+                    for frame in af.decode(streams=stream.index):
+                        buf = torch.from_numpy(frame.to_ndarray())
+                        if buf.shape[0] != stream.channels:
+                            buf = buf.view(-1, stream.channels).t()
+                        frames.append(buf)
+                    wav = torch.cat(frames, dim=1).float()
+                audio = {"waveform": wav.unsqueeze(0), "sample_rate": sr}
+            else:
+                audio = cls._generate_chime()
+
+        preview = ui.PreviewAudio(audio, cls=cls)
+        ui_dict = preview.as_dict()
+        ui_dict["audio_hash"] = [hash(audio["waveform"].sum().item())]
+        return io.NodeOutput(
+            any_input,
+            ui=ui_dict,
+        )
diff --git a/custom_nodes/ComfyUI-KJNodes/nodes/preview_override_node.py b/custom_nodes/ComfyUI-KJNodes/nodes/preview_override_node.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab0e1e76d7573dd2269dc1323bc1e0bfe2c70c5e
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/nodes/preview_override_node.py
@@ -0,0 +1,765 @@
+import base64
+import io as pyio
+import logging
+import queue
+import threading
+import time
+
+import numpy as np
+import torch
+
+import comfy.model_management
+import comfy.patcher_extension
+import latent_preview
+from comfy_api.latest import io
+from PIL import Image, ImageOps
+
+try:
+    from .ltxv_nodes import WrappedPreviewer as _LTXWrappedPreviewer, get_ltx_rgb_factors as _ltx_rgb_factors
+except Exception as e:
+    logging.warning(f"[KJ PreviewOverride] LTX preview helpers unavailable ({e}); LTX previews disabled.")
+    _LTXWrappedPreviewer = None
+    _ltx_rgb_factors = None
+
+
+try:
+    from server import PromptServer
+except ImportError:
+    PromptServer = None
+
+def _suppressed_preview_image(self_, preview_format, x0):
+    return None
+
+
+class _AsyncPreviewEncoder:
+    """Off-thread encoder. Bounded FIFO drops-on-full so the sampler never blocks on us."""
+
+    _STOP = object()
+
+    def __init__(self, max_in_flight=2):
+        self.q = queue.Queue(maxsize=max_in_flight)
+        self.thread = threading.Thread(target=self._run, name="kj_preview_encoder", daemon=True)
+        self.thread.start()
+
+    def submit(self, fn):
+        try:
+            self.q.put_nowait(fn)
+            return True
+        except queue.Full:
+            return False
+
+    def _run(self):
+        while True:
+            item = self.q.get()
+            if item is self._STOP:
+                return
+            try:
+                item()
+            except Exception:
+                logging.exception("[KJ Preview Override] async encoder error")
+
+    def shutdown(self, drain_timeout=5.0):
+        try:
+            self.q.put(self._STOP, timeout=drain_timeout)
+        except queue.Full:
+            pass
+        self.thread.join(timeout=drain_timeout)
+
+
+def _get_core_previewer(load_device, latent_format):
+    # Walk past custom-node hooks on get_previewer to reach the unwrapped core function.
+    fn = latent_preview.get_previewer
+    seen = set()
+    while hasattr(fn, "__wrapped__") and id(fn) not in seen:
+        seen.add(id(fn))
+        fn = fn.__wrapped__
+    return fn(load_device, latent_format)
+
+
+def _decode_video_frames_l2rgb(x0, latent_format, max_frames, stride=1):
+    # Bulk-blocking GPU→CPU copy (not per-frame non_blocking) avoids torn frames at high res.
+    if x0.ndim != 5:
+        return []
+    rgb_factors = getattr(latent_format, "latent_rgb_factors", None)
+    if rgb_factors is None:
+        return []
+    try:
+        reshape = getattr(latent_format, "latent_rgb_factors_reshape", None)
+        if reshape is not None:
+            x0 = reshape(x0)
+        bias = getattr(latent_format, "latent_rgb_factors_bias", None)
+        factors = torch.tensor(rgb_factors, device=x0.device, dtype=x0.dtype).transpose(0, 1)
+        bias_t = torch.tensor(bias, device=x0.device, dtype=x0.dtype) if bias is not None else None
+        x = x0[0]
+        if stride > 1:
+            x = x[:, ::stride]
+        t_total = x.shape[1]
+        if max_frames > 0 and max_frames < t_total:
+            indices = np.linspace(0, t_total - 1, max_frames).round().astype(int).tolist()
+            x = x[:, indices]
+        x = x.movedim(0, -1)
+        rgb = torch.nn.functional.linear(x, factors, bias=bias_t)
+        rgb.add_(1.0).mul_(127.5).clamp_(0, 255)
+        rgb_cpu = rgb.to(torch.uint8).cpu().numpy()
+        return [Image.fromarray(rgb_cpu[i]) for i in range(rgb_cpu.shape[0])]
+    except Exception:
+        return []
+
+
+# PyPI PyAV wheels typically lack NVENC; probe once at import.
+def _probe_nvenc():
+    try:
+        import av  # noqa
+        av.Codec("h264_nvenc", "w")
+        return True
+    except Exception:
+        return False
+
+_NVENC_AVAILABLE = _probe_nvenc()
+
+# NVENC H.264 rejects sub-145×49 inputs at avcodec_open2 — fall back to WebP for small frames.
+_NVENC_MIN_W = 145
+_NVENC_MIN_H = 49
+
+_nvenc_warned = False
+
+
+def _encode_mp4_nvenc(frames, fps, max_res):
+    # Fragmented MP4 so the browser can decode mid-download. Returns (None, 0, 0) on failure
+    # (including too-small-for-NVENC), so caller falls through to WebP.
+    global _nvenc_warned
+    if not frames:
+        return None, 0, 0
+    try:
+        import av
+    except Exception:
+        return None, 0, 0
+    pil_frames = []
+    for f in frames:
+        pf = f if f.mode == "RGB" else f.convert("RGB")
+        if max_res and max_res > 0 and (pf.width > max_res or pf.height > max_res):
+            pf = ImageOps.contain(pf, (max_res, max_res), Image.LANCZOS)
+        pil_frames.append(pf)
+    # yuv420p requires even dimensions.
+    w0, h0 = pil_frames[0].width, pil_frames[0].height
+    out_w, out_h = w0 & ~1, h0 & ~1
+    if (out_w, out_h) != (w0, h0):
+        pil_frames = [pf.resize((out_w, out_h), Image.LANCZOS) for pf in pil_frames]
+    if out_w < _NVENC_MIN_W or out_h < _NVENC_MIN_H:
+        return None, 0, 0
+    # Driver/GPU varies what option combos are accepted; bare preset always works.
+    option_candidates = [
+        {"preset": "p1", "rc": "vbr", "cq": "23"},
+        {"preset": "p1"},
+    ]
+    last_err = None
+    for opts in option_candidates:
+        buf = pyio.BytesIO()
+        try:
+            container = av.open(
+                buf, mode="w", format="mp4",
+                options={"movflags": "frag_keyframe+empty_moov+default_base_moof"},
+            )
+            stream = container.add_stream("h264_nvenc", rate=int(max(1, fps)))
+            stream.width = out_w
+            stream.height = out_h
+            stream.pix_fmt = "yuv420p"
+            stream.options = opts
+            for pf in pil_frames:
+                for pkt in stream.encode(av.VideoFrame.from_image(pf)):
+                    container.mux(pkt)
+            for pkt in stream.encode():
+                container.mux(pkt)
+            container.close()
+            return base64.b64encode(buf.getvalue()).decode("ascii"), out_w, out_h
+        except Exception as e:
+            last_err = e
+            continue
+    if not _nvenc_warned:
+        _nvenc_warned = True
+        logging.warning(f"[KJ PreviewOverride] NVENC MP4 encode failed, using WebP fallback: {last_err}")
+    return None, 0, 0
+
+
+def _encode_animated_webp(frames, fps, quality, max_res):
+    if not frames:
+        return None, 0, 0
+    pil_frames = []
+    for f in frames:
+        pf = f
+        if pf.mode != "RGB":
+            pf = pf.convert("RGB")
+        if max_res and max_res > 0 and (pf.width > max_res or pf.height > max_res):
+            pf = ImageOps.contain(pf, (max_res, max_res), Image.LANCZOS)
+        pil_frames.append(pf)
+    duration_ms = max(1, int(round(1000 / max(1, fps))))
+    buf = pyio.BytesIO()
+    try:
+        pil_frames[0].save(
+            buf,
+            format="WEBP",
+            save_all=True,
+            append_images=pil_frames[1:],
+            duration=duration_ms,
+            loop=0,
+            quality=quality,
+            method=4,
+        )
+    except Exception as e:
+        logging.warning(f"Animated WebP encode failed: {e}")
+        return None, 0, 0
+    return base64.b64encode(buf.getvalue()).decode("ascii"), pil_frames[0].width, pil_frames[0].height
+
+
+def _interp_db_curve(t, xs, ys):
+    # Mirrors sampler_nodes._interp_curve.
+    if t <= xs[0]:
+        return ys[0]
+    if t >= xs[-1]:
+        return ys[-1]
+    for i in range(len(xs) - 1):
+        if xs[i] <= t <= xs[i + 1]:
+            span = xs[i + 1] - xs[i]
+            if span <= 0:
+                return ys[i]
+            f = (t - xs[i]) / span
+            return ys[i] + f * (ys[i + 1] - ys[i])
+    return 0.0
+
+
+def _detect_detail_boost_curve(sampler, model_patcher, sigmas_list):
+    # Amount is already baked into ys by the editor, so peak ys == user-set amount.
+    try:
+        extra = getattr(sampler, "extra_options", None) or {}
+        xs = extra.get("db_curve_xs")
+        ys = extra.get("db_curve_ys")
+        if "db_wrapped_sampler" not in extra or not xs or not ys or len(xs) != len(ys) or len(xs) < 2:
+            return None
+        ms = model_patcher.get_model_object("model_sampling")
+        start_sigma = float(ms.percent_to_sigma(extra.get("db_start_percent", 0.0)))
+        end_sigma = float(ms.percent_to_sigma(extra.get("db_end_percent", 1.0)))
+        # None outside the gate so JS can distinguish "inactive" from "active with value 0".
+        out = []
+        for s in sigmas_list:
+            sig = float(s)
+            if sig <= 0 or start_sigma <= end_sigma or sig >= start_sigma or sig <= end_sigma:
+                out.append(None)
+                continue
+            t = (start_sigma - sig) / (start_sigma - end_sigma)
+            out.append(_interp_db_curve(t, xs, ys))
+        return out
+    except Exception as e:
+        logging.warning(f"[KJ PreviewOverride] DB curve detection failed: {e}")
+        return None
+
+
+def _ltx_decode_to_pil(ltx_previewer, x0_5d, max_frames=None, stride=1):
+    # Pre-shape (B, C, T, H, W) → (B*T, C, H, W); WrappedPreviewer adds the sequence-batch dim.
+    if ltx_previewer is None or x0_5d.ndim != 5:
+        return []
+    if stride > 1:
+        x0_5d = x0_5d[:, :, ::stride]
+    x_moved = x0_5d.movedim(2, 1)  # (B, T, C, H, W) — must take shape AFTER movedim
+    x_in = x_moved.reshape((-1,) + x_moved.shape[-3:])
+    rgb = ltx_previewer.decode_latent_to_preview(x_in)
+    if rgb is None:
+        return []
+    if rgb.ndim == 3:
+        rgb = rgb.unsqueeze(0)
+    if rgb.ndim != 4:
+        return []
+    t_total = rgb.shape[0]
+    if max_frames is not None and 0 < max_frames < t_total:
+        indices = np.linspace(0, t_total - 1, max_frames).round().astype(int).tolist()
+        rgb = rgb[indices]
+    u8 = (rgb * 255).clamp(0, 255).to(torch.uint8).cpu().numpy()
+    return [Image.fromarray(u8[i]) for i in range(u8.shape[0])]
+
+
+def _ltx_full_vae_decode_to_pil(vae, x0_5d, max_frames=None, stride=1):
+    # vae.decode handles device + tiling. Slow vs TAEHV but full quality. Output shape
+    # varies by VAE; we accept (B, T, H, W, C) or (T, H, W, C) and normalize.
+    if vae is None or x0_5d.ndim != 5:
+        return []
+    if stride > 1:
+        x0_5d = x0_5d[:, :, ::stride]
+    try:
+        images = vae.decode(x0_5d)
+    except Exception as e:
+        logging.warning(f"[KJ PreviewOverride] LTX VAE decode failed: {e}")
+        return []
+    if images.ndim == 5:
+        images = images[0]
+    if images.ndim != 4:
+        return []
+    t_total = images.shape[0]
+    if max_frames is not None and 0 < max_frames < t_total:
+        indices = np.linspace(0, t_total - 1, max_frames).round().astype(int).tolist()
+        images = images[indices]
+    u8 = (images.float() * 255).clamp(0, 255).to(torch.uint8).cpu().numpy()
+    return [Image.fromarray(u8[i]) for i in range(u8.shape[0])]
+
+
+def _is_ltx_latent_format(latent_format):
+    return "LTX" in type(latent_format).__name__
+
+
+def _is_ltx2_diffusion_model(model_patcher):
+    # Same probe as ltxv_nodes.OuterSampleCallbackWrapper.
+    try:
+        dm = model_patcher.model.diffusion_model
+        return not getattr(dm, "caption_projection_first_linear", True)
+    except Exception:
+        return False
+
+
+def _ltx_num_keyframes(guider):
+    try:
+        positive = guider.conds.get("positive") if hasattr(guider, "conds") else None
+        if positive and len(positive) > 0:
+            kf = positive[0].get("keyframe_idxs")
+            if kf is not None:
+                return int(torch.unique(kf[0, 0, :, 0]).numel())
+    except Exception:
+        pass
+    return 0
+
+
+def _normalize_ltx_x0(x0, latent_shapes, num_keyframes):
+    # LTX flattens spatial+temporal into a token sequence and may append keyframe latents
+    # at the tail. Restore 5D and trim so downstream previewers see standard video latents.
+    if latent_shapes and len(latent_shapes) > 0:
+        target = latent_shapes[0]
+        if x0.ndim == 3 and len(target) >= 3:
+            cut = 1
+            for d in target[1:]:
+                cut *= int(d)
+            x0 = x0[:, :, :cut].reshape([x0.shape[0]] + list(target)[1:])
+    if num_keyframes > 0 and x0.ndim == 5:
+        x0 = x0[:, :, :-num_keyframes]
+    return x0
+
+
+class _PreviewOverrideWrapper:
+    def __init__(self, max_resolution, node_id, jpeg_quality, suppress_default, preview_frames=1, preview_fps=12, vae=None):
+        self.max_resolution = max_resolution
+        self.node_id = str(node_id) if node_id is not None else None
+        self.jpeg_quality = jpeg_quality
+        self.suppress_default = suppress_default
+        self.preview_frames = preview_frames
+        self.preview_fps = preview_fps
+        self.vae = vae
+        self.frames = []
+
+    def __call__(self, executor, noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes):
+        guider = executor.class_obj
+        model_patcher = guider.model_patcher
+
+        is_ltx = _is_ltx_latent_format(model_patcher.model.latent_format)
+        is_ltx2 = is_ltx and _is_ltx2_diffusion_model(model_patcher)
+        num_keyframes = _ltx_num_keyframes(guider) if is_ltx else 0
+
+        # LTX reuses the LTX-specific node's WrappedPreviewer; we call decode_latent_to_preview
+        # directly per step, bypassing its decode_latent_to_preview_image rate-limiting.
+        # If a non-TAEHV VAE is supplied, decode via vae.decode() for full quality (slower).
+        ltx_previewer = None
+        ltx_full_vae = None
+        vae_restore_device = None
+        if is_ltx:
+            try:
+                factors, bias = _ltx_rgb_factors(is_ltx2)
+                taeltx = None
+                if self.vae is not None:
+                    if self.vae.first_stage_model.__class__.__name__ == "TAEHV":
+                        # TAEHV-LTX decode needs the VAE on GPU; restored at end of __call__.
+                        target_device = comfy.model_management.get_torch_device()
+                        try:
+                            for p in self.vae.first_stage_model.parameters():
+                                vae_restore_device = p.device
+                                break
+                            self.vae.first_stage_model.to(target_device)
+                            taeltx = self.vae
+                        except Exception as e:
+                            logging.warning(f"[KJ PreviewOverride] Could not move TAEHV-LTX to GPU, skipping: {e}")
+                    else:
+                        # Comfy VAE.decode manages its own device — no pin-to-GPU needed.
+                        ltx_full_vae = self.vae
+                ltx_previewer = _LTXWrappedPreviewer(factors, bias, rate=8, taeltx=taeltx)
+            except Exception as e:
+                logging.warning(f"[KJ PreviewOverride] LTX previewer setup failed: {e}")
+
+        previewer = _get_core_previewer(model_patcher.load_device, model_patcher.model.latent_format)
+        # Latent2RGB fallback — used when the active previewer returns a non-PIL result
+        # (e.g. TAEHV/TAESD on a 5D latent). LTX skips this and goes through ltx_previewer.
+        fallback_previewer = None
+        try:
+            lf = model_patcher.model.latent_format
+            rgb_factors = getattr(lf, "latent_rgb_factors", None)
+            if rgb_factors is not None:
+                fallback_previewer = latent_preview.Latent2RGBPreviewer(
+                    rgb_factors,
+                    getattr(lf, "latent_rgb_factors_bias", None),
+                    getattr(lf, "latent_rgb_factors_reshape", None),
+                )
+        except Exception:
+            pass
+
+        original_callback = callback
+        node_id = self.node_id
+        max_res = self.max_resolution
+        quality = self.jpeg_quality
+        self.frames = []
+
+        # N+1 boundaries for N steps: keep them all so the step marker advances through each.
+        sigmas_list = sigmas.detach().cpu().tolist() if sigmas is not None else []
+        # Pre-seed so step 1 has a measurable Δ (model's first transformation from noise → x0).
+        initial_seed_cpu = None
+        try:
+            if sigmas is not None and len(sigmas) > 0:
+                # sigmas often lives on CPU while noise is on CUDA — align before the multiply.
+                s0 = sigmas[0].to(noise.device) if hasattr(sigmas[0], "to") else sigmas[0]
+                seeded = noise * s0
+                if is_ltx:
+                    seeded = _normalize_ltx_x0(seeded, latent_shapes, num_keyframes)
+                initial_seed_cpu = seeded.detach().float().cpu()
+        except Exception as e:
+            logging.warning(f"[KJ PreviewOverride] initial seed Δ pre-fill failed: {e}")
+        state = {"last_x0_cpu": initial_seed_cpu, "last_time": None, "step_ms_window": []}
+        total_steps_init = max(0, len(sigmas_list) - 1)
+
+        # Boundary-0 message: sigmas (required by JS hover handler) plus optional noise preview.
+        if node_id is not None and PromptServer is not None:
+            init_payload = {
+                "node_id": node_id,
+                "step": 0,
+                "total": total_steps_init,
+                "sigma": sigmas_list[0] if sigmas_list else None,
+                "sigmas": sigmas_list,
+            }
+            db_curve = _detect_detail_boost_curve(sampler, model_patcher, sigmas_list)
+            if db_curve is not None:
+                init_payload["db_curve"] = db_curve
+            # Use Latent2RGB (or LTX previewer) directly — the model's default previewer (TAEHV)
+            # slices to one temporal frame and returns a shape PIL can't render on raw noise.
+            try:
+                lf = model_patcher.model.latent_format
+                rgb_factors = getattr(lf, "latent_rgb_factors", None)
+                if sigmas is not None and len(sigmas) > 0:
+                    s0 = sigmas[0].to(noise.device) if hasattr(sigmas[0], "to") else sigmas[0]
+                    init_latent = noise * s0
+                else:
+                    init_latent = noise
+                if is_ltx:
+                    init_latent = _normalize_ltx_x0(init_latent, latent_shapes, num_keyframes)
+                pil_init = None
+                if ltx_previewer is not None and init_latent.ndim == 5:
+                    pil_frames = _ltx_decode_to_pil(ltx_previewer, init_latent, max_frames=1)
+                    pil_init = pil_frames[0] if pil_frames else None
+                elif rgb_factors is not None:
+                    noise_previewer = latent_preview.Latent2RGBPreviewer(
+                        rgb_factors,
+                        getattr(lf, "latent_rgb_factors_bias", None),
+                        getattr(lf, "latent_rgb_factors_reshape", None),
+                    )
+                    out = noise_previewer.decode_latent_to_preview(init_latent)
+                    if isinstance(out, Image.Image):
+                        pil_init = out
+                if pil_init is not None:
+                    if pil_init.mode != "RGB":
+                        pil_init = pil_init.convert("RGB")
+                    if max_res and max_res > 0 and (pil_init.width > max_res or pil_init.height > max_res):
+                        pil_init = ImageOps.contain(pil_init, (max_res, max_res), Image.LANCZOS)
+                    ibuf = pyio.BytesIO()
+                    pil_init.save(ibuf, format="JPEG", quality=quality)
+                    init_payload["image"] = base64.b64encode(ibuf.getvalue()).decode("ascii")
+                    init_payload["w"] = pil_init.width
+                    init_payload["h"] = pil_init.height
+            except Exception as e:
+                logging.warning(f"Initial noise preview failed (sigmas still sent): {e}")
+            PromptServer.instance.send_sync("kj_preview_override", init_payload, PromptServer.instance.client_id)
+
+        encoder = _AsyncPreviewEncoder()
+        animate_video = self.preview_frames > 1
+        anim_frames = self.preview_frames
+        anim_fps = self.preview_fps
+
+
+        def new_callback(step, x0, x, total_steps_):
+            if previewer is not None or fallback_previewer is not None or ltx_previewer is not None:
+                try:
+                    # NEVER rebind x0 — the sampler reuses the same tensor downstream
+                    # (unpack_latents reshapes it). Preview mutations stay on x0_view.
+                    x0_view = x0
+                    if is_ltx:
+                        x0_view = _normalize_ltx_x0(x0_view, latent_shapes, num_keyframes)
+
+                    pil_frames = []
+                    max_pil = anim_frames if animate_video else 1
+                    if ltx_full_vae is not None and x0_view.ndim == 5:
+                        pil_frames = _ltx_full_vae_decode_to_pil(ltx_full_vae, x0_view, max_frames=max_pil)
+                    if not pil_frames and ltx_previewer is not None and x0_view.ndim == 5:
+                        try:
+                            pil_frames = _ltx_decode_to_pil(ltx_previewer, x0_view, max_frames=max_pil)
+                        except Exception as e:
+                            logging.warning(f"LTX preview decode failed: {e}")
+                    if not pil_frames and animate_video and x0_view.ndim == 5 and ltx_previewer is None:
+                        pil_frames = _decode_video_frames_l2rgb(
+                            x0_view, model_patcher.model.latent_format, anim_frames,
+                        )
+
+                    if not pil_frames:
+                        for prev in (previewer, fallback_previewer):
+                            if prev is None:
+                                continue
+                            try:
+                                out = prev.decode_latent_to_preview(x0_view)
+                            except Exception as e:
+                                if prev is previewer:
+                                    logging.warning(f"Active previewer raised, trying Latent2RGB fallback: {e}")
+                                continue
+                            if isinstance(out, Image.Image):
+                                pil_frames = [out]
+                                break
+                            elif prev is previewer:
+                                logging.warning(
+                                    f"Preview override: {type(previewer).__name__} returned "
+                                    f"{type(out).__name__} instead of PIL.Image — falling back to Latent2RGB."
+                                )
+
+                    if not pil_frames:
+                        if original_callback is not None:
+                            original_callback(step, x0, x, total_steps_)
+                        return
+
+                    pil_first = pil_frames[0]
+                    if pil_first.mode != "RGB":
+                        pil_first = pil_first.convert("RGB")
+                        pil_frames[0] = pil_first
+                    # Consumed by GetPreviewOverrideFramesKJ.
+                    self.frames.append(pil_first)
+
+                    if node_id is not None and PromptServer is not None:
+                        # x0_view (not x0) so LTX keyframe padding doesn't dampen the Δ norm.
+                        x0_cpu_now = x0_view.detach().float().cpu()
+                        prev_x0_cpu = state["last_x0_cpu"]
+                        state["last_x0_cpu"] = x0_cpu_now
+
+                        now = time.perf_counter()
+                        step_ms = None
+                        if state["last_time"] is not None:
+                            step_ms = (now - state["last_time"]) * 1000.0
+                            w = state["step_ms_window"]
+                            w.append(step_ms)
+                            if len(w) > 8:
+                                w.pop(0)
+                        state["last_time"] = now
+                        avg_step_ms = (sum(state["step_ms_window"]) / len(state["step_ms_window"])) if state["step_ms_window"] else None
+                        sigma_val = sigmas_list[step] if 0 <= step < len(sigmas_list) else None
+                        sent_step = step + 1
+
+                        def _encode_and_send(
+                            pil_frames=pil_frames, x0_cpu_now=x0_cpu_now, prev_x0_cpu=prev_x0_cpu,
+                            step_ms=step_ms, avg_step_ms=avg_step_ms, sigma_val=sigma_val,
+                            sent_step=sent_step, total_steps_=total_steps_,
+                        ):
+                            if len(pil_frames) > 1:
+                                # NVENC ~8x faster + ~5x smaller than PIL WebP when available.
+                                b64, w_, h_, mime = None, 0, 0, None
+                                if _NVENC_AVAILABLE:
+                                    b64, w_, h_ = _encode_mp4_nvenc(pil_frames, anim_fps, max_res)
+                                    if b64:
+                                        mime = "video/mp4"
+                                if not b64:
+                                    b64, w_, h_ = _encode_animated_webp(pil_frames, anim_fps, quality, max_res)
+                                    mime = "image/webp"
+                            else:
+                                pil_send = pil_frames[0]
+                                if max_res and max_res > 0 and (pil_send.width > max_res or pil_send.height > max_res):
+                                    pil_send = ImageOps.contain(pil_send, (max_res, max_res), Image.LANCZOS)
+                                buf = pyio.BytesIO()
+                                pil_send.save(buf, format="JPEG", quality=quality)
+                                b64 = base64.b64encode(buf.getvalue()).decode("ascii")
+                                w_, h_ = pil_send.width, pil_send.height
+                                mime = "image/jpeg"
+
+                            if not b64:
+                                return
+
+                            delta_v = None
+                            if prev_x0_cpu is not None and prev_x0_cpu.shape == x0_cpu_now.shape:
+                                diff = x0_cpu_now - prev_x0_cpu
+                                delta_v = (diff.norm() / max(1, diff.numel()) ** 0.5).item()
+
+                            PromptServer.instance.send_sync(
+                                "kj_preview_override",
+                                {
+                                    "node_id": node_id,
+                                    "image": b64,
+                                    "mime": mime,
+                                    "w": w_,
+                                    "h": h_,
+                                    "step": sent_step,
+                                    "total": total_steps_,
+                                    "sigma": sigma_val,
+                                    "sigmas": None,
+                                    "delta": delta_v,
+                                    "step_ms": step_ms,
+                                    "avg_step_ms": avg_step_ms,
+                                    "fps": anim_fps if mime in ("video/mp4", "image/webp") else None,
+                                },
+                                PromptServer.instance.client_id,
+                            )
+
+                        encoder.submit(_encode_and_send)
+                except Exception as e:
+                    logging.warning(f"Preview override failed: {e}")
+            if original_callback is not None:
+                original_callback(step, x0, x, total_steps_)
+
+        # Patch every concrete decode_latent_to_preview_image — subclasses like VHS's
+        # WrappedPreviewer override it and would otherwise still emit previews of their own.
+        prev_methods = []
+        if self.suppress_default:
+            targets = [latent_preview.LatentPreviewer]
+            stack = list(latent_preview.LatentPreviewer.__subclasses__())
+            while stack:
+                cls = stack.pop()
+                targets.append(cls)
+                stack.extend(cls.__subclasses__())
+            for cls in targets:
+                if "decode_latent_to_preview_image" in cls.__dict__:
+                    prev_methods.append((cls, cls.__dict__["decode_latent_to_preview_image"]))
+                    cls.decode_latent_to_preview_image = _suppressed_preview_image
+        try:
+            # Seeds step 1's duration measurement (sampling-start → end of step 1).
+            state["last_time"] = time.perf_counter()
+            return executor(noise, latent_image, sampler, sigmas, denoise_mask, new_callback, disable_pbar, seed, latent_shapes=latent_shapes)
+        finally:
+            encoder.shutdown(drain_timeout=5.0)
+            for cls, prev in prev_methods:
+                cls.decode_latent_to_preview_image = prev
+            if vae_restore_device is not None and self.vae is not None:
+                try:
+                    self.vae.first_stage_model.to(vae_restore_device)
+                except Exception:
+                    pass
+
+
+class ModelPreviewOverrideKJ(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="ModelPreviewOverrideKJ",
+            display_name="Model Preview Override",
+            category="KJNodes/sampling",
+            description=(
+                "Adds a dedicated live-preview frame on this node, with overridable max resolution. "
+                "Default ComfyUI preview caps at 512px; this node sends its own preview straight to a "
+                "DOM widget on the node so pixel-space models (Chroma Radiance, ZImage, HiDream-O1, …) "
+                "can be previewed at full sampler resolution."
+            ),
+            inputs=[
+                io.Model.Input("model", tooltip="Model to attach the preview override to."),
+                io.Int.Input(
+                    "max_resolution",
+                    default=1024,
+                    min=0,
+                    max=8192,
+                    step=8,
+                    tooltip="Max preview side in pixels for the live widget. 0 = full sampler resolution (no downscale).",
+                ),
+                io.Int.Input(
+                    "jpeg_quality",
+                    default=80,
+                    min=30,
+                    max=100,
+                    step=1,
+                    tooltip="JPEG quality for the live preview transport.",
+                ),
+                io.Boolean.Input(
+                    "suppress_default_preview",
+                    default=True,
+                    tooltip="Suppress the standard sampler-node preview overlay while sampling, so only this node's frame updates. Progress bar still advances normally.",
+                ),
+                io.Int.Input(
+                    "preview_frames",
+                    default=1,
+                    min=1,
+                    max=1024,
+                    step=1,
+                    tooltip="Frames to sample from each video step's latent for animated preview. "
+                            "1 = single frame (current behavior, fastest). >1 = animated WebP playing back at preview_fps. "
+                            "Only applies to video models (5D latents); ignored for image models.",
+                ),
+                io.Int.Input(
+                    "preview_fps",
+                    default=12,
+                    min=1,
+                    max=60,
+                    step=1,
+                    tooltip="Playback FPS for the animated WebP preview. Ignored when preview_frames=1.",
+                ),
+                io.Vae.Input(
+                    "vae",
+                    optional=True,
+                    tooltip="Optional LTX VAE for true-RGB previews. TAEHV-LTX = fast tiny decode "
+                            "(VAE pinned to GPU). Any other LTX VAE = full-quality decode via "
+                            "vae.decode() — MUCH slower per step.",
+                ),
+            ],
+            outputs=[io.Model.Output(tooltip="Model with preview override attached.")],
+            hidden=[io.Hidden.unique_id],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, model, max_resolution, jpeg_quality, suppress_default_preview, preview_frames, preview_fps, vae=None) -> io.NodeOutput:
+        m = model.clone()
+        m.add_wrapper_with_key(
+            comfy.patcher_extension.WrappersMP.OUTER_SAMPLE,
+            "kj_preview_override",
+            _PreviewOverrideWrapper(
+                max_resolution, cls.hidden.unique_id, jpeg_quality, suppress_default_preview,
+                preview_frames, preview_fps, vae,
+            ),
+        )
+        return io.NodeOutput(m)
+
+
+class GetPreviewOverrideFramesKJ(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="GetPreviewOverrideFramesKJ",
+            display_name="Get Preview Override Frames",
+            category="KJNodes/sampling",
+            description=(
+                "Returns the frames captured by Model Preview Override during the most recent sampling. "
+                "Wire 'model' from Model Preview Override (the same one feeding the sampler) and 'after_sample' "
+                "from after the sampler (LATENT/IMAGE) to enforce correct execution order."
+            ),
+            inputs=[
+                io.Model.Input("model", tooltip="The model output by Model Preview Override (used to locate the captured frames)."),
+                io.MultiType.Input(
+                    "after_sample",
+                    [io.Latent, io.Image],
+                    tooltip="Anything from after the sampler (LATENT or IMAGE). The value is ignored — it just forces this node to run after sampling.",
+                ),
+            ],
+            outputs=[io.Image.Output(display_name="frames")],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, model, after_sample) -> io.NodeOutput:
+        wrappers = model.get_wrappers(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "kj_preview_override")
+        if not wrappers:
+            raise RuntimeError("Get Preview Override Frames: no Model Preview Override wrapper found on this model.")
+        frames = wrappers[-1].frames
+        if not frames:
+            raise RuntimeError("Get Preview Override Frames: no frames captured. Ensure the sampler ran with this model.")
+        tensors = []
+        for pil in frames:
+            arr = np.asarray(pil, dtype=np.float32) / 255.0
+            tensors.append(torch.from_numpy(arr))
+        return io.NodeOutput(torch.stack(tensors, dim=0))
diff --git a/custom_nodes/ComfyUI-KJNodes/nodes/sharpen_nodes.py b/custom_nodes/ComfyUI-KJNodes/nodes/sharpen_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..dad2e8ce6c0bdae0511648eeb3dfeab20aa53bdc
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/nodes/sharpen_nodes.py
@@ -0,0 +1,259 @@
+import torch
+import torch.nn.functional as F
+
+from comfy import model_management
+from comfy_api.latest import io
+
+class ImageSharpenKJ(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ImageSharpenKJ",
+            category="KJNodes/image",
+            display_name="Image Sharpen KJ",
+            search_aliases=["sharpen", "unsharp mask", "deconvolution", "cas", "rcas", "high-pass", "postprocessing"],
+            description="""GPU-accelerated image sharpening with multiple methods.
+
+**RCAS** — AMD's Robust Contrast-Adaptive Sharpening (from FSR).
+Single 5-tap cross filter that adapts to local contrast.
+Minimal artifacts, good for general use with little tuning.
+
+**Adaptive USM** — Unsharp mask with local variance modulation.
+Sharpens detail-rich areas more, flat/noisy areas less.
+More controllable than RCAS via radius and threshold parameters.
+
+**High-Pass** — Extracts high-frequency detail and blends it back.
+Gives a "clarity" enhancement feel. Uses radius to control detail scale.
+
+**Deconvolution** — Richardson-Lucy iterative deconvolution.
+Can recover actual lost detail from blur, not just enhance edges.
+Uses radius as the estimated blur kernel and iterations to control convergence.""",
+            inputs=[
+                io.MatchType.Input("image", io.MatchType.Template("img_or_mask", [io.Image, io.Mask])),
+                io.DynamicCombo.Input("method", options=[
+                    io.DynamicCombo.Option(key="rcas", inputs=[
+                        io.Float.Input("strength", default=0.8, min=0.0, max=1.0, step=0.01,
+                                       tooltip="0 = no sharpening, 1 = full RCAS sharpening."),
+                    ]),
+                    io.DynamicCombo.Option(key="adaptive_usm", inputs=[
+                        io.Float.Input("strength", default=0.5, min=0.0, max=3.0, step=0.01,
+                                       tooltip="Sharpening multiplier. Values above 1.0 give aggressive sharpening."),
+                        io.Float.Input("radius", default=1.0, min=0.5, max=5.0, step=0.1,
+                                       tooltip="Gaussian blur sigma for the unsharp mask. Larger = enhances coarser detail."),
+                        io.Float.Input("threshold", default=0.05, min=0.0, max=1.0, step=0.01,
+                                       tooltip="Noise gate. Higher = only sharpen areas with more texture/detail. 0 = sharpen everything."),
+                    ]),
+                    io.DynamicCombo.Option(key="high_pass", inputs=[
+                        io.Float.Input("strength", default=0.5, min=0.0, max=3.0, step=0.01,
+                                       tooltip="Blend factor for high-frequency detail. Values above 1.0 give a punchier effect."),
+                        io.Float.Input("radius", default=1.0, min=0.5, max=5.0, step=0.1,
+                                       tooltip="Gaussian blur sigma defining the frequency cutoff. Larger = enhances coarser detail."),
+                    ]),
+                    io.DynamicCombo.Option(key="deconvolution", inputs=[
+                        io.Float.Input("strength", default=0.5, min=0.0, max=1.0, step=0.01,
+                                       tooltip="Blend between original (0) and fully deconvolved (1)."),
+                        io.Float.Input("radius", default=1.0, min=0.5, max=5.0, step=0.1,
+                                       tooltip="Sigma of the assumed Gaussian blur to reverse."),
+                        io.Int.Input("iterations", default=10, min=1, max=100, step=1,
+                                     tooltip="Richardson-Lucy iterations. More = sharper but slower, diminishing returns past ~20."),
+                    ]),
+                ]),
+            ],
+            outputs=[
+                io.MatchType.Output(io.MatchType.Template("img_or_mask", [io.Image, io.Mask]), display_name="output"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, image, method) -> io.NodeOutput:
+        selected = method["method"]
+        strength = method.get("strength", 0.5)
+        if strength == 0:
+            return io.NodeOutput(image)
+
+        is_mask = image.ndim == 3  # BHW
+        if is_mask:
+            image = image.unsqueeze(-1)  # BHW -> BHWC with C=1
+
+        radius = method.get("radius", 1.0)
+        threshold = method.get("threshold", 0.05)
+        iterations = method.get("iterations", 10)
+        if selected == "rcas":
+            result = _rcas(image, strength)
+        elif selected == "adaptive_usm":
+            result = _adaptive_usm(image, strength, radius, threshold)
+        elif selected == "high_pass":
+            result = _high_pass(image, strength, radius)
+        else:
+            result = _deconvolution(image, strength, radius, iterations)
+
+        if is_mask:
+            result = result.squeeze(-1)  # BHWC -> BHW
+
+        return io.NodeOutput(result)
+
+
+def _rcas(image: torch.Tensor, strength: float) -> torch.Tensor:
+    """AMD FidelityFX RCAS — 5-tap cross filter with contrast-adaptive lobe."""
+    device = model_management.get_torch_device()
+    intermediate_device = model_management.intermediate_device()
+    dtype = model_management.intermediate_dtype()
+
+    B, H, W, C = image.shape
+    out = torch.empty(B, H, W, C, device=intermediate_device, dtype=dtype)
+    attenuation = strength
+
+    for i in range(B):
+        img = image[i:i+1].to(device=device, dtype=dtype).permute(0, 3, 1, 2)
+        padded = F.pad(img, (1, 1, 1, 1), mode='reflect')
+
+        n = padded[:, :, 0:H, 1:W+1]
+        s = padded[:, :, 2:H+2, 1:W+1]
+        w = padded[:, :, 1:H+1, 0:W]
+        e = padded[:, :, 1:H+1, 2:W+2]
+        center = img
+
+        mn = torch.min(torch.min(torch.min(torch.min(n, s), w), e), center)
+        mx = torch.max(torch.max(torch.max(torch.max(n, s), w), e), center)
+
+        # hitMin = -mn / (4*mx),  hitMax = -(1-mx) / (4*(1-mn))
+        hit_min = mn.div(mx * 4.0 + 1e-6).neg_()
+        hit_max = mx.neg().add_(1.0).div_(mn.neg().add_(1.0).mul_(4.0).add_(1e-6)).neg_()
+
+        torch.max(hit_min, hit_max, out=hit_min)
+        lobe = hit_min.min(dim=1, keepdim=True).values
+
+        lobe.mul_(attenuation)
+        lobe.clamp_(-0.1875, 0.0)
+
+        # (center + lobe*(n+s+e+w)) / (1 + 4*lobe)
+        norm = lobe.mul(4.0).add_(1.0).reciprocal_()
+        neighbors = n + s
+        neighbors.add_(e).add_(w)
+        neighbors.mul_(lobe).add_(center).mul_(norm)
+        neighbors.clamp_(0.0, 1.0)
+
+        out[i:i+1] = neighbors.permute(0, 2, 3, 1).to(device=intermediate_device)
+
+    return out
+
+
+def _gaussian_kernel_1d(sigma: float, device: torch.device, dtype: torch.dtype) -> torch.Tensor:
+    radius = max(int(3.0 * sigma + 0.5), 1)
+    x = torch.arange(-radius, radius + 1, device=device, dtype=dtype)
+    kernel = torch.exp(-0.5 * (x / sigma) ** 2)
+    kernel.div_(kernel.sum())
+    return kernel
+
+
+def _gaussian_blur(img: torch.Tensor, sigma: float) -> torch.Tensor:
+    """Separable Gaussian blur on BCHW tensor."""
+    kernel_1d = _gaussian_kernel_1d(sigma, img.device, img.dtype)
+    k = kernel_1d.shape[0]
+    C = img.shape[1]
+    pad = k // 2
+
+    kh = kernel_1d.view(1, 1, 1, k).expand(C, -1, -1, -1)
+    blurred = F.conv2d(F.pad(img, (pad, pad, 0, 0), mode='reflect'), kh, groups=C)
+
+    kv = kernel_1d.view(1, 1, k, 1).expand(C, -1, -1, -1)
+    blurred = F.conv2d(F.pad(blurred, (0, 0, pad, pad), mode='reflect'), kv, groups=C)
+
+    return blurred
+
+
+def _adaptive_usm(image: torch.Tensor, strength: float, radius: float, threshold: float) -> torch.Tensor:
+    """Unsharp mask modulated by local variance — sharpens texture, skips flat areas."""
+    device = model_management.get_torch_device()
+    intermediate_device = model_management.intermediate_device()
+    dtype = model_management.intermediate_dtype()
+
+    B, H, W, C = image.shape
+    out = torch.empty(B, H, W, C, device=intermediate_device, dtype=dtype)
+    var_sigma = radius * 1.5
+
+    for i in range(B):
+        img = image[i:i+1].to(device=device, dtype=dtype, copy=True).permute(0, 3, 1, 2)
+
+        blurred = _gaussian_blur(img, radius)
+        detail = img - blurred
+
+        # Local variance: E[x^2] - E[x]^2
+        local_mean = _gaussian_blur(img, var_sigma)
+        local_mean_sq = _gaussian_blur(img.square(), var_sigma)
+        local_mean_sq.sub_(local_mean * local_mean).clamp_(min=0.0)
+
+        # Convert to std deviation so threshold is in pixel-intensity scale
+        modulation = local_mean_sq.mean(dim=1, keepdim=True).sqrt_()
+
+        if threshold > 0:
+            modulation.div_(threshold).clamp_(0.0, 1.0)
+        else:
+            modulation.fill_(1.0)
+
+        img.addcmul_(detail, modulation, value=strength).clamp_(0.0, 1.0)
+
+        out[i:i+1] = img.permute(0, 2, 3, 1).to(device=intermediate_device)
+
+    return out
+
+
+def _high_pass(image: torch.Tensor, strength: float, radius: float) -> torch.Tensor:
+    """High-pass sharpening: original + strength * (original - blur)."""
+    device = model_management.get_torch_device()
+    intermediate_device = model_management.intermediate_device()
+    dtype = model_management.intermediate_dtype()
+
+    B, H, W, C = image.shape
+    out = torch.empty(B, H, W, C, device=intermediate_device, dtype=dtype)
+
+    for i in range(B):
+        img = image[i:i+1].to(device=device, dtype=dtype).permute(0, 3, 1, 2)
+
+        low_pass = _gaussian_blur(img, radius)
+        low_pass.neg_().add_(img).mul_(strength).add_(img).clamp_(0.0, 1.0)
+
+        out[i:i+1] = low_pass.permute(0, 2, 3, 1).to(device=intermediate_device)
+
+    return out
+
+
+def _deconvolution(image: torch.Tensor, strength: float, radius: float, iterations: int) -> torch.Tensor:
+    """Richardson-Lucy deconvolution with Gaussian PSF."""
+    device = model_management.get_torch_device()
+    intermediate_device = model_management.intermediate_device()
+    dtype = model_management.intermediate_dtype()
+
+    B, H, W, C = image.shape
+    out = torch.empty(B, H, W, C, device=intermediate_device, dtype=dtype)
+
+    # Gaussian PSF kernel (symmetric, so PSF^T = PSF)
+    kernel_1d = _gaussian_kernel_1d(radius, device, dtype)
+    k = kernel_1d.shape[0]
+    pad = k // 2
+    kh = kernel_1d.view(1, 1, 1, k).expand(C, -1, -1, -1)
+    kv = kernel_1d.view(1, 1, k, 1).expand(C, -1, -1, -1)
+
+    for i in range(B):
+        img = image[i:i+1].to(device=device, dtype=dtype).permute(0, 3, 1, 2)
+        estimate = img.clone()
+
+        for _ in range(iterations):
+            # estimate *= (observed / (estimate * PSF)) * PSF^T
+            blurred = F.conv2d(F.pad(estimate, (pad, pad, 0, 0), mode='reflect'), kh, groups=C)
+            blurred = F.conv2d(F.pad(blurred, (0, 0, pad, pad), mode='reflect'), kv, groups=C)
+
+            blurred.add_(1e-6)
+            torch.div(img, blurred, out=blurred)
+
+            correction = F.conv2d(F.pad(blurred, (pad, pad, 0, 0), mode='reflect'), kh, groups=C)
+            correction = F.conv2d(F.pad(correction, (0, 0, pad, pad), mode='reflect'), kv, groups=C)
+
+            estimate.mul_(correction)
+
+        torch.lerp(img, estimate, strength, out=estimate)
+        estimate.clamp_(0.0, 1.0)
+
+        out[i:i+1] = estimate.permute(0, 2, 3, 1).to(device=intermediate_device)
+
+    return out
diff --git a/custom_nodes/ComfyUI-KJNodes/nodes/triton_vae.py b/custom_nodes/ComfyUI-KJNodes/nodes/triton_vae.py
new file mode 100644
index 0000000000000000000000000000000000000000..0459d95c88659acf5caeca2e0b852d51dd4f75c3
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/nodes/triton_vae.py
@@ -0,0 +1,413 @@
+"""Fused norm+SiLU Triton kernels for VAE decode/encode.
+
+Two architectures are covered, auto-detected by build_object_patches:
+- Wan video VAEs: channel-dim RMSNorm (F.normalize(x, dim=1) * sqrt(C) * gamma)
+  followed by SiLU, wired as Sequential pairs.
+- KL image VAEs (Flux2, SDXL, SD1.5, ...): GroupNorm followed by a shared swish
+  module inside ResnetBlock, plus the GN-only norm_out heads.
+
+The eager chains make 4-5 full memory passes per call; the fused kernels do one
+read+write with fp32 accumulation. The GroupNorm kernel requires channels_last activations, which is
+also what makes channels_last conv layout viable for KL VAEs at all: PyTorch's
+own channels_last GroupNorm kernel is ~4x slower than NCHW, ours is ~4x faster.
+"""
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+try:
+    import triton
+    import triton.language as tl
+except ImportError as e:
+    raise ImportError("PatchTritonVAE requires triton (pip install triton, or triton-windows)") from e
+
+from comfy.ldm.wan.vae import RMS_norm
+from comfy.ldm.modules.diffusionmodules.model import ResnetBlock
+from comfy.ldm.lightricks.vae.pixel_norm import PixelNorm
+from comfy.ldm.lightricks.vae.causal_video_autoencoder import ResnetBlock3D as LTXResnetBlock3D, Encoder as LTXEncoder, Decoder as LTXDecoder
+
+import copy
+import logging
+
+import comfy.model_patcher
+from comfy.patcher_extension import CallbacksMP
+from comfy_api.latest import io
+
+@triton.jit
+def _rms_silu_kernel(x_ptr, g_ptr, out_ptr, C, S, stride_b, eps, scale, rcount, BLOCK_S: tl.constexpr, BLOCK_C: tl.constexpr,
+                     HAS_GAMMA: tl.constexpr = True, SILU: tl.constexpr = True, EPS_INSIDE: tl.constexpr = False):
+    pid_s = tl.program_id(0)
+    pid_b = tl.program_id(1)
+    offs_s = pid_s * BLOCK_S + tl.arange(0, BLOCK_S)
+    mask_s = offs_s < S
+    # int64 offsets: C*S per batch can exceed 2^31 at high resolutions
+    base = x_ptr + pid_b.to(tl.int64) * stride_b
+    acc = tl.zeros([BLOCK_S], dtype=tl.float32)
+    for c0 in range(0, C, BLOCK_C):
+        offs_c = c0 + tl.arange(0, BLOCK_C)
+        mask_c = offs_c < C
+        ptrs = base + offs_c[:, None].to(tl.int64) * S + offs_s[None, :]
+        v = tl.load(ptrs, mask=mask_c[:, None] & mask_s[None, :], other=0.0).to(tl.float32)
+        acc += tl.sum(v * v, axis=0)
+    if EPS_INSIDE:  # pixel_norm: 1/sqrt(mean(x^2) + eps)
+        inv = scale / tl.sqrt(acc * rcount + eps)
+    else:
+        inv = scale / tl.maximum(tl.sqrt(acc), eps)
+    out_base = out_ptr + pid_b.to(tl.int64) * stride_b
+    for c0 in range(0, C, BLOCK_C):
+        offs_c = c0 + tl.arange(0, BLOCK_C)
+        mask_c = offs_c < C
+        m = mask_c[:, None] & mask_s[None, :]
+        ptrs = base + offs_c[:, None].to(tl.int64) * S + offs_s[None, :]
+        v = tl.load(ptrs, mask=m, other=0.0).to(tl.float32)
+        y = v * inv[None, :]
+        if HAS_GAMMA:
+            g = tl.load(g_ptr + offs_c, mask=mask_c, other=0.0).to(tl.float32)
+            y = y * g[:, None]
+        if SILU:
+            y = y * tl.sigmoid(y)
+        tl.store(out_base + offs_c[:, None].to(tl.int64) * S + offs_s[None, :], y.to(out_ptr.dtype.element_ty), mask=m)
+
+
+@triton.jit
+def _rms_silu_cl_kernel(x_ptr, g_ptr, out_ptr, C, ROWS, eps, scale, rcount, BLOCK_S: tl.constexpr, BLOCK_C: tl.constexpr,
+                        HAS_GAMMA: tl.constexpr = True, SILU: tl.constexpr = True, EPS_INSIDE: tl.constexpr = False):
+    pid = tl.program_id(0)
+    offs_s = pid * BLOCK_S + tl.arange(0, BLOCK_S)
+    offs_c = tl.arange(0, BLOCK_C)
+    mask_s = offs_s < ROWS
+    mask_c = offs_c < C
+    m = mask_s[:, None] & mask_c[None, :]
+    # int64 offsets: ROWS*C (= numel) can exceed 2^31 at high resolutions
+    ptrs = x_ptr + offs_s[:, None].to(tl.int64) * C + offs_c[None, :]
+    v = tl.load(ptrs, mask=m, other=0.0).to(tl.float32)
+    acc = tl.sum(v * v, axis=1)
+    if EPS_INSIDE:
+        inv = scale / tl.sqrt(acc * rcount + eps)
+    else:
+        inv = scale / tl.maximum(tl.sqrt(acc), eps)
+    y = v * inv[:, None]
+    if HAS_GAMMA:
+        g = tl.load(g_ptr + offs_c, mask=mask_c, other=0.0).to(tl.float32)
+        y = y * g[None, :]
+    if SILU:
+        y = y * tl.sigmoid(y)
+    tl.store(out_ptr + offs_s[:, None].to(tl.int64) * C + offs_c[None, :], y.to(out_ptr.dtype.element_ty), mask=m)
+
+
+@triton.jit
+def _gn_stats_cl(x_ptr, sum_ptr, sumsq_ptr, S,
+                 G: tl.constexpr, BLOCK_S: tl.constexpr, BLOCK_C: tl.constexpr):
+    pid = tl.program_id(0)
+    b = tl.program_id(1)
+    offs_c = tl.arange(0, BLOCK_C)
+    acc1 = tl.zeros([G], dtype=tl.float32)
+    acc2 = tl.zeros([G], dtype=tl.float32)
+    s0 = pid * BLOCK_S
+    step = tl.num_programs(0) * BLOCK_S
+    while s0 < S:
+        offs_s = s0 + tl.arange(0, BLOCK_S)
+        m = (offs_s < S)[:, None]
+        # int64 offsets: (B*S)*C can exceed 2^31 at high resolutions
+        ptr = x_ptr + (b.to(tl.int64) * S + offs_s[:, None]) * BLOCK_C + offs_c[None, :]
+        v = tl.load(ptr, mask=m, other=0.0).to(tl.float32)
+        v3 = tl.reshape(v, (BLOCK_S, G, BLOCK_C // G))
+        acc1 += tl.sum(tl.sum(v3, axis=2), axis=0)
+        acc2 += tl.sum(tl.sum(v3 * v3, axis=2), axis=0)
+        s0 += step
+    tl.atomic_add(sum_ptr + b * G + tl.arange(0, G), acc1)
+    tl.atomic_add(sumsq_ptr + b * G + tl.arange(0, G), acc2)
+
+
+@triton.jit
+def _gn_silu_apply_cl(x_ptr, sum_ptr, sumsq_ptr, w_ptr, bias_ptr, out_ptr, S, count, eps,
+                      G: tl.constexpr, BLOCK_S: tl.constexpr, BLOCK_C: tl.constexpr, SILU: tl.constexpr):
+    pid = tl.program_id(0)
+    b = tl.program_id(1)
+    CS: tl.constexpr = BLOCK_C // G
+    offs_s = pid * BLOCK_S + tl.arange(0, BLOCK_S)
+    offs_c = tl.arange(0, BLOCK_C)
+    m = (offs_s < S)[:, None]
+    ptr = x_ptr + (b.to(tl.int64) * S + offs_s[:, None]) * BLOCK_C + offs_c[None, :]
+    v = tl.load(ptr, mask=m, other=0.0).to(tl.float32)
+    mean_g = tl.load(sum_ptr + b * G + tl.arange(0, G)) / count
+    var_g = tl.load(sumsq_ptr + b * G + tl.arange(0, G)) / count - mean_g * mean_g
+    var_g = tl.maximum(var_g, 0.0)  # E[x²]-mean² can go slightly negative from fp32 cancellation
+    rstd_g = 1.0 / tl.sqrt(var_g + eps)
+    mean_c = tl.reshape(tl.broadcast_to(mean_g[:, None], (G, CS)), (BLOCK_C,))
+    rstd_c = tl.reshape(tl.broadcast_to(rstd_g[:, None], (G, CS)), (BLOCK_C,))
+    w = tl.load(w_ptr + offs_c).to(tl.float32)
+    bias = tl.load(bias_ptr + offs_c).to(tl.float32)
+    y = (v - mean_c[None, :]) * rstd_c[None, :] * w[None, :] + bias[None, :]
+    if SILU:
+        y = y * tl.sigmoid(y)
+    tl.store(out_ptr + (b.to(tl.int64) * S + offs_s[:, None]) * BLOCK_C + offs_c[None, :],
+             y.to(out_ptr.dtype.element_ty), mask=m)
+
+
+# autotuned variants: benchmark configs once per shape key, then cache the fastest.
+# BLOCK_C of the cl/GN kernels is structural (must hold all of C) so only BLOCK_S/num_warps are tuned.
+_rms_silu_kernel_tuned = triton.autotune(
+    configs=[triton.Config({"BLOCK_S": bs, "BLOCK_C": bc}, num_warps=w)
+             for bs in (128, 256, 512) for bc in (32, 64) for w in (4, 8)],
+    key=["C", "S"])(_rms_silu_kernel)
+
+_rms_silu_cl_kernel_tuned = triton.autotune(
+    configs=[triton.Config({"BLOCK_S": bs}, num_warps=w) for bs in (4, 8, 16, 32, 64) for w in (4, 8)],
+    key=["C", "ROWS"])(_rms_silu_cl_kernel)
+
+_gn_stats_cl_tuned = triton.autotune(
+    configs=[triton.Config({"BLOCK_S": bs}, num_warps=w) for bs in (8, 16, 32, 64) for w in (4, 8)],
+    key=["S"], reset_to_zero=["sum_ptr", "sumsq_ptr"])(_gn_stats_cl)
+
+_gn_silu_apply_cl_tuned = triton.autotune(
+    configs=[triton.Config({"BLOCK_S": bs}, num_warps=w) for bs in (8, 16, 32, 64) for w in (4, 8)],
+    key=["S", "count"])(_gn_silu_apply_cl)
+
+
+def fused_rms_silu(x, gamma, scale, eps=1e-12, autotune=False, silu=True, eps_inside=False):
+    B, C = x.shape[0], x.shape[1]
+    S = x.numel() // (B * C)
+    x = x.contiguous()
+    out = torch.empty_like(x)
+    flags = {"HAS_GAMMA": gamma is not None, "SILU": silu, "EPS_INSIDE": eps_inside}
+    g = gamma if gamma is not None else x
+    if autotune:
+        grid = lambda meta: (triton.cdiv(S, meta["BLOCK_S"]), B)
+        _rms_silu_kernel_tuned[grid](x, g, out, C, S, C * S, eps, scale, 1.0 / C, **flags)
+    else:
+        _rms_silu_kernel[(triton.cdiv(S, 256), B)](x, g, out, C, S, C * S, eps, scale, 1.0 / C,
+                                                   BLOCK_S=256, BLOCK_C=32, num_warps=8, **flags)
+    return out
+
+
+def fused_rms_silu_cl(x, gamma, scale, eps=1e-12, autotune=False, silu=True, eps_inside=False):
+    C = x.shape[1]
+    rows = x.numel() // C
+    out = torch.empty_like(x)
+    BLOCK_C = triton.next_power_of_2(C)
+    flags = {"HAS_GAMMA": gamma is not None, "SILU": silu, "EPS_INSIDE": eps_inside}
+    g = gamma if gamma is not None else x
+    if autotune:
+        grid = lambda meta: (triton.cdiv(rows, meta["BLOCK_S"]),)
+        _rms_silu_cl_kernel_tuned[grid](x, g, out, C, rows, eps, scale, 1.0 / C, BLOCK_C=BLOCK_C, **flags)
+    else:
+        BLOCK_S = max(1, 4096 // BLOCK_C)
+        _rms_silu_cl_kernel[(triton.cdiv(rows, BLOCK_S),)](x, g, out, C, rows, eps, scale, 1.0 / C,
+                                                           BLOCK_S=BLOCK_S, BLOCK_C=BLOCK_C, num_warps=4, **flags)
+    return out
+
+
+def fused_gn_silu_cl(x, weight, bias, groups, eps, silu=True, autotune=False):
+    B, C, H, W = x.shape
+    S = H * W
+    sums = torch.zeros(2, B * groups, device=x.device, dtype=torch.float32)
+    out = torch.empty_like(x)
+    count = S * (C // groups)
+    if autotune:
+        # program count beyond ~1024 only adds same-address atomic contention
+        grid_stats = lambda meta: (min(1024, triton.cdiv(S, meta["BLOCK_S"])), B)
+        _gn_stats_cl_tuned[grid_stats](x, sums[0], sums[1], S, G=groups, BLOCK_C=C)
+        grid_apply = lambda meta: (triton.cdiv(S, meta["BLOCK_S"]), B)
+        _gn_silu_apply_cl_tuned[grid_apply](x, sums[0], sums[1], weight, bias, out, S, count, eps,
+                                            G=groups, BLOCK_C=C, SILU=silu)
+    else:
+        BLOCK_S = max(1, 8192 // C)
+        nprog = min(1024, triton.cdiv(S, BLOCK_S))
+        _gn_stats_cl[(nprog, B)](x, sums[0], sums[1], S, G=groups, BLOCK_S=BLOCK_S, BLOCK_C=C, num_warps=8)
+        _gn_silu_apply_cl[(triton.cdiv(S, BLOCK_S), B)](x, sums[0], sums[1], weight, bias, out, S, count, eps,
+                                                        G=groups, BLOCK_S=BLOCK_S, BLOCK_C=C, SILU=silu, num_warps=8)
+    return out
+
+
+class FusedRMSSiLU(nn.Module):
+    def __init__(self, rms, autotune=False):
+        super().__init__()
+        self.gamma = rms.gamma
+        self.scale = rms.scale
+        self.autotune = autotune
+
+    def forward(self, x):
+        if not x.is_cuda:
+            return F.silu(F.normalize(x, dim=1) * self.scale * self.gamma.to(x))
+        gamma = self.gamma
+        if gamma.device != x.device:  # lowvram partial load keeps params offloaded on cpu
+            gamma = gamma.to(x.device)
+        gamma = gamma.reshape(-1)
+        if not gamma.is_contiguous():
+            gamma = gamma.contiguous()
+        if x.ndim == 5 and x.is_contiguous(memory_format=torch.channels_last_3d) and not x.is_contiguous():
+            return fused_rms_silu_cl(x, gamma, self.scale, autotune=self.autotune)
+        return fused_rms_silu(x, gamma, self.scale, autotune=self.autotune)
+
+
+class FusedPixelNorm(nn.Module):
+    def __init__(self, pn, silu=True, autotune=False):
+        super().__init__()
+        self.eps = pn.eps
+        self.silu = silu
+        self.autotune = autotune
+
+    def forward(self, x):
+        if x.is_cuda:
+            if x.ndim == 5 and x.is_contiguous(memory_format=torch.channels_last_3d) and not x.is_contiguous():
+                return fused_rms_silu_cl(x, None, 1.0, self.eps, autotune=self.autotune, silu=self.silu, eps_inside=True)
+            return fused_rms_silu(x, None, 1.0, self.eps, autotune=self.autotune, silu=self.silu, eps_inside=True)
+        out = x / torch.sqrt(torch.mean(x ** 2, dim=1, keepdim=True) + self.eps)
+        if self.silu:
+            out = F.silu(out)
+        return out
+
+
+class FusedGNSiLU(nn.Module):
+    def __init__(self, gn, silu=True, autotune=False):
+        super().__init__()
+        self.weight = gn.weight
+        self.bias = gn.bias
+        self.num_groups = gn.num_groups
+        self.eps = gn.eps
+        self.silu = silu
+        self.autotune = autotune
+
+    def forward(self, x):
+        C = x.shape[1]
+        G = self.num_groups
+        weight, bias = self.weight, self.bias
+        if weight.device != x.device:  # lowvram partial load keeps params offloaded on cpu
+            weight = weight.to(x.device)
+            bias = bias.to(x.device)
+        if x.is_cuda and x.ndim == 4 and (C & (C - 1)) == 0 and (G & (G - 1)) == 0 and C % G == 0 \
+                and x.is_contiguous(memory_format=torch.channels_last) and not x.is_contiguous():
+            return fused_gn_silu_cl(x, weight, bias, self.num_groups, self.eps, self.silu,
+                                    autotune=self.autotune)
+        out = F.group_norm(x, self.num_groups, weight, bias, self.eps)
+        if self.silu:
+            out = F.silu(out)
+        return out
+
+
+def convert_conv_layout(model, channels_last=True):
+    for mod in model.modules():
+        if isinstance(mod, nn.Conv3d):
+            mod.to(memory_format=torch.channels_last_3d if channels_last else torch.contiguous_format)
+        elif isinstance(mod, nn.Conv2d):
+            mod.to(memory_format=torch.channels_last if channels_last else torch.contiguous_format)
+
+
+def build_object_patches(model, autotune=False):
+    """Object patches for vae.patcher: applied at model load, reverted at unload.
+    Also matches already-fused modules so a loaded+patched model rebuilds cleanly."""
+    patches = {}
+    for name, mod in model.named_modules():
+        if isinstance(mod, nn.Sequential):  # Wan-style RMS_norm+SiLU pairs
+            for i in range(len(mod) - 1):
+                if isinstance(mod[i], RMS_norm) and isinstance(mod[i + 1], nn.SiLU) \
+                        and mod[i].gamma.ndim == 4 and mod[i].bias is None:
+                    patches[f"{name}.{i}"] = FusedRMSSiLU(mod[i], autotune=autotune)
+                    patches[f"{name}.{i + 1}"] = nn.Identity()
+                elif isinstance(mod[i], FusedRMSSiLU) and isinstance(mod[i + 1], nn.Identity):
+                    mod[i].autotune = autotune
+                    patches[f"{name}.{i}"] = mod[i]
+                    patches[f"{name}.{i + 1}"] = mod[i + 1]
+        elif isinstance(mod, ResnetBlock) and not hasattr(mod, "temb_proj"):  # KL-style
+            for norm_name in ("norm1", "norm2"):
+                norm = getattr(mod, norm_name)
+                if isinstance(norm, nn.GroupNorm):
+                    patches[f"{name}.{norm_name}"] = FusedGNSiLU(norm, autotune=autotune)
+                elif isinstance(norm, FusedGNSiLU):
+                    norm.autotune = autotune
+                    patches[f"{name}.{norm_name}"] = norm
+            patches[f"{name}.swish"] = nn.Identity()
+        elif name.endswith("norm_out"):  # KL encoder/decoder head, SiLU applied separately
+            if isinstance(mod, nn.GroupNorm):
+                patches[name] = FusedGNSiLU(mod, silu=False, autotune=autotune)
+            elif isinstance(mod, FusedGNSiLU):
+                mod.autotune = autotune
+                patches[name] = mod
+        elif isinstance(mod, LTXResnetBlock3D):
+            # timestep-conditioned decoder blocks apply a scale-shift between norm and
+            # SiLU, so only the norm itself can be fused there
+            fuse_silu = not mod.timestep_conditioning
+            for norm_name in ("norm1", "norm2"):
+                norm = getattr(mod, norm_name)
+                if isinstance(norm, PixelNorm) and norm.dim == 1:
+                    patches[f"{name}.{norm_name}"] = FusedPixelNorm(norm, silu=fuse_silu, autotune=autotune)
+                elif isinstance(norm, FusedPixelNorm):
+                    norm.autotune = autotune
+                    patches[f"{name}.{norm_name}"] = norm
+            if fuse_silu and f"{name}.norm1" in patches:
+                patches[f"{name}.non_linearity"] = nn.Identity()
+        elif isinstance(mod, (LTXEncoder, LTXDecoder)):  # LTX head: conv_norm_out (+ scale-shift on conditioned decoder) + conv_act
+            fuse_silu = isinstance(mod, LTXEncoder) or not mod.timestep_conditioning
+            if isinstance(mod.conv_norm_out, PixelNorm) and mod.conv_norm_out.dim == 1:
+                patches[f"{name}.conv_norm_out"] = FusedPixelNorm(mod.conv_norm_out, silu=fuse_silu, autotune=autotune)
+            elif isinstance(mod.conv_norm_out, FusedPixelNorm):
+                mod.conv_norm_out.autotune = autotune
+                patches[f"{name}.conv_norm_out"] = mod.conv_norm_out
+            if fuse_silu and f"{name}.conv_norm_out" in patches:
+                patches[f"{name}.conv_act"] = nn.Identity()
+    return patches
+
+
+class PatchTritonVAE(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="PatchTritonVAE",
+            display_name="Patch Triton VAE",
+            category="KJNodes/experimental",
+            is_experimental=True,
+            description="Speeds up VAE decode/encode with fused Triton norm+SiLU kernels and channels_last conv layout. "
+                        "Supported VAEs (auto-detected): Wan 2.1/2.2 video VAEs incl. Qwen-Image (RMSNorm, ~1.4x/1.15x), KL image VAEs "
+                        "such as Flux/Flux2, SDXL and SD1.5 (GroupNorm, ~1.6-1.8x at 2048px), and LTXV/LTX2 video VAEs (PixelNorm; "
+                        "timestep-conditioned decoder blocks get norm-only fusion). Other architectures are not supported. "
+                        "Applied as object patches on a cloned patcher, so it only exists while this VAE is loaded.",
+            inputs=[
+                io.Vae.Input("vae"),
+                io.Boolean.Input("fuse_norm_silu", default=True, tooltip="Replace norm+SiLU chains (RMSNorm for Wan, GroupNorm for KL VAEs) with fused Triton kernels (single pass, fp32 accumulation). Requires triton."),
+                io.Boolean.Input("channels_last", default=True, tooltip="Convert conv weights to channels_last memory format, removing cuDNN layout transposes around every conv. Required for the fused GroupNorm kernel to engage on KL VAEs."),
+                io.Boolean.Input("autotune", default=False, tooltip="Benchmark several kernel block-size configs on first use of each tensor shape and cache the fastest. Brief stutter per new resolution, usually a few percent faster after warmup."),
+            ],
+            outputs=[
+                io.Vae.Output(display_name="vae"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, vae, fuse_norm_silu=True, channels_last=True, autotune=False) -> io.NodeOutput:
+        vae = copy.copy(vae)
+        model = vae.first_stage_model
+        if vae.patcher.is_dynamic():
+            # the dynamic-vram patcher rematerializes weights from pinned host buffers per forward, which would discard channels_last layout
+            model.to(vae.vae_dtype)
+            new_patcher = comfy.model_patcher.ModelPatcher(
+                model, load_device=vae.patcher.load_device, offload_device=vae.patcher.offload_device)
+            # without a parent, LoadedModel flags is_dead when this patcher is GC'd (leak warning + full gc)
+            new_patcher.parent = vae.patcher
+            vae.patcher = new_patcher
+
+            def clear_dynamic_cast_flags(patcher, device_to, lowvram_model_memory, force_patch_weights, full_load):
+                # a dynamic load of the shared model sets these without ever resetting them;
+                for mod in patcher.model.modules():
+                    if hasattr(mod, "comfy_cast_weights") and not hasattr(mod, "prev_comfy_cast_weights"):
+                        mod.comfy_cast_weights = False
+                        mod._v_signature = None
+            vae.patcher.add_callback_with_key(CallbacksMP.ON_LOAD, "wan_vae_fused_clear_cast", clear_dynamic_cast_flags)
+        else:
+            vae.patcher = vae.patcher.clone()
+
+        if fuse_norm_silu:
+            patches = build_object_patches(model, autotune=autotune)
+            if not patches:
+                raise RuntimeError("No fusable norm layers found, this node supports Wan video VAEs, KL image VAEs (Flux2/SDXL/SD1.5) and LTXV/LTX2 video VAEs")
+            for name, obj in patches.items():
+                vae.patcher.add_object_patch(name, obj)
+            logging.info(f"PatchTritonVAE: registered {len(patches)} fused norm object patches")
+
+        if channels_last:
+            # reapply on every load: pinned-host/dynamic-vram weight staging restores the original contiguous layout
+            def reapply_channels_last(patcher, device_to, lowvram_model_memory, force_patch_weights, full_load):
+                convert_conv_layout(patcher.model, channels_last=True)
+            vae.patcher.add_callback_with_key(CallbacksMP.ON_LOAD, "wan_vae_fused_channels_last", reapply_channels_last)
+        else:
+            convert_conv_layout(model, channels_last=False)
+        return io.NodeOutput(vae)
diff --git a/custom_nodes/ComfyUI-KJNodes/utility/fluid.py b/custom_nodes/ComfyUI-KJNodes/utility/fluid.py
new file mode 100644
index 0000000000000000000000000000000000000000..c0691987f5249a031ecbb74329ba513d5788b691
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/utility/fluid.py
@@ -0,0 +1,67 @@
+import numpy as np
+from scipy.ndimage import map_coordinates, spline_filter
+from scipy.sparse.linalg import factorized
+
+from .numerical import difference, operator
+
+
+class Fluid:
+    def __init__(self, shape, *quantities, pressure_order=1, advect_order=3):
+        self.shape = shape
+        self.dimensions = len(shape)
+
+        # Prototyping is simplified by dynamically 
+        # creating advected quantities as needed.
+        self.quantities = quantities
+        for q in quantities:
+            setattr(self, q, np.zeros(shape))
+
+        self.indices = np.indices(shape)
+        self.velocity = np.zeros((self.dimensions, *shape))
+
+        laplacian = operator(shape, difference(2, pressure_order))
+        self.pressure_solver = factorized(laplacian)
+        
+        self.advect_order = advect_order
+
+    def step(self):
+        # Advection is computed backwards in time as described in Stable Fluids.
+        advection_map = self.indices - self.velocity
+
+        # SciPy's spline filter introduces checkerboard divergence.
+        # A linear blend of the filtered and unfiltered fields based
+        # on some value epsilon eliminates this error.
+        def advect(field, filter_epsilon=10e-2, mode='constant'):
+            filtered = spline_filter(field, order=self.advect_order, mode=mode)
+            field = filtered * (1 - filter_epsilon) + field * filter_epsilon
+            return map_coordinates(field, advection_map, prefilter=False, order=self.advect_order, mode=mode)
+
+        # Apply advection to each axis of the
+        # velocity field and each user-defined quantity.
+        for d in range(self.dimensions):
+            self.velocity[d] = advect(self.velocity[d])
+
+        for q in self.quantities:
+            setattr(self, q, advect(getattr(self, q)))
+
+        # Compute the jacobian at each point in the
+        # velocity field to extract curl and divergence.
+        jacobian_shape = (self.dimensions,) * 2
+        partials = tuple(np.gradient(d) for d in self.velocity)
+        jacobian = np.stack(partials).reshape(*jacobian_shape, *self.shape)
+
+        divergence = jacobian.trace()
+
+        # If this curl calculation is extended to 3D, the y-axis value must be negated.
+        # This corresponds to the coefficients of the levi-civita symbol in that dimension.
+        # Higher dimensions do not have a vector -> scalar, or vector -> vector,
+        # correspondence between velocity and curl due to differing isomorphisms
+        # between exterior powers in dimensions != 2 or 3 respectively.
+        curl_mask = np.triu(np.ones(jacobian_shape, dtype=bool), k=1)
+        curl = (jacobian[curl_mask] - jacobian[curl_mask.T]).squeeze()
+
+        # Apply the pressure correction to the fluid's velocity field.
+        pressure = self.pressure_solver(divergence.flatten()).reshape(self.shape)
+        self.velocity -= np.gradient(pressure)
+
+        return divergence, curl, pressure
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-KJNodes/utility/magictex.py b/custom_nodes/ComfyUI-KJNodes/utility/magictex.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6d426f7deb3deb977604dd37581eb4e9fe9e6a9
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/utility/magictex.py
@@ -0,0 +1,95 @@
+"""Generates psychedelic color textures in the spirit of Blender's magic texture shader using Python/Numpy
+
+https://github.com/cheind/magic-texture
+"""
+from typing import Tuple, Optional
+import numpy as np
+
+
+def coordinate_grid(shape: Tuple[int, int], dtype=np.float32):
+    """Returns a three-dimensional coordinate grid of given shape for use in `magic`."""
+    x = np.linspace(-1, 1, shape[1], endpoint=True, dtype=dtype)
+    y = np.linspace(-1, 1, shape[0], endpoint=True, dtype=dtype)
+    X, Y = np.meshgrid(x, y)
+    XYZ = np.stack((X, Y, np.ones_like(X)), -1)
+    return XYZ
+
+
+def random_transform(coords: np.ndarray, rng: np.random.Generator = None):
+    """Returns randomly transformed coordinates"""
+    H, W = coords.shape[:2]
+    rng = rng or np.random.default_rng()
+    m = rng.uniform(-1.0, 1.0, size=(3, 3)).astype(coords.dtype)
+    return (coords.reshape(-1, 3) @ m.T).reshape(H, W, 3)
+
+
+def magic(
+    coords: np.ndarray,
+    depth: Optional[int] = None,
+    distortion: Optional[int] = None,
+    rng: np.random.Generator = None,
+):
+    """Returns color magic color texture.
+
+    The implementation is based on Blender's (https://www.blender.org/) magic
+    texture shader. The following adaptions have been made:
+     - we exchange the nested if-cascade by a probabilistic iterative approach
+
+    Kwargs
+    ------
+    coords: HxWx3 array
+        Coordinates transformed into colors by this method. See
+        `magictex.coordinate_grid` to generate the default.
+    depth: int (optional)
+        Number of transformations applied. Higher numbers lead to more
+        nested patterns. If not specified, randomly sampled.
+    distortion: float (optional)
+        Distortion of patterns. Larger values indicate more distortion,
+        lower values tend to generate smoother patterns. If not specified,
+        randomly sampled.
+    rng: np.random.Generator
+        Optional random generator to draw samples from.
+
+    Returns
+    -------
+    colors: HxWx3 array
+        Three channel color image in range [0,1]
+    """
+    rng = rng or np.random.default_rng()
+    if distortion is None:
+        distortion = rng.uniform(1, 4)
+    if depth is None:
+        depth = rng.integers(1, 5)
+
+    H, W = coords.shape[:2]
+    XYZ = coords
+    x = np.sin((XYZ[..., 0] + XYZ[..., 1] + XYZ[..., 2]) * distortion)
+    y = np.cos((-XYZ[..., 0] + XYZ[..., 1] - XYZ[..., 2]) * distortion)
+    z = -np.cos((-XYZ[..., 0] - XYZ[..., 1] + XYZ[..., 2]) * distortion)
+
+    if depth > 0:
+        x *= distortion
+        y *= distortion
+        z *= distortion
+        y = -np.cos(x - y + z)
+        y *= distortion
+
+    xyz = [x, y, z]
+    fns = [np.cos, np.sin]
+    for _ in range(1, depth):
+        axis = rng.choice(3)
+        fn = fns[rng.choice(2)]
+        signs = rng.binomial(n=1, p=0.5, size=4) * 2 - 1
+
+        xyz[axis] = signs[-1] * fn(
+            signs[0] * xyz[0] + signs[1] * xyz[1] + signs[2] * xyz[2]
+        )
+        xyz[axis] *= distortion
+
+    x, y, z = xyz
+    x /= 2 * distortion
+    y /= 2 * distortion
+    z /= 2 * distortion
+    c = 0.5 - np.stack((x, y, z), -1)
+    np.clip(c, 0, 1.0)
+    return c
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-KJNodes/utility/numerical.py b/custom_nodes/ComfyUI-KJNodes/utility/numerical.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5b88bc63c45d63d8913e56cbd06eb7ab413fe4f
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/utility/numerical.py
@@ -0,0 +1,25 @@
+from functools import reduce
+from itertools import cycle
+from math import factorial
+
+import numpy as np
+import scipy.sparse as sp
+
+
+def difference(derivative, accuracy=1):
+    # Central differences implemented based on the article here:
+    # http://web.media.mit.edu/~crtaylor/calculator.html
+    derivative += 1
+    radius = accuracy + derivative // 2 - 1
+    points = range(-radius, radius + 1)
+    coefficients = np.linalg.inv(np.vander(points))
+    return coefficients[-derivative] * factorial(derivative - 1), points
+
+
+def operator(shape, *differences):
+    # Credit to Philip Zucker for figuring out
+    # that kronsum's argument order is reversed.
+    # Without that bit of wisdom I'd have lost it.
+    differences = zip(shape, cycle(differences))
+    factors = (sp.diags(*diff, shape=(dim,) * 2) for dim, diff in differences)
+    return reduce(lambda a, f: sp.kronsum(f, a, format='csc'), factors)
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-KJNodes/utility/utility.py b/custom_nodes/ComfyUI-KJNodes/utility/utility.py
new file mode 100644
index 0000000000000000000000000000000000000000..fac22950a7fcf07abf904f925cd06b8d13f67cde
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/utility/utility.py
@@ -0,0 +1,88 @@
+import torch
+import numpy as np
+from PIL import Image, ImageColor
+from typing import Union, List
+import logging
+
+# Utility functions from mtb nodes: https://github.com/melMass/comfy_mtb
+def pil2tensor(image: Union[Image.Image, List[Image.Image]]) -> torch.Tensor:
+    if isinstance(image, list):
+        return torch.cat([pil2tensor(img) for img in image], dim=0)
+
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+
+def np2tensor(img_np: Union[np.ndarray, List[np.ndarray]]) -> torch.Tensor:
+    if isinstance(img_np, list):
+        return torch.cat([np2tensor(img) for img in img_np], dim=0)
+
+    return torch.from_numpy(img_np.astype(np.float32) / 255.0).unsqueeze(0)
+
+
+def tensor2np(tensor: torch.Tensor):
+    if len(tensor.shape) == 3:  # Single image
+        return np.clip(255.0 * tensor.cpu().numpy(), 0, 255).astype(np.uint8)
+    else:  # Batch of images
+        return [np.clip(255.0 * t.cpu().numpy(), 0, 255).astype(np.uint8) for t in tensor]
+
+def tensor2pil(image: torch.Tensor) -> List[Image.Image]:
+    batch_count = image.size(0) if len(image.shape) > 3 else 1
+    if batch_count > 1:
+        out = []
+        for i in range(batch_count):
+            out.extend(tensor2pil(image[i]))
+        return out
+
+    return [
+        Image.fromarray(
+            np.clip(255.0 * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+        )
+    ]
+
+def string_to_color(color_string: str) -> List[int]:
+    color_list = [0, 0, 0]  # Default fallback (black)
+
+    if ',' in color_string:
+        # Handle CSV format (e.g., "255, 0, 0" or "255, 0, 0, 128" or "1.0, 0.5, 0.0")
+        try:
+            values = [float(channel.strip()) for channel in color_string.split(',')]
+            # Convert to 0-255 range if values are in 0-1 range
+            if all(0 <= v <= 1 for v in values):
+                color_list = [int(v * 255) for v in values]
+            else:
+                color_list = [int(v) for v in values]
+        except ValueError:
+            logging.warning(f"Invalid color format: {color_string}. Using default black.")
+    elif color_string.startswith('#') or (color_string.lstrip('#').isalnum() and not color_string.lstrip('#').replace('.', '', 1).isdigit()):
+        # Could be Hex format or color name
+        color_string_stripped = color_string.lstrip('#')
+        # Try hex first
+        if len(color_string_stripped) in [6, 8] and all(c in '0123456789ABCDEFabcdef' for c in color_string_stripped):
+            if len(color_string_stripped) == 6:  # #RRGGBB
+                color_list = [int(color_string_stripped[i:i+2], 16) for i in (0, 2, 4)]
+            elif len(color_string_stripped) == 8:  # #RRGGBBAA
+                color_list = [int(color_string_stripped[i:i+2], 16) for i in (0, 2, 4, 6)]
+        else:
+            # Try color name (e.g., "red", "blue", "cyan")
+            try:
+                rgb = ImageColor.getrgb(color_string)
+                color_list = list(rgb)
+            except ValueError:
+                logging.warning(f"Invalid color name or hex format: {color_string}. Using default black.")
+    else:
+        # Handle single value (grayscale) - can be int or float
+        try:
+            value = float(color_string.strip())
+            # Convert to 0-255 range if it's a float between 0-1
+            if 0 <= value <= 1:
+                value = int(value * 255)
+            else:
+                value = int(value)
+            color_list = [value, value, value]
+        except ValueError:
+            logging.warning(f"Invalid color format: {color_string}. Using default black.")
+
+    # Clip values to valid range
+    color_list = np.clip(color_list, 0, 255).tolist()
+
+    return color_list
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/appearance.js b/custom_nodes/ComfyUI-KJNodes/web/js/appearance.js
new file mode 100644
index 0000000000000000000000000000000000000000..9069207e2ca079363e757da234318c76d24806ee
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/appearance.js
@@ -0,0 +1,23 @@
+const { app } = window.comfyAPI.app;
+
+app.registerExtension({
+    name: "KJNodes.appearance",
+        nodeCreated(node) {
+            switch (node.comfyClass) {
+                case "INTConstant":
+                    node.setSize([200, 58]);
+                    node.color = "#1b4669";
+                    node.bgcolor = "#29699c";
+                    break;
+                case "FloatConstant":
+                    node.setSize([200, 58]);
+                    node.color = LGraphCanvas.node_colors.green.color;
+                    node.bgcolor = LGraphCanvas.node_colors.green.bgcolor;
+                    break;
+                case "ConditioningMultiCombine":
+                    node.color = LGraphCanvas.node_colors.brown.color;
+                    node.bgcolor = LGraphCanvas.node_colors.brown.bgcolor;
+                    break;
+            }
+        }
+});
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/canvas_background.js b/custom_nodes/ComfyUI-KJNodes/web/js/canvas_background.js
new file mode 100644
index 0000000000000000000000000000000000000000..530a189628cff5a218effb20914a82d308e8d0a4
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/canvas_background.js
@@ -0,0 +1,476 @@
+const { app } = window.comfyAPI.app;
+
+// ── Helpers ─────────────────────────────────────────────────────────────────
+
+/** Linearly mix two hex colors: 0 = pure a, 1 = pure b. */
+function mixColors(a, b, t) {
+	const parse = (hex) => {
+		if (hex.length === 4) hex = `#${hex[1]}${hex[1]}${hex[2]}${hex[2]}${hex[3]}${hex[3]}`;
+		return [parseInt(hex.slice(1, 3), 16), parseInt(hex.slice(3, 5), 16), parseInt(hex.slice(5, 7), 16)];
+	};
+	const ca = parse(a), cb = parse(b);
+	const r = Math.round(ca[0] + (cb[0] - ca[0]) * t);
+	const g = Math.round(ca[1] + (cb[1] - ca[1]) * t);
+	const bl = Math.round(ca[2] + (cb[2] - ca[2]) * t);
+	return `#${((1 << 24) | (r << 16) | (g << 8) | bl).toString(16).slice(1)}`;
+}
+
+/** Ensure a color string has a leading '#' (PrimeVue ColorPicker omits it). */
+function asHex(color, fallback = "#000000") {
+	if (!color) return fallback;
+	return color.startsWith("#") ? color : `#${color}`;
+}
+
+function getSetting(id, fallback) {
+	return app.ui.settings.getSettingValue(id) ?? fallback;
+}
+
+// ── Pattern generators ──────────────────────────────────────────────────────
+// Each returns an OffscreenCanvas to be used with createPattern("repeat").
+// `size` = tile size, `fg`/`bg` = hex colors, `t` = feature thickness.
+
+function makeDots(size, fg, bg, t) {
+	const c = new OffscreenCanvas(size, size);
+	const ctx = c.getContext("2d");
+	ctx.fillStyle = bg;
+	ctx.fillRect(0, 0, size, size);
+	ctx.fillStyle = fg;
+	ctx.beginPath();
+	ctx.arc(size / 2, size / 2, t * 2, 0, Math.PI * 2);
+	ctx.fill();
+	return c;
+}
+
+function makeGrid(size, fg, bg, t) {
+	const c = new OffscreenCanvas(size, size);
+	const ctx = c.getContext("2d");
+	ctx.fillStyle = bg;
+	ctx.fillRect(0, 0, size, size);
+	ctx.fillStyle = fg;
+	ctx.fillRect(size - t, 0, t, size - t);
+	ctx.fillRect(0, size - t, size, t);
+	return c;
+}
+
+function makeCrossDots(size, fg, bg, t) {
+	const c = new OffscreenCanvas(size, size);
+	const ctx = c.getContext("2d");
+	ctx.fillStyle = bg;
+	ctx.fillRect(0, 0, size, size);
+	const half = size / 2;
+	const arm = Math.max(t + 1, size * 0.08);
+	const ht = t / 2;
+	ctx.fillStyle = fg;
+	ctx.fillRect(half - arm, half - ht, arm * 2, t);
+	ctx.fillRect(half - ht, half - arm, t, arm * 2);
+	return c;
+}
+
+function makeBlueprint(size, fg, bg, t) {
+	const c = new OffscreenCanvas(size, size);
+	const ctx = c.getContext("2d");
+	ctx.fillStyle = bg;
+	ctx.fillRect(0, 0, size, size);
+	const sub = size / 4;
+	const subColor = mixColors(bg, fg, 0.3);
+	ctx.fillStyle = subColor;
+	for (let i = sub; i < size; i += sub) {
+		ctx.fillRect(i, 0, t, size - t);
+		ctx.fillRect(0, i, size, t);
+	}
+	ctx.fillStyle = fg;
+	ctx.fillRect(size - t, 0, t, size - t);
+	ctx.fillRect(0, size - t, size, t);
+	return c;
+}
+
+function makeIsometric(size, fg, bg, t) {
+	const w = size;
+	const h = Math.round(size / 2);
+	const c = new OffscreenCanvas(w, h);
+	const ctx = c.getContext("2d");
+	ctx.fillStyle = bg;
+	ctx.fillRect(0, 0, w, h);
+	ctx.strokeStyle = fg;
+	ctx.lineWidth = t;
+	ctx.beginPath();
+	ctx.moveTo(0, h / 2);
+	ctx.lineTo(w / 2, 0);
+	ctx.lineTo(w, h / 2);
+	ctx.moveTo(0, h / 2);
+	ctx.lineTo(w / 2, h);
+	ctx.lineTo(w, h / 2);
+	ctx.stroke();
+	return c;
+}
+
+function makeHexagons(size, fg, bg, t) {
+	const r = size / 2;
+	const w = r * 3;
+	const h = Math.round(r * Math.sqrt(3));
+	const c = new OffscreenCanvas(w, h);
+	const ctx = c.getContext("2d");
+	ctx.fillStyle = bg;
+	ctx.fillRect(0, 0, w, h);
+	ctx.strokeStyle = fg;
+	ctx.lineWidth = t;
+
+	function hex(cx, cy) {
+		ctx.beginPath();
+		for (let i = 0; i < 6; i++) {
+			const a = Math.PI / 3 * i;
+			const px = cx + r * Math.cos(a);
+			const py = cy + r * Math.sin(a);
+			if (i === 0) ctx.moveTo(px, py);
+			else ctx.lineTo(px, py);
+		}
+		ctx.closePath();
+		ctx.stroke();
+	}
+
+	hex(r, h / 2);
+	hex(r * 2.5, 0);
+	hex(r * 2.5, h);
+	return c;
+}
+
+function makeOctagons(size, fg, bg, t) {
+	const c = new OffscreenCanvas(size, size);
+	const ctx = c.getContext("2d");
+	ctx.fillStyle = bg;
+	ctx.fillRect(0, 0, size, size);
+	ctx.strokeStyle = fg;
+	ctx.lineWidth = t;
+
+	const s = size / (1 + Math.SQRT2);
+	const d = (size - s) / 2;
+
+	ctx.beginPath();
+	ctx.moveTo(d, 0);
+	ctx.lineTo(size - d, 0);
+	ctx.lineTo(size, d);
+	ctx.lineTo(size, size - d);
+	ctx.lineTo(size - d, size);
+	ctx.lineTo(d, size);
+	ctx.lineTo(0, size - d);
+	ctx.lineTo(0, d);
+	ctx.closePath();
+	ctx.stroke();
+	return c;
+}
+
+function makeWaves(size, fg, bg, t) {
+	const c = new OffscreenCanvas(size, size);
+	const ctx = c.getContext("2d");
+	ctx.fillStyle = bg;
+	ctx.fillRect(0, 0, size, size);
+	ctx.lineWidth = t;
+
+	const lines = Math.max(2, Math.round(size / 20));
+	const spacing = size / lines;
+
+	for (let i = 0; i < lines; i++) {
+		const blend = 0.1 + (i % 3) * 0.07;
+		ctx.strokeStyle = mixColors(bg, fg, blend);
+		const baseY = spacing * i + spacing / 2;
+		const amp = spacing * (0.15 + (i % 4) * 0.05);
+
+		ctx.beginPath();
+		for (let x = 0; x <= size; x++) {
+			const y = baseY + Math.sin((x / size) * Math.PI * 2 + i * 0.8) * amp;
+			if (x === 0) ctx.moveTo(x, y);
+			else ctx.lineTo(x, y);
+		}
+		ctx.stroke();
+	}
+	return c;
+}
+
+function makeCarbonFiber(size, fg, bg, t) {
+	const cell = Math.max(2, Math.round(size / 4));
+	const s = cell * 4;
+	const c = new OffscreenCanvas(s, s);
+	const ctx = c.getContext("2d");
+
+	const dark = bg;
+	const light = mixColors(bg, fg, 0.2);
+	const groove = mixColors(bg, fg, 0.07);
+
+	for (let row = 0; row < 4; row++) {
+		for (let col = 0; col < 4; col++) {
+			const phase = (col + row) % 4;
+			ctx.fillStyle = (phase < 2) ? light : dark;
+			ctx.fillRect(col * cell, row * cell, cell, cell);
+		}
+	}
+
+	ctx.fillStyle = groove;
+	const gt = Math.max(1, Math.round(t));
+	for (let i = 1; i < 4; i++) {
+		ctx.fillRect(0, i * cell, s, gt);
+		ctx.fillRect(i * cell, 0, gt, s);
+	}
+	return c;
+}
+
+
+const PATTERNS = {
+	default: null,
+	none: null,
+	dots: makeDots,
+	grid: makeGrid,
+	"cross dots": makeCrossDots,
+	blueprint: makeBlueprint,
+	isometric: makeIsometric,
+	hexagons: makeHexagons,
+	octagons: makeOctagons,
+	waves: makeWaves,
+	"carbon fiber": makeCarbonFiber,
+};
+
+// ── State ───────────────────────────────────────────────────────────────────
+
+let currentType = "default";
+let currentFg = "#444444";
+let currentBg = "#212121";
+let currentSize = 32;
+let currentThickness = 2;
+let dirty = true;
+
+function invalidateCache() {
+	dirty = true;
+	app.canvas?.setDirty(true, true);
+}
+
+
+
+function readSettings() {
+	currentType = getSetting("KJNodes.canvasBg.pattern", "default");
+	currentFg = asHex(getSetting("KJNodes.canvasBg.patternColor", "#444444"));
+	currentBg = asHex(getSetting("KJNodes.canvasBg.bgColor", "#212121"));
+	currentSize = getSetting("KJNodes.canvasBg.scale", 32);
+	currentThickness = getSetting("KJNodes.canvasBg.thickness", 2);
+}
+
+// ── Commands ────────────────────────────────────────────────────────────────
+
+const patternCommands = Object.keys(PATTERNS).map((key) => ({
+	id: `KJNodes.canvasBg.setPattern.${key}`,
+	label: key.charAt(0).toUpperCase() + key.slice(1),
+	menubarLabel: key.charAt(0).toUpperCase() + key.slice(1),
+	function: () => {
+		app.ui.settings.setSettingValue("KJNodes.canvasBg.pattern", key);
+	},
+	active: () => getSetting("KJNodes.canvasBg.pattern", "default") === key,
+}));
+
+// Single source of truth for slider bounds — commands reference these same objects.
+const SCALE_ATTRS = { min: 8, max: 512, step: 4 };
+const THICKNESS_ATTRS = { min: 0.5, max: 20, step: 0.5 };
+
+function stepSetting(id, fallback, attrs, direction) {
+	const cur = getSetting(id, fallback);
+	const next = cur + attrs.step * direction;
+	if (next >= attrs.min && next <= attrs.max) {
+		app.ui.settings.setSettingValue(id, next);
+	}
+}
+
+const scaleCommands = [
+	{
+		id: "KJNodes.canvasBg.increaseScale",
+		label: "Increase pattern scale",
+		menubarLabel: "Increase scale",
+		function: () => stepSetting("KJNodes.canvasBg.scale", 32, SCALE_ATTRS, 1),
+		active: () => false,
+	},
+	{
+		id: "KJNodes.canvasBg.decreaseScale",
+		label: "Decrease pattern scale",
+		menubarLabel: "Decrease scale",
+		function: () => stepSetting("KJNodes.canvasBg.scale", 32, SCALE_ATTRS, -1),
+		active: () => false,
+	},
+];
+
+const thicknessCommands = [
+	{
+		id: "KJNodes.canvasBg.increaseThickness",
+		label: "Increase pattern thickness",
+		menubarLabel: "Increase thickness",
+		function: () => stepSetting("KJNodes.canvasBg.thickness", 2, THICKNESS_ATTRS, 1),
+		active: () => false,
+	},
+	{
+		id: "KJNodes.canvasBg.decreaseThickness",
+		label: "Decrease pattern thickness",
+		menubarLabel: "Decrease thickness",
+		function: () => stepSetting("KJNodes.canvasBg.thickness", 2, THICKNESS_ATTRS, -1),
+		active: () => false,
+	},
+];
+
+// ── Extension ───────────────────────────────────────────────────────────────
+
+app.registerExtension({
+	name: "KJNodes.CanvasBackground",
+
+	commands: [...patternCommands, ...scaleCommands, ...thicknessCommands],
+
+	menuCommands: [
+		{
+			path: ["KJNodes", "Canvas Background"],
+			commands: patternCommands.map((c) => c.id),
+		},
+		{
+			path: ["KJNodes", "Canvas Background"],
+			commands: scaleCommands.map((c) => c.id),
+		},
+		{
+			path: ["KJNodes", "Canvas Background"],
+			commands: thicknessCommands.map((c) => c.id),
+		},
+	],
+
+	settings: [
+		{
+			id: "KJNodes.canvasBg.pattern",
+			name: "Background pattern",
+			category: ["KJNodes", "Canvas Background", "Pattern"],
+			tooltip: "Choose a background pattern for the node graph canvas. 'default' uses LiteGraph's built-in dot grid. 'none' disables all background drawing.",
+			type: "combo",
+			defaultValue: "default",
+			options: Object.keys(PATTERNS),
+			onChange: invalidateCache,
+		},
+		{
+			id: "KJNodes.canvasBg.patternColor",
+			name: "Pattern color",
+			category: ["KJNodes", "Canvas Background", "Pattern color"],
+			tooltip: "Foreground color of the pattern",
+			type: "color",
+			defaultValue: "#444444",
+			onChange: invalidateCache,
+		},
+		{
+			id: "KJNodes.canvasBg.bgColor",
+			name: "Background color",
+			category: ["KJNodes", "Canvas Background", "Background color"],
+			tooltip: "Background fill color behind the pattern",
+			type: "color",
+			defaultValue: "#212121",
+			onChange: invalidateCache,
+		},
+		{
+			id: "KJNodes.canvasBg.scale",
+			name: "Pattern scale",
+			category: ["KJNodes", "Canvas Background", "Pattern scale"],
+			tooltip: "Size of one pattern tile in pixels",
+			type: "slider",
+			defaultValue: 32,
+			attrs: SCALE_ATTRS,
+			onChange: invalidateCache,
+		},
+		{
+			id: "KJNodes.canvasBg.thickness",
+			name: "Pattern thickness",
+			category: ["KJNodes", "Canvas Background", "Pattern thickness"],
+			tooltip: "Thickness of lines and size of dots in pixels",
+			type: "slider",
+			defaultValue: 2,
+			attrs: THICKNESS_ATTRS,
+			onChange: invalidateCache,
+		},
+	],
+
+	setup() {
+		const canvas = app.canvas;
+		if (!canvas) return;
+
+		let patternToken = null; // null = default, "" = none, string = custom
+		// Snapshot palette values lazily on first override so we capture post-palette state
+		let origBackgroundImage = null;
+		let origClearBgColor = null;
+		let origsCaptured = false;
+
+		function captureOriginals() {
+			if (origsCaptured) return;
+			origsCaptured = true;
+			origBackgroundImage = canvas.background_image;
+			origClearBgColor = canvas.clear_background_color;
+		}
+
+		function applyToLiteGraph() {
+			if (!dirty) return;
+			dirty = false;
+			readSettings();
+
+			// If ComfyUI's own background image setting is active, don't interfere
+			if (getSetting("Comfy.Canvas.BackgroundImage", "")) {
+				patternToken = null;
+				return;
+			}
+
+			if (currentType === "default") {
+				if (origsCaptured) {
+					canvas.background_image = origBackgroundImage;
+					canvas.clear_background_color = origClearBgColor;
+					canvas._pattern = undefined;
+					canvas._bg_img = undefined;
+				}
+				patternToken = null;
+				canvas.setDirty(true, true);
+				return;
+			}
+
+			// Capture originals before we overwrite them for the first time
+			captureOriginals();
+
+			if (currentType === "none") {
+				canvas.background_image = "";
+				canvas.clear_background_color = currentBg;
+				canvas._pattern = undefined;
+				canvas._bg_img = undefined;
+				patternToken = "";
+				canvas.setDirty(true, true);
+				return;
+			}
+
+			// Generate tile as OffscreenCanvas — passed directly to LiteGraph's
+			// createPattern which accepts any CanvasImageSource.
+			const gen = PATTERNS[currentType];
+			if (!gen) return;
+
+			const tile = gen(currentSize, currentFg, currentBg, currentThickness);
+			patternToken = `kjbg_${currentType}_${currentSize}_${currentThickness}_${currentFg}_${currentBg}`;
+			tile.name = patternToken;
+
+			canvas.background_image = patternToken;
+			canvas.clear_background_color = currentBg;
+			canvas._bg_img = tile;
+			canvas._pattern = undefined;
+			canvas.setDirty(true, true);
+		}
+
+		applyToLiteGraph();
+
+		// Enforce our values if palette service overwrites them.
+		// Returns false — LiteGraph's native code does all drawing.
+		const origCallback = canvas.onRenderBackground;
+		canvas.onRenderBackground = function (cvs, ctx) {
+			if (origCallback) {
+				const result = origCallback.call(this, cvs, ctx);
+				if (result) return true;
+			}
+
+			applyToLiteGraph();
+
+			// If palette service overwrote our values, mark dirty for next frame.
+			if (!dirty && patternToken !== null && this.background_image !== patternToken) {
+				dirty = true;
+			}
+
+			return false;
+		};
+	},
+});
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/contextmenu.js b/custom_nodes/ComfyUI-KJNodes/web/js/contextmenu.js
new file mode 100644
index 0000000000000000000000000000000000000000..9abccc68afa731ce54bb85a772a81eabc6ec6ca2
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/contextmenu.js
@@ -0,0 +1,182 @@
+const { app } = window.comfyAPI.app;
+
+function addNode(name, nextTo, options) {
+	options = { side: "left", select: true, shiftY: 0, shiftX: 0, ...(options || {}) };
+	const node = LiteGraph.createNode(name);
+	const graph = app.canvas?.graph || app.graph;
+	graph.add(node);
+
+	node.pos = [
+		options.side === "left" ? nextTo.pos[0] - (node.size[0] + options.offset): nextTo.pos[0] + nextTo.size[0] + options.offset,
+		nextTo.pos[1] + options.shiftY,
+	];
+
+	// Automatically connect nodes
+	if (options.side === "left") {
+		// New node on left: connect new node's first output to nextTo's first free input
+		if (node.outputs && node.outputs.length > 0 && nextTo.inputs && nextTo.inputs.length > 0) {
+			for (let i = 0; i < nextTo.inputs.length; i++) {
+				if (!nextTo.inputs[i].link) {
+					node.pos[1] += i * (node.size[1] + 32);
+					node.connect(0, nextTo, i);
+					break;
+				}
+			}
+		}
+	} else {
+		// New node on right: connect nextTo's first free output to new node's first free input
+		if (nextTo.outputs && nextTo.outputs.length > 0 && node.inputs && node.inputs.length > 0) {
+			for (let o = 0; o < nextTo.outputs.length; o++) {
+				if (!nextTo.outputs[o].links || nextTo.outputs[o].links.length === 0) {
+					// Offset vertically by slot index so multiple Set nodes don't overlap
+					node.pos[1] += o * (node.size[1] + 32);
+					for (let i = 0; i < node.inputs.length; i++) {
+						if (!node.inputs[i].link) {
+							nextTo.connect(o, node, i);
+							break;
+						}
+					}
+					break;
+				}
+			}
+		}
+	}
+	
+	if (options.select) {
+		app.canvas.selectNode(node, false);
+	}
+	return node;
+}
+
+// Expose addNode for use in setgetnodes.js
+window.kjNodes = window.kjNodes || {};
+window.kjNodes.addNode = addNode;
+
+app.registerExtension({
+	name: "KJNodes.Contextmenu",
+	settings: [
+		{
+			id: "KJNodes.helpPopup",
+			name: "Help popups",
+			category: ["KJNodes", "General", "Help popups"],
+			tooltip: "Show help popups when hovering over KJNodes",
+			defaultValue: true,
+			type: "boolean",
+		},
+		{
+			id: "KJNodes.showSetGetInConnectionMenu",
+			name: "Show Set/Get in connection menu",
+			category: ["KJNodes", "Set & Get", "Show Set/Get in connection menu"],
+			tooltip: "Add Set/Get entries to the slot connection menu (next to Add Reroute)",
+			type: "boolean",
+			defaultValue: true,
+		},
+	],
+	getNodeMenuItems(node) {
+		try {
+			const items = [];
+
+			if (node.inputs && node.inputs.length > 0) {
+				const selectedForConvert = window.kjNodes.snapshotSelectedNodes(node);
+				items.push(
+					{
+						content: "Add GetNode",
+						callback: () => { addNode("GetNode", node, { side: "left", offset: 30 }); }
+					},
+					{
+						content: "Add SetNode",
+						callback: () => { addNode("SetNode", node, { side: "right", offset: 30 }); }
+					},
+					{
+						content: "Add PreviewAsTextNode",
+						callback: () => { addNode("PreviewAny", node, { side: "right", offset: 30 }); }
+					},
+					{
+						content: "Convert all outputs to Set/Get",
+						callback: () => {
+							for (const n of selectedForConvert) window.kjNodes.convertOutputsToSetGet(node.graph, n);
+						}
+					},
+				);
+			}
+
+			const cls = node.constructor?.comfyClass || node.comfyClass;
+			if (cls && window.kjNodes?.recreateNode) {
+				items.push({
+					content: "Recreate node",
+					has_submenu: true,
+					submenu: {
+						options: [
+							{ content: "Keep widget values", callback: () => window.kjNodes.recreateNode(node, cls, { resetValues: false }) },
+							{ content: "Reset widget values", callback: () => window.kjNodes.recreateNode(node, cls, { resetValues: true }) },
+						],
+					},
+				});
+			}
+
+			return items;
+		} catch (err) {
+			console.error("[KJNodes.Contextmenu] getNodeMenuItems failed:", err);
+			return [];
+		}
+	},
+	async setup(app) {
+		const origShowConnectionMenu = app.canvas.showConnectionMenu.bind(app.canvas);
+
+		// Inject "Add SetNode" / "Add GetNode" into the slot connection dropdown menu.
+		app.canvas.showConnectionMenu = function(optPass) {
+			const showSetGet = app.ui.settings.getSettingValue("KJNodes.showSetGetInConnectionMenu") ?? true;
+			if (!showSetGet) return origShowConnectionMenu(optPass);
+
+			const isFrom = optPass.nodeFrom && optPass.slotFrom != null;
+			const nodeType = isFrom ? "SetNode" : "GetNode";
+			const label = isFrom ? "Add SetNode" : "Add GetNode";
+
+			const OrigCM = LiteGraph.ContextMenu;
+			let interceptActive = true;
+			LiteGraph.ContextMenu = function(options, menuOpts) {
+				LiteGraph.ContextMenu = OrigCM;
+				if (!interceptActive) return new OrigCM(options, menuOpts);
+				interceptActive = false;
+
+				const idx = options.indexOf("Add Reroute");
+				if (idx !== -1) options.splice(idx + 1, 0, label);
+
+				const origCb = menuOpts.callback;
+				menuOpts.callback = function(v, cbOpts, e) {
+					if (v === label) {
+						const node = LiteGraph.createNode(nodeType);
+						if (node) {
+							const graph = app.canvas?.graph || app.graph;
+							node.pos = [optPass.e?.canvasX ?? 0, optPass.e?.canvasY ?? 0];
+							graph.add(node);
+							if (isFrom && optPass.nodeFrom) {
+								const slotIdx = typeof optPass.slotFrom === 'number'
+									? optPass.slotFrom
+									: optPass.nodeFrom.findOutputSlot(optPass.slotFrom.name);
+								optPass.nodeFrom.connect(slotIdx, node, 0);
+							} else if (optPass.nodeTo) {
+								const slotIdx = typeof optPass.slotTo === 'number'
+									? optPass.slotTo
+									: optPass.nodeTo.findInputSlot(optPass.slotTo.name);
+								node.connect(0, optPass.nodeTo, slotIdx);
+							}
+							graph.change();
+							app.canvas.setDirty(true, true);
+						}
+						return;
+					}
+					return origCb?.call(this, v, cbOpts, e);
+				};
+
+				return new OrigCM(options, menuOpts);
+			};
+			LiteGraph.ContextMenu.prototype = OrigCM.prototype;
+
+			const result = origShowConnectionMenu(optPass);
+			LiteGraph.ContextMenu = OrigCM;
+			interceptActive = false;
+			return result;
+		};
+	}
+});
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/editors/editor_base.js b/custom_nodes/ComfyUI-KJNodes/web/js/editors/editor_base.js
new file mode 100644
index 0000000000000000000000000000000000000000..963d2f7cfaea49efe520fabf7dff1581e9a9a705
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/editors/editor_base.js
@@ -0,0 +1,770 @@
+import { addMiddleClickPan, captureVideoFrame, chainCallback, makeUUID, watchImageInputs } from '../utility.js';
+
+export function createEditorStylesheet(id, className) {
+  let styleTag = document.head.querySelector(`#${id}`)
+  if (!styleTag) {
+    styleTag = document.createElement('style')
+    styleTag.type = 'text/css'
+    styleTag.id = id
+    styleTag.innerHTML = `
+      .${className} {
+        position: absolute;
+        font: 12px monospace;
+        line-height: 1.5em;
+        padding: 10px;
+        z-index: 0;
+        overflow: hidden;
+      }
+      .${className} canvas {
+        position: relative;
+        z-index: 2;
+      }
+    `
+    document.head.appendChild(styleTag)
+  }
+}
+
+function styleMenuItem(menuItem) {
+  menuItem.style.display = "block";
+  menuItem.style.padding = "5px";
+  menuItem.style.color = "#FFF";
+  menuItem.style.fontFamily = "Arial, sans-serif";
+  menuItem.style.fontSize = "16px";
+  menuItem.style.textDecoration = "none";
+  menuItem.style.marginBottom = "5px";
+}
+
+function createMenuItem(id, textContent) {
+  let menuItem = document.createElement("a");
+  menuItem.href = "#";
+  menuItem.dataset.menuId = id;
+  menuItem.textContent = textContent;
+  styleMenuItem(menuItem);
+  return menuItem;
+}
+
+function setupMenuItems(contextMenu, menuItems) {
+  menuItems.forEach(mi => {
+    mi.addEventListener('mouseover', function () { this.style.backgroundColor = "gray"; });
+    mi.addEventListener('mouseout', function () { this.style.backgroundColor = "#202020"; });
+    contextMenu.appendChild(mi);
+  });
+}
+
+export function createContextMenuElement(className) {
+  const menu = document.createElement("div");
+  if (className) menu.className = className;
+  menu.id = `context-menu-${Math.random().toString(36).slice(2, 10)}`;
+  menu.style.display = "none";
+  menu.style.position = "absolute";
+  menu.style.backgroundColor = "#202020";
+  menu.style.minWidth = "100px";
+  menu.style.boxShadow = "0px 8px 16px 0px rgba(0,0,0,0.2)";
+  menu.style.zIndex = "100";
+  menu.style.padding = "5px";
+  return menu;
+}
+
+// ─── Base Editor Canvas ───
+
+const maxDisplayDim = 1024;
+const buttonRowHeight = 28;
+
+export class BaseEditorCanvas {
+  constructor(context, reset = false) {
+    this.node = context;
+    this.reset = reset;
+    this.bgImage = null;
+    this.margin = 14;
+    this.dragIndex = -1;
+    this.dragType = null;
+    this.dragOffset = null;
+
+    this._uploadGeneration = 0;
+  }
+
+  // ─── Shared Methods ───
+
+  setNodeWidth(width) {
+    this.node.setSize([width, this.node.size[1]]);
+    const nodeEl = document.querySelector(`[data-node-id="${this.node.id}"]`);
+    if (nodeEl) nodeEl.style.setProperty('--node-width', `${width}px`);
+  }
+
+  // Scale factors: coord space → canvas space
+  get scaleX() { return this.width / this.coordWidth; }
+  get scaleY() { return this.height / this.coordHeight; }
+
+  // Returns mouse position in coord space
+  getLocalMouse(e) {
+    const rect = this.canvas.getBoundingClientRect();
+    const canvasScaleX = this.canvas.width / rect.width;
+    const canvasScaleY = this.canvas.height / rect.height;
+    const canvasX = (e.clientX - rect.left) * canvasScaleX - this.margin;
+    const canvasY = (e.clientY - rect.top) * canvasScaleY - this.margin;
+    return {
+      x: canvasX / this.scaleX,
+      y: canvasY / this.scaleY
+    };
+  }
+
+  // Clamp to coord space
+  clamp(x, y) {
+    return {
+      x: Math.max(0, Math.min(this.coordWidth, x)),
+      y: Math.max(0, Math.min(this.coordHeight, y))
+    };
+  }
+
+  // ─── Canvas Setup ───
+
+  createCanvas(parentElement) {
+    this.canvas = document.createElement('canvas');
+    this.canvas.width = this.width + this.margin * 2;
+    this.canvas.height = this.height + this.margin * 2;
+    this.ctx = this.canvas.getContext('2d');
+    parentElement.appendChild(this.canvas);
+  }
+
+  resizeCanvas() {
+    this.canvas.width = this.width + this.margin * 2;
+    this.canvas.height = this.height + this.margin * 2;
+  }
+
+  // ─── Event Listeners ───
+
+  setupEventListeners() {
+    this._onDragMove = (e) => this.onMouseMove(e);
+    this._onDragEnd = (e) => this.onMouseUp(e);
+    this._removeMiddleClickPan = addMiddleClickPan(this.canvas);
+    this._onCanvasPointerDown = (e) => { e.stopPropagation(); this.onMouseDown(e); };
+    this._onCanvasPointerMove = (e) => this.onMouseMove(e);
+    this._onCanvasContextMenu = (e) => { e.preventDefault(); e.stopPropagation(); };
+    this.canvas.addEventListener('pointerdown', this._onCanvasPointerDown);
+    this.canvas.addEventListener('pointermove', this._onCanvasPointerMove);
+    this.canvas.addEventListener('contextmenu', this._onCanvasContextMenu);
+  }
+
+  removeEventListeners() {
+    if (this.canvas) {
+      this.canvas.removeEventListener('pointerdown', this._onCanvasPointerDown);
+      this.canvas.removeEventListener('pointermove', this._onCanvasPointerMove);
+      this.canvas.removeEventListener('pointermove', this._onDragMove);
+      this.canvas.removeEventListener('pointerup', this._onDragEnd);
+      this.canvas.removeEventListener('contextmenu', this._onCanvasContextMenu);
+    }
+    document.removeEventListener('pointermove', this._onDragMove);
+    document.removeEventListener('pointerup', this._onDragEnd);
+    if (this._removeMiddleClickPan) this._removeMiddleClickPan();
+  }
+
+  startDocumentDrag(e) {
+    if (e && e.pointerId !== undefined) {
+      this.canvas.setPointerCapture(e.pointerId);
+      this._capturedPointerId = e.pointerId;
+      // With pointer capture, events fire on the canvas, not document
+      this.canvas.addEventListener('pointermove', this._onDragMove);
+      this.canvas.addEventListener('pointerup', this._onDragEnd);
+    } else {
+      document.addEventListener('pointermove', this._onDragMove);
+      document.addEventListener('pointerup', this._onDragEnd);
+    }
+  }
+
+  endDrag() {
+    this.dragIndex = -1;
+    this.dragType = null;
+    this.dragOffset = null;
+    this.canvas.style.cursor = 'default';
+    if (this._capturedPointerId !== undefined) {
+      try { this.canvas.releasePointerCapture(this._capturedPointerId); } catch (_) {}
+      this._capturedPointerId = undefined;
+      this.canvas.removeEventListener('pointermove', this._onDragMove);
+      this.canvas.removeEventListener('pointerup', this._onDragEnd);
+    } else {
+      document.removeEventListener('pointermove', this._onDragMove);
+      document.removeEventListener('pointerup', this._onDragEnd);
+    }
+  }
+
+  // ─── Render Batching ───
+
+  render() {
+    if (this._renderPending) return;
+    this._renderPending = true;
+    requestAnimationFrame(() => {
+      this._renderPending = false;
+      this._render();
+    });
+  }
+
+  // Subclasses must implement _render()
+
+  // ─── Common Render Helpers ───
+
+  // Convert coord-space point to canvas-space pixel
+  toCanvas(x, y) {
+    return { x: x * this.scaleX, y: y * this.scaleY };
+  }
+
+  // Call at start of _render() — clears and draws background
+  beginRender() {
+    const ctx = this.ctx;
+    ctx.clearRect(0, 0, this.canvas.width, this.canvas.height);
+    ctx.save();
+    ctx.translate(this.margin, this.margin);
+    ctx.fillStyle = '#222';
+    ctx.fillRect(0, 0, this.width, this.height);
+    ctx.strokeStyle = 'gray';
+    ctx.lineWidth = 2;
+    ctx.strokeRect(0, 0, this.width, this.height);
+    if (this.bgImage) {
+      ctx.drawImage(this.bgImage, 0, 0, this.width, this.height);
+    }
+  }
+
+  endRender() {
+    this.ctx.restore();
+  }
+
+  // ─── Image Handling ───
+
+  handleImageLoad = (img, downscaledImg) => {
+    // Set coord space to image dimensions, rescaling existing points if the space changed
+    const oldCoordW = this.coordWidth, oldCoordH = this.coordHeight;
+    this.coordWidth = img.width;
+    this.coordHeight = img.height;
+    this.widthWidget.value = img.width;
+    this.heightWidget.value = img.height;
+    if (oldCoordW && oldCoordH && (oldCoordW !== img.width || oldCoordH !== img.height)) {
+      this.onCoordSpaceResized?.(oldCoordW, oldCoordH);
+    }
+    this.onImageResize?.(img);
+
+    // Cap display size to the current node width if the user has already resized it,
+    // otherwise fall back to maxDisplayDim. This prevents the node from expanding to
+    // fill the image — instead the image scales to fit the node.
+    const nodeCanvasW = Math.max(64, Math.round(this.node.size[0] - 45));
+    const fitDim = Math.min(nodeCanvasW, maxDisplayDim);
+    let displayW = img.width, displayH = img.height;
+    if (displayW > fitDim || displayH > fitDim) {
+      const scale = fitDim / Math.max(displayW, displayH);
+      displayW = Math.round(displayW * scale);
+      displayH = Math.round(displayH * scale);
+    }
+
+    if (displayW !== this.width || displayH !== this.height) {
+      this.width = displayW;
+      this.height = displayH;
+      this.resizeCanvas();
+
+      // Only expand the node width if it's narrower than the image — never shrink it
+      if (displayW + 45 > this.node.size[0]) this.setNodeWidth(displayW + 45);
+      this.onSizeChanged();
+      if (this.node.graph) {
+        try { this.node.arrange?.(); } catch (_) {}
+        this.node.graph.setDirtyCanvas(true, true);
+      }
+    }
+
+    // Use downscaled image if available to avoid holding full-res in memory
+    this.bgImage = downscaledImg || img;
+    this.render();
+    this.onDataChanged();
+  };
+
+  // resizeCanvas: if true (default), resize editor canvas to match image.
+  // If false, just store the image and update the background without resizing.
+  processImage = (img, { resize = true } = {}) => {
+    const canvas = document.createElement('canvas');
+    const ctx = canvas.getContext('2d');
+    let width = img.width, height = img.height;
+    if (width > maxDisplayDim || height > maxDisplayDim) {
+      const scale = maxDisplayDim / Math.max(width, height);
+      width = Math.round(width * scale);
+      height = Math.round(height * scale);
+    }
+    canvas.width = width;
+    canvas.height = height;
+    ctx.drawImage(img, 0, 0, width, height);
+
+    const { app } = window.comfyAPI.app;
+    const embed = app.ui.settings.getSettingValue("KJNodes.editors.embedBackgroundImage") ?? false;
+
+    // Use the downscaled canvas directly as bgImage — drawImage accepts canvas elements,
+    // avoids a data URL round-trip, and is immediately available (no async decode).
+    const onStored = () => {
+      if (resize) {
+        this.handleImageLoad(img, canvas);
+      } else {
+        this.bgImage = canvas;
+        this.render();
+      }
+    };
+
+    const gen = ++this._uploadGeneration;
+    if (embed) {
+      const base64String = canvas.toDataURL('image/webp', 0.5).replace(/^data:.+?,/, '');
+      if (gen !== this._uploadGeneration) return;
+      this.node.properties.imgData = { type: 'image/webp', base64: base64String };
+      onStored();
+    } else {
+      canvas.toBlob((blob) => {
+        if (gen !== this._uploadGeneration) return;
+        const filename = `editor_bg_${this.node.id}_${Date.now()}.webp`;
+        const formData = new FormData();
+        formData.append('image', blob, filename);
+        formData.append('type', 'temp');
+        formData.append('overwrite', 'true');
+        fetch('/upload/image', { method: 'POST', body: formData })
+          .then(r => r.json())
+          .then(result => {
+            if (gen !== this._uploadGeneration) return;
+            this.node.properties.imgData = { type: 'temp', filename: result.name };
+            onStored();
+          })
+          .catch(e => console.error("Failed to upload editor background:", e));
+      }, 'image/webp', 0.5);
+    }
+  };
+
+  handleImageFile = (file) => {
+    const url = URL.createObjectURL(file);
+    const img = new Image();
+    img.onload = () => { URL.revokeObjectURL(url); this.processImage(img); };
+    img.onerror = () => URL.revokeObjectURL(url);
+    img.src = url;
+  };
+
+  refreshBackgroundImage = () => {
+    const imgData = this.node.properties.imgData;
+    if (!imgData) return;
+
+    const img = new Image();
+    img.onerror = (e) => console.error("Background image failed to load:", e);
+    img.onload = () => {
+      // Just set the background — don't resize canvas, widget values are already correct from serialization
+      this.bgImage = img;
+      this.render();
+    };
+
+    if (imgData.base64) {
+      const mimeType = imgData.type || 'image/png';
+      img.src = `data:${mimeType};base64,${imgData.base64}`;
+    } else if (imgData.filename) {
+      img.src = `/view?filename=${encodeURIComponent(imgData.filename)}&type=temp&no-cache=${Date.now()}`;
+    }
+  };
+
+  // ─── Context Menu Helpers ───
+
+  // Set up document-level listeners for context menu behavior
+  setupContextMenuListeners(editorIdPrefix) {
+    this._onContextMenu = (e) => {
+      if (e.target.closest(`#${editorIdPrefix}-${this.node.uuid}`) ||
+          this.node.contextMenu.contains(e.target)) {
+        e.preventDefault();
+      }
+    };
+    this._onDocClick = (e) => {
+      if (!this.node.contextMenu.contains(e.target)) {
+        this.node.contextMenu.style.display = 'none';
+      }
+    };
+    document.addEventListener('contextmenu', this._onContextMenu);
+    document.addEventListener('click', this._onDocClick);
+  }
+
+  // Clean up previous editor instance
+  cleanupPreviousEditor(context) {
+    if (context.editor) {
+      context.editor.destroy();
+    }
+  }
+
+  // Full cleanup — override in subclass to add additional cleanup
+  destroy() {
+    this._uploadGeneration++;  // Invalidate any pending async image uploads
+    this.removeEventListeners();
+    if (this._onContextMenu) document.removeEventListener('contextmenu', this._onContextMenu);
+    if (this._onDocClick) document.removeEventListener('click', this._onDocClick);
+    if (this._onKeyUp) document.removeEventListener('keyup', this._onKeyUp);
+  }
+
+  // Common menu actions
+  openImageFilePicker() {
+    const fileInput = document.createElement('input');
+    fileInput.type = 'file';
+    fileInput.accept = 'image/*';
+    fileInput.addEventListener('change', (event) => {
+      const file = event.target.files[0];
+      if (file) this.handleImageFile(file);
+    });
+    fileInput.click();
+  }
+
+  clearBackgroundImage() {
+    this.bgImage = null;
+    this.node.properties.imgData = null;
+    this.render();
+  }
+
+  // Find a widget by name on the node
+  findWidget(name) {
+    const w = this.node.widgets.find(w => w.name === name);
+    if (!w) console.warn(`${this.constructor.name}: widget "${name}" not found`);
+    return w;
+  }
+
+  // Show context menu at mouse position
+  showContextMenu(e) {
+    this._updateMenuToggleStates();
+    const menu = this.node.contextMenu;
+    menu.style.display = 'block';
+    menu.style.left = `${e.clientX}px`;
+    menu.style.top = `${e.clientY}px`;
+    // Adjust if menu overflows viewport
+    const rect = menu.getBoundingClientRect();
+    if (rect.right > window.innerWidth) menu.style.left = `${Math.max(0, e.clientX - rect.width)}px`;
+    if (rect.bottom > window.innerHeight) menu.style.top = `${Math.max(0, e.clientY - rect.height)}px`;
+  }
+
+  // ─── Width/Height Resize ───
+
+  setupSizeCallbacks() {
+    this.widthWidget.callback = () => {
+      const oldCoordW = this.coordWidth;
+      this.coordWidth = this.widthWidget.value;
+      this.onCoordSpaceResized(oldCoordW, this.coordHeight);
+      this.render();
+      this.onDataChanged();
+    };
+    this.heightWidget.callback = () => {
+      const oldCoordH = this.coordHeight;
+      this.coordHeight = this.heightWidget.value;
+      this.onCoordSpaceResized(this.coordWidth, oldCoordH);
+      this.render();
+      this.onDataChanged();
+    };
+  }
+
+  // ─── imgData persistence ───
+  // Call once from onNodeCreated to ensure imgData survives save/reload
+  static setupImgDataPersistence(node) {
+    chainCallback(node, "onSerialize", function (o) {
+      if (this.properties.imgData && o.properties) {
+        o.properties.imgData = this.properties.imgData;
+      }
+    });
+    chainCallback(node, "onConfigure", function (info) {
+      if (info?.properties?.imgData) {
+        this.properties.imgData = info.properties.imgData;
+      }
+    });
+  }
+
+  // ─── Node registration helper ───
+  // Call from onNodeCreated to set up the common editor scaffolding.
+  // config: { editorClass, editorKey, heightKey, className, menuItems, hiddenWidgets, initialSize, extraProperties }
+  // editorClass: the editor constructor (e.g. SplineEditor)
+  // editorKey: node property name for the DOM widget (e.g. 'splineEditor')
+  // heightKey: node property name for editor height (e.g. 'splineEditorHeight')
+  // className: CSS class for the editor container
+  // menuItems: { id: { label, action(editor), toggle?(editor) }, ... } — context menu definition
+  // menuClassName: optional CSS class for the context menu
+  // hiddenWidgets: widget names to hide
+  // initialSize: [width, height] for the node
+  // extraProperties: array of [name, default, type] to register via addProperty on first create
+  static setupNode(node, nodeData, config) {
+    const { editorClass, editorKey, heightKey, className, menuItems,
+            menuClassName, hiddenWidgets, initialSize, extraProperties } = config;
+
+    if (!node.properties) node.properties = {};
+    if (node.properties.imgData === undefined) node.properties.imgData = null;
+
+    // File handling — set up once per node, delegate to current editor
+    node.pasteFile = (file) => {
+      if (node.editor && file.type.startsWith("image/")) {
+        node.editor.handleImageFile(file);
+        return true;
+      }
+      return false;
+    };
+    node.onDragOver = (e) => {
+      if (node.editor && e.dataTransfer && e.dataTransfer.items) {
+        return [...e.dataTransfer.items].some(f => f.kind === "file" && f.type.startsWith("image/"));
+      }
+      return false;
+    };
+    node.onDragDrop = (e) => {
+      if (!node.editor) return false;
+      let handled = false;
+      for (const file of e.dataTransfer.files) {
+        if (file.type.startsWith("image/")) {
+          node.editor.handleImageFile(file);
+          handled = true;
+        }
+      }
+      return handled;
+    };
+
+    for (const name of (hiddenWidgets || [])) {
+      const w = node.widgets.find(w => w.name === name);
+      if (w) w.hidden = true;
+    }
+
+    const element = document.createElement("div");
+    node.uuid = makeUUID();
+    element.id = `${className}-${node.uuid}`;
+    node.previewMediaType = 'image';
+
+    node[editorKey] = node.addDOMWidget(nodeData.name, `${editorClass.name}Widget`, element, {
+      serialize: false, hideOnZoom: false,
+      getMinHeight: () => node[heightKey] || 550,
+      getMaxHeight: () => node[heightKey] || 550,
+      getHeight: () => node[heightKey] || 550,
+    });
+    node[heightKey] = 550;
+
+    node.contextMenu = createContextMenuElement(menuClassName);
+    node._menuDef = menuItems;
+    const menuEls = Object.entries(menuItems).map(([id, def]) => createMenuItem(id, def.label || id));
+    setupMenuItems(node.contextMenu, menuEls);
+    document.body.appendChild(node.contextMenu);
+
+    // Shared helper — loads the stored background image into the editor.
+    // alignToImage: if true, resizes the node to fit the image first (old behaviour).
+    //               if false, scales the image to fit the current node size (new behaviour).
+    const _reloadBgImage = (alignToImage) => {
+      const imgData = node.properties.imgData;
+      if (!imgData) return;
+      const img = new Image();
+      img.onload = () => {
+        if (!node.editor) return;
+        if (alignToImage) {
+          // Temporarily lift the node-size cap so processImage can expand the node
+          const savedSize = [node.size[0], node.size[1]];
+          node.setSize([Math.min(img.width, maxDisplayDim) + 45, savedSize[1]]);
+        }
+        node.editor.processImage(img);
+      };
+      if (imgData.base64) {
+        img.src = `data:${imgData.type || 'image/png'};base64,${imgData.base64}`;
+      } else if (imgData.filename) {
+        img.src = `/view?filename=${encodeURIComponent(imgData.filename)}&type=temp&no-cache=${Date.now()}`;
+      }
+    };
+
+    // Two side-by-side buttons in a single DOM widget row.
+    // "Reset canvas" — clears points and re-fits the image to the current node size.
+    // "Align to image" — resizes the node to match the image dimensions (legacy behaviour).
+    const buttonRow = document.createElement("div");
+    buttonRow.style.cssText = "display:flex;gap:4px;width:100%;box-sizing:border-box;";
+    const makeRowBtn = (label, onClick) => {
+      const btn = document.createElement("button");
+      btn.textContent = label;
+      btn.style.cssText = "flex:1;height:24px;padding:0 6px;font:12px sans-serif;cursor:pointer;";
+      btn.addEventListener("click", onClick);
+      return btn;
+    };
+    buttonRow.appendChild(makeRowBtn("Reset canvas", () => {
+      try {
+        node.editor = new editorClass(node, true);
+        _reloadBgImage(false);
+      } catch (error) { console.error(`Error creating ${editorClass.name}:`, error); }
+    }));
+    buttonRow.appendChild(makeRowBtn("Align to image", () => {
+      try {
+        if (!node.editor) node.editor = new editorClass(node);
+        _reloadBgImage(true);
+      } catch (error) { console.error(`Error aligning ${editorClass.name}:`, error); }
+    }));
+    node.addDOMWidget("editor_buttons", "editorButtonRow", buttonRow, {
+      serialize: false, hideOnZoom: false,
+      getMinHeight: () => buttonRowHeight, getMaxHeight: () => buttonRowHeight, getHeight: () => buttonRowHeight,
+    });
+
+    node.setSize(initialSize);
+    node[editorKey].parentEl = document.createElement("div");
+    node[editorKey].parentEl.className = className;
+    node[editorKey].parentEl.id = `${className}-${node.uuid}`;
+    element.appendChild(node[editorKey].parentEl);
+
+    // Auto-create for new nodes; onConfigure cancels this for saved workflows
+    node._autoCreatePending = setTimeout(() => {
+      if (!node.editor) {
+        try {
+          node.editor = new editorClass(node);
+          for (const [name, value, type] of (extraProperties || [])) {
+            node.addProperty(name, value, type);
+          }
+        } catch (error) { console.error(`Error creating ${editorClass.name}:`, error); }
+      }
+    }, 0);
+
+    BaseEditorCanvas.setupImgDataPersistence(node);
+
+    chainCallback(node, "onResize", function () {
+      const editor = this.editor;
+      if (!editor) return;
+      const newWidth = Math.max(64, Math.round(this.size[0] - 45));
+      if (newWidth === editor.width) return;
+
+      // Only change display size — coord space stays the same
+      editor.width = newWidth;
+      editor.height = Math.round(newWidth * (editor.coordHeight / editor.coordWidth));
+
+      editor.resizeCanvas();
+      editor.onSizeChanged();
+      editor.render();
+    });
+
+    chainCallback(node, "onConfigure", function () {
+      clearTimeout(this._autoCreatePending);
+      try { this.editor = new editorClass(this); }
+      catch (error) { console.error(`Error configuring ${editorClass.name}:`, error); }
+      // Allow watchImageInputs to operate only after configure + a delay,
+      // so refreshBackgroundImage has time to restore the saved image first.
+      node._bgWatchReady = false;
+      setTimeout(() => { node._bgWatchReady = true; }, 500);
+    });
+
+    chainCallback(node, "onExecuted", function (message) {
+      let bg_image = message["bg_image"];
+      if (Array.isArray(bg_image)) bg_image = bg_image[0];
+      if (bg_image) {
+        const img = new Image();
+        img.src = `data:image/jpeg;base64,${bg_image}`;
+        img.onload = () => {
+          if (this.editor) this.editor.processImage(img);
+        };
+      }
+    });
+
+    // Load background image from connected source node (LoadImage, VHS, etc.)
+    // _bgWatchReady is false during reload until the editor has restored its saved background.
+    // _bgFromConnectedSource tracks whether the current bg came from a connection (vs execution/paste).
+    node._bgFromConnectedSource = false;
+    node._bgWatchReady = true;  // true for new nodes, set false during onConfigure
+    watchImageInputs(node, "bg_image", (sources) => {
+      if (!node.editor || !node._bgWatchReady) return;
+      const source = sources[0];
+      if (source?.isVideo && source.videoEl) {
+        // Capture the first/current frame from a VHS-style video preview
+        node._bgFromConnectedSource = true;
+        captureVideoFrame(source.videoEl, (canvas) => {
+          if (node.editor) node.editor.processImage(canvas);
+        });
+      } else if (source && !source.isVideo) {
+        node._bgFromConnectedSource = true;
+        const img = new Image();
+        img.crossOrigin = "anonymous";
+        img.onerror = (e) => console.error("[Editor] source image load error:", e);
+        img.onload = () => { if (node.editor) node.editor.processImage(img); };
+        img.src = source.url;
+      } else if (!source && node._bgFromConnectedSource) {
+        node._bgFromConnectedSource = false;
+        node.editor.clearBackgroundImage();
+      }
+    });
+
+    chainCallback(node, "onRemoved", function () {
+      if (this.editor) this.editor.destroy();
+      if (this.contextMenu?.parentNode) this.contextMenu.parentNode.removeChild(this.contextMenu);
+    });
+  }
+
+  // ─── Shared constructor flow ───
+  // Call from subclass constructor after setting up widgets and data.
+  // editorKey: e.g. 'pointsEditor' or 'splineEditor'
+  // heightKey: e.g. 'pointsEditorHeight' or 'splineEditorHeight'
+  // heightOffset: pixels added to canvas height for full node height (e.g. 310 or 460)
+  initEditor(editorKey, heightKey, heightOffset) {
+    this._editorKey = editorKey;
+    this._heightKey = heightKey;
+    this._heightOffset = heightOffset;
+
+    this.createCanvas(this.node[editorKey].element);
+
+    if (this.width > 256) this.setNodeWidth(this.width + 45);
+    this.node[heightKey] = this.height + 40;
+    this.node.setSize([this.node.size[0], this.height + heightOffset + buttonRowHeight]);
+
+    this.setupEventListeners();
+    this.render();
+  }
+
+  // Shared onSizeChanged — uses stored heightKey/heightOffset
+  onSizeChanged() {
+    this.node[this._heightKey] = this.height + 40;
+    this.node.setSize([this.node.size[0], this.height + this._heightOffset + buttonRowHeight]);
+    if (this.node.graph) this.node.graph.setDirtyCanvas(true, true);
+  }
+
+  // Shared constructor preamble — cleanup, reset, context menu, coord/display init
+  initEditorPreamble(editorKey, className) {
+    this._className = className;
+    this.cleanupPreviousEditor(this.node);
+    if (this.reset && this.node[editorKey].element) {
+      this.node[editorKey].element.innerHTML = '';
+    }
+    this.createContextMenu();
+  }
+
+  // Shared coord/display size init from widgets + saved node size
+  initDisplaySize() {
+    this.coordWidth = this.widthWidget.value;
+    this.coordHeight = this.heightWidget.value;
+    const savedWidth = Math.max(64, Math.round(this.node.size[0] - 45));
+    this.width = Math.min(savedWidth, maxDisplayDim);
+    this.height = Math.round(this.width * (this.coordHeight / this.coordWidth));
+  }
+
+  // Shared context menu creation — clone to clear stale listeners, wire up action handlers
+  createContextMenu() {
+    const oldMenu = this.node.contextMenu;
+    const newMenu = oldMenu.cloneNode(true);
+    oldMenu.parentNode.replaceChild(newMenu, oldMenu);
+    this.node.contextMenu = newMenu;
+    this.setupContextMenuListeners(this._className);
+
+    const self = this;
+    newMenu.addEventListener('click', (e) => {
+      e.preventDefault();
+      if (e.target.tagName !== 'A') return;
+      const id = e.target.dataset.menuId;
+      const def = this.node._menuDef[id];
+      if (def?.action) {
+        def.action(self);
+        self._updateMenuToggleStates();
+      }
+      newMenu.style.display = 'none';
+    });
+  }
+
+  // Update toggle item styling from menu definitions
+  _updateMenuToggleStates() {
+    const menuDef = this.node._menuDef;
+    this.node.contextMenu.querySelectorAll('a').forEach(item => {
+      const def = menuDef[item.dataset.menuId];
+      if (def?.toggle) {
+        const on = def.toggle(this);
+        item.style.color = on ? '#4fc3f7' : '#FFF';
+        item.style.borderLeft = on ? '3px solid #4fc3f7' : '3px solid transparent';
+        item.style.paddingLeft = '8px';
+      }
+    });
+  }
+
+  // ─── Hooks for subclasses ───
+  // Override these instead of duplicating logic:
+
+  // Called after data changes (render + update widgets)
+  onDataChanged() {}
+
+  // Called on image load for editor-specific state (e.g., drawRuler = false)
+  onImageResize() {}
+
+  // Called when coord space changes — override to rescale coordinates
+  onCoordSpaceResized(_oldWidth, _oldHeight) {}
+}
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/editors/interpolation.js b/custom_nodes/ComfyUI-KJNodes/web/js/editors/interpolation.js
new file mode 100644
index 0000000000000000000000000000000000000000..728f923109856572ba9f6474e92fd842eb21ad13
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/editors/interpolation.js
@@ -0,0 +1,211 @@
+// ─── Interpolation: SVG path d-string builders ───
+// Ported from protovis basis/cardinal/monotone/hermite algorithms
+
+const Interpolation = {
+  _basisPoint(t0, p0, t1, p1, t2, p2, t3, p3) {
+    return t0 * p0 + t1 * p1 + t2 * p2 + t3 * p3;
+  },
+
+  _pathBasis(p0, p1, p2, p3) {
+    const bp = Interpolation._basisPoint;
+    const x1 = bp(0, p0.x, 2/3, p1.x, 1/3, p2.x, 0, p3.x);
+    const y1 = bp(0, p0.y, 2/3, p1.y, 1/3, p2.y, 0, p3.y);
+    const x2 = bp(0, p0.x, 1/3, p1.x, 2/3, p2.x, 0, p3.x);
+    const y2 = bp(0, p0.y, 1/3, p1.y, 2/3, p2.y, 0, p3.y);
+    const x  = bp(0, p0.x, 1/6, p1.x, 2/3, p2.x, 1/6, p3.x);
+    const y  = bp(0, p0.y, 1/6, p1.y, 2/3, p2.y, 1/6, p3.y);
+    return `C${x1},${y1},${x2},${y2},${x},${y}`;
+  },
+
+  basis(points) {
+    if (points.length <= 2) return Interpolation.linear(points);
+    let d = '';
+    let b0 = points[0], b1 = points[0], b2 = points[0], b3 = points[1];
+    d += Interpolation._pathBasis(b0, b1, b2, b3);
+    for (let i = 2; i < points.length; i++) {
+      b0 = b1; b1 = b2; b2 = b3; b3 = points[i];
+      d += Interpolation._pathBasis(b0, b1, b2, b3);
+    }
+    b0 = b1; b1 = b2; b2 = b3;
+    d += Interpolation._pathBasis(b0, b1, b2, b3);
+    b0 = b1; b1 = b2;
+    d += Interpolation._pathBasis(b0, b1, b2, b3);
+    return d;
+  },
+
+  _cardinalTangents(points, tension) {
+    const alpha = (1 - tension) / 2;
+    const tangents = [];
+    let f = points[0], g = points[1], h = points[2];
+    for (let i = 3; i < points.length; i++) {
+      tangents.push({ x: alpha * (h.x - f.x), y: alpha * (h.y - f.y) });
+      f = g; g = h; h = points[i];
+    }
+    tangents.push({ x: alpha * (h.x - f.x), y: alpha * (h.y - f.y) });
+    return tangents;
+  },
+
+  _hermite(points, tangents) {
+    if (tangents.length < 1 ||
+        (points.length !== tangents.length && points.length !== tangents.length + 2))
+      return '';
+    const quad = points.length !== tangents.length;
+    let d = '';
+    let g = points[0], h = points[1], i = tangents[0], j = i, k = 1;
+
+    if (quad) {
+      d += `Q${h.x - i.x * 2/3},${h.y - i.y * 2/3},${h.x},${h.y}`;
+      g = points[1]; k = 2;
+    }
+    if (tangents.length > 1) {
+      j = tangents[1]; h = points[k]; k++;
+      d += `C${g.x + i.x},${g.y + i.y},${h.x - j.x},${h.y - j.y},${h.x},${h.y}`;
+      for (let idx = 2; idx < tangents.length; idx++, k++) {
+        h = points[k]; j = tangents[idx];
+        d += `S${h.x - j.x},${h.y - j.y},${h.x},${h.y}`;
+      }
+    }
+    if (quad) {
+      const last = points[k];
+      d += `Q${h.x + j.x * 2/3},${h.y + j.y * 2/3},${last.x},${last.y}`;
+    }
+    return d;
+  },
+
+  cardinal(points, tension) {
+    if (points.length <= 2) return Interpolation.linear(points);
+    return Interpolation._hermite(points, Interpolation._cardinalTangents(points, tension));
+  },
+
+  _monotoneTangents(points) {
+    const n = points.length;
+    const d = [], m = [], dx = [];
+    for (let i = 0; i < n - 1; i++) d.push((points[i + 1].y - points[i].y) / (points[i + 1].x - points[i].x));
+    m.push(d[0]);
+    for (let i = 1; i < n - 1; i++) m.push((d[i - 1] + d[i]) / 2);
+    m.push(d[n - 2]);
+    dx.push(points[1].x - points[0].x);
+    for (let i = 1; i < n - 1; i++) dx.push((points[i + 1].x - points[i - 1].x) / 2);
+    dx.push(points[n - 1].x - points[n - 2].x);
+    for (let i = 0; i < n - 1; i++) { if (Math.abs(d[i]) < 1e-7) { m[i] = 0; m[i + 1] = 0; } }
+    for (let i = 0; i < n - 1; i++) {
+      if (Math.abs(m[i]) >= 1e-5 && Math.abs(m[i + 1]) >= 1e-5) {
+        const alpha = m[i] / d[i], beta = m[i + 1] / d[i], sigma = alpha * alpha + beta * beta;
+        if (sigma > 9) { const k = 3 / Math.sqrt(sigma); m[i] = k * alpha * d[i]; m[i + 1] = k * beta * d[i]; }
+      }
+    }
+    const tangents = [];
+    for (let i = 0; i < n; i++) { const denom = 1 + m[i] * m[i]; tangents.push({ x: dx[i] / 3 / denom, y: m[i] * dx[i] / 3 / denom }); }
+    return tangents;
+  },
+
+  monotone(points) {
+    if (points.length <= 2) return Interpolation.linear(points);
+    return Interpolation._hermite(points, Interpolation._monotoneTangents(points));
+  },
+
+  linear(points) { let d = ''; for (let i = 1; i < points.length; i++) d += `L${points[i].x},${points[i].y}`; return d; },
+  stepBefore(points) { let d = ''; for (let i = 1; i < points.length; i++) d += `V${points[i].y}H${points[i].x}`; return d; },
+  stepAfter(points) { let d = ''; for (let i = 1; i < points.length; i++) d += `H${points[i].x}V${points[i].y}`; return d; },
+
+  bezier(points) {
+    let d = '';
+    for (let i = 1; i < points.length; i++) {
+      const prev = points[i - 1], cur = points[i];
+      d += `C${prev.h2x ?? prev.x},${prev.h2y ?? prev.y},${cur.h1x ?? cur.x},${cur.h1y ?? cur.y},${cur.x},${cur.y}`;
+    }
+    return d;
+  },
+
+  ensureBezierHandles(points) {
+    for (let i = 0; i < points.length; i++) {
+      if (points[i].h1x !== undefined) continue;
+      const prev = points[Math.max(0, i - 1)], next = points[Math.min(points.length - 1, i + 1)];
+      const dx = (next.x - prev.x) * 0.25, dy = (next.y - prev.y) * 0.25;
+      points[i].h1x = points[i].x - dx; points[i].h1y = points[i].y - dy;
+      points[i].h2x = points[i].x + dx; points[i].h2y = points[i].y + dy;
+    }
+  },
+
+  buildPathD(points, interpolation, tension) {
+    if (!points || points.length === 0) return '';
+    let d = `M${points[0].x},${points[0].y}`;
+    if (points.length === 1) return d;
+    switch (interpolation) {
+      case 'basis': d += Interpolation.basis(points); break;
+      case 'cardinal': d += Interpolation.cardinal(points, tension); break;
+      case 'monotone': d += Interpolation.monotone(points); break;
+      case 'step-before': d += Interpolation.stepBefore(points); break;
+      case 'step-after': d += Interpolation.stepAfter(points); break;
+      case 'bezier': Interpolation.ensureBezierHandles(points); d += Interpolation.bezier(points); break;
+      case 'linear': default: d += Interpolation.linear(points); break;
+    }
+    return d;
+  },
+
+  drawOnCanvas(ctx, points, interpolation, tension) {
+    if (!points || points.length < 2) return;
+    ctx.beginPath();
+    ctx.moveTo(points[0].x, points[0].y);
+    switch (interpolation) {
+      case 'basis':
+        if (points.length < 3) { ctx.lineTo(points[1].x, points[1].y); break; }
+        Interpolation._drawBasisCanvas(ctx, points); break;
+      case 'cardinal':
+        if (points.length < 3) { ctx.lineTo(points[1].x, points[1].y); break; }
+        Interpolation._drawHermiteCanvas(ctx, points, Interpolation._cardinalTangents(points, tension)); break;
+      case 'monotone':
+        if (points.length < 3) { ctx.lineTo(points[1].x, points[1].y); break; }
+        Interpolation._drawHermiteCanvas(ctx, points, Interpolation._monotoneTangents(points)); break;
+      case 'step-before':
+        for (let i = 1; i < points.length; i++) { ctx.lineTo(points[i - 1].x, points[i].y); ctx.lineTo(points[i].x, points[i].y); } break;
+      case 'step-after':
+        for (let i = 1; i < points.length; i++) { ctx.lineTo(points[i].x, points[i - 1].y); ctx.lineTo(points[i].x, points[i].y); } break;
+      case 'bezier':
+        Interpolation.ensureBezierHandles(points);
+        for (let i = 1; i < points.length; i++) {
+          const prev = points[i - 1], cur = points[i];
+          ctx.bezierCurveTo(prev.h2x ?? prev.x, prev.h2y ?? prev.y, cur.h1x ?? cur.x, cur.h1y ?? cur.y, cur.x, cur.y);
+        } break;
+      case 'linear': default:
+        for (let i = 1; i < points.length; i++) ctx.lineTo(points[i].x, points[i].y); break;
+    }
+  },
+
+  _drawBasisCanvas(ctx, points) {
+    const bp = Interpolation._basisPoint;
+    const drawSeg = (p0, p1, p2, p3) => {
+      ctx.bezierCurveTo(
+        bp(0,p0.x,2/3,p1.x,1/3,p2.x,0,p3.x), bp(0,p0.y,2/3,p1.y,1/3,p2.y,0,p3.y),
+        bp(0,p0.x,1/3,p1.x,2/3,p2.x,0,p3.x), bp(0,p0.y,1/3,p1.y,2/3,p2.y,0,p3.y),
+        bp(0,p0.x,1/6,p1.x,2/3,p2.x,1/6,p3.x), bp(0,p0.y,1/6,p1.y,2/3,p2.y,1/6,p3.y));
+    };
+    let b0 = points[0], b1 = points[0], b2 = points[0], b3 = points[1];
+    drawSeg(b0, b1, b2, b3);
+    for (let i = 2; i < points.length; i++) { b0 = b1; b1 = b2; b2 = b3; b3 = points[i]; drawSeg(b0, b1, b2, b3); }
+    b0 = b1; b1 = b2; b2 = b3; drawSeg(b0, b1, b2, b3);
+    b0 = b1; b1 = b2; drawSeg(b0, b1, b2, b3);
+  },
+
+  _drawHermiteCanvas(ctx, points, tangents) {
+    if (tangents.length < 1 || (points.length !== tangents.length && points.length !== tangents.length + 2)) return;
+    const quad = points.length !== tangents.length;
+    let g = points[0], h = points[1], i = tangents[0], j = i, k = 1;
+    let prevCp2x, prevCp2y;
+    if (quad) { ctx.quadraticCurveTo(h.x - i.x * 2/3, h.y - i.y * 2/3, h.x, h.y); g = points[1]; k = 2; }
+    if (tangents.length > 1) {
+      j = tangents[1]; h = points[k]; k++;
+      prevCp2x = h.x - j.x; prevCp2y = h.y - j.y;
+      ctx.bezierCurveTo(g.x + i.x, g.y + i.y, prevCp2x, prevCp2y, h.x, h.y);
+      for (let idx = 2; idx < tangents.length; idx++, k++) {
+        const prevH = h; h = points[k]; j = tangents[idx];
+        const rcp1x = 2 * prevH.x - prevCp2x, rcp1y = 2 * prevH.y - prevCp2y;
+        prevCp2x = h.x - j.x; prevCp2y = h.y - j.y;
+        ctx.bezierCurveTo(rcp1x, rcp1y, prevCp2x, prevCp2y, h.x, h.y);
+      }
+    }
+    if (quad) { const last = points[k]; ctx.quadraticCurveTo(h.x + j.x * 2/3, h.y + j.y * 2/3, last.x, last.y); }
+  }
+};
+
+export { Interpolation };
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/editors/point_editor_canvas.js b/custom_nodes/ComfyUI-KJNodes/web/js/editors/point_editor_canvas.js
new file mode 100644
index 0000000000000000000000000000000000000000..395825992b8599c83bf8c6c71dccbd18e7b3568a
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/editors/point_editor_canvas.js
@@ -0,0 +1,370 @@
+import { chainCallback, rectHitTest, cursorForBboxMode } from '../utility.js';
+import { BaseEditorCanvas, createEditorStylesheet } from './editor_base.js';
+const { app } = window.comfyAPI.app;
+
+createEditorStylesheet('kj-pointseditor-stylesheet', 'points-editor');
+
+app.registerExtension({
+  name: 'KJNodes.PointEditor',
+
+  async beforeRegisterNodeDef(nodeType, nodeData) {
+    if (nodeData?.name === 'PointsEditor') {
+      chainCallback(nodeType.prototype, "onNodeCreated", function () {
+        BaseEditorCanvas.setupNode(this, nodeData, {
+          editorClass: PointsEditor,
+          editorKey: 'pointsEditor',
+          heightKey: 'pointsEditorHeight',
+          className: 'points-editor',
+          menuItems: {
+            "Load Image":  { action: (ed) => ed.openImageFilePicker() },
+            "Clear Image":  { action: (ed) => ed.clearBackgroundImage() },
+          },
+          hiddenWidgets: ["coordinates", "neg_coordinates", "bboxes"],
+          initialSize: [550, 550],
+          extraProperties: [
+            ["points", this.constructor.type, "string"],
+            ["neg_points", this.constructor.type, "string"],
+          ],
+        });
+      });
+    }
+  }
+});
+
+class PointsEditor extends BaseEditorCanvas {
+  constructor(context, reset = false) {
+    super(context, reset);
+    this.initEditorPreamble('pointsEditor', 'points-editor');
+
+    this.pos_coordWidget = this.findWidget("coordinates");
+    this.neg_coordWidget = this.findWidget("neg_coordinates");
+    this.pointsStoreWidget = this.findWidget("points_store");
+    this.widthWidget = this.findWidget("width");
+    this.heightWidget = this.findWidget("height");
+    this.bboxStoreWidget = this.findWidget("bbox_store");
+    this.bboxWidget = this.findWidget("bboxes");
+
+    this.setupSizeCallbacks();
+    this.pointsStoreWidget.callback = () => {
+      try {
+        const parsed = JSON.parse(this.pointsStoreWidget.value);
+        this.points = parsed.positive || [];
+        this.neg_points = parsed.negative || [];
+      } catch (e) { console.error("Error parsing points data:", e); }
+      this.render();
+      this.updateData();
+    };
+    this.bboxStoreWidget.callback = () => {
+      try { this.bbox = JSON.parse(this.bboxStoreWidget.value); }
+      catch (e) { console.error("Error parsing bbox data:", e); }
+      this.render();
+      this.updateData();
+    };
+
+    this.initDisplaySize();
+    this.points = [];
+    this.neg_points = [];
+    this.bbox = [{}];
+    this.drawing = false;
+    this.selectedIndex = -1;
+    this.pointRadius = Math.max(4, Math.log(Math.max(1, Math.min(this.coordWidth, this.coordHeight))) * 4);
+
+    if (!reset && this.pointsStoreWidget.value !== "") {
+      try {
+        const parsed = JSON.parse(this.pointsStoreWidget.value);
+        this.points = parsed.positive || [];
+        this.neg_points = parsed.negative || [];
+        this.bbox = JSON.parse(this.bboxStoreWidget.value);
+      } catch (e) {
+        console.error("Error parsing stored points:", e);
+        this.points = [{ x: this.coordWidth / 2, y: this.coordHeight / 2 }];
+        this.neg_points = [];
+        this.bbox = [{}];
+      }
+    } else {
+      this.points = [{ x: this.coordWidth / 2, y: this.coordHeight / 2 }];
+      this.neg_points = [];
+      this.pointsStoreWidget.value = JSON.stringify({ positive: this.points, negative: this.neg_points });
+      this.bboxStoreWidget.value = JSON.stringify(this.bbox);
+    }
+
+    this.initEditor('pointsEditor', 'pointsEditorHeight', 310);
+    this.updateData();
+    this.refreshBackgroundImage();
+  }
+
+  onDataChanged() { this.updateData(); }
+
+  onCoordSpaceResized(oldWidth, oldHeight) {
+    const sx = this.coordWidth / oldWidth, sy = this.coordHeight / oldHeight;
+    const clampPt = (p) => { p.x = Math.max(0, Math.min(this.coordWidth, p.x * sx)); p.y = Math.max(0, Math.min(this.coordHeight, p.y * sy)); };
+    for (const p of this.points) clampPt(p);
+    for (const p of this.neg_points) clampPt(p);
+    for (const b of this.bbox) {
+      if (b.startX != null) {
+        b.startX = Math.max(0, Math.min(this.coordWidth, b.startX * sx));
+        b.startY = Math.max(0, Math.min(this.coordHeight, b.startY * sy));
+        b.endX = Math.max(0, Math.min(this.coordWidth, b.endX * sx));
+        b.endY = Math.max(0, Math.min(this.coordHeight, b.endY * sy));
+      }
+    }
+    this.pointRadius = Math.max(4, Math.log(Math.max(1, Math.min(this.coordWidth, this.coordHeight))) * 4);
+  }
+
+  // ─── Hit Testing ───
+
+  findPointAt(x, y, pointsArray) {
+    // Convert pixel radius to coord space
+    const r = this.pointRadius / Math.min(this.scaleX, this.scaleY);
+    for (let i = pointsArray.length - 1; i >= 0; i--) {
+      const p = pointsArray[i];
+      const dx = p.x - x, dy = p.y - y;
+      if (dx * dx + dy * dy <= r * r) return i;
+    }
+    return -1;
+  }
+
+  normBbox() {
+    const b = this.bbox[0];
+    if (!b || Object.keys(b).length === 0) return null;
+    return {
+      x1: Math.min(b.startX, b.endX), y1: Math.min(b.startY, b.endY),
+      x2: Math.max(b.startX, b.endX), y2: Math.max(b.startY, b.endY),
+    };
+  }
+
+  hitTestBbox(mx, my) {
+    const n = this.normBbox();
+    if (!n) return null;
+    return rectHitTest(mx, my, n.x1, n.y1, n.x2, n.y2, 10 / Math.min(this.scaleX, this.scaleY));
+  }
+
+  // ─── Mouse Handlers ───
+
+  onMouseDown(e) {
+    const mouse = this.getLocalMouse(e);
+    const clamped = this.clamp(mouse.x, mouse.y);
+
+    if (e.shiftKey && e.button === 2) {
+      this.neg_points.push({ x: clamped.x, y: clamped.y });
+      this.dragType = 'negative'; this.dragIndex = this.neg_points.length - 1;
+      this.selectedIndex = this.dragIndex;
+      this.dragOffset = { x: 0, y: 0 };
+      this.render(); this.updateData(); this.startDocumentDrag(e); return;
+    }
+    if (e.shiftKey && e.button === 0) {
+      this.points.push({ x: clamped.x, y: clamped.y });
+      this.dragType = 'positive'; this.dragIndex = this.points.length - 1;
+      this.selectedIndex = this.dragIndex;
+      this.dragOffset = { x: 0, y: 0 };
+      this.render(); this.updateData(); this.startDocumentDrag(e); return;
+    }
+
+    if (e.button === 2) {
+      const posIdx = this.findPointAt(mouse.x, mouse.y, this.points);
+      if (posIdx >= 0) { this.points.splice(posIdx, 1); this.render(); this.updateData(); return; }
+      const negIdx = this.findPointAt(mouse.x, mouse.y, this.neg_points);
+      if (negIdx >= 0) { this.neg_points.splice(negIdx, 1); this.render(); this.updateData(); return; }
+      const bboxHit = this.hitTestBbox(mouse.x, mouse.y);
+      if (bboxHit) { this.bbox = [{}]; this.render(); this.updateData(); return; }
+      this.showContextMenu(e);
+      return;
+    }
+
+    if (e.button !== 0) return;
+
+    if (e.ctrlKey) {
+      this.drawing = true;
+      this.dragType = 'bbox-draw';
+      this.bbox[0] = { startX: clamped.x, startY: clamped.y, endX: clamped.x, endY: clamped.y };
+      this.render(); this.startDocumentDrag(e); return;
+    }
+
+    const bboxHit = this.hitTestBbox(mouse.x, mouse.y);
+    if (bboxHit) {
+      const n = this.normBbox();
+      this.bboxAtDragStart = { startX: n.x1, startY: n.y1, endX: n.x2, endY: n.y2 };
+      this.dragStart = { x: mouse.x, y: mouse.y };
+      this.dragType = bboxHit === "move" ? 'bbox-move' : bboxHit;
+      this.dragIndex = 0;
+      this.startDocumentDrag(e); return;
+    }
+
+    const posIdx = this.findPointAt(mouse.x, mouse.y, this.points);
+    if (posIdx >= 0) {
+      this.dragType = 'positive'; this.dragIndex = posIdx; this.selectedIndex = posIdx;
+      this.dragOffset = { x: mouse.x - this.points[posIdx].x, y: mouse.y - this.points[posIdx].y };
+      this.render(); this.startDocumentDrag(e); return;
+    }
+
+    const negIdx = this.findPointAt(mouse.x, mouse.y, this.neg_points);
+    if (negIdx >= 0) {
+      this.dragType = 'negative'; this.dragIndex = negIdx; this.selectedIndex = negIdx;
+      this.dragOffset = { x: mouse.x - this.neg_points[negIdx].x, y: mouse.y - this.neg_points[negIdx].y };
+      this.render(); this.startDocumentDrag(e); return;
+    }
+  }
+
+  onMouseMove(e) {
+    const mouse = this.getLocalMouse(e);
+    const clamped = this.clamp(mouse.x, mouse.y);
+
+    if (!this.drawing && this.dragIndex < 0 && !this.dragType) {
+      const bboxHit = this.hitTestBbox(mouse.x, mouse.y);
+      const bboxCursor = cursorForBboxMode(bboxHit);
+      if (bboxCursor) { this.canvas.style.cursor = bboxCursor; }
+      else if (this.findPointAt(mouse.x, mouse.y, this.points) >= 0 ||
+               this.findPointAt(mouse.x, mouse.y, this.neg_points) >= 0) { this.canvas.style.cursor = "move"; }
+      else { this.canvas.style.cursor = "default"; }
+      return;
+    }
+
+    if (this.dragType === 'bbox-draw') {
+      this.bbox[0].endX = clamped.x; this.bbox[0].endY = clamped.y;
+      this.render(); return;
+    }
+
+    if (this.dragType === 'bbox-move' && this.bboxAtDragStart) {
+      const dx = mouse.x - this.dragStart.x, dy = mouse.y - this.dragStart.y;
+      const bs = this.bboxAtDragStart;
+      const bw = bs.endX - bs.startX, bh = bs.endY - bs.startY;
+      const nx = Math.max(0, Math.min(this.coordWidth - bw, bs.startX + dx));
+      const ny = Math.max(0, Math.min(this.coordHeight - bh, bs.startY + dy));
+      this.bbox[0] = { startX: nx, startY: ny, endX: nx + bw, endY: ny + bh };
+      this.render(); return;
+    }
+
+    if (this.dragType?.startsWith('resize-') && this.bboxAtDragStart) {
+      const bs = this.bboxAtDragStart;
+      const offX = this.dragStart.x - (this.dragType === 'resize-tl' || this.dragType === 'resize-bl' ? bs.startX : bs.endX);
+      const offY = this.dragStart.y - (this.dragType === 'resize-tl' || this.dragType === 'resize-tr' ? bs.startY : bs.endY);
+      const cx = Math.max(0, Math.min(this.coordWidth, mouse.x - offX));
+      const cy = Math.max(0, Math.min(this.coordHeight, mouse.y - offY));
+      if (this.dragType === 'resize-tl') this.bbox[0] = { startX: bs.endX, startY: bs.endY, endX: cx, endY: cy };
+      else if (this.dragType === 'resize-tr') this.bbox[0] = { startX: bs.startX, startY: bs.endY, endX: cx, endY: cy };
+      else if (this.dragType === 'resize-bl') this.bbox[0] = { startX: bs.endX, startY: bs.startY, endX: cx, endY: cy };
+      else this.bbox[0] = { startX: bs.startX, startY: bs.startY, endX: cx, endY: cy };
+      this.render(); return;
+    }
+
+    if (this.dragType === 'positive' && this.dragIndex >= 0 && this.dragIndex < this.points.length) {
+      const ox = this.dragOffset?.x || 0, oy = this.dragOffset?.y || 0;
+      const target = this.clamp(mouse.x - ox, mouse.y - oy);
+      this.points[this.dragIndex] = { x: target.x, y: target.y };
+      this.render();
+    } else if (this.dragType === 'negative' && this.dragIndex >= 0 && this.dragIndex < this.neg_points.length) {
+      const ox = this.dragOffset?.x || 0, oy = this.dragOffset?.y || 0;
+      const target = this.clamp(mouse.x - ox, mouse.y - oy);
+      this.neg_points[this.dragIndex] = { x: target.x, y: target.y };
+      this.render();
+    }
+  }
+
+  onMouseUp() {
+    if (this.drawing || this.dragType) {
+      this.drawing = false;
+      this.bboxAtDragStart = null;
+      this.dragStart = null;
+      this.endDrag();
+      this.updateData();
+    }
+  }
+
+  // ─── Rendering ───
+
+  _render() {
+    const ctx = this.ctx;
+    this.beginRender();
+
+    // Bounding box
+    const nb = this.normBbox();
+    if (nb) {
+      const tl = this.toCanvas(nb.x1, nb.y1), br = this.toCanvas(nb.x2, nb.y2);
+      const bw = br.x - tl.x, bh = br.y - tl.y;
+      ctx.fillStyle = 'rgba(70, 130, 180, 0.3)';
+      ctx.fillRect(tl.x, tl.y, bw, bh);
+      ctx.strokeStyle = 'rgba(0, 0, 0, 0.5)'; ctx.lineWidth = 4;
+      ctx.strokeRect(tl.x, tl.y, bw, bh);
+      ctx.strokeStyle = 'steelblue'; ctx.lineWidth = 2;
+      ctx.strokeRect(tl.x, tl.y, bw, bh);
+      // Center cross
+      const cx = (tl.x + br.x) / 2, cy = (tl.y + br.y) / 2;
+      ctx.strokeStyle = 'steelblue'; ctx.lineWidth = 1;
+      ctx.beginPath(); ctx.moveTo(cx - 5, cy); ctx.lineTo(cx + 5, cy);
+      ctx.moveTo(cx, cy - 5); ctx.lineTo(cx, cy + 5); ctx.stroke();
+      // Corner handles
+      const hs = 5;
+      ctx.fillStyle = 'steelblue';
+      for (const [hx, hy] of [[tl.x, tl.y], [br.x, tl.y], [tl.x, br.y], [br.x, br.y]]) {
+        ctx.fillRect(hx - hs, hy - hs, hs * 2, hs * 2);
+      }
+    }
+
+    // Points
+    this._badgePositions = [];
+    this._drawPoints(ctx, this.points, '#139613', '#07f907', 'positive');
+    // Negative points
+    this._drawPoints(ctx, this.neg_points, '#891616', '#f91111', 'negative');
+
+    this.endRender();
+  }
+
+  _drawPoints(ctx, points, color, selectedColor, type) {
+    const r = this.pointRadius;
+
+    for (let i = 0; i < points.length; i++) {
+      const p = points[i];
+      const cp = this.toCanvas(p.x, p.y);
+      const isSelected = (this.dragType === type && this.dragIndex === i);
+      // Outer circle
+      ctx.beginPath(); ctx.arc(cp.x, cp.y, r, 0, Math.PI * 2);
+      ctx.fillStyle = 'rgba(100, 100, 100, 0.25)'; ctx.fill();
+      ctx.strokeStyle = isSelected ? selectedColor : color; ctx.lineWidth = 2; ctx.stroke();
+      // Center dot
+      ctx.beginPath(); ctx.arc(cp.x, cp.y, 1.5, 0, Math.PI * 2);
+      ctx.fillStyle = 'red'; ctx.fill();
+      // Index badge
+      const label = i.toString();
+      const badgeR = label.length > 1 ? 8 : 7;
+      const off = r * 0.7;
+      const candidates = [
+        { x: cp.x + off, y: cp.y - off },
+        { x: cp.x - off, y: cp.y - off },
+        { x: cp.x + off, y: cp.y + off },
+        { x: cp.x - off, y: cp.y + off },
+      ];
+      const valid = candidates.filter(c => c.x - badgeR >= 0 && c.x + badgeR <= this.width && c.y - badgeR >= 0 && c.y + badgeR <= this.height);
+      const pool = valid.length > 0 ? valid : candidates;
+      let badge = pool[0];
+      const minDist = badgeR * 2.2;
+      for (const c of pool) {
+        const overlaps = this._badgePositions.some(b => Math.abs(c.x - b.x) < minDist && Math.abs(c.y - b.y) < minDist);
+        if (!overlaps) { badge = c; break; }
+      }
+      this._badgePositions.push(badge);
+
+      ctx.font = 'bold 10px sans-serif';
+      ctx.textAlign = 'center'; ctx.textBaseline = 'middle';
+      ctx.beginPath(); ctx.arc(badge.x, badge.y, badgeR, 0, Math.PI * 2);
+      ctx.fillStyle = isSelected ? selectedColor : color; ctx.fill();
+      ctx.fillStyle = '#fff';
+      ctx.fillText(label, badge.x, badge.y + 0.5);
+      ctx.textAlign = 'start'; ctx.textBaseline = 'alphabetic';
+    }
+  }
+
+  // ─── Data ───
+
+  updateData() {
+    const combinedPoints = { positive: this.points || [], negative: this.neg_points || [] };
+    this.pointsStoreWidget.value = JSON.stringify(combinedPoints);
+    this.pos_coordWidget.value = JSON.stringify(this.points || []);
+    this.neg_coordWidget.value = JSON.stringify(this.neg_points || []);
+    if (this.bbox.length !== 0) {
+      let bboxString = JSON.stringify(this.bbox);
+      this.bboxStoreWidget.value = bboxString;
+      this.bboxWidget.value = bboxString;
+    }
+  }
+
+}
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/editors/spline_editor_canvas.js b/custom_nodes/ComfyUI-KJNodes/web/js/editors/spline_editor_canvas.js
new file mode 100644
index 0000000000000000000000000000000000000000..00c943de6c965a5416c3efc4ffcda50493935f69
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/editors/spline_editor_canvas.js
@@ -0,0 +1,720 @@
+import { chainCallback } from '../utility.js';
+import { BaseEditorCanvas, createEditorStylesheet } from './editor_base.js';
+import { Interpolation } from './interpolation.js';
+const { app } = window.comfyAPI.app;
+
+createEditorStylesheet('kj-splineeditor-stylesheet', 'spline-editor');
+
+// ─── Hidden SVG for path sampling (singleton) ───
+let _sharedSampler = null;
+let _samplerRefCount = 0;
+class PathSampler {
+  constructor() {
+    const ns = 'http://www.w3.org/2000/svg';
+    this._svg = document.createElementNS(ns, 'svg');
+    this._svg.style.cssText = 'position:absolute;width:0;height:0;overflow:hidden;pointer-events:none;';
+    this._svg.setAttribute('aria-hidden', 'true');
+    document.body.appendChild(this._svg);
+    this._path = document.createElementNS(ns, 'path');
+    this._svg.appendChild(this._path);
+  }
+  static acquire() {
+    if (_sharedSampler) { _samplerRefCount++; return _sharedSampler; }
+    _sharedSampler = new PathSampler();
+    _samplerRefCount = 1;
+    return _sharedSampler;
+  }
+  setPath(d) { this._path.setAttribute('d', d); }
+  getTotalLength() { return this._path.getTotalLength(); }
+  getPointAtLength(len) { return this._path.getPointAtLength(len); }
+  release() {
+    _samplerRefCount--;
+    if (_samplerRefCount <= 0 && this._svg && this._svg.parentNode) {
+      this._svg.parentNode.removeChild(this._svg);
+      _sharedSampler = null;
+    }
+  }
+  findPointAtX(targetX, pathLength) {
+    let low = 0, high = pathLength, bestPoint = this.getPointAtLength(0);
+    const epsilon = 0.5;
+    while (high - low > epsilon) {
+      const mid = (low + high) / 2, point = this.getPointAtLength(mid);
+      if (Math.abs(point.x - targetX) < Math.abs(bestPoint.x - targetX)) bestPoint = point;
+      if (Math.abs(point.x - targetX) < 0.5) return point;
+      if (point.x < targetX) low = mid; else high = mid;
+    }
+    return bestPoint;
+  }
+}
+
+// ─── Extension Registration ───
+app.registerExtension({
+  name: 'KJNodes.SplineEditor',
+
+  init() {
+    app.ui.settings.addSetting({
+      id: "KJNodes.splineEditor.showControlLines",
+      name: "Show control lines",
+      category: ["KJNodes", "Editors", "Show control lines"],
+      tooltip: "Display straight lines between control points",
+      type: "boolean",
+      defaultValue: false,
+    });
+    app.ui.settings.addSetting({
+      id: "KJNodes.splineEditor.showSamplePoints",
+      name: "Show sample points",
+      category: ["KJNodes", "Editors", "Show sample points"],
+      tooltip: "Display the sampled output points as red dots",
+      type: "boolean",
+      defaultValue: false,
+    });
+    app.ui.settings.addSetting({
+      id: "KJNodes.splineEditor.showArrows",
+      name: "Show direction arrows",
+      category: ["KJNodes", "Editors", "Show direction arrows"],
+      tooltip: "Display chevron arrows on control points showing curve direction",
+      type: "boolean",
+      defaultValue: true,
+    });
+    app.ui.settings.addSetting({
+      id: "KJNodes.editors.embedBackgroundImage",
+      name: "Embed background image in workflow",
+      category: ["KJNodes", "Editors", "Embed background image"],
+      tooltip: "When enabled, editor background images are embedded as base64 in the workflow file (portable, larger files). When disabled, images are stored as temp files on the server (small workflows, survives refresh, but not portable).",
+      type: "boolean",
+      defaultValue: false,
+    });
+  },
+
+  async beforeRegisterNodeDef(nodeType, nodeData) {
+    if (nodeData?.name === 'SplineEditor') {
+      chainCallback(nodeType.prototype, "onNodeCreated", function () {
+        BaseEditorCanvas.setupNode(this, nodeData, {
+          editorClass: SplineEditor,
+          editorKey: 'splineEditor',
+          heightKey: 'splineEditorHeight',
+          className: 'spline-editor',
+          menuClassName: 'spline-editor-context-menu',
+          menuItems: {
+            "Display control lines":  { toggle: (ed) => ed.drawHandles, action: (ed) => { ed.drawHandles = !ed.drawHandles; app.ui.settings.setSettingValue("KJNodes.splineEditor.showControlLines", ed.drawHandles); ed.render(); } },
+            "Display sample points":  { toggle: (ed) => ed.drawSamplePoints, action: (ed) => { ed.drawSamplePoints = !ed.drawSamplePoints; app.ui.settings.setSettingValue("KJNodes.splineEditor.showSamplePoints", ed.drawSamplePoints); ed.updatePath(); } },
+            "Display arrows":         { toggle: (ed) => ed.showChevrons, action: (ed) => { ed.showChevrons = !ed.showChevrons; app.ui.settings.setSettingValue("KJNodes.splineEditor.showArrows", ed.showChevrons); ed.render(); } },
+            "Background image":       { action: (ed) => ed.openImageFilePicker() },
+            "Invert point order":     { action: (ed) => { ed.splines[ed.activeSplineIndex].points.reverse(); ed.render(); ed.updatePath(); } },
+            "Clear Image":            { action: (ed) => { ed.clearBackgroundImage(); ed.drawRuler = true; } },
+            "Add new spline":         { action: (ed) => { const idx = ed.splines.length; ed.splines.push({ points: [{ x: 0, y: ed.coordHeight }, { x: ed.coordWidth / 2, y: ed.coordHeight / 2 }, { x: ed.coordWidth, y: 0 }], color: ed.getSplineColor(idx), name: `Spline ${idx + 1}` }); ed.activeSplineIndex = idx; ed.render(); ed.updatePath(); } },
+            "Add new single point":   { action: (ed) => { const idx = ed.splines.length; ed.splines.push({ points: [{ x: ed.lastContextMenuPos.x, y: ed.lastContextMenuPos.y }], color: ed.getSplineColor(idx), name: `Spline ${idx + 1}`, isSinglePoint: true }); ed.activeSplineIndex = idx; ed.render(); ed.updatePath(); } },
+            "Delete current spline":  { action: (ed) => { if (ed.splines.length > 1) { ed.splines.splice(ed.activeSplineIndex, 1); ed.activeSplineIndex = Math.min(ed.activeSplineIndex, ed.splines.length - 1); ed.render(); ed.updatePath(); } } },
+            "Next spline":            { action: (ed) => { ed.activeSplineIndex = (ed.activeSplineIndex + 1) % ed.splines.length; ed.render(); ed.updatePath(); } },
+          },
+          hiddenWidgets: ["coordinates"],
+          initialSize: [550, 1000],
+          extraProperties: [
+            ["points", this.constructor.type, "string"],
+          ],
+        });
+      });
+    }
+  }
+});
+
+// ─── SplineEditor class ───
+class SplineEditor extends BaseEditorCanvas {
+  constructor(context, reset = false) {
+    super(context, reset);
+    this.initEditorPreamble('splineEditor', 'spline-editor');
+
+    this.sampler = PathSampler.acquire();
+    this.drawSamplePoints = app.ui.settings.getSettingValue("KJNodes.splineEditor.showSamplePoints") ?? false;
+    this.drawHandles = app.ui.settings.getSettingValue("KJNodes.splineEditor.showControlLines") ?? false;
+    this.showChevrons = app.ui.settings.getSettingValue("KJNodes.splineEditor.showArrows") ?? true;
+    this.drawRuler = true;
+
+    this.coordWidget = this.findWidget("coordinates");
+    this.interpolationWidget = this.findWidget("interpolation");
+    this.pointsWidget = this.findWidget("points_to_sample");
+    this.pointsStoreWidget = this.findWidget("points_store");
+    this.tensionWidget = this.findWidget("tension");
+    this.samplingMethodWidget = this.findWidget("sampling_method");
+    this.widthWidget = this.findWidget("mask_width");
+    this.heightWidget = this.findWidget("mask_height");
+
+    this.interpolationWidget.callback = () => this.updatePath(true);
+    this.samplingMethodWidget.callback = () => {
+      if (this.samplingMethod === "controlpoints") this.drawSamplePoints = true;
+      else this.drawSamplePoints = false;
+      if (this.samplingMethod === "path" || this.samplingMethod === "speed") this.showChevrons = true;
+      this.updatePath(true);
+    };
+    this.tensionWidget.callback = () => this.updatePath(true);
+    this.pointsWidget.callback = () => this.updatePath(true);
+    this.setupSizeCallbacks();
+    this.pointsStoreWidget.callback = () => {
+      this.parseSplineData();
+      this.render(); this.updatePath(true);
+    };
+
+    this.initDisplaySize();
+    this.splines = [];
+    this.activeSplineIndex = 0;
+    this.hoverSplineIndex = -1;
+    this.hoverIndex = -1;
+    this.sampledCoords = null;
+    this.lastContextMenuPos = { x: this.coordWidth / 2, y: this.coordHeight / 2 };
+
+    if (!reset && this.pointsStoreWidget.value !== "") {
+      this.parseSplineData();
+    } else {
+      this.initializeDefaultSplines();
+      this.pointsStoreWidget.value = JSON.stringify(this.splines);
+    }
+
+    this._onKeyUp = (e) => {
+      if (e.key === 'Control' && this.subdividePreview) { this.subdividePreview = null; this.render(); }
+    };
+    document.addEventListener('keyup', this._onKeyUp);
+
+    this.initEditor('splineEditor', 'splineEditorHeight', 460);
+    this.updatePath();
+    this.refreshBackgroundImage();
+  }
+
+  // ─── Widget value getters ───
+  get interpolation() { return this.interpolationWidget.value; }
+  get tension() { return this.tensionWidget.value; }
+  get points_to_sample() { return this.pointsWidget.value; }
+  get samplingMethod() { return this.samplingMethodWidget.value; }
+
+  // ─── Base class hooks ───
+
+  destroy() {
+    super.destroy();
+    if (this.sampler) this.sampler.release();
+  }
+
+  onDataChanged() { this.updatePath(true); }
+
+  onImageResize() { this.drawRuler = false; }
+
+  onCoordSpaceResized(oldWidth, oldHeight) {
+    const sx = this.coordWidth / oldWidth, sy = this.coordHeight / oldHeight;
+    const cw = this.coordWidth, ch = this.coordHeight;
+    const clamp = (v, max) => Math.max(0, Math.min(max, v));
+    for (const spline of this.splines) {
+      for (const p of spline.points) {
+        p.x = clamp(p.x * sx, cw); p.y = clamp(p.y * sy, ch);
+        if (p.h1x !== undefined) { p.h1x = clamp(p.h1x * sx, cw); p.h1y = clamp(p.h1y * sy, ch); }
+        if (p.h2x !== undefined) { p.h2x = clamp(p.h2x * sx, cw); p.h2y = clamp(p.h2y * sy, ch); }
+      }
+    }
+  }
+
+  // ─── Spline Helpers ───
+
+  parseSplineData() {
+    try {
+      const parsed = JSON.parse(this.pointsStoreWidget.value);
+      if (Array.isArray(parsed) && parsed.length > 0 && parsed[0].hasOwnProperty('points')) {
+        this.splines = parsed;
+      } else {
+        this.splines = [{ points: parsed, color: "#1f77b4", name: "Spline 1" }];
+      }
+    } catch (e) {
+      console.error("Error parsing spline data:", e);
+      this.initializeDefaultSplines();
+    }
+    if (this.activeSplineIndex >= this.splines.length) {
+      this.activeSplineIndex = Math.max(0, this.splines.length - 1);
+    }
+  }
+
+  initializeDefaultSplines() {
+    this.splines = [{
+      points: [
+        { x: 0, y: this.coordHeight },
+        { x: this.coordWidth / 5 * 2, y: 50 + Math.random() * (this.coordHeight - 100) },
+        { x: this.coordWidth, y: 0 }
+      ],
+      color: this.getSplineColor(0),
+      name: "Spline 1"
+    }];
+  }
+
+  getSplineColor(index) {
+    const colors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b", "#e377c2", "#7f7f7f", "#bcbd22", "#17becf"];
+    return colors[index % colors.length];
+  }
+
+  // ─── Hit Testing ───
+
+  findPointAt(x, y) {
+    const activePoints = this.splines[this.activeSplineIndex]?.points;
+    if (!activePoints) return -1;
+    const r = 12 / Math.min(this.scaleX, this.scaleY);
+    for (let i = activePoints.length - 1; i >= 0; i--) {
+      const dx = activePoints[i].x - x, dy = activePoints[i].y - y;
+      if (dx * dx + dy * dy <= r * r) return i;
+    }
+    return -1;
+  }
+
+  findHandleAt(x, y) {
+    if (this.interpolation !== 'bezier') return null;
+    const activePoints = this.splines[this.activeSplineIndex]?.points;
+    if (!activePoints) return null;
+    const r = 8 / Math.min(this.scaleX, this.scaleY);
+    for (let i = activePoints.length - 1; i >= 0; i--) {
+      const p = activePoints[i];
+      if (i > 0 && p.h1x !== undefined) { const dx = p.h1x - x, dy = p.h1y - y; if (dx * dx + dy * dy <= r * r) return { index: i, type: 'h1' }; }
+      if (i < activePoints.length - 1 && p.h2x !== undefined) { const dx = p.h2x - x, dy = p.h2y - y; if (dx * dx + dy * dy <= r * r) return { index: i, type: 'h2' }; }
+    }
+    return null;
+  }
+
+  findSplineAt(x, y) {
+    const threshold = 15 / Math.min(this.scaleX, this.scaleY);
+    let bestDist = Infinity, bestIdx = -1;
+    for (let si = 0; si < this.splines.length; si++) {
+      const spline = this.splines[si];
+      if (spline.isSinglePoint || (spline.points && spline.points.length === 1)) {
+        const p = spline.points[0], d = Math.sqrt((p.x - x) ** 2 + (p.y - y) ** 2);
+        if (d < threshold && d < bestDist) { bestDist = d; bestIdx = si; }
+        continue;
+      }
+      const pathD = Interpolation.buildPathD(spline.points, this.interpolation, this.tension);
+      this.sampler.setPath(pathD);
+      const len = this.sampler.getTotalLength();
+      const steps = Math.min(200, Math.max(50, Math.round(len / 3)));
+      for (let i = 0; i <= steps; i++) {
+        const pt = this.sampler.getPointAtLength(len * i / steps);
+        const d = Math.sqrt((pt.x - x) ** 2 + (pt.y - y) ** 2);
+        if (d < threshold && d < bestDist) { bestDist = d; bestIdx = si; }
+      }
+    }
+    return bestIdx;
+  }
+
+  findClosestPoints(points, clickedPoint) {
+    if (points.length < 2) return null;
+    let distances = points.map((point, idx) => {
+      const dx = clickedPoint.x - point.x, dy = clickedPoint.y - point.y;
+      return { index: idx, distance: Math.sqrt(dx * dx + dy * dy) };
+    });
+    distances.sort((a, b) => a.distance - b.distance);
+    let i1 = distances[0].index, i2 = distances[1].index;
+    if (i1 > i2) [i1, i2] = [i2, i1];
+    return { point1Index: i1, point2Index: i2 };
+  }
+
+  // ─── Mouse Handlers ───
+
+  onMouseDown(e) {
+    const mouse = this.getLocalMouse(e);
+    const clamped = this.clamp(mouse.x, mouse.y);
+    const activeSpline = this.splines[this.activeSplineIndex];
+    const activePoints = activeSpline?.points;
+
+    if (e.shiftKey && e.button === 0 && activePoints) {
+      activePoints.push({ x: clamped.x, y: clamped.y });
+      this.render(); this.updatePath(); return;
+    }
+    if (e.ctrlKey && e.button === 0 && activePoints && activePoints.length >= 2) {
+      const closest = this.findClosestPoints(activePoints, clamped);
+      if (closest) {
+        const p1 = activePoints[closest.point1Index], p2 = activePoints[closest.point2Index];
+        activePoints.splice(closest.point2Index, 0, { x: (p1.x + p2.x) / 2, y: (p1.y + p2.y) / 2 });
+        this.render(); this.updatePath();
+      }
+      return;
+    }
+
+    if (e.button === 2) {
+      const ptIdx = this.findPointAt(mouse.x, mouse.y);
+      if (activePoints && ptIdx > 0 && ptIdx < activePoints.length - 1) {
+        activePoints.splice(ptIdx, 1); this.render(); this.updatePath(); return;
+      }
+      this.lastContextMenuPos = { x: clamped.x, y: clamped.y };
+      this.showContextMenu(e);
+      return;
+    }
+
+    if (e.button !== 0) return;
+
+    // Bezier handle drag
+    const handle = this.findHandleAt(mouse.x, mouse.y);
+    if (handle && activePoints) {
+      this.dragIndex = handle.index; this.dragType = handle.type;
+      const p = activePoints[handle.index];
+      const hx = handle.type === 'h1' ? p.h1x : p.h2x, hy = handle.type === 'h1' ? p.h1y : p.h2y;
+      this.dragOffset = { x: mouse.x - hx, y: mouse.y - hy };
+      this.render(); this.startDocumentDrag(e); return;
+    }
+
+    // Point drag
+    const ptIdx = this.findPointAt(mouse.x, mouse.y);
+    if (ptIdx >= 0) {
+      this.dragIndex = ptIdx; this.dragType = 'point'; this.hoverIndex = ptIdx;
+      const p = activePoints[ptIdx];
+      this.dragOffset = { x: mouse.x - p.x, y: mouse.y - p.y };
+      this.render(); this.startDocumentDrag(e); return;
+    }
+
+    // Spline selection
+    const splineIdx = this.findSplineAt(mouse.x, mouse.y);
+    if (splineIdx >= 0 && splineIdx !== this.activeSplineIndex) {
+      this.activeSplineIndex = splineIdx; this.render(); this.updatePath(); return;
+    }
+  }
+
+  onMouseMove(e) {
+    const mouse = this.getLocalMouse(e);
+    const clamped = this.clamp(mouse.x, mouse.y);
+
+    // Bezier handle drag
+    if ((this.dragType === 'h1' || this.dragType === 'h2') && this.dragIndex >= 0) {
+      const activePoints = this.splines[this.activeSplineIndex]?.points;
+      if (activePoints && this.dragIndex < activePoints.length) {
+        const p = activePoints[this.dragIndex];
+        const clampH = (hx, hy) => ({ x: Math.max(0, Math.min(this.coordWidth, hx)), y: Math.max(0, Math.min(this.coordHeight, hy)) });
+        const ox = this.dragOffset?.x || 0, oy = this.dragOffset?.y || 0;
+        const prevH1x = p.h1x, prevH1y = p.h1y, prevH2x = p.h2x, prevH2y = p.h2y;
+        if (this.dragType === 'h1') {
+          const c = clampH(mouse.x - ox, mouse.y - oy);
+          p.h1x = c.x; p.h1y = c.y;
+          if (!e.altKey) { const dx = p.x - p.h1x, dy = p.y - p.h1y; const mc = clampH(p.x + dx, p.y + dy); p.h2x = mc.x; p.h2y = mc.y; }
+        } else {
+          const c = clampH(mouse.x - ox, mouse.y - oy);
+          p.h2x = c.x; p.h2y = c.y;
+          if (!e.altKey) { const dx = p.x - p.h2x, dy = p.y - p.h2y; const mc = clampH(p.x + dx, p.y + dy); p.h1x = mc.x; p.h1y = mc.y; }
+        }
+        if (p.h1x === prevH1x && p.h1y === prevH1y && p.h2x === prevH2x && p.h2y === prevH2y) return;
+        this.render();
+      }
+      return;
+    }
+
+    // Point drag
+    if (this.dragType === 'point' && this.dragIndex >= 0) {
+      const activePoints = this.splines[this.activeSplineIndex]?.points;
+      if (activePoints && this.dragIndex < activePoints.length) {
+        const p = activePoints[this.dragIndex];
+        const ox = this.dragOffset?.x || 0, oy = this.dragOffset?.y || 0;
+        const target = this.clamp(mouse.x - ox, mouse.y - oy);
+        if (target.x === p.x && target.y === p.y) return;
+        const dx = target.x - p.x, dy = target.y - p.y;
+        if (this.interpolation === 'bezier' && p.h1x !== undefined) {
+          p.h1x += dx; p.h1y += dy; p.h2x += dx; p.h2y += dy;
+        }
+        p.x = target.x; p.y = target.y;
+        this.render();
+      }
+      return;
+    }
+
+    // Hover detection
+    if (!this.dragType) {
+      const ptIdx = this.findPointAt(mouse.x, mouse.y);
+      const activePoints = this.splines[this.activeSplineIndex]?.points;
+
+      // Ctrl preview
+      if (e.ctrlKey && activePoints && activePoints.length >= 2) {
+        const closest = this.findClosestPoints(activePoints, clamped);
+        if (closest) {
+          const p1 = activePoints[closest.point1Index], p2 = activePoints[closest.point2Index];
+          this.subdividePreview = { x: (p1.x + p2.x) / 2, y: (p1.y + p2.y) / 2 };
+          this.canvas.style.cursor = 'copy'; this.render(); return;
+        }
+      } else if (this.subdividePreview) { this.subdividePreview = null; this.render(); }
+
+      // Handle hover
+      const handleHit = this.findHandleAt(mouse.x, mouse.y);
+      if (handleHit) { this.canvas.style.cursor = 'crosshair'; if (ptIdx !== this.hoverIndex) { this.hoverIndex = ptIdx; this.render(); } return; }
+
+      if (ptIdx !== this.hoverIndex) {
+        this.hoverIndex = ptIdx;
+        this.canvas.style.cursor = ptIdx >= 0 ? 'move' : 'default';
+        this.render();
+      }
+
+      // Spline hover
+      if (ptIdx < 0 && this.splines.length > 1) {
+        const splineIdx = this.findSplineAt(mouse.x, mouse.y);
+        const newSplineHover = splineIdx >= 0 && splineIdx !== this.activeSplineIndex ? splineIdx : -1;
+        if (newSplineHover !== this.hoverSplineIndex) {
+          this.hoverSplineIndex = newSplineHover;
+          this.canvas.style.cursor = newSplineHover >= 0 ? 'pointer' : 'default';
+          this.render();
+        }
+      } else if (this.hoverSplineIndex >= 0) { this.hoverSplineIndex = -1; this.render(); }
+    }
+  }
+
+  onMouseUp() {
+    if (this.dragType) {
+      this.endDrag();
+      this.updatePath();
+    }
+  }
+
+  // ─── Path Building & Sampling ───
+
+  samplePoints(splineIndex, numSamples, samplingMethod) {
+    const spline = this.splines[splineIndex];
+    if (!spline || !spline.points || spline.points.length < 1) return [];
+    if (spline.isSinglePoint || spline.points.length === 1) {
+      const point = spline.points[0];
+      return Array(numSamples).fill().map(() => ({ x: point.x, y: point.y }));
+    }
+    if (numSamples < 2) {
+      const p = spline.points[0];
+      return [{ x: p.x, y: p.y }];
+    }
+    const pathD = Interpolation.buildPathD(spline.points, this.interpolation, this.tension);
+    if (!pathD) return [];
+
+    this.sampler.setPath(pathD);
+    const pathLength = this.sampler.getTotalLength();
+    const points = [];
+
+    if (samplingMethod === "speed") {
+      if (pathLength < 0.001) return [{ x: spline.points[0].x, y: spline.points[0].y }];
+      const controlPoints = spline.points;
+      // Pair each control point with its path position, then sort together
+      const cpWithPos = controlPoints.map(cp => {
+        let bestDist = Infinity, bestPos = 0;
+        for (let pos = 0; pos <= pathLength; pos += pathLength / 100) {
+          const pt = this.sampler.getPointAtLength(pos);
+          const dist = Math.sqrt((pt.x - cp.x) ** 2 + (pt.y - cp.y) ** 2);
+          if (dist < bestDist) { bestDist = dist; bestPos = pos; }
+        }
+        return { cp, pos: bestPos };
+      });
+      cpWithPos.sort((a, b) => a.pos - b.pos);
+      const pathPositions = cpWithPos.map(c => c.pos);
+
+      const densities = [];
+      let totalWeight = 0;
+      for (let i = 0; i < cpWithPos.length - 1; i++) {
+        const segLength = pathPositions[i + 1] - pathPositions[i];
+        const d = 1 / Math.max(segLength, 0.0001);
+        densities.push(d);
+        totalWeight += d;
+      }
+      const cumulativeWeights = [];
+      let cum = 0;
+      for (let i = 0; i < densities.length; i++) {
+        cum += densities[i] / totalWeight;
+        cumulativeWeights.push(cum);
+      }
+      const mapToPath = (t) => {
+        if (t === 0) return 0; if (t === 1) return pathLength;
+        let segIdx = cumulativeWeights.length - 1;
+        for (let i = 0; i < cumulativeWeights.length; i++) { if (t <= cumulativeWeights[i]) { segIdx = i; break; } }
+        const segStart = segIdx > 0 ? cumulativeWeights[segIdx - 1] : 0;
+        const segT = (t - segStart) / (cumulativeWeights[segIdx] - segStart);
+        return pathPositions[segIdx] + segT * (pathPositions[segIdx + 1] - pathPositions[segIdx]);
+      };
+      for (let i = 0; i < numSamples; i++) {
+        const pt = this.sampler.getPointAtLength(mapToPath(i / (numSamples - 1)));
+        points.push({ x: pt.x, y: pt.y });
+      }
+      return points;
+    }
+
+    for (let i = 0; i < numSamples; i++) {
+      let point;
+      if (samplingMethod === "time") {
+        point = this.sampler.findPointAtX((this.coordWidth / (numSamples - 1)) * i, pathLength);
+      } else {
+        point = this.sampler.getPointAtLength((pathLength / (numSamples - 1)) * i);
+      }
+      points.push({ x: point.x, y: point.y });
+    }
+    if (points.length > 0 && spline.points.length > 1) {
+      points[points.length - 1].y = spline.points[spline.points.length - 1].y;
+    }
+    return points;
+  }
+
+  updatePath(allDirty = false) {
+    if (!this.splines || this.splines.length === 0) return;
+
+    // Rebuild cache when spline count changes or full refresh requested
+    if (!this._sampledCache || this._sampledCache.length !== this.splines.length || allDirty) {
+      this._sampledCache = new Array(this.splines.length).fill(null);
+    }
+
+    const method = this.samplingMethod;
+    const useControlPoints = method === "controlpoints";
+    for (let i = 0; i < this.splines.length; i++) {
+      if (useControlPoints) {
+        this._sampledCache[i] = this.splines[i].points;
+      } else if (i === this.activeSplineIndex || !this._sampledCache[i]) {
+        this._sampledCache[i] = this.samplePoints(i, this.points_to_sample, method);
+      }
+    }
+    this.sampledCoords = this._sampledCache[this.activeSplineIndex] || [];
+    this.pointsStoreWidget.value = JSON.stringify(this.splines);
+    if (this.coordWidget) this.coordWidget.value = JSON.stringify(this._sampledCache);
+    this.render();
+  }
+
+  // ─── Rendering ───
+
+  _render() {
+    const ctx = this.ctx;
+    const h = this.coordHeight;
+    this.beginRender();
+
+    // Ruler lines
+    if (this.drawRuler && !this.bgImage) {
+      ctx.strokeStyle = 'rgba(128,128,128,0.3)'; ctx.lineWidth = 1;
+      for (let y = 64; y < h; y += 64) {
+        const cy = y * this.scaleY;
+        ctx.beginPath(); ctx.moveTo(0, cy); ctx.lineTo(this.width, cy); ctx.stroke();
+      }
+    }
+
+    const tc = (x, y) => this.toCanvas(x, y);
+
+    // Draw all splines — use scale transform for curves, toCanvas for discrete elements
+    for (let si = 0; si < this.splines.length; si++) {
+      const spline = this.splines[si];
+      const isActive = si === this.activeSplineIndex, isHover = si === this.hoverSplineIndex;
+
+      if (spline.isSinglePoint || (spline.points && spline.points.length === 1)) {
+        const cp = tc(spline.points[0].x, spline.points[0].y), sz = isActive ? 8 : 6;
+        ctx.fillStyle = spline.color; ctx.fillRect(cp.x - sz, cp.y - sz, sz * 2, sz * 2);
+        ctx.strokeStyle = 'black'; ctx.lineWidth = 2; ctx.strokeRect(cp.x - sz, cp.y - sz, sz * 2, sz * 2);
+        continue;
+      }
+      if (spline.points.length < 2) continue;
+
+      // Build path once, stroke twice (outline + color) to avoid recomputing interpolation
+      const pathD = Interpolation.buildPathD(spline.points, this.interpolation, this.tension);
+      const curvePath = new Path2D(pathD);
+      ctx.save(); ctx.scale(this.scaleX, this.scaleY);
+      ctx.lineWidth = (isActive ? 5 : isHover ? 4 : 3.5) / Math.min(this.scaleX, this.scaleY); ctx.strokeStyle = 'black';
+      ctx.stroke(curvePath);
+      ctx.lineWidth = (isActive ? 3 : isHover ? 2 : 1.5) / Math.min(this.scaleX, this.scaleY); ctx.strokeStyle = spline.color;
+      ctx.stroke(curvePath);
+      ctx.restore();
+    }
+
+    const activeSpline = this.splines[this.activeSplineIndex];
+
+    // Handle lines
+    if (this.drawHandles) {
+      const activePoints = this.splines[this.activeSplineIndex]?.points;
+      if (activePoints && activePoints.length >= 2) {
+        ctx.strokeStyle = '#ff7f0e'; ctx.lineWidth = 1; ctx.beginPath();
+        const c0 = tc(activePoints[0].x, activePoints[0].y);
+        ctx.moveTo(c0.x, c0.y);
+        for (let i = 1; i < activePoints.length; i++) { const ci = tc(activePoints[i].x, activePoints[i].y); ctx.lineTo(ci.x, ci.y); }
+        ctx.stroke();
+      }
+    }
+
+    // Control points for active spline
+    if (activeSpline && activeSpline.points && !activeSpline.isSinglePoint) {
+      const dotRadius = 12;
+      for (let i = 0; i < activeSpline.points.length; i++) {
+        const p = activeSpline.points[i];
+        const cp = tc(p.x, p.y);
+        const isHovered = this.hoverIndex === i;
+        ctx.fillStyle = 'rgba(100, 100, 100, 0.3)';
+        ctx.strokeStyle = isHovered ? '#ff7f0e' : '#1f77b4'; ctx.lineWidth = 2;
+        ctx.beginPath(); ctx.arc(cp.x, cp.y, dotRadius, 0, Math.PI * 2); ctx.fill(); ctx.stroke();
+
+        // Chevron
+        const isEndpoint = i === 0 || i === activeSpline.points.length - 1;
+        const showChevron = this.showChevrons && (
+          (this.samplingMethod === 'path' || this.samplingMethod === 'speed')
+          || (this.samplingMethod === 'time' && isEndpoint));
+
+        if (showChevron && activeSpline.points.length > 1) {
+          let angle = 0;
+          if (i > 0 && i < activeSpline.points.length - 1) { const prev = activeSpline.points[i-1], next = activeSpline.points[i+1]; angle = Math.atan2((next.y - prev.y) * this.scaleY, (next.x - prev.x) * this.scaleX); }
+          else if (i === 0) { const next = activeSpline.points[1]; angle = Math.atan2((next.y - p.y) * this.scaleY, (next.x - p.x) * this.scaleX); }
+          else { const prev = activeSpline.points[i-1]; angle = Math.atan2((p.y - prev.y) * this.scaleY, (p.x - prev.x) * this.scaleX); }
+          ctx.save();
+          ctx.translate(cp.x, cp.y);
+          ctx.rotate(angle);
+          ctx.strokeStyle = isHovered ? '#fff' : 'rgba(255,255,255,0.7)';
+          ctx.lineWidth = 2.5; ctx.lineCap = 'round'; ctx.lineJoin = 'round';
+          ctx.beginPath();
+          ctx.moveTo(-dotRadius * 0.2, -dotRadius * 0.35);
+          ctx.lineTo(dotRadius * 0.3, 0);
+          ctx.lineTo(-dotRadius * 0.2, dotRadius * 0.35);
+          ctx.stroke();
+          ctx.lineCap = 'butt'; ctx.lineJoin = 'miter';
+          ctx.restore();
+        }
+
+        if (isHovered) {
+          ctx.font = '11px monospace';
+          const ax = String(Math.round(p.x)), ay = String(Math.round(p.y));
+          const nx = (p.x / this.coordWidth).toFixed(3), ny = (p.y / this.coordHeight).toFixed(3);
+          const ml = Math.max(ax.length, nx.length), mr = Math.max(ay.length, ny.length);
+          const pre = `${i}: `, L1 = `[${ax.padStart(ml)}, ${ay.padStart(mr)}]`, L2 = `[${nx.padStart(ml)}, ${ny.padStart(mr)}]`;
+          const preW = ctx.measureText(pre).width, totW = preW + Math.max(ctx.measureText(L1).width, ctx.measureText(L2).width);
+          const pad = dotRadius + 5, lineH = 13;
+          let lx = cp.x + pad, ly = cp.y - lineH;
+          if (lx + totW > this.width) lx = cp.x - pad - totW;
+          if (ly - lineH < 0) ly = cp.y + pad;
+          for (const [style, fill] of [['#000', 3], ['#fff', 0]]) {
+            ctx.strokeStyle = style; ctx.lineWidth = fill; ctx.fillStyle = style;
+            const draw = fill ? 'strokeText' : 'fillText';
+            ctx[draw](pre + L1, lx, ly);
+            ctx[draw](L2, lx + preW, ly + lineH);
+          }
+        }
+      }
+    }
+
+    // Bezier handles
+    if (this.interpolation === 'bezier' && activeSpline && activeSpline.points && !activeSpline.isSinglePoint) {
+      const pts = activeSpline.points;
+      Interpolation.ensureBezierHandles(pts);
+      const hr = 5;
+      for (let i = 0; i < pts.length; i++) {
+        const p = pts[i];
+        const cp = tc(p.x, p.y);
+        if (i > 0 && p.h1x !== undefined) {
+          const h1 = tc(p.h1x, p.h1y);
+          ctx.strokeStyle = 'rgba(255,255,255,0.4)'; ctx.lineWidth = 1;
+          ctx.beginPath(); ctx.moveTo(cp.x, cp.y); ctx.lineTo(h1.x, h1.y); ctx.stroke();
+          ctx.fillStyle = (this.dragType === 'h1' && this.dragIndex === i) ? '#fff' : 'rgba(255,255,255,0.8)';
+          ctx.strokeStyle = '#1f77b4'; ctx.lineWidth = 1.5;
+          ctx.beginPath(); ctx.arc(h1.x, h1.y, hr, 0, Math.PI * 2); ctx.fill(); ctx.stroke();
+        }
+        if (i < pts.length - 1 && p.h2x !== undefined) {
+          const h2 = tc(p.h2x, p.h2y);
+          ctx.strokeStyle = 'rgba(255,255,255,0.4)'; ctx.lineWidth = 1;
+          ctx.beginPath(); ctx.moveTo(cp.x, cp.y); ctx.lineTo(h2.x, h2.y); ctx.stroke();
+          ctx.fillStyle = (this.dragType === 'h2' && this.dragIndex === i) ? '#fff' : 'rgba(255,255,255,0.8)';
+          ctx.strokeStyle = '#ff7f0e'; ctx.lineWidth = 1.5;
+          ctx.beginPath(); ctx.arc(h2.x, h2.y, hr, 0, Math.PI * 2); ctx.fill(); ctx.stroke();
+        }
+      }
+    }
+
+    // Sample points
+    if (this.drawSamplePoints && this.sampledCoords) {
+      ctx.fillStyle = 'red'; ctx.strokeStyle = 'black'; ctx.lineWidth = 1;
+      for (const pt of this.sampledCoords) { const cp = tc(pt.x, pt.y); ctx.beginPath(); ctx.arc(cp.x, cp.y, 5, 0, Math.PI * 2); ctx.fill(); ctx.stroke(); }
+    }
+
+    // Subdivide preview
+    if (this.subdividePreview) {
+      const sp = tc(this.subdividePreview.x, this.subdividePreview.y);
+      ctx.beginPath(); ctx.arc(sp.x, sp.y, 10, 0, Math.PI * 2);
+      ctx.strokeStyle = 'rgba(0,255,0,0.3)'; ctx.lineWidth = 4; ctx.stroke();
+      ctx.beginPath(); ctx.arc(sp.x, sp.y, 8, 0, Math.PI * 2);
+      ctx.strokeStyle = '#0f0'; ctx.lineWidth = 2; ctx.stroke();
+    }
+
+    this.endRender();
+  }
+
+}
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/fast_preview.js b/custom_nodes/ComfyUI-KJNodes/web/js/fast_preview.js
new file mode 100644
index 0000000000000000000000000000000000000000..15c99d6d7dfa7570e365390cfff3cb81dfbc8f2f
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/fast_preview.js
@@ -0,0 +1,40 @@
+import { chainCallback } from './utility.js';
+const { app } = window.comfyAPI.app;
+const { api } = window.comfyAPI.api;
+
+let execId = null;
+api.addEventListener("executing", e => { execId = e.detail ?? null; });
+
+app.registerExtension({
+  name: 'KJNodes.FastPreview',
+  async beforeRegisterNodeDef(nodeType, nodeData) {
+    if (nodeData?.name !== 'FastPreview') return;
+    chainCallback(nodeType.prototype, "onNodeCreated", function () {
+      this.setSize([550, 550]);
+      const nodeRef = this;
+
+      const show = blob => {
+        const img = new Image();
+        img.onload = () => { nodeRef.imgs = [img]; nodeRef.setDirtyCanvas(true); };
+        img.src = URL.createObjectURL(blob);
+      };
+
+      const metaHandler = e => {
+        const { blob, nodeId, displayNodeId } = e.detail;
+        if (String(displayNodeId || nodeId) === String(nodeRef.id)) show(blob);
+      };
+      const plainHandler = e => {
+        if (api.serverSupportsFeature?.("supports_preview_metadata")) return;
+        if (String(execId) === String(nodeRef.id)) show(e.detail);
+      };
+
+      api.addEventListener("b_preview_with_metadata", metaHandler);
+      api.addEventListener("b_preview", plainHandler);
+
+      chainCallback(nodeRef, "onRemoved", () => {
+        api.removeEventListener("b_preview_with_metadata", metaHandler);
+        api.removeEventListener("b_preview", plainHandler);
+      });
+    });
+  }
+});
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/fast_preview_batch.js b/custom_nodes/ComfyUI-KJNodes/web/js/fast_preview_batch.js
new file mode 100644
index 0000000000000000000000000000000000000000..f49e47cb3922b2ab04bd1488a846fea5a87cd229
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/fast_preview_batch.js
@@ -0,0 +1,518 @@
+import { chainCallback, addMiddleClickPan, addWheelPassthrough } from './utility.js';
+const { app } = window.comfyAPI.app;
+const { api } = window.comfyAPI.api;
+
+const STYLE_ID = 'kj-batch-preview-style';
+function ensureStyles() {
+    if (document.getElementById(STYLE_ID)) return;
+    const s = document.createElement('style');
+    s.id = STYLE_ID;
+    s.textContent = `
+.kj-bp-root { width:100%; height:100%; position:relative; background:#1a1a1a; overflow:hidden; user-select:none; }
+.kj-bp-grid { width:100%; height:100%; display:block; cursor:pointer; image-rendering:auto; }
+.kj-bp-detail { position:absolute; inset:0; display:none; background:#0d0d0d; }
+.kj-bp-detail.visible { display:flex; flex-direction:column; }
+.kj-bp-detail-canvas { flex:1 1 auto; min-height:0; width:100%; display:block; }
+.kj-bp-bar { display:flex; align-items:center; justify-content:space-between; padding:4px 8px; background:#222; color:#ddd; font:12px sans-serif; }
+.kj-bp-btn { background:#333; color:#ddd; border:1px solid #444; padding:2px 10px; cursor:pointer; font:12px sans-serif; }
+.kj-bp-btn:hover { background:#444; }
+.kj-bp-btn:disabled { opacity:0.4; cursor:default; }
+.kj-bp-scrubber { position:relative; flex:1 1 auto; height:22px; margin:0 8px; background:#2a2a2a; border:1px solid #444; cursor:ew-resize; user-select:none; touch-action:none; }
+.kj-bp-scrubber-fill { position:absolute; top:0; bottom:0; left:0; width:0; background:#3a5a7a; pointer-events:none; }
+.kj-bp-scrubber-text { position:absolute; inset:0; display:flex; align-items:center; justify-content:center; color:#ddd; font:11px sans-serif; pointer-events:none; text-shadow:0 1px 2px rgba(0,0,0,0.6); }
+.kj-bp-status { position:absolute; inset:0; display:flex; align-items:center; justify-content:center; color:#888; font:12px sans-serif; pointer-events:none; }
+`;
+    document.head.appendChild(s);
+}
+
+function pickGridLayout(count, aspect, containerW, containerH) {
+    let best = { cols: 1, rows: count, cellW: 0, cellH: 0, area: 0 };
+    for (let cols = 1; cols <= count; cols++) {
+        const rows = Math.ceil(count / cols);
+        const cellW = containerW / cols;
+        const cellH = containerH / rows;
+        const fitW = Math.min(cellW, cellH * aspect);
+        const fitH = fitW / aspect;
+        const area = fitW * fitH;
+        if (area > best.area) best = { cols, rows, cellW, cellH, area, fitW, fitH };
+    }
+    return best;
+}
+
+app.registerExtension({
+    name: 'KJNodes.FastPreviewBatch',
+    async beforeRegisterNodeDef(nodeType, nodeData) {
+        if (nodeData?.name !== 'FastPreviewBatch') return;
+
+        chainCallback(nodeType.prototype, 'onNodeCreated', function () {
+            ensureStyles();
+            this.setSize([520, 520]);
+
+            const root = document.createElement('div');
+            root.className = 'kj-bp-root';
+
+            const status = document.createElement('div');
+            status.className = 'kj-bp-status';
+            status.textContent = 'No preview yet';
+            root.appendChild(status);
+
+            const gridCanvas = document.createElement('canvas');
+            gridCanvas.className = 'kj-bp-grid';
+            gridCanvas.style.display = 'none';
+            root.appendChild(gridCanvas);
+
+            const detail = document.createElement('div');
+            detail.className = 'kj-bp-detail';
+            const detailCanvas = document.createElement('canvas');
+            detailCanvas.className = 'kj-bp-detail-canvas';
+            const bar = document.createElement('div');
+            bar.className = 'kj-bp-bar';
+            const prevBtn = document.createElement('button');
+            prevBtn.className = 'kj-bp-btn';
+            prevBtn.textContent = '❮';
+            const nextBtn = document.createElement('button');
+            nextBtn.className = 'kj-bp-btn';
+            nextBtn.textContent = '❯';
+            const scrubber = document.createElement('div');
+            scrubber.className = 'kj-bp-scrubber';
+            const scrubberFill = document.createElement('div');
+            scrubberFill.className = 'kj-bp-scrubber-fill';
+            const scrubberText = document.createElement('span');
+            scrubberText.className = 'kj-bp-scrubber-text';
+            scrubber.appendChild(scrubberFill);
+            scrubber.appendChild(scrubberText);
+            const playBtn = document.createElement('button');
+            playBtn.className = 'kj-bp-btn';
+            playBtn.textContent = '▶';
+            const closeBtn = document.createElement('button');
+            closeBtn.className = 'kj-bp-btn';
+            closeBtn.textContent = '✕';
+            bar.appendChild(playBtn);
+            bar.appendChild(prevBtn);
+            bar.appendChild(scrubber);
+            bar.appendChild(nextBtn);
+            bar.appendChild(closeBtn);
+            detail.appendChild(detailCanvas);
+            detail.appendChild(bar);
+            root.appendChild(detail);
+
+            this.addDOMWidget('fast_preview_batch', 'div', root, { serialize: false });
+
+            const removeMiddleClickPan = addMiddleClickPan(root);
+            addWheelPassthrough(root);
+
+            const state = {
+                video: null,
+                strip: null,
+                stripCols: 1,
+                stripCellW: 0,
+                stripCellH: 0,
+                frameCount: 0,
+                fps: 30,
+                thumbW: 0,
+                thumbH: 0,
+                layout: null,
+                gridReady: false,
+                currentIndex: 0,
+                hoverIndex: -1,
+            };
+
+            const setStatus = (msg) => {
+                if (msg) {
+                    status.textContent = msg;
+                    status.style.display = 'flex';
+                } else {
+                    status.style.display = 'none';
+                }
+            };
+
+            const cleanupVideo = () => {
+                if (state.video) {
+                    try { state.video.pause(); } catch (_) { }
+                    state.video.removeAttribute('src');
+                    try { state.video.load(); } catch (_) { }
+                    if (state.video.parentNode) state.video.parentNode.removeChild(state.video);
+                    state.video = null;
+                }
+            };
+
+            const seekToFrame = (i) => new Promise((resolve) => {
+                if (!state.video) return resolve();
+                const t = (i + 0.5) / state.fps;
+                const onSeeked = () => {
+                    state.video.removeEventListener('seeked', onSeeked);
+                    // give the decoder a tick to ensure frame is drawable
+                    requestAnimationFrame(() => resolve());
+                };
+                state.video.addEventListener('seeked', onSeeked);
+                try {
+                    state.video.currentTime = t;
+                } catch (_) {
+                    state.video.removeEventListener('seeked', onSeeked);
+                    resolve();
+                }
+            });
+
+            let drawGeneration = 0;
+            const updateScrubberUI = () => {
+                const t = state.frameCount > 1 ? state.currentIndex / (state.frameCount - 1) : 0;
+                scrubberFill.style.width = (t * 100) + '%';
+                scrubberText.textContent = `frame ${state.currentIndex + 1} / ${state.frameCount}`;
+                prevBtn.disabled = state.currentIndex <= 0;
+                nextBtn.disabled = state.currentIndex >= state.frameCount - 1;
+            };
+            const paintFrameToDetail = () => {
+                const v = state.video;
+                if (!v) return;
+                const w = detailCanvas.clientWidth;
+                const h = detailCanvas.clientHeight;
+                if (w <= 0 || h <= 0) return;
+                const dpr = window.devicePixelRatio || 1;
+                const targetW = Math.max(1, Math.floor(w * dpr));
+                const targetH = Math.max(1, Math.floor(h * dpr));
+                if (detailCanvas.width !== targetW) detailCanvas.width = targetW;
+                if (detailCanvas.height !== targetH) detailCanvas.height = targetH;
+                const ctx = detailCanvas.getContext('2d');
+                ctx.fillStyle = '#0d0d0d';
+                ctx.fillRect(0, 0, detailCanvas.width, detailCanvas.height);
+                const aspect = state.thumbW / state.thumbH;
+                let dw = detailCanvas.width;
+                let dh = dw / aspect;
+                if (dh > detailCanvas.height) {
+                    dh = detailCanvas.height;
+                    dw = dh * aspect;
+                }
+                const dx = (detailCanvas.width - dw) / 2;
+                const dy = (detailCanvas.height - dh) / 2;
+                ctx.drawImage(v, dx, dy, dw, dh);
+            };
+            const drawDetail = async () => {
+                if (!state.video || !state.frameCount) return;
+                updateScrubberUI();
+                const gen = ++drawGeneration;
+                await seekToFrame(state.currentIndex);
+                if (gen !== drawGeneration) return; // superseded by a newer scrub
+                paintFrameToDetail();
+            };
+
+            let playing = false;
+            let playHandle = 0;
+            const updatePlayBtn = () => { playBtn.textContent = playing ? '⏸' : '▶'; };
+            const stopPlayback = () => {
+                if (!playing) return;
+                playing = false;
+                playHandle++;
+                if (state.video) { try { state.video.pause(); } catch (_) { } }
+                updatePlayBtn();
+            };
+            const startPlayback = async () => {
+                if (!state.video || !state.frameCount || playing) return;
+                if (typeof state.video.requestVideoFrameCallback !== 'function') return;
+                if (state.currentIndex >= state.frameCount - 1) state.currentIndex = 0;
+                drawGeneration++; // invalidate any pending drawDetail
+                await seekToFrame(state.currentIndex);
+                paintFrameToDetail();
+                updateScrubberUI();
+                playing = true;
+                updatePlayBtn();
+                state.video.playbackRate = 1;
+                const myHandle = ++playHandle;
+                try { await state.video.play(); } catch (_) { stopPlayback(); return; }
+                if (myHandle !== playHandle) return;
+                const onFrame = (_now, metadata) => {
+                    if (!playing || myHandle !== playHandle) return;
+                    const idx = Math.min(state.frameCount - 1,
+                        Math.floor(metadata.mediaTime * state.fps + 1e-6));
+                    if (idx !== state.currentIndex) {
+                        state.currentIndex = idx;
+                        updateScrubberUI();
+                    }
+                    paintFrameToDetail();
+                    if (idx >= state.frameCount - 1) { stopPlayback(); return; }
+                    state.video.requestVideoFrameCallback(onFrame);
+                };
+                state.video.requestVideoFrameCallback(onFrame);
+            };
+
+            const renderGrid = () => {
+                if (!state.frameCount || !state.strip) return;
+                const containerW = root.clientWidth;
+                const containerH = root.clientHeight;
+                if (containerW <= 0 || containerH <= 0) return;
+                const dpr = window.devicePixelRatio || 1;
+                const aspect = state.thumbW / state.thumbH;
+                const layout = pickGridLayout(state.frameCount, aspect, containerW, containerH);
+                state.layout = layout;
+                gridCanvas.width = Math.floor(containerW * dpr);
+                gridCanvas.height = Math.floor(containerH * dpr);
+                gridCanvas.style.width = containerW + 'px';
+                gridCanvas.style.height = containerH + 'px';
+                const ctx = gridCanvas.getContext('2d');
+                ctx.fillStyle = '#1a1a1a';
+                ctx.fillRect(0, 0, gridCanvas.width, gridCanvas.height);
+
+                const cellW = layout.cellW * dpr;
+                const cellH = layout.cellH * dpr;
+                const fitW = layout.fitW * dpr;
+                const fitH = layout.fitH * dpr;
+                const total = state.frameCount;
+                const sw = state.stripCellW;
+                const sh = state.stripCellH;
+                const sCols = state.stripCols;
+                for (let i = 0; i < total; i++) {
+                    const sx = (i % sCols) * sw;
+                    const sy = Math.floor(i / sCols) * sh;
+                    const dCol = i % layout.cols;
+                    const dRow = Math.floor(i / layout.cols);
+                    const dx = dCol * cellW + (cellW - fitW) / 2;
+                    const dy = dRow * cellH + (cellH - fitH) / 2;
+                    ctx.drawImage(state.strip, sx, sy, sw, sh, dx, dy, fitW, fitH);
+                }
+                if (state.hoverIndex >= 0 && state.hoverIndex < total) {
+                    const i = state.hoverIndex;
+                    const sx = (i % sCols) * sw;
+                    const sy = Math.floor(i / sCols) * sh;
+                    const dCol = i % layout.cols;
+                    const dRow = Math.floor(i / layout.cols);
+                    const cx = dCol * cellW + cellW / 2;
+                    const cy = dRow * cellH + cellH / 2;
+                    const scale = 1.15;
+                    const zw = fitW * scale;
+                    const zh = fitH * scale;
+                    let dx = cx - zw / 2;
+                    let dy = cy - zh / 2;
+                    dx = Math.max(0, Math.min(gridCanvas.width - zw, dx));
+                    dy = Math.max(0, Math.min(gridCanvas.height - zh, dy));
+                    ctx.save();
+                    ctx.shadowColor = 'rgba(0,0,0,0.65)';
+                    ctx.shadowBlur = 14 * dpr;
+                    ctx.drawImage(state.strip, sx, sy, sw, sh, dx, dy, zw, zh);
+                    ctx.restore();
+                    ctx.strokeStyle = '#5a8ec4';
+                    ctx.lineWidth = Math.max(2, 2 * dpr);
+                    ctx.strokeRect(dx + ctx.lineWidth / 2, dy + ctx.lineWidth / 2, zw - ctx.lineWidth, zh - ctx.lineWidth);
+                }
+                state.gridReady = true;
+                setStatus('');
+                gridCanvas.style.display = 'block';
+            };
+
+            const enterDetail = (idx) => {
+                state.currentIndex = idx;
+                detail.classList.add('visible');
+                requestAnimationFrame(drawDetail);
+            };
+            const exitDetail = () => {
+                stopPlayback();
+                detail.classList.remove('visible');
+            };
+
+            playBtn.addEventListener('click', () => {
+                if (playing) stopPlayback();
+                else startPlayback();
+            });
+
+            const cellIndexFromEvent = (e) => {
+                if (!state.gridReady || !state.layout) return -1;
+                const rect = gridCanvas.getBoundingClientRect();
+                if (rect.width <= 0 || rect.height <= 0) return -1;
+                const x = e.clientX - rect.left;
+                const y = e.clientY - rect.top;
+                const cellW = rect.width / state.layout.cols;
+                const cellH = rect.height / state.layout.rows;
+                const col = Math.floor(x / cellW);
+                const row = Math.floor(y / cellH);
+                if (col < 0 || col >= state.layout.cols || row < 0 || row >= state.layout.rows) return -1;
+                const idx = row * state.layout.cols + col;
+                return (idx >= 0 && idx < state.frameCount) ? idx : -1;
+            };
+
+            gridCanvas.addEventListener('click', (e) => {
+                const idx = cellIndexFromEvent(e);
+                if (idx >= 0) enterDetail(idx);
+            });
+
+            gridCanvas.addEventListener('pointermove', (e) => {
+                const idx = cellIndexFromEvent(e);
+                if (idx !== state.hoverIndex) {
+                    state.hoverIndex = idx;
+                    renderGrid();
+                }
+            });
+            gridCanvas.addEventListener('pointerleave', () => {
+                if (state.hoverIndex !== -1) {
+                    state.hoverIndex = -1;
+                    renderGrid();
+                }
+            });
+
+            prevBtn.addEventListener('click', () => {
+                if (state.currentIndex > 0) {
+                    stopPlayback();
+                    state.currentIndex--;
+                    drawDetail();
+                }
+            });
+            nextBtn.addEventListener('click', () => {
+                if (state.currentIndex < state.frameCount - 1) {
+                    stopPlayback();
+                    state.currentIndex++;
+                    drawDetail();
+                }
+            });
+            closeBtn.addEventListener('click', exitDetail);
+
+            let scrubbing = false;
+            const scrubFromEvent = (e) => {
+                if (!state.frameCount) return;
+                const rect = scrubber.getBoundingClientRect();
+                const t = Math.max(0, Math.min(1, (e.clientX - rect.left) / rect.width));
+                const idx = Math.round(t * (state.frameCount - 1));
+                if (idx !== state.currentIndex) {
+                    state.currentIndex = idx;
+                    drawDetail();
+                }
+            };
+            scrubber.addEventListener('pointerdown', (e) => {
+                if (e.button !== 0) return; // let middle/right click bubble (canvas pan, etc.)
+                stopPlayback();
+                scrubbing = true;
+                scrubber.setPointerCapture(e.pointerId);
+                scrubFromEvent(e);
+                e.preventDefault();
+            });
+            scrubber.addEventListener('pointermove', (e) => {
+                if (scrubbing) scrubFromEvent(e);
+            });
+            const endScrub = (e) => {
+                if (!scrubbing) return;
+                scrubbing = false;
+                try { scrubber.releasePointerCapture(e.pointerId); } catch (_) { }
+            };
+            scrubber.addEventListener('pointerup', endScrub);
+            scrubber.addEventListener('pointercancel', endScrub);
+
+            const keyHandler = (e) => {
+                if (!detail.classList.contains('visible')) return;
+                if (e.key === 'Escape') { exitDetail(); e.stopPropagation(); }
+                else if (e.key === ' ') {
+                    if (playing) stopPlayback(); else startPlayback();
+                    e.preventDefault(); e.stopPropagation();
+                }
+                else if (e.key === 'ArrowLeft' && state.currentIndex > 0) {
+                    stopPlayback(); state.currentIndex--; drawDetail(); e.stopPropagation();
+                } else if (e.key === 'ArrowRight' && state.currentIndex < state.frameCount - 1) {
+                    stopPlayback(); state.currentIndex++; drawDetail(); e.stopPropagation();
+                }
+            };
+            window.addEventListener('keydown', keyHandler);
+
+            const ro = new ResizeObserver(() => {
+                if (detail.classList.contains('visible')) {
+                    drawDetail();
+                } else if (state.strip) {
+                    renderGrid();
+                }
+            });
+            ro.observe(root);
+
+            const buildViewUrl = (filename, subfolder, type) => {
+                const params = new URLSearchParams({
+                    filename: filename,
+                    subfolder: subfolder || '',
+                    type: type || 'temp',
+                    t: String(Date.now()),
+                });
+                return api.apiURL(`/view?${params.toString()}`);
+            };
+
+            const loadFromOutput = async (info) => {
+                stopPlayback();
+                cleanupVideo();
+                state.strip = null;
+                state.gridReady = false;
+                gridCanvas.style.display = 'none';
+                setStatus('Loading preview…');
+
+                state.frameCount = info.frame_count;
+                state.fps = info.fps || 30;
+                state.thumbW = info.thumb_w;
+                state.thumbH = info.thumb_h;
+                state.stripCols = info.strip_cols || 1;
+                state.stripCellW = info.strip_cell_w || info.thumb_w;
+                state.stripCellH = info.strip_cell_h || info.thumb_h;
+                if (state.currentIndex >= state.frameCount || state.currentIndex < 0) {
+                    state.currentIndex = 0;
+                }
+
+                const v = document.createElement('video');
+                v.muted = true;
+                v.playsInline = true;
+                v.preload = 'auto';
+                v.style.position = 'absolute';
+                v.style.left = '-99999px';
+                v.style.width = '1px';
+                v.style.height = '1px';
+                document.body.appendChild(v);
+                state.video = v;
+
+                // Video loads in parallel for the detail view; failure is non-fatal
+                // for the grid (strip handles that). Caught to avoid unhandled-rejection.
+                new Promise((resolve, reject) => {
+                    v.addEventListener('loadeddata', resolve, { once: true });
+                    v.addEventListener('error', () => {
+                        cleanupVideo();
+                        reject(new Error('video load failed'));
+                    }, { once: true });
+                    v.src = buildViewUrl(info.filename, info.subfolder, info.type);
+                }).catch(() => { });
+
+                await new Promise((resolve, reject) => {
+                    const img = new Image();
+                    img.onload = () => { state.strip = img; resolve(); };
+                    img.onerror = () => reject(new Error('strip load failed'));
+                    img.src = buildViewUrl(info.strip_filename, info.subfolder, info.type);
+                });
+                renderGrid();
+                if (detail.classList.contains('visible')) {
+                    // Refresh detail canvas with the new video; drawDetail awaits seek,
+                    // which itself queues until the new video element finishes loading.
+                    drawDetail();
+                }
+            };
+
+            const tryLoad = (info, onMissing) => {
+                loadFromOutput(info).catch((err) => {
+                    console.warn('FastPreviewBatch load failed', err);
+                    if (onMissing) onMissing();
+                    else setStatus('Preview failed: ' + (err && err.message || err));
+                });
+            };
+
+            chainCallback(this, 'onExecuted', function (output) {
+                if (!output || !output.kj_batch_preview) return;
+                const info = output.kj_batch_preview[0];
+                if (!info) return;
+                this.properties = this.properties || {};
+                this.properties.kj_batch_preview = info;
+                tryLoad(info);
+            });
+
+            chainCallback(this, 'onConfigure', function () {
+                const info = this.properties && this.properties.kj_batch_preview;
+                if (!info) return;
+                tryLoad(info, () => {
+                    setStatus('Preview unavailable (re-run to regenerate)');
+                    delete this.properties.kj_batch_preview;
+                });
+            });
+
+            chainCallback(this, 'onRemoved', function () {
+                window.removeEventListener('keydown', keyHandler);
+                ro.disconnect();
+                removeMiddleClickPan();
+                cleanupVideo();
+            });
+        });
+    },
+});
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/fillconnect.js b/custom_nodes/ComfyUI-KJNodes/web/js/fillconnect.js
new file mode 100644
index 0000000000000000000000000000000000000000..1903e415eb10237b0dbae50440c9787fbf8d3f5a
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/fillconnect.js
@@ -0,0 +1,225 @@
+const { app } = window.comfyAPI.app;
+import { getSlotPos } from "./utility.js";
+
+/**
+ * Check type compatibility. Returns a match tier:
+ *   2 = exact single-type match, 1 = multi-type or wildcard match, -1 = incompatible
+ */
+function typeMatchTier(outType, inType) {
+	if (!LiteGraph.isValidConnection(outType, inType)) return -1;
+	if (typeof outType === "string" && typeof inType === "string"
+		&& !outType.includes(",") && !inType.includes(",")
+		&& outType.toUpperCase() === inType.toUpperCase()) return 2;
+	return 1;
+}
+
+
+/**
+ * Order selected nodes for connection.
+ * Respects existing links between them (topological sort),
+ * then inserts unconnected nodes by spatial position (left-to-right, top-to-bottom).
+ */
+function orderNodes(nodes, graph) {
+	const selectedIds = new Set(nodes.map(n => n.id));
+	const nodeById = new Map(nodes.map(n => [n.id, n]));
+
+	const outEdges = new Map();
+	const inDegree = new Map();
+	for (const n of nodes) {
+		outEdges.set(n.id, new Set());
+		inDegree.set(n.id, 0);
+	}
+
+	for (const node of nodes) {
+		for (const out of (node.outputs || [])) {
+			for (const linkId of (out.links || [])) {
+				const link = graph.getLink(linkId);
+				if (link && selectedIds.has(link.target_id) && link.target_id !== node.id) {
+					if (!outEdges.get(node.id).has(link.target_id)) {
+						outEdges.get(node.id).add(link.target_id);
+						inDegree.set(link.target_id, inDegree.get(link.target_id) + 1);
+					}
+				}
+			}
+		}
+	}
+
+	const bySpatial = (a, b) => a.pos[0] - b.pos[0] || a.pos[1] - b.pos[1];
+	const queue = nodes.filter(n => inDegree.get(n.id) === 0);
+	queue.sort(bySpatial);
+
+	const ordered = [];
+	while (queue.length > 0) {
+		const node = queue.shift();
+		ordered.push(node);
+		for (const targetId of outEdges.get(node.id)) {
+			const deg = inDegree.get(targetId) - 1;
+			inDegree.set(targetId, deg);
+			if (deg === 0 && nodeById.has(targetId)) {
+				queue.push(nodeById.get(targetId));
+				queue.sort(bySpatial);
+			}
+		}
+	}
+
+	// Cycle fallback: append remaining by position
+	if (ordered.length < nodes.length) {
+		const inOrdered = new Set(ordered.map(n => n.id));
+		const remaining = nodes.filter(n => !inOrdered.has(n.id));
+		remaining.sort(bySpatial);
+		ordered.push(...remaining);
+	}
+
+	return ordered;
+}
+
+/**
+ * Collect all candidate connections and assign globally by closest 2D distance.
+ * Each input and output is used at most once per invocation.
+ */
+function planConnections(ordered) {
+	// Collect all candidates globally across all target nodes
+	const candidates = [];
+
+	for (let b = 1; b < ordered.length; b++) {
+		const nodeB = ordered[b];
+		if (!nodeB.inputs) continue;
+
+		for (let inIdx = 0; inIdx < nodeB.inputs.length; inIdx++) {
+			const inp = nodeB.inputs[inIdx];
+			if (inp.link != null) continue;
+
+			const inPos = getSlotPos(nodeB, true, inIdx);
+			const inputCandidates = [];
+			let hasExact = false;
+
+			const inName = (inp.name || inp.label || "").toLowerCase();
+
+			for (let a = b - 1; a >= 0; a--) {
+				const nodeA = ordered[a];
+				if (!nodeA.outputs) continue;
+
+				for (let outIdx = 0; outIdx < nodeA.outputs.length; outIdx++) {
+					const out = nodeA.outputs[outIdx];
+					const tier = typeMatchTier(out.type, inp.type);
+					if (tier < 0) continue;
+					if (tier === 2) hasExact = true;
+
+					const outName = (out.name || out.label || "").toLowerCase();
+					const nameMatch = inName !== "" && inName === outName ? 1 : 0;
+
+					const outPos = getSlotPos(nodeA, false, outIdx);
+					const dx = outPos[0] - inPos[0];
+					const dy = outPos[1] - inPos[1];
+					inputCandidates.push({
+						targetNode: nodeB,
+						inIdx,
+						sourceNode: nodeA,
+						outIdx,
+						tier,
+						nameMatch,
+						dist: dx * dx + dy * dy,
+					});
+				}
+			}
+
+			for (const c of inputCandidates) {
+				c.hasExact = hasExact;
+			}
+			candidates.push(...inputCandidates);
+		}
+	}
+
+	// Filter out non-exact matches for inputs that have an exact candidate (keep name matches)
+	const filtered = candidates.filter(c => c.tier === 2 || c.nameMatch || !c.hasExact);
+
+	// Sort globally: name match first, then higher type tier, then closest pair
+	filtered.sort((x, y) => (y.nameMatch - x.nameMatch) || (y.tier - x.tier) || (x.dist - y.dist));
+
+	// Greedy global assignment — closest pairs first
+	const planned = [];
+	const usedInputs = new Set();
+	const usedOutputs = new Set();
+
+	for (const c of filtered) {
+		const inKey = `${c.targetNode.id}:${c.inIdx}`;
+		if (usedInputs.has(inKey)) continue;
+		const outKey = `${c.sourceNode.id}:${c.outIdx}`;
+		if (usedOutputs.has(outKey)) continue;
+
+		planned.push({
+			sourceNode: c.sourceNode,
+			outIdx: c.outIdx,
+			targetNode: c.targetNode,
+			inIdx: c.inIdx,
+		});
+		usedInputs.add(inKey);
+		usedOutputs.add(outKey);
+	}
+
+	return planned;
+}
+
+function connectPlanned(planned, graph) {
+	for (const p of planned) {
+		p.sourceNode.connect(p.outIdx, p.targetNode, p.inIdx);
+	}
+	graph.change();
+}
+
+function fillConnectSelected() {
+	if (!app.ui.settings.getSettingValue("KJNodes.fillConnectEnabled")) return;
+
+	const canvas = app.canvas;
+	const graph = canvas.graph;
+	const nodes = Object.values(canvas.selected_nodes || {});
+	if (nodes.length < 2) return;
+
+	const ordered = orderNodes(nodes, graph);
+	const planned = planConnections(ordered);
+
+	if (planned.length > 0) {
+		connectPlanned(planned, graph);
+	}
+
+	if (app.extensionManager?.toast) {
+		app.extensionManager.toast.add({
+			severity: planned.length > 0 ? "info" : "warn",
+			summary: planned.length > 0
+				? `Connected ${planned.length} link${planned.length > 1 ? "s" : ""}`
+				: "No compatible connections found",
+			life: 2000,
+		});
+	}
+}
+
+app.registerExtension({
+	name: "KJNodes.FillConnect",
+
+	settings: [
+		{
+			id: "KJNodes.fillConnectEnabled",
+			name: "Enable Fill-Connect hotkey",
+			category: ["KJNodes", "Fill-Connect"],
+			tooltip: "Fill-connect selected nodes by compatible types",
+			type: "boolean",
+			defaultValue: true,
+		},
+	],
+
+	commands: [
+		{
+			id: "KJNodes.fillConnectSelected",
+			label: "Fill-Connect Selected Nodes",
+			function: fillConnectSelected,
+		},
+	],
+
+	keybindings: [
+		{
+			commandId: "KJNodes.fillConnectSelected",
+			combo: { key: "f" },
+			targetElementId: "graph-canvas",
+		},
+	],
+});
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/fix_node.js b/custom_nodes/ComfyUI-KJNodes/web/js/fix_node.js
new file mode 100644
index 0000000000000000000000000000000000000000..aec5cbd9b621a9a2030a1d040e826b1cd0f9a071
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/fix_node.js
@@ -0,0 +1,93 @@
+const { app } = window.comfyAPI.app;
+
+function snapshotOld(oldNode) {
+    const data = oldNode.serialize();
+    const widgetValuesByName = {};
+    for (const w of oldNode.widgets || []) widgetValuesByName[w.name] = w.value;
+
+    const inputSnapshot = (oldNode.inputs || []).map(i => ({
+        name: i.name,
+        widgetName: i.widget?.name ?? null,
+        linkId: i.link ?? null,
+    }));
+    const outputLinksByName = {};
+    for (const o of oldNode.outputs || []) {
+        if (o.links?.length) outputLinksByName[o.name] = [...o.links];
+    }
+    return { data, widgetValuesByName, inputSnapshot, outputLinksByName };
+}
+
+function applyCosmetics(newNode, old) {
+    if (old.pos) newNode.pos = [...old.pos];
+    if (old.size) {
+        const min = newNode.computeSize?.() || [0, 0];
+        newNode.size = [Math.max(old.size[0], min[0]), Math.max(old.size[1], min[1])];
+    }
+    if (old.title) newNode.title = old.title;
+    if (old.color) newNode.color = old.color;
+    if (old.bgcolor) newNode.bgcolor = old.bgcolor;
+    if (old.flags) newNode.flags = { ...(newNode.flags || {}), ...old.flags };
+    if (typeof old.mode === "number") newNode.mode = old.mode;
+    if (old.properties) newNode.properties = { ...(newNode.properties || {}), ...old.properties };
+}
+
+function fixNode(oldNode, comfyClass, { resetValues = false } = {}) {
+    const graph = app.canvas.graph;
+    const snap = snapshotOld(oldNode);
+
+    const newNode = LiteGraph.createNode(comfyClass);
+    if (!newNode) {
+        console.error(`[KJNodes.FixNode] Unknown node type: ${comfyClass}`);
+        return null;
+    }
+
+    try {
+        graph.add(newNode, false);
+        applyCosmetics(newNode, snap.data);
+
+        // Restore widget values by name (live widgets, not widgets_values array).
+        if (!resetValues) {
+            for (const w of newNode.widgets || []) {
+                if (Object.prototype.hasOwnProperty.call(snap.widgetValuesByName, w.name)) {
+                    try { w.value = snap.widgetValuesByName[w.name]; } catch {}
+                }
+            }
+        }
+
+        // Reconnect inputs by name.
+        for (const inp of snap.inputSnapshot) {
+            if (inp.linkId == null) continue;
+            const link = app.graph.links[inp.linkId];
+            if (!link) continue;
+            const src = app.graph.getNodeById(link.origin_id);
+            const newSlot = newNode.findInputSlot?.(inp.name);
+            if (!src || newSlot == null || newSlot < 0) continue;
+            try { src.connect(link.origin_slot, newNode, newSlot); } catch {}
+        }
+
+        // Reconnect outputs by name.
+        (newNode.outputs || []).forEach((out, i) => {
+            const linkIds = snap.outputLinksByName[out.name];
+            if (!linkIds) return;
+            for (const id of linkIds) {
+                const link = app.graph.links[id];
+                if (!link) continue;
+                const tgt = app.graph.getNodeById(link.target_id);
+                if (!tgt) continue;
+                try { newNode.connect(i, tgt, link.target_slot); } catch {}
+            }
+        });
+
+        graph.remove(oldNode);
+        app.graph.afterChange();
+        requestAnimationFrame(() => app.canvas.setDirty(true, true));
+        return newNode;
+    } catch (err) {
+        console.error("[KJNodes.FixNode] Aborting, rolling back:", err);
+        try { graph.remove(newNode); } catch {}
+        return null;
+    }
+}
+
+window.kjNodes = window.kjNodes || {};
+window.kjNodes.recreateNode = fixNode;
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/hdr_preview.js b/custom_nodes/ComfyUI-KJNodes/web/js/hdr_preview.js
new file mode 100644
index 0000000000000000000000000000000000000000..c7c454f58e4b2f2d82f6114a8cb7c04ff4b7a8b6
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/hdr_preview.js
@@ -0,0 +1,574 @@
+import { chainCallback, addMiddleClickPan, addWheelPassthrough } from './utility.js';
+const { app } = window.comfyAPI.app;
+
+// Shared across all HDR Preview nodes so synced nodes can drive each other.
+const hdrSyncGroup = new Set();
+
+const VERTEX_SHADER = `#version 300 es
+out vec2 v_texCoord;
+void main() {
+    vec2 verts[3] = vec2[](vec2(-1.0, -1.0), vec2(3.0, -1.0), vec2(-1.0, 3.0));
+    v_texCoord = verts[gl_VertexID] * 0.5 + 0.5;
+    v_texCoord.y = 1.0 - v_texCoord.y;
+    gl_Position = vec4(verts[gl_VertexID], 0.0, 1.0);
+}`;
+
+// LogC3 constants ported from ComfyUI-LTXVideo/hdr.py
+const FRAGMENT_SHADER = `#version 300 es
+precision highp float;
+in vec2 v_texCoord;
+out vec4 fragColor;
+uniform sampler2D u_image;
+uniform float u_exposure;
+uniform float u_saturation;
+uniform float u_linearScale;
+uniform int u_space;  // 0 = logc3, 1 = linear
+
+const float LC_A = 5.555556;
+const float LC_B = 0.052272;
+const float LC_C = 0.247190;
+const float LC_D = 0.385537;
+const float LC_E = 5.367655;
+const float LC_F = 0.092809;
+const float LC_CUT = 0.010591;
+const float LC_CUT_LOG = LC_E * LC_CUT + LC_F;  // ~0.14966
+
+vec3 logc3_decompress(vec3 logc) {
+    logc = clamp(logc, 0.0, 1.0);
+    vec3 lin_from_log = (pow(vec3(10.0), (logc - LC_D) / LC_C) - LC_B) / LC_A;
+    vec3 lin_from_lin = (logc - LC_F) / LC_E;
+    vec3 is_log = step(vec3(LC_CUT_LOG), logc);
+    return mix(lin_from_lin, lin_from_log, is_log);
+}
+
+vec3 linear_to_srgb(vec3 x) {
+    vec3 cutoff = vec3(0.0031308);
+    vec3 low = 12.92 * x;
+    vec3 high = 1.055 * pow(max(x, cutoff), vec3(1.0 / 2.4)) - 0.055;
+    return clamp(mix(low, high, step(cutoff, x)), 0.0, 1.0);
+}
+
+vec3 srgb_to_linear(vec3 x) {
+    vec3 cutoff = vec3(0.04045);
+    vec3 low = x / 12.92;
+    vec3 high = pow(max(x, vec3(0.0)) + 0.055, vec3(2.4)) / pow(vec3(1.055), vec3(2.4));
+    return mix(low, high, step(cutoff, x));
+}
+
+void main() {
+    vec3 col = texture(u_image, v_texCoord).rgb;
+    vec3 hdr;
+    if (u_space == 0) {
+        hdr = logc3_decompress(col);
+    } else if (u_space == 1) {
+        hdr = col * u_linearScale;
+    } else {
+        hdr = srgb_to_linear(col);
+    }
+    hdr = max(hdr, vec3(0.0));
+    vec3 exposed = hdr * exp2(u_exposure);
+    float luma = dot(exposed, vec3(0.2126, 0.7152, 0.0722));
+    vec3 saturated = max(mix(vec3(luma), exposed, u_saturation), vec3(0.0));
+    vec3 tm = (u_space == 2) ? clamp(saturated, 0.0, 1.0) : saturated / (1.0 + saturated);
+    fragColor = vec4(linear_to_srgb(tm), 1.0);
+}`;
+
+function compileShader(gl, type, src) {
+    const sh = gl.createShader(type);
+    gl.shaderSource(sh, src);
+    gl.compileShader(sh);
+    if (!gl.getShaderParameter(sh, gl.COMPILE_STATUS)) {
+        const log = gl.getShaderInfoLog(sh) || "shader compile failed";
+        gl.deleteShader(sh);
+        throw new Error(log);
+    }
+    return sh;
+}
+
+function createProgram(gl, vsSrc, fsSrc) {
+    const vs = compileShader(gl, gl.VERTEX_SHADER, vsSrc);
+    const fs = compileShader(gl, gl.FRAGMENT_SHADER, fsSrc);
+    const prog = gl.createProgram();
+    gl.attachShader(prog, vs);
+    gl.attachShader(prog, fs);
+    gl.linkProgram(prog);
+    if (!gl.getProgramParameter(prog, gl.LINK_STATUS)) {
+        const log = gl.getProgramInfoLog(prog) || "program link failed";
+        gl.deleteProgram(prog);
+        throw new Error(log);
+    }
+    gl.deleteShader(vs);
+    gl.deleteShader(fs);
+    return prog;
+}
+
+function loadImageBitmapFromView(filename, type) {
+    return fetch(`/view?filename=${encodeURIComponent(filename)}&type=${type}`)
+        .then(r => r.ok ? r.blob() : null)
+        .then(blob => blob ? createImageBitmap(blob, { colorSpaceConversion: "none", premultiplyAlpha: "none" }) : null)
+        .catch(() => null);
+}
+
+app.registerExtension({
+    name: "KJNodes.HDRPreview",
+
+    async beforeRegisterNodeDef(nodeType, nodeData) {
+        if (nodeData?.name !== "HDRPreviewKJ") return;
+
+        chainCallback(nodeType.prototype, "onNodeCreated", function () {
+            const node = this;
+
+            const state = {
+                frames: [],
+                frameCount: 0,
+                width: 512, height: 512,
+                fps: 24,
+                inputSpace: "logc3",
+                linearScale: 1.0,
+                exposure: 0.0,
+                saturation: 1.0,
+                currentFrame: 0,
+                playing: false,
+                playTimer: null,
+                gl: null, program: null, texture: null, vao: null,
+                uniforms: {},
+                uploadedFrame: -1,
+                needsRender: false,
+                execGen: 0,
+            };
+            node._hdrState = state;
+
+            let controlsHeight = 28;  // measured from DOM after mount; this is the fallback
+            node._widgetHeight = controlsHeight;
+
+            const container = document.createElement("div");
+            container.style.cssText =
+                "position:relative;width:100%;background:#111;display:flex;flex-direction:column;align-items:center;overflow:hidden;";
+
+            const viewport = document.createElement("div");
+            // flex-shrink:0 — legacy container can be shorter than this height; without it the viewport collapses to 0.
+            viewport.style.cssText =
+                "position:relative;width:100%;height:0;background:#000;overflow:hidden;flex-shrink:0;";
+
+            const canvas = document.createElement("canvas");
+            canvas.style.cssText =
+                "display:block;width:100%;height:100%;";
+            viewport.appendChild(canvas);
+
+            const frameRow = document.createElement("div");
+            frameRow.style.cssText =
+                "display:flex;align-items:center;gap:6px;padding:3px 6px;color:#ccc;font-size:11px;background:#1a1a1a;width:100%;box-sizing:border-box;flex:0 0 auto;";
+
+            const playBtn = document.createElement("button");
+            playBtn.type = "button";
+            playBtn.textContent = "▶";
+            playBtn.style.cssText =
+                "background:#333;color:#ccc;border:1px solid #555;cursor:pointer;padding:1px 8px;font-size:11px;min-width:24px;";
+
+            const syncBtn = document.createElement("button");
+            syncBtn.type = "button";
+            syncBtn.textContent = "⛓";
+            syncBtn.title = "Sync playback with other HDR Preview nodes that have sync enabled.";
+            syncBtn.style.cssText =
+                "background:#333;color:#888;border:1px solid #555;cursor:pointer;padding:1px 6px;font-size:11px;min-width:22px;";
+
+            const frameSlider = document.createElement("input");
+            frameSlider.type = "range";
+            frameSlider.min = "0";
+            frameSlider.max = "0";
+            frameSlider.value = "0";
+            frameSlider.step = "1";
+            frameSlider.style.cssText = "flex:1;min-width:40px;accent-color:#5af;";
+
+            const frameLabel = document.createElement("span");
+            frameLabel.textContent = "0/0";
+            frameLabel.style.cssText = "min-width:50px;text-align:right;font-variant-numeric:tabular-nums;";
+
+            frameRow.appendChild(playBtn);
+            frameRow.appendChild(syncBtn);
+            frameRow.appendChild(frameSlider);
+            frameRow.appendChild(frameLabel);
+
+            container.appendChild(viewport);
+            container.appendChild(frameRow);
+
+            const stopProp = (e) => e.stopPropagation();
+            for (const el of [frameSlider, playBtn, syncBtn]) {
+                el.addEventListener("pointerdown", stopProp);
+                el.addEventListener("mousedown", stopProp);
+            }
+
+            addMiddleClickPan(container);
+            addWheelPassthrough(canvas);
+
+            function initGL() {
+                try {
+                    const gl = canvas.getContext("webgl2", { antialias: false, premultipliedAlpha: false, alpha: false });
+                    if (!gl) {
+                        console.error("[HDRPreviewKJ] WebGL2 not available");
+                        return false;
+                    }
+                    const program = createProgram(gl, VERTEX_SHADER, FRAGMENT_SHADER);
+                    gl.useProgram(program);
+
+                    const uniforms = {
+                        u_image: gl.getUniformLocation(program, "u_image"),
+                        u_exposure: gl.getUniformLocation(program, "u_exposure"),
+                        u_saturation: gl.getUniformLocation(program, "u_saturation"),
+                        u_space: gl.getUniformLocation(program, "u_space"),
+                        u_linearScale: gl.getUniformLocation(program, "u_linearScale"),
+                    };
+                    gl.uniform1i(uniforms.u_image, 0);
+
+                    const texture = gl.createTexture();
+                    gl.activeTexture(gl.TEXTURE0);
+                    gl.bindTexture(gl.TEXTURE_2D, texture);
+                    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);
+                    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);
+                    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
+                    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
+
+                    const vao = gl.createVertexArray();
+                    gl.bindVertexArray(vao);
+
+                    state.gl = gl;
+                    state.program = program;
+                    state.texture = texture;
+                    state.vao = vao;
+                    state.uniforms = uniforms;
+                    return true;
+                } catch (err) {
+                    console.error("[HDRPreviewKJ] WebGL init failed:", err);
+                    return false;
+                }
+            }
+
+            function requestRender() {
+                if (state.needsRender) return;
+                state.needsRender = true;
+                requestAnimationFrame(() => {
+                    state.needsRender = false;
+                    render();
+                });
+            }
+
+            function render() {
+                if (!state.gl || !state.frames.length) return;
+                const frame = state.frames[state.currentFrame];
+                if (!frame) return;
+
+                const gl = state.gl;
+                // Backing buffer sized to source resolution for quality; CSS handles display scaling
+                if (canvas.width !== state.width || canvas.height !== state.height) {
+                    canvas.width = state.width;
+                    canvas.height = state.height;
+                }
+                gl.viewport(0, 0, canvas.width, canvas.height);
+                gl.useProgram(state.program);
+                gl.bindVertexArray(state.vao);
+                gl.activeTexture(gl.TEXTURE0);
+                gl.bindTexture(gl.TEXTURE_2D, state.texture);
+
+                if (state.uploadedFrame !== state.currentFrame) {
+                    gl.pixelStorei(gl.UNPACK_FLIP_Y_WEBGL, false);
+                    gl.pixelStorei(gl.UNPACK_ALIGNMENT, 1);
+                    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGB8, gl.RGB, gl.UNSIGNED_BYTE, frame);
+                    state.uploadedFrame = state.currentFrame;
+                }
+
+                gl.uniform1f(state.uniforms.u_exposure, state.exposure);
+                gl.uniform1f(state.uniforms.u_saturation, state.saturation);
+                const spaceIdx = state.inputSpace === "logc3" ? 0 : state.inputSpace === "srgb" ? 2 : 1;
+                gl.uniform1i(state.uniforms.u_space, spaceIdx);
+                gl.uniform1f(state.uniforms.u_linearScale, state.linearScale);
+
+                gl.clearColor(0, 0, 0, 1);
+                gl.clear(gl.COLOR_BUFFER_BIT);
+                gl.drawArrays(gl.TRIANGLES, 0, 3);
+            }
+
+            function stopPlayback() {
+                if (state.playTimer !== null) {
+                    clearInterval(state.playTimer);
+                    state.playTimer = null;
+                }
+                state.playing = false;
+                playBtn.textContent = "▶";
+            }
+
+            function updateFrameLabel() {
+                const total = Math.max(state.frameCount, 1);
+                frameLabel.textContent = `${state.currentFrame + 1}/${total}`;
+            }
+
+            const exposureWidget = node.widgets?.find(w => w.name === "exposure");
+            const saturationWidget = node.widgets?.find(w => w.name === "saturation");
+
+            function hookLiveWidget(widget, stateKey) {
+                if (!widget) return;
+                state[stateKey] = isFinite(widget.value) ? widget.value : state[stateKey];
+                const original = widget.callback;
+                widget.callback = function (value) {
+                    const num = isFinite(value) ? value : state[stateKey];
+                    state[stateKey] = num;
+                    requestRender();
+                    return original?.apply(this, arguments);
+                };
+            }
+            hookLiveWidget(exposureWidget, "exposure");
+            hookLiveWidget(saturationWidget, "saturation");
+
+            // Sync handle: other nodes in the group call these to follow along.
+            let syncEnabled = false;
+            const syncHandle = {
+                setFrameFraction(fraction) {
+                    if (state.frameCount <= 0) return;
+                    const maxIdx = Math.max(state.frameCount - 1, 0);
+                    const frame = Math.min(maxIdx, Math.max(0, Math.round(fraction * maxIdx)));
+                    if (frame === state.currentFrame) return;
+                    state.currentFrame = frame;
+                    frameSlider.value = String(frame);
+                    updateFrameLabel();
+                    requestRender();
+                },
+                showPlaying(play) {
+                    state.playing = play;
+                    playBtn.textContent = play ? "■" : "▶";
+                    if (!play && state.playTimer !== null) {
+                        clearInterval(state.playTimer);
+                        state.playTimer = null;
+                    }
+                },
+            };
+
+            function broadcastFraction() {
+                if (!syncEnabled || state.frameCount <= 1) return;
+                const fraction = state.currentFrame / (state.frameCount - 1);
+                for (const other of hdrSyncGroup) {
+                    if (other !== syncHandle) other.setFrameFraction(fraction);
+                }
+            }
+
+            function broadcastPlaying(play) {
+                if (!syncEnabled) return;
+                for (const other of hdrSyncGroup) {
+                    if (other !== syncHandle) other.showPlaying(play);
+                }
+            }
+
+            syncBtn.addEventListener("click", () => {
+                syncEnabled = !syncEnabled;
+                syncBtn.style.color = syncEnabled ? "#5fa" : "#888";
+                syncBtn.style.borderColor = syncEnabled ? "#5fa" : "#555";
+                if (syncEnabled) {
+                    hdrSyncGroup.add(syncHandle);
+                } else {
+                    hdrSyncGroup.delete(syncHandle);
+                }
+            });
+
+            frameSlider.addEventListener("input", () => {
+                state.currentFrame = parseInt(frameSlider.value, 10) || 0;
+                updateFrameLabel();
+                requestRender();
+                broadcastFraction();
+            });
+            frameSlider.addEventListener("pointerdown", () => {
+                stopPlayback();
+                broadcastPlaying(false);
+            });
+            function persistCurrentFrame() {
+                if (!node.properties?.hdrLastPreview) return;
+                node.properties.hdrLastPreview.current_frame = state.currentFrame;
+                node.graph?.change?.();
+                try { app.extensionManager?.workflow?.activeWorkflow?.changeTracker?.checkState?.(); } catch {}
+            }
+            frameSlider.addEventListener("change", persistCurrentFrame);
+
+            playBtn.addEventListener("click", () => {
+                if (state.playing) {
+                    stopPlayback();
+                    broadcastPlaying(false);
+                    persistCurrentFrame();
+                    return;
+                }
+                if (state.frameCount <= 1) return;
+                // Ensure no other synced node has an active timer — we become the sole driver.
+                if (syncEnabled) {
+                    for (const other of hdrSyncGroup) {
+                        if (other !== syncHandle) other.showPlaying(false);
+                    }
+                }
+                const intervalMs = Math.max(16, 1000 / Math.max(state.fps, 1));
+                state.playing = true;
+                playBtn.textContent = "■";
+                state.playTimer = setInterval(() => {
+                    state.currentFrame = (state.currentFrame + 1) % state.frameCount;
+                    frameSlider.value = String(state.currentFrame);
+                    updateFrameLabel();
+                    requestRender();
+                    broadcastFraction();
+                }, intervalMs);
+                broadcastPlaying(true);
+            });
+
+            const domWidget = node.addDOMWidget("hdr_preview", "hdr_preview", container, {
+                serialize: false,
+                hideOnZoom: false,
+                margin: 0,
+                getMinHeight: () => node._widgetHeight,
+                getMaxHeight: () => node._widgetHeight,
+                getHeight: () => node._widgetHeight,
+            });
+            node.resizable = true;
+
+            // LiteGraph's computeSize uses a fixed per-widget height that ignores DOM widgets — sum manually.
+            const NATIVE_ROW_H = (LiteGraph?.NODE_WIDGET_HEIGHT ?? 20) + 4;
+            const TITLE_H = LiteGraph?.NODE_TITLE_HEIGHT ?? 30;
+            function computeNodeHeight() {
+                let nativeCount = 0;
+                for (const w of node.widgets || []) {
+                    if (w === domWidget) continue;
+                    if (w.hidden || w.type === "converted-widget") continue;
+                    nativeCount++;
+                }
+                return TITLE_H + nativeCount * NATIVE_ROW_H + node._widgetHeight + 8;
+            }
+
+            // Don't gate on state.frames — a fire during async bitmap load would wipe _widgetHeight and collapse the node.
+            const controlsObserver = new ResizeObserver(() => {
+                const measured = frameRow.offsetHeight;
+                if (measured <= 0 || measured === controlsHeight) return;
+                controlsHeight = measured;
+                const viewportPx = parseInt(viewport.style.height, 10) || 0;
+                node._widgetHeight = viewportPx + controlsHeight;
+                node.setSize([node.size[0], computeNodeHeight()]);
+                node.graph?.setDirtyCanvas(true, true);
+            });
+            controlsObserver.observe(frameRow);
+
+            let resizing = false;
+            function resizeToFit() {
+                if (resizing) return;
+                resizing = true;
+                try {
+                    const srcW = state.width || 512, srcH = state.height || 512;
+                    const availW = Math.max(100, node.size[0] - 30);
+                    const ratio = srcH / srcW;
+                    const displayH = Math.round(availW * ratio);
+                    const totalH = displayH + controlsHeight;
+                    viewport.style.width = availW + "px";
+                    viewport.style.height = displayH + "px";
+                    // min-height keeps children visible when ComfyUI sets the container height from a stale computeSize.
+                    container.style.minHeight = totalH + "px";
+                    node._widgetHeight = totalH;
+                    node.setSize([node.size[0], computeNodeHeight()]);
+                    node.graph?.setDirtyCanvas(true, true);
+                } finally {
+                    resizing = false;
+                }
+            }
+
+            chainCallback(node, "onResize", function () {
+                if (state.frames.length) resizeToFit();
+            });
+
+            async function applyPreviewData(data) {
+                const gen = ++state.execGen;
+
+                stopPlayback();
+
+                for (const f of state.frames) {
+                    try { f.close?.(); } catch {}
+                }
+                state.frames = [];
+                state.uploadedFrame = -1;
+
+                state.frameCount = data.frame_count || 0;
+                state.width = data.width || 512;
+                state.height = data.height || 512;
+                state.fps = data.fps || 24;
+                state.inputSpace = data.input_space || "logc3";
+                state.linearScale = data.linear_scale || 1.0;
+                const restoredFrame = Number.isInteger(data.current_frame) ? data.current_frame : 0;
+                state.currentFrame = Math.min(Math.max(0, restoredFrame), Math.max(0, state.frameCount - 1));
+
+                frameSlider.max = String(Math.max(0, state.frameCount - 1));
+                frameSlider.value = String(state.currentFrame);
+                updateFrameLabel();
+
+                if (!state.gl) initGL();
+
+                resizeToFit();
+
+                const bitmaps = await Promise.all(
+                    (data.frames || []).map(f => loadImageBitmapFromView(f.filename, f.type))
+                );
+
+                if (gen !== state.execGen) {
+                    for (const b of bitmaps) {
+                        try { b?.close?.(); } catch {}
+                    }
+                    return;
+                }
+
+                state.frames = bitmaps.filter(Boolean);
+                // Re-assert size in case the ResizeObserver fired mid-await with empty frames.
+                resizeToFit();
+                requestRender();
+            }
+
+            chainCallback(node, "onExecuted", async function (message) {
+                const data = message?.hdr_preview_data?.[0];
+                if (!data) return;
+
+                node.properties = node.properties || {};
+                node.properties.hdrLastPreview = {
+                    frames: data.frames,
+                    width: data.width,
+                    height: data.height,
+                    fps: data.fps,
+                    input_space: data.input_space,
+                    linear_scale: data.linear_scale,
+                    frame_count: data.frame_count,
+                };
+                // Autosave snapshots before execution, so post-execute property updates need an explicit dirty nudge.
+                node.graph?.change?.();
+                try {
+                    const ct = app.extensionManager?.workflow?.activeWorkflow?.changeTracker;
+                    ct?.checkState?.();
+                } catch {}
+                try { app.workflowManager?.activeWorkflow?.changeTracker?.checkState?.(); } catch {}
+
+                await applyPreviewData(data);
+            });
+
+            chainCallback(node, "onConfigure", function () {
+                if (exposureWidget && isFinite(exposureWidget.value)) state.exposure = exposureWidget.value;
+                if (saturationWidget && isFinite(saturationWidget.value)) state.saturation = saturationWidget.value;
+                const saved = node.properties?.hdrLastPreview;
+                if (saved?.frames?.length) applyPreviewData(saved);
+            });
+
+            chainCallback(node, "onRemoved", function () {
+                stopPlayback();
+                controlsObserver.disconnect();
+                hdrSyncGroup.delete(syncHandle);
+                for (const f of state.frames) {
+                    try { f.close?.(); } catch {}
+                }
+                state.frames = [];
+                if (state.gl) {
+                    try {
+                        state.gl.deleteProgram(state.program);
+                        state.gl.deleteTexture(state.texture);
+                        state.gl.deleteVertexArray(state.vao);
+                        const lose = state.gl.getExtension("WEBGL_lose_context");
+                        if (lose) lose.loseContext();
+                    } catch {}
+                    state.gl = null;
+                }
+            });
+        });
+    },
+});
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/help_popup.js b/custom_nodes/ComfyUI-KJNodes/web/js/help_popup.js
new file mode 100644
index 0000000000000000000000000000000000000000..d089fbb11c76ceb7df4066638717b3df546ca3d3
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/help_popup.js
@@ -0,0 +1,468 @@
+const { app } = window.comfyAPI.app;
+
+// code based on mtb nodes by Mel Massadian https://github.com/melMass/comfy_mtb/
+export const loadScript = (
+  FILE_URL,
+  async = true,
+  type = 'text/javascript',
+) => {
+  return new Promise((resolve, reject) => {
+    try {
+      // Check if the script already exists
+      const existingScript = document.querySelector(`script[src="${FILE_URL}"]`)
+      if (existingScript) {
+        resolve({ status: true, message: 'Script already loaded' })
+        return
+      }
+
+      const scriptEle = document.createElement('script')
+      scriptEle.type = type
+      scriptEle.async = async
+      scriptEle.src = FILE_URL
+
+      scriptEle.addEventListener('load', (ev) => {
+        resolve({ status: true })
+      })
+
+      scriptEle.addEventListener('error', (ev) => {
+        reject({
+          status: false,
+          message: `Failed to load the script ${FILE_URL}`,
+        })
+      })
+
+      document.body.appendChild(scriptEle)
+    } catch (error) {
+      reject(error)
+    }
+  })
+}
+
+loadScript('kjweb_async/marked.min.js').catch((e) => {
+  console.error(e)
+})
+loadScript('kjweb_async/purify.min.js').catch((e) => {
+  console.error(e)
+})
+
+const categories = ["KJNodes", "SUPIR", "VoiceCraft", "Marigold", "IC-Light", "WanVideoWrapper"];
+const nodeDescriptions = new Map();
+
+function isHelpPopupEnabled() {
+  return app.ui.settings.getSettingValue("KJNodes.helpPopup") !== false;
+}
+
+app.registerExtension({
+	name: "KJNodes.HelpPopup",
+	async beforeRegisterNodeDef(nodeType, nodeData) {
+    if (!isHelpPopupEnabled()) return;
+		try {
+			categories.forEach(category => {
+        if (nodeData?.category?.startsWith(category)) {
+            if (nodeData.description) {
+              nodeDescriptions.set(nodeData.name, nodeData.description);
+            }
+            addDocumentation(nodeData, nodeType);
+        }
+        else return
+    });
+		} catch (error) {
+			console.error("Error in registering KJNodes.HelpPopup", error);
+		}
+	},
+	nodeCreated(node) {
+    if (!isHelpPopupEnabled()) return;
+    const description = nodeDescriptions.get(node.type) || nodeDescriptions.get(node.comfyClass);
+    if (!description) return;
+    node._kjHelpDescription = description;
+  },
+  setup() {
+    if (!isHelpPopupEnabled()) return;
+    setupHelpObserver();
+  },
+});
+
+const create_documentation_stylesheet = () => {
+    const tag = 'kj-documentation-stylesheet'
+
+    let styleTag = document.getElementById(tag)
+
+    if (!styleTag) {
+      styleTag = document.createElement('style')
+      styleTag.type = 'text/css'
+      styleTag.id = tag
+      styleTag.innerHTML = `
+      .kj-documentation-popup {
+        background: var(--comfy-menu-bg);
+        position: absolute;
+        color: var(--fg-color);
+        font: 12px monospace;
+        line-height: 1.5em;
+        padding: 10px;
+        border-radius: 10px;
+        border-style: solid;
+        border-width: medium;
+        border-color: var(--border-color);
+        z-index: 5;
+        overflow: hidden;
+       }
+       .content-wrapper {
+        overflow: auto;
+        max-height: 100%;
+        /* Scrollbar styling for Chrome */
+        &::-webkit-scrollbar {
+           width: 6px;
+        }
+        &::-webkit-scrollbar-track {
+           background: var(--bg-color);
+        }
+        &::-webkit-scrollbar-thumb {
+           background-color: var(--fg-color);
+           border-radius: 6px;
+           border: 3px solid var(--bg-color);
+        }
+
+        /* Scrollbar styling for Firefox */
+        scrollbar-width: thin;
+        scrollbar-color: var(--fg-color) var(--bg-color);
+        a {
+          color: yellow;
+        }
+        a:visited {
+          color: orange;
+        }
+        a:hover {
+          color: red;
+        }
+       }
+        `
+      document.head.appendChild(styleTag)
+    }
+  }
+
+/**
+ * Creates the documentation popup DOM and wires up resize/close interactions.
+ * Returns { docElement, contentWrapper }.
+ * @param {string} description - Markdown description text
+ * @param {AbortSignal} signal - Signal to clean up event listeners
+ * @param {() => void} onClose - Called when the close button is clicked
+ * @param {{ scaleResize: boolean }} opts - If scaleResize is true, resize deltas are divided by canvas scale
+ */
+function createDocPopup(description, signal, onClose, opts = {}) {
+  create_documentation_stylesheet()
+
+  const docElement = document.createElement('div')
+  const contentWrapper = document.createElement('div')
+  docElement.appendChild(contentWrapper)
+
+  contentWrapper.classList.add('content-wrapper')
+  docElement.classList.add('kj-documentation-popup')
+  // Try ComfyUI's built-in markdown renderer first (available after frontend PR #10700)
+  if (app.extensionManager?.renderMarkdownToHtml) {
+    contentWrapper.innerHTML = app.extensionManager.renderMarkdownToHtml(description)
+  } else if (typeof marked !== 'undefined' && typeof DOMPurify !== 'undefined') {
+    contentWrapper.innerHTML = DOMPurify.sanitize(marked.parse(description))
+  } else {
+    // Fallback: convert markdown links to <a> tags, auto-link bare URLs, preserve line breaks
+    const escaped = description
+      .replace(/&/g, '&amp;').replace(/</g, '&lt;').replace(/>/g, '&gt;')
+      .replace(/\[([^\]]+)\]\((https?:\/\/[^)]+)\)/g, '<a href="$2" target="_blank">$1</a>')
+      .replace(/(^|[^"'])(https?:\/\/[^\s<]+)/g, '$1<a href="$2" target="_blank">$2</a>')
+      .replace(/\n/g, '<br>')
+    contentWrapper.innerHTML = escaped
+  }
+
+  // resize handle
+  const resizeHandle = document.createElement('div')
+  resizeHandle.style.width = '0'
+  resizeHandle.style.height = '0'
+  resizeHandle.style.position = 'absolute'
+  resizeHandle.style.bottom = '0'
+  resizeHandle.style.right = '0'
+  resizeHandle.style.cursor = 'se-resize'
+  const borderColor = getComputedStyle(document.documentElement).getPropertyValue('--border-color').trim()
+  resizeHandle.style.borderTop = '10px solid transparent'
+  resizeHandle.style.borderLeft = '10px solid transparent'
+  resizeHandle.style.borderBottom = `10px solid ${borderColor}`
+  resizeHandle.style.borderRight = `10px solid ${borderColor}`
+  docElement.appendChild(resizeHandle)
+
+  let isResizing = false
+  let startX, startY, startWidth, startHeight
+  resizeHandle.addEventListener('mousedown', (e) => {
+    e.preventDefault()
+    e.stopPropagation()
+    isResizing = true
+    startX = e.clientX
+    startY = e.clientY
+    startWidth = parseInt(document.defaultView.getComputedStyle(docElement).width, 10)
+    startHeight = parseInt(document.defaultView.getComputedStyle(docElement).height, 10)
+  }, { signal })
+
+  document.addEventListener('mousemove', (e) => {
+    if (!isResizing) return
+    const scaleFactor = opts.scaleResize ? app.canvas.ds.scale : 1
+    const newWidth = startWidth + (e.clientX - startX) / scaleFactor
+    const newHeight = startHeight + (e.clientY - startY) / scaleFactor
+    docElement.style.width = `${newWidth}px`
+    docElement.style.height = `${newHeight}px`
+  }, { signal })
+
+  document.addEventListener('mouseup', () => {
+    isResizing = false
+  }, { signal })
+
+  // close button
+  const closeButton = document.createElement('div')
+  closeButton.textContent = '❌'
+  closeButton.style.position = 'absolute'
+  closeButton.style.top = '0'
+  closeButton.style.right = '0'
+  closeButton.style.cursor = 'pointer'
+  closeButton.style.padding = '5px'
+  closeButton.style.color = 'red'
+  closeButton.style.fontSize = '12px'
+  docElement.appendChild(closeButton)
+
+  closeButton.addEventListener('mousedown', (e) => {
+    e.stopPropagation()
+    onClose()
+  }, { signal })
+
+  document.body.appendChild(docElement)
+  return { docElement, contentWrapper }
+}
+
+// ─── Legacy canvas mode (onDrawForeground) ───
+
+/** Add documentation widget to the selected node (legacy canvas rendering) */
+export const addDocumentation = (
+  nodeData,
+  nodeType,
+  opts = { icon_size: 14, icon_margin: 4 },) => {
+
+  opts = opts || {}
+  const iconSize = opts.icon_size ? opts.icon_size : 14
+  const iconMargin = opts.icon_margin ? opts.icon_margin : 4
+  let docElement = null
+  let contentWrapper = null
+
+  if (!nodeData.description) {
+    return
+  }
+
+  const drawFg = nodeType.prototype.onDrawForeground
+  nodeType.prototype.onDrawForeground = function (ctx) {
+    const r = drawFg ? drawFg.apply(this, arguments) : undefined
+    if (this.flags.collapsed) return r
+
+    const x = this.size[0] - iconSize - iconMargin
+
+    // create the popup
+    if (this.show_doc && docElement === null) {
+      const popup = createDocPopup(
+        nodeData.description,
+        this.docCtrl.signal,
+        () => {
+          this.show_doc = !this.show_doc
+          docElement.parentNode.removeChild(docElement)
+          docElement = null
+          contentWrapper = null
+        },
+        { scaleResize: true }
+      )
+      docElement = popup.docElement
+      contentWrapper = popup.contentWrapper
+    }
+    // close the popup
+    else if (!this.show_doc && docElement !== null) {
+      docElement.parentNode.removeChild(docElement)
+      docElement = null
+    }
+    // update position of the popup
+    if (this.show_doc && docElement !== null) {
+      const rect = ctx.canvas.getBoundingClientRect()
+      const scaleX = rect.width / ctx.canvas.width
+      const scaleY = rect.height / ctx.canvas.height
+
+      const transform = new DOMMatrix()
+      .scaleSelf(scaleX, scaleY)
+      .multiplySelf(ctx.getTransform())
+      .translateSelf(this.size[0] * scaleX * Math.max(1.0,window.devicePixelRatio) , 0)
+      .translateSelf(10, -32)
+
+      const scale = new DOMMatrix()
+      .scaleSelf(transform.a, transform.d);
+      const bcr = app.canvas.canvas.getBoundingClientRect()
+
+      const styleObject = {
+        transformOrigin: '0 0',
+        transform: scale,
+        left: `${transform.a + bcr.x + transform.e}px`,
+        top: `${transform.d + bcr.y + transform.f}px`,
+       };
+      Object.assign(docElement.style, styleObject);
+    }
+
+    ctx.save()
+    ctx.translate(x - 2, iconSize - 34)
+    ctx.scale(iconSize / 32, iconSize / 32)
+    ctx.strokeStyle = 'rgba(255,255,255,0.3)'
+    ctx.lineCap = 'round'
+    ctx.lineJoin = 'round'
+    ctx.lineWidth = 2.4
+    ctx.font = 'bold 36px monospace'
+    ctx.fillStyle = 'orange';
+    ctx.fillText('?', 0, 24)
+    ctx.restore()
+    return r
+  }
+
+  // handle clicking of the icon
+  const mouseDown = nodeType.prototype.onMouseDown
+  nodeType.prototype.onMouseDown = function (e, localPos, canvas) {
+    const r = mouseDown ? mouseDown.apply(this, arguments) : undefined
+    const iconX = this.size[0] - iconSize - iconMargin
+    const iconY = iconSize - 34
+    if (
+      localPos[0] > iconX &&
+      localPos[0] < iconX + iconSize &&
+      localPos[1] > iconY &&
+      localPos[1] < iconY + iconSize
+    ) {
+      if (this.show_doc === undefined) {
+        this.show_doc = true
+      } else {
+        this.show_doc = !this.show_doc
+      }
+      if (this.show_doc) {
+        this.docCtrl = new AbortController()
+      } else {
+        this.docCtrl.abort()
+      }
+      return true;
+    }
+    return r;
+  }
+
+  const onRem = nodeType.prototype.onRemoved
+  nodeType.prototype.onRemoved = function () {
+    const r = onRem ? onRem.apply(this, []) : undefined
+    if (docElement) {
+      docElement.remove()
+      docElement = null
+      contentWrapper = null
+    }
+    return r
+  }
+}
+
+// ─── Vue nodes mode (DOM injection via MutationObserver) ───
+
+/** Per-node popup state, keyed by node ID */
+const popupState = new Map()
+
+function getNodeById(nodeId) {
+  return app.graph?.getNodeById(nodeId)
+}
+
+function closeNodePopup(nodeId) {
+  const state = popupState.get(nodeId)
+  if (!state) return
+  if (state.docElement) {
+    state.docElement.remove()
+  }
+  if (state.abortCtrl) {
+    state.abortCtrl.abort()
+  }
+  if (state.animFrame) {
+    cancelAnimationFrame(state.animFrame)
+  }
+  popupState.delete(nodeId)
+}
+
+function openNodePopup(nodeId, description) {
+  closeNodePopup(nodeId)
+  const state = popupState.get(nodeId) || {}
+  popupState.set(nodeId, state)
+
+  state.abortCtrl = new AbortController()
+  const popup = createDocPopup(
+    description,
+    state.abortCtrl.signal,
+    () => closeNodePopup(nodeId),
+    { scaleResize: false }
+  )
+  state.docElement = popup.docElement
+
+  function updatePosition() {
+    if (!state.docElement || !state.docElement.parentNode) return
+    const nodeEl = document.querySelector(`[data-node-id="${nodeId}"]`)
+    if (nodeEl) {
+      const rect = nodeEl.getBoundingClientRect()
+      state.docElement.style.left = `${rect.right + 10}px`
+      state.docElement.style.top = `${rect.top}px`
+    }
+    state.animFrame = requestAnimationFrame(updatePosition)
+  }
+  state.animFrame = requestAnimationFrame(updatePosition)
+}
+
+/** Try to inject a "?" button into a Vue node header */
+function tryInjectHelpButton(header) {
+  if (header.querySelector('.kj-help-btn')) return
+
+  const nodeEl = header.closest('[data-node-id]')
+  if (!nodeEl) return
+  const nodeId = nodeEl.dataset.nodeId
+  const node = getNodeById(nodeId)
+  if (!node) return
+
+  const description = node._kjHelpDescription
+  if (!description) return
+
+  const flexContainer = header.querySelector(':scope > div')
+  if (!flexContainer) return
+
+  const helpBtn = document.createElement('span')
+  helpBtn.className = 'kj-help-btn'
+  helpBtn.textContent = '?'
+  helpBtn.style.cssText = `
+    color: orange;
+    font-weight: bold;
+    font-size: 14px;
+    cursor: pointer;
+    flex-shrink: 0;
+    padding: 0 4px;
+    line-height: 1;
+    user-select: none;
+  `
+  helpBtn.title = 'Show help'
+  helpBtn.addEventListener('click', (e) => {
+    e.stopPropagation()
+    const state = popupState.get(nodeId)
+    if (state?.docElement) {
+      closeNodePopup(nodeId)
+    } else {
+      openNodePopup(nodeId, description)
+    }
+  })
+  flexContainer.appendChild(helpBtn)
+}
+
+/** Observe the DOM for Vue node headers appearing and inject help buttons */
+function setupHelpObserver() {
+  // Inject into any headers already in the DOM
+  document.querySelectorAll('.lg-node-header').forEach(tryInjectHelpButton)
+
+  let pending = false
+  const observer = new MutationObserver(() => {
+    if (pending) return
+    pending = true
+    requestAnimationFrame(() => {
+      pending = false
+      document.querySelectorAll('.lg-node-header:not(:has(.kj-help-btn))').forEach(tryInjectHelpButton)
+    })
+  })
+  observer.observe(document.body, { childList: true, subtree: true })
+}
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/ideogram4_prompt_builder.js b/custom_nodes/ComfyUI-KJNodes/web/js/ideogram4_prompt_builder.js
new file mode 100644
index 0000000000000000000000000000000000000000..626cffed68cbeaf99f738fb4ddf8b605efa356d7
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/ideogram4_prompt_builder.js
@@ -0,0 +1,2544 @@
+import { chainCallback, addMiddleClickPan, addWheelPassthrough, cursorForBboxMode, watchImageInputs, captureVideoFrame } from './utility.js';
+const { app } = window.comfyAPI.app;
+
+const HANDLE = 8;            // hit radius (canvas px) for corners/edges
+const MAX_ELEM_COLORS = 5;   // Ideogram 4 per-element palette cap
+const MAX_STYLE_COLORS = 16; // Ideogram 4 style palette cap
+let copiedBoxes = null;      // internal clipboard for copy/paste of regions (array; shared across nodes)
+
+// Track the most recent generated image so it can be grabbed as a background.
+let lastResultImage = null;
+try {
+  app.api?.addEventListener?.("executed", (e) => {
+    const imgs = e?.detail?.output?.images;
+    if (Array.isArray(imgs) && imgs.length) {
+      lastResultImage = imgs[imgs.length - 1];
+      // Live nodes swap their preview for the full-res final result automatically.
+      for (const n of livePreviewNodes) n._ideoGrabFinal?.();
+    }
+  });
+} catch (e) {}
+function resultViewUrl(img) {
+  const p = new URLSearchParams({ filename: img.filename || "", subfolder: img.subfolder || "", type: img.type || "output" });
+  return "/view?" + p.toString();
+}
+
+// Nodes opted into "live background": feed them the sampling preview frames as they arrive.
+const livePreviewNodes = new Set();
+let _lastPreviewBlob = null;
+function feedPreviewBlob(blob) {
+  // Newer ComfyUI dispatches "b_preview_with_metadata"; older ones only "b_preview" — and both can
+  // fire for the same frame (same Blob), so dedupe by identity to avoid decoding twice.
+  if (!blob || blob === _lastPreviewBlob || !livePreviewNodes.size) return;
+  _lastPreviewBlob = blob;
+  createImageBitmap(blob).then((bmp) => {
+    let used = false;
+    for (const n of livePreviewNodes) { n._ideoSetLiveBg?.(bmp); used = true; }
+    if (!used && bmp.close) bmp.close();
+  }).catch(() => {});
+}
+try {
+  app.api?.addEventListener?.("b_preview", (e) => feedPreviewBlob(e?.detail));
+  app.api?.addEventListener?.("b_preview_with_metadata", (e) => feedPreviewBlob(e?.detail?.blob));
+} catch (e) {}
+
+// Named caption-JSON templates, each stored as its own file server-side via ComfyUI's userdata
+// API (under the ComfyUI user dir) so they persist across browsers/machines and survive clears.
+const TPL_DIR = "kjnodes/ideogram4/templates";
+const tplSafe = (s) => (s || "").replace(/[\/\\:*?"<>|]+/g, "_").trim();   // filesystem-safe name
+const tplFile = (name) => `${TPL_DIR}/${name}.json`;
+async function listTemplateNames() {
+  try {
+    const items = await app.api.listUserDataFullInfo(TPL_DIR);
+    return items.map((it) => it.path.split(/[\\/]/).pop() || "")
+      .filter((f) => /\.json$/i.test(f))
+      .map((f) => f.replace(/\.json$/i, ""))
+      .filter(Boolean).sort((a, b) => a.localeCompare(b));
+  } catch (e) { return []; }
+}
+async function loadTemplate(name) {
+  try { const r = await app.api.getUserData(tplFile(name)); if (r.status === 200) return await r.text(); } catch (e) {}
+  return null;
+}
+async function saveTemplate(name, caption) {
+  try {
+    await app.api.storeUserData(tplFile(name), caption, { overwrite: true, stringify: false, throwOnError: true });
+    return true;
+  } catch (e) { window.alert("Couldn't save the template to the server."); return false; }
+}
+async function deleteTemplate(name) {
+  try { await app.api.deleteUserData(tplFile(name)); } catch (e) {}
+}
+
+// Parse a #rrggbb hex into {r,g,b}, or null if malformed.
+function hexRgb(hex) {
+  const h = (hex || "").replace("#", "");
+  if (h.length < 6) return null;
+  return { r: parseInt(h.slice(0, 2), 16), g: parseInt(h.slice(2, 4), 16), b: parseInt(h.slice(4, 6), 16) };
+}
+// Perceived luminance (0-255) of an {r,g,b}.
+function luminance({ r, g, b }) { return 0.299 * r + 0.587 * g + 0.114 * b; }
+
+// The hex color lightened toward white if too dark, so it stays readable on the dark canvas.
+function readableText(hex) {
+  const c = hexRgb(hex);
+  if (!c) return "#d4d4d4";
+  let { r, g, b } = c;
+  const lum = luminance(c), MIN = 130;
+  if (lum < MIN) {
+    const t = (MIN - lum) / (255 - lum);
+    r = Math.round(r + (255 - r) * t); g = Math.round(g + (255 - g) * t); b = Math.round(b + (255 - b) * t);
+  }
+  return `rgb(${r},${g},${b})`;
+}
+
+// Black or white, whichever contrasts better with the given hex background.
+function textOn(hex) {
+  const c = hexRgb(hex);
+  if (!c) return "#000";
+  return luminance(c) > 140 ? "#000" : "#fff";
+}
+
+// Parse a clipboard string into a normalized #rrggbb, or null if it isn't a color.
+function parseColorString(s) {
+  if (!s) return null;
+  s = s.trim();
+  let m = s.match(/^#?([0-9a-fA-F]{6})$/);
+  if (m) return "#" + m[1].toLowerCase();
+  m = s.match(/^#?([0-9a-fA-F]{3})$/);
+  if (m) { const h = m[1]; return ("#" + h[0] + h[0] + h[1] + h[1] + h[2] + h[2]).toLowerCase(); }
+  m = s.match(/^rgba?\(\s*(\d+)\s*[, ]\s*(\d+)\s*[, ]\s*(\d+)/i);
+  if (m) {
+    const h2 = (n) => Math.max(0, Math.min(255, parseInt(n, 10))).toString(16).padStart(2, "0");
+    return "#" + h2(m[1]) + h2(m[2]) + h2(m[3]);
+  }
+  return null;
+}
+// The palette swatch under the cursor + a setter, so Ctrl+V pastes a color onto it
+// and Ctrl+C copies its hex.
+let hoveredSwatch = null;
+document.addEventListener("keydown", (e) => {
+  if (!hoveredSwatch) return;
+  const ctrl = e.ctrlKey || e.metaKey;
+  if (!ctrl) return;
+  const key = e.key.toLowerCase();
+  if (key !== "v" && key !== "c") return;
+  const ae = document.activeElement;          // don't steal copy/paste from a focused text field
+  if (ae && (ae.tagName === "TEXTAREA" || ae.tagName === "INPUT" || ae.isContentEditable)) return;
+  const target = hoveredSwatch;
+  if (!target.sw || !target.sw.isConnected) return;
+  e.preventDefault(); e.stopPropagation();
+  if (key === "c") {
+    navigator.clipboard?.writeText?.(target.sw.dataset.hex || "").catch(() => {});
+  } else {
+    navigator.clipboard?.readText?.().then((txt) => {
+      const c = parseColorString(txt);
+      if (c && target.sw && target.sw.isConnected) target.setColor(c);
+    }).catch(() => {});
+  }
+}, true);
+
+// The node whose canvas is under the cursor — lets H toggle box visibility on hover (no click needed).
+let hoveredCanvasNode = null;
+document.addEventListener("keydown", (e) => {
+  if (!hoveredCanvasNode) return;
+  if (e.ctrlKey || e.metaKey || e.altKey || (e.key !== "h" && e.key !== "H")) return;
+  const ae = document.activeElement;
+  if (ae && (ae.tagName === "TEXTAREA" || ae.tagName === "INPUT" || ae.isContentEditable)) return;
+  e.preventDefault(); e.stopPropagation();
+  hoveredCanvasNode._toggleHideBoxes?.();
+}, true);
+// Alt held over a canvas: preview the alt-click cycle target even without moving the mouse.
+document.addEventListener("keydown", (e) => { if (hoveredCanvasNode && e.key === "Alt") hoveredCanvasNode._altRefresh?.(true); }, true);
+document.addEventListener("keyup", (e) => { if (hoveredCanvasNode && e.key === "Alt") hoveredCanvasNode._altRefresh?.(false); }, true);
+
+// Pinned dock follows the node: Nodes 2.0 hosts it inside the node element (inherits its transform); legacy is body-fixed.
+const pinnedDocks = new Set();
+const liveDocks = new Set();      // every node with a dock — swept so an orphaned dock can't linger
+let _dockRectNonce = 0;
+window.addEventListener("resize", () => { _dockRectNonce++; wakeDocks(); });   // canvas may shift without pan/zoom
+// Safety net: a dock whose node has left the graph (deleted, workflow cleared) is torn down even if onRemoved didn't fire.
+function sweepOrphanDocks() {
+  for (const n of liveDocks) {
+    if (n.graph?.getNodeById?.(n.id) === n) continue;   // still registered in its graph
+    try { n._dockRO?.disconnect(); } catch (e) {}
+    try { n._visObserver?.disconnect(); } catch (e) {}
+    n._dockEl?.remove();
+    pinnedDocks.delete(n); liveDocks.delete(n);
+  }
+}
+// gr.x/gr.y = offset (graph units) from the node's content top-left (node.pos).
+function applyDockTransform(n) {
+  const c = app.canvas, fl = n._dockEl, gr = n.properties.dockGraph;
+  if (!c || !fl || !gr) return;
+  let nodeEl = null;
+  if (window.LiteGraph?.vueNodesMode && n.id != null) {        // cache; re-query only if it's gone (virtualization)
+    nodeEl = n._dockNodeEl;
+    if (!nodeEl || !nodeEl.isConnected) nodeEl = n._dockNodeEl = document.querySelector(`[data-node-id="${n.id}"]`);
+  }
+  if (nodeEl) {                                                 // host inside the node element → inherits its transform
+    const title = window.LiteGraph?.NODE_TITLE_HEIGHT ?? 30;   // element top is the title bar; node.pos sits below it
+    const sig = "v|" + gr.x + "|" + gr.y;
+    if (fl.parentElement !== nodeEl) {
+      nodeEl.appendChild(fl);
+      fl.style.position = "absolute"; fl.style.transform = ""; fl.style.transformOrigin = ""; fl.style.zIndex = "";
+      n._dockSig = "";
+    }
+    if (n._dockSig !== sig) { fl.style.left = gr.x + "px"; fl.style.top = (title + gr.y) + "px"; n._dockSig = sig; }
+    return;
+  }
+  // Legacy / fallback (no Vue element, e.g. just after a mode switch): body-fixed from node.pos + transform.
+  if (fl.parentElement !== document.body) { document.body.appendChild(fl); fl.style.left = ""; fl.style.top = ""; n._dockSig = ""; }  // re-attach if orphaned
+  if (!n.pos) return;
+  const ds = c.ds, scale = ds.scale, rect = c.canvas.getBoundingClientRect();
+  const baseLeft = rect.left + (n.pos[0] + gr.x + ds.offset[0]) * scale;
+  const baseTop = rect.top + (n.pos[1] + gr.y + ds.offset[1]) * scale;
+  const tf = `translate(${baseLeft}px,${baseTop}px) scale(${scale})`;
+  if (n._dockSig !== tf) { fl.style.position = "fixed"; fl.style.transformOrigin = "top left"; fl.style.transform = tf; n._dockSig = tf; }
+  const order = n.graph?.nodes?.indexOf(n);            // sit at the node DOM-widget layer (not above everything)
+  if (order != null && order >= 0) fl.style.zIndex = String(order);
+}
+// Dock chrome follows the node's color theme (title → header, body → background); falls back to dark when uncolored.
+function applyDockTheme(n) {
+  const fl = n._dockEl; if (!fl) return;
+  const sig = (n.color || "") + "|" + (n.bgcolor || "");
+  if (n._dockTheme === sig) return;
+  n._dockTheme = sig;
+  const set = (k, v) => (v ? fl.style.setProperty(k, v) : fl.style.removeProperty(k));
+  set("--kj-dock-bg", n.bgcolor);
+  set("--kj-dock-head", n.color);
+  set("--kj-dock-border", n.color || n.bgcolor);
+}
+// Event-driven loop: woken by canvas redraws (dirty-gated) + resize, self-stops when settled.
+const DOCK_IDLE_STOP = 6;        // stop after this many frames with no further wake
+let _dockRAF = 0, _dockLoopSig = "", _dockLastMode = null, _dockIdle = 0, _dockWakesInstalled = false;
+function tickDocks() {
+  const c = app.canvas;
+  if (c && pinnedDocks.size) {
+    for (const n of pinnedDocks) applyDockTheme(n);   // cheap: cached, only writes on color change
+    const vue = !!window.LiteGraph?.vueNodesMode;
+    if (vue !== _dockLastMode) {                  // legacy↔2.0 flip rebuilds the node DOM — force re-parent + re-place
+      _dockLastMode = vue; _dockLoopSig = "";
+      for (const n of pinnedDocks) { n._dockSig = ""; n._dockNodeEl = null; }
+    }
+    if (vue) {
+      for (const n of pinnedDocks) {
+        applyDockTransform(n);
+        const w = n._dockWrap;                     // Vue re-mounts the widget into the node body — pull it back
+        if (w && n._dockBody && !n._fullscreen && w.parentElement !== n._dockBody) { n._dockBody.appendChild(w); n._dockSig = ""; }
+      }
+    } else {                                       // legacy: skip rect reads when nothing moved
+      const ds = c.ds;
+      let sig = _dockRectNonce + "|" + ds.scale + "|" + ds.offset[0] + "|" + ds.offset[1];
+      for (const n of pinnedDocks) sig += n.pos ? "|" + n.pos[0] + "," + n.pos[1] : "|";
+      if (sig !== _dockLoopSig) { _dockLoopSig = sig; for (const n of pinnedDocks) applyDockTransform(n); }
+    }
+  }
+  if (pinnedDocks.size && ++_dockIdle < DOCK_IDLE_STOP) _dockRAF = requestAnimationFrame(tickDocks);
+  else { _dockRAF = 0; _dockLoopSig = ""; }       // settled — stop until the next wake
+}
+function wakeDocks() {            // a canvas change happened — run/continue the loop until it settles
+  _dockIdle = 0;
+  if (!_dockRAF && pinnedDocks.size) _dockRAF = requestAnimationFrame(tickDocks);
+}
+function installDockWakes() {     // onDrawForeground only fires when dirty_canvas is set
+  if (_dockWakesInstalled) return;
+  const c = app.canvas; if (!c) return;
+  _dockWakesInstalled = true;
+  chainCallback(c, "onDrawForeground", () => { sweepOrphanDocks(); wakeDocks(); });
+}
+function startDockLoop() { installDockWakes(); wakeDocks(); }
+
+// Pointer-capture drag: filters moves by pointerId, auto-removes its listeners on release, calls onEnd.
+function dragPointer(e, target, onMove, onEnd) {
+  try { target.setPointerCapture(e.pointerId); } catch (er) {}
+  const move = (me) => { if (me.pointerId === e.pointerId) onMove(me); };
+  const end = (ue) => {
+    if (ue.pointerId !== e.pointerId) return;
+    target.removeEventListener("pointermove", move);
+    target.removeEventListener("pointerup", end);
+    target.removeEventListener("pointercancel", end);
+    if (onEnd) onEnd(ue);
+  };
+  target.addEventListener("pointermove", move);
+  target.addEventListener("pointerup", end);
+  target.addEventListener("pointercancel", end);
+}
+
+// Outside-click dismissal for a popup: arm() after showing, disarm() after hiding.
+function outsideDismiss(menu, onDismiss, anchor) {
+  let handler = null;
+  const disarm = () => {
+    if (!handler) return;
+    document.removeEventListener("pointerdown", handler, true);
+    document.removeEventListener("mousedown", handler, true);
+    handler = null;
+  };
+  const arm = () => {
+    disarm();
+    handler = (e) => { if (!menu.contains(e.target) && e.target !== anchor) onDismiss(); };
+    setTimeout(() => {                                  // defer so the opening click itself doesn't dismiss
+      if (!handler) return;
+      document.addEventListener("pointerdown", handler, true);
+      document.addEventListener("mousedown", handler, true);
+    }, 0);
+  };
+  return { arm, disarm };
+}
+
+function injectStyle() {
+  if (document.getElementById("kjideo-style")) return;
+  const s = document.createElement("style");
+  s.id = "kjideo-style";
+  s.textContent = `
+    .kjideo-wrap { display:flex; flex-direction:column; overflow:hidden; position:relative; pointer-events:auto; gap:4px; }
+    .kjideo-cv { flex:1 1 auto; min-height:60px; display:flex; align-items:center; justify-content:center; overflow:hidden; }
+    .kjideo-canvas { cursor:crosshair; display:block; flex:0 0 auto; background:#1a1a1a; border-radius:4px; outline:none; touch-action:none; }
+    .kjideo-bar { display:flex; align-items:center; gap:6px; font:11px sans-serif; color:#aaa; user-select:none; padding:0 2px; flex:0 0 auto; }
+    .kjideo-panel { display:flex; flex-direction:column; gap:5px; padding:6px; background:#262626; border-radius:4px; font:11px sans-serif; color:#bbb; flex:0 0 auto; overflow-y:auto; min-height:0; }
+    .kjideo-split { flex:0 0 auto; height:8px; cursor:ns-resize; position:relative; }
+    .kjideo-split::before { content:""; position:absolute; left:50%; top:50%; transform:translate(-50%,-50%); width:34px; height:3px; background:#555; border-radius:2px; }
+    .kjideo-split:hover::before { background:#46b4e6; }
+    .kjideo-row { display:flex; align-items:center; gap:6px; flex-wrap:wrap; }
+    .kjideo-btn { background:#333; border:1px solid #555; border-radius:4px; color:#bbb; font:11px sans-serif; cursor:pointer; padding:2px 8px; line-height:16px; white-space:nowrap; flex-shrink:0; }
+    .kjideo-btn:hover { border-color:#46b4e6; color:#fff; }
+    .kjideo-btn.active { border-color:#46b4e6; color:#46b4e6; background:#2a3a42; }
+    .kjideo-area { width:100%; box-sizing:border-box; background:#1d1d1d; border:1px solid #444; border-radius:4px; color:#ddd; font:13px monospace; padding:4px 6px; resize:none; min-height:36px; flex:1 1 auto; }
+    .kjideo-sw { width:20px; height:20px; border:1px solid #666; border-radius:3px; cursor:pointer; flex-shrink:0; position:relative; touch-action:none; transition:transform .18s ease, box-shadow .12s ease, opacity .12s ease; }
+    .kjideo-sw:hover { transform:scale(1.2); box-shadow:0 0 0 2px #46b4e6; z-index:3; }
+    .kjideo-sw.dragging { opacity:.4; box-shadow:0 0 0 2px #46b4e6; }
+    body.kjideo-dragging, body.kjideo-dragging * { cursor:move !important; }
+    .kjideo-sw input { position:absolute; opacity:0; width:0; height:0; pointer-events:none; }
+    .kjideo-inline { position:absolute; box-sizing:border-box; background:rgba(18,18,18,0.92); border:2px solid #46b4e6; border-radius:3px; color:#fff; font:13px monospace; padding:3px 4px; resize:none; outline:none; z-index:10; }
+    .kjideo-bbox { width:128px; box-sizing:border-box; background:#1d1d1d; border:1px solid #444; border-radius:4px; color:#bbb; font:11px monospace; padding:2px 5px; }
+    .kjideo-bbox:focus { border-color:#46b4e6; outline:none; color:#fff; }
+    .kjideo-menu { position:fixed; z-index:10000; background:#262626; border:1px solid #555; border-radius:6px; padding:4px; box-shadow:0 6px 20px rgba(0,0,0,0.55); font:12px sans-serif; color:#ddd; max-height:60vh; overflow-y:auto; min-width:210px; max-width:340px; }
+    .kjideo-mhdr { font:11px sans-serif; color:#888; padding:2px 6px 4px; user-select:none; }
+    .kjideo-lrow { display:flex; align-items:center; gap:6px; padding:3px 5px; border-radius:4px; cursor:move; user-select:none; touch-action:none; transition:transform .18s ease, box-shadow .12s ease, opacity .12s ease, background .12s; }
+    .kjideo-lrow:hover { background:#333; }
+    .kjideo-lrow.active { background:#2a3a42; box-shadow:inset 0 0 0 1px #46b4e6; }
+    .kjideo-lrow.dragging { opacity:.4; box-shadow:0 0 0 2px #46b4e6; background:#333; }
+    .kjideo-lsw { width:16px; height:16px; border-radius:3px; border:1px solid #666; flex:0 0 auto; }
+    .kjideo-lnum { font:bold 11px monospace; color:#888; flex:0 0 auto; width:18px; }
+    .kjideo-ltext { flex:1 1 auto; min-width:0; white-space:nowrap; overflow:hidden; text-overflow:ellipsis; }
+    .kjideo-ltext.empty { color:#777; font-style:italic; }
+    .kjideo-lbtn { background:none; border:none; color:#999; cursor:pointer; font:13px sans-serif; line-height:1; padding:2px 5px; border-radius:3px; flex:0 0 auto; }
+    .kjideo-lbtn:hover { color:#fff; background:#444; }
+    .kjideo-lbtn.del:hover { color:#fff; background:#a33; }
+    .kjideo-lbtn.on { background:#3a3320; }
+    .kjideo-lock { filter:grayscale(1); opacity:0.4; }                 /* unlocked: faded grey */
+    .kjideo-lock.on, .kjideo-lock:hover { filter:none; opacity:1; }    /* locked / hover: full colour */
+    .kjideo-lbtn:disabled { opacity:0.25; cursor:default; background:none; }
+    .kjideo-fs { position:fixed; inset:0; z-index:9000; background:rgba(0,0,0,0.72); display:flex; align-items:center; justify-content:center; }
+    .kjideo-fs-inner { position:relative; width:88vw; height:90vh; background:#1a1a1a; border:1px solid #444; border-radius:8px; box-shadow:0 12px 48px rgba(0,0,0,0.6); padding:12px; box-sizing:border-box; }
+    .kjideo-fs-inner .kjideo-wrap { height:100%; }
+    .kjideo-fs-close { position:absolute; top:14px; right:18px; z-index:5; padding:4px 12px; font-size:14px; }
+    .kjideo-dock { position:fixed; z-index:8500; pointer-events:auto; display:flex; flex-direction:column; background:var(--kj-dock-bg,#1a1a1a); border:1px solid var(--kj-dock-border,#555); border-radius:8px; box-shadow:0 8px 30px rgba(0,0,0,0.55); min-width:300px; min-height:240px; overflow:hidden; }
+    .kjideo-rsz { position:absolute; z-index:20; touch-action:none; }
+    .kjideo-rsz.n { top:0; left:11px; right:11px; height:6px; cursor:ns-resize; }
+    .kjideo-rsz.s { bottom:0; left:11px; right:11px; height:6px; cursor:ns-resize; }
+    .kjideo-rsz.e { right:0; top:11px; bottom:11px; width:6px; cursor:ew-resize; }
+    .kjideo-rsz.w { left:0; top:11px; bottom:11px; width:6px; cursor:ew-resize; }
+    .kjideo-rsz.ne { top:0; right:0; width:12px; height:12px; cursor:nesw-resize; }
+    .kjideo-rsz.nw { top:0; left:0; width:12px; height:12px; cursor:nwse-resize; }
+    .kjideo-rsz.se { bottom:0; right:0; width:12px; height:12px; cursor:nwse-resize; }
+    .kjideo-rsz.sw { bottom:0; left:0; width:12px; height:12px; cursor:nesw-resize; }
+    .kjideo-dock.minimized { min-height:0 !important; height:auto !important; }
+    .kjideo-dock.minimized .kjideo-dock-body { display:none; }
+    .kjideo-dock.minimized .kjideo-rsz { display:none; }
+    .kjideo-dock-head { display:flex; align-items:center; gap:6px; padding:4px 8px; background:var(--kj-dock-head,#262626); cursor:move; font:12px sans-serif; color:#ccc; user-select:none; border-bottom:1px solid rgba(0,0,0,0.25); flex:0 0 auto; }
+    .kjideo-dock-head .kjideo-btn { padding:1px 7px; }
+    .kjideo-dock-body { flex:1 1 auto; min-height:0; padding:8px; box-sizing:border-box; overflow:hidden; }
+    .kjideo-dock-body .kjideo-wrap { height:100%; }
+    .kjideo-bgmenu { padding:7px; display:flex; flex-direction:column; gap:7px; min-width:170px; }
+    .kjideo-bgrow { display:flex; align-items:center; gap:8px; }
+    .kjideo-bglbl { color:#888; font:11px sans-serif; flex:0 0 auto; min-width:62px; }
+    .kjideo-trow { padding:2px 4px; border-radius:4px; }
+    .kjideo-trow:hover { background:#333; }
+  `;
+  document.head.appendChild(s);
+}
+
+app.registerExtension({
+  name: "KJNodes.Ideogram4PromptBuilder",
+
+  async beforeRegisterNodeDef(nodeType, nodeData) {
+    if (nodeData?.name !== "Ideogram4PromptBuilderKJ") return;
+    injectStyle();
+
+    chainCallback(nodeType.prototype, "onNodeCreated", function () {
+      const node = this;
+      const findW = (n) => node.widgets?.find((w) => w.name === n);
+      const elementsWidget = findW("elements_data");
+      const stylePaletteWidget = findW("style_palette_data");
+      const bgBrightnessWidget = findW("bg_brightness");
+      const formatWidget = findW("output_format");           // "pretty" | "compact" (set via the Text menu)
+      if (bgBrightnessWidget && typeof bgBrightnessWidget.value !== "number") bgBrightnessWidget.value = 25;
+      const wWidget = findW("width"), hWidget = findW("height");
+      // Hide the data widgets while keeping them serializable.
+      function hideDataWidgets() {
+        for (const w of [elementsWidget, stylePaletteWidget, bgBrightnessWidget, formatWidget]) {
+          if (!w) continue;
+          w.hidden = true;
+          w.computeSize = () => [0, -4];
+        }
+        for (const name of ["elements_data", "style_palette_data", "bg_brightness"]) {
+          const i = node.inputs?.findIndex((inp) => inp.name === name);
+          if (i != null && i !== -1) node.removeInput(i);
+        }
+      }
+      hideDataWidgets();
+
+      node._boxes = [];        // {x,y,w,h normalized 0-1, type, text, desc, palette[]}
+      node._stylePalette = []; // global style color palette (hex[])
+      node._activeIdx = -1;
+      node._drawing = false;
+      node._placing = false;   // duplicate-placement mode: active box follows the cursor until clicked
+      node._dragMode = null;
+      node._dragStartN = null; // mouse-down point, normalized
+      node._boxAtStart = null; // active box snapshot at drag start
+      node._selection = new Set();   // selected box indices (multi-select); always contains _activeIdx
+      node._groupStart = null;       // {idx: {x,y,w,h}} snapshot of all selected boxes at drag start
+      node._pendingCollapse = -1;    // box to collapse selection to if a click (not drag) on a multi-selection
+      node._marquee = null;          // {x0,y0,x,y} rubber-band selection rect (shift-drag), normalized
+      node._hoverTitle = null; // index of the title chip under the cursor
+      node._hoverBox = null;   // index of the box under the cursor
+      node._focused = false;   // editor (DOM) focused — gates the active-box highlight
+      node._selected = false;  // node selected in the graph
+      node._bgImg = null;      // optional reference image shown as the canvas background
+      node._bgManual = false;  // bg set via "use last result" (not the image input)
+      node._lastImported = ""; // last import_json applied to the editor (avoid re-apply)
+      node._areaObservers = [];             // (reserved) live ResizeObservers to disconnect on rebuild
+
+      // ── DOM ──
+      const wrap = document.createElement("div");
+      wrap.className = "kjideo-wrap";
+      const bar = document.createElement("div");
+      bar.className = "kjideo-bar";
+      const hint = document.createElement("span");
+      hint.style.flex = "1";
+      const copyBtn = document.createElement("button");
+      copyBtn.className = "kjideo-btn";
+      copyBtn.textContent = "Copy";
+      copyBtn.title = "Copy the current caption JSON to the clipboard";
+      const importBtn = document.createElement("button");
+      importBtn.className = "kjideo-btn";
+      importBtn.textContent = "Paste";
+      importBtn.title = "Parse a caption JSON (clipboard, else paste prompt) and populate the node";
+      const clearBtn = document.createElement("button");
+      clearBtn.className = "kjideo-btn";
+      clearBtn.textContent = "Clear all";
+      const tokenSpan = document.createElement("span");
+      tokenSpan.style.cssText = "color:#888; white-space:nowrap;";
+      tokenSpan.title = "Rough token estimate (~chars/4). Grey <256, green healthy, orange nearing, red ≥2048 (model cap — will error)";
+      // Compact-output toggle next to the token count (compact is the format Ideogram 4 expects — default on).
+      const compactLbl = document.createElement("label");
+      compactLbl.style.cssText = "display:flex;align-items:center;gap:3px;cursor:pointer;flex:0 0 auto;color:#aaa;white-space:nowrap;";
+      compactLbl.title = "Compact JSON output (the format Ideogram 4 was trained on). Uncheck for pretty/indented. Copy reflects it.";
+      const compactCb = document.createElement("input"); compactCb.type = "checkbox";
+      compactCb.checked = (formatWidget?.value) !== "pretty";   // default: compact
+      stopProp(compactCb);
+      compactCb.addEventListener("change", () => { if (formatWidget) formatWidget.value = compactCb.checked ? "compact" : "pretty"; updateTokens(); });
+      compactLbl.appendChild(compactCb); compactLbl.appendChild(document.createTextNode("compact"));
+      compactLbl._cb = compactCb;
+      const grabBtn = document.createElement("button");
+      grabBtn.className = "kjideo-btn";
+      grabBtn.addEventListener("mousedown", (e) => e.stopPropagation());
+      grabBtn.addEventListener("click", () => { (node._bgManual && node._bgImg) ? node._clearBg() : node._grabResultBg(); });
+      function updateGrabBtn() {
+        const clear = node._bgManual && node._bgImg;
+        grabBtn.textContent = clear ? "Clear BG" : "Grab BG";
+        grabBtn.title = clear ? "Remove the grabbed background"
+          : "Use the last generated image as the background";
+      }
+      const liveLabel = document.createElement("label");
+      liveLabel.style.cssText = "display:flex;align-items:center;gap:3px;flex:0 0 auto;cursor:pointer;";
+      liveLabel.title = "Use the live sampling preview as the background while generating";
+      const liveChk = document.createElement("input");
+      liveChk.type = "checkbox";
+      liveChk.checked = !!node.properties.liveBg;
+      liveChk.addEventListener("mousedown", (e) => e.stopPropagation());
+      liveChk.addEventListener("change", () => {
+        node.properties.liveBg = liveChk.checked;
+        if (liveChk.checked) livePreviewNodes.add(node); else livePreviewNodes.delete(node);
+      });
+      liveLabel.appendChild(liveChk); liveLabel.appendChild(document.createTextNode("Live"));
+      if (liveChk.checked) livePreviewNodes.add(node);
+      const bgSlider = document.createElement("input");
+      bgSlider.type = "range"; bgSlider.min = "0"; bgSlider.max = "100"; bgSlider.step = "1";
+      bgSlider.value = bgBrightnessWidget ? bgBrightnessWidget.value : 25;
+      bgSlider.title = "Background brightness (image or blank canvas)";
+      bgSlider.style.cssText = "width:64px;flex:0 0 auto;";
+      stopProp(bgSlider);
+      bgSlider.addEventListener("input", () => { if (bgBrightnessWidget) bgBrightnessWidget.value = parseInt(bgSlider.value, 10); drawCanvas(); });
+      const guideSel = document.createElement("select");
+      guideSel.className = "kjideo-btn"; guideSel.style.cssText = "flex:0 0 auto;";
+      guideSel.title = "Composition guide overlay (editor view only)";
+      for (const [val, label] of [["none", "no guide"], ["thirds", "thirds"], ["grid", "grid"], ["golden", "golden ratio"], ["spiral", "golden spiral"]]) {
+        const o = document.createElement("option"); o.value = val; o.textContent = label; guideSel.appendChild(o);
+      }
+      guideSel.value = node.properties.guide || "none";
+      stopProp(guideSel);
+      guideSel.addEventListener("change", () => { node.properties.guide = guideSel.value; drawCanvas(); });
+      // Group the background/guide controls into one popup to keep the toolbar tidy.
+      const bgBtn = document.createElement("button");
+      bgBtn.className = "kjideo-btn"; bgBtn.textContent = "Background ▾";
+      bgBtn.title = "Background & guides: live preview, grab/clear, brightness, composition guide";
+      stopProp(bgBtn);
+      const GRID_INV = 130;       // slider shows cell SIZE: position = GRID_INV - divisions (right = larger cells)
+      const gridSlider = document.createElement("input");
+      gridSlider.type = "range"; gridSlider.min = "2"; gridSlider.max = "128"; gridSlider.step = "1";
+      gridSlider.value = GRID_INV - (node.properties.gridSize || 10);
+      gridSlider.style.cssText = "width:90px;flex:0 0 auto;";
+      gridSlider.title = "Grid cell size (drag right for larger cells); also the snap step";
+      stopProp(gridSlider);
+      gridSlider.addEventListener("input", () => { node.properties.gridSize = GRID_INV - parseInt(gridSlider.value, 10); drawCanvas(); });
+      const snapLabel = document.createElement("label");
+      snapLabel.style.cssText = "display:flex;align-items:center;gap:4px;cursor:pointer;";
+      const snapChk = document.createElement("input");
+      snapChk.type = "checkbox"; snapChk.checked = !!node.properties.snap;
+      snapChk.addEventListener("change", () => { node.properties.snap = snapChk.checked; });
+      snapLabel.appendChild(snapChk); snapLabel.appendChild(document.createTextNode("snap to grid"));
+      const guideColor = document.createElement("input");
+      guideColor.type = "color"; guideColor.value = node.properties.guideColor || "#ffffff";
+      guideColor.style.cssText = "width:32px;height:20px;flex:0 0 auto;padding:0;border:1px solid #555;background:none;";
+      stopProp(guideColor);
+      guideColor.addEventListener("input", () => { node.properties.guideColor = guideColor.value; drawCanvas(); });
+      const opacitySlider = document.createElement("input");
+      opacitySlider.type = "range"; opacitySlider.min = "0"; opacitySlider.max = "100"; opacitySlider.step = "1";
+      opacitySlider.value = node.properties.guideOpacity == null ? 100 : node.properties.guideOpacity;
+      opacitySlider.style.cssText = "width:90px;flex:0 0 auto;";
+      opacitySlider.title = "Guide/grid line opacity";
+      stopProp(opacitySlider);
+      opacitySlider.addEventListener("input", () => { node.properties.guideOpacity = parseInt(opacitySlider.value, 10); drawCanvas(); });
+      const bgMenu = document.createElement("div");
+      bgMenu.className = "kjideo-menu kjideo-bgmenu";
+      bgMenu.style.display = "none";
+      const bgRow = (labelText, el) => {
+        const r = document.createElement("div"); r.className = "kjideo-bgrow";
+        if (labelText) { const l = document.createElement("span"); l.className = "kjideo-bglbl"; l.textContent = labelText; r.appendChild(l); }
+        r.appendChild(el); bgMenu.appendChild(r);
+      };
+      bgRow("", liveLabel); bgRow("", grabBtn); bgRow("Brightness", bgSlider);
+      bgRow("Guide", guideSel); bgRow("Grid size", gridSlider); bgRow("", snapLabel);
+      bgRow("Line color", guideColor); bgRow("Line opacity", opacitySlider);
+      document.body.appendChild(bgMenu);
+      node._bgMenu = bgMenu;
+      const bgDismiss = outsideDismiss(bgMenu, () => closeBgMenu(), bgBtn);
+      function closeBgMenu() { bgMenu.style.display = "none"; bgDismiss.disarm(); }
+      bgBtn.addEventListener("click", () => {
+        if (bgMenu.style.display !== "none") { closeBgMenu(); return; }
+        bgMenu.style.display = "";
+        const r = bgBtn.getBoundingClientRect();
+        bgMenu.style.left = Math.max(4, Math.min(r.left, window.innerWidth - bgMenu.offsetWidth - 4)) + "px";
+        bgMenu.style.top = Math.min(r.bottom + 4, window.innerHeight - bgMenu.offsetHeight - 4) + "px";
+        bgDismiss.arm();
+      });
+      // ── Text style popup: show/hide, outline, font size, auto-placement ──
+      const txtBtn = document.createElement("button");
+      txtBtn.className = "kjideo-btn"; txtBtn.textContent = "Text ▾";
+      txtBtn.title = "Region text: show/hide, outline, font size, overlap-avoiding placement";
+      stopProp(txtBtn);
+      const txtToggle = (prop, label, title) => {
+        const l = document.createElement("label");
+        l.style.cssText = "display:flex;align-items:center;gap:4px;cursor:pointer;"; l.title = title || "";
+        const cb = document.createElement("input"); cb.type = "checkbox"; cb.checked = node.properties[prop] !== false;
+        cb.addEventListener("change", () => { node.properties[prop] = cb.checked; drawCanvas(); });
+        l.appendChild(cb); l.appendChild(document.createTextNode(label));
+        l._cb = cb; return l;
+      };
+      const showLbl = txtToggle("showBoxText", "show text", "Draw each region's text inside its box");
+      const strokeLbl = txtToggle("textStroke", "outline", "Dark halo behind the text for legibility");
+      const autoLbl = txtToggle("textAutoPlace", "auto-place", "Stagger overlapping labels to reduce overlap");
+      const sizeSlider = document.createElement("input");
+      sizeSlider.type = "range"; sizeSlider.min = "8"; sizeSlider.max = "22"; sizeSlider.step = "1";
+      sizeSlider.value = node.properties.textSize || 12;
+      sizeSlider.style.cssText = "width:90px;flex:0 0 auto;";
+      sizeSlider.title = "Region text font size";
+      stopProp(sizeSlider);
+      sizeSlider.addEventListener("input", () => { node.properties.textSize = parseInt(sizeSlider.value, 10); drawCanvas(); });
+      const boxOpacSlider = document.createElement("input");
+      boxOpacSlider.type = "range"; boxOpacSlider.min = "0"; boxOpacSlider.max = "100"; boxOpacSlider.step = "1";
+      boxOpacSlider.value = node.properties.boxOpacity == null ? 14 : node.properties.boxOpacity;
+      boxOpacSlider.style.cssText = "width:90px;flex:0 0 auto;";
+      boxOpacSlider.title = "Box fill opacity";
+      stopProp(boxOpacSlider);
+      boxOpacSlider.addEventListener("input", () => { node.properties.boxOpacity = parseInt(boxOpacSlider.value, 10); drawCanvas(); });
+      const txtMenu = document.createElement("div");
+      txtMenu.className = "kjideo-menu kjideo-bgmenu";
+      txtMenu.style.display = "none";
+      const txtRow = (labelText, el) => {
+        const r = document.createElement("div"); r.className = "kjideo-bgrow";
+        if (labelText) { const l = document.createElement("span"); l.className = "kjideo-bglbl"; l.textContent = labelText; r.appendChild(l); }
+        r.appendChild(el); txtMenu.appendChild(r);
+      };
+      txtRow("", showLbl); txtRow("Font size", sizeSlider); txtRow("Box opacity", boxOpacSlider); txtRow("", strokeLbl); txtRow("", autoLbl);
+      document.body.appendChild(txtMenu);
+      node._txtMenu = txtMenu;
+      const txtDismiss = outsideDismiss(txtMenu, () => closeTxtMenu(), txtBtn);
+      function closeTxtMenu() { txtMenu.style.display = "none"; txtDismiss.disarm(); }
+      txtBtn.addEventListener("click", () => {
+        if (txtMenu.style.display !== "none") { closeTxtMenu(); return; }
+        txtMenu.style.display = "";
+        const r = txtBtn.getBoundingClientRect();
+        txtMenu.style.left = Math.max(4, Math.min(r.left, window.innerWidth - txtMenu.offsetWidth - 4)) + "px";
+        txtMenu.style.top = Math.min(r.bottom + 4, window.innerHeight - txtMenu.offsetHeight - 4) + "px";
+        txtDismiss.arm();
+      });
+      // ── Templates popup: save/load named caption JSONs (server-side userdata) ──
+      const tplBtn = document.createElement("button");
+      tplBtn.className = "kjideo-btn"; tplBtn.textContent = "Templates ▾";
+      tplBtn.title = "Save / load the caption JSON as templates (stored on the server ComfyUI\\user\\default\\kjnodes\\ideogram4)";
+      stopProp(tplBtn);
+      const tplMenu = document.createElement("div");
+      tplMenu.className = "kjideo-menu kjideo-bgmenu";
+      tplMenu.style.display = "none";
+      document.body.appendChild(tplMenu);
+      node._tplMenu = tplMenu;
+      const tplDismiss = outsideDismiss(tplMenu, () => closeTplMenu(), tplBtn);
+      function closeTplMenu() { tplMenu.style.display = "none"; tplDismiss.disarm(); }
+      async function buildTplMenu() {
+        tplMenu.innerHTML = "";
+        const saveRow = document.createElement("div"); saveRow.className = "kjideo-bgrow";
+        const saveBtn = document.createElement("button"); saveBtn.className = "kjideo-btn"; saveBtn.textContent = "+ Save as…";
+        saveBtn.addEventListener("click", async () => {
+          const name = tplSafe(window.prompt("Save template as:", "") || "");
+          if (!name) return;
+          const existing = await listTemplateNames();
+          if (existing.includes(name) && !window.confirm(`Overwrite template "${name}"?`)) return;
+          if (await saveTemplate(name, buildCaption())) buildTplMenu();
+        });
+        saveRow.appendChild(saveBtn); tplMenu.appendChild(saveRow);
+        const names = await listTemplateNames();
+        if (!names.length) {
+          const empty = document.createElement("div"); empty.className = "kjideo-mhdr"; empty.textContent = "No templates saved.";
+          tplMenu.appendChild(empty);
+        }
+        for (const name of names) {
+          const row = document.createElement("div"); row.className = "kjideo-bgrow kjideo-trow";
+          const txt = document.createElement("span");
+          txt.className = "kjideo-ltext"; txt.style.cssText = "flex:1 1 auto;cursor:pointer;"; txt.textContent = name; txt.title = "Load " + name + " (replaces everything)";
+          const ins = document.createElement("button"); ins.className = "kjideo-lbtn"; ins.textContent = "⊞"; ins.title = "Insert this template's boxes only into the current canvas";
+          const upd = document.createElement("button"); upd.className = "kjideo-lbtn"; upd.textContent = "⤓"; upd.title = "Save current (overwrite)";
+          const del = document.createElement("button"); del.className = "kjideo-lbtn del"; del.textContent = "✕"; del.title = "Delete template";
+          row.append(txt, ins, upd, del); tplMenu.appendChild(row);
+          txt.addEventListener("click", async () => {
+            const cap = tryParseCaption(await loadTemplate(name));
+            if (!cap) { window.alert("That template isn't a valid caption JSON."); return; }
+            loadCaption(cap); closeTplMenu();
+          });
+          ins.addEventListener("click", async (e) => {
+            e.stopPropagation();
+            const cap = tryParseCaption(await loadTemplate(name));
+            if (!cap) { window.alert("That template isn't a valid caption JSON."); return; }
+            insertCaptionBoxes(cap); closeTplMenu();
+          });
+          upd.addEventListener("click", async (e) => {
+            e.stopPropagation();
+            if (await saveTemplate(name, buildCaption())) buildTplMenu();
+          });
+          del.addEventListener("click", async (e) => {
+            e.stopPropagation();
+            if (!window.confirm(`Delete template "${name}"?`)) return;
+            await deleteTemplate(name); buildTplMenu();
+          });
+        }
+      }
+      tplBtn.addEventListener("click", async () => {
+        if (tplMenu.style.display !== "none") { closeTplMenu(); return; }
+        tplMenu.style.display = "";
+        await buildTplMenu();
+        const r = tplBtn.getBoundingClientRect();
+        tplMenu.style.left = Math.max(4, Math.min(r.left, window.innerWidth - tplMenu.offsetWidth - 4)) + "px";
+        tplMenu.style.top = Math.min(r.bottom + 4, window.innerHeight - tplMenu.offsetHeight - 4) + "px";
+        tplDismiss.arm();
+      });
+      bar.appendChild(hint); bar.appendChild(tokenSpan); bar.appendChild(compactLbl); bar.appendChild(bgBtn); bar.appendChild(txtBtn); bar.appendChild(copyBtn); bar.appendChild(importBtn); bar.appendChild(tplBtn); bar.appendChild(clearBtn);
+      updateGrabBtn();
+
+      // Persistent global style-palette row
+      const styleBar = document.createElement("div");
+      styleBar.className = "kjideo-bar";
+      const styleLbl = document.createElement("span");
+      styleLbl.textContent = "Style colors:";
+      styleBar.appendChild(styleLbl);
+
+      const canvasEl = document.createElement("canvas");
+      canvasEl.className = "kjideo-canvas";
+      canvasEl.tabIndex = 0;                                  // focusable, so it can receive key events
+      canvasEl.title = "Drag to draw · Ctrl-drag force-draw over a box · click to select · shift-drag marquee-select · " +
+        "shift-click toggle · drag a group to move all · alt-click overlap · dbl-click edit · right-click region list · " +
+        "Del remove (all selected) · Ctrl/Cmd+C/V/D copy/paste/duplicate · H hide boxes (view)";
+      const ctx = canvasEl.getContext("2d");
+      addWheelPassthrough(wrap);
+      addMiddleClickPan(canvasEl);
+
+      const panel = document.createElement("div");
+      panel.className = "kjideo-panel";
+      node._panelH = node._panelH || 150;                    // height of the prompt panel (set by the splitter)
+      panel.style.height = node._panelH + "px";
+
+      // Canvas letterbox-fits into a flex container (cvBox), so node resize never aspect-locks the height.
+      const cvBox = document.createElement("div");
+      cvBox.className = "kjideo-cv";
+      cvBox.appendChild(canvasEl);
+
+      // Draggable separator between the canvas and the prompt panel — drag up for more prompt, down for more canvas.
+      const splitter = document.createElement("div");
+      splitter.className = "kjideo-split";
+      splitter.title = "Drag to resize the description area";
+      splitter.addEventListener("pointerdown", (e) => {
+        if (e.button !== 0) return;
+        e.preventDefault(); e.stopPropagation();
+        const scale = (node.properties.dockPinned && app.canvas) ? (app.canvas.ds.scale || 1) : 1;
+        const sy = e.clientY, h0 = panel.offsetHeight;
+        dragPointer(e, splitter, (me) => {
+          const dy = (me.clientY - sy) / scale;
+          const h = Math.max(60, Math.min(h0 - dy, wrap.clientHeight - 110));   // up → bigger panel; keep room for the canvas
+          node._panelH = Math.round(h); panel.style.height = node._panelH + "px";   // persisted via the onSerialize blob
+          fitCanvas();
+        }, flushChange);
+      });
+
+      wrap.appendChild(bar); wrap.appendChild(styleBar); wrap.appendChild(cvBox); wrap.appendChild(splitter); wrap.appendChild(panel);
+
+      const EDITOR_MIN = 220;                                  // fixed floor; the node fills the rest and resizes freely
+      node.ideoEditor = node.addDOMWidget("ideo_editor", "Ideogram4Editor", wrap, {
+        serialize: false, hideOnZoom: false,
+        getMinHeight: () => (node._detached ? 0 : EDITOR_MIN),   // collapse when popped out
+      });
+      node.resizable = true;
+
+      // DOM widgets are HTML layered over the canvas; ComfyUI only repositions/clips them
+      // during a foreground draw. When the node returns from off-screen the element can
+      // briefly float over the canvas until the next draw — force one when it re-enters view.
+      try {
+        node._visObserver = new IntersectionObserver((entries) => {
+          if (entries.some((en) => en.isIntersecting)) {
+            app.canvas?.setDirtyCanvas?.(true, true);
+            drawCanvas();
+          }
+        });
+        node._visObserver.observe(wrap);
+      } catch (e) {}
+
+      // Letterbox-fit the canvas (keeping width/height aspect) into its flex container cvBox.
+      function fitCanvas() {
+        if (!node._detached && wrap.offsetParent === null) return;   // hidden tab — measurements are 0
+        const availW = cvBox.clientWidth, availH = cvBox.clientHeight;
+        if (availW < 4 || availH < 4) return;
+        const aspect = (wWidget?.value || 1) / (hWidget?.value || 1);
+        let cw = availW, ch = cw / aspect;
+        if (ch > availH) { ch = availH; cw = ch * aspect; }
+        canvasEl.style.width = Math.round(cw) + "px";
+        canvasEl.style.height = Math.round(ch) + "px";
+        drawCanvas();
+      }
+      const fitFsCanvas = fitCanvas;                           // dock/fullscreen use the same fitter
+      function syncCanvasToDims() { fitCanvas(); }             // aspect changed (width/height widgets)
+      function fitNode() {
+        if (node._detached) { fitCanvas(); return; }
+        if (wrap.offsetParent === null) return;                // hidden tab / off-screen — skip
+        const minH = node.computeSize()[1];                    // never sit below the floor
+        if (node.size[1] < minH) node.setSize([node.size[0], minH]);
+        fitCanvas();
+      }
+      function detachInto(container) {                        // move the editor out of the node
+        node._wrapHome = wrap.parentNode;
+        container.appendChild(wrap);
+        if (node.ideoEditor) node.ideoEditor.hidden = true;
+        node._detached = true;
+        node._fsInner = container;
+        node._dockWrap = wrap;                               // so the dock can reclaim it if Vue re-mounts the widget
+      }
+      function reattach() {                                   // fullscreen exit → back into the floating dock
+        canvasEl.style.width = ""; canvasEl.style.height = "";
+        const home = node._dockBody || node._wrapHome;
+        if (home) home.appendChild(wrap);
+        node._fsInner = node._dockBody || null;               // stays detached, living in the dock
+        if (node.graph) node.graph.setDirtyCanvas(true, true);
+        requestAnimationFrame(fitCanvas);
+      }
+
+      function onFsEsc(e) { if (e.key === "Escape") { e.preventDefault(); e.stopPropagation(); exitFs(); } }
+      function enterFs() {
+        if (node._fullscreen) return;
+        node._fullscreen = true;
+        const ov = document.createElement("div"); ov.className = "kjideo-fs";
+        const inner = document.createElement("div"); inner.className = "kjideo-fs-inner";
+        ov.appendChild(inner);
+        ov.addEventListener("mousedown", (e) => { if (e.target === ov) exitFs(); });  // backdrop closes
+        document.body.appendChild(ov);
+        node._fsOverlay = ov;
+        detachInto(inner);
+        const closeBtn = document.createElement("button");                            // visible exit (⛶ in the dock header is hidden here)
+        closeBtn.className = "kjideo-btn kjideo-fs-close"; closeBtn.textContent = "✕"; closeBtn.title = "Close (Esc)";
+        stopProp(closeBtn); closeBtn.addEventListener("click", exitFs);
+        ov.appendChild(closeBtn);                                                     // on the backdrop corner, clear of the editor
+        document.addEventListener("keydown", onFsEsc, true);
+        window.addEventListener("resize", fitFsCanvas);
+        requestAnimationFrame(fitFsCanvas);
+      }
+      function exitFs() {
+        if (!node._fullscreen) return;
+        node._fullscreen = false;
+        document.removeEventListener("keydown", onFsEsc, true);
+        window.removeEventListener("resize", fitFsCanvas);
+        node._fsOverlay?.remove(); node._fsOverlay = null;
+        reattach();
+      }
+
+      // ── floating / dockable editor panel ──
+      // Persist the panel geometry — screen-space rect when unpinned, graph-space size when pinned.
+      function saveDockGeom() {
+        const fl = node._dockEl; if (!fl) return;
+        if (node.properties.dockMin) return;                  // collapsed: don't persist the header-only size
+        if (node.properties.dockPinned) {
+          node.properties.dockGraph = node.properties.dockGraph || { x: 0, y: 0 };
+          node.properties.dockGraph.w = fl.offsetWidth; node.properties.dockGraph.h = fl.offsetHeight;
+        } else {
+          const r = fl.getBoundingClientRect();
+          node.properties.dockRect = { x: Math.round(r.left), y: Math.round(r.top), w: Math.round(r.width), h: Math.round(r.height) };
+        }
+      }
+      // Nudge ComfyUI's change tracker (it snapshots on mouseup, which our preventDefault'd drags suppress).
+      function flushChange() { try { window.dispatchEvent(new MouseEvent("mouseup")); } catch (e) {} }
+      function setPinned(on, pinBtn) {
+        const fl = node._dockEl, c = app.canvas; if (!fl || !c) return;
+        const rect = c.canvas.getBoundingClientRect(), ds = c.ds;
+        if (on) {                                          // return to where it was pinned (the saved dockGraph)
+          let g = node.properties.dockGraph;
+          if (!g) {                                        // never pinned before — derive from the current float position
+            const r = fl.getBoundingClientRect();
+            const gx = (r.left - rect.left) / ds.scale - ds.offset[0], gy = (r.top - rect.top) / ds.scale - ds.offset[1];
+            g = node.properties.dockGraph = {
+              x: gx - (node.pos?.[0] ?? 0), y: gy - (node.pos?.[1] ?? 0),
+              w: Math.round(fl.offsetWidth / ds.scale), h: Math.round(fl.offsetHeight / ds.scale),
+            };
+          }
+          node.properties.dockPinned = true;
+          fl.style.left = ""; fl.style.top = "";
+          fl.style.width = g.w + "px"; fl.style.height = g.h + "px";
+          node._dockSig = ""; node._dockNodeEl = null;     // force a fresh place + re-parent
+          pinnedDocks.add(node); startDockLoop();
+          applyDockTransform(node);                        // place this frame (avoid a flash at the old float spot)
+        } else {                                           // freeze current visual rect as a body-fixed screen panel
+          const r = fl.getBoundingClientRect();
+          node.properties.dockPinned = false;
+          pinnedDocks.delete(node);
+          node._dockSig = ""; node._dockNodeEl = null;
+          document.body.appendChild(fl);                   // back out of the node element
+          fl.style.position = "fixed"; fl.style.transform = ""; fl.style.transformOrigin = ""; fl.style.zIndex = "";
+          fl.style.left = Math.round(r.left) + "px"; fl.style.top = Math.round(r.top) + "px";
+          fl.style.width = Math.round(r.width) + "px"; fl.style.height = Math.round(r.height) + "px";
+          node.properties.dockRect = { x: Math.round(r.left), y: Math.round(r.top), w: Math.round(r.width), h: Math.round(r.height) };
+        }
+        if (pinBtn) { pinBtn.classList.toggle("active", on); pinBtn.title = on ? "Unpin from canvas (float in screen)" : "Pin to canvas (move/zoom with the graph)"; }
+        c.setDirtyCanvas(true, true); requestAnimationFrame(fitFsCanvas);
+        flushChange();   // click's mouseup already fired before this handler — snapshot the new state
+      }
+      // Resize from any edge/corner; N/W edges shift the anchor. Units: graph px pinned (÷scale), else screen px.
+      function startDockResize(e, dir, fl) {
+        if (e.button !== 0) return;
+        e.preventDefault(); e.stopPropagation();
+        const pinned = !!node.properties.dockPinned;
+        const scale = (pinned && app.canvas) ? (app.canvas.ds.scale || 1) : 1;
+        const sx = e.clientX, sy = e.clientY, w0 = fl.offsetWidth, h0 = fl.offsetHeight;
+        const gx0 = pinned ? node.properties.dockGraph.x : (parseFloat(fl.style.left) || 0);
+        const gy0 = pinned ? node.properties.dockGraph.y : (parseFloat(fl.style.top) || 0);
+        const MINW = 300, MINH = 240;
+        dragPointer(e, e.currentTarget, (me) => {
+          const dx = (me.clientX - sx) / scale, dy = (me.clientY - sy) / scale;
+          let w = w0, h = h0, gx = gx0, gy = gy0;
+          if (dir.indexOf("e") >= 0) w = w0 + dx;
+          if (dir.indexOf("s") >= 0) h = h0 + dy;
+          if (dir.indexOf("w") >= 0) { w = w0 - dx; gx = gx0 + dx; }
+          if (dir.indexOf("n") >= 0) { h = h0 - dy; gy = gy0 + dy; }
+          if (w < MINW) { if (dir.indexOf("w") >= 0) gx -= (MINW - w); w = MINW; }
+          if (h < MINH) { if (dir.indexOf("n") >= 0) gy -= (MINH - h); h = MINH; }
+          fl.style.width = Math.round(w) + "px"; fl.style.height = Math.round(h) + "px";
+          if (pinned) { node.properties.dockGraph.x = gx; node.properties.dockGraph.y = gy; node._dockSig = ""; applyDockTransform(node); }
+          else { fl.style.left = Math.round(gx) + "px"; fl.style.top = Math.round(gy) + "px"; }
+          fitFsCanvas();
+        }, () => { saveDockGeom(); flushChange(); });
+      }
+      function addDockResizeHandles(fl) {
+        for (const dir of ["n", "s", "e", "w", "ne", "nw", "se", "sw"]) {
+          const h = document.createElement("div");
+          h.className = "kjideo-rsz " + dir;
+          h.addEventListener("pointerdown", (e) => startDockResize(e, dir, fl));
+          fl.appendChild(h);
+        }
+      }
+      function undock() {
+        if (node._docked) return;
+        if (node._fullscreen) exitFs();
+        node._docked = true; node.properties.docked = true;
+        const fl = document.createElement("div"); fl.className = "kjideo-dock";
+        fl.dataset.captureWheel = "true";   // 2.0: let focused inputs in the dock scroll instead of zooming the graph
+        stopProp(fl);   // hosted inside the node element — don't let dock interactions drag/zoom the node
+        const head = document.createElement("div"); head.className = "kjideo-dock-head";
+        const title = document.createElement("span"); title.textContent = "Ideogram 4 editor"; title.style.flex = "1";
+        const minBtn = document.createElement("button"); minBtn.className = "kjideo-btn";
+        stopProp(minBtn);
+        const applyMin = (on) => {                            // collapse the editor to just the header bar
+          node.properties.dockMin = !!on;
+          fl.classList.toggle("minimized", !!on);
+          minBtn.textContent = on ? "▢" : "—";
+          minBtn.title = on ? "Restore editor" : "Minimize editor";
+          if (!on) requestAnimationFrame(fitFsCanvas);        // re-letterbox the canvas after it's visible again
+        };
+        minBtn.addEventListener("click", () => { applyMin(!node.properties.dockMin); flushChange(); });
+        const fsBtn = document.createElement("button"); fsBtn.className = "kjideo-btn"; fsBtn.textContent = "⛶";
+        fsBtn.title = "Open in a larger window (Esc to close)";
+        stopProp(fsBtn); fsBtn.addEventListener("click", () => node._fullscreen ? exitFs() : enterFs());
+        const pinBtn = document.createElement("button"); pinBtn.className = "kjideo-btn"; pinBtn.textContent = "📌";
+        stopProp(pinBtn); pinBtn.addEventListener("click", () => setPinned(!node.properties.dockPinned, pinBtn));
+        head.append(title, fsBtn, minBtn, pinBtn);
+        const body = document.createElement("div"); body.className = "kjideo-dock-body";
+        fl.append(head, body);
+        addDockResizeHandles(fl);
+        document.body.appendChild(fl);
+        node._dockEl = fl; node._dockBody = body; node._dockSig = ""; node._dockNodeEl = null; liveDocks.add(node);
+        applyDockTheme(node);                                 // match the node's color theme right away
+        detachInto(body);
+        // initial geometry / mode
+        const pinned = !!node.properties.dockPinned;
+        if (pinned) {
+          const g = node.properties.dockGraph || { x: 0, y: 0, w: 540, h: 470 };
+          fl.style.position = "absolute"; fl.style.width = g.w + "px"; fl.style.height = g.h + "px";
+          pinnedDocks.add(node); startDockLoop();
+          applyDockTransform(node);                          // parent + place this frame (avoid a flash at 0,0)
+          pinBtn.classList.add("active");
+        } else {
+          const rc = node.properties.dockRect || { x: 90, y: 90, w: 540, h: 470 };
+          fl.style.left = Math.max(0, Math.min(rc.x, window.innerWidth - 80)) + "px";
+          fl.style.top = Math.max(0, Math.min(rc.y, window.innerHeight - 40)) + "px";
+          fl.style.width = rc.w + "px"; fl.style.height = rc.h + "px";
+        }
+        pinBtn.title = pinned ? "Unpin from canvas (float in screen)" : "Pin to canvas (move/zoom with the graph)";
+        applyMin(!!node.properties.dockMin);                  // restore minimized state
+        // drag the panel by its header (graph-space when pinned, screen-space otherwise)
+        head.addEventListener("pointerdown", (e) => {
+          if (e.target === pinBtn || e.target === minBtn || e.target === fsBtn || e.button !== 0) return;
+          e.preventDefault();
+          const sx0 = e.clientX, sy0 = e.clientY;
+          const pinned = node.properties.dockPinned;
+          const scale = pinned ? (app.canvas.ds.scale || 1) : 1;
+          const gx0 = pinned ? node.properties.dockGraph.x : 0, gy0 = pinned ? node.properties.dockGraph.y : 0;
+          const ox = fl.offsetLeft, oy = fl.offsetTop;
+          dragPointer(e, head, (me) => {
+            if (pinned) {                                  // move the panel by writing its transform directly (no full redraw)
+              node.properties.dockGraph.x = gx0 + (me.clientX - sx0) / scale;
+              node.properties.dockGraph.y = gy0 + (me.clientY - sy0) / scale;
+              applyDockTransform(node);
+            } else {
+              fl.style.left = Math.max(0, Math.min(ox + me.clientX - sx0, window.innerWidth - 60)) + "px";
+              fl.style.top = Math.max(0, Math.min(oy + me.clientY - sy0, window.innerHeight - 30)) + "px";
+            }
+          }, () => { saveDockGeom(); flushChange(); });
+        });
+        try { node._dockRO = new ResizeObserver(() => { fitFsCanvas(); saveDockGeom(); }); node._dockRO.observe(fl); } catch (e) {}
+        // editor slot is collapsed (getMinHeight→0) — shrink the node to its widgets
+        requestAnimationFrame(() => { node.setSize([node.size[0], node.computeSize()[1]]); fitFsCanvas(); });
+      }
+      // Pin the editor under the node. `fresh` (new node only) matches the node width; reloads honor the blob.
+      function ensureDocked(fresh) {
+        if (node._docked) return;
+        if (node.properties.dockPinned == null) node.properties.dockPinned = true;
+        const honor = !!node.properties.dockGraph && node._dockGeomRestored;   // only trust blob-restored geometry
+        if (!honor) {
+          // old workflow: reuse its saved (aspect-locked) node height so the canvas isn't tiny
+          const sh = (!fresh && node._savedSize && node._savedSize[1] > 240) ? Math.round(node._savedSize[1]) : 470;
+          node.properties.dockGraph = { x: 0, y: 0, w: 560, h: sh };
+        }
+        undock();
+        if (!honor) requestAnimationFrame(() => {             // no reliable geometry — place a default under the node
+          const g = node.properties.dockGraph;
+          g.x = 0; g.y = node.size[1] + 12;
+          if (fresh) { g.w = Math.round(node.size[0]); if (node._dockEl) node._dockEl.style.width = g.w + "px"; }
+          node._dockSig = ""; applyDockTransform(node); saveDockGeom();
+        });
+      }
+
+      // ── geometry helpers ── (logical CSS px = the displayed canvas size)
+      function logW() { return canvasEl.offsetWidth || 1; }
+      function logH() { return canvasEl.offsetHeight || 1; }
+      function toPx(b) {
+        const W = logW(), H = logH();
+        return { x1: b.x * W, y1: b.y * H, x2: (b.x + b.w) * W, y2: (b.y + b.h) * H };
+      }
+      function mouseN(e) {
+        const r = canvasEl.getBoundingClientRect();
+        return { x: (e.clientX - r.left) / r.width, y: (e.clientY - r.top) / r.height };
+      }
+      function clamp01(v) { return Math.max(0, Math.min(1, v)); }
+      // greedy word-wrap to maxW px (ctx.font must be set by caller)
+      function wrapLines(text, maxW) {
+        const lines = [];
+        for (const para of text.split("\n")) {
+          let line = "";
+          for (const word of para.split(/\s+/)) {
+            if (!word) continue;
+            const test = line ? line + " " + word : word;
+            if (line && ctx.measureText(test).width > maxW) { lines.push(line); line = word; }
+            else line = test;
+          }
+          lines.push(line);
+        }
+        return lines;
+      }
+      function normalizeBox(b) {
+        // collapse negative size to positive top-left + w/h, clamp into canvas
+        let x = b.x, y = b.y, w = b.w, h = b.h;
+        if (w < 0) { x += w; w = -w; }
+        if (h < 0) { y += h; h = -h; }
+        x = clamp01(x); y = clamp01(y);
+        w = Math.min(w, 1 - x); h = Math.min(h, 1 - y);
+        return { ...b, x, y, w: Math.max(0, w), h: Math.max(0, h) };
+      }
+
+      // All boxes under the point, top-first to match draw order: the active box is
+      // drawn last (on top), then the rest by index low→high (index 0 = front).
+      function boxesAt(mN) {
+        const baseRx = HANDLE / logW(), baseRy = HANDLE / logH();
+        const res = [];
+        for (let i = 0; i < node._boxes.length; i++) {
+          const b = node._boxes[i];
+          if (b.locked) continue;                          // locked regions aren't grabbable on the canvas
+          const rx = Math.min(baseRx, b.w / 3), ry = Math.min(baseRy, b.h / 3);  // shrink handles on small boxes so a central move zone remains
+          const mode = rectHitTestN(mN.x, mN.y, b.x, b.y, b.x + b.w, b.y + b.h, rx, ry);
+          if (mode) res.push({ index: i, mode });
+        }
+        const ai = res.findIndex((c) => c.index === node._activeIdx);
+        if (ai > 0) res.unshift(res.splice(ai, 1)[0]);
+        return res;
+      }
+      // Hover / right-click: prefer a resize handle on the active box, else topmost.
+      function hitTest(mN) {
+        const cands = boxesAt(mN);
+        if (!cands.length) return null;
+        return cands.find((c) => c.index === node._activeIdx && c.mode !== "move") || cands[0];
+      }
+      // Tag-chip rects (canvas px), placed to avoid overlapping each other: each
+      // box's tag tries top-left, top-right, bottom-right, bottom-left in turn.
+      function tagRects() {
+        ctx.font = "bold 11px monospace";
+        const W = logW(), H = logH(), h = 14;
+        const placed = [], rects = [];
+        const hits = (a, b) => a.x < b.x + b.w && a.x + a.w > b.x && a.y < b.y + b.h && a.y + a.h > b.y;
+        for (let i = 0; i < node._boxes.length; i++) {
+          const b = node._boxes[i];
+          const x1 = b.x * W, y1 = b.y * H, x2 = (b.x + b.w) * W, y2 = (b.y + b.h) * H;
+          const tag = String(i + 1).padStart(2, "0");
+          const w = ctx.measureText(tag).width + 8;
+          // Box too small to hold the badge → place it just outside (above/below/beside) so it doesn't
+          // cover the box and the box stays grabbable; otherwise tuck it into a free corner inside.
+          const small = (x2 - x1) < w || (y2 - y1) < h;
+          const cands = small
+            ? [[x1, y1 - h - 1], [x1, y2 + 1], [x2 + 1, y1], [x1 - w - 1, y1]]
+            : [[x1, y1], [x2 - w, y1], [x2 - w, y2 - h], [x1, y2 - h]];
+          let pick = cands[0];
+          for (const [cx, cy] of cands) {
+            if (cx < 0 || cy < 0 || cx + w > W || cy + h > H) continue;   // keep on-canvas
+            if (!placed.some((p) => hits({ x: cx, y: cy, w, h }, p))) { pick = [cx, cy]; break; }
+          }
+          const r = { x: pick[0], y: pick[1], w, h, tag };
+          placed.push(r); rects[i] = r;
+        }
+        return rects;
+      }
+      function titleAt(mN) {
+        const px = mN.x * logW(), py = mN.y * logH();
+        const rects = tagRects();
+        for (let i = node._boxes.length - 1; i >= 0; i--) {
+          if (node._boxes[i].locked) continue;             // locked badges aren't interactive
+          const r = rects[i];
+          if (r && px >= r.x && px <= r.x + r.w && py >= r.y && py <= r.y + r.h) return i;
+        }
+        return null;
+      }
+      // In-box text placement: when auto-place is on, put each box's text in whichever vertical slot overlaps
+      // least with the text already placed for other boxes (greedy), so overlapping labels don't stack.
+      function textBlocks() {
+        if (node.properties.showBoxText === false) return [];
+        const { fs, lh } = txtFont();
+        ctx.font = fs + "px monospace";
+        const W = logW(), H = logH(), pad = 4, tagH = 14, auto = node.properties.textAutoPlace !== false;
+        const placed = [], blocks = [];
+        const overlap = (a, b) => Math.max(0, Math.min(a.x + a.w, b.x + b.w) - Math.max(a.x, b.x)) *
+                                  Math.max(0, Math.min(a.y + a.h, b.y + b.h) - Math.max(a.y, b.y));
+        for (let i = 0; i < node._boxes.length; i++) {
+          const b = node._boxes[i];
+          let body = b.desc || "";
+          if (b.type === "text" && b.text) body = `"${b.text}"` + (body ? " — " + body : "");
+          if (!body) { blocks[i] = null; continue; }
+          const x1 = b.x * W, y1 = b.y * H, x2 = (b.x + b.w) * W, y2 = (b.y + b.h) * H;
+          const lines = wrapLines(body, (x2 - x1) - pad * 2);   // full width
+          let lw = 0; for (const ln of lines) lw = Math.max(lw, ctx.measureText(ln).width);
+          const bw = Math.min(x2 - x1, lw + pad * 2), bh = Math.min(y2 - y1, lines.length * lh + 4);
+          // candidates = several vertical slots (more when the box is taller); text spans the width, so only Y varies
+          const top = y1 + ((y2 - y1) > bh + tagH ? tagH : 0), bottom = y2 - bh;
+          const cands = [];
+          if (!auto || bottom <= top) cands.push([x1, top]);    // off, or fills the box → fixed top
+          else {
+            const n = Math.min(6, 2 + Math.floor((bottom - top) / (lh * 2)));
+            for (let k = 0; k < n; k++) cands.push([x1, top + ((bottom - top) * k) / (n - 1)]);
+          }
+          let best = cands[0], bestScore = Infinity;
+          for (const [cx, cy] of cands) {
+            const rect = { x: cx, y: cy, w: bw, h: bh };
+            let s = 0; for (const p of placed) s += overlap(rect, p);
+            if (s < bestScore) { bestScore = s; best = [cx, cy]; if (s === 0) break; }
+          }
+          const r = { x: best[0], y: best[1], w: bw, h: bh, lines, lh, fs, pad };
+          placed.push(r); blocks[i] = r;
+        }
+        return blocks;
+      }
+      // Click selection: active box's resize handle wins (corner resize); then a
+      // title-chip click selects that box (drawn to front); Alt-click cycles the
+      // overlap stack; else the topmost box.
+      function pickForSelection(mN, cycle) {
+        const cands = boxesAt(mN);
+        if (!cands.length) return null;
+        const ah = cands.find((c) => c.index === node._activeIdx && c.mode !== "move");
+        if (ah && !cycle) return ah;
+        const ti = titleAt(mN);
+        if (ti !== null && !cycle) return { index: ti, mode: "move" };
+        if (cycle && cands.length > 1) {
+          // cycle over a stable order (boxesAt moves the active box to the front, which would otherwise ping-pong 2)
+          const ordered = [...cands].sort((a, b) => a.index - b.index);
+          const pos = ordered.findIndex((c) => c.index === node._activeIdx);
+          return ordered[(pos + 1) % ordered.length];
+        }
+        return cands.find((c) => c.index === node._activeIdx && c.mode !== "move") || cands[0];
+      }
+      // normalized variant of rectHitTest with separate x/y radii
+      function rectHitTestN(mx, my, x1, y1, x2, y2, rx, ry) {
+        const h = (cx, cy) => Math.abs(mx - cx) < rx && Math.abs(my - cy) < ry;
+        if (h(x1, y1)) return "resize-tl";
+        if (h(x2, y1)) return "resize-tr";
+        if (h(x1, y2)) return "resize-bl";
+        if (h(x2, y2)) return "resize-br";
+        if (mx >= x1 && mx <= x2 && Math.abs(my - y1) < ry) return "resize-t";
+        if (mx >= x1 && mx <= x2 && Math.abs(my - y2) < ry) return "resize-b";
+        if (my >= y1 && my <= y2 && Math.abs(mx - x1) < rx) return "resize-l";
+        if (my >= y1 && my <= y2 && Math.abs(mx - x2) < rx) return "resize-r";
+        if (mx >= x1 && mx <= x2 && my >= y1 && my <= y2) return "move";
+        return null;
+      }
+
+      function applyDrag(mode, start, dN) {
+        const { x, y, w, h } = start;
+        const dx = dN.x, dy = dN.y;
+        if (mode === "move") {
+          return { ...start, x: clamp01(Math.min(x + dx, 1 - w)), y: clamp01(Math.min(y + dy, 1 - h)) };
+        }
+        if (mode === "draw") {
+          return normalizeBox({ ...start, w: w + dx, h: h + dy });
+        }
+        // resize: move only the dragged edges (clamped to the canvas); the others stay anchored.
+        const suf = mode.slice(7);                 // "tl"|"tr"|"bl"|"br"|"t"|"b"|"l"|"r"
+        let l = x, t = y, r = x + w, b = y + h;
+        if (suf.includes("l")) l = clamp01(l + dx);
+        if (suf.includes("r")) r = clamp01(r + dx);
+        if (suf.includes("t")) t = clamp01(t + dy);
+        if (suf.includes("b")) b = clamp01(b + dy);
+        if (r < l) [l, r] = [r, l];                // crossing the opposite edge flips, no anchor drift
+        if (b < t) [t, b] = [b, t];
+        return { ...start, x: l, y: t, w: r - l, h: b - t };
+      }
+
+      // ── grid / guides ──
+      function gridN() { return Math.max(2, Math.min(128, node.properties.gridSize || 10)); }
+      function txtFont() { const fs = Math.max(6, Math.min(40, node.properties.textSize || 12)); return { fs, lh: Math.round(fs * 1.2) }; }
+      // Whole number of cells per axis so they fill the canvas exactly (no split cells at the edges);
+      // near-square (the target size is rounded to fit). Shared by the grid guide and snap-to-grid.
+      function gridStep() {
+        const W = logW(), H = logH(), target = Math.min(W, H) / gridN();
+        const nx = Math.max(1, Math.round(W / target)), ny = Math.max(1, Math.round(H / target));
+        return { nx, ny, cw: W / nx, ch: H / ny, sx: 1 / nx, sy: 1 / ny };
+      }
+      function guideStroke(a) {
+        const c = hexRgb(node.properties.guideColor || "#ffffff") || { r: 255, g: 255, b: 255 };
+        const op = (node.properties.guideOpacity == null ? 100 : node.properties.guideOpacity) / 100;
+        return `rgba(${c.r},${c.g},${c.b},${(a * op).toFixed(3)})`;
+      }
+      // Snap a box's free edges to the (square) grid when snap-to-grid is on.
+      function snapBox(b, mode) {
+        if (!node.properties.snap) return b;
+        const { sx, sy } = gridStep();
+        const sn = (v, s) => Math.round(v / s) * s;
+        const { x, y, w, h } = b;
+        if (mode === "move") {                          // snap position, preserve size
+          return { ...b, x: clamp01(Math.min(sn(x, sx), 1 - w)), y: clamp01(Math.min(sn(y, sy), 1 - h)) };
+        }
+        const x2 = sn(x + w, sx), y2 = sn(y + h, sy);   // draw/resize: snap the edges
+        return normalizeBox({ ...b, x: sn(x, sx), y: sn(y, sy), w: x2 - sn(x, sx), h: y2 - sn(y, sy) });
+      }
+
+      // ── drawing ──
+      // Golden spiral: largest φ-rectangle fitting the canvas, subdivided into squares with quarter arcs.
+      function goldenSpiral(W, H) {
+        const phi = 1.6180339887;
+        let w, h;
+        if (W >= H) { if (W / H >= phi) { h = H; w = H * phi; } else { w = W; h = W / phi; } }
+        else { if (H / W >= phi) { w = W; h = W * phi; } else { h = H; w = H / phi; } }
+        let x = (W - w) / 2, y = (H - h) / 2;
+        ctx.save();
+        ctx.strokeStyle = guideStroke(0.25); ctx.lineWidth = 1;
+        ctx.strokeRect(x + 0.5, y + 0.5, w, h);
+        ctx.strokeStyle = guideStroke(0.6); ctx.lineWidth = 1.5;
+        let phase = w >= h ? 0 : 1;
+        for (let i = 0; i < 12 && w > 1 && h > 1; i++, phase = (phase + 1) % 4) {
+          const s = Math.min(w, h);
+          let cx, cy, a0, a1;
+          if (phase === 0)      { cx = x + s;     cy = y + s;     a0 = Math.PI;       a1 = Math.PI * 1.5; x += s; w -= s; }
+          else if (phase === 1) { cx = x;         cy = y + s;     a0 = Math.PI * 1.5; a1 = Math.PI * 2;   y += s; h -= s; }
+          else if (phase === 2) { cx = x + w - s; cy = y;         a0 = 0;             a1 = Math.PI * 0.5; w -= s; }
+          else                  { cx = x + w;     cy = y + h - s; a0 = Math.PI * 0.5; a1 = Math.PI;       h -= s; }
+          ctx.beginPath(); ctx.arc(cx, cy, s, a0, a1); ctx.stroke();
+        }
+        ctx.restore();
+      }
+      // Composition guide overlay (rule of thirds / grid / golden ratio / spiral), drawn on the bg.
+      function drawGuide(W, H) {
+        const kind = node.properties.guide;
+        if (!kind || kind === "none") return;
+        if (kind === "spiral") { goldenSpiral(W, H); return; }
+        ctx.save();
+        ctx.lineWidth = 1;
+        if (kind === "grid") {                          // whole cells across both axes (no split cells at edges)
+          ctx.strokeStyle = guideStroke(0.24);
+          const { nx, ny, cw, ch } = gridStep();
+          for (let i = 1; i < nx; i++) { const X = Math.round(i * cw) + 0.5; ctx.beginPath(); ctx.moveTo(X, 0); ctx.lineTo(X, H); ctx.stroke(); }
+          for (let j = 1; j < ny; j++) { const Y = Math.round(j * ch) + 0.5; ctx.beginPath(); ctx.moveTo(0, Y); ctx.lineTo(W, Y); ctx.stroke(); }
+          ctx.restore(); return;
+        }
+        const v = [], h = [];                           // thirds / golden: per-axis fractions (correct at any aspect)
+        if (kind === "thirds") { v.push(1 / 3, 2 / 3); h.push(1 / 3, 2 / 3); }
+        else if (kind === "golden") { const g = 1 / 1.6180339887; v.push(1 - g, g); h.push(1 - g, g); }
+        ctx.strokeStyle = guideStroke(kind === "golden" ? 0.45 : 0.28);
+        for (const fx of v) { const px = Math.round(fx * W) + 0.5; ctx.beginPath(); ctx.moveTo(px, 0); ctx.lineTo(px, H); ctx.stroke(); }
+        for (const fy of h) { const py = Math.round(fy * H) + 0.5; ctx.beginPath(); ctx.moveTo(0, py); ctx.lineTo(W, py); ctx.stroke(); }
+        ctx.restore();
+      }
+      let _rafPending = false;
+      function drawCanvas() {
+        if (_rafPending) return;
+        _rafPending = true;
+        requestAnimationFrame(() => {
+          _rafPending = false;
+          _draw();
+        });
+      }
+      function _draw() {
+        // Size the backing store to display × DPR and draw in logical px (crisp text/lines).
+        const W = logW(), H = logH(), d = window.devicePixelRatio || 1;
+        const bw = Math.round(W * d), bh = Math.round(H * d);
+        if (canvasEl.width !== bw || canvasEl.height !== bh) { canvasEl.width = bw; canvasEl.height = bh; }
+        ctx.setTransform(d, 0, 0, d, 0, 0);
+        ctx.clearRect(0, 0, W, H);
+        let bri = bgBrightnessWidget ? bgBrightnessWidget.value : 25;
+        if (typeof bri !== "number" || isNaN(bri)) bri = 25;       // guard against unset widget value
+        if (node._bgImg) {                                         // reference image, dimmed by brightness
+          ctx.drawImage(node._bgImg, 0, 0, W, H);
+          const dim = 1 - bri / 100;
+          if (dim > 0) { ctx.fillStyle = `rgba(0,0,0,${dim})`; ctx.fillRect(0, 0, W, H); }
+        } else {                                                   // blank canvas grey from brightness
+          const g = Math.round(bri / 100 * 128);
+          ctx.fillStyle = `rgb(${g},${g},${g})`; ctx.fillRect(0, 0, W, H);
+        }
+        drawGuide(W, H);                                          // composition guide overlay
+        if (node._hideBoxes) return;                              // H: temporary background-only view
+        // selection highlight only when the editor is focused or the node is selected
+        const showSel = node._focused || node._selected;
+        const aIdx = showSel ? node._activeIdx : -1;
+        const selSet = new Set(showSel ? node._selection : []);
+        if (aIdx >= 0) selSet.add(aIdx);
+        // index 0 = front (drawn last); selected drawn above non-selected, active last of all
+        const nonSel = node._boxes.map((_, i) => i).filter((i) => !selSet.has(i)).reverse();
+        const selOthers = node._boxes.map((_, i) => i).filter((i) => selSet.has(i) && i !== aIdx).reverse();
+        const order = [...nonSel, ...selOthers];
+        if (aIdx >= 0 && aIdx < node._boxes.length) order.push(aIdx);
+        const tagR = tagRects();                              // collision-avoided tag positions
+        const textB = textBlocks();                           // collision-avoided in-box text positions
+        for (const i of order) {
+          const b = node._boxes[i], active = i === aIdx, selected = selSet.has(i) && !b.locked;  // locked never shows as selected
+          const pal = (b.palette || []).filter(Boolean);
+          const col = pal.length ? pal[0] : "#8c8c8c";       // box color = first palette color, else neutral grey
+          const { x1, y1, x2, y2 } = toPx(b);
+          const w = x2 - x1, h = y2 - y1;
+          const hovered = (i === node._hoverBox && !b.locked) || selected;  // locked boxes don't hover; selected stay highlighted
+          if (selected) {                                    // opaque backing so contents read clearly over boxes behind
+            ctx.fillStyle = "rgba(26,26,26,0.88)";
+            ctx.fillRect(x1, y1, w, h);
+          }
+          const baseA = (node.properties.boxOpacity == null ? 14 : node.properties.boxOpacity) / 100;
+          const fillA = Math.min(1, hovered ? baseA + 0.1 : baseA);   // box-color tint at the chosen opacity
+          ctx.fillStyle = col + Math.round(fillA * 255).toString(16).padStart(2, "0");
+          ctx.fillRect(x1, y1, w, h);
+          if (b.locked) ctx.setLineDash([3, 3]);             // locked: frozen on the canvas
+          else if (b.nobbox) ctx.setLineDash([6, 4]);        // unplaced (no bbox in source)
+          const lw = selected ? 2 : (hovered ? 1.5 : 1);
+          ctx.strokeStyle = col; ctx.lineWidth = lw;
+          ctx.strokeRect(x1 + lw / 2, y1 + lw / 2, w - lw, h - lw);  // inside the box so strip/badge align at y1
+          ctx.setLineDash([]);
+          if (pal.length) {                                  // palette shown as a strip along the top edge
+            const sw = w / pal.length, n = pal.length, sh = 7;
+            for (let p = 0; p < n; p++) {
+              const sx = x1 + Math.round(p * sw);
+              ctx.fillStyle = pal[p];
+              ctx.fillRect(sx, y1, x1 + Math.round((p + 1) * sw) - sx, sh);
+            }
+          }
+          // in-box content (clipped to the box): prompt text (at its de-conflicted corner) + lock badge
+          ctx.save();
+          ctx.beginPath(); ctx.rect(x1, y1, w, h); ctx.clip();
+          const tb = textB[i];
+          if (tb) {
+            const stroke = node.properties.textStroke !== false;
+            ctx.font = tb.fs + "px monospace";
+            if (stroke) { ctx.lineWidth = 3; ctx.lineJoin = "round"; ctx.strokeStyle = "rgba(0,0,0,0.85)"; }  // dark halo
+            ctx.fillStyle = readableText(col);              // box color, lightened if too dark
+            let ty = tb.y + tb.fs;
+            for (const line of tb.lines) {
+              if (stroke) ctx.strokeText(line, tb.x + tb.pad, ty);
+              ctx.fillText(line, tb.x + tb.pad, ty);
+              ty += tb.lh;
+            }
+          }
+          ctx.restore();
+          // tag chip on top, unclipped (sits outside boxes too small to hold it — its collision-avoided position)
+          const tr = tagR[i];
+          ctx.font = "bold 11px monospace";
+          ctx.fillStyle = col;                                // tag chip = box color
+          ctx.fillRect(tr.x, tr.y, tr.w, 14);
+          if (i === node._hoverTitle && !b.locked) {          // hover highlight (never on locked)
+            ctx.fillStyle = "rgba(255,255,255,0.25)"; ctx.fillRect(tr.x, tr.y, tr.w, 14);
+            ctx.strokeStyle = "#fff"; ctx.lineWidth = 1; ctx.strokeRect(tr.x + 0.5, tr.y + 0.5, tr.w - 1, 13);
+          }
+          ctx.fillStyle = textOn(col);
+          ctx.fillText(tr.tag, tr.x + 4, tr.y + 11);
+          if (b.locked) { ctx.font = "11px sans-serif"; ctx.fillStyle = "#ddd"; ctx.fillText("🔒", tr.x + tr.w + 2, tr.y + 11); }  // lock symbol next to the badge
+          if (selected) {                                    // orange selection ring on top (above strip/tag): solid = primary, dashed = others
+            const olw = active ? 2 : 1;
+            ctx.strokeStyle = "#ff8c00"; ctx.lineWidth = olw;
+            if (!active) ctx.setLineDash([5, 3]);
+            ctx.strokeRect(x1 + olw / 2, y1 + olw / 2, w - olw, h - olw);
+            ctx.setLineDash([]);
+          }
+        }
+        const apIdx = node._altPreview ?? -1;                 // Alt-held: ring the box an alt-click would select next
+        if (apIdx >= 0 && apIdx < node._boxes.length) {
+          const { x1, y1, x2, y2 } = toPx(node._boxes[apIdx]);
+          ctx.strokeStyle = "#46b4e6"; ctx.lineWidth = 2; ctx.setLineDash([5, 3]);
+          ctx.strokeRect(x1 + 1, y1 + 1, (x2 - x1) - 2, (y2 - y1) - 2);
+          ctx.setLineDash([]);
+        }
+        if (node._marquee && node._marqueeActive) {           // rubber-band selection rectangle
+          const r = marqueeRect();
+          const mx = r.x * W, my = r.y * H, mw = r.w * W, mh = r.h * H;
+          ctx.fillStyle = "rgba(70,180,230,0.12)";
+          ctx.fillRect(mx, my, mw, mh);
+          ctx.strokeStyle = "#46b4e6"; ctx.lineWidth = 1; ctx.setLineDash([4, 3]);
+          ctx.strokeRect(mx + 0.5, my + 0.5, mw - 1, mh - 1);
+          ctx.setLineDash([]);
+        }
+      }
+
+      // ── serialization ──
+      // Wired AND the source node isn't muted(2)/bypassed(4) — those keep the link but emit nothing.
+      const importConnected = () => {
+        const link = node.graph?.links?.[node.inputs?.find((i) => i.name === "import_json")?.link];
+        return !!link && ![2, 4].includes(node.graph.getNodeById(link.origin_id)?.mode);
+      };
+      function serialize() {                              // saved/restored value: clean boxes
+        if (elementsWidget) elementsWidget.value = node._boxes.length ? JSON.stringify(node._boxes) : "";
+        if (stylePaletteWidget) stylePaletteWidget.value = node._stylePalette.length ? JSON.stringify(node._stylePalette) : "";
+      }
+      // Queue-time value (not the saved value): when a wired import should drive the output — "always"
+      // mode, or empty in "when empty" mode — return a unique marker so ComfyUI can't cache-skip the
+      // node. It then re-executes and pushes the import back via ui, refreshing the editor. The server
+      // treats a non-list elements_data as empty (and ignores it entirely in "always" mode), so the
+      // nonce never affects the output.
+      if (elementsWidget) {
+        elementsWidget.serializeValue = () => {
+          const always = findW("import_mode")?.value === "always";
+          if (importConnected() && (always || !node._boxes.length)) {
+            return JSON.stringify({ _refresh: (node._serialSeq = (node._serialSeq || 0) + 1) });
+          }
+          return node._boxes.length ? JSON.stringify(node._boxes) : "";
+        };
+      }
+
+      function commit() { serialize(); renderPanel(); drawCanvas(); updateTokens(); flushChange(); }  // flush so canvas edits persist without a defocus
+      // Live text edit: persist + repaint + token count, without rebuilding the panel.
+      function touch() { serialize(); drawCanvas(); updateTokens(); }
+
+      function removeBox(i) {
+        node._boxes.splice(i, 1);
+        node._selection = new Set();
+        if (node._boxes.length === 0) node._activeIdx = -1;
+        else if (i <= node._activeIdx) node._activeIdx = Math.max(0, node._activeIdx - 1);
+        if (node._activeIdx >= 0) node._selection.add(node._activeIdx);
+      }
+      // Delete every selected box (or the active one) and reset the selection.
+      function removeSelected() {
+        const idxs = [...node._selection].sort((a, b) => b - a).filter((i) => !node._boxes[i]?.locked);  // keep locked
+        if (!idxs.length && node._activeIdx >= 0 && !node._boxes[node._activeIdx]?.locked) idxs.push(node._activeIdx);
+        if (!idxs.length) return;
+        for (const i of idxs) node._boxes.splice(i, 1);
+        node._selection = new Set();
+        node._activeIdx = node._boxes.length ? Math.min(idxs[idxs.length - 1], node._boxes.length - 1) : -1;
+        if (node._activeIdx >= 0) node._selection.add(node._activeIdx);
+      }
+      // Replace the selection with a single box (used by clicks / programmatic selects).
+      function selectOnly(idx) {
+        node._activeIdx = idx;
+        node._selection = idx >= 0 ? new Set([idx]) : new Set();
+      }
+
+      // ── pointer interaction ──
+      canvasEl.addEventListener("pointerdown", (e) => {
+        if (node._placing) {             // drop the duplicate being placed
+          if (e.button === 0) { placeFollower(mouseN(e)); finishPlacing(); }
+          else cancelPlacing();
+          e.preventDefault(); e.stopPropagation();
+          return;
+        }
+        if (e.button !== 0) return;
+        if (node._hideBoxes) return;     // view-only while boxes are hidden (H)
+        canvasEl.focus();                // so Delete/Backspace targets this editor
+        // capture the pointer so move/up keep coming even when the cursor leaves the node
+        try { canvasEl.setPointerCapture(e.pointerId); } catch (e2) {}
+        node._hoverTitle = null; node._hoverBox = null;  // clear hover highlight while interacting
+        const mN = mouseN(e);
+        // Ctrl/Cmd forces drawing a new box even when starting over an existing one.
+        const hit = (e.ctrlKey || e.metaKey) ? null : pickForSelection(mN, e.altKey);
+        // Touch double-tap opens the inline editor (dblclick may not fire on touch).
+        if (e.pointerType && e.pointerType !== "mouse") {
+          const last = node._lastTap, now = e.timeStamp;
+          if (hit && last && now - last.t < 350 && Math.abs(mN.x - last.x) < 0.03 && Math.abs(mN.y - last.y) < 0.03) {
+            node._lastTap = null; openInlineEditor(hit.index);
+            e.preventDefault(); e.stopPropagation(); return;
+          }
+          node._lastTap = { t: now, x: mN.x, y: mN.y };
+        }
+        node._pendingCollapse = -1;
+        node._groupStart = null;
+        if (e.shiftKey) {                              // shift-drag = marquee select; shift-click = toggle
+          startMarquee(mN, hit ? hit.index : -1);
+          e.preventDefault(); e.stopPropagation();
+          return;
+        }
+        if (hit) {
+          if (!node._selection.has(hit.index)) node._selection = new Set([hit.index]);  // outside selection → pick it
+          else if (node._selection.size > 1) node._pendingCollapse = hit.index;          // click (no drag) collapses
+          node._activeIdx = hit.index;
+          node._dragMode = hit.mode;
+          node._boxAtStart = { ...node._boxes[hit.index] };
+          if (node._selection.size > 1) {                  // snapshot the whole group for group move/resize
+            node._groupStart = {};
+            for (const i of node._selection) node._groupStart[i] = { ...node._boxes[i] };
+          }
+        } else {
+          node._dragMode = "draw";
+          const nb = { x: mN.x, y: mN.y, w: 0, h: 0, type: "obj", text: "", desc: "", palette: [] };
+          node._boxes.push(nb);
+          node._activeIdx = node._boxes.length - 1;
+          node._selection = new Set([node._activeIdx]);
+          node._boxAtStart = { ...nb };
+        }
+        node._drawing = true;
+        node._dragStartN = mN;
+        canvasEl.addEventListener("pointermove", onMove);
+        canvasEl.addEventListener("pointerup", onUp);
+        canvasEl.addEventListener("pointercancel", onUp);   // touch can cancel instead of up
+        e.preventDefault(); e.stopPropagation();
+        drawCanvas();   // panel rebuild/resize deferred to onUp so the canvas doesn't shift mid-drag
+      });
+
+      // Hover targets: with Alt, preview what an alt-click would select next (the box under the current one).
+      function hoverTargets(mN, alt, force) {
+        if (alt && !force) { const pick = pickForSelection(mN, true); return { ti: null, hb: null, ap: pick ? pick.index : -1, hit: null }; }
+        const ti = force ? null : titleAt(mN);
+        const hit = force ? null : hitTest(mN);
+        return { ti, hb: ti != null ? ti : (hit ? hit.index : null), ap: -1, hit };
+      }
+      function applyHover(t) {
+        if (t.ti !== node._hoverTitle || t.hb !== node._hoverBox || t.ap !== (node._altPreview ?? -1)) {
+          node._hoverTitle = t.ti; node._hoverBox = t.hb; node._altPreview = t.ap; drawCanvas();
+        }
+      }
+      canvasEl.addEventListener("pointermove", (e) => {
+        node._lastMouseN = mouseN(e);                        // track cursor for paste-under-cursor
+        if (node._placing) { placeFollower(node._lastMouseN); return; }
+        if (node._drawing || node._marquee || node._hideBoxes) return;
+        const mN = node._lastMouseN;
+        const force = e.ctrlKey || e.metaKey, alt = e.altKey;   // Ctrl/Cmd = force-draw, Alt = cycle preview
+        const t = hoverTargets(mN, alt, force);
+        applyHover(t);
+        canvasEl.style.cursor = (alt && !force) ? (t.ap >= 0 ? "pointer" : "crosshair")
+          : (t.ti != null ? "pointer" : (t.hit ? (cursorForBboxMode(t.hit.mode) || "crosshair") : "crosshair"));
+      });
+      // Alt pressed/released without moving the mouse — refresh the preview from the last cursor position.
+      node._altRefresh = (altDown) => {
+        if (node._drawing || node._marquee || node._placing || node._hideBoxes || !node._lastMouseN) return;
+        applyHover(hoverTargets(node._lastMouseN, altDown, false));
+      };
+      canvasEl.addEventListener("pointerleave", () => {
+        if (hoveredCanvasNode === node) hoveredCanvasNode = null;
+        if (node._hoverTitle !== null || node._hoverBox !== null || (node._altPreview ?? -1) >= 0) {
+          node._hoverTitle = null; node._hoverBox = null; node._altPreview = -1; drawCanvas();
+        }
+      });
+      canvasEl.addEventListener("pointerenter", () => { hoveredCanvasNode = node; });
+      // H (while hovering the canvas): temporary background-only view (not serialized).
+      node._toggleHideBoxes = () => { node._hideBoxes = !node._hideBoxes; drawCanvas(); };
+
+      // ── inline description editing (double-click a region) ──
+      let inlineTa = null;
+      function closeInlineEditor() {
+        if (inlineTa) { inlineTa.remove(); inlineTa = null; }
+      }
+      function openInlineEditor(idx) {
+        closeInlineEditor();
+        const b = node._boxes[idx];
+        if (!b) return;
+        selectOnly(idx);
+        const dw = canvasEl.offsetWidth, dh = canvasEl.offsetHeight;       // CSS display size
+        const ox = canvasEl.offsetLeft, oy = canvasEl.offsetTop;
+        const w = Math.min(dw, Math.max(70, b.w * dw));
+        const h = Math.min(dh, Math.max(42, b.h * dh));
+        // clamp so the editor stays inside the canvas (wrapper is overflow:hidden)
+        const left = Math.max(ox, Math.min(ox + b.x * dw, ox + dw - w));
+        const top = Math.max(oy, Math.min(oy + b.y * dh, oy + dh - h));
+        const ta = document.createElement("textarea");
+        ta.className = "kjideo-inline";
+        ta.value = b.desc || "";
+        ta.style.left = left + "px";
+        ta.style.top = top + "px";
+        ta.style.width = w + "px";
+        ta.style.height = h + "px";
+        ta.style.borderColor = (b.palette || []).find(Boolean) || "#46b4e6";  // first palette color, else accent
+        stopProp(ta);
+        wrap.appendChild(ta);
+        inlineTa = ta;
+        ta.focus(); ta.select();
+        const orig = b.desc || "";
+        let cancelled = false;
+        ta.addEventListener("input", () => { b.desc = ta.value; drawCanvas(); updateTokens(); });
+        ta.addEventListener("keydown", (e) => {
+          e.stopPropagation();
+          if (e.key === "Escape") { cancelled = true; b.desc = orig; ta.blur(); }
+          else if (e.key === "Enter" && (e.ctrlKey || e.metaKey)) ta.blur();
+        });
+        ta.addEventListener("blur", () => {
+          if (!cancelled) b.desc = ta.value;
+          closeInlineEditor();
+          commit();
+        });
+      }
+      canvasEl.addEventListener("dblclick", (e) => {
+        if (node._hideBoxes) return;
+        e.preventDefault(); e.stopPropagation();
+        const cands = boxesAt(mouseN(e));     // edit the active box if it's under the cursor, else topmost
+        const target = cands.find((c) => c.index === node._activeIdx) || cands[0];
+        if (target) openInlineEditor(target.index);
+      });
+
+      // Snapshot the current selection (or the active box) for the clipboard.
+      function copySelection() {
+        const idxs = node._selection.size ? [...node._selection].sort((a, b) => a - b)
+          : (node._activeIdx >= 0 ? [node._activeIdx] : []);
+        return idxs.map((i) => JSON.parse(JSON.stringify(node._boxes[i])));
+      }
+      // Paste the clipboard regions as a group, centered under the cursor, keeping their layout.
+      function pasteBoxes() {
+        if (!copiedBoxes || !copiedBoxes.length) return;
+        const clones = copiedBoxes.map((b) => { const c = JSON.parse(JSON.stringify(b)); delete c.locked; return c; });  // pasted = editable
+        let minx = Infinity, miny = Infinity, maxx = -Infinity, maxy = -Infinity;
+        for (const b of clones) {
+          minx = Math.min(minx, b.x); miny = Math.min(miny, b.y);
+          maxx = Math.max(maxx, b.x + b.w); maxy = Math.max(maxy, b.y + b.h);
+        }
+        const gw = maxx - minx, gh = maxy - miny;
+        const m = node._lastMouseN;
+        let tx = m ? m.x - gw / 2 : minx + 0.03;       // target group top-left
+        let ty = m ? m.y - gh / 2 : miny + 0.03;
+        tx = Math.max(0, Math.min(tx, 1 - gw));         // clamp group into the canvas
+        ty = Math.max(0, Math.min(ty, 1 - gh));
+        const dx = tx - minx, dy = ty - miny;
+        const start = node._boxes.length;
+        for (const b of clones) {
+          b.x = clamp01(b.x + dx); b.y = clamp01(b.y + dy);
+          delete b.nobbox;                              // pasted boxes are placed
+          node._boxes.push(b);
+        }
+        node._selection = new Set();
+        for (let i = start; i < node._boxes.length; i++) node._selection.add(i);
+        node._activeIdx = node._boxes.length - 1;
+        commit(); fitNode();
+      }
+      // Keyboard: Delete removes; Ctrl/Cmd C/V/D copy/paste/duplicate the active region.
+      // Canvas must be focused; stop the event so LiteGraph doesn't act on the node.
+      canvasEl.addEventListener("keydown", (e) => {
+        if (node._placing) {
+          if (e.key === "Escape") { e.preventDefault(); e.stopPropagation(); cancelPlacing(); }
+          return;
+        }
+        if (node._drawing || node._hideBoxes) return;   // view-only while boxes are hidden (H)
+        const ctrl = e.ctrlKey || e.metaKey;
+        if ((e.key === "Delete" || e.key === "Backspace") && node._activeIdx >= 0) {
+          e.preventDefault(); e.stopPropagation();
+          removeSelected(); commit(); fitNode();      // removes all selected (or the active one)
+        } else if (ctrl && e.key === "c" && node._activeIdx >= 0) {
+          e.preventDefault(); e.stopPropagation();
+          copiedBoxes = copySelection();
+        } else if (ctrl && e.key === "v" && copiedBoxes) {
+          e.preventDefault(); e.stopPropagation();
+          pasteBoxes();
+        } else if (ctrl && e.key === "d" && node._activeIdx >= 0) {
+          e.preventDefault(); e.stopPropagation();
+          copiedBoxes = copySelection();
+          pasteBoxes();
+        }
+      });
+
+      function onMove(e) {
+        if (!node._drawing) return;
+        const mN = mouseN(e);
+        const dN = { x: mN.x - node._dragStartN.x, y: mN.y - node._dragStartN.y };
+        if (Math.abs(dN.x) + Math.abs(dN.y) > 0.001) node._pendingCollapse = -1;  // it's a drag, not a click
+        if (node._dragMode === "move" && node._groupStart) {
+          let dx = dN.x, dy = dN.y;                   // clamp delta so the whole group stays in bounds
+          for (const i in node._groupStart) {
+            const s = node._groupStart[i];
+            dx = Math.min(Math.max(dx, -s.x), 1 - s.w - s.x);
+            dy = Math.min(Math.max(dy, -s.y), 1 - s.h - s.y);
+          }
+          if (node.properties.snap) {                 // snap the group's movement to whole grid cells
+            const { sx, sy } = gridStep();
+            dx = Math.round(dx / sx) * sx;
+            dy = Math.round(dy / sy) * sy;
+          }
+          for (const i in node._groupStart) {
+            const s = node._groupStart[i];
+            node._boxes[i] = { ...s, x: s.x + dx, y: s.y + dy };
+            delete node._boxes[i].nobbox;
+          }
+          drawCanvas(); updateBboxLabel();
+          return;
+        }
+        if (node._groupStart && node._dragMode.startsWith("resize")) {
+          // Scale every selected box by the primary's resize, about the handle's fixed edge.
+          const a = node._boxAtStart, na = applyDrag(node._dragMode, a, dN);
+          const suf = node._dragMode.slice(7);       // "tl"|"tr"|"bl"|"br"|"t"|"b"|"l"|"r"
+          const scaleX = (suf.includes("l") || suf.includes("r")) && a.w > 0 ? Math.max(0.02, na.w / a.w) : 1;
+          const scaleY = (suf.includes("t") || suf.includes("b")) && a.h > 0 ? Math.max(0.02, na.h / a.h) : 1;
+          const ax = suf.includes("l") ? a.x + a.w : a.x;   // fixed (anchor) edges
+          const ay = suf.includes("t") ? a.y + a.h : a.y;
+          for (const i in node._groupStart) {
+            const s = node._groupStart[i];
+            node._boxes[i] = normalizeBox({
+              ...s, x: ax + (s.x - ax) * scaleX, y: ay + (s.y - ay) * scaleY,
+              w: s.w * scaleX, h: s.h * scaleY,
+            });
+            delete node._boxes[i].nobbox;
+          }
+          drawCanvas(); updateBboxLabel();
+          return;
+        }
+        const nb = snapBox(applyDrag(node._dragMode, node._boxAtStart, dN), node._dragMode);
+        delete nb.nobbox;            // moving/resizing places the element (gives it a bbox)
+        node._boxes[node._activeIdx] = nb;
+        drawCanvas(); updateBboxLabel();
+      }
+      function onUp() {
+        if (!node._drawing) return;
+        node._drawing = false;
+        canvasEl.removeEventListener("pointermove", onMove);
+        canvasEl.removeEventListener("pointerup", onUp);
+        canvasEl.removeEventListener("pointercancel", onUp);
+        // a click (no drag) on empty space drops the placeholder box and deselects everything
+        const b = node._boxes[node._activeIdx];
+        if (b && (b.w < 0.005 || b.h < 0.005) && node._dragMode === "draw") {
+          node._boxes.splice(node._activeIdx, 1);
+          selectOnly(-1);
+        } else if (node._pendingCollapse >= 0) {     // click (no drag) on a group member → keep only it
+          selectOnly(node._pendingCollapse);
+        }
+        node._pendingCollapse = -1; node._groupStart = null;
+        commit();
+      }
+
+      // ── marquee (rubber-band) selection: shift-drag ──
+      function marqueeRect() {
+        const m = node._marquee;
+        return { x: Math.min(m.x0, m.x), y: Math.min(m.y0, m.y),
+                 w: Math.abs(m.x - m.x0), h: Math.abs(m.y - m.y0) };
+      }
+      function rectsOverlap(r, b) {
+        return r.x < b.x + b.w && r.x + r.w > b.x && r.y < b.y + b.h && r.y + r.h > b.y;
+      }
+      function startMarquee(mN, startHit) {
+        node._marquee = { x0: mN.x, y0: mN.y, x: mN.x, y: mN.y };
+        node._marqueeBase = new Set(node._selection);   // additive: marquee unions with what's selected
+        node._marqueeStartHit = startHit;               // for the shift-click (no drag) toggle fallback
+        node._marqueeActive = false;
+        canvasEl.focus();
+        canvasEl.addEventListener("pointermove", onMarqueeMove);
+        canvasEl.addEventListener("pointerup", onMarqueeUp);
+        canvasEl.addEventListener("pointercancel", onMarqueeUp);
+        drawCanvas();
+      }
+      function onMarqueeMove(e) {
+        if (!node._marquee) return;
+        const mN = mouseN(e);
+        node._marquee.x = mN.x; node._marquee.y = mN.y;
+        if (Math.abs(mN.x - node._marquee.x0) + Math.abs(mN.y - node._marquee.y0) > 0.01) node._marqueeActive = true;
+        if (node._marqueeActive) {
+          const r = marqueeRect();
+          const sel = new Set(node._marqueeBase);
+          node._boxes.forEach((b, i) => { if (!b.locked && rectsOverlap(r, b)) sel.add(i); });   // marquee skips locked
+          node._selection = sel;
+          if (node._activeIdx < 0 || !sel.has(node._activeIdx)) node._activeIdx = sel.size ? [...sel][0] : node._activeIdx;
+        }
+        drawCanvas();
+      }
+      function onMarqueeUp() {
+        canvasEl.removeEventListener("pointermove", onMarqueeMove);
+        canvasEl.removeEventListener("pointerup", onMarqueeUp);
+        canvasEl.removeEventListener("pointercancel", onMarqueeUp);
+        if (!node._marqueeActive && node._marqueeStartHit >= 0) {   // shift-click on a box → toggle it
+          const idx = node._marqueeStartHit;
+          if (node._selection.has(idx) && node._selection.size > 1) {
+            node._selection.delete(idx);
+            if (node._activeIdx === idx) node._activeIdx = node._selection.values().next().value;
+          } else {
+            node._selection.add(idx); node._activeIdx = idx;
+          }
+        }
+        node._marquee = null; node._marqueeActive = false;
+        if (node._activeIdx >= 0 && !node._selection.has(node._activeIdx)) {
+          node._activeIdx = node._selection.size ? [...node._selection][0] : -1;
+        }
+        commit();
+      }
+
+      // ── duplicate placement: the new box follows the cursor until clicked ──
+      function placeFollower(mN) {
+        const b = node._boxes[node._activeIdx];
+        if (!b) return;
+        b.x = clamp01(Math.min(mN.x - b.w / 2, 1 - b.w));
+        b.y = clamp01(Math.min(mN.y - b.h / 2, 1 - b.h));
+        delete b.nobbox;
+        drawCanvas(); updateBboxLabel();
+      }
+      function startPlacing(srcIdx) {
+        const src = node._boxes[srcIdx];
+        if (!src) return;
+        const nb = JSON.parse(JSON.stringify(src));
+        delete nb.nobbox; delete nb.locked;               // a duplicate is editable, not born locked
+        node._boxes.push(nb);
+        selectOnly(node._boxes.length - 1);
+        node._placing = true;
+        canvasEl.focus();
+        canvasEl.style.cursor = "move";
+        serialize(); renderPanel(); drawCanvas(); updateTokens();
+      }
+      function finishPlacing() {
+        if (!node._placing) return;
+        node._placing = false;
+        canvasEl.style.cursor = "crosshair";
+        commit(); fitNode();
+      }
+      function cancelPlacing() {
+        if (!node._placing) return;
+        node._placing = false;
+        canvasEl.style.cursor = "crosshair";
+        removeBox(node._activeIdx);
+        commit(); fitNode();
+      }
+
+      // ── right-click "layers" menu: list / select / delete / duplicate / reorder regions ──
+      function closeLayersMenu() {
+        if (node._layerMenu) { node._layerMenu.remove(); node._layerMenu = null; }
+        node._layerDismiss?.disarm(); node._layerDismiss = null;
+      }
+      function rowLabel(b) {
+        if (b.type === "text") {
+          const t = b.text ? `"${b.text}"` : "";
+          return b.desc ? (t ? t + " — " + b.desc : b.desc) : t;
+        }
+        return b.desc || "";
+      }
+      function openLayersMenu(clientX, clientY) {
+        closeLayersMenu();
+        const menu = document.createElement("div");
+        menu.className = "kjideo-menu";
+        const hdr = document.createElement("div");
+        hdr.className = "kjideo-mhdr";
+        hdr.textContent = "Regions — top = front · click select · drag reorder";
+        // 01 at the top of the list = front-most (drawn on top); see _draw / boxesAt.
+        menu.appendChild(hdr);
+        const list = document.createElement("div");
+        menu.appendChild(list);
+        node._layerMenu = menu;
+
+        const renumber = () => Array.from(list.querySelectorAll(".kjideo-lrow")).forEach((row, k) => {
+          row.querySelector(".kjideo-lnum").textContent = String(k + 1).padStart(2, "0");
+        });
+        function buildRows() {
+          list.innerHTML = "";
+          if (!node._boxes.length) {
+            const empty = document.createElement("div");
+            empty.className = "kjideo-mhdr"; empty.textContent = "No regions yet.";
+            list.appendChild(empty);
+            return;
+          }
+          node._boxes.forEach((b, i) => {
+            const row = document.createElement("div");
+            row.className = "kjideo-lrow" + (i === node._activeIdx ? " active" : "");
+            row._box = b;
+            const sw = document.createElement("div");
+            sw.className = "kjideo-lsw";
+            sw.style.background = (b.palette || []).find(Boolean) || "#8c8c8c";
+            const num = document.createElement("span");
+            num.className = "kjideo-lnum"; num.textContent = String(i + 1).padStart(2, "0");
+            const txt = document.createElement("span");
+            const label = rowLabel(b);
+            txt.className = "kjideo-ltext" + (label ? "" : " empty");
+            txt.textContent = label || (b.type === "text" ? "(text)" : "(empty)");
+            txt.title = label;
+            const lock = document.createElement("button");
+            lock.className = "kjideo-lbtn kjideo-lock" + (b.locked ? " on" : "");
+            lock.textContent = b.locked ? "🔒" : "🔓";
+            lock.title = b.locked ? "Unlock (allow moving/resizing)" : "Lock (freeze on the canvas)";
+            const dup = document.createElement("button");
+            dup.className = "kjideo-lbtn"; dup.textContent = "⧉";
+            dup.title = "Duplicate, then click on the canvas to place";
+            const del = document.createElement("button");
+            del.className = "kjideo-lbtn del"; del.textContent = "✕";
+            del.title = b.locked ? "Unlock to delete" : "Delete region";
+            del.disabled = !!b.locked;
+            row.append(sw, num, txt, lock, dup, del);
+            list.appendChild(row);
+
+            lock.addEventListener("click", (e) => {
+              e.stopPropagation();
+              b.locked = !b.locked;
+              if (b.locked) {                              // a locked box drops out of the selection
+                const idx = node._boxes.indexOf(b);
+                node._selection.delete(idx);
+                if (node._activeIdx === idx) node._activeIdx = node._selection.size ? [...node._selection][0] : -1;
+              }
+              commit(); buildRows();
+            });
+
+            row.addEventListener("click", () => {
+              if (row._dragged) { row._dragged = false; return; }
+              selectOnly(node._boxes.indexOf(b));
+              commit();
+              for (const r of list.querySelectorAll(".kjideo-lrow")) r.classList.toggle("active", r._box === b);
+            });
+            dup.addEventListener("click", (e) => {
+              e.stopPropagation();
+              const idx = node._boxes.indexOf(b);
+              closeLayersMenu();
+              startPlacing(idx);
+            });
+            del.addEventListener("click", (e) => {
+              e.stopPropagation();
+              const idx = node._boxes.indexOf(b);
+              if (idx < 0) return;
+              removeBox(idx); commit(); fitNode();
+              if (!node._boxes.length) { closeLayersMenu(); return; }
+              buildRows();
+            });
+            // drag-reorder (vertical FLIP, mirrors the palette swatch reorder)
+            row.addEventListener("pointerdown", (e) => {
+              if (e.button !== 0 || e.target === lock || e.target === dup || e.target === del) return;
+              e.preventDefault(); e.stopPropagation();
+              const sx = e.clientX, sy = e.clientY;
+              let dragging = false;
+              const move = (me) => {
+                if (!dragging) {
+                  if (Math.abs(me.clientX - sx) + Math.abs(me.clientY - sy) < 4) return;
+                  dragging = true; row.classList.add("dragging"); document.body.classList.add("kjideo-dragging");
+                }
+                for (const other of list.querySelectorAll(".kjideo-lrow")) {
+                  if (other === row) continue;
+                  const r = other.getBoundingClientRect();
+                  if (me.clientY >= r.top && me.clientY <= r.bottom) {
+                    const ref = me.clientY > r.top + r.height / 2 ? other.nextSibling : other;
+                    if (ref === row || ref === row.nextSibling) break;
+                    const els = Array.from(list.querySelectorAll(".kjideo-lrow"));
+                    const prev = els.map((el) => el.getBoundingClientRect().top);
+                    list.insertBefore(row, ref);
+                    els.forEach((el, k) => {                        // FLIP: slide to new positions
+                      const dy = prev[k] - el.getBoundingClientRect().top;
+                      if (!dy) return;
+                      el.style.transition = "none";
+                      el.style.transform = `translateY(${dy}px)`;
+                      el.getBoundingClientRect();                   // flush
+                      el.style.transition = ""; el.style.transform = "";
+                    });
+                    break;
+                  }
+                }
+              };
+              const up = () => {
+                document.removeEventListener("pointermove", move);
+                document.removeEventListener("pointerup", up);
+                document.removeEventListener("pointercancel", up);
+                document.body.classList.remove("kjideo-dragging");
+                if (dragging) {
+                  row.classList.remove("dragging");
+                  row._dragged = true;                             // suppress the trailing click
+                  const active = node._boxes[node._activeIdx];
+                  const order = Array.from(list.querySelectorAll(".kjideo-lrow")).map((el) => el._box);
+                  if (order.length === node._boxes.length) node._boxes = order;
+                  selectOnly(active ? node._boxes.indexOf(active) : -1);  // reorder invalidates multi-select indices
+                  renumber();
+                  commit();
+                }
+              };
+              document.addEventListener("pointermove", move);
+              document.addEventListener("pointerup", up);
+              document.addEventListener("pointercancel", up);
+            });
+          });
+        }
+        buildRows();
+
+        document.body.appendChild(menu);
+        const r = menu.getBoundingClientRect();                    // clamp into the viewport
+        let left = clientX, top = clientY;
+        if (left + r.width > window.innerWidth) left = window.innerWidth - r.width - 4;
+        if (top + r.height > window.innerHeight) top = window.innerHeight - r.height - 4;
+        menu.style.left = Math.max(4, left) + "px";
+        menu.style.top = Math.max(4, top) + "px";
+
+        node._layerDismiss = outsideDismiss(menu, () => closeLayersMenu());
+        node._layerDismiss.arm();
+      }
+
+      canvasEl.addEventListener("contextmenu", (e) => {
+        e.preventDefault(); e.stopPropagation();
+        if (node._placing || node._hideBoxes) return;
+        closeInlineEditor();
+        openLayersMenu(e.clientX, e.clientY);
+      });
+      stopProp(clearBtn);
+      clearBtn.addEventListener("click", () => {
+        closeInlineEditor();
+        node._boxes = []; node._activeIdx = -1; node._selection = new Set(); node._stylePalette = [];
+        node._lastImported = "";
+        commit(); rebuildStylePalette(); fitNode();   // a wired import re-seeds on the next run (serializeValue cache-busts)
+      });
+
+      // ── build caption JSON (mirrors Python key order) ──
+      // pyJson: matches Python _dumps — indent=4, but scalar arrays stay on one line.
+      function pyJson(v, lvl = 0) {
+        if (v === null) return "null";
+        if (typeof v === "number" || typeof v === "boolean") return String(v);
+        if (typeof v === "string") return JSON.stringify(v);
+        const pad = "    ".repeat(lvl + 1), end = "    ".repeat(lvl);
+        if (Array.isArray(v)) {
+          if (!v.length) return "[]";
+          if (v.every((x) => x === null || typeof x !== "object"))   // scalar array → inline
+            return "[" + v.map((x) => pyJson(x, lvl)).join(", ") + "]";
+          return "[\n" + v.map((x) => pad + pyJson(x, lvl + 1)).join(",\n") + "\n" + end + "]";
+        }
+        const keys = Object.keys(v);
+        if (!keys.length) return "{}";
+        return "{\n" + keys.map((k) => pad + JSON.stringify(k) + ": " + pyJson(v[k], lvl + 1)).join(",\n") + "\n" + end + "}";
+      }
+      function getW(name) { const w = findW(name); return w ? w.value : ""; }
+      function cleanPalette(arr) { return (arr || []).filter((c) => c).map((c) => c.toUpperCase()); }
+      function normBboxJS(b) {
+        const c = (v) => Math.max(0, Math.min(1000, Math.round(v * 1000)));
+        let ymin = c(b.y), xmin = c(b.x), ymax = c(b.y + b.h), xmax = c(b.x + b.w);
+        if (ymin > ymax) [ymin, ymax] = [ymax, ymin];
+        if (xmin > xmax) [xmin, xmax] = [xmax, xmin];
+        return [ymin, xmin, ymax, xmax];
+      }
+      function buildCaption() {
+        const cap = {};
+        if ((getW("high_level_description") || "").trim()) cap.high_level_description = getW("high_level_description");
+        const styleW = findW("style");
+        const kind = styleW ? styleW.value : "none";
+        if (kind !== "none") {
+          const sd = { aesthetics: getW("aesthetics"), lighting: getW("lighting") };
+          if (kind === "photo") { sd.photo = getW("style.photo") || ""; sd.medium = getW("medium"); }
+          else { sd.medium = getW("medium"); sd.art_style = getW("style.art_style") || ""; }
+          const pal = cleanPalette(node._stylePalette);
+          if (pal.length) sd.color_palette = pal;
+          cap.style_description = sd;
+        }
+        const elements = node._boxes.map((b) => {
+          const etype = b.type === "text" ? "text" : "obj";
+          const el = { type: etype };
+          if (!b.nobbox) el.bbox = normBboxJS(b);            // unplaced elements omit bbox
+          if (etype === "text") el.text = b.text || "";
+          el.desc = b.desc || "";
+          const pal = cleanPalette(b.palette).slice(0, MAX_ELEM_COLORS);
+          if (pal.length) el.color_palette = pal;
+          return el;
+        });
+        cap.compositional_deconstruction = { background: getW("background"), elements };
+        return (formatWidget?.value) === "pretty" ? pyJson(cap) : JSON.stringify(cap);   // compact by default; matches the node output
+      }
+      // Rough token estimate (~chars/4); exact count needs the Qwen tokenizer.
+      function updateTokens() {
+        const n = Math.ceil(buildCaption().length / 4);
+        tokenSpan.textContent = "~" + n + " tok";
+        // grey <256 (sparse) · green healthy · orange nearing · red ≥2048 (model hard cap)
+        tokenSpan.style.color = n >= 2048 ? "#e05555" : n >= 1792 ? "#e6a23c" : n >= 256 ? "#6cc06c" : "#888";
+      }
+      async function doCopy() {
+        const txt = buildCaption();
+        try { await navigator.clipboard.writeText(txt); copyBtn.textContent = "Copied"; setTimeout(() => (copyBtn.textContent = "Copy"), 900); }
+        catch (e) { window.prompt("Copy the caption JSON:", txt); }
+      }
+      stopProp(copyBtn);
+      copyBtn.addEventListener("click", doCopy);
+
+      // ── import a caption JSON and populate the node ──
+      function setWidgetVal(name, val) {
+        const w = findW(name);
+        if (w) { w.value = val; w.callback?.(val); }
+      }
+      function bboxElemToBox(el, idx) {
+        if (!el || typeof el !== "object") return null;
+        const box = { type: el.type === "text" ? "text" : "obj",
+          text: el.text || "", desc: el.desc || "",
+          palette: Array.isArray(el.color_palette) ? el.color_palette.slice() : [] };
+        const bb = el.bbox;
+        if (Array.isArray(bb) && bb.length === 4) {
+          const [ymin, xmin, ymax, xmax] = bb;
+          box.x = xmin / 1000; box.y = ymin / 1000; box.w = (xmax - xmin) / 1000; box.h = (ymax - ymin) / 1000;
+        } else {
+          // No bbox: "unplaced" element — small placeholder, flagged so export omits bbox.
+          const k = (idx || 0) % 6;
+          box.x = 0.03 + k * 0.035; box.y = 0.03 + k * 0.035; box.w = 0.22; box.h = 0.14;
+          box.nobbox = true;
+        }
+        return box;
+      }
+      function applyCaption(cap) {
+        const cd = (cap && cap.compositional_deconstruction) || {};
+        const els = Array.isArray(cd.elements) ? cd.elements : [];
+        node._boxes = els.map((el, i) => bboxElemToBox(el, i)).filter(Boolean);
+        selectOnly(node._boxes.length ? 0 : -1);
+        setWidgetVal("high_level_description", cap.high_level_description || "");
+        setWidgetVal("background", cd.background || "");
+        const sd = cap.style_description || {};
+        let kind = "none";
+        if (typeof sd.photo === "string") kind = "photo";
+        else if (typeof sd.art_style === "string") kind = "art_style";
+        const styleW = findW("style");
+        if (styleW) styleW.value = kind;            // setter synchronously rebuilds sub-widgets
+        if (kind === "photo") setWidgetVal("style.photo", sd.photo || "");
+        else if (kind === "art_style") setWidgetVal("style.art_style", sd.art_style || "");
+        setWidgetVal("aesthetics", sd.aesthetics || "");
+        setWidgetVal("lighting", sd.lighting || "");
+        setWidgetVal("medium", sd.medium || "");
+        node._stylePalette = Array.isArray(sd.color_palette) ? sd.color_palette.slice() : [];
+      }
+      function tryParseCaption(t) {
+        if (!t) return null;
+        try { const o = JSON.parse(t); return (o && typeof o === "object" && o.compositional_deconstruction) ? o : null; }
+        catch (e) { return null; }
+      }
+      // Apply a parsed caption to the editor and refresh everything.
+      function loadCaption(cap) {
+        closeInlineEditor();
+        applyCaption(cap);
+        syncCanvasToDims(); commit(); rebuildStylePalette(); fitNode();
+      }
+      // Append a caption's regions to the current canvas (keeps existing boxes + caption fields).
+      function insertCaptionBoxes(cap) {
+        closeInlineEditor();
+        const cd = (cap && cap.compositional_deconstruction) || {};
+        const els = Array.isArray(cd.elements) ? cd.elements : [];
+        const added = els.map((el, i) => bboxElemToBox(el, i)).filter(Boolean);
+        if (!added.length) return;
+        const start = node._boxes.length;
+        node._boxes.push(...added);
+        node._selection = new Set();                      // select the inserted regions
+        for (let i = start; i < node._boxes.length; i++) node._selection.add(i);
+        node._activeIdx = node._boxes.length - 1;
+        commit(); fitNode();
+      }
+      async function doImport() {
+        let cap = null, txt = "";
+        try { txt = (await navigator.clipboard.readText() || "").trim(); cap = tryParseCaption(txt); } catch (e) {}
+        if (!cap) { txt = (window.prompt("Paste Ideogram 4 caption JSON:", "") || "").trim(); cap = tryParseCaption(txt); }
+        if (!cap) { if (txt) alert("Not a valid Ideogram 4 caption JSON (needs 'compositional_deconstruction')."); return; }
+        loadCaption(cap);
+      }
+      stopProp(importBtn);
+      importBtn.addEventListener("click", doImport);
+
+      // Populate the editor from a caption pushed back by execute() when import_json
+      // is connected (a connected socket can't be read in the frontend directly).
+      function applyImported(capStr) {
+        if (!capStr) return;
+        // "always" mode re-applies even an unchanged import so the editor snaps back to the
+        // authoritative JSON after edits; "when empty" keeps the guard so edits stick.
+        const always = findW("import_mode")?.value === "always";
+        if (capStr === node._lastImported && !always) return;
+        const cap = tryParseCaption(capStr);
+        if (!cap) return;
+        node._lastImported = capStr;
+        loadCaption(cap);
+      }
+      chainCallback(node, "onExecuted", function (message) {
+        if (message?.caption) applyImported(message.caption[0]);
+        // Seed regions from the bboxes input only when the editor is empty, so user-drawn/edited
+        // regions are never overwritten.
+        if (message?.boxes && !node._boxes.length) {
+          const seeded = JSON.parse(message.boxes[0]);
+          if (Array.isArray(seeded) && seeded.length) {
+            node._boxes = seeded.filter((b) => b && typeof b.x === "number" && typeof b.w === "number");
+            selectOnly(node._boxes.length ? 0 : -1);
+            commit(); fitNode();
+          }
+        }
+        // Reflect resolved width/height (e.g. from connected inputs) in the canvas aspect.
+        // A connected background image governs the aspect itself, so skip then.
+        if (message?.dims && !node._bgImg) {
+          const [w, h] = message.dims;
+          if (wWidget && w) wWidget.value = w;
+          if (hWidget && h) hWidget.value = h;
+          syncCanvasToDims(); fitNode();
+        }
+      });
+
+      // ── property panel ──
+      function stopProp(el) {
+        for (const ev of ["mousedown", "pointerdown", "wheel"]) el.addEventListener(ev, (e) => e.stopPropagation());
+      }
+      // Color swatches: onEdit on change, onStruct on add/remove/reorder. Shared by both palettes.
+      // Pointer-based drag (HTML5 DnD is unreliable inside LiteGraph DOM widgets) with live reorder.
+      function buildSwatchRow(container, arr, max, onEdit, onStruct) {
+        arr.forEach((hex, i) => {
+          const sw = document.createElement("div");
+          sw.className = "kjideo-sw";
+          sw.style.background = hex;
+          sw.dataset.hex = hex;
+          sw.title = "Click edit · drag reorder · Ctrl+C/V copy/paste hex · right-click remove";
+          const inp = document.createElement("input");
+          inp.type = "color"; inp.value = hex;
+          sw.appendChild(inp);
+          container.appendChild(sw);
+          const setColor = (hex2) => { arr[i] = hex2; inp.value = hex2; sw.style.background = hex2; sw.dataset.hex = hex2; onEdit(); };
+          inp.addEventListener("input", () => setColor(inp.value));
+          sw.addEventListener("pointerenter", () => { hoveredSwatch = { sw, setColor }; });
+          sw.addEventListener("pointerleave", () => { if (hoveredSwatch && hoveredSwatch.sw === sw) hoveredSwatch = null; });
+          sw.addEventListener("wheel", (e) => e.stopPropagation());
+          sw.addEventListener("contextmenu", (e) => { e.preventDefault(); e.stopPropagation(); arr.splice(i, 1); onStruct(); });
+          sw.addEventListener("pointerdown", (e) => {
+            if (e.button !== 0) return;
+            e.preventDefault(); e.stopPropagation();
+            try { sw.setPointerCapture(e.pointerId); } catch (e2) {}  // capture so Nodes 2.0's WidgetDOM .stop can't swallow the drag
+            const sx = e.clientX, sy = e.clientY;
+            let dragging = false;
+            const move = (me) => {
+              if (!dragging) {
+                if (Math.abs(me.clientX - sx) + Math.abs(me.clientY - sy) < 4) return;
+                dragging = true; sw.classList.add("dragging"); document.body.classList.add("kjideo-dragging");
+              }
+              for (const other of container.querySelectorAll(".kjideo-sw")) {
+                if (other === sw) continue;
+                const r = other.getBoundingClientRect();
+                if (me.clientX >= r.left && me.clientX <= r.right && me.clientY >= r.top - 6 && me.clientY <= r.bottom + 6) {
+                  const ref = me.clientX > r.left + r.width / 2 ? other.nextSibling : other;
+                  if (ref === sw || ref === sw.nextSibling) break;   // already there
+                  const els = Array.from(container.querySelectorAll(".kjideo-sw"));
+                  const prev = els.map((el) => el.getBoundingClientRect().left);
+                  container.insertBefore(sw, ref);
+                  els.forEach((el, k) => {                            // FLIP: slide to new positions
+                    const dx = prev[k] - el.getBoundingClientRect().left;
+                    if (!dx) return;
+                    el.style.transition = "none";
+                    el.style.transform = `translateX(${dx}px)`;
+                    el.getBoundingClientRect();                       // flush
+                    el.style.transition = ""; el.style.transform = "";
+                  });
+                  break;
+                }
+              }
+            };
+            const up = () => {
+              sw.removeEventListener("pointermove", move);
+              sw.removeEventListener("pointerup", up);
+              sw.removeEventListener("pointercancel", up);
+              document.body.classList.remove("kjideo-dragging");
+              if (dragging) {
+                sw.classList.remove("dragging");
+                const order = Array.from(container.querySelectorAll(".kjideo-sw")).map((el) => el.dataset.hex);
+                if (order.length === arr.length) { arr.length = 0; arr.push(...order); }
+                onStruct();
+              } else {
+                inp.click();                                 // no drag → treat as click, open the picker
+              }
+            };
+            sw.addEventListener("pointermove", move);
+            sw.addEventListener("pointerup", up);
+            sw.addEventListener("pointercancel", up);
+          });
+        });
+        if (arr.length < max) {
+          const add = document.createElement("button");
+          add.className = "kjideo-btn"; add.textContent = "+";
+          add.title = "Add a color (uses the clipboard color if it is one)";
+          stopProp(add);
+          add.addEventListener("click", async () => {
+            let col = "#ffffff";
+            try { const c = parseColorString(await navigator.clipboard.readText()); if (c) col = c; } catch (e) {}
+            arr.push(col); onStruct();
+          });
+          container.appendChild(add);
+        }
+      }
+
+      // Swatch color changed (no add/remove): persist + repaint.
+      function swatchEdit() { serialize(); drawCanvas(); }
+
+      function rebuildStylePalette() {
+        while (styleBar.children.length > 1) styleBar.removeChild(styleBar.lastChild);
+        buildSwatchRow(styleBar, node._stylePalette, MAX_STYLE_COLORS,
+          swatchEdit,
+          () => { swatchEdit(); rebuildStylePalette(); fitNode(); });
+      }
+
+      // Textarea that flexes to fill the prompt panel (whose height is set by the splitter).
+      function makeArea(field, value, placeholder, onInput) {
+        const ta = document.createElement("textarea");
+        ta.className = "kjideo-area";
+        ta.placeholder = placeholder;
+        ta.value = value || "";
+        stopProp(ta);
+        ta.addEventListener("input", onInput);
+        return ta;
+      }
+      let bboxPx = null, bboxGrid = null;   // editable bbox fields: pixels and 0–1000 grid
+      function dims() { return [wWidget ? wWidget.value : 1024, hWidget ? hWidget.value : 1024]; }
+      function boxToPx(b) {                                   // same order as the grid: ymin, xmin, ymax, xmax
+        const [W, H] = dims();
+        return [Math.round(b.y * H), Math.round(b.x * W), Math.round((b.y + b.h) * H), Math.round((b.x + b.w) * W)];
+      }
+      function setField(inp, b, fn) {                          // skip while the field is being edited
+        if (!inp || document.activeElement === inp) return;
+        inp.value = (!b || b.nobbox) ? "" : fn(b).join(", ");
+      }
+      function updateBboxLabel() {
+        const b = node._boxes[node._activeIdx];
+        setField(bboxPx, b, boxToPx);
+        setField(bboxGrid, b, normBboxJS);
+      }
+      function parse4(inp) {
+        const nums = inp.value.split(/[,\s]+/).map(Number).filter((n) => !isNaN(n));
+        return nums.length === 4 ? nums : null;
+      }
+      function commitPxEdit() {
+        const b = node._boxes[node._activeIdx]; if (!b || !bboxPx) return;
+        const nums = parse4(bboxPx); if (!nums) { updateBboxLabel(); return; }
+        const [W, H] = dims();
+        let [ymin, xmin, ymax, xmax] = nums;                  // ymin, xmin, ymax, xmax (matches grid)
+        ymin = Math.max(0, Math.min(H, ymin)); ymax = Math.max(0, Math.min(H, ymax));
+        xmin = Math.max(0, Math.min(W, xmin)); xmax = Math.max(0, Math.min(W, xmax));
+        if (ymin > ymax) [ymin, ymax] = [ymax, ymin];
+        if (xmin > xmax) [xmin, xmax] = [xmax, xmin];
+        b.y = ymin / H; b.x = xmin / W; b.h = (ymax - ymin) / H; b.w = (xmax - xmin) / W;
+        delete b.nobbox; commit(); fitNode();
+      }
+      function commitGridEdit() {
+        const b = node._boxes[node._activeIdx]; if (!b || !bboxGrid) return;
+        const nums = parse4(bboxGrid); if (!nums) { updateBboxLabel(); return; }
+        let [ymin, xmin, ymax, xmax] = nums.map((n) => Math.max(0, Math.min(1000, n)));
+        if (ymin > ymax) [ymin, ymax] = [ymax, ymin];
+        if (xmin > xmax) [xmin, xmax] = [xmax, xmin];
+        b.y = ymin / 1000; b.x = xmin / 1000; b.h = (ymax - ymin) / 1000; b.w = (xmax - xmin) / 1000;
+        delete b.nobbox; commit(); fitNode();
+      }
+      function makeBboxField(placeholder, title, onCommit) {
+        const inp = document.createElement("input");
+        inp.type = "text"; inp.className = "kjideo-bbox";
+        inp.placeholder = placeholder; inp.title = title;
+        stopProp(inp);
+        inp.addEventListener("keydown", (e) => {
+          e.stopPropagation();
+          if (e.key === "Enter") inp.blur();
+          else if (e.key === "Escape") { updateBboxLabel(); inp.blur(); }
+        });
+        inp.addEventListener("change", onCommit);
+        return inp;
+      }
+      function renderPanel() {
+        for (const ro of node._areaObservers) ro.disconnect();
+        node._areaObservers = [];
+        panel.innerHTML = "";
+        const b = node._boxes[node._activeIdx];
+        if (!b) {
+          hint.textContent = "";
+          const p = document.createElement("div");
+          p.style.color = "#888";
+          p.textContent = node._boxes.length ? "Click a region to edit it." : "No regions yet.";
+          panel.appendChild(p);
+          return;
+        }
+        const col = (b.palette || []).find(Boolean) || "#bbb";
+        const selN = node._selection.size;
+        // Build with DOM + style.color (a CSS value) — never innerHTML — since col comes from
+        // box data that may be loaded from an untrusted template/import (avoids HTML injection).
+        hint.textContent = "";
+        const tag = document.createElement("b");
+        tag.style.color = col; tag.textContent = "region " + (node._activeIdx + 1);
+        hint.appendChild(tag);
+        if (selN > 1) {
+          const s = document.createElement("span");
+          s.style.color = "#888"; s.textContent = ` (${selN} selected)`;
+          hint.appendChild(s);
+        }
+
+        // type toggle
+        const typeRow = document.createElement("div");
+        typeRow.className = "kjideo-row";
+        const lbl = document.createElement("span"); lbl.textContent = "type:"; typeRow.appendChild(lbl);
+        for (const t of ["obj", "text"]) {
+          const btn = document.createElement("button");
+          btn.className = "kjideo-btn" + (b.type === t ? " active" : "");
+          btn.textContent = t;
+          stopProp(btn);
+          btn.addEventListener("click", () => { b.type = t; commit(); });
+          typeRow.appendChild(btn);
+        }
+        const pxLbl = document.createElement("span");
+        pxLbl.textContent = "px:"; pxLbl.style.cssText = "margin-left:auto; color:#888;";
+        typeRow.appendChild(pxLbl);
+        bboxPx = makeBboxField("ymin, xmin, ymax, xmax", "Pixel bbox (of the node's width/height): ymin, xmin, ymax, xmax — editable", commitPxEdit);
+        typeRow.appendChild(bboxPx);
+        const gl = document.createElement("span");
+        gl.textContent = "out:"; gl.style.color = "#888";
+        typeRow.appendChild(gl);
+        bboxGrid = makeBboxField("ymin, xmin, ymax, xmax", "Exported bbox on the 0–1000 grid: ymin, xmin, ymax, xmax — editable", commitGridEdit);
+        typeRow.appendChild(bboxGrid);
+        updateBboxLabel();
+        panel.appendChild(typeRow);
+
+        // text (only for text type)
+        if (b.type === "text") {
+          panel.appendChild(makeArea("text", b.text, "text to render (verbatim)",
+            function () { b.text = this.value; touch(); }));
+        }
+
+        panel.appendChild(makeArea("desc", b.desc, "description of this region",
+          function () { b.desc = this.value; touch(); }));
+
+        // palette
+        const palRow = document.createElement("div");
+        palRow.className = "kjideo-row";
+        const pl = document.createElement("span"); pl.textContent = "colors:"; palRow.appendChild(pl);
+        b.palette = b.palette || [];
+        buildSwatchRow(palRow, b.palette, MAX_ELEM_COLORS, swatchEdit, commit);
+        panel.appendChild(palRow);
+      }
+
+      // ── width/height widget callbacks ──
+      for (const w of [wWidget, hWidget]) {
+        if (!w) continue;
+        chainCallback(w, "callback", () => { syncCanvasToDims(); drawCanvas(); fitNode(); });
+      }
+      // Update the token estimate when the caption-level text widgets change.
+      for (const name of ["background", "high_level_description", "aesthetics", "lighting", "medium", "style"]) {
+        const w = findW(name);
+        if (w) chainCallback(w, "callback", () => updateTokens());
+      }
+
+      // ── node resizes freely; the canvas just re-fits the space the widget is given ──
+      let _resizing = false;
+      chainCallback(node, "onResize", function () {
+        if (_resizing || node._detached || wrap.offsetParent === null) return;
+        _resizing = true;
+        fitCanvas();
+        requestAnimationFrame(fitCanvas);   // re-fit after LiteGraph re-arranges widgets on the next draw
+        _resizing = false;
+      });
+
+      // Optional reference image as the canvas background (matches ImageTransformKJ).
+      function loadBg(src) {
+        const img = new Image();
+        img.crossOrigin = "anonymous";
+        img.onload = () => {
+          node._bgImg = img;
+          const r16 = (v) => Math.max(16, Math.round(v / 16) * 16);   // model needs multiples of 16
+          if (wWidget) wWidget.value = r16(img.naturalWidth);          // match canvas aspect to the image
+          if (hWidget) hWidget.value = r16(img.naturalHeight);
+          syncCanvasToDims(); drawCanvas(); fitNode(); updateGrabBtn();
+        };
+        img.src = src;
+      }
+      watchImageInputs(node, "image", (sources) => {
+        if (!sources.length) { if (!node._bgManual) { node._bgImg = null; drawCanvas(); updateGrabBtn(); } return; }
+        node._bgManual = false;                              // a connected image takes over
+        const s = sources[0];
+        if (s.isVideo && s.videoEl) captureVideoFrame(s.videoEl, (cv) => loadBg(cv.toDataURL("image/webp", 0.9)));
+        else if (s.url) loadBg(s.url);
+      });
+      // "Grab BG" button: use the last generated image as the background, or clear it.
+      node._grabResultBg = () => {
+        if (!lastResultImage) { alert("No sampling result yet — run a generation first."); return; }
+        node._bgManual = true;
+        loadBg(resultViewUrl(lastResultImage));
+      };
+      node._clearBg = () => {
+        node._bgManual = false; node._bgImg = null;
+        if (node._liveBmp?.close) { try { node._liveBmp.close(); } catch (e) {} node._liveBmp = null; }
+        drawCanvas(); updateGrabBtn();
+      };
+      // Feed a live sampling-preview frame as the background (no width/height change).
+      node._ideoSetLiveBg = (bmp) => {
+        if (node._liveBmp?.close && node._liveBmp !== bmp) { try { node._liveBmp.close(); } catch (e) {} }
+        node._liveBmp = bmp; node._bgImg = bmp; node._bgManual = true;
+        drawCanvas(); updateGrabBtn();
+      };
+      // After generation, replace the live preview with the full-res final result.
+      node._ideoGrabFinal = () => {
+        if (!lastResultImage) return;
+        if (node._liveBmp?.close) { try { node._liveBmp.close(); } catch (e) {} node._liveBmp = null; }
+        node._bgManual = true;
+        loadBg(resultViewUrl(lastResultImage));
+      };
+
+      // Active-box highlight only while the editor is focused or the node is selected.
+      wrap.addEventListener("focusin", () => { if (!node._focused) { node._focused = true; drawCanvas(); } });
+      wrap.addEventListener("focusout", (e) => {
+        if (!wrap.contains(e.relatedTarget)) { node._focused = false; drawCanvas(); }
+      });
+      chainCallback(node, "onSelected", function () { node._selected = true; drawCanvas(); });
+      chainCallback(node, "onDeselected", function () { node._selected = false; finishPlacing(); closeLayersMenu(); drawCanvas(); });
+
+      chainCallback(node, "onRemoved", function () {
+        livePreviewNodes.delete(node);
+        if (hoveredCanvasNode === node) hoveredCanvasNode = null;
+        if (node._fullscreen) {                       // tear down the popup if open
+          document.removeEventListener("keydown", onFsEsc, true);
+          window.removeEventListener("resize", fitFsCanvas);
+          node._fsOverlay?.remove();
+        }
+        pinnedDocks.delete(node); liveDocks.delete(node);
+        node._dockRO?.disconnect(); node._dockEl?.remove();   // tear down the floating dock if open
+        node._visObserver?.disconnect();
+        if (node._liveBmp?.close) { try { node._liveBmp.close(); } catch (e) {} node._liveBmp = null; }  // release GPU bitmap
+        closeBgMenu(); node._bgMenu?.remove();
+        closeTxtMenu(); node._txtMenu?.remove();
+        closeTplMenu(); node._tplMenu?.remove();
+        closeInlineEditor();
+        closeLayersMenu();
+        for (const ro of node._areaObservers) ro.disconnect();
+        node._areaObservers = [];
+      });
+
+      // ── restore on load ──
+      function _parseBoxes(s) {
+        try {
+          const p = JSON.parse(s);
+          if (Array.isArray(p) && p.some((b) => b && typeof b.x === "number" && typeof b.w === "number")) return p;
+        } catch (e) {}
+        return null;
+      }
+      // Persist editor data by name (robust to widget-order changes across versions).
+      chainCallback(node, "onSerialize", function (o) {
+        if (!o) return;
+        const p = node.properties;
+        o.ideo = { boxes: node._boxes, palette: node._stylePalette, importMode: findW("import_mode")?.value,
+          outputFormat: findW("output_format")?.value,
+          dock: { pinned: p.dockPinned, graph: p.dockGraph, rect: p.dockRect, panelH: node._panelH, min: p.dockMin } };
+      });
+      chainCallback(node, "onConfigure", function (o) {
+        const raw = o && Array.isArray(o.widgets_values) ? o.widgets_values : [];
+        // Restore dock geometry from the blob into node.properties BEFORE ensureDocked runs.
+        node._savedSize = node.size ? [node.size[0], node.size[1]] : null;  // pre-collapse size (old aspect-locked height)
+        const d = o && o.ideo && o.ideo.dock;
+        if (d) {
+          node._dockGeomRestored = true;                     // honor it; old workflows w/o the blob get a default
+          if (d.graph) node.properties.dockGraph = d.graph;
+          if (d.rect) node.properties.dockRect = d.rect;
+          if (d.pinned != null) node.properties.dockPinned = d.pinned;
+          if (d.min != null) node.properties.dockMin = d.min;
+          if (typeof d.panelH === "number") { node._panelH = d.panelH; panel.style.height = d.panelH + "px"; }
+        }
+        // Recover regions: name-keyed blob → named widget → raw saved values (survives
+        // any widget reorder/remap across versions) → live widgets.
+        let boxes = (o && o.ideo && Array.isArray(o.ideo.boxes)) ? o.ideo.boxes : _parseBoxes(elementsWidget?.value || "");
+        if (!boxes) { for (const v of raw) { const b = _parseBoxes(v); if (b) { boxes = b; break; } } }
+        if (!boxes) { for (const w of node.widgets || []) { const b = _parseBoxes(w?.value); if (b) { boxes = b; break; } } }
+        if (boxes) {
+          node._boxes = boxes.filter((b) => b && typeof b.x === "number");
+          selectOnly(node._boxes.length ? 0 : -1);
+        }
+        const isPal = (p) => Array.isArray(p) && p.length && p.every((c) => typeof c === "string" && c[0] === "#");
+        let pal = (o && o.ideo && isPal(o.ideo.palette)) ? o.ideo.palette : null;
+        if (!pal) { try { const p = JSON.parse(stylePaletteWidget?.value || ""); if (isPal(p)) pal = p; } catch (e) {} }
+        if (!pal) { for (const v of raw) { try { const p = JSON.parse(v); if (isPal(p)) { pal = p; break; } } catch (e) {} } }
+        if (pal) node._stylePalette = pal.slice();
+        const imW = findW("import_mode");                     // restore import_mode; coerce to a valid option so
+        if (imW) {                                            // old workflows (saved before this widget) don't fail Combo validation
+          const im = o && o.ideo && o.ideo.importMode, opts = ["when empty", "always"];
+          imW.value = opts.includes(im) ? im : (opts.includes(imW.value) ? imW.value : "when empty");
+        }
+        if (formatWidget) {                                   // restore output format from the blob; default compact unless explicitly pretty
+          formatWidget.value = (o && o.ideo && o.ideo.outputFormat) === "pretty" ? "pretty" : "compact";
+          compactLbl._cb.checked = formatWidget.value === "compact";
+        }
+        node._configured = true;                              // mark as loaded so initial layout keeps the restored size
+        hideDataWidgets();
+        serialize();                                         // realign widget values for Python + future saves
+        if (bgBrightnessWidget) {
+          if (typeof bgBrightnessWidget.value !== "number") bgBrightnessWidget.value = 25;  // old workflows may restore ""
+          bgSlider.value = bgBrightnessWidget.value;
+        }
+        // node.properties is restored after onNodeCreated, so resync the toolbar controls to it.
+        liveChk.checked = !!node.properties.liveBg;
+        if (liveChk.checked) livePreviewNodes.add(node); else livePreviewNodes.delete(node);
+        guideSel.value = node.properties.guide || "none";
+        gridSlider.value = GRID_INV - (node.properties.gridSize || 10);
+        snapChk.checked = !!node.properties.snap;
+        guideColor.value = node.properties.guideColor || "#ffffff";
+        opacitySlider.value = node.properties.guideOpacity == null ? 100 : node.properties.guideOpacity;
+        showLbl._cb.checked = node.properties.showBoxText !== false;
+        strokeLbl._cb.checked = node.properties.textStroke !== false;
+        autoLbl._cb.checked = node.properties.textAutoPlace !== false;
+        sizeSlider.value = node.properties.textSize || 12;
+        boxOpacSlider.value = node.properties.boxOpacity == null ? 14 : node.properties.boxOpacity;
+        syncCanvasToDims();
+        rebuildStylePalette();
+        renderPanel();
+        drawCanvas();
+        updateTokens();
+        requestAnimationFrame(fitNode);
+        requestAnimationFrame(() => ensureDocked(false));    // reload: honor the saved dock geometry, never match node width
+      });
+
+      // initial layout (deferred so size/last_y are settled)
+      setTimeout(() => {
+        hideDataWidgets();
+        if (!node._configured) node.setSize([Math.max(480, node.size[0]), node.size[1]]);  // fresh node: a comfortable default width
+        else if (node.size[0] < 380) node.setSize([380, node.size[1]]);                     // loaded node: just enforce the min
+        syncCanvasToDims();
+        rebuildStylePalette();
+        renderPanel();
+        drawCanvas();
+        updateTokens();
+        if (!node._configured) ensureDocked(true);           // fresh node: pop the dock under it, matching its width
+        fitNode();
+      }, 0);
+    });
+  },
+});
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/image_transform.js b/custom_nodes/ComfyUI-KJNodes/web/js/image_transform.js
new file mode 100644
index 0000000000000000000000000000000000000000..0555a30378b774cbd68b8a0d64a7c0c2653e6f7a
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/image_transform.js
@@ -0,0 +1,2025 @@
+import { chainCallback, addMiddleClickPan, addWheelPassthrough, captureVideoFrame, watchImageInputs, rectHitTest, cursorForBboxMode } from './utility.js';
+const { app } = window.comfyAPI.app;
+
+const BBOX_PALETTE = ["#46b4e6", "#e68246", "#82e646", "#e646b4", "#e6e646", "#46e6c8"];
+const MAX_CANVAS_W = 1024, MAX_CANVAS_H = 768;
+const BBOX_COLORS = BBOX_PALETTE.map((hex) => {
+  const r = parseInt(hex.slice(1, 3), 16), g = parseInt(hex.slice(3, 5), 16), b = parseInt(hex.slice(5, 7), 16);
+  return { rgb: `rgb(${r}, ${g}, ${b})`, gridActive: `rgba(${r}, ${g}, ${b}, 0.35)`,
+    gridInactive: `rgba(${r}, ${g}, ${b}, 0.3)`, tint: `rgba(${r}, ${g}, ${b}, 0.2)` };
+});
+
+function getBboxColor(index, active) {
+  const c = BBOX_COLORS[index % BBOX_COLORS.length];
+  return { border: c.rgb, fill: c.rgb, handle: c.rgb, gridColor: active ? c.gridActive : c.gridInactive, tint: c.tint };
+}
+
+function clampToMaxCanvas(w, h) {
+  if (w > MAX_CANVAS_W || h > MAX_CANVAS_H) {
+    const s = Math.min(MAX_CANVAS_W / w, MAX_CANVAS_H / h);
+    return [Math.round(w * s), Math.round(h * s)];
+  }
+  return [w, h];
+}
+
+function roundDown(val, divBy) {
+  return divBy > 1 ? val - (val % divBy) : val;
+}
+
+app.registerExtension({
+  name: "KJNodes.ImageTransformKJ",
+
+  async beforeRegisterNodeDef(nodeType, nodeData) {
+    if (nodeData?.name !== "ImageTransformKJ") return;
+
+    chainCallback(nodeType.prototype, "onNodeCreated", function () {
+      const node = this;
+
+      const findW = (n) => this.widgets.find((w) => w.name === n);
+      const bboxWidget = findW("bboxes"), twWidget = findW("target_width"), thWidget = findW("target_height");
+      const kpWidget = findW("keep_proportion"), divWidget = findW("divisible_by");
+      const epWidget = findW("extra_padding"), icWidget = findW("invert_crop");
+      bboxWidget.hidden = true;
+
+      const _subWidgetCache = {};
+      function _refreshSubWidgetCache() {
+        if (!node.widgets) return;
+        for (const w of node.widgets) {
+          if (w.name?.includes(".")) _subWidgetCache[w.name] = w;
+        }
+      }
+      function _getSubWidget(name) {
+        let w = _subWidgetCache[name];
+        if (w && node.widgets?.includes(w)) return w;
+        // Cache miss — rescan
+        w = node.widgets?.find(ww => ww.name === name);
+        if (w) _subWidgetCache[name] = w;
+        return w || null;
+      }
+
+      const _tmpCanvas = document.createElement("canvas"), _tmpCtx = _tmpCanvas.getContext("2d");
+      const _mirrorCanvas = document.createElement("canvas"), _mirrorCtx = _mirrorCanvas.getContext("2d");
+      const _rotCanvas = document.createElement("canvas"), _rotCtx = _rotCanvas.getContext("2d");
+      let _rotCacheKey = "";
+      function getRotatedPreview() {
+        if (!node._previewImg) return null;
+        const nw = node._previewImg.naturalWidth;
+        const nh = node._previewImg.naturalHeight;
+        const { w: effW, h: effH } = getEffectiveImageDims(true);
+        // Scale to reasonable preview size
+        let [rw, rh] = clampToMaxCanvas(effW, effH);
+        const key = `${node._previewImg.src}|${node._rotation}|${rw}|${rh}`;
+        if (_rotCacheKey === key) return _rotCanvas;
+        _rotCanvas.width = rw;
+        _rotCanvas.height = rh;
+        _rotCtx.clearRect(0, 0, rw, rh);
+        if (node._rotation === 0) {
+          _rotCtx.drawImage(node._previewImg, 0, 0, rw, rh);
+        } else {
+          const rad = Math.abs(rotRad());
+          const rotW = Math.abs(nw * Math.cos(rad)) + Math.abs(nh * Math.sin(rad));
+          const rotH = Math.abs(nw * Math.sin(rad)) + Math.abs(nh * Math.cos(rad));
+          const fitScale = Math.min(rw / rotW, rh / rotH);
+          _rotCtx.translate(rw / 2, rh / 2);
+          _rotCtx.rotate(rotRad());
+          _rotCtx.drawImage(node._previewImg, -(nw * fitScale) / 2, -(nh * fitScale) / 2, nw * fitScale, nh * fitScale);
+          _rotCtx.setTransform(1, 0, 0, 1, 0, 0);
+        }
+        _rotCacheKey = key;
+        return _rotCanvas;
+      }
+
+      let _frameEp = { top: 0, bottom: 0, left: 0, right: 0, mode: "disabled", color: null, edgeMode: "clamp" };
+      let _framePadXY = { x: 0.5, y: 0.5 };
+
+      const wrapper = document.createElement("div");
+      wrapper.style.cssText = "display:flex;flex-direction:column;overflow:hidden;position:relative;pointer-events:auto;";
+      const canvasEl = document.createElement("canvas");
+      const canvasCtx = canvasEl.getContext("2d");
+      canvasEl.style.cssText = "cursor:crosshair;display:block;width:100%;height:auto;";
+      addWheelPassthrough(wrapper);
+      addMiddleClickPan(canvasEl);
+
+      // Inject slider thumb styles once
+      if (!document.getElementById("kjcrop-slider-style")) {
+        const style = document.createElement("style");
+        style.id = "kjcrop-slider-style";
+        style.textContent = `
+          .kjcrop-slider { -webkit-appearance: none; appearance: none; background: #444; border-radius: 4px; outline: none; }
+          .kjcrop-slider::-webkit-slider-thumb { -webkit-appearance: none; width: 8px; height: 18px; background: #46b4e6; border-radius: 4px; cursor: pointer; border: none; }
+          .kjcrop-slider::-moz-range-thumb { width: 8px; height: 18px; background: #46b4e6; border-radius: 4px; cursor: pointer; border: none; }
+          .kjcrop-btn { background: #333; border: 1px solid #555; border-radius: 4px; color: #bbb; font: 11px sans-serif; cursor: pointer; padding: 2px 8px; line-height: 16px; white-space: nowrap; flex-shrink: 0; transition: border-color 0.15s, color 0.15s, background 0.15s; text-align: center; }
+          .kjcrop-btn:hover { border-color: #46b4e6; color: #fff; background: #3a3a3a; }
+          .kjcrop-btn.active { border-color: #46b4e6; color: #46b4e6; background: #2a3a42; }
+        `;
+        document.head.appendChild(style);
+      }
+      // Grid size slider overlay (purely visual aid, not serialized)
+      const gridBar = document.createElement("div");
+      gridBar.style.cssText = "display:flex;align-items:center;gap:6px;padding:3px 6px;margin-bottom:4px;font:11px sans-serif;color:#aaa;user-select:none;box-sizing:border-box;width:100%;flex:0 0 auto;";
+      const gridLabel = document.createElement("span");
+      gridLabel.textContent = "Grid: off";
+      gridLabel.style.cssText = "min-width:62px;text-align:right;";
+      const gridSlider = document.createElement("input");
+      gridSlider.type = "range";
+      gridSlider.min = "0";
+      gridSlider.max = "256";
+      gridSlider.step = "1";
+      gridSlider.value = node.properties.gridSize ?? "0";
+      gridSlider.className = "kjcrop-slider";
+      gridSlider.style.cssText = "flex:1;height:8px;cursor:pointer;";
+      function updateGridLabel() {
+        const v = parseInt(gridSlider.value);
+        gridLabel.textContent = v > 0 ? `Grid: ${v}px` : "Grid: off";
+      }
+      updateGridLabel();
+      let _gridStops = null;
+      function getGridStops() {
+        if (!node._previewImg) return null;
+        const tw = node._previewImg.naturalWidth;
+        const th = node._previewImg.naturalHeight;
+        const max = parseInt(gridSlider.max);
+        const set = new Set([0]);
+        for (let n = 1; n <= Math.max(tw, th); n++) {
+          const gw = tw / n, gh = th / n;
+          if (gw >= 2 && gw <= max) set.add(Math.round(gw * 10) / 10);
+          if (gh >= 2 && gh <= max) set.add(Math.round(gh * 10) / 10);
+          if (gw < 2 && gh < 2) break;
+        }
+        return [...set].sort((a, b) => a - b);
+      }
+      function invalidateGridStops() { _gridStops = null; }
+      gridSlider.addEventListener("input", () => {
+        let v = parseInt(gridSlider.value);
+        // Snap to divisors of the longer dimension for whole-cell grid
+        if (v > 0 && node._previewImg) {
+          if (!_gridStops) _gridStops = getGridStops();
+          if (_gridStops) {
+            let best = 0, bestDist = Infinity;
+            for (const s of _gridStops) {
+              const d = Math.abs(s - v);
+              if (d < bestDist) { bestDist = d; best = s; }
+            }
+            v = best;
+          }
+        }
+        gridSlider.value = Math.round(v);
+        node.properties.gridSize = Math.round(v);
+        updateGridLabel();
+        drawCanvas();
+      });
+      // Prevent drag events from propagating to the LiteGraph canvas,
+      // but only when not actively dragging a bbox handle
+      for (const evt of ["mousedown", "mousemove", "mouseup"]) {
+        gridSlider.addEventListener(evt, (e) => {
+          if (!node._drawing) e.stopPropagation();
+        });
+      }
+      // Toggle button to disable image preview (draw bboxes on blank canvas)
+      const previewBtn = document.createElement("button");
+      previewBtn.title = "Auto: canvas sized to input image. Manual: canvas sized to target dimensions.";
+      previewBtn.className = "kjcrop-btn";
+      previewBtn.style.width = "56px";
+      if (node.properties.previewEnabled === undefined) node.properties.previewEnabled = true;
+      node._previewEnabled = node.properties.previewEnabled;
+
+      function updatePreviewBtn() {
+        previewBtn.textContent = node._previewEnabled ? "Auto" : "Manual";
+        previewBtn.classList.toggle("active", !node._previewEnabled);
+      }
+      updatePreviewBtn();
+
+      previewBtn.addEventListener("click", () => {
+        node._previewEnabled = !node._previewEnabled;
+        node.properties.previewEnabled = node._previewEnabled;
+        updatePreviewBtn();
+        if (!node._previewEnabled) {
+          updateCanvasFromTargetDims();
+        } else if (node._previewImg) {
+          // Restore canvas to image dimensions
+          const [cw, ch] = clampToMaxCanvas(node._previewImg.naturalWidth, node._previewImg.naturalHeight);
+          setCanvasSize(cw, ch);
+        }
+        drawCanvas();
+      });
+      previewBtn.addEventListener("mousedown", (e) => e.stopPropagation());
+
+      const colorSwatch = document.createElement("div");
+      const colorInput = document.createElement("input");
+      colorInput.type = "color";
+      colorInput.value = node.properties.fillColor || "#000000";
+      colorInput.style.cssText = "position:absolute;opacity:0;width:0;height:0;pointer-events:none;";
+      colorSwatch.style.cssText = "width:18px;height:18px;border:1px solid #666;border-radius:3px;cursor:pointer;flex-shrink:0;background:" + (node.properties.fillColor || "#000000") + ";";
+      colorSwatch.title = "Fill color (used by pad, invert crop, rotation)";
+      colorSwatch.appendChild(colorInput);
+      colorSwatch.addEventListener("click", () => colorInput.click());
+      colorSwatch.addEventListener("mousedown", (e) => e.stopPropagation());
+      colorInput.addEventListener("input", () => {
+        node.properties.fillColor = colorInput.value; colorSwatch.style.background = colorInput.value;
+        updateBboxWidgets(); drawCanvas();
+      });
+
+      function getEffectiveImageDims(expand = true) {
+        const w = node._previewImg.naturalWidth, h = node._previewImg.naturalHeight;
+        if (node._rotation === 0 || !expand) return { w, h };
+        const rad = Math.abs(rotRad());
+        const cosR = Math.cos(rad), sinR = Math.sin(rad);
+        return { w: Math.round(Math.abs(w * cosR) + Math.abs(h * sinR)),
+          h: Math.round(Math.abs(w * sinR) + Math.abs(h * cosR)) };
+      }
+
+      function renderReplicateRotated(destW, destH) {
+        const img = node._previewImg, nw = img.naturalWidth, nh = img.naturalHeight;
+        const pad = Math.max(nw, nh), pw = nw + pad * 2, ph = nh + pad * 2;
+        // Build replicate-padded image on _mirrorCanvas
+        _mirrorCanvas.width = pw; _mirrorCanvas.height = ph;
+        _mirrorCtx.drawImage(img, pad, pad, nw, nh);
+        // Edges (top, bottom, left, right)
+        _mirrorCtx.drawImage(img, 0, 0, nw, 1, pad, 0, nw, pad);
+        _mirrorCtx.drawImage(img, 0, nh - 1, nw, 1, pad, pad + nh, nw, pad);
+        _mirrorCtx.drawImage(img, 0, 0, 1, nh, 0, pad, pad, nh);
+        _mirrorCtx.drawImage(img, nw - 1, 0, 1, nh, pad + nw, pad, pad, nh);
+        // Corners (TL, TR, BL, BR)
+        _mirrorCtx.drawImage(img, 0, 0, 1, 1, 0, 0, pad, pad);
+        _mirrorCtx.drawImage(img, nw - 1, 0, 1, 1, pad + nw, 0, pad, pad);
+        _mirrorCtx.drawImage(img, 0, nh - 1, 1, 1, 0, pad + nh, pad, pad);
+        _mirrorCtx.drawImage(img, nw - 1, nh - 1, 1, 1, pad + nw, pad + nh, pad, pad);
+        // Rotate padded image, crop to original size, scale to dest
+        _tmpCanvas.width = destW; _tmpCanvas.height = destH;
+        _tmpCtx.save();
+        _tmpCtx.scale(destW / nw, destH / nh);
+        _tmpCtx.translate(nw / 2, nh / 2);
+        _tmpCtx.rotate(rotRad());
+        _tmpCtx.drawImage(_mirrorCanvas, -(pw / 2), -(ph / 2));
+        _tmpCtx.restore();
+      }
+
+      // Resize node to fit the current canvas/image. If onlyGrow is true,
+      // the node will only get larger, never shrink (preserves user resize).
+      // If false, the node is set to the exact size needed.
+      function fitNodeToImage(onlyGrow) {
+        if (!node._previewEnabled) {
+          // Manual mode: only grow to fit current canvas, never change width
+          if (onlyGrow && canvasEl.width > 0 && canvasEl.height > 0) {
+            _resizing = true;
+            const displayedH = Math.round(canvasEl.height * ((node.size[0] - 30) / canvasEl.width));
+            node._widgetHeight = displayedH + GRID_BAR_HEIGHT;
+            const aboveH = node.cropEditor?.last_y || 350;
+            const neededH = aboveH + node._widgetHeight + 20;
+            if (node.size[1] < neededH) node.setSize([node.size[0], neededH]);
+            _resizing = false;
+          }
+          return;
+        }
+        if (!node._previewImg) return;
+        _resizing = true;
+        const { w: effW, h: effH } = getEffectiveImageDims(true);
+        const ar = effH / effW;
+        const nodeW = onlyGrow ? node.size[0] : canvasEl.width + 30;
+        const canvasDisplayH = Math.round((nodeW - 30) * ar);
+        node._widgetHeight = canvasDisplayH + GRID_BAR_HEIGHT;
+        const aboveH = node.cropEditor?.last_y || 350;
+        // +20 accounts for DOM widget margin (10px top + 10px bottom)
+        const neededH = aboveH + node._widgetHeight + 20;
+        const newW = onlyGrow ? Math.max(node.size[0], nodeW) : nodeW;
+        const newH = onlyGrow ? Math.max(node.size[1], neededH) : neededH;
+        if (newW !== node.size[0] || newH !== node.size[1]) {
+          node.setSize([newW, newH]);
+        }
+        _resizing = false;
+        // Force reflow and redraw so the layout updates immediately
+        void canvasEl.offsetHeight;
+        if (node.graph) {
+          node.graph.setDirtyCanvas(true, true);
+          requestAnimationFrame(() => {
+            drawCanvas();
+            if (node.graph) node.graph.setDirtyCanvas(true, true);
+          });
+        }
+      }
+
+      function resetRotation() {
+        if (node._rotation === 0) return;
+        node._rotation = 0;
+        node.properties.rotation = 0;
+        restoreCanvasToImage();
+        fitNodeToImage(false);
+        updateBboxWidgets();
+        drawCanvas();
+      }
+
+      const rotateBtn = document.createElement("button");
+      rotateBtn.title = "Toggle rotation cross (drag to rotate image)";
+      rotateBtn.className = "kjcrop-btn";
+      if (node.properties.showRotationCross === undefined) node.properties.showRotationCross = false;
+      node._showRotationCross = node.properties.showRotationCross;
+
+      function updateRotateBtn() {
+        rotateBtn.textContent = "Rotate";
+        rotateBtn.classList.toggle("active", node._showRotationCross);
+      }
+      updateRotateBtn();
+
+      rotateBtn.addEventListener("click", () => {
+        node._showRotationCross = !node._showRotationCross;
+        node.properties.showRotationCross = node._showRotationCross;
+        updateRotateBtn();
+        drawCanvas();
+      });
+      rotateBtn.addEventListener("mousedown", (e) => e.stopPropagation());
+      rotateBtn.addEventListener("contextmenu", (e) => {
+        e.preventDefault();
+        resetRotation();
+      });
+
+      const gridColorInput = document.createElement("input");
+      gridColorInput.type = "color";
+      gridColorInput.value = node.properties.gridColor || "#ffffff";
+      gridColorInput.style.cssText = "position:absolute;opacity:0;width:0;height:0;pointer-events:none;";
+      const gridColorSwatch = document.createElement("div");
+      gridColorSwatch.style.cssText = "width:18px;height:18px;border:1px solid #666;border-radius:3px;cursor:pointer;flex-shrink:0;background:" + (node.properties.gridColor || "#ffffff") + ";";
+      gridColorSwatch.title = "Grid color";
+      gridColorSwatch.appendChild(gridColorInput);
+      gridColorSwatch.addEventListener("click", () => gridColorInput.click());
+      gridColorSwatch.addEventListener("mousedown", (e) => e.stopPropagation());
+      let _gridColorRGB = null;
+      function getGridColorRGB() {
+        const gc = node.properties.gridColor || "#ffffff";
+        if (!_gridColorRGB || _gridColorRGB.hex !== gc) {
+          _gridColorRGB = { hex: gc, r: parseInt(gc.slice(1, 3), 16), g: parseInt(gc.slice(3, 5), 16), b: parseInt(gc.slice(5, 7), 16) };
+        }
+        return _gridColorRGB;
+      }
+      gridColorInput.addEventListener("input", () => {
+        node.properties.gridColor = gridColorInput.value; gridColorSwatch.style.background = gridColorInput.value;
+        _gridColorRGB = null;
+        drawCanvas();
+      });
+
+      for (const el of [previewBtn, rotateBtn, colorSwatch, gridLabel, gridColorSwatch, gridSlider]) gridBar.appendChild(el);
+      wrapper.appendChild(gridBar); wrapper.appendChild(canvasEl);
+
+      const GRID_BAR_HEIGHT = 46;
+      node._widgetHeight = 300 + GRID_BAR_HEIGHT;
+
+      node.cropEditor = this.addDOMWidget("crop_preview", "CropPreviewWidget", wrapper, {
+        serialize: false, hideOnZoom: false,
+        getMinHeight: () => GRID_BAR_HEIGHT + 50,
+      });
+
+      this.resizable = true;
+      // Set default width wider than LiteGraph's default — deferred so onConfigure can set the flag first
+      setTimeout(() => {
+        if (!node._cropConfigured) node.setSize([450, node.size[1]]);
+      }, 0);
+
+      Object.assign(node, { _previewImg: null, _bboxes: [], _activeIdx: -1,
+        _drawing: false, _dragMode: null, _dragStart: null, _bboxAtDragStart: null,
+        _rotation: node.properties.rotation || 0 });
+
+      for (const w of [twWidget, thWidget, kpWidget, divWidget, epWidget, icWidget]) {
+        if (!w) continue;
+        const origCb = w.callback;
+        w.callback = function (...args) {
+          if (origCb) origCb.apply(this, args);
+          updateCanvasFromTargetDims();
+          drawCanvas();
+          fitNodeToCanvas();
+          fitNodeToImage(true);
+          if (w === kpWidget || w === icWidget || w === epWidget) {
+            // Restore values with increasing delays to catch ComfyUI's own default-setting
+            const restoreSaved = () => {
+              hookSubWidgets();
+              _refreshSubWidgetCache();
+              if (w === epWidget && node._savedEpValues && epWidget.value !== "disabled") {
+                for (const [n, v] of Object.entries(node._savedEpValues)) {
+                  const sw = _getSubWidget(`extra_padding.${n}`);
+                  if (sw) sw.value = v;
+                }
+              } else if (w === kpWidget && node._savedKpValues) {
+                for (const [n, v] of Object.entries(node._savedKpValues)) {
+                  const sw = _getSubWidget(`keep_proportion.${n}`);
+                  if (sw) sw.value = v;
+                }
+              }
+              drawCanvas();
+              fitNodeToCanvas();
+              fitNodeToImage(true);
+            };
+            for (const delay of [50, 100, 200, 300, 500, 1000]) {
+              setTimeout(restoreSaved, delay);
+            }
+          }
+        };
+      }
+
+      const hookSubWidgets = () => {
+        _refreshSubWidgetCache();
+        for (const subName of ["keep_proportion.edge_mode", "keep_proportion.pad_x", "keep_proportion.pad_y", "keep_proportion.width_mult", "keep_proportion.height_mult", "extra_padding.pad_top", "extra_padding.pad_bottom", "extra_padding.pad_left", "extra_padding.pad_right", "extra_padding.edge_mode", ]) {
+          const sw = _getSubWidget(subName);
+          if (sw && !sw._cropPreviewHooked) {
+            sw._cropPreviewHooked = true;
+            // Restore saved values from mode switch
+            const shortName = subName.split(".")[1];
+            if (subName.startsWith("extra_padding.") && node._savedEpValues && shortName in node._savedEpValues) {
+              sw.value = node._savedEpValues[shortName];
+            } else if (subName.startsWith("keep_proportion.") && node._savedKpValues && shortName in node._savedKpValues) {
+              sw.value = node._savedKpValues[shortName];
+            }
+            // Hook callback — save values on change so they persist across mode switches
+            const swOrig = sw.callback;
+            sw.callback = function (...a) {
+              if (_suppressSubCallbacks) return;
+              if (subName.startsWith("extra_padding.")) node._savedEpValues[shortName] = sw.value;
+              else if (subName.startsWith("keep_proportion.")) node._savedKpValues[shortName] = sw.value;
+              if (swOrig) swOrig.apply(this, a);
+              drawCanvas();
+            };
+          }
+        }
+      };
+      for (const delay of [50, 150, 300, 500]) {
+        setTimeout(hookSubWidgets, delay);
+      }
+
+      let _lastWidgetSnapshot = "";
+      const _snapshotNames = [
+        "extra_padding", "extra_padding.pad_top", "extra_padding.pad_bottom",
+        "extra_padding.pad_left", "extra_padding.pad_right",
+        "extra_padding.edge_mode",
+        "keep_proportion.pad_x", "keep_proportion.pad_y", "keep_proportion.color",
+        "keep_proportion.edge_mode", "keep_proportion.width_mult", "keep_proportion.height_mult",
+      ];
+      node._savedEpValues = node._savedEpValues || {};
+      node._savedKpValues = node._savedKpValues || {};
+      const origDrawFg = node.onDrawForeground;
+      node.onDrawForeground = function (...args) {
+        if (origDrawFg) origDrawFg.apply(this, args);
+        const snap = _snapshotNames.map(n => _getSubWidget(n)?.value ?? (n === "extra_padding" ? epWidget?.value : "") ?? "").join("|");
+        if (snap !== _lastWidgetSnapshot) {
+          _lastWidgetSnapshot = snap;
+          drawCanvas();
+        }
+      };
+
+      // Restore bboxes from widget
+      restoreBboxesFromWidget();
+
+      function restoreBboxesFromWidget() {
+        if (bboxWidget.value) {
+          try {
+            const parsed = JSON.parse(bboxWidget.value);
+            // New format: { bboxes: [...], rotation: N }
+            if (parsed && !Array.isArray(parsed) && typeof parsed === "object") {
+              if (parsed.bboxes && Array.isArray(parsed.bboxes)) {
+                node._bboxes = parsed.bboxes.filter((b) => b?.startX != null);
+                node._activeIdx = node._bboxes.length > 0 ? 0 : -1;
+              }
+              if (parsed.rotation !== undefined) {
+                node._rotation = parsed.rotation;
+                node.properties.rotation = parsed.rotation;
+              }
+              if (parsed.fillColor) {
+                node.properties.fillColor = parsed.fillColor;
+                colorInput.value = parsed.fillColor;
+                colorSwatch.style.background = parsed.fillColor;
+              }
+            }
+            // Legacy format: [bbox, bbox, ...]
+            else if (Array.isArray(parsed) && parsed.length > 0) {
+              node._bboxes = parsed.filter((b) => b?.startX != null);
+              node._activeIdx = node._bboxes.length > 0 ? 0 : -1;
+            }
+          } catch (e) {}
+        }
+      }
+
+      function normBbox(bbox) {
+        return { x1: Math.min(bbox.startX, bbox.endX), y1: Math.min(bbox.startY, bbox.endY),
+          x2: Math.max(bbox.startX, bbox.endX), y2: Math.max(bbox.startY, bbox.endY) };
+      }
+
+      function getCanvasMouse(e) {
+        const rect = canvasEl.getBoundingClientRect();
+        return { x: (e.clientX - rect.left) * (canvasEl.width / rect.width),
+          y: (e.clientY - rect.top) * (canvasEl.height / rect.height) };
+      }
+
+      function rotRad() { return node._rotation * Math.PI / 180; }
+      function getDivBy() { return divWidget ? divWidget.value : 0; }
+      function getGridSize() { return parseInt(gridSlider.value) || 0; }
+      // Subtract extra padding from target dims for non-pad keep_proportion modes
+      function adjustTargetForPadding(rw, rh, tgtW, tgtH) {
+        const ep = getExtraPadding();
+        if (hasExtraPad(ep) && !ep.isFirst) {
+          if (tgtW > 0) rw = Math.max(1, rw - ep.left - ep.right);
+          if (tgtH > 0) rh = Math.max(1, rh - ep.top - ep.bottom);
+        }
+        return { rw, rh };
+      }
+      function getEffectiveTargetDims(fallbackW, fallbackH) {
+        const divBy = getDivBy();
+        const { w: tgtW, h: tgtH } = getTgt();
+        let { rw, rh } = adjustTargetForPadding(tgtW > 0 ? tgtW : fallbackW, tgtH > 0 ? tgtH : fallbackH, tgtW, tgtH);
+        return { rw: roundDown(rw, divBy), rh: roundDown(rh, divBy) };
+      }
+
+      function getFillColor() { return node.properties.fillColor || "#000000"; }
+      function getTgt() { return { w: twWidget ? twWidget.value : 0, h: thWidget ? thWidget.value : 0 }; }
+      function getKp() { return kpWidget ? kpWidget.value : "stretch"; }
+      function isPadMode(kp) { return kp === "pad_color" || kp === "pad_edge"; }
+      function hasExtraPad(ep) { return ep.top + ep.bottom + ep.left + ep.right > 0; }
+
+      function hitTestRotationCross(mx, my) {
+        if (!node._showRotationCross) return false;
+        const cx = canvasEl.width / 2;
+        const cy = canvasEl.height / 2;
+        const dx = mx - cx;
+        const dy = my - cy;
+        const rad = rotRad();
+        const cosR = Math.cos(rad);
+        const sinR = Math.sin(rad);
+        const perpH = -dx * sinR + dy * cosR;
+        const perpV = dx * cosR + dy * sinR;
+        const dist = Math.sqrt(dx * dx + dy * dy);
+        return dist < 15 || Math.abs(perpH) < 8 || Math.abs(perpV) < 8;
+      }
+
+      function restoreCanvasToImage() {
+        if (!node._previewImg || !node._previewEnabled) return;
+        setCanvasSize(...clampToMaxCanvas(...Object.values(getEffectiveImageDims(true))));
+      }
+
+      function drawPreviewRegion(ctx, rx, ry, rw, rh, dx, dy, dw, dh) {
+        const rp = getRotatedPreview();
+        const sx = _rotCanvas.width / canvasEl.width, sy = _rotCanvas.height / canvasEl.height;
+        ctx.drawImage(rp, rx * sx, ry * sy, rw * sx, rh * sy, dx, dy, dw, dh);
+      }
+
+      function drawStretchOverlay(ctx, ar, rx, ry, rw, rh) {
+        const { pW, pH, pX, pY } = fitAspect(ar, rw, rh, rx, ry);
+        ctx.fillStyle = "rgba(0, 0, 0, 0.5)";
+        ctx.fillRect(rx, ry, rw, rh);
+        if (node._previewEnabled && node._previewImg) drawPreviewRegion(ctx, rx, ry, rw, rh, pX, pY, pW, pH);
+        drawDashedBorder(ctx, pX, pY, pW, pH);
+      }
+
+      // Compute content dimensions after keep_proportion resize, before extra padding
+      function computeContentDims(cropW, cropH) {
+        if (cropW <= 0 || cropH <= 0) return null;
+        const { w: tgtW, h: tgtH } = getTgt();
+        let outW = cropW, outH = cropH;
+        const kp = getKp();
+        // For pad-first non-pad modes, the source is padded before keep_proportion
+        const ep = getExtraPadding();
+        if (ep.isFirst && hasExtraPad(ep) && !isPadMode(kp)) {
+          cropW += ep.left + ep.right;
+          cropH += ep.top + ep.bottom;
+        }
+        if (tgtW > 0 || tgtH > 0) {
+          let { rw, rh } = adjustTargetForPadding(tgtW > 0 ? tgtW : cropW, tgtH > 0 ? tgtH : cropH, tgtW, tgtH);
+          if (kp === "keep_long_edge") {
+            const ratio = Math.min(rw / cropW, rh / cropH);
+            rw = Math.round(cropW * ratio);
+            rh = Math.round(cropH * ratio);
+          } else if (kp === "keep_short_edge") {
+            const ratio = Math.max(rw / cropW, rh / cropH);
+            rw = Math.round(cropW * ratio);
+            rh = Math.round(cropH * ratio);
+          } else if (kp === "total_pixels") {
+            const totalPx = rw * rh;
+            const ar = cropW / cropH;
+            rh = Math.round(Math.sqrt(totalPx / ar));
+            rw = Math.round(Math.sqrt(totalPx * ar));
+          }
+          outW = rw;
+          outH = rh;
+        }
+        if (kp === "multiplier") {
+          const wm = _getSubWidget("keep_proportion.width_mult")?.value || 1.0;
+          const hm = _getSubWidget("keep_proportion.height_mult")?.value || 1.0;
+          outW = Math.round(cropW * wm);
+          outH = Math.round(cropH * hm);
+        }
+        return { w: outW, h: outH };
+      }
+
+      function computeOutputDims(cropW, cropH) {
+        const content = computeContentDims(cropW, cropH);
+        if (!content) return null;
+        let outW = content.w, outH = content.h;
+        // Add extra padding (skip for pad-first — handled on the source image)
+        const ep = getExtraPadding();
+        if (!ep.isFirst) {
+          outW += ep.left + ep.right;
+          outH += ep.top + ep.bottom;
+        }
+        const divBy = getDivBy();
+        outW = roundDown(outW, divBy);
+        outH = roundDown(outH, divBy);
+        return { w: outW, h: outH };
+      }
+
+      function getPadXY() { return _framePadXY; }
+      function getExtraPadding() { return _frameEp; }
+
+      function _readPadXY() {
+        const px = _getSubWidget("keep_proportion.pad_x"), py = _getSubWidget("keep_proportion.pad_y");
+        return { x: px ? px.value : 0.5, y: py ? py.value : 0.5 };
+      }
+
+      let _suppressSubCallbacks = false;
+      function setPadXY(x, y, defer) {
+        x = Math.max(0, Math.min(1, x));
+        y = Math.max(0, Math.min(1, y));
+        _framePadXY = { x, y };
+        if (!defer) {
+          const px = _getSubWidget("keep_proportion.pad_x"), py = _getSubWidget("keep_proportion.pad_y");
+          _suppressSubCallbacks = true;
+          if (px) px.value = x;
+          if (py) py.value = y;
+          _suppressSubCallbacks = false;
+        }
+      }
+
+      function _readExtraPadding() {
+        const get = (name) => _getSubWidget(`extra_padding.${name}`)?.value || 0;
+        const mode = epWidget?.value || "disabled";
+        // pad_color/pad_edge = pad first (before crop), pad_crop_color/pad_crop_edge = pad after crop
+        const isCrop = mode.startsWith("pad_crop_");
+        const baseMode = isCrop ? mode.replace("pad_crop_", "pad_") : mode;
+        return {
+          top: get("pad_top"), bottom: get("pad_bottom"),
+          left: get("pad_left"), right: get("pad_right"),
+          mode: baseMode,
+          isFirst: !isCrop && mode !== "disabled",
+          edgeMode: _getSubWidget("extra_padding.edge_mode")?.value || "clamp",
+        };
+      }
+
+      // Draw extra padding fill around content area (cX,cY,cW,cH) within pad area (pX,pY,pW,pH)
+      // srcImg is the source image, srcX/Y/W/H define the source region
+      // Draw edge/color fill around content area. srcImg must be pre-rendered at its full dimensions.
+      function drawExtraPadFill(ctx, ep, pX, pY, pW, pH, cX, cY, cW, cH, srcImg) {
+        if (ep.mode === "pad_color") {
+          ctx.fillStyle = getFillColor();
+          ctx.fillRect(pX, pY, pW, pH);
+        } else if (ep.mode === "pad_edge") {
+          const em = ep.edgeMode;
+          const sw = srcImg.width, sh = srcImg.height;
+          if (em === "clamp") {
+            const l = cX - pX, r = (pX + pW) - (cX + cW), t = cY - pY, b = (pY + pH) - (cY + cH);
+            if (t > 0) ctx.drawImage(srcImg, 0, 0, sw, 1, cX, pY, cW, t);
+            if (b > 0) ctx.drawImage(srcImg, 0, sh - 1, sw, 1, cX, cY + cH, cW, b);
+            if (l > 0) ctx.drawImage(srcImg, 0, 0, 1, sh, pX, cY, l, cH);
+            if (r > 0) ctx.drawImage(srcImg, sw - 1, 0, 1, sh, cX + cW, cY, r, cH);
+            if (l > 0 && t > 0) ctx.drawImage(srcImg, 0, 0, 1, 1, pX, pY, l, t);
+            if (r > 0 && t > 0) ctx.drawImage(srcImg, sw - 1, 0, 1, 1, cX + cW, pY, r, t);
+            if (l > 0 && b > 0) ctx.drawImage(srcImg, 0, sh - 1, 1, 1, pX, cY + cH, l, b);
+            if (r > 0 && b > 0) ctx.drawImage(srcImg, sw - 1, sh - 1, 1, 1, cX + cW, cY + cH, r, b);
+          } else if (em === "repeat" || em === "mirror") {
+            // Copy srcImg to _mirrorCanvas first — srcImg may be _tmpCanvas
+            const tileW = Math.max(1, Math.round(cW));
+            const tileH = Math.max(1, Math.round(cH));
+            _mirrorCanvas.width = tileW;
+            _mirrorCanvas.height = tileH;
+            _mirrorCtx.drawImage(srcImg, 0, 0, tileW, tileH);
+            let tileCanvas;
+            if (em === "mirror") {
+              _tmpCanvas.width = tileW * 2; _tmpCanvas.height = tileH * 2;
+              _tmpCtx.drawImage(_mirrorCanvas, 0, 0);
+              _tmpCtx.save(); _tmpCtx.translate(_tmpCanvas.width, 0); _tmpCtx.scale(-1, 1); _tmpCtx.drawImage(_mirrorCanvas, 0, 0); _tmpCtx.restore();
+              _tmpCtx.save(); _tmpCtx.translate(0, _tmpCanvas.height); _tmpCtx.scale(1, -1); _tmpCtx.drawImage(_mirrorCanvas, 0, 0); _tmpCtx.restore();
+              _tmpCtx.save(); _tmpCtx.translate(_tmpCanvas.width, _tmpCanvas.height); _tmpCtx.scale(-1, -1); _tmpCtx.drawImage(_mirrorCanvas, 0, 0); _tmpCtx.restore();
+              tileCanvas = _tmpCanvas;
+            } else {
+              tileCanvas = _mirrorCanvas;
+            }
+            const pattern = ctx.createPattern(tileCanvas, "repeat");
+            ctx.save();
+            ctx.beginPath(); ctx.rect(pX, pY, pW, pH); ctx.clip();
+            ctx.translate(cX, cY);
+            ctx.fillStyle = pattern;
+            ctx.fillRect(pX - cX, pY - cY, pW, pH);
+            ctx.restore();
+          }
+        }
+      }
+
+      function fitAspect(targetAR, cw, ch, cx = 0, cy = 0) {
+        if (targetAR > cw / ch) { const pH = cw / targetAR; return { pW: cw, pH, pX: cx, pY: cy + (ch - pH) / 2 }; }
+        const pW = ch * targetAR; return { pW, pH: ch, pX: cx + (cw - pW) / 2, pY: cy };
+      }
+
+      function drawDashedBorder(ctx, x, y, w, h, color = "rgba(255, 180, 60, 0.8)") {
+        ctx.setLineDash([4, 4]); ctx.strokeStyle = color; ctx.lineWidth = 1;
+        ctx.strokeRect(x, y, w, h); ctx.setLineDash([]);
+      }
+
+      // Dim four sides around an inner rect within an outer rect
+      function dimSides(ctx, ox, oy, ow, oh, ix, iy, iw, ih, color = "rgba(255, 80, 80, 0.3)") {
+        ctx.fillStyle = color;
+        ctx.fillRect(ox, oy, ow, iy - oy);                        // top
+        ctx.fillRect(ox, iy + ih, ow, (oy + oh) - (iy + ih));     // bottom
+        ctx.fillRect(ox, iy, ix - ox, ih);                         // left
+        ctx.fillRect(ix + iw, iy, (ox + ow) - (ix + iw), ih);     // right
+      }
+
+      // Compute pad mode layout: fit target AR, compute content rect inside
+      function computePadLayout(rw, rh, cropW, cropH, fitW, fitH, fitX, fitY) {
+        const { pW, pH, pX, pY } = fitAspect(rw / rh, fitW, fitH, fitX, fitY);
+        const imgRatio = Math.min(rw / cropW, rh / cropH);
+        const cW = pW * (cropW * imgRatio / rw), cH = pH * (cropH * imgRatio / rh);
+        const { cX, cY } = computePadContentPos(pX, pY, pW, pH, cW, cH);
+        return { pW, pH, pX, pY, cW, cH, cX, cY };
+      }
+
+      // Render source to _tmpCanvas at given size. If srcImg + source rect provided, draws that region.
+      // Otherwise draws the rotated preview (with replicate rotation for pad_edge mode).
+      // replicate: force replicate-pad rotation (for keep_proportion pad_edge mode)
+      function renderToTmp(w, h, srcImg, sx, sy, sw, sh, replicate) {
+        const ep = getExtraPadding();
+        if (!srcImg && node._rotation !== 0 && (replicate || (ep.mode === "pad_edge" && ep.isFirst))) {
+          renderReplicateRotated(w, h); return;
+        }
+        _tmpCanvas.width = w; _tmpCanvas.height = h;
+        if (srcImg) _tmpCtx.drawImage(srcImg, sx, sy, sw, sh, 0, 0, w, h);
+        else _tmpCtx.drawImage(getRotatedPreview(), 0, 0, _rotCanvas.width, _rotCanvas.height, 0, 0, w, h);
+      }
+
+      function getEdgeEp() {
+        return { mode: "pad_edge", edgeMode: _getSubWidget("keep_proportion.edge_mode")?.value || "clamp" };
+      }
+
+      function computePadContentPos(pX, pY, pW, pH, cW, cH) {
+        const { x, y } = getPadXY();
+        return { cX: pX + (pW - cW) * x, cY: pY + (pH - cH) * y };
+      }
+
+      function getOtherBboxEdges(axis) {
+        const edges = [];
+        for (let i = 0; i < node._bboxes.length; i++) {
+          if (i === node._activeIdx) continue;
+          const { x1, y1, x2, y2 } = normBbox(node._bboxes[i]);
+          edges.push(axis === "x" ? x1 : y1, axis === "x" ? x2 : y2);
+        }
+        return edges;
+      }
+
+      function getEffectiveSnap() {
+        const g = getGridParams();
+        if (g) return { x: g.effGridW, y: g.effGridH };
+        const d = getDivBy();
+        return d > 1 ? { x: d, y: d } : { x: 0, y: 0 };
+      }
+
+      function snapCoord(val, snapSize, scale, canvasDim, otherEdges) {
+        let best = val, bestDist = Infinity;
+        const consider = (c) => { const d = Math.abs(val - c); if (d < bestDist) { bestDist = d; best = c; } };
+        consider(0);
+        consider(canvasDim);
+        if (snapSize > 1) consider(Math.round(val * scale / snapSize) * snapSize / scale);
+        if (otherEdges) for (const edge of otherEdges) consider(edge);
+        return best;
+      }
+
+      // Snap the moving corner(s) of a bbox to grid / edges / other bboxes.
+      function snapBbox(bbox) {
+        if (!bbox) return bbox;
+        const snap = getEffectiveSnap(), scale = getCanvasScale();
+        const xEdges = getOtherBboxEdges("x"), yEdges = getOtherBboxEdges("y");
+        return { startX: snapCoord(bbox.startX, snap.x, scale.x, canvasEl.width, xEdges),
+          startY: snapCoord(bbox.startY, snap.y, scale.y, canvasEl.height, yEdges),
+          endX: snapCoord(bbox.endX, snap.x, scale.x, canvasEl.width, xEdges),
+          endY: snapCoord(bbox.endY, snap.y, scale.y, canvasEl.height, yEdges) };
+      }
+
+      function snapBboxPosition(bbox) {
+        if (!bbox) return bbox;
+        const snap = getEffectiveSnap();
+        const scale = getCanvasScale();
+        const xEdges = getOtherBboxEdges("x");
+        const yEdges = getOtherBboxEdges("y");
+        const { x1, y1 } = normBbox(bbox);
+        const w = Math.abs(bbox.endX - bbox.startX);
+        const h = Math.abs(bbox.endY - bbox.startY);
+        // Snap both the left/top and right/bottom edges, pick whichever is closer
+        const snappedX1 = snapCoord(x1, snap.x, scale.x, canvasEl.width, xEdges);
+        const snappedX2 = snapCoord(x1 + w, snap.x, scale.x, canvasEl.width, xEdges) - w;
+        const snappedX = Math.abs(snappedX1 - x1) <= Math.abs(snappedX2 - x1) ? snappedX1 : snappedX2;
+        const snappedY1 = snapCoord(y1, snap.y, scale.y, canvasEl.height, yEdges);
+        const snappedY2 = snapCoord(y1 + h, snap.y, scale.y, canvasEl.height, yEdges) - h;
+        const snappedY = Math.abs(snappedY1 - y1) <= Math.abs(snappedY2 - y1) ? snappedY1 : snappedY2;
+        return { startX: snappedX, startY: snappedY, endX: snappedX + w, endY: snappedY + h };
+      }
+
+      function constrainAspect(bbox, dragMode) {
+        if (!bbox) return bbox;
+        const { w: tw, h: th } = getTgt();
+        // Target aspect in source-image pixels
+        const srcAR = (tw > 0 && th > 0) ? tw / th : 1;
+        // Convert to preview-pixel aspect by accounting for non-uniform scaling
+        const scale = getCanvasScale();
+        const ar = srcAR * scale.y / scale.x;
+
+        const { x1, y1, x2, y2 } = normBbox(bbox);
+        let bw = x2 - x1;
+        let bh = y2 - y1;
+        if (bw <= 0 || bh <= 0) return bbox;
+
+        // Adjust height to match aspect, keep width
+        const hFromW = bw / ar;
+        const wFromH = bh * ar;
+        // Pick the smaller fit so we don't exceed the drawn extent
+        if (hFromW <= bh) {
+          bh = hFromW;
+        } else {
+          bw = wFromH;
+        }
+
+        // Anchor based on drag mode
+        if (dragMode === "resize-tl") {
+          return { startX: x2 - bw, startY: y2 - bh, endX: x2, endY: y2 };
+        } else if (dragMode === "resize-tr") {
+          return { startX: x1, startY: y2 - bh, endX: x1 + bw, endY: y2 };
+        } else if (dragMode === "resize-bl") {
+          return { startX: x2 - bw, startY: y1, endX: x2, endY: y1 + bh };
+        } else {
+          // resize-br or draw (anchor top-left)
+          return { startX: x1, startY: y1, endX: x1 + bw, endY: y1 + bh };
+        }
+      }
+
+      function removeBbox(index) {
+        node._bboxes.splice(index, 1);
+        if (node._bboxes.length === 0) node._activeIdx = -1;
+        else if (index <= node._activeIdx) node._activeIdx = Math.min(node._activeIdx - (index < node._activeIdx ? 1 : 0), node._bboxes.length - 1);
+      }
+
+      function hitTestBboxes(mx, my) {
+        const order = node._activeIdx >= 0 ? [node._activeIdx] : [];
+        for (let i = 0; i < node._bboxes.length; i++) { if (i !== node._activeIdx) order.push(i); }
+        for (const idx of order) {
+          const { x1, y1, x2, y2 } = normBbox(node._bboxes[idx]);
+          const mode = rectHitTest(mx, my, x1, y1, x2, y2, 10);
+          if (mode) return { index: idx, mode };
+        }
+        return null;
+      }
+
+      canvasEl.addEventListener("mousedown", (e) => {
+        canvasEl.focus();
+        if (e.button === 2) {
+          e.preventDefault();
+          const m = getCanvasMouse(e);
+          // Right-click on rotation cross: reset rotation
+          if (node._rotation !== 0 && hitTestRotationCross(m.x, m.y)) {
+            resetRotation();
+            return;
+          }
+          // Right-click: delete the bbox under cursor, or active bbox
+          const hit = hitTestBboxes(m.x, m.y);
+          if (hit) {
+            removeBbox(hit.index);
+          } else if (node._activeIdx >= 0) {
+            removeBbox(node._activeIdx);
+          }
+          updateBboxWidgets();
+          drawCanvas();
+          return;
+        }
+        if (e.button !== 0) return;
+        const m = getCanvasMouse(e);
+
+        // Check for rotation cross drag (click near the center or any arm)
+        if (hitTestRotationCross(m.x, m.y)) {
+            node._dragMode = "rotate";
+            node._rotateStart = node._rotation;
+            const rect0 = canvasEl.getBoundingClientRect();
+            // Capture screen center once — immune to canvas resize
+            node._rotateCenterX = rect0.left + rect0.width / 2;
+            node._rotateCenterY = rect0.top + rect0.height / 2;
+            node._rotateScreenStartAngle = Math.atan2(
+              e.clientY - node._rotateCenterY,
+              e.clientX - node._rotateCenterX
+            ) * 180 / Math.PI;
+            node._drawing = true;
+            node._dragStart = m;
+            document.addEventListener("mousemove", onDragMove);
+            document.addEventListener("mouseup", onDragEnd);
+            e.preventDefault();
+            e.stopPropagation();
+            return;
+        }
+
+        // Shift+drag: drag content position within padded area
+        if (e.shiftKey) {
+          const kpVal = getKp();
+          const { w: tW2, h: tH2 } = getTgt();
+          const ep = getExtraPadding();
+          const startDrag = (mode, extra) => {
+            node._dragMode = mode; Object.assign(node, extra);
+            node._drawing = true; node._dragStart = m;
+            document.addEventListener("mousemove", onDragMove);
+            document.addEventListener("mouseup", onDragEnd);
+            e.preventDefault(); e.stopPropagation();
+          };
+          if ((tW2 > 0 || tH2 > 0) && isPadMode(kpVal)) {
+            startDrag("pad_drag", { _padDragStart: getPadXY(), _bboxAtDragStart: null }); return;
+          } else if (hasExtraPad(ep)) {
+            startDrag("extra_pad_drag", { _extraPadStart: { ...ep }, _bboxAtDragStart: null }); return;
+          }
+        }
+
+        const hit = hitTestBboxes(m.x, m.y);
+        if (hit) {
+          // Select and interact with existing bbox
+          node._activeIdx = hit.index;
+          node._dragMode = hit.mode;
+          const bbox = node._bboxes[hit.index];
+          const { x1, y1, x2, y2 } = normBbox(bbox);
+          node._bboxAtDragStart = { startX: x1, startY: y1, endX: x2, endY: y2 };
+        } else {
+          // Draw new bbox
+          node._dragMode = "draw";
+          const newBbox = { startX: m.x, startY: m.y, endX: m.x, endY: m.y };
+          node._bboxes.push(newBbox);
+          node._activeIdx = node._bboxes.length - 1;
+          node._bboxAtDragStart = null;
+        }
+        node._drawing = true;
+        node._dragStart = m;
+
+        document.addEventListener("mousemove", onDragMove);
+        document.addEventListener("mouseup", onDragEnd);
+        e.preventDefault();
+        e.stopPropagation();
+      });
+
+      // Hover cursor updates
+      canvasEl.addEventListener("mousemove", (e) => {
+        if (node._drawing) return;
+        const m = getCanvasMouse(e);
+        const hit = hitTestBboxes(m.x, m.y);
+        if (!hit) {
+          if (!node._showRotationCross) {
+            canvasEl.style.cursor = e.shiftKey ? "grab" : "crosshair";
+          } else if (hitTestRotationCross(m.x, m.y)) {
+            canvasEl.style.cursor = "alias";
+          } else if (e.shiftKey) {
+            const kpVal = getKp();
+            const { w: tW, h: tH } = getTgt();
+            const epHover = getExtraPadding();
+            if (((tW > 0 || tH > 0) && isPadMode(kpVal)) || hasExtraPad(epHover)) {
+              canvasEl.style.cursor = "grab";
+            } else {
+              canvasEl.style.cursor = "crosshair";
+            }
+          } else {
+            canvasEl.style.cursor = "crosshair";
+          }
+        } else {
+          canvasEl.style.cursor = cursorForBboxMode(hit.mode) || "crosshair";
+        }
+      });
+
+      function onDragMove(e) {
+        if (!node._drawing) return;
+        if (node._dragMode === "rotate") {
+          // Screen-space atan2 with fixed center (captured at drag start)
+          const sdx = e.clientX - node._rotateCenterX;
+          const sdy = e.clientY - node._rotateCenterY;
+          const screenDist = Math.sqrt(sdx * sdx + sdy * sdy);
+          if (screenDist < 15) {
+            node._rotateStart = node._rotation;
+            node._rotateScreenStartAngle = Math.atan2(sdy, sdx) * 180 / Math.PI;
+            return;
+          }
+          const angleCur = Math.atan2(sdy, sdx) * 180 / Math.PI;
+          let angleDelta = angleCur - node._rotateScreenStartAngle;
+          // Normalize delta to -180..180 to handle atan2 wrap-around
+          while (angleDelta > 180) angleDelta -= 360;
+          while (angleDelta < -180) angleDelta += 360;
+          let newRot = node._rotateStart + angleDelta;
+          if (e.ctrlKey) {
+            newRot = Math.round(newRot / 15) * 15;
+          } else {
+            for (const snap of [0, 90, 180, 270, -90, -180, -270, 360]) {
+              if (Math.abs(newRot - snap) < 3) { newRot = snap; break; }
+            }
+          }
+          while (newRot > 180) newRot -= 360;
+          while (newRot < -180) newRot += 360;
+          node._rotation = newRot;
+          node.properties.rotation = newRot;
+          // Resize canvas to match rotated image dimensions
+          if (node._previewImg && node._previewEnabled) {
+            const { w: ew, h: eh } = getEffectiveImageDims(true);
+            const [rw, rh] = clampToMaxCanvas(ew, eh);
+            if (Math.abs(rw - canvasEl.width) > 3 || Math.abs(rh - canvasEl.height) > 3) {
+              setCanvasSize(rw, rh);
+            }
+          }
+          drawCanvas();
+          return;
+        }
+        if (node._dragMode === "pad_drag") {
+          const m = getCanvasMouse(e);
+          const dx = m.x - node._dragStart.x;
+          const dy = m.y - node._dragStart.y;
+          // Compute the pad preview layout to find how many canvas pixels = full 0-1 range
+          const ep = getExtraPadding();
+          const { w: imgW, h: imgH } = node._previewImg
+            ? getEffectiveImageDims(getKp() !== "pad_edge")
+            : { w: canvasEl.width, h: canvasEl.height };
+          const { rw: baseTw, rh: baseTh } = getEffectiveTargetDims(imgW, imgH);
+          const totalW = baseTw + ep.left + ep.right;
+          const totalH = baseTh + ep.top + ep.bottom;
+          const { pW, pH } = fitAspect(totalW / totalH, canvasEl.width, canvasEl.height);
+          const imgRatio = Math.min(baseTw / imgW, baseTh / imgH);
+          const cW = pW * (imgW * imgRatio / totalW), cH = pH * (imgH * imgRatio / totalH);
+          const rangeX = pW - cW, rangeY = pH - cH;
+          const newX = rangeX > 0 ? node._padDragStart.x + dx / rangeX : 0.5;
+          const newY = rangeY > 0 ? node._padDragStart.y + dy / rangeY : 0.5;
+          setPadXY(newX, newY, true);
+          drawCanvas();
+          return;
+        }
+        if (node._dragMode === "extra_pad_drag") {
+          const m = getCanvasMouse(e);
+          const dx = m.x - node._dragStart.x;
+          const dy = m.y - node._dragStart.y;
+          const s = node._extraPadStart;
+          const totalH = s.top + s.bottom, totalW = s.left + s.right;
+          const fullW = (node._previewImg ? node._previewImg.naturalWidth : canvasEl.width) + totalW;
+          const fullH = (node._previewImg ? node._previewImg.naturalHeight : canvasEl.height) + totalH;
+          const scale = (fullW / fullH > canvasEl.width / canvasEl.height)
+            ? fullW / canvasEl.width : fullH / canvasEl.height;
+          const pixDx = Math.round(dx * scale);
+          const pixDy = Math.round(dy * scale);
+          const newLeft = Math.max(0, Math.min(totalW, s.left + pixDx));
+          const newTop = Math.max(0, Math.min(totalH, s.top + pixDy));
+          for (const [name, val] of [["pad_left", newLeft], ["pad_right", totalW - newLeft],
+              ["pad_top", newTop], ["pad_bottom", totalH - newTop]]) {
+            const w = _getSubWidget(`extra_padding.${name}`);
+            if (w) w.value = val;
+          }
+          drawCanvas();
+          return;
+        }
+        if (node._activeIdx < 0) return;
+        const m = getCanvasMouse(e);
+        const cw = canvasEl.width;
+        const ch = canvasEl.height;
+        let bbox = node._bboxes[node._activeIdx];
+
+        if (node._dragMode === "draw") {
+          bbox.endX = Math.max(0, Math.min(cw, m.x));
+          bbox.endY = Math.max(0, Math.min(ch, m.y));
+        } else if (node._dragMode === "move" && node._bboxAtDragStart) {
+          const dx = m.x - node._dragStart.x;
+          const dy = m.y - node._dragStart.y;
+          const bs = node._bboxAtDragStart;
+          const w = bs.endX - bs.startX;
+          const h = bs.endY - bs.startY;
+          const nx = Math.max(0, Math.min(cw - w, bs.startX + dx));
+          const ny = Math.max(0, Math.min(ch - h, bs.startY + dy));
+          bbox = { startX: nx, startY: ny, endX: nx + w, endY: ny + h };
+        } else if (node._dragMode?.startsWith("resize") && node._bboxAtDragStart) {
+          const bs = node._bboxAtDragStart;
+          const dm = node._dragMode;
+          // Edge resize: only one axis moves
+          if (dm === "resize-t" || dm === "resize-b" || dm === "resize-l" || dm === "resize-r") {
+            const edgeCoord = dm === "resize-t" ? bs.startY : dm === "resize-b" ? bs.endY : dm === "resize-l" ? bs.startX : bs.endX;
+            const isVert = dm === "resize-t" || dm === "resize-b";
+            const off = (isVert ? node._dragStart.y : node._dragStart.x) - edgeCoord;
+            const val = Math.max(0, Math.min(isVert ? ch : cw, (isVert ? m.y : m.x) - off));
+            if (dm === "resize-t") bbox = { startX: bs.startX, startY: val, endX: bs.endX, endY: bs.endY };
+            else if (dm === "resize-b") bbox = { startX: bs.startX, startY: bs.startY, endX: bs.endX, endY: val };
+            else if (dm === "resize-l") bbox = { startX: val, startY: bs.startY, endX: bs.endX, endY: bs.endY };
+            else bbox = { startX: bs.startX, startY: bs.startY, endX: val, endY: bs.endY };
+          } else {
+            // Corner resize
+            let cornerX, cornerY;
+            if (dm === "resize-tl") { cornerX = bs.startX; cornerY = bs.startY; }
+            else if (dm === "resize-tr") { cornerX = bs.endX; cornerY = bs.startY; }
+            else if (dm === "resize-bl") { cornerX = bs.startX; cornerY = bs.endY; }
+            else { cornerX = bs.endX; cornerY = bs.endY; }
+            const offX = node._dragStart.x - cornerX;
+            const offY = node._dragStart.y - cornerY;
+            const cx = Math.max(0, Math.min(cw, m.x - offX));
+            const cy = Math.max(0, Math.min(ch, m.y - offY));
+            if (dm === "resize-tl") bbox = { startX: bs.endX, startY: bs.endY, endX: cx, endY: cy };
+            else if (dm === "resize-tr") bbox = { startX: bs.startX, startY: bs.endY, endX: cx, endY: cy };
+            else if (dm === "resize-bl") bbox = { startX: bs.endX, startY: bs.startY, endX: cx, endY: cy };
+            else if (dm === "resize-br") bbox = { startX: bs.startX, startY: bs.startY, endX: cx, endY: cy };
+          }
+        }
+
+        // Alt: resize symmetrically — for draw, the click point is center;
+        // for resize, the center of the original bbox is the pivot
+        if (e.altKey && node._dragMode !== "move") {
+          let cx, cy;
+          if (node._dragMode === "draw") {
+            cx = node._dragStart.x;
+            cy = node._dragStart.y;
+          } else if (node._bboxAtDragStart) {
+            const bs = node._bboxAtDragStart;
+            cx = (bs.startX + bs.endX) / 2;
+            cy = (bs.startY + bs.endY) / 2;
+          } else {
+            cx = (bbox.startX + bbox.endX) / 2;
+            cy = (bbox.startY + bbox.endY) / 2;
+          }
+          const dx = Math.abs(bbox.endX - cx);
+          const dy = Math.abs(bbox.endY - cy);
+          bbox = {
+            startX: Math.max(0, cx - dx),
+            startY: Math.max(0, cy - dy),
+            endX: Math.min(cw, cx + dx),
+            endY: Math.min(ch, cy + dy),
+          };
+        }
+
+        // Shift: constrain to target aspect ratio (not applicable to edge resize)
+        const isEdgeResize = /^resize-[tblr]$/.test(node._dragMode);
+        if (e.shiftKey && node._dragMode !== "move" && !isEdgeResize) {
+          bbox = constrainAspect(bbox, node._dragMode);
+        }
+
+        // Ctrl: snap to grid in source image space
+        if (e.ctrlKey) {
+          if (node._dragMode === "move") {
+            bbox = snapBboxPosition(bbox);
+          } else if (e.altKey) {
+            // Snap both edges, keep centered
+            const snap = getEffectiveSnap();
+            const scale = getCanvasScale();
+            const xEdges = getOtherBboxEdges("x");
+            const yEdges = getOtherBboxEdges("y");
+            const sx1 = snapCoord(bbox.startX, snap.x, scale.x, cw, xEdges);
+            const sx2 = snapCoord(bbox.endX, snap.x, scale.x, cw, xEdges);
+            const sy1 = snapCoord(bbox.startY, snap.y, scale.y, ch, yEdges);
+            const sy2 = snapCoord(bbox.endY, snap.y, scale.y, ch, yEdges);
+            bbox = { startX: sx1, startY: sy1, endX: sx2, endY: sy2 };
+          } else if (isEdgeResize) {
+            // Only snap the edge being dragged
+            const snap = getEffectiveSnap();
+            const scale = getCanvasScale();
+            const xEdges = getOtherBboxEdges("x"), yEdges = getOtherBboxEdges("y");
+            const dm = node._dragMode;
+            bbox = { ...bbox,
+              ...(dm === "resize-t" && { startY: snapCoord(bbox.startY, snap.y, scale.y, ch, yEdges) }),
+              ...(dm === "resize-b" && { endY: snapCoord(bbox.endY, snap.y, scale.y, ch, yEdges) }),
+              ...(dm === "resize-l" && { startX: snapCoord(bbox.startX, snap.x, scale.x, cw, xEdges) }),
+              ...(dm === "resize-r" && { endX: snapCoord(bbox.endX, snap.x, scale.x, cw, xEdges) }),
+            };
+          } else {
+            bbox = snapBbox(bbox);
+          }
+        }
+
+        node._bboxes[node._activeIdx] = bbox;
+        drawCanvas();
+        e.preventDefault();
+      }
+
+      function onDragEnd(e) {
+        if (!node._drawing) return;
+        const wasRotate = node._dragMode === "rotate";
+        const wasPadDrag = node._dragMode === "pad_drag";
+        Object.assign(node, { _drawing: false, _dragMode: null, _dragStart: null,
+          _bboxAtDragStart: null, _padDragStart: null, _extraPadStart: null });
+        // Flush deferred pad_x/pad_y widget values
+        if (wasPadDrag) setPadXY(_framePadXY.x, _framePadXY.y);
+        document.removeEventListener("mousemove", onDragMove);
+        document.removeEventListener("mouseup", onDragEnd);
+
+        // Snap canvas to exact rotated dimensions on release (during drag we use a threshold to avoid jitter)
+        if (wasRotate) {
+          restoreCanvasToImage();
+          fitNodeToImage(false);
+        }
+
+        if (node._activeIdx >= 0) {
+          const bbox = node._bboxes[node._activeIdx];
+          const { x1, y1, x2, y2 } = normBbox(bbox);
+          if (x2 - x1 < 3 && y2 - y1 < 3) {
+            // Too small — remove it
+            removeBbox(node._activeIdx);
+          } else {
+            node._bboxes[node._activeIdx] = { startX: x1, startY: y1, endX: x2, endY: y2 };
+          }
+        }
+        updateBboxWidgets();
+        drawCanvas();
+      }
+
+      canvasEl.addEventListener("contextmenu", (e) => { e.preventDefault(); e.stopPropagation(); });
+
+      canvasEl.tabIndex = 0;
+      canvasEl.style.outline = "none";
+      canvasEl.addEventListener("keydown", (e) => {
+        if (node._activeIdx < 0) return;
+        const arrows = { ArrowLeft: [-1, 0], ArrowRight: [1, 0], ArrowUp: [0, -1], ArrowDown: [0, 1] };
+        const dir = arrows[e.key];
+        if (!dir) {
+          // Delete key removes active bbox
+          if (e.key === "Delete" || e.key === "Backspace") {
+            e.preventDefault();
+            e.stopPropagation();
+            removeBbox(node._activeIdx);
+            updateBboxWidgets();
+            drawCanvas();
+          }
+          // Tab cycles through bboxes
+          if (e.key === "Tab" && node._bboxes.length > 1) {
+            e.preventDefault();
+            e.stopPropagation();
+            node._activeIdx = (node._activeIdx + (e.shiftKey ? -1 : 1) + node._bboxes.length) % node._bboxes.length;
+            drawCanvas();
+          }
+          return;
+        }
+        e.preventDefault();
+        e.stopPropagation();
+
+        const snap = getEffectiveSnap();
+        const step = e.ctrlKey ? (snap.x > 1 ? snap.x / getCanvasScale().x : 10) : 1;
+        const stepY = e.ctrlKey ? (snap.y > 1 ? snap.y / getCanvasScale().y : 10) : 1;
+        const dx = dir[0] * step;
+        const dy = dir[1] * stepY;
+        const bbox = node._bboxes[node._activeIdx];
+        const { x1, y1, x2, y2 } = normBbox(bbox);
+        const w = x2 - x1, h = y2 - y1;
+        const nx = Math.max(0, Math.min(canvasEl.width - w, x1 + dx));
+        const ny = Math.max(0, Math.min(canvasEl.height - h, y1 + dy));
+        node._bboxes[node._activeIdx] = { startX: nx, startY: ny, endX: nx + w, endY: ny + h };
+        updateBboxWidgets();
+        drawCanvas();
+      });
+
+      function updateBboxWidgets() {
+        const data = {};
+        if (node._rotation !== 0) data.rotation = node._rotation;
+        data.fillColor = getFillColor();
+        if (node._bboxes.length > 0) {
+          data.bboxes = node._bboxes.map((bbox) => {
+            const b = Object.assign({}, bbox);
+            if (node._previewImg) {
+              b.previewWidth = canvasEl.width;
+              b.previewHeight = canvasEl.height;
+            }
+            return b;
+          });
+        }
+        bboxWidget.value = Object.keys(data).length > 0 ? JSON.stringify(data) : "";
+      }
+
+      let _resizing = false;
+      function fitNodeToCanvas() {
+        if (canvasEl.width <= 0 || canvasEl.height <= 0) return;
+        // If in a no-bbox mode with target dims, resize canvas to output AR
+        const kp = getKp();
+        const { w: tgtW, h: tgtH } = getTgt();
+        const hasBboxes = node._bboxes.length > 0;
+        if (!hasBboxes && node._previewImg && node._previewEnabled && node._rotation === 0 && (tgtW > 0 || tgtH > 0) &&
+            (kp === "stretch" || isPadMode(kp))) {
+          const { w: imgW, h: imgH } = getEffectiveImageDims(kp !== "pad_edge");
+          let rw = tgtW > 0 ? tgtW : imgW;
+          let rh = tgtH > 0 ? tgtH : imgH;
+          const outAR = rw / rh;
+          const newH = Math.round(canvasEl.width / outAR);
+          if (Math.abs(newH - canvasEl.height) > 2) {
+            canvasEl.height = newH;
+            canvasEl.style.aspectRatio = `${canvasEl.width} / ${newH}`;
+            _rotCacheKey = "";
+          }
+        } else if (node._previewImg && node._previewEnabled) {
+          // Restore to image AR (including pad-first extra padding if active)
+          let { w: iw, h: ih } = getEffectiveImageDims(true);
+          const ep = getExtraPadding();
+          if (ep.isFirst && (ep.top + ep.bottom + ep.left + ep.right) > 0) {
+            iw += ep.left + ep.right;
+            ih += ep.top + ep.bottom;
+          }
+          const imgAR = iw / ih;
+          const newH = Math.round(canvasEl.width / imgAR);
+          if (Math.abs(newH - canvasEl.height) > 2) {
+            canvasEl.height = newH;
+            canvasEl.style.aspectRatio = `${canvasEl.width} / ${newH}`;
+            _rotCacheKey = "";
+          }
+        }
+        // Update widget height but don't force node size — only grow if needed
+        const nodeW = node.size[0];
+        const displayedW = nodeW - 30;
+        const displayedH = Math.round(canvasEl.height * (displayedW / canvasEl.width));
+        node._widgetHeight = displayedH + GRID_BAR_HEIGHT;
+      }
+
+      function setCanvasSize(cw, ch) {
+        canvasEl.width = cw;
+        canvasEl.height = ch;
+        canvasEl.style.aspectRatio = `${cw} / ${ch}`;
+        node.properties.canvasSize = [cw, ch];
+        node._widgetHeight = ch + GRID_BAR_HEIGHT;
+        if (node.graph) node.graph.setDirtyCanvas(true, true);
+        fitNodeToCanvas();
+      }
+
+      function updateCanvasFromTargetDims() {
+        if (node._previewImg && node._previewEnabled) return;
+        const divBy = getDivBy();
+        const { w: twV, h: thV } = getTgt();
+        let tw = roundDown(twV, divBy);
+        let th = roundDown(thV, divBy);
+        const [cw, ch] = clampToMaxCanvas(tw > 0 ? Math.max(tw, 200) : 400, th > 0 ? Math.max(th, 150) : 300);
+        if (cw !== canvasEl.width || ch !== canvasEl.height) {
+          setCanvasSize(cw, ch);
+        }
+      }
+
+      function getCanvasScale() {
+        if (node._previewImg && node._previewEnabled) {
+          if (node._rotation !== 0) {
+            // When rotated, canvas is sized to rotated bounds — uniform scale
+            const { w: rotW } = getEffectiveImageDims(true);
+            const s = rotW / canvasEl.width;  // same as rotH / canvasEl.height
+            return { x: s, y: s };
+          }
+          let nw = node._previewImg.naturalWidth;
+          let nh = node._previewImg.naturalHeight;
+          // For "pad first" modes, bbox coords map to padded image dimensions
+          const ep = getExtraPadding();
+          if (ep.isFirst && hasExtraPad(ep)) {
+            nw += ep.left + ep.right;
+            nh += ep.top + ep.bottom;
+          }
+          return { x: nw / canvasEl.width, y: nh / canvasEl.height };
+        }
+        const { w: tw, h: th } = getTgt();
+        return { x: tw > 0 ? tw / canvasEl.width : 1, y: th > 0 ? th / canvasEl.height : 1 };
+      }
+
+      function getOutputDims(bbox) {
+        if (!bbox) return null;
+        const { x: sX, y: sY } = getCanvasScale();
+        const { x1, y1, x2, y2 } = normBbox(bbox);
+        const cropW = Math.round((x2 - x1) * sX);
+        const cropH = Math.round((y2 - y1) * sY);
+        const dims = computeOutputDims(cropW, cropH);
+        return dims ? { w: Math.max(1, dims.w), h: Math.max(1, dims.h) } : null;
+      }
+
+      function getGridParams() {
+        const gridSize = getGridSize();
+        if (gridSize <= 1) return null;
+
+        let tw, th;
+        if (node._previewImg && node._previewEnabled) {
+          const dims = getEffectiveImageDims(true);
+          tw = dims.w;
+          th = dims.h;
+        } else {
+          tw = twWidget ? twWidget.value : canvasEl.width;
+          th = thWidget ? thWidget.value : canvasEl.height;
+        }
+        const scaleX = canvasEl.width / tw;
+        const scaleY = canvasEl.height / th;
+        if (gridSize * scaleX < 4 && gridSize * scaleY < 4) return null;
+        // Per-dimension effective grid: no partial cells, cells are near-square
+        const cellsX = Math.max(1, Math.round(tw / gridSize));
+        const cellsY = Math.max(1, Math.round(th / gridSize));
+        const effGridW = tw / cellsX;
+        const effGridH = th / cellsY;
+        return { gridSize, tw, th, scaleX, scaleY, effGridW, effGridH };
+      }
+
+      function drawGrid(ctx, alpha, clipBbox, color) {
+        const g = getGridParams();
+        if (!g) return;
+        const { tw, th, scaleX, scaleY, effGridW, effGridH } = g;
+
+        ctx.save();
+        if (clipBbox) {
+          const { x1, y1, x2, y2 } = normBbox(clipBbox);
+          ctx.beginPath();
+          ctx.rect(x1, y1, x2 - x1, y2 - y1);
+          ctx.clip();
+        }
+
+        const showPreview = node._previewImg && node._previewEnabled;
+        const defaultAlpha = showPreview ? 0.25 : 0.1;
+        if (color) {
+          ctx.strokeStyle = color;
+        } else {
+          const { r, g, b } = getGridColorRGB();
+          ctx.strokeStyle = `rgba(${r}, ${g}, ${b}, ${alpha ?? defaultAlpha})`;
+        }
+        ctx.lineWidth = 1;
+        ctx.beginPath();
+        for (let rx = effGridW; rx < tw - 0.5; rx += effGridW) {
+          const px = Math.round(rx * scaleX) + 0.5;
+          ctx.moveTo(px, 0);
+          ctx.lineTo(px, canvasEl.height);
+        }
+        for (let ry = effGridH; ry < th - 0.5; ry += effGridH) {
+          const py = Math.round(ry * scaleY) + 0.5;
+          ctx.moveTo(0, py);
+          ctx.lineTo(canvasEl.width, py);
+        }
+        ctx.stroke();
+        ctx.restore();
+      }
+
+      function drawRotationCross(ctx) {
+        if (!node._showRotationCross) return;
+        const { width: cw, height: ch } = canvasEl;
+        const armLen = Math.sqrt(cw * cw + ch * ch) / 2;
+        const rad = rotRad();
+        const isActive = node._rotation !== 0;
+        ctx.save();
+        ctx.translate(cw / 2, ch / 2);
+        ctx.rotate(rad);
+        ctx.beginPath();
+        ctx.moveTo(-armLen, 0); ctx.lineTo(armLen, 0);
+        ctx.moveTo(0, -armLen); ctx.lineTo(0, armLen);
+        // Black outline
+        Object.assign(ctx, { strokeStyle: "rgba(0, 0, 0, 0.6)", lineWidth: isActive ? 5 : 4 });
+        ctx.stroke();
+        // Orange fill
+        Object.assign(ctx, { strokeStyle: "rgba(255, 160, 30, 0.8)", lineWidth: isActive ? 3 : 2 });
+        ctx.stroke();
+        if (isActive) {
+          ctx.rotate(-rad);
+          const label = `${node._rotation.toFixed(1)}°`;
+          Object.assign(ctx, { font: "12px sans-serif", textAlign: "center" });
+          const lw = ctx.measureText(label).width + 8;
+          ctx.fillStyle = "rgba(0, 0, 0, 0.6)";
+          ctx.beginPath(); ctx.roundRect(-lw / 2, -34, lw, 16, 3); ctx.fill();
+          ctx.fillStyle = "rgba(255, 160, 30, 1)";
+          ctx.fillText(label, 0, -20);
+        }
+        ctx.restore();
+      }
+
+      function drawBbox(ctx, bbox, index, isActive) {
+        const { x1, y1, x2, y2 } = normBbox(bbox);
+        const w = x2 - x1, h = y2 - y1;
+        const color = getBboxColor(index, isActive);
+        const invertEnabled = icWidget ? icWidget.value === "enabled" : false;
+        if (isActive) {
+          if (invertEnabled) {
+            ctx.fillStyle = getFillColor(); ctx.fillRect(x1, y1, w, h);
+          } else {
+            ctx.fillStyle = "rgba(0, 0, 0, 0.5)"; ctx.beginPath();
+            ctx.rect(0, 0, canvasEl.width, canvasEl.height); ctx.rect(x1, y1, w, h); ctx.fill("evenodd");
+          }
+        }
+        drawGrid(ctx, null, bbox, color.gridColor);
+        if (!isActive) { ctx.fillStyle = color.tint; ctx.fillRect(x1, y1, w, h); }
+
+        if (isActive && !invertEnabled) {
+          const { x: sX, y: sY } = getCanvasScale();
+          const cropW = Math.round(w * sX);
+          const cropH = Math.round(h * sY);
+          const { w: tgtW, h: tgtH } = getTgt();
+          const kp = getKp();
+
+          if (kp === "multiplier") {
+            const wmv = _getSubWidget("keep_proportion.width_mult")?.value || 1.0;
+            const hmv = _getSubWidget("keep_proportion.height_mult")?.value || 1.0;
+            if (wmv !== 1.0 || hmv !== 1.0) {
+              drawStretchOverlay(ctx, (cropW * wmv) / (cropH * hmv), x1, y1, w, h);
+            }
+          }
+
+          if ((tgtW > 0 || tgtH > 0) && kp === "stretch") {
+            const { rw, rh } = getEffectiveTargetDims(cropW, cropH);
+            drawStretchOverlay(ctx, rw / rh, x1, y1, w, h);
+          }
+
+          if ((tgtW > 0 || tgtH > 0) && (kp === "crop" || isPadMode(kp))) {
+            const { rw, rh } = getEffectiveTargetDims(cropW, cropH);
+
+            if (kp === "crop") {
+              const ratio = Math.max(rw / cropW, rh / cropH);
+              const visW = rw / ratio, visH = rh / ratio;
+              const offX = (cropW - visW) / 2, offY = (cropH - visH) / 2;
+              const vx1 = x1 + offX / sX, vy1 = y1 + offY / sY;
+              const vx2 = x1 + (offX + visW) / sX, vy2 = y1 + (offY + visH) / sY;
+              dimSides(ctx, x1, y1, w, h, vx1, vy1, vx2 - vx1, vy2 - vy1);
+              drawDashedBorder(ctx, vx1, vy1, vx2 - vx1, vy2 - vy1, "rgba(255, 255, 100, 0.8)");
+            } else if (isPadMode(kp)) {
+              const { pW, pH, pX, pY, cW, cH, cX, cY } = computePadLayout(rw, rh, cropW, cropH, w, h, x1, y1);
+              ctx.fillStyle = "rgba(0, 0, 0, 0.7)";
+              ctx.fillRect(x1, y1, w, h);
+              if (kp === "pad_edge" && node._previewEnabled && node._previewImg) {
+                renderToTmp(Math.max(1, Math.round(cW)), Math.max(1, Math.round(cH)),
+                  node._previewImg, x1 * sX, y1 * sY, w * sX, h * sY);
+                drawExtraPadFill(ctx, getEdgeEp(), pX, pY, pW, pH, cX, cY, cW, cH, _tmpCanvas);
+              } else if (kp === "pad_color") {
+                ctx.fillStyle = getFillColor();
+                ctx.fillRect(pX, pY, pW, pH);
+              }
+              if (node._previewEnabled) {
+                drawPreviewRegion(ctx, x1, y1, w, h, cX, cY, cW, cH);
+              }
+              drawDashedBorder(ctx, cX, cY, cW, cH, "rgba(100, 200, 255, 0.8)");
+            }
+          }
+
+          const epBbox = getExtraPadding();
+          const padModeHandledBbox = isPadMode(kp) && (tgtW > 0 || tgtH > 0);
+          if (hasExtraPad(epBbox) && !padModeHandledBbox && !invertEnabled && !epBbox.isFirst && node._previewImg) {
+            const epContent = computeContentDims(cropW, cropH);
+            const epCW = epContent ? epContent.w : cropW, epCH = epContent ? epContent.h : cropH;
+            const totalW2 = epCW + epBbox.left + epBbox.right, totalH2 = epCH + epBbox.top + epBbox.bottom;
+            const { pW: pW2, pH: pH2, pX: pX2, pY: pY2 } = fitAspect(totalW2 / totalH2, w, h, x1, y1);
+            ctx.fillStyle = "rgba(0, 0, 0, 0.7)"; ctx.fillRect(x1, y1, w, h);
+            const cW2 = pW2 * (epCW / totalW2), cH2 = pH2 * (epCH / totalH2);
+            const cX2 = pX2 + pW2 * (epBbox.left / totalW2), cY2 = pY2 + pH2 * (epBbox.top / totalH2);
+            // Draw crop fitted within content area (letterbox, don't stretch)
+            const cropAR = cropW / cropH;
+            const { pW: iW2, pH: iH2, pX: iX2, pY: iY2 } = fitAspect(cropAR, cW2, cH2, cX2, cY2);
+            const riW2 = Math.max(1, Math.round(iW2)), riH2 = Math.max(1, Math.round(iH2));
+            renderToTmp(riW2, riH2, node._previewImg, x1 * sX, y1 * sY, w * sX, h * sY);
+            drawExtraPadFill(ctx, epBbox, pX2, pY2, pW2, pH2, cX2, cY2, cW2, cH2, _tmpCanvas);
+            drawPreviewRegion(ctx, x1, y1, w, h, iX2, iY2, iW2, iH2);
+            drawDashedBorder(ctx, cX2, cY2, cW2, cH2, "rgba(100, 200, 255, 0.8)");
+          }
+
+          const { w: outW, h: outH } = getOutputDims(bbox) || { w: cropW, h: cropH };
+          const boxLabel = node._bboxes.length > 1 ? `[${index + 1}] ` : "";
+          const label = (tgtW > 0 || tgtH > 0)
+            ? `${boxLabel}${cropW}×${cropH} → ${outW}×${outH}`
+            : `${boxLabel}${cropW} × ${cropH}`;
+          ctx.font = "12px sans-serif";
+          const lw = ctx.measureText(label).width + 12;
+          const lh = 20;
+          const ly = y1 > lh + 4 ? y1 - lh - 2 : y1 + 4;
+          ctx.fillStyle = "rgba(0, 0, 0, 0.7)";
+          ctx.fillRect(x1, ly, lw, lh);
+          ctx.fillStyle = "#fff";
+          ctx.textAlign = "left";
+          ctx.fillText(label, x1 + 6, ly + 14);
+        } else if (!isActive) {
+          // Inactive: show index label centered in bbox
+          const boxLabel = `${index + 1}`;
+          ctx.font = "bold 20px sans-serif";
+          ctx.fillStyle = color.border;
+          ctx.textAlign = "center";
+          ctx.textBaseline = "middle";
+          ctx.fillText(boxLabel, x1 + w / 2, y1 + h / 2);
+          ctx.textBaseline = "alphabetic";
+        }
+
+        if (isActive) {
+          const cx = (x1 + x2) / 2, cy = (y1 + y2) / 2;
+          Object.assign(ctx, { strokeStyle: color.border, lineWidth: 1 });
+          ctx.beginPath();
+          ctx.moveTo(cx - 5, cy); ctx.lineTo(cx + 5, cy);
+          ctx.moveTo(cx, cy - 5); ctx.lineTo(cx, cy + 5);
+          ctx.stroke();
+        }
+        if (!isActive) { ctx.strokeStyle = "rgba(0, 0, 0, 0.6)"; ctx.lineWidth = 4; ctx.strokeRect(x1, y1, w, h); }
+        ctx.strokeStyle = color.border; ctx.lineWidth = 2; ctx.strokeRect(x1, y1, w, h);
+
+        ctx.fillStyle = color.handle;
+        const hs = isActive ? 5 : 4, s2 = hs * 2;
+        const cx1 = Math.max(hs, Math.min(canvasEl.width - hs, x1));
+        const cx2 = Math.max(hs, Math.min(canvasEl.width - hs, x2));
+        const cy1 = Math.max(hs, Math.min(canvasEl.height - hs, y1));
+        const cy2 = Math.max(hs, Math.min(canvasEl.height - hs, y2));
+        for (const [hx, hy] of [[cx1, cy1], [cx2, cy1], [cx1, cy2], [cx2, cy2]])
+          ctx.fillRect(hx - hs, hy - hs, s2, s2);
+      }
+
+      function drawCanvas() {
+        const ctx = canvasCtx;
+        ctx.clearRect(0, 0, canvasEl.width, canvasEl.height);
+        _frameEp = _readExtraPadding();
+        if (node._dragMode !== "pad_drag") _framePadXY = _readPadXY();
+        const _kpVal = getKp(), { w: _tgtW, h: _tgtH } = getTgt();
+        const hasBboxes = node._bboxes.length > 0;
+        const showPreview = node._previewImg && node._previewEnabled;
+        const _skipFullImage = !hasBboxes && showPreview && isPadMode(_kpVal) && (_tgtW > 0 || _tgtH > 0);
+
+        const _epBase = getExtraPadding();
+        const _isFirst = _epBase.isFirst, _hasFirstPad = _isFirst && hasExtraPad(_epBase);
+        const maybeResize = (cw, ch) => {
+          if (cw !== canvasEl.width || ch !== canvasEl.height) { setCanvasSize(cw, ch); ctx.clearRect(0, 0, cw, ch); }
+        };
+        if (showPreview) {
+          if (_hasFirstPad) {
+            const { w: eiw, h: eih } = getEffectiveImageDims(_epBase.mode !== "pad_edge");
+            maybeResize(...clampToMaxCanvas(eiw + _epBase.left + _epBase.right, eih + _epBase.top + _epBase.bottom));
+          } else if (node._previewImg) {
+            maybeResize(...clampToMaxCanvas(...Object.values(getEffectiveImageDims(true))));
+          }
+        }
+
+        if (showPreview && !_skipFullImage) {
+          if (_hasFirstPad) {
+            const { w: epImgW, h: epImgH } = getEffectiveImageDims(_epBase.mode !== "pad_edge");
+            const totalW = epImgW + _epBase.left + _epBase.right, totalH = epImgH + _epBase.top + _epBase.bottom;
+            const cX = canvasEl.width * (_epBase.left / totalW), cY = canvasEl.height * (_epBase.top / totalH);
+            const cW = canvasEl.width * (epImgW / totalW), cH = canvasEl.height * (epImgH / totalH);
+            const rcW = Math.max(1, Math.round(cW)), rcH = Math.max(1, Math.round(cH));
+            // Render content (with replicate-rotate for pad_edge + rotation)
+            renderToTmp(rcW, rcH);
+            drawExtraPadFill(ctx, _epBase, 0, 0, canvasEl.width, canvasEl.height, cX, cY, cW, cH, _tmpCanvas);
+            drawPreviewRegion(ctx, 0, 0, canvasEl.width, canvasEl.height, cX, cY, cW, cH);
+          } else {
+            if (node._rotation !== 0) { ctx.fillStyle = getFillColor(); ctx.fillRect(0, 0, canvasEl.width, canvasEl.height); }
+            const { pW, pH, pX, pY } = fitAspect(_rotCanvas.width / _rotCanvas.height, canvasEl.width, canvasEl.height);
+            ctx.drawImage(getRotatedPreview(), 0, 0, _rotCanvas.width, _rotCanvas.height, pX, pY, pW, pH);
+          }
+        } else if (!showPreview) {
+          ctx.fillStyle = "#383838"; ctx.fillRect(0, 0, canvasEl.width, canvasEl.height);
+          const divBy = getDivBy(), { w: lw0, h: lh0 } = getTgt();
+          Object.assign(ctx, { fillStyle: "#888", font: "14px sans-serif", textAlign: "center" });
+          ctx.fillText(`${roundDown(lw0, divBy) || canvasEl.width} × ${roundDown(lh0, divBy) || canvasEl.height}`,
+            canvasEl.width / 2, canvasEl.height / 2);
+        }
+
+        // Manual mode: visualize extra padding on the placeholder
+        if (!hasBboxes && !showPreview) {
+          const _ep = getExtraPadding();
+          if (hasExtraPad(_ep)) {
+            const cW0 = canvasEl.width, cH0 = canvasEl.height;
+            const { w: tw0, h: th0 } = getTgt();
+            const rawW = tw0 > 0 ? tw0 : cW0;
+            const rawH = th0 > 0 ? th0 : cH0;
+            const content = computeContentDims(rawW, rawH);
+            const contentW = content ? content.w : rawW;
+            const contentH = content ? content.h : rawH;
+            const totalW = contentW + _ep.left + _ep.right;
+            const totalH = contentH + _ep.top + _ep.bottom;
+            const { pW, pH, pX, pY } = fitAspect(totalW / totalH, cW0, cH0);
+            const cW = pW * (contentW / totalW), cH = pH * (contentH / totalH);
+            const cX = pX + pW * (_ep.left / totalW), cY = pY + pH * (_ep.top / totalH);
+            ctx.fillStyle = "rgba(0, 0, 0, 0.5)";
+            ctx.fillRect(0, 0, cW0, cH0);
+            if (_ep.mode === "pad_color" || _ep.mode === "pad_crop_color") {
+              ctx.fillStyle = getFillColor();
+            } else {
+              ctx.fillStyle = "#555";
+            }
+            ctx.fillRect(pX, pY, pW, pH);
+            ctx.fillStyle = "#383838";
+            ctx.fillRect(cX, cY, cW, cH);
+            drawDashedBorder(ctx, cX, cY, cW, cH, "rgba(100, 200, 255, 0.8)");
+            // Redraw dimension text on top
+            const divBy = getDivBy(), { w: lw0, h: lh0 } = getTgt();
+            Object.assign(ctx, { fillStyle: "#888", font: "14px sans-serif", textAlign: "center" });
+            ctx.fillText(`${roundDown(lw0, divBy) || cW0} × ${roundDown(lh0, divBy) || cH0}`, cX + cW / 2, cY + cH / 2);
+          }
+        }
+
+        // When no bboxes are drawn, show pad/crop preview for the full image (auto mode only)
+        if (!hasBboxes && node._previewEnabled) {
+          const cW0 = canvasEl.width, cH0 = canvasEl.height;
+          if (node._previewImg) {
+            const { w: tgtW, h: tgtH } = getTgt();
+            const kp = getKp();
+            // Multiplier mode: compute effective target from crop dims × multiplier
+            if (kp === "multiplier") {
+              const wmv = _getSubWidget("keep_proportion.width_mult")?.value || 1.0;
+              const hmv = _getSubWidget("keep_proportion.height_mult")?.value || 1.0;
+              if (wmv !== 1.0 || hmv !== 1.0) {
+                const nw = node._previewImg.naturalWidth;
+                const nh = node._previewImg.naturalHeight;
+                drawStretchOverlay(ctx, (nw * wmv) / (nh * hmv), 0, 0, cW0, cH0);
+              }
+            }
+            if ((tgtW > 0 || tgtH > 0) && (kp === "stretch" || isPadMode(kp) || kp === "crop")) {
+              const { w: imgW, h: imgH } = getEffectiveImageDims(kp !== "pad_edge");
+              const { rw, rh } = getEffectiveTargetDims(imgW, imgH);
+
+              if (kp === "stretch") {
+                drawStretchOverlay(ctx, rw / rh, 0, 0, cW0, cH0);
+              } else if (isPadMode(kp)) {
+                const epPad = getExtraPadding();
+                const hasEp = hasExtraPad(epPad);
+                // Full output = target. Content = target - extra padding.
+                const fullTgtW = tgtW > 0 ? tgtW : imgW;
+                const fullTgtH = tgtH > 0 ? tgtH : imgH;
+                const contentTgtW = hasEp ? Math.max(1, fullTgtW - epPad.left - epPad.right) : fullTgtW;
+                const contentTgtH = hasEp ? Math.max(1, fullTgtH - epPad.top - epPad.bottom) : fullTgtH;
+                // Fit the full output to the canvas
+                const { pW: outerW, pH: outerH, pX: outerX, pY: outerY } = fitAspect(fullTgtW / fullTgtH, cW0, cH0);
+                // Content area within the output (offset by padding)
+                const contentW = outerW * (contentTgtW / fullTgtW);
+                const contentH = outerH * (contentTgtH / fullTgtH);
+                const contentX = outerX + outerW * (epPad.left / fullTgtW);
+                const contentY = outerY + outerH * (epPad.top / fullTgtH);
+                // Image is sized to fit the content area, but positioned across the full output via pad_x/pad_y
+                const imgRatio = Math.min(contentTgtW / imgW, contentTgtH / imgH);
+                const scaledW = imgW * imgRatio, scaledH = imgH * imgRatio;
+                const pW = outerW, pH = outerH, pX = outerX, pY = outerY;
+                const cW = outerW * (scaledW / fullTgtW), cH = outerH * (scaledH / fullTgtH);
+                const { cX, cY } = computePadContentPos(outerX, outerY, outerW, outerH, cW, cH);
+                ctx.clearRect(0, 0, cW0, cH0);
+                // Fill outer area (extra padding region)
+                if (hasEp) {
+                  ctx.fillStyle = getFillColor();
+                  ctx.fillRect(outerX, outerY, outerW, outerH);
+                }
+                if (kp === "pad_edge") {
+                  renderToTmp(Math.max(1, Math.round(cW)), Math.max(1, Math.round(cH)),
+                    null, 0, 0, 0, 0, true);
+                  ctx.drawImage(_tmpCanvas, 0, 0, _tmpCanvas.width, _tmpCanvas.height, cX, cY, cW, cH);
+                  drawExtraPadFill(ctx, getEdgeEp(), pX, pY, pW, pH, cX, cY, cW, cH, _tmpCanvas);
+                } else {
+                  ctx.fillStyle = getFillColor();
+                  ctx.fillRect(pX, pY, pW, pH);
+                  drawPreviewRegion(ctx, 0, 0, cW0, cH0, cX, cY, cW, cH);
+                }
+                drawDashedBorder(ctx, cX, cY, cW, cH, "rgba(100, 200, 255, 0.8)");
+                if (hasEp) drawDashedBorder(ctx, contentX, contentY, contentW, contentH, "rgba(255, 200, 100, 0.6)");
+              } else if (kp === "crop") {
+                const ratio = Math.max(rw / imgW, rh / imgH);
+                const visW = rw / ratio, visH = rh / ratio;
+                const offX = (imgW - visW) / 2, offY = (imgH - visH) / 2;
+                const sc = Math.min(cW0 / imgW, cH0 / imgH);
+                const ox = (cW0 - imgW * sc) / 2, oy = (cH0 - imgH * sc) / 2;
+                const vx1 = ox + offX * sc, vy1 = oy + offY * sc;
+                const vw = visW * sc, vh = visH * sc;
+                dimSides(ctx, 0, 0, cW0, cH0, vx1, vy1, vw, vh);
+                drawDashedBorder(ctx, vx1, vy1, vw, vh, "rgba(255, 255, 100, 0.8)");
+              }
+            }
+          }
+          const _ep = getExtraPadding();
+          if (hasExtraPad(_ep) && node._previewImg && !_isFirst && !(isPadMode(_kpVal) && (_tgtW > 0 || _tgtH > 0))) {
+            const { w: rawImgW, h: rawImgH } = getEffectiveImageDims(_ep.mode !== "pad_edge");
+            const content = computeContentDims(rawImgW, rawImgH);
+            const epImgW = content ? content.w : rawImgW;
+            const epImgH = content ? content.h : rawImgH;
+            const totalW = epImgW + _ep.left + _ep.right, totalH = epImgH + _ep.top + _ep.bottom;
+            const { pW, pH, pX, pY } = fitAspect(totalW / totalH, cW0, cH0);
+            ctx.clearRect(0, 0, cW0, cH0);
+            const cW = pW * (epImgW / totalW), cH = pH * (epImgH / totalH);
+            const cX = pX + pW * (_ep.left / totalW), cY = pY + pH * (_ep.top / totalH);
+            // Draw padding fill, then image in content area (same approach as keep_proportion pad mode)
+            if (_ep.mode === "pad_color") {
+              ctx.fillStyle = getFillColor();
+              ctx.fillRect(pX, pY, pW, pH);
+              drawPreviewRegion(ctx, 0, 0, cW0, cH0, cX, cY, cW, cH);
+            } else {
+              // pad_edge: render content for edge fill source
+              const rcW2 = Math.max(1, Math.round(cW)), rcH2 = Math.max(1, Math.round(cH));
+              renderToTmp(rcW2, rcH2, null, 0, 0, 0, 0, _ep.isFirst);
+              drawExtraPadFill(ctx, _ep, pX, pY, pW, pH, cX, cY, cW, cH, _tmpCanvas);
+              drawPreviewRegion(ctx, 0, 0, cW0, cH0, cX, cY, cW, cH);
+            }
+            drawDashedBorder(ctx, cX, cY, cW, cH, "rgba(100, 200, 255, 0.8)");
+          }
+
+          drawGrid(ctx);
+          drawRotationCross(ctx);
+          if (!node._drawing) updateOutputSlotLabels();
+          return;
+        }
+
+        if (node._activeIdx >= 0 && node._activeIdx < node._bboxes.length) {
+          drawBbox(ctx, node._bboxes[node._activeIdx], node._activeIdx, true);
+        }
+        for (let i = 0; i < node._bboxes.length; i++) {
+          if (i === node._activeIdx) continue;
+          drawBbox(ctx, node._bboxes[i], i, false);
+        }
+
+        drawGrid(ctx);
+        drawRotationCross(ctx);
+        if (!node._drawing) updateOutputSlotLabels();
+      }
+
+      let _lastSlotLabelKey = "";
+      function updateOutputSlotLabels() {
+        const activeBbox = (node._activeIdx >= 0 && node._activeIdx < node._bboxes.length)
+          ? node._bboxes[node._activeIdx] : null;
+        let dims = activeBbox ? getOutputDims(activeBbox) : null;
+        if (!dims) {
+          const { w: tW, h: tH } = getTgt();
+          const imgW = node._previewImg ? node._previewImg.naturalWidth
+            : (tW > 0 ? tW : canvasEl.width);
+          const imgH = node._previewImg ? node._previewImg.naturalHeight
+            : (tH > 0 ? tH : canvasEl.height);
+          const computed = computeOutputDims(imgW, imgH);
+          if (computed && computed.w > 0 && computed.h > 0) {
+            dims = computed;
+          }
+        }
+        // Skip expensive DOM/canvas updates if nothing changed
+        const key = `${dims?.w || 0},${dims?.h || 0},${node._bboxes.length}`;
+        if (key === _lastSlotLabelKey) return;
+        _lastSlotLabelKey = key;
+        if (node.outputs) {
+          const hasBboxes = node._bboxes.length > 0;
+          for (let i = 0; i < node.outputs.length; i++) {
+            const out = node.outputs[i];
+            if (!out._origName) {
+              if (out.name?.includes("width") || out.label?.includes("width")) out._origName = "width";
+              else if (out.name?.includes("height") || out.label?.includes("height")) out._origName = "height";
+              else if (out.name === "bbox") out._origName = "bbox";
+              else if (out.name === "bbox_mask") out._origName = "bbox_mask";
+            }
+            if (out._origName === "width") {
+              out.label = dims ? `${dims.w} width` : "width";
+            } else if (out._origName === "height") {
+              out.label = dims ? `${dims.h} height` : "height";
+            }
+            // Identify output/output_mask slots
+            if (!out._origName) {
+              if (out.name === "cropped" || out.name === "output") out._origName = "output";
+              else if (out.name === "cropped_mask" || out.name === "output_mask") out._origName = "output_mask";
+            }
+            // Toggle list icon on output/output_mask
+            if (out._origName === "output" || out._origName === "output_mask") {
+              out.shape = hasBboxes && node._bboxes.length > 1 ? 6 : undefined;
+            }
+            // Visually hide bbox/bbox_mask slots when no bboxes, show with list icon when multiple
+            if (out._origName === "bbox" || out._origName === "bbox_mask") {
+              if (!hasBboxes) {
+                out.label = " ";
+                out.color_off = "transparent";
+                out.color_on = "transparent";
+                out.shape = 6;
+                out.dir = undefined;
+              } else {
+                out.label = out._origName;
+                out.color_off = undefined;
+                out.color_on = undefined;
+                out.shape = node._bboxes.length > 1 ? 6 : undefined;
+              }
+            }
+          }
+          node.setDirtyCanvas(true, true);
+          // Vue nodes mode: hide/show bbox slots via DOM since canvas properties don't apply
+          if (LiteGraph.vueNodesMode && node.id != null) {
+            const nodeEl = document.querySelector(`[data-node-id="${node.id}"]`);
+            if (nodeEl) {
+              const outputSlots = nodeEl.querySelectorAll(".lg-slot--output");
+              for (let i = 0; i < node.outputs.length; i++) {
+                const out = node.outputs[i];
+                if ((out._origName === "bbox" || out._origName === "bbox_mask") && outputSlots[i]) {
+                  outputSlots[i].style.visibility = hasBboxes ? "" : "hidden";
+                  // Update label text directly since Vue may not observe raw property changes
+                  if (hasBboxes) {
+                    const labelEl = outputSlots[i].querySelector("span");
+                    if (labelEl) labelEl.textContent = out._origName;
+                  }
+                }
+              }
+            }
+          }
+        }
+      }
+
+      function loadPreviewImage(src, preserveBbox) {
+        const img = new Image();
+        img.crossOrigin = "anonymous";
+        img.onload = () => {
+          node._previewImg = img;
+          _rotCacheKey = "";
+          invalidateGridStops();
+          restoreCanvasToImage();
+          if (node._previewEnabled && !preserveBbox) {
+            node._bboxes = [];
+            node._activeIdx = -1;
+          }
+          updateBboxWidgets();
+          drawCanvas();
+          fitNodeToImage(preserveBbox);
+        };
+        img.onerror = () => {
+          console.warn("ImageTransformKJ: failed to load preview:", src);
+        };
+        img.src = src;
+      }
+
+      function loadPreviewFromTempFile(filename) {
+        if (!filename) return;
+        loadPreviewImage(`/view?filename=${encodeURIComponent(filename)}&type=temp`, true);
+      }
+
+      function captureVideoFrameToPreview(videoEl, preserveBbox) {
+        captureVideoFrame(videoEl, (canvas) => {
+          loadPreviewImage(canvas.toDataURL("image/webp", 0.9), preserveBbox);
+        });
+      }
+
+      function handleSourceChange(sources, preserveBbox) {
+        if (sources.length === 0) {
+          // Disconnected
+          node._previewImg = null;
+          delete node.properties.previewFile;
+          if (node._previewEnabled) {
+            node._bboxes = [];
+            node._activeIdx = -1;
+            updateBboxWidgets();
+          }
+          updateCanvasFromTargetDims();
+          drawCanvas();
+          return;
+        }
+        const src = sources[0];
+        if (src.isVideo && src.videoEl) {
+          captureVideoFrameToPreview(src.videoEl, preserveBbox);
+        } else if (src.url) {
+          loadPreviewImage(src.url, preserveBbox);
+        }
+      }
+
+      let _configuring = false, _sourceResolved = false;
+      watchImageInputs(node, "image", (sources) => {
+        _sourceResolved = true;
+        handleSourceChange(sources, _configuring);
+      });
+
+      chainCallback(this, "onResize", function () {
+        if (_resizing) return;
+        const availW = Math.max(100, node.size[0] - 30);
+        _resizing = true;
+        if (node._previewEnabled) {
+          // Auto mode: resize canvas pixel buffer to match node width
+          const ar = canvasEl.height / canvasEl.width;
+          const newW = Math.round(availW);
+          const newH = Math.round(availW * ar);
+          if (newW !== canvasEl.width || newH !== canvasEl.height) {
+            canvasEl.width = newW;
+            canvasEl.height = newH;
+            canvasEl.style.aspectRatio = `${newW} / ${newH}`;
+            node.properties.canvasSize = [newW, newH];
+          }
+          node._widgetHeight = newH + GRID_BAR_HEIGHT;
+        } else {
+          // Manual mode: keep pixel buffer fixed, CSS handles display scaling
+          const displayedH = Math.round(canvasEl.height * (availW / canvasEl.width));
+          node._widgetHeight = displayedH + GRID_BAR_HEIGHT;
+        }
+        drawCanvas();
+        _resizing = false;
+      });
+
+      chainCallback(this, "onConfigure", function () {
+        node._cropConfigured = true;
+        if (node.properties.gridSize !== undefined) {
+          gridSlider.value = node.properties.gridSize;
+          updateGridLabel();
+        }
+        if (node.properties.previewEnabled !== undefined) {
+          node._previewEnabled = node.properties.previewEnabled;
+          updatePreviewBtn();
+        }
+        restoreBboxesFromWidget();
+        if (node.properties.rotation !== undefined) node._rotation = node.properties.rotation;
+        if (node.properties.fillColor) {
+          colorInput.value = node.properties.fillColor;
+          colorSwatch.style.background = node.properties.fillColor;
+        }
+        if (node.properties.gridColor) {
+          gridColorInput.value = node.properties.gridColor;
+          gridColorSwatch.style.background = node.properties.gridColor;
+        }
+
+        if (node.properties.canvasSize) {
+          setCanvasSize(node.properties.canvasSize[0], node.properties.canvasSize[1]);
+        }
+
+        _configuring = true;
+        _sourceResolved = false;
+        setTimeout(() => {
+          if (node.properties.gridSize !== undefined) {
+            gridSlider.value = node.properties.gridSize;
+            updateGridLabel();
+          }
+          _configuring = false;
+          if (!_sourceResolved) {
+            if (node.properties.previewFile) {
+              loadPreviewFromTempFile(node.properties.previewFile);
+            } else {
+              updateCanvasFromTargetDims();
+              drawCanvas();
+            }
+          }
+        }, 200);
+      });
+
+      chainCallback(this, "onExecuted", function (message) {
+        const filename = message?.preview_filename?.[0];
+        if (filename) {
+          node.properties.previewFile = filename;
+          loadPreviewFromTempFile(filename);
+        }
+      });
+
+      // Initial draw
+      setCanvasSize(400, 300);
+      drawCanvas();
+    });
+  },
+});
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/jsnodes.js b/custom_nodes/ComfyUI-KJNodes/web/js/jsnodes.js
new file mode 100644
index 0000000000000000000000000000000000000000..a38556a37824c7ce6b70c31c26f6e14fffafba4e
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/jsnodes.js
@@ -0,0 +1,365 @@
+const { app } = window.comfyAPI.app;
+const { applyTextReplacements } = window.comfyAPI.utils;
+
+// ─── Dynamic input slot management for *Multi nodes ───
+// Adds an "Update inputs" button that rebuilds prefix_1..prefix_N slots to match the count widget.
+// Also wired to the count callback so API-format reload (bare callback) restores the slots; the
+// canvas arg from interactive edits is the tell, so scrubbing the count doesn't reflow the node.
+function setupDynamicInputs(node, { type, prefix, countWidget = "inputcount", slotOptions } = {}) {
+    const rebuild = () => {
+        if (!node.inputs) node.inputs = [];
+        const countW = node.widgets?.find(w => w.name === countWidget);
+        if (!countW) return;
+        const target = countW.value;
+        const current = node.inputs.filter(i => i.name?.startsWith(prefix)).length;
+        if (target === current) return;
+        if (target < current) {
+            for (let i = 0; i < current - target; i++) node.removeInput(node.inputs.length - 1);
+        } else {
+            for (let i = current + 1; i <= target; i++) node.addInput(`${prefix}${i}`, type, slotOptions);
+        }
+    };
+    node.addWidget("button", "Update inputs", null, rebuild);
+    const countW = node.widgets?.find(w => w.name === countWidget);
+    if (countW) {
+        const origCb = countW.callback;   // guard: may be nullish
+        countW.callback = function (value, canvas) {
+            const r = origCb ? origCb.apply(this, arguments) : undefined;
+            if (!canvas) rebuild();   // bare = API reload; skip interactive scrub
+            return r;
+        };
+    }
+    return rebuild;
+}
+
+app.registerExtension({
+	name: "KJNodes.jsnodes",
+	async beforeRegisterNodeDef(nodeType, nodeData, app) {
+		if(!nodeData?.category?.startsWith("KJNodes")) {
+			return;
+		  }
+		switch (nodeData.name) {
+			case "ConditioningMultiCombine":
+				nodeType.prototype.onNodeCreated = function () {
+					this.inputs_offset = nodeData.name.includes("selective")?1:0
+					setupDynamicInputs(this, { type: "CONDITIONING", prefix: "conditioning_" });
+				}
+				break;
+			case "ImageBatchMulti":
+			case "ImageAddMulti":
+			case "CrossFadeImagesMulti":
+			case "TransitionImagesMulti":
+				nodeType.prototype.onNodeCreated = function () {
+					setupDynamicInputs(this, { type: "IMAGE", prefix: "image_", slotOptions: {shape: 7} });
+				}
+				break;
+			case "ImageConcatMulti":
+				// Dynamic slots accept MASK too; name-prefix counting handles the mixed types.
+				nodeType.prototype.onNodeCreated = function () {
+					setupDynamicInputs(this, { type: "IMAGE,MASK", prefix: "image_", slotOptions: {shape: 7} });
+				}
+				break;
+			case "MaskBatchMulti":
+				nodeType.prototype.onNodeCreated = function () {
+					setupDynamicInputs(this, { type: "MASK", prefix: "mask_" });
+				}
+				break;
+			
+			case "FluxBlockLoraSelect":
+			case "HunyuanVideoBlockLoraSelect":
+			case "Wan21BlockLoraSelect":
+			case "LTX2BlockLoraSelect":
+				nodeType.prototype.onNodeCreated = function () {
+					this.addWidget("button", "Set all", null, () => {
+						const userInput = prompt("Enter the values to set for widgets (e.g., s0,1,2-7=2.0, d0,1,2-7=2.0, or 1.0):", "");
+						if (userInput) {
+							const regex = /([sd])?(\d+(?:,\d+|-?\d+)*?)?=(\d+(\.\d+)?)/;
+							const match = userInput.match(regex);
+							if (match) {
+								const type = match[1];
+								const indicesPart = match[2];
+								const value = parseFloat(match[3]);
+			
+								let targetWidgets = [];
+								if (type === 's') {
+									targetWidgets = this.widgets.filter(widget => widget.name.includes("single"));
+								} else if (type === 'd') {
+									targetWidgets = this.widgets.filter(widget => widget.name.includes("double"));
+								} else {
+									targetWidgets = this.widgets; // No type specified, all widgets
+								}
+			
+								if (indicesPart) {
+									const indices = indicesPart.split(',').flatMap(part => {
+										if (part.includes('-')) {
+											const [start, end] = part.split('-').map(Number);
+											return Array.from({ length: end - start + 1 }, (_, i) => start + i);
+										}
+										return Number(part);
+									});
+			
+									for (const index of indices) {
+										if (index < targetWidgets.length) {
+											targetWidgets[index].value = value;
+										}
+									}
+								} else {
+									// No indices provided, set value for all target widgets
+									for (const widget of targetWidgets) {
+										widget.value = value;
+									}
+								}
+							} else if (!isNaN(parseFloat(userInput))) {
+								// Single value provided, set it for all widgets
+								const value = parseFloat(userInput);
+								for (const widget of this.widgets) {
+									widget.value = value;
+								}
+							} else {
+								alert("Invalid input format. Please use the format s0,1,2-7=2.0, d0,1,2-7=2.0, or 1.0");
+							}
+						} else {
+							alert("Invalid input. Please enter a value.");
+						}
+					});
+				};
+				break;
+
+			case "GetMaskSizeAndCount":
+				const onGetMaskSizeConnectInput = nodeType.prototype.onConnectInput;
+				nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) {
+					const v = onGetMaskSizeConnectInput? onGetMaskSizeConnectInput.apply(this, arguments): undefined
+					this.outputs[1]["label"] = "width"
+					this.outputs[2]["label"] = "height" 
+					this.outputs[3]["label"] = "count"
+					return v;
+				}
+				const onGetMaskSizeExecuted = nodeType.prototype.onAfterExecuteNode;
+				nodeType.prototype.onExecuted = function(message) {
+					const r = onGetMaskSizeExecuted? onGetMaskSizeExecuted.apply(this,arguments): undefined
+					let values = message["text"].toString().split('x').map(Number);
+					this.outputs[1]["label"] = values[1] + " width"
+					this.outputs[2]["label"] = values[2] + " height" 
+					this.outputs[3]["label"] = values[0] + " count" 
+					return r
+				}
+				break;
+			
+			case "GetImageSizeAndCount":
+				const onGetImageSizeConnectInput = nodeType.prototype.onConnectInput;
+				nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) {
+					console.log(this)
+					const v = onGetImageSizeConnectInput? onGetImageSizeConnectInput.apply(this, arguments): undefined
+					//console.log(this)
+					this.outputs[1]["label"] = "width"
+					this.outputs[2]["label"] = "height" 
+					this.outputs[3]["label"] = "count"
+					return v;
+				}
+				//const onGetImageSizeExecuted = nodeType.prototype.onExecuted;
+				const onGetImageSizeExecuted = nodeType.prototype.onAfterExecuteNode;
+				nodeType.prototype.onExecuted = function(message) {
+					console.log(this)
+					const r = onGetImageSizeExecuted? onGetImageSizeExecuted.apply(this,arguments): undefined
+					let values = message["text"].toString().split('x').map(Number);
+					console.log(values)
+					this.outputs[1]["label"] = values[1] + " width"
+					this.outputs[2]["label"] = values[2] + " height" 
+					this.outputs[3]["label"] = values[0] + " count" 
+					return r
+				}
+				break;
+
+			case "GetLatentSizeAndCount":
+				const onGetLatentConnectInput = nodeType.prototype.onConnectInput;
+				nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) {
+					console.log(this)
+					const v = onGetLatentConnectInput? onGetLatentConnectInput.apply(this, arguments): undefined
+					//console.log(this)
+					this.outputs[1]["label"] = "batch_size"
+					this.outputs[2]["label"] = "channels" 
+					this.outputs[3]["label"] = "frames" 
+					this.outputs[4]["label"] = "height" 
+					this.outputs[5]["label"] = "width"
+					return v;
+				}
+				//const onGetImageSizeExecuted = nodeType.prototype.onExecuted;
+				const onGetLatentSizeExecuted = nodeType.prototype.onAfterExecuteNode;
+				nodeType.prototype.onExecuted = function(message) {
+					console.log(this)
+					const r = onGetLatentSizeExecuted? onGetLatentSizeExecuted.apply(this,arguments): undefined
+					let values = message["text"].toString().split('x').map(Number);
+					console.log(values)
+					this.outputs[1]["label"] = values[0] + " batch"
+					this.outputs[2]["label"] = values[1] + " channels" 
+					this.outputs[3]["label"] = values[2] + " frames" 
+					this.outputs[4]["label"] = values[3] + " height" 
+					this.outputs[5]["label"] = values[4] + " width" 
+					return r
+				}
+				break;
+
+			case "PreviewAnimation":
+				const onPreviewAnimationConnectInput = nodeType.prototype.onConnectInput;
+				nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) {
+					const v = onPreviewAnimationConnectInput? onPreviewAnimationConnectInput.apply(this, arguments): undefined
+					this.title = "Preview Animation"
+					return v;
+				}
+				const onPreviewAnimationExecuted = nodeType.prototype.onAfterExecuteNode;
+				nodeType.prototype.onExecuted = function(message) {
+					const r = onPreviewAnimationExecuted? onPreviewAnimationExecuted.apply(this,arguments): undefined
+					let values = message["text"].toString();
+					this.title = "Preview Animation " + values
+					return r
+				}
+				break;
+
+			case "VRAM_Debug":
+				const onVRAM_DebugConnectInput = nodeType.prototype.onConnectInput;
+				nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) {
+					const v = onVRAM_DebugConnectInput? onVRAM_DebugConnectInput.apply(this, arguments): undefined
+					this.outputs[3]["label"] = "freemem_before"
+					this.outputs[4]["label"] = "freemem_after" 
+					return v;
+				}
+				const onVRAM_DebugExecuted = nodeType.prototype.onAfterExecuteNode;
+				nodeType.prototype.onExecuted = function(message) {
+					const r = onVRAM_DebugExecuted? onVRAM_DebugExecuted.apply(this,arguments): undefined
+					let values = message["text"].toString().split('x');
+					this.outputs[3]["label"] = values[0] + "   freemem_before"
+					this.outputs[4]["label"] = values[1] + "      freemem_after" 
+					return r
+				}
+				break;
+
+			case "JoinStringMulti":
+				const originalOnNodeCreated = nodeType.prototype.onNodeCreated || function() {};
+				nodeType.prototype.onNodeCreated = function () {
+					originalOnNodeCreated.apply(this, arguments);
+					setupDynamicInputs(this, { type: "STRING", prefix: "string_", slotOptions: {shape: 7} });
+				}
+				break;
+			case "SoundReactive":
+				nodeType.prototype.onNodeCreated = function () {
+					let audioContext;
+					let microphoneStream;
+					let animationFrameId;
+					let analyser;
+					let dataArray;
+					let startRangeHz;
+    				let endRangeHz;
+					let smoothingFactor = 0.5;
+					let smoothedSoundLevel = 0;
+				
+					// Function to update the widget value in real-time
+					const updateWidgetValueInRealTime = () => {
+						// Ensure analyser and dataArray are defined before using them
+						if (analyser && dataArray) {
+							analyser.getByteFrequencyData(dataArray);
+
+							const startRangeHzWidget = this.widgets.find(w => w.name === "start_range_hz");
+							if (startRangeHzWidget) startRangeHz = startRangeHzWidget.value;
+							const endRangeHzWidget = this.widgets.find(w => w.name === "end_range_hz");
+							if (endRangeHzWidget) endRangeHz = endRangeHzWidget.value;
+							const smoothingFactorWidget = this.widgets.find(w => w.name === "smoothing_factor");
+							if (smoothingFactorWidget) smoothingFactor = smoothingFactorWidget.value;
+
+							// Calculate frequency bin width (frequency resolution)
+							const frequencyBinWidth = audioContext.sampleRate / analyser.fftSize;	
+							// Convert the widget values from Hz to indices
+							const startRangeIndex = Math.floor(startRangeHz / frequencyBinWidth);
+							const endRangeIndex = Math.floor(endRangeHz / frequencyBinWidth);
+
+							// Function to calculate the average value for a frequency range
+							const calculateAverage = (start, end) => {
+								const sum = dataArray.slice(start, end).reduce((acc, val) => acc + val, 0);
+								const average = sum / (end - start);
+
+								// Apply exponential moving average smoothing
+    							smoothedSoundLevel = (average * (1 - smoothingFactor)) + (smoothedSoundLevel * smoothingFactor);
+								return smoothedSoundLevel;
+							};
+							// Calculate the average levels for each frequency range
+							const soundLevel = calculateAverage(startRangeIndex, endRangeIndex);
+							
+							// Update the widget values
+
+							const lowLevelWidget = this.widgets.find(w => w.name === "sound_level");
+							if (lowLevelWidget) lowLevelWidget.value = soundLevel;
+
+							animationFrameId = requestAnimationFrame(updateWidgetValueInRealTime);
+						}
+					};
+				
+					// Function to start capturing audio from the microphone
+					const startMicrophoneCapture = () => {
+						// Only create the audio context and analyser once
+						if (!audioContext) {
+							audioContext = new (window.AudioContext || window.webkitAudioContext)();
+							// Access the sample rate of the audio context
+							console.log(`Sample rate: ${audioContext.sampleRate}Hz`);
+							analyser = audioContext.createAnalyser();
+							analyser.fftSize = 2048;
+							dataArray = new Uint8Array(analyser.frequencyBinCount);
+							// Get the range values from widgets (assumed to be in Hz)
+							const lowRangeWidget = this.widgets.find(w => w.name === "low_range_hz");
+							if (lowRangeWidget) startRangeHz = lowRangeWidget.value;
+				
+							const midRangeWidget = this.widgets.find(w => w.name === "mid_range_hz");
+							if (midRangeWidget) endRangeHz = midRangeWidget.value;
+						}
+						
+						navigator.mediaDevices.getUserMedia({ audio: true }).then(stream => {
+							microphoneStream = stream;
+							const microphone = audioContext.createMediaStreamSource(stream);
+							microphone.connect(analyser);
+							updateWidgetValueInRealTime();
+						}).catch(error => {
+							console.error('Access to microphone was denied or an error occurred:', error);
+						});
+					};
+				
+					// Function to stop capturing audio from the microphone
+					const stopMicrophoneCapture = () => {
+						if (animationFrameId) {
+							cancelAnimationFrame(animationFrameId);
+						}
+						if (microphoneStream) {
+							microphoneStream.getTracks().forEach(track => track.stop());
+						}
+						if (audioContext) {
+							audioContext.close();
+							// Reset audioContext to ensure it can be created again when starting
+							audioContext = null;
+						}
+					};
+				
+					// Add start button
+					this.addWidget("button", "Start mic capture", null, startMicrophoneCapture);
+				
+					// Add stop button
+					this.addWidget("button", "Stop mic capture", null, stopMicrophoneCapture);
+				};
+			break;
+		case "SaveImageKJ":
+		case "SaveImageWithAlpha":
+		case "SaveStringKJ":
+		case "DecodeAndSaveVideo":
+		case "ModelSaveKJ":
+		case "LoraExtractKJ":
+			const onNodeCreated = nodeType.prototype.onNodeCreated;
+			nodeType.prototype.onNodeCreated = function() {
+				const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : void 0;
+				const widget = this.widgets.find((w) => w.name === "filename_prefix");
+				widget.serializeValue = () => {
+				return applyTextReplacements(app, widget.value);
+				};
+				return r;
+			};
+			break;
+			
+		}	
+		
+	},
+});
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/node_insert.js b/custom_nodes/ComfyUI-KJNodes/web/js/node_insert.js
new file mode 100644
index 0000000000000000000000000000000000000000..468c20265aff1612db6c59339d9db476c50847be
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/node_insert.js
@@ -0,0 +1,449 @@
+const { app } = window.comfyAPI.app;
+import { getSlotPos, clientToCanvas, getNodeAtPoint, typesCompatible, chainCallback } from "./utility.js";
+
+// Max age of the last keydown for it to count as the command's trigger.
+// Beyond this, treat the command as menu-fired (no release tracking).
+const KEYDOWN_MAX_AGE_MS = 100;
+
+const state = {
+	pointerDown: false,
+	hasMoved: false,
+	insertKeyDown: false,
+	activationKey: null,
+	draggedNode: null,
+	startPos: null,
+	insertTargetLink: null,
+	insertSlots: null,
+	insertOriginNode: null,
+	insertDestNode: null,
+	lastScanTime: 0,
+	animating: false,
+};
+
+let lastKeyDown = null;
+let lastKeyDownTime = 0;
+
+function bezierAt(p0, p1, p2, p3, t) {
+	const u = 1 - t;
+	const uu = u * u, uuu = uu * u;
+	const tt = t * t, ttt = tt * t;
+	return [
+		uuu * p0[0] + 3 * uu * t * p1[0] + 3 * u * tt * p2[0] + ttt * p3[0],
+		uuu * p0[1] + 3 * uu * t * p1[1] + 3 * u * tt * p2[1] + ttt * p3[1],
+	];
+}
+
+// Horizontal control-point offset for the link bezier; must match what
+// LiteGraph draws so the hit-test region matches the visible curve.
+const bezierOffsetX = (from, to) => Math.max(Math.abs(to[0] - from[0]) * 0.5, 50);
+
+function findLinkUnderNode(graph, draggedNode) {
+	const bounds = draggedNode.getBounding?.();
+	const nodeX = bounds ? bounds[0] : draggedNode.pos[0];
+	const nodeY = bounds ? bounds[1] : draggedNode.pos[1];
+	const nodeW = bounds ? bounds[2] : (draggedNode.size[0] || 100);
+	const nodeH = bounds ? bounds[3] : (draggedNode.size[1] || 60);
+	const nodeCx = nodeX + nodeW / 2;
+	const nodeCy = nodeY + nodeH / 2;
+
+	let bestLink = null;
+	let bestDist = Infinity;
+
+	const links = graph.links;
+	if (!links) return null;
+
+	for (const link of links.values()) {
+		if (!link) continue;
+		if (link.origin_id === draggedNode.id || link.target_id === draggedNode.id) continue;
+
+		const originNode = graph.getNodeById(link.origin_id);
+		const targetNode = graph.getNodeById(link.target_id);
+		if (!originNode || !targetNode) continue;
+
+		// Reject on node bounds before resolving slot positions — the latter
+		// hits the DOM in Vue mode and is the expensive step.
+		const oW = originNode.size?.[0] || 100, oH = originNode.size?.[1] || 60;
+		const tW = targetNode.size?.[0] || 100, tH = targetNode.size?.[1] || 60;
+		const cMinX = Math.min(originNode.pos[0], targetNode.pos[0]);
+		const cMaxX = Math.max(originNode.pos[0] + oW, targetNode.pos[0] + tW);
+		const cMinY = Math.min(originNode.pos[1], targetNode.pos[1]) - 50;
+		const cMaxY = Math.max(originNode.pos[1] + oH, targetNode.pos[1] + tH) + 50;
+		if (cMaxX < nodeX || cMinX > nodeX + nodeW || cMaxY < nodeY || cMinY > nodeY + nodeH) continue;
+
+		const outPos = getSlotPos(originNode, false, link.origin_slot);
+		const inPos = getSlotPos(targetNode, true, link.target_slot);
+
+		const lMinX = Math.min(outPos[0], inPos[0]);
+		const lMaxX = Math.max(outPos[0], inPos[0]);
+		const lMinY = Math.min(outPos[1], inPos[1]) - 50; // bezier can sag
+		const lMaxY = Math.max(outPos[1], inPos[1]) + 50;
+		if (lMaxX < nodeX || lMinX > nodeX + nodeW || lMaxY < nodeY || lMinY > nodeY + nodeH) continue;
+
+		const offsetX = bezierOffsetX(outPos, inPos);
+		const p0 = outPos;
+		const p1 = [outPos[0] + offsetX, outPos[1]];
+		const p2 = [inPos[0] - offsetX, inPos[1]];
+		const p3 = inPos;
+
+		for (let i = 0; i <= 20; i++) {
+			const pt = bezierAt(p0, p1, p2, p3, i / 20);
+			if (pt[0] >= nodeX && pt[0] <= nodeX + nodeW && pt[1] >= nodeY && pt[1] <= nodeY + nodeH) {
+				const d = Math.hypot(pt[0] - nodeCx, pt[1] - nodeCy);
+				if (d < bestDist) {
+					bestDist = d;
+					bestLink = link;
+				}
+				break;
+			}
+		}
+	}
+	return bestLink;
+}
+
+function findInsertSlots(node, linkType) {
+	const inputSlot = node.inputs?.findIndex(i => i.link == null && typesCompatible(linkType, i.type)) ?? -1;
+	const outputSlot = node.outputs?.findIndex(o => typesCompatible(linkType, o.type)) ?? -1;
+	if (inputSlot === -1 || outputSlot === -1) return null;
+	return { inputSlot, outputSlot };
+}
+
+function executeNodeInsert(canvas, node, link) {
+	const graph = canvas.graph || app.graph;
+
+	const originNode = graph.getNodeById(link.origin_id);
+	const targetNode = graph.getNodeById(link.target_id);
+	if (!originNode || !targetNode) return;
+
+	const slots = findInsertSlots(node, link.type);
+	if (!slots) return;
+
+	const originSlot = link.origin_slot;
+	const targetSlot = link.target_slot;
+
+	targetNode.disconnectInput(targetSlot);
+	originNode.connect(originSlot, node, slots.inputSlot);
+	node.connect(slots.outputSlot, targetNode, targetSlot);
+
+	graph.change();
+	canvas.setDirty(true, true);
+}
+
+function setInsertTarget(link, graph, slots = null) {
+	if (link === state.insertTargetLink) return;
+	// `_dragging` is LiteGraph's own runtime flag for hiding a link mid-rewire.
+	// Not serialized, so save/copy/undo stay safe.
+	if (state.insertTargetLink) delete state.insertTargetLink._dragging;
+	if (link) {
+		link._dragging = true;
+		state.insertSlots = slots ?? findInsertSlots(state.draggedNode, link.type);
+		state.insertOriginNode = graph.getNodeById(link.origin_id);
+		state.insertDestNode = graph.getNodeById(link.target_id);
+	} else {
+		state.insertSlots = null;
+		state.insertOriginNode = null;
+		state.insertDestNode = null;
+	}
+	state.insertTargetLink = link;
+}
+
+function getTypeColor(lgCanvas, slotType) {
+	if (slotType && LGraphCanvas.link_type_colors && LGraphCanvas.link_type_colors[slotType]) {
+		return LGraphCanvas.link_type_colors[slotType];
+	}
+	return lgCanvas.default_link_color || "#AAA";
+}
+
+function drawGhostLink(ctx, from, to, color, alpha, drawTime) {
+	const offsetX = bezierOffsetX(from, to);
+
+	ctx.beginPath();
+	ctx.moveTo(from[0], from[1]);
+	ctx.bezierCurveTo(
+		from[0] + offsetX, from[1],
+		to[0] - offsetX, to[1],
+		to[0], to[1],
+	);
+
+	ctx.strokeStyle = color;
+	ctx.globalAlpha = alpha;
+	ctx.lineWidth = 2.5;
+	ctx.setLineDash([8, 4]);
+	ctx.lineDashOffset = -(drawTime ?? performance.now()) / 50;
+
+	ctx.stroke();
+	ctx.setLineDash([]);
+	ctx.globalAlpha = 1;
+}
+
+function drawSlotHighlight(ctx, pos, color, alpha) {
+	ctx.beginPath();
+	ctx.arc(pos[0], pos[1], 6, 0, Math.PI * 2);
+	ctx.globalAlpha = alpha;
+	ctx.fillStyle = color;
+	ctx.fill();
+	ctx.globalAlpha = 1;
+}
+
+function drawGhostSegment(ctx, from, to, color, now) {
+	drawGhostLink(ctx, from, to, color, 0.8, now);
+	drawSlotHighlight(ctx, from, color, 0.6);
+	drawSlotHighlight(ctx, to, color, 0.6);
+}
+
+function drawInsertPreview(ctx, lgCanvas) {
+	const { insertTargetLink: link, insertSlots: slots, insertOriginNode, insertDestNode, draggedNode } = state;
+	if (!link || !slots || !insertOriginNode || !insertDestNode || !draggedNode) return;
+
+	const now = performance.now();
+	const color = getTypeColor(lgCanvas, link.type);
+
+	drawGhostSegment(ctx,
+		getSlotPos(insertOriginNode, false, link.origin_slot),
+		getSlotPos(draggedNode, true, slots.inputSlot),
+		color, now);
+	drawGhostSegment(ctx,
+		getSlotPos(draggedNode, false, slots.outputSlot),
+		getSlotPos(insertDestNode, true, link.target_slot),
+		color, now);
+}
+
+function startAnimLoop(lgCanvas) {
+	if (state.animating) return;
+	state.animating = true;
+
+	function tick() {
+		const link = state.insertTargetLink;
+		if (!link) {
+			state.animating = false;
+			lgCanvas.setDirty(true, true);
+			return;
+		}
+		// If the link was removed externally (e.g. undo) mid-preview, our
+		// `_dragging` flag has nothing to land on — drop the preview.
+		const graph = lgCanvas.graph || app.graph;
+		const stored = graph?.links?.get(link.id);
+		if (stored !== link) {
+			setInsertTarget(null);
+			state.animating = false;
+			lgCanvas.setDirty(true, true);
+			return;
+		}
+		// Steady-state: only the dash offset changes — front canvas only.
+		lgCanvas.setDirty(true, false);
+		requestAnimationFrame(tick);
+	}
+	requestAnimationFrame(tick);
+}
+
+function clearState() {
+	setInsertTarget(null);
+	state.pointerDown = false;
+	state.hasMoved = false;
+	state.draggedNode = null;
+	state.startPos = null;
+}
+
+app.registerExtension({
+	name: "KJNodes.NodeInsert",
+
+	settings: [
+		{
+			id: "KJNodes.nodeInsertMode",
+			name: "Node insert activation",
+			category: ["KJNodes", "Node Insert", "Activation mode"],
+			tooltip: "Always: dragging a compatible node onto a link previews insertion. Hotkey: only while the hotkey (default: D) is held. Disabled: feature off.",
+			type: "combo",
+			defaultValue: "hotkey",
+			options: ["always", "hotkey", "disabled"],
+		},
+	],
+
+	commands: [
+		{
+			id: "KJNodes.NodeInsertMode",
+			label: "Node insert mode (hold to activate)",
+			active: () => state.insertKeyDown,
+			function: () => {
+				state.insertKeyDown = true;
+				// Snapshot the key that triggered this so any rebind works.
+				state.activationKey = (performance.now() - lastKeyDownTime < KEYDOWN_MAX_AGE_MS)
+					? lastKeyDown
+					: null;
+				if (state.draggedNode) {
+					const graph = app.canvas?.graph || app.graph;
+					if (graph) {
+						const link = findLinkUnderNode(graph, state.draggedNode);
+						if (link && findInsertSlots(state.draggedNode, link.type)) {
+							setInsertTarget(link, graph);
+							startAnimLoop(app.canvas);
+						}
+					}
+				}
+			},
+		},
+	],
+
+	keybindings: [
+		{
+			commandId: "KJNodes.NodeInsertMode",
+			// Default only — release detection captures the actually-triggering
+			// key at command-fire time, so user rebinds work.
+			combo: { key: "d" },
+			targetElementId: "graph-canvas",
+		},
+	],
+
+	async setup() {
+		const lgCanvas = app.canvas;
+		const canvasEl = lgCanvas.canvas;
+
+		// Capture phase so `lastKeyDown` is set before ComfyUI fires the command.
+		document.addEventListener("keydown", (e) => {
+			if (e.repeat) return;
+			lastKeyDown = e.key?.toLowerCase() ?? null;
+			lastKeyDownTime = performance.now();
+		}, true);
+
+		document.addEventListener("keyup", (e) => {
+			const key = e.key?.toLowerCase() ?? null;
+			// Released key can't be a future trigger — invalidate so a stale
+			// keydown can't be picked up by a later menu activation.
+			if (key && key === lastKeyDown) lastKeyDownTime = 0;
+
+			if (!state.insertKeyDown) return;
+			// No activation key (menu-fired) → blur/pointercancel will clean up.
+			if (!state.activationKey || key !== state.activationKey) return;
+			state.insertKeyDown = false;
+			state.activationKey = null;
+			if (state.insertTargetLink) {
+				setInsertTarget(null);
+				lgCanvas.setDirty(false, true);
+			}
+		});
+
+		// Releases outside the window never reach our keyup/pointerup listeners,
+		// so without these the `_dragging` flag and hotkey state can stick.
+		const dropTransient = () => {
+			state.insertKeyDown = false;
+			state.activationKey = null;
+			clearState();
+			lgCanvas.setDirty(false, true);
+		};
+		window.addEventListener("blur", dropTransient);
+		document.addEventListener("visibilitychange", () => {
+			if (document.hidden) dropTransient();
+		});
+		document.addEventListener("pointercancel", dropTransient, true);
+
+		// Scope the Vue-mode node lookup to the graph canvas's container so
+		// stray `[data-node-id]` elements elsewhere can't activate this flow.
+		const graphRoot = canvasEl.parentElement;
+		document.addEventListener("pointerdown", (e) => {
+			if (e.button !== 0) return;
+			const onCanvas = e.target === canvasEl;
+			const vueNodeEl = graphRoot?.contains(e.target)
+				? e.target?.closest?.("[data-node-id]")
+				: null;
+			if (!onCanvas && !vueNodeEl) return;
+
+			state.pointerDown = true;
+			state.hasMoved = false;
+
+			const graph = lgCanvas.graph || app.graph;
+			if (graph) {
+				let node = null;
+				if (vueNodeEl) {
+					const nodeId = parseInt(vueNodeEl.getAttribute("data-node-id"));
+					if (Number.isFinite(nodeId)) node = graph.getNodeById(nodeId);
+				} else {
+					const [cx, cy] = clientToCanvas(lgCanvas, e.clientX, e.clientY);
+					node = getNodeAtPoint(graph, cx, cy);
+				}
+				if (node && (node.inputs || node.outputs)) {
+					state.draggedNode = node;
+					state.startPos = node.pos ? [node.pos[0], node.pos[1]] : null;
+				}
+			}
+		}, true);
+
+		document.addEventListener("pointermove", () => {
+			if (!state.pointerDown) return;
+			if (lgCanvas.connecting_links?.length) return;
+			if (lgCanvas.resizing_node) return;
+			if (lgCanvas.node_widget) return;
+
+			const mode = app.ui.settings.getSettingValue("KJNodes.nodeInsertMode") ?? "always";
+			if (mode === "disabled") return;
+
+			state.hasMoved = true;
+
+			const active = mode === "always" || state.insertKeyDown;
+			if (!active) {
+				setInsertTarget(null);
+				return;
+			}
+
+			if (!state.draggedNode) return;
+
+			// Multi-node drags move several nodes together — inserting one of
+			// them into a link is almost never what the user wants.
+			const selected = lgCanvas.selected_nodes;
+			if (selected) {
+				const count = selected instanceof Map ? selected.size : Object.keys(selected).length;
+				if (count > 1) {
+					setInsertTarget(null);
+					return;
+				}
+			}
+
+			// Vue mode: pointermove fires on the document during widget drags
+			// (sliders etc.) too — require the node itself to have moved.
+			const np = state.draggedNode.pos;
+			if (!np || !state.startPos
+				|| (np[0] === state.startPos[0] && np[1] === state.startPos[1])) {
+				setInsertTarget(null);
+				return;
+			}
+
+			const graph = lgCanvas.graph || app.graph;
+			if (!graph) return;
+
+			const now = performance.now();
+			const nodeCount = graph._nodes?.length ?? 0;
+			const throttle = nodeCount > 200 ? 50 : nodeCount > 100 ? 32 : 16;
+			if (now - state.lastScanTime < throttle) return;
+			state.lastScanTime = now;
+
+			const link = findLinkUnderNode(graph, state.draggedNode);
+			const slots = link ? findInsertSlots(state.draggedNode, link.type) : null;
+			const wasIdle = !state.insertTargetLink;
+
+			if (link && slots) {
+				setInsertTarget(link, graph, slots);
+				if (wasIdle) startAnimLoop(lgCanvas);
+			} else {
+				setInsertTarget(null);
+			}
+		}, true);
+
+		document.addEventListener("pointerup", (e) => {
+			if (e.button !== 0) return;
+			if (!state.pointerDown) return;
+
+			if (state.insertTargetLink && state.draggedNode && state.hasMoved
+				&& !state.draggedNode.flags?.pinned) {
+				delete state.insertTargetLink._dragging;
+				executeNodeInsert(lgCanvas, state.draggedNode, state.insertTargetLink);
+			}
+
+			clearState();
+		}, true);
+
+		// onDrawForeground: ghost paints over the existing (hidden) link.
+		chainCallback(lgCanvas, "onDrawForeground", function (ctx) {
+			if (state.insertTargetLink) {
+				drawInsertPreview(ctx, lgCanvas);
+			}
+		});
+	},
+});
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/nodeswap.js b/custom_nodes/ComfyUI-KJNodes/web/js/nodeswap.js
new file mode 100644
index 0000000000000000000000000000000000000000..d588fed5321cb4caf3fbb12da342732abe58c93e
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/nodeswap.js
@@ -0,0 +1,434 @@
+const { app } = window.comfyAPI.app;
+import { typesCompatible, clientToCanvas, getNodeAtPoint } from "./utility.js";
+
+let swapTargetNode = null;
+let swapDraggedNode = null;
+let swapDragStartPos = null;
+let swapAnimating = false;
+let swapHasMoved = false;
+let swapKeyDown = false;
+
+/** Find the topmost node whose bounding box overlaps with draggedNode */
+function getOverlappingNode(graph, draggedNode) {
+	const ax = draggedNode.pos[0];
+	const ay = draggedNode.pos[1];
+	const aw = draggedNode.size[0] || 100;
+	const ah = draggedNode.size[1] || 60;
+
+	for (let i = graph._nodes.length - 1; i >= 0; i--) {
+		const n = graph._nodes[i];
+		if (n === draggedNode) continue;
+		const bx = n.pos[0];
+		const by = n.pos[1];
+		if (ax < bx + (n.size[0] || 100) && ax + aw > bx &&
+			ay < by + (n.size[1] || 60) && ay + ah > by) {
+			return n;
+		}
+	}
+	return null;
+}
+
+
+function startHighlightAnim(lgCanvas) {
+	if (swapAnimating) return;
+	swapAnimating = true;
+
+	function tick() {
+		if (!swapTargetNode) {
+			swapAnimating = false;
+			return;
+		}
+		lgCanvas.setDirty(false, true);
+		requestAnimationFrame(tick);
+	}
+	requestAnimationFrame(tick);
+}
+
+function clearSwapState() {
+	swapTargetNode = null;
+	swapDraggedNode = null;
+	swapDragStartPos = null;
+	swapHasMoved = false;
+}
+
+function snapshotConnections(graph, node) {
+	const inputs = [];
+	for (let i = 0; i < (node.inputs?.length ?? 0); i++) {
+		const inp = node.inputs[i];
+		if (inp.link == null) continue;
+		const link = graph.getLink(inp.link);
+		if (!link) continue;
+		inputs.push({
+			slotIndex: i,
+			type: inp.type,
+			name: inp.name,
+			originNodeId: link.origin_id,
+			originSlot: link.origin_slot,
+		});
+	}
+
+	const outputs = [];
+	for (let o = 0; o < (node.outputs?.length ?? 0); o++) {
+		const out = node.outputs[o];
+		if (!out.links?.length) continue;
+		const targets = out.links
+			.map((linkId) => graph.getLink(linkId))
+			.filter(Boolean)
+			.map((link) => ({ targetNodeId: link.target_id, targetSlot: link.target_slot }));
+		if (targets.length > 0) {
+			outputs.push({ slotIndex: o, type: out.type, name: out.name, targets });
+		}
+	}
+
+	return { inputs, outputs };
+}
+
+function disconnectAll(node) {
+	if (node.inputs) {
+		for (let i = node.inputs.length - 1; i >= 0; i--) {
+			if (node.inputs[i].link != null) node.disconnectInput(i);
+		}
+	}
+	if (node.outputs) {
+		for (let o = node.outputs.length - 1; o >= 0; o--) {
+			if (node.outputs[o].links?.length) node.disconnectOutput(o);
+		}
+	}
+}
+
+// Map each snapshot entry → a new-node slot. Name matches win first, then sameIndex,
+// then first compatible. Each slot is claimed at most once so fallbacks can't stomp matches.
+function resolveAssignments(snapshots, slots, isFree) {
+	const assignments = new Map();
+	if (!slots || !slots.length) return assignments;
+	const used = new Set();
+
+	for (const snap of snapshots) {
+		if (!snap.name) continue;
+		const nameLower = String(snap.name).toLowerCase();
+		for (let s = 0; s < slots.length; s++) {
+			if (used.has(s) || !isFree(s)) continue;
+			const slotName = slots[s].name;
+			if (slotName && String(slotName).toLowerCase() === nameLower &&
+				typesCompatible(snap.type, slots[s].type)) {
+				assignments.set(snap, s);
+				used.add(s);
+				break;
+			}
+		}
+	}
+
+	for (const snap of snapshots) {
+		if (assignments.has(snap)) continue;
+		const i = snap.slotIndex;
+		if (i < slots.length && !used.has(i) && isFree(i) &&
+			typesCompatible(snap.type, slots[i].type)) {
+			assignments.set(snap, i);
+			used.add(i);
+			continue;
+		}
+		for (let s = 0; s < slots.length; s++) {
+			if (used.has(s) || !isFree(s)) continue;
+			if (typesCompatible(snap.type, slots[s].type)) {
+				assignments.set(snap, s);
+				used.add(s);
+				break;
+			}
+		}
+	}
+
+	return assignments;
+}
+
+// Reconnect external connections from a snapshot onto newNode.
+function reconnectExternal(graph, snapshot, newNode, otherNodeId) {
+	const inputAssignments = resolveAssignments(
+		snapshot.inputs, newNode.inputs || [],
+		(s) => newNode.inputs[s].link == null,
+	);
+	for (const inp of snapshot.inputs) {
+		if (inp.originNodeId === otherNodeId) continue;
+		if (!inputAssignments.has(inp)) continue;
+		const originNode = graph.getNodeById(inp.originNodeId);
+		if (!originNode) continue;
+		originNode.connect(inp.originSlot, newNode, inputAssignments.get(inp));
+	}
+
+	const outputAssignments = resolveAssignments(
+		snapshot.outputs, newNode.outputs || [],
+		() => true,
+	);
+	for (const out of snapshot.outputs) {
+		if (!outputAssignments.has(out)) continue;
+		const bestSlot = outputAssignments.get(out);
+		for (const tgt of out.targets) {
+			if (tgt.targetNodeId === otherNodeId) continue;
+			const targetNode = graph.getNodeById(tgt.targetNodeId);
+			if (!targetNode) continue;
+			newNode.connect(bestSlot, targetNode, tgt.targetSlot);
+		}
+	}
+}
+
+// Reconnect links that were between the two swapped nodes in one direction.
+// A→B becomes B→A, if slot types are compatible.
+function reconnectInternalOneDirection(snap, fromNodeId, srcNode, dstNode) {
+	for (const out of snap.outputs) {
+		for (const tgt of out.targets) {
+			if (tgt.targetNodeId !== fromNodeId) continue;
+			if (!srcNode.outputs || out.slotIndex >= srcNode.outputs.length) continue;
+			if (!dstNode.inputs || tgt.targetSlot >= dstNode.inputs.length) continue;
+			if (!typesCompatible(srcNode.outputs[out.slotIndex].type, dstNode.inputs[tgt.targetSlot].type)) continue;
+			srcNode.connect(out.slotIndex, dstNode, tgt.targetSlot);
+		}
+	}
+}
+
+function getNodeElement(nodeId) {
+	return document.querySelector(`[data-node-id="${nodeId}"]`);
+}
+
+function animateSwapVue(nodeA, toA, nodeB, toB, duration) {
+	const elA = getNodeElement(nodeA.id);
+	const elB = getNodeElement(nodeB.id);
+	const transition = `transform ${duration}ms cubic-bezier(0.65, 0, 0.35, 1)`;
+
+	if (elA) elA.style.transition = transition;
+	if (elB) elB.style.transition = transition;
+
+	nodeA.pos = [toA[0], toA[1]];
+	nodeB.pos = [toB[0], toB[1]];
+	app.canvas.setDirty(true, true);
+
+	setTimeout(() => {
+		if (elA) elA.style.transition = "";
+		if (elB) elB.style.transition = "";
+	}, duration + 16);
+}
+
+function animateSwapCanvas(nodeA, fromA, toA, nodeB, fromB, toB, duration) {
+	const start = performance.now();
+
+	function ease(t) {
+		return t < 0.5 ? 4 * t * t * t : 1 - Math.pow(-2 * t + 2, 3) / 2;
+	}
+
+	function frame(now) {
+		const t = Math.min((now - start) / duration, 1);
+		const e = ease(t);
+		nodeA.pos = [
+			fromA[0] + (toA[0] - fromA[0]) * e,
+			fromA[1] + (toA[1] - fromA[1]) * e,
+		];
+		nodeB.pos = [
+			fromB[0] + (toB[0] - fromB[0]) * e,
+			fromB[1] + (toB[1] - fromB[1]) * e,
+		];
+		app.canvas.setDirty(true, true);
+		if (t < 1) requestAnimationFrame(frame);
+	}
+
+	requestAnimationFrame(frame);
+}
+
+function executeNodeSwap(canvas, nodeA, nodeB, originalPosA) {
+	const graph = canvas.graph || app.graph;
+
+	const snapA = snapshotConnections(graph, nodeA);
+	const snapB = snapshotConnections(graph, nodeB);
+
+	const posA = originalPosA || [nodeA.pos[0], nodeA.pos[1]];
+	const posB = [nodeB.pos[0], nodeB.pos[1]];
+
+	disconnectAll(nodeA);
+	disconnectAll(nodeB);
+
+	reconnectExternal(graph, snapA, nodeB, nodeA.id);
+	reconnectExternal(graph, snapB, nodeA, nodeB.id);
+
+	reconnectInternalOneDirection(snapA, nodeB.id, nodeB, nodeA);
+	reconnectInternalOneDirection(snapB, nodeA.id, nodeA, nodeB);
+
+	if (LiteGraph.vueNodesMode) {
+		animateSwapVue(nodeA, posB, nodeB, posA, 200);
+	} else {
+		animateSwapCanvas(nodeA, [nodeA.pos[0], nodeA.pos[1]], posB,
+			nodeB, [nodeB.pos[0], nodeB.pos[1]], posA, 200);
+	}
+
+	graph.change();
+	canvas.setDirty(true, true);
+}
+
+app.registerExtension({
+	name: "KJNodes.NodeSwap",
+
+	settings: [
+		{
+			id: "KJNodes.nodeSwapEnabled",
+			name: "Enable node swap on drag",
+			category: ["KJNodes", "Node Swap", "Enable"],
+			tooltip: "Hold swap key (default: S, rebindable in Keybindings) and drag a node onto another to swap their positions and reconnect links",
+			type: "boolean",
+			defaultValue: true,
+		},
+	],
+	commands: [
+		{
+			id: "KJNodes.ToggleNodeSwap",
+			label: "Toggle node swap on drag",
+			active: () => app.ui.settings.getSettingValue("KJNodes.nodeSwapEnabled"),
+			function: () => {
+				const cur = app.ui.settings.getSettingValue("KJNodes.nodeSwapEnabled");
+				app.ui.settings.setSettingValue("KJNodes.nodeSwapEnabled", !cur);
+			},
+		},
+		{
+			id: "KJNodes.NodeSwapMode",
+			label: "Node swap mode (hold to activate)",
+			active: () => swapKeyDown,
+			function: () => {
+				swapKeyDown = true;
+				// If already dragging a node, check for overlap immediately
+				// (handles case where node is already on top of another when key is pressed)
+				if (swapDraggedNode) {
+					const graph = app.canvas?.graph || app.graph;
+					if (graph) {
+						swapTargetNode = getOverlappingNode(graph, swapDraggedNode);
+						if (swapTargetNode) startHighlightAnim(app.canvas);
+					}
+				}
+			},
+		},
+	],
+	keybindings: [
+		{
+			commandId: "KJNodes.NodeSwapMode",
+			combo: { key: "s" },
+			targetElementId: "graph-canvas",
+		},
+	],
+
+	async setup() {
+		await new Promise((resolve) => {
+			function check() {
+				if (app.canvas) return resolve();
+				requestAnimationFrame(check);
+			}
+			check();
+		});
+
+		const lgCanvas = app.canvas;
+		const canvasEl = lgCanvas.canvas;
+
+		// The command system handles keydown (sets swapKeyDown = true).
+		// Any keyup clears it since the user must hold the key.
+		document.addEventListener("keyup", () => {
+			if (swapKeyDown) {
+				swapKeyDown = false;
+				if (swapTargetNode) {
+					swapTargetNode = null;
+					lgCanvas.setDirty(false, true);
+				}
+			}
+		});
+
+		// Pointermove/pointerup handlers — only attached while pointer is down.
+		function onPointerMove() {
+			swapHasMoved = true;
+
+			if (!swapKeyDown) {
+				if (swapTargetNode) {
+					swapTargetNode = null;
+					lgCanvas.setDirty(false, true);
+				}
+				return;
+			}
+
+			if (!swapDraggedNode) return;
+
+			const graph = lgCanvas.graph || app.graph;
+			if (!graph) return;
+
+			const prev = swapTargetNode;
+			swapTargetNode = getOverlappingNode(graph, swapDraggedNode);
+
+			if (swapTargetNode && !prev) {
+				startHighlightAnim(lgCanvas);
+			}
+		}
+
+		function onPointerUp(e) {
+			if (e.button !== 0) return;
+
+			document.removeEventListener("pointermove", onPointerMove);
+			document.removeEventListener("pointerup", onPointerUp, true);
+
+			const targetNode = swapTargetNode;
+			const draggedNode = swapDraggedNode;
+			const startPos = swapDragStartPos;
+			const hasMoved = swapHasMoved;
+			clearSwapState();
+
+			if (!hasMoved || !targetNode || !draggedNode || !swapKeyDown) return;
+
+			if (!targetNode.flags?.pinned && !draggedNode.flags?.pinned) {
+				executeNodeSwap(lgCanvas, draggedNode, targetNode, startPos);
+			}
+		}
+
+		// Record clicked node and its original position.
+		// Listen on document to catch events from both the canvas and Vue node overlays.
+		const graphContainer = canvasEl.closest(".graph-canvas-container") || canvasEl.parentElement;
+		document.addEventListener("pointerdown", (e) => {
+			if (e.button !== 0) return;
+			if (!app.ui.settings.getSettingValue("KJNodes.nodeSwapEnabled")) return;
+			if (!graphContainer?.contains(e.target)) return;
+
+			swapHasMoved = false;
+
+			const graph = lgCanvas.graph || app.graph;
+			if (!graph) return;
+
+			const [cx, cy] = clientToCanvas(lgCanvas, e.clientX, e.clientY);
+			const node = getNodeAtPoint(graph, cx, cy);
+
+			if (node && (node.inputs || node.outputs)) {
+				swapDraggedNode = node;
+				swapDragStartPos = [node.pos[0], node.pos[1]];
+				document.addEventListener("pointermove", onPointerMove);
+				document.addEventListener("pointerup", onPointerUp, true);
+			}
+		}, true);
+
+		// Draw pulsing glow outline on the swap target node
+		const origOnDrawBg = lgCanvas.onDrawBackground;
+		lgCanvas.onDrawBackground = function (ctx, ...args) {
+			origOnDrawBg?.call(this, ctx, ...args);
+
+			const target = swapTargetNode;
+			if (!target) return;
+
+			const [x, y] = target.pos;
+			const w = target.size[0] || 100;
+			const h = target.size[1] || 60;
+			const titleH = LiteGraph.NODE_TITLE_HEIGHT || 20;
+			const pulse = 0.5 + 0.3 * Math.sin(Date.now() / 200);
+			const isPinned = target.flags?.pinned;
+			const color = isPinned ? "255, 80, 80" : "100, 200, 255";
+			const pad = 5;
+
+			ctx.save();
+			ctx.strokeStyle = `rgba(${color}, ${pulse})`;
+			ctx.lineWidth = 3;
+			ctx.shadowColor = `rgba(${color}, 0.8)`;
+			ctx.shadowBlur = 15;
+			ctx.beginPath();
+			ctx.roundRect(x - pad, y - titleH - pad, w + pad * 2, h + titleH + pad * 2, 8);
+			ctx.stroke();
+			ctx.fillStyle = `rgba(${color}, ${isPinned ? 0.08 : 0.05})`;
+			ctx.fill();
+			ctx.restore();
+		};
+	},
+});
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/performance.js b/custom_nodes/ComfyUI-KJNodes/web/js/performance.js
new file mode 100644
index 0000000000000000000000000000000000000000..4420dce237fab545326b298cd175b27bd85e67bd
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/performance.js
@@ -0,0 +1,286 @@
+const { app } = window.comfyAPI.app;
+
+const _savedRoundRadius = typeof LiteGraph !== "undefined" ? LiteGraph.ROUND_RADIUS : 8;
+
+function getSetting(id, fallback) {
+	return app.ui.settings.getSettingValue(id) ?? fallback;
+}
+
+function toggleSetting(id) {
+	const cur = app.ui.settings.getSettingValue(id);
+	app.ui.settings.setSettingValue(id, !cur);
+}
+
+app.registerExtension({
+	name: "KJNodes.Performance",
+
+	commands: [
+		{
+			id: "KJNodes.perf.toggleSingleCanvasPan",
+			label: "Single-canvas mode during pan",
+			menubarLabel: "Single-canvas mode during pan",
+			function: () => toggleSetting("KJNodes.perf.singleCanvasPan"),
+			active: () => getSetting("KJNodes.perf.singleCanvasPan", false),
+		},
+		{
+			id: "KJNodes.perf.toggleDisableShadows",
+			label: "Disable node shadows",
+			menubarLabel: "Disable node shadows",
+			function: () => toggleSetting("KJNodes.perf.disableShadows"),
+			active: () => getSetting("KJNodes.perf.disableShadows", false),
+		},
+		{
+			id: "KJNodes.perf.toggleDisableConnectionBorders",
+			label: "Disable connection borders",
+			menubarLabel: "Disable connection borders",
+			function: () => toggleSetting("KJNodes.perf.disableConnectionBorders"),
+			active: () => getSetting("KJNodes.perf.disableConnectionBorders", false),
+		},
+		{
+			id: "KJNodes.perf.toggleDisableRoundedCorners",
+			label: "Disable rounded corners",
+			menubarLabel: "Disable rounded corners",
+			function: () => toggleSetting("KJNodes.perf.disableRoundedCorners"),
+			active: () => getSetting("KJNodes.perf.disableRoundedCorners", false),
+		},
+		{
+			id: "KJNodes.perf.toggleThrottleRenderInfo",
+			label: "Throttle info overlay",
+			menubarLabel: "Throttle info overlay",
+			function: () => toggleSetting("KJNodes.perf.throttleRenderInfo"),
+			active: () => getSetting("KJNodes.perf.throttleRenderInfo", false),
+		},
+	],
+
+	menuCommands: [
+		{
+			path: ["KJNodes", "Performance"],
+			commands: [
+				"KJNodes.perf.toggleSingleCanvasPan",
+				"KJNodes.perf.toggleDisableShadows",
+				"KJNodes.perf.toggleDisableConnectionBorders",
+				"KJNodes.perf.toggleDisableRoundedCorners",
+				"KJNodes.perf.toggleThrottleRenderInfo",
+			],
+		},
+	],
+
+	settings: [
+		{
+			id: "KJNodes.perf.singleCanvasPan",
+			name: "Single-canvas mode during pan",
+			category: ["KJNodes", "Performance", "Single-canvas mode during pan"],
+			tooltip: "Eliminates the expensive bgcanvas-to-canvas copy during panning. Significant improvement without hardware acceleration.",
+			type: "boolean",
+			defaultValue: false,
+			onChange: (value) => {
+				const canvas = app.canvas;
+				if (!canvas) return;
+				if (value) {
+					installSingleCanvasPan(canvas);
+				} else {
+					uninstallSingleCanvasPan(canvas);
+				}
+			},
+		},
+		{
+			id: "KJNodes.perf.disableShadows",
+			name: "Disable node shadows",
+			category: ["KJNodes", "Performance", "Disable node shadows"],
+			tooltip: "Disable shadow rendering on nodes for better performance without hardware acceleration.",
+			type: "boolean",
+			defaultValue: false,
+			onChange: (value) => {
+				if (app.canvas) {
+					app.canvas.render_shadows = !value;
+					app.canvas.setDirty(true, true);
+				}
+			},
+		},
+		{
+			id: "KJNodes.perf.disableConnectionBorders",
+			name: "Disable connection borders",
+			category: ["KJNodes", "Performance", "Disable connection borders"],
+			tooltip: "Disable the outer border stroke on connection lines. Each link draws two strokes instead of one when enabled.",
+			type: "boolean",
+			defaultValue: false,
+			onChange: (value) => {
+				if (app.canvas) {
+					app.canvas.render_connections_border = !value;
+					app.canvas.setDirty(true, true);
+				}
+			},
+		},
+		{
+			id: "KJNodes.perf.disableRoundedCorners",
+			name: "Disable rounded corners",
+			category: ["KJNodes", "Performance", "Disable rounded corners"],
+			tooltip: "Use square corners on nodes instead of rounded. Avoids roundRect calls in software rendering.",
+			type: "boolean",
+			defaultValue: false,
+			onChange: (value) => {
+				if (typeof LiteGraph !== "undefined") {
+					LiteGraph.ROUND_RADIUS = value ? 0 : _savedRoundRadius;
+					app.canvas?.setDirty(true, true);
+				}
+			},
+		},
+		{
+			id: "KJNodes.perf.throttleRenderInfo",
+			name: "Throttle info overlay",
+			category: ["KJNodes", "Performance", "Throttle info overlay"],
+			tooltip: "Cache the FPS/info text overlay and only re-render it a few times per second. Saves ~20ms per frame in software rendering.",
+			type: "boolean",
+			defaultValue: false,
+			onChange: (value) => {
+				const canvas = app.canvas;
+				if (!canvas) return;
+				if (value) {
+					installThrottleRenderInfo(canvas);
+				} else {
+					uninstallThrottleRenderInfo(canvas);
+				}
+			},
+		},
+	],
+
+	setup() {
+		const canvas = app.canvas;
+		if (!canvas) return;
+
+		if (getSetting("KJNodes.perf.singleCanvasPan", false)) {
+			installSingleCanvasPan(canvas);
+		}
+		if (getSetting("KJNodes.perf.disableShadows", false)) {
+			canvas.render_shadows = false;
+		}
+		if (getSetting("KJNodes.perf.disableConnectionBorders", false)) {
+			canvas.render_connections_border = false;
+		}
+		if (getSetting("KJNodes.perf.disableRoundedCorners", false)) {
+			LiteGraph.ROUND_RADIUS = 0;
+		}
+		if (getSetting("KJNodes.perf.throttleRenderInfo", false)) {
+			installThrottleRenderInfo(canvas);
+		}
+	},
+});
+
+// ── Single-canvas mode during pan ───────────────────────────────────────────
+
+let _origDraw = null;
+let _panInstalledOn = null;
+
+function installSingleCanvasPan(canvas) {
+	if (_origDraw && _panInstalledOn === canvas) return;
+	if (_origDraw && _panInstalledOn !== canvas) {
+		// Canvas was replaced — discard stale reference
+		_origDraw = null;
+	}
+	_panInstalledOn = canvas;
+	_origDraw = canvas.draw;
+	let panning = false;
+	let savedBgCanvas = null;
+	let savedBgCtx = null;
+
+	canvas.draw = function (force_canvas, force_bgcanvas) {
+		if (this.dragging_canvas) {
+			if (!panning) {
+				// Flush any pending bg redraw before aliasing to avoid a stale-frame flash
+				if (this.dirty_bgcanvas) {
+					_origDraw.call(this, false, true);
+				}
+				savedBgCanvas = this.bgcanvas;
+				savedBgCtx = this.bgctx;
+				panning = true;
+				this.bgcanvas = this.canvas;
+				this.bgctx = this.ctx;
+			}
+		} else if (panning) {
+			panning = false;
+			this.bgcanvas = savedBgCanvas;
+			this.bgctx = savedBgCtx;
+			this.dirty_bgcanvas = true;
+			this.dirty_canvas = true;
+		}
+		_origDraw.call(this, force_canvas, force_bgcanvas);
+	};
+}
+
+function uninstallSingleCanvasPan(canvas) {
+	if (!_origDraw) return;
+	if (_panInstalledOn === canvas) {
+		canvas.draw = _origDraw;
+	}
+	_origDraw = null;
+	_panInstalledOn = null;
+	canvas.dirty_bgcanvas = true;
+	canvas.dirty_canvas = true;
+}
+
+// ── Throttle renderInfo ─────────────────────────────────────────────────────
+
+let _origRenderInfo = null;
+let _infoCanvas = null;
+let _infoInstalledOn = null;
+
+function installThrottleRenderInfo(canvas) {
+	if (_origRenderInfo && _infoInstalledOn === canvas) return;
+	if (_origRenderInfo && _infoInstalledOn !== canvas) {
+		_origRenderInfo = null;
+		_infoCanvas = null;
+	}
+	_infoInstalledOn = canvas;
+	_origRenderInfo = canvas.renderInfo;
+	_infoCanvas = document.createElement("canvas");
+	let infoCtx = _infoCanvas.getContext("2d");
+	let lastInfoTime = 0;
+	let cachedDpr = 0;
+	let _infoDrawX = 10;
+	let _infoDrawY = 0;
+
+	canvas.renderInfo = function (ctx, x, y) {
+		const dpr = window.devicePixelRatio || 1;
+
+		// Resize offscreen canvas if DPR changed
+		if (dpr !== cachedDpr) {
+			cachedDpr = dpr;
+			_infoCanvas.width = Math.ceil(200 * dpr);
+			_infoCanvas.height = Math.ceil(100 * dpr);
+			infoCtx = _infoCanvas.getContext("2d");
+			infoCtx.scale(dpr, dpr);
+			lastInfoTime = 0;
+		}
+
+		const now = performance.now();
+		if (now - lastInfoTime > 250) {
+			lastInfoTime = now;
+			infoCtx.clearRect(0, 0, 200, 100);
+			// Render with the same x/y the original would use, but offset
+			// into offscreen canvas coordinates
+			const origX = x || 10;
+			const lineHeight = 13;
+			const lineCount = (this.graph ? 5 : 1) + (this.info_text ? 1 : 0);
+			const origY = y || (this.canvas.height / dpr - (lineCount + 1) * lineHeight);
+			// Store the computed position for blitting
+			_infoDrawX = origX;
+			_infoDrawY = origY;
+			// Draw into offscreen at (1, 1), text flows from there
+			_origRenderInfo.call(this, infoCtx, 1, 1);
+		}
+
+		// Blit cached text at the original position
+		ctx.drawImage(_infoCanvas, _infoDrawX - 1, _infoDrawY - 1, 200, 100);
+	};
+}
+
+function uninstallThrottleRenderInfo(canvas) {
+	if (!_origRenderInfo) return;
+	if (_infoInstalledOn === canvas) {
+		canvas.renderInfo = _origRenderInfo;
+	}
+	_origRenderInfo = null;
+	_infoCanvas = null;
+	_infoInstalledOn = null;
+	canvas.setDirty(true, true);
+}
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/play_sound.js b/custom_nodes/ComfyUI-KJNodes/web/js/play_sound.js
new file mode 100644
index 0000000000000000000000000000000000000000..cda30caea56623e650836b77a5e532f47c3cae8c
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/play_sound.js
@@ -0,0 +1,94 @@
+const { app } = window.comfyAPI.app;
+const { api } = window.comfyAPI.api;
+
+app.registerExtension({
+    name: "KJNodes.PlaySound",
+    async beforeRegisterNodeDef(nodeType, nodeData) {
+        if (nodeData.name !== "PlaySoundKJ") return;
+
+        const onExecuted = nodeType.prototype.onExecuted;
+        nodeType.prototype.onExecuted = function (output) {
+            onExecuted?.call(this, output);
+
+            const audios = output?.audio;
+            if (!audios?.length) return;
+
+            const modeWidget = this.widgets?.find(w => w.name === "mode");
+            const volumeWidget = this.widgets?.find(w => w.name === "volume");
+            const durationWidget = this.widgets?.find(w => w.name === "duration");
+            const mode = modeWidget?.value ?? "always";
+            const volume = volumeWidget?.value ?? 0.5;
+            const duration = durationWidget?.value ?? 0;
+
+            // on_change: skip if audio content hasn't changed
+            if (mode === "on_change") {
+                const audioHash = output?.audio_hash?.[0];
+                if (audioHash != null && this._kjLastAudioHash === audioHash) return;
+                this._kjLastAudioHash = audioHash;
+            }
+
+            // Clean up previous state
+            if (this._kjStatusListener) {
+                api.removeEventListener("status", this._kjStatusListener);
+                this._kjStatusListener = null;
+            }
+            clearTimeout(this._kjQueueDebounce);
+            this._kjPendingAudio = null;
+
+            if (this._kjPlayingAudio) {
+                this._kjPlayingAudio.pause();
+                this._kjPlayingAudio = null;
+            }
+            if (this._kjPlayTimer != null) {
+                clearTimeout(this._kjPlayTimer);
+                this._kjPlayTimer = null;
+            }
+
+            const startPlayback = () => {
+                const { filename, subfolder, type } = audios[0];
+                const params = new URLSearchParams({
+                    filename: filename ?? "",
+                    subfolder: subfolder ?? "",
+                    type: type ?? "temp",
+                });
+                const url = api.apiURL(`/view?${params.toString()}`);
+                const audio = new Audio(url);
+                audio.volume = Math.max(0, Math.min(1, volume));
+                audio.play().catch(() => {});
+                this._kjPlayingAudio = audio;
+                if (duration > 0) {
+                    this._kjPlayTimer = setTimeout(() => {
+                        audio.pause();
+                        this._kjPlayingAudio = null;
+                        this._kjPlayTimer = null;
+                    }, duration * 1000);
+                }
+            };
+
+            if (mode === "on_empty_queue") {
+                this._kjPendingAudio = startPlayback;
+                this._kjStatusListener = ({ detail }) => {
+                    const remaining = detail?.exec_info?.queue_remaining ?? 0;
+                    if (remaining === 0) {
+                        // Debounce: confirm queue is truly empty
+                        // (status can briefly show 0 between dispatches)
+                        clearTimeout(this._kjQueueDebounce);
+                        this._kjQueueDebounce = setTimeout(() => {
+                            if (this._kjPendingAudio) {
+                                this._kjPendingAudio();
+                                this._kjPendingAudio = null;
+                            }
+                            api.removeEventListener("status", this._kjStatusListener);
+                            this._kjStatusListener = null;
+                        }, 1000);
+                    } else {
+                        clearTimeout(this._kjQueueDebounce);
+                    }
+                };
+                api.addEventListener("status", this._kjStatusListener);
+            } else {
+                startPlayback();
+            }
+        };
+    },
+});
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/preview_override/preview_override.css b/custom_nodes/ComfyUI-KJNodes/web/js/preview_override/preview_override.css
new file mode 100644
index 0000000000000000000000000000000000000000..c5197d80df27cbc767d677b78cc260e1522f8e0a
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/preview_override/preview_override.css
@@ -0,0 +1,194 @@
+/* Palette matched to ComfyUI-MemoryVisualization. */
+
+.kj-pov-root {
+    width: 100%;
+    height: 100%;
+    position: relative;
+    background: #181818;
+    color: #b0b0b0;
+    font: 12px monospace;
+    display: flex;
+    flex-direction: column;
+    overflow: hidden;
+    user-select: none;
+}
+
+.kj-pov-image-area {
+    flex: 1 1 auto;
+    min-height: 80px;
+    position: relative;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    background: #0a0a0a;
+    overflow: hidden;
+}
+
+/* Absolute-fill so latent-resolution previews scale up rather than staying centred-tiny. */
+.kj-pov-img {
+    position: absolute;
+    inset: 0;
+    width: 100%;
+    height: 100%;
+    object-fit: contain;
+    image-rendering: pixelated;
+    display: block;
+}
+
+.kj-pov-placeholder {
+    position: absolute;
+    inset: 0;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    color: #707070;
+    font: 12px monospace;
+    pointer-events: none;
+}
+
+.kj-pov-scrub {
+    position: absolute;
+    left: 0;
+    right: 0;
+    bottom: 0;
+    height: 6px;
+    background: rgba(0, 0, 0, 0.5);
+    cursor: pointer;
+    z-index: 5;
+    transition: height 80ms ease;
+}
+.kj-pov-scrub:hover { height: 10px; }
+.kj-pov-scrub-fill {
+    height: 100%;
+    width: 0%;
+    background: #e67e22;
+    pointer-events: none;
+}
+.kj-pov-scrub.kj-pov-paused .kj-pov-scrub-fill {
+    background: #707070;
+}
+
+.kj-pov-panel-grip {
+    flex: 0 0 5px;
+    background: #2a2a2a;
+    cursor: ns-resize;
+    position: relative;
+    z-index: 4;
+    user-select: none;
+}
+.kj-pov-panel-grip::after {
+    content: '';
+    position: absolute;
+    left: 50%;
+    top: 50%;
+    transform: translate(-50%, -50%);
+    width: 26px;
+    height: 2px;
+    background: #555;
+    border-radius: 1px;
+}
+.kj-pov-panel-grip:hover { background: #3a3a3a; }
+.kj-pov-panel-grip:hover::after { background: #888; }
+
+.kj-pov-panel {
+    flex: 0 0 auto;
+    height: 140px;
+    min-height: 60px;
+    background: #181818;
+    font: 11px monospace;
+    color: #b0b0b0;
+    display: flex;
+    flex-direction: column;
+    overflow: hidden;
+}
+
+.kj-pov-panel-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    padding: 4px 10px;
+    gap: 8px;
+    background: #202020;
+    border-bottom: 1px solid #2a2a2a;
+}
+.kj-pov-header-left {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+}
+.kj-pov-panel-title { color: #b0b0b0; font-size: 11px; }
+.kj-pov-panel-summary { color: #707070; font-size: 10px; }
+
+.kj-pov-toggle {
+    background: #2a2a2a;
+    color: #888;
+    border: 1px solid #333;
+    border-radius: 3px;
+    padding: 1px 7px;
+    font: 10px monospace;
+    cursor: pointer;
+    user-select: none;
+}
+.kj-pov-toggle:hover { background: #333; color: #b0b0b0; }
+.kj-pov-toggle.kj-pov-on {
+    background: #3a2a1a;
+    color: #e67e22;
+    border-color: #c66e1a;
+}
+
+.kj-pov-graphs-grid {
+    display: flex;
+    align-items: stretch;
+    padding: 6px 6px 7px;
+    gap: 6px;
+    flex: 1 1 auto;
+    min-height: 0;
+    box-sizing: border-box;
+}
+
+.kj-pov-graph-cell {
+    flex: 1 1 0;
+    min-width: 0;
+    display: flex;
+    flex-direction: column;
+    background: #0e0e0e;
+    border: 1px solid #1c1c1c;
+    border-radius: 3px;
+    overflow: hidden;
+}
+
+.kj-pov-graph-head {
+    flex: 0 0 auto;
+    display: flex;
+    justify-content: space-between;
+    align-items: baseline;
+    padding: 3px 6px 2px;
+    background: #161616;
+    border-bottom: 1px solid #1c1c1c;
+    font-size: 10px;
+    line-height: 1.1;
+    gap: 4px;
+}
+
+.kj-pov-graph-label {
+    color: #707070;
+    letter-spacing: 0.05em;
+    flex: 0 0 auto;
+}
+
+.kj-pov-graph-value {
+    color: #b0b0b0;
+    font-size: 10px;
+    text-align: right;
+    flex: 1 1 auto;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+}
+
+.kj-pov-graph-canvas {
+    flex: 1 1 auto;
+    display: block;
+    width: 100%;
+    min-height: 0;
+}
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/preview_override/preview_override.js b/custom_nodes/ComfyUI-KJNodes/web/js/preview_override/preview_override.js
new file mode 100644
index 0000000000000000000000000000000000000000..cc1cf1a8e23e0e38bc0dc2e5dd4d6f64d7ab50cd
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/preview_override/preview_override.js
@@ -0,0 +1,948 @@
+import { chainCallback, addMiddleClickPan, addWheelPassthrough } from '../utility.js';
+const { app } = window.comfyAPI.app;
+const { api } = window.comfyAPI.api;
+
+const STYLE_ID = "kj-pov-stylesheet";
+const _cssUrl = new URL("./preview_override.css", import.meta.url).href;
+function ensureStyles() {
+    if (document.getElementById(STYLE_ID)) return;
+    const link = document.createElement("link");
+    link.id = STYLE_ID;
+    link.rel = "stylesheet";
+    link.href = _cssUrl;
+    document.head.appendChild(link);
+}
+
+// Walks subgraph chain for IDs like "12:7:5". Mirrors getNodeByExecutionId (not exported).
+function findNodeByQualifiedId(rootGraph, qid) {
+    if (!rootGraph || !qid) return null;
+    const parts = String(qid).split(":");
+    let graph = rootGraph;
+    for (let i = 0; i < parts.length - 1; i++) {
+        const parentId = parseInt(parts[i], 10);
+        if (!Number.isFinite(parentId)) return null;
+        const parentNode = graph?.getNodeById?.(parentId);
+        if (!parentNode?.subgraph) return null;
+        graph = parentNode.subgraph;
+    }
+    const leafId = parseInt(parts[parts.length - 1], 10);
+    if (!Number.isFinite(leafId)) return null;
+    return graph?.getNodeById?.(leafId) || null;
+}
+
+api.addEventListener("kj_preview_override", (e) => {
+    const data = e.detail;
+    if (!data || data.node_id == null) return;
+    const node = findNodeByQualifiedId(app.graph, data.node_id);
+    if (node?._kjPreviewHandler) node._kjPreviewHandler(data);
+});
+
+const GRAPH_PAD_X = 4;
+
+function fmt(n, d) {
+    return Number.isFinite(n) ? n.toFixed(d) : "—";
+}
+
+function el(tag, className, parent) {
+    const e = document.createElement(tag);
+    if (className) e.className = className;
+    if (parent) parent.appendChild(e);
+    return e;
+}
+
+function b64ToBlob(b64, mime) {
+    const bin = atob(b64);
+    const arr = new Uint8Array(bin.length);
+    for (let i = 0; i < bin.length; i++) arr[i] = bin.charCodeAt(i);
+    return new Blob([arr], { type: mime });
+}
+
+function syncCanvasDPR(canvas) {
+    const dpr = window.devicePixelRatio || 1;
+    const cssW = canvas.clientWidth || canvas.width;
+    const cssH = canvas.clientHeight || canvas.height;
+    if (canvas.width !== Math.round(cssW * dpr) || canvas.height !== Math.round(cssH * dpr)) {
+        canvas.width = Math.round(cssW * dpr);
+        canvas.height = Math.round(cssH * dpr);
+    }
+    const ctx = canvas.getContext("2d");
+    ctx.setTransform(dpr, 0, 0, dpr, 0, 0);
+    return { ctx, W: cssW, H: cssH };
+}
+
+function drawGridlines(ctx, W, H, padX, padY) {
+    ctx.strokeStyle = "#1e1e1e";
+    ctx.lineWidth = 1;
+    for (let g = 1; g < 4; g++) {
+        const y = Math.round(padY + (g / 4) * (H - 2 * padY)) + 0.5;
+        ctx.beginPath();
+        ctx.moveTo(padX, y);
+        ctx.lineTo(W - padX, y);
+        ctx.stroke();
+    }
+}
+
+// σ + Δ overlaid; each series normalised to its own max for comparable shapes.
+function drawSigmaDeltaGraph(canvas, sigmas, deltas, step, totalSteps, hoverStep, dbCurve, lockedStep) {
+    const { ctx, W, H } = syncCanvasDPR(canvas);
+    const padX = GRAPH_PAD_X, padY = 3;
+    const iW = W - 2 * padX, iH = H - 2 * padY;
+    ctx.clearRect(0, 0, W, H);
+    drawGridlines(ctx, W, H, padX, padY);
+
+    const n = sigmas?.length || 0;
+    const xSteps = Math.max(totalSteps || n, n, deltas?.length || 0);
+    const xAt = i => padX + (i / Math.max(1, xSteps - 1)) * iW;
+
+    let sYAt = null;
+    if (n > 1) {
+        let sMax = -Infinity, sMin = Infinity;
+        for (const s of sigmas) { if (s > sMax) sMax = s; if (s < sMin) sMin = s; }
+        if (sMin > 0) sMin = 0;
+        const sRange = Math.max(sMax - sMin, 1e-6);
+        sYAt = v => padY + (1 - (v - sMin) / sRange) * iH;
+
+        ctx.strokeStyle = "rgba(208, 208, 208, 0.55)";
+        ctx.lineWidth = 1;
+        ctx.setLineDash([3, 3]);
+        ctx.beginPath();
+        for (let i = 0; i < n; i++) {
+            const px = padX + (i / (n - 1)) * iW;
+            const py = sYAt(sigmas[i]);
+            if (i === 0) ctx.moveTo(px, py); else ctx.lineTo(px, py);
+        }
+        ctx.stroke();
+        ctx.setLineDash([]);
+
+        const i = Math.max(0, Math.min(n - 1, step));
+        const mx = padX + (i / Math.max(1, n - 1)) * iW;
+        const my = sYAt(sigmas[i]);
+        ctx.fillStyle = "#d0d0d0";
+        ctx.beginPath();
+        ctx.arc(mx, my, 2.5, 0, Math.PI * 2);
+        ctx.fill();
+    }
+
+    let dYAt = null;
+    if (deltas && deltas.length >= 1) {
+        let dMax = -Infinity;
+        for (const v of deltas) if (Number.isFinite(v) && v > dMax) dMax = v;
+        const dRange = Math.max(dMax, 1e-6);
+        dYAt = v => padY + (1 - v / dRange) * iH;
+
+        // delta[i] is plotted at boundary (i+1); flat-extend delta[0] back to boundary 0.
+        const lastB = deltas.length;
+        ctx.beginPath();
+        ctx.moveTo(xAt(0), H - padY);
+        ctx.lineTo(xAt(0), dYAt(deltas[0]));
+        for (let i = 0; i < deltas.length; i++) ctx.lineTo(xAt(i + 1), dYAt(deltas[i]));
+        ctx.lineTo(xAt(lastB), H - padY);
+        ctx.closePath();
+        ctx.fillStyle = "rgba(230, 126, 34, 0.15)";
+        ctx.fill();
+
+        ctx.strokeStyle = "#e67e22";
+        ctx.lineWidth = 1.3;
+        if (deltas.length === 1) {
+            ctx.fillStyle = "#e67e22";
+            ctx.beginPath();
+            ctx.arc(xAt(1), dYAt(deltas[0]), 2, 0, Math.PI * 2);
+            ctx.fill();
+        } else {
+            ctx.beginPath();
+            for (let i = 0; i < deltas.length; i++) {
+                const px = xAt(i + 1), py = dYAt(deltas[i]);
+                if (i === 0) ctx.moveTo(px, py); else ctx.lineTo(px, py);
+            }
+            ctx.stroke();
+        }
+    }
+
+    // SamplerDetailBoost curve. `null` entries = outside the gate (drawn as faint baseline),
+    // finite entries = active (drawn as the bright cyan line, auto-scaled to its own peak).
+    if (Array.isArray(dbCurve) && dbCurve.length > 1) {
+        let dbMaxAbs = 0;
+        for (const v of dbCurve) if (Number.isFinite(v) && Math.abs(v) > dbMaxAbs) dbMaxAbs = Math.abs(v);
+        const dbXAt = i => padX + (i / Math.max(1, dbCurve.length - 1)) * iW;
+        const baselineY = H - padY - 0.5;
+        const isActive = v => Number.isFinite(v);
+
+        ctx.strokeStyle = "rgba(120, 200, 220, 0.25)";
+        ctx.lineWidth = 1;
+        ctx.beginPath();
+        let inactiveOpen = false;
+        for (let i = 0; i < dbCurve.length; i++) {
+            if (!isActive(dbCurve[i])) {
+                if (!inactiveOpen) { ctx.moveTo(dbXAt(i), baselineY); inactiveOpen = true; }
+                else ctx.lineTo(dbXAt(i), baselineY);
+            } else {
+                inactiveOpen = false;
+            }
+        }
+        ctx.stroke();
+
+        if (dbMaxAbs > 1e-9) {
+            const dbYAt = v => padY + (1 - Math.abs(v) / dbMaxAbs) * iH;
+            ctx.strokeStyle = "rgba(120, 200, 220, 0.85)";
+            ctx.fillStyle = "rgba(120, 200, 220, 0.85)";
+            ctx.lineWidth = 1.2;
+            ctx.beginPath();
+            let segStart = -1;
+            for (let i = 0; i <= dbCurve.length; i++) {
+                const v = i < dbCurve.length ? dbCurve[i] : null;
+                if (isActive(v)) {
+                    if (segStart < 0) { segStart = i; ctx.moveTo(dbXAt(i), dbYAt(v)); }
+                    else ctx.lineTo(dbXAt(i), dbYAt(v));
+                } else if (segStart >= 0) {
+                    if (i - segStart === 1) {
+                        const v0 = dbCurve[segStart];
+                        ctx.fillRect(dbXAt(segStart) - 1, dbYAt(v0) - 1, 2, 2);
+                    }
+                    segStart = -1;
+                }
+            }
+            ctx.stroke();
+        }
+    }
+
+    if (lockedStep != null && lockedStep >= 0 && lockedStep < xSteps) {
+        const lx = xAt(lockedStep) + 0.5;
+        ctx.strokeStyle = "rgba(245, 200, 60, 0.9)";
+        ctx.lineWidth = 1.2;
+        ctx.setLineDash([4, 2]);
+        ctx.beginPath();
+        ctx.moveTo(lx, padY);
+        ctx.lineTo(lx, H - padY);
+        ctx.stroke();
+        ctx.setLineDash([]);
+    }
+
+    if (hoverStep != null && hoverStep >= 0 && hoverStep < xSteps) {
+        const hx = xAt(hoverStep) + 0.5;
+        ctx.strokeStyle = "rgba(208, 208, 208, 0.5)";
+        ctx.lineWidth = 1;
+        ctx.beginPath();
+        ctx.moveTo(hx, padY);
+        ctx.lineTo(hx, H - padY);
+        ctx.stroke();
+        if (sYAt && hoverStep < n) {
+            ctx.fillStyle = "#d0d0d0";
+            ctx.beginPath();
+            ctx.arc(hx - 0.5, sYAt(sigmas[hoverStep]), 2.5, 0, Math.PI * 2);
+            ctx.fill();
+        }
+        // delta[k-1] is plotted at boundary k.
+        if (dYAt && hoverStep >= 1 && (hoverStep - 1) < deltas.length) {
+            ctx.fillStyle = "#e67e22";
+            ctx.beginPath();
+            ctx.arc(hx - 0.5, dYAt(deltas[hoverStep - 1]), 2.5, 0, Math.PI * 2);
+            ctx.fill();
+        }
+    }
+}
+
+// totalSteps fixes the x-axis so the line grows left-to-right, not stretching to fill.
+function drawLineGraph(canvas, values, totalSteps) {
+    const { ctx, W, H } = syncCanvasDPR(canvas);
+    const padX = GRAPH_PAD_X, padY = 3;
+    const iW = W - 2 * padX, iH = H - 2 * padY;
+    ctx.clearRect(0, 0, W, H);
+    drawGridlines(ctx, W, H, padX, padY);
+    if (!values || values.length < 1) return;
+
+    let vMax = -Infinity, vMin = Infinity;
+    for (const v of values) { if (v > vMax) vMax = v; if (v < vMin) vMin = v; }
+    if (vMin > 0) vMin = 0;
+    const vRange = Math.max(vMax - vMin, 1e-6);
+
+    const xSteps = Math.max(totalSteps || values.length, values.length);
+
+    ctx.beginPath();
+    ctx.moveTo(padX, H - padY);
+    for (let i = 0; i < values.length; i++) {
+        const px = padX + (i / Math.max(1, xSteps - 1)) * iW;
+        const py = padY + (1 - (values[i] - vMin) / vRange) * iH;
+        ctx.lineTo(px, py);
+    }
+    ctx.lineTo(padX + ((values.length - 1) / Math.max(1, xSteps - 1)) * iW, H - padY);
+    ctx.closePath();
+    ctx.fillStyle = "rgba(230, 126, 34, 0.15)";
+    ctx.fill();
+
+    ctx.strokeStyle = "#e67e22";
+    ctx.lineWidth = 1.3;
+    ctx.beginPath();
+    for (let i = 0; i < values.length; i++) {
+        const px = padX + (i / Math.max(1, xSteps - 1)) * iW;
+        const py = padY + (1 - (values[i] - vMin) / vRange) * iH;
+        if (i === 0) ctx.moveTo(px, py); else ctx.lineTo(px, py);
+    }
+    ctx.stroke();
+}
+
+app.registerExtension({
+    name: "KJNodes.ModelPreviewOverride",
+    async beforeRegisterNodeDef(nodeType, nodeData) {
+        if (nodeData?.name !== "ModelPreviewOverrideKJ") return;
+
+        chainCallback(nodeType.prototype, "onNodeCreated", function () {
+            ensureStyles();
+            const node = this;
+
+            const root = el("div", "kj-pov-root");
+
+            const imageArea = el("div", "kj-pov-image-area", root);
+            // Double-buffered: decode() on the visible-to-be element so the bitmap is reused.
+            const imgA = el("img", "kj-pov-img", imageArea);
+            const imgB = el("img", "kj-pov-img", imageArea);
+            imgA.draggable = false;
+            imgB.draggable = false;
+            imgB.style.opacity = "0";
+            let visibleImg = imgA;
+            let pendingImg = imgB;
+            const img = imgA;  // alias used by class-toggle code; applied to both buffers
+            // WebP path: ImageDecoder → VideoFrame[] → canvas, driven by a global timer.
+            const videoCanvas = el("canvas", "kj-pov-img", imageArea);
+            videoCanvas.style.opacity = "0";
+            const videoCtx = videoCanvas.getContext("2d");
+            // MP4 path: double-buffered <video> for same no-flash reason as imgA/B.
+            function mkVideo() {
+                const v = el("video", "kj-pov-img", imageArea);
+                v.style.opacity = "0";
+                v.muted = true;
+                v.playsInline = true;
+                v.loop = true;
+                v.autoplay = true;
+                v.disablePictureInPicture = true;
+                return v;
+            }
+            const videoA = mkVideo();
+            const videoB = mkVideo();
+            let visibleVideo = videoA;
+            let pendingVideo = videoB;
+            const placeholder = el("div", "kj-pov-placeholder", imageArea);
+            placeholder.textContent = "waiting for sample…";
+            // Playback scrub bar — hidden until animated content is loaded.
+            const scrubBar = el("div", "kj-pov-scrub", imageArea);
+            const scrubFill = el("div", "kj-pov-scrub-fill", scrubBar);
+            scrubBar.style.display = "none";
+
+            const grip = el("div", "kj-pov-panel-grip", root);
+            grip.title = "Drag to resize panel";
+
+            const panel = el("div", "kj-pov-panel", root);
+            const header = el("div", "kj-pov-panel-header", panel);
+            const headerLeft = el("span", "kj-pov-header-left", header);
+            const headerTitle = el("span", "kj-pov-panel-title", headerLeft);
+            headerTitle.textContent = "Preview Override";
+            const headerSummary = el("span", "kj-pov-panel-summary", header);
+            headerSummary.textContent = "idle";
+
+            node.properties = node.properties || {};
+            if (typeof node.properties.kjPovPanelH === "number") {
+                panel.style.height = node.properties.kjPovPanelH + "px";
+            }
+
+            grip.addEventListener("mousedown", (e) => {
+                if (e.button !== 0) return;
+                e.preventDefault();
+                e.stopPropagation();
+                const startY = e.clientY;
+                const startH = panel.offsetHeight;
+                const move = (ev) => {
+                    const scale = app.canvas?.ds?.scale || 1;
+                    const dy = (ev.clientY - startY) / scale;
+                    const rootRect = root.getBoundingClientRect();
+                    const maxH = Math.max(60, rootRect.height / scale - 80);
+                    const newH = Math.max(60, Math.min(maxH, startH - dy));
+                    panel.style.height = newH + "px";
+                };
+                const up = () => {
+                    document.removeEventListener("mousemove", move);
+                    document.removeEventListener("mouseup", up);
+                    node.properties.kjPovPanelH = panel.offsetHeight;
+                    node.graph?.change?.();
+                };
+                document.addEventListener("mousemove", move);
+                document.addEventListener("mouseup", up);
+            });
+
+            chainCallback(node, "onConfigure", function () {
+                if (typeof node.properties?.kjPovPanelH === "number") {
+                    panel.style.height = node.properties.kjPovPanelH + "px";
+                }
+            });
+
+            const grid = el("div", "kj-pov-graphs-grid", panel);
+
+            function makeCell(labelText) {
+                const cell = el("div", "kj-pov-graph-cell", grid);
+                const head = el("div", "kj-pov-graph-head", cell);
+                const lbl = el("span", "kj-pov-graph-label", head);
+                lbl.textContent = labelText;
+                const valEl = el("span", "kj-pov-graph-value", head);
+                const canvas = el("canvas", "kj-pov-graph-canvas", cell);
+                return { canvas, valEl, lbl };
+            }
+            const sdRow = makeCell("σ / Δ");
+            const timeRow = makeCell("step time (ms)");
+            sdRow.canvas.style.cursor = "crosshair";
+            // sdRow.canvas.title = "Hover to scrub steps · click to lock · ← → to step · click again to unlock";
+            timeRow.canvas.title = "Click to toggle ms ↔ s";
+
+            // Click step-time cell to toggle ms ↔ s. Initial unit auto-picked from first value.
+            let timeUnitSeconds = false;
+            let lastStepMs = null;
+            let lastAvgStepMs = null;
+            let lastTotal = null;
+            let lastStep = null;
+            let lastW = null, lastH = null;
+            function fmtTime(ms) {
+                if (ms == null || !Number.isFinite(ms)) return "—";
+                return timeUnitSeconds ? `${(ms / 1000).toFixed(2)}s` : `${ms.toFixed(0)}ms`;
+            }
+            // history.stepMs[k-1] = step k's duration.
+            function stepTimeForDisplay() {
+                let idx = lastCurrentStep;
+                if (hoverStep != null) idx = hoverStep;
+                else if (lockedStep != null) idx = lockedStep;
+                const tIdx = idx - 1;
+                if (tIdx >= 0 && tIdx < history.stepMs.length) return history.stepMs[tIdx];
+                return null;
+            }
+            function renderTime() {
+                timeRow.lbl.textContent = timeUnitSeconds ? "step time (s)" : "step time (ms)";
+                timeRow.valEl.textContent = fmtTime(stepTimeForDisplay());
+                if (lastAvgStepMs != null && lastTotal != null && lastStep != null) {
+                    const eta = Math.max(0, lastTotal - lastStep) * lastAvgStepMs / 1000;
+                    const avgTxt = timeUnitSeconds
+                        ? `${(lastAvgStepMs / 1000).toFixed(2)}s/step`
+                        : `${lastAvgStepMs.toFixed(0)}ms/step`;
+                    headerSummary.textContent = `${lastW}×${lastH} · ${lastStep}/${lastTotal} · ${avgTxt} · ETA ${eta.toFixed(1)}s`;
+                }
+            }
+            timeRow.canvas.style.cursor = "pointer";
+            timeRow.lbl.style.cursor = "pointer";
+            timeRow.valEl.style.cursor = "pointer";
+            const toggleTimeUnit = (ev) => {
+                ev.stopPropagation();
+                timeUnitSeconds = !timeUnitSeconds;
+                renderTime();
+            };
+            timeRow.canvas.addEventListener("click", toggleTimeUnit);
+            timeRow.lbl.addEventListener("click", toggleTimeUnit);
+            timeRow.valEl.addEventListener("click", toggleTimeUnit);
+
+            // Per-run state. Declared together so helpers/handlers below can close over them.
+            let hoverStep = null;
+            let lockedStep = null;  // click-locked step; survives mouseleave for inspection
+            let lastCurrentStep = -1;
+            const history = { stepMs: [], delta: [] };
+            let cachedSigmas = null;
+            let cachedDbCurve = null;
+            let totalSteps = 0;
+            const stepBlobUrls = [];
+            const stepVideoFrames = [];  // WebP path: per-step VideoFrame[]
+            const stepMp4Urls = [];      // MP4 path: per-step blob URL
+            let liveBlobUrl = null;
+            let liveMp4Url = null;
+            // Global timer never resets between steps — scrub continues at the equivalent elapsed.
+            let playbackStartMs = null;
+            let videoRafId = null;
+            let currentMp4Url = null;
+
+            const SD_LEG_BASE =
+                '<span title="sigma — noise level at this boundary" style="color:#d0d0d0">σ</span>'
+                + ' / '
+                + '<span title="delta — magnitude of x0 change per step (orange fill = filled area, larger spike = bigger change)" style="color:#e67e22">Δ</span>';
+            const SD_LEG_WITH_DB = SD_LEG_BASE
+                + ' / '
+                + '<span title="SamplerDetailBoost curve — per-σ boost applied by the wrapped sampler (peak = amount setting)" style="color:rgb(120,200,220)">DB</span>';
+            function applySdLegend() {
+                sdRow.lbl.innerHTML = cachedDbCurve ? SD_LEG_WITH_DB : SD_LEG_BASE;
+            }
+            applySdLegend();
+
+            // Load into hidden buffer, await its decode(), then opacity-swap. See note on the
+            // imgA/imgB definitions about why decode must run on the soon-to-be-visible element.
+            function showLiveFrame(url) {
+                const target = pendingImg;
+                target.src = url;
+                target.decode().then(() => {
+                    if (hoverStep != null || lockedStep != null) return;
+                    if (liveBlobUrl !== url) return;
+                    if (target !== pendingImg) return;  // a newer frame already swapped
+                    target.style.opacity = "1";
+                    visibleImg.style.opacity = "0";
+                    const prev = visibleImg;
+                    visibleImg = target;
+                    pendingImg = prev;
+                }).catch(() => {});
+            }
+            // Baked WebP durations are dropped; rate comes live from preview_fps.
+            async function decodeAnimatedBlob(blob) {
+                if (typeof ImageDecoder === "undefined") return null;
+                try {
+                    const decoder = new ImageDecoder({ data: blob.stream(), type: blob.type });
+                    await decoder.completed;
+                    const track = decoder.tracks.selectedTrack;
+                    if (!track || track.frameCount <= 1) { decoder.close?.(); return null; }
+                    const frames = [];
+                    for (let i = 0; i < track.frameCount; i++) {
+                        const r = await decoder.decode({ frameIndex: i });
+                        frames.push(r.image);
+                    }
+                    decoder.close?.();
+                    return { frames };
+                } catch {
+                    return null;
+                }
+            }
+            function closeStepVideo(stepIdx) {
+                const v = stepVideoFrames[stepIdx];
+                if (!v) return;
+                for (const f of v.frames) { try { f.close(); } catch {} }
+                stepVideoFrames[stepIdx] = null;
+            }
+            function activeStepIdx() {
+                if (lockedStep != null) return lockedStep;
+                if (hoverStep != null) return hoverStep;
+                return lastCurrentStep;
+            }
+            // Read preview_fps live each tick so the widget retimes playback without re-encoding.
+            const fpsWidget = node.widgets?.find(w => w.name === "preview_fps");
+            function currentFps() {
+                const v = +fpsWidget?.value;
+                return Number.isFinite(v) && v > 0 ? v : 12;
+            }
+
+            // Pause state for the WebP path. MP4 uses videoEl.paused natively.
+            let isPaused = false;
+            let pauseAtMs = 0;
+            function elapsedMs() {
+                if (playbackStartMs == null) return 0;
+                return (isPaused ? pauseAtMs : performance.now()) - playbackStartMs;
+            }
+            // MP4 encode-time fps; scales videoEl.playbackRate for live preview_fps retime.
+            let bakedFps = null;
+
+            function mp4Active() {
+                return currentMp4Url != null && Number.isFinite(visibleVideo.duration) && visibleVideo.duration > 0;
+            }
+            function clipDurationMs() {
+                if (mp4Active()) return visibleVideo.duration * 1000;
+                const v = stepVideoFrames[activeStepIdx()];
+                if (v && v.frames.length > 1) return v.frames.length * (1000 / currentFps());
+                return 0;
+            }
+            function getProgress() {
+                if (mp4Active()) return visibleVideo.currentTime / visibleVideo.duration;
+                const dur = clipDurationMs();
+                if (dur <= 0 || playbackStartMs == null) return 0;
+                return (elapsedMs() % dur) / dur;
+            }
+            function setProgress(pos) {
+                pos = Math.max(0, Math.min(1, pos));
+                const dur = clipDurationMs();
+                if (dur <= 0) return;
+                // Sync the global timer so cross-clip scrub picks up here.
+                const ref = isPaused ? pauseAtMs : performance.now();
+                if (playbackStartMs == null) playbackStartMs = ref;
+                playbackStartMs = ref - pos * dur;
+                if (mp4Active()) {
+                    visibleVideo.currentTime = pos * visibleVideo.duration;
+                }
+            }
+            function togglePause() {
+                const willPause = !isPaused;
+                if (mp4Active()) {
+                    if (willPause) visibleVideo.pause();
+                    else visibleVideo.play().catch(() => {});
+                }
+                if (willPause) {
+                    pauseAtMs = performance.now();
+                    isPaused = true;
+                } else {
+                    if (playbackStartMs != null) playbackStartMs += performance.now() - pauseAtMs;
+                    isPaused = false;
+                }
+                scrubBar.classList.toggle("kj-pov-paused", isPaused);
+            }
+            let scrubRafId = null;
+            function tickScrub() {
+                scrubRafId = requestAnimationFrame(tickScrub);
+                const dur = clipDurationMs();
+                if (dur > 0) {
+                    if (scrubBar.style.display === "none") scrubBar.style.display = "block";
+                    scrubFill.style.width = (getProgress() * 100) + "%";
+                } else if (scrubBar.style.display !== "none") {
+                    scrubBar.style.display = "none";
+                }
+                if (mp4Active() && bakedFps != null && bakedFps > 0) {
+                    const rate = currentFps() / bakedFps;
+                    if (Math.abs(visibleVideo.playbackRate - rate) > 0.001) {
+                        visibleVideo.playbackRate = rate;
+                    }
+                }
+            }
+            scrubRafId = requestAnimationFrame(tickScrub);
+            scrubBar.addEventListener("mousedown", (ev) => {
+                if (ev.button !== 0) return;
+                ev.stopPropagation();
+                ev.preventDefault();
+                const rect = scrubBar.getBoundingClientRect();
+                const seek = (e) => setProgress((e.clientX - rect.left) / rect.width);
+                seek(ev);
+                const move = (e) => seek(e);
+                const up = () => {
+                    document.removeEventListener("mousemove", move);
+                    document.removeEventListener("mouseup", up);
+                };
+                document.addEventListener("mousemove", move);
+                document.addEventListener("mouseup", up);
+            });
+            // Click on the preview frame toggles pause (animated content only).
+            imageArea.addEventListener("click", (ev) => {
+                if (scrubBar.contains(ev.target)) return;
+                if (clipDurationMs() <= 0) return;
+                ev.stopPropagation();
+                togglePause();
+            });
+            function drawCurrentVideoFrame() {
+                const idx = activeStepIdx();
+                const v = stepVideoFrames[idx];
+                if (!v) return false;
+                if (playbackStartMs == null) playbackStartMs = performance.now();
+                const frameDurMs = 1000 / currentFps();
+                const totalMs = v.frames.length * frameDurMs;
+                const elapsed = elapsedMs() % totalMs;
+                const fIdx = Math.min(v.frames.length - 1, Math.floor(elapsed / frameDurMs));
+                const frame = v.frames[fIdx];
+                if (videoCanvas.width !== frame.displayWidth || videoCanvas.height !== frame.displayHeight) {
+                    videoCanvas.width = frame.displayWidth;
+                    videoCanvas.height = frame.displayHeight;
+                }
+                videoCtx.drawImage(frame, 0, 0);
+                return true;
+            }
+            function startVideoLoop() {
+                if (videoRafId != null) return;
+                const tick = () => {
+                    videoRafId = requestAnimationFrame(tick);
+                    if (!drawCurrentVideoFrame()) return;
+                    if (videoCanvas.style.opacity !== "1") {
+                        videoCanvas.style.opacity = "1";
+                        imgA.style.opacity = "0";
+                        imgB.style.opacity = "0";
+                    }
+                };
+                videoRafId = requestAnimationFrame(tick);
+            }
+            function stopVideoLoop() {
+                if (videoRafId != null) { cancelAnimationFrame(videoRafId); videoRafId = null; }
+                videoCanvas.style.opacity = "0";
+                visibleImg.style.opacity = "1";
+            }
+            // seeked → rVFC → double-rAF before hiding old video, to avoid the single-frame black gap.
+            function showMp4(url) {
+                if (url === currentMp4Url) return;
+                currentMp4Url = url;
+                if (playbackStartMs == null) playbackStartMs = performance.now();
+                const target = pendingVideo;
+                target.src = url;
+                const promote = () => {
+                    if (currentMp4Url !== url || target !== pendingVideo) return;
+                    // Re-snap to NOW — initial seek drifted during load/paint. Paused-aware.
+                    try {
+                        const dur = target.duration;
+                        if (Number.isFinite(dur) && dur > 0) {
+                            target.currentTime = (elapsedMs() / 1000) % dur;
+                        }
+                    } catch {}
+                    target.style.opacity = "1";
+                    requestAnimationFrame(() => requestAnimationFrame(() => {
+                        if (target !== visibleVideo && currentMp4Url === url) {
+                            visibleVideo.style.opacity = "0";
+                            visibleVideo.pause();
+                            const prev = visibleVideo;
+                            visibleVideo = target;
+                            pendingVideo = prev;
+                            imgA.style.opacity = "0";
+                            imgB.style.opacity = "0";
+                            videoCanvas.style.opacity = "0";
+                            // Re-pause if user paused before this step swap.
+                            if (isPaused) visibleVideo.pause();
+                        }
+                    }));
+                };
+                const afterSeek = () => {
+                    if (typeof target.requestVideoFrameCallback === "function") {
+                        target.requestVideoFrameCallback(() => promote());
+                    } else {
+                        promote();
+                    }
+                };
+                const onLoaded = () => {
+                    target.removeEventListener("loadeddata", onLoaded);
+                    try {
+                        const dur = target.duration;
+                        if (Number.isFinite(dur) && dur > 0) {
+                            // 'seeked' fires when the new currentTime is decoded and ready.
+                            target.addEventListener("seeked", afterSeek, { once: true });
+                            target.currentTime = (elapsedMs() / 1000) % dur;
+                        } else {
+                            afterSeek();
+                        }
+                        // Must play during load so rVFC fires; we re-pause after promote.
+                        target.play().catch(() => {});
+                    } catch { promote(); }
+                };
+                if (target.readyState >= 2) onLoaded();
+                else target.addEventListener("loadeddata", onLoaded, { once: true });
+            }
+            function hideMp4() {
+                visibleVideo.pause();
+                visibleVideo.style.opacity = "0";
+                pendingVideo.pause();
+                pendingVideo.style.opacity = "0";
+                visibleImg.style.opacity = "1";
+                currentMp4Url = null;
+            }
+            function setStepBlob(stepIdx, blob) {
+                const url = URL.createObjectURL(blob);
+                if (placeholder.parentNode) placeholder.remove();
+                if (blob.type === "video/mp4") {
+                    if (stepMp4Urls[stepIdx]) {
+                        try { URL.revokeObjectURL(stepMp4Urls[stepIdx]); } catch {}
+                    }
+                    stepMp4Urls[stepIdx] = url;
+                    liveMp4Url = url;
+                    if (hoverStep == null && lockedStep == null) showMp4(url);
+                    return;
+                }
+                if (stepBlobUrls[stepIdx]) {
+                    try { URL.revokeObjectURL(stepBlobUrls[stepIdx]); } catch {}
+                }
+                stepBlobUrls[stepIdx] = url;
+                liveBlobUrl = url;
+                if (blob.type === "image/webp") {
+                    decodeAnimatedBlob(blob).then(v => {
+                        if (!v) {
+                            // Single-frame webp — show via the img path.
+                            if (hoverStep == null && lockedStep == null && liveBlobUrl === url) {
+                                showLiveFrame(url);
+                            }
+                            return;
+                        }
+                        closeStepVideo(stepIdx);
+                        stepVideoFrames[stepIdx] = v;
+                        startVideoLoop();
+                    });
+                } else {
+                    if (hoverStep == null && lockedStep == null) {
+                        showLiveFrame(url);
+                    }
+                }
+            }
+            function resetFrames() {
+                for (const u of stepBlobUrls) {
+                    if (u) try { URL.revokeObjectURL(u); } catch {}
+                }
+                stepBlobUrls.length = 0;
+                liveBlobUrl = null;
+                for (let i = 0; i < stepVideoFrames.length; i++) closeStepVideo(i);
+                stepVideoFrames.length = 0;
+                stopVideoLoop();
+                for (const u of stepMp4Urls) {
+                    if (u) try { URL.revokeObjectURL(u); } catch {}
+                }
+                stepMp4Urls.length = 0;
+                liveMp4Url = null;
+                hideMp4();
+                playbackStartMs = null;
+                bakedFps = null;
+                isPaused = false;
+                scrubBar.classList.remove("kj-pov-paused");
+            }
+
+            function updateSdHeader() {
+                // Display priority: hover > locked > live. Hover gets [k] prefix, locked gets 🔒[k].
+                let idx = null;
+                let prefix = "";
+                if (hoverStep != null && cachedSigmas && hoverStep < cachedSigmas.length) {
+                    idx = hoverStep;
+                    prefix = `[${idx}] `;
+                } else if (lockedStep != null && cachedSigmas && lockedStep < cachedSigmas.length) {
+                    idx = lockedStep;
+                    prefix = `🔒[${idx}] `;
+                } else if (lastCurrentStep >= 0 && cachedSigmas) {
+                    idx = Math.min(lastCurrentStep, cachedSigmas.length - 1);
+                }
+                if (idx == null) return;
+                const sig = cachedSigmas[idx];
+                const dIdx = idx - 1;
+                const d = (dIdx >= 0 && dIdx < history.delta.length) ? history.delta[dIdx] : null;
+                let text = `${prefix}${fmt(sig, 3)} / ${fmt(d, 3)}`;
+                if (cachedDbCurve && idx < cachedDbCurve.length) {
+                    text += ` / ${fmt(cachedDbCurve[idx], 3)}`;
+                }
+                sdRow.valEl.textContent = text;
+            }
+
+            function redrawSd() {
+                drawSigmaDeltaGraph(sdRow.canvas, cachedSigmas, history.delta, lastCurrentStep, totalSteps, hoverStep, cachedDbCurve, lockedStep);
+                updateSdHeader();
+                renderTime();
+                // Scrub priority: locked > hover > live. Locked persists past mouseleave.
+                const displayStep = lockedStep != null ? lockedStep : hoverStep;
+                const targetIdx = displayStep != null ? displayStep : lastCurrentStep;
+                if (stepMp4Urls[targetIdx]) {
+                    stopVideoLoop();
+                    showMp4(stepMp4Urls[targetIdx]);
+                } else if (stepVideoFrames[targetIdx]) {
+                    hideMp4();
+                    startVideoLoop();
+                } else if (displayStep != null && stepBlobUrls[displayStep]) {
+                    stopVideoLoop();
+                    hideMp4();
+                    visibleImg.src = stepBlobUrls[displayStep];
+                } else if (liveMp4Url) {
+                    stopVideoLoop();
+                    showMp4(liveMp4Url);
+                } else if (liveBlobUrl) {
+                    stopVideoLoop();
+                    hideMp4();
+                    showLiveFrame(liveBlobUrl);
+                }
+            }
+
+            sdRow.canvas.addEventListener("mousemove", (ev) => {
+                if (!cachedSigmas) return;
+                const rect = sdRow.canvas.getBoundingClientRect();
+                const iW = Math.max(1, rect.width - 2 * GRAPH_PAD_X);
+                const xSteps = Math.max(totalSteps || cachedSigmas.length, cachedSigmas.length, history.delta.length);
+                const fx = (ev.clientX - rect.left - GRAPH_PAD_X) / iW;
+                const idx = Math.max(0, Math.min(xSteps - 1, Math.round(fx * (xSteps - 1))));
+                if (idx !== hoverStep) {
+                    hoverStep = idx;
+                    redrawSd();
+                }
+            });
+            sdRow.canvas.addEventListener("mouseleave", () => {
+                if (hoverStep != null) {
+                    hoverStep = null;
+                    redrawSd();
+                }
+            });
+
+            // Click toggles a lock at the hover position so the preview survives mouseleave.
+            sdRow.canvas.addEventListener("mousedown", (ev) => { ev.stopPropagation(); });
+            sdRow.canvas.addEventListener("click", (ev) => {
+                ev.preventDefault();
+                ev.stopPropagation();
+                if (lockedStep != null) {
+                    lockedStep = null;
+                } else if (cachedSigmas) {
+                    const rect = sdRow.canvas.getBoundingClientRect();
+                    const iW = Math.max(1, rect.width - 2 * GRAPH_PAD_X);
+                    const xSteps = Math.max(totalSteps || cachedSigmas.length, cachedSigmas.length, history.delta.length);
+                    const fx = (ev.clientX - rect.left - GRAPH_PAD_X) / iW;
+                    lockedStep = Math.max(0, Math.min(xSteps - 1, Math.round(fx * (xSteps - 1))));
+                }
+                redrawSd();
+            });
+
+            // Arrow-key scrub: gated on mouseOverPanel so we don't shadow ComfyUI's global keys.
+            let mouseOverPanel = false;
+            root.addEventListener("mouseenter", () => { mouseOverPanel = true; });
+            root.addEventListener("mouseleave", () => { mouseOverPanel = false; });
+            const onKey = (ev) => {
+                if (!mouseOverPanel) return;
+                if (ev.key === " " && clipDurationMs() > 0) {
+                    ev.preventDefault();
+                    ev.stopPropagation();
+                    togglePause();
+                    return;
+                }
+                if (!cachedSigmas) return;
+                if (ev.key !== "ArrowLeft" && ev.key !== "ArrowRight") return;
+                const xSteps = Math.max(totalSteps || cachedSigmas.length, cachedSigmas.length, history.delta.length);
+                const cur = lockedStep != null ? lockedStep : (hoverStep != null ? hoverStep : lastCurrentStep);
+                const next = Math.max(0, Math.min(xSteps - 1, cur + (ev.key === "ArrowRight" ? 1 : -1)));
+                ev.preventDefault();
+                ev.stopPropagation();
+                lockedStep = next;
+                redrawSd();
+            };
+            document.addEventListener("keydown", onKey, true);
+            chainCallback(node, "onRemoved", () => document.removeEventListener("keydown", onKey, true));
+
+            addMiddleClickPan(root);
+            addWheelPassthrough(root);
+
+            node.addDOMWidget("preview", "kj_preview", root, { serialize: false });
+            node.setSize([Math.max(node.size?.[0] ?? 360, 360), Math.max(node.size?.[1] ?? 480, 480)]);
+
+            function resetHistory() {
+                history.stepMs.length = 0;
+                history.delta.length = 0;
+                resetFrames();
+                // Clear hover/lock — setStepBlob skips updates while either is set.
+                hoverStep = null;
+                lockedStep = null;
+                // Allow the next run to re-pick its time unit from the first sample.
+                lastStepMs = null;
+            }
+
+            const handler = (data) => {
+                try {
+                    if (Array.isArray(data.sigmas) && data.sigmas.length > 1) {
+                        cachedSigmas = data.sigmas;
+                        cachedDbCurve = Array.isArray(data.db_curve) ? data.db_curve : null;
+                        resetHistory();
+                        applySdLegend();
+                    }
+                    // Indexed by boundary; step 0 = initial noise (image optional).
+                    if (typeof data.image === "string") {
+                        const mime = typeof data.mime === "string" ? data.mime : "image/jpeg";
+                        if (typeof data.fps === "number" && data.fps > 0) bakedFps = data.fps;
+                        setStepBlob(data.step, b64ToBlob(data.image, mime));
+                    }
+
+                    totalSteps = data.total || totalSteps;
+
+                    if (data.step_ms != null) history.stepMs.push(data.step_ms);
+                    if (data.delta != null) history.delta.push(data.delta);
+
+                    // Auto-pick unit from the first sample so slow steps default to seconds.
+                    if (lastStepMs == null && data.step_ms != null && data.step_ms > 1500) {
+                        timeUnitSeconds = true;
+                    }
+                    lastStepMs = data.step_ms;
+                    lastAvgStepMs = data.avg_step_ms;
+                    lastStep = data.step;
+                    lastTotal = data.total;
+                    lastW = data.w;
+                    lastH = data.h;
+                    // data.step is 1-based; set BEFORE renderTime so stepTimeForDisplay sees it.
+                    lastCurrentStep = data.step;
+                    renderTime();
+
+                    drawSigmaDeltaGraph(sdRow.canvas, cachedSigmas, history.delta, lastCurrentStep, totalSteps, hoverStep, cachedDbCurve);
+                    updateSdHeader();
+                    drawLineGraph(timeRow.canvas, history.stepMs, totalSteps);
+                } catch (err) {
+                    console.warn("[KJNodes.ModelPreviewOverride] preview decode failed:", err);
+                }
+            };
+            node._kjPreviewHandler = handler;
+
+            chainCallback(node, "onRemoved", function () {
+                node._kjPreviewHandler = null;
+                resetFrames();
+                stopVideoLoop();
+                if (scrubRafId != null) cancelAnimationFrame(scrubRafId);
+            });
+        });
+    },
+});
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/screencap_stream.js b/custom_nodes/ComfyUI-KJNodes/web/js/screencap_stream.js
new file mode 100644
index 0000000000000000000000000000000000000000..1fca8b8b5450086cb40849ab941add09d6f58574
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/screencap_stream.js
@@ -0,0 +1,500 @@
+import { addMiddleClickPan, addWheelPassthrough } from "./utility.js";
+const { app } = window.comfyAPI.app;
+
+app.registerExtension({
+	name: "KJNodes.ScreencapStream",
+	async beforeRegisterNodeDef(nodeType, nodeData) {
+		if (nodeData.name !== "ScreencapStream") return;
+
+		nodeType.prototype.onNodeCreated = function () {
+			const node = this;
+			const frameWidget = this.widgets.find(w => w.name === "frame_data");
+			frameWidget.computeSize = () => [0, -4];
+
+			// --- State ---
+			let stream = null;
+			let crop = null;               // { x, y, w, h } in video pixel coords, or null
+			let previewVisible = true;
+			let previewInterval = null;
+			let bitmapInterval = null;
+			let cachedBitmap = null;        // downscaled ImageBitmap for preview
+			let bitmapPending = false;
+			let drag = null;
+			let drawPending = false;
+			let cachedLayout = null;
+			let layoutDirty = true;
+			let updatingFromCrop = false;
+			let captureStartTime = null;
+			let timerInterval = null;
+
+			const EDGE = 8;
+			// separate canvases to avoid conflicts between preview and serialization
+			const serializeCanvas = document.createElement("canvas");
+			const serializeCtx = serializeCanvas.getContext("2d");
+
+			// --- DOM elements ---
+			const video = document.createElement("video");
+			video.autoplay = true;
+			video.muted = true;
+			video.style.cssText = "position:absolute;width:1px;height:1px;opacity:0;pointer-events:none;overflow:hidden;";
+
+			const container = document.createElement("div");
+			container.style.cssText = "width:100%;height:100%;background:#000;border-radius:4px;overflow:hidden;position:relative;";
+			container.appendChild(video);
+
+			const overlay = document.createElement("canvas");
+			overlay.style.cssText = "position:absolute;top:0;left:0;width:100%;height:100%;cursor:crosshair;";
+			container.appendChild(overlay);
+			const overlayCtx = overlay.getContext("2d");
+
+			addMiddleClickPan(container);
+			addWheelPassthrough(container);
+
+			// --- Widget references ---
+			const cropWWidget = node.widgets.find(w => w.name === "crop_width");
+			const cropHWidget = node.widgets.find(w => w.name === "crop_height");
+			const PREVIEW_MAX_RES = 960; // preview-only cap for performance
+
+			// --- Layout ---
+			function invalidateLayout() { layoutDirty = true; }
+
+			function getLayout() {
+				if (!layoutDirty && cachedLayout) return cachedLayout;
+				const oRect = overlay.getBoundingClientRect();
+				const vw = video.videoWidth, vh = video.videoHeight;
+				if (!vw || !vh || !oRect.width || !oRect.height) { cachedLayout = null; return null; }
+				const scale = Math.min(oRect.width / vw, oRect.height / vh);
+				cachedLayout = {
+					scale, vw, vh,
+					offX: (oRect.width - vw * scale) / 2,
+					offY: (oRect.height - vh * scale) / 2,
+				};
+				layoutDirty = false;
+				return cachedLayout;
+			}
+
+			function toVideoCoords(cx, cy) {
+				const L = getLayout();
+				if (!L) return { x: 0, y: 0 };
+				return {
+					x: Math.round(Math.max(0, Math.min(L.vw, (cx - L.offX) / L.scale))),
+					y: Math.round(Math.max(0, Math.min(L.vh, (cy - L.offY) / L.scale))),
+				};
+			}
+
+			function pointerToLocal(e) {
+				const r = overlay.getBoundingClientRect();
+				return { x: e.clientX - r.left, y: e.clientY - r.top };
+			}
+
+			// --- Output dimensions ---
+			// Single source of truth for what the output size will be.
+			function getOutputDimensions() {
+				const vw = video.videoWidth, vh = video.videoHeight;
+				if (!vw || !vh) return null;
+				const sw = crop ? crop.w : vw;
+				const sh = crop ? crop.h : vh;
+				return { w: sw, h: sh };
+			}
+
+			function updateSizeWidgets() {
+				updatingFromCrop = true;
+				const dims = getOutputDimensions();
+				if (dims) {
+					cropWWidget.value = dims.w;
+					cropHWidget.value = dims.h;
+					// clamp max to full capture dimensions
+					if (cropWWidget.options) cropWWidget.options.max = video.videoWidth || dims.w;
+					if (cropHWidget.options) cropHWidget.options.max = video.videoHeight || dims.h;
+				}
+				app.graph.setDirtyCanvas(true);
+				updatingFromCrop = false;
+			}
+
+			function updateCropFromWidgets() {
+				if (updatingFromCrop || !video.videoWidth || !crop) return;
+				const newW = Math.min(cropWWidget.value, video.videoWidth);
+				const newH = Math.min(cropHWidget.value, video.videoHeight);
+				if (newW <= 0 || newH <= 0) return;
+				const cx = crop.x + crop.w / 2;
+				const cy = crop.y + crop.h / 2;
+				let x = Math.round(cx - newW / 2);
+				let y = Math.round(cy - newH / 2);
+				x = Math.max(0, Math.min(x, video.videoWidth - newW));
+				y = Math.max(0, Math.min(y, video.videoHeight - newH));
+				crop = { x, y, w: newW, h: newH };
+				drawOverlay();
+			}
+
+			function clearCrop() {
+				if (video.videoWidth && video.videoHeight) {
+					crop = { x: 0, y: 0, w: video.videoWidth, h: video.videoHeight };
+				} else {
+					crop = null;
+				}
+				drawOverlay();
+				updateSizeWidgets();
+			}
+
+			// --- Cached bitmap ---
+			// Updated every 500ms for preview display.
+			// Downscaled to PREVIEW_MAX_RES for performance.
+			async function updateCachedBitmap() {
+				if (bitmapPending || !stream || !video.videoWidth) return;
+				bitmapPending = true;
+				try {
+					const vw = video.videoWidth, vh = video.videoHeight;
+					if (vw <= 0 || vh <= 0) { bitmapPending = false; return; }
+					// always full frame for preview — crop is drawn as overlay
+					let outW = vw, outH = vh;
+					if (Math.max(vw, vh) > PREVIEW_MAX_RES) {
+						const s = PREVIEW_MAX_RES / Math.max(vw, vh);
+						outW = Math.round(vw * s);
+						outH = Math.round(vh * s);
+					}
+					const bmp = await createImageBitmap(video, {
+						resizeWidth: outW,
+						resizeHeight: outH,
+						resizeQuality: "medium",
+					});
+					if (cachedBitmap) cachedBitmap.close();
+					cachedBitmap = bmp;
+				} catch { /* frame not ready */ }
+				bitmapPending = false;
+			}
+
+			function startBitmapLoop() {
+				stopBitmapLoop();
+				bitmapInterval = setInterval(updateCachedBitmap, 500);
+			}
+			function stopBitmapLoop() {
+				if (bitmapInterval) { clearInterval(bitmapInterval); bitmapInterval = null; }
+			}
+
+			// --- Preview drawing ---
+			function drawOverlay() {
+				if (drawPending) return;
+				drawPending = true;
+				requestAnimationFrame(() => {
+					drawPending = false;
+					renderOverlay();
+				});
+			}
+
+			function renderOverlay() {
+				const r = overlay.getBoundingClientRect();
+				const w = Math.round(r.width), h = Math.round(r.height);
+				if (overlay.width !== w || overlay.height !== h) {
+					overlay.width = w; overlay.height = h;
+					invalidateLayout();
+				}
+				overlayCtx.clearRect(0, 0, w, h);
+				if (!previewVisible) return;
+				const L = getLayout();
+				if (!L) return;
+
+				// draw from cached bitmap if available, else raw video
+				const src = cachedBitmap || video;
+				overlayCtx.drawImage(src, L.offX, L.offY, L.vw * L.scale, L.vh * L.scale);
+
+				if (!crop) return;
+
+				const rx = L.offX + crop.x * L.scale;
+				const ry = L.offY + crop.y * L.scale;
+				const rw = crop.w * L.scale;
+				const rh = crop.h * L.scale;
+
+				// darken outside crop
+				overlayCtx.fillStyle = "rgba(0,0,0,0.5)";
+				overlayCtx.fillRect(0, 0, w, ry);
+				overlayCtx.fillRect(0, ry + rh, w, h - ry - rh);
+				overlayCtx.fillRect(0, ry, rx, rh);
+				overlayCtx.fillRect(rx + rw, ry, w - rx - rw, rh);
+
+				overlayCtx.strokeStyle = "#fff";
+				overlayCtx.lineWidth = 1.5;
+				overlayCtx.setLineDash([4, 3]);
+				overlayCtx.strokeRect(rx, ry, rw, rh);
+				overlayCtx.setLineDash([]);
+			}
+
+			function startPreviewLoop() {
+				stopPreviewLoop();
+				previewInterval = setInterval(() => drawOverlay(), 100);
+			}
+			function stopPreviewLoop() {
+				if (previewInterval) { clearInterval(previewInterval); previewInterval = null; }
+			}
+
+			// --- Hit testing ---
+			function hitTest(lx, ly) {
+				if (!crop) return "draw";
+				const L = getLayout();
+				if (!L) return "draw";
+				const rx = L.offX + crop.x * L.scale;
+				const ry = L.offY + crop.y * L.scale;
+				const rw = crop.w * L.scale;
+				const rh = crop.h * L.scale;
+
+				const nearL = Math.abs(lx - rx) < EDGE;
+				const nearR = Math.abs(lx - (rx + rw)) < EDGE;
+				const nearT = Math.abs(ly - ry) < EDGE;
+				const nearB = Math.abs(ly - (ry + rh)) < EDGE;
+				const inX = lx > rx - EDGE && lx < rx + rw + EDGE;
+				const inY = ly > ry - EDGE && ly < ry + rh + EDGE;
+
+				if (nearT && nearL && inX && inY) return "nw";
+				if (nearT && nearR && inX && inY) return "ne";
+				if (nearB && nearL && inX && inY) return "sw";
+				if (nearB && nearR && inX && inY) return "se";
+				if (nearT && inX) return "n";
+				if (nearB && inX) return "s";
+				if (nearL && inY) return "w";
+				if (nearR && inY) return "e";
+				if (lx > rx && lx < rx + rw && ly > ry && ly < ry + rh) return "move";
+				return "draw";
+			}
+
+			const cursorMap = {
+				nw: "nwse-resize", se: "nwse-resize",
+				ne: "nesw-resize", sw: "nesw-resize",
+				n: "ns-resize", s: "ns-resize",
+				w: "ew-resize", e: "ew-resize",
+				move: "grab", draw: "crosshair",
+			};
+
+			// --- Widget callbacks ---
+			const origWCb = cropWWidget.callback;
+			const origHCb = cropHWidget.callback;
+			cropWWidget.callback = function() { origWCb?.apply(this, arguments); updateCropFromWidgets(); };
+			cropHWidget.callback = function() { origHCb?.apply(this, arguments); updateCropFromWidgets(); };
+
+			// --- Pointer events ---
+			overlay.addEventListener("pointermove", (e) => {
+				if (drag) {
+					e.stopPropagation();
+					e.preventDefault();
+					const p = pointerToLocal(e);
+					const v = toVideoCoords(p.x, p.y);
+					const dx = v.x - drag.startVX;
+					const dy = v.y - drag.startVY;
+					const o = drag.origCrop;
+					const maxW = video.videoWidth, maxH = video.videoHeight;
+
+					if (drag.type === "draw") {
+						let w = Math.abs(v.x - drag.startVX);
+						let h = Math.abs(v.y - drag.startVY);
+						if (e.shiftKey && w > 0 && h > 0) {
+							const aspect = drag.aspect || w / h;
+							drag.aspect = aspect;
+							if (w / h > aspect) w = Math.round(h * aspect);
+							else h = Math.round(w / aspect);
+						} else {
+							drag.aspect = null;
+						}
+						crop = {
+							x: v.x < drag.startVX ? drag.startVX - w : drag.startVX,
+							y: v.y < drag.startVY ? drag.startVY - h : drag.startVY,
+							w, h,
+						};
+					} else if (drag.type === "move") {
+						let nx = o.x + dx, ny = o.y + dy;
+						nx = Math.max(0, Math.min(maxW - o.w, nx));
+						ny = Math.max(0, Math.min(maxH - o.h, ny));
+						crop = { x: nx, y: ny, w: o.w, h: o.h };
+					} else {
+						let { x, y, w, h } = { ...o };
+						if (drag.type.includes("w")) { x = o.x + dx; w = o.w - dx; }
+						if (drag.type.includes("e")) { w = o.w + dx; }
+						if (drag.type.includes("n")) { y = o.y + dy; h = o.h - dy; }
+						if (drag.type.includes("s")) { h = o.h + dy; }
+						if (w < 0) { x += w; w = -w; }
+						if (h < 0) { y += h; h = -h; }
+						if (e.shiftKey && o.w > 0 && o.h > 0 && w > 0 && h > 0) {
+							const aspect = o.w / o.h;
+							if (drag.type.length === 2) {
+								if (w / h > aspect) w = Math.round(h * aspect);
+								else h = Math.round(w / aspect);
+								if (drag.type.includes("w")) x = o.x + o.w - w;
+								if (drag.type.includes("n")) y = o.y + o.h - h;
+							} else if (drag.type === "n" || drag.type === "s") {
+								w = Math.round(h * aspect);
+								x = o.x + Math.round((o.w - w) / 2);
+							} else {
+								h = Math.round(w / aspect);
+								y = o.y + Math.round((o.h - h) / 2);
+							}
+						}
+						x = Math.max(0, x); y = Math.max(0, y);
+						w = Math.min(w, maxW - x); h = Math.min(h, maxH - y);
+						crop = { x, y, w, h };
+					}
+					drawOverlay();
+					updateSizeWidgets();
+					return;
+				}
+				if (!previewVisible || !stream) return;
+				const p = pointerToLocal(e);
+				overlay.style.cursor = cursorMap[hitTest(p.x, p.y)];
+			});
+
+			overlay.addEventListener("pointerdown", (e) => {
+				if (!previewVisible || !stream) return;
+				e.stopPropagation();
+				e.preventDefault();
+				overlay.setPointerCapture(e.pointerId);
+				const p = pointerToLocal(e);
+				const type = hitTest(p.x, p.y);
+				const v = toVideoCoords(p.x, p.y);
+				drag = {
+					type,
+					startVX: v.x, startVY: v.y,
+					origCrop: crop ? { ...crop } : null,
+				};
+				if (type === "move") overlay.style.cursor = "grabbing";
+			});
+
+			overlay.addEventListener("pointerup", (e) => {
+				if (!drag) return;
+				e.stopPropagation();
+				if (drag.type === "move") overlay.style.cursor = "grab";
+				drag = null;
+				if (crop && (crop.w < 5 || crop.h < 5)) {
+						crop = { x: 0, y: 0, w: video.videoWidth, h: video.videoHeight };
+					}
+				drawOverlay();
+				updateSizeWidgets();
+			});
+
+			overlay.addEventListener("dblclick", (e) => {
+				e.stopPropagation();
+				e.preventDefault();
+				clearCrop();
+			});
+
+			overlay.addEventListener("contextmenu", (e) => {
+				e.stopPropagation();
+				e.preventDefault();
+				clearCrop();
+			});
+
+			// --- Resize observer ---
+			const resizeObs = new ResizeObserver(() => { invalidateLayout(); drawOverlay(); });
+			resizeObs.observe(container);
+
+			// --- Timer ---
+			function formatElapsed(ms) {
+				const s = Math.floor(ms / 1000);
+				const m = Math.floor(s / 60);
+				const h = Math.floor(m / 60);
+				const pad = (n) => String(n).padStart(2, "0");
+				if (h > 0) return `${h}:${pad(m % 60)}:${pad(s % 60)}`;
+				return `${m}:${pad(s % 60)}`;
+			}
+
+			function updateBtnTimer() {
+				if (!captureStartTime || !btnWidget) return;
+				const elapsed = formatElapsed(Date.now() - captureStartTime);
+				btnWidget.name = `\uD83D\uDD34 ${elapsed} - Stop capture`;
+				app.graph.setDirtyCanvas(true);
+			}
+
+			function startTimer() {
+				captureStartTime = Date.now();
+				timerInterval = setInterval(updateBtnTimer, 1000);
+				updateBtnTimer();
+			}
+
+			function stopTimer() {
+				if (timerInterval) { clearInterval(timerInterval); timerInterval = null; }
+				captureStartTime = null;
+			}
+
+			// --- Stream management ---
+			let btnWidget = null;
+
+			function stopStream() {
+				stopPreviewLoop();
+				stopBitmapLoop();
+				stopTimer();
+				if (cachedBitmap) { cachedBitmap.close(); cachedBitmap = null; }
+				if (stream) stream.getTracks().forEach(t => t.stop());
+				stream = null;
+				crop = null;
+				video.srcObject = null;
+				if (btnWidget) btnWidget.name = "Start capture";
+				app.graph.setDirtyCanvas(true);
+			}
+
+			btnWidget = this.addWidget("button", "Start capture", null, async () => {
+				if (stream) { stopStream(); return; }
+				try {
+					// capture at native resolution — browser constraints
+					// cause green tint. Preview is downscaled via
+					// createImageBitmap, output goes to Python at full res.
+					stream = await navigator.mediaDevices.getDisplayMedia({
+						video: { frameRate: { ideal: 5, max: 10 } },
+					});
+					video.srcObject = stream;
+					startTimer();
+					const track = stream.getVideoTracks()[0];
+					track.addEventListener("ended", stopStream);
+					video.addEventListener("loadedmetadata", () => {
+						crop = { x: 0, y: 0, w: video.videoWidth, h: video.videoHeight };
+						invalidateLayout();
+						updateSizeWidgets();
+						startBitmapLoop();
+						if (previewVisible) startPreviewLoop();
+					}, { once: true });
+				} catch { /* user cancelled */ }
+			});
+
+			// --- DOM widget ---
+			this.addDOMWidget("preview", "ScreencapPreview", container, {
+				serialize: false,
+				hideOnZoom: false,
+				getMinHeight: () => 200,
+			});
+
+			const previewBtn = this.addWidget("button", "Hide preview", null, () => {
+				previewVisible = !previewVisible;
+				previewBtn.name = previewVisible ? "Hide preview" : "Show preview";
+				if (previewVisible && stream) {
+					invalidateLayout();
+					startBitmapLoop();
+					startPreviewLoop();
+				} else {
+					stopPreviewLoop();
+					stopBitmapLoop();
+					overlayCtx.clearRect(0, 0, overlay.width, overlay.height);
+				}
+				app.graph.setDirtyCanvas(true);
+			});
+
+			// --- Frame serialization ---
+			// Always send full crop resolution to Python.
+			// Python handles downscaling with Lanczos for better quality.
+			frameWidget.serializeValue = () => {
+				if (!stream || !video.videoWidth) return "";
+				const vw = video.videoWidth, vh = video.videoHeight;
+				const sx = crop ? Math.min(crop.x, vw) : 0;
+				const sy = crop ? Math.min(crop.y, vh) : 0;
+				const sw = crop ? Math.min(crop.w, vw - sx) : vw;
+				const sh = crop ? Math.min(crop.h, vh - sy) : vh;
+				if (sw <= 0 || sh <= 0) return "";
+				serializeCanvas.width = sw;
+				serializeCanvas.height = sh;
+				serializeCtx.drawImage(video, sx, sy, sw, sh, 0, 0, sw, sh);
+				return serializeCanvas.toDataURL("image/png");
+			};
+
+			// --- Cleanup ---
+			const origOnRemoved = this.onRemoved;
+			this.onRemoved = function () {
+				stopStream();
+				resizeObs.disconnect();
+				origOnRemoved?.apply(this, arguments);
+			};
+		};
+	},
+});
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/setgetnodes.js b/custom_nodes/ComfyUI-KJNodes/web/js/setgetnodes.js
new file mode 100644
index 0000000000000000000000000000000000000000..cb4c9a4e61a3900ac406d63a5cbe71069fad783c
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/setgetnodes.js
@@ -0,0 +1,1711 @@
+const { app } = window.comfyAPI.app;
+
+// originally based on diffus3's SetGet: https://github.com/diffus3/ComfyUI-extensions
+
+// Nodes that allow you to hide connections for cleaner graphs
+
+let _typeColorMap;
+function setColorAndBgColor(node, type) {
+	if (!_typeColorMap) {
+		_typeColorMap = {
+			"DEFAULT": LGraphCanvas.node_colors.gray,
+			"MODEL": LGraphCanvas.node_colors.blue,
+			"LATENT": LGraphCanvas.node_colors.purple,
+			"VAE": LGraphCanvas.node_colors.red,
+			"WANVAE": LGraphCanvas.node_colors.red,
+			"CONDITIONING": LGraphCanvas.node_colors.brown,
+			"IMAGE": LGraphCanvas.node_colors.pale_blue,
+			"CLIP": LGraphCanvas.node_colors.yellow,
+			"FLOAT": LGraphCanvas.node_colors.green,
+			"MASK": { color: "#1c5715", bgcolor: "#1f401b"},
+			"INT": { color: "#1b4669", bgcolor: "#29699c"},
+			"CONTROL_NET": { color: "#156653", bgcolor: "#1c453b"},
+			"NOISE": { color: "#2e2e2e", bgcolor: "#242121"},
+			"GUIDER": { color: "#3c7878", bgcolor: "#1c453b"},
+			"SAMPLER": { color: "#614a4a", bgcolor: "#3b2c2c"},
+			"SIGMAS": { color: "#485248", bgcolor: "#272e27"},
+		};
+	}
+	const colors = _typeColorMap[type] || LGraphCanvas.node_colors?.gray;
+	if (colors) {
+		node.color = colors.color;
+		node.bgcolor = colors.bgcolor;
+	}
+}
+function getDisablePrefix() {
+	return app.ui.settings.getSettingValue("KJNodes.disablePrefix") ?? false;
+}
+function prefixedTitle(prefix, name) {
+	return (getDisablePrefix() ? "" : prefix + "_") + name;
+}
+function autoColor(node, type) {
+	if (!app.ui.settings.getSettingValue("KJNodes.nodeAutoColor")) return;
+	if (type === '*') { node.color = null; node.bgcolor = null; }
+	else setColorAndBgColor(node, type);
+}
+function addNodeToSelectedOrCursor(nodeType, side) {
+	const selected = Object.values(app.canvas.selected_nodes || {});
+	if (selected.length > 0) {
+		for (const n of selected) window.kjNodes.addNode(nodeType, n, { side, offset: 30 });
+	} else {
+		const canvas = app.canvas, graph = canvas.graph || app.graph;
+		const node = LiteGraph.createNode(nodeType);
+		if (!node) return;
+		node.pos = [canvas.graph_mouse[0], canvas.graph_mouse[1]];
+		graph.add(node);
+		canvas.selectNode(node, false);
+		canvas.setDirty(true, true);
+	}
+}
+// Temporary map for paste rename coordination between Set and Get nodes.
+// Key: old name, Value: new name. Cleared via setTimeout(0) after each paste cycle.
+// This works because both onConfigure calls (Set + Get) fire synchronously within
+// the same paste operation, before the timeout clears the entry.
+const _pasteRenameMap = new Map();
+
+let _forceShowAllLinks = false;
+
+function getShowLinksMode() {
+	if (_forceShowAllLinks) return "always";
+	return app.ui.settings.getSettingValue("KJNodes.showSetGetLinks") ?? "never";
+}
+const LGraphNode = LiteGraph.LGraphNode
+
+// Cross-graph traversal utilities with lexical scoping for subgraph support.
+// Set nodes propagate downward: a Set in a parent graph is visible to all descendant subgraphs.
+// Get nodes look upward: a Get searches its own graph first, then parent, then grandparent, etc.
+// Duplicate names are allowed across unrelated (sibling) subgraphs.
+
+function findRootGraph(graph) {
+	if (!graph) return null;
+	return graph.rootGraph || graph;
+}
+
+// Find which SubgraphNode in parentGraph wraps the given subgraph
+function findSubgraphNodeFor(parentGraph, innerNode) {
+	if (!parentGraph?._nodes || !innerNode?.graph) return null;
+	for (const n of parentGraph._nodes) {
+		if (n.subgraph && n.subgraph === innerNode.graph) return n;
+	}
+	return null;
+}
+
+// Walk from a subgraph up to root, returning [graph, parent, grandparent, ..., root]
+function getGraphAncestors(graph) {
+	if (!graph) return [];
+	const root = findRootGraph(graph);
+	if (!root || graph === root) return [root];
+
+	const chain = [graph];
+	const visited = new Set([graph]);
+	let current = graph;
+	// Walk up: find which SubgraphNode wraps current, then get its parent graph
+	while (current !== root) {
+		let found = false;
+		// Search root nodes
+		for (const n of root._nodes) {
+			if (n.subgraph === current) {
+				chain.push(root);
+				current = root;
+				found = true;
+				break;
+			}
+		}
+		if (found) break;
+		// Search sibling subgraphs (for nested subgraphs)
+		const subgraphs = root._subgraphs || root.subgraphs;
+		if (subgraphs) {
+			for (const sg of subgraphs.values()) {
+				if (sg === current || !sg._nodes) continue;
+				for (const n of sg._nodes) {
+					if (n.subgraph === current) {
+						if (visited.has(sg)) { found = false; break; }
+						visited.add(sg);
+						chain.push(sg);
+						current = sg;
+						found = true;
+						break;
+					}
+				}
+				if (found) break;
+			}
+		}
+		if (!found) {
+			// Can't find parent, add root as fallback
+			if (!chain.includes(root)) chain.push(root);
+			break;
+		}
+	}
+	return chain;
+}
+
+// Get all descendant subgraphs of a graph (children, grandchildren, etc.)
+function getGraphDescendants(graph, _visited) {
+	if (!graph?._nodes) return [];
+	const visited = _visited || new Set();
+	if (visited.has(graph)) return [];
+	visited.add(graph);
+	const descendants = [];
+	for (const n of graph._nodes) {
+		if (n.subgraph && !visited.has(n.subgraph)) {
+			descendants.push(n.subgraph);
+			descendants.push(...getGraphDescendants(n.subgraph, visited));
+		}
+	}
+	return descendants;
+}
+
+// Collect nodes of a type from specific graphs
+function collectNodesOfType(graphs, type) {
+	const results = [];
+	for (const g of graphs) {
+		if (!g?._nodes) continue;
+		for (const node of g._nodes) {
+			if (node.type === type) results.push({ node, graph: g });
+		}
+	}
+	return results;
+}
+
+// Find all nodes of type across ALL graphs (root + all subgraphs). Used for global operations.
+function findAllNodesOfType(graph, type) {
+	const root = findRootGraph(graph);
+	if (!root) return [];
+	const allGraphs = [root];
+	const subgraphs = root._subgraphs || root.subgraphs;
+	if (subgraphs) {
+		for (const sg of subgraphs.values()) allGraphs.push(sg);
+	}
+	return collectNodesOfType(allGraphs, type);
+}
+
+// Scoped setter lookup: search current graph, then ancestors (look up).
+function findSetterByName(graph, name) {
+	if (!name) return null;
+	for (const g of getGraphAncestors(graph)) {
+		if (!g?._nodes) continue;
+		for (const node of g._nodes) {
+			if (node.type === 'SetNode' && node.widgets[0].value === name) {
+				return { node, graph: g };
+			}
+		}
+	}
+	return null;
+}
+
+// Scoped getter lookup: search current graph + descendants (propagate down).
+function findGettersByName(graph, name) {
+	if (!name) return [];
+	const graphs = [graph, ...getGraphDescendants(graph)];
+	return collectNodesOfType(graphs, 'GetNode')
+		.filter(entry => entry.node.widgets[0].value === name);
+}
+
+// Get all visible SetNode names for a GetNode's combo dropdown.
+// Shows names from current graph + ancestors (what's in scope).
+// Also builds a source map for getOptionLabel. This module-level variable is safe
+// because the values getter and getOptionLabel run in the same synchronous render pass.
+let _setNameSourceMap = new Map();
+
+function getVisibleSetNames(graph, filterType) {
+	const sourceMap = new Map();
+	const ancestors = getGraphAncestors(graph);
+	const entries = collectNodesOfType(ancestors, 'SetNode');
+	for (const e of entries) {
+		const name = e.node.widgets[0].value;
+		if (!name) continue;
+		if (filterType && filterType !== '*') {
+			const setType = e.node.inputs[0]?.type;
+			if (setType && setType !== '*') {
+				const filterTypes = String(filterType).split(",");
+				if (!filterTypes.some(ft => ft === setType || setType.split(",").includes(ft))) continue;
+			}
+		}
+		if (!sourceMap.has(name)) {
+			sourceMap.set(name, e.graph === graph ? "local" : "parent");
+		}
+	}
+	_setNameSourceMap = sourceMap;
+	return [...sourceMap.keys()].sort();
+}
+
+// Force every GetNode (root + subgraphs) to swap its widget.options reference so Vue re-reads values.
+function refreshAllGetNodeCombos(graph) {
+	const root = findRootGraph(graph);
+	if (!root) return;
+	const allGraphs = [root];
+	const subgraphs = root._subgraphs || root.subgraphs;
+	if (subgraphs) {
+		for (const sg of subgraphs.values()) allGraphs.push(sg);
+	}
+	for (const g of allGraphs) {
+		if (!g?._nodes) continue;
+		for (const node of g._nodes) {
+			if (node.type === 'GetNode') node._refreshComboOptions?.();
+		}
+	}
+}
+
+// Exposed globally for use in contextmenu.js
+window.kjNodes = window.kjNodes || {};
+window.kjNodes.convertOutputsToSetGet = convertOutputsToSetGet;
+window.kjNodes.snapshotSelectedNodes = snapshotSelectedNodes;
+function convertOutputsToSetGet(graph, node) {
+	if (!graph || !node) return;
+	for (let slotIdx = 0; slotIdx < node.outputs.length; slotIdx++) {
+		const output = node.outputs[slotIdx];
+		if (!output.links || output.links.length === 0) continue;
+
+		const linkType = output.type || "*";
+		const linkName = output.name || linkType;
+
+		// Collect targets and link IDs to remove, skipping existing Set/Get nodes
+		const targets = [];
+		const linksToRemove = [];
+		for (const linkId of [...output.links]) {
+			const link = getLink(graph, linkId);
+			if (link) {
+				const targetNode = graph.getNodeById(link.target_id);
+				if (targetNode && targetNode.type !== 'SetNode' && targetNode.type !== 'GetNode') {
+					targets.push({ targetId: link.target_id, targetSlot: link.target_slot });
+					linksToRemove.push(linkId);
+				}
+			}
+		}
+		if (targets.length === 0) continue;
+
+		// Create Set node
+		const setNode = LiteGraph.createNode("SetNode");
+		if (!setNode) continue;
+		setNode.pos = [
+			node.pos[0] + node.size[0] + 30,
+			node.pos[1] + slotIdx * 60
+		];
+		graph.add(setNode);
+		setNode.flags.collapsed = true;
+
+		for (const linkId of linksToRemove) graph.removeLink(linkId);
+
+		// Connect source → Set node input
+		node.connect(slotIdx, setNode, 0);
+
+		// Set the name widget
+		setNode.widgets[0].value = linkName;
+		setNode.title = prefixedTitle("Set", linkName);
+		setNode.validateName(graph);
+		const finalName = setNode.widgets[0].value;
+		setNode.properties.previousName = finalName;
+
+		// Create a Get node for each target
+		for (const target of targets) {
+			const targetNode = graph.getNodeById(target.targetId);
+			if (!targetNode) continue;
+
+			const getNode = LiteGraph.createNode("GetNode");
+			if (!getNode) continue;
+			getNode.pos = [
+				targetNode.pos[0] - getNode.size[0] - 30,
+				targetNode.pos[1]
+			];
+			graph.add(getNode);
+			getNode.flags.collapsed = true;
+
+			getNode.widgets[0].value = finalName;
+			getNode.onRename();
+
+			getNode.connect(0, targetNode, target.targetSlot);
+		}
+	}
+	app.canvas.setDirty(true, true);
+}
+
+// Snapshot selection immediately (before right-click changes it)
+function snapshotSelectedNodes(node, typeFilter) {
+	const selected = Object.values(app.canvas.selected_nodes || {});
+	// Always include the right-clicked node, even if selection state is inconsistent
+	const nodeSet = new Set(selected);
+	nodeSet.add(node);
+	let nodes = [...nodeSet];
+	if (typeFilter) nodes = nodes.filter(n => n.type === typeFilter);
+	return nodes;
+}
+
+// Tracks nodes flagged with has_errors so they can be cleared on the next alert.
+// Always cleared before repopulating, so it only holds nodes from the most recent alert.
+const _errorNodes = new Set();
+function showAlert(message, nodes) {
+	const nodeList = nodes ? (Array.isArray(nodes) ? nodes : [nodes]) : [];
+	const nodeInfo = nodeList.map(n =>
+		`${n.title || n.type} [${Math.round(n.pos[0])}, ${Math.round(n.pos[1])}]`
+	).join(', ');
+	if (nodeList.length) {
+		// Clear previous error flags
+		for (const n of _errorNodes) n.has_errors = false;
+		_errorNodes.clear();
+		setTimeout(() => {
+			for (const n of nodeList) {
+				n.has_errors = true;
+				_errorNodes.add(n);
+			}
+			app.canvas?.setDirty(true, true);
+		}, 100);
+	}
+	app.extensionManager.toast.add({
+		severity: 'warn',
+		summary: "KJ Set/Get",
+		detail: nodeInfo ? `${message} — ${nodeInfo}` : message,
+		life: 5000,
+	});
+}
+function convertAllSetGetToLinks(graph) {
+	if (!graph) return;
+	const rootGraph = findRootGraph(graph);
+
+	// First pass: handle cross-graph pairs (must run before same-graph
+	// because converting cross-graph creates real links that replace
+	// the Set/Get, and same-graph pass would miss the removed nodes)
+	const allSetEntries = findAllNodesOfType(rootGraph, 'SetNode');
+	for (const { node: setNode, graph: setGraph } of [...allSetEntries]) {
+		const name = setNode.widgets[0].value;
+		if (!name) continue;
+		const allGetEntries = findGettersByName(rootGraph, name);
+		const crossGraphGetters = allGetEntries.filter(e => e.graph !== setGraph);
+		if (crossGraphGetters.length > 0) {
+			convertCrossGraphSetGet(setNode, setGraph, crossGraphGetters);
+		}
+	}
+
+	// Second pass: handle remaining same-graph pairs
+	for (const { node: setNode, graph: g } of [...findAllNodesOfType(rootGraph, 'SetNode')]) {
+		convertSetGetToLinks(g, setNode);
+	}
+}
+
+// Compat shim: older frontends may lack graph.getLink(), and _links may be a Map or
+// plain object depending on the litegraph version. The == null check catches both null
+// and undefined intentionally.
+function getLink(graph, linkId) {
+	if (linkId == null) return null;
+	if (graph.getLink) return graph.getLink(linkId);
+	return graph._links instanceof Map ? graph._links.get(linkId) : graph._links?.[linkId] ?? null;
+}
+
+// Collect {targetId, targetSlot} from an output slot's links
+function collectOutputConnections(graph, output) {
+	const connections = [];
+	if (output?.links) {
+		for (const linkId of [...output.links]) {
+			const link = getLink(graph, linkId);
+			if (link) connections.push({ targetId: link.target_id, targetSlot: link.target_slot });
+		}
+	}
+	return connections;
+}
+
+function convertCrossGraphSetGet(setNode, setGraph, crossGraphGetters) {
+	// Find what's connected to the SetNode's input
+	const setInput = setNode.inputs[0];
+	if (setInput.link == null) return;
+	const sourceLink = getLink(setGraph, setInput.link);
+	if (!sourceLink) return;
+	const sourceNode = setGraph.getNodeById(sourceLink.origin_id);
+	if (!sourceNode) return;
+	const sourceSlot = sourceLink.origin_slot;
+	const linkType = setNode.inputs[0].type || '*';
+
+	for (const { node: getter, graph: getterGraph } of crossGraphGetters) {
+		// Collect getter's downstream connections before removing it
+		const connections = collectOutputConnections(getterGraph, getter.outputs[0]);
+		if (connections.length === 0) {
+			getterGraph.remove(getter);
+			continue;
+		}
+
+		// Determine direction and create SubgraphInput or SubgraphOutput
+		const rootGraph = findRootGraph(setGraph);
+		const sgNodeForGetter = findSubgraphNodeFor(rootGraph, getter);
+		const sgNodeForSetter = findSubgraphNodeFor(rootGraph, setNode);
+
+		if (sgNodeForGetter && setGraph === rootGraph) {
+			// Set in root, Get in subgraph → create SubgraphInput
+			const subgraph = sgNodeForGetter.subgraph;
+			const inputName = setNode.widgets[0].value || linkType;
+			const newInput = subgraph.addInput(inputName, linkType);
+			const inputIndex = subgraph.inputs.indexOf(newInput);
+
+			// Connect source → SubgraphNode's new input in root graph
+			sourceNode.connect(sourceSlot, sgNodeForGetter, inputIndex);
+
+			// Inside subgraph: connect SubgraphInput slot → getter's targets
+			for (const conn of connections) {
+				if (conn.targetId === subgraph.outputNode?.id) {
+					// GetNode fed a SubgraphOutput — bypass it and connect source directly in parent graph.
+					const sgNodeOutput = sgNodeForGetter.outputs[conn.targetSlot];
+					if (sgNodeOutput?.links) {
+						for (const parentLinkId of [...sgNodeOutput.links]) {
+							const parentLink = getLink(rootGraph, parentLinkId);
+							if (parentLink) {
+								const parentTarget = rootGraph.getNodeById(parentLink.target_id);
+								if (parentTarget) {
+									sourceNode.connect(sourceSlot, parentTarget, parentLink.target_slot);
+								}
+							}
+						}
+					}
+					// Remove the now-unnecessary SubgraphOutput
+					const sgOutput = subgraph.outputs[conn.targetSlot];
+					if (sgOutput) {
+						subgraph.removeOutput(sgOutput);
+					}
+				} else {
+					const targetNode = getterGraph.getNodeById(conn.targetId);
+					if (targetNode) {
+						newInput.connect(targetNode.inputs[conn.targetSlot], targetNode);
+					}
+				}
+			}
+
+			// If no connections used the SubgraphInput, remove it
+			if (newInput.linkIds.length === 0) {
+				subgraph.removeInput(newInput);
+			}
+
+			getterGraph.remove(getter);
+
+		} else if (sgNodeForSetter && getterGraph === rootGraph) {
+			// Set in subgraph, Get in root → create SubgraphOutput
+			const subgraph = sgNodeForSetter.subgraph;
+			const outputName = setNode.widgets[0].value || linkType;
+			const newOutput = subgraph.addOutput(outputName, linkType);
+			const outputIndex = subgraph.outputs.indexOf(newOutput);
+
+			// Inside subgraph: connect source output → SubgraphOutput slot
+			newOutput.connect(sourceNode.outputs[sourceSlot], sourceNode);
+
+			// In root graph: connect SubgraphNode's new output → getter's targets
+			for (const conn of connections) {
+				const targetNode = getterGraph.getNodeById(conn.targetId);
+				if (targetNode) {
+					sgNodeForSetter.connect(outputIndex, targetNode, conn.targetSlot);
+				}
+			}
+
+			getterGraph.remove(getter);
+
+		} else {
+			// Both in different subgraphs (sibling) — would need both input and output
+			// Skip for now, too complex
+			console.warn(`KJNodes: Cannot convert cross-graph Set/Get "${setNode.widgets[0].value}" between sibling subgraphs`);
+		}
+	}
+
+	// Remove SetNode if all its getters have been handled (check if any same-graph getters remain)
+	const remainingGetters = setGraph._nodes.filter(
+		n => n.type === 'GetNode' && n.widgets[0].value === setNode.widgets[0].value
+	);
+	if (remainingGetters.length === 0) {
+		setGraph.remove(setNode);
+	}
+}
+
+function convertSetGetToLinks(graph, setNode) {
+	if (!graph || !setNode) return;
+
+	const name = setNode.widgets[0].value;
+	let sameGraphGetters = [];
+	if (name) {
+		const allGetters = findGettersByName(graph, name);
+		const crossGetters = [];
+		for (const entry of allGetters) {
+			if (entry.graph === graph) sameGraphGetters.push(entry.node);
+			else crossGetters.push(entry);
+		}
+		if (crossGetters.length > 0) {
+			convertCrossGraphSetGet(setNode, graph, crossGetters);
+		}
+	}
+
+	// Find the source connected to the Set node's input
+	const setInput = setNode.inputs[0];
+	if (setInput.link == null) return;
+	const sourceLink = getLink(graph, setInput.link);
+	if (!sourceLink) return;
+	const sourceNode = graph.getNodeById(sourceLink.origin_id);
+	if (!sourceNode) return;
+	const sourceSlot = sourceLink.origin_slot;
+
+	// Collect all consumer connections from Get nodes and SetNode's own output passthrough
+	const connections = [];
+	for (const getter of sameGraphGetters) {
+		connections.push(...collectOutputConnections(graph, getter.outputs[0]));
+	}
+	connections.push(...collectOutputConnections(graph, setNode.outputs[0]));
+
+	// Remove all Get nodes (this also removes their links)
+	for (const getter of sameGraphGetters) {
+		graph.remove(getter);
+	}
+	// Remove the Set node
+	graph.remove(setNode);
+
+	// Create direct links from source to each consumer
+	for (const conn of connections) {
+		const targetNode = graph.getNodeById(conn.targetId);
+		if (targetNode) {
+			sourceNode.connect(sourceSlot, targetNode, conn.targetSlot);
+		}
+	}
+
+	app.canvas?.setDirty(true, true);
+}
+
+// region SetNode
+app.registerExtension({
+	name: "KJNodes.SetNode",
+	registerCustomNodes() {
+		class SetNode extends LGraphNode {
+			static title = "Set";
+			static category = "KJNodes";
+			serialize_widgets = true;
+			drawConnection = false;
+			slotColor = "#FFF";
+			canvas = app.canvas;
+
+			constructor(title) {
+				super(title)
+				if (!this.properties) {
+					this.properties = {
+						"previousName": ""
+					};
+				}
+				this.properties["Node name for S&R"] = "SetNode";
+				this.properties["aux_id"] = "kijai/ComfyUI-KJNodes";
+				this.isVirtualNode = true; // This node is purely frontend and does not impact the resulting prompt so should not be serialized
+
+				this.addWidget(
+					"text",
+					"Constant",
+					'',
+					() => {
+						if (!this.graph || app.configuringGraph) return;
+						this.validateName(this.graph);
+						if (this.widgets[0].value !== '') {
+							this.title = prefixedTitle("Set", this.widgets[0].value);
+						}
+						this.update();
+						this.properties.previousName = this.widgets[0].value;
+					},
+					{}
+				)
+
+				this.addInput("*", "*");
+				this.addOutput("*", '*');
+			}
+
+			onConnectionsChange(
+				slotType,
+				slot,
+				isChangeConnect,
+				link_info
+			) {
+				// During graph load, slots are restored by configure — skip side effects
+				if (app.configuringGraph) return;
+				//On Disconnect
+				if (slotType === LiteGraph.INPUT && !isChangeConnect) {
+					const outputConnected = this.outputs[0]?.links?.length > 0;
+					if (outputConnected) {
+						this.inputs[slot].type = this.outputs[0].type;
+						this.inputs[slot].name = this.outputs[0].name;
+					} else {
+						this.inputs[slot].type = '*';
+						this.inputs[slot].name = '*';
+						this.outputs[0].type = '*';
+						this.outputs[0].name = '*';
+						this.title = "Set";
+						this.color = null;
+						this.bgcolor = null;
+					}
+					this.update();
+				}
+				if (slotType === LiteGraph.OUTPUT && !isChangeConnect) {
+					if (this.outputs && this.outputs[slot]) {
+						// Keep type if input has a real connection
+						const inputConnected = this.inputs[0]?.link != null;
+						if (inputConnected) {
+							this.outputs[slot].type = this.inputs[0].type;
+							this.outputs[slot].name = this.inputs[0].name;
+						} else {
+							this.inputs[0].type = '*';
+							this.inputs[0].name = '*';
+							this.outputs[slot].type = '*';
+							this.outputs[slot].name = '*';
+							this.color = null;
+							this.bgcolor = null;
+						}
+					}
+				}
+				//On Connect
+				if (link_info && this.graph && slotType === LiteGraph.INPUT && isChangeConnect) {
+					const resolve = link_info.resolve(this.graph)
+					const resolvedSlot = resolve?.subgraphInput ?? resolve?.output;
+					const type = resolvedSlot?.type;
+					if (type) {
+						if (this.title === "Set"){
+							this.title = prefixedTitle("Set", type);
+						}
+						if (this.widgets[0].value === '' || this.widgets[0].value === '*'){
+							// Determine the initial widget value based on naming setting
+							const namingMode = app.ui.settings.getSettingValue("KJNodes.setGetNaming") ?? "empty";
+							if (namingMode !== "empty") {
+								const link = getLink(this.graph, this.inputs[0]?.link);
+								const sourceNode = link ? this.graph.getNodeById(link.origin_id) : null;
+								const slotName = sourceNode?.outputs?.[link?.origin_slot]?.name || type;
+								switch (namingMode) {
+									case "slot name": this.widgets[0].value = slotName; break;
+									case "slot name (lowercase)": this.widgets[0].value = slotName.toLowerCase(); break;
+									case "slot name (UPPERCASE)": this.widgets[0].value = slotName.toUpperCase(); break;
+								}
+							}
+						}
+
+						this.validateName(this.graph);
+						this.properties.previousName = this.widgets[0].value;
+						this.inputs[0].type = type;
+						this.inputs[0].name = type;
+						this.outputs[0].type = type;
+						this.outputs[0].name = type;
+
+						autoColor(this, type);
+					} else {
+						showAlert(`node ${this.title} input undefined.`, this)
+					}
+				}
+				if (link_info && this.graph && slotType === LiteGraph.OUTPUT && isChangeConnect) {
+					const inputType = this.inputs[0]?.type;
+					if (inputType && inputType !== '*') {
+						this.outputs[0].type = inputType;
+						this.outputs[0].name = inputType;
+					} else {
+						const resolve = link_info.resolve(this.graph);
+						const type = resolve?.input?.type;
+						if (type && type !== '*') {
+							this.inputs[0].type = type;
+							this.inputs[0].name = type;
+							this.outputs[0].type = type;
+							this.outputs[0].name = type;
+							autoColor(this, type);
+						}
+					}
+				}
+
+				//Update either way
+				this.update();
+			}
+
+			// Returns true if the name was changed
+			validateName(graph, sameGraphOnly) {
+				let widgetValue = this.widgets[0].value;
+
+				if (widgetValue !== '') {
+					let tries = 0;
+					const existingValues = new Set();
+
+					// sameGraphOnly: only check the immediate graph (for paste/clone)
+					// otherwise check full scope (own graph + ancestors)
+					const scopeGraphs = sameGraphOnly ? [graph] : getGraphAncestors(graph);
+					const scopedSetNodes = collectNodesOfType(scopeGraphs, 'SetNode');
+					scopedSetNodes.forEach(entry => {
+						if (entry.node !== this) {
+							existingValues.add(entry.node.widgets[0].value);
+						}
+					});
+
+					const originalValue = widgetValue;
+					// Only strip _N suffix during paste to avoid FOO_0_1_2 accumulation.
+					// For manual renames, keep the full name as base (user may intend FOO_3).
+					const baseName = this._justAdded ? widgetValue.replace(/_\d+$/, '') : widgetValue;
+					while (existingValues.has(widgetValue)) {
+						widgetValue = baseName + "_" + tries;
+						tries++;
+					}
+
+					this.widgets[0].value = widgetValue;
+					this.title = prefixedTitle("Set", widgetValue);
+					return widgetValue !== originalValue;
+				}
+				return false;
+			}
+
+			clone() {
+				const cloned = super.clone();
+				cloned.inputs[0].name = '*';
+				cloned.inputs[0].type = '*';
+				cloned.properties.previousName = '';
+				cloned.size = cloned.computeSize();
+				return cloned;
+			}
+
+			onAdded() {
+				this._justAdded = true;
+				// Vue mode doesn't always re-extract GetNode options on SetNode add; skip during workflow load.
+				if (LiteGraph.vueNodesMode && this.graph && !app.configuringGraph) {
+					refreshAllGetNodeCombos(this.graph);
+				}
+			}
+
+			onRemoved() {
+				// Only Vue mode needs the refresh — legacy re-reads the values getter on every click.
+				if (!LiteGraph.vueNodesMode) return;
+				const g = this.graph;
+				if (!g) return;
+				// Defer: onRemoved fires before _nodes is spliced, so getters still see this SetNode.
+				setTimeout(() => refreshAllGetNodeCombos(g), 0);
+			}
+
+			onConfigure() {
+				// Only run paste logic when actually pasting, not during workflow load
+				if (this._justAdded && this.graph && !app.configuringGraph) {
+					const oldName = this.widgets[0].value;
+					this.validateName(this.graph, true);
+					this._justAdded = false;
+					const newName = this.widgets[0].value;
+					if (newName !== oldName) {
+						_pasteRenameMap.set(oldName, newName);
+						// Clear the map after this paste cycle
+						setTimeout(() => _pasteRenameMap.delete(oldName), 0);
+					}
+					// Reset type and color on paste — nothing is connected yet
+					if (this.inputs[0]?.link == null) {
+						this.inputs[0].type = '*';
+						this.inputs[0].name = '*';
+						this.outputs[0].type = '*';
+						this.outputs[0].name = '*';
+						this.color = null;
+						this.bgcolor = null;
+					}
+				}
+				this._justAdded = false;
+			}
+
+			update() {
+				if (!this.graph) return;
+
+				const getters = this.findGetters(this.graph);
+				getters.forEach(getter => {
+					getter.setType(this.inputs[0].type);
+				});
+
+				if (this.widgets[0].value && this.properties.previousName) {
+					const gettersWithPreviousName = this.findGetters(this.graph, true);
+					gettersWithPreviousName.forEach(getter => {
+						getter.setName(this.widgets[0].value);
+					});
+				}
+				app.canvas?.setDirty(true, true);
+			}
+
+			findGetters(graph, checkForPreviousName) {
+				const name = checkForPreviousName ? this.properties.previousName : this.widgets[0].value;
+				if (!name || name === '') return [];
+				// Scoped: searches own graph + descendant subgraphs
+				return findGettersByName(graph, name).map(entry => entry.node);
+			}
+
+			getExtraMenuOptions(_, options) {
+				options.unshift(
+					{
+						content: this.drawConnection ? "Hide connections" : "Show connections",
+						callback: () => {
+							this.drawConnection = !this.drawConnection;
+							const linkType = this.inputs[0].type;
+							this.slotColor = this.canvas.default_connection_color_byType[linkType];
+							this.canvas.setDirty(true, true);
+						},
+					},
+					{
+						content: "Hide all connections",
+						callback: () => {
+							for (const n of this.graph._nodes) {
+								if (n.type === "GetNode" || n.type === "SetNode") n.drawConnection = false;
+							}
+							this.canvas.setDirty(true, true);
+						},
+					
+					},
+				);
+				options.unshift({
+					content: "Convert to links",
+					callback: () => {
+						const graph = this.graph;
+						for (const n of snapshotSelectedNodes(this, 'SetNode')) convertSetGetToLinks(graph, n);
+					},
+				});
+				options.unshift({
+					content: "Add paired GetNode",
+					callback: () => {
+						const graph = this.graph;
+						const getNode = LiteGraph.createNode("GetNode");
+						if (!getNode) return;
+						getNode.pos = [this.pos[0] + this.size[0] + 30, this.pos[1]];
+						graph.add(getNode);
+						// Set the widget value to match — this drives type, color, and connection
+						const name = this.widgets[0].value;
+						if (getNode.widgets?.[0]) {
+							getNode.widgets[0].value = name;
+						}
+						getNode.onRename?.();
+						app.canvas.selectNode(getNode, false);
+						app.canvas.setDirty(true, true);
+					},
+				});
+				// Dynamically add a submenu for all getters
+				const allGettersForMenu = this.findGetters(this.graph);
+				if (allGettersForMenu.length) {
+					let gettersSubmenu = allGettersForMenu.map(getter => {
+						const sameGraph = getter.graph === this.graph;
+						const sgNode = !sameGraph ? findSubgraphNodeFor(this.graph, getter) : null;
+						const label = sameGraph
+							? `${getter.title} id: ${getter.id}`
+							: `${getter.title} (in subgraph${sgNode ? ': ' + (sgNode.title || sgNode.type) : ''})`;
+						return {
+							content: label,
+							callback: () => {
+								if (sameGraph) {
+									this.canvas.centerOnNode(getter);
+									this.canvas.selectNode(getter, false);
+								} else if (sgNode) {
+									this.canvas.openSubgraph(sgNode.subgraph, sgNode);
+									// Center on the getter after entering the subgraph
+									setTimeout(() => {
+										this.canvas.centerOnNode(getter);
+										this.canvas.selectNode(getter, false);
+									}, 0);
+								}
+								this.canvas.setDirty(true, true);
+							},
+						};
+					});
+			
+					options.unshift({
+						content: "Getters",
+						has_submenu: true,
+						submenu: {
+							title: "GetNodes",
+                            options: gettersSubmenu,
+						}
+					});
+				}
+			}
+		}
+
+		LiteGraph.registerNodeType("SetNode", SetNode);
+	},
+});
+
+// region GetNode
+app.registerExtension({
+	name: "KJNodes.GetNode",
+	registerCustomNodes() {
+		class GetNode extends LGraphNode {
+			static title = "Get";
+			static category = "KJNodes";
+			serialize_widgets = true;
+			drawConnection = false;
+			currentSetter = null;
+			canvas = app.canvas;
+
+			constructor(title) {
+				super(title)
+				if (!this.properties) {
+					this.properties = {};
+				}
+				this.properties["Node name for S&R"] = "GetNode";
+				this.properties["aux_id"] = "kijai/ComfyUI-KJNodes";
+				this.isVirtualNode = true; // This node is purely frontend and does not impact the resulting prompt so should not be serialized
+				const comboOptions = {
+					getOptionLabel: (value) => {
+						if (!value) return "";
+						const source = _setNameSourceMap.get(value);
+						if (!source || source === "local") return value;
+						return `${value} (${source})`;
+					},
+				};
+				Object.defineProperty(comboOptions, 'values', {
+					get: () => {
+						if (!this.graph) return [];
+						let filterType = null;
+						if (app.ui.settings.getSettingValue("KJNodes.filterGetNodeOptions") !== false
+							&& this.outputs[0]?.links?.length) {
+							const linkId = this.outputs[0].links[0];
+							const link = getLink(this.graph, linkId);
+							if (link) {
+								const targetNode = this.graph.getNodeById(link.target_id);
+								filterType = targetNode?.inputs?.[link.target_slot]?.type || null;
+							}
+						}
+						return getVisibleSetNames(this.graph, filterType);
+					},
+					enumerable: true,
+					configurable: true
+				});
+				const constantWidget = this.addWidget("combo", "Constant", "", () => { if (!app.configuringGraph) this.onRename(); }, comboOptions);
+				// Legacy-only fix: pass pre-labeled values to ContextMenu so the core filter shows the search input.
+				// Sidesteps ComboWidget's empty-array + getOptionLabel bug. Vue mode handles it natively.
+				const origOnClick = constantWidget.onClick?.bind(constantWidget);
+				constantWidget.onClick = (params) => {
+					if (LiteGraph.vueNodesMode) return origOnClick?.(params);
+					const { e, canvas, node } = params;
+					const x = e.canvasX - node.pos[0];
+					const width = constantWidget.width || node.size[0];
+					if (x < 40) return constantWidget.decrementValue({ e, node, canvas });
+					if (x > width - 40) return constantWidget.incrementValue({ e, node, canvas });
+					const rawValues = comboOptions.values;
+					const labels = rawValues.map(v => comboOptions.getOptionLabel(v) || v);
+					const menu = new LiteGraph.ContextMenu(labels, {
+						scale: Math.max(1, canvas.ds.scale),
+						event: e,
+						className: 'dark',
+						callback: (selectedLabel) => {
+							const idx = labels.indexOf(selectedLabel);
+							if (idx >= 0) constantWidget.setValue(rawValues[idx], { e, node, canvas });
+						}
+					});
+					// Color each entry's left border by its SetNode's type.
+					if (!_typeColorMap) setColorAndBgColor({}, '');   // ensure lazy map is initialized
+					const entries = menu.root?.querySelectorAll('.litemenu-entry');
+					rawValues.forEach((name, i) => {
+						if (!entries?.[i]) return;
+						const setter = findSetterByName(this.graph, name);
+						const type = setter?.node?.inputs?.[0]?.type;
+						const c = _typeColorMap?.[type];
+						const borderColor = canvas.default_connection_color_byType?.[type]
+							|| LGraphCanvas.link_type_colors?.[type]
+							|| (c && (c.groupcolor || c.bgcolor || c.color))
+							|| '#888';
+						entries[i].style.borderLeft = `4px solid ${borderColor}`;
+						entries[i].style.paddingLeft = '8px';
+					});
+				};
+				// Fresh options object (live getter preserved) + remove/re-add to force Vue re-extraction.
+				this._refreshComboOptions = () => {
+					const w = this.widgets?.[0];
+					if (!w) return;
+					const newOpts = { getOptionLabel: comboOptions.getOptionLabel };
+					Object.defineProperty(newOpts, 'values',
+						Object.getOwnPropertyDescriptor(comboOptions, 'values'));
+					w.options = newOpts;
+					const idx = this.widgets.indexOf(w);
+					if (idx >= 0) {
+						this.widgets.splice(idx, 1);
+						this.widgets.splice(idx, 0, w);
+					}
+				};
+				this.addOutput("*", '*');
+			}
+
+			onConnectionsChange() {
+				if (app.configuringGraph) return;
+				this.validateLinks();
+			}
+
+			setName(name) {
+				this.widgets[0].value = name;
+				this.onRename();
+				this.serialize();
+			}
+
+			onRename() {
+				const setter = this.findSetter(this.graph);
+				if (setter) {
+					this.setType(setter.inputs[0].type);
+					this.title = prefixedTitle("Get", setter.widgets[0].value);
+				} else {
+					this.setType('*');
+					const name = this.widgets[0].value;
+					this.title = name ? prefixedTitle("Get", name) : "Get";
+				}
+				app.canvas?.setDirty(true, true);
+			}
+
+			clone() {
+				const cloned = super.clone();
+				cloned.size = cloned.computeSize();
+				return cloned;
+			}
+
+			validateLinks() {
+				if (this.outputs[0].type !== '*' && this.outputs[0].links && this.graph) {
+					this.outputs[0].links.filter(linkId => {
+						const link = getLink(this.graph, linkId);
+						if (!link || !link.type) return false;
+						if (link.type === '*') return false;
+						const targetNode = this.graph.getNodeById(link.target_id);
+						const targetType = targetNode?.inputs?.[link.target_slot]?.type;
+						if (targetType === '*') return false;
+						if (targetType) {
+							const targetTypes = String(targetType).split(",");
+							if (targetTypes.includes(this.outputs[0].type)) return false;
+						}
+						return !link.type.split(",").includes(this.outputs[0].type);
+					}).forEach(linkId => {
+						this.graph.removeLink(linkId);
+					});
+				}
+			}
+
+			setType(type) {
+				this.outputs[0].name = type;
+				this.outputs[0].type = type;
+				this.validateLinks();
+				autoColor(this, type);
+			}
+
+			findSetter(graph) {
+				const name = this.widgets[0].value;
+				// Scoped: searches own graph first, then ancestors
+				const result = findSetterByName(graph, name);
+				return result ? result.node : undefined;
+			}
+
+			goToSetter() {
+				if (!this.currentSetter) return;
+				const setterGraph = this.currentSetter.graph;
+				if (setterGraph && setterGraph !== this.graph) {
+					this.canvas.setGraph(setterGraph);
+					setTimeout(() => {
+						this.canvas.centerOnNode(this.currentSetter);
+						this.canvas.selectNode(this.currentSetter, false);
+						this.canvas.setDirty(true, true);
+					}, 0);
+				} else {
+					this.canvas.centerOnNode(this.currentSetter);
+					this.canvas.selectNode(this.currentSetter, false);
+				}
+			}
+
+			onAdded() {
+				this._justAdded = true;
+			}
+
+			onConfigure() {
+				if (this._justAdded && !app.configuringGraph) {
+					const name = this.widgets[0].value;
+					if (name) {
+						// Check if our paired SetNode was renamed during this paste
+						const newName = _pasteRenameMap.get(name);
+						if (newName) {
+							this.widgets[0].value = newName;
+						}
+						// Restore type/color from setter after paste
+						setTimeout(() => this.onRename(), 0);
+					}
+				}
+				this._justAdded = false;
+			}
+
+			getInputLink(slot) {
+				// Same-graph: standard resolveOutput path.
+				// Cross-graph: handled by resolveVirtualOutput.
+				const name = this.widgets[0].value;
+				if (!name || name === '') return null;
+				const setter = this.graph?._nodes?.find(
+					n => n.type === 'SetNode' && n.widgets[0].value === name
+				);
+				if (setter) {
+					const slotInfo = setter.inputs[slot];
+					if (!slotInfo || slotInfo.link == null) return null;
+					return getLink(this.graph, slotInfo.link);
+				}
+				// Scoped lookup: no setter found in own graph or ancestors
+				if (name && !findSetterByName(this.graph, name)) {
+					showAlert("No SetNode found for " + name + "(" + this.type + ")", this);
+				}
+				return null;
+			}
+
+			resolveVirtualOutput(slot) {
+				const name = this.widgets[0].value;
+				// Scoped lookup: own graph first, then ancestors
+				const result = findSetterByName(this.graph, name);
+				if (!result) return undefined;
+
+				// Same graph — let the standard getInputLink path handle it
+				if (result.graph === this.graph) return undefined;
+
+				// Warn if multiple SetNodes with this name exist in scope
+				const scopeGraphs = getGraphAncestors(this.graph);
+				const scopedSetters = collectNodesOfType(scopeGraphs, 'SetNode')
+					.filter(e => e.node.widgets[0].value === name);
+				if (scopedSetters.length > 1) {
+					showAlert(`Multiple SetNodes named "${name}" found in scope. Rename duplicates or use "Convert to links" to resolve`, [this, ...scopedSetters.map(e => e.node)]);
+					return undefined;
+				}
+
+				const { node: setter, graph: setterGraph } = result;
+				const slotInfo = setter.inputs[slot];
+				if (!slotInfo || slotInfo.link == null) return undefined;
+
+				const link = getLink(setterGraph, slotInfo.link);
+				if (!link) return undefined;
+
+				const sourceNode = setterGraph.getNodeById(link.origin_id);
+				if (!sourceNode) return undefined;
+
+				return { node: sourceNode, slot: link.origin_slot };
+			}
+			getExtraMenuOptions(_, options) {
+				this.currentSetter = this.findSetter(this.graph)
+				if (!this.currentSetter) return
+				const sameGraph = this.currentSetter.graph === this.graph;
+				if (sameGraph) {
+					let menuEntry = this.drawConnection ? "Hide connections" : "Show connections";
+					options.unshift(
+						{
+							content: "Convert to links",
+							callback: () => {
+								const graph = this.graph;
+								const setters = new Set(snapshotSelectedNodes(this, 'GetNode')
+									.map(n => n.findSetter?.(graph)).filter(Boolean));
+								for (const s of setters) convertSetGetToLinks(graph, s);
+							},
+						},
+						{
+							content: "Go to setter",
+							callback: () => {
+								this.goToSetter();
+							},
+						},
+						{
+							content: menuEntry,
+							callback: () => {
+								if (!this.currentSetter) return;
+								const linkType = this.currentSetter.inputs[0].type;
+								// Toggle on the SetNode so the canvas-level hook draws it
+								this.currentSetter.drawConnection = !this.currentSetter.drawConnection;
+								this.currentSetter.slotColor = this.canvas.default_connection_color_byType[linkType];
+								this.drawConnection = this.currentSetter.drawConnection;
+								this.canvas.setDirty(true, true);
+							},
+						},
+					);
+				} else {
+					// Cross-graph setter — navigate to its graph
+					const setterGraph = this.currentSetter.graph;
+					const isRoot = setterGraph === findRootGraph(this.graph);
+					options.unshift(
+						{
+							content: `Go to setter (in ${isRoot ? 'parent graph' : 'subgraph'})`,
+							callback: () => {
+								const canvas = this.canvas;
+								canvas.setGraph(setterGraph);
+								setTimeout(() => {
+									canvas.centerOnNode(this.currentSetter);
+									canvas.selectNode(this.currentSetter, false);
+									canvas.setDirty(true, true);
+								}, 0);
+							},
+						},
+					);
+				}
+			}
+		}
+
+		LiteGraph.registerNodeType("GetNode", GetNode);
+	},
+});
+
+// region UI: commands, keybindings, canvas menu, draw hook, settings
+app.registerExtension({
+	name: "KJNodes.SetGetUI",
+	settings: [
+		{
+			id: "KJNodes.setGetNaming",
+			name: "Default SetNode widget value",
+			category: ["KJNodes", "Set & Get", "Default SetNode widget value"],
+			tooltip: "Initial Constant value when a Set node is first connected to a slot",
+			type: "combo",
+			options: ["empty", "slot name", "slot name (lowercase)", "slot name (UPPERCASE)"],
+			defaultValue: "empty",
+		},
+		{
+			id: "KJNodes.showSetGetLinks",
+			name: "Show links",
+			category: ["KJNodes", "Set & Get", "Show links"],
+			tooltip: "When to show virtual links between Set/Get pairs",
+			type: "combo",
+			options: ["never", "selected", "always"],
+			defaultValue: "never",
+			onChange: () => app.canvas?.setDirty(true, true),
+		},
+		{
+			id: "KJNodes.nodeAutoColor",
+			name: "Auto-color nodes",
+			category: ["KJNodes", "Set & Get", "Auto-color nodes"],
+			tooltip: "Automatically color Set/Get nodes based on their connection type",
+			type: "boolean",
+			defaultValue: true,
+		},
+		{
+			id: "KJNodes.disablePrefix",
+			name: "Disable Set_/Get_ prefix",
+			category: ["KJNodes", "Set & Get", "Disable Set_/Get_ prefix"],
+			tooltip: "Prevents automatically adding Set_ and Get_ prefixes to node titles",
+			defaultValue: false,
+			type: "boolean",
+		},
+		{
+			id: "KJNodes.shiftMiddleClickSetGet",
+			name: "Shift+middle-click creates Set/Get",
+			category: ["KJNodes", "Set & Get", "Shift+middle-click creates Set/Get"],
+			tooltip: "Shift+middle-click on a slot creates a connected SetNode (output) or GetNode (input) instead of Reroute",
+			type: "boolean",
+			defaultValue: true,
+		},
+		{
+			id: "KJNodes.middleClickSetGet",
+			name: "Middle-click creates Set/Get",
+			category: ["KJNodes", "Set & Get", "Middle-click creates Set/Get"],
+			tooltip: "Middle-click on a slot creates Set/Get instead of Reroute (overrides core behavior)",
+			type: "boolean",
+			defaultValue: false,
+		},
+	],
+	commands: [
+		{
+			id: "KJNodes.AddSetNodeToSelected",
+			label: "Add Set node to selected / at cursor",
+			function: () => addNodeToSelectedOrCursor("SetNode", "right"),
+		},
+		{
+			id: "KJNodes.AddGetNodeAtCursor",
+			label: "Add Get node to selected / at cursor",
+			function: () => addNodeToSelectedOrCursor("GetNode", "left"),
+		},
+		{
+			id: "KJNodes.ToggleForceShowSetGetLinks",
+			label: "Toggle force-show all Set/Get connections",
+			function: () => {
+				_forceShowAllLinks = !_forceShowAllLinks;
+				app.canvas.setDirty(true, true);
+			},
+		},
+	],
+	keybindings: [
+		{
+			commandId: "KJNodes.AddSetNodeToSelected",
+			combo: { key: "s", ctrl: true, shift: true },
+			targetElementId: "graph-canvas",
+		},
+		{
+			commandId: "KJNodes.AddGetNodeAtCursor",
+			combo: { key: "g", ctrl: true, shift: true },
+			targetElementId: "graph-canvas",
+		},
+		{
+			commandId: "KJNodes.ToggleForceShowSetGetLinks",
+			combo: { key: "l", ctrl: true, shift: true },
+			targetElementId: "graph-canvas",
+		},
+	],
+	getCanvasMenuItems() {
+		return [
+			{
+				content: "KJNodes",
+				has_submenu: true,
+				submenu: {
+					options: [
+						{
+							content: "Convert outputs on all selected nodes to Set/Get",
+							callback: () => {
+								const selected = Object.values(app.canvas.selected_nodes || {});
+								if (selected.length === 0) return;
+								for (const n of selected) convertOutputsToSetGet(app.graph, n);
+							},
+						},
+						{
+							content: "Convert selected Set/Get to links",
+							callback: () => {
+								const graph = app.graph;
+								const selected = Object.values(app.canvas.selected_nodes || {});
+								const setNodes = selected.filter(n => n.type === 'SetNode');
+								const getNodes = selected.filter(n => n.type === 'GetNode');
+								for (const n of setNodes) convertSetGetToLinks(graph, n);
+								const setters = new Set(getNodes.map(n => n.findSetter?.(graph)).filter(Boolean));
+								for (const s of setters) convertSetGetToLinks(graph, s);
+							},
+						},
+					],
+				},
+			},
+		];
+	},
+	setup() {
+		// Double-click GetNode to jump to its SetNode (works in both legacy and Vue modes)
+		document.addEventListener("dblclick", (e) => {
+			// Don't interfere when a ContextMenu (e.g. combo dropdown) is open
+			if (document.querySelector(".litecontextmenu")) return;
+			const canvas = app.canvas;
+			if (!canvas) return;
+			const selected = Object.values(canvas.selected_nodes || {});
+			if (selected.length !== 1) return;
+			const node = selected[0];
+			if (node.type === "GetNode") {
+				node.currentSetter = node.findSetter(node.graph);
+				if (node.currentSetter) node.goToSetter();
+			}
+		});
+		// Suppress title rename on collapsed GetNodes so double-click navigates instead
+		document.addEventListener("litegraph:canvas", (e) => {
+			if (e.detail?.subType !== 'node-double-click') return;
+			const node = e.detail.node;
+			if (node?.type === "GetNode" && node.flags?.collapsed) {
+				e.stopImmediatePropagation();
+			}
+		});
+
+		// Monkey-patch: no extension hook exists for filtering visible nodes.
+		// Keep Set/Get nodes with visible virtual connections in the visible set
+		// even when offscreen, so the link lines don't get clipped.
+		const originalComputeVisibleNodes = LGraphCanvas.prototype.computeVisibleNodes;
+		LGraphCanvas.prototype.computeVisibleNodes = function () {
+			const visible = originalComputeVisibleNodes.apply(this, arguments);
+			for (const node of this.graph._nodes) {
+				if ((node.type === "SetNode" || node.type === "GetNode") && node.drawConnection && !visible.includes(node)) {
+					visible.push(node);
+				}
+			}
+			return visible;
+		};
+
+		// Monkey-patch: no extension hook exists for custom canvas drawing.
+		// Draws virtual links between Set/Get pairs.
+		// Handles per-node "Show connections", "selected" mode, and "always" mode.
+		const origCanvasOnDrawBackground = app.canvas.onDrawBackground;
+		app.canvas.onDrawBackground = function(ctx, visibleArea) {
+			origCanvasOnDrawBackground?.call(this, ctx, visibleArea);
+
+			const graph = this.graph || app.graph;
+			if (!graph?._nodes) return;
+			const mode = getShowLinksMode();
+
+			// Build set of selected Set/Get names for "selected" mode
+			let selectedNames = null;
+			if (mode === "selected") {
+				const sel = Object.values(this.selected_nodes || {});
+				if (!sel.length) {
+					// No selection — still honor per-node drawConnection
+					if (!graph._nodes.some(n => n.type === 'SetNode' && n.drawConnection)) return;
+				} else {
+					selectedNames = new Set();
+					for (const n of sel) {
+						if (n.type === 'SetNode' || n.type === 'GetNode') {
+							selectedNames.add(n.widgets?.[0]?.value ?? n.title);
+						}
+					}
+					if (!selectedNames.size && !graph._nodes.some(n => n.type === 'SetNode' && n.drawConnection)) return;
+				}
+			} else if (mode === "never") {
+				if (!graph._nodes.some(n => n.type === 'SetNode' && n.drawConnection)) return;
+			}
+
+			// Collect all GetNodes once across current graph + descendants, grouped by name.
+			// This avoids re-discovering descendant graphs and re-scanning nodes per SetNode.
+			const gettersByName = new Map();
+			const scopeGraphs = [graph, ...getGraphDescendants(graph)];
+			for (const g of scopeGraphs) {
+				if (!g?._nodes) continue;
+				for (const node of g._nodes) {
+					if (node.type !== 'GetNode') continue;
+					const n = node.widgets?.[0]?.value;
+					if (!n) continue;
+					let list = gettersByName.get(n);
+					if (!list) { list = []; gettersByName.set(n, list); }
+					list.push(node);
+				}
+			}
+
+			for (const setNode of graph._nodes) {
+				if (setNode.type !== 'SetNode') continue;
+				const name = setNode.widgets?.[0]?.value ?? setNode.title;
+				const showByMode = mode === "always" || (mode === "selected" && selectedNames?.has(name));
+				if (!showByMode && !setNode.drawConnection) continue;
+
+				const allGetters = gettersByName.get(name) || [];
+				const drawTargets = [];
+				const seenSubgraphs = new Set();
+				for (const getter of allGetters) {
+					if (getter.graph === graph) {
+						drawTargets.push(getter);
+					} else {
+						const sgNode = findSubgraphNodeFor(graph, getter);
+						if (sgNode && !seenSubgraphs.has(sgNode)) {
+							seenSubgraphs.add(sgNode);
+							drawTargets.push(sgNode);
+						}
+					}
+				}
+				if (!drawTargets.length) continue;
+
+				const linkType = setNode.inputs[0]?.type;
+				const slotColor = app.canvas.default_connection_color_byType?.[linkType]
+					|| LGraphCanvas.link_type_colors?.[linkType]
+					|| setNode.bgcolor
+					|| (setNode.slotColor !== "#FFF" ? setNode.slotColor : null) || "#AAA";
+
+				const startPos = setNode.getConnectionPos(false, 0);
+
+				for (const target of drawTargets) {
+					const endPos = target.getConnectionPos(true, 0);
+					const highlighted = setNode.is_selected || target.is_selected;
+					const color = highlighted ? "#FFF" : slotColor;
+					app.canvas.renderLink(
+						ctx,
+						startPos,
+						endPos,
+						null,
+						false,
+						null,
+						color,
+						LiteGraph.RIGHT,
+						LiteGraph.LEFT
+					);
+				}
+			}
+		};
+
+		app.ui.settings.addSetting({
+			id: "KJNodes.filterGetNodeOptions",
+			name: "Filter Get node options by type",
+			category: ["KJNodes", "Set & Get", "Filter Get node options by type"],
+			tooltip: "When a Get node is connected, only show Set nodes with compatible types in the dropdown",
+			type: "boolean",
+			defaultValue: true,
+		});
+		app.ui.settings.addSetting({
+			id: "KJNodes.convertAllSetGet",
+			name: "Convert ALL Set/Get to links",
+			category: ["KJNodes", "Set & Get", "Convert ALL Set/Get to links"],
+			tooltip: "Replaces all Set/Get node pairs with direct links, including across subgraph boundaries (irreversible)",
+			type: () => {
+				const btn = document.createElement("button");
+				btn.textContent = "Convert ALL Set/Get to links";
+				btn.style.cssText = "padding: 6px 12px; cursor: pointer;";
+				btn.onclick = () => {
+					if (confirm("This will replace ALL Set/Get pairs with direct links. This is irreversible. Continue?")) {
+						convertAllSetGetToLinks(app.graph);
+						app.canvas?.setDirty(true, true);
+						app.extensionManager.toast.add({
+							severity: 'info',
+							summary: "KJ Set/Get",
+							detail: "All Set/Get nodes converted to direct links",
+							life: 3000,
+						});
+					}
+				};
+				return btn;
+			},
+			defaultValue: false,
+		});
+
+		// Middle-click Set/Get: let the core do slot hit detection, then intercept
+		// createDefaultNodeForSlot to create Set/Get instead of reroute.
+		let _fromMiddleClick = false;
+		let _lastMiddleClickShift = false;
+		const origProcessMiddle = LGraphCanvas.prototype._processMiddleButton;
+		LGraphCanvas.prototype._processMiddleButton = function(e, node) {
+			_fromMiddleClick = true;
+			_lastMiddleClickShift = !!e.shiftKey;
+			const wantSetGet = _lastMiddleClickShift
+				? app.ui.settings.getSettingValue("KJNodes.shiftMiddleClickSetGet") ?? true
+				: app.ui.settings.getSettingValue("KJNodes.middleClickSetGet") ?? false;
+			const saved = LiteGraph.middle_click_slot_add_default_node;
+			if (wantSetGet) {
+				LiteGraph.middle_click_slot_add_default_node = true;
+			} else {
+				// Workaround: core slotDefaults.init() resets this flag to true
+				const coreSetting = app.ui.settings.getSettingValue("Comfy.Node.MiddleClickRerouteNode");
+				if (coreSetting === false) LiteGraph.middle_click_slot_add_default_node = false;
+			}
+			const result = origProcessMiddle.call(this, e, node);
+			LiteGraph.middle_click_slot_add_default_node = saved;
+			return result;
+		};
+		const origCreateDefault = LGraphCanvas.prototype.createDefaultNodeForSlot;
+		LGraphCanvas.prototype.createDefaultNodeForSlot = function(optPass) {
+			if (!_fromMiddleClick) return origCreateDefault.call(this, optPass);
+			_fromMiddleClick = false;
+			const wantSetGet = _lastMiddleClickShift
+				? app.ui.settings.getSettingValue("KJNodes.shiftMiddleClickSetGet") ?? true
+				: app.ui.settings.getSettingValue("KJNodes.middleClickSetGet") ?? false;
+			if (!wantSetGet) return origCreateDefault.call(this, optPass);
+
+			const isFrom = optPass.nodeFrom && optPass.slotFrom !== null;
+			const sourceNode = isFrom ? optPass.nodeFrom : optPass.nodeTo;
+			const slotIndex = isFrom ? optPass.slotFrom : optPass.slotTo;
+			if (!sourceNode || slotIndex == null) return origCreateDefault.call(this, optPass);
+			const graph = this.graph || app.graph;
+			if (!graph) return origCreateDefault.call(this, optPass);
+
+			const newNode = LiteGraph.createNode(isFrom ? "SetNode" : "GetNode");
+			if (!newNode) return false;
+			const bounding = sourceNode.getBounding();
+			newNode.pos = [
+				isFrom ? bounding[0] + bounding[2] + 30 : bounding[0] - 200,
+				optPass.position?.[1] ?? bounding[1]
+			];
+			graph.add(newNode);
+			if (isFrom) sourceNode.connect(slotIndex, newNode, 0);
+			else newNode.connect(0, sourceNode, slotIndex);
+			graph.change();
+			this.setDirty(true, true);
+			return true;
+		};
+	}
+});
+
+// Temporary backwards-compat patch: makes cross-subgraph Set/Get work on frontends
+// that don't natively support resolveVirtualOutput. Remove when no longer needed.
+app.registerExtension({
+	name: "KJNodes.CrossGraphSetGet",
+	setup() {
+		let patched = false;
+
+		const originalGraphToPrompt = app.graphToPrompt.bind(app);
+		app.graphToPrompt = async function(...args) {
+			if (!patched) {
+				try {
+					const subgraphNode = app.graph._nodes.find(n => typeof n.getInnerNodes === 'function');
+					if (subgraphNode) {
+						const tempMap = new Map();
+						const dtos = subgraphNode.getInnerNodes(tempMap, []);
+						if (dtos.length > 0) {
+							const proto = Object.getPrototypeOf(dtos[0]);
+							const DtoClass = proto.constructor;
+							const nativeSource = proto.resolveOutput.toString();
+							const hasNativeSupport = nativeSource.includes('resolveVirtualOutput');
+							console.log(`[KJNodes] Cross-graph Set/Get: frontend native support ${hasNativeSupport ? 'detected, skipping patch' : 'not found, applying patch'}`);
+							if (!hasNativeSupport) {
+								const origResolveOutput = proto.resolveOutput;
+								proto.resolveOutput = function(slot, type, visited) {
+									if (typeof this.node?.resolveVirtualOutput === 'function') {
+										const virtualSource = this.node.resolveVirtualOutput(slot);
+										if (virtualSource) {
+											const inputNodeDto = [...this.nodesByExecutionId.values()]
+												.find(dto => dto instanceof DtoClass && dto.node === virtualSource.node);
+											if (inputNodeDto) {
+												return inputNodeDto.resolveOutput(virtualSource.slot, type, visited);
+											}
+											throw new Error(`KJNodes: No DTO found for cross-graph source node [${virtualSource.node.id}]`);
+										}
+									}
+									return origResolveOutput.call(this, slot, type, visited);
+								};
+							}
+							patched = true;
+						}
+					}
+				} catch (e) {
+					console.warn('[KJNodes] Failed to probe ExecutableNodeDTO for cross-graph patch:', e);
+				}
+			}
+			return originalGraphToPrompt(...args);
+		};
+	}
+});
+
+// Monkey-patch: no hook exists to extend the link context menu.
+// Appends a "Convert to Set/Get" entry by counting DOM menus before/after
+// the original call to find the newly created one. Fragile but unavoidable.
+app.registerExtension({
+	name: "KJNodes.LinkToSetGet",
+	setup() {
+		const originalShowLinkMenu = LGraphCanvas.prototype.showLinkMenu;
+		LGraphCanvas.prototype.showLinkMenu = function(segment, e) {
+			const menusBefore = document.querySelectorAll(".litecontextmenu").length;
+			const result = originalShowLinkMenu.call(this, segment, e);
+
+			const graph = this.graph;
+			if (!graph) return result;
+
+			// Get the full link object to access target info
+			const link = getLink(graph, segment.id);
+			if (!link || link.origin_id == null || link.target_id == null) return result;
+
+			// Find the newly created context menu
+			const menus = document.querySelectorAll(".litecontextmenu");
+			if (menus.length <= menusBefore) return result;
+			const lastMenu = menus[menus.length - 1];
+			if (!lastMenu) return result;
+
+			const entries = lastMenu.querySelector(".litemenu-entry")?.parentElement;
+			if (!entries) return result;
+
+			// Create separator
+			const separator = document.createElement("div");
+			separator.className = "litemenu-entry separator";
+			entries.appendChild(separator);
+
+			// Create menu entry
+			const menuItem = document.createElement("div");
+			menuItem.className = "litemenu-entry submenu";
+			menuItem.textContent = "Convert to Set/Get";
+			entries.appendChild(menuItem);
+
+			const canvas = this;
+			menuItem.addEventListener("click", function() {
+				// Close menu
+				lastMenu.remove();
+
+				const originNode = graph.getNodeById(link.origin_id);
+				const targetNode = graph.getNodeById(link.target_id);
+				if (!originNode || !targetNode) return;
+
+				const outputSlot = originNode.outputs[link.origin_slot];
+				const linkType = outputSlot?.type || "*";
+				const linkName = outputSlot?.name || linkType;
+
+				// Create Set node near the source
+				const setNode = LiteGraph.createNode("SetNode");
+				if (!setNode) return;
+				setNode.pos = [
+					originNode.pos[0] + originNode.size[0] + 30,
+					originNode.pos[1]
+				];
+				graph.add(setNode);
+
+				// Create Get node near the target
+				const getNode = LiteGraph.createNode("GetNode");
+				if (!getNode) return;
+				graph.add(getNode);
+				getNode.pos = [
+					targetNode.pos[0] - getNode.size[0] - 30,
+					targetNode.pos[1]
+				];
+
+				// Remove the original link
+				graph.removeLink(link.id);
+
+				// Connect source -> Set node input
+				originNode.connect(link.origin_slot, setNode, 0);
+
+				// Set the name widget on the Set node
+				setNode.widgets[0].value = linkName;
+				setNode.title = prefixedTitle("Set", linkName);
+				setNode.validateName(graph);
+				setNode.properties.previousName = setNode.widgets[0].value;
+
+				// Update Get node to match
+				const finalName = setNode.widgets[0].value;
+				getNode.widgets[0].value = finalName;
+				getNode.onRename();
+
+				// Connect Get node output -> target node input
+				getNode.connect(0, targetNode, link.target_slot);
+
+				canvas.setDirty(true, true);
+			});
+
+			return result;
+		};
+	}
+});
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/shake_to_disconnect.js b/custom_nodes/ComfyUI-KJNodes/web/js/shake_to_disconnect.js
new file mode 100644
index 0000000000000000000000000000000000000000..3c33c118a700851cfe793262c576745155c11fb7
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/shake_to_disconnect.js
@@ -0,0 +1,221 @@
+const { app } = window.comfyAPI.app;
+
+const state = {
+	pointerDown: false,
+	lastDelta: [0, 0],
+	lastPos: null,
+	reversalTimes: [],
+	lastScanTime: 0,
+	triggered: false,
+};
+
+// Cached from each setting's onChange to avoid per-pointermove lookups.
+const settings = {
+	enabled: true,
+	reversalsNeeded: 3,
+};
+
+// Top-left, not center: center shifts during resize, pos only moves on drag.
+function getNodeAnchor(node) {
+	return [node.pos[0], node.pos[1]];
+}
+
+function getDraggedNodes(lgCanvas) {
+	const items = lgCanvas.selectedItems;
+	if (!items || items.size === 0) return [];
+	return [...items].filter(n => n.inputs || n.outputs);
+}
+
+function nodeHasLinks(node) {
+	if (node.inputs?.some(i => i.link != null)) return true;
+	if (node.outputs?.some(o => o.links?.length > 0)) return true;
+	return false;
+}
+
+// Same end result as deleting all selected nodes except internal links between selected nodes are preserved.
+function executeShakeBreak(draggedNodes, graph, lgCanvas) {
+	if (draggedNodes.length === 0) return;
+
+	const selectedIds = new Set(draggedNodes.map(n => n.id));
+
+	lgCanvas.emitBeforeChange?.();
+	graph.beforeChange?.();
+
+	try {
+		for (const node of draggedNodes) bypassExternal(node, selectedIds, graph);
+		for (const node of draggedNodes) disconnectExternal(node, selectedIds, graph);
+	} finally {
+		graph.afterChange?.();
+		lgCanvas.emitAfterChange?.();
+	}
+
+	lgCanvas.setDirty(true, true);
+
+	state.triggered = true;
+	state.reversalTimes = [];
+}
+
+function bypassExternal(node, selectedIds, graph) {
+	const inputs = node.inputs || [];
+	for (let i = 0; i < inputs.length; i++) {
+		if (inputs[i].link == null) continue;
+		const inLink = graph.getLink(inputs[i].link);
+		if (!inLink || selectedIds.has(inLink.origin_id)) continue;
+		const inNode = graph.getNodeById(inLink.origin_id);
+		if (!inNode) continue;
+
+		traceAndBypass(node, i, inNode, inLink, selectedIds, graph, new Set());
+	}
+}
+
+// At each step, follows output[slot] (matching the input slot we arrived on).
+// Internal targets recurse; external targets get a direct upstream → target link.
+function traceAndBypass(currentNode, slot, inNode, inLink, selectedIds, graph, visited) {
+	const key = `${currentNode.id}:${slot}`;
+	if (visited.has(key)) return;
+	visited.add(key);
+
+	const output = currentNode.outputs?.[slot];
+	if (!output?.links?.length) return;
+
+	for (const outLinkId of [...output.links]) {
+		const outLink = graph.getLink(outLinkId);
+		if (!outLink) continue;
+
+		if (selectedIds.has(outLink.target_id)) {
+			const nextNode = graph.getNodeById(outLink.target_id);
+			if (nextNode) {
+				traceAndBypass(nextNode, outLink.target_slot, inNode, inLink, selectedIds, graph, visited);
+			}
+		} else {
+			const outNode = graph.getNodeById(outLink.target_id);
+			if (outNode) {
+				inNode.connect(inLink.origin_slot, outNode, outLink.target_slot, inLink.parentId);
+			}
+		}
+	}
+}
+
+function disconnectExternal(node, selectedIds, graph) {
+	if (node.inputs) {
+		for (let i = 0; i < node.inputs.length; i++) {
+			const linkId = node.inputs[i].link;
+			if (linkId == null) continue;
+			const link = graph.getLink(linkId);
+			if (!link || selectedIds.has(link.origin_id)) continue;
+			node.disconnectInput(i, true);
+		}
+	}
+	if (node.outputs) {
+		for (let o = 0; o < node.outputs.length; o++) {
+			const links = node.outputs[o].links;
+			if (!links?.length) continue;
+			for (const linkId of [...links]) {
+				const link = graph.getLink(linkId);
+				if (!link || selectedIds.has(link.target_id)) continue;
+				const targetNode = graph.getNodeById(link.target_id);
+				if (targetNode) targetNode.disconnectInput(link.target_slot, true);
+			}
+		}
+	}
+}
+
+function clearState() {
+	state.pointerDown = false;
+	state.lastDelta = [0, 0];
+	state.lastPos = null;
+	state.reversalTimes = [];
+	state.lastScanTime = 0;
+	state.triggered = false;
+}
+
+app.registerExtension({
+	name: "KJNodes.ShakeToDisconnect",
+
+	settings: [
+		{
+			id: "KJNodes.shakeToDisconnect",
+			name: "Enable shake to disconnect",
+			category: ["KJNodes", "Shake to Disconnect", "Enabled"],
+			tooltip: "Shake a selected node back and forth quickly to remove its links. Pass-through bypass is applied (same as deleting the node); when multiple nodes are selected, links between them are preserved and bypass propagates through the chain.",
+			type: "boolean",
+			defaultValue: false,
+			onChange: (v) => { settings.enabled = !!v; },
+		},
+		{
+			id: "KJNodes.shakeReversals",
+			name: "Reversals required",
+			category: ["KJNodes", "Shake to Disconnect", "Reversals required"],
+			tooltip: "Number of back-and-forth direction reversals needed to trigger disconnect. Lower = more sensitive (triggers from a small wiggle), higher = needs a more deliberate shake.",
+			type: "slider",
+			defaultValue: 3,
+			attrs: { min: 2, max: 6, step: 1 },
+			onChange: (v) => { settings.reversalsNeeded = Number(v) || 3; },
+		},
+	],
+
+	setup() {
+		const lgCanvas = app.canvas;
+		const canvasEl = lgCanvas.canvas;
+
+		document.addEventListener("pointerdown", (e) => {
+			if (e.button !== 0) return;
+			// Legacy mode targets the canvas; Vue mode targets a node DOM element.
+			const onCanvas = e.target === canvasEl;
+			const onVueNode = e.target?.closest?.("[data-node-id]");
+			if (!onCanvas && !onVueNode) return;
+
+			state.pointerDown = true;
+			state.lastDelta = [0, 0];
+			state.lastPos = null;
+			state.reversalTimes = [];
+			state.triggered = false;
+		}, true);
+
+		document.addEventListener("pointermove", () => {
+			if (!state.pointerDown) return;
+			if (state.triggered) return;
+			if (lgCanvas.connecting_links?.length) return;
+			if (lgCanvas.resizing_node) return;
+			if (lgCanvas.node_widget) return;
+
+			if (!settings.enabled) return;
+
+			const graph = lgCanvas.graph || app.graph;
+			if (!graph) return;
+
+			const now = performance.now();
+			const nodeCount = graph._nodes?.length ?? 0;
+			const throttle = nodeCount > 200 ? 50 : nodeCount > 100 ? 32 : 16;
+			if (now - state.lastScanTime < throttle) return;
+			state.lastScanTime = now;
+
+			const draggedNodes = getDraggedNodes(lgCanvas);
+			if (draggedNodes.length === 0) return;
+
+			const curPos = getNodeAnchor(draggedNodes[0]);
+			if (state.lastPos) {
+				// Thresholds are in screen-pixels; deltas are in graph-coords, so scale.
+				const scale = lgCanvas.ds?.scale ?? 1;
+				const sdx = (curPos[0] - state.lastPos[0]) * scale;
+				const sdy = (curPos[1] - state.lastPos[1]) * scale;
+				const dot = sdx * state.lastDelta[0] + sdy * state.lastDelta[1];
+				const magnitude = Math.sqrt(sdx * sdx + sdy * sdy);
+				if (dot < 0 && magnitude > 5) state.reversalTimes.push(now);
+				if (magnitude > 2) state.lastDelta = [sdx, sdy];
+			}
+			state.lastPos = curPos;
+			const cutoff = now - 600;
+			while (state.reversalTimes.length && state.reversalTimes[0] < cutoff) state.reversalTimes.shift();
+
+			if (state.reversalTimes.length >= settings.reversalsNeeded && draggedNodes.some(nodeHasLinks)) {
+				executeShakeBreak(draggedNodes, graph, lgCanvas);
+			}
+		}, true);
+
+		document.addEventListener("pointerup", (e) => {
+			if (e.button !== 0) return;
+			clearState();
+		}, true);
+	},
+});
diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/utility.js b/custom_nodes/ComfyUI-KJNodes/web/js/utility.js
new file mode 100644
index 0000000000000000000000000000000000000000..171443ac2ecbe8a4a3bebc1a95192dffa874cc2f
--- /dev/null
+++ b/custom_nodes/ComfyUI-KJNodes/web/js/utility.js
@@ -0,0 +1,271 @@
+const { app } = window.comfyAPI.app;
+
+// ─── General-purpose helpers shared across KJNodes JS ───
+
+export function makeUUID() {
+  let dt = new Date().getTime()
+  const uuid = 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, (c) => {
+    const r = ((dt + Math.random() * 16) % 16) | 0
+    dt = Math.floor(dt / 16)
+    return (c === 'x' ? r : (r & 0x3) | 0x8).toString(16)
+  })
+  return uuid
+}
+
+export function chainCallback(object, property, callback) {
+  if (object == undefined) {
+    console.error("Tried to add callback to non-existant object")
+    return;
+  }
+  if (property in object) {
+    const callback_orig = object[property]
+    object[property] = function () {
+      const r = callback_orig.apply(this, arguments);
+      callback.apply(this, arguments);
+      return r
+    };
+  } else {
+    object[property] = callback;
+  }
+}
+
+export function hideWidgetForGood(node, widget, suffix = '') {
+  widget.origType = widget.type
+  widget.origComputeSize = widget.computeSize
+  widget.origSerializeValue = widget.serializeValue
+  widget.computeSize = () => [0, -4]
+  widget.type = "converted-widget" + suffix
+  if (widget.linkedWidgets) {
+    for (const w of widget.linkedWidgets) {
+      hideWidgetForGood(node, w, ':' + widget.name)
+    }
+  }
+}
+
+// ─── Graph coordinate helpers ───
+
+export function clientToCanvas(lgCanvas, clientX, clientY) {
+  const rect = lgCanvas.canvas.getBoundingClientRect();
+  return [
+    (clientX - rect.left) / lgCanvas.ds.scale - lgCanvas.ds.offset[0],
+    (clientY - rect.top) / lgCanvas.ds.scale - lgCanvas.ds.offset[1],
+  ];
+}
+
+export function getNodeAtPoint(graph, cx, cy) {
+  for (let i = graph._nodes.length - 1; i >= 0; i--) {
+    if (graph._nodes[i].isPointInside(cx, cy)) return graph._nodes[i];
+  }
+  return null;
+}
+
+export function typesCompatible(a, b) {
+  if (a === "*" || b === "*") return true;
+  if (a === b) return true;
+  if (typeof a !== "string" || typeof b !== "string") return false;
+  if (a.toUpperCase() === b.toUpperCase()) return true;
+  // ComfyUI accepts union types like "STRING,INT" — match if any token overlaps
+  if (a.includes(",") || b.includes(",")) {
+    const aTokens = a.toUpperCase().split(",").map(s => s.trim()).filter(Boolean);
+    const bTokens = b.toUpperCase().split(",").map(s => s.trim()).filter(Boolean);
+    if (aTokens.includes("*") || bTokens.includes("*")) return true;
+    return aTokens.some(t => bTokens.includes(t));
+  }
+  return false;
+}
+
+// ─── Slot position helper ───
+// Uses modern API (DOM-registered positions in Vue mode) with legacy fallback
+export function getSlotPos(node, isInput, slotIdx) {
+  if (isInput && node.getInputPos) return node.getInputPos(slotIdx);
+  if (!isInput && node.getOutputPos) return node.getOutputPos(slotIdx);
+  const out = [0, 0];
+  node.getConnectionPos(isInput, slotIdx, out);
+  return out;
+}
+
+// ─── Middle-click pan passthrough for DOM widgets ───
+// Allows panning the LiteGraph canvas via middle-click drag on any DOM element
+export function addMiddleClickPan(element) {
+  const onMouseDown = (e) => {
+    if (e.button !== 1) return;
+    e.preventDefault();
+    const ds = app.canvas?.ds;
+    if (!ds) return;
+    const startX = e.clientX, startY = e.clientY;
+    const startOffsetX = ds.offset[0], startOffsetY = ds.offset[1];
+    const onMove = (me) => {
+      ds.offset[0] = startOffsetX + (me.clientX - startX);
+      ds.offset[1] = startOffsetY + (me.clientY - startY);
+      app.canvas.setDirty(true, true);
+    };
+    const onUp = () => {
+      document.removeEventListener('mousemove', onMove);
+      document.removeEventListener('mouseup', onUp);
+    };
+    document.addEventListener('mousemove', onMove);
+    document.addEventListener('mouseup', onUp);
+  };
+  element.addEventListener('mousedown', onMouseDown);
+  return () => element.removeEventListener('mousedown', onMouseDown);
+}
+
+// ─── Resolve preview image/video from a connected source node ───
+// Walks the graph link to find what's connected to node.inputs[inputSlot]
+// and returns { url, isVideo, videoEl } or null
+export function resolveSourcePreview(node, inputSlot) {
+  if (!node.graph) return null;
+  const input = node.inputs?.[inputSlot];
+  if (!input || input.link == null) return null;
+  const link = node.graph.links?.get(input.link);
+  if (!link) return null;
+  const srcNode = node.graph.getNodeById(link.origin_id);
+  if (!srcNode) return null;
+
+  // Check for VHS-style video preview widget
+  const vpWidget = srcNode.widgets?.find((w) => w.name === "videopreview");
+  if (vpWidget?.videoEl?.src) {
+    return { isVideo: true, videoEl: vpWidget.videoEl };
+  }
+
+  // Look for a LoadImage "image" widget or LoadVideo "video" widget
+  const w = srcNode.widgets?.find((w) => w.name === "image" || w.name === "video");
+  if (w?.value) {
+    let subfolder = "", fname = w.value;
+    const lastSlash = fname.lastIndexOf("/");
+    if (lastSlash >= 0) { subfolder = fname.substring(0, lastSlash); fname = fname.substring(lastSlash + 1); }
+    const isVideo = w.name === "video";
+    const url = `/view?filename=${encodeURIComponent(fname)}&type=input&subfolder=${encodeURIComponent(subfolder)}`;
+    return { url, isVideo };
+  }
+
+  // Fallback: check node.imgs (set after execution)
+  if (srcNode.imgs?.length > 0 && srcNode.imgs[0].src) {
+    return { url: srcNode.imgs[0].src, isVideo: false };
+  }
+  return null;
+}
+
+// ─── Source input watcher ───
+// Watches a named IMAGE input for connection changes and source widget changes.
+// Calls onChange(sources) when connections change or the source node's image/video widget changes.
+// sources: array of { url, isVideo, videoEl } per connected IMAGE input matching inputName (or all if inputName is null).
+// Returns a cleanup function.
+export function watchImageInputs(node, inputName, onChange) {
+  let watchedWidgets = [];
+
+  function unwatchWidgets() {
+    for (const { widget, origCb } of watchedWidgets) widget.callback = origCb;
+    watchedWidgets = [];
+  }
+
+  function resolve() {
+    if (!node.inputs) return [];
+    const slots = inputName
+      ? node.inputs.map((inp, i) => inp.name === inputName ? i : -1).filter(i => i >= 0)
+      : node.inputs.map((inp, i) => inp.type === "IMAGE" ? i : -1).filter(i => i >= 0);
+    return slots.map(i => resolveSourcePreview(node, i)).filter(s => s !== null);
+  }
+
+  function watch() {
+    unwatchWidgets();
+    if (!node.inputs || !node.graph) return;
+    const slots = inputName
+      ? node.inputs.map((inp, i) => inp.name === inputName ? i : -1).filter(i => i >= 0)
+      : node.inputs.map((inp, i) => inp.type === "IMAGE" ? i : -1).filter(i => i >= 0);
+    for (const slotIdx of slots) {
+      const input = node.inputs[slotIdx];
+      if (!input || input.link == null) continue;
+      const link = node.graph.links?.get(input.link);
+      if (!link) continue;
+      const srcNode = node.graph.getNodeById(link.origin_id);
+      if (!srcNode) continue;
+      const w = srcNode.widgets?.find(w => w.name === "image" || w.name === "video");
+      if (!w) continue;
+      const origCb = w.callback;
+      w.callback = function (...args) {
+        if (origCb) origCb.apply(this, args);
+        setTimeout(() => onChange(resolve()), 100);
+      };
+      watchedWidgets.push({ widget: w, origCb });
+    }
+  }
+
+  chainCallback(node, "onConnectionsChange", function (type) {
+    // Only react to input connection changes, not output
+    if (type != null && type !== 1) return;
+    setTimeout(() => {
+      watch();
+      onChange(resolve());
+    }, 100);
+  });
+
+  chainCallback(node, "onRemoved", unwatchWidgets);
+
+  return unwatchWidgets;
+}
+
+// ─── Video frame capture ───
+// Draws the current frame of a video element to a fresh canvas at native resolution.
+// If the video isn't decoded yet (readyState < 2), waits once for "loadeddata" before capturing.
+// Calls callback(canvas) with the resulting canvas. Caller decides whether to use it directly
+// (drawImage accepts canvas) or convert via toDataURL.
+export function captureVideoFrame(videoEl, callback) {
+  const capture = () => {
+    if (!videoEl.videoWidth || !videoEl.videoHeight) return;
+    const c = document.createElement("canvas");
+    c.width = videoEl.videoWidth;
+    c.height = videoEl.videoHeight;
+    c.getContext("2d").drawImage(videoEl, 0, 0);
+    callback(c);
+  };
+  if (videoEl.readyState >= 2) {
+    capture();
+  } else {
+    const onReady = () => { videoEl.removeEventListener("loadeddata", onReady); capture(); };
+    videoEl.addEventListener("loadeddata", onReady);
+  }
+}
+
+// ─── Bounding box hit test ───
+// Tests whether (mx, my) hits a corner handle or the interior of a rect defined by (x1, y1)–(x2, y2).
+// Returns "resize-tl", "resize-tr", "resize-bl", "resize-br", "resize-t", "resize-b", "resize-l", "resize-r", "move", or null.
+export function rectHitTest(mx, my, x1, y1, x2, y2, radius) {
+  const hit = (cx, cy) => Math.abs(mx - cx) < radius && Math.abs(my - cy) < radius;
+  // Corners first (higher priority)
+  if (hit(x1, y1)) return "resize-tl";
+  if (hit(x2, y1)) return "resize-tr";
+  if (hit(x1, y2)) return "resize-bl";
+  if (hit(x2, y2)) return "resize-br";
+  // Edges
+  if (mx >= x1 && mx <= x2 && Math.abs(my - y1) < radius) return "resize-t";
+  if (mx >= x1 && mx <= x2 && Math.abs(my - y2) < radius) return "resize-b";
+  if (my >= y1 && my <= y2 && Math.abs(mx - x1) < radius) return "resize-l";
+  if (my >= y1 && my <= y2 && Math.abs(mx - x2) < radius) return "resize-r";
+  // Interior
+  if (mx >= x1 && mx <= x2 && my >= y1 && my <= y2) return "move";
+  return null;
+}
+
+// Returns the appropriate CSS cursor for a bbox hit mode string.
+export function cursorForBboxMode(mode) {
+  if (mode === "move") return "move";
+  if (mode === "resize-tl" || mode === "resize-br") return "nwse-resize";
+  if (mode === "resize-tr" || mode === "resize-bl") return "nesw-resize";
+  if (mode === "resize-t" || mode === "resize-b") return "ns-resize";
+  if (mode === "resize-l" || mode === "resize-r") return "ew-resize";
+  return null;
+}
+
+// ─── Wheel zoom passthrough for DOM widgets ───
+// Re-dispatches wheel events to the LiteGraph canvas for zoom
+export function addWheelPassthrough(element) {
+  element.addEventListener("wheel", (e) => {
+    const gc = document.getElementById("graph-canvas");
+    if (gc) {
+      gc.dispatchEvent(new WheelEvent(e.type, e));
+      e.preventDefault();
+    }
+  }, { passive: false });
+}
+
diff --git a/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.0/LTX-2_I2V_Distilled_wLora.json b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.0/LTX-2_I2V_Distilled_wLora.json
new file mode 100644
index 0000000000000000000000000000000000000000..6bb986447bc42ac8a79f45bc614946ba61663e9d
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.0/LTX-2_I2V_Distilled_wLora.json
@@ -0,0 +1,3282 @@
+{
+  "id": "5775f45b-ae48-4889-b57c-dbdb70f74381",
+  "revision": 0,
+  "last_node_id": 5214,
+  "last_link_id": 13953,
+  "nodes": [
+    {
+      "id": 5185,
+      "type": "EmptyImage",
+      "pos": [
+        -2667.561287475645,
+        6824.636229314792
+      ],
+      "size": [
+        210,
+        130
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            13943
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1",
+        "Node name for S&R": "EmptyImage"
+      },
+      "widgets_values": [
+        1920,
+        1088,
+        1,
+        0
+      ]
+    },
+    {
+      "id": 5186,
+      "type": "PrimitiveInt",
+      "pos": [
+        -2669.265782707413,
+        7006.326003754771
+      ],
+      "size": [
+        210,
+        82
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "INT",
+          "type": "INT",
+          "links": [
+            13944
+          ]
+        }
+      ],
+      "title": "length",
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1",
+        "Node name for S&R": "PrimitiveInt"
+      },
+      "widgets_values": [
+        121,
+        "fixed"
+      ]
+    },
+    {
+      "id": 5196,
+      "type": "CM_FloatToInt",
+      "pos": [
+        -2673.4965346008653,
+        7259.13385362648
+      ],
+      "size": [
+        214.03773034527285,
+        58
+      ],
+      "flags": {},
+      "order": 13,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "a",
+          "type": "FLOAT",
+          "widget": {
+            "name": "a"
+          },
+          "link": 13948
+        }
+      ],
+      "outputs": [
+        {
+          "name": "INT",
+          "type": "INT",
+          "links": [
+            13949
+          ]
+        }
+      ],
+      "title": "Frame Rate",
+      "properties": {
+        "Node name for S&R": "CM_FloatToInt"
+      },
+      "widgets_values": [
+        0
+      ]
+    },
+    {
+      "id": 5190,
+      "type": "CreateVideo",
+      "pos": [
+        -1150.8629295969438,
+        6768.122273238377
+      ],
+      "size": [
+        210,
+        78
+      ],
+      "flags": {},
+      "order": 20,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 13930
+        },
+        {
+          "name": "audio",
+          "shape": 7,
+          "type": "AUDIO",
+          "link": 13931
+        },
+        {
+          "name": "fps",
+          "type": "FLOAT",
+          "widget": {
+            "name": "fps"
+          },
+          "link": 13932
+        }
+      ],
+      "outputs": [
+        {
+          "name": "VIDEO",
+          "type": "VIDEO",
+          "links": [
+            13946
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1",
+        "Node name for S&R": "CreateVideo"
+      },
+      "widgets_values": [
+        30
+      ]
+    },
+    {
+      "id": 4958,
+      "type": "SaveVideo",
+      "pos": [
+        -908.3162271395863,
+        6765.627599904565
+      ],
+      "size": [
+        632.4072425120453,
+        817.489191202842
+      ],
+      "flags": {},
+      "order": 21,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "video",
+          "type": "VIDEO",
+          "link": 13946
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1"
+      },
+      "widgets_values": [
+        "video/LTX-2",
+        "auto",
+        "auto"
+      ]
+    },
+    {
+      "id": 5184,
+      "type": "PrimitiveFloat",
+      "pos": [
+        -2674.0643381993596,
+        7141.5495111297
+      ],
+      "size": [
+        219.3647216161935,
+        58
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "FLOAT",
+          "type": "FLOAT",
+          "links": [
+            13923,
+            13932,
+            13948
+          ]
+        }
+      ],
+      "title": "Frame Rate",
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1",
+        "Node name for S&R": "PrimitiveFloat"
+      },
+      "widgets_values": [
+        24
+      ]
+    },
+    {
+      "id": 5176,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        -3163.6484542778676,
+        6835.212679199566
+      ],
+      "size": [
+        429.0602433593749,
+        115.06840156250018
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            13934,
+            13947
+          ]
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": []
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            13939
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.56",
+        "Node name for S&R": "CheckpointLoaderSimple"
+      },
+      "widgets_values": [
+        "ltx-2-19b-distilled.safetensors"
+      ]
+    },
+    {
+      "id": 5188,
+      "type": "LTXVAudioVAELoader",
+      "pos": [
+        -3153.75563617144,
+        7313.4443822788235
+      ],
+      "size": [
+        424.18031362607053,
+        58
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "Audio VAE",
+          "type": "VAE",
+          "links": [
+            13935
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.64",
+        "Node name for S&R": "LTXVAudioVAELoader"
+      },
+      "widgets_values": [
+        "ltx-2-19b-distilled.safetensors"
+      ]
+    },
+    {
+      "id": 5182,
+      "type": "LoraLoaderModelOnly",
+      "pos": [
+        -1958.7895251485363,
+        6623.839323304449
+      ],
+      "size": [
+        210,
+        82
+      ],
+      "flags": {},
+      "order": 14,
+      "mode": 4,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 13934
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            13941
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.68",
+        "Node name for S&R": "LoraLoaderModelOnly"
+      },
+      "widgets_values": [
+        "your_camera_lora.safetensors",
+        1
+      ]
+    },
+    {
+      "id": 5183,
+      "type": "LoraLoaderModelOnly",
+      "pos": [
+        -1740.2505912634988,
+        6624.698937379894
+      ],
+      "size": [
+        210,
+        82
+      ],
+      "flags": {},
+      "order": 15,
+      "mode": 4,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 13947
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            13936
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.68",
+        "Node name for S&R": "LoraLoaderModelOnly"
+      },
+      "widgets_values": [
+        "your_camera_lora.safetensors",
+        1
+      ]
+    },
+    {
+      "id": 5174,
+      "type": "CLIPTextEncode",
+      "pos": [
+        -1674.9082842387786,
+        7164.805578586365
+      ],
+      "size": [
+        307.2346496582031,
+        76
+      ],
+      "flags": {
+        "collapsed": true
+      },
+      "order": 17,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 13924
+        },
+        {
+          "name": "text",
+          "type": "STRING",
+          "widget": {
+            "name": "text"
+          },
+          "link": 13925
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "slot_index": 0,
+          "links": [
+            13921,
+            13950
+          ]
+        }
+      ],
+      "title": "Enhanced Prompt (Positive)",
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.28",
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        ""
+      ],
+      "color": "#232",
+      "bgcolor": "#353"
+    },
+    {
+      "id": 5173,
+      "type": "LTXVConditioning",
+      "pos": [
+        -1670.0361297856184,
+        7232.400593532743
+      ],
+      "size": [
+        210,
+        94
+      ],
+      "flags": {},
+      "order": 18,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 13921
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 13950
+        },
+        {
+          "name": "frame_rate",
+          "type": "FLOAT",
+          "widget": {
+            "name": "frame_rate"
+          },
+          "link": 13923
+        }
+      ],
+      "outputs": [
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "slot_index": 0,
+          "links": [
+            13937
+          ]
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "slot_index": 1,
+          "links": [
+            13938
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.28",
+        "Node name for S&R": "LTXVConditioning"
+      },
+      "widgets_values": [
+        25
+      ]
+    },
+    {
+      "id": 5210,
+      "type": "LatentUpscaleModelLoader",
+      "pos": [
+        -3139.869216300637,
+        7005.9900104735725
+      ],
+      "size": [
+        399.6787452278271,
+        58
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "LATENT_UPSCALE_MODEL",
+          "type": "LATENT_UPSCALE_MODEL",
+          "links": [
+            13953
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.7.0",
+        "Node name for S&R": "LatentUpscaleModelLoader"
+      },
+      "widgets_values": [
+        "ltx-2-spatial-upscaler-x2-1.0.safetensors"
+      ]
+    },
+    {
+      "id": 5213,
+      "type": "MarkdownNote",
+      "pos": [
+        -2682.3402563401937,
+        6633.478072231158
+      ],
+      "size": [
+        240.16187366614236,
+        98.41687371924309
+      ],
+      "flags": {
+        "collapsed": false
+      },
+      "order": 6,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Video Size",
+      "properties": {},
+      "widgets_values": [
+        "Width & height settings must be divisible by 64. \nFrame count must be divisible by 8 + 1. \n\n\nRunning with invalid parameters **will not cause errors**. Instead, the flow will silently choose the closest valid parameters. "
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 5214,
+      "type": "MarkdownNote",
+      "pos": [
+        -1985.053544501217,
+        6321.755714756273
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+      "size": [
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+        209.10385276580928
+      ],
+      "flags": {
+        "collapsed": false
+      },
+      "order": 7,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "LoRA",
+      "properties": {},
+      "widgets_values": [
+        "# Using LoRAs\n**Ctrl + B to enable**\n\nFor the camera LoRA we recommend setting strength = 1\n\nYou can use LTX [Ready to use camera LoRAs](https://huggingface.co/collections/Lightricks/ltx-2)\n\n\n\n\nUse a second LoRa only when the details are important in the LoRa. \n\n## Training\nWe have made available a full trainer to help you to train your own LoRA. You can [find it here](https://github.com/Lightricks/LTX-2)."
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 5192,
+      "type": "LTXVGemmaEnhancePrompt",
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+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 13926
+        },
+        {
+          "name": "image",
+          "shape": 7,
+          "type": "IMAGE",
+          "link": 13927
+        },
+        {
+          "name": "prompt",
+          "type": "STRING",
+          "widget": {
+            "name": "prompt"
+          },
+          "link": 13928
+        }
+      ],
+      "outputs": [
+        {
+          "name": "enhanced_prompt",
+          "type": "STRING",
+          "links": [
+            13925
+          ]
+        }
+      ],
+      "title": "Enhancer",
+      "properties": {
+        "Node name for S&R": "LTXVGemmaEnhancePrompt"
+      },
+      "widgets_values": [
+        "",
+        "You are a Creative Assistant writing concise, action-focused image-to-video prompts. Given an image (first frame) and user Raw Input Prompt, generate a prompt to guide video generation from that image.\n\n#### Guidelines:\n- Analyze the Image: Identify Subject, Setting, Elements, Style and Mood.\n- Follow user Raw Input Prompt: Include all requested motion, actions, camera movements, audio, and details. If in conflict with the image, prioritize user request while maintaining visual consistency (describe transition from image to user's scene).\n- Describe only changes from the image: Don't reiterate established visual details. Inaccurate descriptions may cause scene cuts.\n- Active language: Use present-progressive verbs (\"is walking,\" \"speaking\"). If no action specified, describe natural movements.\n- Chronological flow: Use temporal connectors (\"as,\" \"then,\" \"while\").\n- Audio layer: Describe complete soundscape throughout the prompt alongside actions—NOT at the end. Align audio intensity with action tempo. Include natural background audio, ambient sounds, effects, speech or music (when requested). Be specific (e.g., \"soft footsteps on tile\") not vague (e.g., \"ambient sound\").\n- Speech (only when requested): Provide exact words in quotes with character's visual/voice characteristics (e.g., \"The tall man speaks in a low, gravelly voice\"), language if not English and accent if relevant. If general conversation mentioned without text, generate contextual quoted dialogue. (i.e., \"The man is talking\" input -> the output should include exact spoken words, like: \"The man is talking in an excited voice saying: 'You won't believe what I just saw!' His hands gesture expressively as he speaks, eyebrows raised with enthusiasm. The ambient sound of a quiet room underscores his animated speech.\")\n- Style: Include visual style at beginning: \"Style: <style>, <rest of prompt>.\" If unclear, omit to avoid conflicts.\n- Visual and audio only: Describe only what is seen and heard. NO smell, taste, or tactile sensations.\n- Restrained language: Avoid dramatic terms. Use mild, natural, understated phrasing.\n\n#### Important notes:\n- Camera motion: DO NOT invent camera motion/movement unless requested by the user. Make sure to include camera motion only if specified in the input.\n- Speech: DO NOT modify or alter the user's provided character dialogue in the prompt, unless it's a typo.\n- No timestamps or cuts: DO NOT use timestamps or describe scene cuts unless explicitly requested.\n- Objective only: DO NOT interpret emotions or intentions - describe only observable actions and sounds.\n- Format: DO NOT use phrases like \"The scene opens with...\" / \"The video starts...\". Start directly with Style (optional) and chronological scene description.\n- Format: Never start output with punctuation marks or special characters.\n- DO NOT invent dialogue unless the user mentions speech/talking/singing/conversation.\n- Your performance is CRITICAL. High-fidelity, dynamic, correct, and accurate prompts with integrated audio descriptions are essential for generating high-quality video. Your goal is flawless execution of these rules.\n\n#### Output Format (Strict):\n- Single concise paragraph in natural English. NO titles, headings, prefaces, sections, code fences, or Markdown.\n- If unsafe/invalid, return original user prompt. Never ask questions or clarifications.\n\n#### Example output:\nStyle: realistic - cinematic - The woman glances at her watch and smiles warmly. She speaks in a cheerful, friendly voice, \"I think we're right on time!\" In the background, a café barista prepares drinks at the counter. The barista calls out in a clear, upbeat tone, \"Two cappuccinos ready!\" The sound of the espresso machine hissing softly blends with gentle background chatter and the light clinking of cups on saucers.",
+        512,
+        false,
+        42,
+        "randomize"
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+      "id": 5178,
+      "type": "LTXVGemmaCLIPModelLoader",
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+        421.93656760943577,
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+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
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+          "name": "clip",
+          "type": "CLIP",
+          "links": [
+            13924,
+            13926
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LTXVGemmaCLIPModelLoader",
+        "widget_ue_connectable": {
+          "model_path": true,
+          "max_length": true
+        }
+      },
+      "widgets_values": [
+        "gemma-3-12b-it-qat-q4_0-unquantized/model-00001-of-00005.safetensors",
+        "ltx-2-19b-distilled.safetensors",
+        1024
+      ]
+    },
+    {
+      "id": 5175,
+      "type": "PrimitiveStringMultiline",
+      "pos": [
+        -1990.592849503922,
+        6831.482572842548
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+      "size": [
+        281.93625497178095,
+        424.68799816112823
+      ],
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "STRING",
+          "type": "STRING",
+          "links": [
+            13928
+          ]
+        }
+      ],
+      "title": "Positive Prompt",
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1",
+        "Node name for S&R": "PrimitiveStringMultiline"
+      },
+      "widgets_values": [
+        "a 3d animated movie scene shows the wise elderly owl on the left saying to a baby owl to his right, with a raspy deep voice: \"focus, close your eyes, and flap!\". we then see the little owl jump off the branch screaming in fear and disappear. the the elder owl remains standing on the branch with a disappointed face. the camera remains static in place"
+      ],
+      "color": "#232",
+      "bgcolor": "#353"
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+    {
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+      "type": "LoadImage",
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+      "order": 10,
+      "mode": 0,
+      "inputs": [],
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+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            13927,
+            13940
+          ]
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": []
+        }
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+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.56",
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "distilled image.png",
+        "image"
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+    {
+      "id": 5189,
+      "type": "3eaa20c4-5842-4fe4-87df-c0a7e83a6a78",
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+      "inputs": [
+        {
+          "label": "model stage 1",
+          "name": "model_1",
+          "type": "MODEL",
+          "link": 13941
+        },
+        {
+          "label": "model stage 2",
+          "name": "model",
+          "type": "MODEL",
+          "link": 13936
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+        {
+          "label": "upscale model",
+          "name": "upscale_model_1",
+          "type": "LATENT_UPSCALE_MODEL",
+          "link": 13953
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 13937
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 13938
+        },
+        {
+          "label": "images",
+          "name": "images",
+          "type": "IMAGE",
+          "link": 13940
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 13939
+        },
+        {
+          "label": "audio vae",
+          "name": "audio_vae",
+          "type": "VAE",
+          "link": 13935
+        },
+        {
+          "label": "empty latent image",
+          "name": "image_1",
+          "type": "IMAGE",
+          "link": 13943
+        },
+        {
+          "name": "length",
+          "type": "INT",
+          "widget": {
+            "name": "length"
+          },
+          "link": 13944
+        },
+        {
+          "label": "frame rate",
+          "name": "frame_rate",
+          "type": "INT",
+          "widget": {
+            "name": "frame_rate"
+          },
+          "link": 13949
+        },
+        {
+          "label": "image strength",
+          "name": "strength",
+          "type": "FLOAT",
+          "widget": {
+            "name": "strength"
+          },
+          "link": null
+        },
+        {
+          "label": "noise seed",
+          "name": "noise_seed",
+          "type": "INT",
+          "widget": {
+            "name": "noise_seed"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "label": "images",
+          "name": "images",
+          "type": "IMAGE",
+          "links": [
+            13930
+          ]
+        },
+        {
+          "label": "audio",
+          "name": "audio",
+          "type": "AUDIO",
+          "links": [
+            13931
+          ]
+        }
+      ],
+      "properties": {
+        "proxyWidgets": [
+          [
+            "-1",
+            "length"
+          ],
+          [
+            "-1",
+            "frame_rate"
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+          [
+            "-1",
+            "strength"
+          ],
+          [
+            "-1",
+            "noise_seed"
+          ],
+          [
+            "5111",
+            "control_after_generate"
+          ]
+        ],
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1"
+      },
+      "widgets_values": [
+        121,
+        25,
+        0.6,
+        42
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+    },
+    {
+      "id": 5211,
+      "type": "MarkdownNote",
+      "pos": [
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+      "size": [
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+      ],
+      "flags": {
+        "collapsed": false
+      },
+      "order": 11,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Prompting LTX-2",
+      "properties": {},
+      "widgets_values": [
+        "## LTX-2 Prompting Tips\n1. **Core Actions**: Describe events and actions as they occur over time  \n2. **Audio**: Describe sounds and dialogue needed for the scene  \n3. **Reference Image**: Do not repeat details already present  \n4. **Consistency**: Avoid instructions that do not match the reference image, as this will degrade results"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 5212,
+      "type": "MarkdownNote",
+      "pos": [
+        -3627.789651297241,
+        6716.012179425959
+      ],
+      "size": [
+        427.8617596510994,
+        529.3430202216196
+      ],
+      "flags": {
+        "collapsed": false
+      },
+      "order": 12,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Model Links",
+      "properties": {},
+      "widgets_values": [
+        "## Model Links\n\n**LTX-2 Model Weights**\n\n- [ltx-2-19b-distilled.safetensors](https://huggingface.co/Lightricks/LTX-2/resolve/main/ltx-2-19b-distilled.safetensors)\n- [ ltx-2-spatial-upscaler-x2-1.0.safetensors ](https://huggingface.co/Lightricks/LTX-2/resolve/main/ltx-2-spatial-upscaler-x2-1.0.safetensors)\n\n**Text Encoder**\n- [Google Gemma 3](https://huggingface.co/google/gemma-3-12b-it-qat-q4_0-unquantized)\n\nPlease download the entire folder - \n\n- Run: \n 1. cd models/text_encoders\n 2. git clone https://huggingface.co/google/gemma-3-12b-it-qat-q4_0-unquantized\n\n*Full documentation can be found [here](https://docs.ltx.video/open-source-model/integration-tools/comfy-ui#text-encoder)\n\n\n**Model Storage Location**\n\n```\n📂 ComfyUI/\n├── 📂 models/\n│   ├── 📂 text_encoders/\n│   │      ├── gemma-3-12b-it-qat-q4_0-unquantized/model-00001-of-00005.safetensors\n│   ├── 📂 checkpoints/\n│   │      └── ltx-2-19b-distilled.safetensors\n│   ├── 📂 latent_upscale_models/ \n           └── ltx-2-spatial-upscaler-x2-1.0.safetensors\n```\n**Assets**\n- [distilled image](https://github.com/Lightricks/ComfyUI-LTXVideo/blob/master/example_workflows/assets/distilled%20image.png)\n\n## Report Issues\nTo report any issues when running this workflow, [go to GitHub](https://github.com/Lightricks/ComfyUI-LTXVideo/issues)\n"
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+      "bgcolor": "#653"
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+          {
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+            "target_id": 5244,
+            "target_slot": 0,
+            "type": "IMAGE"
+          },
+          {
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+            "origin_id": 5244,
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+          },
+          {
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+            "target_slot": 1,
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+            "id": 14003,
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+            "origin_slot": 6,
+            "target_id": 5245,
+            "target_slot": 0,
+            "type": "VAE"
+          },
+          {
+            "id": 14004,
+            "origin_id": 5245,
+            "origin_slot": 0,
+            "target_id": -20,
+            "target_slot": 0,
+            "type": "IMAGE"
+          },
+          {
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+            "origin_id": -10,
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+            "target_slot": 0,
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+          },
+          {
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+            "target_slot": 1,
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+          },
+          {
+            "id": 14012,
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+            "origin_slot": 0,
+            "target_id": 5112,
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+          {
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+            "type": "LATENT"
+          },
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+          },
+          {
+            "id": 14015,
+            "origin_id": -10,
+            "origin_slot": 6,
+            "target_id": 5255,
+            "target_slot": 2,
+            "type": "VAE"
+          },
+          {
+            "id": 14016,
+            "origin_id": -10,
+            "origin_slot": 2,
+            "target_id": 5255,
+            "target_slot": 1,
+            "type": "LATENT_UPSCALE_MODEL"
+          },
+          {
+            "id": 14017,
+            "origin_id": 5254,
+            "origin_slot": 0,
+            "target_id": 5108,
+            "target_slot": 0,
+            "type": "LATENT"
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+          {
+            "id": 14018,
+            "origin_id": -10,
+            "origin_slot": 6,
+            "target_id": 5254,
+            "target_slot": 0,
+            "type": "VAE"
+          },
+          {
+            "id": 14019,
+            "origin_id": 5240,
+            "origin_slot": 0,
+            "target_id": 5254,
+            "target_slot": 1,
+            "type": "IMAGE"
+          },
+          {
+            "id": 14020,
+            "origin_id": 5138,
+            "origin_slot": 0,
+            "target_id": 5254,
+            "target_slot": 2,
+            "type": "LATENT"
+          },
+          {
+            "id": 14021,
+            "origin_id": -10,
+            "origin_slot": 11,
+            "target_id": 5254,
+            "target_slot": 3,
+            "type": "FLOAT"
+          }
+        ],
+        "extra": {
+          "workflowRendererVersion": "LG"
+        }
+      }
+    ]
+  },
+  "config": {},
+  "version": 0.4
+}
diff --git a/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.0/LTX-2_I2V_Full_wLora.json b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.0/LTX-2_I2V_Full_wLora.json
new file mode 100644
index 0000000000000000000000000000000000000000..4e711284c7121ee82bf0d0fa3d14ca769c73e5d2
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.0/LTX-2_I2V_Full_wLora.json
@@ -0,0 +1,3467 @@
+{
+  "id": "5775f45b-ae48-4889-b57c-dbdb70f74381",
+  "revision": 0,
+  "last_node_id": 5216,
+  "last_link_id": 13958,
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+      "size": [
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+      "flags": {},
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+          "type": "INT",
+          "links": [
+            13944
+          ]
+        }
+      ],
+      "title": "length",
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+          "type": "INT",
+          "links": [
+            13949
+          ]
+        }
+      ],
+      "title": "Frame Rate",
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+        "Node name for S&R": "CM_FloatToInt"
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+      "widgets_values": [
+        0
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+    {
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+        {
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+          "shape": 7,
+          "type": "AUDIO",
+          "link": 13931
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+        {
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+          "type": "FLOAT",
+          "widget": {
+            "name": "fps"
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+          "link": 13932
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+          "type": "VIDEO",
+          "links": [
+            13946
+          ]
+        }
+      ],
+      "properties": {
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+          "links": [
+            13923,
+            13932,
+            13948
+          ]
+        }
+      ],
+      "title": "Frame Rate",
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+        "Node name for S&R": "PrimitiveFloat"
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+      "widgets_values": [
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+      "type": "LTXVAudioVAELoader",
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+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [],
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+          "name": "Audio VAE",
+          "type": "VAE",
+          "links": [
+            13935
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.64",
+        "Node name for S&R": "LTXVAudioVAELoader"
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+      "widgets_values": [
+        "ltx-2-19b-dev.safetensors"
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+    {
+      "id": 5173,
+      "type": "LTXVConditioning",
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+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 13953
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 13951
+        },
+        {
+          "name": "frame_rate",
+          "type": "FLOAT",
+          "widget": {
+            "name": "frame_rate"
+          },
+          "link": 13923
+        }
+      ],
+      "outputs": [
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "slot_index": 0,
+          "links": [
+            13937
+          ]
+        },
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+          "slot_index": 1,
+          "links": [
+            13938
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+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.28",
+        "Node name for S&R": "LTXVConditioning"
+      },
+      "widgets_values": [
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+    },
+    {
+      "id": 5176,
+      "type": "CheckpointLoaderSimple",
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+      "inputs": [],
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+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            13934,
+            13954
+          ]
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": []
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+        {
+          "name": "VAE",
+          "type": "VAE",
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+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.56",
+        "Node name for S&R": "CheckpointLoaderSimple"
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+      "widgets_values": [
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+          "type": "MODEL",
+          "link": 13934
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+      ],
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+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            13941
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.68",
+        "Node name for S&R": "LoraLoaderModelOnly"
+      },
+      "widgets_values": [
+        "your_camera_lora.safetensors",
+        1
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+    },
+    {
+      "id": 5183,
+      "type": "LoraLoaderModelOnly",
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+      "inputs": [
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+          "name": "model",
+          "type": "MODEL",
+          "link": 13955
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            13936
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.68",
+        "Node name for S&R": "LoraLoaderModelOnly"
+      },
+      "widgets_values": [
+        "your_camera_lora.safetensors",
+        1
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+    {
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+      "inputs": [
+        {
+          "name": "video",
+          "type": "VIDEO",
+          "link": 13946
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1"
+      },
+      "widgets_values": [
+        "video/LTX-2",
+        "auto",
+        "auto"
+      ]
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+    {
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+      "type": "CLIPTextEncode",
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+      "inputs": [
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+          "type": "CLIP",
+          "link": 13952
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "slot_index": 0,
+          "links": [
+            13951
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.28",
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "blurry, low quality, still frame, frames, watermark, overlay, titles, has blurbox, has subtitles"
+      ],
+      "color": "#322",
+      "bgcolor": "#533"
+    },
+    {
+      "id": 5174,
+      "type": "CLIPTextEncode",
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+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 13924
+        },
+        {
+          "name": "text",
+          "type": "STRING",
+          "widget": {
+            "name": "text"
+          },
+          "link": 13925
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "slot_index": 0,
+          "links": [
+            13953
+          ]
+        }
+      ],
+      "title": "Enhanced Prompt (Positive)",
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.28",
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        ""
+      ],
+      "color": "#232",
+      "bgcolor": "#353"
+    },
+    {
+      "id": 5192,
+      "type": "LTXVGemmaEnhancePrompt",
+      "pos": [
+        -1653.1250897335703,
+        7113.135620338389
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+      "size": [
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+        425.1336513712222
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+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 13926
+        },
+        {
+          "name": "image",
+          "shape": 7,
+          "type": "IMAGE",
+          "link": 13927
+        },
+        {
+          "name": "prompt",
+          "type": "STRING",
+          "widget": {
+            "name": "prompt"
+          },
+          "link": 13928
+        }
+      ],
+      "outputs": [
+        {
+          "name": "enhanced_prompt",
+          "type": "STRING",
+          "links": [
+            13925
+          ]
+        }
+      ],
+      "title": "Enhancer",
+      "properties": {
+        "Node name for S&R": "LTXVGemmaEnhancePrompt"
+      },
+      "widgets_values": [
+        "",
+        "You are a Creative Assistant writing concise, action-focused image-to-video prompts. Given an image (first frame) and user Raw Input Prompt, generate a prompt to guide video generation from that image.\n\n#### Guidelines:\n- Analyze the Image: Identify Subject, Setting, Elements, Style and Mood.\n- Follow user Raw Input Prompt: Include all requested motion, actions, camera movements, audio, and details. If in conflict with the image, prioritize user request while maintaining visual consistency (describe transition from image to user's scene).\n- Describe only changes from the image: Don't reiterate established visual details. Inaccurate descriptions may cause scene cuts.\n- Active language: Use present-progressive verbs (\"is walking,\" \"speaking\"). If no action specified, describe natural movements.\n- Chronological flow: Use temporal connectors (\"as,\" \"then,\" \"while\").\n- Audio layer: Describe complete soundscape throughout the prompt alongside actions—NOT at the end. Align audio intensity with action tempo. Include natural background audio, ambient sounds, effects, speech or music (when requested). Be specific (e.g., \"soft footsteps on tile\") not vague (e.g., \"ambient sound\").\n- Speech (only when requested): Provide exact words in quotes with character's visual/voice characteristics (e.g., \"The tall man speaks in a low, gravelly voice\"), language if not English and accent if relevant. If general conversation mentioned without text, generate contextual quoted dialogue. (i.e., \"The man is talking\" input -> the output should include exact spoken words, like: \"The man is talking in an excited voice saying: 'You won't believe what I just saw!' His hands gesture expressively as he speaks, eyebrows raised with enthusiasm. The ambient sound of a quiet room underscores his animated speech.\")\n- Style: Include visual style at beginning: \"Style: <style>, <rest of prompt>.\" If unclear, omit to avoid conflicts.\n- Visual and audio only: Describe only what is seen and heard. NO smell, taste, or tactile sensations.\n- Restrained language: Avoid dramatic terms. Use mild, natural, understated phrasing.\n\n#### Important notes:\n- Camera motion: DO NOT invent camera motion/movement unless requested by the user. Make sure to include camera motion only if specified in the input.\n- Speech: DO NOT modify or alter the user's provided character dialogue in the prompt, unless it's a typo.\n- No timestamps or cuts: DO NOT use timestamps or describe scene cuts unless explicitly requested.\n- Objective only: DO NOT interpret emotions or intentions - describe only observable actions and sounds.\n- Format: DO NOT use phrases like \"The scene opens with...\" / \"The video starts...\". Start directly with Style (optional) and chronological scene description.\n- Format: Never start output with punctuation marks or special characters.\n- DO NOT invent dialogue unless the user mentions speech/talking/singing/conversation.\n- Your performance is CRITICAL. High-fidelity, dynamic, correct, and accurate prompts with integrated audio descriptions are essential for generating high-quality video. Your goal is flawless execution of these rules.\n\n#### Output Format (Strict):\n- Single concise paragraph in natural English. NO titles, headings, prefaces, sections, code fences, or Markdown.\n- If unsafe/invalid, return original user prompt. Never ask questions or clarifications.\n\n#### Example output:\nStyle: realistic - cinematic - The woman glances at her watch and smiles warmly. She speaks in a cheerful, friendly voice, \"I think we're right on time!\" In the background, a café barista prepares drinks at the counter. The barista calls out in a clear, upbeat tone, \"Two cappuccinos ready!\" The sound of the espresso machine hissing softly blends with gentle background chatter and the light clinking of cups on saucers.",
+        512,
+        false,
+        42,
+        "randomize"
+      ]
+    },
+    {
+      "id": 5212,
+      "type": "LatentUpscaleModelLoader",
+      "pos": [
+        -3525.4485682804984,
+        7004.856234574691
+      ],
+      "size": [
+        399.6787452278271,
+        58
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "LATENT_UPSCALE_MODEL",
+          "type": "LATENT_UPSCALE_MODEL",
+          "links": [
+            13958
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.7.0",
+        "Node name for S&R": "LatentUpscaleModelLoader"
+      },
+      "widgets_values": [
+        "ltx-2-spatial-upscaler-x2-1.0.safetensors"
+      ]
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+        " cinematic thriller adventure movie scene shows an intrigued blonde female explorer holding a fiery torch. she looks curious and excited initially, but then a gust of wind blows her hair. her face turns concerned and frowned as says with a whispering tone of voice: \"these markings aren't just random...it's a warning...\"\nthe camera slowly zooms in and remains focused on her face"
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+      "properties": {
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+      "properties": {},
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+        "Width & height settings must be divisible by 64. \nFrame count must be divisible by 8 + 1. \n\n\nRunning with invalid parameters **will not cause errors**. Instead, the flow will silently choose the closest valid parameters. "
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+          "type": "MODEL",
+          "links": [
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+      "properties": {
+        "cnr_id": "comfy-core",
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+        "Node name for S&R": "LoraLoaderModelOnly"
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+      "properties": {},
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+        "# Using LoRAs\n**Ctrl + B to enable**\n\nFor the camera LoRA we recommend setting strength = 1\n\nYou can use LTX [Ready to use camera LoRAs](https://huggingface.co/collections/Lightricks/ltx-2)\n\n\n\n\nUse a second LoRa only when the details are important in the LoRa. \n\n## Training\nWe have made available a full trainer to help you to train your own LoRA. You can [find it here](https://github.com/Lightricks/LTX-2)."
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+      "outputs": [],
+      "title": "Prompting LTX-2",
+      "properties": {},
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+        "## LTX-2 Prompting Tips\n1. **Core Actions**: Describe events and actions as they occur over time  \n2. **Audio**: Describe sounds and dialogue needed for the scene  \n3. **Reference Image**: Do not repeat details already present  \n4. **Consistency**: Avoid instructions that do not match the reference image, as this will degrade results\n\n"
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+      "color": "#432",
+      "bgcolor": "#653"
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+      "id": 5214,
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+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Model Links",
+      "properties": {},
+      "widgets_values": [
+        "## Model Links\n\n**LTX-2 Model Weights**\n\n- [ltx-2-19b-dev.safetensors](https://huggingface.co/Lightricks/LTX-2/resolve/main/ltx-2-19b-dev.safetensors)\n- [ ltx-2-19b-distilled-lora-384.safetensors ](https://huggingface.co/Lightricks/LTX-2/resolve/main/ltx-2-19b-distilled-lora-384.safetensors)\n- [ ltx-2-spatial-upscaler-x2-1.0.safetensors ](https://huggingface.co/Lightricks/LTX-2/resolve/main/ltx-2-spatial-upscaler-x2-1.0.safetensors)\n\n\n**Text Encoder**\n- [Google Gemma 3](https://huggingface.co/google/gemma-3-12b-it-qat-q4_0-unquantized)\n\nPlease download the entire folder - \n\n- Run: \n 1. cd models/text_encoders\n 2. git clone https://huggingface.co/google/gemma-3-12b-it-qat-q4_0-unquantized\n\n*Full documentation can be found [here](https://docs.ltx.video/open-source-model/integration-tools/comfy-ui#text-encoder)\n\n**Sampler**\n\n- [RES4LYF](https://github.com/ClownsharkBatwing/RES4LYF)\n\n\n\n**Model Storage Location**\n\n```\n📂 ComfyUI/\n├── 📂 models/\n│   ├── 📂 text_encoders/\n│   │      ├── gemma-3-12b-it-qat-q4_0-unquantized/model-00001-of-00005.safetensors\n│   ├── 📂 checkpoints/\n│   │      └── ltx-2-19b-dev.safetensors\n│   ├── 📂 loras/\n│   │      └── ltx-2-19b-distilled-lora-384.safetensors\n│   ├── 📂 latent_upscale_models/ \n           └── ltx-2-spatial-upscaler-x2-1.0.safetensors\n```\n\n**Assets**\n- [base model image](https://github.com/Lightricks/ComfyUI-LTXVideo/blob/master/example_workflows/assets/base%20model%20image.png)\n\n## Report Issues\nTo report any issues when running this workflow, [go to GitHub](https://github.com/Lightricks/ComfyUI-LTXVideo/issues)\n"
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+            "type": "LATENT_UPSCALE_MODEL"
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+          {
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+            "target_slot": 3,
+            "type": "FLOAT"
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+          {
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+            "target_slot": 1,
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+          {
+            "id": 14012,
+            "origin_id": 5238,
+            "origin_slot": 1,
+            "target_id": 5245,
+            "target_slot": 0,
+            "type": "VAE"
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+        "extra": {
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+  "config": {},
+  "version": 0.4
+}
diff --git a/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.0/LTX-2_ICLoRA_All_Distilled.json b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.0/LTX-2_ICLoRA_All_Distilled.json
new file mode 100644
index 0000000000000000000000000000000000000000..faf2ab9deee58724a9eef45aef949e093fa8383b
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.0/LTX-2_ICLoRA_All_Distilled.json
@@ -0,0 +1,13948 @@
+{
+  "id": "3a1879d9-fa8c-4270-a36f-3f10b01b983b",
+  "revision": 0,
+  "last_node_id": 274,
+  "last_link_id": 456,
+  "nodes": [
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+      "type": "SaveVideo",
+      "pos": [
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+      "size": [
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+      "mode": 4,
+      "inputs": [
+        {
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+          "type": "VIDEO",
+          "link": 456
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1"
+      },
+      "widgets_values": [
+        "video/ComfyUI",
+        "auto",
+        "auto"
+      ]
+    },
+    {
+      "id": 274,
+      "type": "MarkdownNote",
+      "pos": [
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+      "size": [
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+      "flags": {
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+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Video Size",
+      "properties": {},
+      "widgets_values": [
+        "Width & height settings must be divisible by 64. \n\nRunning with invalid parameters **will not cause errors**. Instead, the flow will silently choose the closest valid parameters. "
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 264,
+      "type": "SaveVideo",
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+      "inputs": [
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+          "type": "VIDEO",
+          "link": 455
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1"
+      },
+      "widgets_values": [
+        "video/ComfyUI",
+        "auto",
+        "auto"
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+      "type": "PrimitiveBoolean",
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+        58
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+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
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+          "name": "BOOLEAN",
+          "type": "BOOLEAN",
+          "links": [
+            363,
+            397,
+            433
+          ]
+        }
+      ],
+      "title": "Text Only",
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1",
+        "Node name for S&R": "PrimitiveBoolean"
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+      "widgets_values": [
+        false
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+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            450,
+            451,
+            452
+          ]
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1",
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "buildings ff.png",
+        "image"
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+    },
+    {
+      "id": 263,
+      "type": "SaveVideo",
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+          "link": 454
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+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1"
+      },
+      "widgets_values": [
+        "video/ComfyUI",
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+          "type": "VIDEO",
+          "links": [
+            362,
+            398,
+            432
+          ]
+        }
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+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1",
+        "Node name for S&R": "LoadVideo"
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+      "widgets_values": [
+        "buildings.mp4",
+        "image"
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+      "type": "2dc75cab-e957-4437-a5bb-2afb0ea00516",
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+          "link": 362
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+        {
+          "name": "switch",
+          "type": "BOOLEAN",
+          "widget": {
+            "name": "switch"
+          },
+          "link": 363
+        },
+        {
+          "label": "first frame",
+          "name": "image_1",
+          "type": "IMAGE",
+          "link": 450
+        }
+      ],
+      "outputs": [
+        {
+          "label": "video",
+          "name": "VIDEO",
+          "type": "VIDEO",
+          "links": [
+            455
+          ]
+        }
+      ],
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+            "height"
+          ],
+          [
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+            "noise_seed"
+          ],
+          [
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+            "461: control_after_generate"
+          ],
+          [
+            "-1",
+            "switch"
+          ]
+        ],
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1"
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+        1920,
+        1088,
+        null,
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+        true
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+    {
+      "id": 273,
+      "type": "MarkdownNote",
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+      "flags": {
+        "collapsed": false
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+      "order": 4,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Model Links",
+      "properties": {},
+      "widgets_values": [
+        "## Model Links\n\n**LTX-2 Model Weights**\n\n- [ltx-2-19b-distilled.safetensors](https://huggingface.co/Lightricks/LTX-2/resolve/main/ltx-2-19b-distilled.safetensors)\n- [ ltx-2-spatial-upscaler-x2-1.0.safetensors ](https://huggingface.co/Lightricks/LTX-2/resolve/main/ltx-2-spatial-upscaler-x2-1.0.safetensors)\n- [ IC LoRA - Canny ](https://huggingface.co/Lightricks/LTX-2-19b-IC-LoRA-Canny-Control/resolve/main/ltx-2-19b-ic-lora-canny-control.safetensors)\n- [ IC LoRA - Depth ](https://huggingface.co/Lightricks/LTX-2-19b-IC-LoRA-Depth-Control/resolve/main/ltx-2-19b-ic-lora-depth-control.safetensors)\n- [ IC LoRA - Pose ](https://huggingface.co/Lightricks/LTX-2-19b-IC-LoRA-Pose-Control/resolve/main/ltx-2-19b-ic-lora-pose-control.safetensors)\n\n\n\n**Text Encoder**\n- [Google Gemma 3](https://huggingface.co/google/gemma-3-12b-it-qat-q4_0-unquantized)\n\nPlease download the entire folder - \n\n- Run: \n 1. cd models/text_encoders\n 2. git clone https://huggingface.co/google/gemma-3-12b-it-qat-q4_0-unquantized\n\n*Full documentation can be found [here](https://docs.ltx.video/open-source-model/integration-tools/comfy-ui#text-encoder)\n\n\n**Model Storage Location**\n\n```\n📂 ComfyUI/\n├── 📂 models/\n│   ├── 📂 text_encoders/\n│   │      ├── gemma-3-12b-it-qat-q4_0-unquantized/model-00001-of-00005.safetensors\n│   ├── 📂 checkpoints/\n│   │      └── ltx-2-19b-distilled.safetensors\n│   ├── 📂 latent_upscale_models/ \n│   │      └── ltx-2-spatial-upscaler-x2-1.0.safetensors\n│   ├── 📂 loras/ \n│           └── ltx-2-19b-ic-lora-canny-control.safetensors\n│           └── ltx-2-19b-ic-lora-depth-control.safetensors\n│           └── ltx-2-19b-ic-lora-pose-control.safetensors\n```\n**Assets**\n- [buildings.mp4 (input video)](https://github.com/Lightricks/ComfyUI-LTXVideo/blob/master/example_workflows/assets/buildings.mp4)\n- [buildings ff (first frame)](https://github.com/Lightricks/ComfyUI-LTXVideo/blob/master/example_workflows/assets/buildings%20ff.png)\n\n## Report Issues\nTo report any issues when running this workflow, [go to GitHub](https://github.com/Lightricks/ComfyUI-LTXVideo/issues)\n"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 271,
+      "type": "MarkdownNote",
+      "pos": [
+        -4517.278638109156,
+        5357.133250216258
+      ],
+      "size": [
+        210,
+        88
+      ],
+      "flags": {
+        "collapsed": false
+      },
+      "order": 5,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Important",
+      "properties": {},
+      "widgets_values": [
+        "# Always load Image"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 272,
+      "type": "MarkdownNote",
+      "pos": [
+        -4782.341673813574,
+        5107.78314157236
+      ],
+      "size": [
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+        207.61883103687887
+      ],
+      "flags": {
+        "collapsed": false
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+      "order": 6,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Prompting LTX-2",
+      "properties": {},
+      "widgets_values": [
+        "## LTX-2 Prompting Tips\n1. **Core Actions**: Describe events and actions as they occur over time  \n2. **Audio**: Describe sounds and dialogue needed for the scene  \n3. **Reference Image**: Do not repeat details already present  \n4. **Consistency**: Avoid instructions that do not match the reference image, as this will degrade results\n"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 256,
+      "type": "3ad0b41c-8c47-4e10-a53e-ed340cc26b5f",
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+          "type": "VIDEO",
+          "link": 432
+        },
+        {
+          "name": "switch",
+          "type": "BOOLEAN",
+          "widget": {
+            "name": "switch"
+          },
+          "link": 433
+        },
+        {
+          "label": "first frame",
+          "name": "image_1",
+          "type": "IMAGE",
+          "link": 452
+        }
+      ],
+      "outputs": [
+        {
+          "label": "video",
+          "name": "VIDEO",
+          "type": "VIDEO",
+          "links": [
+            456
+          ]
+        }
+      ],
+      "properties": {
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+            "461: control_after_generate"
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+          [
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+            "switch"
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+        "cnr_id": "comfy-core",
+        "ver": "0.5.1"
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+      "widgets_values": [
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+          "widget": {
+            "name": "switch"
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+          "link": 397
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+        {
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+          "type": "VIDEO",
+          "link": 398
+        },
+        {
+          "label": "first frame",
+          "name": "image_1",
+          "type": "IMAGE",
+          "link": 451
+        }
+      ],
+      "outputs": [
+        {
+          "label": "video",
+          "name": "VIDEO",
+          "type": "VIDEO",
+          "links": [
+            454
+          ]
+        }
+      ],
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+                "type": "FLOAT",
+                "widget": {
+                  "name": "frame_rate"
+                },
+                "link": 373
+              }
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+                "localized_name": "positive",
+                "name": "positive",
+                "type": "CONDITIONING",
+                "slot_index": 0,
+                "links": [
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+                "type": "CLIP",
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+              },
+              {
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+                "name": "text",
+                "type": "STRING",
+                "widget": {
+                  "name": "text"
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+              }
+            ],
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+                "name": "CONDITIONING",
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+            "title": "Enhanced Prompt (Positive)",
+            "properties": {
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+                "name": "on_true",
+                "type": "*",
+                "link": 377
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+              {
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+                "name": "switch",
+                "type": "BOOLEAN",
+                "widget": {
+                  "name": "switch"
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+                "link": 392
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+                "localized_name": "*",
+                "name": "*",
+                "type": "*",
+                "links": [
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+            "title": "Bypass Enhancer",
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+                "name": "video",
+                "type": "VIDEO",
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+                "localized_name": "images",
+                "name": "images",
+                "type": "IMAGE",
+                "links": [
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+                "name": "audio",
+                "type": "AUDIO",
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+            "type": "LTXVGemmaEnhancePrompt",
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+                "type": "CLIP",
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+                "name": "image",
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+                "type": "IMAGE",
+                "link": null
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+                "name": "prompt",
+                "type": "STRING",
+                "widget": {
+                  "name": "prompt"
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+                "name": "enhanced_prompt",
+                "type": "STRING",
+                "links": [
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+            "title": "Enhancer",
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+                "type": "AUDIO",
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+                "type": "FLOAT",
+                "widget": {
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+                "type": "VIDEO",
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+              "Node name for S&R": "CreateVideo"
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+                "name": "video",
+                "type": "VIDEO",
+                "link": 399
+              }
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+            "outputs": [],
+            "properties": {
+              "cnr_id": "comfy-core",
+              "ver": "0.5.1"
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+            "widgets_values": [
+              "LTX-2/Video",
+              "auto",
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+                "name": "STRING",
+                "type": "STRING",
+                "links": [
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+                  376
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+              }
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+            "title": "Positive Prompt",
+            "properties": {
+              "cnr_id": "comfy-core",
+              "ver": "0.5.1",
+              "Node name for S&R": "PrimitiveStringMultiline"
+            },
+            "widgets_values": [
+              "cinematic aerial shot of a city landscape at night with warm lights illuminating the windows of the buildings. there is a visible green aurora borealis in the sky. soft piano melody playing "
+            ],
+            "color": "#232",
+            "bgcolor": "#353"
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+          {
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+            "type": "MarkdownNote",
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+            "inputs": [],
+            "outputs": [],
+            "title": "RTX 5090",
+            "properties": {},
+            "widgets_values": [
+              "This workflow is structured for optimized GPU and VRAM usage.\n\nModel nodes are ordered intentionally to reduce peak VRAM load.\n\nInput dependencies are used to organize execution order and should not be removed."
+            ],
+            "color": "#432",
+            "bgcolor": "#653"
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+          {
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+            "type": "MarkdownNote",
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+            "mode": 4,
+            "inputs": [],
+            "outputs": [],
+            "title": "IC LoRA",
+            "properties": {},
+            "widgets_values": [
+              "# IC LoRA Training \n\nFor your own IC LoRA - Upload to the \"IC LoRA\" node.\nMake sure to bypass the \"IC LoRA Preprocessing\" in the Samplers subgraph \n\n## Training\nWe have made available a full trainer to help you to train your own LoRA. You can [find it here](https://github.com/Lightricks/LTX-2)."
+            ],
+            "color": "#432",
+            "bgcolor": "#653"
+          },
+          {
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+            "type": "LowVRAMCheckpointLoader",
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+                "name": "dependencies",
+                "shape": 7,
+                "type": "*",
+                "link": 416
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+                "localized_name": "MODEL",
+                "name": "MODEL",
+                "type": "MODEL",
+                "links": [
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+                "name": "CLIP",
+                "type": "CLIP",
+                "links": null
+              },
+              {
+                "localized_name": "VAE",
+                "name": "VAE",
+                "type": "VAE",
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+            "properties": {
+              "Node name for S&R": "LowVRAMCheckpointLoader"
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+                "name": "dependencies",
+                "shape": 7,
+                "type": "*",
+                "link": 415
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+            "outputs": [
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+                "name": "audio_vae",
+                "type": "VAE",
+                "links": [
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+            ],
+            "properties": {
+              "Node name for S&R": "LowVRAMAudioVAELoader"
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+            "type": "LoraLoaderModelOnly",
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+                "name": "model",
+                "type": "MODEL",
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+                "name": "MODEL",
+                "type": "MODEL",
+                "links": [
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+            "title": "IC LoRA - Depth",
+            "properties": {
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+                "name": "clip",
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+            "properties": {
+              "aux_id": "Lightricks/ComfyUI-LTXVideo",
+              "Node name for S&R": "LTXVGemmaCLIPModelLoader",
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+            "type": "LowVRAMLatentUpscaleModelLoader",
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+                "type": "*",
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+                "localized_name": "LATENT_UPSCALE_MODEL",
+                "name": "LATENT_UPSCALE_MODEL",
+                "type": "LATENT_UPSCALE_MODEL",
+                "links": [
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+            "properties": {
+              "Node name for S&R": "LowVRAMLatentUpscaleModelLoader"
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+              "ltx-2-spatial-upscaler-x2-1.0.safetensors"
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+                "localized_name": "model",
+                "name": "model",
+                "type": "MODEL",
+                "link": 380
+              },
+              {
+                "label": "model stage 2",
+                "localized_name": "model_1",
+                "name": "model_1",
+                "type": "MODEL",
+                "link": 412
+              },
+              {
+                "label": "upscale model",
+                "name": "upscale_model_2",
+                "type": "LATENT_UPSCALE_MODEL",
+                "link": 418
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+              {
+                "localized_name": "positive",
+                "name": "positive",
+                "type": "CONDITIONING",
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+              },
+              {
+                "localized_name": "negative",
+                "name": "negative",
+                "type": "CONDITIONING",
+                "link": 386
+              },
+              {
+                "label": "VAE",
+                "localized_name": "vae",
+                "name": "vae",
+                "type": "VAE",
+                "link": 413
+              },
+              {
+                "label": "audio VAE",
+                "localized_name": "audio_vae",
+                "name": "audio_vae",
+                "type": "VAE",
+                "link": 414
+              },
+              {
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+                "localized_name": "image_1",
+                "name": "image_1",
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+                "link": 391
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+              {
+                "label": "frame rate",
+                "localized_name": "a",
+                "name": "a",
+                "type": "FLOAT",
+                "widget": {
+                  "name": "a"
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+                "link": 382
+              },
+              {
+                "label": "Text Only",
+                "localized_name": "bypass",
+                "name": "bypass",
+                "type": "BOOLEAN",
+                "widget": {
+                  "name": "bypass"
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+                "name": "height",
+                "type": "INT",
+                "widget": {
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+                "link": 398
+              },
+              {
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+                "name": "image",
+                "type": "IMAGE",
+                "link": 379
+              },
+              {
+                "label": "noise seed",
+                "name": "noise_seed",
+                "type": "INT",
+                "widget": {
+                  "name": "noise_seed"
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+                "link": null
+              },
+              {
+                "label": "image strength",
+                "name": "strength",
+                "type": "FLOAT",
+                "widget": {
+                  "name": "strength"
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+                "link": null
+              }
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+            "outputs": [
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+            "target_id": 253,
+            "target_slot": 1,
+            "type": "MODEL"
+          },
+          {
+            "id": 452,
+            "origin_id": 266,
+            "origin_slot": 2,
+            "target_id": 253,
+            "target_slot": 5,
+            "type": "VAE"
+          },
+          {
+            "id": 453,
+            "origin_id": 267,
+            "origin_slot": 0,
+            "target_id": 253,
+            "target_slot": 2,
+            "type": "LATENT_UPSCALE_MODEL"
+          },
+          {
+            "id": 454,
+            "origin_id": 253,
+            "origin_slot": 2,
+            "target_id": 267,
+            "target_slot": 0,
+            "type": "*"
+          },
+          {
+            "id": 455,
+            "origin_id": 253,
+            "origin_slot": 2,
+            "target_id": 266,
+            "target_slot": 0,
+            "type": "*"
+          },
+          {
+            "id": 456,
+            "origin_id": 253,
+            "origin_slot": 2,
+            "target_id": 268,
+            "target_slot": 0,
+            "type": "*"
+          },
+          {
+            "id": 457,
+            "origin_id": 268,
+            "origin_slot": 0,
+            "target_id": 253,
+            "target_slot": 6,
+            "type": "VAE"
+          }
+        ],
+        "extra": {
+          "workflowRendererVersion": "LG"
+        }
+      }
+    ]
+  },
+  "config": {},
+  "version": 0.4
+}
diff --git a/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.0/LTX-2_ICLoRA_All_Distilled_ref0.5.json b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.0/LTX-2_ICLoRA_All_Distilled_ref0.5.json
new file mode 100644
index 0000000000000000000000000000000000000000..1ef9bbe1cb8b260c0e1b1c4952ed831b4c2024bb
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.0/LTX-2_ICLoRA_All_Distilled_ref0.5.json
@@ -0,0 +1,14193 @@
+{
+  "id": "3a1879d9-fa8c-4270-a36f-3f10b01b983b",
+  "revision": 0,
+  "last_node_id": 274,
+  "last_link_id": 456,
+  "nodes": [
+    {
+      "id": 265,
+      "type": "SaveVideo",
+      "pos": [
+        -3307.3567107528597,
+        5373.118200558858
+      ],
+      "size": [
+        416.2646763195153,
+        452.6478370150671
+      ],
+      "flags": {},
+      "order": 12,
+      "mode": 4,
+      "inputs": [
+        {
+          "name": "video",
+          "type": "VIDEO",
+          "link": 456
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1"
+      },
+      "widgets_values": [
+        "video/ComfyUI",
+        "auto",
+        "auto"
+      ]
+    },
+    {
+      "id": 274,
+      "type": "MarkdownNote",
+      "pos": [
+        -3705.3723898360204,
+        4198.991136202638
+      ],
+      "size": [
+        271.8700736604794,
+        105.6563141843053
+      ],
+      "flags": {
+        "collapsed": false
+      },
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Video Size",
+      "properties": {},
+      "widgets_values": [
+        "Width & height settings must be divisible by 64. \n\nRunning with invalid parameters **will not cause errors**. Instead, the flow will silently choose the closest valid parameters. "
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 264,
+      "type": "SaveVideo",
+      "pos": [
+        -3300.5108091503857,
+        4344.231267080603
+      ],
+      "size": [
+        394.3820764961033,
+        441.7206085664038
+      ],
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "video",
+          "type": "VIDEO",
+          "link": 455
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1"
+      },
+      "widgets_values": [
+        "video/ComfyUI",
+        "auto",
+        "auto"
+      ]
+    },
+    {
+      "id": 201,
+      "type": "PrimitiveBoolean",
+      "pos": [
+        -4281.760208043995,
+        5035.499737380581
+      ],
+      "size": [
+        384.03006973056927,
+        58
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "BOOLEAN",
+          "type": "BOOLEAN",
+          "links": [
+            363,
+            397,
+            433
+          ]
+        }
+      ],
+      "title": "Text Only",
+      "properties": {
+        "Node name for S&R": "PrimitiveBoolean",
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1"
+      },
+      "widgets_values": [
+        false
+      ]
+    },
+    {
+      "id": 263,
+      "type": "SaveVideo",
+      "pos": [
+        -3300.791002295813,
+        4850.00281731496
+      ],
+      "size": [
+        407.5231522848858,
+        444.0448474342347
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+      "flags": {},
+      "order": 11,
+      "mode": 4,
+      "inputs": [
+        {
+          "name": "video",
+          "type": "VIDEO",
+          "link": 454
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1"
+      },
+      "widgets_values": [
+        "video/ComfyUI",
+        "auto",
+        "auto"
+      ]
+    },
+    {
+      "id": 273,
+      "type": "MarkdownNote",
+      "pos": [
+        -4785.564205750212,
+        4455.096649073921
+      ],
+      "size": [
+        474.7453652124491,
+        605.585189911063
+      ],
+      "flags": {
+        "collapsed": false
+      },
+      "order": 2,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Model Links",
+      "properties": {},
+      "widgets_values": [
+        "## Model Links\n\n**LTX-2 Model Weights**\n\n- [ltx-2-19b-distilled.safetensors](https://huggingface.co/Lightricks/LTX-2/resolve/main/ltx-2-19b-distilled.safetensors)\n- [ ltx-2-spatial-upscaler-x2-1.0.safetensors ](https://huggingface.co/Lightricks/LTX-2/resolve/main/ltx-2-spatial-upscaler-x2-1.0.safetensors)\n- [ IC LoRA - Canny ](https://huggingface.co/Lightricks/LTX-2-19b-IC-LoRA-Canny-Control/resolve/main/ltx-2-19b-ic-lora-canny-control.safetensors)\n- [ IC LoRA - Depth ](https://huggingface.co/Lightricks/LTX-2-19b-IC-LoRA-Depth-Control/resolve/main/ltx-2-19b-ic-lora-depth-control.safetensors)\n- [ IC LoRA - Pose ](https://huggingface.co/Lightricks/LTX-2-19b-IC-LoRA-Pose-Control/resolve/main/ltx-2-19b-ic-lora-pose-control.safetensors)\n\n\n\n**Text Encoder**\n- [Google Gemma 3](https://huggingface.co/google/gemma-3-12b-it-qat-q4_0-unquantized)\n\nPlease download the entire folder - \n\n- Run: \n 1. cd models/text_encoders\n 2. git clone https://huggingface.co/google/gemma-3-12b-it-qat-q4_0-unquantized\n\n*Full documentation can be found [here](https://docs.ltx.video/open-source-model/integration-tools/comfy-ui#text-encoder)\n\n\n**Model Storage Location**\n\n```\n📂 ComfyUI/\n├── 📂 models/\n│   ├── 📂 text_encoders/\n│   │      ├── gemma-3-12b-it-qat-q4_0-unquantized/model-00001-of-00005.safetensors\n│   ├── 📂 checkpoints/\n│   │      └── ltx-2-19b-distilled.safetensors\n│   ├── 📂 latent_upscale_models/ \n│   │      └── ltx-2-spatial-upscaler-x2-1.0.safetensors\n│   ├── 📂 loras/ \n│           └── ltx-2-19b-ic-lora-canny-control.safetensors\n│           └── ltx-2-19b-ic-lora-depth-control.safetensors\n│           └── ltx-2-19b-ic-lora-pose-control.safetensors\n```\n**Assets**\n- [buildings.mp4 (input video)](https://github.com/Lightricks/ComfyUI-LTXVideo/blob/master/example_workflows/assets/buildings.mp4)\n- [buildings ff (first frame)](https://github.com/Lightricks/ComfyUI-LTXVideo/blob/master/example_workflows/assets/buildings%20ff.png)\n\n## Report Issues\nTo report any issues when running this workflow, [go to GitHub](https://github.com/Lightricks/ComfyUI-LTXVideo/issues)\n"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 271,
+      "type": "MarkdownNote",
+      "pos": [
+        -4517.278638109156,
+        5357.133250216258
+      ],
+      "size": [
+        210,
+        88
+      ],
+      "flags": {
+        "collapsed": false
+      },
+      "order": 3,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Important",
+      "properties": {},
+      "widgets_values": [
+        "# Always load Image"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 272,
+      "type": "MarkdownNote",
+      "pos": [
+        -4782.341673813574,
+        5107.78314157236
+      ],
+      "size": [
+        471.21870630897365,
+        207.61883103687887
+      ],
+      "flags": {
+        "collapsed": false
+      },
+      "order": 4,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Prompting LTX-2",
+      "properties": {},
+      "widgets_values": [
+        "## LTX-2 Prompting Tips\n1. **Core Actions**: Describe events and actions as they occur over time  \n2. **Audio**: Describe sounds and dialogue needed for the scene  \n3. **Reference Image**: Do not repeat details already present  \n4. **Consistency**: Avoid instructions that do not match the reference image, as this will degrade results\n"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 164,
+      "type": "LoadVideo",
+      "pos": [
+        -4273.650112839631,
+        4496.243204253725
+      ],
+      "size": [
+        386.798828125,
+        458.5515914425977
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "VIDEO",
+          "type": "VIDEO",
+          "links": [
+            362,
+            398,
+            432
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadVideo",
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1"
+      },
+      "widgets_values": [
+        "LTX-2_V2V_00014-audio.mp4",
+        "image"
+      ]
+    },
+    {
+      "id": 200,
+      "type": "LoadImage",
+      "pos": [
+        -4285.129479933403,
+        5143.939169472896
+      ],
+      "size": [
+        400.4874419937255,
+        378.7916882359277
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            450,
+            451,
+            452
+          ]
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage",
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1"
+      },
+      "widgets_values": [
+        "first_frame (1).png",
+        "image"
+      ]
+    },
+    {
+      "id": 224,
+      "type": "2dc75cab-e957-4437-a5bb-2afb0ea00516",
+      "pos": [
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+        4340.026662390727
+      ],
+      "size": [
+        430.6110937678254,
+        456.14251880590064
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "video",
+          "type": "VIDEO",
+          "link": 362
+        },
+        {
+          "name": "switch",
+          "type": "BOOLEAN",
+          "widget": {
+            "name": "switch"
+          },
+          "link": 363
+        },
+        {
+          "label": "first frame",
+          "name": "image_1",
+          "type": "IMAGE",
+          "link": 450
+        }
+      ],
+      "outputs": [
+        {
+          "label": "video",
+          "name": "VIDEO",
+          "type": "VIDEO",
+          "links": [
+            455
+          ]
+        }
+      ],
+      "properties": {
+        "proxyWidgets": [
+          [
+            "212",
+            "value"
+          ],
+          [
+            "-1",
+            "width"
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+          [
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+            "height"
+          ],
+          [
+            "223",
+            "noise_seed"
+          ],
+          [
+            "223",
+            "461: control_after_generate"
+          ],
+          [
+            "-1",
+            "switch"
+          ]
+        ],
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1"
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+      "widgets_values": [
+        null,
+        1920,
+        1024,
+        null,
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+      "flags": {},
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+      "mode": 4,
+      "inputs": [
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+          "name": "switch",
+          "type": "BOOLEAN",
+          "widget": {
+            "name": "switch"
+          },
+          "link": 397
+        },
+        {
+          "name": "video",
+          "type": "VIDEO",
+          "link": 398
+        },
+        {
+          "label": "first frame",
+          "name": "image_1",
+          "type": "IMAGE",
+          "link": 451
+        }
+      ],
+      "outputs": [
+        {
+          "label": "video",
+          "name": "VIDEO",
+          "type": "VIDEO",
+          "links": [
+            454
+          ]
+        }
+      ],
+      "properties": {
+        "proxyWidgets": [
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+            "value"
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+            "height"
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+          [
+            "239",
+            "noise_seed"
+          ],
+          [
+            "239",
+            "461: control_after_generate"
+          ],
+          [
+            "-1",
+            "switch"
+          ]
+        ],
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1"
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+      "widgets_values": [
+        null,
+        1920,
+        1024,
+        null,
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+      "mode": 4,
+      "inputs": [
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+          "type": "VIDEO",
+          "link": 432
+        },
+        {
+          "name": "switch",
+          "type": "BOOLEAN",
+          "widget": {
+            "name": "switch"
+          },
+          "link": 433
+        },
+        {
+          "label": "first frame",
+          "name": "image_1",
+          "type": "IMAGE",
+          "link": 452
+        }
+      ],
+      "outputs": [
+        {
+          "label": "video",
+          "name": "VIDEO",
+          "type": "VIDEO",
+          "links": [
+            456
+          ]
+        }
+      ],
+      "properties": {
+        "proxyWidgets": [
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+            "461: control_after_generate"
+          ],
+          [
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+            "switch"
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+        "cnr_id": "comfy-core",
+        "ver": "0.5.1"
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+      "widgets_values": [
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+        1920,
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+        null,
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+      164,
+      0,
+      224,
+      0,
+      "VIDEO"
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+      2,
+      "IMAGE"
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+      200,
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+      240,
+      2,
+      "IMAGE"
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+      200,
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+      2,
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+      0,
+      265,
+      0,
+      "VIDEO"
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+      "bounding": [
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+      "flags": {},
+      "nodes": [
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+        164,
+        200
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+    {
+      "id": 4,
+      "title": "Switch T2V Or I2V",
+      "bounding": [
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+        4961.8997373805805,
+        420.4874419937255,
+        570.8311203282425
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+      "color": "#3f789e",
+      "font_size": 24,
+      "flags": {},
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+  "definitions": {
+    "subgraphs": [
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+        "version": 1,
+        "state": {
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+          "lastNodeId": 561,
+          "lastLinkId": 1208,
+          "lastRerouteId": 0
+        },
+        "revision": 0,
+        "config": {},
+        "name": "Samplers",
+        "inputNode": {
+          "id": -10,
+          "bounding": [
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+            169.416015625,
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+        "outputNode": {
+          "id": -20,
+          "bounding": [
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+            100
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+        "inputs": [
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+            "id": "c07959d1-4634-401e-a363-cb7e8d7815fc",
+            "name": "model",
+            "type": "MODEL",
+            "linkIds": [
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+            "localized_name": "model",
+            "label": "model stage 1",
+            "pos": [
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+              3595.5393812831066
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+          {
+            "id": "af0d6ebf-673c-44d8-bc26-2777ff0a5f13",
+            "name": "model_1",
+            "type": "MODEL",
+            "linkIds": [
+              1023,
+              1024
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+            "localized_name": "model_1",
+            "label": "model stage 2",
+            "pos": [
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+              3615.5393812831066
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+          {
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+                "type": "FLOAT",
+                "widget": {
+                  "name": "frame_rate"
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+                "name": "positive",
+                "type": "CONDITIONING",
+                "slot_index": 0,
+                "links": [
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+                "name": "clip",
+                "type": "CLIP",
+                "link": 375
+              },
+              {
+                "localized_name": "text",
+                "name": "text",
+                "type": "STRING",
+                "widget": {
+                  "name": "text"
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+                "link": 378
+              }
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+            "outputs": [
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+                "name": "CONDITIONING",
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+                "links": [
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+            "title": "Enhanced Prompt (Positive)",
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+              "Node name for S&R": "CLIPTextEncode",
+              "cnr_id": "comfy-core",
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+                "name": "on_true",
+                "type": "*",
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+                "name": "switch",
+                "type": "BOOLEAN",
+                "widget": {
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+                "localized_name": "*",
+                "name": "*",
+                "type": "*",
+                "links": [
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+            "title": "Bypass Enhancer",
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+                "type": "VIDEO",
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+                "name": "images",
+                "type": "IMAGE",
+                "links": [
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+            "type": "LTXVGemmaEnhancePrompt",
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+                "link": 369
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+                "name": "image",
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+                "type": "IMAGE",
+                "link": null
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+              {
+                "localized_name": "prompt",
+                "name": "prompt",
+                "type": "STRING",
+                "widget": {
+                  "name": "prompt"
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+                "localized_name": "enhanced_prompt",
+                "name": "enhanced_prompt",
+                "type": "STRING",
+                "links": [
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+              }
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+            "title": "Enhancer",
+            "properties": {
+              "Node name for S&R": "LTXVGemmaEnhancePrompt"
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+              "The kid holding an \"LTX-2\" sign with umbrella",
+              "You are a prompt enhancer for a text-to-video model that may be controlled by depth or canny conditioning unknown to you.\n\nYour task is to expand an input caption into a temporally rich, visually expressive video prompt while avoiding any assumptions about scene structure, layout, camera motion, or subject configuration that could contradict an unseen conditioning input.\n\nYou are given:\n\t•\tA text caption describing the scene\n\t•\tNo access to the conditioning input\n\nYou must produce a single continuous paragraph describing how the scene unfolds over time, written to remain compatible with any plausible depth or canny constraint.\n\nCore constraint (highest priority):\n\t•\tStructural agnosticism: Assume that scene geometry, object placement, number of subjects, poses, camera position, and camera motion are fully determined elsewhere. Do NOT invent, alter, or imply any of them.\n\t•\tNo structural commitments: Do NOT add new objects, people, props, architecture, crowd dynamics, entrances/exits, or camera movements unless they are explicitly stated in the input caption.\n\t•\tCamera neutrality: Do NOT specify shot type, framing, or camera motion unless explicitly mentioned in the input caption.\n\t•\tIf unsure, omit: If a description would require assuming unseen structure or motion, leave it out.\n\nGuidelines for enhancement:\n\t•\tFaithful expansion: Enrich the caption without changing its meaning or adding unstated elements.\n\t•\tTemporal dynamics: Focus on timing, continuity, rhythm, and subtle evolution over a few seconds—not new events.\n\t•\tMicro-motion bias: Prefer small, universally compatible motions (breathing, fabric movement, light shifts, ambient motion).\n\t•\tVisual expressiveness (non-structural): You may enhance lighting behavior, color response, surface interaction, atmosphere, and material qualities as long as they do not imply new geometry or layout.\n\t•\tCharacter & action: Describe actions only if explicitly mentioned or safely implied without adding interactions or spatial assumptions.\n\t•\tChronological flow: Use temporal connectors (\"as,\" \"while,\" \"then\") to sequence events.\n\nAudio and dialogue:\n\t•\tAudio layer (always include): Add ambient sounds or music that enhance mood but do NOT imply unseen physical sources, events, or actions. Weave audio naturally into the temporal flow.\n\t•\tDialogue (only if present in input): If dialogue is mentioned, provide exact quoted lines with clear attribution. Specify language or voice traits only if required. Do NOT invent dialogue otherwise.\n\nStyle and realism:\n\t•\tStyle & atmosphere: Artistic tone is allowed only when it does not imply structure, scale, or camera choices.\n\t•\tPhysics realism: Use precise, physically plausible descriptions.\n\t•\tObjective only: Do NOT infer emotions, intentions, or narrative meaning beyond observable motion and sound.\n\t•\tStyle priority: If the user explicitly specifies a visual style (e.g., anime, claymation, watercolor, noir), make that style dominant throughout the prompt. Apply it consistently to lighting, materials, rendering cues, color treatment, and atmosphere, without implying scene structure.\n\nHard constraints:\n\t•\tDo NOT introduce scene cuts, edits, timestamps, or montage language\n\t•\tDo NOT add off-screen events that affect the scene physically\n\t•\tProduce plain prose only\n\nOutput format (strict):\n\t•\tOne continuous paragraph in natural language\n\t•\tNo headings, labels, markdown, code fences, or metadata\n\t•\tAudio must be woven into the paragraph, never separated\n\t•\tMake sure to mention the relevant style if requested, and in that case adjust the entire prompt to match the style, including colors, texture, etc. also start with \"Style: \" and the style name",
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+                "type": "AUDIO",
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+                "type": "FLOAT",
+                "widget": {
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+                "name": "VIDEO",
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+              "Node name for S&R": "CreateVideo",
+              "cnr_id": "comfy-core",
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+            "widgets_values": [
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+                "name": "video",
+                "type": "VIDEO",
+                "link": 399
+              }
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+            "outputs": [],
+            "properties": {
+              "cnr_id": "comfy-core",
+              "ver": "0.5.1"
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+            "widgets_values": [
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+              "auto",
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+                "name": "STRING",
+                "type": "STRING",
+                "links": [
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+                  376
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+            ],
+            "title": "Positive Prompt",
+            "properties": {
+              "Node name for S&R": "PrimitiveStringMultiline",
+              "cnr_id": "comfy-core",
+              "ver": "0.5.1"
+            },
+            "widgets_values": [
+              "a cute monkey jupms on a bed"
+            ],
+            "color": "#232",
+            "bgcolor": "#353"
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+            "type": "MarkdownNote",
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+            "order": 1,
+            "mode": 4,
+            "inputs": [],
+            "outputs": [],
+            "title": "RTX 5090",
+            "properties": {},
+            "widgets_values": [
+              "This workflow is structured for optimized GPU and VRAM usage.\n\nModel nodes are ordered intentionally to reduce peak VRAM load.\n\nInput dependencies are used to organize execution order and should not be removed."
+            ],
+            "color": "#432",
+            "bgcolor": "#653"
+          },
+          {
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+            "type": "MarkdownNote",
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+            "mode": 4,
+            "inputs": [],
+            "outputs": [],
+            "title": "IC LoRA",
+            "properties": {},
+            "widgets_values": [
+              "# IC LoRA Training \n\nFor your own IC LoRA - Upload to the \"IC LoRA\" node.\nMake sure to bypass the \"IC LoRA Preprocessing\" in the Samplers subgraph \n\n## Training\nWe have made available a full trainer to help you to train your own LoRA. You can [find it here](https://github.com/Lightricks/LTX-2)."
+            ],
+            "color": "#432",
+            "bgcolor": "#653"
+          },
+          {
+            "id": 252,
+            "type": "LowVRAMAudioVAELoader",
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+                "name": "dependencies",
+                "shape": 7,
+                "type": "*",
+                "link": 415
+              }
+            ],
+            "outputs": [
+              {
+                "localized_name": "audio_vae",
+                "name": "audio_vae",
+                "type": "VAE",
+                "links": [
+                  414
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+              }
+            ],
+            "properties": {
+              "Node name for S&R": "LowVRAMAudioVAELoader"
+            },
+            "widgets_values": [
+              "ltx-2-19b-distilled.safetensors"
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+            "type": "LTXVGemmaCLIPModelLoader",
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+                "name": "clip",
+                "type": "CLIP",
+                "links": [
+                  369,
+                  375
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+              }
+            ],
+            "properties": {
+              "Node name for S&R": "LTXVGemmaCLIPModelLoader",
+              "aux_id": "Lightricks/ComfyUI-LTXVideo",
+              "ver": "445e087a43b95b57ef765874b6787446ec72546c"
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+            "widgets_values": [
+              "gemma-3-12b-it-qat-q4_0-unquantized/model-00001-of-00005.safetensors",
+              "ltx-2-19b-distilled.safetensors",
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+                "name": "dependencies",
+                "shape": 7,
+                "type": "*",
+                "link": 417
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+            "outputs": [
+              {
+                "localized_name": "LATENT_UPSCALE_MODEL",
+                "name": "LATENT_UPSCALE_MODEL",
+                "type": "LATENT_UPSCALE_MODEL",
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+                "type": "*",
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+                "name": "MODEL",
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+                "name": "CLIP",
+                "type": "CLIP",
+                "links": null
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+              {
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+                "name": "VAE",
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+                "name": "model",
+                "type": "MODEL",
+                "link": 411
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+            ],
+            "outputs": [
+              {
+                "localized_name": "MODEL",
+                "name": "MODEL",
+                "type": "MODEL",
+                "links": []
+              }
+            ],
+            "title": "IC LoRA - Depth",
+            "properties": {
+              "Node name for S&R": "LoraLoaderModelOnly",
+              "cnr_id": "comfy-core",
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+                "localized_name": "model",
+                "name": "model",
+                "type": "MODEL",
+                "link": 421
+              },
+              {
+                "label": "model stage 2",
+                "localized_name": "model_1",
+                "name": "model_1",
+                "type": "MODEL",
+                "link": 412
+              },
+              {
+                "label": "upscale model",
+                "name": "upscale_model_2",
+                "type": "LATENT_UPSCALE_MODEL",
+                "link": 418
+              },
+              {
+                "localized_name": "positive",
+                "name": "positive",
+                "type": "CONDITIONING",
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+              },
+              {
+                "localized_name": "negative",
+                "name": "negative",
+                "type": "CONDITIONING",
+                "link": 386
+              },
+              {
+                "label": "VAE",
+                "localized_name": "vae",
+                "name": "vae",
+                "type": "VAE",
+                "link": 413
+              },
+              {
+                "label": "audio VAE",
+                "localized_name": "audio_vae",
+                "name": "audio_vae",
+                "type": "VAE",
+                "link": 414
+              },
+              {
+                "label": "first frame",
+                "localized_name": "image_1",
+                "name": "image_1",
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+                "localized_name": "a",
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+                "widget": {
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+                "link": 382
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+              {
+                "label": "Text Only",
+                "localized_name": "bypass",
+                "name": "bypass",
+                "type": "BOOLEAN",
+                "widget": {
+                  "name": "bypass"
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+                "name": "image",
+                "type": "IMAGE",
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+              },
+              {
+                "label": "noise seed",
+                "name": "noise_seed",
+                "type": "INT",
+                "widget": {
+                  "name": "noise_seed"
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+                "link": null
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+              {
+                "label": "image strength",
+                "name": "strength",
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+                "widget": {
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+              {
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diff --git a/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.0/LTX-2_T2V_Distilled_wLora.json b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.0/LTX-2_T2V_Distilled_wLora.json
new file mode 100644
index 0000000000000000000000000000000000000000..b0a524d2d9caf357668f1a209f4d7add7517c48c
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.0/LTX-2_T2V_Distilled_wLora.json
@@ -0,0 +1,2837 @@
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+        "You are a Creative Assistant. Given a user's raw input prompt describing a scene or concept, expand it into a detailed video generation prompt with specific visuals and integrated audio to guide a text-to-video model.\n\n#### Guidelines\n- Strictly follow all aspects of the user's raw input: include every element requested (style, visuals, motions, actions, camera movement, audio).\n    - If the input is vague, invent concrete details: lighting, textures, materials, scene settings, etc.\n        - For characters: describe gender, clothing, hair, expressions. DO NOT invent unrequested characters.\n- Use active language: present-progressive verbs (\"is walking,\" \"speaking\"). If no action specified, describe natural movements.\n- Maintain chronological flow: use temporal connectors (\"as,\" \"then,\" \"while\").\n- Audio layer: Describe complete soundscape (background audio, ambient sounds, SFX, speech/music when requested). Integrate sounds chronologically alongside actions. Be specific (e.g., \"soft footsteps on tile\"), not vague (e.g., \"ambient sound is present\").\n- Speech (only when requested): \n    - For ANY speech-related input (talking, conversation, singing, etc.), ALWAYS include exact words in quotes with voice characteristics (e.g., \"The man says in an excited voice: 'You won't believe what I just saw!'\").\n    - Specify language if not English and accent if relevant.\n- Style: Include visual style at the beginning: \"Style: <style>, <rest of prompt>.\" Default to cinematic-realistic if unspecified. Omit if unclear.\n- Visual and audio only: NO non-visual/auditory senses (smell, taste, touch).\n- Restrained language: Avoid dramatic/exaggerated terms. Use mild, natural phrasing.\n    - Colors: Use plain terms (\"red dress\"), not intensified (\"vibrant blue,\" \"bright red\").\n    - Lighting: Use neutral descriptions (\"soft overhead light\"), not harsh (\"blinding light\").\n    - Facial features: Use delicate modifiers for subtle features (i.e., \"subtle freckles\").\n\n#### Important notes: \n- Analyze the user's raw input carefully. In cases of FPV or POV, exclude the description of the subject whose POV is requested.\n- Camera motion: DO NOT invent camera motion unless requested by the user.\n- Speech: DO NOT modify user-provided character dialogue unless it's a typo.\n- No timestamps or cuts: DO NOT use timestamps or describe scene cuts unless explicitly requested.\n- Format: DO NOT use phrases like \"The scene opens with...\". Start directly with Style (optional) and chronological scene description.\n- Format: DO NOT start your response with special characters.\n- DO NOT invent dialogue unless the user mentions speech/talking/singing/conversation.\n- If the user's raw input prompt is highly detailed, chronological and in the requested format: DO NOT make major edits or introduce new elements. Add/enhance audio descriptions if missing.\n\n#### Output Format (Strict):\n- Single continuous paragraph in natural language (English).\n- NO titles, headings, prefaces, code fences, or Markdown.\n- If unsafe/invalid, return original user prompt. Never ask questions or clarifications.\n\nYour output quality is CRITICAL. Generate visually rich, dynamic prompts with integrated audio for high-quality video generation.\n\n#### Example\nInput: \"A woman at a coffee shop talking on the phone\"\nOutput:\nStyle: realistic with cinematic lighting. In a medium close-up, a woman in her early 30s with shoulder-length brown hair sits at a small wooden table by the window. She wears a cream-colored turtleneck sweater, holding a white ceramic coffee cup in one hand and a smartphone to her ear with the other. Ambient cafe sounds fill the space—espresso machine hiss, quiet conversations, gentle clinking of cups. The woman listens intently, nodding slightly, then takes a sip of her coffee and sets it down with a soft clink. Her face brightens into a warm smile as she speaks in a clear, friendly voice, 'That sounds perfect! I'd love to meet up this weekend. How about Saturday afternoon?' She laughs softly—a genuine chuckle—and shifts in her chair. Behind her, other patrons move subtly in and out of focus. 'Great, I'll see you then,' she concludes cheerfully, lowering the phone.",
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+          "type": "STRING",
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+      "title": "Positive Prompt",
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+        "cinematic epic war movie scene shows a medium closeup shot of a fierce muscular Viking warrior standing in place, clad in weathered leather armor and gripping a battle-worn axe, his long, tangled hair billowing against the stormy sky. The scene is set under a foreboding, cloud-heavy sky, casting an eerie, dim light that reflects off his glinting axe. we hear him roar: \"prepare for battle, and eternal glory!\". the camera slowly zooms in on his determined face"
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+      "color": "#232",
+      "bgcolor": "#353"
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+      "type": "LTXVGemmaCLIPModelLoader",
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+          "type": "CLIP",
+          "links": [
+            13961,
+            13962
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+      "properties": {
+        "Node name for S&R": "LTXVGemmaCLIPModelLoader",
+        "widget_ue_connectable": {
+          "model_path": true,
+          "max_length": true
+        }
+      },
+      "widgets_values": [
+        "gemma-3-12b-it-qat-q4_0-unquantized/model-00001-of-00005.safetensors",
+        "ltx-2-19b-distilled.safetensors",
+        1024
+      ]
+    },
+    {
+      "id": 5248,
+      "type": "MarkdownNote",
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+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "LoRA",
+      "properties": {},
+      "widgets_values": [
+        "# Using LoRAs\n**Ctrl + B to enable**\n\nFor the camera LoRA we recommend setting strength = 1\n\nYou can use LTX [Ready to use camera LoRAs](https://huggingface.co/collections/Lightricks/ltx-2)\n\n\n\n\nUse a second LoRa only when the details are important in the LoRa. \n\n## Training\nWe have made available a full trainer to help you to train your own LoRA. You can [find it here](https://github.com/Lightricks/LTX-2)"
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+          "link": 13967
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+          "link": 13968
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+          "label": "VAE",
+          "name": "vae",
+          "type": "VAE",
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+          "type": "INT",
+          "widget": {
+            "name": "noise_seed"
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+          "label": "images",
+          "name": "empty latent image",
+          "type": "IMAGE",
+          "links": [
+            13958
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+        },
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+          "links": [
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+      "properties": {
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+        "ver": "0.5.1"
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+        121,
+        25,
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+    },
+    {
+      "id": 5246,
+      "type": "MarkdownNote",
+      "pos": [
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+        "collapsed": false
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+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Prompting LTX-2",
+      "properties": {},
+      "widgets_values": [
+        "## LTX-2 Prompting Tips\n1. **Core Actions**: Describe events and actions as they occur over time  \n2. **Audio**: Describe sounds and dialogue needed for the scene  \n3. **Consistency**: Avoid instructions that do not match the reference image, as this will degrade results\n"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 5245,
+      "type": "MarkdownNote",
+      "pos": [
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+        7426.935420507221
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+      "size": [
+        419.9816103344465,
+        480.7830408486252
+      ],
+      "flags": {
+        "collapsed": false
+      },
+      "order": 11,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Model Links",
+      "properties": {},
+      "widgets_values": [
+        "## Model Links\n\n**LTX-2 Model Weights**\n\n- [ltx-2-19b-distilled.safetensors](https://huggingface.co/Lightricks/LTX-2/resolve/main/ltx-2-19b-distilled.safetensors)\n- [ ltx-2-spatial-upscaler-x2-1.0.safetensors ](https://huggingface.co/Lightricks/LTX-2/resolve/main/ltx-2-spatial-upscaler-x2-1.0.safetensors)\n\n**Text Encoder**\n- [Google Gemma 3](https://huggingface.co/google/gemma-3-12b-it-qat-q4_0-unquantized)\n\nPlease download the entire folder - \n\n- Run: \n 1. cd models/text_encoders\n 2. git clone https://huggingface.co/google/gemma-3-12b-it-qat-q4_0-unquantized\n\n*Full documentation can be found [here](https://docs.ltx.video/open-source-model/integration-tools/comfy-ui#text-encoder)\n\n**Model Storage Location**\n\n```\n📂 ComfyUI/\n├── 📂 models/\n│   ├── 📂 text_encoders/\n│   │      ├── gemma-3-12b-it-qat-q4_0-unquantized/model-00001-of-00005.safetensors\n│   ├── 📂 checkpoints/\n│   │      └── ltx-2-19b-distilled.safetensors\n│   ├── 📂 latent_upscale_models/ \n           └── ltx-2-spatial-upscaler-x2-1.0.safetensors\n```\n\n## Report Issues\nTo report any issues when running this workflow, [go to GitHub](https://github.com/Lightricks/ComfyUI-LTXVideo/issues)\n"
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diff --git a/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.0/LTX-2_T2V_Full_wLora.json b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.0/LTX-2_T2V_Full_wLora.json
new file mode 100644
index 0000000000000000000000000000000000000000..5f0fd3276b493e720ed223351414f28b1ecea220
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.0/LTX-2_T2V_Full_wLora.json
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+        "Node name for S&R": "CLIPTextEncode",
+        "cnr_id": "comfy-core",
+        "ver": "0.3.28"
+      },
+      "widgets_values": [
+        ""
+      ],
+      "color": "#232",
+      "bgcolor": "#353"
+    },
+    {
+      "id": 5221,
+      "type": "LoraLoaderModelOnly",
+      "pos": [
+        -2435.0393201037195,
+        8163.778513700478
+      ],
+      "size": [
+        210,
+        82
+      ],
+      "flags": {},
+      "order": 17,
+      "mode": 4,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 13958
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            14030
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoraLoaderModelOnly",
+        "cnr_id": "comfy-core",
+        "ver": "0.3.68"
+      },
+      "widgets_values": [
+        "your_camera_lora.safetensors",
+        1
+      ]
+    },
+    {
+      "id": 5222,
+      "type": "LoraLoaderModelOnly",
+      "pos": [
+        -2668.636981453724,
+        8162.466693154896
+      ],
+      "size": [
+        220.14623500416474,
+        90.32621827319144
+      ],
+      "flags": {},
+      "order": 14,
+      "mode": 4,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 13959
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            14033
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoraLoaderModelOnly",
+        "cnr_id": "comfy-core",
+        "ver": "0.3.68"
+      },
+      "widgets_values": [
+        "your_camera_lora.safetensors",
+        1
+      ]
+    },
+    {
+      "id": 5226,
+      "type": "CLIPTextEncode",
+      "pos": [
+        -2655.5275979204835,
+        8707.696034280034
+      ],
+      "size": [
+        313.9271213812535,
+        160.84366176012554
+      ],
+      "flags": {
+        "collapsed": false
+      },
+      "order": 15,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 13963
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "slot_index": 0,
+          "links": [
+            13968
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode",
+        "cnr_id": "comfy-core",
+        "ver": "0.3.28"
+      },
+      "widgets_values": [
+        "blurry, low quality, still frame, frames, watermark, overlay, titles, has blurbox, has subtitles"
+      ],
+      "color": "#322",
+      "bgcolor": "#533"
+    },
+    {
+      "id": 5270,
+      "type": "LatentUpscaleModelLoader",
+      "pos": [
+        -3625.8131648839753,
+        8493.432696308342
+      ],
+      "size": [
+        396.1660435338662,
+        90.46574270449128
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "LATENT_UPSCALE_MODEL",
+          "type": "LATENT_UPSCALE_MODEL",
+          "links": [
+            14038
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LatentUpscaleModelLoader",
+        "cnr_id": "comfy-core",
+        "ver": "0.7.0"
+      },
+      "widgets_values": [
+        "ltx-2-spatial-upscaler-x2-1.0.safetensors"
+      ]
+    },
+    {
+      "id": 5273,
+      "type": "MarkdownNote",
+      "pos": [
+        -2943.241504237536,
+        8161.259137854798
+      ],
+      "size": [
+        240.16187366614236,
+        98.41687371924309
+      ],
+      "flags": {
+        "collapsed": false
+      },
+      "order": 5,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Video Size",
+      "properties": {},
+      "widgets_values": [
+        "Width & height settings must be divisible by 64. \nFrame count must be divisible by 8 + 1. \n\n\nRunning with invalid parameters **will not cause errors**. Instead, the flow will silently choose the closest valid parameters. "
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 5274,
+      "type": "MarkdownNote",
+      "pos": [
+        -2679.7373441625773,
+        7861.17563988318
+      ],
+      "size": [
+        476.19110164532435,
+        207.4316250893462
+      ],
+      "flags": {
+        "collapsed": false
+      },
+      "order": 6,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "LoRA",
+      "properties": {},
+      "widgets_values": [
+        "# Using LoRAs\n**Ctrl + B to enable**\n\nFor the camera LoRA we recommend setting strength = 1\n\nYou can use LTX [Ready to use camera LoRAs](https://huggingface.co/collections/Lightricks/ltx-2)\n\n\n\n\nUse a second LoRa only when the details are important in the LoRa. \n\n## Training\nWe have made available a full trainer to help you to train your own LoRA. You can [find it here](https://github.com/Lightricks/LTX-2)."
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 5227,
+      "type": "LTXVGemmaEnhancePrompt",
+      "pos": [
+        -2302.473950325332,
+        8630.055358449259
+      ],
+      "size": [
+        369.82634110464005,
+        194
+      ],
+      "flags": {
+        "collapsed": true
+      },
+      "order": 16,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 13964
+        },
+        {
+          "name": "image",
+          "shape": 7,
+          "type": "IMAGE",
+          "link": null
+        },
+        {
+          "name": "prompt",
+          "type": "STRING",
+          "widget": {
+            "name": "prompt"
+          },
+          "link": 13984
+        }
+      ],
+      "outputs": [
+        {
+          "name": "enhanced_prompt",
+          "type": "STRING",
+          "links": [
+            13966
+          ]
+        }
+      ],
+      "title": "Enhancer",
+      "properties": {
+        "Node name for S&R": "LTXVGemmaEnhancePrompt"
+      },
+      "widgets_values": [
+        "",
+        "You are a Creative Assistant. Given a user's raw input prompt describing a scene or concept, expand it into a detailed video generation prompt with specific visuals and integrated audio to guide a text-to-video model.\n\n#### Guidelines\n- Strictly follow all aspects of the user's raw input: include every element requested (style, visuals, motions, actions, camera movement, audio).\n    - If the input is vague, invent concrete details: lighting, textures, materials, scene settings, etc.\n        - For characters: describe gender, clothing, hair, expressions. DO NOT invent unrequested characters.\n- Use active language: present-progressive verbs (\"is walking,\" \"speaking\"). If no action specified, describe natural movements.\n- Maintain chronological flow: use temporal connectors (\"as,\" \"then,\" \"while\").\n- Audio layer: Describe complete soundscape (background audio, ambient sounds, SFX, speech/music when requested). Integrate sounds chronologically alongside actions. Be specific (e.g., \"soft footsteps on tile\"), not vague (e.g., \"ambient sound is present\").\n- Speech (only when requested): \n    - For ANY speech-related input (talking, conversation, singing, etc.), ALWAYS include exact words in quotes with voice characteristics (e.g., \"The man says in an excited voice: 'You won't believe what I just saw!'\").\n    - Specify language if not English and accent if relevant.\n- Style: Include visual style at the beginning: \"Style: <style>, <rest of prompt>.\" Default to cinematic-realistic if unspecified. Omit if unclear.\n- Visual and audio only: NO non-visual/auditory senses (smell, taste, touch).\n- Restrained language: Avoid dramatic/exaggerated terms. Use mild, natural phrasing.\n    - Colors: Use plain terms (\"red dress\"), not intensified (\"vibrant blue,\" \"bright red\").\n    - Lighting: Use neutral descriptions (\"soft overhead light\"), not harsh (\"blinding light\").\n    - Facial features: Use delicate modifiers for subtle features (i.e., \"subtle freckles\").\n\n#### Important notes: \n- Analyze the user's raw input carefully. In cases of FPV or POV, exclude the description of the subject whose POV is requested.\n- Camera motion: DO NOT invent camera motion unless requested by the user.\n- Speech: DO NOT modify user-provided character dialogue unless it's a typo.\n- No timestamps or cuts: DO NOT use timestamps or describe scene cuts unless explicitly requested.\n- Format: DO NOT use phrases like \"The scene opens with...\". Start directly with Style (optional) and chronological scene description.\n- Format: DO NOT start your response with special characters.\n- DO NOT invent dialogue unless the user mentions speech/talking/singing/conversation.\n- If the user's raw input prompt is highly detailed, chronological and in the requested format: DO NOT make major edits or introduce new elements. Add/enhance audio descriptions if missing.\n\n#### Output Format (Strict):\n- Single continuous paragraph in natural language (English).\n- NO titles, headings, prefaces, code fences, or Markdown.\n- If unsafe/invalid, return original user prompt. Never ask questions or clarifications.\n\nYour output quality is CRITICAL. Generate visually rich, dynamic prompts with integrated audio for high-quality video generation.\n\n#### Example\nInput: \"A woman at a coffee shop talking on the phone\"\nOutput:\nStyle: realistic with cinematic lighting. In a medium close-up, a woman in her early 30s with shoulder-length brown hair sits at a small wooden table by the window. She wears a cream-colored turtleneck sweater, holding a white ceramic coffee cup in one hand and a smartphone to her ear with the other. Ambient cafe sounds fill the space—espresso machine hiss, quiet conversations, gentle clinking of cups. The woman listens intently, nodding slightly, then takes a sip of her coffee and sets it down with a soft clink. Her face brightens into a warm smile as she speaks in a clear, friendly voice, 'That sounds perfect! I'd love to meet up this weekend. How about Saturday afternoon?' She laughs softly—a genuine chuckle—and shifts in her chair. Behind her, other patrons move subtly in and out of focus. 'Great, I'll see you then,' she concludes cheerfully, lowering the phone.",
+        512,
+        true,
+        2793143611,
+        "randomize"
+      ]
+    },
+    {
+      "id": 5225,
+      "type": "PrimitiveStringMultiline",
+      "pos": [
+        -2658.9981943763814,
+        8349.827956066236
+      ],
+      "size": [
+        312.1591137598489,
+        314.2382942594604
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "STRING",
+          "type": "STRING",
+          "links": [
+            13984
+          ]
+        }
+      ],
+      "title": "Positive Prompt",
+      "properties": {
+        "Node name for S&R": "PrimitiveStringMultiline",
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1"
+      },
+      "widgets_values": [
+        "cinematic drama movie scene shows a closeup of a blonde woman wearing a red sweater hanging out of the open door of a moving train, looking outside and smiling. the camera is fixed to the train's side as the train moves forward on the track. the woman seems excited and says: \" I think we're almost there!\". we hear the train's engine in the background. then, a little boy with brown hair pops his head out of the train along with the woman, looking at her in excitement. he then asks her: \"This is Nana's old village, isn't it?\". she nodes and embraces him joyfully."
+      ],
+      "color": "#232",
+      "bgcolor": "#353"
+    },
+    {
+      "id": 5233,
+      "type": "PrimitiveInt",
+      "pos": [
+        -2930.33867108064,
+        8527.25744685942
+      ],
+      "size": [
+        210,
+        82
+      ],
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "INT",
+          "type": "INT",
+          "links": [
+            14026
+          ]
+        }
+      ],
+      "title": "length",
+      "properties": {
+        "Node name for S&R": "PrimitiveInt",
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1"
+      },
+      "widgets_values": [
+        241,
+        "fixed"
+      ]
+    },
+    {
+      "id": 5223,
+      "type": "CreateVideo",
+      "pos": [
+        -1805.9191518229377,
+        8313.410516500595
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+      "size": [
+        210,
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+      ],
+      "flags": {},
+      "order": 21,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 14036
+        },
+        {
+          "name": "audio",
+          "shape": 7,
+          "type": "AUDIO",
+          "link": 14037
+        },
+        {
+          "name": "fps",
+          "type": "FLOAT",
+          "widget": {
+            "name": "fps"
+          },
+          "link": 13982
+        }
+      ],
+      "outputs": [
+        {
+          "name": "VIDEO",
+          "type": "VIDEO",
+          "links": [
+            13979
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CreateVideo",
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1"
+      },
+      "widgets_values": [
+        30
+      ]
+    },
+    {
+      "id": 5216,
+      "type": "LoraLoaderModelOnly",
+      "pos": [
+        -3197.2762910684833,
+        8383.672331407945
+      ],
+      "size": [
+        210,
+        82
+      ],
+      "flags": {},
+      "order": 13,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 13957
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            13958
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoraLoaderModelOnly",
+        "cnr_id": "comfy-core",
+        "ver": "0.3.68"
+      },
+      "widgets_values": [
+        "ltx-2-19b-distilled-lora-384.safetensors",
+        0.6
+      ]
+    },
+    {
+      "id": 5261,
+      "type": "65e1c5bd-6c3a-4569-960f-5a9656bb6cef",
+      "pos": [
+        -2033.145605075496,
+        8315.679575761673
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+      "size": [
+        210,
+        290
+      ],
+      "flags": {},
+      "order": 20,
+      "mode": 0,
+      "inputs": [
+        {
+          "label": "model stage 1",
+          "name": "model_1",
+          "type": "MODEL",
+          "link": 14033
+        },
+        {
+          "label": "model stage 2",
+          "name": "model",
+          "type": "MODEL",
+          "link": 14030
+        },
+        {
+          "label": "upscale model",
+          "name": "upscale_model_1",
+          "type": "LATENT_UPSCALE_MODEL",
+          "link": 14038
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 14031
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 14032
+        },
+        {
+          "label": "VAE",
+          "name": "vae",
+          "type": "VAE",
+          "link": 14034
+        },
+        {
+          "label": "audio vae",
+          "name": "audio_vae",
+          "type": "VAE",
+          "link": 14028
+        },
+        {
+          "label": "empty latent image",
+          "name": "empty_latent_image",
+          "type": "IMAGE",
+          "link": 14027
+        },
+        {
+          "name": "length",
+          "type": "INT",
+          "widget": {
+            "name": "length"
+          },
+          "link": 14026
+        },
+        {
+          "label": "frame rate",
+          "name": "frame_rate",
+          "type": "INT",
+          "widget": {
+            "name": "frame_rate"
+          },
+          "link": 14029
+        },
+        {
+          "label": "noise seed",
+          "name": "noise_seed",
+          "type": "INT",
+          "widget": {
+            "name": "noise_seed"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "label": "images",
+          "name": "empty latent image",
+          "type": "IMAGE",
+          "links": [
+            14036
+          ]
+        },
+        {
+          "label": "audio",
+          "name": "audio",
+          "type": "AUDIO",
+          "links": [
+            14037
+          ]
+        }
+      ],
+      "properties": {
+        "proxyWidgets": [
+          [
+            "-1",
+            "length"
+          ],
+          [
+            "-1",
+            "frame_rate"
+          ],
+          [
+            "-1",
+            "noise_seed"
+          ],
+          [
+            "5257",
+            "control_after_generate"
+          ]
+        ],
+        "cnr_id": "comfy-core",
+        "ver": "0.5.1"
+      },
+      "widgets_values": [
+        121,
+        25,
+        43
+      ]
+    },
+    {
+      "id": 5272,
+      "type": "MarkdownNote",
+      "pos": [
+        -4097.884850147693,
+        8694.883120289716
+      ],
+      "size": [
+        423.87576766895836,
+        201.4320443144279
+      ],
+      "flags": {
+        "collapsed": false
+      },
+      "order": 9,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Prompting LTX-2",
+      "properties": {},
+      "widgets_values": [
+        "## LTX-2 Prompting Tips\n1. **Core Actions**: Describe events and actions as they occur over time  \n2. **Audio**: Describe sounds and dialogue needed for the scene  \n3. **Consistency**: Avoid instructions that do not match the reference image, as this will degrade results\n"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 5275,
+      "type": "MarkdownNote",
+      "pos": [
+        -4103.084142465534,
+        8076.449221913373
+      ],
+      "size": [
+        425.40198676408454,
+        574.4981796837719
+      ],
+      "flags": {
+        "collapsed": false
+      },
+      "order": 10,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Model Links",
+      "properties": {},
+      "widgets_values": [
+        "## Model Links\n\n**LTX-2 Model Weights**\n\n- [ltx-2-19b-dev.safetensors](https://huggingface.co/Lightricks/LTX-2/resolve/main/ltx-2-19b-dev.safetensors)\n- [ ltx-2-19b-distilled-lora-384.safetensors ](https://huggingface.co/Lightricks/LTX-2/resolve/main/ltx-2-19b-distilled-lora-384.safetensors)\n- [ ltx-2-spatial-upscaler-x2-1.0.safetensors ](https://huggingface.co/Lightricks/LTX-2/resolve/main/ltx-2-spatial-upscaler-x2-1.0.safetensors)\n\n\n**Text Encoder**\n- [Google Gemma 3](https://huggingface.co/google/gemma-3-12b-it-qat-q4_0-unquantized)\n\nPlease download the entire folder - \n\n- Run: \n 1. cd models/text_encoders\n 2. git clone https://huggingface.co/google/gemma-3-12b-it-qat-q4_0-unquantized\n\n*Full documentation can be found [here](https://docs.ltx.video/open-source-model/integration-tools/comfy-ui#text-encoder)\n\n**Sampler**\n\n- [RES4LYF](https://github.com/ClownsharkBatwing/RES4LYF)\n\n\n**Model Storage Location**\n\n```\n📂 ComfyUI/\n├── 📂 models/\n│   ├── 📂 text_encoders/\n│   │      ├── gemma-3-12b-it-qat-q4_0-unquantized/model-00001-of-00005.safetensors\n│   ├── 📂 checkpoints/\n│   │      └── ltx-2-19b-dev.safetensors\n│   ├── 📂 loras/\n│   │      └── ltx-2-19b-distilled-lora-384.safetensors\n│   ├── 📂 latent_upscale_models/ \n           └── ltx-2-spatial-upscaler-x2-1.0.safetensors\n```\n\n## Report Issues\nTo report any issues when running this workflow, [go to GitHub](https://github.com/Lightricks/ComfyUI-LTXVideo/issues)\n"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 5218,
+      "type": "LTXVGemmaCLIPModelLoader",
+      "pos": [
+        -3626.512660851144,
+        8639.80634877081
+      ],
+      "size": [
+        399.66579573119816,
+        106
+      ],
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "links": [
+            13963,
+            13964,
+            13965
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LTXVGemmaCLIPModelLoader",
+        "widget_ue_connectable": {
+          "model_path": true,
+          "max_length": true
+        }
+      },
+      "widgets_values": [
+        "gemma-3-12b-it-qat-q4_0-unquantized/model-00001-of-00005.safetensors",
+        "ltx-2-19b-dev.safetensors",
+        1024
+      ]
+    }
+  ],
+  "links": [
+    [
+      13957,
+      5220,
+      0,
+      5216,
+      0,
+      "MODEL"
+    ],
+    [
+      13958,
+      5216,
+      0,
+      5221,
+      0,
+      "MODEL"
+    ],
+    [
+      13959,
+      5220,
+      0,
+      5222,
+      0,
+      "MODEL"
+    ],
+    [
+      13963,
+      5218,
+      0,
+      5226,
+      0,
+      "CLIP"
+    ],
+    [
+      13964,
+      5218,
+      0,
+      5227,
+      0,
+      "CLIP"
+    ],
+    [
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diff --git a/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.0/LTX-2_V2V_Detailer.json b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.0/LTX-2_V2V_Detailer.json
new file mode 100644
index 0000000000000000000000000000000000000000..2dda0159dc7cea9036057382591bd12cd1317cd3
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.0/LTX-2_V2V_Detailer.json
@@ -0,0 +1,1087 @@
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+        "ltx-2-19b-dev.safetensors",
+        1024
+      ]
+    },
+    {
+      "id": 4903,
+      "type": "MarkdownNote",
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+        1856.581308246473,
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+      ],
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+        476.6587823511936
+      ],
+      "flags": {
+        "collapsed": false
+      },
+      "order": 6,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Model Links",
+      "properties": {},
+      "widgets_values": [
+        "## Model Links\n\n**LTX-2 Model Weights**\n\n- [ltx-2-19b-dev.safetensors](https://huggingface.co/Lightricks/LTX-2/resolve/main/ltx-2-19b-dev.safetensors)\n- [ltx-2-19b-ic-lora-detailer.safetensors](https://huggingface.co/Lightricks/LTX-2-19b-IC-LoRA-Detailer/resolve/main/ltx-2-19b-ic-lora-detailer.safetensors)\n\n**Text Encoder**\n- [Google Gemma 3](https://huggingface.co/google/gemma-3-12b-it-qat-q4_0-unquantized)\n\nPlease download the entire folder - \n\n- Run: \n 1. cd models/text_encoders\n 2. git clone https://huggingface.co/google/gemma-3-12b-it-qat-q4_0-unquantized\n\n*Full documentation can be found [here](https://docs.ltx.video/open-source-model/integration-tools/comfy-ui#text-encoder)\n\n**Model Storage Location**\n\n```\n📂 ComfyUI/\n├── 📂 models/\n│   ├── 📂 text_encoders/\n│   │      ├── gemma-3-12b-it-qat-q4_0-unquantized/model-00001-of-00005.safetensors\n│   ├── 📂 checkpoints/\n│   │      └── ltx-2-19b-dev.safetensors\n│   ├── 📂 loras/\n│   │      ├── ltx-2-19b-ic-lora-detailer.safetensors\n\n```\n\n## Report Issues\nTo report any issues when running this workflow, [go to GitHub](https://github.com/Lightricks/ComfyUI-LTXVideo/issues)\n"
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+}
diff --git a/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.3/LTX-2.3_ICLoRA_HDR_Distilled.json b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.3/LTX-2.3_ICLoRA_HDR_Distilled.json
new file mode 100644
index 0000000000000000000000000000000000000000..e117a064abc1bc7885f0812bb8eb3313941fb259
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.3/LTX-2.3_ICLoRA_HDR_Distilled.json
@@ -0,0 +1,3122 @@
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+          "link": null
+        },
+        {
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+          "name": "codec",
+          "type": "COMBO",
+          "widget": {
+            "name": "codec"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.14.1"
+      },
+      "widgets_values": [
+        "output",
+        "auto",
+        "auto"
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+    {
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+          "name": "a",
+          "shape": 7,
+          "type": "*",
+          "link": 13655
+        },
+        {
+          "localized_name": "b",
+          "name": "b",
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+          "type": "*",
+          "link": null
+        },
+        {
+          "localized_name": "c",
+          "name": "c",
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+          "type": "*",
+          "link": null
+        },
+        {
+          "localized_name": "value",
+          "name": "value",
+          "type": "STRING",
+          "widget": {
+            "name": "value"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "INT",
+          "name": "INT",
+          "type": "INT",
+          "links": [
+            13651
+          ]
+        },
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+          "localized_name": "FLOAT",
+          "name": "FLOAT",
+          "type": "FLOAT",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "SimpleMath+"
+      },
+      "widgets_values": [
+        "a*32"
+      ]
+    },
+    {
+      "id": 5029,
+      "type": "GetImageSize",
+      "pos": [
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+      "order": 23,
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+      "inputs": [
+        {
+          "localized_name": "image",
+          "name": "image",
+          "type": "IMAGE",
+          "link": 13653
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "width",
+          "name": "width",
+          "type": "INT",
+          "links": [
+            13658
+          ]
+        },
+        {
+          "localized_name": "height",
+          "name": "height",
+          "type": "INT",
+          "links": [
+            13659
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+        },
+        {
+          "localized_name": "batch_size",
+          "name": "batch_size",
+          "type": "INT",
+          "links": [
+            13660
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.16.0",
+        "Node name for S&R": "GetImageSize"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 5112,
+      "type": "ResizeImageMaskNode",
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+          "name": "input",
+          "type": "IMAGE,MASK",
+          "link": 13654
+        },
+        {
+          "localized_name": "resize_type",
+          "name": "resize_type",
+          "type": "COMFY_DYNAMICCOMBO_V3",
+          "widget": {
+            "name": "resize_type"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "resize_type.multiple",
+          "name": "resize_type.multiple",
+          "type": "INT",
+          "widget": {
+            "name": "resize_type.multiple"
+          },
+          "link": 13651
+        },
+        {
+          "localized_name": "scale_method",
+          "name": "scale_method",
+          "type": "COMBO",
+          "widget": {
+            "name": "scale_method"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "resized",
+          "name": "resized",
+          "type": "IMAGE",
+          "links": [
+            13653,
+            13661
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.16.0",
+        "Node name for S&R": "ResizeImageMaskNode"
+      },
+      "widgets_values": [
+        "scale to multiple",
+        32,
+        "lanczos"
+      ]
+    },
+    {
+      "id": 5105,
+      "type": "GetVideoComponents",
+      "pos": [
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+      "size": [
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+        66
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+      "flags": {},
+      "order": 18,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "video",
+          "name": "video",
+          "type": "VIDEO",
+          "link": 13669
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "images",
+          "name": "images",
+          "type": "IMAGE",
+          "links": [
+            13654
+          ]
+        },
+        {
+          "localized_name": "audio",
+          "name": "audio",
+          "type": "AUDIO",
+          "links": [
+            13662
+          ]
+        },
+        {
+          "localized_name": "fps",
+          "name": "fps",
+          "type": "FLOAT",
+          "links": [
+            13641
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+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.75",
+        "Node name for S&R": "GetVideoComponents"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 3059,
+      "type": "EmptyLTXVLatentVideo",
+      "pos": [
+        -2399.297235778659,
+        3472.056477015808
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+      "size": [
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+      "flags": {},
+      "order": 24,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "width",
+          "name": "width",
+          "type": "INT",
+          "widget": {
+            "name": "width"
+          },
+          "link": 13658
+        },
+        {
+          "localized_name": "height",
+          "name": "height",
+          "type": "INT",
+          "widget": {
+            "name": "height"
+          },
+          "link": 13659
+        },
+        {
+          "localized_name": "length",
+          "name": "length",
+          "type": "INT",
+          "widget": {
+            "name": "length"
+          },
+          "link": 13660
+        },
+        {
+          "localized_name": "batch_size",
+          "name": "batch_size",
+          "type": "INT",
+          "widget": {
+            "name": "batch_size"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "LATENT",
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            13663
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.43",
+        "Node name for S&R": "EmptyLTXVLatentVideo"
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+      "widgets_values": [
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+        544,
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+        1
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+      "id": 5012,
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+      "order": 25,
+      "mode": 0,
+      "inputs": [
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+          "localized_name": "positive",
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 13403
+        },
+        {
+          "localized_name": "negative",
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 13404
+        },
+        {
+          "localized_name": "vae",
+          "name": "vae",
+          "type": "VAE",
+          "link": 13405
+        },
+        {
+          "localized_name": "latent",
+          "name": "latent",
+          "type": "LATENT",
+          "link": 13663
+        },
+        {
+          "localized_name": "image",
+          "name": "image",
+          "type": "IMAGE",
+          "link": 13661
+        },
+        {
+          "localized_name": "frame_idx",
+          "name": "frame_idx",
+          "type": "INT",
+          "widget": {
+            "name": "frame_idx"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "strength",
+          "name": "strength",
+          "type": "FLOAT",
+          "widget": {
+            "name": "strength"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "latent_downscale_factor",
+          "name": "latent_downscale_factor",
+          "type": "FLOAT",
+          "widget": {
+            "name": "latent_downscale_factor"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "crop",
+          "name": "crop",
+          "type": "COMBO",
+          "widget": {
+            "name": "crop"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "use_tiled_encode",
+          "name": "use_tiled_encode",
+          "type": "BOOLEAN",
+          "widget": {
+            "name": "use_tiled_encode"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "tile_size",
+          "name": "tile_size",
+          "type": "INT",
+          "widget": {
+            "name": "tile_size"
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+          "link": null
+        },
+        {
+          "localized_name": "tile_overlap",
+          "name": "tile_overlap",
+          "type": "INT",
+          "widget": {
+            "name": "tile_overlap"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "positive",
+          "name": "positive",
+          "type": "CONDITIONING",
+          "links": [
+            13409,
+            13413
+          ]
+        },
+        {
+          "localized_name": "negative",
+          "name": "negative",
+          "type": "CONDITIONING",
+          "links": [
+            13410,
+            13414
+          ]
+        },
+        {
+          "localized_name": "latent",
+          "name": "latent",
+          "type": "LATENT",
+          "links": [
+            13647
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LTXAddVideoICLoRAGuide",
+        "cnr_id": "ComfyUI-LTXVideo",
+        "ver": "82bd963cdeb66d023bed8c99324a307020907ef8"
+      },
+      "widgets_values": [
+        0,
+        1,
+        1,
+        "disabled",
+        false,
+        256,
+        64
+      ]
+    },
+    {
+      "id": 5117,
+      "type": "Note",
+      "pos": [
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+        2961.1212206931395
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+      "size": [
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+        88
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": [
+        "Set OPENCV_IO_ENABLE_OPENEXR=1 in your shell before launching ComfyUI, otherwise save_exr will fail"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 5118,
+      "type": "Note",
+      "pos": [
+        493.2978290212085,
+        3170.8005369326115
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+      "size": [
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+        88
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+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": [
+        "EXR output is best viewed in DJV — standard image viewers won't display linear HDR correctly."
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 5119,
+      "type": "Note",
+      "pos": [
+        -4431.933188657127,
+        3275.5592871256463
+      ],
+      "size": [
+        210,
+        88
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": [
+        "equires ltx-2.3-22b-ic-lora-hdr-0.9.safetensors from Lightricks/LTX-2.3-22b-IC-LoRA-HDR. Place in models/loras/"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 5113,
+      "type": "Note",
+      "pos": [
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+        4257.835858092407
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+      "size": [
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+        203.246379625386
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+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": [
+        "The flow will generate the closest 8XF-1 in the input video length\n\nExpected runinng times on H1 machine\n\nFull HD 1080*1920\n 161 frames 100 secs\n 241 frames 152 secs\n \n\n                   "
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 5011,
+      "type": "LTXICLoRALoaderModelOnly",
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+        3243.0693856008756
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+        130.8845841562129
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+      "flags": {},
+      "order": 19,
+      "mode": 0,
+      "inputs": [
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+          "name": "model",
+          "type": "MODEL",
+          "link": 13672
+        },
+        {
+          "localized_name": "lora_name",
+          "name": "lora_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "lora_name"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "strength_model",
+          "name": "strength_model",
+          "type": "FLOAT",
+          "widget": {
+            "name": "strength_model"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "model",
+          "name": "model",
+          "type": "MODEL",
+          "links": [
+            13401
+          ]
+        },
+        {
+          "localized_name": "latent_downscale_factor",
+          "name": "latent_downscale_factor",
+          "type": "FLOAT",
+          "links": [
+            13655
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LTXICLoRALoaderModelOnly",
+        "cnr_id": "ComfyUI-LTXVideo",
+        "ver": "82bd963cdeb66d023bed8c99324a307020907ef8"
+      },
+      "widgets_values": [
+        "ltxv/ltx2/ltx-2.3-22b-ic-lora-hdr-0.9.safetensors",
+        1
+      ]
+    },
+    {
+      "id": 5125,
+      "type": "LTXICLoRALoaderModelOnly",
+      "pos": [
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+        3066.1900360630175
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+      "order": 17,
+      "mode": 0,
+      "inputs": [
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+          "localized_name": "model",
+          "name": "model",
+          "type": "MODEL",
+          "link": 13671
+        },
+        {
+          "localized_name": "lora_name",
+          "name": "lora_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "lora_name"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "strength_model",
+          "name": "strength_model",
+          "type": "FLOAT",
+          "widget": {
+            "name": "strength_model"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "model",
+          "name": "model",
+          "type": "MODEL",
+          "links": [
+            13672
+          ]
+        },
+        {
+          "localized_name": "latent_downscale_factor",
+          "name": "latent_downscale_factor",
+          "type": "FLOAT",
+          "links": []
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LTXICLoRALoaderModelOnly",
+        "cnr_id": "ComfyUI-LTXVideo",
+        "ver": "82bd963cdeb66d023bed8c99324a307020907ef8"
+      },
+      "widgets_values": [
+        "ltxv/ltx2/ltx-2.3-22b-distilled-lora-384-1.1.safetensors",
+        0.5
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+    },
+    {
+      "id": 3940,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
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+        3073.132850297725
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+      "size": [
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+        108.51948797992713
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+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
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+          "name": "ckpt_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "ckpt_name"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "MODEL",
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            13671
+          ]
+        },
+        {
+          "localized_name": "CLIP",
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": []
+        },
+        {
+          "localized_name": "VAE",
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            13405,
+            13619
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.56",
+        "Node name for S&R": "CheckpointLoaderSimple"
+      },
+      "widgets_values": [
+        "ltx-2.3-22b-dev.safetensors"
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+    },
+    {
+      "id": 5114,
+      "type": "LTXVHDRDecodePostprocess",
+      "pos": [
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+        3122.6640852823707
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+      "size": [
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+        174
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+      "flags": {},
+      "order": 30,
+      "mode": 0,
+      "inputs": [
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+          "name": "image",
+          "type": "IMAGE",
+          "link": 13664
+        },
+        {
+          "localized_name": "exposure",
+          "name": "exposure",
+          "shape": 7,
+          "type": "FLOAT",
+          "widget": {
+            "name": "exposure"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "save_exr",
+          "name": "save_exr",
+          "shape": 7,
+          "type": "BOOLEAN",
+          "widget": {
+            "name": "save_exr"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "output_dir",
+          "name": "output_dir",
+          "shape": 7,
+          "type": "STRING",
+          "widget": {
+            "name": "output_dir"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "filename_prefix",
+          "name": "filename_prefix",
+          "shape": 7,
+          "type": "STRING",
+          "widget": {
+            "name": "filename_prefix"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "half_precision",
+          "name": "half_precision",
+          "shape": 7,
+          "type": "BOOLEAN",
+          "widget": {
+            "name": "half_precision"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "tonemapped",
+          "name": "tonemapped",
+          "type": "IMAGE",
+          "links": []
+        },
+        {
+          "localized_name": "hdr_linear",
+          "name": "hdr_linear",
+          "type": "IMAGE",
+          "links": [
+            13667
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LTXVHDRDecodePostprocess"
+      },
+      "widgets_values": [
+        7.1,
+        true,
+        "output/hdr_exr3",
+        "frame",
+        true
+      ]
+    },
+    {
+      "id": 5121,
+      "type": "Note",
+      "pos": [
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+        2965.4285923622097
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+      "size": [
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+        88
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+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": [
+        "in case of OOM use tile_size of 768\ntemporal size 16"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
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diff --git a/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.3/LTX-2.3_ICLoRA_Lipdub_Two_Stage_Distilled.json b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.3/LTX-2.3_ICLoRA_Lipdub_Two_Stage_Distilled.json
new file mode 100644
index 0000000000000000000000000000000000000000..6d907703232da148353f4a5599c5f453caf30275
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.3/LTX-2.3_ICLoRA_Lipdub_Two_Stage_Distilled.json
@@ -0,0 +1,3587 @@
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+          "label": "Audio VAE",
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+      "properties": {
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+          "name": "CLIP",
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+          "type": "CONDITIONING",
+          "slot_index": 0,
+          "links": [
+            7065
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+        }
+      ],
+      "title": "CLIP Text Encode (Positive Prompt)",
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.28",
+        "Node name for S&R": "CLIPTextEncode"
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+        "A sweaty, determined woman in a black racing suit and helmet drives a race car, her hands on the steering wheel, speaking in Spanish saying: \"Box, box, vuelta ahora. Una ventaja ya en la entrada, Cambio.\""
+      ],
+      "color": "#232",
+      "bgcolor": "#353"
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+      "type": "MarkdownNote",
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+      "properties": {},
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+        "# How to Prompt a Good Lipdub/Rephrase\n\nThis workflow supports both **dubbing** (translating speech into another language) and **rephrasing** (changing what the speaker says while keeping the same language). In both cases, the model will generate new lip movements and audio to match the text you provide.\n\n## 1. Include the translated words in your prompt\n\nThe prompt should contain the translated words you want the speaker to say. The model will **not** translate it for you.\n\n## 2. Use native script\n\nUse the alphabet of the language you want the model to speak - for example, Cyrillic for Russian or Chinese characters for Chinese.\n\n## 3. Single speaker only\n\nThe current LoRA supports only one speaker.\n\n## 4. Match the length of the original for the best audio\n\nTry to keep your translation close to the length of the original dialogue.\n\n- **Too long** — The model might skip words.\n- **Too short** — The output might sound slow and unnatural.\n\n> **Tip:** Since there are many ways to say the same thing, play around with your wording until the length is right.\n"
+      ],
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+      "bgcolor": "#653"
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+      "mode": 0,
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+      "properties": {},
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+        "The `latent_downscale_factor` output from the IC-LoRA Loader is intentionally ignored. The lipdub LoRA was trained with `reference_downscale_factor = 1` (full resolution), so no spatial downscaling of the reference is needed. For reference on handling other values, see `LTX-2.3_ICLoRA_Union_Control_Distilled.json`"
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+          "name": "height",
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diff --git a/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.3/LTX-2.3_ICLoRA_Motion_Track_Distilled.json b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.3/LTX-2.3_ICLoRA_Motion_Track_Distilled.json
new file mode 100644
index 0000000000000000000000000000000000000000..f8c1ca4620087acfe139c6ab89bb499e87b9e5cb
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.3/LTX-2.3_ICLoRA_Motion_Track_Distilled.json
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diff --git a/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.3/LTX-2.3_ICLoRA_Union_Control_Distilled.json b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.3/LTX-2.3_ICLoRA_Union_Control_Distilled.json
new file mode 100644
index 0000000000000000000000000000000000000000..b4b7112ee28d616b965352e1c69d0462fcf8e5d9
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.3/LTX-2.3_ICLoRA_Union_Control_Distilled.json
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diff --git a/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.3/LTX-2.3_T2V_I2V_Single_Stage_Distilled_Full.json b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.3/LTX-2.3_T2V_I2V_Single_Stage_Distilled_Full.json
new file mode 100644
index 0000000000000000000000000000000000000000..1358422d7928c00b002ceeb1390822c9e5f58aee
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.3/LTX-2.3_T2V_I2V_Single_Stage_Distilled_Full.json
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diff --git a/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.3/LTX-2.3_T2V_I2V_Two_Stage_Distilled.json b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.3/LTX-2.3_T2V_I2V_Two_Stage_Distilled.json
new file mode 100644
index 0000000000000000000000000000000000000000..6387f884824030575e0c64ffd30330e98288e77c
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.3/LTX-2.3_T2V_I2V_Two_Stage_Distilled.json
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diff --git a/custom_nodes/ComfyUI-LTXVideo/gemma_configs/gemma3cfg.json b/custom_nodes/ComfyUI-LTXVideo/gemma_configs/gemma3cfg.json
new file mode 100644
index 0000000000000000000000000000000000000000..fa6deb0ee8a60f38c5b7b9f9b4dfe9e08fc353de
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/gemma_configs/gemma3cfg.json
@@ -0,0 +1,62 @@
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diff --git a/custom_nodes/ComfyUI-LTXVideo/gemma_configs/preprocessor_config.json b/custom_nodes/ComfyUI-LTXVideo/gemma_configs/preprocessor_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..b1e00fc184f61b698181821169c6374cd5813e5c
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/gemma_configs/preprocessor_config.json
@@ -0,0 +1,29 @@
+{
+  "do_convert_rgb": null,
+  "do_normalize": true,
+  "do_pan_and_scan": null,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
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+    0.5,
+    0.5
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+  "image_processor_type": "Gemma3ImageProcessor",
+  "image_seq_length": 256,
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "pan_and_scan_max_num_crops": null,
+  "pan_and_scan_min_crop_size": null,
+  "pan_and_scan_min_ratio_to_activate": null,
+  "processor_class": "Gemma3Processor",
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 896,
+    "width": 896
+  }
+}
diff --git a/custom_nodes/ComfyUI-LTXVideo/gemma_configs/processor_config.json b/custom_nodes/ComfyUI-LTXVideo/gemma_configs/processor_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..453c7966d4b5d0b4a317c585989f64c58c2a6bf0
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/gemma_configs/processor_config.json
@@ -0,0 +1,4 @@
+{
+  "image_seq_length": 256,
+  "processor_class": "Gemma3Processor"
+}
diff --git a/custom_nodes/ComfyUI-LTXVideo/gemma_configs/tokenizer_config.json b/custom_nodes/ComfyUI-LTXVideo/gemma_configs/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..72c9f6bc082907666631052305438b5e7db2a8e2
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/gemma_configs/tokenizer_config.json
@@ -0,0 +1,51346 @@
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+      "single_word": false,
+      "special": false
+    },
+    "262097": {
+      "content": "<unused6195>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262098": {
+      "content": "<unused6196>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262099": {
+      "content": "<unused6197>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262100": {
+      "content": "<unused6198>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262101": {
+      "content": "<unused6199>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262102": {
+      "content": "<unused6200>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262103": {
+      "content": "<unused6201>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262104": {
+      "content": "<unused6202>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262105": {
+      "content": "<unused6203>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262106": {
+      "content": "<unused6204>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262107": {
+      "content": "<unused6205>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262108": {
+      "content": "<unused6206>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262109": {
+      "content": "<unused6207>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262110": {
+      "content": "<unused6208>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262111": {
+      "content": "<unused6209>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262112": {
+      "content": "<unused6210>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262113": {
+      "content": "<unused6211>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262114": {
+      "content": "<unused6212>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262115": {
+      "content": "<unused6213>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262116": {
+      "content": "<unused6214>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262117": {
+      "content": "<unused6215>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262118": {
+      "content": "<unused6216>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262119": {
+      "content": "<unused6217>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262120": {
+      "content": "<unused6218>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262121": {
+      "content": "<unused6219>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262122": {
+      "content": "<unused6220>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262123": {
+      "content": "<unused6221>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262124": {
+      "content": "<unused6222>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262125": {
+      "content": "<unused6223>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262126": {
+      "content": "<unused6224>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262127": {
+      "content": "<unused6225>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262128": {
+      "content": "<unused6226>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262129": {
+      "content": "<unused6227>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262130": {
+      "content": "<unused6228>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262131": {
+      "content": "<unused6229>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262132": {
+      "content": "<unused6230>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262133": {
+      "content": "<unused6231>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262134": {
+      "content": "<unused6232>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262135": {
+      "content": "<unused6233>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262136": {
+      "content": "<unused6234>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262137": {
+      "content": "<unused6235>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262138": {
+      "content": "<unused6236>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262139": {
+      "content": "<unused6237>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262140": {
+      "content": "<unused6238>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262141": {
+      "content": "<unused6239>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262142": {
+      "content": "<unused6240>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262143": {
+      "content": "<unused6241>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "262144": {
+      "content": "<image_soft_token>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "boi_token": "<start_of_image>",
+  "bos_token": "<bos>",
+  "chat_template": "{{ bos_token }}\n{%- if messages[0]['role'] == 'system' -%}\n    {%- if messages[0]['content'] is string -%}\n        {%- set first_user_prefix = messages[0]['content'] + '\n\n' -%}\n    {%- else -%}\n        {%- set first_user_prefix = messages[0]['content'][0]['text'] + '\n\n' -%}\n    {%- endif -%}\n    {%- set loop_messages = messages[1:] -%}\n{%- else -%}\n    {%- set first_user_prefix = \"\" -%}\n    {%- set loop_messages = messages -%}\n{%- endif -%}\n{%- for message in loop_messages -%}\n    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}\n        {{ raise_exception(\"Conversation roles must alternate user/assistant/user/assistant/...\") }}\n    {%- endif -%}\n    {%- if (message['role'] == 'assistant') -%}\n        {%- set role = \"model\" -%}\n    {%- else -%}\n        {%- set role = message['role'] -%}\n    {%- endif -%}\n    {{ '<start_of_turn>' + role + '\n' + (first_user_prefix if loop.first else \"\") }}\n    {%- if message['content'] is string -%}\n        {{ message['content'] | trim }}\n    {%- elif message['content'] is iterable -%}\n        {%- for item in message['content'] -%}\n            {%- if item['type'] == 'image' -%}\n                {{ '<start_of_image>' }}\n            {%- elif item['type'] == 'text' -%}\n                {{ item['text'] | trim }}\n            {%- endif -%}\n        {%- endfor -%}\n    {%- else -%}\n        {{ raise_exception(\"Invalid content type\") }}\n    {%- endif -%}\n    {{ '<end_of_turn>\n' }}\n{%- endfor -%}\n{%- if add_generation_prompt -%}\n    {{'<start_of_turn>model\n'}}\n{%- endif -%}\n",
+  "clean_up_tokenization_spaces": false,
+  "eoi_token": "<end_of_image>",
+  "eos_token": "<eos>",
+  "extra_special_tokens": {
+    "boi_token": "<start_of_image>",
+    "eoi_token": "<end_of_image>",
+    "image_token": "<image_soft_token>"
+  },
+  "image_token": "<image_soft_token>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "processor_class": "Gemma3Processor",
+  "sp_model_kwargs": null,
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "GemmaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}
diff --git a/custom_nodes/ComfyUI-LTXVideo/guiders/__init__.py b/custom_nodes/ComfyUI-LTXVideo/guiders/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69377272d6693e00de5637604f8e62dfb6a5775
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/guiders/__init__.py
@@ -0,0 +1,2 @@
+from .multimodal_guider import MultimodalGuiderNode  # noqa: F401
+from .parameters import GuiderParametersNode  # noqa: F401
diff --git a/custom_nodes/ComfyUI-LTXVideo/guiders/multimodal_guider.py b/custom_nodes/ComfyUI-LTXVideo/guiders/multimodal_guider.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d964e40f8b4f13153fd33fc21c79545a6d797f0
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/guiders/multimodal_guider.py
@@ -0,0 +1,311 @@
+from typing import List
+
+import comfy.samplers
+import torch
+from comfy.model_patcher import ModelPatcher
+from comfy.patcher_extension import CallbacksMP
+
+from ..nodes_registry import comfy_node
+from ..stg import STGBlockWrapper, STGFlag
+from .parameters import GuiderParameters, Modality
+
+
+class MultimodalGuider(comfy.samplers.CFGGuider):
+    def __init__(
+        self,
+        model: ModelPatcher,
+        parameters: dict[str, GuiderParameters],
+        skip_blocks: List[int],
+    ):
+        model = model.clone()
+        self.current_step = 0
+        self.last_denoised_v = None
+        self.last_denoised_a = None
+
+        model.add_callback_with_key(
+            CallbacksMP.ON_PRE_RUN, "mm_guider_on_pre_run", self.reset_current_step
+        )
+        super().__init__(model)
+
+        self.stg_flag = STGFlag(
+            do_skip=False,
+            skip_layers=skip_blocks,
+        )
+
+        self.patch_model(model, self.stg_flag)
+        self.parameters = parameters
+
+    def reset_current_step(self, model_patcher=None):
+        self.current_step = 0
+        self.last_denoised_v = None
+        self.last_denoised_a = None
+
+    @classmethod
+    def patch_model(cls, model: ModelPatcher, stg_flag: STGFlag):
+        transformer_blocks = cls.get_transformer_blocks(model)
+
+        for i, block in enumerate(transformer_blocks):
+            model.set_model_patch_replace(
+                STGBlockWrapper(block, stg_flag, i), "dit", "double_block", i
+            )
+
+    @staticmethod
+    def get_transformer_blocks(model: ModelPatcher):
+        diffusion_model = model.get_model_object("diffusion_model")
+        key = "diffusion_model.transformer_blocks"
+        if diffusion_model.__class__.__name__ == "LTXVTransformer3D":
+            key = "diffusion_model.transformer.transformer_blocks"
+        return model.get_model_object(key)
+
+    def set_conds(self, positive, negative):
+        self.inner_set_conds({"positive": positive, "negative": negative})
+
+    def calc_stg_indexes(
+        self,
+        run_vx: bool,
+        run_ax: bool,
+        audio_ptb: bool,
+        video_ptb: bool,
+    ):
+        stg_indexes = set()
+        num_self_attns = 0
+        if run_vx:
+            num_self_attns += 1
+        if run_ax:
+            num_self_attns += 1
+
+        video_attn_idx = 0
+        audio_attn_idx = 0 if num_self_attns == 1 else 2
+
+        if video_ptb:
+            stg_indexes.add(video_attn_idx)
+        if audio_ptb:
+            stg_indexes.add(audio_attn_idx)
+
+        return list(stg_indexes)
+
+    def unpack_latents(self, x: torch.Tensor):
+        latent_shapes = (
+            self.conds.get("positive", {})[0]
+            .get("model_conds", {})
+            .get("latent_shapes", None)
+            .cond
+        )
+        vx, ax = comfy.utils.unpack_latents(x, latent_shapes)
+        return vx, ax
+
+    def pack_latents(self, vx: torch.Tensor, ax: torch.Tensor):
+        x, latent_shapes = comfy.utils.pack_latents([vx, ax])
+        return x, latent_shapes
+
+    def predict_noise(
+        self,
+        x: torch.Tensor,
+        timestep: torch.Tensor,
+        model_options: dict = {},
+        seed=None,
+    ):
+        # in CFGGuider.predict_noise, we call sampling_function(), which uses cfg_function() to compute pos & neg
+        # but we'd rather do a single batch of sampling pos, neg, and perturbed, so we call calc_cond_batch([perturbed,pos,neg]) directly
+        current_step = self.current_step
+        self.current_step = current_step + 1
+
+        positive_cond = self.conds.get("positive", None)
+        negative_cond = self.conds.get("negative", None)
+
+        audio_params: GuiderParameters = self.parameters.get(
+            Modality.AUDIO.value, GuiderParameters()
+        )
+        video_params: GuiderParameters = self.parameters.get(
+            Modality.VIDEO.value, GuiderParameters()
+        )
+
+        run_vx = not video_params.do_skip(current_step)
+        run_ax = not audio_params.do_skip(current_step)
+        run_a2v = video_params.do_cross_attn(current_step)
+        run_v2a = audio_params.do_cross_attn(current_step)
+
+        vx, ax = self.unpack_latents(x)
+        if not run_vx:
+            vx = self.last_denoised_v
+        if not run_ax:
+            ax = self.last_denoised_a
+        x, _ = self.pack_latents(vx, ax)
+
+        if not run_vx and not run_ax:
+            return x
+
+        try:
+            model_options["transformer_options"]["run_vx"] = run_vx
+            model_options["transformer_options"]["run_ax"] = run_ax
+            model_options["transformer_options"]["a2v_cross_attn"] = run_a2v
+            model_options["transformer_options"]["v2a_cross_attn"] = run_v2a
+            noise_pred_pos = comfy.samplers.calc_cond_batch(
+                self.inner_model,
+                [positive_cond],
+                x,
+                timestep,
+                model_options,
+            )[0]
+        finally:
+            del model_options["transformer_options"]["run_vx"]
+            del model_options["transformer_options"]["run_ax"]
+            del model_options["transformer_options"]["a2v_cross_attn"]
+            del model_options["transformer_options"]["v2a_cross_attn"]
+        v_noise_pred_pos, a_noise_pred_pos = self.unpack_latents(noise_pred_pos)
+
+        a_noise_pred_neg, v_noise_pred_neg = 0, 0
+        a_noise_pred_perturbed, v_noise_pred_perturbed = 0, 0
+        a_noise_pred_modality, v_noise_pred_modality = 0, 0
+
+        if any(params.do_uncond() for params in [audio_params, video_params]):
+            try:
+                model_options["transformer_options"]["run_vx"] = run_vx
+                model_options["transformer_options"]["run_ax"] = run_ax
+                model_options["transformer_options"]["a2v_cross_attn"] = run_a2v
+                model_options["transformer_options"]["v2a_cross_attn"] = run_v2a
+                noise_pred_neg = comfy.samplers.calc_cond_batch(
+                    self.inner_model,
+                    [negative_cond],
+                    x,
+                    timestep,
+                    model_options,
+                )[0]
+                v_noise_pred_neg, a_noise_pred_neg = self.unpack_latents(noise_pred_neg)
+            finally:
+                del model_options["transformer_options"]["run_vx"]
+                del model_options["transformer_options"]["run_ax"]
+                del model_options["transformer_options"]["a2v_cross_attn"]
+                del model_options["transformer_options"]["v2a_cross_attn"]
+
+        if any(params.do_perturbed() for params in [audio_params, video_params]):
+            try:
+                stg_indexes = self.calc_stg_indexes(
+                    run_vx,
+                    run_ax and ax.numel() > 0,
+                    audio_params.perturb_attn,
+                    video_params.perturb_attn,
+                )
+                model_options["transformer_options"]["run_vx"] = run_vx
+                model_options["transformer_options"]["run_ax"] = run_ax
+                model_options["transformer_options"]["a2v_cross_attn"] = run_a2v
+                model_options["transformer_options"]["v2a_cross_attn"] = run_v2a
+                model_options["transformer_options"]["ptb_index"] = 0
+                model_options["transformer_options"]["stg_indexes"] = stg_indexes
+                self.stg_flag.do_skip = True
+                noise_pred_perturbed = comfy.samplers.calc_cond_batch(
+                    self.inner_model,
+                    [positive_cond],
+                    x,
+                    timestep,
+                    model_options,
+                )[0]
+                v_noise_pred_perturbed, a_noise_pred_perturbed = self.unpack_latents(
+                    noise_pred_perturbed
+                )
+            finally:
+                self.stg_flag.do_skip = False
+                del model_options["transformer_options"]["ptb_index"]
+                del model_options["transformer_options"]["run_vx"]
+                del model_options["transformer_options"]["run_ax"]
+                del model_options["transformer_options"]["a2v_cross_attn"]
+                del model_options["transformer_options"]["v2a_cross_attn"]
+                del model_options["transformer_options"]["stg_indexes"]
+
+        if any(params.do_modality() for params in [audio_params, video_params]):
+            try:
+                model_options["transformer_options"]["run_vx"] = run_vx
+                model_options["transformer_options"]["run_ax"] = run_ax
+                model_options["transformer_options"]["a2v_cross_attn"] = False
+                model_options["transformer_options"]["v2a_cross_attn"] = False
+                noise_pred_modality = comfy.samplers.calc_cond_batch(
+                    self.inner_model,
+                    [positive_cond],
+                    x,
+                    timestep,
+                    model_options,
+                )[0]
+                v_noise_pred_modality, a_noise_pred_modality = self.unpack_latents(
+                    noise_pred_modality
+                )
+            finally:
+                del model_options["transformer_options"]["a2v_cross_attn"]
+                del model_options["transformer_options"]["v2a_cross_attn"]
+                del model_options["transformer_options"]["run_vx"]
+                del model_options["transformer_options"]["run_ax"]
+
+        if run_vx:
+            vx = video_params.calculate(
+                v_noise_pred_pos,
+                v_noise_pred_neg,
+                v_noise_pred_perturbed,
+                v_noise_pred_modality,
+            )
+        else:
+            vx = self.last_denoised_v
+
+        if run_ax:
+            ax = audio_params.calculate(
+                a_noise_pred_pos,
+                a_noise_pred_neg,
+                a_noise_pred_perturbed,
+                a_noise_pred_modality,
+            )
+        else:
+            ax = self.last_denoised_a
+
+        x, _ = self.pack_latents(vx, ax)
+
+        # normally this would be done in cfg_function, but we skipped
+        # that for efficiency: we can compute the noise predictions in
+        # a single call to calc_cond_batch() (rather than two)
+        # so we replicate the hook here
+        for fn in model_options.get("sampler_post_cfg_function", []):
+            args = {
+                "denoised": x,
+                "cond": positive_cond,
+                "uncond": negative_cond,
+                "model": self.inner_model,
+                "uncond_denoised": noise_pred_neg,
+                "cond_denoised": noise_pred_pos,
+                "sigma": timestep,
+                "model_options": model_options,
+                "input": x,
+                # not in the original call in samplers.py:cfg_function, but made available for future hooks
+                "perturbed_cond": positive_cond,
+                "perturbed_cond_denoised": noise_pred_perturbed,
+            }
+            x = fn(args)
+
+        self.last_denoised_v, self.last_denoised_a = self.unpack_latents(x)
+        return x
+
+
+@comfy_node(name="MultimodalGuider")
+class MultimodalGuiderNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "positive": ("CONDITIONING",),
+                "negative": ("CONDITIONING",),
+                "parameters": ("GUIDER_PARAMETERS",),
+                "skip_blocks": (
+                    "STRING",
+                    {"default": "", "multiline": True},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("GUIDER",)
+
+    FUNCTION = "get_guider"
+    CATEGORY = "lightricks/LTXV"
+
+    def get_guider(self, model, positive, negative, parameters, skip_blocks):
+        skip_blocks = [int(n.strip()) for n in skip_blocks.split(",") if n.strip()]
+        guider = MultimodalGuider(model, parameters, skip_blocks)
+        guider.set_conds(positive, negative)
+        guider.raw_conds = (positive, negative)
+        return (guider,)
diff --git a/custom_nodes/ComfyUI-LTXVideo/guiders/parameters.py b/custom_nodes/ComfyUI-LTXVideo/guiders/parameters.py
new file mode 100644
index 0000000000000000000000000000000000000000..89b94ec304b5c668db2d57fe86e4b8a27e3c1321
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/guiders/parameters.py
@@ -0,0 +1,168 @@
+import math
+from enum import Enum
+
+from ..nodes_registry import comfy_node
+
+
+class Modality(Enum):
+    VIDEO = "VIDEO"
+    AUDIO = "AUDIO"
+
+
+class GuiderParameters:
+    def __init__(
+        self,
+        cfg_scale: float = 1.0,
+        stg_scale: float = 0.0,
+        perturb_attn: bool = True,
+        rescale_scale: float = 0.0,
+        modality_scale: float = 1.0,
+        skip_step: int = 0,
+        cross_attn: bool = True,
+        cfg_zero_star: bool = False,
+        zero_init_sigma: float = 1.0,
+    ):
+        self.cfg_scale = cfg_scale
+        self.stg_scale = stg_scale
+        self.perturb_attn = perturb_attn
+        self.rescale_scale = rescale_scale
+        self.modality_scale = modality_scale
+        self.skip_step = skip_step
+        self.cross_attn = cross_attn
+        self.cfg_zero_star = cfg_zero_star
+        self.zero_init_sigma = zero_init_sigma
+
+    def __str__(self):
+        return f"cfg_scale: {self.cfg_scale}, stg_scale: {self.stg_scale}, rescale_scale: {self.rescale_scale}, modality_scale: {self.modality_scale}"
+
+    def __repr__(self):
+        return f"cfg_scale: {self.cfg_scale}, stg_scale: {self.stg_scale}, rescale_scale: {self.rescale_scale}, modality_scale: {self.modality_scale}"
+
+    def calculate(
+        self, noise_pred_pos, noise_pred_neg, noise_pred_pertubed, noise_pred_modality
+    ):
+        noise_pred = (
+            noise_pred_pos
+            + (self.cfg_scale - 1) * (noise_pred_pos - noise_pred_neg)
+            + self.stg_scale * (noise_pred_pos - noise_pred_pertubed)
+            + (self.modality_scale - 1) * (noise_pred_pos - noise_pred_modality)
+        )
+
+        if self.rescale_scale != 0:
+            factor = noise_pred_pos.std() / noise_pred.std()
+            factor = self.rescale_scale * factor + (1 - self.rescale_scale)
+            noise_pred = noise_pred * factor
+
+        return noise_pred
+
+    def do_uncond(self):
+        return not math.isclose(self.cfg_scale, 1.0)
+
+    def do_perturbed(self):
+        return not math.isclose(self.stg_scale, 0.0)
+
+    def do_modality(self):
+        return not math.isclose(self.modality_scale, 1.0)
+
+    def do_skip(self, step: int) -> bool:
+        if self.skip_step == 0:
+            return False
+
+        return step % (self.skip_step + 1) != 0
+
+    def do_cross_attn(self, step: int) -> bool:
+        return self.cross_attn and not self.do_skip(step)
+
+
+@comfy_node(name="GuiderParameters")
+class GuiderParametersNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "modality": (
+                    [m.value for m in Modality],
+                    {
+                        "default": Modality.VIDEO.value,
+                    },
+                ),
+                "cfg": (
+                    "FLOAT",
+                    {
+                        "default": 1.0,
+                        "min": 0.0,
+                        "max": 100.0,
+                        "step": 0.1,
+                        "round": 0.01,
+                    },
+                ),
+                "stg": (
+                    "FLOAT",
+                    {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01},
+                ),
+                "perturb_attn": (
+                    "BOOLEAN",
+                    {"default": True},
+                ),
+                "rescale": (
+                    "FLOAT",
+                    {"default": 0.7, "min": 0.0, "max": 100.0, "step": 0.01},
+                ),
+                "modality_scale": (
+                    "FLOAT",
+                    {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.01},
+                ),
+                "skip_step": (
+                    "INT",
+                    {"default": 0, "min": 0, "max": 100, "step": 1},
+                ),
+                "cross_attn": (
+                    "BOOLEAN",
+                    {"default": True},
+                ),
+            },
+            "optional": {
+                "parameters": (
+                    "GUIDER_PARAMETERS",
+                    {"default": None},
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("GUIDER_PARAMETERS",)
+
+    FUNCTION = "get_parameters"
+    CATEGORY = "lightricks/LTXV"
+
+    def get_parameters(
+        self,
+        modality,
+        cfg,
+        stg,
+        perturb_attn,
+        rescale,
+        modality_scale,
+        skip_step,
+        cross_attn,
+        parameters=None,
+    ):
+        parameters = parameters.copy() if parameters is not None else {}
+
+        if modality in parameters:
+            raise ValueError(f"Modality {modality} already exists in parameters")
+
+        parameters.update(
+            {
+                modality: GuiderParameters(
+                    cfg,
+                    stg,
+                    perturb_attn,
+                    rescale,
+                    modality_scale,
+                    skip_step,
+                    cross_attn,
+                )
+            }
+        )
+
+        return (parameters,)
diff --git a/custom_nodes/ComfyUI-LTXVideo/presets/stg_advanced_presets.json b/custom_nodes/ComfyUI-LTXVideo/presets/stg_advanced_presets.json
new file mode 100644
index 0000000000000000000000000000000000000000..b71395ed268124c3f9b974d02527e8e10d720152
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/presets/stg_advanced_presets.json
@@ -0,0 +1,55 @@
+[
+    {
+        "name": "Custom"
+    },
+    {
+        "name": "13b Dynamic",
+        "skip_steps_sigma_threshold": 0.997,
+        "cfg_star_rescale": true,
+        "sigmas": [1.0, 0.9933, 0.9850, 0.9767, 0.9008, 0.6180],
+        "cfg_values": [1, 6, 8, 6, 1, 1],
+        "stg_scale_values": [0, 4, 4, 4, 2, 1],
+        "stg_rescale_values": [1, 0.5, 0.5, 1, 1, 1],
+        "stg_layers_indices": [[11, 25, 35, 39], [22, 35, 39], [28], [28], [28], [28]]
+    },
+    {
+        "name": "13b Balanced",
+        "skip_steps_sigma_threshold": 0.998,
+        "cfg_star_rescale": true,
+        "sigmas": [1.0, 0.9933, 0.9850, 0.9767, 0.9008, 0.6180],
+        "cfg_values": [1, 6, 8, 6, 1, 1],
+        "stg_scale_values": [0, 4, 4, 4, 2, 1],
+        "stg_rescale_values": [1, 0.5, 0.5, 1, 1, 1],
+        "stg_layers_indices": [[12], [12], [5], [5], [28], [29]]
+    },
+    {
+        "name": "13b Upscale",
+        "skip_steps_sigma_threshold": 0.997,
+        "cfg_star_rescale": true,
+        "sigmas": [1.0, 0.9933, 0.9850, 0.9767, 0.9008, 0.6180],
+        "cfg_values": [1, 1, 1, 1, 1, 1],
+        "stg_scale_values": [1, 1, 1, 1, 1, 1],
+        "stg_rescale_values": [1, 1, 1, 1, 1, 1],
+        "stg_layers_indices": [[42], [42], [42], [42], [42], [42]]
+    },
+    {
+        "name": "13b Distilled",
+        "skip_steps_sigma_threshold": 0.997,
+        "cfg_star_rescale": true,
+        "sigmas": [1.0],
+        "cfg_values": [1],
+        "stg_scale_values": [0],
+        "stg_rescale_values": [1],
+        "stg_layers_indices": [[25]]
+    },
+    {
+        "name": "2b",
+        "skip_steps_sigma_threshold": 0.997,
+        "cfg_star_rescale": true,
+        "sigmas": [1.0, 0.9933, 0.9850, 0.9767, 0.9008, 0.6180],
+        "cfg_values": [4, 4, 4, 4, 1, 1],
+        "stg_scale_values": [2, 2, 2, 2, 1, 0],
+        "stg_rescale_values": [1, 1, 1, 1, 1, 1],
+        "stg_layers_indices": [[14], [14], [14], [14], [14], [14]]
+    }
+]
diff --git a/custom_nodes/ComfyUI-LTXVideo/system_prompts/gemma_i2v_system_prompt.txt b/custom_nodes/ComfyUI-LTXVideo/system_prompts/gemma_i2v_system_prompt.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0d677244f76bd0666c4b3a8c301779207e39e625
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/system_prompts/gemma_i2v_system_prompt.txt
@@ -0,0 +1,30 @@
+You are a Creative Assistant writing concise, action-focused image-to-video prompts. Given an image (first frame) and user Raw Input Prompt, generate a prompt to guide video generation from that image.
+
+#### Guidelines:
+- Analyze the Image: Identify Subject, Setting, Elements, Style and Mood.
+- Follow user Raw Input Prompt: Include all requested motion, actions, camera movements, audio, and details. If in conflict with the image, prioritize user request while maintaining visual consistency (describe transition from image to user's scene).
+- Describe only changes from the image: Don't reiterate established visual details. Inaccurate descriptions may cause scene cuts.
+- Active language: Use present-progressive verbs ("is walking," "speaking"). If no action specified, describe natural movements.
+- Chronological flow: Use temporal connectors ("as," "then," "while").
+- Audio layer: Describe complete soundscape throughout the prompt alongside actions—NOT at the end. Align audio intensity with action tempo. Include natural background audio, ambient sounds, effects, speech or music (when requested). Be specific (e.g., "soft footsteps on tile") not vague (e.g., "ambient sound").
+- Speech (only when requested): Provide exact words in quotes with character's visual/voice characteristics (e.g., "The tall man speaks in a low, gravelly voice"), language if not English and accent if relevant. If general conversation mentioned without text, generate contextual quoted dialogue. (i.e., "The man is talking" input -> the output should include exact spoken words, like: "The man is talking in an excited voice saying: 'You won't believe what I just saw!' His hands gesture expressively as he speaks, eyebrows raised with enthusiasm. The ambient sound of a quiet room underscores his animated speech.")
+- Style: Include visual style at beginning: "Style: <style>, <rest of prompt>." If unclear, omit to avoid conflicts.
+- Visual and audio only: Describe only what is seen and heard. NO smell, taste, or tactile sensations.
+- Restrained language: Avoid dramatic terms. Use mild, natural, understated phrasing.
+
+#### Important notes:
+- Camera motion: DO NOT invent camera motion/movement unless requested by the user. Make sure to include camera motion only if specified in the input.
+- Speech: DO NOT modify or alter the user's provided character dialogue in the prompt, unless it's a typo.
+- No timestamps or cuts: DO NOT use timestamps or describe scene cuts unless explicitly requested.
+- Objective only: DO NOT interpret emotions or intentions - describe only observable actions and sounds.
+- Format: DO NOT use phrases like "The scene opens with..." / "The video starts...". Start directly with Style (optional) and chronological scene description.
+- Format: Never start output with punctuation marks or special characters.
+- DO NOT invent dialogue unless the user mentions speech/talking/singing/conversation.
+- Your performance is CRITICAL. High-fidelity, dynamic, correct, and accurate prompts with integrated audio descriptions are essential for generating high-quality video. Your goal is flawless execution of these rules.
+
+#### Output Format (Strict):
+- Single concise paragraph in natural English. NO titles, headings, prefaces, sections, code fences, or Markdown.
+- If unsafe/invalid, return original user prompt. Never ask questions or clarifications.
+
+#### Example output:
+Style: realistic - cinematic - The woman glances at her watch and smiles warmly. She speaks in a cheerful, friendly voice, "I think we're right on time!" In the background, a café barista prepares drinks at the counter. The barista calls out in a clear, upbeat tone, "Two cappuccinos ready!" The sound of the espresso machine hissing softly blends with gentle background chatter and the light clinking of cups on saucers.
diff --git a/custom_nodes/ComfyUI-LTXVideo/system_prompts/gemma_t2v_system_prompt.txt b/custom_nodes/ComfyUI-LTXVideo/system_prompts/gemma_t2v_system_prompt.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e8642019c15ad0102ac43bd31db478eee8f53a4f
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/system_prompts/gemma_t2v_system_prompt.txt
@@ -0,0 +1,40 @@
+You are a Creative Assistant. Given a user's raw input prompt describing a scene or concept, expand it into a detailed video generation prompt with specific visuals and integrated audio to guide a text-to-video model.
+
+#### Guidelines
+- Strictly follow all aspects of the user's raw input: include every element requested (style, visuals, motions, actions, camera movement, audio).
+    - If the input is vague, invent concrete details: lighting, textures, materials, scene settings, etc.
+        - For characters: describe gender, clothing, hair, expressions. DO NOT invent unrequested characters.
+- Use active language: present-progressive verbs ("is walking," "speaking"). If no action specified, describe natural movements.
+- Maintain chronological flow: use temporal connectors ("as," "then," "while").
+- Audio layer: Describe complete soundscape (background audio, ambient sounds, SFX, speech/music when requested). Integrate sounds chronologically alongside actions. Be specific (e.g., "soft footsteps on tile"), not vague (e.g., "ambient sound is present").
+- Speech (only when requested):
+    - For ANY speech-related input (talking, conversation, singing, etc.), ALWAYS include exact words in quotes with voice characteristics (e.g., "The man says in an excited voice: 'You won't believe what I just saw!'").
+    - Specify language if not English and accent if relevant.
+- Style: Include visual style at the beginning: "Style: <style>, <rest of prompt>." Default to cinematic-realistic if unspecified. Omit if unclear.
+- Visual and audio only: NO non-visual/auditory senses (smell, taste, touch).
+- Restrained language: Avoid dramatic/exaggerated terms. Use mild, natural phrasing.
+    - Colors: Use plain terms ("red dress"), not intensified ("vibrant blue," "bright red").
+    - Lighting: Use neutral descriptions ("soft overhead light"), not harsh ("blinding light").
+    - Facial features: Use delicate modifiers for subtle features (i.e., "subtle freckles").
+
+#### Important notes:
+- Analyze the user's raw input carefully. In cases of FPV or POV, exclude the description of the subject whose POV is requested.
+- Camera motion: DO NOT invent camera motion unless requested by the user.
+- Speech: DO NOT modify user-provided character dialogue unless it's a typo.
+- No timestamps or cuts: DO NOT use timestamps or describe scene cuts unless explicitly requested.
+- Format: DO NOT use phrases like "The scene opens with...". Start directly with Style (optional) and chronological scene description.
+- Format: DO NOT start your response with special characters.
+- DO NOT invent dialogue unless the user mentions speech/talking/singing/conversation.
+- If the user's raw input prompt is highly detailed, chronological and in the requested format: DO NOT make major edits or introduce new elements. Add/enhance audio descriptions if missing.
+
+#### Output Format (Strict):
+- Single continuous paragraph in natural language (English).
+- NO titles, headings, prefaces, code fences, or Markdown.
+- If unsafe/invalid, return original user prompt. Never ask questions or clarifications.
+
+Your output quality is CRITICAL. Generate visually rich, dynamic prompts with integrated audio for high-quality video generation.
+
+#### Example
+Input: "A woman at a coffee shop talking on the phone"
+Output:
+Style: realistic with cinematic lighting. In a medium close-up, a woman in her early 30s with shoulder-length brown hair sits at a small wooden table by the window. She wears a cream-colored turtleneck sweater, holding a white ceramic coffee cup in one hand and a smartphone to her ear with the other. Ambient cafe sounds fill the space—espresso machine hiss, quiet conversations, gentle clinking of cups. The woman listens intently, nodding slightly, then takes a sip of her coffee and sets it down with a soft clink. Her face brightens into a warm smile as she speaks in a clear, friendly voice, 'That sounds perfect! I'd love to meet up this weekend. How about Saturday afternoon?' She laughs softly—a genuine chuckle—and shifts in her chair. Behind her, other patrons move subtly in and out of focus. 'Great, I'll see you then,' she concludes cheerfully, lowering the phone.
diff --git a/custom_nodes/ComfyUI-LTXVideo/tricks/__init__.py b/custom_nodes/ComfyUI-LTXVideo/tricks/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..75ebaa430c04efac6b58ff81d53306d2b24afff5
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/tricks/__init__.py
@@ -0,0 +1,54 @@
+from .nodes.attn_bank_nodes import (
+    LTXAttentionBankNode,
+    LTXAttentioOverrideNode,
+    LTXPrepareAttnInjectionsNode,
+)
+from .nodes.attn_override_node import LTXAttnOverrideNode
+from .nodes.latent_guide_node import AddLatentGuideNode
+from .nodes.ltx_feta_enhance_node import LTXFetaEnhanceNode
+from .nodes.ltx_flowedit_nodes import LTXFlowEditCFGGuiderNode, LTXFlowEditSamplerNode
+from .nodes.ltx_inverse_model_pred_nodes import (
+    LTXForwardModelSamplingPredNode,
+    LTXReverseModelSamplingPredNode,
+)
+from .nodes.ltx_pag_node import LTXPerturbedAttentionNode
+from .nodes.modify_ltx_model_node import ModifyLTXModelNode
+from .nodes.rectified_sampler_nodes import (
+    LTXRFForwardODESamplerNode,
+    LTXRFReverseODESamplerNode,
+)
+
+NODE_CLASS_MAPPINGS = {
+    "ModifyLTXModel": ModifyLTXModelNode,
+    "AddLatentGuide": AddLatentGuideNode,
+    "LTXForwardModelSamplingPred": LTXForwardModelSamplingPredNode,
+    "LTXReverseModelSamplingPred": LTXReverseModelSamplingPredNode,
+    "LTXRFForwardODESampler": LTXRFForwardODESamplerNode,
+    "LTXRFReverseODESampler": LTXRFReverseODESamplerNode,
+    "LTXAttentionBank": LTXAttentionBankNode,
+    "LTXPrepareAttnInjections": LTXPrepareAttnInjectionsNode,
+    "LTXAttentioOverride": LTXAttentioOverrideNode,
+    "LTXPerturbedAttention": LTXPerturbedAttentionNode,
+    "LTXAttnOverride": LTXAttnOverrideNode,
+    "LTXFlowEditCFGGuider": LTXFlowEditCFGGuiderNode,
+    "LTXFlowEditSampler": LTXFlowEditSamplerNode,
+    "LTXFetaEnhance": LTXFetaEnhanceNode,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ModifyLTXModel": "Modify LTX Model",
+    "AddLatentGuide": "Add LTX Latent Guide",
+    "LTXAddImageGuide": "Add LTX Image Guide",
+    "LTXForwardModelSamplingPred": "LTX Forward Model Pred",
+    "LTXReverseModelSamplingPred": "LTX Reverse Model Pred",
+    "LTXRFForwardODESampler": "LTX Rf-Inv Forward Sampler",
+    "LTXRFReverseODESampler": "LTX Rf-Inv Reverse Sampler",
+    "LTXAttentionBank": "LTX Attention Bank",
+    "LTXPrepareAttnInjections": "LTX Prepare Attn Injection",
+    "LTXAttentioOverride": "LTX Attn Block Override",
+    "LTXPerturbedAttention": "LTX Apply Perturbed Attention",
+    "LTXAttnOverride": "LTX Attention Override",
+    "LTXFlowEditCFGGuider": "LTX Flow Edit CFG Guider",
+    "LTXFlowEditSampler": "LTX Flow Edit Sampler",
+    "LTXFetaEnhance": "LTX Feta Enhance",
+}
diff --git a/custom_nodes/ComfyUI-LTXVideo/tricks/modules/ltx_model.py b/custom_nodes/ComfyUI-LTXVideo/tricks/modules/ltx_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..05d337f9728af6d34d956f91aebb69d30360a533
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/tricks/modules/ltx_model.py
@@ -0,0 +1,257 @@
+import math
+
+import comfy.ldm.common_dit
+import comfy.ldm.modules.attention
+import torch
+from comfy.ldm.lightricks.model import (
+    BasicTransformerBlock,
+    LTXVModel,
+    apply_rotary_emb,
+)
+from comfy.ldm.lightricks.symmetric_patchifier import latent_to_pixel_coords
+from torch import nn
+
+from ..utils.feta_enhance_utils import get_feta_scores
+
+
+class LTXModifiedCrossAttention(nn.Module):
+    def forward(self, x, context=None, mask=None, pe=None, transformer_options={}):
+        context = x if context is None else context
+        context_v = x if context is None else context
+
+        step = transformer_options.get("step", -1)
+        total_steps = transformer_options.get("total_steps", 0)
+        attn_bank = transformer_options.get("attn_bank", None)
+        sample_mode = transformer_options.get("sample_mode", None)
+        if attn_bank is not None and self.idx in attn_bank["block_map"]:
+            len_conds = len(transformer_options["cond_or_uncond"])
+            pred_order = transformer_options["pred_order"]
+            if (
+                sample_mode == "forward"
+                and total_steps - step - 1 < attn_bank["save_steps"]
+            ):
+                step_idx = f"{pred_order}_{total_steps-step-1}"
+                attn_bank["block_map"][self.idx][step_idx] = x.cpu()
+            elif sample_mode == "reverse" and step < attn_bank["inject_steps"]:
+                step_idx = f"{pred_order}_{step}"
+                inject_settings = attn_bank.get("inject_settings", {})
+                if len(inject_settings) > 0:
+                    inj = (
+                        attn_bank["block_map"][self.idx][step_idx]
+                        .to(x.device)
+                        .repeat(len_conds, 1, 1)
+                    )
+                if "q" in inject_settings:
+                    x = inj
+                if "k" in inject_settings:
+                    context = inj
+                if "v" in inject_settings:
+                    context_v = inj
+
+        q = self.to_q(x)
+        k = self.to_k(context)
+        v = self.to_v(context_v)
+
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+
+        if pe is not None:
+            q = apply_rotary_emb(q, pe)
+            k = apply_rotary_emb(k, pe)
+
+        feta_score = None
+        if (
+            transformer_options.get("feta_weight", 0) > 0
+            and self.idx in transformer_options["feta_layers"]["layers"]
+        ):
+            feta_score = get_feta_scores(q, k, self.heads, transformer_options)
+
+        alt_attn_fn = (
+            transformer_options.get("patches_replace", {})
+            .get("layer", {})
+            .get(("self_attn", self.idx), None)
+        )
+        if alt_attn_fn is not None:
+            out = alt_attn_fn(
+                q,
+                k,
+                v,
+                self.heads,
+                attn_precision=self.attn_precision,
+                transformer_options=transformer_options,
+            )
+        elif mask is None:
+            out = comfy.ldm.modules.attention.optimized_attention(
+                q, k, v, self.heads, attn_precision=self.attn_precision
+            )
+        else:
+            out = comfy.ldm.modules.attention.optimized_attention_masked(
+                q, k, v, self.heads, mask, attn_precision=self.attn_precision
+            )
+
+        if feta_score is not None:
+            out *= feta_score
+
+        return self.to_out(out)
+
+
+class LTXModifiedBasicTransformerBlock(BasicTransformerBlock):
+    def forward(
+        self,
+        x,
+        context=None,
+        attention_mask=None,
+        timestep=None,
+        pe=None,
+        transformer_options={},
+    ):
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
+            self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype)
+            + timestep.reshape(
+                x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1
+            )
+        ).unbind(dim=2)
+        x += (
+            self.attn1(
+                comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa,
+                pe=pe,
+                transformer_options=transformer_options,
+            )
+            * gate_msa
+        )
+
+        x += self.attn2(x, context=context, mask=attention_mask)
+
+        y = comfy.ldm.common_dit.rms_norm(x) * (1 + scale_mlp) + shift_mlp
+        x += self.ff(y) * gate_mlp
+
+        return x
+
+
+class LTXVModelModified(LTXVModel):
+
+    def forward(
+        self,
+        x,
+        timestep,
+        context,
+        attention_mask,
+        frame_rate=25,
+        transformer_options={},
+        keyframe_idxs=None,
+        **kwargs,
+    ):
+        patches_replace = transformer_options.get("patches_replace", {})
+
+        orig_shape = list(x.shape)
+
+        x, latent_coords = self.patchifier.patchify(x)
+        pixel_coords = latent_to_pixel_coords(
+            latent_coords=latent_coords,
+            scale_factors=self.vae_scale_factors,
+            causal_fix=self.causal_temporal_positioning,
+        )
+
+        if keyframe_idxs is not None:
+            pixel_coords[:, :, -keyframe_idxs.shape[2] :] = keyframe_idxs
+
+        fractional_coords = pixel_coords.to(torch.float32)
+        fractional_coords[:, 0] = fractional_coords[:, 0] * (1.0 / frame_rate)
+
+        x = self.patchify_proj(x)
+        timestep = timestep * 1000.0
+
+        if attention_mask is not None and not torch.is_floating_point(attention_mask):
+            attention_mask = (attention_mask - 1).to(x.dtype).reshape(
+                (attention_mask.shape[0], 1, -1, attention_mask.shape[-1])
+            ) * torch.finfo(x.dtype).max
+
+        pe = self._precompute_freqs_cis(
+            fractional_coords, dim=self.inner_dim, out_dtype=x.dtype
+        )
+
+        batch_size = x.shape[0]
+        timestep, embedded_timestep = self.adaln_single(
+            timestep.flatten(),
+            {"resolution": None, "aspect_ratio": None},
+            batch_size=batch_size,
+            hidden_dtype=x.dtype,
+        )
+        # Second dimension is 1 or number of tokens (if timestep_per_token)
+        timestep = timestep.view(batch_size, -1, timestep.shape[-1])
+        embedded_timestep = embedded_timestep.view(
+            batch_size, -1, embedded_timestep.shape[-1]
+        )
+
+        # 2. Blocks
+        if self.caption_projection is not None:
+            batch_size = x.shape[0]
+            context = self.caption_projection(context)
+            context = context.view(batch_size, -1, x.shape[-1])
+
+        blocks_replace = patches_replace.get("dit", {})
+        for i, block in enumerate(self.transformer_blocks):
+            if ("double_block", i) in blocks_replace:
+
+                def block_wrap(args):
+                    out = {}
+                    out["img"] = block(
+                        args["img"],
+                        context=args["txt"],
+                        attention_mask=args["attention_mask"],
+                        timestep=args["vec"],
+                        pe=args["pe"],
+                    )
+                    return out
+
+                out = blocks_replace[("double_block", i)](
+                    {
+                        "img": x,
+                        "txt": context,
+                        "attention_mask": attention_mask,
+                        "vec": timestep,
+                        "pe": pe,
+                    },
+                    {"original_block": block_wrap},
+                )
+                x = out["img"]
+            else:
+                x = block(
+                    x,
+                    context=context,
+                    attention_mask=attention_mask,
+                    timestep=timestep,
+                    pe=pe,
+                    transformer_options=transformer_options,
+                )
+
+        # 3. Output
+        scale_shift_values = (
+            self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype)
+            + embedded_timestep[:, :, None]
+        )
+        shift, scale = scale_shift_values[:, :, 0], scale_shift_values[:, :, 1]
+        x = self.norm_out(x)
+        # Modulation
+        x = x * (1 + scale) + shift
+        x = self.proj_out(x)
+
+        x = self.patchifier.unpatchify(
+            latents=x,
+            output_height=orig_shape[3],
+            output_width=orig_shape[4],
+            output_num_frames=orig_shape[2],
+            out_channels=orig_shape[1] // math.prod(self.patchifier.patch_size),
+        )
+
+        return x
+
+
+def inject_model(diffusion_model):
+    diffusion_model.__class__ = LTXVModelModified
+    for idx, transformer_block in enumerate(diffusion_model.transformer_blocks):
+        transformer_block.__class__ = LTXModifiedBasicTransformerBlock
+        transformer_block.idx = idx
+        transformer_block.attn1.__class__ = LTXModifiedCrossAttention
+        transformer_block.attn1.idx = idx
+    return diffusion_model
diff --git a/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/attn_bank_nodes.py b/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/attn_bank_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a482df069c4f37fcdb01a4e22ac8d19c4a2f5fc
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/attn_bank_nodes.py
@@ -0,0 +1,90 @@
+from ..utils.attn_bank import AttentionBank
+
+
+class LTXAttentionBankNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "save_steps": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
+                "blocks": ("STRING", {"multiline": True}),
+            }
+        }
+
+    RETURN_TYPES = ("ATTN_BANK",)
+    FUNCTION = "build"
+
+    CATEGORY = "ltxtricks"
+
+    def build(self, save_steps, blocks=""):
+        block_map = {}
+        block_list = blocks.split(",")
+        for block in block_list:
+            block_idx = int(block)
+            block_map[block_idx] = {}
+
+        bank = AttentionBank(save_steps, block_map)
+        return (bank,)
+
+
+class LTXPrepareAttnInjectionsNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "latent": ("LATENT",),
+                "attn_bank": ("ATTN_BANK",),
+                "query": ("BOOLEAN", {"default": False}),
+                "key": ("BOOLEAN", {"default": False}),
+                "value": ("BOOLEAN", {"default": False}),
+                "inject_steps": (
+                    "INT",
+                    {"default": 0, "min": 0, "max": 1000, "step": 1},
+                ),
+            },
+            "optional": {"blocks": ("LTX_BLOCKS",)},
+        }
+
+    RETURN_TYPES = ("LATENT", "ATTN_INJ")
+    FUNCTION = "prepare"
+
+    CATEGORY = "fluxtapoz"
+
+    def prepare(self, latent, attn_bank, query, key, value, inject_steps, blocks=None):
+        if inject_steps > attn_bank["save_steps"]:
+            raise ValueError("Can not inject more steps than were saved.")
+        attn_bank = AttentionBank(
+            attn_bank["save_steps"], attn_bank["block_map"], inject_steps
+        )
+        attn_bank["inject_settings"] = set([])
+        if query:
+            attn_bank["inject_settings"].add("q")
+        if key:
+            attn_bank["inject_settings"].add("k")
+        if value:
+            attn_bank["inject_settings"].add("v")
+
+        if blocks is not None:
+            attn_bank["block_map"] = {**attn_bank["block_map"]}
+            for key in list(attn_bank["block_map"].keys()):
+                if key not in blocks:
+                    del attn_bank["block_map"][key]
+
+        # Hack to force order of operations in ComfyUI graph
+        return (latent, attn_bank)
+
+
+class LTXAttentioOverrideNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"blocks": ("STRING", {"multiline": True})}}
+
+    RETURN_TYPES = ("LTX_BLOCKS",)
+    FUNCTION = "build"
+
+    CATEGORY = "ltxtricks"
+
+    def build(self, blocks=""):
+        block_set = set(list(int(block) for block in blocks.split(",")))
+
+        return (block_set,)
diff --git a/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/attn_override_node.py b/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/attn_override_node.py
new file mode 100644
index 0000000000000000000000000000000000000000..421484ce5880d25a571f40db5adb4eaec8edb2d6
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/attn_override_node.py
@@ -0,0 +1,30 @@
+def is_integer(string):
+    try:
+        int(string)
+        return True
+    except ValueError:
+        return False
+
+
+class LTXAttnOverrideNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "layers": ("STRING", {"multiline": True}),
+            }
+        }
+
+    RETURN_TYPES = ("ATTN_OVERRIDE",)
+    FUNCTION = "build"
+
+    CATEGORY = "ltxtricks/attn"
+
+    def build(self, layers):
+        layers_map = set([])
+        for block in layers.split(","):
+            block = block.strip()
+            if is_integer(block):
+                layers_map.add(int(block))
+
+        return ({"layers": layers_map},)
diff --git a/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/latent_guide_node.py b/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/latent_guide_node.py
new file mode 100644
index 0000000000000000000000000000000000000000..652a689136a221ef303db34f24dd0e113137cf3c
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/latent_guide_node.py
@@ -0,0 +1,40 @@
+import comfy_extras.nodes_lt as nodes_lt
+
+
+class AddLatentGuideNode(nodes_lt.LTXVAddGuide):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "latent": ("LATENT",),
+                "image_latent": ("LATENT",),
+                "index": ("INT", {"default": 0, "min": -1, "max": 9999, "step": 1}),
+                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0}),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL", "LATENT")
+    RETURN_NAMES = ("model", "latent")
+
+    CATEGORY = "ltxtricks"
+    FUNCTION = "generate"
+
+    def generate(self, model, latent, image_latent, index, strength):
+        noise_mask = nodes_lt.get_noise_mask(latent)
+        latent = latent["samples"]
+
+        image_latent = image_latent["samples"]
+
+        latent, noise_mask = self.replace_latent_frames(
+            latent,
+            noise_mask,
+            image_latent,
+            index,
+            strength,
+        )
+
+        return (
+            model,
+            {"samples": latent, "noise_mask": noise_mask},
+        )
diff --git a/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/ltx_feta_enhance_node.py b/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/ltx_feta_enhance_node.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f6a0c920f645c1af90b68dfd2bcaf3849a9b973
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/ltx_feta_enhance_node.py
@@ -0,0 +1,36 @@
+DEFAULT_ATTN = {
+    "layers": [i for i in range(0, 100, 1)],
+}
+
+
+class LTXFetaEnhanceNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "feta_weight": (
+                    "FLOAT",
+                    {"default": 4, "min": -100.0, "max": 100.0, "step": 0.01},
+                ),
+            },
+            "optional": {"attn_override": ("ATTN_OVERRIDE",)},
+        }
+
+    RETURN_TYPES = ("MODEL",)
+
+    CATEGORY = "ltxtricks"
+    FUNCTION = "apply"
+
+    def apply(self, model, feta_weight, attn_override=DEFAULT_ATTN):
+        model = model.clone()
+
+        model_options = model.model_options.copy()
+        transformer_options = model_options["transformer_options"].copy()
+
+        transformer_options["feta_weight"] = feta_weight
+        transformer_options["feta_layers"] = attn_override
+        model_options["transformer_options"] = transformer_options
+
+        model.model_options = model_options
+        return (model,)
diff --git a/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/ltx_flowedit_nodes.py b/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/ltx_flowedit_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..de902c3db49e8f68c3b902bc3724d9cfc8df0cb7
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/ltx_flowedit_nodes.py
@@ -0,0 +1,180 @@
+import torch
+from comfy.samplers import KSAMPLER, CFGGuider, sampling_function
+from tqdm import trange
+
+
+class FlowEditGuider(CFGGuider):
+    def __init__(self, model_patcher):
+        super().__init__(model_patcher)
+        self.cfgs = {}
+
+    def set_conds(self, **kwargs):
+        self.inner_set_conds(kwargs)
+
+    def set_cfgs(self, **kwargs):
+        self.cfgs = {**kwargs}
+
+    def predict_noise(self, x, timestep, model_options={}, seed=None):
+        latent_type = model_options["transformer_options"]["latent_type"]
+        positive = self.conds.get(f"{latent_type}_positive", None)
+        negative = self.conds.get(f"{latent_type}_negative", None)
+        cfg = self.cfgs.get(latent_type, self.cfg)
+        return sampling_function(
+            self.inner_model,
+            x,
+            timestep,
+            negative,
+            positive,
+            cfg,
+            model_options=model_options,
+            seed=seed,
+        )
+
+
+class LTXFlowEditCFGGuiderNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "source_pos": ("CONDITIONING",),
+                "source_neg": ("CONDITIONING",),
+                "target_pos": ("CONDITIONING",),
+                "target_neg": ("CONDITIONING",),
+                "source_cfg": (
+                    "FLOAT",
+                    {"default": 2, "min": 0, "max": 0xFFFFFFFFFFFFFFFF, "step": 0.01},
+                ),
+                "target_cfg": (
+                    "FLOAT",
+                    {"default": 4.5, "min": 0, "max": 0xFFFFFFFFFFFFFFFF, "step": 0.01},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("GUIDER",)
+
+    FUNCTION = "get_guider"
+    CATEGORY = "ltxtricks"
+
+    def get_guider(
+        self,
+        model,
+        source_pos,
+        source_neg,
+        target_pos,
+        target_neg,
+        source_cfg,
+        target_cfg,
+    ):
+        guider = FlowEditGuider(model)
+        guider.set_conds(
+            source_positive=source_pos,
+            source_negative=source_neg,
+            target_positive=target_pos,
+            target_negative=target_neg,
+        )
+        guider.set_cfgs(source=source_cfg, target=target_cfg)
+        return (guider,)
+
+
+def get_flowedit_sample(skip_steps, refine_steps, seed):
+    generator = torch.manual_seed(seed)
+
+    @torch.no_grad()
+    def flowedit_sample(
+        model, x_init, sigmas, extra_args=None, callback=None, disable=None
+    ):
+        extra_args = {} if extra_args is None else extra_args
+
+        model_options = extra_args.get("model_options", {})
+        transformer_options = model_options.get("transformer_options", {})
+        transformer_options = {**transformer_options}
+        model_options["transformer_options"] = transformer_options
+        extra_args["model_options"] = model_options
+        denoise_mask = extra_args.get("denoise_mask", 1)
+        if denoise_mask is None:
+            denoise_mask = 1
+        else:
+            extra_args["denoise_mask"] = torch.ones_like(denoise_mask)
+
+        source_extra_args = {
+            **extra_args,
+            "model_options": {
+                "transformer_options": {**transformer_options, "latent_type": "source"}
+            },
+        }
+
+        sigmas = sigmas[skip_steps:]
+
+        x_tgt = x_init.clone()
+        N = len(sigmas) - 1
+        s_in = x_init.new_ones([x_init.shape[0]])
+
+        for i in trange(N, disable=disable):
+            sigma = sigmas[i]
+            noise = torch.randn(x_init.shape, generator=generator).to(x_init.device)
+
+            zt_src = (1 - sigma) * x_init + sigma * noise
+
+            if i < N - refine_steps:
+                zt_tgt = x_tgt + (zt_src - x_init) * denoise_mask
+                transformer_options["latent_type"] = "source"
+                source_extra_args["model_options"]["transformer_options"][
+                    "latent_type"
+                ] = "source"
+                vt_src = model(zt_src, sigma * s_in, **source_extra_args)
+            else:
+                if i == N - refine_steps:
+                    x_tgt = x_tgt + (zt_src - x_init) * denoise_mask
+                zt_tgt = x_tgt
+                vt_src = 0
+
+            transformer_options["latent_type"] = "target"
+            vt_tgt = model(zt_tgt, sigma * s_in, **extra_args)
+
+            v_delta = (vt_tgt - vt_src) * denoise_mask
+            x_tgt += (sigmas[i + 1] - sigmas[i]) * v_delta
+
+            if callback is not None:
+                callback(
+                    {
+                        "x": x_tgt,
+                        "denoised": x_tgt,
+                        "i": i + skip_steps,
+                        "sigma": sigmas[i],
+                        "sigma_hat": sigmas[i],
+                    }
+                )
+
+        return x_tgt
+
+    return flowedit_sample
+
+
+class LTXFlowEditSamplerNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "skip_steps": (
+                    "INT",
+                    {"default": 4, "min": 0, "max": 0xFFFFFFFFFFFFFFFF},
+                ),
+                "refine_steps": (
+                    "INT",
+                    {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF},
+                ),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF}),
+            },
+            "optional": {},
+        }
+
+    RETURN_TYPES = ("SAMPLER",)
+    FUNCTION = "build"
+
+    CATEGORY = "ltxtricks"
+
+    def build(self, skip_steps, refine_steps, seed):
+        sampler = KSAMPLER(get_flowedit_sample(skip_steps, refine_steps, seed))
+        return (sampler,)
diff --git a/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/ltx_inverse_model_pred_nodes.py b/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/ltx_inverse_model_pred_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..db081ff6034dbb9c1af74f4c93bf441bd8b01fea
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/ltx_inverse_model_pred_nodes.py
@@ -0,0 +1,91 @@
+import comfy.latent_formats
+import comfy.model_sampling
+import comfy.sd
+
+
+class InverseCONST:
+    def calculate_input(self, sigma, noise):
+        return noise
+
+    def calculate_denoised(self, sigma, model_output, model_input):
+        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+        return model_output
+
+    def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
+        return latent_image
+
+    def inverse_noise_scaling(self, sigma, latent):
+        return latent
+
+
+class LTXForwardModelSamplingPredNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "ltxtricks"
+
+    def patch(self, model):
+        m = model.clone()
+
+        sampling_base = comfy.model_sampling.ModelSamplingFlux
+        sampling_type = InverseCONST
+
+        class ModelSamplingAdvanced(sampling_base, sampling_type):
+            pass
+
+        model_sampling = ModelSamplingAdvanced(model.model.model_config)
+        model_sampling.set_parameters(shift=1.15)
+        m.add_object_patch("model_sampling", model_sampling)
+        return (m,)
+
+
+class ReverseCONST:
+    def calculate_input(self, sigma, noise):
+        return noise
+
+    def calculate_denoised(self, sigma, model_output, model_input):
+        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+        return model_output  # model_input - model_output * sigma
+
+    def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
+        return latent_image
+
+    def inverse_noise_scaling(self, sigma, latent):
+        return latent / (1.0 - sigma)
+
+
+class LTXReverseModelSamplingPredNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "ltxtricks"
+
+    def patch(self, model):
+        m = model.clone()
+
+        sampling_base = comfy.model_sampling.ModelSamplingFlux
+        sampling_type = ReverseCONST
+
+        class ModelSamplingAdvanced(sampling_base, sampling_type):
+            pass
+
+        model_sampling = ModelSamplingAdvanced(model.model.model_config)
+        model_sampling.set_parameters(shift=1.15)
+        m.add_object_patch("model_sampling", model_sampling)
+        return (m,)
diff --git a/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/ltx_pag_node.py b/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/ltx_pag_node.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b1bee36380de9f1cbf5bb995c277c809c493398
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/ltx_pag_node.py
@@ -0,0 +1,149 @@
+import math
+
+import comfy.model_patcher
+import comfy.samplers
+import torch
+import torch.nn.functional as F
+from comfy.ldm.modules.attention import optimized_attention
+from einops import rearrange
+
+DEFAULT_PAG_LTX = {"layers": set([14])}
+
+
+def gaussian_blur_2d(img, kernel_size, sigma):
+    height = img.shape[-1]
+    kernel_size = min(kernel_size, height - (height % 2 - 1))
+    ksize_half = (kernel_size - 1) * 0.5
+
+    x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size)
+
+    pdf = torch.exp(-0.5 * (x / sigma).pow(2))
+
+    x_kernel = pdf / pdf.sum()
+    x_kernel = x_kernel.to(device=img.device, dtype=img.dtype)
+
+    kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :])
+    kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1])
+
+    padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2]
+
+    img = F.pad(img, padding, mode="reflect")
+    img = F.conv2d(img, kernel2d, groups=img.shape[-3])
+
+    return img
+
+
+class LTXPerturbedAttentionNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "scale": (
+                    "FLOAT",
+                    {
+                        "default": 2.0,
+                        "min": 0.0,
+                        "max": 100.0,
+                        "step": 0.01,
+                        "round": 0.01,
+                    },
+                ),
+                "rescale": (
+                    "FLOAT",
+                    {
+                        "default": 0.5,
+                        "min": 0.0,
+                        "max": 100.0,
+                        "step": 0.01,
+                        "round": 0.01,
+                    },
+                ),
+                "cfg": (
+                    "FLOAT",
+                    {
+                        "default": 3.0,
+                        "min": 0.0,
+                        "max": 100.0,
+                        "step": 0.01,
+                        "round": 0.01,
+                    },
+                ),
+            },
+            "optional": {
+                "attn_override": ("ATTN_OVERRIDE",),
+                # "attn_type": (["PAG", "SEG"],),
+            },
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "ltxtricks/attn"
+
+    def patch(
+        self, model, scale, rescale, cfg, attn_override=DEFAULT_PAG_LTX, attn_type="PAG"
+    ):
+        m = model.clone()
+
+        def pag_fn(q, k, v, heads, attn_precision=None, transformer_options=None):
+            return v
+
+        def seg_fn(q, k, v, heads, attn_precision=None, transformer_options=None):
+            _, sequence_length, _ = q.shape
+            b, c, f, h, w = transformer_options["original_shape"]
+
+            q = rearrange(q, "b (f h w) d -> b (f d) w h", h=h, w=w)
+            kernel_size = math.ceil(6 * scale) + 1 - math.ceil(6 * scale) % 2
+            q = gaussian_blur_2d(q, kernel_size, scale)
+            q = rearrange(q, "b (f d) w h -> b (f h w) d", f=f)
+            return optimized_attention(q, k, v, heads, attn_precision=attn_precision)
+
+        def post_cfg_function(args):
+            model = args["model"]
+
+            cond_pred = args["cond_denoised"]
+            uncond_pred = args["uncond_denoised"]
+
+            len_conds = 1 if args.get("uncond", None) is None else 2
+
+            cond = args["cond"]
+            sigma = args["sigma"]
+            model_options = args["model_options"].copy()
+            x = args["input"]
+
+            if scale == 0:
+                if len_conds == 1:
+                    return cond_pred
+                return uncond_pred + (cond_pred - uncond_pred)
+
+            attn_fn = pag_fn if attn_type == "PAG" else seg_fn
+            for block_idx in attn_override["layers"]:
+                model_options = comfy.model_patcher.set_model_options_patch_replace(
+                    model_options, attn_fn, "layer", "self_attn", int(block_idx)
+                )
+
+            (perturbed,) = comfy.samplers.calc_cond_batch(
+                model, [cond], x, sigma, model_options
+            )
+
+            # if len_conds == 1:
+            #     output = cond_pred + scale * (cond_pred - pag)
+            # else:
+            #     output = cond_pred + (scale-1.0) * (cond_pred - uncond_pred) + scale * (cond_pred - pag)
+
+            output = (
+                uncond_pred
+                + cfg * (cond_pred - uncond_pred)
+                + scale * (cond_pred - perturbed)
+            )
+            if rescale > 0:
+                factor = cond_pred.std() / output.std()
+                factor = rescale * factor + (1 - rescale)
+                output = output * factor
+
+            return output
+
+        m.set_model_sampler_post_cfg_function(post_cfg_function)
+
+        return (m,)
diff --git a/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/modify_ltx_model_node.py b/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/modify_ltx_model_node.py
new file mode 100644
index 0000000000000000000000000000000000000000..40b7fbbf780b02dc97dfd223eba2b802169494d9
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/modify_ltx_model_node.py
@@ -0,0 +1,20 @@
+from ..modules.ltx_model import inject_model
+
+
+class ModifyLTXModelNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+
+    CATEGORY = "ltxtricks"
+    FUNCTION = "modify"
+
+    def modify(self, model):
+        model.model.diffusion_model = inject_model(model.model.diffusion_model)
+        return (model,)
diff --git a/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/rectified_sampler_nodes.py b/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/rectified_sampler_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d44801cbef6165bb7bf7e73363b0fc64168460e
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/rectified_sampler_nodes.py
@@ -0,0 +1,275 @@
+import torch
+from comfy.samplers import KSAMPLER
+from tqdm import trange
+
+
+def generate_trend_values(steps, start_time, end_time, eta, eta_trend):
+    eta_values = [0] * steps
+
+    if eta_trend == "constant":
+        for i in range(start_time, end_time):
+            eta_values[i] = eta
+    elif eta_trend == "linear_increase":
+        for i in range(start_time, end_time):
+            progress = (i - start_time) / (end_time - start_time - 1)
+            eta_values[i] = eta * progress
+    elif eta_trend == "linear_decrease":
+        for i in range(start_time, end_time):
+            progress = 1 - (i - start_time) / (end_time - start_time - 1)
+            eta_values[i] = eta * progress
+
+    return eta_values
+
+
+def get_sample_forward(
+    gamma, start_step, end_step, gamma_trend, seed, attn_bank=None, order="first"
+):
+    # Controlled Forward ODE (Algorithm 1)
+    generator = torch.Generator()
+    generator.manual_seed(seed)
+
+    @torch.no_grad()
+    def sample_forward(model, y0, sigmas, extra_args=None, callback=None, disable=None):
+        if attn_bank is not None:
+            for block_idx in attn_bank["block_map"]:
+                attn_bank["block_map"][block_idx].clear()
+
+        extra_args = {} if extra_args is None else extra_args
+        model_options = extra_args.get("model_options", {})
+        model_options = {**model_options}
+        transformer_options = model_options.get("transformer_options", {})
+        transformer_options = {
+            **transformer_options,
+            "total_steps": len(sigmas) - 1,
+            "sample_mode": "forward",
+            "attn_bank": attn_bank,
+        }
+        model_options["transformer_options"] = transformer_options
+        extra_args["model_options"] = model_options
+
+        Y = y0.clone()
+        y1 = torch.randn(Y.shape, generator=generator).to(y0.device)
+        N = len(sigmas) - 1
+        s_in = y0.new_ones([y0.shape[0]])
+        gamma_values = generate_trend_values(
+            N, start_step, end_step, gamma, gamma_trend
+        )
+        for i in trange(N, disable=disable):
+            transformer_options["step"] = i
+            sigma = sigmas[i]
+            sigma_next = sigmas[i + 1]
+            t_i = model.inner_model.inner_model.model_sampling.timestep(sigmas[i])
+
+            conditional_vector_field = (y1 - Y) / (1 - t_i)
+
+            transformer_options["pred_order"] = "first"
+            pred = model(
+                Y, s_in * sigmas[i], **extra_args
+            )  # this implementation takes sigma instead of timestep
+
+            if order == "second":
+                transformer_options["pred_order"] = "second"
+                img_mid = Y + (sigma_next - sigma) / 2 * pred
+                sigma_mid = sigma + (sigma_next - sigma) / 2
+                pred_mid = model(img_mid, s_in * sigma_mid, **extra_args)
+
+                first_order = (pred_mid - pred) / ((sigma_next - sigma) / 2)
+                pred = pred + gamma_values[i] * (conditional_vector_field - pred)
+                # first_order = first_order + gamma_values[i] * (conditional_vector_field - first_order)
+                Y = (
+                    Y
+                    + (sigma_next - sigma) * pred
+                    + 0.5 * (sigma_next - sigma) ** 2 * first_order
+                )
+            else:
+                pred = pred + gamma_values[i] * (conditional_vector_field - pred)
+                Y = Y + pred * (sigma_next - sigma)
+
+            if callback is not None:
+                callback(
+                    {"x": Y, "denoised": Y, "i": i, "sigma": sigma, "sigma_hat": sigma}
+                )
+
+        return Y
+
+    return sample_forward
+
+
+def get_sample_reverse(
+    latent_image, eta, start_time, end_time, eta_trend, attn_bank=None, order="first"
+):
+    # Controlled Reverse ODE (Algorithm 2)
+    @torch.no_grad()
+    def sample_reverse(model, y1, sigmas, extra_args=None, callback=None, disable=None):
+        extra_args = {} if extra_args is None else extra_args
+        model_options = extra_args.get("model_options", {})
+        model_options = {**model_options}
+        transformer_options = model_options.get("transformer_options", {})
+        transformer_options = {
+            **transformer_options,
+            "total_steps": len(sigmas) - 1,
+            "sample_mode": "reverse",
+            "attn_bank": attn_bank,
+        }
+        model_options["transformer_options"] = transformer_options
+        extra_args["model_options"] = model_options
+
+        X = y1.clone()
+        N = len(sigmas) - 1
+        y0 = latent_image.clone().to(y1.device)
+        s_in = y0.new_ones([y0.shape[0]])
+        eta_values = generate_trend_values(N, start_time, end_time, eta, eta_trend)
+        for i in trange(N, disable=disable):
+            transformer_options["step"] = i
+            t_i = 1 - model.inner_model.inner_model.model_sampling.timestep(sigmas[i])
+            sigma = sigmas[i]
+            sigma_prev = sigmas[i + 1]
+
+            conditional_vector_field = (y0 - X) / (1 - t_i)
+
+            transformer_options["pred_order"] = "first"
+            pred = model(
+                X, sigma * s_in, **extra_args
+            )  # this implementation takes sigma instead of timestep
+
+            if order == "second":
+                transformer_options["pred_order"] = "second"
+                img_mid = X + (sigma_prev - sigma) / 2 * pred
+                sigma_mid = sigma + (sigma_prev - sigma) / 2
+                pred_mid = model(img_mid, s_in * sigma_mid, **extra_args)
+
+                first_order = (pred_mid - pred) / ((sigma_prev - sigma) / 2)
+                pred = -pred + eta_values[i] * (conditional_vector_field + pred)
+
+                first_order = -first_order + eta_values[i] * (
+                    conditional_vector_field + first_order
+                )
+                X = (
+                    X
+                    + (sigma - sigma_prev) * pred
+                    + 0.5 * (sigma - sigma_prev) ** 2 * first_order
+                )
+            else:
+                controlled_vector_field = -pred + eta_values[i] * (
+                    conditional_vector_field + pred
+                )
+                X = X + controlled_vector_field * (sigma - sigma_prev)
+
+            if callback is not None:
+                callback(
+                    {
+                        "x": X,
+                        "denoised": X,
+                        "i": i,
+                        "sigma": sigmas[i],
+                        "sigma_hat": sigmas[i],
+                    }
+                )
+
+        return X
+
+    return sample_reverse
+
+
+class LTXRFForwardODESamplerNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "gamma": (
+                    "FLOAT",
+                    {"default": 0.5, "min": 0.0, "max": 100.0, "step": 0.01},
+                ),
+                "start_step": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
+                "end_step": ("INT", {"default": 5, "min": 0, "max": 1000, "step": 1}),
+                "gamma_trend": (["linear_decrease", "linear_increase", "constant"],),
+            },
+            "optional": {
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF}),
+                "attn_bank": ("ATTN_BANK",),
+                "order": (["first", "second"],),
+            },
+        }
+
+    RETURN_TYPES = ("SAMPLER",)
+    FUNCTION = "build"
+
+    CATEGORY = "ltxtricks"
+
+    def build(
+        self,
+        gamma,
+        start_step,
+        end_step,
+        gamma_trend,
+        seed=0,
+        attn_bank=None,
+        order="first",
+    ):
+        sampler = KSAMPLER(
+            get_sample_forward(
+                gamma,
+                start_step,
+                end_step,
+                gamma_trend,
+                seed,
+                attn_bank=attn_bank,
+                order=order,
+            )
+        )
+
+        return (sampler,)
+
+
+class LTXRFReverseODESamplerNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "latent_image": ("LATENT",),
+                "eta": (
+                    "FLOAT",
+                    {"default": 0.8, "min": 0.0, "max": 100.0, "step": 0.01},
+                ),
+                "start_step": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
+                "end_step": ("INT", {"default": 15, "min": 0, "max": 1000, "step": 1}),
+            },
+            "optional": {
+                "eta_trend": (["linear_decrease", "linear_increase", "constant"],),
+                "attn_inj": ("ATTN_INJ",),
+                "order": (["first", "second"],),
+            },
+        }
+
+    RETURN_TYPES = ("SAMPLER",)
+    FUNCTION = "build"
+
+    CATEGORY = "ltxtricks"
+
+    def build(
+        self,
+        model,
+        latent_image,
+        eta,
+        start_step,
+        end_step,
+        eta_trend="constant",
+        attn_inj=None,
+        order="first",
+    ):
+        process_latent_in = model.get_model_object("process_latent_in")
+        latent_image = process_latent_in(latent_image["samples"])
+        sampler = KSAMPLER(
+            get_sample_reverse(
+                latent_image,
+                eta,
+                start_step,
+                end_step,
+                eta_trend,
+                attn_bank=attn_inj,
+                order=order,
+            )
+        )
+
+        return (sampler,)
diff --git a/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/rf_edit_sampler_nodes.py b/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/rf_edit_sampler_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..d843bbf13debaf89f8543470a1589609b2befeda
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/tricks/nodes/rf_edit_sampler_nodes.py
@@ -0,0 +1,376 @@
+import torch
+from comfy.samplers import KSAMPLER
+from tqdm import trange
+
+
+def get_sample_forward(attn_bank, save_steps, single_layers, double_layers):
+    @torch.no_grad()
+    def sample_forward(model, x, sigmas, extra_args=None, callback=None, disable=None):
+        attn_bank.clear()
+        attn_bank["save_steps"] = save_steps
+
+        extra_args = {} if extra_args is None else extra_args
+
+        model_options = extra_args.get("model_options", {})
+        model_options = {**model_options}
+        transformer_options = model_options.get("transformer_options", {})
+        transformer_options = {**transformer_options}
+        model_options["transformer_options"] = transformer_options
+        extra_args["model_options"] = model_options
+
+        N = len(sigmas) - 1
+        s_in = x.new_ones([x.shape[0]])
+        for i in trange(N, disable=disable):
+            sigma = sigmas[i]
+            sigma_next = sigmas[i + 1]
+
+            if N - i - 1 < save_steps:
+                attn_bank[N - i - 1] = {"first": {}, "mid": {}}
+
+            transformer_options["rfedit"] = {
+                "step": N - i - 1,
+                "process": "forward" if N - i - 1 < save_steps else None,
+                "pred": "first",
+                "bank": attn_bank,
+                "single_layers": single_layers,
+                "double_layers": double_layers,
+            }
+
+            pred = model(x, s_in * sigma, **extra_args)
+
+            transformer_options["rfedit"] = {
+                "step": N - i - 1,
+                "process": "forward" if N - i - 1 < save_steps else None,
+                "pred": "mid",
+                "bank": attn_bank,
+                "single_layers": single_layers,
+                "double_layers": double_layers,
+            }
+
+            img_mid = x + (sigma_next - sigma) / 2 * pred
+            sigma_mid = sigma + (sigma_next - sigma) / 2
+            pred_mid = model(img_mid, s_in * sigma_mid, **extra_args)
+
+            first_order = (pred_mid - pred) / ((sigma_next - sigma) / 2)
+            x = (
+                x
+                + (sigma_next - sigma) * pred
+                + 0.5 * (sigma_next - sigma) ** 2 * first_order
+            )
+
+            if callback is not None:
+                callback(
+                    {
+                        "x": x,
+                        "denoised": x,
+                        "i": i,
+                        "sigma": sigmas[i],
+                        "sigma_hat": sigmas[i],
+                    }
+                )
+
+        return x
+
+    return sample_forward
+
+
+def get_sample_reverse(attn_bank, inject_steps, single_layers, double_layers):
+    @torch.no_grad()
+    def sample_reverse(model, x, sigmas, extra_args=None, callback=None, disable=None):
+        if inject_steps > attn_bank["save_steps"]:
+            raise ValueError(
+                f'You must save at least as many steps as you want to inject. save_steps: {attn_bank["save_steps"]}, inject_steps: {inject_steps}'
+            )
+
+        extra_args = {} if extra_args is None else extra_args
+
+        model_options = extra_args.get("model_options", {})
+        model_options = {**model_options}
+        transformer_options = model_options.get("transformer_options", {})
+        transformer_options = {**transformer_options}
+        model_options["transformer_options"] = transformer_options
+        extra_args["model_options"] = model_options
+
+        N = len(sigmas) - 1
+        s_in = x.new_ones([x.shape[0]])
+        for i in trange(N, disable=disable):
+            sigma = sigmas[i]
+            sigma_prev = sigmas[i + 1]
+
+            transformer_options["rfedit"] = {
+                "step": i,
+                "process": "reverse" if i < inject_steps else None,
+                "pred": "first",
+                "bank": attn_bank,
+                "single_layers": single_layers,
+                "double_layers": double_layers,
+            }
+
+            pred = model(x, s_in * sigma, **extra_args)
+
+            transformer_options["rfedit"] = {
+                "step": i,
+                "process": "reverse" if i < inject_steps else None,
+                "pred": "mid",
+                "bank": attn_bank,
+                "single_layers": single_layers,
+                "double_layers": double_layers,
+            }
+
+            img_mid = x + (sigma_prev - sigma) / 2 * pred
+            sigma_mid = sigma + (sigma_prev - sigma) / 2
+            pred_mid = model(img_mid, s_in * sigma_mid, **extra_args)
+
+            first_order = (pred_mid - pred) / ((sigma_prev - sigma) / 2)
+            x = (
+                x
+                + (sigma_prev - sigma) * pred
+                + 0.5 * (sigma_prev - sigma) ** 2 * first_order
+            )
+
+            if callback is not None:
+                callback(
+                    {
+                        "x": x,
+                        "denoised": x,
+                        "i": i,
+                        "sigma": sigmas[i],
+                        "sigma_hat": sigmas[i],
+                    }
+                )
+
+        return x
+
+    return sample_reverse
+
+
+DEFAULT_SINGLE_LAYERS = {}
+for i in range(38):
+    DEFAULT_SINGLE_LAYERS[f"{i}"] = i > 19
+
+DEFAULT_DOUBLE_LAYERS = {}
+for i in range(19):
+    DEFAULT_DOUBLE_LAYERS[f"{i}"] = False
+
+
+class FlowEditForwardSamplerNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "save_steps": (
+                    "INT",
+                    {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF},
+                ),
+            },
+            "optional": {
+                "single_layers": ("SINGLE_LAYERS",),
+                "double_layers": ("DOUBLE_LAYERS",),
+            },
+        }
+
+    RETURN_TYPES = ("SAMPLER", "ATTN_INJ")
+    FUNCTION = "build"
+
+    CATEGORY = "fluxtapoz"
+
+    def build(
+        self,
+        save_steps,
+        single_layers=DEFAULT_SINGLE_LAYERS,
+        double_layers=DEFAULT_DOUBLE_LAYERS,
+    ):
+        attn_bank = {}
+        sampler = KSAMPLER(
+            get_sample_forward(attn_bank, save_steps, single_layers, double_layers)
+        )
+
+        return (sampler, attn_bank)
+
+
+# class FlowEditReverseSamplerNode:
+#    @classmethod
+#    def INPUT_TYPES(s):
+#        return {
+#            "required": {
+#                "attn_inj": ("ATTN_INJ",),
+#                "latent_image": ("LATENT",),
+#                "eta": (
+#                    "FLOAT",
+#                    {"default": 0.8, "min": 0.0, "max": 100.0, "step": 0.01},
+#                ),
+#                "start_step": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
+#                "end_step": ("INT", {"default": 5, "min": 0, "max": 1000, "step": 1}),
+#            },
+#            "optional": {},
+#        }
+#
+#    RETURN_TYPES = ("SAMPLER",)
+#    FUNCTION = "build"
+#
+#    CATEGORY = "fluxtapoz"
+#
+#    def build(self, latent_image, eta, start_step, end_step):
+#        sampler = KSAMPLER(
+#            get_sample_reverse(attn_inj, inject_steps, single_layers, double_layers)
+#        )
+#        return (sampler,)
+
+
+def get_sample_reverse2(attn_bank, inject_steps, single_layers, double_layers):
+    @torch.no_grad()
+    def sample_reverse(model, x, sigmas, extra_args=None, callback=None, disable=None):
+        if inject_steps > attn_bank["save_steps"]:
+            raise ValueError(
+                f'You must save at least as many steps as you want to inject. save_steps: {attn_bank["save_steps"]}, inject_steps: {inject_steps}'
+            )
+
+        extra_args = {} if extra_args is None else extra_args
+
+        model_options = extra_args.get("model_options", {})
+        model_options = {**model_options}
+        transformer_options = model_options.get("transformer_options", {})
+        transformer_options = {**transformer_options}
+        model_options["transformer_options"] = transformer_options
+        extra_args["model_options"] = model_options
+
+        N = len(sigmas) - 1
+        s_in = x.new_ones([x.shape[0]])
+        for i in trange(N, disable=disable):
+            sigma = sigmas[i]
+            sigma_prev = sigmas[i + 1]
+
+            transformer_options["rfedit"] = {
+                "step": i,
+                "process": "reverse" if i < inject_steps else None,
+                "pred": "first",
+                "bank": attn_bank,
+                "single_layers": single_layers,
+                "double_layers": double_layers,
+            }
+
+            pred = model(x, s_in * sigma, **extra_args)
+
+            transformer_options["rfedit"] = {
+                "step": i,
+                "process": "reverse" if i < inject_steps else None,
+                "pred": "mid",
+                "bank": attn_bank,
+                "single_layers": single_layers,
+                "double_layers": double_layers,
+            }
+
+            img_mid = x + (sigma_prev - sigma) / 2 * pred
+            sigma_mid = sigma + (sigma_prev - sigma) / 2
+            pred_mid = model(img_mid, s_in * sigma_mid, **extra_args)
+
+            first_order = (pred_mid - pred) / ((sigma_prev - sigma) / 2)
+            x = (
+                x
+                + (sigma_prev - sigma) * pred
+                + 0.5 * (sigma_prev - sigma) ** 2 * first_order
+            )
+
+            if callback is not None:
+                callback(
+                    {
+                        "x": x,
+                        "denoised": x,
+                        "i": i,
+                        "sigma": sigmas[i],
+                        "sigma_hat": sigmas[i],
+                    }
+                )
+
+        return x
+
+    return sample_reverse
+
+
+class FlowEdit2ReverseSamplerNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "attn_inj": ("ATTN_INJ",),
+                "inject_steps": (
+                    "INT",
+                    {"default": 0, "min": 0, "max": 1000, "step": 1},
+                ),
+            },
+            "optional": {
+                "single_layers": ("SINGLE_LAYERS",),
+                "double_layers": ("DOUBLE_LAYERS",),
+            },
+        }
+
+    RETURN_TYPES = ("SAMPLER",)
+    FUNCTION = "build"
+
+    CATEGORY = "ltxtricks"
+
+    def build(
+        self,
+        attn_inj,
+        inject_steps,
+        single_layers=DEFAULT_SINGLE_LAYERS,
+        double_layers=DEFAULT_DOUBLE_LAYERS,
+    ):
+        sampler = KSAMPLER(
+            get_sample_reverse(attn_inj, inject_steps, single_layers, double_layers)
+        )
+        return (sampler,)
+
+
+class PrepareAttnBankNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "latent": ("LATENT",),
+                "attn_inj": ("ATTN_INJ",),
+            }
+        }
+
+    RETURN_TYPES = ("LATENT", "ATTN_INJ")
+    FUNCTION = "prepare"
+
+    CATEGORY = "ltxtricks"
+
+    def prepare(self, latent, attn_inj):
+        # Hack to force order of operations in ComfyUI graph
+        return (latent, attn_inj)
+
+
+class RFSingleBlocksOverrideNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        layers = {}
+        for i in range(38):
+            layers[f"{i}"] = ("BOOLEAN", {"default": i > 19})
+        return {"required": layers}
+
+    RETURN_TYPES = ("SINGLE_LAYERS",)
+    FUNCTION = "build"
+
+    CATEGORY = "ltxtricks"
+
+    def build(self, *args, **kwargs):
+        return (kwargs,)
+
+
+class RFDoubleBlocksOverrideNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        layers = {}
+        for i in range(19):
+            layers[f"{i}"] = ("BOOLEAN", {"default": False})
+        return {"required": layers}
+
+    RETURN_TYPES = ("DOUBLE_LAYERS",)
+    FUNCTION = "build"
+
+    CATEGORY = "ltxtricks"
+
+    def build(self, *args, **kwargs):
+        return (kwargs,)
diff --git a/custom_nodes/ComfyUI-LTXVideo/tricks/utils/attn_bank.py b/custom_nodes/ComfyUI-LTXVideo/tricks/utils/attn_bank.py
new file mode 100644
index 0000000000000000000000000000000000000000..47efe98d3726a2365c47345f903ecbb5b511201d
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/tricks/utils/attn_bank.py
@@ -0,0 +1,16 @@
+class AttentionBank:
+    def __init__(self, save_steps, block_map, inject_steps=None):
+        self._data = {
+            "save_steps": save_steps,
+            "block_map": block_map,
+            "inject_steps": inject_steps,
+        }
+
+    def __getitem__(self, key):
+        return self._data[key]
+
+    def __setitem__(self, key, value):
+        self._data[key] = value
+
+    def get(self, key, default=None):
+        return self._data.get(key, default)
diff --git a/custom_nodes/ComfyUI-LTXVideo/tricks/utils/feta_enhance_utils.py b/custom_nodes/ComfyUI-LTXVideo/tricks/utils/feta_enhance_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..3af9e8b0c389a0a8cd684b481a5c7fd090fbd066
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/tricks/utils/feta_enhance_utils.py
@@ -0,0 +1,55 @@
+import torch
+from einops import rearrange
+
+
+def _feta_score(query_image, key_image, head_dim, num_frames, enhance_weight):
+    scale = head_dim**-0.5
+    query_image = query_image * scale
+    attn_temp = query_image @ key_image.transpose(-2, -1)  # translate attn to float32
+    attn_temp = attn_temp.to(torch.float32)
+    attn_temp = attn_temp.softmax(dim=-1)
+
+    # Reshape to [batch_size * num_tokens, num_frames, num_frames]
+    attn_temp = attn_temp.reshape(-1, num_frames, num_frames)
+
+    # Create a mask for diagonal elements
+    diag_mask = torch.eye(num_frames, device=attn_temp.device).bool()
+    diag_mask = diag_mask.unsqueeze(0).expand(attn_temp.shape[0], -1, -1)
+
+    # Zero out diagonal elements
+    attn_wo_diag = attn_temp.masked_fill(diag_mask, 0)
+
+    # Calculate mean for each token's attention matrix
+    # Number of off-diagonal elements per matrix is n*n - n
+    num_off_diag = num_frames * num_frames - num_frames
+    mean_scores = attn_wo_diag.sum(dim=(1, 2)) / num_off_diag
+
+    enhance_scores = mean_scores.mean() * (num_frames + enhance_weight)
+    enhance_scores = enhance_scores.clamp(min=1)
+    return enhance_scores
+
+
+def get_feta_scores(img_q, img_k, num_heads, transformer_options):
+    num_frames = transformer_options["original_shape"][2]
+    _, ST, dim = img_q.shape
+    head_dim = dim // num_heads
+    spatial_dim = ST // num_frames
+
+    query_image = rearrange(
+        img_q,
+        "B (T S) (N C) -> (B S) N T C",
+        T=num_frames,
+        S=spatial_dim,
+        N=num_heads,
+        C=head_dim,
+    )
+    key_image = rearrange(
+        img_k,
+        "B (T S) (N C) -> (B S) N T C",
+        T=num_frames,
+        S=spatial_dim,
+        N=num_heads,
+        C=head_dim,
+    )
+    weight = transformer_options.get("feta_weight", 0)
+    return _feta_score(query_image, key_image, head_dim, num_frames, weight)
diff --git a/custom_nodes/ComfyUI-LTXVideo/tricks/utils/latent_guide.py b/custom_nodes/ComfyUI-LTXVideo/tricks/utils/latent_guide.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1968b442f40f3cd6abcafdc6bade617048f0108
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/tricks/utils/latent_guide.py
@@ -0,0 +1,8 @@
+import torch
+
+
+class LatentGuide(torch.nn.Module):
+    def __init__(self, latent: torch.Tensor, index) -> None:
+        super().__init__()
+        self.index = index
+        self.register_buffer("latent", latent)
diff --git a/custom_nodes/ComfyUI-LTXVideo/tricks/utils/module_utils.py b/custom_nodes/ComfyUI-LTXVideo/tricks/utils/module_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..528f7ee06a6efe0d2d24a2d4af5ec46bde6932ab
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/tricks/utils/module_utils.py
@@ -0,0 +1,6 @@
+def isinstance_str(x: object, cls_name: str):
+    for _cls in x.__class__.__mro__:
+        if _cls.__name__ == cls_name:
+            return True
+
+    return False
diff --git a/custom_nodes/ComfyUI-LTXVideo/tricks/utils/noise_utils.py b/custom_nodes/ComfyUI-LTXVideo/tricks/utils/noise_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..db92cf9c8669cf696972280f01ba705577ea4df5
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/tricks/utils/noise_utils.py
@@ -0,0 +1,23 @@
+def get_alphacumprod(sigma):
+    return 1 / ((sigma * sigma) + 1)
+
+
+def add_noise(src_latent, noise, sigma):
+    alphas_cumprod = get_alphacumprod(sigma)
+
+    sqrt_alpha_prod = alphas_cumprod**0.5
+    sqrt_alpha_prod = sqrt_alpha_prod.flatten()
+    while len(sqrt_alpha_prod.shape) < len(src_latent.shape):
+        sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
+
+    sqrt_one_minus_alpha_prod = (1 - alphas_cumprod) ** 0.5
+    sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
+    while len(sqrt_one_minus_alpha_prod.shape) < len(src_latent.shape):
+        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
+
+    noisy_samples = sqrt_alpha_prod * src_latent + sqrt_one_minus_alpha_prod * noise
+    return noisy_samples
+
+
+def add_noise_flux(src_latent, noise, sigma):
+    return sigma * noise + (1.0 - sigma) * src_latent
diff --git a/custom_nodes/ComfyUI-LTXVideo/web/js/guider_parameters_advanced.js b/custom_nodes/ComfyUI-LTXVideo/web/js/guider_parameters_advanced.js
new file mode 100644
index 0000000000000000000000000000000000000000..8c999d90747d2c0950bd9dee02a78e4fbdff1fd1
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/web/js/guider_parameters_advanced.js
@@ -0,0 +1,346 @@
+import { app } from "../../../scripts/app.js";
+
+const PREFIXES = ["video", "audio"];
+const STRING_FIELDS = [
+    "sigmas", "cfg", "stg", "rescale", "modality_scale",
+    "perturb_attn", "skip_step", "cross_attn",
+    "cfg_zero_star",
+];
+const FIELD_LABELS = {
+    sigmas: "Sigmas",
+    cfg: "CFG",
+    stg: "STG",
+    rescale: "Rescale",
+    modality_scale: "Modality Scale",
+    perturb_attn: "Perturb Attn",
+    skip_step: "Skip Step",
+    cross_attn: "Cross Attn",
+    cfg_zero_star: "CFG Zero*",
+};
+
+const _origProps = {};
+
+function toggleWidget(node, widget, show) {
+    if (!widget) return;
+    if (!_origProps[widget.name]) {
+        _origProps[widget.name] = {
+            origComputeSize: widget.computeSize,
+        };
+    }
+    const origSize = node.size;
+
+    widget.hidden = !show;
+    widget.computeSize = show
+        ? _origProps[widget.name].origComputeSize
+        : () => [0, -3.3];
+
+    widget.linkedWidgets?.forEach((w) => toggleWidget(node, w, show));
+
+    const height = show
+        ? Math.max(node.computeSize()[1], origSize[1])
+        : node.computeSize()[1];
+    node.setSize([node.size[0], height]);
+
+    if (show) delete widget.computedHeight;
+    else widget.computedHeight = 0;
+}
+
+function toggleModalityWidgets(node, prefix, enabled) {
+    for (const w of node.widgets) {
+        if (
+            w.name.startsWith(prefix + "_") &&
+            w.name !== prefix + "_enabled"
+        ) {
+            toggleWidget(node, w, enabled);
+        }
+    }
+}
+
+function injectMultilineLabels(node) {
+    const labelMap = {};
+    for (const prefix of PREFIXES) {
+        for (const field of STRING_FIELDS) {
+            const key = prefix + "_" + field;
+            labelMap[key] = prefix.toUpperCase() + " " + FIELD_LABELS[field];
+        }
+    }
+
+    function applyLabels() {
+        let allDone = true;
+        for (const w of node.widgets) {
+            if (!(w.name in labelMap)) continue;
+            // w.element is the <textarea> itself, not the wrapper div
+            const textarea = w.element;
+            if (!textarea || textarea.tagName !== "TEXTAREA") {
+                allDone = false;
+                continue;
+            }
+            const container = textarea.parentElement;
+            if (!container) {
+                allDone = false;
+                continue;
+            }
+            if (container.querySelector(".guider-param-label")) continue;
+
+            textarea.placeholder = labelMap[w.name];
+
+            const label = document.createElement("div");
+            label.className = "guider-param-label";
+            label.textContent = labelMap[w.name];
+            label.style.cssText =
+                "font-size:10px;font-weight:600;color:#bbb;" +
+                "padding:0 4px;pointer-events:none;" +
+                "position:absolute;top:-14px;left:0;z-index:1;";
+            container.style.overflow = "visible";
+            container.insertBefore(label, textarea);
+        }
+        return allDone;
+    }
+
+    let retries = 0;
+    function tryApply() {
+        if (applyLabels() || retries > 30) return;
+        retries++;
+        requestAnimationFrame(tryApply);
+    }
+    requestAnimationFrame(tryApply);
+}
+
+const MOD_PREFIX_MAP = { VIDEO: "video", AUDIO: "audio" };
+
+function populateWidgets(node, data) {
+    for (const [modName, fields] of Object.entries(data)) {
+        const prefix = MOD_PREFIX_MAP[modName];
+        if (!prefix) continue;
+        for (const [field, value] of Object.entries(fields)) {
+            const w = node.widgets.find(
+                (w) => w.name === prefix + "_" + field
+            );
+            if (w) w.value = value;
+        }
+    }
+}
+
+async function importXlsxValues(node, filename) {
+    try {
+        const resp = await fetch("/guider_params/import", {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({ filename }),
+        });
+        if (!resp.ok) return;
+        const data = await resp.json();
+        populateWidgets(node, data);
+    } catch (err) {
+        console.error("XLSX import error:", err);
+    }
+}
+
+function setupUploadButton(node) {
+    const fileInput = document.createElement("input");
+    fileInput.type = "file";
+    fileInput.accept = ".xlsx";
+    fileInput.style.display = "none";
+    document.body.appendChild(fileInput);
+
+    fileInput.addEventListener("change", async () => {
+        if (!fileInput.files || fileInput.files.length === 0) return;
+        const file = fileInput.files[0];
+        const formData = new FormData();
+        formData.append("image", file, file.name);
+        formData.append("type", "input");
+        formData.append("overwrite", "true");
+
+        try {
+            const resp = await fetch("/upload/image", {
+                method: "POST",
+                body: formData,
+            });
+            if (!resp.ok) {
+                console.error("XLSX upload failed:", resp.statusText);
+                return;
+            }
+            const result = await resp.json();
+            const uploadedName = result.name;
+
+            await importXlsxValues(node, uploadedName);
+        } catch (err) {
+            console.error("XLSX upload error:", err);
+        }
+        fileInput.value = "";
+    });
+
+    node.addWidget("button", "Upload XLSX", "Upload XLSX", () => {
+        fileInput.click();
+    });
+}
+
+function hasConnectedInput(node, widgetName) {
+    if (!node.inputs) return false;
+    for (const inp of node.inputs) {
+        if (inp.widget && inp.widget.name === widgetName && inp.link != null) {
+            return true;
+        }
+    }
+    return false;
+}
+
+function collectModalityParams(node) {
+    const resolved = node._resolvedValues || {};
+    const modalities = {};
+    for (const prefix of PREFIXES) {
+        const enabledWidget = node.widgets.find(
+            (w) => w.name === prefix + "_enabled"
+        );
+        if (!enabledWidget || !enabledWidget.value) continue;
+        const fields = {};
+        const resolvedMod = resolved[prefix] || {};
+        for (const field of STRING_FIELDS) {
+            const widgetName = prefix + "_" + field;
+            if (hasConnectedInput(node, widgetName) && field in resolvedMod) {
+                fields[field] = resolvedMod[field];
+            } else {
+                const w = node.widgets.find(
+                    (w) => w.name === widgetName
+                );
+                if (w) fields[field] = w.value;
+            }
+        }
+        const zisName = prefix + "_zero_init_sigma";
+        if (hasConnectedInput(node, zisName) && "zero_init_sigma" in resolvedMod) {
+            fields["zero_init_sigma"] = resolvedMod["zero_init_sigma"];
+        } else {
+            const zisWidget = node.widgets.find(
+                (w) => w.name === zisName
+            );
+            if (zisWidget) fields["zero_init_sigma"] = String(zisWidget.value);
+        }
+        modalities[prefix === "video" ? "VIDEO" : "AUDIO"] = fields;
+    }
+    return modalities;
+}
+
+function setupExportButton(node) {
+    node.addWidget("button", "Export XLSX", "Export XLSX", async () => {
+        const filename = prompt("Enter filename for XLSX export:", "preset");
+        if (!filename) return;
+
+        const modalities = collectModalityParams(node);
+        if (Object.keys(modalities).length === 0) {
+            alert("No modalities enabled. Enable at least one.");
+            return;
+        }
+
+        try {
+            const resp = await fetch("/guider_params/export", {
+                method: "POST",
+                headers: { "Content-Type": "application/json" },
+                body: JSON.stringify({ filename, modalities }),
+            });
+            const result = await resp.json();
+            if (!resp.ok) {
+                alert("Export failed: " + (result.error || resp.statusText));
+                return;
+            }
+
+            const url =
+                "/view?filename=" +
+                encodeURIComponent(result.filename) +
+                "&type=" +
+                encodeURIComponent(result.type);
+            window.open(url, "_blank");
+        } catch (err) {
+            console.error("Export error:", err);
+            alert("Export failed: " + err.message);
+        }
+    });
+}
+
+app.registerExtension({
+    name: "ltx.guider_parameters_advanced",
+
+    async nodeCreated(node) {
+        if (node.comfyClass !== "GuiderParametersAdvanced") {
+            return;
+        }
+
+        // Hide internal widgets that buttons manage
+        const saveWidget = node.widgets.find(
+            (w) => w.name === "save_filename"
+        );
+        if (saveWidget) toggleWidget(node, saveWidget, false);
+
+        setupUploadButton(node);
+        setupExportButton(node);
+        injectMultilineLabels(node);
+
+        for (const prefix of PREFIXES) {
+            const enabledWidget = node.widgets.find(
+                (w) => w.name === prefix + "_enabled"
+            );
+            if (!enabledWidget) continue;
+
+            const origCallback = enabledWidget.callback;
+            enabledWidget.callback = function (...args) {
+                if (origCallback) origCallback.apply(this, args);
+                toggleModalityWidgets(node, prefix, enabledWidget.value);
+            };
+
+            toggleModalityWidgets(node, prefix, enabledWidget.value);
+        }
+    },
+
+    async beforeRegisterNodeDef(nodeType, nodeData) {
+        if (nodeData.name !== "GuiderParametersAdvanced") {
+            return;
+        }
+
+        const origOnExecuted = nodeType.prototype.onExecuted;
+
+        nodeType.prototype.onExecuted = function (message) {
+            if (origOnExecuted) {
+                origOnExecuted.apply(this, arguments);
+            }
+
+            if (
+                Array.isArray(message.resolved_values) &&
+                message.resolved_values.length > 0
+            ) {
+                this._resolvedValues = message.resolved_values[0];
+            }
+
+            if (
+                Array.isArray(message.xlsx_download) &&
+                message.xlsx_download.length > 0
+            ) {
+                const info = message.xlsx_download[0];
+                const url =
+                    "/view?filename=" +
+                    encodeURIComponent(info.filename) +
+                    "&type=" +
+                    encodeURIComponent(info.type);
+
+                let linkWidget = this.widgets.find(
+                    (w) => w.name === "_download_link"
+                );
+                if (!linkWidget) {
+                    linkWidget = this.addWidget(
+                        "button",
+                        "_download_link",
+                        "Download XLSX",
+                        () => {
+                            window.open(url, "_blank");
+                        }
+                    );
+                }
+                linkWidget.label = "Download: " + info.filename;
+                linkWidget.callback = () => {
+                    window.open(url, "_blank");
+                };
+
+                this.setSize(this.computeSize());
+            }
+
+        };
+    },
+});
diff --git a/custom_nodes/ComfyUI-LTXVideo/web/js/sparse_track_editor.js b/custom_nodes/ComfyUI-LTXVideo/web/js/sparse_track_editor.js
new file mode 100644
index 0000000000000000000000000000000000000000..dcd5c2b3ebecb816e0da71ed101274f22a684544
--- /dev/null
+++ b/custom_nodes/ComfyUI-LTXVideo/web/js/sparse_track_editor.js
@@ -0,0 +1,715 @@
+import { app } from "../../../scripts/app.js";
+
+// ---------------------------------------------------------------------------
+// Catmull-Rom spline interpolation (written from scratch)
+// ---------------------------------------------------------------------------
+
+function catmullRom(p0, p1, p2, p3, t) {
+  const t2 = t * t;
+  const t3 = t2 * t;
+  return {
+    x:
+      0.5 *
+      (2 * p1.x +
+        (-p0.x + p2.x) * t +
+        (2 * p0.x - 5 * p1.x + 4 * p2.x - p3.x) * t2 +
+        (-p0.x + 3 * p1.x - 3 * p2.x + p3.x) * t3),
+    y:
+      0.5 *
+      (2 * p1.y +
+        (-p0.y + p2.y) * t +
+        (2 * p0.y - 5 * p1.y + 4 * p2.y - p3.y) * t2 +
+        (-p0.y + 3 * p1.y - 3 * p2.y + p3.y) * t3),
+  };
+}
+
+function interpolateSpline(controlPoints, numSamples) {
+  if (controlPoints.length === 0) return [];
+  if (controlPoints.length === 1) {
+    return Array.from({ length: numSamples }, () => ({ ...controlPoints[0] }));
+  }
+  if (controlPoints.length === 2) {
+    const [a, b] = controlPoints;
+    return Array.from({ length: numSamples }, (_, i) => {
+      const t = i / (numSamples - 1);
+      return { x: a.x + (b.x - a.x) * t, y: a.y + (b.y - a.y) * t };
+    });
+  }
+  // Pad with phantom endpoints for Catmull-Rom
+  const pts = [
+    controlPoints[0],
+    ...controlPoints,
+    controlPoints[controlPoints.length - 1],
+  ];
+  const nSeg = pts.length - 3;
+  const result = [];
+  for (let i = 0; i < numSamples; i++) {
+    const gT = (i / (numSamples - 1)) * nSeg;
+    const seg = Math.min(Math.floor(gT), nSeg - 1);
+    const lT = gT - seg;
+    result.push(catmullRom(pts[seg], pts[seg + 1], pts[seg + 2], pts[seg + 3], lT));
+  }
+  return result;
+}
+
+// ---------------------------------------------------------------------------
+// Visual constants
+// ---------------------------------------------------------------------------
+
+const SPLINE_COLORS = [
+  "#ef4444", "#22c55e", "#3b82f6", "#f59e0b",
+  "#a855f7", "#06b6d4", "#f97316", "#84cc16",
+];
+const POINT_RADIUS = 5;
+const ACTIVE_POINT_RADIUS = 7;
+const HIT_TOLERANCE = 14;
+const CURVE_LINE_WIDTH = 2.5;
+const CANVAS_MIN_HEIGHT = 128;
+
+// ---------------------------------------------------------------------------
+// Helpers
+// ---------------------------------------------------------------------------
+
+function dist(a, b) {
+  return Math.hypot(a.x - b.x, a.y - b.y);
+}
+
+function hideWidget(w) {
+  if (!w) return;
+  w.hidden = true;
+  w._origComputeSize = w.computeSize;
+  w.computeSize = () => [0, -3.3];
+  w.computedHeight = 0;
+}
+
+function closestSegment(pts, p) {
+  let best = Infinity;
+  let bestIdx = 0;
+  for (let i = 0; i < pts.length - 1; i++) {
+    const a = pts[i];
+    const b = pts[i + 1];
+    const dx = b.x - a.x;
+    const dy = b.y - a.y;
+    const len2 = dx * dx + dy * dy;
+    let t = len2 === 0 ? 0 : ((p.x - a.x) * dx + (p.y - a.y) * dy) / len2;
+    t = Math.max(0, Math.min(1, t));
+    const proj = { x: a.x + t * dx, y: a.y + t * dy };
+    const d = dist(proj, p);
+    if (d < best) {
+      best = d;
+      bestIdx = i;
+    }
+  }
+  return { dist: best, segIndex: bestIdx };
+}
+
+// ---------------------------------------------------------------------------
+// Extension registration
+// ---------------------------------------------------------------------------
+
+app.registerExtension({
+  name: "LTXVideo.SparseTrackEditor",
+
+  async nodeCreated(node) {
+    if (node.comfyClass !== "LTXVSparseTrackEditor") return;
+    initEditor(node);
+  },
+
+  async beforeRegisterNodeDef(nodeType, nodeData) {
+    if (nodeData?.name !== "LTXVSparseTrackEditor") return;
+
+    const origExecuted = nodeType.prototype.onExecuted;
+    nodeType.prototype.onExecuted = function (data) {
+      origExecuted?.apply(this, arguments);
+      if (data?.bg_image?.[0]) {
+        loadBgImage(this, data.bg_image[0]);
+      }
+    };
+
+    const origConfigure = nodeType.prototype.onConfigure;
+    nodeType.prototype.onConfigure = function (info) {
+      origConfigure?.apply(this, arguments);
+      if (this._ed) {
+        reloadState(this);
+      }
+    };
+
+    const origRemoved = nodeType.prototype.onRemoved;
+    nodeType.prototype.onRemoved = function () {
+      origRemoved?.apply(this, arguments);
+      if (this._ed) {
+        cancelAnimationFrame(this._ed.rafId);
+        if (this._ed._docClickHandler) {
+          document.removeEventListener("click", this._ed._docClickHandler);
+        }
+        this._ed = null;
+      }
+    };
+  },
+});
+
+// ---------------------------------------------------------------------------
+// Editor initialisation
+// ---------------------------------------------------------------------------
+
+function initEditor(node) {
+  const pointsW = node.widgets.find((w) => w.name === "points_store");
+  const coordsW = node.widgets.find((w) => w.name === "coordinates");
+  hideWidget(pointsW);
+  hideWidget(coordsW);
+
+  const ed = {
+    splines: [],
+    active: 0,
+    imgW: 1024,
+    imgH: 576,
+    bgImg: null,
+    drag: null,
+    hover: null,
+    canvas: null,
+    ctx: null,
+    menu: null,
+    dirty: true,
+    rafId: null,
+  };
+  node._ed = ed;
+
+  // Restore saved state
+  try {
+    const saved = JSON.parse(pointsW.value);
+    if (Array.isArray(saved) && saved.length > 0) {
+      ed.splines = saved;
+    }
+  } catch (_) {
+    /* first run */
+  }
+  if (ed.splines.length === 0) {
+    ed.splines = [
+      [
+        { x: ed.imgW * 0.3, y: ed.imgH * 0.5 },
+        { x: ed.imgW * 0.7, y: ed.imgH * 0.5 },
+      ],
+    ];
+  }
+
+  // DOM
+  const container = document.createElement("div");
+  container.style.position = "relative";
+  container.style.minHeight = `${CANVAS_MIN_HEIGHT}px`;
+
+  const canvas = document.createElement("canvas");
+  canvas.width = 1024;
+  canvas.height = 576;
+  canvas.style.width = "100%";
+  canvas.style.display = "block";
+  canvas.style.background = "#1a1a2e";
+  canvas.style.borderRadius = "4px";
+  canvas.style.cursor = "default";
+  canvas.style.pointerEvents = "auto";
+  canvas.style.touchAction = "none";
+  container.appendChild(canvas);
+  ed.canvas = canvas;
+  ed.ctx = canvas.getContext("2d");
+
+  const menu = buildContextMenu();
+  container.appendChild(menu);
+  ed.menu = menu;
+
+  const widget = node.addDOMWidget("track_editor", "TrackEditorWidget", container, {
+    serialize: false,
+    hideOnZoom: false,
+  });
+  widget.computeSize = () => {
+    const w = node.size[0];
+    const aspect = node._ed.imgH / node._ed.imgW;
+    const h = Math.max(CANVAS_MIN_HEIGHT, Math.round(w * aspect));
+    return [w, h + 10];
+  };
+
+  setupEvents(node);
+  syncWidgets(node);
+  startRenderLoop(node);
+
+  requestAnimationFrame(() => {
+    node.setSize?.(node.computeSize());
+    app.graph?.setDirtyCanvas(true, true);
+  });
+}
+
+// ---------------------------------------------------------------------------
+// State reload (fires on onConfigure, i.e. workflow load)
+// ---------------------------------------------------------------------------
+
+function reloadState(node) {
+  const ed = node._ed;
+  const pointsW = node.widgets.find((w) => w.name === "points_store");
+  try {
+    const saved = JSON.parse(pointsW?.value);
+    if (Array.isArray(saved) && saved.length > 0) {
+      ed.splines = saved;
+    }
+  } catch (_) {
+    /* keep current splines */
+  }
+  ed.dirty = true;
+  syncWidgets(node);
+}
+
+// ---------------------------------------------------------------------------
+// Background image loading
+// ---------------------------------------------------------------------------
+
+function loadBgImage(node, b64) {
+  const ed = node._ed;
+  const img = new window.Image();
+  img.onload = () => {
+    const firstLoad = ed.bgImg === null;
+    ed.bgImg = img;
+    ed.imgW = img.width;
+    ed.imgH = img.height;
+    if (firstLoad && ed.splines.length === 1 && ed.splines[0].length === 2) {
+      ed.splines[0][0] = { x: ed.imgW * 0.3, y: ed.imgH * 0.5 };
+      ed.splines[0][1] = { x: ed.imgW * 0.7, y: ed.imgH * 0.5 };
+    }
+    ed.dirty = true;
+    syncWidgets(node);
+    requestAnimationFrame(() => {
+      node.setSize?.(node.computeSize());
+      app.graph?.setDirtyCanvas(true, true);
+    });
+  };
+  img.src = `data:image/jpeg;base64,${b64}`;
+}
+
+// ---------------------------------------------------------------------------
+// Coordinate transforms  (image space <-> canvas pixel space)
+// ---------------------------------------------------------------------------
+
+function getScale(node) {
+  const ed = node._ed;
+  const c = ed.canvas;
+  const scaleX = c.width / ed.imgW;
+  const scaleY = c.height / ed.imgH;
+  return Math.min(scaleX, scaleY);
+}
+
+function imgToCanvas(node, p) {
+  const s = getScale(node);
+  return { x: p.x * s, y: p.y * s };
+}
+
+function canvasToImg(node, p) {
+  const s = getScale(node);
+  return { x: p.x / s, y: p.y / s };
+}
+
+function mouseToCanvas(node, e) {
+  const rect = node._ed.canvas.getBoundingClientRect();
+  return {
+    x: (e.clientX - rect.left) * (node._ed.canvas.width / rect.width),
+    y: (e.clientY - rect.top) * (node._ed.canvas.height / rect.height),
+  };
+}
+
+// ---------------------------------------------------------------------------
+// Hit-testing
+// ---------------------------------------------------------------------------
+
+function hitTestPoint(node, canvasPos) {
+  const ed = node._ed;
+  const dpr = window.devicePixelRatio || 1;
+  const tol = HIT_TOLERANCE * dpr;
+  for (let si = 0; si < ed.splines.length; si++) {
+    for (let pi = 0; pi < ed.splines[si].length; pi++) {
+      const cp = imgToCanvas(node, ed.splines[si][pi]);
+      if (dist(cp, canvasPos) < tol) {
+        return { si, pi };
+      }
+    }
+  }
+  return null;
+}
+
+// ---------------------------------------------------------------------------
+// Context menu
+// ---------------------------------------------------------------------------
+
+function buildContextMenu() {
+  const menu = document.createElement("div");
+  Object.assign(menu.style, {
+    display: "none",
+    position: "absolute",
+    background: "#252530",
+    border: "1px solid #555",
+    borderRadius: "6px",
+    padding: "4px 0",
+    zIndex: "1000",
+    boxShadow: "0 4px 12px rgba(0,0,0,0.4)",
+    minWidth: "160px",
+    fontFamily: "system-ui, sans-serif",
+    fontSize: "13px",
+  });
+  return menu;
+}
+
+function showMenu(node, canvasEvt, imgPos) {
+  const ed = node._ed;
+  const menu = ed.menu;
+  menu.innerHTML = "";
+
+  const hit = hitTestPoint(node, mouseToCanvas(node, canvasEvt));
+
+  const items = [];
+  items.push({
+    label: "Add Point at Cursor",
+    action: () => {
+      ed.splines[ed.active].push({ x: imgPos.x, y: imgPos.y });
+      ed.dirty = true;
+      syncWidgets(node);
+    },
+  });
+  items.push({
+    label: "Subdivide Nearest Segment",
+    action: () => {
+      const spline = ed.splines[ed.active];
+      if (spline.length < 2) return;
+      const { segIndex } = closestSegment(spline, imgPos);
+      const a = spline[segIndex];
+      const b = spline[segIndex + 1];
+      const mid = { x: (a.x + b.x) / 2, y: (a.y + b.y) / 2 };
+      spline.splice(segIndex + 1, 0, mid);
+      ed.dirty = true;
+      syncWidgets(node);
+    },
+  });
+  items.push({ separator: true });
+  items.push({
+    label: "New Spline",
+    action: () => {
+      ed.splines.push([
+        { x: imgPos.x - 40, y: imgPos.y },
+        { x: imgPos.x + 40, y: imgPos.y },
+      ]);
+      ed.active = ed.splines.length - 1;
+      ed.dirty = true;
+      syncWidgets(node);
+    },
+  });
+  items.push({
+    label: "New Static Spline",
+    action: () => {
+      ed.splines.push([{ x: imgPos.x, y: imgPos.y }]);
+      ed.active = ed.splines.length - 1;
+      ed.dirty = true;
+      syncWidgets(node);
+    },
+  });
+  items.push({
+    label: "Delete Spline",
+    action: () => {
+      if (ed.splines.length <= 1) return;
+      ed.splines.splice(ed.active, 1);
+      ed.active = Math.min(ed.active, ed.splines.length - 1);
+      ed.dirty = true;
+      syncWidgets(node);
+    },
+    disabled: ed.splines.length <= 1,
+  });
+  if (hit) {
+    const spline = ed.splines[hit.si];
+    if (spline.length > 1) {
+      items.push({ separator: true });
+      items.push({
+        label: "Delete Point",
+        action: () => {
+          spline.splice(hit.pi, 1);
+          ed.dirty = true;
+          syncWidgets(node);
+        },
+      });
+    }
+  }
+
+  for (const item of items) {
+    if (item.separator) {
+      const hr = document.createElement("div");
+      hr.style.borderTop = "1px solid #444";
+      hr.style.margin = "4px 8px";
+      menu.appendChild(hr);
+      continue;
+    }
+    const el = document.createElement("div");
+    el.textContent = item.label;
+    const isDisabled = item.disabled;
+    Object.assign(el.style, {
+      padding: "6px 14px",
+      cursor: isDisabled ? "default" : "pointer",
+      color: isDisabled ? "#666" : "#ddd",
+      whiteSpace: "nowrap",
+    });
+    if (!isDisabled) {
+      el.addEventListener("mouseenter", () => (el.style.background = "#3a3a4a"));
+      el.addEventListener("mouseleave", () => (el.style.background = "none"));
+      el.addEventListener("click", (e) => {
+        e.stopPropagation();
+        menu.style.display = "none";
+        item.action();
+      });
+    }
+    menu.appendChild(el);
+  }
+
+  const rect = node._ed.canvas.getBoundingClientRect();
+  const containerRect = node._ed.canvas.parentElement.getBoundingClientRect();
+  menu.style.left = `${canvasEvt.clientX - containerRect.left}px`;
+  menu.style.top = `${canvasEvt.clientY - containerRect.top}px`;
+  menu.style.display = "block";
+}
+
+// ---------------------------------------------------------------------------
+// Event handling
+// ---------------------------------------------------------------------------
+
+function setupEvents(node) {
+  const ed = node._ed;
+  const canvas = ed.canvas;
+
+  canvas.addEventListener("pointerdown", (e) => {
+    ed.menu.style.display = "none";
+    if (e.button === 2) return; // right-click handled by contextmenu
+    if (e.button !== 0) return;
+    e.preventDefault();
+    e.stopPropagation();
+    canvas.setPointerCapture(e.pointerId);
+
+    const cp = mouseToCanvas(node, e);
+    const hit = hitTestPoint(node, cp);
+    if (hit) {
+      ed.active = hit.si;
+      ed.drag = { ...hit, pointerId: e.pointerId };
+      canvas.style.cursor = "grabbing";
+      ed.dirty = true;
+    }
+  });
+
+  canvas.addEventListener("pointermove", (e) => {
+    const cp = mouseToCanvas(node, e);
+
+    if (ed.drag) {
+      e.preventDefault();
+      e.stopPropagation();
+      const imgP = canvasToImg(node, cp);
+      imgP.x = Math.max(0, Math.min(ed.imgW, imgP.x));
+      imgP.y = Math.max(0, Math.min(ed.imgH, imgP.y));
+      ed.splines[ed.drag.si][ed.drag.pi] = imgP;
+      ed.dirty = true;
+      syncWidgets(node);
+      return;
+    }
+
+    const hit = hitTestPoint(node, cp);
+    if (hit) {
+      canvas.style.cursor = "grab";
+      ed.hover = hit;
+    } else {
+      canvas.style.cursor = "default";
+      ed.hover = null;
+    }
+    ed.dirty = true;
+  });
+
+  canvas.addEventListener("pointerup", (e) => {
+    if (ed.drag) {
+      e.preventDefault();
+      e.stopPropagation();
+      canvas.releasePointerCapture(e.pointerId);
+      ed.drag = null;
+      canvas.style.cursor = "default";
+      ed.dirty = true;
+      syncWidgets(node);
+    }
+  });
+
+  canvas.addEventListener("pointerleave", () => {
+    ed.hover = null;
+    ed.dirty = true;
+  });
+
+  canvas.addEventListener("lostpointercapture", () => {
+    if (ed.drag) {
+      ed.drag = null;
+      canvas.style.cursor = "default";
+      ed.dirty = true;
+      syncWidgets(node);
+    }
+  });
+
+  canvas.addEventListener("contextmenu", (e) => {
+    e.preventDefault();
+    e.stopPropagation();
+    const cp = mouseToCanvas(node, e);
+    const imgP = canvasToImg(node, cp);
+    showMenu(node, e, imgP);
+  });
+
+  const docClickHandler = (e) => {
+    if (!ed.menu.contains(e.target)) {
+      ed.menu.style.display = "none";
+    }
+  };
+  document.addEventListener("click", docClickHandler);
+  ed._docClickHandler = docClickHandler;
+}
+
+// ---------------------------------------------------------------------------
+// Widget synchronisation
+// ---------------------------------------------------------------------------
+
+function syncWidgets(node) {
+  const ed = node._ed;
+  const pointsW = node.widgets.find((w) => w.name === "points_store");
+  const coordsW = node.widgets.find((w) => w.name === "coordinates");
+  const samplesW = node.widgets.find((w) => w.name === "points_to_sample");
+
+  if (pointsW) pointsW.value = JSON.stringify(ed.splines);
+
+  const numSamples = samplesW ? samplesW.value : 121;
+  const interpolated = ed.splines.map((sp) => interpolateSpline(sp, numSamples));
+  // Round to integers for pixel-level coordinates
+  const rounded = interpolated.map((track) =>
+    track.map((p) => ({ x: Math.round(p.x), y: Math.round(p.y) }))
+  );
+  if (coordsW) coordsW.value = JSON.stringify(rounded);
+}
+
+// ---------------------------------------------------------------------------
+// Rendering
+// ---------------------------------------------------------------------------
+
+function startRenderLoop(node) {
+  const ed = node._ed;
+
+  function loop() {
+    ed.rafId = requestAnimationFrame(loop);
+    resizeCanvas(node);
+    if (!ed.dirty) return;
+    ed.dirty = false;
+    render(node);
+  }
+
+  loop();
+}
+
+function resizeCanvas(node) {
+  const ed = node._ed;
+  const canvas = ed.canvas;
+  const dispW = canvas.clientWidth || node.size?.[0] || 400;
+
+  const aspect = ed.imgH / ed.imgW;
+  const dispH = Math.max(CANVAS_MIN_HEIGHT, Math.round(dispW * aspect));
+
+  canvas.style.height = `${dispH}px`;
+
+  const dpr = window.devicePixelRatio || 1;
+  const bufW = Math.round(dispW * dpr);
+  const bufH = Math.round(dispH * dpr);
+
+  if (canvas.width !== bufW || canvas.height !== bufH) {
+    canvas.width = bufW;
+    canvas.height = bufH;
+    ed.dirty = true;
+  }
+}
+
+function render(node) {
+  const ed = node._ed;
+  const ctx = ed.ctx;
+  const c = ed.canvas;
+
+  ctx.clearRect(0, 0, c.width, c.height);
+
+  // Background image
+  if (ed.bgImg) {
+    const s = getScale(node);
+    ctx.globalAlpha = 0.85;
+    ctx.drawImage(ed.bgImg, 0, 0, ed.imgW * s, ed.imgH * s);
+    ctx.globalAlpha = 1.0;
+  }
+
+  const samplesW = node.widgets.find((w) => w.name === "points_to_sample");
+  const numSamples = samplesW ? samplesW.value : 121;
+  const dpr = window.devicePixelRatio || 1;
+
+  // Draw each spline
+  for (let si = 0; si < ed.splines.length; si++) {
+    const isActive = si === ed.active;
+    const color = SPLINE_COLORS[si % SPLINE_COLORS.length];
+    const spline = ed.splines[si];
+
+    // Interpolated curve
+    if (spline.length >= 2) {
+      const curve = interpolateSpline(spline, Math.max(numSamples, 60));
+      ctx.beginPath();
+      const p0 = imgToCanvas(node, curve[0]);
+      ctx.moveTo(p0.x, p0.y);
+      for (let i = 1; i < curve.length; i++) {
+        const p = imgToCanvas(node, curve[i]);
+        ctx.lineTo(p.x, p.y);
+      }
+      ctx.strokeStyle = color;
+      ctx.lineWidth = (isActive ? CURVE_LINE_WIDTH * 1.4 : CURVE_LINE_WIDTH) * dpr;
+      ctx.globalAlpha = isActive ? 1.0 : 0.5;
+      ctx.stroke();
+      ctx.globalAlpha = 1.0;
+    }
+
+    // Control points
+    for (let pi = 0; pi < spline.length; pi++) {
+      const cp = imgToCanvas(node, spline[pi]);
+      const isHov = ed.hover && ed.hover.si === si && ed.hover.pi === pi;
+      const isDrag = ed.drag && ed.drag.si === si && ed.drag.pi === pi;
+      const baseR =
+        isHov || isDrag
+          ? ACTIVE_POINT_RADIUS
+          : isActive
+            ? POINT_RADIUS
+            : POINT_RADIUS * 0.8;
+      const r = baseR * dpr;
+
+      // Outer ring
+      ctx.beginPath();
+      ctx.arc(cp.x, cp.y, r + 2 * dpr, 0, Math.PI * 2);
+      ctx.fillStyle = isActive ? "#fff" : "rgba(255,255,255,0.5)";
+      ctx.fill();
+
+      // Inner fill
+      ctx.beginPath();
+      ctx.arc(cp.x, cp.y, r, 0, Math.PI * 2);
+      ctx.fillStyle = color;
+      ctx.fill();
+
+      // Point number label
+      if (isActive) {
+        ctx.fillStyle = "#fff";
+        ctx.font = `bold ${Math.round(11 * dpr)}px system-ui`;
+        ctx.textAlign = "center";
+        ctx.fillText(String(pi), cp.x, cp.y - r - 4 * dpr);
+      }
+    }
+  }
+
+  // Active spline indicator
+  if (ed.splines.length > 1) {
+    const dpr = window.devicePixelRatio || 1;
+    ctx.fillStyle = "rgba(0,0,0,0.5)";
+    ctx.fillRect(4 * dpr, 4 * dpr, 130 * dpr, 20 * dpr);
+    ctx.fillStyle = SPLINE_COLORS[ed.active % SPLINE_COLORS.length];
+    ctx.font = `bold ${11 * dpr}px system-ui`;
+    ctx.textAlign = "left";
+    ctx.fillText(
+      `Spline ${ed.active + 1} / ${ed.splines.length}`,
+      10 * dpr,
+      18 * dpr
+    );
+  }
+}
diff --git a/custom_nodes/ComfyUI-Manager/.github/workflows/publish-to-pypi.yml b/custom_nodes/ComfyUI-Manager/.github/workflows/publish-to-pypi.yml
new file mode 100644
index 0000000000000000000000000000000000000000..f96c027a20266ff3a7d5b140a7dfd0380c2c49de
--- /dev/null
+++ b/custom_nodes/ComfyUI-Manager/.github/workflows/publish-to-pypi.yml
@@ -0,0 +1,58 @@
+name: Publish to PyPI
+
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - draft-v4
+    paths:
+      - "pyproject.toml"
+
+jobs:
+  build-and-publish:
+    runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'ltdrdata' || github.repository_owner == 'Comfy-Org' }}
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.9'
+
+      - name: Install build dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install build twine
+
+      - name: Get current version
+        id: current_version
+        run: |
+          CURRENT_VERSION=$(grep -oP 'version = "\K[^"]+' pyproject.toml)
+          echo "version=$CURRENT_VERSION" >> $GITHUB_OUTPUT
+          echo "Current version: $CURRENT_VERSION"
+
+      - name: Build package
+        run: python -m build
+
+      - name: Create GitHub Release
+        id: create_release
+        uses: softprops/action-gh-release@v2
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        with:
+          files: dist/*
+          tag_name: v${{ steps.current_version.outputs.version }}
+          draft: false
+          prerelease: false
+          generate_release_notes: true
+
+      - name: Publish to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          password: ${{ secrets.PYPI_TOKEN }}
+          skip-existing: true
+          verbose: true 
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-Manager/.github/workflows/publish.yml b/custom_nodes/ComfyUI-Manager/.github/workflows/publish.yml
new file mode 100644
index 0000000000000000000000000000000000000000..463ecca9ef06e4c6a8dbd47fcf8acb9335eeef13
--- /dev/null
+++ b/custom_nodes/ComfyUI-Manager/.github/workflows/publish.yml
@@ -0,0 +1,25 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main-blocked
+    paths:
+      - "pyproject.toml"
+
+permissions:
+  issues: write
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'ltdrdata' }}
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@v1
+        with:
+          ## Add your own personal access token to your Github Repository secrets and reference it here.
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
diff --git a/custom_nodes/ComfyUI-Manager/.github/workflows/ruff.yml b/custom_nodes/ComfyUI-Manager/.github/workflows/ruff.yml
new file mode 100644
index 0000000000000000000000000000000000000000..4c1a025948b4f4102d162e9a791d1f5fbf4f168c
--- /dev/null
+++ b/custom_nodes/ComfyUI-Manager/.github/workflows/ruff.yml
@@ -0,0 +1,23 @@
+name: Python Linting
+
+on: [push, pull_request]
+
+jobs:
+  ruff:
+    name: Run Ruff
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: 3.x
+
+    - name: Install Ruff
+      run: pip install ruff
+
+    - name: Run Ruff
+      run: ruff check .
diff --git a/custom_nodes/ComfyUI-Manager/node_db/dev/custom-node-list.json b/custom_nodes/ComfyUI-Manager/node_db/dev/custom-node-list.json
new file mode 100644
index 0000000000000000000000000000000000000000..44d4e5bcf3c47bf46203ee19443dd0ec6af63916
--- /dev/null
+++ b/custom_nodes/ComfyUI-Manager/node_db/dev/custom-node-list.json
@@ -0,0 +1,12417 @@
+{
+    "custom_nodes": [
+        {
+            "author": "7akoro",
+            "title": "comfyui-prompt-enhancer [NAME CONFLICT]",
+            "reference": "https://github.com/7akoro/comfyui-prompt-enhancer",
+            "files": [
+                "https://github.com/7akoro/comfyui-prompt-enhancer"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI extension for efficient prompt editing with weight adjustment, tag movement, auto-completion, and more."
+        },
+        {
+            "author": "kongsheng",
+            "title": "ComfyUI-GeminiImage [NAME CONFLICT]",
+            "reference": "https://github.com/kongsheng/ComfyUI-GeminiImage",
+            "files": [
+                "https://github.com/kongsheng/ComfyUI-GeminiImage"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom nodes for Gemini image generation service supporting text-to-image and image-to-image with Gemini 2.0 models."
+        },
+        {
+            "author": "MohammadAboulEla",
+            "title": "ComfyUI-BiRefNet [NAME CONFLICT]",
+            "reference": "https://github.com/MohammadAboulEla/ComfyUI-BiRefNet",
+            "files": [
+                "https://github.com/MohammadAboulEla/ComfyUI-BiRefNet"
+            ],
+            "install_type": "git-clone",
+            "description": "A simple ComfyUI custom node for background removal using the BiRefNet model."
+        },
+        {
+            "author": "florestefano1975",
+            "title": "ComfyUI-Prompt-Library [NAME CONFLICT]",
+            "reference": "https://github.com/florestefano1975/ComfyUI-Prompt-Library",
+            "files": [
+                "https://github.com/florestefano1975/ComfyUI-Prompt-Library"
+            ],
+            "install_type": "git-clone",
+            "description": "A fully-featured prompt manager for ComfyUI with categories, search, tags, and a random prompt node for automatic prompt variation."
+        },
+        {
+            "author": "JosDigby",
+            "title": "ComfyUI-DigbyTools",
+            "reference": "https://github.com/JosDigby/ComfyUI-DigbyTools",
+            "files": [
+                "https://github.com/JosDigby/ComfyUI-DigbyTools"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom node pack with Keyframer and LTX VAE Latent prep nodes for keyframe animation workflows. (Description by CC)"
+        },
+        {
+            "author": "ivanravinovitch",
+            "title": "AutoCropByColor",
+            "reference": "https://github.com/ivanravinovitch/AutoCropByColor",
+            "files": [
+                "https://github.com/ivanravinovitch/AutoCropByColor"
+            ],
+            "install_type": "git-clone",
+            "description": "Utility nodes for automatic image cropping by color and scaling to megapixel dimensions. (Description by CC)"
+        },
+        {
+            "author": "brbbbq",
+            "title": "ComfyUI-WanMoveSVI_FLF_v2",
+            "reference": "https://github.com/brbbbq/ComfyUI-WanMoveSVI_FLF_v2",
+            "files": [
+                "https://github.com/brbbbq/ComfyUI-WanMoveSVI_FLF_v2"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node implementing WAN-Move Sparse Video Interpolation with frame latency features (FLF v2). (Description by CC)"
+        },
+        {
+            "author": "krystal",
+            "title": "[WIP] ComfyUI KrystalNodes",
+            "reference": "https://github.com/Krystal-0/ComfyUI-KrystalNodes",
+            "files": [
+                "https://github.com/Krystal-0/ComfyUI-KrystalNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "A small pack of utility nodes including CLIP/VAE fallbacks, Anima model detection, and multi image preview.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "TechnoWarrior2",
+            "title": "comfyui-live-preview [WIP]",
+            "reference": "https://github.com/TechnoWarrior2/comfyui-live-preview",
+            "files": [
+                "https://github.com/TechnoWarrior2/comfyui-live-preview"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node displaying large floating overlay window with live denoising step previews as image generates, similar to A1111 and Forge.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "HuajunPeng1982",
+            "title": "comfyui-grsai-storyboard",
+            "reference": "https://github.com/HuajunPeng1982/comfyui-grsai-storyboard",
+            "files": [
+                "https://github.com/HuajunPeng1982/comfyui-grsai-storyboard"
+            ],
+            "install_type": "git-clone",
+            "description": "Storyboard generation node for ComfyUI using AI-assisted visual narrative planning. (Description by CC)"
+        },
+        {
+            "author": "Arch666Angel",
+            "title": "ComfyUI-Geometry-Nodes [WIP]",
+            "reference": "https://github.com/Arch666Angel/ComfyUI-Geometry-Nodes",
+            "files": [
+                "https://github.com/Arch666Angel/ComfyUI-Geometry-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Geometric mask generation and manipulation nodes providing color selection, pattern painting, and procedural layer features. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "alldaygameover",
+            "title": "ComfyUI-PanonramaViewer [WIP]",
+            "reference": "https://github.com/alldaygameover/ComfyUI-PanonramaViewer",
+            "files": [
+                "https://github.com/alldaygameover/ComfyUI-PanonramaViewer"
+            ],
+            "install_type": "git-clone",
+            "description": "A simple custom node package for ComfyUI that renders a perspective camera view from an equirectangular 360 panorama image.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Sen-sou",
+            "title": "Comfyui-Anima-Regional-Conditioning [WIP]",
+            "reference": "https://github.com/Sen-sou/Comfyui-Anima-Regional-Conditioning",
+            "files": [
+                "https://github.com/Sen-sou/Comfyui-Anima-Regional-Conditioning"
+            ],
+            "install_type": "git-clone",
+            "description": "Regional conditioning custom nodes for Anima models in ComfyUI.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "GitHug",
+            "title": "ComfyQueue",
+            "reference": "https://github.com/GitHug/ComfyQueue",
+            "files": [
+                "https://github.com/GitHug/ComfyQueue"
+            ],
+            "install_type": "git-clone",
+            "description": "Sequential prompt queue node for ComfyUI — serves multiline prompts one at a time."
+        },
+        {
+            "author": "duycao2506",
+            "title": "ComfyUI-Local-PiperTTS [WIP]",
+            "reference": "https://github.com/duycao2506/ComfyUI-Local-PiperTTS",
+            "files": [
+                "https://github.com/duycao2506/ComfyUI-Local-PiperTTS"
+            ],
+            "install_type": "git-clone",
+            "description": "Convert text-to-speech inside ComfyUI using Piper TTS engine with support for multiple voices via Onnx models.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "n0va39",
+            "title": "ComfyUI-LatentSpectralExpand [WIP]",
+            "reference": "https://github.com/n0va39/ComfyUI-LatentSpectralExpand",
+            "files": [
+                "https://github.com/n0va39/ComfyUI-LatentSpectralExpand"
+            ],
+            "install_type": "git-clone",
+            "description": "Experimental implementation of latent spectral expansion and high-frequency noise injection techniques from the SPEED paper, enabling frequency-domain upscaling with progressive noise injection in ComfyUI. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "BlackSnowSkill",
+            "title": "ComfyUI-BSS_ModelMixSampler [WIP]",
+            "reference": "https://github.com/BlackSnowSkill/ComfyUI-BSS_ModelMixSampler",
+            "files": [
+                "https://github.com/BlackSnowSkill/ComfyUI-BSS_ModelMixSampler"
+            ],
+            "install_type": "git-clone",
+            "description": "Advanced custom node for chaining and alternating denoising steps between two models with identical latent architecture within a single generation process.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "UnlimitedEditing",
+            "title": "comfyui-turntable-render",
+            "reference": "https://github.com/UnlimitedEditing/comfyui-turntable-render",
+            "files": [
+                "https://github.com/UnlimitedEditing/comfyui-turntable-render"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI node for turntable rendering workflows. (Description by CC)"
+        },
+        {
+            "author": "IAmAshishNegi",
+            "title": "comfyui_tensor_parallel_v3",
+            "reference": "https://github.com/IAmAshishNegi/comfyui_tensor_parallel_v3",
+            "files": [
+                "https://github.com/IAmAshishNegi/comfyui_tensor_parallel_v3"
+            ],
+            "install_type": "git-clone",
+            "description": "Tensor parallelization node for ComfyUI optimization. (Description by CC)"
+        },
+        {
+            "author": "aidengit7",
+            "title": "ComfyUI_WanVaceTiledSampler",
+            "reference": "https://github.com/aidengit7/ComfyUI_WanVaceTiledSampler",
+            "files": [
+                "https://github.com/aidengit7/ComfyUI_WanVaceTiledSampler"
+            ],
+            "install_type": "git-clone",
+            "description": "WanVaceTiledSampler node for image generation. (Description by CC)"
+        },
+        {
+            "author": "alru",
+            "title": "ComfyUI-Alru-Tools",
+            "reference": "https://github.com/alru/ComfyUI-Alru-Tools",
+            "files": [
+                "https://github.com/alru/ComfyUI-Alru-Tools"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom nodes providing text encoding, sampler selection, scheduler selection, and widget value fetching utilities. (Description by CC)"
+        },
+        {
+            "author": "anveshane",
+            "title": "comfyui-turboteacache",
+            "reference": "https://github.com/anveshane/comfyui-turboteacache",
+            "files": [
+                "https://github.com/anveshane/comfyui-turboteacache"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for TurboTeaCache functionality. (Description by CC)"
+        },
+        {
+            "author": "ayataka0nk",
+            "title": "ayataka-comfyui-nodes",
+            "reference": "https://github.com/ayataka0nk/ayataka-comfyui-nodes",
+            "files": [
+                "https://github.com/ayataka0nk/ayataka-comfyui-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI utility nodes for string operations and resolution management. (Description by CC)"
+        },
+        {
+            "author": "CCSSNE",
+            "title": "ComfyUI-AKI-DeepFree",
+            "reference": "https://github.com/CCSSNE/ComfyUI-AKI-DeepFree",
+            "files": [
+                "https://github.com/CCSSNE/ComfyUI-AKI-DeepFree"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node for AKI DeepFree extreme cleanup functionality. (Description by CC)"
+        },
+        {
+            "author": "ccssu",
+            "title": "ComfyUI-Black-Image-Interceptor",
+            "reference": "https://github.com/ccssu/ComfyUI-Black-Image-Interceptor",
+            "files": [
+                "https://github.com/ccssu/ComfyUI-Black-Image-Interceptor"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI extension that detects and halts execution when black images are encountered, with logging capabilities. (Description by CC)"
+        },
+        {
+            "author": "Chigozie619",
+            "title": "ComfyUI_NB",
+            "reference": "https://github.com/Chigozie619/ComfyUI_NB",
+            "files": [
+                "https://github.com/Chigozie619/ComfyUI_NB"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node for NanaBananaPro node functionality. (Description by CC)"
+        },
+        {
+            "author": "ckinpdx",
+            "title": "ComfyUI-FPSChange",
+            "reference": "https://github.com/ckinpdx/ComfyUI-FPSChange",
+            "files": [
+                "https://github.com/ckinpdx/ComfyUI-FPSChange"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for image batch FPS changes. (Description by CC)"
+        },
+        {
+            "author": "emniko",
+            "title": "ComfyUI-HPSv2",
+            "reference": "https://github.com/emniko/ComfyUI-HPSv2",
+            "files": [
+                "https://github.com/emniko/ComfyUI-HPSv2"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node implementing HPSv2 model for image evaluation and quality assessment. (Description by CC)"
+        },
+        {
+            "author": "ervinne13",
+            "title": "comfyui-prompt-library",
+            "reference": "https://github.com/ervinne13/comfyui-prompt-library",
+            "files": [
+                "https://github.com/ervinne13/comfyui-prompt-library"
+            ],
+            "install_type": "git-clone",
+            "description": "Prompt library node for ComfyUI. (Description by CC)"
+        },
+        {
+            "author": "fedetanco22",
+            "title": "comfyui-notebooklm-main",
+            "reference": "https://github.com/fedetanco22/comfyui-notebooklm-main",
+            "files": [
+                "https://github.com/fedetanco22/comfyui-notebooklm-main"
+            ],
+            "install_type": "git-clone",
+            "description": "Generates scripts from notebooks using NotebookLM. (Description by CC)"
+        },
+        {
+            "author": "fedetanco22",
+            "title": "comfyui_morpheus_NanoBanana_Mask-main",
+            "reference": "https://github.com/fedetanco22/comfyui_morpheus_NanoBanana_Mask-main",
+            "files": [
+                "https://github.com/fedetanco22/comfyui_morpheus_NanoBanana_Mask-main"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for batch image cropping, editing prompts, and mask generation. (Description by CC)"
+        },
+        {
+            "author": "FloranceYeh",
+            "title": "ComfyUI_F_nodes",
+            "reference": "https://github.com/FloranceYeh/ComfyUI_F_nodes",
+            "files": [
+                "https://github.com/FloranceYeh/ComfyUI_F_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes providing dynamic switching, relay, and sampling preset functionality for workflow control and flexibility. (Description by CC)"
+        },
+        {
+            "author": "g37613182-tech",
+            "title": "comfyui-auto-text-layout",
+            "reference": "https://github.com/g37613182-tech/comfyui-auto-text-layout",
+            "files": [
+                "https://github.com/g37613182-tech/comfyui-auto-text-layout"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node for automatic text layout and positioning. (Description by CC)"
+        },
+        {
+            "author": "g37613182-tech",
+            "title": "comfyui-auto-text-layout-v2",
+            "reference": "https://github.com/g37613182-tech/comfyui-auto-text-layout-v2",
+            "files": [
+                "https://github.com/g37613182-tech/comfyui-auto-text-layout-v2"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node providing automatic text layout functionality. (Description by CC)"
+        },
+        {
+            "author": "gazingstars",
+            "title": "ComfyUI-CFGParallel",
+            "reference": "https://github.com/gazingstars123/ComfyUI-CFGParallel",
+            "files": [
+                "https://github.com/gazingstars123/ComfyUI-CFGParallel"
+            ],
+            "install_type": "git-clone",
+            "description": "Parallel CFG processing for ComfyUI using model replication across 2 GPUs"
+        },
+        {
+            "author": "gnt2001",
+            "title": "ComfyUI-GntsNodes",
+            "reference": "https://github.com/gnt2001/ComfyUI-GntsNodes",
+            "files": [
+                "https://github.com/gnt2001/ComfyUI-GntsNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI utility nodes for random string generation and image resizing operations. (Description by CC)"
+        },
+        {
+            "author": "hshheyman478-svg",
+            "title": "comfyui-sliding-rife",
+            "reference": "https://github.com/hshheyman478-svg/comfyui-sliding-rife",
+            "files": [
+                "https://github.com/hshheyman478-svg/comfyui-sliding-rife"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node for sliding window RIFE frame interpolation. (Description by CC)"
+        },
+        {
+            "author": "huihui-v",
+            "title": "ComfyUI-WanInfiniteTalkToVideo-offset",
+            "reference": "https://github.com/huihui-v/ComfyUI-WanInfiniteTalkToVideo-offset",
+            "files": [
+                "https://github.com/huihui-v/ComfyUI-WanInfiniteTalkToVideo-offset"
+            ],
+            "install_type": "git-clone",
+            "description": "WAN infinite talk-to-video node for ComfyUI. (Description by CC)"
+        },
+        {
+            "author": "Jalen-Brunson",
+            "title": "ComfyUI-PoseCleanFace",
+            "reference": "https://github.com/Jalen-Brunson/ComfyUI-PoseCleanFace",
+            "files": [
+                "https://github.com/Jalen-Brunson/ComfyUI-PoseCleanFace"
+            ],
+            "install_type": "git-clone",
+            "description": "Pose redrawing nodes for cleaning faces in ComfyUI workflows. (Description by CC)"
+        },
+        {
+            "author": "Jalen-Brunson",
+            "title": "ComfyUI-vace-step-schedule",
+            "reference": "https://github.com/Jalen-Brunson/ComfyUI-vace-step-schedule",
+            "files": [
+                "https://github.com/Jalen-Brunson/ComfyUI-vace-step-schedule"
+            ],
+            "install_type": "git-clone",
+            "description": "VACE step scheduling and latent manipulation nodes for ComfyUI video workflows. (Description by CC)"
+        },
+        {
+            "author": "jsbct",
+            "title": "ComfyUI-TextToBatchByDelimiter",
+            "reference": "https://github.com/jsbct/ComfyUI-TextToBatchByDelimiter",
+            "files": [
+                "https://github.com/jsbct/ComfyUI-TextToBatchByDelimiter"
+            ],
+            "install_type": "git-clone",
+            "description": "Text processing node that converts input text into batches using delimiter-based splitting. (Description by CC)"
+        },
+        {
+            "author": "lamianlbe",
+            "title": "ComfyUI-Mediapipe-FaceMesh",
+            "reference": "https://github.com/lamianlbe/ComfyUI-Mediapipe-FaceMesh",
+            "files": [
+                "https://github.com/lamianlbe/ComfyUI-Mediapipe-FaceMesh"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for face mesh detection using multiple methods (3DDFA_V2, FaceAlignment, InsightFace, Mediapipe). (Description by CC)"
+        },
+        {
+            "author": "lamianlbe",
+            "title": "ComfyUI-ReFace",
+            "reference": "https://github.com/lamianlbe/ComfyUI-ReFace",
+            "files": [
+                "https://github.com/lamianlbe/ComfyUI-ReFace"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node plugin for facial replacement operations. (Description by CC)"
+        },
+        {
+            "author": "lyeeedar",
+            "title": "Afnm Remove Background",
+            "reference": "https://github.com/Lyeeedar/comfyui-afnm-rmbg",
+            "files": [
+                "https://github.com/Lyeeedar/comfyui-afnm-rmbg"
+            ],
+            "install_type": "git-clone",
+            "description": "Background removal node for Afnm game asset generation using RMBG-2.0 / BiRefNet."
+        },
+        {
+            "author": "mavibirdesmi",
+            "title": "ComfyUI-CUDABuggy",
+            "reference": "https://github.com/mavibirdesmi/ComfyUI-CUDABuggy",
+            "files": [
+                "https://github.com/mavibirdesmi/ComfyUI-CUDABuggy"
+            ],
+            "install_type": "git-clone",
+            "description": "CUDA bug testing node for ComfyUI. (Description by CC)"
+        },
+        {
+            "author": "molbal",
+            "title": "ComfyUI-molbals-light-presets",
+            "reference": "https://github.com/molbal/ComfyUI-molbals-light-presets",
+            "files": [
+                "https://github.com/molbal/ComfyUI-molbals-light-presets"
+            ],
+            "install_type": "git-clone",
+            "description": "Prompt styling preset node for ComfyUI. (Description by CC)"
+        },
+        {
+            "author": "mozhaa",
+            "title": "ComfyUI-Danbooru-Random",
+            "reference": "https://github.com/mozhaa/ComfyUI-Danbooru-Random",
+            "files": [
+                "https://github.com/mozhaa/ComfyUI-Danbooru-Random"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for filtering and sampling images from Danbooru. (Description by CC)"
+        },
+        {
+            "author": "mozhaa",
+            "title": "ComfyUI-Mask-Interpolation",
+            "reference": "https://github.com/mozhaa/ComfyUI-Mask-Interpolation",
+            "files": [
+                "https://github.com/mozhaa/ComfyUI-Mask-Interpolation"
+            ],
+            "install_type": "git-clone",
+            "description": "Provides mask interpolation nodes using Gaussian mixture models for ComfyUI workflows. (Description by CC)"
+        },
+        {
+            "author": "n1ckfg",
+            "title": "ComfyUI-K2P",
+            "reference": "https://github.com/n1ckfg/ComfyUI-K2P",
+            "files": [
+                "https://github.com/n1ckfg/ComfyUI-K2P"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node for converting keypoints to pin coordinates. (Description by CC)"
+        },
+        {
+            "author": "namemechan",
+            "title": "comfyui_at_mix_node",
+            "reference": "https://github.com/namemechan/comfyui_at_mix_node",
+            "files": [
+                "https://github.com/namemechan/comfyui_at_mix_node"
+            ],
+            "install_type": "git-clone",
+            "description": "Attention mixing node for ComfyUI workflows. (Description by CC)"
+        },
+        {
+            "author": "Parsa-Sha",
+            "title": "ComfyUI-IP-Tools",
+            "reference": "https://github.com/Parsa-Sha/ComfyUI-IP-Tools",
+            "files": [
+                "https://github.com/Parsa-Sha/ComfyUI-IP-Tools"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for switching and concatenating image processing operations. (Description by CC)"
+        },
+        {
+            "author": "PauldeLavallaz",
+            "title": "comfyui-notebooklm",
+            "reference": "https://github.com/PauldeLavallaz/comfyui-notebooklm",
+            "files": [
+                "https://github.com/PauldeLavallaz/comfyui-notebooklm"
+            ],
+            "install_type": "git-clone",
+            "description": "NotebookLM script generator node for ComfyUI. (Description by CC)"
+        },
+        {
+            "author": "peterkickasspeter-civit",
+            "title": "ComfyUI-ZImageTurboProgressiveLockedUpscale",
+            "reference": "https://github.com/peterkickasspeter-civit/ComfyUI-ZImageTurboProgressiveLockedUpscale",
+            "files": [
+                "https://github.com/peterkickasspeter-civit/ComfyUI-ZImageTurboProgressiveLockedUpscale"
+            ],
+            "install_type": "git-clone",
+            "description": "Progressive locked upscale image processing node for ComfyUI. (Description by CC)"
+        },
+        {
+            "author": "pocotaru",
+            "title": "comfyui_poco_tools",
+            "reference": "https://github.com/pocotaru/comfyui_poco_tools",
+            "files": [
+                "https://github.com/pocotaru/comfyui_poco_tools"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI utility nodes including SamplerPipeIn, SamplerPipeOut, and VAENames for workflow management. (Description by CC)"
+        },
+        {
+            "author": "pollockjj",
+            "title": "JAXTools",
+            "reference": "https://github.com/pollockjj/ComfyUI-JAXTools",
+            "files": [
+                "https://github.com/pollockjj/ComfyUI-JAXTools"
+            ],
+            "install_type": "git-clone",
+            "description": "JAX-accelerated image processing nodes for ComfyUI via dm_pix sealed worker isolation"
+        },
+        {
+            "author": "ptmff",
+            "title": "ComfyUI-Nodes8734",
+            "reference": "https://github.com/ptmff/ComfyUI-Nodes8734",
+            "files": [
+                "https://github.com/ptmff/ComfyUI-Nodes8734"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom nodes providing MyFirstNode functionality. (Description by CC)"
+        },
+        {
+            "author": "rdanalex",
+            "title": "Comfyui-Realism-Scorer",
+            "reference": "https://github.com/rdanalex/Comfyui-Realism-Scorer",
+            "files": [
+                "https://github.com/rdanalex/Comfyui-Realism-Scorer"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for scoring image realism. (Description by CC)"
+        },
+        {
+            "author": "romandev-codex",
+            "title": "ComfyUI-DevTools",
+            "reference": "https://github.com/romandev-codex/ComfyUI-DevTools",
+            "files": [
+                "https://github.com/romandev-codex/ComfyUI-DevTools"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI development tools providing frame processing and passthrough nodes. (Description by CC)"
+        },
+        {
+            "author": "sintspiden",
+            "title": "comfyui-id-lora-gguf",
+            "reference": "https://github.com/sintspiden/comfyui-id-lora-gguf",
+            "files": [
+                "https://github.com/sintspiden/comfyui-id-lora-gguf"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for GGUF-backed ID-LoRA-2.3 audio+video generation with speaker identity transfer"
+        },
+        {
+            "author": "sy00186",
+            "title": "Comfyui-Dashboard",
+            "reference": "https://github.com/sy00186/Comfyui-Dashboard",
+            "files": [
+                "https://github.com/sy00186/Comfyui-Dashboard"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node providing global monitoring functionality. (Description by CC)"
+        },
+        {
+            "author": "vmxy",
+            "title": "comfyui-enhance-prompt",
+            "reference": "https://github.com/vmxy/comfyui-enhance-prompt",
+            "files": [
+                "https://github.com/vmxy/comfyui-enhance-prompt"
+            ],
+            "install_type": "git-clone",
+            "description": "Prompt enhancement node for ComfyUI workflows. (Description by CC)"
+        },
+        {
+            "author": "vovler",
+            "title": "comfyui_tensorrt2",
+            "reference": "https://github.com/vovler/ComfyUI_TensorRT2",
+            "files": [
+                "https://github.com/vovler/ComfyUI_TensorRT2"
+            ],
+            "install_type": "git-clone",
+            "description": "TensorRT Node for ComfyUI\\nThis node enables the best performance on NVIDIA RTX™ Graphics Cards (GPUs) for Stable Diffusion by leveraging NVIDIA TensorRT."
+        },
+        {
+            "author": "whenSunSet",
+            "title": "ComfyUI-FaceAnalysisNode",
+            "reference": "https://github.com/whenSunSet/ComfyUI-FaceAnalysisNode",
+            "files": [
+                "https://github.com/whenSunSet/ComfyUI-FaceAnalysisNode"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom node for ComfyUI providing face analysis functionality. (Description by CC)"
+        },
+        {
+            "author": "WindyCityDave",
+            "title": "ComfyUI-PeftLoraLoader",
+            "reference": "https://github.com/WindyCityDave/ComfyUI-PeftLoraLoader",
+            "files": [
+                "https://github.com/WindyCityDave/ComfyUI-PeftLoraLoader"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for loading PEFT LoRA models. (Description by CC)"
+        },
+        {
+            "author": "Fuhze73",
+            "title": "comfyui-painteri2vadvanced-tiledvae [WIP]",
+            "reference": "https://github.com/Fuhze73/ComfyUI-PainterI2VAdvanced-TiledVAE",
+            "files": [
+                "https://github.com/Fuhze73/ComfyUI-PainterI2VAdvanced-TiledVAE"
+            ],
+            "install_type": "git-clone",
+            "description": "Fusion of PainterI2VAdvanced (motion enhancement + color correction + dual-sampler) with Tiled VAE encoding and First/Last Frame support for Wan2.2 I2V workflows\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "elrandolo",
+            "title": "El Generator Klein [WIP]",
+            "reference": "https://github.com/Fuhze73/ComfyUI-ElGenerator-Klein",
+            "files": [
+                "https://github.com/Fuhze73/ComfyUI-ElGenerator-Klein"
+            ],
+            "install_type": "git-clone",
+            "description": "El Generator Klein v2 — 8-node character/scene editor for FLUX.2 Klein 9B. Natural language prose. Photographic realism. Amateur/candid/casual focus. Boolean switches. None defaults.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "batch-iterator",
+            "title": "Batch Iterator [WIP]",
+            "reference": "https://github.com/Latentnaut/ComfyUI-Batch-Iterator",
+            "files": [
+                "https://github.com/Latentnaut/ComfyUI-Batch-Iterator"
+            ],
+            "install_type": "git-clone",
+            "description": "Batch processing node for iterating base images with reference images in nested folder structures\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "nerdywhiskers",
+            "title": "ComfyUI-Whisker-Nodes [WIP]",
+            "reference": "https://github.com/nerdywhiskers/ComfyUI-Whisker-Nodes",
+            "files": [
+                "https://github.com/nerdywhiskers/ComfyUI-Whisker-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Image and mask compositing utilities for ComfyUI: background removal, sprite sheets, strip masks, ratio masks, cube grids, and image offsetting\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "well-made",
+            "title": "Wan-SVI2Pro-FLF [WIP]",
+            "reference": "https://github.com/Well-Made/ComfyUI-Wan-SVI2Pro-FLF",
+            "files": [
+                "https://github.com/Well-Made/ComfyUI-Wan-SVI2Pro-FLF"
+            ],
+            "install_type": "git-clone",
+            "description": "First/Last Frame control for SVI 2 PRO\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "zhoul1",
+            "title": "comfy-alpha_outline-magick [WIP]",
+            "reference": "https://github.com/zhoul1/comfy-alpha_outline-magick",
+            "files": [
+                "https://github.com/zhoul1/comfy-alpha_outline-magick"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom output node for ComfyUI that removes external background pixels from image corners using BFS flood fill, automatically closes single-pixel gaps in outlines, and saves results as transparent RGBA PNG files. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "ChenXi-Core",
+            "title": "custom_quad_picker [WIP]",
+            "reference": "https://github.com/ChenXi-Core/custom_quad_picker",
+            "files": [
+                "https://github.com/ChenXi-Core/custom_quad_picker"
+            ],
+            "install_type": "git-clone",
+            "description": "Quadrilateral picker node for ComfyUI. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "fedetanco22",
+            "title": "ComfyUI-AudioDuration-main [WIP]",
+            "reference": "https://github.com/fedetanco22/ComfyUI-AudioDuration-main",
+            "files": [
+                "https://github.com/fedetanco22/ComfyUI-AudioDuration-main"
+            ],
+            "install_type": "git-clone",
+            "description": "Audio duration analysis node. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "lunaaispace-eng",
+            "title": "LLM_Prompt [WIP]",
+            "reference": "https://github.com/lunaaispace-eng/LLM_Prompt",
+            "files": [
+                "https://github.com/lunaaispace-eng/LLM_Prompt"
+            ],
+            "install_type": "git-clone",
+            "description": "LLM prompt node for ComfyUI. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "lyeeedar",
+            "title": "Keep Largest Blob [WIP]",
+            "reference": "https://github.com/Lyeeedar/comfyui-keep-largest-blob",
+            "files": [
+                "https://github.com/Lyeeedar/comfyui-keep-largest-blob"
+            ],
+            "install_type": "git-clone",
+            "description": "Removes disconnected alpha regions from RGBA images, keeping only the largest contiguous blob. Useful for cleaning up background-removed game assets.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "outliner",
+            "title": "ComfyUI-QwenRemoveBackground [WIP]",
+            "reference": "https://github.com/outliner/ComfyUI-QwenRemoveBackground",
+            "files": [
+                "https://github.com/outliner/ComfyUI-QwenRemoveBackground"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI image editing node using Qwen for background removal from images. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "spyderweb47",
+            "title": "ComfyUI-Automation-Toolkit [WIP]",
+            "reference": "https://github.com/spyderweb47/ComfyUI-Automation-Toolkit",
+            "files": [
+                "https://github.com/spyderweb47/ComfyUI-Automation-Toolkit"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI plugin with node PromptCycler for automation. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "jaskirat05",
+            "title": "ComfyUI-MultiDepth [WIP]",
+            "reference": "https://github.com/jaskirat05/ComfyUI-MultiDepth",
+            "files": [
+                "https://github.com/jaskirat05/ComfyUI-MultiDepth"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node pack with three separate depth estimation nodes: Metric3D (model-zoo via huggingface), UniDepth v1/v2 (model-zoo), and Facebook DepthLM Official style (local pretrained).\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "2omegaXv",
+            "title": "comfyui_face_router [WIP]",
+            "reference": "https://github.com/2omegaXv/comfyui_face_router",
+            "files": [
+                "https://github.com/2omegaXv/comfyui_face_router"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node that routes detected face SEGS to separate outputs by matching each face crop to text prompts via CLIP image-text cosine similarity.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Agnuxo1",
+            "title": "silicon-comfyui-node [WIP]",
+            "reference": "https://github.com/Agnuxo1/silicon-comfyui-node",
+            "files": [
+                "https://github.com/Agnuxo1/silicon-comfyui-node"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node that embeds and verifies cryptographic watermarks in images using SiliconSignature technology.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Bigornh2o",
+            "title": "ComfyUI-Bigornh2oSmartResolution [WIP]",
+            "reference": "https://github.com/Bigornh2o/ComfyUI-Bigornh2oSmartResolution",
+            "files": [
+                "https://github.com/Bigornh2o/ComfyUI-Bigornh2oSmartResolution"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node that calculates width and height from a requested aspect ratio and megapixel count, but features an intelligent mathematical clamp to ensure the long side never exceeds a user-defined maximum safe value.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "ces",
+            "title": "ComfyUI-CES [WIP]",
+            "reference": "https://github.com/Clybius/ComfyUI-CES",
+            "files": [
+                "https://github.com/Clybius/ComfyUI-CES"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI Conditioning Enhancement Suite - Advanced conditioning manipulation nodes\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "craftopiastudio",
+            "title": "ComfyUI-WorkflowOrganizer [WIP]",
+            "reference": "https://github.com/CraftopiaStudio/ComfyUI-WorkflowOrganizer",
+            "files": [
+                "https://github.com/CraftopiaStudio/ComfyUI-WorkflowOrganizer"
+            ],
+            "install_type": "git-clone",
+            "description": "Drag-and-drop workflow organization for the ComfyUI sidebar\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "danmano",
+            "title": "ComfyUI-Danbooru-LLM [WIP]",
+            "reference": "https://github.com/danmano/ComfyUI-Danbooru-LLM",
+            "files": [
+                "https://github.com/danmano/ComfyUI-Danbooru-LLM"
+            ],
+            "install_type": "git-clone",
+            "description": "LLM + Live Danbooru API with 7 presets, Regional Prompting, AnimateDiff, Auto LoRA tools, Impact Pack integration (full V3)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "DrTimAI",
+            "title": "drtim_my_node_pack_playground [WIP]",
+            "reference": "https://github.com/DrTimAI/drtim_my_node_pack_playground",
+            "files": [
+                "https://github.com/DrTimAI/drtim_my_node_pack_playground"
+            ],
+            "install_type": "git-clone",
+            "description": "Node pack providing dropdown menus for selecting LLMs (Mistral/Openrouter API) and aspect ratios in ComfyUI workflows. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Egorkkk",
+            "title": "ComfyUI-WOFTSAM-PlanarTracker [WIP]",
+            "reference": "https://github.com/Egorkkk/ComfyUI-WOFTSAM-PlanarTracker",
+            "files": [
+                "https://github.com/Egorkkk/ComfyUI-WOFTSAM-PlanarTracker"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node that runs WOFTSAM planar tracking using externally provided per-frame masks and outputs tracked planar corners as JSON.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "gasdyueer",
+            "title": "comfyui-pctextencode-region-composer [WIP]",
+            "reference": "https://github.com/gasdyueer/comfyui-pctextencode-region-composer",
+            "files": [
+                "https://github.com/gasdyueer/comfyui-pctextencode-region-composer"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node providing visual region prompt editor for generating prompts compatible with ComfyUI Prompt Control syntax for area-based attention. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "GGBroX",
+            "title": "ComfyUI-Router [WIP]",
+            "reference": "https://github.com/GGBroX/ComfyUI-Router",
+            "files": [
+                "https://github.com/GGBroX/ComfyUI-Router"
+            ],
+            "install_type": "git-clone",
+            "description": "A small ComfyUI node suite for clean channel-based routing.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "ih455",
+            "title": "ComfyUI-Gemini3-Resolution-Snapper [WIP]",
+            "reference": "https://github.com/ih455/ComfyUI-Gemini3-Resolution-Snapper",
+            "files": [
+                "https://github.com/ih455/ComfyUI-Gemini3-Resolution-Snapper"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom node that automatically matches input image aspect ratios to safe standard resolutions optimized for the Nano Banana Pro model. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "InformedViewz",
+            "title": "ComfyUI-InformedViewz [WIP]",
+            "reference": "https://github.com/InformedViewz/ComfyUI-InformedViewz",
+            "files": [
+                "https://github.com/InformedViewz/ComfyUI-InformedViewz"
+            ],
+            "install_type": "git-clone",
+            "description": "Dropdown selector node for image dimension presets optimized for Flux and Qwen generation models. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "ivanprimestars",
+            "title": "ComfyUI_Brilliance_iOSPort [WIP]",
+            "reference": "https://github.com/ivanprimestars/ComfyUI_Brilliance_iOSPort",
+            "files": [
+                "https://github.com/ivanprimestars/ComfyUI_Brilliance_iOSPort"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom ComfyUI node that ports the provided ReShade shader into a ComfyUI image-processing node.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "ivanprimestars",
+            "title": "comfyui_iphone_look [WIP]",
+            "reference": "https://github.com/ivanprimestars/comfyui_iphone_look",
+            "files": [
+                "https://github.com/ivanprimestars/comfyui_iphone_look"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI node for iPhone-like photo postprocessing.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "JackZhaoYK",
+            "title": "ComfyUI-Shared [WIP]",
+            "reference": "https://github.com/JackZhaoYK/ComfyUI-Shared",
+            "files": [
+                "https://github.com/JackZhaoYK/ComfyUI-Shared"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI nodes for vehicle detection, image cropping/placement, and masked blending. Designed for car image processing pipelines.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "jeremy-strang",
+            "title": "Visual Model Merge [WIP]",
+            "reference": "https://github.com/jeremy-strang/ComfyUI-VisualModelMerge",
+            "files": [
+                "https://github.com/jeremy-strang/ComfyUI-VisualModelMerge"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node that provides an interactive curve editor for SDXL model block weight merging.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "KoaZhang",
+            "title": "comfyui_danbooru_tag_filter [WIP]",
+            "reference": "https://github.com/KoaZhang/comfyui_danbooru_tag_filter",
+            "files": [
+                "https://github.com/KoaZhang/comfyui_danbooru_tag_filter"
+            ],
+            "install_type": "git-clone",
+            "description": "Danbooru tag classification and filtering nodes for ComfyUI. Filter tagger output by category with built-in classification data covering ~35000 tags. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "kurtgamer01",
+            "title": "comfyui-aspect-to-preset [WIP]",
+            "reference": "https://github.com/kurtgamer01/comfyui-aspect-to-preset",
+            "files": [
+                "https://github.com/kurtgamer01/comfyui-aspect-to-preset"
+            ],
+            "install_type": "git-clone",
+            "description": "A lightweight utility node for ComfyUI that detects aspect ratio of input images and selects preset resolutions with multiplier support.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "MengYe-Z",
+            "title": "ComfyUI-MultiPointWhiteBalance [WIP]",
+            "reference": "https://github.com/MengYe-Z/ComfyUI-MultiPointWhiteBalance",
+            "files": [
+                "https://github.com/MengYe-Z/ComfyUI-MultiPointWhiteBalance"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node pack for e-commerce product photography with precise white balance and exposure correction using multi-point sampling.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Mervent",
+            "title": "comfyui-fast-lut [WIP]",
+            "reference": "https://github.com/Mervent/comfyui-fast-lut",
+            "files": [
+                "https://github.com/Mervent/comfyui-fast-lut"
+            ],
+            "install_type": "git-clone",
+            "description": "GPU-accelerated .cube LUT node for ComfyUI. Applies 3D and 1D LUTs using trilinear interpolation — no CPU round-trips, no NumPy in the hot path.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Merzmensch",
+            "title": "comfyui-stylegan2-explorer [WIP]",
+            "reference": "https://github.com/Merzmensch/comfyui-stylegan2-explorer",
+            "files": [
+                "https://github.com/Merzmensch/comfyui-stylegan2-explorer"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom nodes for ComfyUI that bring StyleGAN2 latent space exploration directly into your workflow graph.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "MONKEYFOREVER2",
+            "title": "comfyui-quantum-spectral-nodes [WIP]",
+            "reference": "https://github.com/MONKEYFOREVER2/comfyui-quantum-spectral-nodes",
+            "files": [
+                "https://github.com/MONKEYFOREVER2/comfyui-quantum-spectral-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Spectral Latent Enhance node for ComfyUI that applies frequency-domain post-processing to latents before VAE decode to improve image sharpness and quality.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "paragdhersarepaisewala",
+            "title": "Prompt-Cycler-Node---ComfyUI-Custom-Node [WIP]",
+            "reference": "https://github.com/paragdhersarepaisewala/Prompt-Cycler-Node---ComfyUI-Custom-Node",
+            "files": [
+                "https://github.com/paragdhersarepaisewala/Prompt-Cycler-Node---ComfyUI-Custom-Node"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node that cycles through multiline prompts with persistent seed state per node.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "pineambassador",
+            "title": "ComfyUI-ID-Lora-Pine [WIP]",
+            "reference": "https://github.com/pineambassador/ComfyUI-ID-Lora-Pine",
+            "files": [
+                "https://github.com/pineambassador/ComfyUI-ID-Lora-Pine"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI implementation of ID-LoRA: Identity-Driven Audio-Video Personalization with In-Context LoRA for audio-video personalization.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Sheldongg",
+            "title": "ComfyUI-RefineAnything [WIP]",
+            "reference": "https://github.com/Sheldongg/ComfyUI-RefineAnything",
+            "files": [
+                "https://github.com/Sheldongg/ComfyUI-RefineAnything"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for region-specific image refinement with Qwen Image Edit 2511 and the RefineAnything LoRA.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "SUPi5460",
+            "title": "comfyui-smartprompt [WIP]",
+            "reference": "https://github.com/SUPi5460/ComfyUI-SmartPrompt",
+            "files": [
+                "https://github.com/SUPi5460/ComfyUI-SmartPrompt"
+            ],
+            "install_type": "git-clone",
+            "description": "Smart prompt generation nodes for ComfyUI - theme-based tags, pose selection, and BREAK builder\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "sy00186",
+            "title": "Comfyui-Vates-vae [WIP]",
+            "reference": "https://github.com/sy00186/Comfyui-Vates-vae",
+            "files": [
+                "https://github.com/sy00186/Comfyui-Vates-vae"
+            ],
+            "install_type": "git-clone",
+            "description": "Vates distributed VAE decoder for ComfyUI with Rust-based pixel pooling, parallel encoding via Rayon, and multi-format export support. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Toraong",
+            "title": "ComfyUI-Latent-Resizer-Tools [WIP]",
+            "reference": "https://github.com/Toraong/ComfyUI-Latent-Resizer-Tools",
+            "files": [
+                "https://github.com/Toraong/ComfyUI-Latent-Resizer-Tools"
+            ],
+            "install_type": "git-clone",
+            "description": "Resizes latent images to a 1M megapixel base and rounds dimensions to the nearest multiple of 64.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "vanillasoap",
+            "title": "ComfyUI-Seamless-Texture [WIP]",
+            "reference": "https://github.com/vanillasoap/ComfyUI-Seamless-Texture",
+            "files": [
+                "https://github.com/vanillasoap/ComfyUI-Seamless-Texture"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom nodes for ComfyUI that handle texture delighting (removing baked lighting/shadows) and seamless tiling for production-ready PBR textures.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "wikinikiwings",
+            "title": "ComfyUI-simple_image_nodes [WIP]",
+            "reference": "https://github.com/wikinikiwings/ComfyUI-simple_image_nodes",
+            "files": [
+                "https://github.com/wikinikiwings/ComfyUI-simple_image_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Simple Image Accumulator - accumulates images and delivers them as a sheet.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "wulalalaaaaa",
+            "title": "Comfyui_Consistency_xiling [WIP]",
+            "reference": "https://github.com/wulalalaaaaa/Comfyui_Consistency_xiling",
+            "files": [
+                "https://github.com/wulalalaaaaa/Comfyui_Consistency_xiling"
+            ],
+            "install_type": "git-clone",
+            "description": "A framework-first ComfyUI custom node pack for character and style consistency, featuring character lock, style lock, and combined consistency with iterative enhancement via AnimeDiff and character consistency methods. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "yipeeka",
+            "title": "ComfyUI-ACE-Step [WIP]",
+            "reference": "https://github.com/yipeeka/ComfyUI-ACE-Step",
+            "files": [
+                "https://github.com/yipeeka/ComfyUI-ACE-Step"
+            ],
+            "install_type": "git-clone",
+            "description": "Extends the built-in ACE-Step 1.5 nodes with Repaint / Extend, Cover (Style Transfer), Multi-Region Repaint, Extract BGM (WIP), and Lego / Retarget (WIP) modes — without modifying any ComfyUI core files.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Ynead",
+            "title": "ksampler_config [WIP]",
+            "reference": "https://github.com/Ynead/ksampler_config",
+            "files": [
+                "https://github.com/Ynead/ksampler_config"
+            ],
+            "install_type": "git-clone",
+            "description": "Centralized configuration node for ComfyUI that sets sampler parameters once and feeds them to every KSampler in the workflow, designed for switching between model types. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Ynead",
+            "title": "xy_ksampler_plot [WIP]",
+            "reference": "https://github.com/Ynead/xy_ksampler_plot",
+            "files": [
+                "https://github.com/Ynead/xy_ksampler_plot"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI node that generates full XY grids of images by independently varying parameters like LoRA selection, seed, LoRA weight, CFG, or steps.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "IsacEkeroth",
+            "title": "comfyui-ffmpeg-audiospeed [NAME CONFLICT]",
+            "reference": "https://github.com/IsacEkeroth/ComfyUI-Audio-Speed",
+            "files": [
+                "https://github.com/IsacEkeroth/ComfyUI-Audio-Speed"
+            ],
+            "install_type": "git-clone",
+            "description": "Standalone ComfyUI AudioSpeed node using FFmpeg atempo"
+        },
+        {
+            "author": "cnoellert",
+            "title": "ComfyUI CorridorKey [NAME CONFLICT]",
+            "reference": "https://github.com/cnoellert/comfyui-corridorkey",
+            "files": [
+                "https://github.com/cnoellert/comfyui-corridorkey"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for CorridorKey neural green screen keying"
+        },
+        {
+            "author": "DanielBartolic",
+            "title": "comfyui-custom-nodes [NAME CONFLICT]",
+            "reference": "https://github.com/DanielBartolic/comfyui-custom-nodes",
+            "files": [
+                "https://github.com/DanielBartolic/comfyui-custom-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Collection of useful custom nodes for ComfyUI including Save Image with Caption node for saving images and prompts."
+        },
+        {
+            "author": "wxytjustb",
+            "title": "ComfyUI-CustomNodes [NAME CONFLICT]",
+            "reference": "https://github.com/wxytjustb/ComfyUI-CustomNodes",
+            "files": [
+                "https://github.com/wxytjustb/ComfyUI-CustomNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI AI node system supporting multiple API providers including Google Gemini, OpenAI, OpenRouter, and VectorEngine with configuration-driven provider and model management."
+        },
+        {
+            "author": "romandev-codex",
+            "title": "ComfyUI-Downloader [NAME CONFLICT]",
+            "reference": "https://github.com/romandev-codex/ComfyUI-Downloader",
+            "files": [
+                "https://github.com/romandev-codex/ComfyUI-Downloader"
+            ],
+            "install_type": "git-clone",
+            "description": "A powerful extension for ComfyUI that adds a built-in model downloader with modal interface, parallel downloading, and security features for downloading models, LoRAs, and VAEs."
+        },
+        {
+            "author": "Latentnaut",
+            "title": "ComfyUI-Image-Selector [NAME CONFLICT/WIP]",
+            "reference": "https://github.com/Latentnaut/ComfyUI-Image-Selector",
+            "files": [
+                "https://github.com/Latentnaut/ComfyUI-Image-Selector"
+            ],
+            "install_type": "git-clone",
+            "description": "Image selector node for ComfyUI.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "saganaki22",
+            "title": "Kitten-TTS [NAME CONFLICT]",
+            "reference": "https://github.com/Saganaki22/ComfyUI-KittenTTS",
+            "files": [
+                "https://github.com/Saganaki22/ComfyUI-KittenTTS"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for KittenTTS - Ultra-lightweight text-to-speech (CUDA / CPU)"
+        },
+        {
+            "author": "opj161",
+            "title": "comfyui-comfyui-lmstudio [NAME CONFLICT]",
+            "reference": "https://github.com/opj161/comfyui-lmstudio",
+            "files": [
+                "https://github.com/opj161/comfyui-lmstudio"
+            ],
+            "install_type": "git-clone",
+            "description": "These custom nodes acts as a bidirectional bridge between ComfyUI's synchronous/tensor-based ecosystem and LM Studio's asynchronous/text-based inference engine."
+        },
+        {
+            "author": "AJbeckliy",
+            "title": "ComfyUI-MatAnyone2 [NAME CONFLICT]",
+            "reference": "https://github.com/AJbeckliy/ComfyUI-MatAnyone2",
+            "files": [
+                "https://github.com/AJbeckliy/ComfyUI-MatAnyone2"
+            ],
+            "install_type": "git-clone",
+            "description": "MatAnyone2 node pack for video matting in ComfyUI providing end-to-end workflow for video processing with transparency export options."
+        },
+        {
+            "author": "starsfriday",
+            "title": "ComfyUI-MatAnyone2 [NAME CONFLICT]",
+            "reference": "https://github.com/starsFriday/ComfyUI-MatAnyone2",
+            "files": [
+                "https://github.com/starsFriday/ComfyUI-MatAnyone2"
+            ],
+            "install_type": "git-clone",
+            "description": "MatAnyone2's method for segmenting the characters in the video"
+        },
+        {
+            "author": "local",
+            "title": "ComfyUI Downloader [NAME CONFLICT]",
+            "reference": "https://github.com/TomBelfast/ComfyUI-Model-Downloader",
+            "files": [
+                "https://github.com/TomBelfast/ComfyUI-Model-Downloader"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI extension that finds missing workflow models and installs them into the right folders."
+        },
+        {
+            "author": "Ckrest",
+            "title": "comfyui-ollama-nodes [NAME CONFLICT]",
+            "reference": "https://github.com/Ckrest/comfyui-ollama-nodes",
+            "files": [
+                "https://github.com/Ckrest/comfyui-ollama-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Local LLM integration for ComfyUI via Ollama with full parameter control"
+        },
+        {
+            "author": "eokoo",
+            "title": "ComfyUI-Photopea [NAME CONFLICT]",
+            "reference": "https://github.com/eokoo/ComfyUI-Photopea",
+            "files": [
+                "https://github.com/eokoo/ComfyUI-Photopea"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node interface for editing images directly using embedded online Photopea, allowing real-time image modifications within workflows."
+        },
+        {
+            "author": "gzsiang",
+            "title": "[LOCALE ISSUE] ComfyUI-VolcEngine",
+            "id": "comfyui-volcengine",
+            "reference": "https://github.com/gzsiang/ComfyUI-VolcEngine",
+            "files": [
+                "https://github.com/gzsiang/ComfyUI-VolcEngine"
+            ],
+            "install_type": "git-clone",
+            "description": "VolcEngine (火山引擎) video and image generation nodes. Supports Seedance (text-to-video, image-to-video with audio reference) and Seedream (text-to-image, image-to-image) models via Ark API."
+        },
+        {
+            "author": "ketle-man",
+            "title": "[LOCALE ISSUE] ComfyUI Image Feeder",
+            "id": "comfyui-image-feeder",
+            "reference": "https://github.com/ketle-man/comfyui-image-feeder",
+            "files": [
+                "https://github.com/ketle-man/comfyui-image-feeder"
+            ],
+            "install_type": "git-clone",
+            "description": "A node for looping through images in a folder with a visual 3-pane library UI, preset support, and i18n (EN/JA/ZH)."
+        },
+        {
+            "author": "ethanfel",
+            "title": "Comfyui-Return-Run-Logic",
+            "reference": "https://github.com/ethanfel/Comfyui-Return-Run-Logic",
+            "files": [
+                "https://github.com/ethanfel/Comfyui-Return-Run-Logic"
+            ],
+            "install_type": "git-clone",
+            "description": "Adds a toggle button to quickly switch between Run and Run (Instant) modes without opening the queue button dropdown menu."
+        },
+        {
+            "author": "outrun32",
+            "title": "comfyui-chat-assistant [WIP]",
+            "reference": "https://github.com/outrun32/comfyui-chat-assistant",
+            "files": [
+                "https://github.com/outrun32/comfyui-chat-assistant"
+            ],
+            "install_type": "git-clone",
+            "description": "A streamlined ComfyUI extension that adds an AI-powered chat interface to the sidebar for converting complex briefs/specs into generation-ready prompts. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "ComfyuiGY",
+            "title": "Comfyui-Memory-Clear",
+            "reference": "https://github.com/ComfyuiGY/Comfyui-Memory-Clear",
+            "files": [
+                "https://github.com/ComfyuiGY/Comfyui-Memory-Clear"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node for clearing memory. (Description by CC)"
+        },
+        {
+            "author": "lying1324a-glitch",
+            "title": "comfyuiautofirelaod",
+            "reference": "https://github.com/lying1324a-glitch/comfyuiautofirelaod",
+            "files": [
+                "https://github.com/lying1324a-glitch/comfyuiautofirelaod"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI auto-fire loader extension. (Description by CC)"
+        },
+        {
+            "author": "skkut",
+            "title": "ComfyUI-detailed-jobstatus [WIP]",
+            "reference": "https://github.com/skkut/ComfyUI-detailed-jobstatus",
+            "files": [
+                "https://github.com/skkut/ComfyUI-detailed-jobstatus"
+            ],
+            "install_type": "git-clone",
+            "description": "A simple ComfyUI extension to show a execution timer.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "bob-bar-b-que",
+            "title": "ComfyUI CLIP Token Counter [WIP]",
+            "reference": "https://github.com/Bob-Bar-B-Que/ComfyUI_clip_token_counter",
+            "files": [
+                "https://github.com/Bob-Bar-B-Que/ComfyUI_clip_token_counter"
+            ],
+            "install_type": "git-clone",
+            "description": "Shows real-time token count and adds a copy button to CLIPTextEncode nodes in ComfyUI.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Lisa-Mays",
+            "title": "wan-model-downloader",
+            "reference": "https://github.com/Lisa-Mays/wan-model-downloader",
+            "files": [
+                "https://github.com/Lisa-Mays/wan-model-downloader"
+            ],
+            "install_type": "git-clone",
+            "description": "Auto-downloads required models on startup."
+        },
+        {
+            "author": "fengye12",
+            "title": "ComfyUI_XL_OpenAPI",
+            "reference": "https://github.com/fengye12/ComfyUI_XL_OpenAPI",
+            "files": [
+                "https://github.com/fengye12/ComfyUI_XL_OpenAPI"
+            ],
+            "install_type": "git-clone",
+            "description": "OpenAPI wrapper for ComfyUI XL, providing standardized API access. (Description by CC)"
+        },
+        {
+            "author": "scruffynerf",
+            "title": "_0_comfyui_transformers_fix",
+            "reference": "https://github.com/scruffynerf/_0_comfyui_transformers_fix",
+            "files": [
+                "https://github.com/scruffynerf/_0_comfyui_transformers_fix"
+            ],
+            "install_type": "git-clone",
+            "description": "Quick patch that solves compatibility problems caused by MeshGraphormer-DepthMapPreprocessor by injecting missing functions into transformers."
+        },
+        {
+            "author": "pixelmavenai",
+            "title": "comfyui-custom-branding",
+            "reference": "https://github.com/pixelmavenai/comfyui-custom-branding",
+            "files": [
+                "https://github.com/pixelmavenai/comfyui-custom-branding"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom branding for ComfyUI tab title and favicon"
+        },
+        {
+            "author": "ka4ok424",
+            "title": "ComfyUI-NodeLibrary",
+            "reference": "https://github.com/ka4ok424/ComfyUI-NodeLibrary",
+            "files": [
+                "https://github.com/ka4ok424/ComfyUI-NodeLibrary"
+            ],
+            "install_type": "git-clone",
+            "description": "No ComfyUI nodes detected. May not be a ComfyUI custom node repository."
+        },
+        {
+            "author": "skkut",
+            "title": "ComfyUI-Auto-DarkMode [WIP]",
+            "reference": "https://github.com/skkut/ComfyUI-Auto-DarkMode",
+            "files": [
+                "https://github.com/skkut/ComfyUI-Auto-DarkMode"
+            ],
+            "install_type": "git-clone",
+            "description": "Lightweight ComfyUI extension that automatically detects and matches your system's Light/Dark theme in real-time.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "xWris3",
+            "title": "jus_multifruit_comfyui",
+            "reference": "https://github.com/xWris3/jus_multifruit_comfyui",
+            "files": [
+                "https://github.com/xWris3/jus_multifruit_comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "Collection of custom nodes."
+        },
+        {
+            "author": "synthetai",
+            "title": "ComfyUI-ToolBox [NAME CONFLICT]",
+            "reference": "https://github.com/synthetai/ComfyUI-ToolBox",
+            "files": [
+                "https://github.com/synthetai/ComfyUI-ToolBox"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of utility nodes for ComfyUI, including audio/video processing, file uploads, and AI image generation."
+        },
+        {
+            "author": "davidjshin1",
+            "title": "ComfyUI-NanobananaPro [NAME CONFLICT]",
+            "reference": "https://github.com/davidjshin1/ComfyUI-NanobananaPro",
+            "files": [
+                "https://github.com/davidjshin1/ComfyUI-NanobananaPro"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node providing NanoBananaPro image generation functionality, likely a variant or extension of the Nano Banana Gemini-based image generation nodes. (Description by CC)"
+        },
+        {
+            "author": "suburban123",
+            "title": "ComfyUI-DAP [NAME CONFLICT]",
+            "reference": "https://github.com/suburban123/ComfyUI-DAP",
+            "files": [
+                "https://github.com/suburban123/ComfyUI-DAP"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom nodes for depth estimation using the DAP (Depth Anything Plus) model, with standard and advanced estimation modes and a dedicated model loader. (Description by CC)"
+        },
+        {
+            "author": "thomashollier",
+            "title": "comfyUI-DAP [UNSAFE/NAME CONFLICT]",
+            "reference": "https://github.com/thomashollier/comfyUI-DAP",
+            "files": [
+                "https://github.com/thomashollier/comfyUI-DAP"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for DAP (Depth Any Panoramas), DA-2 (Depth Anything in Any Direction), and PanDA (Panoramic Depth Anything) panoramic depth estimation[w/This nodepack has a path traversal vulnerability]"
+        },
+        {
+            "author": "visualbruno",
+            "title": "ComfyUI-Tools [UNSAFE/NAME CONFLICT]",
+            "reference": "https://github.com/visualbruno/ComfyUI-Tools",
+            "files": [
+                "https://github.com/visualbruno/ComfyUI-Tools"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node providing tools for FBX to SMPL renaming operations. (Description by CC)[w/This nodepack has a path traversal vulnerability]"
+        },
+        {
+            "author": "DarioFT",
+            "title": "ComfyUI-Qwen3-TTS [UNSAFE/NAME CONFLICT]",
+            "reference": "https://github.com/DarioFT/ComfyUI-Qwen3-TTS",
+            "files": [
+                "https://github.com/DarioFT/ComfyUI-Qwen3-TTS"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node suite for Qwen3-TTS, supporting 1.7B and 0.6B models, Custom Voice, Voice Design, Voice Cloning and Fine-Tuning.[w/This nodepack has a path traversal vulnerability]"
+        },
+        {
+            "author": "ai-joe-git",
+            "title": "ComfyUI-Qwen3-TTS  [UNSAFE/NAME CONFLICT]",
+            "reference": "https://github.com/ai-joe-git/ComfyUI-Qwen3-TTS",
+            "files": [
+                "https://github.com/ai-joe-git/ComfyUI-Qwen3-TTS"
+            ],
+            "install_type": "git-clone",
+            "description": "High-quality Text-to-Speech nodes for ComfyUI using Qwen3-TTS models. Supports voice cloning, voice design, and custom voices with Intel Arc XPU optimization.\nNOTE: The files in the repo are not organized.[w/This nodepack has a path traversal vulnerability]"
+        },
+        {
+            "author": "amenoyoya",
+            "title": "ComfyUI-Qwen3-TTS [NAME CONFLICT]",
+            "reference": "https://github.com/amenoyoya/ComfyUI-Qwen3-TTS",
+            "files": [
+                "https://github.com/amenoyoya/ComfyUI-Qwen3-TTS"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node package for Qwen3-TTS, providing high-quality Text-to-Speech (TTS) with Custom Voice and Voice Cloning capabilities."
+        },
+        {
+            "author": "NaomiVK",
+            "title": "comfyui-qwen3-tts [NAME CONFLICT]",
+            "reference": "https://github.com/NaomiVK/comfyui-qwen3-tts",
+            "files": [
+                "https://github.com/NaomiVK/comfyui-qwen3-tts"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI nodes integrating Qwen3-TTS for voice cloning, voice design from natural language descriptions, preset speakers, and multilingual speech synthesis. (Description by CC)"
+        },
+        {
+            "author": "Meisoftcoltd",
+            "title": "ComfyUI-UVR5 [NAME CONFLICT]",
+            "reference": "https://github.com/Meisoftcoltd/ComfyUI-UVR5",
+            "files": [
+                "https://github.com/Meisoftcoltd/ComfyUI-UVR5"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom nodes integrating UVR5 (Ultimate Vocal Remover) for audio/video extraction and MDX-Net-based vocal separation, supporting batch processing and GPU-accelerated audio quality. (Description by CC)"
+        },
+        {
+            "author": "mcaishao123",
+            "title": "ComfyUI-lut [UNSAFE/NAME CONFLICT]",
+            "reference": "https://github.com/mcaishao123/ComfyUI-lut",
+            "files": [
+                "https://github.com/mcaishao123/ComfyUI-lut"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for loading and applying Adobe Lightroom .lrtemplate presets as image filters. Supports exposure, contrast, tone curves, HSL, split toning, vignette, grain, and more.[w/This nodepack has a path traversal vulnerability]"
+        },
+        {
+            "author": "iloveuav",
+            "title": "ComfyUI-AudioTools [UNSAFE/NAME CONFLICT]",
+            "reference": "https://github.com/iloveuav/ComfyUI-AudioTools",
+            "files": [
+                "https://github.com/iloveuav/ComfyUI-AudioTools"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI audio extension providing HTTP upload/preview endpoints and nodes for reading/writing audio in workflows, enabling automation pipelines like N8N to integrate audio into ComfyUI. (Description by CC)\nNOTE: The files in the repo are not organized.[w/This nodepack has a path traversal vulnerability]"
+        },
+        {
+            "author": "benjiyaya",
+            "title": "ComfyUI-Spectrum [WIP/NAME CONFLICT]",
+            "reference": "https://github.com/benjiyaya/ComfyUI-Spectrum",
+            "files": [
+                "https://github.com/benjiyaya/ComfyUI-Spectrum"
+            ],
+            "install_type": "git-clone",
+            "description": "Training-free diffusion sampling acceleration by forecasting denoiser outputs with Chebyshev polynomials and ridge regression, works with any ComfyUI sampler and backbone.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "jinxishe",
+            "title": "ComfyUI-AudioX [NAME CONFLICT]",
+            "reference": "https://github.com/jinxishe/ComfyUI-AudioX",
+            "files": [
+                "https://github.com/jinxishe/ComfyUI-AudioX"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom nodes for AudioX/AudioX-MAF video-to-audio generation"
+        },
+        {
+            "author": "Ahmed791996",
+            "title": "SplatViwer_comfyUI",
+            "reference": "https://github.com/Ahmed791996/SplatViwer_comfyUI",
+            "files": [
+                "https://github.com/Ahmed791996/SplatViwer_comfyUI"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI plugin for Gaussian splatting visualization. (Description by CC)"
+        },
+        {
+            "author": "Apache0ne",
+            "title": "ComfyUI-SaveLoadConditioning",
+            "reference": "https://github.com/Apache0ne/ComfyUI-SaveLoadConditioning",
+            "files": [
+                "https://github.com/Apache0ne/ComfyUI-SaveLoadConditioning"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom nodes for saving and loading conditioning data. (Description by CC)"
+        },
+        {
+            "author": "Krish-701",
+            "title": "Comfyui_nodes_v01",
+            "reference": "https://github.com/Krish-701/Comfyui_nodes_v01",
+            "files": [
+                "https://github.com/Krish-701/Comfyui_nodes_v01"
+            ],
+            "install_type": "git-clone",
+            "description": "Provides RKMultiImage custom nodes for ComfyUI. (Description by CC)"
+        },
+        {
+            "author": "Momediada97",
+            "title": "ComfyUI-ImageIfNotBlack [WIP]",
+            "reference": "https://github.com/Momediada97/ComfyUI-ImageIfNotBlack",
+            "files": [
+                "https://github.com/Momediada97/ComfyUI-ImageIfNotBlack"
+            ],
+            "install_type": "git-clone",
+            "description": "A lightweight custom node for ComfyUI that detects whether an input image is completely black.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "PauldeLavallaz",
+            "title": "comfyui-fabric-lipsync",
+            "reference": "https://github.com/PauldeLavallaz/comfyui-fabric-lipsync",
+            "files": [
+                "https://github.com/PauldeLavallaz/comfyui-fabric-lipsync"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for lip-sync and text-to-video generation. (Description by CC)"
+        },
+        {
+            "author": "StartHua",
+            "title": "Comfyui_yolo26 [WIP]",
+            "reference": "https://github.com/StartHua/Comfyui_yolo26",
+            "files": [
+                "https://github.com/StartHua/Comfyui_yolo26"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for local YOLO object detection and pose estimation with mask generation and customizable body part selection.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Veritas-bit",
+            "title": "ComfyUI-StyleSelector [WIP]",
+            "reference": "https://github.com/Veritas-bit/ComfyUI-StyleSelector",
+            "files": [
+                "https://github.com/Veritas-bit/ComfyUI-StyleSelector"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for style selection with styles folder configuration. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "ethanfel",
+            "title": "ComfyUI-CFG-CTRL [WIP]",
+            "reference": "https://github.com/ethanfel/ComfyUI-CFG-CTRL",
+            "files": [
+                "https://github.com/ethanfel/ComfyUI-CFG-CTRL"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI node implementing SMC-CFG (Sliding Mode Control CFG) that replaces standard linear CFG with a nonlinear sliding mode controller for stable guidance at any CFG scale. NOT WORKING - Work in progress. (Description by CC)"
+        },
+        {
+            "author": "unobtuse",
+            "title": "comfyui-topaz-ai-upscale",
+            "reference": "https://github.com/unobtuse/comfyui-topaz-ai-upscale",
+            "files": [
+                "https://github.com/unobtuse/comfyui-topaz-ai-upscale"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom nodes for upscaling images using Topaz Photo AI's Autopilot engine via the CLI."
+        },
+        {
+            "author": "unobtuse",
+            "title": "comfyui-topaz-gigapixel",
+            "reference": "https://github.com/unobtuse/comfyui-topaz-gigapixel",
+            "files": [
+                "https://github.com/unobtuse/comfyui-topaz-gigapixel"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom nodes for upscaling images using Topaz Gigapixel AI's CLI."
+        },
+        {
+            "author": "mozhaa",
+            "title": "ComfyUI-Latent-Transform",
+            "reference": "https://github.com/mozhaa/ComfyUI-Latent-Transform",
+            "files": [
+                "https://github.com/mozhaa/ComfyUI-Latent-Transform"
+            ],
+            "install_type": "git-clone",
+            "description": "Latent space transformation nodes providing mathematical operations like add, blend, blur, gaussian noise, multiply, sharpen, and wave effects. (Description by CC)"
+        },
+        {
+            "author": "Lumiyumi",
+            "title": "comfyui_stringcombineX",
+            "reference": "https://github.com/Lumiyumi/comfyui_stringcombineX",
+            "files": [
+                "https://github.com/Lumiyumi/comfyui_stringcombineX"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for string combination with extensible class structure for custom variations. (Description by CC)"
+        },
+        {
+            "author": "ayylmao1773",
+            "title": "ComfyUI-switch-aspect",
+            "reference": "https://github.com/ayylmao1773/ComfyUI-switch-aspect",
+            "files": [
+                "https://github.com/ayylmao1773/ComfyUI-switch-aspect"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node for automatic portrait/landscape aspect ratio switching based on input dimensions."
+        },
+        {
+            "author": "tester4488",
+            "title": "mc_audio",
+            "reference": "https://github.com/tester4488/mc_audio",
+            "files": [
+                "https://github.com/tester4488/mc_audio"
+            ],
+            "install_type": "git-clone",
+            "description": "mc audio info"
+        },
+        {
+            "author": "LiJT",
+            "title": "ComfyUI-JT_Tools",
+            "reference": "https://github.com/LiJT/ComfyUI-JT_Tools",
+            "files": [
+                "https://github.com/LiJT/ComfyUI-JT_Tools"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI-JT_Tools custom nodes."
+        },
+        {
+            "author": "ThanaritKanjanametawatAU",
+            "title": "comfyui_runninghub_nanobanana",
+            "reference": "https://github.com/ThanaritKanjanametawatAU/comfyui_runninghub_nanobanana",
+            "files": [
+                "https://github.com/ThanaritKanjanametawatAU/comfyui_runninghub_nanobanana"
+            ],
+            "install_type": "git-clone",
+            "description": "RunningHub Nano Banana Pro API node for ComfyUI"
+        },
+        {
+            "author": "Echoflare",
+            "title": "ComfyUI-Reverse-Proxy-Fix",
+            "reference": "https://github.com/Echoflare/ComfyUI-Reverse-Proxy-Fix",
+            "files": [
+                "https://github.com/Echoflare/ComfyUI-Reverse-Proxy-Fix"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node for fixing the \"405 Method Not Allowed\" issue on certain endpoints when reverse proxying ComfyUI."
+        },
+        {
+            "author": "Ronnasayd",
+            "title": "comfyui-nodes",
+            "reference": "https://github.com/Ronnasayd/comfyui-nodes",
+            "files": [
+                "https://github.com/Ronnasayd/comfyui-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of custom nodes for ComfyUI"
+        },
+        {
+            "author": "vjumpkung",
+            "title": "comfyui-mosaic-blur [NAME CONFLICT]",
+            "reference": "https://github.com/vjumpkung/comfyui-mosaic-blur",
+            "files": [
+                "https://github.com/vjumpkung/comfyui-mosaic-blur"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for applying mosaic blur effects. (Description by CC)"
+        },
+        {
+            "author": "danieljanata",
+            "title": "ComfyUI-phone_compression_ultimate [WIP]",
+            "reference": "https://github.com/danieljanata/ComfyUI-phone_compression_ultimate",
+            "files": [
+                "https://github.com/danieljanata/ComfyUI-phone_compression_ultimate"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node that simulates realistic smartphone camera compression and processing artifacts.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Norsnow",
+            "title": "comfyui-image-ratio-preset [WIP]",
+            "reference": "https://github.com/Norsnow/comfyui-image-ratio-preset",
+            "files": [
+                "https://github.com/Norsnow/comfyui-image-ratio-preset"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node for quickly setting image aspect ratios with presets and custom dimensions, directly generating latent tensors.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "svyatojdismas",
+            "title": "ComfyUI-StDismas [WIP]",
+            "reference": "https://github.com/svyatojdismas/ComfyUI-StDismas",
+            "files": [
+                "https://github.com/svyatojdismas/ComfyUI-StDismas"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom nodes for ComfyUI. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "OtwakO",
+            "title": "ComfyUI-UsefulTools",
+            "reference": "https://github.com/OtwakO/ComfyUI-UsefulTools",
+            "files": [
+                "https://github.com/OtwakO/ComfyUI-UsefulTools"
+            ],
+            "install_type": "git-clone",
+            "description": "Extract tags from your original prompts by giving it a substring or an array of substrings."
+        },
+        {
+            "author": "vito0131",
+            "title": "ComfyUI_SmartContentCrop [WIP]",
+            "reference": "https://github.com/vito0131/ComfyUI_SmartContentCrop",
+            "files": [
+                "https://github.com/vito0131/ComfyUI_SmartContentCrop"
+            ],
+            "install_type": "git-clone",
+            "description": "Smart content cropping with circle processing capabilities. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Vollond",
+            "title": "ComfyUI-LatentCutPlus",
+            "reference": "https://github.com/Vollond/ComfyUI-LatentCutPlus",
+            "files": [
+                "https://github.com/Vollond/ComfyUI-LatentCutPlus"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node: LatentCutPlus (slice latents with amount=-1 to end)"
+        },
+        {
+            "author": "gaonprime-labs",
+            "title": "nordy-custom-node-ComfyUI-NormalOverlapBandBlend",
+            "reference": "https://github.com/gaonprime-labs/nordy-custom-node-ComfyUI-NormalOverlapBandBlend",
+            "files": [
+                "https://github.com/gaonprime-labs/nordy-custom-node-ComfyUI-NormalOverlapBandBlend"
+            ],
+            "install_type": "git-clone",
+            "description": "Normal map blending node for overlapping band effects in ComfyUI. (Description by CC)"
+        },
+        {
+            "author": "muriellee1x",
+            "title": "ComfyUI-Mysterious-node3",
+            "reference": "https://github.com/muriellee1x/ComfyUI-Mysterious-node3",
+            "files": [
+                "https://github.com/muriellee1x/ComfyUI-Mysterious-node3"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom node VideoTimeRemapSpeedPresets for ComfyUI. (Description by CC)"
+        },
+        {
+            "author": "muriellee1x",
+            "title": "ComfyUI-Mysterious-node2 [WIP]",
+            "reference": "https://github.com/muriellee1x/ComfyUI-Mysterious-node2",
+            "files": [
+                "https://github.com/muriellee1x/ComfyUI-Mysterious-node2"
+            ],
+            "install_type": "git-clone",
+            "description": "Professional green/blue screen chroma keying nodes inspired by After Effects Keylight plugin, supporting automatic/manual key color detection and advanced spillage suppression. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Mexes1978",
+            "title": "Film Photography Prompt Styler [WIP]",
+            "reference": "https://github.com/Mexes1978/film_prompt_styler",
+            "files": [
+                "https://github.com/Mexes1978/film_prompt_styler"
+            ],
+            "install_type": "git-clone",
+            "description": "Photography history education tool node with 8 core categories including film stocks, lenses, lighting, genres, print styles, eras, technical processes, and photographer styles for intentional artistic choices.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "RaeZhLiu",
+            "title": "comfy_ark",
+            "reference": "https://github.com/RaeZhLiu/comfy_ark",
+            "files": [
+                "https://github.com/RaeZhLiu/comfy_ark"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for Ark functionality. (Description by CC)"
+        },
+        {
+            "author": "harishcmgit",
+            "title": "comfyui_sp [WIP]",
+            "reference": "https://github.com/harishcmgit/comfyui_sp",
+            "files": [
+                "https://github.com/harishcmgit/comfyui_sp"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for seamless pattern generation. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Omega-L",
+            "title": "comfyUI-prompts",
+            "reference": "https://github.com/Omega-L/comfyUI-prompts",
+            "files": [
+                "https://github.com/Omega-L/comfyUI-prompts"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI Frontend Vue Basic is custom node that demonstrate how to use vue as frontend framework along with primevue and vue-i18n, cooperating with ComfyUI API. Needs ComfyUI Frontend 1.25.0 or later."
+        },
+        {
+            "author": "vjumpkung",
+            "title": "comfyui-mosaic-blur [NAME CONFLICT]",
+            "reference": "https://github.com/vjumpkung/comfyui-mosaic-blur",
+            "files": [
+                "https://github.com/vjumpkung/comfyui-mosaic-blur"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for applying mosaic blur effects. (Description by CC)"
+        },
+        {
+            "author": "richardctw",
+            "title": "ComfyUI-ImageFillBackgroundColor",
+            "reference": "https://github.com/richardctw/ComfyUI-ImageFillBackgroundColor",
+            "files": [
+                "https://github.com/richardctw/ComfyUI-ImageFillBackgroundColor"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: ImageFillBackgroundColor"
+        },
+        {
+            "author": "zxq790909-maker",
+            "title": "ComfyUI-wangyi-OLED [WIP]",
+            "reference": "https://github.com/zxq790909-maker/ComfyUI-wangyi-OLED",
+            "files": [
+                "https://github.com/zxq790909-maker/ComfyUI-wangyi-OLED"
+            ],
+            "install_type": "git-clone",
+            "description": "An extension that highlights the currently running node in ComfyUI with a strong neon outline and breathing effect for easier workflow navigation.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "starsFriday",
+            "title": "ComfyUI-Qwen3-TTS [NAME CONFLICT/WIP]",
+            "reference": "https://github.com/starsFriday/ComfyUI-Qwen3-TTS",
+            "files": [
+                "https://github.com/starsFriday/ComfyUI-Qwen3-TTS"
+            ],
+            "install_type": "git-clone",
+            "description": "Sound-related nodes for the Qwen3-TTS project\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "RicoMeng",
+            "title": "CartoonGenComfyui-PromtGenerator",
+            "reference": "https://github.com/RicoMeng/CartoonGenComfyui-PromtGenerator",
+            "files": [
+                "https://github.com/RicoMeng/CartoonGenComfyui-PromtGenerator"
+            ],
+            "install_type": "git-clone",
+            "description": "Istanbul 80s Prompt Generator Node for ComfyUI. (Description by CC)"
+        },
+        {
+            "author": "N3uR0TiCV0iD",
+            "title": "ComfyUI-ModelListTools",
+            "reference": "https://github.com/N3uR0TiCV0iD/ComfyUI-ModelListTools",
+            "files": [
+                "https://github.com/N3uR0TiCV0iD/ComfyUI-ModelListTools"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: CLIPModelSelectionNode, CLIPVisionModelSelectionNode, CheckpointSelectionNode, DiffusionModelSelectionNode, FilteredCLIPModelsNode, ... (13 total)"
+        },
+        {
+            "author": "pollockjj",
+            "title": "ComfyUI-StabilityTest",
+            "reference": "https://github.com/pollockjj/ComfyUI-StabilityTest",
+            "files": [
+                "https://github.com/pollockjj/ComfyUI-StabilityTest"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI stability testing node. (Description by CC)"
+        },
+        {
+            "author": "Tr1dae",
+            "title": "ComfyUI-MobileSAM [WIP]",
+            "reference": "https://github.com/Tr1dae/ComfyUI-MobileSAM",
+            "files": [
+                "https://github.com/Tr1dae/ComfyUI-MobileSAM"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node for text-guided image segmentation using GroundingDINO and MobileSAM to segment objects in images using natural language prompts.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Vov1ch",
+            "title": "ComfyUI_GLMImage",
+            "reference": "https://github.com/Vov1ch/ComfyUI_GLMImage",
+            "files": [
+                "https://github.com/Vov1ch/ComfyUI_GLMImage"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for GLM image generation, image-to-image translation, and flexible input handling. (Description by CC)"
+        },
+        {
+            "author": "shin131002",
+            "title": "ComfyUI-Prompt-Preset-Selector [WIP]",
+            "reference": "https://github.com/shin131002/ComfyUI-Prompt-Preset-Selector",
+            "files": [
+                "https://github.com/shin131002/ComfyUI-Prompt-Preset-Selector"
+            ],
+            "install_type": "git-clone",
+            "description": "Flexible preset selector with YAML support, advanced keyword filtering, and hierarchical key search\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "fogyisland",
+            "title": "Comfy_Show_StringText [WIP]",
+            "reference": "https://github.com/fogyisland/Comfy_Show_StringText",
+            "files": [
+                "https://github.com/fogyisland/Comfy_Show_StringText"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node for text display that implements string data output. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Leecoahs",
+            "title": "ComfyUI_LeeNodes",
+            "reference": "https://github.com/Leecoahs/ComfyUI_LeeNodes",
+            "files": [
+                "https://github.com/Leecoahs/ComfyUI_LeeNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for image processing including grayscale combining, overlaying, texture inpainting. (Description by CC)"
+        },
+        {
+            "author": "tdrminglin",
+            "title": "Comfyui-hymotionbridge",
+            "reference": "https://github.com/tdrminglin/Comfyui-hymotionbridge",
+            "files": [
+                "https://github.com/tdrminglin/Comfyui-hymotionbridge"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: HYMotionToNLFBridge, HYMotionToSCAILBridge"
+        },
+        {
+            "author": "NakanoSanku",
+            "title": "ComfyUI-Gemini [NAME CONFLICT]",
+            "reference": "https://github.com/NakanoSanku/ComfyUI-Gemini",
+            "files": [
+                "https://github.com/NakanoSanku/ComfyUI-Gemini"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI nodes for Google Gemini AI with text generation and image generation capabilities using the Python SDK."
+        },
+        {
+            "author": "Saganaki22",
+            "title": "ComfyUI-NovaSR",
+            "reference": "https://github.com/Saganaki22/ComfyUI-NovaSR",
+            "files": [
+                "https://github.com/Saganaki22/ComfyUI-NovaSR"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node for NovaSR - Ultra-fast audio super resolution (3600x realtime, 50KB model)"
+        },
+        {
+            "author": "GokuHiki",
+            "title": "ComfyUI-Goku-Tools",
+            "reference": "https://github.com/GokuHiki/ComfyUI-Goku-Tools",
+            "files": [
+                "https://github.com/GokuHiki/ComfyUI-Goku-Tools"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom nodes including FirstNonFalse utility. (Description by CC)"
+        },
+        {
+            "author": "endman100",
+            "title": "ComfyUI_tensor_script [WIP]",
+            "reference": "https://github.com/endman100/ComfyUI_tensor_script",
+            "files": [
+                "https://github.com/endman100/ComfyUI_tensor_script"
+            ],
+            "install_type": "git-clone",
+            "description": "Utility nodes for tensor manipulation including squeeze, unsqueeze, and shape operations. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "nekotxt",
+            "title": "ComfyUI-NTX-support-nodes [WIP]",
+            "reference": "https://github.com/nekotxt/ComfyUI-NTX-support-nodes",
+            "files": [
+                "https://github.com/nekotxt/ComfyUI-NTX-support-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Utility nodes to support the creation of pipes\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "killshotttttt",
+            "title": "ComfyUI-killshotttttt [WIP]",
+            "reference": "https://github.com/killshotttttt/ComfyUI-killshotttttt",
+            "files": [
+                "https://github.com/killshotttttt/ComfyUI-killshotttttt"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node for ComfyUI that integrates with the Meshy AI API for Image-to-Image transformations using nano-banana models.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "bozkut",
+            "title": "ComfyUI-Prompt-Expander [NAME CONFLICT]",
+            "reference": "https://github.com/bozkut/ComfyUI-Prompt-Expander",
+            "files": [
+                "https://github.com/bozkut/ComfyUI-Prompt-Expander"
+            ],
+            "install_type": "git-clone",
+            "description": "An LLM-powered prompt expansion node for ComfyUI. Transform simple prompts into detailed, high-quality image generation prompts."
+        },
+        {
+            "author": "my-xz-org",
+            "title": "comfyui_xz_nodes",
+            "reference": "https://github.com/my-xz-org/comfyui_xz_nodes",
+            "files": [
+                "https://github.com/my-xz-org/comfyui_xz_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: XZImageToText"
+        },
+        {
+            "author": "tackcrypto1031",
+            "title": "tk_comfyui_SimpleSize [WIP]",
+            "reference": "https://github.com/tackcrypto1031/tk_comfyui_SimpleSize",
+            "files": [
+                "https://github.com/tackcrypto1031/tk_comfyui_SimpleSize"
+            ],
+            "install_type": "git-clone",
+            "description": "A professional and intelligent aspect ratio and resolution selector for ComfyUI.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "hashms0a",
+            "title": "ComfyUI-Qwen-Multi-Angle-Camera-Nodes [WIP]",
+            "reference": "https://github.com/hashms0a/ComfyUI-Qwen-Multi-Angle-Camera-Nodes",
+            "files": [
+                "https://github.com/hashms0a/ComfyUI-Qwen-Multi-Angle-Camera-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Comprehensive custom nodes for controlling camera angles with Qwen-Image-Edit-2511-Multiple-Angles-LoRA, supporting all 96 camera positions with basic/advanced selection, orbital animation, elevation sweep, and pre-defined animation paths.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "jceme",
+            "title": "Comfy_Extensions [WIP]",
+            "reference": "https://github.com/jceme/Comfy_Extensions",
+            "files": [
+                "https://github.com/jceme/Comfy_Extensions"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI extension pack offering image dimension handling, aspect ratio presets, FPS utilities, inpainting, and prompt customization. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "saltchicken",
+            "title": "ComfyUI-Interactive-Cropper",
+            "reference": "https://github.com/saltchicken/ComfyUI-Interactive-Cropper",
+            "files": [
+                "https://github.com/saltchicken/ComfyUI-Interactive-Cropper"
+            ],
+            "install_type": "git-clone",
+            "description": "Interactive image cropping node with visual interface. (Description by CC)"
+        },
+        {
+            "author": "shenymce",
+            "title": "ComfyUI-JsonViewer [WIP]",
+            "reference": "https://github.com/shenymce/ComfyUI-JsonViewer",
+            "files": [
+                "https://github.com/shenymce/ComfyUI-JsonViewer"
+            ],
+            "install_type": "git-clone",
+            "description": "OpenPose JSON data visualization for displaying pose keypoint data exported from ComfyUI workflow nodes. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "qimi-dev",
+            "title": "ComfyUI-Qimi-Tiler",
+            "reference": "https://github.com/qimi-dev/ComfyUI-Qimi-Tiler",
+            "files": [
+                "https://github.com/qimi-dev/ComfyUI-Qimi-Tiler"
+            ],
+            "install_type": "git-clone",
+            "description": "Image tiling utility nodes for splitting and combining image tiles in ComfyUI. (Description by CC)"
+        },
+        {
+            "author": "maTORIx",
+            "title": "ComfyUI-KeypointsToImage",
+            "reference": "https://github.com/maTORIx/ComfyUI-KeypointsToImage",
+            "files": [
+                "https://github.com/maTORIx/ComfyUI-KeypointsToImage"
+            ],
+            "install_type": "git-clone",
+            "description": "Node for converting keypoints to images. (Description by CC)"
+        },
+        {
+            "author": "IO-AtelierTech",
+            "title": "comfyui-video-utils [NAME CONFLICT]",
+            "reference": "https://github.com/IO-AtelierTech/comfyui-video-utils",
+            "files": [
+                "https://github.com/IO-AtelierTech/comfyui-video-utils"
+            ],
+            "install_type": "git-clone",
+            "description": "Provides utility nodes for video operations, particularly for FFmpeg integration and workflow connectivity."
+        },
+        {
+            "author": "PozzettiAndrea",
+            "title": "ComfyUI-TRELLIS2 [NAME CONFLICT]",
+            "reference": "https://github.com/PozzettiAndrea/ComfyUI-TRELLIS2",
+            "files": [
+                "https://github.com/PozzettiAndrea/ComfyUI-TRELLIS2"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom nodes for TRELLIS.2 - Microsoft's image-to-3D generation model for creating high-quality 3D meshes with PBR materials."
+        },
+        {
+            "author": "colorAi",
+            "title": "comfyui-prompt-manager [NAME CONFLICT]",
+            "reference": "https://github.com/colorAi/comfyui-prompt-manager",
+            "files": [
+                "https://github.com/colorAi/comfyui-prompt-manager"
+            ],
+            "install_type": "git-clone",
+            "description": "A modern, powerful, and easy-to-use Prompt Manager extension for ComfyUI."
+        },
+        {
+            "author": "Yukinoshita-Yukinoe",
+            "title": "ComfyUI-SenseVoice [NAME CONFLICT]",
+            "reference": "https://github.com/Yukinoshita-Yukinoe/ComfyUI-SenseVoice",
+            "files": [
+                "https://github.com/Yukinoshita-Yukinoe/ComfyUI-SenseVoice"
+            ],
+            "install_type": "git-clone",
+            "description": "Audio processing node providing SenseVoice speech recognition, audio splitting, and SRT generation for ComfyUI. (Description by CC)"
+        },
+        {
+            "author": "satyam-fp",
+            "title": "ComfyUI-GeminiImage [NAME CONFLICT]",
+            "reference": "https://github.com/satyam-fp/ComfyUI-GeminiImage",
+            "files": [
+                "https://github.com/satyam-fp/ComfyUI-GeminiImage"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom ComfyUI node package that integrates Google Gemini API for AI-powered image generation and enhancement."
+        },
+        {
+            "author": "ai-joe-git",
+            "title": "ComfyUI-Chatterbox [NAME CONFLICT]",
+            "reference": "https://github.com/ai-joe-git/ComfyUI-Chatterbox",
+            "files": [
+                "https://github.com/ai-joe-git/ComfyUI-Chatterbox"
+            ],
+            "install_type": "git-clone",
+            "description": "Chatterbox TTS integration for ComfyUI - Voice cloning with 3 model variants."
+        },
+        {
+            "author": "Sergey004",
+            "title": "ComfyUI-Telegram-Sender [UNSAFE/NAME CONFLICT]",
+            "reference": "https://github.com/Sergey004/ComfyUI-Telegram-Sender",
+            "files": [
+                "https://github.com/Sergey004/ComfyUI-Telegram-Sender"
+            ],
+            "install_type": "git-clone",
+            "description": "Does the same thing as comfyui_image_metadata_extension but is slightly more modern, sending the output to Telegram and downloading unnecessary metadata at the same time.[w/This nodepack contains a path traversal vulnerability.]"
+        },
+        {
+            "author": "cedarconnor",
+            "title": "ComfyUI-DAP [UNSAFE/NAME CONFLICT]",
+            "reference": "https://github.com/cedarconnor/ComfyUI-DAP",
+            "files": [
+                "https://github.com/cedarconnor/ComfyUI-DAP"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for DAP (Depth Any Panoramas) - panoramic depth estimation[w/This nodepack contains a path traversal vulnerability.]"
+        },
+        {
+            "author": "AhiruNeko",
+            "title": "ComfyUI-MiniTools [UNSAFE/NAME CONFLICT]",
+            "reference": "https://github.com/AhiruNeko/ComfyUI-MiniTools",
+            "files": [
+                "https://github.com/AhiruNeko/ComfyUI-MiniTools"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI plugins[w/This nodepack contains a path traversal vulnerability.]"
+        },
+        {
+            "author": "861289009",
+            "title": "comfyui_video_node [WIP]",
+            "reference": "https://github.com/861289009/comfyui_video_node",
+            "files": [
+                "https://github.com/861289009/comfyui_video_node"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI node for creating smooth fade transitions between two video segments using hex color effects. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "w3rc",
+            "title": "lpips-similarity-comfyui",
+            "reference": "https://github.com/w3rc/lpips-similarity-comfyui",
+            "files": [
+                "https://github.com/w3rc/lpips-similarity-comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: GetSimilarity, LPIPSSimilarity"
+        },
+        {
+            "author": "StevenBaby",
+            "title": "comfyui-tools",
+            "reference": "https://github.com/StevenBaby/comfyui-tools",
+            "files": [
+                "https://github.com/StevenBaby/comfyui-tools"
+            ],
+            "install_type": "git-clone",
+            "description": "IntParameterNode: A Node contain 4 int parameters to quick switch input to other nodes."
+        },
+        {
+            "author": "zhu798542746",
+            "title": "comfyui_model [UNSAFE]",
+            "reference": "https://github.com/zhu798542746/comfyui_model",
+            "files": [
+                "https://github.com/zhu798542746/comfyui_model"
+            ],
+            "install_type": "git-clone",
+            "description": "A powerful tool for managing and exporting ComfyUI plugins and models with an elegant side panel interface. (Description by CC) [w/This node pack has a vulnerability that allows it to access (or exfiltrate) data from custom nodes installed remotely.]"
+        },
+        {
+            "author": "simonri",
+            "title": "ComfyUI-SimonNodes",
+            "reference": "https://github.com/simonri/ComfyUI-SimonNodes",
+            "files": [
+                "https://github.com/simonri/ComfyUI-SimonNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Provides seed upscaling and image cropping utilities for ComfyUI workflows. (Description by CC)"
+        },
+        {
+            "author": "j-pyxal",
+            "title": "ComfyUI-Lattice-Manim [WIP]",
+            "reference": "https://github.com/j-pyxal/ComfyUI-Lattice-Manim",
+            "files": [
+                "https://github.com/j-pyxal/ComfyUI-Lattice-Manim"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI extension integrating Manim for animation rendering.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "kanttouchthis",
+            "title": "ComfyUI-SDNQ [NAME CONFLICT]",
+            "reference": "https://github.com/kanttouchthis/ComfyUI-SDNQ",
+            "files": [
+                "https://github.com/kanttouchthis/ComfyUI-SDNQ"
+            ],
+            "install_type": "git-clone",
+            "description": "SDNQ support for ComfyUI."
+        },
+        {
+            "author": "SparknightLLC",
+            "title": "ComfyUI-GraphConstantFolder",
+            "reference": "https://github.com/SparknightLLC/ComfyUI-GraphConstantFolder",
+            "files": [
+                "https://github.com/SparknightLLC/ComfyUI-GraphConstantFolder"
+            ],
+            "install_type": "git-clone",
+            "description": "Improves performance of large workflows by rewriting the submitted prompt graph before validation to constant-fold switch/selector nodes and optionally prune now-unreachable branches."
+        },
+        {
+            "author": "bryanlholland1",
+            "title": "comfyui-app-bridge [WIP]",
+            "reference": "https://github.com/bryanlholland1/comfyui-app-bridge",
+            "files": [
+                "https://github.com/bryanlholland1/comfyui-app-bridge"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for sending images to the companion iOS/macOS/visionOS app\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "HalfADog",
+            "title": "ComfyUI-Mask2JSON",
+            "reference": "https://github.com/HalfADog/ComfyUI-Mask2JSON",
+            "files": [
+                "https://github.com/HalfADog/ComfyUI-Mask2JSON"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for converting masks to contour JSON format with visualization capabilities. (Description by CC)"
+        },
+        {
+            "author": "flywhale-666",
+            "title": "ComfyUI_pixel_snapping [WIP]",
+            "reference": "https://github.com/flywhale-666/ComfyUI_pixel_snapping",
+            "files": [
+                "https://github.com/flywhale-666/ComfyUI_pixel_snapping"
+            ],
+            "install_type": "git-clone",
+            "description": "SIFT-based image alignment, intelligent mask cropping and restoration for ComfyUI\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "fangg2000",
+            "title": "comfyui_fgtools [WIP]",
+            "reference": "https://github.com/fangg2000/comfyui_fgtools",
+            "files": [
+                "https://github.com/fangg2000/comfyui_fgtools"
+            ],
+            "install_type": "git-clone",
+            "description": "Personal utility tools for ComfyUI. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "wandaijin",
+            "title": "ComfyUI-PaddleOCR [NAME CONFLICT]",
+            "reference": "https://github.com/wandaijin/ComfyUI-PaddleOCR",
+            "files": [
+                "https://github.com/wandaijin/ComfyUI-PaddleOCR"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of custom nodes for ComfyUI"
+        },
+        {
+            "author": "supaidauen",
+            "title": "ComfyUI-supaidauen [WIP]",
+            "reference": "https://github.com/supaidauen/ComfyUI-supaidauen",
+            "files": [
+                "https://github.com/supaidauen/ComfyUI-supaidauen"
+            ],
+            "install_type": "git-clone",
+            "description": "Collection of custom ComfyUI nodes including VRAM management, image processing, latent manipulation, character I/O, and advanced sampling utilities. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "huhu-tiger",
+            "title": "ComfyUI-RemoteResource",
+            "reference": "https://github.com/huhu-tiger/ComfyUI-RemoteResource",
+            "files": [
+                "https://github.com/huhu-tiger/ComfyUI-RemoteResource"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node for loading images from remote sources. (Description by CC)"
+        },
+        {
+            "author": "nomadop",
+            "title": "ComfyUI-Video-Matting [NAME CONFLICT]",
+            "reference": "https://github.com/nomadop/ComfyUI-Video-Matting",
+            "files": [
+                "https://github.com/nomadop/ComfyUI-Video-Matting"
+            ],
+            "install_type": "git-clone",
+            "description": "Modular video matting nodes supporting multiple models (RVM, MODNet, U2Net, RMBG-2.0) and video object segmentation with Cutie and edge refinement via ViTMatte."
+        },
+        {
+            "author": "agavesunset",
+            "title": "Comfyui_SiliconFlow_AgaveSunset",
+            "reference": "https://github.com/agavesunset/Comfyui_SiliconFlow_AgaveSunset",
+            "files": [
+                "https://github.com/agavesunset/Comfyui_SiliconFlow_AgaveSunset"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: SiliconFlowLoader_AS, SiliconFlowSampler_AS"
+        },
+        {
+            "author": "IIEleven11",
+            "title": "ComfyUI-Dataset_Maker [WIP]",
+            "reference": "https://github.com/IIEleven11/ComfyUI-Dataset_Maker",
+            "files": [
+                "https://github.com/IIEleven11/ComfyUI-Dataset_Maker"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom node pack that automates dataset creation by generating images for concept lists, each with a specific LoRA. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "bhaveek424",
+            "title": "ComfyUI-HMNodes",
+            "reference": "https://github.com/bhaveek424/ComfyUI-HMNodes",
+            "files": [
+                "https://github.com/bhaveek424/ComfyUI-HMNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for audio processing, image enhancement, and AI-powered prompting including FFT analysis, automatic white balance, lens effects, and realism optimization. (Description by CC)"
+        },
+        {
+            "author": "raohammad",
+            "title": "ComfyUI-VTUtilNodes [WIP]",
+            "reference": "https://github.com/raohammad/ComfyUI-VTUtilNodes",
+            "files": [
+                "https://github.com/raohammad/ComfyUI-VTUtilNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of utility custom nodes for ComfyUI.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "yamanacn",
+            "title": "ComfyUI-ImageMask-Random-Sync-Picker",
+            "reference": "https://github.com/yamanacn/ComfyUI-ImageMask-Random-Sync-Picker",
+            "files": [
+                "https://github.com/yamanacn/ComfyUI-ImageMask-Random-Sync-Picker"
+            ],
+            "install_type": "git-clone",
+            "description": "Node for randomly selecting and synchronizing image masks with selectable options. (Description by CC)"
+        },
+        {
+            "author": "tdrminglin",
+            "title": "ComfyUI_SceneSplitter",
+            "reference": "https://github.com/tdrminglin/ComfyUI_SceneSplitter",
+            "files": [
+                "https://github.com/tdrminglin/ComfyUI_SceneSplitter"
+            ],
+            "install_type": "git-clone",
+            "description": "Scene detection and splitting nodes for ComfyUI enabling frame-level scene detection and start frame tracking. (Description by CC)"
+        },
+        {
+            "author": "starsFriday",
+            "title": "ComfyUI-KLingAI-OmniVideo [WIP]",
+            "reference": "https://github.com/starsFriday/ComfyUI-KLingAI-OmniVideo",
+            "files": [
+                "https://github.com/starsFriday/ComfyUI-KLingAI-OmniVideo"
+            ],
+            "install_type": "git-clone",
+            "description": "Five API nodes for KLingAI's OmniVideo (O1) usage\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Hifunyo",
+            "title": "comfyui_google_ai",
+            "reference": "https://github.com/Hifunyo/comfyui_google_ai",
+            "files": [
+                "https://github.com/Hifunyo/comfyui_google_ai"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI integration node for Google AI image generation capabilities. (Description by CC)"
+        },
+        {
+            "author": "nschpy",
+            "title": "ComfyUI_MovisAdapter [UNSAFE]",
+            "reference": "https://github.com/nschpy/ComfyUI_MovisAdapter",
+            "files": [
+                "https://github.com/nschpy/ComfyUI_MovisAdapter"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of custom nodes for ComfyUI[w/This nodepack contains a node that has a vulnerability allowing write to arbitrary file paths.]"
+        },
+        {
+            "author": "hgh086",
+            "title": "Comfyui-HghImage",
+            "reference": "https://github.com/hgh086/Comfyui-HghImage",
+            "files": [
+                "https://github.com/hgh086/Comfyui-HghImage"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for image comparison functionality. (Description by CC)"
+        },
+        {
+            "author": "Ginolazy",
+            "title": "ComfyUI-FluxKontextImageCompensate [WIP]",
+            "reference": "https://github.com/Ginolazy/ComfyUI-FluxKontextImageCompensate",
+            "files": [
+                "https://github.com/Ginolazy/ComfyUI-FluxKontextImageCompensate"
+            ],
+            "install_type": "git-clone",
+            "description": "A focused ComfyUI plugin to handle the vertical stretching issue introduced by the Flux Kontext model.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "IO-AtelierTech",
+            "title": "comfyui-genai-connectors [WIP]",
+            "reference": "https://github.com/IO-AtelierTech/comfyui-genai-connectors",
+            "files": [
+                "https://github.com/IO-AtelierTech/comfyui-genai-connectors"
+            ],
+            "install_type": "git-clone",
+            "description": "The ComfyUI-fal-Connector is a tool designed to provide an integration between ComfyUI and fal. This extension allows users to execute their ComfyUI workflows directly on [a/fal.ai](https://fal.ai/). This enables users to leverage the computational power and resources provided by fal.ai for running their ComfyUI workflows.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "PozzettiAndrea",
+            "title": "ComfyUI-MVDUST3R [UNSAFE]",
+            "reference": "https://github.com/PozzettiAndrea/ComfyUI-MVDUST3R",
+            "files": [
+                "https://github.com/PozzettiAndrea/ComfyUI-MVDUST3R"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for MVDUST3R multi-view 3D reconstruction[w/This nodepack contains a node that has a vulnerability allowing write to arbitrary file paths.]"
+        },
+        {
+            "author": "ProjectAtlantis-dev",
+            "title": "comfyui-atlantis-json [UNSAFE]",
+            "reference": "https://github.com/ProjectAtlantis-dev/comfyui-atlantis-json",
+            "files": [
+                "https://github.com/ProjectAtlantis-dev/comfyui-atlantis-json"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI nodes for JSON processing and transcription workflows, including text-to-JSON conversion, SRT subtitle parsing, and file saving. (Description by CC)[w/This nodepack contains a node that has a vulnerability allowing write to arbitrary file paths.]"
+        },
+        {
+            "author": "Smyshnikof",
+            "title": "ComfyUI-PresetDownloadManager [UNSAFE]",
+            "reference": "https://github.com/Smyshnikof/ComfyUI-PresetDownloadManager",
+            "files": [
+                "https://github.com/Smyshnikof/ComfyUI-PresetDownloadManager"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom ComfyUI node for managing and downloading models from HuggingFace with preset support[w/This nodepack contains a node that has a vulnerability allowing write to arbitrary file paths.]"
+        },
+        {
+            "author": "gulajawalegit",
+            "title": "ComfyUI-Telegram-Sender [UNSAFE]",
+            "reference": "https://github.com/gulajawalegit/ComfyUI-Telegram-Sender",
+            "files": [
+                "https://github.com/gulajawalegit/ComfyUI-Telegram-Sender"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for sending generated videos to Telegram, enabling direct output sharing to messaging platforms. (Description by CC)[w/This nodepack contains a node that has a vulnerability allowing write to arbitrary file paths.]"
+        },
+        {
+            "author": "Laolilzp",
+            "title": "Laoli3D [UNSAFE]",
+            "reference": "https://github.com/Laolilzp/Laoli3D",
+            "files": [
+                "https://github.com/Laolilzp/Laoli3D"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI 3D pose editor enabling visual character manipulation and ControlNet image generation for precise AI figure control without prompt description. (Description by CC)[w/This nodepack contains a node that has a vulnerability allowing write to arbitrary file paths.]"
+        },
+        {
+            "author": "Goldlionren",
+            "title": "ComfyUI_Bridge_fabric [UNSAFE]",
+            "reference": "https://github.com/Goldlionren/ComfyUI_Bridge_fabric",
+            "files": [
+                "https://github.com/Goldlionren/ComfyUI_Bridge_fabric"
+            ],
+            "install_type": "git-clone",
+            "description": "AI Compute Fabric bridge for ComfyUI enabling distributed CLIP, VAE, and ControlNet inference across local and remote machines. (Description by CC)[w/This nodepack contains a node that has a vulnerability allowing write to arbitrary file paths.]"
+        },
+        {
+            "author": "CypherNaught-0x",
+            "title": "ComfyUI-StarVector [WIP]",
+            "reference": "https://github.com/CypherNaught-0x/ComfyUI-StarVector",
+            "files": [
+                "https://github.com/CypherNaught-0x/ComfyUI-StarVector"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom nodes for SVG generation using StarVector models\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Clivey1234",
+            "title": "ComfyUI_FBX_Import [UNSAFE]",
+            "reference": "https://github.com/Clivey1234/ComfyUI_FBX_Import",
+            "files": [
+                "https://github.com/Clivey1234/ComfyUI_FBX_Import"
+            ],
+            "install_type": "git-clone",
+            "description": "Convert FBX animations into ControlNet OpenPose images for driving AI video generation with motion from any animation source. (Description by CC)[w/This nodepack has a vulnerability that allows arbitrary code execution remotely.]"
+        },
+        {
+            "author": "TobiasGlaubach",
+            "title": "ComfyUI-TG_PyCode [UNSAFE]",
+            "reference": "https://github.com/TobiasGlaubach/ComfyUI-TG_PyCode",
+            "files": [
+                "https://github.com/TobiasGlaubach/ComfyUI-TG_PyCode"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node library with an editor and nodes to run Python code within ComfyUI workflows.[w/This nodepack has a vulnerability that allows arbitrary code execution remotely.]"
+        },
+        {
+            "author": "jchiotaka",
+            "title": "ComfyUI-ClarityAI-Upscaler",
+            "reference": "https://github.com/jchiotaka/ComfyUI-ClarityAI-Upscaler",
+            "files": [
+                "https://github.com/jchiotaka/ComfyUI-ClarityAI-Upscaler"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI upscaler nodes including ClarityCreativeUpscaler, ClarityCrystalUpscaler, and ClarityFluxUpscaler. (Description by CC)"
+        },
+        {
+            "author": "tpc2233",
+            "title": "ComfyUI-TP-IMtalker [WIP]",
+            "reference": "https://github.com/tpc2233/ComfyUI-TP-IMtalker",
+            "files": [
+                "https://github.com/tpc2233/ComfyUI-TP-IMtalker"
+            ],
+            "install_type": "git-clone",
+            "description": "Comfy UI nodes for IMtalker to run native weights.)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "yuyu0218yu",
+            "title": "comfyui-NXCM-tool [UNSAFE]",
+            "reference": "https://github.com/yuyu0218yu/comfyui-NXCM-tool",
+            "files": [
+                "https://github.com/yuyu0218yu/comfyui-NXCM-tool"
+            ],
+            "install_type": "git-clone",
+            "description": "Next-generation creative media encryption toolkit for ComfyUI providing image and video encryption with AES-256-CTR + HMAC-SHA256, metadata preservation, and batch processing support. (Description by CC) [w/hardcoded encryption key]"
+        },
+        {
+            "author": "SergeyKarleev",
+            "title": "comfyui-textutils [WIP]",
+            "reference": "https://github.com/SergeyKarleev/comfyui-textutils",
+            "files": [
+                "https://github.com/SergeyKarleev/comfyui-textutils"
+            ],
+            "install_type": "git-clone",
+            "description": "Small utility nodes for ComfyUI text workflows.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "love530love",
+            "title": "ComfyUI-TorchMonitor [WIP]",
+            "reference": "https://github.com/love530love/ComfyUI-TorchMonitor",
+            "files": [
+                "https://github.com/love530love/ComfyUI-TorchMonitor"
+            ],
+            "install_type": "git-clone",
+            "description": "Fixed-position real-time monitor for ComfyUI displaying CPU, RAM, VRAM, and GPU temperature metrics with zero configuration and single-file installation.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Enferlain",
+            "title": "ComfyUI-SamplerCustom-3Decimals",
+            "reference": "https://github.com/Enferlain/ComfyUI-SamplerCustom-3Decimals",
+            "files": [
+                "https://github.com/Enferlain/ComfyUI-SamplerCustom-3Decimals"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI sampler node with custom 3 decimals precision for sampling parameters. (Description by CC)"
+        },
+        {
+            "author": "lfelipegg",
+            "title": "lfgg_custom_nodes_comfyui [WIP]",
+            "reference": "https://github.com/lfelipegg/lfgg_custom_nodes_comfyui",
+            "files": [
+                "https://github.com/lfelipegg/lfgg_custom_nodes_comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "LFGG custom nodes for ComfyUI providing resolution-first utilities, latent sizing by aspect ratio, and divisibility-aware image processing.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "saltchicken",
+            "title": "ComfyUI-Selector",
+            "reference": "https://github.com/saltchicken/ComfyUI-Selector",
+            "files": [
+                "https://github.com/saltchicken/ComfyUI-Selector"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node providing simple selector functionality. (Description by CC)"
+        },
+        {
+            "author": "saltchicken",
+            "title": "ComfyUI-Video-Utils",
+            "reference": "https://github.com/saltchicken/ComfyUI-Video-Utils",
+            "files": [
+                "https://github.com/saltchicken/ComfyUI-Video-Utils"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for video processing including FinalFrameSelector and VideoMerge functionality. (Description by CC)"
+        },
+        {
+            "author": "EricRorich",
+            "title": "ComfyUI-MegaTran-cutom-node [WIP]",
+            "reference": "https://github.com/EricRorich/ComfyUI-MegaTran-cutom-node",
+            "files": [
+                "https://github.com/EricRorich/ComfyUI-MegaTran-cutom-node"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node applying image transformations (scaling, rotation, translation) around a pivot point with optional canvas expansion and pivot visualization. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "OhSeongHyeon",
+            "title": "comfyui-random-image-size",
+            "reference": "https://github.com/OhSeongHyeon/comfyui-random-image-size",
+            "files": [
+                "https://github.com/OhSeongHyeon/comfyui-random-image-size"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI Random Image Size"
+        },
+        {
+            "author": "Enferlain",
+            "title": "ComfyUI-Model-Comparison [WIP]",
+            "reference": "https://github.com/Enferlain/ComfyUI-Model-Comparison",
+            "files": [
+                "https://github.com/Enferlain/ComfyUI-Model-Comparison"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node for comparing multiple models side-by-side. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "AMTPorn",
+            "title": "comfyui_amt",
+            "reference": "https://github.com/AMTPorn/comfyui_amt",
+            "files": [
+                "https://github.com/AMTPorn/comfyui_amt"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: AMTStringDeduplication"
+        },
+        {
+            "author": "gajjar4",
+            "title": "ComfyUI-Qwen-Image-i2L [UNSAFE]",
+            "reference": "https://github.com/gajjar4/ComfyUI-Qwen-Image-i2L",
+            "files": [
+                "https://github.com/gajjar4/ComfyUI-Qwen-Image-i2L"
+            ],
+            "install_type": "git-clone",
+            "description": "A fully optimized ComfyUI custom node for Qwen-Image-i2L (Image-to-LoRA) that extracts style, composition, or details from images and saves them as lightweight LoRA files with intelligent VRAM optimization. (Description by CC)[w/This nodepack contains a node that has a vulnerability allowing write to arbitrary file paths.]"
+        },
+        {
+            "author": "edvardtoth",
+            "title": "ComfyUI-ETNodes",
+            "reference": "https://github.com/edvardtoth/ComfyUI-ETNodes",
+            "files": [
+                "https://github.com/edvardtoth/ComfyUI-ETNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: ETNodes-Color-Selector, ETNodes-Gemini-API-Image, ETNodes-Gemini-API-Text, ETNodes-List-Items, ETNodes-List-Selector, ETNodes-Text-Previe"
+        },
+        {
+            "author": "jtydhr88",
+            "title": "ComfyUI-PolotnoCanvasEditor [UNSAFE]",
+            "reference": "https://github.com/jtydhr88/ComfyUI-PolotnoCanvasEditor",
+            "files": [
+                "https://github.com/jtydhr88/ComfyUI-PolotnoCanvasEditor"
+            ],
+            "install_type": "git-clone",
+            "description": "Integrates Polotno Canvas Editor into ComfyUI for advanced image editing and design.[w/This nodepack contains a path traversal vulnerability.]"
+        },
+        {
+            "author": "Taremin",
+            "title": "comfyui-remove-print",
+            "reference": "https://github.com/Taremin/comfyui-remove-print",
+            "files": [
+                "https://github.com/Taremin/comfyui-remove-print"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI extension for removing or suppressing print statements in node output. (Description by CC)"
+        },
+        {
+            "author": "JiangAogo",
+            "title": "ComfyUI-Gemini-API [WIP]",
+            "reference": "https://github.com/JiangAogo/ComfyUI-Gemini-API",
+            "files": [
+                "https://github.com/JiangAogo/ComfyUI-Gemini-API"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom nodes for Google Gemini API integration, supporting both text generation (LLM) and image generation.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "AprEcho",
+            "title": "ComfyUI-RandomSeed",
+            "reference": "https://github.com/AprEcho/ComfyUI-RandomSeed",
+            "files": [
+                "https://github.com/AprEcho/ComfyUI-RandomSeed"
+            ],
+            "install_type": "git-clone",
+            "description": "Generates random seed values for ComfyUI workflows. (Description by CC)"
+        },
+        {
+            "author": "Spicely",
+            "title": "ComfyUI-Luma [WIP]",
+            "reference": "https://github.com/Spicely/ComfyUI-Luma",
+            "files": [
+                "https://github.com/Spicely/ComfyUI-Luma"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI utility providing video and audio processing capabilities including text watermarking, audio-video separation, and audio-to-subtitle conversion. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "SSYCloud",
+            "title": "comfyui-ssy-syncapi [WIP]",
+            "reference": "https://github.com/SSYCloud/comfyui-ssy-syncapi",
+            "files": [
+                "https://github.com/SSYCloud/comfyui-ssy-syncapi"
+            ],
+            "install_type": "git-clone",
+            "description": "Powerful ComfyUI custom node collection providing 4 dedicated nodes to access SSY Cloud synchronous image generation and processing models including Google Gemini, ByteDance Doubao, OpenAI, and image enhancement APIs. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "LMietkiewicz",
+            "title": "HiggsfieldAPI_Node",
+            "reference": "https://github.com/LMietkiewicz/HiggsfieldAPI_Node",
+            "files": [
+                "https://github.com/LMietkiewicz/HiggsfieldAPI_Node"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for Higgsfield API integration. (Description by CC)"
+        },
+        {
+            "author": "starsFriday",
+            "title": "ComfyUI-LongCat-Image [WIP]",
+            "reference": "https://github.com/starsFriday/ComfyUI-LongCat-Image",
+            "files": [
+                "https://github.com/starsFriday/ComfyUI-LongCat-Image"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI wrapper nodes for the LongCat-Image text-to-image and image-editing pipelines with Python 3.12 support.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "logicalor",
+            "title": "comfyui_mv_adapter [WIP]",
+            "reference": "https://github.com/logicalor/comfyui_mv_adapter",
+            "files": [
+                "https://github.com/logicalor/comfyui_mv_adapter"
+            ],
+            "install_type": "git-clone",
+            "description": "MV-Adapter nodes for ComfyUI - Multi-view image generation\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "rafstahelin",
+            "title": "ComfyUI_KieNanoBananaPro",
+            "reference": "https://github.com/rafstahelin/ComfyUI_KieNanoBananaPro",
+            "files": [
+                "https://github.com/rafstahelin/ComfyUI_KieNanoBananaPro"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for processing and filtering using KieNanoBananaPro algorithm. (Description by CC)"
+        },
+        {
+            "author": "jinchanz",
+            "title": "ComfyUI-Midjourney",
+            "reference": "https://github.com/jinchanz/ComfyUI-Midjourney",
+            "files": [
+                "https://github.com/jinchanz/ComfyUI-Midjourney"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI integration for Midjourney API with nodes for submitting requests, polling results, and extracting JSON data. (Description by CC)"
+        },
+        {
+            "author": "saltchicken",
+            "title": "ComfyUI-Local-Loader",
+            "reference": "https://github.com/saltchicken/ComfyUI-Local-Loader",
+            "files": [
+                "https://github.com/saltchicken/ComfyUI-Local-Loader"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom nodes for loading images from specified directories and file paths. (Description by CC)"
+        },
+        {
+            "author": "satyasairazole",
+            "title": "ComfyUI-Turbandetection [WIP]",
+            "reference": "https://github.com/satyasairazole/ComfyUI-Turbandetection",
+            "files": [
+                "https://github.com/satyasairazole/ComfyUI-Turbandetection"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node for detecting turbans in images using computer vision. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "dougbtv",
+            "title": "comfyui-vllm-omni",
+            "reference": "https://github.com/dougbtv/comfyui-vllm-omni",
+            "files": [
+                "https://github.com/dougbtv/comfyui-vllm-omni"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for vLLM-Omni text-to-image generation"
+        },
+        {
+            "author": "u5dev",
+            "title": "ComfyUI_u5_EasyScripter [UNSAFE]",
+            "reference": "https://github.com/u5dev/ComfyUI_u5_EasyScripter",
+            "files": [
+                "https://github.com/u5dev/ComfyUI_u5_EasyScripter"
+            ],
+            "install_type": "git-clone",
+            "description": "EASY VBA-style script for ComfyUI. Anything you want be in 1 node. Conditional branching, iteration, prompt generation, parameter adjustment, memory release, file input/output, Using HTTP RestAPI ... with 100+ built-in functions![w/This nodepack has RCE, SSRF, and arbitrary file read vulnerabilities.]"
+        },
+        {
+            "author": "stalkervr",
+            "title": "ComfyUI-StalkerVr",
+            "reference": "https://github.com/stalkervr/ComfyUI-StalkerVr",
+            "files": [
+                "https://github.com/stalkervr/ComfyUI-StalkerVr"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom nodes for image processing, aspect ratio fixing, batch cropping, grid manipulation, JSON handling, and value extraction. (Description by CC)"
+        },
+        {
+            "author": "baoanhng",
+            "title": "ComfyUI-utils",
+            "reference": "https://github.com/baoanhng/ComfyUI-utils",
+            "files": [
+                "https://github.com/baoanhng/ComfyUI-utils"
+            ],
+            "install_type": "git-clone",
+            "description": "Provides text utility nodes (TextJoiner, TextSplitter) for ComfyUI workflows. (Description by CC)"
+        },
+        {
+            "author": "xuchenxu168",
+            "title": "comfyui_meituan_image [WIP]",
+            "reference": "https://github.com/xuchenxu168/comfyui_meituan_image",
+            "files": [
+                "https://github.com/xuchenxu168/comfyui_meituan_image"
+            ],
+            "install_type": "git-clone",
+            "description": "Generate high-quality images from text prompts with excellent Chinese text rendering，Edit images using natural language instructions..\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "saltchicken",
+            "title": "ComfyUI-Identity-Mixer",
+            "reference": "https://github.com/saltchicken/ComfyUI-Identity-Mixer",
+            "files": [
+                "https://github.com/saltchicken/ComfyUI-Identity-Mixer"
+            ],
+            "install_type": "git-clone",
+            "description": "Mixes multiple identity LoRAs with normalized strength."
+        },
+        {
+            "author": "saltchicken",
+            "title": "ComfyUI-Prompter",
+            "reference": "https://github.com/saltchicken/ComfyUI-Prompter",
+            "files": [
+                "https://github.com/saltchicken/ComfyUI-Prompter"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node providing customizable prompt generation capabilities. (Description by CC)"
+        },
+        {
+            "author": "JBKing514",
+            "title": "map_comfyui [WIP]",
+            "reference": "https://github.com/JBKing514/map_comfyui",
+            "files": [
+                "https://github.com/JBKing514/map_comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node implementing the Manifold Alignment Protocol (MAP) within ComfyUI, transforming diffusion sampling into a measurable and visualizable geometric process. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "binarystatic",
+            "title": "ComfyUI-BinarystaticMasterSeed",
+            "reference": "https://github.com/binarystatic/ComfyUI-BinarystaticMasterSeed",
+            "files": [
+                "https://github.com/binarystatic/ComfyUI-BinarystaticMasterSeed"
+            ],
+            "install_type": "git-clone",
+            "description": "BinarystaticMasterSeed node for ComfyUI. (Description by CC)"
+        },
+        {
+            "author": "Aruntd008",
+            "title": "ComfyUI_SeamlessPattern [WIP]",
+            "reference": "https://github.com/Aruntd008/ComfyUI_SeamlessPattern",
+            "files": [
+                "https://github.com/Aruntd008/ComfyUI_SeamlessPattern"
+            ],
+            "install_type": "git-clone",
+            "description": "SeamlessPatternNode for ComfyUI. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "SilentLuxRay",
+            "title": "ComfyUI-Furrey-Super-Prompt [WIP]",
+            "reference": "https://github.com/SilentLuxRay/ComfyUI-Furrey-Super-Prompt",
+            "files": [
+                "https://github.com/SilentLuxRay/ComfyUI-Furrey-Super-Prompt"
+            ],
+            "install_type": "git-clone",
+            "description": "A personalized all-in-one node for ComfyUI that simplifies prompt management and LoRA handling with automatic translation to English. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Rayen21",
+            "title": "ComfyUI-PromptLinePlus [WIP]",
+            "reference": "https://github.com/Rayen21/ComfyUI-PromptLinePlus",
+            "files": [
+                "https://github.com/Rayen21/ComfyUI-PromptLinePlus"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node that splits multi-line prompts by line, enabling batch image generation with each line triggering one execution and supporting custom prompt boxes. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "c1660181647-hash",
+            "title": "ComfyUI-MM-Visual-Encryption",
+            "reference": "https://github.com/c1660181647-hash/ComfyUI-MM-Visual-Encryption",
+            "files": [
+                "https://github.com/c1660181647-hash/ComfyUI-MM-Visual-Encryption"
+            ],
+            "install_type": "git-clone",
+            "description": "A visual noise encryption custom node for ComfyUI, supporting Image and Video privacy protection."
+        },
+        {
+            "author": "charlierz",
+            "title": "comfyui-charlierz",
+            "reference": "https://github.com/charlierz/comfyui-charlierz",
+            "files": [
+                "https://github.com/charlierz/comfyui-charlierz"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: BackgroundColor, ScaleDimensions"
+        },
+        {
+            "author": "lrzjason",
+            "title": "Comfyui-DiffusersUtils [WIP]",
+            "reference": "https://github.com/lrzjason/Comfyui-DiffusersUtils",
+            "files": [
+                "https://github.com/lrzjason/Comfyui-DiffusersUtils"
+            ],
+            "install_type": "git-clone",
+            "description": "A set of nodes which provide flexible inference using diffusers in comfyui env. (Description by CC)"
+        },
+        {
+            "author": "Toxic1228",
+            "title": "Eleven-labs-comfyui-sts",
+            "reference": "https://github.com/Toxic1228/Eleven-labs-comfyui-sts",
+            "files": [
+                "https://github.com/Toxic1228/Eleven-labs-comfyui-sts"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI integration node for Eleven Labs text-to-speech service (requires API key). (Description by CC)"
+        },
+        {
+            "author": "NeoTech",
+            "title": "comfyui-laserprep",
+            "reference": "https://github.com/NeoTech/comfyui-laserprep",
+            "files": [
+                "https://github.com/NeoTech/comfyui-laserprep"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node implementing laser preparation functionality with LaserPrep node. (Description by CC)"
+        },
+        {
+            "author": "Enferlain",
+            "title": "ComfyUI-extra-schedulers [WIP]",
+            "reference": "https://github.com/Enferlain/ComfyUI-extra-schedulers",
+            "files": [
+                "https://github.com/Enferlain/ComfyUI-extra-schedulers"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom nodes providing additional scheduler implementations for advanced sampling control. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "tiange-tree",
+            "title": "BLUEAI_ComfyUI_OpenAI",
+            "reference": "https://github.com/tiange-tree/BLUEAI_ComfyUI_OpenAI",
+            "files": [
+                "https://github.com/tiange-tree/BLUEAI_ComfyUI_OpenAI"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: BLUEAI_OpenAI_Node"
+        },
+        {
+            "author": "nestflow",
+            "title": "ComfyUI-WanPlus",
+            "reference": "https://github.com/nestflow/ComfyUI-WanPlus",
+            "files": [
+                "https://github.com/nestflow/ComfyUI-WanPlus"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for video frame manipulation and image-to-video conversion. (Description by CC)"
+        },
+        {
+            "author": "twdockery",
+            "title": "ComfyUI_Prompt_Batch_Generator",
+            "reference": "https://github.com/twdockery/ComfyUI_Prompt_Batch_Generator",
+            "files": [
+                "https://github.com/twdockery/ComfyUI_Prompt_Batch_Generator"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom nodes for batch image generation with Stable Diffusion 1.5, optimized for low VRAM systems. (Description by CC)"
+        },
+        {
+            "author": "tuxiansheng-ld",
+            "title": "Comfyui-tuxiansheng-nodes",
+            "reference": "https://github.com/tuxiansheng-ld/Comfyui-tuxiansheng-nodes",
+            "files": [
+                "https://github.com/tuxiansheng-ld/Comfyui-tuxiansheng-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: StringToListNode"
+        },
+        {
+            "author": "quinteroac",
+            "title": "comfyui_api_executor_nodes",
+            "reference": "https://github.com/quinteroac/comfyui_api_executor_nodes",
+            "files": [
+                "https://github.com/quinteroac/comfyui_api_executor_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom nodes for ComfyUI that enable workflow execution via API (internal or external), as well as input/output handling and workflow selection."
+        },
+        {
+            "author": "Chang-Jin-Lee",
+            "title": "ComfyUI-PromptMixer-AI [WIP]",
+            "reference": "https://github.com/Chang-Jin-Lee/ComfyUI-PromptMixer-AI",
+            "files": [
+                "https://github.com/Chang-Jin-Lee/ComfyUI-PromptMixer-AI"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node collection for unified control of checkpoints, steps, CFG, samplers, LoRA and prompt parameters with local LLM integration. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "leacvikas0",
+            "title": "ComfyUI-Presence [WIP]",
+            "reference": "https://github.com/leacvikas0/ComfyUI-Presence",
+            "files": [
+                "https://github.com/leacvikas0/ComfyUI-Presence"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: BeautifulTextNode, FluxAdaptiveInjector, InspectNode, PresenceDirector, PresenceDirectorFireworks, PresenceDirectorVertex, PresenceSaver, UnaliverBundlePreview, UnaliverNode, UnaliverPlanner, UnaliverStepIterato, ...\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Kraven1109",
+            "title": "ComfyUI-Llama [NAME CONFLICT]",
+            "reference": "https://github.com/Kraven1109/ComfyUI-Llama",
+            "files": [
+                "https://github.com/Kraven1109/ComfyUI-Llama"
+            ],
+            "install_type": "git-clone",
+            "description": "Lightweight ComfyUI plugin exposing llama.cpp-based one-shot Qwen VQA nodes."
+        },
+        {
+            "author": "xiaoxidashen",
+            "title": "comfyui_my_utils",
+            "reference": "https://github.com/xiaoxidashen/comfyui_my_utils",
+            "files": [
+                "https://github.com/xiaoxidashen/comfyui_my_utils"
+            ],
+            "install_type": "git-clone",
+            "description": "Guide and utilities for creating ComfyUI custom nodes with image/video preview functionality. (Description by CC)"
+        },
+        {
+            "author": "agavesunset",
+            "title": "ComfyUI_LoRA_Tracker",
+            "reference": "https://github.com/agavesunset/ComfyUI_LoRA_Tracker",
+            "files": [
+                "https://github.com/agavesunset/ComfyUI_LoRA_Tracker"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node for tracking and displaying LoRA parameters. (Description by CC)"
+        },
+        {
+            "author": "SleazySleaze",
+            "title": "aesthetic-persona-comfyui-node",
+            "reference": "https://github.com/SleazySleaze/aesthetic-persona-comfyui-node",
+            "files": [
+                "https://github.com/SleazySleaze/aesthetic-persona-comfyui-node"
+            ],
+            "install_type": "git-clone",
+            "description": "Node providing aesthetic persona parsing capabilities for ComfyUI. (Description by CC)"
+        },
+        {
+            "author": "xtanqn",
+            "title": "comfyui-xishen [WIP]",
+            "reference": "https://github.com/xtanqn/comfyui-xishen",
+            "files": [
+                "https://github.com/xtanqn/comfyui-xishen"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node for ComfyUI that generates random numbers as text output.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "heyburns",
+            "title": "ComfyUI-Logic-Redux [WIP]",
+            "reference": "https://github.com/heyburns/ComfyUI-Logic-Redux",
+            "files": [
+                "https://github.com/heyburns/ComfyUI-Logic-Redux"
+            ],
+            "install_type": "git-clone",
+            "description": "Validation-friendly rewrite of ComfyUI Logic nodes with drop-in compatibility, featuring compare, int/float/bool/string pass-through, ternary logic, and debug nodes. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Mohamed-Sakr",
+            "title": "ComfyUI-SimpleMarkdown [UNSAFE]",
+            "reference": "https://github.com/Mohamed-Sakr/ComfyUI-SimpleMarkdown",
+            "files": [
+                "https://github.com/Mohamed-Sakr/ComfyUI-SimpleMarkdown"
+            ],
+            "install_type": "git-clone",
+            "description": "A simple markdown node for ComfyUI[w/This nodepack has a frontend vulnerability.]"
+        },
+        {
+            "author": "starsFriday",
+            "title": "ComfyUI-Tracker-Person [WIP]",
+            "reference": "https://github.com/starsFriday/ComfyUI-Tracker-Person",
+            "files": [
+                "https://github.com/starsFriday/ComfyUI-Tracker-Person"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node using YOLO instance segmentation to track specific people in video frames with color histogram re-identification for robust tracking across occlusions. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "judian17",
+            "title": "ComfyUI-MIDI-3D [WIP]",
+            "reference": "https://github.com/judian17/ComfyUI-MIDI-3D",
+            "files": [
+                "https://github.com/judian17/ComfyUI-MIDI-3D"
+            ],
+            "install_type": "git-clone",
+            "description": "Partial implementation of the MIDI-3D project for ComfyUI with multi-view normal/depth map generation and texture baking. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "IAFFeng",
+            "title": "Comfyui_XF_Custom_Actual-Node",
+            "reference": "https://github.com/IAFFeng/Comfyui_XF_Custom_Actual-Node",
+            "files": [
+                "https://github.com/IAFFeng/Comfyui_XF_Custom_Actual-Node"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node providing GeminiMatting functionality for image matting operations. (Description by CC)"
+        },
+        {
+            "author": "LvyuanW",
+            "title": "ComfyUI-6yuan [WIP]",
+            "reference": "https://github.com/LvyuanW/ComfyUI-6yuan",
+            "files": [
+                "https://github.com/LvyuanW/ComfyUI-6yuan"
+            ],
+            "install_type": "git-clone",
+            "description": "A curated set of ComfyUI nodes under 6yuan namespace."
+        },
+        {
+            "author": "wciq1208",
+            "title": "even-comfyui-plugin",
+            "reference": "https://github.com/wciq1208/even-comfyui-plugin",
+            "files": [
+                "https://github.com/wciq1208/even-comfyui-plugin"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Gemini, NanoBanana"
+        },
+        {
+            "author": "Fuhze73",
+            "title": "ComfyUI-PainterI2V-StartEndImage-Tiled [WIP]",
+            "reference": "https://github.com/Fuhze73/ComfyUI-PainterI2V-StartEndImage-Tiled",
+            "files": [
+                "https://github.com/Fuhze73/ComfyUI-PainterI2V-StartEndImage-Tiled"
+            ],
+            "install_type": "git-clone",
+            "description": "Wan2.2 image-to-video enhancement node optimized for 4-step LoRAs, reducing slow-motion and enhancing camera movement. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "stevanisya",
+            "title": "comfyui_txt_selector  [WIP]",
+            "reference": "https://github.com/stevanisya/comfyui_txt_selector",
+            "files": [
+                "https://github.com/stevanisya/comfyui_txt_selector"
+            ],
+            "install_type": "git-clone",
+            "description": "A simple ComfyUI node that lets you choose one text from up to 10 inputs.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Isi-dev",
+            "title": "ComfyUI_DeleteModelPassthrough_ExecutionFlowControl [WIP]",
+            "reference": "https://github.com/Isi-dev/ComfyUI_DeleteModelPassthrough_ExecutionFlowControl",
+            "files": [
+                "https://github.com/Isi-dev/ComfyUI_DeleteModelPassthrough_ExecutionFlowControl"
+            ],
+            "install_type": "git-clone",
+            "description": "Memory management custom node that deletes specific models from VRAM and RAM while passing through other input types unchanged, useful for low-memory environments. (Description by CC)"
+        },
+        {
+            "author": "K1maran",
+            "title": "ComfyUI-Kimaran",
+            "reference": "https://github.com/K1maran/ComfyUI-Kimaran",
+            "files": [
+                "https://github.com/K1maran/ComfyUI-Kimaran"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI nodes for preset latent image generation including EmptyLatentImagePresetsAIO, FLUX, QWEN, and SD1.5 variants. (Description by CC)"
+        },
+        {
+            "author": "jorin91",
+            "title": "ComfyUI-JSG-Utils [UNSAFE]",
+            "reference": "https://github.com/jorin91/ComfyUI-JSG-Utils",
+            "files": [
+                "https://github.com/jorin91/ComfyUI-JSG-Utils"
+            ],
+            "install_type": "git-clone",
+            "description": "Utility nodes for ComfyUI including file operations, recursive image finding, and path parsing. (Description by CC)[w/This nodepack contains a vulnerability that allows remote deletion of files at arbitrary paths.]"
+        },
+        {
+            "author": "vadimcro",
+            "title": "External-Image-Revised-with-MASK",
+            "reference": "https://github.com/vadimcro/External-Image-Revised-with-MASK",
+            "files": [
+                "https://github.com/vadimcro/External-Image-Revised-with-MASK"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI external image node with mask support for image composition and masking workflows. (Description by CC)"
+        },
+        {
+            "author": "martinken",
+            "title": "ComfyUI-KMNodes",
+            "reference": "https://github.com/martinken/ComfyUI-KMNodes",
+            "files": [
+                "https://github.com/martinken/ComfyUI-KMNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: KM_Aspect_Ratio_Selector, KM_Color_Correct, KM_Downscale_Image, KM_Merge_Images, KM_Safe_Mask_Bounds, KM_Safe_SEGS_Bounds, KM_Video_Image_Color_Match, KM_WanVideoToVideo, WanImageToVide, ..."
+        },
+        {
+            "author": "sahibalejandro",
+            "title": "ComfyUI Sahib Nodes",
+            "reference": "https://github.com/sahibalejandro/comfyui-sahib-nodes",
+            "files": [
+                "https://github.com/sahibalejandro/comfyui-sahib-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of custom nodes for ComfyUI"
+        },
+        {
+            "author": "pznodes",
+            "title": "MotionCapture [UNSAFE]",
+            "reference": "https://github.com/PozzettiAndrea/ComfyUI-MotionCapture",
+            "files": [
+                "https://github.com/PozzettiAndrea/ComfyUI-MotionCapture"
+            ],
+            "install_type": "git-clone",
+            "description": "Motion Capture nodes for ComfyUI: SMPL data/retargeting using GVHMR. This node is a very first draft, putting it out there in the hope some blender whizzes can help me figure out retargeting *prayer emoji*[w/This nodepack includes an endpoint that access files from arbitrary paths.]"
+        },
+        {
+            "author": "pznodes",
+            "title": "SAM3DObjects [UNSAFE]",
+            "reference": "https://github.com/PozzettiAndrea/ComfyUI-SAM3DObjects",
+            "files": [
+                "https://github.com/PozzettiAndrea/ComfyUI-SAM3DObjects"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI wrapper for SAM3DObjects: extract 3D meshes/gaussians from pictures. This node is a very first draft, putting it out there in the hope people help me with the development. I hate shipping broken code but I have some hope at least some linux people will manage to use it.[w/This nodepack includes an endpoint that access files from arbitrary paths.]"
+        },
+        {
+            "author": "nachoman",
+            "title": "nachoman Custom Nodes [WIP]",
+            "reference": "https://github.com/eddiehavila/comfyui-nachoman",
+            "files": [
+                "https://github.com/eddiehavila/comfyui-nachoman"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: NACHOMAN_AudioCropTime, NACHOMAN_AutoQueueController, NACHOMAN_ChunkIndexController, NACHOMAN_CombineAllChunks, NACHOMAN_Extract_Frame_Number, NACHOMAN_FullSongAnalyzerV4, NACHOMAN_GetPromptByIndex, NACHOMAN_GetRunIndexFromJson, NACHOMAN_IndexedPromptChunker, NACHOMAN_IndexedPromptChunkerV2, NACHOMAN_LoadAudioSplitDynamic, NACHOMAN_LoadAudioSplitUpload, NACHOMAN_LoadAudioSplit_HUMO, NACHOMAN_LoadAudioSplit_HUMO_Transcribe, NACHOMAN_LoadSingleAudioChunk, NACHOMAN_LoadVideos, NACHOMAN_PostRunIndexStepper, NACHOMAN_SaveVideoChunkWithIndex, NACHOMAN_TranscribeText, NACHOMAN_VideoSplitte, ...\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "xuchenxu168",
+            "title": "ComfyUI-Gemini-3 [UNSAFE/WIP]",
+            "reference": "https://github.com/xuchenxu168/ComfyUI-Gemini-3",
+            "files": [
+                "https://github.com/xuchenxu168/ComfyUI-Gemini-3"
+            ],
+            "install_type": "git-clone",
+            "description": "Full-featured, high-performance Google Gemini 3 integration for ComfyUI. This plugin implements **all** Gemini 3 capabilities with optimized performance.\nNOTE: The files in the repo are not organized.[w/This nodepack has a vulnerability that allows arbitrary code execution remotely.]"
+        },
+        {
+            "author": "starsFriday",
+            "title": "ComfyUI-Audio-Subtitle [WIP]",
+            "reference": "https://github.com/starsFriday/ComfyUI-Audio-Subtitle",
+            "files": [
+                "https://github.com/starsFriday/ComfyUI-Audio-Subtitle"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI node that automatically transcribes audio using OpenAI Whisper and hardcodes subtitles onto video frames using FFmpeg with customizable fonts, colors, styles, and positioning. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "TheLocalLab",
+            "title": "ComfyUI-Kandinsky-GGUF [WIP]",
+            "reference": "https://github.com/TheLocalLab/ComfyUI-Kandinsky-GGUF",
+            "files": [
+                "https://github.com/TheLocalLab/ComfyUI-Kandinsky-GGUF"
+            ],
+            "install_type": "git-clone",
+            "description": "A fork of Ada123-a’s ComfyUI-Kandinsky nodes for Kandinsky 5 DiT. Enables high-quality video generation from text (T2V) and images (I2V) with advanced text-guided controls. \nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "JosDigby",
+            "title": "ComfyUI-DigbyWan",
+            "reference": "https://github.com/JosDigby/ComfyUI-DigbyWan",
+            "files": [
+                "https://github.com/JosDigby/ComfyUI-DigbyWan"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: ImageBatchLoopExtract, WanMiddleFrameToVideo, WanVACEVideoSmoother"
+        },
+        {
+            "author": "UltraNoob-NazoGiken",
+            "title": "ComfyUI-TOML-Tools [WIP]",
+            "reference": "https://github.com/UltraNoob-NazoGiken/ComfyUI-TOML-Tools",
+            "files": [
+                "https://github.com/UltraNoob-NazoGiken/ComfyUI-TOML-Tools"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: CreateTomlData, GetTomlValue, LoadToml, MergeTomlData, SaveTom, ...\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "frost-byte",
+            "title": "fb-tools",
+            "reference": "https://github.com/frost-byte/fbTools",
+            "files": [
+                "https://github.com/frost-byte/fbTools"
+            ],
+            "install_type": "git-clone",
+            "description": "A collect of custom nodes for ComfyUI"
+        },
+        {
+            "author": "Aruntd008",
+            "title": "ComfyUI_blender_render",
+            "reference": "https://github.com/Aruntd008/ComfyUI_blender_render",
+            "files": [
+                "https://github.com/Aruntd008/ComfyUI_blender_render"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Blender Render (Auto-Setup)"
+        },
+        {
+            "author": "mertgoksel",
+            "title": "comfyui-noot-nodes [WIP]",
+            "reference": "https://github.com/mertgoksel/ComfyUI-Noot-Nodes",
+            "files": [
+                "https://github.com/mertgoksel/ComfyUI-Noot-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes i made to suit my needs, currently includes a tag randomizer and a stagedksampler which iterates sampler steps and upscaling."
+        },
+        {
+            "author": "egormly",
+            "title": "ComfyUI-EG_Tools [WIP]",
+            "reference": "https://github.com/egormly/ComfyUI-EG_Tools",
+            "files": [
+                "https://github.com/egormly/ComfyUI-EG_Tools"
+            ],
+            "install_type": "git-clone",
+            "description": "Quality-of-life utility nodes for ComfyUI providing smart switches for dynamic data routing, path helpers for consistent file naming, and image utilities including Qwen vision model preparation.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "huhu-tiger",
+            "title": "ComfyUI-RemoteDownload",
+            "reference": "https://github.com/huhu-tiger/ComfyUI-RemoteDownload",
+            "files": [
+                "https://github.com/huhu-tiger/ComfyUI-RemoteDownload"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: LoadImageFromRemote"
+        },
+        {
+            "author": "basenc",
+            "title": "Comfyui-Nodes-basenc [UNSAFE]",
+            "reference": "https://github.com/basenc/Comfyui-Nodes-basenc",
+            "files": [
+                "https://github.com/basenc/Comfyui-Nodes-basenc"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Custom OpenAI Chat Completion, Environment Variable, Eval, JSON Path Select, JSON Path Select, Wan Video Size, ... [w/This nodepack has a vulnerability that allows arbitrary file contents to be read remotely.]"
+        },
+        {
+            "author": "jkaarlehto",
+            "title": "ComfyUI-GetWorkflowName [WIP]",
+            "reference": "https://github.com/jKaarlehto/ComfyUI-GetWorkflowName",
+            "files": [
+                "https://github.com/jKaarlehto/ComfyUI-GetWorkflowName"
+            ],
+            "install_type": "git-clone",
+            "description": "Adds a node that outputs the workflow's name as a string\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "newraina",
+            "title": "comfyui-photoshop-v2 [WIP]",
+            "reference": "https://github.com/newraina/comfyui-photoshop-v2",
+            "files": [
+                "https://github.com/newraina/comfyui-photoshop-v2"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Photoshop ComfyUI Plugin, Send To Photoshop, ClipPass, modelPass, Photoshop RemoteConnection, ..."
+        },
+        {
+            "author": "eddyhhlure1Eddy",
+            "title": "ComfyUI-QwenImageWrapper [WIP]",
+            "reference": "https://github.com/eddyhhlure1Eddy/ComfyUI-QwenImageWrapper",
+            "files": [
+                "https://github.com/eddyhhlure1Eddy/ComfyUI-QwenImageWrapper"
+            ],
+            "install_type": "git-clone",
+            "description": "Integrated Qwen-Image node for ComfyUI with all-in-one model loading, 4 LoRA slots, memory optimization via BlockSwap reducing VRAM usage by 30-60%, and multiple quantization options.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "tori29umai0123",
+            "title": "ComfyUI-SDXLGenerateFromTextFile [UNSAFE]",
+            "reference": "https://github.com/tori29umai0123/ComfyUI-SDXLGenerateFromTextFile",
+            "files": [
+                "https://github.com/tori29umai0123/ComfyUI-SDXLGenerateFromTextFile"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node for batch image generation from text file prompts with configurable resolution, quantity, and model settings for Stable Diffusion workflows. (Description by CC)[w/This nodepack has a vulnerability that allows arbitrary file contents to be read remotely.]"
+        },
+        {
+            "author": "unphased",
+            "title": "code-nodes [UNSAFE]",
+            "reference": "https://github.com/unphased/code-nodes",
+            "files": [
+                "https://github.com/unphased/code-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "This repository provides two lightweight execution helpers for ComfyUI graphs: Shell Code, Python Code[w/This nodepack has a vulnerability that allows arbitrary code execution remotely.]"
+        },
+        {
+            "author": "marduk191",
+            "title": "comfyui_cmdtls [UNSAFE]",
+            "reference": "https://github.com/marduk191/comfyui_cmdtls",
+            "files": [
+                "https://github.com/marduk191/comfyui_cmdtls"
+            ],
+            "install_type": "git-clone",
+            "description": "A comprehensive set of ComfyUI custom nodes for file system operations, file browsing, upload/download, and shell command execution.[w/This nodepack has a vulnerability that allows arbitrary code execution remotely.]"
+        },
+        {
+            "author": "AIExplorer25",
+            "title": "ComfyUI_HuggingfaceHelper",
+            "reference": "https://github.com/AIExplorer25/ComfyUI_HuggingfaceHelper",
+            "files": [
+                "https://github.com/AIExplorer25/ComfyUI_HuggingfaceHelper"
+            ],
+            "install_type": "git-clone",
+            "description": "This repo has different functions to help dealing with huggingface data"
+        },
+        {
+            "author": "ilslli",
+            "title": "Comfyui-IV2Z",
+            "reference": "https://github.com/ilslli/Comfyui-IV2Z",
+            "files": [
+                "https://github.com/ilslli/Comfyui-IV2Z"
+            ],
+            "install_type": "git-clone",
+            "description": "Modify the VideoCombine node of [a/ComfyUI-VideoHelperSuite](https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite) so that, after encoding the video, it outputs the result as a compressed archive."
+        },
+        {
+            "author": "PladsElsker",
+            "title": "comfyui-krita [WIP]",
+            "reference": "https://github.com/PladsElsker/comfyui-krita",
+            "files": [
+                "https://github.com/PladsElsker/comfyui-krita"
+            ],
+            "install_type": "git-clone",
+            "description": "Minimal Krita extension and ComfyUI custom nodes for integrating both UIs together with workflow exposure and compositing capabilities. NOTE: The project is in active development (WIP). (Description by CC)"
+        },
+        {
+            "author": "max-dingsda",
+            "title": "ComfyUI-PromptLoop [WIP]",
+            "reference": "https://github.com/max-dingsda/ComfyUI-PromptLoop",
+            "files": [
+                "https://github.com/max-dingsda/ComfyUI-PromptLoop"
+            ],
+            "install_type": "git-clone",
+            "description": "Batch process multiple prompts from text files in a single ComfyUI queue job - supports prompt loops from files or multi-line text with optional start index and limit controls.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Gergzilla",
+            "title": "Slim-Comfy-Pack [private]",
+            "reference": "https://github.com/Gergzilla/Slim-Comfy-Pack",
+            "files": [
+                "https://github.com/Gergzilla/Slim-Comfy-Pack"
+            ],
+            "install_type": "git-clone",
+            "description": "A slimmed down collection of ComfyUI nodes either from elsewhere, custom creation or updated independently. (Description by CC)"
+        },
+        {
+            "author": "Sida Liu",
+            "title": "ComfyUI-Notebook [UNSAFE]",
+            "id": "notebook",
+            "reference": "https://github.com/liusida/ComfyUI-Notebook",
+            "files": [
+                "https://github.com/liusida/ComfyUI-Notebook"
+            ],
+            "install_type": "git-clone",
+            "description": "A Jupyter-style custom node for executing Python code and plotting within ComfyUI workflows.[w/This extension allows remote command execution.]"
+        },
+        {
+            "author": "pznodes",
+            "title": "ComfyUI-UniRig [WIP]",
+            "reference": "https://github.com/PozzettiAndrea/ComfyUI-UniRig",
+            "files": [
+                "https://github.com/PozzettiAndrea/ComfyUI-UniRig"
+            ],
+            "install_type": "git-clone",
+            "description": "WORK IN PROGRESS. Automatic skeleton extraction for ComfyUI using UniRig (SIGGRAPH 2025). Self-contained with bundled Blender."
+        },
+        {
+            "author": "pznodes",
+            "title": "CADabra [WIP]",
+            "reference": "https://github.com/PozzettiAndrea/ComfyUI-CADabra",
+            "files": [
+                "https://github.com/PozzettiAndrea/ComfyUI-CADabra"
+            ],
+            "install_type": "git-clone",
+            "description": "WORK IN PROGRESS. CAD file processing nodes for ComfyUI with interactive 3D OCC CAD viewer. Load STEP, IGES, BREP files and generate meshes using Gmsh."
+        },
+        {
+            "author": "pznodes",
+            "title": "Hunyuan3D-Part [WIP]",
+            "reference": "https://github.com/PozzettiAndrea/ComfyUI-Hunyuan3D-Part",
+            "files": [
+                "https://github.com/PozzettiAndrea/ComfyUI-Hunyuan3D-Part"
+            ],
+            "install_type": "git-clone",
+            "description": "WORK IN PROGRESS. Hunyuan3D-Part nodes for ComfyUI: 3D part segmentation with P3-SAM and high-fidelity part generation with X-Part diffusion."
+        },
+        {
+            "author": "hhelloe",
+            "title": "comfyuiWeightOnlyQuant [WIP]",
+            "reference": "https://github.com/hhelloe/comfyuiWeightOnlyQuant",
+            "files": [
+                "https://github.com/hhelloe/comfyuiWeightOnlyQuant"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Weight-Only Quantize (Linear)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "KXSR",
+            "title": "ComfyUI-Easy-s3",
+            "reference": "https://github.com/KXSR/ComfyUI-Easy-s3",
+            "files": [
+                "https://github.com/KXSR/ComfyUI-Easy-s3"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: 'URL: Load Media', 'Image: Save To Disk (No UI)', 'S3: Upload Presigned', ..."
+        },
+        {
+            "author": "akashch1512",
+            "title": "ComfyUI_UguuUploader",
+            "reference": "https://github.com/akashch1512/ComfyUI_UguuUploader",
+            "files": [
+                "https://github.com/akashch1512/ComfyUI_UguuUploader"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Uguu.se Video Uploader\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "vadimcro",
+            "title": "string_by_index",
+            "reference": "https://github.com/vadimcro/string_by_index",
+            "files": [
+                "https://github.com/vadimcro/string_by_index"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node that lets you output a string, based on int index number on input"
+        },
+        {
+            "author": "sickbraintwo",
+            "title": "WextraX [WIP]",
+            "reference": "https://github.com/sickbraintwo/WextraX",
+            "files": [
+                "https://github.com/sickbraintwo/WextraX"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom nodes for LoRA management, including LoRA name extraction and tag filtering utilities. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "hhelloe",
+            "title": "ComfyuiQuantizerToFp8 [WIP]",
+            "reference": "https://github.com/hhelloe/ComfyuiQuantizerToFp8",
+            "files": [
+                "https://github.com/hhelloe/ComfyuiQuantizerToFp8"
+            ],
+            "install_type": "git-clone",
+            "description": "Linear quantizer node for ComfyUI that converts models to FP8 precision format for reduced memory usage and faster inference.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "nobinBB",
+            "title": "comfyui-samenodes",
+            "reference": "https://github.com/nobinBB/comfyui-samenodes",
+            "files": [
+                "https://github.com/nobinBB/comfyui-samenodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Batch Image Processor, Float to String, Float to String with Prefix, LoRA Wildcard Generator"
+        },
+        {
+            "author": "MushroomFleet",
+            "title": "ComfyUI-SCP-nodes [UNSAFE]",
+            "reference": "https://github.com/MushroomFleet/ComfyUI-SCP-nodes",
+            "files": [
+                "https://github.com/MushroomFleet/ComfyUI-SCP-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom ComfyUI node that automatically saves generated images locally and uploads them to a remote server via SCP (Secure Copy Protocol).[w/This nodepack includes an endpoint that access files from arbitrary paths.]"
+        },
+        {
+            "author": "jonstreeter",
+            "title": "Archive Output [UNSAFE]",
+            "id": "archive-output",
+            "reference": "https://github.com/jonstreeter/comfyui-archive-output",
+            "files": [
+                "https://github.com/jonstreeter/comfyui-archive-output"
+            ],
+            "install_type": "git-clone",
+            "description": "Archive and organize your generated images into date-structured folders, and compress PNG images to JPEG/WebP with full workflow metadata preservation, saving 85-95% disk space.[w/This nodepack includes an endpoint that access files from arbitrary paths.]"
+        },
+        {
+            "author": "xuchenxu168",
+            "title": "ComfyUI-Qwen3-VL [WIP]",
+            "id": "qwen3-vl",
+            "reference": "https://github.com/xuchenxu168/ComfyUI_Qwen3-VL",
+            "files": [
+                "https://github.com/xuchenxu168/ComfyUI_Qwen3-VL"
+            ],
+            "install_type": "git-clone",
+            "description": "Enhanced Qwen3-VL integration for ComfyUI with direct image and video support.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "cedarconnor",
+            "title": "HunyuanWorld-Mirror ComfyUI Node Pack [WIP]",
+            "reference": "https://github.com/cedarconnor/ComfyUI-HunyuanWorld-Mirror",
+            "files": [
+                "https://github.com/cedarconnor/ComfyUI-HunyuanWorld-Mirror"
+            ],
+            "install_type": "git-clone",
+            "description": "Transform 2D images into 3D worlds using Tencent's HunyuanWorld-Mirror model directly in ComfyUI."
+        },
+        {
+            "author": "zRicmuriellee1xh",
+            "title": "ComfyUI-Logic-Switch",
+            "reference": "https://github.com/muriellee1x/ComfyUI-Logic-Switch",
+            "files": [
+                "https://github.com/muriellee1x/ComfyUI-Logic-Switch"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: random string selector"
+        },
+        {
+            "author": "zRich",
+            "title": "ComfyUI-zRich-ObjectCutter",
+            "reference": "https://github.com/zRich/ComfyUI-zRich-ObjectCutter",
+            "files": [
+                "https://github.com/zRich/ComfyUI-zRich-ObjectCutter"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: zRich Object Cutter (MASK to Transparent), SAM2 Auto Layer Extractor"
+        },
+        {
+            "author": "ajcampbell1333",
+            "title": "BudgetGuard for Artists [WIP]",
+            "reference": "https://github.com/ajcampbell1333/BudgetGuard_Artists",
+            "files": [
+                "https://github.com/ajcampbell1333/BudgetGuard_Artists"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node that provides cost estimation and budget management for NVIDIA NIM nodes across multiple cloud providers."
+        },
+        {
+            "author": "muriellee1x",
+            "title": "ComfyUI-switch-angles",
+            "reference": "https://github.com/muriellee1x/ComfyUI-switch-angles",
+            "files": [
+                "https://github.com/muriellee1x/ComfyUI-switch-angles"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Camera Prompt Generator, ..."
+        },
+        {
+            "author": "APZmedia",
+            "title": "ComfyUI Folder Parser [UNSAFE]",
+            "reference": "https://github.com/APZmedia/ComfyUI-folder-parser",
+            "files": [
+                "https://github.com/APZmedia/ComfyUI-folder-parser"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node that parses files from a folder with filtering and sorting capabilities. Outputs file paths for use in other ComfyUI nodes.[w/This nodepack contains a path traversal vulnerability.]"
+        },
+        {
+            "author": "Seb-Lis",
+            "title": "comfyui-seed-selector",
+            "reference": "https://github.com/Seb-Lis/comfyui-seed-selector",
+            "files": [
+                "https://github.com/Seb-Lis/comfyui-seed-selector"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Seed Selector (INT), My Seed Selector (INT), Seed Selector with Display, ..."
+        },
+        {
+            "author": "Amor718",
+            "title": "AmorForge",
+            "reference": "https://github.com/Amor718/AmorForge",
+            "files": [
+                "https://github.com/Amor718/AmorForge"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Amor's Image WebpSaver, Amor's Image Resizer, Amor's Text Translator, Amor's CogView Image Generator, ..."
+        },
+        {
+            "author": "hujuying",
+            "title": "ComfyUI-QwenVL3-image-Plus [WIP]",
+            "reference": "https://github.com/hujuying/ComfyUI-QwenVL3-image-Plus",
+            "files": [
+                "https://github.com/hujuying/ComfyUI-QwenVL3-image-Plus"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node designed for ComfyUI that integrates the **Qwen3-VL-4B-Instruct-FP8** vision-language model for efficient image understanding and description. It supports flexible prompt-combination features to enhance the accuracy and diversity of image descriptions.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "r-vage",
+            "title": "ComfyUI_Eclipse [UNSAFE]",
+            "reference": "https://github.com/r-vage/ComfyUI_Eclipse",
+            "files": [
+                "https://github.com/r-vage/ComfyUI_Eclipse"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom nodes and utilities for workflow building, type conversions, checkpoint/pipe loaders and file utilities.[w/This nodepack includes an endpoint that access files from arbitrary paths.]"
+        },
+        {
+            "author": "lyra-ai",
+            "title": "lyra-nodes",
+            "reference": "https://github.com/lyra-ai/lyra-nodes",
+            "files": [
+                "https://github.com/lyra-ai/lyra-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Lyra – 🎞️ Image Audio Fusion, Lyra – 🌐 Load Audio from URL, Lyra – 🖼️ Load Image from URL"
+        },
+        {
+            "author": "VectorASD",
+            "title": "ComfyUI-VectorASD",
+            "reference": "https://github.com/VectorASD/ComfyUI-VectorASD",
+            "files": [
+                "https://github.com/VectorASD/ComfyUI-VectorASD"
+            ],
+            "install_type": "git-clone",
+            "description": "VectorASD's custom nodes for ComfyUI"
+        },
+        {
+            "author": "laobaishu1",
+            "title": "ComfyUI-JSON-Upload-Iterator [UNSAFE]",
+            "reference": "https://github.com/laobaishu1/ComfyUI-JSON-Upload-Iterator",
+            "files": [
+                "https://github.com/laobaishu1/ComfyUI-JSON-Upload-Iterator"
+            ],
+            "install_type": "git-clone",
+            "description": "A JSON upload and iterator node for ComfyUI that supports uploading JSON storyboard files and outputs each field sequentially.[w/This nodepack includes an endpoint that access files from arbitrary paths.]"
+        },
+        {
+            "author": "dibowei1980",
+            "title": "Comfyui-QwenEditUtilsFromRapid",
+            "reference": "https://github.com/dibowei1980/Comfyui-QwenEditUtilsFromRapid",
+            "files": [
+                "https://github.com/dibowei1980/Comfyui-QwenEditUtilsFromRapid"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: TextEncodeQwenImageEdit_FromRapid, TextEncodeQwenImageEditPlus_FromRapid"
+        },
+        {
+            "author": "minujeong",
+            "title": "comfyui-oklch-color-picker",
+            "reference": "https://github.com/minujeong/comfyui-oklch-color-picker",
+            "files": [
+                "https://github.com/minujeong/comfyui-oklch-color-picker"
+            ],
+            "install_type": "git-clone",
+            "description": "provide OKLCH space color picker as Comfy UI node"
+        },
+        {
+            "author": "gaonprime-lab",
+            "title": "ComfyUI_Nordy",
+            "reference": "https://github.com/gaonprime-labs/ComfyUI_Nordy",
+            "files": [
+                "https://github.com/gaonprime-labs/ComfyUI_Nordy"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Save Image S3 PresignedUrl (Nordy), Memory Test"
+        },
+        {
+            "author": "mittimi",
+            "title": "ComfyUI_mittimiLoadPresetLite [UNSAFE]",
+            "reference": "https://github.com/mittimi/ComfyUI_mittimiLoadPresetLite",
+            "files": [
+                "https://github.com/mittimi/ComfyUI_mittimiLoadPresetLite"
+            ],
+            "install_type": "git-clone",
+            "description": "This node can easily switch between models and prompts by saving presets.[w/This nodepack includes an endpoint that access files from arbitrary paths.]"
+        },
+        {
+            "author": "WenerSantos3",
+            "title": "ComfyUI-BlueeUtils [UNSAFE]",
+            "reference": "https://github.com/WenerSantos3/ComfyUI-BlueeUtils",
+            "files": [
+                "https://github.com/WenerSantos3/ComfyUI-BlueeUtils"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Bluee Utils - Delete File, blueeInput, Bluee Template String, Bluee Utils - Download Temp, Bluee Utils - Read Temp Image, Bluee Utils - Read Temp Audio, bluee - file exists, Bluee - Combine Images + Audio (H.264 MP4)[w/This nodepack includes an endpoint that access files from arbitrary paths.]"
+        },
+        {
+            "author": "Rosegun007",
+            "title": "ComfyUI-Pandora",
+            "reference": "https://github.com/Rosegun007/ComfyUI-Pandora",
+            "files": [
+                "https://github.com/Rosegun007/ComfyUI-Pandora"
+            ],
+            "install_type": "git-clone",
+            "description": "A simple and user-friendly ComfyUI plugin that offers advanced text rendering, streamlined bridge-style image preview, and various convenient mask tools — primarily designed for ComfyUI tutorial creators."
+        },
+        {
+            "author": "fuselayer",
+            "title": "comfyui-libcom-image-composition",
+            "reference": "https://github.com/fuselayer/comfyui-libcom-image-composition",
+            "files": [
+                "https://github.com/fuselayer/comfyui-libcom-image-composition"
+            ],
+            "install_type": "git-clone",
+            "description": "port of bcmi/libcom and other image composition (harmonization, shadow generation) utilities"
+        },
+        {
+            "author": "Long-form-AI-video-generation",
+            "title": "ComfyUI_vaceFramepack",
+            "reference": "https://github.com/Long-form-AI-video-generation/ComfyUI_vaceFramepack",
+            "files": [
+                "https://github.com/Long-form-AI-video-generation/ComfyUI_vaceFramepack"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: WanVACE FramePack Sampler 2"
+        },
+        {
+            "author": "ifilipis",
+            "title": "ComfyUI-Auto-Prompt-Engineer",
+            "reference": "https://github.com/ifilipis/ComfyUI-Auto-Prompt-Engineer",
+            "files": [
+                "https://github.com/ifilipis/ComfyUI-Auto-Prompt-Engineer"
+            ],
+            "install_type": "git-clone",
+            "description": "Automated prompt engineering using LLMs"
+        },
+        {
+            "author": "JHBOY-ha",
+            "title": "ComfyUI-jh-essential",
+            "reference": "https://github.com/JHBOY-ha/ComfyUI-jh-essential",
+            "files": [
+                "https://github.com/JHBOY-ha/ComfyUI-jh-essential"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Start Timer, End Timer"
+        },
+        {
+            "author": "APZmedia",
+            "title": "Joshua Brand Assets Loader [private]",
+            "reference": "https://github.com/APZmedia/Joshua-brand-assets-loader",
+            "files": [
+                "https://github.com/APZmedia/Joshua-brand-assets-loader"
+            ],
+            "install_type": "git-clone",
+            "description": "A comprehensive brand asset loader for ComfyUI that handles logos, fonts, and color palettes with dual loading modes (API and manual)."
+        },
+        {
+            "author": "GoddessLabs",
+            "title": "ComfyUI-GoddessLabs-NodePack",
+            "reference": "https://github.com/GoddessLabs/ComfyUI-GoddessLabs-NodePack",
+            "files": [
+                "https://github.com/GoddessLabs/ComfyUI-GoddessLabs-NodePack"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom node pack for ComfyUI This nodepack helps to conveniently enhance images through Detector, Detailer, Upscaler, Pipe, and more."
+        },
+        {
+            "author": "Beinsezii",
+            "title": "DEET: Diffusion Entropy Equalization Transform [WIP]",
+            "reference": "https://github.com/Beinsezii/DEET",
+            "files": [
+                "https://github.com/Beinsezii/DEET"
+            ],
+            "install_type": "git-clone",
+            "description": "A simple transform to regulate the noise as it flows in and out of a diffusion model. Currently implemented as a nn.Module forward hook for Diffusers and as a node for ComfyUI"
+        },
+        {
+            "author": "woitec",
+            "title": "ComfyUI_ImageDimensions [WIP]",
+            "reference": "https://github.com/woitec/ComfyUI_ImageDimensions",
+            "files": [
+                "https://github.com/woitec/ComfyUI_ImageDimensions"
+            ],
+            "install_type": "git-clone",
+            "description": "Node for extraction of dimensions from an upstream-loaded tensor image.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "PopeyesBiscuit",
+            "title": "ComfyUI-WebSocketLoRAExporter",
+            "reference": "https://github.com/PopeyesBiscuit/ComfyUI-WebSocketLoRAExporter",
+            "files": [
+                "https://github.com/PopeyesBiscuit/ComfyUI-WebSocketLoRAExporter"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Simple LoRA Loader, ..."
+        },
+        {
+            "author": "PopeyesBiscuit",
+            "title": "ComfyUI Delete Folder Nodes [UNSAFE]",
+            "reference": "https://github.com/PopeyesBiscuit/ComfyUI-DeleteFolder",
+            "files": [
+                "https://github.com/PopeyesBiscuit/ComfyUI-DeleteFolder"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom nodes for clearing folders in the input or output folder."
+        },
+        {
+            "author": "hndrr",
+            "title": "Comfyui-SceneDetect [WIP]",
+            "reference": "https://github.com/hndrr/Comfyui-SceneDetect",
+            "files": [
+                "https://github.com/hndrr/Comfyui-SceneDetect"
+            ],
+            "install_type": "git-clone",
+            "description": "Comfyui-SceneDetect is a ComfyUI custom node that uses PySceneDetect to locate scene boundaries in a video and emit one representative frame per scene as an `IMAGE` batch. It also returns per-scene metadata as JSON (`STRING`) and the total number of detected scenes (`INT`)."
+        },
+        {
+            "author": "zRich",
+            "title": "sam2_layer_node",
+            "reference": "https://github.com/zRich/sam2_layer_node",
+            "files": [
+                "https://github.com/zRich/sam2_layer_node"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI Sam2 Layer Extract"
+        },
+        {
+            "author": "promptingpixels",
+            "title": "ComfyUI-DreamOmni2-GGUF [WIP]",
+            "reference": "https://github.com/xuchenxu168/Comfyui_Prompt_Edit",
+            "files": [
+                "https://github.com/xuchenxu168/Comfyui_Prompt_Edit"
+            ],
+            "install_type": "git-clone",
+            "description": "Prompt Edit is a custom node for ComfyUI that allows you to pause workflow execution, edit text prompts, and then continue. This is very useful for scenarios where you need to dynamically adjust prompts during the generation process.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "promptingpixels",
+            "title": "Simple CSV Parser [UNSAFE]",
+            "reference": "https://github.com/content-and-code/ComfyUI-SimpleCSVParse",
+            "files": [
+                "https://github.com/content-and-code/ComfyUI-SimpleCSVParse"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node for parsing CSV files with positive and negative prompts[w/This nodepack includes an endpoint that access files from arbitrary paths.]"
+        },
+        {
+            "author": "PauldeLavallaz",
+            "title": "comfyui_morpheus_NanoBanana_Mask [WIP]",
+            "reference": "https://github.com/PauldeLavallaz/comfyui_morpheus_NanoBanana_Mask",
+            "files": [
+                "https://github.com/PauldeLavallaz/comfyui_morpheus_NanoBanana_Mask"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Morpheus · Batch Images + crop image, Morpheus · NanoBanana Mask, Morpheus · Image Editing Prompt, ...\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "cedarconnor",
+            "title": "ComfyUI Motion Transfer Pack [WIP]",
+            "reference": "https://github.com/cedarconnor/ComfyUI_MotionTransfer",
+            "files": [
+                "https://github.com/cedarconnor/ComfyUI_MotionTransfer"
+            ],
+            "install_type": "git-clone",
+            "description": "Transfer motion from low-resolution AI-generated videos to ultra-high-resolution still images (up to 16K+).\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "theshubzworld",
+            "title": "ComfyUI-NvidiaVision [WIP]",
+            "reference": "https://github.com/theshubzworld/ComfyUI-NvidiaVision",
+            "files": [
+                "https://github.com/theshubzworld/ComfyUI-NvidiaVision"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for NVIDIA AI Vision and Text models\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "MadPonyInteractive",
+            "title": "ComfyUi-MpiNodes [WIP]",
+            "reference": "https://github.com/MadPonyInteractive/ComfyUi-MpiNodes",
+            "files": [
+                "https://github.com/MadPonyInteractive/ComfyUi-MpiNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Mpi Batch Text Replace, Mpi Prompt Chain Selector, Mpi Preset Selector, Mpi Prompt Override Seed, Mpi If Else Probability Inverted, ...\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "fantaskiss",
+            "title": "ComfyUI-Qwen3_VQA_enhanced [WIP]",
+            "reference": "https://github.com/fantaskiss/ComfyUI-Qwen3_VQA_enhanced",
+            "files": [
+                "https://github.com/fantaskiss/ComfyUI-Qwen3_VQA_enhanced"
+            ],
+            "install_type": "git-clone",
+            "description": "Enhancement of the original qwen3_VQA node. Other functions remain unchanged, with only the automatic scanning of the prompt_generator folder added. No new node group is created; only additions and modifications are made to the original node group."
+        },
+        {
+            "author": "pacchikAI",
+            "title": "comfyui_pacchik_window [UNSAFE]",
+            "reference": "https://github.com/pacchikAI/comfyui_pacchik_window",
+            "files": [
+                "https://github.com/pacchikAI/comfyui_pacchik_window"
+            ],
+            "install_type": "git-clone",
+            "description": "A simple ComfyUI node that takes any window you have as an Input to ComfyUI node\nNOTE: WORKS IN WINDOWS ONLY[w/This nodepack has a feature that can retrieve your screen information remotely.]"
+        },
+        {
+            "author": "yamanacn",
+            "title": "ComfyUI-QwenVL3-image [WIP]",
+            "reference": "https://github.com/yamanacn/ComfyUI-QwenVL3-image",
+            "files": [
+                "https://github.com/yamanacn/ComfyUI-QwenVL3-image"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node for ComfyUI that integrates the Qwen3-VL-4B-Instruct-FP8 vision-language model for efficient image understanding and description.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "DeyanSrossturkhahov",
+            "title": "ComfyUI Primitive Mesh Generator [WIP]",
+            "reference": "https://github.com/rossturk/comfyui-primitive-mesh",
+            "files": [
+                "https://github.com/rossturk/comfyui-primitive-mesh"
+            ],
+            "install_type": "git-clone",
+            "description": "Image-to-vector art generator for ComfyUI - Convert photographs into artistic collages composed of geometric shapes using a greedy optimization algorithm.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "DeyanShahov",
+            "title": "Dynamic Prompt Selector for ComfyUI [WIP]",
+            "reference": "https://github.com/DeyanShahov/ComfyUI_RedFox",
+            "files": [
+                "https://github.com/DeyanShahov/ComfyUI_RedFox"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node for ComfyUI that allows you to dynamically select parts from large text prompts split by delimiters. The node supports 5 independent tabs (each like a mini-prompt selector), various selection behaviors, and persists state across ComfyUI restarts.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "addddd2",
+            "title": "AI_Generated_nodes",
+            "reference": "https://github.com/addddd2/AI_Generated_nodes",
+            "files": [
+                "https://github.com/addddd2/AI_Generated_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: ImageScaleToTotalPixelsRound64, ImageBlendLighter, ImageOffset, RGBtoRYGCBM, RYGCBMtoRGB, ExtractImageChannel, MatchRYGCBMColors, TextCommaToWeighted, TextCommaToRandomWeighted, RGBtoLAB, LABtoRGB, ImageMirrorPad, ImageCropBorders, ImageStitcher, ImageScaleToQwen, ..."
+        },
+        {
+            "author": "henjicc",
+            "title": "ComfyUI-CC-ImageLoader [UNSAFE]",
+            "reference": "https://github.com/henjicc/ComfyUI-CC-ImageLoader",
+            "files": [
+                "https://github.com/henjicc/ComfyUI-CC-ImageLoader"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI-CC-ImageLoader is an enhanced image loading node designed for ComfyUI. It is developed based on two excellent projects: ComfyUI-Thumbnails and ComfyUI_Local_Media_Manager.[w/This nodepack includes an endpoint that access files from arbitrary paths.]"
+        },
+        {
+            "author": "A1rCHAN",
+            "title": "Eric's Prompt Enhancers for ComfyUI# Eric's Prompt Enhancers for ComfyUI",
+            "reference": "https://github.com/A1rCHAN/ComfyUI_A1rSpace",
+            "files": [
+                "https://github.com/A1rCHAN/ComfyUI_A1rSpace"
+            ],
+            "install_type": "git-clone",
+            "description": "A comfyui custom node package used by myself. "
+        },
+        {
+            "author": "franklydegenerate",
+            "title": "WAN Resolution Helper [WIP]",
+            "reference": "https://github.com/franklydegenerate/ComfyUI-WAN-Resolution-Helper",
+            "files": [
+                "https://github.com/franklydegenerate/ComfyUI-WAN-Resolution-Helper"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node that outputs WAN 2.2-ready sizes by preserving aspect ratio, capping the long side, and rounding both dimensions to multiples of 16 pixels to reduce artifacts.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "RM2-0",
+            "title": "ComfyUI-Upscale",
+            "reference": "https://github.com/RM2-0/ComfyUI-Upscale",
+            "files": [
+                "https://github.com/RM2-0/ComfyUI-Upscale"
+            ],
+            "install_type": "git-clone",
+            "description": "Efficient upscaling node with FP16 and batch processing support for ComfyUI"
+        },
+        {
+            "author": "light-and-ray",
+            "title": "Minimalistic-Comfy-Wrapper-WebUI",
+            "reference": "https://github.com/light-and-ray/Minimalistic-Comfy-Wrapper-WebUI",
+            "files": [
+                "https://github.com/light-and-ray/Minimalistic-Comfy-Wrapper-WebUI"
+            ],
+            "install_type": "git-clone",
+            "description": "An alternative additional non-node based UI for ComfyUI, that dynamically adapts to your workflows - you only need to change the titles of nodes that you want to see in the Minimalistic webui, and click 'Refresh' button"
+        },
+        {
+            "author": "bakker",
+            "title": "BK_Utils [WIP]",
+            "reference": "https://github.com/bakker/BK_Utils",
+            "files": [
+                "https://github.com/bakker/BK_Utils"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Same Pixel Resolution Calculator, .."
+        },
+        {
+            "author": "garciaone",
+            "title": "g_one_toolkit",
+            "reference": "https://github.com/garciaone/g_one_toolkit",
+            "files": [
+                "https://github.com/garciaone/g_one_toolkit"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of quality of life nodes"
+        },
+        {
+            "author": "banini1841",
+            "title": "BigModelPipe [WIP]",
+            "reference": "https://github.com/banini1841/BigModelPipe",
+            "files": [
+                "https://github.com/banini1841/BigModelPipe"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: BigModelPipeIn, BigModelPipeOut"
+        },
+        {
+            "author": "comfyui-wiki",
+            "title": "ComfyUI-SelectionFocus [WIP]",
+            "reference": "https://github.com/comfyui-wiki/ComfyUI-SelectionFocus",
+            "files": [
+                "https://github.com/comfyui-wiki/ComfyUI-SelectionFocus"
+            ],
+            "install_type": "git-clone",
+            "description": "A powerful ComfyUI extension that helps you focus on selected nodes by highlighting their connections while dimming or hiding unrelated links\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "pizurny",
+            "title": "ComfyUI Feedback Sampler [WIP]",
+            "reference": "https://github.com/pizurny/Comfyui-FeedbackSampler",
+            "files": [
+                "https://github.com/pizurny/Comfyui-FeedbackSampler"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom ComfyUI sampler for creating Deforum-style zoom animations through iterative feedback loop."
+        },
+        {
+            "author": "FlowX Team",
+            "title": "FlowX Preprocess [UNSAFE]",
+            "reference": "https://github.com/cgf120/comfyui-flowx-preprocess",
+            "files": [
+                "https://github.com/cgf120/comfyui-flowx-preprocess"
+            ],
+            "install_type": "git-clone",
+            "description": "FlowX Preprocess Node for ComfyUI - Export workflow configurations with input parameters[w/This nodepack includes an endpoint that deletes files from arbitrary paths.]"
+        },
+        {
+            "author": "jpwgad",
+            "title": "jpwgad",
+            "reference": "https://github.com/jpwgad/jpwgad-comfy",
+            "files": [
+                "https://github.com/jpwgad/jpwgad-comfy"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Image Size Input, Date/Time based output path"
+        },
+        {
+            "author": "octapus8085",
+            "title": "OpenAI-comfyui-O",
+            "reference": "https://github.com/octapus8085/OpenAI-comfyui-O",
+            "files": [
+                "https://github.com/octapus8085/OpenAI-comfyui-O"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: AI API → Image"
+        },
+        {
+            "author": "Immac",
+            "title": "Immac's Tools",
+            "reference": "https://github.com/Immac/ComfyUI-ImmacTools",
+            "files": [
+                "https://github.com/Immac/ComfyUI-ImmacTools"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Concatenate Sigmas Node, Splice Sigmas At %"
+        },
+        {
+            "author": "ThuckMaBaws",
+            "title": "TMB Camera Modifier for ComfyUI [WIP]",
+            "reference": "https://github.com/ThuckMaBaws/TMB-Camera-Modifier",
+            "files": [
+                "https://github.com/ThuckMaBaws/TMB-Camera-Modifier"
+            ],
+            "install_type": "git-clone",
+            "description": "This custom node adds a powerful camera-based modifier system to ComfyUI, enabling rich, authentic prompt generation for image workflows. It’s designed for creators who want to simulate real-world camera setups, film types, and stylistic traits — all with flexible filtering and randomization.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "pfpb",
+            "title": "comfyui-badnodes",
+            "reference": "https://github.com/pfpb/comfyui-badnodes",
+            "files": [
+                "https://github.com/pfpb/comfyui-badnodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: ReplaceEmptyMasksWithLastMasks, ..."
+        },
+        {
+            "author": "isurulkh",
+            "title": "ComfyUI-Lovis-Node [WIP]",
+            "reference": "https://github.com/isurulkh/ComfyUI-Lovis-Node",
+            "files": [
+                "https://github.com/isurulkh/ComfyUI-Lovis-Node"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom nodes for text and audio processing in ComfyUI.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "doc",
+            "title": "doc-utils",
+            "reference": "https://github.com/doc-packages/comfyui-doc-utils",
+            "files": [
+                "https://github.com/doc-packages/comfyui-doc-utils"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: DOC_SaveImageAndAddToHistory, DOC_RandomPromptChoice"
+        },
+        {
+            "author": "obisin",
+            "title": "FSampler for ComfyUI — Fast Skips via Epsilon Extrapolation [WIP]",
+            "reference": "https://github.com/obisin/ComfyUI-FSampler",
+            "files": [
+                "https://github.com/obisin/ComfyUI-FSampler"
+            ],
+            "install_type": "git-clone",
+            "description": "FSampler is a training‑free, sampler‑agnostic acceleration layer for diffusion sampling that reduces model calls by predicting each step’s epsilon (noise) from recent real calls and feeding it into the existing integrator. It provides fixed history modes (h2/h3/h4) and an aggressive adaptive mode that, per step, builds two predictions (e.g., h3 vs h2), compares their predicted next states to get a relative error, and skips the model call when that error is below a hardcoded tolerance. Predicted epsilons are validated and scaled by a universal learning stabilizer L, skips are bounded by guard rails, and the sampler math (Euler, RES 2M/2S, DDIM, DPM++ 2M/2S, LMS) is unchanged.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "remhesneb",
+            "title": "comfyui-remote-media",
+            "reference": "https://github.com/remhesneb/comfyui-remote-media",
+            "files": [
+                "https://github.com/remhesneb/comfyui-remote-media"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Load Image by URL, Load Video by URL"
+        },
+        {
+            "author": "takoyaki1118",
+            "title": "ComfyUI-Kakimoji-Layer",
+            "reference": "https://github.com/takoyaki1118/ComfyUI-Kakimoji-Layer",
+            "files": [
+                "https://github.com/takoyaki1118/ComfyUI-Kakimoji-Layer"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Kakimoji Editor (Live Preview)"
+        },
+        {
+            "author": "ethanfel",
+            "title": "ComfyUI-Mamad8 [WIP]",
+            "reference": "https://github.com/ethanfel/ComfyUI-Mamad8-QwenEdit-fix",
+            "files": [
+                "https://github.com/ethanfel/ComfyUI-Mamad8-QwenEdit-fix"
+            ],
+            "install_type": "git-clone",
+            "description": "Router nodes for optimized batch processing\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "wTechArtist",
+            "title": "ComfyUI-VVL-Tools",
+            "reference": "https://github.com/wTechArtist/ComfyUI-VVL-Tools",
+            "files": [
+                "https://github.com/wTechArtist/ComfyUI-VVL-Tools"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: VVL For Loop Start (Async), VVL For Loop End (Async),  VVL List Chunk (Async), VVL Batch Anything Dynamic, VVL Tensor List Preview, VVL JSON Object Deduplicator, ..."
+        },
+        {
+            "author": "Goldlionren",
+            "title": "ComfyUI-CleanSlate [WIP]",
+            "reference": "https://github.com/Goldlionren/ComfyUI-CleanSlate",
+            "files": [
+                "https://github.com/Goldlionren/ComfyUI-CleanSlate"
+            ],
+            "install_type": "git-clone",
+            "description": "CleanSlate: Post-Sample Cleanup — put it between Sampler and VAE Decode to free memory aggressively.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "systemaiofinterest-wq",
+            "title": "ComfyUI-GoogleFX",
+            "reference": "https://github.com/systemaiofinterest-wq/ComfyUI-GoogleFX",
+            "files": [
+                "https://github.com/systemaiofinterest-wq/ComfyUI-GoogleFX"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Google Imagen 3.5, Google Imagen R2I, Google Imagen Nano Banana x4"
+        },
+        {
+            "author": "alisson-anjos",
+            "title": "ComfyUI-Workarounds [WIP]",
+            "reference": "https://github.com/alisson-anjos/ComfyUI-Workarounds",
+            "files": [
+                "https://github.com/alisson-anjos/ComfyUI-Workarounds"
+            ],
+            "install_type": "git-clone",
+            "description": "This repository provides practical ComfyUI nodes for fast, planar face overlay without non-linear warping, plus flexible region masks to transfer the full head (face + hair + eyebrows + eyes + nose + mouth) or any combination of regions. It also includes optional color matching and JSON diagnostics to integrate with downstream logic.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "adamdavidcole",
+            "title": "ComfyUI XY Video Plot Suite [WIP]",
+            "reference": "https://github.com/adamdavidcole/comfyui-video-xy-plot",
+            "files": [
+                "https://github.com/adamdavidcole/comfyui-video-xy-plot"
+            ],
+            "install_type": "git-clone",
+            "description": "A suite of ComfyUI nodes for generating parameter sweep video matrices with labeled grids. Create comprehensive visual comparisons of how different parameter combinations affect your video generation results.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "clubmate",
+            "title": "comfyui-boll-nodes",
+            "reference": "https://github.com/clubmate/comfyui-boll-nodes",
+            "files": [
+                "https://github.com/clubmate/comfyui-boll-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Get Last Image, Remove Last Image, WAN Config"
+        },
+        {
+            "author": "vunguyen-oss",
+            "title": "ComfyUI-ccsrv2 [WIP]",
+            "reference": "https://github.com/vunguyen-oss/ComfyUI-ccsrv2",
+            "files": [
+                "https://github.com/vunguyen-oss/ComfyUI-ccsrv2"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: CCSR Upscale"
+        },
+        {
+            "author": "kesslerdev",
+            "title": "kesslerdev_comfyui_nodes [WIP/UNSAFE]",
+            "reference": "https://github.com/kesslerdev/kesslerdev_comfyui_nodes",
+            "files": [
+                "https://github.com/kesslerdev/kesslerdev_comfyui_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: S3 Upload File, File Explorer, Save Image Export Path, Save Video Export Path, Save Audio Export Path, S3 Combine Images to Video, Multi-Line Text Input, ...\nNOTE: The files in the repo are not organized.[w/This extension has a vulnerability that allows arbitrary access to local files from remote.]"
+        },
+        {
+            "author": "kesslerdev",
+            "title": "Comfyui : kesslerdev_comfyui_nodes [UNSAFE]",
+            "reference": "https://github.com/kesslerdev/kesslerdev_comfyui_nodes",
+            "files": [
+                "https://github.com/kesslerdev/kesslerdev_comfyui_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Multiline Text Choice (Kesslerdev), Environment Variable Getter (Kesslerdev)[w/This nodepack includes a node that can remotely read arbitrary environment variables.]"
+        },
+        {
+            "author": "panchovial-max",
+            "title": "ComfyUI-ListHelper [NAME CONFLICT]",
+            "reference": "https://github.com/panchovial-max/ComfyUI-ListHelper",
+            "files": [
+                "https://github.com/panchovial-max/ComfyUI-ListHelper"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of custom nodes for ComfyUI that provides enhanced list manipulation and AI integration capabilities."
+        },
+        {
+            "author": "Eric7758",
+            "title": "ComfyUI-IAT [WIP]",
+            "reference": "https://github.com/Eric7758/ComfyUI-IAT",
+            "files": [
+                "https://github.com/Eric7758/ComfyUI-IAT"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI plugin for image & text utilities with Qwen translator and prompt optimizer\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "NeuroWaifu",
+            "title": "ComfyUI.Node.MediaSuite",
+            "reference": "https://github.com/NeuroWaifu/ComfyUI.Node.MediaSuite",
+            "files": [
+                "https://github.com/NeuroWaifu/ComfyUI.Node.MediaSuite"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Image Resize, Video Resize, Resize Calculator, Mask Operations, Mask Blur, Mask Threshold, Dominant Color, Color Palette, Shadows/Highlights/Midtones, Tone Curve, Color Grading, Upscale Video (using Model), Get Video Size, Get Video Info, Compare Video Size, Math Operation, Math Function, Boolean Logic, Compare Numbers, Convert Number, Random Number, Clamp, Remap, Smooth, Constants, ..."
+        },
+        {
+            "author": "BizaNator",
+            "title": "BrainDead_QwenCharacterEditor [WIP]",
+            "reference": "https://github.com/BizaNator/BrainDead_QwenCharacterEditor",
+            "files": [
+                "https://github.com/BizaNator/BrainDead_QwenCharacterEditor"
+            ],
+            "install_type": "git-clone",
+            "description": "Created by BizaNator for the BrainDeadGuild community. These professional-grade character editing tools for ComfyUI specialize in identity preservation and multi-reference image control.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "sk-palani",
+            "title": "ComfyUI_Simpler",
+            "reference": "https://github.com/sk-palani/ComfyUI_Simpler",
+            "files": [
+                "https://github.com/sk-palani/ComfyUI_Simpler"
+            ],
+            "install_type": "git-clone",
+            "description": "This repository contains custom nodes designed for the ComfyUI framework, focusing on simplification improvements. These nodes aim to make tasks easier and simple"
+        },
+        {
+            "author": "SushantSingh-23-01",
+            "title": "comfyui_flowrider_nodes [UNSAFE]",
+            "reference": "https://github.com/SushantSingh-23-01/comfyui_flowrider_nodes",
+            "files": [
+                "https://github.com/SushantSingh-23-01/comfyui_flowrider_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Collection of nodes performing random utility functions like: JPG Saver - Save images in jpg format. Resolution Selector - Selects from standard resolutions with aspect ratios.[w/This extension has a vulnerability that allows arbitrary access to local files from remote.]"
+        },
+        {
+            "author": "YokoYokoTEC",
+            "title": "comfyui-sequential-prompt-file [UNSAFE]",
+            "reference": "https://github.com/YokoYokoTEC/comfyui-sequential-prompt-file",
+            "files": [
+                "https://github.com/YokoYokoTEC/comfyui-sequential-prompt-file"
+            ],
+            "install_type": "git-clone",
+            "description": "This is the file-based version of comfyui-sequential-prompt. For detailed usage instructions, please refer to the following link: [a/https://note.com/alive_gibbon2712/n/n0eb671383b30](https://note.com/alive_gibbon2712/n/n0eb671383b30)[w/This extension has a vulnerability that allows arbitrary access to local files from remote.]"
+        },
+        {
+            "author": "YokoYokoTEC",
+            "title": "ComfyUI Batch Sequence Node [UNSAFE]",
+            "reference": "https://github.com/YokoYokoTEC/comfyui-batch-sequence",
+            "files": [
+                "https://github.com/YokoYokoTEC/comfyui-batch-sequence"
+            ],
+            "install_type": "git-clone",
+            "description": "This custom node is a toolkit designed to help you run batch processing (such as sequential image generation) more smoothly in ComfyUI.[w/This extension has a vulnerability that allows arbitrary access to local files from remote.]"
+        },
+        {
+            "author": "kaski23",
+            "title": "Kaskis_Comfy_Nodes",
+            "reference": "https://github.com/kaski23/Kaskis_Comfy_Nodes",
+            "files": [
+                "https://github.com/kaski23/Kaskis_Comfy_Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Load Image (with Filename), Load Video (with Filename), String Split & Select, Int → String, Pair Switch, Unbroken-Video-Handler, Unbroken-Controlvideo-Handler, ..."
+        },
+        {
+            "author": "asaddi",
+            "title": "Allan's ComfyUI nodes",
+            "reference": "https://github.com/asaddi/allans-comfy-nodes",
+            "files": [
+                "https://github.com/asaddi/allans-comfy-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Parameter Experimentation, Lists, Image Utilities, Utilities, Buses (Combiners/Splitters), Prompting, Switches, Tabular Data, Debugging, Third-Party Models"
+        },
+        {
+            "author": "Suzu008",
+            "title": "ComfyUI-TTYd",
+            "reference": "https://github.com/Suzu008/ComfyUI-TTYd",
+            "files": [
+                "https://github.com/Suzu008/ComfyUI-TTYd"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI extension for web-based terminal access via side panel"
+        },
+        {
+            "author": "Mets3D",
+            "title": "Mets-Nodes",
+            "reference": "https://github.com/Mets3D/mets-nodes",
+            "files": [
+                "https://github.com/Mets3D/mets-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Random nodes for now, may split them out later into properly maintained repos."
+        },
+        {
+            "author": "downlifted",
+            "title": "ComfyUI_GroqPrompt [WIP]",
+            "reference": "https://github.com/downlifted/ComfyUI_GroqPrompt",
+            "files": [
+                "https://github.com/downlifted/ComfyUI_GroqPrompt"
+            ],
+            "install_type": "git-clone",
+            "description": " Groq API Access in ComfyUI, Free Prompt Creation!\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "kijai",
+            "title": "ComfyUI-WanAnimatePreprocess [WIP]",
+            "reference": "https://github.com/kijai/ComfyUI-WanAnimatePreprocess",
+            "files": [
+                "https://github.com/kijai/ComfyUI-WanAnimatePreprocess"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for WanAnimate model input preprocessing"
+        },
+        {
+            "author": "charlie-otoy",
+            "title": "ComfyUI-otoySKW-SystemMetrics",
+            "reference": "https://github.com/charlie-otoy/ComfyUI-otoySKW-SystemMetrics",
+            "files": [
+                "https://github.com/charlie-otoy/ComfyUI-otoySKW-SystemMetrics"
+            ],
+            "install_type": "git-clone",
+            "description": "System monitoring API endpoints for ComfyUI by otoy SKW"
+        },
+        {
+            "author": "lihaoyun6",
+            "title": "ComfyUI-lhyNodes",
+            "reference": "https://github.com/lihaoyun6/ComfyUI-lhyNodes",
+            "files": [
+                "https://github.com/lihaoyun6/ComfyUI-lhyNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Mask to SAM2Coordinates, String Format, String Format (Advanced), CSV RandomPicker, CSV RandomPicker (Advanced)"
+        },
+        {
+            "author": "orion4d",
+            "title": "Orion4D MasterPrompt Suite for ComfyUI [UNSAFE]",
+            "reference": "https://github.com/orion4d/Orion4D_MasterPrompt",
+            "files": [
+                "https://github.com/orion4d/Orion4D_MasterPrompt"
+            ],
+            "install_type": "git-clone",
+            "description": "Welcome to the MasterPrompt suite, a collection of custom nodes for ComfyUI designed to supercharge your text, list, and JSON manipulation workflows. Whether you need to dynamically load styles from files, mix concepts with weighted probabilities, format complex prompts, or handle JSON data, this suite provides the necessary tools with intuitive and powerful user interfaces. As a bonus I have included a basic pack of thematic lists generated with gpt5, have fun![w/This extension has a vulnerability that allows arbitrary access to local files from remote.]"
+        },
+        {
+            "author": "adigayung",
+            "title": "comfyui_PlutoNode",
+            "reference": "https://github.com/adigayung/comfyui_PlutoNode",
+            "files": [
+                "https://github.com/adigayung/comfyui_PlutoNode"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: 'Pluto : Auto Crop Faces', 'Pluto : Composite Image',  'Pluto : Float Math', 'Pluto : GetSizeFromImage', 'Pluto : Text Append', ..."
+        },
+        {
+            "author": "lu64k",
+            "title": "ks_nodes",
+            "reference": "https://github.com/lu64k/ks_nodes",
+            "files": [
+                "https://github.com/lu64k/ks_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: KS Text_String, KS Random File Name, KS Save Text, KS load text, KS get time int, KS json float range filter, KS json array constrains filter, KS json key replace, KS json value eliminator, KS json extract key and value, KS json key random, KS Json Count, KS Word Frequency Statistics, KS Load Images From Folder KS, KS Json To String, KS JsonlReader, KS JsonKeyReplacer, KS JsonKeyExtractor, KS_merge_json_node, KS_make_json_node, KS_qwen_image_api, KS_any_payload_image, KS JsonlFolderMatchReader, KS_image_metadata_node"
+        },
+        {
+            "author": "DavidJBarnes",
+            "title": "comfyui-djb-utils",
+            "reference": "https://github.com/DavidJBarnes/comfyui-djb-utils",
+            "files": [
+                "https://github.com/DavidJBarnes/comfyui-djb-utils"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Working Filename Extractor, Direct Image Filename Input"
+        },
+        {
+            "author": "husw725",
+            "title": "comfyui_alta_nodes",
+            "reference": "https://github.com/husw725/comfyui_alta_nodes",
+            "files": [
+                "https://github.com/husw725/comfyui_alta_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: 'Alta:SaveImagePlus', 'Alta:LoadImagesPath', 'Alta:LoadVideoPath', 'Alta:LoadVideosFromFolder', 'Alta:VideoCombine', ..."
+        },
+        {
+            "author": "LukeCoulson1",
+            "title": "comfyui-sundryutils",
+            "reference": "https://github.com/orpheus-gaze/comfyui-sundryutils",
+            "files": [
+                "https://github.com/orpheus-gaze/comfyui-sundryutils"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: 'Return DateString', 'Sharpen, RealGrain & Autocontrast', 'Apply HaldClut'"
+        },
+        {
+            "author": "dcduanchao",
+            "title": "comfyui_video_http",
+            "reference": "https://github.com/dcduanchao/comfyui_video_http",
+            "files": [
+                "https://github.com/dcduanchao/comfyui_video_http"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Video Upload Node"
+        },
+        {
+            "author": "AIGCZero",
+            "title": "AIGCZero-comfyui-Qwen_edit-zero",
+            "reference": "https://github.com/AIGCZero/AIGCZero-comfyui-Qwen_edit-zero",
+            "files": [
+                "https://github.com/AIGCZero/AIGCZero-comfyui-Qwen_edit-zero"
+            ],
+            "install_type": "git-clone",
+            "description": "A node that resolves the pixel offset issue in Qwen_edit."
+        },
+        {
+            "author": "SlackinJack",
+            "title": "multigpu_diffusion_comfyui",
+            "reference": "https://github.com/SlackinJack/multigpu_diffusion_comfyui",
+            "files": [
+                "https://github.com/SlackinJack/multigpu_diffusion_comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: GGUFSelector, CheckpointSelector, SchedulerSelector, LoraSelector, MultiLoraJoiner, VAESelector, MotionModuleSelector, MotionAdapterSelector, MotionAdapterLoraSelector, ControlNetSelector, IPAdapterSelector, EncodePromptWithCompel, HostConfig, AsyncDiffConfig, AsyncDiffADSampler, AsyncDiffSDSampler, AsyncDiffSDUpscaleSampler, AsyncDiffSVDSampler, DistrifuserConfig, DistrifuserSDSampler, xDiTConfig, xDiTSampler, xDiTUSPConfig, xDiTUSPImageSampler, xDiTUSPVideoSampler"
+        },
+        {
+            "author": "dmitry-guskov",
+            "title": "ComfyUI_light_image_ops",
+            "reference": "https://github.com/dmitry-guskov/ComfyUI_light_image_ops",
+            "files": [
+                "https://github.com/dmitry-guskov/ComfyUI_light_image_ops"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Light Scale to Side, Light Batch to List, Light List to Batch, Wan Latents Resize To Size, Wan Embeds Resize Spatial (Packed), Wan Latents Resize + Normalize + Noise"
+        },
+        {
+            "author": "78Ventures",
+            "title": "ComfyUI-Tortu [WIP/UNSAFE]",
+            "reference": "https://github.com/78Ventures/ComfyUI-Tortu",
+            "files": [
+                "https://github.com/78Ventures/ComfyUI-Tortu"
+            ],
+            "install_type": "git-clone",
+            "description": "Opinionated custom nodes and ready-to-use workflows for dataset prep and portrait/headshot pipelines in ComfyUI.\nNOTE: The files in the repo are not organized.[w/This extension has a vulnerability that allows arbitrary access to local files from remote.]"
+        },
+        {
+            "author": "ches2010",
+            "title": "comfyui_aliyundrive_uploader",
+            "reference": "https://github.com/ches2010/comfyui_aliyundrive_uploader",
+            "files": [
+                "https://github.com/ches2010/comfyui_aliyundrive_uploader"
+            ],
+            "install_type": "git-clone",
+            "description": "comfyui_aliyundrive_uploader"
+        },
+        {
+            "author": "CooperCorona",
+            "title": "comfy-auto-unload",
+            "reference": "https://github.com/CooperCorona/comfy-auto-unload",
+            "files": [
+                "https://github.com/CooperCorona/comfy-auto-unload"
+            ],
+            "install_type": "git-clone",
+            "description": "unloads models from VRAM in comfyui after a set period of time"
+        },
+        {
+            "author": "polygoningenieur",
+            "title": "ComfyUI-Diffusion-SDXL-Video",
+            "reference": "https://github.com/Polygoningenieur/ComfyUI-Diffusion-SDXL-Video",
+            "files": [
+                "https://github.com/Polygoningenieur/ComfyUI-Diffusion-SDXL-Video"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node for a frame by frame Diffusion."
+        },
+        {
+            "author": "ric-y",
+            "title": "ComfyUI Datadog Monitor [WIP]",
+            "reference": "https://github.com/ric-yu/comfyui-datadog-monitor",
+            "files": [
+                "https://github.com/ric-yu/comfyui-datadog-monitor"
+            ],
+            "install_type": "git-clone",
+            "description": "Minimal custom node that enables comprehensive Datadog APM tracing and profiling for ComfyUI.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "synchronicity-labs",
+            "title": "ComfyUI Sync Lipsync Node",
+            "reference": "https://github.com/synchronicity-labs/sync-comfyui",
+            "files": [
+                "https://github.com/synchronicity-labs/sync-comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: WanVideo T5 Apply Soft Prefix"
+        },
+        {
+            "author": "NSFW-API",
+            "title": "ComfyUI-WanSoftPrefix",
+            "reference": "https://github.com/NSFW-API/ComfyUI-WanSoftPrefix",
+            "files": [
+                "https://github.com/NSFW-API/ComfyUI-WanSoftPrefix"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Resize Frame, Pad Batch to 4n+1, Trim Padded Batch, Get Image Dimensions, Slot Frame, ..."
+        },
+        {
+            "author": "rishipandey125",
+            "title": "ComfyUI-StyleFrame-Nodes",
+            "reference": "https://github.com/rishipandey125/ComfyUI-StyleFrame-Nodes",
+            "files": [
+                "https://github.com/rishipandey125/ComfyUI-StyleFrame-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Resize Frame, Pad Batch to 4n+1, Trim Padded Batch, Get Image Dimensions, Slot Frame, ..."
+        },
+        {
+            "author": "nicolabergamascahkimkoohi",
+            "title": "ComfyUI-OSS-Upload [UNSAFE]",
+            "reference": "https://github.com/ahkimkoo/ComfyUI-OSS-Upload",
+            "files": [
+                "https://github.com/ahkimkoo/ComfyUI-OSS-Upload"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI plugin for uploading generated images and videos to Alibaba Cloud OSS (Object Storage Service).[w/This extension has a vulnerability that allows arbitrary access to local files from remote.]"
+        },
+        {
+            "author": "threecrowco",
+            "title": "ComfyUI-FlowMatchScheduler [WIP]",
+            "reference": "https://github.com/threecrowco/ComfyUI-FlowMatchScheduler",
+            "files": [
+                "https://github.com/threecrowco/ComfyUI-FlowMatchScheduler"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: FlowMatch → SIGMAS\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "NimbleWing",
+            "title": "ComfyUI-NW",
+            "reference": "https://github.com/NimbleWing/ComfyUI-NW",
+            "files": [
+                "https://github.com/NimbleWing/ComfyUI-NW"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node for a frame by frame Diffusion."
+        },
+        {
+            "author": "hujuying",
+            "title": "comfyui_gemini_banana_api [WIP]",
+            "reference": "https://github.com/hujuying/comfyui_gemini_banana_api",
+            "files": [
+                "https://github.com/hujuying/comfyui_gemini_banana_api"
+            ],
+            "install_type": "git-clone",
+            "description": "A powerful ComfyUI plugin that supports image editing and generation using the Gemini API, featuring multiple API key rotation and secure storage.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "pft-ChenKu",
+            "title": "ComfyUI_system-dev [WIP]",
+            "reference": "https://github.com/pft-ChenKu/ComfyUI_system-dev",
+            "files": [
+                "https://github.com/pft-ChenKu/ComfyUI_system-dev"
+            ],
+            "install_type": "git-clone",
+            "description": "Collect Vram ram and Time\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "z604159435g",
+            "title": "comfyui_random_prompt_plugin [WIP]",
+            "reference": "https://github.com/z604159435g/comfyui_random_prompt_plugin",
+            "files": [
+                "https://github.com/z604159435g/comfyui_random_prompt_plugin"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI natural language prompt plugin specifically designed for generating realistic photos of Caucasian women. Completely restructured to produce smooth natural language paragraphs instead of CLIP-style formatting.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "edisonchan",
+            "title": "ComfyUI-Sysinfo",
+            "reference": "https://github.com/edisonchan/ComfyUI-Sysinfo",
+            "files": [
+                "https://github.com/edisonchan/ComfyUI-Sysinfo"
+            ],
+            "install_type": "git-clone",
+            "description": "A simple ComfyUI custom node for checking system CUDA information and the PyTorch version."
+        },
+        {
+            "author": "Goldlionren",
+            "title": "comfyui-spawner-schedulers",
+            "reference": "https://github.com/spawner1145/comfyui-spawner-schedulers",
+            "files": [
+                "https://github.com/spawner1145/comfyui-spawner-schedulers"
+            ],
+            "install_type": "git-clone",
+            "description": "This plugin provides additional scheduler options for ComfyUI. Currently, it includes a smooth scheduler designed to optimize the quality and consistency of generated images."
+        },
+        {
+            "author": "nicolabergamaschi",
+            "title": "ComfyUI_XLweb [UNSAFE]",
+            "reference": "https://github.com/853587221/ComfyUI_XLweb",
+            "files": [
+                "https://github.com/853587221/ComfyUI_XLweb"
+            ],
+            "install_type": "git-clone",
+            "description": "An elegant web frontend for ComfyUI that simplifies complex node workflows.[w/This extension has a vulnerability that allows arbitrary access to local files from remote.]"
+        },
+        {
+            "author": "nicolabergamaschi",
+            "title": "ComfyUI-Dynamic-Lora-Loader",
+            "reference": "https://github.com/BARKEM-JC/ComfyUI-Dynamic-Lora-Loader",
+            "files": [
+                "https://github.com/BARKEM-JC/ComfyUI-Dynamic-Lora-Loader"
+            ],
+            "install_type": "git-clone",
+            "description": "A simple dynamic lora loader, supports hundreds of loras at once by loading on demand and automatically adjusting weights + prompt injection."
+        },
+        {
+            "author": "kuailefengnan2024",
+            "title": "Comfyui_Layer",
+            "reference": "https://github.com/kuailefengnan2024/Comfyui_Layer",
+            "files": [
+                "https://github.com/kuailefengnan2024/Comfyui_Layer"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Vision API Node"
+        },
+        {
+            "author": "tony-zn",
+            "title": "comfyui-zn-pycode [UNSAFE]",
+            "id": "comfyui-zn-pycode",
+            "reference": "https://github.com/tony-zn/comfyui-zn-pycode",
+            "files": [
+                "https://github.com/tony-zn/comfyui-zn-pycode"
+            ],
+            "install_type": "git-clone",
+            "description": "This node allows you to run custom Python code for flexible data handling. It supports up to 20 input parameters and 20 output results, with unused slots automatically showing or hiding based on whether the parameter and result slots are connected."
+        },
+        {
+            "author": "Solankimayursinh",
+            "title": "PMSnodes [WIP]",
+            "reference": "https://github.com/Solankimayursinh/PMSnodes",
+            "files": [
+                "https://github.com/Solankimayursinh/PMSnodes"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom nodes for ComfyUI to Load audio in Base64 format and Send Audio to Websocket in Base64 Format for creating API of Audio related AI\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "vasilmitov",
+            "title": "ComfyUI-SeedSnapShotManager [WIP]",
+            "reference": "https://github.com/vasilmitov/ComfyUI-SeedSnapShotManager",
+            "files": [
+                "https://github.com/vasilmitov/ComfyUI-SeedSnapShotManager"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node for saving and restoring random seeds. Useful for workflow reproducibility, experimentation, and quickly trying different variations.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Juste-Leo2",
+            "title": "ComfyUI-Arduino [WIP]",
+            "reference": "https://github.com/Juste-Leo2/ComfyUI-Arduino",
+            "files": [
+                "https://github.com/Juste-Leo2/ComfyUI-Arduino"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI-Arduino aims to bridge the gap between ComfyUI's powerful generative AI workflows and the physical world through Arduino. This project allows you to design, code, and interact with Arduino boards directly from your ComfyUI nodes, opening up possibilities for AI-driven robotics, physical generative art, interactive installations, and more."
+        },
+        {
+            "author": "Nienai666",
+            "title": "ComfyUI-NanoBanana-ImageGenerator",
+            "reference": "https://github.com/Nienai666/comfyui-encrypt-image-main",
+            "files": [
+                "https://github.com/Nienai666/comfyui-encrypt-image-main"
+            ],
+            "install_type": "git-clone",
+            "description": "This is an image encryption extension for ComfyUI. If you load this extension, images will be stored in an encrypted form and cannot be previewed normally."
+        },
+        {
+            "author": "Madygnomo",
+            "title": "ComfyUI-NanoBanana-ImageGenerator",
+            "reference": "https://github.com/Madygnomo/ComfyUI-NanoBanana-ImageGenerator",
+            "files": [
+                "https://github.com/Madygnomo/ComfyUI-NanoBanana-ImageGenerator"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: NanoBanana Image Generator"
+        },
+        {
+            "author": "jerryname2022",
+            "title": "MegaTTS 3 [WIP]",
+            "reference": "https://github.com/jerryname2022/ComfyUI-MegaTTS3",
+            "files": [
+                "https://github.com/jerryname2022/ComfyUI-MegaTTS3"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: MegaTTS3 Model Loader, MegaTTS3 Zero Shot, Text Editor\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "punicfaith",
+            "title": "ComfyUI-GoogleAIStudio",
+            "reference": "https://github.com/punicfaith/ComfyUI-GoogleAIStudio",
+            "files": [
+                "https://github.com/punicfaith/ComfyUI-GoogleAIStudio"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Google Gemini Prompt"
+        },
+        {
+            "author": "numq",
+            "title": "comfyui-camera-capture-node",
+            "reference": "https://github.com/numq/comfyui-camera-capture-node",
+            "files": [
+                "https://github.com/numq/comfyui-camera-capture-node"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Camera Capture"
+        },
+        {
+            "author": "maoper11",
+            "title": "ComfyUI_Inteliweb_nodes",
+            "reference": "https://github.com/maoper11/comfyui_inteliweb_nodes",
+            "files": [
+                "https://github.com/maoper11/comfyui_inteliweb_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "System Check (Inteliweb): RAM/VRAM live, Free RAM/VRAM buttons, Py Libs. + Photopea Editor integration (menu contextual)."
+        },
+        {
+            "author": "casterpollux",
+            "title": "ComfyUI Affine Transform Mapper [WIP]",
+            "reference": "https://github.com/GuardSkill/ComfyUI-AffineImage",
+            "files": [
+                "https://github.com/GuardSkill/ComfyUI-AffineImage"
+            ],
+            "install_type": "git-clone",
+            "description": "Insert one image into specific location on another image by affine transform.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "casterpollux",
+            "title": "ComfyUI USO Custom Node [WIP]",
+            "reference": "https://github.com/casterpollux/ComfyUI-USO",
+            "files": [
+                "https://github.com/casterpollux/ComfyUI-USO"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node implementation of ByteDance's USO (Unified Style and Subject-Driven Generation) model, which enables advanced style transfer and subject preservation using FLUX.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "numq",
+            "title": "comfyui-camera-capture-node",
+            "reference": "https://github.com/numq/comfyui-camera-capture-node",
+            "files": [
+                "https://github.com/numq/comfyui-camera-capture-node"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Camera Capture"
+        },
+        {
+            "author": "pickles",
+            "title": "PyPromptGenerator [UNSAFE]",
+            "reference": "https://github.com/pickles/ComfyUI-PyPromptGenerator",
+            "files": [
+                "https://github.com/pickles/ComfyUI-PyPromptGenerator"
+            ],
+            "install_type": "git-clone",
+            "description": "Generate positve/negative prompt via python script. [w/This node allows you to execute arbitrary code via the workflow.]"
+        },
+        {
+            "author": "bmgjet",
+            "title": "ComfyUI GPU Power Limit Node",
+            "reference": "https://github.com/bmgjet/comfyui-powerlimit",
+            "files": [
+                "https://github.com/bmgjet/comfyui-powerlimit"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom ComfyUI node to change NVIDIA GPU power limits dynamically while passing through any input. NOTE: Windows only.[w/This node warns if ComfyUI is not running as administrator/root — it does not attempt to elevate the process.]"
+        },
+        {
+            "author": "Karlmeister",
+            "title": "comfyui-karlmeister-nodes-suit",
+            "reference": "https://github.com/Karlmeister/comfyui-karlmeister-nodes-suit",
+            "files": [
+                "https://github.com/Karlmeister/comfyui-karlmeister-nodes-suit"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Seed with Filename Generator, KSampler Config Selector, KSampler Config Selector With Tuple Output, KSampler Config Tuple, Text Concatenator, A If Not None., Split a string. "
+        },
+        {
+            "author": "ServiceStack",
+            "title": "classifier-agent",
+            "reference": "https://github.com/ServiceStack/classifier-agent",
+            "files": [
+                "https://github.com/ServiceStack/classifier-agent"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Classifiy Image, Classifiy Audio"
+        },
+        {
+            "author": "elfatherbrown",
+            "title": "Real-CUGAN ComfyUI Custom Node [WIP]",
+            "reference": "https://github.com/elfatherbrown/comfyui-realcugan-node",
+            "files": [
+                "https://github.com/elfatherbrown/comfyui-realcugan-node"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node implementation for Real-CUGAN anime/illustration upscaling models from Bilibili's AI Lab.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "sthao42",
+            "title": "ComfyUI Melodkeet TTS",
+            "reference": "https://github.com/sthao42/comfyui-melodkeet-tts",
+            "files": [
+                "https://github.com/sthao42/comfyui-melodkeet-tts"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node for ComfyUI that provides a simple and direct way to use OpenAI-compatible Text-to-Speech (TTS) services."
+        },
+        {
+            "author": "Jairodaniel-17",
+            "title": "ComfyUI-traductor-offline",
+            "reference": "https://github.com/Jairodaniel-17/ComfyUI-traductor-offline",
+            "files": [
+                "https://github.com/Jairodaniel-17/ComfyUI-traductor-offline"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Traductor: CLIP Texto (EN↔ES), Traductor: Prompt Texto (EN↔ES)"
+        },
+        {
+            "author": "aesethtics",
+            "title": "ComfyUI-OpenPoser [WIP]",
+            "reference": "https://github.com/aesethtics/ComfyUI-OpenPoser",
+            "files": [
+                "https://github.com/aesethtics/ComfyUI-OpenPoser"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI-OpenPoser is a custom node extension for ComfyUI that provides an interactive pose editor.\nIt allows you to freely define 18 OpenPose-style keypoints (joints) directly in the ComfyUI front-end without bone-length constraints! The node generates an image tensor representing the skeleton that can be passed to models which support OpenPose/DWPose/etc input."
+        },
+        {
+            "author": "dead-matrix",
+            "title": "ComfyUI-RMBG-Custom",
+            "reference": "https://github.com/dead-matrix/ComfyUI-RMBG-Custom",
+            "files": [
+                "https://github.com/dead-matrix/ComfyUI-RMBG-Custom"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: RMBG Custom"
+        },
+        {
+            "author": "gmammolo",
+            "title": "comfyui-gmammolo",
+            "reference": "https://github.com/gmammolo/comfyui-gmammolo",
+            "files": [
+                "https://github.com/gmammolo/comfyui-gmammolo"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Simple Textbox, Filter Text Prompt, Check Image Generation"
+        },
+        {
+            "author": "543872524",
+            "title": "ComfyUI_crdong",
+            "reference": "https://github.com/543872524/ComfyUI_crdong",
+            "files": [
+                "https://github.com/543872524/ComfyUI_crdong"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: INT Constant, Simple Int Math Handle, Simple Json Array Handle, Simple Json Object Handle,  Select Image Size, Video Time & FPS, Video Frame Size, Wan22 Step Handle, Prompt Selector String, Prompt Example Node, Prompt Join or List, Prompt List, CRD Audio Length Node, ..."
+        },
+        {
+            "author": "Saganaki22",
+            "title": "ComfyUI YTDL Nodes [WIP]",
+            "reference": "https://github.com/Saganaki22/ComfyUI-ytdl_nodes",
+            "files": [
+                "https://github.com/Saganaki22/ComfyUI-ytdl_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI nodes for downloading, converting, and previewing audio/video from YouTube and 1,000+ other platforms"
+        },
+        {
+            "author": "comfyscript",
+            "title": "ComfyUI-CloudClient",
+            "reference": "https://github.com/comfyscript/ComfyUI-CloudClient",
+            "files": [
+                "https://github.com/comfyscript/ComfyUI-CloudClient"
+            ],
+            "install_type": "git-clone",
+            "description": "Design to Easily Remote Operate ComfyUI in the Cloud"
+        },
+        {
+            "author": "RobbertB80",
+            "title": "ComfyUI SharePoint/OneDrive Upload Node [UNSAFE]",
+            "reference": "https://github.com/RobbertB80/ComfyUI-SharePoint-Upload",
+            "files": [
+                "https://github.com/RobbertB80/ComfyUI-SharePoint-Upload"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node for ComfyUI that automatically uploads generated images to SharePoint or OneDrive document libraries.[w/This nodepack contains a node that can write files to an arbitrary path.]"
+        },
+        {
+            "author": "KoinnAI",
+            "title": "ComfyUI Dynamic Prompting Simplified [WIP]",
+            "reference": "https://github.com/KoinnAI/ComfyUI-DynPromptSimplified",
+            "files": [
+                "https://github.com/KoinnAI/ComfyUI-DynPromptSimplified"
+            ],
+            "install_type": "git-clone",
+            "description": "A minimal dynamic prompting + mirrored wildcards node for ComfyUI.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "jtrue",
+            "title": "MaskTools",
+            "reference": "https://github.com/jtrue/ComfyUI-MaskTools",
+            "files": [
+                "https://github.com/jtrue/ComfyUI-MaskTools"
+            ],
+            "install_type": "git-clone",
+            "description": "Pixel-selection tools (masks) for ComfyUI — modular."
+        },
+        {
+            "author": "nadushu",
+            "title": "comfyui-handy-nodes [UNSAFE]",
+            "reference": "https://github.com/nadushu/comfyui-handy-nodes",
+            "files": [
+                "https://github.com/nadushu/comfyui-handy-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Empty Random Latent Image, Filename Prompt Extractor, My Image Save, Queue Batch Fixed Seed, Text Cleaner, Text Splitter[w/This nodepack contains a node that can write files to an arbitrary path.]"
+        },
+        {
+            "author": "borisfaley",
+            "title": "ComfyUI-ACES-EXR-OCIOr [UNSAFE]",
+            "reference": "https://github.com/borisfaley/ComfyUI-ACES-EXR-OCIO",
+            "files": [
+                "https://github.com/borisfaley/ComfyUI-ACES-EXR-OCIO"
+            ],
+            "install_type": "git-clone",
+            "description": "Save images and videos in ACESCg or ACES-2065-1[w/This nodepack contains a node that can write files to an arbitrary path.]"
+        },
+        {
+            "author": "NSFW-API",
+            "title": "ComfyUI-Embedding-Delta-Adapter",
+            "reference": "https://github.com/NSFW-API/ComfyUI-Embedding-Delta-Adapter",
+            "files": [
+                "https://github.com/NSFW-API/ComfyUI-Embedding-Delta-Adapter"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Load EmbDelta Adapter, Apply EmbDelta (WAN TextEmbeds)"
+        },
+        {
+            "author": "clcimir",
+            "title": "FileTo64",
+            "reference": "https://github.com/clcimir/FileTo64",
+            "files": [
+                "https://github.com/clcimir/FileTo64"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI FileTo64"
+        },
+        {
+            "author": "LittleTechPomp",
+            "title": "comfyui-pixxio",
+            "reference": "https://github.com/LittleTechPomp/comfyui-pixxio",
+            "files": [
+                "https://github.com/LittleTechPomp/comfyui-pixxio"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Load Image from Pixx.io, Auto-Upload Image to Pixxio Collection"
+        },
+        {
+            "author": "RomanticQq",
+            "title": "ComfyUI-Groudingdino-Sam",
+            "reference": "https://github.com/RomanticQq/ComfyUI-Groudingdino-Sam",
+            "files": [
+                "https://github.com/RomanticQq/ComfyUI-Groudingdino-Sam"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: GroundingDino, GroundedSam2CutGaussian"
+        },
+        {
+            "author": "Firetheft",
+            "title": "ComfyUI Local Media Manager [UNSAFE]",
+            "reference": "https://github.com/Firetheft/ComfyUI_Local_Media_Manager",
+            "files": [
+                "https://github.com/Firetheft/ComfyUI_Local_Media_Manager"
+            ],
+            "install_type": "git-clone",
+            "description": "The Ultimate Local File Manager for Images, Videos, and Audio in ComfyUI.[w/This nodepack provides functionality to access files through an endpoint.]"
+        },
+        {
+            "author": "Omario92",
+            "title": "ComfyUI-OmarioNodes",
+            "reference": "https://github.com/Omario92/ComfyUI-OmarioNodes",
+            "files": [
+                "https://github.com/Omario92/ComfyUI-OmarioNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Dual Endpoint Color Blend (by Frames)"
+        },
+        {
+            "author": "locphan201",
+            "title": "ComfyUI-Alter-Nodes",
+            "reference": "https://github.com/locphan201/ComfyUI-Alter-Nodes",
+            "files": [
+                "https://github.com/locphan201/ComfyUI-Alter-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Alter MMAudio Config, Alter MMAudio Model Loader, Alter MMAudio Feature Utils, Alter MMAudio Sampler"
+        },
+        {
+            "author": "mrCodinghero",
+            "title": "ComfyUI File Transfer Plugin (comfyui-rsync-plugin) [UNSAFE]",
+            "reference": "https://github.com/tg-tjmitchell/comfyui-rsync-plugin",
+            "files": [
+                "https://github.com/tg-tjmitchell/comfyui-rsync-plugin"
+            ],
+            "install_type": "git-clone",
+            "description": "Lightweight helper for using rsync and rclone from ComfyUI with a dedicated UI panel. This repository contains Python wrappers for file transfer CLI tools and a ComfyUI plugin that adds a user-friendly panel for file transfer operations."
+        },
+        {
+            "author": "mrCodinghero",
+            "title": "ComfyUI-Codinghero",
+            "reference": "https://github.com/mrCodinghero/ComfyUI-Codinghero",
+            "files": [
+                "https://github.com/mrCodinghero/ComfyUI-Codinghero"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Image Size, Video Settings"
+        },
+        {
+            "author": "Vsolon",
+            "title": "ComfyUI-CBZ-Pack [UNSAFE]",
+            "reference": "https://github.com/Vsolon/ComfyUI-CBZ-Pack",
+            "files": [
+                "https://github.com/Vsolon/ComfyUI-CBZ-Pack"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes for Handling CBZ MetaData and Images as List or Bash.[w/This nodepack contains a node that has a vulnerability allowing access to arbitrary file paths.]"
+        },
+        {
+            "author": "odedgranot",
+            "title": "ComfyUI Video Save Node [UNSAFE]",
+            "reference": "https://github.com/odedgranot/comfyui_video_save_node",
+            "files": [
+                "https://github.com/odedgranot/comfyui_video_save_node"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom ComfyUI node that saves video outputs as H.264 .mp4 files with unique naming and returns the file path as a string.[w/This nodepack contains a node that has a vulnerability allowing write to arbitrary file paths.]"
+        },
+        {
+            "author": "odedgranot",
+            "title": "ComfyUI FFmpeg Node [UNSAFE]",
+            "reference": "https://github.com/odedgranot/comfyui-ffmpeg-node",
+            "files": [
+                "https://github.com/odedgranot/comfyui-ffmpeg-node"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom ComfyUI node that allows you to run FFmpeg commands directly within your ComfyUI workflows. [w/This nodepack contains a vulnerability that allows remote code execution.]"
+        },
+        {
+            "author": "viik420",
+            "title": "Model Copy Node for ComfyUI [UNSAFE]",
+            "reference": "https://github.com/apeirography/ModelCopyNode",
+            "files": [
+                "https://github.com/apeirography/ModelCopyNode"
+            ],
+            "install_type": "git-clone",
+            "description": "A simple ComfyUI custom node that copies model files from the models/ folder to the output/ folder.[w/This nodepack has a vulnerability that allows writing files to arbitrary paths.]"
+        },
+        {
+            "author": "viik420",
+            "title": "AdvancedModelDownloader [UNSAFE]",
+            "reference": "https://github.com/viik420/AdvancedModelDownloader",
+            "files": [
+                "https://github.com/viik420/AdvancedModelDownloader"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node for ComfyUI that adds a powerful, integrated downloader to the main menu, complete with an automatic update checker.[w/This nodepack provides functionality to access files through an endpoint.]"
+        },
+        {
+            "author": "DenRakEiw",
+            "title": "Comfyui-Aspect-Ratio-Processor [WIP]",
+            "reference": "https://github.com/DenRakEiw/Comfyui-Aspect-Ratio-Processor",
+            "files": [
+                "https://github.com/DenRakEiw/Comfyui-Aspect-Ratio-Processor"
+            ],
+            "install_type": "git-clone",
+            "description": "Comfyui Aspect Ratio Processor 2:3 / 3:2\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "yuvraj108c",
+            "title": "ComfyUI HYPIR [NAME CONFLICT]",
+            "reference": "https://github.com/yuvraj108c/ComfyUI-HYPIR",
+            "files": [
+                "https://github.com/yuvraj108c/ComfyUI-HYPIR"
+            ],
+            "install_type": "git-clone",
+            "description": "This project is a ComfyUI wrapper for [a/HYPIR](https://github.com/XPixelGroup/HYPIR) (Harnessing Diffusion-Yielded Score Priors for Image Restoration)"
+        },
+        {
+            "author": "miabrahams",
+            "title": "ComfyUI-WebAutomation [UNSAFE]",
+            "reference": "https://github.com/miabrahams/ComfyUI-WebAutomation",
+            "files": [
+                "https://github.com/miabrahams/ComfyUI-WebAutomation"
+            ],
+            "install_type": "git-clone",
+            "description": "Automation for ComfyUI Web UI [w/This nodepack provides functionality to access files through an endpoint.]"
+        },
+        {
+            "author": "Rizzlord",
+            "title": "ComfyUI-SeqTex",
+            "reference": "https://github.com/Rizzlord/ComfyUI-SeqTex",
+            "files": [
+                "https://github.com/Rizzlord/ComfyUI-SeqTex"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: SeqTex Load Mesh, SeqTex Loader, SeqTex Step 1: Process Mesh, SeqTex Step 2: Generate Condition, SeqTex Step 3: Generate Texture, SeqTex Step 4: Apply Texture to Trimesh"
+        },
+        {
+            "author": "BiodigitalJaz",
+            "title": "ComfyUI-Dafaja-Nodes [WIP]",
+            "reference": "https://github.com/BiodigitalJaz/ComfyUI-Dafaja-Nodes",
+            "files": [
+                "https://github.com/BiodigitalJaz/ComfyUI-Dafaja-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI nodes for 3D mesh processing and STL export\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "ervinne13",
+            "title": "ComfyUI-Metadata-Hub",
+            "reference": "https://github.com/ervinne13/ComfyUI-Metadata-Hub",
+            "files": [
+                "https://github.com/ervinne13/ComfyUI-Metadata-Hub"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Metadata Hub, Save Image With Metadata"
+        },
+        {
+            "author": "mico-world",
+            "title": "comfyui_mico_node",
+            "reference": "https://github.com/mico-world/comfyui_mico_node",
+            "files": [
+                "https://github.com/mico-world/comfyui_mico_node"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: HF UNET Loader"
+        },
+        {
+            "author": "GuusF",
+            "title": "Comfyui_CrazyMaths [WIP]",
+            "reference": "https://github.com/GuusF/Comfyui_CrazyMaths",
+            "files": [
+                "https://github.com/GuusF/Comfyui_CrazyMaths"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom nodepack with a bunch of nodes that helps you generate fun math paterns directly inside of comfyui for masking or other reasons.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "TimothyCMeehan",
+            "title": "ComfyUI CK3 Presets",
+            "reference": "https://github.com/TimothyCMeehan/comfyui-ck3-presets",
+            "files": [
+                "https://github.com/TimothyCMeehan/comfyui-ck3-presets"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom nodes for Crusader Kings III modding - size presets, image resize, style helpers"
+        },
+        {
+            "author": "driftjohnson",
+            "title": "DaimalyadNodes [WIP]",
+            "reference": "https://github.com/MushroomFleet/DJZ-Nodes",
+            "files": [
+                "https://github.com/MushroomFleet/DJZ-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "AspectSize and 100 more nodes\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "tnil25",
+            "title": "ComfyUI-TJNodes [WIP]",
+            "reference": "https://github.com/tnil25/ComfyUI-TJNodes",
+            "files": [
+                "https://github.com/tnil25/ComfyUI-TJNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Point Tracker\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "zhu733756",
+            "title": "ivan_knows [UNSAFE]",
+            "reference": "https://github.com/Babiduba/ivan_knows",
+            "files": [
+                "https://github.com/Babiduba/ivan_knows"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Role Selector, Save Absolute. [w/This nodepack contains a node that has a vulnerability allowing write to arbitrary file paths.]"
+        },
+        {
+            "author": "zhu733756",
+            "title": "Comfyui-Anything-Converter [UNSAFE]",
+            "reference": "https://github.com/zhu733756/Comfyui-Anything-Converter",
+            "files": [
+                "https://github.com/zhu733756/Comfyui-Anything-Converter"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a custom node extension designed for ComfyUI, providing JSON/TEXT/IMG handling functionality etc.[w/This nodepack contains a node that has a vulnerability allowing write to arbitrary file paths.]"
+        },
+        {
+            "author": "twj515895394",
+            "title": "ComfComfyUI-LowMemVideoSuite [UNSAFE]",
+            "reference": "https://github.com/twj515895394/ComfyUI-LowMemVideoSuite",
+            "files": [
+                "https://github.com/twj515895394/ComfyUI-LowMemVideoSuite"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a low-memory video composition plugin designed for ComfyUI, which uses FFmpeg to combine disk-stored frame images into a video, avoiding loading all frames into memory at once.[w/This nodepack contains a node that has a vulnerability allowing write to arbitrary file paths.]"
+        },
+        {
+            "author": "chenpipi0807",
+            "title": "ComfyUI-InstantCharacterFlux [WIP]",
+            "reference": "https://github.com/chenpipi0807/ComfyUI-InstantCharacterFlux",
+            "files": [
+                "https://github.com/chenpipi0807/ComfyUI-InstantCharacterFlux"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: IC → FLUX One-Knob, IC Strength Controller (InstantCharacter → FLUX), Load IC Weights, Load SigLIP Vision, Load DINOv2 Vision, Encode Reference Image (InstantCharacter)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Yuan-ManX",
+            "title": "ComfyUI-Step1X-Edit [NAME CONFLICT]",
+            "reference": "https://github.com/Yuan-ManX/ComfyUI-Step1X-Edit",
+            "files": [
+                "https://github.com/Yuan-ManX/ComfyUI-Step1X-Edit"
+            ],
+            "install_type": "git-clone",
+            "description": "Make Step1X-Edit avialbe in ComfyUI."
+        },
+        {
+            "author": "hben35096",
+            "title": "hben35096/ComfyUI-ToolBox [NAME CONFLICT]",
+            "id": "hben-toolbox",
+            "reference": "https://github.com/hben35096/ComfyUI-ToolBox",
+            "files": [
+                "https://github.com/hben35096/ComfyUI-ToolBox"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of utility nodes for ComfyUI, including audio/video processing, file uploads, and AI image generation."
+        },
+        {
+            "author": "locphan201",
+            "title": "ComfyUI-Alternatives",
+            "reference": "https://github.com/locphan201/ComfyUI-Alternatives",
+            "files": [
+                "https://github.com/locphan201/ComfyUI-Alternatives"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: LoraPreLoader, LoraApplier"
+        },
+        {
+            "author": "tg-tjmitchell",
+            "title": "ComfyUI Manager Package Lister",
+            "reference": "https://github.com/tg-tjmitchell/comfyui-custom-node-lister",
+            "files": [
+                "https://github.com/tg-tjmitchell/comfyui-custom-node-lister"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node that lists installed custom nodepackages in ComfyUI Manager compatible format, providing the exact package names and install commands for sharing or reinstalling."
+        },
+        {
+            "author": "duckmartians",
+            "title": "Duck_Nodes [UNSAFE]",
+            "reference": "https://github.com/duckmartians/Duck_Nodes",
+            "files": [
+                "https://github.com/duckmartians/Duck_Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Load data from Google Sheets, Google Docs, Excel, Word, and TXT with built-in login system for ComfyUI.[w/This nodepack contains a node with a vulnerability that allows reading files from arbitrary paths.]"
+        },
+        {
+            "author": "xsai-collab",
+            "title": "ComfyUI-CombineVideoAndSubtitle",
+            "reference": "https://github.com/xsai-collab/ComfyUI-CombineVideoAndSubtitle",
+            "files": [
+                "https://github.com/xsai-collab/ComfyUI-CombineVideoAndSubtitle"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Combine Video and Subtitle"
+        },
+        {
+            "author": "Lovzu",
+            "title": "ComfyUI-Qwen [NAME CONFLICT]",
+            "reference": "https://github.com/Lovzu/ComfyUI-Qwen",
+            "files": [
+                "https://github.com/Lovzu/ComfyUI-Qwen"
+            ],
+            "install_type": "git-clone",
+            "description": "This custom node Qwen3 designed to integrate with a GPT-based system under the category GPT_QWEN/Qwen. It serves as an interface to interact with the Qwen language model, specifically the 'Qwen/Qwen3-4B-Instruct-2507' variant."
+        },
+        {
+            "author": "MatthewClayHarrison",
+            "title": "MetaMan - Universal AI Image Metadata Manager [UNSAFE]",
+            "reference": "https://github.com/MatthewClayHarrison/ComfyUI-MetaMan",
+            "files": [
+                "https://github.com/MatthewClayHarrison/ComfyUI-MetaMan"
+            ],
+            "install_type": "git-clone",
+            "description": "First universal metadata system for AI image generation, with template-driven architecture allowing easy extension to new services; comprehensive dependency tracking with automatic download resolution; lossless conversion between platform formats where possible; future-proof design with extensible schema and validation system.[w/This nodepack has a vulnerability that allows remote access to arbitrary file paths.]"
+        },
+        {
+            "author": "idoru",
+            "title": "Filestash Upload Node [UNSAFE]",
+            "reference": "https://github.com/idoru/ComfyUI-SKCFI-NetworkFileIO",
+            "files": [
+                "https://github.com/idoru/ComfyUI-SKCFI-NetworkFileIO"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for uploading files to Filestash server.[w/This nodepack has a vulnerability that allows remote access to arbitrary file paths.]"
+        },
+        {
+            "author": "HWDigi",
+            "title": "Camera Factory Station [WIP]",
+            "reference": "https://github.com/HWDigi/Camera_Factory_Station_comfyui",
+            "files": [
+                "https://github.com/HWDigi/Camera_Factory_Station_comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "Universal Photography & Visual Enhancement Suite for ComfyUI\nThe most comprehensive collection of 5 specialized nodes providing 600+ professional options for complete photography coverage. Designed to handle everything anyone needs to create professional images across all formats, platforms, and industries - from basic snapshots to high-end commercial photography."
+        },
+        {
+            "author": "sschleis",
+            "title": "sschl-comfyui-notes",
+            "reference": "https://github.com/sschleis/sschl-comfyui-notes",
+            "files": [
+                "https://github.com/sschleis/sschl-comfyui-notes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Add Numbers, Float to String, Input Text, Show Text, Combine Strings, Text Appender, SSchl Text Encoder, Character, Connector"
+        },
+        {
+            "author": "xgfone",
+            "title": "ComfyUI_RasterCardMaker",
+            "reference": "https://github.com/xgfone/ComfyUI_RasterCardMaker",
+            "files": [
+                "https://github.com/xgfone/ComfyUI_RasterCardMaker"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Raster Card Maker"
+        },
+        {
+            "author": "majocola",
+            "title": "Standbybutton",
+            "reference": "https://github.com/majocola/comfyui-standbybutton",
+            "files": [
+                "https://github.com/majocola/comfyui-standbybutton"
+            ],
+            "install_type": "git-clone",
+            "description": "A Simple NODE for a Standbybutton in ComyUi. It works also with the webinterface."
+        },
+        {
+            "author": "Dream-Pixels-Forge",
+            "title": "ComfyUI-Mzikart-Vocal [WIP]",
+            "reference": "https://github.com/Dream-Pixels-Forge/ComfyUI-Mzikart-Vocal",
+            "files": [
+                "https://github.com/Dream-Pixels-Forge/ComfyUI-Mzikart-Vocal"
+            ],
+            "install_type": "git-clone",
+            "description": "Vocals mastering nodes for ComfyUI\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Dream-Pixels-Forge",
+            "title": "ComfyUI-RendArt-Nodes",
+            "reference": "https://github.com/Dream-Pixels-Forge/ComfyUI-RendArt-Nodes",
+            "files": [
+                "https://github.com/Dream-Pixels-Forge/ComfyUI-RendArt-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: RendArt Ultimate, RendArt Pro (Legacy)"
+        },
+        {
+            "author": "Karniverse",
+            "title": "ComfyUI-Randomselector",
+            "reference": "https://github.com/Karniverse/ComfyUI-Randomselector",
+            "files": [
+                "https://github.com/Karniverse/ComfyUI-Randomselector"
+            ],
+            "install_type": "git-clone",
+            "description": "A node that dynamically accepts multiple inputs of the same type and selects one based on choice or randomly."
+        },
+        {
+            "author": "thaakeno",
+            "title": "comfyui-universal-asset-downloader [UNSAFE/WIP]",
+            "reference": "https://github.com/thaakeno/comfyui-universal-asset-downloader",
+            "files": [
+                "https://github.com/thaakeno/comfyui-universal-asset-downloader"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node for ComfyUI that intelligently downloads assets from Civitai, Hugging Face, and MEGA.\nNOTE: The files in the repo are not organized.[w/This nodepack has a vulnerability that allows remote access to arbitrary file paths.]"
+        },
+        {
+            "author": "77oussam",
+            "title": "Alo77 - ComfyUI Custom Nodes Collection [WIP]",
+            "reference": "https://github.com/77oussam/Aio77-Comfyui",
+            "files": [
+                "https://github.com/77oussam/Aio77-Comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "A comprehensive collection of three powerful ComfyUI custom nodes for advanced image processing workflows.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "xgfone",
+            "title": "ComfyUI_FaceToMask",
+            "reference": "https://github.com/xgfone/ComfyUI_FaceToMask",
+            "files": [
+                "https://github.com/xgfone/ComfyUI_FaceToMask"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Face To Mask(Copy)"
+        },
+        {
+            "author": "JasonW146",
+            "title": "JasonW146",
+            "reference": "https://github.com/pururin777/ComfyUI-Manual-Openpose",
+            "files": [
+                "https://github.com/pururin777/ComfyUI-Manual-Openpose"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node that provides the ability to manually map out Controlnet Openpose landmarks for a batch of images."
+        },
+        {
+            "author": "slezica",
+            "title": "slezica/ComfyUI Personal Nodes",
+            "reference": "https://github.com/slezica/comfyui-personal",
+            "files": [
+                "https://github.com/slezica/comfyui-personal"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node collection for ComfyUI containing simplified workflow nodes and enhanced UI features for my personal use."
+        },
+        {
+            "author": "boggerrr1110",
+            "title": "Boggerrr Nodes [WIP]",
+            "reference": "https://github.com/mamamia1110/comfyui-boggerrr-nodes",
+            "files": [
+                "https://github.com/mamamia1110/comfyui-boggerrr-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "A node for comfyui to use seedream3.0 and seededit3.0\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "jonathan-bryant",
+            "title": "ComfyUI-ImageStraightener [WIP]",
+            "reference": "https://github.com/jonathan-bryant/ComfyUI-ImageStraightener",
+            "files": [
+                "https://github.com/jonathan-bryant/ComfyUI-ImageStraightener"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node that automatically detects and corrects image tilt/rotation to straighten images. This node uses computer vision techniques to detect lines in the image and calculate the optimal rotation angle to straighten the image.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "adithis197",
+            "title": "ComfyUI-multimodal-CaptionToVideoGen [WIP]",
+            "reference": "https://github.com/adithis197/ComfyUI-multimodal-CaptionToVideoGen",
+            "files": [
+                "https://github.com/adithis197/ComfyUI-multimodal-CaptionToVideoGen"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for video generation using a music prompt to generate audio.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "adithis197",
+            "title": "ComfyUI-Caption_to_audio [WIP]",
+            "reference": "https://github.com/adithis197/ComfyUI-Caption_to_audio",
+            "files": [
+                "https://github.com/adithis197/ComfyUI-Caption_to_audio"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node that converts image description to an appropriate prompt for music generation.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "alistairallan",
+            "title": "ComfyUI-skin-retouch",
+            "reference": "https://github.com/alistairallan/ComfyUI-skin-retouch",
+            "files": [
+                "https://github.com/alistairallan/ComfyUI-skin-retouch"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of custom nodes for ComfyUI"
+        },
+        {
+            "author": "sprited-ai",
+            "title": "Sprited ComfyUI Nodes [WIP]",
+            "reference": "https://github.com/sprited-ai/sprited-comfyui-nodes",
+            "files": [
+                "https://github.com/sprited-ai/sprited-comfyui-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of custom nodes for ComfyUI"
+        },
+        {
+            "author": "trashkollector",
+            "title": "ComfyUI-TKVideoZoom [WIP]",
+            "reference": "https://github.com/trashkollector/TKVideoZoom",
+            "files": [
+                "https://github.com/trashkollector/TKVideoZoom"
+            ],
+            "install_type": "git-clone",
+            "description": "Various Zoom/Slide effects for Video\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "tankenyuen-ola",
+            "title": "comfyui-wanvideo-scheduler-loop",
+            "reference": "https://github.com/tankenyuen-ola/comfyui-wanvideo-scheduler-loop",
+            "files": [
+                "https://github.com/tankenyuen-ola/comfyui-wanvideo-scheduler-loop"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: WanVideo Scheduler Selector, WanVideo Scheduler Loop, WanVideo Scheduler Info"
+        },
+        {
+            "author": "ElyZeng",
+            "title": "ComfyUI-Translator [NAME CONFLICT]",
+            "reference": "https://github.com/ElyZeng/ComfyUI-Translator",
+            "files": [
+                "https://github.com/ElyZeng/ComfyUI-Translator"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI node to translate text between multiple languages using Argos Translate."
+        },
+        {
+            "author": "lggcfx2020",
+            "title": "ComfyUI-LGGCFX-Tools",
+            "reference": "https://github.com/lggcfx2020/ComfyUI-LGGCFX-Tools",
+            "files": [
+                "https://github.com/lggcfx2020/ComfyUI-LGGCFX-Tools"
+            ],
+            "install_type": "git-clone",
+            "description": "A small tool for calculating the frame number and total frames of a video. Originally required four nodes, but has been merged into a single node for convenience. Includes quick resolution selection and landscape mode switching."
+        },
+        {
+            "author": "206811",
+            "title": "ComfyUI_ZhipuAIO",
+            "reference": "https://github.com/206811/ComfyUI_ZhipuAIO",
+            "files": [
+                "https://github.com/206811/ComfyUI_ZhipuAIO"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: ZhipuAI AIO Config, ZhipuAI Translator, ZhipuAI GLM-4V Vision\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "ahmedbana",
+            "title": "upload-to-azure",
+            "reference": "https://github.com/ahmedbana/upload-to-azure",
+            "files": [
+                "https://github.com/ahmedbana/upload-to-azure"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI Upload to Azure Node"
+        },
+        {
+            "author": "Inoriac",
+            "title": "comfyui-HandDetect",
+            "reference": "https://github.com/Inoriac/comfyui-HandDetect",
+            "files": [
+                "https://github.com/Inoriac/comfyui-HandDetect"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: A custom node for ComfyUI that performs hand detection, implemented with the YOLOv8 model, supporting both hand detection and mask generation."
+        },
+        {
+            "author": "eggsbenedicto",
+            "title": "DiffusionRenderer-ComfyUI [WIP]",
+            "reference": "https://github.com/eggsbenedicto/DiffusionRenderer-ComfyUI",
+            "files": [
+                "https://github.com/eggsbenedicto/DiffusionRenderer-ComfyUI"
+            ],
+            "install_type": "git-clone",
+            "description": "Experimental wrapper for diffusion-renderer in ComfyUI.\nNOTE: Currently unfinished and non-functioning. Will update"
+        },
+        {
+            "author": "system-out-cho",
+            "title": "displayHistory [WIP]",
+            "reference": "https://github.com/system-out-cho/displayHistory_ComfyUI",
+            "files": [
+                "https://github.com/system-out-cho/displayHistory_ComfyUI"
+            ],
+            "install_type": "git-clone",
+            "description": "A node that displays any node's history\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "lazybuttalented",
+            "title": "ComfyUI_LBT [WIP]",
+            "reference": "https://github.com/lazybuttalented/ComfyUI_LBT",
+            "files": [
+                "https://github.com/lazybuttalented/ComfyUI_LBT"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node to program the image & text processing flows.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "xgfone",
+            "title": "ComfyUI_PromptLogoCleaner",
+            "reference": "https://github.com/xgfone/ComfyUI_PromptLogoCleaner",
+            "files": [
+                "https://github.com/xgfone/ComfyUI_PromptLogoCleaner"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Prompt Cleaner (Remove Logo Words)"
+        },
+        {
+            "author": "dexintenebri",
+            "title": "comfyui_voxel_nodes [WIP]",
+            "reference": "https://github.com/dexintenebri/comfyui_voxel_nodes",
+            "files": [
+                "https://github.com/dexintenebri/comfyui_voxel_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Extracts RGB and depthmap from image to create 3D Voxels, for integration into MagicaVoxel and Unity Engine\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "1H-hobit",
+            "title": "ComfyComfyUI_InternVL3 [WIP]",
+            "reference": "https://github.com/1H-hobit/ComfyUI_InternVL3",
+            "files": [
+                "https://github.com/1H-hobit/ComfyUI_InternVL3"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes to use InternVL3"
+        },
+        {
+            "author": "zhuanvi",
+            "title": "ComfyUI-ZVNodes [WIP]",
+            "reference": "https://github.com/zhuanvi/ComfyUI-ZVNodes",
+            "files": [
+                "https://github.com/zhuanvi/ComfyUI-ZVNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Load One Image (Directory), Save Image (Directory), Count Image (Directory), Json Reader, Json List Node, Json List Length, Json List Indexer, Json List Slicer, Triangle Character Layout, Json List To Mask, Random Select From List, Join List, ..."
+        },
+        {
+            "author": "Filexor",
+            "title": "File_x_dynamic_prompt2",
+            "reference": "https://github.com/Filexor/File_x_dynamic_prompt2",
+            "files": [
+                "https://github.com/Filexor/File_x_dynamic_prompt2"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node for random prompt generation"
+        },
+        {
+            "author": "FileSystem Manager Team",
+            "title": "Comfyui-FileSytem-Manager",
+            "reference": "https://github.com/bleash-dev/Comfyui-FileSytem-Manager",
+            "files": [
+                "https://github.com/bleash-dev/Comfyui-FileSytem-Manager"
+            ],
+            "install_type": "git-clone",
+            "description": "Comprehensive file system management for ComfyUI with multiple upload sources"
+        },
+        {
+            "author": "thavocado",
+            "title": "comfyui-danbooru-lookup",
+            "reference": "https://github.com/thavocado/comfyui-danbooru-lookup",
+            "files": [
+                "https://github.com/thavocado/comfyui-danbooru-lookup"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node that performs FAISS cosine similarity lookup on Danbooru embeddings using multiple input modes: CLIP conditioning, images with WD14 tagging, or text tags.[w/This nodepack installs its dependencies automatically during execution.]"
+        },
+        {
+            "author": "love2hina-net",
+            "title": "ComfyUI-Local-Translator",
+            "reference": "https://github.com/love2hina-net/ComfyUI-Local-Translator",
+            "files": [
+                "https://github.com/love2hina-net/ComfyUI-Local-Translator"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a text translation node using a local SLM (Microsoft Phi-4) for ComfyUI."
+        },
+        {
+            "author": "DenRakEiw",
+            "title": "Denrakeiw Nodes [WIP]",
+            "reference": "https://github.com/DenRakEiw/DenRakEiw_Nodes",
+            "files": [
+                "https://github.com/DenRakEiw/DenRakEiw_Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom nodepack for ComfyUI that provides utility nodes for image generation and manipulation.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "ahmedbana",
+            "title": "json-creator [WIP]",
+            "reference": "https://github.com/ahmedbana/json-creator",
+            "files": [
+                "https://github.com/ahmedbana/json-creator"
+            ],
+            "install_type": "git-clone",
+            "description": "Create Json for ComfyUI\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "ahmedbana",
+            "title": "File-Rename [UNSAFE]",
+            "reference": "https://github.com/ahmedbana/File-Rename",
+            "files": [
+                "https://github.com/ahmedbana/File-Rename"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom ComfyUI nodepackage that allows you to rename files with incremented numbers based on various mathematical operations. Includes both basic and advanced functionality.[w/This nodepack includes a node that can rename files to arbitrary paths.]"
+        },
+        {
+            "author": "ahmedbana",
+            "title": "ComfyUI_AC_FUNV7-FLUX- [WIP]",
+            "reference": "https://github.com/A719689614/ComfyUI_AC_FUNV7-FLUX-",
+            "files": [
+                "https://github.com/A719689614/ComfyUI_AC_FUNV7-FLUX-"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: AC_Super_UNET(FLUX), AC_Super_CLIP(FLUX)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "broumbroum",
+            "title": "comfyui-time-system [WIP]",
+            "reference": "https://github.com/broumbroum/comfyui-time-system",
+            "files": [
+                "https://github.com/broumbroum/comfyui-time-system"
+            ],
+            "install_type": "git-clone",
+            "description": "Package that adds nodes to retrieve the system date and time.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "blepping",
+            "title": "ComfyUI 'dum' samplers [WIP]",
+            "reference": "https://github.com/blepping/comfyui_dum_samplers",
+            "files": [
+                "https://github.com/blepping/comfyui_dum_samplers"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of random, experimental (and most likely 'dum') samplers for ComfyUI."
+        },
+        {
+            "author": "crimro-se",
+            "title": "ComfyUI-CascadedGaze",
+            "reference": "https://github.com/crimro-se/ComfyUI-CascadedGaze",
+            "files": [
+                "https://github.com/crimro-se/ComfyUI-CascadedGaze"
+            ],
+            "install_type": "git-clone",
+            "description": "Two custom nodes that bring the CascadedGaze image denoising model architecture to ComfyUI."
+        },
+        {
+            "author": "RamonGuthrie",
+            "title": "ComfyUI-RBG-LoRA-Converter [UNSAFE]",
+            "reference": "https://github.com/RamonGuthrie/ComfyUI-RBG-LoraConverter",
+            "files": [
+                "https://github.com/RamonGuthrie/ComfyUI-RBG-LoraConverter"
+            ],
+            "install_type": "git-clone",
+            "description": "A node for converting LoRA (Low-Rank Adaptation) keys in ComfyUI.[w/This nodepack contains a node that has a vulnerability allowing write to arbitrary file paths.]"
+        },
+        {
+            "author": "Estanislao-Oviedo",
+            "title": "ComfyUI-CustomNodes [NAME CONFLICT]",
+            "reference": "https://github.com/Estanislao-Oviedo/ComfyUI-CustomNodes",
+            "files": [
+                "https://github.com/Estanislao-Oviedo/ComfyUI-CustomNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Load Image Folder (Custom), Make Batch from Single Image (Custom)"
+        },
+        {
+            "author": "ctf05",
+            "title": "ComfyUI-AudioDuration",
+            "reference": "https://github.com/ctf05/ComfyUI-AudioDuration",
+            "files": [
+                "https://github.com/ctf05/ComfyUI-AudioDuration"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Audio Duration, Audio Overlay (Mix)"
+        },
+        {
+            "author": "soliton",
+            "title": "Watermark Detection YOLO Custom Node [WIP]",
+            "reference": "https://github.com/Soliton80/ComfyUI-Watermark-Detection-YOLO",
+            "files": [
+                "https://github.com/Soliton80/ComfyUI-Watermark-Detection-YOLO"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom watermark detection using rained on 24,558 watermark images YOLO11 model for ComfyUI\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Jpzz",
+            "title": "IxiWorks StoryBoard Nodes [WIP]",
+            "reference": "https://github.com/IXIWORKS-KIMJUNGHO/comfyui-ixiworks",
+            "files": [
+                "https://github.com/IXIWORKS-KIMJUNGHO/comfyui-ixiworks"
+            ],
+            "install_type": "git-clone",
+            "description": "StoryBoard nodes for ComfyUI - Parse JSON templates and build prompts for generative movie creation\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "siyonomicon",
+            "title": "ComfyUI-Pin",
+            "reference": "https://github.com/siyonomicon/ComfyUI-Pin",
+            "files": [
+                "https://github.com/siyonomicon/ComfyUI-Pin"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Pin Grid Node"
+        },
+        {
+            "author": "rakete",
+            "title": "comfyui-rakete",
+            "reference": "https://github.com/rakete/comfyui-rakete",
+            "files": [
+                "https://github.com/rakete/comfyui-rakete"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Get Widget or Default Value, GPU Garbage Collector, Build String from Widget Values"
+        },
+        {
+            "author": "boricuapab",
+            "title": "ComfyUI-Bori-KontextPresets [WIP]",
+            "reference": "https://github.com/boricuapab/ComfyUI-Bori-KontextPresets",
+            "files": [
+                "https://github.com/boricuapab/ComfyUI-Bori-KontextPresets"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a custom node for ComfyUI that uses the Kontext Presets.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "sh570655308",
+            "title": "Comfyui-RayNodes [WIP]",
+            "reference": "https://github.com/sh570655308/Comfyui-RayNodes",
+            "files": [
+                "https://github.com/sh570655308/Comfyui-RayNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Bracketed Tag-Index Merger, Florence2 Tag Processor, Image List Converter, Image Selector, Mask Blackener, Mask Applier and Combiner, Mask Processor, Tag Array to Lines, Tag-Index Merger, Grabber Tag Processor, Image Resizer, Save Image Websocket, Border Mask, SaturationAdjuster, ...\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Rocky-Lee-001",
+            "title": "ComfyUI_SZtools",
+            "reference": "https://github.com/Rocky-Lee-001/ComfyUI_SZtools",
+            "files": [
+                "https://github.com/Rocky-Lee-001/ComfyUI_SZtools"
+            ],
+            "install_type": "git-clone",
+            "description": "This project is the comfyui implementation of ComfyUI_SZtools, a labeling and naming tool developed for Kontext's local training package T2ITrainer.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "gorillaframeai",
+            "title": "GF_pixtral_node [WIP]",
+            "reference": "https://github.com/gorillaframeai/GF_pixtral_node",
+            "files": [
+                "https://github.com/gorillaframeai/GF_pixtral_node"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: GF Mistral & Pixtral"
+        },
+        {
+            "author": "enlo",
+            "title": "ComfyUI-CheckpointSettings",
+            "reference": "https://github.com/enlo/ComfyUI-CheckpointSettings",
+            "files": [
+                "https://github.com/enlo/ComfyUI-CheckpointSettings"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node created to fulfill a personal need I thought of while playing around with ComfyUI — 'I want to save checkpoint names and KSampler settings together and randomly switch between them for fun.'"
+        },
+        {
+            "author": "Mzikart",
+            "title": "ComfyUI-Mzikart-Player [WIP]",
+            "reference": "https://github.com/Dream-Pixels-Forge/ComfyUI-Mzikart-Player",
+            "files": [
+                "https://github.com/Dream-Pixels-Forge/ComfyUI-Mzikart-Player"
+            ],
+            "install_type": "git-clone",
+            "description": "Interactive audio player for ComfyUI\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "babydjac",
+            "title": "comfyui-grok-ponyxl [WIP]",
+            "reference": "https://github.com/babydjac/comfyui-grok-ponyxl",
+            "files": [
+                "https://github.com/babydjac/comfyui-grok-ponyxl"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: GrokPonyXLPrompter\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "MarkFreeDom168",
+            "title": "ComfyUI-image-load-url [WIP]",
+            "reference": "https://github.com/MarkFreeDom168/ComfyUI-image-load-url",
+            "files": [
+                "https://github.com/MarkFreeDom168/ComfyUI-image-load-url"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Load Image From URL/Base64, Load Mask From URL/Base64, Load img and mask from url\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "realm-weaver",
+            "title": "Tile Seamstress 360° [WIP]",
+            "reference": "https://github.com/realm-weaver/ComfyUI-tile-seamstress-360",
+            "files": [
+                "https://github.com/realm-weaver/ComfyUI-tile-seamstress-360"
+            ],
+            "install_type": "git-clone",
+            "description": "Tile Seamstress 360 is a set of tools for fixing seams & poles in 360° panoramic equirectangular images inside ComfyUI."
+        },
+        {
+            "author": "jisenhua",
+            "title": "ComfyUI-yolov5-face [WIP]",
+            "reference": "https://github.com/UmutGuzel/tryvariantai-comfyui",
+            "files": [
+                "https://github.com/UmutGuzel/tryvariantai-comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Fill Transparency, Mask Expand Border, Mask Expand Border (Advanced), Mask to Transparent, Debug Mask Visualizer, White to Transparent, White Detector\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "visualbruno",
+            "title": "ComfyUI-QRemeshify",
+            "reference": "https://github.com/visualbruno/ComfyUI-QRemeshify",
+            "files": [
+                "https://github.com/visualbruno/ComfyUI-QRemeshify"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: QRemeshify"
+        },
+        {
+            "author": "jisenhua",
+            "title": "ComfyUI-yolov5-face [WIP]",
+            "reference": "https://github.com/JiSenHua/ComfyUI-yolov5-face",
+            "files": [
+                "https://github.com/JiSenHua/ComfyUI-yolov5-face"
+            ],
+            "install_type": "git-clone",
+            "description": "A YOLOv5 face detection project for ComfyUI.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "zopieux",
+            "title": "ComfyUI-zopi [UNSAFE]",
+            "reference": "https://github.com/zopieux/ComfyUI-zopi",
+            "files": [
+                "https://github.com/zopieux/ComfyUI-zopi"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Eval Python, Load TensortRT + checkpoint + CLIP + VAE [w/This nodepack contains a vulnerability that allows remote code execution.]"
+        },
+        {
+            "author": "przewodo",
+            "title": "ComfyUI-Przewodo-Utils [WIP]",
+            "reference": "https://github.com/przewodo/ComfyUI-Przewodo-Utils",
+            "files": [
+                "https://github.com/przewodo/ComfyUI-Przewodo-Utils"
+            ],
+            "install_type": "git-clone",
+            "description": "Utilities to make it easy to develop advanced Workflows without having to use a lot of nodes for simple stuff.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "hulipanpan",
+            "title": "Comfyui_tuteng [WIP]",
+            "reference": "https://github.com/hulipanpan/Comfyui_tuteng",
+            "files": [
+                "https://github.com/hulipanpan/Comfyui_tuteng"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Tuteng Mj, Tuteng Mj Style, Tuteng Upload, Tuteng Mj Upscale, Tuteng Mj Vary/Zoom, Tuteng Kling Text2Video, Tuteng Kling Image2Video, Tuteng Kling Video Extend, Tuteng Gemini API, Tuteng Doubao SeedEdit, Tuteng ChatGPT API, Tuteng Jimeng API, Tuteng GPT-Image-1 Edit, ...\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "PaleBloodq",
+            "title": "ComfyUI-HFTransformers",
+            "reference": "https://github.com/PaleBloodq/ComfyUI-HFTransformers",
+            "files": [
+                "https://github.com/PaleBloodq/ComfyUI-HFTransformers"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: HFT Pipeline Loader, HFT Classifier, HFT Classification Selector, HFT Object Detector, HFT Image to Text, HFT Depth Estimator"
+        },
+        {
+            "author": "whmc76",
+            "title": "ComfyUI-AudioSuiteAdvanced [WIP]",
+            "reference": "https://github.com/whmc76/ComfyUI-AudioSuiteAdvanced",
+            "files": [
+                "https://github.com/whmc76/ComfyUI-AudioSuiteAdvanced"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI plugin for processing long text files and generating speech, supporting features such as audio separation, text segmentation, and audio merging.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Letz-AI",
+            "title": "ComfyUI-LetzAI [UNSAFE]",
+            "reference": "https://github.com/Letz-AI/ComfyUI-LetzAI",
+            "files": [
+                "https://github.com/Letz-AI/ComfyUI-LetzAI"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI Node for LetzAI Image Generation[w/The API key is embedded in the workflow.]"
+        },
+        {
+            "author": "ZhouNLP",
+            "title": "comfyui_LK_selfuse",
+            "reference": "https://github.com/LK-168/comfyui_LK_selfuse",
+            "files": [
+                "https://github.com/LK-168/comfyui_LK_selfuse"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Mask Diff, Mask Connected Remove, Mask Get Max, Mask Filter with Rate, InspectModelArchitecture, Print Sigma, Adv Scheduler, LK_MaskToSEGS, LK_SegsAdjust, String Filter, String Remove Duplicate, String Modify, ... \nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "junhe421",
+            "title": "comfyui_batch_image_processor [WIP]",
+            "reference": "https://github.com/junhe421/comfyui_batch_image_processor",
+            "files": [
+                "https://github.com/junhe421/comfyui_batch_image_processor"
+            ],
+            "install_type": "git-clone",
+            "description": "A Kontext Bench-style ComfyUI image difference analysis node that supports instruction-based prompt generation and batch TXT editing.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Tr1dae",
+            "title": "ComfyUI-CustomNodes-MVM",
+            "reference": "https://github.com/Tr1dae/ComfyUI-CustomNodes-MVM",
+            "files": [
+                "https://github.com/Tr1dae/ComfyUI-CustomNodes-MVM"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Load Image From Folder MVM, Load Guidance Images From Folder MVM, Load Text From Folder MVM"
+        },
+        {
+            "author": "Vkabuto23",
+            "title": "ComfyUI Custom Nodes: OpenRouter & Ollama [UNSAFE]",
+            "reference": "https://github.com/Vkabuto23/comfyui_openrouter_ollama",
+            "files": [
+                "https://github.com/Vkabuto23/comfyui_openrouter_ollama"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI Custom Nodes: OpenRouter & Ollama[w/The API key is embedded in the workflow.]"
+        },
+        {
+            "author": "subnet99",
+            "title": "ComfyUI-URLLoader",
+            "reference": "https://github.com/subnet99/ComfyUI-URLLoader",
+            "files": [
+                "https://github.com/subnet99/ComfyUI-URLLoader"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI plugin for downloading and loading media files from URLs."
+        },
+        {
+            "author": "bikiam",
+            "title": "Comfyui_AudioRecoder",
+            "reference": "https://github.com/bikiam/Comfyui_AudioRecoder",
+            "files": [
+                "https://github.com/bikiam/Comfyui_AudioRecoder"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: AUDIO Recorder"
+        },
+        {
+            "author": "saulchiu",
+            "title": "comfyui-saul-plugin [WIP]",
+            "reference": "https://github.com/saulchiu/comfy_saul_plugin",
+            "files": [
+                "https://github.com/saulchiu/comfy_saul_plugin"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Cutting Video\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "wasilone11",
+            "title": "comfyui-sync-translate-node",
+            "reference": "https://github.com/wasilone11/comfyui-sync-translate-node",
+            "files": [
+                "https://github.com/wasilone11/comfyui-sync-translate-node"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Sync.so Translator"
+        },
+        {
+            "author": "ashllay",
+            "title": "ComfyUI_MoreComfy",
+            "reference": "https://github.com/ashllay/ComfyUI_MoreComfy",
+            "files": [
+                "https://github.com/ashllay/ComfyUI_MoreComfy"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: MC Switch Seed, MC Switch Image, MC Switch String, MC Alter Seed, MC Set Tile Size, MC Get Image Size, MC Get Image Min Max, MC Multi Concat, MC Multi Concat(Advanced), MC Noise"
+        },
+        {
+            "author": "gaowei-space",
+            "title": "ComfyUI Doubao LLM [WIP]",
+            "reference": "https://github.com/gaowei-space/ComfyUI-Doubao-LLM",
+            "files": [
+                "https://github.com/gaowei-space/ComfyUI-Doubao-LLM"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for Doubao (ByteDance) LLM and Vision Language Model integration\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "BrettMedia",
+            "title": "comfyui-bhtools [WIP]",
+            "reference": "https://github.com/BrettMedia/comfyui-bhtools",
+            "files": [
+                "https://github.com/BrettMedia/comfyui-bhtools"
+            ],
+            "install_type": "git-clone",
+            "description": "A suite of creative tools designed to help AI artists with continuity, brainstorming, and workflow optimization. Born from real-world needs during my AI journey, these nodes solve common pain points in creative workflows.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "XiaoHeiziGGG",
+            "title": "ComfyUI-Gemini-Kontext [WIP]",
+            "reference": "https://github.com/XiaoHeiziGGG/ComfyUI-Gemini-Kontext",
+            "files": [
+                "https://github.com/XiaoHeiziGGG/ComfyUI-Gemini-Kontext"
+            ],
+            "install_type": "git-clone",
+            "description": "Google Gemini API powered translation nodes for ComfyUI\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Bwebbfx",
+            "title": "ComfyUI Face Parsing Nodes [WIP]",
+            "reference": "https://github.com/Bwebbfx/ComfyUI_FaceParsing",
+            "files": [
+                "https://github.com/Bwebbfx/ComfyUI_FaceParsing"
+            ],
+            "install_type": "git-clone",
+            "description": "This package provides ComfyUI nodes for face parsing using BiSeNet (from yakhyo/face-parsing), supporting batch and video workflows.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "orion4d",
+            "title": "Unified List Selector for ComfyUI [UNSAFE]",
+            "reference": "https://github.com/orion4d/ComfyUI_unified_list_selector",
+            "files": [
+                "https://github.com/orion4d/ComfyUI_unified_list_selector"
+            ],
+            "install_type": "git-clone",
+            "description": "This project is a custom node for ComfyUI that allows you to dynamically load lists from text (.txt) or CSV (.csv) files and select an item to use in your workflow. It features a manual selection mode (via a dropdown list) and a random selection mode, as well as the ability to add prefixes and suffixes to the selected text.[w/This nodepack contains a node with a vulnerability that allows reading files from arbitrary paths.]"
+        },
+        {
+            "author": "kongds1999",
+            "title": "ComfyUI_was_image",
+            "reference": "https://github.com/kongds1999/ComfyUI_was_image",
+            "files": [
+                "https://github.com/kongds1999/ComfyUI_was_image"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Replace Color By Palette, ConvertGrayToImage"
+        },
+        {
+            "author": "zl9739379",
+            "title": "ComfyUI Qwen Vision Language API Node [NAME CONFLICT]",
+            "reference": "https://github.com/zl9739379/comfyui-qwen-vl-api",
+            "files": [
+                "https://github.com/zl9739379/comfyui-qwen-vl-api"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node for describing images using Qwen Vision Language models through OpenAI-compatible APIs."
+        },
+        {
+            "author": "bikiam",
+            "title": "ComfyUi_WhisperGTranslate",
+            "reference": "https://github.com/bikiam/ComfyUi_WhisperGTranslate",
+            "files": [
+                "https://github.com/bikiam/ComfyUi_WhisperGTranslate"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Whisper + AudioTranslate, Google Translate Node"
+        },
+        {
+            "author": "edgerunner",
+            "title": "ComfyUI Queue Manager [WIP]",
+            "reference": "https://github.com/QuietNoise/ComfyUI-Queue-Manager",
+            "files": [
+                "https://github.com/QuietNoise/ComfyUI-Queue-Manager"
+            ],
+            "install_type": "git-clone",
+            "description": "An extension supporting more streamlined prompt queue management."
+        },
+        {
+            "author": "fylrid2",
+            "title": "lockValue",
+            "reference": "https://github.com/fylrid2/comfyui_lock_previous_value",
+            "files": [
+                "https://github.com/fylrid2/comfyui_lock_previous_value"
+            ],
+            "install_type": "git-clone",
+            "description": "Allows the locking of a nodes value\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "XiaoHeiziGGG",
+            "title": "ComfyUI Gemini Translator [WIP]",
+            "reference": "https://github.com/XiaoHeiziGGG/ComfyUI-GeminiTranslator",
+            "files": [
+                "https://github.com/XiaoHeiziGGG/ComfyUI-GeminiTranslator"
+            ],
+            "install_type": "git-clone",
+            "description": "The API node library of gemini can be translated and recognized.The API node library of gemini can be translated and recognized.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "DiffusionWave-YT",
+            "title": "DiffusionWave_PickResolution [WIP]",
+            "reference": "https://github.com/DiffusionWave-YT/DiffusionWave_PickResolution",
+            "files": [
+                "https://github.com/DiffusionWave-YT/DiffusionWave_PickResolution"
+            ],
+            "install_type": "git-clone",
+            "description": "Change of resolutions for ComfyUI and Upscalers\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "PeterMikhai",
+            "title": "DoomFLUX Nodes [WIP]",
+            "reference": "https://github.com/PeterMikhai/Doom_Flux_NodePack",
+            "files": [
+                "https://github.com/PeterMikhai/Doom_Flux_NodePack"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom nodes for FLUX models, including a loader and specialized samplers for standard and inpaint generation.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "maque",
+            "title": "comfyui_video_BC [WIP]",
+            "reference": "https://github.com/JioJe/comfyui_video_BC",
+            "files": [
+                "https://github.com/JioJe/comfyui_video_BC"
+            ],
+            "install_type": "git-clone",
+            "description": "Batch load video nodes and save videos in custom paths\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "ZHO-ZHO-ZHO",
+            "title": "ComfyUI-Gemini [NAME CONFLICT]",
+            "id": "gemini",
+            "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Gemini",
+            "files": [
+                "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Gemini"
+            ],
+            "install_type": "git-clone",
+            "description": "Using Gemini-pro & Gemini-pro-vision in ComfyUI."
+        },
+        {
+            "author": "No-22-Github",
+            "title": "ComfyUI_SaveImageCustom",
+            "reference": "https://github.com/No-22-Github/ComfyUI_SaveImageCustom",
+            "files": [
+                "https://github.com/No-22-Github/ComfyUI_SaveImageCustom"
+            ],
+            "install_type": "git-clone",
+            "description": "Easy save image with dir+name"
+        },
+        {
+            "author": "jiafuzeng",
+            "title": "comfyui-fishSpeech",
+            "reference": "https://github.com/jiafuzeng/comfyui-fishSpeech",
+            "files": [
+                "https://github.com/jiafuzeng/comfyui-fishSpeech"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Fish-Speech Loader, Fish-Speech TTS, Fish-Speech Audio Preview"
+        },
+        {
+            "author": "bleash-dev",
+            "title": "ComfyUI-Auth-Manager",
+            "reference": "https://github.com/bleash-dev/ComfyUI-Auth-Manager",
+            "files": [
+                "https://github.com/bleash-dev/ComfyUI-Auth-Manager"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node that provides email/password authentication for ComfyUI pods with a beautiful modal interface."
+        },
+        {
+            "author": "filliptm",
+            "title": "ComfyUI_Fill-Node-Loader [WIP]",
+            "reference": "https://github.com/filliptm/ComfyUI_Fill-Node-Loader",
+            "files": [
+                "https://github.com/filliptm/ComfyUI_Fill-Node-Loader"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI plugin to simplify loading and managing custom nodes with a sidebar interface."
+        },
+        {
+            "author": "diogod",
+            "title": "Comfy Inpainting Works [WIP]",
+            "reference": "https://github.com/diodiogod/Comfy-Inpainting-Works",
+            "files": [
+                "https://github.com/diodiogod/Comfy-Inpainting-Works"
+            ],
+            "install_type": "git-clone",
+            "description": "Go to the top menu>Workflow>Browse Templates. This is a collection of my Inpainting workflows for Flux (expanded and COMPACT) + others. Previously called: 'Proper Flux Control-Net inpainting and/or outpainting with batch size - Alimama or Flux Fill'. By installing this 'node' you can always keep them up to date by updating on the manager. This is not a new custom node. You will still need to install all other custom nodes used on the workflows. You will also find my 'Flux LoRA Block Weights Preset Tester' here as well.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Malloc-pix",
+            "title": "comfyui-QwenVL",
+            "reference": "https://github.com/Malloc-pix/comfyui-QwenVL",
+            "files": [
+                "https://github.com/Malloc-pix/comfyui-QwenVL"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Qwen2.5VL, Qwen2.5"
+        },
+        {
+            "author": "artifyfun",
+            "title": "ComfyUI-JS [UNSAFE]",
+            "reference": "https://github.com/artifyfun/ComfyUI-JS",
+            "files": [
+                "https://github.com/artifyfun/ComfyUI-JS"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node capable of executing JavaScript code: it takes JavaScript code as input and outputs the execution result.[w/This extension has an XSS vulnerability that can be triggered through workflow execution.]"
+        },
+        {
+            "author": "OgreLemonSoup",
+            "title": "ComfyUI-Notes-manager",
+            "reference": "https://github.com/OgreLemonSoup/ComfyUI-Notes-manager",
+            "files": [
+                "https://github.com/OgreLemonSoup/ComfyUI-Notes-manager"
+            ],
+            "install_type": "git-clone",
+            "description": "This extension provides the note feature."
+        },
+        {
+            "author": "WozStudios",
+            "title": "ComfyUI-WozNodes",
+            "reference": "https://github.com/WozStudios/ComfyUI-WozNodes",
+            "files": [
+                "https://github.com/WozStudios/ComfyUI-WozNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Trim Image Batch, Create Image Batch, Select Image Batch by Mask, Advanced Batch Creator"
+        },
+        {
+            "author": "vovler",
+            "title": "comfyui-vovlertools",
+            "reference": "https://github.com/vovler/ComfyUI-vovlerTools",
+            "files": [
+                "https://github.com/vovler/ComfyUI-vovlerTools"
+            ],
+            "install_type": "git-clone",
+            "description": "Advanced ComfyUI nodes for WD14 tagging, image filtering, and CLIP to TensorRT conversion"
+        },
+        {
+            "author": "ELiZswe",
+            "title": "ComfyUI-ELiZTools",
+            "reference": "https://github.com/ELiZswe/ComfyUI-ELiZTools",
+            "files": [
+                "https://github.com/ELiZswe/ComfyUI-ELiZTools"
+            ],
+            "install_type": "git-clone",
+            "description": "ELIZ Tools"
+        },
+        {
+            "author": "yamanacn",
+            "title": "comfyui_qwenbbox",
+            "reference": "https://github.com/yamanacn/comfyui_qwenbbox",
+            "files": [
+                "https://github.com/yamanacn/comfyui_qwenbbox"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Load Qwen Model (v2), Qwen Bbox Detection, Prepare BBox for SAM (v2)"
+        },
+        {
+            "author": "mikheys",
+            "title": "ComfyUI-mikheys",
+            "reference": "https://github.com/mikheys/ComfyUI-mikheys",
+            "files": [
+                "https://github.com/mikheys/ComfyUI-mikheys"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: WAN Optimal Resolution Selector, WAN Show Image Dimensions"
+        },
+        {
+            "author": "yamanacn",
+            "title": "comfyui_qwen_object [WIP]",
+            "reference": "https://github.com/yamanacn/comfyui_qwen_object",
+            "files": [
+                "https://github.com/yamanacn/comfyui_qwen_object"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a custom node for ComfyUI that integrates the Qwen vision model for tasks such as object detection.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "neverbiasu",
+            "title": "ComfyUI-Show-o [WIP]",
+            "reference": "https://github.com/neverbiasu/ComfyUI-Show-o",
+            "files": [
+                "https://github.com/neverbiasu/ComfyUI-Show-o"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Show-o Model Loader, Show-o Text to Image, Show-o Image Captioning, Show-o Image Inpainting"
+        },
+        {
+            "author": "zyquon",
+            "title": "ComfyUI Stash",
+            "reference": "https://github.com/zyquon/ComfyUI-Stash",
+            "files": [
+                "https://github.com/zyquon/ComfyUI-Stash"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes to use Stash within Comfy workflows"
+        },
+        {
+            "author": "tankenyuen-ola",
+            "title": "comfyui-env-variable-reader [UNSAFE]",
+            "reference": "https://github.com/tankenyuen-ola/comfyui-env-variable-reader",
+            "files": [
+                "https://github.com/tankenyuen-ola/comfyui-env-variable-reader"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Environment Variable Reader [w/Installing this node may expose environment variables that contain sensitive information such as API keys.]"
+        },
+        {
+            "author": "ftf001-tech",
+            "title": "ComfyUI-Lucian [WIP]",
+            "reference": "https://github.com/ftf001-tech/ComfyUI-ExternalLLMDetector",
+            "files": [
+                "https://github.com/ftf001-tech/ComfyUI-ExternalLLMDetector"
+            ],
+            "install_type": "git-clone",
+            "description": "These nodes allow you to configure LLM API connections, send images with custom prompts, and convert the LLM's JSON bounding box responses into a format compatible with segmentation nodes like SAM2\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "LucianGnn",
+            "title": "ComfyUI-Lucian [WIP]",
+            "reference": "https://github.com/LucianGnn/ComfyUI-Lucian",
+            "files": [
+                "https://github.com/LucianGnn/ComfyUI-Lucian"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Audio Duration Calculator\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "akatz-ai",
+            "title": "ComfyUI-Execution-Inversion",
+            "reference": "https://github.com/akatz-ai/ComfyUI-Execution-Inversion",
+            "files": [
+                "https://github.com/akatz-ai/ComfyUI-Execution-Inversion"
+            ],
+            "install_type": "git-clone",
+            "description": "Contains nodes related to the new execution inversion engine in ComfyUI. nodepack originally from [a/https://github.com/BadCafeCode/execution-inversion-demo-comfyui](https://github.com/BadCafeCode/execution-inversion-demo-comfyui)"
+        },
+        {
+            "author": "mamorett",
+            "title": "comfyui_minicpm_vision",
+            "reference": "https://github.com/mamorett/comfyui_minicpm_vision",
+            "files": [
+                "https://github.com/mamorett/comfyui_minicpm_vision"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: MiniCPM Vision GGUF"
+        },
+        {
+            "author": "BigStationW",
+            "title": "flowmatch_scheduler-comfyui",
+            "reference": "https://github.com/BigStationW/flowmatch_scheduler-comfyui",
+            "files": [
+                "https://github.com/BigStationW/flowmatch_scheduler-comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: FlowMatchSigmas"
+        },
+        {
+            "author": "franky519",
+            "title": "ComfyUI Face Four Image Matcher [WIP]",
+            "reference": "https://github.com/franky519/comfyui_fnckc_Face_analysis",
+            "files": [
+                "https://github.com/franky519/comfyui_fnckc_Face_analysis"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for four face image matching and face swap control\nNOTE: Invalid pyproject.toml"
+        },
+        {
+            "author": "bleash-dev",
+            "title": "Comfyui-Iddle-Checker",
+            "reference": "https://github.com/bleash-dev/Comfyui-Idle-Checker",
+            "files": [
+                "https://github.com/bleash-dev/Comfyui-Idle-Checker"
+            ],
+            "install_type": "git-clone",
+            "description": "front extension for idle checker"
+        },
+        {
+            "author": "fangg2000",
+            "title": "ComfyUI-StableAudioFG [WIP]",
+            "reference": "https://github.com/fangg2000/ComfyUI-StableAudioFG",
+            "files": [
+                "https://github.com/fangg2000/ComfyUI-StableAudioFG"
+            ],
+            "install_type": "git-clone",
+            "description": "The ComfyUI plugin for stable-audio (supports offline use)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "hdfhssg",
+            "title": "comfyui_EvoSearch [WIP]",
+            "reference": "https://github.com/hdfhssg/comfyui_EvoSearch",
+            "files": [
+                "https://github.com/hdfhssg/comfyui_EvoSearch"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: EvoSearch_FLUX, EvoSearch_SD21, EvoSearch_WAN, EvolutionScheduleGenerator, GuidanceRewardsGenerator"
+        },
+        {
+            "author": "simonjaq",
+            "title": "ComfyUI-sjnodes",
+            "reference": "https://github.com/simonjaq/ComfyUI-sjnodes",
+            "files": [
+                "https://github.com/simonjaq/ComfyUI-sjnodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Some modified ComfyUI custom nodes"
+        },
+        {
+            "author": "xzuyn",
+            "title": "xzuynodes-ComfyUI",
+            "reference": "https://github.com/xzuyn/ComfyUI-xzuynodes",
+            "files": [
+                "https://github.com/xzuyn/ComfyUI-xzuynodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: First/Last Frame (XZ), Resize Image (Original KJ), Resize Image (XZ), CLIP Text Encode (XZ), Load CLIP (XZ), TripleCLIPLoader (XZ), WanImageToVideo (XZ)"
+        },
+        {
+            "author": "gilons",
+            "title": "ComfyUI-GoogleDrive-Downloader [UNSAFE]",
+            "reference": "https://github.com/gilons/ComfyUI-GoogleDrive-Downloader",
+            "files": [
+                "https://github.com/gilons/ComfyUI-GoogleDrive-Downloader"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for downloading files from Google Drive.[w/There is a vulnerability that allows saving a remote file to an arbitrary local path.]"
+        },
+        {
+            "author": "moonwhaler",
+            "title": "ComfyUI-FileBrowserAPI [UNSAFE]",
+            "reference": "https://github.com/GalactusX31/ComfyUI-FileBrowserAPI",
+            "files": [
+                "https://github.com/GalactusX31/ComfyUI-FileBrowserAPI"
+            ],
+            "install_type": "git-clone",
+            "description": "A general-purpose, dependency-free File and Folder Browser API for ComfyUI custom nodes.[w/path traversal vulnerability]"
+        },
+        {
+            "author": "DreamsInAutumn",
+            "title": "ComfyUI-Autumn-LLM-Nodes",
+            "reference": "https://github.com/DreamsInAutumn/ComfyUI-Autumn-LLM-Nodes",
+            "files": [
+                "https://github.com/DreamsInAutumn/ComfyUI-Autumn-LLM-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Gemini-Image-To-Prompt, Gemini-Prompt-Builder, LLM-Prompt-Builder"
+        },
+        {
+            "author": "alexgenovese",
+            "title": "ComfyUI-Reica",
+            "reference": "https://github.com/alexgenovese/ComfyUI-Reica",
+            "files": [
+                "https://github.com/alexgenovese/ComfyUI-Reica"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: 'Reica GCP: Read Image', 'Reica GCP: Write Image & Get URL', 'Reica Text Image Display', 'Reica Read Image URL', 'Reica URL Image Loader Filename', 'Reica API: Send HTTP Notification', 'Insert Anything'"
+        },
+        {
+            "author": "yichengup",
+            "title": "ComfyUI-Transition",
+            "reference": "https://github.com/yichengup/ComfyUI-Transition",
+            "files": [
+                "https://github.com/yichengup/ComfyUI-Transition"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Linear Transition, Gradient Transition, Dual Line Transition, Sequence Transition, Circular Transition, Circular Sequence Transition"
+        },
+        {
+            "author": "wildminder",
+            "title": "ComfyUI-MagCache [NAME CONFLICT|WIP]",
+            "reference": "https://github.com/wildminder/ComfyUI-MagCache",
+            "files": [
+                "https://github.com/wildminder/ComfyUI-MagCache"
+            ],
+            "install_type": "git-clone",
+            "description": "official implementation of [zehong-ma/MagCache](https://github.com/zehong-ma/MagCache) for ComfyUI"
+        },
+        {
+            "author": "laubsauger",
+            "title": "ComfyUI Storyboard [WIP]",
+            "reference": "https://github.com/laubsauger/comfyui-storyboard",
+            "files": [
+                "https://github.com/laubsauger/comfyui-storyboard"
+            ],
+            "install_type": "git-clone",
+            "description": "This custom node for ComfyUI provides a markdown renderer to display formatted text and notes within your workflow."
+        },
+        {
+            "author": "grokuku",
+            "title": "ComfyUI-Holaf-Terminal [UNSAFE]",
+            "reference": "https://github.com/grokuku/ComfyUI-Holaf-Utilities",
+            "files": [
+                "https://github.com/grokuku/ComfyUI-Holaf-Utilities"
+            ],
+            "install_type": "git-clone",
+            "description": "Interactive Terminal in a node for ComfyUI[w/This custom extension provides a remote web-based shell (terminal) interface to the machine running the ComfyUI server. By installing and using this extension, you are opening a direct, powerful, and potentially dangerous access point to your system.]"
+        },
+        {
+            "author": "usrname0",
+            "title": "ComfyUI-AllergicPack [WIP]",
+            "reference": "https://github.com/usrname0/ComfyUI-AllergicPack",
+            "files": [
+                "https://github.com/usrname0/ComfyUI-AllergicPack"
+            ],
+            "install_type": "git-clone",
+            "description": "This package is not ready for primetime but I'm making it public anyway. If I'm using the node then I'm putting it here. Might make it more official later. Use at your own risk."
+        },
+        {
+            "author": "cesilk10",
+            "title": "cesilk-comfyui-nodes",
+            "reference": "https://github.com/cesilk10/cesilk-comfyui-nodes",
+            "files": [
+                "https://github.com/cesilk10/cesilk-comfyui-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Save and Upload to S3, SDXL Image Sizes"
+        },
+        {
+            "author": "COcisuts",
+            "title": "CObot-ComfyUI-WhisperToTranscription [WIP]",
+            "reference": "https://github.com/COcisuts/CObot-ComfyUI-WhisperToTranscription",
+            "files": [
+                "https://github.com/COcisuts/CObot-ComfyUI-WhisperToTranscription"
+            ],
+            "install_type": "git-clone",
+            "description": "CObot-ComfyUI-WhisperToTranscription\nNOTE: missing requirements.txt"
+        },
+        {
+            "author": "jinchanz",
+            "title": "ComfyUI-AliCloud-Bailian [WIP]",
+            "reference": "https://github.com/jinchanz/ComfyUI-AliCloud-Bailian",
+            "files": [
+                "https://github.com/jinchanz/ComfyUI-AliCloud-Bailian"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a collection of custom nodes for invoking Alibaba Cloud's DashScope API within ComfyUI.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Yukinoshita-Yukinoe",
+            "title": "ComfyUI-KontextOfficialNode",
+            "reference": "https://github.com/Yukinoshita-Yukinoe/ComfyUI-KontextOfficialNode",
+            "files": [
+                "https://github.com/Yukinoshita-Yukinoe/ComfyUI-KontextOfficialNode"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Kontext Text-to-Image (Official Max), Kontext Image Editing (Official Max)"
+        },
+        {
+            "author": "takoyaki1118",
+            "title": "ComfyUI_PromptExtractor",
+            "reference": "https://github.com/takoyaki1118/ComfyUI_PromptExtractor",
+            "files": [
+                "https://github.com/takoyaki1118/ComfyUI_PromptExtractor"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Custom Load Image With Path, Prompt Extractor Node"
+        },
+        {
+            "author": "littleowl",
+            "title": "ComfyUI-MV-HECV",
+            "reference": "https://github.com/littleowl/ComfyUI-MV-HECV",
+            "files": [
+                "https://github.com/littleowl/ComfyUI-MV-HECV"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI export of 3D Videos and Images Compatible with VR / XR, including the AVP."
+        },
+        {
+            "author": "BinglongLi",
+            "title": "ComfyUI_ToolsForAutomask",
+            "reference": "https://github.com/BinglongLi/ComfyUI_ToolsForAutomask",
+            "files": [
+                "https://github.com/BinglongLi/ComfyUI_ToolsForAutomask"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Directional Mask Expansion, Remove Small Regions Mask, Precise Subtract Mask, Precise Add Mask, Closing Mask, Opening Mask, Conditional Mask Selector, Prune Thin Branches Mask, Mask Fill Gaps Convex Hull"
+        },
+        {
+            "author": "strhwste",
+            "title": "CSV Utils [WIP]",
+            "reference": "https://github.com/strhwste/comfyui_csv_utils",
+            "files": [
+                "https://github.com/strhwste/comfyui_csv_utils"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom CSV handling nodes for ComfyUI\nNOTE: invalid pyproject.toml"
+        },
+        {
+            "author": "retech995",
+            "title": "ComfyUI_SaveImageBulk [UNSAFE]",
+            "reference": "https://github.com/retech995/Save_Florence2_Bulk_Prompts",
+            "files": [
+                "https://github.com/retech995/Save_Florence2_Bulk_Prompts"
+            ],
+            "install_type": "git-clone",
+            "description": "This comfyui node helps save image[w/This nodepack contains a node that can write files to an arbitrary path.]"
+        },
+        {
+            "author": "etng",
+            "title": "ComfyUI-Heartbeat [UNSAFE]",
+            "reference": "https://github.com/etng/ComfyUI-Heartbeat",
+            "files": [
+                "https://github.com/etng/ComfyUI-Heartbeat"
+            ],
+            "install_type": "git-clone",
+            "description": "A plugin for ComfyUI that sends periodic heartbeat requests to a configured gateway, including system information and node status."
+        },
+        {
+            "author": "Novavision0313",
+            "title": "ComfyUI-NVVS [WIP]",
+            "reference": "https://github.com/Novavision0313/ComfyUI-NVVS",
+            "files": [
+                "https://github.com/Novavision0313/ComfyUI-NVVS"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI plugin customized by NOVEVISION\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "zackabrams",
+            "title": "ComfyUI-KeySyncWrapper [WIP]",
+            "reference": "https://github.com/zackabrams/ComfyUI-KeySyncWrapper",
+            "files": [
+                "https://github.com/zackabrams/ComfyUI-KeySyncWrapper"
+            ],
+            "install_type": "git-clone",
+            "description": "implementation of KeySync in ComfyUI"
+        },
+        {
+            "author": "godric8",
+            "title": "ComfyUI_Step1X-Edit [NAME CONFLICT]",
+            "reference": "https://github.com/godric8/ComfyUI_Step1X-Edit",
+            "files": [
+                "https://github.com/godric8/ComfyUI_Step1X-Edit"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for Step1X-Edit"
+        },
+        {
+            "author": "violet0927",
+            "title": "ComfyUI-Direct3DS2 [WIP]",
+            "reference": "https://github.com/y4my4my4m/ComfyUI_Direct3DS2",
+            "files": [
+                "https://github.com/y4my4my4m/ComfyUI_Direct3DS2"
+            ],
+            "install_type": "git-clone",
+            "description": "Direct3D-S2 plugin for ComfyUI. [w/Doesn't work yet]"
+        },
+        {
+            "author": "gamtruliar",
+            "title": "ComfyUI-N_SwapInput [UNSAFE]",
+            "reference": "https://github.com/gamtruliar/ComfyUI-N_SwapInput",
+            "files": [
+                "https://github.com/gamtruliar/ComfyUI-N_SwapInput"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a simple tool for swapping input folders with custom suffix in comfy-UI[w/]This nodepack performs deletion operations on local files and contains a vulnerability that allows arbitrary paths to be deleted."
+        },
+        {
+            "author": "bulldog68",
+            "title": "ComfyUI_FMJ [WIP]",
+            "reference": "https://github.com/bulldog68/ComfyUI_FMJ",
+            "files": [
+                "https://github.com/bulldog68/ComfyUI_FMJ"
+            ],
+            "install_type": "git-clone",
+            "description": "Generate random prompts easily for FMJ.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "pictorialink",
+            "title": "comfyui-static-resource[UNSAFE]",
+            "reference": "https://github.com/pictorialink/ComfyUI-static-resource",
+            "files": [
+                "https://github.com/pictorialink/ComfyUI-static-resource"
+            ],
+            "install_type": "git-clone",
+            "description": "Use model bending to push your model beyond its visuals' limits. These nodes allow you to apply transformations to the intemediate densoising steps during sampling, e.g. add, multiplty, scale, rotate, dilate, erode ..etc.[w/This nodepack includes a feature that allows downloading remote files to arbitrary local paths. This is a vulnerability that can lead to Remote Code Execution.]"
+        },
+        {
+            "author": "brace-great",
+            "title": "comfyui-mc [WIP]",
+            "reference": "https://github.com/brace-great/comfyui-mc",
+            "files": [
+                "https://github.com/brace-great/comfyui-mc"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: IncrementCounterOnMatch\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "blueraincoatli",
+            "title": "ComfyModelCleaner [WIP]",
+            "reference": "https://github.com/blueraincoatli/ComfyUI-Model-Cleaner",
+            "files": [
+                "https://github.com/blueraincoatli/ComfyUI-Model-Cleaner"
+            ],
+            "install_type": "git-clone",
+            "description": "This plugin helps identify and clean up unused model files in ComfyUI installations. It analyzes workflows, custom nodes, and model usage to safely identify redundant files."
+        },
+        {
+            "author": "avocadori",
+            "title": "ComfyUI Audio Amplitude Converter [WIP]",
+            "reference": "https://github.com/avocadori/ComfyUI-AudioAmplitudeConverter",
+            "files": [
+                "https://github.com/avocadori/ComfyUI-AudioAmplitudeConverter"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a high-performance custom node for ComfyUI that performs audio amplitude conversion.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "wTechArtist",
+            "title": "ComfyUI_VVL_VideoCamera",
+            "reference": "https://github.com/wTechArtist/ComfyUI_VVL_VideoCamera",
+            "files": [
+                "https://github.com/wTechArtist/ComfyUI_VVL_VideoCamera"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: VVL Video Camera Estimator, VVL Video Frame Extractor"
+        },
+        {
+            "author": "wTechArtist",
+            "title": "ComfyUI_VVL_Segmentation [WIP]",
+            "reference": "https://github.com/wTechArtist/ComfyUI_VVL_Segmentation",
+            "files": [
+                "https://github.com/wTechArtist/ComfyUI_VVL_Segmentation"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: VVL Mask2Former Panoptic (Enhanced), VVL OneFormer Universal Segmentation\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "lum3on",
+            "title": "comfyui_RollingDepth [WIP]",
+            "reference": "https://github.com/lum3on/comfyui_RollingDepth",
+            "files": [
+                "https://github.com/lum3on/comfyui_RollingDepth"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyuI Needs longer to start the first time, because the mode gets downloaded.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "abuzreq",
+            "title": "ComfyUI Model Bending [UNSAFE]",
+            "reference": "https://github.com/abuzreq/ComfyUI-Model-Bending",
+            "files": [
+                "https://github.com/abuzreq/ComfyUI-Model-Bending"
+            ],
+            "install_type": "git-clone",
+            "description": "Use model bending to push your model beyond its visuals' limits. These nodes allow you to apply transformations to the intemediate densoising steps during sampling, e.g. add, multiplty, scale, rotate, dilate, erode ..etc.[w/This nodepack contains a vulnerability that allows remote code execution.]"
+        },
+        {
+            "author": "Stable Diffusion VN",
+            "title": "SDVN Comfy node [UNSAFE]",
+            "id": "SDVN",
+            "reference": "https://github.com/StableDiffusionVN/SDVN_Comfy_node",
+            "files": [
+                "https://github.com/StableDiffusionVN/SDVN_Comfy_node"
+            ],
+            "install_type": "git-clone",
+            "description": "Update IC Lora Layout Support Node[w/This nodepack contains a vulnerability that allows remote code execution.]"
+        },
+        {
+            "author": "Sephrael",
+            "title": "comfyui_caption-around-image",
+            "reference": "https://github.com/Sephrael/comfyui_caption-around-image",
+            "files": [
+                "https://github.com/Sephrael/comfyui_caption-around-image"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: A comfyUI node to create captions around a generated image with the ability to dynamically include generation parameters"
+        },
+        {
+            "author": "yincangshiwei",
+            "title": "ComfyUI-SEQLToolNode",
+            "reference": "https://github.com/yincangshiwei/ComfyUI-SEQLToolNode",
+            "files": [
+                "https://github.com/yincangshiwei/ComfyUI-SEQLToolNode"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: ImageCropAlphaNode (Image), CanvasFusionNode (Image)"
+        },
+        {
+            "author": "gabe-init",
+            "title": "comfyui_ui_render [UNSAFE]",
+            "reference": "https://github.com/gabe-init/comfyui_ui_render",
+            "files": [
+                "https://github.com/gabe-init/comfyui_ui_render"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI HTML Renderer Node - Display rich HTML content within ComfyUI nodes[w/This nodepack contains nodes that potentially have XSS vulnerabilities.]"
+        },
+        {
+            "author": "gabe-init",
+            "title": "ComfyUI LM Studio Node [WIP]",
+            "reference": "https://github.com/gabe-init/ComfyUI-LM-Studio",
+            "files": [
+                "https://github.com/gabe-init/ComfyUI-LM-Studio"
+            ],
+            "install_type": "git-clone",
+            "description": "A powerful ComfyUI custom node that seamlessly integrates LM Studio's local language models into your ComfyUI workflows. This node supports both text-only and multimodal (text + image) inputs, making it perfect for complex AI-driven creative workflows.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "LyazS",
+            "title": "ComfyUI-aznodes",
+            "reference": "https://github.com/LyazS/ComfyUI-aznodes",
+            "files": [
+                "https://github.com/LyazS/ComfyUI-aznodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: CrossFadeImageSequence, SaveImageAZ"
+        },
+        {
+            "author": "truebillyblue",
+            "title": "lC.ComfyUI_epistemic_nodes [WIP]",
+            "reference": "https://github.com/truebillyblue/lC.ComfyUI_epistemic_nodes",
+            "files": [
+                "https://github.com/truebillyblue/lC.ComfyUI_epistemic_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: lC L1 Startle, lC L2 FrameClick, lC L3 KeymapClick, lC L4 AnchorClick, lC L5 FieldClick, lC L6 ReflectBoom, lC Epistemic Pipeline (L1-L7), Create PBI (lC), Query PBIs (lC), Update PBI (lC), lC API LLM Agent, lC Web LLM Agent, ...\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "aklevecz",
+            "title": "ComfyUI-AutoPrompt [WIP]",
+            "reference": "https://github.com/aklevecz/ComfyUI-AutoPrompt",
+            "files": [
+                "https://github.com/aklevecz/ComfyUI-AutoPrompt"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Ollama Prompt Generator, Ollama Model Lister, Ollama Chat, Text Display"
+        },
+        {
+            "author": "AlexYez",
+            "title": "ComfyUI Timesaver Nodes",
+            "reference": "https://github.com/AlexYez/comfyui-timesaver",
+            "files": [
+                "https://github.com/AlexYez/comfyui-timesaver"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes from [Timesaver](https://github.com/AlexYez/comfyui-timesaver)."
+        },
+        {
+            "author": "aa-parky",
+            "title": "pipemind-comfyui",
+            "reference": "https://github.com/aa-parky/pipemind-comfyui",
+            "files": [
+                "https://github.com/aa-parky/pipemind-comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Random Line from File (Seeded), Keyword Prompt Composer, Simple Prompt Combiner (5x), Boolean Switch (Any), Select Line from TxT (Any), Multiline Text Input, Flux 2M Aspect Ratios, SDXL Aspect Ratios, Room Mapper, ..."
+        },
+        {
+            "author": "pacchikAI",
+            "title": "ImagePromptBatch [UNSAFE]",
+            "reference": "https://github.com/pacchikAI/ImagePromptBatch",
+            "files": [
+                "https://github.com/pacchikAI/ImagePromptBatch"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Load Image and Prompt[w/This includes a node that can read the contents of a `.csv` file from an arbitrary path.]"
+        },
+        {
+            "author": "papcorns",
+            "title": "ComfyUI-Papcorns-Node-UploadToGCS",
+            "reference": "https://github.com/papcorns/ComfyUI-Papcorns-Node-UploadToGCS",
+            "files": [
+                "https://github.com/papcorns/ComfyUI-Papcorns-Node-UploadToGCS"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Upload Image To GCS"
+        },
+        {
+            "author": "ZHO-ZHO-ZHO",
+            "title": "Qwen-2.5 in ComfyUI [NAME CONFLICT]",
+            "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Qwen",
+            "files": [
+                "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Qwen"
+            ],
+            "install_type": "git-clone",
+            "description": "Using Qwen-2.5 in ComfyUI"
+        },
+        {
+            "author": "Charonartist",
+            "title": "gabe-init [WIP]",
+            "reference": "https://github.com/gabe-init/ComfyUI-Repo-Eater",
+            "files": [
+                "https://github.com/gabe-init/ComfyUI-Repo-Eater"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node that ingests GitHub repositories and outputs their content as text along with token count.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Mervent",
+            "title": "comfyui-yaml-prompt",
+            "reference": "https://github.com/Mervent/comfyui-yaml-prompt",
+            "files": [
+                "https://github.com/Mervent/comfyui-yaml-prompt"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: YAMLPromptParser"
+        },
+        {
+            "author": "dhpdong",
+            "title": "ComfyUI-IPAdapter-Flux-Repair",
+            "reference": "https://github.com/dhpdong/ComfyUI-IPAdapter-Flux-Repair",
+            "files": [
+                "https://github.com/dhpdong/ComfyUI-IPAdapter-Flux-Repair"
+            ],
+            "install_type": "git-clone",
+            "description": "The IPAdapter-Flux node may cause some GPU memory to not be properly released during multiple inferences or when alternating between two nodes, eventually leading to a memory overflow. This project addresses and fixes that issue."
+        },
+        {
+            "author": "Mervent",
+            "title": "comfyui-telegram-send",
+            "reference": "https://github.com/Mervent/comfyui-telegram-send",
+            "files": [
+                "https://github.com/Mervent/comfyui-telegram-send"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: TelegramSend, TelegramReply"
+        },
+        {
+            "author": "MuAIGC",
+            "title": "DMXAPI Nodes [WIP]",
+            "reference": "https://github.com/MuAIGC/ComfyUI-DMXAPI_mmx",
+            "files": [
+                "https://github.com/MuAIGC/ComfyUI-DMXAPI_mmx"
+            ],
+            "install_type": "git-clone",
+            "description": "DMXAPI integration for ComfyUI with Seedream-3.0 text-to-image model\nNOTE: invalid pyproject.toml"
+        },
+        {
+            "author": "Hapseleg",
+            "title": "This n that (Hapse)",
+            "reference": "https://github.com/Hapseleg/ComfyUI-This-n-That",
+            "files": [
+                "https://github.com/Hapseleg/ComfyUI-This-n-That"
+            ],
+            "install_type": "git-clone",
+            "description": "Comfyui custom nodes I use for... This n That..."
+        },
+        {
+            "author": "matDobek",
+            "title": "ComfyUI_duck",
+            "reference": "https://github.com/matDobek/ComfyUI_duck",
+            "files": [
+                "https://github.com/matDobek/ComfyUI_duck"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Combine Images (duck)"
+        },
+        {
+            "author": "wTechArtist",
+            "title": "ComfyUI_vvl_BBOX",
+            "reference": "https://github.com/wTechArtist/ComfyUI_vvl_BBOX",
+            "files": [
+                "https://github.com/wTechArtist/ComfyUI_vvl_BBOX"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: vvl BBox Input"
+        },
+        {
+            "author": "zhengxyz123",
+            "title": "zhengxyz123/ComfyUI-CLIPSeg [NAME CONFLICT]",
+            "reference": "https://github.com/zhengxyz123/ComfyUI-CLIPSeg",
+            "files": [
+                "https://github.com/zhengxyz123/ComfyUI-CLIPSeg"
+            ],
+            "install_type": "git-clone",
+            "description": "Using CLIPSeg model to generate masks for image inpainting tasks based on text or image prompts."
+        },
+        {
+            "author": "Alazuaka",
+            "title": "ComfyUI Image Analysis Toolkit [WIP]",
+            "reference": "https://github.com/ThatGlennD/ComfyUI-Image-Analysis-Tools",
+            "files": [
+                "https://github.com/ThatGlennD/ComfyUI-Image-Analysis-Tools"
+            ],
+            "install_type": "git-clone",
+            "description": "A suite of custom ComfyUI nodes built to evaluate and diagnose the technical qualities of images—especially those generated by AI models. Rather than creating visuals, these tools measure them, offering precise insights into sharpness, noise, exposure, color balance, and more.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "trampolin",
+            "title": "comfy-ui-scryfall",
+            "reference": "https://github.com/trampolin/comfy-ui-scryfall",
+            "files": [
+                "https://github.com/trampolin/comfy-ui-scryfall"
+            ],
+            "install_type": "git-clone",
+            "description": "Some ComfyUI nodes to fetch cards from scryfall"
+        },
+        {
+            "author": "pomelyu",
+            "title": "cy-prompt-tools",
+            "reference": "https://github.com/pomelyu/cy-prompt-tools",
+            "files": [
+                "https://github.com/pomelyu/cy-prompt-tools"
+            ],
+            "install_type": "git-clone",
+            "description": "prompt tools for comfyui"
+        },
+        {
+            "author": "Alazuaka",
+            "title": "ES_nodes for ComfyUI by Alazuka [WIP]",
+            "reference": "https://github.com/Alazuaka/comfyui-lora-stack-node",
+            "files": [
+                "https://github.com/Alazuaka/comfyui-lora-stack-node"
+            ],
+            "install_type": "git-clone",
+            "description": "Node for LoRA stack management in ComfyUI\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "PabloGrant",
+            "title": "comfyui-giraffe-test-panel",
+            "reference": "https://github.com/PabloGrant/comfyui-giraffe-test-panel",
+            "files": [
+                "https://github.com/PabloGrant/comfyui-giraffe-test-panel"
+            ],
+            "install_type": "git-clone",
+            "description": "General-purpose test node. [w/Use at your own risk. No warranties. No guaranteed support or future updates. Feel free to fork, but remember to share in case anyone else can benefit.]"
+        },
+        {
+            "author": "lrzjason",
+            "title": "Comfyui-Condition-Utils [WIP]",
+            "reference": "https://github.com/lrzjason/Comfyui-Condition-Utils",
+            "files": [
+                "https://github.com/lrzjason/Comfyui-Condition-Utils"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of utility nodes for handling condition tensors in ComfyUI."
+        },
+        {
+            "author": "gordon123",
+            "title": "ComfyUI_DreamBoard [WIP]",
+            "reference": "https://github.com/gordon123/ComfyUI_DreamBoard",
+            "files": [
+                "https://github.com/gordon123/ComfyUI_DreamBoard"
+            ],
+            "install_type": "git-clone",
+            "description": "for making storyboard UNDERCONSTRUCTION!"
+        },
+        {
+            "author": "erosDiffusion",
+            "title": "Select key from JSON (Alpha) [UNSAFE]",
+            "reference": "https://github.com/erosDiffusion/ComfyUI-enricos-json-file-load-and-value-selector",
+            "files": [
+                "https://github.com/erosDiffusion/ComfyUI-enricos-json-file-load-and-value-selector"
+            ],
+            "install_type": "git-clone",
+            "description": "this node lists json files in the ComfyUI input folder[w/If this nodepack is installed and the server is running with remote access enabled, it can read the contents of JSON files located in arbitrary paths.]"
+        },
+        {
+            "author": "yichengup",
+            "title": "ComfyUI-YCNodes_Advance",
+            "reference": "https://github.com/yichengup/ComfyUI-YCNodes_Advance",
+            "files": [
+                "https://github.com/yichengup/ComfyUI-YCNodes_Advance"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Face Detector Selector, YC Human Parts Ultra(Advance), Color Match (YC)"
+        },
+        {
+            "author": "maizerrr",
+            "title": "comfyui-code-nodes",
+            "reference": "https://github.com/maizerrr/comfyui-code-nodes",
+            "files": [
+                "https://github.com/maizerrr/comfyui-code-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: BBox Drawer, BBox Parser, Dummy Passthrough Node, Batch Images (up to 5), Mask Editor, OpenAI GPT-Image-1 Node, GhatGPT Node"
+        },
+        {
+            "author": "virallover",
+            "title": "comfyui-virallover",
+            "reference": "https://github.com/virallover/comfyui-virallover",
+            "files": [
+                "https://github.com/virallover/comfyui-virallover"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Download and Load Lora Model Only, Depth Fitter, Brightness Correction, Edge Noise, Feathered Sharpen, Concat Horizontal With Mask"
+        },
+        {
+            "author": "jax-explorer",
+            "title": "ComfyUI-DreamO",
+            "reference": "https://github.com/jax-explorer/ComfyUI-DreamO",
+            "files": [
+                "https://github.com/jax-explorer/ComfyUI-DreamO"
+            ],
+            "install_type": "git-clone",
+            "description": "[a/https://github.com/bytedance/DreamO](https://github.com/bytedance/DreamO]) ComfyUI Warpper"
+        },
+        {
+            "author": "SKBv0",
+            "title": "Retro Engine Node for ComfyUI",
+            "reference": "https://github.com/SKBv0/ComfyUI-RetroEngine",
+            "files": [
+                "https://github.com/SKBv0/ComfyUI-RetroEngine"
+            ],
+            "install_type": "git-clone",
+            "description": "This custom node integrates [a/EmulatorJS](https://github.com/EmulatorJS/EmulatorJS) into ComfyUI, allowing you to run retro games and capture their screens for your image generation workflows."
+        },
+        {
+            "author": "brace-great",
+            "title": "comfyui-eim",
+            "reference": "https://github.com/brace-great/comfyui-eim",
+            "files": [
+                "https://github.com/brace-great/comfyui-eim"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: EncryptImage"
+        },
+        {
+            "author": "p1atdev",
+            "title": "comfyui-aesthetic-predictor",
+            "reference": "https://github.com/p1atdev/comfyui-aesthetic-predictor",
+            "files": [
+                "https://github.com/p1atdev/comfyui-aesthetic-predictor"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Load Aesthetic Predictor, Predict Aesthetic Score"
+        },
+        {
+            "author": "barakapa",
+            "title": "barakapa-nodes",
+            "reference": "https://github.com/barakapa/barakapa-nodes",
+            "files": [
+                "https://github.com/barakapa/barakapa-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Compare and save unique workflows, count tokens in prompt, and other utility."
+        },
+        {
+            "author": "VictorLopes643",
+            "title": "ComfyUI-Video-Dataset-Tools [WIP]",
+            "reference": "https://github.com/VictorLopes643/ComfyUI-Video-Dataset-Tools",
+            "files": [
+                "https://github.com/VictorLopes643/ComfyUI-Video-Dataset-Tools"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Video Frame Extractor, Image Frame Saver\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "George0726",
+            "title": "ComfyUI-video-accessory [WIP]",
+            "reference": "https://github.com/George0726/ComfyUI-video-accessory",
+            "files": [
+                "https://github.com/George0726/ComfyUI-video-accessory"
+            ],
+            "install_type": "git-clone",
+            "description": "accessory nodes for video generation"
+        },
+        {
+            "author": "bheins",
+            "title": "ComfyUI-glb-to-stl [WIP]",
+            "reference": "https://github.com/maurorilla/ComfyUI-MisterMR-Nodes",
+            "files": [
+                "https://github.com/maurorilla/ComfyUI-MisterMR-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of custom nodes for ComfyUI that add drawing capabilities to your workflow.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "TheJorseman",
+            "title": "IntrinsicCompositingClean-ComfyUI",
+            "reference": "https://github.com/TheJorseman/IntrinsicCompositingClean-ComfyUI",
+            "files": [
+                "https://github.com/TheJorseman/IntrinsicCompositingClean-ComfyUI"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: DepthModelLoader, NormalsModelLoader, IntrinsicModelLoader, AlbedoModelLoader, ReshadingModelLoader, ReshadingProcessor, ...\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "bheins",
+            "title": "ComfyUI-glb-to-stl [WIP]",
+            "reference": "https://github.com/bheins/ComfyUI-glb-to-stl",
+            "files": [
+                "https://github.com/bheins/ComfyUI-glb-to-stl"
+            ],
+            "install_type": "git-clone",
+            "description": "GLB conversion to STL node for ComfyUI\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "3dmindscapper",
+            "title": "ComfyUI-Sam-Mesh [WIP]",
+            "reference": "https://github.com/3dmindscapper/ComfyUI-Sam-Mesh",
+            "files": [
+                "https://github.com/3dmindscapper/ComfyUI-Sam-Mesh"
+            ],
+            "install_type": "git-clone",
+            "description": "comfyui implementation of SaMesh segmentation of 3d meshes\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "shinich39",
+            "title": "comfyui-run-js [UNSAFE]",
+            "reference": "https://github.com/shinich39/comfyui-run-js",
+            "files": [
+                "https://github.com/shinich39/comfyui-run-js"
+            ],
+            "description": "Manipulate workflow via javascript on node.",
+            "install_type": "git-clone"
+        },
+        {
+            "author": "fangg2000",
+            "title": "ComfyUI-SenseVoice [WIP]",
+            "reference": "https://github.com/fangg2000/ComfyUI-SenseVoice",
+            "files": [
+                "https://github.com/fangg2000/ComfyUI-SenseVoice"
+            ],
+            "description": "A comfyui node plug-in developed based on the SenseVoise project, and a simple recording node.\nNOTE: The files in the repo are not organized.",
+            "install_type": "git-clone"
+        },
+        {
+            "author": "risunobushi",
+            "title": "ComfyUI_FaceMesh_Eyewear_Mask",
+            "reference": "https://github.com/risunobushi/ComfyUI_FaceMesh_Eyewear_Mask",
+            "files": [
+                "https://github.com/risunobushi/ComfyUI_FaceMesh_Eyewear_Mask"
+            ],
+            "description": "NODES: Face Mesh Eyewear Mask, MediaPipe Face to Mask, OpenPose Eyewear Mask (DWPose), Mask From Facial Keypoints",
+            "install_type": "git-clone"
+        },
+        {
+            "author": "machinesarenotpeople",
+            "title": "comfyui-energycost",
+            "reference": "https://github.com/machinesarenotpeople/comfyui-energycost",
+            "files": [
+                "https://github.com/machinesarenotpeople/comfyui-energycost"
+            ],
+            "description": "NODES: Energy Cost Timer, Energy Cost Calculator",
+            "install_type": "git-clone"
+        },
+        {
+            "author": "xqqe",
+            "title": "honey_nodes [WIP]",
+            "reference": "https://github.com/xqqe/honey_nodes",
+            "files": [
+                "https://github.com/xqqe/honey_nodes"
+            ],
+            "description": "honey nodes for comfyui\nNOTE: The files in the repo are not organized.",
+            "install_type": "git-clone"
+        },
+        {
+            "author": "Raidez",
+            "title": "Kuniklo Collection",
+            "reference": "https://github.com/Raidez/comfyui-kuniklo-collection",
+            "files": [
+                "https://github.com/Raidez/comfyui-kuniklo-collection"
+            ],
+            "description": "NODES: Properties, Apply SVG to Image",
+            "install_type": "git-clone"
+        },
+        {
+            "author": "ronaldstg",
+            "title": "comfyui-plus-integrations [WIP]",
+            "reference": "https://github.com/ronalds-eu/comfyui-plus-integrations",
+            "files": [
+                "https://github.com/ronalds-eu/comfyui-plus-integrations"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Image Pass Through, Upload Image to S3\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "kevin314",
+            "title": "ComfyUI-FastVideo",
+            "reference": "https://github.com/kevin314/ComfyUI-FastVideo",
+            "files": [
+                "https://github.com/kevin314/ComfyUI-FastVideo"
+            ],
+            "description": "NODES: Video Generator, Inference Args, VAE Config, Text Encoder Config, DIT Config",
+            "install_type": "git-clone"
+        },
+        {
+            "author": "benda1989",
+            "title": "Comfyui lama remover [WIP]",
+            "reference": "https://github.com/benda1989/WaterMarkRemover_ComfyUI",
+            "files": [
+                "https://github.com/benda1989/WaterMarkRemover_ComfyUI"
+            ],
+            "install_type": "git-clone",
+            "description": "A very simple ComfyUI node to remove item like image/video with mask watermark\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "3dmindscapper",
+            "title": "ComfyUI-PartField [WIP]",
+            "reference": "https://github.com/3dmindscapper/ComfyUI-PartField",
+            "files": [
+                "https://github.com/3dmindscapper/ComfyUI-PartField"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI implementation of the partfield nvidea segmentation models\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "StaffsGull",
+            "title": "comfyui_scene_builder [WIP]",
+            "reference": "https://github.com/StaffsGull/comfyui_scene_builder",
+            "files": [
+                "https://github.com/StaffsGull/comfyui_scene_builder"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: CharacterBuilderNode, ClothingItemNode, ClothingMergerNode, EnvironmentBuilderNode, MergeCharactersNode, PhotoStyleBuilderNode, SceneCombinerNode\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "gagaprince",
+            "title": "ComfyUI_gaga_utils",
+            "reference": "https://github.com/gagaprince/ComfyUI_gaga_utils",
+            "files": [
+                "https://github.com/gagaprince/ComfyUI_gaga_utils"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: GagaGetFileList, GagaGetStringListSize, GagaSplitStringToList, GagaTest, GagaBatchStringReplace"
+        },
+        {
+            "author": "ftechmax",
+            "title": "ComfyUI-NovaKit-Pack",
+            "reference": "https://github.com/ftechmax/ComfyUI-NovaKit-Pack",
+            "files": [
+                "https://github.com/ftechmax/ComfyUI-NovaKit-Pack"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Count Tokens"
+        },
+        {
+            "author": "jtydhr88",
+            "title": "ComfyUI Frontend Vue Basic [WIP]",
+            "reference": "https://github.com/jtydhr88/ComfyUI_frontend_vue_basic",
+            "files": [
+                "https://github.com/jtydhr88/ComfyUI_frontend_vue_basic"
+            ],
+            "install_type": "git-clone",
+            "description": "A demonstration custom node that showcases how to integrate Vue as a frontend framework within ComfyUI, complete with PrimeVue components and vue-i18n support."
+        },
+        {
+            "author": "silent-rain",
+            "title": "ComfyUI-SilentRain",
+            "reference": "https://github.com/silent-rain/ComfyUI-SilentRain",
+            "files": [
+                "https://github.com/silent-rain/ComfyUI-SilentRain"
+            ],
+            "install_type": "git-clone",
+            "description": "Ecological extension of comfyui using Rust language."
+        },
+        {
+            "author": "EricRollei",
+            "title": "Comfy-Metadata-System [WIP]",
+            "reference": "https://github.com/EricRollei/Comfy-Metadata-System",
+            "files": [
+                "https://github.com/EricRollei/Comfy-Metadata-System"
+            ],
+            "install_type": "git-clone",
+            "description": "Series of custom Comfyui Nodes that collects and saves metadata to embedded (png, jpg) as well as optional xmp and txt sidecars and database"
+        },
+        {
+            "author": "turskeli",
+            "title": "comfyui-SetWallpaper",
+            "reference": "https://github.com/turskeli/comfyui-SetWallpaper",
+            "files": [
+                "https://github.com/turskeli/comfyui-SetWallpaper"
+            ],
+            "install_type": "git-clone",
+            "description": "Simple wallpaper node for ComfyUI. Curently only supports Windows OS"
+        },
+        {
+            "author": "Sophylax",
+            "title": "ComfyUI-ReferenceMerge",
+            "reference": "https://github.com/Sophylax/ComfyUI-ReferenceMerge",
+            "files": [
+                "https://github.com/Sophylax/ComfyUI-ReferenceMerge"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Combine Images and Mask, Restitch Combined Crop"
+        },
+        {
+            "author": "bandido37",
+            "title": "Kaggle ComfyUI Local Save Node [WIP]",
+            "reference": "https://github.com/bandido37/comfyui-kaggle-local-save",
+            "files": [
+                "https://github.com/bandido37/comfyui-kaggle-local-save"
+            ],
+            "install_type": "git-clone",
+            "description": "This custom node for ComfyUI allows you to save generated images directly to your local PC instead of Kaggle's cloud output folder.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "springjk",
+            "title": "Psutil Container Memory Patch",
+            "reference": "https://github.com/springjk/ComfyUI-Psutil-Container-Memory-Patch",
+            "files": [
+                "https://github.com/springjk/ComfyUI-Psutil-Container-Memory-Patch"
+            ],
+            "install_type": "git-clone",
+            "description": "Make ComfyUI get correct memory information in the container (psutil monkey path)"
+        },
+        {
+            "author": "songtianhui",
+            "title": "ComfyUI-DMM [WIP]",
+            "reference": "https://github.com/songtianhui/ComfyUI-DMM",
+            "files": [
+                "https://github.com/songtianhui/ComfyUI-DMM"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: DMMLoader, DMMApply"
+        },
+        {
+            "author": "leon-etienne",
+            "title": "ComfyUI_Scoring-Nodes",
+            "reference": "https://github.com/leon-etienne/ComfyUI_Scoring-Nodes",
+            "files": [
+                "https://github.com/leon-etienne/ComfyUI_Scoring-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Text Similarity (CLIP), Image Similarity (CLIP), Multi Text→Image Similarity, Multi Image→Text Similarity, Aesthetic Score, Multi Aesthetic Comparison"
+        },
+        {
+            "author": "tanmoy-it",
+            "title": "comfyuiCustomNode",
+            "reference": "https://github.com/tanmoy-it/comfyuiCustomNode",
+            "files": [
+                "https://github.com/tanmoy-it/comfyuiCustomNode"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Download Image (Direct/No Save)"
+        },
+        {
+            "author": "Jingwen-genies",
+            "title": "comfyui-genies-nodes",
+            "reference": "https://github.com/Jingwen-genies/comfyui-genies-nodes",
+            "files": [
+                "https://github.com/Jingwen-genies/comfyui-genies-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Genies Pose Estimation, Genies Scale Face by Keypoints, Get V Channel from HSV, Select RGB by Mask"
+        },
+        {
+            "author": "Tawbaware",
+            "title": "ComfyUI-Tawbaware [WIP]",
+            "reference": "https://github.com/Tawbaware/ComfyUI-Tawbaware",
+            "files": [
+                "https://github.com/Tawbaware/ComfyUI-Tawbaware"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of custom nodes for ComfyUI\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "lucafoscili",
+            "title": "LF Nodes [UNSAFE]",
+            "reference": "https://github.com/lucafoscili/lf-nodes",
+            "files": [
+                "https://github.com/lucafoscili/lf-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom nodes with a touch of extra UX, including: history for primitives, JSON manipulation, logic switches with visual feedback, LLM chat... and more!\n[w/This nodepack contains a node with a vulnerability that allows arbitrary code execution.]"
+        },
+        {
+            "author": "jerryname2022",
+            "title": "ComfyUI-Real-ESRGAN [WIP]",
+            "reference": "https://github.com/jerryname2022/ComfyUI-Real-ESRGAN",
+            "files": [
+                "https://github.com/jerryname2022/ComfyUI-Real-ESRGAN"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Real-ESRGAN Model Loader, GFPGAN Model Loader, Real-ESRGAN Image Generator, GFPGAN Image Generator"
+        },
+        {
+            "author": "mm-akhtar",
+            "title": "comfyui-mask-selector-node",
+            "reference": "https://github.com/mm-akhtar/comfyui-mask-selector-node",
+            "files": [
+                "https://github.com/mm-akhtar/comfyui-mask-selector-node"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Mask Selector"
+        },
+        {
+            "author": "ryanontheinside",
+            "title": "ComfyUI-Livepeer [WIP]",
+            "reference": "https://github.com/ryanontheinside/ComfyUI-Livepeer",
+            "files": [
+                "https://github.com/ryanontheinside/ComfyUI-Livepeer"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI extension that provides integration with [a/Livepeer](https://livepeer.org/)'s AI services allowing for both sync and async generation."
+        },
+        {
+            "author": "newraina",
+            "title": "ComfyUI-Remote-Save-Image [UNSAFE]",
+            "reference": "https://github.com/newraina/ComfyUI-Remote-Save-Image",
+            "files": [
+                "https://github.com/newraina/ComfyUI-Remote-Save-Image"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node for ComfyUI that allows uploading generated images to any HTTP endpoint.[w/This node allows any users to send any locally stored image to a specified URL.]"
+        },
+        {
+            "author": "SXQBW",
+            "title": "ComfyUI-Qwen-VLM [WIP]",
+            "reference": "https://github.com/SXQBW/ComfyUI-Qwen3",
+            "files": [
+                "https://github.com/SXQBW/ComfyUI-Qwen3"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: QwenVLM"
+        },
+        {
+            "author": "kijai",
+            "title": "ComfyUI-FramePackWrapper [WIP]",
+            "reference": "https://github.com/kijai/ComfyUI-FramePackWrapper",
+            "files": [
+                "https://github.com/kijai/ComfyUI-FramePackWrapper"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI Wrapper for FramePack by lllyasviel"
+        },
+        {
+            "author": "WaiyanLing",
+            "title": "ComfyUI-Tracking [WIP]",
+            "reference": "https://github.com/WaiyanLing/ComfyUI-Tracking",
+            "files": [
+                "https://github.com/WaiyanLing/ComfyUI-Tracking"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI-Tracking This nodepack helps to conveniently collect invocation data from workflows for further study.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "vladp0727",
+            "title": "ComfyUI Simple Image Tools [WIP]",
+            "reference": "https://github.com/vladp0727/Comfyui-with-Furniture",
+            "files": [
+                "https://github.com/vladp0727/Comfyui-with-Furniture"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Get Mask From Alpha, Get Quadrilateral Outfit\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "ryanontheinside",
+            "title": "ComfyUI MineWorld Nodes [WIP]",
+            "reference": "https://github.com/ryanontheinside/ComfyUI-MineWorld",
+            "files": [
+                "https://github.com/ryanontheinside/ComfyUI-MineWorld"
+            ],
+            "install_type": "git-clone",
+            "description": "This extension integrates Microsoft's MineWorld - an interactive world model for Minecraft - into ComfyUI.\nMineWorld allows you to generate interactive Minecraft gameplay based on actions you provide, creating realistic Minecraft gameplay videos."
+        },
+        {
+            "author": "SanDiegoDude",
+            "title": "HiDreamSampler for ComfyUI [WIP]",
+            "reference": "https://github.com/SanDiegoDude/ComfyUI-HiDream-Sampler",
+            "files": [
+                "https://github.com/SanDiegoDude/ComfyUI-HiDream-Sampler"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom ComfyUI node for generating images using the HiDream AI model.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "ZenAI-Vietnam",
+            "title": "ComfyUI_InfiniteYou [NAME CONFLICT]",
+            "reference": "https://github.com/ZenAI-Vietnam/ComfyUI_InfiniteYou",
+            "files": [
+                "https://github.com/ZenAI-Vietnam/ComfyUI_InfiniteYou"
+            ],
+            "install_type": "git-clone",
+            "description": "An implementation of InfiniteYou for ComfyUI. Native support for [a/InfiniteYou](https://github.com/bytedance/InfiniteYou) in ComfyUI, designed by the ZenAI team."
+        },
+        {
+            "author": "filipemeneses",
+            "title": "ComfyUI_html [UNSAFE]",
+            "reference": "https://github.com/filipemeneses/ComfyUI_html",
+            "files": [
+                "https://github.com/filipemeneses/ComfyUI_html"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes to manipulate HTML.[w/This extension poses a risk of XSS vulnerability.]"
+        },
+        {
+            "author": "m-ai-studio",
+            "title": "mai-prompt-progress",
+            "reference": "https://github.com/m-ai-studio/mai-prompt-progress",
+            "files": [
+                "https://github.com/m-ai-studio/mai-prompt-progress"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI extensions for sending prompt progress to webhook"
+        },
+        {
+            "author": "ashllay",
+            "title": "ComfyUI_MoreComfy",
+            "reference": "https://github.com/ashllay/ComfyUI_MoreComfy",
+            "files": [
+                "https://github.com/ashllay/ComfyUI_MoreComfy"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: MC Switch Seed/Image/Latent/Model/String, MC Alter Seed, MC Set Tile Size, MC Get Image Size, MC Multi Concat"
+        },
+        {
+            "author": "gordon1chuge2623",
+            "title": "ComfyUI_seal_migration [WIP]",
+            "reference": "https://github.com/chuge26/ComfyUI_seal_migration",
+            "files": [
+                "https://github.com/chuge26/ComfyUI_seal_migration"
+            ],
+            "install_type": "git-clone",
+            "description": "This project implements stamp migration in PDF files based on ComfyUI, allowing stamps from specified pages of a source PDF to be transferred to specified pages of a target PDF.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "gordon123",
+            "title": "ComfyUI_srt2speech [WIP]",
+            "reference": "https://github.com/gordon123/ComfyUI_srt2speech",
+            "files": [
+                "https://github.com/gordon123/ComfyUI_srt2speech"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI_srt2speech"
+        },
+        {
+            "author": "hnmr293",
+            "title": "ComfyUI-SamOne - one-step sampling",
+            "reference": "https://github.com/hnmr293/ComfyUI-SamOne",
+            "files": [
+                "https://github.com/hnmr293/ComfyUI-SamOne"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a node that advances sampling by just one step in ComfyUI."
+        },
+        {
+            "author": "rphmeier",
+            "title": "comfyui-videodepthanything",
+            "reference": "https://github.com/rphmeier/comfyui-videodepthanything",
+            "files": [
+                "https://github.com/rphmeier/comfyui-videodepthanything"
+            ],
+            "install_type": "git-clone",
+            "description": "VideoDepthAnything nodes for ComfyUI"
+        },
+        {
+            "author": "benmizrahi",
+            "title": "ComfyGCS [WIP]",
+            "reference": "https://github.com/benmizrahi/ComfyGCS",
+            "files": [
+                "https://github.com/benmizrahi/ComfyGCS"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyGCS is a robust read/write plugin for Google Cloud Storage, designed to simplify interaction with GCS buckets in your projects.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "dogcomplex",
+            "title": "ComfyUI-LOKI [WIP]",
+            "reference": "https://github.com/dogcomplex/ComfyUI-LOKI",
+            "files": [
+                "https://github.com/dogcomplex/ComfyUI-LOKI"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Glamour\nNOTE: This nodepack installs pip dependencies outside the control of ComfyUI-Manager."
+        },
+        {
+            "author": "hunzmusic",
+            "title": "Comfyui-CraftsMan3DWrapper [WIP]",
+            "reference": "https://github.com/hunzmusic/Comfyui-CraftsMan3DWrapper",
+            "files": [
+                "https://github.com/hunzmusic/Comfyui-CraftsMan3DWrapper"
+            ],
+            "install_type": "git-clone",
+            "description": "A wrapper for CraftsMan\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "tzsoulcap",
+            "title": "ComfyUI-SaveImg-W-MetaData",
+            "reference": "https://github.com/tzsoulcap/ComfyUI-SaveImg-W-MetaData",
+            "files": [
+                "https://github.com/tzsoulcap/ComfyUI-SaveImg-W-MetaData"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: CAP Checkpoint Selector, CAP Save Image w/Metadata, CAP Load Image with Metadata, CAP Tag Image, CAP Sampler Selector, CAP Scheduler Selector, CAP Seed Generator, CAP String Literal, CAP Width/Height Literal, CAP Cfg Literal, CAP Int Literal"
+        },
+        {
+            "author": "hylarucoder",
+            "title": "comfyui-copilot",
+            "reference": "https://github.com/hylarucoder/comfyui-copilot",
+            "files": [
+                "https://github.com/hylarucoder/comfyui-copilot"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Eagle Image Node for PNGInfo, SDXL Resolution Presets (ws), SDXL Prompt Styler, SDXL Prompt Styler Advanced"
+        },
+        {
+            "author": "AlejandroTuzzi",
+            "title": "TUZZI-ByPass [WIP]",
+            "reference": "https://github.com/AlejandroTuzzi/TUZZI-ByPass",
+            "files": [
+                "https://github.com/AlejandroTuzzi/TUZZI-ByPass"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom nodes for automated AI pipelines\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "oxysoft",
+            "title": "Comfy-Compel",
+            "reference": "https://github.com/oxysoft/Comfy-Compel",
+            "files": [
+                "https://github.com/oxysoft/Comfy-Compel"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: CLIP Embed (Compel)"
+        },
+        {
+            "author": "QingLuanWithoutHeart",
+            "title": "ComfyUI File/Image Utils Nodes [UNSAFE]",
+            "reference": "https://github.com/QingLuanWithoutHeart/comfyui-file-image-utils",
+            "files": [
+                "https://github.com/QingLuanWithoutHeart/comfyui-file-image-utils"
+            ],
+            "install_type": "git-clone",
+            "description": "This custom node set provides useful utilities for file operations and image loading in ComfyUI."
+        },
+        {
+            "author": "pmarmotte2",
+            "title": "VibeVoiceSelector [WIP]",
+            "reference": "https://github.com/pmarmotte2/Comfyui-VibeVoiceSelector",
+            "files": [
+                "https://github.com/pmarmotte2/Comfyui-VibeVoiceSelector"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Vibe Voice Selector"
+        },
+        {
+            "author": "Temult",
+            "title": "TWanVideoSigmaSampler: EXPERIMENTAL [WIP]",
+            "reference": "https://github.com/Temult/TWanSigmaSampler",
+            "files": [
+                "https://github.com/Temult/TWanSigmaSampler"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node that modifies the WanVideoSampler to accept an external sigma schedule. Allows for customized and non-standard noise schedules in Wan 2.1 video generation workflow.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "wordbrew",
+            "title": "WAN Control Nodes for ComfyUI [WIP]",
+            "reference": "https://github.com/wordbrew/comfyui-wan-control-nodes",
+            "files": [
+                "https://github.com/wordbrew/comfyui-wan-control-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "This pack provides enhanced control nodes for working with Wan video models in ComfyUI. It is under active development and may change regularly, or may not. Depends entirely on my free time and waning interest. Please don't come to rely on it for anything, but you are welcome to improve on it.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "techtruth",
+            "title": "ComfyUI-Dreambooth",
+            "reference": "https://github.com/techtruth/ComfyUI-Dreambooth",
+            "files": [
+                "https://github.com/techtruth/ComfyUI-Dreambooth"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Dreambooth Trainer"
+        },
+        {
+            "author": "438443467",
+            "title": "ComfyUI-SanMian-Nodes",
+            "reference": "https://github.com/438443467/ComfyUI-SanMian-Nodes",
+            "files": [
+                "https://github.com/438443467/ComfyUI-SanMian-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Add Text To Image, Adjust Hex Brightness, Adjust Transparency By Mask, Align Images with Mask, Align Restore Json, Binarize Mask, Blend ICLight, ..."
+        },
+        {
+            "author": "alexgenovese",
+            "title": "ComfyUI-Reica",
+            "reference": "https://github.com/alexgenovese/ComfyUI-Reica",
+            "files": [
+                "https://github.com/alexgenovese/ComfyUI-Reica"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Reica Text Image Display, Flux Image Generator, Reica GCP: Read Image, Reica GCP: Write Image & Get URL, Reica API: Send HTTP Notification"
+        },
+        {
+            "author": "Stable-X",
+            "title": "ComfyUI-Hi3DGen",
+            "reference": "https://github.com/Stable-X/ComfyUI-Hi3DGen",
+            "files": [
+                "https://github.com/Stable-X/ComfyUI-Hi3DGen"
+            ],
+            "install_type": "git-clone",
+            "description": "This extension integrates [a/Hi3DGen](https://github.com/Stable-X/Hi3DGen) into ComfyUI, allowing user to generate high-fidelity 3D geometry generation from Images.[w/If the *sageattention* package is installed, this nodepack causes problems.]"
+        },
+        {
+            "author": "stiffy-committee",
+            "title": "comfyui-stiffy-nodes",
+            "reference": "https://github.com/V-woodpecker-V/comfyui-stiffy-nodes",
+            "files": [
+                "https://github.com/V-woodpecker-V/comfyui-stiffy-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: StiffyPrompter, StiffyPersistentPrompter, StiffyDecoder, StiffyDebugger, ..."
+        },
+        {
+            "author": "chetusangolgi",
+            "title": "Comfyui-supabase",
+            "reference": "https://github.com/chetusangolgi/Comfyui-supabase",
+            "files": [
+                "https://github.com/chetusangolgi/Comfyui-supabase"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Watch Supabase Bucket, Upload Image to Supabase"
+        },
+        {
+            "author": "rickyars",
+            "title": "sd-cn-animation",
+            "reference": "https://github.com/rickyars/sd-cn-animation",
+            "files": [
+                "https://github.com/rickyars/sd-cn-animation"
+            ],
+            "install_type": "git-clone",
+            "description": "SD-CN animation for Comfyui"
+        },
+        {
+            "author": "daracazamea",
+            "title": "DCNodess [WIP]",
+            "reference": "https://github.com/daracazamea/comfyUI-DCNodes",
+            "files": [
+                "https://github.com/daracazamea/comfyUI-DCNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Start Timer (Pass-Through), Get Generation Time, Manual Trigger, Flux: Resolution Picker, SDXL: Resolution Picker\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "hunzmusic",
+            "title": "ComfyUI-Hunyuan3DTools [WIP]",
+            "reference": "https://github.com/hunzmusic/ComfyUI-Hunyuan3DTools",
+            "files": [
+                "https://github.com/hunzmusic/ComfyUI-Hunyuan3DTools"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Hy3DTools Render Specific View, Hy3DTools Back-Project Inpaint\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "grokuku",
+            "title": "Holaf Custom Nodes for ComfyUI",
+            "reference": "https://github.com/grokuku/ComfyUI-Holaf",
+            "files": [
+                "https://github.com/grokuku/ComfyUI-Holaf"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Neurogrid Overload, Tile Calculator, Slice Calculator, Save Image, Tiled KSampler, KSampler, Image Comparer, Upscale, Overlay, Resolution Preset, Benchmark Runner, Benchmark Plotter, Benchmark Loader"
+        },
+        {
+            "author": "Burgstall-labs",
+            "title": "ComfyUI-BS_FalAi-API-Video [WIP]",
+            "reference": "https://github.com/Burgstall-labs/ComfyUI-BS_FalAi-API-Video",
+            "files": [
+                "https://github.com/Burgstall-labs/ComfyUI-BS_FalAi-API-Video"
+            ],
+            "install_type": "git-clone",
+            "description": "Experimental ComfyUI Custom Node for generating videos using various FAL AI API endpoints.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "uauaouau",
+            "title": "Mycraft [WIP]",
+            "reference": "https://github.com/sorption-dev/mycraft-comfyui",
+            "files": [
+                "https://github.com/sorption-dev/mycraft-comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "Mycraft provides a limitless storyboard experience for image generation, powered by the ComfyUI API.\nEach container functions as an independent ComfyUI workflow, Supports workflows (text-to-text) and fine-tuning (image-to-image), Supports workflow customization."
+        },
+        {
+            "author": "zhaorishuai",
+            "title": "ComfyUI-StoryboardDistributor",
+            "reference": "https://github.com/zhaorishuai/ComfyUI-StoryboardDistributor",
+            "files": [
+                "https://github.com/zhaorishuai/ComfyUI-StoryboardDistributor"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI plugin that automatically assigns storyboard content to 9 storyboard nodes."
+        },
+        {
+            "author": "alexgenovese",
+            "title": "ComfyUI-Diffusion-4k [WIP]",
+            "reference": "https://github.com/alexgenovese/ComfyUI-Diffusion-4k",
+            "files": [
+                "https://github.com/alexgenovese/ComfyUI-Diffusion-4k"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node implementation of the Diffusion 4K research paper.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "KERRY-YUAN",
+            "title": "Python_Executor [UNSAFE]",
+            "id": "PythonExecutor",
+            "reference": "https://github.com/KERRY-YUAN/ComfyUI_Python_Executor",
+            "files": [
+                "https://github.com/KERRY-YUAN/ComfyUI_Python_Executor"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes: Provides nodes to execute arbitrary Python code snippets and Resize images directly within ComfyUI workflows. [w/This node allows you to execute arbitrary code via the workflow.]"
+        },
+        {
+            "author": "ashllay",
+            "title": "ComfyUI_MoreComfy",
+            "reference": "https://github.com/ashllay/ComfyUI_MoreComfy",
+            "files": [
+                "https://github.com/ashllay/ComfyUI_MoreComfy"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: MC Switch Seed, MC Alter Seed, MC Set Tile Size, MC Multi Concat"
+        },
+        {
+            "author": "ayaoayaoayaoaya",
+            "title": "ComfyUI-KLUT-DeepSeek-API [WIP]",
+            "reference": "https://github.com/ayaoayaoayaoaya/ComfyUI-KLUT-DeepSeek-API",
+            "files": [
+                "https://github.com/ayaoayaoayaoaya/ComfyUI-KLUT-DeepSeek-API"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of utility / quality-of-life nodes for ComfyUI. Probably only useful to me.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "olyyarm",
+            "title": "ComfyUI-VLMStudio",
+            "reference": "https://github.com/KurtHokke/ComfyUI_KurtHokke_Nodes",
+            "files": [
+                "https://github.com/KurtHokke/ComfyUI_KurtHokke_Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Node_BOOL/INT/Float, BooleanToPipe, BooleanFromPipe, ExpMath, ExpMathDual/Quad, ...."
+        },
+        {
+            "author": "olyyarm",
+            "title": "ComfyUI-VLMStudio",
+            "reference": "https://github.com/olyyarm/ComfyUI-VLMStudio",
+            "files": [
+                "https://raw.githubusercontent.com/olyyarm/ComfyUI-VLMStudio/refs/heads/master/vlm_visionary_node_v3_.py"
+            ],
+            "install_type": "copy",
+            "description": "NODES: GemmaMultimodalAnalyzer"
+        },
+        {
+            "author": "apetitbois",
+            "title": "nova_utils",
+            "reference": "https://github.com/apetitbois/nova_utils",
+            "files": [
+                "https://github.com/apetitbois/nova_utils"
+            ],
+            "install_type": "git-clone",
+            "description": "Nova utils for ComfyUI"
+        },
+        {
+            "author": "sugarkwork",
+            "title": "comfyui_my_img_util",
+            "reference": "https://github.com/sugarkwork/comfyui_my_img_util",
+            "files": [
+                "https://github.com/sugarkwork/comfyui_my_img_util"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Simple Image Rotate"
+        },
+        {
+            "author": "sugarkwork",
+            "title": "comfyui-trtools",
+            "reference": "https://github.com/sugarkwork/comfyui-trtools",
+            "files": [
+                "https://github.com/sugarkwork/comfyui-trtools"
+            ],
+            "install_type": "git-clone",
+            "description": "TensorRT Tools for ComfyUI"
+        },
+        {
+            "author": "DonutsDelivery",
+            "title": "ComfyUI-DonutDetailer",
+            "reference": "https://github.com/DonutsDelivery/ComfyUI-DonutNodes",
+            "files": [
+                "https://github.com/DonutsDelivery/ComfyUI-DonutNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "This is an experimental node I made to mimick the 'adjust' in A1111 Supermerger [a/https://github.com/hako-mikan/sd-webui-supermerger?tab=readme-ov-file#adjust](https://github.com/hako-mikan/sd-webui-supermerger?tab=readme-ov-file#adjust)."
+        },
+        {
+            "author": "ZenAI-Vietnam",
+            "title": "ComfyUI-gemini-IG",
+            "reference": "https://github.com/ZenAI-Vietnam/ComfyUI-gemini-IG",
+            "files": [
+                "https://github.com/ZenAI-Vietnam/ComfyUI-gemini-IG"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Gemini Image Generation, Gemini Text Generation"
+        },
+        {
+            "author": "hunzmusic",
+            "title": "comfyui-hnznodes",
+            "reference": "https://github.com/hunzmusic/comfyui-hnznodes",
+            "files": [
+                "https://github.com/hunzmusic/comfyui-hnznodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Combine Channels Grayscale, Reorder Image Batch, Male Character Prompt"
+        },
+        {
+            "author": "cidiro",
+            "title": "cid-node-pack",
+            "reference": "https://github.com/cidiro/cid-node-pack",
+            "files": [
+                "https://github.com/cidiro/cid-node-pack"
+            ],
+            "install_type": "git-clone",
+            "description": "A lightweight nodepack for ComfyUI that adds a few handy nodes that I use in my workflows"
+        },
+        {
+            "author": "CeeVeeR",
+            "title": "ComfyUi-Text-Tiler",
+            "reference": "https://github.com/CeeVeeR/ComfyUi-Text-Tiler",
+            "files": [
+                "https://github.com/CeeVeeR/ComfyUi-Text-Tiler"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Text Tiler"
+        },
+        {
+            "author": "Dreamshot-io",
+            "title": "ComfyUI-Extend-Resolution",
+            "reference": "https://github.com/Dreamshot-io/ComfyUI-Extend-Resolution",
+            "files": [
+                "https://github.com/Dreamshot-io/ComfyUI-Extend-Resolution"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Resolution Padding"
+        },
+        {
+            "author": "l1yongch1",
+            "title": "ComfyUI-YcNodes",
+            "reference": "https://github.com/l1yongch1/ComfyUI-YcNodes",
+            "files": [
+                "https://github.com/l1yongch1/ComfyUI-YcNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: RemoveHighlightAndBlur, RoundedCorners, PaddingAccordingToBackground\npersonal custom nodes for learning"
+        },
+        {
+            "author": "GeekatplayStudio",
+            "title": "PMSnodes [WIP]",
+            "reference": "https://github.com/GeekatplayStudio/ComfyUI_Toolbox",
+            "files": [
+                "https://github.com/GeekatplayStudio/ComfyUI_Toolbox"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom nodes for ComfyUI to Load audio in Base64 format and Send Audio to Websocket in Base64 Format for creating API of Audio related AI\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "rhinoflavored",
+            "title": "comfyui_QT",
+            "reference": "https://github.com/rhinoflavored/comfyui_QT",
+            "files": [
+                "https://github.com/rhinoflavored/comfyui_QT"
+            ],
+            "install_type": "git-clone",
+            "description": "bunch of image manipulation nodes....\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "ricklove",
+            "title": "ComfyUI-AutoSeg-SAM2",
+            "reference": "https://github.com/ricklove/ComfyUI-AutoSeg-SAM2",
+            "files": [
+                "https://github.com/ricklove/ComfyUI-AutoSeg-SAM2"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: AutoSeg-SAM2 Batch Segmentation"
+        },
+        {
+            "author": "JoeAu",
+            "title": "ComfyUI-PythonNode [UNSAFE]",
+            "reference": "https://github.com/JoeAu/ComfyUI-PythonNode",
+            "files": [
+                "https://github.com/JoeAu/ComfyUI-PythonNode"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom ComfyUI node that allows users to execute arbitrary Python code with a single input (value) and output (result), enabling flexible processing of the input value using any Python code before assigning the final result to result. It also captures print() output and exceptions for debugging.[w/This node is an unsafe node that includes the capability to execute arbitrary python script.]"
+        },
+        {
+            "author": "smthemex",
+            "title": "ComfyUI_GPT_SoVITS_Lite",
+            "reference": "https://github.com/smthemex/ComfyUI_GPT_SoVITS_Lite",
+            "files": [
+                "https://github.com/smthemex/ComfyUI_GPT_SoVITS_Lite"
+            ],
+            "install_type": "git-clone",
+            "description": "[a/GPT_SoVITS](https://github.com/RVC-Boss/GPT-SoVITS) infer only for ComfyUI users\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Nambi24",
+            "title": "ComfyUI-Save_Image",
+            "reference": "https://github.com/Nambi24/ComfyUI-Save_Image",
+            "files": [
+                "https://github.com/Nambi24/ComfyUI-Save_Image"
+            ],
+            "description": "NODES: Save Image With Subfolder, Extract Last Path Component\nNOTE: The files in the repo are not organized.",
+            "install_type": "git-clone"
+        },
+        {
+            "author": "sugarkwork",
+            "title": "comfyui_image_crop",
+            "reference": "https://github.com/sugarkwork/comfyui_image_crop",
+            "files": [
+                "https://github.com/sugarkwork/comfyui_image_crop"
+            ],
+            "description": "NODES: CropTransparent, RestoreCrop, ExpandMultiple, CropReapply",
+            "install_type": "git-clone"
+        },
+        {
+            "author": "AkiEvansDev",
+            "title": "ComfyUI-Tools",
+            "reference": "https://github.com/AkiEvansDev/ComfyUI-Tools",
+            "files": [
+                "https://github.com/AkiEvansDev/ComfyUI-Tools"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom nodes for basic actions."
+        },
+        {
+            "author": "longzoho",
+            "title": "ComfyUI-Qdrant-Saver",
+            "reference": "https://github.com/longzoho/ComfyUI-Qdrant-Saver",
+            "files": [
+                "https://github.com/longzoho/ComfyUI-Qdrant-Saver"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: QDrant Saver Node"
+        },
+        {
+            "author": "RUFFY-369",
+            "title": "ComfyUI-FeatureBank",
+            "reference": "https://github.com/RUFFY-369/ComfyUI-FeatureBank",
+            "files": [
+                "https://github.com/RUFFY-369/ComfyUI-FeatureBank"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: FeatureBankAttentionProcessor"
+        },
+        {
+            "author": "Pablerdo",
+            "title": "ComfyUI-Sa2VAWrapper [WIP]",
+            "reference": "https://github.com/Pablerdo/ComfyUI-Sa2VAWrapper",
+            "files": [
+                "https://github.com/Pablerdo/ComfyUI-Sa2VAWrapper"
+            ],
+            "install_type": "git-clone",
+            "description": "Wrapper for the Sa2VA model"
+        },
+        {
+            "author": "aria1th",
+            "title": "ComfyUI-camietagger-onnx",
+            "reference": "https://github.com/aria1th/ComfyUI-camietagger-onnx",
+            "files": [
+                "https://github.com/aria1th/ComfyUI-camietagger-onnx"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Camie Tagger"
+        },
+        {
+            "author": "zjkhurry",
+            "title": "comfyui_MetalFX [WIP]",
+            "reference": "https://github.com/zjkhurry/comfyui_MetalFX",
+            "files": [
+                "https://github.com/zjkhurry/comfyui_MetalFX"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node for ComfyUI that enables high-quality image and video upscaling using Apple MetalFX technology.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "RoyKillington",
+            "title": "Miscomfy Nodes [WIP]",
+            "reference": "https://github.com/RoyKillington/miscomfy-nodes",
+            "files": [
+                "https://github.com/RoyKillington/miscomfy-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "A repo of custom nodes for ComfyUI, from interacting with certain APIs to whatever other miscellanea I end up making"
+        },
+        {
+            "author": "Elypha",
+            "title": "ComfyUI-Prompt-Helper [WIP]",
+            "reference": "https://github.com/Elypha/ComfyUI-Prompt-Helper",
+            "files": [
+                "https://github.com/Elypha/ComfyUI-Prompt-Helper"
+            ],
+            "install_type": "git-clone",
+            "description": "Concat conditions and prompts for ComfyUI"
+        },
+        {
+            "author": "StoryWalker",
+            "title": "comfyui_flux_collection_advanced [WIP]",
+            "reference": "https://github.com/StoryWalker/comfyui_flux_collection_advanced",
+            "files": [
+                "https://github.com/StoryWalker/comfyui_flux_collection_advanced"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a collection focused in give a little more flexibility in the use of Flux models."
+        },
+        {
+            "author": "OSAnimate",
+            "title": "ComfyUI-SpriteSheetMaker [WIP]",
+            "reference": "https://github.com/OSAnimate/ComfyUI-SpriteSheetMaker",
+            "files": [
+                "https://github.com/OSAnimate/ComfyUI-SpriteSheetMaker"
+            ],
+            "install_type": "git-clone",
+            "description": "The sprite sheet maker node is a simple way to create sprite sheets and image grids.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "BuffMcBigHuge",
+            "title": "ComfyUI-Buff-Nodes [WIP]",
+            "reference": "https://github.com/BuffMcBigHuge/ComfyUI-Buff-Nodes",
+            "files": [
+                "https://github.com/BuffMcBigHuge/ComfyUI-Buff-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Several quality-of-life batch operation and string manipulation nodes."
+        },
+        {
+            "author": "ritikvirus",
+            "title": "ComfyUI Terminal Command Node [UNSAFE]",
+            "reference": "https://github.com/ritikvirus/comfyui-terminal-modal-node",
+            "files": [
+                "https://github.com/ritikvirus/comfyui-terminal-modal-node"
+            ],
+            "install_type": "git-clone",
+            "description": "This repository provides a custom ComfyUI node that lets you execute arbitrary terminal commands directly from the ComfyUI interface. [w/This extension allows remote command execution.]"
+        },
+        {
+            "author": "pixuai",
+            "title": "ComfyUI-PixuAI",
+            "reference": "https://github.com/pixuai/ComfyUI-PixuAI",
+            "files": [
+                "https://github.com/pixuai/ComfyUI-PixuAI"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of ComfyUI nodes designed to streamline prompt creation, organization, and discovery - making your workflows faster and more intuitive."
+        },
+        {
+            "author": "techidsk",
+            "title": "comfyui_molook_nodes [WIP]",
+            "reference": "https://github.com/techidsk/comfyui_molook_nodes",
+            "files": [
+                "https://github.com/techidsk/comfyui_molook_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Some extra nodes"
+        },
+        {
+            "author": "Northerner1",
+            "title": "ComfyUI_North_Noise [WIP]",
+            "reference": "https://github.com/Northerner1/ComfyUI_North_Noise",
+            "files": [
+                "https://github.com/Northerner1/ComfyUI_North_Noise"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: North Noise"
+        },
+        {
+            "author": "ManuShamil",
+            "title": "ComfyUI_BodyEstimation_Nodes",
+            "reference": "https://github.com/ManuShamil/ComfyUI_BodyEstimation_Nodes",
+            "files": [
+                "https://github.com/ManuShamil/ComfyUI_BodyEstimation_Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: CogitareLabsPoseIDExtractor"
+        },
+        {
+            "author": "MockbaTheBorg",
+            "title": "ComfyUI-Mockba",
+            "reference": "https://github.com/MockbaTheBorg/ComfyUI-Mockba",
+            "files": [
+                "https://github.com/MockbaTheBorg/ComfyUI-Mockba"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Image Batch/Flip/Rotate/Subtract/Dither, Barcode, Select, ..."
+        },
+        {
+            "author": "jcomeme",
+            "title": "AsunaroTools",
+            "reference": "https://github.com/jcomeme/ComfyUI-AsunaroTools",
+            "files": [
+                "https://github.com/jcomeme/ComfyUI-AsunaroTools"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of custom nodes for ComfyUI"
+        },
+        {
+            "author": "ZHO-ZHO-ZHO",
+            "title": "ComfyUI Wan2.1 [WIP]",
+            "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Wan-ZHO",
+            "files": [
+                "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Wan-ZHO"
+            ],
+            "install_type": "git-clone",
+            "description": "It’s estimated that ComfyUI itself will support it soon, so go ahead and give it a try!"
+        },
+        {
+            "author": "ltdrdata",
+            "title": "comfyui-unsafe-torch [UNSAFE]",
+            "reference": "https://github.com/ltdrdata/comfyui-unsafe-torch",
+            "files": [
+                "https://github.com/ltdrdata/comfyui-unsafe-torch"
+            ],
+            "install_type": "git-clone",
+            "description": "disable torch.load's `weigths_only`"
+        },
+        {
+            "author": "muvich3n",
+            "title": "ComfyUI-Crop-Border",
+            "reference": "https://github.com/muvich3n/ComfyUI-Crop-Border",
+            "files": [
+                "https://github.com/muvich3n/ComfyUI-Crop-Border"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Crop Image Borders"
+        },
+        {
+            "author": "masmullin2000",
+            "title": "ComfyUI-MMYolo",
+            "reference": "https://github.com/masmullin2000/ComfyUI-MMYolo",
+            "files": [
+                "https://github.com/masmullin2000/ComfyUI-MMYolo"
+            ],
+            "install_type": "git-clone",
+            "description": "A comfy node to find faces and output a mask"
+        },
+        {
+            "author": "Yeonri",
+            "title": "ComfyUI_LLM_Are_You_Listening [WIP]",
+            "reference": "https://github.com/Yeonri/ComfyUI_LLM_Are_You_Listening",
+            "files": [
+                "https://github.com/Yeonri/ComfyUI_LLM_Are_You_Listening"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: AYL_Node, AYL_GGUF_Node, AYL_API_Node\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "altkeyproject",
+            "title": "Dream Painter [WIP]",
+            "reference": "https://github.com/alt-key-project/comfyui-dream-painter",
+            "files": [
+                "https://github.com/alt-key-project/comfyui-dream-painter"
+            ],
+            "install_type": "git-clone",
+            "description": "Provide utilities for 2D image generation and processing."
+        },
+        {
+            "author": "kimara-ai",
+            "title": "ComfyUI-Kimara-AI-Image-From-URL [WIP]",
+            "reference": "https://github.com/kimara-ai/ComfyUI-Kimara-AI-Image-From-URL",
+            "files": [
+                "https://github.com/kimara-ai/ComfyUI-Kimara-AI-Image-From-URL"
+            ],
+            "install_type": "git-clone",
+            "description": "Load image from URL and downscale to desired megapixels. Set megapixels to 0 for no downscaling."
+        },
+        {
+            "author": "krisshen2021",
+            "title": "comfyui_OpenRouterNodes [WIP]",
+            "reference": "https://github.com/krisshen2021/comfyui_OpenRouterNodes",
+            "files": [
+                "https://github.com/krisshen2021/comfyui_OpenRouterNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "LLM custom nodes for comfyui\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Velour-Fog",
+            "title": "comfy-latent-nodes [UNSAFE]",
+            "reference": "https://github.com/Velour-Fog/comfy-latent-nodes",
+            "files": [
+                "https://github.com/Velour-Fog/comfy-latent-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes to save and load a latent to a specified directory. Saves time for doing operations on a latent such as upscaling without having to re-trigger the creation of the original latent.[w/This node can write files to an arbitrary path.]"
+        },
+        {
+            "author": "jgbyte",
+            "title": "ComfyUI-RandomCube [WIP]",
+            "reference": "https://github.com/jgbyte/ComfyUI-RandomCube",
+            "files": [
+                "https://github.com/jgbyte/ComfyUI-RandomCube"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: RandomCubeGrid"
+        },
+        {
+            "author": "thot-experiment",
+            "title": "comfy-live-preview [WIP]",
+            "reference": "https://github.com/thot-experiment/comfy-live-preview",
+            "files": [
+                "https://github.com/thot-experiment/comfy-live-preview"
+            ],
+            "install_type": "git-clone",
+            "description": "external live preview plugin for ComfyUI"
+        },
+        {
+            "author": "AhBumm",
+            "title": "ComfyUI-Upscayl",
+            "reference": "https://github.com/AhBumm/ComfyUI-Upscayl",
+            "files": [
+                "https://github.com/AhBumm/ComfyUI-Upscayl"
+            ],
+            "nodename_pattern": "\\(BillBum\\)$",
+            "install_type": "git-clone",
+            "description": "NODES: Upscayl Upscaler"
+        },
+        {
+            "author": "NEZHA625",
+            "title": "ComfyUI-tools-by-dong [UNSAFE]",
+            "reference": "https://github.com/NEZHA625/ComfyUI-tools-by-dong",
+            "files": [
+                "https://github.com/NEZHA625/ComfyUI-tools-by-dong"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: HuggingFaceUploadNode, ImageDownloader, LoraIterator, FileMoveNode, InputDetectionNode, ...\nNOTE: The files in the repo are not organized.[w/This nodepack includes nodes that can modify arbitrary files.]"
+        },
+        {
+            "author": "if-ai",
+            "title": "ComfyUI-IF_Zonos [WIP]",
+            "reference": "https://github.com/if-ai/ComfyUI-IF_Zonos",
+            "files": [
+                "https://github.com/if-ai/ComfyUI-IF_Zonos"
+            ],
+            "install_type": "git-clone",
+            "description": "Zonos for ComfyUI"
+        },
+        {
+            "author": "grinlau18",
+            "title": "Xiser_Nodes [WIP]",
+            "reference": "https://github.com/grinlau18/ComfyUI_XISER_Nodes",
+            "files": [
+                "https://github.com/grinlau18/ComfyUI_XISER_Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom nodes for customizing workflows\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "LAOGOU-666",
+            "title": "Comfyui_StartPatch [UNSAFE]",
+            "reference": "https://github.com/LAOGOU-666/Comfyui_StartPatch",
+            "files": [
+                "https://github.com/LAOGOU-666/Comfyui_StartPatch"
+            ],
+            "install_type": "git-clone",
+            "description": "This patch plugin optimizes the node information processing mechanism of the ComfyUI server, significantly improving server performance and response speed. It greatly reduces the browser page initialization waiting time. [w/Since this patch modifies key functions of ComfyUI, it is highly likely to cause compatibility issues.]"
+        },
+        {
+            "author": "badmike",
+            "title": "Prompt Factory [CONFLICT]",
+            "reference": "https://github.com/badmike/comfyui-prompt-factory",
+            "files": [
+                "https://github.com/badmike/comfyui-prompt-factory"
+            ],
+            "install_type": "git-clone",
+            "description": "A modular system that adds randomness to prompt generation [w/This nodepack is causing a name conflict with https://github.com/satche/comfyui-prompt-factory]"
+        },
+        {
+            "author": "owengillett",
+            "title": "ComfyUI-tilefusion",
+            "reference": "https://github.com/owengillett/ComfyUI-tilefusion",
+            "files": [
+                "https://github.com/owengillett/ComfyUI-tilefusion"
+            ],
+            "install_type": "git-clone",
+            "description": "Helper nodes for generating seamless tiles."
+        },
+        {
+            "author": "Scaryplasmon",
+            "title": "ComfTrellis [WIP]",
+            "reference": "https://github.com/Scaryplasmon/ComfTrellis",
+            "files": [
+                "https://github.com/Scaryplasmon/ComfTrellis"
+            ],
+            "install_type": "git-clone",
+            "description": "1 click install to run Trellis in ComfyUI\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "fangziheng2321",
+            "title": "comfyuinode_chopmask [WIP]",
+            "reference": "https://github.com/fangziheng2321/comfyuinode_chopmask",
+            "files": [
+                "https://github.com/fangziheng2321/comfyuinode_chopmask"
+            ],
+            "install_type": "git-clone",
+            "description": "a custom comfyui node for '/fooocusinpaint_upload'\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "D1-3105",
+            "title": "ComfyUI-VideoStream",
+            "reference": "https://github.com/D1-3105/ComfyUI-VideoStream",
+            "files": [
+                "https://github.com/D1-3105/ComfyUI-VideoStream"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: FloWWeaverExportSingleFrameGRPC"
+        },
+        {
+            "author": "wirytiox",
+            "title": "ComfyUI-Qwen [CONFLICT]",
+            "reference": "https://github.com/mr-krak3n/ComfyUI-Qwen",
+            "files": [
+                "https://github.com/mr-krak3n/ComfyUI-Qwen"
+            ],
+            "install_type": "git-clone",
+            "description": "This repository contains custom nodes for ComfyUI, designed to facilitate working with language models such as Qwen2.5 and DeepSeek. [w/This nodepack is causing a name conflict with https://github.com/ZHO-ZHO-ZHO/ComfyUI-Qwen]"
+        },
+        {
+            "author": "hiusdev",
+            "title": "ComfyUI_Lah_Toffee",
+            "reference": "https://github.com/hiusdev/ComfyUI_Lah_Toffee",
+            "files": [
+                "https://github.com/hiusdev/ComfyUI_Lah_Toffee"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Lah LoadVideoRandom"
+        },
+        {
+            "author": "hdfhssg",
+            "title": "ComfyUI_pxtool [WIP]",
+            "reference": "https://github.com/hdfhssg/ComfyUI_pxtool",
+            "files": [
+                "https://github.com/hdfhssg/ComfyUI_pxtool"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a custom plugin node for ComfyUI that modifies and extends some features from existing projects. The main implementations include:\n* Reproducing some features of the [a/Stable-Diffusion-Webui-Civitai-Helper](https://github.com/zixaphir/Stable-Diffusion-Webui-Civitai-Helper) project within ComfyUI\n* Implementing a feature to randomly generate related prompt words by referencing the [a/noob-wiki dataset](https://huggingface.co/datasets/Laxhar/noob-wiki/tree/main)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "franky519",
+            "title": "comfyui-redux-style",
+            "reference": "https://github.com/franky519/comfyui-redux-style",
+            "files": [
+                "https://github.com/franky519/comfyui-redux-style"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Style Model Grid, Style Model Apply, Style Model Advanced"
+        },
+        {
+            "author": "rishipandey125",
+            "title": "ComfyUI-FramePacking [WIP]",
+            "reference": "https://github.com/rishipandey125/ComfyUI-FramePacking",
+            "files": [
+                "https://github.com/rishipandey125/ComfyUI-FramePacking"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Add Grid Boundaries, Pack Frames, Unpack Frames, Resize Frame"
+        },
+        {
+            "author": "Northerner1",
+            "title": "ComfyUI_North_Noise [WIP]",
+            "reference": "https://github.com/Northerner1/ComfyUI_North_Noise",
+            "files": [
+                "https://github.com/Northerner1/ComfyUI_North_Noise"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Unsampler"
+        },
+        {
+            "author": "kimara-ai",
+            "title": "ComfyUI-Kimara-AI-Image-From-URL [WIP]",
+            "reference": "https://github.com/kimara-ai/ComfyUI-Kimara-AI-Image-From-URL",
+            "files": [
+                "https://github.com/kimara-ai/ComfyUI-Kimara-AI-Image-From-URL"
+            ],
+            "install_type": "git-clone",
+            "description": "Load image from URL and downscale to desired megapixels. Set megapixels to 0 for no downscaling."
+        },
+        {
+            "author": "tc8M4lF3s88",
+            "title": "comfy-tif-support",
+            "reference": "https://github.com/M4lF3s/comfy-tif-support",
+            "files": [
+                "https://github.com/M4lF3s/comfy-tif-support"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Load TIFF"
+        },
+        {
+            "author": "tc888",
+            "title": "ComfyUI_Save_Flux_Image",
+            "reference": "https://github.com/tc888/ComfyUI_Save_Flux_Image",
+            "files": [
+                "https://github.com/tc888/ComfyUI_Save_Flux_Image"
+            ],
+            "install_type": "git-clone",
+            "description": "Customized version of comfyui-image-save tailored for saving Flux images"
+        },
+        {
+            "author": "xinyiSS",
+            "title": "CombineMasksNode",
+            "reference": "https://github.com/xinyiSS/CombineMasksNode",
+            "files": [
+                "https://github.com/xinyiSS/CombineMasksNode"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Combine Masks Node"
+        },
+        {
+            "author": "osuiso-depot",
+            "title": "comfyui-keshigom_custom",
+            "reference": "https://github.com/osuiso-depot/comfyui-keshigom_custom",
+            "files": [
+                "https://github.com/osuiso-depot/comfyui-keshigom_custom"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: RegexpChopper, FLIP-W/H Selector, FLIP-W/H SelectorConst, TextFind, ckpt_Loader_Simple, True-or-False, myStringNode"
+        },
+        {
+            "author": "LucipherDev",
+            "title": "ComfyUI-Sentinel [WIP]",
+            "reference": "https://github.com/LucipherDev/ComfyUI-Sentinel",
+            "files": [
+                "https://github.com/LucipherDev/ComfyUI-Sentinel"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI Extension for Advanced Security. Implements login, multi-user registration, IP filtering, and user-specific input/output directories.[w/WARN:While ComfyUI Sentinel enhances security for ComfyUI, it does not guarantee absolute protection. Security is about risk mitigation, not elimination. Users are responsible for implementing their own security measures.]"
+        },
+        {
+            "author": "threadedblue",
+            "title": "MLXnodes [WIP]",
+            "reference": "https://github.com/threadedblue/MLXnodes",
+            "files": [
+                "https://github.com/threadedblue/MLXnodes"
+            ],
+            "install_type": "git-clone",
+            "description": "A port of MLX Examples to ComfyUI custom_nodes. These are intended to run on a macOS M1.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "jschoormans",
+            "title": "Comfy-InterestingPixels [WIP]",
+            "reference": "https://github.com/jschoormans/Comfy-InterestingPixels",
+            "files": [
+                "https://github.com/jschoormans/Comfy-InterestingPixels"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Shareable Image Slider, Random Palette\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "jschoormans",
+            "title": "ComfyUI-TexturePacker [WIP]",
+            "reference": "https://github.com/kijai/ComfyUI-TexturePacker",
+            "files": [
+                "https://github.com/jschoormans/Comfy-InterestingPixels"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node to use PyTexturePacker\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "MickeyJ",
+            "title": "ComfyUI_mickster_nodes [WIP]",
+            "reference": "https://github.com/MickeyJ/ComfyUI_mickster_nodes",
+            "files": [
+                "https://github.com/MickeyJ/ComfyUI_mickster_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of custom nodes for ComfyUI, focusing on image handling and LoRA training."
+        },
+        {
+            "author": "gold24park",
+            "title": "loki-comfyui-node",
+            "reference": "https://github.com/gold24park/loki-comfyui-node",
+            "files": [
+                "https://github.com/gold24park/loki-comfyui-node"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Get Image Luminance, Get Dominant Color, Overlay Text"
+        },
+        {
+            "author": "hayden-fr",
+            "title": "ComfyUI-Image-Browsing [USAFE]",
+            "id": "image-browsing",
+            "reference": "https://github.com/hayden-fr/ComfyUI-Image-Browsing",
+            "files": [
+                "https://github.com/hayden-fr/ComfyUI-Image-Browsing"
+            ],
+            "install_type": "git-clone",
+            "description": "Image Browsing: browsing, download and delete."
+        },
+        {
+            "author": "molbal",
+            "title": "comfy-url-fetcher [WIP]",
+            "reference": "https://github.com/molbal/comfy-url-fetcher",
+            "files": [
+                "https://github.com/molbal/comfy-url-fetcher"
+            ],
+            "install_type": "git-clone",
+            "description": "Fetches URLs"
+        },
+        {
+            "author": "neverbiasu",
+            "title": "ComfyUI_Output_as_Input",
+            "reference": "https://github.com/a-und-b/ComfyUI_Output_as_Input",
+            "files": [
+                "https://github.com/a-und-b/ComfyUI_Output_as_Input"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a simple custom ComfyUI node that allows you to easily use recent output images as input in your workflows. It does not allow image uploads on purpose and does not require any additional dependencies.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "neverbiasu",
+            "title": "ComfyUI-DeepSeek",
+            "reference": "https://github.com/neverbiasu/ComfyUI-DeepSeek",
+            "files": [
+                "https://github.com/neverbiasu/ComfyUI-DeepSeek"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: DeepSeek Caller"
+        },
+        {
+            "author": "Krish-701",
+            "title": "RK_Comfyui",
+            "reference": "https://github.com/Krish-701/RK_Comfyui",
+            "files": [
+                "https://github.com/Krish-701/RK_Comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: RK Excel File State Looper, RK Accumulate Text, RK Advanced Script Finder, RK CSV File State Looper, RK Read Excel Row, RK Sequential Image Viewer, RK Concatenate Text, RK Write Text, RK Save Image, RK Seed Loop"
+        },
+        {
+            "author": "Kayarte",
+            "title": "Time Series Nodes for ComfyUI [Experimental]",
+            "reference": "https://github.com/Kayarte/Time-Series-Nodes-for-ComfyUI",
+            "files": [
+                "https://github.com/Kayarte/Time-Series-Nodes-for-ComfyUI"
+            ],
+            "install_type": "git-clone",
+            "description": "Basic nodes for time series analysis in ComfyUI. Currently in early development."
+        },
+        {
+            "author": "comfyuiblog",
+            "title": "deepseek_prompt_generator_comfyui [WIP]",
+            "reference": "https://github.com/comfyuiblog/deepseek_prompt_generator_comfyui",
+            "files": [
+                "https://github.com/comfyuiblog/deepseek_prompt_generator_comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "Prompt Expansion for Stable Diffusion, using Deepseek API.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "risunobushi",
+            "title": "ComfyUI_HEXtoRGB",
+            "reference": "https://github.com/risunobushi/ComfyUI_HEXtoRGB",
+            "files": [
+                "https://github.com/risunobushi/ComfyUI_HEXtoRGB"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Hex to RGB Converter"
+        },
+        {
+            "author": "EmanueleUniroma2",
+            "title": "ComfyUI-FLAC-to-WAV [WIP]",
+            "reference": "https://github.com/EmanueleUniroma2/ComfyUI-FLAC-to-WAV",
+            "files": [
+                "https://github.com/EmanueleUniroma2/ComfyUI-FLAC-to-WAV"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node to convert flac files to wav inside comfy UI\nComfyUI Custom Node: FLAC to WAV Converter Welcome to the ComfyUI Custom Node: FLAC to WAV Converter repository! This project provides a custom node for ComfyUI that allows you to easily convert .flac audio files to .wav format, making it simpler to work with a variety of audio tools and applications.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "eyekayem",
+            "title": "comfyui_runway_gen3",
+            "reference": "https://github.com/eyekayem/comfyui_runway_gen3",
+            "files": [
+                "https://github.com/eyekayem/comfyui_runway_gen3"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Runway Video Gen, Runway Video Preview"
+        },
+        {
+            "author": "StartHua",
+            "title": "Comfyui_CXH_joy_caption [SECURITY SCREENING]",
+            "reference": "https://github.com/StartHua/Comfyui_CXH_joy_caption",
+            "files": [
+                "https://github.com/StartHua/Comfyui_CXH_joy_caption"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Joy_caption_load, Joy_caption\nNOTE:This nodepack has been transitioned to a security screening status due to policy."
+        },
+        {
+            "author": "kijai",
+            "title": "ComfyUI-ComfyUI-Hunyuan3DWrapper [WIP]",
+            "reference": "https://github.com/kijai/ComfyUI-Hunyuan3DWrapper",
+            "files": [
+                "https://github.com/kijai/ComfyUI-Hunyuan3DWrapper"
+            ],
+            "install_type": "git-clone",
+            "description": "Wrapper nodes for https://github.com/Tencent/Hunyuan3D-2, additional installation steps needed, please check the github repository"
+        },
+        {
+            "author": "7BEII",
+            "title": "comfyui-promptbymood [WIP]",
+            "reference": "https://github.com/7BEII/Comfyui_PDuse",
+            "files": [
+                "https://github.com/7BEII/Comfyui_PDuse"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: PD_json_group_fontsize, PD_Incremental_JSON, PD_removeword, PD_Image Crop Location, PD_ImageConcanate, PD_FileName_refixer\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "RLW-Chars",
+            "title": "comfyui-promptbymood [WIP]",
+            "reference": "https://github.com/RLW-Chars/comfyui-promptbymood",
+            "files": [
+                "https://github.com/RLW-Chars/comfyui-promptbymood"
+            ],
+            "install_type": "git-clone",
+            "description": "A plugin for ComfyUI to create random prompts.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "mohamedsobhi777",
+            "title": "ComfyUI-FramerComfy [WIP]",
+            "reference": "https://github.com/mohamedsobhi777/ComfyUI-FramerComfy",
+            "files": [
+                "https://github.com/mohamedsobhi777/ComfyUI-FramerComfy"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: FramerComfy Input String/Number/Image/Float/Boolean/Image, ...\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "naderzare",
+            "title": "comfyui-inodes",
+            "reference": "https://github.com/naderzare/comfyui-inodes",
+            "files": [
+                "https://github.com/naderzare/comfyui-inodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: If-Else, Multiline Split, Azure AI API"
+        },
+        {
+            "author": "sizzlebop",
+            "title": "ComfyUI LLM Prompt Enhancer [WIP]",
+            "reference": "https://github.com/pinkpixel-dev/comfyui-llm-prompt-enhancer",
+            "files": [
+                "https://github.com/pinkpixel-dev/comfyui-llm-prompt-enhancer"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI node for enhancing prompts using various LLM providers\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "a-One-Fan",
+            "title": "ComfyUI-Blenderesque-Nodes [WIP]",
+            "reference": "https://github.com/a-One-Fan/ComfyUI-Blenderesque-Nodes",
+            "files": [
+                "https://github.com/a-One-Fan/ComfyUI-Blenderesque-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Blender-like nodes for ComfyUI."
+        },
+        {
+            "author": "yanhuifair",
+            "title": "comfyui-deepseek [WIP]",
+            "reference": "https://github.com/yanhuifair/comfyui-deepseek",
+            "files": [
+                "https://github.com/yanhuifair/comfyui-deepseek"
+            ],
+            "install_type": "git-clone",
+            "description": "nodes for deepseek api\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "IfnotFr",
+            "title": "ComfyUI-Ifnot-Pack",
+            "reference": "https://github.com/IfnotFr/ComfyUI-Ifnot-Pack",
+            "files": [
+                "https://github.com/IfnotFr/ComfyUI-Ifnot-Pack"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Face Crop, [w/A pack of custom nodes used in my projects. Not intended to be used by other persons as the usage is not documented. But if something interests you in this repository, go for it !]"
+        },
+        {
+            "author": "KihongK",
+            "title": "ComfyUI-RoysNodes [WIP]",
+            "reference": "https://github.com/KihongK/comfyui-roysnodes",
+            "files": [
+                "https://github.com/KihongK/comfyui-roysnodes"
+            ],
+            "install_type": "git-clone",
+            "description": "WIP custom nodes for Creation of AI images & videos"
+        },
+        {
+            "author": "catboxanon",
+            "title": "ComfyUI-Pixelsmith [WIP]",
+            "reference": "https://github.com/catboxanon/ComfyUI-Pixelsmith",
+            "files": [
+                "https://github.com/catboxanon/ComfyUI-Pixelsmith"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Pixelsmith"
+        },
+        {
+            "author": "hunterssl",
+            "title": "ComfyUI_SSLNodes",
+            "reference": "https://github.com/hunterssl/ComfyUI_SSLNodes",
+            "files": [
+                "https://github.com/hunterssl/ComfyUI_SSLNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: SSL Load Json, SSL Get Json Keys Count, SSL Load Checkpoint By Name, SSL Random Num In Loop, SSL Save Image Outside"
+        },
+        {
+            "author": "ammahmoudi",
+            "title": "ComfyUI-Legendary-Nodes",
+            "reference": "https://github.com/ammahmoudi/ComfyUI-Legendary-Nodes",
+            "files": [
+                "https://github.com/ammahmoudi/ComfyUI-Legendary-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Legendary Lora URL Loader, Legendary Lora URL Loader"
+        },
+        {
+            "author": "yichengup",
+            "title": "Comfyui-NodeSpark",
+            "reference": "https://github.com/yichengup/Comfyui-NodeSpark",
+            "files": [
+                "https://github.com/yichengup/Comfyui-NodeSpark"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Image Circle Warp, Image Stretch, Image Wave Warp, Liquify Effect"
+        },
+        {
+            "author": "kijai",
+            "title": "ComfyUI-VideoNoiseWarp [WIP]",
+            "reference": "https://github.com/kijai/ComfyUI-VideoNoiseWarp",
+            "files": [
+                "https://github.com/kijai/ComfyUI-VideoNoiseWarp"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes to generate noise from video for [a/https://github.com/Eyeline-Research/Go-with-the-Flow](https://github.com/Eyeline-Research/Go-with-the-Flow)"
+        },
+        {
+            "author": "muvich3n",
+            "title": "ComfyUI-Claude-I2T",
+            "reference": "https://github.com/muvich3n/ComfyUI-Claude-I2T",
+            "files": [
+                "https://github.com/muvich3n/ComfyUI-Claude-I2T"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Claude Image to Prompt Generator"
+        },
+        {
+            "author": "maekawataiki",
+            "title": "ComfyUI-ALB-Login",
+            "reference": "https://github.com/maekawataiki/ComfyUI-ALB-Login",
+            "files": [
+                "https://github.com/maekawataiki/ComfyUI-ALB-Login"
+            ],
+            "install_type": "git-clone",
+            "description": "Auth library to inspect token provided by ALB to protect ComfyUI."
+        },
+        {
+            "author": "ArmandAlbert",
+            "title": "Kwai_font_comfyui",
+            "reference": "https://github.com/ArmandAlbert/Kwai_font_comfyui",
+            "files": [
+                "https://github.com/ArmandAlbert/Kwai_font_comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Kwaifont_Resnet50_Runner, Kwaifont_Resnet50_Loader, Kwaifont_Resnet101_Runner, Kwaifont_Resnet101_Loader, Kwaifont_Image_Cropper"
+        },
+        {
+            "author": "PATATAJEC",
+            "title": "Patatajec-Nodes [WIP]",
+            "reference": "https://github.com/PATATAJEC/ComfyUI-PatatajecNodes",
+            "files": [
+                "https://github.com/PATATAJEC/ComfyUI-PatatajecNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Path Tool, Color Match Falloff, Sequence Content Zoom, Sequence Blend, Color Picker"
+        },
+        {
+            "author": "sourceful-official",
+            "title": "comfyui-sourceful-official",
+            "reference": "https://github.com/sourceful-official/comfyui-sourceful-official",
+            "files": [
+                "https://github.com/sourceful-official/comfyui-sourceful-official"
+            ],
+            "description": "NODES: SourcefulOfficialComfyuiIncontextThreePanels, FalFluxLoraSourcefulOfficial, FalIcLightV2SourcefulOfficial",
+            "install_type": "git-clone"
+        },
+        {
+            "author": "Alvaroeai",
+            "title": "ComfyUI-SunoAI-Mds",
+            "reference": "https://github.com/Alvaroeai/ComfyUI-SunoAI-Mds",
+            "files": [
+                "https://github.com/Alvaroeai/ComfyUI-SunoAI-Mds"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Suno Generate, Suno Download, Suno Proxy Generate, Suno Proxy Download"
+        },
+        {
+            "author": "parmarjh",
+            "title": "ComfyUI-MochiWrapper-I2V [WIP]",
+            "reference": "https://github.com/parmarjh/ComfyUI-MochiWrapper-I2V",
+            "files": [
+                "https://github.com/parmarjh/ComfyUI-MochiWrapper-I2V"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI wrapper nodes for [a/Mochi](https://github.com/genmoai/models) video generator"
+        },
+        {
+            "author": "Symbiomatrix",
+            "title": "Comfyui-Sort-Files",
+            "reference": "https://github.com/Symbiomatrix/Comfyui-Sort-Files",
+            "files": [
+                "https://github.com/Symbiomatrix/Comfyui-Sort-Files"
+            ],
+            "install_type": "git-clone",
+            "description": "Monkeypatch file sort to date modified or custom instead of lexicographic."
+        },
+        {
+            "author": "x3bits",
+            "title": "ComfyUI-Power-Flow [UNSAFE]",
+            "reference": "https://github.com/x3bits/ComfyUI-Power-Flow",
+            "files": [
+                "https://github.com/x3bits/ComfyUI-Power-Flow"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI nodepackage that introduces common programming logic to enhance the flexibility of ComfyUI workflows. It supports features such as function definition and execution, 'for' loops, 'while' loops, and Python code execution.\n[w/This extension allows the execution of arbitrary Python code from a workflow.]"
+        },
+        {
+            "author": "EmilioPlumed",
+            "title": "ComfyUI-Math [WIP]",
+            "reference": "https://github.com/EmilioPlumed/ComfyUI-Math",
+            "files": [
+                "https://github.com/EmilioPlumed/ComfyUI-Math"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom nodes that take 2 float inputs and calculates greatest common denominator and least common multiple, returning them as ints.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "mliand",
+            "title": "ComfyUI-Calendar-Node [WIP]",
+            "reference": "https://github.com/mliand/ComfyUI-Calendar-Node",
+            "files": [
+                "https://github.com/mliand/ComfyUI-Calendar-Node"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node for Comfyui to create a Calendar like grid\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "phamngoctukts",
+            "title": "ComyUI-Tupham",
+            "reference": "https://github.com/phamngoctukts/ComyUI-Tupham",
+            "files": [
+                "https://github.com/phamngoctukts/ComyUI-Tupham"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Ghép Ảnh, Multi Prompt v2.0, Condition Upscale, Multi sampler, Run node selected"
+        },
+        {
+            "author": "5x00",
+            "title": "ComfyUI-Prompt-Plus [WIP]",
+            "reference": "https://github.com/5x00/ComfyUI-Prompt-Plus",
+            "files": [
+                "https://github.com/5x00/ComfyUI-Prompt-Plus"
+            ],
+            "install_type": "git-clone",
+            "description": "Prompt Plus is a collection of LLM and VLM nodes that make prompting easier for image and video generation.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "aria1th",
+            "title": "ComfyUI-CairoSVG",
+            "reference": "https://github.com/aria1th/ComfyUI-CairoSVG",
+            "files": [
+                "https://github.com/aria1th/ComfyUI-CairoSVG"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: VectorizedUpscaleScaling, VectorizedUpscaleSize"
+        },
+        {
+            "author": "gitmylo",
+            "title": "FlowNodes [WIP]",
+            "reference": "https://github.com/gitmylo/FlowNodes",
+            "files": [
+                "https://github.com/gitmylo/FlowNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI nodepack containing nodes for basic programming logic."
+        },
+        {
+            "author": "chengzeyi",
+            "title": "Comfy-WaveSpeed [WIP]",
+            "reference": "https://github.com/chengzeyi/Comfy-WaveSpeed",
+            "files": [
+                "https://github.com/chengzeyi/Comfy-WaveSpeed"
+            ],
+            "install_type": "git-clone",
+            "description": "The all in one inference optimization solution for ComfyUI, universal, flexible, and fast."
+        },
+        {
+            "author": "zyd232",
+            "title": "ComfyUI-zyd232-Nodes",
+            "reference": "https://github.com/zyd232/ComfyUI-zyd232-Nodes",
+            "files": [
+                "https://github.com/zyd232/ComfyUI-zyd232-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Image Pixels Compare, Save Preview Images"
+        },
+        {
+            "author": "yanhuifair",
+            "title": "ComfyUI-FairLab",
+            "reference": "https://github.com/yanhuifair/ComfyUI-FairLab",
+            "files": [
+                "https://github.com/yanhuifair/ComfyUI-FairLab"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: CLIP Text Encode Translated, Translate String, Load Image From Folder, Save String To Folder, Fix UTF-8 String, String Combine, String Field, Download Image, Save Images To Folder, Save Image To Folder, Image Resize, ..."
+        },
+        {
+            "author": "nomcycle",
+            "title": "ComfyUI_Cluster [WIP]",
+            "reference": "https://github.com/nomcycle/ComfyUI_Cluster",
+            "files": [
+                "https://github.com/nomcycle/ComfyUI_Cluster"
+            ],
+            "install_type": "git-clone",
+            "description": "Very early W.I.P of clustered ComfyUI inference."
+        },
+        {
+            "author": "waynepimpzhang",
+            "title": "FindBrightestSpot [WIP]",
+            "reference": "https://github.com/waynepimpzhang/comfyui-opencv-brightestspot",
+            "files": [
+                "https://github.com/waynepimpzhang/comfyui-opencv-brightestspot"
+            ],
+            "install_type": "git-clone",
+            "description": "Analyze the image to find the x and y coordinates of the brightest point.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "power88",
+            "title": "ComfyUI-PDiD-Nodes [WIP]",
+            "reference": "https://github.com/power88/ComfyUI-PDiD-Nodes",
+            "files": [
+                "https://github.com/power88/ComfyUI-PDiD-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Get Image Size, Check Character Tag, Nearest SDXL Resolution divided by 64, Get Image Main Color, Blend Images, List Operations, Make Image Gray.\nNOTE: not working"
+        },
+        {
+            "author": "FinetunersAI",
+            "title": "ComfyUI Finetuners [WIP]",
+            "reference": "https://github.com/FinetunersAI/finetuners",
+            "files": [
+                "https://github.com/FinetunersAI/finetuners"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of utility nodes for ComfyUI to enhance your workflow.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "sourceful-official",
+            "title": "ComfyUI_InstructPixToPixConditioningLatent [WIP]",
+            "reference": "https://github.com/sourceful-official/ComfyUI_InstructPixToPixConditioningLatent",
+            "files": [
+                "https://github.com/sourceful-official/ComfyUI_InstructPixToPixConditioningLatent"
+            ],
+            "description": "ComfyUI-ComfyUI_InstructPixToPixConditioningLatent\nNOTE:invalid pyproject.toml",
+            "install_type": "git-clone"
+        },
+        {
+            "author": "fritzprix",
+            "title": "ComfyUI-LLM-Utils [WIP]",
+            "reference": "https://github.com/fritzprix/ComfyUI-LLM-Utils",
+            "files": [
+                "https://github.com/fritzprix/ComfyUI-LLM-Utils"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of utility nodes for ComfyUI focused on text and LLM-related operations\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "ciga2011",
+            "title": "ComfyUI-AppGen [UNSAFE]",
+            "reference": "https://github.com/ciga2011/ComfyUI-AppGen",
+            "files": [
+                "https://github.com/ciga2011/ComfyUI-AppGen"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI nodepack designed to generate and edit Single Page Applications (SPAs) using natural language.[w/This extension allows arbitrary JavaScript code to be executed through the execution of workflows.]"
+        },
+        {
+            "author": "DraconicDragon",
+            "title": "ComfyUI e621 booru Toolkit",
+            "reference": "https://github.com/DraconicDragon/ComfyUI_e621_booru_toolkit",
+            "files": [
+                "https://github.com/DraconicDragon/ComfyUI_e621_booru_toolkit"
+            ],
+            "install_type": "git-clone",
+            "description": "WIP. Nodes: Fetch e621/danbooru image and/or tags etc from a given URL; Get the Wiki entry for a tag through a button press."
+        },
+        {
+            "author": "Grey3016",
+            "title": "Save2Icon",
+            "reference": "https://github.com/Grey3016/Save2Icon",
+            "files": [
+                "https://github.com/Grey3016/Save2Icon"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Save2Icon"
+        },
+        {
+            "author": "Chargeuk",
+            "title": "ComfyUI-vts-nodes [WIP]",
+            "reference": "https://github.com/Chargeuk/ComfyUI-vts-nodes",
+            "files": [
+                "https://github.com/Chargeuk/ComfyUI-vts-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Clean Text, Color Mask To Mask, Conditioning Set Batch Mask, Merge Delimited Text, Reduce Batch Size, Text To Batch Prompt, To Text, "
+        },
+        {
+            "author": "ryanontheinside",
+            "title": "ComfyUI_YoloNasObjectDetection_Tensorrt [WIP]",
+            "reference": "https://github.com/ryanontheinside/ComfyUI_YoloNasObjectDetection_Tensorrt",
+            "files": [
+                "https://github.com/ryanontheinside/ComfyUI_YoloNasObjectDetection_Tensorrt"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI YOLO NAS Object Detection with TensorRT"
+        },
+        {
+            "author": "steelan9199",
+            "title": "ComfyUI-Teeth [UNSAFE]",
+            "reference": "https://github.com/steelan9199/ComfyUI-Teeth",
+            "files": [
+                "https://github.com/steelan9199/ComfyUI-Teeth"
+            ],
+            "install_type": "git-clone",
+            "description": "Run Python code, Outline, List, Four-quadrant grid, Nine-square grid[w/This extension poses a risk of executing arbitrary commands through workflow execution. Please be cautious.]"
+        },
+        {
+            "author": "aiden1020",
+            "title": "ComfyUI_Artcoder [WIP]",
+            "reference": "https://github.com/aiden1020/ComfyUI_Artcoder",
+            "files": [
+                "https://github.com/aiden1020/ComfyUI_Artcoder"
+            ],
+            "install_type": "git-clone",
+            "description": "This project is a custom node for ComfyUI that uses [a/ArtCoder](https://arxiv.org/abs/2011.07815) [CVPR 2021] to refine videos generated by [a/AnimateDiff](https://arxiv.org/abs/2307.04725) [ICLR2024 Spotlight] or the other video. The node is to transform these videos into functional QR code videos that can be scanned.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "A4P7J1N7M05OT",
+            "title": "ComfyUI-ManualSigma",
+            "reference": "https://github.com/A4P7J1N7M05OT/ComfyUI-ManualSigma",
+            "files": [
+                "https://github.com/A4P7J1N7M05OT/ComfyUI-ManualSigma"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Manual Sigma"
+        },
+        {
+            "author": "neverbiasu",
+            "title": "ComfyUI-StereoCrafter [WIP]",
+            "reference": "https://github.com/neverbiasu/ComfyUI-StereoCrafter",
+            "files": [
+                "https://github.com/neverbiasu/ComfyUI-StereoCrafter"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Depth Splatting Model Loader, Depth Splatting Node, Inpainting Inference Node"
+        },
+        {
+            "author": "watarika",
+            "title": "ComfyUI-exit [UNSAFE]",
+            "reference": "https://github.com/watarika/ComfyUI-exit",
+            "files": [
+                "https://github.com/watarika/ComfyUI-exit"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom node to handle text.[w/This custom node includes a custom node that can terminate ComfyUI.]"
+        },
+        {
+            "author": "watarika",
+            "title": "ComfyUI-Text-Utility [UNSAFE]",
+            "reference": "https://github.com/watarika/ComfyUI-Text-Utility",
+            "files": [
+                "https://github.com/watarika/ComfyUI-Text-Utility"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom node to handle text.[w/This nodepack contains a custom node that poses a security risk by providing the ability to read text from arbitrary paths.]"
+        },
+        {
+            "author": "mehbebe",
+            "title": "ComfyLoraGallery [WIP]",
+            "reference": "https://github.com/mehbebe/ComfyLoraGallery",
+            "files": [
+                "https://github.com/mehbebe/ComfyLoraGallery"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node for ComfyUI that will provide a gallery style lora selector similar to the 'lora' tab in Automatic1111."
+        },
+        {
+            "author": "karthikg-09",
+            "title": "ComfyUI-KG09 [WIP]",
+            "reference": "https://github.com/karthikg-09/ComfyUI-3ncrypt",
+            "files": [
+                "https://github.com/karthikg-09/ComfyUI-3ncrypt"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Save Image+[w/The web extension of this nodepack modifies part of ComfyUI's asset files.]"
+        },
+        {
+            "author": "glamorfleet0i",
+            "title": "ComfyUI Firewall",
+            "reference": "https://github.com/glamorfleet0i/ComfyUI-Firewall",
+            "files": [
+                "https://github.com/glamorfleet0i/ComfyUI-Firewall"
+            ],
+            "install_type": "git-clone",
+            "description": "A very basic firewall-like middleware that restricts access to your ComfyUI server based on a list of specified IP addresses. As this is configured as middleware, the firewall will restrict both the web UI and any API endpoints."
+        },
+        {
+            "author": "warshanks",
+            "title": "Shank-Tools",
+            "reference": "https://github.com/warshanks/Shank-Tools",
+            "files": [
+                "https://github.com/warshanks/Shank-Tools"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Tile Calculator, Resolution Divider, Height & Width"
+        },
+        {
+            "author": "BaronVonBoolean",
+            "title": "ComfyUI-FileOps [UNSAFE]",
+            "reference": "https://github.com/BaronVonBoolean/ComfyUI-FileOps",
+            "files": [
+                "https://github.com/BaronVonBoolean/ComfyUI-FileOps"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: File Mv, File Path, File Dir.\n[w/This is dangerous as it provides the ability to manipulate arbitrary user files.]"
+        },
+        {
+            "author": "JissiChoi",
+            "title": "ComfyUI-Jissi-List [WIP]",
+            "reference": "https://github.com/JissiChoi/ComfyUI-Jissi-List",
+            "files": [
+                "https://github.com/JissiChoi/ComfyUI-Jissi-List"
+            ],
+            "install_type": "git-clone",
+            "description": "Data List Management for ComfyUI\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Maxim-Dey",
+            "title": "ComfyUI-MS_Tools [WIP]",
+            "reference": "https://github.com/Maxim-Dey/ComfyUI-MaksiTools",
+            "files": [
+                "https://github.com/Maxim-Dey/ComfyUI-MaksiTools"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: MS Time Measure NodeMaksiTools"
+        },
+        {
+            "author": "krich-cto",
+            "title": "ComfyUI Flow Control [UNSTABLE]",
+            "reference": "https://github.com/krich-cto/ComfyUI-Flow-Control",
+            "files": [
+                "https://github.com/krich-cto/ComfyUI-Flow-Control"
+            ],
+            "install_type": "git-clone",
+            "description": "This is an Extension for ComfyUI. This project will help you control the flow logic via many controls.[w/Installing this custom node currently causes a conflict with the UnetLoaderGGUF of ComfyUI-GGUF.]"
+        },
+        {
+            "author": "dihan",
+            "title": "ComfyUI Random Keypoints for InstantID [WIP]",
+            "reference": "https://github.com/dihan/comfyui-random-kps",
+            "files": [
+                "https://github.com/dihan/comfyui-random-kps"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node for ComfyUI that generates random facial keypoints compatible with InstantID.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "emranemran",
+            "title": "ComfyUI-FasterLivePortrait",
+            "reference": "https://github.com/emranemran/ComfyUI-FasterLivePortrait",
+            "files": [
+                "https://github.com/emranemran/ComfyUI-FasterLivePortrait"
+            ],
+            "install_type": "git-clone",
+            "description": "Improve mouth tracking with live AI Video"
+        },
+        {
+            "author": "kandy",
+            "title": "ComfyUI-KAndy",
+            "reference": "https://github.com/kandy/ComfyUI-KAndy",
+            "files": [
+                "https://github.com/kandy/ComfyUI-KAndy"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Civit Prompt API, Load Image From Url, Civit Images API, KAndyNoiseCondition, KAndyImagesByCss"
+        },
+        {
+            "author": "StartHua",
+            "title": "Comfyui_leffa",
+            "reference": "https://github.com/StartHua/Comfyui_leffa",
+            "files": [
+                "https://github.com/StartHua/Comfyui_leffa"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: CXH_Leffa_Viton_Load, CXH_Leffa_Viton_Run"
+        },
+        {
+            "author": "logtd",
+            "title": "ComfyUI-HunyuanLoom [WIP]",
+            "id": "comfyui-42lux",
+            "reference": "https://github.com/logtd/ComfyUI-HunyuanLoom",
+            "files": [
+                "https://github.com/logtd/ComfyUI-HunyuanLoom"
+            ],
+            "install_type": "git-clone",
+            "description": "A set of nodes to edit videos using the Hunyuan Video model"
+        },
+        {
+            "author": "watarika",
+            "title": "ComfyUI-exit [UNSAFE]",
+            "reference": "https://github.com/watarika/ComfyUI-exit",
+            "files": [
+                "https://github.com/watarika/ComfyUI-exit"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node that terminates ComfyUI after a specified number of seconds. Use this node if you want Google Colab to automatically terminate after mass generation. It is necessary to disconnect and delete the Google Colab runtime on the Notebook side."
+        },
+        {
+            "author": "backearth1",
+            "title": "Comfyui-MiniMax-Video [WIP]",
+            "reference": "https://github.com/backearth1/Comfyui-MiniMax-Video",
+            "files": [
+                "https://github.com/backearth1/Comfyui-MiniMax-Video"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI extension that integrates MiniMax AI's image-to-video and text-to-video generation capabilities, allowing users to easily convert static images into dynamic videos.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "FinetunersAI",
+            "title": "Fast Group Link [WIP]",
+            "id": "fast-group-link",
+            "reference": "https://github.com/FinetunersAI/comfyui-fast-group-link",
+            "files": [
+                "https://github.com/FinetunersAI/comfyui-fast-group-link"
+            ],
+            "install_type": "git-clone",
+            "description": "Link and control ComfyUI groups with a simple ON/OFF toggle. Control multiple groups at once with an easy-to-use interface.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "kijai",
+            "title": "ComfyUI-MMAudio [WIP]",
+            "reference": "https://github.com/kijai/ComfyUI-MMAudio",
+            "files": [
+                "https://github.com/kijai/ComfyUI-MMAudio"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes to use [a/MMAudio](https://github.com/hkchengrex/MMAudio)"
+        },
+        {
+            "author": "kuschanow",
+            "title": "ComfyUI-SD-Slicer",
+            "reference": "https://github.com/kuschanow/ComfyUI-SD-Slicer",
+            "files": [
+                "https://github.com/kuschanow/ComfyUI-SD-Slicer"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Slicer"
+        },
+        {
+            "author": "ralonsobeas",
+            "title": "ComfyUI-HDRConversion [WIP]",
+            "reference": "https://github.com/ralonsobeas/ComfyUI-HDRConversion",
+            "files": [
+                "https://github.com/ralonsobeas/ComfyUI-HDRConversion"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Generate HDR image"
+        },
+        {
+            "author": "Matrix-King-Studio",
+            "title": "ComfyUI-MoviePy",
+            "reference": "https://github.com/Matrix-King-Studio/ComfyUI-MoviePy",
+            "files": [
+                "https://github.com/Matrix-King-Studio/ComfyUI-MoviePy"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Image Clip Node, Audio Duration Node, Save Video Node"
+        },
+        {
+            "author": "oxysoft",
+            "title": "ComfyUI-uiapi",
+            "reference": "https://github.com/oxysoft/ComfyUI-uiapi",
+            "files": [
+                "https://github.com/oxysoft/ComfyUI-uiapi"
+            ],
+            "install_type": "git-clone",
+            "description": "UIAPI is an intermediate and frontend plugin which allow communicating with the Comfy webui through server connection. This saves the need to export a workflow.json and instead directly sending a queue command to the frontend. This way, the user can experiment in realtime as they are running some professional industry or rendering software which uses UIAPI / ComfyUI as a backend. There is no way to switch seamlessly between UIAPI and regular server connection - though as of late summer 2023 it was inferior to use the server connection because the server would constantly unload models and start from scratch, and the schema of the workfow json was completely different and much less convenient, losing crucial information for efficient querying of nodes and assigning data dynamically."
+        },
+        {
+            "author": "hotpot-killer",
+            "title": "ComfyUI_AlexNodes",
+            "reference": "https://github.com/hotpot-killer/ComfyUI_AlexNodes",
+            "files": [
+                "https://github.com/hotpot-killer/ComfyUI_AlexNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: InstructPG - editing images with text prompt, ...\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "pschroedl",
+            "title": "ComfyUI-StreamDiffusion",
+            "reference": "https://github.com/pschroedl/ComfyUI-StreamDiffusion",
+            "files": [
+                "https://github.com/pschroedl/ComfyUI-StreamDiffusion"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: StreamDiffusionConfig, StreamDiffusionAccelerationSampler, StreamDiffusionLoraLoader, StreamDiffusionAccelerationConfig, StreamDiffusionSimilarityFilterConfig, StreamDiffusionModelLoader, ..."
+        },
+        {
+            "author": "Junst",
+            "title": "ComfyUI-PNG2SVG2PNG",
+            "reference": "https://github.com/Junst/ComfyUI-PNG2SVG2PNG",
+            "files": [
+                "https://github.com/Junst/ComfyUI-PNG2SVG2PNG"
+            ],
+            "description": "NODES:PNG2SVG2PNG",
+            "install_type": "git-clone"
+        },
+        {
+            "author": "animEEEmpire",
+            "title": "ComfyUI-Animemory-Loader",
+            "reference": "https://github.com/animEEEmpire/ComfyUI-Animemory-Loader",
+            "files": [
+                "https://github.com/animEEEmpire/ComfyUI-Animemory-Loader"
+            ],
+            "install_type": "git-clone",
+            "description": "AniMemory-alpha Custom Node for ComfyUI"
+        },
+        {
+            "author": "ShahFaisalWani",
+            "title": "ComfyUI-Mojen-Nodeset",
+            "reference": "https://github.com/ShahFaisalWani/ComfyUI-Mojen-Nodeset",
+            "files": [
+                "https://github.com/ShahFaisalWani/ComfyUI-Mojen-Nodeset"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of powerful, versatile, and community-driven custom nodes for ComfyUI, designed to elevate AI workflows!"
+        },
+        {
+            "author": "grimli333",
+            "title": "ComfyUI_Grim",
+            "reference": "https://github.com/grimli333/ComfyUI_Grim",
+            "files": [
+                "https://github.com/grimli333/ComfyUI_Grim"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Generate a unique filename and folder name, Format Strings with Two Inputs"
+        },
+        {
+            "author": "risunobushi",
+            "title": "ComfyUI_FocusMask",
+            "reference": "https://github.com/risunobushi/ComfyUI_FocusMask",
+            "files": [
+                "https://github.com/risunobushi/ComfyUI_FocusMask"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Extract Focus Mask"
+        },
+        {
+            "author": "RicherdLee",
+            "title": "comfyui-oss-image-save [WIP]",
+            "reference": "https://github.com/RicherdLee/comfyui-oss-image-save",
+            "files": [
+                "https://github.com/RicherdLee/comfyui-oss-image-save"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: SaveImageOSS."
+        },
+        {
+            "author": "Matrix-King-Studio",
+            "title": "ComfyUI-MoviePy",
+            "reference": "https://github.com/Matrix-King-Studio/ComfyUI-MoviePy",
+            "files": [
+                "https://github.com/Matrix-King-Studio/ComfyUI-MoviePy"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Image Clip Node, Audio Duration Node, Save Video Node,..."
+        },
+        {
+            "author": "Big Idea Technology",
+            "title": "ComfyUI-Movie-Tools [WIP]",
+            "reference": "https://github.com/Big-Idea-Technology/ComfyUI-Movie-Tools",
+            "files": [
+                "https://github.com/Big-Idea-Technology/ComfyUI-Movie-Tools"
+            ],
+            "install_type": "git-clone",
+            "description": "Movie Tools is a set of custom nodes, designed to simplify saving and loading batches of images with enhanced functionality like subfolder management and batch image handling."
+        },
+        {
+            "author": "ArthusLiang",
+            "title": "comfyui-face-remap [WIP]",
+            "reference": "https://github.com/ArthusLiang/comfyui-face-remap",
+            "files": [
+                "https://github.com/ArthusLiang/comfyui-face-remap"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: FaceRemap\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "trithemius",
+            "title": "ComfyUI-SmolVLM [WIP]",
+            "reference": "https://github.com/mamorett/ComfyUI-SmolVLM",
+            "files": [
+                "https://github.com/mamorett/ComfyUI-SmolVLM"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes to use SmolVLM for image tagging and captioning.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "anze",
+            "title": "ComfyUI-OIDN [WIP]",
+            "reference": "https://github.com/Anze-/ComfyUI-OIDN",
+            "files": [
+                "https://github.com/Anze-/ComfyUI-OIDN"
+            ],
+            "install_type": "git-clone",
+            "description": "ComyUI wrapper for Intel OIDN image denoising\nWARNING! : this is a development repo, usage in production environments is not advised! Bugs are to be expected."
+        },
+        {
+            "author": "hay86",
+            "title": "ComfyUI AceNodes [UNSAFE]",
+            "reference": "https://github.com/hay86/ComfyUI_AceNodes",
+            "files": [
+                "https://github.com/hay86/ComfyUI_AceNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Some useful custom nodes that are not included in ComfyUI core yet.\nNOTE: Vulnerability discovered. Not being managed."
+        },
+        {
+            "author": "dowands",
+            "title": "AddMaskForICLora",
+            "reference": "https://github.com/dowands/ComfyUI-AddMaskForICLora",
+            "files": [
+                "https://github.com/dowands/ComfyUI-AddMaskForICLora"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Add Mask For IC Lora x"
+        },
+        {
+            "author": "exectails",
+            "title": "Scripting",
+            "id": "et_scripting [UNSAFE]",
+            "reference": "https://github.com/exectails/comfyui-et_scripting",
+            "files": [
+                "https://github.com/exectails/comfyui-et_scripting"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes that can be used to write Python scripts directly on a node. Useful for quick prototyping and testing, at the cost of security.[w/This extension allows the execution of arbitrary Python code from a workflow.]"
+        },
+        {
+            "author": "AIFSH",
+            "title": "UltralightDigitalHuman-ComfyUI",
+            "reference": "https://github.com/AIFSH/UltralightDigitalHuman-ComfyUI",
+            "files": [
+                "https://github.com/AIFSH/UltralightDigitalHuman-ComfyUI"
+            ],
+            "install_type": "git-clone",
+            "description": "a custom node for [a/Ultralight-Digital-Human](https://github.com/anliyuan/Ultralight-Digital-Human)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "StartHua",
+            "title": "Comfyui_Flux_Style_Ctr [WIP]",
+            "reference": "https://github.com/StartHua/Comfyui_Flux_Style_Ctr",
+            "files": [
+                "https://github.com/StartHua/Comfyui_Flux_Style_Ctr"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:CXH_StyleModelApply\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "miragecoa",
+            "title": "ComfyUI-LLM-Evaluation [WIP]",
+            "reference": "https://github.com/miragecoa/ComfyUI-LLM-Evaluation",
+            "files": [
+                "https://github.com/miragecoa/ComfyUI-LLM-Evaluation"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:Load File, Select Item by Index, Select Item by Key, JSONToListNode, MathOperationNode, F1ScoreNode, AccuracyNode, ..."
+        },
+        {
+            "author": "WASasquatch",
+            "title": "ASTERR [UNSAFE]",
+            "id": "asterr",
+            "reference": "https://github.com/WASasquatch/ASTERR",
+            "files": [
+                "https://github.com/WASasquatch/ASTERR"
+            ],
+            "install_type": "git-clone",
+            "description": "Abstract Syntax Trees Evaluated Restricted Run (ASTERR) is a Python Script executor for ComfyUI. [w/Warning:ASTERR runs Python Code from a Web Interface! It is highly recommended to run this in a closed-off environment, as it could have potential security risks.]"
+        },
+        {
+            "author": "BenjaMITM",
+            "title": "ComfyUI_On_The_Fly_Wildcards [WIP]",
+            "reference": "https://github.com/BenjaMITM/ComfyUI_On_The_Fly_Wildcards",
+            "files": [
+                "https://github.com/BenjaMITM/ComfyUI_On_The_Fly_Wildcards"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:Wildcard Creator, Wildcard Loader, Wildcard Selector, Display String.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "celll1",
+            "title": "cel_sampler [WIP]",
+            "reference": "https://github.com/celll1/cel_sampler",
+            "files": [
+                "https://github.com/celll1/cel_sampler"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:Latent Value Tracker\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "DataCTE",
+            "title": "ComfyUI-DataVoid-nodes [WIP]",
+            "reference": "https://github.com/DataCTE/ComfyUI-DataVoid-nodes",
+            "files": [
+                "https://github.com/DataCTE/ComfyUI-DataVoid-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of custom nodes for ComfyUI focused on model merging and style adaptation.[w/It may cause a lot of node conflicts with comfyui_ipadapter_plus.]"
+        },
+        {
+            "author": "minhtuannhn",
+            "title": "comfyui-gemini-studio [WIP]",
+            "reference": "https://github.com/minhtuannhn/comfyui-gemini-studio",
+            "files": [
+                "https://github.com/minhtuannhn/comfyui-gemini-studio"
+            ],
+            "install_type": "git-clone",
+            "description": "comfyui-gemini-studio[w/This extension uses the legacy method of copying JS.]"
+        },
+        {
+            "author": "artem-konevskikh",
+            "title": "ComfyUI Video Processing Nodes [WIP]",
+            "reference": "https://github.com/artem-konevskikh/comfyui-split-merge-video",
+            "files": [
+                "https://github.com/artem-konevskikh/comfyui-split-merge-video"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom nodes for ComfyUI that add video splitting and merging capabilities with crossfade transitions."
+        },
+        {
+            "author": "Poseidon-fan",
+            "title": "ComfyUI-fileCleaner [UNSAFE]",
+            "reference": "https://github.com/Poseidon-fan/ComfyUI-fileCleaner",
+            "files": [
+                "https://github.com/Poseidon-fan/ComfyUI-fileCleaner"
+            ],
+            "install_type": "git-clone",
+            "description": "In production environments, images are usually saved on storage servers such as S3, rather than local folders. So the method of placing images in local folders using ComfyUI's native LoadImage and SaveImage nodes cannot be used as a deployment service method, but can only be used as a temporary storage place for images. This requires a way to delete images from the input and output folders.\nThis node is used to delete images from the input and output folders. It is recommended to use this node through api call in the backend after the image processing is completed.[w/Users can use the file deletion feature through the workflow.]"
+        },
+        {
+            "author": "yorkane",
+            "title": "Comfy UI Robe Nodes [UNSAFE]",
+            "reference": "https://github.com/RobeSantoro/ComfyUI-RobeNodes",
+            "files": [
+                "https://github.com/RobeSantoro/ComfyUI-RobeNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: List Video Path Node, List Image Path Node\nThis is a collection of utility nodes for the ComfyUI stable diffusion client that provides enhanced file path handling capabilities.[w/Users will be able to access images from arbitrary paths through the workflow.]"
+        },
+        {
+            "author": "Clybius",
+            "title": "ComfyUI-FluxDeCLIP",
+            "reference": "https://github.com/Clybius/ComfyUI-FluxDeCLIP",
+            "files": [
+                "https://github.com/Clybius/ComfyUI-FluxDeCLIP"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:FluxDeCLIPCheckpointLoader"
+        },
+        {
+            "author": "ZHO-ZHO-ZHO",
+            "title": "ComfyUI-BiRefNet-ZHO [BROKEN]",
+            "id": "birefnet",
+            "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-BiRefNet-ZHO",
+            "files": [
+                "https://github.com/ZHO-ZHO-ZHO/ComfyUI-BiRefNet-ZHO"
+            ],
+            "install_type": "git-clone",
+            "description": "Better version for [a/BiRefNet](https://github.com/zhengpeng7/birefnet) in ComfyUI | Both img and video.\nNOTE: You need to do [a/manual patch](https://github.com/ZHO-ZHO-ZHO/ComfyUI-BiRefNet-ZHO/issues/20)"
+        },
+        {
+            "author": "trashgraphicard",
+            "title": "Albedo-Sampler-for-ComfyUI",
+            "reference": "https://github.com/trashgraphicard/Albedo-Sampler-for-ComfyUI",
+            "files": [
+                "https://github.com/trashgraphicard/Albedo-Sampler-for-ComfyUI"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:Sample Image, Make Seamless Tile"
+        },
+        {
+            "author": "Anze-",
+            "title": "ComfyUI_deepDeband [WIP]",
+            "reference": "https://github.com/Anze-/ComfyUI_deepDeband",
+            "files": [
+                "https://github.com/Anze-/ComfyUI_deepDeband"
+            ],
+            "install_type": "git-clone",
+            "description": "ComyUI wrapper for RaymondLZhou/deepDeband image and video debanding\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "bmad4ever",
+            "title": "Bmad Nodes [UNSAFE]",
+            "id": "bmad",
+            "reference": "https://github.com/bmad4ever/comfyui_bmad_nodes",
+            "files": [
+                "https://github.com/bmad4ever/comfyui_bmad_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "This custom node offers the following functionalities: API support for setting up API requests, computer vision primarily for masking or collages, and general utility to streamline workflow setup or implement essential missing features.\nNOTE: Vulnerability discovered. Not being managed."
+        },
+        {
+            "author": "suncat2ps",
+            "title": "ComfyUI-SaveImgNextcloud",
+            "reference": "https://github.com/suncat2ps/ComfyUI-SaveImgNextcloud",
+            "files": [
+                "https://github.com/suncat2ps/ComfyUI-SaveImgNextcloud"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Save Image to Nextcloud"
+        },
+        {
+            "author": "DoctorDiffusion",
+            "title": "ComfyUI-Flashback",
+            "reference": "https://github.com/DoctorDiffusion/ComfyUI-Flashback",
+            "files": [
+                "https://github.com/DoctorDiffusion/ComfyUI-Flashback"
+            ],
+            "description": "NODES:Latent Export, Latent Import, Latent Loop",
+            "install_type": "git-clone"
+        },
+        {
+            "author": "sswink",
+            "title": "comfyui-lingshang",
+            "reference": "https://github.com/sswink/comfyui-lingshang",
+            "files": [
+                "https://github.com/sswink/comfyui-lingshang"
+            ],
+            "description": "NODES:LS_SaveImageToOss, LS_LoadMaskFromUrl, LS_DigImageByMask, LS_ALY_Seg_Utils, LS_ALY_UploadToOssAndGetUrl, LS_GrowMaskWithBlur, LS_ALY_Seg_Body_Utils, LS_ALY_Seg_Common_Utils, LS_ALY_Seg_Clothes_Utils, LS_ALY_Seg_Body_Utils_Return_crop, ...",
+            "install_type": "git-clone"
+        },
+        {
+            "author": "AICodeFactory",
+            "title": "ComfyUI-Viva",
+            "reference": "https://github.com/AICodeFactory/ComfyUI-Viva",
+            "files": [
+                "https://github.com/AICodeFactory/ComfyUI-Viva"
+            ],
+            "description": "NODES:HttpTrigger (Viva), HttpTrigger (Image), HttpTrigger (Common)",
+            "install_type": "git-clone"
+        },
+        {
+            "author": "LogicAI",
+            "title": "ComfyUI-MagicAI [UNSAFE]",
+            "reference": "https://github.com/lcolok/ComfyUI-MagicAI",
+            "files": [
+                "https://github.com/lcolok/ComfyUI-MagicAI"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:Mask Size Calculator (MagicAI), Universal Mask Converter (MagicAI), Python Execution (MagicAI), Extract JSON From Text Node(MagicAI)\n[w/This extension allows the execution of arbitrary Python code from a workflow.]"
+        },
+        {
+            "author": "aria1th",
+            "title": "ComfyUI-SkipCFGSigmas",
+            "reference": "https://github.com/aria1th/ComfyUI-SkipCFGSigmas",
+            "files": [
+                "https://github.com/aria1th/ComfyUI-SkipCFGSigmas"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: CFGControl_SKIPCFG"
+        },
+        {
+            "author": "Clelstyn",
+            "title": "ComfyUI-Inpaint_with_Detailer",
+            "reference": "https://github.com/Clelstyn/ComfyUI-Inpaint_with_Detailer",
+            "files": [
+                "https://github.com/Clelstyn/ComfyUI-Inpaint_with_Detailer"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:Masked Resize Image, Paste Masked Image, Filter And Blur Mask"
+        },
+        {
+            "author": "Looking-Glass",
+            "title": "LKG-ComfyUI",
+            "reference": "https://github.com/Looking-Glass/LKG-ComfyUI",
+            "files": [
+                "https://github.com/Looking-Glass/LKG-ComfyUI"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:Side by Side Node, Bridge Preview Node, Load Folder, Scale Maintain Aspect Ratio Node, "
+        },
+        {
+            "author": "xiaoyumu",
+            "title": "ComfyUI-XYNodes",
+            "reference": "https://github.com/xiaoyumu/ComfyUI-XYNodes",
+            "files": [
+                "https://github.com/xiaoyumu/ComfyUI-XYNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:PrimitiveBBOX."
+        },
+        {
+            "author": "ainanoha",
+            "title": "etm_comfyui_nodes",
+            "reference": "https://github.com/ainanoha/etm_comfyui_nodes",
+            "files": [
+                "https://github.com/ainanoha/etm_comfyui_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:LETM Save Image, ETM Load Image From Local"
+        },
+        {
+            "author": "m-ai-studio",
+            "title": "mai-prompt-progress",
+            "reference": "https://github.com/m-ai-studio/mai-prompt-progress",
+            "files": [
+                "https://github.com/m-ai-studio/mai-prompt-progress"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI extensions for sending prompt progress to webhook"
+        },
+        {
+            "author": "neeltheninja",
+            "title": "ComfyUI-TempFileDeleter [UNSAFE]",
+            "reference": "https://github.com/neeltheninja/ComfyUI-TempFileDeleter",
+            "files": [
+                "https://github.com/neeltheninja/ComfyUI-TempFileDeleter"
+            ],
+            "install_type": "git-clone",
+            "description": "This node is designed to streamline your workflow in ComfyUI by efficiently cleaning up temporary files on each run. This node takes no input. You can specify 'on' or 'off' in the node itself, or just bypass to not use use it.[w/This node can delete any files in the folder mentioned in 'folder_path' in the node. Be aware of this and change the folder path correctly before running any workflow with this node. I will NOT be responsible for wrongly deleted files because you didn't read this beforehand.]"
+        },
+        {
+            "author": "kylegrover",
+            "title": "comfyui-python-cowboy [UNSAFE]",
+            "reference": "https://github.com/kylegrover/comfyui-python-cowboy",
+            "files": [
+                "https://github.com/kylegrover/comfyui-python-cowboy"
+            ],
+            "install_type": "git-clone",
+            "description": "run python code in comfyui\nuses codemirror for nice syntax highlighting\nNOTE: based on ComfyUI-nidefawl[w/This node is an unsafe node that includes the capability to execute arbitrary python script.]"
+        },
+        {
+            "author": "kijai",
+            "title": "ComfyUI-MochiWrapper [WIP]",
+            "reference": "https://github.com/kijai/ComfyUI-MochiWrapper",
+            "files": [
+                "https://github.com/kijai/ComfyUI-MochiWrapper"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI wrapper nodes for [a/Mochi](https://github.com/genmoai/models) video generator"
+        },
+        {
+            "author": "leadbreak",
+            "title": "Face Aging [WIP]",
+            "reference": "https://github.com/leadbreak/comfyui-faceaging",
+            "files": [
+                "https://github.com/leadbreak/comfyui-faceaging"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a comfyui custom node version of [a/Age Transformation](https://github.com/yuval-alaluf/SAM).\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "downlifted",
+            "title": "ComfyUI_BWiZ_Nodes [WIP]",
+            "reference": "https://github.com/downlifted/ComfyUI_BWiZ_Nodes",
+            "files": [
+                "https://github.com/downlifted/ComfyUI_BWiZ_Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:CaptainWebhook, CaptainWebhook-Email, CaptainWebhook-Push, BWIZ_AdvancedLoadImageBatch, BWIZ_ErrorDetector, BWIZ_HFRepoBatchLoader, BWIZ_NotificationSound.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "blurymind",
+            "title": "cozy-fireplace [WIP]",
+            "reference": "https://github.com/blurymind/cozy-fireplace",
+            "files": [
+                "https://github.com/blurymind/cozy-fireplace"
+            ],
+            "install_type": "git-clone",
+            "description": "Cozy fireplace is a ComfyUI workflow prompter that brings a localhost server frontend for existing workflows created in ComfyUi. Just place your favorite or lovingly crafted workflows in a folder and cozy fireplace will let you select and run any of them (export them as API type in comfyui) It's a cozy UI that scales all the way down to mobile phone devices - to let you prompt your beefy pc at home with your smartphone."
+        },
+        {
+            "author": "kxh",
+            "title": "ComfyUI-ImageUpscaleWithModelMultipleTimes",
+            "reference": "https://github.com/kxh/ComfyUI-ImageUpscaleWithModelMultipleTimes",
+            "files": [
+                "https://github.com/kxh/ComfyUI-ImageUpscaleWithModelMultipleTimes"
+            ],
+            "install_type": "git-clone",
+            "description": "Upscale image with model multiple times !"
+        },
+        {
+            "author": "rouxianmantou",
+            "title": "comfyui-rxmt-nodes",
+            "reference": "https://github.com/rouxianmantou/comfyui-rxmt-nodes",
+            "files": [
+                "https://github.com/rouxianmantou/comfyui-rxmt-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Check Value Type, Why Prompt Text"
+        },
+        {
+            "author": "SirVeggie",
+            "title": "SirVeggie/Custom nodes for ComfyUI",
+            "reference": "https://github.com/SirVeggie/comfyui-sv-nodes",
+            "files": [
+                "https://github.com/SirVeggie/comfyui-sv-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:SV-SimpleText, SV-PromptProcessing, SV-PromptProcessingRecursive, SV-PromptProcessingAdvanced, SV-PromptProcessingEncode,..."
+        },
+        {
+            "author": "artisanalcomputing",
+            "title": "artcpu-custom-nodes",
+            "reference": "https://github.com/artisanalcomputing/ComfyUI-Custom-Nodes",
+            "files": [
+                "https://github.com/artisanalcomputing/ComfyUI-Custom-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:Random Video Mixer, Spotify Canvas Generator, Video Writer\ncustom comfyui nodes for audio/visual purposes# ComfyUI-Custom-Nodes"
+        },
+        {
+            "author": "kxh",
+            "title": "ComfyUI-sam2",
+            "reference": "https://github.com/kxh/ComfyUI-sam2",
+            "files": [
+                "https://github.com/kxh/ComfyUI-sam2"
+            ],
+            "install_type": "git-clone",
+            "description": "use semantic tag to segment any element in an image, output a mask.\nNOTE: Repo name is conflicting with neverbiasu/ComfyUI-SAM2"
+        },
+        {
+            "author": "AIFSH",
+            "title": "UtilNodes-ComfyUI [WIP]",
+            "reference": "https://github.com/AIFSH/UtilNodes-ComfyUI",
+            "files": [
+                "https://github.com/AIFSH/UtilNodes-ComfyUI"
+            ],
+            "install_type": "git-clone",
+            "description": "here put custom input nodes such as text,video...\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "alchemist-novaro",
+            "title": "ComfyUI-Simple-Image-Tools [WIP]",
+            "reference": "https://github.com/alchemist-novaro/ComfyUI-Simple-Image-Tools",
+            "files": [
+                "https://github.com/alchemist-novaro/ComfyUI-Simple-Image-Tools"
+            ],
+            "install_type": "git-clone",
+            "description": "Get mask from image based on alpha (Get Mask From Alpha)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "galoreware",
+            "title": "ComfyUI-GaloreNodes [WIP]",
+            "reference": "https://github.com/galoreware/ComfyUI-GaloreNodes",
+            "files": [
+                "https://github.com/galoreware/ComfyUI-GaloreNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Color and Image related nodes for ComfyUI."
+        },
+        {
+            "author": "lgldlk",
+            "title": "ComfyUI-img-tiler",
+            "reference": "https://github.com/lgldlk/ComfyUI-img-tiler",
+            "files": [
+                "https://github.com/lgldlk/ComfyUI-img-tiler"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:TilerImage, TilerSelect, TileMaker, ImageListTileMaker"
+        },
+        {
+            "author": "wilzamguerrero",
+            "title": "Comfyui-zZzZz [UNSAFE]",
+            "reference": "https://github.com/wilzamguerrero/Comfyui-zZzZz",
+            "files": [
+                "https://github.com/wilzamguerrero/Comfyui-zZzZz"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:Download Z, Compress Z, Move Z, Delete Z, Rename Z, Create Z, Infinite Z, Share Screen Z"
+        },
+        {
+            "author": "alchemist-novaro",
+            "title": "Affine Transform ComfyUI Node [WIP]",
+            "reference": "https://github.com/alchemist-novaro/ComfyUI-Affine-Transform",
+            "files": [
+                "https://github.com/alchemist-novaro/ComfyUI-Affine-Transform"
+            ],
+            "install_type": "git-clone",
+            "description": "This node output the image that are transfromed by affine matrix what is made according to 4 points of output.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "ruka-game",
+            "title": "ComfyUI RukaLib [WIP]",
+            "reference": "https://github.com/ruka-game/rukalib_comfyui",
+            "files": [
+                "https://github.com/ruka-game/rukalib_comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Ruka Prompt Enhancer, Ruka Debug Probe.\nMy utilities for comfy (WIP / ollama is required for LLM nodes)"
+        },
+        {
+            "author": "MythicalChu",
+            "title": "ComfyUI-APG_ImYourCFGNow",
+            "reference": "https://github.com/MythicalChu/ComfyUI-APG_ImYourCFGNow",
+            "files": [
+                "https://github.com/MythicalChu/ComfyUI-APG_ImYourCFGNow"
+            ],
+            "install_type": "git-clone",
+            "description": "Use this node like a RescaleCFG node, ... modelIn -> ThisNode -> ModelOut ... -> KSampler\n'scale' acts like your CFG, your CFG doesn't do anything anymore white this node is active. See paper [a/https://arxiv.org/pdf/2410.02416](https://arxiv.org/pdf/2410.02416) for instructions about the other parameters. (Pages 20-21)"
+        },
+        {
+            "author": "okg21",
+            "title": "VLLMVisionChatNode",
+            "reference": "https://github.com/okg21/VLLMVisionChatNode",
+            "files": [
+                "https://raw.githubusercontent.com/okg21/VLLMVisionChatNode/refs/heads/main/VLLMVisionChatNode.py"
+            ],
+            "pip": [
+                "openai",
+                "numpy"
+            ],
+            "install_type": "copy",
+            "description": "This platform extension provides ZhipuAI nodes, enabling you to configure a workflow for online video generation."
+        },
+        {
+            "author": "netanelben",
+            "title": "comfyui-photobooth-customnode",
+            "reference": "https://github.com/netanelben/comfyui-photobooth-customnode",
+            "files": [
+                "https://github.com/netanelben/comfyui-photobooth-customnode"
+            ],
+            "install_type": "git-clone",
+            "description": "comfyui-photobooth-customnode"
+        },
+        {
+            "author": "netanelben",
+            "title": "comfyui-text2image-customnode",
+            "reference": "https://github.com/netanelben/comfyui-text2image-customnode",
+            "files": [
+                "https://github.com/netanelben/comfyui-text2image-customnode"
+            ],
+            "install_type": "git-clone",
+            "description": "comfyui-text2image-customnode"
+        },
+        {
+            "author": "netanelben",
+            "title": "comfyui-camera2image-customnode",
+            "reference": "https://github.com/netanelben/comfyui-camera2image-customnode",
+            "files": [
+                "https://github.com/netanelben/comfyui-camera2image-customnode"
+            ],
+            "install_type": "git-clone",
+            "description": "comfyui-camera2image-customnode"
+        },
+        {
+            "author": "netanelben",
+            "title": "comfyui-image2image-customnode",
+            "reference": "https://github.com/netanelben/comfyui-image2image-customnode",
+            "files": [
+                "https://github.com/netanelben/comfyui-image2image-customnode"
+            ],
+            "install_type": "git-clone",
+            "description": "comfyui-image2image-customnode"
+        },
+        {
+            "author": "JayLyu",
+            "title": "ComfyUI_BaiKong_Node",
+            "id": "baikong",
+            "reference": "https://github.com/JayLyu/ComfyUI_BaiKong_Node",
+            "files": [
+                "https://github.com/JayLyu/ComfyUI_BaiKong_Node"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes for advanced color manipulation and image processing: BK Img To Color, BK Color Selector, BK Color Contrast, BK Color Limit, BK Color Luminance, BK Gradient Image, and BK Image Aspect Filter.\n[w/requirements.txt is broken.]"
+        },
+        {
+            "author": "ZHO-ZHO-ZHO",
+            "title": "ComfyUI Llama 3.1 [WIP]",
+            "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Llama-3-2",
+            "files": [
+                "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Llama-3-2"
+            ],
+            "install_type": "git-clone",
+            "description": "Using Llama-3-1 in ComfyUI"
+        },
+        {
+            "author": "netanelben",
+            "title": "comfyui-text2image-customnode [WIP]",
+            "reference": "https://github.com/netanelben/comfyui-text2image-customnode",
+            "files": [
+                "https://github.com/netanelben/comfyui-text2image-customnode"
+            ],
+            "install_type": "git-clone",
+            "description": "text2image web extension"
+        },
+        {
+            "author": "leeguandong",
+            "title": "ComfyUI_AliControlnetInpainting [WIP]",
+            "reference": "https://github.com/leeguandong/ComfyUI_AliControlnetInpainting",
+            "files": [
+                "https://github.com/leeguandong/ComfyUI_AliControlnetInpainting"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes to use AliControlnetInpainting"
+        },
+        {
+            "author": "jordancoult",
+            "title": "ComfyUI_HelpfulNodes",
+            "reference": "https://github.com/jordancoult/ComfyUI_HelpfulNodes",
+            "files": [
+                "https://github.com/jordancoult/ComfyUI_HelpfulNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Prep Crop Around Keypoints"
+        },
+        {
+            "author": "ashishsaini",
+            "title": "comfyui_segformer_b2_sleeves",
+            "reference": "https://github.com/ashishsaini/comfyui-segment-clothing-sleeves",
+            "files": [
+                "https://github.com/ashishsaini/comfyui-segment-clothing-sleeves"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:segformer_b2_sleeves"
+        },
+        {
+            "author": "io-club",
+            "title": "ComfyUI-LuminaNext [WIP]",
+            "reference": "https://github.com/io-club/ComfyUI-LuminaNext",
+            "files": [
+                "https://github.com/io-club/ComfyUI-LuminaNext"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: GemmaClipLoader"
+        },
+        {
+            "author": "shadowcz007",
+            "title": "Comfyui-EzAudio [WIP]",
+            "reference": "https://github.com/shadowcz007/Comfyui-EzAudio",
+            "files": [
+                "https://github.com/shadowcz007/Comfyui-EzAudio"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: EZ Generate Audio, EZ Load Model\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "neo0801",
+            "title": "my-comfy-node",
+            "reference": "https://github.com/neo0801/my-comfy-node",
+            "files": [
+                "https://github.com/neo0801/my-comfy-node"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:Deep Mosaic Get Image Mosaic Mask, Deep Mosaic Get Video Mosaic Mask, Deep Mosaic Remove Image Mosaic, Deep Mosaic Remove Video Mosaic"
+        },
+        {
+            "author": "nikkuexe",
+            "title": "List Data Helper Nodes",
+            "reference": "https://github.com/paulhoux/Smart-Prompting",
+            "files": [
+                "https://github.com/paulhoux/Smart-Prompting"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom nodes for ComfyUI, allowing you to more easily manipulate text and create good prompts.[w/The use of outdated front extension techniques results in remnants being left behind during uninstallation.]"
+        },
+        {
+            "author": "nikkuexe",
+            "title": "List Data Helper Nodes",
+            "reference": "https://github.com/nikkuexe/ComfyUI-ListDataHelpers",
+            "files": [
+                "https://github.com/nikkuexe/ComfyUI-ListDataHelpers"
+            ],
+            "install_type": "git-clone",
+            "description": "A set of custom nodes for handling lists in ComfyUI."
+        },
+        {
+            "author": "Fannovel16",
+            "title": "ComfyUI-AppIO",
+            "reference": "https://github.com/Fannovel16/ComfyUI-AppIO",
+            "files": [
+                "https://github.com/Fannovel16/ComfyUI-AppIO"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:AppIO_StringInput, AppIO_ImageInput, AppIO_StringOutput, AppIO_ImageOutput"
+        },
+        {
+            "author": "seancheung",
+            "title": "comfyui-creative-nodes",
+            "reference": "https://github.com/seancheung/comfyui-creative-nodes",
+            "files": [
+                "https://github.com/seancheung/comfyui-creative-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:Stop Flow, Skip From Flow, Skip To Flow, Resolution Selector, ResolutionXL Selector"
+        },
+        {
+            "author": "AlexXi19",
+            "title": "ComfyUI-OpenAINode",
+            "reference": "https://github.com/AlexXi19/ComfyUI-OpenAINode",
+            "files": [
+                "https://github.com/AlexXi19/ComfyUI-OpenAINode"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI-OpenAINode is a user-friendly node that serves as an interface to the OpenAI Models.[w/Repo name conflict with Electrofried/ComfyUI-OpenAINode]"
+        },
+        {
+            "author": "IgPoly",
+            "title": "ComfyUI-igTools",
+            "reference": "https://github.com/IgPoly/ComfyUI-igTools",
+            "files": [
+                "https://github.com/IgPoly/ComfyUI-igTools"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:IGT Simple Tiles Calc"
+        },
+        {
+            "author": "Ryota",
+            "title": "Ryota's Nodes",
+            "reference": "https://github.com/lichenhao/Comfyui_Ryota",
+            "files": [
+                "https://github.com/lichenhao/Comfyui_Ryota"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:CombineTexts, FontLoader, DrawText, TxtFileLoader, SaveTxtFile, SwitchModelClip, SwitchAnyInputs, Reroute2, Reroute3"
+        },
+        {
+            "author": "Soppatorsk",
+            "title": "comfyui_img_to_ascii [WIP]",
+            "reference": "https://github.com/Soppatorsk/comfyui_img_to_ascii",
+            "files": [
+                "https://github.com/Soppatorsk/comfyui_img_to_ascii"
+            ],
+            "install_type": "git-clone",
+            "description": "Basic functionality for converting an image to ASCII art returned as a png image based on [a/ascii_magic](https://github.com/LeandroBarone/python-ascii_magic)"
+        },
+        {
+            "author": "Lilien86",
+            "title": "Comfyui_Latent_Interpolation [WIP]",
+            "reference": "https://github.com/Lilien86/Comfyui_Latent_Interpolation",
+            "files": [
+                "https://github.com/Lilien86/Comfyui_Latent_Interpolation"
+            ],
+            "install_type": "git-clone",
+            "description": "Hey everyone it's my Custom ComfyUI Nodes Pack repository! This project contains a collection of custom nodes designed to extend the functionality of ComfyUI. These nodes offer capabilities and new creative possibilities, especially in the realms of latent space manipulation and interpolation.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "haodman",
+            "title": "ComfyUI_Rain",
+            "reference": "https://github.com/haodman/ComfyUI_Rain",
+            "files": [
+                "https://github.com/haodman/ComfyUI_Rain"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:Rain_ValueSwitch, Rain_Math, Rain_IntToFloat, Rain_ImageSize."
+        },
+        {
+            "author": "bananasss00",
+            "title": "Comfyui-PyExec [UNSAFE]",
+            "reference": "https://github.com/bananasss00/Comfyui-PyExec",
+            "files": [
+                "https://github.com/bananasss00/Comfyui-PyExec"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:PyExec.[w/This node allows access to arbitrary files through the workflow, which could pose a security threat.]"
+        },
+        {
+            "author": "jgbrblmd",
+            "title": "ComfyUI-ComfyFluxSize [WIP]",
+            "reference": "https://github.com/jgbrblmd/ComfyUI-ComfyFluxSize",
+            "files": [
+                "https://github.com/jgbrblmd/ComfyUI-ComfyFluxSize"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:ComfyFlux Size\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "ilovejohnwhite",
+            "title": "Kolors Awesome Prompts [WIP]",
+            "reference": "https://github.com/ilovejohnwhite/Tracer",
+            "files": [
+                "https://github.com/ilovejohnwhite/Tracer"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Image Load TTK, SuckerPunch, LinkMasterNode, PixelPerfectResolution, ImageGenResolutionFromImage, ImageGenResolutionFromLatent, HintImageEnchance\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "shuanshtalon468uan",
+            "title": "ComfyUI-Rpg-Architect [WIP]",
+            "reference": "https://github.com/talon468/ComfyUI-Rpg-Architect",
+            "files": [
+                "https://github.com/talon468/ComfyUI-Rpg-Architect"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom Node for ComfyUI to create RPG Characters\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "shuanshuan",
+            "title": "ComfyUI_CheckPointLoader_Ext [WIP]",
+            "reference": "https://github.com/shuanshuan/ComfyUI_CheckPointLoader_Ext",
+            "files": [
+                "https://github.com/shuanshuan/ComfyUI_CheckPointLoader_Ext"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:Checkpoint Loader Ext"
+        },
+        {
+            "author": "123jimin",
+            "title": "ComfyUI MobileForm [WIP]",
+            "reference": "https://github.com/123jimin/ComfyUI-MobileForm",
+            "files": [
+                "https://github.com/123jimin/ComfyUI-MobileForm"
+            ],
+            "install_type": "git-clone",
+            "description": "MobileForm is an extension for ComfyUI, providing simple form for any workflows, suitable for use on mobile phones.[w/Currently MobileForm is in a PoC state; expect bugs and breaking changes.]"
+        },
+        {
+            "author": "go-package-lab",
+            "title": "ComfyUI-Tools-Video-Combine [WIP]",
+            "reference": "https://github.com/go-package-lab/ComfyUI-Tools-Video-Combine",
+            "files": [
+                "https://github.com/go-package-lab/ComfyUI-Tools-Video-Combine"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:LoadAudioUrl, VideoWatermark"
+        },
+        {
+            "author": "zhongpei",
+            "title": "Comfyui_image2prompt",
+            "id": "img2prompt",
+            "reference": "https://github.com/zhongpei/Comfyui_image2prompt",
+            "files": [
+                "https://github.com/zhongpei/Comfyui_image2prompt"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Image to Text, Loader Image to Text Model.[w/This custom node may break dependencies by reinstalling the torch package.]"
+        },
+        {
+            "author": "APZmedia",
+            "title": "comfyui-textools [WIP]",
+            "reference": "https://github.com/APZmedia/comfyui-textools",
+            "files": [
+                "https://github.com/APZmedia/comfyui-textools"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI-textools is a collection of custom nodes designed for use with ComfyUI. These nodes enhance text processing capabilities, including applying rich text overlays on images and cleaning file names for safe and consistent file management.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Comfy Org",
+            "title": "ComfyUI_devtools [WIP]",
+            "reference": "https://github.com/Comfy-Org/ComfyUI_devtools",
+            "files": [
+                "https://github.com/Comfy-Org/ComfyUI_devtools"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI developer tools (Custom Node)"
+        },
+        {
+            "author": "ThisModernDay",
+            "title": "ComfyUI Instructor Ollama",
+            "reference": "https://github.com/ThisModernDay/ComfyUI-InstructorOllama",
+            "files": [
+                "https://github.com/ThisModernDay/ComfyUI-InstructorOllama"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI Nodes for interacting with Ollama using the Instructor. Library to provide structured output from your LLM. To use this properly, you would need a running Ollama server reachable from the host that is running ComfyUI.\nNOTE: The files in the repo are not organized, which may lead to update issues."
+        },
+        {
+            "author": "gioferreira",
+            "title": "ComfyUI-Molde-Utils",
+            "reference": "https://github.com/gioferreira/ComfyUI-Molde-Utils",
+            "files": [
+                "https://github.com/gioferreira/ComfyUI-Molde-Utils"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI-Molde-Utils is a utility library designed to provide various helper functions for working with UI elements. This project includes modules for handling bezier curves and color conversions.\nNOTE: The files in the repo are not organized, which may lead to update issues."
+        },
+        {
+            "author": "kijai",
+            "title": "ComfyUI nodes for VEnhancer [WIP]",
+            "reference": "https://github.com/kijai/ComfyUI-VEnhancer",
+            "files": [
+                "https://github.com/kijai/ComfyUI-VEnhancer"
+            ],
+            "install_type": "git-clone",
+            "description": "Original repo: [a/https://github.com/Vchitect/VEnhancer](https://github.com/Vchitect/VEnhancer)"
+        },
+        {
+            "author": "jimstudt",
+            "title": "Jim's ComfyUI Nodes [WIP]",
+            "reference": "https://github.com/jimstudt/ComfyUI-Jims-Nodes",
+            "files": [
+                "https://github.com/jimstudt/ComfyUI-Jims-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Zoom and Enhance Nodes, Text To String List, Choose String, Define Word, Lookup Word, Substitute Words, Dictionary to JSON, JSON file to Dictionary, JSON to Dictionary, Load Image And Info From Path, CubbyHack, Image to Solid Background"
+        },
+        {
+            "author": "ChrisColeTech",
+            "title": "ComfyUI-Get-Random-File [UNSAFE]",
+            "reference": "https://github.com/ChrisColeTech/ComfyUI-Get-Random-File",
+            "files": [
+                "https://github.com/ChrisColeTech/ComfyUI-Get-Random-File"
+            ],
+            "install_type": "git-clone",
+            "description": "Gets a random file from a directory. Returns the filpath as a STRING. [w/This node allows access to arbitrary files through the workflow, which could pose a security threat.]"
+        },
+        {
+            "author": "neeltheninja",
+            "title": "ComfyUI-TextOverlay",
+            "reference": "https://github.com/neeltheninja/ComfyUI-TextOverlay",
+            "files": [
+                "https://github.com/neeltheninja/ComfyUI-TextOverlay"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node for ComfyUI that adds text overlay to images, with options for text and background color, opacity, vertical positioning, and custom font selection. [w/Name conflict with munkyfoot/ComfyUI-TextOverlay. Cannot install simulatenously.]"
+        },
+        {
+            "author": "comfyanonymous",
+            "title": "ComfyUI_bitsandbytes_NF4 [EXPERIMENTAL]",
+            "reference": "https://github.com/comfyanonymous/ComfyUI_bitsandbytes_NF4",
+            "files": [
+                "https://github.com/comfyanonymous/ComfyUI_bitsandbytes_NF4"
+            ],
+            "install_type": "git-clone",
+            "description": "A quickly written custom node that uses code from Forge to support the nf4 flux dev checkpoint and nf4 flux schnell checkpoint.\nRequires installing bitsandbytes.\nMake sure your ComfyUI is updated.\nThe node is: CheckpointLoaderNF4, just plug it in your flux workflow instead of the regular one.[w/NF4 checckpoint doesn't support LoRA.]"
+        },
+        {
+            "author": "kijai",
+            "title": "ComfyUI-EasyAnimateWrapper [WIP]",
+            "reference": "https://github.com/kijai/ComfyUI-EasyAnimateWrapper",
+            "files": [
+                "https://github.com/kijai/ComfyUI-EasyAnimateWrapper"
+            ],
+            "install_type": "git-clone",
+            "description": "EasyAnimateWrapper for ComfyUI"
+        },
+        {
+            "author": "logtd",
+            "title": "ComfyUI-Veevee [WIP]",
+            "reference": "https://github.com/logtd/ComfyUI-Veevee",
+            "files": [
+                "https://github.com/logtd/ComfyUI-Veevee"
+            ],
+            "install_type": "git-clone",
+            "description": "A Video2Video framework for text2image models in ComfyUI. Supports SD1.5 and SDXL."
+        },
+        {
+            "author": "chrisdreid",
+            "title": "ComfyUI_EnvAutopsyAPI Debugger [UNSAFE]",
+            "id": "chrisdreid",
+            "reference": "https://github.com/chrisdreid/ComfyUI_EnvAutopsyAPI",
+            "files": [
+                "https://github.com/chrisdreid/ComfyUI_EnvAutopsyAPI"
+            ],
+            "install_type": "git-clone",
+            "description": "A powerful debugging tool designed to provide in-depth analysis of your environment and dependencies by exposing API endpoints. This tool allows you to inspect environment variables, pip packages, python info and dependency trees, making it easier to diagnose and resolve issues in your ComfyUI setup.[w/This tool may expose sensitive system information if used on a public server]"
+        },
+        {
+            "author": "denislov",
+            "title": "Comfyui_AutoSurvey",
+            "reference": "https://github.com/denislov/Comfyui_AutoSurvey",
+            "files": [
+                "https://github.com/denislov/Comfyui_AutoSurvey"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:AutoSurvey, WriteOutline, WriteSection, ChatModel, QueryKnowledge, ManageDatabase, AddDoc2Knowledge"
+        },
+        {
+            "author": "leoleelxh",
+            "title": "ComfyUI-MidjourneyNode-leoleexh",
+            "reference": "https://github.com/leoleelxh/ComfyUI-MidjourneyNode-leoleexh",
+            "files": [
+                "https://github.com/leoleelxh/ComfyUI-MidjourneyNode-leoleexh"
+            ],
+            "install_type": "git-clone",
+            "description": "This node allows ComfyUI to easily integrate with Midjourney, utilizing the ultra-high quality of Midjourney and the powerful control of SD to provide more convenient capabilities for AIGC.\nNOTE: This node relies on the midjourney proxy project and requires API deployment in advance. For detailed installation, please refer to the instructions of the project. https://github.com/novicezk/midjourney-proxy"
+        },
+        {
+            "author": "kijai",
+            "title": "ComfyUI-FollowYourEmojiWrapper [WIP]",
+            "reference": "https://github.com/kijai/ComfyUI-FollowYourEmojiWrapper",
+            "files": [
+                "https://github.com/kijai/ComfyUI-FollowYourEmojiWrapper"
+            ],
+            "install_type": "git-clone",
+            "description": "Original repo: [a/https://github.com/mayuelala/FollowYourEmoji](https://github.com/mayuelala/FollowYourEmoji)"
+        },
+        {
+            "author": "haomole",
+            "title": "Comfyui-SadTalker",
+            "reference": "https://github.com/haomole/Comfyui-SadTalker",
+            "files": [
+                "https://github.com/haomole/Comfyui-SadTalker"
+            ],
+            "install_type": "git-clone",
+            "description": "[a/SadTalker](https://github.com/OpenTalker/SadTalker) for ComfyUI"
+        },
+        {
+            "author": "hotpizzatactics",
+            "title": "ComfyUI-WaterMark-Detector",
+            "id": "watermark-detector",
+            "reference": "https://github.com/hotpizzatactics/ComfyUI-WaterMark-Detector",
+            "files": [
+                "https://github.com/hotpizzatactics/ComfyUI-WaterMark-Detector"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:CLAHE Enhancement, High Pass Filter, Edge Detection, Combine Enhancements, Adaptive Thresholding, Morphological Operations, Gray Color Enhancement, Improved Gray Color Enhancement, Texture Enhancement, Denoising Filter, Flexible Combine Enhancements."
+        },
+        {
+            "author": "BetaDoggo",
+            "title": "ComfyUI-LogicGates",
+            "id": "logicgates",
+            "reference": "https://github.com/BetaDoggo/ComfyUI-LogicGates",
+            "files": [
+                "https://github.com/BetaDoggo/ComfyUI-LogicGates"
+            ],
+            "install_type": "git-clone",
+            "description": "Binary Nodes, Byte Nodes, ..."
+        },
+        {
+            "author": "shadowcz007",
+            "title": "comfyui-hydit",
+            "reference": "https://github.com/shadowcz007/comfyui-hydit-lowvram",
+            "files": [
+                "https://github.com/shadowcz007/comfyui-hydit-lowvram"
+            ],
+            "install_type": "git-clone",
+            "description": "HunYuan Diffusers Nodes"
+        },
+        {
+            "author": "walterFeng",
+            "title": "ComfyUI-Image-Utils",
+            "reference": "https://github.com/walterFeng/ComfyUI-Image-Utils",
+            "files": [
+                "https://github.com/walterFeng/ComfyUI-Image-Utils"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes: Calculate Image Brightness"
+        },
+        {
+            "author": "zml-ai",
+            "title": "comfyui-hydit",
+            "reference": "https://github.com/zml-ai/comfyui-hydit",
+            "files": [
+                "https://github.com/zml-ai/comfyui-hydit"
+            ],
+            "install_type": "git-clone",
+            "description": "The ComfyUI code is under review in the official repository. Meanwhile, a temporary version is available below for immediate community use. We welcome users to try our workflow and appreciate any inquiries or suggestions."
+        },
+        {
+            "author": "melMass",
+            "title": "ComfyUI-Lygia",
+            "id": "lygia",
+            "reference": "https://github.com/melMass/ComfyUI-Lygia",
+            "files": [
+                "https://github.com/melMass/ComfyUI-Lygia"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: LygiaProgram, LygiaUniforms"
+        },
+        {
+            "author": "SpaceWarpStudio",
+            "title": "ComfyUI_Remaker_FaceSwap",
+            "reference": "https://github.com/SpaceWarpStudio/ComfyUI_Remaker_FaceSwap",
+            "files": [
+                "https://github.com/SpaceWarpStudio/ComfyUI_Remaker_FaceSwap"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Remaker Face Swap"
+        },
+        {
+            "author": "VisionExp",
+            "title": "ve_custom_comfyui_nodes",
+            "reference": "https://github.com/VisionExp/ve_custom_comfyui_nodes",
+            "files": [
+                "https://github.com/VisionExp/ve_custom_comfyui_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:LoadImgFromInputUrl"
+        },
+        {
+            "author": "StartHua",
+            "title": "Comfyui_CXH_CRM",
+            "id": "csdmt-cxh",
+            "reference": "https://github.com/StartHua/Comfyui_CSDMT_CXH",
+            "files": [
+                "https://github.com/StartHua/Comfyui_CSDMT_CXH"
+            ],
+            "install_type": "git-clone",
+            "description": "Node:CSD_Makeup\nNOTE:You need to download [a/pre-trained model file](https://github.com/StartHua/Comfyui_CSDMT_CXH)."
+        },
+        {
+            "author": "ZHO-ZHO-ZHO",
+            "title": "ComfyUI-AuraSR-ZHO",
+            "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-AuraSR-ZHO",
+            "files": [
+                "https://github.com/ZHO-ZHO-ZHO/ComfyUI-AuraSR-ZHO"
+            ],
+            "install_type": "git-clone",
+            "description": "AuraSR in ComfyUI for img & video\n[w/If the custom_nodes path is not under ComfyUI, be careful as it may not install properly.]"
+        },
+        {
+            "author": "tom-doerr",
+            "title": "DSPy Nodes [WIP]",
+            "reference": "https://github.com/tom-doerr/dspy_nodes",
+            "files": [
+                "https://github.com/tom-doerr/dspy_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "This is an attempt to make all DSPy features available in ComfyUI. Using an UI to devlop DSPy programs should be way faster since it makes it easier to see what is happening and allows to quickly iterate on the DSPy program structure."
+        },
+        {
+            "author": "Grant-CP",
+            "title": "ComfyUI-LivePortraitKJ-MPS",
+            "reference": "https://github.com/Grant-CP/ComfyUI-LivePortraitKJ-MPS",
+            "files": [
+                "https://github.com/Grant-CP/ComfyUI-LivePortraitKJ-MPS"
+            ],
+            "install_type": "git-clone",
+            "description": "If you wish to incorporate these changes into your repo, feel free to open an issue and ask. The commits should be pretty clear, and I also label almost all changes with #HACK so a full text search will work too.\nPlease let me know if you decide to incorporate any of these changes into your LivePortrait implementation so I can direct people to you repository. I do not intend to maintain this repo.\nSome operations are simply not supported on MPS and I didn't rewrite them. Most of my changes are just to .cuda calls and that sort of thing. Many operations are still done on CPU, so don't expect awesome performance."
+        },
+        {
+            "author": "thderoo",
+            "title": "_topfun_s_nodes",
+            "reference": "https://github.com/thderoo/ComfyUI-_topfun_s_nodes",
+            "files": [
+                "https://github.com/thderoo/ComfyUI-_topfun_s_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Conditioning Perturbation"
+        },
+        {
+            "author": "willblaschko",
+            "title": "ComfyUI-Unload-Models",
+            "reference": "https://github.com/willblaschko/ComfyUI-Unload-Models",
+            "files": [
+                "https://github.com/willblaschko/ComfyUI-Unload-Models"
+            ],
+            "install_type": "git-clone",
+            "description": "This repository provides developers with a way to better manage their ComfyUI model memory. It includes nodes that allow developers to either unload all models or unload one model at a time. These nodes are designed as pass-through nodes, so they can be used anywhere in the flow. The nodes can be found in the 'Unload Model' section.[w/These are massive hammers, and it could be possible to break things, please don't use them if you need finesse.]"
+        },
+        {
+            "author": "AIFSH",
+            "title": "ComfyUI-OpenDIT [WIP]",
+            "id": "opendit",
+            "reference": "https://github.com/AIFSH/ComfyUI-OpenDIT",
+            "files": [
+                "https://github.com/AIFSH/ComfyUI-OpenDIT"
+            ],
+            "install_type": "git-clone",
+            "description": "make [a/OpenDIT](https://github.com/NUS-HPC-AI-Lab/OpenDiT) avaliable in ComfyUI"
+        },
+        {
+            "author": "alexisrolland",
+            "title": "alexisrolland/ComfyUI-AuraSR",
+            "id": "aurasr-alexisrolland",
+            "reference": "https://github.com/alexisrolland/ComfyUI-AuraSR",
+            "files": [
+                "https://github.com/alexisrolland/ComfyUI-AuraSR"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI nodes to run [a/fal-ai/AuraSR](https://huggingface.co/fal-ai/AuraSR) model.[w/This node cannot be installed simultaneously with AIFSH/ComfyUI-AuraSR due to overlapping repository names.]"
+        },
+        {
+            "author": "linhusyung",
+            "title": "ComfyUI Build and Train Your Network [WIP]",
+            "id": "cfgpp",
+            "reference": "https://github.com/linhusyung/comfyui-Build-and-train-your-network",
+            "files": [
+                "https://github.com/linhusyung/comfyui-Build-and-train-your-network"
+            ],
+            "install_type": "git-clone",
+            "description": "Stable Diffusion is an image generation technique based on diffusion models. Its core idea involves simulating diffusion processes by iteratively adding noise and using neural networks to predict and remove the noise, thereby generating high-quality images. This approach is not limited to image generation; with appropriate network architecture and training data, it can be adapted for various other tasks. The application of neural networks extends beyond image generation. By adjusting network structures and loss functions, neural networks can also perform tasks such as classification and regression. This flexibility makes neural networks a powerful tool for handling a wide range of machine learning tasks. This project aims to expand custom neural network layers (such as linear layers, convolutional layers, etc.) within ComfyUI and provide simplified task training functionalities. Through this project, users can easily construct custom neural network layers and perform training in ComfyUI using a graphical interface."
+        },
+        {
+            "author": "Fucci-Mateo",
+            "title": "ComfyUI-Airtable [WIP]",
+            "id": "airtable",
+            "reference": "https://github.com/Fucci-Mateo/ComfyUI-Airtable",
+            "files": [
+                "https://github.com/Fucci-Mateo/ComfyUI-Airtable"
+            ],
+            "install_type": "git-clone",
+            "description": "A simple node to load image from local path or http url. You can find this node from 'image' category."
+        },
+        {
+            "author": "majorsauce",
+            "title": "comfyui_indieTools [WIP]",
+            "id": "indie-tools",
+            "reference": "https://github.com/majorsauce/comfyui_indieTools",
+            "files": [
+                "https://github.com/majorsauce/comfyui_indieTools"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:[Indie] Cut by Mask, [Indie] Paste Image, [Indie] Local Scale, [Indie] Solidify, [Indie] Yolo Detector.[w/Install may fail due to invliad requirements.txt file]"
+        },
+        {
+            "author": "AIFSH",
+            "title": "ComfyUI-ViViD",
+            "id": "vivid",
+            "reference": "https://github.com/AIFSH/ComfyUI-ViViD",
+            "files": [
+                "https://github.com/AIFSH/ComfyUI-ViViD"
+            ],
+            "install_type": "git-clone",
+            "description": "a comfyui custom node for ViViD"
+        },
+        {
+            "author": "nat-chan",
+            "title": "comfyui-paint",
+            "reference": "https://github.com/nat-chan/comfyui-paint",
+            "files": [
+                "https://github.com/nat-chan/comfyui-paint"
+            ],
+            "install_type": "git-clone",
+            "description": "comfyui-paint\n[w/You need to clone submodule manually after clone. There is permission issue.]"
+        },
+        {
+            "author": "prabinpebam",
+            "title": "anyPython [UNSAFE]",
+            "reference": "https://github.com/prabinpebam/anyPython",
+            "files": [
+                "https://github.com/prabinpebam/anyPython"
+            ],
+            "install_type": "git-clone",
+            "description": "This node was inspired by AnyNode. I wanted to have a node where I can paste any python script and execute it. That way I can use this node in combination with a Custom node like the Ollama node that can generate the python code and feed into this node. This also makes it much easier to debug or modify the code iteratively. As of the current version, I've created separate nodes for no input, 1 input and 2 inputs. The input also currently takes only sting as input. Let me know in the discussion how you would use this node.\n[w/This extension allows the execution of arbitrary Python code from a workflow.]"
+        },
+        {
+            "author": "kijai",
+            "title": "ComfyUI DiffSynth wrapper nodes",
+            "id": "diffsynth-wrapper",
+            "reference": "https://github.com/kijai/ComfyUI-DiffSynthWrapper",
+            "files": [
+                "https://github.com/kijai/ComfyUI-DiffSynthWrapper"
+            ],
+            "install_type": "git-clone",
+            "description": "Currently only the new extended SVD model 'ExVideo' is supported.\nOriginal repo:[a/https://github.com/modelscope/DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio)"
+        },
+        {
+            "author": "AllenEdgarPoe",
+            "title": "ComfyUI-Xorbis-nodes [WIP]",
+            "id": "xorbis",
+            "reference": "https://github.com/AllenEdgarPoe/ComfyUI-Xorbis-nodes",
+            "files": [
+                "https://github.com/AllenEdgarPoe/ComfyUI-Xorbis-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "This repository is for MuseumX Update. We use ComfyUI as our framework, and the nodes are built for my comfort.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "mikeymcfish",
+            "title": "LaserCutterFull and Deptherize Nodes",
+            "id": "fishtools",
+            "reference": "https://github.com/mikeymcfish/FishTools",
+            "files": [
+                "https://github.com/mikeymcfish/FishTools"
+            ],
+            "install_type": "git-clone",
+            "description": "This repository contains two custom nodes, LaserCutterFull and Deptherize, designed for use in image processing workflows. The LaserCutterFull node processes input images to generate layers for laser cutting, while the Deptherize node converts SVG data into a depth map image."
+        },
+        {
+            "author": "pzzmyc",
+            "title": "comfyui-sd3-simple-simpletuner",
+            "id": "simpletuner",
+            "reference": "https://github.com/pzzmyc/comfyui-sd3-simple-simpletuner",
+            "files": [
+                "https://github.com/pzzmyc/comfyui-sd3-simple-simpletuner"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:sd3 simple simpletuner by hhy."
+        },
+        {
+            "author": "horidream",
+            "title": "ComfyUI-Horidream",
+            "id": "horidream",
+            "reference": "https://github.com/horidream/ComfyUI-Horidream",
+            "files": [
+                "https://github.com/horidream/ComfyUI-Horidream"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Pass Through With Sound."
+        },
+        {
+            "author": "kijai",
+            "title": "ComfyUI-DiffusersSD3Wrapper",
+            "id": "diffusers-sd3-wrapper",
+            "reference": "https://github.com/kijai/ComfyUI-DiffusersSD3Wrapper",
+            "files": [
+                "https://github.com/kijai/ComfyUI-DiffusersSD3Wrapper"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Load SD3DiffusersPipeline, SD3 ControlNet Sampler"
+        },
+        {
+            "author": "redhottensors",
+            "title": "ComfyUI-ODE",
+            "id": "ode",
+            "reference": "https://github.com/redhottensors/ComfyUI-ODE",
+            "files": [
+                "https://github.com/redhottensors/ComfyUI-ODE"
+            ],
+            "install_type": "git-clone",
+            "description": "ODE Solvers for ComfyUI\nThis node enables use of torchdiffeq ODE solvers with models.  Intended for use with Stable Diffusion 3 and similar flow models."
+        },
+        {
+            "author": "maruhidd",
+            "title": "Transparent Background for ComfyUI",
+            "id": "transparent-bg",
+            "reference": "https://github.com/maruhidd/ComfyUI_Transparent-Background",
+            "files": [
+                "https://github.com/maruhidd/ComfyUI_Transparent-Background"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Remove Background, Fill Transparent"
+        },
+        {
+            "author": "ejektaflex",
+            "title": "ComfyUI - Ty",
+            "id": "ty-nodes",
+            "reference": "https://github.com/ejektaflex/ComfyUI-Ty",
+            "files": [
+                "https://github.com/ejektaflex/ComfyUI-Ty"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Lora Block Weight Regex Loader"
+        },
+        {
+            "author": "jtydhr88",
+            "title": "ComfyUI-Unique3D [WIP]",
+            "id": "unique3d",
+            "reference": "https://github.com/jtydhr88/ComfyUI-Unique3D",
+            "files": [
+                "https://github.com/jtydhr88/ComfyUI-Unique3D"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI Unique3D is custom nodes that running [a/AiuniAI/Unique3D](https://github.com/AiuniAI/Unique3D) into ComfyUI."
+        },
+        {
+            "author": "kycg",
+            "title": "comfyui-Kwtoolset",
+            "id": "kwtoolset",
+            "reference": "https://github.com/kycg/comfyui-Kwtoolset",
+            "files": [
+                "https://github.com/kycg/comfyui-Kwtoolset"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:KwtoolsetLoraLoaderwithpreview, KwtoolsetCheckpointLoaderwithpreview, KwtoolsetLoadCheckpointsBatch, KwtoolsetGrowMaskPlus, KwtoolsetGetHipMask, KwtoolsetGetHipMasktest, KwtoolsetGetImageSize, KWPositiveString, KWNagetiveString, KWanywhereString, KwtoolsetChangeOpenpose, ..."
+        },
+        {
+            "author": "mashb1t",
+            "title": "ComfyUI mashb1t nodes",
+            "id": "mashb1t",
+            "reference": "https://github.com/mashb1t/comfyui-nodes-mashb1t",
+            "files": [
+                "https://github.com/mashb1t/comfyui-nodes-mashb1t"
+            ],
+            "install_type": "git-clone",
+            "description": "This Python script is an optional add-on to the Comfy UI stable diffusion client."
+        },
+        {
+            "author": "immersiveexperience",
+            "title": "ie-comfyui-color-nodes",
+            "reference": "https://github.com/immersiveexperience/ie-comfyui-color-nodes",
+            "files": [
+                "https://github.com/immersiveexperience/ie-comfyui-color-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI nodes for simple color correction."
+        },
+        {
+            "author": "LZpenguin",
+            "title": "ComfyUI-Text",
+            "id": "comfy-text",
+            "reference": "https://github.com/LZpenguin/ComfyUI-Text",
+            "files": [
+                "https://github.com/LZpenguin/ComfyUI-Text"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Add_text_by_mask.[w/This custom node cannot be installed simultaneously as it has the same repository name as MarkoCa1/ComfyUI-Text.]"
+        },
+        {
+            "author": "norgeous",
+            "title": "UI Builder [WIP]",
+            "id": "norgeous",
+            "reference": "https://github.com/norgeous/ComfyUI-UI-Builder",
+            "files": [
+                "https://github.com/norgeous/ComfyUI-UI-Builder"
+            ],
+            "install_type": "git-clone",
+            "description": "Alternative configurable React UI overlay for Comfy UI."
+        },
+        {
+            "author": "Shinsplat",
+            "title": "ComfyUI-Shinsplat [UNSAFE]",
+            "id": "shinsplat",
+            "reference": "https://github.com/Shinsplat/ComfyUI-Shinsplat",
+            "files": [
+                "https://github.com/Shinsplat/ComfyUI-Shinsplat"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes: Clip Text Encode (Shinsplat), Clip Text Encode SDXL (Shinsplat), Lora Loader (Shinsplat).\n[w/This extension poses a risk of executing arbitrary commands through workflow execution. Please be cautious.]"
+        },
+        {
+            "author": "hy134300",
+            "title": "comfyui-hydit",
+            "reference": "https://github.com/hy134300/comfyui-hydit",
+            "files": [
+                "https://github.com/hy134300/comfyui-hydit"
+            ],
+            "install_type": "git-clone",
+            "description": "This repository contains a customized node and workflow designed specifically for HunYuan DIT. The official tests conducted on DDPM, DDIM, and DPMMS have consistently yielded results that align with those obtained through the Diffusers library. However, it's important to note that we cannot assure the consistency of results from other ComfyUI native samplers with the Diffusers inference. We cordially invite users to explore our workflow and are open to receiving any inquiries or suggestions you may have."
+        },
+        {
+            "author": "corbin-hayden13",
+            "title": "ComfyUI-Better-Dimensions",
+            "id": "better-dim",
+            "reference": "https://github.com/corbin-hayden13/ComfyUI-Better-Dimensions",
+            "files": [
+                "https://github.com/corbin-hayden13/ComfyUI-Better-Dimensions"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:BetterImageDimensions, SDXLDimensions, PureRatio"
+        },
+        {
+            "author": "endman100",
+            "title": "ComfyUI-augmentation",
+            "id": "augmentation",
+            "reference": "https://github.com/endman100/ComfyUI-augmentation",
+            "files": [
+                "https://github.com/endman100/ComfyUI-augmentation"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:RamdomFlipImage (endman100)"
+        },
+        {
+            "author": "endman100",
+            "title": "ComfyUI Nodes: SaveConditioning and LoadConditioning",
+            "id": "save-load-conditioning",
+            "reference": "https://github.com/endman100/ComfyUI-SaveAndLoadPromptCondition",
+            "files": [
+                "https://github.com/endman100/ComfyUI-SaveAndLoadPromptCondition"
+            ],
+            "install_type": "git-clone",
+            "description": "The SaveConditioning node is designed to save conditioning data to binary files. This is useful for storing and reusing conditioning information across different sessions or applications.\n[w/This node can only handle very limited conditioning at the text prompt level.]"
+        },
+        {
+            "author": "kijai",
+            "title": "ComfyUI-CV-VAE",
+            "id": "cv-vae",
+            "reference": "https://github.com/kijai/ComfyUI-CV-VAE",
+            "files": [
+                "https://github.com/kijai/ComfyUI-CV-VAE"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:CV_VAE_Load, CV_VAE_Encode, CV_VAE_Decode"
+        },
+        {
+            "author": "GentlemanHu",
+            "title": "ComfyUI Notifier",
+            "id": "notifier",
+            "reference": "https://github.com/GentlemanHu/ComfyUI-Notifier",
+            "files": [
+                "https://github.com/GentlemanHu/ComfyUI-Notifier"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:GentlemanHu_Notifier"
+        },
+        {
+            "author": "jimmm-ai",
+            "title": "TimeUi a ComfyUI Timeline Node System [WIP]",
+            "id": "timeline",
+            "reference": "https://github.com/jimmm-ai/TimeUi-a-ComfyUi-Timeline-Node",
+            "files": [
+                "https://github.com/jimmm-ai/TimeUi-a-ComfyUi-Timeline-Node"
+            ],
+            "install_type": "git-clone",
+            "description": "I've been working on the UX/UI of a timeline custom node system for ComfyUI over the past two weeks. The goal is to create a timeline similar to video/animation editing tools, without relying on traditional timeframe code. You can effortlessly add, delete, or rearrange rows, providing a streamlined user experience."
+        },
+        {
+            "author": "StartHua",
+            "title": "Comfyui_CXH_CRM",
+            "id": "cxh-crm",
+            "reference": "https://github.com/StartHua/Comfyui_CXH_CRM",
+            "files": [
+                "https://github.com/StartHua/Comfyui_CXH_CRM"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:CRM"
+        },
+        {
+            "author": "comfypod",
+            "title": "ComfyUI-Comflow",
+            "id": "comflow",
+            "reference": "https://github.com/comfypod/ComfyUI-Comflow",
+            "files": [
+                "https://github.com/comfypod/ComfyUI-Comflow"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI-Comflow."
+        },
+        {
+            "author": "FoundD-oka",
+            "title": "ComfyUI KISEKAE-OOTD",
+            "id": "kisekae-ootd",
+            "reference": "https://github.com/FoundD-oka/ComfyUI-kisekae-OOTD",
+            "files": [
+                "https://github.com/FoundD-oka/ComfyUI-kisekae-OOTD"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:LoadOOTDPipeline, LoadOOTDPipelineHub, LoadOOTDPipelineHub."
+        },
+        {
+            "author": "bruce007lee",
+            "title": "comfyui-tiny-utils",
+            "id": "tiny-utils",
+            "reference": "https://github.com/bruce007lee/comfyui-tiny-utils",
+            "files": [
+                "https://github.com/bruce007lee/comfyui-tiny-utils"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:FaceAlign, FaceAlignImageProcess, FaceAlignMaskProcess, ImageFillColorByMask, CropImageByMask, LoadImageAdvance, ImageTransposeAdvance, ImageSAMMask"
+        },
+        {
+            "author": "brycegoh",
+            "title": "brycegoh/comfyui-custom-nodes",
+            "reference": "https://github.com/brycegoh/comfyui-custom-nodes",
+            "files": [
+                "https://github.com/brycegoh/comfyui-custom-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:MaskAreaComparisonSegment, FillMaskedArea, OCRAndMask, CombineTwoImageIntoOne"
+        },
+        {
+            "author": "LykosAI",
+            "title": "ComfyUI Nodes for Inference.Core",
+            "id": "inference-core",
+            "reference": "https://github.com/LykosAI/ComfyUI-Inference-Core-Nodes",
+            "files": [
+                "https://github.com/LykosAI/ComfyUI-Inference-Core-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Primary Nodes for Inference.Core and Stability Matrix. With a focus on not impacting startup performance and using fully qualified Node names. [w/This custom node is likely to conflict with many other nodes.]"
+        },
+        {
+            "author": "tracerstar",
+            "title": "comfyui-p5js-node",
+            "id": "p5js",
+            "reference": "https://github.com/tracerstar/comfyui-p5js-node",
+            "files": [
+                "https://github.com/tracerstar/comfyui-p5js-node"
+            ],
+            "install_type": "git-clone",
+            "description": "A simple proof of concept node to pass a p5js canvas through ComfyUI for img2img generation use."
+        },
+        {
+            "author": "chaojie",
+            "title": "ComfyUI-mobvoi-openapi",
+            "id": "mobvoi-openapi",
+            "reference": "https://github.com/chaojie/ComfyUI-mobvoi-openapi",
+            "files": [
+                "https://github.com/chaojie/ComfyUI-mobvoi-openapi"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:MobvoiOpenapiMetamanText, MobvoiOpenapiMetamanAudio, MobvoiOpenapiTts, HtmlViewer, OssUploadImage, OssUploadAudio"
+        },
+        {
+            "author": "immersiveexperience",
+            "title": "ie-comfyui-color-nodes",
+            "id": "ie-color-nodes",
+            "reference": "https://github.com/immersiveexperience/ie-comfyui-color-nodes",
+            "files": [
+                "https://github.com/immersiveexperience/ie-comfyui-color-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI nodes for simple color correction."
+        },
+        {
+            "author": "beyastard",
+            "title": "ComfyUI_BeySoft",
+            "reference": "https://github.com/beyastard/ComfyUI_BeySoft",
+            "files": [
+                "https://github.com/beyastard/ComfyUI_BeySoft"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:BeySoft"
+        },
+        {
+            "author": "christian-byrne",
+            "title": "🌌 Infinite Parallax Nodes [WIP]",
+            "reference": "https://github.com/christian-byrne/infinite-zoom-parallax-nodes",
+            "files": [
+                "https://github.com/christian-byrne/infinite-zoom-parallax-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Parallax Config, Load Parallax Frame, Save Parallax Object Layers, Layer Shifter for Parallax Outpainting, Save Parallax Frame, Shrink and Pad for Outpainting, Create Infinite Zoom Video"
+        },
+        {
+            "author": "flyingdogsoftware",
+            "title": "Gyre for ComfyUI",
+            "id": "gyre",
+            "reference": "https://github.com/flyingdogsoftware/gyre_for_comfyui",
+            "files": [
+                "https://github.com/flyingdogsoftware/gyre_for_comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:BackgroundRemoval, GyreLoopStart, GyreLoopEnd, GyreIfElse"
+        },
+        {
+            "author": "githubYiheng",
+            "title": "comfyui_median_filter",
+            "id": "median-filter",
+            "reference": "https://github.com/githubYiheng/comfyui_median_filter",
+            "files": [
+                "https://github.com/githubYiheng/comfyui_median_filter"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Apply Median Filter. [w/This has been updated to be equivalent to the comfyui_kmeans_filter node. Mistake?]"
+        },
+        {
+            "author": "nat-chan",
+            "title": "comfyui-exec [UNSAFE]",
+            "id": "evalnode",
+            "reference": "https://github.com/nat-chan/comfyui-exec",
+            "files": [
+                "https://github.com/nat-chan/comfyui-exec"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:EvalNode [w/Please do not use the node that executes arbitrary code and outputs in any type, as it is dangerous.]"
+        },
+        {
+            "author": "haofanwang",
+            "title": "ComfyUI-InstantStyle",
+            "id": "instantstyle",
+            "reference": "https://github.com/haofanwang/ComfyUI-InstantStyle",
+            "files": [
+                "https://github.com/haofanwang/ComfyUI-InstantStyle"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:PromptLoader, BaseModelLoader, InstantStyleLoader, InstantStyleGenerationNode"
+        },
+        {
+            "author": "jp0215",
+            "title": "comfyUI_padding-resize_node",
+            "reference": "https://github.com/jp0215/comfyUI_padding-resize_node",
+            "files": [
+                "https://raw.githubusercontent.com/jp0215/comfyUI_padding-resize_node/main/PaddingNode.py",
+                "https://raw.githubusercontent.com/jp0215/comfyUI_padding-resize_node/main/ResizeNode.py"
+            ],
+            "install_type": "copy",
+            "description": "Padding image to 8x: input image and mask, if the side length is not an integer multiple of 8, expand the side length to the smallest multiple of 8 greater than the original side length. Output padding image and mask. Resize to the origin: input the generated image and the original image, crop the generated image to the size of the original image, output the cropped image."
+        },
+        {
+            "author": "Quasimondo",
+            "title": "ComfyUI-QuasimondoNodes [WIP]",
+            "id": "quasimondo-nodes",
+            "reference": "https://github.com/Quasimondo/ComfyUI-QuasimondoNodes",
+            "files": [
+                "https://github.com/Quasimondo/ComfyUI-QuasimondoNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Custom Shader, Spring Mesh"
+        },
+        {
+            "author": "TSFSean",
+            "title": "ComfyUI-TSFNodes",
+            "id": "tsfnodes",
+            "reference": "https://github.com/TSFSean/ComfyUI-TSFNodes",
+            "files": [
+                "https://github.com/TSFSean/ComfyUI-TSFNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:GyroOSC"
+        },
+        {
+            "author": "blib-la",
+            "title": "ComfyUI-Captain-Extensions",
+            "id": "captain",
+            "reference": "https://github.com/blib-la/ComfyUI-Captain-Extensions",
+            "files": [
+                "https://github.com/blib-la/ComfyUI-Captain-Extensions"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI extensions for better [a/Captain](https://github.com/blib-la/captain) integration."
+        },
+        {
+            "author": "ejektaflex",
+            "title": "ComfyUI-Ty",
+            "reference": "https://github.com/ejektaflex/ComfyUI-Ty",
+            "files": [
+                "https://github.com/ejektaflex/ComfyUI-Ty"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Lora Block Weight Regex Loader"
+        },
+        {
+            "author": "christian-byrne",
+            "title": "Python Interpreter ComfyUI Node [UNSAFE]",
+            "reference": "https://github.com/christian-byrne/python-interpreter-node",
+            "files": [
+                "https://github.com/christian-byrne/python-interpreter-node"
+            ],
+            "install_type": "git-clone",
+            "description": "For debugging, parsing data, generating random values, converting types, testing custom nodes faster.\nReference and use variables in the code using the same names as the inputs in the UI\nWrapper class around tensors with operator overloading for doing common image manipulation tasks.I might remove this aspect\n[w/This extension allows you to run programs through Python code in your workflow, which may not be secure. Use with caution.]"
+        },
+        {
+            "author": "sofakid",
+            "title": "dandy [UNSAFE]",
+            "reference": "https://github.com/sofakid/dandy",
+            "files": [
+                "https://github.com/sofakid/dandy"
+            ],
+            "install_type": "git-clone",
+            "description": "Dandy is a JavaScript bridge for ComfyUI. It includes everything you need to make JavaScript enabled extensions, or just load and code in little editor nodes right in ComfyUI.[w/This code can cause security issues because it allows for the execution of arbitrary JavaScript input.]"
+        },
+        {
+            "author": "shadowcz007",
+            "title": "ComfyUI-PuLID [TEST]",
+            "reference": "https://github.com/shadowcz007/ComfyUI-PuLID-Test",
+            "files": [
+                "https://github.com/shadowcz007/ComfyUI-PuLID-Test"
+            ],
+            "install_type": "git-clone",
+            "description": "[a/PuLID](https://github.com/ToTheBeginning/PuLID) ComfyUI native implementation."
+        },
+        {
+            "author": "sangeet",
+            "title": "Simple Frontend For ComfyUI workflow [TEST]",
+            "reference": "https://github.com/sangeet/testui",
+            "files": [
+                "https://github.com/sangeet/testui"
+            ],
+            "install_type": "git-clone",
+            "description": "A simple base front-end for text-to-image workflow in ComfyUI. Meant to serve as a base to be modified for future complex workflows"
+        },
+        {
+            "author": "jtscmw01",
+            "title": "ComfyUI-DiffBIR",
+            "id": "diffbir",
+            "reference": "https://github.com/jtscmw01/ComfyUI-DiffBIR",
+            "files": [
+                "https://github.com/jtscmw01/ComfyUI-DiffBIR"
+            ],
+            "install_type": "git-clone",
+            "description": "This extension provides [a/DiffBIR](https://github.com/XPixelGroup/DiffBIR) feature."
+        },
+        {
+            "author": "ericbeyer",
+            "title": "guidance_interval",
+            "reference": "https://github.com/ericbeyer/guidance_interval",
+            "files": [
+                "https://github.com/ericbeyer/guidance_interval"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Guidance Interval\nNOTE: Because the sampling function is replaced, you must restart after executing this custom node to restore the original state."
+        },
+        {
+            "author": "oztrkoguz",
+            "title": "Kosmos2_BBox_Cutter Models",
+            "reference": "https://github.com/oztrkoguz/ComfyUI_Kosmos2_BBox_Cutter",
+            "files": [
+                "https://github.com/oztrkoguz/ComfyUI_Kosmos2_BBox_Cutter"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:KosmosLoader, Kosmos2SamplerSimple, Write"
+        },
+        {
+            "author": "ZHO-ZHO-ZHO",
+            "title": "ComfyUI-PuLID-ZHO [WIP]",
+            "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-PuLID-ZHO",
+            "files": [
+                "https://github.com/ZHO-ZHO-ZHO/ComfyUI-PuLID-ZHO"
+            ],
+            "install_type": "git-clone",
+            "description": "Unofficial implementation of [a/PuLID](https://github.com/ToTheBeginning/PuLID)（diffusers） for ComfyUI"
+        },
+        {
+            "author": "longgui0318",
+            "title": "comfyui-one-more-step [WIP]",
+            "reference": "https://github.com/longgui0318/comfyui-one-more-step",
+            "files": [
+                "https://github.com/longgui0318/comfyui-one-more-step"
+            ],
+            "install_type": "git-clone",
+            "description": "[a/(OMS)mhh0318/OneMoreStep](https://github.com/mhh0318/OneMoreStep) comfyui support ."
+        },
+        {
+            "author": "unknown",
+            "title": "CLIPTextEncodeAndEnhancev4 (shirazdesigner)",
+            "reference": "https://github.com/shirazdesigner/CLIPTextEncodeAndEnhancev4",
+            "files": [
+                "https://github.com/shirazdesigner/CLIPTextEncodeAndEnhancev4"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:CLIPTextEncodeAndEnhance.\nNOTE:Translation:This is a wrapper that simply makes it easy to install an existing node via git."
+        },
+        {
+            "author": "Video3DGenResearch",
+            "title": "ComfyUI Batch Input Node",
+            "reference": "https://github.com/Video3DGenResearch/comfyui-batch-input-node",
+            "files": [
+                "https://github.com/Video3DGenResearch/comfyui-batch-input-node"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:BatchInputText"
+        },
+        {
+            "author": "kijai",
+            "title": "ComfyUI nodes to use DeepSeek-VL",
+            "reference": "https://github.com/kijai/ComfyUI-DeepSeek-VL",
+            "files": [
+                "https://github.com/kijai/ComfyUI-DeepSeek-VL"
+            ],
+            "install_type": "git-clone",
+            "description": "[a/https://huggingface.co/deepseek-ai](https://huggingface.co/deepseek-ai)"
+        },
+        {
+            "author": "GentlemanHu",
+            "title": "ComfyUI-Notifier",
+            "reference": "https://github.com/GentlemanHu/ComfyUI-Notifier",
+            "files": [
+                "https://github.com/GentlemanHu/ComfyUI-Notifier"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:GentlemanHu_Notifier"
+        },
+        {
+            "author": "nat-chan",
+            "title": "Transceiver📡",
+            "reference": "https://github.com/nat-chan/transceiver",
+            "files": [
+                "https://github.com/nat-chan/transceiver"
+            ],
+            "install_type": "git-clone",
+            "description": "Why? When processing a large number of requests, the SaveImage and LoadImage nodes may be IO-limited, and using shared memory improves performance by passing images only through memory access, not through IO."
+        },
+        {
+            "author": "DrMWeigand",
+            "title": "ComfyUI_LineBreakInserter",
+            "reference": "https://github.com/DrMWeigand/ComfyUI_LineBreakInserter",
+            "files": [
+                "https://github.com/DrMWeigand/ComfyUI_LineBreakInserter"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Line Break Inserter"
+        },
+        {
+            "author": "WilliamStanford",
+            "title": "visuallabs_comfyui_nodes",
+            "reference": "https://github.com/WilliamStanford/ComfyUI-VisualLabs",
+            "files": [
+                "https://github.com/WilliamStanford/ComfyUI-VisualLabs"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:PointStringFromFloatArray"
+        },
+        {
+            "author": "bruce007lee",
+            "title": "comfyui-cleaner",
+            "reference": "https://github.com/bruce007lee/comfyui-cleaner",
+            "files": [
+                "https://github.com/bruce007lee/comfyui-cleaner"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:cleaner"
+        },
+        {
+            "author": "ExponentialML",
+            "title": "ComfyUI_LiveDirector (WIP)",
+            "reference": "https://github.com/ExponentialML/ComfyUI_LiveDirector",
+            "files": [
+                "https://github.com/ExponentialML/ComfyUI_LiveDirector"
+            ],
+            "install_type": "git-clone",
+            "description": "Experimental method to use reference video to drive motion in generations without training in ComfyUI."
+        },
+        {
+            "author": "hy134300",
+            "title": "comfyui-hb-node",
+            "reference": "https://github.com/hy134300/comfyui-hb-node",
+            "files": [
+                "https://github.com/hy134300/comfyui-hb-node"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:sound voice, text concat, latent to list, movie generate, movie batch, hy save image, generate story"
+        },
+        {
+            "author": "gameltb",
+            "title": "io_comfyui",
+            "reference": "https://github.com/gameltb/io_comfyui",
+            "files": [
+                "https://github.com/gameltb/io_comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "Let Blender work with ComfyUI by ComfyScript. This addon is still in development."
+        },
+        {
+            "author": "Jiffies-64",
+            "title": "ComfyUI-SaveImagePlus",
+            "reference": "https://github.com/Jiffies-64/ComfyUI-SaveImagePlus",
+            "files": [
+                "https://github.com/Jiffies-64/ComfyUI-SaveImagePlus"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:SaveImagePlus"
+        },
+        {
+            "author": "kadirnar",
+            "title": "ComfyUI-Adapter [WIP]",
+            "reference": "https://github.com/kadirnar/ComfyUI-Adapter",
+            "files": [
+                "https://github.com/kadirnar/ComfyUI-Adapter"
+            ],
+            "install_type": "git-clone",
+            "description": "WIP"
+        },
+        {
+            "author": "Beinsezii",
+            "title": "comfyui-amd-go-fast",
+            "reference": "https://github.com/Beinsezii/comfyui-amd-go-fast",
+            "files": [
+                "https://github.com/Beinsezii/comfyui-amd-go-fast"
+            ],
+            "install_type": "git-clone",
+            "description": "This contains all-in-one 'principled' nodes for T2I, I2I, refining, and scaling. Additionally it has many tools for directly manipulating the color of latents, high res fix math, and scripted image post-processing."
+        },
+        {
+            "author": "sugarkwork",
+            "title": "comfyui_psd [WIP]",
+            "reference": "https://github.com/sugarkwork/comfyui_psd",
+            "files": [
+                "https://github.com/sugarkwork/comfyui_psd"
+            ],
+            "install_type": "git-clone",
+            "description": "Not working yet."
+        },
+        {
+            "author": "SadaleNet",
+            "title": "ComfyUI Port for Google's Prompt-to-Prompt",
+            "reference": "https://github.com/SadaleNet/ComfyUI-Prompt-To-Prompt",
+            "files": [
+                "https://github.com/SadaleNet/ComfyUI-Prompt-To-Prompt"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a PoC port of [a/Google's Prompt-to-Prompt](https://github.com/google/prompt-to-prompt/) to ComfyUI. It isn't feature complete. But it's good enough for evaluating if prompt-to-prompt is of any good."
+        },
+        {
+            "author": "stavsap",
+            "title": "ComfyUI Ollama [WIP]",
+            "reference": "https://github.com/stavsap/ComfyUI-React-SDK",
+            "files": [
+                "https://github.com/stavsap/ComfyUI-React-SDK"
+            ],
+            "install_type": "git-clone",
+            "description": "This project is for building React application as an overlay upon ComfyUI.\nProviding and ability to provide desired UI with ComfyUI API and workflows.\nInspired by: [a/https://github.com/cubiq/Comfy_Dungeon](https://github.com/cubiq/Comfy_Dungeon)"
+        },
+        {
+            "author": "cubiq",
+            "title": "Comfy Dungeon [WIP]",
+            "reference": "https://github.com/cubiq/Comfy_Dungeon",
+            "files": [
+                "https://github.com/cubiq/Comfy_Dungeon"
+            ],
+            "install_type": "git-clone",
+            "description": "Build D&D Character Portraits with ComfyUI.\nIMPORTANT: At the moment this is mostly a tech demo to show how to build a web app on top of ComfyUI. The code is very messy and the application doesn't guaratee consistent results."
+        },
+        {
+            "author": "dfl",
+            "title": "comfyui-stylegan",
+            "reference": "https://github.com/dfl/comfyui-stylegan",
+            "files": [
+                "https://github.com/dfl/comfyui-stylegan"
+            ],
+            "install_type": "git-clone",
+            "description": "Generator for StyleGAN 3"
+        },
+        {
+            "author": "A719689614",
+            "title": "ComfyUI_AC_FUNV8Beta1",
+            "reference": "https://github.com/A719689614/ComfyUI_AC_FUNV8Beta1",
+            "files": [
+                "https://github.com/A719689614/ComfyUI_AC_FUNV8Beta1"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:AC_Super_Controlnet/Checkpoint/Loras/Lora&LCM/KSampler/UpKSampler/SaveImage/PreviewImage/CKPT&LCM/CLIPEN/EmptLatent, AC_FUN_SUPER_LARGE, AC_Super_Come_Ckpt, AC_Super_Come_Lora"
+        },
+        {
+            "author": "houdinii",
+            "title": "comfy-magick [WIP]",
+            "reference": "https://github.com/houdinii/comfy-magick",
+            "files": [
+                "https://github.com/houdinii/comfy-magick"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a way to implement ImageMagick functionality in ComfyUI, which is generally PIL (pillow) based. I'm not sure the best way to handle this, as batch images make it a lot more complex, but the general idea will be two nodes to translate the IMAGE type, a torch.tensor of shape [batch, height, width, channels], or [1, 600, 800, 3] for a single 800x600 image, into/from a wand Image object."
+        },
+        {
+            "author": "tjorbogarden",
+            "title": "my-useful-comfyui-custom-nodes",
+            "reference": "https://github.com/tjorbogarden/my-useful-comfyui-custom-nodes",
+            "files": [
+                "https://github.com/tjorbogarden/my-useful-comfyui-custom-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:My-Image Sizer, KSamplerSDXLAdvanced."
+        },
+        {
+            "author": "DeTK",
+            "title": "ComfyUI Node Switcher",
+            "reference": "https://github.com/DeTK/ComfyUI-Switch",
+            "files": [
+                "https://github.com/DeTK/ComfyUI-Switch"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:NodeSwitch."
+        },
+        {
+            "author": "GrindHouse66",
+            "title": "GH Tools for ComfyUI",
+            "reference": "https://github.com/GrindHouse66/ComfyUI-GH_Tools",
+            "files": [
+                "https://github.com/GrindHouse66/ComfyUI-GH_Tools"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:GH Tools Image Sizer, GH Tools Simple Scale. Simple quality of life Tools for ComfyUI. Basically, If it makes my life easier, it will be here. The list will grow over time."
+        },
+        {
+            "author": "Beinsezii",
+            "title": "comfyui-amd-go-fast",
+            "reference": "https://github.com/Beinsezii/comfyui-amd-go-fast",
+            "files": [
+                "https://github.com/Beinsezii/comfyui-amd-go-fast"
+            ],
+            "install_type": "git-clone",
+            "description": "See details: [a/link](https://github.com/Beinsezii/comfyui-amd-go-fast?tab=readme-ov-file)"
+        },
+        {
+            "author": "SeedV",
+            "title": "ComfyUI-SeedV-Nodes [UNSAFE]",
+            "reference": "https://github.com/SeedV/ComfyUI-SeedV-Nodes",
+            "files": [
+                "https://github.com/SeedV/ComfyUI-SeedV-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Script.\n[w/This extension poses a risk of executing arbitrary commands through workflow execution. Please be cautious.]"
+        },
+        {
+            "author": "mut-ex",
+            "title": "ComfyUI GLIGEN GUI Node",
+            "reference": "https://github.com/mut-ex/comfyui-gligengui-node",
+            "files": [
+                "https://github.com/mut-ex/comfyui-gligengui-node"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a simple, straightforward ComfyUI node to be used along with the [a/GLIGEN GUI](https://github.com/mut-ex/gligen-gui) I developed.\nNOTE:[a/Make sure you have the GLIGEN GUI up and running](https://github.com/mut-ex/gligen-gui/tree/main)"
+        },
+        {
+            "author": "unanan",
+            "title": "ComfyUI-Dist [WIP]",
+            "reference": "https://github.com/unanan/ComfyUI-Dist",
+            "files": [
+                "https://github.com/unanan/ComfyUI-Dist"
+            ],
+            "install_type": "git-clone",
+            "description": "For distributed processing ComfyUI workflows within a local area network.\nNot Finished Yet."
+        },
+        {
+            "author": "NicholasKao1029",
+            "title": "comfyui-hook",
+            "reference": "https://github.com/NicholasKao1029/comfyui-hook",
+            "files": [
+                "https://github.com/NicholasKao1029/comfyui-hook"
+            ],
+            "install_type": "git-clone",
+            "description": "This extension provides additional API"
+        },
+        {
+            "author": "Extraltodeus",
+            "title": "Conditioning-token-experiments-for-ComfyUI",
+            "reference": "https://github.com/Extraltodeus/Conditioning-token-experiments-for-ComfyUI",
+            "files": [
+                "https://github.com/Extraltodeus/Conditioning-token-experiments-for-ComfyUI"
+            ],
+            "install_type": "git-clone",
+            "description": "I made these nodes for experimenting so it's far from perfect but at least it is entertaining!\nIt uses cosine similarities or smallest euclidean distances to find the closest tokens."
+        },
+        {
+            "author": "shadowcz007",
+            "title": "comfyui-llamafile [WIP]",
+            "reference": "https://github.com/shadowcz007/comfyui-sd-prompt-mixlab",
+            "files": [
+                "https://github.com/shadowcz007/comfyui-sd-prompt-mixlab"
+            ],
+            "install_type": "git-clone",
+            "description": "This node is an experimental node aimed at exploring the collaborative way of human-machine creation."
+        },
+        {
+            "author": "gameltb",
+            "title": "ComfyUI paper playground",
+            "reference": "https://github.com/gameltb/ComfyUI_paper_playground",
+            "files": [
+                "https://github.com/gameltb/ComfyUI_paper_playground"
+            ],
+            "install_type": "git-clone",
+            "description": "Evaluate some papers in ComfyUI, just playground.\nNOTE: Various models need to be installed, so please visit the repository to check."
+        },
+        {
+            "author": "huizhang0110",
+            "title": "ComfyUI_Easy_Nodes_hui",
+            "reference": "https://github.com/huizhang0110/ComfyUI_Easy_Nodes_hui",
+            "files": [
+                "https://github.com/huizhang0110/ComfyUI_Easy_Nodes_hui"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:EasyEmptyLatentImage"
+        },
+        {
+            "author": "tuckerdarby",
+            "title": "ComfyUI-TDNodes [WIP]",
+            "reference": "https://github.com/tuckerdarby/ComfyUI-TDNodes",
+            "files": [
+                "https://github.com/tuckerdarby/ComfyUI-TDNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:KSampler (RAVE), KSampler (TF), Object Tracker, KSampler Batched, Video Tracker Prompt, TemporalNet Preprocessor, Instance Tracker Prompt, Instance Diffusion Loader, Hand Tracker Node"
+        },
+        {
+            "author": "shadowcz007",
+            "title": "comfyui-CLIPSeg",
+            "reference": "https://github.com/shadowcz007/comfyui-CLIPSeg",
+            "files": [
+                "https://github.com/shadowcz007/comfyui-CLIPSeg"
+            ],
+            "install_type": "git-clone",
+            "description": "Download [a/CLIPSeg](https://huggingface.co/CIDAS/clipseg-rd64-refined/tree/main), move to : models/clipseg"
+        },
+        {
+            "author": "stutya",
+            "title": "ComfyUI-Terminal [UNSAFE]",
+            "reference": "https://github.com/stutya/ComfyUI-Terminal",
+            "files": [
+                "https://github.com/stutya/ComfyUI-Terminal"
+            ],
+            "install_type": "git-clone",
+            "description": "Run Terminal Commands from ComfyUI.\n[w/This extension poses a risk of executing arbitrary commands through workflow execution. Please be cautious.]"
+        },
+        {
+            "author": "marcueberall",
+            "title": "ComfyUI-BuildPath",
+            "reference": "https://github.com/marcueberall/ComfyUI-BuildPath",
+            "files": [
+                "https://github.com/marcueberall/ComfyUI-BuildPath"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes: Build Path Adv."
+        },
+        {
+            "author": "LotzF",
+            "title": "ComfyUI simple ChatGPT completion [UNSAFE]",
+            "reference": "https://github.com/LotzF/ComfyUI-Simple-Chat-GPT-completion",
+            "files": [
+                "https://github.com/LotzF/ComfyUI-Simple-Chat-GPT-completion"
+            ],
+            "install_type": "git-clone",
+            "description": "A simple node to request ChatGPT completions. [w/Do not share your workflows including the API key! I'll take no responsibility for your leaked keys.]"
+        },
+        {
+            "author": "17Retoucher",
+            "title": "ComfyUI_Fooocus",
+            "reference": "https://github.com/17Retoucher/ComfyUI_Fooocus",
+            "files": [
+                "https://github.com/17Retoucher/ComfyUI_Fooocus"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom nodes that help reproduce image generation in Fooocus."
+        },
+        {
+            "author": "BadCafeCode",
+            "title": "execution-inversion-demo-comfyui",
+            "reference": "https://github.com/BadCafeCode/execution-inversion-demo-comfyui",
+            "files": [
+                "https://github.com/BadCafeCode/execution-inversion-demo-comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "execution-inversion-demo-comfyui"
+        },
+        {
+            "author": "prodogape",
+            "title": "ComfyUI-clip-interrogator [WIP]",
+            "reference": "https://github.com/prodogape/ComfyUI-clip-interrogator",
+            "files": [
+                "https://github.com/prodogape/ComfyUI-clip-interrogator"
+            ],
+            "install_type": "git-clone",
+            "description": "Unofficial ComfyUI extension of clip-interrogator"
+        },
+        {
+            "author": "poisenbery",
+            "title": "NudeNet-Detector-Provider [WIP]",
+            "reference": "https://github.com/poisenbery/NudeNet-Detector-Provider",
+            "files": [
+                "https://github.com/poisenbery/NudeNet-Detector-Provider"
+            ],
+            "install_type": "git-clone",
+            "description": "BBOX Detector Provider for NudeNet. Bethesda version of NudeNet V3 detector provider to work with Impact Pack ComfyUI."
+        },
+        {
+            "author": "LarryJane491",
+            "title": "ComfyUI-ModelUnloader",
+            "reference": "https://github.com/LarryJane491/ComfyUI-ModelUnloader",
+            "files": [
+                "https://github.com/LarryJane491/ComfyUI-ModelUnloader"
+            ],
+            "install_type": "git-clone",
+            "description": "A simple custom node that unloads all models. Useful for developers or users who want to free some memory."
+        },
+        {
+            "author": "MrAdamBlack",
+            "title": "CheckProgress [WIP]",
+            "reference": "https://github.com/MrAdamBlack/CheckProgress",
+            "files": [
+                "https://github.com/MrAdamBlack/CheckProgress"
+            ],
+            "install_type": "git-clone",
+            "description": "I was looking for a node to put in place to ensure my prompt etc where going as expected before the rest of the flow executed. To end the session, I just return the input image as None (see expected error). Recommend using it alongside PreviewImage, then output to the rest of the flow and Save Image."
+        },
+        {
+            "author": "birnam",
+            "title": "Gen Data Tester [WIP]",
+            "reference": "https://github.com/birnam/ComfyUI-GenData-Pack",
+            "files": [
+                "https://github.com/birnam/ComfyUI-GenData-Pack"
+            ],
+            "install_type": "git-clone",
+            "description": "This answers the itch for being able to easily paste [a/CivitAI.com](https://civitai.com/) generated data (or other simple metadata) into Comfy in a way that makes it easy to test with multiple checkpoints."
+        },
+        {
+            "author": "nidefawl",
+            "title": "ComfyUI-nidefawl [UNSAFE]",
+            "reference": "https://github.com/nidefawl/ComfyUI-nidefawl",
+            "files": [
+                "https://github.com/nidefawl/ComfyUI-nidefawl"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:PythonScript, BlendImagesWithBoundedMasks, CropImagesWithMasks, VAELoaderDataType, ModelSamplerTonemapNoiseTest, gcLatentTunnel, ReferenceOnlySimple, EmptyImageWithColor, MaskFromColor, SetLatentCustomNoise, LatentToImage, ImageToLatent, LatentScaledNoise, DisplayAnyType, SamplerCustomCallback, CustomCallback, SplitCustomSigmas, SamplerDPMPP_2M_SDE_nidefawl, LatentPerlinNoise.<BR>[w/This node is an unsafe node that includes the capability to execute arbitrary python script.]"
+        },
+        {
+            "author": "foglerek",
+            "title": "comfyui-cem-tools",
+            "reference": "https://github.com/foglerek/comfyui-cem-tools",
+            "files": [
+                "https://github.com/foglerek/comfyui-cem-tools"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:ProcessImageBatch"
+        },
+        {
+            "author": "komojini",
+            "title": "ComfyUI_Prompt_Template_CustomNodes",
+            "reference": "https://github.com/komojini/ComfyUI_Prompt_Template_CustomNodes",
+            "files": [
+                "https://raw.githubusercontent.com/komojini/ComfyUI_Prompt_Template_CustomNodes/main/prompt_with_template.py"
+            ],
+            "install_type": "copy",
+            "description": "Nodes:Prompt with Template"
+        },
+        {
+            "author": "talesofai",
+            "title": "comfyui-supersave [WIP]",
+            "reference": "https://github.com/talesofai/comfyui-supersave",
+            "files": [
+                "https://github.com/talesofai/comfyui-supersave"
+            ],
+            "install_type": "git-clone",
+            "description": "WIP"
+        },
+        {
+            "author": "Sai-ComfyUI",
+            "title": "ComfyUI-MS-Nodes [WIP]",
+            "reference": "https://github.com/Sai-ComfyUI/ComfyUI-MS-Nodes",
+            "files": [
+                "https://github.com/Sai-ComfyUI/ComfyUI-MS-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "WIP"
+        },
+        {
+            "author": "eigenpunk",
+            "title": "ComfyUI-audio",
+            "reference": "https://github.com/eigenpunk/ComfyUI-audio",
+            "files": [
+                "https://github.com/eigenpunk/ComfyUI-audio"
+            ],
+            "install_type": "git-clone",
+            "description": "generative audio tools for ComfyUI. highly experimental-expect things to break."
+        },
+        {
+            "author": "Jaxkr",
+            "title": "comfyui-terminal-command [UNSAFE]",
+            "reference": "https://github.com/Jaxkr/comfyui-terminal-command",
+            "files": [
+                "https://github.com/Jaxkr/comfyui-terminal-command"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes: Run Terminal Command. [w/This node is an unsafe node that includes the capability to execute terminal commands.]"
+        },
+        {
+            "author": "BlueDangerX",
+            "title": "ComfyUI-BDXNodes [WIP]",
+            "reference": "https://github.com/BlueDangerX/ComfyUI-BDXNodes",
+            "files": [
+                "https://github.com/BlueDangerX/ComfyUI-BDXNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes: Node Jumper. Various quality of life testing nodes"
+        },
+        {
+            "author": "romeobuilderotti",
+            "title": "ComfyUI-EZ-Pipes",
+            "reference": "https://github.com/romeobuilderotti/ComfyUI-EZ-Pipes",
+            "files": [
+                "https://github.com/romeobuilderotti/ComfyUI-EZ-Pipes"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI-EZ-Pipes is a set of custom pipe nodes for ComfyUI. It provides a set of Input/Edit/Output nodes for each pipe type."
+        },
+        {
+            "author": "wormley",
+            "title": "comfyui-wormley-nodes",
+            "reference": "https://github.com/wormley/comfyui-wormley-nodes",
+            "files": [
+                "https://github.com/wormley/comfyui-wormley-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes: CheckpointVAELoaderSimpleText, CheckpointVAESelectorText, LoRA_Tag_To_Stack"
+        },
+        {
+            "author": "Brandelan",
+            "title": "ComfyUI_bd_customNodes",
+            "reference": "https://github.com/Brandelan/ComfyUI_bd_customNodes",
+            "files": [
+                "https://github.com/Brandelan/ComfyUI_bd_customNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes: BD Random Range, BD Settings, BD Sequencer."
+        },
+        {
+            "author": "Jordach",
+            "title": "comfy-consistency-vae",
+            "reference": "https://github.com/Jordach/comfy-consistency-vae",
+            "files": [
+                "https://github.com/Jordach/comfy-consistency-vae"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes: Comfy_ConsistencyVAE"
+        },
+        {
+            "author": "gameltb",
+            "title": "ComfyUI_stable_fast",
+            "reference": "https://github.com/gameltb/ComfyUI_stable_fast",
+            "files": [
+                "https://github.com/gameltb/ComfyUI_stable_fast"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:ApplyStableFastUnet. Experimental usage of stable-fast."
+        },
+        {
+            "author": "jn-jairo",
+            "title": "jn_node_suite_comfyui [WIP]",
+            "reference": "https://github.com/jn-jairo/jn_node_suite_comfyui",
+            "files": [
+                "https://github.com/jn-jairo/jn_node_suite_comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "Image manipulation nodes, Temperature control nodes, Tiling nodes, Primitive and operation nodes, ..."
+        },
+        {
+            "author": "laksjdjf",
+            "title": "ssd-1b-comfyui",
+            "reference": "https://github.com/laksjdjf/ssd-1b-comfyui",
+            "files": [
+                "https://github.com/laksjdjf/ssd-1b-comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "Experimental node for SSD-1B. This node is not need for latest comfyui."
+        },
+        {
+            "author": "flowtyone",
+            "title": "comfyui-flowty-lcm",
+            "reference": "https://github.com/flowtyone/comfyui-flowty-lcm",
+            "files": [
+                "https://github.com/flowtyone/comfyui-flowty-lcm"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a comfyui early testing node for LCM, adapted from [a/https://github.com/0xbitches/sd-webui-lcm](https://github.com/0xbitches/sd-webui-lcm). It uses the diffusers backend unfortunately and not comfy's model loading mechanism. But the intention here is just to be able to execute lcm inside comfy.\nNOTE: 0xbitches's 'Latent Consistency Model for ComfyUI' is original implementation."
+        },
+        {
+            "author": "doucx",
+            "title": "ComfyUI_WcpD_Utility_Kit",
+            "reference": "https://github.com/doucx/ComfyUI_WcpD_Utility_Kit",
+            "files": [
+                "https://github.com/doucx/ComfyUI_WcpD_Utility_Kit"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes: MergeStrings, ExecStrAsCode, RandnLatentImage. [w/NOTE: This extension includes the ability to execute code as a string in nodes. Be cautious during installation, as it can pose a security risk.]"
+        },
+        {
+            "author": "WSJUSA",
+            "title": "pre-comfyui-stablsr",
+            "reference": "https://github.com/WSJUSA/Comfyui-StableSR",
+            "files": [
+                "https://github.com/WSJUSA/Comfyui-StableSR"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a development respository for debugging migration of StableSR to Comfyui"
+        },
+        {
+            "author": "Dr.Lt.Data",
+            "title": "ComfyUI-Workflow-Component [WIP]",
+            "reference": "https://github.com/ltdrdata/ComfyUI-Workflow-Component",
+            "files": [
+                "https://github.com/ltdrdata/ComfyUI-Workflow-Component"
+            ],
+            "install_type": "git-clone",
+            "description": "This extension provides the capability to use ComfyUI Workflow as a component and the ability to use the Image Refiner functionality based on components. NOTE: This is an experimental extension feature with no consideration for backward compatibility and can be highly unstable."
+        },
+        {
+            "author": "Humming",
+            "title": "ComfyUI-Model-Downloader",
+            "reference": "https://github.com/Hanmingsan/ComfyUI-Model-Downloader",
+            "files": [
+                "https://github.com/Hanmingsan/ComfyUI-Model-Downloader"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a extention that provides model downloading features on remote hosts. However the code is still being actively developed, and stability is not guaranteed."
+        }
+    ]
+}
diff --git a/custom_nodes/ComfyUI-Manager/node_db/dev/extension-node-map.json b/custom_nodes/ComfyUI-Manager/node_db/dev/extension-node-map.json
new file mode 100644
index 0000000000000000000000000000000000000000..6c94f252bdf8b13f25002ba0fde59b873e235440
--- /dev/null
+++ b/custom_nodes/ComfyUI-Manager/node_db/dev/extension-node-map.json
@@ -0,0 +1,16376 @@
+{
+    "https://github.com/17Retoucher/ComfyUI_Fooocus": [
+        [
+            "BasicScheduler",
+            "CLIPLoader",
+            "CLIPMergeSimple",
+            "CLIPSave",
+            "CLIPSetLastLayer",
+            "CLIPTextEncode",
+            "CLIPTextEncodeSDXL",
+            "CLIPTextEncodeSDXLRefiner",
+            "CLIPVisionEncode",
+            "CLIPVisionLoader",
+            "Canny",
+            "CheckpointLoader",
+            "CheckpointLoaderSimple",
+            "CheckpointSave",
+            "ConditioningAverage",
+            "ConditioningCombine",
+            "ConditioningConcat",
+            "ConditioningSetArea",
+            "ConditioningSetAreaPercentage",
+            "ConditioningSetMask",
+            "ConditioningSetTimestepRange",
+            "ConditioningZeroOut",
+            "ControlNetApply",
+            "ControlNetApplyAdvanced",
+            "ControlNetLoader",
+            "CropMask",
+            "DiffControlNetLoader",
+            "DiffusersLoader",
+            "DualCLIPLoader",
+            "EmptyImage",
+            "EmptyLatentImage",
+            "ExponentialScheduler",
+            "FeatherMask",
+            "FlipSigmas",
+            "Fooocus Controlnet",
+            "Fooocus Hirefix",
+            "Fooocus KSampler",
+            "Fooocus Loader",
+            "Fooocus LoraStack",
+            "Fooocus PreKSampler",
+            "Fooocus negative",
+            "Fooocus positive",
+            "Fooocus stylesSelector",
+            "FreeU",
+            "FreeU_V2",
+            "GLIGENLoader",
+            "GLIGENTextBoxApply",
+            "GrowMask",
+            "HyperTile",
+            "HypernetworkLoader",
+            "ImageBatch",
+            "ImageBlend",
+            "ImageBlur",
+            "ImageColorToMask",
+            "ImageCompositeMasked",
+            "ImageCrop",
+            "ImageInvert",
+            "ImageOnlyCheckpointLoader",
+            "ImagePadForOutpaint",
+            "ImageQuantize",
+            "ImageScale",
+            "ImageScaleBy",
+            "ImageScaleToTotalPixels",
+            "ImageSharpen",
+            "ImageToMask",
+            "ImageUpscaleWithModel",
+            "InvertMask",
+            "JoinImageWithAlpha",
+            "KSampler",
+            "KSamplerAdvanced",
+            "KSamplerSelect",
+            "KarrasScheduler",
+            "LatentAdd",
+            "LatentBatch",
+            "LatentBlend",
+            "LatentComposite",
+            "LatentCompositeMasked",
+            "LatentCrop",
+            "LatentFlip",
+            "LatentFromBatch",
+            "LatentInterpolate",
+            "LatentMultiply",
+            "LatentRotate",
+            "LatentSubtract",
+            "LatentUpscale",
+            "LatentUpscaleBy",
+            "LoadImage",
+            "LoadImageMask",
+            "LoadLatent",
+            "LoraLoader",
+            "LoraLoaderModelOnly",
+            "MaskComposite",
+            "MaskToImage",
+            "ModelMergeAdd",
+            "ModelMergeBlocks",
+            "ModelMergeSimple",
+            "ModelMergeSubtract",
+            "ModelSamplingContinuousEDM",
+            "ModelSamplingDiscrete",
+            "PatchModelAddDownscale",
+            "PerpNeg",
+            "PolyexponentialScheduler",
+            "PorterDuffImageComposite",
+            "PreviewImage",
+            "RebatchImages",
+            "RebatchLatents",
+            "RepeatImageBatch",
+            "RepeatLatentBatch",
+            "RescaleCFG",
+            "SDTurboScheduler",
+            "SVD_img2vid_Conditioning",
+            "SamplerCustom",
+            "SamplerDPMPP_2M_SDE",
+            "SamplerDPMPP_SDE",
+            "SaveAnimatedPNG",
+            "SaveAnimatedWEBP",
+            "SaveImage",
+            "SaveLatent",
+            "SelfAttentionGuidance",
+            "SetLatentNoiseMask",
+            "SolidMask",
+            "SplitImageWithAlpha",
+            "SplitSigmas",
+            "StableZero123_Conditioning",
+            "StyleModelApply",
+            "StyleModelLoader",
+            "TomePatchModel",
+            "UNETLoader",
+            "UpscaleModelLoader",
+            "VAEDecode",
+            "VAEDecodeTiled",
+            "VAEEncode",
+            "VAEEncodeForInpaint",
+            "VAEEncodeTiled",
+            "VAELoader",
+            "VAESave",
+            "VPScheduler",
+            "VideoLinearCFGGuidance",
+            "unCLIPCheckpointLoader",
+            "unCLIPConditioning"
+        ],
+        {
+            "title_aux": "ComfyUI_Fooocus"
+        }
+    ],
+    "https://github.com/1H-hobit/ComfyUI_InternVL3": [
+        [
+            "BBOXES_XYXY_Converter",
+            "DynamicPreprocess",
+            "Florence2toCoordinates_hb",
+            "InternVLHFInference",
+            "InternVLModelLoader",
+            "hb_Number_Counter"
+        ],
+        {
+            "title_aux": "ComfyComfyUI_InternVL3 [WIP]"
+        }
+    ],
+    "https://github.com/206811/ComfyUI_ZhipuAIO": [
+        [
+            "ZhipuAIOConfigNode",
+            "ZhipuAIO_VisionNode",
+            "ZhipuTranslateNode"
+        ],
+        {
+            "title_aux": "ComfyUI_ZhipuAIO"
+        }
+    ],
+    "https://github.com/2omegaXv/comfyui_face_router": [
+        [
+            "FaceSEGSRouter"
+        ],
+        {
+            "title_aux": "comfyui_face_router [WIP]"
+        }
+    ],
+    "https://github.com/3dmindscapper/ComfyUI-PartField": [
+        [
+            "PartFieldClustering",
+            "PartFieldExportParts",
+            "PartFieldInference",
+            "PartFieldModelDownLoader",
+            "PartFieldSplitter",
+            "PartFieldViewer"
+        ],
+        {
+            "title_aux": "ComfyUI-PartField [WIP]"
+        }
+    ],
+    "https://github.com/3dmindscapper/ComfyUI-Sam-Mesh": [
+        [
+            "SamMeshExporter",
+            "SamMeshExporter+SamMesh",
+            "SamMeshLoader",
+            "SamMeshLoader+SamMesh",
+            "SamMeshRenderer",
+            "SamMeshRenderer+SamMesh",
+            "SamMeshSegmenter",
+            "SamMeshSegmenter+SamMesh",
+            "SamModelDownloader",
+            "SamModelDownloader+SamMesh"
+        ],
+        {
+            "title_aux": "ComfyUI-Sam-Mesh [WIP]"
+        }
+    ],
+    "https://github.com/438443467/ComfyUI-SanMian-Nodes": [
+        [
+            "FaceAlignPro",
+            "FaceAlignProRestore",
+            "SANMIN Adapt Coordinates",
+            "SanmiKSampler",
+            "sanmi AddTextToImage",
+            "sanmi Adjust Transparency By Mask",
+            "sanmi AdjustHexBrightness",
+            "sanmi Align Images with Mask",
+            "sanmi BinarizeMask",
+            "sanmi BlendICLight",
+            "sanmi Chinese To Character",
+            "sanmi ColorOverlayOnMask",
+            "sanmi Compare",
+            "sanmi CompareV2",
+            "sanmi Counter",
+            "sanmi CreateTxtForImages",
+            "sanmi Filter Prompt Words",
+            "sanmi Float",
+            "sanmi Florence2toCoordinates",
+            "sanmi Get Content From Excel",
+            "sanmi Get LastPathComponent",
+            "sanmi Get Mask White Region Size",
+            "sanmi GetFilePath",
+            "sanmi GetMostCommonColor",
+            "sanmi ImageBatchSplitter",
+            "sanmi Image_Rotate",
+            "sanmi Int90",
+            "sanmi IntToBOOLEAN",
+            "sanmi Load Image Batch",
+            "sanmi LoadImageFromPath",
+            "sanmi LoadImagesanmi",
+            "sanmi Mask To Box",
+            "sanmi MaskToBboxes",
+            "sanmi MaskWhiteRatioAnalyzer",
+            "sanmi Path Captioner",
+            "sanmi Path Change",
+            "sanmi Read Image Prompt",
+            "sanmi RectMaskAnalyzer",
+            "sanmi Reduce Mask",
+            "sanmi RestoreJson",
+            "sanmi Sanmi_Text_Concatenate",
+            "sanmi Save Image To Local",
+            "sanmi SimpleWildcards",
+            "sanmi SortTheMasksLeftRight",
+            "sanmi SortTheMasksSize",
+            "sanmi Special Counter",
+            "sanmi StrToPinYin",
+            "sanmi String Counter",
+            "sanmi String Counter V2",
+            "sanmi StringToBox",
+            "sanmi Time",
+            "sanmi Upscale And Keep Original Size"
+        ],
+        {
+            "title_aux": "ComfyUI-SanMian-Nodes"
+        }
+    ],
+    "https://github.com/543872524/ComfyUI_crdong": [
+        [
+            "CRDAudioLengthNode",
+            "CRDNodesImageSelector",
+            "INTConstant",
+            "PromptBatchMulti",
+            "PromptExampleNode",
+            "PromptJoinOrList",
+            "PromptList",
+            "PromptSelectorList",
+            "PromptSelectorStr",
+            "PromptSelectorStrV2",
+            "SelectImageSize",
+            "SimpleIntMathHandle",
+            "SimpleJsonArrayHandle",
+            "SimpleJsonObjectHandle",
+            "TestExtendComfyNode",
+            "VideoFrameSize",
+            "VideoSizeAndFps",
+            "VideoTimeAndFPS",
+            "Wan22StepHandle"
+        ],
+        {
+            "title_aux": "ComfyUI_crdong"
+        }
+    ],
+    "https://github.com/5x00/ComfyUI-Prompt-Plus": [
+        [
+            "LoadAPI",
+            "LoadCustomModel",
+            "LoadFlorenceModel",
+            "Prompt",
+            "RunAPIVLM",
+            "RunCustomVLM",
+            "TriggerToPromptAPI",
+            "TriggerToPromptCustom",
+            "TriggerToPromptSimple"
+        ],
+        {
+            "title_aux": "ComfyUI-Prompt-Plus [WIP]"
+        }
+    ],
+    "https://github.com/77oussam/Aio77-Comfyui": [
+        [
+            "LoadImages-77",
+            "LoadImages-77-Simple",
+            "Outline-77"
+        ],
+        {
+            "title_aux": "Alo77 - ComfyUI Custom Nodes Collection [WIP]"
+        }
+    ],
+    "https://github.com/78Ventures/ComfyUI-Tortu": [
+        [
+            "BC_DETECT_FACE_ORIENTATION",
+            "BC_EXIF_WRITER",
+            "BC_IMAGE_LORA_CONFORM",
+            "BC_LOAD_IMAGES",
+            "BC_LORA_DEFINE",
+            "BC_LORA_METADATA",
+            "BC_LORA_TRAIN",
+            "BC_SAVE_IMAGES",
+            "IF_LoadImagesS",
+            "TORTU_DETECT_DUPLICATES",
+            "TORTU_DETECT_EMOTION",
+            "TORTU_DIRECTORY_ORGANIZER",
+            "TORTU_EMOTION_DETECTION",
+            "TORTU_METADATA_WRITER",
+            "TORTU_ORGANIZE_IMAGES",
+            "TORTU_WRITE_EXIF"
+        ],
+        {
+            "title_aux": "ComfyUI-Tortu [WIP/UNSAFE]"
+        }
+    ],
+    "https://github.com/7BEII/Comfyui_PDuse": [
+        [
+            "CustomAddLabel",
+            "Empty_Line",
+            "ImageAddBackground",
+            "ImageBlendText",
+            "ImageBlendV1",
+            "ImageGridSplitter",
+            "Load_Images_Advance",
+            "Load_Images_V1",
+            "PD Image Resize_V1",
+            "PDIMAGE_ImageCombine",
+            "PDIMAGE_LongerSize",
+            "PDImageConcante",
+            "PDImageCropLocation_V2",
+            "PDImageResize",
+            "PDImageResizeV2",
+            "PDImageResizeV3",
+            "PDJSON_BatchJsonIncremental",
+            "PDJSON_Group",
+            "PDRatioSelector",
+            "PDStringConcate",
+            "PDStringInput",
+            "PDTEXT_SAVE_PATH",
+            "PD_AitoolkitTrainingRedux",
+            "PD_BatchCropBlackBorder",
+            "PD_CaptionJSONEditor",
+            "PD_CaptionJSONParser",
+            "PD_CropBorder",
+            "PD_Extract_Image",
+            "PD_GetImageRatio",
+            "PD_GetImageSize",
+            "PD_ImageCompress",
+            "PD_ImageFileTraining",
+            "PD_ImageListForSort",
+            "PD_ImageListForSortWithMetadata",
+            "PD_ImageSearch",
+            "PD_Image_Crop_Location",
+            "PD_Image_Rotate_v1",
+            "PD_Image_centerCrop",
+            "PD_JoinStringMultiLine",
+            "PD_LoadImageMetadata",
+            "PD_LoadImageWithMeta",
+            "PD_LoadImagesPath",
+            "PD_LoadTextsFromDir",
+            "PD_LoadTextsFromZip",
+            "PD_MASK_SELECTION",
+            "PD_Mask Selector",
+            "PD_Mask Selector By Area Left",
+            "PD_MaskFillHoles",
+            "PD_MaskRemoveSmallObjects",
+            "PD_Maskfenkai",
+            "PD_ModelLoraLoader",
+            "PD_RemoveBlackBackground",
+            "PD_RemoveColorWords",
+            "PD_RemoveWhiteBorder",
+            "PD_RenameV2",
+            "PD_SAVE_PATH2",
+            "PD_SaveTextPathAndName",
+            "PD_ShowText",
+            "PD_StringLineCount",
+            "PD_StringToCaptionJSON",
+            "PD_TensorTraining",
+            "PD_Text Overlay Node",
+            "PD_TextListPack",
+            "PD_TextListSort",
+            "PD_TextListStringAddWord",
+            "PD_TextListUnpack",
+            "PD_TxtBatchRename",
+            "PD_UnMultBlackBackground",
+            "PD_ZIP_Packingsave",
+            "PD_del_word",
+            "PD_empty_word",
+            "PD_if",
+            "PD_image_coversaver",
+            "PD_image_resize_by_ratio",
+            "PD_image_to_text_v1",
+            "PD_imagesave_path",
+            "PD_load_image_v1",
+            "PD_lora_loader",
+            "PD_name_replacewordorder",
+            "PD_number_star",
+            "PD_number_start",
+            "PD_random_prompt",
+            "PD_rename_image",
+            "PDbananaImagesizeByRatio",
+            "PDimage_corp_v1",
+            "PDimage_corp_v2",
+            "PDimage_dual_batch_by_list",
+            "PDimage_dual_batch_v1",
+            "mask_edge_selector"
+        ],
+        {
+            "title_aux": "comfyui-promptbymood [WIP]"
+        }
+    ],
+    "https://github.com/861289009/comfyui_video_node": [
+        [
+            "VideoAddAudio",
+            "VideoFadeHex2In",
+            "VideoFirstFrame",
+            "VideoForceFpsCfr",
+            "VideoMerge30FpsAudio",
+            "VideoMerge30FpsNoAudio",
+            "VideoProbe",
+            "VideoTrimEndFrames",
+            "VideoTrimStartFrames",
+            "VideoUrlLastFrame",
+            "VideoUrlSplitCfr"
+        ],
+        {
+            "title_aux": "comfyui_video_node [WIP]"
+        }
+    ],
+    "https://github.com/A1rCHAN/ComfyUI_A1rSpace": [
+        [
+            "ModeCollector",
+            "SeedControl",
+            "SizeCanvas",
+            "WidgetCollector"
+        ],
+        {
+            "title_aux": "Eric's Prompt Enhancers for ComfyUI# Eric's Prompt Enhancers for ComfyUI"
+        }
+    ],
+    "https://github.com/A4P7J1N7M05OT/ComfyUI-ManualSigma": [
+        [
+            "ManualSigma"
+        ],
+        {
+            "title_aux": "ComfyUI-ManualSigma"
+        }
+    ],
+    "https://github.com/A719689614/ComfyUI_AC_FUNV7-FLUX-": [
+        [
+            "AC_Super_CLIP(FLUX)",
+            "AC_Super_UNET(FLUX)"
+        ],
+        {
+            "title_aux": "ComfyUI_AC_FUNV7-FLUX- [WIP]"
+        }
+    ],
+    "https://github.com/A719689614/ComfyUI_AC_FUNV8Beta1": [
+        [
+            "AC_FUN_SUPER_DESIGN_LARGE",
+            "AC_FUN_SUPER_LARGE",
+            "AC_Super_CKPT&LCM",
+            "AC_Super_CLIPEN",
+            "AC_Super_Checkpoint",
+            "AC_Super_Controlnet",
+            "AC_Super_EmptLatent",
+            "AC_Super_KSampler",
+            "AC_Super_Lora&LCM",
+            "AC_Super_Loras",
+            "AC_Super_MaskScale",
+            "AC_Super_MaskScaleBy",
+            "AC_Super_PreviewImage",
+            "AC_Super_PreviewMask",
+            "AC_Super_SaveImage",
+            "AC_Super_UpKSampler"
+        ],
+        {
+            "title_aux": "ComfyUI_AC_FUNV8Beta1"
+        }
+    ],
+    "https://github.com/AICodeFactory/ComfyUI-Viva": [
+        [
+            "HttpTrigger_Common",
+            "HttpTrigger_Image",
+            "HttpTrigger_Viva"
+        ],
+        {
+            "title_aux": "ComfyUI-Viva"
+        }
+    ],
+    "https://github.com/AIExplorer25/ComfyUI_HuggingfaceHelper": [
+        [
+            "GPTOSSHelper",
+            "GoogleSearchFromList",
+            "HuggingFaceModelCardReader",
+            "HuggingFacePromptBuilder",
+            "HuggingFaceTrendingModels",
+            "JinaPageScraper",
+            "PTagExtractor",
+            "QwenResponseExtract"
+        ],
+        {
+            "title_aux": "ComfyUI_HuggingfaceHelper"
+        }
+    ],
+    "https://github.com/AIFSH/ComfyUI-OpenDIT": [
+        [
+            "DITModelLoader",
+            "DITPromptNode",
+            "DiffVAELoader",
+            "LattePipeLineNode",
+            "OpenSoraNode",
+            "OpenSoraPlanPipeLineNode",
+            "PABConfigNode",
+            "PreViewVideo",
+            "SchedulerLoader",
+            "T5EncoderLoader",
+            "T5TokenizerLoader"
+        ],
+        {
+            "title_aux": "ComfyUI-OpenDIT [WIP]"
+        }
+    ],
+    "https://github.com/AIFSH/ComfyUI-ViViD": [
+        [
+            "LoadImagePath",
+            "LoadVideo",
+            "PreViewVideo",
+            "ViViD_Node"
+        ],
+        {
+            "title_aux": "ComfyUI-ViViD"
+        }
+    ],
+    "https://github.com/AIFSH/UltralightDigitalHuman-ComfyUI": [
+        [
+            "InferUltralightDigitalHumanNode",
+            "TrainUltralightDigitalHumanNode"
+        ],
+        {
+            "title_aux": "UltralightDigitalHuman-ComfyUI"
+        }
+    ],
+    "https://github.com/AIFSH/UtilNodes-ComfyUI": [
+        [
+            "GetRGBEmptyImgae",
+            "LoadVideo",
+            "PreViewVideo",
+            "PromptTextNode"
+        ],
+        {
+            "title_aux": "UtilNodes-ComfyUI [WIP]"
+        }
+    ],
+    "https://github.com/AIGCZero/AIGCZero-comfyui-Qwen_edit-zero": [
+        [
+            "TextEncodeQwenImageEdit_zero"
+        ],
+        {
+            "title_aux": "AIGCZero-comfyui-Qwen_edit-zero"
+        }
+    ],
+    "https://github.com/AMTPorn/comfyui_amt": [
+        [
+            "AMTCountTagsInDirectory",
+            "AMTEmbedTagFrequencySafetensors",
+            "AMTMultiConditionRegexReplace",
+            "AMTReplaceAndMaybeDropString",
+            "AMTStringDeduplication"
+        ],
+        {
+            "title_aux": "comfyui_amt"
+        }
+    ],
+    "https://github.com/APZmedia/ComfyUI-folder-parser": [
+        [
+            "APZFolderParser"
+        ],
+        {
+            "author": "Pablo Apiolazza",
+            "description": "A ComfyUI custom node for parsing and filtering files from a folder with sorting capabilities.",
+            "nickname": "ComfyUI Folder Parser",
+            "title": "ComfyUI Folder Parser",
+            "title_aux": "ComfyUI Folder Parser [UNSAFE]"
+        }
+    ],
+    "https://github.com/APZmedia/Joshua-brand-assets-loader": [
+        [
+            "APZmediaBrandAssetLoader",
+            "APZmediaBrandAssetReader",
+            "APZmediaColorPalette",
+            "APZmediaColorPaletteSelector",
+            "APZmediaFontSelector",
+            "APZmediaGlobalBrandAccess",
+            "APZmediaGlobalBrandStatus",
+            "APZmediaGradientOverlay",
+            "APZmediaLogoOverlay",
+            "APZmediaLogoPlacement",
+            "APZmediaSolidColor",
+            "APZmediaURLImageLoader",
+            "POISmartCrop"
+        ],
+        {
+            "title_aux": "Joshua Brand Assets Loader [private]"
+        }
+    ],
+    "https://github.com/APZmedia/comfyui-textools": [
+        [
+            "APZ/MarkdownTextOverlay",
+            "APZmediaBrandMarkdownTextOverlay",
+            "APZmediaBrandRichTextOverlay",
+            "APZmediaBrandRichTextOverlayV2",
+            "APZmediaImageMarkdownTextOverlay",
+            "APZmediaImageRichTextOverlay",
+            "APZmediaImageRichTextOverlayV2"
+        ],
+        {
+            "author": "Pablo Apiolazza",
+            "description": "This extension provides rich text overlay functionalities, color management, and text parsing utilities for ComfyUI.",
+            "nickname": "ComfyUI Text Tools",
+            "title": "ComfyUI APZmedia Text Tools",
+            "title_aux": "comfyui-textools [WIP]"
+        }
+    ],
+    "https://github.com/Aero-Ex/comfyui_diffswap": [
+        [
+            "DiffSwapNode"
+        ],
+        {
+            "title_aux": "comfyui_diffswap"
+        }
+    ],
+    "https://github.com/Agnuxo1/silicon-comfyui-node": [
+        [
+            "SiliconSignatureEmbed",
+            "SiliconSignatureVerify"
+        ],
+        {
+            "title_aux": "silicon-comfyui-node [WIP]"
+        }
+    ],
+    "https://github.com/AhBumm/ComfyUI-Upscayl": [
+        [
+            "Upscayl Upscaler"
+        ],
+        {
+            "nodename_pattern": "\\(BillBum\\)$",
+            "title_aux": "ComfyUI-Upscayl"
+        }
+    ],
+    "https://github.com/Ahmed791996/SplatViwer_comfyUI": [
+        [
+            "GaussianSplatViewer"
+        ],
+        {
+            "title_aux": "SplatViwer_comfyUI"
+        }
+    ],
+    "https://github.com/AkiEvansDev/ComfyUI-Tools": [
+        [
+            "AE.AnySwitch",
+            "AE.AnyTypeSwitch",
+            "AE.BRIARemBG",
+            "AE.BRIARemBGAdvanced",
+            "AE.ChangeSamplerConfig",
+            "AE.CheckpointList",
+            "AE.CheckpointLoader",
+            "AE.CompareFloat",
+            "AE.CompareInt",
+            "AE.ControlNetApplyWithConfig",
+            "AE.ControlNetConfig",
+            "AE.DisplayAny",
+            "AE.ExtractControlNetConfig",
+            "AE.ExtractHiresFixConfig",
+            "AE.ExtractImg2ImgConfig",
+            "AE.ExtractOutpaintConfig",
+            "AE.ExtractSamplerConfig",
+            "AE.Float",
+            "AE.FloatList",
+            "AE.FloatSwitch",
+            "AE.FloatToInt",
+            "AE.GaussianBlurMask",
+            "AE.GetImageSize",
+            "AE.GetLatentSize",
+            "AE.GroupsMuter",
+            "AE.HiresFixConfig",
+            "AE.ImageAdjustment",
+            "AE.ImageBlank",
+            "AE.ImageBlendMask",
+            "AE.ImageBlendMode",
+            "AE.ImageCannyFilter",
+            "AE.ImageDragonFilter",
+            "AE.ImageHighPassFilter",
+            "AE.ImageLevels",
+            "AE.ImageLucySharpen",
+            "AE.ImagePixelate",
+            "AE.ImagePowerNoise",
+            "AE.ImageStyleFilter",
+            "AE.Img2ImgConfig",
+            "AE.InpaintWithModel",
+            "AE.Int",
+            "AE.IntList",
+            "AE.IntSwitch",
+            "AE.IntToFloat",
+            "AE.KSamplerHiresFixWithConfig",
+            "AE.KSamplerImg2ImgWithConfig",
+            "AE.KSamplerInpaintWithConfig",
+            "AE.KSamplerInpaintWithConfigAndImage",
+            "AE.KSamplerOutpaintWithConfig",
+            "AE.KSamplerOutpaintWithConfigAndImage",
+            "AE.KSamplerWithConfig",
+            "AE.LoadImageFromPath",
+            "AE.LoadInpaintModel",
+            "AE.LoraLoader",
+            "AE.LorasList",
+            "AE.LorasLoader",
+            "AE.MathFloat",
+            "AE.MathInt",
+            "AE.OutpaintConfig",
+            "AE.OutpaintWithModel",
+            "AE.OutpaintWithModelAndConfig",
+            "AE.PromptConfig",
+            "AE.Range",
+            "AE.RangeList",
+            "AE.SDXLConditions",
+            "AE.SDXLConditionsWithHires",
+            "AE.SDXLConfig",
+            "AE.SDXLPrompt",
+            "AE.SDXLPromptWithHires",
+            "AE.SDXLRegionalPrompt",
+            "AE.SDXLRegionalPromptWithHires",
+            "AE.SamplerConfig",
+            "AE.SamplerList",
+            "AE.SaveImage",
+            "AE.SchedulerList",
+            "AE.Seed",
+            "AE.String",
+            "AE.StringConcat",
+            "AE.StringEquals",
+            "AE.StringLength",
+            "AE.StringList",
+            "AE.StringReplace",
+            "AE.StringSwitch",
+            "AE.Text",
+            "AE.ToString",
+            "AE.ToStringConcat",
+            "AE.UpscaleLatentBy",
+            "AE.VAEEncodeInpaintConditioning",
+            "AE.XYRange"
+        ],
+        {
+            "title_aux": "ComfyUI-Tools"
+        }
+    ],
+    "https://github.com/Alazuaka/comfyui-lora-stack-node": [
+        [
+            "AlazukaCheckpoint",
+            "EsLoraSet"
+        ],
+        {
+            "title_aux": "ES_nodes for ComfyUI by Alazuka [WIP]"
+        }
+    ],
+    "https://github.com/AlejandroTuzzi/TUZZI-ByPass": [
+        [
+            "LinkSuppressor",
+            "SequentialTextReaderAuto",
+            "TUZZI-AppendToMasterVideo",
+            "TUZZI-Bypasser",
+            "TUZZI-DataloungeScraper",
+            "TUZZI-DirectoryImagePromptReader",
+            "TUZZI-ElevenLabsTTS",
+            "TUZZI-GeminiNode",
+            "TUZZI-GroqNode",
+            "TUZZI-ImageAudioToVideo",
+            "TUZZI-ImageExtractorSaver",
+            "TUZZI-LineCounter",
+            "TUZZI-LinkSuppressor",
+            "TUZZI-NumberLines",
+            "TUZZI-PlosArticleScraper",
+            "TUZZI-RangedSelectorText5",
+            "TUZZI-RangedSelectorTitleURL10",
+            "TUZZI-RangedSelectorTitleURL5",
+            "TUZZI-RedditPostExtractor",
+            "TUZZI-SaveVideo",
+            "TUZZI-SequentialTextReader",
+            "TUZZI-SequentialTextReaderAuto",
+            "TUZZI-SmartAudioVisualComposer",
+            "TUZZI-TVTropesScraper",
+            "TUZZI-TextFormatter",
+            "TUZZI-TextFormatterPlus",
+            "TUZZI-TextTranslatorExporter",
+            "TUZZI-TextTruncatorPlus",
+            "TUZZI-YouTubeCommentExtractor",
+            "TUZZI-YouTubeSubtitleExtractor"
+        ],
+        {
+            "title_aux": "TUZZI-ByPass [WIP]"
+        }
+    ],
+    "https://github.com/AlexXi19/ComfyUI-OpenAINode": [
+        [
+            "ImageWithPrompt",
+            "TextWithPrompt"
+        ],
+        {
+            "title_aux": "ComfyUI-OpenAINode"
+        }
+    ],
+    "https://github.com/AlexYez/comfyui-timesaver": [
+        [
+            "TS Cube to Equirectangular",
+            "TS Equirectangular to Cube",
+            "TS Files Downloader",
+            "TS Youtube Chapters",
+            "TSAutoTileSize",
+            "TSCropToMask",
+            "TSRestoreFromCrop",
+            "TSWhisper",
+            "TS_Animation_Preview",
+            "TS_AudioLoader",
+            "TS_AudioPreview",
+            "TS_BGRM_BiRefNet",
+            "TS_BatchPromptLoader",
+            "TS_CPULoraMerger",
+            "TS_Color_Grade",
+            "TS_Color_Match",
+            "TS_Despill",
+            "TS_FilePathLoader",
+            "TS_FilmGrain",
+            "TS_Film_Emulation",
+            "TS_FloatSlider",
+            "TS_Frame_Interpolation",
+            "TS_GetImageMegapixels",
+            "TS_GetImageSizeSide",
+            "TS_ImageBatchCut",
+            "TS_ImageBatchToImageList",
+            "TS_ImageListToImageBatch",
+            "TS_ImagePromptInjector",
+            "TS_ImageResize",
+            "TS_ImageTileMerger",
+            "TS_ImageTileSplitter",
+            "TS_Int_Slider",
+            "TS_Keyer",
+            "TS_LTX_FirstLastFrame",
+            "TS_LamaCleanup",
+            "TS_Math_Int",
+            "TS_Matting_ViTMatte",
+            "TS_ModelConverter",
+            "TS_ModelConverterAdvanced",
+            "TS_ModelConverterAdvancedDirect",
+            "TS_ModelScanner",
+            "TS_MultiReference",
+            "TS_MusicStems",
+            "TS_PromptBuilder",
+            "TS_Qwen3_VL_V3",
+            "TS_QwenCanvas",
+            "TS_QwenSafeResize",
+            "TS_RTX_Upscaler",
+            "TS_ResolutionSelector",
+            "TS_SAM_MediaLoader",
+            "TS_SileroStress",
+            "TS_SileroTTS",
+            "TS_Smart_Switch",
+            "TS_StylePromptSelector",
+            "TS_SuperPrompt",
+            "TS_VideoDepthNode",
+            "TS_Video_Upscale_With_Model",
+            "TS_WAN_SafeResize"
+        ],
+        {
+            "title_aux": "ComfyUI Timesaver Nodes"
+        }
+    ],
+    "https://github.com/AllenEdgarPoe/ComfyUI-Xorbis-nodes": [
+        [
+            "Add Human Styler",
+            "ConcaveHullImage",
+            "Convert Monochrome",
+            "ImageBWPostprocessor",
+            "ImageBWPreprocessor",
+            "Inpaint Crop Xo",
+            "LoadData",
+            "Mask Aligned bbox for ConcaveHull",
+            "Mask Aligned bbox for Inpainting",
+            "Mask Aligned bbox for Inpainting2",
+            "Mask Square bbox for Inpainting",
+            "One Image Compare",
+            "RT4KSR Loader",
+            "RandomPromptStyler",
+            "Save Log Info",
+            "Save log info",
+            "Three Image Compare",
+            "Upscale RT4SR"
+        ],
+        {
+            "title_aux": "ComfyUI-Xorbis-nodes [WIP]"
+        }
+    ],
+    "https://github.com/Alvaroeai/ComfyUI-SunoAI-Mds": [
+        [
+            "Mideas_SunoAI_AudioManager",
+            "Mideas_SunoAI_Generator",
+            "Mideas_SunoAI_ProxyDownloadNode",
+            "Mideas_SunoAI_ProxyNode"
+        ],
+        {
+            "title_aux": "ComfyUI-SunoAI-Mds"
+        }
+    ],
+    "https://github.com/Amor718/AmorForge": [
+        [
+            "CogViewImageGenerator",
+            "ImageToWebpSaver",
+            "SimpleImageResizer",
+            "SimpleImageResizerNode",
+            "TextTranslator",
+            "TextTranslatorNode"
+        ],
+        {
+            "title_aux": "AmorForge"
+        }
+    ],
+    "https://github.com/Anze-/ComfyUI-OIDN": [
+        [
+            "OIDN Denoise",
+            "denoiseOIDN"
+        ],
+        {
+            "title_aux": "ComfyUI-OIDN [WIP]"
+        }
+    ],
+    "https://github.com/Anze-/ComfyUI_deepDeband": [
+        [
+            "deepDeband Inference",
+            "deepDebandInference"
+        ],
+        {
+            "title_aux": "ComfyUI_deepDeband [WIP]"
+        }
+    ],
+    "https://github.com/Apache0ne/ComfyUI-SaveLoadConditioning": [
+        [
+            "LoadConditioningNode",
+            "SaveConditioningNode"
+        ],
+        {
+            "title_aux": "ComfyUI-SaveLoadConditioning"
+        }
+    ],
+    "https://github.com/AprEcho/ComfyUI-RandomSeed": [
+        [
+            "RandomSeed"
+        ],
+        {
+            "title_aux": "ComfyUI-RandomSeed"
+        }
+    ],
+    "https://github.com/Arch666Angel/ComfyUI-Geometry-Nodes": [
+        [
+            "ColorListIndexSelector",
+            "GeometricMaskGenerator",
+            "MaskInvertMergeLogic",
+            "MaskMergeLogic",
+            "PatternLayerPainter",
+            "ProceduralLayerPainter",
+            "RandomizedGeometricMaskGenerator"
+        ],
+        {
+            "title_aux": "ComfyUI-Geometry-Nodes [WIP]"
+        }
+    ],
+    "https://github.com/ArmandAlbert/Kwai_font_comfyui": [
+        [
+            "Kwaifont_Image_Cropper",
+            "Kwaifont_Resnet101_Loader",
+            "Kwaifont_Resnet101_Runner",
+            "Kwaifont_Resnet50_Loader",
+            "Kwaifont_Resnet50_Runner"
+        ],
+        {
+            "title_aux": "Kwai_font_comfyui"
+        }
+    ],
+    "https://github.com/ArthusLiang/comfyui-face-remap": [
+        [
+            "FaceRemap"
+        ],
+        {
+            "title_aux": "comfyui-face-remap [WIP]"
+        }
+    ],
+    "https://github.com/Aruntd008/ComfyUI_SeamlessPattern": [
+        [
+            "SeamlessPatternNode"
+        ],
+        {
+            "title_aux": "ComfyUI_SeamlessPattern [WIP]"
+        }
+    ],
+    "https://github.com/Aruntd008/ComfyUI_blender_render": [
+        [
+            "Blender Render Node",
+            "BlenderRenderNode"
+        ],
+        {
+            "title_aux": "ComfyUI_blender_render"
+        }
+    ],
+    "https://github.com/BARKEM-JC/ComfyUI-Dynamic-Lora-Loader": [
+        [
+            "DynamicLoraBlockWeights",
+            "DynamicLoraConfig",
+            "DynamicLoraConfigCombiner",
+            "DynamicLoraEmbedding",
+            "DynamicLoraKeyword",
+            "DynamicLoraLoader",
+            "DynamicLoraOffset"
+        ],
+        {
+            "title_aux": "ComfyUI-Dynamic-Lora-Loader"
+        }
+    ],
+    "https://github.com/Babiduba/ivan_knows": [
+        [
+            "RoleSelector",
+            "SaveImageToAbsolutePath"
+        ],
+        {
+            "title_aux": "ivan_knows [UNSAFE]"
+        }
+    ],
+    "https://github.com/BadCafeCode/execution-inversion-demo-comfyui": [
+        [
+            "AccumulateNode",
+            "AccumulationGetItemNode",
+            "AccumulationGetLengthNode",
+            "AccumulationHeadNode",
+            "AccumulationSetItemNode",
+            "AccumulationTailNode",
+            "AccumulationToListNode",
+            "BoolOperationNode",
+            "ComponentInput",
+            "ComponentMetadata",
+            "ComponentOutput",
+            "DebugPrint",
+            "ExecutionBlocker",
+            "FloatConditions",
+            "ForLoopClose",
+            "ForLoopOpen",
+            "IntConditions",
+            "IntMathOperation",
+            "InversionDemoAdvancedPromptNode",
+            "InversionDemoLazyConditional",
+            "InversionDemoLazyIndexSwitch",
+            "InversionDemoLazyMixImages",
+            "InversionDemoLazySwitch",
+            "ListToAccumulationNode",
+            "MakeListNode",
+            "StringConditions",
+            "ToBoolNode",
+            "WhileLoopClose",
+            "WhileLoopOpen"
+        ],
+        {
+            "title_aux": "execution-inversion-demo-comfyui"
+        }
+    ],
+    "https://github.com/BaronVonBoolean/ComfyUI-FileOps": [
+        [
+            "File Mv",
+            "File Path",
+            "FileMv",
+            "Make Dir"
+        ],
+        {
+            "title_aux": "ComfyUI-FileOps [UNSAFE]"
+        }
+    ],
+    "https://github.com/Beinsezii/DEET": [
+        [
+            "DEET"
+        ],
+        {
+            "title_aux": "DEET: Diffusion Entropy Equalization Transform [WIP]"
+        }
+    ],
+    "https://github.com/BenjaMITM/ComfyUI_On_The_Fly_Wildcards": [
+        [
+            "Display String",
+            "Wildcard Creator",
+            "Wildcard Loader",
+            "Wildcard Selector"
+        ],
+        {
+            "title_aux": "ComfyUI_On_The_Fly_Wildcards [WIP]"
+        }
+    ],
+    "https://github.com/BetaDoggo/ComfyUI-LogicGates": [
+        [
+            "AND",
+            "BitMemory",
+            "BoolToString",
+            "ByteMemory",
+            "ByteToBits",
+            "CreateBYTE",
+            "CreateByte",
+            "NAND",
+            "NOR",
+            "NOT",
+            "ON",
+            "OR",
+            "SWITCH",
+            "XNOR",
+            "XOR"
+        ],
+        {
+            "title_aux": "ComfyUI-LogicGates"
+        }
+    ],
+    "https://github.com/Big-Idea-Technology/ComfyUI-Movie-Tools": [
+        [
+            "LoadImagesFromSubdirsBatch",
+            "SaveImagesWithSubfolder"
+        ],
+        {
+            "title_aux": "ComfyUI-Movie-Tools [WIP]"
+        }
+    ],
+    "https://github.com/BigStationW/flowmatch_scheduler-comfyui": [
+        [
+            "FlowMatchSigmas"
+        ],
+        {
+            "title_aux": "flowmatch_scheduler-comfyui"
+        }
+    ],
+    "https://github.com/Bigornh2o/ComfyUI-Bigornh2oSmartResolution": [
+        [
+            "SmartFluxResolution"
+        ],
+        {
+            "title_aux": "ComfyUI-Bigornh2oSmartResolution [WIP]"
+        }
+    ],
+    "https://github.com/BinglongLi/ComfyUI_ToolsForAutomask": [
+        [
+            "Closing Mask",
+            "Conditional Mask Selector",
+            "Directional Mask Expansion",
+            "Mask Fill Gaps Convex Hull",
+            "Opening Mask",
+            "Precise Add Mask",
+            "Precise Subtract Mask",
+            "Prune Thin Branches Mask",
+            "Remove Small Regions Mask"
+        ],
+        {
+            "title_aux": "ComfyUI_ToolsForAutomask"
+        }
+    ],
+    "https://github.com/BiodigitalJaz/ComfyUI-Dafaja-Nodes": [
+        [
+            "DafajaBackgroundRemover",
+            "DafajaExportSTL",
+            "DafajaMeshGenerator",
+            "DafajaMeshInfo",
+            "DafajaMeshNormalizer",
+            "DafajaMeshProcessor",
+            "DafajaSTLValidator",
+            "DafajaStemsMidiSeparator",
+            "DafajaVAEDecoder"
+        ],
+        {
+            "title_aux": "ComfyUI-Dafaja-Nodes [WIP]"
+        }
+    ],
+    "https://github.com/BizaNator/BrainDead_QwenCharacterEditor": [
+        [
+            "TextEncodeQwenImageEditEnhanced",
+            "TextEncodeQwenImageEditIdentity",
+            "TextEncodeQwenImageEditMulti",
+            "TextEncodeQwenImageEditWeighted",
+            "TextEncodeQwenImageT2ICustom"
+        ],
+        {
+            "title_aux": "BrainDead_QwenCharacterEditor [WIP]"
+        }
+    ],
+    "https://github.com/BlackSnowSkill/ComfyUI-BSS_ModelMixSampler": [
+        [
+            "ModelMixSampler_BSS",
+            "ModelMixVAEDecode_BSS"
+        ],
+        {
+            "title_aux": "ComfyUI-BSS_ModelMixSampler [WIP]"
+        }
+    ],
+    "https://github.com/BlueDangerX/ComfyUI-BDXNodes": [
+        [
+            "BDXTestInt"
+        ],
+        {
+            "author": "BlueDangerX",
+            "title": "BDXNodes",
+            "title_aux": "ComfyUI-BDXNodes [WIP]"
+        }
+    ],
+    "https://github.com/Brandelan/ComfyUI_bd_customNodes": [
+        [
+            "BD Float to String",
+            "BD Int to String",
+            "BD Random Range",
+            "BD Random Settings",
+            "BD Sequencer",
+            "BD Settings",
+            "bd_FloatRangeSlider",
+            "bd_FloatToString",
+            "bd_IntToString",
+            "bd_RandomizeSettings",
+            "bd_Sequencer"
+        ],
+        {
+            "title_aux": "ComfyUI_bd_customNodes"
+        }
+    ],
+    "https://github.com/BrettMedia/comfyui-bhtools": [
+        [
+            "CinematicSceneDirectorTools",
+            "EndOfWorkflowClearingBHTools",
+            "PromptInferenceBHTools",
+            "SaveImageVideoBHTools"
+        ],
+        {
+            "title_aux": "comfyui-bhtools [WIP]"
+        }
+    ],
+    "https://github.com/BuffMcBigHuge/ComfyUI-Buff-Nodes": [
+        [
+            "BatchRaftOpticalFlowNode",
+            "ConsoleOutput",
+            "FilePathSelectorFromDirectory",
+            "FrameRateModulator",
+            "LoadTextLineFromFile",
+            "MostRecentFileSelector",
+            "MultilineTextSplitter",
+            "RaftOpticalFlowNode",
+            "StringProcessor",
+            "TwoImageConcatenator",
+            "VideoTransitionBatchMerger"
+        ],
+        {
+            "title_aux": "ComfyUI-Buff-Nodes [WIP]"
+        }
+    ],
+    "https://github.com/Burgstall-labs/ComfyUI-BS_FalAi-API-Video": [
+        [
+            "FalAILipSyncNode",
+            "FalAPIOmniProNode",
+            "FalAPIVideoGeneratorI2V",
+            "FalAPIVideoGeneratorT2V",
+            "FalI2V",
+            "FalLipSync",
+            "FalOmniPro",
+            "FalOmniProV2",
+            "FalT2V"
+        ],
+        {
+            "title_aux": "ComfyUI-BS_FalAi-API-Video [WIP]"
+        }
+    ],
+    "https://github.com/Bwebbfx/ComfyUI_FaceParsing": [
+        [
+            "FaceParsingInfer",
+            "FaceParsingLoader",
+            "FacePartMask"
+        ],
+        {
+            "title_aux": "ComfyUI Face Parsing Nodes [WIP]"
+        }
+    ],
+    "https://github.com/CCSSNE/ComfyUI-AKI-DeepFree": [
+        [
+            "AKIDeepFreeExtremeCleanup"
+        ],
+        {
+            "title_aux": "ComfyUI-AKI-DeepFree"
+        }
+    ],
+    "https://github.com/COcisuts/CObot-ComfyUI-WhisperToTranscription": [
+        [
+            "CobotWhisperToTransciption"
+        ],
+        {
+            "title_aux": "CObot-ComfyUI-WhisperToTranscription [WIP]"
+        }
+    ],
+    "https://github.com/CeeVeeR/ComfyUi-Text-Tiler": [
+        [
+            "Text Tiler"
+        ],
+        {
+            "title_aux": "ComfyUi-Text-Tiler"
+        }
+    ],
+    "https://github.com/Chang-Jin-Lee/ComfyUI-PromptMixer-AI": [
+        [
+            "AutoModelQuality",
+            "FavoritePromptMixer",
+            "FreeAIPrompt",
+            "FreeAIPromptGenerate",
+            "Load_Text_Batch_Simple"
+        ],
+        {
+            "title_aux": "ComfyUI-PromptMixer-AI [WIP]"
+        }
+    ],
+    "https://github.com/Chargeuk/ComfyUI-vts-nodes": [
+        [
+            "VAELoader VTS",
+            "VTS Add Text To list",
+            "VTS Calculate Upscale Amount",
+            "VTS Clean Text",
+            "VTS Clean Text List",
+            "VTS Clear Ram",
+            "VTS Clip Text Encode",
+            "VTS Color Mask To Mask",
+            "VTS Colour Match",
+            "VTS Colour Match Advanced",
+            "VTS Colour Match First Frames",
+            "VTS Colour Match First Frames Bad Highlights",
+            "VTS Compare Latents",
+            "VTS Conditioning Set Batch Mask",
+            "VTS Convert Latents",
+            "VTS Count Characters",
+            "VTS Create Character Mask",
+            "VTS Delete Saved Images",
+            "VTS Fix Image Tags",
+            "VTS Frame Interpolate",
+            "VTS Image Batch Extend With Overlap",
+            "VTS Image Composite Masked",
+            "VTS Image Composite Masked Old",
+            "VTS Image From Batch",
+            "VTS Image To Disk",
+            "VTS Image Upscale With Model",
+            "VTS Images Crop From Masks",
+            "VTS Images Scale",
+            "VTS Images Scale To Min",
+            "VTS KSampler",
+            "VTS Latent Batch To List",
+            "VTS Latent List To Batch",
+            "VTS Looping K Sampler",
+            "VTS Math",
+            "VTS Merge Delimited Text",
+            "VTS Merge Text",
+            "VTS Merge Text Lists",
+            "VTS Prompt Batcher",
+            "VTS Random Prompt Builder",
+            "VTS Reduce Batch Size",
+            "VTS Render People Kps",
+            "VTS Repeat Text As List",
+            "VTS Replace Text In List",
+            "VTS SeedList",
+            "VTS Sharpen",
+            "VTS TAE Video Decode",
+            "VTS TAE Video Encode",
+            "VTS To Boolean",
+            "VTS To List",
+            "VTS To Text",
+            "VTS To Text List",
+            "VTS VAE Decode Tiled",
+            "VTS VAE Encode",
+            "VTS VAE Encode Tiled",
+            "VTSGenericImageWrapper",
+            "VTSLTXAddVideoICLoRAGuide",
+            "VTS_BBox_To_Normalized_Text_List",
+            "VTS_Load_Pose_Keypoints",
+            "Vts Text To Batch Prompt"
+        ],
+        {
+            "title_aux": "ComfyUI-vts-nodes [WIP]"
+        }
+    ],
+    "https://github.com/ChenXi-Core/custom_quad_picker": [
+        [
+            "QuadrilateralPicker"
+        ],
+        {
+            "title_aux": "custom_quad_picker [WIP]"
+        }
+    ],
+    "https://github.com/Chigozie619/ComfyUI_NB": [
+        [
+            "NanaBananaPro"
+        ],
+        {
+            "title_aux": "ComfyUI_NB"
+        }
+    ],
+    "https://github.com/ChrisColeTech/ComfyUI-Get-Random-File": [
+        [
+            "Get Image File By Index",
+            "Get Video File By Index",
+            "Random File Path",
+            "Random Image Path",
+            "Random Video Path"
+        ],
+        {
+            "title_aux": "ComfyUI-Get-Random-File [UNSAFE]"
+        }
+    ],
+    "https://github.com/Ckrest/comfyui-ollama-nodes": [
+        [
+            "OllamaChat",
+            "OllamaModelList",
+            "OllamaQuery"
+        ],
+        {
+            "title_aux": "comfyui-ollama-nodes [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/Clelstyn/ComfyUI-Inpaint_with_Detailer": [
+        [
+            "FilterAndBlurMask",
+            "MaskedResizeImage",
+            "PasteMaskedImage"
+        ],
+        {
+            "title_aux": "ComfyUI-Inpaint_with_Detailer"
+        }
+    ],
+    "https://github.com/Clivey1234/ComfyUI_FBX_Import": [
+        [
+            "BatchListResize",
+            "FBX_CameraDirector",
+            "FBX_Canny_Blender",
+            "FBX_Depth_Blender",
+            "FBX_Extraction",
+            "FBX_ImageBatchNumberOverlay",
+            "FBX_ImageResInfo",
+            "FBX_Info"
+        ],
+        {
+            "title_aux": "ComfyUI_FBX_Import [UNSAFE]"
+        }
+    ],
+    "https://github.com/Clybius/ComfyUI-CES": [
+        [
+            "CES_ConditioningFormula",
+            "CES_ConditioningSVDEnhancer"
+        ],
+        {
+            "title_aux": "ComfyUI-CES [WIP]"
+        }
+    ],
+    "https://github.com/Clybius/ComfyUI-FluxDeCLIP": [
+        [
+            "FluxDeCLIPCheckpointLoader"
+        ],
+        {
+            "title_aux": "ComfyUI-FluxDeCLIP"
+        }
+    ],
+    "https://github.com/Comfy-Org/ComfyUI_devtools": [
+        [
+            "DevToolsButtonWidget",
+            "DevToolsChartWidget",
+            "DevToolsColorPickerWidget",
+            "DevToolsDeprecatedNode",
+            "DevToolsDisabledWidgets",
+            "DevToolsErrorRaiseNode",
+            "DevToolsErrorRaiseNodeWithMessage",
+            "DevToolsExperimentalNode",
+            "DevToolsFileUploadSingleWidget",
+            "DevToolsFileUploadWidget",
+            "DevToolsGalleriaWidget",
+            "DevToolsImageCompareSideBySideWidget",
+            "DevToolsImageCompareWidget",
+            "DevToolsImageWidget",
+            "DevToolsInputTextWidget",
+            "DevToolsIntSliderWidget",
+            "DevToolsLoadAnimatedImageTest",
+            "DevToolsLongComboDropdown",
+            "DevToolsMultiSelectNode",
+            "DevToolsMultiSelectWidget",
+            "DevToolsNodeWithBooleanInput",
+            "DevToolsNodeWithDefaultInput",
+            "DevToolsNodeWithForceInput",
+            "DevToolsNodeWithOnlyOptionalInput",
+            "DevToolsNodeWithOptionalComboInput",
+            "DevToolsNodeWithOptionalInput",
+            "DevToolsNodeWithOutputCombo",
+            "DevToolsNodeWithOutputList",
+            "DevToolsNodeWithSeedInput",
+            "DevToolsNodeWithStringInput",
+            "DevToolsNodeWithUnionInput",
+            "DevToolsNodeWithV2ComboInput",
+            "DevToolsNodeWithValidation",
+            "DevToolsObjectPatchNode",
+            "DevToolsRemoteWidgetNode",
+            "DevToolsRemoteWidgetNodeWithControlAfterRefresh",
+            "DevToolsRemoteWidgetNodeWithParams",
+            "DevToolsRemoteWidgetNodeWithRefresh",
+            "DevToolsRemoteWidgetNodeWithRefreshButton",
+            "DevToolsSelectButtonWidget",
+            "DevToolsSelectWidget",
+            "DevToolsSimpleSlider",
+            "DevToolsSliderWidget",
+            "DevToolsTextareaWidget",
+            "DevToolsToggleSwitchWidget",
+            "DevToolsTreeSelectMultiWidget",
+            "DevToolsTreeSelectWidget"
+        ],
+        {
+            "title_aux": "ComfyUI_devtools [WIP]"
+        }
+    ],
+    "https://github.com/ComfyUI-concat/images_to_api_gemini": [
+        [
+            "ConcatImagesToAPIGeminiMPOnly",
+            "ConcatTwoImagesRaw",
+            "GridCollageARCols"
+        ],
+        {}
+    ],
+    "https://github.com/ComfyUI/Accessories": [
+        [
+            "ACC_AnyCast",
+            "AccMakeListNode",
+            "GetMaskDimensions",
+            "GetRandomDimensions",
+            "isImageEmpty",
+            "isMaskEmpty"
+        ],
+        {}
+    ],
+    "https://github.com/ComfyUI/CondsUtils": [
+        [
+            "InsertAttnConds",
+            "InsertTEConds",
+            "LoadReduxEmb",
+            "LoadTEConds",
+            "SaveCondsEmb",
+            "SaveReduxEmb"
+        ],
+        {}
+    ],
+    "https://github.com/ComfyUI/DiffsynthPause": [
+        [
+            "nynxz.Patch.ZImageDiffsynthControlnetPause"
+        ],
+        {}
+    ],
+    "https://github.com/ComfyUI/LoHalo": [
+        [
+            "LohaloHighFidelityScaler",
+            "LohaloKernelScalingNode"
+        ],
+        {}
+    ],
+    "https://github.com/ComfyUI/PromptAttention": [
+        [
+            "CLIPTextEncodeAttentionBias"
+        ],
+        {}
+    ],
+    "https://github.com/ComfyUI/ReduxEmbedToolkit": [
+        [
+            "InsertAttnConds",
+            "InsertTEConds",
+            "LoadReduxEmb",
+            "LoadTEConds",
+            "SaveCondsEmb",
+            "SaveReduxEmb"
+        ],
+        {}
+    ],
+    "https://github.com/ComfyUI/SamplingUtils": [
+        [
+            "AdjustedResolutionParameters",
+            "BonusPromptPresets",
+            "CameraShotPresets",
+            "EditOpPresets",
+            "EditTargetPresets",
+            "Frakturpad",
+            "GetJsonKeyValue",
+            "IdeographicLinePad",
+            "IdeographicSentencePad",
+            "IdeographicTagPad",
+            "ImageBlendByMask",
+            "ImageMatchProperties",
+            "Image_Color_Noise",
+            "InstructPromptPresets",
+            "IntegerRangeRandom",
+            "JoinerPadding",
+            "LlTokenizerOptions",
+            "ModifyMask",
+            "OpticalFlowComposite",
+            "PrimitiveRandomInt",
+            "PrimitiveRandomIntRange",
+            "PrimitiveStaticInt",
+            "SU_LoadImageDirectory",
+            "SU_LoadImagePath",
+            "SamplingParameters",
+            "SystemMessagePresets",
+            "TagNormalizeCombine",
+            "TextEncodeFlux2SystemPrompt",
+            "TextEncodeKleinSystemPrompt",
+            "TextEncodeSystemPrompt",
+            "TextEncodeZITSystemPrompt",
+            "TextEncodeZImageThinkPrompt",
+            "TextOverlayNode",
+            "UNETLoaderAsync",
+            "UnFrakturPad",
+            "UnifiedPresets",
+            "VLMSysInstrAdvPresets",
+            "VLMSysInstrPresets",
+            "VLMSysQueryAddPresets"
+        ],
+        {}
+    ],
+    "https://github.com/ComfyUI/Text_Processor": [
+        [
+            "AddTextToImage",
+            "AdvancedTextFilter",
+            "TextInput",
+            "TextScraper",
+            "TextStorage",
+            "Wildcards",
+            "WildcardsAdv"
+        ],
+        {}
+    ],
+    "https://github.com/CraftopiaStudio/ComfyUI-WorkflowOrganizer": [
+        [
+            "WorkflowOrganizerInfo"
+        ],
+        {
+            "title_aux": "ComfyUI-WorkflowOrganizer [WIP]"
+        }
+    ],
+    "https://github.com/CypherNaught-0x/ComfyUI-StarVector": [
+        [
+            "LoadSVG",
+            "SVGPreview",
+            "SVGPreviewFromString",
+            "SVGToString",
+            "SaveSVG",
+            "StarVectorImage2SVG",
+            "StarVectorModelLoader",
+            "StringToSVG"
+        ],
+        {
+            "title_aux": "ComfyUI-StarVector [WIP]"
+        }
+    ],
+    "https://github.com/D1-3105/ComfyUI-VideoStream": [
+        [
+            "FloWWeaverExportSingleFrameGRPC",
+            "FloWWeaverFrameNoBroker"
+        ],
+        {
+            "title_aux": "ComfyUI-VideoStream"
+        }
+    ],
+    "https://github.com/DataCTE/ComfyUI-DataVoid-nodes": [
+        [
+            "IPAAdapterFaceIDBatch",
+            "IPAdapter",
+            "IPAdapterAdvanced",
+            "IPAdapterBatch",
+            "IPAdapterClipVisionEnhancer",
+            "IPAdapterClipVisionEnhancerBatch",
+            "IPAdapterCombineEmbeds",
+            "IPAdapterCombineParams",
+            "IPAdapterCombineWeights",
+            "IPAdapterEmbeds",
+            "IPAdapterEmbedsBatch",
+            "IPAdapterEncoder",
+            "IPAdapterFaceID",
+            "IPAdapterFaceIDKolors",
+            "IPAdapterFromParams",
+            "IPAdapterInsightFaceLoader",
+            "IPAdapterLoadEmbeds",
+            "IPAdapterMS",
+            "IPAdapterModelLoader",
+            "IPAdapterNoise",
+            "IPAdapterPreciseComposition",
+            "IPAdapterPreciseCompositionBatch",
+            "IPAdapterPreciseStyleTransfer",
+            "IPAdapterPreciseStyleTransferBatch",
+            "IPAdapterPromptScheduleFromWeightsStrategy",
+            "IPAdapterRegionalConditioning",
+            "IPAdapterSameEnergy",
+            "IPAdapterSaveEmbeds",
+            "IPAdapterStyleComposition",
+            "IPAdapterStyleCompositionBatch",
+            "IPAdapterTiled",
+            "IPAdapterTiledBatch",
+            "IPAdapterUnifiedLoader",
+            "IPAdapterUnifiedLoaderCommunity",
+            "IPAdapterUnifiedLoaderFaceID",
+            "IPAdapterWeights",
+            "IPAdapterWeightsFromStrategy",
+            "MegaMergeSDXL",
+            "PrepImageForClipVision"
+        ],
+        {
+            "title_aux": "ComfyUI-DataVoid-nodes [WIP]"
+        }
+    ],
+    "https://github.com/DavidJBarnes/comfyui-djb-utils": [
+        [
+            "DirectImageFilenameInput",
+            "ResolutionDetectorFromFilename",
+            "WAN21ModelTracker",
+            "WorkingFilenameExtractor"
+        ],
+        {
+            "title_aux": "comfyui-djb-utils"
+        }
+    ],
+    "https://github.com/DeTK/ComfyUI-Switch": [
+        [
+            "NodeSwitch"
+        ],
+        {
+            "title_aux": "ComfyUI Node Switcher"
+        }
+    ],
+    "https://github.com/DenRakEiw/Comfyui-Aspect-Ratio-Processor": [
+        [
+            "AspectRatioProcessor"
+        ],
+        {
+            "title_aux": "Comfyui-Aspect-Ratio-Processor [WIP]"
+        }
+    ],
+    "https://github.com/DenRakEiw/DenRakEiw_Nodes": [
+        [
+            "ColorGeneratorNode",
+            "ConditioningInspector",
+            "FluxLayerDiffuseConditioningFix",
+            "FluxLayerDiffuseDecoderSimple",
+            "FluxLayerDiffuseEmptyConditioning",
+            "FluxLayerDiffuseInfo",
+            "FluxLayerDiffuseStandaloneLoader",
+            "LatentColorMatch",
+            "LatentColorMatchSimple",
+            "LatentImageAdjust",
+            "LoadImageSequence",
+            "LoadImageSequenceInfo",
+            "MultiImageAspectRatioComposer",
+            "OptimizedWanVAEUpscaler",
+            "PreviewTransparentImage",
+            "SaveTransparentImage",
+            "TransparentImageInfo",
+            "WanNNLatentUpscaler"
+        ],
+        {
+            "title_aux": "Denrakeiw Nodes [WIP]"
+        }
+    ],
+    "https://github.com/DeyanShahov/ComfyUI_RedFox": [
+        [
+            "DynamicPromptSelector"
+        ],
+        {
+            "title_aux": "Dynamic Prompt Selector for ComfyUI [WIP]"
+        }
+    ],
+    "https://github.com/DiffusionWave-YT/DiffusionWave_PickResolution": [
+        [
+            "Blacklist_String_DiffusionWave \ud83c\udf0a",
+            "ImageBatchMulti_DiffusionWave",
+            "ImageSimpleSaver_DiffusionWave",
+            "Int_PickResolution_DiffusionWave \ud83c\udf0a",
+            "LoadImagesFromFolder_DiffusionWave",
+            "MergeImages_DiffusionWave",
+            "Order_String_Tags_DiffusionWave \ud83c\udf0a",
+            "OverlayImages_DiffusionWave",
+            "PickResolution_DiffusionWave \ud83c\udf0a",
+            "PromptExpression_DiffusionWave \ud83c\udf0a",
+            "RemoveBackgroundByColor_DiffusionWave",
+            "ResizeLongestSide_DiffusionWave",
+            "Seed__DiffusionWave \ud83c\udf0a"
+        ],
+        {
+            "title_aux": "DiffusionWave_PickResolution [WIP]"
+        }
+    ],
+    "https://github.com/DoctorDiffusion/ComfyUI-Flashback": [
+        [
+            "LatentExport",
+            "LatentImport",
+            "LatentLoop"
+        ],
+        {
+            "title_aux": "ComfyUI-Flashback"
+        }
+    ],
+    "https://github.com/DonutsDelivery/ComfyUI-DonutNodes": [
+        [
+            "ApplyLBW //Inspire",
+            "Donut Block Calibration",
+            "Donut Detailer",
+            "Donut Detailer 2",
+            "Donut Detailer 4",
+            "Donut Detailer LoRA 5",
+            "Donut Detailer XL Blocks",
+            "Donut Frequency Analysis",
+            "Donut Sharpener",
+            "Donut Sharpener (from reference)",
+            "Donut Simple Calibration",
+            "DonutApplyLoRAStack",
+            "DonutAutoGamma",
+            "DonutAutoWhiteBalance",
+            "DonutCAS",
+            "DonutCacheDebug",
+            "DonutCheckpointSave",
+            "DonutClipEncode",
+            "DonutDetailerZIT",
+            "DonutFaceDetailer",
+            "DonutFillerClip",
+            "DonutFillerModel",
+            "DonutGammaCorrection",
+            "DonutHiRaLoAm",
+            "DonutHistogramStretch",
+            "DonutHotReload",
+            "DonutImageReporter",
+            "DonutLoRACivitAIInfo",
+            "DonutLoRAHashLookup",
+            "DonutLoRALibrary",
+            "DonutLoRAStack",
+            "DonutLoraStackCombine",
+            "DonutManualCleanup",
+            "DonutModelSave",
+            "DonutMultiModelSampler",
+            "DonutOpenCivitAI",
+            "DonutPromptInjection",
+            "DonutPromptReceiver",
+            "DonutSDXLTeaCache",
+            "DonutSDXLTeaCacheStats",
+            "DonutSampler",
+            "DonutSampler (Advanced)",
+            "DonutTiledUpscale",
+            "DonutUniversalDetailer",
+            "DonutWidenMergeCLIP",
+            "DonutWidenMergeUNet",
+            "LoadLBW //Inspire",
+            "LoraBlockInfo //Inspire",
+            "LoraLoaderBlockWeight //Inspire",
+            "MakeLBW //Inspire",
+            "ModelMergeZIT",
+            "ModelMergeZITBlocks",
+            "SaveLBW //Inspire",
+            "XY Input: Lora Block Weight //Inspire"
+        ],
+        {
+            "title_aux": "ComfyUI-DonutDetailer"
+        }
+    ],
+    "https://github.com/DrMWeigand/ComfyUI_LineBreakInserter": [
+        [
+            "LineBreakInserter"
+        ],
+        {
+            "title_aux": "ComfyUI_LineBreakInserter"
+        }
+    ],
+    "https://github.com/DrTimAI/drtim_my_node_pack_playground": [
+        [
+            "StringSelector",
+            "StringSelectorAspectRatio",
+            "StringSelectorOR"
+        ],
+        {
+            "title_aux": "drtim_my_node_pack_playground [WIP]"
+        }
+    ],
+    "https://github.com/DraconicDragon/ComfyUI_e621_booru_toolkit": [
+        [
+            "BTK_PixAITaggerNode",
+            "GetAIBooruPost",
+            "GetAnyBooruPostAdv",
+            "GetBooruPost",
+            "GetDanbooruPost",
+            "GetE621Post",
+            "GetGelbooruPost",
+            "TagWikiFetch"
+        ],
+        {
+            "title_aux": "ComfyUI e621 booru Toolkit"
+        }
+    ],
+    "https://github.com/Dream-Pixels-Forge/ComfyUI-Mzikart-Player": [
+        [
+            "MzikartPlayerNode"
+        ],
+        {
+            "title_aux": "ComfyUI-Mzikart-Player [WIP]"
+        }
+    ],
+    "https://github.com/Dream-Pixels-Forge/ComfyUI-Mzikart-Vocal": [
+        [
+            "MasteringCombinerNode",
+            "MzikartNoiseCutter",
+            "MzikartPlayerNode",
+            "VocalCompressorNode",
+            "VocalDeesserNode",
+            "VocalDoublerNode",
+            "VocalEQNode",
+            "VocalLimiterNode",
+            "VocalProcessorNode",
+            "VocalReverbNode"
+        ],
+        {
+            "title_aux": "ComfyUI-Mzikart-Vocal [WIP]"
+        }
+    ],
+    "https://github.com/Dream-Pixels-Forge/ComfyUI-RendArt-Nodes": [
+        [
+            "RendArtNode",
+            "RendArtUltimateNode"
+        ],
+        {
+            "title_aux": "ComfyUI-RendArt-Nodes"
+        }
+    ],
+    "https://github.com/DreamsInAutumn/ComfyUI-Autumn-LLM-Nodes": [
+        [
+            "GeminiImageToPrompt",
+            "GeminiPromptBuilder",
+            "LLMPromptBuilder"
+        ],
+        {
+            "title_aux": "ComfyUI-Autumn-LLM-Nodes"
+        }
+    ],
+    "https://github.com/Dreamshot-io/ComfyUI-Extend-Resolution": [
+        [
+            "ResolutionPadding"
+        ],
+        {
+            "title_aux": "ComfyUI-Extend-Resolution"
+        }
+    ],
+    "https://github.com/ELiZswe/ComfyUI-ELiZTools": [
+        [
+            "ELiZMeshUVWrap"
+        ],
+        {
+            "title_aux": "ComfyUI-ELiZTools"
+        }
+    ],
+    "https://github.com/Egorkkk/ComfyUI-WOFTSAM-PlanarTracker": [
+        [
+            "WOFTSAM_Corners_Track"
+        ],
+        {
+            "title_aux": "ComfyUI-WOFTSAM-PlanarTracker [WIP]"
+        }
+    ],
+    "https://github.com/ElyZeng/ComfyUI-Translator": [
+        [
+            "TextTranslatorNode"
+        ],
+        {
+            "title_aux": "ComfyUI-Translator [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/Elypha/ComfyUI-Prompt-Helper": [
+        [
+            "PromptHelper_CombineConditioning",
+            "PromptHelper_ConcatConditioning",
+            "PromptHelper_ConcatString",
+            "PromptHelper_EncodeMultiStringCombine",
+            "PromptHelper_FormatString",
+            "PromptHelper_LoadImageBatchFromDir",
+            "PromptHelper_LoadImageListFromDir",
+            "PromptHelper_LoadPreset",
+            "PromptHelper_LoadPresetAdvanced",
+            "PromptHelper_SaveImageToDir",
+            "PromptHelper_String",
+            "PromptHelper_StringMultiLine",
+            "PromptHelper_WeightedPrompt"
+        ],
+        {
+            "title_aux": "ComfyUI-Prompt-Helper [WIP]"
+        }
+    ],
+    "https://github.com/EmanueleUniroma2/ComfyUI-FLAC-to-WAV": [
+        [
+            "AudioToWavConverter"
+        ],
+        {
+            "title_aux": "ComfyUI-FLAC-to-WAV [WIP]"
+        }
+    ],
+    "https://github.com/EmilioPlumed/ComfyUI-Math": [
+        [
+            "GreatestCommonDenominator",
+            "LowestCommonMultiple"
+        ],
+        {
+            "title_aux": "ComfyUI-Math [WIP]"
+        }
+    ],
+    "https://github.com/Enferlain/ComfyUI-Model-Comparison": [
+        [
+            "ModelComparisoner"
+        ],
+        {
+            "title_aux": "ComfyUI-Model-Comparison [WIP]"
+        }
+    ],
+    "https://github.com/Enferlain/ComfyUI-SamplerCustom-3Decimals": [
+        [
+            "SamplerCustom3Decimals"
+        ],
+        {
+            "title_aux": "ComfyUI-SamplerCustom-3Decimals"
+        }
+    ],
+    "https://github.com/Enferlain/ComfyUI-extra-schedulers": [
+        [
+            "BetaSchedulerV2C",
+            "BetaSchedulerV2CRaw",
+            "BetaSchedulerV3"
+        ],
+        {
+            "title_aux": "ComfyUI-extra-schedulers [WIP]"
+        }
+    ],
+    "https://github.com/Eric7758/ComfyUI-IAT": [
+        [
+            "Base64ToImageNode by IAT",
+            "FloatInputNode by IAT",
+            "GPTReversePrompt by IAT",
+            "ImageColorPaletteExtractor by IAT",
+            "ImageMatchSize by IAT",
+            "ImageResizeLongestSide by IAT",
+            "ImageResizeToSDXL by IAT",
+            "ImageSize by IAT",
+            "IntInputNode by IAT",
+            "Qwen35DatasetRAGReversePrompt by IAT",
+            "Qwen35PromptEnhancer by IAT",
+            "Qwen35ReversePrompt by IAT",
+            "QwenKontextTranslator by IAT",
+            "QwenTranslator by IAT",
+            "SaveAudioMP3WithCover by IAT",
+            "SeedGeneratorNode by IAT",
+            "SmartPathBuilderNode by IAT",
+            "TextInputNode by IAT"
+        ],
+        {
+            "title_aux": "ComfyUI-IAT [WIP]"
+        }
+    ],
+    "https://github.com/EricRollei/Comfy-Metadata-System": [
+        [
+            "EnhancedMetadataFilterNode_V2",
+            "Eric_Duplicate_Image_Finder_v021",
+            "Eric_Image_Sorter_V13",
+            "Eric_Keyword_Sorter_V6",
+            "Eric_Metadata_Debugger_V2",
+            "Eric_Metadata_Entry_V2",
+            "Eric_Metadata_Filter_V2",
+            "Eric_Metadata_Query_V3",
+            "MetadataAwareSaveImage_v099",
+            "MetadataConsolidatorNode_V2",
+            "PngInfoDiagnosticV3",
+            "PngMetadataExtractorV3",
+            "TextOverlayNode_v04"
+        ],
+        {
+            "title_aux": "Comfy-Metadata-System [WIP]"
+        }
+    ],
+    "https://github.com/EricRorich/ComfyUI-MegaTran-cutom-node": [
+        [
+            "Mega tran",
+            "Megatran"
+        ],
+        {
+            "title_aux": "ComfyUI-MegaTran-cutom-node [WIP]"
+        }
+    ],
+    "https://github.com/Estanislao-Oviedo/ComfyUI-CustomNodes": [
+        [
+            "Attention couple",
+            "AttentionCouple",
+            "CombineVideoClips",
+            "LoadImageFolder",
+            "MakeBatchFromSingleImage",
+            "RegionConditionMerge",
+            "RegionConditionSpecPct",
+            "RegionConditionSpecPx",
+            "WanVideoVaceSeamlessJoin"
+        ],
+        {
+            "title_aux": "ComfyUI-CustomNodes [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/ExponentialML/ComfyUI_LiveDirector": [
+        [
+            "LiveDirector"
+        ],
+        {
+            "title_aux": "ComfyUI_LiveDirector (WIP)"
+        }
+    ],
+    "https://github.com/Extraltodeus/Conditioning-token-experiments-for-ComfyUI": [
+        [
+            "Automatic wildcards",
+            "Conditioning (Cosine similarities)",
+            "Conditioning (Maximum absolute)",
+            "Conditioning (Maximum absolute) text inputs",
+            "Conditioning (Scale by absolute sum)",
+            "Conditioning merge clip g/l",
+            "Conditioning similar tokens recombine",
+            "Conditioning to text",
+            "Quick and dirty text encode",
+            "encode_all_tokens_SDXL"
+        ],
+        {
+            "title_aux": "Conditioning-token-experiments-for-ComfyUI"
+        }
+    ],
+    "https://github.com/Fannovel16/ComfyUI-AppIO": [
+        [
+            "AppIO_FitResizeImage",
+            "AppIO_ImageInput",
+            "AppIO_ImageInputFromID",
+            "AppIO_ImageOutput",
+            "AppIO_IntegerInput",
+            "AppIO_ResizeInstanceAndPaste",
+            "AppIO_ResizeInstanceImageMask",
+            "AppIO_StringInput",
+            "AppIO_StringOutput"
+        ],
+        {
+            "title_aux": "ComfyUI-AppIO"
+        }
+    ],
+    "https://github.com/Filexor/File_x_dynamic_prompt2": [
+        [
+            "File_x_DynamicPrompt2",
+            "File_x_DynamicPrompt2 with States IO"
+        ],
+        {
+            "title_aux": "File_x_dynamic_prompt2"
+        }
+    ],
+    "https://github.com/FinetunersAI/comfyui-fast-group-link": [
+        [
+            "FastGroupLink"
+        ],
+        {
+            "title_aux": "Fast Group Link [WIP]"
+        }
+    ],
+    "https://github.com/FinetunersAI/finetuners": [
+        [
+            "AutoImageResize",
+            "GroupLink",
+            "VariablesInjector"
+        ],
+        {
+            "title_aux": "ComfyUI Finetuners [WIP]"
+        }
+    ],
+    "https://github.com/Firetheft/ComfyUI_Local_Media_Manager": [
+        [
+            "LocalMediaManagerNode",
+            "SelectOriginalAudioNode",
+            "SelectOriginalImageNode",
+            "SelectOriginalVideoNode"
+        ],
+        {
+            "title_aux": "ComfyUI Local Media Manager [UNSAFE]"
+        }
+    ],
+    "https://github.com/FloranceYeh/ComfyUI_F_nodes": [
+        [
+            "F_CLIPTextSwitchEncode",
+            "F_DynamicMultiSwitch",
+            "F_DynamicRelay",
+            "F_DynamicSwitch",
+            "F_KSamplerPreset"
+        ],
+        {
+            "title_aux": "ComfyUI_F_nodes"
+        }
+    ],
+    "https://github.com/FoundD-oka/ComfyUI-kisekae-OOTD": [
+        [
+            "LoadOOTDPipeline",
+            "LoadOOTDPipelineHub",
+            "OOTDGenerate"
+        ],
+        {
+            "title_aux": "ComfyUI KISEKAE-OOTD"
+        }
+    ],
+    "https://github.com/Fucci-Mateo/ComfyUI-Airtable": [
+        [
+            "Push pose to Airtable"
+        ],
+        {
+            "title_aux": "ComfyUI-Airtable [WIP]"
+        }
+    ],
+    "https://github.com/Fuhze73/ComfyUI-ElGenerator-Klein": [
+        [
+            "Klein_Combine",
+            "Klein_Face",
+            "Klein_NSFW",
+            "Klein_People",
+            "Klein_Pose",
+            "Klein_Scene",
+            "Klein_Subject",
+            "Klein_Wardrobe"
+        ],
+        {
+            "title_aux": "El Generator Klein [WIP]"
+        }
+    ],
+    "https://github.com/Fuhze73/ComfyUI-PainterI2V-StartEndImage-Tiled": [
+        [
+            "PainterI2V",
+            "PainterI2VTiled"
+        ],
+        {
+            "title_aux": "ComfyUI-PainterI2V-StartEndImage-Tiled [WIP]"
+        }
+    ],
+    "https://github.com/Fuhze73/ComfyUI-PainterI2VAdvanced-TiledVAE": [
+        [
+            "PainterI2VAdvancedTiledVAE"
+        ],
+        {
+            "title_aux": "comfyui-painteri2vadvanced-tiledvae [WIP]"
+        }
+    ],
+    "https://github.com/GGBroX/ComfyUI-Router": [
+        [
+            "GGBro Channel Selector",
+            "GGBro Get",
+            "GGBro Router",
+            "GGBro Router IN",
+            "GGBro Router OUT",
+            "GGBro Set"
+        ],
+        {
+            "title_aux": "ComfyUI-Router [WIP]"
+        }
+    ],
+    "https://github.com/GalactusX31/ComfyUI-FileBrowserAPI": [
+        [
+            "PathSelectorNode"
+        ],
+        {
+            "title_aux": "ComfyUI-FileBrowserAPI [UNSAFE]"
+        }
+    ],
+    "https://github.com/GeekatplayStudio/ComfyUI_Toolbox": [
+        [
+            "ModelAspectRatioSelector"
+        ],
+        {
+            "title_aux": "PMSnodes [WIP]"
+        }
+    ],
+    "https://github.com/GentlemanHu/ComfyUI-Notifier": [
+        [
+            "GentlemanHu_Notifier"
+        ],
+        {
+            "title_aux": "ComfyUI-Notifier"
+        }
+    ],
+    "https://github.com/George0726/ComfyUI-video-accessory": [
+        [
+            "VideoAcc_CameraTrajectoryAdvance",
+            "VideoAcc_CameraTrajectoryRecam",
+            "VideoAcc_ImageResizeAdvanced",
+            "VideoAcc_ImageUpscaleVideo",
+            "VideoAcc_Kontext_removal",
+            "VideoAcc_LoadImage",
+            "VideoAcc_LoadVideo",
+            "VideoAcc_SaveMP4",
+            "VideoAcc_imageSize"
+        ],
+        {
+            "title_aux": "ComfyUI-video-accessory [WIP]"
+        }
+    ],
+    "https://github.com/Gergzilla/Slim-Comfy-Pack": [
+        [
+            "SC Apply LoRA Stack",
+            "SC Aspect Ratio",
+            "SC LoRA Stack",
+            "SC Load LoRA",
+            "SC SD1.5 Aspect Ratio",
+            "SC SDXL Aspect Ratio",
+            "SC Seed"
+        ],
+        {
+            "author": "Gergz",
+            "description": "Custom collection of nodes and custom nodes for ComfyUI v0.6.38+",
+            "nickname": "Slim Comfy Pack",
+            "title": "Slim Comfy Pack",
+            "title_aux": "Slim-Comfy-Pack [private]"
+        }
+    ],
+    "https://github.com/Ginolazy/ComfyUI-FluxKontextImageCompensate": [
+        [
+            "FluxKontextImageCompensate",
+            "FluxKontextImageRestore"
+        ],
+        {
+            "title_aux": "ComfyUI-FluxKontextImageCompensate [WIP]"
+        }
+    ],
+    "https://github.com/GitHug/ComfyQueue": [
+        [
+            "PromptQueue"
+        ],
+        {
+            "title_aux": "ComfyQueue"
+        }
+    ],
+    "https://github.com/GoddessLabs/ComfyUI-GoddessLabs-NodePack": [
+        [
+            "GoddessLabsDestroyAndRecreate",
+            "GoddessLabsFolderSelector",
+            "GoddessLabsNodeStateManager"
+        ],
+        {
+            "author": "GoddessLabs",
+            "description": "Custom nodes to conveniently enhance images through Detector, Detailer, Upscaler, Pipe, and more.",
+            "nickname": "GoddessLabs",
+            "title": "\u2764\ufe0f\u200d\ud83d\udd25\ud83d\udc8a GoddessLabs Toolset",
+            "title_aux": "ComfyUI-GoddessLabs-NodePack"
+        }
+    ],
+    "https://github.com/GokuHiki/ComfyUI-Goku-Tools": [
+        [
+            "FirstNonFalse"
+        ],
+        {
+            "title_aux": "ComfyUI-Goku-Tools"
+        }
+    ],
+    "https://github.com/Goldlionren/ComfyUI-CleanSlate": [
+        [
+            "CleanSlateCleanup"
+        ],
+        {
+            "title_aux": "ComfyUI-CleanSlate [WIP]"
+        }
+    ],
+    "https://github.com/Goldlionren/ComfyUI_Bridge_fabric": [
+        [
+            "LocalCLIPClient",
+            "RemoteCLIPHostSaver"
+        ],
+        {
+            "title_aux": "ComfyUI_Bridge_fabric [UNSAFE]"
+        }
+    ],
+    "https://github.com/Grant-CP/ComfyUI-LivePortraitKJ-MPS": [
+        [
+            "DownloadAndLoadLivePortraitModels",
+            "LivePortraitProcess"
+        ],
+        {
+            "title_aux": "ComfyUI-LivePortraitKJ-MPS"
+        }
+    ],
+    "https://github.com/Grey3016/Save2Icon": [
+        [
+            "ConvertToIconNode"
+        ],
+        {
+            "title_aux": "Save2Icon"
+        }
+    ],
+    "https://github.com/GrindHouse66/ComfyUI-GH_Tools": [
+        [
+            "GHImg_Sizer",
+            "GHSimple_Scale"
+        ],
+        {
+            "title_aux": "GH Tools for ComfyUI"
+        }
+    ],
+    "https://github.com/GuardSkill/ComfyUI-AffineImage": [
+        [
+            "CanvasFourPointSelector",
+            "PerspectiveScreenMapper"
+        ],
+        {
+            "title_aux": "ComfyUI Affine Transform Mapper [WIP]"
+        }
+    ],
+    "https://github.com/GuusF/Comfyui_CrazyMaths": [
+        [
+            "AttractorAlphaMask",
+            "EquationAlphaMask",
+            "FractalNoiseAlphaMask",
+            "HarmonographAlphaMask",
+            "MathAlphaMask",
+            "QuasicrystalAlphaMask",
+            "SierpinskiAlphaMask",
+            "VoronoiAlphaMask"
+        ],
+        {
+            "title_aux": "Comfyui_CrazyMaths [WIP]"
+        }
+    ],
+    "https://github.com/HWDigi/Camera_Factory_Station_comfyui": [
+        [
+            "FactoryCameraOperator",
+            "FactoryColorHarmonist",
+            "FactoryLightingStudio",
+            "FactoryProductPhotographer",
+            "FactorySizeOptimizer"
+        ],
+        {
+            "title_aux": "Camera Factory Station [WIP]"
+        }
+    ],
+    "https://github.com/HalfADog/ComfyUI-Mask2JSON": [
+        [
+            "ContourVisualizer",
+            "MaskToContourJSON"
+        ],
+        {
+            "title_aux": "ComfyUI-Mask2JSON"
+        }
+    ],
+    "https://github.com/Hanmingsan/ComfyUI-Model-Downloader": [
+        [
+            "CivitaiDownloader",
+            "HFDownloader"
+        ],
+        {
+            "title_aux": "ComfyUI-Model-Downloader"
+        }
+    ],
+    "https://github.com/Hapseleg/ComfyUI-This-n-That": [
+        [
+            "Show Prompt (Hapse)",
+            "Show Prompt TnT",
+            "Simple Ratio Selector (Hapse)",
+            "Simple Ratio Selector TnT",
+            "Simple Seed Selector TnT"
+        ],
+        {
+            "title_aux": "This n that (Hapse)"
+        }
+    ],
+    "https://github.com/Hifunyo/comfyui_google_ai": [
+        [
+            "GoogleAIGenerateImage"
+        ],
+        {
+            "title_aux": "comfyui_google_ai"
+        }
+    ],
+    "https://github.com/HuajunPeng1982/comfyui-grsai-storyboard": [
+        [
+            "GrsaiStoryboardGenerator"
+        ],
+        {
+            "title_aux": "comfyui-grsai-storyboard"
+        }
+    ],
+    "https://github.com/IAFFeng/Comfyui_XF_Custom_Actual-Node": [
+        [
+            "GeminiMattingNode"
+        ],
+        {
+            "title_aux": "Comfyui_XF_Custom_Actual-Node"
+        }
+    ],
+    "https://github.com/IAmAshishNegi/comfyui_tensor_parallel_v3": [
+        [
+            "TensorParallelV3Node"
+        ],
+        {
+            "title_aux": "comfyui_tensor_parallel_v3"
+        }
+    ],
+    "https://github.com/IIEleven11/ComfyUI-Dataset_Maker": [
+        [
+            "ConceptList",
+            "DatasetGenerator",
+            "DatasetLoraLoader",
+            "DatasetPromptBuilder",
+            "LoraList"
+        ],
+        {
+            "title_aux": "ComfyUI-Dataset_Maker [WIP]"
+        }
+    ],
+    "https://github.com/IO-AtelierTech/comfyui-genai-connectors": [
+        [
+            "BooleanInput_fal",
+            "FloatInput_fal",
+            "IntegerInput_fal",
+            "LoadImageFromURL_fal",
+            "RemoteCheckpointLoader_fal",
+            "RemoteLoraLoader_fal",
+            "SaveImage_fal",
+            "StringInput_fal"
+        ],
+        {
+            "title_aux": "comfyui-genai-connectors [WIP]"
+        }
+    ],
+    "https://github.com/IO-AtelierTech/comfyui-video-utils": [
+        [
+            "GetVideoPath",
+            "SaveVideoGetPath"
+        ],
+        {
+            "title_aux": "comfyui-video-utils [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/IXIWORKS-KIMJUNGHO/comfyui-ixiworks": [
+        [
+            "BuildCharacterPromptNode",
+            "BuildPromptNode",
+            "JsonParserNode",
+            "MergeStringsNode",
+            "SelectIndexNode"
+        ],
+        {
+            "title_aux": "IxiWorks StoryBoard Nodes [WIP]"
+        }
+    ],
+    "https://github.com/IfnotFr/ComfyUI-Ifnot-Pack": [
+        [
+            "Face Crop",
+            "Face Crop Mouth",
+            "Get Beard Mask"
+        ],
+        {
+            "title_aux": "ComfyUI-Ifnot-Pack"
+        }
+    ],
+    "https://github.com/IgPoly/ComfyUI-igTools": [
+        [
+            "IGT_AspectRatioResizer",
+            "IGT_FloatMinMax",
+            "IGT_ImageResizer",
+            "IGT_ImageTilesCalc",
+            "IGT_IntMinMax",
+            "IGT_LoadImageBatch",
+            "IGT_LoadSingleImage",
+            "IGT_SimpleTilesCalc",
+            "IGT_TelegramSender"
+        ],
+        {
+            "title_aux": "ComfyUI-igTools"
+        }
+    ],
+    "https://github.com/Immac/ComfyUI-ImmacTools": [
+        [
+            "ConcatenateSigmasImmacTools",
+            "ForwardAnyImmacTools",
+            "ForwardConditioningImmacTools",
+            "ForwardModelImmacTools",
+            "MatchContrastImmacTools",
+            "ResampleSigmasImmacTools",
+            "SkipEveryNthImagesImmacTools",
+            "SpliceSigmasAtImmacTools",
+            "SwitchImmacTools"
+        ],
+        {
+            "title_aux": "Immac's Tools"
+        }
+    ],
+    "https://github.com/InformedViewz/ComfyUI-InformedViewz": [
+        [
+            "InformedViewz Image Sizes"
+        ],
+        {
+            "title_aux": "ComfyUI-InformedViewz [WIP]"
+        }
+    ],
+    "https://github.com/Inoriac/comfyui-HandDetect": [
+        [
+            "HandMaskGenerator"
+        ],
+        {
+            "title_aux": "comfyui-HandDetect"
+        }
+    ],
+    "https://github.com/IsacEkeroth/ComfyUI-Audio-Speed": [
+        [
+            "AudioSpeed"
+        ],
+        {
+            "title_aux": "comfyui-ffmpeg-audiospeed [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/Isi-dev/ComfyUI_DeleteModelPassthrough_ExecutionFlowControl": [
+        [
+            "ControlledCLIPTextEncode",
+            "ControlledControlNetLoader",
+            "ControlledModelPatchLoader",
+            "ControlledUNETLoader",
+            "ControlledUnetLoaderGGUF",
+            "ControlledVAELoader",
+            "DeleteModelPassthrough",
+            "DeleteModelPassthroughLight",
+            "SmartClipDeleter"
+        ],
+        {
+            "title_aux": "ComfyUI_DeleteModelPassthrough_ExecutionFlowControl [WIP]"
+        }
+    ],
+    "https://github.com/JBKing514/map_comfyui": [
+        [
+            "MAP_Pro_Suite"
+        ],
+        {
+            "title_aux": "map_comfyui [WIP]"
+        }
+    ],
+    "https://github.com/JHBOY-ha/ComfyUI-jh-essential": [
+        [
+            "EndTimer",
+            "StartTimer"
+        ],
+        {
+            "title_aux": "ComfyUI-jh-essential"
+        }
+    ],
+    "https://github.com/JackZhaoYK/ComfyUI-Shared": [
+        [
+            "ApplyMaskedBlendNode",
+            "ImagePlacementNode",
+            "YoloPreprocessor"
+        ],
+        {
+            "title_aux": "ComfyUI-Shared [WIP]"
+        }
+    ],
+    "https://github.com/Jairodaniel-17/ComfyUI-traductor-offline": [
+        [
+            "CLIPTextTranslateNode",
+            "PromptTextTranslateNode"
+        ],
+        {
+            "title_aux": "ComfyUI-traductor-offline"
+        }
+    ],
+    "https://github.com/Jalen-Brunson/ComfyUI-PoseCleanFace": [
+        [
+            "PoseRedrawCleanFace"
+        ],
+        {
+            "title_aux": "ComfyUI-PoseCleanFace"
+        }
+    ],
+    "https://github.com/Jalen-Brunson/ComfyUI-vace-step-schedule": [
+        [
+            "CfgScheduleModelPatch",
+            "LatentNoiseMaskInspect",
+            "LatentNoiseMaskRebase",
+            "LatentPrependRefFrame",
+            "LatentTailContinuation",
+            "LoraStepScheduleLoader",
+            "MaskPrependRefFrame",
+            "VaceAutoTrimLatent",
+            "VaceScheduleKSampler",
+            "VaceScheduleModelPatch",
+            "WanPixelMaskToLatentMask",
+            "WanVaceToVideoScheduled"
+        ],
+        {
+            "title_aux": "ComfyUI-vace-step-schedule"
+        }
+    ],
+    "https://github.com/Jaxkr/comfyui-terminal-command": [
+        [
+            "Terminal"
+        ],
+        {
+            "title_aux": "comfyui-terminal-command [UNSAFE]"
+        }
+    ],
+    "https://github.com/JayLyu/ComfyUI_BaiKong_Node": [
+        [
+            "BK_ColorContrast",
+            "BK_ColorLimit",
+            "BK_ColorLuminance",
+            "BK_ColorSelector",
+            "BK_GradientImage",
+            "BK_ImageAspectFilter",
+            "BK_ImageList",
+            "BK_ImageRandomLayout",
+            "BK_ImageRectLayout",
+            "BK_Img2Color",
+            "BK_PrintImage"
+        ],
+        {
+            "title_aux": "ComfyUI_BaiKong_Node"
+        }
+    ],
+    "https://github.com/JiSenHua/ComfyUI-yolov5-face": [
+        [
+            "FaceDetect"
+        ],
+        {
+            "title_aux": "ComfyUI-yolov5-face [WIP]"
+        }
+    ],
+    "https://github.com/JiangAogo/ComfyUI-Gemini-API": [
+        [
+            "GeminiImageGenerator",
+            "GeminiLLM"
+        ],
+        {
+            "title_aux": "ComfyUI-Gemini-API [WIP]"
+        }
+    ],
+    "https://github.com/Jiffies-64/ComfyUI-SaveImagePlus": [
+        [
+            "SaveImagePlus"
+        ],
+        {
+            "title_aux": "ComfyUI-SaveImagePlus"
+        }
+    ],
+    "https://github.com/Jingwen-genies/comfyui-genies-nodes": [
+        [
+            "GeniesPoseEstimation",
+            "GeniesRGBToHSV",
+            "GeniesScaleFaceByKeypoints",
+            "GeniesSelectRGBByMask"
+        ],
+        {
+            "title_aux": "comfyui-genies-nodes"
+        }
+    ],
+    "https://github.com/JioJe/comfyui_video_BC": [
+        [
+            "TextBatchIndexer",
+            "TextBatchLoader",
+            "TextBatchReplace",
+            "TextBatchSaver",
+            "VideoCombine",
+            "VideoSequenceProcessor"
+        ],
+        {
+            "title_aux": "comfyui_video_BC [WIP]"
+        }
+    ],
+    "https://github.com/JissiChoi/ComfyUI-Jissi-List": [
+        [
+            "JissiFloatList",
+            "JissiList",
+            "JissiMatching",
+            "JissiMultiplePrompts",
+            "JissiText",
+            "JissiTextFileToListDisplay",
+            "JissiTextTemplate",
+            "JissiView"
+        ],
+        {
+            "title_aux": "ComfyUI-Jissi-List [WIP]"
+        }
+    ],
+    "https://github.com/Jordach/comfy-consistency-vae": [
+        [
+            "Comfy_ConsistencyVAE"
+        ],
+        {
+            "title_aux": "comfy-consistency-vae"
+        }
+    ],
+    "https://github.com/JosDigby/ComfyUI-DigbyTools": [
+        [
+            "DigbyKeyframer",
+            "DigbyLTXVLatentPrep"
+        ],
+        {
+            "title_aux": "ComfyUI-DigbyTools"
+        }
+    ],
+    "https://github.com/JosDigby/ComfyUI-DigbyWan": [
+        [
+            "DigbyLoopClose",
+            "DigbyLoopOpen",
+            "DigbyLoopRetrieveImages",
+            "DigbyLoopStoreImages",
+            "DigbyLoopVariables",
+            "DigbyLoopVariablesInit",
+            "DigbyWan22MiddleFrameToVideo",
+            "DigbyWan22SmoothVideoTransition",
+            "DigbyWanMoeKSampler",
+            "DigbyWanMoeKSamplerBasic",
+            "DigbyWanVACEVideoBridge",
+            "DigbyWanVACEVideoExtend",
+            "DigbyWanVACEVideoSmooth",
+            "LTX2_AVLatent"
+        ],
+        {
+            "title_aux": "ComfyUI-DigbyWan"
+        }
+    ],
+    "https://github.com/Junst/ComfyUI-PNG2SVG2PNG": [
+        [
+            "PNG2SVG2PNG"
+        ],
+        {
+            "title_aux": "ComfyUI-PNG2SVG2PNG"
+        }
+    ],
+    "https://github.com/Juste-Leo2/ComfyUI-Arduino": [
+        [
+            "ArduinoAnalogWrite",
+            "ArduinoCompileUpload",
+            "ArduinoCreateVariable",
+            "ArduinoDelay",
+            "ArduinoDigitalWrite",
+            "ArduinoReceiver",
+            "ArduinoSender",
+            "ArduinoTarget",
+            "ArduinoVariableInfo"
+        ],
+        {
+            "title_aux": "ComfyUI-Arduino [WIP]"
+        }
+    ],
+    "https://github.com/K1maran/ComfyUI-Kimaran": [
+        [
+            "EmptyLatentImagePresetsAIO",
+            "EmptyLatentImagePresetsFLUX",
+            "EmptyLatentImagePresetsQWEN",
+            "EmptyLatentImagePresetsSD15",
+            "KSamplerSetup"
+        ],
+        {
+            "title_aux": "ComfyUI-Kimaran"
+        }
+    ],
+    "https://github.com/KERRY-YUAN/ComfyUI_Python_Executor": [
+        [
+            "NodePython"
+        ],
+        {
+            "title_aux": "Python_Executor [UNSAFE]"
+        }
+    ],
+    "https://github.com/KXSR/ComfyUI-Easy-s3": [
+        [
+            "LoadFromUrl",
+            "S3UploadPresigned",
+            "SaveImageToDisk",
+            "WeaverAnimator"
+        ],
+        {
+            "title_aux": "ComfyUI-Easy-s3"
+        }
+    ],
+    "https://github.com/Karlmeister/comfyui-karlmeister-nodes-suit": [
+        [
+            "A_IfNotNone",
+            "KSamplerConfigSelector",
+            "KSamplerConfigSelector_Tuple",
+            "KSamplerConfigUnpack",
+            "SeedFilenameGenerator",
+            "SplitString",
+            "TextConcatenator"
+        ],
+        {
+            "title_aux": "comfyui-karlmeister-nodes-suit"
+        }
+    ],
+    "https://github.com/Karniverse/ComfyUI-Randomselector": [
+        [
+            "DynamicMultiSelector"
+        ],
+        {
+            "title_aux": "ComfyUI-Randomselector"
+        }
+    ],
+    "https://github.com/Kayarte/Time-Series-Nodes-for-ComfyUI": [
+        [
+            "DomainTimeSeriesPrep",
+            "TimeSeriesLoader",
+            "TimeSeriesPredictor"
+        ],
+        {
+            "title_aux": "Time Series Nodes for ComfyUI [Experimental]"
+        }
+    ],
+    "https://github.com/KihongK/comfyui-roysnodes": [
+        [
+            "CLIPMultiTextEncode",
+            "Create_ConditionLoRA",
+            "Create_ConditionLoRA_MainPrompt",
+            "Load_ConditionLoRA",
+            "OpenAI_Summarize",
+            "Unzip_ConditionLoRA"
+        ],
+        {
+            "title_aux": "ComfyUI-RoysNodes [WIP]"
+        }
+    ],
+    "https://github.com/KoaZhang/comfyui_danbooru_tag_filter": [
+        [
+            "DanbooruTagCategoryFilter"
+        ],
+        {
+            "title_aux": "comfyui_danbooru_tag_filter [WIP]"
+        }
+    ],
+    "https://github.com/KoinnAI/ComfyUI-DynPromptSimplified": [
+        [
+            "DynPromptExpand"
+        ],
+        {
+            "title_aux": "ComfyUI Dynamic Prompting Simplified [WIP]"
+        }
+    ],
+    "https://github.com/Kraven1109/ComfyUI-Llama": [
+        [
+            "ComfyLLamaPreviewText",
+            "ComfyLLamaPromptBuilder",
+            "ComfyLLamaSaveText",
+            "ComfyLLamaServer",
+            "ComfyLLamaServerConfig",
+            "ComfyLLamaTextConcat",
+            "ComfyLLamaTextInput",
+            "GGUFLoader",
+            "HFModelLoader",
+            "HFModelUnloader",
+            "HFTextInference",
+            "HFVLInference",
+            "MediaPathsLoader"
+        ],
+        {
+            "title_aux": "ComfyUI-Llama [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/Krish-701/Comfyui_nodes_v01": [
+        [
+            "RKMultiImageV01",
+            "RKMultiImageV02"
+        ],
+        {
+            "title_aux": "Comfyui_nodes_v01"
+        }
+    ],
+    "https://github.com/Krish-701/RK_Comfyui": [
+        [
+            "RK_Accumulate_Text_Multiline",
+            "RK_Accumulate_Text_Multiline_Numbered",
+            "RK_Advanced_Script_Finder",
+            "RK_CSV_File_State_Looper_v01",
+            "RK_CSV_File_State_Looper_v02",
+            "RK_Calc",
+            "RK_Concatenate_Text",
+            "RK_Excel_File_State_Looper",
+            "RK_ImageViewer",
+            "RK_Read_Excel_Row",
+            "RK_Ultra",
+            "RK_Write_Text",
+            "RK_seed",
+            "rk_save_image",
+            "rk_save_image_v01"
+        ],
+        {
+            "title_aux": "RK_Comfyui"
+        }
+    ],
+    "https://github.com/Krystal-0/ComfyUI-KrystalNodes": [
+        [
+            "CLIPFallback",
+            "IsAnimaModel",
+            "MultiImagePreview",
+            "VAEFallback"
+        ],
+        {
+            "title_aux": "[WIP] ComfyUI KrystalNodes"
+        }
+    ],
+    "https://github.com/KurtHokke/ComfyUI_KurtHokke_Nodes": [
+        [
+            "AIO_Tuner_Pipe",
+            "ApplyCondsExtraOpts",
+            "BooleanFromPipe",
+            "BooleanToPipe",
+            "COND_ExtraOpts",
+            "COND_ExtraOpts_2",
+            "COND_SET_STRENGTH_ExtraOpts",
+            "ChainTextEncode",
+            "CkptPipe",
+            "EmptyLatentSize",
+            "EmptyLatentSize64",
+            "ExpMath",
+            "ExpMathDual",
+            "ExpMathQuad",
+            "LoadUnetAndClip",
+            "MergeExtraOpts",
+            "ModelPipe1",
+            "ModelPipe2",
+            "NoModel_CkptLoader",
+            "NoNegExtraOpts",
+            "Node_BOOL",
+            "Node_Float",
+            "Node_INT",
+            "Node_RandomRange",
+            "Node_String",
+            "Node_StringMultiline",
+            "SEED_ExtraOpts",
+            "SamplerCustomAdvanced_Pipe",
+            "SamplerSel",
+            "SchedulerSel",
+            "SedOnString",
+            "UnetClipLoraLoader",
+            "UnetClipLoraLoaderBasic",
+            "VAE_ExtraOpts",
+            "ViewExtraOpts",
+            "batchsize_ExtraOpts",
+            "get_lora_metadata",
+            "re_sub_str",
+            "str_str",
+            "str_str_str_str"
+        ],
+        {
+            "title_aux": "ComfyUI-VLMStudio"
+        }
+    ],
+    "https://github.com/LK-168/comfyui_LK_selfuse": [
+        [
+            "Adv Scheduler",
+            "Artist Add Prefix",
+            "CaompareDebug",
+            "EasyUse_XYPlot_Lora_Folder",
+            "Efficient_XYplot_LoRA_Batch",
+            "InspectModelArchitecture",
+            "LK_MaskToSEGS",
+            "LK_SegsAdjust",
+            "Mask Connected Remove",
+            "Mask Diff",
+            "Mask Filter with Rate",
+            "Mask Get Max",
+            "Pick From String",
+            "Print Sigma",
+            "Random Choice",
+            "Read Text File",
+            "SaveImageTensor",
+            "SaveMaskTensor",
+            "String Filter",
+            "String Filter v2",
+            "String Modify",
+            "String Modify Simple",
+            "String Remove Duplicate",
+            "String Replace 10",
+            "String Switch",
+            "String To Save"
+        ],
+        {
+            "title_aux": "comfyui_LK_selfuse"
+        }
+    ],
+    "https://github.com/LMietkiewicz/HiggsfieldAPI_Node": [
+        [
+            "HiggsfieldAPI"
+        ],
+        {
+            "title_aux": "HiggsfieldAPI_Node"
+        }
+    ],
+    "https://github.com/LZpenguin/ComfyUI-Text": [
+        [
+            "Add_text_by_mask"
+        ],
+        {
+            "title_aux": "ComfyUI-Text"
+        }
+    ],
+    "https://github.com/Laolilzp/Laoli3D": [
+        [
+            "Laoli_3DPoseEditor"
+        ],
+        {
+            "title_aux": "Laoli3D [UNSAFE]"
+        }
+    ],
+    "https://github.com/LarryJane491/ComfyUI-ModelUnloader": [
+        [
+            "Model Unloader"
+        ],
+        {
+            "title_aux": "ComfyUI-ModelUnloader"
+        }
+    ],
+    "https://github.com/Latentnaut/ComfyUI-Batch-Iterator": [
+        [
+            "BaseReferenceBatchIterator",
+            "SimpleBatchIterator"
+        ],
+        {
+            "title_aux": "Batch Iterator [WIP]"
+        }
+    ],
+    "https://github.com/Latentnaut/ComfyUI-Image-Selector": [
+        [
+            "ImageSelector"
+        ],
+        {
+            "title_aux": "ComfyUI-Image-Selector [NAME CONFLICT/WIP]"
+        }
+    ],
+    "https://github.com/Leecoahs/ComfyUI_LeeNodes": [
+        [
+            "CombineGrayscalePak",
+            "ImageOverlaySimple",
+            "TextureInpaintingPrep",
+            "TextureInpaintingRestore"
+        ],
+        {
+            "title_aux": "ComfyUI_LeeNodes"
+        }
+    ],
+    "https://github.com/Letz-AI/ComfyUI-LetzAI": [
+        [
+            "LetzAI Generator"
+        ],
+        {
+            "title_aux": "ComfyUI-LetzAI [UNSAFE]"
+        }
+    ],
+    "https://github.com/LiJT/ComfyUI-JT_Tools": [
+        [
+            "ImagePadToMultiple",
+            "IsSolidColorImage",
+            "JTPromptPresetOutput",
+            "PolynomialColorMatch",
+            "UpstreamStateDetector"
+        ],
+        {
+            "title_aux": "ComfyUI-JT_Tools"
+        }
+    ],
+    "https://github.com/Lilien86/Comfyui_Latent_Interpolation": [
+        [
+            "Latent Interpolator Multi"
+        ],
+        {
+            "title_aux": "Comfyui_Latent_Interpolation [WIP]"
+        }
+    ],
+    "https://github.com/LittleTechPomp/comfyui-pixxio": [
+        [
+            "AutoUploadImageToPixxioCollection",
+            "LoadImageFromPixxioAPI"
+        ],
+        {
+            "title_aux": "comfyui-pixxio"
+        }
+    ],
+    "https://github.com/Long-form-AI-video-generation/ComfyUI_vaceFramepack": [
+        [
+            "DownloadAndLoadWav2VecModel",
+            "FantasyTalkingModelLoader",
+            "FantasyTalkingWav2VecEmbeds",
+            "MultiTalkModelLoader",
+            "MultiTalkWav2VecEmbeds",
+            "ReCamMasterPoseVisualizer",
+            "WanVACEVideoFramepackSampler2",
+            "WanVideoATITracks",
+            "WanVideoATITracksVisualize",
+            "WanVideoATI_comfy",
+            "WanVideoContextSelector",
+            "WanVideoControlnet",
+            "WanVideoControlnetLoader",
+            "WanVideoDiffusionForcingSampler",
+            "WanVideoFunCameraEmbeds",
+            "WanVideoImageToVideoMultiTalk",
+            "WanVideoReCamMasterCameraEmbed",
+            "WanVideoReCamMasterDefaultCamera",
+            "WanVideoReCamMasterGenerateOrbitCamera",
+            "WanVideoUni3C_ControlnetLoader",
+            "WanVideoUni3C_embeds",
+            "WanVideoUniAnimateDWPoseDetector",
+            "WanVideoUniAnimatePoseInput"
+        ],
+        {
+            "title_aux": "ComfyUI_vaceFramepack"
+        }
+    ],
+    "https://github.com/Looking-Glass/LKG-ComfyUI": [
+        [
+            "BridgePreview",
+            "LoadFolder",
+            "ScaleAndMaintainAspect",
+            "SideBySide"
+        ],
+        {
+            "title_aux": "LKG-ComfyUI"
+        }
+    ],
+    "https://github.com/LotzF/ComfyUI-Simple-Chat-GPT-completion": [
+        [
+            "AzureChatGptCompletion",
+            "ChatGPTCompletion"
+        ],
+        {
+            "title_aux": "ComfyUI simple ChatGPT completion [UNSAFE]"
+        }
+    ],
+    "https://github.com/LucianGnn/ComfyUI-Lucian": [
+        [
+            "AudioDurationCalculator"
+        ],
+        {
+            "title_aux": "ComfyUI-Lucian [WIP]"
+        }
+    ],
+    "https://github.com/Lumiyumi/comfyui_stringcombineX": [
+        [
+            "StringCombine10"
+        ],
+        {
+            "title_aux": "comfyui_stringcombineX"
+        }
+    ],
+    "https://github.com/LvyuanW/ComfyUI-6yuan": [
+        [
+            "6yuan_DrawTextNode",
+            "ColorKMeans",
+            "CropAlpha",
+            "PosterColorEngine",
+            "PosterLogoPlacer",
+            "PosterMaskGenerator",
+            "PosterTemplateLoader",
+            "PosterTextRenderer",
+            "RemoveBackground",
+            "RemoveBackgroundAdvanced",
+            "ShapeLanguageSummary",
+            "ShrinkImage",
+            "SimpleTextLayout",
+            "StringPreview"
+        ],
+        {
+            "title_aux": "ComfyUI-6yuan [WIP]"
+        }
+    ],
+    "https://github.com/LyazS/ComfyUI-aznodes": [
+        [
+            "CrossFadeImageSequence",
+            "ImageGrayscaleAZ",
+            "SaveImageAZ"
+        ],
+        {
+            "title_aux": "ComfyUI-aznodes"
+        }
+    ],
+    "https://github.com/Lyeeedar/comfyui-afnm-rmbg": [
+        [
+            "AfnmRMBG"
+        ],
+        {
+            "title_aux": "Afnm Remove Background"
+        }
+    ],
+    "https://github.com/Lyeeedar/comfyui-keep-largest-blob": [
+        [
+            "KeepLargestBlob"
+        ],
+        {
+            "title_aux": "Keep Largest Blob [WIP]"
+        }
+    ],
+    "https://github.com/LykosAI/ComfyUI-Inference-Core-Nodes": [
+        [
+            "AIO_Preprocessor",
+            "AnimalPosePreprocessor",
+            "AnimeFace_SemSegPreprocessor",
+            "AnimeLineArtPreprocessor",
+            "AnyLineArtPreprocessor_aux",
+            "BAE-NormalMapPreprocessor",
+            "BinaryPreprocessor",
+            "CannyEdgePreprocessor",
+            "ColorPreprocessor",
+            "ControlNetAuxSimpleAddText",
+            "ControlNetPreprocessorSelector",
+            "DSINE-NormalMapPreprocessor",
+            "DWPreprocessor",
+            "DensePosePreprocessor",
+            "DepthAnythingPreprocessor",
+            "DepthAnythingV2Preprocessor",
+            "DiffusionEdge_Preprocessor",
+            "ExecuteAllControlNetPreprocessors",
+            "FacialPartColoringFromPoseKps",
+            "FakeScribblePreprocessor",
+            "HEDPreprocessor",
+            "HintImageEnchance",
+            "ImageGenResolutionFromImage",
+            "ImageGenResolutionFromLatent",
+            "ImageIntensityDetector",
+            "ImageLuminanceDetector",
+            "InpaintPreprocessor",
+            "LayeredDiffusionApply",
+            "LayeredDiffusionCondApply",
+            "LayeredDiffusionCondJointApply",
+            "LayeredDiffusionDecode",
+            "LayeredDiffusionDecodeRGBA",
+            "LayeredDiffusionDecodeSplit",
+            "LayeredDiffusionDiffApply",
+            "LayeredDiffusionJointApply",
+            "LeReS-DepthMapPreprocessor",
+            "LineArtPreprocessor",
+            "LineartStandardPreprocessor",
+            "M-LSDPreprocessor",
+            "Manga2Anime_LineArt_Preprocessor",
+            "MaskOptFlow",
+            "MediaPipe-FaceMeshPreprocessor",
+            "MeshGraphormer+ImpactDetector-DepthMapPreprocessor",
+            "MeshGraphormer-DepthMapPreprocessor",
+            "Metric3D-DepthMapPreprocessor",
+            "Metric3D-NormalMapPreprocessor",
+            "MiDaS-DepthMapPreprocessor",
+            "MiDaS-NormalMapPreprocessor",
+            "ModelMergeBlockNumber",
+            "ModelMergeSDXL",
+            "ModelMergeSDXLDetailedTransformers",
+            "ModelMergeSDXLTransformers",
+            "ModelSamplerTonemapNoiseTest",
+            "OneFormer-ADE20K-SemSegPreprocessor",
+            "OneFormer-COCO-SemSegPreprocessor",
+            "OpenposePreprocessor",
+            "PiDiNetPreprocessor",
+            "PixelPerfectResolution",
+            "PromptExpansion",
+            "PyraCannyPreprocessor",
+            "ReferenceOnlySimple",
+            "RenderAnimalKps",
+            "RenderPeopleKps",
+            "RescaleClassifierFreeGuidanceTest",
+            "SAMPreprocessor",
+            "SavePoseKpsAsJsonFile",
+            "ScribblePreprocessor",
+            "Scribble_PiDiNet_Preprocessor",
+            "Scribble_XDoG_Preprocessor",
+            "SemSegPreprocessor",
+            "ShufflePreprocessor",
+            "TEEDPreprocessor",
+            "TEED_Preprocessor",
+            "TTPlanet_TileGF_Preprocessor",
+            "TTPlanet_TileSimple_Preprocessor",
+            "TilePreprocessor",
+            "TonemapNoiseWithRescaleCFG",
+            "UniFormer-SemSegPreprocessor",
+            "Unimatch_OptFlowPreprocessor",
+            "UpperBodyTrackingFromPoseKps",
+            "Zoe-DepthMapPreprocessor",
+            "Zoe_DepthAnythingPreprocessor"
+        ],
+        {
+            "author": "tstandley",
+            "title_aux": "ComfyUI Nodes for Inference.Core"
+        }
+    ],
+    "https://github.com/M4lF3s/comfy-tif-support": [
+        [
+            "Load TIFF",
+            "Save TIFF"
+        ],
+        {
+            "title_aux": "comfy-tif-support"
+        }
+    ],
+    "https://github.com/MONKEYFOREVER2/comfyui-quantum-spectral-nodes": [
+        [
+            "SpectralLatentEnhance"
+        ],
+        {
+            "title_aux": "comfyui-quantum-spectral-nodes [WIP]"
+        }
+    ],
+    "https://github.com/MadPonyInteractive/ComfyUi-MpiNodes": [
+        [
+            "MpiAddImageToList",
+            "MpiAnyInvSwitch",
+            "MpiAnySwitch",
+            "MpiAspectRatio",
+            "MpiBatchTextReplace",
+            "MpiBboxToMask",
+            "MpiBoolean",
+            "MpiBooleanCompare",
+            "MpiChoiceSelector",
+            "MpiClearVram",
+            "MpiCombo",
+            "MpiCombo1",
+            "MpiCombo10",
+            "MpiCombo2",
+            "MpiCombo3",
+            "MpiCombo4",
+            "MpiCombo5",
+            "MpiCombo6",
+            "MpiCombo7",
+            "MpiCombo8",
+            "MpiCombo9",
+            "MpiComboMaker",
+            "MpiCompare",
+            "MpiConvert",
+            "MpiDisplayPrompt",
+            "MpiDoubleLora",
+            "MpiExpoFloat",
+            "MpiFloat",
+            "MpiFromCheckpoint",
+            "MpiGetImageAtIndex",
+            "MpiGridDimensions",
+            "MpiHasOptions",
+            "MpiIfElse",
+            "MpiIfElseInverted",
+            "MpiIfElseProbability",
+            "MpiIfElseProbabilityInverted",
+            "MpiInt",
+            "MpiJsonLoad",
+            "MpiJsonSave",
+            "MpiListCount",
+            "MpiListRange",
+            "MpiLogger",
+            "MpiLoraModel",
+            "MpiLoraModelClip",
+            "MpiLoraSwitch",
+            "MpiMaskDebugInfo",
+            "MpiMath",
+            "MpiMultiTextX2",
+            "MpiMultiTextX3",
+            "MpiMultiTextX4",
+            "MpiMultiTextX5",
+            "MpiNormalizeValue",
+            "MpiPresetSelector",
+            "MpiPromptBridge",
+            "MpiPromptChainSelector",
+            "MpiPromptList",
+            "MpiPromptListJoin",
+            "MpiPromptListSelector",
+            "MpiPromptOverride",
+            "MpiPromptOverrideSeed",
+            "MpiPromptProcessor",
+            "MpiRandPromptGen",
+            "MpiRandPromptGenOrder",
+            "MpiRandPromptGenOverrideList",
+            "MpiRandPromptGenOverrideSection",
+            "MpiRandPromptGenSave",
+            "MpiRoundToMultiple",
+            "MpiRoundToMultipleRes",
+            "MpiScaledDimensions",
+            "MpiSimpleBoolean",
+            "MpiString",
+            "MpiStringInvSwitch",
+            "MpiText",
+            "MpiTextListJoin",
+            "MpiTextListReplace",
+            "MpiToChekPoint",
+            "MpiWanFrames",
+            "MpiWanSeconds"
+        ],
+        {
+            "title_aux": "ComfyUi-MpiNodes [WIP]"
+        }
+    ],
+    "https://github.com/Madygnomo/ComfyUI-NanoBanana-ImageGenerator": [
+        [
+            "Google-Gemini"
+        ],
+        {
+            "title_aux": "ComfyUI-NanoBanana-ImageGenerator"
+        }
+    ],
+    "https://github.com/Malloc-pix/comfyui-QwenVL": [
+        [
+            "Qwen2.5",
+            "Qwen2.5VL"
+        ],
+        {
+            "title_aux": "comfyui-QwenVL"
+        }
+    ],
+    "https://github.com/ManuShamil/ComfyUI_BodyEstimation_Nodes": [
+        [
+            "CogitareLabsPoseIDExtractor"
+        ],
+        {
+            "title_aux": "ComfyUI_BodyEstimation_Nodes"
+        }
+    ],
+    "https://github.com/MarkFreeDom168/ComfyUI-image-load-url": [
+        [
+            "LoadImageAndMaskFromURLOrBase64",
+            "LoadImageFromURLOrBase64",
+            "LoadMaskFromURLOrBase64"
+        ],
+        {
+            "title_aux": "ComfyUI-image-load-url [WIP]"
+        }
+    ],
+    "https://github.com/Matrix-King-Studio/ComfyUI-MoviePy": [
+        [
+            "AudioDurationNode",
+            "ImageClipNode",
+            "SaveVideoNode"
+        ],
+        {
+            "title_aux": "ComfyUI-MoviePy"
+        }
+    ],
+    "https://github.com/MatthewClayHarrison/ComfyUI-MetaMan": [
+        [
+            "MetaManEmbedAndSave",
+            "MetaManExtractComponents",
+            "MetaManLoadImage"
+        ],
+        {
+            "title_aux": "MetaMan - Universal AI Image Metadata Manager [UNSAFE]"
+        }
+    ],
+    "https://github.com/Maxim-Dey/ComfyUI-MaksiTools": [
+        [
+            "\ud83d\udd22 Return Boolean",
+            "\ud83d\udd22 Return Float",
+            "\ud83d\udd22 Return Integer",
+            "\ud83d\udd22 Return Multiline String",
+            "\ud83d\udd27 Time Measure Node"
+        ],
+        {
+            "title_aux": "ComfyUI-MS_Tools [WIP]"
+        }
+    ],
+    "https://github.com/Meisoftcoltd/ComfyUI-UVR5": [
+        [
+            "LoadSimpleAudio",
+            "UVRExtractor"
+        ],
+        {
+            "title_aux": "ComfyUI-UVR5 [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/MengYe-Z/ComfyUI-MultiPointWhiteBalance": [
+        [
+            "ApplyExposureGain",
+            "ApplyWhiteBalanceGain",
+            "ExposureMatchFromPoint",
+            "GaussianBlur",
+            "GrayBalanceFromPoint"
+        ],
+        {
+            "title_aux": "ComfyUI-MultiPointWhiteBalance [WIP]"
+        }
+    ],
+    "https://github.com/Mervent/comfyui-fast-lut": [
+        [
+            "ApplyLUTFast"
+        ],
+        {
+            "title_aux": "comfyui-fast-lut [WIP]"
+        }
+    ],
+    "https://github.com/Mervent/comfyui-telegram-send": [
+        [
+            "TelegramReply",
+            "TelegramSend"
+        ],
+        {
+            "title_aux": "comfyui-telegram-send"
+        }
+    ],
+    "https://github.com/Mervent/comfyui-yaml-prompt": [
+        [
+            "YAMLPromptParser"
+        ],
+        {
+            "title_aux": "comfyui-yaml-prompt"
+        }
+    ],
+    "https://github.com/Merzmensch/comfyui-stylegan2-explorer": [
+        [
+            "StyleGAN2Interpolate",
+            "StyleGAN2LatentWalk",
+            "StyleGAN2ModelLoader",
+            "StyleGAN2Sampler"
+        ],
+        {
+            "title_aux": "comfyui-stylegan2-explorer [WIP]"
+        }
+    ],
+    "https://github.com/Mets3D/mets-nodes": [
+        [
+            "AdjustImageNode",
+            "AutoExtractTags",
+            "DownloadCivitaiModel",
+            "ExtraCheckpointData",
+            "ExtractTagFromString",
+            "MergeData",
+            "PrepareCheckpoint",
+            "PrepareLoRA",
+            "PromptTidy",
+            "RegexNode",
+            "RenderPass",
+            "RenderPass_Face",
+            "RenderPass_Prepare",
+            "SplitData",
+            "StableRandomChoiceNode",
+            "TagStacker",
+            "TagTweaker",
+            "Wan22DualLoRA",
+            "Wan22DualModel",
+            "Wan22LoRAStacker",
+            "Wan22Render"
+        ],
+        {
+            "title_aux": "Mets-Nodes"
+        }
+    ],
+    "https://github.com/Mexes1978/film_prompt_styler": [
+        [
+            "FilmPromptStyler"
+        ],
+        {
+            "title_aux": "Film Photography Prompt Styler [WIP]"
+        }
+    ],
+    "https://github.com/MickeyJ/ComfyUI_mickster_nodes": [
+        [
+            "Image Size Scaled",
+            "ImageSwitchSelect"
+        ],
+        {
+            "title_aux": "ComfyUI_mickster_nodes [WIP]"
+        }
+    ],
+    "https://github.com/Mohamed-Sakr/ComfyUI-SimpleMarkdown": [
+        [
+            "SimpleMarkdownPreview"
+        ],
+        {
+            "title_aux": "ComfyUI-SimpleMarkdown [UNSAFE]"
+        }
+    ],
+    "https://github.com/MohammadAboulEla/ComfyUI-BiRefNet": [
+        [
+            "BiRefNet_Node"
+        ],
+        {
+            "title_aux": "ComfyUI-BiRefNet [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/Momediada97/ComfyUI-ImageIfNotBlack": [
+        [
+            "ImageIfNotBlack"
+        ],
+        {
+            "title_aux": "ComfyUI-ImageIfNotBlack [WIP]"
+        }
+    ],
+    "https://github.com/MrAdamBlack/CheckProgress": [
+        [
+            "CHECK_PROGRESS",
+            "Example"
+        ],
+        {
+            "title_aux": "CheckProgress [WIP]"
+        }
+    ],
+    "https://github.com/MuAIGC/ComfyUI-DMXAPI_mmx": [
+        [
+            "Qwen3_TTS_Design_MMX",
+            "Qwen3_TTS_MultiChar_MMX",
+            "Qwen3_TTS_MultiChar_Preset_MMX",
+            "Qwen3_TTS_Preset_MMX",
+            "QwenTTSDesignPreset_MMX"
+        ],
+        {
+            "title_aux": "DMXAPI Nodes [WIP]"
+        }
+    ],
+    "https://github.com/MushroomFleet/ComfyUI-SCP-nodes": [
+        [
+            "SaveImageSCP"
+        ],
+        {
+            "title_aux": "ComfyUI-SCP-nodes [UNSAFE]"
+        }
+    ],
+    "https://github.com/MushroomFleet/DJZ-Nodes": [
+        [
+            "AnamorphicEffect",
+            "AspectSize",
+            "AspectSizeV2",
+            "BatchAlphaComposite",
+            "BatchOffset",
+            "BatchRangeInsert",
+            "BatchRangeSwap",
+            "BatchThief",
+            "BlackBarsV1",
+            "BlackBarsV2",
+            "BlackBarsV3",
+            "BorderCompositeAlpha",
+            "BracketCleaner",
+            "CRT_Effect_v1",
+            "CaptionsToPromptList",
+            "CathodeRayEffect",
+            "ClassicFilmEffect",
+            "CombineAudio",
+            "DJZ-LoadLatent",
+            "DJZ-LoadLatentV2",
+            "DJZColorWheel",
+            "DJZDatamosh",
+            "DJZDatamoshV2",
+            "DJZImageScaleToMegabytes",
+            "DJZImageScaleToTotalPixels",
+            "DJZ_ParallaxV1",
+            "DatasetWordcloud",
+            "DeadPixelEffect",
+            "DepthBasedPixelization",
+            "DinskyPlus",
+            "DinskyPlusV2",
+            "DjzDatabendingV1",
+            "DjzDatamoshV3",
+            "DjzDatamoshV4",
+            "DjzDatamoshV5",
+            "DjzDatamoshV6",
+            "DjzDatamoshV7",
+            "DjzDatamoshV8",
+            "FFXFADEORAMA",
+            "FilmGateWeave",
+            "FilmGrainEffect",
+            "FilmGrainEffect_v2",
+            "FishEyeEffect",
+            "FishEyeV2",
+            "FractalGenerator",
+            "FractalGeneratorV2",
+            "FractalGeneratorV3",
+            "GSL_Filter_V1",
+            "HalationBloom",
+            "ImageInterleavedUpscaler",
+            "ImageInterleavedUpscalerV2",
+            "ImageSizeAdjuster",
+            "ImageSizeAdjusterV2",
+            "ImageSizeAdjusterV3",
+            "JitterEffect",
+            "KeyframeBasedUpscalerV1",
+            "KinescopeEffectV1",
+            "LensLeaks",
+            "LoadTextDirectory",
+            "LoadVideoBatchFrame",
+            "LoadVideoDirectory",
+            "LoadVideoDirectoryV2",
+            "MotionBlending",
+            "NoiseFactory",
+            "NoiseFactoryV2",
+            "NoiseFactoryV3",
+            "NonSquarePixelsV1",
+            "PanavisionLensV2",
+            "ParametricMeshGen",
+            "ParametricMeshGenV2",
+            "PartTimer",
+            "ProjectFilePathNode",
+            "ProjectFolderPathNode",
+            "PromptCleaner",
+            "PromptCleanerV2",
+            "PromptDupeRemover",
+            "PromptDupeRemoverV2",
+            "PromptInject",
+            "PromptInjectV2",
+            "PromptSwap",
+            "RetroVideoText",
+            "SaveText",
+            "ScreensaverGenerator",
+            "ScreensaverGeneratorV2",
+            "ScreensaverGeneratorV3",
+            "SequentialNumberGenerator",
+            "StringChaos",
+            "StringMatch",
+            "StringPainter",
+            "StringPainterV2",
+            "StringWeights",
+            "Technicolor3Strip_v1",
+            "Technicolor3Strip_v2",
+            "ThinkSeeker",
+            "ThreeToneStyler",
+            "TrianglesPlus",
+            "TrianglesPlusV2",
+            "UncleanSpeech",
+            "VGA_Effect_v1",
+            "VHS_Effect_V3",
+            "VHS_Effect_v1",
+            "VHS_Effect_v2",
+            "VideoBitClamp",
+            "VideoChromaticAberration",
+            "VideoCorridorV1",
+            "VideoCubeV1",
+            "VideoFilmDamage",
+            "VideoInterlaceFastV4",
+            "VideoInterlaceGANV3",
+            "VideoInterlaced",
+            "VideoInterlacedV2",
+            "VideoMazeV1",
+            "VideoMazeV2",
+            "VideoNoiseFactory",
+            "VideoPyramidV1",
+            "VideoRingPainter",
+            "VideoTemperatureV1",
+            "VideoText",
+            "VideoTextV2",
+            "VideoTimecode",
+            "VideoTrails",
+            "VideoTrailsV2",
+            "VideoVignettingV1",
+            "VoiceEffects",
+            "VoiceEffects2",
+            "WaveletCompose",
+            "WaveletDecompose",
+            "WinampViz",
+            "WinampVizV2",
+            "ZenakiVideoPromptV1",
+            "ZenkaiAmbienceAudioV1",
+            "ZenkaiControlPromptV1",
+            "ZenkaiControlPromptV2",
+            "ZenkaiDepthPrompt",
+            "ZenkaiImagePromptV1",
+            "ZenkaiImagePromptV2",
+            "ZenkaiPoseMap",
+            "ZenkaiPrompt",
+            "ZenkaiPromptV2",
+            "ZenkaiPromptV3",
+            "ZenkaiPromptV4",
+            "ZenkaiPromptV5",
+            "ZenkaiSceneVideoV1",
+            "ZenkaiVideoPose",
+            "ZenkaiWildcard",
+            "ZenkaiWildcardV2",
+            "Zenkai_IMPv1",
+            "djzTiling",
+            "djzTilingV2"
+        ],
+        {
+            "author": "DJZ-Nodes",
+            "title_aux": "DaimalyadNodes [WIP]"
+        }
+    ],
+    "https://github.com/MythicalChu/ComfyUI-APG_ImYourCFGNow": [
+        [
+            "APG_ImYourCFGNow"
+        ],
+        {
+            "title_aux": "ComfyUI-APG_ImYourCFGNow"
+        }
+    ],
+    "https://github.com/N3uR0TiCV0iD/ComfyUI-ModelListTools": [
+        [
+            "CLIPModelSelectionNode",
+            "CLIPVisionModelSelectionNode",
+            "CheckpointSelectionNode",
+            "DiffusionModelSelectionNode",
+            "FilteredCLIPModelsNode",
+            "FilteredCLIPVisionModelsNode",
+            "FilteredCheckpointsNode",
+            "FilteredDiffusionModelsNode",
+            "FilteredLoRAModelsNode",
+            "FilteredTextEncodersNode",
+            "LoRAModelSelectionNode",
+            "LoadLoRAListNode",
+            "TextEncoderSelectionNode"
+        ],
+        {
+            "title_aux": "ComfyUI-ModelListTools"
+        }
+    ],
+    "https://github.com/NEZHA625/ComfyUI-tools-by-dong": [
+        [
+            "Banana\u8282\u70b9",
+            "DeepSeek\u8282\u70b9",
+            "JSON\u5355\u952e\u503c\u6784\u5efa\u8282\u70b9",
+            "JSON\u53d6\u503c\u8282\u70b9",
+            "JSON\u5408\u5e76\u8282\u70b9",
+            "JSON\u591a\u952e\u503c\u6784\u5efa\u8282\u70b9",
+            "QwenVL\u8282\u70b9",
+            "Sora2\u8282\u70b9",
+            "\u4ece\u6587\u672c\u5217\u8868\u83b7\u53d6\u6587\u672c\u8282\u70b9",
+            "\u4ed9\u5bab\u4e91\u73af\u5883\u53d8\u91cf\u8282\u70b9",
+            "\u4fdd\u5b58\u56fe\u50cf\u8282\u70b9",
+            "\u4fdd\u5b58\u6587\u672c\u8282\u70b9",
+            "\u5206\u8fa8\u7387\u8282\u70b9",
+            "\u5220\u9664\u6587\u4ef6\u8282\u70b9",
+            "\u538b\u7f29\u8282\u70b9",
+            "\u53d6\u6574\u51fd\u6570\u8282\u70b9",
+            "\u56fe\u50cf\u8fed\u4ee3\u8282\u70b9",
+            "\u5916\u8865\u753b\u677f\u8282\u70b9",
+            "\u5b57\u7b26\u5904\u7406\u8282\u70b9",
+            "\u5b57\u7b26\u5b58\u5728\u6027\u68c0\u6d4b\u8282\u70b9",
+            "\u5bab\u683c\u56fe\u5207\u5206\u8282\u70b9",
+            "\u5c0f\u663e\u5b58\u68c0\u6d4b\u8282\u70b9",
+            "\u6570\u636e\u5b58\u5728\u6027\u68c0\u6d4b\u8282\u70b9",
+            "\u6587\u4ef6\u4e0b\u8f7d\u8282\u70b9",
+            "\u6587\u4ef6\u5939\u8fed\u4ee3\u8282\u70b9",
+            "\u6587\u672c\u8f93\u5165\u8282\u70b9",
+            "\u6a21\u578b\u4e0b\u8f7d\u8282\u70b9",
+            "\u6bb5\u843d\u6570\u8ba1\u7b97\u8282\u70b9",
+            "\u79fb\u52a8\u6587\u4ef6\u8282\u70b9",
+            "\u7ffb\u8bd1\u8282\u70b9",
+            "\u83b7\u53d6\u56fe\u50cf\u5217\u8868\u8282\u70b9",
+            "\u83b7\u53d6\u89c6\u9891\u8def\u5f84\u8282\u70b9",
+            "\u8ba1\u7b97\u6587\u4ef6\u6570\u91cf\u8282\u70b9",
+            "\u8def\u5f84\u52a0\u5165\u8282\u70b9",
+            "\u901a\u7528API\u8282\u70b9",
+            "\u903b\u8f91\u8282\u70b9",
+            "\u91cd\u547d\u540d\u8282\u70b9",
+            "\u968f\u673a\u6570\u8282\u70b9",
+            "\u97f3\u9891\u65f6\u957f\u8ba1\u7b97\u8282\u70b9"
+        ],
+        {
+            "title_aux": "ComfyUI-tools-by-dong [UNSAFE]"
+        }
+    ],
+    "https://github.com/NSFW-API/ComfyUI-Embedding-Delta-Adapter": [
+        [
+            "ApplyEmbDeltaWANTextEmbeds",
+            "LoadEmbDeltaAdapter"
+        ],
+        {
+            "title_aux": "ComfyUI-Embedding-Delta-Adapter"
+        }
+    ],
+    "https://github.com/NSFW-API/ComfyUI-WanSoftPrefix": [
+        [
+            "WanVideoT5ApplySoftPrefix"
+        ],
+        {
+            "title_aux": "ComfyUI-WanSoftPrefix"
+        }
+    ],
+    "https://github.com/Nambi24/ComfyUI-Save_Image": [
+        [
+            "ExtractLastPathComponent",
+            "ListSubfoldersNode",
+            "SaveImageNode"
+        ],
+        {
+            "title_aux": "ComfyUI-Save_Image"
+        }
+    ],
+    "https://github.com/NaomiVK/comfyui-qwen3-tts": [
+        [
+            "Qwen3TTS_CustomVoice",
+            "Qwen3TTS_ModelLoader",
+            "Qwen3TTS_VoiceClone",
+            "Qwen3TTS_VoiceClonePrompt",
+            "Qwen3TTS_VoiceDesign",
+            "Qwen3TTS_VoiceDesignToClone",
+            "Qwen3TTS_WhisperTranscribe"
+        ],
+        {
+            "title_aux": "comfyui-qwen3-tts [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/NeoTech/comfyui-laserprep": [
+        [
+            "LaserPrep"
+        ],
+        {
+            "title_aux": "comfyui-laserprep"
+        }
+    ],
+    "https://github.com/NeuroWaifu/ComfyUI.Node.MediaSuite": [
+        [
+            "BooleanLogic",
+            "Clamp",
+            "ColorGrading",
+            "ColorPalette",
+            "CompareNumbers",
+            "CompareVideoSize",
+            "Constants",
+            "ConvertNumber",
+            "DominantColor",
+            "GetVideoInfo",
+            "GetVideoSize",
+            "ImageResize",
+            "MaskBlur",
+            "MaskOperations",
+            "MaskThreshold",
+            "MathFunction",
+            "MathOperation",
+            "RandomNumber",
+            "Remap",
+            "ResizeCalculator",
+            "ShadowsHighlightsMidtones",
+            "Smooth",
+            "ToneCurve",
+            "VideoResize",
+            "VideoUpscaleWithModel"
+        ],
+        {
+            "title_aux": "ComfyUI.Node.MediaSuite"
+        }
+    ],
+    "https://github.com/Nienai666/comfyui-encrypt-image-main": [
+        [
+            "DecryptImageFromFile",
+            "EncryptImage",
+            "\u89e3\u5bc6\u56fe\u7247"
+        ],
+        {
+            "title_aux": "ComfyUI-NanoBanana-ImageGenerator"
+        }
+    ],
+    "https://github.com/NimbleWing/ComfyUI-NW": [
+        [
+            "ResolutionSelector"
+        ],
+        {
+            "title_aux": "ComfyUI-NW"
+        }
+    ],
+    "https://github.com/No-22-Github/ComfyUI_SaveImageCustom": [
+        [
+            "SaveUtility: SaveImageCustom"
+        ],
+        {
+            "title_aux": "ComfyUI_SaveImageCustom"
+        }
+    ],
+    "https://github.com/Norsnow/comfyui-image-ratio-preset": [
+        [
+            "ImageRatioPreset"
+        ],
+        {
+            "title_aux": "comfyui-image-ratio-preset [WIP]"
+        }
+    ],
+    "https://github.com/Northerner1/ComfyUI_North_Noise": [
+        [
+            "North_Noise"
+        ],
+        {
+            "title_aux": "ComfyUI_North_Noise [WIP]"
+        }
+    ],
+    "https://github.com/Novavision0313/ComfyUI-NVVS": [
+        [
+            "AllBlackMaskValidator",
+            "DirectionSelector",
+            "FullBodyDetection",
+            "HighlightIndexSelector",
+            "MaskCoverageAnalysis",
+            "RegexTextExtractor",
+            "StringSplit",
+            "StringStrip"
+        ],
+        {
+            "title_aux": "ComfyUI-NVVS [WIP]"
+        }
+    ],
+    "https://github.com/OSAnimate/ComfyUI-SpriteSheetMaker": [
+        [
+            "ImageGridNode",
+            "SpriteSheetMaker"
+        ],
+        {
+            "title_aux": "ComfyUI-SpriteSheetMaker [WIP]"
+        }
+    ],
+    "https://github.com/OhSeongHyeon/comfyui-random-image-size": [
+        [
+            "BFParameters",
+            "BFParametersSimple",
+            "CheckpointComboParameter",
+            "CheckpointLoaderWithOuputDirByModelName",
+            "CyclicInteger",
+            "DetailerSchedulerComboParameter",
+            "KsamplerSamplersComboParameter",
+            "KsamplerSchedulersComboParameter",
+            "OuputDirByModelName",
+            "RandomImageSizeAdvanced",
+            "RandomImageSizeAdvancedYAML",
+            "RandomInteger",
+            "UnetComboParameter",
+            "UniqueStringList",
+            "UniqueStringListAdvanced",
+            "VAEComboParameter"
+        ],
+        {
+            "title_aux": "comfyui-random-image-size"
+        }
+    ],
+    "https://github.com/Omario92/ComfyUI-OmarioNodes": [
+        [
+            "DualEndpointColorBlendScheduler",
+            "GemmaAPITextEncode",
+            "LightLeaksTransition",
+            "LoadConditioning",
+            "LoadInpaintCropCache",
+            "MaskClampedCrop",
+            "MaskClampedCropSticky",
+            "SaveConditioning",
+            "SaveImagePlus",
+            "SaveInpaintCropCache"
+        ],
+        {
+            "title_aux": "ComfyUI-OmarioNodes"
+        }
+    ],
+    "https://github.com/Omega-L/comfyUI-prompts": [
+        [
+            "comfyUI-prompt"
+        ],
+        {
+            "title_aux": "comfyUI-prompts"
+        }
+    ],
+    "https://github.com/OtwakO/ComfyUI-UsefulTools": [
+        [
+            "TagConcatenator",
+            "TagExtractor"
+        ],
+        {
+            "title_aux": "ComfyUI-UsefulTools"
+        }
+    ],
+    "https://github.com/PATATAJEC/ComfyUI-PatatajecNodes": [
+        [
+            "ColorMatchFalloff",
+            "ColorPicker",
+            "ImageDifferenceToAplpha",
+            "PathTool",
+            "SequenceBlend",
+            "SequenceContentZoom"
+        ],
+        {
+            "title_aux": "Patatajec-Nodes [WIP]"
+        }
+    ],
+    "https://github.com/Pablerdo/ComfyUI-Sa2VAWrapper": [
+        [
+            "GetCaptionFromImages"
+        ],
+        {
+            "title_aux": "ComfyUI-Sa2VAWrapper [WIP]"
+        }
+    ],
+    "https://github.com/PabloGrant/comfyui-giraffe-test-panel": [
+        [
+            "DebugHelloNode",
+            "GiraffeTestPanel"
+        ],
+        {
+            "title_aux": "comfyui-giraffe-test-panel"
+        }
+    ],
+    "https://github.com/PaleBloodq/ComfyUI-HFTransformers": [
+        [
+            "HFTCaptioner",
+            "HFTClassificationSelector",
+            "HFTClassifier",
+            "HFTDepthEstimator",
+            "HFTLoader",
+            "HFTObjectDetector"
+        ],
+        {
+            "title_aux": "ComfyUI-HFTransformers"
+        }
+    ],
+    "https://github.com/Parsa-Sha/ComfyUI-IP-Tools": [
+        [
+            "Switch Concatenate",
+            "SwitchConcatenate"
+        ],
+        {
+            "title_aux": "ComfyUI-IP-Tools"
+        }
+    ],
+    "https://github.com/PauldeLavallaz/comfyui-fabric-lipsync": [
+        [
+            "FabricLipsync",
+            "FabricTextToVideo"
+        ],
+        {
+            "title_aux": "comfyui-fabric-lipsync"
+        }
+    ],
+    "https://github.com/PauldeLavallaz/comfyui-notebooklm": [
+        [
+            "NotebookLM_ScriptGenerator"
+        ],
+        {
+            "title_aux": "comfyui-notebooklm"
+        }
+    ],
+    "https://github.com/PauldeLavallaz/comfyui_morpheus_NanoBanana_Mask": [
+        [
+            "MorpheusBatchImagesCropV25Fix",
+            "MorpheusImageEditingPrompt",
+            "MorpheusNanoBananaMaskGeminiV25Fix"
+        ],
+        {
+            "title_aux": "comfyui_morpheus_NanoBanana_Mask [WIP]"
+        }
+    ],
+    "https://github.com/PeterMikhai/Doom_Flux_NodePack": [
+        [
+            "DoomFluxInpaintSampler",
+            "DoomFluxLoader",
+            "DoomFluxLoader_GGUF",
+            "DoomFluxSampler",
+            "DoomFluxSamplerAdvanced",
+            "DoomFluxSuperLoader"
+        ],
+        {
+            "title_aux": "DoomFLUX Nodes [WIP]"
+        }
+    ],
+    "https://github.com/PladsElsker/comfyui-krita": [
+        [
+            "KritaSaveImage"
+        ],
+        {
+            "title_aux": "comfyui-krita [WIP]"
+        }
+    ],
+    "https://github.com/Polygoningenieur/ComfyUI-Diffusion-SDXL-Video": [
+        [
+            "DiffusionSDXLFrameByFrame"
+        ],
+        {
+            "title_aux": "ComfyUI-Diffusion-SDXL-Video"
+        }
+    ],
+    "https://github.com/PopeyesBiscuit/ComfyUI-DeleteFolder": [
+        [
+            "DeleteInputFolder",
+            "DeleteOutputFolder"
+        ],
+        {
+            "title_aux": "ComfyUI Delete Folder Nodes [UNSAFE]"
+        }
+    ],
+    "https://github.com/PopeyesBiscuit/ComfyUI-WebSocketLoRAExporter": [
+        [
+            "SimpleLoRALoader",
+            "WebSocket_LoRA_Exporter"
+        ],
+        {
+            "title_aux": "ComfyUI-WebSocketLoRAExporter"
+        }
+    ],
+    "https://github.com/Poseidon-fan/ComfyUI-fileCleaner": [
+        [
+            "Clean input and output file"
+        ],
+        {
+            "title_aux": "ComfyUI-fileCleaner [UNSAFE]"
+        }
+    ],
+    "https://github.com/PozzettiAndrea/ComfyUI-CADabra": [
+        [
+            "BrepGeneration",
+            "CADAnalysisViewer",
+            "CADBoundingBox",
+            "CADCheckOverlappingFaces",
+            "CADCurvature",
+            "CADCurvePlotter",
+            "CADEdgeAnalysis",
+            "CADEdgeDetailAnalyzer",
+            "CADEdgeViewer",
+            "CADEdgeViewerVTK",
+            "CADExtractFaces",
+            "CADFaceAnalysis",
+            "CADFixDegenerateFaces",
+            "CADGetFilename",
+            "CADHealShape",
+            "CADHierarchyTree",
+            "CADMergeVertices",
+            "CADPrimitiveReconstruction",
+            "CADProjectFaces2D",
+            "CADROISelector",
+            "CADRaytracerBVH",
+            "CADSave",
+            "CADSewFaces",
+            "CADSplineViewer",
+            "CADSplitComponents",
+            "CADTransform",
+            "CAD_Load",
+            "CAD_Load_From_Glob",
+            "CAD_Mesh",
+            "CAD_Quality_Metrics",
+            "FloatMath",
+            "LoadPointCloudXYZ",
+            "MeshFaceSegmentation",
+            "MeshSegmentToPointCloud",
+            "PointCloudSegmentation",
+            "PreviewCADBatch",
+            "PreviewCADOCC",
+            "PrimitiveFitting",
+            "QuadRemesh"
+        ],
+        {
+            "title_aux": "CADabra [WIP]"
+        }
+    ],
+    "https://github.com/PozzettiAndrea/ComfyUI-Hunyuan3D-Part": [
+        [
+            "ClearAllModelCaches",
+            "ComputeMeshFeatures",
+            "ExplodedMeshViewer",
+            "Hunyuan3DPreviewBoundingBoxes",
+            "LoadBoundingBoxes",
+            "LoadP3SAMSegmentor",
+            "LoadSonataEncoder",
+            "LoadXPartModels",
+            "P3SAMSegmentMesh",
+            "SaveBoundingBoxes",
+            "XPartGenerateParts"
+        ],
+        {
+            "title_aux": "Hunyuan3D-Part [WIP]"
+        }
+    ],
+    "https://github.com/PozzettiAndrea/ComfyUI-MVDUST3R": [
+        [
+            "BlurDetection",
+            "ExportMesh",
+            "ExportPointCloud",
+            "LoadMVDUST3RModel",
+            "MVDUST3DVisualizer",
+            "MVDUST3RGridMesh",
+            "MVDUST3RInference",
+            "PrepareImages"
+        ],
+        {
+            "title_aux": "ComfyUI-MVDUST3R [UNSAFE]"
+        }
+    ],
+    "https://github.com/PozzettiAndrea/ComfyUI-MotionCapture": [
+        [
+            "BVHViewer",
+            "BVHtoFBX",
+            "CompareSMPLtoBVH",
+            "CompareSkeletons",
+            "ExtractRestPose",
+            "FBXAnimationViewer",
+            "FBXPreview",
+            "GVHMRInference",
+            "LoadBVHFromFolder",
+            "LoadCameraTrajectory",
+            "LoadFBXCharacter",
+            "LoadGVHMRModels",
+            "LoadMixamoCharacter",
+            "LoadSMPL",
+            "MocapPreviewRiggedMesh",
+            "SMPLCameraViewer",
+            "SMPLRetargetToSMPL",
+            "SMPLToFBX",
+            "SMPLToGLB",
+            "SMPLToMixamo",
+            "SMPLViewer",
+            "SMPLtoBVH",
+            "SaveSMPL"
+        ],
+        {
+            "title_aux": "MotionCapture [UNSAFE]"
+        }
+    ],
+    "https://github.com/PozzettiAndrea/ComfyUI-SAM3DObjects": [
+        [
+            "LoadSAM3DModel",
+            "SAM3DExportPLY",
+            "SAM3DExportPLYBatch",
+            "SAM3DGaussianDecode",
+            "SAM3DGenerateSLAT",
+            "SAM3DMeshDecode",
+            "SAM3DSceneGenerate",
+            "SAM3DTextureBake",
+            "SAM3D_DepthEstimate",
+            "SAM3D_PoseOptimization",
+            "SAM3D_PreviewPointCloud",
+            "SAM3D_ProjectionOverlay",
+            "SAM3D_ScenePoseOptimize"
+        ],
+        {
+            "title_aux": "SAM3DObjects [UNSAFE]"
+        }
+    ],
+    "https://github.com/PozzettiAndrea/ComfyUI-TRELLIS2": [
+        [
+            "LoadTrellis2Models",
+            "Trellis2ApplyGuidanceInterval",
+            "Trellis2DecodeSSLatent",
+            "Trellis2Empty3DLatent",
+            "Trellis2EncodeMesh",
+            "Trellis2ExportGLB",
+            "Trellis2ExportTrimesh",
+            "Trellis2GetConditioning",
+            "Trellis2ImageToShape",
+            "Trellis2LoadMesh",
+            "Trellis2LoadSSFlowModel",
+            "Trellis2MultiViewImageToShape",
+            "Trellis2ProcessMesh",
+            "Trellis2RasterizePBR",
+            "Trellis2RefineMesh",
+            "Trellis2RemoveBackground",
+            "Trellis2RenderPreview",
+            "Trellis2RenderVideo",
+            "Trellis2SSConditioning",
+            "Trellis2ShapeToMesh",
+            "Trellis2ShapeToTexturedMesh",
+            "Trellis2Simplify",
+            "Trellis2TextureMesh",
+            "Trellis2UVUnwrap"
+        ],
+        {
+            "title_aux": "ComfyUI-TRELLIS2 [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/PozzettiAndrea/ComfyUI-UniRig": [
+        [
+            "MIAAutoRig",
+            "MIALoadModel",
+            "UniRigApplyAnimation",
+            "UniRigApplySkinningMLNew",
+            "UniRigAutoRig",
+            "UniRigCompareSkeletons",
+            "UniRigDebugSkeleton",
+            "UniRigExportPosedFBX",
+            "UniRigExtractSkeletonNew",
+            "UniRigLoadMesh",
+            "UniRigLoadModel",
+            "UniRigLoadRiggedMesh",
+            "UniRigLoadSkeletonModel",
+            "UniRigLoadSkinningModel",
+            "UniRigOrientationCheck",
+            "UniRigPreviewRiggedMesh",
+            "UniRigSaveMesh",
+            "UniRigViewRigging"
+        ],
+        {
+            "title_aux": "ComfyUI-UniRig [WIP]"
+        }
+    ],
+    "https://github.com/ProjectAtlantis-dev/comfyui-atlantis-json": [
+        [
+            "SRTToJson",
+            "SaveJSON",
+            "TextToJsonInput"
+        ],
+        {
+            "title_aux": "comfyui-atlantis-json [UNSAFE]"
+        }
+    ],
+    "https://github.com/QingLuanWithoutHeart/comfyui-file-image-utils": [
+        [
+            "FileManagerV2",
+            "IfTextEquals",
+            "LoadImageFromPath"
+        ],
+        {
+            "title_aux": "ComfyUI File/Image Utils Nodes [UNSAFE]"
+        }
+    ],
+    "https://github.com/Quasimondo/ComfyUI-QuasimondoNodes": [
+        [
+            "CPPN Generator",
+            "Color Match",
+            "Coordinates From Mask",
+            "Custom Shader",
+            "Distance Map",
+            "Folder Queue Manager",
+            "Image Blend by Mask (Batch)",
+            "Image Noise Generator",
+            "Image to Optical Flow",
+            "Perlin Noise Generator",
+            "Preview Mask",
+            "Random Image Generator",
+            "Shift Mask",
+            "Slit Scan",
+            "Spring Mesh",
+            "Temporal Blur",
+            "Video Queue Manager"
+        ],
+        {
+            "title_aux": "ComfyUI-QuasimondoNodes [WIP]"
+        }
+    ],
+    "https://github.com/QuietNoise/ComfyUI-Queue-Manager": [
+        [
+            "Workflow Name"
+        ],
+        {
+            "title_aux": "ComfyUI Queue Manager [WIP]"
+        }
+    ],
+    "https://github.com/RLW-Chars/comfyui-promptbymood": [
+        [
+            "Prompt By Mood"
+        ],
+        {
+            "title_aux": "comfyui-promptbymood [WIP]"
+        }
+    ],
+    "https://github.com/RM2-0/ComfyUI-Upscale": [
+        [
+            "BatchImageUpscaleWithModel"
+        ],
+        {
+            "title_aux": "ComfyUI-Upscale"
+        }
+    ],
+    "https://github.com/RUFFY-369/ComfyUI-FeatureBank": [
+        [
+            "FeatureBankAttentionProcessor"
+        ],
+        {
+            "title_aux": "ComfyUI-FeatureBank"
+        }
+    ],
+    "https://github.com/RaeZhLiu/comfy_ark": [
+        [
+            "ArkSeedreamNode"
+        ],
+        {
+            "title_aux": "comfy_ark"
+        }
+    ],
+    "https://github.com/Raidez/comfyui-kuniklo-collection": [
+        [
+            "ApplySVG2Image",
+            "Properties"
+        ],
+        {
+            "title_aux": "Kuniklo Collection"
+        }
+    ],
+    "https://github.com/RamonGuthrie/ComfyUI-RBG-LoraConverter": [
+        [
+            "RBGLoraKeyConverterNode"
+        ],
+        {
+            "title_aux": "ComfyUI-RBG-LoRA-Converter [UNSAFE]"
+        }
+    ],
+    "https://github.com/Rayen21/ComfyUI-PromptLinePlus": [
+        [
+            "PromptLinePlus"
+        ],
+        {
+            "title_aux": "ComfyUI-PromptLinePlus [WIP]"
+        }
+    ],
+    "https://github.com/RicherdLee/comfyui-oss-image-save": [
+        [
+            "SaveImageOSS"
+        ],
+        {
+            "title_aux": "comfyui-oss-image-save [WIP]"
+        }
+    ],
+    "https://github.com/RicoMeng/CartoonGenComfyui-PromtGenerator": [
+        [
+            "Istanbul80sPromptGenerator"
+        ],
+        {
+            "title_aux": "CartoonGenComfyui-PromtGenerator"
+        }
+    ],
+    "https://github.com/Rizzlord/ComfyUI-SeqTex": [
+        [
+            "SeqTex_DisplaceMesh",
+            "SeqTex_Load_Mesh",
+            "SeqTex_Loader",
+            "SeqTex_ProjectTexture",
+            "SeqTex_Step1_ProcessMesh",
+            "SeqTex_Step2_GenerateCondition",
+            "SeqTex_Step3_GenerateTexture",
+            "SeqTex_Step4_SaveMesh",
+            "SeqTex_TensorsToImages"
+        ],
+        {
+            "title_aux": "ComfyUI-SeqTex"
+        }
+    ],
+    "https://github.com/RobbertB80/ComfyUI-SharePoint-Upload": [
+        [
+            "SharePointUploadNode"
+        ],
+        {
+            "title_aux": "ComfyUI SharePoint/OneDrive Upload Node [UNSAFE]"
+        }
+    ],
+    "https://github.com/RobeSantoro/ComfyUI-RobeNodes": [
+        [
+            "AudioWeights to FadeMask \ud83d\udc24",
+            "GeminiBanana \ud83c\udf4c",
+            "Get File Name \ud83d\udc24",
+            "Image Input Switch \ud83d\udc24",
+            "Indices Generator \ud83d\udc24",
+            "Latent Input Switch \ud83d\udc24",
+            "List Image Path \ud83d\udc24",
+            "List Model Path \ud83d\udc24",
+            "List Video Path \ud83d\udc24",
+            "Peaks Weights Generator \ud83d\udc24",
+            "Save Image (JPEG) \ud83d\udc24",
+            "easy loadImageBase64",
+            "grokImage"
+        ],
+        {
+            "title_aux": "Comfy UI Robe Nodes [UNSAFE]"
+        }
+    ],
+    "https://github.com/Rocky-Lee-001/ComfyUI_SZtools": [
+        [
+            "RSZImageTaggerSave",
+            "ShanZhuDepthVAEEncode",
+            "ShanZhuLoadImagesFromDirList",
+            "ShanZhuTextJoin",
+            "ShanZhuTextSaver",
+            "ShanzhuDepthVAEDecoder",
+            "ShanzhuLujingTIFSaver",
+            "ShanzhuTifSaver",
+            "ShanzhuVAEDecodeSmooth",
+            "TSZImageTaggerSave"
+        ],
+        {
+            "title_aux": "ComfyUI_SZtools"
+        }
+    ],
+    "https://github.com/RomanticQq/ComfyUI-Groudingdino-Sam": [
+        [
+            "grounded_sam2_cut",
+            "grounded_sam2_cut_gaussian",
+            "groundingdino",
+            "groundingdino_clip",
+            "groundingdino_clip_2",
+            "groundingdino_draw_bbox"
+        ],
+        {
+            "title_aux": "ComfyUI-Groudingdino-Sam"
+        }
+    ],
+    "https://github.com/Ronnasayd/comfyui-nodes": [
+        [
+            "AspectRatioCrop",
+            "ConditioningExtendFrames",
+            "ConditioningShapeDebug",
+            "LatentExtendFrames",
+            "LatentPrependCache",
+            "LatentShapeDebug",
+            "PixelatedBorderNode",
+            "VaceControlPrepare",
+            "VideoLatentMask",
+            "VideoSegmentPrepare",
+            "VideoSegmentSave",
+            "WanImagesToVideo"
+        ],
+        {
+            "title_aux": "comfyui-nodes"
+        }
+    ],
+    "https://github.com/Rosegun007/ComfyUI-Pandora": [
+        [
+            "Image Fill Color | Pandora",
+            "Image From File | Pandora",
+            "Mask to Image | Pandora",
+            "Preview Image | Pandora",
+            "Preview Mask | Pandora",
+            "Text On Image | Pandora"
+        ],
+        {
+            "title_aux": "ComfyUI-Pandora"
+        }
+    ],
+    "https://github.com/RoyKillington/miscomfy-nodes": [
+        [
+            "VeniceUpscale"
+        ],
+        {
+            "title_aux": "Miscomfy Nodes [WIP]"
+        }
+    ],
+    "https://github.com/SKBv0/ComfyUI-RetroEngine": [
+        [
+            "RetroEngineNode"
+        ],
+        {
+            "title_aux": "Retro Engine Node for ComfyUI"
+        }
+    ],
+    "https://github.com/SSYCloud/comfyui-ssy-syncapi": [
+        [
+            "SSYBytedanceProcessor",
+            "SSYDoubaoGenerator",
+            "SSYGoogleGenerator",
+            "SSYOpenAIGenerator"
+        ],
+        {
+            "title_aux": "comfyui-ssy-syncapi [WIP]"
+        }
+    ],
+    "https://github.com/SUPi5460/ComfyUI-SmartPrompt": [
+        [
+            "PromptBreakBuilder",
+            "RandomPoseSelector",
+            "RandomThemeTagSelector"
+        ],
+        {
+            "title_aux": "comfyui-smartprompt [WIP]"
+        }
+    ],
+    "https://github.com/SXQBW/ComfyUI-Qwen3": [
+        [
+            "QwenVisionParser"
+        ],
+        {
+            "title_aux": "ComfyUI-Qwen-VLM [WIP]"
+        }
+    ],
+    "https://github.com/SadaleNet/ComfyUI-Prompt-To-Prompt": [
+        [
+            "CLIPTextEncodePromptToPrompt",
+            "KSamplerPromptToPrompt",
+            "KSamplerPromptToPromptAttentionMapLogger",
+            "LocalBlendLayerPresetPromptToPrompt"
+        ],
+        {
+            "title_aux": "ComfyUI Port for Google's Prompt-to-Prompt"
+        }
+    ],
+    "https://github.com/Saganaki22/ComfyUI-KittenTTS": [
+        [
+            "KittenTTS"
+        ],
+        {
+            "title_aux": "Kitten-TTS [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/Saganaki22/ComfyUI-NovaSR": [
+        [
+            "NovaSR"
+        ],
+        {
+            "title_aux": "ComfyUI-NovaSR"
+        }
+    ],
+    "https://github.com/Saganaki22/ComfyUI-ytdl_nodes": [
+        [
+            "YTDLDownloader",
+            "YTDLLinksInput",
+            "YTDLPreview",
+            "YTDLPreviewAudio"
+        ],
+        {
+            "title_aux": "ComfyUI YTDL Nodes [WIP]"
+        }
+    ],
+    "https://github.com/Sai-ComfyUI/ComfyUI-MS-Nodes": [
+        [
+            "FloatMath",
+            "MS_Boolean",
+            "MS_Float",
+            "MS_GenerateSeed",
+            "MS_NP_Vector3",
+            "PowerFractalCrossHatchNode",
+            "PowerFractalNoiseNode",
+            "VectorMath"
+        ],
+        {
+            "title_aux": "ComfyUI-MS-Nodes [WIP]"
+        }
+    ],
+    "https://github.com/SanDiegoDude/ComfyUI-HiDream-Sampler": [
+        [
+            "HiDreamImg2Img",
+            "HiDreamResolutionSelect",
+            "HiDreamSampler",
+            "HiDreamSamplerAdvanced"
+        ],
+        {
+            "title_aux": "HiDreamSampler for ComfyUI [WIP]"
+        }
+    ],
+    "https://github.com/Scaryplasmon/ComfTrellis": [
+        [
+            "LoadTrellisModel",
+            "RembgSquare",
+            "SaveGLBFile",
+            "TrellisGrid",
+            "TrellisInference"
+        ],
+        {
+            "title_aux": "ComfTrellis [WIP]"
+        }
+    ],
+    "https://github.com/Seb-Lis/comfyui-seed-selector": [
+        [
+            "MySeedSelectorInt",
+            "SeedSelectorInt",
+            "SeedSelectorIntWithDisplay"
+        ],
+        {
+            "title_aux": "comfyui-seed-selector"
+        }
+    ],
+    "https://github.com/SeedV/ComfyUI-SeedV-Nodes": [
+        [
+            "ALL_Model_UnLoader(SEEDV)",
+            "AdvancedScript",
+            "CheckpointLoaderSimpleShared //SeedV",
+            "ControlNetLoaderAdvancedShared",
+            "HandleImage(SEEDV)",
+            "LoraLoader //SeedV",
+            "Script",
+            "Switch_Any(SEEDV)",
+            "TCD_Sampler(SEEDV)",
+            "nunchakuLoraAdapter(SEEDV)"
+        ],
+        {
+            "title_aux": "ComfyUI-SeedV-Nodes [UNSAFE]"
+        }
+    ],
+    "https://github.com/Sen-sou/Comfyui-Anima-Regional-Conditioning": [
+        [
+            "AnimaConditioningRegion",
+            "ApplyAnimaRegionalConditioningPatch"
+        ],
+        {
+            "title_aux": "Comfyui-Anima-Regional-Conditioning [WIP]"
+        }
+    ],
+    "https://github.com/Sephrael/comfyui_caption-around-image": [
+        [
+            "CaptionAroundImageSmart",
+            "PrintPromptValues",
+            "PromptProvider",
+            "TextboxReferenceNode"
+        ],
+        {
+            "title_aux": "comfyui_caption-around-image"
+        }
+    ],
+    "https://github.com/SergeyKarleev/comfyui-textutils": [
+        [
+            "TextLinesBatch"
+        ],
+        {
+            "title_aux": "comfyui-textutils [WIP]"
+        }
+    ],
+    "https://github.com/ShahFaisalWani/ComfyUI-Mojen-Nodeset": [
+        [
+            "MojenAnalyzeProcessor",
+            "MojenAspectRatio",
+            "MojenImageLoader",
+            "MojenNSFWClassifier",
+            "MojenNSFWClassifierSave",
+            "MojenStringLength",
+            "MojenStyleExtractor",
+            "MojenTagProcessor",
+            "MojenTransparentBg"
+        ],
+        {
+            "title_aux": "ComfyUI-Mojen-Nodeset"
+        }
+    ],
+    "https://github.com/Sheldongg/ComfyUI-RefineAnything": [
+        [
+            "RefineAnything"
+        ],
+        {
+            "title_aux": "ComfyUI-RefineAnything [WIP]"
+        }
+    ],
+    "https://github.com/Shinsplat/ComfyUI-Shinsplat": [
+        [
+            "Clip Text Encode (Shinsplat)",
+            "Clip Text Encode ALT (Shinsplat)",
+            "Clip Text Encode SD3 (Shinsplat)",
+            "Clip Text Encode SDXL (Shinsplat)",
+            "Clip Text Encode T5 (Shinsplat)",
+            "Clip Tokens Encode (Shinsplat)",
+            "Green Box (Shinsplat)",
+            "Hex To Other (Shinsplat)",
+            "KSampler (Shinsplat)",
+            "Lora Loader (Shinsplat)",
+            "Nupoma (Shinsplat)",
+            "Seed (Shinsplat)",
+            "Shinsplat_CLIPTextEncodeFlux",
+            "String Interpolated (Shinsplat)",
+            "Sum Wrap (Shinsplat)",
+            "Tensor Toys (Shinsplat)",
+            "Test Node (Shinsplat)",
+            "Text To Tokens (Shinsplat)",
+            "Text To Tokens SD3 (Shinsplat)",
+            "Variables (Shinsplat)"
+        ],
+        {
+            "author": "Shinsplat",
+            "description": "",
+            "nickname": "shinsplat",
+            "title": "Shinsplat",
+            "title_aux": "ComfyUI-Shinsplat [UNSAFE]"
+        }
+    ],
+    "https://github.com/SilentLuxRay/ComfyUI-Furrey-Super-Prompt": [
+        [
+            "FurreySimplePrompt",
+            "FurreySuperPrompt"
+        ],
+        {
+            "title_aux": "ComfyUI-Furrey-Super-Prompt [WIP]"
+        }
+    ],
+    "https://github.com/SirVeggie/comfyui-sv-nodes": [
+        [
+            "SV-AccumulateNode",
+            "SV-AccumulationGetItemNode",
+            "SV-AccumulationGetLengthNode",
+            "SV-AccumulationHeadNode",
+            "SV-AccumulationSetItemNode",
+            "SV-AccumulationTailNode",
+            "SV-AccumulationToListNode",
+            "SV-AnyToAny",
+            "SV-ApplyCurve",
+            "SV-ApplyCurveFromStep",
+            "SV-AssertNotNone",
+            "SV-BasicParams",
+            "SV-BasicParamsCustom",
+            "SV-BasicParamsOutput",
+            "SV-BasicParamsPlus",
+            "SV-BasicParamsStartEnd",
+            "SV-BooleanNot",
+            "SV-CacheObject",
+            "SV-CheckNone",
+            "SV-CheckNoneNot",
+            "SV-ClearCustomCaches",
+            "SV-CompressConds",
+            "SV-ConditioningReroute",
+            "SV-ConsolePrint",
+            "SV-ConsolePrintLoop",
+            "SV-ConsolePrintMulti",
+            "SV-CurveFromEquation",
+            "SV-DefaultBoolean",
+            "SV-DefaultFloat",
+            "SV-DefaultInt",
+            "SV-DefaultString",
+            "SV-DefaultValue",
+            "SV-EquationCompare",
+            "SV-FloatRerouteForSubnodes",
+            "SV-FlowBlocker",
+            "SV-FlowGate",
+            "SV-FlowGateMulti",
+            "SV-FlowNode",
+            "SV-FlowPipeCombine",
+            "SV-FlowPipeInput",
+            "SV-FlowPipeInputIndex",
+            "SV-FlowPipeInputKey",
+            "SV-FlowPipeInputKeyTuple",
+            "SV-FlowPipeInputLarge",
+            "SV-FlowPipeInputModel",
+            "SV-FlowPipeInputParams",
+            "SV-FlowPipeOutput",
+            "SV-FlowPipeOutputIndex",
+            "SV-FlowPipeOutputKey",
+            "SV-FlowPipeOutputKeyTuple",
+            "SV-FlowPipeOutputLarge",
+            "SV-FlowPipeOutputModel",
+            "SV-FlowPipeOutputParams",
+            "SV-ForLoopClose",
+            "SV-ForLoopOpen",
+            "SV-HashItems",
+            "SV-HashModel",
+            "SV-HashObject",
+            "SV-IfBranch",
+            "SV-ImageReroute",
+            "SV-InputSelect",
+            "SV-InputSelectBoolean",
+            "SV-InputSelectCompare",
+            "SV-IntMathOperation",
+            "SV-ListToAccumulationNode",
+            "SV-LoadTextFile",
+            "SV-LoraSeparator",
+            "SV-ManualCache",
+            "SV-MathAddInt",
+            "SV-MathCompare",
+            "SV-MathOperation",
+            "SV-MetadataJson",
+            "SV-ModelName",
+            "SV-ModelReroute",
+            "SV-NormalizeImageSize",
+            "SV-NormalizeImageSize64",
+            "SV-NormalizeSamples",
+            "SV-PadImage",
+            "SV-PromptPlusModel",
+            "SV-PromptPlusModelOutput",
+            "SV-PromptProcessing",
+            "SV-PromptProcessingAdvanced",
+            "SV-PromptProcessingEncode",
+            "SV-PromptProcessingEncodeList",
+            "SV-PromptProcessingGetCond",
+            "SV-PromptProcessingPromptControl",
+            "SV-PromptProcessingRecursive",
+            "SV-PromptProcessingSimple",
+            "SV-PromptProcessingVars",
+            "SV-ResolutionSelector",
+            "SV-ResolutionSelector2",
+            "SV-ResolutionSelector2Output",
+            "SV-SamplerNameToSampler",
+            "SV-SaveTextFile",
+            "SV-SigmaAsFloat",
+            "SV-SigmaConcat",
+            "SV-SigmaContinue",
+            "SV-SigmaContinueLinear",
+            "SV-SigmaEmpty",
+            "SV-SigmaLength",
+            "SV-SigmaOneStep",
+            "SV-SigmaRange",
+            "SV-SigmaRemap",
+            "SV-SigmaReroute",
+            "SV-SigmaReverse",
+            "SV-SigmaStartEnd",
+            "SV-SigmaStrength",
+            "SV-SimpleText",
+            "SV-StringCombine",
+            "SV-StringSeparator",
+            "SV-SwapValues",
+            "SV-TimerEnd",
+            "SV-TimerStart",
+            "SV-ToBoolNode",
+            "SV-UnitFloat",
+            "SV-ValueRepeater",
+            "SV-VariableClear",
+            "SV-VariableGet",
+            "SV-VariableSet",
+            "SV-WhileLoopClose",
+            "SV-WhileLoopOpen",
+            "SV-WildcardLoader",
+            "SV-WildcardProcessing",
+            "SV-WildcardString"
+        ],
+        {
+            "title_aux": "SirVeggie/Custom nodes for ComfyUI"
+        }
+    ],
+    "https://github.com/SlackinJack/multigpu_diffusion_comfyui": [
+        [
+            "ApplyPipeline",
+            "AsyncDiffConfig",
+            "AttentionBackendConfig",
+            "BalancedConfig",
+            "BitsAndBytesQuantizationConfig",
+            "CheckpointSelector",
+            "CloseHost",
+            "CompileConfig",
+            "CreateHost",
+            "DDIMScheduler",
+            "DDPMScheduler",
+            "DEISMultistepScheduler",
+            "DPMSolverMultistepScheduler",
+            "DPMSolverSinglestepScheduler",
+            "EncodePromptWithCompel",
+            "EulerAncestralDiscreteScheduler",
+            "EulerDiscreteScheduler",
+            "FlowMatchScheduler",
+            "FlowMatchSchedulerConfig",
+            "FluxSampler",
+            "GroupOffloadConfig",
+            "HeunDiscreteScheduler",
+            "KDPM2AncestralDiscreteScheduler",
+            "KDPM2DiscreteScheduler",
+            "LMSDiscreteScheduler",
+            "LoraSelector",
+            "ModelSelector",
+            "MultiLoraJoiner",
+            "OffloadConfig",
+            "OffloadPipeline",
+            "PNDMScheduler",
+            "QuantizationConfig",
+            "QuantoQuantizationConfig",
+            "SDNQQuantizationConfig",
+            "SDSampler",
+            "SDUpscaleSampler",
+            "SVDSampler",
+            "SingleConfig",
+            "SleepHost",
+            "TCDScheduler",
+            "TorchAOQuantizationConfig",
+            "TorchConfig",
+            "UniPCMultistepScheduler",
+            "UnsafeModelSelector",
+            "WanSampler",
+            "ZImageSampler"
+        ],
+        {
+            "title_aux": "multigpu_diffusion_comfyui"
+        }
+    ],
+    "https://github.com/SleazySleaze/aesthetic-persona-comfyui-node": [
+        [
+            "AestheticPersonaParser"
+        ],
+        {
+            "title_aux": "aesthetic-persona-comfyui-node"
+        }
+    ],
+    "https://github.com/Smyshnikof/ComfyUI-PresetDownloadManager": [
+        [
+            "PresetDownloadManager"
+        ],
+        {
+            "title_aux": "ComfyUI-PresetDownloadManager [UNSAFE]"
+        }
+    ],
+    "https://github.com/Solankimayursinh/PMSnodes": [
+        [
+            "InputAnalyzer",
+            "LoadBase64Audio",
+            "LoadImageBase64",
+            "LoadMaskBase64",
+            "PMSLoadText",
+            "PMSSendAudio",
+            "PMSSendImage"
+        ],
+        {
+            "title_aux": "PMSnodes [WIP]"
+        }
+    ],
+    "https://github.com/Soliton80/ComfyUI-Watermark-Detection-YOLO": [
+        [
+            "WatermarkDetector",
+            "WatermarkDetectorLoader"
+        ],
+        {
+            "title_aux": "Watermark Detection YOLO Custom Node [WIP]"
+        }
+    ],
+    "https://github.com/Sophylax/ComfyUI-ReferenceMerge": [
+        [
+            "InpaintRegionRestitcher",
+            "ReferenceInpaintComposite"
+        ],
+        {
+            "title_aux": "ComfyUI-ReferenceMerge"
+        }
+    ],
+    "https://github.com/Soppatorsk/comfyui_img_to_ascii": [
+        [
+            "FirstNode",
+            "Img_to_ASCII"
+        ],
+        {
+            "title_aux": "comfyui_img_to_ascii [WIP]"
+        }
+    ],
+    "https://github.com/SpaceWarpStudio/ComfyUI_Remaker_FaceSwap": [
+        [
+            "RemakerFaceSwap"
+        ],
+        {
+            "title_aux": "ComfyUI_Remaker_FaceSwap"
+        }
+    ],
+    "https://github.com/Spicely/ComfyUI-Luma": [
+        [
+            "AddVideoTextWatermark",
+            "GetDeviceType",
+            "LoadAudioByUrl",
+            "LoadImageByUrl",
+            "LoadVideoByUrl",
+            "SeparateVideoAudio",
+            "Wav2Srt"
+        ],
+        {
+            "title_aux": "ComfyUI-Luma [WIP]"
+        }
+    ],
+    "https://github.com/Stable-X/ComfyUI-Hi3DGen": [
+        [
+            "DifferenceExtractorNode",
+            "DownloadAndLoadStableXModel",
+            "IF_TrellisCheckpointLoader",
+            "IF_TrellisImageTo3D",
+            "StableXProcessImage"
+        ],
+        {
+            "title_aux": "ComfyUI-Hi3DGen"
+        }
+    ],
+    "https://github.com/StableDiffusionVN/SDVN_Comfy_node": [
+        [
+            "InpaintCropImproved",
+            "InpaintStitchImproved",
+            "SDVM Image List Repeat",
+            "SDVN API chatbot",
+            "SDVN Any From List",
+            "SDVN Any Input Type",
+            "SDVN Any List",
+            "SDVN Any Repeat",
+            "SDVN Any Show",
+            "SDVN AnyDownload List",
+            "SDVN Apply Kontext Reference",
+            "SDVN Apply Style Model",
+            "SDVN Auto Generate",
+            "SDVN AutoSwitch",
+            "SDVN Boolean",
+            "SDVN CLIP Download",
+            "SDVN CLIP Text Encode",
+            "SDVN CLIP Text Encode Simple",
+            "SDVN CLIPVision Download",
+            "SDVN Checkpoint Download",
+            "SDVN Checkpoint Download List",
+            "SDVN ControlNet Download",
+            "SDVN Controlnet Apply",
+            "SDVN Crop By Ratio",
+            "SDVN DALL-E Generate Image",
+            "SDVN Dic Convert",
+            "SDVN DiffsynthControlNet Apply",
+            "SDVN DiffsynthUnionLora Apply",
+            "SDVN DualCLIP Download",
+            "SDVN Easy IPAdapter weight",
+            "SDVN Empty Latent Ratio",
+            "SDVN Exif check",
+            "SDVN Fast Groups Bypasser",
+            "SDVN Fill Background",
+            "SDVN Filter List",
+            "SDVN Flip Image",
+            "SDVN Float Slider",
+            "SDVN GPT Image",
+            "SDVN Gemini 3 Pro Image",
+            "SDVN Gemini 3.1 Flash Image",
+            "SDVN Gemini Flash 2 Image",
+            "SDVN Get Mask Size",
+            "SDVN Google Colab Disconnect",
+            "SDVN Google Imagen",
+            "SDVN IC Lora Layout",
+            "SDVN IC Lora Layout Crop",
+            "SDVN IPAdapterModel Download",
+            "SDVN Image Adjust",
+            "SDVN Image Chooser",
+            "SDVN Image Color Match",
+            "SDVN Image Film Grain",
+            "SDVN Image HSL",
+            "SDVN Image Info",
+            "SDVN Image Layout",
+            "SDVN Image Repeat",
+            "SDVN Image Scraper",
+            "SDVN Image Size",
+            "SDVN Image White Balance",
+            "SDVN ImageGallery",
+            "SDVN Inpaint",
+            "SDVN Inpaint Crop",
+            "SDVN InstantIDModel Download",
+            "SDVN Int Slider",
+            "SDVN Join Parameter",
+            "SDVN KSampler",
+            "SDVN Load Checkpoint",
+            "SDVN Load Checkpoint Filter",
+            "SDVN Load Google Sheet",
+            "SDVN Load Image",
+            "SDVN Load Image Folder",
+            "SDVN Load Image From List",
+            "SDVN Load Image Ultimate",
+            "SDVN Load Image Url",
+            "SDVN Load Lora",
+            "SDVN Load Lora Filter",
+            "SDVN Load Text",
+            "SDVN LoadPinterest",
+            "SDVN Logic",
+            "SDVN Loop Inpaint Stitch",
+            "SDVN Lora Download",
+            "SDVN Lora info",
+            "SDVN Mask Layout Fit",
+            "SDVN Mask Regions",
+            "SDVN Mask To Transparent Color",
+            "SDVN Menu Option",
+            "SDVN Menu Option Extra",
+            "SDVN Merge Flux",
+            "SDVN Merge SD1",
+            "SDVN Merge SDXL",
+            "SDVN Metadata Check",
+            "SDVN Model Export",
+            "SDVN Model Merge",
+            "SDVN Model info editor",
+            "SDVN ModelPatch Download",
+            "SDVN Nano Banana",
+            "SDVN Overlay Images",
+            "SDVN Overlay Mask Color Image",
+            "SDVN Pipe In",
+            "SDVN Pipe Out",
+            "SDVN Pipe Out All",
+            "SDVN QuadrupleCLIP Download",
+            "SDVN QwenEdit TextEncoder",
+            "SDVN QwenEdit TextEncoder Plus",
+            "SDVN RGBA to RGB",
+            "SDVN Random Prompt",
+            "SDVN Run Python Code",
+            "SDVN Run Test",
+            "SDVN Save Image",
+            "SDVN Save Text",
+            "SDVN Seed",
+            "SDVN Simple Any Input",
+            "SDVN Slider 1",
+            "SDVN Slider 100",
+            "SDVN Split Tile",
+            "SDVN Stitch Tile",
+            "SDVN StyleModel Download",
+            "SDVN Styles",
+            "SDVN Switch",
+            "SDVN Translate",
+            "SDVN UNET Download",
+            "SDVN UPscale Latent",
+            "SDVN Upscale Image",
+            "SDVN UpscaleModel Download",
+            "SDVN VAE Download",
+            "SDVN Yolo8 Seg"
+        ],
+        {
+            "title_aux": "SDVN Comfy node [UNSAFE]"
+        }
+    ],
+    "https://github.com/StaffsGull/comfyui_scene_builder": [
+        [
+            "CharacterBuilderNode",
+            "ClothingItemNode",
+            "ClothingMergerNode",
+            "EnvironmentBuilderNode",
+            "MergeCharactersNode",
+            "PhotoStyleBuilderNode",
+            "SceneCombinerNode"
+        ],
+        {
+            "title_aux": "comfyui_scene_builder [WIP]"
+        }
+    ],
+    "https://github.com/StartHua/Comfyui_CSDMT_CXH": [
+        [
+            "CSD"
+        ],
+        {
+            "title_aux": "Comfyui_CXH_CRM"
+        }
+    ],
+    "https://github.com/StartHua/Comfyui_CXH_CRM": [
+        [
+            "CRM"
+        ],
+        {
+            "title_aux": "Comfyui_CXH_CRM"
+        }
+    ],
+    "https://github.com/StartHua/Comfyui_CXH_joy_caption": [
+        [
+            "CXH_DownloadAndLoadFlorence2Model",
+            "CXH_Florence2Run",
+            "CXH_HG_Model_Load",
+            "CXH_IC_Lora_Florence2Run",
+            "CXH_IC_lora_reversal",
+            "CXH_Ic_lora_Joy_batch",
+            "CXH_Min2_6_classifiy",
+            "CXH_Min2_6_prompt_Run",
+            "CXH_MinCP3_4B_Chat",
+            "CXH_MinCP3_4B_Load",
+            "CXH_SmolVlm_Load",
+            "CXH_SmolVlm_Run",
+            "Joy_caption",
+            "Joy_caption_alpha_batch",
+            "Joy_caption_alpha_batch_Dirs",
+            "Joy_caption_alpha_load",
+            "Joy_caption_alpha_prompt",
+            "Joy_caption_alpha_run",
+            "Joy_caption_load"
+        ],
+        {
+            "title_aux": "Comfyui_CXH_joy_caption [SECURITY SCREENING]"
+        }
+    ],
+    "https://github.com/StartHua/Comfyui_Flux_Style_Ctr": [
+        [
+            "CXH_StyleModelApply"
+        ],
+        {
+            "title_aux": "Comfyui_Flux_Style_Ctr [WIP]"
+        }
+    ],
+    "https://github.com/StartHua/Comfyui_leffa": [
+        [
+            "CXH_Leffa_Viton_Load",
+            "CXH_Leffa_Viton_Run"
+        ],
+        {
+            "title_aux": "Comfyui_leffa"
+        }
+    ],
+    "https://github.com/StartHua/Comfyui_yolo26": [
+        [
+            "YoloPoseAdvancedNode",
+            "YoloPoseNode",
+            "YoloWorldNode"
+        ],
+        {
+            "title_aux": "Comfyui_yolo26 [WIP]"
+        }
+    ],
+    "https://github.com/StevenBaby/comfyui-tools": [
+        [
+            "IntParameterNode"
+        ],
+        {
+            "title_aux": "comfyui-tools"
+        }
+    ],
+    "https://github.com/SushantSingh-23-01/comfyui_flowrider_nodes": [
+        [
+            "FilmGrainVFX",
+            "SaveCustomResolutions",
+            "SaveImageCustom",
+            "SelectCustomResolutions"
+        ],
+        {
+            "title_aux": "comfyui_flowrider_nodes [UNSAFE]"
+        }
+    ],
+    "https://github.com/Symbiomatrix/Comfyui-Sort-Files": [
+        [
+            "ImageSaverSBM",
+            "SortControlSBM",
+            "StringToFloatSBM",
+            "VideoSeriesMergerSBM"
+        ],
+        {
+            "author": "SBM",
+            "title_aux": "Comfyui-Sort-Files"
+        }
+    ],
+    "https://github.com/TSFSean/ComfyUI-TSFNodes": [
+        [
+            "GyroOSC"
+        ],
+        {
+            "title_aux": "ComfyUI-TSFNodes"
+        }
+    ],
+    "https://github.com/Tawbaware/ComfyUI-Tawbaware": [
+        [
+            "LatentBlendGradient",
+            "ReverseLatentBatch",
+            "WanVideoReCamMasterGenerateOrbitCameraEx"
+        ],
+        {
+            "title_aux": "ComfyUI-Tawbaware [WIP]"
+        }
+    ],
+    "https://github.com/TechnoWarrior2/comfyui-live-preview": [
+        [
+            "AlexLivePreview"
+        ],
+        {
+            "title_aux": "comfyui-live-preview [WIP]"
+        }
+    ],
+    "https://github.com/Temult/TWanSigmaSampler": [
+        [
+            "TWanVideoSigmaSampler"
+        ],
+        {
+            "title_aux": "TWanVideoSigmaSampler: EXPERIMENTAL [WIP]"
+        }
+    ],
+    "https://github.com/ThanaritKanjanametawatAU/comfyui_runninghub_nanobanana": [
+        [
+            "RH_NanoBananaPro"
+        ],
+        {
+            "title_aux": "comfyui_runninghub_nanobanana"
+        }
+    ],
+    "https://github.com/ThatGlennD/ComfyUI-Image-Analysis-Tools": [
+        [
+            "Blur Detection",
+            "BlurDetection",
+            "Clipping Analysis",
+            "ClippingAnalysis",
+            "Color Cast Detector",
+            "Color Harmony Analyzer",
+            "Color Temperature Estimator",
+            "ColorCastDetector",
+            "ColorHarmonyAnalyzer",
+            "ColorTemperatureEstimator",
+            "Contrast Analysis",
+            "ContrastAnalysis",
+            "Defocus Analysis",
+            "DefocusAnalysis",
+            "Edge Density Analysis",
+            "EdgeDensityAnalysis",
+            "Entropy Analysis",
+            "EntropyAnalysis",
+            "Noise Estimation",
+            "NoiseEstimation",
+            "RGB Histogram Renderer",
+            "RGBHistogramRenderer",
+            "Sharpness / Focus Score",
+            "SharpnessFocusScore"
+        ],
+        {
+            "title_aux": "ComfyUI Image Analysis Toolkit [WIP]"
+        }
+    ],
+    "https://github.com/TheJorseman/IntrinsicCompositingClean-ComfyUI": [
+        [
+            "AlbedoHarmonizer",
+            "AlbedoModelLoader",
+            "CompleteRelighting",
+            "CompositeNormalsCalculator",
+            "DepthEstimator",
+            "DepthModelLoader",
+            "ExtractInvShading",
+            "ExtractSmallBgShd",
+            "HarmonizedImageCreator",
+            "ImageResizer",
+            "ImageResizerNP",
+            "ImageResizerNPMASK",
+            "IntrinsicDecomposer",
+            "IntrinsicModelLoader",
+            "LightCoeffExtractor",
+            "LoadImagePIL",
+            "MaskApplier",
+            "MaskGenerator",
+            "MaskPreprocessor",
+            "NormalsExtractor",
+            "NormalsModelLoader",
+            "ReshadingModelLoader",
+            "ReshadingProcessor"
+        ],
+        {
+            "title_aux": "IntrinsicCompositingClean-ComfyUI"
+        }
+    ],
+    "https://github.com/TheLocalLab/ComfyUI-Kandinsky-GGUF": [
+        [
+            "EmptyKandinskyV5_Latent",
+            "KandinskyV5_ImageToVideoLatent",
+            "KandinskyV5_Loader",
+            "KandinskyV5_PruneFrames",
+            "KandinskyV5_Sampler",
+            "KandinskyV5_TextEncode",
+            "KandinskyV5_VAEDecode",
+            "KandinskyV5_VAELoader"
+        ],
+        {
+            "title_aux": "ComfyUI-Kandinsky-GGUF [WIP]"
+        }
+    ],
+    "https://github.com/ThisModernDay/ComfyUI-InstructorOllama": [
+        [
+            "OllamaInstructorNode"
+        ],
+        {
+            "title_aux": "ComfyUI Instructor Ollama"
+        }
+    ],
+    "https://github.com/ThuckMaBaws/TMB-Camera-Modifier": [
+        [
+            "Camera Modifier Selector"
+        ],
+        {
+            "title_aux": "TMB Camera Modifier for ComfyUI [WIP]"
+        }
+    ],
+    "https://github.com/TimothyCMeehan/comfyui-ck3-presets": [
+        [
+            "CK3 Resize Image",
+            "CK3ResizeImage",
+            "CK3SizePreset"
+        ],
+        {
+            "title_aux": "ComfyUI CK3 Presets"
+        }
+    ],
+    "https://github.com/TobiasGlaubach/ComfyUI-TG_PyCode": [
+        [
+            "TG_PyCall",
+            "TG_PyCallKey",
+            "TG_PyEditor",
+            "TG_PyEval",
+            "TG_PyEvalNamespace",
+            "TG_PyGetInstalledLibs",
+            "TG_PyImportLibs"
+        ],
+        {
+            "title_aux": "ComfyUI-TG_PyCode [UNSAFE]"
+        }
+    ],
+    "https://github.com/Toraong/ComfyUI-Latent-Resizer-Tools": [
+        [
+            "LatentResizeTo1M",
+            "LatentResizeTo64"
+        ],
+        {
+            "title_aux": "ComfyUI-Latent-Resizer-Tools [WIP]"
+        }
+    ],
+    "https://github.com/Toxic1228/Eleven-labs-comfyui-sts": [
+        [
+            "ElevenLabs_SpeechToSpeech"
+        ],
+        {
+            "title_aux": "Eleven-labs-comfyui-sts"
+        }
+    ],
+    "https://github.com/Tr1dae/ComfyUI-CustomNodes-MVM": [
+        [
+            "LoadImageFromFolderMVM",
+            "LoadImagesFromFolderRandomMVM",
+            "LoadImagesFromRelativePathRandomMVM",
+            "LoadTextFromFolderMVM"
+        ],
+        {
+            "title_aux": "ComfyUI-CustomNodes-MVM"
+        }
+    ],
+    "https://github.com/Tr1dae/ComfyUI-MobileSAM": [
+        [
+            "EasyMobileSAM"
+        ],
+        {
+            "title_aux": "ComfyUI-MobileSAM [WIP]"
+        }
+    ],
+    "https://github.com/UltraNoob-NazoGiken/ComfyUI-TOML-Tools": [
+        [
+            "CreateTomlData",
+            "GetTomlValue",
+            "LoadToml",
+            "MergeTomlData",
+            "SaveToml"
+        ],
+        {
+            "title_aux": "ComfyUI-TOML-Tools [WIP]"
+        }
+    ],
+    "https://github.com/UmutGuzel/tryvariantai-comfyui": [
+        [
+            "Base64DecodeNode",
+            "BiRefNetSegmentationNode",
+            "DebugMaskNode",
+            "FillTransparencyNode",
+            "ImageToMaskNode",
+            "MaskCleanupNode",
+            "MaskExpandBorder",
+            "MaskExpandBorderAdvanced",
+            "MaskFromContour",
+            "MaskFromContoursOpenCV",
+            "MaskMattingNode",
+            "MaskMorphNode",
+            "MaskShrinkNode",
+            "MaskToImageNode",
+            "MaskToTransparentNode",
+            "RGBAtoRGBNode",
+            "SimpleWhiteDetectorNode",
+            "WhiteToTransparentNode"
+        ],
+        {
+            "title_aux": "ComfyUI-yolov5-face [WIP]"
+        }
+    ],
+    "https://github.com/UnlimitedEditing/comfyui-turntable-render": [
+        [
+            "TurntableRenderNode"
+        ],
+        {
+            "title_aux": "comfyui-turntable-render"
+        }
+    ],
+    "https://github.com/V-woodpecker-V/comfyui-stiffy-nodes": [
+        [
+            "StiffyComboNode",
+            "StiffyComplexPresetNode",
+            "StiffyDecoderNode",
+            "StiffySimplePresetNode"
+        ],
+        {
+            "title_aux": "comfyui-stiffy-nodes"
+        }
+    ],
+    "https://github.com/VectorASD/ComfyUI-VectorASD": [
+        [
+            "ASD_CLIPLoader",
+            "ASD_CLIPSplitter",
+            "ASD_CLIPTextEncode",
+            "ASD_CompareAnything",
+            "ASD_JoinString",
+            "ASD_JoinStringMulti",
+            "ASD_JoinStringMultiPrefix",
+            "ASD_JoinStringPrefix"
+        ],
+        {
+            "title_aux": "ComfyUI-VectorASD"
+        }
+    ],
+    "https://github.com/Velour-Fog/comfy-latent-nodes": [
+        [
+            "CustomLoadLatent",
+            "CustomSaveLatent"
+        ],
+        {
+            "title_aux": "comfy-latent-nodes [UNSAFE]"
+        }
+    ],
+    "https://github.com/Veritas-bit/ComfyUI-StyleSelector": [
+        [
+            "MyUnifiedSelector"
+        ],
+        {
+            "title_aux": "ComfyUI-StyleSelector [WIP]"
+        }
+    ],
+    "https://github.com/VictorLopes643/ComfyUI-Video-Dataset-Tools": [
+        [
+            "VideoFrameExtractor",
+            "VideoFrameSaver"
+        ],
+        {
+            "title_aux": "ComfyUI-Video-Dataset-Tools [WIP]"
+        }
+    ],
+    "https://github.com/Video3DGenResearch/comfyui-batch-input-node": [
+        [
+            "BatchImageAndPrompt",
+            "BatchInputCSV",
+            "BatchInputText"
+        ],
+        {
+            "title_aux": "ComfyUI Batch Input Node"
+        }
+    ],
+    "https://github.com/VisionExp/ve_custom_comfyui_nodes": [
+        [
+            "Asset Image",
+            "LoadImgFromInputUrl",
+            "Render3d",
+            "assets/Asset Image",
+            "render3d/Render Node"
+        ],
+        {
+            "title_aux": "ve_custom_comfyui_nodes"
+        }
+    ],
+    "https://github.com/Vkabuto23/comfyui_openrouter_ollama": [
+        [
+            "OllamaNode",
+            "OllamaNodeExperimental",
+            "OllamaVisionNode",
+            "OllamaVisionNodeExperimental",
+            "OpenRouterNode",
+            "OpenRouterNodeExperimental",
+            "OpenRouterVisionNode",
+            "OpenRouterVisionNodeExperimental"
+        ],
+        {
+            "title_aux": "ComfyUI Custom Nodes: OpenRouter & Ollama [UNSAFE]"
+        }
+    ],
+    "https://github.com/Vollond/ComfyUI-LatentCutPlus": [
+        [
+            "DebugAny",
+            "LTXVEmptyLatentAudioDebug",
+            "LTXVSpatioTemporalTiledVAEDecode_DirectEncode",
+            "LTXVTiledVAEDecode",
+            "LatentCutPlus",
+            "LatentDebugInfo"
+        ],
+        {
+            "title_aux": "ComfyUI-LatentCutPlus"
+        }
+    ],
+    "https://github.com/Vov1ch/ComfyUI_GLMImage": [
+        [
+            "GLMImageGenerate",
+            "GLMImageImageToImage",
+            "GLMImageSDNQ_FlexibleInput",
+            "GLMImageSDNQ_Generate",
+            "GLMImageSDNQ_I2I_Standalone",
+            "GLMImageSDNQ_ImageToImage",
+            "GLMImageSDNQ_LoadPipe",
+            "GLMImageSDNQ_MultiI2I_Standalone",
+            "GLMImageSDNQ_MultiImageToImage",
+            "GLMImageSDNQ_T2I_Standalone"
+        ],
+        {
+            "title_aux": "ComfyUI_GLMImage"
+        }
+    ],
+    "https://github.com/Vsolon/ComfyUI-CBZ-Pack": [
+        [
+            "CBZ Preview Any",
+            "CBZCollector",
+            "CBZCollectorPassthrough",
+            "CBZUnpacker",
+            "CBZUnpackerPassthrough",
+            "DirToCBZ",
+            "DirToCBZPassthrough",
+            "ExportCBZ",
+            "ExportCBZPassthrough"
+        ],
+        {
+            "title_aux": "ComfyUI-CBZ-Pack [UNSAFE]"
+        }
+    ],
+    "https://github.com/WASasquatch/ASTERR": [
+        [
+            "ASTERR",
+            "SaveASTERR"
+        ],
+        {
+            "title_aux": "ASTERR [UNSAFE]"
+        }
+    ],
+    "https://github.com/WSJUSA/Comfyui-StableSR": [
+        [
+            "ColorFix",
+            "StableSRUpscalerPipe"
+        ],
+        {
+            "author": "WSJUSA",
+            "description": "This module enables StableSR in Comgfyui. Ported work of sd-webui-stablesr. Original work for Auotmaatic1111 version of this module and StableSR credit to LIightChaser and Jianyi Wang.",
+            "nickname": "StableSR",
+            "title": "StableSR",
+            "title_aux": "pre-comfyui-stablsr"
+        }
+    ],
+    "https://github.com/WaiyanLing/ComfyUI-Tracking": [
+        [
+            "WorkflowStats"
+        ],
+        {
+            "title_aux": "ComfyUI-Tracking [WIP]"
+        }
+    ],
+    "https://github.com/Well-Made/ComfyUI-Wan-SVI2Pro-FLF": [
+        [
+            "WanCutLastSlot",
+            "WanImageToVideoSVIProFLF"
+        ],
+        {
+            "title_aux": "Wan-SVI2Pro-FLF [WIP]"
+        }
+    ],
+    "https://github.com/WenerSantos3/ComfyUI-BlueeUtils": [
+        [
+            "BlueeCombineImagesAudioH264",
+            "BlueeFileExists",
+            "BlueeTemplateString",
+            "BlueeUtilsDelete",
+            "BlueeUtilsDownloadTemp",
+            "BlueeUtilsReadTempAudio",
+            "BlueeUtilsReadTempImage",
+            "blueeInput"
+        ],
+        {
+            "title_aux": "ComfyUI-BlueeUtils [UNSAFE]"
+        }
+    ],
+    "https://github.com/WilliamStanford/ComfyUI-VisualLabs": [
+        [
+            "CreateFadeMaskAdvancedVL",
+            "PointStringFromFloatArray",
+            "RescaleFloatArray",
+            "StringFromFloatArray"
+        ],
+        {
+            "title_aux": "visuallabs_comfyui_nodes"
+        }
+    ],
+    "https://github.com/WindyCityDave/ComfyUI-PeftLoraLoader": [
+        [
+            "ACEPeftLoraLoader"
+        ],
+        {
+            "title_aux": "ComfyUI-PeftLoraLoader"
+        }
+    ],
+    "https://github.com/WozStudios/ComfyUI-WozNodes": [
+        [
+            "CreateImageBatch",
+            "ImageBatchSelectByMask",
+            "ImageBatchTrim",
+            "ImageBatcherByIndexProV2"
+        ],
+        {
+            "title_aux": "ComfyUI-WozNodes"
+        }
+    ],
+    "https://github.com/XiaoHeiziGGG/ComfyUI-Gemini-Kontext": [
+        [
+            "GeminiBatchTranslator",
+            "GeminiImageAnalyzer",
+            "GeminiKontextOptimizer",
+            "GeminiTranslator"
+        ],
+        {
+            "title_aux": "ComfyUI-Gemini-Kontext [WIP]"
+        }
+    ],
+    "https://github.com/XiaoHeiziGGG/ComfyUI-GeminiTranslator": [
+        [
+            "GeminiBatchTranslator",
+            "GeminiTranslator"
+        ],
+        {
+            "title_aux": "ComfyUI Gemini Translator [WIP]"
+        }
+    ],
+    "https://github.com/Yeonri/ComfyUI_LLM_Are_You_Listening": [
+        [
+            "AYL_API_Node",
+            "AYL_GGUF_Node",
+            "AYL_Node"
+        ],
+        {
+            "title_aux": "ComfyUI_LLM_Are_You_Listening [WIP]"
+        }
+    ],
+    "https://github.com/Ynead/ksampler_config": [
+        [
+            "KSamplerConfig"
+        ],
+        {
+            "title_aux": "ksampler_config [WIP]"
+        }
+    ],
+    "https://github.com/Ynead/xy_ksampler_plot": [
+        [
+            "XYKSamplerPlot"
+        ],
+        {
+            "title_aux": "xy_ksampler_plot [WIP]"
+        }
+    ],
+    "https://github.com/YokoYokoTEC/comfyui-batch-sequence": [
+        [
+            "BatchCounter|YokoYokoTEC",
+            "LineRead|YokoYokoTEC"
+        ],
+        {
+            "title_aux": "ComfyUI Batch Sequence Node [UNSAFE]"
+        }
+    ],
+    "https://github.com/YokoYokoTEC/comfyui-sequential-prompt-file": [
+        [
+            "File",
+            "Find"
+        ],
+        {
+            "title_aux": "comfyui-sequential-prompt-file [UNSAFE]"
+        }
+    ],
+    "https://github.com/Yuan-ManX/ComfyUI-Step1X-Edit": [
+        [
+            "GenerateEditedImage",
+            "LoadAEModel",
+            "LoadDITModel",
+            "LoadImagePath",
+            "LoadQwenVLModel",
+            "Prompt",
+            "SaveEditedImage"
+        ],
+        {
+            "title_aux": "ComfyUI-Step1X-Edit [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/Yukinoshita-Yukinoe/ComfyUI-KontextOfficialNode": [
+        [
+            "KontextImageEditingOfficialAPI_Max",
+            "KontextTextToImageOfficialAPI_Max"
+        ],
+        {
+            "title_aux": "ComfyUI-KontextOfficialNode"
+        }
+    ],
+    "https://github.com/ZHO-ZHO-ZHO/ComfyUI-AuraSR-ZHO": [
+        [
+            "AuraSR_Lterative_Zho",
+            "AuraSR_ModelLoader_Zho",
+            "AuraSR_Zho"
+        ],
+        {
+            "title_aux": "ComfyUI-AuraSR-ZHO"
+        }
+    ],
+    "https://github.com/ZHO-ZHO-ZHO/ComfyUI-BiRefNet-ZHO": [
+        [
+            "BiRefNet_ModelLoader_Zho",
+            "BiRefNet_Zho"
+        ],
+        {
+            "title_aux": "ComfyUI-BiRefNet-ZHO [BROKEN]"
+        }
+    ],
+    "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Gemini": [
+        [
+            "ConcatText_Zho",
+            "DisplayText_Zho",
+            "Gemini_15P_API_S_Advance_Zho",
+            "Gemini_15P_API_S_Chat_Advance_Zho",
+            "Gemini_API_Chat_Zho",
+            "Gemini_API_S_Chat_Zho",
+            "Gemini_API_S_Vsion_ImgURL_Zho",
+            "Gemini_API_S_Zho",
+            "Gemini_API_Vsion_ImgURL_Zho",
+            "Gemini_API_Zho",
+            "Gemini_FileUpload_API_S_Zho",
+            "Gemini_File_API_S_Zho"
+        ],
+        {
+            "title_aux": "ComfyUI-Gemini [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/ZHO-ZHO-ZHO/ComfyUI-PuLID-ZHO": [
+        [
+            "PuLID_Zho"
+        ],
+        {
+            "title_aux": "ComfyUI-PuLID-ZHO [WIP]"
+        }
+    ],
+    "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Wan-ZHO": [
+        [
+            "WANMODELS_Generation_Zho",
+            "WANMODELS_ModelLoader_Zho",
+            "WanT2V_Generation_Zho",
+            "WanT2V_ModelLoader_Zho"
+        ],
+        {
+            "title_aux": "ComfyUI Wan2.1 [WIP]"
+        }
+    ],
+    "https://github.com/ZenAI-Vietnam/ComfyUI-gemini-IG": [
+        [
+            "Gemini Image Generation",
+            "Gemini Text Generation"
+        ],
+        {
+            "title_aux": "ComfyUI-gemini-IG"
+        }
+    ],
+    "https://github.com/ZenAI-Vietnam/ComfyUI_InfiniteYou": [
+        [
+            "FaceCombine",
+            "FaceSwap_InfiniteYou",
+            "InfiniteYouApply"
+        ],
+        {
+            "title_aux": "ComfyUI_InfiniteYou [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/a-One-Fan/ComfyUI-Blenderesque-Nodes": [
+        [
+            "BlenderBlackbody",
+            "BlenderBrickTexture",
+            "BlenderBrightnessContrast",
+            "BlenderCheckerTexture",
+            "BlenderClamp",
+            "BlenderCombineColor",
+            "BlenderCombineXYZ",
+            "BlenderCrop",
+            "BlenderGamma",
+            "BlenderGradientTexture",
+            "BlenderHueSaturationValue",
+            "BlenderInvertColor",
+            "BlenderLensDistortion",
+            "BlenderMapRange",
+            "BlenderMapUV",
+            "BlenderMath",
+            "BlenderMix",
+            "BlenderNoiseTexture",
+            "BlenderRGB",
+            "BlenderRGBtoBW",
+            "BlenderRotate",
+            "BlenderScale",
+            "BlenderSeparateColor",
+            "BlenderSeparateXYZ",
+            "BlenderSetAlpha",
+            "BlenderTransform",
+            "BlenderTranslate",
+            "BlenderUV",
+            "BlenderValue",
+            "BlenderVectorMath",
+            "BlenderVoronoiTexture",
+            "BlenderWavelength"
+        ],
+        {
+            "title_aux": "ComfyUI-Blenderesque-Nodes [WIP]"
+        }
+    ],
+    "https://github.com/a-und-b/ComfyUI_Output_as_Input": [
+        [
+            "OutputAsInput"
+        ],
+        {
+            "title_aux": "ComfyUI_Output_as_Input"
+        }
+    ],
+    "https://github.com/aa-parky/pipemind-comfyui": [
+        [
+            "BatchImageLoadInput",
+            "BatchImageLoadOutput",
+            "BooleanSwitchAny",
+            "EnhancedKeywordPromptComposer",
+            "KeywordPromptComposer",
+            "LoadTxtFile",
+            "PipemindDisplayAny",
+            "PipemindFlux2MAspectRatio",
+            "PipemindLoraLoader",
+            "PipemindMultilineTextInput",
+            "PipemindQwenAspectRatio",
+            "PipemindSDXL15AspectRatio",
+            "PipemindSaveImageWTxt",
+            "PipemindShowText",
+            "PipemindShowTextFind",
+            "PipemindTokenCounter",
+            "RandomLineFromDropdown",
+            "SelectLineFromDropdown",
+            "SimplePromptCombiner"
+        ],
+        {
+            "title_aux": "pipemind-comfyui"
+        }
+    ],
+    "https://github.com/abuzreq/ComfyUI-Model-Bending": [
+        [
+            "Add Noise Module (Bending)",
+            "Add Scalar Module (Bending)",
+            "Apply To Subset (Bending)",
+            "ApplyBendsFromJSON",
+            "Compute PCA",
+            "ConditioningApplyOperation",
+            "Dilation Module (Bending)",
+            "Erosion Module (Bending)",
+            "Fourier Amplify Module (Bending)",
+            "Gradient Module (Bending)",
+            "HSpace Bending",
+            "InteractiveBendingWebUI",
+            "Latent Operation (Add Noise)",
+            "Latent Operation (Add Scalar)",
+            "Latent Operation (Custom)",
+            "Latent Operation (Multiply Scalar)",
+            "Latent Operation (Rotate)",
+            "Latent Operation (Threshold)",
+            "Latent Operation To Module",
+            "LatentApplyOperationCFGToStep",
+            "LoRA Bending",
+            "LoRA Bending (list)",
+            "Model Bending",
+            "Model Bending (SD Blocks)",
+            "Model Bending (SD Layers)",
+            "Model Inspector",
+            "Model VAE Bending",
+            "Model VAE Inspector",
+            "Multiply Scalar Module (Bending)",
+            "NoiseVariations",
+            "Rotate Module (Bending)",
+            "Scale Module (Bending)",
+            "Sobel Module (Bending)",
+            "Threshold Module (Bending)",
+            "Visualize Feature Map"
+        ],
+        {
+            "title_aux": "ComfyUI Model Bending [UNSAFE]"
+        }
+    ],
+    "https://github.com/adamdavidcole/comfyui-video-xy-plot": [
+        [
+            "XYPlotCollectVideo",
+            "XYPlotDirectorySetup",
+            "XYPlotGetValues",
+            "XYPlotSetup",
+            "XYPlotVideoGrid"
+        ],
+        {
+            "title_aux": "ComfyUI XY Video Plot Suite [WIP]"
+        }
+    ],
+    "https://github.com/addddd2/AI_Generated_nodes": [
+        [
+            "ExtractImageChannel",
+            "ImageBlendLighter",
+            "ImageCropBorders",
+            "ImageMirrorPad",
+            "ImageOffset",
+            "ImageScaleToQwen",
+            "ImageScaleToTotalPixelsRound64",
+            "ImageStitcher",
+            "LABtoRGB",
+            "LTXVLatentConcat",
+            "LTXVLatentTrim",
+            "LatentBatchToLatentList",
+            "LatentListToLatentBatch",
+            "MatchRYGCBMColors",
+            "PiDImagesTileMerge",
+            "PiDLatentTileSplit",
+            "RGBtoLAB",
+            "RGBtoRYGCBM",
+            "RYGCBMtoRGB",
+            "RandomDecreasing01Sequence",
+            "TextCommaToRandomWeighted",
+            "TextCommaToWeighted"
+        ],
+        {
+            "title_aux": "AI_Generated_nodes"
+        }
+    ],
+    "https://github.com/adigayung/comfyui_PlutoNode": [
+        [
+            "CompositeImageNode",
+            "FloatArithmeticNode",
+            "FloatToIntNode",
+            "IntArithmeticNode",
+            "PlutoAutoCropFaces",
+            "PlutoAutoSaveImage",
+            "PlutoEmptyLatentByInput",
+            "PlutoGetSizeFromImage",
+            "PreviewImageXX",
+            "TextAppendNode",
+            "TextReplaceNode",
+            "TextToImageNode"
+        ],
+        {
+            "title_aux": "comfyui_PlutoNode"
+        }
+    ],
+    "https://github.com/adithis197/ComfyUI-Caption_to_audio": [
+        [
+            "TextToMusicGenAudio"
+        ],
+        {
+            "title_aux": "ComfyUI-Caption_to_audio [WIP]"
+        }
+    ],
+    "https://github.com/adithis197/ComfyUI-multimodal-CaptionToVideoGen": [
+        [
+            "CaptionToMusicPromptLLM"
+        ],
+        {
+            "title_aux": "ComfyUI-multimodal-CaptionToVideoGen [WIP]"
+        }
+    ],
+    "https://github.com/aesethtics/ComfyUI-OpenPoser": [
+        [
+            "OpenPoser"
+        ],
+        {
+            "title_aux": "ComfyUI-OpenPoser [WIP]"
+        }
+    ],
+    "https://github.com/agavesunset/ComfyUI_LoRA_Tracker": [
+        [
+            "LoRAParameterOverlay"
+        ],
+        {
+            "title_aux": "ComfyUI_LoRA_Tracker"
+        }
+    ],
+    "https://github.com/agavesunset/Comfyui_SiliconFlow_AgaveSunset": [
+        [
+            "SiliconFlowLoader_AS",
+            "SiliconFlowSampler_AS"
+        ],
+        {
+            "title_aux": "Comfyui_SiliconFlow_AgaveSunset"
+        }
+    ],
+    "https://github.com/ahkimkoo/ComfyUI-OSS-Upload": [
+        [
+            "OSSAudioUploader",
+            "OSSFileUploader",
+            "OSSImageUploader",
+            "OSSVideoUploader"
+        ],
+        {
+            "title_aux": "ComfyUI-OSS-Upload [UNSAFE]"
+        }
+    ],
+    "https://github.com/ahmedbana/File-Rename": [
+        [
+            "AdvancedFileRenameNode",
+            "FileRenameNode"
+        ],
+        {
+            "title_aux": "File-Rename [UNSAFE]"
+        }
+    ],
+    "https://github.com/ahmedbana/json-creator": [
+        [
+            "JsonCreator"
+        ],
+        {
+            "title_aux": "json-creator [WIP]"
+        }
+    ],
+    "https://github.com/ahmedbana/upload-to-azure": [
+        [
+            "AzureBlobUploader"
+        ],
+        {
+            "title_aux": "upload-to-azure"
+        }
+    ],
+    "https://github.com/ai-joe-git/ComfyUI-Chatterbox": [
+        [
+            "ChatterboxLoadReferenceAudio",
+            "ChatterboxPresets",
+            "ChatterboxSaveAudio",
+            "ChatterboxTTSNode"
+        ],
+        {
+            "title_aux": "ComfyUI-Chatterbox [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/aiden1020/ComfyUI_Artcoder": [
+        [
+            "ArtCoder"
+        ],
+        {
+            "title_aux": "ComfyUI_Artcoder [WIP]"
+        }
+    ],
+    "https://github.com/aidengit7/ComfyUI_WanVaceTiledSampler": [
+        [
+            "WanVaceTiledSampler"
+        ],
+        {
+            "title_aux": "ComfyUI_WanVaceTiledSampler"
+        }
+    ],
+    "https://github.com/ainanoha/etm_comfyui_nodes": [
+        [
+            "ETM_LoadImageFromLocal",
+            "ETM_SaveImage"
+        ],
+        {
+            "title_aux": "etm_comfyui_nodes"
+        }
+    ],
+    "https://github.com/ajcampbell1333/BudgetGuard_Artists": [
+        [
+            "BudgetGuardNode"
+        ],
+        {
+            "title_aux": "BudgetGuard for Artists [WIP]"
+        }
+    ],
+    "https://github.com/akashch1512/ComfyUI_UguuUploader": [
+        [
+            "UguuUploader"
+        ],
+        {
+            "title_aux": "ComfyUI_UguuUploader"
+        }
+    ],
+    "https://github.com/aklevecz/ComfyUI-AutoPrompt": [
+        [
+            "OllamaChat",
+            "OllamaModelLister",
+            "OllamaPromptGenerator",
+            "TextDisplay"
+        ],
+        {
+            "title_aux": "ComfyUI-AutoPrompt [WIP]"
+        }
+    ],
+    "https://github.com/alchemist-novaro/ComfyUI-Affine-Transform": [
+        [
+            "AffineTransform"
+        ],
+        {
+            "title_aux": "Affine Transform ComfyUI Node [WIP]"
+        }
+    ],
+    "https://github.com/alchemist-novaro/ComfyUI-Simple-Image-Tools": [
+        [
+            "GetMaskFromAlpha",
+            "GetQuadrilateralOutfit"
+        ],
+        {
+            "title_aux": "ComfyUI-Simple-Image-Tools [WIP]"
+        }
+    ],
+    "https://github.com/alexgenovese/ComfyUI-Diffusion-4k": [
+        [
+            "FluxImageGenerator"
+        ],
+        {
+            "title_aux": "ComfyUI-Diffusion-4k [WIP]"
+        }
+    ],
+    "https://github.com/alexgenovese/ComfyUI-Reica": [
+        [
+            "ReicaGCPReadImageNode",
+            "ReicaGCPWriteImageNode",
+            "ReicaHTTPCustomRequest",
+            "ReicaHTTPNotification",
+            "ReicaInsertAnythingNode",
+            "ReicaLoadLoopImagesFromURLs",
+            "ReicaLoadLoopImagesFromURLsSkipErrors",
+            "ReicaReadImageUrl",
+            "ReicaSmartResizer",
+            "ReicaTextImageDisplay",
+            "ReicaTryOffDiffGenerator",
+            "ReicaTryOffDiffLoader",
+            "ReicaURLImageLoader"
+        ],
+        {
+            "title_aux": "ComfyUI-Reica"
+        }
+    ],
+    "https://github.com/alexisrolland/ComfyUI-AuraSR": [
+        [
+            "LoadAuraSR",
+            "RunAuraSR"
+        ],
+        {
+            "title_aux": "alexisrolland/ComfyUI-AuraSR"
+        }
+    ],
+    "https://github.com/alisson-anjos/ComfyUI-Workarounds": [
+        [
+            "FlowMatchAutoConfig",
+            "FlowMatchGuide",
+            "FlowMatchScheduler",
+            "FlowMatchSchedulerPresets",
+            "SkinToneColorMatch",
+            "SkinToneDetector",
+            "WA_Face3DProjection",
+            "WA_FaceLandmarkDetector",
+            "WA_FaceMaskCreator",
+            "WA_FaceRegionOptions",
+            "WA_GeometricFaceAlign",
+            "WA_ImageComposite",
+            "WA_ImageTransform",
+            "WA_PlanarFaceOverlay",
+            "WA_SkinToneColorMatch",
+            "WA_SkinToneDetector"
+        ],
+        {
+            "title_aux": "ComfyUI-Workarounds [WIP]"
+        }
+    ],
+    "https://github.com/alistairallan/ComfyUI-skin-retouch": [
+        [
+            "SkinRetouching"
+        ],
+        {
+            "title_aux": "ComfyUI-skin-retouch"
+        }
+    ],
+    "https://github.com/alldaygameover/ComfyUI-PanonramaViewer": [
+        [
+            "Panorama 360 Camera Viewer"
+        ],
+        {
+            "title_aux": "ComfyUI-PanonramaViewer [WIP]"
+        }
+    ],
+    "https://github.com/alru/ComfyUI-Alru-Tools": [
+        [
+            "AlruCLIPTextEncodeNegative",
+            "AlruCLIPTextEncodePositive",
+            "AlruFetchWidgetValue",
+            "AlruKSamplerPlus",
+            "AlruSelectCheckpoint",
+            "AlruSelectSampler",
+            "AlruSelectScheduler"
+        ],
+        {
+            "title_aux": "ComfyUI-Alru-Tools"
+        }
+    ],
+    "https://github.com/alt-key-project/comfyui-dream-painter": [
+        [
+            "Bitmap AND [DPaint]",
+            "Bitmap Crop Center [DPaint]",
+            "Bitmap Dimensions [DPaint]",
+            "Bitmap Edge Detect [DPaint]",
+            "Bitmap Expand Canvas [DPaint]",
+            "Bitmap Invert [DPaint]",
+            "Bitmap OR [DPaint]",
+            "Bitmap Resize [DPaint]",
+            "Bitmap Rotate [DPaint]",
+            "Bitmap To Image & Mask [DPaint]",
+            "Bitmap XOR [DPaint]",
+            "Draw Shape As Bitmap [DPaint]",
+            "Image To Bitmap [DPaint]",
+            "Random Number Generator [DPaint]",
+            "Shape Center & Fit [DPaint]",
+            "Shape Combiner [DPaint]",
+            "Shape Copycat Tool [DPaint]",
+            "Shape Find Bounds [DPaint]",
+            "Shape Flip [DPaint]",
+            "Shape Grid [DPaint]",
+            "Shape Resize [DPaint]",
+            "Shape Rotate [DPaint]",
+            "Shape of Circular Rays [DPaint]",
+            "Shape of N-Polygon [DPaint]",
+            "Shape of Rectangle [DPaint]",
+            "Shape of Star [DPaint]"
+        ],
+        {
+            "title_aux": "Dream Painter [WIP]"
+        }
+    ],
+    "https://github.com/ammahmoudi/ComfyUI-Legendary-Nodes": [
+        [
+            "Legendary Dataset Saver",
+            "Legendary Image URL Loader",
+            "Legendary Lora URL Loader"
+        ],
+        {
+            "title_aux": "ComfyUI-Legendary-Nodes"
+        }
+    ],
+    "https://github.com/animEEEmpire/ComfyUI-Animemory-Loader": [
+        [
+            "AnimemoryNode"
+        ],
+        {
+            "title_aux": "ComfyUI-Animemory-Loader"
+        }
+    ],
+    "https://github.com/anveshane/comfyui-turboteacache": [
+        [
+            "TurboTeaCache"
+        ],
+        {
+            "title_aux": "comfyui-turboteacache"
+        }
+    ],
+    "https://github.com/apeirography/ModelCopyNode": [
+        [
+            "ModelCopyNode"
+        ],
+        {
+            "title_aux": "Model Copy Node for ComfyUI [UNSAFE]"
+        }
+    ],
+    "https://github.com/apetitbois/nova_utils": [
+        [
+            "jsonParser"
+        ],
+        {
+            "title_aux": "nova_utils"
+        }
+    ],
+    "https://github.com/aria1th/ComfyUI-SkipCFGSigmas": [
+        [
+            "CFGControl_SKIPCFG"
+        ],
+        {
+            "title_aux": "ComfyUI-SkipCFGSigmas"
+        }
+    ],
+    "https://github.com/aria1th/ComfyUI-camietagger-onnx": [
+        [
+            "CamieTagger"
+        ],
+        {
+            "title_aux": "ComfyUI-camietagger-onnx"
+        }
+    ],
+    "https://github.com/artem-konevskikh/comfyui-split-merge-video": [
+        [
+            "VideoMerger",
+            "VideoSplitter"
+        ],
+        {
+            "title_aux": "ComfyUI Video Processing Nodes [WIP]"
+        }
+    ],
+    "https://github.com/artifyfun/ComfyUI-JS": [
+        [
+            "JavascriptExecutor",
+            "JavascriptExecutorMultiOutput"
+        ],
+        {
+            "title_aux": "ComfyUI-JS [UNSAFE]"
+        }
+    ],
+    "https://github.com/artisanalcomputing/ComfyUI-Custom-Nodes": [
+        [
+            "RandomVideoMixer",
+            "SpotifyCanvasGenerator",
+            "VideoWriter"
+        ],
+        {
+            "title_aux": "artcpu-custom-nodes"
+        }
+    ],
+    "https://github.com/asaddi/allans-comfy-nodes": [
+        [
+            "BatchImageLoader",
+            "ControlBus",
+            "DepthAnythingV2",
+            "DepthAnythingV2Model",
+            "DumpToConsole",
+            "EmptyLatentImageSelector",
+            "FlattenImageAlpha",
+            "FloatLatch",
+            "FloatList",
+            "FloatListStepSize",
+            "ImageBuffer",
+            "ImageCropSquare",
+            "ImageDimensions",
+            "ImageMaskSwitch2",
+            "ImageMaskSwitch4",
+            "ImageRouter",
+            "ImageSequenceList5",
+            "ImageSwitch2",
+            "IntegerLatch",
+            "JSONExtractNumber",
+            "JSONExtractString",
+            "LPIPSModel",
+            "LPIPSRun",
+            "ListCounter",
+            "MakeImageGrid",
+            "MaskBlur",
+            "MixNoise",
+            "ModelSwitch2",
+            "PathJoin",
+            "PathRelativeTo",
+            "PathSplit",
+            "PresetText",
+            "PrivateLoraStack",
+            "PrivateSeed",
+            "RandomCombo2",
+            "RepeatFloatList",
+            "RepeatIntList",
+            "RepeatStringList",
+            "ReproducibleWildcards",
+            "ResolutionChooser",
+            "SaveComicBookArchive",
+            "SaveTabular",
+            "SeedList",
+            "SimpleBus",
+            "StringSequenceList2",
+            "StringSequenceList5",
+            "TabularJoin",
+            "VAESwitch2",
+            "VersatileTextEncode",
+            "WDv3Model",
+            "WDv3Tagger",
+            "WriteTextImage"
+        ],
+        {
+            "title_aux": "Allan's ComfyUI nodes"
+        }
+    ],
+    "https://github.com/ashishsaini/comfyui-segment-clothing-sleeves": [
+        [
+            "segformer_b2_sleeves"
+        ],
+        {
+            "title_aux": "comfyui_segformer_b2_sleeves"
+        }
+    ],
+    "https://github.com/ashllay/ComfyUI_MoreComfy": [
+        [
+            "MC Alter Seed",
+            "MC Get Image Min Max",
+            "MC Get Image Size",
+            "MC Multi Concat",
+            "MC Multi Concat(Advanced)",
+            "MC Noise",
+            "MC Set Tile Size",
+            "MC Switch Image",
+            "MC Switch Latent",
+            "MC Switch Model",
+            "MC Switch Noise",
+            "MC Switch Seed",
+            "MC Switch String",
+            "MC Swtich Latent(Multi)",
+            "MC Swtich Sampler(Multi)",
+            "MC Swtich Sigmas(Multi)"
+        ],
+        {
+            "title_aux": "ComfyUI_MoreComfy"
+        }
+    ],
+    "https://github.com/avocadori/ComfyUI-AudioAmplitudeConverter": [
+        [
+            "NormalizeAmpToFloatNode"
+        ],
+        {
+            "title_aux": "ComfyUI Audio Amplitude Converter [WIP]"
+        }
+    ],
+    "https://github.com/ayaoayaoayaoaya/ComfyUI-KLUT-DeepSeek-API": [
+        [
+            "KLUTDeepSeekAPI"
+        ],
+        {
+            "title_aux": "ComfyUI-KLUT-DeepSeek-API [WIP]"
+        }
+    ],
+    "https://github.com/ayataka0nk/ayataka-comfyui-nodes": [
+        [
+            "Concatenate String",
+            "Concatenate String4",
+            "Concatenate String8",
+            "Illustrious XL Resolutions",
+            "SDXL Recommended Resolutions",
+            "Switch String Case12",
+            "Switch String Case16",
+            "Switch String Case8"
+        ],
+        {
+            "title_aux": "ayataka-comfyui-nodes"
+        }
+    ],
+    "https://github.com/ayylmao1773/ComfyUI-switch-aspect": [
+        [
+            "CustomizableAspectRatioSwap"
+        ],
+        {
+            "title_aux": "ComfyUI-switch-aspect"
+        }
+    ],
+    "https://github.com/babydjac/comfyui-grok-ponyxl": [
+        [
+            "GrokPonyXLPrompter"
+        ],
+        {
+            "title_aux": "comfyui-grok-ponyxl [WIP]"
+        }
+    ],
+    "https://github.com/backearth1/Comfyui-MiniMax-Video": [
+        [
+            "ImageToPrompt",
+            "MiniMaxAIAPIClient",
+            "MiniMaxImage2Video",
+            "MiniMaxImageGenerator",
+            "MiniMaxPreviewVideo"
+        ],
+        {
+            "title_aux": "Comfyui-MiniMax-Video [WIP]"
+        }
+    ],
+    "https://github.com/bakker/BK_Utils": [
+        [
+            "DynamicGroupSwitchMulti",
+            "FileNameDefinition",
+            "FluxPromptSaver",
+            "FluxTextSampler",
+            "IsOneOfGroupsActive",
+            "ModelName",
+            "SamePixelResolutionCalculator",
+            "ZImageTurboResolutions"
+        ],
+        {
+            "title_aux": "BK_Utils [WIP]"
+        }
+    ],
+    "https://github.com/bananasss00/Comfyui-PyExec": [
+        [
+            "PyExec",
+            "PyExec_Output",
+            "PyExec_OutputIsList",
+            "PyExec_OutputIsValue"
+        ],
+        {
+            "author": "SeniorPioner",
+            "description": "Comfyui runtime python code execution",
+            "nickname": "PyExec",
+            "title": "PyExec",
+            "title_aux": "Comfyui-PyExec [UNSAFE]"
+        }
+    ],
+    "https://github.com/bandido37/comfyui-kaggle-local-save": [
+        [
+            "KaggleLocalSaveNode"
+        ],
+        {
+            "title_aux": "Kaggle ComfyUI Local Save Node [WIP]"
+        }
+    ],
+    "https://github.com/banini1841/BigModelPipe": [
+        [
+            "BigModelPipeIn",
+            "BigModelPipeOut"
+        ],
+        {
+            "title_aux": "BigModelPipe [WIP]"
+        }
+    ],
+    "https://github.com/baoanhng/ComfyUI-utils": [
+        [
+            "TextJoiner",
+            "TextJoinerTags",
+            "TextSplitter"
+        ],
+        {
+            "title_aux": "ComfyUI-utils"
+        }
+    ],
+    "https://github.com/barakapa/barakapa-nodes": [
+        [
+            "brkp_ConcatenateString",
+            "brkp_ConvertToString",
+            "brkp_CountTokens",
+            "brkp_DirectoryImageCounter",
+            "brkp_SaveImage",
+            "brkp_SaveWorkflow"
+        ],
+        {
+            "title_aux": "barakapa-nodes"
+        }
+    ],
+    "https://github.com/basenc/Comfyui-Nodes-basenc": [
+        [
+            "CustomOpenAIResponse",
+            "DimensionsPresetPicker",
+            "EnvVarNode",
+            "Eval",
+            "JMESPathSelect",
+            "RescaleToDimensions",
+            "ResponseInputAppend",
+            "WanVideoSize"
+        ],
+        {
+            "title_aux": "Comfyui-Nodes-basenc [UNSAFE]"
+        }
+    ],
+    "https://github.com/benda1989/WaterMarkRemover_ComfyUI": [
+        [
+            "Remover",
+            "VideoRemover"
+        ],
+        {
+            "title_aux": "Comfyui lama remover [WIP]"
+        }
+    ],
+    "https://github.com/benjiyaya/ComfyUI-Spectrum": [
+        [
+            "SpectrumModelPatcher"
+        ],
+        {
+            "title_aux": "ComfyUI-Spectrum [WIP/NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/benmizrahi/ComfyGCS": [
+        [
+            "LoadImageGCS",
+            "SaveImageGCS"
+        ],
+        {
+            "title_aux": "ComfyGCS [WIP]"
+        }
+    ],
+    "https://github.com/beyastard/ComfyUI_BeySoft": [
+        [
+            "BeySoft"
+        ],
+        {
+            "title_aux": "ComfyUI_BeySoft"
+        }
+    ],
+    "https://github.com/bhaveek424/ComfyUI-HMNodes": [
+        [
+            "Analogizer",
+            "FFTSurgeon",
+            "HearmemanAI_Prompter",
+            "Realism_AdaptiveGrain",
+            "Realism_AutoWB",
+            "Realism_LensEffects",
+            "Realism_MicroContrast",
+            "Realism_SpectrumMatch"
+        ],
+        {
+            "title_aux": "ComfyUI-HMNodes"
+        }
+    ],
+    "https://github.com/bheins/ComfyUI-glb-to-stl": [
+        [
+            "GLBToSTLNode"
+        ],
+        {
+            "title_aux": "ComfyUI-glb-to-stl [WIP]"
+        }
+    ],
+    "https://github.com/bikiam/ComfyUi_WhisperGTranslate": [
+        [
+            "GoogleTranslateNode",
+            "WhisperAudioTranslateNode"
+        ],
+        {
+            "title_aux": "ComfyUi_WhisperGTranslate"
+        }
+    ],
+    "https://github.com/bikiam/Comfyui_AudioRecoder": [
+        [
+            "BikiAudioRecorderNode"
+        ],
+        {
+            "title_aux": "Comfyui_AudioRecoder"
+        }
+    ],
+    "https://github.com/binarystatic/ComfyUI-BinarystaticMasterSeed": [
+        [
+            "BinarystaticMasterSeed"
+        ],
+        {
+            "title_aux": "ComfyUI-BinarystaticMasterSeed"
+        }
+    ],
+    "https://github.com/birnam/ComfyUI-GenData-Pack": [
+        [
+            "Checkpoint From String \ud83d\udc69\u200d\ud83d\udcbb",
+            "Checkpoint Rerouter \ud83d\udc69\u200d\ud83d\udcbb",
+            "Checkpoint Selector Stacker \ud83d\udc69\u200d\ud83d\udcbb",
+            "Checkpoint Selector \ud83d\udc69\u200d\ud83d\udcbb",
+            "Checkpoint to String \ud83d\udc69\u200d\ud83d\udcbb",
+            "Crop Recombine \ud83d\udc69\u200d\ud83d\udcbb",
+            "Crop|IP|Inpaint|SDXL \ud83d\udc69\u200d\ud83d\udcbb",
+            "Decode GenData \ud83d\udc69\u200d\ud83d\udcbb",
+            "Encode GenData \ud83d\udc69\u200d\ud83d\udcbb",
+            "GenData Stacker \ud83d\udc69\u200d\ud83d\udcbb",
+            "IPAdapterApply",
+            "IPAdapterApplyEncoded",
+            "IPAdapterApplyFaceID",
+            "IPAdapterBatchEmbeds",
+            "IPAdapterEncoder",
+            "IPAdapterLoadEmbeds",
+            "IPAdapterModelLoader",
+            "IPAdapterSaveEmbeds",
+            "IPAdapterTilesMasked",
+            "InsightFaceLoader",
+            "LoRA Stack to String \ud83d\udc69\u200d\ud83d\udcbb",
+            "LoRA Stacker From Prompt \ud83d\udc69\u200d\ud83d\udcbb",
+            "Load Checkpoints From File \ud83d\udc69\u200d\ud83d\udcbb",
+            "Load GenData From Dir \ud83d\udc69\u200d\ud83d\udcbb",
+            "Parse GenData \ud83d\udc69\u200d\ud83d\udcbb",
+            "PrepImageForClipVision",
+            "PrepImageForInsightFace",
+            "Provide GenData \ud83d\udc69\u200d\ud83d\udcbb",
+            "Save Image From GenData \ud83d\udc69\u200d\ud83d\udcbb",
+            "VAE From String \ud83d\udc69\u200d\ud83d\udcbb",
+            "VAE to String \ud83d\udc69\u200d\ud83d\udcbb",
+            "\u00d7 Product CheckpointXGenDatas \ud83d\udc69\u200d\ud83d\udcbb"
+        ],
+        {
+            "title_aux": "Gen Data Tester [WIP]"
+        }
+    ],
+    "https://github.com/bleash-dev/Comfyui-FileSytem-Manager": [
+        [
+            "custom_nodes",
+            "web_extensions"
+        ],
+        {
+            "title_aux": "Comfyui-FileSytem-Manager"
+        }
+    ],
+    "https://github.com/blepping/comfyui_dum_samplers": [
+        [
+            "BatchMergeSampler",
+            "ButcherTableauSampler",
+            "CacheAwareEulerSampler",
+            "CyclePaddingSampler",
+            "HistorySampler",
+            "LetMeHelpYouSampler",
+            "PingPongSampler",
+            "RestlessScheduler",
+            "SimilarityAncestralEulerSampler",
+            "SimilarityClampEulerSampler"
+        ],
+        {
+            "title_aux": "ComfyUI 'dum' samplers [WIP]"
+        }
+    ],
+    "https://github.com/blueraincoatli/ComfyUI-Model-Cleaner": [
+        [
+            "InteractiveModelCleanerNode",
+            "ModelScannerNode"
+        ],
+        {
+            "title_aux": "ComfyModelCleaner [WIP]"
+        }
+    ],
+    "https://github.com/bmad4ever/comfyui_bmad_nodes": [
+        [
+            "AdaptiveThresholding",
+            "Add String To Many",
+            "AddAlpha",
+            "AdjustRect",
+            "AnyToAny",
+            "BoundingRect (contours)",
+            "BuildColorRangeAdvanced (hsv)",
+            "BuildColorRangeHSV (hsv)",
+            "CLAHE",
+            "CLIPEncodeMultiple",
+            "CLIPEncodeMultipleAdvanced",
+            "ChameleonMask",
+            "CheckpointLoader (dirty)",
+            "CheckpointLoaderSimple (dirty)",
+            "Color (RGB)",
+            "Color (hexadecimal)",
+            "Color Clip",
+            "Color Clip (advanced)",
+            "Color Clip ADE20k",
+            "ColorDictionary",
+            "ColorDictionary (custom)",
+            "Conditioning Grid (cond)",
+            "Conditioning Grid (string)",
+            "Conditioning Grid (string) Advanced",
+            "Contour To Mask",
+            "Contours",
+            "ControlNetHadamard",
+            "ControlNetHadamard (manual)",
+            "ConvertImg",
+            "CopyMakeBorder",
+            "CreateRequestMetadata",
+            "DistanceTransform",
+            "Draw Contour(s)",
+            "EqualizeHistogram",
+            "ExtendColorList",
+            "ExtendCondList",
+            "ExtendFloatList",
+            "ExtendImageList",
+            "ExtendIntList",
+            "ExtendLatentList",
+            "ExtendMaskList",
+            "ExtendModelList",
+            "ExtendStringList",
+            "FadeMaskEdges",
+            "Filter Contour",
+            "FindComplementaryColor",
+            "FindThreshold",
+            "FlatLatentsIntoSingleGrid",
+            "Framed Mask Grab Cut",
+            "Framed Mask Grab Cut 2",
+            "FromListGet1Color",
+            "FromListGet1Cond",
+            "FromListGet1Float",
+            "FromListGet1Image",
+            "FromListGet1Int",
+            "FromListGet1Latent",
+            "FromListGet1Mask",
+            "FromListGet1Model",
+            "FromListGet1String",
+            "FromListGetColors",
+            "FromListGetConds",
+            "FromListGetFloats",
+            "FromListGetImages",
+            "FromListGetInts",
+            "FromListGetLatents",
+            "FromListGetMasks",
+            "FromListGetModels",
+            "FromListGetStrings",
+            "Get Contour from list",
+            "Get Models",
+            "Get Prompt",
+            "Hue Mode (InRange hsv)",
+            "HypernetworkLoader (dirty)",
+            "ImageBatchToList",
+            "InRange (hsv)",
+            "Inpaint",
+            "Input/String to Int Array",
+            "KMeansColor",
+            "Load 64 Encoded Image",
+            "LoraLoader (dirty)",
+            "MaskGrid N KSamplers Advanced",
+            "MaskOuterBlur",
+            "Merge Latent Batch Gridwise",
+            "MonoMerge",
+            "MorphologicOperation",
+            "MorphologicSkeletoning",
+            "NaiveAutoKMeansColor",
+            "OtsuThreshold",
+            "RGB to HSV",
+            "Rect Grab Cut",
+            "Remap",
+            "RemapBarrelDistortion",
+            "RemapFromInsideParabolas",
+            "RemapFromQuadrilateral (homography)",
+            "RemapInsideParabolas",
+            "RemapInsideParabolasAdvanced",
+            "RemapPinch",
+            "RemapReverseBarrelDistortion",
+            "RemapStretch",
+            "RemapToInnerCylinder",
+            "RemapToOuterCylinder",
+            "RemapToQuadrilateral",
+            "RemapWarpPolar",
+            "Repeat Into Grid (image)",
+            "Repeat Into Grid (latent)",
+            "RequestInputs",
+            "SampleColorHSV",
+            "Save Image (api)",
+            "SeamlessClone",
+            "SeamlessClone (simple)",
+            "SetRequestStateToComplete",
+            "String",
+            "String to Float",
+            "String to Integer",
+            "ToColorList",
+            "ToCondList",
+            "ToFloatList",
+            "ToImageList",
+            "ToIntList",
+            "ToLatentList",
+            "ToMaskList",
+            "ToModelList",
+            "ToStringList",
+            "UnGridify (image)",
+            "VAEEncodeBatch"
+        ],
+        {
+            "title_aux": "Bmad Nodes [UNSAFE]"
+        }
+    ],
+    "https://github.com/bmgjet/comfyui-powerlimit": [
+        [
+            "SetPowerLimitNode"
+        ],
+        {
+            "title_aux": "ComfyUI GPU Power Limit Node"
+        }
+    ],
+    "https://github.com/boricuapab/ComfyUI-Bori-KontextPresets": [
+        [
+            "Bori Kontext Presets"
+        ],
+        {
+            "title_aux": "ComfyUI-Bori-KontextPresets [WIP]"
+        }
+    ],
+    "https://github.com/borisfaley/ComfyUI-ACES-EXR-OCIO": [
+        [
+            "ACESEXRSaveOCIO"
+        ],
+        {
+            "title_aux": "ComfyUI-ACES-EXR-OCIOr [UNSAFE]"
+        }
+    ],
+    "https://github.com/bozkut/ComfyUI-Prompt-Expander": [
+        [
+            "PromptExpanderNode",
+            "PromptExpanderSimple"
+        ],
+        {
+            "title_aux": "ComfyUI-Prompt-Expander [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/brace-great/comfyui-eim": [
+        [
+            "EncryptImage"
+        ],
+        {
+            "title_aux": "comfyui-eim"
+        }
+    ],
+    "https://github.com/brace-great/comfyui-mc": [
+        [
+            "IncrementCounterOnMatch"
+        ],
+        {
+            "title_aux": "comfyui-mc [WIP]"
+        }
+    ],
+    "https://github.com/brbbbq/ComfyUI-WanMoveSVI_FLF_v2": [
+        [
+            "WanMoveSVI_FLF_Decode",
+            "WanMoveSVI_FLF_Encode",
+            "WanMoveSVI_FLF_Stitch",
+            "WanMoveSVI_FLF_Visualize",
+            "WanMoveSVI_FLF_v2"
+        ],
+        {
+            "title_aux": "ComfyUI-WanMoveSVI_FLF_v2"
+        }
+    ],
+    "https://github.com/broumbroum/comfyui-time-system": [
+        [
+            "DayTimeNode",
+            "HourTimeNode",
+            "MinuteTimeNode",
+            "MonthTimeNode",
+            "SecondTimeNode",
+            "YearTimeNode"
+        ],
+        {
+            "title_aux": "comfyui-time-system [WIP]"
+        }
+    ],
+    "https://github.com/bruce007lee/comfyui-cleaner": [
+        [
+            "cleaner"
+        ],
+        {
+            "title_aux": "comfyui-cleaner"
+        }
+    ],
+    "https://github.com/bruce007lee/comfyui-tiny-utils": [
+        [
+            "CropImageByMask",
+            "FaceAlign",
+            "FaceAlignImageProcess",
+            "FaceAlignMaskProcess",
+            "ImageFillColorByMask",
+            "ImageSAMMask",
+            "ImageTransposeAdvance",
+            "LoadImageAdvance"
+        ],
+        {
+            "title_aux": "comfyui-tiny-utils"
+        }
+    ],
+    "https://github.com/bryanlholland1/comfyui-app-bridge": [
+        [
+            "SendToApp"
+        ],
+        {
+            "title_aux": "comfyui-app-bridge [WIP]"
+        }
+    ],
+    "https://github.com/brycegoh/comfyui-custom-nodes": [
+        [
+            "CombineTwoImageIntoOne",
+            "FillMaskedArea",
+            "MaskAreaComparisonSegment",
+            "OCRAndMask"
+        ],
+        {
+            "title_aux": "brycegoh/comfyui-custom-nodes"
+        }
+    ],
+    "https://github.com/bulldog68/ComfyUI_FMJ": [
+        [
+            "FMJCreaPrompt",
+            "FMJKontext"
+        ],
+        {
+            "title_aux": "ComfyUI_FMJ [WIP]"
+        }
+    ],
+    "https://github.com/c1660181647-hash/ComfyUI-MM-Visual-Encryption": [
+        [
+            "MM_Image_Decrypt",
+            "MM_Image_Encrypt",
+            "MM_Video_Decrypt",
+            "MM_Video_Encrypt"
+        ],
+        {
+            "title_aux": "ComfyUI-MM-Visual-Encryption"
+        }
+    ],
+    "https://github.com/casterpollux/ComfyUI-USO": [
+        [
+            "USOImageEncoder",
+            "USOLatentToImage",
+            "USOModelLoader",
+            "USOSampler"
+        ],
+        {
+            "title_aux": "ComfyUI USO Custom Node [WIP]"
+        }
+    ],
+    "https://github.com/catboxanon/ComfyUI-Pixelsmith": [
+        [
+            "Pixelsmith"
+        ],
+        {
+            "title_aux": "ComfyUI-Pixelsmith [WIP]"
+        }
+    ],
+    "https://github.com/ccssu/ComfyUI-Black-Image-Interceptor": [
+        [
+            "HaltAndLogOnBlackImage"
+        ],
+        {
+            "title_aux": "ComfyUI-Black-Image-Interceptor"
+        }
+    ],
+    "https://github.com/cedarconnor/ComfyUI-DAP": [
+        [
+            "DAP_BatchFromFolder",
+            "DAP_Inference",
+            "DAP_SaveDepthBatch",
+            "DAP_Setup"
+        ],
+        {
+            "title_aux": "ComfyUI-DAP [UNSAFE/NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/cedarconnor/ComfyUI-HunyuanWorld-Mirror": [
+        [
+            "HWMInference",
+            "LoadHunyuanWorldMirrorModel",
+            "PreprocessImagesForHWM",
+            "Save3DGaussians",
+            "SaveCOLMAPReconstruction",
+            "SaveCameraParams",
+            "SaveDepthMap",
+            "SavePointCloud",
+            "View3DInBrowser",
+            "VisualizeDepth",
+            "VisualizeNormals"
+        ],
+        {
+            "title_aux": "HunyuanWorld-Mirror ComfyUI Node Pack [WIP]"
+        }
+    ],
+    "https://github.com/cedarconnor/ComfyUI_MotionTransfer": [
+        [
+            "AdaptiveTessellate",
+            "BarycentricWarp",
+            "BidirectionalFlowExtractor",
+            "DepthEstimator",
+            "FlowSRRefine",
+            "FlowToSTMap",
+            "HiResWriter",
+            "MeshBuilder2D",
+            "MeshFromCoTracker",
+            "ProxyReprojector",
+            "RAFTFlowExtractor",
+            "SequentialMotionTransfer",
+            "TemporalConsistency",
+            "TileWarp16K"
+        ],
+        {
+            "title_aux": "ComfyUI Motion Transfer Pack [WIP]"
+        }
+    ],
+    "https://github.com/celll1/cel_sampler": [
+        [
+            "latent_tracker"
+        ],
+        {
+            "title_aux": "cel_sampler [WIP]"
+        }
+    ],
+    "https://github.com/cesilk10/cesilk-comfyui-nodes": [
+        [
+            "CESILK_OpenAIChat",
+            "CESILK_OpenAIImageBatchGenerator",
+            "CESILK_OpenAIImageDescriptionToTextfile",
+            "CESILK_SaveAndUploadToGoogleDrive",
+            "CESILK_SaveAndUploadToS3",
+            "CESILK_SdxlImageSizes"
+        ],
+        {
+            "title_aux": "cesilk-comfyui-nodes"
+        }
+    ],
+    "https://github.com/chaojie/ComfyUI-mobvoi-openapi": [
+        [
+            "HtmlViewer",
+            "MobvoiOpenapiMetamanAudio",
+            "MobvoiOpenapiMetamanText",
+            "MobvoiOpenapiTts",
+            "OssUploadAudio",
+            "OssUploadImage"
+        ],
+        {
+            "title_aux": "ComfyUI-mobvoi-openapi"
+        }
+    ],
+    "https://github.com/charlierz/comfyui-charlierz": [
+        [
+            "BackgroundColor",
+            "EstimateTextTokens",
+            "LlamaCppChat",
+            "LlamaCppVisionChat",
+            "PromptHelper",
+            "PromptHelperFillApply",
+            "PromptHelperFillRequest",
+            "ScaleDimensions",
+            "WildcardProcessor"
+        ],
+        {
+            "title_aux": "comfyui-charlierz"
+        }
+    ],
+    "https://github.com/chengzeyi/Comfy-WaveSpeed": [
+        [
+            "ApplyFBCacheOnModel",
+            "EnhancedCompileModel",
+            "EnhancedLoadDiffusionModel",
+            "VelocatorCompileModel",
+            "VelocatorLoadAndQuantizeClip",
+            "VelocatorLoadAndQuantizeDiffusionModel",
+            "VelocatorQuantizeModel"
+        ],
+        {
+            "title_aux": "Comfy-WaveSpeed [WIP]"
+        }
+    ],
+    "https://github.com/chenpipi0807/ComfyUI-InstantCharacterFlux": [
+        [
+            "EncodeRefImageIC",
+            "ICFluxOneKnob",
+            "ICStrengthController",
+            "LoadDINOv2Vision",
+            "LoadICWeights",
+            "LoadSigLIPVision"
+        ],
+        {
+            "title_aux": "ComfyUI-InstantCharacterFlux [WIP]"
+        }
+    ],
+    "https://github.com/ches2010/comfyui_aliyundrive_uploader": [
+        [
+            "AliyunDriveCloudUploadNode",
+            "AliyunDriveOptimizedUploadNode",
+            "AliyunDriveUploadNode",
+            "SimpleUploadToAliyunDrive",
+            "UploadTo115",
+            "UploadToAliyunDrive",
+            "UploadToOSSNode"
+        ],
+        {
+            "title_aux": "comfyui_aliyundrive_uploader"
+        }
+    ],
+    "https://github.com/chetusangolgi/Comfyui-supabase": [
+        [
+            "SupabaseAudioUploader",
+            "SupabaseGLBUploader",
+            "SupabaseImageUploader",
+            "SupabaseTableWatcherNode"
+        ],
+        {
+            "title_aux": "Comfyui-supabase"
+        }
+    ],
+    "https://github.com/christian-byrne/infinite-zoom-parallax-nodes": [
+        [
+            "Create Parallax Video",
+            "Layer Shifter for Parallax Outpainting",
+            "Load Parallax Frame",
+            "Parallax Config",
+            "Save Parallax Frame",
+            "Shrink and Pad for Outpainting"
+        ],
+        {
+            "title_aux": "\ud83c\udf0c Infinite Parallax Nodes [WIP]"
+        }
+    ],
+    "https://github.com/christian-byrne/python-interpreter-node": [
+        [
+            "Exec Python Code Script"
+        ],
+        {
+            "title_aux": "Python Interpreter ComfyUI Node [UNSAFE]"
+        }
+    ],
+    "https://github.com/chuge26/ComfyUI_seal_migration": [
+        [
+            "PDFLoader",
+            "PDFSaver",
+            "SealMigration"
+        ],
+        {
+            "title_aux": "ComfyUI_seal_migration [WIP]"
+        }
+    ],
+    "https://github.com/cidiro/cid-node-pack": [
+        [
+            "CidAnyBuffer",
+            "CidAnySync",
+            "CidLoadImageFromDir",
+            "CidSaveImage",
+            "CidWildcardProcessor"
+        ],
+        {
+            "title_aux": "cid-node-pack"
+        }
+    ],
+    "https://github.com/ciga2011/ComfyUI-AppGen": [
+        [
+            "AG_APP_EDIT",
+            "AG_APP_GEN",
+            "AG_APP_SANDBOX",
+            "AG_CODER_LLM"
+        ],
+        {
+            "title_aux": "ComfyUI-AppGen [UNSAFE]"
+        }
+    ],
+    "https://github.com/ckinpdx/ComfyUI-FPSChange": [
+        [
+            "ImageBatchChangeFPS"
+        ],
+        {
+            "title_aux": "ComfyUI-FPSChange"
+        }
+    ],
+    "https://github.com/clcimir/FileTo64": [
+        [
+            "FileToBase641"
+        ],
+        {
+            "title_aux": "FileTo64"
+        }
+    ],
+    "https://github.com/clubmate/comfyui-boll-nodes": [
+        [
+            "GetLastImage",
+            "RemoveLastImage",
+            "WANConfig"
+        ],
+        {
+            "title_aux": "comfyui-boll-nodes"
+        }
+    ],
+    "https://github.com/cnoellert/comfyui-corridorkey": [
+        [
+            "CK_Despeckle",
+            "CK_Despill",
+            "CK_Keyer",
+            "CK_LinearToSRGB",
+            "CK_ModelLoader",
+            "CK_SRGBToLinear"
+        ],
+        {
+            "title_aux": "ComfyUI CorridorKey [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/colorAi/comfyui-prompt-manager": [
+        [
+            "PromptManagerNode"
+        ],
+        {
+            "title_aux": "comfyui-prompt-manager [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/comfyanonymous/ComfyUI": [
+        [
+            "APG",
+            "ARVideoI2V",
+            "AddNoise",
+            "AlignYourStepsScheduler",
+            "AudioAdjustVolume",
+            "AudioConcat",
+            "AudioEncoderEncode",
+            "AudioEncoderLoader",
+            "AudioEqualizer3Band",
+            "AudioMerge",
+            "AutogrowNamesTestNode",
+            "AutogrowPrefixTestNode",
+            "BasicGuider",
+            "BasicScheduler",
+            "BatchImagesMasksLatentsNode",
+            "BatchImagesNode",
+            "BatchLatentsNode",
+            "BatchMasksNode",
+            "BeebleSwitchXImageEdit",
+            "BeebleSwitchXVideoEdit",
+            "BetaSamplingScheduler",
+            "BriaImageEditNode",
+            "BriaRemoveImageBackground",
+            "BriaRemoveVideoBackground",
+            "BriaTransparentVideoBackground",
+            "BriaVideoGreenScreen",
+            "BriaVideoReplaceBackground",
+            "ByteDance2FirstLastFrameNode",
+            "ByteDance2ReferenceNode",
+            "ByteDance2TextToVideoNode",
+            "ByteDanceCreateImageAsset",
+            "ByteDanceCreateVideoAsset",
+            "ByteDanceFirstLastFrameNode",
+            "ByteDanceImageNode",
+            "ByteDanceImageReferenceNode",
+            "ByteDanceImageToVideoNode",
+            "ByteDanceSeedNode",
+            "ByteDanceSeedreamNode",
+            "ByteDanceSeedreamNodeV2",
+            "ByteDanceTextToVideoNode",
+            "CFGGuider",
+            "CFGNorm",
+            "CFGOverride",
+            "CFGZeroStar",
+            "CLIPAttentionMultiply",
+            "CLIPLoader",
+            "CLIPMergeAdd",
+            "CLIPMergeSimple",
+            "CLIPMergeSubtract",
+            "CLIPSave",
+            "CLIPSetLastLayer",
+            "CLIPTextEncode",
+            "CLIPTextEncodeControlnet",
+            "CLIPTextEncodeFlux",
+            "CLIPTextEncodeHiDream",
+            "CLIPTextEncodeHunyuanDiT",
+            "CLIPTextEncodeKandinsky5",
+            "CLIPTextEncodeLumina2",
+            "CLIPTextEncodePixArtAlpha",
+            "CLIPTextEncodeSD3",
+            "CLIPTextEncodeSDXL",
+            "CLIPTextEncodeSDXLRefiner",
+            "CLIPVisionEncode",
+            "CLIPVisionLoader",
+            "Canny",
+            "CaseConverter",
+            "CheckpointLoader",
+            "CheckpointLoaderSimple",
+            "CheckpointSave",
+            "ChromaRadianceOptions",
+            "ClaudeNode",
+            "ColorToRGBInt",
+            "ColorTransfer",
+            "ComboOptionTestNode",
+            "ComfyAndNode",
+            "ComfyMathExpression",
+            "ComfyNotNode",
+            "ComfyNumberConvert",
+            "ComfyOrNode",
+            "ComfySoftSwitchNode",
+            "ComfySwitchNode",
+            "ConditioningAverage",
+            "ConditioningCombine",
+            "ConditioningConcat",
+            "ConditioningSetArea",
+            "ConditioningSetAreaPercentage",
+            "ConditioningSetAreaPercentageVideo",
+            "ConditioningSetAreaStrength",
+            "ConditioningSetMask",
+            "ConditioningSetTimestepRange",
+            "ConditioningStableAudio",
+            "ConditioningZeroOut",
+            "ContextWindowsManual",
+            "ControlNetApply",
+            "ControlNetApplyAdvanced",
+            "ControlNetApplySD3",
+            "ControlNetInpaintingAliMamaApply",
+            "ControlNetLoader",
+            "ConvertStringToComboNode",
+            "CosmosImageToVideoLatent",
+            "CosmosPredict2ImageToVideoLatent",
+            "CreateCameraInfo",
+            "CreateList",
+            "CreateVideo",
+            "CropByBBoxes",
+            "CropMask",
+            "CurveEditor",
+            "CustomCombo",
+            "DCTestNode",
+            "DiffControlNetLoader",
+            "DifferentialDiffusion",
+            "DiffusersLoader",
+            "DisableNoise",
+            "DrawBBoxes",
+            "DualCFGGuider",
+            "DualCLIPLoader",
+            "DualModelGuider",
+            "EasyCache",
+            "ElevenLabsAudioIsolation",
+            "ElevenLabsInstantVoiceClone",
+            "ElevenLabsSpeechToSpeech",
+            "ElevenLabsSpeechToText",
+            "ElevenLabsTextToDialogue",
+            "ElevenLabsTextToSoundEffects",
+            "ElevenLabsTextToSpeech",
+            "ElevenLabsVoiceSelector",
+            "EmptyARVideoLatent",
+            "EmptyAceStep1.5LatentAudio",
+            "EmptyAceStepLatentAudio",
+            "EmptyAudio",
+            "EmptyChromaRadianceLatentImage",
+            "EmptyCosmosLatentVideo",
+            "EmptyFlux2LatentImage",
+            "EmptyHiDreamO1LatentImage",
+            "EmptyHunyuanImageLatent",
+            "EmptyHunyuanLatentVideo",
+            "EmptyImage",
+            "EmptyLTXVLatentVideo",
+            "EmptyLatentAudio",
+            "EmptyLatentHunyuan3Dv2",
+            "EmptyLatentImage",
+            "EmptyMochiLatentVideo",
+            "EmptyQwenImageLayeredLatentImage",
+            "EmptySD3LatentImage",
+            "Epsilon Scaling",
+            "ExponentialScheduler",
+            "ExtendIntermediateSigmas",
+            "FeatherMask",
+            "File3DToSplat",
+            "FlipSigmas",
+            "Flux2ImageNode",
+            "Flux2ProImageNode",
+            "Flux2Scheduler",
+            "FluxDisableGuidance",
+            "FluxEraseNode",
+            "FluxGuidance",
+            "FluxKVCache",
+            "FluxKontextImageScale",
+            "FluxKontextMultiReferenceLatentMethod",
+            "FluxKontextProImageNode",
+            "FluxProExpandNode",
+            "FluxProFillNode",
+            "FluxProUltraImageNode",
+            "FluxVTONode",
+            "FrameInterpolate",
+            "FrameInterpolationModelLoader",
+            "FreSca",
+            "FreeU",
+            "FreeU_V2",
+            "GITSScheduler",
+            "GLIGENLoader",
+            "GLIGENTextBoxApply",
+            "GLSLShader",
+            "GeminiImage2Node",
+            "GeminiImageNode",
+            "GeminiInputFiles",
+            "GeminiNanoBanana2",
+            "GeminiNanoBanana2V2",
+            "GeminiNode",
+            "GeminiNodeV2",
+            "GenerateTracks",
+            "GetICLoRAParameters",
+            "GetImageSize",
+            "GetSplatCount",
+            "GetVideoComponents",
+            "GrokImageEditNode",
+            "GrokImageEditNodeV2",
+            "GrokImageNode",
+            "GrokVideoEditNode",
+            "GrokVideoExtendNode",
+            "GrokVideoNode",
+            "GrokVideoReferenceNode",
+            "GrowMask",
+            "HappyHorseImageToVideoApi",
+            "HappyHorseReferenceVideoApi",
+            "HappyHorseTextToVideoApi",
+            "HappyHorseVideoEditApi",
+            "HiDreamO1PatchSeamSmoothing",
+            "HiDreamO1ReferenceImages",
+            "HitPawGeneralImageEnhance",
+            "HitPawVideoEnhance",
+            "Hunyuan3Dv2Conditioning",
+            "Hunyuan3Dv2ConditioningMultiView",
+            "HunyuanImageToVideo",
+            "HunyuanRefinerLatent",
+            "HunyuanVideo15ImageToVideo",
+            "HunyuanVideo15LatentUpscaleWithModel",
+            "HunyuanVideo15SuperResolution",
+            "HyperTile",
+            "HypernetworkLoader",
+            "Ideogram4Scheduler",
+            "IdeogramV1",
+            "IdeogramV2",
+            "IdeogramV3",
+            "IdeogramV4",
+            "ImageAddNoise",
+            "ImageBatch",
+            "ImageBlend",
+            "ImageBlur",
+            "ImageColorToMask",
+            "ImageCompare",
+            "ImageCompositeMasked",
+            "ImageCrop",
+            "ImageCropV2",
+            "ImageFlip",
+            "ImageFromBatch",
+            "ImageHistogram",
+            "ImageInvert",
+            "ImageMergeTileList",
+            "ImageOnlyCheckpointLoader",
+            "ImageOnlyCheckpointSave",
+            "ImagePadForOutpaint",
+            "ImageProcessingNode",
+            "ImageQuantize",
+            "ImageRGBToYUV",
+            "ImageRotate",
+            "ImageScale",
+            "ImageScaleBy",
+            "ImageScaleToMaxDimension",
+            "ImageScaleToTotalPixels",
+            "ImageSharpen",
+            "ImageStitch",
+            "ImageToMask",
+            "ImageUpscaleWithModel",
+            "ImageYUVToRGB",
+            "InpaintModelConditioning",
+            "InstructPixToPixConditioning",
+            "InvertBooleanNode",
+            "InvertMask",
+            "JoinAudioChannels",
+            "JoinImageWithAlpha",
+            "JsonExtractString",
+            "KSampler",
+            "KSamplerAdvanced",
+            "KSamplerSelect",
+            "Kandinsky5ImageToVideo",
+            "KarrasScheduler",
+            "KlingAvatarNode",
+            "KlingCameraControlI2VNode",
+            "KlingCameraControlT2VNode",
+            "KlingCameraControls",
+            "KlingDualCharacterVideoEffectNode",
+            "KlingFirstLastFrameNode",
+            "KlingImage2VideoNode",
+            "KlingImageGenerationNode",
+            "KlingImageToVideoWithAudio",
+            "KlingLipSyncAudioToVideoNode",
+            "KlingLipSyncTextToVideoNode",
+            "KlingMotionControl",
+            "KlingOmniProEditVideoNode",
+            "KlingOmniProFirstLastFrameNode",
+            "KlingOmniProImageNode",
+            "KlingOmniProImageToVideoNode",
+            "KlingOmniProTextToVideoNode",
+            "KlingOmniProVideoToVideoNode",
+            "KlingSingleImageVideoEffectNode",
+            "KlingStartEndFrameNode",
+            "KlingTextToVideoNode",
+            "KlingTextToVideoWithAudio",
+            "KlingVideoExtendNode",
+            "KlingVideoNode",
+            "KlingVirtualTryOnNode",
+            "Krea2ImageNode",
+            "Krea2StyleReferenceNode",
+            "LTXAVTextEncoderLoader",
+            "LTXVAddGuide",
+            "LTXVAudioVAEDecode",
+            "LTXVAudioVAELoader",
+            "LTXVConcatAVLatent",
+            "LTXVConditioning",
+            "LTXVCropGuides",
+            "LTXVEmptyLatentAudio",
+            "LTXVImgToVideo",
+            "LTXVImgToVideoInplace",
+            "LTXVLatentUpsampler",
+            "LTXVPreprocess",
+            "LTXVReferenceAudio",
+            "LTXVScheduler",
+            "LTXVSeparateAVLatent",
+            "LaplaceScheduler",
+            "LatentAdd",
+            "LatentApplyOperation",
+            "LatentApplyOperationCFG",
+            "LatentBatch",
+            "LatentBatchSeedBehavior",
+            "LatentBlend",
+            "LatentComposite",
+            "LatentCompositeMasked",
+            "LatentConcat",
+            "LatentCrop",
+            "LatentCut",
+            "LatentCutToBatch",
+            "LatentFlip",
+            "LatentFromBatch",
+            "LatentInterpolate",
+            "LatentMultiply",
+            "LatentOperationSharpen",
+            "LatentOperationTonemapReinhard",
+            "LatentRotate",
+            "LatentSubtract",
+            "LatentUpscale",
+            "LatentUpscaleBy",
+            "LatentUpscaleModelLoader",
+            "LazyCache",
+            "Load3D",
+            "LoadAudio",
+            "LoadBackgroundRemovalModel",
+            "LoadImage",
+            "LoadImageDataSetFromFolder",
+            "LoadImageMask",
+            "LoadImageOutput",
+            "LoadImageTextDataSetFromFolder",
+            "LoadLatent",
+            "LoadMediaPipeFaceLandmarker",
+            "LoadMoGeModel",
+            "LoadTrainingDataset",
+            "LoadVideo",
+            "LoraLoader",
+            "LoraLoaderBypass",
+            "LoraLoaderBypassModelOnly",
+            "LoraLoaderModelOnly",
+            "LoraModelLoader",
+            "LoraSave",
+            "LossGraphNode",
+            "LotusConditioning",
+            "LtxvApiImageToVideo",
+            "LtxvApiTextToVideo",
+            "LumaConceptsNode",
+            "LumaImageEditNode2",
+            "LumaImageModifyNode",
+            "LumaImageNode",
+            "LumaImageNode2",
+            "LumaImageToVideoNode",
+            "LumaReferenceNode",
+            "LumaVideoNode",
+            "MagnificImageRelightNode",
+            "MagnificImageSkinEnhancerNode",
+            "MagnificImageStyleTransferNode",
+            "MagnificImageUpscalerCreativeNode",
+            "MagnificImageUpscalerPreciseV2Node",
+            "Mahiro",
+            "MakeTrainingDataset",
+            "ManualSigmas",
+            "MaskComposite",
+            "MaskPreview",
+            "MaskToImage",
+            "MediaPipeFaceLandmarker",
+            "MediaPipeFaceMask",
+            "MediaPipeFaceMeshVisualize",
+            "MergeSplat",
+            "MeshyAnimateModelNode",
+            "MeshyImageToModelNode",
+            "MeshyMultiImageToModelNode",
+            "MeshyRefineNode",
+            "MeshyRigModelNode",
+            "MeshyTextToModelNode",
+            "MeshyTextureNode",
+            "MinimaxHailuoVideoNode",
+            "MinimaxImageToVideoNode",
+            "MinimaxSubjectToVideoNode",
+            "MinimaxTextToVideoNode",
+            "MoGeInference",
+            "MoGePanoramaInference",
+            "MoGePointMapToMesh",
+            "MoGeRender",
+            "ModelComputeDtype",
+            "ModelMergeAdd",
+            "ModelMergeAuraflow",
+            "ModelMergeBlocks",
+            "ModelMergeCosmos14B",
+            "ModelMergeCosmos7B",
+            "ModelMergeCosmosPredict2_14B",
+            "ModelMergeCosmosPredict2_2B",
+            "ModelMergeFlux1",
+            "ModelMergeLTXV",
+            "ModelMergeMochiPreview",
+            "ModelMergeQwenImage",
+            "ModelMergeSD1",
+            "ModelMergeSD2",
+            "ModelMergeSD35_Large",
+            "ModelMergeSD3_2B",
+            "ModelMergeSDXL",
+            "ModelMergeSimple",
+            "ModelMergeSubtract",
+            "ModelMergeWAN2_1",
+            "ModelNoiseScale",
+            "ModelPatchLoader",
+            "ModelSamplingAuraFlow",
+            "ModelSamplingContinuousEDM",
+            "ModelSamplingContinuousV",
+            "ModelSamplingDiscrete",
+            "ModelSamplingFlux",
+            "ModelSamplingLTXV",
+            "ModelSamplingSD3",
+            "ModelSamplingStableCascade",
+            "ModelSave",
+            "Morphology",
+            "MultiGPU_Options",
+            "MultiGPU_WorkUnits",
+            "NAGuidance",
+            "NormalizeVideoLatentStart",
+            "OpenAIChatConfig",
+            "OpenAIChatNode",
+            "OpenAIDalle2",
+            "OpenAIDalle3",
+            "OpenAIGPTImage1",
+            "OpenAIGPTImageNodeV2",
+            "OpenAIInputFiles",
+            "OpenAIVideoSora2",
+            "OpenRouterLLMNode",
+            "OpticalFlowLoader",
+            "OptimalStepsScheduler",
+            "Painter",
+            "PatchModelAddDownscale",
+            "PerpNeg",
+            "PerpNegGuider",
+            "PerturbedAttentionGuidance",
+            "PhotoMakerEncode",
+            "PhotoMakerLoader",
+            "PiDConditioning",
+            "PixverseImageToVideoNode",
+            "PixverseTemplateNode",
+            "PixverseTextToVideoNode",
+            "PixverseTransitionVideoNode",
+            "PolyexponentialScheduler",
+            "PorterDuffImageComposite",
+            "Preview3D",
+            "Preview3DAdvanced",
+            "PreviewAny",
+            "PreviewAudio",
+            "PreviewGaussianSplat",
+            "PreviewImage",
+            "PreviewPointCloud",
+            "PrimitiveBoolean",
+            "PrimitiveBoundingBox",
+            "PrimitiveFloat",
+            "PrimitiveInt",
+            "PrimitiveString",
+            "PrimitiveStringMultiline",
+            "QuadrupleCLIPLoader",
+            "QuiverImageToSVGNode",
+            "QuiverTextToSVGNode",
+            "QwenImageDiffsynthControlnet",
+            "RTDETR_detect",
+            "RandomNoise",
+            "RebatchImages",
+            "RebatchLatents",
+            "RecordAudio",
+            "RecraftColorRGB",
+            "RecraftControls",
+            "RecraftCreateStyleNode",
+            "RecraftCrispUpscaleNode",
+            "RecraftImageInpaintingNode",
+            "RecraftImageToImageNode",
+            "RecraftRemoveBackgroundNode",
+            "RecraftReplaceBackgroundNode",
+            "RecraftStyleV3InfiniteStyleLibrary",
+            "RecraftStyleV3RealisticImage",
+            "RecraftTextToImageNode",
+            "RecraftTextToVectorNode",
+            "RecraftV4TextToImageNode",
+            "RecraftV4TextToVectorNode",
+            "RecraftVectorizeImageNode",
+            "ReferenceLatent",
+            "ReferenceTimbreAudio",
+            "RegexExtract",
+            "RegexMatch",
+            "RegexReplace",
+            "RemoveBackground",
+            "RenderSplat",
+            "RenormCFG",
+            "RepeatImageBatch",
+            "RepeatLatentBatch",
+            "ReplaceVideoLatentFrames",
+            "RescaleCFG",
+            "ResizeAndPadImage",
+            "ResizeImageMaskNode",
+            "ResolutionBucket",
+            "ResolutionSelector",
+            "ReveImageCreateNode",
+            "ReveImageEditNode",
+            "ReveImageRemixNode",
+            "Rodin3D_Detail",
+            "Rodin3D_Gen2",
+            "Rodin3D_Gen25_Image",
+            "Rodin3D_Gen25_Text",
+            "Rodin3D_Regular",
+            "Rodin3D_Sketch",
+            "Rodin3D_Smooth",
+            "RunwayFirstLastFrameNode",
+            "RunwayImageToVideoNodeGen3a",
+            "RunwayImageToVideoNodeGen4",
+            "RunwayTextToImageNode",
+            "SAM3_Detect",
+            "SAM3_TrackPreview",
+            "SAM3_TrackToMask",
+            "SAM3_VideoTrack",
+            "SDPoseDrawKeypoints",
+            "SDPoseFaceBBoxes",
+            "SDPoseKeypointExtractor",
+            "SDTurboScheduler",
+            "SD_4XUpscale_Conditioning",
+            "SUPIRApply",
+            "SV3D_Conditioning",
+            "SVD_img2vid_Conditioning",
+            "SamplerARVideo",
+            "SamplerCustom",
+            "SamplerCustomAdvanced",
+            "SamplerDPMAdaptative",
+            "SamplerDPMPP_2M_SDE",
+            "SamplerDPMPP_2S_Ancestral",
+            "SamplerDPMPP_3M_SDE",
+            "SamplerDPMPP_SDE",
+            "SamplerER_SDE",
+            "SamplerEulerAncestral",
+            "SamplerEulerAncestralCFGPP",
+            "SamplerEulerCFGpp",
+            "SamplerLCM",
+            "SamplerLCMUpscale",
+            "SamplerLMS",
+            "SamplerSASolver",
+            "SamplerSEEDS2",
+            "SamplingPercentToSigma",
+            "SaveAnimatedPNG",
+            "SaveAnimatedWEBP",
+            "SaveAudio",
+            "SaveAudioAdvanced",
+            "SaveAudioMP3",
+            "SaveAudioOpus",
+            "SaveGLB",
+            "SaveImage",
+            "SaveImageAdvanced",
+            "SaveImageDataSetToFolder",
+            "SaveImageTextDataSetToFolder",
+            "SaveImageWebsocket",
+            "SaveLatent",
+            "SaveLoRA",
+            "SaveSVGNode",
+            "SaveTrainingDataset",
+            "SaveVideo",
+            "SaveWEBM",
+            "ScaleROPE",
+            "SelectCLIPDevice",
+            "SelectModelDevice",
+            "SelectVAEDevice",
+            "SelfAttentionGuidance",
+            "SetFirstSigma",
+            "SetLatentNoiseMask",
+            "SetUnionControlNetType",
+            "ShuffleImageTextDataset",
+            "SkipLayerGuidanceDiT",
+            "SkipLayerGuidanceDiTSimple",
+            "SkipLayerGuidanceSD3",
+            "SolidMask",
+            "SoniloTextToMusic",
+            "SoniloVideoToMusic",
+            "SplatToFile3D",
+            "SplatToMesh",
+            "SplitAudioChannels",
+            "SplitImageToTileList",
+            "SplitImageWithAlpha",
+            "SplitSigmas",
+            "SplitSigmasDenoise",
+            "StabilityAudioInpaint",
+            "StabilityAudioToAudio",
+            "StabilityStableImageSD_3_5Node",
+            "StabilityStableImageUltraNode",
+            "StabilityTextToAudio",
+            "StabilityUpscaleConservativeNode",
+            "StabilityUpscaleCreativeNode",
+            "StabilityUpscaleFastNode",
+            "StableCascade_EmptyLatentImage",
+            "StableCascade_StageB_Conditioning",
+            "StableCascade_StageC_VAEEncode",
+            "StableCascade_SuperResolutionControlnet",
+            "StableZero123_Conditioning",
+            "StableZero123_Conditioning_Batched",
+            "StringCompare",
+            "StringConcatenate",
+            "StringContains",
+            "StringFormat",
+            "StringLength",
+            "StringReplace",
+            "StringSubstring",
+            "StringTrim",
+            "StubConstantImage",
+            "StubFloat",
+            "StubImage",
+            "StubInt",
+            "StubMask",
+            "StyleModelApply",
+            "StyleModelLoader",
+            "T5TokenizerOptions",
+            "TCFG",
+            "TemporalScoreRescaling",
+            "Tencent3DPartNode",
+            "Tencent3DTextureEditNode",
+            "TencentImageToModelNode",
+            "TencentModelTo3DUVNode",
+            "TencentSmartTopologyNode",
+            "TencentTextToModelNode",
+            "TestAccumulateNode",
+            "TestAccumulationGetItemNode",
+            "TestAccumulationGetLengthNode",
+            "TestAccumulationHeadNode",
+            "TestAccumulationSetItemNode",
+            "TestAccumulationTailNode",
+            "TestAccumulationToListNode",
+            "TestAsyncBatchProcessing",
+            "TestAsyncConcurrentLimit",
+            "TestAsyncError",
+            "TestAsyncLazyCheck",
+            "TestAsyncProgressUpdate",
+            "TestAsyncResourceUser",
+            "TestAsyncTimeout",
+            "TestAsyncValidation",
+            "TestAsyncValidationError",
+            "TestBoolOperationNode",
+            "TestCustomIsChanged",
+            "TestCustomValidation1",
+            "TestCustomValidation2",
+            "TestCustomValidation3",
+            "TestCustomValidation4",
+            "TestCustomValidation5",
+            "TestDynamicAsyncGeneration",
+            "TestDynamicDependencyCycle",
+            "TestExecutionBlocker",
+            "TestFloatConditions",
+            "TestForLoopClose",
+            "TestForLoopOpen",
+            "TestIntConditions",
+            "TestIntMathOperation",
+            "TestIsChangedWithConstants",
+            "TestLazyMixImages",
+            "TestListToAccumulationNode",
+            "TestMakeListNode",
+            "TestMixedExpansionReturns",
+            "TestOutputNodeWithSocketOutput",
+            "TestParallelSleep",
+            "TestSamplingInExpansion",
+            "TestSleep",
+            "TestStringConditions",
+            "TestSyncError",
+            "TestSyncProgressUpdate",
+            "TestToBoolNode",
+            "TestVariadicAverage",
+            "TestWhileLoopClose",
+            "TestWhileLoopOpen",
+            "TextEncodeAceStepAudio",
+            "TextEncodeAceStepAudio1.5",
+            "TextEncodeHunyuanVideo_ImageToVideo",
+            "TextEncodeQwenImageEdit",
+            "TextEncodeQwenImageEditPlus",
+            "TextEncodeZImageOmni",
+            "TextGenerate",
+            "TextProcessingNode",
+            "ThresholdMask",
+            "TomePatchModel",
+            "TopazImageEnhance",
+            "TopazVideoEnhance",
+            "TopazVideoEnhanceV2",
+            "TorchCompileModel",
+            "TrainLoraNode",
+            "TransformSplat",
+            "TrimAudioDuration",
+            "TrimVideoLatent",
+            "TripleCLIPLoader",
+            "TripoConversionNode",
+            "TripoImageToModelNode",
+            "TripoMultiviewToModelNode",
+            "TripoP1ImageToModelNode",
+            "TripoP1MultiviewToModelNode",
+            "TripoP1TextToModelNode",
+            "TripoRefineNode",
+            "TripoRetargetNode",
+            "TripoRigNode",
+            "TripoSplatConditioning",
+            "TripoSplatPreprocessImage",
+            "TripoSplatSamplingPreview",
+            "TripoTextToModelNode",
+            "TripoTextureNode",
+            "UNETLoader",
+            "UNetCrossAttentionMultiply",
+            "UNetSelfAttentionMultiply",
+            "UNetTemporalAttentionMultiply",
+            "USOStyleReference",
+            "UpscaleModelLoader",
+            "VAEDecode",
+            "VAEDecodeAudio",
+            "VAEDecodeAudioTiled",
+            "VAEDecodeHunyuan3D",
+            "VAEDecodeTiled",
+            "VAEDecodeTripoSplat",
+            "VAEEncode",
+            "VAEEncodeAudio",
+            "VAEEncodeForInpaint",
+            "VAEEncodeTiled",
+            "VAELoader",
+            "VAESave",
+            "VOIDInpaintConditioning",
+            "VOIDQuadmaskPreprocess",
+            "VOIDSampler",
+            "VOIDWarpedNoise",
+            "VOIDWarpedNoiseSource",
+            "VPScheduler",
+            "Veo3FirstLastFrameNode",
+            "Veo3VideoGenerationNode",
+            "VeoVideoGenerationNode",
+            "Video Slice",
+            "VideoLinearCFGGuidance",
+            "VideoTriangleCFGGuidance",
+            "Vidu2ImageToVideoNode",
+            "Vidu2ReferenceVideoNode",
+            "Vidu2StartEndToVideoNode",
+            "Vidu2TextToVideoNode",
+            "Vidu3ImageToVideoNode",
+            "Vidu3StartEndToVideoNode",
+            "Vidu3TextToVideoNode",
+            "ViduExtendVideoNode",
+            "ViduImageToVideoNode",
+            "ViduMultiFrameVideoNode",
+            "ViduReferenceVideoNode",
+            "ViduStartEndToVideoNode",
+            "ViduTextToVideoNode",
+            "VoxelToMesh",
+            "VoxelToMeshBasic",
+            "Wan22FunControlToVideo",
+            "Wan22ImageToVideoLatent",
+            "Wan2ImageToVideoApi",
+            "Wan2ReferenceVideoApi",
+            "Wan2TextToVideoApi",
+            "Wan2VideoContinuationApi",
+            "Wan2VideoEditApi",
+            "WanAnimateToVideo",
+            "WanCameraEmbedding",
+            "WanCameraImageToVideo",
+            "WanDancerEncodeAudio",
+            "WanDancerPadKeyframes",
+            "WanDancerPadKeyframesList",
+            "WanDancerVideo",
+            "WanFirstLastFrameToVideo",
+            "WanFunControlToVideo",
+            "WanFunInpaintToVideo",
+            "WanHuMoImageToVideo",
+            "WanImageToImageApi",
+            "WanImageToVideo",
+            "WanImageToVideoApi",
+            "WanInfiniteTalkToVideo",
+            "WanMoveConcatTrack",
+            "WanMoveTrackToVideo",
+            "WanMoveTracksFromCoords",
+            "WanMoveVisualizeTracks",
+            "WanPhantomSubjectToVideo",
+            "WanReferenceVideoApi",
+            "WanSCAILToVideo",
+            "WanSoundImageToVideo",
+            "WanSoundImageToVideoExtend",
+            "WanTextToImageApi",
+            "WanTextToVideoApi",
+            "WanTrackToVideo",
+            "WanVaceToVideo",
+            "WavespeedFlashVSRNode",
+            "WavespeedImageUpscaleNode",
+            "WebcamCapture",
+            "ZImageFunControlnet",
+            "unCLIPCheckpointLoader",
+            "unCLIPConditioning",
+            "wanBlockSwap"
+        ],
+        {
+            "title_aux": "ComfyUI"
+        }
+    ],
+    "https://github.com/comfyanonymous/ComfyUI_bitsandbytes_NF4": [
+        [
+            "CheckpointLoaderNF4"
+        ],
+        {
+            "title_aux": "ComfyUI_bitsandbytes_NF4 [EXPERIMENTAL]"
+        }
+    ],
+    "https://github.com/comfypod/ComfyUI-Comflow": [
+        [
+            "ComflowInputBoolean",
+            "ComflowInputCheckpoint",
+            "ComflowInputImage",
+            "ComflowInputImageAlpha",
+            "ComflowInputImageBatch",
+            "ComflowInputLora",
+            "ComflowInputNumber",
+            "ComflowInputNumberInt",
+            "ComflowInputNumberSlider",
+            "ComflowInputText",
+            "ComflowInputVid",
+            "ComflowInputVideo",
+            "ComflowWebsocketImageInput",
+            "ComflowWebsocketImageOutput"
+        ],
+        {
+            "description": "",
+            "nickname": "Comflow",
+            "title": "comflow",
+            "title_aux": "ComfyUI-Comflow"
+        }
+    ],
+    "https://github.com/comfyscript/ComfyUI-CloudClient": [
+        [
+            "ClientImageDownloadNode",
+            "ClientVideoDownloadNode",
+            "RemoteImageLoader",
+            "ServerMemoryImageNode",
+            "TemporaryImagePreview",
+            "UniversalDataToAudioVideo",
+            "UniversalDataToImage"
+        ],
+        {
+            "title_aux": "ComfyUI-CloudClient"
+        }
+    ],
+    "https://github.com/comfyuiblog/deepseek_prompt_generator_comfyui": [
+        [
+            "DeepSeek_Prompt_Generator"
+        ],
+        {
+            "title_aux": "deepseek_prompt_generator_comfyui [WIP]"
+        }
+    ],
+    "https://github.com/content-and-code/ComfyUI-SimpleCSVParse": [
+        [
+            "SimpleCSVParser"
+        ],
+        {
+            "title_aux": "Simple CSV Parser [UNSAFE]"
+        }
+    ],
+    "https://github.com/corbin-hayden13/ComfyUI-Better-Dimensions": [
+        [
+            "BetterImageDimensions",
+            "PureRatio",
+            "SDXLDimensions"
+        ],
+        {
+            "title_aux": "ComfyUI-Better-Dimensions"
+        }
+    ],
+    "https://github.com/crimro-se/ComfyUI-CascadedGaze": [
+        [
+            "CGDenoiseImage",
+            "CGDenoiseModelLoader"
+        ],
+        {
+            "title_aux": "ComfyUI-CascadedGaze"
+        }
+    ],
+    "https://github.com/ctf05/ComfyUI-AudioDuration": [
+        [
+            "SimpleAudioDuration",
+            "SimpleAudioOverlay"
+        ],
+        {
+            "title_aux": "ComfyUI-AudioDuration"
+        }
+    ],
+    "https://github.com/danieljanata/ComfyUI-phone_compression_ultimate": [
+        [
+            "PhoneCompressionUltimate"
+        ],
+        {
+            "title_aux": "ComfyUI-phone_compression_ultimate [WIP]"
+        }
+    ],
+    "https://github.com/danmano/ComfyUI-Danbooru-LLM": [
+        [
+            "DanbooruLLMPromptGenerator"
+        ],
+        {
+            "title_aux": "ComfyUI-Danbooru-LLM [WIP]"
+        }
+    ],
+    "https://github.com/daracazamea/comfyUI-DCNodes": [
+        [
+            "FluxResolutionPicker",
+            "GetGenerationTime",
+            "ManualTrigger",
+            "SDXLResolutionPicker",
+            "StartTimerPassThrough"
+        ],
+        {
+            "title_aux": "DCNodess [WIP]"
+        }
+    ],
+    "https://github.com/davidjshin1/ComfyUI-NanobananaPro": [
+        [
+            "NanoBananaPro"
+        ],
+        {
+            "title_aux": "ComfyUI-NanobananaPro [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/dcduanchao/comfyui_video_http": [
+        [
+            "Translator Node",
+            "Video Upload Node"
+        ],
+        {
+            "title_aux": "comfyui_video_http"
+        }
+    ],
+    "https://github.com/dead-matrix/ComfyUI-RMBG-Custom": [
+        [
+            "RMBG_Custom"
+        ],
+        {
+            "title_aux": "ComfyUI-RMBG-Custom"
+        }
+    ],
+    "https://github.com/denislov/Comfyui_AutoSurvey": [
+        [
+            "AddDoc2Knowledge",
+            "AutoSurvey",
+            "ChatModel",
+            "ComfyMilvus",
+            "ComfyWeaviate",
+            "ManageDatabase",
+            "MilvusScheme",
+            "MsField",
+            "QueryKnowledge",
+            "WcProperty",
+            "WcPropertyComb",
+            "WriteOutline",
+            "WriteSection"
+        ],
+        {
+            "title_aux": "Comfyui_AutoSurvey"
+        }
+    ],
+    "https://github.com/dexintenebri/comfyui_voxel_nodes": [
+        [
+            "AutoVoxelScaler",
+            "DepthEstimationNode",
+            "DepthToVox",
+            "MultiViewVoxelFusion",
+            "OptimizedVoxelizationNode",
+            "ProceduralChunkedWFC3DTerrain",
+            "ShapeCompletionNode",
+            "VoxelExportNode",
+            "VoxelModelLoader",
+            "VoxelPreview",
+            "VoxelPreviewNode",
+            "VoxelToGLB",
+            "WFC3DTerrainGenerator"
+        ],
+        {
+            "title_aux": "comfyui_voxel_nodes [WIP]"
+        }
+    ],
+    "https://github.com/dfl/comfyui-stylegan": [
+        [
+            "BatchAverageStyleGANLatents",
+            "BlendStyleGANLatents",
+            "GenerateStyleGANLatent",
+            "LoadStyleGAN",
+            "LoadStyleGANLatentImg",
+            "SaveStyleGANLatentImg",
+            "StyleGANInversion",
+            "StyleGANLatentFromBatch",
+            "StyleGANSampler"
+        ],
+        {
+            "title_aux": "comfyui-stylegan"
+        }
+    ],
+    "https://github.com/dhpdong/ComfyUI-IPAdapter-Flux-Repair": [
+        [
+            "SamplerCustomAdvancedPlus",
+            "SeedPlus",
+            "UnetLoaderGGUFPlus"
+        ],
+        {
+            "title_aux": "ComfyUI-IPAdapter-Flux-Repair"
+        }
+    ],
+    "https://github.com/dibowei1980/Comfyui-QwenEditUtilsFromRapid": [
+        [
+            "TextEncodeQwenImageEditPlus_FromRapid",
+            "TextEncodeQwenImageEdit_FromRapid"
+        ],
+        {
+            "title_aux": "Comfyui-QwenEditUtilsFromRapid"
+        }
+    ],
+    "https://github.com/dihan/comfyui-random-kps": [
+        [
+            "RandomFaceKeypoints"
+        ],
+        {
+            "title_aux": "ComfyUI Random Keypoints for InstantID [WIP]"
+        }
+    ],
+    "https://github.com/dmitry-guskov/ComfyUI_light_image_ops": [
+        [
+            "LightBatchToList",
+            "LightListToBatch",
+            "LightScaleToSide",
+            "LightWanEmbedsResizeSpatialPacked",
+            "LightWanLatentsResizeNormalizeNoise",
+            "LightWanLatentsResizeToSize"
+        ],
+        {
+            "title_aux": "ComfyUI_light_image_ops"
+        }
+    ],
+    "https://github.com/doc-packages/comfyui-doc-utils": [
+        [
+            "DOC_RandomPromptChoice",
+            "DOC_SaveImageAndAddToHistory"
+        ],
+        {
+            "title_aux": "doc-utils"
+        }
+    ],
+    "https://github.com/dogcomplex/ComfyUI-LOKI": [
+        [
+            "EvaluateRelevanceLLM",
+            "FilterNodesLLM",
+            "LLMQueryAPI",
+            "LLMQueryAPIBatch",
+            "LokiGlamour",
+            "LokiListAvailableNodes",
+            "LokiListInstalledNodes",
+            "LokiTweetScraper",
+            "LokiTweetThreadScraper",
+            "LokiTweetUserScraper",
+            "TwitterScraper",
+            "TwitterThreadScraper",
+            "TwitterUserScraper",
+            "\u270d\ufe0fScribe (LOKI)"
+        ],
+        {
+            "title_aux": "ComfyUI-LOKI [WIP]"
+        }
+    ],
+    "https://github.com/doucx/ComfyUI_WcpD_Utility_Kit": [
+        [
+            "BlackImage",
+            "CopyImage(Wayland)",
+            "ExecStrAsCode",
+            "MergeStrings",
+            "YamlToPrompt"
+        ],
+        {
+            "title_aux": "ComfyUI_WcpD_Utility_Kit"
+        }
+    ],
+    "https://github.com/dougbtv/comfyui-vllm-omni": [
+        [
+            "VLLMImageEdit",
+            "VLLMTextToImage"
+        ],
+        {
+            "title_aux": "comfyui-vllm-omni"
+        }
+    ],
+    "https://github.com/dowands/ComfyUI-AddMaskForICLora": [
+        [
+            "AddMaskForICLora"
+        ],
+        {
+            "title_aux": "AddMaskForICLora"
+        }
+    ],
+    "https://github.com/downlifted/ComfyUI_BWiZ_Nodes": [
+        [
+            "BWIZInteractiveLogMonitor",
+            "BWIZ_AdvancedLoadImageBatch",
+            "BWIZ_CaptainWebhook",
+            "BWIZ_CaptainWebhook-Email",
+            "BWIZ_ComfyEmail",
+            "BWIZ_ErrorDetector",
+            "BWIZ_HFRepoBatchLoader",
+            "BWIZ_NotificationSound"
+        ],
+        {
+            "title_aux": "ComfyUI_BWiZ_Nodes [WIP]"
+        }
+    ],
+    "https://github.com/downlifted/ComfyUI_GroqPrompt": [
+        [
+            "GroqAPIKeyManager",
+            "GroqAPIKeyProvider",
+            "GroqArtPromptEnhancer",
+            "GroqArtPromptGenerator",
+            "GroqLLMNode",
+            "GroqMusicToArtPrompter",
+            "GroqStyleTransferPrompter",
+            "GroqWorkflowHelper"
+        ],
+        {
+            "title_aux": "ComfyUI_GroqPrompt [WIP]"
+        }
+    ],
+    "https://github.com/duckmartians/Duck_Nodes": [
+        [
+            "Duck_AddTextOverlay",
+            "Duck_EmptyLatentImage",
+            "Duck_LoadExcelRow",
+            "Duck_LoadGoogleDocLine",
+            "Duck_LoadGoogleSheetOneRow",
+            "Duck_LoadWordLine",
+            "Duck_PromptLoader",
+            "Duck_QwenAspectRatios",
+            "Duck_TextReplacer"
+        ],
+        {
+            "title_aux": "Duck_Nodes [UNSAFE]"
+        }
+    ],
+    "https://github.com/duycao2506/ComfyUI-Local-PiperTTS": [
+        [
+            "PiperTTS"
+        ],
+        {
+            "title_aux": "ComfyUI-Local-PiperTTS [WIP]"
+        }
+    ],
+    "https://github.com/eddiehavila/comfyui-nachoman": [
+        [
+            "NACHOMAN_AudioCropTime",
+            "NACHOMAN_AutoQueueController",
+            "NACHOMAN_ChunkIndexController",
+            "NACHOMAN_CombineAllChunks",
+            "NACHOMAN_Extract_Frame_Number",
+            "NACHOMAN_FullSongAnalyzerV4",
+            "NACHOMAN_GetPromptByIndex",
+            "NACHOMAN_GetRunIndexFromJson",
+            "NACHOMAN_IndexedPromptChunker",
+            "NACHOMAN_IndexedPromptChunkerV2",
+            "NACHOMAN_LoadAudioSplitDynamic",
+            "NACHOMAN_LoadAudioSplitUpload",
+            "NACHOMAN_LoadAudioSplit_HUMO",
+            "NACHOMAN_LoadAudioSplit_HUMO_Transcribe",
+            "NACHOMAN_LoadSingleAudioChunk",
+            "NACHOMAN_LoadVideos",
+            "NACHOMAN_PostRunIndexStepper",
+            "NACHOMAN_PromptSplitter",
+            "NACHOMAN_SaveVideoChunkWithIndex",
+            "NACHOMAN_TranscribeText",
+            "NACHOMAN_VideoSplitter"
+        ],
+        {
+            "title_aux": "nachoman Custom Nodes [WIP]"
+        }
+    ],
+    "https://github.com/eddyhhlure1Eddy/ComfyUI-QwenImageWrapper": [
+        [
+            "CLIPLoader",
+            "CLIPSetLastLayer",
+            "CLIPTextEncode",
+            "CLIPVisionEncode",
+            "CLIPVisionLoader",
+            "CheckpointLoader",
+            "CheckpointLoaderSimple",
+            "ConditioningAverage",
+            "ConditioningCombine",
+            "ConditioningConcat",
+            "ConditioningSetArea",
+            "ConditioningSetAreaPercentage",
+            "ConditioningSetAreaStrength",
+            "ConditioningSetMask",
+            "ConditioningSetTimestepRange",
+            "ConditioningZeroOut",
+            "ControlNetApply",
+            "ControlNetApplyAdvanced",
+            "ControlNetLoader",
+            "DiffControlNetLoader",
+            "DiffusersLoader",
+            "DualCLIPLoader",
+            "EmptyImage",
+            "EmptyLatentImage",
+            "GLIGENLoader",
+            "GLIGENTextBoxApply",
+            "GetImageSize",
+            "ImageBatch",
+            "ImageBlend",
+            "ImageBlur",
+            "ImageCrop",
+            "ImageInvert",
+            "ImagePadForOutpaint",
+            "ImageQuantize",
+            "ImageScale",
+            "ImageScaleBy",
+            "ImageScaleToTotalPixels",
+            "ImageSharpen",
+            "ImageStitch",
+            "ImageUpscaleWithModel",
+            "InpaintModelConditioning",
+            "KSampler",
+            "KSamplerAdvanced",
+            "LatentBlend",
+            "LatentComposite",
+            "LatentCrop",
+            "LatentFlip",
+            "LatentFromBatch",
+            "LatentRotate",
+            "LatentUpscale",
+            "LatentUpscaleBy",
+            "LoadImage",
+            "LoadImageMask",
+            "LoadImageOutput",
+            "LoadLatent",
+            "LoraLoader",
+            "LoraLoaderModelOnly",
+            "PreviewImage",
+            "RepeatLatentBatch",
+            "SaveImage",
+            "SaveLatent",
+            "SetLatentNoiseMask",
+            "StyleModelApply",
+            "StyleModelLoader",
+            "UNETLoader",
+            "UpscaleModelLoader",
+            "VAEDecode",
+            "VAEDecodeTiled",
+            "VAEEncode",
+            "VAEEncodeForInpaint",
+            "VAEEncodeTiled",
+            "VAELoader",
+            "eddy_batch_image_generator",
+            "eddy_batch_prompt",
+            "eddy_image_blend",
+            "eddy_qwen_CLIPLoader",
+            "eddy_qwen_CLIPTextEncode",
+            "eddy_qwen_EmptySD3LatentImage",
+            "eddy_qwen_KSampler",
+            "eddy_qwen_LoraLoaderModelOnly",
+            "eddy_qwen_ModelSamplingAuraFlow",
+            "eddy_qwen_SaveImage",
+            "eddy_qwen_UNETLoader",
+            "eddy_qwen_VAEDecode",
+            "eddy_qwen_VAELoader",
+            "eddy_qwen_image_blockswap",
+            "unCLIPCheckpointLoader",
+            "unCLIPConditioning"
+        ],
+        {
+            "title_aux": "ComfyUI-QwenImageWrapper [WIP]"
+        }
+    ],
+    "https://github.com/edisonchan/ComfyUI-Sysinfo": [
+        [
+            "SysInfoDisplay"
+        ],
+        {
+            "title_aux": "ComfyUI-Sysinfo"
+        }
+    ],
+    "https://github.com/edvardtoth/ComfyUI-ETNodes": [
+        [
+            "ETNodes-Color-Selector",
+            "ETNodes-Gemini-API-Image",
+            "ETNodes-Gemini-API-Text",
+            "ETNodes-List-Items",
+            "ETNodes-List-Selector",
+            "ETNodes-Text-Preview"
+        ],
+        {
+            "title_aux": "ComfyUI-ETNodes"
+        }
+    ],
+    "https://github.com/eggsbenedicto/DiffusionRenderer-ComfyUI": [
+        [
+            "Cosmos1ForwardRenderer",
+            "Cosmos1InverseRenderer",
+            "LoadDiffusionRendererModel",
+            "LoadHDRImage",
+            "VAEPassthroughTest"
+        ],
+        {
+            "title_aux": "DiffusionRenderer-ComfyUI [WIP]"
+        }
+    ],
+    "https://github.com/egormly/ComfyUI-EG_Tools": [
+        [
+            "EG_AppendSubfolder",
+            "EG_GetDateTime",
+            "EG_ImageInfo",
+            "EG_IncrementCounter",
+            "EG_ListFiles",
+            "EG_PathExists",
+            "EG_PathIndexFilename",
+            "EG_PathIndexPrefix",
+            "EG_PathParts",
+            "EG_ProjectRoot",
+            "EG_SaveImage",
+            "EG_WithNewExtension"
+        ],
+        {
+            "title_aux": "ComfyUI-EG_Tools [WIP]"
+        }
+    ],
+    "https://github.com/eigenpunk/ComfyUI-audio": [
+        [
+            "ApplyVoiceFixer",
+            "AudioSampleRate",
+            "BatchAudio",
+            "BlendAudio",
+            "ClipAudioRegion",
+            "CombineImageWithAudio",
+            "ConcatAudio",
+            "ConvertAudio",
+            "FilterAudio",
+            "FlattenAudioBatch",
+            "HifiGANApply",
+            "HifiGANLoader",
+            "HifiGANModelParams",
+            "InvertAudioPhase",
+            "MusicgenGenerate",
+            "MusicgenHFGenerate",
+            "MusicgenHFLoader",
+            "MusicgenLoader",
+            "NormalizeAudio",
+            "ResampleAudio",
+            "SpectrogramImage",
+            "Tacotron2Generate",
+            "Tacotron2Loader",
+            "ToMelSpectrogram",
+            "TortoiseTTSGenerate",
+            "TortoiseTTSLoader",
+            "TrimAudio",
+            "TrimAudioSamples",
+            "TrimSilence",
+            "VALLEXGenerator",
+            "VALLEXLoader",
+            "VALLEXVoicePromptFromAudio",
+            "VALLEXVoicePromptLoader",
+            "WaveGlowApply",
+            "WaveGlowLoader"
+        ],
+        {
+            "title_aux": "ComfyUI-audio"
+        }
+    ],
+    "https://github.com/ejektaflex/ComfyUI-Ty": [
+        [
+            "Lora Block Weight Regex Loader // Ty"
+        ],
+        {
+            "title_aux": "ComfyUI-Ty"
+        }
+    ],
+    "https://github.com/elfatherbrown/comfyui-realcugan-node": [
+        [
+            "RealCUGANUpscaler"
+        ],
+        {
+            "title_aux": "Real-CUGAN ComfyUI Custom Node [WIP]"
+        }
+    ],
+    "https://github.com/emniko/ComfyUI-HPSv2": [
+        [
+            "HPSv2Node"
+        ],
+        {
+            "title_aux": "ComfyUI-HPSv2"
+        }
+    ],
+    "https://github.com/emranemran/ComfyUI-FasterLivePortrait": [
+        [
+            "FasterLivePortraitProcess",
+            "LoadFasterLivePortraitModels"
+        ],
+        {
+            "title_aux": "ComfyUI-FasterLivePortrait"
+        }
+    ],
+    "https://github.com/endman100/ComfyUI-SaveAndLoadPromptCondition": [
+        [
+            "Load Contditioning",
+            "LoadContditioning",
+            "Save Conditioning",
+            "SaveConditioning"
+        ],
+        {
+            "title_aux": "ComfyUI Nodes: SaveConditioning and LoadConditioning"
+        }
+    ],
+    "https://github.com/endman100/ComfyUI-augmentation": [
+        [
+            "RamdomFlipImage (endman100)"
+        ],
+        {
+            "title_aux": "ComfyUI-augmentation"
+        }
+    ],
+    "https://github.com/endman100/ComfyUI_tensor_script": [
+        [
+            "DictGetItem",
+            "LatentSqueeze",
+            "LatentUnsqueeze",
+            "ListGetIndex",
+            "TensorEmpty",
+            "TensorGetShape",
+            "TensorShowShape",
+            "TensorSqueeze",
+            "TensorUnsqueeze"
+        ],
+        {
+            "title_aux": "ComfyUI_tensor_script [WIP]"
+        }
+    ],
+    "https://github.com/enlo/ComfyUI-CheckpointSettings": [
+        [
+            "CheckPointSettingsListMerger",
+            "CheckPointSettingsPack",
+            "CheckPointSettingsRandomSelector",
+            "CheckPointSettingsTie",
+            "CheckPointSettingsToFilename",
+            "CheckPointSettingsUnpack",
+            "RandomChoiceNumber"
+        ],
+        {
+            "title_aux": "ComfyUI-CheckpointSettings"
+        }
+    ],
+    "https://github.com/eokoo/ComfyUI-Photopea": [
+        [
+            "PhotopeaNode"
+        ],
+        {
+            "title_aux": "ComfyUI-Photopea [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/ericbeyer/guidance_interval": [
+        [
+            "Guidance Interval"
+        ],
+        {
+            "title_aux": "guidance_interval"
+        }
+    ],
+    "https://github.com/erosDiffusion/ComfyUI-enricos-json-file-load-and-value-selector": [
+        [
+            "SelectorNode"
+        ],
+        {
+            "title_aux": "Select key from JSON (Alpha) [UNSAFE]"
+        }
+    ],
+    "https://github.com/ervinne13/ComfyUI-Metadata-Hub": [
+        [
+            "Metadata Hub",
+            "Save Image With Metadata"
+        ],
+        {
+            "title_aux": "ComfyUI-Metadata-Hub"
+        }
+    ],
+    "https://github.com/ervinne13/comfyui-prompt-library": [
+        [
+            "PromptLibraryExtraNode",
+            "PromptLibraryNode"
+        ],
+        {
+            "title_aux": "comfyui-prompt-library"
+        }
+    ],
+    "https://github.com/ethanfel/ComfyUI-CFG-CTRL": [
+        [
+            "SMCCFGCtrl"
+        ],
+        {
+            "title_aux": "ComfyUI-CFG-CTRL [WIP]"
+        }
+    ],
+    "https://github.com/ethanfel/ComfyUI-Mamad8-QwenEdit-fix": [
+        [
+            "CLIPTextEncodeMamad8",
+            "CompiledModelRouter",
+            "ConditioningRouter",
+            "ImageRouter",
+            "LatentRouter",
+            "ModelRouter",
+            "QwenImagePreprocessMamad8",
+            "TextEncodeQwenImageEditPlusMamad8",
+            "WeightSwappingRouter"
+        ],
+        {
+            "title_aux": "ComfyUI-Mamad8 [WIP]"
+        }
+    ],
+    "https://github.com/exectails/comfyui-et_scripting": [
+        [
+            "ETPythonTextScript3Node"
+        ],
+        {
+            "title_aux": "Scripting"
+        }
+    ],
+    "https://github.com/eyekayem/comfyui_runway_gen3": [
+        [
+            "RunwayVideoGenerator",
+            "RunwayVideoPreview"
+        ],
+        {
+            "title_aux": "comfyui_runway_gen3"
+        }
+    ],
+    "https://github.com/fangg2000/ComfyUI-SenseVoice": [
+        [
+            "STTNode",
+            "VoiceRecorderNode"
+        ],
+        {
+            "title_aux": "ComfyUI-SenseVoice [WIP]"
+        }
+    ],
+    "https://github.com/fangg2000/ComfyUI-StableAudioFG": [
+        [
+            "LoadStableAudioModel",
+            "StableAudioFG"
+        ],
+        {
+            "author": "lks-ai",
+            "description": "A Simple integration of Stable Audio Diffusion with knobs and stuff!",
+            "nickname": "stableaudio",
+            "title": "StableAudioSampler",
+            "title_aux": "ComfyUI-StableAudioFG [WIP]"
+        }
+    ],
+    "https://github.com/fangg2000/comfyui_fgtools": [
+        [
+            "DeepSeekChat",
+            "DoubaoChat",
+            "InpaintConcat",
+            "InpaintCut",
+            "IsEmptyString",
+            "PriorityLLMNode",
+            "SwitchString"
+        ],
+        {
+            "author": "lks-ai",
+            "description": "A Simple integration of Stable Audio Diffusion with knobs and stuff!",
+            "nickname": "stableaudio",
+            "title": "StableAudioSampler",
+            "title_aux": "comfyui_fgtools [WIP]"
+        }
+    ],
+    "https://github.com/fangziheng2321/comfyuinode_chopmask": [
+        [
+            "cus_chopmask"
+        ],
+        {
+            "title_aux": "comfyuinode_chopmask [WIP]"
+        }
+    ],
+    "https://github.com/fantaskiss/ComfyUI-Qwen3_VQA_enhanced": [
+        [
+            "ImageLoader",
+            "MultiplePathsInput",
+            "Qwen3_VQA",
+            "Qwen3_VQA_Enhanced",
+            "VideoLoader"
+        ],
+        {
+            "title_aux": "ComfyUI-Qwen3_VQA_enhanced [WIP]"
+        }
+    ],
+    "https://github.com/fedetanco22/ComfyUI-AudioDuration-main": [
+        [
+            "AudioDuration"
+        ],
+        {
+            "title_aux": "ComfyUI-AudioDuration-main [WIP]"
+        }
+    ],
+    "https://github.com/fedetanco22/comfyui-notebooklm-main": [
+        [
+            "NotebookLM_ScriptGenerator"
+        ],
+        {
+            "title_aux": "comfyui-notebooklm-main"
+        }
+    ],
+    "https://github.com/fedetanco22/comfyui_morpheus_NanoBanana_Mask-main": [
+        [
+            "MorpheusBatchImagesCropV25Fix",
+            "MorpheusImageEditingPrompt",
+            "MorpheusNanoBananaMaskGeminiV25Fix"
+        ],
+        {
+            "title_aux": "comfyui_morpheus_NanoBanana_Mask-main"
+        }
+    ],
+    "https://github.com/filipemeneses/ComfyUI_html": [
+        [
+            "HtmlDownload",
+            "HtmlPreview",
+            "LoadHtml",
+            "SaveHtml",
+            "SingleImageToBase64",
+            "SingleImageToBase64KeepMetadata"
+        ],
+        {
+            "title_aux": "ComfyUI_html [UNSAFE]"
+        }
+    ],
+    "https://github.com/florestefano1975/ComfyUI-Prompt-Library": [
+        [
+            "PromptLibraryNode",
+            "PromptLibraryRandomNode"
+        ],
+        {
+            "title_aux": "ComfyUI-Prompt-Library [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/flowtyone/comfyui-flowty-lcm": [
+        [
+            "LCMSampler"
+        ],
+        {
+            "title_aux": "comfyui-flowty-lcm"
+        }
+    ],
+    "https://github.com/flyingdogsoftware/gyre_for_comfyui": [
+        [
+            "BackgroundRemoval",
+            "GyreIfElse",
+            "GyreLoopEnd",
+            "GyreLoopStart"
+        ],
+        {
+            "title_aux": "Gyre for ComfyUI"
+        }
+    ],
+    "https://github.com/flywhale-666/ComfyUI_pixel_snapping": [
+        [
+            "FaceHandCrop",
+            "PixelSnapping",
+            "PowerfulColorAlignment",
+            "PowerfulMaskCrop",
+            "PowerfulMaskRestore"
+        ],
+        {
+            "title_aux": "ComfyUI_pixel_snapping [WIP]"
+        }
+    ],
+    "https://github.com/foglerek/comfyui-cem-tools": [
+        [
+            "ProcessImageBatch"
+        ],
+        {
+            "title_aux": "comfyui-cem-tools"
+        }
+    ],
+    "https://github.com/fogyisland/Comfy_Show_StringText": [
+        [
+            "ComfyUIShowText"
+        ],
+        {
+            "title_aux": "Comfy_Show_StringText [WIP]"
+        }
+    ],
+    "https://github.com/franklydegenerate/ComfyUI-WAN-Resolution-Helper": [
+        [
+            "WANResolutionHelperV2"
+        ],
+        {
+            "title_aux": "WAN Resolution Helper [WIP]"
+        }
+    ],
+    "https://github.com/franky519/comfyui-redux-style": [
+        [
+            "StyleModelAdvanced",
+            "StyleModelApplySimple",
+            "StyleModelConditioner",
+            "StyleModelGridVisualizer"
+        ],
+        {
+            "title_aux": "comfyui-redux-style"
+        }
+    ],
+    "https://github.com/franky519/comfyui_fnckc_Face_analysis": [
+        [
+            "FaceAnalysisModels",
+            "FaceFourImageMatcher"
+        ],
+        {
+            "title_aux": "ComfyUI Face Four Image Matcher [WIP]"
+        }
+    ],
+    "https://github.com/fritzprix/ComfyUI-LLM-Utils": [
+        [
+            "WeightedDict",
+            "WeightedDictConcat",
+            "WeightedDictInput",
+            "WeightedDictSelect",
+            "WeightedDictSelectGroup",
+            "WeightedDictToPrompt"
+        ],
+        {
+            "title_aux": "ComfyUI-LLM-Utils [WIP]"
+        }
+    ],
+    "https://github.com/frost-byte/fbTools": [
+        [
+            "CaptionModelUnloader",
+            "DatasetCaptionEditor",
+            "DatasetCaptionViewer",
+            "DatasetCaptioner",
+            "DatasetExportSummary",
+            "FBTextEncodeQwenImageEditPlus",
+            "LibberApply",
+            "LibberManager",
+            "MaskProcessor",
+            "MultiLoraLoader",
+            "NodeInputSelect",
+            "OpaqueAlpha",
+            "PromptComposer",
+            "QwenAspectRatio",
+            "SAMPreprocessNHWC",
+            "SceneCreate",
+            "SceneInput",
+            "SceneMaskDefinition",
+            "SceneOutput",
+            "ScenePromptManager",
+            "SceneSave",
+            "SceneSelect",
+            "SceneUpdate",
+            "SceneView",
+            "SceneWanVideoLoraMultiSave",
+            "StoryCreate",
+            "StoryEdit",
+            "StoryLoad",
+            "StorySave",
+            "StorySceneBatch",
+            "StorySceneImageSave",
+            "StoryScenePick",
+            "StoryVideoBatch",
+            "StoryView",
+            "SubdirLister",
+            "TailEnhancePro",
+            "TailSplit"
+        ],
+        {
+            "title_aux": "fb-tools"
+        }
+    ],
+    "https://github.com/ftechmax/ComfyUI-NovaKit-Pack": [
+        [
+            "CountTokens"
+        ],
+        {
+            "title_aux": "ComfyUI-NovaKit-Pack"
+        }
+    ],
+    "https://github.com/ftf001-tech/ComfyUI-ExternalLLMDetector": [
+        [
+            "ExternalLLMDetectorBboxesConvert",
+            "ExternalLLMDetectorMainProcess",
+            "ExternalLLMDetectorSettings"
+        ],
+        {
+            "title_aux": "ComfyUI-Lucian [WIP]"
+        }
+    ],
+    "https://github.com/fuselayer/comfyui-libcom-image-composition": [
+        [
+            "HarmonyScore",
+            "ImageHarmonization",
+            "ImageHarmonizationAuto"
+        ],
+        {
+            "title_aux": "comfyui-libcom-image-composition"
+        }
+    ],
+    "https://github.com/fylrid2/comfyui_lock_previous_value": [
+        [
+            "Lock"
+        ],
+        {
+            "title_aux": "lockValue"
+        }
+    ],
+    "https://github.com/g37613182-tech/comfyui-auto-text-layout": [
+        [
+            "AutoTextLayout"
+        ],
+        {
+            "title_aux": "comfyui-auto-text-layout"
+        }
+    ],
+    "https://github.com/g37613182-tech/comfyui-auto-text-layout-v2": [
+        [
+            "AutoTextLayout"
+        ],
+        {
+            "title_aux": "comfyui-auto-text-layout-v2"
+        }
+    ],
+    "https://github.com/gabe-init/ComfyUI-LM-Studio": [
+        [
+            "LMStudioNode"
+        ],
+        {
+            "title_aux": "ComfyUI LM Studio Node [WIP]"
+        }
+    ],
+    "https://github.com/gabe-init/ComfyUI-Repo-Eater": [
+        [
+            "RepoEaterNode"
+        ],
+        {
+            "title_aux": "gabe-init [WIP]"
+        }
+    ],
+    "https://github.com/gabe-init/comfyui_ui_render": [
+        [
+            "HTMLRendererNode"
+        ],
+        {
+            "title_aux": "comfyui_ui_render [UNSAFE]"
+        }
+    ],
+    "https://github.com/gagaprince/ComfyUI_gaga_utils": [
+        [
+            "GagaAddStringArray",
+            "GagaBatchStringReplace",
+            "GagaGetDirList",
+            "GagaGetFileList",
+            "GagaGetImageInfoByUpload",
+            "GagaGetImageInfoWithUrl",
+            "GagaGetImageWithPath",
+            "GagaGetStringArrayByIndex",
+            "GagaGetStringArraySize",
+            "GagaGetStringListSize",
+            "GagaPythonScript",
+            "GagaSaveImageToPath",
+            "GagaSaveImageWithInfo",
+            "GagaSaveImagesToGif",
+            "GagaSplitStringToList",
+            "GagaStringListToArray",
+            "GagaTest"
+        ],
+        {
+            "title_aux": "ComfyUI_gaga_utils"
+        }
+    ],
+    "https://github.com/gajjar4/ComfyUI-Qwen-Image-i2L": [
+        [
+            "QwenI2L_Apply",
+            "QwenI2L_PipelineLoader",
+            "QwenI2L_Save"
+        ],
+        {
+            "title_aux": "ComfyUI-Qwen-Image-i2L [UNSAFE]"
+        }
+    ],
+    "https://github.com/galoreware/ComfyUI-GaloreNodes": [
+        [
+            "GNI_HEX_TO_COLOR",
+            "GNI_RGB_TO_COLOR",
+            "GN_COLOR_TO_INT",
+            "GN_IO_GET_FILENAME",
+            "GN_MASK_TO_IMAGE",
+            "GN_SNAP_RESIZE"
+        ],
+        {
+            "title_aux": "ComfyUI-GaloreNodes [WIP]"
+        }
+    ],
+    "https://github.com/gameltb/ComfyUI_stable_fast": [
+        [
+            "ApplyStableFastUnet",
+            "ApplyTensorRTControlNet",
+            "ApplyTensorRTUnet",
+            "ApplyTensorRTVaeDecoder"
+        ],
+        {
+            "title_aux": "ComfyUI_stable_fast"
+        }
+    ],
+    "https://github.com/gamtruliar/ComfyUI-N_SwapInput": [
+        [
+            "N_SwapInput"
+        ],
+        {
+            "title_aux": "ComfyUI-N_SwapInput [UNSAFE]"
+        }
+    ],
+    "https://github.com/gaonprime-labs/ComfyUI_Nordy": [
+        [
+            "DetailInjectAfterUpscale",
+            "LaplacianVarianceScore",
+            "MemoryTest",
+            "SaveImageS3PresignedUrlNordy",
+            "TextureDensityMetrics",
+            "TileMergeByMeta",
+            "TileMergeByMetaWOMaskBlending",
+            "TileSplitByFactor"
+        ],
+        {
+            "title_aux": "ComfyUI_Nordy"
+        }
+    ],
+    "https://github.com/gaonprime-labs/nordy-custom-node-ComfyUI-NormalOverlapBandBlend": [
+        [
+            "NormalOverlapBandBlend"
+        ],
+        {
+            "title_aux": "nordy-custom-node-ComfyUI-NormalOverlapBandBlend"
+        }
+    ],
+    "https://github.com/gaowei-space/ComfyUI-Doubao-LLM": [
+        [
+            "DoubaoAPI",
+            "DoubaoConfig",
+            "DoubaoTextChat",
+            "DoubaoVisionChat"
+        ],
+        {
+            "title_aux": "ComfyUI Doubao LLM [WIP]"
+        }
+    ],
+    "https://github.com/garciaone/g_one_toolkit": [
+        [
+            "g-one Face Tracking Features",
+            "g-one Face Tracking Masks"
+        ],
+        {
+            "title_aux": "g_one_toolkit"
+        }
+    ],
+    "https://github.com/gasdyueer/comfyui-pctextencode-region-composer": [
+        [
+            "PCTextEncode_RegionComposer"
+        ],
+        {
+            "title_aux": "comfyui-pctextencode-region-composer [WIP]"
+        }
+    ],
+    "https://github.com/gazingstars123/ComfyUI-CFGParallel": [
+        [
+            "CfgParallel",
+            "VerboseDebug"
+        ],
+        {
+            "title_aux": "ComfyUI-CFGParallel"
+        }
+    ],
+    "https://github.com/gilons/ComfyUI-GoogleDrive-Downloader": [
+        [
+            "custom_nodes"
+        ],
+        {
+            "title_aux": "ComfyUI-GoogleDrive-Downloader [UNSAFE]"
+        }
+    ],
+    "https://github.com/gioferreira/ComfyUI-Molde-Utils": [
+        [
+            "BezierMapping"
+        ],
+        {
+            "title_aux": "ComfyUI-Molde-Utils"
+        }
+    ],
+    "https://github.com/githubYiheng/comfyui_median_filter": [
+        [
+            "ImageMedianFilter"
+        ],
+        {
+            "title_aux": "comfyui_median_filter"
+        }
+    ],
+    "https://github.com/gmammolo/comfyui-gmammolo": [
+        [
+            "CheckImageGen",
+            "FilterConditioningPrompt",
+            "FilterTextPrompt",
+            "SimpleTextbox"
+        ],
+        {
+            "title_aux": "comfyui-gmammolo"
+        }
+    ],
+    "https://github.com/gnt2001/ComfyUI-GntsNodes": [
+        [
+            "RandomStringGenerator",
+            "ResizeImageByTotalPixels"
+        ],
+        {
+            "title_aux": "ComfyUI-GntsNodes"
+        }
+    ],
+    "https://github.com/go-package-lab/ComfyUI-Tools-Video-Combine": [
+        [
+            "Tools:CopyFile",
+            "Tools:Image2video",
+            "Tools:LoadAudioUrl",
+            "Tools:PreviewVideo",
+            "Tools:SetString",
+            "Tools:SetValue",
+            "Tools:VideoWatermark"
+        ],
+        {
+            "title_aux": "ComfyUI-Tools-Video-Combine [WIP]"
+        }
+    ],
+    "https://github.com/godric8/ComfyUI_Step1X-Edit": [
+        [
+            "Step1XEdit"
+        ],
+        {
+            "title_aux": "ComfyUI_Step1X-Edit [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/gold24park/loki-comfyui-node": [
+        [
+            "Base64ToImage",
+            "DominantColor",
+            "ImageLuminance",
+            "ImageToBase64",
+            "OverlayText"
+        ],
+        {
+            "title_aux": "loki-comfyui-node"
+        }
+    ],
+    "https://github.com/gordon123/ComfyUI_DreamBoard": [
+        [
+            "PromptExtraNode",
+            "StoryboardNode"
+        ],
+        {
+            "title_aux": "ComfyUI_DreamBoard [WIP]"
+        }
+    ],
+    "https://github.com/gordon123/ComfyUI_srt2speech": [
+        [
+            "GetSubtitleByIndex",
+            "MergeAllWave",
+            "SaveWavNode"
+        ],
+        {
+            "title_aux": "ComfyUI_srt2speech [WIP]"
+        }
+    ],
+    "https://github.com/gorillaframeai/GF_pixtral_node": [
+        [
+            "GF_MistralPixtralNode"
+        ],
+        {
+            "title_aux": "GF_pixtral_node [WIP]"
+        }
+    ],
+    "https://github.com/grimli333/ComfyUI_Grim": [
+        [
+            "GenerateFileName",
+            "TwoStringsFormat"
+        ],
+        {
+            "title_aux": "ComfyUI_Grim"
+        }
+    ],
+    "https://github.com/grinlau18/ComfyUI_XISER_Nodes": [
+        [
+            "BaseFromListGetOneV3",
+            "XISER_Canvas",
+            "XIS_BooleanSwitch",
+            "XIS_CanvasConfig",
+            "XIS_CanvasMaskProcessor",
+            "XIS_CompositorProcessor",
+            "XIS_CoordinatePath",
+            "XIS_CropImage",
+            "XIS_CurveEditor",
+            "XIS_DynamicImageInputs",
+            "XIS_DynamicKSampler",
+            "XIS_DynamicPackImages",
+            "XIS_FloatSwitch",
+            "XIS_Float_Slider",
+            "XIS_INT_Slider",
+            "XIS_IPAStyleSettings",
+            "XIS_IfDataIsNone",
+            "XIS_ImageAdjustAndBlend",
+            "XIS_ImageManager",
+            "XIS_ImageMaskMirror",
+            "XIS_ImagePreview",
+            "XIS_ImagePuzzle",
+            "XIS_ImageSwitch",
+            "XIS_IntSwitch",
+            "XIS_InvertMask",
+            "XIS_IsThereAnyData",
+            "XIS_KSamplerSettingsNode",
+            "XIS_KSamplerSettingsUnpackNode",
+            "XIS_LLMOrchestrator",
+            "XIS_Label",
+            "XIS_LatentBlendNode",
+            "XIS_LoadImage",
+            "XIS_MaskBatchProcessor",
+            "XIS_MaskCompositeOperation",
+            "XIS_MaskSwitch",
+            "XIS_MergePackImages",
+            "XIS_MultiPointGradient",
+            "XIS_MultiPromptSwitch",
+            "XIS_MultipleAnglesPrompt",
+            "XIS_PSDLayerExtractor",
+            "XIS_PackImages",
+            "XIS_PromptProcessor",
+            "XIS_PromptsWithSwitches",
+            "XIS_QwenVLInference",
+            "XIS_ReorderImageMaskGroups",
+            "XIS_ResizeImageOrMask",
+            "XIS_ResizeToDivisible",
+            "XIS_ResolutionSelector",
+            "XIS_SetColor",
+            "XIS_ShapeAndText",
+            "XIS_ShapeData",
+            "XIS_StringListMerger",
+            "XIS_StringSwitch",
+            "XIS_UnpackImages",
+            "XIS_VGMOrchestrator"
+        ],
+        {
+            "title_aux": "Xiser_Nodes [WIP]"
+        }
+    ],
+    "https://github.com/grokuku/ComfyUI-Holaf": [
+        [
+            "HolafAutoSelectX2",
+            "HolafBundleCreator",
+            "HolafBundleExtractor",
+            "HolafBypasser",
+            "HolafGroupBypasser",
+            "HolafImageAdjustment",
+            "HolafImageBatchSlice",
+            "HolafImageComparer",
+            "HolafInstagramResize",
+            "HolafKSampler",
+            "HolafLoadImageVideo",
+            "HolafLutGenerator",
+            "HolafLutSaver",
+            "HolafMaskToBoolean",
+            "HolafNucleusImage",
+            "HolafOverlayNode",
+            "HolafRemote",
+            "HolafRemoteSelector",
+            "HolafResolutionPreset",
+            "HolafSaveMedia",
+            "HolafTextBox",
+            "HolafTiledKSampler",
+            "HolafToText",
+            "HolafVideoPreview",
+            "UpscaleImageHolaf"
+        ],
+        {
+            "title_aux": "Holaf Custom Nodes for ComfyUI"
+        }
+    ],
+    "https://github.com/grokuku/ComfyUI-Holaf-Utilities": [
+        [
+            "HolafRemoteComparer"
+        ],
+        {
+            "title_aux": "ComfyUI-Holaf-Terminal [UNSAFE]"
+        }
+    ],
+    "https://github.com/gulajawalegit/ComfyUI-Telegram-Sender": [
+        [
+            "TelegramSendImage",
+            "TelegramSendVideo"
+        ],
+        {
+            "title_aux": "ComfyUI-Telegram-Sender [UNSAFE]"
+        }
+    ],
+    "https://github.com/gzsiang/ComfyUI-VolcEngine": [
+        [
+            "VolcEngineImageGeneration",
+            "VolcEngineImageToVideo",
+            "VolcEngineTextToVideo"
+        ],
+        {
+            "title_aux": "[LOCALE ISSUE] ComfyUI-VolcEngine"
+        }
+    ],
+    "https://github.com/haodman/ComfyUI_Rain": [
+        [
+            "Rain_ImageSize",
+            "Rain_IntToFloat",
+            "Rain_Math",
+            "Rain_ValueSwitch"
+        ],
+        {
+            "title_aux": "ComfyUI_Rain"
+        }
+    ],
+    "https://github.com/haofanwang/ComfyUI-InstantStyle": [
+        [
+            "BaseModelLoader",
+            "InstantStyleGenerationNode",
+            "InstantStyleLoader",
+            "PromptLoader"
+        ],
+        {
+            "title_aux": "ComfyUI-InstantStyle"
+        }
+    ],
+    "https://github.com/haomole/Comfyui-SadTalker": [
+        [
+            "LoadRefVideo",
+            "SadTalker",
+            "ShowAudio",
+            "ShowText",
+            "ShowVideo"
+        ],
+        {
+            "title_aux": "Comfyui-SadTalker"
+        }
+    ],
+    "https://github.com/harishcmgit/comfyui_sp": [
+        [
+            "SeamlessPatternNode"
+        ],
+        {
+            "title_aux": "comfyui_sp [WIP]"
+        }
+    ],
+    "https://github.com/hashms0a/ComfyUI-Qwen-Multi-Angle-Camera-Nodes": [
+        [
+            "QwenMultiAngleBatchSelector",
+            "QwenMultiAngleCameraAdvanced",
+            "QwenMultiAngleCameraBasic",
+            "QwenMultiAngleCameraDistanceTransition",
+            "QwenMultiAngleCameraElevationSweep",
+            "QwenMultiAngleCameraFromIndex",
+            "QwenMultiAngleCameraInfo",
+            "QwenMultiAngleCameraOrbit",
+            "QwenMultiAngleCameraPath",
+            "QwenMultiAngleCameraRandom",
+            "QwenMultiAnglePromptCombiner"
+        ],
+        {
+            "title_aux": "ComfyUI-Qwen-Multi-Angle-Camera-Nodes [WIP]"
+        }
+    ],
+    "https://github.com/hay86/ComfyUI_AceNodes": [
+        [
+            "ACE_AnyInputSwitchBool",
+            "ACE_AnyInputToAny",
+            "ACE_AudioCrop",
+            "ACE_AudioLoad",
+            "ACE_AudioPlay",
+            "ACE_AudioSave",
+            "ACE_Expression_Eval",
+            "ACE_Float",
+            "ACE_ImageColorFix",
+            "ACE_ImageConstrain",
+            "ACE_ImageFaceCrop",
+            "ACE_ImageGetSize",
+            "ACE_ImageLoadFromCloud",
+            "ACE_ImageMakeSlideshow",
+            "ACE_ImagePixelate",
+            "ACE_ImageQA",
+            "ACE_ImageRemoveBackground",
+            "ACE_ImageSaveToCloud",
+            "ACE_Integer",
+            "ACE_MaskBlur",
+            "ACE_OpenAI_GPT_Chat",
+            "ACE_OpenAI_GPT_IMAGE",
+            "ACE_OpenAI_GPT_TTS",
+            "ACE_Seed",
+            "ACE_Text",
+            "ACE_TextConcatenate",
+            "ACE_TextGoogleTranslate",
+            "ACE_TextInputSwitch2Way",
+            "ACE_TextInputSwitch4Way",
+            "ACE_TextInputSwitch8Way",
+            "ACE_TextList",
+            "ACE_TextLoad",
+            "ACE_TextPreview",
+            "ACE_TextSave",
+            "ACE_TextSelector",
+            "ACE_TextToResolution",
+            "ACE_TextTranslate",
+            "ACE_VideoConcat",
+            "ACE_VideoLoad",
+            "ACE_VideoPreview"
+        ],
+        {
+            "title_aux": "ComfyUI AceNodes [UNSAFE]"
+        }
+    ],
+    "https://github.com/hben35096/ComfyUI-ToolBox": [
+        [
+            "AutoDLDownload",
+            "CreatePaths",
+            "FolderDeleter",
+            "FolderViewe",
+            "PathOutput"
+        ],
+        {
+            "title_aux": "hben35096/ComfyUI-ToolBox [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/hdfhssg/ComfyUI_pxtool": [
+        [
+            "ArtistLoader",
+            "CivitaiHelper",
+            "DanbooruCharacterTag",
+            "E621CharacterTag",
+            "NegativeTag",
+            "PX_Seed",
+            "QualityTag",
+            "RandomArtists",
+            "RandomArtistsAdvanced",
+            "RandomTag"
+        ],
+        {
+            "title_aux": "ComfyUI_pxtool [WIP]"
+        }
+    ],
+    "https://github.com/hdfhssg/comfyui_EvoSearch": [
+        [
+            "EvoSearch_FLUX",
+            "EvoSearch_SD21",
+            "EvoSearch_WAN",
+            "EvolutionScheduleGenerator",
+            "GuidanceRewardsGenerator"
+        ],
+        {
+            "title_aux": "comfyui_EvoSearch [WIP]"
+        }
+    ],
+    "https://github.com/henjicc/ComfyUI-CC-ImageLoader": [
+        [
+            "LoadImage"
+        ],
+        {
+            "title_aux": "ComfyUI-CC-ImageLoader [UNSAFE]"
+        }
+    ],
+    "https://github.com/heyburns/ComfyUI-Logic-Redux": [
+        [
+            "Bool-\ud83d\udd2c",
+            "Compare-\ud83d\udd2c",
+            "DebugPrint-\ud83d\udd2c",
+            "Float-\ud83d\udd2c",
+            "If ANY execute A else B",
+            "If ANY execute A else B-\ud83d\udd2c",
+            "If ANY return A else B-\ud83d\udd2c",
+            "Int-\ud83d\udd2c",
+            "String-\ud83d\udd2c"
+        ],
+        {
+            "title_aux": "ComfyUI-Logic-Redux [WIP]"
+        }
+    ],
+    "https://github.com/hgh086/Comfyui-HghImage": [
+        [
+            "GuanghuanImageComparer"
+        ],
+        {
+            "title_aux": "Comfyui-HghImage"
+        }
+    ],
+    "https://github.com/hhelloe/ComfyuiQuantizerToFp8": [
+        [
+            "FP8OnlineQuantizer",
+            "FP8QuantizeFormat",
+            "ModelToStateDict",
+            "SaveAsSafeTensor"
+        ],
+        {
+            "title_aux": "ComfyuiQuantizerToFp8 [WIP]"
+        }
+    ],
+    "https://github.com/hhelloe/comfyuiWeightOnlyQuant": [
+        [
+            "WeightOnlyQuantize"
+        ],
+        {
+            "title_aux": "comfyuiWeightOnlyQuant [WIP]"
+        }
+    ],
+    "https://github.com/hiusdev/ComfyUI_Lah_Toffee": [
+        [
+            "LoadVideoRandom"
+        ],
+        {
+            "title_aux": "ComfyUI_Lah_Toffee"
+        }
+    ],
+    "https://github.com/hndrr/Comfyui-SceneDetect": [
+        [
+            "PySceneDetectToImages"
+        ],
+        {
+            "title_aux": "Comfyui-SceneDetect [WIP]"
+        }
+    ],
+    "https://github.com/hnmr293/ComfyUI-SamOne": [
+        [
+            "Latent",
+            "SamplerOne"
+        ],
+        {
+            "title_aux": "ComfyUI-SamOne - one-step sampling"
+        }
+    ],
+    "https://github.com/horidream/ComfyUI-Horidream": [
+        [
+            "PassThroughWithSound"
+        ],
+        {
+            "title_aux": "ComfyUI-Horidream"
+        }
+    ],
+    "https://github.com/hotpizzatactics/ComfyUI-WaterMark-Detector": [
+        [
+            "AdaptiveThresholding",
+            "AdvancedWatermarkEnhancement",
+            "AdvancedWaveletWatermarkEnhancement",
+            "CLAHEEnhancement",
+            "CombineEnhancements",
+            "ComprehensiveImageEnhancement",
+            "DenoisingFilter",
+            "EdgeDetection",
+            "FlexibleCombineEnhancements",
+            "HighPassFilter",
+            "ImprovedGrayColorEnhancement",
+            "MorphologicalOperations",
+            "TextureEnhancement",
+            "WatermarkEnhancement"
+        ],
+        {
+            "title_aux": "ComfyUI-WaterMark-Detector"
+        }
+    ],
+    "https://github.com/hotpot-killer/ComfyUI_AlexNodes": [
+        [
+            "InstructPG"
+        ],
+        {
+            "title_aux": "ComfyUI_AlexNodes"
+        }
+    ],
+    "https://github.com/houdinii/comfy-magick": [
+        [
+            "AdaptiveBlur",
+            "AdaptiveSharpen",
+            "AddNoise",
+            "BlueShift",
+            "Blur",
+            "Charcoal",
+            "Colorize",
+            "CropByAspectRatio",
+            "Despeckle",
+            "Edge",
+            "Emboss",
+            "FX",
+            "GaussianBlur",
+            "Implode",
+            "Kuwahara",
+            "MotionBlur",
+            "RotationalBlur",
+            "SelectiveBlur",
+            "Sepia",
+            "Shade",
+            "Sharpen",
+            "Sketch",
+            "Solarize",
+            "Spread",
+            "Stereogram",
+            "Swirl",
+            "Tint",
+            "UnsharpMask",
+            "Vignette",
+            "WaveletDenoise"
+        ],
+        {
+            "title_aux": "comfy-magick [WIP]"
+        }
+    ],
+    "https://github.com/hshheyman478-svg/comfyui-sliding-rife": [
+        [
+            "SlidingWindowRIFE"
+        ],
+        {
+            "title_aux": "comfyui-sliding-rife"
+        }
+    ],
+    "https://github.com/huhu-tiger/ComfyUI-RemoteDownload": [
+        [
+            "LoadImageFromRemote"
+        ],
+        {
+            "title_aux": "ComfyUI-RemoteDownload"
+        }
+    ],
+    "https://github.com/huhu-tiger/ComfyUI-RemoteResource": [
+        [
+            "LoadImageFromRemote"
+        ],
+        {
+            "title_aux": "ComfyUI-RemoteResource"
+        }
+    ],
+    "https://github.com/huihui-v/ComfyUI-WanInfiniteTalkToVideo-offset": [
+        [
+            "WanInfiniteTalkToVideo_offset"
+        ],
+        {
+            "title_aux": "ComfyUI-WanInfiniteTalkToVideo-offset"
+        }
+    ],
+    "https://github.com/huizhang0110/ComfyUI_Easy_Nodes_hui": [
+        [
+            "EasyBgRemover",
+            "EasyBgRemover_ModelLoader",
+            "EasyControlNetApply",
+            "EasyControlNetLoader",
+            "EasyEmptyLatentImage",
+            "EasyLatentToCondition",
+            "EasyLoadImage"
+        ],
+        {
+            "title_aux": "ComfyUI_Easy_Nodes_hui"
+        }
+    ],
+    "https://github.com/hujuying/ComfyUI-QwenVL3-image-Plus": [
+        [
+            "QwenVL3ImagePlus",
+            "QwenVLUnloadModelPlus"
+        ],
+        {
+            "title_aux": "ComfyUI-QwenVL3-image-Plus [WIP]"
+        }
+    ],
+    "https://github.com/hujuying/comfyui_gemini_banana_api": [
+        [
+            "GeminiImageEditNode",
+            "GeminiImageEditV2Node"
+        ],
+        {
+            "title_aux": "comfyui_gemini_banana_api [WIP]"
+        }
+    ],
+    "https://github.com/hulipanpan/Comfyui_tuteng": [
+        [
+            "TutengChatGPTApi",
+            "TutengGeminiAPI",
+            "TutengJimengApi",
+            "TutengJimengVideoApi",
+            "TutengSeededit",
+            "Tuteng_Flux_Kontext",
+            "Tuteng_Flux_Kontext_Edit",
+            "Tuteng_Flux_Kontext_bfl",
+            "Tuteng_Google_Veo3",
+            "Tuteng_Mj",
+            "Tuteng_Mju",
+            "Tuteng_Mjv",
+            "Tuteng_gpt_image_1",
+            "Tuteng_gpt_image_1_edit",
+            "Tuteng_kling_image2video",
+            "Tuteng_kling_text2video",
+            "Tuteng_kling_videoPreview",
+            "Tuteng_lip_sync",
+            "Tuteng_mjstyle",
+            "Tuteng_upload",
+            "Tuteng_video_extend"
+        ],
+        {
+            "title_aux": "Comfyui_tuteng [WIP]"
+        }
+    ],
+    "https://github.com/hunterssl/ComfyUI_SSLNodes": [
+        [
+            "SSLGetJsonKeysCount",
+            "SSLLoadCheckpointByName",
+            "SSLLoadJson",
+            "SSLRandomNumInLoop",
+            "SSLSaveImageOutside"
+        ],
+        {
+            "title_aux": "ComfyUI_SSLNodes"
+        }
+    ],
+    "https://github.com/hunzmusic/ComfyUI-Hunyuan3DTools": [
+        [
+            "Hy3DTools_BackProjectInpaint",
+            "Hy3DTools_RenderSpecificView"
+        ],
+        {
+            "title_aux": "ComfyUI-Hunyuan3DTools [WIP]"
+        }
+    ],
+    "https://github.com/hunzmusic/Comfyui-CraftsMan3DWrapper": [
+        [
+            "CraftsManDoraVAEGenerator",
+            "DecodeCraftsManLatents",
+            "LoadCraftsManPipeline",
+            "PreprocessImageCraftsMan",
+            "SampleCraftsManLatents",
+            "SaveCraftsManMesh"
+        ],
+        {
+            "title_aux": "Comfyui-CraftsMan3DWrapper [WIP]"
+        }
+    ],
+    "https://github.com/hunzmusic/comfyui-hnznodes": [
+        [
+            "CombineChannelsGrayscale",
+            "ImageBatchReorder",
+            "MaleCharacterPromptGenerator"
+        ],
+        {
+            "title_aux": "comfyui-hnznodes"
+        }
+    ],
+    "https://github.com/husw725/comfyui_alta_nodes": [
+        [
+            "Alta:API_LipSync",
+            "Alta:API_ProcessVideo",
+            "Alta:API_TTS",
+            "Alta:API_TranscribeAudio",
+            "Alta:API_Translate",
+            "Alta:Add(Math)",
+            "Alta:AddInt(Math)",
+            "Alta:BuildFilePath",
+            "Alta:ComboWrapper(Logic)",
+            "Alta:CompareFolders(File)",
+            "Alta:DeleteAudioFromMemory",
+            "Alta:DeleteFile(Util)",
+            "Alta:FileDownloader",
+            "Alta:GetFaceMask",
+            "Alta:GetFileFolder",
+            "Alta:GetFilenameNoExt",
+            "Alta:GetFilenameWithExt",
+            "Alta:GetImageAndPath",
+            "Alta:GetImageByIndex",
+            "Alta:GetStringByIndex",
+            "Alta:IfOnly(Logic)",
+            "Alta:Int2Str(Math)",
+            "Alta:JSONKeyExtractor(Util)",
+            "Alta:ListAllFiles",
+            "Alta:ListElement(Util)",
+            "Alta:ListFilesByExt",
+            "Alta:ListLength(Util)",
+            "Alta:LoadAudioByPath",
+            "Alta:LoadAudioFromMemory",
+            "Alta:LoadFilesFromFolder",
+            "Alta:LoadImage",
+            "Alta:LoadImageFromPath",
+            "Alta:LoadImageWithPath",
+            "Alta:LoadImagesPath",
+            "Alta:LoadVideo",
+            "Alta:LoadVideoPath",
+            "Alta:LoadVideosFromFolder",
+            "Alta:MergeNodes",
+            "Alta:MoveFile(File)",
+            "Alta:MultiRoute",
+            "Alta:OpenAIImages2Image",
+            "Alta:ReadImageFromFile",
+            "Alta:ReadStringFromFile",
+            "Alta:RegexMatch(Util)",
+            "Alta:SaveAudioToPath",
+            "Alta:SaveImage",
+            "Alta:SaveImagePlus",
+            "Alta:SaveStringToFile",
+            "Alta:SpeakerDiarization",
+            "Alta:StoreAudioInMemory",
+            "Alta:StrToNum(Math)",
+            "Alta:StringPlus(Util)",
+            "Alta:Sub(Math)",
+            "Alta:SubInt(Math)",
+            "Alta:SyncLipsyncNode(path)",
+            "Alta:SyncLipsyncNode(url)",
+            "Alta:VideoCombine",
+            "Alta:YOLOFaceModelCore",
+            "Alta:writeStringToFile",
+            "Alta:\u5373\u68a6API\u56fe\u751f\u89c6\u9891",
+            "Alta:\u5373\u68a6API\u56fe\u751f\u89c6\u9891\u7ed3\u679c\u67e5\u8be2",
+            "Alta:\u5373\u68a6API\u6587\u751f\u56fe",
+            "Alta:\u5373\u68a6Web\u56fe\u751f\u89c6\u9891"
+        ],
+        {
+            "title_aux": "comfyui_alta_nodes"
+        }
+    ],
+    "https://github.com/hy134300/comfyui-hb-node": [
+        [
+            "generate story",
+            "hy save image",
+            "latent to list",
+            "movie batch",
+            "movie generate",
+            "sound voice",
+            "text concat"
+        ],
+        {
+            "title_aux": "comfyui-hb-node"
+        }
+    ],
+    "https://github.com/hy134300/comfyui-hydit": [
+        [
+            "DiffusersCLIPLoader",
+            "DiffusersCheckpointLoader",
+            "DiffusersClipTextEncode",
+            "DiffusersControlNetLoader",
+            "DiffusersLoraLoader",
+            "DiffusersModelMakeup",
+            "DiffusersPipelineLoader",
+            "DiffusersSampler",
+            "DiffusersSchedulerLoader",
+            "DiffusersVAELoader"
+        ],
+        {
+            "title_aux": "comfyui-hydit"
+        }
+    ],
+    "https://github.com/hylarucoder/comfyui-copilot": [
+        [
+            "EagleImageNode",
+            "SDXLPromptStyler",
+            "SDXLPromptStylerAdvanced",
+            "SDXLResolutionPresets"
+        ],
+        {
+            "title_aux": "comfyui-copilot"
+        }
+    ],
+    "https://github.com/idoru/ComfyUI-SKCFI-NetworkFileIO": [
+        [
+            "FilestashUploadNode",
+            "HttpUploadNode",
+            "MultipartFileHTTPUploadNode"
+        ],
+        {
+            "title_aux": "Filestash Upload Node [UNSAFE]"
+        }
+    ],
+    "https://github.com/if-ai/ComfyUI-IF_Zonos": [
+        [
+            "IF_ZonosTTS"
+        ],
+        {
+            "title_aux": "ComfyUI-IF_Zonos [WIP]"
+        }
+    ],
+    "https://github.com/ifilipis/ComfyUI-Auto-Prompt-Engineer": [
+        [
+            "DirectorGemini",
+            "ImageRouterSink",
+            "LatestImageSource"
+        ],
+        {
+            "title_aux": "ComfyUI-Auto-Prompt-Engineer"
+        }
+    ],
+    "https://github.com/ih455/ComfyUI-Gemini3-Resolution-Snapper": [
+        [
+            "Gemini3ResolutionSnapper"
+        ],
+        {
+            "title_aux": "ComfyUI-Gemini3-Resolution-Snapper [WIP]"
+        }
+    ],
+    "https://github.com/ilovejohnwhite/Tracer": [
+        [
+            "BillyGoatNode",
+            "EcstaticNode",
+            "HintImageEnchance",
+            "Image Load TTK",
+            "ImageGenResolutionFromImage",
+            "ImageGenResolutionFromLatent",
+            "KillMeNode",
+            "LinkMasterNode",
+            "OkayBuddyNode",
+            "OutlineRealNode",
+            "OutlineStandardNode",
+            "PixelPerfectResolution",
+            "SuckerPunch",
+            "UWU_Preprocessor",
+            "VooDooNode"
+        ],
+        {
+            "title_aux": "Kolors Awesome Prompts [WIP]"
+        }
+    ],
+    "https://github.com/iloveuav/ComfyUI-AudioTools": [
+        [
+            "AudioToBase64Websocket",
+            "LoadAudioBySource",
+            "SaveAudioWebsocket",
+            "UploadAudioOrBase64ToURL"
+        ],
+        {
+            "title_aux": "ComfyUI-AudioTools [UNSAFE/NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/ilslli/Comfyui-IV2Z": [
+        [
+            "IV2Z_VideoCombine"
+        ],
+        {
+            "title_aux": "Comfyui-IV2Z"
+        }
+    ],
+    "https://github.com/immersiveexperience/ie-comfyui-color-nodes": [
+        [
+            "Average Color",
+            "Complementary Color",
+            "Hex Color to Image",
+            "Hex to Color Name",
+            "Random String"
+        ],
+        {
+            "title_aux": "ie-comfyui-color-nodes"
+        }
+    ],
+    "https://github.com/io-club/ComfyUI-LuminaNext": [
+        [
+            "GemmaClipLoader"
+        ],
+        {
+            "title_aux": "ComfyUI-LuminaNext [WIP]"
+        }
+    ],
+    "https://github.com/isurulkh/ComfyUI-Lovis-Node": [
+        [
+            "AudioDurationNode",
+            "LineCountNode",
+            "TextToSingleLineNode"
+        ],
+        {
+            "title_aux": "ComfyUI-Lovis-Node [WIP]"
+        }
+    ],
+    "https://github.com/ivanprimestars/ComfyUI_Brilliance_iOSPort": [
+        [
+            "BrillianceIOSPort"
+        ],
+        {
+            "title_aux": "ComfyUI_Brilliance_iOSPort [WIP]"
+        }
+    ],
+    "https://github.com/ivanprimestars/comfyui_iphone_look": [
+        [
+            "iPhoneLookProcessor"
+        ],
+        {
+            "title_aux": "comfyui_iphone_look [WIP]"
+        }
+    ],
+    "https://github.com/ivanravinovitch/AutoCropByColor": [
+        [
+            "AutoCropByColor",
+            "ScaleImageToMegapixels"
+        ],
+        {
+            "title_aux": "AutoCropByColor"
+        }
+    ],
+    "https://github.com/j-pyxal/ComfyUI-Lattice-Manim": [
+        [
+            "ManimAudioCaptionNode",
+            "ManimDataVisualizationNode",
+            "ManimScriptNode",
+            "ManimTimelineSceneNode"
+        ],
+        {
+            "title_aux": "ComfyUI-Lattice-Manim [WIP]"
+        }
+    ],
+    "https://github.com/jKaarlehto/ComfyUI-GetWorkflowName": [
+        [
+            "GetWorkflowName"
+        ],
+        {
+            "title_aux": "ComfyUI-GetWorkflowName [WIP]"
+        }
+    ],
+    "https://github.com/jaskirat05/ComfyUI-MultiDepth": [
+        [
+            "DepthLMDepthNode",
+            "Metric3Dv2DepthNode",
+            "UniDepthV2DepthNode"
+        ],
+        {
+            "title_aux": "ComfyUI-MultiDepth [WIP]"
+        }
+    ],
+    "https://github.com/jax-explorer/ComfyUI-DreamO": [
+        [
+            "BgRmModelLoad",
+            "DreamOGenerate",
+            "DreamOLoadModel",
+            "DreamOLoadModelFromLocal",
+            "FaceModelLoad"
+        ],
+        {
+            "title_aux": "ComfyUI-DreamO"
+        }
+    ],
+    "https://github.com/jceme/Comfy_Extensions": [
+        [
+            "DimensionResize",
+            "FpsHelper",
+            "ImageAspectPresets",
+            "ImageAspectResize",
+            "ImageDim",
+            "ImageSizePresets",
+            "InpaintPrepare",
+            "InpaintRestore",
+            "NegativePromptCustomizer",
+            "PositivePromptCustomizer"
+        ],
+        {
+            "title_aux": "Comfy_Extensions [WIP]"
+        }
+    ],
+    "https://github.com/jchiotaka/ComfyUI-ClarityAI-Upscaler": [
+        [
+            "ClarityCreativeUpscaler",
+            "ClarityCrystalUpscaler",
+            "ClarityFluxUpscaler"
+        ],
+        {
+            "title_aux": "ComfyUI-ClarityAI-Upscaler"
+        }
+    ],
+    "https://github.com/jcomeme/ComfyUI-AsunaroTools": [
+        [
+            "AsunaroAnd",
+            "AsunaroAutomaticSexPrompter",
+            "AsunaroBatchImageLoader",
+            "AsunaroIfBiggerThanZero",
+            "AsunaroIfContain",
+            "AsunaroIfSame",
+            "AsunaroImageLoader",
+            "AsunaroIntToStr",
+            "AsunaroOr",
+            "AsunaroPromptStripper",
+            "AsunaroRandomDice",
+            "AsunaroResolutions",
+            "AsunaroSave",
+            "AsunaroTextConcatenator",
+            "AsunaroWildCard"
+        ],
+        {
+            "title_aux": "AsunaroTools"
+        }
+    ],
+    "https://github.com/jeremy-strang/ComfyUI-VisualModelMerge": [
+        [
+            "VisualModelMergeSDXL"
+        ],
+        {
+            "title_aux": "Visual Model Merge [WIP]"
+        }
+    ],
+    "https://github.com/jerryname2022/ComfyUI-MegaTTS3": [
+        [
+            "MegaTTS3ModelLoader",
+            "MegaTTS3ZeroShot",
+            "TextEditor"
+        ],
+        {
+            "title_aux": "MegaTTS 3 [WIP]"
+        }
+    ],
+    "https://github.com/jerryname2022/ComfyUI-Real-ESRGAN": [
+        [
+            "GFPGANImageGenerator",
+            "GFPGANModelLoader",
+            "ImageMergeGenerator",
+            "InpaintingLamaImageGenerator",
+            "InpaintingLamaModelLoader",
+            "RealESRGANImageGenerator",
+            "RealESRGANModelLoader"
+        ],
+        {
+            "title_aux": "ComfyUI-Real-ESRGAN [WIP]"
+        }
+    ],
+    "https://github.com/jgbrblmd/ComfyUI-ComfyFluxSize": [
+        [
+            "ComfyFluxSize"
+        ],
+        {
+            "title_aux": "ComfyUI-ComfyFluxSize [WIP]"
+        }
+    ],
+    "https://github.com/jgbyte/ComfyUI-RandomCube": [
+        [
+            "RandomCubeGrid"
+        ],
+        {
+            "title_aux": "ComfyUI-RandomCube [WIP]"
+        }
+    ],
+    "https://github.com/jiafuzeng/comfyui-fishSpeech": [
+        [
+            "FishSpeechAudioDurationNode",
+            "FishSpeechAudioPreview",
+            "FishSpeechLoader",
+            "FishSpeechTTS"
+        ],
+        {
+            "title_aux": "comfyui-fishSpeech"
+        }
+    ],
+    "https://github.com/jimmm-ai/TimeUi-a-ComfyUi-Timeline-Node": [
+        [
+            "jimmm.ai.TimelineUI"
+        ],
+        {
+            "title_aux": "TimeUi a ComfyUI Timeline Node System [WIP]"
+        }
+    ],
+    "https://github.com/jimstudt/ComfyUI-Jims-Nodes": [
+        [
+            "ChooseFromStringList",
+            "Cubby",
+            "DefineWord",
+            "DictFromJSON",
+            "DictionaryToJSON",
+            "ImageToSolidBackground",
+            "JSONTODictionary",
+            "JSONToDictionary",
+            "LiftFromBackground",
+            "LoadImageAndInfoFromPath",
+            "LookupWord",
+            "ReplaceWords",
+            "TextToStringList",
+            "ZoomFocus"
+        ],
+        {
+            "title_aux": "Jim's ComfyUI Nodes [WIP]"
+        }
+    ],
+    "https://github.com/jinchanz/ComfyUI-AliCloud-Bailian": [
+        [
+            "BailianAPI",
+            "BailianAPIPoll",
+            "BailianAPISubmit",
+            "MaletteJSONExtractor",
+            "MaletteJSONModifier",
+            "MaletteVirtualTryOn"
+        ],
+        {
+            "title_aux": "ComfyUI-AliCloud-Bailian [WIP]"
+        }
+    ],
+    "https://github.com/jinchanz/ComfyUI-Midjourney": [
+        [
+            "MidjourneyAPI",
+            "MidjourneyAPIPoll",
+            "MidjourneyAPISubmit",
+            "MidjourneyJSONExtractor"
+        ],
+        {
+            "title_aux": "ComfyUI-Midjourney"
+        }
+    ],
+    "https://github.com/jinxishe/ComfyUI-AudioX": [
+        [
+            "AudioXImagesToAudio",
+            "AudioXModelLoader",
+            "AudioXVideoToAudio"
+        ],
+        {
+            "title_aux": "ComfyUI-AudioX [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/jn-jairo/jn_node_suite_comfyui": [
+        [
+            "JN_AreaInfo",
+            "JN_AreaNormalize",
+            "JN_AreaWidthHeight",
+            "JN_AreaXY",
+            "JN_Blip",
+            "JN_BlipLoader",
+            "JN_BooleanOperation",
+            "JN_Condition",
+            "JN_CoolDown",
+            "JN_CoolDownOutput",
+            "JN_CropFace",
+            "JN_DatetimeFormat",
+            "JN_DatetimeInfo",
+            "JN_DatetimeNow",
+            "JN_Dump",
+            "JN_DumpOutput",
+            "JN_FaceRestoreModelLoader",
+            "JN_FaceRestoreWithModel",
+            "JN_FirstActive",
+            "JN_ImageAddMask",
+            "JN_ImageBatch",
+            "JN_ImageCenterArea",
+            "JN_ImageCrop",
+            "JN_ImageGrid",
+            "JN_ImageInfo",
+            "JN_ImageSharpness",
+            "JN_ImageSquare",
+            "JN_ImageUncrop",
+            "JN_KSampler",
+            "JN_KSamplerAdvancedParams",
+            "JN_KSamplerFaceRestoreParams",
+            "JN_KSamplerResizeInputParams",
+            "JN_KSamplerResizeMaskAreaParams",
+            "JN_KSamplerResizeOutputParams",
+            "JN_KSamplerSeamlessParams",
+            "JN_KSamplerTileParams",
+            "JN_LoadImageDirectory",
+            "JN_LogicOperation",
+            "JN_MaskInfo",
+            "JN_MathOperation",
+            "JN_MathOperationArray",
+            "JN_PrimitiveArrayInfo",
+            "JN_PrimitiveBatchToArray",
+            "JN_PrimitiveBoolean",
+            "JN_PrimitiveFloat",
+            "JN_PrimitiveInt",
+            "JN_PrimitivePrompt",
+            "JN_PrimitiveString",
+            "JN_PrimitiveStringMultiline",
+            "JN_PrimitiveStringToArray",
+            "JN_PrimitiveToArray",
+            "JN_PrimitiveToBoolean",
+            "JN_PrimitiveToFloat",
+            "JN_PrimitiveToInt",
+            "JN_PrimitiveToString",
+            "JN_RemoveBackground",
+            "JN_Seamless",
+            "JN_SeamlessBorder",
+            "JN_SeamlessBorderCrop",
+            "JN_SelectItem",
+            "JN_Sleep",
+            "JN_SleepOutput",
+            "JN_SliceOperation",
+            "JN_StopIf",
+            "JN_StopIfOutput",
+            "JN_TextConcatenation",
+            "JN_TextReplace",
+            "JN_TimedeltaFormat",
+            "JN_TimedeltaInfo",
+            "JN_VAEPatch"
+        ],
+        {
+            "title_aux": "jn_node_suite_comfyui [WIP]"
+        }
+    ],
+    "https://github.com/jonathan-bryant/ComfyUI-ImageStraightener": [
+        [
+            "AutoStraightenImage"
+        ],
+        {
+            "title_aux": "ComfyUI-ImageStraightener [WIP]"
+        }
+    ],
+    "https://github.com/jonstreeter/comfyui-archive-output": [
+        [
+            "ArchiveOutputWorkflow"
+        ],
+        {
+            "title_aux": "Archive Output [UNSAFE]"
+        }
+    ],
+    "https://github.com/jordancoult/ComfyUI_HelpfulNodes": [
+        [
+            "JCo_CropAroundKPS"
+        ],
+        {
+            "title_aux": "ComfyUI_HelpfulNodes"
+        }
+    ],
+    "https://github.com/jorin91/ComfyUI-JSG-Utils": [
+        [
+            "JSGBoolSwitch",
+            "JSGBoolToString",
+            "JSGCaptionBuilder",
+            "JSGCombineStrings",
+            "JSGCreateBlankMetadata",
+            "JSGDeleteFilePassAny",
+            "JSGDeleteFilePassImage",
+            "JSGDeleteFilePassString",
+            "JSGFindImagePathsRecursive",
+            "JSGFindImagesRecursiveList",
+            "JSGFormattedStringViewer",
+            "JSGImageSizeCalc",
+            "JSGIteratorInputs",
+            "JSGIteratorList",
+            "JSGLoadImageFromPath",
+            "JSGLoraStackLoader",
+            "JSGLoraStackLoaderAdvanced",
+            "JSGMatchStringList",
+            "JSGModelInfo",
+            "JSGNormalizeNumber",
+            "JSGObjectBuilder",
+            "JSGObjectUnpack",
+            "JSGParsePath",
+            "JSGRandomBool",
+            "JSGRandomColorHSVA",
+            "JSGRandomPromptBuilder",
+            "JSGRandomPromptBuilderV2",
+            "JSGRandomStringChoice",
+            "JSGRandomStringChoiceList",
+            "JSGRemoveMetadata",
+            "JSGSaveImage",
+            "JSGSetMetadataField",
+            "JSGSetMetadataFields",
+            "JSGSetStandardMetadataField",
+            "JSGValueStepper"
+        ],
+        {
+            "title_aux": "ComfyUI-JSG-Utils [UNSAFE]"
+        }
+    ],
+    "https://github.com/jpwgad/jpwgad-comfy": [
+        [
+            "AutoSelector",
+            "OutputPath",
+            "SizeInput"
+        ],
+        {
+            "title_aux": "jpwgad"
+        }
+    ],
+    "https://github.com/jsbct/ComfyUI-TextToBatchByDelimiter": [
+        [
+            "TextToBatchByDelimiter"
+        ],
+        {
+            "title_aux": "ComfyUI-TextToBatchByDelimiter"
+        }
+    ],
+    "https://github.com/jschoormans/Comfy-InterestingPixels": [
+        [
+            "Random Palette",
+            "Shareable Image Slider"
+        ],
+        {
+            "title_aux": "ComfyUI-TexturePacker [WIP]"
+        }
+    ],
+    "https://github.com/jtrue/ComfyUI-MaskTools": [
+        [
+            "ColorToMask",
+            "MaskColorRange"
+        ],
+        {
+            "description": "Pixel-selection tools (masks) for ComfyUI \u2014 modular.",
+            "nickname": "Mask",
+            "title": "MaskTools",
+            "title_aux": "MaskTools"
+        }
+    ],
+    "https://github.com/jtscmw01/ComfyUI-DiffBIR": [
+        [
+            "DiffBIR_sample",
+            "DiffBIR_sample_advanced",
+            "Simple_load",
+            "Stage1_load",
+            "Stage2_load"
+        ],
+        {
+            "title_aux": "ComfyUI-DiffBIR"
+        }
+    ],
+    "https://github.com/jtydhr88/ComfyUI-Unique3D": [
+        [
+            "Unique3DLoadPipeline",
+            "Unique3DRun - Geo Reconstruct",
+            "Unique3DRun - MVPrediction"
+        ],
+        {
+            "title_aux": "ComfyUI-Unique3D [WIP]"
+        }
+    ],
+    "https://github.com/jtydhr88/ComfyUI_frontend_vue_basic": [
+        [
+            "vue-basic"
+        ],
+        {
+            "title_aux": "ComfyUI Frontend Vue Basic [WIP]"
+        }
+    ],
+    "https://github.com/judian17/ComfyUI-MIDI-3D": [
+        [
+            "MIDI3DAllPassRenderer",
+            "MIDI3DInstanceCropper",
+            "MIDI3DLoader",
+            "MIDI3DSampler",
+            "MIDI3DTextureBaker"
+        ],
+        {
+            "title_aux": "ComfyUI-MIDI-3D [WIP]"
+        }
+    ],
+    "https://github.com/junhe421/comfyui_batch_image_processor": [
+        [
+            "BatchImageDifferenceToPromptFiles",
+            "custom_nodes"
+        ],
+        {
+            "title_aux": "comfyui_batch_image_processor [WIP]"
+        }
+    ],
+    "https://github.com/kadirnar/ComfyUI-Adapter": [
+        [
+            "GarmentSegLoader"
+        ],
+        {
+            "title_aux": "ComfyUI-Adapter [WIP]"
+        }
+    ],
+    "https://github.com/kandy/ComfyUI-KAndy": [
+        [
+            "KAndyTaggerModelLoader",
+            "KAndyWD14Tagger",
+            "KPromtGen",
+            "KandyLoad",
+            "KandySave",
+            "KandySimplePrompt"
+        ],
+        {
+            "title_aux": "ComfyUI-KAndy"
+        }
+    ],
+    "https://github.com/kanttouchthis/ComfyUI-SDNQ": [
+        [
+            "SDNQLoader"
+        ],
+        {
+            "title_aux": "ComfyUI-SDNQ [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/karthikg-09/ComfyUI-3ncrypt": [
+        [
+            "Enhanced Save Image",
+            "Markdown Editor"
+        ],
+        {
+            "title_aux": "ComfyUI-KG09 [WIP]"
+        }
+    ],
+    "https://github.com/kaski23/Kaskis_Comfy_Nodes": [
+        [
+            "AudioResampler",
+            "CollectKeyedVideosNode",
+            "CollectVideosNode",
+            "ConformAudio",
+            "ConformVideo",
+            "ImageDelay",
+            "IntToString",
+            "KlingVideoHandler",
+            "LoadImageWithFilename",
+            "LoadVideoWithFilename",
+            "PairSwitchNode",
+            "PreviewImageLimited",
+            "ResampleVideoNearest",
+            "StringSplitSelect",
+            "Unbroken-Video-Handler",
+            "VideoHandler",
+            "WanVaceInputConform",
+            "WorkflowLoggerNode"
+        ],
+        {
+            "title_aux": "Kaskis_Comfy_Nodes"
+        }
+    ],
+    "https://github.com/kesslerdev/kesslerdev_comfyui_nodes": [
+        [
+            "KesslerDev_AlwaysSeed",
+            "KesslerDev_EnvironmentVariableGetter",
+            "KesslerDev_LoadImageAndFilename",
+            "KesslerDev_LoadImagesFromFolder",
+            "KesslerDev_MultilineTextChoice"
+        ],
+        {
+            "title_aux": "Comfyui : kesslerdev_comfyui_nodes [UNSAFE]"
+        }
+    ],
+    "https://github.com/ketle-man/comfyui-image-feeder": [
+        [
+            "ImageFeeder"
+        ],
+        {
+            "title_aux": "[LOCALE ISSUE] ComfyUI Image Feeder"
+        }
+    ],
+    "https://github.com/kevin314/ComfyUI-FastVideo": [
+        [
+            "DITConfig",
+            "InferenceArgs",
+            "LoadImagePath",
+            "TextEncoderConfig",
+            "VAEConfig",
+            "VideoGenerator"
+        ],
+        {
+            "title_aux": "ComfyUI-FastVideo"
+        }
+    ],
+    "https://github.com/kijai/ComfyUI-CV-VAE": [
+        [
+            "CV_VAE_Decode",
+            "CV_VAE_Encode",
+            "CV_VAE_Load"
+        ],
+        {
+            "title_aux": "ComfyUI-CV-VAE"
+        }
+    ],
+    "https://github.com/kijai/ComfyUI-DeepSeek-VL": [
+        [
+            "deepseek_vl_inference",
+            "deepseek_vl_model_loader"
+        ],
+        {
+            "title_aux": "ComfyUI nodes to use DeepSeek-VL"
+        }
+    ],
+    "https://github.com/kijai/ComfyUI-DiffSynthWrapper": [
+        [
+            "DiffSynthSampler",
+            "DownloadAndLoadDiffSynthExVideoSVD"
+        ],
+        {
+            "title_aux": "ComfyUI DiffSynth wrapper nodes"
+        }
+    ],
+    "https://github.com/kijai/ComfyUI-DiffusersSD3Wrapper": [
+        [
+            "LoadSD3DiffusersPipeline",
+            "SD3ControlNetSampler"
+        ],
+        {
+            "title_aux": "ComfyUI-DiffusersSD3Wrapper"
+        }
+    ],
+    "https://github.com/kijai/ComfyUI-EasyAnimateWrapper": [
+        [
+            "DownloadAndLoadEasyAnimateModel",
+            "EasyAnimateDecode",
+            "EasyAnimateImageEncoder",
+            "EasyAnimateResize",
+            "EasyAnimateSampler",
+            "EasyAnimateTextEncode"
+        ],
+        {
+            "title_aux": "ComfyUI-EasyAnimateWrapper [WIP]"
+        }
+    ],
+    "https://github.com/kijai/ComfyUI-FollowYourEmojiWrapper": [
+        [
+            "DownloadAndLoadFYEModel",
+            "FYECLIPEncode",
+            "FYEClipEmbedToComfy",
+            "FYELandmarkEncode",
+            "FYELandmarkToComfy",
+            "FYEMediaPipe",
+            "FYESampler",
+            "FYESamplerLong"
+        ],
+        {
+            "title_aux": "ComfyUI-FollowYourEmojiWrapper [WIP]"
+        }
+    ],
+    "https://github.com/kijai/ComfyUI-FramePackWrapper": [
+        [
+            "DownloadAndLoadFramePackModel",
+            "FramePackFindNearestBucket",
+            "FramePackLoraSelect",
+            "FramePackSampler",
+            "FramePackSingleFrameSampler",
+            "FramePackTorchCompileSettings",
+            "LoadFramePackModel"
+        ],
+        {
+            "title_aux": "ComfyUI-FramePackWrapper [WIP]"
+        }
+    ],
+    "https://github.com/kijai/ComfyUI-Hunyuan3DWrapper": [
+        [
+            "CV2InpaintTexture",
+            "DownloadAndLoadHy3DDelightModel",
+            "DownloadAndLoadHy3DPaintModel",
+            "Hy3DApplyTexture",
+            "Hy3DBPT",
+            "Hy3DBakeFromMultiview",
+            "Hy3DCameraConfig",
+            "Hy3DDelightImage",
+            "Hy3DDiffusersSchedulerConfig",
+            "Hy3DExportMesh",
+            "Hy3DFastSimplifyMesh",
+            "Hy3DGenerateMesh",
+            "Hy3DGenerateMeshMultiView",
+            "Hy3DGetMeshPBRTextures",
+            "Hy3DIMRemesh",
+            "Hy3DLoadMesh",
+            "Hy3DMeshInfo",
+            "Hy3DMeshUVWrap",
+            "Hy3DMeshVerticeInpaintTexture",
+            "Hy3DModelLoader",
+            "Hy3DNvdiffrastRenderer",
+            "Hy3DPostprocessMesh",
+            "Hy3DRenderMultiView",
+            "Hy3DRenderMultiViewDepth",
+            "Hy3DRenderSingleView",
+            "Hy3DSampleMultiView",
+            "Hy3DSetMeshPBRAttributes",
+            "Hy3DSetMeshPBRTextures",
+            "Hy3DTorchCompileSettings",
+            "Hy3DUploadMesh",
+            "Hy3DVAEDecode",
+            "Hy3DVAELoader",
+            "Hy3D_2_1SimpleMeshGen",
+            "MESHToTrimesh",
+            "TrimeshToMESH"
+        ],
+        {
+            "title_aux": "ComfyUI-ComfyUI-Hunyuan3DWrapper [WIP]"
+        }
+    ],
+    "https://github.com/kijai/ComfyUI-MMAudio": [
+        [
+            "MMAudioFeatureUtilsLoader",
+            "MMAudioModelLoader",
+            "MMAudioSampler",
+            "MMAudioVoCoderLoader"
+        ],
+        {
+            "title_aux": "ComfyUI-MMAudio [WIP]"
+        }
+    ],
+    "https://github.com/kijai/ComfyUI-MochiWrapper": [
+        [
+            "DownloadAndLoadMochiModel",
+            "MochiDecode",
+            "MochiDecodeSpatialTiling",
+            "MochiFasterCache",
+            "MochiImageEncode",
+            "MochiLatentPreview",
+            "MochiModelLoader",
+            "MochiSampler",
+            "MochiSigmaSchedule",
+            "MochiTextEncode",
+            "MochiTorchCompileSettings",
+            "MochiVAEEncoderLoader",
+            "MochiVAELoader"
+        ],
+        {
+            "title_aux": "ComfyUI-MochiWrapper [WIP]"
+        }
+    ],
+    "https://github.com/kijai/ComfyUI-VEnhancer": [
+        [
+            "DownloadAndLoadVEnhancerModel",
+            "VEnhancerSampler",
+            "VEnhancerUnpad"
+        ],
+        {
+            "title_aux": "ComfyUI nodes for VEnhancer [WIP]"
+        }
+    ],
+    "https://github.com/kijai/ComfyUI-VideoNoiseWarp": [
+        [
+            "GetWarpedNoiseFromVideo",
+            "GetWarpedNoiseFromVideoAnimateDiff",
+            "GetWarpedNoiseFromVideoCogVideoX",
+            "GetWarpedNoiseFromVideoHunyuan"
+        ],
+        {
+            "title_aux": "ComfyUI-VideoNoiseWarp [WIP]"
+        }
+    ],
+    "https://github.com/kijai/ComfyUI-WanAnimatePreprocess": [
+        [
+            "DrawViTPose",
+            "OnnxDetectionModelLoader",
+            "PoseAndFaceDetection",
+            "PoseDetectionOneToAllAnimation",
+            "PoseRetargetPromptHelper"
+        ],
+        {
+            "title_aux": "ComfyUI-WanAnimatePreprocess [WIP]"
+        }
+    ],
+    "https://github.com/killshotttttt/ComfyUI-killshotttttt": [
+        [
+            "MeshyImageToImage"
+        ],
+        {
+            "title_aux": "ComfyUI-killshotttttt [WIP]"
+        }
+    ],
+    "https://github.com/kimara-ai/ComfyUI-Kimara-AI-Image-From-URL": [
+        [
+            "KimaraAIImageFromURL"
+        ],
+        {
+            "title_aux": "ComfyUI-Kimara-AI-Image-From-URL [WIP]"
+        }
+    ],
+    "https://github.com/kongds1999/ComfyUI_was_image": [
+        [
+            "BBoxToCoco",
+            "BBoxToXYWH",
+            "BBoxVisNode",
+            "Check Person In Text",
+            "CocoToNumber",
+            "ConvertGrayToImage",
+            "ConvertToDict",
+            "CustomUltralyticsModelLoader",
+            "Generate Color Palette",
+            "GetImageSize",
+            "ImageResizeAdvanced",
+            "Replace Color By Palette",
+            "UltralyticsCheckCategory",
+            "UltralyticsInference",
+            "UltralyticsInference_face_detection",
+            "UltralyticsListIndexToNames",
+            "UltralyticsModelLoader",
+            "UltralyticsVisualization",
+            "ViewText"
+        ],
+        {
+            "title_aux": "ComfyUI_was_image"
+        }
+    ],
+    "https://github.com/krich-cto/ComfyUI-Flow-Control": [
+        [
+            "CLIPLoaderGGUF",
+            "DualCLIPLoaderGGUF",
+            "FlowCheckpointPresetLoader",
+            "FlowClipCondition",
+            "FlowClipTextEncode",
+            "FlowConditioningAutoSwitch",
+            "FlowFluxPresetLoader",
+            "FlowGate",
+            "FlowImageAutoBatch",
+            "FlowImageCondition",
+            "FlowKSampler",
+            "FlowLatentAutoBatch",
+            "FlowLatentCondition",
+            "FlowLoraLoader",
+            "FlowLoraLoaderModelOnly",
+            "FlowModelManager",
+            "FlowSaveImage",
+            "QuadrupleCLIPLoaderGGUF",
+            "TripleCLIPLoaderGGUF",
+            "UnetLoaderGGUF",
+            "UnetLoaderGGUFAdvanced"
+        ],
+        {
+            "title_aux": "ComfyUI Flow Control [UNSTABLE]"
+        }
+    ],
+    "https://github.com/krisshen2021/comfyui_OpenRouterNodes": [
+        [
+            "OpenRouterOAINode_Infer",
+            "OpenRouterOAINode_Models",
+            "OpenRouterOAINode_hunyuanPrompt",
+            "OpenRouterOAINode_txt2imgPrompt"
+        ],
+        {
+            "title_aux": "comfyui_OpenRouterNodes [WIP]"
+        }
+    ],
+    "https://github.com/kuailefengnan2024/Comfyui_Layer": [
+        [
+            "VisionAPIPluginNode"
+        ],
+        {
+            "title_aux": "Comfyui_Layer"
+        }
+    ],
+    "https://github.com/kurtgamer01/comfyui-aspect-to-preset": [
+        [
+            "AspectToPresetRes"
+        ],
+        {
+            "title_aux": "comfyui-aspect-to-preset [WIP]"
+        }
+    ],
+    "https://github.com/kuschanow/ComfyUI-SD-Slicer": [
+        [
+            "SdSlicer"
+        ],
+        {
+            "title_aux": "ComfyUI-SD-Slicer"
+        }
+    ],
+    "https://github.com/kxh/ComfyUI-ImageUpscaleWithModelMultipleTimes": [
+        [
+            "ImageUpscaleWithModelMultipleTimes"
+        ],
+        {
+            "title_aux": "ComfyUI-ImageUpscaleWithModelMultipleTimes"
+        }
+    ],
+    "https://github.com/kxh/ComfyUI-sam2": [
+        [
+            "Segment"
+        ],
+        {
+            "title_aux": "ComfyUI-sam2"
+        }
+    ],
+    "https://github.com/kycg/comfyui-Kwtoolset": [
+        [
+            "KWImageResizeByLongerSide",
+            "KWNagetiveString",
+            "KWPositiveString",
+            "KWShowAnything",
+            "KWanywhereString",
+            "KwtoolsetChangeOpenpose",
+            "KwtoolsetCheckpointLoaderwithpreview",
+            "KwtoolsetConditioningSelect",
+            "KwtoolsetGetHipMask",
+            "KwtoolsetGetHipMasktest",
+            "KwtoolsetGetImageSize",
+            "KwtoolsetGrowMaskPlus",
+            "KwtoolsetImageSelect",
+            "KwtoolsetLoadCheckpointsBatch",
+            "KwtoolsetLoraLoaderwithpreview",
+            "KwtoolsetMaskAdd",
+            "KwtoolsetModelSelect",
+            "LatentMatch"
+        ],
+        {
+            "title_aux": "comfyui-Kwtoolset"
+        }
+    ],
+    "https://github.com/kylegrover/comfyui-python-cowboy": [
+        [
+            "PythonScript"
+        ],
+        {
+            "title_aux": "comfyui-python-cowboy [UNSAFE]"
+        }
+    ],
+    "https://github.com/laksjdjf/ssd-1b-comfyui": [
+        [
+            "SSD-1B-Loader"
+        ],
+        {
+            "title_aux": "ssd-1b-comfyui"
+        }
+    ],
+    "https://github.com/lamianlbe/ComfyUI-Mediapipe-FaceMesh": [
+        [
+            "3DDFA_V2-FaceMesh",
+            "FaceAlignment-FaceMesh",
+            "InsightFace-FaceMesh",
+            "Mediapipe-FaceMesh"
+        ],
+        {
+            "title_aux": "ComfyUI-Mediapipe-FaceMesh"
+        }
+    ],
+    "https://github.com/lamianlbe/ComfyUI-ReFace": [
+        [
+            "ReFaceCrop",
+            "ReFaceLoopEnd",
+            "ReFaceLoopStart"
+        ],
+        {
+            "title_aux": "ComfyUI-ReFace"
+        }
+    ],
+    "https://github.com/laobaishu1/ComfyUI-JSON-Upload-Iterator": [
+        [
+            "JSONUploadIterator"
+        ],
+        {
+            "title_aux": "ComfyUI-JSON-Upload-Iterator [UNSAFE]"
+        }
+    ],
+    "https://github.com/laubsauger/comfyui-storyboard": [
+        [
+            "MarkdownRenderer",
+            "NodeInspector",
+            "OpenAIAPIKeyManager",
+            "OpenAIAdvancedConfiguration",
+            "OpenAIChatGPT"
+        ],
+        {
+            "author": "laub",
+            "description": "A ComfyUI node to display markdown text, and other storyboard features.",
+            "nickname": "Storyboard",
+            "title": "ComfyUI Storyboard",
+            "title_aux": "ComfyUI Storyboard [WIP]"
+        }
+    ],
+    "https://github.com/lazybuttalented/ComfyUI_LBT": [
+        [
+            "LBT_BooleanAND",
+            "LBT_CombineImagesFromBatch",
+            "LBT_CombineImagesFromList",
+            "LBT_CropByMask",
+            "LBT_GetFolderInfo",
+            "LBT_ListInfo",
+            "LBT_LoadImageFromPath",
+            "LBT_LoadImagesFromFolder",
+            "LBT_LoadImagesFromList",
+            "LBT_LoadMultilineText",
+            "LBT_LoadTextFromFolder",
+            "LBT_LoadVideoFromFolder",
+            "LBT_SaveImage",
+            "LBT_SaveVideo",
+            "LBT_ShowTextEditable",
+            "LBT_StringToList",
+            "LBT_SwitchNoPause",
+            "LBT_TextImageLibraryComparison",
+            "LBT_TextKeywordMatch"
+        ],
+        {
+            "title_aux": "ComfyUI_LBT [WIP]"
+        }
+    ],
+    "https://github.com/leacvikas0/ComfyUI-Presence": [
+        [
+            "FluxAdaptiveInjector",
+            "GaussianNoisePadding",
+            "PresenceDirector",
+            "PresencePaddingTester",
+            "PresencePreview",
+            "PresenceSaver"
+        ],
+        {
+            "title_aux": "ComfyUI-Presence [WIP]"
+        }
+    ],
+    "https://github.com/leadbreak/comfyui-faceaging": [
+        [
+            "AgeTransformationNode"
+        ],
+        {
+            "title_aux": "Face Aging [WIP]"
+        }
+    ],
+    "https://github.com/leeguandong/ComfyUI_AliControlnetInpainting": [
+        [
+            "AliInpaintingsampler",
+            "EcomXL_AddFG",
+            "EcomXL_Condition",
+            "EcomXL_Controlnet_ModelLoader",
+            "EcomXL_LoadImage",
+            "EcomXL_SDXL_Inpaint_ModelLoader",
+            "Flux_Controlnet_ModelLoader",
+            "Flux_Inpainting_ModelLoader",
+            "SD3_Controlnet_ModelLoader",
+            "SD3_Inpainting_ModelLoader"
+        ],
+        {
+            "title_aux": "ComfyUI_AliControlnetInpainting [WIP]"
+        }
+    ],
+    "https://github.com/leoleelxh/ComfyUI-MidjourneyNode-leoleexh": [
+        [
+            "MidjourneyGenerateNode",
+            "MidjourneyUpscaleNode"
+        ],
+        {
+            "title_aux": "ComfyUI-MidjourneyNode-leoleexh"
+        }
+    ],
+    "https://github.com/leon-etienne/ComfyUI_Scoring-Nodes": [
+        [
+            "AestheticScore",
+            "ImageSimilarity",
+            "MultiAestheticScore",
+            "MultiImageToTextSimilarity",
+            "MultiTextToImageSimilarity",
+            "TextSimilarity"
+        ],
+        {
+            "title_aux": "ComfyUI_Scoring-Nodes"
+        }
+    ],
+    "https://github.com/lfelipegg/lfgg_custom_nodes_comfyui": [
+        [
+            "LfggImageResolutionByRatio",
+            "LfggLatentSizeByRatio",
+            "LfggPixelBudgetLatentSize"
+        ],
+        {
+            "title_aux": "lfgg_custom_nodes_comfyui [WIP]"
+        }
+    ],
+    "https://github.com/lggcfx2020/ComfyUI-LGGCFX-Tools": [
+        [
+            "LGGCFX_ImagePreview",
+            "LGGCFX_audio",
+            "LGGCFX_prompt",
+            "LGGCFX_resolution",
+            "LGGCFX_time_frame",
+            "VRAMReserver"
+        ],
+        {
+            "title_aux": "ComfyUI-LGGCFX-Tools"
+        }
+    ],
+    "https://github.com/lgldlk/ComfyUI-img-tiler": [
+        [
+            "PC ImageListTileMaker",
+            "PC TileMaker",
+            "PC TilerImage",
+            "PC TilerSelect"
+        ],
+        {
+            "title_aux": "ComfyUI-img-tiler"
+        }
+    ],
+    "https://github.com/lihaoyun6/ComfyUI-lhyNodes": [
+        [
+            "AnyToAny",
+            "BlockifyMask_lhy",
+            "CLIPName",
+            "CLIPNameGGUF",
+            "CLIPVisionName",
+            "CSVRandomPicker",
+            "CSVRandomPickerAdv",
+            "CheckpointName",
+            "CodeableString",
+            "ControlNetName",
+            "CudaDevicePatcher",
+            "DrawMaskOnImage_lhy",
+            "DrawViTPose_lhy",
+            "DynamicParameterPanel",
+            "GrowMask_lhy",
+            "ImageBatchtoImageList",
+            "ImageBatchtoImages",
+            "ImageOffset",
+            "ImageOverlay_lhy",
+            "ImageScaleToTotalPixelsAdv",
+            "LivePreviewer",
+            "LoadImageBatch",
+            "LoadZipBatch",
+            "LoraName",
+            "MaskToSAMCoords",
+            "MaskToSAMCoordsV2",
+            "MorseCode",
+            "ParameterUnpacker",
+            "PoseReformer",
+            "QueueHandler",
+            "RemoteClipClient",
+            "RemoteClipServer",
+            "RequestURL",
+            "SaveImageAsZip",
+            "SaveTextAsZip",
+            "StrFormat",
+            "StrFormatAdv",
+            "UNETName",
+            "UNETNameGGUF",
+            "UpscaleModelName",
+            "VAEName",
+            "WanAnimateBestFrameWindow",
+            "WanAnimateMaskPreprocessor",
+            "YoloFaceReformer",
+            "noneNode"
+        ],
+        {
+            "title_aux": "ComfyUI-lhyNodes"
+        }
+    ],
+    "https://github.com/linhusyung/comfyui-Build-and-train-your-network": [
+        [
+            "Conv_layer",
+            "Normalization_layer",
+            "activation_function",
+            "create_dataset",
+            "create_intput",
+            "create_model",
+            "create_training_task",
+            "forward_test",
+            "linear_layer",
+            "pooling_layer",
+            "pre_train_layer",
+            "res_connect",
+            "show_dimensions",
+            "view_layer"
+        ],
+        {
+            "title_aux": "ComfyUI Build and Train Your Network [WIP]"
+        }
+    ],
+    "https://github.com/littleowl/ComfyUI-MV-HECV": [
+        [
+            "CombineSideBySide",
+            "DepthResize",
+            "StereoShift_Fast"
+        ],
+        {
+            "title_aux": "ComfyUI-MV-HECV"
+        }
+    ],
+    "https://github.com/liusida/ComfyUI-Notebook": [
+        [
+            "NotebookCell",
+            "NotebookForceRerun",
+            "PreviewHTML"
+        ],
+        {
+            "title_aux": "ComfyUI-Notebook [UNSAFE]"
+        }
+    ],
+    "https://github.com/locphan201/ComfyUI-Alter-Nodes": [
+        [
+            "AlterMMAudioConfig",
+            "AlterMMAudioFeatureUtils",
+            "AlterMMAudioModelLoader",
+            "AlterMMAudioSampler"
+        ],
+        {
+            "title_aux": "ComfyUI-Alter-Nodes"
+        }
+    ],
+    "https://github.com/locphan201/ComfyUI-Alternatives": [
+        [
+            "Lora Applier",
+            "Lora PreLoader",
+            "LoraApplier",
+            "LoraPreLoader"
+        ],
+        {
+            "title_aux": "ComfyUI-Alternatives"
+        }
+    ],
+    "https://github.com/logicalor/comfyui_mv_adapter": [
+        [
+            "MVAdapterBackgroundRemoval",
+            "MVAdapterCameraEmbed",
+            "MVAdapterClearVRAM",
+            "MVAdapterI2MVSampler",
+            "MVAdapterImageGrid",
+            "MVAdapterImagePreprocess",
+            "MVAdapterLoRALoader",
+            "MVAdapterModelSetup",
+            "MVAdapterPipelineLoader",
+            "MVAdapterReferencePreprocess",
+            "MVAdapterSchedulerConfig",
+            "MVAdapterSplitViews",
+            "MVAdapterT2MVSampler",
+            "MVAdapterVAEDecode",
+            "MVAdapterViewSelector"
+        ],
+        {
+            "title_aux": "comfyui_mv_adapter [WIP]"
+        }
+    ],
+    "https://github.com/logtd/ComfyUI-HunyuanLoom": [
+        [
+            "ConfigureModifiedHY",
+            "HYApplyRegionalConds",
+            "HYAttnOverride",
+            "HYCreateRegionalCond",
+            "HYFetaEnhance",
+            "HYFlowEditGuider",
+            "HYFlowEditGuiderAdv",
+            "HYFlowEditGuiderCFG",
+            "HYFlowEditGuiderCFGAdv",
+            "HYFlowEditSampler",
+            "HYForwardODESampler",
+            "HYInverseModelSamplingPred",
+            "HYReverseModelSamplingPred",
+            "HYReverseODESampler",
+            "HyVideoFlowEditSamplerWrapper"
+        ],
+        {
+            "title_aux": "ComfyUI-HunyuanLoom [WIP]"
+        }
+    ],
+    "https://github.com/logtd/ComfyUI-Veevee": [
+        [
+            "ApplyVVModel",
+            "FlowConfig",
+            "FlowGetFlow",
+            "GetRaftFlow",
+            "InjectionConfig",
+            "PivotConfig",
+            "RaveConfig",
+            "SCAConfig",
+            "TemporalConfig",
+            "VVGetRaftFlow",
+            "VVSamplerSampler",
+            "VVUnsamplerSampler"
+        ],
+        {
+            "title_aux": "ComfyUI-Veevee [WIP]"
+        }
+    ],
+    "https://github.com/longgui0318/comfyui-one-more-step": [
+        [
+            "Calculate More Step Latent",
+            "Load More Step Model"
+        ],
+        {
+            "title_aux": "comfyui-one-more-step [WIP]"
+        }
+    ],
+    "https://github.com/longzoho/ComfyUI-Qdrant-Saver": [
+        [
+            "QDrantSaver"
+        ],
+        {
+            "title_aux": "ComfyUI-Qdrant-Saver"
+        }
+    ],
+    "https://github.com/love2hina-net/ComfyUI-Local-Translator": [
+        [
+            "LocalTranslator"
+        ],
+        {
+            "title_aux": "ComfyUI-Local-Translator"
+        }
+    ],
+    "https://github.com/lrzjason/Comfyui-Condition-Utils": [
+        [
+            "LoadCondition",
+            "LoadConditionFromLoras",
+            "SaveCondition"
+        ],
+        {
+            "title_aux": "Comfyui-Condition-Utils [WIP]"
+        }
+    ],
+    "https://github.com/lrzjason/Comfyui-DiffusersUtils": [
+        [
+            "DiffusersGenPriorTokens",
+            "DiffusersGenPriorTokensDebug",
+            "DiffusersImageEditGenerator",
+            "DiffusersImageGenerator",
+            "DiffusersLoadLoraOnly",
+            "DiffusersLoraLayersOperation",
+            "DiffusersLoraLoader",
+            "DiffusersLoraStatViewer",
+            "DiffusersLoraUnloader",
+            "DiffusersMergeLoraToPipeline",
+            "DiffusersModelLoader",
+            "DiffusersPipeline",
+            "DiffusersPipelineBuilder",
+            "DiffusersPreprocessorLoader",
+            "DiffusersSampling",
+            "DiffusersSaveLora",
+            "DiffusersTextEncode",
+            "DiffusersTextEncoderLoader",
+            "DiffusersTokenizerLoader",
+            "DiffusersTransformerLoader",
+            "DiffusersVAELoader",
+            "LoadLoraOnly",
+            "LoraLayersOperation",
+            "LoraStatViewer",
+            "MergeLoraToTransformer",
+            "SaveLora",
+            "TextEncodeDiffusersLongCat",
+            "TextEncodeDiffusersLongCatCached",
+            "TextEncodeDiffusersLongCatImageEdit"
+        ],
+        {
+            "title_aux": "Comfyui-DiffusersUtils [WIP]"
+        }
+    ],
+    "https://github.com/ltdrdata/ComfyUI-Workflow-Component": [
+        [
+            "ComboToString",
+            "ExecutionBlocker",
+            "ExecutionControlString",
+            "ExecutionOneOf",
+            "ExecutionSwitch",
+            "InputUnzip",
+            "InputZip",
+            "LoopControl",
+            "LoopCounterCondition",
+            "OptionalTest",
+            "TensorToCPU"
+        ],
+        {
+            "title_aux": "ComfyUI-Workflow-Component [WIP]"
+        }
+    ],
+    "https://github.com/lu64k/ks_nodes": [
+        [
+            "KS Json Count",
+            "KS Json To String",
+            "KS JsonKeyExtractor",
+            "KS JsonKeyReplacer",
+            "KS JsonlFolderMatchReader",
+            "KS Load Images From Folder",
+            "KS Random File Name",
+            "KS Save Text",
+            "KS Text_String",
+            "KS get time int",
+            "KS json array constrains filter",
+            "KS json float range filter",
+            "KS json value eliminator",
+            "KS load text",
+            "KS_Json_Extract_Key_And_Value_3ways",
+            "KS_Json_Key_Random_3ways",
+            "KS_Json_Key_Replace_3ways",
+            "KS_Json_loader",
+            "KS_Save_JSON",
+            "KS_any_payload_image",
+            "KS_image_metadata_node",
+            "KS_make_json_node",
+            "KS_merge_json_node"
+        ],
+        {
+            "title_aux": "ks_nodes"
+        }
+    ],
+    "https://github.com/lucafoscili/lf-nodes": [
+        [
+            "LF_BackgroundRemover",
+            "LF_Blend",
+            "LF_BlobToImage",
+            "LF_Bloom",
+            "LF_BlurImages",
+            "LF_Boolean",
+            "LF_Brightness",
+            "LF_CaptionImageWD14",
+            "LF_CharacterImpersonator",
+            "LF_CheckpointSelector",
+            "LF_CivitAIMetadataSetup",
+            "LF_Clarity",
+            "LF_ColorAnalysis",
+            "LF_CompareImages",
+            "LF_Contrast",
+            "LF_ControlPanel",
+            "LF_CreateMask",
+            "LF_Desaturation",
+            "LF_DetectRegions",
+            "LF_DiffusionModelSelector",
+            "LF_DisplayBoolean",
+            "LF_DisplayFloat",
+            "LF_DisplayInteger",
+            "LF_DisplayJSON",
+            "LF_DisplayPrimitiveAsJSON",
+            "LF_DisplayString",
+            "LF_EmbeddingSelector",
+            "LF_EmptyImage",
+            "LF_ExtractPromptFromLoraTag",
+            "LF_ExtractString",
+            "LF_FilmGrain",
+            "LF_Float",
+            "LF_GaussianBlur",
+            "LF_GeminiAPI",
+            "LF_GetRandomKeyFromJSON",
+            "LF_GetValueFromJSON",
+            "LF_ImageClassifier",
+            "LF_ImageHistogram",
+            "LF_ImageListFromJSON",
+            "LF_ImageToSVG",
+            "LF_ImagesEditingBreakpoint",
+            "LF_ImagesSlideshow",
+            "LF_Inpaint",
+            "LF_InpaintAdvanced",
+            "LF_Integer",
+            "LF_IsLandscape",
+            "LF_JSONPromptCombinator",
+            "LF_KeywordCounter",
+            "LF_KeywordToggleFromJSON",
+            "LF_LLMChat",
+            "LF_LLMMessenger",
+            "LF_LUTApplication",
+            "LF_LUTGeneration",
+            "LF_Line",
+            "LF_LoadAndEditImages",
+            "LF_LoadCLIPSegModel",
+            "LF_LoadFileOnce",
+            "LF_LoadImages",
+            "LF_LoadLocalJSON",
+            "LF_LoadLoraTags",
+            "LF_LoadMetadata",
+            "LF_LoadWD14Model",
+            "LF_LoadWD14Onnx",
+            "LF_LoraAndEmbeddingSelector",
+            "LF_LoraSelector",
+            "LF_MarkdownDocGenerator",
+            "LF_MathOperation",
+            "LF_MultipleImageResizeForWeb",
+            "LF_Notify",
+            "LF_ONNXSelector",
+            "LF_OpenAIAPI",
+            "LF_ParsePromptWithLoraTags",
+            "LF_RandomBoolean",
+            "LF_RegexReplace",
+            "LF_RegionExtractor",
+            "LF_RegionMask",
+            "LF_ResizeImageByEdge",
+            "LF_ResizeImageToDimension",
+            "LF_ResizeImageToSquare",
+            "LF_ResolutionSwitcher",
+            "LF_SamplerSelector",
+            "LF_Saturation",
+            "LF_SaveImageForCivitAI",
+            "LF_SaveJSON",
+            "LF_SaveMarkdown",
+            "LF_SaveSVG",
+            "LF_SaveText",
+            "LF_SchedulerSelector",
+            "LF_Sepia",
+            "LF_SequentialSeedsGenerator",
+            "LF_SetValueInJSON",
+            "LF_ShuffleJSONKeys",
+            "LF_Something2Number",
+            "LF_Something2String",
+            "LF_SortJSONKeys",
+            "LF_SortTags",
+            "LF_SplitTone",
+            "LF_StabilityAPI",
+            "LF_String",
+            "LF_StringReplace",
+            "LF_StringTemplate",
+            "LF_StringToJSON",
+            "LF_SwitchFloat",
+            "LF_SwitchImage",
+            "LF_SwitchInteger",
+            "LF_SwitchJSON",
+            "LF_SwitchMask",
+            "LF_SwitchString",
+            "LF_TiledSuperRes",
+            "LF_TiltShift",
+            "LF_UnsharpMask",
+            "LF_UpdateUsageStatistics",
+            "LF_UploadImages",
+            "LF_UpscaleModelSelector",
+            "LF_UrandomSeedGenerator",
+            "LF_UsageStatistics",
+            "LF_VAEDecode",
+            "LF_VAEEncode",
+            "LF_VAESelector",
+            "LF_Vibrance",
+            "LF_ViewImages",
+            "LF_ViewSVGs",
+            "LF_Vignette",
+            "LF_WallOfText",
+            "LF_WriteJSON"
+        ],
+        {
+            "title_aux": "LF Nodes [UNSAFE]"
+        }
+    ],
+    "https://github.com/lum3on/comfyui_RollingDepth": [
+        [
+            "RollingDepthNode"
+        ],
+        {
+            "title_aux": "comfyui_RollingDepth [WIP]"
+        }
+    ],
+    "https://github.com/lunaaispace-eng/LLM_Prompt": [
+        [
+            "GrokImageAPINode",
+            "GrokImageEditAPINode",
+            "GrokVideoAPINode",
+            "GrokVideoEditAPINode",
+            "GrokVideoExtendAPINode",
+            "GrokVideoReferenceAPINode",
+            "LLMPrompt",
+            "LLMPromptAPI"
+        ],
+        {
+            "title_aux": "LLM_Prompt [WIP]"
+        }
+    ],
+    "https://github.com/lyra-ai/lyra-nodes": [
+        [
+            "LyraAudioVisualizer",
+            "LyraCdnUploader",
+            "LyraCharacterSearch",
+            "LyraFilenameBuilder",
+            "LyraImageAudioFusion",
+            "LyraLoadAudioFromURL",
+            "LyraLoadImageFromURL",
+            "LyraStringToTypes"
+        ],
+        {
+            "title_aux": "lyra-nodes"
+        }
+    ],
+    "https://github.com/maTORIx/ComfyUI-KeypointsToImage": [
+        [
+            "KeypointsToImage"
+        ],
+        {
+            "title_aux": "ComfyUI-KeypointsToImage"
+        }
+    ],
+    "https://github.com/machinesarenotpeople/comfyui-energycost": [
+        [
+            "TimeCostEndNode",
+            "TimeStartNode"
+        ],
+        {
+            "title_aux": "comfyui-energycost"
+        }
+    ],
+    "https://github.com/maizerrr/comfyui-code-nodes": [
+        [
+            "BBoxDrawNode",
+            "BBoxParseNode",
+            "DummyNode",
+            "ImageBatchNode",
+            "MaskEditorNode",
+            "OpenAIGPTImageNode",
+            "OpenAIQueryNode"
+        ],
+        {
+            "title_aux": "comfyui-code-nodes"
+        }
+    ],
+    "https://github.com/majocola/comfyui-standbybutton": [
+        [
+            "StandbyButton"
+        ],
+        {
+            "title_aux": "Standbybutton"
+        }
+    ],
+    "https://github.com/majorsauce/comfyui_indieTools": [
+        [
+            "IndCutByMask",
+            "IndLocalScale",
+            "IndPastImage",
+            "IndSolidify",
+            "IndYoloDetector"
+        ],
+        {
+            "title_aux": "comfyui_indieTools [WIP]"
+        }
+    ],
+    "https://github.com/mamamia1110/comfyui-boggerrr-nodes": [
+        [
+            "SeedEdit3",
+            "Seedream3"
+        ],
+        {
+            "title_aux": "Boggerrr Nodes [WIP]"
+        }
+    ],
+    "https://github.com/mamorett/ComfyUI-SmolVLM": [
+        [
+            "Smolvlm_Caption_Analyzer",
+            "Smolvlm_Flux_CLIPTextEncode",
+            "Smolvlm_SaveTags",
+            "Smolvlm_Tagger"
+        ],
+        {
+            "title_aux": "ComfyUI-SmolVLM [WIP]"
+        }
+    ],
+    "https://github.com/mamorett/comfyui_minicpm_vision": [
+        [
+            "MiniCPMVisionGGUF"
+        ],
+        {
+            "title_aux": "comfyui_minicpm_vision"
+        }
+    ],
+    "https://github.com/maoper11/comfyui_inteliweb_nodes": [
+        [
+            "InteliwebSystemCheck"
+        ],
+        {
+            "title_aux": "ComfyUI_Inteliweb_nodes"
+        }
+    ],
+    "https://github.com/marcueberall/ComfyUI-BuildPath": [
+        [
+            "Build Path Adv"
+        ],
+        {
+            "title_aux": "ComfyUI-BuildPath"
+        }
+    ],
+    "https://github.com/marduk191/comfyui_cmdtls": [
+        [
+            "DirectoryCreator",
+            "FileBrowser",
+            "FileCopy",
+            "FileDelete",
+            "FileReader",
+            "FileWriter",
+            "ShellExecutor"
+        ],
+        {
+            "title_aux": "comfyui_cmdtls [UNSAFE]"
+        }
+    ],
+    "https://github.com/martinken/ComfyUI-KMNodes": [
+        [
+            "KM_Aspect_Ratio_Selector",
+            "KM_Aspect_Ratio_Selector2",
+            "KM_CFGGuider",
+            "KM_Color_Correct",
+            "KM_Color_Match",
+            "KM_Downscale_Image",
+            "KM_Extract_Images",
+            "KM_Merge_Images",
+            "KM_Resize_Image",
+            "KM_Resize_Image_With_Model",
+            "KM_Safe_Mask_Bounds",
+            "KM_Safe_SEGS_Bounds",
+            "KM_Select_Every_Nth_Image",
+            "KM_Select_Image",
+            "KM_Split_Images",
+            "KM_Video_Blend",
+            "KM_Video_Image_Color_Match",
+            "KM_WanVideoToVideo",
+            "WanImageToVideo"
+        ],
+        {
+            "title_aux": "ComfyUI-KMNodes"
+        }
+    ],
+    "https://github.com/maruhidd/ComfyUI_Transparent-Background": [
+        [
+            "FillTransparentNode",
+            "RemoveBackgroundNode"
+        ],
+        {
+            "title_aux": "Transparent Background for ComfyUI"
+        }
+    ],
+    "https://github.com/mashb1t/comfyui-nodes-mashb1t": [
+        [
+            "mashb1t: LoadImage"
+        ],
+        {
+            "title_aux": "ComfyUI mashb1t nodes"
+        }
+    ],
+    "https://github.com/masmullin2000/ComfyUI-MMYolo": [
+        [
+            "MMFace_Finder"
+        ],
+        {
+            "title_aux": "ComfyUI-MMYolo"
+        }
+    ],
+    "https://github.com/matDobek/ComfyUI_duck": [
+        [
+            "Combine Images (duck)"
+        ],
+        {
+            "title_aux": "ComfyUI_duck"
+        }
+    ],
+    "https://github.com/maurorilla/ComfyUI-MisterMR-Nodes": [
+        [
+            "AddLogo",
+            "AddSingleObject",
+            "AddSingleText",
+            "ColorNode",
+            "Log",
+            "PromptSelector",
+            "SaveImageAndText"
+        ],
+        {
+            "title_aux": "ComfyUI-glb-to-stl [WIP]"
+        }
+    ],
+    "https://github.com/mavibirdesmi/ComfyUI-CUDABuggy": [
+        [
+            "CUDABuggyNode"
+        ],
+        {
+            "title_aux": "ComfyUI-CUDABuggy"
+        }
+    ],
+    "https://github.com/max-dingsda/ComfyUI-PromptLoop": [
+        [
+            "PromptLoopFromFile",
+            "PromptLoopFromText",
+            "PromptLoopInfo",
+            "PromptLoopSaveImage"
+        ],
+        {
+            "title_aux": "ComfyUI-PromptLoop [WIP]"
+        }
+    ],
+    "https://github.com/mcaishao123/ComfyUI-lut": [
+        [
+            "CUBE LUT Apply",
+            "CUBE LUT Loader",
+            "CUBE LUT Search Loader",
+            "LRTemplate Apply",
+            "LRTemplate Loader",
+            "LRTemplate Manual",
+            "LRTemplate Search Loader",
+            "Preset Catalog Generator"
+        ],
+        {
+            "title_aux": "ComfyUI-lut [UNSAFE/NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/mehbebe/ComfyLoraGallery": [
+        [
+            "LoraGallery"
+        ],
+        {
+            "title_aux": "ComfyLoraGallery [WIP]"
+        }
+    ],
+    "https://github.com/melMass/ComfyUI-Lygia": [
+        [
+            "LygiaProgram",
+            "LygiaUniforms"
+        ],
+        {
+            "title_aux": "ComfyUI-Lygia"
+        }
+    ],
+    "https://github.com/mertgoksel/ComfyUI-Noot-Nodes": [
+        [
+            "StagedKSampler",
+            "TagRandomizer"
+        ],
+        {
+            "title_aux": "comfyui-noot-nodes [WIP]"
+        }
+    ],
+    "https://github.com/mico-world/comfyui_mico_node": [
+        [
+            "HFUNETLoader"
+        ],
+        {
+            "title_aux": "comfyui_mico_node"
+        }
+    ],
+    "https://github.com/mikeymcfish/FishTools": [
+        [
+            "AnaglyphCreator",
+            "AnaglyphCreatorPro",
+            "Deptherize",
+            "LaserCutterFull",
+            "ShadowMap"
+        ],
+        {
+            "author": "Fish",
+            "description": "This extension provides tools for generating laser cutter ready files and other fun stuff",
+            "nickname": "FishTools",
+            "title": "FishTools",
+            "title_aux": "LaserCutterFull and Deptherize Nodes"
+        }
+    ],
+    "https://github.com/mikheys/ComfyUI-mikheys": [
+        [
+            "WanImageDimensions",
+            "WanOptimalResolution",
+            "\u0418\u043c\u044f\u0414\u043b\u044fComfyUI"
+        ],
+        {
+            "title_aux": "ComfyUI-mikheys"
+        }
+    ],
+    "https://github.com/minhtuannhn/comfyui-gemini-studio": [
+        [
+            "GetFileNameFromURL"
+        ],
+        {
+            "title_aux": "comfyui-gemini-studio [WIP]"
+        }
+    ],
+    "https://github.com/minujeong/comfyui-oklch-color-picker": [
+        [
+            "OKLCH",
+            "OKLCHPicker"
+        ],
+        {
+            "title_aux": "comfyui-oklch-color-picker"
+        }
+    ],
+    "https://github.com/miragecoa/ComfyUI-LLM-Evaluation": [
+        [
+            "AccuracyNode",
+            "ClearVRAM",
+            "DeleteFile",
+            "DownloadHuggingFaceModel",
+            "F1ScoreNode",
+            "FirstNode",
+            "JSONToListNode",
+            "JsonResultGenerator",
+            "LLMLocalLoader",
+            "LoadFileNode",
+            "MathOperationNode",
+            "MyNode",
+            "PullOllamaModel",
+            "SelectItemByIndexNode",
+            "SelectItemByKeyNode",
+            "StringCombiner",
+            "StringPatternEnforcer",
+            "StringScraper",
+            "UpdateLLMResultToJson",
+            "WriteToJson"
+        ],
+        {
+            "title_aux": "ComfyUI-LLM-Evaluation [WIP]"
+        }
+    ],
+    "https://github.com/mittimi/ComfyUI_mittimiLoadPresetLite": [
+        [
+            "CombineParamDataLiteMittimi",
+            "LoadCheckpointLiteMittimi",
+            "LoadImageParamLiteMittimi",
+            "LoadSetParamLiteMittimi",
+            "LoraLoaderExtractedPromptLiteMittimi",
+            "LoraLoaderModelOnlyExtractedPromptLiteMittimi",
+            "SaveImageParamLiteMittimi",
+            "SaveParamToPresetLiteMittimi",
+            "TextToConditioningLiteMittimi"
+        ],
+        {
+            "author": "mittimi",
+            "description": "This node can easily switch between models and prompts by saving presets.",
+            "nickname": "mittimiLoadPresetLite",
+            "title": "mittimiLoadPresetLite",
+            "title_aux": "ComfyUI_mittimiLoadPresetLite [UNSAFE]"
+        }
+    ],
+    "https://github.com/mliand/ComfyUI-Calendar-Node": [
+        [
+            "Comfy Calendar Node",
+            "SportyStyleNode"
+        ],
+        {
+            "title_aux": "ComfyUI-Calendar-Node [WIP]"
+        }
+    ],
+    "https://github.com/mm-akhtar/comfyui-mask-selector-node": [
+        [
+            "Mask Selector"
+        ],
+        {
+            "title_aux": "comfyui-mask-selector-node"
+        }
+    ],
+    "https://github.com/mohamedsobhi777/ComfyUI-FramerComfy": [
+        [
+            "FramerComfyBooleanInputNode",
+            "FramerComfyFloatInputNode",
+            "FramerComfyInputImageNode",
+            "FramerComfyInputNumberNode",
+            "FramerComfyInputStringNode",
+            "FramerComfySaveImageNode"
+        ],
+        {
+            "title_aux": "ComfyUI-FramerComfy [WIP]"
+        }
+    ],
+    "https://github.com/molbal/ComfyUI-molbals-light-presets": [
+        [
+            "PromptStylerAppender"
+        ],
+        {
+            "title_aux": "ComfyUI-molbals-light-presets"
+        }
+    ],
+    "https://github.com/molbal/comfy-url-fetcher": [
+        [
+            "URL Fetcher"
+        ],
+        {
+            "title_aux": "comfy-url-fetcher [WIP]"
+        }
+    ],
+    "https://github.com/mozhaa/ComfyUI-Danbooru-Random": [
+        [
+            "DanbooruFilterNode",
+            "DanbooruSampleNode"
+        ],
+        {
+            "title_aux": "ComfyUI-Danbooru-Random"
+        }
+    ],
+    "https://github.com/mozhaa/ComfyUI-Latent-Transform": [
+        [
+            "LT: Add",
+            "LT: Blend",
+            "LT: Blur",
+            "LT: Gaussian Noise",
+            "LT: Multiply",
+            "LT: Sharpen",
+            "LT: Wave"
+        ],
+        {
+            "title_aux": "ComfyUI-Latent-Transform"
+        }
+    ],
+    "https://github.com/mozhaa/ComfyUI-Mask-Interpolation": [
+        [
+            "GaussianMixtureToMask",
+            "InterpolateGaussianMixture",
+            "MaskToGaussianMixture"
+        ],
+        {
+            "title_aux": "ComfyUI-Mask-Interpolation"
+        }
+    ],
+    "https://github.com/mr-krak3n/ComfyUI-Qwen": [
+        [
+            "Qwen2_Chat_Zho",
+            "Qwen2_ModelLoader_Zho",
+            "Qwen2_Zho"
+        ],
+        {
+            "title_aux": "ComfyUI-Qwen [CONFLICT]"
+        }
+    ],
+    "https://github.com/mrCodinghero/ComfyUI-Codinghero": [
+        [
+            "Basic Settings",
+            "Flux.2 Settings",
+            "Image Size Calculator",
+            "Model Selector",
+            "RES4LYF Flux.2 Settings",
+            "Settings",
+            "Upscale Settings Calculator"
+        ],
+        {
+            "title_aux": "ComfyUI-Codinghero"
+        }
+    ],
+    "https://github.com/muriellee1x/ComfyUI-Logic-Switch": [
+        [
+            "RandomStringSelector"
+        ],
+        {
+            "title_aux": "ComfyUI-Logic-Switch"
+        }
+    ],
+    "https://github.com/muriellee1x/ComfyUI-Mysterious-node2": [
+        [
+            "Key Edge Args (V2.3.6fixE2_clean)",
+            "Key Matte Math Args (V2.3.6fixE2_clean)",
+            "Key Protect Highlights Args (V2.3.6fixE2_clean)",
+            "Key Sampler Args (V2.3.6fixE2_clean)",
+            "Key Spill/Algo Args (V2.3.6fixE2_clean)",
+            "KeylightCoreHubV3"
+        ],
+        {
+            "title_aux": "ComfyUI-Mysterious-node2 [WIP]"
+        }
+    ],
+    "https://github.com/muriellee1x/ComfyUI-Mysterious-node3": [
+        [
+            "VideoTimeRemapSpeedPresets"
+        ],
+        {
+            "title_aux": "ComfyUI-Mysterious-node3"
+        }
+    ],
+    "https://github.com/muriellee1x/ComfyUI-switch-angles": [
+        [
+            "CameraPromptGenerator"
+        ],
+        {
+            "title_aux": "ComfyUI-switch-angles"
+        }
+    ],
+    "https://github.com/mut-ex/comfyui-gligengui-node": [
+        [
+            "GLIGEN_GUI"
+        ],
+        {
+            "title_aux": "ComfyUI GLIGEN GUI Node"
+        }
+    ],
+    "https://github.com/muvich3n/ComfyUI-Claude-I2T": [
+        [
+            "ClaudeImageToPrompt"
+        ],
+        {
+            "title_aux": "ComfyUI-Claude-I2T"
+        }
+    ],
+    "https://github.com/muvich3n/ComfyUI-Crop-Border": [
+        [
+            "CropImageBorder"
+        ],
+        {
+            "title_aux": "ComfyUI-Crop-Border"
+        }
+    ],
+    "https://github.com/my-xz-org/comfyui_xz_nodes": [
+        [
+            "XZImageToText",
+            "XZLlmResponse"
+        ],
+        {
+            "title_aux": "comfyui_xz_nodes"
+        }
+    ],
+    "https://github.com/n0va39/ComfyUI-LatentSpectralExpand": [
+        [
+            "LSESegmentSigmaPlanner",
+            "LSESegmentSigmas2",
+            "LSESegmentSigmas3",
+            "LSEStagePrepare",
+            "LatentSpectralExpand",
+            "SplitSigmaArrayDenoiseLSE",
+            "SplitSigmaArrayLSE"
+        ],
+        {
+            "title_aux": "ComfyUI-LatentSpectralExpand [WIP]"
+        }
+    ],
+    "https://github.com/n1ckfg/ComfyUI-K2P": [
+        [
+            "KeypointsToPinNode"
+        ],
+        {
+            "title_aux": "ComfyUI-K2P"
+        }
+    ],
+    "https://github.com/naderzare/comfyui-inodes": [
+        [
+            "IAzureAiApi",
+            "ICutStrings",
+            "IFinalizeProject",
+            "IIfElse",
+            "ILLMExecute",
+            "ILLMExecute2",
+            "ILoadAzureAiApi",
+            "ILoadOllamaApi",
+            "IMergeImages",
+            "IMultilineSplitToStrings",
+            "IPassImage",
+            "IPostProcessLLMResponse",
+            "IPromptGenerator",
+            "IRandomChoiceToStrings",
+            "ISaveImage",
+            "ISaveText",
+            "IStringsCounter",
+            "IStringsToFile",
+            "IStringsToString",
+            "ITimesToStrings",
+            "IUploadToGoogleDrive",
+            "IZipImages"
+        ],
+        {
+            "title_aux": "comfyui-inodes"
+        }
+    ],
+    "https://github.com/nadushu/comfyui-handy-nodes": [
+        [
+            "EmptyRandomLatentImage",
+            "FilenamePromptExtractor",
+            "My Image Save",
+            "QueueBatchFixedSeed",
+            "TextCleaner",
+            "TextSplitter"
+        ],
+        {
+            "title_aux": "comfyui-handy-nodes [UNSAFE]"
+        }
+    ],
+    "https://github.com/namemechan/comfyui_at_mix_node": [
+        [
+            "AtMix"
+        ],
+        {
+            "title_aux": "comfyui_at_mix_node"
+        }
+    ],
+    "https://github.com/neeltheninja/ComfyUI-TempFileDeleter": [
+        [
+            "TempCleaner"
+        ],
+        {
+            "title_aux": "ComfyUI-TempFileDeleter [UNSAFE]"
+        }
+    ],
+    "https://github.com/neeltheninja/ComfyUI-TextOverlay": [
+        [
+            "TextOverlay"
+        ],
+        {
+            "title_aux": "ComfyUI-TextOverlay"
+        }
+    ],
+    "https://github.com/nekotxt/ComfyUI-NTX-support-nodes": [
+        [
+            "ApplyLoraStack",
+            "AutogrowNode",
+            "CLIPTextEncodeWithCutoff",
+            "CollectModelNtxdata",
+            "ComplexPrompt",
+            "ConvertLoraStackToString",
+            "ConvertLoraStringToStack",
+            "CreateImageLatent",
+            "DictGet",
+            "DictSet",
+            "DictSetMulti",
+            "DictSetMultipleStrings",
+            "DownloadModelsList",
+            "DynamicComboNode",
+            "DynamicTwinNode",
+            "FileNameTemplate",
+            "GenerationDataGet",
+            "GenerationDataMaxSize",
+            "GenerationDataSet",
+            "ImageGridNode",
+            "ListCount",
+            "ListGet",
+            "ListSet",
+            "LoadCharInfo",
+            "LoadCharacterInfo",
+            "LoadCheckpointInfo",
+            "LoadCustomVae",
+            "LoraStack",
+            "MergeLoraStacks",
+            "PipeBase",
+            "PromptChainer",
+            "ReplaceTextParameters",
+            "RerouteBase",
+            "SaveMultipleImages",
+            "SwitchAny",
+            "SwitchNode",
+            "TestNode"
+        ],
+        {
+            "title_aux": "ComfyUI-NTX-support-nodes [WIP]"
+        }
+    ],
+    "https://github.com/neo0801/my-comfy-node": [
+        [
+            "DeepMosaicGetImageMosaicMask",
+            "DeepMosaicGetVideoMosaicMask",
+            "DeepMosaicRemoveImageMosaic",
+            "DeepMosaicRemoveVideoMosaic"
+        ],
+        {
+            "title_aux": "my-comfy-node"
+        }
+    ],
+    "https://github.com/nerdywhiskers/ComfyUI-Whisker-Nodes": [
+        [
+            "BGRemoveCompose",
+            "RandomCubeGrid",
+            "RatioMask",
+            "SpriteSheetGenerator",
+            "StripMaskGenerator",
+            "bg_remove_compose",
+            "offset_image",
+            "ratio_mask",
+            "sprite_sheet",
+            "strip_masks"
+        ],
+        {
+            "title_aux": "ComfyUI-Whisker-Nodes [WIP]"
+        }
+    ],
+    "https://github.com/nestflow/ComfyUI-WanPlus": [
+        [
+            "WanImageToVideoKeepFrames",
+            "WanImageToVideoMask"
+        ],
+        {
+            "title_aux": "ComfyUI-WanPlus"
+        }
+    ],
+    "https://github.com/neverbiasu/ComfyUI-DeepSeek": [
+        [
+            "DeepSeekCaller"
+        ],
+        {
+            "title_aux": "ComfyUI-DeepSeek"
+        }
+    ],
+    "https://github.com/neverbiasu/ComfyUI-Show-o": [
+        [
+            "ShowoImageCaptioning",
+            "ShowoImageInpainting",
+            "ShowoModelLoader",
+            "ShowoTextToImage"
+        ],
+        {
+            "title_aux": "ComfyUI-Show-o [WIP]"
+        }
+    ],
+    "https://github.com/neverbiasu/ComfyUI-StereoCrafter": [
+        [
+            "DepthSplattingModelLoader",
+            "DepthSplattingNode",
+            "InpaintingInferenceNode"
+        ],
+        {
+            "title_aux": "ComfyUI-StereoCrafter [WIP]"
+        }
+    ],
+    "https://github.com/newraina/ComfyUI-Remote-Save-Image": [
+        [
+            "RemoteImageSaver"
+        ],
+        {
+            "title_aux": "ComfyUI-Remote-Save-Image [UNSAFE]"
+        }
+    ],
+    "https://github.com/newraina/comfyui-photoshop-v2": [
+        [
+            "ClipPass",
+            "PhotoshopConnection",
+            "PhotoshopToComfyUI",
+            "SendToPhotoshop",
+            "modelPass",
+            "\ud83d\udd39 Photoshop RemoteConnection",
+            "\ud83d\udd39ClipPass",
+            "\ud83d\udd39Floats",
+            "\ud83d\udd39Photoshop ComfyUI Plugin",
+            "\ud83d\udd39Photoshop Images",
+            "\ud83d\udd39Photoshop Strings",
+            "\ud83d\udd39Reroute - Anything Everywhere",
+            "\ud83d\udd39SeedManager",
+            "\ud83d\udd39SendTo Photoshop Plugin",
+            "\ud83d\udd39modelPass"
+        ],
+        {
+            "title_aux": "comfyui-photoshop-v2 [WIP]"
+        }
+    ],
+    "https://github.com/nidefawl/ComfyUI-nidefawl": [
+        [
+            "BlendImagesWithBoundedMasks",
+            "CropImagesWithMasks",
+            "CustomCallback",
+            "DisplayAnyType",
+            "EmptyImageWithColor",
+            "ImageToLatent",
+            "LatentPerlinNoise",
+            "LatentScaledNoise",
+            "LatentToImage",
+            "MaskFromColor",
+            "ModelSamplerTonemapNoiseTest",
+            "PythonScript",
+            "ReferenceOnlySimple",
+            "SamplerCustomCallback",
+            "SamplerDPMPP_2M_SDE_nidefawl",
+            "SetLatentCustomNoise",
+            "SplitCustomSigmas",
+            "VAELoaderDataType",
+            "gcLatentTunnel"
+        ],
+        {
+            "title_aux": "ComfyUI-nidefawl [UNSAFE]"
+        }
+    ],
+    "https://github.com/nikkuexe/ComfyUI-ListDataHelpers": [
+        [
+            "List Difference",
+            "VHS Output Filter",
+            "VHS_VideoOutputFilter"
+        ],
+        {
+            "title_aux": "List Data Helper Nodes"
+        }
+    ],
+    "https://github.com/nobinBB/comfyui-samenodes": [
+        [
+            "A1111PromptSplitter",
+            "BatchImageCompressor",
+            "BatchImageProcessor",
+            "CivitaiLoraSearcher",
+            "EmbeddingPathResolver",
+            "EmbeddingWildcardGenerator",
+            "ExtractPromptFromImage",
+            "FloatToString",
+            "FloatToStringWithPrefix",
+            "GetComfyInputPath",
+            "ImageFormatConverter",
+            "ImagesToPdf",
+            "ImpactWildcardProcessorSeed",
+            "LoRATagPowerLoaderExtended",
+            "LoRATextDualInput",
+            "LoraToCivitaiUrl",
+            "LoraWildcardGenerator",
+            "LoraWildcardGeneratorV2",
+            "RepeatTextLines",
+            "SDPromptSaverWithCompression",
+            "SeedStepN",
+            "TIPONobinCustom",
+            "TextSplit3"
+        ],
+        {
+            "title_aux": "comfyui-samenodes"
+        }
+    ],
+    "https://github.com/nomadop/ComfyUI-Video-Matting": [
+        [
+            "AlphaCombine",
+            "AlphaCurveBlend",
+            "AlphaDeflicker",
+            "ApplyAlpha",
+            "CutieLoader",
+            "CutieProcess",
+            "FlickerformerDeflicker",
+            "FlickerformerLoader",
+            "FrameSelector",
+            "ImageSequencePackager",
+            "MODNetInference",
+            "MODNetLoader",
+            "MaskToGrayscaleImage",
+            "OpticalFlowComputer",
+            "PreviewSlider",
+            "RAFTLoader",
+            "RMBGInference",
+            "RMBGLoader",
+            "RVMInference",
+            "RVMLoader",
+            "SpatialAlphaBlend",
+            "TrimapVisualize",
+            "TwoPassBlend",
+            "U2NetInference",
+            "U2NetLoader",
+            "ViTMatteLoader",
+            "ViTMatteRefine"
+        ],
+        {
+            "title_aux": "ComfyUI-Video-Matting [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/nomcycle/ComfyUI_Cluster": [
+        [
+            "ClusterBroadcastLoadedImage",
+            "ClusterBroadcastTensor",
+            "ClusterEndSubgraph",
+            "ClusterExecuteCurrentWorkflow",
+            "ClusterExecuteWorkflow",
+            "ClusterFanInImages",
+            "ClusterFanOutImage",
+            "ClusterFanOutLatent",
+            "ClusterFanOutMask",
+            "ClusterFinallyFree",
+            "ClusterFlattenBatchedImageList",
+            "ClusterFreeNow",
+            "ClusterGatherImages",
+            "ClusterGatherLatents",
+            "ClusterGatherMasks",
+            "ClusterGetInstanceWorkItemFromBatch",
+            "ClusterInfo",
+            "ClusterInsertAtIndex",
+            "ClusterListenTensorBroadcast",
+            "ClusterSplitBatchToList",
+            "ClusterStartSubgraph",
+            "ClusterStridedReorder",
+            "ClusterUseSubgraph"
+        ],
+        {
+            "title_aux": "ComfyUI_Cluster [WIP]"
+        }
+    ],
+    "https://github.com/nschpy/ComfyUI_MovisAdapter": [
+        [
+            "MPA Brightness Effect",
+            "MPA Combine Videos",
+            "MPA Contrast Effect",
+            "MPA Speed Effect",
+            "MPA Text Overlay",
+            "MPA Video Transition"
+        ],
+        {
+            "title_aux": "ComfyUI_MovisAdapter [UNSAFE]"
+        }
+    ],
+    "https://github.com/numq/comfyui-camera-capture-node": [
+        [
+            "CameraCapture"
+        ],
+        {
+            "title_aux": "comfyui-camera-capture-node"
+        }
+    ],
+    "https://github.com/obisin/ComfyUI-FSampler": [
+        [
+            "FSampler",
+            "FSamplerAdvanced",
+            "FSamplerSelect"
+        ],
+        {
+            "title_aux": "FSampler for ComfyUI \u2014 Fast Skips via Epsilon Extrapolation [WIP]"
+        }
+    ],
+    "https://github.com/octapus8085/OpenAI-comfyui-O": [
+        [
+            "AIImageNode",
+            "APIImageNode"
+        ],
+        {
+            "title_aux": "OpenAI-comfyui-O"
+        }
+    ],
+    "https://github.com/odedgranot/comfyui-ffmpeg-node": [
+        [
+            "FFmpegNode"
+        ],
+        {
+            "title_aux": "ComfyUI FFmpeg Node [UNSAFE]"
+        }
+    ],
+    "https://github.com/odedgranot/comfyui_video_save_node": [
+        [
+            "VideoSaveNode"
+        ],
+        {
+            "title_aux": "ComfyUI Video Save Node [UNSAFE]"
+        }
+    ],
+    "https://github.com/opj161/comfyui-lmstudio": [
+        [
+            "LMStudio_Unified_Chat"
+        ],
+        {
+            "title_aux": "comfyui-comfyui-lmstudio [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/orion4d/ComfyUI_unified_list_selector": [
+        [
+            "UnifiedListSelector"
+        ],
+        {
+            "title_aux": "Unified List Selector for ComfyUI [UNSAFE]"
+        }
+    ],
+    "https://github.com/orion4d/Orion4D_MasterPrompt": [
+        [
+            "list_selector_pro",
+            "mp_file_txt_pro",
+            "mp_folder_merge_lines",
+            "mp_format",
+            "mp_json_format",
+            "mp_json_kv_lines",
+            "mp_json_merge",
+            "mp_json_pick",
+            "mp_list_editor",
+            "mp_replace",
+            "mp_super_show_text",
+            "mp_wrap",
+            "multi_list_mixer",
+            "text_field_mixer",
+            "text_prompt_mixer"
+        ],
+        {
+            "title_aux": "Orion4D MasterPrompt Suite for ComfyUI [UNSAFE]"
+        }
+    ],
+    "https://github.com/orpheus-gaze/comfyui-sundryutils": [
+        [
+            "\u2728 Apply HaldClut",
+            "\u2728 Return DateString",
+            "\u2728 Sharpen, RealGrain & Autocontrast"
+        ],
+        {
+            "title_aux": "comfyui-sundryutils"
+        }
+    ],
+    "https://github.com/osuiso-depot/comfyui-keshigom_custom": [
+        [
+            "KANI_Checkpoint_Loader_From_String",
+            "KANI_MathExpression",
+            "KANI_Multiplexer",
+            "KANI_ShowAnything",
+            "KANI_TextFind",
+            "KANI_TrueorFalse",
+            "RegExTextChopper",
+            "ResolutionSelector",
+            "ResolutionSelectorConst",
+            "StringNodeClass"
+        ],
+        {
+            "title_aux": "comfyui-keshigom_custom"
+        }
+    ],
+    "https://github.com/outliner/ComfyUI-QwenRemoveBackground": [
+        [
+            "QwenImageEditRemoveBackground"
+        ],
+        {
+            "title_aux": "ComfyUI-QwenRemoveBackground [WIP]"
+        }
+    ],
+    "https://github.com/owengillett/ComfyUI-tilefusion": [
+        [
+            "VideoGridCombine"
+        ],
+        {
+            "title_aux": "ComfyUI-tilefusion"
+        }
+    ],
+    "https://github.com/oxysoft/Comfy-Compel": [
+        [
+            "CLIPEmbedCompel"
+        ],
+        {
+            "title_aux": "Comfy-Compel"
+        }
+    ],
+    "https://github.com/oztrkoguz/ComfyUI_Kosmos2_BBox_Cutter": [
+        [
+            "Kosmos2SamplerSimple",
+            "KosmosLoader",
+            "Write"
+        ],
+        {
+            "title_aux": "Kosmos2_BBox_Cutter Models"
+        }
+    ],
+    "https://github.com/p1atdev/comfyui-aesthetic-predictor": [
+        [
+            "LoadAestheticPredictorNode",
+            "PredictAestheticScore"
+        ],
+        {
+            "title_aux": "comfyui-aesthetic-predictor"
+        }
+    ],
+    "https://github.com/pacchikAI/ImagePromptBatch": [
+        [
+            "LoadImageandPrompt"
+        ],
+        {
+            "title_aux": "ImagePromptBatch [UNSAFE]"
+        }
+    ],
+    "https://github.com/pacchikAI/comfyui_pacchik_window": [
+        [
+            "DynamicSelectionNode",
+            "JsonSelectionNode",
+            "StaticSelectionNode"
+        ],
+        {
+            "title_aux": "comfyui_pacchik_window [UNSAFE]"
+        }
+    ],
+    "https://github.com/panchovial-max/ComfyUI-ListHelper": [
+        [
+            "AudioListCombine",
+            "ListFilter",
+            "ListTransform",
+            "NumberListGenerator",
+            "PromptSplitByDelimiter"
+        ],
+        {
+            "title_aux": "ComfyUI-ListHelper [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/papcorns/ComfyUI-Papcorns-Node-UploadToGCS": [
+        [
+            "UploadImageToGCS"
+        ],
+        {
+            "title_aux": "ComfyUI-Papcorns-Node-UploadToGCS"
+        }
+    ],
+    "https://github.com/paragdhersarepaisewala/Prompt-Cycler-Node---ComfyUI-Custom-Node": [
+        [
+            "PromptLineCycler"
+        ],
+        {
+            "title_aux": "Prompt-Cycler-Node---ComfyUI-Custom-Node [WIP]"
+        }
+    ],
+    "https://github.com/parmarjh/ComfyUI-MochiWrapper-I2V": [
+        [
+            "DownloadAndLoadMochiModel",
+            "MochiDecode",
+            "MochiDecodeSpatialTiling",
+            "MochiFasterCache",
+            "MochiImageEncode",
+            "MochiLatentPreview",
+            "MochiModelLoader",
+            "MochiSampler",
+            "MochiSigmaSchedule",
+            "MochiTextEncode",
+            "MochiTorchCompileSettings",
+            "MochiVAEEncoderLoader",
+            "MochiVAELoader"
+        ],
+        {
+            "title_aux": "ComfyUI-MochiWrapper-I2V [WIP]"
+        }
+    ],
+    "https://github.com/paulhoux/Smart-Prompting": [
+        [
+            "SaveImageWithPrefix",
+            "TextAppend",
+            "TextCharacterSelector",
+            "TextEncodeReusable",
+            "TextFile",
+            "TextNegatives",
+            "TextSearchReplace",
+            "TextString",
+            "TextStyleSelector"
+        ],
+        {
+            "title_aux": "List Data Helper Nodes"
+        }
+    ],
+    "https://github.com/peterkickasspeter-civit/ComfyUI-ZImageTurboProgressiveLockedUpscale": [
+        [
+            "ZImageTurboProgressiveLockedUpscale"
+        ],
+        {
+            "title_aux": "ComfyUI-ZImageTurboProgressiveLockedUpscale"
+        }
+    ],
+    "https://github.com/pfpb/comfyui-badnodes": [
+        [
+            "ReplaceEmptyMasksWithLastMasks"
+        ],
+        {
+            "title_aux": "comfyui-badnodes"
+        }
+    ],
+    "https://github.com/pft-ChenKu/ComfyUI_system-dev": [
+        [
+            "TY_ExecutionTime"
+        ],
+        {
+            "title_aux": "ComfyUI_system-dev [WIP]"
+        }
+    ],
+    "https://github.com/phamngoctukts/ComyUI-Tupham": [
+        [
+            "AreaCondition_v2",
+            "ConditionUpscale",
+            "MultiLatent",
+            "Runnodeselected",
+            "ghepanh"
+        ],
+        {
+            "title_aux": "ComyUI-Tupham"
+        }
+    ],
+    "https://github.com/pickles/ComfyUI-PyPromptGenerator": [
+        [
+            "PyPromptFileGeneratorNode",
+            "PyPromptGeneratorNode"
+        ],
+        {
+            "title_aux": "PyPromptGenerator [UNSAFE]"
+        }
+    ],
+    "https://github.com/pictorialink/ComfyUI-static-resource": [
+        [
+            "StaticResource"
+        ],
+        {
+            "title_aux": "comfyui-static-resource[UNSAFE]"
+        }
+    ],
+    "https://github.com/pineambassador/ComfyUI-ID-Lora-Pine": [
+        [
+            "IDLoRAAudioNoiseInjector",
+            "IDLoRAGuider",
+            "IDLoRAPrepareAudioReference",
+            "IDLoRAPrepareVideo",
+            "IDLoRAPromptFormatter"
+        ],
+        {
+            "title_aux": "ComfyUI-ID-Lora-Pine [WIP]"
+        }
+    ],
+    "https://github.com/pinkpixel-dev/comfyui-llm-prompt-enhancer": [
+        [
+            "PromptEnhancer"
+        ],
+        {
+            "title_aux": "ComfyUI LLM Prompt Enhancer [WIP]"
+        }
+    ],
+    "https://github.com/pixuai/ComfyUI-PixuAI": [
+        [
+            "PromptSearch"
+        ],
+        {
+            "title_aux": "ComfyUI-PixuAI"
+        }
+    ],
+    "https://github.com/pizurny/Comfyui-FeedbackSampler": [
+        [
+            "FeedbackSampler"
+        ],
+        {
+            "title_aux": "ComfyUI Feedback Sampler [WIP]"
+        }
+    ],
+    "https://github.com/pmarmotte2/Comfyui-VibeVoiceSelector": [
+        [
+            "VibeVoiceSelector"
+        ],
+        {
+            "title_aux": "VibeVoiceSelector [WIP]"
+        }
+    ],
+    "https://github.com/pocotaru/comfyui_poco_tools": [
+        [
+            "SamplerPipeIn",
+            "SamplerPipeOut",
+            "VAENames"
+        ],
+        {
+            "title_aux": "comfyui_poco_tools"
+        }
+    ],
+    "https://github.com/poisenbery/NudeNet-Detector-Provider": [
+        [
+            "NudeNetDetectorProvider"
+        ],
+        {
+            "title_aux": "NudeNet-Detector-Provider [WIP]"
+        }
+    ],
+    "https://github.com/pollockjj/ComfyUI-JAXTools": [
+        [
+            "JAX_AdjustBrightness",
+            "JAX_AdjustContrast",
+            "JAX_AdjustGamma",
+            "JAX_AdjustHue",
+            "JAX_FlipUpDown",
+            "JAX_GaussianBlur",
+            "JAX_StyleTransfer"
+        ],
+        {
+            "title_aux": "JAXTools"
+        }
+    ],
+    "https://github.com/pomelyu/cy-prompt-tools": [
+        [
+            "CY_LLM",
+            "CY_LoadPrompt",
+            "CY_LoadPrompt4",
+            "CY_LoadPromptPro",
+            "CY_PromptComposer",
+            "CY_TextBox"
+        ],
+        {
+            "title_aux": "cy-prompt-tools"
+        }
+    ],
+    "https://github.com/power88/ComfyUI-PDiD-Nodes": [
+        [
+            "Blend Images",
+            "BlendTwoImages",
+            "Check Character Tag",
+            "CheckAnimeCharacter",
+            "CollectImageSize",
+            "Get Image Colors",
+            "Get image size",
+            "GetImageMainColor",
+            "List Operations",
+            "ListOperation",
+            "Make Image Gray",
+            "Nearest SDXL Resolution divided by 64",
+            "NearestSDXLResolutionby64",
+            "RemoveSaturation"
+        ],
+        {
+            "title_aux": "ComfyUI-PDiD-Nodes [WIP]"
+        }
+    ],
+    "https://github.com/prabinpebam/anyPython": [
+        [
+            "Any Python"
+        ],
+        {
+            "author": "prabinpebam",
+            "description": "This node can execute Python operations with user-confirmed risk management",
+            "nickname": "anyPython",
+            "title": "anyPython v0.0.3",
+            "title_aux": "anyPython [UNSAFE]"
+        }
+    ],
+    "https://github.com/prodogape/ComfyUI-clip-interrogator": [
+        [
+            "ComfyUIClipInterrogator"
+        ],
+        {
+            "title_aux": "ComfyUI-clip-interrogator [WIP]"
+        }
+    ],
+    "https://github.com/przewodo/ComfyUI-Przewodo-Utils": [
+        [
+            "przewodo AppendToAnyList",
+            "przewodo BatchImagesFromPath",
+            "przewodo CompareNumbersToCombo",
+            "przewodo DebugLatentShapes",
+            "przewodo FloatIfElse",
+            "przewodo HasInputvalue",
+            "przewodo ImageScaleFactor",
+            "przewodo ImageSizer",
+            "przewodo IsInputDisabled",
+            "przewodo LTXVAEDriftFix",
+            "przewodo LtxKeyFrameIndexes",
+            "przewodo SendFirstValidValue",
+            "przewodo SwapAnyComparison",
+            "przewodo SwapAnyCondition",
+            "przewodo SwapImageComparison",
+            "przewodo TimeToFrames",
+            "przewodo WanFirstLastFirstFrameToVideo",
+            "przewodo WanGetMaxImageResolutionByAspectRatio",
+            "przewodo WanImageToVideoAdvancedSampler",
+            "przewodo WanModelTypeSelector",
+            "przewodo WanPromptChunkStacker",
+            "przewodo WanVideoEnhanceAVideo",
+            "przewodo WanVideoGenerationModeSelector",
+            "przewodo WanVideoLoraStack",
+            "przewodo WanVideoVaeDecode"
+        ],
+        {
+            "title_aux": "ComfyUI-Przewodo-Utils [WIP]"
+        }
+    ],
+    "https://github.com/pschroedl/ComfyUI-StreamDiffusion": [
+        [
+            "StreamDiffusionAdvancedConfig",
+            "StreamDiffusionCheckpointLoader",
+            "StreamDiffusionConfig",
+            "StreamDiffusionLPCheckpointLoader",
+            "StreamDiffusionLoraLoader",
+            "StreamDiffusionPrebuiltConfig",
+            "StreamDiffusionSampler",
+            "StreamDiffusionTensorRTEngineLoader"
+        ],
+        {
+            "title_aux": "ComfyUI-StreamDiffusion"
+        }
+    ],
+    "https://github.com/ptmff/ComfyUI-Nodes8734": [
+        [
+            "MyFirstNode"
+        ],
+        {
+            "title_aux": "ComfyUI-Nodes8734"
+        }
+    ],
+    "https://github.com/punicfaith/ComfyUI-GoogleAIStudio": [
+        [
+            "BatchImageNormalizer",
+            "GoogleGeminiPrompt",
+            "NanobananaNode"
+        ],
+        {
+            "title_aux": "ComfyUI-GoogleAIStudio"
+        }
+    ],
+    "https://github.com/pururin777/ComfyUI-Manual-Openpose": [
+        [
+            "Manual Openpose Setter"
+        ],
+        {
+            "title_aux": "JasonW146"
+        }
+    ],
+    "https://github.com/pzzmyc/comfyui-sd3-simple-simpletuner": [
+        [
+            "sd not very simple simpletuner by hhy",
+            "simpletunerpro"
+        ],
+        {
+            "title_aux": "comfyui-sd3-simple-simpletuner"
+        }
+    ],
+    "https://github.com/qimi-dev/ComfyUI-Qimi-Tiler": [
+        [
+            "Qimi-ImageToTiles",
+            "Qimi-TilesImage"
+        ],
+        {
+            "title_aux": "ComfyUI-Qimi-Tiler"
+        }
+    ],
+    "https://github.com/quinteroac/comfyui_api_executor_nodes": [
+        [
+            "AnyTo",
+            "ImageInputCombiner",
+            "ImageInputPair",
+            "IntInputCombiner",
+            "IntInputPair",
+            "RemoteWorkflowAPIExecutor",
+            "SimpleText",
+            "SimpleWorkflowAPIExecutor",
+            "TextImageToImageWorkflowSelector",
+            "TextImageToVideoWorkflowSelector",
+            "TextInputCombiner",
+            "TextInputPair",
+            "TextToImageWorkflowSelector",
+            "TextToVideoWorkflowSelector",
+            "VideoInputPair",
+            "WorkflowAPIExecutor",
+            "YandereRandomTags"
+        ],
+        {
+            "title_aux": "comfyui_api_executor_nodes"
+        }
+    ],
+    "https://github.com/r-vage/ComfyUI_Eclipse": [
+        [
+            "Add Filename Prefix [Eclipse]",
+            "Add Folder [Eclipse]",
+            "Add Watermark Image [Eclipse]",
+            "Any Dual-Switch Purge [Eclipse]",
+            "Any Dual-Switch [Eclipse]",
+            "Any Multi-Switch Lazy Purge [Eclipse]",
+            "Any Multi-Switch Lazy [Eclipse]",
+            "Any Multi-Switch Purge [Eclipse]",
+            "Any Multi-Switch [Eclipse]",
+            "Any Passer Purge [Eclipse]",
+            "Any Passer [Eclipse]",
+            "Audio Passer [Eclipse]",
+            "Basic Pipe Passer [Eclipse]",
+            "Batch Interleave [Eclipse]",
+            "Batch Slice [Eclipse]",
+            "Boolean Passer [Eclipse]",
+            "Boolean [Eclipse]",
+            "CLIP Loader [Eclipse]",
+            "CLIP Text Encode [Eclipse]",
+            "CLIPLoaderGGUF",
+            "Checkpoint Loader Small (Pipe) [Eclipse]",
+            "Checkpoint Loader Small [Eclipse]",
+            "Clip Passer [Eclipse]",
+            "Color Match [Eclipse]",
+            "Combine Video Clips [Eclipse]",
+            "Concat Pipe Multi [Eclipse]",
+            "Conditioning Passer [Eclipse]",
+            "Conditioning Zero Out [Eclipse]",
+            "Context Image [Eclipse]",
+            "Context Video (WVW) [Eclipse]",
+            "Context Video [Eclipse]",
+            "ControlNet Passer [Eclipse]",
+            "ControlNet Set Union Types (Flux) [Eclipse]",
+            "Convert Primitive [Eclipse]",
+            "Convert To Batch [Eclipse]",
+            "Convert to List [Eclipse]",
+            "Custom Size [Eclipse]",
+            "Detailer Pipe Passer [Eclipse]",
+            "Detection to Bboxes [Eclipse]",
+            "DualCLIPLoaderGGUF",
+            "Fast Bypasser [Eclipse]",
+            "Fast Groups Bypasser [Eclipse]",
+            "Fast Groups Muter [Eclipse]",
+            "Fast Mode Switcher [Eclipse]",
+            "Fast Mode Toggle [Eclipse]",
+            "Fast Muter [Eclipse]",
+            "Float Passer [Eclipse]",
+            "Float [Eclipse]",
+            "Folder Path [Eclipse]",
+            "Generation Data (Gated) [Eclipse]",
+            "Generation Data [Eclipse]",
+            "Get First Image [Eclipse]",
+            "Get Last Image [Eclipse]",
+            "IF A Else B Fallback [Eclipse]",
+            "IF A Else B [Eclipse]",
+            "IO Checkpoint Loader [Eclipse]",
+            "IO Checkpoint Loader v2 [Eclipse]",
+            "IO Load Image [Eclipse]",
+            "Image Align Size [Eclipse]",
+            "Image Batch Extend With Overlap [Eclipse]",
+            "Image Batch Strip [Eclipse]",
+            "Image Comparer [Eclipse]",
+            "Image Convert [Eclipse]",
+            "Image Crop by Mask [Eclipse]",
+            "Image Filter Adjustments [Eclipse]",
+            "Image Passer [Eclipse]",
+            "Image Rescale [Eclipse]",
+            "Image Resize [Eclipse]",
+            "Image Resolution [Eclipse]",
+            "Image Selector [Eclipse]",
+            "Image Soften [Eclipse]",
+            "Image Upscale With Model [Eclipse]",
+            "Image with FX [Eclipse]",
+            "Inset & Crop [Eclipse]",
+            "Int Passer [Eclipse]",
+            "Integer Generate [Eclipse]",
+            "Integer [Eclipse]",
+            "Join [Eclipse]",
+            "Keep Calculator [Eclipse]",
+            "Latent Passer [Eclipse]",
+            "Load Audio [Eclipse]",
+            "Load Batch From Folder [Eclipse]",
+            "Load Directory Settings [Eclipse]",
+            "Load Image (Metadata Pipe) [Eclipse]",
+            "Load Image (Pipe) [Eclipse]",
+            "Load Image From Folder (Pipe) [Eclipse]",
+            "Load Image From Folder [Eclipse]",
+            "Load Image from Path (Metadata Pipe) [Eclipse]",
+            "Load Image from Path (Metadata) [Eclipse]",
+            "Loop Calculator Audio [Eclipse]",
+            "Loop Calculator [Eclipse]",
+            "Lora Stack [Eclipse]",
+            "Lora Stack apply [Eclipse]",
+            "Lora Stack to String [Eclipse]",
+            "Mask Passer [Eclipse]",
+            "Merge Strings [Eclipse]",
+            "Mode Bridge Get [Eclipse]",
+            "Mode Bridge Set [Eclipse]",
+            "Mode Bridge [Eclipse]",
+            "Mode Relay [Eclipse]",
+            "Model Loader Pipe [Eclipse]",
+            "Model Loader [Eclipse]",
+            "Model Passer [Eclipse]",
+            "Mute / Bypass Repeater [Eclipse]",
+            "Node Collector [Eclipse]",
+            "None [Eclipse]",
+            "Nunchaku PuLID Apply [Eclipse]",
+            "Nunchaku PuLID Loader [Eclipse]",
+            "Pipe 12CH Any [Eclipse]",
+            "Pipe 24CH Any [Eclipse]",
+            "Pipe 36CH Any [Eclipse]",
+            "Pipe IO Sampler Settings [Eclipse]",
+            "Pipe IO Sampler Settings v2 [Eclipse]",
+            "Pipe IO Sampler Settings v2.1 [Eclipse]",
+            "Pipe IO Sampler Settings v2.2 [Eclipse]",
+            "Pipe Out Checkpoint Loader [Eclipse]",
+            "Pipe Out LM Advanced Options [Eclipse]",
+            "Pipe Out LM Advanced Options [SmartLML]",
+            "Pipe Out Load Directory Settings [Eclipse]",
+            "Pipe Out Load Image (Metadata Pipe) [Eclipse]",
+            "Pipe Out Sampler Settings [Eclipse]",
+            "Pipe Out Smart Folder [Eclipse]",
+            "Pipe Out VC Name Generator [Eclipse]",
+            "Pipe Out WanVideo Setup [Eclipse]",
+            "Pipe Passer [Eclipse]",
+            "Preview Image (DOM) [Eclipse]",
+            "Preview Image [Eclipse]",
+            "Preview Mask [Eclipse]",
+            "Preview Video [Eclipse]",
+            "Prompt Styler [Eclipse]",
+            "QuadrupleCLIPLoaderGGUF",
+            "RAM Cleanup [Eclipse]",
+            "RIFE Multiplier [Eclipse]",
+            "Read Prompt Files [Eclipse]",
+            "Replace String [Eclipse]",
+            "Replace String v2 [Eclipse]",
+            "Replace String v3 [Eclipse]",
+            "Resolution Scale [Eclipse]",
+            "SEGS Passer [Eclipse]",
+            "SEGS Preview [Eclipse]",
+            "Sampler Selection [Eclipse]",
+            "Sampler Settings NI [Eclipse]",
+            "Sampler Settings NI v2 [Eclipse]",
+            "Sampler Settings NI+Seed [Eclipse]",
+            "Sampler Settings NI+Seed v2 [Eclipse]",
+            "Sampler Settings NI+Seed v2.1 [Eclipse]",
+            "Sampler Settings Small [Eclipse]",
+            "Sampler Settings Small+Seed [Eclipse]",
+            "Sampler Settings [Eclipse]",
+            "Sampler Settings+Seed [Eclipse]",
+            "Sampler Settings+Seed v2 [Eclipse]",
+            "Save Images [Eclipse]",
+            "Save Images v2 [Eclipse]",
+            "Save Prompt [Eclipse]",
+            "Save Video [Eclipse]",
+            "Seamless Join Video Clips [Eclipse]",
+            "Seed 32-bit [Eclipse]",
+            "Seed [Eclipse]",
+            "Show Any [Eclipse]",
+            "Show Text [Eclipse]",
+            "Smart Detection [Eclipse]",
+            "Smart Detection [SML]",
+            "Smart Folder [Eclipse]",
+            "Smart Folder v2 [Eclipse]",
+            "Smart LM Loader [Eclipse]",
+            "Smart Language Model Loader v2 [Eclipse]",
+            "Smart Language Model Loader v2 [SmartLML]",
+            "Smart Language Model Loader v3 [SmartLML]",
+            "Smart Loader Basic [Eclipse]",
+            "Smart Loader Basic v2 [Eclipse]",
+            "Smart Loader Plus [Eclipse]",
+            "Smart Loader Plus v2 [Eclipse]",
+            "Smart Loader [Eclipse]",
+            "Smart Loader v2 [Eclipse]",
+            "Smart Model Loader [Eclipse]",
+            "Smart Model Loader [SML]",
+            "Smart Prompt [Eclipse]",
+            "Smart Prompt v2 [Eclipse]",
+            "Smart Sampler Settings [Eclipse]",
+            "Smart Sampler Settings v2 [Eclipse]",
+            "Stop [Eclipse]",
+            "String DeDuplicate [Eclipse]",
+            "String Dual [Eclipse]",
+            "String Multiline List [Eclipse]",
+            "String Multiline [Eclipse]",
+            "String Passer [Eclipse]",
+            "String [Eclipse]",
+            "String from List [Eclipse]",
+            "Text Image with FX [Eclipse]",
+            "Tile Assembly [Eclipse]",
+            "Tile Decode & Assembly [Eclipse]",
+            "Tile Split [Eclipse]",
+            "TripleCLIPLoaderGGUF",
+            "UnetLoaderGGUF",
+            "UnetLoaderGGUFAdvanced",
+            "Universal Block Swap [Eclipse]",
+            "VAE Loader Video+Audio [Eclipse]",
+            "VAE Loader [Eclipse]",
+            "VAE Passer [Eclipse]",
+            "VC-Filename Generator I [Eclipse]",
+            "VC-Filename Generator II [Eclipse]",
+            "VRAM Cleanup [Eclipse]",
+            "Video Frame Consistency [Eclipse]",
+            "Video Resolution [Eclipse]",
+            "WAN Model Passer [Eclipse]",
+            "WanVideo Setup [Eclipse]",
+            "Widget to String [Eclipse]",
+            "Wildcard Processor [Eclipse]"
+        ],
+        {
+            "title_aux": "ComfyUI_Eclipse [UNSAFE]"
+        }
+    ],
+    "https://github.com/rafstahelin/ComfyUI_KieNanoBananaPro": [
+        [
+            "KieNanoBananaPro",
+            "KieNanoBananaProBatch"
+        ],
+        {
+            "title_aux": "ComfyUI_KieNanoBananaPro"
+        }
+    ],
+    "https://github.com/rakete/comfyui-rakete": [
+        [
+            "rakete.BuildString",
+            "rakete.GetWidgetValue",
+            "rakete.GpuGarbageCollector",
+            "rakete.JoinStrings",
+            "rakete.SelectSamplerName"
+        ],
+        {
+            "author": "Rakete",
+            "description": "Rakete Comfy Custom Nodes",
+            "nickname": "Rakete Nodes",
+            "title": "Rakete Nodes",
+            "title_aux": "comfyui-rakete"
+        }
+    ],
+    "https://github.com/ralonsobeas/ComfyUI-HDRConversion": [
+        [
+            "HDRConversion"
+        ],
+        {
+            "title_aux": "ComfyUI-HDRConversion [WIP]"
+        }
+    ],
+    "https://github.com/raohammad/ComfyUI-VTUtilNodes": [
+        [
+            "JSONKeyExtractor",
+            "JSONListIterator",
+            "JSONQueue",
+            "JSONQueueOutput",
+            "JSONQueueSignal",
+            "SignalCounter",
+            "SimpleCounter",
+            "TextToJSON"
+        ],
+        {
+            "title_aux": "ComfyUI-VTUtilNodes [WIP]"
+        }
+    ],
+    "https://github.com/rdanalex/Comfyui-Realism-Scorer": [
+        [
+            "RealismScoreNode",
+            "VideoRealismScoreNode"
+        ],
+        {
+            "title_aux": "Comfyui-Realism-Scorer"
+        }
+    ],
+    "https://github.com/realm-weaver/ComfyUI-tile-seamstress-360": [
+        [
+            "RW_EquirectangularMask",
+            "RW_EquirectangularSphericalRotate",
+            "RW_PostSeamlessUpscale",
+            "RW_PreSeamlessUpscale",
+            "RW_ResizeToProperRatio",
+            "RW_SegmentBlend"
+        ],
+        {
+            "author": "realm-weaver",
+            "description": "Tools for touching up seams and poles on 360\u00b0 panoramic images and flat tilings.",
+            "nickname": "ts360",
+            "title": "Tile Seamstress 360",
+            "title_aux": "Tile Seamstress 360\u00b0 [WIP]"
+        }
+    ],
+    "https://github.com/redhottensors/ComfyUI-ODE": [
+        [
+            "ODESamplerSelect"
+        ],
+        {
+            "author": "RedHotTensors",
+            "description": "Adaptive ODE Solvers for ComfyUI",
+            "nickname": "ComfyUI-ODE",
+            "title": "ComfyUI-ODE",
+            "title_aux": "ComfyUI-ODE"
+        }
+    ],
+    "https://github.com/remhesneb/comfyui-remote-media": [
+        [
+            "LoadImageByUrl",
+            "LoadVideoByUrl"
+        ],
+        {
+            "title_aux": "comfyui-remote-media"
+        }
+    ],
+    "https://github.com/retech995/Save_Florence2_Bulk_Prompts": [
+        [
+            "SaveTextFlorence"
+        ],
+        {
+            "title_aux": "ComfyUI_SaveImageBulk [UNSAFE]"
+        }
+    ],
+    "https://github.com/rhinoflavored/comfyui_QT": [
+        [
+            "CSVDataMatcher",
+            "QTAutoCropByNPS",
+            "QTExcelImageReader",
+            "QTExcelReader",
+            "QTRandomSelectString",
+            "QTStringWrappingByNumber",
+            "QT_Alpha_Yaxis_Node",
+            "QT_AntiAliasing_Node",
+            "QT_Batch_Anything_Node",
+            "QT_Center_Rotation",
+            "QT_Character_Height_Difference",
+            "QT_Character_Size_Node",
+            "QT_Color_Image_Loop",
+            "QT_Content_Location_Node",
+            "QT_Crop_Alpha",
+            "QT_Crop_Alpha_V2",
+            "QT_Curves_Node",
+            "QT_Dictionary_Node",
+            "QT_Elements_Into_List_Node",
+            "QT_Float_To_Int",
+            "QT_Image_Array",
+            "QT_Image_Array_Circle",
+            "QT_Image_Array_Rectangle",
+            "QT_Image_Overlay",
+            "QT_Image_Overlay_BOOLEAN",
+            "QT_Image_Overlay_Rotation",
+            "QT_Image_Overlay_V2",
+            "QT_Image_Overlay_V3",
+            "QT_Image_Sorting_Node",
+            "QT_Image_Upscale_And_Crop_Node",
+            "QT_Image_Upscale_And_Crop_Node_V2",
+            "QT_Image_Upscale_And_Crop_Node_V3",
+            "QT_Image_Upscale_Node",
+            "QT_Image_Vision_Center_Node",
+            "QT_Join_Image_List_Node",
+            "QT_Line_Break",
+            "QT_Line_Break_V2",
+            "QT_List_Length",
+            "QT_List_Picker",
+            "QT_List_To_String",
+            "QT_Mask_Mix_Node",
+            "QT_Merge_Into_List_Node",
+            "QT_Pageturn_Node",
+            "QT_Pattern_Fill",
+            "QT_Piecewise_Function_Node",
+            "QT_Polar_Coordinate_Conversion_Node",
+            "QT_Rounded_Corner",
+            "QT_SUPIR_Upscale",
+            "QT_Simple_Text_Image_V2",
+            "QT_Sorting_Node",
+            "QT_Split_List_Node",
+            "QT_Split_List_Node_V2",
+            "QT_Split_Mask_Node",
+            "QT_Split_String",
+            "QT_String_Horizontal_To_Vertical",
+            "QT_String_To_List",
+            "QT_String_Wrapping_By_Number",
+            "QT_Text_Input_Switch_Node",
+            "QT_Text_Overlay_V2",
+            "QT_Text_To_Bool_Node",
+            "QT_Tilt_Transform",
+            "QT_Translucent_Node",
+            "QT_Vertical_Text_Overlay",
+            "QT_Video_Combine_Node"
+        ],
+        {
+            "title_aux": "comfyui_QT"
+        }
+    ],
+    "https://github.com/ric-yu/comfyui-datadog-monitor": [
+        [
+            "DatadogMemoryProfiler"
+        ],
+        {
+            "title_aux": "ComfyUI Datadog Monitor [WIP]"
+        }
+    ],
+    "https://github.com/richardctw/ComfyUI-ImageFillBackgroundColor": [
+        [
+            "ImageFillBackgroundColor"
+        ],
+        {
+            "title_aux": "ComfyUI-ImageFillBackgroundColor"
+        }
+    ],
+    "https://github.com/ricklove/ComfyUI-AutoSeg-SAM2": [
+        [
+            "AutoSegSAM2Node"
+        ],
+        {
+            "title_aux": "ComfyUI-AutoSeg-SAM2"
+        }
+    ],
+    "https://github.com/rickyars/sd-cn-animation": [
+        [
+            "SDCNAnimation",
+            "SDCNAnimationAdvanced"
+        ],
+        {
+            "title_aux": "sd-cn-animation"
+        }
+    ],
+    "https://github.com/rishipandey125/ComfyUI-FramePacking": [
+        [
+            "Batch Keyframes",
+            "Get Image Dimensions",
+            "Image Mix RGB",
+            "Pad Batch to 4n+1",
+            "Resize Frame",
+            "Slot Frame",
+            "Threshold Image",
+            "Trim Padded Batch"
+        ],
+        {
+            "title_aux": "ComfyUI-FramePacking [WIP]"
+        }
+    ],
+    "https://github.com/rishipandey125/ComfyUI-StyleFrame-Nodes": [
+        [
+            "Batch Keyframes",
+            "Canny Edge",
+            "Combine RGB Channels",
+            "Create Empty Frames",
+            "Crop Guide Frames",
+            "Get Image Dimensions",
+            "Image Grayscale",
+            "LTXVMultiKeyframeGuide",
+            "LTXVMultiLatentGuide",
+            "Load Image Folder",
+            "Load Images",
+            "Load Images From List",
+            "Load Media Sequence",
+            "Pad Batch to 4n+1",
+            "Pad Batch to kn+1",
+            "Repeat Frames",
+            "Resample Frames",
+            "Resize Frame",
+            "Save Image Folder",
+            "Select Image From Batch",
+            "Slot Frame",
+            "Split RGB Channels",
+            "String To Float List",
+            "Threshold Image",
+            "Trim Padded Batch"
+        ],
+        {
+            "title_aux": "ComfyUI-StyleFrame-Nodes"
+        }
+    ],
+    "https://github.com/risunobushi/ComfyUI_FaceMesh_Eyewear_Mask": [
+        [
+            "FaceMeshEyewearMask",
+            "MaskFromFacialKeypoints",
+            "MediaPipeFaceToMask",
+            "OpenPoseEyewearMask"
+        ],
+        {
+            "title_aux": "ComfyUI_FaceMesh_Eyewear_Mask"
+        }
+    ],
+    "https://github.com/risunobushi/ComfyUI_FocusMask": [
+        [
+            "FocusMaskExtractor",
+            "FocusOutlineExtractor"
+        ],
+        {
+            "title_aux": "ComfyUI_FocusMask"
+        }
+    ],
+    "https://github.com/risunobushi/ComfyUI_HEXtoRGB": [
+        [
+            "HexToRGB"
+        ],
+        {
+            "title_aux": "ComfyUI_HEXtoRGB"
+        }
+    ],
+    "https://github.com/ritikvirus/comfyui-terminal-modal-node": [
+        [
+            "terminal_node"
+        ],
+        {
+            "title_aux": "ComfyUI Terminal Command Node [UNSAFE]"
+        }
+    ],
+    "https://github.com/romandev-codex/ComfyUI-DevTools": [
+        [
+            "FixFrames",
+            "LastFrameFromFrames",
+            "PassThrough10",
+            "PassThroughAny"
+        ],
+        {
+            "title_aux": "ComfyUI-DevTools"
+        }
+    ],
+    "https://github.com/ronalds-eu/comfyui-plus-integrations": [
+        [
+            "ImagePassThrough",
+            "ImageToS3"
+        ],
+        {
+            "title_aux": "comfyui-plus-integrations [WIP]"
+        }
+    ],
+    "https://github.com/rossturk/comfyui-primitive-mesh": [
+        [
+            "PrimitiveMeshNode"
+        ],
+        {
+            "title_aux": "ComfyUI Primitive Mesh Generator [WIP]"
+        }
+    ],
+    "https://github.com/rouxianmantou/comfyui-rxmt-nodes": [
+        [
+            "CheckValueTypeNode",
+            "ConvertStrToIntNode",
+            "TextCombineWithCommaNode",
+            "WhyPromptTextNode"
+        ],
+        {
+            "title_aux": "comfyui-rxmt-nodes"
+        }
+    ],
+    "https://github.com/rphmeier/comfyui-videodepthanything": [
+        [
+            "VideoDepthAnythingLoader",
+            "VideoDepthAnythingProcess"
+        ],
+        {
+            "title_aux": "comfyui-videodepthanything"
+        }
+    ],
+    "https://github.com/ruka-game/rukalib_comfyui": [
+        [
+            "RukaDebugProbe",
+            "RukaPromptEnhancer"
+        ],
+        {
+            "title_aux": "ComfyUI RukaLib [WIP]"
+        }
+    ],
+    "https://github.com/ryanontheinside/ComfyUI-Livepeer": [
+        [
+            "BatchInfo",
+            "BatchIterator",
+            "LivepeerA2T",
+            "LivepeerAudioJobGetter",
+            "LivepeerI2I",
+            "LivepeerI2T",
+            "LivepeerI2V",
+            "LivepeerImageJobGetter",
+            "LivepeerLLM",
+            "LivepeerLive2Video",
+            "LivepeerSegment",
+            "LivepeerT2I",
+            "LivepeerT2S",
+            "LivepeerTextJobGetter",
+            "LivepeerUpscale",
+            "LivepeerVideoJobGetter"
+        ],
+        {
+            "title_aux": "ComfyUI-Livepeer [WIP]"
+        }
+    ],
+    "https://github.com/ryanontheinside/ComfyUI-MineWorld": [
+        [
+            "MineWorldGenerateFrame",
+            "MineWorldGenerateSequence",
+            "MineWorldInitialState",
+            "MineWorldModelLoader"
+        ],
+        {
+            "title_aux": "ComfyUI MineWorld Nodes [WIP]"
+        }
+    ],
+    "https://github.com/ryanontheinside/ComfyUI_YoloNasObjectDetection_Tensorrt": [
+        [
+            "YoloNasDetectionTensorrt"
+        ],
+        {
+            "title_aux": "ComfyUI_YoloNasObjectDetection_Tensorrt [WIP]"
+        }
+    ],
+    "https://github.com/sahibalejandro/comfyui-sahib-nodes": [
+        [
+            "ChooseResolution"
+        ],
+        {
+            "title_aux": "ComfyUI Sahib Nodes"
+        }
+    ],
+    "https://github.com/saltchicken/ComfyUI-Identity-Mixer": [
+        [
+            "IdentityLoraMixer",
+            "IdentityLoraMixerDirectory",
+            "IdentityLoraMixerStack",
+            "IdentityLoraMixerStackDirectory"
+        ],
+        {
+            "title_aux": "ComfyUI-Identity-Mixer"
+        }
+    ],
+    "https://github.com/saltchicken/ComfyUI-Interactive-Cropper": [
+        [
+            "InteractiveCropNode"
+        ],
+        {
+            "title_aux": "ComfyUI-Interactive-Cropper"
+        }
+    ],
+    "https://github.com/saltchicken/ComfyUI-Local-Loader": [
+        [
+            "LoadImageFromDir",
+            "LoadImageFromOutput",
+            "LoadImageFromPath",
+            "LoadSingleImageFromPath",
+            "LoadVideoFromOutput"
+        ],
+        {
+            "title_aux": "ComfyUI-Local-Loader"
+        }
+    ],
+    "https://github.com/saltchicken/ComfyUI-Prompter": [
+        [
+            "PromptTemplateManager"
+        ],
+        {
+            "title_aux": "ComfyUI-Prompter"
+        }
+    ],
+    "https://github.com/saltchicken/ComfyUI-Selector": [
+        [
+            "SimpleSelectorNode"
+        ],
+        {
+            "title_aux": "ComfyUI-Selector"
+        }
+    ],
+    "https://github.com/saltchicken/ComfyUI-Video-Utils": [
+        [
+            "FinalFrameSelector",
+            "FirstNFramesSelector",
+            "LastNFramesSelector",
+            "NthFirstFrameSelector",
+            "NthLastFrameSelector",
+            "PreviewImageWithCounter",
+            "RemoveFirstAndLastFrame",
+            "RemoveFirstFrame",
+            "VideoMerge"
+        ],
+        {
+            "title_aux": "ComfyUI-Video-Utils"
+        }
+    ],
+    "https://github.com/satyam-fp/ComfyUI-GeminiImage": [
+        [
+            "GeminiImageEnhance",
+            "GeminiTextToImage"
+        ],
+        {
+            "title_aux": "ComfyUI-GeminiImage [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/satyasairazole/ComfyUI-Turbandetection": [
+        [
+            "TurbanDetectorNode"
+        ],
+        {
+            "title_aux": "ComfyUI-Turbandetection [WIP]"
+        }
+    ],
+    "https://github.com/saulchiu/comfy_saul_plugin": [
+        [
+            "Blend Images",
+            "Change the camera pose of config file",
+            "Compute Keypoints Similarity",
+            "Cutting Video",
+            "End Node",
+            "Extract .webp from Folder",
+            "Extract the First Frame",
+            "Find the most similar webp",
+            "Fuse People and Cigarette",
+            "Get Pose",
+            "Patch Pose to People",
+            "Saul-Plugin",
+            "Smoking Auto Label"
+        ],
+        {
+            "title_aux": "comfyui-saul-plugin [WIP]"
+        }
+    ],
+    "https://github.com/seancheung/comfyui-creative-nodes": [
+        [
+            "CreativeSkipFromFlow",
+            "CreativeSkipToFlow",
+            "CreativeStopFlow",
+            "ResolutionSelector",
+            "ResolutionXLSelector"
+        ],
+        {
+            "title_aux": "comfyui-creative-nodes"
+        }
+    ],
+    "https://github.com/sh570655308/Comfyui-RayNodes": [
+        [
+            "ActionUnitDescription",
+            "ActionUnitDetection",
+            "BorderMask",
+            "BracketedTagIndexMerger",
+            "DescriptionGen",
+            "EmotiEffLibAnalysis",
+            "EmotionAnalysis",
+            "FaceAnalysis",
+            "FaceExtract",
+            "Florence2TagProcessor",
+            "GrabberTagProcessor",
+            "HSEmotionAnalysis",
+            "HighlightOverlay",
+            "ImageListConverter",
+            "ImageResizer",
+            "ImageSelector",
+            "KeywordFilter",
+            "KeywordFilterLoRA",
+            "MaskApplierAndCombiner",
+            "MaskBlackener",
+            "MaskMerger",
+            "MaskProcessor",
+            "MultiEngineEmotion",
+            "PixelCountScaler",
+            "SaturationAdjuster",
+            "SaveImageWebsocket",
+            "TagArrayToLines",
+            "TagIndexMerger"
+        ],
+        {
+            "title_aux": "Comfyui-RayNodes [WIP]"
+        }
+    ],
+    "https://github.com/shadowcz007/ComfyUI-PuLID-Test": [
+        [
+            "ApplyPulid",
+            "PulidEvaClipLoader",
+            "PulidInsightFaceLoader",
+            "PulidModelLoader"
+        ],
+        {
+            "title_aux": "ComfyUI-PuLID [TEST]"
+        }
+    ],
+    "https://github.com/shadowcz007/Comfyui-EzAudio": [
+        [
+            "EZGenerateAudioNode",
+            "EZLoadModelNode"
+        ],
+        {
+            "title_aux": "Comfyui-EzAudio [WIP]"
+        }
+    ],
+    "https://github.com/shadowcz007/comfyui-CLIPSeg": [
+        [
+            "CLIPSeg_",
+            "CombineMasks_"
+        ],
+        {
+            "title_aux": "comfyui-CLIPSeg"
+        }
+    ],
+    "https://github.com/shadowcz007/comfyui-hydit-lowvram": [
+        [
+            "DiffusersCLIPLoader",
+            "DiffusersCheckpointLoader",
+            "DiffusersClipTextEncode",
+            "DiffusersControlNetLoader",
+            "DiffusersLoraLoader",
+            "DiffusersModelMakeup",
+            "DiffusersPipelineLoader",
+            "DiffusersSampler",
+            "DiffusersSchedulerLoader"
+        ],
+        {
+            "title_aux": "comfyui-hydit"
+        }
+    ],
+    "https://github.com/shenymce/ComfyUI-JsonViewer": [
+        [
+            "JsonViewer"
+        ],
+        {
+            "title_aux": "ComfyUI-JsonViewer [WIP]"
+        }
+    ],
+    "https://github.com/shin131002/ComfyUI-Prompt-Preset-Selector": [
+        [
+            "PromptPresetSelector",
+            "PromptPresetSelectorWithWildcard"
+        ],
+        {
+            "title_aux": "ComfyUI-Prompt-Preset-Selector [WIP]"
+        }
+    ],
+    "https://github.com/shinich39/comfyui-run-js": [
+        [
+            "RunJS"
+        ],
+        {
+            "author": "shinich39",
+            "description": "Manipulate workflow via javascript on node.",
+            "nickname": "comfyui-run-js",
+            "title": "comfyui-run-js",
+            "title_aux": "comfyui-run-js [UNSAFE]"
+        }
+    ],
+    "https://github.com/shirazdesigner/CLIPTextEncodeAndEnhancev4": [
+        [
+            "CLIPTextEncodeAndEnhance"
+        ],
+        {
+            "title_aux": "CLIPTextEncodeAndEnhancev4 (shirazdesigner)"
+        }
+    ],
+    "https://github.com/shuanshuan/ComfyUI_CheckPointLoader_Ext": [
+        [
+            "CheckpointLoaderExt"
+        ],
+        {
+            "title_aux": "ComfyUI_CheckPointLoader_Ext [WIP]"
+        }
+    ],
+    "https://github.com/sickbraintwo/WextraX": [
+        [
+            "loraName",
+            "tag"
+        ],
+        {
+            "title_aux": "WextraX [WIP]"
+        }
+    ],
+    "https://github.com/silent-rain/ComfyUI-SilentRain": [
+        [
+            "Example"
+        ],
+        {
+            "title_aux": "ComfyUI-SilentRain"
+        }
+    ],
+    "https://github.com/simonjaq/ComfyUI-sjnodes": [
+        [
+            "AudioStackWithVolume",
+            "CrossFadeVideo",
+            "FixedLengthAudioSequencer",
+            "InpaintCropImprovedGPU",
+            "InpaintStitchImprovedGPU",
+            "JsonArrayExtractor",
+            "LoadImageFromPath",
+            "LoadStitcherFromFile",
+            "LogCRec709Convert",
+            "PadAudioToLength",
+            "SJAlwaysChanged",
+            "SJPathListDirLive",
+            "SaveStitcherToFile",
+            "SmartVideoTrim",
+            "SmoothTemporalMask",
+            "WanVideoVACEExtend"
+        ],
+        {
+            "title_aux": "ComfyUI-sjnodes"
+        }
+    ],
+    "https://github.com/simonri/ComfyUI-SimonNodes": [
+        [
+            "SR_CLIPTextEncodeCached",
+            "SR_ImageResize",
+            "SR_RIFE",
+            "SR_Seed",
+            "SR_SeedVR_Upscale",
+            "SR_UltralyticsCrop",
+            "SR_UltralyticsModelLoader",
+            "SR_WanResolution",
+            "SR_WanSampler"
+        ],
+        {
+            "title_aux": "ComfyUI-SimonNodes"
+        }
+    ],
+    "https://github.com/sintspiden/comfyui-id-lora-gguf": [
+        [
+            "IDLoraGGUFModelLoader",
+            "IDLoraGGUFPromptEncoder",
+            "IDLoraGGUFSampler"
+        ],
+        {
+            "title_aux": "comfyui-id-lora-gguf"
+        }
+    ],
+    "https://github.com/siyonomicon/ComfyUI-Pin": [
+        [
+            "PinGridNode"
+        ],
+        {
+            "title_aux": "ComfyUI-Pin"
+        }
+    ],
+    "https://github.com/sk-palani/ComfyUI_Simpler": [
+        [
+            "Image_Preparations",
+            "InputSelector",
+            "Input_Image"
+        ],
+        {
+            "title_aux": "ComfyUI_Simpler"
+        }
+    ],
+    "https://github.com/slezica/comfyui-personal": [
+        [
+            "GenerateImage",
+            "OwlDetector",
+            "ScaleSigmas",
+            "UpscaleImage",
+            "UseCheckpoint",
+            "UseControlNet",
+            "UseIPAdapter",
+            "UseImage",
+            "UseInfiniteYou",
+            "UseInstantID",
+            "UseLora",
+            "UseStyleModel"
+        ],
+        {
+            "title_aux": "slezica/ComfyUI Personal Nodes"
+        }
+    ],
+    "https://github.com/smthemex/ComfyUI_GPT_SoVITS_Lite": [
+        [
+            "GPT_SoVITS_LoadModel",
+            "GPT_SoVITS_Sampler"
+        ],
+        {
+            "title_aux": "ComfyUI_GPT_SoVITS_Lite"
+        }
+    ],
+    "https://github.com/sofakid/dandy": [
+        [
+            "DandyBooleanCollector",
+            "DandyBooleanPreview",
+            "DandyBooleanSplitter",
+            "DandyCss",
+            "DandyCssLoader",
+            "DandyEditorSettings",
+            "DandyFloat",
+            "DandyFloatCollector",
+            "DandyFloatPreview",
+            "DandyFloatSplitter",
+            "DandyGradient",
+            "DandyHtml",
+            "DandyHtmlLoader",
+            "DandyImageCollector",
+            "DandyInt",
+            "DandyIntCollector",
+            "DandyIntPreview",
+            "DandyIntSplitter",
+            "DandyJs",
+            "DandyJsLoader",
+            "DandyJson",
+            "DandyJsonLoader",
+            "DandyLand",
+            "DandyMaskCollector",
+            "DandyP5JsDraw",
+            "DandyP5JsLoader",
+            "DandyP5JsSetup",
+            "DandyPixelsJs",
+            "DandyPixiJs",
+            "DandyPrompt",
+            "DandyString",
+            "DandyStringArrayCollector",
+            "DandyStringArraySplitter",
+            "DandyStringCatCollector",
+            "DandyStringPreview",
+            "DandyUrlLoader",
+            "DandyWasmLoader",
+            "DandyYaml",
+            "DandyYamlLoader"
+        ],
+        {
+            "title_aux": "dandy [UNSAFE]"
+        }
+    ],
+    "https://github.com/songtianhui/ComfyUI-DMM": [
+        [
+            "DMMApply",
+            "DMMLoader"
+        ],
+        {
+            "title_aux": "ComfyUI-DMM [WIP]"
+        }
+    ],
+    "https://github.com/sourceful-official/ComfyUI_InstructPixToPixConditioningLatent": [
+        [
+            "InstructPixToPixConditioningLatent"
+        ],
+        {
+            "title_aux": "ComfyUI_InstructPixToPixConditioningLatent [WIP]"
+        }
+    ],
+    "https://github.com/sourceful-official/comfyui-sourceful-official": [
+        [
+            "FalFluxLoraSourcefulOfficial",
+            "FalIcLightV2SourcefulOfficial",
+            "SourcefulOfficialComfyuiIncontextThreePanels"
+        ],
+        {
+            "title_aux": "comfyui-sourceful-official"
+        }
+    ],
+    "https://github.com/sprited-ai/sprited-comfyui-nodes": [
+        [
+            "BiRefNetBackgroundRemoval",
+            "FlattenImageList",
+            "LoopExtractorNodeV2",
+            "LoopExtractorNodeV3",
+            "MakeGrid",
+            "PixelRGBStats",
+            "SliceBatch",
+            "SliceLatents",
+            "SpriteDXAntiCorruptionV1",
+            "SpriteDX_ParseInt",
+            "SpritedMakeGrid",
+            "URLToVideo",
+            "VideoDownloader",
+            "VideoEvenShotSplitter"
+        ],
+        {
+            "title_aux": "Sprited ComfyUI Nodes [WIP]"
+        }
+    ],
+    "https://github.com/spyderweb47/ComfyUI-Automation-Toolkit": [
+        [
+            "PromptCycler"
+        ],
+        {
+            "title_aux": "ComfyUI-Automation-Toolkit [WIP]"
+        }
+    ],
+    "https://github.com/sschleis/sschl-comfyui-notes": [
+        [
+            "AddNumbers",
+            "Character",
+            "CharacterPrompt",
+            "CharacterPromptWithLora",
+            "CharacterPromptWithLoraWithDualModel",
+            "CombineStrings",
+            "Connector",
+            "FloatToStr",
+            "Gallery",
+            "GroupManager",
+            "InputText",
+            "OneMScale",
+            "ResFinder",
+            "SDXLResFinder",
+            "SSchlTextEncoder",
+            "ShowText",
+            "Switch",
+            "TextAppender"
+        ],
+        {
+            "title_aux": "sschl-comfyui-notes"
+        }
+    ],
+    "https://github.com/sswink/comfyui-lingshang": [
+        [
+            "LS_ALY_Seg_Body_Utils",
+            "LS_ALY_Seg_Body_Utils_Return_crop",
+            "LS_ALY_Seg_Clothes_Utils",
+            "LS_ALY_Seg_Common_Utils",
+            "LS_ALY_Seg_Utils",
+            "LS_ALY_UploadToOssAndGetUrl",
+            "LS_DigImageByMask",
+            "LS_GrowMaskWithBlur",
+            "LS_LoadMaskFromUrl",
+            "LS_SaveImageToOss"
+        ],
+        {
+            "title_aux": "comfyui-lingshang"
+        }
+    ],
+    "https://github.com/stalkervr/ComfyUI-StalkerVr": [
+        [
+            "CalculateFrameCount",
+            "CurrentDateTime",
+            "FileSavePath",
+            "FormatDatePath",
+            "GenerateCreationTime",
+            "ImageCropper",
+            "ImageDesiredResolution",
+            "ImageGetSize",
+            "ImageGridCropper",
+            "ImageLoadWithMetadata",
+            "ImageRatioResizer",
+            "ImageSaveWithMetadata",
+            "ImageWatermark",
+            "ImagesLoadWithMetadata",
+            "JsonBuilder",
+            "JsonDeserializeObject",
+            "JsonFieldRemover",
+            "JsonFieldReplaceExtend",
+            "JsonFieldValueExtractor",
+            "JsonFormat",
+            "JsonMinify",
+            "JsonPairInput",
+            "JsonPathLoader",
+            "JsonPromptToTextPromptConverter",
+            "JsonSerializeObject",
+            "LlamaCppTextGenerator",
+            "Logger",
+            "LoraLoaderExtended",
+            "LoraLoaderExtendedBatch",
+            "SaveTextFile",
+            "SaveVideoWithMetadata",
+            "StringBuilder",
+            "StringNormalize",
+            "StringWrapper",
+            "SwitchAny",
+            "TextWatermark",
+            "WanVideoEnhanceMotionAdvanced",
+            "WanVideoEnhanceMotionAdvancedKJ",
+            "WanVideoLoraCivitAIDownloader",
+            "WanVideoLoraPairCreator",
+            "WanVideoLoraPairSelect",
+            "YAMLLoadPrompt",
+            "YAMLSavePrompt"
+        ],
+        {
+            "title_aux": "ComfyUI-StalkerVr"
+        }
+    ],
+    "https://github.com/starsFriday/ComfyUI-Audio-Subtitle": [
+        [
+            "AudioSubtitle"
+        ],
+        {
+            "title_aux": "ComfyUI-Audio-Subtitle [WIP]"
+        }
+    ],
+    "https://github.com/starsFriday/ComfyUI-KLingAI-OmniVideo": [
+        [
+            "KlingFirstEndFrame",
+            "KlingImage2Video",
+            "KlingText2Video",
+            "KlingVideoEdit",
+            "KlingVideoExtend"
+        ],
+        {
+            "title_aux": "ComfyUI-KLingAI-OmniVideo [WIP]"
+        }
+    ],
+    "https://github.com/starsFriday/ComfyUI-LongCat-Image": [
+        [
+            "LongCatImageEdit",
+            "LongCatImageModelLoader",
+            "LongCatImageTextToImage"
+        ],
+        {
+            "title_aux": "ComfyUI-LongCat-Image [WIP]"
+        }
+    ],
+    "https://github.com/starsFriday/ComfyUI-MatAnyone2": [
+        [
+            "LoadVideoForMatAnyone2",
+            "MatAnyone2",
+            "SaveMatAnyone2TransparentMOV",
+            "SaveMatAnyone2TransparentWebM",
+            "SaveMatAnyone2Video"
+        ],
+        {
+            "title_aux": "ComfyUI-MatAnyone2 [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/starsFriday/ComfyUI-Qwen3-TTS": [
+        [
+            "Qwen3TTSLoader",
+            "Qwen3TTSRealVoiceClone",
+            "Qwen3TTSVoiceClone",
+            "Qwen3TTSVoiceDesign"
+        ],
+        {
+            "title_aux": "ComfyUI-Qwen3-TTS [NAME CONFLICT/WIP]"
+        }
+    ],
+    "https://github.com/starsFriday/ComfyUI-Tracker-Person": [
+        [
+            "YoloTrackNode"
+        ],
+        {
+            "title_aux": "ComfyUI-Tracker-Person [WIP]"
+        }
+    ],
+    "https://github.com/steelan9199/ComfyUI-Teeth": [
+        [
+            "teeth FindContours",
+            "teeth Gemini2",
+            "teeth GetFirstSeg",
+            "teeth GetValueByIndexFromList",
+            "teeth ImageGridLines",
+            "teeth LoadTextFile",
+            "teeth RunPythonCode",
+            "teeth SaveTextFile",
+            "teeth SplitGridImage",
+            "teeth TextSplitByDelimiter"
+        ],
+        {
+            "title_aux": "ComfyUI-Teeth [UNSAFE]"
+        }
+    ],
+    "https://github.com/stevanisya/comfyui_txt_selector": [
+        [
+            "TextSelector"
+        ],
+        {
+            "title_aux": "comfyui_txt_selector  [WIP]"
+        }
+    ],
+    "https://github.com/sthao42/comfyui-melodkeet-tts": [
+        [
+            "MelodkeetTTS"
+        ],
+        {
+            "title_aux": "ComfyUI Melodkeet TTS"
+        }
+    ],
+    "https://github.com/strhwste/comfyui_csv_utils": [
+        [
+            "ExtractFromJSON",
+            "SearchCSVByRow",
+            "WriteCSVByRow"
+        ],
+        {
+            "title_aux": "CSV Utils [WIP]"
+        }
+    ],
+    "https://github.com/stutya/ComfyUI-Terminal": [
+        [
+            "Terminal"
+        ],
+        {
+            "title_aux": "ComfyUI-Terminal [UNSAFE]"
+        }
+    ],
+    "https://github.com/subnet99/ComfyUI-URLLoader": [
+        [
+            "URLAudioDownloader",
+            "URLImageDownloader"
+        ],
+        {
+            "title_aux": "ComfyUI-URLLoader"
+        }
+    ],
+    "https://github.com/sugarkwork/comfyui-trtools": [
+        [
+            "TRTUpscaler",
+            "TRTagger"
+        ],
+        {
+            "title_aux": "comfyui-trtools"
+        }
+    ],
+    "https://github.com/sugarkwork/comfyui_image_crop": [
+        [
+            "CropReapply",
+            "CropTransparent",
+            "ExpandMultiple",
+            "RestoreCrop"
+        ],
+        {
+            "title_aux": "comfyui_image_crop"
+        }
+    ],
+    "https://github.com/sugarkwork/comfyui_my_img_util": [
+        [
+            "Auto Image Selector",
+            "Image Resize And Crop",
+            "Image Trim Edges",
+            "OpenCVDenoiseColored",
+            "Simple Image Rotate"
+        ],
+        {
+            "title_aux": "comfyui_my_img_util"
+        }
+    ],
+    "https://github.com/sugarkwork/comfyui_psd": [
+        [
+            "Convert PSD to Image",
+            "PSDConvert",
+            "PSDLayer",
+            "PSDSave",
+            "Save PSD"
+        ],
+        {
+            "title_aux": "comfyui_psd [WIP]"
+        }
+    ],
+    "https://github.com/suncat2ps/ComfyUI-SaveImgNextcloud": [
+        [
+            "SaveImageNextcloud"
+        ],
+        {
+            "title_aux": "ComfyUI-SaveImgNextcloud"
+        }
+    ],
+    "https://github.com/supaidauen/ComfyUI-supaidauen": [
+        [
+            "ClearVRAM",
+            "ImageBatchToCount",
+            "Integer",
+            "KSampler_Advanced_Calculator",
+            "Latent_Switcher",
+            "Subject_Detection_and_Interrupt",
+            "Supaidauen_Add_RunID",
+            "Supaidauen_Character_IO",
+            "Supaidauen_Create_DummyRandomInt",
+            "Supaidauen_Create_Filename",
+            "Supaidauen_GenerateRandomImagePadding",
+            "Supaidauen_ImagePadding",
+            "Supaidauen_Image_Compositor",
+            "Supaidauen_ImpactEndAtStepModelControl",
+            "Supaidauen_LoadImageFromPath_input",
+            "Supaidauen_Normalized_Float_Slider",
+            "Supaidauen_Passthrough_CLIP",
+            "Supaidauen_Passthrough_IMAGE",
+            "Supaidauen_Passthrough_LATENT",
+            "Supaidauen_Passthrough_MASK",
+            "Supaidauen_Passthrough_STRING",
+            "Supaidauen_Passthrough_VAE",
+            "Supaidauen_Prompt_Consolidator",
+            "Supaidauen_Recursive_Uspcaler",
+            "Supaidauen_Text_Concat",
+            "Supaidauen_Text_Replace",
+            "Supaidauen_Text_Wildcard",
+            "Supaidauen_Text_w_Options_Replace_LoRA",
+            "Supaidauen_ZippedPromptFromTextAdvanced"
+        ],
+        {
+            "title_aux": "ComfyUI-supaidauen [WIP]"
+        }
+    ],
+    "https://github.com/svyatojdismas/ComfyUI-StDismas": [
+        [
+            "BBoxRescale",
+            "BatchImageCropByMaskAdvanced_StDismas",
+            "BatchImageUncropByMaskAdvanced_StDismas",
+            "CSVIndexPicker_StDismas",
+            "DualKSamplerCore",
+            "DualKSamplerCoreAdvanced",
+            "DualKSamplerCoreAdvancedAlt",
+            "DualWVSamplerWanLightning",
+            "DualWVSamplerWanLightningAdvancedAlt",
+            "ExpandMaskBySides",
+            "ExpandMaskWithFeathering",
+            "IntDivisibleBy_StDismas",
+            "MaskToBBox",
+            "MultiStringSelector_StDismas",
+            "ReplaceImagesInBatchIndexed",
+            "SetDimension_StDismas",
+            "SetDuration_StDismas"
+        ],
+        {
+            "title_aux": "ComfyUI-StDismas [WIP]"
+        }
+    ],
+    "https://github.com/sy00186/Comfyui-Dashboard": [
+        [
+            "VatesGlobalMonitor"
+        ],
+        {
+            "title_aux": "Comfyui-Dashboard"
+        }
+    ],
+    "https://github.com/sy00186/Comfyui-Vates-vae": [
+        [
+            "VatesVAECollector",
+            "VatesVAESplitter",
+            "VatesVAEWorker"
+        ],
+        {
+            "title_aux": "Comfyui-Vates-vae [WIP]"
+        }
+    ],
+    "https://github.com/synchronicity-labs/sync-comfyui": [
+        [
+            "SyncApiKeyNode",
+            "SyncAudioInputNode",
+            "SyncLipsyncMainNode",
+            "SyncLipsyncOutputNode",
+            "SyncVideoInputNode"
+        ],
+        {
+            "title_aux": "ComfyUI Sync Lipsync Node"
+        }
+    ],
+    "https://github.com/system-out-cho/displayHistory_ComfyUI": [
+        [
+            "Client Proxy",
+            "DisplayHistory",
+            "Example",
+            "Image Selector"
+        ],
+        {
+            "title_aux": "displayHistory [WIP]"
+        }
+    ],
+    "https://github.com/systemaiofinterest-wq/ComfyUI-GoogleFX": [
+        [
+            "GoogleImagen31Node",
+            "GoogleImagen35Node",
+            "GoogleImagenR2INode",
+            "GoogleNanoBananaNode"
+        ],
+        {
+            "title_aux": "ComfyUI-GoogleFX"
+        }
+    ],
+    "https://github.com/tackcrypto1031/tk_comfyui_SimpleSize": [
+        [
+            "TK_SimpleSize"
+        ],
+        {
+            "title_aux": "tk_comfyui_SimpleSize [WIP]"
+        }
+    ],
+    "https://github.com/takoyaki1118/ComfyUI-Kakimoji-Layer": [
+        [
+            "KakimojiEditor"
+        ],
+        {
+            "title_aux": "ComfyUI-Kakimoji-Layer"
+        }
+    ],
+    "https://github.com/takoyaki1118/ComfyUI_PromptExtractor": [
+        [
+            "CustomLoadImageWithPathNode",
+            "PromptExtractorNode"
+        ],
+        {
+            "title_aux": "ComfyUI_PromptExtractor"
+        }
+    ],
+    "https://github.com/talon468/ComfyUI-Rpg-Architect": [
+        [
+            "ComfyUI Rpg Architect \ud83e\ude84"
+        ],
+        {
+            "title_aux": "ComfyUI-Rpg-Architect [WIP]"
+        }
+    ],
+    "https://github.com/tankenyuen-ola/comfyui-env-variable-reader": [
+        [
+            "EnvironmentVariableNode"
+        ],
+        {
+            "title_aux": "comfyui-env-variable-reader [UNSAFE]"
+        }
+    ],
+    "https://github.com/tankenyuen-ola/comfyui-wanvideo-scheduler-loop": [
+        [
+            "FloatRangeLoop",
+            "ParametersRangeLoop",
+            "WanVideoAllParametersLoop",
+            "WanVideoSchedulerInfo",
+            "WanVideoSchedulerLoop"
+        ],
+        {
+            "title_aux": "comfyui-wanvideo-scheduler-loop"
+        }
+    ],
+    "https://github.com/tanmoy-it/comfyuiCustomNode": [
+        [
+            "DownloadImageDataUrl"
+        ],
+        {
+            "title_aux": "comfyuiCustomNode"
+        }
+    ],
+    "https://github.com/tc888/ComfyUI_Save_Flux_Image": [
+        [
+            "Cfg Literal",
+            "Int Literal",
+            "Sampler Select",
+            "Save Flux Image with Metadata",
+            "Scheduler Select",
+            "Seed Gen",
+            "String Literal",
+            "Unet Select",
+            "Width/Height Literal"
+        ],
+        {
+            "title_aux": "ComfyUI_Save_Flux_Image"
+        }
+    ],
+    "https://github.com/tdrminglin/ComfyUI_SceneSplitter": [
+        [
+            "SceneDetectSplitter",
+            "SceneStartFramesNode"
+        ],
+        {
+            "title_aux": "ComfyUI_SceneSplitter"
+        }
+    ],
+    "https://github.com/tdrminglin/Comfyui-hymotionbridge": [
+        [
+            "HYMotionToNLFBridge",
+            "HYMotionToSCAILBridge"
+        ],
+        {
+            "title_aux": "Comfyui-hymotionbridge"
+        }
+    ],
+    "https://github.com/techidsk/comfyui_molook_nodes": [
+        [
+            "ImageOutpaintPadding(Molook)",
+            "MaskExpand(Molook)",
+            "OpenAIProvider(Molook)"
+        ],
+        {
+            "title_aux": "comfyui_molook_nodes [WIP]"
+        }
+    ],
+    "https://github.com/techtruth/ComfyUI-Dreambooth": [
+        [
+            "DreamboothNode"
+        ],
+        {
+            "title_aux": "ComfyUI-Dreambooth"
+        }
+    ],
+    "https://github.com/tester4488/mc_audio": [
+        [
+            "MCAudioInfo"
+        ],
+        {
+            "title_aux": "mc_audio"
+        }
+    ],
+    "https://github.com/tg-tjmitchell/comfyui-custom-node-lister": [
+        [
+            "CustomNodeLister"
+        ],
+        {
+            "title_aux": "ComfyUI Manager Package Lister"
+        }
+    ],
+    "https://github.com/tg-tjmitchell/comfyui-rsync-plugin": [
+        [
+            "FileTransferHelperNode"
+        ],
+        {
+            "title_aux": "ComfyUI File Transfer Plugin (comfyui-rsync-plugin) [UNSAFE]"
+        }
+    ],
+    "https://github.com/thaakeno/comfyui-universal-asset-downloader": [
+        [
+            "UniversalAssetDownloader"
+        ],
+        {
+            "title_aux": "comfyui-universal-asset-downloader [UNSAFE/WIP]"
+        }
+    ],
+    "https://github.com/thavocado/comfyui-danbooru-lookup": [
+        [
+            "DanbooruFAISSLookup",
+            "DanbooruFAISSLookupAdvanced",
+            "WD14ToConditioning"
+        ],
+        {
+            "author": "ComfyUI Danbooru Lookup",
+            "description": "ComfyUI node that performs FAISS cosine similarity lookup on Danbooru embeddings using CLIP conditioning inputs.",
+            "nickname": "Danbooru Lookup",
+            "title": "Danbooru FAISS Lookup",
+            "title_aux": "comfyui-danbooru-lookup"
+        }
+    ],
+    "https://github.com/thderoo/ComfyUI-_topfun_s_nodes": [
+        [
+            "ConditioningPerturbation",
+            "TextGenerator"
+        ],
+        {
+            "title_aux": "_topfun_s_nodes"
+        }
+    ],
+    "https://github.com/theshubzworld/ComfyUI-NvidiaVision": [
+        [
+            "\u2728 NVIDIA Prompt Generator (Text) \u2728",
+            "\ud83d\udc41\ufe0f\u200d\ud83d\udde8\ufe0f NVIDIA Vision Node \ud83d\udc41\ufe0f\u200d\ud83d\udde8\ufe0f"
+        ],
+        {
+            "title_aux": "ComfyUI-NvidiaVision [WIP]"
+        }
+    ],
+    "https://github.com/thomashollier/comfyUI-DAP": [
+        [
+            "DA2DepthEstimation",
+            "DA2ModelLoader",
+            "DAPDepthEstimation",
+            "DAPDepthVisualize",
+            "DAPModelLoader",
+            "DAPPointCloudExport",
+            "DepthGamma",
+            "DepthToNormals",
+            "PanDADepthEstimation",
+            "PanDAModelLoader"
+        ],
+        {
+            "title_aux": "comfyUI-DAP [UNSAFE/NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/threadedblue/MLXnodes": [
+        [
+            "MLXImg2Img",
+            "MLXText2Image"
+        ],
+        {
+            "title_aux": "MLXnodes [WIP]"
+        }
+    ],
+    "https://github.com/threecrowco/ComfyUI-FlowMatchScheduler": [
+        [
+            "FlowMatchSigmas"
+        ],
+        {
+            "title_aux": "ComfyUI-FlowMatchScheduler [WIP]"
+        }
+    ],
+    "https://github.com/tiange-tree/BLUEAI_ComfyUI_OpenAI": [
+        [
+            "BLUEAI_OpenAI_Node"
+        ],
+        {
+            "title_aux": "BLUEAI_ComfyUI_OpenAI"
+        }
+    ],
+    "https://github.com/tjorbogarden/my-useful-comfyui-custom-nodes": [
+        [
+            "ImageSizer",
+            "KSamplerSDXLAdvanced",
+            "My-ImageSizer"
+        ],
+        {
+            "title_aux": "my-useful-comfyui-custom-nodes"
+        }
+    ],
+    "https://github.com/tnil25/ComfyUI-TJNodes": [
+        [
+            "ExpandMaskDir",
+            "OverlayMaskNode",
+            "Tracker"
+        ],
+        {
+            "title_aux": "ComfyUI-TJNodes [WIP]"
+        }
+    ],
+    "https://github.com/tom-doerr/dspy_nodes": [
+        [
+            "Accepted Examples Viewer",
+            "Dataset Reader",
+            "DynamicOptionsNode",
+            "Few Shot CoT",
+            "Few Shot Control",
+            "Few Shot Review",
+            "FewShotReview",
+            "FewShotReviewServer",
+            "Model",
+            "Predict",
+            "Print Hello World",
+            "ShowText|pysssss",
+            "String List Viewer",
+            "String Splitter",
+            "StringReverser",
+            "StringSplitter",
+            "Text Field",
+            "Text Output"
+        ],
+        {
+            "title_aux": "DSPy Nodes [WIP]"
+        }
+    ],
+    "https://github.com/tony-zn/comfyui-zn-pycode": [
+        [
+            "ZnPyCode: CustomCode"
+        ],
+        {
+            "title_aux": "comfyui-zn-pycode [UNSAFE]"
+        }
+    ],
+    "https://github.com/tori29umai0123/ComfyUI-SDXLGenerateFromTextFile": [
+        [
+            "GenerateFromTextFile"
+        ],
+        {
+            "title_aux": "ComfyUI-SDXLGenerateFromTextFile [UNSAFE]"
+        }
+    ],
+    "https://github.com/tpc2233/ComfyUI-TP-IMtalker": [
+        [
+            "IMTalkerAudioDriven",
+            "IMTalkerLoader",
+            "IMTalkerVideoDriven"
+        ],
+        {
+            "title_aux": "ComfyUI-TP-IMtalker [WIP]"
+        }
+    ],
+    "https://github.com/tracerstar/comfyui-p5js-node": [
+        [
+            "HYPE_P5JSImage"
+        ],
+        {
+            "title_aux": "comfyui-p5js-node"
+        }
+    ],
+    "https://github.com/trampolin/comfy-ui-scryfall": [
+        [
+            "ScryfallCardInfoNode",
+            "ScryfallDecklistParserNode",
+            "ScryfallImageExtractorNode",
+            "ScryfallSearchNode"
+        ],
+        {
+            "title_aux": "comfy-ui-scryfall"
+        }
+    ],
+    "https://github.com/trashgraphicard/Albedo-Sampler-for-ComfyUI": [
+        [
+            "Make Seamless Tile",
+            "Sample Image"
+        ],
+        {
+            "title_aux": "Albedo-Sampler-for-ComfyUI"
+        }
+    ],
+    "https://github.com/trashkollector/TKVideoZoom": [
+        [
+            "TKVideoFuse",
+            "TKVideoSmoothLooper",
+            "TKVideoSpeedZones",
+            "TKVideoStitcher",
+            "TKVideoZoom"
+        ],
+        {
+            "title_aux": "ComfyUI-TKVideoZoom [WIP]"
+        }
+    ],
+    "https://github.com/truebillyblue/lC.ComfyUI_epistemic_nodes": [
+        [
+            "AddApplicationNode",
+            "AddInterpretationNode",
+            "AddObservationNode",
+            "CreatePbiNode",
+            "CreateRDSOTMComponentNode",
+            "GetMadaObjectNode",
+            "InitiateOiaNode",
+            "InitiateRDSOTMCycleNode",
+            "LcADKConfigNode",
+            "LcADKGuiInteractionNode",
+            "LcAddCommentToPbiNode",
+            "LcAnchorClickNode",
+            "LcApiLlmAgentNode",
+            "LcApplyDoneNode",
+            "LcEpistemicPipelineNode",
+            "LcFieldClickNode",
+            "LcFrameClickNode",
+            "LcGetPbiDetailsNode",
+            "LcKeymapClickNode",
+            "LcLinkPbiNode",
+            "LcMemWriteNode",
+            "LcReflectBoomNode",
+            "LcStartleNode",
+            "LcWebLlmAgentNode",
+            "QueryPbisNode",
+            "ShowTextNode",
+            "StoreMadaObjectNode",
+            "UpdatePbiNode",
+            "ViewOiaCycleNode",
+            "ViewRDSOTMCycleDetailsNode"
+        ],
+        {
+            "title_aux": "lC.ComfyUI_epistemic_nodes [WIP]"
+        }
+    ],
+    "https://github.com/tuckerdarby/ComfyUI-TDNodes": [
+        [
+            "HandTrackerNode",
+            "InstanceDiffusionLoader",
+            "InstanceTrackerPrompt",
+            "KSamplerBatchedNode",
+            "KSamplerRAVE",
+            "KSamplerTF",
+            "TemporalNetPreprocessor",
+            "TrackerNode",
+            "VideoTrackerPromptNode"
+        ],
+        {
+            "title_aux": "ComfyUI-TDNodes [WIP]"
+        }
+    ],
+    "https://github.com/turskeli/comfyui-SetWallpaper": [
+        [
+            "SetWallpaper"
+        ],
+        {
+            "title_aux": "comfyui-SetWallpaper"
+        }
+    ],
+    "https://github.com/tuxiansheng-ld/Comfyui-tuxiansheng-nodes": [
+        [
+            "HTMLScreenshotterNode",
+            "KimiTableToJSONNode",
+            "StringToListNode",
+            "TableRendererNode",
+            "TableStyleConfigNode"
+        ],
+        {
+            "title_aux": "Comfyui-tuxiansheng-nodes"
+        }
+    ],
+    "https://github.com/twdockery/ComfyUI_Prompt_Batch_Generator": [
+        [
+            "PromptBatchIterator"
+        ],
+        {
+            "title_aux": "ComfyUI_Prompt_Batch_Generator"
+        }
+    ],
+    "https://github.com/twj515895394/ComfyUI-LowMemVideoSuite": [
+        [
+            "FFmpeg \u89c6\u9891\u5408\u6210\uff08\u4f4e\u5185\u5b58\uff09 / FFmpegVideoCombineLowMem",
+            "SaveImageWebsocket",
+            "\u4fdd\u5b58\u5355\u5e27\u5230\u78c1\u76d8 / SaveSingleFrameToDisk",
+            "\u6279\u91cf\u4fdd\u5b58\u5e27\u5230\u78c1\u76d8 / SaveFrameBatchToDisk"
+        ],
+        {
+            "title_aux": "ComfComfyUI-LowMemVideoSuite [UNSAFE]"
+        }
+    ],
+    "https://github.com/tzsoulcap/ComfyUI-SaveImg-W-MetaData": [
+        [
+            "CAP Cfg Literal",
+            "CAP Checkpoint Selector",
+            "CAP Int Literal",
+            "CAP Load Image with Metadata",
+            "CAP Sampler Selector",
+            "CAP Save Image w/Metadata",
+            "CAP Scheduler Selector",
+            "CAP Seed Generator",
+            "CAP String Literal",
+            "CAP Tag Image",
+            "CAP Width/Height Literal"
+        ],
+        {
+            "title_aux": "ComfyUI-SaveImg-W-MetaData"
+        }
+    ],
+    "https://github.com/u5dev/ComfyUI_u5_EasyScripter": [
+        [
+            "comfyUI_u5_easyscripter",
+            "u5_CLIPLoader",
+            "u5_CLIPVisionLoader",
+            "u5_CheckpointLoader",
+            "u5_ControlNetLoader",
+            "u5_GLIGENLoader",
+            "u5_LoraLoader",
+            "u5_StyleModelLoader",
+            "u5_UNETLoader",
+            "u5_VAELoader"
+        ],
+        {
+            "title_aux": "ComfyUI_u5_EasyScripter [UNSAFE]"
+        }
+    ],
+    "https://github.com/unanan/ComfyUI-Dist": [
+        [
+            "LoadImageFromLAN",
+            "LoadImageFromURL"
+        ],
+        {
+            "title_aux": "ComfyUI-Dist [WIP]"
+        }
+    ],
+    "https://github.com/unobtuse/comfyui-topaz-ai-upscale": [
+        [
+            "TopazPhotoAISettings",
+            "TopazPhotoAIUpscale"
+        ],
+        {
+            "title_aux": "comfyui-topaz-ai-upscale"
+        }
+    ],
+    "https://github.com/unobtuse/comfyui-topaz-gigapixel": [
+        [
+            "TopazGigapixelSettings",
+            "TopazGigapixelUpscale"
+        ],
+        {
+            "title_aux": "comfyui-topaz-gigapixel"
+        }
+    ],
+    "https://github.com/unphased/code-nodes": [
+        [
+            "ImageBatcherByIndexProV2",
+            "PythonCodeNode",
+            "ShellCodeNode"
+        ],
+        {
+            "title_aux": "code-nodes [UNSAFE]"
+        }
+    ],
+    "https://github.com/usrname0/ComfyUI-AllergicPack": [
+        [
+            "FolderFileCounter",
+            "IncrementorPlus",
+            "MasterBatcher",
+            "RememberMeNode"
+        ],
+        {
+            "title_aux": "ComfyUI-AllergicPack [WIP]"
+        }
+    ],
+    "https://github.com/vadimcro/External-Image-Revised-with-MASK": [
+        [
+            "ComfyUIDeployExternalImageRevised"
+        ],
+        {
+            "title_aux": "External-Image-Revised-with-MASK"
+        }
+    ],
+    "https://github.com/vadimcro/string_by_index": [
+        [
+            "StringByIndex"
+        ],
+        {
+            "title_aux": "string_by_index"
+        }
+    ],
+    "https://github.com/vanillasoap/ComfyUI-Seamless-Texture": [
+        [
+            "SeamlessTex_AlphaBlend",
+            "SeamlessTex_BatchProcessMaps",
+            "SeamlessTex_CLAHE",
+            "SeamlessTex_DelightAndTile",
+            "SeamlessTex_FlattenLuminance",
+            "SeamlessTex_HighPassDelight",
+            "SeamlessTex_MirrorQuad",
+            "SeamlessTex_MultibandBlend",
+            "SeamlessTex_OffsetBlend",
+            "SeamlessTex_RemoveGradient",
+            "SeamlessTex_SeamHeatmap",
+            "SeamlessTex_TilePreview"
+        ],
+        {
+            "title_aux": "ComfyUI-Seamless-Texture [WIP]"
+        }
+    ],
+    "https://github.com/vasilmitov/ComfyUI-SeedSnapShotManager": [
+        [
+            "SeedSSManager"
+        ],
+        {
+            "title_aux": "ComfyUI-SeedSnapShotManager [WIP]"
+        }
+    ],
+    "https://github.com/viik420/AdvancedModelDownloader": [
+        [
+            "AdvancedDownloader"
+        ],
+        {
+            "title_aux": "AdvancedModelDownloader [UNSAFE]"
+        }
+    ],
+    "https://github.com/virallover/comfyui-virallover": [
+        [
+            "BrightnessCorrectionNode",
+            "ConcatHorizontalWithMask",
+            "DeHaloAlphaWithMaskTorch",
+            "DepthFitter",
+            "DownloadAndLoadLoraModelOnly",
+            "EdgeNoise",
+            "FeatheredSharpen",
+            "IterativeDeHaloAlphaWithMaskTorch",
+            "SkinColorFusion"
+        ],
+        {
+            "title_aux": "comfyui-virallover"
+        }
+    ],
+    "https://github.com/visualbruno/ComfyUI-QRemeshify": [
+        [
+            "QRemeshify"
+        ],
+        {
+            "title_aux": "ComfyUI-QRemeshify"
+        }
+    ],
+    "https://github.com/vito0131/ComfyUI_SmartContentCrop": [
+        [
+            "SimpleCircleProcessor",
+            "SmartContentCrop"
+        ],
+        {
+            "title_aux": "ComfyUI_SmartContentCrop [WIP]"
+        }
+    ],
+    "https://github.com/vjumpkung/comfyui-mosaic-blur": [
+        [
+            "MosaicBlur"
+        ],
+        {
+            "title_aux": "comfyui-mosaic-blur [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/vladp0727/Comfyui-with-Furniture": [
+        [
+            "GetMaskFromAlpha",
+            "GetQuadrilateralOutfit"
+        ],
+        {
+            "title_aux": "ComfyUI Simple Image Tools [WIP]"
+        }
+    ],
+    "https://github.com/vmxy/comfyui-enhance-prompt": [
+        [
+            "PromptEnhancer"
+        ],
+        {
+            "title_aux": "comfyui-enhance-prompt"
+        }
+    ],
+    "https://github.com/vovler/ComfyUI-vovlerTools": [
+        [
+            "WD14BlackListLoader",
+            "WD14TaggerAndImageFilterer",
+            "WD14TensorRTModelLoader",
+            "WDTaggerONNXtoTENSORRT"
+        ],
+        {
+            "title_aux": "comfyui-vovlertools"
+        }
+    ],
+    "https://github.com/vovler/ComfyUI_TensorRT2": [
+        [
+            "SDXL_VAE_TENSORRT_CONVERTER",
+            "SDXL_VAE_TENSORRT_LOADER_DECODER",
+            "UNET_TENSORRT_CONVERTER",
+            "UNET_TENSORRT_CONVERTER_INT8",
+            "UNET_TENSORRT_LOADER",
+            "VAE_TENSORRT_CONVERTER",
+            "VAE_TENSORRT_LOADER"
+        ],
+        {
+            "title_aux": "comfyui_tensorrt2"
+        }
+    ],
+    "https://github.com/vunguyen-oss/ComfyUI-ccsrv2": [
+        [
+            "CCSRUpscale"
+        ],
+        {
+            "title_aux": "ComfyUI-ccsrv2 [WIP]"
+        }
+    ],
+    "https://github.com/w3rc/lpips-similarity-comfyui": [
+        [
+            "GetSimilarity",
+            "LPIPSSimilarity"
+        ],
+        {
+            "title_aux": "lpips-similarity-comfyui"
+        }
+    ],
+    "https://github.com/wTechArtist/ComfyUI-VVL-Tools": [
+        [
+            "ApplyUrlsToJson",
+            "BlenderModelAligner",
+            "BlenderSmartModelScalerBatch",
+            "DimensionReorderAndScale",
+            "DynamicBatchAnything",
+            "Enhanced3DRenderer",
+            "IndexOffsetAdjuster",
+            "IndexUrlPairDeduplicator",
+            "JsonArrayElementFieldExtractor",
+            "JsonArrayFieldUniformSetter",
+            "JsonCompressor",
+            "JsonExtractSubjectNamesScales",
+            "JsonFieldDeleter",
+            "JsonFieldRenamer",
+            "JsonMarkdownCleaner",
+            "JsonObjectDeduplicator",
+            "JsonObjectMerger",
+            "JsonObjectOutputUrlCheck",
+            "JsonObjectSplitter",
+            "JsonRotationScaleAdjuster",
+            "JsonScaleMaxAdjuster",
+            "ModelTransformParameters",
+            "RoomBboxGeneratorNode",
+            "TensorListPreview",
+            "TextCombineMulti",
+            "VVL compare",
+            "VVL forLoopEnd",
+            "VVL forLoopStart",
+            "VVL listChunk",
+            "VVL listConstruct",
+            "VVL listGetItem",
+            "VVL listLength",
+            "VVL mathInt",
+            "VVL whileLoopEnd",
+            "VVL whileLoopStart",
+            "VVL_Load_Text_Batch",
+            "async_sleep_v1"
+        ],
+        {
+            "title_aux": "ComfyUI-VVL-Tools"
+        }
+    ],
+    "https://github.com/wTechArtist/ComfyUI_VVL_Segmentation": [
+        [
+            "Mask2FormerPanoptic",
+            "OneFormerPanoptic"
+        ],
+        {
+            "title_aux": "ComfyUI_VVL_Segmentation [WIP]"
+        }
+    ],
+    "https://github.com/wTechArtist/ComfyUI_VVL_VideoCamera": [
+        [
+            "ImageSequenceCameraEstimator",
+            "VVLColmapMVSDepthNode"
+        ],
+        {
+            "title_aux": "ComfyUI_VVL_VideoCamera"
+        }
+    ],
+    "https://github.com/wTechArtist/ComfyUI_vvl_BBOX": [
+        [
+            "vvl_BBoxInput"
+        ],
+        {
+            "title_aux": "ComfyUI_vvl_BBOX"
+        }
+    ],
+    "https://github.com/walterFeng/ComfyUI-Image-Utils": [
+        [
+            "Calculate Image Brightness",
+            "Calculate Image Contrast",
+            "Calculate Image Saturation",
+            "Color Similarity Checker",
+            "Crop Mask Util",
+            "Displace Filter",
+            "Image Fix (tensor shape convert)",
+            "Load Image (By Url)",
+            "Mask Refine (Aliyun)"
+        ],
+        {
+            "title_aux": "ComfyUI-Image-Utils"
+        }
+    ],
+    "https://github.com/wandaijin/ComfyUI-PaddleOCR": [
+        [
+            "OcrBox"
+        ],
+        {
+            "title_aux": "ComfyUI-PaddleOCR [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/warshanks/Shank-Tools": [
+        [
+            "HeightWidth",
+            "ResolutionDivider",
+            "TileCalculator"
+        ],
+        {
+            "title_aux": "Shank-Tools"
+        }
+    ],
+    "https://github.com/wasilone11/comfyui-sync-translate-node": [
+        [
+            "SyncTranslateInputNode",
+            "SyncTranslateNode"
+        ],
+        {
+            "title_aux": "comfyui-sync-translate-node"
+        }
+    ],
+    "https://github.com/watarika/ComfyUI-Text-Utility": [
+        [
+            "ConditionalTagProcessorNode",
+            "LoadTextFile",
+            "ParsePromptCustom",
+            "ParsePromptFull",
+            "ProcessWildcard",
+            "PromptsFromTextbox",
+            "RemoveComments",
+            "ReplaceVariables",
+            "ReplaceVariablesAndProcessWildcard",
+            "SaveTextFile",
+            "StringsFromTextbox",
+            "StringsToList"
+        ],
+        {
+            "title_aux": "ComfyUI-Text-Utility [UNSAFE]"
+        }
+    ],
+    "https://github.com/watarika/ComfyUI-exit": [
+        [
+            "ExitComfyUI",
+            "ExitWhenLastBatchConfirm",
+            "FetchApi"
+        ],
+        {
+            "title_aux": "ComfyUI-exit [UNSAFE]"
+        }
+    ],
+    "https://github.com/waynepimpzhang/comfyui-opencv-brightestspot": [
+        [
+            "FindBrightestSpot"
+        ],
+        {
+            "title_aux": "FindBrightestSpot [WIP]"
+        }
+    ],
+    "https://github.com/wciq1208/even-comfyui-plugin": [
+        [
+            "Gemini",
+            "NanoBanana"
+        ],
+        {
+            "title_aux": "even-comfyui-plugin"
+        }
+    ],
+    "https://github.com/whenSunSet/ComfyUI-FaceAnalysisNode": [
+        [
+            "FaceAnalysisAdvancedNode_Custom"
+        ],
+        {
+            "title_aux": "ComfyUI-FaceAnalysisNode"
+        }
+    ],
+    "https://github.com/whmc76/ComfyUI-AudioSuiteAdvanced": [
+        [
+            "AudioConcatenateFree",
+            "AudioSeparation",
+            "CharacterVocalExtractor",
+            "CharacterVocalExtractorMultiTrack",
+            "CombineAudioFromList",
+            "IndexSelectFromList",
+            "ListLength",
+            "LongTextSplitter",
+            "MakeAudioBatch",
+            "MultiSpeakerSpeechToText",
+            "SubtitleFileLoader"
+        ],
+        {
+            "title_aux": "ComfyUI-AudioSuiteAdvanced [WIP]"
+        }
+    ],
+    "https://github.com/wikinikiwings/ComfyUI-simple_image_nodes": [
+        [
+            "ImageCropFromCoords",
+            "SimpleImageAccumulator"
+        ],
+        {
+            "title_aux": "ComfyUI-simple_image_nodes [WIP]"
+        }
+    ],
+    "https://github.com/wildminder/ComfyUI-MagCache": [
+        [
+            "MagCache"
+        ],
+        {
+            "title_aux": "ComfyUI-MagCache [NAME CONFLICT|WIP]"
+        }
+    ],
+    "https://github.com/willblaschko/ComfyUI-Unload-Models": [
+        [
+            "DeleteAnyObject",
+            "UnloadAllModels",
+            "UnloadOneModel"
+        ],
+        {
+            "title_aux": "ComfyUI-Unload-Models"
+        }
+    ],
+    "https://github.com/wilzamguerrero/Comfyui-zZzZz": [
+        [
+            "CaptureZNode",
+            "CompressFolderNode",
+            "CreateZNode",
+            "DeleteZNode",
+            "DownloadFileNode",
+            "InfiniteZNode",
+            "MoveZNode",
+            "RenameZNode",
+            "VideoZNode",
+            "ZFShareScreen"
+        ],
+        {
+            "title_aux": "Comfyui-zZzZz [UNSAFE]"
+        }
+    ],
+    "https://github.com/woitec/ComfyUI_ImageDimensions": [
+        [
+            "ImageDimensionsExtractorFloat",
+            "ImageDimensionsExtractorInt"
+        ],
+        {
+            "title_aux": "ComfyUI_ImageDimensions [WIP]"
+        }
+    ],
+    "https://github.com/wordbrew/comfyui-wan-control-nodes": [
+        [
+            "WanWeightedControlToVideo"
+        ],
+        {
+            "title_aux": "WAN Control Nodes for ComfyUI [WIP]"
+        }
+    ],
+    "https://github.com/wormley/comfyui-wormley-nodes": [
+        [
+            "CheckpointVAELoaderSimpleText",
+            "CheckpointVAESelectorText",
+            "LoRA_Tag_To_Stack"
+        ],
+        {
+            "title_aux": "comfyui-wormley-nodes"
+        }
+    ],
+    "https://github.com/wulalalaaaaa/Comfyui_Consistency_xiling": [
+        [
+            "ApplyCharacterConsistency",
+            "ApplyStyleConsistency",
+            "CharacterReferenceAnalyzer",
+            "CharacterStyleController",
+            "ConsistencyDebugViewer",
+            "StyleReferenceAnalyzer"
+        ],
+        {
+            "title_aux": "Comfyui_Consistency_xiling [WIP]"
+        }
+    ],
+    "https://github.com/xWris3/jus_multifruit_comfyui": [
+        [
+            "DebugPythonTypesToConsole",
+            "IntToString",
+            "StringToInt"
+        ],
+        {
+            "title_aux": "jus_multifruit_comfyui"
+        }
+    ],
+    "https://github.com/xgfone/ComfyUI_FaceToMask": [
+        [
+            "FaceToMaskCopy"
+        ],
+        {
+            "title_aux": "ComfyUI_FaceToMask"
+        }
+    ],
+    "https://github.com/xgfone/ComfyUI_PromptLogoCleaner": [
+        [
+            "PromptLogoCleaner"
+        ],
+        {
+            "title_aux": "ComfyUI_PromptLogoCleaner"
+        }
+    ],
+    "https://github.com/xgfone/ComfyUI_RasterCardMaker": [
+        [
+            "RasterCardMaker"
+        ],
+        {
+            "title_aux": "ComfyUI_RasterCardMaker"
+        }
+    ],
+    "https://github.com/xiaoxidashen/comfyui_my_utils": [
+        [
+            "VideoSplitCombine"
+        ],
+        {
+            "title_aux": "comfyui_my_utils"
+        }
+    ],
+    "https://github.com/xiaoyumu/ComfyUI-XYNodes": [
+        [
+            "AdjustImageColor",
+            "AppyColorToImage",
+            "PrimitiveBBOX",
+            "StringToBBOX"
+        ],
+        {
+            "title_aux": "ComfyUI-XYNodes"
+        }
+    ],
+    "https://github.com/xinyiSS/CombineMasksNode": [
+        [
+            "CombineMasksNode"
+        ],
+        {
+            "title_aux": "CombineMasksNode"
+        }
+    ],
+    "https://github.com/xqqe/honey_nodes": [
+        [
+            "ExtractLoRAName",
+            "Honey Lora Loader",
+            "HoneyBatchAspectRatio",
+            "HoneyLoraStackTags",
+            "HoneyTextConcat",
+            "Honey_LoRAStackRandom",
+            "Honey_LoRATags",
+            "LoraLoaderx",
+            "SmLoraLoader",
+            "Small Lora Loader",
+            "TagAdder"
+        ],
+        {
+            "title_aux": "honey_nodes [WIP]"
+        }
+    ],
+    "https://github.com/xsai-collab/ComfyUI-CombineVideoAndSubtitle": [
+        [
+            "CombineVideosFromFolder",
+            "GetSubtitlesFromVideo",
+            "MergeVideoAndSubtitle"
+        ],
+        {
+            "title_aux": "ComfyUI-CombineVideoAndSubtitle"
+        }
+    ],
+    "https://github.com/xtanqn/comfyui-xishen": [
+        [
+            "BatchSizeControl",
+            "XishenCommonPromptNode",
+            "XishenCommonResolutionNode",
+            "XishenPromptEditNode",
+            "XishenRandomIntegerNode",
+            "XishenRemoveEmptyLinesNode",
+            "XishenShutdownTimerAdvanced",
+            "XishenShutdownTimerNode",
+            "XishenSmartDisplayNode",
+            "XishenThemePromptNode",
+            "\ud83c\udf49Image-\u9897\u7c92\u8d28\u611f",
+            "\ud83c\udf61Qwen-\u5c3a\u5bf8\u9884\u8bbe",
+            "\ud83c\udf65Qwen-\u6253\u5149\u9884\u8bbe",
+            "\ud83c\udf6dImage-\u6cdb\u5149\u6548\u679c",
+            "\ud83c\udf70Qwen-\u955c\u5934\u9884\u8bbe"
+        ],
+        {
+            "title_aux": "comfyui-xishen [WIP]"
+        }
+    ],
+    "https://github.com/xuchenxu168/ComfyUI-Gemini-3": [
+        [
+            "Gemini3Chat",
+            "Gemini3ContextCache",
+            "Gemini3FunctionCalling",
+            "Gemini3ImageEditorPrompt",
+            "Gemini3MultiModalAnalysis",
+            "Gemini3SafetySettings",
+            "Gemini3StreamingGeneration",
+            "Gemini3StructuredOutput",
+            "Gemini3TextGeneration",
+            "Gemini3UseCachedContent",
+            "Gemini3WithCodeExecution",
+            "Gemini3WithGoogleSearch"
+        ],
+        {
+            "title_aux": "ComfyUI-Gemini-3 [UNSAFE/WIP]"
+        }
+    ],
+    "https://github.com/xuchenxu168/ComfyUI_Qwen3-VL": [
+        [
+            "CombineImagesForQwen3VL",
+            "LoadImageForQwen3VL",
+            "LoadVideo",
+            "LoadVideoForQwen3VL",
+            "LoadVideoURL",
+            "Qwen3VLAPIAdvanced",
+            "Qwen3VLAPINode",
+            "Qwen3VLProcessor",
+            "Qwen3VLResponseFormatter",
+            "TextPromptForQwen3VL"
+        ],
+        {
+            "title_aux": "ComfyUI-Qwen3-VL [WIP]"
+        }
+    ],
+    "https://github.com/xuchenxu168/Comfyui_Prompt_Edit": [
+        [
+            "Prompt_Edit"
+        ],
+        {
+            "title_aux": "ComfyUI-DreamOmni2-GGUF [WIP]"
+        }
+    ],
+    "https://github.com/xuchenxu168/comfyui_meituan_image": [
+        [
+            "MeituanLongCatEdit",
+            "MeituanLongCatLoader",
+            "MeituanLongCatT2I"
+        ],
+        {
+            "title_aux": "comfyui_meituan_image [WIP]"
+        }
+    ],
+    "https://github.com/xzuyn/ComfyUI-xzuynodes": [
+        [
+            "CLIPLoaderXZ",
+            "CLIPTextEncodeXZ",
+            "DualCLIPLoaderXZ",
+            "FirstLastFrameXZ",
+            "ImageResizeKJ",
+            "ImageResizeXZ",
+            "ModelSamplingFlux2XZ",
+            "SelfGuidanceXZ",
+            "TextEncodeQwenImageEditSimpleXZ",
+            "TextEncodeQwenImageEditXZ",
+            "TripleCLIPLoaderXZ",
+            "VAEDecodeTiledXZ",
+            "VAEDecodeXZ",
+            "WanImageToVideoXZ"
+        ],
+        {
+            "title_aux": "xzuynodes-ComfyUI"
+        }
+    ],
+    "https://github.com/y4my4my4m/ComfyUI_Direct3DS2": [
+        [
+            "Direct3DS2ModelDownloader",
+            "Direct3DS2Node"
+        ],
+        {
+            "title_aux": "ComfyUI-Direct3DS2 [WIP]"
+        }
+    ],
+    "https://github.com/yamanacn/ComfyUI-ImageMask-Random-Sync-Picker": [
+        [
+            "ImageMaskRandomSelector"
+        ],
+        {
+            "title_aux": "ComfyUI-ImageMask-Random-Sync-Picker"
+        }
+    ],
+    "https://github.com/yamanacn/ComfyUI-QwenVL3-image": [
+        [
+            "QwenVL3_image",
+            "QwenVLUnloadModel"
+        ],
+        {
+            "title_aux": "ComfyUI-QwenVL3-image [WIP]"
+        }
+    ],
+    "https://github.com/yamanacn/comfyui_qwen_object": [
+        [
+            "BBoxToSAM",
+            "DetectObject",
+            "LoadQwenModel",
+            "SortBBox"
+        ],
+        {
+            "title_aux": "comfyui_qwen_object [WIP]"
+        }
+    ],
+    "https://github.com/yamanacn/comfyui_qwenbbox": [
+        [
+            "BBoxToSAM_v2",
+            "LoadQwenModel_v2",
+            "QwenBbox"
+        ],
+        {
+            "title_aux": "comfyui_qwenbbox"
+        }
+    ],
+    "https://github.com/yanhuifair/ComfyUI-FairLab": [
+        [
+            "ASCIICharNode",
+            "AddNode",
+            "AppendTagsNode",
+            "AspectRatiosNode",
+            "Base64ToImageNode",
+            "DetailMapNode",
+            "DivideNode",
+            "DownloadImageNode",
+            "ExcludeTagsNode",
+            "FillAlphaNode",
+            "FixUTF8StringNode",
+            "FloatNode",
+            "FloatToIntNode",
+            "IfNode",
+            "ImageRemoveAlphaNode",
+            "ImageShapeNode",
+            "ImageSizeNode",
+            "ImageToBase64Node",
+            "ImageToVideoNode",
+            "ImagesCatNode",
+            "ImagesIndexNode",
+            "ImagesRangeNode",
+            "IntNode",
+            "IntToFloatNode",
+            "LoadImageBatchFromDirectoryNode",
+            "LoadImageFromDirectoryNode",
+            "LoadImageFromURLNode",
+            "LoadStringFromDirectoryNode",
+            "LoadStringNode",
+            "LoraLoaderDualNode",
+            "MaskMapNode",
+            "MaxNode",
+            "MinNode",
+            "ModulationDirectionNode",
+            "ModulationNode",
+            "MultiplyIntNode",
+            "MultiplyNode",
+            "NumberNode",
+            "OutpaintingPadNode",
+            "PerfectPixelNode",
+            "PrependTagsNode",
+            "PrintAnyNode",
+            "PrintImageNode",
+            "PureColorImageNode",
+            "PythonScriptNode",
+            "RangeStringListNode",
+            "RangeStringNode",
+            "ResizeImageNode",
+            "RoughnessToSmoothnessNode",
+            "SaveImageToDirectoryNode",
+            "SaveImageToFolderNode",
+            "SaveStringToDirectoryNode",
+            "ShowStringNode",
+            "StringAppendNode",
+            "StringNode",
+            "StringTranslateNode",
+            "SubtractNode",
+            "UniqueTagsNode",
+            "VideoToImageNode"
+        ],
+        {
+            "title_aux": "ComfyUI-FairLab"
+        }
+    ],
+    "https://github.com/yanhuifair/comfyui-deepseek": [
+        [
+            "DeepSeekChatNode",
+            "DeepSeekChatProNode",
+            "DeepSeekReasonerNode"
+        ],
+        {
+            "title_aux": "comfyui-deepseek [WIP]"
+        }
+    ],
+    "https://github.com/yichengup/ComfyUI-Transition": [
+        [
+            "CircularSequenceTransition",
+            "CircularTransition",
+            "DualLineTransition",
+            "GradientTransition",
+            "LinearTransition",
+            "SequenceTransition"
+        ],
+        {
+            "title_aux": "ComfyUI-Transition"
+        }
+    ],
+    "https://github.com/yichengup/ComfyUI-YCNodes_Advance": [
+        [
+            "FaceDetectorSelector",
+            "HumanPartsUltra",
+            "ImageUpscaleWithModel_Optimized",
+            "UpscaleModelLoader_Optimized",
+            "YC Color Match",
+            "YCFaceAlignToCanvas",
+            "YCFaceAlignToCanvasV2",
+            "YCFaceAlignToReference",
+            "YCFaceAnalysisModels",
+            "ycFaceMaskCreator"
+        ],
+        {
+            "title_aux": "ComfyUI-YCNodes_Advance"
+        }
+    ],
+    "https://github.com/yichengup/Comfyui-NodeSpark": [
+        [
+            "ImageCircleWarp",
+            "ImageStretch",
+            "ImageWaveWarp",
+            "LiquifyNode"
+        ],
+        {
+            "title_aux": "Comfyui-NodeSpark"
+        }
+    ],
+    "https://github.com/yincangshiwei/ComfyUI-SEQLToolNode": [
+        [
+            "CanvasFusionNode",
+            "ImageCropByAlpha"
+        ],
+        {
+            "title_aux": "ComfyUI-SEQLToolNode"
+        }
+    ],
+    "https://github.com/yipeeka/ComfyUI-ACE-Step": [
+        [
+            "AceStep15CoverConditioning",
+            "AceStep15ExtractConditioning",
+            "AceStep15LatentFromAudio",
+            "AceStep15LegoConditioning",
+            "AceStep15MultiRegionRepaint",
+            "AceStep15NullConditioning",
+            "AceStep15RepaintConditioning"
+        ],
+        {
+            "title_aux": "ComfyUI-ACE-Step [WIP]"
+        }
+    ],
+    "https://github.com/yuvraj108c/ComfyUI-HYPIR": [
+        [
+            "HYPIRProcess",
+            "LoadHYPIRModel",
+            "LoadTheraModel"
+        ],
+        {
+            "title_aux": "ComfyUI HYPIR [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/yuyu0218yu/comfyui-NXCM-tool": [
+        [
+            "HD_EncryptImage",
+            "HD_EncryptVideo",
+            "VideoToGarbledImage"
+        ],
+        {
+            "title_aux": "comfyui-NXCM-tool [UNSAFE]"
+        }
+    ],
+    "https://github.com/z604159435g/comfyui_random_prompt_plugin": [
+        [
+            "NaturalLanguagePromptGenerator",
+            "PromptCacheCleaner"
+        ],
+        {
+            "title_aux": "comfyui_random_prompt_plugin [WIP]"
+        }
+    ],
+    "https://github.com/zRich/ComfyUI-zRich-ObjectCutter": [
+        [
+            "SAM2AutoLayer",
+            "zRichObjectCutter"
+        ],
+        {
+            "title_aux": "ComfyUI-zRich-ObjectCutter"
+        }
+    ],
+    "https://github.com/zRich/sam2_layer_node": [
+        [
+            "SAM2AutoLayer"
+        ],
+        {
+            "title_aux": "sam2_layer_node"
+        }
+    ],
+    "https://github.com/zackabrams/ComfyUI-KeySyncWrapper": [
+        [
+            "KeySyncAdvanced",
+            "KeySyncWrapper"
+        ],
+        {
+            "title_aux": "ComfyUI-KeySyncWrapper [WIP]"
+        }
+    ],
+    "https://github.com/zhaorishuai/ComfyUI-StoryboardDistributor": [
+        [
+            "StoryboardDistributor"
+        ],
+        {
+            "title_aux": "ComfyUI-StoryboardDistributor"
+        }
+    ],
+    "https://github.com/zhengxyz123/ComfyUI-CLIPSeg": [
+        [
+            "CLIPSegImage",
+            "CLIPSegText"
+        ],
+        {
+            "title_aux": "zhengxyz123/ComfyUI-CLIPSeg [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/zhongpei/Comfyui_image2prompt": [
+        [
+            "CLIP AdvancedTextEncode|fofo",
+            "CLIP PromptConditioning|fofo",
+            "Image2Text",
+            "Image2TextWithTags",
+            "ImageBatchToList|fofo",
+            "ImageRewardScore|fofo",
+            "LoadImage2TextModel",
+            "LoadImageRewardScoreModel|fofo",
+            "LoadT5Model|fofo",
+            "LoadText2PromptModel",
+            "ShowText|fofo",
+            "T5QuantizationConfig|fofo",
+            "T5Text2Prompt|fofo",
+            "Text2GPTPrompt",
+            "Text2Prompt",
+            "TextBox|fofo",
+            "Translate2Chinese|fofo"
+        ],
+        {
+            "title_aux": "Comfyui_image2prompt"
+        }
+    ],
+    "https://github.com/zhoul1/comfy-alpha_outline-magick": [
+        [
+            "AlphaOutlineMagick"
+        ],
+        {
+            "title_aux": "comfy-alpha_outline-magick [WIP]"
+        }
+    ],
+    "https://github.com/zhu733756/Comfyui-Anything-Converter": [
+        [
+            "FileConverter.FileDictConverter",
+            "FileConverter.FileSplitter",
+            "FileConverter.LineConverter",
+            "ImageCoverter.LoadImage2Kontext",
+            "ImageCoverter.SaveImage",
+            "JsonCoverter.JsonCombiner",
+            "JsonCoverter.JsonParser",
+            "JsonCoverter.JsonPromptProcessor",
+            "TextConverter.PromptTemplateText"
+        ],
+        {
+            "title_aux": "Comfyui-Anything-Converter [UNSAFE]"
+        }
+    ],
+    "https://github.com/zhu798542746/comfyui_model": [
+        [
+            "ModelManagerNode"
+        ],
+        {
+            "title_aux": "comfyui_model [UNSAFE]"
+        }
+    ],
+    "https://github.com/zhuanvi/ComfyUI-ZVNodes": [
+        [
+            "BBOXFormatConverterZV",
+            "FFmpegImageSlideShowZV",
+            "ImageCounterNodeZV",
+            "JoinListZV",
+            "JsonListIndexerZV",
+            "JsonListLengthZV",
+            "JsonListNodeZV",
+            "JsonListSlicerZV",
+            "JsonListToMaskZV",
+            "JsonReaderZV",
+            "JsonToSrtConverterZV",
+            "LineNumberGeneratorZV",
+            "LoadAudioFromDirZV",
+            "LoadImageFromDirZV",
+            "LoadImageFromUrlZV",
+            "LoadTxtFromDirZV",
+            "LoadVideoFromDirZV",
+            "MangaCleanerInferenceZV",
+            "MangaCleanerModelLoaderZV",
+            "MangaCleanerPostProcessZV",
+            "MultiLineConditionalZV",
+            "MultiLineOperationZV",
+            "PatternFillNodeZV",
+            "ProductionDisplacementMapNodeZV",
+            "PromptSelectorWithTimeoutZV",
+            "RandomSelectListZV",
+            "SaveImageToPathZV",
+            "SaveTxtToPathZV",
+            "SaveVideoToPathZV",
+            "StringToPathZV",
+            "SubFolderScannerZV",
+            "TriangleCharacterLayoutZV",
+            "TxtCounterNodeZV",
+            "UniversalBBOXToMaskZV",
+            "VideoCounterNodeZV",
+            "VideoGeneratorFFmpegZV",
+            "VideoSceneDetectorZV",
+            "VideoSpeedZV"
+        ],
+        {
+            "title_aux": "ComfyUI-ZVNodes [WIP]"
+        }
+    ],
+    "https://github.com/zjkhurry/comfyui_MetalFX": [
+        [
+            "metalFXImg"
+        ],
+        {
+            "title_aux": "comfyui_MetalFX [WIP]"
+        }
+    ],
+    "https://github.com/zl9739379/comfyui-qwen-vl-api": [
+        [
+            "VL_QwenDescribeImage"
+        ],
+        {
+            "title_aux": "ComfyUI Qwen Vision Language API Node [NAME CONFLICT]"
+        }
+    ],
+    "https://github.com/zopieux/ComfyUI-zopi": [
+        [
+            "EvalPython",
+            "LoadTensortRTAndCheckpoint",
+            "SizeSelector"
+        ],
+        {
+            "title_aux": "ComfyUI-zopi [UNSAFE]"
+        }
+    ],
+    "https://github.com/zyd232/ComfyUI-zyd232-Nodes": [
+        [
+            "zyd232 ImagesPixelsCompare",
+            "zyd232 MaskBatchBlend",
+            "zyd232_SavePreviewImages"
+        ],
+        {
+            "title_aux": "ComfyUI-zyd232-Nodes"
+        }
+    ],
+    "https://github.com/zyquon/ComfyUI-Stash": [
+        [
+            "StashImage",
+            "StashNode",
+            "VRFaceRectify",
+            "VRFaceUnrectify"
+        ],
+        {
+            "title_aux": "ComfyUI Stash"
+        }
+    ],
+    "https://raw.githubusercontent.com/komojini/ComfyUI_Prompt_Template_CustomNodes/main/prompt_with_template.py": [
+        [
+            "ObjectPromptWithTemplate",
+            "PromptWithTemplate"
+        ],
+        {
+            "title_aux": "ComfyUI_Prompt_Template_CustomNodes"
+        }
+    ],
+    "https://raw.githubusercontent.com/okg21/VLLMVisionChatNode/refs/heads/main/VLLMVisionChatNode.py": [
+        [
+            "VLLMVisionChatNode"
+        ],
+        {
+            "title_aux": "VLLMVisionChatNode"
+        }
+    ],
+    "https://raw.githubusercontent.com/olyyarm/ComfyUI-VLMStudio/refs/heads/master/vlm_visionary_node_v3_.py": [
+        [
+            "GemmaMultimodalAnalyzer"
+        ],
+        {
+            "title_aux": "ComfyUI-VLMStudio"
+        }
+    ]
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-Manager/node_db/dev/github-stats.json b/custom_nodes/ComfyUI-Manager/node_db/dev/github-stats.json
new file mode 100644
index 0000000000000000000000000000000000000000..9b520bd08dfc7e90bd37cce58623299c15107afc
--- /dev/null
+++ b/custom_nodes/ComfyUI-Manager/node_db/dev/github-stats.json
@@ -0,0 +1,6212 @@
+{
+    "https://github.com/123jimin/ComfyUI-MobileForm": {
+        "stars": 13,
+        "last_update": "2025-04-06 13:36:29",
+        "author_account_age_days": 5480
+    },
+    "https://github.com/17Retoucher/ComfyUI_Fooocus": {
+        "stars": 57,
+        "last_update": "2024-02-24 07:33:29",
+        "author_account_age_days": 886
+    },
+    "https://github.com/1H-hobit/ComfyUI_InternVL3": {
+        "stars": 2,
+        "last_update": "2025-10-20 07:24:30",
+        "author_account_age_days": 642
+    },
+    "https://github.com/206811/ComfyUI_ZhipuAIO": {
+        "stars": 3,
+        "last_update": "2025-07-29 10:11:45",
+        "author_account_age_days": 2222
+    },
+    "https://github.com/2omegaXv/comfyui_face_router": {
+        "stars": 0,
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+    "https://github.com/wTechArtist/ComfyUI_VVL_Segmentation": {
+        "stars": 0,
+        "last_update": "2025-05-29 05:25:00",
+        "author_account_age_days": 2075
+    },
+    "https://github.com/wTechArtist/ComfyUI_VVL_VideoCamera": {
+        "stars": 0,
+        "last_update": "2025-06-12 02:11:03",
+        "author_account_age_days": 2075
+    },
+    "https://github.com/wTechArtist/ComfyUI_vvl_BBOX": {
+        "stars": 0,
+        "last_update": "2025-05-21 12:14:07",
+        "author_account_age_days": 2075
+    },
+    "https://github.com/walterFeng/ComfyUI-Image-Utils": {
+        "stars": 3,
+        "last_update": "2025-03-25 14:36:37",
+        "author_account_age_days": 3490
+    },
+    "https://github.com/wandaijin/ComfyUI-PaddleOCR": {
+        "stars": 0,
+        "last_update": "2025-12-31 05:41:25",
+        "author_account_age_days": 4134
+    },
+    "https://github.com/warshanks/Shank-Tools": {
+        "stars": 0,
+        "last_update": "2025-01-26 03:39:09",
+        "author_account_age_days": 4198
+    },
+    "https://github.com/wasilone11/comfyui-sync-translate-node": {
+        "stars": 0,
+        "last_update": "2025-07-19 03:26:55",
+        "author_account_age_days": 2920
+    },
+    "https://github.com/watarika/ComfyUI-Text-Utility": {
+        "stars": 2,
+        "last_update": "2025-11-29 09:12:35",
+        "author_account_age_days": 2447
+    },
+    "https://github.com/watarika/ComfyUI-exit": {
+        "stars": 0,
+        "last_update": "2025-09-28 23:38:02",
+        "author_account_age_days": 2447
+    },
+    "https://github.com/waynepimpzhang/comfyui-opencv-brightestspot": {
+        "stars": 0,
+        "last_update": "2025-01-05 06:04:53",
+        "author_account_age_days": 4505
+    },
+    "https://github.com/wciq1208/even-comfyui-plugin": {
+        "stars": 0,
+        "last_update": "2025-11-22 08:49:45",
+        "author_account_age_days": 4294
+    },
+    "https://github.com/whenSunSet/ComfyUI-FaceAnalysisNode": {
+        "stars": 0,
+        "last_update": "2026-04-13 02:06:55",
+        "author_account_age_days": 3794
+    },
+    "https://github.com/whmc76/ComfyUI-AudioSuiteAdvanced": {
+        "stars": 29,
+        "last_update": "2026-01-10 15:04:49",
+        "author_account_age_days": 1166
+    },
+    "https://github.com/wikinikiwings/ComfyUI-simple_image_nodes": {
+        "stars": 0,
+        "last_update": "2026-03-09 21:33:34",
+        "author_account_age_days": 723
+    },
+    "https://github.com/wildminder/ComfyUI-MagCache": {
+        "stars": 8,
+        "last_update": "2025-06-13 20:56:49",
+        "author_account_age_days": 4947
+    },
+    "https://github.com/wildminder/ComfyUI-VoxCPM": {
+        "stars": 478,
+        "last_update": "2026-04-24 12:30:42",
+        "author_account_age_days": 4947
+    },
+    "https://github.com/willblaschko/ComfyUI-Unload-Models": {
+        "stars": 27,
+        "last_update": "2024-06-30 10:07:40",
+        "author_account_age_days": 5306
+    },
+    "https://github.com/wilzamguerrero/Comfyui-zZzZz": {
+        "stars": 2,
+        "last_update": "2026-01-31 12:38:39",
+        "author_account_age_days": 1402
+    },
+    "https://github.com/woitec/ComfyUI_ImageDimensions": {
+        "stars": 0,
+        "last_update": "2025-10-26 19:13:31",
+        "author_account_age_days": 3581
+    },
+    "https://github.com/wordbrew/comfyui-wan-control-nodes": {
+        "stars": 6,
+        "last_update": "2025-06-19 23:37:04",
+        "author_account_age_days": 1327
+    },
+    "https://github.com/wormley/comfyui-wormley-nodes": {
+        "stars": 0,
+        "last_update": "2023-11-12 19:05:11",
+        "author_account_age_days": 3188
+    },
+    "https://github.com/wulalalaaaaa/Comfyui_Consistency_xiling": {
+        "stars": 0,
+        "last_update": "2026-05-19 07:45:23",
+        "author_account_age_days": 1700
+    },
+    "https://github.com/wxytjustb/ComfyUI-CustomNodes": {
+        "stars": 0,
+        "last_update": "2026-03-01 08:45:45",
+        "author_account_age_days": 4856
+    },
+    "https://github.com/x3bits/ComfyUI-Power-Flow": {
+        "stars": 3,
+        "last_update": "2025-01-14 14:20:35",
+        "author_account_age_days": 4097
+    },
+    "https://github.com/xWris3/jus_multifruit_comfyui": {
+        "stars": 0,
+        "last_update": "2026-03-22 19:57:36",
+        "author_account_age_days": 2975
+    },
+    "https://github.com/xgfone/ComfyUI_FaceToMask": {
+        "stars": 1,
+        "last_update": "2025-08-05 05:25:47",
+        "author_account_age_days": 4972
+    },
+    "https://github.com/xgfone/ComfyUI_PromptLogoCleaner": {
+        "stars": 0,
+        "last_update": "2025-07-28 05:28:42",
+        "author_account_age_days": 4972
+    },
+    "https://github.com/xgfone/ComfyUI_RasterCardMaker": {
+        "stars": 0,
+        "last_update": "2025-08-06 03:31:46",
+        "author_account_age_days": 4972
+    },
+    "https://github.com/xiaoxidashen/comfyui_my_utils": {
+        "stars": 0,
+        "last_update": "2025-12-02 05:58:50",
+        "author_account_age_days": 3247
+    },
+    "https://github.com/xiaoyumu/ComfyUI-XYNodes": {
+        "stars": 0,
+        "last_update": "2024-12-05 07:07:30",
+        "author_account_age_days": 4731
+    },
+    "https://github.com/xinyiSS/CombineMasksNode": {
+        "stars": 0,
+        "last_update": "2025-02-08 04:35:18",
+        "author_account_age_days": 1166
+    },
+    "https://github.com/xqqe/honey_nodes": {
+        "stars": 0,
+        "last_update": "2026-01-19 22:13:19",
+        "author_account_age_days": 2416
+    },
+    "https://github.com/xsai-collab/ComfyUI-CombineVideoAndSubtitle": {
+        "stars": 1,
+        "last_update": "2025-08-19 12:58:37",
+        "author_account_age_days": 1040
+    },
+    "https://github.com/xtanqn/comfyui-xishen": {
+        "stars": 0,
+        "last_update": "2025-12-11 08:31:37",
+        "author_account_age_days": 2346
+    },
+    "https://github.com/xuchenxu168/ComfyUI-Gemini-3": {
+        "stars": 4,
+        "last_update": "2025-11-20 09:43:19",
+        "author_account_age_days": 547
+    },
+    "https://github.com/xuchenxu168/ComfyUI_Qwen3-VL": {
+        "stars": 13,
+        "last_update": "2025-11-15 16:46:04",
+        "author_account_age_days": 547
+    },
+    "https://github.com/xuchenxu168/Comfyui_Prompt_Edit": {
+        "stars": 68,
+        "last_update": "2025-11-11 09:42:32",
+        "author_account_age_days": 547
+    },
+    "https://github.com/xuchenxu168/comfyui_meituan_image": {
+        "stars": 1,
+        "last_update": "2025-12-11 03:39:03",
+        "author_account_age_days": 547
+    },
+    "https://github.com/xzuyn/ComfyUI-xzuynodes": {
+        "stars": 0,
+        "last_update": "2026-03-25 18:47:13",
+        "author_account_age_days": 3835
+    },
+    "https://github.com/y4my4my4m/ComfyUI_Direct3DS2": {
+        "stars": 6,
+        "last_update": "2025-06-01 04:29:47",
+        "author_account_age_days": 4349
+    },
+    "https://github.com/yamanacn/ComfyUI-ImageMask-Random-Sync-Picker": {
+        "stars": 0,
+        "last_update": "2025-12-30 14:41:25",
+        "author_account_age_days": 2034
+    },
+    "https://github.com/yamanacn/ComfyUI-QwenVL3-image": {
+        "stars": 9,
+        "last_update": "2025-10-23 01:57:46",
+        "author_account_age_days": 2034
+    },
+    "https://github.com/yamanacn/comfyui_qwen_object": {
+        "stars": 0,
+        "last_update": "2025-06-20 12:24:28",
+        "author_account_age_days": 2034
+    },
+    "https://github.com/yamanacn/comfyui_qwenbbox": {
+        "stars": 0,
+        "last_update": "2025-06-21 03:00:01",
+        "author_account_age_days": 2034
+    },
+    "https://github.com/yanhuifair/ComfyUI-FairLab": {
+        "stars": 2,
+        "last_update": "2026-01-22 03:12:25",
+        "author_account_age_days": 4276
+    },
+    "https://github.com/yanhuifair/comfyui-deepseek": {
+        "stars": 4,
+        "last_update": "2025-04-08 09:14:25",
+        "author_account_age_days": 4276
+    },
+    "https://github.com/yichengup/ComfyUI-Transition": {
+        "stars": 3,
+        "last_update": "2025-07-12 07:15:38",
+        "author_account_age_days": 839
+    },
+    "https://github.com/yichengup/ComfyUI-YCNodes_Advance": {
+        "stars": 20,
+        "last_update": "2026-01-04 18:27:21",
+        "author_account_age_days": 839
+    },
+    "https://github.com/yichengup/Comfyui-NodeSpark": {
+        "stars": 6,
+        "last_update": "2025-01-20 14:20:36",
+        "author_account_age_days": 839
+    },
+    "https://github.com/yincangshiwei/ComfyUI-SEQLToolNode": {
+        "stars": 0,
+        "last_update": "2025-05-28 10:06:17",
+        "author_account_age_days": 4345
+    },
+    "https://github.com/yipeeka/ComfyUI-ACE-Step": {
+        "stars": 0,
+        "last_update": "2026-03-09 17:52:50",
+        "author_account_age_days": 3652
+    },
+    "https://github.com/yuvraj108c/ComfyUI-HYPIR": {
+        "stars": 3,
+        "last_update": "2025-08-18 06:04:36",
+        "author_account_age_days": 2867
+    },
+    "https://github.com/yuyu0218yu/comfyui-NXCM-tool": {
+        "stars": 1,
+        "last_update": "2025-12-22 02:54:56",
+        "author_account_age_days": 1280
+    },
+    "https://github.com/z604159435g/comfyui_random_prompt_plugin": {
+        "stars": 0,
+        "last_update": "2025-09-08 15:15:23",
+        "author_account_age_days": 582
+    },
+    "https://github.com/zRich/ComfyUI-zRich-ObjectCutter": {
+        "stars": 0,
+        "last_update": "2025-11-07 03:49:30",
+        "author_account_age_days": 4960
+    },
+    "https://github.com/zRich/sam2_layer_node": {
+        "stars": 0,
+        "last_update": "2025-10-25 06:00:16",
+        "author_account_age_days": 4960
+    },
+    "https://github.com/zackabrams/ComfyUI-KeySyncWrapper": {
+        "stars": 3,
+        "last_update": "2025-06-21 17:46:04",
+        "author_account_age_days": 3044
+    },
+    "https://github.com/zhaorishuai/ComfyUI-StoryboardDistributor": {
+        "stars": 7,
+        "last_update": "2025-04-01 08:10:16",
+        "author_account_age_days": 2953
+    },
+    "https://github.com/zhengxyz123/ComfyUI-CLIPSeg": {
+        "stars": 5,
+        "last_update": "2025-05-20 12:40:03",
+        "author_account_age_days": 2369
+    },
+    "https://github.com/zhongpei/Comfyui_image2prompt": {
+        "stars": 386,
+        "last_update": "2025-06-06 23:41:46",
+        "author_account_age_days": 4173
+    },
+    "https://github.com/zhoul1/comfy-alpha_outline-magick": {
+        "stars": 0,
+        "last_update": "2026-04-02 12:06:43",
+        "author_account_age_days": 4575
+    },
+    "https://github.com/zhu733756/Comfyui-Anything-Converter": {
+        "stars": 1,
+        "last_update": "2025-09-10 08:24:29",
+        "author_account_age_days": 2967
+    },
+    "https://github.com/zhu798542746/comfyui_model": {
+        "stars": 0,
+        "last_update": "2026-01-05 11:44:52",
+        "author_account_age_days": 3242
+    },
+    "https://github.com/zhuanvi/ComfyUI-ZVNodes": {
+        "stars": 0,
+        "last_update": "2026-06-08 00:57:17",
+        "author_account_age_days": 3872
+    },
+    "https://github.com/zjkhurry/comfyui_MetalFX": {
+        "stars": 1,
+        "last_update": "2025-03-05 07:07:17",
+        "author_account_age_days": 3695
+    },
+    "https://github.com/zl9739379/comfyui-qwen-vl-api": {
+        "stars": 0,
+        "last_update": "2025-07-02 12:53:51",
+        "author_account_age_days": 1295
+    },
+    "https://github.com/zml-ai/comfyui-hydit": {
+        "stars": 3,
+        "last_update": "2024-08-07 09:37:09",
+        "author_account_age_days": 2697
+    },
+    "https://github.com/zopieux/ComfyUI-zopi": {
+        "stars": 0,
+        "last_update": "2026-04-15 18:20:23",
+        "author_account_age_days": 6243
+    },
+    "https://github.com/zxq790909-maker/ComfyUI-wangyi-OLED": {
+        "stars": 3,
+        "last_update": "2026-01-24 05:04:40",
+        "author_account_age_days": 301
+    },
+    "https://github.com/zyd232/ComfyUI-zyd232-Nodes": {
+        "stars": 2,
+        "last_update": "2025-12-14 12:53:28",
+        "author_account_age_days": 4338
+    },
+    "https://github.com/zyquon/ComfyUI-Stash": {
+        "stars": 0,
+        "last_update": "2026-06-05 22:50:19",
+        "author_account_age_days": 434
+    },
+    "ttps://github.com/GraftingRayman/Border-Distance-Mapper": {
+        "stars": 1,
+        "last_update": "2025-11-17 17:49:31",
+        "author_account_age_days": 877
+    }
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-Manager/node_db/dev/model-list.json b/custom_nodes/ComfyUI-Manager/node_db/dev/model-list.json
new file mode 100644
index 0000000000000000000000000000000000000000..8e3e1dc4858a08aa46190aa53ba320d565206cf4
--- /dev/null
+++ b/custom_nodes/ComfyUI-Manager/node_db/dev/model-list.json
@@ -0,0 +1,3 @@
+{
+  "models": []
+}
diff --git a/custom_nodes/ComfyUI-Manager/node_db/dev/scan.sh b/custom_nodes/ComfyUI-Manager/node_db/dev/scan.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ac8c4255b608064f5a362e01532f97a733fbc67b
--- /dev/null
+++ b/custom_nodes/ComfyUI-Manager/node_db/dev/scan.sh
@@ -0,0 +1,3 @@
+#!/bin/bash
+rm ~/.tmp/dev/*.py > /dev/null 2>&1
+python ../../scanner.py ~/.tmp/dev $*
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-Manager/node_db/forked/custom-node-list.json b/custom_nodes/ComfyUI-Manager/node_db/forked/custom-node-list.json
new file mode 100644
index 0000000000000000000000000000000000000000..589243083120e4ffeb706e61e7d3c74e4db3462a
--- /dev/null
+++ b/custom_nodes/ComfyUI-Manager/node_db/forked/custom-node-list.json
@@ -0,0 +1,194 @@
+{
+    "custom_nodes": [
+        {
+            "author": "Fossiel",
+            "title": "ComfyUI-MultiGPU-Patched",
+            "reference": "https://github.com/Fossiel/ComfyUI-MultiGPU-Patched",
+            "files": [
+                "https://github.com/Fossiel/ComfyUI-MultiGPU-Patched"
+            ],
+            "install_type": "git-clone",
+            "description": "Patched fork of ComfyUI-MultiGPU providing universal .safetensors and GGUF multi-GPU distribution with DisTorch 2.0 engine, model-driven allocation options (bytes/ratio modes), WanVideoWrapper integration, and up to 10% faster GGUF inference. (Description by CC)"
+        },        
+        {
+            "author": "synchronicity-labs",
+            "title": "ComfyUI Sync Lipsync Node",
+            "reference": "https://github.com/synchronicity-labs/sync-comfyui",
+            "files": [
+                "https://github.com/synchronicity-labs/sync-comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "This custom node allows you to perform audio-video lip synchronization inside ComfyUI using a simple interface."
+        },
+        {
+            "author": "joaomede",
+            "title": "ComfyUI-Unload-Model-Fork",
+            "reference": "https://github.com/joaomede/ComfyUI-Unload-Model-Fork",
+            "files": [
+                "https://github.com/joaomede/ComfyUI-Unload-Model-Fork"
+            ],
+            "install_type": "git-clone",
+            "description": "For unloading a model or all models, using the memory management that is already present in ComfyUI. Copied from [a/https://github.com/willblaschko/ComfyUI-Unload-Models](https://github.com/willblaschko/ComfyUI-Unload-Models) but without the unnecessary extra stuff."
+        },
+        {
+            "author": "SanDiegoDude",
+            "title": "ComfyUI-HiDream-Sampler [WIP]",
+            "reference": "https://github.com/SanDiegoDude/ComfyUI-HiDream-Sampler",
+            "files": [
+                "https://github.com/SanDiegoDude/ComfyUI-HiDream-Sampler"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of enhanced nodes for ComfyUI that provide powerful additional functionality to your workflows.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "PramaLLC",
+            "title": "ComfyUI BEN - Background Erase Network",
+            "reference": "https://github.com/PramaLLC/BEN2_ComfyUI",
+            "files": [
+                "https://github.com/PramaLLC/BEN2_ComfyUI"
+            ],
+            "install_type": "git-clone",
+            "description": "Remove backgrounds from images with [a/BEN2](https://huggingface.co/PramaLLC/BEN2) in ComfyUI\nOriginal repo: [a/https://github.com/DoctorDiffusion/ComfyUI-BEN](https://github.com/DoctorDiffusion/ComfyUI-BEN)"
+        },
+        {
+            "author": "BlenderNeko",
+            "title": "ltdrdata/ComfyUI_TiledKSampler",
+            "reference": "https://github.com/ltdrdata/ComfyUI_TiledKSampler",
+            "files": [
+                "https://github.com/ltdrdata/ComfyUI_TiledKSampler"
+            ],
+            "install_type": "git-clone",
+            "description": "PR for [a/https://github.com/BlenderNeko/ComfyUI_TiledKSampler/pull/59](https://github.com/BlenderNeko/ComfyUI_TiledKSampler/pull/59)"
+        },
+        {
+            "author": "leeooo001",
+            "title": "ComfyUI-leo-Hamer",
+            "reference": "https://github.com/leeooo001/ComfyUI-leo-Hamer",
+            "files": [
+                "https://github.com/leeooo001/ComfyUI-leo-Hamer"
+            ],
+            "install_type": "git-clone",
+            "description": "Unoffice Hamer-ComfyUI by leo\nNOTE:base on [a/hamer](https://github.com/geopavlakos/hamer)"
+        },
+        {
+            "author": "leeooo001",
+            "title": "ComfyUI-leo-GVHMR",
+            "reference": "https://github.com/leeooo001/ComfyUI-leo-GVHMR",
+            "files": [
+                "https://github.com/leeooo001/ComfyUI-leo-GVHMR"
+            ],
+            "install_type": "git-clone",
+            "description": "Unoffice Hamer-ComfyUI by leo\nNOTE:base on [a/GVHMR](https://github.com/zju3dv/GVHMR)"
+        },
+        {
+            "author": "leeooo001",
+            "title": "RealisDance-ComfyUI",
+            "reference": "https://github.com/leeooo001/ComfyUI-leo-RealisDance",
+            "files": [
+                "https://github.com/leeooo001/ComfyUI-leo-RealisDance"
+            ],
+            "install_type": "git-clone",
+            "description": "Unoffice RealisDance-ComfyUI by leo\nNOTE:base on [a/RealisDance](https://github.com/damo-cv/RealisDance), modified on [a/RealisDanceComfyui](https://github.com/AIFSH/RealisDance-ComfyUI)"
+        },
+        {
+            "author": "jags111",
+            "title": "NyaamZ/efficiency-nodes-ED",
+            "reference": "https://github.com/NyaamZ/efficiency-nodes-ED",
+            "files": [
+                "https://github.com/NyaamZ/efficiency-nodes-ED"
+            ],
+            "install_type": "git-clone",
+            "description": "This forked repo supports efficiency-nodes-comfyui. Additional features."
+        },
+        {
+            "author": "SeaArtLab",
+            "title": "zer0int/ComfyUI-Long-CLIP",
+            "reference": "https://github.com/zer0int/ComfyUI-Long-CLIP",
+            "files": [
+                "https://github.com/zer0int/ComfyUI-Long-CLIP"
+            ],
+            "install_type": "git-clone",
+            "description": "This forked repo supports FLUX.1 not only SD1.5, SDXL."
+        },
+        {
+            "author": "meimeilook",
+            "title": "ComfyUI_IPAdapter_plus.old [backward compatbility]",
+            "reference": "https://github.com/meimeilook/ComfyUI_IPAdapter_plus.old",
+            "files": [
+                "https://github.com/meimeilook/ComfyUI_IPAdapter_plus.old"
+            ],
+            "install_type": "git-clone",
+            "description": "This repo is created to provide backward compatibility for workflows configured with the old IPAdapter."
+        },
+        {
+            "author": "ZHO-ZHO-ZHO",
+            "title": "Dr.Lt.Data/ComfyUI-YoloWorld-EfficientSAM",
+            "reference": "https://github.com/ltdrdata/ComfyUI-YoloWorld-EfficientSAM",
+            "files": [
+                "https://github.com/ltdrdata/ComfyUI-YoloWorld-EfficientSAM"
+            ],
+            "install_type": "git-clone",
+            "description": "This fork includes [a/PR32](https://github.com/ZHO-ZHO-ZHO/ComfyUI-YoloWorld-EfficientSAM/pull/32)"
+        },
+        {
+            "author": "ertu110",
+            "title": "sdxl_prompt_style",
+            "reference": "https://github.com/ertu110/sdxl_prompt_style",
+            "files": [
+                "https://github.com/ertu110/sdxl_prompt_style"
+            ],
+            "install_type": "git-clone",
+            "description": "This project is a complete benchmark [a/https://github.com/twri/sdxl_prompt_styler](https://github.com/twri/sdxl_prompt_styler) A large amount of code inside comes from https://github.com/twri/sdxl_prompt_styler Project and [a/https://www.nodecafe.org/package/pythongosssss_ComfyUI-Custom-Scripts](https://www.nodecafe.org/package/pythongosssss_ComfyUI-Custom-Scripts) project\nThe functionality of this project is related to https://github.com/twri/sdxl_prompt_styler Highly overlapping, the only purpose of creating this project is because there are too many styles when selecting, resulting in a long and inconvenient dropdown box. Therefore, To address this issue, this project has added a secondary menu to the style."
+        },
+        {
+            "author": "gustproof",
+            "title": "ComfyUI_IPAdapter_plus_Style_Components",
+            "reference": "https://github.com/gustproof/ComfyUI_IPAdapter_plus_Style_Components",
+            "files": [
+                "https://github.com/gustproof/ComfyUI_IPAdapter_plus_Style_Components"
+            ],
+            "install_type": "git-clone",
+            "description": "Style Components is an IP-Adapter model conditioned on anime styles. The style embeddings can either be extracted from images or created manually. This repo currently only supports the SDXL model trained on AutismmixPony."
+        },
+        {
+            "author": "gameltb",
+            "title": "comfyui-stablsr",
+            "reference": "https://github.com/gameltb/Comfyui-StableSR",
+            "files": [
+                "https://github.com/gameltb/Comfyui-StableSR"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a development respository for debugging migration of StableSR to ComfyUI\n\nNOTE:Forked from [https://github.com/gameltb/Comfyui-StableSR]\nPut the StableSR [a/webui_786v_139.ckpt](https://huggingface.co/Iceclear/StableSR/resolve/main/webui_768v_139.ckpt) model into Comyfui/models/stablesr/, Put the StableSR [a/stablesr_768v_000139.ckpt](https://huggingface.co/Iceclear/StableSR/resolve/main/stablesr_768v_000139.ckpt) model into Comyfui/models/checkpoints/"
+        },
+        {
+            "author": "city96",
+            "title": "Efficient-Large-Model/Extra Models for ComfyUI",
+            "reference": "https://github.com/Efficient-Large-Model/ComfyUI_ExtraModels",
+            "files": [
+                "https://github.com/Efficient-Large-Model/ComfyUI_ExtraModels"
+            ],
+            "install_type": "git-clone",
+            "description": "A forked version of ComfyUI_ExtraModels. (modified by Efficient-Large-Model)"
+        },
+        {
+            "author": "Pablerdo",
+            "title": "ComfyUI-PSNodes",
+            "reference": "https://github.com/Pablerdo/ComfyUI-PSNodes",
+            "files": [
+                "https://github.com/Pablerdo/ComfyUI-PSNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "A fork of KJNodes for ComfyUI.\nVarious quality of life -nodes for ComfyUI, mostly just visual stuff to improve usability"
+        },
+        {
+            "author": "huixingyun",
+            "title": "ComfyUI-SoundFlow",
+            "reference": "https://github.com/huixingyun/ComfyUI-SoundFlow",
+            "files": [
+                "https://github.com/huixingyun/ComfyUI-SoundFlow"
+            ],
+            "install_type": "git-clone",
+            "description": "forked from https://github.com/fredconex/ComfyUI-SoundFlow (removed)"
+        }
+    ]
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-Manager/node_db/forked/extension-node-map.json b/custom_nodes/ComfyUI-Manager/node_db/forked/extension-node-map.json
new file mode 100644
index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b
--- /dev/null
+++ b/custom_nodes/ComfyUI-Manager/node_db/forked/extension-node-map.json
@@ -0,0 +1 @@
+{}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-Manager/node_db/forked/model-list.json b/custom_nodes/ComfyUI-Manager/node_db/forked/model-list.json
new file mode 100644
index 0000000000000000000000000000000000000000..8e3e1dc4858a08aa46190aa53ba320d565206cf4
--- /dev/null
+++ b/custom_nodes/ComfyUI-Manager/node_db/forked/model-list.json
@@ -0,0 +1,3 @@
+{
+  "models": []
+}
diff --git a/custom_nodes/ComfyUI-Manager/node_db/forked/scan.sh b/custom_nodes/ComfyUI-Manager/node_db/forked/scan.sh
new file mode 100644
index 0000000000000000000000000000000000000000..5d8d8c48b6e3f48dc1491738c1226f574909c05d
--- /dev/null
+++ b/custom_nodes/ComfyUI-Manager/node_db/forked/scan.sh
@@ -0,0 +1,4 @@
+#!/bin/bash
+source ../../../../venv/bin/activate
+rm .tmp/*.py > /dev/null
+python ../../scanner.py
diff --git a/custom_nodes/ComfyUI-Manager/node_db/legacy/alter-list.json b/custom_nodes/ComfyUI-Manager/node_db/legacy/alter-list.json
new file mode 100644
index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b
--- /dev/null
+++ b/custom_nodes/ComfyUI-Manager/node_db/legacy/alter-list.json
@@ -0,0 +1 @@
+{}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-Manager/node_db/legacy/custom-node-list.json b/custom_nodes/ComfyUI-Manager/node_db/legacy/custom-node-list.json
new file mode 100644
index 0000000000000000000000000000000000000000..f30a0239b55654890704067834927cf1a1e8f6c9
--- /dev/null
+++ b/custom_nodes/ComfyUI-Manager/node_db/legacy/custom-node-list.json
@@ -0,0 +1,5843 @@
+{
+    "custom_nodes": [
+        {
+            "author": "Aero-Ex",
+            "title": "ComfyUI Vision LLM Analyzer Node [REMOVED]",
+            "reference": "https://github.com/Aero-Ex/ComfyUI-Vision-LLM-Analyzer",
+            "files": [
+                "https://github.com/Aero-Ex/ComfyUI-Vision-LLM-Analyzer"
+            ],
+            "install_type": "git-clone",
+            "description": "This repository contains a powerful and versatile custom node for ComfyUI that seamlessly integrates with OpenAI-compatible Large Language Models (LLMs), including multimodal (vision-enabled) models like GPT-4o.\nThis single node allows you to perform both text generation and image analysis, making it an essential tool for advanced prompt engineering and creative automation."
+        },
+        {
+            "author": "Aero-Ex",
+            "title": "comfyui_diffswap [REMOVED]",
+            "reference": "https://github.com/Aero-Ex/comfyui_diffswap",
+            "files": [
+                "https://github.com/Aero-Ex/comfyui_diffswap"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: DiffSwap"
+        },
+        {
+            "author": "Sakura-nee",
+            "title": "ComfyUI_Save2Discord [REMOVED]",
+            "reference": "https://github.com/Sakura-nee/ComfyUI_Save2Discord",
+            "files": [
+                "https://github.com/Sakura-nee/ComfyUI_Save2Discord"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Send Generated Image To Discord Webhook.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "pixaroma",
+            "title": "ComfyUI-Pixaroma [REMOVED]",
+            "id": "comfyui-pixaroma",
+            "reference": "https://github.com/pixaroma/ComfyUI-Pixaroma",
+            "files": [
+                "https://github.com/pixaroma/ComfyUI-Pixaroma"
+            ],
+            "install_type": "git-clone",
+            "description": "Creative suite for ComfyUI with 3D Builder, AudioReact, Image Composer, Paint Studio, Save Mp4, Image Crop, Image Compare, Note Pixaroma, Label, Resolution, Preview Image, and Align Pixaroma (canvas snap & alignment guides). Layers, transforms, AI background removal, audio-reactive video, and more."
+        },
+        {
+            "author": "moose-lab",
+            "title": "ComfyUI-GPT [REMOVED]",
+            "reference": "https://github.com/moose-lab/ComfyUI-GPT",
+            "files": [
+                "https://github.com/moose-lab/ComfyUI-GPT"
+            ],
+            "install_type": "git-clone",
+            "description": "Help comfy deisgner develope custom nodes by foreground GUI without any coding knowledge, complementing the workflow what you design with LLM automatically"
+        },
+        {
+            "author": "Jjulianadv",
+            "title": "Wild Divide [REMOVED]",
+            "reference": "https://github.com/Julian-adv/WildDivide",
+            "files": [
+                "https://github.com/Julian-adv/WildDivide"
+            ],
+            "install_type": "git-clone",
+            "description": "This extension provides the ability to build prompts using wildcards for each region of a split image."
+        },
+        {
+            "author": "btitkin",
+            "title": "Random Prompt Builder [REMOVED]",
+            "id": "random-prompt-builder",
+            "reference": "https://github.com/btitkin/ComfyUI-RandomPromptBuilder",
+            "files": [
+                "https://github.com/btitkin/ComfyUI-RandomPromptBuilder"
+            ],
+            "install_type": "git-clone",
+            "description": "Advanced AI-powered prompt generation using local GGUF models. Generate detailed, structured prompts with character controls, style presets, and model-specific formatting for Pony, SDXL, Flux, and more. Supports GPU acceleration and runs completely offline."
+        },
+        {
+            "author": "JamesDanielDoss",
+            "title": "JamesDossAI-EnhancedLinks [REMOVED]",
+            "reference": "https://github.com/JamesDanielDoss/JamesDossAI-EnhancedLinks",
+            "files": [
+                "https://github.com/JamesDanielDoss/JamesDossAI-EnhancedLinks"
+            ],
+            "install_type": "git-clone",
+            "description": "Simple animated arrows traveling along workflow links (input → output)"
+        },
+        {
+            "author": "JamesDanielDoss",
+            "title": "Doss-Node-Suite [REMOVED]",
+            "reference": "https://github.com/JamesDanielDoss/Doss-Node-Suite",
+            "files": [
+                "https://github.com/JamesDanielDoss/Doss-Node-Suite"
+            ],
+            "install_type": "git-clone",
+            "description": "A beginner-friendly custom node suite for ComfyUI focused on prompt helpers, workflow organization, and creator tools."
+        },
+        {
+            "author": "AlcAI-AnimeHaven",
+            "title": "AlcAI Nodes for ComfyUI [REMOVED]",
+            "reference": "https://github.com/AlcAI-AnimeHaven/AlcAI-nodes",
+            "files": [
+                "https://github.com/AlcAI-AnimeHaven/AlcAI-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "AlcAI Nodes is a collection of custom nodes for ComfyUI, a modular interface for image generation with Stable Diffusion. This project focuses on tools optimized for creating anime-style images and stylized effects, inspired by the AnimeHaven universe. It aims to simplify workflows for artists and AI creativity enthusiasts."
+        },
+        {
+            "author": "HailXD",
+            "title": "comfyui-random-artist [REMOVED]",
+            "reference": "https://github.com/HailXD/comfyui-random-artist",
+            "files": [
+                "https://github.com/HailXD/comfyui-random-artist"
+            ],
+            "install_type": "git-clone",
+            "description": "Generates random artist styles for ComfyUI workflows. (Description by CC)"
+        },
+        {
+            "author": "usman2003",
+            "title": "ComfyUI-Gender-Classifier [REMOVED]",
+            "reference": "https://github.com/usman2003/ComfyUI-Gender-Classifier",
+            "files": [
+                "https://github.com/usman2003/ComfyUI-Gender-Classifier"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Gender Classification"
+        },
+        {
+            "author": "Anonymzx",
+            "title": "ComfyUI-Indonesia-TTS [WIP] [REMOVED]",
+            "reference": "https://github.com/Anonymzx/ComfyUI-Indonesia-TTS",
+            "files": [
+                "https://github.com/Anonymzx/ComfyUI-Indonesia-TTS"
+            ],
+            "description": "Repositori ini menyediakan integrasi model Text-to-Speech (TTS) Bahasa Indonesia dari Facebook (MMS-TTS-IND) ke dalam ComfyUI, sehingga Anda dapat langsung menyintesis suara berbahasa Indonesia dengan kontrol penuh via antarmuka node-based.\nNOTE: The files in the repo are not organized.",
+            "install_type": "git-clone"
+        },
+        {
+            "author": "ssspace1",
+            "title": "SSpack_ComfyUI [REMOVED]",
+            "reference": "https://github.com/ssspace1/SSpack_ComfyUI",
+            "files": [
+                "https://github.com/ssspace1/SSpack_ComfyUI"
+            ],
+            "install_type": "git-clone",
+            "description": "Compact, UI-friendly nodes for ComfyUI with LoRA/checkpoint selectors with thumbnails, text utilities, image helpers, and a lightweight cache cleaner script."
+        },
+        {
+            "author": "denyazzolin",
+            "title": "comfyui-abs-int [REMOVED]",
+            "reference": "https://github.com/denyazzolin/comfyui-abs-int",
+            "files": [
+                "https://github.com/denyazzolin/comfyui-abs-int"
+            ],
+            "install_type": "git-clone",
+            "description": "This node receives an int and returns the positive version of it in any case."
+        },
+        {
+            "author": "denyazzolin",
+            "title": "comfyui-null-checker [REMOVED]",
+            "reference": "https://github.com/denyazzolin/comfyui-null-checker",
+            "files": [
+                "https://github.com/denyazzolin/comfyui-null-checker"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node for null value checking. (Description by CC)"
+        },
+        {
+            "author": "denyazzolin",
+            "title": "comfyui-null-audio-checker [REMOVED]",
+            "reference": "https://github.com/denyazzolin/comfyui-null-audio-checker",
+            "files": [
+                "https://github.com/denyazzolin/comfyui-null-audio-checker"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for detecting and handling null audio streams in workflows. (Description by CC)"
+        },
+        {
+            "author": "l2dnjsrud",
+            "title": "ComfyUI-BlueMangaPanelSplitter [REMOVED]",
+            "reference": "https://github.com/l2dnjsrud/ComfyUI-BlueMangaPanelSplitter",
+            "files": [
+                "https://github.com/l2dnjsrud/ComfyUI-BlueMangaPanelSplitter"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for splitting blue-pencil manga manuscript pages into story panels."
+        },
+        {
+            "author": "ThepExcel",
+            "title": "Multiline Text Choice Node for ComfyUI [REMOVED]",
+            "reference": "https://github.com/ThepExcel/aiangelgallery-comfyui",
+            "files": [
+                "https://github.com/ThepExcel/aiangelgallery-comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "This custom node for ComfyUI allows users to input multiline text and select a specific line by its number. The node processes the input and returns the selected line along with its index."
+        },
+        {
+            "author": "zhupeter010903",
+            "title": "ComfyUI-prompt-library [REMOVED]",
+            "reference": "https://github.com/zhupeter010903/ComfyUI-prompt-library",
+            "files": [
+                "https://github.com/zhupeter010903/ComfyUI-prompt-library"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Grouped Prompt Node, ..."
+        },
+        {
+            "author": "meshmesh-io",
+            "title": "mm-comfyui-loopback [REMOVED]",
+            "id": "mm-loopback",
+            "reference": "https://github.com/meshmesh-io/mm-comfyui-loopback",
+            "files": [
+                "https://github.com/meshmesh-io/mm-comfyui-loopback"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Loop, LoopStart, LoopEnd, LoopStart_SEGIMAGE, LoopEnd_SEGIMAGE"
+        },
+        {
+            "author": "devzeroLL",
+            "title": "comfyui-lxj-Node [REMOVED]",
+            "reference": "https://github.com/devzeroLL/comfyui-lxj-Node",
+            "files": [
+                "https://github.com/devzeroLL/comfyui-lxj-Node"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: lxj_ImageBatch14, lxj_TextBatch14"
+        },
+        {
+            "author": "Suzu008",
+            "title": "ComfyUI-ImageCritic [REMOVED]",
+            "reference": "https://github.com/Suzu008/ComfyUI-ImageCritic",
+            "files": [
+                "https://github.com/Suzu008/ComfyUI-ImageCritic"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for image analysis and evaluation. (Description by CC)"
+        },
+        {
+            "author": "LucianoCirino",
+            "title": "ComfyUI-invAIder-Nodes [REMOVED]",
+            "reference": "https://github.com/LucianoCirino/ComfyUI-invAIder-Nodes",
+            "files": [
+                "https://github.com/LucianoCirino/ComfyUI-invAIder-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom nodes I've created for my own personal workflows. Use at your own discretion.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "lordwedggie",
+            "title": "xcpNodes [WIP] [REMOVED]",
+            "reference": "https://github.com/lordwedggie/xcpNodes",
+            "files": [
+                "https://github.com/lordwedggie/xcpNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Slider nodes based on Smirnov75's codes [a/https://github.com/Smirnov75/ComfyUI-mxToolkit](https://github.com/Smirnov75/ComfyUI-mxToolkit)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "fya16838",
+            "title": "Comfyui-Xiufu [REMOVED]",
+            "reference": "https://github.com/fya16838/Comfyui-Xiufu",
+            "files": [
+                "https://github.com/fya16838/Comfyui-Xiufu"
+            ],
+            "install_type": "git-clone",
+            "description": "Fix ComfyUI models and nodes after importing workflows from others. (Description by CC)"
+        },
+        {
+            "author": "daveand",
+            "title": "ComfyUI-daveand-nodes [REMOVED]",
+            "reference": "https://github.com/daveand/ComfyUI-daveand-nodes",
+            "files": [
+                "https://github.com/daveand/ComfyUI-daveand-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "ModelConfigSelector node for managing checkpoint configurations with manual override support and batch sampler parameters. (Description by CC)"
+        },
+        {
+            "author": "PozzettiAndrea",
+            "title": "ComfyUI-MeshCraft [REMOVED]",
+            "reference": "https://github.com/PozzettiAndrea/ComfyUI-MeshCraft",
+            "files": [
+                "https://github.com/PozzettiAndrea/ComfyUI-MeshCraft"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node that crops images to specified dimensions from a center point with optional offset"
+        },
+        {
+            "author": "PozzettiAndrea",
+            "title": "ComfyUI-HunyuanX [REMOVED]",
+            "reference": "https://github.com/PozzettiAndrea/ComfyUI-HunyuanX",
+            "files": [
+                "https://github.com/PozzettiAndrea/ComfyUI-HunyuanX"
+            ],
+            "install_type": "git-clone",
+            "description": "Hunyuan 3D 2.1 image-to-3D generation nodes for ComfyUI with PBR texturing and model caching. Granular control for research."
+        },
+        {
+            "author": "orpheus-gaze",
+            "title": "comfyui-s2-guidance-test [REMOVED]",
+            "reference": "https://github.com/orpheus-gaze/comfyui-s2-guidance-test",
+            "files": [
+                "https://github.com/orpheus-gaze/comfyui-s2-guidance-test"
+            ],
+            "install_type": "git-clone",
+            "description": "An attempted implementation of S2 guidance for DIT diffusion models in ComfyUI, currently hardcoded for Flux model architecture to improve image sharpness and brightness."
+        },
+        {
+            "author": "abdullahceylan",
+            "title": "AC Image Optimizer [REMOVED]",
+            "reference": "https://github.com/abdullahceylan/ac-comfyui-image-optimizer",
+            "files": [
+                "https://github.com/abdullahceylan/ac-comfyui-image-optimizer"
+            ],
+            "install_type": "git-clone",
+            "description": "A powerful ComfyUI custom node for optimizing and compressing images with support for multiple formats (JPEG, WebP, PNG) and configurable quality settings."
+        },
+        {
+            "author": "yojimbodayne",
+            "title": "ComfyUI-Dropbox-API [WIP] [REMOVED]",
+            "reference": "https://github.com/yojimbodayne/ComfyUI-Dropbox-API",
+            "files": [
+                "https://github.com/yojimbodayne/ComfyUI-Dropbox-API"
+            ],
+            "install_type": "git-clone",
+            "description": "This custom nodepackage for ComfyUI allows users to interact with Dropbox API, enabling image, text, and video uploads, downloads, and management directly from ComfyUI workflows.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "tfernd",
+            "title": "Auto CPU Offload for ComfyUI [WIP] [REMOVED]",
+            "reference": "https://github.com/tfernd/ComfyUI-AutoCPUOffload",
+            "files": [
+                "https://github.com/tfernd/ComfyUI-AutoCPUOffload"
+            ],
+            "install_type": "git-clone",
+            "description": "This extension introduces an 'Auto CPU Offload' node designed to reduce GPU VRAM usage by automatically offloading model components to the CPU. It intelligently manages the movement of model layers between the GPU and CPU, aiming to keep only the necessary parts in VRAM during inference."
+        },
+        {
+            "author": "stalkervr",
+            "title": "Custom Path Nodes for ComfyUI [UNSAFE] [REMOVED]",
+            "reference": "https://github.com/stalkervr/comfyui-custom-path-nodes",
+            "files": [
+                "https://github.com/stalkervr/comfyui-custom-path-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes for path handling and image cropping.[w/This nodepack contains a node that has a vulnerability allowing access to arbitrary file paths.]"
+        },
+        {
+            "author": "Charonartist",
+            "title": "ComfyUI-send-eagle-pro [REMOVED]",
+            "reference": "https://github.com/Charonartist/ComfyUI-send-eagle-pro_2",
+            "files": [
+                "https://github.com/Charonartist/ComfyUI-send-eagle-pro_2"
+            ],
+            "install_type": "git-clone",
+            "description": "Eagle integration extension node for ComfyUI (Pro version)"
+        },
+        {
+            "author": "Charonartist",
+            "title": "ComfyUI LoRA Random Selector [REMOVED]",
+            "reference": "https://github.com/Charonartist/comfyui-lora-random-selector",
+            "files": [
+                "https://github.com/Charonartist/comfyui-lora-random-selector"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: WanMoeKSampler, WanMoeKSamplerAdvanced"
+        },
+        {
+            "author": "bpmpnaito",
+            "title": "comfyui-imageviewer [REMOVED]",
+            "reference": "https://github.com/bpmpnaito/comfyui-imageviewer",
+            "files": [
+                "https://github.com/bpmpnaito/comfyui-imageviewer"
+            ],
+            "install_type": "git-clone",
+            "description": "Image viewer node that displays preview of images directly in the node."
+        },
+        {
+            "author": "leonardomiramondi",
+            "title": "Flux Context ComfyUI Node [REMOVED]",
+            "reference": "https://github.com/leonardomiramondi/flux-context-comfyui",
+            "files": [
+                "https://github.com/leonardomiramondi/flux-context-comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node for Flux Context (Kontext) image editing"
+        },
+        {
+            "author": "Charonartist",
+            "title": "comfyui-lmstudio-conversation [REMOVED]",
+            "reference": "https://github.com/Charonartist/comfyui-lmstudio-conversation",
+            "files": [
+                "https://github.com/Charonartist/comfyui-lmstudio-conversation"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom nodes for LM Studio integration with conversation history support "
+        },
+        {
+            "author": "Charonartist",
+            "title": "comfyui-last-frame-extractor [REMOVED]",
+            "reference": "https://github.com/Charonartist/comfyui-last-frame-extractor",
+            "files": [
+                "https://github.com/Charonartist/comfyui-last-frame-extractor"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a ComfyUI custom node that extracts the last frame (image) from an input image batch. It is particularly useful when you want to obtain the final scene or frame in a video generation workflow."
+        },
+        {
+            "author": "Charonartist",
+            "title": "ComfyUI Tag Remover [REMOVED]",
+            "reference": "https://github.com/Charonartist/comfyui-tag-remover",
+            "files": [
+                "https://github.com/Charonartist/comfyui-tag-remover"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for removing specified tags and their content from text"
+        },
+        {
+            "author": "Charonartist",
+            "title": "ComfyUI Smart Resize Node [REMOVED]",
+            "reference": "https://github.com/Charonartist/comfyui-smart-resize-node",
+            "files": [
+                "https://github.com/Charonartist/comfyui-smart-resize-node"
+            ],
+            "install_type": "git-clone",
+            "description": "Smart resize node for ComfyUI that handles portrait/landscape images with short/long side specification"
+        },
+        {
+            "author": "Charonartist",
+            "title": "ComfyUI Auto LoRA [REMOVED]",
+            "reference": "https://github.com/Charonartist/comfyui-auto-lora-v2",
+            "files": [
+                "https://github.com/Charonartist/comfyui-auto-lora-v2"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a ComfyUI custom node that automatically detects trigger words from text prompts and applies the corresponding LoRA models."
+        },
+        {
+            "author": "qlikpetersen",
+            "title": "ComfyUI-AI_Tools [UNSAFE] [REMOVED]",
+            "reference": "https://github.com/qlikpetersen/ComfyUI-AI_Tools",
+            "files": [
+                "https://github.com/qlikpetersen/ComfyUI-AI_Tools"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: DoLogin, HttpRequest, Json2String, String2Json, CreateListString, CreateListJSON, Query_OpenAI, Image_Attachment, JSON_Attachment, String_Attachment, RunPython\n[w/This nodepack contains a node with a vulnerability that allows arbitrary code execution.]"
+        },
+        {
+            "author": "AiSatan",
+            "title": "ComfyUI_CSM [NAME CONFLICT] [REMOVED]",
+            "reference": "https://github.com/AiSatan/ComfyUI_CSM",
+            "files": [
+                "https://github.com/AiSatan/ComfyUI_CSM"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI node for the CSM model featuring text-to-speech, voice cloning, and automatic model downloading from Hugging Face."
+        },
+        {
+            "author": "shiertier",
+            "title": "ComfyUI-TeaCache-Lumina [REMOVED]",
+            "reference": "https://github.com/shiertier/ComfyUI-TeaCache-lumina2",
+            "files": [
+                "https://github.com/shiertier/ComfyUI-TeaCache-lumina2"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI Node Implementation: TeaCache Acceleration Specifically Designed for the Lumina Model"
+        },
+        {
+            "author": "Daniel Lewis",
+            "title": "ComfyUI-TTS [REMOVED]",
+            "reference": "https://github.com/daniel-lewis-ab/ComfyUI-TTS",
+            "files": [
+                "https://github.com/daniel-lewis-ab/ComfyUI-TTS"
+            ],
+            "install_type": "git-clone",
+            "description": "Text To Speech (TTS) for ComfyUI"
+        },
+        {
+            "author": "Daniel Lewis",
+            "title": "ComfyUI-Llama [REMOVED]",
+            "reference": "https://github.com/daniel-lewis-ab/ComfyUI-Llama",
+            "files": [
+                "https://github.com/daniel-lewis-ab/ComfyUI-Llama"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a set of nodes to interact with llama-cpp-python"
+        },
+        {
+            "author": "Solarish",
+            "title": "fyUI-MidnightLook [REMOVED]",
+            "reference": "https://github.com/Solarish/ComfyUI-MidnightLook",
+            "files": [
+                "https://github.com/Solarish/ComfyUI-MidnightLook"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom node for MidnightLook.com\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "pdale-boop",
+            "title": "Network Guardian [REMOVED]",
+            "reference": "https://github.com/pdale-boop/Comfyui-Network-Guardian",
+            "files": [
+                "https://github.com/pdale-boop/Comfyui-Network-Guardian"
+            ],
+            "install_type": "git-clone",
+            "description": "Protects ComfyUI workflows from network interruptions with automatic retry and fallback"
+        },
+        {
+            "author": "rodpl",
+            "title": "comfyui-asset-manager [INCOMPLETE]",
+            "reference": "https://github.com/rodpl/comfyui-asset-manager",
+            "files": [
+                  "https://github.com/rodpl/comfyui-asset-manager"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI Asset Manager for managing assets in ComfyUI"
+        },
+        {
+            "author": "DailyMok",
+            "title": "ComfyUI-PromptMixerNode [REMOVED]",
+            "reference": "https://github.com/DailyMok/ComfyUI-PromptMixerNode",
+            "files": [
+                "https://github.com/DailyMok/ComfyUI-PromptMixerNode"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for prompt mixing with PromptMixerDaily node. (Description by CC)"
+        },
+        {
+            "author": "IvanZhd",
+            "title": "comfyui-codeformer [REMOVED]",
+            "reference": "https://github.com/IvanZhd/comfyui-codeformer",
+            "files": [
+                "https://github.com/IvanZhd/comfyui-codeformer"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Image Inverter"
+        },
+        {
+            "author": "Kur0butiMegane",
+            "title": "Comfyui-StringUtils [REMOVED]",
+            "reference": "https://github.com/Kur0butiMegane/Comfyui-StringUtils2",
+            "files": [
+                "https://github.com/Kur0butiMegane/Comfyui-StringUtils2"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Normalizer, Splitter, Selector, XML Parser, XML Parser, Make Property, Add XML Tag, Is String Empty, Cond Passthrough, CLIP Passthrough, ClipRegion Passthrough, Scheduler Selector (Impact), Scheduler Selector (Inspire), Save Text, XML to Cutoff"
+        },
+        {
+            "author": "rookiestar28",
+            "title": "ComfyUI_Security_Audit [REMOVED]",
+            "reference": "https://github.com/rookiestar28/ComfyUI_Security_Audit",
+            "files": [
+                "https://github.com/rookiestar28/ComfyUI_Security_Audit"
+            ],
+            "install_type": "git-clone",
+            "description": "A lightweight, dual-layer security extension for ComfyUI using AST-based static analysis and runtime monitoring to detect malicious code in custom nodes."
+        },
+        {
+            "author": "hw5511",
+            "title": "Woohee HF Upscaler Loader [REMOVED]",
+            "reference": "https://github.com/hw5511/comfyui_hf_upscaler_loader",
+            "files": [
+                "https://github.com/hw5511/comfyui_hf_upscaler_loader"
+            ],
+            "install_type": "git-clone",
+            "description": "Load upscale models directly from Hugging Face Hub in ComfyUI"
+        },
+        {
+            "author": "arthur",
+            "title": "Video Output Bridge [REMOVED]",
+            "reference": "https://github.com/arthurtravers/ComfyUI-VideoOutputBridge",
+            "files": [
+                "https://github.com/arthurtravers/ComfyUI-VideoOutputBridge"
+            ],
+            "install_type": "git-clone",
+            "description": "Bridge VHS_VideoCombine video outputs to standard image payloads for serverless runners (RunPod, Modal). Enables automatic S3 uploads of MP4/WebP files in worker-comfyui deployments."
+        },
+        {
+            "author": "UmeAiRT",
+            "title": "ComfyUI-UmeAiRT-Sync [REMOVED]",
+            "reference": "https://github.com/UmeAiRT/ComfyUI-UmeAiRT-Sync",
+            "files": [
+                "https://github.com/UmeAiRT/ComfyUI-UmeAiRT-Sync"
+            ],
+            "install_type": "git-clone",
+            "description": "Automatic workflow synchronizer/loader for UmeAiRT Workflows (Flux, SDXL, WAN, etc.)."
+        },
+        {
+            "author": "UmeAiRT",
+            "title": "UmeAiRT-Toolkit [REMOVED]",
+            "reference": "https://github.com/UmeAiRT/ComfyUI-UmeAiRT-Toolkit",
+            "files": [
+                "https://github.com/UmeAiRT/ComfyUI-UmeAiRT-Toolkit"
+            ],
+            "install_type": "git-clone",
+            "description": "A Wireless, Nodes 2.0 Ready, and Aesthetic Toolkit for ComfyUI."
+        },
+        {
+            "author": "sln77",
+            "title": "ComfyUI-Camie-Tagger [REMOVED]",
+            "reference": "https://github.com/sln77/ComfyUI-Camie-Tagger",
+            "files": [
+                "https://github.com/sln77/ComfyUI-Camie-Tagger"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node integration for the Camie tagger v2 model from Hugging Face for image tagging. (Description by CC)"
+        },
+        {
+            "author": "vegaflowltd",
+            "title": "Vega Flow V8.9 — Temporal Stabilisation [REMOVED]",
+            "id": "comfyui-vegaflow",
+            "reference": "https://github.com/vegaflowltd/ComfyUI-VegaFlow",
+            "files": [
+                "https://github.com/vegaflowltd/ComfyUI-VegaFlow"
+            ],
+            "install_type": "git-clone",
+            "description": "Professional temporal stabilisation engine. Eliminates flicker, luminance drift, and frame-to-frame inconsistency in AI-generated and traditional video footage."
+        },
+        {
+            "author": "seanhan19911990-source",
+            "title": "ComfyUI-LTX2-Visual-LoRA [REMOVED",
+            "reference": "https://github.com/seanhan19911990-source/ComfyUI-LTX2-Visual-LoRA",
+            "files": [
+                "https://github.com/seanhan19911990-source/ComfyUI-LTX2-Visual-LoRA"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node that filters LTX-2 LoRAs to strip out audio-weight interference, ensuring clean, high-fidelity sound while maintaining visual fine-tuning."
+        },
+        {
+            "author": "chrisgoringe",
+            "title": "Comfy Controller [REMOVED",
+            "id": "cg-comfycontroller",
+            "reference": "https://github.com/chrisgoringe/cg-controller",
+            "files": [
+                "https://github.com/chrisgoringe/cg-controller"
+            ],
+            "install_type": "git-clone",
+            "description": "Quickly and easily build a GUI on top of your workflow. Gather just the nodes that you want to see, with no spaghetti, onto controller panels, leaving your workflow untouched in the background."
+        },
+        {
+            "author": "Ada123-a",
+            "title": "ComfyUI-Kandinsky [REMOVED]",
+            "reference": "https://github.com/Ada123-a/ComfyUI-Kandinsky",
+            "files": [
+                "https://github.com/Ada123-a/ComfyUI-Kandinsky"
+            ],
+            "install_type": "git-clone",
+            "description": "Kandinsky 2/20B ComfyUI support with FP8/GGUF/blockswap support."
+        },
+        {
+            "author": "lgldlk",
+            "title": "ComfyUI-PSD-Replace [REMOVED]",
+            "reference": "https://github.com/lgldlk/ComfyUI-PSD-Replace",
+            "files": [
+                "https://github.com/lgldlk/ComfyUI-PSD-Replace"
+            ],
+            "install_type": "git-clone",
+            "description": "One click replacement of smart objects or layers in PSD"
+        },
+        {
+            "author": "Rizzlord",
+            "title": "ComfyUI-RizzNodes [REMOVED]",
+            "reference": "https://github.com/Rizzlord/ComfyUI-RizzNodes",
+            "files": [
+                "https://github.com/Rizzlord/ComfyUI-RizzNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "RizzNodes for ComfyUI Welcome to RizzNodes, a collection of custom nodes for ComfyUI designed to streamline various workflows, from loading images and models in batches to dynamic prompt generation and memory management."
+        },
+        {
+            "author": "palant",
+            "title": "Image Resize for ComfyUI [REMOVED]",
+            "id": "image-resize",
+            "reference": "https://github.com/palant/image-resize-comfyui",
+            "files": [
+                "https://github.com/palant/image-resize-comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "This custom node provides various tools for resizing images. The goal is resizing without distorting proportions, yet without having to perform any calculations with the size of the original image. If a mask is present, it is resized and modified along with the image."
+        },
+        {
+            "author": "palant",
+            "title": "Integrated Nodes for ComfyUI [REMOVED]",
+            "reference": "https://github.com/palant/integrated-nodes-comfyui",
+            "files": [
+                "https://github.com/palant/integrated-nodes-comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "This tool will turn entire workflows or parts of them into single integrated nodes. In a way, it is similar to the Node Templates functionality but hides the inner structure. This is useful if all you want is to reuse and quickly configure a bunch of nodes without caring how they are interconnected."
+        },
+        {
+            "author": "zfkun",
+            "title": "ComfyUI_zfkun [REMOVED]",
+            "reference": "https://github.com/zfkun/ComfyUI_zfkun",
+            "files": [
+                "https://github.com/zfkun/ComfyUI_zfkun"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of nodes for common tools, including text preview, text translation (multi-platform, multi-language), image loader, webcamera capture."
+        },
+        {
+            "author": "xXAdonesXx",
+            "title": "NodeGPT",
+            "id": "nodegpt",
+            "reference": "https://github.com/xXAdonesXx/NodeGPT",
+            "reference2": "https://github.com/antonym-git/NodeGPT",
+            "files": [
+                "https://github.com/xXAdonesXx/NodeGPT"
+            ],
+            "install_type": "git-clone",
+            "description": "Implementation of AutoGen inside ComfyUI. This repository is under development, and not everything is functioning correctly yet."
+        },
+        {
+            "author": "basenc",
+            "title": "ComfyUI Environment Variable Node [UNSAFE/REMOVED]",
+            "reference": "https://github.com/basenc/Comfyui-EnvVarNode",
+            "files": [
+                "https://github.com/basenc/Comfyui-EnvVarNode"
+            ],
+            "install_type": "git-clone",
+            "description": "Exposes environment variables inside ComfyUI workflows. Use it to keep API keys and other secrets out of your graph files while still wiring them into nodes that need them.[w/This nodepack has a vulnerability that allows reading arbitrary environment variables.]"
+        },
+        {
+            "author": "Bharanidharan",
+            "title": "iSeeBetter Node for ComfyUI [REMOVED]",
+            "reference": "https://github.com/llikethat/ComfyUI-iseebetter",
+            "files": [
+                "https://github.com/llikethat/ComfyUI-iseebetter"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom Node to implement iSeeBetter upscaling method."
+        },
+        {
+            "author": "uczensokratesa",
+            "title": "ComfyUI-Gemini-VAE-Fix [REMOVED]",
+            "reference": "https://github.com/uczensokratesa/ComfyUI-Gemini-VAE-Fix",
+            "files": [
+                "https://github.com/uczensokratesa/ComfyUI-Gemini-VAE-Fix"
+            ],
+            "install_type": "git-clone",
+            "description": "Advanced VAE decoder node with audio sync precision and crash-proof memory management for long videos. (Description by CC)"
+        },
+        {
+            "author": "uczensokratesa",
+            "title": "ComfyUI-UniversalSmartVAE [REMOVED]",
+            "reference": "https://github.com/uczensokratesa/ComfyUI-UniversalSmartVAE",
+            "files": [
+                "https://github.com/uczensokratesa/ComfyUI-UniversalSmartVAE"
+            ],
+            "install_type": "git-clone",
+            "description": "VRAM-safe VAE decoder for video latents in ComfyUI. Decodes 5D video latents in temporal batches to prevent GPU memory explosions."
+        },
+        {
+            "author": "uczensokratesa",
+            "title": "ComfyUI-Claude-VAE [REMOVED]",
+            "reference": "https://github.com/uczensokratesa/ComfyUI-Claude-VAE",
+            "files": [
+                "https://github.com/uczensokratesa/ComfyUI-Claude-VAE"
+            ],
+            "install_type": "git-clone",
+            "description": "VAE Decode node that solves VRAM problems with mathematically precise temporal stitching, maintaining perfect frame counts across batch sizes for video generation."
+        },
+        {
+            "author": "siray-ai",
+            "title": "siray-comfyui [REMOVED]",
+            "reference": "https://github.com/siray-ai/siray-comfyui",
+            "files": [
+                "https://github.com/siray-ai/siray-comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI nodes for Siray image/video models with dynamic schema-based inputs, authentication, and video streaming. (Description by CC)"
+        },
+        {
+            "author": "1hew",
+            "title": "ComfyUI-1hewHungryStudio [REMOVED]",
+            "reference": "https://github.com/1hew/ComfyUI-1hewHungryStudio",
+            "files": [
+                "https://github.com/1hew/ComfyUI-1hewHungryStudio"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI nodes for image cloning and shape drawing operations. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Oct7",
+            "title": "ComfyUI-LaplaMask [REMOVED]",
+            "reference": "https://github.com/Oct7/ComfyUI-LaplaMask",
+            "files": [
+                "https://github.com/Oct7/ComfyUI-LaplaMask"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Blur→Mask"
+        },
+        {
+            "author": "aliabougazia",
+            "title": "comfyui_pt_security_scanner [REMOVED]",
+            "reference": "https://github.com/aliabougazia/comfyui_pt_security_scanner",
+            "files": [
+                "https://github.com/aliabougazia/comfyui_pt_security_scanner"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node that scans PyTorch model files for potential security vulnerabilities without executing them."
+        },
+        {
+            "author": "Matthew-X",
+            "title": "Workflow Importer [REMOVED]",
+            "id": "comfyui-workflow_importer",
+            "reference": "https://github.com/Matthew-X/comfyui-workflow_importer",
+            "files": [
+                "https://github.com/Matthew-X/comfyui-workflow_importer"
+            ],
+            "install_type": "git-clone",
+            "js_path": "workflow_importer",
+            "description": "Import ComfyUI workflows from images with embedded metadata. Adds a toolbar Import button, drag-and-drop dialog and Ctrl+Shift+I shortcut. Supports legacy and new UIs, common image formats, opens each image in a new workflow tab."
+        },
+        {
+            "author": "silveroxides",
+            "title": "ComfyUI_Hybrid-Scaled_fp8-Loader [REMOVED]",
+            "reference": "https://github.com/silveroxides/ComfyUI_Hybrid-Scaled_fp8-Loader",
+            "files": [
+                "https://github.com/silveroxides/ComfyUI_Hybrid-Scaled_fp8-Loader"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom Loader for scaled_fp8 models with hybrid precision layers."
+        },
+        {
+            "author": "AnastasiyaW",
+            "title": "ComfyUI-SmartScreenshotCleaner [REMOVED]",
+            "reference": "https://github.com/AnastasiyaW/ComfyUI-SmartScreenshotCleaner",
+            "files": [
+                "https://github.com/AnastasiyaW/ComfyUI-SmartScreenshotCleaner"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes for removing UI elements from screenshots by detecting and masking icons, buttons, and text. (Description by CC)"
+        },
+        {
+            "author": "Raykosan",
+            "title": "ComfyUI_RS-SaturationNode [REMOVED]",
+            "reference": "https://github.com/Raykosan/ComfyUI_RS-SaturationNode",
+            "files": [
+                "https://github.com/Raykosan/ComfyUI_RS-SaturationNode"
+            ],
+            "install_type": "git-clone",
+            "description": "Professional image saturation control with artifact and highlight protection."
+        },
+        {
+            "author": "juehackr",
+            "title": "comfyui_fk_server [BANNED]",
+            "id": "fk-server",
+            "reference": "https://github.com/juehackr/comfyui_fk_server",
+            "files": [
+                "https://github.com/juehackr/comfyui_fk_server"
+            ],
+            "install_type": "git-clone",
+            "description": "🤗🤗🤗Comfyui Universal Translation Plugin (no longer requires adding various nodes, directly add translation function on the existing nodes), allowing Comfyui to support Chinese input and automatic translation for any long text input box, while adding error translation function (calling Baidu Translate), achieving translation freedom![w/This nodepack turns your ComfyUI server into a resource in the cloud.]"
+        },
+        {
+            "author": "Slix-M-Lestragg",
+            "title": "comfyui-enhanced [WIP]",
+            "reference": "https://github.com/Slix-M-Lestragg/comfyui-enhanced",
+            "files": [
+                "https://github.com/Slix-M-Lestragg/comfyui-enhanced"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of enhanced nodes for ComfyUI that provide powerful additional functionality to your workflows.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Linsoo",
+            "title": "ComfyUI-Linsoo-Custom-Nodes",
+            "reference": "https://github.com/Linsoo/ComfyUI-Linsoo-Custom-Nodes",
+            "files": [
+                "https://github.com/Linsoo/ComfyUI-Linsoo-Custom-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Linsoo Save Image, Linsoo Load Image (In development.. not working), Linsoo Empty Latent Image, Linsoo Multi Inputs, Linsoo Multi Outputs"
+        },
+        {
+            "author": "shinich39",
+            "title": "comfyui-nothing-happened",
+            "reference": "httphttps://github.com/shinich39/comfyui-nothing-happened",
+            "files": [
+               "https://github.com/shinich39/comfyui-nothing-happened"
+            ],
+            "description": "Save image and keep metadata.",
+            "install_type": "git-clone"
+        },
+        {
+            "author": "AhBumm",
+            "title": "ComfyUI_MangaLineExtraction",
+            "reference": "https://github.com/AhBumm/ComfyUI_MangaLineExtraction-hf",
+            "files": [
+               "https://github.com/AhBumm/ComfyUI_MangaLineExtraction-hf"
+            ],
+            "description": "p1atdev/MangaLineExtraction-hf as a node in comfyui",
+            "install_type": "git-clone"
+        },
+        {
+            "author": "fuzr0dah",
+            "title": "comfyui-sceneassembly",
+            "reference": "https://github.com/fuzr0dah/comfyui-sceneassembly",
+            "files": [
+                "https://github.com/fuzr0dah/comfyui-sceneassembly"
+            ],
+            "install_type": "git-clone",
+            "description": "A bunch of nodes I created that I also find useful."
+        },
+        {
+            "author": "amamisonlyuser",
+            "title": "MixvtonComfyui [WIP]",
+            "reference": "https://github.com/amamisonlyuser/MixvtonComfyui",
+            "files": [
+                "https://github.com/amamisonlyuser/MixvtonComfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: CXH_Leffa_Viton_Load, CXH_Leffa_Viton_Run\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "A4P7J1N7M05OT",
+            "title": "ComfyUI-VAELoaderSDXLmod",
+            "reference": "https://github.com/A4P7J1N7M05OT/ComfyUI-VAELoaderSDXLmod",
+            "files": [
+                "https://github.com/A4P7J1N7M05OT/ComfyUI-VAELoaderSDXLmod"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Modified SDXL VAE Loader, Empty Latent Image Variable"
+        },
+        {
+            "author": "pixixai",
+            "title": "ComfyUI_Pixix-Tools [UNSAFE/WIP]",
+            "reference": "https://github.com/pixixai/ComfyUI_pixixTools",
+            "files": [
+                "https://github.com/pixixai/ComfyUI_pixixTools"
+            ],
+            "install_type": "git-clone",
+            "description": "Load Text (from folder)\nNOTE: The files in the repo are not organized.[w/The contents of files from arbitrary paths can be read remotely through this node.]"
+        },
+        {
+            "author": "brandonkish",
+            "title": "comfyUI-extractable-text [WIP]",
+            "reference": "https://github.com/brandonkish/comfyUI-extractable-text",
+            "files": [
+                "https://github.com/brandonkish/comfyUI-extractable-text"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Save Image With Description, Save Image To Folder, Load Image With Description, LoRA Testing Node, Get Smaller Of Two Numbers, Get Larger Of Two Numbers\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Randomwalkforest",
+            "title": "Comfyui-Koi-Toolkit",
+            "reference": "https://github.com/Randomwalkforest/Comfyui-Koi-Toolkit",
+            "files": [
+                "https://github.com/Randomwalkforest/Comfyui-Koi-Toolkit"
+            ],
+            "install_type": "git-clone",
+            "description": "Koi Toolkit provides advanced nodes"
+        },
+        {
+            "author": "lfelipegg",
+            "title": "lfgg_custom_nodes [WIP]",
+            "reference": "https://github.com/lfelipegg/lfgg_custom_nodes",
+            "files": [
+                "https://github.com/lfelipegg/lfgg_custom_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: ModelMergeCombos\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Dehypnotic",
+            "title": "Save MP3 node for ComfyUI [UNSAFE]",
+            "reference": "https://github.com/Dehypnotic/comfyui-save-mp3",
+            "files": [
+                "https://github.com/Dehypnotic/comfyui-save-mp3"
+            ],
+            "install_type": "git-clone",
+            "description": "Simple, flexible MP3 saver with bitrate options and handy path/filename templates.[w/This nodepack contains a node that has a vulnerability allowing write to arbitrary file paths.]"
+        },
+        {
+            "author": "octapus8085",
+            "title": "OpenAI-comfyui-O",
+            "reference": "https://github.com/Spicely/Comfyui-File-Utils",
+            "files": [
+                "https://github.com/Spicely/Comfyui-File-Utils"
+            ],
+            "install_type": "git-clone",
+            "description": "This plugin provides multiple file-handling and utility nodes for ComfyUI, including: image saving, audio saving, video saving, video composition, audio-to-subtitle conversion, and random number generation nodes. These nodes not only process files but also return their absolute file paths.\nNOTE: The files in the repo are not organized.[w/This nodepack contains a node that has a vulnerability allowing write to arbitrary file paths.]"
+        },
+        {
+            "author": "pizurny",
+            "title": "ComfyUI-Just-DWPose [WIP]",
+            "reference": "https://github.com/pizurny/ComfyUI-Just-DWPose",
+            "files": [
+                "https://github.com/pizurny/ComfyUI-Just-DWPose"
+            ],
+            "install_type": "git-clone",
+            "description": "An advanced DWPose annotator for ComfyUI with TorchScript and ONNX backends, featuring comprehensive pose detection, bone validation, temporal smoothing, and custom visualization tools."
+        },
+        {
+            "author": "aistudynow",
+            "title": "comfyui-wilddragon-nodes [WIP]",
+            "reference": "https://github.com/aistudynow/comfyui-wilddragon-nodes",
+            "files": [
+                "https://github.com/aistudynow/comfyui-wilddragon-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Image Face Crop (2025), Person Selector, Pose Face Filter, Person BBox Tracker, Face Restore & Blend, ...\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "rzasharp79",
+            "title": "ComfyUI--SolarFlare",
+            "reference": "https://github.com/rzasharp79/ComfyUI--SolarFlare",
+            "files": [
+                "https://github.com/rzasharp79/ComfyUI--SolarFlare"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Qwen Image, ..."
+        },
+        {
+            "author": "90cube",
+            "title": "CBCanvas Node for ComfyUI [WIP]",
+            "reference": "https://github.com/90cube/Comfyui-CBcanvas",
+            "files": [
+                "https://github.com/90cube/Comfyui-CBcanvas"
+            ],
+            "install_type": "git-clone",
+            "description": "Canvas node with intelligent aspect ratio control - 13 preset aspect ratios from ultra-wide (21:9) to ultra-tall (9:21).\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "nohikomiso",
+            "title": "ComfyUI-ImageFolderPicker [UNSAFE]",
+            "reference": "https://github.com/nohikomiso/ComfyUI-ImageFolderPicker",
+            "files": [
+                "https://github.com/nohikomiso/ComfyUI-ImageFolderPicker"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI node for browsing local server folders and selecting images via thumbnail display in a grid interface. (Description by CC)[w/This nodepack has a vulnerability that allows it to retrieve a list of files from arbitrary paths.]"
+        },
+        {
+            "author": "PozzettiAndrea",
+            "title": "ComfyUI-CameraAnalysis",
+            "reference": "https://github.com/PozzettiAndrea/ComfyUI-CameraAnalysis",
+            "files": [
+                "https://github.com/PozzettiAndrea/ComfyUI-CameraAnalysis"
+            ],
+            "install_type": "git-clone",
+            "description": "Extracts camera intrinsic parameters from image EXIF data."
+        },
+        {
+            "author": "LSDJesus",
+            "title": "ComfyUI-Luna-Collection [WIP]",
+            "reference": "https://github.com/LSDJesus/ComfyUI-Luna-Collection",
+            "files": [
+                "https://github.com/LSDJesus/ComfyUI-Luna-Collection"
+            ],
+            "install_type": "git-clone",
+            "description": "Collection of finetuned and custom nodes for Pyrite and I\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "dorpxam",
+            "title": "ComfyUI-LTXVideoLoRA [REMOVED]",
+            "reference": "https://github.com/dorpxam/ComfyUI-LTXVideoLoRA",
+            "files": [
+                "https://github.com/dorpxam/ComfyUI-LTXVideoLoRA"
+            ],
+            "install_type": "git-clone",
+            "description": "A set of custom nodes enabling LoRA support for LTX Video"
+        },
+        {
+            "author": "dorpxam",
+            "title": "ComfyUI-FramePack-F1-T2V [REMOVED]",
+            "reference": "https://github.com/dorpxam/ComfyUI-FramePack-F1-T2V",
+            "files": [
+                "https://github.com/dorpxam/ComfyUI-FramePack-F1-T2V"
+            ],
+            "install_type": "git-clone",
+            "description": "A set of custom nodes enabling Text-to-Video support for FramePack-F1"
+        },
+        {
+            "author": "A4P7J1N7M05OT",
+            "title": "ComfyUI-AutoColorGimp [REMOVED]",
+            "reference": "https://github.com/A4P7J1N7M05OT/ComfyUI-AutoColorGimp",
+            "files": [
+                "https://github.com/A4P7J1N7M05OT/ComfyUI-AutoColorGimp"
+            ],
+            "install_type": "git-clone",
+            "description": "Shamelessly copied the code to auto color correct the image like in gimp from this answer: [a/https://stackoverflow.com/a/56365560/4561887](https://stackoverflow.com/a/56365560/4561887)"
+        },
+        {
+            "author": "SijieMei",
+            "title": "ComfyUI-Prompt-History [REMOVED]",
+            "reference": "https://github.com/SijieMei/ComfyUI-promptHistory",
+            "files": [
+                "https://github.com/SijieMei/ComfyUI-promptHistory"
+            ],
+            "install_type": "git-clone",
+            "description": "Save prompt history and reselect"
+        },
+        {
+            "author": "Carasibana",
+            "title": "ComfyUI-PolyMask [REMOVED]",
+            "reference": "https://github.com/Carasibana/ComfyUI-PolyMask",
+            "files": [
+                "https://github.com/Carasibana/ComfyUI-PolyMask"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for loading images and drawing polygon masks interactively on them"
+        },
+        {
+            "author": "easelhub",
+            "title": "ComfyUI_EaselHub_Nodes [REMOVED]",
+            "reference": "https://github.com/easelhub/ComfyUI_EaselHub_Nodes",
+            "files": [
+                "https://github.com/easelhub/ComfyUI_EaselHub_Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Collection of ComfyUI nodes for image processing, model management, and prompt generation including BiRefNet, Florence2Tagger, and image manipulation tools. (Description by CC)"
+        },
+        {
+            "author": "Randomwalkforest",
+            "title": "Comfyui-Koi-Toolkit [REMOVED]",
+            "reference": "https://github.com/Randomwalkforest/Comfyui-Koi-Toolkit",
+            "files": [
+                "https://github.com/Randomwalkforest/Comfyui-Koi-Toolkit"
+            ],
+            "install_type": "git-clone",
+            "description": "Koi Toolkit provides advanced nodes"
+        },
+        {
+            "author": "A4P7J1N7M05OT",
+            "title": "ComfyUI-VAELoaderSDXLmod [REMOVED]",
+            "reference": "https://github.com/A4P7J1N7M05OT/ComfyUI-VAELoaderSDXLmod",
+            "files": [
+                "https://github.com/A4P7J1N7M05OT/ComfyUI-VAELoaderSDXLmod"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Modified SDXL VAE Loader, Empty Latent Image Variable"
+        },
+        {
+            "author": "Slix-M-Lestragg",
+            "title": "comfyui-enhanced [REMOVED]",
+            "reference": "https://github.com/Slix-M-Lestragg/comfyui-enhanced",
+            "files": [
+                "https://github.com/Slix-M-Lestragg/comfyui-enhanced"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of enhanced nodes for ComfyUI that provide powerful additional functionality to your workflows.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "MTheUnexpected-1",
+            "title": "ModuSnap-ComfyUI-Manager [REMOVED]",
+            "reference": "https://github.com/MTheUnexpected-1/ModuSnap-ComfyUI-Manager",
+            "files": [
+                "https://github.com/MTheUnexpected-1/ModuSnap-ComfyUI-Manager"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom-node bridge between ComfyUI and Modusnap Engine manager APIs"
+        },
+        {
+            "author": "da2el-ai",
+            "title": "D2 Steps [REMOVED]",
+            "id": "d2steps",
+            "reference": "https://github.com/da2el-ai/ComfyUI-d2-steps",
+            "files": [
+                "https://github.com/da2el-ai/ComfyUI-d2-steps"
+            ],
+            "install_type": "git-clone",
+            "description": "A handy custom node for using Refiner (switching to a different checkpoint midway) When you specify the end of the base checkpoint, you can extract refiner_start which is end + 1. The output is fixed as an INT, so it can be passed to the handy custom node, Anything Everywhere? Since it only outputs a numerical value, it can also be used for other purposes."
+        },
+        {
+            "author": "da2el-ai",
+            "title": "D2 Size Selector [REMOVED]",
+            "id": "size-selector",
+            "reference": "https://github.com/da2el-ai/ComfyUI-d2-size-selector",
+            "files": [
+                "https://github.com/da2el-ai/ComfyUI-d2-size-selector"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a custom node that allows you to easily call up and set image size presets. Settings can be made by editing the included config.yaml. It is almost identical to Comfyroll Studio's CR AspectRatio. I created it because I wanted to easily edit the presets."
+        },
+        {
+            "author": "da2el-ai",
+            "title": "D2 XYPlot Utils [REMOVED]",
+            "reference": "https://github.com/da2el-ai/ComfyUI-d2-xyplot-utils",
+            "files": [
+                "https://github.com/da2el-ai/ComfyUI-d2-xyplot-utils"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom node for using Prompt S/R in XY Plot\nAlso includes nodes for listing generic parameters like seed and cfg\nEasy to manipulate as elements are separated by line breaks\nDesigned for use with the XY Plot custom node qq-nodes-comfyui, but may work with other custom nodes as well"
+        },
+        {
+            "author": "Lavah000",
+            "title": "ComfyUI Advanced Generation Pack (AGP) [REMOVED]",
+            "reference": "https://github.com/Lavah000/ComfyUI-AdvGenPack",
+            "files": [
+                "https://github.com/Lavah000/ComfyUI-AdvGenPack"
+            ],
+            "install_type": "git-clone",
+            "description": "Production-ready custom nodes for advanced diffusion workflows. Modular implementation grounded in proven techniques from leading ComfyUI repositories."
+        },
+        {
+            "author": "itom0717",
+            "title": "ComfyUI-Prompt-Selector [REMOVED]",
+            "reference": "https://github.com/itom0717/ComfyUI-Prompt-Selector",
+            "files": [
+                "https://github.com/itom0717/ComfyUI-Prompt-Selector"
+            ],
+            "install_type": "git-clone",
+            "description": "No ComfyUI nodes detected. May not be a ComfyUI custom node repository."
+        },
+        {
+            "author": "xela-io",
+            "title": "ComfyUI-HairDetailer [REMOVED]",
+            "reference": "https://github.com/xela-io/ComfyUI-HairDetailer",
+            "files": [
+                "https://github.com/xela-io/ComfyUI-HairDetailer"
+            ],
+            "install_type": "git-clone",
+            "description": "Comprehensive custom node pack for detecting hair regions, creating precise masks, and enhancing hair details in images with 6 specialized nodes for various hair processing workflows. (Description by CC)"
+        },
+        {
+            "author": "xela-io",
+            "title": "ComfyUI-RegionalPrompting [REMOVED]",
+            "reference": "https://github.com/xela-io/ComfyUI-RegionalPrompting",
+            "files": [
+                "https://github.com/xela-io/ComfyUI-RegionalPrompting"
+            ],
+            "install_type": "git-clone",
+            "description": "Regional Prompting for ComfyUI - assign different prompts to different image areas."
+        },
+        {
+            "author": "andygill",
+            "title": "comfyui-sunflower-nodes [REMOVED]",
+            "reference": "https://github.com/andygill/comfyui-sunflower-nodes",
+            "files": [
+                "https://github.com/andygill/comfyui-sunflower-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for 3D visualization."
+        },
+        {
+            "author": "CarlMarkswx",
+            "title": "ComfyUI-Mirror-Download [UNSAFE/REMOVED]",
+            "reference": "https://github.com/CarlMarkswx/ComfyUI-Mirror-Download",
+            "files": [
+                "https://github.com/CarlMarkswx/ComfyUI-Mirror-Download"
+            ],
+            "install_type": "git-clone",
+            "description": "Plugin that adds a mirror download button for accessing Hugging Face models via Chinese mirror, with automatic detection and progress tracking. (Description by CC)[w/This nodepack contains a node that has a vulnerability allowing write to arbitrary file paths.]"
+        },
+        {
+            "author": "anilstream",
+            "title": "ComfyUI-NanoBananaPro [REMOVED]",
+            "reference": "https://github.com/anilstream/ComfyUI-NanoBananaPro",
+            "files": [
+                "https://github.com/anilstream/ComfyUI-NanoBananaPro"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI node implementing basic functionality with NanoBananaBasicNode. (Description by CC)"
+        },
+        {
+            "author": "anilstream",
+            "title": "ComfyUI-Ideogram-Character [REMOVED]",
+            "reference": "https://github.com/anilstream/ComfyUI-Ideogram-Character",
+            "files": [
+                "https://github.com/anilstream/ComfyUI-Ideogram-Character"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI node for generating consistent character images using Ideogram API v3's character reference feature. (Description by CC)"
+        },
+        {
+            "author": "FemReiyaDev",
+            "title": "ComfyUI-ReiyaNodes [REMOVED]",
+            "reference": "https://github.com/FemReiyaDev/ComfyUI-ReiyaNodes",
+            "files": [
+                "https://github.com/FemReiyaDev/ComfyUI-ReiyaNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI nodes for image stitching"
+        },
+        {
+            "author": "ncky",
+            "title": "ComfyUI-Qwen3-TTS [REMOVED]",
+            "reference": "https://github.com/ncky/ComfyUI-Qwen3-TTS",
+            "files": [
+                "https://github.com/ncky/ComfyUI-Qwen3-TTS"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom node that wraps Qwen3-TTS voice cloning for synthesizing speech in a cloned voice. (Description by CC)"
+        },
+        {
+            "author": "artokun",
+            "title": "ComfyUI-Stereo-Gaussian [REMOVED]",
+            "reference": "https://github.com/artokun/ComfyUI-Stereo-Gaussian",
+            "files": [
+                "https://github.com/artokun/ComfyUI-Stereo-Gaussian"
+            ],
+            "install_type": "git-clone",
+            "description": "Stereoscopic VR rendering from 3D Gaussian Splats for ComfyUI"
+        },
+        {
+            "author": "Ysthara",
+            "title": "ComfyUI-Resolution-AR-Node",
+            "reference": "https://github.com/Ysthara/ComfyUI-Resolution-AR-Node",
+            "files": [
+                "https://github.com/Ysthara/ComfyUI-Resolution-AR-Node"
+            ],
+            "install_type": "git-clone",
+            "description": "A simple ComfyUI utility node that calculates image width and height from a target longest-side value and an aspect ratio."
+        },
+        {
+            "author": "brandonkish",
+            "title": "comfyUI-extractable-text [REMOVED]",
+            "reference": "https://github.com/brandonkish/comfyUI-extractable-text",
+            "files": [
+                "https://github.com/brandonkish/comfyUI-extractable-text"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Save Image With Description, Save Image To Folder, Load Image With Description, LoRA Testing Node, Get Smaller Of Two Numbers, Get Larger Of Two Numbers\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "techzuhaib",
+            "title": "ComfyUI-CacheImageNode [REMOVED]",
+            "reference": "https://github.com/techzuhaib/ComfyUI-CacheImageNode",
+            "files": [
+                "https://github.com/techzuhaib/ComfyUI-CacheImageNode"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: CacheImageNode"
+        },
+        {
+            "author": "techzuhaib",
+            "title": "FaceCLIP-ComfyUI [REMOVED]",
+            "reference": "https://github.com/techzuhaib/FaceCLIP-ComfyUI",
+            "files": [
+                "https://github.com/techzuhaib/FaceCLIP-ComfyUI"
+            ],
+            "install_type": "git-clone",
+            "description": "FaceCLIP nodes for ComfyUI providing joint embeddings from aligned face images plus text prompts, and identity-preserving image synthesis using fine-tuned SDXL UNet. (Description by CC)"
+        },
+        {
+            "author": "SlothHole",
+            "title": "Imitatoes [REMOVED]",
+            "reference": "https://github.com/SlothHole/Imitatoes",
+            "files": [
+                "https://github.com/SlothHole/Imitatoes"
+            ],
+            "install_type": "git-clone",
+            "description": "Build a ComfyUI \"self-improving\" workflow where each render is evaluated by a local vision LLM, which then edits the prompt/parameters and triggers another run."
+        },
+        {
+            "author": "teskor-hub",
+            "title": "comfyui-teskors-utils [REMOVED]",
+            "reference": "https://github.com/teskor-hub/comfyui-teskors-utils",
+            "files": [
+                "https://github.com/teskor-hub/comfyui-teskors-utils"
+            ],
+            "install_type": "git-clone",
+            "description": "DWpose smoothing and rendering node (body, face, hands)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Dehypnotic",
+            "title": "Save MP3 node for ComfyUI [UNSAFE/REMOVED]",
+            "reference": "https://github.com/Dehypnotic/comfyui-save-mp3",
+            "files": [
+                "https://github.com/Dehypnotic/comfyui-save-mp3"
+            ],
+            "install_type": "git-clone",
+            "description": "Simple, flexible MP3 saver with bitrate options and handy path/filename templates.[w/This nodepack contains a node that has a vulnerability allowing write to arbitrary file paths.]"
+        },
+        {
+            "author": "jluo-github",
+            "title": "comfyui-easy-resize [REMOVED]",
+            "reference": "https://github.com/jluo-github/comfyui-easy-resize",
+            "files": [
+                "https://github.com/jluo-github/comfyui-easy-resize"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom nodes for quick image size selection with curated presets\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "joe002",
+            "title": "comfyui-rtx-remix [UNSAFE/REMOVED]",
+            "reference": "https://github.com/joe002/comfyui-rtx-remix",
+            "files": [
+                "https://github.com/joe002/comfyui-rtx-remix"
+            ],
+            "install_type": "git-clone",
+            "description": "RTX Remix texture pipeline integration nodes for ComfyUI[w/This nodepack contains a path traversal vulnerability.]"
+        },
+        {
+            "author": "patientx",
+            "title": "CFZ-SwitchMenu [REMOVED]",
+            "reference": "https://github.com/patientx/CFZ-SwitchMenu",
+            "files": [
+                "https://github.com/patientx/CFZ-SwitchMenu"
+            ],
+            "install_type": "git-clone",
+            "description": "Adds a menu button that switches between old and new style ComfyUI menus."
+        },
+        {
+            "author": "ved-gaur",
+            "title": "ComfyUI-Sink-Nodes [REMOVED]",
+            "reference": "https://github.com/ved-gaur/ComfyUI-Sink-Nodes",
+            "files": [
+                "https://github.com/ved-gaur/ComfyUI-Sink-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI sink nodes that accept inputs and intentionally do nothing, forcing models to load into memory without running inference. (Description by CC)"
+        },
+        {
+            "author": "A043-studios",
+            "title": "ComfyUI Deforum-X-Flux Nodes [REMOVED]",
+            "reference": "https://github.com/A043-studios/comfyui-deforum-x-flux-nodes",
+            "files": [
+                "https://github.com/A043-studios/comfyui-deforum-x-flux-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Professional video animation nodes for ComfyUI based on Deforum-X-Flux research"
+        },
+        {
+            "author": "linksluckytime",
+            "title": "comfyui_snacknodes [REMOVED]",
+            "reference": "https://github.com/linksluckytime/comfyui_snacknodes",
+            "files": [
+                "https://github.com/linksluckytime/comfyui_snacknodes"
+            ],
+            "install_type": "git-clone",
+            "description": "A comprehensive collection of ComfyUI nodes designed to reduce reliance on multiple third-party node packages."
+        },
+        {
+            "author": "Nynxz",
+            "title": "ComfyUI_DiffsynthPause [REMOVED]",
+            "reference": "https://github.com/Nynxz/ComfyUI_DiffsynthPause",
+            "files": [
+                "https://github.com/Nynxz/ComfyUI_DiffsynthPause"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for controlling Diffsynth checkpoint pausing behavior during image generation workflows. (Description by CC)"
+        },
+        {
+            "author": "Angelsan720",
+            "title": "attention_bench [REMOVED]",
+            "reference": "https://github.com/Angelsan720/attention_bench",
+            "files": [
+                "https://github.com/Angelsan720/attention_bench"
+            ],
+            "install_type": "git-clone",
+            "description": "A simple ComfyUI node example project to help beginners learn how to develop ComfyUI nodes."
+        },
+        {
+            "author": "balu112121",
+            "title": "Comfyui_Hivision_ID_photo_layout [REMOVED]",
+            "reference": "https://github.com/balu112121/Comfyui_Hivision_ID_photo_layout",
+            "files": [
+                "https://github.com/balu112121/Comfyui_Hivision_ID_photo_layout"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for ID photo layout with support for custom paper sizes, orientation, rows/columns, spacing, margins, and grid display. (Description by CC)"
+        },
+        {
+            "author": "ainewsto",
+            "title": "Comfyui_Comfly_v2 [REMOVED]",
+            "reference": "https://github.com/ainewsto/Comfyui_Comfly_v2",
+            "files": [
+                "https://github.com/ainewsto/Comfyui_Comfly_v2"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Comfly_Mj, Comfly_mjstyle, Comfly_upload, Comfly_Mju, Comfly_Mjv, Comfly_kling_text2video, Comfly_kling_image2video, Comfly_video_extend, Comfly_lip_sync, Comfly_kling_videoPreview, Comfly Gemini API, Comfly Doubao SeedEdit, Comfly ChatGPT Api,Comfly Jimeng API, Comfly_gpt_image_1_edit, Comfly_gpt_image_1"
+        },        
+        {
+            "author": "thoddnn",
+            "title": "ComfyUI MLX Nodes [REMOVED]",
+            "reference": "https://github.com/thoddnn/ComfyUI-MLX",
+            "files": [
+                "https://github.com/thoddnn/ComfyUI-MLX"
+            ],
+            "install_type": "git-clone",
+            "description": "Faster workflows for ComfyUI users on Mac with Apple silicon"
+        },
+        {
+            "author": "90cube",
+            "title": "CBCanvas Node for ComfyUI [REMOVED]",
+            "reference": "https://github.com/90cube/Comfyui-CBcanvas",
+            "files": [
+                "https://github.com/90cube/Comfyui-CBcanvas"
+            ],
+            "install_type": "git-clone",
+            "description": "Canvas node with intelligent aspect ratio control - 13 preset aspect ratios from ultra-wide (21:9) to ultra-tall (9:21).\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "inflamously",
+            "title": "comfyui-prompt-enhancer [DEPRECATED]",
+            "reference": "https://github.com/inflamously/comfyui-prompt-enhancer",
+            "files": [
+                "https://github.com/inflamously/comfyui-prompt-enhancer"
+            ],
+            "install_type": "git-clone",
+            "description": "A crazy node that pragmatically just enhances a given prompt with various descriptions in the hope that the image quality just increase and prompting just gets easier."
+        },
+        {
+            "author": "tudal",
+            "title": "Hakkun-ComfyUI-nodes [REMOVED]",
+            "reference": "https://github.com/tudal/Hakkun-ComfyUI-nodes",
+            "files": [
+                "https://raw.githubusercontent.com/tudal/Hakkun-ComfyUI-nodes/main/hakkun_nodes.py"
+            ],
+            "install_type": "copy",
+            "description": "Mainly its prompt generating by custom syntax. Prompt Parser, Prompt tags, Random Line, Calculate Upscale, Image size to string, Type Converter, Image Resize To Height/Width, Load Random Image, Load Text"
+        },
+        {
+            "author": "huyl3-cpu",
+            "title": "comfyui_segment_anything_a100 [REMOVED]",
+            "reference": "https://github.com/huyl3-cpu/comfyui_segment_anything_a100",
+            "files": [
+                "https://github.com/huyl3-cpu/comfyui_segment_anything_a100"
+            ],
+            "install_type": "git-clone",
+            "description": "Heavily optimized A100 GPU fork of ComfyUI Segment Anything using BF16 precision, VRAM Locking, and Zero-Copy GPU processing for massive batch processing."
+        },
+        {
+            "author": "PauldeLavallaz",
+            "title": "comfyui_morpheus_model_management [REMOVED]",
+            "reference": "https://github.com/PauldeLavallaz/comfyui_morpheus_model_management",
+            "files": [
+                "https://github.com/PauldeLavallaz/comfyui_morpheus_model_management"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodo personalizzato per ComfyUI che consente di sfogliare una libreria locale di immagini di talent con interfaccia gallery, filtri avanzati e output multipli. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "laboratoiresonore",
+            "title": "ComfyUI_PerformanceLab [REMOVED]",
+            "reference": "https://github.com/laboratoiresonore/ComfyUI_PerformanceLab",
+            "files": [
+                "https://github.com/laboratoiresonore/ComfyUI_PerformanceLab"
+            ],
+            "install_type": "git-clone",
+            "description": "Make any ComfyUI workflow faster, use less VRAM, or produce better quality - with AI assistance"
+        },
+        {
+            "author": "Linsoo",
+            "title": "ComfyUI-Linsoo-Custom-Nodes [REMOVED]",
+            "reference": "https://github.com/Linsoo/ComfyUI-Linsoo-Custom-Nodes",
+            "files": [
+                "https://github.com/Linsoo/ComfyUI-Linsoo-Custom-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Linsoo Save Image, Linsoo Load Image (In development.. not working), Linsoo Empty Latent Image, Linsoo Multi Inputs, Linsoo Multi Outputs"
+        },
+        {
+            "author": "PauldeLavallaz",
+            "title": "ComfyUI-concat_images_to_api_gemini [REMOVED]",
+            "reference": "https://github.com/PauldeLavallaz/ComfyUI-concat_images_to_api_gemini",
+            "files": [
+                "https://github.com/PauldeLavallaz/ComfyUI-concat_images_to_api_gemini"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Concat Images → Gemini (MP only), Concat Two Images (RAW passthrough), Grid Collage AR Cols, ..."
+        },
+        {
+            "author": "aistudynow",
+            "title": "comfyui-wilddragon-nodes [REMOVED]",
+            "reference": "https://github.com/aistudynow/comfyui-wilddragon-nodes",
+            "files": [
+                "https://github.com/aistudynow/comfyui-wilddragon-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Image Face Crop (2025), Person Selector, Pose Face Filter, Person BBox Tracker, Face Restore & Blend, ...\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "Wladimir Palant",
+            "title": "image-resize-comfyui [REMOVED]",
+            "reference": "https://github.com/ussoewwin/image_resize_comfyui",
+            "files": [
+                "https://github.com/ussoewwin/image_resize_comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "Advanced image resizing node for ComfyUI with aspect ratio preservation and mask support"
+        },
+        {
+            "author": "umisetokikaze",
+            "title": "comfyui_mergekit [REMOVED]",
+            "reference": "https://github.com/umisetokikaze/comfyui_mergekit",
+            "files": [
+                "https://github.com/umisetokikaze/comfyui_mergekit"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:DefineSaveName, SetModels, get_skip, LoadLR, LoadTarget, SetTokenizer, Merge, SetLayer, SetModels"
+        },
+        {
+            "author": "LSDJesus",
+            "title": "ComfyUI-Luna-Collection [REMOVED]",
+            "reference": "https://github.com/LSDJesus/ComfyUI-Luna-Collection",
+            "files": [
+                "https://github.com/LSDJesus/ComfyUI-Luna-Collection"
+            ],
+            "install_type": "git-clone",
+            "description": "Collection of finetuned and custom nodes for Pyrite and I\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "r3dial",
+            "title": "Redial Discomphy - Discord Integration for ComfyUI [REMOVED]",
+            "reference": "https://github.com/r3dial/redial-discomphy",
+            "files": [
+                "https://github.com/r3dial/redial-discomphy"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node for ComfyUI that enables direct posting of images, videos, and messages to Discord channels. This node seamlessly integrates your ComfyUI workflows with Discord communication, allowing you to automatically share your generated content."
+        },
+        {
+            "author": "EricRorich",
+            "title": "ComfyUI-Parametric-Face-Canvas [REMOVED]",
+            "reference": "https://github.com/EricRorich/ComfyUI-Parametric-Face-Canvas",
+            "files": [
+                "https://github.com/EricRorich/ComfyUI-Parametric-Face-Canvas"
+            ],
+            "install_type": "git-clone",
+            "description": "Generates a parametric 3D face wireframe and renders it as a 2D image with adjustable facial proportions and camera orientation for use in AI pipelines.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "pixixai",
+            "title": "ComfyUI_Pixix-Tools [UNSAFE/REMOVED]",
+            "reference": "https://github.com/pixixai/ComfyUI_pixixTools",
+            "files": [
+                "https://github.com/pixixai/ComfyUI_pixixTools"
+            ],
+            "install_type": "git-clone",
+            "description": "Load Text (from folder)\nNOTE: The files in the repo are not organized.[w/The contents of files from arbitrary paths can be read remotely through this node.]"
+        },
+        {
+            "author": "fllywaay",
+            "title": "Comfyui-TextLine-counter [REMOVED]",
+            "reference": "https://github.com/zpengcom/Comfyui-TextLine-counter",
+            "files": [
+                "https://github.com/zpengcom/Comfyui-TextLine-counter"
+            ],
+            "install_type": "git-clone",
+            "description": "A simple multi-line text processing tool, such as line count statistics, ignoring blank lines, etc."
+        },
+        {
+            "author": "daveand",
+            "title": "ComfyUI-daveand-utils [REMOVED]",
+            "reference": "https://github.com/daveand/ComfyUI-daveand-utils",
+            "files": [
+                "https://github.com/daveand/ComfyUI-daveand-utils"
+            ],
+            "install_type": "git-clone",
+            "description": "Utility nodes including ModelConfigSelector for saving checkpoint configurations and managing manual sampler overrides. (Description by CC)"
+        },
+        {
+            "author": "tristanvdb",
+            "title": "ComfyUI-toolset [REMOVED]",
+            "reference": "https://github.com/tristanvdb/ComfyUI-toolset",
+            "files": [
+                "https://github.com/tristanvdb/ComfyUI-toolset"
+            ],
+            "install_type": "git-clone",
+            "description": "Human-in-the-loop image selection tool for ComfyUI workflows using a Flask web server, enabling users to pause workflows and interactively select images via a web browser interface."
+        },
+        {
+            "author": "chuchu114514",
+            "title": "comfyui_proportion_solver [REMOVED]",
+            "reference": "https://github.com/chuchu114514/comfyui_proportion_solver",
+            "files": [
+                "https://github.com/chuchu114514/comfyui_proportion_solver"
+            ],
+            "install_type": "git-clone",
+            "description": "This plugin includes two core nodes designed to handle proportion optimization tasks of varying complexity"
+        },
+        {
+            "author": "chuchu114514",
+            "title": "comfyui_text_list_stepper [REMOVED]",
+            "reference": "https://github.com/chuchu114514/comfyui_text_list_stepper",
+            "files": [
+                "https://github.com/chuchu114514/comfyui_text_list_stepper"
+            ],
+            "install_type": "git-clone",
+            "description": "Used for batch extraction of prompt words."
+        },
+        {
+            "author": "balu112121",
+            "title": "ComfyUI URL Image Loader [REMOVED]",
+            "reference": "https://github.com/balu112121/comfyui-LoadImageFromURL",
+            "files": [
+                "https://github.com/balu112121/comfyui-LoadImageFromURL"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom ComfyUI node for loading images directly from URLs for AI image generation workflows."
+        },
+        {
+            "author": "huyl3-cpu",
+            "title": "ComfyUI_A100_Ultimate_Optimizer [REMOVED]",
+            "reference": "https://github.com/huyl3-cpu/ComfyUI_A100_Ultimate_Optimizer",
+            "files": [
+                "https://github.com/huyl3-cpu/ComfyUI_A100_Ultimate_Optimizer"
+            ],
+            "install_type": "git-clone",
+            "description": "A100 GPU batch processing and optimization node for ComfyUI. (Description by CC)"
+        },
+        {
+            "author": "BlackVortexAI",
+            "title": "BV Nodes [DEPRECATED]",
+            "reference": "https://github.com/BlackVortexAI/ComfyUI-BVortexNodes",
+            "files": [
+                "https://github.com/BlackVortexAI/ComfyUI-BVortexNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "This repository contains a user-defined node for ComfyUI, currently there are nodes for capturing captions. But will be expanded in the future."
+        },        
+        {
+            "author": "scott-createplay",
+            "title": "ComfyUI_frontend_tools [REMOVED]",
+            "reference": "https://github.com/scott-createplay/ComfyUI_frontend_tools",
+            "files": [
+                "https://github.com/scott-createplay/ComfyUI_frontend_tools"
+            ],
+            "install_type": "git-clone",
+            "description": "A comprehensive utility suite for ComfyUI that helps maintain clean, organized workflows with node cleaner, layout tools, HUD projection, and wireless connection management.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "yutrodimitri-ship-it",
+            "title": "ComfyUI-YUTRO-CastingStudio-v2 [REMOVED]",
+            "reference": "https://github.com/yutrodimitri-ship-it/ComfyUI-YUTRO-CastingStudio-v2",
+            "files": [
+                "https://github.com/yutrodimitri-ship-it/ComfyUI-YUTRO-CastingStudio-v2"
+            ],
+            "install_type": "git-clone",
+            "description": "A professional modular suite of nodes for ComfyUI designed for virtual casting agencies, professional photographers, and content creators to generate high-quality model portfolios efficiently. (Description by CC)\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "amamisonlyuser",
+            "title": "MixvtonComfyui [REMOVED]",
+            "reference": "https://github.com/amamisonlyuser/MixvtonComfyui",
+            "files": [
+                "https://github.com/amamisonlyuser/MixvtonComfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: CXH_Leffa_Viton_Load, CXH_Leffa_Viton_Run\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "AhBumm",
+            "title": "ComfyUI_MangaLineExtraction [REMOVED]",
+            "reference": "https://github.com/AhBumm/ComfyUI_MangaLineExtraction-hf",
+            "files": [
+               "https://github.com/AhBumm/ComfyUI_MangaLineExtraction-hf"
+            ],
+            "description": "p1atdev/MangaLineExtraction-hf as a node in comfyui",
+            "install_type": "git-clone"
+        },        
+        {
+            "author": "danieljanata",
+            "title": "ComfyUI-QwenVL-Override [REMOVED]",
+            "reference": "https://github.com/danieljanata/ComfyUI-QwenVL-Override",
+            "files": [
+                "https://github.com/danieljanata/ComfyUI-QwenVL-Override"
+            ],
+            "install_type": "git-clone",
+            "description": "Adds two nodes that reuse upstream ComfyUI-QwenVL presets but add a runtime override that can be wired/unwired without getting stuck."
+        },        
+        {
+            "author": "Futureversecom",
+            "title": "ComfyUI-JEN [REMOVED]",
+            "reference": "https://github.com/futureversecom/ComfyUI-JEN",
+            "files": [
+                "https://github.com/futureversecom/ComfyUI-JEN"
+            ],
+            "install_type": "git-clone",
+            "description": "Comfy UI custom nodes for JEN music generation powered by Futureverse"
+        },
+        {
+            "author": "TheBill2001",
+            "title": "comfyui-upscale-by-model [REMOVED]",
+            "reference": "https://github.com/TheBill2001/comfyui-upscale-by-model",
+            "files": [
+                "https://github.com/TheBill2001/comfyui-upscale-by-model"
+            ],
+            "install_type": "git-clone",
+            "description": "This custom node allow upscaling an image by a factor using a model."
+        },
+        {
+            "author": "XYMikky12138",
+            "title": "ComfyUI-NanoBanana-inpaint [REMOVED]",
+            "reference": "https://github.com/XYMikky12138/ComfyUI-NanoBanana-inpaint",
+            "files": [
+                "https://github.com/XYMikky12138/ComfyUI-NanoBanana-inpaint"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for API-based inpainting (Gemini, Imagen) with aspect ratio constraints, smart cropping, resize fitting, intelligent paste-back with transparency support. (Description by CC)"
+        },
+        {
+            "author": "Blonicx",
+            "title": "ComfyUI-Rework-X [REMOVED]",
+            "id": "rework-x",
+            "reference": "https://github.com/Blonicx/ComfyUI-X-Rework",
+            "files": [
+                "https://github.com/Blonicx/ComfyUI-X-Rework"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a plugin for ComfyUI that adds new Util Nodes and Nodes for easier image creation and sharing."
+        },
+        {
+            "author": "scott-createplay",
+            "title": "ComfyUI_video_essentials [REMOVED]",
+            "reference": "https://github.com/scott-createplay/ComfyUI_video_essentials",
+            "files": [
+                "https://github.com/scott-createplay/ComfyUI_video_essentials"
+            ],
+            "install_type": "git-clone",
+            "description": "Essential video processing nodes for ComfyUI"
+        },
+        {
+            "author": "thnikk",
+            "title": "comfyui-thnikk-utils [REMOVED]",
+            "reference": "https://github.com/thnikk/comfyui-thnikk-utils",
+            "files": [
+                "https://github.com/thnikk/comfyui-thnikk-utils"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes to clean up your workflow."
+        },
+        {
+            "author": "Tr1dae",
+            "title": "LoRA Matcher Nodes for ComfyUI [REMOVED]",
+            "reference": "https://github.com/Tr1dae/ComfyUI-LoraPromptMatcher",
+            "files": [
+                "https://github.com/Tr1dae/ComfyUI-LoraPromptMatcher"
+            ],
+            "install_type": "git-clone",
+            "description": "This custom node provides two different approaches to automatically match text prompts with LoRA models using their descriptions."
+        },
+
+        {
+            "author": "jkhayiying",
+            "title": "ImageLoadFromLocalOrUrl Node for ComfyUI [REMOVED]",
+            "id": "JkhaImageLoaderPathOrUrl",
+            "reference": "https://gitee.com/yyh915/jkha-load-img",
+            "files": [
+                "https://gitee.com/yyh915/jkha-load-img"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a node to load an image from local path or url."
+        },
+        {
+            "author": "pizurny",
+            "title": "ComfyUI-Just-DWPose [REMOVED]",
+            "reference": "https://github.com/pizurny/ComfyUI-Just-DWPose",
+            "files": [
+                "https://github.com/pizurny/ComfyUI-Just-DWPose"
+            ],
+            "install_type": "git-clone",
+            "description": "An advanced DWPose annotator for ComfyUI with TorchScript and ONNX backends, featuring comprehensive pose detection, bone validation, temporal smoothing, and custom visualization tools."
+        },
+        {
+            "author": "lfelipegg",
+            "title": "lfgg_custom_nodes [REMOVED]",
+            "reference": "https://github.com/lfelipegg/lfgg_custom_nodes",
+            "files": [
+                "https://github.com/lfelipegg/lfgg_custom_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: ModelMergeCombos\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "AndSni",
+            "title": "Comfy-FL-Nodes [REMOVED]",
+            "reference": "https://github.com/AndSni/Comfy-FL-Nodes",
+            "files": [
+                "https://github.com/AndSni/Comfy-FL-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Generates human characters for commerce applications in ComfyUI. (Description by CC)"
+        },
+        {
+            "author": "neeltheninja",
+            "title": "ComfyUI-ControlNeXt [REMOVED]",
+            "reference": "https://github.com/neverbiasu/ComfyUI-ControlNeXt",
+            "files": [
+                "https://github.com/neverbiasu/ComfyUI-ControlNeXt"
+            ],
+            "install_type": "git-clone",
+            "description": "In progress🚧"
+        },
+        {
+            "author": "TheArtOfficial",
+            "title": "ComfyUI-Nitra [REMOVED]",
+            "reference": "https://github.com/TheArtOfficial/ComfyUI-Nitra",
+            "files": [
+                "https://github.com/TheArtOfficial/ComfyUI-Nitra"
+            ],
+            "install_type": "git-clone",
+            "description": "Nitra custom node for ComfyUI"
+        },        
+        {
+            "author": "AngelCookies",
+            "title": "ComfyUI-Seed-Tracker [REMOVED]",
+            "reference": "https://github.com/AngelCookies/ComfyUI-Seed-Tracker",
+            "files": [
+                "https://github.com/AngelCookies/ComfyUI-Seed-Tracker"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI extension that tracks random seeds throughout your image generation workflows"
+        },
+        {
+            "author": "shinich39",
+            "title": "comfyui-nothing-happened [REMOVED]",
+            "reference": "httphttps://github.com/shinich39/comfyui-nothing-happened",
+            "files": [
+               "https://github.com/shinich39/comfyui-nothing-happened"
+            ],
+            "description": "Save image and keep metadata.",
+            "install_type": "git-clone"
+        },
+        {
+            "author": "ashtar1984",
+            "title": "comfyui-switch-bypass-mute-by-group [REMOVED]",
+            "reference": "https://github.com/ashtar1984/comfyui-switch-bypass-mute-by-group",
+            "files": [
+                "https://github.com/ashtar1984/comfyui-switch-bypass-mute-by-group"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for group-based node switching, bypassing, and muting control. (Description by CC)"
+        },
+        {
+            "author": "wallen0322",
+            "title": "ComfyUI-TTM-WAN22 [REMOVED]",
+            "reference": "https://github.com/wallen0322/ComfyUI-TTM-WAN22",
+            "files": [
+                "https://github.com/wallen0322/ComfyUI-TTM-WAN22"
+            ],
+            "install_type": "git-clone",
+            "description": "TTM (Time-to-Move) node for ComfyUI enabling motion-controlled video generation with Wan2.2 models using dual-clock denoising for independent background and object animation control."
+        },
+        {
+            "author": "cdanielp",
+            "title": "COMFYUI_PROMPTMODELS [REMOVED]",
+            "reference": "https://github.com/cdanielp/COMFYUI_PROMPTMODELS",
+            "files": [
+                "https://github.com/cdanielp/COMFYUI_PROMPTMODELS"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom nodes for ComfyUI by PROMPTMODELS."
+        },
+        {
+            "author": "mcrataobrabo",
+            "title": "comfyui-smart-lora-downloader - Automatically Fetch Missing LoRAs [REMOVED]",
+            "reference": "https://github.com/mcrataobrabo/comfyui-smart-lora-downloader",
+            "files": [
+                "https://github.com/mcrataobrabo/comfyui-smart-lora-downloader"
+            ],
+            "install_type": "git-clone",
+            "description": "Automatically detect and download missing LoRAs for ComfyUI workflows"
+        },
+        {
+            "author": "KANAsho34636",
+            "title": "ComfyUI-NaturalSort-ImageLoader [REMOVED]",
+            "reference": "https://github.com/KANAsho34636/ComfyUI-NaturalSort-ImageLoader",
+            "files": [
+                "https://github.com/KANAsho34636/ComfyUI-NaturalSort-ImageLoader"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom image loader node supporting natural number sorting with multiple sort modes (natural, lexicographic, modification time, creation time, reverse natural). (Description by CC)"
+        },
+        {
+            "author": "johninthewinter",
+            "title": "comfyui-fal-flux-2-John [REMOVED]",
+            "reference": "https://github.com/johninthewinter/comfyui-fal-flux-2-John",
+            "files": [
+                "https://github.com/johninthewinter/comfyui-fal-flux-2-John"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom nodes for ComfyUI that integrate with fal.ai's FLUX 2 and FLUX 1 LoRA APIs for text-to-image generation."
+        },
+        {
+            "author": "LargeModGames",
+            "title": "ComfyUI LoRA Auto Downloader [REMOVED]",
+            "reference": "https://github.com/LargeModGames/comfyui-smart-lora-downloader",
+            "files": [
+                "https://github.com/LargeModGames/comfyui-smart-lora-downloader"
+            ],
+            "install_type": "git-clone",
+            "description": "Automatically download missing LoRAs from CivitAI and detect missing LoRAs in workflows. Features smart directory detection and easy installation."
+        },
+        {
+            "author": "DiffusionWave",
+            "title": "PickResolution_DiffusionWave [DEPRECATED]",
+            "reference": "https://github.com/DiffusionWave/PickResolution_DiffusionWave",
+            "files": [
+                "https://github.com/DiffusionWave/PickResolution_DiffusionWave"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node for ComfyUI that allows selecting a base resolution, applying a custom scaling value based on FLOAT (up to 10 decimal places), and adding an extra integer value. Outputs include both INT and FLOAT resolutions, making it perfect for you to play around with."
+        },
+        {
+            "author": "geltz",
+            "title": "ComfyUI-geltz [REMOVED]",
+            "reference": "https://github.com/geltz/ComfyUI-geltz",
+            "files": [
+                "https://github.com/geltz/ComfyUI-geltz"
+            ],
+            "install_type": "git-clone",
+            "description": "Various custom nodes; guidance, latents, sampling, tokenization, etc."
+        },
+        {
+            "author": "anilsathyan7",
+            "title": "ComfyUI-Crystal-Upscaler [REMOVED]",
+            "reference": "https://github.com/anilsathyan7/ComfyUI-Crystal-Upscaler",
+            "files": [
+                "https://github.com/anilsathyan7/ComfyUI-Crystal-Upscaler"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for image upscaling using crystal upscaling technology. (Description by CC)"
+        },
+        {
+            "author": "nohikomiso",
+            "title": "ComfyUI-ImageFolderPicker [REMOVED/UNSAFE]",
+            "reference": "https://github.com/nohikomiso/ComfyUI-ImageFolderPicker",
+            "files": [
+                "https://github.com/nohikomiso/ComfyUI-ImageFolderPicker"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI node for browsing local server folders and selecting images via thumbnail display in a grid interface. (Description by CC)[w/This nodepack has a vulnerability that allows it to retrieve a list of files from arbitrary paths.]"
+        },
+        {
+            "author": "rzasharp79",
+            "title": "ComfyUI--SolarFlare [REMOVED]",
+            "reference": "https://github.com/rzasharp79/ComfyUI--SolarFlare",
+            "files": [
+                "https://github.com/rzasharp79/ComfyUI--SolarFlare"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Qwen Image, ..."
+        },
+        {
+            "author": "shinich39",
+            "title": "comfyui-no-one-above-me [REMOVED]",
+            "reference": "https://github.com/shinich39/comfyui-no-one-above-me",
+            "files": [
+                "https://github.com/shinich39/comfyui-no-one-above-me"
+            ],
+            "install_type": "git-clone",
+            "description": "Fix node to top."
+        },
+        {
+            "author": "octapus8085",
+            "title": "OpenAI-comfyui-O [REMOVED]",
+            "reference": "https://github.com/Spicely/Comfyui-File-Utils",
+            "files": [
+                "https://github.com/Spicely/Comfyui-File-Utils"
+            ],
+            "install_type": "git-clone",
+            "description": "This plugin provides multiple file-handling and utility nodes for ComfyUI, including: image saving, audio saving, video saving, video composition, audio-to-subtitle conversion, and random number generation nodes. These nodes not only process files but also return their absolute file paths.\nNOTE: The files in the repo are not organized.[w/This nodepack contains a node that has a vulnerability allowing write to arbitrary file paths.]"
+        },        
+        {
+            "author": "yemanou",
+            "title": "NABA Image (Gemini REST) Node [REMOVED]",
+            "reference": "https://github.com/yemanou/ComfyUI-NABA",
+            "files": [
+                "https://github.com/yemanou/ComfyUI-NABA"
+            ],
+            "install_type": "git-clone",
+            "description": "Simplified Gemini 2.5 Flash Image Preview node for ComfyUI. REST-only for stability, two optional reference images, padded aspect ratio resizing (no stretching), and basic sampling controls. All extra debug layers, SDK path, multi-seed, and legacy compatibility code removed to avoid crashes."
+        },
+        {
+            "author": "comrender",
+            "title": "ComfyUI-Nano-Banana-Resizer [REMOVED]",
+            "reference": "https://github.com/comrender/ComfyUI-Nano-Banana-Resizer",
+            "files": [
+                "https://github.com/comrender/ComfyUI-Nano-Banana-Resizer"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node that automatically calculates optimal output dimensions for Google's Nano Banana image editing model, supporting 22 aspect ratio buckets and ensuring pixel-perfect outputs without shifting or cropping."
+        },
+        {
+            "author": "comrender",
+            "title": "ComfyUI-edge-match-checker [REMOVED]",
+            "reference": "https://github.com/comrender/ComfyUI-edge-match-checker",
+            "files": [
+                "https://github.com/comrender/ComfyUI-edge-match-checker"
+            ],
+            "install_type": "git-clone",
+            "description": "Node comparing two image masks or images with adjustable overlap threshold (default 95%) for detecting minor shifts and mismatches in proportions, suitable for automated post-processing validation. (Description by CC)"
+        },
+        {
+            "author": "comrender",
+            "title": "ComfyUI-gpt5_image_text [REMOVED]",
+            "reference": "https://github.com/comrender/ComfyUI-gpt5_image_text",
+            "files": [
+                "https://github.com/comrender/ComfyUI-gpt5_image_text"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node for vision + text analysis using GPT-5 and GPT-4o with direct API key input, system prompt, temperature, max tokens, and multi-image support."
+        },
+        {
+            "author": "PozzettiAndrea",
+            "title": "ComfyUI-CameraAnalysis [REMOVED]",
+            "reference": "https://github.com/PozzettiAndrea/ComfyUI-CameraAnalysis",
+            "files": [
+                "https://github.com/PozzettiAndrea/ComfyUI-CameraAnalysis"
+            ],
+            "install_type": "git-clone",
+            "description": "Extracts camera intrinsic parameters from image EXIF data."
+        },
+        {
+            "author": "fuzr0dah",
+            "title": "comfyui-sceneassembly [REMOVED]",
+            "reference": "https://github.com/fuzr0dah/comfyui-sceneassembly",
+            "files": [
+                "https://github.com/fuzr0dah/comfyui-sceneassembly"
+            ],
+            "install_type": "git-clone",
+            "description": "A bunch of nodes I created that I also find useful."
+        },
+        {
+            "author": "rslosch",
+            "title": "ComfyUI-EZ_Prompts [REMOVED]",
+            "reference": "https://github.com/rslosch/ComfyUI-EZ_Prompts",
+            "files": [
+                "https://github.com/rslosch/ComfyUI-EZ_Prompts"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node extension that provides easy-to-use prompt templates and wildcards for AI image generation."
+        },
+        {
+            "author": "hvppycoding",
+            "title": "hvppyflow [REMOVED]",
+            "reference": "https://github.com/hvppycoding/hvppyflow",
+            "files": [
+                "https://github.com/hvppycoding/hvppyflow"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for Automated Workflow"
+        },        
+        {
+            "author": "cedarconnor",
+            "title": "ComfyUI-GEN3C-Gsplat [REMOVED]",
+            "reference": "https://github.com/cedarconnor/ComfyUI-GEN3C-Gsplat",
+            "files": [
+                "https://github.com/cedarconnor/ComfyUI-GEN3C-Gsplat"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom ComfyUI node pack that bridges Cosmos/GEN3C video generation with in-graph Gaussian Splat (3DGS) training. It adds camera/trajectory tooling, dataset exporters, and two training backends (Nerfstudio CLI wrapper and an in-process gsplat optimizer) so artists can go from prompt to splat entirely inside ComfyUI.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "dowa-git",
+            "title": "comfyui-dowa [REMOVED]",
+            "reference": "https://github.com/dowa-git/comfyui-dowa",
+            "files": [
+                "https://github.com/dowa-git/comfyui-dowa"
+            ],
+            "install_type": "git-clone",
+            "description": "Professional navigation bar widget for ComfyUI with JWT-based user authentication, workflow templates, and team collaboration features in a purple gradient design."
+        },
+        {
+            "author": "Fablestarexpanse",
+            "title": "Timer-Node-Comfyui [REMOVED]",
+            "reference": "https://github.com/Fablestarexpanse/Timer-Node-Comfyui",
+            "files": [
+                "https://github.com/Fablestarexpanse/Timer-Node-Comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom ComfyUI node that displays live processing time in a red digital countdown clock format, perfect for monitoring image generation times and tracking performance between workflow nodes."
+        },
+        {
+            "author": "cedarconnor",
+            "title": "ComfyUI-OmniX [REMOVED]",
+            "reference": "https://github.com/cedarconnor/ComfyUI-OmniX",
+            "files": [
+                "https://github.com/cedarconnor/ComfyUI-OmniX"
+            ],
+            "install_type": "git-clone",
+            "description": "Extract comprehensive scene properties from 360-degree equirectangular panoramas, including depth, normals, and PBR materials, using OmniX adapters with Flux."
+        },
+        {
+            "author": "cedarconnor",
+            "title": "ComfyUI-DiT360 [REMOVED]",
+            "reference": "https://github.com/cedarconnor/ComfyUI-DiT360",
+            "files": [
+                "https://github.com/cedarconnor/ComfyUI-DiT360"
+            ],
+            "install_type": "git-clone",
+            "description": "Generate high-fidelity 360-degree panoramic images using the DiT360 diffusion transformer model in ComfyUI."
+        },
+        {
+            "author": "PozzettiAndrea",
+            "title": "ComfyUI-AnyTop [REMOVED]",
+            "reference": "https://github.com/PozzettiAndrea/ComfyUI-AnyTop",
+            "files": [
+                "https://github.com/PozzettiAndrea/ComfyUI-AnyTop"
+            ],
+            "install_type": "git-clone",
+            "description": "Standalone ComfyUI custom nodes for AnyTop - Universal Motion Generation for Any Skeleton Topology."
+        },
+        {
+            "author": "penposs",
+            "title": "ComfyUI-Banana-Node [REMOVED]",
+            "reference": "https://github.com/penposs/ComfyUI-Banana-Node",
+            "files": [
+                "https://github.com/penposs/ComfyUI-Banana-Node"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node for ComfyUI that generates images using Google’s Gemini 2.5 Flash Image Preview API."
+        },
+        {
+            "author": "spiralmountain",
+            "title": "ComfyUI_HDNodes [REMOVED]",
+            "reference": "https://github.com/spiralmountain/ComfyUI_HDNodes",
+            "files": [
+                "https://github.com/spiralmountain/ComfyUI_HDNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom nodes for ComfyUI that enable video generation using ByteDance's Seedance model via [a/Fal.ai](https://fal.ai/)."
+        },
+        {
+            "author": "fredconex",
+            "title": "Sync Edit [REMOVED]",
+            "reference": "https://github.com/fredconex/ComfyUI-SyncEdit",
+            "files": [
+                "https://github.com/fredconex/ComfyUI-SyncEdit"
+            ],
+            "install_type": "git-clone",
+            "description": "This node allow to intercept changes on the input string and choose between use the current one or sync with incoming new one."
+        },
+        {
+            "author": "fredconex",
+            "title": "ComfyUI-SoundFlow [REMOVED]",
+            "reference": "https://github.com/fredconex/ComfyUI-SoundFlow",
+            "files": [
+                "https://github.com/fredconex/ComfyUI-SoundFlow"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a bunch of nodes for ComfyUI to help with sound work."
+        },
+        {
+            "author": "fredconex",
+            "title": "SongBloom [REMOVED]",
+            "reference": "https://github.com/fredconex/ComfyUI-SongBloom",
+            "files": [
+                "https://github.com/fredconex/ComfyUI-SongBloom"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI Nodes for SongBloom"
+        },
+        {
+            "author": "EQXai",
+            "title": "ComfyUI_EQX [REMOVED]",
+            "reference": "https://github.com/EQXai/ComfyUI_EQX",
+            "files": [
+                "https://github.com/EQXai/ComfyUI_EQX"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: SaveImage_EQX, File Image Selector, Load Prompt From File - EQX, LoraStackEQX_random, Extract Filename - EQX, Extract LORA name - EQX, NSFW Detector EQX, NSFW Detector Advanced EQX"
+        },
+        {
+            "author": "wizdroid",
+            "title": "Wizdroid ComfyUI Outfit Selection [REMOVED]",
+            "reference": "https://github.com/wizdroid/wizdroid-fashionista",
+            "files": [
+                "https://github.com/wizdroid/wizdroid-fashionista"
+            ],
+            "install_type": "git-clone",
+            "description": "A comprehensive outfit generation system for ComfyUI with AI-powered prompt enhancement and dynamic outfit composition."
+        },
+        {
+            "author": "enternalsaga",
+            "title": "NBA-ComfyUINode [REMOVED]",
+            "reference": "https://github.com/enternalsaga/NBA-ComfyUINode-public",
+            "files": [
+                "https://github.com/enternalsaga/NBA-ComfyUINode-public"
+            ],
+            "install_type": "git-clone",
+            "description": "Version 1.2.1 - Dependency cleanup and archived LineSelector node\nA comprehensive collection of custom nodes for ComfyUI, providing advanced image processing, workflow control, and utility functions to enhance your AI image generation workflows."
+        },
+        {
+            "author": "sselpah",
+            "title": "ComfyUI-sselpah-nodes [REMOVED]",
+            "reference": "https://github.com/sselpah/ComfyUI-sselpah-nodes",
+            "files": [
+                "https://github.com/sselpah/ComfyUI-sselpah-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Extension of IPAdapter implementation by cubiq and whoever contributed to that repository"
+        },
+        {
+            "author": "vsaan212",
+            "title": "ComfyUI Text Split Node [REMOVED]",
+            "reference": "https://github.com/vsaan212/Comfy-ui-textsplit",
+            "files": [
+                "https://github.com/vsaan212/Comfy-ui-textsplit"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom ComfyUI node that splits text into multiple outputs for feeding complex multi-scene renders. This node allows you to dynamically control the number of splits and use custom separators."
+        },
+        {
+            "author": "EnragedAntelope",
+            "title": "ComfyUI-Doubutsu-Describer [DEPRECATED]",
+            "reference": "https://github.com/EnragedAntelope/ComfyUI-Doubutsu-Describer",
+            "files": [
+                "https://github.com/EnragedAntelope/ComfyUI-Doubutsu-Describer"
+            ],
+            "install_type": "git-clone",
+            "description": "This custom node for ComfyUI allows you to use the Doubutsu small VLM model to describe images. Credit and further information on Doubutsu: [a/https://huggingface.co/qresearch/doubutsu-2b-pt-756](https://huggingface.co/qresearch/doubutsu-2b-pt-756)"
+        },
+        {
+            "author": "vsaan212",
+            "title": "ComfyUI Subject Selector [DEPRECATED]",
+            "reference": "https://github.com/vsaan212/ComfyUI_subjectselector",
+            "files": [
+                "https://github.com/vsaan212/ComfyUI_subjectselector"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI_subjectselector is a custom ComfyUI node that allows you to manage and select text-based subject descriptions directly from the workflow UI. This node was designed to pair seamlessly with [a/ComfyUI-textsplit](https://github.com/vsaan212/Comfy-ui-textsplit), providing a clean, modular way to feed descriptive text prompts into your generation pipeline."
+        },
+        {
+            "author": "agxagi",
+            "title": "Autoregressive Transformer and Rolling Diffusion Sampler for ComfyUI [REMOVED]",
+            "reference": "https://github.com/agxagi/ComfyUI-GPT4o-Image-Gen-FLUX-DEV",
+            "files": [
+                "https://github.com/agxagi/ComfyUI-GPT4o-Image-Gen-FLUX-DEV"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom ComfyUI node that implements an Autoregressive Transformer and Rolling Diffusion-like Decoder using Black Forest Lab's Flux-Dev model for accurate text-to-image generation, similar to GPT-4o's image generation approach.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "walke2019",
+            "title": "ComfyUI-GGUF-VisionLM [REMOVED]",
+            "reference": "https://github.com/walke2019/ComfyUI-GGUF-VisionLM",
+            "files": [
+                "https://github.com/walke2019/ComfyUI-GGUF-VisionLM"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for running GGUF quantized Qwen2.5-VL models using llama.cpp"
+        },
+        {
+            "author": "neocrz",
+            "title": "comfyui-tinyae [REMOVED]",
+            "reference": "https://github.com/neocrz/comfyui-tinyae",
+            "files": [
+                "https://github.com/neocrz/comfyui-tinyae"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: TinyAE Encode (Image/Video), TinyAE Decode (Image/Video), TinyAE Encode Tiled (Image), TinyAE Decode Tiled (Image)"
+        },
+        {
+            "author": "Laser-one",
+            "title": "ComfyUI-align-pose [REMOVED]",
+            "reference": "https://github.com/Laser-one/ComfyUI-align-pose",
+            "files": [
+                "https://github.com/Laser-one/ComfyUI-align-pose"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:align pose"
+        },
+        {
+            "author": "nomadoor",
+            "title": "ComfyUI Video Stabilizer",
+            "reference": "https://github.com/nomadoor/ComfyUI-Video-Stabilizer",
+            "files": [
+                "https://github.com/nomadoor/ComfyUI-Video-Stabilizer"
+            ],
+            "install_type": "git-clone",
+            "description": "Two complementary stabiliser nodes for ComfyUI: Video Stabilizer (Classic), Video Stabilizer (Flow)"
+        },
+        {
+            "author": "0xhappydev",
+            "title": "comfyui-qwen-image-tools",
+            "reference": "https://github.com/0xhappydev/comfyui-qwen-image-tools",
+            "files": [
+                "https://github.com/0xhappydev/comfyui-qwen-image-tools"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom nodes for Qwen-Image-Edit with multi-image support, more flexibility around the vision transformer (qwen2.5-vl), custom system prompts, and some other experimental things to come."
+        },
+        {
+            "author": "Rathius-Saranoth",
+            "title": "Rathius ComfyUI Nodes",
+            "reference": "https://github.com/Rathius-Saranoth/rathius-comfyui-nodes",
+            "files": [
+                "https://github.com/Rathius-Saranoth/rathius-comfyui-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom nodes for ComfyUI by Rathius"
+        },
+        {
+            "author": "Hiero207",
+            "title": "Hiero-Nodes [REMOVED]",
+            "id": "hiero",
+            "reference": "https://github.com/Hiero207/ComfyUI-Hiero-Nodes",
+            "files": [
+                "https://github.com/Hiero207/ComfyUI-Hiero-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Post to Discord w/ Webhook"
+        },
+        {
+            "author": "NeoDroleDeGueule",
+            "title": "comfyui-image-mixer",
+            "reference": "https://github.com/NeoDroleDeGueule/comfyui-image-mixer [REMOVED]",
+            "files": [
+                "https://github.com/NeoDroleDeGueule/comfyui-image-mixer"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node that blends two images in latent space using a mix factor slider."
+        },
+        {
+            "author": "Glarus-akash",
+            "title": "ComfyUI_Image_Upscaler [REMOVED]",
+            "reference": "https://github.com/Glarus-akash/ComfyUI_Image_Upscaler",
+            "files": [
+                "https://github.com/Glarus-akash/ComfyUI_Image_Upscaler"
+            ],
+            "install_type": "git-clone",
+            "description": "Welcome to the Image Upscaler & Restorer project! This tool utilizes the [a/GFPGAN](https://github.com/TencentARC/GFPGAN) algorithm to enhance and restore images, providing a seamless way to improve image quality."
+        },
+        {
+            "author": "styletransfer",
+            "title": "Sequential Group Controller for ComfyUI [REMOVED]",
+            "reference": "https://github.com/styletransfer/ComfyUI_SequentialGroupController",
+            "files": [
+                "https://github.com/styletransfer/ComfyUI_SequentialGroupController"
+            ],
+            "install_type": "git-clone",
+            "description": "Control which groups execute based on iteration ranges - a simplified alternative to complex conditional branching workflows."
+        },
+        {
+            "author": "xl0",
+            "title": "q_tools [REMOVED]",
+            "reference": "https://github.com/xl0/q_tools",
+            "files": [
+                "https://github.com/xl0/q_tools"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: QLoadLatent, QLinearScheduler, QPreviewLatent, QGaussianLatent, QUniformLatent, QKSampler"
+        },
+        {
+            "author": "tmode-1960",
+            "title": "comfyui-ta-nodes-pack [REMOVED]",
+            "reference": "https://github.com/tmode-1960/comfyui-ta-nodes-pack",
+            "files": [
+                "https://github.com/tmode-1960/comfyui-ta-nodes-pack"
+            ],
+            "install_type": "git-clone",
+            "description": "Model loaders with an additional model name output"
+        },
+        {
+            "author": "Shadetail",
+            "title": "Eagleshadow Custom Nodes [REMOVED]",
+            "id": "eagleshadow",
+            "reference": "https://github.com/Shadetail/ComfyUI_Eagleshadow",
+            "files": [
+                "https://github.com/Shadetail/ComfyUI_Eagleshadow"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom nodes for ComfyUI by Eagleshadow."
+        },
+        {
+            "author": "manycore-maas",
+            "title": "ComfyUI-SpatialGen [REMOVED]",
+            "reference": "https://github.com/manycore-maas/ComfyUI-SpatialGen",
+            "files": [
+                "https://github.com/manycore-maas/ComfyUI-SpatialGen"
+            ],
+            "install_type": "git-clone",
+            "description": "Scene Viewer of SpatialGen"
+        },
+        {
+            "author": "YinBailiang",
+            "title": "MergeBlockWeighted_fo_ComfyUI [REMOVED]",
+            "id": "mergeblockweighted_fo_comfyui",
+            "reference": "https://github.com/YinBailiang/MergeBlockWeighted_fo_ComfyUI",
+            "files": [
+                "https://github.com/YinBailiang/MergeBlockWeighted_fo_ComfyUI"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes: MergeBlockWeighted"
+        },
+        {
+            "author": "facok",
+            "title": "ComfyUI-FokToolset [REMOVED]",
+            "reference": "https://github.com/facok/ComfyUI-FokToolset",
+            "files": [
+                "https://github.com/facok/ComfyUI-FokToolset"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Fok Preprocess Ref Image (Phantom)"
+        },
+        {
+            "author": "Elawphant",
+            "title": "ComfyUI-MusicGen [WIP]",
+            "id": "musicgen",
+            "reference": "https://github.com/Elawphant/ComfyUI-MusicGen",
+            "files": [
+                "https://github.com/Elawphant/ComfyUI-MusicGen"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI for Meta MusicGen."
+        },
+        {
+            "author": "isaac-mcfadyen",
+            "title": "ComfyUI-QwenClip [REMOVED]",
+            "reference": "https://github.com/isaac-mcfadyen/ComfyUI-QwenClip",
+            "files": [
+                "https://github.com/isaac-mcfadyen/ComfyUI-QwenClip"
+            ],
+            "install_type": "git-clone",
+            "description": "A variety of random text encoder tools intended for use with ComfyUI and Qwen Image/Qwen Image Edit. More (may) be added as I try out various modifications to Qwen Image."
+        },
+        {
+            "author": "Gaotian",
+            "title": "KLComfyUI-Nodes [REMOVED]",
+            "reference": "https://github.com/Gaotian-cpu/KLComfyUI-Nodes",
+            "files": [
+                "https://github.com/Gaotian-cpu/KLComfyUI-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Single Video_Img Callback"
+        },
+        {
+            "author": "geltz",
+            "title": "Momentum Guidance for ComfyUI [REMOVED]",
+            "reference": "https://github.com/geltz/ComfyUI-MomentumGuidance",
+            "files": [
+                "https://github.com/geltz/ComfyUI-MomentumGuidance"
+            ],
+            "install_type": "git-clone",
+            "description": "Momentum Guidance (MG) is a training-free guidance method that reduces computational cost by 40% compared to standard guidance techniques like CFG or PAG."
+        },
+        {
+            "author": "GeekyGhost",
+            "title": "Studio42 Image, Audio, and Video Editing Suite for ComfyUI [REMOVED]",
+            "reference": "https://github.com/GeekyGhost/24oiduts-ComfyUI",
+            "files": [
+                "https://github.com/GeekyGhost/24oiduts-ComfyUI"
+            ],
+            "install_type": "git-clone",
+            "description": "Studio42 is a comprehensive suite of advanced custom nodes that brings professional-grade image and video editing capabilities to ComfyUI. Designed for efficiency, quality, and creative flexibility, this suite provides cutting-edge background removal, layer composition, and patch manipulation tools used in modern VFX and content creation workflows."
+        },
+        {
+            "author": "rvage",
+            "title": "ComfyUI-RvTools-X [REMOVED]",
+            "reference": "https://github.com/r-vage/ComfyUI-RvTools-X",
+            "files": [
+                "https://github.com/r-vage/ComfyUI-RvTools-X"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom nodes and utilities for workflow building, type conversions, checkpoint/pipe loaders and file utilities."
+        },
+        {
+            "author": "heyburns",
+            "title": "LinxUtil [REMOVED]",
+            "reference": "https://github.com/heyburns/LinxUtil",
+            "files": [
+                "https://github.com/heyburns/LinxUtil"
+            ],
+            "install_type": "git-clone",
+            "description": "Utility nodes for ComfyUI. Created solely for my own use case, shared as a courtesy only.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "fcanfora",
+            "title": "comfyui-camera-tools [REMOVED]",
+            "reference": "https://github.com/fcanfora/comfyui-camera-tools",
+            "files": [
+                "https://github.com/fcanfora/comfyui-camera-tools"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Load Camera From File, Load 3D, Load 3D - Animation, Preview 3D, Preview 3D - Animation"
+        },
+        {
+            "author": "ziwang-com",
+            "title": "comfyui-deepseek-r1 [REMOVED]",
+            "reference": "https://github.com/ziwang-com/comfyui-deepseek-r1",
+            "files": [
+                "https://github.com/ziwang-com/comfyui-deepseek-r1"
+            ],
+            "install_type": "git-clone",
+            "description": "Comfyui-deepseek-r1 Node Plugin"
+        },
+        {
+            "author": "leeguandong",
+            "title": "ComfyUI nodes to use Xverse [REMOVED]",
+            "reference": "https://github.com/leeguandong/ComfyUI_Xverse",
+            "files": [
+                "https://github.com/leeguandong/ComfyUI_Xverse"
+            ],
+            "install_type": "git-clone",
+            "description": "The ComfyUI version of [a/XVerse](https://github.com/bytedance/XVerse)"
+        },
+        {
+            "author": "Dehypnotic",
+            "title": "Dehypnotic Save nodes [REMOVED]",
+            "reference": "https://github.com/Dehypnotic/comfyui-dehypnotic-save-nodes",
+            "files": [
+                "https://github.com/Dehypnotic/comfyui-dehypnotic-save-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Save toolkit for audio, image, and video: MP3 audio export with VBR/CBR options, multi-format image saving with workflow/thumbnail metadata, and video + frame encoding (MP4/MKV/WEBM/MOV) — all sharing whitelist-safe paths and rich placeholder templating."
+        },
+        {
+            "author": "znuost10",
+            "title": "comfyui-multi-float-output [REMOVED]",
+            "reference": "https://github.com/znuost10/comfyui-multi-float-output",
+            "files": [
+                "https://github.com/znuost10/comfyui-multi-float-output"
+            ],
+            "install_type": "git-clone",
+            "description": "System monitoring API endpoints for ComfyUI by otoy SKW"
+        },
+        {
+            "author": "rakki194",
+            "title": "ComfyUI_WolfSigmas [UNSAFE/REMOVED]",
+            "reference": "https://github.com/rakki194/ComfyUI_WolfSigmas",
+            "files": [
+                "https://github.com/rakki194/ComfyUI_WolfSigmas"
+            ],
+            "install_type": "git-clone",
+            "description": "This custom nodepack for ComfyUI provides a suite of tools for generating and manipulating sigma schedules for diffusion models. These nodes are particularly useful for fine-tuning the sampling process, experimenting with different step counts, and adapting schedules for specific models.[w/Security Warning: Remote Code Execution]"
+        },
+        {
+            "author": "Maff3u",
+            "title": "MattiaNodes - Points Editor On Cropped [REMOVED]",
+            "reference": "https://github.com/Maff3u/MattiaNodes",
+            "files": [
+                "https://github.com/Maff3u/MattiaNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "A standalone ComfyUI custom node for interactive coordinate editing with crop factor correction.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "rakki194",
+            "title": "ComfyUI-ImageCompare [REMOVED]",
+            "reference": "https://github.com/rakki194/ComfyUI-ImageCompare",
+            "files": [
+                "https://github.com/rakki194/ComfyUI-ImageCompare"
+            ],
+            "install_type": "git-clone",
+            "description": "A simple custom node for ComfyUI that allows you to compare two images (or batches of images) side-by-side within the UI."
+        },
+        {
+            "author": "APZmedia",
+            "title": "NormalMapLightEstimator [REMOVED]",
+            "reference": "https://github.com/APZmedia/Comfyui-LightDirection-estimation",
+            "files": [
+                "https://github.com/APZmedia/Comfyui-LightDirection-estimation"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node for estimating light direction and quality from normal maps using luma masking. The system analyzes surface normals to infer lighting information for downstream tasks like adaptive relighting, directional masking, or stylized effects."
+        },
+        {
+            "author": "APZmedia",
+            "title": "ComfyUI APZmedia PSD Tools [REMOVED]",
+            "reference": "https://github.com/APZmedia/APZmedia-ComfyUI-PSDtools",
+            "files": [
+                "https://github.com/APZmedia/APZmedia-ComfyUI-PSDtools"
+            ],
+            "install_type": "git-clone",
+            "description": "This extension provides PSD layer saving functionalities with mask support for ComfyUI."
+        },
+        {
+            "author": "huixingyun",
+            "title": "ComfyUI-HX-Pimg [REMOVED]",
+            "reference": "https://github.com/huixingyun/ComfyUI-HX-Pimg",
+            "files": [
+                "https://github.com/huixingyun/ComfyUI-HX-Pimg"
+            ],
+            "install_type": "git-clone",
+            "description": "Some custom nodes used for pimg (a comfyui controller deployed in huixingyun)."
+        },
+        {
+            "author": "rvage",
+            "title": "ComfyUI-RvToolsX [REMOVED]",
+            "reference": "https://github.com/r-vage/ComfyUI-RvToolsX",
+            "files": [
+                "https://github.com/r-vage/ComfyUI-RvToolsX"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom nodes and utilities for workflow building, type conversions, checkpoint/pipe loaders and file utilities."
+        },
+        {
+            "author": "usman2003",
+            "title": "ComfyUI-RaceDetect [REMOVED]",
+            "reference": "https://github.com/usman2003/ComfyUI-RaceDetect",
+            "files": [
+                "https://github.com/usman2003/ComfyUI-RaceDetect"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Race Detection V2"
+        },
+        {
+            "author": "lihaoyun6",
+            "title": "ComfyUI-CSV-Random-Picker [REMOVED]",
+            "reference": "https://github.com/lihaoyun6/ComfyUI-CSV-Random-Picker",
+            "files": [
+                "https://github.com/lihaoyun6/ComfyUI-CSV-Random-Picker"
+            ],
+            "install_type": "git-clone",
+            "description": "String random picker for ComfyUI"
+        },
+        {
+            "author": "r-vage",
+            "title": "ComfyUI-RvTools_v2 [REMOVED]",
+            "reference": "https://github.com/r-vage/ComfyUI-RvTools_v2",
+            "files": [
+                "https://github.com/r-vage/ComfyUI-RvTools_v2"
+            ],
+            "install_type": "git-clone",
+            "description": "this node contains a lot of small little helpers like switches, passers and selectors that i use a lot to build my workflows."
+        },
+        {
+            "author": "Yuxi Liu",
+            "title": "comfyui-ddu [REMOVED]",
+            "reference": "https://github.com/YL-Lyx/Comfyui-ddu-toolchain",
+            "files": [
+                "https://github.com/YL-Lyx/Comfyui-ddu-toolchain"
+            ],
+            "install_type": "git-clone",
+            "description": "ai-driven toolchain for digital design and fabrication "
+        },
+        {
+            "author": "lu64k",
+            "title": "SK-Nodes [REMOVED]",
+            "reference": "https://github.com/lu64k/SK-Nodes",
+            "files": [
+                "https://github.com/lu64k/SK-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:image select, Load AnyLLM, Ask LLM, OpenAI DAlle Node, SK Text_String, SK Random File Name"
+        },
+        {
+            "author": "SiggEye",
+            "title": "FaceCanon — Consistent Faces at Any Resolution [REMOVED]",
+            "reference": "https://github.com/SiggEye/FaceCanon",
+            "files": [
+                "https://github.com/SiggEye/FaceCanon"
+            ],
+            "install_type": "git-clone",
+            "description": "FaceCanon scales a detected face to a canonical pixel size, lets you run your favorite face detailer at that sweet spot, then maps the result back into the original image with seamless blending. The payoff is consistent face style no matter the input resolution or framing."
+        },
+        {
+            "author": "AlfredClark",
+            "title": "ComfyUI-ModelSpec [REMOVED]",
+            "reference": "https://github.com/AlfredClark/ComfyUI-ModelSpec",
+            "files": [
+                "https://github.com/AlfredClark/ComfyUI-ModelSpec"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI model metadata editing nodes."
+        },
+        {
+            "author": "VraethrDalkr",
+            "title": "ComfyUI-ProgressiveBlend [REMOVED]",
+            "reference": "https://github.com/VraethrDalkr/ComfyUI-ProgressiveBlend",
+            "files": [
+                "https://github.com/VraethrDalkr/ComfyUI-ProgressiveBlend"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of custom nodes for ComfyUI that enable progressive blending and color matching effects across image batches/video frames."
+        },
+        {
+            "author": "xmarked-ai",
+            "title": "ComfyUI_misc [REMOVED]",
+            "reference": "https://github.com/xmarked-ai/ComfyUI_misc",
+            "files": [
+                "https://github.com/xmarked-ai/ComfyUI_misc"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Ace IntegerX, Ace FloatX, Ace Color FixX, White Balance X, Depth Displace X, Empty Latent X, KSampler Combo X, ..."
+        },
+        {
+            "author": "sm079",
+            "title": "ComfyUI-Face-Detection [REMOVED]",
+            "reference": "https://github.com/sm079/ComfyUI-Face-Detection",
+            "files": [
+                "https://github.com/sm079/ComfyUI-Face-Detection"
+            ],
+            "install_type": "git-clone",
+            "description": "face detection nodes for comfyui"
+        },
+        {
+            "author": "42lux",
+            "title": "ComfyUI-42lux [REMOVED]",
+            "reference": "https://github.com/42lux/ComfyUI-42lux",
+            "files": [
+                "https://github.com/42lux/ComfyUI-42lux"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of custom nodes for ComfyUI focused on enhanced sampling, model optimization, and quality improvements."
+        },
+        {
+            "author": "lucak5s",
+            "title": "ComfyUI GFPGAN [REMOVED]",
+            "reference": "https://github.com/lucak5s/comfyui_gfpgan",
+            "files": [
+                "https://github.com/lucak5s/comfyui_gfpgan"
+            ],
+            "install_type": "git-clone",
+            "description": "Face restoration with GFPGAN."
+        },
+        {
+            "author": "impactframes",
+            "title": "IF_AI_tools [DEPRECATED]",
+            "id": "impactframes-tools",
+            "reference": "https://github.com/if-ai/ComfyUI-IF_AI_tools",
+            "files": [
+                "https://github.com/if-ai/ComfyUI-IF_AI_tools"
+            ],
+            "install_type": "git-clone",
+            "description": "Various AI tools to use in Comfy UI. Starting with VL and prompt making tools using Ollma as backend will evolve as I find time."
+        },
+        {
+            "author": "netroxin",
+            "title": "comfyui_netro [REMOVED]",
+            "reference": "https://github.com/netroxin/comfyui_netro",
+            "files": [
+                "https://github.com/netroxin/comfyui_netro"
+            ],
+            "install_type": "git-clone",
+            "description": "#Camera Movement Prompt Node for ComfyUI\nThis custom node script for ComfyUI generates descriptive camera movement prompts based on user-selected movement options for Wan2.2"
+        },
+        {
+            "author": "aistudynow",
+            "title": "comfyui-HunyuanImage-2.1 [REMOVED]",
+            "reference": "https://github.com/aistudynow/comfyui-HunyuanImage-2.1",
+            "files": [
+                "https://github.com/aistudynow/comfyui-HunyuanImage-2.1"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Load HunyuanImage DiT, Load HunyuanImage VAE, Load HunyuanImage Dual Text Encoder, HunyuanImage Sampler, HunyuanImage VAE Decode, HunyuanImage CLIP Text Encode, Empty HunyuanImage Latent Image"
+        },
+        {
+            "author": "SlackinJack",
+            "title": "distrifuser_comfyui [DEPRECATED]",
+            "reference": "https://github.com/SlackinJack/distrifuser_comfyui",
+            "files": [
+                "https://github.com/SlackinJack/distrifuser_comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "[a/Distrifuser](https://github.com/mit-han-lab/distrifuser) sampler node for ComfyUI\n"
+        },
+        {
+            "author": "SlackinJack",
+            "title": "asyncdiff_comfyui [DEPRECATED]",
+            "reference": "https://github.com/SlackinJack/asyncdiff_comfyui",
+            "files": [
+                "https://github.com/SlackinJack/asyncdiff_comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "AsyncDiff node for ComfyUI"
+        },
+        {
+            "author": "TheBill2001",
+            "title": "Save Images with Captions [REMOVED]",
+            "reference": "https://github.com/TheBill2001/ComfyUI-Save-Image-Caption",
+            "files": [
+                "https://github.com/TheBill2001/ComfyUI-Save-Image-Caption"
+            ],
+            "install_type": "git-clone",
+            "description": "Provide two custom nodes to load and save images with captions as separate files."
+        },
+        {
+            "author": "ShmuelRonen",
+            "title": "ComfyUI Flux 1.1 Ultra & Raw Node [REMOVED]",
+            "reference": "https://github.com/ShmuelRonen/ComfyUI_Flux_1.1_RAW_API",
+            "files": [
+                "https://github.com/ShmuelRonen/ComfyUI_Flux_1.1_RAW_API"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI custom node for Black Forest Labs' FLUX 1.1 [pro] API, supporting both regular and Ultra modes with optional Raw mode."
+        },
+        {
+            "author": "mattwilliamson",
+            "title": "ComfyUI AI GameDev Nodes [UNSAFE/REMOVED]",
+            "reference": "https://github.com/mattwilliamson/comfyui-ai-gamedev",
+            "files": [
+                "https://github.com/mattwilliamson/comfyui-ai-gamedev"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI nodes for AI-powered game asset generation, providing a comprehensive toolkit for game developers to create 3D models, animations, and audio assets using state-of-the-art AI models.[w/This node pack has an implementation that dynamically generates scripts.]"
+        },
+        {
+            "author": "manifestations",
+            "title": "ComfyUI Outfit Nodes [DEPRECATED]",
+            "reference": "https://github.com/manifestations/comfyui-outfit",
+            "files": [
+                "https://github.com/manifestations/comfyui-outfit"
+            ],
+            "install_type": "git-clone",
+            "description": "Advanced, professional outfit and makeup generation nodes for ComfyUI, with dynamic UI and AI-powered prompt formatting."
+        },
+        {
+            "author": "Poukpalaova",
+            "title": "ComfyUI-FRED-Nodes [DEPRECATED]",
+            "reference": "https://github.com/Poukpalaova/ComfyUI-FRED-Nodes",
+            "files": [
+                "https://github.com/Poukpalaova/ComfyUI-FRED-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Multiple nodes that ease the process.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "cwebbi1",
+            "title": "VoidCustomNodes [REMOVED]",
+            "reference": "https://github.com/cwebbi1/VoidCustomNodes",
+            "files": [
+                "https://github.com/cwebbi1/VoidCustomNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:Prompt Parser, String Combiner"
+        },
+        {
+            "author": "Shellishack",
+            "title": "ComfyUI Remote Media Loaders [REMOVED]",
+            "reference": "https://github.com/Shellishack/comfyui-remote-media-loaders",
+            "files": [
+                "https://github.com/Shellishack/comfyui-remote-media-loaders"
+            ],
+            "install_type": "git-clone",
+            "description": "Load media (image/video/audio) from remote URL"
+        },
+       {
+            "author": "D3lUX3I",
+            "title": "VideoPromptEnhancer [REMOVED]",
+            "reference": "https://github.com/D3lUX3I/ComfyUI-VideoPromptEnhancer",
+            "files": [
+                "https://github.com/D3lUX3I/ComfyUI-VideoPromptEnhancer"
+            ],
+            "install_type": "git-clone",
+            "description": "This node generates a professional prompt from an input text for modern video AI models (e.g., Alibaba Wan 2.2) via the OpenRouter API."
+        },
+        {
+            "author": "perilli",
+            "title": "apw_nodes [REMOVED]",
+            "reference": "https://github.com/alessandroperilli/APW_Nodes",
+            "files": [
+                "https://github.com/alessandroperilli/APW_Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node suite to augment the capabilities of the [a/AP Workflows for ComfyUI](https://perilli.com/ai/comfyui/)\nNOTE: See [a/Open Creative Studio Nodes](https://github.com/alessandroperilli/OCS_Nodes)"
+        },
+        {
+            "author": "greengerong",
+            "title": "ComfyUI-Lumina-Video [REMOVED]",
+            "reference": "https://github.com/greengerong/ComfyUI-Lumina-Video",
+            "files": [
+                "https://github.com/greengerong/ComfyUI-Lumina-Video"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a video generation plugin implementation for ComfyUI based on the Lumina Video model."
+        },
+        {
+            "author": "SatadalAI",
+            "title": "Combined Upscale Node for ComfyUI [REMOVED]",
+            "reference": "https://github.com/SatadalAI/SATA_UtilityNode",
+            "files": [
+                "https://github.com/SatadalAI/SATA_UtilityNode"
+            ],
+            "install_type": "git-clone",
+            "description": "Combined_Upscale is a custom ComfyUI node designed for high-quality image enhancement workflows. It intelligently combines model-based upscaling with efficient CPU-based resizing, offering granular control over output dimensions and quality. Ideal for asset pipelines, UI prototyping, and generative workflows.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "netroxin",
+            "title": "Netro_wildcards [REMOVED]",
+            "reference": "https://github.com/netroxin/comfyui_netro_wildcards",
+            "files": [
+                "https://github.com/netroxin/comfyui_netro_wildcards"
+            ],
+            "install_type": "git-clone",
+            "description": "Since I used 'simple wildcards' from Vanilla and it no longer works with the new Comfy UI version for me, I created an alternative. This CustomNode takes the entire contents of your wildcards-folder(comfyui wildcards) and creates a node for each one."
+        },
+        {
+            "author": "takoyaki1118",
+            "title": "ComfyUI-MangaTools [REMOVED]",
+            "reference": "https://github.com/takoyaki1118/ComfyUI-MangaTools",
+            "files": [
+                "https://github.com/takoyaki1118/ComfyUI-MangaTools"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Manga Panel Detector, Manga Panel Dispatcher, GateImage, MangaPageAssembler"
+        },
+        {
+            "author": "lucasgattas",
+            "title": "comfyui-egregora-regional [REMOVED]",
+            "reference": "https://github.com/lucasgattas/comfyui-egregora-regional",
+            "files": [
+                "https://github.com/lucasgattas/comfyui-egregora-regional"
+            ],
+            "install_type": "git-clone",
+            "description": "Image Tile Split with Region-Aware Prompting for ComfyUI"
+        },
+        {
+            "author": "lucasgattas",
+            "title": "comfyui-egregora-tiled [REMOVED]",
+            "reference": "https://github.com/lucasgattas/comfyui-egregora-tiled",
+            "files": [
+                "https://github.com/lucasgattas/comfyui-egregora-tiled"
+            ],
+            "install_type": "git-clone",
+            "description": "Tiled regional prompting + tiled VAE decode with seam-free blending for ComfyUI"
+        },
+        {
+            "author": "Seedsa",
+            "title": "ComfyUI Fooocus Nodes [REMOVED]",
+            "id": "fooocus-nodes",
+            "reference": "https://github.com/Seedsa/Fooocus_Nodes",
+            "files": [
+                "https://github.com/Seedsa/Fooocus_Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "This extension provides image generation features based on Fooocus."
+        },
+        {
+            "author": "zhilemann",
+            "title": "ComfyUI-moondream2 [REMOVED]",
+            "reference": "https://github.com/zhilemann/ComfyUI-moondream2",
+            "files": [
+                "https://github.com/zhilemann/ComfyUI-moondream2"
+            ],
+            "install_type": "git-clone",
+            "description": "nodes for nightly moondream2 VLM inference\nsupports only captioning and visual queries at the moment"
+        },
+        {
+            "author": "shinich39",
+            "title": "comfyui-textarea-is-shit [REMOVED]",
+            "reference": "https://github.com/shinich39/comfyui-textarea-is-shit",
+            "files": [
+               "https://github.com/shinich39/comfyui-textarea-is-shit"
+            ],
+            "description": "HTML gives me a textarea like piece of shit.",
+            "install_type": "git-clone"
+        },
+        {
+            "author": "shinich39",
+            "title": "comfyui-poor-textarea [REMOVED]",
+            "reference": "https://github.com/shinich39/comfyui-poor-textarea",
+            "files": [
+                "https://github.com/shinich39/comfyui-poor-textarea"
+            ],
+            "install_type": "git-clone",
+            "description": "Add commentify, indentation, auto-close brackets in textarea."
+        },
+        {
+            "author": "InfiniNode",
+            "title": "Comfyui-InfiniNode-Model-Suite [UNSAFE/REMOVED]",
+            "reference": "https://github.com/InfiniNode/Comfyui-InfiniNode-Model-Suite",
+            "files": [
+                "https://github.com/InfiniNode/Comfyui-InfiniNode-Model-Suite"
+            ],
+            "install_type": "git-clone",
+            "description": "Welcome to the InfiniNode Model Suite, a custom node pack for ComfyUI that transforms the process of manipulating generative AI models. Our suite is a direct implementation of the 'GUI-Based Key Converter Development Plan,' designed to remove technical barriers for advanced AI practitioners and integrate seamlessly with existing image generation pipelines.[w/This node pack contains a node that has a vulnerability allowing write to arbitrary file paths.]"
+        },
+        {
+            "author": "Avalre",
+            "title": "ComfyUI-avaNodes [REMOVED]",
+            "reference": "https://github.com/Avalre/ComfyUI-avaNodes",
+            "files": [
+                "https://github.com/Avalre/ComfyUI-avaNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "These nodes were created to personalize/optimize several ComfyUI nodes for my own use. You can replicate the functionality of most of my nodes by some combination of default ComfyUI nodes and custom nodes from other developers."
+        },
+        {
+            "author": "Alectriciti",
+            "title": "comfyui-creativeprompts [REMOVED]",
+            "reference": "https://github.com/Alectriciti/comfyui-creativeprompts",
+            "files": [
+                "https://github.com/Alectriciti/comfyui-creativeprompts"
+            ],
+            "install_type": "git-clone",
+            "description": "A creative alternative to dynamicprompts"
+        },
+        {
+            "author": "flybirdxx",
+            "title": "ComfyUI Sliding Window [REMOVED]",
+            "reference": "https://github.com/PixWizardry/ComfyUI_Sliding_Window",
+            "files": [
+                "https://github.com/PixWizardry/ComfyUI_Sliding_Window"
+            ],
+            "install_type": "git-clone",
+            "description": "This set of nodes provides a powerful sliding window or 'tiling' technique for processing long videos and animations in ComfyUI. It allows you to work on animations that are longer than your VRAM would typically allow by breaking the job into smaller, overlapping chunks and seamlessly blending them back together."
+        },
+        {
+            "author": "SykkoAtHome",
+            "title": "Sykko Tools for ComfyUI [REMOVED]",
+            "reference": "https://github.com/SykkoAtHome/ComfyUI_SykkoTools",
+            "files": [
+                "https://github.com/SykkoAtHome/ComfyUI_SykkoTools"
+             ],
+             "install_type": "git-clone",
+             "description": "Utilities for working with camera animations inside ComfyUI. The repository currently provides a node for loading camera motion from ASCII FBX files and a corresponding command line helper for debugging."
+        },
+        {
+            "author": "hananbeer",
+            "title": "node_dev - ComfyUI Node Development Helper [REMOVED]",
+            "reference": "https://github.com/hananbeer/node_dev",
+            "files": [
+                "https://github.com/hananbeer/node_dev"
+            ],
+            "install_type": "git-clone",
+            "description": "Browse to this endpoint to reload custom nodes for more streamlined development:\nhttp://127.0.0.1:8188/node_dev/reload/<module_name>"
+        },
+        {
+            "author": "Charonartist",
+            "title": "Comfyui_gemini_tts_node [REMOVED]",
+            "reference": "https://github.com/Charonartist/Comfyui_gemini_tts_node",
+            "files": [
+                "https://github.com/Charonartist/Comfyui_gemini_tts_node"
+            ],
+            "install_type": "git-clone",
+            "description": "This custom node is a ComfyUI node for generating speech from text using the Gemini 2.5 Flash Preview TTS API."
+        },
+        {
+            "author": "squirrel765",
+            "title": "lorasubdirectory [REMOVED]",
+            "reference": "https://github.com/andrewsthomasj/lorasubdirectory",
+            "files": [
+                "https://github.com/andrewsthomasj/lorasubdirectory"
+            ],
+            "install_type": "git-clone",
+            "description": "only show dropdown of loras ina a given subdirectory"
+        },
+        {
+            "author": "shingo1228",
+            "title": "ComfyUI-send-Eagle(slim) [REVMOED]",
+            "id": "send-eagle",
+            "reference": "https://github.com/shingo1228/ComfyUI-send-eagle-slim",
+            "files": [
+                "https://github.com/shingo1228/ComfyUI-send-eagle-slim"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Send Webp Image to Eagle. This is an extension node for ComfyUI that allows you to send generated images in webp format to Eagle. This extension node is a re-implementation of the Eagle linkage functions of the previous ComfyUI-send-Eagle node, focusing on the functions required for this node."
+        },
+        {
+            "author": "shingo1228",
+            "title": "ComfyUI-SDXL-EmptyLatentImage [REVMOED]",
+            "id": "sdxl-emptylatent",
+            "reference": "https://github.com/shingo1228/ComfyUI-SDXL-EmptyLatentImage",
+            "files": [
+                "https://github.com/shingo1228/ComfyUI-SDXL-EmptyLatentImage"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:SDXL Empty Latent Image. An extension node for ComfyUI that allows you to select a resolution from the pre-defined json files and output a Latent Image."
+        },
+        {
+            "author": "chaunceyyann",
+            "title": "ComfyUI Image Processing Nodes [REMOVED]",
+            "reference": "https://github.com/chaunceyyann/comfyui-image-processing-nodes",
+            "files": [
+                "https://github.com/chaunceyyann/comfyui-image-processing-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of custom nodes for ComfyUI focused on image processing operations."
+        },
+        {
+            "author": "OgreLemonSoup",
+            "title": "Gallery&Tabs [DEPRECATED]",
+            "id": "LoadImageGallery",
+            "reference": "https://github.com/OgreLemonSoup/ComfyUI-Load-Image-Gallery",
+            "files": [
+                "https://github.com/OgreLemonSoup/ComfyUI-Load-Image-Gallery"
+            ],
+            "install_type": "git-clone",
+            "description": "Adds a gallery to the Load Image node and tabs for Load Checkpoint/Lora/etc nodes"
+        },
+        {
+            "author": "11dogzi",
+            "title": "Qwen-Image ComfyUI [REMOVED]",
+            "reference": "https://github.com/11dogzi/Comfyui-Qwen-Image",
+            "files": [
+                "https://github.com/11dogzi/Comfyui-Qwen-Image"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a custom node package that integrates the Qwen-Image model into ComfyUI."
+        },
+        {
+            "author": "BAIS1C",
+            "title": "ComfyUI-AudioDuration [REMOVED]",
+            "reference": "https://github.com/BAIS1C/ComfyUI_BASICDancePoser",
+            "files": [
+                "https://github.com/BAIS1C/ComfyUI_BASICDancePoser"
+            ],
+            "install_type": "git-clone",
+            "description": "Node to extract Dance poses from Music to control Video Generations.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "BAIS1C",
+            "title": "ComfyUI_BASICSAdvancedDancePoser [REMOVED]",
+            "reference": "https://github.com/BAIS1C/ComfyUI_BASICSAdvancedDancePoser",
+            "files": [
+                "https://github.com/BAIS1C/ComfyUI_BASICSAdvancedDancePoser"
+            ],
+            "install_type": "git-clone",
+            "description": "Professional COCO-WholeBody 133-keypoint dance animation system for ComfyUI"
+        },
+        {
+            "author": "fablestudio",
+            "title": "ComfyUI-Showrunner-Utils [REMOVED]",
+            "reference": "https://github.com/fablestudio/ComfyUI-Showrunner-Utils",
+            "files": [
+                "https://github.com/fablestudio/ComfyUI-Showrunner-Utils"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Align Face, Generate Timestamp, GetMostCommonColors, Alpha Crop and Position Image, Shrink Image"
+        },
+        {
+            "author": "skayka",
+            "title": "ComfyUI-DreamFit [REMOVED]",
+            "reference": "https://github.com/skayka/ComfyUI-DreamFit",
+            "files": [
+                "https://github.com/skayka/ComfyUI-DreamFit"
+            ],
+            "install_type": "git-clone",
+            "description": "Garment-centric human generation nodes for ComfyUI using DreamFit with Flux.\nDreamFit is a powerful adapter system that enhances Flux models with garment-aware generation capabilities, enabling high-quality fashion and clothing generation."
+        },
+        {
+            "author": "domenecmiralles",
+            "title": "obobo_nodes [REMOVED]",
+            "reference": "https://github.com/domenecmiralles/obobo_nodes",
+            "files": [
+                "https://github.com/domenecmiralles/obobo_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of custom nodes for ComfyUI that provide various input and output capabilities."
+        },
+        {
+            "author": "NicholasKao1029",
+            "title": "comfyui-pixxio [REMOVED]",
+            "reference": "https://github.com/NicholasKao1029/comfyui-pixxio",
+            "files": [
+                "https://github.com/NicholasKao1029/comfyui-pixxio"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Auto-Upload Image to Pixxio Collection, Load Image from Pixx.io"
+        },
+        {
+            "author": "ComfyUI-Workflow",
+            "title": "ComfyUI OpenAI Nodes [REMOVED]",
+            "reference": "https://github.com/ComfyUI-Workflow/ComfyUI-OpenAI",
+            "files": [
+                "https://github.com/ComfyUI-Workflow/ComfyUI-OpenAI"
+            ],
+            "install_type": "git-clone",
+            "description": "By utilizing OpenAI's powerful vision models, this node enables you to incorporate state-of-the-art image understanding into your ComfyUI projects with minimal setup."
+        },
+        {
+            "author": "dionren",
+            "title": "Export Workflow With Cyuai Api Available Nodes [REMOVED]",
+            "id": "comfyUI-Pro-Export-Tool",
+            "reference": "https://github.com/dionren/ComfyUI-Pro-Export-Tool",
+            "files": [
+                "https://github.com/dionren/ComfyUI-Pro-Export-Tool"
+            ],
+            "install_type": "git-clone",
+            "description": "This is a node to convert workflows to cyuai api available nodes."
+        },
+        {
+            "author": "1H-hobit",
+            "title": "ComfyUI_InternVL3 [REMOVED]",
+            "reference": "https://github.com/1H-hobit/ComfyUI_InternVL3",
+            "files": [
+                "https://github.com/1H-hobit/ComfyUI_InternVL3"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI for [a/InternVL](https://github.com/OpenGVLab/InternVL)"
+        },
+        {
+            "author": "spacepxl",
+            "title": "ComfyUI-Florence-2 [DEPRECATED]",
+            "id": "florence2-spacepxl",
+            "reference": "https://github.com/spacepxl/ComfyUI-Florence-2",
+            "files": [
+                "https://github.com/spacepxl/ComfyUI-Florence-2"
+            ],
+            "install_type": "git-clone",
+            "description": "[a/https://huggingface.co/microsoft/Florence-2-large-ft](https://huggingface.co/microsoft/Florence-2-large-ft)\nLarge or base model, support for captioning and bbox task modes, more coming soon."
+        },
+        {
+            "author": "xxxxxxxxxxxc",
+            "title": "flux-kontext-diff-merge [REMOVED]",
+            "reference": "https://github.com/xxxxxxxxxxxc/flux-kontext-diff-merge",
+            "files": [
+                "https://github.com/xxxxxxxxxxxc/flux-kontext-diff-merge"
+            ],
+            "install_type": "git-clone",
+            "description": "Preserve image quality with flux-kontext-diff-merge. This ComfyUI node merges only changed areas from AI edits, ensuring clarity and detail."
+        },
+        {
+            "author": "TechnoByteJS",
+            "title": "TechNodes [REMOVED]",
+            "id": "technodes",
+            "reference": "https://github.com/TechnoByteJS/ComfyUI-TechNodes",
+            "files": [
+                "https://github.com/TechnoByteJS/ComfyUI-TechNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for merging, testing and more.\nNOTE: SDNext Merge, VAE Merge, MBW Layers, Repeat VAE, Quantization."
+        },
+        {
+            "author": "DDDDEEP",
+            "title": "ComfyUI-DDDDEEP [REMOVED]",
+            "reference": "https://github.com/DDDDEEP/ComfyUI-DDDDEEP",
+            "files": [
+                "https://github.com/DDDDEEP/ComfyUI-DDDDEEP"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: AutoWidthHeight, ReturnIntSeed, OppositeBool, PromptItemCollection"
+        },
+        {
+            "author": "manifestations",
+            "title": "ComfyUI Ethnic Outfits Custom Nodes [REMOVED]",
+            "reference": "https://github.com/manifestations/comfyui-outfits",
+            "files": [
+                "https://github.com/manifestations/comfyui-outfits"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI nodes for generating outfit prompts representing diverse ethnicities, cultures, and regions. Uses extensible JSON data for clothing, accessories, and poses, with “random/disabled” dropdowns for flexibility. Advanced prompt engineering is supported via Ollama LLM integration. Easily add new regions, ethnicities, or cultures by updating data files and creating lightweight node wrappers. Designed for artists, researchers, and developers seeking culturally rich, customizable prompt generation in ComfyUI workflows."
+        },
+        {
+            "author": "MitoshiroPJ",
+            "title": "ComfyUI Slothful Attention [REMOVED]",
+            "reference": "https://github.com/MitoshiroPJ/comfyui_slothful_attention",
+            "files": [
+                "https://github.com/MitoshiroPJ/comfyui_slothful_attention"
+            ],
+            "install_type": "git-clone",
+            "description": "This custom node allow controlling output without training. The reducing method is similar to [a/Spatial-Reduction Attention](https://paperswithcode.com/method/spatial-reduction-attention)."
+        },
+        {
+            "author": "MitoshiroPJ",
+            "title": "comfyui_focal_sampler [REMOVED]",
+            "reference": "https://github.com/MitoshiroPJ/comfyui_focal_sampler",
+            "files": [
+                "https://github.com/MitoshiroPJ/comfyui_focal_sampler"
+            ],
+            "install_type": "git-clone",
+            "description": "Apply additional sampling to specific area"
+        },
+        {
+            "author": "manifestations",
+            "title": "ComfyUI Ethnic Outfit & Prompt Enhancer Nodes [REMOVED]",
+            "reference": "https://github.com/manifestations/comfyui-indian-outfit",
+            "files": [
+                "https://github.com/manifestations/comfyui-indian-outfit"
+            ],
+            "install_type": "git-clone",
+            "description": "Features:\n* Extensive options for Indian, Indonesian, and international clothing, jewelry, accessories, and styles\n* Multiple jewelry and accessory fields (with material support: gold, diamond, silver, leather, beads, etc.)\n* Support for tattoos, henna, hair styles, poses, shot types, lighting, and photography genres\n* Seamless prompt expansion using your own Ollama LLM instance\n* Modular, extensible JSON data files for easy customization"
+        },
+        {
+            "author": "coVISIONSld",
+            "title": "ComfyUI-OmniGen2 [REMOVED]",
+            "reference": "https://github.com/coVISIONSld/ComfyUI-OmniGen2",
+            "files": [
+                "https://github.com/coVISIONSld/ComfyUI-OmniGen2"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI-OmniGen2 is a custom node package for the OmniGen2 model, enabling advanced text-to-image generation and visual understanding."
+        },
+        {
+            "author": "S4MUEL-404",
+            "title": "ComfyUI-S4Tool-Image-Overlay [REMOVED]",
+            "reference": "https://github.com/S4MUEL-404/ComfyUI-S4Tool-Image-Overlay",
+            "files": [
+                "https://github.com/S4MUEL-404/ComfyUI-S4Tool-Image-Overlay"
+            ],
+            "install_type": "git-clone",
+            "description": "Quickly set up image overlay effects"
+        },
+        {
+            "author": "akspa0",
+            "title": "ComfyUI-FapMixPlus [REMOVED]",
+            "reference": "https://github.com/akspa0/ComfyUI-FapMixPlus",
+            "files": [
+                "https://github.com/akspa0/ComfyUI-FapMixPlus"
+            ],
+            "install_type": "git-clone",
+            "description": "This is an audio processing script that applies soft limiting, optional loudness normalization, and optional slicing for transcription. It can also produce stereo-mixed outputs with optional audio appended to the end. The script organizes processed files into structured folders with sanitized filenames and retains original timestamps for continuity."
+        },
+        {
+            "author": "RedmondAI",
+            "title": "comfyui-tools [UNSAFE]",
+            "reference": "https://github.com/RedmondAI/comfyui-tools",
+            "files": [
+                "https://github.com/RedmondAI/comfyui-tools"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom extensions for ComfyUI used by the Redmond3D VFX team.[w/This node pack has a vulnerability that allows it to create files at arbitrary paths.]"
+        },
+        {
+            "author": "S4MUEL-404",
+            "title": "Image Position Blend [REMOVED]",
+            "id": "ComfyUI-Image-Position-Blend",
+            "version": "1.1",
+            "reference": "https://github.com/S4MUEL-404/ComfyUI-Image-Position-Blend",
+            "files": [
+                "https://github.com/S4MUEL-404/ComfyUI-Image-Position-Blend"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node for conveniently adjusting the overlay position of two images."
+        },
+        {
+            "author": "S4MUEL-404",
+            "title": "ComfyUI-Text-On-Image [REMOVED]",
+            "id": "ComfyUI-Text-On-Image",
+            "reference": "https://github.com/S4MUEL-404/ComfyUI-Text-On-Image",
+            "files": [
+                "https://github.com/S4MUEL-404/ComfyUI-Text-On-Image"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node for ComfyUI that allows users to add text overlays to images with customizable size, font, position, and shadow."
+        },
+        {
+            "author": "S4MUEL-404",
+            "title": "ComfyUI-Prompts-Selector [REMOVED]",
+            "reference": "https://github.com/S4MUEL-404/ComfyUI-Prompts-Selector",
+            "files": [
+                "https://github.com/S4MUEL-404/ComfyUI-Prompts-Selector"
+            ],
+            "install_type": "git-clone",
+            "description": "Quickly select preset prompts and merge them"
+        },
+        {
+            "author": "juntaosun",
+            "title": "ComfyUI_open_nodes [REMOVED]",
+             "reference": "https://github.com/juntaosun/ComfyUI_open_nodes",
+             "files": [
+                 "https://github.com/juntaosun/ComfyUI_open_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI open nodes by juntaosun."
+        },
+        {
+            "author": "perilli",
+            "title": "apw_nodes [DEPRECATED]",
+            "reference": "https://github.com/alessandroperilli/apw_nodes",
+            "files": [
+                "https://github.com/alessandroperilli/apw_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node suite to augment the capabilities of the [a/AP Workflows for ComfyUI](https://perilli.com/ai/comfyui/)[w/'APW_Nodes' has been newly added in place of 'apw_nodes'.]"
+        },
+        {
+            "author": "markuryy",
+            "title": "ComfyUI Spiritparticle Nodes [REMOVED]",
+            "reference": "https://github.com/markuryy/comfyui-spiritparticle",
+            "files": [
+                "https://github.com/markuryy/comfyui-spiritparticle"
+            ],
+            "install_type": "git-clone",
+            "description": "A node pack by spiritparticle."
+        },
+        {
+            "author": "SpaceKendo",
+            "title": "Text to video for Stable Video Diffusion in ComfyUI [REMOVED]",
+            "id": "svd-txt2vid",
+            "reference": "https://github.com/SpaceKendo/ComfyUI-svd_txt2vid",
+            "files": [
+                "https://github.com/SpaceKendo/ComfyUI-svd_txt2vid"
+            ],
+            "install_type": "git-clone",
+            "description": "This is node replaces the init_image conditioning for the [a/Stable Video Diffusion](https://github.com/Stability-AI/generative-models) image to video model with text embeds, together with a conditioning frame. The conditioning frame is a set of latents."
+        },
+        {
+            "author": "vovler",
+            "title": "ComfyUI Civitai Helper Extension [REMOVED]",
+            "reference": "https://github.com/vovler/comfyui-civitaihelper",
+            "files": [
+                "https://github.com/vovler/comfyui-civitaihelper"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI extension for parsing Civitai PNG workflows and automatically downloading missing models"
+        },
+        {
+            "author": "DriftJohnson",
+            "title": "DJZ-Nodes [REMOVED]",
+            "id": "DJZ-Nodes",
+            "reference": "https://github.com/MushroomFleet/DJZ-Nodes",
+            "files": [
+                "https://github.com/MushroomFleet/DJZ-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "AspectSize and other nodes"
+        },
+        {
+            "author": "DriftJohnson",
+            "title": "KokoroTTS Node [REMOVED]",
+            "reference": "https://github.com/MushroomFleet/DJZ-KokoroTTS",
+            "files": [
+                "https://github.com/MushroomFleet/DJZ-KokoroTTS"
+            ],
+            "install_type": "git-clone",
+            "description": "This node provides advanced text-to-speech functionality powered by KokoroTTS. Follow the instructions below to install, configure, and use the node within your portable ComfyUI installation."
+        },
+        {
+            "author": "MushroomFleet",
+            "title": "SVG Suite for ComfyUI [REMOVED]",
+            "reference": "https://github.com/MushroomFleet/svg-suite",
+            "files": [
+                "https://github.com/MushroomFleet/svg-suite"
+            ],
+            "install_type": "git-clone",
+            "description": "SVG Suite is an advanced set of nodes for converting images to SVG in ComfyUI, expanding upon the functionality of ComfyUI-ToSVG."
+        },
+        {
+            "author": "joeriben",
+            "title": "AI4ArtsEd Ollama Prompt Node [DEPRECATED]",
+            "reference": "https://github.com/joeriben/ai4artsed_comfyui",
+            "files": [
+                "https://github.com/joeriben/ai4artsed_comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "Experimental nodes for ComfyUI. For more, see [a/https://kubi-meta.de/ai4artsed](https://kubi-meta.de/ai4artsed) A custom ComfyUI node for stylistic and cultural transformation of input text using local LLMs served via Ollama. This node allows you to combine a free-form prompt (e.g. translation, poetic recoding, genre shift) with externally supplied text in the ComfyUI graph. The result is processed via an Ollama-hosted model and returned as plain text."
+        },
+        {
+            "author": "bento234",
+            "title": "ComfyUI-bento-toolbox [REMOVED]",
+            "reference": "https://github.com/bento234/ComfyUI-bento-toolbox",
+            "files": [
+                "https://github.com/bento234/ComfyUI-bento-toolbox"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Tile Prompt Distributor"
+        },
+        {
+            "author": "yichengup",
+            "title": "ComfyUI-VideoBlender [REMOVED]",
+            "reference": "https://github.com/yichengup/ComfyUI-VideoBlender",
+            "files": [
+                "https://github.com/yichengup/ComfyUI-VideoBlender"
+            ],
+            "install_type": "git-clone",
+            "description": "Video clip mixing"
+        },
+        {
+            "author": "xl0",
+            "title": "latent-tools [REMOVED]",
+            "reference": "https://github.com/xl0/latent-tools",
+            "files": [
+                "https://github.com/xl0/latent-tools"
+            ],
+            "install_type": "git-clone",
+            "description": "Visualize and manipulate the latent space in ComfyUI"
+        },
+        {
+            "author": "Conor-Collins",
+            "title": "ComfyUI-CoCoTools [REMOVED]",
+            "reference": "https://github.com/Conor-Collins/coco_tools",
+            "files": [
+                "https://github.com/Conor-Collins/coco_tools"
+             ],
+             "install_type": "git-clone",
+             "description": "A set of custom nodes for ComfyUI providing advanced image processing, file handling, and utility functions."
+        },
+        {
+            "author": "theUpsider",
+            "title": "ComfyUI-Logic [DEPRECATED]",
+            "id": "comfy-logic",
+            "reference": "https://github.com/theUpsider/ComfyUI-Logic",
+            "files": [
+                "https://github.com/theUpsider/ComfyUI-Logic"
+            ],
+            "install_type": "git-clone",
+            "description": "An extension to ComfyUI that introduces logic nodes and conditional rendering capabilities."
+        },
+        {
+            "author": "Malloc-pix",
+            "title": "comfyui_qwen2.4_vl_node [REMOVED]",
+            "reference": "https://github.com/Malloc-pix/comfyui_qwen2.4_vl_node",
+            "files": [
+                "https://github.com/Malloc-pix/comfyui_qwen2.4_vl_node"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: CogVLM2 Captioner, CLIP Dynamic Text Encode(cy)"
+        },
+        {
+            "author": "inyourdreams-studio",
+            "title": "ComfyUI-RBLM [REMOVED]",
+            "reference": "https://github.com/inyourdreams-studio/comfyui-rblm",
+            "files": [
+                "https://github.com/inyourdreams-studio/comfyui-rblm"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node pack for ComfyUI that provides text manipulation nodes."
+        },
+        {
+            "author": "dream-computing",
+            "title": "SyntaxNodes - Image Processing Effects for ComfyUI [REMOVED]",
+            "reference": "https://github.com/dream-computing/syntax-nodes",
+            "files": [
+                "https://github.com/dream-computing/syntax-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of custom nodes for ComfyUI designed to apply various image processing effects, stylizations, and analyses."
+        },
+        {
+            "author": "UD1sto",
+            "title": "plugin-utils-nodes [DEPRECATED]",
+            "reference": "https://github.com/its-DeFine/plugin-utils-nodes",
+            "files": [
+                "https://github.com/its-DeFine/plugin-utils-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Compare Images (SimHash), Image Selector, Temporal Consistency, Update Image Reference, Frame Blend."
+        },
+        {
+            "author": "hanyingcho",
+            "title": "ComfyUI LLM Promp [REMOVED]",
+            "reference": "https://github.com/hanyingcho/comfyui-llmprompt",
+            "files": [
+                "https://github.com/hanyingcho/comfyui-llmprompt"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Load llm, Generate Text with LLM, Inference Qwen2VL, Inference Qwen2"
+        },
+        {
+            "author": "WASasquatch",
+            "title": "WAS Node Suite [DEPRECATED]",
+            "id": "was",
+            "reference": "https://github.com/WASasquatch/was-node-suite-comfyui",
+            "pip": ["numba"],
+            "files": [
+                "https://github.com/WASasquatch/was-node-suite-comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "A node suite for ComfyUI with many new nodes, such as image processing, text processing, and more."
+        },
+        {
+            "author": "TOM1063",
+            "title": "ComfyUI-SamuraiTools [REMOVED]",
+            "reference": "https://github.com/TOM1063/ComfyUI-SamuraiTools",
+            "files": [
+                "https://github.com/TOM1063/ComfyUI-SamuraiTools"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node for switching integer values based on boolean conditions"
+        },
+        {
+            "author": "whitemoney293",
+            "title": "ComfyUI-MediaUtilities [REMOVED]",
+            "reference": "https://github.com/ThanaritKanjanametawatAU/ComfyUI-MediaUtilities",
+            "files": [
+                "https://github.com/ThanaritKanjanametawatAU/ComfyUI-MediaUtilities"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom nodes for loading and previewing media from URLs in ComfyUI."
+        },
+        {
+            "author": "pureexe",
+            "title": "DiffusionLight-ComfyUI [REMOVED]",
+            "reference": "https://github.com/pureexe/DiffusionLight-ComfyUI",
+            "files": [
+                "https://github.com/pureexe/DiffusionLight-ComfyUI"
+            ],
+            "install_type": "git-clone",
+            "description": "DiffusionLight (Turbo) implemented in ComfyUI"
+        },
+        {
+            "author": "gondar-software",
+            "title": "comfyui-custom-padding [REMOVED]",
+            "reference": "https://github.com/gondar-software/comfyui-custom-padding",
+            "files": [
+                "https://github.com/gondar-software/comfyui-custom-padding"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Adaptive image padding, Adaptive image unpadding"
+        },
+        {
+            "author": "Charonartist",
+            "title": "ComfyUI-EagleExporter [REMOVED]",
+            "reference": "https://github.com/Charonartist/ComfyUI-EagleExporter",
+            "files": [
+                "https://github.com/Charonartist/ComfyUI-EagleExporter"
+            ],
+            "install_type": "git-clone",
+            "description": "This is an extension that automatically saves video files generated with ComfyUI's 'video combine' extension to the Eagle library."
+        },
+        {
+            "author": "pomePLaszlo-collablyu",
+            "title": "comfyui_ejam [REMOVED]",
+            "reference": "https://github.com/PLaszlo-collab/comfyui_ejam",
+            "files": [
+                "https://github.com/PLaszlo-collab/comfyui_ejam"
+            ],
+            "install_type": "git-clone",
+            "description": "Ejam nodes for comfyui"
+        },
+        {
+            "author": "jonnydolake",
+            "title": "ComfyUI-AIR-Nodes [REMOVED]",
+            "reference": "https://github.com/jonnydolake/ComfyUI-AIR-Nodes",
+            "files": [
+                "https://github.com/jonnydolake/ComfyUI-AIR-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: String List To Prompt Schedule, Force Minimum Batch Size, Target Location (Crop), Target Location (Paste), Image Composite Chained, Match Image Count To Mask Count, Random Character Prompts, Parallax Test, Easy Parallax, Parallax GPU Test"
+        },
+        {
+            "author": "solution9th",
+            "title": "Comfyui_mobilesam [REMOVED]",
+            "reference": "https://github.com/solution9th/Comfyui_mobilesam",
+            "files": [
+                "https://github.com/solution9th/Comfyui_mobilesam"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Mobile SAM Model Loader, Mobile SAM Detector, Mobile SAM Predictor"
+        },
+        {
+            "author": "syaofox",
+            "title": "ComfyUI_fnodes [REMOVED]",
+            "reference": "https://github.com/syaofox/ComfyUI_fnodes",
+            "files": [
+                "https://github.com/syaofox/ComfyUI_fnodes"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI_fnodes is a collection of custom nodes designed for ComfyUI. These nodes provide additional functionality that can enhance your ComfyUI workflows.\nFile manipulation tools, Image resizing tools, IPAdapter tools, Image processing tools, Mask tools, Face analysis tools, Sampler tools, Miscellaneous tools"
+        },
+        {
+            "author": "Hangover3832",
+            "title": "ComfyUI-Hangover-Moondream [DEPRECATED]",
+            "reference": "https://github.com/Hangover3832/ComfyUI-Hangover-Moondream",
+            "files": [
+                "https://github.com/Hangover3832/ComfyUI-Hangover-Moondream"
+            ],
+            "install_type": "git-clone",
+            "description": "Moondream is a lightweight multimodal large language model.\n[w/WARN:Additional python code will be downloaded from huggingface and executed. You have to trust this creator if you want to use this node!]"
+        },
+        {
+            "author": "Hangover3832",
+            "title": "Recognize Anything Model (RAM) for ComfyUI [DEPRECATED]",
+            "reference": "https://github.com/Hangover3832/ComfyUI-Hangover-Recognize_Anything",
+            "files": [
+                "https://github.com/Hangover3832/ComfyUI-Hangover-Recognize_Anything"
+            ],
+            "install_type": "git-clone",
+            "description": "This is an image recognition node for ComfyUI based on the RAM++ model from [a/xinyu1205](https://huggingface.co/xinyu1205).\nThis node outputs a string of tags with all the recognized objects and elements in the image in English or Chinese language.\nFor image tagging and captioning."
+        },
+        {
+            "author": "Hangover3832",
+            "title": "ComfyUI-Hangover-Nodes [DEPRECATED]",
+            "reference": "https://github.com/Hangover3832/ComfyUI-Hangover-Nodes",
+            "files": [
+                "https://github.com/Hangover3832/ComfyUI-Hangover-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes: MS kosmos-2 Interrogator, Save Image w/o Metadata, Image Scale Bounding Box. An implementation of Microsoft [a/kosmos-2](https://huggingface.co/microsoft/kosmos-2-patch14-224) image to text transformer."
+        },
+        {
+            "author": "SirLatore",
+            "title": "ComfyUI-IPAdapterWAN [REMOVED]",
+            "reference": "https://github.com/SirLatore/ComfyUI-IPAdapterWAN",
+            "files": [
+                "https://github.com/SirLatore/ComfyUI-IPAdapterWAN"
+            ],
+            "install_type": "git-clone",
+            "description": "This extension adapts the [a/InstantX IP-Adapter for SD3.5-Large](https://huggingface.co/InstantX/SD3.5-Large-IP-Adapter) to work with Wan 2.1 and other UNet-based video/image models in ComfyUI.\nUnlike the original SD3 version (which depends on joint_blocks from MMDiT), this version performs sampling-time identity conditioning by dynamically injecting into attention layers — making it compatible with models like Wan 2.1, AnimateDiff, and other non-SD3 pipelines."
+        },
+        {
+            "author": "Jpzz",
+            "title": "ComfyUI-VirtualInteraction [UNSAFE/REMOVED]",
+            "reference": "https://github.com/Jpzz/ComfyUI-VirtualInteraction",
+            "files": [
+                "https://github.com/Jpzz/ComfyUI-VirtualInteraction"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: virtual interaction custom node when using generative movie\n[w/This nodepack contains a node which is reading arbitrary excel file.]"
+        },
+        {
+            "author": "satche",
+            "title": "Prompt Factory [REMOVED]",
+            "reference": "https://github.com/satche/comfyui-prompt-factory",
+            "files": [
+                "https://github.com/satche/comfyui-prompt-factory"
+            ],
+            "install_type": "git-clone",
+            "description": "A modular system that adds randomness to prompt generation"
+        },
+        {
+            "author": "MITCAP",
+            "title": "ComfyUI OpenAI DALL-E 3 Node [REMOVED]",
+            "reference": "https://github.com/MITCAP/OpenAI-ComfyUI",
+            "files": [
+                "https://github.com/MITCAP/OpenAI-ComfyUI"
+            ],
+            "install_type": "git-clone",
+            "description": "This project provides custom nodes for ComfyUI that integrate with OpenAI's DALL-E 3 and GPT-4o models. The nodes allow users to generate images and describe images using OpenAI's API.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "raspie10032",
+            "title": "ComfyUI NAI Prompt Converter [REMOVED]",
+            "reference": "https://github.com/raspie10032/ComfyUI_RS_NAI_Local_Prompt_converter",
+            "files": [
+                "https://github.com/raspie10032/ComfyUI_RS_NAI_Local_Prompt_converter"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node extension for ComfyUI that enables conversion between different prompt formats: NovelAI V4, ComfyUI, and old NovelAI."
+        },
+        {
+            "author": "holchan",
+            "title": "ComfyUI-ModelDownloader [REMOVED]",
+            "reference": "https://github.com/holchan/ComfyUI-ModelDownloader",
+            "files": [
+                "https://github.com/holchan/ComfyUI-ModelDownloader"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI node to download models(Checkpoints and LoRA) from external links and act as an output standalone node."
+        },
+        {
+            "author": "Kur0butiMegane",
+            "title": "Comfyui-StringUtils [DEPRECATED]",
+            "reference": "https://github.com/Kur0butiMegane/Comfyui-StringUtils",
+            "files": [
+                "https://github.com/Kur0butiMegane/Comfyui-StringUtils"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Prompt Normalizer, String Splitter, String Line Selector, Extract Markup Value"
+        },
+        {
+            "author": "Apache0ne",
+            "title": "ComfyUI-LantentCompose [REMOVED]",
+            "reference": "https://github.com/Apache0ne/ComfyUI-LantentCompose",
+            "files": [
+                "https://github.com/Apache0ne/ComfyUI-LantentCompose"
+            ],
+            "install_type": "git-clone",
+            "description": "Interpolate sdxl latents using slerp with and without a mask. use with unsample nodes for best effect.\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "jax-explorer",
+            "title": "ComfyUI-H-flow [REMOVED]",
+            "reference": "https://github.com/jax-explorer/ComfyUI-H-flow",
+            "files": [
+                "https://github.com/jax-explorer/ComfyUI-H-flow"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Wan2-1 Image To Video, LLM Task, Save Image, Save Video, Show Text, FluxPro Ultra, IdeogramV2 Turbo, Runway Image To Video, Kling Image To Video, Replace Text, Join Text, Test Image, Test Text"
+        },
+        {
+            "author": "Apache0ne",
+            "title": "SambaNova [REMOVED]",
+            "id": "SambaNovaAPI",
+            "reference": "https://github.com/Apache0ne/SambaNova",
+            "files": [
+                "https://github.com/Apache0ne/SambaNova"
+            ],
+            "install_type": "git-clone",
+            "description": "Super Fast LLM's llama3.1-405B,70B,8B and more"
+        },
+        {
+            "author": "Apache0ne",
+            "title": "ComfyUI-EasyUrlLoader [REMOVED]",
+            "id": "easy-url-loader",
+            "reference": "https://github.com/Apache0ne/ComfyUI-EasyUrlLoader",
+            "files": [
+                "https://github.com/Apache0ne/ComfyUI-EasyUrlLoader"
+            ],
+            "install_type": "git-clone",
+            "description": "A simple YT downloader node for ComfyUI using video Urls. Can be used with VHS nodes etc."
+        },
+        {
+            "author": "nxt5656",
+            "title": "ComfyUI-Image2OSS [REMOVED]",
+            "reference": "https://github.com/nxt5656/ComfyUI-Image2OSS",
+            "files": [
+                "https://github.com/nxt5656/ComfyUI-Image2OSS"
+            ],
+            "install_type": "git-clone",
+            "description": "Upload the image to Alibaba Cloud OSS."
+        },
+        {
+            "author": "ainewsto",
+            "title": "Comfyui_Comfly",
+            "reference": "https://github.com/ainewsto/Comfyui_Comfly",
+            "files": [
+                "https://github.com/ainewsto/Comfyui_Comfly"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Comfly_Mj, Comfly_mjstyle, Comfly_upload, Comfly_Mju, Comfly_Mjv, Comfly_kling_videoPreview\nNOTE: Comfyui_Comfly_v2 is introduced."
+        },
+        {
+            "author": "shinich39",
+            "title": "comfyui-to-inpaint",
+            "reference": "https://github.com/shinich39/comfyui-to-inpaint",
+            "files": [
+                "https://github.com/shinich39/comfyui-to-inpaint"
+            ],
+            "install_type": "git-clone",
+            "description": "Send preview image to inpaint workflow."
+        },
+        {
+            "author": "magic-quill",
+            "title": "ComfyUI_MagicQuill [NOT MAINTAINED]",
+            "id": "MagicQuill",
+            "reference": "https://github.com/magic-quill/ComfyUI_MagicQuill",
+            "files": [
+                "https://github.com/magic-quill/ComfyUI_MagicQuill"
+            ],
+            "install_type": "git-clone",
+            "description": "Towards GPT-4 like large language and visual assistant.\nNOTE: The current version has not been maintained for a long time and does not work. Please use https://github.com/brantje/ComfyUI_MagicQuill instead."
+        },
+        {
+            "author": "shinich39",
+            "title": "comfyui-event-handler [USAFE/REMOVED]",
+            "reference": "https://github.com/shinich39/comfyui-event-handler",
+            "files": [
+                "https://github.com/shinich39/comfyui-event-handler"
+            ],
+            "install_type": "git-clone",
+            "description": "Javascript code will run when an event fires. [w/This node allows you to execute arbitrary JavaScript code as input for the workflow.]"
+        },
+        {
+            "author": "Moooonet",
+            "title": "ComfyUI-ArteMoon [REMOVED]",
+            "reference": "https://github.com/Moooonet/ComfyUI-ArteMoon",
+            "files": [
+                "https://github.com/Moooonet/ComfyUI-ArteMoon"
+            ],
+            "install_type": "git-clone",
+            "description": "This plugin works with [a/IF_AI_Tools](https://github.com/if-ai/ComfyUI-IF_AI_tools) to build a workflow in ComfyUI that uses AI to assist in generating prompts."
+        },
+        {
+            "author": "ryanontheinside",
+            "title": "ComfyUI-MediaPipe-Vision [REMOVED]",
+            "reference": "https://github.com/ryanontheinside/ComfyUI-MediaPipe-Vision",
+            "files": [
+                "https://github.com/ryanontheinside/ComfyUI-MediaPipe-Vision"
+            ],
+            "install_type": "git-clone",
+            "description": "A centralized wrapper of all MediaPipe vision tasks for ComfyUI."
+        },
+        {
+            "author": "shinich39",
+            "title": "comfyui-textarea-command [REMOVED]",
+            "reference": "https://github.com/shinich39/comfyui-textarea-command",
+            "files": [
+                "https://github.com/shinich39/comfyui-textarea-command"
+            ],
+            "install_type": "git-clone",
+            "description": "Add command and comment in textarea. (e.g. // Disabled line)"
+        },
+        {
+            "author": "shinich39",
+            "title": "comfyui-parse-image [REMOVED]",
+            "reference": "https://github.com/shinich39/comfyui-parse-image",
+            "files": [
+                "https://github.com/shinich39/comfyui-parse-image"
+            ],
+            "install_type": "git-clone",
+            "description": "Extract metadata from image."
+        },
+        {
+            "author": "shinich39",
+            "title": "comfyui-put-image [REMOVED]",
+            "reference": "https://github.com/shinich39/comfyui-put-image",
+            "files": [
+                "https://github.com/shinich39/comfyui-put-image"
+            ],
+            "install_type": "git-clone",
+            "description": "Load image from directory."
+        },
+        {
+            "author": "fredconex",
+            "title": "TripoSG Nodes for ComfyUI [REMOVED]",
+            "reference": "https://github.com/fredconex/ComfyUI-TripoSG",
+            "files": [
+                "https://github.com/fredconex/ComfyUI-TripoSG"
+            ],
+            "install_type": "git-clone",
+            "description": "Created by Alfredo Fernandes inspired by Hunyuan3D nodes by Kijai. This extension adds TripoSG 3D mesh generation capabilities to ComfyUI, allowing you to generate 3D meshes from a single image using the TripoSG model."
+        },
+        {
+            "author": "fredconex",
+            "title": "ComfyUI-PaintTurbo [REMOVED]",
+            "reference": "https://github.com/fredconex/ComfyUI-PaintTurbo",
+            "files": [
+                "https://github.com/fredconex/ComfyUI-PaintTurbo"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Hunyuan3D Texture Mesh"
+        },
+        {
+            "author": "zhuanqianfish",
+            "title": "TaesdDecoder [REMOVED]",
+            "reference": "https://github.com/zhuanqianfish/TaesdDecoder",
+            "files": [
+                "https://github.com/zhuanqianfish/TaesdDecoder"
+            ],
+            "install_type": "git-clone",
+            "description": "use TAESD decoded image.you need donwload taesd_decoder.pth and taesdxl_decoder.pth to vae_approx folder first.\n It will result in a slight loss of image quality and a significant decrease in peak video memory during decoding."
+        },
+        {
+            "author": "myAiLemon",
+            "title": "MagicAutomaticPicture [REMOVED]",
+            "reference": "https://github.com/myAiLemon/MagicAutomaticPicture",
+            "files": [
+                "https://github.com/myAiLemon/MagicAutomaticPicture"
+            ],
+            "install_type": "git-clone",
+            "description": "A comfyui node package that can generate pictures and automatically save positive prompts and eliminate unwanted prompts"
+        },
+        {
+            "author": "thisiseddy-ab",
+            "title": "ComfyUI-Edins-Ultimate-Pack [REMOVED]",
+            "reference": "https://github.com/thisiseddy-ab/ComfyUI-Edins-Ultimate-Pack",
+            "files": [
+                "https://github.com/thisiseddy-ab/ComfyUI-Edins-Ultimate-Pack"
+            ],
+            "install_type": "git-clone",
+            "description": "Well i needet a Tiled Ksampler that still works for Comfy UI there were none so i made one, in this Package i will put all Nodes i will develop for Comfy Ui still in beta alot will change.."
+        },
+        {
+            "author": "Davros666",
+            "title": "safetriggers [REMOVED]",
+            "reference": "https://github.com/Davros666/safetriggers",
+            "files": [
+                "https://github.com/Davros666/safetriggers"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI Nodes for READING TRIGGERS, TRIGGER-WORDS, TRIGGER-PHRASES FROM LoRAs"
+        },
+        {
+            "author": "cubiq",
+            "title": "Simple Math [REMOVED]",
+            "id": "simplemath",
+            "reference": "https://github.com/cubiq/ComfyUI_SimpleMath",
+            "files": [
+                "https://github.com/cubiq/ComfyUI_SimpleMath"
+            ],
+            "install_type": "git-clone",
+            "description": "custom node for ComfyUI to perform simple math operations"
+        },
+        {
+            "author": "lucafoscili",
+            "title": "LF Nodes [DEPRECATED]",
+            "reference": "https://github.com/lucafoscili/comfyui-lf",
+            "files": [
+                "https://github.com/lucafoscili/comfyui-lf"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom nodes with a touch of extra UX, including: history for primitives, JSON manipulation, logic switches with visual feedback, LLM chat... and more!"
+        },
+        {
+            "author": "AI2lab",
+            "title": "comfyUI-tool-2lab [REMOVED]",
+            "id": "tool-2lab",
+            "reference": "https://github.com/AI2lab/comfyUI-tool-2lab",
+            "files": [
+                "https://github.com/AI2lab/comfyUI-tool-2lab"
+            ],
+            "install_type": "git-clone",
+            "description": "tool set for developing workflow and publish to web api server"
+        },
+        {
+            "author": "AI2lab",
+            "title": "comfyUI-DeepSeek-2lab [REMOVED]",
+            "id": "deepseek",
+            "reference": "https://github.com/AI2lab/comfyUI-DeepSeek-2lab",
+            "files": [
+                "https://github.com/AI2lab/comfyUI-DeepSeek-2lab"
+            ],
+            "install_type": "git-clone",
+            "description": "Unofficial implementation of DeepSeek for ComfyUI"
+        },
+        {
+            "author": "AI2lab",
+            "title": "comfyUI-kling-api-2lab [REMOVED]",
+            "reference": "https://github.com/AI2lab/comfyUI-kling-api-2lab",
+            "files": [
+                "https://github.com/AI2lab/comfyUI-kling-api-2lab"
+            ],
+            "install_type": "git-clone",
+            "description": "Unofficial implementation of KLing for ComfyUI"
+        },
+        {
+            "author": "ZhiHui6",
+            "title": "comfyui_zhihui_nodes [REMOVED]",
+            "reference": "https://github.com/ZhiHui6/comfyui_zhihui_nodes",
+            "files": [
+                "https://github.com/ZhiHui6/comfyui_zhihui_nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Prompt Preset, Video Batch Loader, Video Combiner"
+        },
+        {
+            "author": "ImagineerNL",
+            "title": "comfyui_potrace_svg [REMOVED]",
+            "reference": "https://github.com/ImagineerNL/comfyui_potrace_svg",
+            "files": [
+                "https://github.com/ImagineerNL/comfyui_potrace_svg"
+            ],
+            "install_type": "git-clone",
+            "description": "This project converts raster images into SVG format using the Potrace library."
+        },
+        {
+            "author": "kayselmecnun",
+            "title": "ComfyUI-Qwen-25-VL [REMOVED]",
+            "reference": "https://github.com/kayselmecnun/ComfyUI-Qwen-25-VL",
+            "files": [
+                "https://github.com/kayselmecnun/ComfyUI-Qwen-25-VL"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom Comfy UI node for using Qwen2.5-VL-3B/7B-Instruct models"
+        },
+        {
+            "author": "IfnotFr",
+            "title": "⚡ ComfyUI Connect [REMOVED]",
+            "reference": "https://github.com/IfnotFr/ComfyUI-Connect",
+            "files": [
+                "https://github.com/IfnotFr/ComfyUI-Connect"
+            ],
+            "install_type": "git-clone",
+            "description": "Transform your ComfyUI into a powerful API, exposing all your saved workflows as ready-to-use HTTP endpoints."
+        },
+        {
+            "author": "ginlov",
+            "title": "segment_to_mask_comfyui [REMOVED]",
+            "reference": "https://github.com/ginlov/segment_to_mask_comfyui",
+            "files": [
+                "https://github.com/ginlov/segment_to_mask_comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:SegToMask"
+        },
+        {
+            "author": "TGu-97",
+            "title": "TGu Utilities [REMOVED]",
+            "id": "tgu",
+            "reference": "https://github.com/TGu-97/ComfyUI-TGu-utils",
+            "files": [
+                "https://github.com/TGu-97/ComfyUI-TGu-utils"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes: MPN Switch, MPN Reroute, PN Switch. This is a set of custom nodes for ComfyUI. Mainly focus on control switches."
+        },
+        {
+            "author": "IfnotFr",
+            "title": "ComfyUI-Connect [REMOVED]",
+            "reference": "https://github.com/IfnotFr/ComfyUI-Connect",
+            "files": [
+                "https://github.com/IfnotFr/ComfyUI-Connect"
+            ],
+            "install_type": "git-clone",
+            "description": "Transform your ComfyUI into a powerful API, exposing all your saved workflows as ready-to-use HTTP endpoints."
+        },
+        {
+            "author": "KurtHokke",
+            "title": "ComfyUI_KurtHokke-Nodes [REMOVED]",
+            "reference": "https://github.com/KurtHokke/ComfyUI_KurtHokke-Nodes",
+            "files": [
+                "https://github.com/KurtHokke/ComfyUI_KurtHokke-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI_KurtHokke-Nodes"
+        },
+        {
+            "author": "SpatialDeploy",
+            "title": "ComfyUI-Voxels [REMOVED]",
+            "reference": "https://github.com/SpatialDeploy/ComfyUI-Voxels",
+            "files": [
+                "https://github.com/SpatialDeploy/ComfyUI-Voxels"
+            ],
+            "install_type": "git-clone",
+            "description": "Tools for creating voxel based videos"
+        },
+        {
+            "author": "shinich39",
+            "title": "comfyui-group-selection [REMOVED]",
+            "reference": "https://github.com/shinich39/comfyui-group-selection",
+            "files": [
+                "https://github.com/shinich39/comfyui-group-selection"
+            ],
+            "install_type": "git-clone",
+            "description": "Create a new group of nodes."
+        },
+        {
+            "author": "shinich39",
+            "title": "connect-from-afar [REMOVED]",
+            "reference": "https://github.com/shinich39/comfyui-connect-from-afar",
+            "files": [
+                "https://github.com/shinich39/comfyui-connect-from-afar"
+            ],
+            "install_type": "git-clone",
+            "description": "Connect a new link from out of screen."
+        },
+        {
+            "author": "shinich39",
+            "title": "comfyui-local-db [REMOVED]",
+            "reference": "https://github.com/shinich39/comfyui-local-db",
+            "files": [
+                "https://github.com/shinich39/comfyui-local-db"
+            ],
+            "install_type": "git-clone",
+            "description": "Store text to Key-Values pair json."
+        },
+        {
+            "author": "shinich39",
+            "title": "comfyui-model-db [REMOVED]",
+            "reference": "https://github.com/shinich39/comfyui-model-db",
+            "files": [
+                "https://github.com/shinich39/comfyui-model-db"
+            ],
+            "install_type": "git-clone",
+            "description": "Store settings by model."
+        },
+        {
+            "author": "shinich39",
+            "title": "comfyui-target-search [REMOVED]",
+            "reference": "https://github.com/shinich39/comfyui-target-search",
+            "files": [
+                "https://github.com/shinich39/comfyui-target-search"
+            ],
+            "install_type": "git-clone",
+            "description": "Move canvas to target on dragging connection."
+        },
+        {
+            "author": "chrisgoringe",
+            "title": "Image chooser [DEPRECATED]",
+            "id": "image-chooser",
+            "reference": "https://github.com/chrisgoringe/cg-image-picker",
+            "files": [
+                "https://github.com/chrisgoringe/cg-image-picker"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node that pauses the flow while you choose which image (or latent) to pass on to the rest of the workflow."
+        },
+        {
+            "author": "weilin9999",
+            "title": "WeiLin-ComfyUI-prompt-all-in-one [DEPRECATED]",
+            "id": "prompt-all-in-one",
+            "reference": "https://github.com/weilin9999/WeiLin-ComfyUI-prompt-all-in-one",
+            "files": [
+                "https://github.com/weilin9999/WeiLin-ComfyUI-prompt-all-in-one"
+            ],
+            "install_type": "git-clone",
+            "description": "Write prompt words like WebUI"
+        },
+        {
+            "author": "svetozarov",
+            "title": "AS_GeminiCaptioning Node [REMOVED]",
+            "reference": "https://github.com/svetozarov/AS_GeminiCaptioning",
+            "files": [
+                "https://github.com/svetozarov/AS_GeminiCaptioning"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI node that combines an image with simple text parameters to create a prompt, sends it to the Google Gemini API via the google-generativeai SDK, and returns the generated text response along with the original prompt and an execution log"
+        },
+        {
+            "author": "shinich39",
+            "title": "comfyui-load-image-in-seq [REMOVED]",
+            "reference": "https://github.com/shinich39/comfyui-load-image-in-seq",
+            "files": [
+                "https://github.com/shinich39/comfyui-load-image-in-seq"
+            ],
+            "install_type": "git-clone",
+            "description": "This node is load png image sequentially with metadata. Only supported for PNG format that has been created by ComfyUI.[w/renamed from comfyui-load-image-39. You need to remove previous one and reinstall to this.]"
+        },
+        {
+            "author": "shinich39",
+            "title": "comfyui-model-metadata [REMOVED]",
+            "reference": "https://github.com/shinich39/comfyui-model-metadata",
+            "files": [
+                "https://github.com/shinich39/comfyui-model-metadata"
+            ],
+            "install_type": "git-clone",
+            "description": "Print model metadata on note node"
+        },
+        {
+            "author": "shinich39",
+            "title": "comfyui-view-recommendations [REMOVED]",
+            "reference": "https://github.com/shinich39/comfyui-view-recommendations",
+            "files": [
+                "https://github.com/shinich39/comfyui-view-recommendations"
+            ],
+            "install_type": "git-clone",
+            "description": "Load model generation data from civitai."
+        },
+        {
+            "author": "jonstreeter",
+            "title": "Comfyui-PySceneDetect [REMOVED]",
+            "reference": "https://github.com/jonstreeter/Comfyui-PySceneDetect",
+            "files": [
+                "https://github.com/jonstreeter/Comfyui-PySceneDetect"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: PySceneDetect Video Processor"
+        },
+        {
+            "author": "muxueChen",
+            "title": "ComfyUI-NTQwen25-VL [REMOVED]",
+            "reference": "https://github.com/muxueChen/ComfyUI-NTQwen25-VL",
+            "files": [
+                "https://github.com/muxueChen/ComfyUI-NTQwen25-VL"
+            ],
+            "install_type": "git-clone",
+            "description": "Qwen25-VL is a plugin for ComfyU"
+        },
+        {
+            "author": "Makki_Shizu",
+            "title": "ComfyUI-SaveAnimatedGIF [DEPRECATED]",
+            "id": "SaveAnimatedGIF",
+            "reference": "https://github.com/MakkiShizu/ComfyUI-SaveAnimatedGIF",
+            "files": [
+                "https://github.com/MakkiShizu/ComfyUI-SaveAnimatedGIF"
+            ],
+            "install_type": "git-clone",
+            "description": "Save animated GIF format nodes in ComfyUI"
+        },
+        {
+            "author": "l1yongch1",
+            "title": "ComfyUI_PhiCaption [REMOVED]",
+            "reference": "https://github.com/l1yongch1/ComfyUI_PhiCaption",
+            "files": [
+                "https://github.com/l1yongch1/ComfyUI_PhiCaption"
+            ],
+            "install_type": "git-clone",
+            "description": "In addition to achieving conventional single-image, single-round reverse engineering, it can also achieve single-image multi-round and multi-image single-round reverse engineering. Moreover, the Phi model has a better understanding of prompts."
+        },
+        {
+            "author": "nova-florealis",
+            "title": "comfyui-alien [REMOVED]",
+            "reference": "https://github.com/nova-florealis/comfyui-alien",
+            "files": [
+                "https://github.com/nova-florealis/comfyui-alien"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Text to Text (LLM), Text Output, Convert to Markdown, List Display (Debug)"
+        },
+        {
+            "author": "PluMaZero",
+            "title": "ComfyUI-SpaceFlower [REMOVED]",
+            "reference": "https://github.com/PluMaZero/ComfyUI-SpaceFlower",
+            "files": [
+                "https://github.com/PluMaZero/ComfyUI-SpaceFlower"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes: SpaceFlower_Prompt, SpaceFlower_HangulPrompt, ..."
+        },
+        {
+            "author": "vahidzxc",
+            "title": "ComfyUI-My-Handy-Nodes [REMOVED]",
+            "reference": "https://github.com/vahidzxc/ComfyUI-My-Handy-Nodes",
+            "files": [
+                "https://github.com/vahidzxc/ComfyUI-My-Handy-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:VahCropImage"
+        },
+        {
+            "author": "Samulebotin",
+            "title": "ComfyUI-FreeVC_wrapper [REMOVED]",
+            "reference": "https://github.com/Samulebotin/ComfyUI-FreeVC_wrapper",
+            "files": [
+                "https://github.com/Samulebotin/ComfyUI-FreeVC_wrapper"
+            ],
+            "install_type": "git-clone",
+            "description": "A voice conversion extension node for ComfyUI based on FreeVC, enabling high-quality voice conversion capabilities within the ComfyUI framework."
+        },
+        {
+            "author": "GoingAI1998",
+            "title": "ComfyUI Web Canvas Node [REMOVED]",
+            "reference": "https://github.com/GoingAI1998/Comfyui_imgcanvas",
+            "files": [
+                "https://github.com/GoingAI1998/Comfyui_imgcanvas"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI_imgcanvas At present, I have not used the useful comfyui custom node about layer mixing, and I have written a comfyui runtime automatic pop-up window for layer editing node"
+        },
+        {
+            "author": "807502278",
+            "title": "ComfyUI_TensorRT_Merge [REMOVED]",
+            "reference": "https://github.com/807502278/ComfyUI_TensorRT_Merge",
+            "files": [
+                "https://github.com/807502278/ComfyUI_TensorRT_Merge"
+            ],
+            "install_type": "git-clone",
+            "description": "Non diffusion models supported by TensorRT, merged Comfyui plugin, added onnx automatic download and trt model conversion nodes."
+        },
+        {
+            "author": "logtd",
+            "title": "ComfyUI-LTXTricks [DEPRECATED]",
+            "reference": "https://github.com/logtd/ComfyUI-LTXTricks",
+            "files": [
+                "https://github.com/logtd/ComfyUI-LTXTricks"
+            ],
+            "install_type": "git-clone",
+            "description": "A set of nodes that provide additional controls for the LTX Video model"
+        },
+        {
+            "author": "JichaoLiang",
+            "title": "Immortal_comfyUI [REMOVED]",
+            "reference": "https://github.com/JichaoLiang/Immortal_comfyUI",
+            "files": [
+                "https://github.com/JichaoLiang/Immortal_comfyUI"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:ImNewNode, ImAppendNode, MergeNode, SetProperties, SaveToDirectory, batchNodes, redirectToNode, SetEvent, ..."
+        },
+        {
+            "author": "Rvage0815",
+            "title": "ComfyUI-RvTools [REMOVED]",
+            "reference": "https://github.com/Rvage0815/ComfyUI-RvTools",
+            "files": [
+                "https://github.com/Rvage0815/ComfyUI-RvTools"
+            ],
+            "install_type": "git-clone",
+            "description": "this node contains a lot of small little helpers like switches, passers and selectors that i use a lot to build my workflows."
+        },
+        {
+            "author": "Rvage0815",
+            "title": "RvTComfyUI-RvTools_v2 [REMOVED]",
+            "reference": "https://github.com/Rvage0815/ComfyUI-RvTools_v2",
+            "files": [
+                "https://github.com/Rvage0815/ComfyUI-RvTools_v2"
+            ],
+            "install_type": "git-clone",
+            "description": "this node contains a lot of small little helpers like switches, passers and selectors that i use a lot to build my workflows."
+        },
+        {
+            "author": "scottmudge",
+            "title": "ComfyUI_BiscuitNodes [REMOVED]",
+            "reference": "https://github.com/scottmudge/ComfyUI_BiscuitNodes",
+            "files": [
+                "https://github.com/scottmudge/ComfyUI_BiscuitNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Load Image From Path Using File Selector"
+        },
+        {
+            "author": "thanhduong0213929",
+            "title": "ComfyUI-DeepUnlock [REMOVED]",
+            "reference": "https://github.com/thanhduong0213929/ComfyUI-DeepUnlock",
+            "files": [
+                "https://github.com/thanhduong0213929/ComfyUI-DeepUnlock"
+            ],
+            "install_type": "git-clone",
+            "description": "DeepFuze is a state-of-the-art deep learning tool that seamlessly integrates with ComfyUI to revolutionize facial transformations, lipsyncing, video generation, voice cloning, face swapping, and lipsync translation. Leveraging advanced algorithms, DeepFuze enables users to combine audio and video with unparalleled realism, ensuring perfectly synchronized facial movements. This innovative solution is ideal for content creators, animators, developers, and anyone seeking to elevate their video editing projects with sophisticated AI-driven features."
+        },
+        {
+            "author": "pathway8-sudo",
+            "title": "RMBG [REMOVED]",
+            "reference": "https://github.com/pathway8-sudo/RMBG",
+            "files": [
+                "https://github.com/pathway8-sudo/RMBG"
+            ],
+            "install_type": "git-clone",
+            "description": "This repository provides a custom node for ComfyUI, leveraging the BriaRMBG model to remove backgrounds from images and output a transparent PNG."
+        },
+        {
+            "author": "iris-Neko",
+            "title": "ComfyUI_ascii_art [REMOVED]",
+            "reference": "https://github.com/iris-Neko/ComfyUI_ascii_art",
+            "files": [
+                "https://github.com/iris-Neko/ComfyUI_ascii_art"
+             ],
+             "install_type": "git-clone",
+             "description": "ComfyUI node for [a/ASCII art controlnet](https://civitai.com/models/986392)"
+        },
+        {
+            "author": "apesplat",
+            "title": "ezXY scripts and nodes [NOT MAINTAINED]",
+            "id": "ezxy",
+            "reference": "https://github.com/GMapeSplat/ComfyUI_ezXY",
+            "files": [
+                "https://github.com/GMapeSplat/ComfyUI_ezXY"
+            ],
+            "install_type": "git-clone",
+            "description": "Extensions/Patches: Enables linking float and integer inputs and ouputs. Values are automatically cast to the correct type and clamped to the correct range. Works with both builtin and custom nodes.[w/NOTE: This repo patches ComfyUI's validate_inputs and map_node_over_list functions while running. May break depending on your version of ComfyUI. Can be deactivated in config.yaml.]Nodes: A collection of nodes for facilitating the generation of XY plots. Capable of plotting changes over most primitive values.[w/Does not work with current version of Comfyui]"
+        },
+        {
+            "author": "mie",
+            "title": "ComfyUI_JanusProCaption [REMOVED]",
+            "reference": "https://github.com/MieMieeeee/ComfyUI-JanusProCaption",
+            "files": [
+                "https://github.com/MieMieeeee/ComfyUI-JanusProCaption"
+            ],
+            "install_type": "git-clone",
+            "description": "Describe image or create caption files using Janus Pro Model"
+        },
+        {
+            "author": "Njbx",
+            "title": "ComfyUI-blockswap [REMOVED]",
+            "reference": "https://github.com/Njbx/ComfyUI-blockswap",
+            "files": [
+                "https://github.com/Njbx/ComfyUI-blockswap"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Block Swap"
+        },
+        {
+            "author": "T8star1984",
+            "title": "comfyui-purgevram [REMOVED]",
+            "reference": "https://github.com/T8star1984/comfyui-purgevram",
+            "files": [
+                "https://github.com/T8star1984/comfyui-purgevram"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:PurgeVRAM.\nCan be added after any node to clean up vram and memory"
+        },
+        {
+            "author": "zmwv823",
+            "title": "ComfyUI-VideoDiffusion [REMOVED]",
+            "reference": "https://github.com/zmwv823/ComfyUI-VideoDiffusion",
+            "files": [
+                "https://github.com/zmwv823/ComfyUI-VideoDiffusion"
+            ],
+            "install_type": "git-clone",
+            "description": "[a/LatentSync](https://github.com/bytedance/LatentSync) and [a/Sonic](https://github.com/jixiaozhong/Sonic). [w/Just for study purpose. It's not for directly use, u should know how to fix issues.]"
+        },
+        {
+            "author": "NyaamZ",
+            "title": "Get Booru Tag ExtendeD [REMOVED]",
+            "reference": "https://github.com/NyaamZ/ComfyUI-GetBooruTag-ED",
+            "files": [
+               "https://github.com/NyaamZ/ComfyUI-GetBooruTag-ED"
+            ],
+            "description": "Get tag from Booru site.",
+            "install_type": "git-clone"
+        },
+        {
+            "author": "lingha",
+            "title": "comfyui_kj [REMOVED]",
+            "id": "comfyui_kj",
+            "reference": "https://github.com/XieChengYuan/comfyui_kj",
+            "files": [
+                "https://github.com/XieChengYuan/comfyui_kj"
+            ],
+            "install_type": "git-clone",
+            "description": "comfyui_kj, A tool that can package workflows into projects and publish them to a WeChat Mini Program named Kaji, allowing charges to be collected from users."
+        },
+        {
+            "author": "myAiLemon",
+            "title": "MagicGetPromptAutomatically [REMOVED]",
+            "reference": "https://github.com/myAiLemon/MagicGetPromptAutomatically",
+            "files": [
+                "https://github.com/myAiLemon/MagicGetPromptAutomatically"
+            ],
+            "install_type": "git-clone",
+            "description": "A plug-in that can automatically generate pictures and save txt files in comfyui"
+        },
+        {
+            "author": "ryanontheinside",
+            "title": "ComfyUI_ScavengerHunt [REMOVED]",
+            "reference": "https://github.com/ryanontheinside/ComfyUI_ScavengerHunt",
+            "files": [
+                "https://github.com/ryanontheinside/ComfyUI_ScavengerHunt"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Compare Image Similarity (ResNet), Compare Image Similarity (CLIP), Compare Image Types\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "vpakarinen",
+            "title": "ComfyUI-GenerationTimer [REMOVED]",
+            "reference": "https://github.com/vpakarinen/ComfyUI-GenerationTimer",
+            "files": [
+                "https://github.com/vpakarinen/ComfyUI-GenerationTimer"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Generation Timer, Image Timer, Timer Display"
+        },
+        {
+            "author": "RedRayz",
+            "title": "ComfyUI-Danbooru-To-WD [REMOVED]",
+            "id": "danbooru2wd",
+            "reference": "https://github.com/RedRayz/ComfyUI-Danbooru-To-WD",
+            "files": [
+                "https://github.com/RedRayz/ComfyUI-Danbooru-To-WD"
+            ],
+            "install_type": "git-clone",
+            "description": "Converts booru tags to a format suitable for Waifu Diffusion(or Danbooru based models)."
+        },
+        {
+            "author": "alexgenovese",
+            "title": "comfyui_CfgPlusPlus [REMOVED]",
+            "id": "cfgpp",
+            "reference": "https://gitea.com/NotEvilGirl/cfgpp",
+            "files": [
+                "https://gitea.com/NotEvilGirl/cfgpp"
+            ],
+            "install_type": "git-clone",
+            "description": "CFG++ implemented according to [a/https://cfgpp-diffusion.github.io](https://cfgpp-diffusion.github.io). Basically modified DDIM sampler that makes sampling work at low CFG values (0 ~ 2). Read the CFG++ paper for more details"
+        },
+        {
+            "author": "hosterosi",
+            "title": "ComfyUI OpenAI Node",
+            "reference": "https://github.com/hosterosi/ComfyUI_OpenAI [REMOVED]",
+            "files": [
+                "https://github.com/hosterosi/ComfyUI_OpenAI"
+            ],
+            "install_type": "git-clone",
+            "description": "This custom node for ComfyUI allows users to input multiline text and select a specific line by its number. The node processes the input and returns the selected line along with its index."
+        },
+        {
+            "author": "Gourieff",
+            "title": "ReActor Node for ComfyUI [DISABLED]",
+            "id": "reactor",
+            "reference": "https://github.com/Gourieff/comfyui-reactor-node",
+            "files": [
+                "https://github.com/Gourieff/comfyui-reactor-node"
+            ],
+            "install_type": "git-clone",
+            "description": "The Fast and Simple 'roop-like' Face Swap Extension Node for ComfyUI, based on ReActor (ex Roop-GE) SD-WebUI Face Swap Extension"
+        },
+        {
+            "author": "prismwastaken",
+            "title": "prism-tools [REMOVED]",
+            "reference": "https://github.com/prismwastaken/prism-comfyui-tools",
+            "files": [
+                "https://github.com/prismwastaken/prism-comfyui-tools"
+            ],
+            "install_type": "git-clone",
+            "description": "prism-tools"
+        },
+        {
+            "author": "42lux",
+            "title": "ComfyUI-safety-checker [DEPRECATED]",
+            "reference": "https://github.com/42lux/ComfyUI-safety-checker",
+            "files": [
+                "https://github.com/42lux/ComfyUI-safety-checker"
+            ],
+            "install_type": "git-clone",
+            "description": "A NSFW/Safety Checker Node for ComfyUI."
+        },
+        {
+            "author": "riverolls",
+            "title": "ComfyUI-FJDH",
+            "reference": "https://github.com/riverolls/ComfyUI-FJDH [REMOVED]",
+            "files": [
+                "https://github.com/riverolls/ComfyUI-FJDH"
+            ],
+            "install_type": "git-clone",
+            "description": "bbox tools, image tools, mask generators, point tools"
+        },
+        {
+            "author": "jetchopper",
+            "title": "ComfyUI-GeneraNodes",
+            "id": "genera",
+            "reference": "https://github.com/evolox/ComfyUI-GeneraNodes [REMOVED]",
+            "files": [
+                "https://github.com/evolox/ComfyUI-GeneraNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Genera custom nodes and extensions"
+        },
+        {
+            "author": "Pos13",
+            "title": "Cyclist [DEPRECATED]",
+            "id": "cyclist",
+            "reference": "https://github.com/Pos13/comfyui-cyclist",
+            "files": [
+                "https://github.com/Pos13/comfyui-cyclist"
+            ],
+            "install_type": "git-clone",
+            "description": "This extension provides tools to iterate generation results between runs. In general, it's for cycles."
+        },
+        {
+            "author": "leiweiqiang",
+            "title": "ComfyUI-TRA",
+            "id": "tra",
+            "reference": "https://github.com/leiweiqiang/ComfyUI-TRA",
+            "files": [
+                "https://github.com/leiweiqiang/ComfyUI-TRA"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:TCL EbSynth, TCL Extract Frames (From File), TCL Extract Frames (From Video), TCL Combine Frames, TCL Save Video (From Frames)"
+        },
+        {
+            "author": "ai-business-hql",
+            "title": "comfyUIAgent [REMOVED]",
+            "reference": "https://github.com/ai-business-hql/comfyUIAgent",
+            "files": [
+                "https://github.com/ai-business-hql/comfyUIAgent"
+            ],
+            "install_type": "git-clone",
+            "description": "test"
+        },
+        {
+            "author": "daqingliu",
+            "title": "ComfyUI-SaveImageOSS [REMOVED]",
+            "reference": "https://github.com/daqingliu/ComfyUI-SaveImageOSS",
+            "files": [
+                "https://github.com/daqingliu/ComfyUI-SaveImageOSS"
+            ],
+            "install_type": "git-clone",
+            "description": "Save images directly to URL, e.g., OSS. Just input the url in the text box!"
+        },
+        {
+            "author": "shinich39",
+            "title": "comfyui-textarea-keybindings [REMOVED]",
+            "reference": "https://github.com/shinich39/comfyui-textarea-keybindings",
+            "files": [
+                "https://github.com/shinich39/comfyui-textarea-keybindings"
+            ],
+            "install_type": "git-clone",
+            "description": "Add keybindings to textarea."
+        },
+        {
+            "author": "shinich39",
+            "title": "comfyui-load-image-with-cmd [REMOVED]",
+            "reference": "https://github.com/shinich39/comfyui-load-image-with-cmd",
+            "files": [
+                "https://github.com/shinich39/comfyui-load-image-with-cmd"
+            ],
+            "install_type": "git-clone",
+            "description": "Load image and partially workflow with javascript."
+        },
+        {
+            "author": "neuratech-ai",
+            "title": "ComfyUI-MultiGPU [NOT MAINTAINED]",
+            "reference": "https://github.com/neuratech-ai/ComfyUI-MultiGPU",
+            "files": [
+                "https://github.com/neuratech-ai/ComfyUI-MultiGPU"
+            ],
+            "install_type": "git-clone",
+            "description": "Experimental nodes for using multiple GPUs in a single ComfyUI workflow.\nThis extension adds new nodes for model loading that allow you to specify the GPU to use for each model. It monkey patches the memory management of ComfyUI in a hacky way and is neither a comprehensive solution nor a well-tested one. Use at your own risk.\nNote that this does not add parallelism. The workflow steps are still executed sequentially just on different GPUs. Any potential speedup comes from not having to constantly load and unload models from VRAM."
+        },
+        {
+            "author": "jefferyharrell",
+            "title": "ComfyUI-JHXMP [REMOVED]",
+            "reference": "https://github.com/jefferyharrell/ComfyUI-JHXMP",
+            "files": [
+                "https://github.com/jefferyharrell/ComfyUI-JHXMP"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: Save Image With XMP Metadata"
+        },
+        {
+            "author": "viperyl",
+            "title": "ComfyUI-BiRefNet [NOT MAINTAINED]",
+            "id": "comfyui-birefnet",
+            "reference": "https://github.com/viperyl/ComfyUI-BiRefNet",
+            "files": [
+                "https://github.com/viperyl/ComfyUI-BiRefNet"
+            ],
+            "install_type": "git-clone",
+            "description": "Bilateral Reference Network achieves SOTA result in multi Salient Object Segmentation dataset, this repo pack BiRefNet as ComfyUI nodes, and make this SOTA model easier use for everyone."
+        },
+        {
+            "author": "asagi4",
+            "title": "ComfyUI prompt control (LEGACY VERSION)",
+            "reference": "https://github.com/asagi4/comfyui-prompt-control-legacy",
+            "files": [
+                "https://github.com/asagi4/comfyui-prompt-control-legacy"
+            ],
+            "install_type": "git-clone",
+            "description": "WARNING: These nodes exist only to reproduce old workflows. They are unmaintained See https://github.com/asagi4/comfyui-prompt-control for the revised, current version of prompt control."
+        },
+        {
+            "author": "doomy23",
+            "title": "ComfyUI-D00MYsNodes [REMOVED]",
+            "reference": "https://github.com/doomy23/ComfyUI-D00MYsNodes",
+            "files": [
+                "https://github.com/doomy23/ComfyUI-D00MYsNodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes: Images_Converter, Show_Text, Strings_From_List, Save_Text, Random_Images, Load_Images_From_Paths, JSPaint."
+        },
+        {
+            "author": "kadirnar",
+            "title": "comfyui_hub [REMOVED]",
+            "reference": "https://github.com/kadirnar/comfyui_hub",
+            "files": [
+                "https://github.com/kadirnar/comfyui_hub"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of nodes randomly selected and gathered, related to noise. NOTE: SD-Advanced-Noise, noise_latent_perlinpinpin, comfy-plasma"
+        },
+        {
+            "author": "SaltAI",
+            "title": "SaltAI_AudioViz [REMOVED]",
+            "id": "saltai-audioviz",
+            "reference": "https://github.com/get-salt-AI/SaltAI_AudioViz",
+            "files": [
+                "https://github.com/get-salt-AI/SaltAI_AudioViz"
+            ],
+            "install_type": "git-clone",
+            "description": "SaltAI AudioViz contains ComfyUI nodes for generating complex audio reactive visualizations"
+        },
+        {
+            "author": "SaltAI",
+            "title": "SaltAI-Open-Resources [REMOVED]",
+            "id": "saltai-open-resource",
+            "reference": "https://github.com/get-salt-AI/SaltAI",
+            "pip": ["numba"],
+            "files": [
+                "https://github.com/get-salt-AI/SaltAI"
+            ],
+            "install_type": "git-clone",
+            "description": "This repository is a collection of open-source nodes and workflows for ComfyUI, a dev tool that allows users to create node-based workflows often powered by various AI models to do pretty much anything.\nOur mission is to seamlessly connect people and organizations with the world’s foremost AI innovations, anywhere, anytime. Our vision is to foster a flourishing AI ecosystem where the world’s best developers can build and share their work, thereby redefining how software is made, pushing innovation forward, and ensuring as many people as possible can benefit from the positive promise of AI technologies.\nWe believe that ComfyUI is a powerful tool that can help us achieve our mission and vision, by enabling anyone to explore the possibilities and limitations of AI models in a visual and interactive way, without coding if desired.\nWe hope that by sharing our nodes and workflows, we can inspire and empower more people to create amazing AI-powered content with ComfyUI."
+        },
+        {
+            "author": "SaltAI",
+            "title": "SaltAI_Language_Toolkit [REMOVED]",
+            "id": "saltai_language_toolkit",
+            "reference": "https://github.com/get-salt-AI/SaltAI_Language_Toolkit",
+            "files": [
+                "https://github.com/get-salt-AI/SaltAI_Language_Toolkit"
+            ],
+            "install_type": "git-clone",
+            "description": "The project integrates the Retrieval Augmented Generation (RAG) tool [a/Llama-Index](https://www.llamaindex.ai/), [a/Microsoft's AutoGen](https://microsoft.github.io/autogen/), and [a/LlaVA-Next](https://github.com/LLaVA-VL/LLaVA-NeXT) with ComfyUI's adaptable node interface, enhancing the functionality and user experience of the platform."
+        },
+        {
+            "author": "zmwv823",
+            "title": "ComfyUI-Sana [DEPRECATED]",
+            "reference": "https://github.com/zmwv823/ComfyUI-Sana",
+            "files": [
+                "https://github.com/zmwv823/ComfyUI-Sana"
+            ],
+            "install_type": "git-clone",
+            "description": "Unofficial custom-node for [a/SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformer](https://github.com/NVlabs/Sana)\n[w/A init node with lots of bugs, do not try unless interested.]"
+        },
+        {
+            "author": "ACE-innovate",
+            "title": "seg-node [REMOVED]",
+            "reference": "https://github.com/ACE-innovate/seg-node",
+            "files": [
+                "https://github.com/ACE-innovate/seg-node"
+            ],
+            "install_type": "git-clone",
+            "description": "hf cloth seg custom node for comfyui\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "zefu-lu",
+            "title": "ComfyUI_InstantX_SD35_Large_IPAdapter [REMOVED]",
+            "id": "comfyui-instantx-sd3-5-large-ipadapter",
+            "reference": "https://github.com/zefu-lu/ComfyUI-InstantX-SD3_5-Large-IPAdapter",
+            "files": [
+                "https://github.com/zefu-lu/ComfyUI-InstantX-SD3_5-Large-IPAdapter"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI node for using InstantX SD3.5-Large IPAdapter"
+        },
+        {
+            "author": "HentaiGirlfriendDotCom",
+            "title": "comfyui-highlight-connections [REMOVED]",
+            "reference": "https://github.com/HentaiGirlfriendDotCom/comfyui-highlight-connections",
+            "files": [
+                "https://github.com/HentaiGirlfriendDotCom/comfyui-highlight-connections"
+            ],
+            "install_type": "git-clone",
+            "description": "A node that can be dropped into a group. When a node is then clicked within that group, all nodes and connections in that group get greyed out and the connections from the clicked node go bright red."
+        },
+        {
+            "author": "huangyangke",
+            "title": "ComfyUI-Kolors-IpadapterFaceId [DEPRECATED]",
+            "reference": "https://github.com/huangyangke/ComfyUI-Kolors-IpadapterFaceId",
+            "files": [
+                "https://github.com/huangyangke/ComfyUI-Kolors-IpadapterFaceId"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:kolors_ipadapter_faceid\nNOTE: The files in the repo are not organized."
+        },
+        {
+            "author": "zmwv823",
+            "title": "ComfyUI_Ctrlora [DEPRECATED]",
+            "reference": "https://github.com/zmwv823/ComfyUI_Ctrlora",
+            "files": [
+                "https://github.com/zmwv823/ComfyUI_Ctrlora"
+            ],
+            "install_type": "git-clone",
+            "description": "Unofficial custom_node for [a/xyfJASON/ctrlora](https://github.com/xyfJASON/ctrlora)."
+        },
+        {
+            "author": "Fannovel16",
+            "title": "ComfyUI Loopchain [DEPRECATED]",
+            "id": "loopchain",
+            "reference": "https://github.com/Fannovel16/ComfyUI-Loopchain",
+            "files": [
+                "https://github.com/Fannovel16/ComfyUI-Loopchain"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of nodes which can be useful for animation in ComfyUI. The main focus of this extension is implementing a mechanism called loopchain. A loopchain in this case is the chain of nodes only executed repeatly in the workflow. If a node chain contains a loop node from this extension, it will become a loop chain."
+        },
+        {
+            "author": "DonBaronFactory",
+            "title": "ComfyUI-Cre8it-Nodes [DEPRECATED]",
+            "reference": "https://github.com/DonBaronFactory/ComfyUI-Cre8it-Nodes",
+            "files": [
+                "https://github.com/DonBaronFactory/ComfyUI-Cre8it-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:CRE8IT Serial Prompter, CRE8IT Apply Serial Prompter, CRE8IT Image Sizer. A few simple nodes to facilitate working wiht ComfyUI Workflows"
+        },
+        {
+            "author": "thecooltechguy",
+            "title": "ComfyUI-ComfyRun [DEPRECATED/UNSAFE]",
+            "reference": "https://github.com/thecooltechguy/ComfyUI-ComfyRun",
+            "files": [
+                "https://github.com/thecooltechguy/ComfyUI-ComfyRun"
+            ],
+            "install_type": "git-clone",
+            "description": "The easiest way to run & share any ComfyUI workflow [a/https://comfyrun.com](https://comfyrun.com)\nNOTE: Vulnerability discovered. Not being managed."
+        },
+        {
+            "author": "Cardoso-topdev",
+            "title": "comfyui_meshanything_v1 [REMOVED]",
+            "reference": "https://github.com/Cardoso-topdev/comfyui_meshanything_v1",
+            "files": [
+                "https://github.com/Cardoso-topdev/comfyui_meshanything_v1"
+            ],
+            "install_type": "git-clone",
+            "description": "MeshAnything V2: Artist-Created Mesh Generation With Adjacent Mesh Tokenization"
+        },
+        {
+            "author": "palant",
+            "title": "Extended Save Image for ComfyUI [DEPRECATED]",
+            "reference": "https://github.com/palant/extended-saveimage-comfyui",
+            "files": [
+                "https://github.com/palant/extended-saveimage-comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "This custom node is largely identical to the usual Save Image but allows saving images also in JPEG and WEBP formats, the latter with both lossless and lossy compression. Metadata is embedded in the images as usual, and the resulting images can be used to load a workflow."
+        },
+        {
+            "author": "1038lab",
+            "title": "ComfyUI-GPT2P [REMOVED]",
+            "id": "gpt2p",
+            "reference": "https://github.com/1038lab/ComfyUI-GPT2P",
+            "files": [
+                "https://github.com/1038lab/ComfyUI-GPT2P"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI Node - Hugging Face repositories GTP2 Prompt"
+        },
+        {
+            "author": "yushan777",
+            "title": "Y7 Nodes for ComfyUI [REMOVED]",
+            "id": "y7nodes",
+            "reference": "https://github.com/yushan777/ComfyUI-Y7Nodes",
+            "files": [
+                "https://github.com/yushan777/ComfyUI-Y7Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Count_Tokens_(Y7)"
+        },
+        {
+            "author": "city96",
+            "title": "SD-Advanced-Noise [DEPRECATED]",
+            "id": "adv-noise",
+            "reference": "https://github.com/city96/SD-Advanced-Noise",
+            "files": [
+                "https://github.com/city96/SD-Advanced-Noise"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes: LatentGaussianNoise, MathEncode. An experimental custom node that generates latent noise directly by utilizing the linear characteristics of the latent space."
+        },
+        {
+            "author": "shockz0rz",
+            "title": "InterpolateEverything [DEPRECATED]",
+            "id": "interpolate-everything",
+            "reference": "https://github.com/shockz0rz/ComfyUI_InterpolateEverything",
+            "files": [
+                "https://github.com/shockz0rz/ComfyUI_InterpolateEverything"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes: Interpolate Poses, Interpolate Lineart, ... Custom nodes for interpolating between, well, everything in the Stable Diffusion ComfyUI."
+        },
+        {
+            "author": "svdC1",
+            "title": "LoRa Dataset Tools [REMOVED]",
+            "reference": "https://github.com/svdC1/comfy-ui-lora-dataset-tools",
+            "files": [
+                "https://github.com/svdC1/comfy-ui-lora-dataset-tools"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:Directory Loader, Filter Images Without Faces, Detect Faces and Draw Detection Box"
+        },
+        {
+            "author": "MiddleKD",
+            "title": "ComfyUI-default-workflow-setter [REMOVED]",
+            "reference": "https://github.com/MiddleKD/ComfyUI-default-workflow-setter",
+            "files": [
+                "https://github.com/MiddleKD/ComfyUI-default-workflow-setter"
+            ],
+            "install_type": "git-clone",
+            "description": "Default workflow setter"
+        },
+        {
+            "author": "Firetheft",
+            "title": "ComfyUI-Flux-Prompt-Tools [REMOVED]",
+            "reference": "https://github.com/Firetheft/ComfyUI-Flux-Prompt-Tools",
+            "files": [
+                "https://github.com/Firetheft/ComfyUI-Flux-Prompt-Tools"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:Flux Prompt Enhance, Flux Prompt Gemini Flash, Flux Prompt Generator, MiniCPM V2.6 Int4"
+        },
+        {
+            "author": "jtydhr88",
+            "title": "ComfyUI Unique3D [DEPRECATED]",
+            "id": "unique3d",
+            "reference": "https://github.com/jtydhr88/ComfyUI-Unique3D",
+            "files": [
+                "https://github.com/jtydhr88/ComfyUI-Unique3D"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI Unique3D is custom nodes that running AiuniAI/Unique3D into ComfyUI[w/Please follow readme to install with ComfyUI embedded python.]"
+        },
+        {
+            "author": "mpiquero7164",
+            "title": "SaveImgPrompt [DEPRECATED]",
+            "id": "save-imgprompt",
+            "reference": "https://github.com/mpiquero7164/ComfyUI-SaveImgPrompt",
+            "files": [
+                "https://github.com/mpiquero7164/ComfyUI-SaveImgPrompt"
+            ],
+            "install_type": "git-clone",
+            "description": "Save a png or jpeg and option to save prompt/workflow in a text or json file for each image in Comfy + Workflow loading."
+        },
+        {
+            "author": "guoyk93",
+            "title": "y.k.'s ComfyUI node suite [DEPRECATED]",
+            "id": "yks",
+            "reference": "https://github.com/yankeguo-deprecated/yk-node-suite-comfyui",
+            "files": [
+                "https://github.com/yankeguo-deprecated/yk-node-suite-comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes: YKImagePadForOutpaint, YKMaskToImage"
+        },
+        {
+            "author": "adityathiru",
+            "title": "ComfyUI LLMs [REMOVED]",
+            "reference": "https://github.com/adityathiru/ComfyUI-LLMs",
+            "files": [
+                "https://github.com/adityathiru/ComfyUI-LLMs"
+            ],
+            "install_type": "git-clone",
+            "description": "Goal: To enable folks to rapidly build complex workflows with LLMs\nNOTE:☠️ This is experimental and not recommended to use in a production environment (yet!)"
+        },
+        {
+            "author": "DannyStone1999",
+            "title": "ComfyUI-Depth2Mask [REMOVED]",
+            "reference": "https://github.com/DannyStone1999/ComfyUI-Depth2Mask",
+            "files": [
+                "https://github.com/DannyStone1999/ComfyUI-Depth2Mask/raw/main/Depth2Mask.py"
+            ],
+            "install_type": "copy",
+            "description": "Nodes:Depth2Mask"
+        },
+        {
+            "author": "syaofox",
+            "title": "ComfyUI_FoxTools [REMOVED]",
+            "reference": "https://github.com/syaofox/ComfyUI_FoxTools",
+            "files": [
+                "https://github.com/syaofox/ComfyUI_FoxTools"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:BatchImageFromList, Load Face Occlusion Model, Create Face Mask, Simple FaceAlign, Cacul FaceAlign, Gen Blurbord, Face Align, Face Rotate, ImageAdd, LoadImageList, SaveImage Plus, RegTextFind"
+        },
+        {
+            "author": "AIFSH",
+            "title": "SeedVC-ComfyUI [REMOVED]",
+            "reference": "https://github.com/AIFSH/SeedVC-ComfyUI",
+            "files": [
+                "https://github.com/AIFSH/SeedVC-ComfyUI"
+            ],
+            "install_type": "git-clone",
+            "description": "a custom node for [a/seed-vc](https://github.com/Plachtaa/seed-vc)"
+        },
+        {
+            "author": "jazhang00",
+            "title": "ComfyUI Node for Slicedit [REMOVED]",
+            "reference": "https://github.com/jazhang00/ComfyUI-Slicedit",
+            "files": [
+                "https://github.com/jazhang00/ComfyUI-Slicedit"
+            ],
+            "install_type": "git-clone",
+            "description": "Slicedit main page: [a/https://matankleiner.github.io/slicedit/](https://matankleiner.github.io/slicedit/). Use Slicedit with ComfyUI."
+        },
+        {
+            "author": "rklaffehn",
+            "title": "rk-comfy-nodes [REMOVED]",
+            "id": "rknodes",
+            "reference": "https://github.com/rklaffehn/rk-comfy-nodes",
+            "files": [
+                "https://github.com/rklaffehn/rk-comfy-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes: RK_CivitAIMetaChecker, RK_CivitAIAddHashes."
+        },
+        {
+            "author": "Extraltodeus",
+            "title": "CLIP-Token-Injection [REMOVED]",
+            "reference": "https://github.com/Extraltodeus/CLIP-Token-Injection",
+            "files": [
+                "https://github.com/Extraltodeus/CLIP-Token-Injection"
+            ],
+            "install_type": "git-clone",
+            "description": "These nodes are to edit the text vectors of CLIP models, so to customize how the prompts will be interpreted. You could see it as either customisation, 'one token prompt' up to some limitation and a way to mess with how the text will be interpreted. The edited CLIP can then be saved, or as well the edited tokens themselves. The shared example weights does not contain any image-knowledge but the text vector of the words affected."
+        },
+        {
+            "author": "openart",
+            "title": "openart-comfyui-deploy [REMOVED]",
+            "id": "openart-comfyui-deploy",
+            "reference": "https://github.com/kulsisme/openart-comfyui-deploy",
+            "files": [
+                "https://github.com/kulsisme/openart-comfyui-deploy"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES: External Boolean (ComfyUI Deploy), External Checkpoint (ComfyUI Deploy), External Image (ComfyUI Deploy), External Video (ComfyUI Deploy x VHS), OpenArt Text, Image Websocket Output (ComfyDeploy), ..."
+        },
+        {
+            "author": "mittimi",
+            "title": "ComfyUI_mittimiLoadPreset [DEPRECATED]",
+            "id": "comfyui-mittimi-load-preset",
+            "reference": "https://github.com/mittimi/ComfyUI_mittimiLoadPreset",
+            "files": [
+                "https://github.com/mittimi/ComfyUI_mittimiLoadPreset"
+            ],
+            "install_type": "git-clone",
+            "description": "The system selects and loads preset."
+        },
+        {
+            "author": "jinljin",
+            "title": "ComfyUI-Talking-Head [REMOVED]",
+            "reference": "https://github.com/jinljin/ComfyUI-ElevenlabsAndDID-Combine",
+            "files": [
+                "https://github.com/jinljin/ComfyUI-ElevenlabsAndDID-Combine"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI-Talking-Head"
+        },
+        {
+            "author": "jh-leon-kim",
+            "title": "ComfyUI-JHK-utils [REMOVED]",
+            "id": "jhk",
+            "reference": "https://github.com/jh-leon-kim/ComfyUI-JHK-utils",
+            "files": [
+                "https://github.com/jh-leon-kim/ComfyUI-JHK-utils"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:JHK_Utils_LoadEmbed, JHK_Utils_string_merge, JHK_Utils_ImageRemoveBackground"
+        },
+        {
+            "author": "ilovejohnwhite",
+            "title": "TatToolkit [REMOVED]",
+            "reference": "https://github.com/ilovejohnwhite/UncleBillyGoncho",
+            "files": [
+                "https://github.com/ilovejohnwhite/UncleBillyGoncho"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:UWU TTK Preprocessor, Pixel Perfect Resolution, Generation Resolution From Image, Generation Resolution From Latent, Enchance And Resize Hint Images, ..."
+        },
+        {
+            "author": "hzchet",
+            "title": "ComfyUI_QueueGeneration [REMOVED]",
+            "reference": "https://github.com/hzchet/ComfyUI_QueueGeneration",
+            "files": [
+                "https://github.com/hzchet/ComfyUI_QueueGeneration"
+            ],
+            "install_type": "git-clone",
+            "description": "NODES:Queue Img2Vid Generation"
+        },
+        {
+            "author": "ader47",
+            "title": "ComfyUI-MeshHamer [REMOVED]",
+            "reference": "https://github.com/ader47/comfyui_meshhamer",
+            "files": [
+                "https://github.com/ader47/comfyui_meshhamer"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:MeshHamer Hand Refiner. See also: [a/HaMeR: Hand Mesh Recovery](https://github.com/geopavlakos/hamer/tree/main)"
+        },
+        {
+            "author": "SEkINVR",
+            "title": "ComfyUI-Animator",
+            "reference": "https://github.com/SEkINVR/ComfyUI-Animator [REMOVED]",
+            "files": [
+                "https://github.com/SEkINVR/ComfyUI-Animator"
+            ],
+            "install_type": "git-clone",
+            "description": "This custom node for ComfyUI provides full-body animation capabilities, including facial rigging, various lighting styles, and green screen output."
+        },
+        {
+            "author": "ZHO-ZHO-ZHO",
+            "title": "ComfyUI-AnyText [NOT MAINTAINED]",
+            "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-AnyText",
+            "files": [
+                "https://github.com/ZHO-ZHO-ZHO/ComfyUI-AnyText"
+            ],
+            "install_type": "git-clone",
+            "description": "Unofficial implementation of [a/AnyText](https://github.com/tyxsspa/AnyText/tree/825bcc54687206b15bd7e28ba1a8b095989d58e3) for ComfyUI（EXP）"
+        },
+        {
+            "author": "shinich39",
+            "title": "comfyui-pkg39 [DEPRECATED]",
+            "reference": "https://github.com/shinich39/comfyui-pkg39",
+            "files": [
+                "https://github.com/shinich39/comfyui-pkg39"
+            ],
+            "install_type": "git-clone",
+            "description": "This package has created for generate image from generated image and embedded workflow."
+        },
+        {
+            "author": "dnl13",
+            "title": "ComfyUI-dnl13-seg [DEPRECATED]",
+            "reference": "https://github.com/dnl13/ComfyUI-dnl13-seg",
+            "files": [
+                "https://github.com/dnl13/ComfyUI-dnl13-seg"
+            ],
+            "install_type": "git-clone",
+            "description": "After discovering @storyicon implementation here of Segment Anything, I realized its potential as a powerful tool for ComfyUI if implemented correctly. I delved into the SAM and Dino models. The following is my own adaptation of sam_hq for ComfyUI."
+        },
+        {
+            "author": "1038lab",
+            "title": "ComfyUI-latentSizeSelector [REMOVED]",
+            "id": "ComfyUI-latentSizeSelector",
+            "reference": "https://github.com/1038lab/ComfyUI_LatentSizeSelector",
+            "files": [
+                "https://github.com/1038lab/ComfyUI_LatentSizeSelector"
+            ],
+            "install_type": "git-clone",
+            "description": "You'll get a new node Latent Size Selector, you can pick the x and y sizes from a list."
+        },
+        {
+            "author": "hy134300",
+            "title": "ComfyUI-PhotoMaker-V2 [REMOVED]",
+            "reference": "https://github.com/hy134300/ComfyUI-PhotoMaker-V2",
+            "files": [
+                "https://github.com/hy134300/ComfyUI-PhotoMaker-V2"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes for PhotoMaker-V2"
+        },
+        {
+            "author": "neverbiasu",
+            "title": "ComfyUI ImageCaptioner [REMOVED]",
+            "reference": "https://github.com/neverbiasu/ComfyUI-ImageCaptioner",
+            "files": [
+                "https://github.com/neverbiasu/ComfyUI-ImageCaptioner"
+            ],
+            "install_type": "git-clone",
+            "description": "A ComfyUI extension for generating captions for your images. Runs on your own system, no external services used, no filter."
+        },
+        {
+            "author": "mingqizhang",
+            "title": "ComfyUI_InSPyResNet_zmq [REMOVED]",
+            "id": "inspy",
+            "reference": "https://github.com/mingqizhang/ComfyUI_InSPyResNet_zmq",
+            "files": [
+                "https://github.com/mingqizhang/ComfyUI_InSPyResNet_zmq"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:INSPY removebg ModelLoader, INSPY RMBG"
+        },
+        {
+            "author": "mingqizhang",
+            "title": "ComfyUI_AEMatter_zmq [REMOVED]",
+            "id": "aematter",
+            "reference": "https://github.com/mingqizhang/ComfyUI_AEMatter_zmq",
+            "files": [
+                "https://github.com/mingqizhang/ComfyUI_AEMatter_zmq"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:AEMatter_ModelLoader, Create_Trimap, AEMatter_Apply, Mask_Transfor, Replace_Background, Gaussian_Filter, Guide_Filter, Improved_Aplha_Composite"
+        },
+        {
+            "author": "bradsec",
+            "title": "ComfyUI_StringTools [REMOVED]",
+            "id": "stringtools",
+            "reference": "https://github.com/bradsec/ComfyUI_StringTools",
+            "files": [
+                "https://github.com/bradsec/ComfyUI_StringTools"
+            ],
+            "install_type": "git-clone",
+            "description": "Some simple string tools to modify text and strings in ComfyUI."
+        },
+        {
+            "author": "Millyarde",
+            "title": "Pomfy - Photoshop and ComfyUI 2-way sync [REMOVED]",
+            "reference": "https://github.com/Millyarde/Pomfy",
+            "files": [
+                "https://github.com/Millyarde/Pomfy"
+            ],
+            "install_type": "git-clone",
+            "description": "Photoshop custom nodes inside of ComfyUi, send and get data via Photoshop UXP plugin for cross platform support"
+        },
+        {
+            "author": "turkyden",
+            "title": "ComfyUI-Sticker [REMOVED]",
+            "reference": "https://github.com/turkyden/ComfyUI-Sticker",
+            "files": [
+                "https://github.com/turkyden/ComfyUI-Sticker"
+            ],
+            "install_type": "git-clone",
+            "description": "image to sticker"
+        },
+        {
+            "author": "turkyden",
+            "title": "ComfyUI-Comic [REMOVED]",
+            "id": "comic",
+            "reference": "https://github.com/turkyden/ComfyUI-Comic",
+            "files": [
+                "https://github.com/turkyden/ComfyUI-Comic"
+            ],
+            "install_type": "git-clone",
+            "description": "a comfyui plugin for image to comic"
+        },
+        {
+            "author": "turkyden",
+            "title": "ComfyUI-Avatar [REMOVED]",
+            "id": "avatar",
+            "reference": "https://github.com/turkyden/ComfyUI-Avatar",
+            "files": [
+                "https://github.com/turkyden/ComfyUI-Avatar"
+            ],
+            "install_type": "git-clone",
+            "description": "a comfyui plugin for image to avatar"
+        },
+        {
+            "author": "bvhari",
+            "title": "LatentToRGB [DEPRECATED]",
+            "id": "latent2rgb",
+            "reference": "https://github.com/bvhari/ComfyUI_LatentToRGB",
+            "files": [
+                "https://github.com/bvhari/ComfyUI_LatentToRGB"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI custom node to convert latent to RGB.\nNOTE:This repo has been archived because ComfyUI natively has similar functionality now"
+        },
+        {
+            "author": "Kaharos94",
+            "title": "ComfyUI-Saveaswebp [DEPRECATED]",
+            "id": "save-webp",
+            "reference": "https://github.com/Kaharos94/ComfyUI-Saveaswebp",
+            "files": [
+                "https://github.com/Kaharos94/ComfyUI-Saveaswebp"
+            ],
+            "install_type": "git-clone",
+            "description": "Save a picture as Webp file in Comfy + Workflow loading"
+        },
+        {
+            "author": "udi0510",
+            "title": "comfyui-slicer [REMOVED]",
+            "id": "slicer",
+            "reference": "https://github.com/udi0510/comfyui-slicer",
+            "files": [
+                "https://github.com/udi0510/comfyui-slicer"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:SlicerNode"
+        },
+        {
+            "author": "logtd",
+            "title": "ComfyUI-FLATTEN [REMOVED]",
+            "id": "flatten",
+            "reference": "https://github.com/logtd/ComfyUI-FlattenFlow",
+            "files": [
+                "https://github.com/logtd/ComfyUI-FlattenFlow"
+            ],
+            "install_type": "git-clone",
+            "description": "An alternate trajectory processor for ComfyUI-FLATTEN\nNOTE:When using this trajectory type FLATTEN will use roughly 1/4 VRAM and be ~20% faster at the cost of some consistency (especially when injection_steps are low)."
+        },
+        {
+            "author": "MackinationsAi",
+            "title": "ComfyUi_Stuctured-Outputs [REMOVED]",
+            "id": "struct-output",
+            "reference": "https://github.com/MackinationsAi/ComfyUi_Stuctured-Outputs",
+            "files": [
+                "https://github.com/MackinationsAi/ComfyUi_Stuctured-Outputs"
+            ],
+            "install_type": "git-clone",
+            "description": "This repository contains a custom node for ComfyUI that allows users to save generative image outputs with custom filenames and folder structures. The filenames are padded to four digits, and the positive and negative prompts are embedded in the image metadata."
+        },
+        {
+            "author": "laksjdjf",
+            "title": "attention-couple-ComfyUI [DEPRECATED]",
+            "id": "attention-couple",
+            "reference": "https://github.com/laksjdjf/attention-couple-ComfyUI",
+            "files": [
+                "https://github.com/laksjdjf/attention-couple-ComfyUI"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Attention couple. This is a custom node that manipulates region-specific prompts. While vanilla ComfyUI employs an area specification method based on latent couples, this node divides regions using attention layers within UNet.\nNOTE: This has been integrated with cgem156-ComfyUI."
+        },
+        {
+            "author": "phineas-pta",
+            "title": "comfy-trt-test [DEPRECATED]",
+            "reference": "https://github.com/phineas-pta/comfy-trt-test",
+            "files": [
+                "https://github.com/phineas-pta/comfy-trt-test"
+            ],
+            "install_type": "git-clone",
+            "description": "Test project for ComfyUI TensorRT Support.\nNOT WORKING YET.\nnot automatic yet, do not use ComfyUI-Manager to install !!!.\nnot beginner-friendly yet, still intended to technical users\nNOTE: The reason for registration in the Manager is for guidance, and for detailed installation instructions, please visit the repository.\nNOTE: Use 'TensorRT Node for ComfyUI' instead of this."
+        },
+        {
+            "author": "dezi-ai",
+            "title": "ComfyUI Animate LCM [NOT MAINTAINED]",
+            "reference": "https://github.com/dezi-ai/ComfyUI-AnimateLCM",
+            "files": [
+                "https://github.com/dezi-ai/ComfyUI-AnimateLCM"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI implementation for [a/AnimateLCM](https://animatelcm.github.io/) [[a/paper](https://arxiv.org/abs/2402.00769)].\b[w/This extension includes a large number of nodes imported from the existing custom nodes, increasing the likelihood of conflicts.]"
+        },
+        {
+            "author": "christian-byrne",
+            "title": "elimination-nodes [REMOVED]",
+            "reference": "https://github.com/christian-byrne/elimination-nodes",
+            "files": [
+                "https://github.com/christian-byrne/elimination-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Paste Cutout on Base Image"
+        },
+        {
+            "author": "Levy1417",
+            "title": "Universal-Data-Processing-Kit [UNSAFE] [REMOVED]",
+            "reference": "https://github.com/Levy1417/Universal-Data-Processing-Kit",
+            "files": [
+                "https://github.com/Levy1417/Universal-Data-Processing-Kit"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:DPK - Any Eval, DPK - Extract Array, DPK - Run External Program, DPK - Any Literals, DPK - Set Node States, DPK - Realtime Text Preview, DPK - Dynamic Action, DPK - Object To Json, DPK - Json To Object\n[w/This extension includes the ability to execute arbitrary code and programs.]"
+        },
+        {
+            "author": "liusida",
+            "title": "ComfyUI-Sida-Remove-Image [UNSAFE] [REMOVED]",
+            "reference": "https://github.com/liusida/ComfyUI-Sida-Remove-Image",
+            "files": [
+                "https://github.com/liusida/ComfyUI-Sida-Remove-Image"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes: LoadImageWithPrivacy, RemoveImage.[w/This extension is not secure because it provides the capability to delete files from arbitrary paths.]"
+        },
+        {
+            "author": "88IO",
+            "title": "ComfyUI Image Reordering Plugins [REMOVED]",
+            "reference": "https://github.com/88IO/ComfyUI-ImageReorder",
+            "files": [
+                "https://github.com/88IO/ComfyUI-ImageReorder"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node reorder multiple image frames based on indexes or curves."
+        },
+        {
+            "author": "jtydhr88",
+            "title": "ComfyUI-InstantMesh [DEPRECATED]",
+            "id": "instant-mesh",
+            "reference": "https://github.com/jtydhr88/ComfyUI-InstantMesh",
+            "files": [
+                "https://github.com/jtydhr88/ComfyUI-InstantMesh"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI InstantMesh is custom nodes that running TencentARC/InstantMesh into ComfyUI, this extension depends on ComfyUI-3D-Pack. Please refer to Readme carefully to install.\nNOTE: This repo is archived due to ComfyUI-3D-Pack supports InstantMesh, please check 3D-Pack directly if you need it"
+        },
+        {
+            "author": "biegert",
+            "title": "CLIPSeg [NOT MAINTAINED]",
+            "id": "clipseg",
+            "reference": "https://github.com/biegert/ComfyUI-CLIPSeg",
+            "files": [
+                "https://github.com/biegert/ComfyUI-CLIPSeg/raw/main/custom_nodes/clipseg.py"
+            ],
+            "install_type": "copy",
+            "description": "The CLIPSeg node generates a binary mask for a given input image and text prompt."
+        },
+        {
+            "author": "tankucc1no",
+            "title": "ComfyUI-Dragdiffusion [REMOVED]",
+            "id": "dragdiffusion",
+            "reference": "https://github.com/tankucc1no/ComfyUI-Dragdiffusion",
+            "files": [
+                "https://github.com/tankucc1no/ComfyUI-Dragdiffusion"
+            ],
+            "install_type": "git-clone",
+            "description": "Implementation of [a/Dragdiffusion](https://github.com/Yujun-Shi/DragDiffusion) in ComfyUI."
+        },
+        {
+            "author": "wibur0620",
+            "title": "ComfyUI Ollama (wibur) [REMOVED]",
+            "id": "ollama-wibur",
+            "reference": "https://github.com/wibur0620/comfyui-ollama-wibur",
+            "files": [
+                "https://github.com/wibur0620/comfyui-ollama-wibur"
+            ],
+            "install_type": "git-clone",
+            "description": "Custom ComfyUI Nodes for interacting with [a/Ollama](https://ollama.com/) using the ollama python client.\nIntegrate the power of LLMs into ComfyUI workflows easily or just experiment with GPT.\nNOTE: To use this properly, you would need a running Ollama server reachable from the host that is running ComfyUI."
+        },
+        {
+            "author": "IKHOR",
+            "title": "ikhor-nodes [REMOVED]",
+            "reference": "https://github.com/IKHOR/ComfyUI-IKHOR-Jam-Nodes",
+            "files": [
+                "https://github.com/IKHOR/ComfyUI-IKHOR-Jam-Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:LoadFromS3, LoadBatchFromS3, SaveToS3, SaveBatchToS3"
+        },
+        {
+            "author": "kijai",
+            "title": "ComfyUI wrapper nodes for IC-light [DEPRECATED]",
+            "reference": "https://github.com/kijai/ComfyUI-IC-Light-Wrapper",
+            "files": [
+                "https://github.com/kijai/ComfyUI-IC-Light-Wrapper"
+            ],
+            "install_type": "git-clone",
+            "description": "Stopped. Original repo: [a/https://github.com/lllyasviel/IC-Light](https://github.com/lllyasviel/IC-Light)"
+        },
+        {
+            "author": "thedyze",
+            "title": "Save Image Extended for ComfyUI",
+            "reference": "https://github.com/thedyze/save-image-extended-comfyui",
+            "files": [
+                "https://github.com/thedyze/save-image-extended-comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "Customize the information saved in file- and folder names. Use the values of sampler parameters as part of file or folder names. Save your positive & negative prompt as entries in a JSON (text) file, in each folder.\n[w/This custom node has not been maintained for a long time. Please use an alternative node from the default channel.]"
+        },
+        {
+            "author": "ExponentialML",
+            "title": "ComfyUI_ELLA [DEPRECATED]",
+            "reference": "https://github.com/ExponentialML/ComfyUI_ELLA",
+            "files": [
+                "https://github.com/ExponentialML/ComfyUI_ELLA"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI Implementaion of ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment.[w/Officially implemented here: [a/https://github.com/TencentQQGYLab/ComfyUI-ELLA](https://github.com/TencentQQGYLab/ComfyUI-ELLA)]"
+        },
+        {
+            "author": "shinich39",
+            "title": "comfyui-text-pipe-39 [DEPRECATED]",
+            "reference": "https://github.com/shinich39/comfyui-text-pipe-39",
+            "files": [
+                "https://github.com/shinich39/comfyui-text-pipe-39"
+            ],
+            "install_type": "git-clone",
+            "description": "Modify text by condition."
+        },
+        {
+            "author": "Big Idea Technology",
+            "title": "Image Text Overlay Node for ComfyUI [DEPRECATED]",
+            "reference": "https://github.com/Big-Idea-Technology/ComfyUI_Image_Text_Overlay",
+            "files": [
+                "https://github.com/Big-Idea-Technology/ComfyUI_Image_Text_Overlay"
+            ],
+            "install_type": "git-clone",
+            "description": "Please note that the ImageTextOverlay project is no longer supported and has been moved to a new repository. For ongoing developments, contributions, and issues, please refer to the new repository at: [a/https://github.com/Big-Idea-Technology/ComfyUI-Book-Tools](https://github.com/Big-Idea-Technology/ComfyUI-Book-Tools)"
+        },
+        {
+            "author": "mlinmg",
+            "title": "LaMa Preprocessor [DEPRECATED]",
+            "reference": "https://github.com/mlinmg/ComfyUI-LaMA-Preprocessor",
+            "files": [
+                "https://github.com/mlinmg/ComfyUI-LaMA-Preprocessor"
+            ],
+            "install_type": "git-clone",
+            "description": "A LaMa prerocessor for ComfyUI. This preprocessor finally enable users to generate coherent inpaint and outpaint prompt-free. The best results are given on landscapes, not so much in drawings/animation."
+        },
+        {
+            "author": "CapsAdmin",
+            "title": "ComfyUI-Euler-Smea-Dyn-Sampler [DEPRECATED]",
+            "reference": "https://github.com/CapsAdmin/ComfyUI-Euler-Smea-Dyn-Sampler",
+            "files": [
+                "https://github.com/CapsAdmin/ComfyUI-Euler-Smea-Dyn-Sampler"
+            ],
+            "install_type": "git-clone",
+            "description": "Just a comfyui version of [a/Euler Smea Dyn Sampler](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler). It adds the sampler directly to existing samplers."
+        },
+        {
+            "author": "BlakeOne",
+            "title": "ComfyUI FastImageListToImageBatch [REMOVED]",
+            "reference": "https://github.com/BlakeOne/ComfyUI-FastImageListToImageBatch",
+            "files": [
+                "https://github.com/BlakeOne/ComfyUI-FastImageListToImageBatch"
+            ],
+            "install_type": "git-clone",
+            "description": "Quickly convert a list of images to a batch of images. All images must be the same size. Great for long videos."
+        },
+        {
+            "author": "ggpid",
+            "title": "idpark_custom_node [REMOVED]",
+            "reference": "https://github.com/ggpid/idpark_custom_node",
+            "files": [
+                "https://github.com/ggpid/idpark_custom_node"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Load Image from S3, Save Image to S3, Generate SAM, Generate FastSAM, Cut by Mask fixed"
+        },
+        {
+            "author": "Davemane42",
+            "title": "Visual Area Conditioning / Latent composition [DEPRECATED]",
+            "reference": "https://github.com/Davemane42/ComfyUI_Dave_CustomNode",
+            "files": [
+                "https://github.com/Davemane42/ComfyUI_Dave_CustomNode"
+            ],
+            "install_type": "git-clone",
+            "description": "This tool provides custom nodes that allow visualization and configuration of area conditioning and latent composite."
+        },
+        {
+            "author": "laksjdjf",
+            "title": "LoRA-Merger-ComfyUI [DEPRECATED]",
+            "reference": "https://github.com/laksjdjf/LoRA-Merger-ComfyUI",
+            "files": [
+                "https://github.com/laksjdjf/LoRA-Merger-ComfyUI"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Load LoRA Weight Only, Load LoRA from Weight, Merge LoRA, Save LoRA. This extension provides nodes for merging LoRA."
+        },
+        {
+            "author": "kinfolk0117",
+            "title": "TiledIPAdapter [DEPRECATED]",
+            "reference": "https://github.com/kinfolk0117/ComfyUI_TiledIPAdapter",
+            "files": [
+                "https://github.com/kinfolk0117/ComfyUI_TiledIPAdapter"
+            ],
+            "install_type": "git-clone",
+            "description": "Proof of concent on how to use IPAdapter to control tiled upscaling. NOTE: You need to have 'ComfyUI_IPAdapter_plus' installed."
+        },
+        {
+            "author": "XINZHANG-ops",
+            "title": "comfyui-xin-nodes [REMOVED]",
+            "reference": "https://github.com/XINZHANG-ops/comfyui-xin-nodes",
+            "files": [
+                "https://github.com/XINZHANG-ops/comfyui-xin-nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:ImageSizeClassifer, RandomInt, ShowValue"
+        },
+        {
+            "author": "ssitu",
+            "title": "NestedNodeBuilder [DEPRECATED]",
+            "reference": "https://github.com/ssitu/ComfyUI_NestedNodeBuilder",
+            "files": [
+                "https://github.com/ssitu/ComfyUI_NestedNodeBuilder"
+            ],
+            "install_type": "git-clone",
+            "description": "This extension provides the ability to combine multiple nodes into a single node.\nNOTE:An identical feature now exists in ComfyUI. Additionally, this extension is largely broken with the recent versions of the codebase, so please use the built-in feature for group nodes."
+        },
+        {
+            "author": "ccvv804",
+            "title": "ComfyUI StableCascade using diffusers for Low VRAM [DEPRECATED]",
+            "reference": "https://github.com/ccvv804/ComfyUI-DiffusersStableCascade-LowVRAM",
+            "files": [
+                "https://github.com/ccvv804/ComfyUI-DiffusersStableCascade-LowVRAM"
+            ],
+            "install_type": "git-clone",
+            "description": "Works with RTX 4070ti 12GB.\nSimple quick wrapper for [a/https://huggingface.co/stabilityai/stable-cascade](https://huggingface.co/stabilityai/stable-cascade)\nComfy is going to implement this properly soon, this repo is just for quick testing for the impatient!"
+        },
+        {
+            "author": "kijai",
+            "title": "ComfyUI StableCascade using diffusers [DEPRECATED]",
+            "reference": "https://github.com/kijai/ComfyUI-DiffusersStableCascade",
+            "files": [
+                "https://github.com/kijai/ComfyUI-DiffusersStableCascade"
+            ],
+            "install_type": "git-clone",
+            "description": "Simple quick wrapper for [a/https://huggingface.co/stabilityai/stable-cascade](https://huggingface.co/stabilityai/stable-cascade)\nComfy is going to implement this properly soon, this repo is just for quick testing for the impatient!"
+        },
+        {
+            "author": "solarpush",
+            "title": "comfyui_sendimage_node [REMOVED]",
+            "reference": "https://github.com/solarpush/comfyui_sendimage_node",
+            "files": [
+                "https://github.com/solarpush/comfyui_sendimage_node"
+            ],
+            "install_type": "git-clone",
+            "description": "Send images to the pod."
+        },
+        {
+            "author": "azazeal04",
+            "title": "ComfyUI-Styles",
+            "reference": "https://github.com/azazeal04/ComfyUI-Styles",
+            "files": [
+                "https://github.com/azazeal04/ComfyUI-Styles"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes:Anime_Styler, Fantasy_Styler, Gothic_Styler, Line_Art_Styler, Movie_Poster_Styler, Punk_Styler, Travel_Poster_Styler. This extension offers 8 art style nodes, each of which includes approximately 50 individual style variations.\n\nNOTE: Due to the dynamic nature of node name definitions, ComfyUI-Manager cannot recognize the node list from this extension. The Missing nodes and Badge features are not available for this extension.\nNOTE: This extension is removed. Users who were previously using this node should install ComfyUI-styles-all instead."
+        },
+        {
+            "author": "hnmr293",
+            "title": "ComfyUI-nodes-hnmr",
+            "reference": "https://github.com/hnmr293/ComfyUI-nodes-hnmr",
+            "files": [
+                "https://github.com/hnmr293/ComfyUI-nodes-hnmr"
+            ],
+            "install_type": "git-clone",
+            "description": "Provide various custom nodes for Latent, Sampling, Model, Loader, Image, Text"
+        },
+        {
+            "author": "bvhari",
+            "title": "ComfyUI_PerpNeg [WIP]",
+            "reference": "https://github.com/bvhari/ComfyUI_PerpNeg",
+            "files": [
+                "https://github.com/bvhari/ComfyUI_PerpNeg"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes: KSampler (Advanced + Perp-Neg). Implementation of [a/Perp-Neg](https://perp-neg.github.io/)\nIncludes Tonemap and CFG Rescale optionsComfyUI custom node to convert latent to RGB.[w/WARNING: Experimental code, might have incompatibilities and edge cases.]\nNOTE: In the latest version of ComfyUI, this extension is included as built-in."
+        },
+        {
+            "author": "laksjdjf",
+            "title": "IPAdapter-ComfyUI",
+            "reference": "https://github.com/laksjdjf/IPAdapter-ComfyUI",
+            "files": [
+                "https://github.com/laksjdjf/IPAdapter-ComfyUI"
+            ],
+            "install_type": "git-clone",
+            "description": "This custom nodes provides loader of the IP-Adapter model.[w/NOTE: To use this extension node, you need to download the [a/ip-adapter_sd15.bin](https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15.bin) file and place it in the %%**custom_nodes/IPAdapter-ComfyUI/models**%% directory. Additionally, you need to download the 'Clip vision model' from the 'Install models' menu as well.]\nNOTE: Use ComfyUI_IPAdapter_plus instead of this."
+        },
+        {
+            "author": "RockOfFire",
+            "title": "CR Animation Nodes",
+            "reference": "https://github.com/RockOfFire/CR_Animation_Nodes",
+            "files": [
+                "https://github.com/RockOfFire/CR_Animation_Nodes"
+            ],
+            "install_type": "git-clone",
+            "description": "A comprehensive suite of nodes to enhance your animations. These nodes include some features similar to Deforum, and also some new ideas.<BR>NOTE: This node is merged into Comfyroll Custom Nodes."
+        },
+        {
+            "author": "tkoenig89",
+            "title": "Load Image with metadata",
+            "reference": "https://github.com/tkoenig89/ComfyUI_Load_Image_With_Metadata",
+            "files": [
+                "https://github.com/tkoenig89/ComfyUI_Load_Image_With_Metadata"
+            ],
+            "install_type": "git-clone",
+            "description": "A custom node for comfy ui to read generation data from images (prompt, seed, size...). This could be used when upscaling generated images to use the original prompt and seed."
+        },
+        {
+            "author": "LucianoCirino",
+            "title": "Efficiency Nodes for ComfyUI [LEGACY]",
+            "reference": "https://github.com/LucianoCirino/efficiency-nodes-comfyui",
+            "files": [
+                "https://github.com/LucianoCirino/efficiency-nodes-comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "A collection of ComfyUI custom nodes to help streamline workflows and reduce total node count.<BR>NOTE: This repository is the original repository but is no longer maintained. Please use the forked version by jags."
+        },
+        {
+            "author": "GeLi1989",
+            "title": "roop nodes for ComfyUI",
+            "reference": "https://github.com/GeLi1989/GK-beifen-ComfyUI_roop",
+            "files": [
+                "https://github.com/GeLi1989/GK-beifen-ComfyUI_roop"
+            ],
+            "install_type": "git-clone",
+            "description": "ComfyUI nodes for the roop A1111 webui script. NOTE: Need to download <a href='https://github.com/SumDeusVitae/FaceSwap_v01/raw/main/inswapper_128.onnx' target='blank'/>model</a> to use this node. NOTE: This is removed."
+        },
+        {
+            "author": "ProDALOR",
+            "title": "comfyui_u2net",
+            "reference": "https://github.com/ProDALOR/comfyui_u2net",
+            "files": [
+                "https://github.com/ProDALOR/comfyui_u2net"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes: Load U2Net model, U2Net segmentation, To mask, Segmentation to mask, U2NetBaseNormalization, U2NetMaxNormalization. NOTE: This is removed."
+        },
+        {
+            "author": "FizzleDorf",
+            "title": "AIT",
+            "reference": "https://github.com/FizzleDorf/AIT",
+            "files": [
+                "https://github.com/FizzleDorf/AIT"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes: Load AITemplate, Load AITemplate (ControlNet), VAE Decode (AITemplate), VAE Encode (AITemplate), VAE Encode (AITemplate, Inpaint). Experimental usage of AITemplate. NOTE: This is deprecated extension. Use <b>ComfyUI-AIT</b> instead of this."
+        },
+        {
+            "author": "chenbaiyujason",
+            "title": "sc-node-comfyui",
+            "reference": "https://github.com/chenbaiyujason/sc-node-comfyui",
+            "files": [
+                "https://github.com/chenbaiyujason/sc-node-comfyui"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes for GPT interaction and text manipulation"
+        },
+        {
+            "author": "asd417",
+            "title": "CheckpointTomeLoader",
+            "reference": "https://github.com/asd417/tomeSD_for_Comfy",
+            "files": [
+                "https://github.com/ltdrdata/ComfyUI-tomeSD-installer"
+            ],
+            "install_type": "git-clone",
+            "description": "tomeSD(https://github.com/dbolya/tomesd) applied to ComfyUI stable diffusion UI using custom node. <B>Note:In vanilla ComfyUI, the TomePatchModel node is provided as a built-in feature.</B>"
+        },
+        {
+            "author": "gamert",
+            "title": "ComfyUI_tagger",
+            "reference": "https://github.com/gamert/ComfyUI_tagger",
+            "pip": ["gradio"],
+            "files": [
+                "https://github.com/gamert/ComfyUI_tagger"
+            ],
+            "install_type": "git-clone",
+            "description": "Nodes: CLIPTextEncodeTaggerDD, ImageTaggerDD. <p style='background-color: black; color: red;'>WARNING: Installing the current version is causing an issue where ComfyUI fails to start.</p>"
+        },
+        {
+            "author": "Fannovel16",
+            "title": "ControlNet Preprocessors",
+            "reference": "https://github.com/Fannovel16/comfy_controlnet_preprocessors",
+            "files": [
+                "https://github.com/Fannovel16/comfy_controlnet_preprocessors"
+            ],
+            "install_type": "git-clone",
+            "description": "ControlNet Preprocessors. (To use this extension, you need to download the required model file from <B>Install Models</B>)<BR> <p style='background-color: black; color: red;'>NOTE: Please uninstall this custom node and instead install 'ComfyUI's ControlNet Auxiliary Preprocessors' from the default channel.<BR>To use nodes belonging to <B>controlnet v1</b> such as <B>Canny_Edge_Preprocessor, MIDAS_Depth_Map_Preprocessor, Uniformer_SemSegPreprocessor, etc.</B>, you need to copy the <font color='white'>config.yaml.example</font> file to <font color='white'>config.yaml</font> and change <font color='white'>skip_v1: True</font> to <font color='white'>skip_v1: False</font>.</p>"
+        },
+        {
+            "author": "comfyanonymous",
+            "title": "ComfyUI_experiments/sampler_tonemap",
+            "reference": "https://github.com/comfyanonymous/ComfyUI_experiments",
+            "files": [
+                "https://github.com/comfyanonymous/ComfyUI_experiments/raw/master/sampler_tonemap.py"
+            ],
+            "install_type": "copy",
+            "description": "ModelSamplerTonemapNoiseTest a node that makes the sampler use a simple tonemapping algorithm to tonemap the noise. It will let you use higher CFG without breaking the image. To using higher CFG lower the multiplier value. Similar to Dynamic Thresholding extension of A1111. "
+        },
+        {
+            "author": "comfyanonymous",
+            "title": "ComfyUI_experiments/sampler_rescalecfg",
+            "reference": "https://github.com/comfyanonymous/ComfyUI_experiments",
+            "files": [
+                "https://github.com/comfyanonymous/ComfyUI_experiments/raw/master/sampler_rescalecfg.py"
+            ],
+            "install_type": "copy",
+            "description": "RescaleClassifierFreeGuidance improves the problem of images being degraded by high CFG.To using higher CFG lower the multiplier value. Similar to Dynamic Thresholding extension of A1111. (<a href='https://arxiv.org/abs/2305.08891'/>reference paper</a>)<p style='background-color: black; color: red;'>It is recommended to use the integrated custom nodes in the default channel for update support rather than installing individual nodes.</p>"
+        },
+        {
+            "author": "comfyanonymous",
+            "title": "ComfyUI_experiments/advanced_model_merging",
+            "reference": "https://github.com/comfyanonymous/ComfyUI_experiments",
+            "files": [
+                "https://github.com/comfyanonymous/ComfyUI_experiments/raw/master/advanced_model_merging.py"
+            ],
+            "install_type": "copy",
+            "description": "This provides a detailed model merge feature based on block weight. ModelMergeBlock, in vanilla ComfyUI, allows for adjusting the ratios of input/middle/output layers, but this node provides ratio adjustments for all blocks within each layer.<p style='background-color: black; color: red;'>It is recommended to use the integrated custom nodes in the default channel for update support rather than installing individual nodes.</p>"
+        },
+        {
+            "author": "comfyanonymous",
+            "title": "ComfyUI_experiments/sdxl_model_merging",
+            "reference": "https://github.com/comfyanonymous/ComfyUI_experiments",
+            "files": [
+                "https://github.com/comfyanonymous/ComfyUI_experiments/raw/master/sdxl_model_merging.py"
+            ],
+            "install_type": "copy",
+            "description": "These nodes provide the capability to merge SDXL base models.<p style='background-color: black; color: red;'>It is recommended to use the integrated custom nodes in the default channel for update support rather than installing individual nodes.</p>"
+        },
+        {
+            "author": "comfyanonymous",
+            "title": "ComfyUI_experiments/reference_only",
+            "reference": "https://github.com/comfyanonymous/ComfyUI_experiments",
+            "files": [
+                "https://github.com/comfyanonymous/ComfyUI_experiments/raw/master/reference_only.py"
+            ],
+            "install_type": "copy",
+            "description": "This node provides functionality corresponding to Reference only in Controlnet.<p style='background-color: black; color: red;'>It is recommended to use the integrated custom nodes in the default channel for update support rather than installing individual nodes.</p>"
+        }
+    ]
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-Manager/node_db/legacy/extension-node-map.json b/custom_nodes/ComfyUI-Manager/node_db/legacy/extension-node-map.json
new file mode 100644
index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b
--- /dev/null
+++ b/custom_nodes/ComfyUI-Manager/node_db/legacy/extension-node-map.json
@@ -0,0 +1 @@
+{}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-Manager/node_db/legacy/model-list.json b/custom_nodes/ComfyUI-Manager/node_db/legacy/model-list.json
new file mode 100644
index 0000000000000000000000000000000000000000..1f870f11f31e0ea8162e386a9e61ad0f6ddb64d4
--- /dev/null
+++ b/custom_nodes/ComfyUI-Manager/node_db/legacy/model-list.json
@@ -0,0 +1,148 @@
+{
+  "models": [
+    {
+      "name": "Inswapper-fp16 (face swap) [REMOVED]",
+      "type": "insightface",
+      "base": "inswapper",
+      "save_path": "insightface",
+      "description": "Checkpoint of the insightface swapper model\n(used by ComfyUI-FaceSwap, comfyui-reactor-node, CharacterFaceSwap,\nComfyUI roop and comfy_mtb)",
+      "reference": "https://github.com/facefusion/facefusion-assets",
+      "filename": "inswapper_128_fp16.onnx",
+      "url": "https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128_fp16.onnx",
+      "size": "277.7MB"
+    },
+    {
+      "name": "Inswapper (face swap) [REMOVED]",
+      "type": "insightface",
+      "base": "inswapper",
+      "save_path": "insightface",
+      "description": "Checkpoint of the insightface swapper model\n(used by ComfyUI-FaceSwap, comfyui-reactor-node, CharacterFaceSwap,\nComfyUI roop and comfy_mtb)",
+      "reference": "https://github.com/facefusion/facefusion-assets",
+      "filename": "inswapper_128.onnx",
+      "url": "https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128.onnx",
+      "size": "555.3MB"
+    },
+    {
+      "name": "pfg-novel-n10.pt",
+      "type": "PFG",
+      "base": "SD1.5",
+      "save_path": "custom_nodes/pfg-ComfyUI/models",
+      "description": "Pressing 'install' directly downloads the model from the pfg-ComfyUI/models extension node. (Note: Requires ComfyUI-Manager V0.24 or above)",
+      "reference": "https://huggingface.co/furusu/PFG",
+      "filename": "pfg-novel-n10.pt",
+      "url": "https://huggingface.co/furusu/PFG/resolve/main/pfg-novel-n10.pt",
+      "size": "23.6MB"
+    },
+    {
+      "name": "pfg-wd14-n10.pt",
+      "type": "PFG",
+      "base": "SD1.5",
+      "save_path": "custom_nodes/pfg-ComfyUI/models",
+      "description": "Pressing 'install' directly downloads the model from the pfg-ComfyUI/models extension node. (Note: Requires ComfyUI-Manager V0.24 or above)",
+      "reference": "https://huggingface.co/furusu/PFG",
+      "filename": "pfg-wd14-n10.pt",
+      "url": "https://huggingface.co/furusu/PFG/resolve/main/pfg-wd14-n10.pt",
+      "size": "31.5MB"
+    },
+    {
+      "name": "pfg-wd15beta2-n10.pt",
+      "type": "PFG",
+      "base": "SD1.5",
+      "save_path": "custom_nodes/pfg-ComfyUI/models",
+      "description": "Pressing 'install' directly downloads the model from the pfg-ComfyUI/models extension node. (Note: Requires ComfyUI-Manager V0.24 or above)",
+      "reference": "https://huggingface.co/furusu/PFG",
+      "filename": "pfg-wd15beta2-n10.pt",
+      "url": "https://huggingface.co/furusu/PFG/resolve/main/pfg-wd15beta2-n10.pt",
+      "size": "31.5MB"
+    },
+    {
+      "name": "shape_predictor_68_face_landmarks.dat [Face Analysis]",
+      "type": "Shape Predictor",
+      "base": "DLIB",
+      "save_path": "custom_nodes/comfyui_faceanalysis/dlib",
+      "description": "To use the Face Analysis for ComfyUI custom node, installation of this model is needed.",
+      "reference": "https://huggingface.co/matt3ounstable/dlib_predictor_recognition/tree/main",
+      "filename": "shape_predictor_68_face_landmarks.dat",
+      "url": "https://huggingface.co/matt3ounstable/dlib_predictor_recognition/resolve/main/shape_predictor_68_face_landmarks.dat",
+      "size": "99.7MB"
+    },
+    {
+      "name": "dlib_face_recognition_resnet_model_v1.dat [Face Analysis]",
+      "type": "Face Recognition",
+      "base": "DLIB",
+      "save_path": "custom_nodes/comfyui_faceanalysis/dlib",
+      "description": "To use the Face Analysis for ComfyUI custom node, installation of this model is needed.",
+      "reference": "https://huggingface.co/matt3ounstable/dlib_predictor_recognition/tree/main",
+      "filename": "dlib_face_recognition_resnet_model_v1.dat",
+      "url": "https://huggingface.co/matt3ounstable/dlib_predictor_recognition/resolve/main/dlib_face_recognition_resnet_model_v1.dat",
+      "size": "22.5MB"
+    },
+    {
+      "name": "ID-Animator/animator.ckpt",
+      "type": "ID-Animator",
+      "base": "SD1.5",
+      "save_path": "custom_nodes/comfyui_id_animator/models",
+      "description": "ID-Animator checkpoint",
+      "reference": "https://huggingface.co/spaces/ID-Animator/ID-Animator",
+      "filename": "animator.ckpt",
+      "url": "https://huggingface.co/spaces/ID-Animator/ID-Animator/resolve/main/animator.ckpt",
+      "size": "247.3MB"
+    },
+    {
+      "name": "ID-Animator/mm_sd_v15_v2.ckpt",
+      "type": "ID-Animator",
+      "base": "SD1.5",
+      "save_path": "custom_nodes/comfyui_id_animator/models/animatediff_models",
+      "description": "AnimateDiff checkpoint for ID-Animator",
+      "reference": "https://huggingface.co/spaces/ID-Animator/ID-Animator",
+      "filename": "mm_sd_v15_v2.ckpt",
+      "url": "https://huggingface.co/spaces/ID-Animator/ID-Animator/resolve/main/mm_sd_v15_v2.ckpt",
+      "size": "1.82GB"
+    },
+    {
+      "name": "ID-Animator/image_encoder",
+      "type": "ID-Animator",
+      "base": "SD1.5",
+      "save_path": "custom_nodes/comfyui_id_animator/models/image_encoder",
+      "description": "CLIP Image encoder for ID-Animator",
+      "reference": "https://huggingface.co/spaces/ID-Animator/ID-Animator",
+      "filename": "model.safetensors",
+      "url": "https://huggingface.co/spaces/ID-Animator/ID-Animator/resolve/main/image_encoder/model.safetensors",
+      "size": "2.53GB"
+    },
+    {
+      "name": "Doubiiu/ToonCrafter model checkpoint",
+      "type": "checkpoint",
+      "base": "ToonCrafter",
+      "save_path": "custom_nodes/comfyui-tooncrafter/ToonCrafter/checkpoints/tooncrafter_512_interp_v1",
+      "description": "ToonCrafter checkpoint model for ComfyUI-ToonCrafter",
+      "reference": "https://huggingface.co/Doubiiu/ToonCrafter/tree/main",
+      "filename": "model.ckpt",
+      "url": "https://huggingface.co/Doubiiu/ToonCrafter/resolve/main/model.ckpt",
+      "size": "10.5GB"
+    },
+
+    {
+      "name": "BAAI/SegGPT",
+      "type": "SegGPT",
+      "base": "SegGPT",
+      "save_path": "custom_nodes/comfyui-seggpt",
+      "description": "SegGPT",
+      "reference": "https://huggingface.co/BAAI/SegGPT",
+      "filename": "seggpt_vit_large.pth",
+      "url": "https://huggingface.co/BAAI/SegGPT/resolve/main/seggpt_vit_large.pth",
+      "size": "1.48GB"
+    },
+    {
+      "name": "kohya-ss/ControlNet-LLLite: SDXL Canny Anime",
+      "type": "controlnet",
+      "base": "SDXL",
+      "save_path": "custom_nodes/ControlNet-LLLite-ComfyUI/models",
+      "description": "An extremely compactly designed controlnet model (a.k.a. ControlNet-LLLite). Note: The model structure is highly experimental and may be subject to change in the future.",
+      "reference": "https://huggingface.co/kohya-ss/controlnet-lllite",
+      "filename": "controllllite_v01032064e_sdxl_canny_anime.safetensors",
+      "url": "https://huggingface.co/kohya-ss/controlnet-lllite/resolve/main/controllllite_v01032064e_sdxl_canny_anime.safetensors",
+      "size": "46.2MB"
+    }
+  ]
+}
diff --git a/custom_nodes/ComfyUI-Manager/node_db/tutorial/scan.sh b/custom_nodes/ComfyUI-Manager/node_db/tutorial/scan.sh
new file mode 100644
index 0000000000000000000000000000000000000000..6e3e6dafd1f031eeee2ed170b3ba1b9cf731f924
--- /dev/null
+++ b/custom_nodes/ComfyUI-Manager/node_db/tutorial/scan.sh
@@ -0,0 +1,3 @@
+#!/bin/bash
+rm ~/.tmp/dev/*.py > /dev/null 2>&1
+python ../../scanner.py ~/.tmp/tutorial
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/.github/workflows/publish.yml b/custom_nodes/ComfyUI-VideoHelperSuite/.github/workflows/publish.yml
new file mode 100644
index 0000000000000000000000000000000000000000..27bcdb11695ea19286da34cd5f36cf75155a4dbb
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/.github/workflows/publish.yml
@@ -0,0 +1,24 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+    paths:
+      - "pyproject.toml"
+
+permissions:
+  issues: write
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'Kosinkadink' }}
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@v1
+        with:
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }} ## Add your own personal access token to your Github Repository secrets and reference it here.
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/testframework/README.md b/custom_nodes/ComfyUI-VideoHelperSuite/testframework/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..93cd503563080c9c7d785d419bef7dddc4c11cd8
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/testframework/README.md
@@ -0,0 +1,5 @@
+Code to automate execution of the tests and evaluate the results.  
+Distributed as a `custom node`,  and can be installed by copying or simlinking to the `custom_nodes` directory.  
+Requires that ffprobe be available and added to the path. Note that imageio-ffmpeg does not bundle ffprobe.
+
+When installed, it adds a new sidebar tab to automate running one, or a folder of tests. This requires that the `Use new menu and workflow management` setting not be disabled
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/testframework/__init__.py b/custom_nodes/ComfyUI-VideoHelperSuite/testframework/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c75b11c164596ef873898dd843700c7164cc0932
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/testframework/__init__.py
@@ -0,0 +1,6 @@
+from . import server
+NODE_CLASS_MAPPINGS = {}
+NODE_DISPLAY_NAME_MAPPINGS = {}
+
+WEB_DIRECTORY = "./web"
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/testframework/server.py b/custom_nodes/ComfyUI-VideoHelperSuite/testframework/server.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9c20ea3eab31bab288d6089a89410ff409d43ad
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/testframework/server.py
@@ -0,0 +1,60 @@
+import subprocess
+import json
+import os
+import torch
+import shutil
+
+import server
+import folder_paths
+
+web = server.web
+
+@server.PromptServer.instance.routes.post("/VHS_test")
+async def test(request):
+    try:
+        req_data = await request.json()
+        output = req_data['output']['gifs'][0]
+        filename = output['filename']
+        typ = output['type']
+        base_args = ["ffprobe", "-v", "error", '-count_packets', "-show_entries", "stream", "-of", "json"]
+        video = folder_paths.get_annotated_filepath(f'{filename} [{typ}]')
+        vprobe = json.loads(subprocess.run(base_args + ['-select_streams', 'v:0', video],
+                             capture_output=True, check=True).stdout)['streams'][0]
+        aprobe = json.loads(subprocess.run(base_args + ['-select_streams', 'a:0', video],
+                             capture_output=True, check=True).stdout)['streams']
+        probe = {'video': vprobe}
+        if len(aprobe) > 0:
+            probe['audio'] = aprobe[0]
+        errors = []
+        compare = None
+        for test in req_data['tests']:
+            if test['type'] == 'compare':
+                compare = test
+                continue
+            key = test['key']
+            expected = test['value']
+            actual = probe[test['type']][key]
+            if expected != actual:
+                #Consider always dumping type?
+                errors.append(f'{key}: {expected} != {actual}')
+        if len(errors) == 0 and compare is not None:
+            if not os.path.exists(compare['filename']):
+                os.makedirs(os.path.split(compare['filename'])[0], exist_ok=True)
+                shutil.copy(video, compare['filename'])
+                print("Missing comparison file has been initialized from output:", os.path.abspath(compare['filename']))
+            else:
+                #NOTE: This does not include the full memory optimizations of VHS
+                #Tests should be small
+                #TODO: Figure out way to do opacity comparison. May need to do blending in python
+                #(easy, but slower and more memory intensive)
+                diff = subprocess.run(['ffmpeg', '-v', 'error', '-i', video, '-i', compare['filename'], '-filter_complex', 'blend=all_mode=grainextract', '-pix_fmt', 'rgb24', '-f', 'rawvideo', '-'], stdout=subprocess.PIPE, check=True).stdout
+                diff = torch.frombuffer(diff, dtype=torch.uint8).to(dtype=torch.float32).div_(255)
+                #diff = diff.reshape((-1,4))
+                d = (diff-0.5).abs().sum()/diff.size(0)
+                if d > compare['tolerance']:
+                    errors.append(f'Similarity is outside specified tolerance: {d}')
+                else:
+                    print('d:', d)
+        return web.json_response(errors)
+    except Exception as e:
+        return web.json_response(str(e))
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/testframework/web/js/testRunner.js b/custom_nodes/ComfyUI-VideoHelperSuite/testframework/web/js/testRunner.js
new file mode 100644
index 0000000000000000000000000000000000000000..521a2204b9a2a3202441818461231872ea8a15c8
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/testframework/web/js/testRunner.js
@@ -0,0 +1,96 @@
+
+import {app} from "../../../scripts/app.js";
+import {api} from "../../../scripts/api.js";
+
+let watched_nodes = {}
+let resolve = undefined
+let testURL = api.apiURL("/VHS_test")
+let errors = []
+api.addEventListener("executed", async function ({detail}) {
+    if (watched_nodes && watched_nodes[detail?.node]) {
+        if (detail?.output?.unfinished_batch) {
+            return
+        }
+        let requestBody = {tests: watched_nodes[detail.node], output: detail.output}
+        try {
+            let req = await fetch(api.apiURL("/VHS_test"),
+                {method: "POST", body: JSON.stringify(requestBody)});
+            let testResult = await req.json()
+            if (testResult.length != 0) {
+                errors.push(testResult)
+            }
+        } catch(e) {
+            errors.push(e)
+        }
+        if (!(watched_nodes.length -= 1)) {
+            resolve()
+        }
+    }
+});
+
+const workflowService = app.extensionManager.workflow
+
+async function runTest(file) {
+    if (!file?.name?.endsWith(".json")) {
+        return false
+    }
+    let workflow = JSON.parse(await file.text())
+    await app.loadGraphData(workflow)
+    //NOTE: API is not used so workflow data is actually processed
+    watched_nodes = workflow.tests
+    errors = []
+    let p = new Promise((r) => resolve = r)
+    await app.queuePrompt()
+    //block until execution completes
+    await p
+    watched_nodes = {}
+    if (errors.length > 0) {
+        app.ui.dialog.show("Failed " + errors.length + " tests:\n" + errors)
+        return true
+    }
+    await workflowService.closeWorkflow(workflowService.activeWorkflow, {warnIfUnsaved: false})
+    return false
+}
+let iconOverride = document.createElement("style")
+iconOverride.innerHTML = `.VHSTestIcon:before {content: '🧪';}`
+document.body.append(iconOverride)
+
+let testSidebar = {id: 'VHStest', title: 'VHS Test', icon: 'VHSTestIcon', type: 'custom',
+    render: (e) => {
+        e.innerHTML = `Select a folder containing tests
+        <input>
+        Or select a single test
+        <input>
+            `
+
+        const folderInput = e.children[0]
+        const fileInput = e.children[1]
+        Object.assign(folderInput, {
+            type: "file",
+            webkitdirectory: true,
+            onchange: async function() {
+                const startTime = Date.now()
+                let failedTests = false
+                for(const file of this.files) {
+                    failedTests ||= await runTest(file)
+                }
+                this.value=""
+                if (!failedTests) {
+                    console.log("All tests passed in " + ((Date.now() - startTime)/1000) + "s")
+                }
+            },
+        });
+        Object.assign(fileInput, {
+            type: "file",
+            accept: ".json",
+            onchange: async function() {
+                if (this.files.length) {
+                    if(!(await runTest(this.files[0]))) {
+                        console.log("Test complete")
+                    }
+                    this.value=""
+                }
+            },
+        });
+    }}
+app.extensionManager.registerSidebarTab(testSidebar)
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/tests/README.md b/custom_nodes/ComfyUI-VideoHelperSuite/tests/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..ec025a7d169c7783f34523c63a0a6ae9a2e00fdb
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/tests/README.md
@@ -0,0 +1 @@
+Workflows for automated testing of VHS. Most include an additional tests key to check the properties or perform comparisons on node outputs
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/tests/audio.json b/custom_nodes/ComfyUI-VideoHelperSuite/tests/audio.json
new file mode 100644
index 0000000000000000000000000000000000000000..cae3d766731f7fe3ae4866f6fa8c0cc86a2ca46d
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/tests/audio.json
@@ -0,0 +1,313 @@
+{
+  "id": "07b812b5-5037-4878-90bc-32d3a1f36619",
+  "revision": 0,
+  "last_node_id": 7,
+  "last_link_id": 5,
+  "nodes": [
+    {
+      "id": 5,
+      "type": "VHS_VideoCombine",
+      "pos": [
+        732,
+        -23
+      ],
+      "size": [
+        210,
+        334
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 2
+        },
+        {
+          "name": "audio",
+          "shape": 7,
+          "type": "AUDIO",
+          "link": 3
+        },
+        {
+          "name": "meta_batch",
+          "shape": 7,
+          "type": "VHS_BatchManager",
+          "link": null
+        },
+        {
+          "name": "vae",
+          "shape": 7,
+          "type": "VAE",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "Filenames",
+          "type": "VHS_FILENAMES",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_VideoCombine"
+      },
+      "widgets_values": {
+        "frame_rate": 8,
+        "loop_count": 0,
+        "filename_prefix": "AnimateDiff",
+        "format": "video/webm",
+        "pix_fmt": "yuv420p",
+        "crf": 20,
+        "save_metadata": true,
+        "trim_to_audio": false,
+        "pingpong": false,
+        "save_output": false,
+        "videopreview": {
+          "hidden": false,
+          "paused": false,
+          "params": {}
+        }
+      }
+    },
+    {
+      "id": 6,
+      "type": "VHS_VideoCombine",
+      "pos": [
+        503,
+        363
+      ],
+      "size": [
+        210,
+        334
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 4
+        },
+        {
+          "name": "audio",
+          "shape": 7,
+          "type": "AUDIO",
+          "link": 5
+        },
+        {
+          "name": "meta_batch",
+          "shape": 7,
+          "type": "VHS_BatchManager",
+          "link": null
+        },
+        {
+          "name": "vae",
+          "shape": 7,
+          "type": "VAE",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "Filenames",
+          "type": "VHS_FILENAMES",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_VideoCombine"
+      },
+      "widgets_values": {
+        "frame_rate": 8,
+        "loop_count": 0,
+        "filename_prefix": "AnimateDiff",
+        "format": "video/h264-mp4",
+        "pix_fmt": "yuv420p",
+        "crf": 19,
+        "save_metadata": true,
+        "trim_to_audio": false,
+        "pingpong": false,
+        "save_output": false,
+        "videopreview": {
+          "hidden": false,
+          "paused": false,
+          "params": {}
+        }
+      }
+    },
+    {
+      "id": 4,
+      "type": "VHS_LoadVideoPath",
+      "pos": [
+        29,
+        16
+      ],
+      "size": [
+        221.27618408203125,
+        413.1552734375
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "meta_batch",
+          "shape": 7,
+          "type": "VHS_BatchManager",
+          "link": null
+        },
+        {
+          "name": "vae",
+          "shape": 7,
+          "type": "VAE",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            2,
+            4
+          ]
+        },
+        {
+          "name": "frame_count",
+          "type": "INT",
+          "links": null
+        },
+        {
+          "name": "audio",
+          "type": "AUDIO",
+          "links": [
+            3
+          ]
+        },
+        {
+          "name": "video_info",
+          "type": "VHS_VIDEOINFO",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_LoadVideoPath"
+      },
+      "widgets_values": {
+        "video": "input/bigbuckbunny.mp4",
+        "force_rate": 8,
+        "custom_width": 0,
+        "custom_height": 0,
+        "frame_load_cap": 30,
+        "skip_first_frames": 0,
+        "select_every_nth": 1,
+        "format": "AnimateDiff",
+        "videopreview": {
+          "hidden": false,
+          "paused": false,
+          "params": {
+            "filename": "input/bigbuckbunny.mp4",
+            "type": "path",
+            "format": "video/mp4",
+            "force_rate": 8,
+            "custom_width": 0,
+            "custom_height": 0,
+            "frame_load_cap": 30,
+            "skip_first_frames": 0,
+            "select_every_nth": 1
+          }
+        }
+      }
+    },
+    {
+      "id": 7,
+      "type": "VHS_LoadAudio",
+      "pos": [
+        83,
+        564
+      ],
+      "size": [
+        218.93820190429688,
+        126
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "audio",
+          "type": "AUDIO",
+          "links": [
+            5
+          ]
+        },
+        {
+          "name": "duration",
+          "type": "FLOAT",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_LoadAudio"
+      },
+      "widgets_values": {
+        "audio_file": "input/bigbuckbunny.mp4",
+        "seek_seconds": 0,
+        "duration": 5
+      }
+    }
+  ],
+  "links": [
+    [
+      2,
+      4,
+      0,
+      5,
+      0,
+      "IMAGE"
+    ],
+    [
+      3,
+      4,
+      2,
+      5,
+      1,
+      "AUDIO"
+    ],
+    [
+      4,
+      4,
+      0,
+      6,
+      0,
+      "IMAGE"
+    ],
+    [
+      5,
+      7,
+      0,
+      6,
+      1,
+      "AUDIO"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "frontendVersion": "1.25.0",
+    "VHS_latentpreview": true,
+    "VHS_latentpreviewrate": 0,
+    "VHS_MetadataImage": true,
+    "VHS_KeepIntermediate": true
+  },
+  "version": 0.4,
+  "tests": {
+      "5": [{"type": "audio", "key": "nb_read_packets", "value": "177"}],
+      "6": [{"type": "audio", "key": "nb_read_packets", "value": "176"}],
+      "length": 2
+  }
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/tests/batch4x4.json b/custom_nodes/ComfyUI-VideoHelperSuite/tests/batch4x4.json
new file mode 100644
index 0000000000000000000000000000000000000000..34b9647329642a9e5ca233a60b8bbf2ca8f72aca
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/tests/batch4x4.json
@@ -0,0 +1,535 @@
+{
+  "last_node_id": 18,
+  "last_link_id": 28,
+  "nodes": [
+    {
+      "id": 16,
+      "type": "SolidMask",
+      "pos": [
+        38,
+        1066
+      ],
+      "size": {
+        "0": 315,
+        "1": 106
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": [
+            18
+          ],
+          "slot_index": 0,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "SolidMask"
+      },
+      "widgets_values": [
+        1,
+        512,
+        512
+      ]
+    },
+    {
+      "id": 15,
+      "type": "ImageCompositeMasked",
+      "pos": [
+        412.0800030517579,
+        591.4099975585939
+      ],
+      "size": {
+        "0": 315,
+        "1": 146
+      },
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "destination",
+          "type": "IMAGE",
+          "link": 16
+        },
+        {
+          "name": "source",
+          "type": "IMAGE",
+          "link": 17
+        },
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 18
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            19
+          ],
+          "slot_index": 0,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ImageCompositeMasked"
+      },
+      "widgets_values": [
+        0,
+        0,
+        false
+      ]
+    },
+    {
+      "id": 18,
+      "type": "VHS_BatchManager",
+      "pos": [
+        329.8000881958008,
+        -87.40008056640622
+      ],
+      "size": {
+        "0": 315,
+        "1": 58
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "VHS_BatchManager",
+          "type": "VHS_BatchManager",
+          "links": [
+            24,
+            25,
+            26,
+            27
+          ],
+          "slot_index": 0,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_BatchManager"
+      },
+      "widgets_values": {
+        "frames_per_batch": 4,
+        "count": 2
+      }
+    },
+    {
+      "id": 10,
+      "type": "VHS_LoadVideoPath",
+      "pos": [
+        24,
+        102
+      ],
+      "size": [
+        320,
+        420.7188019966722
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "meta_batch",
+          "type": "VHS_BatchManager",
+          "link": 25,
+          "slot_index": 0
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            16,
+            22
+          ],
+          "slot_index": 0,
+          "shape": 3
+        },
+        {
+          "name": "frame_count",
+          "type": "INT",
+          "links": null,
+          "shape": 3
+        },
+        {
+          "name": "audio",
+          "type": "AUDIO",
+          "links": [
+            28
+          ],
+          "slot_index": 2,
+          "shape": 3
+        },
+        {
+          "name": "video_info",
+          "type": "VHS_VIDEOINFO",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_LoadVideoPath"
+      },
+      "widgets_values": {
+        "video": "input/bigbuckbunny.mp4",
+        "force_rate": 8,
+        "force_size": "512x?",
+        "custom_width": 512,
+        "custom_height": 512,
+        "frame_load_cap": 16,
+        "skip_first_frames": 64,
+        "select_every_nth": 1,
+        "videopreview": {
+          "hidden": false,
+          "paused": false,
+          "params": {
+            "frame_load_cap": 16,
+            "skip_first_frames": 64,
+            "force_rate": 8,
+            "filename": "input/bigbuckbunny.mp4",
+            "type": "path",
+            "format": "video/mp4",
+            "select_every_nth": 1,
+            "force_size": "512x?"
+          }
+        }
+      }
+    },
+    {
+      "id": 14,
+      "type": "VHS_LoadVideoPath",
+      "pos": [
+        26,
+        598
+      ],
+      "size": [
+        320,
+        444.7188019966722
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "meta_batch",
+          "type": "VHS_BatchManager",
+          "link": 26,
+          "slot_index": 0
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            17
+          ],
+          "slot_index": 0,
+          "shape": 3
+        },
+        {
+          "name": "frame_count",
+          "type": "INT",
+          "links": null,
+          "shape": 3
+        },
+        {
+          "name": "audio",
+          "type": "VHS_AUDIO",
+          "links": [],
+          "slot_index": 2,
+          "shape": 3
+        },
+        {
+          "name": "video_info",
+          "type": "VHS_VIDEOINFO",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_LoadVideoPath"
+      },
+      "widgets_values": {
+        "video": "input/bigbuckbunny.mp4",
+        "force_rate": 8,
+        "force_size": "Custom Width",
+        "custom_width": 384,
+        "custom_height": 512,
+        "frame_load_cap": 16,
+        "skip_first_frames": 64,
+        "select_every_nth": 1,
+        "videopreview": {
+          "hidden": false,
+          "paused": false,
+          "params": {
+            "frame_load_cap": 16,
+            "skip_first_frames": 64,
+            "force_rate": 8,
+            "filename": "input/bigbuckbunny.mp4",
+            "type": "path",
+            "format": "video/mp4",
+            "select_every_nth": 1,
+            "force_size": "512x?"
+          }
+        }
+      }
+    },
+    {
+      "id": 11,
+      "type": "VHS_VideoCombine",
+      "pos": [
+        762,
+        646
+      ],
+      "size": [
+        320,
+        492.75
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 19
+        },
+        {
+          "name": "audio",
+          "type": "AUDIO",
+          "link": 28
+        },
+        {
+          "name": "meta_batch",
+          "type": "VHS_BatchManager",
+          "link": 27,
+          "slot_index": 2
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "Filenames",
+          "type": "VHS_FILENAMES",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_VideoCombine"
+      },
+      "widgets_values": {
+        "frame_rate": 8,
+        "loop_count": 0,
+        "filename_prefix": "AnimateDiff",
+        "format": "video/h264-mp4",
+        "pix_fmt": "yuv420p",
+        "crf": 19,
+        "save_metadata": true,
+        "pingpong": false,
+        "save_output": false,
+        "videopreview": {
+          "hidden": false,
+          "paused": false,
+          "params": {
+            "filename": "AnimateDiff_00005-audio.mp4",
+            "subfolder": "",
+            "type": "temp",
+            "format": "video/h264-mp4",
+            "frame_rate": 8
+          }
+        }
+      }
+    },
+    {
+      "id": 17,
+      "type": "VHS_VideoCombine",
+      "pos": [
+        756,
+        100
+      ],
+      "size": [
+        320,
+        492.75
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 22
+        },
+        {
+          "name": "audio",
+          "type": "VHS_AUDIO",
+          "link": null
+        },
+        {
+          "name": "meta_batch",
+          "type": "VHS_BatchManager",
+          "link": 24,
+          "slot_index": 2
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "Filenames",
+          "type": "VHS_FILENAMES",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_VideoCombine"
+      },
+      "widgets_values": {
+        "frame_rate": 8,
+        "loop_count": 0,
+        "filename_prefix": "AnimateDiff",
+        "format": "video/webm",
+        "pix_fmt": "yuv420p",
+        "crf": 20,
+        "save_metadata": true,
+        "pingpong": false,
+        "save_output": false,
+        "videopreview": {
+          "hidden": false,
+          "paused": false,
+          "params": {
+            "filename": "AnimateDiff_00004.webm",
+            "subfolder": "",
+            "type": "temp",
+            "format": "video/webm",
+            "frame_rate": 8
+          }
+        }
+      }
+    }
+  ],
+  "links": [
+    [
+      16,
+      10,
+      0,
+      15,
+      0,
+      "IMAGE"
+    ],
+    [
+      17,
+      14,
+      0,
+      15,
+      1,
+      "IMAGE"
+    ],
+    [
+      18,
+      16,
+      0,
+      15,
+      2,
+      "MASK"
+    ],
+    [
+      19,
+      15,
+      0,
+      11,
+      0,
+      "IMAGE"
+    ],
+    [
+      22,
+      10,
+      0,
+      17,
+      0,
+      "IMAGE"
+    ],
+    [
+      24,
+      18,
+      0,
+      17,
+      2,
+      "VHS_BatchManager"
+    ],
+    [
+      25,
+      18,
+      0,
+      10,
+      0,
+      "VHS_BatchManager"
+    ],
+    [
+      26,
+      18,
+      0,
+      14,
+      0,
+      "VHS_BatchManager"
+    ],
+    [
+      27,
+      18,
+      0,
+      11,
+      2,
+      "VHS_BatchManager"
+    ],
+    [
+      28,
+      10,
+      2,
+      11,
+      1,
+      "VHS_AUDIO"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4,
+  "tests": {
+      "17": [{"type": "video", "key": "nb_read_packets", "value": "16"}],
+      "11": [{"type": "video", "key": "nb_read_packets", "value": "16"},
+          {"type": "compare", "filename": "custom_nodes/ComfyUI-VideoHelperSuite/tests/outputs/batch.mp4", "tolerance": 0.02}
+      ],
+      "length": 1
+  }
+}
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/tests/converted-format-input.json b/custom_nodes/ComfyUI-VideoHelperSuite/tests/converted-format-input.json
new file mode 100644
index 0000000000000000000000000000000000000000..e9722580df540b89a4b9185260a8de180ddd63aa
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/tests/converted-format-input.json
@@ -0,0 +1,253 @@
+{
+  "last_node_id": 19,
+  "last_link_id": 20,
+  "nodes": [
+    {
+      "id": 18,
+      "type": "PrimitiveNode",
+      "pos": [
+        318,
+        618
+      ],
+      "size": [
+        210,
+        82
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "INT",
+          "type": "INT",
+          "links": [
+            18
+          ],
+          "widget": {
+            "name": "crf"
+          }
+        }
+      ],
+      "properties": {
+        "Run widget replace on values": false
+      },
+      "widgets_values": [
+        60,
+        "fixed"
+      ]
+    },
+    {
+      "id": 11,
+      "type": "LoadImage",
+      "pos": [
+        260.4530029296875,
+        233.2003173828125
+      ],
+      "size": [
+        315,
+        314
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            19
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "example.png",
+        "image"
+      ]
+    },
+    {
+      "id": 17,
+      "type": "VHS_VideoCombine",
+      "pos": [
+        733.3749389648438,
+        338.28924560546875
+      ],
+      "size": [
+        222.91415405273438,
+        522.9141845703125
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 19
+        },
+        {
+          "name": "audio",
+          "type": "AUDIO",
+          "link": null,
+          "shape": 7
+        },
+        {
+          "name": "meta_batch",
+          "type": "VHS_BatchManager",
+          "link": null,
+          "shape": 7
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": null,
+          "shape": 7
+        },
+        {
+          "name": "crf",
+          "type": "INT",
+          "link": 18,
+          "widget": {
+            "name": "crf"
+          }
+        },
+        {
+          "name": "pix_fmt",
+          "type": [
+            "yuv420p",
+            "yuv420p10le"
+          ],
+          "link": 20,
+          "widget": {
+            "name": "pix_fmt"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "Filenames",
+          "type": "VHS_FILENAMES",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_VideoCombine"
+      },
+      "widgets_values": {
+        "frame_rate": 8,
+        "loop_count": 0,
+        "filename_prefix": "AnimateDiff",
+        "format": "video/h264-mp4",
+        "pix_fmt": "yuv420p10le",
+        "crf": 60,
+        "save_metadata": true,
+        "pingpong": false,
+        "save_output": false,
+        "videopreview": {
+          "hidden": false,
+          "paused": false,
+          "params": {
+            "filename": "AnimateDiff_00001.mp4",
+            "subfolder": "",
+            "type": "temp",
+            "format": "video/h264-mp4",
+            "frame_rate": 8
+          },
+          "muted": false
+        }
+      }
+    },
+    {
+      "id": 19,
+      "type": "PrimitiveNode",
+      "pos": [
+        300,
+        760
+      ],
+      "size": [
+        290,
+        110
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "COMBO",
+          "type": "COMBO",
+          "links": [
+            20
+          ],
+          "widget": {
+            "name": "pix_fmt"
+          }
+        }
+      ],
+      "properties": {
+        "Run widget replace on values": false
+      },
+      "widgets_values": [
+        "yuv420p10le",
+        "fixed",
+        ""
+      ]
+    }
+  ],
+  "links": [
+    [
+      18,
+      18,
+      0,
+      17,
+      4,
+      "INT"
+    ],
+    [
+      19,
+      11,
+      0,
+      17,
+      0,
+      "IMAGE"
+    ],
+    [
+      20,
+      19,
+      0,
+      17,
+      5,
+      [
+        "yuv420p",
+        "yuv420p10le"
+      ]
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.8264462809917354,
+      "offset": [
+        45.8650452880864,
+        -157.46987175292935
+      ]
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+  },
+  "version": 0.4,
+  "tests": {
+      "17": [{"type": "video", "key": "pix_fmt", "value": "yuv420p10le"}
+      ],
+      "length": 1
+  }
+}
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/tests/converted-input.json b/custom_nodes/ComfyUI-VideoHelperSuite/tests/converted-input.json
new file mode 100644
index 0000000000000000000000000000000000000000..41a93a049ea95ea9ddda92ed3a5152b55581938f
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/tests/converted-input.json
@@ -0,0 +1,307 @@
+{
+  "last_node_id": 20,
+  "last_link_id": 23,
+  "nodes": [
+    {
+      "id": 18,
+      "type": "VHS_VideoInfoLoaded",
+      "pos": [
+        424,
+        427
+      ],
+      "size": {
+        "0": 304.79998779296875,
+        "1": 106
+      },
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "video_info",
+          "type": "VHS_VIDEOINFO",
+          "link": 19
+        }
+      ],
+      "outputs": [
+        {
+          "name": "fps🟦",
+          "type": "FLOAT",
+          "links": [
+            20
+          ],
+          "slot_index": 0,
+          "shape": 3
+        },
+        {
+          "name": "frame_count🟦",
+          "type": "INT",
+          "links": null,
+          "shape": 3
+        },
+        {
+          "name": "duration🟦",
+          "type": "FLOAT",
+          "links": null,
+          "shape": 3
+        },
+        {
+          "name": "width🟦",
+          "type": "INT",
+          "links": null,
+          "shape": 3
+        },
+        {
+          "name": "height🟦",
+          "type": "INT",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_VideoInfoLoaded"
+      },
+      "widgets_values": {}
+    },
+    {
+      "id": 17,
+      "type": "VHS_VideoCombine",
+      "pos": [
+        783,
+        223
+      ],
+      "size": [
+        315,
+        286
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 21
+        },
+        {
+          "name": "audio",
+          "type": "VHS_AUDIO",
+          "link": null
+        },
+        {
+          "name": "meta_batch",
+          "type": "VHS_BatchManager",
+          "link": null
+        },
+        {
+          "name": "frame_rate",
+          "type": "FLOAT",
+          "link": 20,
+          "widget": {
+            "name": "frame_rate"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "Filenames",
+          "type": "VHS_FILENAMES",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_VideoCombine"
+      },
+      "widgets_values": {
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+        "loop_count": 0,
+        "filename_prefix": "AnimateDiff",
+        "format": "video/h264-mp4",
+        "pix_fmt": "yuv420p",
+        "crf": 19,
+        "save_metadata": true,
+        "pingpong": false,
+        "save_output": false,
+        "videopreview": {
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+          "paused": false,
+          "params": {
+            "filename": "AnimateDiff_00001.mp4",
+            "subfolder": "",
+            "type": "temp",
+            "format": "video/h264-mp4"
+          }
+        }
+      }
+    },
+    {
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+      "type": "VHS_LoadVideoPath",
+      "pos": [
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+        230
+      ],
+      "size": [
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+        449.5
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+      "flags": {},
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+      "inputs": [
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+          "link": null
+        },
+        {
+          "name": "frame_load_cap",
+          "type": "INT",
+          "link": 23,
+          "slot_index": 1,
+          "widget": {
+            "name": "frame_load_cap"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            21
+          ],
+          "slot_index": 0,
+          "shape": 3
+        },
+        {
+          "name": "frame_count",
+          "type": "INT",
+          "links": null,
+          "shape": 3
+        },
+        {
+          "name": "audio",
+          "type": "VHS_AUDIO",
+          "links": null,
+          "shape": 3
+        },
+        {
+          "name": "video_info",
+          "type": "VHS_VIDEOINFO",
+          "links": [
+            19
+          ],
+          "slot_index": 3,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_LoadVideoPath"
+      },
+      "widgets_values": {
+        "video": "input/leader.webm",
+        "force_rate": 0,
+        "force_size": "Disabled",
+        "custom_width": 512,
+        "custom_height": 512,
+        "frame_load_cap": 64,
+        "skip_first_frames": 0,
+        "select_every_nth": 1,
+        "videopreview": {
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+          "paused": false,
+          "params": {
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+            "skip_first_frames": 0,
+            "force_rate": 0,
+            "filename": "input/leader.webm",
+            "type": "path",
+            "format": "video/webm",
+            "select_every_nth": 1
+          }
+        }
+      }
+    },
+    {
+      "id": 20,
+      "type": "PrimitiveNode",
+      "pos": [
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+        170
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+      "size": {
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+        "1": 80
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "INT",
+          "type": "INT",
+          "links": [
+            23
+          ],
+          "widget": {
+            "name": "frame_load_cap"
+          }
+        }
+      ],
+      "properties": {
+        "Run widget replace on values": false
+      },
+      "widgets_values": [
+        64,
+        "fixed"
+      ]
+    }
+  ],
+  "links": [
+    [
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+      16,
+      3,
+      18,
+      0,
+      "VHS_VIDEOINFO"
+    ],
+    [
+      20,
+      18,
+      0,
+      17,
+      3,
+      "FLOAT"
+    ],
+    [
+      21,
+      16,
+      0,
+      17,
+      0,
+      "IMAGE"
+    ],
+    [
+      23,
+      20,
+      0,
+      16,
+      1,
+      "INT"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4,
+  "tests": {
+      "17": [{"type": "video", "key": "width", "value": 1440},
+          {"type": "video", "key": "height", "value": 1080},
+          {"type": "video", "key": "nb_read_packets", "value": "64"}
+      ],
+      "length": 1
+  }
+}
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/tests/loop.json b/custom_nodes/ComfyUI-VideoHelperSuite/tests/loop.json
new file mode 100644
index 0000000000000000000000000000000000000000..aa5dd4ecdaac29d04b71dbf3bbc8349f2d1210af
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/tests/loop.json
@@ -0,0 +1,178 @@
+{
+  "last_node_id": 3,
+  "last_link_id": 1,
+  "nodes": [
+    {
+      "id": 1,
+      "type": "VHS_LoadVideo",
+      "pos": {
+        "0": 54,
+        "1": 89
+      },
+      "size": [
+        260,
+        460
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "meta_batch",
+          "type": "VHS_BatchManager",
+          "link": null
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            1
+          ],
+          "slot_index": 0,
+          "shape": 3
+        },
+        {
+          "name": "frame_count",
+          "type": "INT",
+          "links": null,
+          "shape": 3
+        },
+        {
+          "name": "audio",
+          "type": "VHS_AUDIO",
+          "links": null,
+          "shape": 3
+        },
+        {
+          "name": "video_info",
+          "type": "VHS_VIDEOINFO",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_LoadVideo"
+      },
+      "widgets_values": {
+        "video": "leader.webm",
+        "force_rate": 8,
+        "force_size": "Disabled",
+        "custom_width": 304,
+        "custom_height": 312,
+        "frame_load_cap": 16,
+        "skip_first_frames": 1,
+        "select_every_nth": 1,
+        "choose video to upload": "image",
+        "videopreview": {
+          "hidden": false,
+          "paused": false,
+          "params": {
+            "frame_load_cap": 16,
+            "skip_first_frames": 1,
+            "force_rate": 8,
+            "filename": "leader.webm",
+            "type": "input",
+            "format": "video/mp4",
+            "force_size": "410.4x?",
+            "select_every_nth": 1
+          }
+        }
+      }
+    },
+    {
+      "id": 3,
+      "type": "VHS_VideoCombine",
+      "pos": {
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+        "1": 222
+      },
+      "size": [
+        320,
+        550
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 1
+        },
+        {
+          "name": "audio",
+          "type": "AUDIO",
+          "link": null
+        },
+        {
+          "name": "meta_batch",
+          "type": "VHS_BatchManager",
+          "link": null
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "Filenames",
+          "type": "VHS_FILENAMES",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_VideoCombine"
+      },
+      "widgets_values": {
+        "frame_rate": 8,
+        "loop_count": 1,
+        "filename_prefix": "AnimateDiff",
+        "format": "video/h264-mp4",
+        "pix_fmt": "yuv420p",
+        "crf": 19,
+        "save_metadata": true,
+        "pingpong": false,
+        "save_output": false,
+        "videopreview": {
+          "hidden": false,
+          "paused": false,
+          "params": {
+            "filename": "AnimateDiff_00005.mp4",
+            "subfolder": "",
+            "type": "temp",
+            "format": "video/h264-mp4",
+            "frame_rate": 8
+          }
+        }
+      }
+    }
+  ],
+  "links": [
+    [
+      1,
+      1,
+      0,
+      3,
+      0,
+      "IMAGE"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4,
+  "tests": {
+      "3": [{"type": "video", "key": "nb_read_packets", "value": "32"}],
+      "length": 1
+  }
+}
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/tests/old-prores.json b/custom_nodes/ComfyUI-VideoHelperSuite/tests/old-prores.json
new file mode 100644
index 0000000000000000000000000000000000000000..17e77c1547663d39b5356ac15d74f807c69dc671
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/tests/old-prores.json
@@ -0,0 +1,423 @@
+{
+  "id": "bc7448ba-118f-4226-95ab-32227992f954",
+  "revision": 0,
+  "last_node_id": 6,
+  "last_link_id": 4,
+  "nodes": [
+    {
+      "id": 1,
+      "type": "VHS_LoadVideo",
+      "pos": [
+        54,
+        89
+      ],
+      "size": [
+        245.1999969482422,
+        492.7751770019531
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "meta_batch",
+          "shape": 7,
+          "type": "VHS_BatchManager",
+          "link": null
+        },
+        {
+          "name": "vae",
+          "shape": 7,
+          "type": "VAE",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "slot_index": 0,
+          "links": [
+            1,
+            2,
+            3,
+            4
+          ]
+        },
+        {
+          "name": "frame_count",
+          "type": "INT",
+          "links": null
+        },
+        {
+          "name": "audio",
+          "type": "AUDIO",
+          "links": null
+        },
+        {
+          "name": "video_info",
+          "type": "VHS_VIDEOINFO",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_LoadVideo"
+      },
+      "widgets_values": {
+        "video": "leader.webm",
+        "force_rate": 8,
+        "custom_width": 512,
+        "custom_height": 0,
+        "frame_load_cap": 64,
+        "skip_first_frames": 1,
+        "select_every_nth": 1,
+        "format": "AnimateDiff",
+        "choose video to upload": "image",
+        "videopreview": {
+          "hidden": false,
+          "paused": false,
+          "params": {
+            "frame_load_cap": 64,
+            "skip_first_frames": 0,
+            "force_rate": 8,
+            "filename": "leader.webm",
+            "type": "input",
+            "format": "video/mp4",
+            "select_every_nth": 1
+          }
+        }
+      }
+    },
+    {
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+      "type": "VHS_VideoCombine",
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+        136.90997314453125
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+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
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+          "type": "IMAGE",
+          "link": 2
+        },
+        {
+          "name": "audio",
+          "shape": 7,
+          "type": "AUDIO",
+          "link": null
+        },
+        {
+          "name": "meta_batch",
+          "shape": 7,
+          "type": "VHS_BatchManager",
+          "link": null
+        },
+        {
+          "name": "vae",
+          "shape": 7,
+          "type": "VAE",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "Filenames",
+          "type": "VHS_FILENAMES",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_VideoCombine"
+      },
+      "widgets_values": {
+        "frame_rate": 8,
+        "loop_count": 0,
+        "filename_prefix": "AnimateDiff",
+        "format": "video/ProRes",
+        "profile": "2",
+        "pingpong": false,
+        "save_output": false,
+        "videopreview": {
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+          "paused": false,
+          "params": {
+            "filename": "AnimateDiff_00004.mov",
+            "subfolder": "",
+            "type": "temp",
+            "format": "video/ProRes",
+            "frame_rate": 8,
+            "workflow": "AnimateDiff_00004.png"
+          }
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+      "inputs": [
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+        {
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+          "type": "AUDIO",
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+        {
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+          "shape": 7,
+          "type": "VHS_BatchManager",
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+        {
+          "name": "vae",
+          "shape": 7,
+          "type": "VAE",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "Filenames",
+          "type": "VHS_FILENAMES",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_VideoCombine"
+      },
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+        "loop_count": 0,
+        "filename_prefix": "AnimateDiff",
+        "format": "video/ProRes",
+        "profile": "1",
+        "pingpong": false,
+        "save_output": false,
+        "videopreview": {
+          "hidden": false,
+          "paused": false,
+          "params": {
+            "filename": "AnimateDiff_00001.mov",
+            "subfolder": "",
+            "type": "temp",
+            "format": "video/ProRes",
+            "frame_rate": 8,
+            "workflow": "AnimateDiff_00001.png"
+          }
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+      }
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+      "mode": 0,
+      "inputs": [
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+        {
+          "name": "audio",
+          "shape": 7,
+          "type": "AUDIO",
+          "link": null
+        },
+        {
+          "name": "meta_batch",
+          "shape": 7,
+          "type": "VHS_BatchManager",
+          "link": null
+        },
+        {
+          "name": "vae",
+          "shape": 7,
+          "type": "VAE",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "Filenames",
+          "type": "VHS_FILENAMES",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_VideoCombine"
+      },
+      "widgets_values": {
+        "frame_rate": 8,
+        "loop_count": 0,
+        "filename_prefix": "AnimateDiff",
+        "format": "video/ProRes",
+        "profile": "3",
+        "pingpong": false,
+        "save_output": false,
+        "videopreview": {
+          "hidden": false,
+          "paused": false,
+          "params": {
+            "filename": "AnimateDiff_00002.mov",
+            "subfolder": "",
+            "type": "temp",
+            "format": "video/ProRes",
+            "frame_rate": 8,
+            "workflow": "AnimateDiff_00002.png"
+          }
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+      }
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+    {
+      "id": 6,
+      "type": "VHS_VideoCombine",
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+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 4
+        },
+        {
+          "name": "audio",
+          "shape": 7,
+          "type": "AUDIO",
+          "link": null
+        },
+        {
+          "name": "meta_batch",
+          "shape": 7,
+          "type": "VHS_BatchManager",
+          "link": null
+        },
+        {
+          "name": "vae",
+          "shape": 7,
+          "type": "VAE",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "Filenames",
+          "type": "VHS_FILENAMES",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_VideoCombine"
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+      "widgets_values": {
+        "frame_rate": 8,
+        "loop_count": 0,
+        "filename_prefix": "AnimateDiff",
+        "format": "video/ProRes",
+        "profile": "4",
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+        "save_output": false,
+        "videopreview": {
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+          "paused": false,
+          "params": {
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+            "subfolder": "",
+            "type": "temp",
+            "format": "video/ProRes",
+            "frame_rate": 8
+          }
+        }
+      }
+    }
+  ],
+  "links": [
+    [
+      1,
+      1,
+      0,
+      3,
+      0,
+      "IMAGE"
+    ],
+    [
+      2,
+      1,
+      0,
+      4,
+      0,
+      "IMAGE"
+    ],
+    [
+      3,
+      1,
+      0,
+      5,
+      0,
+      "IMAGE"
+    ],
+    [
+      4,
+      1,
+      0,
+      6,
+      0,
+      "IMAGE"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "frontendVersion": "1.17.0",
+    "VHS_latentpreview": true,
+    "VHS_latentpreviewrate": 0,
+    "VHS_MetadataImage": true,
+    "VHS_KeepIntermediate": true
+  },
+  "version": 0.4,
+  "tests": {
+      "6": [{"type": "video", "key": "pix_fmt", "value": "yuv444p12le"}],
+      "length": 1
+  }
+}
\ No newline at end of file
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/tests/old-vae-conversion.json b/custom_nodes/ComfyUI-VideoHelperSuite/tests/old-vae-conversion.json
new file mode 100644
index 0000000000000000000000000000000000000000..e751918b6473cae2f38536d8433bc40b2283a3f6
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/tests/old-vae-conversion.json
@@ -0,0 +1,229 @@
+{
+  "last_node_id": 10,
+  "last_link_id": 9,
+  "nodes": [
+    {
+      "id": 7,
+      "type": "VHS_VideoCombine",
+      "pos": [
+        746,
+        309
+      ],
+      "size": [
+        320,
+        468.7188019966722
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "LATENT",
+          "link": 9
+        },
+        {
+          "name": "audio",
+          "type": "AUDIO",
+          "link": null
+        },
+        {
+          "name": "meta_batch",
+          "type": "VHS_BatchManager",
+          "link": null
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 8
+        }
+      ],
+      "outputs": [
+        {
+          "name": "Filenames",
+          "type": "VHS_FILENAMES",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_VideoCombine"
+      },
+      "widgets_values": {
+        "frame_rate": 8,
+        "loop_count": 0,
+        "filename_prefix": "AnimateDiff",
+        "format": "video/webm",
+        "crf": 20,
+        "save_metadata": true,
+        "pingpong": false,
+        "save_output": false,
+        "videopreview": {
+          "hidden": false,
+          "paused": false,
+          "params": {
+            "filename": "AnimateDiff_00001.webm",
+            "subfolder": "",
+            "type": "temp",
+            "format": "video/webm",
+            "frame_rate": 8
+          }
+        }
+      }
+    },
+    {
+      "id": 6,
+      "type": "VHS_LoadVideoPath",
+      "pos": [
+        171,
+        239
+      ],
+      "size": [
+        320,
+        420.7188019966722
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "meta_batch",
+          "type": "VHS_BatchManager",
+          "link": null
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 7,
+          "slot_index": 1
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            9
+          ],
+          "slot_index": 0,
+          "shape": 3
+        },
+        {
+          "name": "frame_count",
+          "type": "INT",
+          "links": null,
+          "shape": 3
+        },
+        {
+          "name": "audio",
+          "type": "AUDIO",
+          "links": [],
+          "slot_index": 2,
+          "shape": 3
+        },
+        {
+          "name": "video_info",
+          "type": "VHS_VIDEOINFO",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_LoadVideoPath"
+      },
+      "widgets_values": {
+        "video": "input/bigbuckbunny.mp4",
+        "force_rate": 8,
+        "force_size": "Disabled",
+        "custom_width": 512,
+        "custom_height": 512,
+        "frame_load_cap": 64,
+        "skip_first_frames": 0,
+        "select_every_nth": 1,
+        "videopreview": {
+          "hidden": false,
+          "paused": false,
+          "params": {
+            "frame_load_cap": 64,
+            "skip_first_frames": 0,
+            "force_rate": 8,
+            "filename": "input/bigbuckbunny.mp4",
+            "type": "path",
+            "format": "video/mp4",
+            "select_every_nth": 1
+          }
+        }
+      }
+    },
+    {
+      "id": 10,
+      "type": "VAELoader",
+      "pos": [
+        -42,
+        88
+      ],
+      "size": {
+        "0": 315,
+        "1": 58
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            7,
+            8
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAELoader"
+      },
+      "widgets_values": [
+        "taesd"
+      ]
+    }
+  ],
+  "links": [
+    [
+      7,
+      10,
+      0,
+      6,
+      1,
+      "VAE"
+    ],
+    [
+      8,
+      10,
+      0,
+      7,
+      3,
+      "VAE"
+    ],
+    [
+      9,
+      6,
+      0,
+      7,
+      0,
+      "LATENT"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4,
+  "tests": {
+      "7": [{"type": "video", "key": "width", "value": 1920},
+          {"type": "video", "key": "height", "value": 1080},
+          {"type": "video", "key": "nb_read_packets", "value": "64"}
+      ],
+      "length": 1
+  }
+}
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/tests/simple.json b/custom_nodes/ComfyUI-VideoHelperSuite/tests/simple.json
new file mode 100644
index 0000000000000000000000000000000000000000..4d7f8ddd68a24cc90519ecd127daae9d8ab81b85
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/tests/simple.json
@@ -0,0 +1,134 @@
+{
+  "last_node_id": 3,
+  "last_link_id": 1,
+  "nodes": [
+    {
+      "id": 1,
+      "type": "VHS_LoadVideo",
+      "pos": [
+        54,
+        89
+      ],
+      "size": [
+        235.1999969482422,
+        384.56999829610186
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            1
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "frame_count",
+          "type": "INT",
+          "links": null,
+          "shape": 3
+        },
+        {
+          "name": "audio",
+          "type": "VHS_AUDIO",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_LoadVideo"
+      },
+      "widgets_values": {
+        "video": "leader.webm",
+        "force_rate": 8,
+        "force_size": "Custom Width",
+        "custom_width": 304,
+        "custom_height": 312,
+        "frame_load_cap": 16,
+        "skip_first_frames": 1,
+        "select_every_nth": 1,
+        "choose video to upload": "image",
+        "videopreview": {
+          "hidden": false,
+          "paused": false,
+          "params": {
+            "frame_load_cap": 0,
+            "skip_first_frames": 0,
+            "force_rate": 0,
+            "filename": "leader.webm",
+            "type": "input",
+            "format": "video/mp4",
+            "force_size": "410.4x?",
+            "select_every_nth": 1
+          }
+        }
+      }
+    },
+    {
+      "id": 3,
+      "type": "VHS_VideoCombine",
+      "pos": [
+        629,
+        222
+      ],
+      "size": {
+        "0": 315,
+        "1": 250
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 1
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "Node name for S&R": "VHS_VideoCombine"
+      },
+      "widgets_values": {
+        "frame_rate": 8,
+        "loop_count": 0,
+        "filename_prefix": "AnimateDiff",
+        "format": "video/webm",
+        "pingpong": false,
+        "save_image": false,
+        "crf": 20,
+        "save_metadata": false,
+        "audio_file": "",
+        "videopreview": {
+          "hidden": false,
+          "paused": false
+        }
+      }
+    }
+  ],
+  "links": [
+    [
+      1,
+      1,
+      0,
+      3,
+      0,
+      "IMAGE"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4,
+  "tests": {
+      "3": [{"type": "video", "key": "width", "value": 304},
+          {"type": "video", "key": "height", "value": 232},
+          {"type": "compare", "filename": "custom_nodes/ComfyUI-VideoHelperSuite/tests/outputs/simple.webm", "tolerance": 0.02}
+      ],
+      "length": 1
+  }
+}
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/16bit-png.json b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/16bit-png.json
new file mode 100644
index 0000000000000000000000000000000000000000..b768bdbcfe8950cb7bde37d02444f0191ad29f51
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/16bit-png.json
@@ -0,0 +1,9 @@
+{
+    "main_pass":
+    [
+        "-n",
+        "-pix_fmt", "rgba64"
+    ],
+    "input_color_depth": "16bit",
+    "extension": "%03d.png"
+}
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/8bit-png.json b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/8bit-png.json
new file mode 100644
index 0000000000000000000000000000000000000000..88b27a75f9c979f18979ec46da8c8843a82e0759
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/8bit-png.json
@@ -0,0 +1,7 @@
+{
+    "main_pass":
+    [
+        "-n"
+    ],
+    "extension": "%03d.png"
+}
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/ProRes.json b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/ProRes.json
new file mode 100644
index 0000000000000000000000000000000000000000..eddd3acf931c5ab3486197c18ba88a3959d3e1d6
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/ProRes.json
@@ -0,0 +1,22 @@
+{
+    "main_pass":
+    [
+        "-n", "-c:v", "prores_ks",
+        "-profile:v", [["$profile"]],
+        ["profile", {
+            "lt": [[]], "1": [[]], "standard": [[]], "2": [[]], "hq": [[]], "3": [[]],
+            "4": ["has_alpha", {"True": [["-pix_fmt", "yuva444p10le"]],
+                "False": [["-pix_fmt", "yuv444p10le"]]}],
+            "4444": ["has_alpha", {"True": [["-pix_fmt", "yuva444p10le"]],
+                "False": [["-pix_fmt", "yuv444p10le"]]}],
+            "4444xq": ["has_alpha", {"True": [["-pix_fmt", "yuva444p10le"]],
+                "False": [["-pix_fmt", "yuv444p10le"]]}]
+        }],
+        "-vf", "scale=out_color_matrix=bt709",
+        "-colorspace", "bt709", "-color_primaries", "bt709", "-color_trc", "bt709"
+    ],
+    "fake_trc": "bt709",
+    "audio_pass": ["-c:a", "pcm_s16le"],
+    "extension": "mov",
+    "extra_widgets": [["profile", ["lt", "standard", "hq", "4444", "4444xq"], {"default": "hq"}]]
+}
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/av1-webm.json b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/av1-webm.json
new file mode 100644
index 0000000000000000000000000000000000000000..659ea166fb140e18109f09f3d5710363af9c2294
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/av1-webm.json
@@ -0,0 +1,16 @@
+{
+    "main_pass":
+    [
+        "-n", "-c:v", "libsvtav1",
+        "-pix_fmt", ["pix_fmt", ["yuv420p10le", "yuv420p"]],
+        "-crf", ["crf","INT", {"default": 23, "min": 0, "max": 100, "step": 1}],
+        "-vf", "scale=out_color_matrix=bt709",
+        "-color_range", "tv", "-colorspace", "bt709", "-color_primaries", "bt709", "-color_trc", "bt709"
+    ],
+    "fake_trc": "bt709",
+    "audio_pass": ["-c:a", "libopus"],
+    "input_color_depth": ["input_color_depth", ["8bit", "16bit"]],
+    "save_metadata": ["save_metadata", "BOOLEAN", {"default": true}],
+    "extension": "webm",
+    "environment": {"SVT_LOG": "1"}
+}
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/ffmpeg-gif.json b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/ffmpeg-gif.json
new file mode 100644
index 0000000000000000000000000000000000000000..3ca0865033c7dabc06176c737652f375e7badb27
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/ffmpeg-gif.json
@@ -0,0 +1,8 @@
+{
+    "main_pass":
+    [
+        "-n",
+        "-filter_complex", ["dither", ["bayer", "heckbert", "floyd_steinberg", "sierra2", "sierra2_4a", "sierra3", "burkes", "atkinson", "none"], {"default": "sierra2_4a"}, "[0:v] split [a][b]; [a] palettegen=reserve_transparent=on:transparency_color=ffffff [p]; [b][p] paletteuse=dither=$val"]
+    ],
+    "extension": "gif"
+}
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/ffv1-mkv.json b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/ffv1-mkv.json
new file mode 100644
index 0000000000000000000000000000000000000000..3248e5fc6425feeb1d55ccdb8bd8d49cb0e3401c
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/ffv1-mkv.json
@@ -0,0 +1,18 @@
+{
+    "main_pass": [
+        "-n",
+        "-c:v", "ffv1",
+        "-level", ["level", ["0", "1", "3"], {"default": "3"}],
+        "-coder", ["coder", ["0", "1", "2"], {"default": "1"}],
+        "-context", ["context", ["0", "1"], {"default": "1"}],
+        "-g", ["gop_size", "INT", {"default": 1, "min": 1, "max": 300, "step": 1}],
+        "-slices", ["slices", ["4", "6", "9", "12", "16", "20", "24", "30"], {"default": "16"}],
+        "-slicecrc", ["slicecrc", ["0", "1"], {"default": "1"}],
+        "-pix_fmt", ["pix_fmt", ["rgba64le", "bgra", "yuv420p", "yuv422p", "yuv444p", "yuva420p", "yuva422p", "yuva444p", "yuv420p10le", "yuv422p10le", "yuv444p10le", "yuv420p12le", "yuv422p12le", "yuv444p12le", "yuv420p14le", "yuv422p14le", "yuv444p14le", "yuv420p16le", "yuv422p16le", "yuv444p16le", "gray", "gray10le", "gray12le", "gray16le"], {"default": "rgba64le"}]
+    ],
+    "audio_pass": ["-c:a", "flac"],
+    "save_metadata": ["save_metadata", "BOOLEAN", {"default": true}],
+    "trim_to_audio": ["trim_to_audio", "BOOLEAN", {"default": false}],
+    "input_color_depth": "16bit",
+    "extension": "mkv"
+}
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/gifski.json b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/gifski.json
new file mode 100644
index 0000000000000000000000000000000000000000..c96b358ab0ae59dbd97c52a699c8ee9825c5d351
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/gifski.json
@@ -0,0 +1,12 @@
+{
+    "main_pass":
+    [
+        "-pix_fmt", "yuv444p",
+        "-vf", "scale=out_color_matrix=bt709:out_range=pc",
+        "-color_range", "pc"
+    ],
+    "extension": "gif",
+    "gifski_pass": [
+        "-Q", ["quality","INT", {"default": 90, "min": 1, "max": 100, "step": 1}]
+    ]
+}
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/h264-mp4.json b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/h264-mp4.json
new file mode 100644
index 0000000000000000000000000000000000000000..4aa0bd57c6a4cc65334d82e5cdaa7aadb8372321
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/h264-mp4.json
@@ -0,0 +1,15 @@
+{
+    "main_pass":
+    [
+        "-n", "-c:v", "libx264",
+        "-pix_fmt", ["pix_fmt", ["yuv420p", "yuv420p10le"]],
+        "-crf", ["crf","INT", {"default": 19, "min": 0, "max": 100, "step": 1}],
+        "-vf", "scale=out_color_matrix=bt709",
+        "-color_range", "tv", "-colorspace", "bt709", "-color_primaries", "bt709", "-color_trc", "bt709"
+    ],
+    "fake_trc": "bt709",
+    "audio_pass": ["-c:a", "aac", "-movflags", "use_metadata_tags"],
+    "save_metadata": ["save_metadata", "BOOLEAN", {"default": true}],
+    "trim_to_audio": ["trim_to_audio", "BOOLEAN", {"default": false}],
+    "extension": "mp4"
+}
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/h265-mp4.json b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/h265-mp4.json
new file mode 100644
index 0000000000000000000000000000000000000000..559c35073edf9ed74baec0eeffc56fde2b0438c5
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/h265-mp4.json
@@ -0,0 +1,17 @@
+{
+    "main_pass":
+    [
+        "-n", "-c:v", "libx265",
+        "-vtag", "hvc1",
+        "-pix_fmt", ["pix_fmt", ["yuv420p10le", "yuv420p"]],
+        "-crf", ["crf","INT", {"default": 22, "min": 0, "max": 100, "step": 1}],
+        "-preset", "medium",
+        "-x265-params", "log-level=quiet",
+        "-vf", "scale=out_color_matrix=bt709",
+        "-color_range", "tv", "-colorspace", "bt709", "-color_primaries", "bt709", "-color_trc", "bt709"
+    ],
+    "fake_trc": "bt709",
+    "audio_pass": ["-c:a", "aac", "-movflags", "use_metadata_tags"],
+    "save_metadata": ["save_metadata", "BOOLEAN", {"default": true}],
+    "extension": "mp4"
+}
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/nvenc_av1-mp4.json b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/nvenc_av1-mp4.json
new file mode 100644
index 0000000000000000000000000000000000000000..ed8294fcf0c9cc1a4780dc6e2454913a969f533b
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/nvenc_av1-mp4.json
@@ -0,0 +1,15 @@
+{
+    "main_pass":
+    [
+        "-n", "-c:v", "av1_nvenc",
+        "-pix_fmt", ["pix_fmt", ["yuv420p", "p010le"]],
+        "-vf", "scale=out_color_matrix=bt709",
+        "-color_range", "tv", "-colorspace", "bt709", "-color_primaries", "bt709", "-color_trc", "bt709"
+    ],
+    "fake_trc": "bt709",
+    "audio_pass": ["-c:a", "aac", "-movflags", "use_metadata_tags"],
+    "bitrate": ["bitrate","INT", {"default": 10, "min": 1, "max": 999, "step": 1 }],
+    "megabit": ["megabit","BOOLEAN", {"default": true}],
+    "save_metadata": ["save_metadata", "BOOLEAN", {"default": true}],
+    "extension": "mp4"
+}
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/nvenc_h264-mp4.json b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/nvenc_h264-mp4.json
new file mode 100644
index 0000000000000000000000000000000000000000..f8676304effd31fb41ae13dd972d92736b122009
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/nvenc_h264-mp4.json
@@ -0,0 +1,15 @@
+{
+    "main_pass":
+    [
+        "-n", "-c:v", "h264_nvenc",
+        "-pix_fmt", ["pix_fmt", ["yuv420p", "p010le"]],
+        "-vf", "scale=out_color_matrix=bt709",
+        "-color_range", "tv", "-colorspace", "bt709", "-color_primaries", "bt709", "-color_trc", "bt709"
+    ],
+    "fake_trc": "bt709",
+    "audio_pass": ["-c:a", "aac", "-movflags", "use_metadata_tags"],
+    "bitrate": ["bitrate","INT", {"default": 10, "min": 1, "max": 999, "step": 1 }],
+    "megabit": ["megabit","BOOLEAN", {"default": true}],
+    "save_metadata": ["save_metadata", "BOOLEAN", {"default": true}],
+    "extension": "mp4"
+}
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/nvenc_hevc-mp4.json b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/nvenc_hevc-mp4.json
new file mode 100644
index 0000000000000000000000000000000000000000..a374ecde4d0e0c2a2f0042c282f8b1915e307aca
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/nvenc_hevc-mp4.json
@@ -0,0 +1,16 @@
+{
+    "main_pass":
+    [
+        "-n", "-c:v", "hevc_nvenc",
+        "-vtag", "hvc1",
+        "-pix_fmt", ["pix_fmt", ["yuv420p", "p010le"]],
+        "-vf", "scale=out_color_matrix=bt709",
+        "-color_range", "tv", "-colorspace", "bt709", "-color_primaries", "bt709", "-color_trc", "bt709"
+    ],
+    "fake_trc": "bt709",
+    "audio_pass": ["-c:a", "aac", "-movflags", "use_metadata_tags"],
+    "bitrate": ["bitrate","INT", {"default": 10, "min": 1, "max": 999, "step": 1 }],
+    "megabit": ["megabit","BOOLEAN", {"default": true}],
+    "save_metadata": ["save_metadata", "BOOLEAN", {"default": true}],
+    "extension": "mp4"
+}
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/webm.json b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/webm.json
new file mode 100644
index 0000000000000000000000000000000000000000..6323f619ad2850ba60cd5e31b32cdfc060e030c9
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/webm.json
@@ -0,0 +1,16 @@
+{
+    "main_pass":
+    [
+        "-n",
+        "-pix_fmt", ["pix_fmt",["yuv420p","yuva420p"]],
+        "-crf", ["crf","INT", {"default": 20, "min": 0, "max": 100, "step": 1}],
+        "-b:v", "0",
+        "-vf", "scale=out_color_matrix=bt709",
+        "-color_range", "tv", "-colorspace", "bt709", "-color_primaries", "bt709", "-color_trc", "bt709"
+    ],
+    "fake_trc": "bt709",
+    "audio_pass": ["-c:a", "libvorbis"],
+    "save_metadata": ["save_metadata", "BOOLEAN", {"default": true}],
+    "trim_to_audio": ["trim_to_audio", "BOOLEAN", {"default": false}],
+    "extension": "webm"
+}
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/batched_nodes.py b/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/batched_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..624bb84f73cce1d8e51abf4fef0b80367ad4b9a3
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/batched_nodes.py
@@ -0,0 +1,56 @@
+import torch
+from nodes import VAEEncode
+from comfy.utils import ProgressBar
+
+
+class VAEDecodeBatched:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "samples": ("LATENT", ),
+                "vae": ("VAE", ),
+                "per_batch": ("INT", {"default": 16, "min": 1})
+                }
+            }
+    
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/batched nodes"
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "decode"
+
+    def decode(self, vae, samples, per_batch):
+        decoded = []
+        pbar = ProgressBar(samples["samples"].shape[0])
+        for start_idx in range(0, samples["samples"].shape[0], per_batch):
+            decoded.append(vae.decode(samples["samples"][start_idx:start_idx+per_batch]))
+            pbar.update(per_batch)
+        return (torch.cat(decoded, dim=0), )
+
+
+class VAEEncodeBatched:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "pixels": ("IMAGE", ), "vae": ("VAE", ),
+                "per_batch": ("INT", {"default": 16, "min": 1})
+                }
+            }
+    
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/batched nodes"
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "encode"
+
+    def encode(self, vae, pixels, per_batch):
+        t = []
+        pbar = ProgressBar(pixels.shape[0])
+        for start_idx in range(0, pixels.shape[0], per_batch):
+            try:
+                sub_pixels = vae.vae_encode_crop_pixels(pixels[start_idx:start_idx+per_batch])
+            except:
+                sub_pixels = VAEEncode.vae_encode_crop_pixels(pixels[start_idx:start_idx+per_batch])
+            t.append(vae.encode(sub_pixels[:,:,:,:3]))
+            pbar.update(per_batch)
+        return ({"samples": torch.cat(t, dim=0)}, )
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/documentation.py b/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/documentation.py
new file mode 100644
index 0000000000000000000000000000000000000000..857de6c6a5b4a3917f49d0b1c5c142f274203f22
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/documentation.py
@@ -0,0 +1,616 @@
+from .logger import logger
+
+def image(src):
+    return f'<img src={src} loading=lazy style="width: 0px; min-width: 100%">'
+def video(src):
+    return f'<video preload="none" src={src} muted loop controls controlslist="nodownload noremoteplayback noplaybackrate" style="width: 0px; min-width: 100%" class="VHS_loopedvideo">'
+def short_desc(desc):
+    return f'<div id=VHS_shortdesc>{desc}</div>'
+
+def format_each(desc, **kwargs):
+    if isinstance(desc, dict):
+        res = {}
+        for k,v in desc.items():
+            res[format_each(k, **kwargs)] = format_each(v, **kwargs)
+        return res
+    if isinstance(desc, list):
+        res = []
+        for v in desc:
+            res.append(format_each(v, **kwargs))
+        return res
+    return desc.format(**kwargs)
+def format_type(desc, lower, lowers=None, upper=None, uppers=None, cap=None):
+    """Utility function for nodes with image/latent/mask variants"""
+    if lowers is None:
+        lowers = lower + 's'
+    if cap is None:
+        cap = lower.capitalize()
+    if upper is None:
+        upper = lower.upper()
+    if uppers is None:
+        uppers = lowers.upper()
+    return format_each(desc, lower=lower, lowers=lowers, upper=upper, uppers=uppers, cap=cap)
+
+common_descriptions = {
+  'merge_strategy': [
+      'Determines what the output resolution will be if input resolutions don\'t match',
+      {'match A': 'Always use the resolution for A',
+      'match B': 'Always use the resolution for B',
+      'match smaller': 'Pick the smaller resolution by area',
+      'match larger': 'Pick the larger resolution by area',
+      }],
+  'scale_method': [
+    'Determines what method to use if scaling is required',
+ ],
+  'crop_method': 'When sizes don\'t match, should the resized image have it\'s aspect ratio changed, or be cropped to maintain aspect ratio',
+  'VHS_PATH': [
+    'This is a VHS_PATH input. When edited, it provides a list of possible valid files or directories',
+    video('https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite/assets/4284322/729b7185-1fca-41d8-bc8d-a770bb2a5ce6'),
+    'The current top-most completion may be selected with Tab',
+    'You can navigate up a directory by pressing Ctrl+B (or Ctrl+W if supported by browser)',
+    'The filter on suggested file types can be disabled by pressing Ctrl+G.',
+    'If converted to an input, this functions as a string',
+      ],
+    "GetCount": ['Get {cap} Count 🎥🅥🅗🅢', short_desc('Return the number of {lowers} in an input as an INT'),
+    {'Inputs': {
+        '{lowers}': 'The input {lower}',
+        },
+     'Outputs': {
+         'count': 'The number of {lowers} in the input',
+        },
+    }],
+    "SelectEveryNth": ['Select Every Nth {cap} 🎥🅥🅗🅢', short_desc('Keep only 1 {lower} for every interval'),
+    {'Inputs': {
+        '{lowers}': 'The input {lower}',
+        },
+     'Outputs': {
+         '{upper}': 'The output {lowers}',
+         'count': 'The number of {lowers} in the input',
+        },
+     'Widgets':{
+         'select_every_nth': 'The interval from which one frame is kept. 1 means no frames are skipped.',
+         'skip_first_{lowers}': 'A number of frames which that is skipped from the start. This applies before select_every_nth. As a result, multiple copies of the node can each have a different skip_first_frames to divide the {lower} into groups'
+        },
+    }],
+}
+
+descriptions = {
+  'VHS_VideoCombine': ['Video Combine 🎥🅥🅗🅢', short_desc('Combine an image sequence into a video'), {
+    'Inputs': {
+        'images': 'The images to be turned into a video',
+        'audio':'(optional) audio to add to the video',
+        'meta_batch': '(optional) Connect to a Meta Batch manager to divide extremely long image sequences into sub batches. See the documentation for Meta Batch Manager',
+        'vae':['(optional) If provided, the node will take latents as input instead of images. This drastically reduces the required RAM (not VRAM) when working with long (100+ frames) sequences',
+               "Unlike on Load Video, this isn't always a strict upgrade over using a standalone VAE Decode.",
+               "If you have multiple Video Combine outputs, then the VAE decode will be performed for each output node increasing execution time",
+               "If you make any change to output settings on the Video Combine (such as changing the output format), the VAE decode will be performed again as the decoded result is (by design) not cached",
+               ]
+        },
+    'Widgets':{
+        'frame_rate': 'The frame rate which will be used for the output video. Consider converting this to an input and connecting this to a Load Video with Video Info(Loaded)->fps. When including audio, failure to properly set this will result in audio desync',
+        'loop_count': 'The number of additional times the video should repeat. Can cause performance issues when used with long (100+ frames) sequences',
+        'filename_prefix': 'A prefix to add to the name of the output filename. This can include subfolders or format strings.',
+        'format': 'The output format to use. Formats starting with, \'image\' are saved with PIL, but formats starting with \'video\' utilize the video_formats system. \'video\' options require ffmpeg and selecting one frequently adds additional options to the node.',
+        'pingpong': 'Play the video normally, then repeat the video in reverse so that it \'pingpongs\' back and forth. This is frequently used to minimize the appearance of skips on very short animations.',
+        'save_output': 'Specifies if output files should be saved to the output folder, or the temporary output folder',
+         'videopreview': 'Displays a preview for the processed result. If advanced previews is enabled, the output is always converted to a format viewable from the browser. If the video has audio, it will also be previewed when moused over. Additional preview options can be accessed with right click.',
+        },
+    'Common Format Widgets': {
+        'crf': 'Determines how much to prioritize quality over filesize. Numbers vary between formats, but on each format that includes it, the default value provides visually loss less output',
+        'pix_fmt': ['The pixel format to use for output. Alternative options will often have higher quality at the cost of increased file size and reduced compatibility with external software.', {
+            'yuv420p': 'The most common and default format',
+            'yuv420p10le': 'Use 10 bit color depth. This can improve color quality when combined with 16bit input color depth',
+            'yuva420p': 'Include transparency in the output video'
+            }],
+        'input_color_depth': 'VHS supports outputting 16bit images. While this produces higher quality output, the difference usually isn\'t visible without postprocessing and it significantly increases file size and processing time.',
+        'save_metadata': 'Determines if metadata for the workflow should be included in the output video file',
+        }
+    }],
+  'VHS_LoadVideo': ['Load Video 🎥🅥🅗🅢', short_desc('Loads a video from the input folder'),
+    {'Inputs': {
+        'meta_batch': '(optional) Connect to a Meta Batch manager to divide extremely long sequences into sub batches. See the documentation for Meta Batch Manager',
+        'vae': ['(optional) If provided the node will output latents instead of images. This drastically reduces the required RAM (not VRAM) when working with long (100+ frames) sequences',
+                'Using this is strongly encouraged unless connecting to a node that requires a blue image connection such as Apply Controllnet',
+                ],
+        },
+     'Outputs': {
+         'IMAGE': 'The loaded images',
+         'frame_count': 'The length of images just returned',
+         'audio': 'The audio from the loaded video',
+         'video_info': 'Exposes additional info about the video such as the source frame rate, or the total length',
+         'LATENT': 'The loaded images pre-converted to latents. Only available when a vae is connected',
+         },
+     'Widgets': {
+         'video': 'The video file to be loaded. Lists all files with a video extension in the ComfyUI/Input folder',
+         'force_rate': 'Drops or duplicates frames so that the produced output has the target frame rate. Many motion models are trained on videos of a specific frame rate and will give better results if input matches that frame rate. If set to 0, all frames are returned. May give unusual results with inputs that have a variable frame rate like animated gifs. Reducing this value can also greatly reduce the execution time and memory requirements.',
+         'force_size': 'Previously was used to provide suggested resolutions. Instead, custom_width and custom_height can be disabled by setting to 0.',
+         'custom_width': 'Allows for an arbitrary width to be entered, cropping to maintain aspect ratio if both are set',
+         'custom_height': 'Allows for an arbitrary height to be entered, cropping to maintain aspect ratio if both are set',
+         'frame_load_cap': 'The maximum number of frames to load. If 0, all frames are loaded.',
+         'skip_first_frames': 'A number of frames which are discarded before producing output.',
+         'select_every_nth': 'Similar to frame rate. Keeps only the first of every n frames and discard the rest. Has better compatibility with variable frame rate inputs such as gifs. When combined with force_rate, select_every_nth_applies after force_rate so the resulting output has a frame rate equivalent to force_rate/select_every_nth. select_every_nth does not apply to skip_first_frames',
+         'format': 'Updates other widgets so that only values supported by the given format can be entered and provides recommended defaults.',
+         'choose video to upload': 'An upload button is provided to upload local files to the input folder',
+         'videopreview': 'Displays a preview for the selected video input. If advanced previews is enabled, this preview will reflect the frame_load_cap, force_rate, skip_first_frames, and select_every_nth values chosen. If the video has audio, it will also be previewed when moused over. Additional preview options can be accessed with right click.',
+         }
+        }],
+  'VHS_LoadVideoFFmpeg': ['Load Video FFmpeg 🎥🅥🅗🅢', short_desc('Loads a video from the input folder using ffmpeg instead of opencv'),
+    'Provides faster execution speed, transparency support, and allows specifying start time in seconds',
+    {'Inputs': {
+        'meta_batch': '(optional) Connect to a Meta Batch manager to divide extremely long sequences into sub batches. See the documentation for Meta Batch Manager',
+        'vae': ['(optional) If provided the node will output latents instead of images. This drastically reduces the required RAM (not VRAM) when working with long (100+ frames) sequences',
+                'Using this is strongly encouraged unless connecting to a node that requires a blue image connection such as Apply Controllnet',
+                ],
+        },
+     'Outputs': {
+         'IMAGE': 'The loaded images',
+         'mask': 'Transparency data from the loaded video',
+         'audio': 'The audio from the loaded video',
+         'video_info': 'Exposes additional info about the video such as the source frame rate, or the total length',
+         'LATENT': 'The loaded images pre-converted to latents. Only available when a vae is connected',
+         },
+     'Widgets': {
+         'video': 'The video file to be loaded. Lists all files with a video extension in the ComfyUI/Input folder',
+         'force_rate': 'Drops or duplicates frames so that the produced output has the target frame rate. Many motion models are trained on videos of a specific frame rate and will give better results if input matches that frame rate. If set to 0, all frames are returned. May give unusual results with inputs that have a variable frame rate like animated gifs. Reducing this value can also greatly reduce the execution time and memory requirements.',
+         'force_size': 'Previously was used to provide suggested resolutions. Instead, custom_width and custom_height can be disabled by setting to 0.',
+         'custom_width': 'Allows for an arbitrary width to be entered, cropping to maintain aspect ratio if both are set',
+         'custom_height': 'Allows for an arbitrary height to be entered, cropping to maintain aspect ratio if both are set',
+         'frame_load_cap': 'The maximum number of frames to load. If 0, all frames are loaded.',
+         'start_time': 'A timestamp, in seconds from the start of the video, to start loading frames from. ',
+         'format': 'Updates other widgets so that only values supported by the given format can be entered and provides recommended defaults.',
+         'choose video to upload': 'An upload button is provided to upload local files to the input folder',
+         'videopreview': 'Displays a preview for the selected video input. If advanced previews is enabled, this preview will reflect the frame_load_cap, force_rate, skip_first_frames, and select_every_nth values chosen. If the video has audio, it will also be previewed when moused over. Additional preview options can be accessed with right click.',
+         }
+        }],
+  'VHS_LoadVideoPath': ['Load Video (Path) 🎥🅥🅗🅢', short_desc('Loads a video from an arbitrary path'),
+    {'Inputs': {
+        'meta_batch': '(optional) Connect to a Meta Batch manager to divide extremely long sequences into sub batches. See the documentation for Meta Batch Manager',
+        'vae': ['(optional) If provided the node will output latents instead of images. This drastically reduces the required RAM (not VRAM) when working with long (100+ frames) sequences',
+                'Using this is strongly encouraged unless connecting to a node that requires a blue image connection such as Apply Controllnet',
+                ],
+        },
+     'Outputs': {
+         'IMAGE': 'The loaded images',
+         'frame_count': 'The length of images just returned',
+         'audio': 'The audio from the loaded video',
+         'video_info': 'Exposes additional info about the video such as the source frame rate, or the total length',
+         'LATENT': 'The loaded images pre-converted to latents. Only available when a vae is connected',
+         },
+     'Widgets': {
+         'video': ['The video file to be loaded.', 'You can also select an image to load it as a single frame'] + common_descriptions['VHS_PATH'],
+         'force_rate': 'Drops or duplicates frames so that the produced output has the target frame rate. Many motion models are trained on videos of a specific frame rate and will give better results if input matches that frame rate. If set to 0, all frames are returned. May give unusual results with inputs that have a variable frame rate like animated gifs. Reducing this value can also greatly reduce the execution time and memory requirements.',
+         'force_size': 'Previously was used to provide suggested resolutions. Instead, custom_width and custom_height can be disabled by setting to 0.',
+         'custom_width': 'Allows for an arbitrary width to be entered, cropping to maintain aspect ratio if both are set',
+         'custom_height': 'Allows for an arbitrary height to be entered, cropping to maintain aspect ratio if both are set',
+         'frame_load_cap': 'The maximum number of frames to load. If 0, all frames are loaded.',
+         'skip_first_frames': 'A number of frames which are discarded before producing output.',
+         'select_every_nth': 'Similar to frame rate. Keeps only the first of every n frames and discard the rest. Has better compatibility with variable frame rate inputs such as gifs. When combined with force_rate, select_every_nth_applies after force_rate so the resulting output has a frame rate equivalent to force_rate/select_every_nth. select_every_nth does not apply to skip_first_frames',
+         'format': 'Updates other widgets so that only values supported by the given format can be entered and provides recommended defaults.',
+         'videopreview': 'Displays a preview for the selected video input. Will only be shown if Advanced Previews is enabled. This preview will reflect the frame_load_cap, force_rate, skip_first_frames, and select_every_nth values chosen. If the video has audio, it will also be previewed when moused over. Additional preview options can be accessed with right click.',
+         }
+        }],
+  'VHS_LoadVideoFFmpegPath': ['Load Video FFmpeg (Path) 🎥🅥🅗🅢', short_desc('Loads a video from an arbitrary path using ffmpeg instead of opencv'),
+    'Provides faster execution speed, transparency support, and allows specifying start time in seconds',
+    {'Inputs': {
+        'meta_batch': '(optional) Connect to a Meta Batch manager to divide extremely long sequences into sub batches. See the documentation for Meta Batch Manager',
+        'vae': ['(optional) If provided the node will output latents instead of images. This drastically reduces the required RAM (not VRAM) when working with long (100+ frames) sequences',
+                'Using this is strongly encouraged unless connecting to a node that requires a blue image connection such as Apply Controllnet',
+                ],
+        },
+     'Outputs': {
+         'IMAGE': 'The loaded images',
+         'mask': 'Transparency data from the loaded video',
+         'audio': 'The audio from the loaded video',
+         'video_info': 'Exposes additional info about the video such as the source frame rate, or the total length',
+         'LATENT': 'The loaded images pre-converted to latents. Only available when a vae is connected',
+         },
+     'Widgets': {
+         'video': ['The video file to be loaded.', 'You can also select an image to load it as a single frame'] + common_descriptions['VHS_PATH'],
+         'force_rate': 'Drops or duplicates frames so that the produced output has the target frame rate. Many motion models are trained on videos of a specific frame rate and will give better results if input matches that frame rate. If set to 0, all frames are returned. May give unusual results with inputs that have a variable frame rate like animated gifs. Reducing this value can also greatly reduce the execution time and memory requirements.',
+         'force_size': 'Previously was used to provide suggested resolutions. Instead, custom_width and custom_height can be disabled by setting to 0.',
+         'custom_width': 'Allows for an arbitrary width to be entered, cropping to maintain aspect ratio if both are set',
+         'custom_height': 'Allows for an arbitrary height to be entered, cropping to maintain aspect ratio if both are set',
+         'frame_load_cap': 'The maximum number of frames to load. If 0, all frames are loaded.',
+         'skip_first_frames': 'A number of frames which are discarded before producing output.',
+         'select_every_nth': 'Similar to frame rate. Keeps only the first of every n frames and discard the rest. Has better compatibility with variable frame rate inputs such as gifs. When combined with force_rate, select_every_nth_applies after force_rate so the resulting output has a frame rate equivalent to force_rate/select_every_nth. select_every_nth does not apply to skip_first_frames',
+         'format': 'Updates other widgets so that only values supported by the given format can be entered and provides recommended defaults.',
+         'videopreview': 'Displays a preview for the selected video input. Will only be shown if Advanced Previews is enabled. This preview will reflect the frame_load_cap, force_rate, skip_first_frames, and select_every_nth values chosen. If the video has audio, it will also be previewed when moused over. Additional preview options can be accessed with right click.',
+         }
+        }],
+  'VHS_LoadImages': ['Load Images 🎥🅥🅗🅢', short_desc('Loads a sequence of images from a subdirectory of the input folder'),
+    {'Inputs': {
+        'meta_batch': '(optional) Connect to a Meta Batch manager to divide extremely long sequences into sub batches. See the documentation for Meta Batch Manager',
+        },
+     'Outputs': {
+         'IMAGE': 'The loaded images',
+         'MASK': 'The alpha channel of the loaded images.',
+         'frame_count': 'The length of images just returned',
+         },
+     'Widgets': {
+         'directory': 'The directory images will be loaded from. Filtered to process jpg, png, ppm, bmp, tif, and webp files',
+         'image_load_cap': 'The maximum number of images to load. If 0, all images are loaded.',
+         'start_time': 'A timestamp, in seconds from the start of the video, to start loading frames from. ',
+         'choose folder to upload': 'An upload button is provided to upload a local folder containing images to the input folder',
+         'videopreview': 'Displays a preview for the selected video input. Will only be shown if Advanced Previews is enabled. This preview will reflect the image_load_cap, skip_first_images, and select_every_nth values chosen. Additional preview options can be accessed with right click.',
+         }
+        }],
+  'VHS_LoadImagesPath': ['Load Images (Path) 🎥🅥🅗🅢', short_desc('Loads a sequence of images from an arbitrary path'),
+    {'Inputs': {
+        'meta_batch': '(optional) Connect to a Meta Batch manager to divide extremely long sequences into sub batches. See the documentation for Meta Batch Manager',
+        },
+     'Outputs': {
+         'IMAGE': 'The loaded images',
+         'MASK': 'The alpha channel of the loaded images.',
+         'frame_count': 'The length of images just returned',
+         },
+     'Widgets': {
+         'directory': ['The directory images will be loaded from. Filtered to process jpg, png, ppm, bmp, tif, and webp files'] + common_descriptions['VHS_PATH'],
+         'image_load_cap': 'The maximum number of images to load. If 0, all images are loaded.',
+         'skip_first_images': 'A number of images which are discarded before producing output.',
+         'select_every_nth': 'Keeps only the first of every n frames and discard the rest.',
+         'videopreview': 'Displays a preview for the selected video input. Will only be shown if Advanced Previews is enabled. This preview will reflect the image_load_cap, skip_first_images, and select_every_nth values chosen. Additional preview options can be accessed with right click.',
+         }
+        }],
+  'VHS_LoadImagePath': ['Load Image (Path) 🎥🅥🅗🅢', short_desc('Load a single image from a given path'),
+    {'Inputs': {
+        'vae': '(optional) If provided the node will output latents instead of images.',
+        },
+     'Outputs': {
+         'IMAGE': 'The loaded images',
+         'MASK': 'The alpha channel of the loaded images.',
+         },
+     'Widgets': {
+         'image': ['The image file to be loaded.'] + common_descriptions['VHS_PATH'],
+         'force_size': ['Allows for conveniently scaling the input without requiring an additional node. Provides options to maintain aspect ratio or conveniently target common training formats for Animate Diff', {'custom_width': 'Allows for an arbitrary width to be entered, cropping to maintain aspect ratio if both are set',
+               'custom_height': 'Allows for an arbitrary height to be entered, cropping to maintain aspect ratio if both are set'}],
+         'videopreview': 'Displays a preview for the selected video input. Will only be shown if Advanced Previews is enabled. This preview will reflect the image_load_cap, skip_first_images, and select_every_nth values chosen. Additional preview options can be accessed with right click.',
+         }
+        }],
+  "VHS_LoadAudio": ['Load Audio (Path) 🎥🅥🅗🅢', short_desc('Loads an audio file from an arbitrary path'),
+    {'Outputs': {
+         'audio': 'The loaded audio',
+         },
+     'Widgets': {
+         'audio_file': ['The audio file to be loaded.'] + common_descriptions['VHS_PATH'],
+         'seek_seconds': 'An offset from the start of the sound file that the audio should start from',
+         }
+        }],
+  "VHS_LoadAudioUpload": ['Load Audio (Upload) 🎥🅥🅗🅢', short_desc('Loads an audio file from the input directory'),
+    "Very similar in functionality to the built-in LoadAudio. It was originally added before VHS swapped to use Comfy's internal AUDIO format, but provides the additional options for start time and duration",
+    {'Outputs': {
+         'audio': 'The loaded audio',
+         },
+     'Widgets': {
+         'audio': 'The audio file to be loaded.',
+         'start_time': 'An offset from the start of the sound file that the audio should start from',
+         'duration': 'A maximum limit for the audio. Disabled if 0',
+         'choose audio to upload': 'An upload button is provided to upload an audio file to the input folder',
+         }
+        }],
+  "VHS_AudioToVHSAudio": ['Audio to legacy VHS_AUDIO 🎥🅥🅗🅢', short_desc('utility function for compatibility with external nodes'),
+    "VHS used to use an internal VHS_AUDIO format for routing audio between inputs and outputs. This format was intended to only be used internally and was designed with a focus on performance over ease of use. Since ComfyUI now has an internal AUDIO format, VHS now uses this format. However, some custom node packs were made that are external to both ComfyUI and VHS that use VHS_AUDIO. This node was added so that those external nodes can still function",
+    {'Inputs': {
+        'audio': 'An input in the standardized AUDIO format',
+        },
+     'Outputs': {
+         'vhs_audio': 'An output in the legacy VHS_AUDIO format for use with external nodes',
+         },
+        }],
+  "VHS_VHSAudioToAudio": ['Legacy VHS_AUDIO to Audio 🎥🅥🅗🅢', short_desc('utility function for compatibility with external nodes'),
+    "VHS used to use an internal VHS_AUDIO format for routing audio between inputs and outputs. This format was intended to only be used internally and was designed with a focus on performance over ease of use. Since ComfyUI now has an internal AUDIO format, VHS now uses this format. However, some custom node packs were made that are external to both ComfyUI and VHS that use VHS_AUDIO. This node was added so that those external nodes can still function",
+    {'Inputs': {
+        'vhs_audio': 'An input in the legacy VHS_AUDIO format produced by an external node',
+        },
+     'Outputs': {
+         'vhs_audio': 'An output in the standardized AUDIO format',
+         },
+        }],
+  "VHS_PruneOutputs": ['Prune Outputs 🎥🅥🅗🅢', short_desc('Automates deletion of undesired outputs from a Video Combine node.'),
+    'Video Combine produces a number of file outputs in addition to the final output. Some of these, such as a video file without audio included, are implementation limitations and are not feasible to solve. As an alternative, the Prune Outputs node is added to automate the deletion of these file outputs if they are not desired',
+    {'Inputs': {
+        'filenames': 'A connection from a Video Combine node to indicate which outputs should be pruned',
+        },
+     'Widgets': {
+         'options': ['Which files should be deleted',
+             {'Intermediate': 'Delete any files that were required for intermediate processing but are not the final output, like the no-audio output file when audio is included',
+              'Intermediate and Utility': 'Delete all produced files that aren\'t the final output, including the first frame png',
+         }]}
+     }],
+  "VHS_BatchManager": ['Meta Batch Manager 🎥🅥🅗🅢', short_desc('Split the processing of a very long video into sets of smaller Meta Batches'),
+    "The Meta Batch Manager allows for extremely long input videos to be processed when all other methods for fitting the content in RAM fail. It does not effect VRAM usage.",
+    "It must be connected to at least one Input (a Load Video or Load Images) AND at least one Video Combine",
+    image("https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite/assets/4284322/7cb3fb7e-59d8-4cb2-a09f-9c6698de8b1f"),
+    "It functions by holding both the inputs and ouputs open between executions, and automatically requeue's the workflow until one of the inputs is unable to provide additional images.",
+    "Because each sub execution only contains a subset of the total frames, each sub execution creates a hard window which temporal smoothing can not be applied across. This results in jumps in the output.",
+    {'Outputs': {
+         'meta_batch': 'Add all connected nodes to this Meta Batch',
+         },
+     'Widgets': {
+         'frames_per_batch': 'How many frames to process for each sub execution. If loading as image, each frame will use about 50MB of RAM (not VRAM), and this can safely be set in the 100-1000 range, depending on available memory. When loading and combining from latent space (no blue image noodles exist), this value can be much higher, around the 2,000 to 20,000 range',
+         }
+        }],
+  "VHS_VideoInfo": ['Video Info 🎥🅥🅗🅢', short_desc('Splits information on a video into a numerous outputs'),
+    {'Inputs': {
+        'video_info': 'A connection to a Load Video node',
+        },
+     'Outputs': {
+         'source_fps🟨': 'The frame rate of the video',
+         'source_frame_count🟨': 'How many total frames the video contains before accounting for frame rate or select_every_nth',
+         'source_duration🟨': 'The length of images just returned in seconds',
+         'source_width🟨': 'The width',
+         'source_height🟨': 'The height',
+         'loaded_fps🟦': 'The frame rate after accounting for force_rate and select_every_nth. This output is of particular use as it can be connected to the converted frame_rate input of a Video Combine node to ensure audio remains synchronized.',
+         'loaded_frame_count🟦': 'The number of frames returned by the current execution. Identical to the frame_count returned by the node itself',
+         'loaded_duration🟦': 'The duration in seconds of returned images after accounting for frame_load_cap',
+         'loaded_width🟦': 'The width of the video after scaling. These coordinates are in image space even if loading to latent space',
+         'loaded_height🟦': 'The height of the video after scaling. These coordinates are in image space even if loading to latent space',
+         },
+        }],
+  "VHS_VideoInfoSource": ['Video Info Source 🎥🅥🅗🅢', short_desc('Splits information on a video into a numerous outputs describing the file itself without accounting for load options'),
+    {'Inputs': {
+        'video_info': 'A connection to a Load Video node',
+        },
+     'Outputs': {
+         'source_fps🟨': 'The frame rate of the video',
+         'source_frame_count🟨': 'How many total frames the video contains before accounting for frame rate or select_every_nth',
+         'source_duration🟨': 'The length of images just returned in seconds',
+         'source_width🟨': 'The original width',
+         'source_height🟨': 'The original height',
+         }
+     }],
+  "VHS_VideoInfoLoaded": ['Video Info Loaded 🎥🅥🅗🅢', short_desc('Splits information on a video into a numerous outputs describing the file itself after accounting for load options'),
+    {'Inputs': {
+        'video_info': 'A connection to a Load Video node',
+        },
+     'Outputs': {
+         'loaded_fps🟦': 'The frame rate after accounting for force_rate and select_every_nth. This output is of particular use as it can be connected to the converted frame_rate input of a Video Combine node to ensure audio remains synchronized.',
+         'loaded_frame_count🟦': 'The number of frames returned by the current execution. Identical to the frame_count returned by the node itself',
+         'loaded_duration🟦': 'The duration in seconds of returned images after accounting for frame_load_cap',
+         'loaded_width🟦': 'The width of the video after scaling. This is the dimension of the corresponding image even if loading as a latent directly',
+         'loaded_height🟦': 'The height of the video after scaling. This is the dimension of the corresponding image even if loading as a latent directly',
+         }
+     }],
+  "VHS_SelectFilename": ['VAE Select Filename 🎥🅥🅗🅢', short_desc('Select a single filename from the VHS_FILENAMES output by a Video Combine and return it as a string'),
+    'Take care when combining this node with Prune Outputs. The VHS_FILENAMES object is immutable and will always contain the full list of output files, but execution order is undefined behavior (currently, Prune Outputs will generally execute first) and SelectFilename may return a path to a file that no longer exists.',
+    {'Inputs': {
+        'filenames': 'A VHS_FILENAMES from a Video Combine node',
+        },
+     'Outputs': {
+         'filename': 'A string representation of the full output path for the chosen file',
+        },
+     'Widgets': {
+         'index': 'The index of which file should be selected. The default, -1, chooses the most complete output',
+        },
+     }],
+    # Batched Nodes
+  "VHS_VAEEncodeBatched": ['VAE Encode Batched 🎥🅥🅗🅢', short_desc('Encode images as latents with a manually specified batch size.'),
+    "Some people have ran into VRAM issues when encoding or decoding large batches of images. As a workaround, this node lets you manually set a batch size when encoding images.",
+    "Unless these issues have been encountered, it is simpler to use the native VAE Encode or to encode directly from a Load Video",
+    {'Inputs': {
+        'pixels': 'The images to be encoded.',
+        'vae': 'The VAE to use when encoding.',
+        },
+     'Outputs': {
+         'LATENT': 'The encoded latents.',
+        },
+     'Widgets': {
+         'per_batch': 'The maximum number of images to encode in each batch.',
+        },
+     }],
+  "VHS_VAEDecodeBatched": ['VAE Decode Batched 🎥🅥🅗🅢', short_desc('Decode latents to images with a manually specified batch size'),
+    "Some people have ran into VRAM issues when encoding or decoding large batches of images. As a workaround, this node lets you manually set a batch size when decoding latents.",
+    "Unless these issues have been encountered, it is simpler to use the native VAE Decode or to decode from a Video Combine directly",
+    {'Inputs': {
+        'samples': 'The latents to be decoded.',
+        'vae': 'The VAE to use when decoding.',
+        },
+     'Outputs': {
+         'IMAGE': 'The decoded images.',
+        },
+     'Widgets': {
+         'per_batch': 'The maximum number of images to decode in each batch.',
+        },
+     }],
+    # Latent and Image nodes
+  "VHS_SplitLatents": ['Split Latents 🎥🅥🅗🅢', short_desc('Split a set of latents into two groups'),
+    {'Inputs': {
+        'latents': 'The latents to be split.',
+        },
+     'Outputs': {
+         'LATENT_A': 'The first group of latents',
+         'A_count': 'The number of latents in group A. This will be equal to split_index unless the latents input has length less than split_index',
+         'LATENT_B': 'The second group of latents',
+         'B_count': 'The number of latents in group B'
+        },
+     'Widgets': {
+        'split_index': 'The index of the first latent that will be in the second output groups.',
+        },
+
+    }],
+    "VHS_SplitImages": ['Split Images 🎥🅥🅗🅢', short_desc('Split a set of images into two groups'),
+    {'Inputs': {
+        'images': 'The images to be split.',
+        },
+     'Outputs': {
+         'IMAGE_A': 'The first group of images',
+         'A_count': 'The number of images in group A. This will be equal to split_index unless the images input has length less than split_index',
+         'IMAGE_B': 'The second group of images',
+         'B_count': 'The number of images in group B'
+        },
+     'Widgets': {
+        'split_index': 'The index of the first latent that will be in the second output groups.',
+        },
+
+    }],
+    "VHS_SplitMasks": ['Split Masks 🎥🅥🅗🅢', short_desc('Split a set of masks into two groups'),
+    {'Inputs': {
+        'mask': 'The masks to be split.',
+        },
+     'Outputs': {
+         'MASK_A': 'The first group of masks',
+         'A_count': 'The number of masks in group A. This will be equal to split_index unless the mask input has length less than split_index',
+         'MASK_B': 'The second group of masks',
+         'B_count': 'The number of masks in group B'
+        },
+     'Widgets': {
+        'split_index': 'The index of the first latent that will be in the second output groups.',
+        },
+
+    }],
+    "VHS_MergeLatents": ['Merge Latents 🎥🅥🅗🅢', short_desc('Combine two groups of latents into a single group of latents'),
+    {'Inputs': {
+        'latents_A': 'The first group of latents',
+        'latents_B': 'The first group of latents',
+        },
+     'Outputs': {
+         'LATENT': 'The combined group of latents',
+         'count': 'The length of the combined group',
+        },
+     'Widgets': {
+        'merge_strategy': common_descriptions['merge_strategy'],
+        'scale_method': common_descriptions['scale_method'],
+        'crop': common_descriptions['crop_method'],
+        },
+
+    }],
+    "VHS_MergeImages": ['Merge Images 🎥🅥🅗🅢', short_desc('Combine two groups of images into a single group of images'),
+    {'Inputs': {
+        'images_A': 'The first group of images',
+        'images_B': 'The first group of images',
+        },
+     'Outputs': {
+         'IMAGE': 'The combined group of images',
+         'count': 'The length of the combined group',
+        },
+     'Widgets': {
+        'merge_strategy': common_descriptions['merge_strategy'],
+        'scale_method': common_descriptions['scale_method'],
+        'crop': common_descriptions['crop_method'],
+        },
+
+    }],
+    "VHS_MergeMasks": ['Merge Masks 🎥🅥🅗🅢', short_desc('Combine two groups of masks into a single group of masks'),
+    {'Inputs': {
+        'mask_A': 'The first group of masks',
+        'mask_B': 'The first group of masks',
+        },
+     'Outputs': {
+         'MASK': 'The combined group of masks',
+         'count': 'The length of the combined group',
+        },
+     'Widgets': {
+        'merge_strategy': common_descriptions['merge_strategy'],
+        'scale_method': common_descriptions['scale_method'],
+        'crop': common_descriptions['crop_method'],
+        },
+
+    }],
+    "VHS_GetLatentCount": format_type(common_descriptions['GetCount'], 'latent'),
+    "VHS_GetImageCount": format_type(common_descriptions['GetCount'], 'image'),
+    "VHS_GetMaskCount": format_type(common_descriptions['GetCount'], 'mask'),
+    "VHS_DuplicateLatents": ['Repeat Latents 🎥🅥🅗🅢', short_desc('Append copies of a latent to itself so it repeats'),
+    {'Inputs': {
+        'latents': 'The latents to be repeated',
+        },
+     'Outputs': {
+         'LATENT': 'The latent with repeats',
+         'count': 'The number of latents in the output. Equal to the length of the input latent * multiply_by',
+        },
+     'Widgets': {
+        'multiply_by': 'Controls the number of times the latent should repeat. 1, the default, means no change.',
+        },
+    }],
+    "VHS_DuplicateImages": ['Repeat Images 🎥🅥🅗🅢', short_desc('Append copies of a image to itself so it repeats'),
+    {'Inputs': {
+        'IMAGES': 'The image to be repeated',
+        },
+     'Outputs': {
+         'IMAGE': 'The image with repeats',
+         'count': 'The number of image in the output. Equal to the length of the input image * multiply_by',
+        },
+     'Widgets': {
+        'multiply_by': 'Controls the number of times the mask should repeat. 1, the default, means no change.',
+        },
+    }],
+    "VHS_DuplicateMasks": ['Repeat Masks 🎥🅥🅗🅢', short_desc('Append copies of a mask to itself so it repeats'),
+    {'Inputs': {
+        'masks': 'The masks to be repeated',
+        },
+     'Outputs': {
+         'LATENT': 'The mask with repeats',
+         'count': 'The number of mask in the output. Equal to the length of the input mask * multiply_by',
+        },
+     'Widgets': {
+        'multiply_by': 'Controls the number of times the mask should repeat. 1, the default, means no change.',
+        },
+    }],
+    "VHS_SelectEveryNthLatent": format_type(common_descriptions['SelectEveryNth'], 'latent'),
+    "VHS_SelectEveryNthImage": format_type(common_descriptions['SelectEveryNth'], 'image'),
+    #TODO: fix discrepency of input being mask instead of masks?
+    "VHS_SelectEveryNthMask": format_type(common_descriptions['SelectEveryNth'], 'mask', lowers='mask'),
+    #TODO: port documentation for select nodes to new system
+    #"VHS_SelectLatents": None,
+    #"VHS_SelectImages": None,
+    #"VHS_SelectMasks": None,
+  "VHS_Unbatch": ['Unbatch 🎥🅥🅗🅢', short_desc('Unbatch a list of items into a single concatenated item'),
+    "Useful for when you want a single video output from a complex workflow",
+    "Has no relation to the Meta Batch system of VHS",
+    {'Inputs': {
+        'batched': 'Any input which may or may not be batched',
+        },
+     'Outputs': {
+         'unbatched': 'A single output element. Torch tensors are concatenated across dim 0, all other types are added which functions as concatenation for strings and arrays, but may give undesired results for other types',
+        },
+    }],
+  "VHS_SelectLatest": ['Select Latest 🎥🅥🅗🅢', short_desc('Experimental virtual node to select the most recently modified file from a given folder'),
+    "Assists in the creation of workflows where outputs from one execution are used elsewhere in subsequent executions.",
+    {'Inputs': {
+        'filename_prefix': 'A path which can consist of a combination of folders and a prefix which candidate files must match',
+        'filename_postfix': 'A string which chich the selected file must end with. Useful for limiting to a target extension.',
+        },
+     'Outputs': {
+         'Filename': 'A string representing a file path to the most recently modified file.',
+        },
+    }],
+}
+
+def as_html(entry, depth=0):
+    if isinstance(entry, dict):
+        size = 0.8 if depth < 2 else 1
+        html = ''
+        for k in entry:
+            if k == "collapsed":
+                continue
+            collapse_single = k.endswith("_collapsed")
+            if collapse_single:
+                name = k[:-len("_collapsed")]
+            else:
+                name = k
+            collapse_flag = ' VHS_precollapse' if entry.get("collapsed", False) or collapse_single else ''
+            html += f'<div vhs_title=\"{name}\" style=\"display: flex; font-size: {size}em\" class=\"VHS_collapse{collapse_flag}\"><div style=\"color: #AAA; height: 1.5em;\">[<span style=\"font-family: monospace\">-</span>]</div><div style=\"width: 100%\">{name}: {as_html(entry[k], depth=depth+1)}</div></div>'
+        return html
+    if isinstance(entry, list):
+        if depth == 0:
+            depth += 1
+            size = .8
+        else:
+            size = 1
+        html = ''
+        html += entry[0]
+        for i in entry[1:]:
+            html += f'<div style=\"font-size: {size}em\">{as_html(i, depth=depth)}</div>'
+        return html
+    return str(entry)
+
+def format_descriptions(nodes):
+    for k in descriptions:
+        if k.endswith("_collapsed"):
+            k = k[:-len("_collapsed")]
+        nodes[k].DESCRIPTION = as_html(descriptions[k])
+    undocumented_nodes = []
+    for k in nodes:
+        if not hasattr(nodes[k], "DESCRIPTION"):
+            undocumented_nodes.append(k)
+    if len(undocumented_nodes) > 0:
+        logger.warning('Some nodes have not been documented %s', undocumented_nodes)
+
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/image_latent_nodes.py b/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/image_latent_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..62b4e3e6452b51b42e3e49852d27a1bb9921edc2
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/image_latent_nodes.py
@@ -0,0 +1,554 @@
+from torch import Tensor
+import torch
+
+import comfy.utils
+
+from .utils import BIGMIN, BIGMAX, select_indexes_from_str, convert_str_to_indexes, select_indexes
+
+
+class MergeStrategies:
+    MATCH_A = "match A"
+    MATCH_B = "match B"
+    MATCH_SMALLER = "match smaller"
+    MATCH_LARGER = "match larger"
+
+    list_all = [MATCH_A, MATCH_B, MATCH_SMALLER, MATCH_LARGER]
+
+
+class ScaleMethods:
+    NEAREST_EXACT = "nearest-exact"
+    BILINEAR = "bilinear"
+    AREA = "area"
+    BICUBIC = "bicubic"
+    BISLERP = "bislerp"
+
+    list_all = [NEAREST_EXACT, BILINEAR, AREA, BICUBIC, BISLERP]
+
+
+class CropMethods:
+    DISABLED = "disabled"
+    CENTER = "center"
+
+    list_all = [DISABLED, CENTER]
+
+
+class SplitLatents:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                    "latents": ("LATENT",),
+                    "split_index": ("INT", {"default": 0, "step": 1, "min": BIGMIN, "max": BIGMAX}),
+                },
+            }
+    
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/latent"
+
+    RETURN_TYPES = ("LATENT", "INT", "LATENT", "INT")
+    RETURN_NAMES = ("LATENT_A", "A_count", "LATENT_B", "B_count")
+    FUNCTION = "split_latents"
+
+    def split_latents(self, latents: dict[str, Tensor], split_index: int):
+        latents_len = len(latents["samples"])
+        group_a = latents.copy()
+        group_b = latents.copy()
+        for key, val in latents.items():
+            if type(val) == Tensor and len(val) == latents_len:
+                group_a[key] = latents[key][:split_index]
+                group_b[key] = latents[key][split_index:]
+        return (group_a, group_a["samples"].size(0), group_b, group_b["samples"].size(0))
+
+
+class SplitImages:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                    "images": ("IMAGE",),
+                    "split_index": ("INT", {"default": 0, "step": 1, "min": BIGMIN, "max": BIGMAX}),
+                },
+            }
+    
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/image"
+
+    RETURN_TYPES = ("IMAGE", "INT", "IMAGE", "INT")
+    RETURN_NAMES = ("IMAGE_A", "A_count", "IMAGE_B", "B_count")
+    FUNCTION = "split_images"
+
+    def split_images(self, images: Tensor, split_index: int):
+        group_a = images[:split_index]
+        group_b = images[split_index:]
+        return (group_a, group_a.size(0), group_b, group_b.size(0))
+
+
+class SplitMasks:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                    "mask": ("MASK",),
+                    "split_index": ("INT", {"default": 0, "step": 1, "min": BIGMIN, "max": BIGMAX}),
+                },
+            }
+    
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/mask"
+
+    RETURN_TYPES = ("MASK", "INT", "MASK", "INT")
+    RETURN_NAMES = ("MASK_A", "A_count", "MASK_B", "B_count")
+    FUNCTION = "split_masks"
+
+    def split_masks(self, mask: Tensor, split_index: int):
+        group_a = mask[:split_index]
+        group_b = mask[split_index:]
+        return (group_a, group_a.size(0), group_b, group_b.size(0))
+
+
+class MergeLatents:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "latents_A": ("LATENT",),
+                "latents_B": ("LATENT",),
+                "merge_strategy": (MergeStrategies.list_all,),
+                "scale_method": (ScaleMethods.list_all,),
+                "crop": (CropMethods.list_all,),
+            }
+        }
+    
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/latent"
+
+    RETURN_TYPES = ("LATENT", "INT",)
+    RETURN_NAMES = ("LATENT", "count",)
+    FUNCTION = "merge"
+
+    def merge(self, latents_A: dict, latents_B: dict, merge_strategy: str, scale_method: str, crop: str):
+        latents = []
+        latents_A = latents_A.copy()["samples"]
+        latents_B = latents_B.copy()["samples"]
+
+        # TODO: handle other properties on latents besides just "samples"
+        # if not same dimensions, do scaling
+        if latents_A.shape[3] != latents_B.shape[3] or latents_A.shape[2] != latents_B.shape[2]:
+            A_size = latents_A.shape[3] * latents_A.shape[2]
+            B_size = latents_B.shape[3] * latents_B.shape[2]
+            # determine which to use
+            use_A_as_template = True
+            if merge_strategy == MergeStrategies.MATCH_A:
+                pass
+            elif merge_strategy == MergeStrategies.MATCH_B:
+                use_A_as_template = False
+            elif merge_strategy in (MergeStrategies.MATCH_SMALLER, MergeStrategies.MATCH_LARGER):
+                if A_size <= B_size:
+                    use_A_as_template = True if merge_strategy == MergeStrategies.MATCH_SMALLER else False
+            # apply scaling
+            if use_A_as_template:
+                latents_B = comfy.utils.common_upscale(latents_B, latents_A.shape[3], latents_A.shape[2], scale_method, crop)
+            else:
+                latents_A = comfy.utils.common_upscale(latents_A, latents_B.shape[3], latents_B.shape[2], scale_method, crop)
+
+        latents.append(latents_A)
+        latents.append(latents_B)
+
+        merged = {"samples": torch.cat(latents, dim=0)}
+        return (merged, len(merged["samples"]),)
+
+
+class MergeImages:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "images_A": ("IMAGE",),
+                "images_B": ("IMAGE",),
+                "merge_strategy": (MergeStrategies.list_all,),
+                "scale_method": (ScaleMethods.list_all,),
+                "crop": (CropMethods.list_all,),
+            }
+        }
+    
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/image"
+
+    RETURN_TYPES = ("IMAGE", "INT",)
+    RETURN_NAMES = ("IMAGE", "count",)
+    FUNCTION = "merge"
+
+    def merge(self, images_A: Tensor, images_B: Tensor, merge_strategy: str, scale_method: str, crop: str):
+        images = []
+        # if not same dimensions, do scaling
+        if images_A.shape[3] != images_B.shape[3] or images_A.shape[2] != images_B.shape[2]:
+            images_A = images_A.movedim(-1,1)
+            images_B = images_B.movedim(-1,1)
+
+            A_size = images_A.shape[3] * images_A.shape[2]
+            B_size = images_B.shape[3] * images_B.shape[2]
+            # determine which to use
+            use_A_as_template = True
+            if merge_strategy == MergeStrategies.MATCH_A:
+                pass
+            elif merge_strategy == MergeStrategies.MATCH_B:
+                use_A_as_template = False
+            elif merge_strategy in (MergeStrategies.MATCH_SMALLER, MergeStrategies.MATCH_LARGER):
+                if A_size <= B_size:
+                    use_A_as_template = True if merge_strategy == MergeStrategies.MATCH_SMALLER else False
+            # apply scaling
+            if use_A_as_template:
+                images_B = comfy.utils.common_upscale(images_B, images_A.shape[3], images_A.shape[2], scale_method, crop)
+            else:
+                images_A = comfy.utils.common_upscale(images_A, images_B.shape[3], images_B.shape[2], scale_method, crop)
+            images_A = images_A.movedim(1,-1)
+            images_B = images_B.movedim(1,-1)
+
+        images.append(images_A)
+        images.append(images_B)
+        all_images = torch.cat(images, dim=0)
+        return (all_images, all_images.size(0),)
+
+
+class MergeMasks:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "mask_A": ("MASK",),
+                "mask_B": ("MASK",),
+                "merge_strategy": (MergeStrategies.list_all,),
+                "scale_method": (ScaleMethods.list_all,),
+                "crop": (CropMethods.list_all,),
+            }
+        }
+    
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/mask"
+
+    RETURN_TYPES = ("MASK", "INT",)
+    RETURN_NAMES = ("MASK", "count",)
+    FUNCTION = "merge"
+
+    def merge(self, mask_A: Tensor, mask_B: Tensor, merge_strategy: str, scale_method: str, crop: str):
+        masks = []
+        # if not same dimensions, do scaling
+        if mask_A.shape[2] != mask_B.shape[2] or mask_A.shape[1] != mask_B.shape[1]:
+            A_size = mask_A.shape[2] * mask_A.shape[1]
+            B_size = mask_B.shape[2] * mask_B.shape[1]
+            # determine which to use
+            use_A_as_template = True
+            if merge_strategy == MergeStrategies.MATCH_A:
+                pass
+            elif merge_strategy == MergeStrategies.MATCH_B:
+                use_A_as_template = False
+            elif merge_strategy in (MergeStrategies.MATCH_SMALLER, MergeStrategies.MATCH_LARGER):
+                if A_size <= B_size:
+                    use_A_as_template = True if merge_strategy == MergeStrategies.MATCH_SMALLER else False
+            # add dimension where image channels would be expected to work with common_upscale
+            mask_A = torch.unsqueeze(mask_A, 1)
+            mask_B = torch.unsqueeze(mask_B, 1)
+            # apply scaling
+            if use_A_as_template:
+                mask_B = comfy.utils.common_upscale(mask_B, mask_A.shape[3], mask_A.shape[2], scale_method, crop)
+            else:
+                mask_A = comfy.utils.common_upscale(mask_A, mask_B.shape[3], mask_B.shape[2], scale_method, crop)
+            # undo dimension increase
+            mask_A = torch.squeeze(mask_A, 1)
+            mask_B = torch.squeeze(mask_B, 1)
+
+        masks.append(mask_A)
+        masks.append(mask_B)
+        all_masks = torch.cat(masks, dim=0)
+        return (all_masks, all_masks.size(0),)
+
+
+class SelectEveryNthLatent:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                    "latents": ("LATENT",),
+                    "select_every_nth": ("INT", {"default": 1, "min": 1, "max": BIGMAX, "step": 1}),
+                    "skip_first_latents": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
+                },
+            }
+    
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/latent"
+
+    RETURN_TYPES = ("LATENT", "INT",)
+    RETURN_NAMES = ("LATENT", "count",)
+    FUNCTION = "select_latents"
+
+    def select_latents(self, latents: dict[str, Tensor], select_every_nth: int, skip_first_latents: int):
+        latents = latents.copy()
+        latents_len = len(latents["samples"])
+        for key, val in latents.items():
+            if type(val) == Tensor and len(val) == latents_len:
+                latents[key] = val[skip_first_latents::select_every_nth]
+        return (latents, latents["samples"].size(0))
+    
+
+class SelectEveryNthImage:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                    "images": ("IMAGE",),
+                    "select_every_nth": ("INT", {"default": 1, "min": 1, "max": BIGMAX, "step": 1}),
+                    "skip_first_images": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
+                    
+                },
+            }
+    
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/image"
+
+    RETURN_TYPES = ("IMAGE", "INT",)
+    RETURN_NAMES = ("IMAGE", "count",)
+    FUNCTION = "select_images"
+
+    def select_images(self, images: Tensor, select_every_nth: int, skip_first_images: int):
+        sub_images = images[skip_first_images::select_every_nth]
+        return (sub_images, sub_images.size(0))
+    
+
+class SelectEveryNthMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                    "mask": ("MASK",),
+                    "select_every_nth": ("INT", {"default": 1, "min": 1, "max": BIGMAX, "step": 1}),
+                    "skip_first_masks": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
+                },
+            }
+    
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/mask"
+
+    RETURN_TYPES = ("MASK", "INT",)
+    RETURN_NAMES = ("MASK", "count",)
+    FUNCTION = "select_masks"
+
+    def select_masks(self, mask: Tensor, select_every_nth: int, skip_first_masks: int):
+        sub_mask = mask[skip_first_masks::select_every_nth]
+        return (sub_mask, sub_mask.size(0))
+
+
+class GetLatentCount:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "latents": ("LATENT",),
+            }
+        }
+    
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/latent"
+
+    RETURN_TYPES = ("INT",)
+    RETURN_NAMES = ("count",)
+    FUNCTION = "count_input"
+
+    def count_input(self, latents: dict):
+        return (latents["samples"].size(0),)
+
+
+class GetImageCount:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+            }
+        }
+    
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/image"
+
+    RETURN_TYPES = ("INT",)
+    RETURN_NAMES = ("count",)
+    FUNCTION = "count_input"
+
+    def count_input(self, images: Tensor):
+        return (images.size(0),)
+    
+
+class GetMaskCount:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "mask": ("MASK",),
+            }
+        }
+    
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/mask"
+
+    RETURN_TYPES = ("INT",)
+    RETURN_NAMES = ("count",)
+    FUNCTION = "count_input"
+
+    def count_input(self, mask: Tensor):
+        return (mask.size(0),)
+
+
+class RepeatLatents:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "latents": ("LATENT",),
+                "multiply_by": ("INT", {"default": 1, "min": 1, "max": BIGMAX, "step": 1})
+            }
+        }
+    
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/latent"
+
+    RETURN_TYPES = ("LATENT", "INT",)
+    RETURN_NAMES = ("LATENT", "count",)
+    FUNCTION = "duplicate_input"
+
+    def duplicate_input(self, latents: dict[str, Tensor], multiply_by: int):
+        latents = latents.copy()
+        latents_len = len(latents["samples"])
+        for key, val in latents.items():
+            if type(val) == Tensor and len(val) == latents_len:
+                full_latents = []
+                for _ in range(0, multiply_by):
+                    full_latents.append(latents[key])
+                latents[key] = torch.cat(full_latents, dim=0)
+        return (latents, latents["samples"].size(0),)
+
+
+class RepeatImages:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "multiply_by": ("INT", {"default": 1, "min": 1, "max": BIGMAX, "step": 1})
+            }
+        }
+    
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/image"
+
+    RETURN_TYPES = ("IMAGE", "INT",)
+    RETURN_NAMES = ("IMAGE", "count",)
+    FUNCTION = "duplicate_input"
+
+    def duplicate_input(self, images: Tensor, multiply_by: int):
+        full_images = []
+        for n in range(0, multiply_by):
+            full_images.append(images)
+        new_images = torch.cat(full_images, dim=0)
+        return (new_images, new_images.size(0),)
+
+
+class RepeatMasks:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "mask": ("MASK",),
+                "multiply_by": ("INT", {"default": 1, "min": 1, "max": BIGMAX, "step": 1})
+            }
+        }
+    
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/mask"
+
+    RETURN_TYPES = ("MASK", "INT",)
+    RETURN_NAMES = ("MASK", "count",)
+    FUNCTION = "duplicate_input"
+
+    def duplicate_input(self, mask: Tensor, multiply_by: int):
+        full_masks = []
+        for n in range(0, multiply_by):
+            full_masks.append(mask)
+        new_mask = torch.cat(full_masks, dim=0)
+        return (new_mask, new_mask.size(0),)
+
+
+select_description = """Use comma-separated indexes to select items in the given order.
+Supports negative indexes, python-style ranges (end index excluded),
+as well as range step.
+
+Acceptable entries (assuming 16 items provided, so idxs 0 to 15 exist):
+0         -> Returns [0]
+-1        -> Returns [15]
+0, 1, 13  -> Returns [0, 1, 13]
+0:5, 13   -> Returns [0, 1, 2, 3, 4, 13]
+0:-1      -> Returns [0, 1, 2, ..., 13, 14]
+0:5:-1    -> Returns [4, 3, 2, 1, 0]
+0:5:2     -> Returns [0, 2, 4]
+::-1     -> Returns [15, 14, 13, ..., 2, 1, 0]
+"""
+class SelectLatents:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                    "latent": ("LATENT",),
+                    "indexes": ("STRING", {"default": "0"}),
+                    "err_if_missing": ("BOOLEAN", {"default": True}),
+                    "err_if_empty": ("BOOLEAN", {"default": True}),
+                },
+            }
+    
+    DESCRIPTION = select_description
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/latent"
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "select"
+
+    def select(self, latent: dict[str, Tensor], indexes: str, err_if_missing: bool, err_if_empty: bool):
+        # latents are a dict and may contain different stuff (like noise_mask), so need to account for it all
+        latent = latent.copy()
+        latents_len = len(latent["samples"])
+        real_idxs = convert_str_to_indexes(indexes, latents_len, allow_missing=not err_if_missing)
+        if err_if_empty and len(real_idxs) == 0:
+            raise Exception(f"Nothing was selected based on indexes found in '{indexes}'.")
+        for key, val in latent.items():
+            if type(val) == Tensor and len(val) == latents_len:
+                latent[key] = select_indexes(val, real_idxs)
+        return (latent,)
+
+
+class SelectImages:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                    "image": ("IMAGE",),
+                    "indexes": ("STRING", {"default": "0"}),
+                    "err_if_missing": ("BOOLEAN", {"default": True}),
+                    "err_if_empty": ("BOOLEAN", {"default": True}),
+                },
+            }
+    
+    DESCRIPTION = select_description
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/image"
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "select"
+
+    def select(self, image: Tensor, indexes: str, err_if_missing: bool, err_if_empty: bool):
+        to_return = select_indexes_from_str(input_obj=image, indexes=indexes,
+                                        err_if_missing=err_if_missing, err_if_empty=err_if_empty)
+        to_return_type = type(to_return)
+        return (to_return,)
+
+
+class SelectMasks:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                    "mask": ("MASK",),
+                    "indexes": ("STRING", {"default": "0"}),
+                    "err_if_missing": ("BOOLEAN", {"default": True}),
+                    "err_if_empty": ("BOOLEAN", {"default": True}),
+                },
+            }
+    
+    DESCRIPTION = select_description
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/mask"
+
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "select"
+
+    def select(self, mask: Tensor, indexes: str, err_if_missing: bool, err_if_empty: bool):
+        return (select_indexes_from_str(input_obj=mask, indexes=indexes,
+                                        err_if_missing=err_if_missing, err_if_empty=err_if_empty),)
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/latent_preview.py b/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/latent_preview.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0a22b8ad028fb9de25cb9818b857a6e4aa0f3e1
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/latent_preview.py
@@ -0,0 +1,114 @@
+from PIL import Image
+import time
+import io
+import struct
+from threading import Thread
+import torch.nn.functional as F
+import torch
+
+import latent_preview
+import server
+serv = server.PromptServer.instance
+
+from .utils import hook
+
+rates_table = {'Mochi': 24//6, 'LTXV': 24//8, 'HunyuanVideo': 24//4,
+               'Cosmos1CV8x8x8': 24//8, 'Wan21': 16//4, 'Wan22': 24//4}
+
+class WrappedPreviewer(latent_preview.LatentPreviewer):
+    def __init__(self, previewer, rate=8):
+        self.first_preview = True
+        self.last_time = 0
+        self.c_index = 0
+        self.rate = rate
+        if hasattr(previewer, 'taesd'):
+            self.taesd = previewer.taesd
+        elif hasattr(previewer, 'latent_rgb_factors'):
+            self.latent_rgb_factors = previewer.latent_rgb_factors
+            self.latent_rgb_factors_bias = previewer.latent_rgb_factors_bias
+            self.latent_rgb_factors_reshape = getattr(previewer, 'latent_rgb_factors_reshape', None)
+        else:
+            raise Exception('Unsupported preview type for VHS animated previews')
+
+    def decode_latent_to_preview_image(self, preview_format, x0):
+        if x0.ndim == 5:
+            #Keep batch major
+            x0 = x0.movedim(2,1)
+            x0 = x0.reshape((-1,)+x0.shape[-3:])
+        num_images = x0.size(0)
+        new_time = time.time()
+        num_previews = int((new_time - self.last_time) * self.rate)
+        self.last_time = self.last_time + num_previews/self.rate
+        if num_previews > num_images:
+            num_previews = num_images
+        elif num_previews <= 0:
+            return None
+        if self.first_preview:
+            self.first_preview = False
+            serv.send_sync('VHS_latentpreview', {'length':num_images, 'rate': self.rate, 'id': serv.last_node_id})
+            self.last_time = new_time + 1/self.rate
+        if self.c_index + num_previews > num_images:
+            x0 = x0.roll(-self.c_index, 0)[:num_previews]
+        else:
+            x0 = x0[self.c_index:self.c_index + num_previews]
+        Thread(target=self.process_previews, args=(x0, self.c_index,
+                                                   num_images)).run()
+        self.c_index = (self.c_index + num_previews) % num_images
+        return None
+    def process_previews(self, image_tensor, ind, leng):
+        image_tensor = self.decode_latent_to_preview(image_tensor)
+        if image_tensor.size(1) > 512 or image_tensor.size(2) > 512:
+            image_tensor = image_tensor.movedim(-1,0)
+            if image_tensor.size(2) < image_tensor.size(3):
+                height = (512 * image_tensor.size(2)) // image_tensor.size(3)
+                image_tensor = F.interpolate(image_tensor, (height,512), mode='bilinear')
+            else:
+                width = (512 * image_tensor.size(3)) // image_tensor.size(2)
+                image_tensor = F.interpolate(image_tensor, (512, width), mode='bilinear')
+            image_tensor = image_tensor.movedim(0,-1)
+        previews_ubyte = (((image_tensor + 1.0) / 2.0).clamp(0, 1)  # change scale from -1..1 to 0..1
+                         .mul(0xFF)  # to 0..255
+                         ).to(device="cpu", dtype=torch.uint8)
+        for preview in previews_ubyte:
+            i = Image.fromarray(preview.numpy())
+            message = io.BytesIO()
+            message.write((1).to_bytes(length=4, byteorder='big')*2)
+            message.write(ind.to_bytes(length=4, byteorder='big'))
+            message.write(struct.pack('16p', serv.last_node_id.encode('ascii')))
+            i.save(message, format="JPEG", quality=95, compress_level=1)
+            #NOTE: send sync already uses call_soon_threadsafe
+            serv.send_sync(server.BinaryEventTypes.PREVIEW_IMAGE,
+                           message.getvalue(), serv.client_id)
+            ind = (ind + 1) % leng
+    def decode_latent_to_preview(self, x0):
+        if hasattr(self, 'taesd'):
+            x_sample = self.taesd.decode(x0).movedim(1, 3)
+            return x_sample
+        else:
+            if self.latent_rgb_factors_reshape is not None:
+                x0 = self.latent_rgb_factors_reshape(x0)
+            self.latent_rgb_factors = self.latent_rgb_factors.to(dtype=x0.dtype, device=x0.device)
+            if self.latent_rgb_factors_bias is not None:
+                self.latent_rgb_factors_bias = self.latent_rgb_factors_bias.to(dtype=x0.dtype, device=x0.device)
+            latent_image = F.linear(x0.movedim(1, -1), self.latent_rgb_factors,
+                                    bias=self.latent_rgb_factors_bias)
+            return latent_image
+
+@hook(latent_preview, 'get_previewer')
+def get_latent_video_previewer(device, latent_format, *args, **kwargs):
+    node_id = serv.last_node_id
+    previewer = get_latent_video_previewer.__wrapped__(device, latent_format, *args, **kwargs)
+    try:
+        extra_info = next(serv.prompt_queue.currently_running.values().__iter__()) \
+                [3]['extra_pnginfo']['workflow']['extra']
+        prev_setting = extra_info.get('VHS_latentpreview', False)
+        if extra_info.get('VHS_latentpreviewrate', 0) != 0:
+            rate_setting = extra_info['VHS_latentpreviewrate']
+        else:
+            rate_setting = rates_table.get(latent_format.__class__.__name__, 8)
+    except:
+        #For safety since there's lots of keys, any of which can fail
+        prev_setting = False
+    if not prev_setting or not hasattr(previewer, "decode_latent_to_preview"):
+        return previewer
+    return WrappedPreviewer(previewer, rate_setting)
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/load_images_nodes.py b/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/load_images_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..43f3387c4f907d7372c207f7f1aeb626c27b836b
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/load_images_nodes.py
@@ -0,0 +1,206 @@
+import os
+import hashlib
+import numpy as np
+import torch
+from PIL import Image, ImageOps
+import itertools
+
+import folder_paths
+from comfy.k_diffusion.utils import FolderOfImages
+from comfy.utils import common_upscale, ProgressBar
+from .logger import logger
+from .utils import BIGMAX, calculate_file_hash, get_sorted_dir_files_from_directory, validate_path, strip_path
+
+
+def is_changed_load_images(directory: str, image_load_cap: int = 0, skip_first_images: int = 0, select_every_nth: int = 1, **kwargs):
+    if not os.path.isdir(directory):
+            return False
+        
+    dir_files = get_sorted_dir_files_from_directory(directory, skip_first_images, select_every_nth, FolderOfImages.IMG_EXTENSIONS)
+    if image_load_cap != 0:
+        dir_files = dir_files[:image_load_cap]
+
+    m = hashlib.sha256()
+    for filepath in dir_files:
+        m.update(calculate_file_hash(filepath).encode()) # strings must be encoded before hashing
+    return m.digest().hex()
+
+
+def validate_load_images(directory: str):
+    if not os.path.isdir(directory):
+            return f"Directory '{directory}' cannot be found."
+    dir_files = os.listdir(directory)
+    if len(dir_files) == 0:
+        return f"No files in directory '{directory}'."
+
+    return True
+
+def images_generator(directory: str, image_load_cap: int = 0, skip_first_images: int = 0, select_every_nth: int = 1, meta_batch=None, unique_id=None):
+    if not os.path.isdir(directory):
+        raise FileNotFoundError(f"Directory '{directory} cannot be found.")
+    dir_files = get_sorted_dir_files_from_directory(directory, skip_first_images, select_every_nth, FolderOfImages.IMG_EXTENSIONS)
+
+    if len(dir_files) == 0:
+        raise FileNotFoundError(f"No files in directory '{directory}'.")
+    if image_load_cap > 0:
+        dir_files = dir_files[:image_load_cap]
+    sizes = {}
+    has_alpha = False
+    for image_path in dir_files:
+        i = Image.open(image_path)
+        #exif_transpose can only ever rotate, but rotating can swap width/height
+        i = ImageOps.exif_transpose(i)
+        has_alpha |= 'A' in i.getbands()
+        count = sizes.get(i.size, 0)
+        sizes[i.size] = count +1
+    size = max(sizes.items(), key=lambda x: x[1])[0]
+    yield size[0], size[1], has_alpha
+    if meta_batch is not None:
+        yield min(image_load_cap, len(dir_files)) or len(dir_files)
+
+    iformat = "RGBA" if has_alpha else "RGB"
+    def load_image(file_path):
+        i = Image.open(file_path)
+        i = ImageOps.exif_transpose(i)
+        i = i.convert(iformat)
+        i = np.array(i, dtype=np.float32)
+        #This nonsense provides a nearly 50% speedup on my system
+        torch.from_numpy(i).div_(255)
+        if i.shape[0] != size[1] or i.shape[1] != size[0]:
+            i = torch.from_numpy(i).movedim(-1, 0).unsqueeze(0)
+            i = common_upscale(i, size[0], size[1], "lanczos", "center")
+            i = i.squeeze(0).movedim(0, -1).numpy()
+        if has_alpha:
+            i[:,:,-1] = 1 - i[:,:,-1]
+        return i
+
+    total_images = len(dir_files)
+    processed_images = 0
+    pbar = ProgressBar(total_images)
+    images =  map(load_image, dir_files)
+    try:
+        prev_image = next(images)
+        while True:
+            next_image = next(images)
+            yield prev_image
+            processed_images += 1
+            pbar.update_absolute(processed_images, total_images)
+            prev_image = next_image
+    except StopIteration:
+        pass
+    if meta_batch is not None:
+        meta_batch.inputs.pop(unique_id)
+        meta_batch.has_closed_inputs = True
+    if prev_image is not None:
+        yield prev_image
+
+
+def load_images(directory: str, image_load_cap: int = 0, skip_first_images: int = 0, select_every_nth: int = 1, meta_batch=None, unique_id=None):
+    if meta_batch is None or unique_id not in meta_batch.inputs:
+        gen = images_generator(directory, image_load_cap, skip_first_images, select_every_nth, meta_batch, unique_id)
+        (width, height, has_alpha) = next(gen)
+        if meta_batch is not None:
+            meta_batch.inputs[unique_id] = (gen, width, height, has_alpha)
+            meta_batch.total_frames = min(meta_batch.total_frames, next(gen))
+    else:
+        gen, width, height, has_alpha = meta_batch.inputs[unique_id]
+
+    if meta_batch is not None:
+        gen = itertools.islice(gen, meta_batch.frames_per_batch)
+    images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (height, width, 3 + has_alpha)))))
+    if has_alpha:
+        #tensors are not continuous. Rewrite will be required if this is an issue
+        masks = images[:,:,:,3]
+        images = images[:,:,:,:3]
+    else:
+        masks = torch.zeros((images.size(0), 64, 64), dtype=torch.float32, device="cpu")
+    if len(images) == 0:
+        raise FileNotFoundError(f"No images could be loaded from directory '{directory}'.")
+    return images, masks, images.size(0)
+
+class LoadImagesFromDirectoryUpload:
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        directories = []
+        for item in os.listdir(input_dir):
+            if not os.path.isfile(os.path.join(input_dir, item)) and item != "clipspace":
+                directories.append(item)
+        return {
+            "required": {
+                "directory": (directories,),
+            },
+            "optional": {
+                "image_load_cap": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
+                "skip_first_images": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
+                "select_every_nth": ("INT", {"default": 1, "min": 1, "max": BIGMAX, "step": 1}),
+                "meta_batch": ("VHS_BatchManager",),
+            },
+            "hidden": {
+                "unique_id": "UNIQUE_ID"
+            },
+        }
+    
+    RETURN_TYPES = ("IMAGE", "MASK", "INT")
+    RETURN_NAMES = ("IMAGE", "MASK", "frame_count")
+    FUNCTION = "load_images"
+
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
+
+    def load_images(self, directory: str, **kwargs):
+        directory = folder_paths.get_annotated_filepath(strip_path(directory))
+        return load_images(directory, **kwargs)
+    
+    @classmethod
+    def IS_CHANGED(s, directory: str, **kwargs):
+        directory = folder_paths.get_annotated_filepath(strip_path(directory))
+        return is_changed_load_images(directory, **kwargs)
+
+    @classmethod
+    def VALIDATE_INPUTS(s, directory: str, **kwargs):
+        directory = folder_paths.get_annotated_filepath(strip_path(directory))
+        return validate_load_images(directory)
+
+
+class LoadImagesFromDirectoryPath:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "directory": ("STRING", {"placeholder": "X://path/to/images", "vhs_path_extensions": []}),
+            },
+            "optional": {
+                "image_load_cap": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
+                "skip_first_images": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
+                "select_every_nth": ("INT", {"default": 1, "min": 1, "max": BIGMAX, "step": 1}),
+                "meta_batch": ("VHS_BatchManager",),
+            },
+            "hidden": {
+                "unique_id": "UNIQUE_ID"
+            },
+        }
+    
+    RETURN_TYPES = ("IMAGE", "MASK", "INT")
+    RETURN_NAMES = ("IMAGE", "MASK", "frame_count")
+    FUNCTION = "load_images"
+
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
+
+    def load_images(self, directory: str, **kwargs):
+        directory = strip_path(directory)
+        if directory is None or validate_load_images(directory) != True:
+            raise Exception("directory is not valid: " + directory)
+
+        return load_images(directory, **kwargs)
+    
+    @classmethod
+    def IS_CHANGED(s, directory: str, **kwargs):
+        if directory is None:
+            return "input"
+        return is_changed_load_images(directory, **kwargs)
+
+    @classmethod
+    def VALIDATE_INPUTS(s, directory: str, **kwargs):
+        if directory is None:
+            return True
+        return validate_load_images(strip_path(directory))
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/load_video_nodes.py b/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/load_video_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..b23e40f535c8166ae05fa6a541d159cdadb368f7
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/load_video_nodes.py
@@ -0,0 +1,671 @@
+import os
+import itertools
+import numpy as np
+import torch
+from PIL import Image, ImageOps
+import cv2
+import psutil
+import subprocess
+import re
+import time
+
+import folder_paths
+from comfy.utils import common_upscale, ProgressBar
+import nodes
+from comfy.k_diffusion.utils import FolderOfImages
+from .logger import logger
+from .utils import BIGMAX, DIMMAX, calculate_file_hash, get_sorted_dir_files_from_directory,\
+        lazy_get_audio, hash_path, validate_path, strip_path, try_download_video,  \
+        is_url, imageOrLatent, ffmpeg_path, ENCODE_ARGS, floatOrInt
+
+
+video_extensions = ['webm', 'mp4', 'mkv', 'gif', 'mov']
+
+VHSLoadFormats = {
+    'None': {},
+    'AnimateDiff': {'target_rate': 8, 'dim': (8,0,512,512)},
+    'Mochi': {'target_rate': 24, 'dim': (16,0,848,480), 'frames':(6,1)},
+    'LTXV': {'target_rate': 24, 'dim': (32,0,768,512), 'frames':(8,1)},
+    'Hunyuan': {'target_rate': 24, 'dim': (16,0,848,480), 'frames':(4,1)},
+    'Cosmos': {'target_rate': 24, 'dim': (16,0,1280,704), 'frames':(8,1)},
+    'Wan': {'target_rate': 16, 'dim': (8,0,832,480), 'frames':(4,1)},
+}
+"""
+External plugins may add additional formats to nodes.VHSLoadFormats
+In addition to shorthand options, direct widget names will map a given dict to options.
+Adding a third arguement to a frames tuple can enable strict checks on number
+of loaded frames, i.e (8,1,True)
+"""
+if not hasattr(nodes, 'VHSLoadFormats'):
+    nodes.VHSLoadFormats = {}
+
+def get_load_formats():
+    #TODO: check if {**extra_config.VHSLoafFormats, **VHSLoadFormats} has minimum version
+    formats = {}
+    formats.update(nodes.VHSLoadFormats)
+    formats.update(VHSLoadFormats)
+    return (list(formats.keys()),
+            {'default': 'AnimateDiff', 'formats': formats})
+def get_format(format):
+    if format in VHSLoadFormats:
+        return VHSLoadFormats[format]
+    return nodes.VHSLoadFormats.get(format, {})
+
+def is_gif(filename) -> bool:
+    file_parts = filename.split('.')
+    return len(file_parts) > 1 and file_parts[-1] == "gif"
+
+
+def target_size(width, height, custom_width, custom_height, downscale_ratio=8) -> tuple[int, int]:
+    if downscale_ratio is None:
+        downscale_ratio = 8
+    if custom_width == 0 and custom_height ==  0:
+        pass
+    elif custom_height == 0:
+        height *= custom_width/width
+        width = custom_width
+    elif custom_width == 0:
+        width *= custom_height/height
+        height = custom_height
+    else:
+        width = custom_width
+        height = custom_height
+    width = int(width/downscale_ratio + 0.5) * downscale_ratio
+    height = int(height/downscale_ratio + 0.5) * downscale_ratio
+    return (width, height)
+
+def cv_frame_generator(video, force_rate, frame_load_cap, skip_first_frames,
+                       select_every_nth, meta_batch=None, unique_id=None):
+    video_cap = cv2.VideoCapture(video)
+    if not video_cap.isOpened() or not video_cap.grab():
+        raise ValueError(f"{video} could not be loaded with cv.")
+
+    # extract video metadata
+    fps = video_cap.get(cv2.CAP_PROP_FPS)
+    width = int(video_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(video_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    total_frames = int(video_cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    duration = total_frames / fps
+
+    width = 0
+
+    if width <=0 or height <=0:
+        _, frame = video_cap.retrieve()
+        height, width, _ = frame.shape
+
+    # set video_cap to look at start_index frame
+    total_frame_count = 0
+    total_frames_evaluated = -1
+    frames_added = 0
+    base_frame_time = 1 / fps
+    prev_frame = None
+
+    if force_rate == 0:
+        target_frame_time = base_frame_time
+    else:
+        target_frame_time = 1/force_rate
+
+    if total_frames > 0:
+        if force_rate != 0:
+            yieldable_frames = int(total_frames / fps * force_rate)
+        else:
+            yieldable_frames = total_frames
+        if select_every_nth:
+            yieldable_frames //= select_every_nth
+        if frame_load_cap != 0:
+            yieldable_frames =  min(frame_load_cap, yieldable_frames)
+    else:
+        yieldable_frames = 0
+    yield (width, height, fps, duration, total_frames, target_frame_time, yieldable_frames)
+    pbar = ProgressBar(yieldable_frames)
+    time_offset=target_frame_time
+    while video_cap.isOpened():
+        if time_offset < target_frame_time:
+            is_returned = video_cap.grab()
+            # if didn't return frame, video has ended
+            if not is_returned:
+                break
+            time_offset += base_frame_time
+        if time_offset < target_frame_time:
+            continue
+        time_offset -= target_frame_time
+        # if not at start_index, skip doing anything with frame
+        total_frame_count += 1
+        if total_frame_count <= skip_first_frames:
+            continue
+        else:
+            total_frames_evaluated += 1
+
+        # if should not be selected, skip doing anything with frame
+        if total_frames_evaluated%select_every_nth != 0:
+            continue
+
+        # opencv loads images in BGR format (yuck), so need to convert to RGB for ComfyUI use
+        # follow up: can videos ever have an alpha channel?
+        # To my testing: No. opencv has no support for alpha
+        unused, frame = video_cap.retrieve()
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        # convert frame to comfyui's expected format
+        # TODO: frame contains no exif information. Check if opencv2 has already applied
+        frame = np.array(frame, dtype=np.float32)
+        torch.from_numpy(frame).div_(255)
+        if prev_frame is not None:
+            inp  = yield prev_frame
+            if inp is not None:
+                #ensure the finally block is called
+                return
+        prev_frame = frame
+        frames_added += 1
+        if pbar is not None:
+            pbar.update_absolute(frames_added, yieldable_frames)
+        # if cap exists and we've reached it, stop processing frames
+        if frame_load_cap > 0 and frames_added >= frame_load_cap:
+            break
+    if meta_batch is not None:
+        meta_batch.inputs.pop(unique_id)
+        meta_batch.has_closed_inputs = True
+    if prev_frame is not None:
+        yield prev_frame
+
+def ffmpeg_frame_generator(video, force_rate, frame_load_cap, start_time,
+                           custom_width, custom_height, downscale_ratio=8,
+                           meta_batch=None, unique_id=None):
+    args_input = ["-i", video]
+    args_dummy = [ffmpeg_path] + args_input +['-c', 'copy', '-frames:v', '1', "-f", "null", "-"]
+    size_base = None
+    fps_base = None
+    try:
+        dummy_res = subprocess.run(args_dummy, stdout=subprocess.DEVNULL,
+                                 stderr=subprocess.PIPE, check=True)
+    except subprocess.CalledProcessError as e:
+        raise Exception("An error occurred in the ffmpeg subprocess:\n" \
+                + e.stderr.decode(*ENCODE_ARGS))
+    lines = dummy_res.stderr.decode(*ENCODE_ARGS)
+    if "Video: vp9 " in lines:
+        args_input = ["-c:v", "libvpx-vp9"] + args_input
+        args_dummy = [ffmpeg_path] + args_input +['-c', 'copy', '-frames:v', '1', "-f", "null", "-"]
+        try:
+            dummy_res = subprocess.run(args_dummy, stdout=subprocess.DEVNULL,
+                                     stderr=subprocess.PIPE, check=True)
+        except subprocess.CalledProcessError as e:
+            raise Exception("An error occurred in the ffmpeg subprocess:\n" \
+                    + e.stderr.decode(*ENCODE_ARGS))
+        lines = dummy_res.stderr.decode(*ENCODE_ARGS)
+
+    for line in lines.split('\n'):
+        match = re.search("^ *Stream .* Video.*, ([1-9]|\\d{2,})x(\\d+)", line)
+        if match is not None:
+            size_base = [int(match.group(1)), int(match.group(2))]
+            fps_match = re.search(", ([\\d\\.]+) fps", line)
+            if fps_match:
+                fps_base = float(fps_match.group(1))
+            else:
+                fps_base = 1
+            alpha = re.search("(yuva|rgba|bgra|gbra)", line) is not None
+            break
+    else:
+        raise Exception("Failed to parse video/image information. FFMPEG output:\n" + lines)
+
+    durs_match = re.search("Duration: (\\d+:\\d+:\\d+\\.\\d+),", lines)
+    if durs_match:
+        durs = durs_match.group(1).split(':')
+        duration = int(durs[0])*3600 + int(durs[1])*60 + float(durs[2])
+    else:
+        duration = 0
+
+    if start_time > 0:
+        if start_time > 4:
+            post_seek = ['-ss', '4']
+            args_input = ['-ss', str(start_time - 4)] + args_input
+        else:
+            post_seek = ['-ss', str(start_time)]
+    else:
+        post_seek = []
+    args_all_frames = [ffmpeg_path, "-v", "error", "-an"] + \
+            args_input + ["-pix_fmt", "rgba64le"] + post_seek
+
+    vfilters = []
+    if force_rate != 0:
+        vfilters.append("fps=fps="+str(force_rate))
+    if custom_width != 0 or custom_height != 0:
+        size = target_size(size_base[0], size_base[1], custom_width,
+                           custom_height, downscale_ratio=downscale_ratio)
+        ar = float(size[0])/float(size[1])
+        if abs(size_base[0]*ar-size_base[1]) >= 1:
+            #Aspect ratio is changed. Crop to new aspect ratio before scale
+            vfilters.append(f"crop=if(gt({ar}\\,a)\\,iw\\,ih*{ar}):if(gt({ar}\\,a)\\,iw/{ar}\\,ih)")
+        size_arg = ':'.join(map(str,size))
+        vfilters.append(f"scale={size_arg}")
+    else:
+        size = size_base
+    if len(vfilters) > 0:
+        args_all_frames += ["-vf", ",".join(vfilters)]
+    yieldable_frames = (force_rate or fps_base)*duration
+    if frame_load_cap > 0:
+        args_all_frames += ["-frames:v", str(frame_load_cap)]
+        yieldable_frames = min(yieldable_frames, frame_load_cap)
+    yield (size_base[0], size_base[1], fps_base, duration, fps_base * duration,
+           1/(force_rate or fps_base), yieldable_frames, size[0], size[1], alpha)
+
+    args_all_frames += ["-f", "rawvideo", "-"]
+    pbar = ProgressBar(yieldable_frames)
+    try:
+        with subprocess.Popen(args_all_frames, stdout=subprocess.PIPE) as proc:
+            #Manually buffer enough bytes for an image
+            bpi = size[0] * size[1] * 8
+            current_bytes = bytearray(bpi)
+            current_offset=0
+            prev_frame = None
+            while True:
+                bytes_read = proc.stdout.read(bpi - current_offset)
+                if bytes_read is None:#sleep to wait for more data
+                    time.sleep(.1)
+                    continue
+                if len(bytes_read) == 0:#EOF
+                    break
+                current_bytes[current_offset:len(bytes_read)] = bytes_read
+                current_offset+=len(bytes_read)
+                if current_offset == bpi:
+                    if prev_frame is not None:
+                        yield prev_frame
+                        pbar.update(1)
+                    prev_frame = np.frombuffer(current_bytes, dtype=np.dtype(np.uint16).newbyteorder("<")).reshape(size[1], size[0], 4) / (2**16-1)
+                    if not alpha:
+                        prev_frame = prev_frame[:, :, :-1]
+                    current_offset = 0
+    except BrokenPipeError as e:
+        raise Exception("An error occured in the ffmpeg subprocess:\n" \
+                + proc.stderr.read().decode(*ENCODE_ARGS))
+    if meta_batch is not None:
+        meta_batch.inputs.pop(unique_id)
+        meta_batch.has_closed_inputs = True
+    if prev_frame is not None:
+        yield prev_frame
+
+#Python 3.12 adds an itertools.batched, but it's easily replicated for legacy support
+def batched(it, n):
+    while batch := tuple(itertools.islice(it, n)):
+        yield batch
+def batched_vae_encode(images, vae, frames_per_batch):
+    for batch in batched(images, frames_per_batch):
+        image_batch = torch.from_numpy(np.array(batch))
+        yield from vae.encode(image_batch).numpy()
+def resized_cv_frame_gen(custom_width, custom_height, downscale_ratio, **kwargs):
+    gen = cv_frame_generator(**kwargs)
+    info =  next(gen)
+    width, height = info[0], info[1]
+    frames_per_batch = (1920 * 1080 * 16) // (width * height) or 1
+    if kwargs.get('meta_batch', None) is not None:
+        frames_per_batch = min(frames_per_batch, kwargs['meta_batch'].frames_per_batch)
+    if custom_width != 0 or custom_height != 0 or downscale_ratio is not None:
+        new_size = target_size(width, height, custom_width, custom_height, downscale_ratio)
+        yield (*info, new_size[0], new_size[1], False)
+        if new_size[0] != width or new_size[1] != height:
+            def rescale(frame):
+                s = torch.from_numpy(np.fromiter(frame, np.dtype((np.float32, (height, width, 3)))))
+                s = s.movedim(-1,1)
+                s = common_upscale(s, new_size[0], new_size[1], "lanczos", "center")
+                return s.movedim(1,-1).numpy()
+            yield from itertools.chain.from_iterable(map(rescale, batched(gen, frames_per_batch)))
+            return
+    else:
+        yield (*info, info[0], info[1], False)
+    yield from gen
+
+def load_video(meta_batch=None, unique_id=None, memory_limit_mb=None, vae=None,
+               generator=resized_cv_frame_gen, format='None',  **kwargs):
+    if 'force_size' in kwargs:
+        kwargs.pop('force_size')
+        logger.warn("force_size has been removed. Did you reload the webpage after updating?")
+    format = get_format(format)
+    kwargs['video'] = strip_path(kwargs['video'])
+    if vae is not None:
+        downscale_ratio = getattr(vae, "downscale_ratio", 8)
+    else:
+        downscale_ratio = format.get('dim', (1,))[0]
+    if meta_batch is None or unique_id not in meta_batch.inputs:
+        gen = generator(meta_batch=meta_batch, unique_id=unique_id, downscale_ratio=downscale_ratio, **kwargs)
+        (width, height, fps, duration, total_frames, target_frame_time, yieldable_frames, new_width, new_height, alpha) = next(gen)
+
+        if meta_batch is not None:
+            meta_batch.inputs[unique_id] = (gen, width, height, fps, duration, total_frames, target_frame_time, yieldable_frames, new_width, new_height, alpha)
+            if yieldable_frames:
+                meta_batch.total_frames = min(meta_batch.total_frames, yieldable_frames)
+
+    else:
+        (gen, width, height, fps, duration, total_frames, target_frame_time, yieldable_frames, new_width, new_height, alpha) = meta_batch.inputs[unique_id]
+
+    memory_limit = None
+    if memory_limit_mb is not None:
+        memory_limit *= 2 ** 20
+    else:
+        #TODO: verify if garbage collection should be performed here.
+        #leaves ~128 MB unreserved for safety
+        try:
+            memory_limit = (psutil.virtual_memory().available + psutil.swap_memory().free) - 2 ** 27
+        except:
+            logger.warn("Failed to calculate available memory. Memory load limit has been disabled")
+            memory_limit = BIGMAX
+    if vae is not None:
+        #space required to load as f32, exist as latent with wiggle room, decode to f32
+        max_loadable_frames = int(memory_limit//(width*height*3*(4+4+1/10)))
+    else:
+        #TODO: use better estimate for when vae is not None
+        #Consider completely ignoring for load_latent case?
+        max_loadable_frames = int(memory_limit//(width*height*3*(.1)))
+    if meta_batch is not None:
+        if 'frames' in format:
+            if meta_batch.frames_per_batch % format['frames'][0] != format['frames'][1]:
+                error = (meta_batch.frames_per_batch - format['frames'][1]) % format['frames'][0]
+                suggested = meta_batch.frames_per_batch - error
+                if error > format['frames'][0] / 2:
+                    suggested += format['frames'][0]
+                raise RuntimeError(f"The chosen frames per batch is incompatible with the selected format. Try {suggested}")
+        if meta_batch.frames_per_batch > max_loadable_frames:
+            raise RuntimeError(f"Meta Batch set to {meta_batch.frames_per_batch} frames but only {max_loadable_frames} can fit in memory")
+        gen = itertools.islice(gen, meta_batch.frames_per_batch)
+    else:
+        original_gen = gen
+        gen = itertools.islice(gen, max_loadable_frames)
+    frames_per_batch = (1920 * 1080 * 16) // (width * height) or 1
+    if vae is not None:
+        gen = batched_vae_encode(gen, vae, frames_per_batch)
+        vw,vh = new_width//downscale_ratio, new_height//downscale_ratio
+        channels = getattr(vae, 'latent_channels', 4)
+        images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (channels,vh,vw)))))
+    else:
+        #Some minor wizardry to eliminate a copy and reduce max memory by a factor of ~2
+        images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (new_height, new_width, 4 if alpha else 3)))))
+    if meta_batch is None and memory_limit is not None:
+        try:
+            next(original_gen)
+            raise RuntimeError(f"Memory limit hit after loading {len(images)} frames. Stopping execution.")
+        except StopIteration:
+            pass
+    if len(images) == 0:
+        raise RuntimeError("No frames generated")
+    if 'frames' in format and len(images) % format['frames'][0] != format['frames'][1]:
+        err_msg = f"The number of frames loaded {len(images)}, does not match the requirements of the currently selected format."
+        if len(format['frames']) > 2 and format['frames'][2]:
+            raise RuntimeError(err_msg)
+        div, mod = format['frames'][:2]
+        frames = (len(images) - mod) // div * div + mod
+        images = images[:frames]
+        #Commenting out log message since it's displayed in UI. consider further
+        #logger.warn(err_msg + f" Output has been truncated to {len(images)} frames.")
+    if 'start_time' in kwargs:
+        start_time = kwargs['start_time']
+    else:
+        start_time = kwargs['skip_first_frames'] * target_frame_time
+    target_frame_time *= kwargs.get('select_every_nth', 1)
+    #Setup lambda for lazy audio capture
+    audio = lazy_get_audio(kwargs['video'], start_time, kwargs['frame_load_cap']*target_frame_time)
+    #Adjust target_frame_time for select_every_nth
+    video_info = {
+        "source_fps": fps,
+        "source_frame_count": total_frames,
+        "source_duration": duration,
+        "source_width": width,
+        "source_height": height,
+        "loaded_fps": 1/target_frame_time,
+        "loaded_frame_count": len(images),
+        "loaded_duration": len(images) * target_frame_time,
+        "loaded_width": new_width,
+        "loaded_height": new_height,
+    }
+    if vae is None:
+        return (images, len(images), audio, video_info)
+    else:
+        return ({"samples": images}, len(images), audio, video_info)
+
+
+
+class LoadVideoUpload:
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        files = []
+        for f in os.listdir(input_dir):
+            if os.path.isfile(os.path.join(input_dir, f)):
+                file_parts = f.split('.')
+                if len(file_parts) > 1 and (file_parts[-1].lower() in video_extensions):
+                    files.append(f)
+        return {"required": {
+                    "video": (sorted(files),),
+                    "force_rate": (floatOrInt, {"default": 0, "min": 0, "max": 60, "step": 1, "disable": 0}),
+                    "custom_width": ("INT", {"default": 0, "min": 0, "max": DIMMAX, 'disable': 0}),
+                    "custom_height": ("INT", {"default": 0, "min": 0, "max": DIMMAX, 'disable': 0}),
+                    "frame_load_cap": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1, "disable": 0}),
+                    "skip_first_frames": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
+                    "select_every_nth": ("INT", {"default": 1, "min": 1, "max": BIGMAX, "step": 1}),
+                    },
+                "optional": {
+                    "meta_batch": ("VHS_BatchManager",),
+                    "vae": ("VAE",),
+                     "format": get_load_formats(),
+                },
+                "hidden": {
+                    "force_size": "STRING",
+                    "unique_id": "UNIQUE_ID"
+                },
+                }
+
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
+
+    RETURN_TYPES = (imageOrLatent, "INT", "AUDIO", "VHS_VIDEOINFO")
+    RETURN_NAMES = ("IMAGE", "frame_count", "audio", "video_info")
+
+    FUNCTION = "load_video"
+
+    def load_video(self, **kwargs):
+        kwargs['video'] = folder_paths.get_annotated_filepath(strip_path(kwargs['video']))
+        return load_video(**kwargs)
+
+    @classmethod
+    def IS_CHANGED(s, video, **kwargs):
+        image_path = folder_paths.get_annotated_filepath(video)
+        return calculate_file_hash(image_path)
+
+    @classmethod
+    def VALIDATE_INPUTS(s, video):
+        if not folder_paths.exists_annotated_filepath(video):
+            return "Invalid video file: {}".format(video)
+        return True
+
+
+class LoadVideoPath:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "video": ("STRING", {"placeholder": "X://insert/path/here.mp4", "vhs_path_extensions": video_extensions}),
+                "force_rate": (floatOrInt, {"default": 0, "min": 0, "max": 60, "step": 1, "disable": 0}),
+                "custom_width": ("INT", {"default": 0, "min": 0, "max": DIMMAX, 'disable': 0}),
+                "custom_height": ("INT", {"default": 0, "min": 0, "max": DIMMAX, 'disable': 0}),
+                "frame_load_cap": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1, "disable": 0}),
+                "skip_first_frames": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
+                "select_every_nth": ("INT", {"default": 1, "min": 1, "max": BIGMAX, "step": 1}),
+            },
+            "optional": {
+                "meta_batch": ("VHS_BatchManager",),
+                "vae": ("VAE",),
+                "format": get_load_formats(),
+            },
+            "hidden": {
+                "force_size": "STRING",
+                "unique_id": "UNIQUE_ID"
+            },
+        }
+
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
+
+    RETURN_TYPES = (imageOrLatent, "INT", "AUDIO", "VHS_VIDEOINFO")
+    RETURN_NAMES = ("IMAGE", "frame_count", "audio", "video_info")
+
+    FUNCTION = "load_video"
+
+    def load_video(self, **kwargs):
+        if kwargs['video'] is None or validate_path(kwargs['video']) != True:
+            raise Exception("video is not a valid path: " + kwargs['video'])
+        if is_url(kwargs['video']):
+            kwargs['video'] = try_download_video(kwargs['video']) or kwargs['video']
+        return load_video(**kwargs)
+
+    @classmethod
+    def IS_CHANGED(s, video, **kwargs):
+        return hash_path(video)
+
+    @classmethod
+    def VALIDATE_INPUTS(s, video):
+        return validate_path(video, allow_none=True)
+
+class LoadVideoFFmpegUpload:
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        files = []
+        for f in os.listdir(input_dir):
+            if os.path.isfile(os.path.join(input_dir, f)):
+                file_parts = f.split('.')
+                if len(file_parts) > 1 and (file_parts[-1].lower() in video_extensions):
+                    files.append(f)
+        return {"required": {
+                    "video": (sorted(files),),
+                    "force_rate": (floatOrInt, {"default": 0, "min": 0, "max": 60, "step": 1, "disable": 0}),
+                    "custom_width": ("INT", {"default": 0, "min": 0, "max": DIMMAX, 'disable': 0}),
+                    "custom_height": ("INT", {"default": 0, "min": 0, "max": DIMMAX, 'disable': 0}),
+                    "frame_load_cap": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1, "disable": 0}),
+                    "start_time": ("FLOAT", {"default": 0, "min": 0, "max": BIGMAX, "step": .001, "widgetType": "VHSTIMESTAMP"}),
+                    },
+                "optional": {
+                    "meta_batch": ("VHS_BatchManager",),
+                    "vae": ("VAE",),
+                     "format": get_load_formats(),
+                },
+                "hidden": {
+                    "force_size": "STRING",
+                    "unique_id": "UNIQUE_ID"
+
+                },
+                }
+
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
+
+    RETURN_TYPES = (imageOrLatent, "MASK", "AUDIO", "VHS_VIDEOINFO")
+    RETURN_NAMES = ("IMAGE", "mask", "audio", "video_info")
+
+    FUNCTION = "load_video"
+
+    def load_video(self, **kwargs):
+        kwargs['video'] = folder_paths.get_annotated_filepath(strip_path(kwargs['video']))
+        image, _, audio, video_info =  load_video(**kwargs, generator=ffmpeg_frame_generator)
+        if image.size(3) == 4:
+            return (image[:,:,:,:3], 1-image[:,:,:,3], audio, video_info)
+        return (image, torch.zeros(image.size(0), 64, 64, device="cpu"), audio, video_info)
+
+    @classmethod
+    def IS_CHANGED(s, video, **kwargs):
+        image_path = folder_paths.get_annotated_filepath(video)
+        return calculate_file_hash(image_path)
+
+    @classmethod
+    def VALIDATE_INPUTS(s, video):
+        if not folder_paths.exists_annotated_filepath(video):
+            return "Invalid video file: {}".format(video)
+        return True
+
+
+class LoadVideoFFmpegPath:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "video": ("STRING", {"placeholder": "X://insert/path/here.mp4", "vhs_path_extensions": video_extensions}),
+                "force_rate": (floatOrInt, {"default": 0, "min": 0, "max": 60, "step": 1, "disable": 0}),
+                "custom_width": ("INT", {"default": 0, "min": 0, "max": DIMMAX, 'disable': 0}),
+                "custom_height": ("INT", {"default": 0, "min": 0, "max": DIMMAX, 'disable': 0}),
+                "frame_load_cap": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1, "disable": 0}),
+                "start_time": ("FLOAT", {"default": 0, "min": 0, "max": BIGMAX, "step": .001, "widgetType": "VHSTIMESTAMP"}),
+            },
+            "optional": {
+                "meta_batch": ("VHS_BatchManager",),
+                "vae": ("VAE",),
+                "format": get_load_formats(),
+            },
+            "hidden": {
+                "force_size": "STRING",
+                "unique_id": "UNIQUE_ID"
+            },
+        }
+
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
+
+    RETURN_TYPES = (imageOrLatent, "MASK", "AUDIO", "VHS_VIDEOINFO")
+    RETURN_NAMES = ("IMAGE", "mask", "audio", "video_info")
+
+    FUNCTION = "load_video"
+
+    def load_video(self, **kwargs):
+        if kwargs['video'] is None or validate_path(kwargs['video']) != True:
+            raise Exception("video is not a valid path: " + kwargs['video'])
+        if is_url(kwargs['video']):
+            kwargs['video'] = try_download_video(kwargs['video']) or kwargs['video']
+        image, _, audio, video_info =  load_video(**kwargs, generator=ffmpeg_frame_generator)
+        if isinstance(image, dict):
+            return (image, None, audio, video_info)
+        if image.size(3) == 4:
+            return (image[:,:,:,:3], 1-image[:,:,:,3], audio, video_info)
+        return (image, torch.zeros(image.size(0), 64, 64, device="cpu"), audio, video_info)
+
+    @classmethod
+    def IS_CHANGED(s, video, **kwargs):
+        return hash_path(video)
+
+    @classmethod
+    def VALIDATE_INPUTS(s, video):
+        return validate_path(video, allow_none=True)
+
+class LoadImagePath:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("STRING", {"placeholder": "X://insert/path/here.png", "vhs_path_extensions": list(FolderOfImages.IMG_EXTENSIONS)}),
+                "custom_width": ("INT", {"default": 0, "min": 0, "max": DIMMAX, "step": 8, 'disable': 0}),
+                "custom_height": ("INT", {"default": 0, "min": 0, "max": DIMMAX, "step": 8, 'disable': 0}),
+            },
+            "optional": {
+                "vae": ("VAE",),
+            },
+            "hidden": {
+                "force_size": "STRING",
+            },
+        }
+
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
+
+    RETURN_TYPES = (imageOrLatent, "MASK")
+    RETURN_NAMES = ("IMAGE", "mask")
+
+    FUNCTION = "load_image"
+
+    def load_image(self, **kwargs):
+        if kwargs['image'] is None or validate_path(kwargs['image']) != True:
+            raise Exception("image is not a valid path: " + kwargs['image'])
+        kwargs.update({'video':  kwargs['image'], 'force_rate': 0, 'frame_load_cap': 0,
+                      'start_time': 0})
+        kwargs.pop('image')
+        image, _, _, _ =  load_video(**kwargs, generator=ffmpeg_frame_generator)
+        if isinstance(image, dict):
+            return (image, None)
+        if image.size(3) == 4:
+            return (image[:,:,:,:3], 1-image[:,:,:,3])
+        return (image, torch.zeros(image.size(0), 64, 64, device="cpu"))
+
+    @classmethod
+    def IS_CHANGED(s, image, **kwargs):
+        return hash_path(image)
+
+    @classmethod
+    def VALIDATE_INPUTS(s, image):
+        return validate_path(image, allow_none=True)
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/logger.py b/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e7b8d64bda275608ba6bf8ee28d2a2112e3e2be
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/logger.py
@@ -0,0 +1,36 @@
+import sys
+import copy
+import logging
+
+
+class ColoredFormatter(logging.Formatter):
+    COLORS = {
+        "DEBUG": "\033[0;36m",  # CYAN
+        "INFO": "\033[0;32m",  # GREEN
+        "WARNING": "\033[0;33m",  # YELLOW
+        "ERROR": "\033[0;31m",  # RED
+        "CRITICAL": "\033[0;37;41m",  # WHITE ON RED
+        "RESET": "\033[0m",  # RESET COLOR
+    }
+
+    def format(self, record):
+        colored_record = copy.copy(record)
+        levelname = colored_record.levelname
+        seq = self.COLORS.get(levelname, self.COLORS["RESET"])
+        colored_record.levelname = f"{seq}{levelname}{self.COLORS['RESET']}"
+        return super().format(colored_record)
+
+
+# Create a new logger
+logger = logging.getLogger("VideoHelperSuite")
+logger.propagate = False
+
+# Add handler if we don't have one.
+if not logger.handlers:
+    handler = logging.StreamHandler(sys.stdout)
+    handler.setFormatter(ColoredFormatter("[%(name)s] - %(levelname)s - %(message)s"))
+    logger.addHandler(handler)
+
+# Configure logger
+loglevel = logging.INFO
+logger.setLevel(loglevel)
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/nodes.py b/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..4389f5de534d0fdc66fdc922257166125e26e444
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/nodes.py
@@ -0,0 +1,1120 @@
+import os
+import sys
+import json
+import subprocess
+import numpy as np
+import re
+import datetime
+from typing import List
+import torch
+from PIL import Image, ExifTags
+from PIL.PngImagePlugin import PngInfo
+from pathlib import Path
+from string import Template
+import itertools
+import functools
+
+import folder_paths
+from .logger import logger
+from .image_latent_nodes import *
+from .load_video_nodes import LoadVideoUpload, LoadVideoPath, LoadVideoFFmpegUpload, LoadVideoFFmpegPath, LoadImagePath
+from .load_images_nodes import LoadImagesFromDirectoryUpload, LoadImagesFromDirectoryPath
+from .batched_nodes import VAEEncodeBatched, VAEDecodeBatched
+from .utils import ffmpeg_path, get_audio, hash_path, validate_path, requeue_workflow, \
+        gifski_path, calculate_file_hash, strip_path, try_download_video, is_url, \
+        imageOrLatent, BIGMAX, merge_filter_args, ENCODE_ARGS, floatOrInt, cached, \
+        ContainsAll
+from comfy.utils import ProgressBar
+
+if 'VHS_video_formats' not in folder_paths.folder_names_and_paths:
+    folder_paths.folder_names_and_paths["VHS_video_formats"] = ((),{".json"})
+if len(folder_paths.folder_names_and_paths['VHS_video_formats'][1]) == 0:
+    folder_paths.folder_names_and_paths["VHS_video_formats"][1].add(".json")
+audio_extensions = ['mp3', 'mp4', 'wav', 'ogg']
+
+def flatten_list(l):
+    ret = []
+    for e in l:
+        if isinstance(e, list):
+            ret.extend(e)
+        else:
+            ret.append(e)
+    return ret
+
+def iterate_format(video_format, for_widgets=True):
+    """Provides an iterator over widgets, or arguments"""
+    def indirector(cont, index):
+        if isinstance(cont[index], list) and (not for_widgets
+          or len(cont[index])> 1 and not isinstance(cont[index][1], dict)):
+            inp = yield cont[index]
+            if inp is not None:
+                cont[index] = inp
+                yield
+    for k in video_format:
+        if k == "extra_widgets":
+            if for_widgets:
+                yield from video_format["extra_widgets"]
+        elif k.endswith("_pass"):
+            for i in range(len(video_format[k])):
+                yield from indirector(video_format[k], i)
+            if not for_widgets:
+                video_format[k] = flatten_list(video_format[k])
+        else:
+            yield from indirector(video_format, k)
+
+base_formats_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "video_formats")
+@cached(5)
+def get_video_formats():
+    format_files = {}
+    for format_name in folder_paths.get_filename_list("VHS_video_formats"):
+        format_files[format_name] = folder_paths.get_full_path("VHS_video_formats", format_name)
+    for item in os.scandir(base_formats_dir):
+        if not item.is_file() or not item.name.endswith('.json'):
+            continue
+        format_files[item.name[:-5]] = item.path
+    formats = []
+    format_widgets = {}
+    for format_name, path in format_files.items():
+        with open(path, 'r') as stream:
+            video_format = json.load(stream)
+        if "gifski_pass" in video_format and gifski_path is None:
+            #Skip format
+            continue
+        widgets = list(iterate_format(video_format))
+        formats.append("video/" + format_name)
+        if (len(widgets) > 0):
+            format_widgets["video/"+ format_name] = widgets
+    return formats, format_widgets
+
+def apply_format_widgets(format_name, kwargs):
+    if os.path.exists(os.path.join(base_formats_dir, format_name + ".json")):
+        video_format_path = os.path.join(base_formats_dir, format_name + ".json")
+    else:
+        video_format_path = folder_paths.get_full_path("VHS_video_formats", format_name)
+    with open(video_format_path, 'r') as stream:
+        video_format = json.load(stream)
+    for w in iterate_format(video_format):
+        if w[0] not in kwargs:
+            if len(w) > 2 and 'default' in w[2]:
+                default = w[2]['default']
+            else:
+                if type(w[1]) is list:
+                    default = w[1][0]
+                else:
+                    #NOTE: This doesn't respect max/min, but should be good enough as a fallback to a fallback to a fallback
+                    default = {"BOOLEAN": False, "INT": 0, "FLOAT": 0, "STRING": ""}[w[1]]
+            kwargs[w[0]] = default
+            logger.warn(f"Missing input for {w[0]} has been set to {default}")
+    wit = iterate_format(video_format, False)
+    for w in wit:
+        while isinstance(w, list):
+            if len(w) == 1:
+                #TODO: mapping=kwargs should be safer, but results in key errors, investigate why
+                w = [Template(x).substitute(**kwargs) for x in w[0]]
+                break
+            elif isinstance(w[1], dict):
+                w = w[1][str(kwargs[w[0]])]
+            elif len(w) > 3:
+                w = Template(w[3]).substitute(val=kwargs[w[0]])
+            else:
+                w = str(kwargs[w[0]])
+        wit.send(w)
+    return video_format
+
+def tensor_to_int(tensor, bits):
+    tensor = tensor.cpu().numpy() * (2**bits-1) + 0.5
+    return np.clip(tensor, 0, (2**bits-1))
+def tensor_to_shorts(tensor):
+    return tensor_to_int(tensor, 16).astype(np.uint16)
+def tensor_to_bytes(tensor):
+    return tensor_to_int(tensor, 8).astype(np.uint8)
+
+def ffmpeg_process(args, video_format, video_metadata, file_path, env):
+
+    res = None
+    frame_data = yield
+    total_frames_output = 0
+    if video_format.get('save_metadata', 'False') != 'False':
+        os.makedirs(folder_paths.get_temp_directory(), exist_ok=True)
+        metadata_path = os.path.join(folder_paths.get_temp_directory(), "metadata.txt")
+        #metadata from file should  escape = ; # \ and newline
+        def escape_ffmpeg_metadata(key, value):
+            value = str(value)
+            value = value.replace("\\","\\\\")
+            value = value.replace(";","\\;")
+            value = value.replace("#","\\#")
+            value = value.replace("=","\\=")
+            value = value.replace("\n","\\\n")
+            return f"{key}={value}"
+
+        with open(metadata_path, "w") as f:
+            f.write(";FFMETADATA1\n")
+            if "prompt" in video_metadata:
+                f.write(escape_ffmpeg_metadata("prompt", json.dumps(video_metadata["prompt"])) + "\n")
+            if "workflow" in video_metadata:
+                f.write(escape_ffmpeg_metadata("workflow", json.dumps(video_metadata["workflow"])) + "\n")
+            for k, v in video_metadata.items():
+                if k not in ["prompt", "workflow"]:
+                    f.write(escape_ffmpeg_metadata(k, json.dumps(v)) + "\n")
+
+        m_args = args[:1] + ["-i", metadata_path] + args[1:] + ["-metadata", "creation_time=now", "-movflags", "use_metadata_tags"]
+        with subprocess.Popen(m_args + [file_path], stderr=subprocess.PIPE,
+                              stdin=subprocess.PIPE, env=env) as proc:
+            try:
+                while frame_data is not None:
+                    proc.stdin.write(frame_data)
+                    #TODO: skip flush for increased speed
+                    frame_data = yield
+                    total_frames_output+=1
+                proc.stdin.flush()
+                proc.stdin.close()
+                res = proc.stderr.read()
+            except BrokenPipeError as e:
+                err = proc.stderr.read()
+                #Check if output file exists. If it does, the re-execution
+                #will also fail. This obscures the cause of the error
+                #and seems to never occur concurrent to the metadata issue
+                if os.path.exists(file_path):
+                    raise Exception("An error occurred in the ffmpeg subprocess:\n" \
+                            + err.decode(*ENCODE_ARGS))
+                #Res was not set
+                print(err.decode(*ENCODE_ARGS), end="", file=sys.stderr)
+                logger.warn("An error occurred when saving with metadata")
+    if res != b'':
+        with subprocess.Popen(args + [file_path], stderr=subprocess.PIPE,
+                              stdin=subprocess.PIPE, env=env) as proc:
+            try:
+                while frame_data is not None:
+                    proc.stdin.write(frame_data)
+                    frame_data = yield
+                    total_frames_output+=1
+                proc.stdin.flush()
+                proc.stdin.close()
+                res = proc.stderr.read()
+            except BrokenPipeError as e:
+                res = proc.stderr.read()
+                raise Exception("An error occurred in the ffmpeg subprocess:\n" \
+                        + res.decode(*ENCODE_ARGS))
+    yield total_frames_output
+    if len(res) > 0:
+        print(res.decode(*ENCODE_ARGS), end="", file=sys.stderr)
+
+def gifski_process(args, dimensions, frame_rate, video_format, file_path, env):
+    frame_data = yield
+    with subprocess.Popen(args + video_format['main_pass'] + ['-f', 'yuv4mpegpipe', '-'],
+                          stderr=subprocess.PIPE, stdin=subprocess.PIPE,
+                          stdout=subprocess.PIPE, env=env) as procff:
+        with subprocess.Popen([gifski_path] + video_format['gifski_pass']
+                              + ['-W', f'{dimensions[0]}', '-H', f'{dimensions[1]}']
+                              + ['-r', f'{frame_rate}']
+                              + ['-q', '-o', file_path, '-'], stderr=subprocess.PIPE,
+                              stdin=procff.stdout, stdout=subprocess.PIPE,
+                              env=env) as procgs:
+            try:
+                while frame_data is not None:
+                    procff.stdin.write(frame_data)
+                    frame_data = yield
+                procff.stdin.flush()
+                procff.stdin.close()
+                resff = procff.stderr.read()
+                resgs = procgs.stderr.read()
+                outgs = procgs.stdout.read()
+            except BrokenPipeError as e:
+                procff.stdin.close()
+                resff = procff.stderr.read()
+                resgs = procgs.stderr.read()
+                raise Exception("An error occurred while creating gifski output\n" \
+                        + "Make sure you are using gifski --version >=1.32.0\nffmpeg: " \
+                        + resff.decode(*ENCODE_ARGS) + '\ngifski: ' + resgs.decode(*ENCODE_ARGS))
+    if len(resff) > 0:
+        print(resff.decode(*ENCODE_ARGS), end="", file=sys.stderr)
+    if len(resgs) > 0:
+        print(resgs.decode(*ENCODE_ARGS), end="", file=sys.stderr)
+    #should always be empty as the quiet flag is passed
+    if len(outgs) > 0:
+        print(outgs.decode(*ENCODE_ARGS))
+
+def to_pingpong(inp):
+    if not hasattr(inp, "__getitem__"):
+        inp = list(inp)
+    yield from inp
+    for i in range(len(inp)-2,0,-1):
+        yield inp[i]
+
+class VideoCombine:
+    @classmethod
+    def INPUT_TYPES(s):
+        ffmpeg_formats, format_widgets = get_video_formats()
+        format_widgets["image/webp"] = [['lossless', "BOOLEAN", {'default': True}]]
+        return {
+            "required": {
+                "images": (imageOrLatent,),
+                "frame_rate": (
+                    floatOrInt,
+                    {"default": 8, "min": 1, "step": 1},
+                ),
+                "loop_count": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}),
+                "filename_prefix": ("STRING", {"default": "AnimateDiff"}),
+                "format": (["image/gif", "image/webp"] + ffmpeg_formats, {'formats': format_widgets}),
+                "pingpong": ("BOOLEAN", {"default": False}),
+                "save_output": ("BOOLEAN", {"default": True}),
+            },
+            "optional": {
+                "audio": ("AUDIO",),
+                "meta_batch": ("VHS_BatchManager",),
+                "vae": ("VAE",),
+            },
+            "hidden": ContainsAll({
+                "prompt": "PROMPT",
+                "extra_pnginfo": "EXTRA_PNGINFO",
+                "unique_id": "UNIQUE_ID"
+            }),
+        }
+
+    RETURN_TYPES = ("VHS_FILENAMES",)
+    RETURN_NAMES = ("Filenames",)
+    OUTPUT_NODE = True
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
+    FUNCTION = "combine_video"
+
+    def combine_video(
+        self,
+        frame_rate: int,
+        loop_count: int,
+        images=None,
+        latents=None,
+        filename_prefix="AnimateDiff",
+        format="image/gif",
+        pingpong=False,
+        save_output=True,
+        prompt=None,
+        extra_pnginfo=None,
+        audio=None,
+        unique_id=None,
+        manual_format_widgets=None,
+        meta_batch=None,
+        vae=None,
+        **kwargs
+    ):
+        if latents is not None:
+            images = latents
+        if images is None:
+            return ((save_output, []),)
+        if vae is not None:
+            if isinstance(images, dict):
+                images = images['samples']
+            else:
+                vae = None
+
+        if isinstance(images, torch.Tensor) and images.size(0) == 0:
+            return ((save_output, []),)
+        num_frames = len(images)
+        pbar = ProgressBar(num_frames)
+        if vae is not None:
+            downscale_ratio = getattr(vae, "downscale_ratio", 8)
+            width = images.size(-1)*downscale_ratio
+            height = images.size(-2)*downscale_ratio
+            frames_per_batch = (1920 * 1080 * 16) // (width * height) or 1
+            #Python 3.12 adds an itertools.batched, but it's easily replicated for legacy support
+            def batched(it, n):
+                while batch := tuple(itertools.islice(it, n)):
+                    yield batch
+            def batched_encode(images, vae, frames_per_batch):
+                for batch in batched(iter(images), frames_per_batch):
+                    image_batch = torch.from_numpy(np.array(batch))
+                    yield from vae.decode(image_batch)
+            images = batched_encode(images, vae, frames_per_batch)
+            first_image = next(images)
+            #repush first_image
+            images = itertools.chain([first_image], images)
+            #A single image has 3 dimensions. Discard higher dimensions
+            while len(first_image.shape) > 3:
+                first_image = first_image[0]
+        else:
+            first_image = images[0]
+            images = iter(images)
+        # get output information
+        output_dir = (
+            folder_paths.get_output_directory()
+            if save_output
+            else folder_paths.get_temp_directory()
+        )
+        (
+            full_output_folder,
+            filename,
+            _,
+            subfolder,
+            _,
+        ) = folder_paths.get_save_image_path(filename_prefix, output_dir)
+        output_files = []
+
+        metadata = PngInfo()
+        video_metadata = {}
+        if prompt is not None:
+            metadata.add_text("prompt", json.dumps(prompt))
+            video_metadata["prompt"] = prompt
+        if extra_pnginfo is not None:
+            for x in extra_pnginfo:
+                metadata.add_text(x, json.dumps(extra_pnginfo[x]))
+                video_metadata[x] = extra_pnginfo[x]
+            extra_options = extra_pnginfo.get('workflow', {}).get('extra', {})
+        else:
+            extra_options = {}
+        metadata.add_text("CreationTime", datetime.datetime.now().isoformat(" ")[:19])
+
+        if meta_batch is not None and unique_id in meta_batch.outputs:
+            (counter, output_process) = meta_batch.outputs[unique_id]
+        else:
+            # comfy counter workaround
+            max_counter = 0
+
+            # Loop through the existing files
+            matcher = re.compile(f"{re.escape(filename)}_(\\d+)\\D*\\..+", re.IGNORECASE)
+            for existing_file in os.listdir(full_output_folder):
+                # Check if the file matches the expected format
+                match = matcher.fullmatch(existing_file)
+                if match:
+                    # Extract the numeric portion of the filename
+                    file_counter = int(match.group(1))
+                    # Update the maximum counter value if necessary
+                    if file_counter > max_counter:
+                        max_counter = file_counter
+
+            # Increment the counter by 1 to get the next available value
+            counter = max_counter + 1
+            output_process = None
+
+        # save first frame as png to keep metadata
+        first_image_file = f"{filename}_{counter:05}.png"
+        file_path = os.path.join(full_output_folder, first_image_file)
+        if extra_options.get('VHS_MetadataImage', True) != False:
+            Image.fromarray(tensor_to_bytes(first_image)).save(
+                file_path,
+                pnginfo=metadata,
+                compress_level=4,
+            )
+        output_files.append(file_path)
+
+        format_type, format_ext = format.split("/")
+        if format_type == "image":
+            if meta_batch is not None:
+                raise Exception("Pillow('image/') formats are not compatible with batched output")
+            image_kwargs = {}
+            if format_ext == "gif":
+                image_kwargs['disposal'] = 2
+            if format_ext == "webp":
+                #Save timestamp information
+                exif = Image.Exif()
+                exif[ExifTags.IFD.Exif] = {36867: datetime.datetime.now().isoformat(" ")[:19]}
+                image_kwargs['exif'] = exif
+                image_kwargs['lossless'] = kwargs.get("lossless", True)
+            file = f"{filename}_{counter:05}.{format_ext}"
+            file_path = os.path.join(full_output_folder, file)
+            if pingpong:
+                images = to_pingpong(images)
+            def frames_gen(images):
+                for i in images:
+                    pbar.update(1)
+                    yield Image.fromarray(tensor_to_bytes(i))
+            frames = frames_gen(images)
+            # Use pillow directly to save an animated image
+            next(frames).save(
+                file_path,
+                format=format_ext.upper(),
+                save_all=True,
+                append_images=frames,
+                duration=round(1000 / frame_rate),
+                loop=loop_count,
+                compress_level=4,
+                **image_kwargs
+            )
+            output_files.append(file_path)
+        else:
+            # Use ffmpeg to save a video
+            if ffmpeg_path is None:
+                raise ProcessLookupError(f"ffmpeg is required for video outputs and could not be found.\nIn order to use video outputs, you must either:\n- Install imageio-ffmpeg with pip,\n- Place a ffmpeg executable in {os.path.abspath('')}, or\n- Install ffmpeg and add it to the system path.")
+
+            if manual_format_widgets is not None:
+                logger.warn("Format args can now be passed directly. The manual_format_widgets argument is now deprecated")
+                kwargs.update(manual_format_widgets)
+
+            has_alpha = first_image.shape[-1] == 4
+            kwargs["has_alpha"] = has_alpha
+            video_format = apply_format_widgets(format_ext, kwargs)
+            dim_alignment = video_format.get("dim_alignment", 2)
+            if (first_image.shape[1] % dim_alignment) or (first_image.shape[0] % dim_alignment):
+                #output frames must be padded
+                to_pad = (-first_image.shape[1] % dim_alignment,
+                          -first_image.shape[0] % dim_alignment)
+                padding = (to_pad[0]//2, to_pad[0] - to_pad[0]//2,
+                           to_pad[1]//2, to_pad[1] - to_pad[1]//2)
+                padfunc = torch.nn.ReplicationPad2d(padding)
+                def pad(image):
+                    image = image.permute((2,0,1))#HWC to CHW
+                    padded = padfunc(image.to(dtype=torch.float32))
+                    return padded.permute((1,2,0))
+                images = map(pad, images)
+                dimensions = (-first_image.shape[1] % dim_alignment + first_image.shape[1],
+                              -first_image.shape[0] % dim_alignment + first_image.shape[0])
+                logger.warn("Output images were not of valid resolution and have had padding applied")
+            else:
+                dimensions = (first_image.shape[1], first_image.shape[0])
+            if pingpong:
+                if meta_batch is not None:
+                    logger.error("pingpong is incompatible with batched output")
+                images = to_pingpong(images)
+                if num_frames > 2:
+                    num_frames += num_frames -2
+                    pbar.total = num_frames
+            if loop_count > 0:
+                loop_args = ["-vf", "loop=loop=" + str(loop_count)+":size=" + str(num_frames)]
+            else:
+                loop_args = []
+            if video_format.get('input_color_depth', '8bit') == '16bit':
+                images = map(tensor_to_shorts, images)
+                if has_alpha:
+                    i_pix_fmt = 'rgba64'
+                else:
+                    i_pix_fmt = 'rgb48'
+            else:
+                images = map(tensor_to_bytes, images)
+                if has_alpha:
+                    i_pix_fmt = 'rgba'
+                else:
+                    i_pix_fmt = 'rgb24'
+            file = f"{filename}_{counter:05}.{video_format['extension']}"
+            file_path = os.path.join(full_output_folder, file)
+            bitrate_arg = []
+            bitrate = video_format.get('bitrate')
+            if bitrate is not None:
+                bitrate_arg = ["-b:v", str(bitrate) + "M" if video_format.get('megabit') == 'True' else str(bitrate) + "K"]
+            args = [ffmpeg_path, "-v", "error", "-f", "rawvideo", "-pix_fmt", i_pix_fmt,
+                    # The image data is in an undefined generic RGB color space, which in practice means sRGB.
+                    # sRGB has the same primaries and matrix as BT.709, but a different transfer function (gamma),
+                    # called by the sRGB standard name IEC 61966-2-1. However, video hosting platforms like YouTube
+                    # standardize on full BT.709 and will convert the colors accordingly. This last minute change
+                    # in colors can be confusing to users. We can counter it by lying about the transfer function
+                    # on a per format basis, i.e. for video we will lie to FFmpeg that it is already BT.709. Also,
+                    # because the input data is in RGB (not YUV) it is more efficient (fewer scale filter invocations)
+                    # to specify the input color space as RGB and then later, if the format actually wants YUV,
+                    # to convert it to BT.709 YUV via FFmpeg's -vf "scale=out_color_matrix=bt709".
+                    "-color_range", "pc", "-colorspace", "rgb", "-color_primaries", "bt709",
+                    "-color_trc", video_format.get("fake_trc", "iec61966-2-1"),
+                    "-s", f"{dimensions[0]}x{dimensions[1]}", "-r", str(frame_rate), "-i", "-"] \
+                    + loop_args
+
+            images = map(lambda x: x.tobytes(), images)
+            env=os.environ.copy()
+            if  "environment" in video_format:
+                env.update(video_format["environment"])
+
+            if "pre_pass" in video_format:
+                if meta_batch is not None:
+                    #Performing a prepass requires keeping access to all frames.
+                    #Potential solutions include keeping just output frames in
+                    #memory or using 3 passes with intermediate file, but
+                    #very long gifs probably shouldn't be encouraged
+                    raise Exception("Formats which require a pre_pass are incompatible with Batch Manager.")
+                images = [b''.join(images)]
+                os.makedirs(folder_paths.get_temp_directory(), exist_ok=True)
+                in_args_len = args.index("-i") + 2 # The index after ["-i", "-"]
+                pre_pass_args = args[:in_args_len] + video_format['pre_pass']
+                merge_filter_args(pre_pass_args)
+                try:
+                    subprocess.run(pre_pass_args, input=images[0], env=env,
+                                   capture_output=True, check=True)
+                except subprocess.CalledProcessError as e:
+                    raise Exception("An error occurred in the ffmpeg prepass:\n" \
+                            + e.stderr.decode(*ENCODE_ARGS))
+            if "inputs_main_pass" in video_format:
+                in_args_len = args.index("-i") + 2 # The index after ["-i", "-"]
+                args = args[:in_args_len] + video_format['inputs_main_pass'] + args[in_args_len:]
+
+            if output_process is None:
+                if 'gifski_pass' in video_format:
+                    format = 'image/gif'
+                    output_process = gifski_process(args, dimensions, frame_rate, video_format, file_path, env)
+                    audio = None
+                else:
+                    args += video_format['main_pass'] + bitrate_arg
+                    merge_filter_args(args)
+                    output_process = ffmpeg_process(args, video_format, video_metadata, file_path, env)
+                #Proceed to first yield
+                output_process.send(None)
+                if meta_batch is not None:
+                    meta_batch.outputs[unique_id] = (counter, output_process)
+
+            for image in images:
+                pbar.update(1)
+                output_process.send(image)
+            if meta_batch is not None:
+                requeue_workflow((meta_batch.unique_id, not meta_batch.has_closed_inputs))
+            if meta_batch is None or meta_batch.has_closed_inputs:
+                #Close pipe and wait for termination.
+                try:
+                    total_frames_output = output_process.send(None)
+                    output_process.send(None)
+                except StopIteration:
+                    pass
+                if meta_batch is not None:
+                    meta_batch.outputs.pop(unique_id)
+                    if len(meta_batch.outputs) == 0:
+                        meta_batch.reset()
+            else:
+                #batch is unfinished
+                #TODO: Check if empty output breaks other custom nodes
+                return {"ui": {"unfinished_batch": [True]}, "result": ((save_output, []),)}
+
+            output_files.append(file_path)
+
+
+            a_waveform = None
+            if audio is not None:
+                try:
+                    #safely check if audio produced by VHS_LoadVideo actually exists
+                    a_waveform = audio['waveform']
+                except:
+                    pass
+            if a_waveform is not None:
+                # Create audio file if input was provided
+                output_file_with_audio = f"{filename}_{counter:05}-audio.{video_format['extension']}"
+                output_file_with_audio_path = os.path.join(full_output_folder, output_file_with_audio)
+                if "audio_pass" not in video_format:
+                    logger.warn("Selected video format does not have explicit audio support")
+                    video_format["audio_pass"] = ["-c:a", "libopus"]
+
+
+                # FFmpeg command with audio re-encoding
+                #TODO: expose audio quality options if format widgets makes it in
+                #Reconsider forcing apad/shortest
+                channels = audio['waveform'].size(1)
+                min_audio_dur = total_frames_output / frame_rate + 1
+                if video_format.get('trim_to_audio', 'False') != 'False':
+                    apad = []
+                else:
+                    apad = ["-af", "apad=whole_dur="+str(min_audio_dur)]
+                mux_args = [ffmpeg_path, "-v", "error", "-n", "-i", file_path,
+                            "-ar", str(audio['sample_rate']), "-ac", str(channels),
+                            "-f", "f32le", "-i", "-", "-c:v", "copy"] \
+                            + video_format["audio_pass"] \
+                            + apad + ["-shortest", output_file_with_audio_path]
+
+                audio_data = audio['waveform'].squeeze(0).transpose(0,1) \
+                        .numpy().tobytes()
+                merge_filter_args(mux_args, '-af')
+                try:
+                    res = subprocess.run(mux_args, input=audio_data,
+                                         env=env, capture_output=True, check=True)
+                except subprocess.CalledProcessError as e:
+                    raise Exception("An error occured in the ffmpeg subprocess:\n" \
+                            + e.stderr.decode(*ENCODE_ARGS))
+                if res.stderr:
+                    print(res.stderr.decode(*ENCODE_ARGS), end="", file=sys.stderr)
+                output_files.append(output_file_with_audio_path)
+                #Return this file with audio to the webui.
+                #It will be muted unless opened or saved with right click
+                file = output_file_with_audio
+        if extra_options.get('VHS_KeepIntermediate', True) == False:
+            for intermediate in output_files[1:-1]:
+                if os.path.exists(intermediate):
+                    os.remove(intermediate)
+        preview = {
+                "filename": file,
+                "subfolder": subfolder,
+                "type": "output" if save_output else "temp",
+                "format": format,
+                "frame_rate": frame_rate,
+                "workflow": first_image_file,
+                "fullpath": output_files[-1],
+            }
+        if num_frames == 1 and 'png' in format and '%03d' in file:
+            preview['format'] = 'image/png'
+            preview['filename'] = file.replace('%03d', '001')
+        return {"ui": {"gifs": [preview]}, "result": ((save_output, output_files),)}
+
+class LoadAudio:
+    @classmethod
+    def INPUT_TYPES(s):
+        #Hide ffmpeg formats if ffmpeg isn't available
+        return {
+            "required": {
+                "audio_file": ("STRING", {"default": "input/", "vhs_path_extensions": ['wav','mp3','ogg','m4a','flac']}),
+                },
+            "optional" : {
+                "seek_seconds": ("FLOAT", {"default": 0, "min": 0, "widgetType": "VHSTIMESTAMP"}),
+                "duration": ("FLOAT" , {"default": 0, "min": 0, "max": 10000000, "step": 0.01, "widgetType": "VHSTIMESTAMP"}),
+                          }
+        }
+
+    RETURN_TYPES = ("AUDIO", "FLOAT")
+    RETURN_NAMES = ("audio", "duration")
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/audio"
+    FUNCTION = "load_audio"
+    def load_audio(self, audio_file, seek_seconds=0, duration=0):
+        audio_file = strip_path(audio_file)
+        if audio_file is None or validate_path(audio_file) != True:
+            raise Exception("audio_file is not a valid path: " + audio_file)
+        if is_url(audio_file):
+            audio_file = try_download_video(audio_file) or audio_file
+        #Eagerly fetch the audio since the user must be using it if the
+        #node executes, unlike Load Video
+        audio = get_audio(audio_file, start_time=seek_seconds, duration=duration)
+        loaded_duration = audio['waveform'].size(2)/audio['sample_rate']
+        return (audio, loaded_duration)
+
+    @classmethod
+    def IS_CHANGED(s, audio_file, **kwargs):
+        return hash_path(audio_file)
+
+    @classmethod
+    def VALIDATE_INPUTS(s, audio_file, **kwargs):
+        return validate_path(audio_file, allow_none=True)
+
+class LoadAudioUpload:
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        files = []
+        for f in os.listdir(input_dir):
+            if os.path.isfile(os.path.join(input_dir, f)):
+                file_parts = f.split('.')
+                if len(file_parts) > 1 and (file_parts[-1] in audio_extensions):
+                    files.append(f)
+        return {"required": {
+                    "audio": (sorted(files),),},
+                "optional": {
+                    "start_time": ("FLOAT" , {"default": 0, "min": 0, "max": 10000000, "step": 0.01, "widgetType": "VHSTIMESTAMP"}),
+                    "duration": ("FLOAT" , {"default": 0, "min": 0, "max": 10000000, "step": 0.01, "widgetType": "VHSTIMESTAMP"}),
+                     },
+                }
+
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/audio"
+
+    RETURN_TYPES = ("AUDIO", "FLOAT")
+    RETURN_NAMES = ("audio", "duration")
+    FUNCTION = "load_audio"
+
+    def load_audio(self, start_time=0, duration=0, **kwargs):
+        audio_file = folder_paths.get_annotated_filepath(strip_path(kwargs['audio']))
+        if audio_file is None or validate_path(audio_file) != True:
+            raise Exception("audio_file is not a valid path: " + audio_file)
+
+        audio = get_audio(audio_file, start_time, duration)
+        loaded_duration = audio['waveform'].size(2)/audio['sample_rate']
+        return (audio, loaded_duration)
+
+    @classmethod
+    def IS_CHANGED(s, audio, **kwargs):
+        audio_file = folder_paths.get_annotated_filepath(strip_path(audio))
+        return hash_path(audio_file)
+
+    @classmethod
+    def VALIDATE_INPUTS(s, audio, **kwargs):
+        audio_file = folder_paths.get_annotated_filepath(strip_path(audio))
+        return validate_path(audio_file, allow_none=True)
+class AudioToVHSAudio:
+    """Legacy method for external nodes that utilized VHS_AUDIO,
+    VHS_AUDIO is deprecated as a format and should no longer be used"""
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"audio": ("AUDIO",)}}
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/audio"
+
+    RETURN_TYPES = ("VHS_AUDIO", )
+    RETURN_NAMES = ("vhs_audio",)
+    FUNCTION = "convert_audio"
+
+    def convert_audio(self, audio):
+        ar = str(audio['sample_rate'])
+        ac = str(audio['waveform'].size(1))
+        mux_args = [ffmpeg_path, "-f", "f32le", "-ar", ar, "-ac", ac,
+                    "-i", "-", "-f", "wav", "-"]
+
+        audio_data = audio['waveform'].squeeze(0).transpose(0,1) \
+                .numpy().tobytes()
+        try:
+            res = subprocess.run(mux_args, input=audio_data,
+                                 capture_output=True, check=True)
+        except subprocess.CalledProcessError as e:
+            raise Exception("An error occured in the ffmpeg subprocess:\n" \
+                    + e.stderr.decode(*ENCODE_ARGS))
+        if res.stderr:
+            print(res.stderr.decode(*ENCODE_ARGS), end="", file=sys.stderr)
+        return (lambda: res.stdout,)
+
+class VHSAudioToAudio:
+    """Legacy method for external nodes that utilized VHS_AUDIO,
+    VHS_AUDIO is deprecated as a format and should no longer be used"""
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"vhs_audio": ("VHS_AUDIO",)}}
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/audio"
+
+    RETURN_TYPES = ("AUDIO", )
+    RETURN_NAMES = ("audio",)
+    FUNCTION = "convert_audio"
+
+    def convert_audio(self, vhs_audio):
+        if not vhs_audio or not vhs_audio():
+            raise Exception("audio input is not valid")
+        args = [ffmpeg_path, "-i", '-']
+        try:
+            res =  subprocess.run(args + ["-f", "f32le", "-"], input=vhs_audio(),
+                                  capture_output=True, check=True)
+            audio = torch.frombuffer(bytearray(res.stdout), dtype=torch.float32)
+        except subprocess.CalledProcessError as e:
+            raise Exception("An error occured in the ffmpeg subprocess:\n" \
+                    + e.stderr.decode(*ENCODE_ARGS))
+        match = re.search(', (\\d+) Hz, (\\w+), ',res.stderr.decode(*ENCODE_ARGS))
+        if match:
+            ar = int(match.group(1))
+            #NOTE: Just throwing an error for other channel types right now
+            #Will deal with issues if they come
+            ac = {"mono": 1, "stereo": 2}[match.group(2)]
+        else:
+            ar = 44100
+            ac = 2
+        audio = audio.reshape((-1,ac)).transpose(0,1).unsqueeze(0)
+        return ({'waveform': audio, 'sample_rate': ar},)
+
+class PruneOutputs:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                    "filenames": ("VHS_FILENAMES",),
+                    "options": (["Intermediate", "Intermediate and Utility"],)
+                    }
+                }
+
+    RETURN_TYPES = ()
+    OUTPUT_NODE = True
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
+    FUNCTION = "prune_outputs"
+
+    def prune_outputs(self, filenames, options):
+        if len(filenames[1]) == 0:
+            return ()
+        assert(len(filenames[1]) <= 3 and len(filenames[1]) >= 2)
+        delete_list = []
+        if options in ["Intermediate", "Intermediate and Utility", "All"]:
+            delete_list += filenames[1][1:-1]
+        if options in ["Intermediate and Utility", "All"]:
+            delete_list.append(filenames[1][0])
+        if options in ["All"]:
+            delete_list.append(filenames[1][-1])
+
+        output_dirs = [folder_paths.get_output_directory(),
+                       folder_paths.get_temp_directory()]
+        for file in delete_list:
+            #Check that path is actually an output directory
+            if (os.path.commonpath([output_dirs[0], file]) != output_dirs[0]) \
+                    and (os.path.commonpath([output_dirs[1], file]) != output_dirs[1]):
+                        raise Exception("Tried to prune output from invalid directory: " + file)
+            if os.path.exists(file):
+                os.remove(file)
+        return ()
+
+class BatchManager:
+    def __init__(self, frames_per_batch=-1):
+        self.frames_per_batch = frames_per_batch
+        self.inputs = {}
+        self.outputs = {}
+        self.unique_id = None
+        self.has_closed_inputs = False
+        self.total_frames = float('inf')
+    def reset(self):
+        self.close_inputs()
+        for key in self.outputs:
+            if getattr(self.outputs[key][-1], "gi_suspended", False):
+                try:
+                    self.outputs[key][-1].send(None)
+                except StopIteration:
+                    pass
+        self.__init__(self.frames_per_batch)
+    def has_open_inputs(self):
+        return len(self.inputs) > 0
+    def close_inputs(self):
+        for key in self.inputs:
+            if getattr(self.inputs[key][-1], "gi_suspended", False):
+                try:
+                    self.inputs[key][-1].send(1)
+                except StopIteration:
+                    pass
+        self.inputs = {}
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                    "frames_per_batch": ("INT", {"default": 16, "min": 1, "max": BIGMAX, "step": 1})
+                    },
+                "hidden": {
+                    "prompt": "PROMPT",
+                    "unique_id": "UNIQUE_ID"
+                },
+                }
+
+    RETURN_TYPES = ("VHS_BatchManager",)
+    RETURN_NAMES = ("meta_batch",)
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
+    FUNCTION = "update_batch"
+
+    def update_batch(self, frames_per_batch, prompt=None, unique_id=None):
+        if unique_id is not None and prompt is not None:
+            requeue = prompt[unique_id]['inputs'].get('requeue', 0)
+        else:
+            requeue = 0
+        if requeue == 0:
+            self.reset()
+            self.frames_per_batch = frames_per_batch
+            self.unique_id = unique_id
+        else:
+            num_batches = (self.total_frames+self.frames_per_batch-1)//frames_per_batch
+            print(f'Meta-Batch {requeue}/{num_batches}')
+        #onExecuted seems to not be called unless some message is sent
+        return (self,)
+
+
+class VideoInfo:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                    "video_info": ("VHS_VIDEOINFO",),
+                    }
+                }
+
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
+
+    RETURN_TYPES = ("FLOAT","INT", "FLOAT", "INT", "INT", "FLOAT","INT", "FLOAT", "INT", "INT")
+    RETURN_NAMES = (
+        "source_fps🟨",
+        "source_frame_count🟨",
+        "source_duration🟨",
+        "source_width🟨",
+        "source_height🟨",
+        "loaded_fps🟦",
+        "loaded_frame_count🟦",
+        "loaded_duration🟦",
+        "loaded_width🟦",
+        "loaded_height🟦",
+    )
+    FUNCTION = "get_video_info"
+
+    def get_video_info(self, video_info):
+        keys = ["fps", "frame_count", "duration", "width", "height"]
+
+        source_info = []
+        loaded_info = []
+
+        for key in keys:
+            source_info.append(video_info[f"source_{key}"])
+            loaded_info.append(video_info[f"loaded_{key}"])
+
+        return (*source_info, *loaded_info)
+
+
+class VideoInfoSource:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                    "video_info": ("VHS_VIDEOINFO",),
+                    }
+                }
+
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
+
+    RETURN_TYPES = ("FLOAT","INT", "FLOAT", "INT", "INT",)
+    RETURN_NAMES = (
+        "fps🟨",
+        "frame_count🟨",
+        "duration🟨",
+        "width🟨",
+        "height🟨",
+    )
+    FUNCTION = "get_video_info"
+
+    def get_video_info(self, video_info):
+        keys = ["fps", "frame_count", "duration", "width", "height"]
+
+        source_info = []
+
+        for key in keys:
+            source_info.append(video_info[f"source_{key}"])
+
+        return (*source_info,)
+
+
+class VideoInfoLoaded:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                    "video_info": ("VHS_VIDEOINFO",),
+                    }
+                }
+
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
+
+    RETURN_TYPES = ("FLOAT","INT", "FLOAT", "INT", "INT",)
+    RETURN_NAMES = (
+        "fps🟦",
+        "frame_count🟦",
+        "duration🟦",
+        "width🟦",
+        "height🟦",
+    )
+    FUNCTION = "get_video_info"
+
+    def get_video_info(self, video_info):
+        keys = ["fps", "frame_count", "duration", "width", "height"]
+
+        loaded_info = []
+
+        for key in keys:
+            loaded_info.append(video_info[f"loaded_{key}"])
+
+        return (*loaded_info,)
+
+class SelectFilename:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"filenames": ("VHS_FILENAMES",), "index": ("INT", {"default": -1, "step": 1, "min": -1})}}
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES =("Filename",)
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
+    FUNCTION = "select_filename"
+
+    def select_filename(self, filenames, index):
+        return (filenames[1][index],)
+class Unbatch:
+    class Any(str):
+        def __ne__(self, other):
+            return False
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"batched": ("*",)}}
+    RETURN_TYPES = (Any('*'),)
+    INPUT_IS_LIST = True
+    RETURN_NAMES =("unbatched",)
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
+    FUNCTION = "unbatch"
+    def unbatch(self, batched):
+        if isinstance(batched[0], torch.Tensor):
+            return (torch.cat(batched),)
+        if isinstance(batched[0], dict):
+            out = batched[0].copy()
+            if 'samples' in out:
+                out['samples'] = torch.cat([x['samples'] for x in batched])
+            if 'waveform' in out:
+                out['waveform'] = torch.cat([x['waveform'] for x in batched])
+            out.pop('batch_index', None)
+            return (out,)
+        return (functools.reduce(lambda x,y: x+y, batched),)
+    @classmethod
+    def VALIDATE_INPUTS(cls, input_types):
+        return True
+class SelectLatest:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"filename_prefix": ("STRING", {'default': 'output/AnimateDiff', 'vhs_path_extensions': []}),
+                             "filename_postfix": ("STRING", {"placeholder": ".webm"})}}
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES =("Filename",)
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
+    FUNCTION = "select_latest"
+    EXPERIMENTAL = True
+
+    def select_latest(self, filename_prefix, filename_postfix):
+        assert False, "Not Reachable"
+
+NODE_CLASS_MAPPINGS = {
+    "VHS_VideoCombine": VideoCombine,
+    "VHS_LoadVideo": LoadVideoUpload,
+    "VHS_LoadVideoPath": LoadVideoPath,
+    "VHS_LoadVideoFFmpeg": LoadVideoFFmpegUpload,
+    "VHS_LoadVideoFFmpegPath": LoadVideoFFmpegPath,
+    "VHS_LoadImagePath": LoadImagePath,
+    "VHS_LoadImages": LoadImagesFromDirectoryUpload,
+    "VHS_LoadImagesPath": LoadImagesFromDirectoryPath,
+    "VHS_LoadAudio": LoadAudio,
+    "VHS_LoadAudioUpload": LoadAudioUpload,
+    "VHS_AudioToVHSAudio": AudioToVHSAudio,
+    "VHS_VHSAudioToAudio": VHSAudioToAudio,
+    "VHS_PruneOutputs": PruneOutputs,
+    "VHS_BatchManager": BatchManager,
+    "VHS_VideoInfo": VideoInfo,
+    "VHS_VideoInfoSource": VideoInfoSource,
+    "VHS_VideoInfoLoaded": VideoInfoLoaded,
+    "VHS_SelectFilename": SelectFilename,
+    # Batched Nodes
+    "VHS_VAEEncodeBatched": VAEEncodeBatched,
+    "VHS_VAEDecodeBatched": VAEDecodeBatched,
+    # Latent and Image nodes
+    "VHS_SplitLatents": SplitLatents,
+    "VHS_SplitImages": SplitImages,
+    "VHS_SplitMasks": SplitMasks,
+    "VHS_MergeLatents": MergeLatents,
+    "VHS_MergeImages": MergeImages,
+    "VHS_MergeMasks": MergeMasks,
+    "VHS_GetLatentCount": GetLatentCount,
+    "VHS_GetImageCount": GetImageCount,
+    "VHS_GetMaskCount": GetMaskCount,
+    "VHS_DuplicateLatents": RepeatLatents,
+    "VHS_DuplicateImages": RepeatImages,
+    "VHS_DuplicateMasks": RepeatMasks,
+    "VHS_SelectEveryNthLatent": SelectEveryNthLatent,
+    "VHS_SelectEveryNthImage": SelectEveryNthImage,
+    "VHS_SelectEveryNthMask": SelectEveryNthMask,
+    "VHS_SelectLatents": SelectLatents,
+    "VHS_SelectImages": SelectImages,
+    "VHS_SelectMasks": SelectMasks,
+    "VHS_Unbatch": Unbatch,
+    "VHS_SelectLatest": SelectLatest,
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "VHS_VideoCombine": "Video Combine 🎥🅥🅗🅢",
+    "VHS_LoadVideo": "Load Video (Upload) 🎥🅥🅗🅢",
+    "VHS_LoadVideoPath": "Load Video (Path) 🎥🅥🅗🅢",
+    "VHS_LoadVideoFFmpeg": "Load Video FFmpeg (Upload) 🎥🅥🅗🅢",
+    "VHS_LoadVideoFFmpegPath": "Load Video FFmpeg (Path) 🎥🅥🅗🅢",
+    "VHS_LoadImagePath": "Load Image (Path) 🎥🅥🅗🅢",
+    "VHS_LoadImages": "Load Images (Upload) 🎥🅥🅗🅢",
+    "VHS_LoadImagesPath": "Load Images (Path) 🎥🅥🅗🅢",
+    "VHS_LoadAudio": "Load Audio (Path)🎥🅥🅗🅢",
+    "VHS_LoadAudioUpload": "Load Audio (Upload)🎥🅥🅗🅢",
+    "VHS_AudioToVHSAudio": "Audio to legacy VHS_AUDIO🎥🅥🅗🅢",
+    "VHS_VHSAudioToAudio": "Legacy VHS_AUDIO to Audio🎥🅥🅗🅢",
+    "VHS_PruneOutputs": "Prune Outputs 🎥🅥🅗🅢",
+    "VHS_BatchManager": "Meta Batch Manager 🎥🅥🅗🅢",
+    "VHS_VideoInfo": "Video Info 🎥🅥🅗🅢",
+    "VHS_VideoInfoSource": "Video Info (Source) 🎥🅥🅗🅢",
+    "VHS_VideoInfoLoaded": "Video Info (Loaded) 🎥🅥🅗🅢",
+    "VHS_SelectFilename": "Select Filename 🎥🅥🅗🅢",
+    # Batched Nodes
+    "VHS_VAEEncodeBatched": "VAE Encode Batched 🎥🅥🅗🅢",
+    "VHS_VAEDecodeBatched": "VAE Decode Batched 🎥🅥🅗🅢",
+    # Latent and Image nodes
+    "VHS_SplitLatents": "Split Latents 🎥🅥🅗🅢",
+    "VHS_SplitImages": "Split Images 🎥🅥🅗🅢",
+    "VHS_SplitMasks": "Split Masks 🎥🅥🅗🅢",
+    "VHS_MergeLatents": "Merge Latents 🎥🅥🅗🅢",
+    "VHS_MergeImages": "Merge Images 🎥🅥🅗🅢",
+    "VHS_MergeMasks": "Merge Masks 🎥🅥🅗🅢",
+    "VHS_GetLatentCount": "Get Latent Count 🎥🅥🅗🅢",
+    "VHS_GetImageCount": "Get Image Count 🎥🅥🅗🅢",
+    "VHS_GetMaskCount": "Get Mask Count 🎥🅥🅗🅢",
+    "VHS_DuplicateLatents": "Repeat Latents 🎥🅥🅗🅢",
+    "VHS_DuplicateImages": "Repeat Images 🎥🅥🅗🅢",
+    "VHS_DuplicateMasks": "Repeat Masks 🎥🅥🅗🅢",
+    "VHS_SelectEveryNthLatent": "Select Every Nth Latent 🎥🅥🅗🅢",
+    "VHS_SelectEveryNthImage": "Select Every Nth Image 🎥🅥🅗🅢",
+    "VHS_SelectEveryNthMask": "Select Every Nth Mask 🎥🅥🅗🅢",
+    "VHS_SelectLatents": "Select Latents 🎥🅥🅗🅢",
+    "VHS_SelectImages": "Select Images 🎥🅥🅗🅢",
+    "VHS_SelectMasks": "Select Masks 🎥🅥🅗🅢",
+    "VHS_Unbatch":  "Unbatch 🎥🅥🅗🅢",
+    "VHS_SelectLatest": "Select Latest 🎥🅥🅗🅢",
+}
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/server.py b/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/server.py
new file mode 100644
index 0000000000000000000000000000000000000000..f67e342400eb250b731245554bbd495864719f8f
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/server.py
@@ -0,0 +1,294 @@
+import server
+import folder_paths
+import os
+import subprocess
+import re
+
+import asyncio
+import av
+
+from .utils import is_url, get_sorted_dir_files_from_directory, ffmpeg_path, \
+        validate_sequence, is_safe_path, strip_path, try_download_video, ENCODE_ARGS
+from comfy.k_diffusion.utils import FolderOfImages
+
+
+web = server.web
+
+@server.PromptServer.instance.routes.get("/vhs/viewvideo")
+@server.PromptServer.instance.routes.get("/viewvideo")
+async def view_video(request):
+    query = request.rel_url.query
+    path_res = await resolve_path(query)
+    if isinstance(path_res, web.Response):
+        return path_res
+    file, filename, output_dir = path_res
+
+    if ffmpeg_path is None:
+        #Don't just return file, that provides  arbitrary read access to any file
+        if is_safe_path(output_dir, strict=True):
+            return web.FileResponse(path=file)
+
+    frame_rate = query.get('frame_rate', 8)
+    if query.get('format', 'video') == "folder":
+        os.makedirs(folder_paths.get_temp_directory(), exist_ok=True)
+        concat_file = os.path.join(folder_paths.get_temp_directory(), "image_sequence_preview.txt")
+        skip_first_images = int(query.get('skip_first_images', 0))
+        select_every_nth = int(query.get('select_every_nth', 1)) or 1
+        valid_images = get_sorted_dir_files_from_directory(file, skip_first_images, select_every_nth, FolderOfImages.IMG_EXTENSIONS)
+        if len(valid_images) == 0:
+            return web.Response(status=204)
+        with open(concat_file, "w") as f:
+            f.write("ffconcat version 1.0\n")
+            for path in valid_images:
+                f.write("file '" + os.path.abspath(path) + "'\n")
+                f.write("duration 0.125\n")
+        in_args = ["-safe", "0", "-i", concat_file]
+    else:
+        in_args = ["-i", file]
+        if '%' in file:
+            in_args = ['-framerate', str(frame_rate)] + in_args
+    #Do prepass to pull info
+    #breaks skip_first frames if this default is ever actually needed
+    base_fps = 30
+    try:
+        proc = await asyncio.create_subprocess_exec(ffmpeg_path, *in_args, '-t',
+                                   '0','-f', 'null','-', stdout=subprocess.PIPE,
+                                    stderr=subprocess.PIPE, stdin=subprocess.DEVNULL)
+        _, res_stderr = await proc.communicate()
+
+        match = re.search(': Video: (\\w+) .+, (\\d+) fps,', res_stderr.decode(*ENCODE_ARGS))
+        if match:
+            base_fps = float(match.group(2))
+            if match.group(1) == 'vp9':
+                #force libvpx for transparency
+                in_args = ['-c:v', 'libvpx-vp9'] + in_args
+    except subprocess.CalledProcessError as e:
+        print("An error occurred in the ffmpeg prepass:\n" \
+                + e.stderr.decode(*ENCODE_ARGS))
+        return web.Response(status=500)
+    vfilters = []
+    target_rate = float(query.get('force_rate', 0)) or base_fps
+    modified_rate = target_rate / (float(query.get('select_every_nth',1)) or 1)
+    start_time = 0
+    if 'start_time' in query:
+        start_time = float(query['start_time'])
+    elif float(query.get('skip_first_frames', 0)) > 0:
+        start_time = float(query.get('skip_first_frames'))/target_rate
+        if start_time > 1/modified_rate:
+            start_time += 1/modified_rate
+    if start_time > 0:
+        if start_time > 4:
+            post_seek = ['-ss', '4']
+            pre_seek = ['-ss', str(start_time - 4)]
+        else:
+            post_seek = ['-ss', str(start_time)]
+            pre_seek = []
+    else:
+        pre_seek = []
+        post_seek = []
+
+    args = [ffmpeg_path, "-v", "error"] + pre_seek + in_args + post_seek
+    if target_rate != 0:
+        args += ['-r', str(modified_rate)]
+    if query.get('force_size','Disabled') != "Disabled":
+        size = query['force_size'].split('x')
+        if size[0] == '?' or size[1] == '?':
+            size[0] = "-2" if size[0] == '?' else f"'min({size[0]},iw)'"
+            size[1] = "-2" if size[1] == '?' else f"'min({size[1]},ih)'"
+        else:
+            #Aspect ratio is likely changed. A more complex command is required
+            #to crop the output to the new aspect ratio
+            ar = float(size[0])/float(size[1])
+            vfilters.append(f"crop=if(gt({ar}\\,a)\\,iw\\,ih*{ar}):if(gt({ar}\\,a)\\,iw/{ar}\\,ih)")
+        size = ':'.join(size)
+        vfilters.append(f"scale={size}")
+    if len(vfilters) > 0:
+        args += ["-vf", ",".join(vfilters)]
+    if float(query.get('frame_load_cap', 0)) > 0:
+        args += ["-frames:v", query['frame_load_cap'].split('.')[0]]
+    #TODO:reconsider adding high frame cap/setting default frame cap on node
+    if query.get('deadline', 'realtime') == 'good':
+        deadline = 'good'
+    else:
+        deadline = 'realtime'
+
+    args += ['-c:v', 'libvpx-vp9','-deadline', deadline, '-cpu-used', '8', '-f', 'webm', '-']
+
+    try:
+        proc = await asyncio.create_subprocess_exec(*args, stdout=subprocess.PIPE,
+                                                    stdin=subprocess.DEVNULL)
+        try:
+            resp = web.StreamResponse()
+            resp.content_type = 'video/webm'
+            resp.headers["Content-Disposition"] = f"filename=\"{filename}\""
+            await resp.prepare(request)
+            while len(bytes_read := await proc.stdout.read(2**20)) != 0:
+                await resp.write(bytes_read)
+            #Of dubious value given frequency of kill calls, but more correct
+            await proc.wait()
+        except (ConnectionResetError, ConnectionError) as e:
+            proc.kill()
+    except BrokenPipeError as e:
+        pass
+    return resp
+@server.PromptServer.instance.routes.get("/vhs/viewaudio")
+async def view_audio(request):
+    query = request.rel_url.query
+    path_res = await resolve_path(query)
+    if isinstance(path_res, web.Response):
+        return path_res
+    file, filename, output_dir = path_res
+    if ffmpeg_path is None:
+        #Don't just return file, that provides  arbitrary read access to any file
+        if is_safe_path(output_dir, strict=True):
+            return web.FileResponse(path=file)
+
+    in_args = ["-i", file]
+    start_time = 0
+    if 'start_time' in query:
+        start_time = float(query['start_time'])
+    args = [ffmpeg_path, "-v", "error", '-vn'] + in_args + ['-ss', str(start_time)]
+    if float(query.get('duration', 0)) > 0:
+        args += ['-t', str(query['duration'])]
+    if query.get('deadline', 'realtime') == 'good':
+        deadline = 'good'
+    else:
+        deadline = 'realtime'
+
+    args += ['-c:a', 'libopus','-deadline', deadline, '-cpu-used', '8', '-f', 'webm', '-']
+    try:
+        proc = await asyncio.create_subprocess_exec(*args, stdout=subprocess.PIPE,
+                                                    stdin=subprocess.DEVNULL)
+        try:
+            resp = web.StreamResponse()
+            resp.content_type = 'audio/webm'
+            resp.headers["Content-Disposition"] = f"filename=\"{filename}\""
+            await resp.prepare(request)
+            while len(bytes_read := await proc.stdout.read(2**20)) != 0:
+                await resp.write(bytes_read)
+            #Of dubious value given frequency of kill calls, but more correct
+            await proc.wait()
+        except (ConnectionResetError, ConnectionError) as e:
+            proc.kill()
+    except BrokenPipeError as e:
+        pass
+    return resp
+
+query_cache = {}
+@server.PromptServer.instance.routes.get("/vhs/queryvideo")
+async def query_video(request):
+    query = request.rel_url.query
+    filepath = await resolve_path(query)
+    #TODO: cache lookup
+    if isinstance(filepath, web.Response):
+        return filepath
+    filepath = filepath[0]
+    if filepath.endswith(".webp"):
+        # ffmpeg doesn't support decoding animated WebP https://trac.ffmpeg.org/ticket/4907
+        return web.json_response({})
+    if filepath in query_cache and query_cache[filepath][0] == os.stat(filepath).st_mtime:
+        source = query_cache[filepath][1]
+    else:
+        source = {}
+        try:
+            with av.open(filepath) as cont:
+                stream = cont.streams.video[0]
+                source['fps'] = float(stream.average_rate)
+                source['duration'] = float(cont.duration / av.time_base)
+
+                if stream.codec_context.name == 'vp9':
+                    cc = av.Codec('libvpx-vp9', 'r').create()
+                else:
+                    cc = stream
+                def fit():
+                    for packet in cont.demux(video=0):
+                        yield from cc.decode(packet)
+                frame = next(fit())
+
+                source['size'] = [frame.width, frame.height]
+                source['alpha'] = 'a' in frame.format.name
+                source['frames'] = stream.metadata.get('NUMBER_OF_FRAMES', round(source['duration'] * source['fps']))
+                query_cache[filepath] = (os.stat(filepath).st_mtime, source)
+        except Exception:
+            pass
+    if not 'frames' in source:
+        return web.json_response({})
+    loaded = {}
+    loaded['duration'] = source['duration']
+    loaded['duration'] -= float(query.get('start_time',0))
+    loaded['fps'] = float(query.get('force_rate', 0)) or source.get('fps',1)
+    loaded['duration'] -= int(query.get('skip_first_frames', 0)) / loaded['fps']
+    loaded['fps'] /= int(query.get('select_every_nth', 1)) or 1
+    loaded['frames'] = round(loaded['duration'] * loaded['fps'])
+    return web.json_response({'source': source, 'loaded': loaded})
+
+async def resolve_path(query):
+    if "filename" not in query:
+        return web.Response(status=204)
+    filename = query["filename"]
+
+    #Path code misformats urls on windows and must be skipped
+    if is_url(filename):
+        file = await asyncio.to_thread(try_download_video, filename) or file
+        filname, output_dir = os.path.split(file)
+        return file, filename, output_dir
+    else:
+        filename, output_dir = folder_paths.annotated_filepath(filename)
+
+        type = query.get("type", "output")
+        if type == "path":
+            #special case for path_based nodes
+            #NOTE: output_dir may be empty, but non-None
+            output_dir, filename = os.path.split(strip_path(filename))
+        if output_dir is None:
+            output_dir = folder_paths.get_directory_by_type(type)
+
+        if output_dir is None:
+            return web.Response(status=204)
+
+        if not is_safe_path(output_dir):
+            return web.Response(status=204)
+
+        if "subfolder" in query:
+            output_dir = os.path.join(output_dir, query["subfolder"])
+
+        filename = os.path.basename(filename)
+        file = os.path.join(output_dir, filename)
+
+        if not os.path.exists(file):
+            return web.Response(status=204)
+        if query.get('format', 'video') == 'folder':
+            if not os.path.isdir(file):
+                return web.Response(status=204)
+        else:
+            if not os.path.isfile(file) and not validate_sequence(file):
+                    return web.Response(status=204)
+        return file, filename, output_dir
+
+@server.PromptServer.instance.routes.get("/vhs/getpath")
+@server.PromptServer.instance.routes.get("/getpath")
+async def get_path(request):
+    query = request.rel_url.query
+    if "path" not in query:
+        return web.Response(status=204)
+    #NOTE: path always ends in `/`, so this is functionally an lstrip
+    path = os.path.abspath(strip_path(query["path"]))
+
+    if not os.path.exists(path) or not is_safe_path(path):
+        return web.json_response([])
+
+    #Use get so None is default instead of keyerror
+    valid_extensions = query.get("extensions")
+    valid_items = []
+    for item in os.scandir(path):
+        try:
+            if item.is_dir():
+                valid_items.append(item.name + "/")
+                continue
+            if valid_extensions is None or item.name.split(".")[-1].lower() in valid_extensions:
+                valid_items.append(item.name)
+        except OSError:
+            #Broken symlinks can throw a very unhelpful "Invalid argument"
+            pass
+    valid_items.sort(key=lambda f: os.stat(os.path.join(path,f)).st_mtime)
+    return web.json_response(valid_items)
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/utils.py b/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b35ad47fa3d1412773ab561598031ba6a3b38bc
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/utils.py
@@ -0,0 +1,443 @@
+import hashlib
+import os
+from typing import Iterable
+import shutil
+import subprocess
+import re
+import time
+from collections.abc import Mapping
+from typing import Union
+import functools
+import torch
+from torch import Tensor
+
+import server
+from .logger import logger
+import folder_paths
+
+BIGMIN = -(2**53-1)
+BIGMAX = (2**53-1)
+
+DIMMAX = 8192
+
+ENCODE_ARGS = ("utf-8", 'backslashreplace')
+
+def ffmpeg_suitability(path):
+    try:
+        version = subprocess.run([path, "-version"], check=True,
+                                 capture_output=True).stdout.decode(*ENCODE_ARGS)
+    except:
+        return 0
+    score = 0
+    #rough layout of the importance of various features
+    simple_criterion = [("libvpx", 20),("264",10), ("265",3),
+                        ("svtav1",5),("libopus", 1)]
+    for criterion in simple_criterion:
+        if version.find(criterion[0]) >= 0:
+            score += criterion[1]
+    #obtain rough compile year from copyright information
+    copyright_index = version.find('2000-2')
+    if copyright_index >= 0:
+        copyright_year = version[copyright_index+6:copyright_index+9]
+        if copyright_year.isnumeric():
+            score += int(copyright_year)
+    return score
+
+class MultiInput(str):
+    def __new__(cls, string, allowed_types="*"):
+        res = super().__new__(cls, string)
+        res.allowed_types=allowed_types
+        return res
+    def __ne__(self, other):
+        if self.allowed_types == "*" or other == "*":
+            return False
+        return other not in self.allowed_types
+imageOrLatent = MultiInput("IMAGE", ["IMAGE", "LATENT"])
+floatOrInt = MultiInput("FLOAT", ["FLOAT", "INT"])
+
+class ContainsAll(dict):
+    def __contains__(self, other):
+        return True
+    def __getitem__(self, key):
+        return super().get(key, (None, {}))
+
+if "VHS_FORCE_FFMPEG_PATH" in os.environ:
+    ffmpeg_path = os.environ.get("VHS_FORCE_FFMPEG_PATH")
+else:
+    ffmpeg_paths = []
+    try:
+        from imageio_ffmpeg import get_ffmpeg_exe
+        imageio_ffmpeg_path = get_ffmpeg_exe()
+        ffmpeg_paths.append(imageio_ffmpeg_path)
+    except:
+        if "VHS_USE_IMAGEIO_FFMPEG" in os.environ:
+            raise
+        logger.warn("Failed to import imageio_ffmpeg")
+    if "VHS_USE_IMAGEIO_FFMPEG" in os.environ:
+        ffmpeg_path = imageio_ffmpeg_path
+    else:
+        system_ffmpeg = shutil.which("ffmpeg")
+        if system_ffmpeg is not None:
+            ffmpeg_paths.append(system_ffmpeg)
+        if os.path.isfile("ffmpeg"):
+            ffmpeg_paths.append(os.path.abspath("ffmpeg"))
+        if os.path.isfile("ffmpeg.exe"):
+            ffmpeg_paths.append(os.path.abspath("ffmpeg.exe"))
+        if len(ffmpeg_paths) == 0:
+            logger.error("No valid ffmpeg found.")
+            ffmpeg_path = None
+        elif len(ffmpeg_paths) == 1:
+            #Evaluation of suitability isn't required, can take sole option
+            #to reduce startup time
+            ffmpeg_path = ffmpeg_paths[0]
+        else:
+            ffmpeg_path = max(ffmpeg_paths, key=ffmpeg_suitability)
+gifski_path = os.environ.get("VHS_GIFSKI", None)
+if gifski_path is None:
+    gifski_path = os.environ.get("JOV_GIFSKI", None)
+    if gifski_path is None:
+        gifski_path = shutil.which("gifski")
+ytdl_path = os.environ.get("VHS_YTDL", None) or shutil.which('yt-dlp') \
+        or shutil.which('youtube-dl')
+download_history = {}
+def try_download_video(url):
+    if ytdl_path is None:
+        return None
+    if url in download_history:
+        return download_history[url]
+    os.makedirs(folder_paths.get_temp_directory(), exist_ok=True)
+    #Format information could be added to only download audio for Load Audio,
+    #but this gets hairy if same url is also used for video.
+    #Best to just always keep defaults
+    #dl_format = ['-f', 'ba'] if is_audio else []
+    try:
+        res = subprocess.run([ytdl_path, "--print", "after_move:filepath",
+                              "-P", folder_paths.get_temp_directory(), url],
+                             capture_output=True, check=True)
+        #strip newline
+        file = res.stdout.decode(*ENCODE_ARGS)[:-1]
+    except subprocess.CalledProcessError as e:
+        raise Exception("An error occurred in the yt-dl process:\n" \
+                + e.stderr.decode(*ENCODE_ARGS))
+        file = None
+    download_history[url] = file
+    return file
+
+def is_safe_path(path, strict=False):
+    if "VHS_STRICT_PATHS" not in os.environ and not strict:
+        return True
+    basedir = os.path.abspath('.')
+    try:
+        common_path = os.path.commonpath([basedir, path])
+    except:
+        #Different drive on windows
+        return False
+    return common_path == basedir
+
+def get_sorted_dir_files_from_directory(directory: str, skip_first_images: int=0, select_every_nth: int=1, extensions: Iterable=None):
+    directory = strip_path(directory)
+    dir_files = os.listdir(directory)
+    dir_files = sorted(dir_files)
+    dir_files = [os.path.join(directory, x) for x in dir_files]
+    dir_files = list(filter(lambda filepath: os.path.isfile(filepath), dir_files))
+    # filter by extension, if needed
+    if extensions is not None:
+        extensions = list(extensions)
+        new_dir_files = []
+        for filepath in dir_files:
+            ext = "." + filepath.split(".")[-1]
+            if ext.lower() in extensions:
+                new_dir_files.append(filepath)
+        dir_files = new_dir_files
+    # start at skip_first_images
+    dir_files = dir_files[skip_first_images:]
+    dir_files = dir_files[0::select_every_nth]
+    return dir_files
+
+
+# modified from https://stackoverflow.com/questions/22058048/hashing-a-file-in-python
+def calculate_file_hash(filename: str, hash_every_n: int = 1):
+    #Larger video files were taking >.5 seconds to hash even when cached,
+    #so instead the modified time from the filesystem is used as a hash
+    h = hashlib.sha256()
+    h.update(filename.encode())
+    h.update(str(os.path.getmtime(filename)).encode())
+    return h.hexdigest()
+
+prompt_queue = server.PromptServer.instance.prompt_queue
+def requeue_workflow_unchecked():
+    """Requeues the current workflow without checking for multiple requeues"""
+    currently_running = prompt_queue.currently_running
+    value = next(iter(currently_running.values()))
+    
+    # Handle both old (5 values) and new (6 values) ComfyUI versions
+    if len(value) == 6:
+        (_, prompt_id, prompt, extra_data, outputs_to_execute, _) = value
+    else:
+        (_, prompt_id, prompt, extra_data, outputs_to_execute) = value
+    
+    #Ensure batch_managers are marked stale
+    prompt = prompt.copy()
+    for uid in prompt:
+        if prompt[uid]['class_type'] == 'VHS_BatchManager':
+            prompt[uid]['inputs']['requeue'] = prompt[uid]['inputs'].get('requeue',0)+1
+
+    #execution.py has guards for concurrency, but server doesn't.
+    #TODO: Check that this won't be an issue
+    number = -server.PromptServer.instance.number
+    server.PromptServer.instance.number += 1
+    prompt_id = str(server.uuid.uuid4())
+    # Put back with 6 elements to match what ComfyUI expects
+    sensitive = value[5] if len(value) > 5 else {}
+    prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute, sensitive))
+
+requeue_guard = [None, 0, 0, {}]
+
+def requeue_workflow(requeue_required=(-1,True)):
+    assert(len(prompt_queue.currently_running) == 1)
+    global requeue_guard
+    
+    value = next(iter(prompt_queue.currently_running.values()))
+    
+    # Handle both old (5 values) and new (6 values) ComfyUI versions
+    if len(value) == 6:
+        (run_number, _, prompt, extra_data, outputs_to_execute, _) = value
+    else:
+        (run_number, _, prompt, extra_data, outputs_to_execute) = value
+    
+    if requeue_guard[0] != run_number:
+        #Calculate a count of how many outputs are managed by a batch manager
+        managed_outputs=0
+        for bm_uid in prompt:
+            if prompt[bm_uid]['class_type'] == 'VHS_BatchManager':
+                for output_uid in prompt:
+                    if prompt[output_uid]['class_type'] in ["VHS_VideoCombine"]:
+                        for inp in prompt[output_uid]['inputs'].values():
+                            if inp == [bm_uid, 0]:
+                                managed_outputs+=1
+        requeue_guard = [run_number, 0, managed_outputs, {}]
+    requeue_guard[1] = requeue_guard[1]+1
+    requeue_guard[3][requeue_required[0]] = requeue_required[1]
+    if requeue_guard[1] == requeue_guard[2] and max(requeue_guard[3].values()):
+        requeue_workflow_unchecked()
+
+def get_audio(file, start_time=0, duration=0):
+    args = [ffmpeg_path, "-i", file]
+    if start_time > 0:
+        args += ["-ss", str(start_time)]
+    if duration > 0:
+        args += ["-t", str(duration)]
+    try:
+        #TODO: scan for sample rate and maintain
+        res =  subprocess.run(args + ["-f", "f32le", "-"],
+                              capture_output=True, check=True)
+        audio = torch.frombuffer(bytearray(res.stdout), dtype=torch.float32)
+        match = re.search(', (\\d+) Hz, (\\w+), ',res.stderr.decode(*ENCODE_ARGS))
+    except subprocess.CalledProcessError as e:
+        raise Exception(f"VHS failed to extract audio from {file}:\n" \
+                + e.stderr.decode(*ENCODE_ARGS))
+    if match:
+        ar = int(match.group(1))
+        #NOTE: Just throwing an error for other channel types right now
+        #Will deal with issues if they come
+        ac = {"mono": 1, "stereo": 2}[match.group(2)]
+    else:
+        ar = 44100
+        ac = 2
+    audio = audio.reshape((-1,ac)).transpose(0,1).unsqueeze(0)
+    return {'waveform': audio, 'sample_rate': ar}
+
+class LazyAudioMap(Mapping):
+    def __init__(self, file, start_time, duration):
+        self.file = file
+        self.start_time=start_time
+        self.duration=duration
+        self._dict=None
+    def __getitem__(self, key):
+        if self._dict is None:
+            self._dict = get_audio(self.file, self.start_time, self.duration)
+        return self._dict[key]
+    def __iter__(self):
+        if self._dict is None:
+            self._dict = get_audio(self.file, self.start_time, self.duration)
+        return iter(self._dict)
+    def __len__(self):
+        if self._dict is None:
+            self._dict = get_audio(self.file, self.start_time, self.duration)
+        return len(self._dict)
+def lazy_get_audio(file, start_time=0, duration=0, **kwargs):
+    return LazyAudioMap(file, start_time, duration)
+
+def is_url(url):
+    return url.split("://")[0] in ["http", "https"]
+
+def validate_sequence(path):
+    #Check if path is a valid ffmpeg sequence that points to at least one file
+    (path, file) = os.path.split(path)
+    if not os.path.isdir(path):
+        return False
+    match = re.search('%0?\\d+d', file)
+    if not match:
+        return False
+    seq = match.group()
+    if seq == '%d':
+        seq = '\\\\d+'
+    else:
+        seq = '\\\\d{%s}' % seq[1:-1]
+    file_matcher = re.compile(re.sub('%0?\\d+d', seq, file))
+    for file in os.listdir(path):
+        if file_matcher.fullmatch(file):
+            return True
+    return False
+
+def strip_path(path):
+    #This leaves whitespace inside quotes and only a single "
+    #thus ' ""test"' -> '"test'
+    #consider path.strip(string.whitespace+"\"")
+    #or weightier re.fullmatch("[\\s\"]*(.+?)[\\s\"]*", path).group(1)
+    path = path.strip()
+    if path.startswith("\""):
+        path = path[1:]
+    if path.endswith("\""):
+        path = path[:-1]
+    return path
+def hash_path(path):
+    if path is None:
+        return "input"
+    if is_url(path):
+        return "url"
+    if not os.path.isfile(path):
+        return "DNE"
+    return calculate_file_hash(strip_path(path))
+
+
+def validate_path(path, allow_none=False, allow_url=True):
+    if path is None:
+        return allow_none
+    if is_url(path):
+        #Probably not feasible to check if url resolves here
+        if not allow_url:
+            return "URLs are unsupported for this path"
+        return is_safe_path(path)
+    if not os.path.isfile(strip_path(path)):
+        return "Invalid file path: {}".format(path)
+    return is_safe_path(path)
+
+
+def validate_index(index: int, length: int=0, is_range: bool=False, allow_negative=False, allow_missing=False) -> int:
+    # if part of range, do nothing
+    if is_range:
+        return index
+    # otherwise, validate index
+    # validate not out of range - only when latent_count is passed in
+    if length > 0 and index > length-1 and not allow_missing:
+        raise IndexError(f"Index '{index}' out of range for {length} item(s).")
+    # if negative, validate not out of range
+    if index < 0:
+        if not allow_negative:
+            raise IndexError(f"Negative indeces not allowed, but was '{index}'.")
+        conv_index = length+index
+        if conv_index < 0 and not allow_missing:
+            raise IndexError(f"Index '{index}', converted to '{conv_index}' out of range for {length} item(s).")
+        index = conv_index
+    return index
+
+
+def convert_to_index_int(raw_index: str, length: int=0, is_range: bool=False, allow_negative=False, allow_missing=False) -> int:
+    try:
+        return validate_index(int(raw_index), length=length, is_range=is_range, allow_negative=allow_negative, allow_missing=allow_missing)
+    except ValueError as e:
+        raise ValueError(f"Index '{raw_index}' must be an integer.", e)
+
+
+def convert_str_to_indexes(indexes_str: str, length: int=0, allow_missing=False) -> list[int]:
+    if not indexes_str:
+        return []
+    int_indexes = list(range(0, length))
+    allow_negative = length > 0
+    chosen_indexes = []
+    # parse string - allow positive ints, negative ints, and ranges separated by ':'
+    groups = indexes_str.split(",")
+    groups = [g.strip() for g in groups]
+    for g in groups:
+        # parse range of indeces (e.g. 2:16)
+        if ':' in g:
+            index_range = g.split(":", 2)
+            index_range = [r.strip() for r in index_range]
+
+            start_index = index_range[0]
+            if len(start_index) > 0:
+                start_index = convert_to_index_int(start_index, length=length, is_range=True, allow_negative=allow_negative, allow_missing=allow_missing)
+            else:
+                start_index = 0
+            end_index = index_range[1]
+            if len(end_index) > 0:
+                end_index = convert_to_index_int(end_index, length=length, is_range=True, allow_negative=allow_negative, allow_missing=allow_missing)
+            else:
+                end_index = length
+            # support step as well, to allow things like reversing, every-other, etc.
+            step = 1
+            if len(index_range) > 2:
+                step = index_range[2]
+                if len(step) > 0:
+                    step = convert_to_index_int(step, length=length, is_range=True, allow_negative=True, allow_missing=True)
+                else:
+                    step = 1
+            # if latents were passed in, base indeces on known latent count
+            if len(int_indexes) > 0:
+                chosen_indexes.extend(int_indexes[start_index:end_index][::step])
+            # otherwise, assume indeces are valid
+            else:
+                chosen_indexes.extend(list(range(start_index, end_index, step)))
+        # parse individual indeces
+        else:
+            chosen_indexes.append(convert_to_index_int(g, length=length, allow_negative=allow_negative, allow_missing=allow_missing))
+    return chosen_indexes
+
+
+def select_indexes(input_obj: Union[Tensor, list], idxs: list):
+    if type(input_obj) == Tensor:
+        return input_obj[idxs]
+    else:
+        return [input_obj[i] for i in idxs]
+
+def merge_filter_args(args, ftype="-vf"):
+    #TODO This doesn't account for filter_complex
+    #Will likely need to convert all filters to filter complex in the future
+    #But that requires source/output deduplication
+    try:
+        start_index = args.index(ftype)+1
+        index = start_index
+        while True:
+            index = args.index(ftype, index)
+            args[start_index] += ',' + args[index+1]
+            args.pop(index)
+            args.pop(index)
+    except ValueError:
+        pass
+
+def select_indexes_from_str(input_obj: Union[Tensor, list], indexes: str, err_if_missing=True, err_if_empty=True):
+    real_idxs = convert_str_to_indexes(indexes, len(input_obj), allow_missing=not err_if_missing)
+    if err_if_empty and len(real_idxs) == 0:
+        raise Exception(f"Nothing was selected based on indexes found in '{indexes}'.")
+    return select_indexes(input_obj, real_idxs)
+
+def hook(obj, attr):
+    def dec(f):
+        f = functools.update_wrapper(f, getattr(obj,attr))
+        setattr(obj,attr,f)
+        return f
+    return dec
+
+def cached(duration):
+    def dec(f):
+        cached_ret = None
+        cache_time = 0
+        def cached_func():
+            nonlocal cache_time, cached_ret
+            if time.time() > cache_time + duration or cached_ret is None:
+                cache_time = time.time()
+                cached_ret = f()
+            return cached_ret
+        return cached_func
+    return dec
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/web/js/VHS.core.js b/custom_nodes/ComfyUI-VideoHelperSuite/web/js/VHS.core.js
new file mode 100644
index 0000000000000000000000000000000000000000..fcbf03ffc7eb517e8a0a7b5ce045aa9bee5d8e43
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/web/js/VHS.core.js
@@ -0,0 +1,2570 @@
+import { app } from '../../../scripts/app.js'
+import { api } from '../../../scripts/api.js'
+import { setWidgetConfig } from '../../../extensions/core/widgetInputs.js'
+import { applyTextReplacements } from "../../../scripts/utils.js";
+
+function chainCallback(object, property, callback) {
+    if (object == undefined) {
+        //This should not happen.
+        console.error("Tried to add callback to non-existant object")
+        return;
+    }
+    if (property in object && object[property]) {
+        const callback_orig = object[property]
+        object[property] = function () {
+            const r = callback_orig.apply(this, arguments);
+            return callback.apply(this, arguments) ?? r
+        };
+    } else {
+        object[property] = callback;
+    }
+}
+
+function getNodeById(id, graph=app.graph) {
+    let cg = graph
+    let node = undefined
+    for (let sid of (''+id).split(':')) {
+        node = cg?.getNodeById?.(sid)
+        cg = node?.subgraph
+    }
+    return node
+}
+
+const convDict = {
+    VHS_LoadImages : ["directory", null, "image_load_cap", "skip_first_images", "select_every_nth"],
+    VHS_LoadImagesPath : ["directory", "image_load_cap", "skip_first_images", "select_every_nth"],
+    VHS_VideoCombine : ["frame_rate", "loop_count", "filename_prefix", "format", "pingpong", "save_image"],
+    VHS_LoadVideo : ["video", "force_rate", "force_size", "frame_load_cap", "skip_first_frames", "select_every_nth"],
+    VHS_LoadVideoPath : ["video", "force_rate", "force_size", "frame_load_cap", "skip_first_frames", "select_every_nth"],
+};
+const renameDict  = {VHS_VideoCombine : {save_output : "save_image"}}
+function useKVState(nodeType) {
+    chainCallback(nodeType.prototype, "onNodeCreated", function () {
+        chainCallback(this, "onConfigure", function(info) {
+            if (!this.widgets) {
+                //Node has no widgets, there is nothing to restore
+                return
+            }
+            if (typeof(info.widgets_values) != "object") {
+                //widgets_values is in some unknown inactionable format
+                return
+            }
+            let widgetDict = info.widgets_values
+            if (info.widgets_values.length) {
+                //widgets_values is in the old list format
+                if (this.type in convDict) {
+                    //widget does not have a conversion format provided
+                    let convList = convDict[this.type];
+                    if(info.widgets_values.length >= convList.length) {
+                        //has all required fields
+                        widgetDict = {}
+                        for (let i = 0; i < convList.length; i++) {
+                            if(!convList[i]) {
+                                //Element should not be processed (upload button on load image sequence)
+                                continue
+                            }
+                            widgetDict[convList[i]] = info.widgets_values[i];
+                        }
+                    } else {
+                        //widgets_values is missing elements marked as required
+                        //let it fall through to failure state
+                    }
+                }
+            }
+            if ('force_size' in widgetDict) {
+                //force size has been phased out, Migrate state
+                if (widgetDict.force_size.includes?.('x')) {
+                    let sizes = widgetDict.force_size.split('x')
+                    if (sizes[0] != '?') {
+                        widgetDict.custom_width = parseInt(sizes[0])
+                    } else {
+                        widgetDict.custom_width = 0
+                    }
+                    if (sizes[1] != '?') {
+                        widgetDict.custom_height = parseInt(sizes[1])
+                    } else {
+                        widgetDict.custom_height = 0
+                    }
+                } else {
+                    if (['Disabled', 'Custom Height'].includes(widgetDict.force_size)) {
+                        widgetDict.custom_width = 0
+                    }
+                    if (['Disabled', 'Custom Width'].includes(widgetDict.force_size)) {
+                        widgetDict.custom_height = 0
+                    }
+                }
+            }
+            if (widgetDict.videopreview?.params?.force_size) {
+                delete widgetDict.videopreview.params.force_size
+            }
+            let inputs = {}
+            for (let i of this.inputs) {
+                inputs[i.name] = i
+            }
+            if (widgetDict.length == undefined) {
+                for (let w of this.widgets) {
+                    if (w.type =="button") {
+                        continue
+                    }
+                    if (w.name in widgetDict) {
+                        w.value = widgetDict[w.name];
+                        w.callback?.(w.value)
+                    } else {
+                        //Check for a legacy name that needs migrating
+                        if (this.type in renameDict && w.name in renameDict[this.type]) {
+                            if (renameDict[this.type][w.name] in widgetDict) {
+                                w.value = widgetDict[renameDict[this.type][w.name]]
+                                w.callback?.(w.value)
+                                continue
+                            }
+                        }
+                        //attempt to restore default value
+                        let inputs = LiteGraph.getNodeType(this.type).nodeData.input;
+                        let initialValue = null;
+                        if (inputs?.required?.hasOwnProperty(w.name)) {
+                            if (inputs.required[w.name][1]?.hasOwnProperty("default")) {
+                                initialValue = inputs.required[w.name][1].default;
+                            } else if (inputs.required[w.name][0].length) {
+                                initialValue = inputs.required[w.name][0][0];
+                            }
+                        } else if (inputs?.optional?.hasOwnProperty(w.name)) {
+                            if (inputs.optional[w.name][1]?.hasOwnProperty("default")) {
+                                initialValue = inputs.optional[w.name][1].default;
+                            } else if (inputs.optional[w.name][0].length) {
+                                initialValue = inputs.optional[w.name][0][0];
+                            }
+                        }
+                        if (initialValue) {
+                            w.value = initialValue;
+                            w.callback?.(w.value)
+                        }
+                    }
+                    if (w.name in inputs && w.config) {
+                        setWidgetConfig(inputs[w.name], w.config)
+                    }
+                }
+            } else {
+                //Saved data was not a map made by this method
+                //and a conversion dict for it does not exist
+                //It's likely an array and that has been blindly applied
+                if (info?.widgets_values?.length != this.widgets.length) {
+                    //Widget could not have restored properly
+                    //Note if multiple node loads fail, only the latest error dialog displays
+                    app.ui.dialog.show("Failed to restore node: " + this.title + "\nPlease remove and re-add it.")
+                    this.bgcolor = "#C00"
+                }
+            }
+        });
+        chainCallback(this, "onSerialize", function(info) {
+            info.widgets_values = {};
+            if (!this.widgets) {
+                //object has no widgets, there is nothing to store
+                return;
+            }
+            for (let w of this.widgets) {
+                info.widgets_values[w.name] = w.value;
+            }
+        });
+    })
+}
+var helpDOM = app.VHSHelp;
+if (!app.VHSHelp) {
+    helpDOM = document.createElement("div");
+    app.VHSHelp = helpDOM
+} else {
+    app.extensionManager.dialog
+      .showErrorDialog('Please check your custom_nodes directory and manually remove the duplicate.',
+                       { title: 'Duplicate VHS install detected' })
+    throw new Error('Duplicate VHS install detected. Check your custom_nodes directory')
+}
+function initHelpDOM() {
+    let parentDOM = document.createElement("div");
+    parentDOM.className = "VHS_floatinghelp"
+    document.body.appendChild(parentDOM)
+    parentDOM.appendChild(helpDOM)
+    helpDOM.className = "litegraph";
+    let scrollbarStyle = document.createElement('style');
+    scrollbarStyle.innerHTML = `
+            .VHS_floatinghelp {
+                scrollbar-width: 6px;
+                scrollbar-color: #0003  #0000;
+                &::-webkit-scrollbar {
+                    background: transparent;
+                    width: 6px;
+                }
+                &::-webkit-scrollbar-thumb {
+                    background: #0005;
+                    border-radius: 20px
+                }
+                &::-webkit-scrollbar-button {
+                    display: none;
+                }
+            }
+            .VHS_loopedvideo::-webkit-media-controls-mute-button {
+                display:none;
+            }
+            .VHS_loopedvideo::-webkit-media-controls-fullscreen-button {
+                display:none;
+            }
+    `
+    scrollbarStyle.id = 'scroll-properties'
+    parentDOM.appendChild(scrollbarStyle)
+    chainCallback(app.canvas, "onDrawForeground", function (ctx, visible_rect){
+        let n = helpDOM.node
+        if (!n || !n?.graph) {
+            parentDOM.style['left'] = '-5000px'
+            return
+        }
+        //draw : function(ctx, node, widgetWidth, widgetY, height) {
+        //update widget position, even if off screen
+        const transform = ctx.getTransform();
+        const scale = app.canvas.ds.scale;//gets the litegraph zoom
+        //calculate coordinates with account for browser zoom
+        const bcr = app.canvas.canvas.getBoundingClientRect()
+        const x = transform.e*scale/transform.a + bcr.x;
+        const y = transform.f*scale/transform.a + bcr.y;
+        //TODO: text reflows at low zoom. investigate alternatives
+        Object.assign(parentDOM.style, {
+            left: (x+(n.pos[0] + n.size[0]+15)*scale) + "px",
+            top: (y+(n.pos[1]-LiteGraph.NODE_TITLE_HEIGHT)*scale) + "px",
+            width: "400px",
+            minHeight: "100px",
+            maxHeight: "600px",
+            overflowY: 'scroll',
+            transformOrigin: '0 0',
+            transform: 'scale(' + scale + ',' + scale +')',
+            fontSize: '18px',
+            backgroundColor: LiteGraph.NODE_DEFAULT_BGCOLOR,
+            boxShadow: '0 0 10px black',
+            borderRadius: '4px',
+            padding: '3px',
+            zIndex: 3,
+            position: "absolute",
+            display: 'inline',
+        });
+    });
+    function setCollapse(el, doCollapse) {
+        if (doCollapse) {
+            el.children[0].children[0].innerHTML = '+'
+            Object.assign(el.children[1].style, {
+                color: '#CCC',
+                overflowX: 'hidden',
+                width: '0px',
+                minWidth: 'calc(100% - 20px)',
+                textOverflow: 'ellipsis',
+                whiteSpace: 'nowrap',
+            })
+            for (let child of el.children[1].children) {
+                if (child.style.display != 'none'){
+                    child.origDisplay = child.style.display
+                }
+                child.style.display = 'none'
+            }
+        } else {
+            el.children[0].children[0].innerHTML = '-'
+            Object.assign(el.children[1].style, {
+                color: '',
+                overflowX: '',
+                width: '100%',
+                minWidth: '',
+                textOverflow: '',
+                whiteSpace: '',
+            })
+            for (let child of el.children[1].children) {
+                child.style.display = child.origDisplay
+            }
+        }
+    }
+    helpDOM.collapseOnClick = function() {
+        let doCollapse = this.children[0].innerHTML == '-'
+        setCollapse(this.parentElement, doCollapse)
+    }
+    helpDOM.selectHelp = function(name, value) {
+        //attempt to navigate to name in help
+        function collapseUnlessMatch(items,t) {
+            var match = items.querySelector('[vhs_title="' + t + '"]')
+            if (!match) {
+                for (let i of items.children) {
+                    if (i.innerHTML.slice(0,t.length+5).includes(t)) {
+                        match = i
+                        break
+                    }
+                }
+            }
+            if (!match) {
+                return null
+            }
+            //For longer documentation items with fewer collapsable elements,
+            //scroll to make sure the entirety of the selected item is visible
+            //This has the unfortunate side effect of trying to scroll the main
+            //window if the documentation windows is forcibly offscreen,
+            //but it's easy to simply scroll the main window back and seems to
+            //have no visual side effects
+            match.scrollIntoView(false)
+            window.scrollTo(0,0)
+            for (let i of items.querySelectorAll('.VHS_collapse')) {
+                if (i.contains(match)) {
+                    setCollapse(i, false)
+                } else {
+                    setCollapse(i, true)
+                }
+            }
+            return match
+        }
+        let target = collapseUnlessMatch(helpDOM, name)
+        if (target && value) {
+            collapseUnlessMatch(target, value)
+        }
+    }
+    let titleContext = document.createElement("canvas").getContext("2d")
+    titleContext.font = app.canvas.title_text_font;
+    helpDOM.calculateTitleLength = function(text) {
+        return titleContext.measureText(text).width
+    }
+    helpDOM.addHelp = function(node, nodeType, description) {
+        if (!description) {
+            return
+        }
+        //Pad computed size for the clickable question mark
+        let originalComputeSize = node.computeSize
+        node.computeSize = function() {
+            let size = originalComputeSize.apply(this, arguments)
+            if (!this.title) {
+                return size
+            }
+            let title_width = helpDOM.calculateTitleLength(this.title)
+            size[0] = Math.max(size[0], title_width + LiteGraph.NODE_TITLE_HEIGHT*2)
+            return size
+        }
+
+        node.description = description
+        chainCallback(node, "onDrawForeground", function (ctx) {
+            if (this?.flags?.collapsed) {
+                return
+            }
+            //draw question mark
+            ctx.save()
+            ctx.font = 'bold 20px Arial'
+            ctx.fillText("?", this.size[0]-17, -8)
+            ctx.restore()
+        })
+        chainCallback(node, "onMouseDown", function (e, pos, canvas) {
+            if (this?.flags?.collapsed) {
+                return
+            }
+            //On click would be preferred, but this'll be good enough
+            if (pos[1] < 0 && pos[0] + LiteGraph.NODE_TITLE_HEIGHT > this.size[0]) {
+                //corner question mark clicked
+                if (helpDOM.node == this) {
+                    helpDOM.node = undefined
+                } else {
+                    helpDOM.node = this;
+                    helpDOM.innerHTML = this.description || "no help provided "
+                    for (let e of helpDOM.querySelectorAll('.VHS_collapse')) {
+                        e.children[0].onclick = helpDOM.collapseOnClick
+                        e.children[0].style.cursor = 'pointer'
+                    }
+                    for (let e of helpDOM.querySelectorAll('.VHS_precollapse')) {
+                        setCollapse(e, true)
+                    }
+                    for (let e of helpDOM.querySelectorAll('.VHS_loopedvideo')) {
+                        e?.play()
+                    }
+                    helpDOM.parentElement.scrollTo(0,0)
+                }
+                return true
+            }
+        })
+        let timeout = null
+        chainCallback(node, "onMouseMove", function (e, pos, canvas) {
+            if (timeout) {
+                clearTimeout(timeout)
+                timeout = null
+            }
+            if (helpDOM.node != this) {
+                return
+            }
+            timeout = setTimeout(() => {
+                let n = this
+                if (pos[0] > 0 && pos[0] < n.size[0]
+                    && pos[1] > 0 && pos[1] < n.size[1]) {
+                    //TODO: provide help specific to element clicked
+                    let inputRows = Math.max(n.inputs?.length || 0, n.outputs?.length || 0)
+                    if (pos[1] < LiteGraph.NODE_SLOT_HEIGHT * inputRows) {
+                        let row = Math.floor((pos[1] - 7) / LiteGraph.NODE_SLOT_HEIGHT)
+                        if (pos[0] < n.size[0]/2) {
+                            if (row < n.inputs.length) {
+                                helpDOM.selectHelp(n.inputs[row].name)
+                            }
+                        } else {
+                            if (row < n.outputs.length) {
+                                helpDOM.selectHelp(n.outputs[row].name)
+                            }
+                        }
+                    } else {
+                        //probably widget, but widgets have variable height.
+                        let basey = LiteGraph.NODE_SLOT_HEIGHT * inputRows + 6
+                        for (let w of n.widgets) {
+                            if (w.y) {
+                                basey = w.y
+                            }
+                            let wheight = LiteGraph.NODE_WIDGET_HEIGHT+4
+                            if (w.computeSize) {
+                                wheight = w.computeSize(n.size[0])[1]
+                            }
+                            if (pos[1] < basey + wheight) {
+                                helpDOM.selectHelp(w.name, w.value)
+                                break
+                            }
+                            basey += wheight
+                        }
+                    }
+                }
+            }, 500)
+        })
+        chainCallback(node, "onMouseLeave", function (e, pos, canvas) {
+            if (timeout) {
+                clearTimeout(timeout)
+                timeout = null
+            }
+        });
+    }
+}
+
+function fitHeight(node) {
+    node.setSize([node.size[0], node.computeSize([node.size[0], node.size[1]])[1]])
+    node?.graph?.setDirtyCanvas(true);
+}
+function startDraggingItems(node, pointer) {
+    app.canvas.emitBeforeChange()
+    app.canvas.graph?.beforeChange()
+    // Ensure that dragging is properly cleaned up, on success or failure.
+    pointer.finally = () => {
+      app.canvas.isDragging = false
+      app.canvas.graph?.afterChange()
+      app.canvas.emitAfterChange()
+    }
+    app.canvas.processSelect(node, pointer.eDown, true)
+    app.canvas.isDragging = true
+}
+function processDraggedItems(e) {
+    if (e.shiftKey || LiteGraph.alwaysSnapToGrid)
+      app.canvas?.graph?.snapToGrid(app.canvas.selectedItems)
+    app.canvas.dirty_canvas = true
+    app.canvas.dirty_bgcanvas = true
+    app.canvas.onNodeMoved?.(findFirstNode(app.canvas.selectedItems))
+}
+function allowDragFromWidget(widget) {
+    widget.onPointerDown = function(pointer, node) {
+        pointer.onDragStart = () => startDraggingItems(node, pointer)
+        pointer.onDragEnd = processDraggedItems
+        app.canvas.dirty_canvas = true
+        return true
+    }
+}
+
+//Cloud specific auth code. Short circuits if not on cloud
+async function getAuthHeader() {
+  try {
+    const authStore = await api.getAuthStore()
+    return authStore ? await authStore.getAuthHeader() : null
+  } catch (error) {
+    console.warn('Failed to get auth header:', error)
+    return null
+  }
+}
+
+async function uploadFile(file, progressCallback) {
+    try {
+        // Wrap file in formdata so it includes filename
+        const body = new FormData();
+        const i = file.webkitRelativePath.lastIndexOf('/');
+        const subfolder = file.webkitRelativePath.slice(0,i+1)
+        const new_file = new File([file], file.name, {
+            type: file.type,
+            lastModified: file.lastModified,
+        });
+        body.append("image", new_file);
+        if (i > 0) {
+            body.append("subfolder", subfolder);
+        }
+        const url = api.apiURL("/upload/image")
+        const resp = await new Promise((resolve) => {
+            let req = new XMLHttpRequest()
+            req.upload.onprogress = (e) => progressCallback?.(e.loaded/e.total)
+            req.onload = () => resolve(req)
+            req.open('post', url, true)
+            getAuthHeader().then((headers) => {
+                headers ??= {}
+                for (const key in headers)
+                    req.setRequestHeader(key, headers[key])
+                req.send(body)
+            })
+        })
+
+        if (resp.status !== 200) {
+            alert(resp.status + " - " + resp.statusText);
+        }
+        return resp
+    } catch (error) {
+        alert(error);
+    }
+}
+
+function addVAEOutputToggle(nodeType, nodeData) {
+    chainCallback(nodeType.prototype, "onNodeCreated", function() {
+        this.reject_ue_connection = (input) => input?.name == "vae"
+    })
+    chainCallback(nodeType.prototype, "onConnectionsChange", function(contype, slot, iscon, linfo) {
+        let slotType = this.inputs[slot]?.type
+        if (contype == LiteGraph.INPUT && slotType == "VAE") {
+            if (iscon && linfo) {
+                if (this.linkTimeout) {
+                    clearTimeout(this.linkTimeout)
+                    this.linkTimeout = false
+                } else if (this.outputs[0].type == "IMAGE") {
+                    this.linkTimeout = setTimeout(() => {
+                        if (this.outputs[0].type != "IMAGE") {
+                            return
+                        }
+                        this.linkTimeout = false
+                        this.disconnectOutput(0);
+                    }, 50)
+                }
+                this.outputs[0].name = 'LATENT';
+                this.outputs[0].type = 'LATENT';
+            } else{
+                if (this.outputs[0].type == "LATENT") {
+                    this.linkTimeout = setTimeout(() => {
+                        this.linkTimeout = false
+                        this.disconnectOutput(0);
+                    }, 50)
+                }
+                this.outputs[0].name = "IMAGE";
+                this.outputs[0].type = "IMAGE";
+            }
+        }
+    });
+}
+function addVAEInputToggle(nodeType, nodeData) {
+    chainCallback(nodeType.prototype, "onNodeCreated", function() {
+        this.reject_ue_connection = (input) => input?.name == "vae"
+    })
+    chainCallback(nodeType.prototype, "onConnectionsChange", function(contype, slot, iscon, linf) {
+        if (contype == LiteGraph.INPUT && slot == 3 && this.inputs[3].type == "VAE") {
+            if (iscon && linf) {
+                if (this.linkTimeout) {
+                    clearTimeout(this.linkTimeout)
+                    this.linkTimeout = false
+                } else if (this.inputs[0].type == "IMAGE") {
+                    this.linkTimeout = setTimeout(() => {
+                        //workaround for out of order loading
+                        if (this.inputs[0].type != "IMAGE") {
+                            return
+                        }
+                        this.linkTimeout = false
+                        this.disconnectInput(0);
+                    }, 50)
+                }
+                this.inputs[0].type = 'LATENT';
+            } else {
+                if (this.inputs[0].type == "LATENT") {
+                    this.linkTimeout = setTimeout(() => {
+                        this.linkTimeout = false
+                        this.disconnectInput(0);
+                    }, 50)
+                }
+                this.inputs[0].type = "IMAGE";
+            }
+        }
+    });
+}
+function cloneType(nodeType, nodeData) {
+    chainCallback(nodeType.prototype, "onNodeCreated", function() {
+        this.changeOutputType = function (new_type) {
+            this.linkTimeout = setTimeout(() => {
+                this.linkTimeout = false
+                if (this.outputs[0].type != new_type) {
+                    this.outputs[0].type = new_type
+                    //check and potentially remove links
+                    if (!this.outputs[0].links) {
+                        return
+                    }
+                    let removed_links = []
+                    for (let link_id of this.outputs[0].links) {
+                        let link = app.graph.links[link_id]
+                        if (!link)
+                            debugger
+                        let target_node = app.graph.getNodeById(link.target_id)
+                        let target_input = target_node.inputs[link.target_slot]
+                        let keep = LiteGraph.isValidConnection(new_type, target_input.type)
+                        if (!keep) {
+                            link.disconnect(app.graph, 'input')
+                            removed_links.push(link_id)
+                        }
+                        target_node.onConnectionsChange?.(LiteGraph.INPUT,
+                            link.target_slot, keep, link, target_input)
+                    }
+                    this.outputs[0].links = this.outputs[0].links
+                        .filter((v) => !removed_links.includes(v))
+                }
+            }, 50)
+        }
+        this.changeOutputType("VHS_DUMMY_NONE")
+    });
+    chainCallback(nodeType.prototype, "onConnectionsChange", function(contype, slot, iscon, linf) {
+        if (contype == LiteGraph.INPUT && slot == 0) {
+            let new_type = "VHS_DUMMY_NONE"
+            if (iscon && linf) {
+                new_type = app.graph.getNodeById(linf.origin_id).outputs[linf.origin_slot].type
+            }
+            if (this.linkTimeout) {
+                clearTimeout(this.linkTimeout)
+            }
+            this.changeOutputType(new_type)
+        }
+    });
+}
+
+function addDateFormatting(nodeType, field, timestamp_widget = false) {
+    chainCallback(nodeType.prototype, "onNodeCreated", function() {
+        const widget = this.widgets.find((w) => w.name === field);
+        widget.serializeValue = () => {
+            return applyTextReplacements(app, widget.value);
+        };
+    });
+}
+function addTimestampWidget(nodeType, nodeData, targetWidget) {
+    const newWidgets = {};
+    for (let key in nodeData.input.required) {
+        if (key == targetWidget) {
+            //TODO: account for duplicate entries?
+            newWidgets["timestamp_directory"] = ["BOOLEAN", {"default": true}]
+        }
+        newWidgets[key] = nodeData.input.required[key];
+    }
+    nodeDta.input.required = newWidgets;
+    chainCallback(nodeType.prototype, "onNodeCreated", function () {
+        const directoryWidget = this.widgets.find((w) => w.name === "directory_name");
+        const timestampWidget = this.widgets.find((w) => w.name === "timestamp_directory");
+        directoryWidget.serializeValue = () => {
+            if (timestampWidget.value) {
+                //ignore actual value and return timestamp
+                return formatDate("yyyy-MM-ddThh:mm:ss", new Date());
+            }
+            return directoryWidget.value
+        };
+        timestampWidget._value = value;
+        Object.definteProperty(timestampWidget, "value", {
+            set : function(value) {
+                this._value = value;
+                directoryWidget.disabled = value;
+            },
+            get : function() {
+                return this._value;
+            }
+        });
+    });
+}
+function initializeLoadFormat(nodeType, nodeData) {
+    if (!nodeData?.input?.optional?.format) {
+        return
+    }
+    chainCallback(nodeType.prototype, "onNodeCreated", function() {
+        let node = this
+        let formatWidget = this.widgets.find((w) => w.name === "format")
+        formatWidget.options.formats = nodeData.input.optional.format[1].formats
+        let base = {}
+        for (let widget of this.widgets) {
+           if (['force_rate', 'custom_width', 'custom_height',
+               'frame_load_cap'].includes(widget.name)) {
+               //TODO: filter these options?
+               base[widget.name] = widget.options
+           }
+        }
+        chainCallback(formatWidget, "callback", function(value) {
+            let format = this.options.formats[value]
+            if (!format) {
+                return
+            }
+            if ('target_rate' in format) {
+                format.force_rate = {'reset': format.target_rate}
+            }
+            if ('dim' in format) {
+                format.custom_width = {'step': format.dim[0], 'mod': format.dim[1]}
+                format.custom_height = {'step': format.dim[0], 'mod': format.dim[1]}
+                if (format.dim[2]) {
+                    format.custom_width.reset = format.dim[2]
+                }
+                if (format.dim[3]) {
+                    format.custom_height.reset = format.dim[3]
+                }
+            }
+            if ('frames' in format) {
+                format.frame_load_cap = {'step': format.frames[0], 'mod': format.frames[1]}
+            }
+            for (let widget of node.widgets) {
+                if (widget.name in base) {
+                    let wasDefault = widget.options?.reset == widget.value
+                    widget.options = Object.assign({}, base[widget.name], format[widget.name])
+                    if (wasDefault && widget.options.reset != undefined) {
+                        widget.value = widget.options.reset
+                    }
+                    widget.callback(widget.value)
+                }
+            }
+
+        });
+        let capWidget = this.widgets.find((w) => w.name === "frame_load_cap")
+        capWidget.annotation = (value, width) => {
+            let max_frames = this.video_query?.loaded?.frames
+            if (!max_frames || value && value < max_frames) {
+                return
+            }
+            let format = formatWidget.options.formats[formatWidget.value]
+            const div = format?.frames?.[0] ?? 1
+            const mod = format?.frames?.[1] ?? 0
+            let loadable_frames = max_frames
+            if ((max_frames % div) != mod) {
+                loadable_frames = ((max_frames - mod)/div|0) * div + mod
+            }
+            return loadable_frames + "\u21FD"
+        }
+        let rateWidget = this.widgets.find((w) => w.name === "force_rate")
+        rateWidget.annotation = (value, width) => {
+            if (value == 0 && this.video_query?.source?.fps != undefined) {
+                return roundToPrecision(this.video_query.source.fps, 2) + "\u21FD"
+            }
+        }
+    });
+}
+
+function addUploadWidget(nodeType, nodeData, widgetName, type="video") {
+    let accept = {'video': ["video/webm","video/mp4","video/x-matroska","image/gif"],
+                  'audio': ["audio/mpeg","audio/wav","audio/x-wav","audio/ogg"]}
+    chainCallback(nodeType.prototype, "onNodeCreated", function() {
+        const node = this
+        const pathWidget = this.widgets.find((w) => w.name === widgetName);
+        const fileInput = document.createElement("input");
+        chainCallback(this, "onRemoved", () => {
+            fileInput?.remove();
+        });
+        if (type == "folder") {
+            Object.assign(fileInput, {
+                type: "file",
+                style: "display: none",
+                webkitdirectory: true,
+                onchange: async () => {
+                    const directory = fileInput.files[0].webkitRelativePath;
+                    const i = directory.lastIndexOf('/');
+                    if (i <= 0) {
+                        throw "No directory found";
+                    }
+                    const path = directory.slice(0,directory.lastIndexOf('/'))
+                    if (pathWidget.options.values.includes(path)) {
+                        alert("A folder of the same name already exists");
+                        return;
+                    }
+                    let successes = 0;
+                    const onProg = (p) => this.progress = (successes + p) / fileInput.files.length
+                    for(const file of fileInput.files) {
+                        if ((await uploadFile(file, onProg)).status == 200) {
+                            successes++;
+                        } else {
+                            this.progress = undefined
+                            //Upload failed, but some prior uploads may have succeeded
+                            //Stop future uploads to prevent cascading failures
+                            //and only add to list if an upload has succeeded
+                            if (successes > 0) {
+                                break
+                            } else {
+                                return;
+                            }
+                        }
+                    }
+                    this.progress = undefined
+                    pathWidget.options.values.push(path);
+                    pathWidget.value = path;
+                    if (pathWidget.callback) {
+                        pathWidget.callback(path)
+                    }
+                },
+            });
+        } else {
+            let accept = {'video': ["video/webm","video/mp4","video/x-matroska","image/gif"],
+                          'audio': ["audio/mpeg","audio/wav","audio/x-wav","audio/ogg"]}[type]
+            async function doUpload(file) {
+                let resp = await uploadFile(file, (p) => node.progress = p)
+                node.progress = undefined
+                if (resp.status != 200) {
+                    return false
+                }
+                const filename = JSON.parse(resp.responseText).name;
+                pathWidget.options.values.push(filename);
+                pathWidget.value = filename;
+                if (pathWidget.callback) {
+                    pathWidget.callback(filename)
+                }
+                return true
+            }
+            Object.assign(fileInput, {
+                type: "file",
+                accept: accept.join(','),
+                style: "display: none",
+                onchange: async () => {
+                    if (fileInput.files.length) {
+                        return await doUpload(fileInput.files[0])
+                    }
+                },
+            });
+            this.onDragOver = (e) => !!e?.dataTransfer?.types?.includes?.('Files')
+            this.onDragDrop = async function(e) {
+                if (!e?.dataTransfer?.types?.includes?.('Files')) {
+                    return false
+                }
+                //TODO: Allow dragging multiple files at once?
+                const item = e.dataTransfer?.files?.[0]
+                if (accept.includes(item?.type)) {
+                    return await doUpload(item)
+                }
+                return false
+            }
+        }
+        document.body.append(fileInput);
+        let uploadWidget = this.addWidget("button", "choose " + type + " to upload", "image", () => {
+            //clear the active click event
+            app.canvas.node_widget = null
+
+            fileInput.click();
+        });
+        uploadWidget.options.serialize = false;
+
+
+    });
+}
+function addAudioPreview(nodeType, isInput=true) {
+    chainCallback(nodeType.prototype, "onNodeCreated", function() {
+        var element = document.createElement("audio");
+        element.controls = true
+        element.style['width'] = "100%"
+        element.style['minHeight'] = "50px"
+        const previewNode = this;
+        var previewWidget = this.addDOMWidget("audiopreview", "preview", element, {
+            serialize: false,
+            hideOnZoom: true,
+            getValue() {
+                return element.value;
+            },
+            setValue(v) {
+                element.value = v;
+            },
+        });
+        previewWidget.computeSize = function(width) {
+            return [width, 50];
+        }
+        var timeout = null;
+        this.updateParameters = (params, force_update) => {
+            if (!previewWidget.value.params) {
+                if(typeof(previewWidget.value) != 'object') {
+                    previewWidget.value =  {}
+                }
+                previewWidget.value.params = {}
+            }
+            Object.assign(previewWidget.value.params, params)
+            if (!force_update &&
+                app.ui.settings.getSettingValue("VHS.AdvancedPreviews") == 'Never') {
+                return;
+            }
+            if (timeout) {
+                clearTimeout(timeout);
+            }
+            if (force_update) {
+                previewWidget.updateSource();
+            } else {
+                timeout = setTimeout(() => previewWidget.updateSource(),100);
+            }
+        };
+        previewWidget.updateSource = function () {
+            if (this.value.params == undefined) {
+                return;
+            }
+            let params =  {}
+            let advp = app.ui.settings.getSettingValue("VHS.AdvancedPreviews")
+            if (advp == 'Never') {
+                advp = false
+            } else if (advp == 'Input Only') {
+                advp = isInput
+            } else {
+                advp = true
+            }
+            Object.assign(params, this.value.params);//shallow copy
+            params.timestamp = Date.now()
+            if (!advp) {
+                element.src = api.apiURL('/view?' + new URLSearchParams(params));
+            } else {
+                params.deadline = app.ui.settings.getSettingValue("VHS.AdvancedPreviewsDeadline")
+                element.src = api.apiURL('/vhs/viewaudio?' + new URLSearchParams(params));
+            }
+        }
+        previewWidget.callback = previewWidget.updateSource
+
+
+        //setup widget tracking
+        function update(key) {
+            return function(value) {
+                let params = {}
+                params[key] = this.value
+                previewNode?.updateParameters(params)
+            }
+        }
+        let widgetMap = { 'seek_seconds': 'start_time', 'duration': 'duration',
+            'start_time': 'start_time' }
+        for (let widget of this.widgets) {
+            if (widget.name in widgetMap) {
+                if (typeof(widgetMap[widget.name]) == 'function') {
+                    chainCallback(widget, "callback", widgetMap[widget.name]);
+                } else {
+                    chainCallback(widget, "callback", update(widgetMap[widget.name]))
+                }
+            }
+            if (widget.type != "button") {
+                widget.callback?.(widget.value)
+            }
+        }
+    });
+}
+
+function addVideoPreview(nodeType, isInput=true) {
+    chainCallback(nodeType.prototype, "onNodeCreated", function() {
+        var element = document.createElement("div");
+        const previewNode = this;
+        var previewWidget = this.addDOMWidget("videopreview", "preview", element, {
+            serialize: false,
+            hideOnZoom: false,
+            getValue() {
+                return element.value;
+            },
+            setValue(v) {
+                element.value = v;
+            },
+        });
+        allowDragFromWidget(previewWidget)
+        previewWidget.computeSize = function(width) {
+            if (this.aspectRatio && !this.parentEl.hidden) {
+                let height = (previewNode.size[0]-20)/ this.aspectRatio + 10;
+                if (!(height > 0)) {
+                    height = 0;
+                }
+                this.computedHeight = height + 10;
+                return [width, height];
+            }
+            return [width, -4];//no loaded src, widget should not display
+        }
+        element.addEventListener('contextmenu', (e)  => {
+            e.preventDefault()
+            return app.canvas._mousedown_callback(e)
+        }, true);
+        element.addEventListener('pointerdown', (e)  => {
+            e.preventDefault()
+            return app.canvas._mousedown_callback(e)
+        }, true);
+        element.addEventListener('mousewheel', (e)  => {
+            e.preventDefault()
+            return app.canvas._mousewheel_callback(e)
+        }, true);
+        element.addEventListener('pointermove', (e)  => {
+            e.preventDefault()
+            return app.canvas._mousemove_callback(e)
+        }, true);
+        element.addEventListener('pointerup', (e)  => {
+            e.preventDefault()
+            return app.canvas._mouseup_callback(e)
+        }, true);
+        element.addEventListener('dragover', (e) => {
+            //A little hacky, but allows drag events onto the preview itself
+            e.preventDefault();
+            e.dataTransfer.dropEffect = "copy";
+            app.dragOverNode = this
+        })
+        previewWidget.value = {hidden: false, paused: false, params: {},
+            muted: app.ui.settings.getSettingValue("VHS.DefaultMute")}
+        previewWidget.parentEl = document.createElement("div");
+        previewWidget.parentEl.className = "vhs_preview";
+        previewWidget.parentEl.style['width'] = "100%"
+        element.appendChild(previewWidget.parentEl);
+        previewWidget.videoEl = document.createElement("video");
+        previewWidget.videoEl.controls = false;
+        previewWidget.videoEl.loop = true;
+        previewWidget.videoEl.muted = true;
+        previewWidget.videoEl.style['width'] = "100%"
+        previewWidget.videoEl.addEventListener("loadedmetadata", () => {
+
+            previewWidget.aspectRatio = previewWidget.videoEl.videoWidth / previewWidget.videoEl.videoHeight;
+            fitHeight(this);
+        });
+        previewWidget.videoEl.addEventListener("error", () => {
+            //TODO: consider a way to properly notify the user why a preview isn't shown.
+            previewWidget.parentEl.hidden = true;
+            fitHeight(this);
+        });
+        previewWidget.videoEl.onmouseenter =  () => {
+            previewWidget.videoEl.muted = previewWidget.value.muted
+        };
+        previewWidget.videoEl.onmouseleave = () => {
+            previewWidget.videoEl.muted = true;
+        };
+
+        previewWidget.imgEl = document.createElement("img");
+        previewWidget.imgEl.style['width'] = "100%"
+        previewWidget.imgEl.hidden = true;
+        previewWidget.imgEl.onload = () => {
+            previewWidget.aspectRatio = previewWidget.imgEl.naturalWidth / previewWidget.imgEl.naturalHeight;
+            fitHeight(this);
+        };
+        previewWidget.parentEl.appendChild(previewWidget.videoEl)
+        previewWidget.parentEl.appendChild(previewWidget.imgEl)
+        var timeout = null;
+        this.updateParameters = (params, force_update) => {
+            if (!previewWidget.value.params) {
+                if(typeof(previewWidget.value) != 'object') {
+                    previewWidget.value =  {hidden: false, paused: false}
+                }
+                previewWidget.value.params = {}
+            }
+            if (!Object.entries(params).some(([k,v]) => previewWidget.value.params[k] !== v)) {
+                return
+            }
+            Object.assign(previewWidget.value.params, params)
+            if (!force_update &&
+                app.ui.settings.getSettingValue("VHS.AdvancedPreviews") == 'Never') {
+                return;
+            }
+            if (timeout) {
+                clearTimeout(timeout);
+            }
+            if (force_update) {
+                previewWidget.updateSource();
+            } else {
+                timeout = setTimeout(() => previewWidget.updateSource(),100);
+            }
+        };
+        previewWidget.updateSource = function () {
+            if (this.value.params == undefined) {
+                return;
+            }
+            let params =  {}
+            let advp = app.ui.settings.getSettingValue("VHS.AdvancedPreviews")
+            if (advp == 'Never') {
+                advp = false
+            } else if (advp == 'Input Only') {
+                advp = isInput
+            } else {
+                advp = true
+            }
+            Object.assign(params, this.value.params);//shallow copy
+            params.timestamp = Date.now()
+            this.parentEl.hidden = this.value.hidden;
+            if (params.format?.split('/')[0] == 'video'
+                || advp && (params.format?.split('/')[1] == 'gif')
+                || params.format == 'folder') {
+
+                this.videoEl.autoplay = !this.value.paused && !this.value.hidden;
+                if (!advp) {
+                    this.videoEl.src = api.apiURL('/view?' + new URLSearchParams(params));
+                } else {
+                    let target_width = (previewNode.size[0]-20)*2 || 256;
+                    let minWidth = app.ui.settings.getSettingValue("VHS.AdvancedPreviewsMinWidth")
+                    if (target_width < minWidth) {
+                        target_width = minWidth
+                    }
+                    if (!params.custom_width || !params.custom_height) {
+                        params.force_size = target_width+"x?"
+                    } else {
+                        let ar = params.custom_width/params.custom_height
+                        params.force_size = target_width+"x"+(target_width/ar)
+                    }
+                    params.deadline = app.ui.settings.getSettingValue("VHS.AdvancedPreviewsDeadline")
+                    this.videoEl.src = api.apiURL('/vhs/viewvideo?' + new URLSearchParams(params));
+                }
+                this.videoEl.hidden = false;
+                this.imgEl.hidden = true;
+            } else if (params.format?.split('/')[0] == 'image'){
+                //Is animated image
+                this.imgEl.src = api.apiURL('/view?' + new URLSearchParams(params));
+                this.videoEl.hidden = true;
+                this.imgEl.hidden = false;
+            }
+            delete previewNode.video_query
+            const doQuery = async () => {
+                if (!previewWidget?.value?.params?.filename) {
+                    return
+                }
+                let qurl = api.apiURL('/vhs/queryvideo?' + new URLSearchParams(previewWidget.value.params))
+                let query = undefined
+                try {
+                    let query_res = await fetch(qurl)
+                    query = await query_res.json()
+                } catch(e) {
+                    return
+                }
+                previewNode.video_query = query
+            }
+            doQuery()
+        }
+        previewWidget.callback = previewWidget.updateSource
+        previewWidget.parentEl.appendChild(previewWidget.videoEl)
+        previewWidget.parentEl.appendChild(previewWidget.imgEl)
+    });
+}
+let copiedPath = undefined
+function addPreviewOptions(nodeType) {
+    chainCallback(nodeType.prototype, "getExtraMenuOptions", function(_, options) {
+        // The intended way of appending options is returning a list of extra options,
+        // but this isn't used in widgetInputs.js and would require
+        // less generalization of chainCallback
+        let optNew = []
+        const previewWidget = this.widgets.find((w) => w.name === "videopreview");
+
+        let url = null
+        if (previewWidget.videoEl?.hidden == false && previewWidget.videoEl.src) {
+            if (['input', 'output', 'temp'].includes(previewWidget.value.params.type)) {
+                //Use full quality video
+                url = api.apiURL('/view?' + new URLSearchParams(previewWidget.value.params));
+                //Workaround for 16bit png: Just do first frame
+                url = url.replace('%2503d', '001')
+            }
+        } else if (previewWidget.imgEl?.hidden == false && previewWidget.imgEl.src) {
+            url = previewWidget.imgEl.src;
+            url = new URL(url);
+        }
+        if (this.video_query?.source) {
+            let info_string = this.video_query.source.size.join('x') +
+                              '@' + this.video_query.source.fps + 'fps ' +
+                              this.video_query.source.frames + 'frames'
+            optNew.push({content: info_string, disabled: true})
+        }
+        if (url) {
+            optNew.push(
+                {
+                    content: "Open preview",
+                    callback: () => {
+                        window.open(url, "_blank")
+                    },
+                },
+                {
+                    content: "Save preview",
+                    callback: () => {
+                        const a = document.createElement("a");
+                        a.href = url;
+                        a.setAttribute("download", previewWidget.value.params.filename);
+                        document.body.append(a);
+                        a.click();
+                        requestAnimationFrame(() => a.remove());
+                    },
+                }
+            );
+            if (previewWidget.value.params.fullpath) {
+                copiedPath = previewWidget.value.params.fullpath
+                const blob = new Blob([previewWidget.value.params.fullpath],
+                    { type: 'text/plain'})
+                optNew.push({
+                    content: "Copy output filepath",
+                    callback: async () => {
+                        await navigator.clipboard.write([
+                            new ClipboardItem({
+                                'text/plain': blob
+                            })])}
+                });
+            }
+            if (previewWidget.value.params.workflow) {
+                let wParams = {...previewWidget.value.params,
+                    filename: previewWidget.value.params.workflow}
+                let wUrl = api.apiURL('/view?' + new URLSearchParams(wParams));
+                optNew.push({
+                    content: "Save workflow image",
+                    callback: () => {
+                        const a = document.createElement("a");
+                        a.href = wUrl;
+                        a.setAttribute("download", previewWidget.value.params.workflow);
+                        document.body.append(a);
+                        a.click();
+                        requestAnimationFrame(() => a.remove());
+                    }
+                });
+            }
+        }
+        const PauseDesc = (previewWidget.value.paused ? "Resume" : "Pause") + " preview";
+        if(previewWidget.videoEl.hidden == false) {
+            optNew.push({content: PauseDesc, callback: () => {
+                //animated images can't be paused and are more likely to cause performance issues.
+                //changing src to a single keyframe is possible,
+                //For now, the option is disabled if an animated image is being displayed
+                if(previewWidget.value.paused) {
+                    previewWidget.videoEl?.play();
+                } else {
+                    previewWidget.videoEl?.pause();
+                }
+                previewWidget.value.paused = !previewWidget.value.paused;
+            }});
+        }
+        //TODO: Consider hiding elements if no video preview is available yet.
+        //It would reduce confusion at the cost of functionality
+        //(if a video preview lags the computer, the user should be able to hide in advance)
+        const visDesc = (previewWidget.value.hidden ? "Show" : "Hide") + " preview";
+        optNew.push({content: visDesc, callback: () => {
+            if (!previewWidget.videoEl.hidden && !previewWidget.value.hidden) {
+                previewWidget.videoEl.pause();
+            } else if (previewWidget.value.hidden && !previewWidget.videoEl.hidden && !previewWidget.value.paused) {
+                previewWidget.videoEl.play();
+            }
+            previewWidget.value.hidden = !previewWidget.value.hidden;
+            previewWidget.parentEl.hidden = previewWidget.value.hidden;
+            fitHeight(this);
+
+        }});
+        optNew.push({content: "Sync preview", callback: () => {
+            //TODO: address case where videos have varying length
+            //Consider a system of sync groups which are opt-in?
+            for (let p of document.getElementsByClassName("vhs_preview")) {
+                for (let child of p.children) {
+                    if (child.tagName == "VIDEO") {
+                        child.currentTime=0;
+                    } else if (child.tagName == "IMG") {
+                        child.src = child.src;
+                    }
+                }
+            }
+        }});
+        const muteDesc = (previewWidget.value.muted ? "Unmute" : "Mute") + " Preview"
+        optNew.push({content: muteDesc, callback: () => {
+            previewWidget.value.muted = !previewWidget.value.muted
+        }})
+        if(options.length > 0 && options[0] != null && optNew.length > 0) {
+            optNew.push(null);
+        }
+        options.unshift(...optNew);
+    });
+}
+function addFormatWidgets(nodeType, nodeData) {
+    chainCallback(nodeType.prototype, "onNodeCreated", function() {
+        var formatWidget = null;
+        var formatWidgetIndex = -1;
+        for(let i = 0; i < this.widgets.length; i++) {
+            if (this.widgets[i].name === "format"){
+                formatWidget = this.widgets[i];
+                formatWidgetIndex = i+1;
+                break
+            }
+        }
+        let formatWidgetsCount = 0;
+        chainCallback(formatWidget, "callback", (value) => {
+            const formats = (LiteGraph.registered_node_types[this.type]
+                ?.nodeData?.input?.required?.format?.[1]?.formats)
+            let newWidgets = [];
+            if (formats?.[value]) {
+                let formatWidgets = formats[value]
+                for (let wDef of formatWidgets) {
+                    let type = wDef[2]?.widgetType ?? wDef[1]
+                    if (Array.isArray(type)) {
+                        type = "COMBO"
+                    }
+                    app.widgets[type](this, wDef[0], wDef.slice(1), app)
+                    let w = this.widgets.pop()
+                    w.config = wDef.slice(1)
+                    newWidgets.push(w)
+                }
+            }
+            let removed = this.widgets.splice(formatWidgetIndex,
+                                            formatWidgetsCount, ...newWidgets);
+            let newNames = new Set(newWidgets.map((w) => w.name))
+            for (let w of removed) {
+                w?.onRemove?.()
+                if (w.name in newNames) {
+                    continue
+                }
+                //I do not like the performance of this, but it's safe
+                let slot = this.inputs.findIndex((i) => i.name == w.name)
+                if (slot >= 0) {
+                    this.removeInput(slot)
+                }
+            }
+            for (let w of newWidgets) {
+                let existingInput = this.inputs.find((i) => i.name == w.name)
+                if (existingInput) {
+                    setWidgetConfig(existingInput, w.config)
+                    //TODO: Consider forcing disconnection if props change?
+                } else {
+                    //NOTE: config is applied in wrapped addInput call
+                    this.addInput(w.name, w.config[0], {widget: {name: w.name}})
+                }
+            }
+            fitHeight(this);
+            formatWidgetsCount = newWidgets.length;
+        });
+    });
+}
+function addLoadCommon(nodeType, nodeData) {
+    addVideoPreview(nodeType);
+    initializeLoadFormat(nodeType, nodeData)
+    addPreviewOptions(nodeType);
+    chainCallback(nodeType.prototype, "onNodeCreated", function() {
+        //widget.callback adds unused arguements which need culling
+        const node = this
+        function update(key) {
+            return function(value) {
+                let params = {}
+                params[key] = this.value
+                node?.updateParameters(params)
+            }
+        }
+        let prior_ar = -2
+        const widthWidget = this.widgets.find((w) => w.name === "custom_width");
+        const heightWidget = this.widgets.find((w) => w.name === "custom_height");
+        function updateAR(value) {
+            let new_ar = -1
+            if (widthWidget.value & heightWidget.value) {
+                new_ar = widthWidget.value / heightWidget.value
+            }
+            if (new_ar != prior_ar) {
+                node?.updateParameters({'custom_width': widthWidget.value,
+                    'custom_height': heightWidget.value})
+                prior_ar = new_ar
+            }
+        }
+        const offsetWidget = this.widgets.find((w) => w.name === "start_time");
+        if (offsetWidget) {
+            Object.defineProperty(offsetWidget.options, "step", {
+                set : (value) => {},
+                get : () => {
+                    return 1 / (this.video_query?.loaded?.fps ?? 1)
+                }
+            })
+        }
+        let widgetMap = {'frame_load_cap': 'frame_load_cap',
+            'skip_first_frames': 'skip_first_frames', 'select_every_nth': 'select_every_nth',
+            'start_time': 'start_time', 'force_rate': 'force_rate',
+            'custom_width': updateAR, 'custom_height': updateAR,
+            'image_load_cap': 'image_load_cap', 'skip_first_images': 'skip_first_images'
+        }
+        for (let widget of this.widgets) {
+            if (widget.name in widgetMap) {
+                if (typeof(widgetMap[widget.name]) == 'function') {
+                    chainCallback(widget, "callback", widgetMap[widget.name]);
+                } else {
+                    chainCallback(widget, "callback", update(widgetMap[widget.name]))
+                }
+            }
+            if (widget.type != "button") {
+                widget.callback?.(widget.value)
+            }
+        }
+    });
+}
+
+function path_stem(path) {
+    let i = path.lastIndexOf("/");
+    if (i >= 0) {
+        return [path.slice(0,i+1),path.slice(i+1)];
+    }
+    return ["",path];
+}
+function searchBox(event, [x,y], node) {
+    //Ensure only one dialogue shows at a time
+    if (this.prompt)
+        return;
+    this.prompt = true;
+
+    let pathWidget = this;
+    let dialog = document.createElement("div");
+    dialog.className = "litegraph litesearchbox graphdialog rounded"
+    dialog.innerHTML = '<span class="name">Path</span> <input autofocus="" type="text" class="value"><button class="rounded">OK</button><div class="helper"></div>'
+    dialog.close = () => {
+        dialog.remove();
+    }
+    document.body.append(dialog);
+    if (app.canvas.ds.scale > 1) {
+        dialog.style.transform = "scale(" + app.canvas.ds.scale + ")";
+    }
+    var name_element = dialog.querySelector(".name");
+    var input = dialog.querySelector(".value");
+    var options_element = dialog.querySelector(".helper");
+    input.value = pathWidget.value;
+
+    var timeout = null;
+    let last_path = null;
+    let extensions = pathWidget.options.vhs_path_extensions
+
+    input.addEventListener("keydown", (e) => {
+        dialog.is_modified = true;
+        if (e.keyCode == 27) {
+            //ESC
+            dialog.close();
+        } else if (e.keyCode == 13 && e.target.localName != "textarea") {
+            pathWidget.value = input.value;
+            if (pathWidget.callback) {
+                pathWidget.callback(pathWidget.value);
+            }
+            dialog.close();
+        } else {
+            if (e.keyCode == 9) {
+                //TAB
+                input.value = last_path + options_element.firstChild.innerText;
+                e.preventDefault();
+                e.stopPropagation();
+            } else if (e.ctrlKey && (e.keyCode == 87 || e.keyCode == 66)) {
+                //Ctrl+w or Ctrl+b
+                //most browsers won't support, but it's good QOL for those that do
+                input.value = path_stem(input.value.slice(0,-1))[0]
+                e.preventDefault();
+                e.stopPropagation();
+            } else if (e.ctrlKey && e.keyCode == 71) {
+                //Ctrl+g
+                //Temporarily disables extension filtering to show all files
+                e.preventDefault();
+                e.stopPropagation();
+                extensions = undefined
+                last_path = null;
+            }
+            if (timeout) {
+                clearTimeout(timeout);
+            }
+            timeout = setTimeout(updateOptions, 10);
+            return;
+        }
+        this.prompt=false;
+        e.preventDefault();
+        e.stopPropagation();
+    });
+
+    var button = dialog.querySelector("button");
+    button.addEventListener("click", (e) => {
+        pathWidget.value = input.value;
+        if (pathWidget.callback) {
+            pathWidget.callback(pathWidget.value);
+        }
+        //unsure why dirty is set here, but not on enter-key above
+        node.graph.setDirtyCanvas(true);
+        dialog.close();
+        this.prompt = false;
+    });
+    var rect = app.canvas.canvas.getBoundingClientRect();
+    var offsetx = -20;
+    var offsety = -20;
+    if (rect) {
+        offsetx -= rect.left;
+        offsety -= rect.top;
+    }
+
+    if (event) {
+        dialog.style.left = event.clientX + offsetx + "px";
+        dialog.style.top = event.clientY + offsety + "px";
+    } else {
+        dialog.style.left = canvas.width * 0.5 + offsetx + "px";
+        dialog.style.top = canvas.height * 0.5 + offsety + "px";
+    }
+    //Search code
+    let options = []
+    function addResult(name, isDir) {
+        let el = document.createElement("div");
+        el.innerText = name;
+        el.className = "litegraph lite-search-item";
+        if (isDir) {
+            el.className += " is-dir";
+            el.addEventListener("click", (e) => {
+                input.value = last_path+name
+                if (timeout) {
+                    clearTimeout(timeout);
+                }
+            timeout = setTimeout(updateOptions, 10);
+            });
+        } else {
+            el.addEventListener("click", (e) => {
+                pathWidget.value = last_path+name;
+                if (pathWidget.callback) {
+                    pathWidget.callback(pathWidget.value);
+                }
+                dialog.close();
+                pathWidget.prompt = false;
+            });
+        }
+        options_element.appendChild(el);
+    }
+    async function updateOptions() {
+        timeout = null;
+        let [path, remainder] = path_stem(input.value);
+        if (last_path != path) {
+            //fetch options.  Must block execution here, so update should be async?
+            let params = {path : path}
+            if (extensions) {
+                params.extensions = extensions
+            }
+            let optionsURL = api.apiURL('/vhs/getpath?' + new URLSearchParams(params));
+            try {
+                let resp = await fetch(optionsURL);
+                options = await resp.json();
+                options = options.map((o) => o.replace('.','\0'))
+                options = options.sort()
+                options = options.map((o) => o.replace('\0','.'))
+            } catch(e) {
+                options = []
+            }
+            last_path = path;
+        }
+        options_element.innerHTML = '';
+        //filter options based on remainder
+        for (let option of options) {
+            if (option.startsWith(remainder)) {
+                let isDir = option.endsWith('/')
+                addResult(option, isDir);
+            }
+        }
+    }
+
+    setTimeout(async function() {
+        input.focus();
+        await updateOptions();
+    }, 10);
+
+    return dialog;
+}
+function button_action(widget) {
+  if (
+    widget.options?.reset == undefined &&
+    widget.options?.disable == undefined
+  ) {
+    return 'None'
+  }
+  if (
+    widget.options.reset != undefined &&
+    widget.value != widget.options.reset
+  ) {
+    return 'Reset'
+  }
+  if (
+    widget.options.disable != undefined &&
+    widget.value != widget.options.disable
+  ) {
+    return 'Disable'
+  }
+  if (widget.options.reset != undefined) {
+    return 'No Reset'
+  }
+  return 'No Disable'
+}
+function fitText(ctx, text, maxLength) {
+    if (maxLength <= 0) {
+        return ['', 0]
+    }
+    let fullLength = ctx.measureText(text).width
+    if (fullLength < maxLength) {
+        return [text, fullLength]
+    }
+    //determine approx safe cutoff
+    let cutoff = maxLength / fullLength * text.length | 0
+    let shortened = text.slice(0, Math.max(0, cutoff - 2)) + '…'
+    return [shortened, ctx.measureText(shortened).width]
+}
+function fitPath(ctx, path, maxLength) {
+    let fullLength = ctx.measureText(path).width
+    if (fullLength < maxLength) {
+        return [path, fullLength]
+    }
+    //determine approx safe cutoff
+    let len = (maxLength / fullLength * path.length | 0) - 1
+
+    let displayPath = ''
+    let filename = path_stem(path)[1]
+    if (filename.length > len-2) {
+        //may all fit, but can't squeeze more info
+         displayPath = filename.substr(0,len);
+    } else {
+        //TODO: find solution for windows, path[1] == ':'?
+        let isAbs = path[0] == '/';
+        let partial = path.substr(path.length - (isAbs ? len-2:len-1))
+        let cutoff = partial.indexOf('/');
+        if (cutoff < 0) {
+            //Can occur, but there isn't a nicer way to format
+            displayPath = path.substr(path.length-len);
+        } else {
+            displayPath = (isAbs ? '/…':'…') + partial.substr(cutoff);
+        }
+    }
+    return [displayPath, ctx.measureText(displayPath).width]
+}
+function roundToPrecision(num, precision) {
+    let strnum = Number(num).toFixed(precision)
+    let deci = strnum.indexOf('.')
+    if (deci > 0) {
+        let i = strnum.length - 1
+        while (i > deci && strnum[i] == '0') {
+            i--
+        }
+        if (i == deci) {
+            i--
+        }
+        return strnum.slice(0, i+1)
+    }
+    return strnum
+}
+function inner_value_change(widget, value, node, pos) {
+  widget.value = value
+  if (widget.options?.property && widget.options.property in node.properties) {
+    node.setProperty(widget.options.property, value)
+  }
+  if (widget.callback) {
+    widget.callback(widget.value, app.canvas, node, event)
+  }
+}
+function drawAnnotated(ctx, node, widget_width, y, H) {
+  const litegraph_base = LiteGraph
+  // In vueNodes mode, always show text since Vue renders at 1:1 scale
+  const show_text = LiteGraph.vueNodesMode || app.canvas.ds.scale >= (app.canvas.low_quality_zoom_threshold ?? 0.5)
+  const margin = 15
+  ctx.strokeStyle = litegraph_base.WIDGET_OUTLINE_COLOR
+  ctx.fillStyle = litegraph_base.WIDGET_BGCOLOR
+  ctx.beginPath()
+  if (show_text)
+    ctx.roundRect(margin, y, widget_width - margin * 2, H, [H * 0.5])
+  else ctx.rect(margin, y, widget_width - margin * 2, H)
+  ctx.fill()
+  if (show_text) {
+    if (!this.disabled) ctx.stroke()
+    const button = button_action(this)
+    if (button != 'None') {
+      ctx.save()
+      if (button.startsWith('No ')) {
+        ctx.fillStyle = litegraph_base.WIDGET_OUTLINE_COLOR
+        ctx.strokeStyle = litegraph_base.WIDGET_OUTLINE_COLOR
+      } else {
+        ctx.fillStyle = litegraph_base.WIDGET_TEXT_COLOR
+        ctx.strokeStyle = litegraph_base.WIDGET_TEXT_COLOR
+      }
+      ctx.beginPath()
+      if (button.endsWith('Reset')) {
+        ctx.arc(widget_width - margin - 26, y + H/2, 4, Math.PI*3/2, Math.PI)
+        ctx.stroke()
+        ctx.beginPath()
+        ctx.moveTo(widget_width - margin - 26, y + H/2 - 1.5)
+        ctx.lineTo(widget_width - margin - 26, y + H/2 - 6.5)
+        ctx.lineTo(widget_width - margin - 30, y + H/2 - 3.5)
+        ctx.fill()
+      } else {
+        ctx.arc(widget_width - margin - 26, y + H/2, 4, Math.PI*2/3, Math.PI*8/3)
+        ctx.moveTo(widget_width - margin - 26 - 8 ** .5, y + H/2 + 8 ** .5)
+        ctx.lineTo(widget_width - margin - 26 + 8 ** .5, y + H/2 - 8 ** .5)
+        ctx.stroke()
+      }
+      ctx.restore()
+    }
+    ctx.fillStyle = litegraph_base.WIDGET_TEXT_COLOR
+    if (!this.disabled) {
+      ctx.beginPath()
+      ctx.moveTo(margin + 16, y + 5)
+      ctx.lineTo(margin + 6, y + H * 0.5)
+      ctx.lineTo(margin + 16, y + H - 5)
+      ctx.fill()
+      ctx.beginPath()
+      ctx.moveTo(widget_width - margin - 16, y + 5)
+      ctx.lineTo(widget_width - margin - 6, y + H * 0.5)
+      ctx.lineTo(widget_width - margin - 16, y + H - 5)
+      ctx.fill()
+    }
+    let freeWidth = widget_width - (40 + margin * 2 + 20)
+    let [valueText, valueWidth] = fitText(ctx, (this.displayValue?.() ?? ""), freeWidth)
+    freeWidth -= valueWidth
+
+    ctx.textAlign = 'left'
+    ctx.fillStyle = litegraph_base.WIDGET_SECONDARY_TEXT_COLOR
+    if (freeWidth > 20) {
+      let [name, nameWidth] = fitText(ctx, this.label || this.name, freeWidth)
+      freeWidth -= nameWidth
+      ctx.fillText(name, margin * 2 + 5, y + H * 0.7)
+    }
+
+    let value_offset = margin * 2 + 20
+    ctx.textAlign = 'right'
+    if (this.options.unit) {
+      ctx.fillStyle = litegraph_base.WIDGET_OUTLINE_COLOR
+      let [unitText, unitWidth] = fitText(ctx, this.options.unit, freeWidth)
+      if (unitText == this.options.unit) {
+        ctx.fillText(this.options.unit, widget_width - value_offset, y + H * 0.7)
+        value_offset += unitWidth
+        freeWidth -= unitWidth
+      }
+    }
+    ctx.fillStyle = litegraph_base.WIDGET_TEXT_COLOR
+    ctx.fillText(valueText, widget_width - value_offset, y + H * 0.7)
+    ctx.fillStyle = litegraph_base.WIDGET_SECONDARY_TEXT_COLOR
+
+
+    let annotation = ''
+    if (this.annotation) {
+      annotation = this.annotation(this.value, freeWidth)
+    } else if (
+      this.options.annotation &&
+      this.value in this.options.annotation
+    ) {
+      annotation = this.options.annotation[this.value]
+    }
+    if (annotation) {
+      ctx.fillStyle = litegraph_base.WIDGET_OUTLINE_COLOR
+      let [annoDisplay, annoWidth] = fitText(ctx, annotation, freeWidth)
+      ctx.fillText(
+        annoDisplay,
+        widget_width - 5 - valueWidth - value_offset,
+        y + H * 0.7
+      )
+    }
+  }
+}
+function mouseAnnotated(event, [x, y], node) {
+    //NOTE: Mouse actions contain no history element.
+    //This can cause overlapping actions since each triggers on different event type (down/move/up)
+    //TODO: Consider further rework
+    const widget_width = this.width || node.size[0]
+    const old_value = this.value
+    const margin = 15
+    let isButton = 0
+    if (x > margin + 6 && x < margin + 16) {
+        isButton = -1
+    } else if (x > widget_width - margin - 16 & x < widget_width - margin - 6) {
+        isButton = 1
+    } else if (x > widget_width - margin - 34 && x < widget_width - margin - 18) {
+        isButton = 2
+    }
+    var allow_scroll = true
+    if (allow_scroll && event.type == 'pointermove') {
+        if (event.deltaX)
+            this.value += event.deltaX * (this.options.step || 1)
+        if (this.options.min != null && this.value < this.options.min) {
+            this.value = this.options.min
+        }
+        if (this.options.max != null && this.value > this.options.max) {
+            this.value = this.options.max
+        }
+    } else if (event.type == 'pointerdown') {
+        const buttonType = button_action(this)
+        if (isButton == 2) {
+            if (buttonType == 'Reset') {
+                this.value = this.options.reset
+            } else if (buttonType == 'Disable') {
+                this.value = this.options.disable
+            }
+        } else {
+            this.value += isButton * (this.options.step || 1)
+            if (this.options.min != null && this.value < this.options.min) {
+                this.value = this.options.min
+            }
+            if (this.options.max != null && this.value > this.options.max) {
+                this.value = this.options.max
+            }
+        }
+    } //end mousedown
+    else if (event.type == 'pointerup') {
+        if (event.click_time < 200 && !isButton) {
+            const d_callback = (v) => {
+                this.value = this.parseValue?.(v) ?? Number(v)
+                inner_value_change(this, this.value, node, [x, y])
+            }
+            const dialog = app.canvas.prompt(
+                'Value',
+                this.value,//TODO: Consider making this displayValue?
+                d_callback,
+                event
+            )
+            const input = dialog.querySelector(".value")
+            input.addEventListener("keydown", (e) => {
+                if (e.keyCode == 9) {
+                    e.preventDefault();
+                    e.stopPropagation();
+                    d_callback(input.value)
+                    dialog.close()
+                    node?.graph?.setDirtyCanvas(true);
+                    let i = node.widgets.findIndex((w) => w == this)
+                    if (e.shiftKey)
+                        i--
+                    else
+                        i++
+                    if (node.widgets[i]?.type == "VHS.ANNOTATED") {//restrict to annotatedNUmbers
+                        node.widgets[i]?.mouse(event, [x, y+24], node)
+                    }
+                }
+            })
+        }
+    }
+
+    if (old_value != this.value)
+        setTimeout(
+            function () {
+                inner_value_change(this, this.value, node, [x, y])
+            }.bind(this),
+            20
+        )
+    return true
+}
+let latentPreviewNodes = new Set()
+app.registerExtension({
+    name: "VideoHelperSuite.Core",
+    settings: [
+      {
+        id: 'VHS.AdvancedPreviews',
+        category: ['🎥🅥🅗🅢', 'Previews', 'Advanced Previews'],
+        name: 'Advanced Previews',
+        tooltip: 'Automatically transcode previews on request. Required for advanced functionality',
+        type: 'combo',
+        options: ['Never', 'Always', 'Input Only'],
+        defaultValue: 'Input Only',
+      },
+      {
+        id: 'VHS.AdvancedPreviewsMinWidth',
+        category: ['🎥🅥🅗🅢', 'Previews', 'Min Width'],
+        name: 'Minimum preview width',
+        tooltip: 'Advanced previews have their resolution downscaled to the node size for performance. While a node can be resized to increase preview quality, a minimum width can be set that previews won\'t be downscaled beneath. Preveiws will never be upscaled, so this can safely be set large.',
+        type: 'number',
+        attrs: {
+          min: 0,
+          step: 1,
+          max: 3840,
+        },
+        defaultValue: 0,
+      },
+      {
+        id: 'VHS.AdvancedPreviewsDeadline',
+        category: ['🎥🅥🅗🅢', 'Previews', 'Deadline'],
+        name: 'Deadline',
+        tooltip: 'Determines how much time can be spent when encoding advanced previews. Realtime results in reduced quality, but good will likely cause the preview to stutter as initial generation occurs',
+        type: 'combo',
+        options: ['realtime', 'good'],
+        defaultValue: 'realtime',
+      },
+      {
+        id: 'VHS.AdvancedPreviewsDefaultMute',
+        category: ['🎥🅥🅗🅢', 'Previews', 'Default Mute'],
+        name: 'Mute videos by default',
+        type: 'boolean',
+        defaultValue: false,
+      },
+      {
+        id: 'VHS.LatentPreview',
+        category: ['🎥🅥🅗🅢', 'Sampling', 'Latent Previews'],
+        name: 'Display animated previews when sampling',
+        type: 'boolean',
+        defaultValue: false,
+        onChange(value) {
+            if (!value) {
+                //Remove any previewWidgets
+                for (let id of latentPreviewNodes) {
+                    let n = app.graph.getNodeById(id)
+                    let i = n?.widgets?.findIndex((w) => w.name == 'vhslatentpreview')
+                    if (i >= 0) {
+                        n.widgets.splice(i,1)[0].onRemove()
+                    }
+                }
+                latentPreviewNodes = new Set()
+            }
+        },
+      },
+      {
+        id: "VHS.LatentPreviewRate",
+        category: ['🎥🅥🅗🅢', 'Sampling', 'Latent Preview Rate'],
+        name: "Playback rate override.",
+        type: 'number',
+        attrs: {
+          min: 0,
+          step: 1,
+          max: 60
+        },
+        tooltip:
+          'Force a specific frame rate for the playback of latent frames. This should not be confused with the output frame rate and will not match for video models.',
+        defaultValue: 0,
+      },
+      {
+        id: 'VHS.MetadataImage',
+        category: ['🎥🅥🅗🅢', 'Output', 'MetadataImage'],
+        name: 'Save png of first frame for metadata',
+        type: 'boolean',
+        defaultValue: true,
+      },
+      {
+        id: 'VHS.KeepIntermediate',
+        category: ['🎥🅥🅗🅢', 'Output', 'Keep Intermediate'],
+        name: 'Keep required intermediate files after sucessful execution',
+        type: 'boolean',
+        defaultValue: true,
+      },
+    ],
+
+    async beforeRegisterNodeDef(nodeType, nodeData, app) {
+        if(nodeData?.name?.startsWith("VHS_")) {
+            useKVState(nodeType);
+            if (nodeData.description) {
+                let description = nodeData.description
+                let el = document.createElement("div")
+                el.innerHTML = description
+                if (!el.children.length) {
+                    //Is plaintext. Do minor convenience formatting
+                    let chunks = description.split('\n')
+                    nodeData.description = chunks[0]
+                    description = chunks.join('<br>')
+                } else {
+                    nodeData.description = el.querySelector('#VHS_shortdesc')?.innerHTML || el.children[1]?.firstChild?.innerHTML
+                }
+                chainCallback(nodeType.prototype, "onNodeCreated", function () {
+                    helpDOM.addHelp(this, nodeType, description)
+                    this.setSize(this.computeSize())
+                })
+            }
+            //set widgetType to use VHS widgets where possible
+            for(let inp of Object.values({...nodeData.input?.required, ...nodeData.input?.optional})) {
+                if (["INT", "FLOAT"].includes(inp[0])) {
+                    if (!inp[1]) {
+                        inp[1] = {}
+                    }
+                    inp[1].widgetType ??= "VHS" + inp[0]
+                }
+            }
+            chainCallback(nodeType.prototype, "onNodeCreated", function () {
+                let new_widgets = []
+                if (this.widgets) {
+                    for (let w of this.widgets) {
+                        let input = this.constructor.nodeData.input
+                        let config = input?.required[w.name] ?? input.optional[w.name]
+                        if (!config) {
+                            continue
+                        }
+                        if (w?.type == "text" && config[1].vhs_path_extensions) {
+                            new_widgets.push(app.widgets.VHSPATH({}, w.name, ["VHSPATH", config[1]]));
+                        } else {
+                            new_widgets.push(w)
+                        }
+                    }
+                    this.widgets = new_widgets;
+                }
+                const originalAddInput = this.addInput;
+                this.addInput = function(name, type, options) {
+                    if (options.widget) {
+                        //Is Converted Widget
+                        const widget = this.widgets.find((w) => w.name == name)
+                        if (widget?.config) {
+                            //Has override for type
+                            type = widget.config[0]
+                            if (type == 'FLOAT') {
+                                type = "FLOAT,INT"
+                            }
+                            setWidgetConfig(options, widget.config)
+                        }
+                    }
+                    return originalAddInput.apply(this, [name, type, options])
+                }
+            });
+        }
+        if (nodeData?.name == "VHS_LoadImages") {
+            addUploadWidget(nodeType, nodeData, "directory", "folder");
+            chainCallback(nodeType.prototype, "onNodeCreated", function() {
+                const pathWidget = this.widgets.find((w) => w.name === "directory");
+                chainCallback(pathWidget, "callback", (value) => {
+                    if (!value) {
+                        return;
+                    }
+                    let params = {filename : value, type : "input", format: "folder"};
+                    this.updateParameters(params, true);
+                });
+            });
+            addLoadCommon(nodeType, nodeData);
+        } else if (nodeData?.name == "VHS_LoadImagesPath") {
+            chainCallback(nodeType.prototype, "onNodeCreated", function() {
+                const pathWidget = this.widgets.find((w) => w.name === "directory");
+                chainCallback(pathWidget, "callback", (value) => {
+                    if (!value) {
+                        return;
+                    }
+                    let params = {filename : value, type : "path", format: "folder"};
+                    this.updateParameters(params, true);
+                });
+            });
+            addLoadCommon(nodeType, nodeData);
+        } else if (nodeData?.name == "VHS_LoadVideo" || nodeData?.name == "VHS_LoadVideoFFmpeg") {
+            chainCallback(nodeType.prototype, "onNodeCreated", function() {
+                const pathWidget = this.widgets.find((w) => w.name === "video");
+                chainCallback(pathWidget, "callback", (value) => {
+                    if (!value) {
+                        return;
+                    }
+                    let parts = ["input", value];
+                    let extension_index = parts[1].lastIndexOf(".");
+                    let extension = parts[1].slice(extension_index+1);
+                    let format = "video"
+                    if (["gif", "webp", "avif"].includes(extension)) {
+                        format = "image"
+                    }
+                    format += "/" + extension;
+                    let params = {filename : parts[1], type : parts[0], format: format};
+                    this.updateParameters(params, true);
+                });
+            });
+            addUploadWidget(nodeType, nodeData, "video");
+            addLoadCommon(nodeType, nodeData);
+            addVAEOutputToggle(nodeType, nodeData);
+        } else if (nodeData?.name == "VHS_LoadAudio") {
+            addAudioPreview(nodeType)
+            chainCallback(nodeType.prototype, "onNodeCreated", function() {
+                const pathWidget = this.widgets.find((w) => w.name === "audio_file");
+                chainCallback(pathWidget, "callback", (filename) => {
+                    this.updateParameters({filename, type: 'path'}, true);
+                });
+            });
+        } else if (nodeData?.name == "VHS_LoadAudioUpload") {
+            addUploadWidget(nodeType, nodeData, "audio", "audio");
+            addAudioPreview(nodeType)
+            chainCallback(nodeType.prototype, "onNodeCreated", function() {
+                const pathWidget = this.widgets.find((w) => w.name === "audio");
+                chainCallback(pathWidget, "callback", (filename) => {
+                    if (!filename) return
+                    let params = {filename, type : "input"};
+                    this.updateParameters(params, true);
+                });
+            });
+        } else if (nodeData?.name == "VHS_LoadVideoPath" || nodeData?.name == "VHS_LoadVideoFFmpegPath") {
+            chainCallback(nodeType.prototype, "onNodeCreated", function() {
+                const pathWidget = this.widgets.find((w) => w.name === "video");
+                chainCallback(pathWidget, "callback", (value) => {
+                    let extension_index = value.lastIndexOf(".");
+                    let extension = value.slice(extension_index+1);
+                    let format = "video"
+                    if (["gif", "webp", "avif"].includes(extension)) {
+                        format = "image"
+                    }
+                    format += "/" + extension;
+                    let params = {filename : value, type: "path", format: format};
+                    this.updateParameters(params, true);
+                });
+            });
+            addLoadCommon(nodeType, nodeData);
+            addVAEOutputToggle(nodeType, nodeData);
+        } else if (nodeData?.name == "VHS_LoadImagePath") {
+            addLoadCommon(nodeType, nodeData);
+            addVAEOutputToggle(nodeType, nodeData);
+            chainCallback(nodeType.prototype, "onNodeCreated", function() {
+                const pathWidget = this.widgets.find((w) => w.name === "image");
+                chainCallback(pathWidget, "callback", (value) => {
+                    let extension_index = value.lastIndexOf(".");
+                    let extension = value.slice(extension_index+1);
+                    let format = "video" +  "/" + extension;
+                    let params = {filename : value, type: "path", format: format};
+                    this.updateParameters(params, true);
+                });
+            });
+        } else if (nodeData?.name == "VHS_VideoCombine") {
+            addDateFormatting(nodeType, "filename_prefix");
+            chainCallback(nodeType.prototype, "onExecuted", function(message) {
+                if (message?.gifs) {
+                    this.updateParameters(message.gifs[0], true);
+                }
+            });
+            addVideoPreview(nodeType, false);
+            addPreviewOptions(nodeType);
+            addFormatWidgets(nodeType, nodeData);
+            addVAEInputToggle(nodeType, nodeData)
+        } else if (nodeData?.name == "VHS_SaveImageSequence") {
+            //Disabled for safety as VHS_SaveImageSequence is not currently merged
+            //addDateFormating(nodeType, "directory_name", timestamp_widget=true);
+            //addTimestampWidget(nodeType, nodeData, "directory_name")
+        } else if (nodeData?.name == "VHS_BatchManager") {
+            chainCallback(nodeType.prototype, "onNodeCreated", function() {
+                this.widgets.push({name: "count", type: "dummy", value: 0,
+                    computeSize: () => {return [0,-4]},
+                    afterQueued: function() {this.value++;}});
+            });
+        } else if (nodeData?.name == "VHS_Unbatch") {
+            cloneType(nodeType, nodeData)
+        } else if (nodeData?.name == "VHS_SelectLatest") {
+            chainCallback(nodeType.prototype, "onNodeCreated", function() {
+                this.isVirtualNode = true
+                chainCallback(this, "onConnectionsChange", function (contype, slot, iscon, linfo) {
+                    if (iscon) {
+                        this.update_links()
+                    }
+                })
+
+                this.update_links = function(extraLinks = []) {
+                    if (!this.outputs[0].links?.length) return
+
+                    function get_links(node) {
+                        let links = []
+                        for (const l of node.outputs[0].links) {
+                            const linkInfo = node.graph.links[l]
+                            const n = node.graph.getNodeById(linkInfo.target_id)
+                            if (n.type == 'Reroute') {
+                                links = links.concat(get_links(n))
+                            } else {
+                                links.push(l)
+                            }
+                        }
+                        return links
+                    }
+
+                    let links = [
+                        ...get_links(this).map((l) => this.graph.links[l]),
+                        ...extraLinks
+                    ]
+                    let v = this.latest_file
+                    if (!v) {
+                        return
+                    }
+
+                    // For each output link copy our value over the original widget value
+                    for (const linkInfo of links) {
+                        const node = this.graph.getNodeById(linkInfo.target_id)
+                        const input = node.inputs[linkInfo.target_slot]
+                        const widgetName = input.widget.name
+                        const widget = node.widgets.find((w) => w.name === widgetName)
+                        if (widget) {
+                            widget.value = v
+                            if (widget.callback) {
+                                widget.callback( widget.value, app.canvas,
+                                    node, app.canvas.graph_mouse, {})
+                            }
+                        }
+                    }
+                }
+                let fetch_files = async () => {
+                    let [path, remainder] = path_stem(this.widgets[0].value)
+                    let params = {path : path}
+                    let optionsURL = api.apiURL('/vhs/getpath?' + new URLSearchParams(params));
+                    let options = []
+                    try {
+                        let resp = await fetch(optionsURL);
+                        options = await resp.json();
+                    } catch(e) {}
+                    options = options.filter((file) => file.startsWith(remainder) && file.endsWith(this.widgets[1].value))
+                    if (options.length && this.latest_file != options[options.length-1]) {
+                        this.latest_file = path + options[options.length-1]
+                        this.update_links()
+                    }
+                }
+                this.widgets[0].callback = fetch_files
+                this.widgets[1].callback = fetch_files
+                this.onPromptExecuted  = fetch_files
+                this.applyToGraph = this.update_links
+            })
+        }
+    },
+    async getCustomWidgets() {
+        return {
+            VHSPATH(node, inputName, inputData) {
+                let w = {
+                    name : inputName,
+                    type : "VHS.PATH",
+                    value : "",
+                    draw : function(ctx, node, widget_width, y, H) {
+                        //Adapted from litegraph.core.js:drawNodeWidgets
+                        var show_text = app.canvas.ds.scale >= (app.canvas.low_quality_zoom_threshold ?? 0.5)
+                        var margin = 15;
+                        var text_color = LiteGraph.WIDGET_TEXT_COLOR;
+                        var secondary_text_color = LiteGraph.WIDGET_SECONDARY_TEXT_COLOR;
+                        ctx.textAlign = "left";
+                        ctx.strokeStyle = LiteGraph.WIDGET_OUTLINE_COLOR;
+                        ctx.fillStyle = LiteGraph.WIDGET_BGCOLOR;
+                        ctx.beginPath();
+                        if (show_text)
+                            ctx.roundRect(margin, y, widget_width - margin * 2, H, [H * 0.5]);
+                        else
+                            ctx.rect( margin, y, widget_width - margin * 2, H );
+                        ctx.fill();
+                        if (show_text) {
+                            if(!this.disabled)
+                                ctx.stroke();
+                            ctx.save();
+                            ctx.beginPath();
+                            ctx.rect(margin, y, widget_width - margin * 2, H);
+                            ctx.clip();
+
+                            //ctx.stroke();
+                            let freeWidth = widget_width - (margin * 2 + 40)
+                            ctx.fillStyle = secondary_text_color;
+                            const label = this.label || this.name;
+                            if (label != null) {
+                                let [labelDisplay, labelWidth] = fitText(ctx, label, freeWidth)
+                                freeWidth -= labelWidth
+                                ctx.fillText(labelDisplay, margin * 2, y + H * 0.7);
+                            }
+                            ctx.fillStyle = this.value ? text_color : '#777';
+                            ctx.textAlign = "right";
+                            let disp_text = fitPath(ctx, String(this.value || this.options.placeholder), freeWidth)[0]
+                            ctx.fillText(disp_text, widget_width - margin * 2, y + H * 0.7);
+                            ctx.restore();
+                        }
+                    },
+                    mouse : searchBox,
+                    options : {},
+                };
+                if (inputData.length > 1) {
+                    w.options = inputData[1]
+                    if (inputData[1].default) {
+                        w.value = inputData[1].default;
+                    }
+                }
+
+                if (!node.widgets) {
+                    node.widgets = [];
+                }
+                node.widgets.push(w);
+                return w;
+            },
+            VHSFLOAT(node, inputName, inputData) {
+                let w = {
+                    name: inputName,
+                    type: "VHS.ANNOTATED",
+                    value: inputData[1]?.default ?? 0,
+                    draw: drawAnnotated,
+                    mouse: mouseAnnotated,
+                    computeSize(width) {
+                        return [width, 20]
+                    },
+                    callback(v) {
+                        if (this.options.round) {
+                            //TODO adopt ComfyUI_frontend#4291?
+                            v = Math.round((v + Number.EPSILON) /
+                                this.options.round) * this.options.round
+                        }
+                        if (this.options.max && v > this.options.max) {
+                            v = this.options.max
+                        }
+                        if (this.options.min && v < this.options.max) {
+                            v = this.options.min
+                        }
+                        this.value = v
+                    },
+                    config: inputData,
+                    displayValue: function() {
+                        return roundToPrecision(this.value, this.options.precision ?? 3)
+                    },
+                    options: Object.assign({},  inputData[1])
+                }
+                if (!node.widgets) {
+                    node.widgets = []
+                }
+                node.widgets.push(w)
+                return w
+            },
+            VHSINT(node, inputName, inputData) {
+                let w = {
+                    name: inputName,
+                    type: "VHS.ANNOTATED",
+                    value: inputData[1]?.default ?? 0,
+                    draw: drawAnnotated,
+                    mouse: mouseAnnotated,
+                    computeSize(width) {
+                        return [width, 20]
+                    },
+                    callback(v) {
+                        if (this.options.max && v > this.options.max) {
+                            v = this.options.max
+                        }
+                        if (this.options.min && v < this.options.min) {
+                            v = this.options.min
+                        }
+                        if (v == 0) {
+                            return
+                        }
+                        const s = this.options.step
+                        let sh = this.options.mod ?? 0
+                        this.value = Math.round((v - sh) / s) * s + sh
+                    },
+                    config: inputData,
+                    displayValue: function() {
+                        return this.value | 0
+                    },
+                    options: Object.assign({},  inputData[1])
+                }
+                if (!node.widgets) {
+                    node.widgets = []
+                }
+                node.widgets.push(w)
+                return w
+            },
+            VHSTIMESTAMP(node, inputName, inputData) {
+                let w = {
+                    name: inputName,
+                    type: "VHS.TIMESTAMP",
+                    value: inputData[1]?.default ?? 0,
+                    draw: drawAnnotated,
+                    mouse: mouseAnnotated,
+                    computeSize(width) {
+                        return [width, 20]
+                    },
+                    parseValue(v) {
+                        if (typeof(v) == "string") {
+                            let val = 0
+                            for (let chunk of  v.split(":")) {
+                                val = val * 60 + parseFloat(chunk)
+                            }
+                            return val
+                        }
+                    },
+                    callback(v) {},
+                    config: inputData,
+                    options: Object.assign({}, inputData[1]),
+                    displayValue() {
+                        let seconds = this.value
+                        let hours = seconds / 3600 | 0
+                        seconds -= 3600 * hours
+                        let minutes = seconds / 60 | 0
+                        seconds -= 60 * minutes
+                        let display = ""
+                        if (hours > 0) {
+                            display += hours + ":"
+                        }
+                        if (hours > 0 || minutes > 0) {
+                            if (hours > 0) {
+                                minutes = (''+minutes).padStart(2,'0')
+                            }
+                            display += minutes + ":"
+                        }
+                        seconds = roundToPrecision(seconds, 4)
+                        if ((seconds[1] == '.' || seconds.length == 1) && (minutes > 0 || hours > 0)) {
+                            seconds = '0'+seconds
+                        }
+                        display += seconds
+                        return display
+                    }
+                }
+                if (!node.widgets) {
+                    node.widgets = []
+                }
+                node.widgets.push(w)
+                return w
+            },
+        }
+    },
+    async loadedGraphNode(node) {
+        //Check and migrate inputs named batch_manager from old workflows
+        if (node.type?.startsWith("VHS_") && node.inputs) {
+            const batchInput = node.inputs.find((i) => i.name == "batch_manager")
+            if (batchInput) {
+                batchInput.name = "meta_batch"
+            }
+        }
+    },
+    async beforeConfigureGraph(graphData, missingNodeTypes) {
+        if(helpDOM?.node) {
+            helpDOM.node = undefined
+        }
+    },
+    async setup() {
+        let originalGraphToPrompt = app.graphToPrompt
+        let graphToPrompt = async function() {
+            let res = await originalGraphToPrompt.apply(this, arguments);
+            res.workflow.extra['VHS_latentpreview'] = app.ui.settings.getSettingValue("VHS.LatentPreview")
+            res.workflow.extra['VHS_latentpreviewrate'] = app.ui.settings.getSettingValue("VHS.LatentPreviewRate")
+            res.workflow.extra['VHS_MetadataImage'] = app.ui.settings.getSettingValue("VHS.MetadataImage")
+            res.workflow.extra['VHS_KeepIntermediate'] = app.ui.settings.getSettingValue("VHS.KeepIntermediate")
+            return res
+        }
+        app.graphToPrompt = graphToPrompt
+        //Add a handler for pasting video data
+        document.addEventListener('paste', async (e) => {
+            if (!e.target.classList.contains('litegraph') &&
+                !e.target.classList.contains('graph-canvas-container')) {
+                    return
+                }
+            let data = e.clipboardData || window.clipboardData
+            let filepath = data.getData('text/plain')
+            let video
+            for (const item of data.items) {
+                if (item.type.startsWith('video/')) {
+                    video = item
+                    break
+                }
+            }
+            if (filepath && copiedPath == filepath) {
+                //Add a Load Video (Path) and populate filepath
+                const pastedNode = LiteGraph.createNode('VHS_LoadVideoPath')
+                app.canvas.graph.add(pastedNode)
+                pastedNode.pos[0] = app.canvas.graph_mouse[0]
+                pastedNode.pos[1] = app.canvas.graph_mouse[1]
+                pastedNode.widgets[0].value = filepath
+                pastedNode.widgets[0].callback?.(filepath)
+            } else if (video && false) {
+                //Disabled due to lack of testing
+                //Add a Load Video (Upload), then upload the file, then select the file
+                const pastedNode = LiteGraph.createNode('VHS_LoadVideo')
+                app.canvas.graph.add(pastedNode)
+                pastedNode.pos[0] = app.canvas.graph_mouse[0]
+                pastedNode.pos[1] = app.canvas.graph_mouse[1]
+                const pathWidget = pastedNode.widgets[0]
+                //TODO: upload to pasted dir?
+                const blob = video.getAsFile()
+                const resp = await uploadFile(blob)
+                if (resp.status != 200) {
+                    //upload failed and file can not be added to options
+                    return;
+                }
+                const filename = (await resp.json()).name;
+                pathWidget.options.values.push(filename);
+                pathWidget.value = filename;
+                pathWidget.callback?.(filename)
+            } else {
+                return
+            }
+            e.preventDefault()
+            e.stopImmediatePropagation()
+            return false
+        }, true)
+    },
+    async init() {
+        if (app.ui.settings.getSettingValue("VHS.AdvancedPreviews") == true) {
+            app.ui.settings.setSettingValue("VHS.AdvancedPreviews", 'Always')
+        }
+        if (app.ui.settings.getSettingValue("VHS.AdvancedPreviews") == false) {
+            app.ui.settings.setSettingValue("VHS.AdvancedPreviews", 'Never')
+        }
+        if (app.VHSHelp != helpDOM) {
+            helpDOM = app.VHSHelp
+        } else {
+            initHelpDOM()
+        }
+        let e = app.extensions.filter((w) => w.name == 'UVR5.AudioPreviewer')
+        if (e.length) {
+            let orig = e[0].beforeRegisterNodeDef
+            e[0].beforeRegisterNodeDef = function(nodeType, nodeData, app) {
+                if(!nodeData?.name?.startsWith("VHS_")) {
+                    return orig.apply(this, arguments);
+                }
+            }
+        }
+    },
+});
+let previewImages = []
+api.addEventListener('executing', ({ detail }) => {
+    if (detail === null) {
+        for (let graph of [app.graph, ...app.graph.subgraphs.values()]) {
+            for (let node of graph._nodes) {
+                if (node.type.startsWith("VHS_")) {
+                    node.onPromptExecuted?.()
+                }
+            }
+        }
+    }
+})
+function getLatentPreviewCtx(id, width, height) {
+    const node = getNodeById(id)
+    if (!node) {
+        return undefined
+    }
+
+    let previewWidget = node.widgets.find((w) => w.name == "vhslatentpreview")
+    if (!previewWidget) {
+        //check for and remove any native preview
+        let nativePreview = node.widgets.findIndex((w) => w.name == '$$canvas-image-preview')
+        if (nativePreview >= 0) {
+            node.imgs = []
+            node.widgets.splice(nativePreview,1)
+        }
+        let canvasEl = document.createElement("canvas")
+        canvasEl.style.width = "100%"
+        previewWidget = node.addDOMWidget("vhslatentpreview", "vhscanvas", canvasEl, {
+            serialize: false,
+            hideOnZoom: false,
+        });
+        previewWidget.serialize = false
+        allowDragFromWidget(previewWidget)
+        canvasEl.addEventListener('contextmenu', (e)  => {
+            e.preventDefault()
+            return app.canvas._mousedown_callback(e)
+        }, true);
+        canvasEl.addEventListener('pointerdown', (e)  => {
+            e.preventDefault()
+            return app.canvas._mousedown_callback(e)
+        }, true);
+        canvasEl.addEventListener('mousewheel', (e)  => {
+            e.preventDefault()
+            return app.canvas._mousewheel_callback(e)
+        }, true);
+        canvasEl.addEventListener('pointermove', (e)  => {
+            e.preventDefault()
+            return app.canvas._mousemove_callback(e)
+        }, true);
+        canvasEl.addEventListener('pointerup', (e)  => {
+            e.preventDefault()
+            return app.canvas._mouseup_callback(e)
+        }, true);
+
+        previewWidget.computeSize = function(width) {
+            if (this.aspectRatio) {
+                let height = (node.size[0]-20)/ this.aspectRatio + 10;
+                if (!(height > 0)) {
+                    height = 0;
+                }
+                this.computedHeight = height + 10;
+                return [width, height];
+            }
+            return [width, -4];//no loaded src, widget should not display
+        }
+    }
+    let canvasEl = previewWidget.element
+    if (!previewWidget.ctx || canvasEl.width != width
+        || canvasEl.height != height) {
+        previewWidget.aspectRatio = width / height
+        canvasEl.width = width
+        canvasEl.height = height
+        fitHeight(node)
+    }
+    return canvasEl.getContext("2d")
+}
+let animateIntervals = {}
+function beginLatentPreview(id, previewImages, rate) {
+    latentPreviewNodes.add(id)
+    if (animateIntervals[id]) {
+        clearTimeout(animateIntervals[id])
+    }
+    let displayIndex = 0
+    let node = getNodeById(id)
+    //While progress is safely cleared on execution completion.
+    //Initial progress must be started here to avoid a race condition
+    node.progress = 0
+    animateIntervals[id] = setInterval(() => {
+        if (getNodeById(id)?.progress == undefined
+            || app.canvas.graph.rootGraph != node.graph.rootGraph) {
+            clearTimeout(animateIntervals[id])
+            delete animateIntervals[id]
+            return
+        }
+        if (!previewImages[displayIndex]) {
+            return
+        }
+        getLatentPreviewCtx(id, previewImages[displayIndex].width,
+            previewImages[displayIndex].height)?.drawImage?.(previewImages[displayIndex],0,0)
+        displayIndex = (displayIndex + 1) % previewImages.length
+    }, 1000/rate);
+
+}
+let previewImagesDict = {}
+api.addEventListener('VHS_latentpreview', ({ detail }) => {
+    if (detail.id == null) {
+        return
+    }
+    let previewImages = previewImagesDict[detail.id] = []
+    previewImages.length = detail.length
+
+    let idParts = detail.id.split(':')
+    for (let i=1; i <= idParts.length; i++) {
+        let id = idParts.slice(0,i).join(':')
+        beginLatentPreview(id, previewImages, detail.rate)
+    }
+});
+let td = new TextDecoder()
+api.addEventListener('b_preview', async (e) => {
+    if (Object.keys(animateIntervals).length == 0) {
+        return
+    }
+    e.preventDefault()
+    e.stopImmediatePropagation()
+    e.stopPropagation()
+    const dv = new DataView(await e.detail.slice(0,24).arrayBuffer())
+    const index = dv.getUint32(4)
+    const idlen = dv.getUint8(8)
+    const id = td.decode(dv.buffer.slice(9,9+idlen))
+    previewImagesDict[id][index] = await window.createImageBitmap(e.detail.slice(24))
+    return false
+}, true);
diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/web/js/videoinfo.js b/custom_nodes/ComfyUI-VideoHelperSuite/web/js/videoinfo.js
new file mode 100644
index 0000000000000000000000000000000000000000..b1a9871067e07dc09b37a8dd666ce531c2c5f56b
--- /dev/null
+++ b/custom_nodes/ComfyUI-VideoHelperSuite/web/js/videoinfo.js
@@ -0,0 +1,101 @@
+import { app } from '../../../scripts/app.js'
+
+
+function getVideoMetadata(file) {
+    return new Promise((r) => {
+        const reader = new FileReader();
+        reader.onload = (event) => {
+            const videoData = new Uint8Array(event.target.result);
+            const dataView = new DataView(videoData.buffer);
+
+            let decoder = new TextDecoder();
+            // Check for known valid magic strings
+            if (dataView.getUint32(0) == 0x1A45DFA3) {
+                //webm/mkv (both use EBML/Matroska format)
+                //see http://wiki.webmproject.org/webm-metadata/global-metadata
+                //and https://www.matroska.org/technical/elements.html
+                //contrary to specs, tag seems consistently at start
+                //COMMENT + 0x4487 + packed length?
+                //length 0x8d8 becomes 0x48d8
+                //
+                //description for variable length ints https://github.com/ietf-wg-cellar/ebml-specification/blob/master/specification.markdown
+                let offset = 4 + 8; //COMMENT is 7 chars + 1 to realign
+                while(offset < videoData.length-16) {
+                    //Check for text tags
+                    if (dataView.getUint16(offset) == 0x4487) {
+                        //check that name of tag is COMMENT
+                        const name = String.fromCharCode(...videoData.slice(offset-7,offset));
+                        if (name === "COMMENT") {
+                            let vint = dataView.getUint32(offset+2);
+                            let n_octets = Math.clz32(vint)+1;
+                            if (n_octets < 4) {//250MB sanity cutoff
+                                let length = (vint >> (8*(4-n_octets))) & ~(1 << (7*n_octets));
+                                const content = decoder.decode(videoData.slice(offset+2+n_octets, offset+2+n_octets+length));
+                                let json = JSON.parse(content);
+                                r(json);
+                                return;
+                            }
+                        }
+                    }
+                    offset+=1;
+                }
+            } else if (dataView.getUint32(4) == 0x66747970 && dataView.getUint32(8) == 0x69736F6D) {
+                //mp4
+                //see https://developer.apple.com/documentation/quicktime-file-format
+                //Seems to make no guarantee for alignment
+                let offset = videoData.length-4;
+                while (offset > 16) {//rough safe guess
+                    if (dataView.getUint32(offset) == 0x64617461) {//any data tag
+                        if (dataView.getUint32(offset - 8) == 0xa9636d74) {//cmt data tag
+                            let type = dataView.getUint32(offset+4); //seemingly 1
+                            let locale = dataView.getUint32(offset+8); //seemingly 0
+                            let size = dataView.getUint32(offset-4) - 4*4;
+                            const content = decoder.decode(videoData.slice(offset+12, offset+12+size));
+                            const json = JSON.parse(content);
+                            r(json);
+                            return;
+                        }
+                    }
+
+                    offset-=1;
+                }
+            } else {
+                console.error("Unknown magic: " + dataView.getUint32(0))
+            }
+            r();
+            return;
+        };
+
+        reader.readAsArrayBuffer(file);
+    });
+}
+function isVideoFile(file) {
+    if (file?.name?.endsWith(".webm")) {
+        return true;
+    }
+    if (file?.name?.endsWith(".mp4")) {
+        return true;
+    }
+    if (file?.name?.endsWith(".mkv")) {
+        return true;
+    }
+
+    return false;
+}
+
+let originalHandleFile = app.handleFile;
+app.handleFile = handleFile;
+let fileInput = document.getElementById("comfy-file-input")
+//hijack comfy-file-input to allow webm/mp4/mkv
+fileInput.accept += ",video/webm,video/mp4,video/x-matroska";
+
+async function handleFile(file) {
+    if (file?.type?.startsWith("video/") || isVideoFile(file)) {
+        const videoInfo = await getVideoMetadata(file);
+        if (videoInfo?.workflow) {
+            await app.loadGraphData(videoInfo.workflow);
+            return
+        }
+    }
+    return await originalHandleFile.apply(this, arguments);
+}
diff --git a/custom_nodes/Gemma4RealisticIdeogramPrompt/references/._gemma4_prompt_gen.upstream.py b/custom_nodes/Gemma4RealisticIdeogramPrompt/references/._gemma4_prompt_gen.upstream.py
new file mode 100644
index 0000000000000000000000000000000000000000..79457a8b9932a537da5b9517afff47d1ec1a52bb
Binary files /dev/null and b/custom_nodes/Gemma4RealisticIdeogramPrompt/references/._gemma4_prompt_gen.upstream.py differ
diff --git a/custom_nodes/Gemma4RealisticIdeogramPrompt/references/gemma4_prompt_gen.upstream.py b/custom_nodes/Gemma4RealisticIdeogramPrompt/references/gemma4_prompt_gen.upstream.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3457b60eb021a25f2be5ea243cd8f88b83f5aab
--- /dev/null
+++ b/custom_nodes/Gemma4RealisticIdeogramPrompt/references/gemma4_prompt_gen.upstream.py
@@ -0,0 +1,4104 @@
+"""
+Gemma4PromptGen - ComfyUI Node (v1.0)
+=======================================
+Multi-model prompt engineer powered by local Gemma 4 via llama-server.
+
+  IMAGE MODELS (single image output):
+    • Flux.1  — natural language, subject-first, cinematographic
+    • SDXL    — booru-style comma-separated tags, quality headers, neg prompt
+    • Pony XL — booru tags with score/rating prefix, e621 style
+    • SD 1.5  — classic SD weighted natural language
+
+  VIDEO MODELS (temporal / motion output):
+    • LTX 2.3 — cinematic arc: BEGINNING/MIDDLE/END, audio layer, LTX-native
+    • Wan 2.2  — motion-first, camera language, 80-120w, optional I2V grounding
+
+Modes:
+  PREVIEW  — Flushes VRAM, calls llama-server, stores prompt, halts pipeline.
+  SEND     — Outputs stored prompt, kills llama-server process, frees VRAM.
+
+Backend: llama-server (llama.cpp) running Gemma 4 31B abliterated GGUF locally.
+llama-server must be running before PREVIEW is triggered.
+
+All models support: NSFW content, image grounding (I2V/I2I), character lock,
+environment presets, dialogue injection, seed-controlled randomness.
+
+Part of the LoRa-Daddy toolkit.
+"""
+
+import json
+import os
+import random
+import re
+import subprocess
+import tempfile
+import threading
+import time
+import urllib.request
+import urllib.error
+
+# ══════════════════════════════════════════════════════════════════════════
+#  LORA PRESETS
+#  Activated via toggle — injects a fixed shot structure into the instruction.
+# ══════════════════════════════════════════════════════════════════════════
+LORA_PRESETS = {
+    "None": None,
+    "💦 Blink — Titjob": {
+        "trigger": "JMPCT",
+        "base_caption": (
+            "JMPCT, woman posing and smiling at camera, "
+            "sudden jumpcut to a woman lying down performing a titjob, "
+            "looking up at the camera with an expressive face."
+        ),
+        "system_prompt_override": (
+            "You enhance a base caption for LTX Video 2.3. "
+            "Output ONE short paragraph only — no preamble, no explanation, nothing before or after.\n\n"
+            "RULES — non-negotiable:\n"
+            "1. START your output with exactly: JMPCT,\n"
+            "2. Keep the structure: posing shot then sudden jumpcut then titjob shot\n"
+            "3. Add ONE short detail about the subject appearance from the image if provided\n"
+            "4. Add ONE dialogue line in quotes in the posing shot — sexy and contextual to who she is\n"
+            "5. Add ONE dialogue line in quotes in the titjob shot — breathless and in the moment\n"
+            "6. End with a single brief audio sentence\n"
+            "7. TOTAL OUTPUT: 4 to 6 sentences MAX. Short. Dense. No flowery prose.\n"
+            "8. Do NOT add extra scenes, camera moves, or beats beyond the base caption structure.\n\n"
+            "BASE CAPTION TO ENHANCE:\n"
+            "JMPCT, woman posing and smiling at camera, sudden jumpcut to a woman lying down "
+            "performing a titjob, looking up at the camera with an expressive face.\n\n"
+            "Output only the enhanced prompt. Begin with JMPCT,"
+        ),
+        "dialogue_open_pool": [
+            "Hey... come closer...",
+            "I've been waiting for you...",
+            "You like what you see...?",
+            "Don't be shy...",
+            "Come here...",
+        ],
+        "dialogue_close_pool": [
+            "yes... just like that... don't stop...",
+            "mmm... feels so good...",
+            "keep going... please...",
+            "you feel amazing...",
+            "don't you dare stop...",
+        ],
+    },
+}
+
+
+# ══════════════════════════════════════════════════════════════════════════
+#  ENVIRONMENT PRESETS
+#  Each value: (location, lighting, sound)
+#  None = LLM decides.  "RANDOM" = seed-picked at runtime.
+# ══════════════════════════════════════════════════════════════════════════
+ENVIRONMENT_PRESETS = {
+    "None — LLM decides": None,
+    "🎲 Random — seed picks": "RANDOM",
+
+    # ── NATURAL ──────────────────────────────────────────────────────────
+    "🏖 Beach — golden hour": (
+        "wide open beach at golden hour, warm amber light raking low across wet sand, "
+        "shallow surf foaming in irregular sheets over the flat shore, "
+        "distant horizon blurred with sea haze, seaweed and shell fragments at the tide line, "
+        "salt crust on every exposed surface, damp sand firm underfoot then soft further up the beach",
+        "warm directional sidelight from the low sun, long soft shadows stretching inland, "
+        "orange-gold palette with deep blue shadows pooling in the wet sand troughs",
+        "rolling waves building and collapsing, wind-carried spray hissing across the sand, "
+        "distant gulls, the hollow clap of a wave folding on itself"),
+
+    "🏔 Mountain peak — dawn": (
+        "exposed mountain summit at first light, vast sky opening below in every direction, "
+        "cold thin air, bare grey-brown rock underfoot fractured into angular plates, "
+        "pale blue and rose light spreading from the east across cloud layers far below, "
+        "distant ranges stretching to a gently curved horizon, breath visible in the cold",
+        "cold directional dawn light from the east, high contrast, no fill light, "
+        "long purple shadows from every ridge and rock formation, rose-to-blue sky gradient",
+        "wind building and fading in slow gusts, deep silence between them, "
+        "the creak of cold rock contracting, faint echo from the valley below"),
+
+    "🌲 Dense forest — diffused green": (
+        "deep forest interior, canopy dense and fully closed 20 metres overhead, "
+        "light filtering down in soft broken columns through layered leaves, "
+        "moss-covered ground, ferns at knee height filling every gap between roots, "
+        "standing water in root depressions reflecting green light back upward, "
+        "bark textured with lichen and fungal rings, the space between trunks creating receding depth",
+        "diffused green-filtered light with no hard shadows, uniform soft fill from the canopy above, "
+        "every surface tinted with reflected chlorophyll green",
+        "birdsong in overlapping species layers, wind audible in the canopy but absent at ground level, "
+        "a dry leaf shifting somewhere unseen, distant running water"),
+
+    "🌊 Underwater — shallow reef": (
+        "shallow tropical reef underwater, clear turquoise water with 20-metre visibility, "
+        "shafts of broken sunlight refracting through the rippling surface in caustic patterns, "
+        "staghorn and brain coral formations in soft focus below, "
+        "small fish holding station in the gentle current, everything moving in slow surge rhythm",
+        "caustic light patterns dancing across every surface from above, "
+        "high-key teal-blue overall, darker blue fading into depth below",
+        "muffled pressure, the steady rise of bubbles, distant boat hull drone, "
+        "the creak of coral in the current"),
+
+    "🌧 Rain-soaked city street — night": (
+        "rain-soaked urban street at night, wet asphalt reflecting neon signs "
+        "in elongated distorted colour streaks, steam rising from iron grates in the road, "
+        "pools of amber streetlight surrounded by dark, blurred traffic in background, "
+        "awnings dripping, gutters running",
+        "neon colour reflections in puddles — red, blue, white, amber — "
+        "cool blue ambient fill, warm sodium overhead streetlamps",
+        "rain on pavement in constant hiss, distant traffic, "
+        "wet tyre sound on asphalt, footsteps echoing under an awning"),
+
+    "🏜 Desert — midday heat": (
+        "open desert at midday, bleached pale sand extending to a dead-flat horizon, "
+        "air rippling with heat shimmer low above the ground, "
+        "sky a brilliant white-blue with no cloud, no shade, no landmarks, "
+        "surface cracked into geometric plates closer to the foreground",
+        "brutal overhead sun, harsh vertical top-light with zero shadow relief, "
+        "bleached palette — near-white sand, white-blue sky, black under anything that casts shade",
+        "silence — then wind — then silence again, fine sand skittering across the crust"),
+
+    "🌌 Night sky — open field": (
+        "open field under a fully clear night sky, grass running to a dark horizon, "
+        "the Milky Way arcing overhead in a dense band of blue-white stars, "
+        "no artificial light source, ground-level detail barely visible in deep blue-black ambient",
+        "starlight only, near-black ambient, faint blue-grey top-light from the sky itself, "
+        "the Milky Way core casting a measurable soft gradient",
+        "crickets in continuous layers, light wind through the grass, "
+        "a frog somewhere, the profound silence beneath everything"),
+
+    "🌁 Rooftop — city at night": (
+        "high rooftop at night, city skyline spreading in every direction below, "
+        "warm glow rising from the streets like a second horizon, "
+        "wind at this height, ventilation stacks and water tanks breaking the flat roof surface, "
+        "a parapet at the edge with the drop visible beyond it",
+        "city glow from below as warm amber fill, cool blue sky above, "
+        "backlit silhouette potential against the lit skyline",
+        "distant city hum rising and falling, wind, "
+        "an occasional siren rising from far below and fading"),
+
+    "✈ Plane cockpit — cruising altitude": (
+        "aircraft cockpit at cruising altitude, instrument panel spread in amber and green glow, "
+        "black sky through the windshield, stars visible above the cloud layer, "
+        "the vibration and low hum of engines constant beneath everything, "
+        "oxygen mask clips and circuit breakers detailed on the overhead panel",
+        "instrument panel glow from below — warm amber dials, green digital readouts — "
+        "cool black from the windshield, no natural light",
+        "engine hum constant and enveloping, radio static between calls, "
+        "pressurised air hiss from the vents, the occasional click of switches"),
+
+    # ── INTERIOR ─────────────────────────────────────────────────────────
+    "🏠 Bedroom — warm evening": (
+        "warm bedroom interior in the evening, a single bedside lamp casting a pool of amber light, "
+        "soft shadow in the far corners, bed linen slightly rumpled with the weight of use, "
+        "curtains drawn against the dark outside, a glass of water on the nightstand",
+        "warm tungsten point source from the bedside lamp, soft falloff, "
+        "intimate amber glow, deep shadow beyond its reach",
+        "rain against the window glass if it's raining, or the distant low hum of the city through double glazing, "
+        "the bed shifting under weight, fabric sliding on fabric, "
+        "a phone on the nightstand screen briefly lighting then going dark, "
+        "breathing — the rhythm and depth of it — the only sound that belongs to the room itself"),
+
+    "🛁 Bathroom — steam and tile": (
+        "steam-filled bathroom, a hot shower running behind frosted glass, "
+        "white tile walls beaded with condensation, mirror completely fogged over, "
+        "damp warm air thick enough to see, a folded towel on the rail, "
+        "soap residue on the tile floor",
+        "diffused warm light through frosted glass — soft, hazy, no hard edges, "
+        "the steam itself lit from within",
+        "shower hiss steady behind glass, water hitting tile, "
+        "a slow drip from the tap, muffled echo in the tiled space"),
+
+    "🪟 Penthouse — floor-to-ceiling glass": (
+        "high-floor penthouse interior with floor-to-ceiling glass on two walls, "
+        "city spread far below, clean minimal interior — low furniture in dark leather and pale stone, "
+        "daylight flooding in from the glass wall, the room reflected in the glass at certain angles",
+        "natural daylight through glass — even, cool, diffused by height and haze — "
+        "city providing a continuous ambient glow from below at night",
+        "near-silence — the city thirty floors below reduced to a formless low frequency hum, "
+        "the building's HVAC cycling barely audible, glass creaking faintly in wind at this height, "
+        "ice settling in a glass, the sound of someone's breathing amplified by the quiet, "
+        "and the occasional deep resonant vibration of the building itself moving"),
+
+    "🎹 Jazz club — late night": (
+        "intimate jazz club late at night, low ceiling with exposed brickwork, "
+        "small stage lit warm at the far end, tables pressed close together, "
+        "a candle stub on each table burning low, smoke visible in the stage light, "
+        "a bar along one wall with backlit bottles",
+        "warm tungsten stage wash, candle fill table by table, "
+        "deep shadow in the corners and upper walls",
+        "a jazz trio — upright bass, brushed snare, and a tenor saxophone — playing a slow blues "
+        "at the far end of the room, the saxophone filling the space and bending at the end of each phrase, "
+        "the bassist walking the changes in a low steady pulse, brushes on the snare barely louder than breathing, "
+        "a glass set down on the bar between phrases, low conversation that stops "
+        "when the sax player leans into a long held note, "
+        "the specific intimate acoustic of a low ceiling that puts the music right inside the chest"),
+
+    "🚂 Train — moving through night": (
+        "train carriage moving at night, window showing dark landscape "
+        "with scattered lights passing in rhythm, warm interior against the cold black outside, "
+        "moving reflections of the carriage interior in the glass, "
+        "seats in worn fabric, the rhythmic sway of the carriage",
+        "warm interior tungsten against total black window exterior, "
+        "moving reflections layered over the dark passing world",
+        "rhythmic track click accelerating and decelerating on curves, "
+        "engine vibration through the floor, the world passing outside muffled by glass"),
+
+    "💊 Underground club — strobes and bass": (
+        "underground club at full capacity, strobes cutting the dark in sharp white intervals, "
+        "bass pressure felt in the chest before it is heard, crowd pressed together in the dark, "
+        "a DJ booth visible through smoke at the far end, coloured wash lights sweeping low",
+        "stroboscopic white cuts, colour wash through smoke — purple, red, blue — "
+        "near-black between flashes, faces caught in freeze-frame light",
+        "bass at physical volume, the crowd as a breathing mass of sound, "
+        "the specific compression of a room built for this volume"),
+
+    "🏢 Office — after hours": (
+        "corporate office after hours, desks empty and personal items abandoned mid-day, "
+        "flat cold overhead fluorescent across an open-plan floor, "
+        "city visible through floor-to-ceiling glass on one wall, "
+        "the quality of silence that fills a building after everyone has left",
+        "flat cold fluorescent overhead, warm city glow through the glass, "
+        "clinical blue-white palette, long shadows from desk furniture",
+        "air conditioning hum at low frequency, a distant elevator, "
+        "the silence of an empty building with one person in it"),
+
+    "🚗 Car — moving at night": (
+        "car interior at night, moving through a lit city, streetlights sweeping "
+        "through the windows in rhythmic pulses of amber and shadow, "
+        "dashboard instruments glowing warm from below, city blurred and wet outside, "
+        "the close interior smell of upholstery and warm electronics",
+        "rhythmic streetlight sweeps through the windows, "
+        "warm dashboard glow from below, moving pattern of light and shadow across interior surfaces",
+        "engine, tyres on wet road, city muffled by glass, "
+        "faint radio under everything"),
+
+    # ── ICONIC LOCATIONS ─────────────────────────────────────────────────
+    "🏰 Big Ben — Westminster at night": (
+        "standing directly beneath the Elizabeth Tower on the Westminster Bridge approach, "
+        "the illuminated clock face filling the upper frame, warm floodlit limestone glowing gold "
+        "against a deep navy sky, the Thames visible beyond the stone parapet, "
+        "black iron lampposts lining the bridge behind, black cabs and buses passing in soft blur",
+        "warm sodium floodlighting on the tower face, cold blue ambient sky, "
+        "wet stone reflecting gold below, the clock face its own light source",
+        "distant Big Ben chime on the quarter, Thames wind across the bridge, "
+        "traffic crossing behind, footsteps on stone"),
+
+    "🗽 Times Square — peak night": (
+        "standing at the centre of Times Square at 2am, surrounded by skyscrapers "
+        "sheathed in animated LED billboards — saturated reds, whites, yellows cascading down the canyon walls, "
+        "NASDAQ ticker scrolling, yellow cabs streaming through the intersection, "
+        "tourists in every direction, steam rising from road grates",
+        "total ambient saturation — no single source, light arriving simultaneously from every direction, "
+        "colour-shifting as the billboards cycle",
+        "traffic, crowd hum, distant busker, NYPD siren one block over, "
+        "the specific sound of a city that never quietens"),
+
+    "🗼 Eiffel Tower — sparkling midnight": (
+        "standing on the Champ de Mars facing the Eiffel Tower at midnight, "
+        "the hourly light show in full effect — 20,000 gold bulbs sparkling in random sequence "
+        "across the iron lattice, the Seine to the left catching the glow, "
+        "Parisian apartment blocks framing both sides, a few couples on the lawn nearby",
+        "gold sparkle wash from the tower varying every second, "
+        "deep blue ambient sky, distant street lamp orange at the park edges",
+        "city ambience, wind across the park, the faint metallic creak of the iron structure, "
+        "distant traffic on the quai"),
+
+    "🌉 Golden Gate Bridge — fog morning": (
+        "standing mid-span on the Golden Gate Bridge walkway, "
+        "thick morning fog rolling in from the Pacific and swallowing the south tower completely, "
+        "only the top third of the north tower visible above the fog line, "
+        "the bridge roadway disappearing into white in both directions, "
+        "the bay invisible below, cold salt air, the bridge's suspension cables vanishing into cloud",
+        "flat diffuse fog light — directionless, grey-white, no shadows, "
+        "every surface equally softened, the towers fading to silhouette then to nothing",
+        "wind through the cables producing a low resonant hum that changes pitch with gusts, "
+        "foghorn in the bay, distant muffled traffic"),
+
+    "🏯 Japanese shrine — early morning": (
+        "ancient Shinto shrine at first light, stone torii gate at the entrance "
+        "casting a long shadow down the gravel path, stone lanterns lining both sides, "
+        "cedar trees so tall the canopy closes overhead, "
+        "moss on every stone surface, a single paper lantern still lit from overnight at the main hall",
+        "cool blue pre-dawn light filtering through cedar, "
+        "warm paper lantern glow at the gate, raking first light beginning on the gravel",
+        "wind through cedar boughs, gravel shifting underfoot, "
+        "distant temple bell, water dripping from a stone basin"),
+
+    "🌆 Tokyo Shibuya crossing — night": (
+        "the Shibuya scramble crossing at night between signal changes, "
+        "hundreds of people streaming in every direction simultaneously, "
+        "Shibuya 109 building and its neon crown directly ahead, "
+        "rain-slicked asphalt reflecting every sign and screen in doubled colour, "
+        "7-Eleven and Starbucks glowing warm through steam",
+        "neon and LED saturation from every angle — amber, white, red, blue — no hard shadows, "
+        "everything doubled in the wet ground",
+        "crossing signal tone, crowd footsteps, idling cars, "
+        "distant J-pop from a store entrance, the specific density of Shibuya at night"),
+
+    "🌊 Amalfi Coast — cliff road": (
+        "narrow coastal road cut directly into the Amalfi cliff face, "
+        "turquoise Mediterranean far below catching direct sun and breaking white on the rocks, "
+        "no barrier on the seaward side of the road, "
+        "lemon groves terraced into the hillside above, "
+        "a white-painted village visible across the bay in the afternoon haze",
+        "Mediterranean full sun — hard, directional, high contrast, "
+        "deep shadows in the cliff cuts, warm gold on the road surface",
+        "sea wind, waves far below, a distant scooter engine, "
+        "cicadas in the lemon trees above"),
+
+    "🏖 Maldives — overwater bungalow at dusk": (
+        "wooden deck extending directly over the lagoon from an overwater bungalow, "
+        "water below so clear the sand and coral are visible in turquoise and white, "
+        "dusk turning the horizon to a band of orange fading through pink to violet, "
+        "the Indian Ocean completely flat, other bungalows in a line behind, "
+        "a rope ladder descending from the deck edge into the glowing water",
+        "last light warm orange from the horizon, cool violet sky above, "
+        "water reflecting both colours simultaneously",
+        "water lapping at the stilts below the deck in slow irregular rhythm, "
+        "a wind chime on the bungalow moving in the sea breeze, "
+        "a distant boat engine somewhere out on the lagoon, "
+        "the reef making its evening clicks and pops beneath the surface, "
+        "a fruit bat passing overhead, and underneath all of it the oceanic silence of open water at dusk"),
+
+    "🎪 Coachella — main stage sunset": (
+        "main Coachella stage at golden hour, the Indio desert stretching to the horizon behind the crowd, "
+        "mountains blue and distant in the haze, the stage framed by its giant LED screen "
+        "showing warm amber graphics matching the sunset, "
+        "tens of thousands on the flat desert floor, dust haze in the air, flags and totems swaying",
+        "golden hour desert sun from the west, warm amber fill from the stage screens, "
+        "everything amber-soaked and backlit",
+        "festival crowd roar, bass from the PA crossing the desert, "
+        "the dry desert wind, helicopter overhead"),
+
+    "🌃 Seoul Han River bridge — night": (
+        "walking the pedestrian lane of the Banpo Bridge at night, "
+        "Seoul's skyline reflected in the Han River below in a long shimmering stripe, "
+        "the Moonlight Rainbow Fountain arcing jets of lit water from the bridge rail, "
+        "apartment towers in every direction, Namsan Tower with its crown visible on the hill",
+        "bridge lighting warm white, fountain colour wash cycling, "
+        "Seoul skyline ambient glow on the water surface",
+        "water jets from the fountain, Han River wind, "
+        "distant city, a passing tour boat"),
+
+    "🏔 High-altitude snowfield": (
+        "open snowfield at high altitude, no trees, no shelter, "
+        "snow surface wind-sculpted into slow sastrugi waves, "
+        "a single ridge of darker rock breaking the white in the far distance, "
+        "sky a deep near-violet blue at this altitude, "
+        "breath visible in long plumes, footstep tracks the only mark on the surface",
+        "flat overcast bounce off the snow — sourceless, directionless white light, "
+        "everything equally lit, no shadows, the snow itself the only light source",
+        "wind — and nothing else — occasionally a snow grain skittering across the surface crust"),
+
+    "🚇 NYC subway platform — 3am": (
+        "empty New York City subway platform at 3am, "
+        "tiled walls in grimy institutional cream and brown, "
+        "fluorescent tubes overhead with one flickering on a slow cycle, "
+        "gum-stained concrete, yellow warning stripe at the platform edge, "
+        "a distant rumble building to a full roar as a train approaches and passes without stopping",
+        "flat fluorescent overhead, one tube flickering, "
+        "the train's headlight briefly sweeping the tunnel end",
+        "train rumble building and fading, platform PA echo, "
+        "a distant busker's note floating from the next platform"),
+
+    "🌅 Santorini caldera — dawn": (
+        "whitewashed terrace on the caldera rim in Santorini at first light, "
+        "the volcanic caldera dropping sheer below, the Aegean spread to the horizon in deep blue, "
+        "blue-domed churches clustered on the clifftop in the middle distance, "
+        "bougainvillea cascading over the terrace wall in magenta",
+        "first light pale gold on the white walls, deep blue sea and sky, "
+        "magenta flower accent, the white walls almost glowing",
+        "Aegean wind, a distant church bell, a boat engine somewhere far below"),
+
+    "🏟 Empty stadium — floodlit night": (
+        "standing alone on the pitch of a major football stadium at night, no crowd, "
+        "the four giant floodlight rigs pouring hard white light down onto the turf, "
+        "stands empty in darkness beyond the light line, "
+        "the pitch surface wet from sprinklers, scoreboard dark",
+        "four-point overhead flood — hard white industrial light, "
+        "deep shadow in the empty stands beyond the light boundary",
+        "floodlight hum at low constant frequency, wind across the open bowl, "
+        "a single flag snapping on the roof"),
+
+    "🎻 Vienna opera house — empty stage": (
+        "standing alone on the stage of the Vienna State Opera between performances, "
+        "grand proscenium arch overhead, six tiers of red velvet boxes receding into darkness, "
+        "a single work light — a bare bulb on a stand — the only source on stage, "
+        "the ghost light casting long shadows across the boards",
+        "single bare bulb ghost light — hard, warm, tungsten — "
+        "everything else in dense theatrical dark, the boxes invisible",
+        "the ghost light's single bulb humming faintly at low frequency, "
+        "the vast room holding its breath — the acoustic of 2000 empty velvet seats absorbing all reflection, "
+        "a board creaking once under shifting weight, "
+        "the heating system deep in the walls ticking, "
+        "and the profound specific silence of a concert hall built for music "
+        "when the music has stopped — a silence with shape and texture"),
+
+    "🌿 Amazon jungle interior": (
+        "deep Amazon rainforest interior with no sky visible, "
+        "canopy 40 metres overhead and fully closed, "
+        "light arriving only as occasional single shafts breaking through the layers, "
+        "forest floor a tangle of buttress roots and fern, "
+        "something moving in the mid-canopy unseen and continuous",
+        "green-filtered indirect light, permanent green shade, "
+        "occasional single shaft of direct sun breaking through, "
+        "everything in the same flat green ambient",
+        "constant insect layer at full volume — the Amazon roar — "
+        "bird calls cutting through, distant water, drip from leaves"),
+
+    "🧊 Ice hotel — Lapland": (
+        "interior of an ice hotel room in Lapland in deep winter, "
+        "walls, ceiling and furniture carved entirely from glacier ice, "
+        "sleeping reindeer skins draped over ice bed frames, "
+        "the walls faintly glowing blue-white from ice thickness, "
+        "breath visible in every shot, everything translucent",
+        "ambient blue-white glow through the ice walls — sourceless, cold, crystalline — "
+        "no artificial light, the ice itself luminous",
+        "near-total silence — only the creak of settling ice and breath, "
+        "occasionally the distant howl of wind outside"),
+
+    "🏬 Tokyo convenience store — 3am": (
+        "Lawson or 7-Eleven interior in Tokyo at 3am, completely deserted, "
+        "fluorescent lights at full brightness, every shelf perfectly faced and stocked, "
+        "hot foods rotating in their case by the register, "
+        "rain audible on the pavement outside, "
+        "the automatic door briefly opening to let in cold air and admit no one",
+        "flat harsh fluorescent overhead — clinical white, no shadows, "
+        "everything overlit in that specific convenience store way",
+        "refrigerator hum, hot case motor, rain outside, "
+        "the door's pneumatic hiss and seal"),
+
+    "🛕 Angkor Wat — golden hour": (
+        "standing at the western causeway of Angkor Wat at sunrise, "
+        "the five towers reflected in the rectangular moat below, "
+        "warm orange light catching every carved sandstone spire, "
+        "jungle visible above the outer walls in every direction, "
+        "lotus blossoms floating on the moat surface, a monk crossing in the distance",
+        "direct low sunrise orange from the east, long shadows down the causeway, "
+        "warm pink sky reflected in still water",
+        "jungle birds, water lapping the moat edge, distant chanting, "
+        "the complete stillness of early morning before tourists arrive"),
+
+    # ── HORROR ───────────────────────────────────────────────────────────
+    "🏚 Abandoned building — dark interior": (
+        "derelict interior — a former house or institution stripped back to bare structure, "
+        "plaster fallen from walls exposing dark brick, floorboards rotted through in patches, "
+        "a single doorway open to a deeper corridor beyond, debris underfoot, "
+        "curtains torn and hanging at a broken window, rust stains tracking down every wall",
+        "single motivated light source only — a torch beam, a crack of moonlight through a board, "
+        "a bare bulb on a frayed wire just barely working — everything beyond its reach is near-black",
+        "structural settling sounds — a distant creak, something dripping, wind through a gap, "
+        "the specific silence of a space that hasn't had a person in it for years — then it does"),
+
+    "🏥 Hospital corridor — fluorescent night": (
+        "long hospital corridor at night, linoleum floor with a worn central track, "
+        "institutional cream walls with a dado rail at waist height, "
+        "a row of numbered doors receding in both directions, one door ajar at the far end, "
+        "an overturned wheelchair near the nurse's station, a clipboard on the floor",
+        "overhead fluorescent strip lights — two out, one flickering at irregular intervals, "
+        "the working ones casting cold blue-white, long green-tinged shadows on the floor",
+        "the flicker hum of the failing fluorescent, distant HVAC, a door somewhere closing softly, "
+        "the squeak of something on the linoleum floor at the far end of the corridor"),
+
+    "🌲 Haunted woods — dead of night": (
+        "dense forest at night, canopy completely blocking the sky, "
+        "bare or near-bare trees with high branches interlocked overhead, "
+        "root-broken ground underfoot, a faint path barely distinguishable from the surrounding forest floor, "
+        "mist at knee height in a clearing visible through the trees ahead, "
+        "a structure — the suggestion of one — barely visible in the dark beyond the clearing",
+        "no ambient light — torch beam only, or moonlight arriving at odd angles through gaps in the canopy, "
+        "blue-black shadow everywhere the light doesn't reach, mist catching and holding any beam",
+        "wind in the upper canopy — audible but not felt at ground level — "
+        "an owl somewhere, a branch snapping under weight in a direction the camera hasn't looked yet"),
+
+    # ── SPAGHETTI WESTERN ────────────────────────────────────────────────
+    "🏜 Ghost town — high noon standoff": (
+        "abandoned western town, main street wide enough for a wagon, "
+        "false-front wooden buildings on both sides — general store, saloon, sheriff's office — "
+        "all long-abandoned, paint peeling, a tumbleweed lodged against a hitching post, "
+        "dust rising from the street in a slow gust, shutters banging on a broken hinge, "
+        "a single figure at each end of the street, heat shimmer between them",
+        "brutal noon sun directly overhead — no shadow, no relief, every surface bleached near-white, "
+        "sky a deep saturated blue with no cloud, the sun itself the only light source",
+        "wind — nothing else — then the wind stops — then silence so deep the heartbeat is audible — "
+        "a shutter bangs once — and then nothing again"),
+
+    "🌵 Open desert — late afternoon heat": (
+        "flat desert extending to a dead horizon in every direction, "
+        "cracked salt flats closer, red dust further out, a distant mesa barely distinguishable from sky, "
+        "a single dead tree on the left edge of frame, a buzzard circling high, "
+        "heat shimmer turning the horizon liquid, no road, no structure, no shade anywhere",
+        "late afternoon sun at 20° — long amber shadows stretching hard to the left, "
+        "warm orange-red on every surface, deep purple shadow in any depression, "
+        "sky transitioning from pale blue at zenith to deep amber at the horizon",
+        "wind carrying fine dust, a distant hawk, the creak of the dead tree, "
+        "and total silence underneath everything — the silence of a landscape indifferent to people"),
+
+    "🍺 Frontier saloon — dusk interior": (
+        "interior of a frontier-era saloon, long bar of bare wood on the left, "
+        "a mirror behind it age-spotted and dark, bottles in uneven rows, "
+        "six or seven tables with mismatched chairs, sawdust on the floor, "
+        "a piano in the far corner, a staircase to rooms above, "
+        "wanted posters on the wall beside the door, dust motes in the late light",
+        "late sun through two windows — long amber shafts cutting through dust, "
+        "oil lamp practicals on the bar already lit against the coming dark, "
+        "deep shadow in the corners and beneath the staircase",
+        "an upright piano in the corner playing a ragtime waltz — slightly out of tune on the high strings, "
+        "the pianist visible only as a silhouette — someone drinking alone at the bar, "
+        "a chair scraping on floorboards, spurs on the wooden floor as someone stands, "
+        "a glass set down hard on the bar top, the staircase creaking under descending weight, "
+        "and underneath it all the wind outside finding every gap in the timber walls"),
+
+    # ── DREAMCORE / LIMINAL ──────────────────────────────────────────────
+    "🛒 Empty shopping mall — fluorescent liminal": (
+        "large shopping mall completely empty of people, long corridors of shuttered storefronts "
+        "stretching in both directions, the shutters all down and locked, "
+        "a few abandoned planters with dead or fake plants, "
+        "a central atrium with a dry fountain, escalators running with no one on them, "
+        "the carpet slightly different patterns at each junction suggesting years of piecemeal replacement",
+        "overhead fluorescent grid — full brightness, slightly blue-white, no shadows anywhere, "
+        "the specific flat even light of a space designed for commerce that no longer happens",
+        "the escalators' constant mechanical hum, the HVAC cycling, "
+        "a distant jingle from a speaker playing to no one, "
+        "footsteps that shouldn't be there echoing from somewhere further in"),
+
+    "🏫 School corridor — after hours": (
+        "secondary school corridor at night, lockers running the full length of both walls, "
+        "some hanging open, one with a torn photo still attached to the inside door, "
+        "classroom doors with small rectangular windows, the rooms dark beyond them, "
+        "emergency exit sign at the far end the only non-fluorescent light source, "
+        "a forgotten backpack on the floor, a classroom door ajar showing empty desks",
+        "overhead fluorescent at half — the end nearest the exit sign off, "
+        "creating a gradient from lit to near-dark toward the emergency exit's green cast",
+        "the fluorescent buzz, a locker door swinging slightly in a draught, "
+        "the distant sound of something institutional — a boiler, a clock — "
+        "and the specific silence of a building built for noise now completely empty"),
+
+    "🟨 Backrooms — endless yellow corridors": (
+        "an infinite office-like corridor of consistent beige-yellow walls and carpet, "
+        "no windows, no doors visible, the corridor turning at irregular intervals, "
+        "the same carpet pattern repeating indefinitely, "
+        "fluorescent panels in the dropped ceiling, some working some not, "
+        "a faint wet-carpet smell implied by the visual texture of the aging floor covering, "
+        "the horizon of each corridor always the same distance away regardless of movement",
+        "flat fluorescent from the ceiling panels — no shadows, no depth cues, "
+        "the light slightly yellow-green from the aging panels, uniformly too bright",
+        "a low persistent hum from the lighting and from something deeper in the structure, "
+        "no echo — the space absorbs sound — "
+        "and the sound of footsteps that are yours and also slightly delayed"),
+
+    # ── ACTION / BLOCKBUSTER ─────────────────────────────────────────────
+    "🏙 Rooftop chase — night city": (
+        "rooftop of a city building at night, air conditioning units and water tanks "
+        "creating obstacles across the flat roof, gravel underfoot, "
+        "the edge with its low parapet visible ahead, the city sprawling below and beyond, "
+        "the roof of the next building slightly lower and a gap between them, "
+        "wet from recent rain, puddles on the flat membrane roof catching city glow",
+        "city ambient glow from every direction as orange fill, "
+        "cool blue from the night sky above, practical rooftop lights on the equipment, "
+        "the edge of the roof backlit by the city below it",
+        "city noise rising from below, wind at height, footsteps on gravel carrying clearly, "
+        "a helicopter somewhere — its searchlight sweeping — "
+        "and the impact sounds of bodies on metal and concrete"),
+
+    "🏭 Industrial warehouse — emergency lighting": (
+        "large industrial warehouse interior, steel-frame structure with a high corrugated ceiling, "
+        "abandoned equipment and crated goods on wooden pallets creating a maze of cover, "
+        "concrete floor with oil stains and painted navigation lines, "
+        "a mezzanine level accessible by metal stairs on the far side, "
+        "tall narrow windows at ceiling height letting in fractured moonlight",
+        "standard lighting failed — emergency strips only at floor level in red, "
+        "moonlight through the ceiling windows in diagonal shafts through dust, "
+        "torchlight as a moving motivated source, deep shadow between every structure",
+        "the metal structure ticking as it cools, "
+        "every footstep echoing in the high ceiling space, "
+        "something mechanical still running somewhere — a pump, a conveyor — and then stopping"),
+
+    "🛣 Rain-soaked highway — car chase": (
+        "a six-lane highway at night, rain heavy enough to reduce visibility to 50 metres, "
+        "headlights of other vehicles forming blurred streaks in the wet, "
+        "the road surface a sheet of reflected white and amber, "
+        "crash barriers on both sides, an overpass ahead, "
+        "the subject vehicle threading between slower traffic at high speed",
+        "headlight white from every direction reflected in the wet asphalt, "
+        "amber sodium from the highway gantries above, "
+        "police or pursuit lighting in blue-red in the rear-view mirror",
+        "tyre roar on wet tarmac at speed, rain on the roof and windscreen, "
+        "the engine at high revs, the blast of air as a vehicle is overtaken, "
+        "a distant siren growing closer"),
+
+    # ── COOKING SHOW ─────────────────────────────────────────────────────
+    "👨‍🍳 Professional kitchen — service": (
+        "commercial kitchen at full service, stainless steel surfaces everywhere, "
+        "six burner ranges with active flames, a pass at the far end where plates are assembled, "
+        "the section system visible — hot section, cold section, pastry at the back, "
+        "multiple cooks in whites moving with practised urgency, "
+        "steam rising from multiple pans, heat visible as shimmer above the ranges, "
+        "orders called from the pass, the specific controlled chaos of a kitchen at capacity",
+        "overhead fluorescent on stainless — hard, bright, clinical, no shadows — "
+        "the flames from the burners providing warm orange counter-light from below, "
+        "the pass lit separately in clean white for plating",
+        "the roar of extractor fans overhead, burner flames under pans, "
+        "the call-and-response of the pass, metal on metal, the hiss of liquid hitting a hot pan"),
+
+    "🍳 Home kitchen — morning light": (
+        "domestic kitchen in morning light, an island counter in the centre, "
+        "a window above the sink showing a garden or street outside, "
+        "used chopping board, a few ingredients out on the counter, "
+        "a pan on the hob with a tea towel draped nearby, "
+        "the specific lived-in quality of a kitchen used every day",
+        "natural morning light through the window — soft, directional, warm white — "
+        "the window as the key source, shadows soft to the left of everything, "
+        "under-cabinet lighting on if it's still early, adding warm fill to the counter",
+        "the hob ticking as it heats, the extractor fan at low, "
+        "a radio somewhere in the house, the knife on the board, "
+        "water coming to the boil"),
+
+    # ── WES ANDERSON ─────────────────────────────────────────────────────
+    "🏨 Grand hotel lobby — Wes Anderson": (
+        "a grand hotel lobby of the early-to-mid 20th century, perfectly symmetrical from the camera's position, "
+        "a long reception desk centred at the far end, two matching staircases curving up on either side, "
+        "a chandelier centred in the ceiling, patterned carpet in a geometric repeat, "
+        "a bellboy standing perfectly still at the left, an identical one at the right, "
+        "framed portraits evenly spaced on the walls, a revolving door centred in the entrance behind camera",
+        "warm amber from the chandelier and wall sconces — even, sourceless-feeling, "
+        "the light itself part of the symmetry — no shadow falls asymmetrically",
+        "a grandfather clock ticking in precise four-four time, the revolving door cycling at the entrance "
+        "with its exact pneumatic sweep and click, a telephone on the front desk ringing twice and stopping, "
+        "a bellboy's trolley wheels on marble in perfect straight lines, "
+        "someone at the piano in the adjacent salon playing something from 1932 in a major key, "
+        "the specific hush of a lobby where every sound is permitted but nothing is loud"),
+
+    "🏘 Pastel townhouse street — afternoon": (
+        "a street of terraced townhouses each a different pastel colour — "
+        "pale yellow, dusty rose, sage green, powder blue — in a repeating sequence, "
+        "perfectly maintained window boxes with matching flowers, "
+        "a pavement of identical grey cobbles, "
+        "a bicycle of a matching pastel colour leaning against a door on the left, "
+        "a letter box, a brass knocker, and a doormat all perfectly centred on each door",
+        "flat overcast afternoon — no directional shadow, the pastels fully saturated and even, "
+        "the colour of each house reading cleanly against the white of the sky",
+        "a bicycle bell ringing once at exactly the right moment, a distant tram on its fixed route, "
+        "a window opening on the second floor of the sage-green house — precisely — and closing again, "
+        "someone practising scales on a woodwind instrument somewhere behind a wall, "
+        "the sound of a letterbox closing, footsteps on cobble in a specific rhythm, "
+        "and then complete symmetric silence"),
+
+    # ── K-DRAMA ───────────────────────────────────────────────────────────
+    "🌆 Seoul rooftop — dusk golden hour": (
+        "rooftop of a Seoul apartment building at dusk, "
+        "laundry lines with clothes barely visible in the fading light, "
+        "water tanks and ventilation boxes, a small garden of potted plants in one corner, "
+        "the city below spreading to every horizon, apartment towers lit in warm evening windows, "
+        "the Han River a faint dark band in the mid-distance, "
+        "two folding chairs and a small table — recently used",
+        "dusk: the last directional light gone, sky a gradient of deep rose to cool indigo at the zenith, "
+        "the city's warm amber rising from below like a second horizon, "
+        "a street lamp on the access staircase providing the only warm key light",
+        "city hum from below, wind at rooftop height carrying K-indie or lo-fi from an open window several floors down, "
+        "a distant siren absorbed into traffic, the creak of a laundry line wire, "
+        "the specific rooftop silence that sits just above the city's noise floor — "
+        "present enough to feel alone, close enough to feel held"),
+
+    "🌸 Cherry blossom park — midday": (
+        "a park with cherry blossom trees in full bloom, "
+        "petals continuously falling in the light wind, "
+        "a stone path through the trees, wooden benches at intervals, "
+        "other people visible in soft focus at the edges — couples, families — "
+        "the blossom so dense it forms a soft ceiling overhead, "
+        "petals accumulating in drifts against the kerb of the path",
+        "filtered overhead light through the blossom canopy — soft pink-white, directionless, "
+        "everything in the scene faintly lit from above through the petals, "
+        "no hard shadows, skin luminous in the diffused light",
+        "wind through the blossom — a collective soft rustle — "
+        "petals landing on surfaces with barely any sound, "
+        "distant park sounds softened by the canopy, someone laughing"),
+
+    "🛋 Modern Seoul apartment — evening": (
+        "interior of a modern Seoul apartment, open-plan living and kitchen area, "
+        "floor-to-ceiling glass on one wall showing the Seoul skyline at evening, "
+        "minimal furniture — a sofa, a low table, a kitchen island in white and grey — "
+        "everything clean and considered, a single personal object on the table "
+        "suggesting the room is lived in, a glass of water recently placed",
+        "evening: the skyline outside providing ambient warm orange glow through the glass, "
+        "interior lighting warm and low — a single floor lamp, no overhead lights, "
+        "the glass wall doubling every interior light source in its reflection",
+        "the city muffled by the glass — a distant siren, traffic below — "
+        "the HVAC at low, the specific silence of a well-insulated modern apartment, "
+        "and whatever the scene between the people in it generates"),
+
+    # ── NIGHTLIFE / ADULT VENUES ─────────────────────────────────────────
+    "💃 Strip club — main floor": (
+        "strip club interior at full operation, a raised centre stage with a brass pole "
+        "catching coloured light, mirrored wall behind the stage doubling everything, "
+        "leather booths arranged in a horseshoe around the stage, VIP rope section off to one side, "
+        "a long bar with backlit shelves of bottles along the far wall, "
+        "scattered tables between stage and bar, each with a small candle flickering in red glass, "
+        "smoke machine haze hanging at waist height, a DJ booth tucked in the corner",
+        "stage wash cycling slow between magenta, violet, and warm amber — hard spots on the pole, "
+        "UV strips along the stage edge making white fabric glow, "
+        "deep shadow in the booths beyond the stage light spill, "
+        "the mirrored wall creating infinite depth behind the performer",
+        "bass-heavy RnB or trap at medium volume, ice in glasses, "
+        "low conversation from the booths, heels on the stage surface, "
+        "the specific sound of a room designed to keep you looking at the centre"),
+
+    "🔒 Private booth — POV": (
+        "POV from a man seated in a strip club private booth, "
+        "camera locked at seated eye height looking slightly upward, "
+        "black leather seat visible at the lower edge of frame, "
+        "a curtain of dark velvet or beaded strands half-drawn behind the performer, "
+        "the booth is small — the performer fills the frame at arm's length, "
+        "a low table to one side with a drink, the main club visible only as blurred colour and movement "
+        "through the curtain gap, a small wall-mounted speaker, dim recessed light overhead",
+        "single overhead recessed downlight — warm amber, tight pool, directly above the performance space, "
+        "everything outside the light pool near-black, "
+        "the performer lit from above with strong shadow below the chin and cheekbones, "
+        "occasional colour bleed — magenta, blue — leaking through the curtain from the main floor",
+        "bass from the main floor muffled through the curtain, "
+        "the booth speaker playing its own quieter track, breathing audible at this proximity, "
+        "fabric shifting, the creak of leather seating, ice settling in the glass"),
+
+    # ── BEACHES / OUTDOOR SOCIAL ─────────────────────────────────────────
+    "🌴 LA beach — Venice / Santa Monica": (
+        "Venice Beach boardwalk spilling onto wide flat sand in late afternoon golden hour, "
+        "the Pacific glinting hard silver-gold to the horizon, palm trees in a line along the boardwalk, "
+        "skaters and cyclists in soft-focus background on the bike path, "
+        "muscle beach gym frames visible further down, graffiti walls and vendor stalls along the walk, "
+        "lifeguard tower in classic white and red, crowds scattered across the sand — towels, coolers, "
+        "someone playing volleyball, the Santa Monica pier and its ferris wheel visible in the distant haze",
+        "golden hour California sun — warm, low, directional from the west over the ocean, "
+        "long shadows stretching inland, everything backlit and rim-lit, "
+        "skin glowing warm, sunglasses catching flare, the specific amber-pink LA light",
+        "waves on the shore in steady rhythm, crowd noise from the boardwalk, "
+        "a boombox somewhere playing hip-hop, skate wheels on concrete, "
+        "seagulls, distant laughter, the Venice Beach energy that never fully quiets down"),
+
+    "🍹 Ibiza pool party — golden hour": (
+        "infinity pool at a cliff-edge villa in Ibiza at golden hour, "
+        "the Mediterranean spread below in deep blue, white-washed walls and terracotta tiles, "
+        "the pool overflowing its edge into the view, DJ setup under a white canopy, "
+        "people in the water and on daybeds around the pool, champagne in ice buckets, "
+        "string lights not yet lit waiting for dusk, smoke from a grill drifting across",
+        "direct golden hour sun from the west — hard, warm, every water droplet catching it, "
+        "skin glistening, pool surface a sheet of shifting gold, "
+        "white surfaces bouncing light everywhere as natural fill",
+        "deep house from the DJ at medium volume, water splashing, laughter, "
+        "glasses clinking, the wind off the Mediterranean, "
+        "the specific sound of an afternoon that knows it's about to become a night"),
+
+    "🏄 Bondi Beach — bright midday": (
+        "Bondi Beach at midday from the promenade level looking down the crescent of sand, "
+        "the ocean a vivid turquoise with white breakers rolling in regular sets, "
+        "hundreds of people on the sand, surfers in the water, the iconic red and yellow lifeguard flags, "
+        "the sandstone headland at each end of the crescent, Norfolk pines along the promenade, "
+        "the Icebergs pool visible cut into the rocks at the south end",
+        "harsh Australian midday sun — overhead, no shadow relief, high UV, "
+        "bleached sand near-white, ocean almost too bright to look at, "
+        "everything saturated and high-contrast, sunscreen-sheen on skin",
+        "surf crash in steady sets, crowd buzz, lifeguard whistle, "
+        "someone's portable speaker, seagulls fighting over chips, "
+        "the specific roar of a packed beach at the height of summer"),
+
+    # ── MOODY / CINEMATIC INTERIORS ──────────────────────────────────────
+    "🕯 Candlelit loft — exposed brick": (
+        "open loft apartment with exposed brick walls and timber ceiling beams, "
+        "the only light from clusters of pillar candles — on the floor, on shelves, on a low table, "
+        "thirty or forty flames creating overlapping pools of warm amber, "
+        "a large bed with dark linen visible in the back half of the space, "
+        "a freestanding cast-iron bathtub near the windows, "
+        "tall industrial windows showing the city at night but curtained with sheer fabric",
+        "candlelight only — warm amber from multiple low sources, "
+        "flames creating soft moving shadows on the brick, "
+        "the candles reflected in the dark window glass, deep shadow above the beam line",
+        "candle flames guttering in a draught, distant city through the glass, "
+        "the creak of old timber, fabric shifting, "
+        "the specific intimate quiet of a room lit only by fire"),
+
+    "🚿 Rain shower — glass-walled bathroom": (
+        "large walk-in rain shower with floor-to-ceiling glass walls on two sides, "
+        "a single oversized showerhead directly overhead raining straight down, "
+        "steam filling the upper half of the glass enclosure, "
+        "water streaming in sheets down the glass, "
+        "dark slate tile floor and walls, recessed warm LED strip at floor level, "
+        "a bench built into the back wall, the bathroom beyond the glass visible but soft through steam",
+        "recessed warm LED strip at floor level casting upward through the steam and water, "
+        "overhead downlight diffused through the rain and mist, "
+        "everything soft-edged and glowing, skin wet and catching every light source",
+        "rain shower hiss from directly overhead — enveloping, constant, "
+        "water hitting slate, steam, breathing amplified by the glass enclosure, "
+        "the specific acoustics of a tiled glass box"),
+
+    "🪩 Hotel rooftop bar — city night": (
+        "rooftop bar on a high-end hotel, the city skyline as the backdrop on three sides, "
+        "the bar itself a long backlit slab of marble or onyx, cocktails in progress, "
+        "low seating clusters — velvet and brass — arranged around fire pit tables, "
+        "a small pool or water feature reflecting the city lights, "
+        "well-dressed people at the edges, a DJ playing from a minimal booth, "
+        "string lights and pendant fixtures overhead creating warm islands of light",
+        "warm practical lighting from the bar, fire pits, and string lights, "
+        "city skyline ambient glow as backdrop, "
+        "the sky a deep dark blue with the city preventing true black",
+        "cocktail bar sounds — shaker, ice, glass on marble, low conversation, "
+        "deep house at low volume from the DJ, wind at height, "
+        "the city far below as a continuous ambient hum"),
+
+    # ── TRANSPORT / MOTION ───────────────────────────────────────────────
+    "🛥 Yacht deck — open ocean sunset": (
+        "aft deck of a motor yacht at sunset, teak deck underfoot, "
+        "the wake stretching back white and straight to the horizon, "
+        "open ocean in every direction — deep blue turning to copper near the sun, "
+        "the stern rail and a pair of chaise lounges, champagne in a bucket lashed to the rail, "
+        "the upper flybridge visible above casting a shadow across the back half of the deck, "
+        "sea spray occasionally reaching the lower deck",
+        "direct sunset from the stern — warm copper-gold, hard rim light on everything facing aft, "
+        "deep blue shadow on the forward side, the wake itself catching the light, "
+        "skin lit warm from behind, face in soft reflected ocean fill",
+        "engine vibration through the deck, wind, the hull cutting water, "
+        "wake turbulence behind, a halyard clinking somewhere, "
+        "the deep isolation of being the only thing on the ocean"),
+
+    "🏎 Supercar interior — night drive": (
+        "interior of a low-slung supercar at night — Lamborghini, McLaren, or similar — "
+        "the cockpit tight and low, carbon fibre dash and centre console, "
+        "the instrument cluster glowing warm amber behind the flat-bottom steering wheel, "
+        "city lights streaking past through the low windshield, "
+        "LED ambient strips along the door sills in cool blue, the seats deep bucket-shaped, "
+        "the road surface visible through the windshield blurred with speed",
+        "instrument cluster glow from below — warm amber, "
+        "LED ambient strips in cool blue along the sills, "
+        "city light streaking through the glass in rhythmic pulses, "
+        "the driver's face lit from below and from the passing city",
+        "engine note — a specific high-RPM mechanical scream behind and below the seats, "
+        "tyres on asphalt, wind noise at speed, "
+        "the turbo spool between shifts, city sound entering and leaving in doppler pulses"),
+
+    # ── RAW / GRITTY ─────────────────────────────────────────────────────
+    "🏨 Cheap motel room — neon through blinds": (
+        "single-room motel interior at night, a queen bed with a thin patterned bedspread, "
+        "wood-veneer furniture, a CRT TV on the dresser, venetian blinds at the window "
+        "casting horizontal neon stripes — red and blue — across the bed and opposite wall, "
+        "the bathroom door ajar showing harsh fluorescent inside, "
+        "a bag on the floor, car headlights occasionally sweeping across the ceiling",
+        "neon from outside through the blinds — alternating red and blue in horizontal bands, "
+        "harsh bathroom fluorescent spilling through the cracked door as a single cold stripe, "
+        "headlight sweeps across the ceiling at irregular intervals, "
+        "the room itself has no light on — everything lit from outside or the bathroom",
+        "the neon sign buzzing outside the window, ice machine humming through the wall, "
+        "distant traffic on the highway, a door slamming somewhere in the building, "
+        "the specific acoustic of thin walls and a parking lot outside"),
+
+    "🏗 Industrial warehouse — night": (
+        "cavernous warehouse interior at night, concrete floor cracked and oil-stained, "
+        "steel columns running in a grid to the far wall, high corrugated roof lost in shadow, "
+        "a few industrial pendant lights still working casting hard pools on the floor, "
+        "loading dock doors along one wall — one rolled halfway up showing the dark yard outside, "
+        "a car parked inside with its headlights on cutting two beams through the dust",
+        "hard pools of light from the industrial pendants — warm sodium orange, "
+        "car headlights cutting white beams through floating dust, "
+        "deep black shadow between the light pools, the roof invisible",
+        "echo — everything echoes in here, footsteps, voices, the drip from a pipe, "
+        "a distant generator running, wind through the half-open loading dock, "
+        "the specific reverb of a concrete box fifty metres long"),
+
+    # ── RURAL / EQUESTRIAN ───────────────────────────────────────────────
+    "🐴 Horse stable — warm afternoon": (
+        "centre aisle of a large horse stable, stalls lining both sides with wooden half-doors, "
+        "horses visible in several stalls — heads over the doors, ears forward, watching, "
+        "the aisle floor compacted earth and straw, hay bales stacked against the far wall, "
+        "tack and bridles hanging from iron hooks between stalls, "
+        "afternoon light streaming through the open barn doors at the far end "
+        "in long golden shafts full of floating dust and hay particles, "
+        "the timber roof beams high overhead with swallows nesting in the crossbeams",
+        "warm directional afternoon sun from the open barn doors — long golden shafts cutting the aisle, "
+        "the stalls in warm shadow, straw on the floor catching the light, "
+        "dust motes and hay particles suspended in every beam of light, "
+        "deep amber warmth throughout, cool shadow in the stalls themselves",
+        "horses shifting weight in their stalls — hooves on straw, a snort, "
+        "a tail swishing against wood, the creak of a stall door, "
+        "swallows above, distant meadow sounds from outside the barn, "
+        "the deep quiet underneath everything that says countryside"),
+
+    "🐴 Horse stable — night lantern": (
+        "horse stable at night, the aisle lit by a single hanging lantern "
+        "swaying gently from a roof beam, casting moving amber light and shadow, "
+        "stalls on both sides — horses dozing, one head visible over a door, "
+        "straw deep on the aisle floor, a saddle resting on a stand by the far wall, "
+        "the barn doors closed against the dark, a gap at the top showing stars, "
+        "a wool blanket folded on a hay bale, the smell of horse and leather implied by every surface",
+        "single hanging lantern — warm amber, swaying, casting moving shadows "
+        "that shift across the stall doors and the roof beams, "
+        "everything beyond the lantern's reach in deep warm darkness, "
+        "the horses' eyes catching the light from inside their stalls",
+        "a horse breathing slow and heavy in the nearest stall, straw rustling, "
+        "the lantern chain creaking with its sway, a horse stamping once, "
+        "wind outside the closed doors, an owl somewhere beyond the barn, "
+        "the complete rural silence that makes every small sound distinct"),
+
+    "🌾 Barn interior — hay loft": (
+        "upper hay loft of a large timber barn, the floor thick with loose hay and straw, "
+        "a loft door open to the countryside showing fields stretching to the horizon, "
+        "the roof beams close overhead — rough-hewn timber, iron bolts, cobwebs, "
+        "bales stacked against the back wall, a pitchfork leaning in the corner, "
+        "the loft edge with a wooden rail looking down to the barn floor below, "
+        "golden late-afternoon light flooding through the open loft door",
+        "golden hour sun pouring through the open loft door — directional, warm, "
+        "every piece of hay in the air backlit and glowing, "
+        "the light hitting the loose straw on the floor and turning it to gold, "
+        "deep shadow against the back wall behind the bales",
+        "wind through the open loft door, hay shifting, "
+        "birds in the rafters, distant farm sounds — a tractor, a dog, "
+        "the creak of the old timber structure, the countryside beyond the door"),
+
+    "🏡 Farmhouse kitchen — early morning": (
+        "large farmhouse kitchen at dawn, an Aga or wood-burning range against one wall "
+        "radiating warmth, a scrubbed pine table in the centre with mismatched chairs, "
+        "a window over the sink showing fields in early mist, "
+        "copper pans hanging from a ceiling rack, a stone floor with a woven rug, "
+        "a collie asleep in a basket by the range, a mug of tea steaming on the table",
+        "cold blue dawn light through the window mixing with warm orange from the range, "
+        "the two colour temperatures meeting in the middle of the kitchen, "
+        "her face lit warm from one side and cool from the other",
+        "the range ticking as it heats, a clock on the wall, "
+        "birdsong building outside, the dog breathing in its basket, "
+        "a kettle not yet boiling, the specific deep quiet of a farmhouse before the day starts"),
+
+    # ── EXPERIMENTAL — ULTRA DETAIL ──────────────────────────────────────
+    "🚀 [EXPERIMENTAL] Rocket launch pad — close range countdown": (
+        "launch pad complex at T-minus 5 seconds, the rocket a 70-metre column of white-painted steel "
+        "and composite panels rising directly in front of the camera at a distance of 300 metres, "
+        "close enough that the full rocket does not fit in frame — "
+        "the camera is angled upward capturing the lower third of the vehicle: "
+        "the engine cluster, the launch mount arms still clamped at the base, "
+        "the flame trench below filled with the water suppression system in full flow — "
+        "a white curtain of steam already billowing upward and outward from the base, "
+        "the rocket body showing condensation streaks from the cryogenic propellant "
+        "running down the pale exterior in irregular rivulets, "
+        "launch mount service arms still attached at multiple levels — "
+        "each arm a steel structure 3 metres wide with utility connections and umbilical feeds, "
+        "the hold-down bolts visible at the base still engaged, "
+        "at T-3 the engine ignition sequence begins — "
+        "a pale blue-white flame appears at the base of the engine cluster, "
+        "not yet at full thrust, building in a rapid sequence visible as a brightening bloom "
+        "that lights the steam cloud from within, "
+        "at T-0 the hold-down bolts release and the full engine thrust registers — "
+        "the steam cloud erupts outward in every direction from the flame trench, "
+        "the rocket lifts — slowly at first, the enormous mass requiring two full seconds "
+        "to clear the launch tower, the service arms swinging away, "
+        "the engine exhaust plume expanding below as the vehicle accelerates, "
+        "the ground shaking visible as camera vibration, "
+        "debris — small gravel, dust, paper — lifting off the ground around the camera position, "
+        "the sky above the rocket clearing to a deep blue as the vehicle climbs",
+        "pre-launch: harsh white xenon floodlights from the launch tower illuminating the rocket "
+        "in cold clinical light against a pre-dawn dark blue sky, "
+        "T-0: the engine ignition creating its own light source — "
+        "a blue-white core at 3500 degrees transitioning to orange at the plume edges, "
+        "the entire scene converting from flood-lit industrial to fire-lit in under one second, "
+        "the steam cloud lit orange from within as the plume expands through it, "
+        "everything above the plume still in pre-dawn blue while everything below is orange-white fire",
+        "the countdown from an unseen speaker — each number distinct and flat, "
+        "the water suppression system as continuous white noise building in volume, "
+        "at ignition a sound that arrives as physical pressure before it arrives as audio — "
+        "a crackling roar that builds from a distant rumble to a full chest-compression event "
+        "in under three seconds, the ground vibration arriving through whatever surface the camera contacts, "
+        "the steam cloud hissing, the hold-down release as a mechanical crack lost in the engine noise, "
+        "and after the vehicle clears the tower the sound continuing to build "
+        "as the full plume establishes and the rocket accelerates away"),
+
+    "🚕 [EXPERIMENTAL] Fake taxi — parked, discrete location": (
+        "interior of a taxi cab parked in a quiet layby or side street, engine off, "
+        "the vehicle a standard four-door sedan with a taxi livery — "
+        "yellow or black depending on city, a roof sign unlit since the meter is off, "
+        "back seat wide enough for two with worn dark fabric upholstery, "
+        "a cigarette burn on the left armrest, a pine air freshener hanging from the rear-view mirror, "
+        "a partition of scratched perspex between front and back seat "
+        "with a small sliding cash window currently open, "
+        "the driver turned around in the front seat with one arm over the headrest, "
+        "facing the back, "
+        "the vehicle is stationary — no road movement, no engine vibration, "
+        "parked somewhere deliberately quiet: a darkened layby off a main road, "
+        "a side street behind commercial buildings, a car park with one working light at the far end, "
+        "the windows fogging slightly from body heat inside the sealed car, "
+        "occasional headlights from passing traffic sweeping through the rear window "
+        "and crossing the interior before disappearing, "
+        "the city or countryside outside muffled and indifferent through the closed doors, "
+        "the back seat functionally private — no pedestrians, no other vehicles stopped nearby, "
+        "the taxi meter display dark on the dashboard, "
+        "a dashcam mounted on the windscreen, its small red recording light visible in the mirror",
+        "ambient light from outside the parked vehicle — "
+        "a distant streetlamp providing a low amber fill through the rear and side windows, "
+        "headlights from passing traffic creating sweeping white flares at irregular intervals "
+        "that fully illuminate the interior for half a second before returning to dim amber, "
+        "the dashcam LED a small constant red point in the upper frame, "
+        "the partition perspex catching light and creating a faint reflection of the back seat "
+        "visible to the driver — and to the camera",
+        "near silence of a parked vehicle in a quiet location — "
+        "the engine cooling with irregular metallic ticks, "
+        "distant road traffic as a low continuous presence that rises when a vehicle passes close "
+        "and fades back to nothing, "
+        "the slight creak of the suspension under shifting weight, "
+        "fabric moving against leather and fabric, "
+        "the pine freshener swinging against the mirror on any movement, "
+        "and the specific sealed acoustic of a car interior "
+        "where every sound is close and contained"),
+
+    "🚁 [EXPERIMENTAL] Flying car interior — neon megalopolis night": (
+        "interior of a luxury flying car cockpit suspended 800 metres above a sprawling megalopolis at 2am, "
+        "the canopy glass a seamless wraparound bubble giving unobstructed 270-degree views of the city below, "
+        "every direction filled with other flying vehicles at different altitudes — delivery drones in tight formation lanes, "
+        "heavy freight barges with blinking amber warning lights drifting slowly through the mid-tier, "
+        "sleek personal vehicles weaving the upper express corridors in streaks of white and red light, "
+        "the city surface 800 metres below is a continuous carpet of neon — magenta, cyan, gold, white — "
+        "interrupted by the dark canyons between tower blocks that plunge into unlit depths, "
+        "holographic advertising pillars rising from rooftops project rotating brand logos into the low cloud layer, "
+        "rain is hitting the canopy glass constantly, each droplet refracting the city below into smeared colour streaks "
+        "that run sideways as the vehicle banks, the interior of the cockpit is tight and deliberately minimal — "
+        "a curved dashboard of brushed obsidian inlaid with haptic control surfaces glowing in soft amber and blue, "
+        "the pilot seat in worn dark leather with silver stitching, a cracked personal screen mounted centre showing "
+        "navigation overlays and atmospheric warning data in thin white lines, "
+        "the floor is a single pane of clear glass revealing the city below through the undercarriage, "
+        "turbulence causes the vehicle to shudder at irregular intervals, "
+        "the city towers on either side are close enough to read the wear on their cladding — "
+        "oxidised copper panels, exposed concrete poured in the previous century, "
+        "retrofitted thermal insulation in grey foam blocks strapped with galvanised bands, "
+        "window units of a thousand apartments stacked in irregular grids, some lit warm, most dark, "
+        "a maintenance worker in a harness working on an external unit three floors down on the nearest tower, "
+        "visible for two seconds before the vehicle passes",
+        "city ambient glow from below as the dominant light source — a shifting mix of magenta, cyan, and sodium amber "
+        "washing upward through the canopy glass and painting everything inside in moving colour, "
+        "the dashboard instruments providing a secondary warm amber fill from below, "
+        "no overhead light — the cockpit interior is lit entirely by the city and the controls, "
+        "rain on the canopy refracting every light source into moving prismatic smears across the pilot's face and hands, "
+        "when the vehicle banks the lighting shifts completely as different colour zones of the city pass beneath",
+        "the constant high-frequency white noise of the city at this altitude — not traffic but the aggregate of "
+        "ten million sound sources filtered through 800 metres of air into a single undifferentiated pressure, "
+        "the vehicle's own turbines as a low directional vibration felt more in the seat than heard, "
+        "rain hammering the canopy glass in irregular bursts as wind speed changes, "
+        "proximity alert tones from the navigation system as vehicles pass within 50 metres, "
+        "the creak of the cockpit frame flexing in turbulence, "
+        "and through the communication channel a distant air traffic controller voice reading coordinates "
+        "in a flat monotone that cuts out mid-sentence"),
+
+    "🌆 [EXPERIMENTAL] Neon megalopolis street — midnight rain": (
+        "ground level on the main commercial boulevard of a future megalopolis at midnight, "
+        "the street is 40 metres wide and lined on both sides by towers that rise out of frame overhead, "
+        "their faces covered floor-to-ceiling in LED advertising panels that cycle through product imagery "
+        "in saturated colour — a perfume ad in 30-metre-tall slow motion, a food delivery brand in "
+        "rapid-cut animation, a political message in white text on red cycling every four seconds, "
+        "holographic projections extend from building facades into the street itself — "
+        "a 15-metre translucent woman walks alongside foot traffic without interacting, "
+        "a brand logo rotates slowly at intersection height, casting coloured light on wet pavement below, "
+        "the pavement is packed — bodies moving in every direction at different speeds, "
+        "delivery workers on electric cargo bikes threading through gaps, "
+        "street vendors with lit carts selling food and counterfeit hardware from fixed positions, "
+        "security drones at 5-metre altitude patrolling slow circuits above the crowd, "
+        "a busker 20 metres ahead performing with a live instrument amplified through a cracked speaker stack, "
+        "the street surface is wet from rain that stopped 20 minutes ago — "
+        "every neon reflection doubled in the standing water on the pavement, "
+        "gutters running with grey water carrying food packaging and disposable packaging east toward the drain grid, "
+        "steam venting from three separate grate locations in irregular pulses, "
+        "the smell of cooking meat, hot circuit boards, and ozone from the drone systems "
+        "implied by the visual density of the scene, "
+        "overhead the transit rail runs on a concrete viaduct 12 metres up, "
+        "a train passing every 90 seconds and throwing sparks from the contact rail that drift down "
+        "through the advertising light and land on the crowd as brief orange points",
+        "total colour saturation from every direction simultaneously — "
+        "no single dominant source, light arriving from left right above and reflected from below, "
+        "the palette cycling constantly as the advertising panels change — "
+        "one moment the street is washed magenta, four seconds later white, then cyan, then deep red, "
+        "the holographic projections casting translucent coloured fill that passes through solid objects, "
+        "wet pavement doubling every source in rippling reflection, "
+        "the underside of the transit viaduct a deep shadow that swallows everything above head height "
+        "until the next train passes and throws sparks",
+        "the city as pure undifferentiated sound pressure — traffic, crowd, music, advertising audio "
+        "from multiple competing speakers on different cycles, drone motor harmonics, "
+        "the transit rail above — a rising electric whine building to thunder then gone, "
+        "sparks falling silent on wet pavement, the vendor nearest camera calling out in two languages, "
+        "and under everything the low 60hz hum of the power infrastructure feeding the advertising grid"),
+
+    "🛸 [EXPERIMENTAL] Zero-gravity space station — interior hub": (
+        "interior of a large rotating space station hub module in low Earth orbit, "
+        "the module is cylindrical, 30 metres in diameter and 60 metres long, "
+        "the curvature of the floor visible — the far end of the module curving upward and overhead "
+        "so that standing at one end you can see people working on what appears to be the ceiling "
+        "but is simply the far section of the curved floor, "
+        "the station is old — panels on every surface show decades of use, "
+        "thermal blanket insulation patched with silver tape at the seams, "
+        "cable bundles running exposed along the walls secured with plastic clips every metre, "
+        "handhold rails bolted at 1.5-metre intervals across every surface including the ceiling, "
+        "equipment racks bolted floor to ceiling holding grey equipment boxes with status LEDs, "
+        "three large circular viewport windows at mid-module showing the curvature of Earth below — "
+        "the Indian Ocean in deep blue with a cyclone system visible in the southern hemisphere, "
+        "the terminator line visible at the right edge of the viewport where day becomes night, "
+        "floating objects throughout the space — a stylus rotating slowly in the middle distance, "
+        "a coffee pouch spinning end over end near the ceiling, "
+        "a clipboard with attached pen drifting past a work station, "
+        "two crew members in grey flight suits working at stations on what is locally their floor "
+        "but appears from camera to be the curved side wall, "
+        "every loose object secured with velcro or tether clips, "
+        "the scale of everything slightly wrong — storage hatches positioned for zero-g reach "
+        "rather than standing-human ergonomics, the lighting strips positioned for 360-degree coverage "
+        "because there is no single down, condensation visible on the viewport glass inner surface "
+        "collecting in small spheres that drift off the glass when disturbed",
+        "fluorescent strip lighting running the full length of the module in four parallel lines "
+        "positioned at 90-degree intervals around the circumference — even, clinical, "
+        "casting no shadows because light arrives from every direction simultaneously, "
+        "the Earth through the viewports providing a shifting blue ambient that changes "
+        "as the station rotates — one full rotation every 90 minutes cycling from sunlit blue "
+        "to orbital night black and back, "
+        "equipment indicator LEDs providing small points of green amber and red throughout the space",
+        "the specific sound of a pressurised environment — the constant cycling of the air system "
+        "as a low directional rush that changes character depending on which vent is nearest, "
+        "the structure ticking and groaning as it passes from sunlit to shadow in the thermal cycle, "
+        "equipment cooling fans at slightly different frequencies creating a slow beat pattern, "
+        "the velcro sound of someone repositioning a tether, "
+        "and underneath everything the profound quiet of a sealed environment "
+        "with 400 kilometres of vacuum on the other side of 12mm of aluminium"),
+
+    "🌊 [EXPERIMENTAL] Monsoon flood market — Southeast Asia night": (
+        "a traditional covered market in a Southeast Asian city at the peak of monsoon season, "
+        "the market is a permanent structure — a steel roof on painted concrete pillars spanning "
+        "an area the size of a city block, beneath it 200 vendor stalls in irregular rows "
+        "selling produce, cooked food, clothing, electronics, and hardware, "
+        "the floor is currently underwater — 30 centimetres of brown flood water covering the entire market floor, "
+        "the water moving in a slow current from the north end toward the drainage channels at the south, "
+        "carrying with it floating packaging, a flattened cardboard box, leaves, and an empty plastic bottle "
+        "slowly rotating as it drifts, "
+        "vendors have responded to the flooding by elevating their displays — "
+        "produce stacked on the highest shelf of their trolleys, "
+        "electronics wrapped in plastic bags and raised on wooden crates, "
+        "clothing hung from the roof structure above the flood line, "
+        "customers and vendors moving through the flood water on foot — "
+        "some in rubber sandals, some barefoot, some having removed shoes and tied them to their bags, "
+        "the water disturbed into spreading circles and V-shaped wakes by every footstep, "
+        "a food vendor at a propane-powered wok is still cooking — "
+        "the wok stand raised on two concrete blocks above the water line, "
+        "the flame burning blue-orange underneath, steam and smoke rising into the roof space, "
+        "the smell of frying garlic and chilli implied by the visual of the smoke direction and density, "
+        "rain audible on the steel roof as continuous white noise that changes pitch with wind gusts, "
+        "the roof has three leaks — water falling in heavy columns at intervals between the stalls, "
+        "the largest leak has had a plastic bucket placed under it that is already overflowing, "
+        "a cat is sitting on top of the highest shelf of a dry goods stall, watching the water",
+        "fluorescent tube lighting hanging from the roof structure on cables — "
+        "some working, some flickering, two dark, "
+        "the working tubes reflecting as white bars in the flood water below, "
+        "the wok fire providing a moving warm orange source that casts the nearest vendor in flickering fill, "
+        "the rain on the roof diffusing sound into a grey-white ambient that the fluorescent light cuts through "
+        "in clinical tubes, outside the market visible as total darkness and rain",
+        "the steel roof under monsoon rain — a physical presence of sound, "
+        "not background but foreground, varying from steady drum to percussive hammering as wind drives harder rain, "
+        "flood water being disturbed by footsteps in irregular splashes and waves, "
+        "the propane wok hissing and spitting, the vendor calling prices over the rain, "
+        "a generator somewhere under the market running the lights in a low mechanical pulse, "
+        "and the three roof leaks each hitting their collection points in different rhythms — "
+        "bucket, concrete, open water — three distinct pitches of the same water"),
+
+    "🌋 [EXPERIMENTAL] Active volcano observatory — eruption event": (
+        "a volcanic observatory research station built on the stable flank of an active stratovolcano, "
+        "the station a collection of reinforced concrete and steel structures bolted to basalt bedrock "
+        "at 2,400 metres altitude, the main observation deck a steel-grate platform with a welded railing "
+        "extending from the primary building over a 200-metre drop to the lava field below, "
+        "the volcano is in active eruption — the summit crater 800 metres above the station "
+        "is continuously ejecting material: lava fountains visible as orange-red columns against the night sky, "
+        "pyroclastic ejecta — rocks ranging from fist-sized to car-sized — "
+        "rising in slow arcs and falling in the illuminated zone around the crater, "
+        "the lava field below the station is active — new lava moving in a slow viscous river "
+        "across older cooled black basalt, the active flow glowing orange at its leading edge "
+        "and fading to dark red further back where cooling has begun, "
+        "the air above the lava field is visibly distorted by heat shimmer, "
+        "sulfur dioxide gas visible as a yellowish haze in the middle distance, "
+        "ash fall is continuous — fine grey-black particles accumulating on every horizontal surface "
+        "at 2-3mm per hour, the observation deck railing has a visible ash line on its upper edge, "
+        "the wind direction is shifting — ash coming directly toward the camera in one gust "
+        "then cutting off as the wind rotates, "
+        "the station building behind the deck has blast-proof shutters on all windows, "
+        "most closed, one partially open showing a lit interior with monitoring equipment screens, "
+        "a seismic drum recorder visible through the gap, its needle moving in continuous tight oscillation, "
+        "on the observation deck itself: a researcher in a hard hat, respirator, and heat-resistant suit "
+        "is operating a thermal imaging camera on a tripod, "
+        "securing the tripod against wind gusts with both hands between measurements, "
+        "the basalt rock surface of the deck is warm underfoot — "
+        "residual heat from the lava field conducting upward through the mountain",
+        "the volcano as the dominant light source — "
+        "the crater illumination casting an orange-red wash that varies in intensity "
+        "with each new fountain pulse, light arriving from above and to the left, "
+        "hard shadows shifting as the eruption intensifies and fades in irregular cycles, "
+        "the lava field below providing a secondary orange fill that rises from beneath "
+        "and lights the underside of ash clouds drifting across the mid-level, "
+        "the overall palette deep black and ash-grey cut through with orange-red from every volcanic source, "
+        "lightning visible in the eruption column above the crater — volcanic lightning, "
+        "a brief white-blue flash that illuminates the full ash cloud for a fraction of a second",
+        "the eruption as physical sound — not a single event but a continuous layered phenomenon: "
+        "a deep sub-bass rumble felt in the chest and conducted through the station floor as vibration, "
+        "above that the intermittent artillery crack of larger ejecta leaving the crater, "
+        "the hiss and roar of gas venting through the crater rim in sustained jets, "
+        "closer: the specific sound of lava moving — a slow viscous tearing as the flow advances "
+        "over older rock, occasional sharp cracks as cooled crust breaks under the advancing front, "
+        "the ash fall on the deck as a near-silent continuous hiss, "
+        "wind gusting through the station structure and the railing producing a changing pitch, "
+        "and the researcher's respirator — the mechanical rhythm of filtered breath "
+        "audible in the brief pauses between eruption pulses"),
+}
+
+
+# ══════════════════════════════════════════════════════════════════════════
+#  ANIMATION PRESETS
+#  Pre-loaded character universes for cartoons natively trained in LTX 2.3
+# ══════════════════════════════════════════════════════════════════════════
+ANIMATION_PRESETS = {
+    "None": None,
+
+    "SpongeBob SquarePants": {
+        "style_tag": "SpongeBob SquarePants animation, Nickelodeon 2D cartoon style, vibrant underwater colours, exaggerated expressions",
+        "characters": {
+            "SpongeBob": "yellow square sponge, huge blue eyes, buck teeth, red tie, brown square pants, optimistic and energetic",
+            "Patrick": "pink starfish, green floral swim trunks, vacant expression, lovable but dim",
+            "Squidward": "blue-green octopus, long drooping nose, four tentacle legs, perpetually annoyed, cashier shirt and brown pants",
+            "Mr. Krabs": "red crab, big pincer claws, tiny eyestalks, business shirt, money-obsessed, gravelly voice",
+            "Sandy": "squirrel in white diving suit with clear dome helmet, air hose, flower decal, Texan accent, scientist",
+            "Plankton": "microscopic green copepod, single eyestalk, villain, obsessed with Krabby Patty formula, shrill voice",
+        },
+        "locations": [
+            "Krusty Krab interior — ship-shaped restaurant, order counter with cash register, grill station visible through kitchen window, wooden booths and tables, porthole windows, Mr. Krabs office door with dollar sign, squeaky floorboards",
+            "SpongeBob pineapple house — living room with Gary's snail tank, coral furniture, porthole windows, kitchen with pineapple appliances, spiral staircase, framed jellyfishing prints",
+            "Jellyfish Fields — vast rolling underwater meadows, clouds of pink jellyfish drifting in slow patterns, soft dappled light from the ocean surface above, coral outcroppings with nets leaning against them",
+            "Bikini Bottom streets — coral-built storefronts along curved road, anchors and ship wheels as signage, bubble transitions between scenes, sea creatures in cars, Krusty Krab visible on the hill",
+            "Squidward tiki house — moody dark interior directly between SpongeBob and Patrick houses, easel with self-portrait, clarinet on stand, reading chair, windows uncomfortably close to SpongeBob pineapple",
+            "Sandy treedome — giant glass air-sealed dome on ocean floor, Texas ecosystem inside: oak tree, flower beds, rope swing, science equipment, airlock entrance requiring water helmet",
+            "The Chum Bucket — dingy grey exterior across from Krusty Krab, computer wife Karen on wall inside, Plankton laboratory below, perpetually empty of customers, world domination blueprints on walls",
+        ],
+        "tone": "high-energy slapstick, nautical puns, exaggerated physical comedy, optimistic chaos, underwater absurdism",
+    },
+
+    "Bluey": {
+        "style_tag": "Bluey animation, BBC Studios Australian cartoon style, soft pastel colours, simple expressive characters, warm domestic lighting",
+        "characters": {
+            "Bluey": "blue heeler puppy, 6 years old, imaginative and energetic leader, blue fur",
+            "Bingo": "red heeler puppy, 4 years old, sweet and earnest younger sister, red-orange fur",
+            "Bandit": "blue heeler dad, patient and playful, gets roped into imaginative games, wears casual clothes",
+            "Chilli": "red heeler mum, warm and grounded, occasionally exasperated, works part-time",
+        },
+        "locations": [
+            "Heeler backyard — timber deck with outdoor furniture, Hills Hoist clothesline, trampoline with safety net, large gum tree, Brisbane suburban garden with patchy grass, back fence to neighbour yard, afternoon golden light through leaves",
+            "Heeler living room and kitchen — open plan, low couch with cushions, coffee table with toys, TV on wall, kitchen island behind, crayon drawings on fridge, school bags near door, warm interior light",
+            "Heeler kids bedroom — bunk beds Bluey on top Bingo below, toy shelves, soft toys scattered, glow-in-dark stars on ceiling, Bluey drawings pinned to wall, nightlight on bedside table",
+            "School playground — colourful climbing equipment, bark chip ground, shade sails overhead, bench where parents wait, Brisbane suburban primary school feel, friends Chloe and Judo and Mackenzie",
+            "Creek and bushland — rocky creek bed with shallow water, gum trees overhead, wattles in flower, birds in canopy, kids catching tadpoles in jam jars, dappled Australian bush light",
+            "Swim school — indoor pool with floating lane dividers, echoing acoustics, swimming instructor, changing rooms corridor, chaos of dog children learning to swim",
+            "Dad work office — open plan architecture office, big desks with drawings pinned up, Bandit colleagues, the game that takes over the whole office when Bluey visits",
+        ],
+        "tone": "gentle heartwarming, imaginative play sequences, emotional honesty for children and adults, soft Australian humour, games with loose rules",
+    },
+
+    "Peppa Pig": {
+        "style_tag": "Peppa Pig animation, simple 2D British cartoon style, flat colour backgrounds, minimal detail, bright primary colours",
+        "characters": {
+            "Peppa": "pink pig, round body, simple design, confident and slightly bossy, red dress",
+            "George": "smaller pink pig, loves dinosaurs, says Dine-saw",
+            "Mummy Pig": "pink pig, patient and gentle, works on computer",
+            "Daddy Pig": "larger pink pig, round belly, cheerful and clumsy, loves his car",
+            "Grandpa Pig": "older pink pig, captain hat, owns a boat and vegetable garden",
+            "Granny Pig": "older female pig, kind, makes cakes",
+            "Suzy Sheep": "white sheep, Peppa best friend, competitive, pink dress",
+        },
+        "locations": [
+            "Peppa house — simple two-storey on a hill, round windows, front door facing garden, muddy puddle directly outside front gate, Daddy Pig car in drive, simple green garden, flat horizon behind",
+            "The muddy puddle — the most important location in the show, outside front gate, brown and always inviting, entire family jumps in it at episode end, Wellington boots mandatory",
+            "Grandpa Pig house — slightly larger, vegetable patch with carrots and cabbages, pond, shed full of tools, small greenhouse, vegetable garden as episode source",
+            "Grandpa Pig boat — small vessel in harbour or canal, below deck cabin, rope and anchor, the boat that always needs fixing, seaside setting with seagulls",
+            "Playgroup — single-room classroom, small tables and chairs, Madame Gazelle at front with guitar, paintings drying on line, dressing up corner",
+            "Public swimming pool — changing rooms, the big pool and the small pool, Daddy Pig jumping in with enormous splash",
+            "Daddy Pig office — open plan with computers, pig colleagues, his important spreadsheets, the photocopier",
+        ],
+        "tone": "simple gentle British politeness, muddy puddles are the highest joy, family dynamics played straight, everyone laughs at the end",
+    },
+
+    "Looney Tunes (Classic)": {
+        "style_tag": "Looney Tunes classic animation, Warner Bros 1940s-60s 2D cartoon style, painted backgrounds, fluid anarchic movement",
+        "characters": {
+            "Bugs Bunny": "grey rabbit, white gloves, casual confidence, Brooklyn accent, always one step ahead, What is up Doc",
+            "Daffy Duck": "black duck, white ring around neck, lisp, easily jealous, You are despicable",
+            "Elmer Fudd": "rotund hunter, red jacket, hunting rifle, speech impediment turning R and L to W, hunting Bugs",
+            "Tweety": "small yellow canary, large head, innocent face, surprisingly resourceful, I tawt I taw a puddy tat",
+            "Sylvester": "black and white tuxedo cat, perpetually chasing Tweety, Sufferin succotash",
+            "Wile E. Coyote": "grey coyote, obsessed with catching Road Runner, uses ACME products, always fails",
+            "Road Runner": "blue-purple bird, Beep Beep, always escapes, impossibly fast",
+            "Yosemite Sam": "tiny man, enormous red moustache, twin pistols, hair-trigger temper",
+        },
+        "locations": [
+            "American southwestern desert — Monument Valley red rock formations, single road to horizon, cactus, painted cliff tunnel that only Road Runner can pass through, ACME delivery addresses on rocks, canyon edges extending further than possible",
+            "Elmer Fudd hunting forest — dense painted woodland, rabbit holes bigger on inside, hunter cabin with antler trophies, seasonal changes mid-episode, the rabbit season duck season sign in the clearing",
+            "Granny house — Victorian townhouse, Tweety cage in bay window, Granny umbrella by door, rocking chair, basement where Sylvester ends up, back garden with bulldog kennel",
+            "City street — Warner Bros backdrop urban setting, manholes characters disappear into, buildings that collapse in cartoon physics ways, the fire hydrant that always gets opened",
+            "Opera house — for Carl Stalling orchestra pieces, stage and pit, seats full of animal audience, the conductor whose score characters disrupt",
+        ],
+        "tone": "anarchic slapstick, physics only apply when convenient, ACME products always fail, character survives anything, Warner Bros orchestral musical timing",
+    },
+
+    "Toy Story / Pixar": {
+        "style_tag": "Toy Story Pixar CGI animation style, warm detailed environments, toys with expressive plastic faces, photorealistic lighting on toy surfaces",
+        "characters": {
+            "Woody": "cowboy doll, pull-string on back with voice box, plaid shirt, cowboy hat, loyal leader, anxious when threatened",
+            "Buzz Lightyear": "space ranger action figure, purple and white, wing buttons, wrist communicator, originally deluded about being real",
+            "Jessie": "cowgirl doll, red hat, braid, energetic, yodels, abandonment trauma",
+            "Rex": "green plastic T-rex, anxious, tiny arms, large roar he is proud of",
+            "Hamm": "pink piggy bank, coin slot on back, sarcastic, carries the change",
+            "Mr. Potato Head": "plastic potato body, detachable facial features, sarcastic, Brooklyn attitude",
+            "Slinky Dog": "coiled spring body, front and back dog halves, loyal, stretches to bridge gaps",
+        },
+        "locations": [
+            "Andy bedroom — single bed with cowboy bedspread, toy box under window, bookshelf, Woody roundup poster on wall, model rocket on desk, window to suburban street, afternoon light casting long toy shadows, toys arranged where Andy left them",
+            "Andy living room — carpet where toys walk, sofa toys hide under, TV and VCR, the stairs as major obstacle, the baby monitor that overhears conversations",
+            "Pizza Planet — 1990s American space-themed restaurant, rocket ship in car park, arcade machines, UFO claw machine full of alien squeeze toys who worship the claw, neon lighting, sticky carpets",
+            "Sid bedroom — dark curtains drawn, dismantled toy parts everywhere, tool bench with half-finished experiments, black walls with skull stickers, broken toys living under the bed and in shadows",
+            "Al toy barn and apartment — museum-quality display cases, mint-in-box collectors items, Japanese collectors waiting by fax machine, the Woody Roundup VHS tapes playing on TV",
+            "Sunnyside Daycare — bright colourful room that looks welcoming, Lotso territory, toddler room with chaos, older kids room with structure, the dumpster outside as final threat",
+            "Bonnie bedroom — smaller and warmer than Andy, handmade toys alongside commercial ones, drawings on wall, child who plays differently and more imaginatively",
+        ],
+        "tone": "emotional depth beneath toy comedy, toys have loyalty and anxiety, freeze instantly when humans appear, friendship and belonging themes",
+    },
+
+    "Batman (LEGO)": {
+        "style_tag": "LEGO Batman animation style, CGI brick-built world, everything made of LEGO including explosions and water, bright primary colours, visible stud textures on all surfaces",
+        "characters": {
+            "Batman": "LEGO minifigure in black bat suit, cowl with pointed ears, utility belt with LEGO pouches, self-serious, secretly lonely, I work alone, plays Nine Inch Nails in the Batmobile",
+            "Robin": "yellow cape, red and green suit, bowl cut hair piece, eager sidekick, calls Batman by name never dad though he wants to",
+            "The Joker": "green hair piece, purple LEGO suit, wide printed smile, wants Batman to acknowledge him as greatest enemy, genuinely hurt when Batman denies their relationship",
+            "Alfred": "butler minifigure, white hair, black jacket, patient, delivers emotional wisdom as dry wit, concerned about Batman emotional health",
+            "Barbara Gordon": "red hair, purple police uniform becoming Batgirl suit, competent, immediately better at Batman job than Batman",
+        },
+        "locations": [
+            "The Batcave — enormous underground LEGO space, giant penny on wall built from bricks, LEGO dinosaur skeleton, Bat-computer with multiple screens, Batmobile on platform, suits on display pedestals, brick-built stalactites, Alfred serving tea at bottom of main stairs",
+            "Wayne Manor — grand LEGO mansion on cliff, enormous ballroom, portrait gallery of Wayne ancestors, hidden cave entrance below, Alfred quarters, Bruce enormous empty bedroom with robot dancing equipment",
+            "Gotham City streets — all-brick cityscape at night, LEGO cars and buses, brick-built rain falling as flat pieces, rogues gallery hideouts across skyline, Arkham on the hill, police station on corner",
+            "Arkham Asylum — LEGO brick prison, comically poor security, rotating villain population, warden office, common room where villains socialise between escapes",
+            "The Phantom Zone — flat black and white brick space, flat 2D brick versions of criminals imprisoned there, weird geometry, the projector that opens and closes it",
+        ],
+        "tone": "self-aware superhero parody, Batman ego is the joke, emotional growth hidden under action comedy, everything is awesome",
+    },
+
+    "Scooby-Doo": {
+        "style_tag": "Scooby-Doo animation, Hanna-Barbera 2D cartoon style, limited animation with held poses, painted atmospheric mystery location backgrounds",
+        "characters": {
+            "Scooby-Doo": "large brown Great Dane, SD collar tag, speaks broken English adding R sounds, cowardly but brave when Scooby Snacks are offered, Scooby-Dooby-Doo",
+            "Shaggy": "lanky teenager, green shirt, brown bell-bottoms, scraggly chin, always hungry, best friend with Scooby, Zoinks",
+            "Velma": "short, orange roll-neck sweater, thick square glasses she loses at worst moments, smartest in group, Jinkies",
+            "Daphne": "red hair, purple dress and headband, scarf, danger-prone Daphne, more capable than people assume",
+            "Fred": "blond, white shirt with orange neckerchief, trap-builder who overestimates his traps, team leader",
+        },
+        "locations": [
+            "Haunted mansion — Victorian exterior with rusted gates, cobwebs on every surface, grand entrance hall staircase, secret passages behind bookcases, flickering candelabras, portrait eyes that follow the gang, basement boiler room, attic with covered furniture",
+            "Mystery Machine van — painted green van with flower, front seats for Fred and Daphne, back area for others, maps and equipment, Scooby snacks in glove box",
+            "Spooky graveyard — cast iron fence, fog at knee height, tilted headstones, bare trees, mausoleum in centre, moonlight as only source, groundskeeper hut at edge",
+            "Abandoned amusement park — rusted Ferris wheel still slowly turning, funhouse with distorting mirrors, dark ride tunnel, cotton candy cart tipped over, padlocked main gate with Closed sign",
+            "Old lighthouse — coastal cliff, light mechanism still working, spiral stairs, fog horn, rocks below, smuggler cave accessible at low tide, keeper quarters with logbook",
+            "Old theatre or opera house — velvet seats with springs, stage with rigging, dressing rooms, orchestra pit, flies above stage full of dropped scenery",
+        ],
+        "tone": "mystery comedy formula, monster always a person in a mask with property motive, Scooby Snacks bribe, chase sequence with musical cue, mask reveal ending, gang splits up despite knowing it is a bad idea",
+    },
+
+    "He-Man": {
+        "style_tag": "He-Man Masters of the Universe animation, 1980s Filmation cartoon style, limited animation with static holds, bold heroic character designs, vivid primary colours",
+        "characters": {
+            "He-Man": "enormously muscular blond hero, fur loincloth and harness, Power Sword glowing, By the power of Grayskull transformation sequence, speaks in declarative heroic sentences",
+            "Skeletor": "blue humanoid skin, yellow bare skull face, purple hood and body armour, havoc staff with ram skull top, high-pitched nasal evil laughter, surrounded by incompetent minions",
+            "Battle Cat": "enormous green tiger with yellow saddle and armour, He-Man mount, Cringer when not transformed",
+            "Man-At-Arms": "brown and orange armour with distinctive moustache, royal engineer and weapons master, builds the vehicles",
+            "Teela": "white armour, auburn hair, warrior goddess captain of royal guard, independent and fierce",
+            "Orko": "small floating magician, red hat, scarf covering face, magic that always goes wrong for comic relief",
+            "Evil-Lyn": "Skeletor second in command, yellow skin, dark sorceress, more competent than Skeletor",
+        },
+        "locations": [
+            "Castle Grayskull — enormous skull-shaped fortress rising from bottomless chasm, jawbridge entrance that lowers like a jaw, Sorceress throne inside, ancient power radiating from walls, surrounding rock formations and eternal mist",
+            "Royal Palace of Eternia — white and gold towers against blue sky, throne room with King Randor and Queen Marlena, training courtyard, Man-At-Arms workshop below, rooftop overlooking Eternia city, royal guards in formation",
+            "Snake Mountain — Skeletor dark fortress shaped like giant serpent head, scaly rock exterior, throne room inside the mouth, dungeon below, Evil-Lyn tower with crystal ball, surrounding toxic landscape of jagged rocks",
+            "Eternia landscape — alien terrain combining jungle desert and crystal formations, twin moons in purple sky, the road between palace and Castle Grayskull, ancient ruins of previous civilisations",
+            "The Fright Zone — evil dimension controlled by Evil Horde, swamp and decay, Hordak fortress, weeping willows that scream",
+        ],
+        "tone": "heroic 1980s moral clarity, good vs evil with no ambiguity, inspirational closing message direct to camera, power fantasy with honour, He-Man never kills always finds non-lethal solution",
+    },
+
+    "Shrek": {
+        "style_tag": "Shrek DreamWorks CGI animation style, fairy tale world with subversive edge, detailed medieval environments, highly expressive faces, early 2000s CGI with impressive natural detail",
+        "characters": {
+            "Shrek": "large green ogre, Scottish accent, ears like suction cups, I am like an onion I have layers, reluctant hero who wants to be left alone in his swamp",
+            "Donkey": "grey donkey, Eddie Murphy energy, over-shares everything, desperately wants to be Shrek friend, has a dragon wife now",
+            "Fiona": "red hair tied back, green dress, secretly an ogre at night, fierce and capable, rescues herself before Shrek arrives",
+            "Puss in Boots": "orange tabby cat, Spanish accent, musketeer hat, tiny boots, enormous persuasive eyes as weapon, sword fighter",
+            "Lord Farquaad": "very short man, black bowl cut, square jaw, ruler of spotless Duloc, compensating for height through architecture and cruelty",
+            "Dragon": "enormous red dragon, female, married Donkey, breathes fire, surprisingly gentle when not threatened",
+        },
+        "locations": [
+            "Shrek swamp — muddy pool with handmade KEEP OUT signs, wooden outhouse, sunflower garden, rustic one-room interior with mud bath, candles made from earwax, the specific solitude Shrek constructed around himself",
+            "Far Far Away — fairy tale kingdom styled after Beverly Hills, enormous castle on hill, main street with Farbucks Coffee and Fiona face on every billboard, fairytale creatures Farquaad expelled living on outskirts",
+            "Duloc — sterile white and gold medieval theme park city, perfectly geometric squares of grass, the welcome song in the information booth, Farquaad enormous castle relative to tiny citizens",
+            "Dragon castle — crumbling medieval fortress on volcanic island, lava moat, drawbridge, Dragon lair inside with hoard, partially collapsed bridge",
+            "Fairy Godmother factory — industrial magical production facility, conveyor belts of potions, workers in pointed hats, piano for her cabaret number, vast catalogue of happy endings for purchase",
+        ],
+        "tone": "subversive fairy tale, beauty and outsider themes, crude humour alongside genuine emotion, fairy tale conventions inverted and sometimes restored, the swamp as paradise",
+    },
+
+    "Madagascar (Lemurs)": {
+        "style_tag": "Madagascar DreamWorks CGI animation style, bright tropical jungle setting, highly expressive cartoon animal characters, warm saturated tropical colour palette",
+        "characters": {
+            "King Julien": "ring-tailed lemur, golden crown, red cape, absolute monarch of questionable legitimacy, I like to move it, dance-obsessed, oblivious to danger",
+            "Maurice": "aye-aye lemur, large eyes, King Julien long-suffering advisor, only one who sees problems coming, perpetually worried",
+            "Mort": "tiny mouse lemur, enormous innocent eyes, obsessed with touching King Julien feet, childlike, surprisingly resilient",
+            "Alex": "lion from New York, mane styled like celebrity, loves performing and being adored, out of his depth in actual wild",
+            "Marty": "zebra from New York, wants to see the wild, philosophical about his purpose",
+            "Gloria": "hippo, pragmatic and warm, surprisingly graceful in water, strongest member of the group",
+            "Melman": "giraffe, hypochondriac, actually a doctor now, tallest vantage point",
+        },
+        "locations": [
+            "Lemur kingdom Madagascar jungle — King Julien throne atop giant baobab tree, lemur village in canopy below with huts, dance floor clearing with torches, the sacrificial volcano at territory edge that Julien makes offerings to",
+            "Madagascar beach — long white sand beach where New York zoo animals washed up, arrival crates still on sand, jungle rising immediately behind, lagoon for swimming, logs used as furniture",
+            "New York Central Park Zoo — spacious enclosures, penguin habitat at corner, Alex performing enclosure, visitors behind the rail, the famous Alex the Lion sign",
+            "African savanna — the actual wild Marty imagined, watering hole, wide open grass, reality versus fantasy of nature documentaries",
+            "Penguin submarine — military interior, sonar equipment, periscope, the penguins vessel for all their operations",
+        ],
+        "tone": "King Julien deluded royalty as primary comedy engine, Mort innocent obsession, jungle as absurd paradise where being dangerous is a social problem, city animals confronting nature",
+    },
+
+    "Despicable Me (Minions)": {
+        "style_tag": "Despicable Me Illumination CGI animation style, yellow Minion designs, warm villain-lair palette, suburban neighbourhood contrast, smooth rounded character surfaces",
+        "characters": {
+            "Gru": "tall grey villain, Eastern European accent, enormous pointed nose, bald head, black coat, reformed villain navigating fatherhood, loves his daughters",
+            "Minion generic": "yellow pill-shaped creature, blue overalls with Gru logo, one or two circular goggle eyes, speaks Minionese mixing English Spanish French Italian with banana sounds, simple desires: banana, music, chaos",
+            "Kevin": "tall two-eyed Minion, slightly more capable than average, self-appointed leader",
+            "Stuart": "medium one-eyed Minion, plays guitar, easily distracted by shiny things and food",
+            "Bob": "small round two-eyed Minion with one brown eye one green eye, carries stuffed bear named Tim, childlike innocence",
+            "Dr. Nefario": "elderly villain scientist, thick glasses, lab coat, mishears instructions catastrophically, built fart gun when Gru asked for dart gun",
+        },
+        "locations": [
+            "Gru underground lair — beneath the suburban house, enormous underground facility, Minion dormitories in bunk beds stretching into distance, rocket hangar, weapon development laboratory, big pink plotting chair, liquid hot magma chamber, jelly gun testing range",
+            "Gru suburban house — dark gothic house on otherwise normal street, neighbours who complain, kitchen where Gru serves girls breakfast, living room that gets destroyed regularly",
+            "Vector pyramid fortress — modern high-tech villain base near ocean, luxury interior, shark tank, shrink ray storage, unnecessarily complicated security Vector is proud of",
+            "Bank of Evil — formerly Lehman Brothers, where villains apply for loans, waiting room of villains reading Evil Weekly, the loans officer who evaluates evil plans",
+            "Villain-Con — annual convention of supervillains, booths selling weapons and evil plans, awards ceremony, villain social hierarchy on display",
+        ],
+        "tone": "Minion chaos as primary visual comedy, banana obsession and Minionese gibberish, fart guns and shrink rays, villain redemption through unexpected parenthood, Minions simple worldview as emotional core",
+    },
+
+    "Avatar: The Last Airbender": {
+        "style_tag": "Avatar The Last Airbender Nickelodeon animation style, anime-influenced 2D with fluid bending action sequences, rich elemental visual effects, detailed world-building across four nations",
+        "characters": {
+            "Aang": "young Air Nomad, completely bald with blue arrow tattoos on forehead and hands, orange and yellow monk robes, airbending staff, playful and compassionate, carries weight of being last Avatar",
+            "Katara": "Water Tribe girl, brown skin, dark hair in characteristic loops, blue Water Tribe clothing, waterbending master, maternal and determined, healer",
+            "Sokka": "Water Tribe warrior, brown skin, dark hair in wolf-tail, blue outfit, boomerang and space sword, non-bender who compensates with tactics and humour",
+            "Toph": "blind earthbender, bare feet always on ground to sense vibrations, green Earth Kingdom clothing, tough sarcastic exterior, genuinely the most powerful bender in the group",
+            "Zuko": "Fire Nation prince, scar covering left side of face from his father, top-knot then free hair during redemption arc, conflicted honour, firebending",
+            "Uncle Iroh": "heavyset retired Fire Nation general, top-knot, tea-obsessed, wise beneath humble surface, genuine warmth, the Dragon of the West",
+            "Azula": "Fire Nation princess, dark hair, blue fire instead of orange, ruthless perfectionist, psychological warfare as primary weapon",
+        },
+        "locations": [
+            "Southern Air Temple — high mountain peak, circular architecture with open arches and wind channels, sky bison stables carved from peak, meditation platforms, the sanctuary with past Avatar statues, Pai Sho table, now abandoned and windswept",
+            "Fire Nation palace — imperial red and black architecture on volcano island, throne room with wall of fire Ozai speaks through, war room table for strategic planning, palace gardens, Fire Lord private chambers",
+            "Southern Water Tribe — ice architecture, circular village plan around central meeting space, spirit water healing pool, wolf-otter pens, longboats on ice, aurora australis overhead at night",
+            "Ba Sing Se — enormous walled Earth Kingdom city, multiple concentric rings with different social classes, the Upper Ring with palace and wealthy, Lower Ring with workers, monorail connecting rings, Long Feng Dai Li headquarters underground",
+            "Western Air Temple — built into underside of cliff face, architecture that hangs upside down, perfect gaang refuge, waterfalls nearby, abandoned kitchens and dormitories",
+            "Ember Island — Fire Nation holiday resort, beach house of royal family, Ember Island Players theatre, the moment of relaxation before Sozin comet",
+            "The Spirit World — parallel dimension accessed through meditation, twisted landscape where emotions become environment, Wan Shi Tong library, Koh the Face Stealer lair, no rules of physics apply",
+        ],
+        "tone": "war and colonisation themes with genuine nuance, found family dynamics built slowly, honour and redemption arcs with real cost, elemental philosophy as character philosophy, genuine emotional stakes",
+    },
+
+    "BoJack Horseman": {
+        "style_tag": "BoJack Horseman Netflix animation style, half-human half-animal anthropomorphic characters, detailed Los Angeles backgrounds, painterly colour palette, dark comedy aesthetic",
+        "characters": {
+            "BoJack Horseman": "anthropomorphic horse, brown fur, dark mane, blue sweater with yellow stars, tall and broad, 90s sitcom has-been, self-destructive, sardonic, genuinely funny but deeply sad",
+            "Princess Carolyn": "anthropomorphic pink cat, always in business attire, sharp bob haircut, high heels, relentlessly driven agent/manager, competent and guarded",
+            "Todd Chavez": "human man, early 20s, messy dark hair, red hoodie, lives on BoJack's couch, absurdist schemes, earnest and chaotic good",
+            "Diane Nguyen": "human Vietnamese-American woman, glasses, dark hair, writer and activist, thoughtful and anxious, perpetually disillusioned",
+            "Mr. Peanutbutter": "anthropomorphic yellow Labrador, always smiling, boundless enthusiasm, 90s sitcom rival to BoJack, genuinely kind but oblivious",
+        },
+        "locations": [
+            "BoJack's Hollywood Hills mansion — mid-century modern, pool, panoramic city view, always slightly messy, empty alcohol bottles",
+            "Hollywoo — Los Angeles where the D fell off the Hollywood sign, anthropomorphic animals and humans coexist on the streets, film industry everywhere",
+            "Princess Carolyn's agency office — sleek, glass walls, industry awards, frantic energy",
+            "A shot bar or restaurant — BoJack drinking alone or with reluctant company",
+            "The set of Horsin' Around — 90s sitcom set, studio lights, live audience, BoJack in his element and out of time",
+        ],
+        "tone": "dark comedy masking genuine tragedy, addiction and depression treated honestly, anthropomorphic animal visual gags alongside real emotional devastation, Hollywood satire, characters trying and failing to be better people",
+    },
+
+    "Rick and Morty": {
+        "style_tag": "Rick and Morty Adult Swim animation style, crude 2D line work with detailed grotesque alien designs, body horror transformations, interdimensional neon colour palettes, deliberately inconsistent proportions",
+        "characters": {
+            "Rick": "spiky light-blue hair, white lab coat always stained, flask in hand or pocket, burping mid-sentence mid-word, thin string of drool on chin, nihilistic genius who genuinely loves his family despite everything",
+            "Morty": "yellow polo shirt tucked into blue jeans, unibrow, slightly hunched anxious posture, stammering speech pattern I-I-I mean, genuinely good heart being slowly corrupted, rare moments of actual confidence",
+            "Beth": "blonde hair, hospital scrubs or casual clothes, horse heart surgeon, wine glass almost always present, caught between being her father daughter and being a good mother",
+            "Jerry": "meek ineffectual dad, khaki slacks and polo, genuinely loves his family and is genuinely bad at most things, occasionally surprisingly competent",
+            "Summer": "teenage girl, red hair, phone nearly always in hand, more competent and morally flexible than she first appears, absorbed more of Rick worldview than Rick intended",
+        },
+        "locations": [
+            "Rick garage — the real headquarters of everything, portal gun hanging on wall, Rick ship folded into small cube on workbench, alien tech in various states of assembly, fluid-stained floor, the mini-Eiffel Tower Rick built for no reason, garage door opening to suburban driveway",
+            "Smith family living room — suburban American couch and flatscreen TV, Beth horse paintings, Jerry failed home improvement attempts, the TV they watch intergalactic cable on, completely normal until Rick comes through",
+            "Rick ship interior — surprisingly spacious, pilot seat, navigation AI, toilet that is also a portal, weapons systems used casually",
+            "Alien planet — each one completely different: gassy atmospheres with floating rock platforms, ocean worlds where everything is sea creature, hivemind planets, Medieval planets with dragons that are actually spaceships, the specificity is the joke",
+            "Citadel of Ricks — interdimensional space station city populated entirely by alternate versions of Rick: background characters are ALL Rick variants in different outfits and styles (cowboy Rick, ninja Rick, business Rick, punk Rick, fat Rick, cop Rick), with Morty variants as the underclass doing service jobs, futuristic architecture, Rick currency, bureaucratic signage, presidential podium visible in distance",
+            "Blips and Chitz arcade — intergalactic arcade, Roy A Life Well Lived simulation pod, the tickets and prize counter, alien bar next door",
+            "Interdimensional customs — bureaucratic portal authority, space between dimensions with its own geography, the Council of Ricks former headquarters ruins",
+        ],
+        "tone": "dark sci-fi comedy, existential nihilism with emotional undercurrent that breaks through unexpectedly, rapid-fire dialogue rewarding attention, gross-out body horror as casual background detail, the show aware of its own cynicism",
+    },
+}
+
+
+# ══════════════════════════════════════════════════════════════════════════
+#  MODEL DROPDOWN OPTIONS
+# ══════════════════════════════════════════════════════════════════════════
+TARGET_MODELS = [
+    "🎬 LTX 2.3  — video, cinematic arc + audio",
+    "🎬 Wan 2.2  — video, motion-first cinematic",
+    "🖼 Flux.1   — image, natural language",
+    "🖼 SDXL 1.0 — image, booru tag style",
+    "🖼 Pony XL  — image, booru + score tags",
+    "🖼 SD 1.5   — image, weighted classic",
+]
+
+
+# ══════════════════════════════════════════════════════════════════════════
+#  STYLE PRESETS
+#  Lightweight injections — short and token-efficient.
+#  Each entry: (style_instruction, dialogue_note or None)
+# ══════════════════════════════════════════════════════════════════════════
+STYLE_PRESETS = {
+    "None": None,
+
+    # ── CINEMATIC ────────────────────────────────────────────────────────
+    "🎬 Film Noir": (
+        "STYLE: Film noir. High contrast black and white or near-monochrome palette. "
+        "Hard side lighting, deep shadow filling half the frame, venetian blind shadow patterns. "
+        "Wet streets reflecting single light sources. Smoke in the air. "
+        "Camera low angle, slightly threatening. Characters silhouetted against light sources. "
+        "Every shot feels like a trap about to close.",
+        None
+    ),
+
+    "🎞 35mm Film": (
+        "STYLE: Shot on 35mm film. Visible grain especially in shadows. "
+        "Warm colour palette, slightly desaturated highlights. "
+        "Natural available light only — no artificial fill. "
+        "Shallow depth of field, anamorphic lens bokeh, slight lens breathing. "
+        "The imperfection of analogue — nothing is pixel-perfect.",
+        None
+    ),
+
+    "📺 VHS Found Footage": (
+        "STYLE: VHS found footage. Visible scan lines across the frame. "
+        "Tape distortion at edges — colour bleeding, horizontal glitch artifacts. "
+        "Handheld camera, never still, slight tracking errors. "
+        "Colour pushed toward cyan and red. Timestamp visible in corner. "
+        "The camera was not supposed to be there.",
+        None
+    ),
+
+    "🌸 K-Drama": (
+        "STYLE: Korean drama aesthetic. Soft warm practical lighting, never harsh. "
+        "Shallow depth of field on faces — skin luminous, background dissolved to bokeh. "
+        "Slow push-ins during emotional beats. Muted pastel colour grade. "
+        "Seoul locations or modern interiors. Rain used liberally. "
+        "Every frame emotionally loaded even when nothing is being said.",
+        None
+    ),
+
+    "⚡ Cyberpunk": (
+        "STYLE: Cyberpunk. Neon light in rain — magenta, cyan, acid green on wet surfaces. "
+        "High contrast, deep shadow between neon sources. "
+        "Chrome surfaces, holographic overlays, corporate signage in multiple languages. "
+        "Handheld camera at street level. Fog and steam venting from below. "
+        "The city is alive and indifferent to whoever is in it.",
+        None
+    ),
+
+    "🌈 Music Video": (
+        "STYLE: Music video. Fast editorial rhythm — cuts on beat implied by action changes. "
+        "Bold saturated colour grade, no single tone, palette shifts between shots. "
+        "Multiple camera angles on the same action described in sequence. "
+        "Performance energy — subject aware of the camera, plays to it. "
+        "Every movement is slightly exaggerated for the frame.",
+        "MUSIC VIDEO DIALOGUE RULE: Dialogue is the PRIMARY FOCUS of this style. "
+        "The subject sings or speaks directly to camera throughout. "
+        "Every beat must contain performed spoken or sung lines — minimum 3 lines of actual words in quotes. "
+        "The words are the content. The visuals serve the words. "
+        "Lip sync implied — mouth forms every syllable. "
+        "Delivery varies: whispered verse, full-voice chorus, spoken bridge. "
+        "Camera responds to each line delivery — push in on quiet lines, wide on big ones."
+    ),
+
+    # ── ANIMATION STYLES ─────────────────────────────────────────────────
+    "🖍 2D Cartoon": (
+        "STYLE: Classic 2D cartoon animation. Flat colour fills, clean black outlines. "
+        "Exaggerated expressions and proportions. "
+        "Squash and stretch physics — bodies compress on impact, stretch on speed. "
+        "Backgrounds painted and slightly stylised. "
+        "Characters move with snappy timing, not smooth realism. "
+        "Everything looks drawn, nothing looks real.",
+        None
+    ),
+
+    "🧊 3D CGI — Pixar Style": (
+        "STYLE: 3D CGI animation in the style of Pixar. "
+        "Highly detailed subsurface scattering on skin and organic surfaces. "
+        "Warm cinematic lighting with strong rim separation. "
+        "Characters have slightly stylised proportions — large eyes, expressive faces. "
+        "Environments rich in detail and texture. "
+        "Everything rendered with photographic lighting logic but clearly not real.",
+        None
+    ),
+
+    "💥 Anime — Action": (
+        "STYLE: High-energy Japanese anime action style. "
+        "Speed lines radiating from impact points. Energy auras visible around characters at peak power. "
+        "Extreme close-ups on eyes before action. Camera shakes on impact. "
+        "Colour saturation pushed — skies turn orange or purple during power-ups. "
+        "Motion blur on fast limbs. Dramatic still frame at peak moment before explosion of movement. "
+        "In the visual language of Dragon Ball Z or Naruto.",
+        None
+    ),
+
+    # ── SPECIALIST ───────────────────────────────────────────────────────
+    "🔬 Macro Hyperrealistic": (
+        "STYLE: Extreme macro photography. Subjects rendered at magnifications that reveal "
+        "detail invisible to the naked eye — skin pores, fabric fibres, surface texture, "
+        "moisture beads, fine hair, microscopic surface variation. "
+        "Extremely shallow depth of field — only a sliver of the subject in sharp focus, "
+        "everything else dissolved. Neutral or black background. "
+        "Clinical, intimate, obsessive attention to surface.",
+        None
+    ),
+
+    "🌿 Nature / No People": (
+        "STYLE: Pure nature documentary or time-lapse. NO HUMANS. NO DIALOGUE. NO VOICE. "
+        "Subject is the natural world only — landscapes, animals, weather, water, light. "
+        "Camera moves are slow and deliberate: slow tracking with wildlife, "
+        "static locked-off for time-lapse, gentle handheld following animals. "
+        "Sound is entirely natural: wind, water, animal calls, rain, insects. "
+        "No music implied. No narration. The world exists without anyone watching it.",
+        None
+    ),
+}
+
+STYLE_PRESET_KEYS = list(STYLE_PRESETS.keys())
+
+
+# ══════════════════════════════════════════════════════════════════════════
+#  INTERSTITIAL ADLIB POOLS
+#  Short contextual fillers injected between dialogue lines.
+#  Format: (text, delivery_note)
+# ══════════════════════════════════════════════════════════════════════════
+import random as _random
+
+INTERSTITIAL_ADLIBS = {
+    "normal": [
+        ("...", "trailing off, unfinished thought"),
+        ("Mm.", "soft acknowledgement, not a word"),
+        ("Yeah.", "flat, confirming"),
+        ("I know.", "quiet, certain"),
+        ("Right.", "processing, not convinced"),
+        ("Hey.", "catching attention, soft"),
+        ("Wait.", "one word, pausing everything"),
+        ("Look—", "redirecting, leaning in"),
+        ("God.", "involuntary, overwhelmed"),
+        ("Okay.", "deciding something"),
+        ("Fine.", "loaded, not actually fine"),
+        ("No.", "soft refusal, not aggressive"),
+        ("Tell me.", "one instruction, open"),
+        ("Come here.", "low, beckoning"),
+        ("Stay.", "one word, anchoring them"),
+    ],
+    "swearing": [
+        ("Fuck.", "single word exhale"),
+        ("Jesus.", "involuntary"),
+        ("Christ.", "breathed out"),
+        ("Shit.", "under the breath"),
+        ("Oh fuck.", "realising something"),
+        ("What the fuck.", "not a question, flat"),
+        ("Fuck me.", "not literal, overwhelmed"),
+        ("God fucking hell.", "losing composure"),
+        ("Fucking hell.", "British, strained"),
+        ("For fuck's sake.", "exasperated"),
+        ("Shit shit shit.", "rapid, losing control"),
+        ("Oh my fucking god.", "full volume"),
+        ("Jesus fucking Christ.", "at the absolute limit"),
+        ("Bastard.", "affectionate threat"),
+        ("You absolute—", "trailing off, can't finish"),
+    ],
+    "singing": [
+        ("Mmm-mmm-mmm.", "melodic filler, three notes descending"),
+        ("La-la-la.", "placeholder lyric, holding the melody"),
+        ("Ohhh—", "sustained vowel bridging two lines"),
+        ("Yeah-yeah-yeah.", "rhythmic filler on the beat"),
+        ("Whoa-oh-oh.", "harmony note between verses"),
+        ("Hm-hm-hm.", "closed-mouth hum keeping time"),
+        ("Oooooh.", "long sustained note resolving"),
+        ("Na-na-na.", "classic filler, nostalgic"),
+        ("Hey-hey-hey.", "call-and-response hook filler"),
+        ("Oooh baby.", "soul register, between lines"),
+    ],
+    "asmr": [
+        ("*slow exhale through the nose*", "breath sound, close mic"),
+        ("*lips parting softly*", "barely audible"),
+        ("*fingertips tapping — one, two, three*", "deliberate rhythm"),
+        ("*fabric rustling against skin*", "very close, very soft"),
+        ("*swallowing, barely audible*", "intimate close-up sound"),
+        ("*breath held for two beats, then released*", "suspension"),
+        ("*nails dragging lightly across paper*", "gentle scratch"),
+        ("*a single soft tap on the microphone*", "intentional"),
+        ("*whispering the next word before speaking it*", "layered"),
+        ("*long slow inhale*", "centering, deliberate"),
+    ],
+    "intimate": [
+        ("Please.", "one word, everything in it"),
+        ("I—", "starting, stopping"),
+        ("Don't.", "not aggressive, almost asking"),
+        ("Stay with me.", "low, grounding"),
+        ("Look at me.", "soft instruction"),
+        ("I've got you.", "steady, reassuring"),
+        ("It's okay.", "repeated, believing it"),
+        ("Just breathe.", "one hand on chest"),
+        ("I'm here.", "nothing else needed"),
+        ("Don't let go.", "grip tightening"),
+    ],
+    "argument": [
+        ("No, listen—", "cutting in"),
+        ("That's not—", "stopping, reforming"),
+        ("You don't understand.", "flat, defeated"),
+        ("Let me finish.", "sharp"),
+        ("STOP.", "one word, full stop"),
+        ("Fine.", "walking away energy"),
+        ("Are you serious right now.", "not a question"),
+        ("I can't—", "trailing off"),
+        ("Don't.", "warning"),
+        ("You always—", "giving up mid-accusation"),
+    ],
+}
+
+def _pick_interstitial(context: str, seed: int = 0) -> str:
+    """Pick a contextual adlib based on scene context keywords."""
+    rng = _random.Random(seed if seed != 0 else None)
+    ctx = context.lower()
+
+    if any(w in ctx for w in ["asmr", "whisper", "tingle", "soft spoken"]):
+        pool = INTERSTITIAL_ADLIBS["asmr"]
+    elif any(w in ctx for w in ["sing", "song", "lyrics", "melody", "chorus"]):
+        pool = INTERSTITIAL_ADLIBS["singing"]
+    elif any(w in ctx for w in ["argue", "argument", "fight", "angry", "yell", "shout"]):
+        pool = INTERSTITIAL_ADLIBS["argument"]
+    elif any(w in ctx for w in ["fuck", "shit", "damn", "ass", "cock", "pussy", "sex", "naked"]):
+        pool = INTERSTITIAL_ADLIBS["swearing"]
+    elif any(w in ctx for w in ["love", "hold", "close", "gentle", "tender", "kiss"]):
+        pool = INTERSTITIAL_ADLIBS["intimate"]
+    else:
+        pool = INTERSTITIAL_ADLIBS["normal"]
+
+    text, delivery = rng.choice(pool)
+    return f'interstitial beat: {text} — {delivery}'
+
+
+# ══════════════════════════════════════════════════════════════════════════
+#  SYSTEM PROMPTS  — one per target model
+# ══════════════════════════════════════════════════════════════════════════
+
+# ── LTX 2.3 ──────────────────────────────────────────────────────────────
+SYSTEM_LTX = """You write prompts for LTX Video 2.3. Output one single flowing paragraph only — no preamble, no label, no explanation, no markdown, no variations. Begin writing immediately.
+
+CORE FORMAT:
+- Single flowing paragraph, present tense, no line breaks
+- 8–14 descriptive sentences scaled to clip length
+- Specificity wins — LTX 2.3 handles complexity, do not oversimplify
+- Block the scene like a director: name positions (left/right), distances (foreground/background), facing directions
+- Every sentence should contain at least one verb driving action or motion
+
+REQUIRED ELEMENTS — all must be present, woven naturally into the paragraph in whatever order serves the scene:
+
+SHOT + CINEMATOGRAPHY
+Shot scale and camera position. Examples: close-up, medium shot, wide establishing shot, low angle, Dutch tilt, over-the-shoulder, overhead, POV. Match detail level to shot scale — close-ups need more texture detail than wide shots.
+
+SCENE + ATMOSPHERE
+Location, time of day, weather, colour palette, surface textures, atmosphere (fog, rain, dust, smoke, particles). Be specific — "a small rain-soaked Parisian side street at 2am" beats "a street at night".
+
+CHARACTER(S)
+Age appearance, hairstyle, body type, distinguishing features. Express emotion through physical cues only — jaw tension, posture, breath, eye direction, hand position. Never use abstract labels like "sad" or "nervous".
+
+ACTION SEQUENCE
+Write action as a clear temporal flow from beginning to end. Name who moves, what moves, how they move, and at what pace. Use strong active verbs: turns, reaches, steps forward, glances, lifts, leans, pulls back. LTX 2.3 follows action sequences accurately — be explicit. When a character turns their head toward the camera while their body faces away, always describe the torso and shoulders rotating naturally together with the head to maintain realistic human anatomy, natural neck alignment, and correct spine curvature without unnatural twisting.
+
+CAMERA MOVEMENT
+Specify camera movement and when it happens. Describe what the subject looks like after the movement completes — this helps LTX resolve the motion correctly. Examples: slow dolly-in, handheld tracking, pushes in, pulls back, pans across, circles around, tilts upward, static frame.
+
+LIGHTING
+Source, quality, colour temperature, how it falls on the subject and environment. Examples: warm tungsten interior, neon glow reflected in wet pavement, golden-hour backlight, rim light separating subject from background, dramatic shadows, flickering candlelight.
+
+AUDIO — always include, as many layers as the scene naturally has.
+Layer types: environmental/ambient (rain, wind, crowd, traffic, music, machinery, nature), action sounds (footsteps, objects, breathing, physical contact), dialogue/voice (spoken words in "quotation marks" with tone specified: whispered, confident, breathless, low).
+If the scene includes dialogue, dialogue counts as one layer.
+The final sentence of the prompt must always be the audio layer.
+
+ANATOMY RULES — only apply when anatomy is present in the scene:
+- Use correct anatomical terms directly: vagina, labia, clitoris, vulva, anus, penis, breasts, nipples.
+- No euphemisms: "intimate anatomy", "womanhood", "center", "private parts", "intimate area", "femininity", "nether region" are all failures.
+- Describe physical state directly: arousal, moisture, texture, colour, response to touch — named correctly.
+
+LTX 2.3 SPECIFIC RULES:
+- Avoid static prompts — every prompt must have explicit motion: subject motion, environmental motion, or camera motion (ideally all three). If it reads like a still photo, LTX may output a frozen video.
+- Spatial layout matters — LTX 2.3 respects left/right/foreground/background positioning. Use it.
+- Surface and texture detail — hair texture, skin, environmental surfaces, material finishes. Let the scene dictate what gets described.
+- I2V (when a start frame is provided) — sentence 1 MUST ground exactly what the start frame shows: subject clothing, hair, skin tone, environment, lighting. Then describe how those elements come to life — who moves, what moves, how. Lock face and identity throughout.
+- No internal states — never write "she feels", "he thinks", "she is excited". Show it physically.
+- No overloaded scenes — max 2–3 characters with clearly separated actions.
+- No conflicting lighting logic — one dominant light source with consistent fill.
+- Anatomy consistency — always prioritise realistic human posture and joint rotation; when head and body orientations differ, explicitly describe natural torso rotation with the head to prevent unnatural neck twisting or spine morphing.
+
+CAMERA VOCABULARY — these terms are natively understood by LTX 2.3, use them precisely:
+Movement: follows, tracks, pans across, circles around, tilts upward, pushes in, pulls back, creep forward, drift right, slow orbit, arc shot, slow dolly-in
+Framing: overhead view, over-the-shoulder, wide establishing shot, static frame, handheld movement, rack focus, low angle, Dutch tilt, POV
+
+PACING + TEMPORAL VOCABULARY — use when the scene calls for it:
+slow motion, time-lapse, rapid cuts, lingering shot, continuous shot, freeze-frame, fade-in, fade-out, seamless transition, sudden stop
+
+SCALE VOCABULARY — single words that set scene scale, use where appropriate:
+expansive, epic, intimate, claustrophobic
+
+FILM + VISUAL VOCABULARY — confirmed LTX-native style terms:
+film grain, lens flare, motion blur, depth of field, shallow depth of field, particle systems
+
+AUDIO VOCABULARY — confirmed LTX-native voice and sound descriptors:
+whisper, mutter, shout, scream, resonant, gravelly, breathless, crisp, muffled, distorted, room tone, ambient hum
+
+QUALITY TERMS — weave these naturally into the paragraph where they fit the sentence, do not tack them on as a standalone closing list:
+cinematic, ultra-detailed, sharp focus, photorealistic, masterpiece, maintains realistic human anatomy and natural joint rotation throughout
+
+DIALOGUE FORMAT — STRICT:
+All spoken words use this format: she says: words here, or he says: words here.
+NEVER wrap spoken words in quotation marks. No "quotes" around dialogue. Ever.
+Wrong: she says "hey baby" — Right: she says: hey baby,
+Wrong: he responds, "well I only have 1$" — Right: he says: well I only have 1$,
+
+Output only the prompt. Nothing before it, nothing after it."""
+
+# ── LTX 2.3 — Screenplay mode ────────────────────────────────────────────
+SYSTEM_LTX_SCREENPLAY = """Write a prompt for LTX Video 2.3 in screenplay format. No preamble, no explanation. Begin immediately with the first character.
+
+OUTPUT — write these sections in order, separated by a blank line. Do NOT write any section headers or labels. Do not write "CHARACTERS", "SCENE", "ACTION + DIALOGUE" or any other label. Just the content.
+
+SECTION 1 — one separate paragraph per character, blank line between them.
+Invent a name, age, and full physical description for every character the user did not describe. Be specific: first name, age, hair colour and length, eye colour, skin tone, build, notable physical features. One character per paragraph, nothing else on that line.
+Example output for two characters:
+Becky, 21. Long natural blonde hair, blue eyes, pale skin, slim build, medium full breasts, small waist, soft hands.
+
+John, 34. Short dark hair, brown eyes, light brown skin, medium-athletic build, broad shoulders, defined chest and abs.
+
+SECTION 2 — one paragraph describing the location.
+Time of day, light source and colour temperature, surface textures, atmosphere, ambient sound. Specific and grounded.
+Example: A softly lit bedroom at night. Warm amber bedside lamp casting long shadows across white cotton sheets. Dark hardwood floor, city noise muffled behind closed curtains, the low hum of traffic outside.
+
+SECTION 3 onwards — one paragraph per action beat, blank line between each.
+Each beat: physical action in present tense, dialogue in "quotes" with voice quality noted, camera move and what it finds, dominant sound. 2–4 sentences per beat. Alternate between characters. Keep actions physically simple — hip movement, weight shifts, reaching, turning, leaning. Do not write complex choreography. Do not write a label before each beat. Just write the paragraph and leave a blank line.
+Only write as many beats as the duration needs. When done, stop — do not write a trailing label or empty section."""
+
+# ── LTX 2.3 — Shot Script mode ───────────────────────────────────────────
+SYSTEM_LTX_SHOTSCRIPT = """Write a production shot script for LTX Video 2.3. No preamble, no explanation. Begin immediately with the starting image description.
+
+CHARACTERS — derive from the scene. Could be a woman, a man, two men, a creature, a robot, a kettle, anything. Use whatever label fits: Woman, Man, Voice, Creature, Machine, the character name — whatever makes sense. Never default to Woman if the scene has other subjects.
+
+OUTPUT FORMAT — follow exactly:
+
+Starting Image Description:
+The scene opens on the exact image: [one sentence grounding exactly what the start frame shows — all subjects, state/clothing/position, environment, lighting]
+
+[0-7 sec]
+[Action paragraph — 3 to 5 sentences. Present tense. Physical action only. Who moves, what moves, how fast, direction, physical response. Camera framing and one camera move included.]
+Background Sounds: [specific ambient and action sounds, comma-separated]
+[Subject A label] ([voice quality]): "[their line or sound]"
+[Subject B label] ([voice quality]): "[their line or sound]"
+
+[8-14 sec]
+[same structure — escalate from previous segment]
+Background Sounds: [...]
+[Subject A label] ([voice quality]): "[line]"
+[Subject B label] ([voice quality]): "[line]"
+
+[continue segments at 7-second intervals until clip duration is covered]
+
+End of [X]-second scene.
+
+TIMING RULES:
+- Segments use whole numbers only — never decimals. [0-7 sec], [8-14 sec], [15-21 sec], [22-28 sec] etc.
+- Each segment header format is exactly: [START-END sec] — square brackets, space before sec, nothing else on that line.
+- Segment length is 6-7 seconds. Final segment ends at the clip total duration rounded to whole seconds.
+
+DIALOGUE RULES:
+- Every segment gets one voice line per subject present in the scene.
+- If a subject has no words, they still get a line — a sound, breath, grunt, creak, hiss, whatever fits them.
+- Voice quality options: breathy, gasping, low, whispered, sharp, trembling, ecstatic, laughing, moaning, crying out, flat, nervous, confident, amused, mechanical, distorted.
+- Never stack multiple lines for the same subject in one segment.
+- All subjects must get a line every segment — nobody goes silent.
+
+CONTENT RULES:
+- Action paragraphs: physical only, no internal states, no abstract emotion labels.
+- Correct anatomical terms where relevant. No euphemisms.
+- Final segment must hit a clear peak — physical, emotional, or dramatic.
+- No section headers like BLOCK 1 or CHARACTERS. Just the content in the format above.
+
+Output only the shot script. Nothing before Starting Image Description: and nothing after End of X-second scene."""
+
+# ── Wan 2.2 ──────────────────────────────────────────────────────────────
+SYSTEM_WAN = """You write prompts for Wan 2.2, a video diffusion model optimised for cinematic motion, camera control, and physical realism. Output one paragraph of 80-120 words — no preamble, no label, no markdown.
+
+═══ WAN 2.2 PROMPT STRUCTURE ═══
+Write in this order:
+1. SUBJECT — who/what, precise physical description, clothing, expression
+2. SCENE — location with texture, surface detail, colour palette
+3. MOTION — what moves and exactly how: speed, direction, energy. Use active power verbs. Be explicit about body motion, environmental motion, and camera motion separately.
+4. CAMERA — standard cinematography terms: wide shot, close-up, slow dolly-in, tracking shot, whip pan, push in, arc, tilt. State start position and movement.
+5. LIGHTING — source, quality, colour temperature, how it hits skin and surfaces
+6. AESTHETIC — style tags: "photoreal 4K", "film grain 16mm", "anamorphic bokeh", "teal-and-orange grade"
+
+═══ MOTION LANGUAGE ═══
+Wan 2.2 understands motion at a deep level. Use power verbs: burst, surge, snap, rush, drift, sweep, cascade, ripple.
+Distinguish: subject motion / environmental motion / camera motion — name all three.
+Foreground/background parallax: "subject walks forward while background trees sway independently".
+Speed modifiers: slow-motion, gradual, sudden, instant, sustained.
+
+═══ I2V GROUNDING (when an image is provided) ═══
+Describe ONLY motion — not what is visible (the image provides that). Animate the existing elements.
+Lock face and identity: "face and eyes remain stable, only [element] moves".
+Negative guidance: morphing, warping, face deformation, flickering, inconsistent lighting.
+
+═══ NSFW ═══
+Direct anatomical terms. Motion physics explicit: weight, bounce, thrust, grip, rhythm.
+Camera language: push in to close-up, slow arc around subject, rack focus face to body.
+Lighting: warm amber finds skin curves, rim light separates subject from background.
+
+═══ UNDRESSING ═══
+Motion-first. Every garment removal described as physical action with direction, speed, and material response.
+T-SHIRT: hands grip hem, slow upward pull, fabric rising over skin, hair catching then cascading.
+BRA: fingers reach behind, clasp releases, straps drawn off shoulders one by one, cups fall away.
+PANTIES: thumbs hook waistband, slow push down over hips and thighs, fabric dropping to ankles."""
+
+# ── Flux.1 ────────────────────────────────────────────────────────────────
+SYSTEM_FLUX = """You write prompts for Flux.1, a text-to-image model that uses natural language (not keyword lists). It has a T5-XXL text encoder and weights earlier tokens more heavily. Output one structured paragraph — no preamble, no label, no markdown.
+
+═══ FLUX.1 PROMPT STRUCTURE (follow this order exactly) ═══
+1. SUBJECT — what is the image of? State first, every time.
+2. ACTION / POSE — what is the subject doing?
+3. ENVIRONMENT — where is this happening? Specific, named, physically grounded.
+4. LIGHTING — source, quality, colour temperature, how it falls on the subject.
+5. STYLE / TECHNICAL — camera body, lens, focal length, f-stop, film stock, colour grade, artistic movement.
+6. MOOD — emotional atmosphere, one or two words woven into the description.
+
+═══ FLUX.1 RULES ═══
+- Natural language sentences. NO keyword lists. NO prompt weights (no parentheses with numbers).
+- Do NOT use "white background" — causes blur artefacts.
+- Subject first — CLIP weights earlier tokens heavily. Burying the subject at the end is the most common mistake.
+- Be specific and organised. Describe elements in a logical spatial order.
+- One cohesive style — do not mix conflicting aesthetics (e.g. cyberpunk + medieval).
+- For text in the image: use quotation marks around the exact text string.
+- Describe spatial relationships explicitly: "in front of", "visible through the window", "behind the subject".
+
+EXAMPLE STRUCTURE:
+"Close-up portrait of [subject with specific physical details], [action/pose], [specific named location with texture and light quality], [lighting description], shot on [camera] with [lens], [film stock or grade], [mood]."
+
+═══ NSFW ═══
+Natural language, anatomically precise, physically grounded descriptions.
+Lighting and composition described exactly as you would a non-NSFW shot — just with explicit subject matter.
+State position, action, body response, camera framing, and lighting all in coherent natural sentences.
+
+═══ UNDRESSING ═══
+Describe the moment in the undressing sequence — the physical state of the garment, the body's response, the lighting on skin. Static image: pick the most visually powerful moment in the sequence and describe it as a held frame."""
+
+# ── SDXL 1.0 ─────────────────────────────────────────────────────────────
+SYSTEM_SDXL = """You write prompts for SDXL 1.0 and its fine-tunes (Juggernaut XL, RealVisXL, etc.). These models respond best to comma-separated tag-style prompts with quality headers, NOT long natural language paragraphs. Output ONLY the prompt tags and a negative prompt section — no explanation, no markdown, no intro.
+
+═══ SDXL TAG PROMPT STRUCTURE ═══
+Output exactly this format:
+
+POSITIVE:
+[quality tags], [subject], [clothing/state], [action/pose], [shot type], [location], [lighting], [style/medium], [additional detail tags]
+
+NEGATIVE:
+[negative tags]
+
+═══ QUALITY HEADER (always start with these) ═══
+masterpiece, best quality, ultra-detailed, 8k, photorealistic, sharp focus
+
+═══ TAG ORDERING (most important first — CLIP reads earlier tokens with more weight) ═══
+1. Quality meta tags
+2. Subject (1girl / 1boy / 1woman / couple / etc.)
+3. Physical description (hair colour, eye colour, skin tone, body type)
+4. Clothing or lack thereof — be explicit for NSFW
+5. Action / pose / expression
+6. Shot type (close-up, full body, cowgirl shot, from above, from below, dutch angle, pov)
+7. Location / background
+8. Lighting (studio lighting, rim light, ambient occlusion, volumetric light, neon, golden hour)
+9. Style tags (hyperrealistic, cinematic, film grain, bokeh, depth of field)
+10. Camera (shot on Canon EOS R5, 85mm lens, f/1.4)
+
+═══ SDXL TAG DEPTH — BE THOROUGH ═══
+Generate at minimum 30-45 tags. Cover face details (eye colour, expression, lips), hair (colour, length, style), body (build, skin tone), clothing (every garment, colour, material), pose, shot type, location with surface texture, lighting (source + effect on skin), and style/camera tags. More specific = better results.
+- Use spaces NOT underscores (SDXL CLIP was trained on natural language, spaces work better than danbooru underscores)
+- Prompt weights work: use (tag:1.3) to emphasise, (tag:0.7) to reduce
+- Negative prompt is ESSENTIAL — always output one
+- No sentence structure needed — tags separated by commas only
+
+═══ STANDARD NEGATIVE PROMPT (always include, add to as needed) ═══
+worst quality, bad quality, low quality, lowres, blurry, jpeg artifacts, deformed, bad anatomy, bad hands, missing fingers, extra limbs, watermark, signature, text, logo, cropped, out of frame, ugly, duplicate, mutilated, poorly drawn face
+
+═══ NSFW POSITIVE TAGS ═══
+Use explicit anatomical tag terms directly. State: body position, body parts visible, action occurring, shot framing.
+Example structure: 1woman, nude, [body description], [explicit action], [position], [shot type], explicit, nsfw
+
+NSFW NEGATIVE additions: censored, mosaic censoring, censor bar, blurred, covered"""
+
+# ── Pony XL ───────────────────────────────────────────────────────────────
+SYSTEM_PONY = """You write prompts for Pony Diffusion XL v6 and Pony-based fine-tunes (Autismix, Hassaku XL, etc.). These models use a hybrid of Danbooru booru tags and e621 tags, with a mandatory score/rating prefix. Output ONLY the prompt — no explanation, no markdown, no intro.
+
+═══ PONY XL PROMPT STRUCTURE ═══
+Output exactly this format:
+
+POSITIVE:
+[score prefix], [rating tag], [subject tags], [physical tags], [clothing/state tags], [action/pose tags], [shot/framing tags], [location tags], [lighting tags], [style tags], [quality tags]
+
+NEGATIVE:
+[negative tags]
+
+═══ MANDATORY SCORE PREFIX (always first) ═══
+score_9, score_8_up, score_7_up
+
+═══ RATING TAGS (choose one based on content) ═══
+SFW content: rating_safe
+Suggestive content: rating_questionable
+Explicit content: rating_explicit
+
+═══ BOORU TAG STYLE ═══
+- Use Danbooru / e621 tag format: underscores for multi-word tags (long_hair, blue_eyes, full_body)
+- Comma-separated, no sentences
+- Tags are case-sensitive in some models — use lowercase
+- Subject count tags: 1girl, 1boy, 2girls, couple, group
+- Prompt weights work with parentheses: (long_hair:1.3)
+
+═══ TAG DEPTH — BE THOROUGH ═══
+Generate at minimum 35-50 tags in the positive prompt. Cover ALL of these layers:
+- Score + rating (3 tags)
+- Subject count (1 tag)
+- Face: eye colour, eye shape, eyebrows, lips, expression (5+ tags)
+- Hair: colour, length, style, texture (4+ tags)
+- Body: build, skin tone, any notable features (3+ tags)
+- Clothing: every garment named, colour, material (4+ tags) — or nudity state if applicable
+- Pose + action: specific body position, limb placement (3+ tags)
+- Shot framing: distance, angle, perspective (2+ tags)
+- Location: specific named place + surface + atmosphere (4+ tags)
+- Lighting: source, quality, colour temp, effect on skin (3+ tags)
+- Style + quality tail (4+ tags)
+
+═══ PHYSICAL / CLOTHING TAGS ═══
+Hair: [colour]_hair, [length]_hair, [style]_hair (e.g. long_black_hair, messy_bun)
+Eyes: [colour]_eyes, [shape]_eyes
+Body: large_breasts, slim_waist, muscular, petite, tall, short
+Clothing state: fully_clothed, partially_clothed, topless, bottomless, nude, naked
+
+═══ ACTION / POSE TAGS ═══
+standing, sitting, lying, kneeling, crouching, leaning, spread_legs, on_all_fours, cowgirl_position, missionary
+
+═══ SHOT / FRAMING TAGS ═══
+close-up, portrait, full_body, cowgirl_shot, from_above, from_below, from_behind, dutch_angle, pov, selfie
+
+═══ QUALITY TAIL (always end positive with) ═══
+absurdres, highres, very_aesthetic, newest
+
+═══ NSFW TAGS ═══
+After rating_explicit: use explicit Danbooru anatomical tags directly.
+Explicit action tags: sex, penetration, vaginal, anal, oral, handjob, fingering, cumshot, creampie, etc.
+Position tags: missionary, cowgirl_position, doggy_style, reverse_cowgirl, standing_sex, mating_press
+
+═══ STANDARD NEGATIVE ═══
+worst_quality, bad_quality, lowres, bad_anatomy, bad_hands, missing_fingers, watermark, signature, censored, blurry, jpeg_artifacts, ugly"""
+
+# ── SD 1.5 ────────────────────────────────────────────────────────────────
+SYSTEM_SD15 = """You write prompts for Stable Diffusion 1.5 and its fine-tunes (Realistic Vision, DreamShaper, AbsoluteReality, etc.). SD 1.5 uses a 75-token CLIP limit — keep positive prompts under 75 tokens. Use weighted natural language with prompt emphasis syntax. Output ONLY the prompt — no explanation, no markdown.
+
+═══ SD 1.5 PROMPT STRUCTURE ═══
+Output exactly this format:
+
+POSITIVE:
+[quality header], [subject description], [action/pose], [location], [lighting], [style], [technical tags]
+
+NEGATIVE:
+[negative tags]
+
+═══ SD 1.5 TOKEN LIMIT RULES ═══
+- Hard limit: 75 tokens per segment (roughly 60-70 words)
+- Exceed 75 tokens and quality drops — the model batches in groups of 75
+- Prioritise: subject + action + quality > location > style
+- Drop less important details before exceeding the limit
+
+═══ SD 1.5 RICHNESS — pack detail into every token ═══
+Work right up to the 75-token limit. Cover: subject physical description (hair, eyes, skin, body), clothing or lack thereof, action/pose, specific named location, lighting source + effect, style. Use weights on the 3-4 most important elements. Never leave tokens on the table — a sparse prompt is a wasted prompt.
+(tag:1.3) — increases attention (max practical: 1.5)
+(tag:0.7) — decreases attention
+[tag] — slight decrease
+{tag} — slight increase (A1111 syntax)
+
+═══ QUALITY HEADER (always first) ═══
+(masterpiece:1.2), (best quality:1.1), ultra-detailed, 8k, photorealistic
+
+═══ SD 1.5 STYLE KEYWORDS ═══
+Realism: photorealistic, hyperrealistic, cinematic, film grain, RAW photo, analog photography
+Artistic: digital art, oil painting, concept art, illustration, anime style
+Lighting: (dramatic lighting:1.2), volumetric light, rim light, golden hour, neon glow, studio lighting
+
+═══ NSFW ═══
+SD 1.5 is well-trained on NSFW content — explicit tags work well.
+Use: (nude:1.2), explicit, [anatomical terms], [position], [action], [body part focus]
+Weight explicit elements slightly: (large breasts:1.1), (spread legs:1.2)
+
+═══ STANDARD NEGATIVE (keep under 75 tokens) ═══
+worst quality, bad quality, blurry, low resolution, deformed, bad anatomy, extra limbs, missing fingers, watermark, text, ugly, duplicate, out of frame"""
+
+
+# ══════════════════════════════════════════════════════════════════════════
+#  SYSTEM PROMPT ROUTER
+# ══════════════════════════════════════════════════════════════════════════
+def get_system_prompt(target_model: str, screenplay_mode: bool = False,
+                      shotscript_mode: bool = False,
+                      animation_preset: str = "None") -> str:
+    if "LTX" in target_model:
+        if shotscript_mode:
+            base = SYSTEM_LTX_SHOTSCRIPT
+        elif screenplay_mode:
+            base = SYSTEM_LTX_SCREENPLAY
+        else:
+            base = SYSTEM_LTX
+    elif "Wan" in target_model:
+        base = SYSTEM_WAN
+    elif "Flux" in target_model:
+        base = SYSTEM_FLUX
+    elif "SDXL" in target_model:
+        base = SYSTEM_SDXL
+    elif "Pony" in target_model:
+        base = SYSTEM_PONY
+    elif "SD 1.5" in target_model:
+        base = SYSTEM_SD15
+    else:
+        base = SYSTEM_FLUX
+
+    # Prepend animation style tag at the very top of system prompt
+    if animation_preset and animation_preset != "None":
+        preset = ANIMATION_PRESETS.get(animation_preset)
+        if preset:
+            style_tag = preset.get("style_tag", "")
+            if style_tag:
+                base = (
+                    f"RENDER STYLE — START YOUR PROMPT WITH THIS: {style_tag}\n"
+                    f"The very first words of your output must name the animation style. "
+                    f"Example opening: '{style_tag.split(',')[0]}, ...' — then continue with the scene.\n"
+                    f"Do NOT describe photorealistic skin, film cameras, or live-action lighting.\n"
+                    f"Do NOT end with 'cinematic, ultra-detailed, sharp focus, photorealistic, masterpiece' — replace with the animation style tag instead.\n"
+                    f"STRICT NO-REPEAT RULE: If a line of dialogue appears in the action, do NOT quote it again in the audio layer. Name it by reference only: 'the spoken exchange between Rick and Morty' — never reprint the words.\n\n"
+                ) + base
+    else:
+        pass  # quality tail stays for non-animation prompts
+
+    return base
+
+
+def is_video_model(target_model: str) -> bool:
+    return "LTX" in target_model or "Wan" in target_model
+
+
+def has_audio(target_model: str) -> bool:
+    return "LTX" in target_model
+
+
+# ══════════════════════════════════════════════════════════════════════════
+#  HELPERS — llama-server auto-detect/install + GGUF scanner
+# ══════════════════════════════════════════════════════════════════════════
+
+LLAMA_INSTALL_DIR = r"C:\llama"
+LLAMA_EXE         = r"C:\llama\llama-server.exe"
+MODELS_DIR        = r"C:\models"
+LLAMA_PORT        = 8080
+LLAMA_RELEASE_URL = (
+    "https://github.com/ggml-org/llama.cpp/releases/download/b8846/"
+    "llama-b8846-bin-win-cuda-cu13.1-x64.zip"
+)
+
+
+def _scan_models_folder() -> list:
+    """Return list of .gguf filenames in C:\\models\\, or a placeholder."""
+    if not os.path.isdir(MODELS_DIR):
+        try:
+            os.makedirs(MODELS_DIR, exist_ok=True)
+        except Exception:
+            pass
+    try:
+        ggufs = sorted([f for f in os.listdir(MODELS_DIR) if f.lower().endswith(".gguf") and "mmproj" not in f.lower()])
+        return ggufs if ggufs else ["No GGUFs found in C:\\models\\"]
+    except Exception:
+        return ["No GGUFs found in C:\\models\\"]
+
+
+# ══════════════════════════════════════════════════════════════════════════
+#  NODE CLASS
+# ══════════════════════════════════════════════════════════════════════════
+
+class Gemma4PromptGen:
+    """
+    Multi-model prompt engineer with Gemma 4 llama-server backend.
+
+    Supports: LTX 2.3, Wan 2.2, Flux.1, SDXL 1.0, Pony XL, SD 1.5.
+    All models support NSFW content, image grounding, character lock, environments.
+
+    PREVIEW: flushes VRAM, calls llama-server, stores prompt, halts pipeline.
+    SEND:    outputs stored prompt, kills llama-server process, frees VRAM.
+    """
+
+    _last_prompt = ""
+    _last_neg     = ""
+    _last_qc      = ""
+    _last_trigger = ""
+    _llama_process = None
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        env_keys = list(ENVIRONMENT_PRESETS.keys())
+        return {
+            "required": {
+                "mode": (["PREVIEW", "SEND"],),
+                "target_model": (
+                    TARGET_MODELS,
+                    {
+                        "default": TARGET_MODELS[0],
+                        "tooltip": (
+                            "Which model to generate the prompt FOR. "
+                            "Each model has a completely different prompt style: "
+                            "LTX 2.3 = cinematic arc + audio, "
+                            "Wan 2.2 = motion-first, "
+                            "Flux.1 = natural language, "
+                            "SDXL = booru tag style, "
+                            "Pony XL = score + rating prefix booru tags, "
+                            "SD 1.5 = weighted classic."
+                        ),
+                    }
+                ),
+                "instruction": (
+                    "STRING",
+                    {
+                        "multiline": True,
+                        "default": "a woman in a red dress standing on a rain-soaked rooftop at night",
+                        "placeholder": "Describe the scene — characters, action, mood, position, clothing...",
+                    },
+                ),
+                # ── MOST USED ──────────────────────────────────────────────
+                "🌍 environment": (env_keys, {
+                    "default": "None — LLM decides",
+                    "tooltip": "Location preset — injects rich location, lighting and sound context. Video models use all three layers; image models use location + lighting.",
+                }),
+                "🎬 content_gate": (
+                    ["Auto", "SFW", "NSFW"],
+                    {
+                        "default": "Auto",
+                        "tooltip": (
+                            "Content gate — hard override for how ambiguous language is interpreted.\n"
+                            "Auto: context decides. NSFW keywords → explicit, clean keywords → clean.\n"
+                            "SFW: forces the cleanest possible interpretation of everything. "
+                            "\"woman rubs her pussy\" → she is petting a cat. "
+                            "No sexual content regardless of wording. Wildcards draw from SFW pools only.\n"
+                            "NSFW: forces explicit interpretation of everything. "
+                            "\"woman strokes her cat\" → she is not stroking a cat. "
+                            "Ambiguous language is always read as sexual. Wildcards go filthy."
+                        ),
+                    },
+                ),
+                "⚡ energy": (
+                    ["Auto", "Fun", "Intense", "Extreme"],
+                    {
+                        "default": "Intense",
+                        "tooltip": (
+                            "Scene energy dial. "
+                            "Auto: reads your scene and picks — comedy gets Fun, action gets Extreme, romance gets Intense, etc. "
+                            "Fun: light, playful, loose camera, laughing dialogue, reactions exaggerated. "
+                            "Intense: focused, charged, tight camera, direct dialogue, stakes feel real. "
+                            "Extreme: maximum everything — CAPS shouting at peak moments, physical extremity, no restraint, profanity where it fits. "
+                            "Context-aware: reads your scene type and responds accordingly."
+                        ),
+                    },
+                ),
+                "🖼️ use_image": ("BOOLEAN", {
+                    "default": False,
+                    "tooltip": (
+                        "Enable image grounding — activates vision input for the model.\n"
+                        "• first_frame only: standard I2V grounding (describe what to animate).\n"
+                        "• first_frame + last_frame: bracketed mode — model writes the journey between them.\n"
+                        "• video_frames: video continuation mode — model samples frames and writes a prompt "
+                        "that continues from where the clip ends.\n"
+                        "Requires at least one image pin connected and mmproj GGUF alongside the model."
+                    ),
+                }),
+                # ── SCENE STYLE ────────────────────────────────────────────
+                "💬 dialogue": (
+                    ["Off", "Auto", "More", "Unleashed"],
+                    {
+                        "default": "Off",
+                        "tooltip": (
+                            "Dialogue density. Off: none. "
+                            "Auto: injected when contextually appropriate. "
+                            "More: required every beat, 3-4 lines minimum. "
+                            "Unleashed: dialogue IS the video — every sentence has spoken words, characters talk the entire runtime."
+                        ),
+                    },
+                ),
+                "🎨 style_preset": (
+                    STYLE_PRESET_KEYS,
+                    {
+                        "default": "None",
+                        "tooltip": (
+                            "Visual style preset — shifts colour palette, camera language, and rendering aesthetic. "
+                            "Film Noir, Cyberpunk, VHS, K-Drama, Music Video, 2D Cartoon, 3D CGI, Anime Action, "
+                            "Macro Hyperrealistic, Nature/No People. Independent of animation and environment."
+                        ),
+                    },
+                ),
+                "🎭 animation_preset": (
+                    list(ANIMATION_PRESETS.keys()),
+                    {
+                        "default": "None",
+                        "tooltip": (
+                            "Animation preset — pre-loads character names, locations, and tone from iconic cartoons "
+                            "trained natively in LTX 2.3. Separate from environment — this is about the CHARACTER UNIVERSE. "
+                            "Select a show then describe your scene using character names."
+                        ),
+                    },
+                ),
+                # ── LESS USED ──────────────────────────────────────────────
+                "🎞 lora_preset": (
+                    list(LORA_PRESETS.keys()),
+                    {
+                        "default": "None",
+                        "tooltip": (
+                            "LoRA preset — activates a fixed shot structure for a specific trained LoRA. "
+                            "None = standard freestyle generation. "
+                            "Blink Titjob = JMPCT jumpcut structure with auto dialogue injected around the subject."
+                        ),
+                    },
+                ),
+                "📝 screenplay_mode": ("BOOLEAN", {
+                    "default": False,
+                    "tooltip": (
+                        "LTX 2.3 only. Instead of one flowing paragraph, generates a structured "
+                        "screenplay-style prompt: Characters block, Scene block, then alternating "
+                        "Action+Dialogue beats. Invents names, ages, and physical details for any "
+                        "unspecified characters. Good for scenes with dialogue and multiple beats."
+                    ),
+                }),
+                "🎬 shotscript_mode": ("BOOLEAN", {
+                    "default": False,
+                    "tooltip": (
+                        "LTX 2.3 only. Generates a time-coded production shot script: "
+                        "Starting Image Description block, then [0-6 sec] / [7-13 sec] etc. segments, "
+                        "each with action paragraph, Background Sounds line, and dialogue/sound line. "
+                        "Designed for musubi-tuner AV training captions and LTX I2V workflows. "
+                        "Overrides screenplay_mode if both are on."
+                    ),
+                }),
+            },
+            "optional": {
+                # ── IMAGE INPUT ────────────────────────────────────────────
+                "image": ("IMAGE", {
+                    "tooltip": (
+                        "Reference image for I2V / I2I grounding. Connect any IMAGE tensor — "
+                        "load image node, VAE decoded frame, anything. "
+                        "Requires use_image toggle ON and mmproj GGUF alongside the model. "
+                        "The node encodes the first frame of the batch to JPEG and sends it to vision."
+                    ),
+                }),
+                # ── FREQUENTLY TUNED ──────────────────────────────────────
+                "👤 character": ("STRING", {
+                    "multiline": False,
+                    "default": "",
+                    "tooltip": (
+                        "Character lock — paste your LoRA trigger or character description. "
+                        "e.g. 'tall Korean woman, short black bob, white dress shirt, red lips'. "
+                        "Used exactly as written."
+                    ),
+                }),
+                "🎞️ frame_count": ("INT", {
+                    "default": 257, "min": 1, "max": 2000, "step": 1,
+                    "tooltip": "LTX/Wan frame count @ 25fps. 257 = ~10s. Only used for video models.",
+                }),
+                "🎯 pov_mode": (
+                    ["Off", "POV Female", "POV Male"],
+                    {
+                        "default": "Off",
+                        "tooltip": (
+                            "First-person POV mode. Camera IS the viewer's eyes. "
+                            "POV Female: viewer is the woman — sees her own hands, body, perspective. "
+                            "POV Male: viewer is the man — sees his own hands extending into scene. "
+                            "Everything described from that perspective only."
+                        ),
+                    },
+                ),
+                "🌡️ temperature": (
+                    ["Focused (0.7)", "Default (1.0)", "Creative (1.2)", "Unhinged (1.4)"],
+                    {
+                        "default": "Default (1.0)",
+                        "tooltip": (
+                            "LLM sampling temperature — controls output randomness and creativity.\n"
+                            "Focused (0.7): consistent, structured, less drift — good for booru tags and tight character descriptions.\n"
+                            "Default (1.0): balanced — creative but coherent. Works for everything.\n"
+                            "Creative (1.2): more unexpected word choices, richer variation, occasional weirdness.\n"
+                            "Unhinged (1.4): the model goes wherever it wants — surprising results, occasional garbage."
+                        ),
+                    },
+                ),
+                "🎲 seed_mode": (
+                    ["Random", "Increment", "Fixed"],
+                    {
+                        "default": "Random",
+                        "tooltip": (
+                            "Seed behaviour. "
+                            "Random: new seed every run. "
+                            "Increment: seed increases by 1 each run. "
+                            "Fixed: always uses the seed value below."
+                        ),
+                    },
+                ),
+                "🌱 seed": ("INT", {
+                    "default": 0, "min": 0, "max": 2**31 - 1, "step": 1,
+                    "tooltip": "Seed value. Used directly in Fixed mode, as starting point in Increment mode, ignored in Random mode.",
+                }),
+                # ── BACKEND CONFIG ─────────────────────────────────────────
+                "🖥️ llama_server_url": (
+                    "STRING",
+                    {
+                        "default": "http://127.0.0.1:8080",
+                        "tooltip": "llama-server base URL. Default: http://127.0.0.1:8080",
+                    },
+                ),
+                "🧠 gguf_model": (
+                    _scan_models_folder(),
+                    {
+                        "default": _scan_models_folder()[0],
+                        "tooltip": "GGUF model from C:\\models\\. Add files there and restart ComfyUI to refresh.",
+                    },
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "STRING", "STRING", "STRING", "STRING",)
+    RETURN_NAMES = ("preview_prompt", "send_prompt", "neg_prompt", "qc_report", "lora_trigger",)
+    FUNCTION = "execute"
+    CATEGORY = "LoRa-Daddy/Gemma4"
+    OUTPUT_NODE = True
+
+    def execute(self, **kwargs):
+        # ── Resolve all inputs from kwargs ───────────────────────────────
+        # INPUT_TYPES uses emoji-prefixed keys (e.g. "🌍 environment") which are
+        # valid dict keys but cannot be Python kwarg names.  ComfyUI passes them
+        # through **kwargs, so we normalise here by stripping any leading emoji /
+        # non-ASCII prefix up to and including the first space.
+        def _kw(primary, *aliases, default=None):
+            """Return the first matching value from kwargs, trying primary key,
+            then bare snake_case fallback, then aliases."""
+            for key in (primary, *aliases):
+                if key in kwargs:
+                    return kwargs[key]
+            return default
+
+        mode              = _kw("mode")
+        target_model      = _kw("target_model")
+        instruction       = _kw("instruction", default="")
+        environment       = _kw("🌍 environment",    "environment",       default="None — LLM decides")
+        content_gate      = _kw("🎬 content_gate",   "content_gate",      default="Auto")
+        energy            = _kw("⚡ energy",          "energy",            default="Intense")
+        use_image         = _kw("🖼️ use_image",       "use_image",         default=False)
+        dialogue          = _kw("💬 dialogue",        "dialogue",          default="Off")
+        style_preset      = _kw("🎨 style_preset",    "style_preset",      default="None")
+        animation_preset  = _kw("🎭 animation_preset","animation_preset",  default="None")
+        lora_preset       = _kw("🎞 lora_preset",     "lora_preset",       default="None")
+        screenplay_mode   = _kw("📝 screenplay_mode", "screenplay_mode",   default=False)
+        shotscript_mode   = _kw("🎬 shotscript_mode", "shotscript_mode",   default=False)
+        # removed: wildcards, caption_bridge, word_target, auto_retry
+        wildcards         = False
+        caption_bridge    = False
+        word_target       = 0
+        auto_retry        = False
+        # optional inputs
+        image             = _kw("image",                                    default=None)
+        character         = _kw("👤 character",       "character",         default="")
+        frame_count       = _kw("🎞️ frame_count",     "frame_count",       default=257)
+        pov_mode          = _kw("🎯 pov_mode",        "pov_mode",          default="Off")
+        temperature       = _kw("🌡️ temperature",     "temperature",       default="Default (1.0)")
+        seed_mode         = _kw("🎲 seed_mode",        "seed_mode",         default="Random")
+        seed              = _kw("🌱 seed",             "seed",              default=0)
+        llama_server_url  = _kw("🖥️ llama_server_url","llama_server_url",  default="http://127.0.0.1:8080")
+        gguf_model        = _kw("🧠 gguf_model",      "gguf_model",        default="")
+
+        # ── Seed resolution ──────────────────────────────────────────────
+        if seed_mode == "Random":
+            import random as _rand
+            seed = _rand.randint(0, 2**31 - 1)
+        elif seed_mode == "Increment":
+            seed = (seed + 1) % (2**31)
+        # Fixed: use seed as-is
+
+        # ── LoRA preset injection ─────────────────────────────────────────
+        _lora_trigger    = ""
+        _lora_sys_override = None
+        if lora_preset and lora_preset != "None":
+            preset_def = LORA_PRESETS.get(lora_preset)
+            if preset_def:
+                _lora_trigger      = preset_def["trigger"]
+                _lora_sys_override = preset_def.get("system_prompt_override")
+                # Instruction becomes the user's subject description (or blank = "woman")
+                # The base caption is baked into the system prompt override
+                subject = instruction.strip() or "woman"
+                instruction = (
+                    f"Subject from image: {subject}\n"
+                    f"Make the dialogue contextual to this subject's appearance and character."
+                    if subject != "woman" else
+                    "Enhance the base caption. Make dialogue natural and contextual to what you see in the image."
+                )
+                # Cap word_target to keep it tight
+                word_target = 80
+                print(f"[Gemma4PromptGen] LoRA preset '{lora_preset}' — trigger: {_lora_trigger}, system override active")
+
+        if not llama_server_url or not llama_server_url.strip():
+            llama_server_url = "http://127.0.0.1:8080"
+        llama_server_url = llama_server_url.rstrip("/")
+
+        # Resolve full model path
+        models_dir = r"C:\models"
+        if gguf_model and gguf_model != "No GGUFs found in C:\\models\\":
+            model_path = os.path.join(models_dir, gguf_model)
+        else:
+            # fallback — first gguf in C:\models
+            found = [f for f in os.listdir(models_dir) if f.endswith(".gguf")] if os.path.isdir(models_dir) else []
+            if not found:
+                return ("❌ No GGUF files found in C:\\models\\. Add a GGUF and restart ComfyUI.", "", "", "", "",)
+            model_path = os.path.join(models_dir, found[0])
+
+        # ── PREVIEW MODE ────────────────────────────────────────────────
+        if mode == "PREVIEW":
+            # Flush ComfyUI models from VRAM first
+            try:
+                import comfy.model_management as _mm
+                _mm.unload_all_models()
+                _mm.soft_empty_cache()
+            except Exception:
+                pass
+            try:
+                import torch
+                if torch.cuda.is_available():
+                    torch.cuda.empty_cache()
+                    torch.cuda.ipc_collect()
+            except Exception:
+                pass
+
+            # Store use_image so _ensure_llama_running can access it
+            self._use_image = use_image and image is not None
+
+            # Auto-find or install llama-server
+            llama_exe = self._find_or_install_llama()
+            if llama_exe.startswith("❌"):
+                return (llama_exe, "", "", "", "",)
+
+            # Boot llama-server
+            boot_status = self._ensure_llama_running(llama_server_url, llama_exe, model_path)
+            print(f"[Gemma4PromptGen] {boot_status}")
+            if boot_status.startswith("❌"):
+                return (boot_status, "", "", "", "",)
+
+            # ── Image grounding — single pin, simple path ─────────────────
+            image_paths = []
+            image_mode  = "none"
+
+            if use_image and image is not None:
+                try:
+                    # Always use the first frame of whatever batch is connected
+                    frame = image[0:1] if image.ndim == 4 else image
+                    path  = self._tensor_to_tempfile(frame)
+                    image_paths.append(path)
+                    image_mode = "single"
+                    print(f"[Gemma4PromptGen] Image grounding active — encoded 1 frame")
+                except Exception as e:
+                    print(f"[Gemma4PromptGen] Image encode failed: {e}")
+
+            print(f"[Gemma4PromptGen] Image mode: {image_mode}, paths: {len(image_paths)}")
+
+            # Wildcard override — replace instruction with random assembled scene
+            # Passes original instruction for content detection + anchoring
+            if wildcards:
+                try:
+                    import sys as _sys
+                    import os as _os
+                    _node_dir = _os.path.dirname(_os.path.abspath(__file__))
+                    if _node_dir not in _sys.path:
+                        _sys.path.insert(0, _node_dir)
+                    from wildcard_suite_gemma4 import build_wildcard_injection
+                    instruction = build_wildcard_injection(
+                        seed=seed,
+                        energy=energy,
+                        instruction=instruction,
+                        content_gate=content_gate,
+                    )
+                    print(f'[Gemma4PromptGen] Wildcards ON — injected:\n{instruction}')
+                except Exception as e:
+                    print(f'[Gemma4PromptGen] Wildcard error: {e} — using original instruction')
+
+            # Resolve temperature float from widget string
+            _temp_map = {
+                "Focused (0.7)":  0.7,
+                "Default (1.0)":  1.0,
+                "Creative (1.2)": 1.2,
+                "Unhinged (1.4)": 1.4,
+            }
+            _temperature_float = _temp_map.get(temperature, 1.0)
+
+            # Build message — use LoRA system prompt override if active
+            system_prompt = _lora_sys_override if _lora_sys_override else get_system_prompt(target_model, screenplay_mode, shotscript_mode, animation_preset)
+            combined = self._build_message(
+                instruction, system_prompt, target_model, environment,
+                frame_count, dialogue, character, seed, image_paths,
+                screenplay_mode, pov_mode, animation_preset, energy,
+                style_preset, word_target, content_gate=content_gate,
+                image_mode=image_mode, caption_bridge=caption_bridge,
+                shotscript_mode=shotscript_mode
+            )
+
+            # Generate
+            neg_prompt = ""   # will be populated by _clean_output if model outputs NEGATIVE: block
+            prompt = self._call_llama(combined, system_prompt, llama_server_url, image_paths,
+                                      frame_count, target_model, word_target,
+                                      temperature_override=_temperature_float)
+
+            # ── Hard-staple LoRA trigger to front of output ───────────────
+            if _lora_trigger and not prompt.startswith("❌") and not prompt.startswith("⚠️"):
+                # Strip any stray trigger the model may have included, then prepend clean
+                p = prompt.strip()
+                if p.upper().startswith(_lora_trigger.upper()):
+                    p = p[len(_lora_trigger):].lstrip(" ,")
+                prompt = f"{_lora_trigger}, {p}"
+
+            # Clean up temp images
+            for p in image_paths:
+                if p and os.path.exists(p):
+                    try:
+                        os.unlink(p)
+                    except Exception:
+                        pass
+
+            prompt, neg_prompt = self._clean_output(prompt, screenplay_mode=(screenplay_mode and "LTX" in target_model), shotscript_mode=(shotscript_mode and "LTX" in target_model))
+
+            # Guard: if cleaning stripped everything (e.g. model only emitted junk/labels),
+            # surface a clear retryable error rather than passing a blank string downstream
+            if not prompt.startswith("❌") and not prompt.startswith("⚠️") and not prompt.strip():
+                prompt = "⚠️ Model returned a blank prompt after cleaning. Re-queue to retry."
+
+            # ── Quality check + optional auto-retry ──────────────────────
+            qc_report = ""
+            if not prompt.startswith("❌") and not prompt.startswith("⚠️"):
+                qc_passed, qc_report, qc_score = self._check_prompt_quality(
+                    prompt, dialogue, energy, frame_count, target_model
+                )
+                print(f"[Gemma4PromptGen] {qc_report}")
+
+                if not qc_passed and auto_retry:
+                    print(f"[Gemma4PromptGen] QC failed ({qc_score}/100) — firing retry with boosted temperature...")
+                    retry_prompt = self._call_llama(
+                        combined, system_prompt, llama_server_url, None,
+                        frame_count, target_model, word_target,
+                        temperature_override=min(1.4, _temperature_float + 0.2)
+                    )
+                    retry_prompt, retry_neg = self._clean_output(
+                        retry_prompt,
+                        screenplay_mode=(screenplay_mode and "LTX" in target_model),
+                        shotscript_mode=(shotscript_mode and "LTX" in target_model)
+                    )
+                    if not retry_prompt.startswith("❌") and not retry_prompt.startswith("⚠️"):
+                        _, retry_report, retry_score = self._check_prompt_quality(
+                            retry_prompt, dialogue, energy, frame_count, target_model
+                        )
+                        print(f"[Gemma4PromptGen] Retry {retry_report}")
+                        if retry_score >= qc_score:
+                            prompt = retry_prompt
+                            neg_prompt = retry_neg
+                            qc_report  = retry_report
+                            print(f"[Gemma4PromptGen] Retry kept ({retry_score} >= {qc_score})")
+                        else:
+                            print(f"[Gemma4PromptGen] Original kept (retry {retry_score} < {qc_score})")
+                    else:
+                        print(f"[Gemma4PromptGen] Retry errored — keeping original")
+
+                Gemma4PromptGen._last_prompt   = prompt
+                Gemma4PromptGen._last_neg      = neg_prompt
+                Gemma4PromptGen._last_qc       = qc_report
+                Gemma4PromptGen._last_trigger  = _lora_trigger
+            else:
+                print(f"[Gemma4PromptGen] Generation error — QC skipped")
+
+            print(f"\n{'='*60}")
+            print(f"GEMMA4 PROMPT GEN — {target_model}")
+            print(f"PREVIEW (stored — switch to SEND when ready):")
+            print(f"{'='*60}")
+            print(prompt[:600] + ("..." if len(prompt) > 600 else ""))
+            print(f"{'='*60}\n")
+
+            def _delayed_interrupt():
+                time.sleep(1.5)
+                interrupted = False
+                # Method 1 — current ComfyUI API
+                try:
+                    import comfy.model_management as mm
+                    mm.interrupt_current_processing()
+                    interrupted = True
+                    print("[Gemma4PromptGen] Pipeline interrupted via model_management.")
+                except Exception:
+                    pass
+                # Method 2 — legacy nodes API
+                if not interrupted:
+                    try:
+                        from nodes import interrupt_processing
+                        interrupt_processing()
+                        interrupted = True
+                        print("[Gemma4PromptGen] Pipeline interrupted via nodes API.")
+                    except Exception:
+                        pass
+                # Method 3 — direct server flag
+                if not interrupted:
+                    try:
+                        import server
+                        server.PromptServer.instance.last_node_id = None
+                        import execution
+                        execution.interrupt_processing_bool = True
+                        print("[Gemma4PromptGen] Pipeline interrupted via execution flag.")
+                    except Exception:
+                        pass
+            threading.Thread(target=_delayed_interrupt, daemon=True).start()
+
+            return (prompt, "", neg_prompt, qc_report, _lora_trigger,)
+
+        # ── SEND MODE ────────────────────────────────────────────────────
+        else:
+            if not Gemma4PromptGen._last_prompt:
+                return ("", "❌ No prompt stored yet. Run PREVIEW first.", "", "", "",)
+
+            final_prompt = Gemma4PromptGen._last_prompt
+            self._kill_llama_server()
+            return (final_prompt, final_prompt, Gemma4PromptGen._last_neg, Gemma4PromptGen._last_qc, Gemma4PromptGen._last_trigger,)
+
+    # ── Image utility ─────────────────────────────────────────────────────
+
+    def _tensor_to_tempfile(self, image_tensor) -> str:
+        """Convert a ComfyUI IMAGE tensor (B, H, W, C) to a temp JPEG for LLM context.
+
+        768px JPEG quality=90 matches the captioner tool settings — consistent vision
+        quality across both tools without blowing the context window.
+        """
+        import numpy as np
+        from PIL import Image as PILImage
+
+        frame = image_tensor[0] if image_tensor.ndim == 4 else image_tensor
+        arr = (frame.cpu().numpy() * 255).clip(0, 255).astype(np.uint8)
+        pil_img = PILImage.fromarray(arr, mode="RGB")
+
+        # 768px matches captioner resolution — enough for the LLM to read subject/scene.
+        max_side = 768
+        w, h = pil_img.size
+        if max(w, h) > max_side:
+            scale = max_side / max(w, h)
+            pil_img = pil_img.resize((int(w * scale), int(h * scale)), PILImage.LANCZOS)
+
+        tmp = tempfile.NamedTemporaryFile(suffix=".jpg", delete=False)
+        pil_img.save(tmp.name, format="JPEG", quality=90, optimize=True)  # matches captioner
+        tmp.close()
+        return tmp.name
+
+    # ── Message builder ───────────────────────────────────────────────────
+
+    def _build_message(self, instruction, system_prompt, target_model,
+                       environment, frame_count, dialogue, character, seed,
+                       image_paths=None, screenplay_mode=False, pov_mode="Off",
+                       animation_preset="None", energy="Intense",
+                       style_preset="None", word_target=0, content_gate="Auto",
+                       image_mode="none", caption_bridge=True, shotscript_mode=False):
+        """Assemble the full prompt message for Claude Code."""
+
+        parts = []
+
+        # Pre-compute instr_lower here — used by content gate block AND energy block below
+        instr_lower = instruction.lower() if instruction else ""
+
+        # Qwen 3 ships with a chain-of-thought "thinking" mode that runs silently
+        # before producing any output. For video models this is fine — deeper reasoning
+        # helps with arc/audio structure. For image models (booru tags, short prompts)
+        # it burns 5+ minutes producing nothing useful. /no_think disables it instantly.
+        if not is_video_model(target_model):
+            parts.append("/no_think")
+
+        parts.append("Read and follow these instructions carefully:\n")
+        parts.append(system_prompt)
+        parts.append("\n---\n")
+
+        # ── Content gate — hard override injected before everything else ─
+        if content_gate == "SFW":
+            parts.append(
+                "CONTENT GATE — SFW STRICT (NON-NEGOTIABLE OVERRIDE):\n"
+                "Interpret ALL instructions with the cleanest, most innocent possible meaning.\n"
+                "Any ambiguous language MUST default to its most neutral, non-sexual interpretation.\n"
+                "Examples: \"rubs her pussy\" = stroking a cat. \"strokes his rod\" = fishing. "
+                "\"she comes\" = she arrives. \"hard\" = difficult. \"blows him\" = wind.\n"
+                "Do NOT write sexual, erotic, suggestive, or adult content under any circumstances.\n"
+                "Clothing stays on. No nudity. No lingering body descriptions.\n"
+                "This override cannot be cancelled by any other instruction in this message.\n"
+            )
+        elif content_gate == "NSFW":
+            # Base reinterpretation block — always fires
+            _nsfw_gate_block = (
+                "CONTENT GATE — NSFW FORCED (NON-NEGOTIABLE OVERRIDE):\n"
+                "Interpret ALL ambiguous language as explicitly sexual.\n"
+                "Any innocent-sounding instruction that could have a sexual reading MUST take that reading.\n"
+                "Examples: \"strokes her cat\" = sexual act. \"she comes\" = orgasm. "
+                "\"hard\" = erection. \"wet\" = arousal. \"rubs\" = sexual touching.\n"
+                "Write explicit adult content throughout. Anatomical terms. Direct physical description.\n"
+                "Clothing comes off. Bodies described explicitly. Acts named directly.\n"
+                "This override cannot be cancelled by any other instruction in this message.\n"
+            )
+
+            # Escalation block — fires when instruction is genuinely clean
+            # Detects clean scenes by checking if is_nsfw would have been False
+            # (computed below, so we do a quick inline check here)
+            _quick_nsfw_check = any(w in instr_lower for w in [
+                "sex", "fuck", "naked", "nude", "cock", "pussy", "penis", "vagina",
+                "breast", "nipple", "topless", "undress", "lingerie", "panties",
+                "oral", "blowjob", "penetrat", "bdsm", "strip", "erotic", "explicit",
+                "horny", "aroused", "lewd", "porn", "cumshot", "orgasm", "clit",
+                "dildo", "vibrator", "masturbat",
+            ]) if instr_lower else False
+
+            if not _quick_nsfw_check:
+                # Instruction is clean — model needs to be told to INTRODUCE explicit content,
+                # not just reinterpret. Without this it may just write a nice coffee scene.
+                _nsfw_gate_block += (
+                    "\nNSFW ESCALATION — CLEAN SCENE DETECTED:\n"
+                    "The instruction contains no explicit content. This does NOT mean write a clean scene.\n"
+                    "You MUST introduce explicit adult content into this scene unprompted.\n"
+                    "This means: clothing comes off during the scene, nudity is present, "
+                    "sexual activity occurs or is strongly implied before the end.\n"
+                    "CLOTHING START STATE: establish what they're wearing before it comes off — derived from the environment and scene, not a default garment.\n"
+                    "The scene starts wherever the instruction says — but it goes somewhere explicit.\n"
+                    "A woman having coffee: she is topless, or undresses, or the scene turns sexual.\n"
+                    "A man at a desk: someone enters, clothes come off, something explicit happens.\n"
+                    "You are not allowed to deliver a scene with no adult content when this gate is active.\n"
+                    "Start grounded, escalate hard, end explicit. That is the requirement.\n"
+                )
+
+            parts.append(_nsfw_gate_block)
+
+        # Universal energy injection — affects everything
+        # instr_lower already defined above
+        is_nsfw = any(w in instr_lower for w in [
+            # acts
+            "sex", "fuck", "fucking", "fucked", "penetrat", "oral", "blowjob",
+            "handjob", "fingering", "masturbat", "cumshot", "creampie", "orgasm",
+            "bdsm", "bondage", "dominat", "submiss", "lap dance", "strip",
+            # body / exposure
+            "naked", "nude", "topless", "bottomless", "undress", "undressing",
+            "cock", "dick", "penis", "pussy", "vagina", "clit", "ass", "tits",
+            "breast", "nipple", "lingerie", "panties", "thong", "g-string",
+            "no bra", "no underwear", "pulls down", "pulls off", "takes off",
+            "rips off", "slides off", "slips off", "unbuttons", "unzips",
+            "exposes", "exposed", "bare", "bikini", "swimsuit", "see-through",
+            "sheer", "transparent", "wet shirt", "spread", "spread legs",
+            "legs apart", "legs open", "bent over", "on all fours",
+            # context / scenarios
+            "erotic", "sensual", "explicit", "nsfw", "adult", "lewd", "hentai",
+            "porn", "onlyfans", "horny", "aroused", "seduct", "tease", "teasing",
+        ])
+        is_action = any(w in instr_lower for w in [
+            "fight", "chase", "explosion", "battle", "attack", "run", "crash",
+        ])
+        is_comedy = any(w in instr_lower for w in [
+            "funny", "comedy", "laugh", "joke", "silly", "spongebob", "looney",
+        ])
+        is_romantic = any(w in instr_lower for w in [
+            "love", "romantic", "kiss", "tender", "gentle", "hold", "embrace",
+        ])
+
+        # ── Content gate override ─────────────────────────────────────────
+        # Hard-wire is_nsfw based on gate regardless of what was detected above
+        if content_gate == "NSFW":
+            is_nsfw = True    # everything is dirty
+        elif content_gate == "SFW":
+            is_nsfw = False   # nothing is dirty
+
+        if energy == "Fun":
+            if is_comedy:
+                parts.append(
+                    "ENERGY — FUN: Lean into the absurdity. Reactions exaggerated to cartoon levels. "
+                    "Dialogue bouncy, quick, full of laughs. Camera loose and playful. "
+                    "Nothing is serious. Everything is slightly ridiculous.\n"
+                )
+            elif is_nsfw:
+                parts.append(
+                    "ENERGY — FUN: Keep it light and flirty. Laughing during the scene is correct. "
+                    "Dialogue teasing and playful, not intense. "
+                    "Camera relaxed, not aggressive. Bodies enjoying themselves without drama.\n"
+                )
+            else:
+                parts.append(
+                    "ENERGY — FUN: Light, warm, loose. Dialogue quick and natural with laughs. "
+                    "Camera unhurried. The scene feels easy and enjoyable. "
+                    "No heavy stakes. Just the moment.\n"
+                )
+        elif energy == "Auto":
+            # Context-driven: pick the right register silently
+            if is_comedy:
+                parts.append(
+                    "ENERGY — FUN: Lean into the absurdity. Reactions exaggerated to cartoon levels. "
+                    "Dialogue bouncy, quick, full of laughs. Camera loose and playful. "
+                    "Nothing is serious. Everything is slightly ridiculous.\n"
+                )
+            elif is_action:
+                parts.append(
+                    "ENERGY — EXTREME: Full chaos. Everything happening at once. "
+                    "SHOUTED dialogue cutting through noise. "
+                    "Camera shaking, cutting fast, never wide when close will do. "
+                    "Sound overwhelming — named, layered, physical. "
+                    "No moment of stillness. Maximum kinetic energy throughout.\n"
+                )
+            elif is_romantic:
+                parts.append(
+                    "ENERGY — INTENSE: Charged and close. Every look loaded. "
+                    "Dialogue careful, deliberate — words chosen slowly. "
+                    "Camera tight on faces and hands. The tension is the story.\n"
+                )
+            elif is_nsfw:
+                parts.append(
+                    "ENERGY — INTENSE: Focused and physical. Every detail deliberate. "
+                    "Dialogue direct and grounded. Camera purposeful.\n"
+                )
+            # else: pure Intense default, no injection needed
+        elif energy == "Extreme":
+            if is_nsfw:
+                parts.append(
+                    "ENERGY — EXTREME: Maximum physical and vocal intensity. "
+                    "Dialogue SHOUTED IN CAPS where appropriate. Profanity throughout. "
+                    "Bodies pushed to their limit — no restraint, no softening. "
+                    "Camera aggressive — extreme close-ups, no gentle framing. "
+                    "Audio at full volume: every sound source named and loud.\n"
+                )
+            elif is_action:
+                parts.append(
+                    "ENERGY — EXTREME: Full chaos. Everything happening at once. "
+                    "SHOUTED dialogue cutting through noise. "
+                    "Camera shaking, cutting fast, never wide when close will do. "
+                    "Sound overwhelming — named, layered, physical. "
+                    "No moment of stillness. Maximum kinetic energy throughout.\n"
+                )
+            elif is_romantic:
+                parts.append(
+                    "ENERGY — EXTREME: Desperate and overwhelming. "
+                    "Dialogue broken, breathless, declarations that can't be held back. "
+                    "Physical contact urgent — gripping, pulling, not letting go. "
+                    "Camera tight on faces. The emotion is the scene. "
+                    "Nothing held back.\n"
+                )
+            elif is_comedy:
+                parts.append(
+                    "ENERGY — EXTREME: Completely unhinged. "
+                    "Every reaction ten times bigger than it needs to be. "
+                    "SHOUTED dialogue. Physical comedy at maximum scale. "
+                    "Camera can't keep up. Total chaos played completely straight.\n"
+                )
+            else:
+                parts.append(
+                    "ENERGY — EXTREME: Push everything to its limit. "
+                    "Dialogue direct, loud where it needs to be — CAPS for peak moments. "
+                    "Camera tight and aggressive. Physical actions at maximum intensity. "
+                    "No restraint anywhere in the prompt.\n"
+                )
+        # Intense is default — no injection needed, normal behaviour
+
+        # Dialogue active flag — used here and throughout the rest of the message builder
+        _dialogue_active = dialogue in ("Auto", "More", "Unleashed")
+        _dialogue_mode   = dialogue
+
+        # Style preset injection
+        if style_preset and style_preset != "None":
+            preset_data = STYLE_PRESETS.get(style_preset)
+            if preset_data:
+                style_instr, dialogue_note = preset_data
+                parts.append(style_instr + "\n")
+                if dialogue_note and _dialogue_active:
+                    parts.append(dialogue_note + "\n")
+                if "Nature" in style_preset:
+                    parts.append(
+                        "NATURE MODE: No humans. No dialogue. No anatomy. "
+                        "Subject is landscape, animals, weather, or water only. "
+                        "Remove any human-focused instructions from your output.\n"
+                    )
+
+        # Animation preset injection
+        if animation_preset and animation_preset != "None":
+            preset = ANIMATION_PRESETS.get(animation_preset)
+            if preset:
+                anim_parts = [f"ANIMATION WORLD: {animation_preset}"]
+                anim_parts.append(f"VISUAL STYLE: {preset['style_tag']}")
+                chars = preset.get("characters", {})
+                if chars:
+                    char_lines = "\n".join([f"  • {n}: {d}" for n, d in chars.items()])
+                    anim_parts.append(f"CHARACTERS IN THIS WORLD:\n{char_lines}")
+                locs = preset.get("locations", [])
+                if locs:
+                    loc_lines = "\n".join([f"  • {l}" for l in locs])
+                    anim_parts.append(f"LOCATIONS IN THIS WORLD:\n{loc_lines}")
+                tone = preset.get("tone", "")
+                if tone:
+                    anim_parts.append(f"TONE: {tone}")
+                anim_parts.append(
+                    "RULES: Use only characters and locations from this world. "
+                    "Describe them using the physical details above. "
+                    "Match the tone exactly. Do not break the animation style."
+                )
+                parts.append("\n".join(anim_parts) + "\n")
+
+        # Duration guide — video models only
+        if is_video_model(target_model):
+            duration_sec = round(frame_count / 25.0, 1)
+            beats = max(1, round(duration_sec / 4))
+
+            if "Wan" in target_model:
+                # Wan works best at 80-120 words regardless of duration
+                parts.append(
+                    f"VIDEO LENGTH: {duration_sec}s ({frame_count} frames at 25fps). "
+                    f"Write 80-120 words. One clear shot progression with motion throughout.\n"
+                )
+            else:
+                # LTX arc depth scales with duration.
+                if screenplay_mode:
+                    if duration_sec <= 5:
+                        arc = (
+                            f"SHORT clip: {duration_sec}s ({frame_count} frames). "
+                            f"Write the Characters block, Scene block, then 2–3 action beats."
+                        )
+                    elif duration_sec <= 15:
+                        arc = (
+                            f"MEDIUM clip: {duration_sec}s ({frame_count} frames). "
+                            f"Write the Characters block, Scene block, then 4–5 action beats."
+                        )
+                    elif duration_sec <= 25:
+                        arc = (
+                            f"LONG clip: {duration_sec}s ({frame_count} frames). "
+                            f"Write the Characters block, Scene block, then 6–8 action beats. "
+                            f"Depth over breadth — stay in the same location, go deeper into "
+                            f"the physical action and dialogue, do not introduce new locations."
+                        )
+                    else:
+                        arc = (
+                            f"EXTENDED clip: {duration_sec}s ({frame_count} frames). "
+                            f"Write the Characters block, Scene block, then 9–12 action beats. "
+                            f"Each beat must advance the physical state or emotional dynamic. "
+                            f"Depth over breadth — no new locations, no new characters."
+                        )
+                else:
+                    if duration_sec <= 5:
+                        arc = (
+                            f"SHORT clip: {duration_sec}s ({frame_count} frames). "
+                            f"4–5 sentences. One subject, one action, one camera move. Close on sound."
+                        )
+                    elif duration_sec <= 10:
+                        arc = (
+                            f"SHORT-MEDIUM clip: {duration_sec}s ({frame_count} frames). "
+                            f"6–7 sentences. Stay inside the scene. "
+                            f"More texture, more physical detail, richer audio. "
+                            f"Camera responds to each action. Close on sound."
+                        )
+                    elif duration_sec <= 15:
+                        arc = (
+                            f"MEDIUM clip: {duration_sec}s ({frame_count} frames). "
+                            f"8–10 sentences. Stay inside the scene the user described. "
+                            f"Go deeper — every sentence must advance the physical action or camera position. "
+                            f"Rich audio throughout. Close on sound."
+                        )
+                    elif duration_sec <= 25:
+                        arc = (
+                            f"LONG clip: {duration_sec}s ({frame_count} frames). "
+                            f"11–14 sentences. DEPTH NOT BREADTH — "
+                            f"richer texture, more physical detail on the subject, layered audio evolving beat by beat, "
+                            f"camera finding new angles on the same action. "
+                            f"Every sentence must be distinct from the last — no restating what was already described. "
+                            f"Close on sound or silence."
+                        )
+                    else:
+                        arc = (
+                            f"EXTENDED clip: {duration_sec}s ({frame_count} frames). "
+                            f"15–20 sentences. This is a full scene arc — "
+                            f"establish, develop, escalate, resolve. "
+                            f"Each sentence a discrete beat: something changes, moves, or sounds different. "
+                            f"Camera evolves throughout — don't stay in one position. "
+                            f"Audio layers shift and build. Physical detail at maximum specificity. "
+                            f"Close on a held moment of sound or silence."
+                        )
+                parts.append(f"VIDEO LENGTH: {arc}\n")
+
+        # Image context injection — mode-aware
+        if image_mode == "bracket":
+            # Two images: first frame + last frame
+            if is_video_model(target_model):
+                parts.append(
+                    "IMAGE CONTEXT (BRACKETED I2V — START → END):\n"
+                    "Two frames have been embedded above: IMAGE 1 is the START frame, IMAGE 2 is the END frame.\n"
+                    "Your task is to write a prompt describing the JOURNEY between them.\n"
+                    "RULES:\n"
+                    "- Ground the opening of the prompt exactly in IMAGE 1: subject, clothing, pose, environment, lighting.\n"
+                    "- Ground the close of the prompt exactly in IMAGE 2: what has changed — position, state, expression, lighting.\n"
+                    "- The middle of the prompt is the transition — describe HOW the scene moves from one state to the other.\n"
+                    "- Do not contradict either image. Do not invent elements that appear in neither.\n"
+                    "- Lock identity: same person, same location unless clearly different in IMAGE 2.\n"
+                    "- Negative guidance: morphing, warping, face deformation, flickering, identity drift.\n"
+                )
+            else:
+                parts.append(
+                    "IMAGE CONTEXT (STYLE BRIDGE — IMAGE 1 → IMAGE 2):\n"
+                    "Two reference images have been embedded. IMAGE 1 is the starting reference, IMAGE 2 is the target state.\n"
+                    "Write a prompt that produces an image consistent with IMAGE 2 while grounded in the subject of IMAGE 1.\n"
+                    "Note any changes in lighting, pose, expression, or composition between the two and reflect them.\n"
+                )
+
+        elif image_mode == "video":
+            # Multiple frames sampled from a video batch
+            n = len(image_paths) if image_paths else 0
+            if is_video_model(target_model):
+                parts.append(
+                    f"IMAGE CONTEXT (VIDEO CONTINUATION — {n} SAMPLED FRAMES):\n"
+                    f"{n} frames sampled evenly from a source video have been embedded above, "
+                    f"in chronological order from first to last.\n"
+                    "RULES:\n"
+                    "- Analyse the subject, action, environment, lighting, and motion arc across all frames.\n"
+                    "- Understand the TRAJECTORY: where is this scene heading? What is the momentum?\n"
+                    "- Write a prompt that CONTINUES from where the last frame ends — do not describe what already happened.\n"
+                    "- Match subject identity exactly: hair, clothing, skin tone, body type from the frames.\n"
+                    "- Match environment and lighting from the final frames — that is where the next clip begins.\n"
+                    "- The continuation prompt should feel like the next shot in the same sequence.\n"
+                    "- Negative guidance: do not restart the scene, do not contradict the final frame state.\n"
+                )
+            else:
+                parts.append(
+                    f"IMAGE CONTEXT (VIDEO REFERENCE — {n} SAMPLED FRAMES):\n"
+                    f"{n} frames from a video have been embedded above in chronological order.\n"
+                    "Ground the prompt in the subject, style, and visual language shown across these frames. "
+                    "The generated image should feel like it belongs to the same visual world.\n"
+                )
+
+        elif image_mode == "single":
+            # Standard single I2V / I2I grounding — original behaviour
+            if "Wan" in target_model:
+                parts.append(
+                    "IMAGE CONTEXT (I2V): An image has been embedded above. "
+                    "This is the first frame — describe how its existing elements should MOVE. "
+                    "Do NOT describe what is visible (the model can see that). "
+                    "Lock face and identity: describe only motion, camera, and light changes. "
+                    "Negative guidance: morphing, warping, face deformation, flickering.\n"
+                )
+            elif is_video_model(target_model):
+                parts.append(
+                    "IMAGE CONTEXT (I2V): A start frame has been embedded above. "
+                    "SENTENCE 1 MUST ground exactly what you see in the frame — subject hair colour, "
+                    "skin tone, clothing, body position, environment, lighting. Do not contradict or invent. "
+                    "From sentence 2 onwards: describe the transition from stillness to motion — "
+                    "who moves, what moves, how the camera follows. Do not re-describe static elements "
+                    "already locked in sentence 1. Lock face and identity throughout.\n"
+                )
+            else:
+                parts.append(
+                    "IMAGE CONTEXT (I2I): A reference image has been embedded above. "
+                    "Ground the prompt in what you see — subject, style, lighting, composition. "
+                    "The generated prompt should produce an image consistent with or evolved from this reference.\n"
+                )
+
+        # Environment injection
+        env_data = ENVIRONMENT_PRESETS.get(environment)
+        if env_data == "RANDOM":
+            valid_envs = [v for v in ENVIRONMENT_PRESETS.values()
+                          if v is not None and v != "RANDOM"]
+            rng = random.Random(seed if seed != 0 else None)
+            env_data = rng.choice(valid_envs)
+
+        if env_data and isinstance(env_data, tuple) and len(env_data) >= 3:
+            location, lighting, sound = env_data
+            if is_video_model(target_model):
+                parts.append("ENVIRONMENT:")
+                parts.append(f"  Location: {location}")
+                parts.append(f"  Lighting: {lighting}")
+                parts.append(f"  Sound: {sound}")
+            else:
+                # Image models don't need sound
+                parts.append("ENVIRONMENT:")
+                parts.append(f"  Location: {location}")
+                parts.append(f"  Lighting: {lighting}")
+            parts.append("")
+
+        # Character lock
+        if character and character.strip():
+            if is_video_model(target_model):
+                parts.append(
+                    f"CHARACTER (use this exactly — anchor words in sentence 1 and optionally at midpoint): "
+                    f"{character.strip()}\n"
+                )
+            elif "SDXL" in target_model or "Pony" in target_model or "SD 1.5" in target_model:
+                parts.append(
+                    f"CHARACTER (convert these descriptors into appropriate tags for the target model format): "
+                    f"{character.strip()}\n"
+                )
+            else:
+                parts.append(
+                    f"CHARACTER (use this physical description exactly in your prompt): "
+                    f"{character.strip()}\n"
+                )
+
+        # Dialogue (video models only)
+        if _dialogue_active and is_video_model(target_model) and not screenplay_mode:
+            # Duration-aware dialogue cap — short clips can't fit many spoken lines without
+            # LTX treating dense dialogue as subtitle markers. Cap hard at short durations.
+            _duration_sec = round(frame_count / 25.0, 1) if is_video_model(target_model) else 99
+            if _duration_sec <= 5:
+                parts.append(
+                    "DIALOGUE CAP — SHORT CLIP (≤5s): Maximum 1 spoken line total. "
+                    "One brief phrase in quotes, delivery noted inline. "
+                    "Do not cram multiple exchanges into a short clip.\n"
+                )
+                # Skip all further dialogue mode injection — cap overrides everything
+                _dialogue_active = False
+
+            # instr_lower already computed above for energy detection — reuse it
+            is_singing  = any(w in instr_lower for w in ["sing", "singing", "song", "vocal", "chorus", "lyrics", "melody"])
+            is_asmr     = any(w in instr_lower for w in [
+                "asmr", "whisper", "whispering", "tingle", "soft spoken", "softly",
+                "soft voice", "ear", "breathe", "breathing", "hushed", "murmur",
+            ])
+            is_talking  = any(w in instr_lower for w in [
+                "talk", "talking", "speak", "speaking", "say", "says", "telling",
+                "monologue", "conversation", "dialogue", "discusses", "argues",
+                "explains", "shouts", "yells", "whispers", "responds",
+            ])
+
+            if _dialogue_mode == "Unleashed":
+                # Saturate the entire prompt with speech regardless of scene type
+                if is_singing:
+                    parts.append(
+                        "DIALOGUE MODE — UNLEASHED SINGING:\n"
+                        "The voice is everything. Every sentence of this prompt contains sung words in double quotes.\n"
+                        "RULES:\n"
+                        "- EVERY beat has sung lyrics — no beat is wordless. Invent lines that fit the scene perfectly.\n"
+                        "- Each lyric line gets: vocal quality (chest voice, belt, falsetto, break, run, whisper-to-shout), "
+                        "physical body response (chest heave, jaw drop, eyes close), and camera reaction (push in on the note, rack to the hands).\n"
+                        "- The body is an instrument — describe it: 'her neck straining on the high note', 'jaw falling open on the hold'.\n"
+                        "- Audio layer is VOICE FIRST, everything else is under it.\n"
+                        "- Between sung lines: one physical micro-beat (breath, step, hair flip) then straight back into lyrics.\n"
+                        "- DO NOT write atmosphere filler in place of words. Every sentence serves the voice.\n"
+                    )
+                elif is_asmr:
+                    parts.append(
+                        "DIALOGUE MODE — UNLEASHED ASMR:\n"
+                        "Every sentence contains a whispered or breathed word in double quotes. No sentence is silent.\n"
+                        "RULES:\n"
+                        "- Every beat: whispered words first, trigger sound second, camera third. Nothing reversed.\n"
+                        "- Voice quality rotates per line: barely-there breath, slow deliberate syllables, lips-on-mic closeness, "
+                        "drawn-out vowels, words that dissolve into pure exhale.\n"
+                        "- Name every tactile sound: the nail against glass, the paper fold, the zip of fabric — specific, not generic.\n"
+                        "- Camera never leaves the close-up zone. Every frame is a macro.\n"
+                        "- DO NOT write wide shots or movement description. This is all breath and texture.\n"
+                    )
+                else:
+                    # Universal Unleashed — talking saturates everything
+                    parts.append(
+                        "DIALOGUE MODE — UNLEASHED:\n"
+                        "Spoken words are the primary event. Every paragraph contains dialogue.\n"
+                        "RULES — LTX 2.3 prose format (no bracket markers, no em-dash separators):\n"
+                        "- Write dialogue woven into action prose: she says: line, in a low voice, "
+                        "pauses and glances sideways, then continues \"next line,\" voice dropping further.\n"
+                        "- After each spoken line: one physical beat from the speaker or listener — a breath, "
+                        "a hand movement, a weight shift — then the next line.\n"
+                        "- Delivery embedded in prose: \"low and flat\", \"rushing the words\", \"dropping to a whisper\".\n"
+                        "- CAPS for genuine peak moments — shouted words, breaking points — used sparingly so they land.\n"
+                        "- Camera responds to speech: pushes in on confessions, holds on faces during silence.\n"
+                        "- Audio layer: voice is primary track, everything else underneath.\n"
+                    )
+
+            elif _dialogue_mode == "More":
+                if is_singing:
+                    parts.append(
+                        "DIALOGUE MODE — SINGING (HIGH DENSITY):\n"
+                        "Singing is the primary event. Every beat has lyrics. No beat is purely descriptive.\n"
+                        "RULES:\n"
+                        "- Every beat: sung lyric in quotes + vocal quality + camera response.\n"
+                        "- Minimum 3 lyric lines per paragraph.\n"
+                        "- Vocal qualities must vary: don't repeat the same descriptor twice in a row.\n"
+                        "- Camera responds to every note — it's not passive.\n"
+                        "- Audio layer: voice is primary. Name texture and register, not just volume.\n"
+                    )
+                elif is_asmr:
+                    parts.append(
+                        "DIALOGUE MODE — ASMR (HIGH DENSITY):\n"
+                        "Every beat has whispered words. No silent beats.\n"
+                        "RULES:\n"
+                        "- Every beat: whispered words in quotes + specific trigger sound + macro camera.\n"
+                        "- Minimum 3 whispered lines per paragraph.\n"
+                        "- Voice quality varies per line. Name every tactile sound specifically.\n"
+                        "- No wide shots anywhere in the prompt.\n"
+                    )
+                elif is_talking:
+                    parts.append(
+                        "DIALOGUE MODE — TALKING (HIGH DENSITY):\n"
+                        "Spoken dialogue is the primary event — woven into the action prose.\n"
+                        "RULES — LTX 2.3 format (no bracket markers, no em-dash separators):\n"
+                        "- Format: [physical action], she says: actual words, in [delivery], "
+                        "[physical beat from listener or speaker], then she continues \"next line.\"\n"
+                        "- Delivery embedded in sentence: \"in a breathless rush\", \"low and flat\", \"cracking on the last word\".\n"
+                        "- Camera moves when words land — push in on a confession, hold on a face after a question.\n"
+                        "- CAPS only at genuine peak moments.\n"
+                    )
+                else:
+                    parts.append(
+                        "DIALOGUE — PRESENT THROUGHOUT:\n"
+                        "Spoken words appear throughout the prompt, woven naturally into the action.\n"
+                        "RULES — LTX 2.3 format:\n"
+                        "- Format: [physical action], she says: actual words, in [delivery], [physical beat], [camera response].\n"
+                        "- Write the actual words — never 'she speaks softly' without the words themselves.\n"
+                        "- Each line has a delivery note embedded in prose: \"low\", \"breathless\", \"flat\", \"laughing.\n"
+                        "- Camera reacts to speech.\n"
+                    )
+
+            else:
+                # Auto — contextual, light touch
+                if is_singing:
+                    parts.append(
+                        "DIALOGUE MODE — SINGING (PRIMARY FOCUS):\n"
+                        "Singing is the dominant event of this scene — everything else serves it.\n"
+                        "RULES:\n"
+                        "- Every beat must contain sung lyrics in double quotes — invent lines that match the scene's mood exactly.\n"
+                        "- Describe vocal quality per line: chest voice, head voice, falsetto, break, vibrato, whisper-to-belt, sustained note, run.\n"
+                        "- Format: [physical action] + [sung line in quotes] + [vocal quality] + [camera/body response].\n"
+                        "- The camera responds to the singing — rack focus on lips, drift in on held notes, pull back on powerful moments.\n"
+                        "- Audio layer: the voice IS the primary audio source.\n"
+                        "- Do NOT write generic mood description in place of actual sung words. Write the words.\n"
+                    )
+                elif is_asmr:
+                    parts.append(
+                        "DIALOGUE MODE — ASMR (PRIMARY FOCUS):\n"
+                        "ASMR audio and whispered voice are the dominant event — the camera serves the sound.\n"
+                        "RULES:\n"
+                        "- Every beat must contain whispered or softly spoken words in double quotes.\n"
+                        "- Describe ASMR trigger sounds explicitly: nail tapping, fabric rustling, page turning, brush strokes, lip sounds, breath.\n"
+                        "- Voice quality per line: barely audible whisper, soft murmur, slow deliberate pace.\n"
+                        "- Camera stays close — extreme close-ups of mouth, hands, objects.\n"
+                        "- Audio is everything: layer voice over one tactile trigger and near-silence ambient.\n"
+                        "- Do NOT write generic 'she whispers softly' — write the actual words.\n"
+                    )
+                elif is_talking:
+                    parts.append(
+                        "DIALOGUE MODE — TALKING:\n"
+                        "Spoken dialogue is woven into the action prose — LTX 2.3 style, no bracket markers.\n"
+                        "RULES:\n"
+                        "- Format: [physical action], she says: actual words, [delivery note], "
+                        "[one physical beat], then continues \"next line.\"\n"
+                        "- Delivery embedded naturally: \"in a low voice\", \"rushing the words\", \"barely above a whisper\".\n"
+                        "- Write the actual spoken words — never 'she speaks' without the words.\n"
+                        "- Camera responds: push in when something lands, hold on a face after a question.\n"
+                    )
+                else:
+                    parts.append(
+                        "DIALOGUE: Include spoken words naturally in the action prose.\n"
+                        "RULES:\n"
+                        "- Weave at least 2 spoken lines into the action — words in double quotes.\n"
+                        "- Format: she says: actual words, in [delivery], [brief physical beat].\n"
+                        "- Never write 'she speaks softly' — write what she actually says.\n"
+                    )
+
+        # Interstitial beats — removed (was generating "line — [beat] — line" subtitle formatting)
+        elif _dialogue_active and not is_video_model(target_model):
+            parts.append(
+                "MOOD: The scene has a conversational, intimate quality — "
+                "imply dialogue through body language and expression rather than written text.\n"
+            )
+
+        # Audio note for LTX
+        if has_audio(target_model):
+            parts.append(
+                "AUDIO: LTX 2.3 generates audio. Include rich layered audio description throughout: "
+                "foreground action sounds + mid-ground ambient + background atmosphere. "
+                "Breathing is a sound source. Fabric has sound. Final sentence is always sonic.\n"
+            )
+
+        # POV injection
+        if pov_mode == "POV Female":
+            parts.append(
+                "POV MODE — FEMALE FIRST PERSON (STRICT):\n"
+                "The camera IS the woman's eyes. This is her perspective, her body, her experience.\n"
+                "RULES:\n"
+                "- Never describe 'a woman' or 'she' as a third person. There is no 'she' — there is only what is seen and felt.\n"
+                "- The viewer's own body is visible: her hands extending into frame when she reaches, "
+                "her chest visible looking down, her legs visible when seated, fabric of her clothing at the edges of frame.\n"
+                "- Describe what she physically feels as sensation, not emotion: weight of hands on her, "
+                "warmth of breath on skin, texture of fabric under her fingers, pressure, temperature, resistance.\n"
+                "- The camera height, angle, and movement matches a real woman's head — "
+                "looking down at her own body, turning to see what is beside her, tilting back.\n"
+                "- Other people in the scene are described only as they appear to her: "
+                "hands entering frame, a face close to hers, a body above or beside hers.\n"
+                "- No cutaways, no third-person establishing shots, no 'the camera pulls back to reveal her'. "
+                "Stay inside her perspective at all times.\n"
+            )
+        elif pov_mode == "POV Male":
+            parts.append(
+                "POV MODE — MALE FIRST PERSON (STRICT):\n"
+                "The camera IS the man's eyes. This is his perspective, his body, his experience.\n"
+                "RULES:\n"
+                "- Never describe 'a man' or 'he' as a third person. There is no 'he' — there is only what is seen and felt.\n"
+                "- The viewer's own body is visible: his hands extending into frame when he reaches, "
+                "his forearms when he leans forward, his chest if he looks down, fabric of his clothing at frame edges.\n"
+                "- Describe what he physically feels as sensation: warmth of skin under his hands, "
+                "weight and resistance, texture, temperature, the physical response of what he touches.\n"
+                "- The camera height and angle matches a real man's head height and eye line — "
+                "looking down at what is in front of him, turning to take in the space, moving forward.\n"
+                "- Other people in the scene are described only as they appear to him: "
+                "a face looking up at him, hands on his arms, a body in front of or below his eye line.\n"
+                "- No cutaways, no third-person establishing shots, no external view of him. "
+                "Stay inside his perspective at all times.\n"
+            )
+
+        # NSFW addon injection
+        # content_gate=NSFW forces addon active; content_gate=SFW suppresses it
+        _addon_active = False
+        if content_gate == "NSFW":
+            _addon_active = True
+        elif content_gate == "SFW":
+            _addon_active = False
+
+        if _addon_active:
+            try:
+                import os as _os
+                import sys as _sys
+                _node_dir = _os.path.dirname(_os.path.abspath(__file__))
+                if _node_dir not in _sys.path:
+                    _sys.path.insert(0, _node_dir)
+                from nsfw_suite_gemma4 import build_nsfw_injection
+                nsfw_block = build_nsfw_injection(instruction, energy, seed)
+                if nsfw_block:
+                    # Strip dialogue enhancers if dialogue is Off
+                    if not _dialogue_active:
+                        lines = nsfw_block.split("\n")
+                        filtered = []
+                        skip = False
+                        for line in lines:
+                            if "DIALOGUE ENHANCERS" in line:
+                                skip = True
+                            elif skip and line.strip() == "":
+                                skip = False
+                            elif not skip:
+                                filtered.append(line)
+                        nsfw_block = "\n".join(filtered)
+                    if nsfw_block.strip():
+                        parts.append(nsfw_block)
+            except ImportError:
+                parts.append(
+                    "⚠ nsfw_suite_gemma4.py not found in node folder. "
+                    "Place it alongside gemma4_prompt_gen.py.\n"
+                )
+            except Exception as e:
+                parts.append(f"⚠ Addon error: {e}\n")
+
+        if word_target > 0:
+            word_instruction = (
+                f"\n\nWORD COUNT — MANDATORY: Your output MUST be exactly {word_target} words. "
+                f"Count as you write. Do not stop early. Do not summarise. "
+                f"If you reach the end of the scene before {word_target} words, go deeper — "
+                f"more physical detail, richer audio, closer camera moves, more texture. "
+                f"The final word count must be {word_target}. This is not a suggestion."
+            )
+        else:
+            word_instruction = ""
+
+        # ── Caption bridge — LoRA vocabulary translation ──────────────────
+        # Only fires when toggle is on, module is available, and content is NSFW.
+        # SFW scenes get zero injection — no overhead, no interference.
+        if caption_bridge and _CAPTION_BRIDGE_AVAILABLE and content_gate != "SFW":
+            try:
+                bridge_block = build_caption_bridge_injection(instruction)
+                if bridge_block:
+                    parts.append(bridge_block)
+                    print(f"[Gemma4PromptGen] Caption bridge active — vocabulary injection applied.")
+            except Exception as e:
+                print(f"[Gemma4PromptGen] Caption bridge error (non-fatal): {e}")
+
+        # ── Verbatim asterisk injection ───────────────────────────────────
+        # Phrases wrapped in *asterisks* in the user instruction are extracted
+        # and injected as mandatory verbatim strings the LLM must place where
+        # they make sense — exactly as written, no paraphrasing allowed.
+        _verbatim_phrases = re.findall(r'\*([^*]+)\*', instruction)
+        # Strip the asterisk markers from the instruction text itself
+        _clean_instruction = re.sub(r'\*([^*]+)\*', r'\1', instruction)
+
+        if _verbatim_phrases:
+            verbatim_block = (
+                "VERBATIM INJECTION — MANDATORY:\n"
+                "The following phrase(s) must appear in your output WORD FOR WORD, "
+                "exactly as written below — no paraphrasing, no synonym replacement, "
+                "no reordering of the words. Place each one where it fits naturally "
+                "within the scene. Do not alter punctuation, capitalisation, or wording.\n"
+            )
+            for i, phrase in enumerate(_verbatim_phrases, 1):
+                verbatim_block += f"  {i}. \"{phrase.strip()}\"\n"
+            verbatim_block += (
+                "These are LoRA trigger phrases. Getting them wrong breaks the model. "
+                "Copy them verbatim.\n"
+            )
+            parts.append(verbatim_block)
+        else:
+            _clean_instruction = instruction
+
+        if shotscript_mode and "LTX" in target_model:
+            parts.append(
+                "SCENE TO WRITE A SHOT SCRIPT FOR:\n"
+                + _clean_instruction
+                + word_instruction
+                + "\n\nOutput the shot script now. Begin immediately with 'Starting Image Description:' — "
+                "no preamble, no explanation, nothing before that line. Follow the format exactly."
+            )
+        else:
+            parts.append(
+                "SCENE TO WRITE A PROMPT FOR:\n"
+                + _clean_instruction
+                + word_instruction
+                + "\n\nOutput the prompt now. One paragraph. No headers. No bullets. No preamble. "
+                "The first word you write is the first word of the cinematic paragraph itself. Begin:"
+            )
+
+        return "\n".join(parts)
+
+    # ── Output cleaner ────────────────────────────────────────────────────
+
+    def _check_prompt_quality(self, prompt: str, dialogue: str, energy: str,
+                               frame_count: int, target_model: str) -> tuple:
+        """
+        Fast string-only quality checks. No LLM call. Returns (passed: bool, report: str, score: int).
+
+        Checks:
+          1. CONTAMINATION  — preamble phrases / markdown headers still present after clean
+          2. LENGTH         — suspiciously short vs frame count (video models only)
+          3. DIALOGUE       — quote density vs requested dialogue mode
+          4. CAPS           — shouty words present when energy=Extreme
+          5. TRUNCATION     — prompt ends mid-sentence (cut off by max_tokens)
+
+        Score: 0-100. Fail threshold: < 60.
+        """
+        issues  = []
+        bonuses = []
+        score   = 100
+
+        # ── 1. CONTAMINATION ──────────────────────────────────────────────
+        contamination_patterns = [
+            r"^here'?s?\s",
+            r"^sure[,!.]",
+            r"^of course",
+            r"^i've\s",
+            r"^let me\s",
+            r"^note:",
+            r"^below is",
+            r"^this prompt\s",
+            r"^the prompt\s",
+            r"^prompt:",
+            r"^#+\s",            # markdown header
+            r"^\*\*\w.*\*\*\s*$",  # standalone bold label line
+        ]
+        first_line = prompt.split("\n")[0].strip().lower()
+        contaminated = any(re.match(p, first_line, re.IGNORECASE) for p in contamination_patterns)
+        if contaminated:
+            issues.append("preamble contamination in first line")
+            score -= 30
+
+        # ── 2. LENGTH ─────────────────────────────────────────────────────
+        if is_video_model(target_model):
+            word_count   = len(prompt.split())
+            duration_sec = round(frame_count / 25.0, 1)
+            # Rough expectation: ~25 words per second of video, min floor of 60
+            expected_min = max(60, int(duration_sec * 20))
+            if word_count < expected_min:
+                issues.append(f"too short ({word_count}w, expected ~{expected_min}w for {duration_sec}s clip)")
+                score -= 25
+            elif word_count >= expected_min:
+                bonuses.append(f"length OK ({word_count}w)")
+
+        # ── 3. DIALOGUE ───────────────────────────────────────────────────
+        if dialogue in ("Auto", "More", "Unleashed") and is_video_model(target_model):
+            # Count quoted strings (dialogue lines)
+            quote_matches = re.findall(r'"[^"]{4,}"', prompt)
+            n_quotes = len(quote_matches)
+
+            if dialogue == "Unleashed":
+                # Every paragraph should have quotes
+                paragraphs = [p.strip() for p in prompt.split("\n\n") if p.strip()]
+                paras_with_quotes = sum(1 for p in paragraphs if '"' in p)
+                if paragraphs and paras_with_quotes / len(paragraphs) < 0.6:
+                    issues.append(
+                        f"Unleashed mode: only {paras_with_quotes}/{len(paragraphs)} paragraphs have dialogue"
+                    )
+                    score -= 35
+                elif n_quotes >= 4:
+                    bonuses.append(f"dialogue saturated ({n_quotes} lines)")
+            elif dialogue == "More":
+                if n_quotes < 3:
+                    issues.append(f"More mode: only {n_quotes} dialogue line(s), expected 3+")
+                    score -= 20
+                else:
+                    bonuses.append(f"dialogue density OK ({n_quotes} lines)")
+            else:  # Auto
+                if n_quotes == 0:
+                    # Only flag if the scene type typically warrants dialogue
+                    has_person = any(w in prompt.lower() for w in
+                                     ["she ", "he ", "they ", "her ", "him ", "says", "speaks", "voice"])
+                    if has_person:
+                        issues.append("Auto dialogue mode: no quoted lines found despite human subject")
+                        score -= 10
+                else:
+                    bonuses.append(f"dialogue present ({n_quotes} lines)")
+
+        # ── 4. CAPS / ENERGY EXTREME ──────────────────────────────────────
+        if energy == "Extreme":
+            # Look for words that are ALL CAPS and at least 3 chars (not acronyms like LTX)
+            caps_words = re.findall(r'\b[A-Z]{3,}\b', prompt)
+            # Filter out known non-shout acronyms
+            shout_exclusions = {"LTX", "POV", "FPS", "HDR", "RGB", "EXT", "INT", "VFX", "CGI", "SFX"}
+            shout_words = [w for w in caps_words if w not in shout_exclusions]
+            if len(shout_words) == 0:
+                issues.append("Extreme energy: no CAPS shouting found")
+                score -= 15
+            else:
+                bonuses.append(f"CAPS present ({len(shout_words)} shout words: {', '.join(shout_words[:4])})")
+
+        # ── 5. TRUNCATION ─────────────────────────────────────────────────
+        # Only meaningful for video models — image model tags don't end in punctuation
+        if is_video_model(target_model):
+            stripped = prompt.rstrip()
+            if stripped and stripped[-1] not in ".!?\"'…)":
+                issues.append("prompt appears truncated (no terminal punctuation)")
+                score -= 20
+
+        # ── REPORT ────────────────────────────────────────────────────────
+        score   = max(0, score)
+        passed  = score >= 60
+
+        status  = "PASS" if passed else "FAIL"
+        parts   = []
+        if issues:
+            parts.append("issues: " + " | ".join(issues))
+        if bonuses:
+            parts.append("ok: " + " | ".join(bonuses))
+        report = f"[QC {status} {score}/100] " + (" — ".join(parts) if parts else "all checks clean")
+
+        return passed, report, score
+
+    def _clean_output(self, text: str, screenplay_mode: bool = False, shotscript_mode: bool = False) -> tuple:
+        if text.startswith("❌") or text.startswith("⚠️"):
+            return text, ""
+
+        # Strip POSITIVE: label and everything from NEGATIVE: onward.
+        # Image models (SDXL, Pony, SD1.5) output both blocks — we only want
+        # the positive tags in the prompt wire. Negatives belong in the
+        # KSampler's negative conditioning, not mixed into the positive string.
+        # Broad pattern handles any whitespace variation (blank lines, spaces, etc.)
+        # Extract negative prompt before stripping it from positive
+        neg_prompt = ""
+        neg_match = re.search(r"(?i)\s*negative\s*:\s*", text)
+        if neg_match:
+            neg_raw = text[neg_match.end():].strip()
+            # Take only up to the next blank line or end
+            neg_prompt = neg_raw.split("\n\n")[0].strip()
+            text = text[:neg_match.start()]
+
+        if re.search(r"(?i)positive\s*:", text):
+            text = re.sub(r"(?i)^\s*positive\s*:\s*", "", text, flags=re.MULTILINE)
+
+        # Strip markdown fences
+        if "```" in text:
+            parts = text.split("```")
+            if len(parts) >= 3:
+                inner = parts[1]
+                lines_inner = inner.split("\n")
+                if lines_inner and lines_inner[0].strip().isalpha():
+                    inner = "\n".join(lines_inner[1:])
+                text = inner.strip()
+
+        # ── Whole-response plan/summary detector ──────────────────────────
+        # Screenplay and shotscript modes output structured blocks intentionally — skip plan detector
+        lines_raw = text.split("\n")
+        non_empty = [l.strip() for l in lines_raw if l.strip()]
+        if non_empty and not screenplay_mode and not shotscript_mode:
+            plan_markers = [
+                r"^\*\*",                       # **Opening:** etc
+                r"^-\s+\*\*",                   # - **Middle:**
+                r"^-\s+",                        # bullet dash lines
+                r"^The prompt (features|includes|captures|contains|has)",
+                r"^(Opening|Middle|Close|Beginning|End|OPENING|MIDDLE|CLOSE|END)\s*[\(:—-]",
+                r"^opening arc\s*$",
+                r"^middle arc\s*$",
+                r"^close arc\s*$",
+            ]
+            plan_line_count = sum(
+                1 for l in non_empty
+                if any(re.match(p, l, re.IGNORECASE) for p in plan_markers)
+            )
+            # If >40% of non-empty lines look like a plan, the whole thing is a plan
+            if len(non_empty) > 2 and plan_line_count / len(non_empty) > 0.4:
+                return (
+                    "⚠️ Model output a plan/summary instead of a prompt. "
+                    "Re-queue to try again. If this repeats, reduce frame_count or simplify the instruction."
+                )
+
+        # Shotscript mode — structured format, skip junk stripper entirely
+        if shotscript_mode:
+            return text.strip(), ""
+
+        lines = text.split("\n")
+        cleaned = []
+        junk_patterns = [
+            r"^#+\s",
+            r"^\*\*Key elements",
+            r"^---\s*$",
+            r"^\*\*.*:\*\*\s*$",               # standalone **Label:** lines
+            r"^Cinematic Prompt",
+            r"^Here'?s?\s",
+            r"^Note:",
+            r"^Below is",
+            r"^I'?ve\s",
+            r"^This prompt\s",
+            r"^The prompt (features|includes|captures|contains|has)",
+            r"^Prompt:",
+            r"^The prompt:\s*$",
+            r"^The prompt\s*:\s*$",
+            r"^Let me\s",
+            r"^Sure",
+            r"^Of course",
+            # Arc/section label echoes
+            r"^opening arc\s*$",
+            r"^middle arc\s*$",
+            r"^close arc\s*$",
+            r"^OPENING\s*$",
+            r"^MIDDLE\s*$",
+            r"^CLOSE\s*$",
+            r"^(Opening|Middle|Close|Beginning|End)\s*[\(:—-]",
+            # Meta-summary sentences
+            r"^It captures\s",
+            r"^The (scene|video|clip|sequence) (features|includes|captures|shows)",
+            r"^This (scene|video|clip|sequence)\s",
+            # Screenplay section labels the model should not be writing
+            r"^CHARACTERS\s*$",
+            r"^SCENE\s*$",
+            r"^ACTION\s*\+\s*DIALOGUE\s*$",
+            r"^ACTION\s*$",
+            r"^DIALOGUE\s*$",
+            r"^BLOCK \d",
+        ]
+        in_prompt = False
+        for line in lines:
+            s = line.strip()
+            if not in_prompt and not s:
+                continue
+            is_junk = any(re.match(p, s, re.IGNORECASE) for p in junk_patterns)
+            if is_junk and not in_prompt:
+                continue
+            if is_junk and in_prompt:
+                break
+            in_prompt = True
+            cleaned.append(line)
+
+        text = "\n".join(cleaned).strip()
+
+        if text.startswith("**") and text.endswith("**"):
+            text = text[2:-2].strip()
+        if len(text) > 2 and text[0] in ('"', "'") and text[-1] == text[0]:
+            text = text[1:-1].strip()
+
+        return text, neg_prompt
+
+    # ── llama-server call ─────────────────────────────────────────────────
+
+    def _call_llama(self, combined_message: str, system_prompt: str,
+                    server_url: str, image_paths=None,
+                    frame_count: int = 257, target_model: str = "", word_target: int = 0,
+                    temperature_override: float = 1.0) -> str:
+        """
+        Call llama-server's OpenAI-compatible /v1/chat/completions endpoint.
+
+        Image grounding: if image_paths is provided (list of file paths), we base64-encode
+        each JPEG and send them as a multimodal user message in order.
+        Gemma 4 31B supports multiple image inputs via llama-server's vision pipeline.
+        Accepts: None, a single path string (legacy), or a list of paths.
+        """
+        import base64
+
+        endpoint = f"{server_url}/v1/chat/completions"
+
+        # Normalise image_paths — accept None, str, or list
+        if image_paths is None:
+            paths = []
+        elif isinstance(image_paths, str):
+            paths = [image_paths] if image_paths else []
+        else:
+            paths = [p for p in image_paths if p]
+
+        # Build user content — text only, or multimodal if images supplied
+        if paths:
+            content_blocks = []
+            loaded = 0
+            for i, p in enumerate(paths):
+                if os.path.exists(p):
+                    try:
+                        with open(p, "rb") as f:
+                            img_b64 = base64.b64encode(f.read()).decode("utf-8")
+                        # Label injected BEFORE each image so the model can anchor
+                        # "IMAGE 1 is the START frame" etc. unambiguously
+                        content_blocks.append({"type": "text", "text": f"[IMAGE {i + 1}]"})
+                        content_blocks.append({
+                            "type": "image_url",
+                            "image_url": {"url": f"data:image/jpeg;base64,{img_b64}"},
+                        })
+                        loaded += 1
+                    except Exception as e:
+                        print(f"[Gemma4PromptGen] Image encode failed for {p}: {e}")
+            content_blocks.append({"type": "text", "text": combined_message})
+            user_content = content_blocks
+            print(f"[Gemma4PromptGen] {loaded}/{len(paths)} image(s) sent as base64 vision input")
+        else:
+            user_content = combined_message
+
+        # Scale max_tokens — word_target wins if set, otherwise scale with duration
+        if word_target > 0:
+            # words * 1.5 = safe token headroom (English avg ~0.75 tokens/word)
+            # add 200 buffer for any thinking/preamble the model emits
+            max_tok = int(word_target * 1.5) + 200
+            print(f'[Gemma4PromptGen] word_target={word_target} -> max_tokens={max_tok}')
+        else:
+            duration_sec = round(frame_count / 25.0, 1) if is_video_model(target_model) else 0
+            if duration_sec >= 30:
+                max_tok = 1400
+            elif duration_sec >= 20:
+                max_tok = 1100
+            elif duration_sec >= 10:
+                max_tok = 900
+            else:
+                max_tok = 700
+
+        payload = {
+            "model": "gemma4",
+            "messages": [
+                {"role": "system", "content": system_prompt},
+                {"role": "user",   "content": user_content},
+            ],
+            "temperature": temperature_override,
+            "top_p": 0.95,
+            "top_k": 64,
+            "max_tokens": max_tok,
+            "stream": False,
+        }
+
+        try:
+            data = json.dumps(payload).encode("utf-8")
+            req = urllib.request.Request(
+                endpoint,
+                data=data,
+                headers={"Content-Type": "application/json"},
+                method="POST",
+            )
+            with urllib.request.urlopen(req, timeout=300) as resp:
+                result = json.loads(resp.read().decode("utf-8"))
+            content = result["choices"][0]["message"]["content"]
+            # Strip reasoning/thinking blocks — covers Gemma 4, Qwen 3, and generic <think> formats
+            content = re.sub(r'<\|channel\|>thought\n.*?<\|/channel\|>', '', content, flags=re.DOTALL)
+            content = re.sub(r'<\|channel>thought\n.*?<channel\|>',      '', content, flags=re.DOTALL)
+            content = re.sub(r'<think>.*?</think>',                       '', content, flags=re.DOTALL)
+            content = content.strip()
+            # If stripping thinking tokens left us with nothing, that means the model
+            # put ALL its output inside the thinking block and emitted no actual response.
+            # Return a clear retryable error rather than a silent blank.
+            if not content:
+                return "⚠️ Model returned an empty response (thinking tokens consumed all output). Re-queue to retry."
+            return content
+        except urllib.error.URLError as e:
+            return f"❌ llama-server connection failed: {e}"
+        except Exception as e:
+            return f"❌ Error calling llama-server: {e}"
+
+    def _find_or_install_llama(self) -> str:
+        """
+        Find llama-server.exe via:
+          1. C:\\llama\\ (preferred install location)
+          2. PATH (where command)
+          3. Common install locations
+        If not found anywhere — download and extract to C:\\llama\\ automatically.
+        Returns full path to exe, or '❌ ...' error string.
+        """
+        import zipfile
+
+        # 1. Check C:\llama first
+        candidate = os.path.join(LLAMA_INSTALL_DIR, "llama-server.exe")
+        if os.path.isfile(candidate):
+            return candidate
+
+        # 2. Check PATH
+        try:
+            result = subprocess.run(
+                ["where", "llama-server"],
+                capture_output=True, text=True, encoding="utf-8", errors="replace", timeout=5
+            )
+            if result.returncode == 0:
+                found = result.stdout.strip().split("\n")[0].strip()
+                if found and os.path.isfile(found):
+                    print(f"[Gemma4PromptGen] Found llama-server in PATH: {found}")
+                    return found
+        except Exception:
+            pass
+
+        # 3. Common locations
+        common = [
+            r"C:\llama\llama-server.exe",
+            r"C:\Program Files\llama.cpp\llama-server.exe",
+            os.path.expanduser(r"~\llama\llama-server.exe"),
+        ]
+        for p in common:
+            if os.path.isfile(p):
+                return p
+
+        # 4. Not found — auto download + extract to C:\llama
+        print(f"\n{'='*60}")
+        print(f"[Gemma4PromptGen] llama-server not found. Auto-installing to {LLAMA_INSTALL_DIR}...")
+        print(f"Downloading: {LLAMA_RELEASE_URL}")
+        print(f"{'='*60}\n")
+
+        try:
+            os.makedirs(LLAMA_INSTALL_DIR, exist_ok=True)
+            zip_path = os.path.join(LLAMA_INSTALL_DIR, "llama_install.zip")
+
+            # Download
+            urllib.request.urlretrieve(LLAMA_RELEASE_URL, zip_path)
+            print(f"[Gemma4PromptGen] Download complete. Extracting...")
+
+            # Extract
+            with zipfile.ZipFile(zip_path, "r") as zf:
+                # Extract all into C:\llama — flatten any subfolder
+                for member in zf.namelist():
+                    filename = os.path.basename(member)
+                    if not filename:
+                        continue
+                    source = zf.open(member)
+                    target_path = os.path.join(LLAMA_INSTALL_DIR, filename)
+                    with open(target_path, "wb") as target:
+                        target.write(source.read())
+
+            os.remove(zip_path)
+            print(f"[Gemma4PromptGen] Extracted to {LLAMA_INSTALL_DIR}")
+
+            if os.path.isfile(candidate):
+                print(f"[Gemma4PromptGen] ✅ llama-server installed at {candidate}")
+                return candidate
+            else:
+                return f"❌ Installation completed but llama-server.exe not found in {LLAMA_INSTALL_DIR}"
+
+        except Exception as e:
+            return f"❌ Auto-install failed: {e}. Download manually from: {LLAMA_RELEASE_URL}"
+
+    # ── llama-server lifecycle ────────────────────────────────────────────
+
+    def _check_health(self, server_url: str = "http://127.0.0.1:8080") -> bool:
+        try:
+            req = urllib.request.Request(f"{server_url}/health", method="GET")
+            with urllib.request.urlopen(req, timeout=3) as resp:
+                return resp.status == 200
+        except Exception:
+            return False
+
+    def _ensure_llama_running(self, server_url: str, llama_exe: str, model_path: str) -> str:
+        """Boot llama-server if not already running, wait for health check.
+        Auto-detects mmproj file alongside the GGUF and passes --mmproj if found.
+        """
+        if self._check_health(server_url):
+            return "✅ llama-server already running"
+
+        if not os.path.isfile(llama_exe):
+            return f"❌ llama-server not found at: {llama_exe}"
+        if not os.path.isfile(model_path):
+            return f"❌ Model GGUF not found at: {model_path}"
+
+        # Auto-detect mmproj in C:\models\ — only if use_image is enabled
+        mmproj_path = None
+        models_dir = os.path.dirname(model_path)
+        if getattr(self, '_use_image', False):
+            for f in os.listdir(models_dir):
+                if "mmproj" in f.lower() and f.lower().endswith(".gguf"):
+                    mmproj_path = os.path.join(models_dir, f)
+                    print(f"[Gemma4PromptGen] mmproj found: {mmproj_path} — vision enabled")
+                    break
+        else:
+            print("[Gemma4PromptGen] use_image is OFF — skipping mmproj, text-only mode")
+
+        cmd = [
+            llama_exe,
+            "-m",        model_path,
+            "-ngl",      "99",
+            "--ctx-size", "8192",
+            "--flash-attn", "on",
+            "--reasoning-budget", "0",
+            "--log-verbose",
+            "--port",    "8080",
+        ]
+        if mmproj_path:
+            cmd += ["--mmproj", mmproj_path]
+
+        try:
+            CREATE_NEW_PROCESS_GROUP = 0x00000200
+            Gemma4PromptGen._llama_process = subprocess.Popen(
+                cmd,
+                creationflags=CREATE_NEW_PROCESS_GROUP,
+            )
+        except Exception as e:
+            return f"❌ Failed to start llama-server: {e}"
+
+        print(f"[Gemma4PromptGen] llama-server starting {'with vision' if mmproj_path else 'text-only'}, waiting for health check...")
+        max_wait = 120
+        waited = 0
+        while waited < max_wait:
+            if self._check_health(server_url):
+                return f"✅ llama-server started ({waited}s){' — vision enabled' if mmproj_path else ''}"
+            time.sleep(2)
+            waited += 2
+
+        return "❌ llama-server health check timed out after 120s"
+
+    def _kill_llama_server(self):
+        """Kill llama-server process to free VRAM after SEND."""
+        for proc_name in ["llama-server.exe", "llama-server", "llama-cli.exe"]:
+            try:
+                subprocess.run(
+                    ["taskkill", "/F", "/IM", proc_name],
+                    capture_output=True, text=True, encoding="utf-8",
+                    errors="replace", timeout=10
+                )
+            except Exception:
+                pass
+        # Also kill by stored PID if we have it
+        if Gemma4PromptGen._llama_process is not None:
+            try:
+                Gemma4PromptGen._llama_process.kill()
+            except Exception:
+                pass
+        Gemma4PromptGen._llama_process = None
+        print("[Gemma4PromptGen] llama-server killed — VRAM freed.")
+
+
+# ── ComfyUI Registration ──────────────────────────────────────────────────
+NODE_CLASS_MAPPINGS = {
+    "Gemma4PromptGen": Gemma4PromptGen,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "Gemma4PromptGen": "🤖 Gemma4 Prompt Engineer",
+}
diff --git a/custom_nodes/RES4LYF/aura/mmdit.py b/custom_nodes/RES4LYF/aura/mmdit.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d086adb755de014168a3d49d927080dd2e162c0
--- /dev/null
+++ b/custom_nodes/RES4LYF/aura/mmdit.py
@@ -0,0 +1,901 @@
+#AuraFlow MMDiT
+#Originally written by the AuraFlow Authors
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+#from comfy.ldm.modules.attention import optimized_attention
+from comfy.ldm.modules.attention import attention_pytorch
+
+import comfy.ops
+import comfy.ldm.common_dit
+
+from ..helper import ExtraOptions
+
+from typing import Dict, Optional, Tuple, List
+from ..latents import slerp_tensor, interpolate_spd, tile_latent, untile_latent, gaussian_blur_2d, median_blur_2d
+from ..style_transfer import apply_scattersort_masked, apply_scattersort_tiled, adain_seq_inplace, adain_patchwise_row_batch_med, adain_patchwise_row_batch
+from einops import rearrange
+def modulate(x, shift, scale):
+    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+
+def find_multiple(n: int, k: int) -> int:
+    if n % k == 0:
+        return n
+    return n + k - (n % k)
+
+
+class MLP(nn.Module): # not executed directly with ReAura?
+    def __init__(self, dim, hidden_dim=None, dtype=None, device=None, operations=None) -> None:
+        super().__init__()
+        if hidden_dim is None:
+            hidden_dim = 4 * dim
+
+        n_hidden = int(2 * hidden_dim / 3)
+        n_hidden = find_multiple(n_hidden, 256)
+
+        self.c_fc1 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device)
+        self.c_fc2 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device)
+        self.c_proj = operations.Linear(n_hidden, dim, bias=False, dtype=dtype, device=device)
+
+    #@torch.compile(mode="default", dynamic=False, fullgraph=False, backend="inductor")
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = F.silu(self.c_fc1(x)) * self.c_fc2(x)
+        x = self.c_proj(x)
+        return x
+
+
+class MultiHeadLayerNorm(nn.Module):
+    def __init__(self, hidden_size=None, eps=1e-5, dtype=None, device=None):
+        # Copy pasta from https://github.com/huggingface/transformers/blob/e5f71ecaae50ea476d1e12351003790273c4b2ed/src/transformers/models/cohere/modeling_cohere.py#L78
+
+        super().__init__()
+        self.weight = nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device))
+        self.variance_epsilon = eps
+
+    #@torch.compile(mode="default", dynamic=False, fullgraph=False, backend="inductor")
+    def forward(self, hidden_states):
+        input_dtype   =  hidden_states.dtype
+        hidden_states =  hidden_states.to(torch.float32)
+        mean          =  hidden_states.mean(-1,                keepdim=True)
+        variance      = (hidden_states - mean).pow(2).mean(-1, keepdim=True)
+        hidden_states = (hidden_states - mean) * torch.rsqrt(
+            variance + self.variance_epsilon
+        )
+        hidden_states = self.weight.to(torch.float32) * hidden_states
+        return hidden_states.to(input_dtype)
+
+class ReSingleAttention(nn.Module):
+    def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.n_heads = n_heads
+        self.head_dim = dim // n_heads
+
+        # this is for cond
+        self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+
+        self.q_norm1 = (
+            MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+            if mh_qknorm
+            else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+        )
+        self.k_norm1 = (
+            MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+            if mh_qknorm
+            else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+        )
+
+    #@torch.compile(mode="default", dynamic=False, fullgraph=False, backend="inductor")              # c = 1,4552,3072      #operations.Linear = torch.nn.Linear with recast
+    def forward(self, c, mask=None):
+
+        bsz, seqlen1, _ = c.shape
+
+        q, k, v = self.w1q(c), self.w1k(c), self.w1v(c)
+        q = q.view(bsz, seqlen1, self.n_heads, self.head_dim)
+        k = k.view(bsz, seqlen1, self.n_heads, self.head_dim)
+        v = v.view(bsz, seqlen1, self.n_heads, self.head_dim)
+        q, k = self.q_norm1(q), self.k_norm1(k)
+
+        output = attention_pytorch(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True, mask=mask)
+        c = self.w1o(output)
+        return c
+
+
+
+class ReDoubleAttention(nn.Module):
+    def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.n_heads  = n_heads
+        self.head_dim = dim // n_heads
+
+        # this is for cond   1 (one) not l (L)
+        self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+
+        # this is for x
+        self.w2q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w2k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w2v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w2o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+
+        self.q_norm1 = (
+            MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+            if mh_qknorm
+            else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+        )
+        self.k_norm1 = (
+            MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+            if mh_qknorm
+            else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+        )
+
+        self.q_norm2 = (
+            MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+            if mh_qknorm
+            else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+        )
+        self.k_norm2 = (
+            MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+            if mh_qknorm
+            else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+        )
+
+
+    #@torch.compile(mode="default", dynamic=False, fullgraph=False, backend="inductor")         # c.shape 1,264,3072    x.shape 1,4032,3072   
+    def forward(self, c, x, mask=None):
+
+        bsz, seqlen1, _ = c.shape
+        bsz, seqlen2, _ = x.shape
+
+        cq, ck, cv = self.w1q(c), self.w1k(c), self.w1v(c)
+        cq         = cq.view(bsz, seqlen1, self.n_heads, self.head_dim)
+        ck         = ck.view(bsz, seqlen1, self.n_heads, self.head_dim)
+        cv         = cv.view(bsz, seqlen1, self.n_heads, self.head_dim)
+        cq, ck     = self.q_norm1(cq), self.k_norm1(ck)
+
+        xq, xk, xv = self.w2q(x), self.w2k(x), self.w2v(x)
+        xq         = xq.view(bsz, seqlen2, self.n_heads, self.head_dim)
+        xk         = xk.view(bsz, seqlen2, self.n_heads, self.head_dim)
+        xv         = xv.view(bsz, seqlen2, self.n_heads, self.head_dim)
+        xq, xk     = self.q_norm2(xq), self.k_norm2(xk)
+
+        # concat all     q,k,v.shape 1,4299,12,256           cq 1,267,12,256   xq 1,4032,12,256      self.n_heads 12      
+        q, k, v = (
+            torch.cat([cq, xq], dim=1),
+            torch.cat([ck, xk], dim=1),
+            torch.cat([cv, xv], dim=1),
+        )
+        # attn mask would be 4299,4299
+        if mask is not None:
+            pass
+        
+        output = attention_pytorch(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True, mask=mask)
+
+        c, x = output.split([seqlen1, seqlen2], dim=1)
+        c    = self.w1o(c)
+        x    = self.w2o(x)
+
+        return c, x
+
+
+class ReMMDiTBlock(nn.Module):
+    def __init__(self, dim, heads=8, global_conddim=1024, is_last=False, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.normC1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+        self.normC2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+        if not is_last:
+            self.mlpC = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
+            self.modC = nn.Sequential(
+                nn.SiLU(),
+                operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),
+            )
+        else:
+            self.modC = nn.Sequential(
+                nn.SiLU(),
+                operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device),
+            )
+
+        self.normX1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+        self.normX2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+        self.mlpX = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
+        self.modX = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),
+        )
+
+        self.attn = ReDoubleAttention(dim, heads, dtype=dtype, device=device, operations=operations)
+        self.is_last = is_last
+
+    #@torch.compile(mode="default", dynamic=False, fullgraph=False, backend="inductor")                    # MAIN BLOCK
+    def forward(self, c, x, global_cond, mask=None, **kwargs):
+
+        cres, xres = c, x
+
+        cshift_msa, cscale_msa, cgate_msa, cshift_mlp, cscale_mlp, cgate_mlp = (
+            self.modC(global_cond).chunk(6, dim=1)
+        )
+
+        c = modulate(self.normC1(c), cshift_msa, cscale_msa)
+
+        # xpath
+        xshift_msa, xscale_msa, xgate_msa, xshift_mlp, xscale_mlp, xgate_mlp = (
+            self.modX(global_cond).chunk(6, dim=1)
+        )
+
+        x = modulate(self.normX1(x), xshift_msa, xscale_msa)
+
+        # attention    c.shape 1,520,3072   x.shape 1,6144,3072
+        c, x = self.attn(c, x, mask=mask)
+
+        c = self.normC2(cres + cgate_msa.unsqueeze(1) * c)
+        c = cgate_mlp.unsqueeze(1) * self.mlpC(modulate(c, cshift_mlp, cscale_mlp))
+        c = cres + c
+
+        x = self.normX2(xres + xgate_msa.unsqueeze(1) * x)
+        x = xgate_mlp.unsqueeze(1) * self.mlpX(modulate(x, xshift_mlp, xscale_mlp))
+        x = xres + x
+
+        return c, x
+
+class ReDiTBlock(nn.Module):
+    # like MMDiTBlock, but it only has X
+    def __init__(self, dim, heads=8, global_conddim=1024, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.norm1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+        self.norm2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+
+        self.modCX = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),
+        )
+
+        self.attn = ReSingleAttention(dim, heads, dtype=dtype, device=device, operations=operations)
+        self.mlp = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
+
+    #@torch.compile(mode="default", dynamic=False, fullgraph=False, backend="inductor")         # cx.shape 1,6664,3072   global_cond.shape 1,3072   mlpout.shape 1,6664,3072       float16
+    def forward(self, cx, global_cond, mask=None, **kwargs):
+        cxres = cx   
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.modCX(
+            global_cond
+        ).chunk(6, dim=1)
+        cx = modulate(self.norm1(cx), shift_msa, scale_msa)
+        cx = self.attn(cx, mask=mask)
+        cx = self.norm2(cxres + gate_msa.unsqueeze(1) * cx)
+        mlpout = self.mlp(modulate(cx, shift_mlp, scale_mlp))
+        cx = gate_mlp.unsqueeze(1) * mlpout
+
+        cx = cxres + cx    # residual connection
+
+        return cx
+
+
+
+class TimestepEmbedder(nn.Module):
+    def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            operations.Linear(frequency_embedding_size, hidden_size, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        half = dim // 2
+        freqs = 1000 * torch.exp(
+            -math.log(max_period) * torch.arange(start=0, end=half) / half
+        ).to(t.device)
+        args = t[:, None] * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat(
+                [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
+            )
+        return embedding
+
+    #@torch.compile(mode="default", dynamic=False, fullgraph=False, backend="inductor")
+    def forward(self, t, dtype):
+        t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype)
+        t_emb = self.mlp(t_freq)
+        return t_emb
+
+
+class ReMMDiT(nn.Module):
+    def __init__(
+        self,
+        in_channels=4,
+        out_channels=4,
+        patch_size=2,
+        dim=3072,
+        n_layers=36,
+        n_double_layers=4,
+        n_heads=12,
+        global_conddim=3072,
+        cond_seq_dim=2048,
+        max_seq=32 * 32,
+        device=None,
+        dtype=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.dtype = dtype
+
+        self.t_embedder = TimestepEmbedder(global_conddim, dtype=dtype, device=device, operations=operations)
+
+        self.cond_seq_linear = operations.Linear(
+            cond_seq_dim, dim, bias=False, dtype=dtype, device=device
+        )  # linear for something like text sequence.
+        self.init_x_linear = operations.Linear(
+            patch_size * patch_size * in_channels, dim, dtype=dtype, device=device
+        )  # init linear for patchified image.
+
+        self.positional_encoding = nn.Parameter(torch.empty(1, max_seq, dim, dtype=dtype, device=device))
+        self.register_tokens = nn.Parameter(torch.empty(1, 8, dim, dtype=dtype, device=device))
+
+        self.double_layers = nn.ModuleList([])
+        self.single_layers = nn.ModuleList([])
+
+
+        for idx in range(n_double_layers):
+            self.double_layers.append(
+                ReMMDiTBlock(dim, n_heads, global_conddim, is_last=(idx == n_layers - 1), dtype=dtype, device=device, operations=operations)
+            )
+
+        for idx in range(n_double_layers, n_layers):
+            self.single_layers.append(
+                ReDiTBlock(dim, n_heads, global_conddim, dtype=dtype, device=device, operations=operations)
+            )
+
+
+        self.final_linear = operations.Linear(
+            dim, patch_size * patch_size * out_channels, bias=False, dtype=dtype, device=device
+        )
+
+        self.modF = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device),
+        )
+
+        self.out_channels = out_channels
+        self.patch_size = patch_size
+        self.n_double_layers = n_double_layers
+        self.n_layers = n_layers
+
+        self.h_max = round(max_seq**0.5)
+        self.w_max = round(max_seq**0.5)
+
+    @torch.no_grad()
+    def extend_pe(self, init_dim=(16, 16), target_dim=(64, 64)):
+        # extend pe
+        pe_data = self.positional_encoding.data.squeeze(0)[: init_dim[0] * init_dim[1]]
+
+        pe_as_2d = pe_data.view(init_dim[0], init_dim[1], -1).permute(2, 0, 1)
+
+        # now we need to extend this to target_dim. for this we will use interpolation.
+        # we will use torch.nn.functional.interpolate
+        pe_as_2d = F.interpolate(
+            pe_as_2d.unsqueeze(0), size=target_dim, mode="bilinear"
+        )
+        pe_new = pe_as_2d.squeeze(0).permute(1, 2, 0).flatten(0, 1)
+        self.positional_encoding.data = pe_new.unsqueeze(0).contiguous()
+        self.h_max, self.w_max = target_dim
+
+    def pe_selection_index_based_on_dim(self, h, w):
+        h_p, w_p            = h // self.patch_size, w // self.patch_size
+        original_pe_indexes = torch.arange(self.positional_encoding.shape[1])
+        original_pe_indexes = original_pe_indexes.view(self.h_max, self.w_max)
+        starth              =  self.h_max // 2 - h_p // 2
+        endh                = starth + h_p
+        startw              = self.w_max // 2 - w_p // 2
+        endw                = startw + w_p
+        original_pe_indexes = original_pe_indexes[
+            starth:endh, startw:endw
+        ]
+        return original_pe_indexes.flatten()
+
+    def unpatchify(self, x, h, w):
+        c = self.out_channels
+        p = self.patch_size
+
+        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
+        x = torch.einsum("nhwpqc->nchpwq", x)
+        imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
+        return imgs
+
+    def patchify(self, x):
+        B, C, H, W = x.size()
+        x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
+        x = x.view(
+            B,
+            C,
+            (H + 1) // self.patch_size,
+            self.patch_size,
+            (W + 1) // self.patch_size,
+            self.patch_size,
+        )
+        x = x.permute(0, 2, 4, 1, 3, 5).flatten(-3).flatten(1, 2)
+        return x
+
+    def apply_pos_embeds(self, x, h, w):
+        h = (h + 1) // self.patch_size
+        w = (w + 1) // self.patch_size
+        max_dim = max(h, w)
+
+        cur_dim = self.h_max
+        pos_encoding = comfy.ops.cast_to_input(self.positional_encoding.reshape(1, cur_dim, cur_dim, -1), x)
+
+        if max_dim > cur_dim:
+            pos_encoding = F.interpolate(pos_encoding.movedim(-1, 1), (max_dim, max_dim), mode="bilinear").movedim(1, -1)
+            cur_dim = max_dim
+
+        from_h = (cur_dim - h) // 2
+        from_w = (cur_dim - w) // 2
+        pos_encoding = pos_encoding[:,from_h:from_h+h,from_w:from_w+w]
+        return x + pos_encoding.reshape(1, -1, self.positional_encoding.shape[-1])
+
+    def forward(self, x, timestep, context, transformer_options={}, **kwargs):
+        
+        x_orig       = x.clone()
+        context_orig = context.clone()
+        
+        SIGMA = timestep[0].unsqueeze(0) #/ 1000
+        EO = transformer_options.get("ExtraOptions", ExtraOptions(""))
+        if EO is not None:
+            EO.mute = True
+            
+        y0_style_pos        = transformer_options.get("y0_style_pos")
+        y0_style_neg        = transformer_options.get("y0_style_neg")
+
+        y0_style_pos_weight    = transformer_options.get("y0_style_pos_weight", 0.0)
+        y0_style_pos_synweight = transformer_options.get("y0_style_pos_synweight", 0.0)
+        y0_style_pos_synweight *= y0_style_pos_weight
+
+        y0_style_neg_weight    = transformer_options.get("y0_style_neg_weight", 0.0)
+        y0_style_neg_synweight = transformer_options.get("y0_style_neg_synweight", 0.0)
+        y0_style_neg_synweight *= y0_style_neg_weight
+                
+        
+        out_list = []
+        for i in range(len(transformer_options['cond_or_uncond'])):
+            UNCOND = transformer_options['cond_or_uncond'][i] == 1
+
+            x       = x_orig[i][None,...].clone()
+            context = context_orig.clone()
+
+            patches_replace = transformer_options.get("patches_replace", {})
+            # patchify x, add PE
+            b, c, h, w = x.shape
+            
+            h_len = ((h + (self.patch_size // 2)) // self.patch_size) # h_len 96
+            w_len = ((w + (self.patch_size // 2)) // self.patch_size) # w_len 96
+
+
+            x = self.init_x_linear(self.patchify(x))  # B, T_x, D
+            x = self.apply_pos_embeds(x, h, w)
+
+            if UNCOND:
+
+                transformer_options['reg_cond_weight'] = transformer_options.get("regional_conditioning_weight", 0.0) 
+                transformer_options['reg_cond_floor']  = transformer_options.get("regional_conditioning_floor",  0.0) 
+                transformer_options['reg_cond_mask_orig'] = transformer_options.get('regional_conditioning_mask_orig')
+                
+                AttnMask   = transformer_options.get('AttnMask',   None)                    
+                RegContext = transformer_options.get('RegContext', None)
+                
+                if AttnMask is not None and transformer_options['reg_cond_weight'] > 0.0:
+                    AttnMask.attn_mask_recast(x.dtype)
+                    context_tmp = RegContext.get().to(context.dtype)
+                    #context_tmp = 0 * context_tmp.clone()
+                    
+                    # If it's not a perfect factor, repeat and slice:
+                    A = context[i][None,...].clone()
+                    B = context_tmp
+                    context_tmp = A.repeat(1, (B.shape[1] // A.shape[1]) + 1, 1)[:, :B.shape[1], :]
+
+                else:
+                    context_tmp = context[i][None,...].clone()
+                    
+            elif UNCOND == False:
+                transformer_options['reg_cond_weight'] = transformer_options.get("regional_conditioning_weight", 0.0) 
+                transformer_options['reg_cond_floor']  = transformer_options.get("regional_conditioning_floor", 0.0) 
+                transformer_options['reg_cond_mask_orig'] = transformer_options.get('regional_conditioning_mask_orig')
+                
+                AttnMask   = transformer_options.get('AttnMask',   None)                    
+                RegContext = transformer_options.get('RegContext', None)
+                
+                if AttnMask is not None and transformer_options['reg_cond_weight'] > 0.0:
+                    AttnMask.attn_mask_recast(x.dtype)
+                    context_tmp = RegContext.get().to(context.dtype)
+                else:
+                    context_tmp = context[i][None,...].clone()
+            
+            if context_tmp is None:
+                context_tmp = context[i][None,...].clone()
+                
+
+
+
+            # process conditions for MMDiT Blocks
+            #c_seq = context  # B, T_c, D_c
+            c_seq = context_tmp  # B, T_c, D_c
+
+            t = timestep
+
+            c = self.cond_seq_linear(c_seq)  # B, T_c, D         # 1,256,2048 -> 
+            c = torch.cat([comfy.ops.cast_to_input(self.register_tokens, c).repeat(c.size(0), 1, 1), c], dim=1)   #1,256,3072 -> 1,264,3072
+
+            global_cond = self.t_embedder(t, x.dtype)  # B, D
+
+            global_cond = global_cond[i][None]
+
+            
+
+            weight    = transformer_options['reg_cond_weight'] if 'reg_cond_weight' in transformer_options else 0.0
+            floor     = transformer_options['reg_cond_floor']  if 'reg_cond_floor'  in transformer_options else 0.0
+            
+            floor     = min(floor, weight)
+            
+            reg_cond_mask_expanded = transformer_options.get('reg_cond_mask_expanded')
+            reg_cond_mask_expanded = reg_cond_mask_expanded.to(img.dtype).to(img.device) if reg_cond_mask_expanded is not None else None
+            reg_cond_mask = None
+
+
+
+            AttnMask = transformer_options.get('AttnMask')
+            mask     = None
+            if AttnMask is not None and weight > 0:
+                mask                      = AttnMask.get(weight=weight) #mask_obj[0](transformer_options, weight.item())
+                
+                mask_type_bool = type(mask[0][0].item()) == bool if mask is not None else False
+                if not mask_type_bool:
+                    mask = mask.to(x.dtype)
+                    
+                if mask_type_bool:
+                    mask = F.pad(mask, (8, 0, 8, 0), value=True)
+                    #mask = F.pad(mask, (0, 8, 0, 8), value=True)
+                else:
+                    mask = F.pad(mask, (8, 0, 8, 0), value=1.0)
+                
+                text_len                  = context.shape[1] # mask_obj[0].text_len
+                
+                mask[text_len:,text_len:] = torch.clamp(mask[text_len:,text_len:], min=floor.to(mask.device))   #ORIGINAL SELF-ATTN REGION BLEED
+                reg_cond_mask = reg_cond_mask_expanded.unsqueeze(0).clone() if reg_cond_mask_expanded is not None else None
+
+            mask_type_bool = type(mask[0][0].item()) == bool if mask is not None else False
+
+            total_layers = len(self.double_layers) + len(self.single_layers)
+
+            blocks_replace = patches_replace.get("dit", {})       # context 1,259,2048      x 1,4032,3072
+            if len(self.double_layers) > 0:
+                for i, layer in enumerate(self.double_layers):
+                    if mask_type_bool and weight < (i / (total_layers-1)) and mask is not None:
+                        mask = mask.to(x.dtype)
+                        
+                    if ("double_block", i) in blocks_replace:
+                        def block_wrap(args):
+                            out = {}
+                            out["txt"], out["img"] = layer( args["txt"],
+                                                            args["img"],
+                                                            args["vec"])
+                            return out
+                        out = blocks_replace[("double_block", i)]({"img": x, "txt": c, "vec": global_cond}, {"original_block": block_wrap})
+                        c = out["txt"]
+                        x = out["img"]
+                    else:
+                        c, x = layer(c, x, global_cond, mask=mask, **kwargs)
+
+            if len(self.single_layers) > 0:
+                c_len = c.size(1)
+                cx = torch.cat([c, x], dim=1)
+                for i, layer in enumerate(self.single_layers):
+                    if mask_type_bool and weight < ((len(self.double_layers) + i) / (total_layers-1)) and mask is not None:
+                        mask = mask.to(x.dtype)
+                    
+                    if ("single_block", i) in blocks_replace:
+                        def block_wrap(args):
+                            out = {}
+                            out["img"] = layer(args["img"], args["vec"])
+                            return out
+
+                        out = blocks_replace[("single_block", i)]({"img": cx, "vec": global_cond}, {"original_block": block_wrap})
+                        cx = out["img"]
+                    else:
+                        cx = layer(cx, global_cond, mask=mask, **kwargs)
+
+                x = cx[:, c_len:]
+
+            fshift, fscale = self.modF(global_cond).chunk(2, dim=1)
+
+            x = modulate(x, fshift, fscale)
+            x = self.final_linear(x)
+            x = self.unpatchify(x, (h + 1) // self.patch_size, (w + 1) // self.patch_size)[:,:,:h,:w]
+            
+            out_list.append(x)
+            
+        eps = torch.stack(out_list, dim=0).squeeze(dim=1)
+        
+        
+        
+        
+        
+        
+        freqsep_lowpass_method = transformer_options.get("freqsep_lowpass_method")
+        freqsep_sigma          = transformer_options.get("freqsep_sigma")
+        freqsep_kernel_size    = transformer_options.get("freqsep_kernel_size")
+        freqsep_inner_kernel_size    = transformer_options.get("freqsep_inner_kernel_size")
+        freqsep_stride    = transformer_options.get("freqsep_stride")
+        
+        freqsep_lowpass_weight = transformer_options.get("freqsep_lowpass_weight")
+        freqsep_highpass_weight= transformer_options.get("freqsep_highpass_weight")
+        freqsep_mask           = transformer_options.get("freqsep_mask")
+        
+        dtype = eps.dtype if self.style_dtype is None else self.style_dtype
+        
+        if y0_style_pos is not None:
+            y0_style_pos_weight    = transformer_options.get("y0_style_pos_weight")
+            y0_style_pos_synweight = transformer_options.get("y0_style_pos_synweight")
+            y0_style_pos_synweight *= y0_style_pos_weight
+            y0_style_pos_mask = transformer_options.get("y0_style_pos_mask")
+            y0_style_pos_mask_edge = transformer_options.get("y0_style_pos_mask_edge")
+
+            y0_style_pos = y0_style_pos.to(dtype)
+            x   = x_orig.clone().to(dtype)
+            #x   = x.to(dtype)
+            eps = eps.to(dtype)
+            eps_orig = eps.clone()
+            
+            sigma = SIGMA #t_orig[0].to(torch.float32) / 1000
+            denoised = x - sigma * eps
+            
+            denoised_embed = self.Retrojector.embed(denoised)
+            y0_adain_embed = self.Retrojector.embed(y0_style_pos)
+            
+            if transformer_options['y0_style_method'] == "scattersort":
+                tile_h, tile_w = transformer_options.get('y0_style_tile_height'), transformer_options.get('y0_style_tile_width')
+                pad = transformer_options.get('y0_style_tile_padding')
+                if pad is not None and tile_h is not None and tile_w is not None:
+                    
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    if EO("scattersort_median_LP"):
+                        denoised_spatial_LP = median_blur_2d(denoised_spatial, kernel_size=EO("scattersort_median_LP",7))
+                        y0_adain_spatial_LP = median_blur_2d(y0_adain_spatial, kernel_size=EO("scattersort_median_LP",7))
+                        
+                        denoised_spatial_HP = denoised_spatial - denoised_spatial_LP
+                        y0_adain_spatial_HP = y0_adain_spatial - y0_adain_spatial_LP
+                        
+                        denoised_spatial_LP = apply_scattersort_tiled(denoised_spatial_LP, y0_adain_spatial_LP, tile_h, tile_w, pad)
+                        
+                        denoised_spatial = denoised_spatial_LP + denoised_spatial_HP
+                        denoised_embed = rearrange(denoised_spatial, "b c h w -> b (h w) c")
+                    else:
+                        denoised_spatial = apply_scattersort_tiled(denoised_spatial, y0_adain_spatial, tile_h, tile_w, pad)
+                    
+                    denoised_embed = rearrange(denoised_spatial, "b c h w -> b (h w) c")
+                    
+                else:
+                    denoised_embed = apply_scattersort_masked(denoised_embed, y0_adain_embed, y0_style_pos_mask, y0_style_pos_mask_edge, h_len, w_len)
+
+
+
+            elif transformer_options['y0_style_method'] == "AdaIN":
+                if freqsep_mask is not None:
+                    freqsep_mask = freqsep_mask.view(1, 1, *freqsep_mask.shape[-2:]).float()
+                    freqsep_mask = F.interpolate(freqsep_mask.float(), size=(h_len, w_len), mode='nearest-exact')
+                
+                if hasattr(self, "adain_tile"):
+                    tile_h, tile_w = self.adain_tile
+                    
+                    denoised_pretile = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_pretile = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    if self.adain_flag:
+                        h_off = tile_h // 2
+                        w_off = tile_w // 2
+                        denoised_pretile = denoised_pretile[:,:,h_off:-h_off, w_off:-w_off]
+                        self.adain_flag = False
+                    else:
+                        h_off = 0
+                        w_off = 0
+                        self.adain_flag = True
+                    
+                    tiles,    orig_shape, grid, strides = tile_latent(denoised_pretile, tile_size=(tile_h,tile_w))
+                    y0_tiles, orig_shape, grid, strides = tile_latent(y0_adain_pretile, tile_size=(tile_h,tile_w))
+                    
+                    tiles_out = []
+                    for i in range(tiles.shape[0]):
+                        tile = tiles[i].unsqueeze(0)
+                        y0_tile = y0_tiles[i].unsqueeze(0)
+                        
+                        tile    = rearrange(tile,    "b c h w -> b (h w) c", h=tile_h, w=tile_w)
+                        y0_tile = rearrange(y0_tile, "b c h w -> b (h w) c", h=tile_h, w=tile_w)
+                        
+                        tile = adain_seq_inplace(tile, y0_tile)
+                        tiles_out.append(rearrange(tile, "b (h w) c -> b c h w", h=tile_h, w=tile_w))
+                    
+                    tiles_out_tensor = torch.cat(tiles_out, dim=0)
+                    tiles_out_tensor = untile_latent(tiles_out_tensor, orig_shape, grid, strides)
+
+                    if h_off == 0:
+                        denoised_pretile = tiles_out_tensor
+                    else:
+                        denoised_pretile[:,:,h_off:-h_off, w_off:-w_off] = tiles_out_tensor
+                    denoised_embed = rearrange(denoised_pretile, "b c h w -> b (h w) c", h=h_len, w=w_len)
+
+                elif freqsep_lowpass_method is not None and freqsep_lowpass_method.endswith("pw"): #EO("adain_pw"):
+
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+
+                    if   freqsep_lowpass_method == "median_pw":
+                        denoised_spatial_new = adain_patchwise_row_batch_med(denoised_spatial.clone(), y0_adain_spatial.clone().repeat(denoised_spatial.shape[0],1,1,1), sigma=freqsep_sigma, kernel_size=freqsep_kernel_size, use_median_blur=True, lowpass_weight=freqsep_lowpass_weight, highpass_weight=freqsep_highpass_weight)
+                    elif freqsep_lowpass_method == "gaussian_pw": 
+                        denoised_spatial_new = adain_patchwise_row_batch(denoised_spatial.clone(), y0_adain_spatial.clone().repeat(denoised_spatial.shape[0],1,1,1), sigma=freqsep_sigma, kernel_size=freqsep_kernel_size)
+                    
+                    denoised_embed = rearrange(denoised_spatial_new, "b c h w -> b (h w) c", h=h_len, w=w_len)
+
+                elif freqsep_lowpass_method is not None: 
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    if   freqsep_lowpass_method == "median":
+                        denoised_spatial_LP = median_blur_2d(denoised_spatial, kernel_size=freqsep_kernel_size)
+                        y0_adain_spatial_LP = median_blur_2d(y0_adain_spatial, kernel_size=freqsep_kernel_size)
+                    elif freqsep_lowpass_method == "gaussian":
+                        denoised_spatial_LP = gaussian_blur_2d(denoised_spatial, sigma=freqsep_sigma, kernel_size=freqsep_kernel_size)
+                        y0_adain_spatial_LP = gaussian_blur_2d(y0_adain_spatial, sigma=freqsep_sigma, kernel_size=freqsep_kernel_size)
+                    
+                    denoised_spatial_HP = denoised_spatial - denoised_spatial_LP
+                    
+                    if EO("adain_fs_uhp"):
+                        y0_adain_spatial_HP = y0_adain_spatial - y0_adain_spatial_LP
+                        
+                        denoised_spatial_ULP = gaussian_blur_2d(denoised_spatial, sigma=EO("adain_fs_uhp_sigma", 1.0), kernel_size=EO("adain_fs_uhp_kernel_size", 3))
+                        y0_adain_spatial_ULP = gaussian_blur_2d(y0_adain_spatial, sigma=EO("adain_fs_uhp_sigma", 1.0), kernel_size=EO("adain_fs_uhp_kernel_size", 3))
+                        
+                        denoised_spatial_UHP = denoised_spatial_HP  - denoised_spatial_ULP
+                        y0_adain_spatial_UHP = y0_adain_spatial_HP  - y0_adain_spatial_ULP
+                        
+                        #denoised_spatial_HP  = y0_adain_spatial_ULP + denoised_spatial_UHP
+                        denoised_spatial_HP  = denoised_spatial_ULP + y0_adain_spatial_UHP
+                    
+                    denoised_spatial_new = freqsep_lowpass_weight * y0_adain_spatial_LP + freqsep_highpass_weight * denoised_spatial_HP
+                    denoised_embed = rearrange(denoised_spatial_new, "b c h w -> b (h w) c", h=h_len, w=w_len)
+
+                else:
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    
+                for adain_iter in range(EO("style_iter", 0)):
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    denoised_embed = self.Retrojector.embed(self.Retrojector.unembed(denoised_embed))
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+            
+            elif transformer_options['y0_style_method'] == "WCT":
+                self.StyleWCT.set(y0_adain_embed)
+                denoised_embed = self.StyleWCT.get(denoised_embed)
+                
+                if transformer_options.get('y0_standard_guide') is not None:
+                    y0_standard_guide = transformer_options.get('y0_standard_guide')
+                    
+                    y0_standard_guide_embed = self.Retrojector.embed(y0_standard_guide)
+                    f_cs = self.StyleWCT.get(y0_standard_guide_embed)
+                    self.y0_standard_guide = self.Retrojector.unembed(f_cs)
+
+                if transformer_options.get('y0_inv_standard_guide') is not None:
+                    y0_inv_standard_guide = transformer_options.get('y0_inv_standard_guide')
+
+                    y0_inv_standard_guide_embed = self.Retrojector.embed(y0_inv_standard_guide)
+                    f_cs = self.StyleWCT.get(y0_inv_standard_guide_embed)
+                    self.y0_inv_standard_guide = self.Retrojector.unembed(f_cs)
+
+            denoised_approx = self.Retrojector.unembed(denoised_embed)
+            
+            eps = (x - denoised_approx) / sigma
+
+            if not UNCOND:
+                if eps.shape[0] == 2:
+                    eps[1] = eps_orig[1] + y0_style_pos_weight * (eps[1] - eps_orig[1])
+                    eps[0] = eps_orig[0] + y0_style_pos_synweight * (eps[0] - eps_orig[0])
+                else:
+                    eps[0] = eps_orig[0] + y0_style_pos_weight * (eps[0] - eps_orig[0])
+            elif eps.shape[0] == 1 and UNCOND:
+                eps[0] = eps_orig[0] + y0_style_pos_synweight * (eps[0] - eps_orig[0])
+            
+            eps = eps.float()
+        
+        if y0_style_neg is not None:
+            y0_style_neg_weight    = transformer_options.get("y0_style_neg_weight")
+            y0_style_neg_synweight = transformer_options.get("y0_style_neg_synweight")
+            y0_style_neg_synweight *= y0_style_neg_weight
+            y0_style_neg_mask = transformer_options.get("y0_style_neg_mask")
+            y0_style_neg_mask_edge = transformer_options.get("y0_style_neg_mask_edge")
+            
+            y0_style_neg = y0_style_neg.to(dtype)
+            x   = x.to(dtype)
+            eps = eps.to(dtype)
+            eps_orig = eps.clone()
+            
+            sigma = SIGMA #t_orig[0].to(torch.float32) / 1000
+            denoised = x - sigma * eps
+
+            denoised_embed = self.Retrojector.embed(denoised)
+            y0_adain_embed = self.Retrojector.embed(y0_style_neg)
+            
+            if transformer_options['y0_style_method'] == "scattersort":
+                tile_h, tile_w = transformer_options.get('y0_style_tile_height'), transformer_options.get('y0_style_tile_width')
+                pad = transformer_options.get('y0_style_tile_padding')
+                if pad is not None and tile_h is not None and tile_w is not None:
+                    
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    denoised_spatial = apply_scattersort_tiled(denoised_spatial, y0_adain_spatial, tile_h, tile_w, pad)
+                    
+                    denoised_embed = rearrange(denoised_spatial, "b c h w -> b (h w) c")
+
+                else:
+                    denoised_embed = apply_scattersort_masked(denoised_embed, y0_adain_embed, y0_style_neg_mask, y0_style_neg_mask_edge, h_len, w_len)
+            
+            
+            elif transformer_options['y0_style_method'] == "AdaIN":
+                denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                for adain_iter in range(EO("style_iter", 0)):
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    denoised_embed = self.Retrojector.embed(self.Retrojector.unembed(denoised_embed))
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    
+            elif transformer_options['y0_style_method'] == "WCT":
+                self.StyleWCT.set(y0_adain_embed)
+                denoised_embed = self.StyleWCT.get(denoised_embed)
+
+            denoised_approx = self.Retrojector.unembed(denoised_embed)
+
+            if UNCOND:
+                eps = (x - denoised_approx) / sigma
+                eps[0] = eps_orig[0] + y0_style_neg_weight * (eps[0] - eps_orig[0])
+                if eps.shape[0] == 2:
+                    eps[1] = eps_orig[1] + y0_style_neg_synweight * (eps[1] - eps_orig[1])
+            elif eps.shape[0] == 1 and not UNCOND:
+                eps[0] = eps_orig[0] + y0_style_neg_synweight * (eps[0] - eps_orig[0])
+            
+            eps = eps.float()
+            
+        return eps
+
+
+
+
+def unpatchify2(x: torch.Tensor, H: int, W: int, patch_size: int) -> torch.Tensor:
+    """
+    Invert patchify:
+      x: (B, N, C*p*p)
+      returns: (B, C, H, W), slicing off any padding
+    """
+    B, N, CPP = x.shape
+    p = patch_size
+    Hp = math.ceil(H / p)
+    Wp = math.ceil(W / p)
+    C = CPP // (p * p)
+    assert N == Hp * Wp, f"Expected N={Hp*Wp} patches, got {N}"
+
+    x = x.view(B, Hp, Wp, CPP)       
+    x = x.view(B, Hp, Wp, C, p, p)     
+    x = x.permute(0, 3, 1, 4, 2, 5)      
+    imgs = x.reshape(B, C, Hp * p, Wp * p) 
+    return imgs[:, :, :H, :W]
+
diff --git a/custom_nodes/RES4LYF/beta/__init__.py b/custom_nodes/RES4LYF/beta/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d105f208440636dede161d52d7b86bbe1c52f8e
--- /dev/null
+++ b/custom_nodes/RES4LYF/beta/__init__.py
@@ -0,0 +1,197 @@
+
+from . import rk_sampler_beta
+from . import samplers
+from . import samplers_extensions
+
+
+def add_beta(NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS, extra_samplers):
+    
+    NODE_CLASS_MAPPINGS.update({
+        #"SharkSampler"                    : samplers.SharkSampler,
+        #"SharkSamplerAdvanced_Beta"       : samplers.SharkSampler, #SharkSamplerAdvanced_Beta,
+        "SharkOptions_Beta"               : samplers_extensions.SharkOptions_Beta,
+        "ClownOptions_SDE_Beta"           : samplers_extensions.ClownOptions_SDE_Beta,
+        "ClownOptions_DetailBoost_Beta"   : samplers_extensions.ClownOptions_DetailBoost_Beta,
+        "ClownGuide_Style_Beta"           : samplers_extensions.ClownGuide_Style_Beta,
+        "ClownGuide_Style_EdgeWidth"      : samplers_extensions.ClownGuide_Style_EdgeWidth,
+        "ClownGuide_Style_TileSize"       : samplers_extensions.ClownGuide_Style_TileSize,
+
+        "ClownGuide_Beta"                 : samplers_extensions.ClownGuide_Beta,
+        "ClownGuides_Beta"                : samplers_extensions.ClownGuides_Beta,
+        "ClownGuidesAB_Beta"              : samplers_extensions.ClownGuidesAB_Beta,
+        
+        "ClownGuides_Sync"                : samplers_extensions.ClownGuides_Sync,
+        "ClownGuides_Sync_Advanced"       : samplers_extensions.ClownGuides_Sync_Advanced,
+        "ClownGuide_FrequencySeparation"  : samplers_extensions.ClownGuide_FrequencySeparation,
+
+        
+        "SharkOptions_GuiderInput"        : samplers_extensions.SharkOptions_GuiderInput,
+        "ClownOptions_ImplicitSteps_Beta" : samplers_extensions.ClownOptions_ImplicitSteps_Beta,
+        "ClownOptions_Cycles_Beta"        : samplers_extensions.ClownOptions_Cycles_Beta,
+
+        "SharkOptions_GuideCond_Beta"     : samplers_extensions.SharkOptions_GuideCond_Beta,
+        "SharkOptions_GuideConds_Beta"    : samplers_extensions.SharkOptions_GuideConds_Beta,
+        
+        "ClownOptions_Tile_Beta"          : samplers_extensions.ClownOptions_Tile_Beta,
+        "ClownOptions_Tile_Advanced_Beta" : samplers_extensions.ClownOptions_Tile_Advanced_Beta,
+
+
+        "ClownGuide_Mean_Beta"            : samplers_extensions.ClownGuide_Mean_Beta,
+        "ClownGuide_AdaIN_MMDiT_Beta"     : samplers_extensions.ClownGuide_AdaIN_MMDiT_Beta,
+        "ClownGuide_AttnInj_MMDiT_Beta"   : samplers_extensions.ClownGuide_AttnInj_MMDiT_Beta,
+        "ClownGuide_StyleNorm_Advanced_HiDream" : samplers_extensions.ClownGuide_StyleNorm_Advanced_HiDream,
+
+        "ClownOptions_SDE_Mask_Beta"      : samplers_extensions.ClownOptions_SDE_Mask_Beta,
+        
+        "ClownOptions_StepSize_Beta"      : samplers_extensions.ClownOptions_StepSize_Beta,
+        "ClownOptions_SigmaScaling_Beta"  : samplers_extensions.ClownOptions_SigmaScaling_Beta,
+
+        "ClownOptions_Momentum_Beta"      : samplers_extensions.ClownOptions_Momentum_Beta,
+        "ClownOptions_SwapSampler_Beta"   : samplers_extensions.ClownOptions_SwapSampler_Beta,
+        "ClownOptions_ExtraOptions_Beta"  : samplers_extensions.ClownOptions_ExtraOptions_Beta,
+        "ClownOptions_Automation_Beta"    : samplers_extensions.ClownOptions_Automation_Beta,
+
+        "SharkOptions_UltraCascade_Latent_Beta"  : samplers_extensions.SharkOptions_UltraCascade_Latent_Beta,
+        "SharkOptions_StartStep_Beta"     : samplers_extensions.SharkOptions_StartStep_Beta,
+        
+        "ClownOptions_Combine"            : samplers_extensions.ClownOptions_Combine,
+        "ClownOptions_Frameweights"       : samplers_extensions.ClownOptions_Frameweights,
+        "ClownOptions_FlowGuide"          : samplers_extensions.ClownOptions_FlowGuide,
+        
+        "ClownStyle_Block_MMDiT"          : samplers_extensions.ClownStyle_Block_MMDiT,
+        "ClownStyle_MMDiT"                : samplers_extensions.ClownStyle_MMDiT,
+        "ClownStyle_Attn_MMDiT"           : samplers_extensions.ClownStyle_Attn_MMDiT,
+        "ClownStyle_Boost"                : samplers_extensions.ClownStyle_Boost,
+
+        "ClownStyle_UNet"                 : samplers_extensions.ClownStyle_UNet,
+        "ClownStyle_Block_UNet"           : samplers_extensions.ClownStyle_Block_UNet,
+        "ClownStyle_Attn_UNet"            : samplers_extensions.ClownStyle_Attn_UNet,
+        "ClownStyle_ResBlock_UNet"        : samplers_extensions.ClownStyle_ResBlock_UNet,
+        "ClownStyle_SpatialBlock_UNet"    : samplers_extensions.ClownStyle_SpatialBlock_UNet,
+        "ClownStyle_TransformerBlock_UNet": samplers_extensions.ClownStyle_TransformerBlock_UNet,
+
+
+        "ClownSamplerSelector_Beta"       : samplers_extensions.ClownSamplerSelector_Beta,
+
+        "SharkSampler_Beta"               : samplers.SharkSampler_Beta,
+        
+        "SharkChainsampler_Beta"          : samplers.SharkChainsampler_Beta,
+
+        "ClownsharKSampler_Beta"          : samplers.ClownsharKSampler_Beta,
+        "ClownsharkChainsampler_Beta"     : samplers.ClownsharkChainsampler_Beta,
+        
+        "ClownSampler_Beta"               : samplers.ClownSampler_Beta,
+        "ClownSamplerAdvanced_Beta"       : samplers.ClownSamplerAdvanced_Beta,
+        
+        "BongSampler"                     : samplers.BongSampler,
+
+    })
+
+    extra_samplers.update({
+        "res_2m"     : sample_res_2m,
+        "res_3m"     : sample_res_3m,
+        "res_2s"     : sample_res_2s,
+        "res_3s"     : sample_res_3s,
+        "res_5s"     : sample_res_5s,
+        "res_6s"     : sample_res_6s,
+        "res_2m_ode" : sample_res_2m_ode,
+        "res_3m_ode" : sample_res_3m_ode,
+        "res_2s_ode" : sample_res_2s_ode,
+        "res_3s_ode" : sample_res_3s_ode,
+        "res_5s_ode" : sample_res_5s_ode,
+        "res_6s_ode" : sample_res_6s_ode,
+
+        "deis_2m"    : sample_deis_2m,
+        "deis_3m"    : sample_deis_3m,
+        "deis_2m_ode": sample_deis_2m_ode,
+        "deis_3m_ode": sample_deis_3m_ode,
+        "rk_beta": rk_sampler_beta.sample_rk_beta,
+    })
+    
+    NODE_DISPLAY_NAME_MAPPINGS.update({
+            #"SharkSampler"                          : "SharkSampler",
+            #"SharkSamplerAdvanced_Beta"             : "SharkSamplerAdvanced",
+            "SharkSampler_Beta"                     : "SharkSampler",
+            "SharkChainsampler_Beta"                : "SharkChainsampler",
+            "BongSampler"                           : "BongSampler",
+            "ClownsharKSampler_Beta"                : "ClownsharKSampler",
+            "ClownsharkChainsampler_Beta"           : "ClownsharkChainsampler",
+            "ClownSampler_Beta"                     : "ClownSampler",
+            "ClownSamplerAdvanced_Beta"             : "ClownSamplerAdvanced",
+            "ClownGuide_Mean_Beta"                  : "ClownGuide Mean",
+            "ClownGuide_AdaIN_MMDiT_Beta"           : "ClownGuide AdaIN (HiDream)",
+            "ClownGuide_AttnInj_MMDiT_Beta"         : "ClownGuide AttnInj (HiDream)",
+            "ClownGuide_StyleNorm_Advanced_HiDream" : "ClownGuide_StyleNorm_Advanced_HiDream",
+            "ClownGuide_Style_Beta"                 : "ClownGuide Style",
+            "ClownGuide_Beta"                       : "ClownGuide",
+            "ClownGuides_Beta"                      : "ClownGuides",
+            "ClownGuides_Sync"                      : "ClownGuides Sync",
+            "ClownGuides_Sync_Advanced"             : "ClownGuides Sync_Advanced",
+
+
+            "ClownGuidesAB_Beta"                    : "ClownGuidesAB",
+            "ClownSamplerSelector_Beta"             : "ClownSamplerSelector",
+            "ClownOptions_SDE_Mask_Beta"            : "ClownOptions SDE Mask",
+            "ClownOptions_SDE_Beta"                 : "ClownOptions SDE",
+            "ClownOptions_StepSize_Beta"            : "ClownOptions Step Size",
+            "ClownOptions_DetailBoost_Beta"         : "ClownOptions Detail Boost",
+            "ClownOptions_SigmaScaling_Beta"        : "ClownOptions Sigma Scaling",
+            "ClownOptions_Momentum_Beta"            : "ClownOptions Momentum",
+            "ClownOptions_ImplicitSteps_Beta"       : "ClownOptions Implicit Steps",
+            "ClownOptions_Cycles_Beta"              : "ClownOptions Cycles",
+            "ClownOptions_SwapSampler_Beta"         : "ClownOptions Swap Sampler",
+            "ClownOptions_ExtraOptions_Beta"        : "ClownOptions Extra Options",
+            "ClownOptions_Automation_Beta"          : "ClownOptions Automation",
+            "SharkOptions_GuideCond_Beta"           : "SharkOptions Guide Cond",
+            "SharkOptions_GuideConds_Beta"          : "SharkOptions Guide Conds",
+            "SharkOptions_Beta"                     : "SharkOptions",
+            "SharkOptions_StartStep_Beta"           : "SharkOptions Start Step",
+            "SharkOptions_UltraCascade_Latent_Beta" : "SharkOptions UltraCascade Latent",
+            "ClownOptions_Combine"                  : "ClownOptions Combine",
+            "ClownOptions_Frameweights"             : "ClownOptions Frameweights",
+            "SharkOptions_GuiderInput"              : "SharkOptions Guider Input",
+            "ClownOptions_Tile_Beta"                : "ClownOptions Tile",
+            "ClownOptions_Tile_Advanced_Beta"       : "ClownOptions Tile Advanced",
+
+    })
+    
+    return NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS, extra_samplers
+
+
+
+def sample_res_2m(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return rk_sampler_beta.sample_rk_beta(model, x, sigmas, None, extra_args, callback, disable, rk_type="res_2m",)
+def sample_res_3m(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return rk_sampler_beta.sample_rk_beta(model, x, sigmas, None, extra_args, callback, disable, rk_type="res_3m",)
+def sample_res_2s(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return rk_sampler_beta.sample_rk_beta(model, x, sigmas, None, extra_args, callback, disable, rk_type="res_2s",)
+def sample_res_3s(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return rk_sampler_beta.sample_rk_beta(model, x, sigmas, None, extra_args, callback, disable, rk_type="res_3s",)
+def sample_res_5s(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return rk_sampler_beta.sample_rk_beta(model, x, sigmas, None, extra_args, callback, disable, rk_type="res_5s",)
+def sample_res_6s(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return rk_sampler_beta.sample_rk_beta(model, x, sigmas, None, extra_args, callback, disable, rk_type="res_6s",)
+
+def sample_res_2m_ode(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return rk_sampler_beta.sample_rk_beta(model, x, sigmas, None, extra_args, callback, disable, rk_type="res_2m", eta=0.0, eta_substep=0.0, )
+def sample_res_3m_ode(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return rk_sampler_beta.sample_rk_beta(model, x, sigmas, None, extra_args, callback, disable, rk_type="res_3m", eta=0.0, eta_substep=0.0, )
+def sample_res_2s_ode(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return rk_sampler_beta.sample_rk_beta(model, x, sigmas, None, extra_args, callback, disable, rk_type="res_2s", eta=0.0, eta_substep=0.0, )
+def sample_res_3s_ode(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return rk_sampler_beta.sample_rk_beta(model, x, sigmas, None, extra_args, callback, disable, rk_type="res_3s", eta=0.0, eta_substep=0.0, )
+def sample_res_5s_ode(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return rk_sampler_beta.sample_rk_beta(model, x, sigmas, None, extra_args, callback, disable, rk_type="res_5s", eta=0.0, eta_substep=0.0, )
+def sample_res_6s_ode(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return rk_sampler_beta.sample_rk_beta(model, x, sigmas, None, extra_args, callback, disable, rk_type="res_6s", eta=0.0, eta_substep=0.0, )
+
+def sample_deis_2m(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return rk_sampler_beta.sample_rk_beta(model, x, sigmas, None, extra_args, callback, disable, rk_type="deis_2m",)
+def sample_deis_3m(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return rk_sampler_beta.sample_rk_beta(model, x, sigmas, None, extra_args, callback, disable, rk_type="deis_3m",)
+
+def sample_deis_2m_ode(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return rk_sampler_beta.sample_rk_beta(model, x, sigmas, None, extra_args, callback, disable, rk_type="deis_2m", eta=0.0, eta_substep=0.0, )
+def sample_deis_3m_ode(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return rk_sampler_beta.sample_rk_beta(model, x, sigmas, None, extra_args, callback, disable, rk_type="deis_3m", eta=0.0, eta_substep=0.0, )
+
diff --git a/custom_nodes/RES4LYF/beta/constants.py b/custom_nodes/RES4LYF/beta/constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..4523001e63bc29d809478fa6a3980c7de05b075d
--- /dev/null
+++ b/custom_nodes/RES4LYF/beta/constants.py
@@ -0,0 +1,62 @@
+MAX_STEPS = 10000
+
+
+IMPLICIT_TYPE_NAMES = [
+    "rebound",
+    "retro-eta",
+    "bongmath",
+    "predictor-corrector",
+]
+
+
+
+
+GUIDE_MODE_NAMES_BETA_SIMPLE = [
+    "flow",
+    "sync",
+    "lure",
+    "data",
+    "epsilon",
+    "inversion",
+    "pseudoimplicit",
+    "fully_pseudoimplicit",
+    "none",
+]
+
+FRAME_WEIGHTS_CONFIG_NAMES = [
+    "frame_weights",
+    "frame_weights_inv",
+    "frame_targets"
+]
+
+FRAME_WEIGHTS_DYNAMICS_NAMES = [
+    "constant",
+    "linear",
+    "ease_out",
+    "ease_in",
+    "middle",
+    "trough",
+]
+
+
+FRAME_WEIGHTS_SCHEDULE_NAMES = [
+    "moderate_early",
+    "moderate_late",
+    "fast_early",
+    "fast_late",
+    "slow_early",
+    "slow_late",
+]
+
+
+
+GUIDE_MODE_NAMES_PSEUDOIMPLICIT = [
+    "pseudoimplicit",
+    "pseudoimplicit_cw",
+    "pseudoimplicit_projection",
+    "pseudoimplicit_projection_cw",
+    "fully_pseudoimplicit",
+    "fully_pseudoimplicit_projection",
+    "fully_pseudoimplicit_cw", 
+    "fully_pseudoimplicit_projection_cw"
+]
diff --git a/custom_nodes/RES4LYF/beta/deis_coefficients.py b/custom_nodes/RES4LYF/beta/deis_coefficients.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e2e4c0053ac639dcb06cbca0c240c1d3b1c472b
--- /dev/null
+++ b/custom_nodes/RES4LYF/beta/deis_coefficients.py
@@ -0,0 +1,123 @@
+# Adapted from: https://github.com/zju-pi/diff-sampler/blob/main/gits-main/solver_utils.py
+# fixed the calcs for "rhoab" which suffered from an off-by-one error and made some other minor corrections
+
+import torch
+import numpy as np
+
+# A pytorch reimplementation of DEIS (https://github.com/qsh-zh/deis).
+#############################
+### Utils for DEIS solver ###
+#############################
+#----------------------------------------------------------------------------
+# Transfer from the input time (sigma) used in EDM to that (t) used in DEIS.
+
+def edm2t(edm_steps, epsilon_s=1e-3, sigma_min=0.002, sigma_max=80):
+    vp_sigma = lambda beta_d, beta_min: lambda t: (np.e ** (0.5 * beta_d * (t ** 2) + beta_min * t) - 1) ** 0.5
+    vp_sigma_inv = lambda beta_d, beta_min: lambda sigma: ((beta_min ** 2 + 2 * beta_d * (sigma ** 2 + 1).log()).sqrt() - beta_min) / beta_d
+    vp_beta_d = 2 * (np.log(torch.tensor(sigma_min).cpu() ** 2 + 1) / epsilon_s - np.log(torch.tensor(sigma_max).cpu() ** 2 + 1)) / (epsilon_s - 1)
+    vp_beta_min = np.log(torch.tensor(sigma_max).cpu() ** 2 + 1) - 0.5 * vp_beta_d
+    t_steps = vp_sigma_inv(vp_beta_d.clone().detach().cpu(), vp_beta_min.clone().detach().cpu())(edm_steps.clone().detach().cpu())
+    return t_steps, vp_beta_min, vp_beta_d + vp_beta_min
+
+#----------------------------------------------------------------------------
+
+def cal_poly(prev_t, j, taus):
+    poly = 1
+    for k in range(prev_t.shape[0]):
+        if k == j:
+            continue
+        poly *= (taus - prev_t[k]) / (prev_t[j] - prev_t[k])
+    return poly
+
+#----------------------------------------------------------------------------
+# Transfer from t to alpha_t.
+
+def t2alpha_fn(beta_0, beta_1, t):
+    return torch.exp(-0.5 * t ** 2 * (beta_1 - beta_0) - t * beta_0)
+
+#----------------------------------------------------------------------------
+
+def cal_integrand(beta_0, beta_1, taus):
+    with torch.inference_mode(mode=False):
+        taus = taus.clone()
+        beta_0 = beta_0.clone()
+        beta_1 = beta_1.clone()
+        with torch.enable_grad():
+            taus.requires_grad_(True)
+            alpha = t2alpha_fn(beta_0, beta_1, taus)
+            log_alpha = alpha.log()
+            log_alpha.sum().backward()
+            d_log_alpha_dtau = taus.grad
+    integrand = -0.5 * d_log_alpha_dtau / torch.sqrt(alpha * (1 - alpha))
+    return integrand
+
+#----------------------------------------------------------------------------
+
+def get_deis_coeff_list(t_steps, max_order, N=10000, deis_mode='tab'):
+    """
+    Get the coefficient list for DEIS sampling.
+
+    Args:
+        t_steps: A pytorch tensor. The time steps for sampling.
+        max_order: A `int`. Maximum order of the solver. 1 <= max_order <= 4
+        N: A `int`. Use how many points to perform the numerical integration when deis_mode=='tab'.
+        deis_mode: A `str`. Select between 'tab' and 'rhoab'. Type of DEIS.
+    Returns:
+        A pytorch tensor. A batch of generated samples or sampling trajectories if return_inters=True.
+    """
+    if deis_mode == 'tab':
+        t_steps, beta_0, beta_1 = edm2t(t_steps)
+        C = []
+        for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])):
+            order = min(i+1, max_order)
+            if order == 1:
+                C.append([])
+            else:
+                taus = torch.linspace(t_cur, t_next, N)   # split the interval for integral approximation
+                dtau = (t_next - t_cur) / N
+                prev_t = t_steps[[i - k for k in range(order)]]
+                coeff_temp = []
+                integrand = cal_integrand(beta_0, beta_1, taus)
+                for j in range(order):
+                    poly = cal_poly(prev_t, j, taus)
+                    coeff_temp.append(torch.sum(integrand * poly) * dtau)
+                C.append(coeff_temp)
+
+    elif deis_mode == 'rhoab':
+        # Analytical solution, second order
+        def get_def_integral_2(a, b, start, end, c):
+            coeff = (end**3 - start**3) / 3 - (end**2 - start**2) * (a + b) / 2 + (end - start) * a * b
+            return coeff / ((c - a) * (c - b))
+
+        # Analytical solution, third order
+        def get_def_integral_3(a, b, c, start, end, d):
+            coeff = (end**4 - start**4) / 4 - (end**3 - start**3) * (a + b + c) / 3 \
+                    + (end**2 - start**2) * (a*b + a*c + b*c) / 2 - (end - start) * a * b * c
+            return coeff / ((d - a) * (d - b) * (d - c))
+
+        C = []
+        for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])):
+            order = min(i+1, max_order) #fixed order calcs
+            if order == 1:
+                C.append([])
+            else:
+                prev_t = t_steps[[i - k for k in range(order+1)]]
+                if order == 2:
+                    coeff_cur = ((t_next - prev_t[1])**2 - (t_cur - prev_t[1])**2) / (2 * (t_cur - prev_t[1]))
+                    coeff_prev1 = (t_next - t_cur)**2 / (2 * (prev_t[1] - t_cur))
+                    coeff_temp = [coeff_cur, coeff_prev1]
+                elif order == 3:
+                    coeff_cur = get_def_integral_2(prev_t[1], prev_t[2], t_cur, t_next, t_cur)
+                    coeff_prev1 = get_def_integral_2(t_cur, prev_t[2], t_cur, t_next, prev_t[1])
+                    coeff_prev2 = get_def_integral_2(t_cur, prev_t[1], t_cur, t_next, prev_t[2])
+                    coeff_temp = [coeff_cur, coeff_prev1, coeff_prev2]
+                elif order == 4:
+                    coeff_cur = get_def_integral_3(prev_t[1], prev_t[2], prev_t[3], t_cur, t_next, t_cur)
+                    coeff_prev1 = get_def_integral_3(t_cur, prev_t[2], prev_t[3], t_cur, t_next, prev_t[1])
+                    coeff_prev2 = get_def_integral_3(t_cur, prev_t[1], prev_t[3], t_cur, t_next, prev_t[2])
+                    coeff_prev3 = get_def_integral_3(t_cur, prev_t[1], prev_t[2], t_cur, t_next, prev_t[3])
+                    coeff_temp = [coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3]
+                C.append(coeff_temp)
+ 
+    return C
+
diff --git a/custom_nodes/RES4LYF/beta/noise_classes.py b/custom_nodes/RES4LYF/beta/noise_classes.py
new file mode 100644
index 0000000000000000000000000000000000000000..781f0f6635bc8aeed2e0fb0839a821adf9cc154f
--- /dev/null
+++ b/custom_nodes/RES4LYF/beta/noise_classes.py
@@ -0,0 +1,707 @@
+import torch
+import torch.nn.functional as F
+
+from torch               import nn, Tensor, Generator, lerp
+from torch.nn.functional import unfold
+from torch.distributions import StudentT, Laplace
+
+import numpy as np
+import pywt
+import functools
+
+from typing import Callable, Tuple
+from math   import pi
+
+from comfy.k_diffusion.sampling import BrownianTreeNoiseSampler
+
+from ..res4lyf import RESplain
+
+# Set this to "True" if you have installed OpenSimplex. Recommended to install without dependencies due to conflicting packages: pip3 install opensimplex --no-deps 
+OPENSIMPLEX_ENABLE = False
+
+if OPENSIMPLEX_ENABLE:
+    from opensimplex import OpenSimplex
+
+class PrecisionTool:
+    def __init__(self, cast_type='fp64'):
+        self.cast_type = cast_type
+
+    def cast_tensor(self, func):
+        @functools.wraps(func)
+        def wrapper(*args, **kwargs):
+            if self.cast_type not in ['fp64', 'fp32', 'fp16']:
+                return func(*args, **kwargs)
+
+            target_device = None
+            for arg in args:
+                if torch.is_tensor(arg):
+                    target_device = arg.device
+                    break
+            if target_device is None:
+                for v in kwargs.values():
+                    if torch.is_tensor(v):
+                        target_device = v.device
+                        break
+            
+        # recursively zs_recast tensors in nested dictionaries
+            def cast_and_move_to_device(data):
+                if torch.is_tensor(data):
+                    if self.cast_type == 'fp64':
+                        return data.to(torch.float64).to(target_device)
+                    elif self.cast_type == 'fp32':
+                        return data.to(torch.float32).to(target_device)
+                    elif self.cast_type == 'fp16':
+                        return data.to(torch.float16).to(target_device)
+                elif isinstance(data, dict):
+                    return {k: cast_and_move_to_device(v) for k, v in data.items()}
+                return data
+
+            new_args = [cast_and_move_to_device(arg) for arg in args]
+            new_kwargs = {k: cast_and_move_to_device(v) for k, v in kwargs.items()}
+            
+            return func(*new_args, **new_kwargs)
+        return wrapper
+
+    def set_cast_type(self, new_value):
+        if new_value in ['fp64', 'fp32', 'fp16']:
+            self.cast_type = new_value
+        else:
+            self.cast_type = 'fp64'
+
+precision_tool = PrecisionTool(cast_type='fp64')
+
+
+def noise_generator_factory(cls, **fixed_params):
+    def create_instance(**kwargs):
+        params = {**fixed_params, **kwargs}
+        return cls(**params)
+    return create_instance
+
+def like(x):
+    return {'size': x.shape, 'dtype': x.dtype, 'layout': x.layout, 'device': x.device}
+
+def scale_to_range(x, scaled_min = -1.73, scaled_max = 1.73): #1.73 is roughly the square root of 3
+    return scaled_min + (x - x.min()) * (scaled_max - scaled_min) / (x.max() - x.min())
+
+def normalize(x):
+    return (x - x.mean())/ x.std()
+
+class NoiseGenerator:
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None):
+        self.seed = seed
+
+        if x is not None:
+            self.x      = x
+            self.size   = x.shape
+            self.dtype  = x.dtype
+            self.layout = x.layout
+            self.device = x.device
+        else:   
+            self.x      = torch.zeros(size, dtype, layout, device)
+
+        # allow overriding parameters imported from latent 'x' if specified
+        if size is not None:
+            self.size   = size
+        if dtype is not None:
+            self.dtype  = dtype
+        if layout is not None:
+            self.layout = layout
+        if device is not None:
+            self.device = device
+
+        self.sigma_max = sigma_max.to(device) if isinstance(sigma_max, torch.Tensor) else sigma_max
+        self.sigma_min = sigma_min.to(device) if isinstance(sigma_min, torch.Tensor) else sigma_min
+
+        self.last_seed = seed #- 1 #adapt for update being called during initialization, which increments last_seed
+        
+        if generator is None:
+            self.generator = torch.Generator(device=self.device).manual_seed(seed)
+        else:
+            self.generator = generator
+
+    def __call__(self):
+        raise NotImplementedError("This method got clownsharked!")
+    
+    def update(self, **kwargs):
+        
+        #if not isinstance(self, BrownianNoiseGenerator):
+        #    self.last_seed += 1
+                    
+        updated_values = []
+        for attribute_name, value in kwargs.items():
+            if value is not None:
+                setattr(self, attribute_name, value)
+            updated_values.append(getattr(self, attribute_name))
+        return tuple(updated_values)
+
+
+
+class BrownianNoiseGenerator(NoiseGenerator):
+    def __call__(self, *, sigma=None, sigma_next=None, **kwargs):
+        return BrownianTreeNoiseSampler(self.x, self.sigma_min, self.sigma_max, seed=self.seed, cpu = self.device.type=='cpu')(sigma, sigma_next)
+
+
+
+class FractalNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                alpha=0.0, k=1.0, scale=0.1): 
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.update(alpha=alpha, k=k, scale=scale)
+
+    def __call__(self, *, alpha=None, k=None, scale=None, **kwargs):
+        self.update(alpha=alpha, k=k, scale=scale)
+        self.last_seed += 1
+        
+        if len(self.size) == 5:
+            b, c, t, h, w = self.size
+        else:
+            b, c, h, w = self.size
+        
+        noise = torch.normal(mean=0.0, std=1.0, size=self.size, dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator)
+        
+        y_freq = torch.fft.fftfreq(h, 1/h, device=self.device)
+        x_freq = torch.fft.fftfreq(w, 1/w, device=self.device)
+
+        if len(self.size) == 5:
+            t_freq = torch.fft.fftfreq(t, 1/t, device=self.device)
+            freq = torch.sqrt(t_freq[:, None, None]**2 + y_freq[None, :, None]**2 + x_freq[None, None, :]**2).clamp(min=1e-10)
+        else:
+            freq = torch.sqrt(y_freq[:, None]**2 + x_freq[None, :]**2).clamp(min=1e-10)
+        
+        spectral_density = self.k / torch.pow(freq, self.alpha * self.scale)
+        spectral_density[0, 0] = 0
+
+        noise_fft = torch.fft.fftn(noise)
+        modified_fft = noise_fft * spectral_density
+        noise = torch.fft.ifftn(modified_fft).real
+
+        return noise / torch.std(noise)
+    
+    
+
+class SimplexNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                scale=0.01):
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.noise = OpenSimplex(seed=seed)
+        self.scale = scale
+        
+    def __call__(self, *, scale=None, **kwargs):
+        self.update(scale=scale)
+        self.last_seed += 1
+        
+        if len(self.size) == 5:
+            b, c, t, h, w = self.size
+        else:
+            b, c, h, w = self.size
+
+        noise_array = self.noise.noise3array(np.arange(w),np.arange(h),np.arange(c))
+        self.noise = OpenSimplex(seed=self.noise.get_seed()+1)
+        
+        noise_tensor = torch.from_numpy(noise_array).to(self.device)
+        noise_tensor = torch.unsqueeze(noise_tensor, dim=0)
+        if len(self.size) == 5:
+            noise_tensor = torch.unsqueeze(noise_tensor, dim=0)
+        
+        return noise_tensor / noise_tensor.std()
+        #return normalize(scale_to_range(noise_tensor))
+
+
+
+class HiresPyramidNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                discount=0.7, mode='nearest-exact'):
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.update(discount=discount, mode=mode)
+
+    def __call__(self, *, discount=None, mode=None, **kwargs):
+        self.update(discount=discount, mode=mode)
+        self.last_seed += 1
+
+        if len(self.size) == 5:
+            b, c, t, h, w = self.size
+            orig_h, orig_w, orig_t = h, w, t
+            u = nn.Upsample(size=(orig_h, orig_w, orig_t), mode=self.mode).to(self.device)
+        else:
+            b, c, h, w = self.size
+            orig_h, orig_w = h, w
+            orig_t = t = 1
+            u = nn.Upsample(size=(orig_h, orig_w), mode=self.mode).to(self.device)
+
+        noise = ((torch.rand(size=self.size, dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator) - 0.5) * 2 * 1.73)
+
+        for i in range(4):
+            r = torch.rand(1, device=self.device, generator=self.generator).item() * 2 + 2
+            h, w = min(orig_h * 15, int(h * (r ** i))), min(orig_w * 15, int(w * (r ** i)))
+            if len(self.size) == 5:
+                t = min(orig_t * 15, int(t * (r ** i)))
+                new_noise = torch.randn((b, c, t, h, w), dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator)
+            else:
+                new_noise = torch.randn((b, c, h, w), dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator)
+
+            upsampled_noise = u(new_noise)
+            noise += upsampled_noise * self.discount ** i
+            
+            if h >= orig_h * 15 or w >= orig_w * 15 or t >= orig_t * 15:
+                break  # if resolution is too high
+        
+        return noise / noise.std()
+
+
+
+class PyramidNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                discount=0.8, mode='nearest-exact'):
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.update(discount=discount, mode=mode)
+
+    def __call__(self, *, discount=None, mode=None, **kwargs):
+        self.update(discount=discount, mode=mode)
+        self.last_seed += 1
+
+        x = torch.zeros(self.size, dtype=self.dtype, layout=self.layout, device=self.device)
+
+        if len(self.size) == 5:
+            b, c, t, h, w = self.size
+            orig_h, orig_w, orig_t = h, w, t
+        else:
+            b, c, h, w = self.size
+            orig_h, orig_w = h, w
+
+        r = 1
+        for i in range(5):
+            r *= 2
+
+            if len(self.size) == 5:
+                scaledSize = (b, c, t * r, h * r, w * r)
+                origSize = (orig_h, orig_w, orig_t)
+            else:
+                scaledSize = (b, c, h * r, w * r)
+                origSize = (orig_h, orig_w)
+
+            x += torch.nn.functional.interpolate(
+                torch.normal(mean=0, std=0.5 ** i, size=scaledSize, dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator),
+                size=origSize, mode=self.mode
+            ) * self.discount ** i
+        return x / x.std()
+
+
+
+class InterpolatedPyramidNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                discount=0.7, mode='nearest-exact'):
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.update(discount=discount, mode=mode)
+
+    def __call__(self, *, discount=None, mode=None, **kwargs):
+        self.update(discount=discount, mode=mode)
+        self.last_seed += 1
+
+        if len(self.size) == 5:
+            b, c, t, h, w = self.size
+            orig_t, orig_h, orig_w = t, h, w
+        else:
+            b, c, h, w = self.size
+            orig_h, orig_w = h, w
+            t = orig_t = 1
+
+        noise = ((torch.rand(size=self.size, dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator) - 0.5) * 2 * 1.73)
+        multipliers = [1]
+
+        for i in range(4):
+            r = torch.rand(1, device=self.device, generator=self.generator).item() * 2 + 2
+            h, w = min(orig_h * 15, int(h * (r ** i))), min(orig_w * 15, int(w * (r ** i)))
+            
+            if len(self.size) == 5:
+                t = min(orig_t * 15, int(t * (r ** i)))
+                new_noise = torch.randn((b, c, t, h, w), dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator)
+                upsampled_noise = nn.functional.interpolate(new_noise, size=(orig_t, orig_h, orig_w), mode=self.mode)
+            else:
+                new_noise = torch.randn((b, c, h, w), dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator)
+                upsampled_noise = nn.functional.interpolate(new_noise, size=(orig_h, orig_w), mode=self.mode)
+
+            noise += upsampled_noise * self.discount ** i
+            multipliers.append(        self.discount ** i)
+            
+            if h >= orig_h * 15 or w >= orig_w * 15 or (len(self.size) == 5 and t >= orig_t * 15):
+                break  # if resolution is too high
+        
+        noise = noise / sum([m ** 2 for m in multipliers]) ** 0.5 
+        return noise / noise.std()
+
+
+
+class CascadeBPyramidNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                levels=10, mode='nearest', size_range=[1,16]):
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.update(epsilon=x, levels=levels, mode=mode, size_range=size_range)
+
+    def __call__(self, *, levels=10, mode='nearest', size_range=[1,16], **kwargs):
+        self.update(levels=levels, mode=mode)
+        if len(self.size) == 5:
+            raise NotImplementedError("CascadeBPyramidNoiseGenerator is not implemented for 5D tensors (eg. video).") 
+        self.last_seed += 1
+
+        b, c, h, w = self.size
+
+        epsilon = torch.randn(self.size, dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator)
+        multipliers = [1]
+        for i in range(1, levels):
+            m = 0.75 ** i
+
+            h, w = int(epsilon.size(-2) // (2 ** i)), int(epsilon.size(-2) // (2 ** i))
+            if size_range is None or (size_range[0] <= h <= size_range[1] or size_range[0] <= w <= size_range[1]):
+                offset = torch.randn(epsilon.size(0), epsilon.size(1), h, w, device=self.device, generator=self.generator)
+                epsilon = epsilon + torch.nn.functional.interpolate(offset, size=epsilon.shape[-2:], mode=self.mode) * m
+                multipliers.append(m)
+
+            if h <= 1 or w <= 1:
+                break
+        epsilon = epsilon / sum([m ** 2 for m in multipliers]) ** 0.5 #divides the epsilon tensor by the square root of the sum of the squared multipliers.
+
+        return epsilon
+
+
+class UniformNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                mean=0.0, scale=1.73):
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.update(mean=mean, scale=scale)
+
+    def __call__(self, *, mean=None, scale=None, **kwargs):
+        self.update(mean=mean, scale=scale)
+        self.last_seed += 1
+
+        noise = torch.rand(self.size, dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator)
+
+        return self.scale * 2 * (noise - 0.5) + self.mean
+
+class GaussianNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                mean=0.0, std=1.0):
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.update(mean=mean, std=std)
+
+    def __call__(self, *, mean=None, std=None, **kwargs):
+        self.update(mean=mean, std=std)
+        self.last_seed += 1
+
+        noise = torch.randn(self.size, dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator)
+
+        return (noise - noise.mean()) / noise.std()
+
+class GaussianBackwardsNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                mean=0.0, std=1.0):
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.update(mean=mean, std=std)
+
+    def __call__(self, *, mean=None, std=None, **kwargs):
+        self.update(mean=mean, std=std)
+        self.last_seed += 1
+        RESplain("GaussianBackwards last seed:", self.generator.initial_seed())
+        self.generator.manual_seed(self.generator.initial_seed() - 1)
+        noise = torch.randn(self.size, dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator)
+
+        return (noise - noise.mean()) / noise.std()
+
+class LaplacianNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                loc=0, scale=1.0):
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.update(loc=loc, scale=scale)
+
+    def __call__(self, *, loc=None, scale=None, **kwargs):
+        self.update(loc=loc, scale=scale)
+        self.last_seed += 1
+
+        # b, c, h, w = self.size
+        # orig_h, orig_w = h, w
+
+        noise = torch.randn(self.size, dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator) / 4.0
+
+        rng_state = torch.random.get_rng_state()
+        torch.manual_seed(self.generator.initial_seed())
+        laplacian_noise = Laplace(loc=self.loc, scale=self.scale).rsample(self.size).to(self.device)
+        self.generator.manual_seed(self.generator.initial_seed() + 1)
+        torch.random.set_rng_state(rng_state)
+
+        noise += laplacian_noise
+        return noise / noise.std()
+
+class StudentTNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                loc=0, scale=0.2, df=1):
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.update(loc=loc, scale=scale, df=df)
+
+    def __call__(self, *, loc=None, scale=None, df=None, **kwargs):
+        self.update(loc=loc, scale=scale, df=df)
+        self.last_seed += 1
+
+        # b, c, h, w = self.size
+        # orig_h, orig_w = h, w
+
+        rng_state = torch.random.get_rng_state()
+        torch.manual_seed(self.generator.initial_seed())
+
+        noise = StudentT(loc=self.loc, scale=self.scale, df=self.df).rsample(self.size)
+        if not isinstance(self, BrownianNoiseGenerator):
+            self.last_seed += 1
+                    
+        s = torch.quantile(noise.flatten(start_dim=1).abs(), 0.75, dim=-1)
+        
+        if len(self.size) == 5:
+            s = s.reshape(*s.shape, 1, 1, 1, 1)
+        else:
+            s = s.reshape(*s.shape, 1, 1, 1)
+
+        noise = noise.clamp(-s, s)
+
+        noise_latent = torch.copysign(torch.pow(torch.abs(noise), 0.5), noise).to(self.device)
+
+        self.generator.manual_seed(self.generator.initial_seed() + 1)
+        torch.random.set_rng_state(rng_state)
+        return (noise_latent - noise_latent.mean()) / noise_latent.std()
+
+class WaveletNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                wavelet='haar'):
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.update(wavelet=wavelet)
+
+    def __call__(self, *, wavelet=None, **kwargs):
+        self.update(wavelet=wavelet)
+        self.last_seed += 1
+
+        # b, c, h, w = self.size
+        # orig_h, orig_w = h, w
+
+        # noise for spatial dimensions only
+        coeffs = pywt.wavedecn(torch.randn(self.size, dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator).to('cpu'), wavelet=self.wavelet, mode='periodization')
+        noise = pywt.waverecn(coeffs, wavelet=self.wavelet, mode='periodization')
+        noise_tensor = torch.tensor(noise, dtype=self.dtype, device=self.device)
+
+        noise_tensor = (noise_tensor - noise_tensor.mean()) / noise_tensor.std()
+        return noise_tensor
+
+class PerlinNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                detail=0.0):
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.update(detail=detail)
+
+    @staticmethod
+    def get_positions(block_shape: Tuple[int, int]) -> Tensor:
+        bh, bw = block_shape
+        positions = torch.stack(
+            torch.meshgrid(
+                [(torch.arange(b) + 0.5) / b for b in (bw, bh)],
+                indexing="xy",
+            ),
+            -1,
+        ).view(1, bh, bw, 1, 1, 2)
+        return positions
+
+    @staticmethod
+    def unfold_grid(vectors: Tensor) -> Tensor:
+        batch_size, _, gpy, gpx = vectors.shape
+        return (
+            unfold(vectors, (2, 2))
+            .view(batch_size, 2, 4, -1)
+            .permute(0, 2, 3, 1)
+            .view(batch_size, 4, gpy - 1, gpx - 1, 2)
+        )
+
+    @staticmethod
+    def smooth_step(t: Tensor) -> Tensor:
+        return t * t * (3.0 - 2.0 * t)
+
+    @staticmethod
+    def perlin_noise_tensor(
+        self,
+        vectors: Tensor, positions: Tensor, step: Callable = None
+    ) -> Tensor:
+        if step is None:
+            step = self.smooth_step
+
+        batch_size = vectors.shape[0]
+        # grid height, grid width
+        gh, gw = vectors.shape[2:4]
+        # block height, block width
+        bh, bw = positions.shape[1:3]
+
+        for i in range(2):
+            if positions.shape[i + 3] not in (1, vectors.shape[i + 2]):
+                raise Exception(
+                    f"Blocks shapes do not match: vectors ({vectors.shape[1]}, {vectors.shape[2]}), positions {gh}, {gw})"
+                )
+
+        if positions.shape[0] not in (1, batch_size):
+            raise Exception(
+                f"Batch sizes do not match: vectors ({vectors.shape[0]}), positions ({positions.shape[0]})"
+            )
+
+        vectors = vectors.view(batch_size, 4, 1, gh * gw, 2)
+        positions = positions.view(positions.shape[0], bh * bw, -1, 2)
+
+        step_x = step(positions[..., 0])
+        step_y = step(positions[..., 1])
+
+        row0 = lerp(
+            (vectors[:, 0] * positions).sum(dim=-1),
+            (vectors[:, 1] * (positions - positions.new_tensor((1, 0)))).sum(dim=-1),
+            step_x,
+        )
+        row1 = lerp(
+            (vectors[:, 2] * (positions - positions.new_tensor((0, 1)))).sum(dim=-1),
+            (vectors[:, 3] * (positions - positions.new_tensor((1, 1)))).sum(dim=-1),
+            step_x,
+        )
+        noise = lerp(row0, row1, step_y)
+        return (
+            noise.view(
+                batch_size,
+                bh,
+                bw,
+                gh,
+                gw,
+            )
+            .permute(0, 3, 1, 4, 2)
+            .reshape(batch_size, gh * bh, gw * bw)
+        )
+
+    def perlin_noise(
+        self,
+        grid_shape: Tuple[int, int],
+        out_shape: Tuple[int, int],
+        batch_size: int = 1,
+        generator: Generator = None,
+        *args,
+        **kwargs,
+    ) -> Tensor:
+        gh, gw = grid_shape         # grid height and width
+        oh, ow = out_shape        # output height and width
+        bh, bw = oh // gh, ow // gw        # block height and width
+
+        if oh != bh * gh:
+            raise Exception(f"Output height {oh} must be divisible by grid height {gh}")
+        if ow != bw * gw != 0:
+            raise Exception(f"Output width {ow} must be divisible by grid width {gw}")
+
+        angle = torch.empty(
+            [batch_size] + [s + 1 for s in grid_shape], device=self.device, *args, **kwargs
+        ).uniform_(to=2.0 * pi, generator=self.generator)
+        # random vectors on grid points
+        vectors = self.unfold_grid(torch.stack((torch.cos(angle), torch.sin(angle)), dim=1))
+        # positions inside grid cells [0, 1)
+        positions = self.get_positions((bh, bw)).to(vectors)
+        return self.perlin_noise_tensor(self, vectors, positions).squeeze(0)
+
+    def __call__(self, *, detail=None, **kwargs):
+        self.update(detail=detail) #currently unused
+        self.last_seed += 1
+        if len(self.size) == 5:
+            b, c, t, h, w = self.size
+            noise = torch.randn(self.size, dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator) / 2.0
+            
+            for tt in range(t):
+                for i in range(2):
+                    perlin_slice = self.perlin_noise((h, w), (h, w), batch_size=c, generator=self.generator).to(self.device)
+                    perlin_expanded = perlin_slice.unsqueeze(0).unsqueeze(2)
+                    time_slice = noise[:, :, tt:tt+1, :, :]
+                    noise[:, :, tt:tt+1, :, :] += perlin_expanded
+        else:
+            b, c, h, w = self.size
+            #orig_h, orig_w = h, w
+
+            noise = torch.randn(self.size, dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator) / 2.0
+            for i in range(2):
+                noise += self.perlin_noise((h, w), (h, w), batch_size=c, generator=self.generator).to(self.device)
+                
+        return noise / noise.std()
+    
+from functools import partial
+
+NOISE_GENERATOR_CLASSES = {
+    "fractal"               :                         FractalNoiseGenerator,
+    "gaussian"              :                         GaussianNoiseGenerator,
+    "gaussian_backwards"    :                         GaussianBackwardsNoiseGenerator,
+    "uniform"               :                         UniformNoiseGenerator,
+    "pyramid-cascade_B"     :                         CascadeBPyramidNoiseGenerator,
+    "pyramid-interpolated"  :                         InterpolatedPyramidNoiseGenerator,
+    "pyramid-bilinear"      : noise_generator_factory(PyramidNoiseGenerator,      mode='bilinear'),
+    "pyramid-bicubic"       : noise_generator_factory(PyramidNoiseGenerator,      mode='bicubic'),   
+    "pyramid-nearest"       : noise_generator_factory(PyramidNoiseGenerator,      mode='nearest'),  
+    "hires-pyramid-bilinear": noise_generator_factory(HiresPyramidNoiseGenerator, mode='bilinear'),
+    "hires-pyramid-bicubic" : noise_generator_factory(HiresPyramidNoiseGenerator, mode='bicubic'),   
+    "hires-pyramid-nearest" : noise_generator_factory(HiresPyramidNoiseGenerator, mode='nearest'),  
+    "brownian"              :                         BrownianNoiseGenerator,
+    "laplacian"             :                         LaplacianNoiseGenerator,
+    "studentt"              :                         StudentTNoiseGenerator,
+    "wavelet"               :                         WaveletNoiseGenerator,
+    "perlin"                :                         PerlinNoiseGenerator,
+}
+
+
+NOISE_GENERATOR_CLASSES_SIMPLE = {
+    "none"                  :                         GaussianNoiseGenerator,
+    "brownian"              :                         BrownianNoiseGenerator,
+    "gaussian"              :                         GaussianNoiseGenerator,
+    "gaussian_backwards"    :                         GaussianBackwardsNoiseGenerator,
+    "laplacian"             :                         LaplacianNoiseGenerator,
+    "perlin"                :                         PerlinNoiseGenerator,
+    "studentt"              :                         StudentTNoiseGenerator,
+    "uniform"               :                         UniformNoiseGenerator,
+    "wavelet"               :                         WaveletNoiseGenerator,
+    "brown"                 : noise_generator_factory(FractalNoiseGenerator,      alpha=2.0),
+    "pink"                  : noise_generator_factory(FractalNoiseGenerator,      alpha=1.0),
+    "white"                 : noise_generator_factory(FractalNoiseGenerator,      alpha=0.0),
+    "blue"                  : noise_generator_factory(FractalNoiseGenerator,      alpha=-1.0),
+    "violet"                : noise_generator_factory(FractalNoiseGenerator,      alpha=-2.0),
+    "ultraviolet_A"         : noise_generator_factory(FractalNoiseGenerator,      alpha=-3.0),
+    "ultraviolet_B"         : noise_generator_factory(FractalNoiseGenerator,      alpha=-4.0),
+    "ultraviolet_C"         : noise_generator_factory(FractalNoiseGenerator,      alpha=-5.0),
+
+    "hires-pyramid-bicubic" : noise_generator_factory(HiresPyramidNoiseGenerator, mode='bicubic'),   
+    "hires-pyramid-bilinear": noise_generator_factory(HiresPyramidNoiseGenerator, mode='bilinear'),
+    "hires-pyramid-nearest" : noise_generator_factory(HiresPyramidNoiseGenerator, mode='nearest'),  
+    "pyramid-bicubic"       : noise_generator_factory(PyramidNoiseGenerator,      mode='bicubic'),   
+    "pyramid-bilinear"      : noise_generator_factory(PyramidNoiseGenerator,      mode='bilinear'),
+    "pyramid-nearest"       : noise_generator_factory(PyramidNoiseGenerator,      mode='nearest'),  
+    "pyramid-interpolated"  :                         InterpolatedPyramidNoiseGenerator,
+    "pyramid-cascade_B"     :                         CascadeBPyramidNoiseGenerator,
+}                        
+
+if OPENSIMPLEX_ENABLE:
+    NOISE_GENERATOR_CLASSES.update({
+        "simplex": SimplexNoiseGenerator,
+    })
+
+NOISE_GENERATOR_NAMES = tuple(NOISE_GENERATOR_CLASSES.keys())
+NOISE_GENERATOR_NAMES_SIMPLE = tuple(NOISE_GENERATOR_CLASSES_SIMPLE.keys())
+
+
+@precision_tool.cast_tensor
+def prepare_noise(latent_image, seed, noise_type, noise_inds=None, alpha=1.0, k=1.0): # adapted from comfy/sample.py: https://github.com/comfyanonymous/ComfyUI
+    #optional arg skip can be used to skip and discard x number of noise generations for a given seed
+    noise_func = NOISE_GENERATOR_CLASSES.get(noise_type)(x=latent_image, seed=seed, sigma_min=0.0291675, sigma_max=14.614642)                                          # WARNING: HARDCODED SDXL SIGMA RANGE!
+
+    if noise_type == "fractal":
+        noise_func.alpha = alpha
+        noise_func.k = k
+
+    # from here until return is very similar to comfy/sample.py 
+    if noise_inds is None:
+        return noise_func(sigma=14.614642, sigma_next=0.0291675)
+
+    unique_inds, inverse = np.unique(noise_inds, return_inverse=True)
+    noises = []
+    for i in range(unique_inds[-1]+1):
+        noise = noise_func(size = [1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, device=latent_image.device)
+        if i in unique_inds:
+            noises.append(noise)
+    noises = [noises[i] for i in inverse]
+    noises = torch.cat(noises, axis=0)
+    return noises
diff --git a/custom_nodes/RES4LYF/beta/phi_functions.py b/custom_nodes/RES4LYF/beta/phi_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..66986f0488c8c1fd45f046757750c68bd84ab7fd
--- /dev/null
+++ b/custom_nodes/RES4LYF/beta/phi_functions.py
@@ -0,0 +1,140 @@
+import torch
+import math
+from typing import Optional
+
+
+# Remainder solution
+def _phi(j, neg_h):
+    remainder = torch.zeros_like(neg_h)
+
+    for k in range(j): 
+        remainder += (neg_h)**k / math.factorial(k)
+    phi_j_h = ((neg_h).exp() - remainder) / (neg_h)**j
+
+    return phi_j_h
+
+def calculate_gamma(c2, c3):
+    return (3*(c3**3) - 2*c3) / (c2*(2 - 3*c2))
+
+# Exact analytic solution originally calculated by Clybius. https://github.com/Clybius/ComfyUI-Extra-Samplers/tree/main
+def _gamma(n: int,) -> int:
+    """
+    https://en.wikipedia.org/wiki/Gamma_function
+    for every positive integer n,
+    Γ(n) = (n-1)!
+    """
+    return math.factorial(n-1)
+
+def _incomplete_gamma(s: int, x: float, gamma_s: Optional[int] = None) -> float:
+    """
+    https://en.wikipedia.org/wiki/Incomplete_gamma_function#Special_values
+    if s is a positive integer,
+    Γ(s, x) = (s-1)!*∑{k=0..s-1}(x^k/k!)
+    """
+    if gamma_s is None:
+        gamma_s = _gamma(s)
+
+    sum_: float = 0
+    # {k=0..s-1} inclusive
+    for k in range(s):
+        numerator: float = x**k
+        denom: int = math.factorial(k)
+        quotient: float = numerator/denom
+        sum_ += quotient
+    incomplete_gamma_: float = sum_ * math.exp(-x) * gamma_s
+    return incomplete_gamma_
+
+def phi(j: int, neg_h: float, ):
+    """
+    For j={1,2,3}: you could alternatively use Kat's phi_1, phi_2, phi_3 which perform fewer steps
+
+    Lemma 1
+    https://arxiv.org/abs/2308.02157
+    ϕj(-h) = 1/h^j*∫{0..h}(e^(τ-h)*(τ^(j-1))/((j-1)!)dτ)
+
+    https://www.wolframalpha.com/input?i=integrate+e%5E%28%CF%84-h%29*%28%CF%84%5E%28j-1%29%2F%28j-1%29%21%29d%CF%84
+    = 1/h^j*[(e^(-h)*(-τ)^(-j)*τ(j))/((j-1)!)]{0..h}
+    https://www.wolframalpha.com/input?i=integrate+e%5E%28%CF%84-h%29*%28%CF%84%5E%28j-1%29%2F%28j-1%29%21%29d%CF%84+between+0+and+h
+    = 1/h^j*((e^(-h)*(-h)^(-j)*h^j*(Γ(j)-Γ(j,-h)))/(j-1)!)
+    = (e^(-h)*(-h)^(-j)*h^j*(Γ(j)-Γ(j,-h))/((j-1)!*h^j)
+    = (e^(-h)*(-h)^(-j)*(Γ(j)-Γ(j,-h))/(j-1)!
+    = (e^(-h)*(-h)^(-j)*(Γ(j)-Γ(j,-h))/Γ(j)
+    = (e^(-h)*(-h)^(-j)*(1-Γ(j,-h)/Γ(j))
+
+    requires j>0
+    """
+    assert j > 0
+    gamma_: float = _gamma(j)
+    incomp_gamma_: float = _incomplete_gamma(j, neg_h, gamma_s=gamma_)
+    phi_: float = math.exp(neg_h) * neg_h**-j * (1-incomp_gamma_/gamma_)
+    return phi_
+
+
+
+from mpmath import mp, mpf, factorial, exp
+
+
+mp.dps = 80   # e.g. 80 decimal digits (~ float256)
+
+def phi_mpmath_series(j: int, neg_h: float) -> float:
+    """
+    Arbitrary‐precision phi_j(-h) via the remainder‐series definition,
+    using mpmath’s mpf and factorial.
+    """
+    j = int(j)
+    z = mpf(float(neg_h))    
+    S = mp.mpf('0')    # S = sum_{k=0..j-1} z^k / k!
+    for k in range(j):
+        S += (z**k) / factorial(k)
+    phi_val = (exp(z) - S) / (z**j)
+    return float(phi_val)
+
+
+
+class Phi:
+    def __init__(self, h, c, analytic_solution=False): 
+        self.h = h
+        self.c = c
+        self.cache = {}  
+        if analytic_solution:
+            #self.phi_f = superphi
+            self.phi_f = phi_mpmath_series
+            self.h = mpf(float(h))
+            self.c = [mpf(c_val) for c_val in c]
+            #self.c = c
+            #self.phi_f = phi
+        else:
+            self.phi_f = phi
+            #self.phi_f = _phi  # remainder method
+
+    def __call__(self, j, i=-1):
+        if (j, i) in self.cache:
+            return self.cache[(j, i)]
+
+        if i < 0:
+            c = 1
+        else:
+            c = self.c[i - 1]
+            if c == 0:
+                self.cache[(j, i)] = 0
+                return 0
+
+        if j == 0 and type(c) in {float, torch.Tensor}:
+            result = math.exp(float(-self.h * c))
+        else:
+            result = self.phi_f(j, -self.h * c)
+
+        self.cache[(j, i)] = result
+
+        return result
+
+
+
+from mpmath import mp, mpf, gamma, gammainc
+
+def superphi(j: int, neg_h: float, ):
+    gamma_: float = gamma(j)
+    incomp_gamma_: float = gamma_ - gammainc(j, 0, float(neg_h))
+    phi_: float = float(math.exp(float(neg_h)) * neg_h**-j) * (1-incomp_gamma_/gamma_)
+    return float(phi_)
+
diff --git a/custom_nodes/RES4LYF/beta/rk_coefficients_beta.py b/custom_nodes/RES4LYF/beta/rk_coefficients_beta.py
new file mode 100644
index 0000000000000000000000000000000000000000..a804cfaac1205bfeb51a332596465f8ce605a79b
--- /dev/null
+++ b/custom_nodes/RES4LYF/beta/rk_coefficients_beta.py
@@ -0,0 +1,3307 @@
+import torch
+from torch import Tensor
+
+import copy
+import math
+from mpmath import mp, mpf, factorial, exp
+mp.dps = 80
+from typing          import Optional, Callable, Tuple, Dict, Any, Union, TYPE_CHECKING, TypeVar
+
+from .deis_coefficients import get_deis_coeff_list
+from .phi_functions import phi, Phi, calculate_gamma
+
+from ..helper import ExtraOptions, get_extra_options_kv, extra_options_flag
+
+
+from itertools import permutations, combinations
+import random
+
+from einops import rearrange, einsum
+from ..res4lyf import get_display_sampler_category
+
+# Samplers with free parameters (c1, c2, c3)
+# 1 2 3       
+#   X   res_2s
+#   X X res_3s
+#   X   res_3s_alt
+#   X   res_3s_strehmel_weiner
+#   X   dpmpp_2s                  (dpmpp_sde_2s has c2=1.0)
+#   X X dpmpp_3s
+# X X   irk_exp_diag_2s
+
+RK_EXPONENTIAL_PREFIXES = (
+    "res", 
+    "dpmpp", 
+    "ddim", 
+    "pec", 
+    "etdrk", 
+    "lawson", 
+    "abnorsett",
+    )
+
+def is_exponential(rk_type:str) -> bool:
+    return rk_type.startswith(RK_EXPONENTIAL_PREFIXES)
+
+RK_SAMPLER_NAMES_BETA_FOLDERS = ["none",
+                    "multistep/res_2m",
+                    "multistep/res_3m",
+                    
+                    "multistep/dpmpp_2m",
+                    "multistep/dpmpp_3m",
+
+                    "multistep/abnorsett_2m",
+                    "multistep/abnorsett_3m",
+                    "multistep/abnorsett_4m",
+
+                    "multistep/deis_2m",
+                    "multistep/deis_3m", 
+                    "multistep/deis_4m",
+                    
+                    "exponential/res_2s_rkmk2e", 
+                    "exponential/res_2s", 
+                    "exponential/res_2s_stable", 
+                    "exponential/res_3s",
+                    "exponential/res_3s_non-monotonic",
+                    "exponential/res_3s_alt",
+                    "exponential/res_3s_cox_matthews",
+                    "exponential/res_3s_lie",
+                    "exponential/res_3s_sunstar",
+
+                    "exponential/res_3s_strehmel_weiner",
+                    "exponential/res_4s_krogstad",
+                    "exponential/res_4s_krogstad_alt",
+                    "exponential/res_4s_strehmel_weiner",
+                    "exponential/res_4s_strehmel_weiner_alt",
+                    "exponential/res_4s_cox_matthews",
+                    "exponential/res_4s_cfree4",
+                    "exponential/res_4s_friedli",
+                    "exponential/res_4s_minchev",
+                    "exponential/res_4s_munthe-kaas",
+
+                    "exponential/res_5s",
+                    "exponential/res_5s_hochbruck-ostermann",
+                    "exponential/res_6s",
+                    "exponential/res_8s",
+                    "exponential/res_8s_alt",
+
+                    "exponential/res_10s",
+                    "exponential/res_15s",
+                    "exponential/res_16s",
+                    
+                    "exponential/etdrk2_2s",
+                    "exponential/etdrk3_a_3s",
+                    "exponential/etdrk3_b_3s",
+                    "exponential/etdrk4_4s",
+                    "exponential/etdrk4_4s_alt",
+                    
+                    "exponential/dpmpp_2s",
+                    "exponential/dpmpp_sde_2s",
+                    "exponential/dpmpp_3s",
+                    
+                    "exponential/lawson2a_2s",
+                    "exponential/lawson2b_2s",
+
+                    "exponential/lawson4_4s",
+                    "exponential/lawson41-gen_4s",
+                    "exponential/lawson41-gen-mod_4s",
+
+                    "exponential/ddim",
+                    
+                    "hybrid/pec423_2h2s",
+                    "hybrid/pec433_2h3s",
+                    
+                    "hybrid/abnorsett2_1h2s",
+                    "hybrid/abnorsett3_2h2s",
+                    "hybrid/abnorsett4_3h2s",
+                    
+                    
+                    "hybrid/lawson42-gen-mod_1h4s",
+                    "hybrid/lawson43-gen-mod_2h4s",
+                    "hybrid/lawson44-gen-mod_3h4s",
+                    "hybrid/lawson45-gen-mod_4h4s",
+                    
+                    "linear/ralston_2s",
+                    "linear/ralston_3s",
+                    "linear/ralston_4s", 
+                    
+
+                    
+                    "linear/midpoint_2s",
+                    "linear/heun_2s", 
+                    "linear/heun_3s", 
+                    
+                    "linear/houwen-wray_3s",
+                    "linear/kutta_3s", 
+                    "linear/ssprk3_3s",
+                    "linear/ssprk4_4s",
+                    
+                    "linear/rk38_4s",
+                    "linear/rk4_4s", 
+                    "linear/rk5_7s",
+                    "linear/rk6_7s",
+
+                    "linear/bogacki-shampine_4s",
+                    "linear/bogacki-shampine_7s",
+
+                    "linear/dormand-prince_6s", 
+                    "linear/dormand-prince_13s", 
+
+                    "linear/tsi_7s",
+                    #"verner_robust_16s",
+
+                    "linear/euler",
+                    
+                    "diag_implicit/irk_exp_diag_2s",
+                    
+                    "diag_implicit/kraaijevanger_spijker_2s",
+                    "diag_implicit/qin_zhang_2s",
+                    
+                    "diag_implicit/pareschi_russo_2s",
+                    "diag_implicit/pareschi_russo_alt_2s",
+                    
+                    "diag_implicit/crouzeix_2s",
+                    "diag_implicit/crouzeix_3s",
+                    "diag_implicit/crouzeix_3s_alt",
+                    
+                    "fully_implicit/gauss-legendre_2s",
+                    "fully_implicit/gauss-legendre_3s", 
+                    "fully_implicit/gauss-legendre_4s",
+                    "fully_implicit/gauss-legendre_4s_alternating_a",
+                    "fully_implicit/gauss-legendre_4s_ascending_a",
+                    "fully_implicit/gauss-legendre_4s_alt",
+                    "fully_implicit/gauss-legendre_5s",                    
+                    "fully_implicit/gauss-legendre_5s_ascending",
+                    #"gauss-legendre_diag_8s",
+
+                    
+                    "fully_implicit/radau_ia_2s",
+                    "fully_implicit/radau_ia_3s",
+
+                    "fully_implicit/radau_iia_2s",
+                    "fully_implicit/radau_iia_3s",
+                    "fully_implicit/radau_iia_3s_alt",
+                    "fully_implicit/radau_iia_5s",
+                    "fully_implicit/radau_iia_7s",
+                    "fully_implicit/radau_iia_9s",
+                    "fully_implicit/radau_iia_11s",
+                    
+                    "fully_implicit/lobatto_iiia_2s",
+                    "fully_implicit/lobatto_iiia_3s", 
+                    "fully_implicit/lobatto_iiia_4s",
+                    "fully_implicit/lobatto_iiib_2s",
+                    "fully_implicit/lobatto_iiib_3s",
+                    "fully_implicit/lobatto_iiib_4s",
+
+                    "fully_implicit/lobatto_iiic_2s",
+                    "fully_implicit/lobatto_iiic_3s",
+                    "fully_implicit/lobatto_iiic_4s",
+
+                    "fully_implicit/lobatto_iiic_star_2s",
+                    "fully_implicit/lobatto_iiic_star_3s",
+                    "fully_implicit/lobatto_iiid_2s",
+                    "fully_implicit/lobatto_iiid_3s",
+                    
+                    ]
+
+
+
+RK_SAMPLER_NAMES_BETA_NO_FOLDERS = []
+for orig_sampler_name in RK_SAMPLER_NAMES_BETA_FOLDERS[1:]:
+    sampler_name = orig_sampler_name.split("/")[-1] if "/" in orig_sampler_name else orig_sampler_name
+    RK_SAMPLER_NAMES_BETA_NO_FOLDERS.append(sampler_name)
+
+IRK_SAMPLER_NAMES_BETA_FOLDERS = ["none", "use_explicit"]
+for orig_sampler_name in RK_SAMPLER_NAMES_BETA_FOLDERS[1:]:
+    if "implicit" in orig_sampler_name and "/" in orig_sampler_name:
+        IRK_SAMPLER_NAMES_BETA_FOLDERS.append(orig_sampler_name)
+
+IRK_SAMPLER_NAMES_BETA_NO_FOLDERS = []
+for orig_sampler_name in IRK_SAMPLER_NAMES_BETA_FOLDERS[1:]:
+    sampler_name = orig_sampler_name.split("/")[-1] if "/" in orig_sampler_name else orig_sampler_name
+    IRK_SAMPLER_NAMES_BETA_NO_FOLDERS.append(sampler_name)
+
+RK_SAMPLER_FOLDER_MAP = {}
+for orig_sampler_name in RK_SAMPLER_NAMES_BETA_FOLDERS:
+    if "/" in orig_sampler_name:
+        folder, sampler_name = orig_sampler_name.rsplit("/", 1)
+    else:
+        folder = ""
+        sampler_name = orig_sampler_name
+    RK_SAMPLER_FOLDER_MAP[sampler_name] = folder
+
+IRK_SAMPLER_FOLDER_MAP = {}
+for orig_sampler_name in IRK_SAMPLER_NAMES_BETA_FOLDERS:
+    if "/" in orig_sampler_name:
+        folder, sampler_name = orig_sampler_name.rsplit("/", 1)
+    else:
+        folder = ""
+        sampler_name = orig_sampler_name
+    IRK_SAMPLER_FOLDER_MAP[sampler_name] = folder
+
+class DualFormatList(list):
+    """list that can match items with or without category prefixes."""
+    def __contains__(self, item):
+        if super().__contains__(item):
+            return True
+
+        if isinstance(item, str) and "/" in item:
+            base_name = item.split("/")[-1]
+            return any(name.endswith(base_name) for name in self)
+
+        return any(isinstance(opt, str) and opt.endswith("/" + item) for opt in self)
+
+def get_sampler_name_list(nameOnly = False) -> list:
+    sampler_name_list = []
+    for sampler_name in RK_SAMPLER_FOLDER_MAP:
+        if get_display_sampler_category() and not nameOnly:
+            folder_name = RK_SAMPLER_FOLDER_MAP[sampler_name]
+            full_sampler_name = f"{folder_name}/{sampler_name}"
+        else:
+            full_sampler_name = sampler_name
+        if full_sampler_name[0] == "/":
+            full_sampler_name = full_sampler_name[1:]
+        sampler_name_list.append(full_sampler_name)
+    return DualFormatList(sampler_name_list)
+
+def get_default_sampler_name(nameOnly = False) -> str:
+    default_sampler_name = "res_2m"
+    #find the key associated with the default value
+    for sampler_name in RK_SAMPLER_FOLDER_MAP:
+        if sampler_name == default_sampler_name:
+            if get_display_sampler_category() and not nameOnly:
+                folder_name = RK_SAMPLER_FOLDER_MAP[sampler_name]
+                return f"{folder_name}/{default_sampler_name}"
+            else:
+                return default_sampler_name
+    return default_sampler_name
+
+def get_implicit_sampler_name_list(nameOnly = False) -> list:
+    implicit_sampler_name_list = []
+    for sampler_name in IRK_SAMPLER_FOLDER_MAP:
+        if get_display_sampler_category() and not nameOnly:
+            folder_name = IRK_SAMPLER_FOLDER_MAP[sampler_name]
+            full_sampler_name = f"{folder_name}/{sampler_name}"
+        else:
+            full_sampler_name = sampler_name
+        if full_sampler_name[0] == "/":
+            full_sampler_name = full_sampler_name[1:]
+        implicit_sampler_name_list.append(full_sampler_name)
+    return DualFormatList(implicit_sampler_name_list)
+
+def get_default_implicit_sampler_name(nameOnly = False) -> str:
+    default_sampler_value = "explicit_diagonal"
+    #find the key associated with the default value
+    for sampler_name in IRK_SAMPLER_FOLDER_MAP:
+        if sampler_name == default_sampler_value:
+            if get_display_sampler_category() and not nameOnly:
+                folder_name = IRK_SAMPLER_FOLDER_MAP[sampler_name]
+                return f"{folder_name}/{default_sampler_value}"
+            else:
+                return default_sampler_value
+    return default_sampler_value
+
+def get_full_sampler_name(sampler_name_in: str) -> str:
+    if "/" in sampler_name_in and sampler_name_in[0] != "/":
+        return sampler_name_in
+    for sampler_name in RK_SAMPLER_FOLDER_MAP:
+        if sampler_name == sampler_name_in:
+            folder_name = RK_SAMPLER_FOLDER_MAP[sampler_name]
+            return f"{folder_name}/{sampler_name}"
+    return sampler_name
+
+def process_sampler_name(sampler_name_in):
+    processed_name = sampler_name_in.split("/")[-1] if "/" in sampler_name_in else sampler_name_in
+    full_sampler_name = get_full_sampler_name(sampler_name_in)
+
+    if sampler_name_in.startswith("fully_implicit") or sampler_name_in.startswith("diag_implicit"):
+        implicit_sampler_name = processed_name
+        sampler_name = "euler"
+    else:
+        sampler_name = processed_name
+        implicit_sampler_name = "use_explicit"
+
+    return sampler_name, implicit_sampler_name
+
+
+
+
+alpha_crouzeix = (2/(3**0.5)) * math.cos(math.pi / 18)
+gamma_crouzeix = (1/(3**0.5)) * math.cos(math.pi / 18) + 1/2 # Crouzeix & Raviart 1980; A-stable; pg 100 in Solving Ordinary Differential Equations II
+delta_crouzeix = 1 / (6 * (2 * gamma_crouzeix - 1)**2)       # Crouzeix & Raviart 1980; A-stable; pg 100 in Solving Ordinary Differential Equations II
+
+rk_coeff = {
+    "gauss-legendre_diag_8s": ( # https://github.com/SciML/IRKGaussLegendre.jl/blob/master/src/IRKCoefficients.jl Antoñana, M., Makazaga, J., Murua, Ander. "Reducing and monitoring round-off error propagation for symplectic implicit Runge-Kutta schemes." Numerical Algorithms. 2017.
+    [
+        [
+            0.5,
+            0,0,0,0,0,0,0,
+        ],
+        [
+            1.0818949631055814971365081647359309e00,
+            0.5,
+            0,0,0,0,0,0,
+        ],
+        [
+            9.5995729622205494766003095439844678e-01,
+            1.0869589243008327233290709646162480e00,
+            0.5,
+            0,0,0,0,0,
+        ],
+        [
+            1.0247213458032003748680445816450829e00,
+            9.5505887369737431186016905653386876e-01,
+            1.0880938387323083134422138713913203e00,
+            0.5,
+            0,0,0,0,
+        ],
+        [
+            9.8302382676362890697311829123888390e-01,
+            1.0287597754747493109782305570410685e00,
+            9.5383453518519996588326911440754302e-01,
+            1.0883471611098277842507073806008045e00,
+            0.5,
+            0,0,0,
+        ],
+        [
+            1.0122259141132982060539425317219435e00,
+            9.7998287236359129082628958290257329e-01,
+            1.0296038730649779374630125982121223e00,
+            9.5383453518519996588326911440754302e-01,
+            1.0880938387323083134422138713913203e00,
+            0.5,
+            0,0,
+        ],
+        [
+            9.9125143323080263118822334698608777e-01,
+            1.0140743558891669291459735166525994e00,
+            9.7998287236359129082628958290257329e-01,
+            1.0287597754747493109782305570410685e00,
+            9.5505887369737431186016905653386876e-01,
+            1.0869589243008327233290709646162480e00,
+            0.5,
+            0,
+        ],
+        [
+            1.0054828082532158826793409353214951e00,
+            9.9125143323080263118822334698608777e-01,
+            1.0122259141132982060539425317219435e00,
+            9.8302382676362890697311829123888390e-01,
+            1.0247213458032003748680445816450829e00,
+            9.5995729622205494766003095439844678e-01,
+            1.0818949631055814971365081647359309e00,
+            0.5,
+        ],
+    ],
+    [
+        [
+        5.0614268145188129576265677154981094e-02, 
+        1.1119051722668723527217799721312045e-01,
+        1.5685332293894364366898110099330067e-01,
+        1.8134189168918099148257522463859781e-01,
+        1.8134189168918099148257522463859781e-01,
+        1.5685332293894364366898110099330067e-01,
+        1.1119051722668723527217799721312045e-01,
+        5.0614268145188129576265677154981094e-02,]
+    ],
+    [
+        1.9855071751231884158219565715263505e-02, # 0.019855071751231884158219565715263505
+        1.0166676129318663020422303176208480e-01,
+        2.3723379504183550709113047540537686e-01,
+        4.0828267875217509753026192881990801e-01,
+        5.9171732124782490246973807118009203e-01,
+        7.6276620495816449290886952459462321e-01,
+        8.9833323870681336979577696823791522e-01,
+        9.8014492824876811584178043428473653e-01,
+    ]
+    ),
+    
+    
+    "gauss-legendre_5s": (
+    [
+        [4563950663 / 32115191526, 
+         (310937500000000 / 2597974476091533 + 45156250000 * (739**0.5) / 8747388808389), 
+         (310937500000000 / 2597974476091533 - 45156250000 * (739**0.5) / 8747388808389),
+         (5236016175 / 88357462711 + 709703235 * (739**0.5) / 353429850844),
+         (5236016175 / 88357462711 - 709703235 * (739**0.5) / 353429850844)],
+        
+        [(4563950663 / 32115191526 - 38339103 * (739**0.5) / 6250000000),
+         (310937500000000 / 2597974476091533 + 9557056475401 * (739**0.5) / 3498955523355600000),
+         (310937500000000 / 2597974476091533 - 14074198220719489 * (739**0.5) / 3498955523355600000),
+         (5236016175 / 88357462711 + 5601362553163918341 * (739**0.5) / 2208936567775000000000),
+         (5236016175 / 88357462711 - 5040458465159165409 * (739**0.5) / 2208936567775000000000)],
+        
+        [(4563950663 / 32115191526 + 38339103 * (739**0.5) / 6250000000),
+         (310937500000000 / 2597974476091533 + 14074198220719489 * (739**0.5) / 3498955523355600000),
+         (310937500000000 / 2597974476091533 - 9557056475401 * (739**0.5) / 3498955523355600000),
+         (5236016175 / 88357462711 + 5040458465159165409 * (739**0.5) / 2208936567775000000000),
+         (5236016175 / 88357462711 - 5601362553163918341 * (739**0.5) / 2208936567775000000000)],
+        
+        [(4563950663 / 32115191526 - 38209 * (739**0.5) / 7938810),
+         (310937500000000 / 2597974476091533 - 359369071093750 * (739**0.5) / 70145310854471391),
+         (310937500000000 / 2597974476091533 - 323282178906250 * (739**0.5) / 70145310854471391),
+         (5236016175 / 88357462711 - 470139 * (739**0.5) / 1413719403376),
+         (5236016175 / 88357462711 - 44986764863 * (739**0.5) / 21205791050640)],
+        
+        [(4563950663 / 32115191526 + 38209 * (739**0.5) / 7938810),
+         (310937500000000 / 2597974476091533 + 359369071093750 * (739**0.5) / 70145310854471391),
+         (310937500000000 / 2597974476091533 + 323282178906250 * (739**0.5) / 70145310854471391),
+         (5236016175 / 88357462711 + 44986764863 * (739**0.5) / 21205791050640),
+         (5236016175 / 88357462711 + 470139 * (739**0.5) / 1413719403376)],
+    ],
+    [
+        
+        [
+        4563950663 / 16057595763,
+        621875000000000 / 2597974476091533,
+        621875000000000 / 2597974476091533,
+        10472032350 / 88357462711,
+        10472032350 / 88357462711]
+    ],
+    [
+        1 / 2,
+        1 / 2 - 99 * (739**0.5) / 10000,  # smallest  # 0.06941899716778028758987101075583196       
+        1 / 2 + 99 * (739**0.5) / 10000,  # largest
+        1 / 2 - (739**0.5) / 60,
+        1 / 2 + (739**0.5) / 60
+    ]
+    ),
+    
+    "gauss-legendre_5s_ascending": (
+    [
+        [(4563950663 / 32115191526 - 38339103 * (739**0.5) / 6250000000),
+         (310937500000000 / 2597974476091533 + 9557056475401 * (739**0.5) / 3498955523355600000),
+         (310937500000000 / 2597974476091533 - 14074198220719489 * (739**0.5) / 3498955523355600000),
+         (5236016175 / 88357462711 + 5601362553163918341 * (739**0.5) / 2208936567775000000000),
+         (5236016175 / 88357462711 - 5040458465159165409 * (739**0.5) / 2208936567775000000000)],
+        
+        
+        [(4563950663 / 32115191526 - 38209 * (739**0.5) / 7938810),
+         (310937500000000 / 2597974476091533 - 359369071093750 * (739**0.5) / 70145310854471391),
+         (310937500000000 / 2597974476091533 - 323282178906250 * (739**0.5) / 70145310854471391),
+         (5236016175 / 88357462711 - 470139 * (739**0.5) / 1413719403376),
+         (5236016175 / 88357462711 - 44986764863 * (739**0.5) / 21205791050640)],
+        
+        [4563950663 / 32115191526, 
+         (310937500000000 / 2597974476091533 + 45156250000 * (739**0.5) / 8747388808389), 
+         (310937500000000 / 2597974476091533 - 45156250000 * (739**0.5) / 8747388808389),
+         (5236016175 / 88357462711 + 709703235 * (739**0.5) / 353429850844),
+         (5236016175 / 88357462711 - 709703235 * (739**0.5) / 353429850844)],
+
+        
+        [(4563950663 / 32115191526 + 38209 * (739**0.5) / 7938810),
+         (310937500000000 / 2597974476091533 + 359369071093750 * (739**0.5) / 70145310854471391),
+         (310937500000000 / 2597974476091533 + 323282178906250 * (739**0.5) / 70145310854471391),
+         (5236016175 / 88357462711 + 44986764863 * (739**0.5) / 21205791050640),
+         (5236016175 / 88357462711 + 470139 * (739**0.5) / 1413719403376)],
+        
+        
+        [(4563950663 / 32115191526 + 38339103 * (739**0.5) / 6250000000),
+         (310937500000000 / 2597974476091533 + 14074198220719489 * (739**0.5) / 3498955523355600000),
+         (310937500000000 / 2597974476091533 - 9557056475401 * (739**0.5) / 3498955523355600000),
+         (5236016175 / 88357462711 + 5040458465159165409 * (739**0.5) / 2208936567775000000000),
+         (5236016175 / 88357462711 - 5601362553163918341 * (739**0.5) / 2208936567775000000000)],
+    ],
+    [
+        
+        [621875000000000 / 2597974476091533,
+        10472032350 / 88357462711,
+
+        4563950663 / 16057595763,
+        
+        10472032350 / 88357462711,
+        621875000000000 / 2597974476091533,]
+    ],
+    [
+        1 / 2 - 99 * (739**0.5) / 10000,  # smallest  # 0.06941899716778028758987101075583196       
+        1 / 2 - (739**0.5) / 60,
+        1 / 2,
+
+
+        1 / 2 + (739**0.5) / 60,
+        
+        1 / 2 + 99 * (739**0.5) / 10000,  # largest
+    ]
+    ),
+    "gauss-legendre_4s_alt": ( # https://ijstre.com/Publish/072016/371428231.pdf Four Point Gauss Quadrature Runge – Kuta Method Of Order 8 For Ordinary Differential Equations
+        [
+            [1633/18780 - 71*206**0.5/96717000,
+            134689/939000 - 927*206**0.5/78250,
+            171511/939000 - 927*206**0.5/78250,
+            1633/18780 - 121979*206**0.5/19343400,],            
+            [7623/78250 - 1629507*206**0.5/257912000,
+            347013/21284000,
+            -118701/4256800,
+            7623/78250 + 1629507*206**0.5/257912000,],          
+            [8978/117375 + 1629507*206**0.5/257912000,
+            4520423/12770400,
+            10410661/63852000,
+            8978/117375 + 1629507*206**0.5/257912000,],            
+            [1633/18780 + 121979*206**0.5/19343400,
+            134689/939000 + 927*206**0.5/78250,
+            171511/939000 + 927*206**0.5/78250,
+            1633/18780 + 71*206**0.5/96717000,],       
+        ],
+        [    
+            [1633/9390,
+            1531/4695,
+            1531/4695,
+            1633/9390,]                                        
+        ],
+        [
+            1/2 - 3*206**0.5 / 100,  # 0.06941899716778028758987101075583196                          
+            33/100,                                         
+            67/100,                                        
+            1/2 + 3*206**0.5 / 100,                                         
+        ]
+    ),
+    "gauss-legendre_4s": (
+        [
+            [1/4, 1/4 - 15**0.5 / 6, 1/4 + 15**0.5 / 6, 1/4],            
+            [1/4 + 15**0.5 / 6, 1/4, 1/4 - 15**0.5 / 6, 1/4],          
+            [1/4, 1/4 + 15**0.5 / 6, 1/4, 1/4 - 15**0.5 / 6],            
+            [1/4 - 15**0.5 / 6, 1/4, 1/4 + 15**0.5 / 6, 1/4],       
+        ],
+        [    
+            [
+            1/8, 
+            3/8, 
+            3/8, 
+            1/8,]                                        
+        ],
+        [
+            1/2 - 15**0.5 / 10,      # 0.11270166537925831148207346002176004                               
+            1/2 + 15**0.5 / 10,                                         
+            1/2 + 15**0.5 / 10,                                        
+            1/2 - 15**0.5 / 10                                         
+        ]
+    ),
+    "gauss-legendre_4s_alternating_a": (
+        [
+            [1/4, 1/4 - 15**0.5 / 6, 1/4 + 15**0.5 / 6, 1/4],            
+            [1/4 + 15**0.5 / 6, 1/4, 1/4 - 15**0.5 / 6, 1/4],   
+            [1/4 - 15**0.5 / 6, 1/4, 1/4 + 15**0.5 / 6, 1/4],       
+            [1/4, 1/4 + 15**0.5 / 6, 1/4, 1/4 - 15**0.5 / 6],            
+        ],
+        [    
+            [
+            1/8, 
+            3/8, 
+            1/8, 
+            3/8,]                                        
+        ],
+        [
+            1/2 - 15**0.5 / 10,      # 0.11270166537925831148207346002176004                               
+            1/2 + 15**0.5 / 10,     
+            1/2 - 15**0.5 / 10,                                         
+            1/2 + 15**0.5 / 10,                                        
+        ]
+    ),
+    "gauss-legendre_4s_ascending_a": (
+        [
+            [1/4 - 15**0.5 / 6,   1/4,               1/4 + 15**0.5 / 6, 1/4],              
+            [1/4,                 1/4 - 15**0.5 / 6, 1/4 + 15**0.5 / 6, 1/4],            
+            [1/4,                 1/4 + 15**0.5 / 6, 1/4,               1/4 - 15**0.5 / 6],    
+            [1/4 + 15**0.5 / 6,   1/4,               1/4 - 15**0.5 / 6, 1/4],          
+
+        ],
+        [
+            [
+            1/8, 
+            3/8, 
+            1/8,
+            3/8,]                                        
+        ],
+        [
+            1/2 - 15**0.5 / 10,                                         
+            1/2 - 15**0.5 / 10,                                     
+            1/2 + 15**0.5 / 10,                                         
+            1/2 + 15**0.5 / 10,                                        
+        ]
+    ),
+
+    "gauss-legendre_3s": ( # Kunzmann-Butcher, IRK, order 6 https://www.math.umd.edu/~mariakc/SymplecticMethods.pdf
+        [
+            [5/36, 2/9 - 15**0.5 / 15, 5/36 - 15**0.5 / 30],
+            [5/36 + 15**0.5 / 24, 2/9, 5/36 - 15**0.5 / 24],
+            [5/36 + 15**0.5 / 30, 2/9 + 15**0.5 / 15, 5/36],
+        ],
+        [
+            [5/18, 4/9, 5/18]
+        ],
+        [1/2 - 15**0.5 / 10, 1/2, 1/2 + 15**0.5 / 10]   # 0.11270166537925831148207346002176004
+    ),
+    "gauss-legendre_2s": ( # Hammer-Hollingsworth, IRK, order 4 https://www.math.umd.edu/~mariakc/SymplecticMethods.pdf
+        [
+            [1/4, 1/4 - 3**0.5 / 6],
+            [1/4 + 3**0.5 / 6, 1/4],
+        ],
+        [
+            [1/2, 1/2],
+        ],
+        [1/2 - 3**0.5 / 6, 1/2 + 3**0.5 / 6] # 0.21132486540518711774542560974902127    # 1/2 - (1/2)*(1/3**0.5)       1/2 + (1/2)*(1/3**0.5)
+    ),
+
+    "radau_iia_4s": (
+        [    
+            [],
+            [],
+            [],
+            [],
+        ],
+        [
+            [1/4, 1/4, 1/4, 1/4],
+        ],
+        [(1/11)*(4-6**0.5), (1/11)*(4+6**0.5), 1/2, 1]
+    ),
+    
+    "radau_iia_11s": ( # https://github.com/ryanelandt/Radau.jl
+        [    
+            [0.015280520789530369, -0.0057824996781311875, 0.00438010324638053, -0.0036210375473319026, 0.003092977042211754, -0.0026728314041491816, 0.0023050911672361017, -0.001955651803123845, 0.001593873849612843, -0.0011728625554916522, 0.00046993032567176855],
+            [0.03288397668119629, 0.03451351173940448, -0.009285420023734383, 0.00641324617083941, -0.005095455838865143, 0.0042460913690415955, -0.0035876743372353984, 0.003006834900018004, -0.0024326697483255453, 0.0017827773828584467, -0.0007131464180496306],
+            [0.029332502147155125, 0.0741624250777296, 0.0511486756872502, -0.012005023334430185, 0.00777794727524923, -0.005944695307870806, 0.004802655736401176, -0.003923600687657003, 0.003127328539609814, -0.0022731432208609507, 0.0009063777304940358],
+            [0.03111455337650569, 0.06578995121943092, 0.10929962691877611, 0.06381051663919307, -0.013853591907177828, 0.008557435524870741, -0.0063076358492939275, 0.004913357548166058, -0.0038139969541068734, 0.0027334306074068546, -0.0010839711153145738],
+            [0.03005269275666326, 0.07011284530154153, 0.09714692306747527, 0.1353916024839275, 0.07147107644479529, -0.014710238851905252, 0.008733191499420551, -0.00619941303527863, 0.004591640852897801, -0.003213330884490774, 0.001262857250740274],
+            [0.030728073929609766, 0.06751925856657341, 0.10334060375222286, 0.12083525997663601, 0.1503267876654705, 0.07350931976920085, -0.014512880052768446, 0.008296645645701008, -0.0056128275038367864, 0.003766229774466616, -0.001457705807615146],
+            [0.030292022376401242, 0.06914472100762357, 0.09972096441656238, 0.12801064060853223, 0.13493180383303127, 0.15289670039157693, 0.06975993047996924, -0.013274545709987746, 0.007258767272883859, -0.0044843888202694155, 0.0016878458203415244],
+            [0.03056654381836576, 0.06813851028407998, 0.10188107030389015, 0.12403361149690655, 0.14211431622263265, 0.13829395377418516, 0.14289135336320447, 0.06052636121446275, -0.011077739682117822, 0.005598667203856668, -0.0019877269625674446],
+            [0.030406629901865028, 0.06871880785022819, 0.10066095698900927, 0.12619527453091425, 0.13848875677027936, 0.14450773783254642, 0.13065188915037962, 0.1211140113707743, 0.046555483263607714, -0.008026200095719123, 0.002437640226261747],
+            [0.030484119381553945, 0.06843924691254653, 0.10124184869598654, 0.1251873187759311, 0.14011843430039864, 0.14190386755377057, 0.13500342651951197, 0.11262869537051934, 0.08930604389562254, 0.028969664972192485, -0.0033116985395201413],
+            [0.03046254890606557, 0.06851684106660112, 0.10108155427001221, 0.1254626888485642, 0.13968066655169153, 0.14258278197050367, 0.1339335430948421, 0.11443306192448831, 0.08565880960332992, 0.04992304095398403, 0.008264462809917356],
+        ],
+        [
+            [0.03046254890606557, 0.06851684106660112, 0.10108155427001221, 0.1254626888485642, 0.13968066655169153, 0.14258278197050367, 0.1339335430948421, 0.11443306192448831, 0.08565880960332992, 0.04992304095398403, 0.008264462809917356],
+        ],
+        [0.011917613432415597, 0.061732071877148124, 0.14711144964307024, 0.26115967600845624, 0.39463984688578685, 0.5367387657156606, 0.6759444616766651, 0.8009789210368988, 0.9017109877901468, 0.9699709678385136, 1.0]
+    ),
+    
+    "radau_iia_9s": ( # https://github.com/ryanelandt/Radau.jl
+        [    
+            [0.022788378793458776, -0.008589639752938945, 0.0064510291769951465, -0.00525752869975012, 0.004388833809361376, -0.0036512155536904674, 0.0029404882137526148, -0.002149274163882554, 0.0008588433240576261],
+            [0.04890795244749932, 0.05070205048082808, -0.013523807196021316, 0.009209373774305071, -0.0071557133175369604, 0.005747246699432309, -0.004542582976394536, 0.003288161681791406, -0.0013090736941094112],
+            [0.04374276009157137, 0.10830189290274023, 0.07291956593742897, -0.016879877210016055, 0.010704551844802781, -0.007901946479238777, 0.005991406942179993, -0.0042480244399873135, 0.0016781498061495626],
+            [0.04624923745394712, 0.09656073072680009, 0.1542987697900386, 0.0867193693031384, -0.018451639643617873, 0.011036658729835513, -0.007673280940281649, 0.005228224999889903, -0.00203590583647778],
+            [0.044834436586910234, 0.10230684968594175, 0.13821763419236816, 0.18126393468214014, 0.09043360059943564, -0.018085063366782478, 0.010193387903855565, -0.006405265418866323, 0.0024271699384239612],
+            [0.045658755719323395, 0.09914547048938806, 0.14574704049699233, 0.16364828123387398, 0.18594458734451902, 0.08361326023153276, -0.015809936146309538, 0.00813825269404473, -0.002910469207795258],
+            [0.045200600187797244, 0.10085370671832047, 0.1419422367945749, 0.17118947183876332, 0.1697833861700019, 0.16776829117327952, 0.06707903432249304, -0.011792230536025322, 0.0036092462886493657],
+            [0.045416516657427734, 0.10006040244594375, 0.143652840987038, 0.16801908098069296, 0.17556076841841367, 0.15588627045003361, 0.12889391351650395, 0.04281082602522101, -0.004934574771244536],
+            [0.04535725246164146, 0.10027664901227598, 0.1431933481786156, 0.16884698348796479, 0.1741365013864833, 0.158421887835219, 0.12359468910229653, 0.0738270095231577, 0.012345679012345678],
+        ],
+        [
+            [0.04535725246164146, 0.10027664901227598, 0.1431933481786156, 0.16884698348796479, 0.1741365013864833, 0.158421887835219, 0.12359468910229653, 0.0738270095231577, 0.012345679012345678],
+        ],
+        [0.01777991514736345, 0.09132360789979396, 0.21430847939563075, 0.37193216458327233, 0.5451866848034267, 0.7131752428555694, 0.8556337429578544, 0.9553660447100302, 1.0]
+    ),
+    
+    "radau_iia_7s": ( # https://github.com/ryanelandt/Radau.jl
+        [    
+            [0.03754626499392133, -0.0140393345564604, 0.0103527896007423, -0.008158322540275011, 0.006388413879534685, -0.004602326779148656, 0.0018289425614706437],
+            [0.08014759651561897, 0.08106206398589154, -0.021237992120711036, 0.014000291238817119, -0.010234185730090163, 0.0071534651513645905, -0.0028126393724067235],
+            [0.0720638469418819, 0.17106835498388662, 0.10961456404007211, -0.024619871728984055, 0.014760377043950817, -0.009575259396791401, 0.0036726783971383057],
+            [0.07570512581982441, 0.15409015514217114, 0.2271077366732024, 0.11747818703702478, -0.023810827153044174, 0.012709985533661206, -0.004608844281289633],
+            [0.07391234216319184, 0.16135560761594242, 0.2068672415521042, 0.23700711534269422, 0.10308679353381345, -0.018854139152580447, 0.0058589009748887914],
+            [0.07470556205979623, 0.1583072238724687, 0.21415342326720002, 0.21987784703186003, 0.19875212168063527, 0.06926550160550914, -0.00811600819772829],
+            [0.07449423555601031, 0.15910211573365074, 0.21235188950297781, 0.22355491450728324, 0.19047493682211558, 0.1196137446126562, 0.02040816326530612],
+        ],
+        [
+            [0.07449423555601031, 0.15910211573365074, 0.21235188950297781, 0.22355491450728324, 0.19047493682211558, 0.1196137446126562, 0.02040816326530612],
+        ],
+        [0.029316427159784893, 0.1480785996684843, 0.3369846902811543, 0.5586715187715501, 0.7692338620300545, 0.9269456713197411, 1.0]
+    ),
+    
+    "radau_iia_5s": ( # https://github.com/ryanelandt/Radau.jl
+        [    
+            [0.07299886431790333, -0.02673533110794557, 0.018676929763984353, -0.01287910609330644, 0.005042839233882015],
+            [0.15377523147918246, 0.14621486784749352, -0.03644456890512809, 0.02123306311930472, -0.007935579902728777],
+            [0.14006304568480987, 0.29896712949128346, 0.16758507013524895, -0.03396910168661774, 0.010944288744192253],
+            [0.14489430810953477, 0.2765000687601592, 0.32579792291042103, 0.12875675325490976, -0.015708917378805327],
+            [0.14371356079122594, 0.28135601514946207, 0.31182652297574126, 0.22310390108357075, 0.04],
+        ],
+        [
+            [0.14371356079122594, 0.28135601514946207, 0.31182652297574126, 0.22310390108357075, 0.04],
+        ],
+        [0.05710419611451768, 0.2768430136381238, 0.5835904323689168, 0.8602401356562195, 1.0]
+    ),
+    "radau_iia_3s": ( 
+        [    
+            [11/45 - 7*6**0.5 / 360, 37/225 - 169*6**0.5 / 1800, -2/225 + 6**0.5 / 75],
+            [37/225 + 169*6**0.5 / 1800, 11/45 + 7*6**0.5 / 360, -2/225 - 6**0.5 / 75],
+            [4/9 - 6**0.5 / 36, 4/9 + 6**0.5 / 36, 1/9],
+        ],
+        [
+            [4/9 - 6**0.5 / 36, 4/9 + 6**0.5 / 36, 1/9],
+        ],
+        [2/5 - 6**0.5 / 10, 2/5 + 6**0.5 / 10, 1.]
+    ),
+    "radau_iia_3s_alt": ( # https://www.unige.ch/~hairer/preprints/coimbra.pdf (page 7) Ehle [Eh69] and Axelsson [Ax69]
+        [    
+            [(88 - 7*6**0.5) / 360, (296 - 169*6**0.5) / 1800, (-2 + 3 * 6**0.5) / 225],
+            [(296 + 169*6**0.5) / 1800, (88 + 7*6**0.5) / 360, (-2 - 3*6**0.5) / 225],
+            [(16 - 6**0.5) / 36, (16 + 6**0.5) / 36, 1/9],
+        ],
+        [
+            [
+            (16 - 6**0.5) / 36, 
+            (16 + 6**0.5) / 36, 
+            1/9],
+        ],
+        [
+        (4 - 6**0.5) / 10, 
+        (4 + 6**0.5) / 10, 
+        1.]
+    ),
+    "radau_iia_2s": (
+        [    
+            [5/12, -1/12],
+            [3/4, 1/4],
+        ],
+        [
+            [3/4, 1/4],
+        ],
+        [1/3, 1]
+    ),
+    "radau_ia_3s": (
+        [    
+            [1/9, (-1-6**0.5)/18, (-1+6**0.5)/18],
+            [1/9, 11/45 + 7*6**0.5/360, 11/45-43*6**0.5/360],
+            [1/9, 11/45-43*6**0.5/360, 11/45 + 7*6**0.5/360],
+        ],
+        [
+            [1/9, 4/9 + 6**0.5/36, 4/9 - 6**0.5/36],
+        ],
+        [0, 3/5-6**0.5/10, 3/5+6**0.5/10]
+    ),
+    "radau_ia_2s": (
+        [    
+            [1/4, -1/4],
+            [1/4, 5/12],
+        ],
+        [
+            [1/4, 3/4],
+        ],
+        [0, 2/3]
+    ),
+    "lobatto_iiia_4s": ( #6th order
+        [    
+            [0, 0, 0, 0],
+            [(11+5**0.5)/120,   (25-5**0.5)/120, (25-13*5**0.5)/120, (-1+5**0.5)/120],
+            [(11-5**0.5)/120,   (25+13*5**0.5)/120, (25+5**0.5)/120, (-1-5**0.5)/120],
+            [1/12, 5/12, 5/12, 1/12],
+        ],
+        [
+            [1/12, 5/12, 5/12, 1/12],
+        ],
+        [0, (5-5**0.5)/10, (5+5**0.5)/10, 1]
+    ),
+    "lobatto_iiib_4s": ( #6th order
+        [    
+            [1/12, (-1-5**0.5)/24, (-1+5**0.5)/24, 0],
+            [1/12,   (25+5**0.5)/120, (25-13*5**0.5)/120, 0],
+            [1/12,   (25+13*5**0.5)/120, (25-5**0.5)/120, 0],
+            [1/12, (11-5**0.5)/24, (11+5**0.5)/24, 0],
+        ],
+        [
+            [1/12, 5/12, 5/12, 1/12],
+        ],
+        [0, (5-5**0.5)/10, (5+5**0.5)/10, 1]
+    ),
+    "lobatto_iiic_4s": ( #6th order
+        [    
+            [1/12, (-5**0.5)/12, (5**0.5)/12, -1/12],
+            [1/12,   1/4, (10-7*5**0.5)/60, (5**0.5)/60],
+            [1/12,   (10+7*5**0.5)/60, 1/4, (-5**0.5)/60],
+            [1/12, 5/12, 5/12, 1/12],
+        ],
+        [
+            [1/12, 5/12, 5/12, 1/12],
+        ],
+        [0, (5-5**0.5)/10, (5+5**0.5)/10, 1]
+    ),
+    "lobatto_iiia_3s": (
+        [    
+            [0, 0, 0],
+            [5/24, 1/3, -1/24],
+            [1/6, 2/3, 1/6],
+        ],
+        [
+            [1/6, 2/3, 1/6],
+        ],
+        [0, 1/2, 1]
+    ),
+    "lobatto_iiia_2s": (
+        [    
+            [0, 0],
+            [1/2, 1/2],
+        ],
+        [
+            [1/2, 1/2],
+        ],
+        [0, 1]
+    ),
+    
+    
+    
+    "lobatto_iiib_3s": (
+        [    
+            [1/6, -1/6, 0],
+            [1/6, 1/3, 0],
+            [1/6, 5/6, 0],
+        ],
+        [
+            [1/6, 2/3, 1/6],
+        ],
+        [0, 1/2, 1]
+    ),
+    "lobatto_iiib_2s": (
+        [    
+            [1/2, 0],
+            [1/2, 0],
+        ],
+        [
+            [1/2, 1/2],
+        ],
+        [0, 1]
+    ),
+
+    "lobatto_iiic_3s": (
+        [    
+            [1/6, -1/3, 1/6],
+            [1/6, 5/12, -1/12],
+            [1/6, 2/3, 1/6],
+        ],
+        [
+            [1/6, 2/3, 1/6],
+        ],
+        [0, 1/2, 1]
+    ),
+    "lobatto_iiic_2s": (
+        [    
+            [1/2, -1/2],
+            [1/2, 1/2],
+        ],
+        [
+            [1/2, 1/2],
+        ],
+        [0, 1]
+    ),
+    
+
+    "lobatto_iiic_star_3s": (
+        [    
+            [0, 0, 0],
+            [1/4, 1/4, 0],
+            [0, 1, 0],
+        ],
+        [
+            [1/6, 2/3, 1/6],
+        ],
+        [0, 1/2, 1]
+    ),
+    "lobatto_iiic_star_2s": (
+        [    
+            [0, 0],
+            [1, 0],
+        ],
+        [
+            [1/2, 1/2],
+        ],
+        [0, 1]
+    ),
+    
+    "lobatto_iiid_3s": (
+        [    
+            [1/6, 0, -1/6],
+            [1/12, 5/12, 0],
+            [1/2, 1/3, 1/6],
+        ],
+        [
+            [1/6, 2/3, 1/6],
+        ],
+        [0, 1/2, 1]
+    ),
+    "lobatto_iiid_2s": (
+        [    
+            [1/2, 1/2],
+            [-1/2, 1/2],
+        ],
+        [
+            [1/2, 1/2],
+        ],
+        [0, 1]
+    ),
+
+    "kraaijevanger_spijker_2s": ( #overshoots step
+        [    
+            [1/2, 0],
+            [-1/2, 2],
+        ],
+        [
+            [-1/2, 3/2],
+        ],
+        [1/2, 3/2]
+    ),
+    
+    "qin_zhang_2s": (
+        [    
+            [1/4, 0],
+            [1/2, 1/4],
+        ],
+        [
+            [1/2, 1/2],
+        ],
+        [1/4, 3/4]
+    ),
+
+    "pareschi_russo_2s": (
+        [    
+            [(1-2**0.5/2), 0],
+            [1-2*(1-2**0.5/2), (1-2**0.5/2)],
+        ],
+        [
+            [1/2, 1/2],
+        ],
+        [(1-2**0.5/2), 1-(1-2**0.5/2)]
+    ),
+
+    "pareschi_russo_alt_2s": (
+        [    
+            [(1-2**0.5/2), 0],
+            [1-(1-2**0.5/2), (1-2**0.5/2)],
+        ],
+        [
+            [1-(1-2**0.5/2), (1-2**0.5/2)],
+        ],
+        [(1-2**0.5/2), 1]
+    ),
+
+    "crouzeix_3s_alt": ( # Crouzeix & Raviart 1980; A-stable; pg 100 in Solving Ordinary Differential Equations II
+        [
+            [gamma_crouzeix, 0, 0],
+            [1/2 - gamma_crouzeix, gamma_crouzeix, 0],
+            [2*gamma_crouzeix, 1-4*gamma_crouzeix, gamma_crouzeix],
+        ],
+        [
+            [delta_crouzeix, 1-2*delta_crouzeix, delta_crouzeix],
+        ],
+        [gamma_crouzeix,   1/2,   1-gamma_crouzeix],
+    ),
+    
+    "crouzeix_3s": (
+        [
+            [(1+alpha_crouzeix)/2, 0, 0],
+            [-alpha_crouzeix/2, (1+alpha_crouzeix)/2, 0],
+            [1+alpha_crouzeix, -(1+2*alpha_crouzeix), (1+alpha_crouzeix)/2],
+        ],
+        [
+            [1/(6*alpha_crouzeix**2), 1-(1/(3*alpha_crouzeix**2)), 1/(6*alpha_crouzeix**2)],
+        ],
+        [(1+alpha_crouzeix)/2,   1/2,   (1-alpha_crouzeix)/2],
+    ),
+    
+    "crouzeix_2s": (
+        [
+            [1/2 + 3**0.5 / 6, 0],
+            [-(3**0.5 / 3), 1/2 + 3**0.5 / 6]
+        ],
+        [
+            [1/2, 1/2],
+        ],
+        [1/2 + 3**0.5 / 6, 1/2 - 3**0.5 / 6],
+    ),
+    "verner_13s": ( #verner9. some values are missing, need to revise
+        [
+            [],
+        ],
+        [
+            [],
+        ],
+        [
+            0.03462,
+            0.09702435063878045,
+            0.14553652595817068,
+            0.561,
+            0.22900791159048503,
+            0.544992088409515,
+            0.645,
+            0.48375,
+            0.06757,
+            0.25,
+            0.6590650618730999,
+            0.8206,
+            0.9012,
+        ]
+    ),
+    "verner_robust_16s": (
+        [
+            [],
+            [0.04],
+            [-0.01988527319182291, 0.11637263332969652],
+            [0.0361827600517026, 0, 0.10854828015510781],
+            [2.272114264290177, 0, -8.526886447976398, 6.830772183686221],
+            [0.050943855353893744, 0, 0, 0.1755865049809071, 0.007022961270757467],
+            [0.1424783668683285, 0, 0, -0.3541799434668684, 0.07595315450295101, 0.6765157656337123],
+            [0.07111111111111111, 0, 0, 0, 0, 0.3279909287605898, 0.24089796012829906],
+            [0.07125, 0, 0, 0, 0, 0.32688424515752457, 0.11561575484247544, -0.03375],
+            [0.0482267732246581, 0, 0, 0, 0, 0.039485599804954, 0.10588511619346581, -0.021520063204743093, -0.10453742601833482],
+            [-0.026091134357549235, 0, 0, 0, 0, 0.03333333333333333, -0.1652504006638105, 0.03434664118368617, 0.1595758283215209, 0.21408573218281934],
+            [-0.03628423396255658, 0, 0, 0, 0, -1.0961675974272087, 0.1826035504321331, 0.07082254444170683, -0.02313647018482431, 0.2711204726320933, 1.3081337494229808],
+            [-0.5074635056416975, 0, 0, 0, 0, -6.631342198657237, -0.2527480100908801, -0.49526123800360955, 0.2932525545253887, 1.440108693768281, 6.237934498647056, 0.7270192054526988],
+            [0.6130118256955932, 0, 0, 0, 0, 9.088803891640463, -0.40737881562934486, 1.7907333894903747, 0.714927166761755, -1.4385808578417227, -8.26332931206474, -1.537570570808865, 0.34538328275648716],
+            [-1.2116979103438739, 0, 0, 0, 0, -19.055818715595954, 1.263060675389875, -6.913916969178458, -0.6764622665094981, 3.367860445026608, 18.00675164312591, 6.83882892679428, -1.0315164519219504, 0.4129106232130623],
+            [2.1573890074940536, 0, 0, 0, 0, 23.807122198095804, 0.8862779249216555, 13.139130397598764, -2.604415709287715, -5.193859949783872, -20.412340711541507, -12.300856252505723, 1.5215530950085394],
+        ],
+        [
+            0.014588852784055396, 0, 0, 0, 0, 0, 0, 0.0020241978878893325, 0.21780470845697167,
+            0.12748953408543898, 0.2244617745463132, 0.1787254491259903, 0.07594344758096556,
+            0.12948458791975614, 0.029477447612619417, 0
+        ],
+        [
+            0, 0.04, 0.09648736013787361, 0.1447310402068104, 0.576, 0.2272326564618766,
+            0.5407673435381234, 0.64, 0.48, 0.06754, 0.25, 0.6770920153543243, 0.8115,
+            0.906, 1, 1
+        ],
+    ),
+
+    "dormand-prince_13s": ( #non-monotonic
+        [
+            [],
+            [1/18],
+            [1/48, 1/16],
+            [1/32, 0, 3/32],
+            [5/16, 0, -75/64, 75/64],
+            [3/80, 0, 0, 3/16, 3/20],
+            [29443841/614563906, 0, 0, 77736538/692538347, -28693883/1125000000, 23124283/1800000000],
+            [16016141/946692911, 0, 0, 61564180/158732637, 22789713/633445777, 545815736/2771057229, -180193667/1043307555],
+            [39632708/573591083, 0, 0, -433636366/683701615, -421739975/2616292301, 100302831/723423059, 790204164/839813087, 800635310/3783071287],
+            [246121993/1340847787, 0, 0, -37695042795/15268766246, -309121744/1061227803, -12992083/490766935, 6005943493/2108947869, 393006217/1396673457, 123872331/1001029789],
+            [-1028468189/846180014, 0, 0, 8478235783/508512852, 1311729495/1432422823, -10304129995/1701304382, -48777925059/3047939560, 15336726248/1032824649, -45442868181/3398467696, 3065993473/597172653],
+            [185892177/718116043, 0, 0, -3185094517/667107341, -477755414/1098053517, -703635378/230739211, 5731566787/1027545527, 5232866602/850066563, -4093664535/808688257, 3962137247/1805957418, 65686358/487910083],
+            [403863854/491063109, 0, 0, -5068492393/434740067, -411421997/543043805, 652783627/914296604, 11173962825/925320556, -13158990841/6184727034, 3936647629/1978049680, -160528059/685178525, 248638103/1413531060],
+        ],
+        [
+            [14005451/335480064, 0, 0, 0, 0, -59238493/1068277825, 181606767/758867731, 561292985/797845732, -1041891430/1371343529, 760417239/1151165299, 118820643/751138087, -528747749/2220607170, 1/4],
+        ],
+        [0, 1/18, 1/12, 1/8, 5/16, 3/8, 59/400, 93/200, 5490023248 / 9719169821, 13/20, 1201146811 / 1299019798, 1, 1],
+    ),
+    "dormand-prince_6s": (
+        [
+            [],
+            [1/5],
+            [3/40, 9/40],
+            [44/45, -56/15, 32/9],
+            [19372/6561, -25360/2187, 64448/6561, -212/729],
+            [9017/3168, -355/33, 46732/5247, 49/176, -5103/18656],
+        ],
+        [
+            [35/384, 0, 500/1113, 125/192, -2187/6784, 11/84],
+        ],
+        [0, 1/5, 3/10, 4/5, 8/9, 1],
+    ),
+    "bogacki-shampine_7s": ( #5th order
+        [
+            [],
+            [1/6],
+            [2/27, 4/27],
+            [183/1372, -162/343, 1053/1372],
+            [68/297, -4/11, 42/143, 1960/3861],
+            [597/22528, 81/352, 63099/585728, 58653/366080, 4617/20480],
+            [174197/959244, -30942/79937, 8152137/19744439, 666106/1039181, -29421/29068, 482048/414219],
+        ],
+        [
+            [587/8064, 0, 4440339/15491840, 24353/124800, 387/44800, 2152/5985, 7267/94080],
+        ],
+        [0, 1/6, 2/9, 3/7, 2/3, 3/4, 1] 
+    ),
+    "bogacki-shampine_4s": ( #5th order
+        [
+            [],
+            [1/2],
+            [0, 3/4],
+            [2/9, 1/3, 4/9],
+        ],
+        [
+            [2/9, 1/3, 4/9, 0],
+        ],
+        [0, 1/2, 3/4, 1] 
+    ),
+    "tsi_7s": ( #5th order 
+        [
+            [],
+            [0.161],
+            [-0.008480655492356989, 0.335480655492357],
+            [2.8971530571054935, -6.359448489975075, 4.3622954328695815],
+            [5.325864828439257, -11.748883564062828, 7.4955393428898365, -0.09249506636175525],
+            [5.86145544294642, -12.92096931784711, 8.159367898576159, -0.071584973281401, -0.02826905039406838],
+            [0.09646076681806523, 0.01, 0.4798896504144996, 1.379008574103742, -3.290069515436081, 2.324710524099774],
+        ],
+        [
+            [0.09646076681806523, 0.01, 0.4798896504144996, 1.379008574103742, -3.290069515436081, 2.324710524099774, 0.0],
+        ],
+        [0.0, 0.161, 0.327, 0.9, 0.9800255409045097, 1.0, 1.0],
+    ),
+    "rk6_7s": ( #non-monotonic #5th order
+        [
+            [],
+            [1/3],
+            [0, 2/3],
+            [1/12, 1/3, -1/12],
+            [-1/16, 9/8, -3/16, -3/8],
+            [0, 9/8, -3/8, -3/4, 1/2],
+            [9/44, -9/11, 63/44, 18/11, 0, -16/11],
+        ],
+        [
+            [11/120, 0, 27/40, 27/40, -4/15, -4/15, 11/120],
+        ],
+        [0, 1/3, 2/3, 1/3, 1/2, 1/2, 1],
+    ),
+    "rk5_7s": ( #5th order
+        [
+            [],
+            [1/5],
+            [3/40, 9/40],
+            [44/45, -56/15, 32/9],
+            [19372/6561, -25360/2187, 64448/6561, 212/729], #flipped 212 sign
+            [-9017/3168, -355/33, 46732/5247, 49/176, -5103/18656],
+            [35/384, 0, 500/1113, 125/192, -2187/6784, 11/84],
+        ],
+        [
+            [5179/57600, 0, 7571/16695, 393/640, -92097/339200, 187/2100, 1/40],
+        ],
+        [0, 1/5, 3/10, 4/5, 8/9, 1, 1],
+    ),
+    "ssprk4_4s": ( #non-monotonic #https://link.springer.com/article/10.1007/s41980-022-00731-x
+        [ 
+            [],
+            [1/2],
+            [1/2, 1/2],
+            [1/6, 1/6, 1/6],
+        ],
+        [
+            [1/6, 1/6, 1/6, 1/2],
+        ],
+        [0, 1/2, 1, 1/2],
+    ),
+    "rk4_4s": (
+        [
+            [],
+            [1/2],
+            [0, 1/2],
+            [0, 0, 1],
+        ],
+        [
+            [1/6, 1/3, 1/3, 1/6],
+        ],
+        [0, 1/2, 1/2, 1],
+    ),
+    "rk38_4s": (
+        [
+            [],
+            [1/3],
+            [-1/3, 1],
+            [1, -1, 1],
+        ],
+        [
+            [1/8, 3/8, 3/8, 1/8],
+        ],
+        [0, 1/3, 2/3, 1],
+    ),
+    "ralston_4s": (
+        [
+            [],
+            [2/5],
+            [(-2889+1428 * 5**0.5)/1024,   (3785-1620 * 5**0.5)/1024],
+            [(-3365+2094 * 5**0.5)/6040,   (-975-3046 * 5**0.5)/2552,  (467040+203968*5**0.5)/240845],
+        ],
+        [
+            [(263+24*5**0.5)/1812, (125-1000*5**0.5)/3828, (3426304+1661952*5**0.5)/5924787, (30-4*5**0.5)/123],
+        ],
+        [0, 2/5, (14-3 * 5**0.5)/16, 1],
+    ),
+    "heun_3s": (
+        [
+            [],
+            [1/3],
+            [0, 2/3],
+        ],
+        [
+            [1/4, 0, 3/4],
+        ],
+        [0, 1/3, 2/3],
+    ),
+    "kutta_3s": (
+        [
+            [],
+            [1/2],
+            [-1, 2],
+        ],
+        [
+            [1/6, 2/3, 1/6],
+        ],
+        [0, 1/2, 1],
+    ),
+    "ralston_3s": (
+        [
+            [],
+            [1/2],
+            [0, 3/4],
+        ],
+        [
+            [2/9, 1/3, 4/9],
+        ],
+        [0, 1/2, 3/4],
+    ),
+    "houwen-wray_3s": (
+        [
+            [],
+            [8/15],
+            [1/4, 5/12],
+        ],
+        [
+            [1/4, 0, 3/4],
+        ],
+        [0, 8/15, 2/3],
+    ),
+    "ssprk3_3s": ( #non-monotonic
+        [
+            [],
+            [1],
+            [1/4, 1/4],
+        ],
+        [
+            [1/6, 1/6, 2/3],
+        ],
+        [0, 1, 1/2], 
+    ),
+    "midpoint_2s": (
+        [
+            [],
+            [1/2],
+        ],
+        [
+            [0, 1],
+        ],
+        [0, 1/2],
+    ),
+    "heun_2s": (
+        [
+            [],
+            [1],
+        ],
+        [
+            [1/2, 1/2],
+        ],
+        [0, 1],
+    ),
+    "ralston_2s": (
+        [
+            [],
+            [2/3],
+        ],
+        [
+            [1/4, 3/4],
+        ],
+        [0, 2/3],
+    ),
+    "euler": (
+        [
+            [],
+        ],
+        [
+            [1],
+        ],
+        [0],
+    ),
+}
+
+
+
+def get_rk_methods_beta(rk_type       : str,
+                        h             : Tensor,
+                        c1            : float            = 0.0,
+                        c2            : float            = 0.5,
+                        c3            : float            = 1.0,
+                        h_prev        : Optional[Tensor] = None,
+                        step          : int              = 0,
+                        sigmas        : Optional[Tensor] = None,
+                        sigma         : Optional[Tensor] = None,
+                        sigma_next    : Optional[Tensor] = None,
+                        sigma_down    : Optional[Tensor] = None,
+                        extra_options : Optional[str]    = None
+                        ):
+    
+    FSAL             = False
+    multistep_stages = 0
+    hybrid_stages    = 0
+    u                = None
+    v                = None
+    
+    EO                            = ExtraOptions(extra_options)
+    use_analytic_solution         = not EO("disable_analytic_solution")
+    multistep_initial_sampler     = EO("multistep_initial_sampler", "", debugMode=1)
+    multistep_fallback_sampler    = EO("multistep_fallback_sampler", "")
+    multistep_extra_initial_steps = EO("multistep_extra_initial_steps", 1)
+    
+    #if RK_Method_Beta.is_exponential(rk_type): 
+    if rk_type.startswith(("res", "dpmpp", "ddim", "pec", "etdrk", "lawson")): 
+        h_no_eta = -torch.log(sigma_next/sigma)
+        h_prev1_no_eta = -torch.log(sigmas[step]/sigmas[step-1]) if step >= 1 else None
+        h_prev2_no_eta = -torch.log(sigmas[step]/sigmas[step-2]) if step >= 2 else None
+        h_prev3_no_eta = -torch.log(sigmas[step]/sigmas[step-3]) if step >= 3 else None
+        h_prev4_no_eta = -torch.log(sigmas[step]/sigmas[step-4]) if step >= 4 else None
+
+    else:
+        h_no_eta = sigma_next - sigma
+        h_prev1_no_eta = sigmas[step] - sigmas[step-1] if step >= 1 else None
+        h_prev2_no_eta = sigmas[step] - sigmas[step-2] if step >= 2 else None
+        h_prev3_no_eta = sigmas[step] - sigmas[step-3] if step >= 3 else None
+        h_prev4_no_eta = sigmas[step] - sigmas[step-4] if step >= 4 else None
+        
+    if type(c1) == torch.Tensor:
+        c1 = c1.item()
+    if type(c2) == torch.Tensor:
+        c2 = c2.item()
+    if type(c3) == torch.Tensor:
+        c3 = c3.item()
+
+    if c1 == -1:
+        c1 = random.uniform(0, 1)
+    if c2 == -1:
+        c2 = random.uniform(0, 1)
+    if c3 == -1:
+        c3 = random.uniform(0, 1)
+        
+    if rk_type[:4] == "deis": 
+        order = int(rk_type[-2])
+        if step < order + multistep_extra_initial_steps:
+            if order == 4:
+                #rk_type = "res_4s_strehmel_weiner"
+                rk_type = "ralston_4s"
+                rk_type = multistep_initial_sampler if multistep_initial_sampler else rk_type
+                order = 3
+            elif order == 3:
+                #rk_type = "res_3s"
+                rk_type = "ralston_3s"
+                rk_type = multistep_initial_sampler if multistep_initial_sampler else rk_type
+            elif order == 2:
+                #rk_type = "res_2s"
+                rk_type = "ralston_2s"
+                rk_type = multistep_initial_sampler if multistep_initial_sampler else rk_type
+        else:
+            rk_type = "deis"
+            multistep_stages = order-1
+    
+    if rk_type[-2:] == "2m": #multistep method
+        rk_type = rk_type[:-2] + "2s"
+        #if h_prev is not None and step >= 1: 
+        if h_no_eta < 1.0:
+            if step >= 1 + multistep_extra_initial_steps:
+                multistep_stages = 1
+                c2 = (-h_prev1_no_eta / h_no_eta).item()
+            else:
+                rk_type = multistep_initial_sampler if multistep_initial_sampler else rk_type
+            if rk_type.startswith("abnorsett"):
+                rk_type = "res_2s"
+                rk_type = multistep_initial_sampler if multistep_initial_sampler else rk_type
+        else:
+            #rk_type = "res_2s"
+            rk_type = "euler" if sigma < 0.1 else "res_2s"
+            rk_type = multistep_fallback_sampler if multistep_fallback_sampler else rk_type
+            
+    if rk_type[-2:] == "3m": #multistep method
+        rk_type = rk_type[:-2] + "3s"
+        #if h_prev2 is not None and step >= 2: 
+        if h_no_eta < 1.0:
+            if step >= 2 + multistep_extra_initial_steps:
+                multistep_stages = 2
+
+                c2 = (-h_prev1_no_eta / h_no_eta).item()
+                c3 = (-h_prev2_no_eta / h_no_eta).item()      
+            else:
+                rk_type = multistep_initial_sampler if multistep_initial_sampler else rk_type 
+            if rk_type.startswith("abnorsett"):
+                rk_type = "res_3s"
+                rk_type = multistep_initial_sampler if multistep_initial_sampler else rk_type
+        else:
+            #rk_type = "res_3s"
+            rk_type = "euler" if sigma < 0.1 else "res_3s"
+            rk_type = multistep_fallback_sampler if multistep_fallback_sampler else rk_type
+            
+    if rk_type[-2:] == "4m": #multistep method
+        rk_type = rk_type[:-2] + "4s"
+        #if h_prev2 is not None and step >= 2: 
+        if h_no_eta < 1.0:
+            if step >= 3 + multistep_extra_initial_steps:
+                multistep_stages = 3
+
+                c2 = (-h_prev1_no_eta / h_no_eta).item()
+                c3 = (-h_prev2_no_eta / h_no_eta).item()
+                # WOULD NEED A C4 (POW) TO IMPLEMENT RES_4M IF IT EXISTED
+            else:
+                rk_type = multistep_initial_sampler if multistep_initial_sampler else rk_type
+            if rk_type == "res_4s":
+                rk_type = "res_4s_strehmel_weiner"
+                rk_type = multistep_initial_sampler if multistep_initial_sampler else rk_type
+            if rk_type.startswith("abnorsett"):
+                rk_type = "res_4s_strehmel_weiner"
+                rk_type = multistep_initial_sampler if multistep_initial_sampler else rk_type
+        else:
+            #rk_type = "res_4s_strehmel_weiner"
+            rk_type = "euler" if sigma < 0.1 else "res_4s_strehmel_weiner"
+            rk_type = multistep_fallback_sampler if multistep_fallback_sampler else rk_type
+                        
+    if rk_type[-3] == "h" and rk_type[-1] == "s": #hybrid method 
+        if step < int(rk_type[-4]) + multistep_extra_initial_steps:
+            rk_type = "res_" + rk_type[-2:]
+            rk_type = multistep_initial_sampler if multistep_initial_sampler else rk_type
+        else:
+            hybrid_stages = int(rk_type[-4])  #+1 adjustment needed?
+        if rk_type == "res_4s":
+            rk_type = "res_4s_strehmel_weiner"
+            rk_type = multistep_initial_sampler if multistep_initial_sampler else rk_type
+        if rk_type == "res_1s":
+            rk_type = "res_2s"
+            rk_type = multistep_initial_sampler if multistep_initial_sampler else rk_type
+
+    if rk_type in rk_coeff:
+        a, b, ci = copy.deepcopy(rk_coeff[rk_type])
+        
+        a = [row + [0] * (len(ci) - len(row)) for row in a]
+
+    match rk_type:
+        case "deis": 
+            coeff_list = get_deis_coeff_list(sigmas, multistep_stages+1, deis_mode="rhoab")
+            coeff_list = [[elem / h for elem in inner_list] for inner_list in coeff_list]
+            if multistep_stages == 1:
+                b1, b2 = coeff_list[step]
+                a = [
+                        [0, 0],
+                        [0, 0],
+                ]
+                b = [
+                        [b1, b2],
+                ]
+                ci = [0, 0]
+            if multistep_stages == 2:
+                b1, b2, b3 = coeff_list[step]
+                a = [
+                        [0, 0, 0],
+                        [0, 0, 0],
+                        [0, 0, 0],
+                ]
+                b = [
+                        [b1, b2, b3],
+                ]
+                ci = [0, 0, 0]
+            if multistep_stages == 3:
+                b1, b2, b3, b4 = coeff_list[step]
+                a = [
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0],
+                ]
+                b = [
+                    [b1, b2, b3, b4],
+                ]
+                ci = [0, 0, 0, 0]
+            if multistep_stages > 0:
+                for i in range(len(b[0])): 
+                    b[0][i] *= ((sigma_down - sigma) / (sigma_next - sigma))
+
+        case "dormand-prince_6s":
+            FSAL = True
+
+        case "ddim":
+            b1 = phi(1, -h)
+            a = [
+                    [0],
+            ]
+            b = [
+                    [b1],
+            ]
+            ci = [0]
+
+        case "res_2s":
+            c2 = float(get_extra_options_kv("c2", str(c2), extra_options))
+
+            ci = [0, c2]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            a2_1 = c2 * φ(1,2)
+            b2 = φ(2)/c2
+            b1 = φ(1) - b2
+
+            a = [
+                    [0,0],
+                    [a2_1, 0],
+            ]
+            b = [
+                    [b1, b2],
+            ]
+
+        case "res_2s_stable":
+            c2 = 1.0 #float(get_extra_options_kv("c2", str(c2), extra_options))
+
+            ci = [0, c2]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            a2_1 = c2 * φ(1,2)
+            b2 = φ(2)/c2
+            b1 = φ(1) - b2
+
+            a = [
+                    [0,0],
+                    [a2_1, 0],
+            ]
+            b = [
+                    [b1, b2],
+            ]
+
+        case "res_2s_rkmk2e":
+
+            ci = [0, 1]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            b2 = φ(2)
+
+            a = [
+                    [0,0],
+                    [0, 0],
+            ]
+            b = [
+                    [0, b2],
+            ]
+
+            gen_first_col_exp(a, b, ci, φ)
+
+
+
+        case "abnorsett2_1h2s":
+
+            c1, c2 = 0, 1
+            ci = [c1, c2]
+            φ = Phi(h, ci, use_analytic_solution)
+
+            b1 = φ(1) #+ φ(2)
+
+            a = [
+                    [0, 0],
+                    [0, 0],
+            ]
+            b = [
+                    [0, 0],
+            ]
+
+            if extra_options_flag("h_prev_h_h_no_eta", extra_options):
+                φ1 = Phi(h_prev1_no_eta * h/h_no_eta, ci)
+            elif extra_options_flag("h_only", extra_options):
+                φ1 = Phi(h, ci, use_analytic_solution)
+            else:
+                φ1 = Phi(h_prev1_no_eta, ci)
+
+            u1 = -φ1(2)
+            v1 = -φ1(2)
+
+            u = [
+                    [0, 0],
+                    [u1, 0],
+            ]
+            v = [
+                    [v1, 0],
+            ]
+
+            gen_first_col_exp_uv(a, b, ci, u, v, φ) 
+
+
+
+        case "abnorsett_2m":
+
+            c1, c2 = 0, 1
+            ci = [c1, c2]
+            φ = Phi(h, ci, use_analytic_solution)
+
+            a = [
+                    [0, 0],
+                    [0, 0],
+            ]
+            b = [
+                    [0, -φ(2)],
+            ]
+
+            gen_first_col_exp(a, b, ci, φ) 
+
+
+        case "abnorsett_3m":
+
+            c1, c2, c3 = 0, 0, 1
+            ci = [c1, c2, c3]
+            φ = Phi(h, ci, use_analytic_solution)
+
+            a = [
+                    [0, 0, 0],
+                    [0, 0, 0],
+                    [0, 0, 0],
+            ]
+            b = [
+                    [0, -2*φ(2) - 2*φ(3), (1/2)*φ(2) + φ(3)],
+            ]
+
+            gen_first_col_exp(a, b, ci, φ) 
+
+
+
+        case "abnorsett_4m":
+
+            c1, c2, c3, c4 = 0, 0, 0, 1
+            ci = [c1, c2, c3, c4]
+            φ = Phi(h, ci, use_analytic_solution)
+
+            a = [
+                    [0, 0, 0, 0],
+                    [0, 0, 0, 0],
+                    [0, 0, 0, 0],
+                    [0, 0, 0, 0],
+            ]
+            b = [
+                    [0, 
+                    -3*φ(2) - 5*φ(3) - 3*φ(4),
+                    (3/2)*φ(2) + 4*φ(3) + 3*φ(4),
+                    (-1/3)*φ(2) - φ(3) - φ(4),
+                    ],
+            ]
+
+            gen_first_col_exp(a, b, ci, φ) 
+
+
+        case "abnorsett3_2h2s":
+            
+            c1,c2 = 0,1
+            ci = [c1, c2]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            b2 = 0
+
+            a = [
+                    [0, 0],
+                    [0, 0],
+            ]
+            b = [
+                    [0, 0],
+            ]
+            
+            if extra_options_flag("h_prev_h_h_no_eta", extra_options):
+                φ1 = Phi(h_prev1_no_eta * h/h_no_eta, ci)
+                φ2 = Phi(h_prev2_no_eta * h/h_no_eta, ci)
+            elif extra_options_flag("h_only", extra_options):
+                φ1 = Phi(h, ci, use_analytic_solution)
+                φ2 = Phi(h, ci, use_analytic_solution)
+            else:
+                φ1 = Phi(h_prev1_no_eta, ci)
+                φ2 = Phi(h_prev2_no_eta, ci)
+                
+            u2_1 = -2*φ1(2) - 2*φ1(3)
+            u2_2 = (1/2)*φ2(2) + φ2(3)
+            
+            v1 = u2_1 # -φ1(2) + φ1(3) + 3*φ1(4)
+            v2 = u2_2 # (1/6)*φ2(2) - φ2(4)
+            
+            u = [
+                    [   0,    0],
+                    [u2_1, u2_2],
+            ]
+            v = [
+                    [v1, v2],
+            ]
+            
+            gen_first_col_exp_uv(a, b, ci, u, v, φ)
+            
+
+
+        case "pec423_2h2s": #https://ora.ox.ac.uk/objects/uuid:cc001282-4285-4ca2-ad06-31787b540c61/files/m611df1a355ca243beb09824b70e5e774
+            
+            c1,c2 = 0,1
+            ci = [c1, c2]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            b2 = (1/3)*φ(2) + φ(3) + φ(4)
+
+            a = [
+                    [0, 0],
+                    [0, 0],
+            ]
+            b = [
+                    [0, b2],
+            ]
+            
+            if extra_options_flag("h_prev_h_h_no_eta", extra_options):
+                φ1 = Phi(h_prev1_no_eta * h/h_no_eta, ci)
+                φ2 = Phi(h_prev2_no_eta * h/h_no_eta, ci)
+            elif extra_options_flag("h_only", extra_options):
+                φ1 = Phi(h, ci, use_analytic_solution)
+                φ2 = Phi(h, ci, use_analytic_solution)
+            else:
+                φ1 = Phi(h_prev1_no_eta, ci)
+                φ2 = Phi(h_prev2_no_eta, ci)
+                
+            u2_1 = -2*φ1(2) - 2*φ1(3)
+            u2_2 = (1/2)*φ2(2) + φ2(3)
+            
+            v1 = -φ1(2) + φ1(3) + 3*φ1(4)
+            v2 = (1/6)*φ2(2) - φ2(4)
+            
+            u = [
+                    [   0,    0],
+                    [u2_1, u2_2],
+            ]
+            v = [
+                    [v1, v2],
+            ]
+            
+            gen_first_col_exp_uv(a, b, ci, u, v, φ)
+            
+
+
+
+        case "pec433_2h3s": #https://ora.ox.ac.uk/objects/uuid:cc001282-4285-4ca2-ad06-31787b540c61/files/m611df1a355ca243beb09824b70e5e774
+            
+            c1,c2,c3 = 0, 1, 1
+            ci = [c1,c2,c3]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            a3_2 = (1/3)*φ(2) + φ(3) + φ(4)
+            
+            b2 = 0
+            b3 = (1/3)*φ(2) + φ(3) + φ(4)
+
+            a = [
+                    [0,    0, 0],
+                    [0,    0, 0],
+                    [0, a3_2, 0],
+            ]
+            b = [
+                    [0, b2, b3],
+            ]
+            
+            if extra_options_flag("h_prev_h_h_no_eta", extra_options):
+                φ1 = Phi(h_prev1_no_eta * h/h_no_eta, ci)
+                φ2 = Phi(h_prev2_no_eta * h/h_no_eta, ci)
+            elif extra_options_flag("h_only", extra_options):
+                φ1 = Phi(h, ci, use_analytic_solution)
+                φ2 = Phi(h, ci, use_analytic_solution)
+            else:
+                φ1 = Phi(h_prev1_no_eta, ci)
+                φ2 = Phi(h_prev2_no_eta, ci)
+                
+            u2_1 = -2*φ1(2) - 2*φ1(3)
+            u3_1 =   -φ1(2) +   φ1(3) + 3*φ1(4)
+            v1   =   -φ1(2) +   φ1(3) + 3*φ1(4)
+            
+            u2_2 = (1/2)*φ2(2) + φ2(3)
+            u3_2 = (1/6)*φ2(2) - φ2(4)
+            v2   = (1/6)*φ2(2) - φ2(4)
+
+            
+            u = [
+                    [   0,    0, 0],
+                    [u2_1, u2_2, 0],
+                    [u3_1, u3_2, 0],
+            ]
+            v = [
+                    [v1, v2, 0],
+            ]
+            
+            gen_first_col_exp_uv(a, b, ci, u, v, φ)
+
+
+            
+        case "res_3s":
+            c2 = float(get_extra_options_kv("c2", str(c2), extra_options))
+            c3 = float(get_extra_options_kv("c3", str(c3), extra_options))
+            
+            ci = [0,c2,c3]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            gamma = calculate_gamma(c2, c3)
+
+            a3_2 = gamma * c2 * φ(2,2) + (c3 ** 2 / c2) * φ(2, 3)
+            
+            b3 = (1 / (gamma * c2 + c3)) * φ(2)   
+            b2 = gamma * b3  #simplified version of: b2 = (gamma / (gamma * c2 + c3)) * phi_2_h  
+            
+            a = [
+                    [0, 0, 0],
+                    [0, 0, 0],
+                    [0, a3_2, 0],
+            ]
+            b = [
+                    [0, b2, b3],
+            ]
+            
+            a, b = gen_first_col_exp(a,b,ci,φ)
+            
+        case "res_3s_non-monotonic":
+            c2 = float(get_extra_options_kv("c2", "1.0", extra_options))
+            c3 = float(get_extra_options_kv("c3", "0.5", extra_options))
+            
+            ci = [0,c2,c3]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            gamma = calculate_gamma(c2, c3)
+
+            a3_2 = gamma * c2 * φ(2,2) + (c3 ** 2 / c2) * φ(2, 3)
+            
+            b3 = (1 / (gamma * c2 + c3)) * φ(2)   
+            b2 = gamma * b3  #simplified version of: b2 = (gamma / (gamma * c2 + c3)) * phi_2_h  
+            
+            a = [
+                    [0, 0, 0],
+                    [0, 0, 0],
+                    [0, a3_2, 0],
+            ]
+            b = [
+                    [0, b2, b3],
+            ]
+            
+            a, b = gen_first_col_exp(a,b,ci,φ)
+            
+            
+        case "res_3s_alt":
+            c2 = 1/3
+            c2 = float(get_extra_options_kv("c2", str(c2), extra_options))
+            
+            c1,c2,c3 = 0, c2, 2/3
+            ci = [c1,c2,c3]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            a = [
+                    [0, 0,                   0],
+                    [0, 0,                   0],
+                    [0, (4/(9*c2)) * φ(2,3), 0],
+            ]
+            b = [
+                    [0, 0, (1/c3)*φ(2)],
+            ]
+            
+            a, b = gen_first_col_exp(a,b,ci,φ)
+            
+        case "res_3s_strehmel_weiner": # 
+            c2 = 1/2
+            c2 = float(get_extra_options_kv("c2", str(c2), extra_options))
+
+            ci = [0,c2,1]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            a = [
+                    [0, 0, 0],
+                    [0, 0, 0],
+                    [0, (1/c2) * φ(2,3), 0],
+            ]
+            b = [
+                    [0, 0, φ(2)],
+            ]
+            
+            a, b = gen_first_col_exp(a,b,ci,φ)
+            
+            
+        case "res_3s_cox_matthews": # Cox & Matthews; known as ETD3RK
+            c2 = 1/2 # must be 1/2
+            ci = [0,c2,1]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            a = [
+                    [0, 0, 0],
+                    [0, 0, 0],
+                    [0, (1/c2) * φ(1,3), 0],  # paper said 2 * φ(1,3), but this is the same and more consistent with res_3s_strehmel_weiner
+            ]
+            b = [
+                    [0, 
+                    -8*φ(3) + 4*φ(2),
+                    4*φ(3) - φ(2)],
+            ]
+            
+            a, b = gen_first_col_exp(a,b,ci,φ)
+            
+        case "res_3s_lie": # Lie; known as ETD2CF3
+            c1,c2,c3 = 0, 1/3, 2/3
+            ci = [c1,c2,c3]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            a = [
+                    [0, 0, 0],
+                    [0, 0, 0],
+                    [0, (4/3)*φ(2,3), 0],  # paper said 2 * φ(1,3), but this is the same and more consistent with res_3s_strehmel_weiner
+            ]
+            b = [
+                    [0, 
+                    6*φ(2) - 18*φ(3),
+                    (-3/2)*φ(2) + 9*φ(3)],
+            ]
+            
+            a, b = gen_first_col_exp(a,b,ci,φ)
+        
+        case "res_3s_sunstar": # https://arxiv.org/pdf/2410.00498 pg 5 (tableau 2.7)
+            c1,c2,c3 = 0, 1/3, 2/3
+            ci = [c1,c2,c3]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            a = [
+                    [0, 0, 0],
+                    [0, 0, 0],
+                    [0, (8/9)*φ(2,3), 0],  # paper said 2 * φ(1,3), but this is the same and more consistent with res_3s_strehmel_weiner
+            ]
+            b = [
+                    [0, 
+                    0,
+                    (3/2)*φ(2)],
+            ]
+            
+            a, b = gen_first_col_exp(a,b,ci,φ)
+        
+        
+        case "res_4s_cox_matthews": # weak 4th order, Cox & Matthews; unresolved issue, see below
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            a2_1 = c2 * φ(1,2)
+            a3_2 = c3 * φ(1,3)
+            a4_1 = (1/2) * φ(1,3) * (φ(0,3) - 1) # φ(0,3) == torch.exp(-h*c3)
+            a4_3 = φ(1,3)
+
+            b1 = φ(1) - 3*φ(2) + 4*φ(3)
+
+            b2 = 2*φ(2) - 4*φ(3)
+            b3 = 2*φ(2) - 4*φ(3)
+            b4 = 4*φ(3) - φ(2)
+
+            a = [
+                    [0,    0,0,0],
+                    [a2_1, 0,0,0],
+                    [0, a3_2,0,0],
+                    [a4_1, 0, a4_3,0],
+            ]
+            b = [
+                    [b1, b2, b3, b4],
+            ]
+            
+            
+        case "res_4s_cfree4": # weak 4th order, Cox & Matthews; unresolved issue, see below
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            a2_1 = c2 * φ(1,2)
+            a3_2 = c3 * φ(1,2)
+            a4_1 = (1/2) * φ(1,2) * (φ(0,2) - 1) # φ(0,3) == torch.exp(-h*c3)
+            a4_3 = φ(1,2)
+
+            b1 = (1/2)*φ(1) - (1/3)*φ(1,2)
+
+            b2 = (1/3)*φ(1)
+            b3 = (1/3)*φ(1)
+            b4 = -(1/6)*φ(1) + (1/3)*φ(1,2)
+
+            a = [
+                    [0,    0,0,0],
+                    [a2_1, 0,0,0],
+                    [0, a3_2,0,0],
+                    [a4_1, 0, a4_3,0],
+            ]
+            b = [
+                    [b1, b2, b3, b4],
+            ]
+
+        case "res_4s_friedli": # https://ora.ox.ac.uk/objects/uuid:cc001282-4285-4ca2-ad06-31787b540c61/files/m611df1a355ca243beb09824b70e5e774
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            a3_2 = 2*φ(2,2)
+            a4_2 = -(26/25)*φ(1) +  (2/25)*φ(2)
+            a4_3 =  (26/25)*φ(1) + (48/25)*φ(2)
+
+
+            b2 = 0
+            b3 = 4*φ(2) - 8*φ(3)
+            b4 =  -φ(2) + 4*φ(3)
+
+            a = [
+                    [0, 0,0,0],
+                    [0, 0,0,0],
+                    [0, a3_2,0,0],
+                    [0, a4_2, a4_3,0],
+            ]
+            b = [
+                    [0, b2, b3, b4],
+            ]
+
+            a, b = gen_first_col_exp(a,b,ci,φ)
+
+        case "res_4s_munthe-kaas": # unstable RKMK4t
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci, use_analytic_solution)
+
+            a = [
+                    [0, 0,      0,        0],
+                    [c2*φ(1,2), 0,      0,        0],
+                    [(h/8)*φ(1,2), (1/2)*(1-h/4)*φ(1,2), 0,        0],
+                    [0, 0,      φ(1), 0],
+            ]
+            b = [
+                    [
+                    (1/6)*φ(1)*(1+h/2),
+                    (1/3)*φ(1),
+                    (1/3)*φ(1),
+                    (1/6)*φ(1)*(1-h/2)
+                    ],
+            ]
+
+        case "res_4s_krogstad": # weak 4th order, Krogstad
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            a = [
+                    [0, 0,      0,        0],
+                    [0, 0,      0,        0],
+                    [0, φ(2,3), 0,        0],
+                    [0, 0,      2*φ(2,4), 0],
+            ]
+            b = [
+                    [
+                    0, 
+                    2*φ(2) - 4*φ(3),
+                    2*φ(2) - 4*φ(3),
+                    -φ(2)  + 4*φ(3)
+                    ],
+            ]
+            
+            #a = [row + [0] * (len(ci) - len(row)) for row in a]
+            a, b = gen_first_col_exp(a,b,ci,φ)
+            
+        case "res_4s_krogstad_alt": # weak 4th order, Krogstad https://ora.ox.ac.uk/objects/uuid:cc001282-4285-4ca2-ad06-31787b540c61/files/m611df1a355ca243beb09824b70e5e774
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            a = [
+                    [0, 0,        0,      0],
+                    [0, 0,        0,      0],
+                    [0, 4*φ(2,2), 0,      0],
+                    [0, 0,        2*φ(2), 0],
+            ]
+            b = [
+                    [
+                    0, 
+                    2*φ(2) - 4*φ(3),
+                    2*φ(2) - 4*φ(3),
+                    -φ(2)  + 4*φ(3)
+                    ],
+            ]
+            
+            #a = [row + [0] * (len(ci) - len(row)) for row in a]
+            a, b = gen_first_col_exp(a,b,ci,φ)
+            
+        case "res_4s_minchev": # https://ora.ox.ac.uk/objects/uuid:cc001282-4285-4ca2-ad06-31787b540c61/files/m611df1a355ca243beb09824b70e5e774
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            a3_2 = (4/25)*φ(1,2) + (24/25)*φ(2,2)
+            a4_2 = (21/5)*φ(2) - (108/5)*φ(3)
+            a4_3 = (1/20)*φ(1) - (33/10)*φ(2) + (123/5)*φ(3)
+
+
+            b2 = -(1/10)*φ(1) +  (1/5)*φ(2) - 4*φ(3) + 12*φ(4)
+            b3 =  (1/30)*φ(1) + (23/5)*φ(2) - 8*φ(3) -  4*φ(4)
+            b4 =  (1/30)*φ(1) -  (7/5)*φ(2) + 6*φ(3) -  4*φ(4)
+
+            a = [
+                    [0, 0,0,0],
+                    [0, 0,0,0],
+                    [0, a3_2,0,0],
+                    [0, 0, a4_3,0],
+            ]
+            b = [
+                    [0, b2, b3, b4],
+            ]
+
+            a, b = gen_first_col_exp(a,b,ci,φ)
+            
+        case "res_4s_strehmel_weiner": # weak 4th order, Strehmel & Weiner
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            a = [
+                    [0, 0,         0,        0],
+                    [0, 0,         0,        0],
+                    [0, c3*φ(2,3), 0,        0],
+                    [0, -2*φ(2,4), 4*φ(2,4), 0],
+            ]
+            b = [
+                    [
+                    0, 
+                    0,
+                    4*φ(2) - 8*φ(3), 
+                    -φ(2) +  4*φ(3)
+                    ],
+            ]
+            
+            a, b = gen_first_col_exp(a,b,ci,φ)
+            
+        case "res_4s_strehmel_weiner_alt": # weak 4th order, Strehmel & Weiner https://ora.ox.ac.uk/objects/uuid:cc001282-4285-4ca2-ad06-31787b540c61/files/m611df1a355ca243beb09824b70e5e774
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            a = [
+                    [0, 0,        0,      0],
+                    [0, 0,        0,      0],
+                    [0, 2*φ(2,2), 0,      0],
+                    [0,  -2*φ(2), 4*φ(2), 0],
+            ]
+            b = [
+                    [
+                    0, 
+                    0,
+                    4*φ(2) - 8*φ(3), 
+                    -φ(2) +  4*φ(3)
+                    ],
+            ]
+            
+            a, b = gen_first_col_exp(a,b,ci,φ)
+            
+            
+        case "lawson2a_2s": # based on midpoint rule, stiff order 1 https://cds.cern.ch/record/848126/files/cer-002531460.pdf
+            c1,c2 = 0,1/2
+            ci = [c1, c2]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            a2_1 = c2 * φ(0,2)
+            b2 = φ(0,2)
+            b1 = 0
+
+            a = [
+                    [0,0],
+                    [a2_1, 0],
+            ]
+            b = [
+                    [b1, b2],
+            ]
+
+        case "lawson2b_2s": # based on trapezoidal rule, stiff order 1 https://cds.cern.ch/record/848126/files/cer-002531460.pdf
+            c1,c2 = 0,1
+            ci = [c1, c2]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            a2_1 = φ(0)
+            b2 = 1/2
+            b1 = (1/2)*φ(0)
+
+            a = [
+                    [0,0],
+                    [a2_1, 0],
+            ]
+            b = [
+                    [b1, b2],
+            ]
+
+
+        case "lawson4_4s": 
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            a2_1 = c2 * φ(0,2)
+            a3_2 = 1/2
+            a4_3 = φ(0,2)
+            
+            b1 = (1/6) * φ(0)
+            b2 = (1/3) * φ(0,2)
+            b3 = (1/3) * φ(0,2)
+            b4 = 1/6
+
+            a = [
+                    [0,    0,    0,    0],
+                    [a2_1, 0,    0,    0],
+                    [0,    a3_2, 0,    0],
+                    [0,    0,    a4_3, 0],
+            ]
+            b = [
+                    [b1,b2,b3,b4],
+            ]
+
+        case "lawson41-gen_4s": # GenLawson4 https://ora.ox.ac.uk/objects/uuid:cc001282-4285-4ca2-ad06-31787b540c61/files/m611df1a355ca243beb09824b70e5e774
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci, use_analytic_solution)
+
+
+            a3_2 = 1/2
+            a4_3 = φ(0,2)
+            
+            b2 = (1/3) * φ(0,2)
+            b3 = (1/3) * φ(0,2)
+            b4 = 1/6
+
+            a = [
+                    [0, 0,        0, 0],
+                    [0, 0,          0,        0],
+                    [0, a3_2, 0,        0],
+                    [0, 0, a4_3, 0],
+            ]
+            b = [
+                    [0,
+                    b2,
+                    b3,
+                    b4,],
+            ]
+
+            a, b = gen_first_col_exp(a,b,ci,φ)
+
+        case "lawson41-gen-mod_4s": # GenLawson4 https://ora.ox.ac.uk/objects/uuid:cc001282-4285-4ca2-ad06-31787b540c61/files/m611df1a355ca243beb09824b70e5e774
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci, use_analytic_solution)
+
+
+            a3_2 = 1/2
+            a4_3 = φ(0,2)
+            
+            b2 = (1/3) * φ(0,2)
+            b3 = (1/3) * φ(0,2)
+            b4 = φ(2) - (1/3)*φ(0,2)
+
+            a = [
+                    [0, 0,        0, 0],
+                    [0, 0,          0,        0],
+                    [0, a3_2, 0,        0],
+                    [0, 0, a4_3, 0],
+            ]
+            b = [
+                    [0,
+                    b2,
+                    b3,
+                    b4,],
+            ]
+
+            a, b = gen_first_col_exp(a,b,ci,φ)
+
+
+
+        case "lawson42-gen-mod_1h4s": # GenLawson4 https://ora.ox.ac.uk/objects/uuid:cc001282-4285-4ca2-ad06-31787b540c61/files/m611df1a355ca243beb09824b70e5e774
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci, use_analytic_solution)
+
+            a3_2 = 1/2
+            a4_3 = φ(0,2)
+            
+            b2 = (1/3) * φ(0,2)
+            b3 = (1/3) * φ(0,2)
+            b4 = (1/2)*φ(2) + φ(3) - (1/4)*φ(0,2)
+
+            a = [
+                    [0, 0,    0, 0],
+                    [0, 0,    0, 0],
+                    [0, a3_2, 0, 0],
+                    [0, 0, a4_3, 0],
+            ]
+            b = [
+                    [0, b2, b3, b4,],
+            ]
+
+            if extra_options_flag("h_prev_h_h_no_eta", extra_options):
+                φ1 = Phi(h_prev1_no_eta * h/h_no_eta, ci, use_analytic_solution)
+            elif extra_options_flag("h_only", extra_options):
+                φ1 = Phi(h, ci, use_analytic_solution)
+            else:
+                φ1 = Phi(h_prev1_no_eta, ci, use_analytic_solution)
+
+            u2_1 = -φ1(2,2)
+            u3_1 = -φ1(2,2) + 1/4
+            u4_1 = -φ1(2) + (1/2)*φ1(0,2)
+            v1 = -(1/2)*φ1(2) + φ1(3) + (1/12)*φ1(0,2)
+
+            u = [
+                    [   0, 0, 0, 0],
+                    [u2_1, 0, 0, 0],
+                    [u3_1, 0, 0, 0],
+                    [u4_1, 0, 0, 0],
+            ]
+            v = [
+                    [v1, 0, 0, 0,],
+            ]
+
+            a, b = gen_first_col_exp_uv(a,b,ci,u,v,φ)
+
+
+
+        case "lawson43-gen-mod_2h4s": # GenLawson4 https://ora.ox.ac.uk/objects/uuid:cc001282-4285-4ca2-ad06-31787b540c61/files/m611df1a355ca243beb09824b70e5e774
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci, use_analytic_solution)
+
+            a3_2 = 1/2
+            a4_3 = φ(0,2)
+            
+            b3 = b2 = (1/3) * a4_3
+            b4 = (1/3)*φ(2) + φ(3) + φ(4) - (5/24)*φ(0,2)
+
+            a = [
+                    [0, 0,    0, 0],
+                    [0, 0,    0, 0],
+                    [0, a3_2, 0, 0],
+                    [0, 0, a4_3, 0],
+            ]
+            b = [
+                    [0, b2, b3, b4,],
+            ]
+
+            if extra_options_flag("h_prev_h_h_no_eta", extra_options):
+                φ1 = Phi(h_prev1_no_eta * h/h_no_eta, ci, use_analytic_solution)
+                φ2 = Phi(h_prev2_no_eta * h/h_no_eta, ci, use_analytic_solution)
+            elif extra_options_flag("h_only", extra_options):
+                φ1 = Phi(h, ci, use_analytic_solution)
+                φ2 = Phi(h, ci, use_analytic_solution)
+            else:
+                φ1 = Phi(h_prev1_no_eta, ci, use_analytic_solution)
+                φ2 = Phi(h_prev2_no_eta, ci, use_analytic_solution)
+
+            u2_1 = -2*φ1(2,2) - 2*φ1(3,2)
+            u3_1 = -2*φ1(2,2) - 2*φ1(3,2) + 5/8
+            u4_1 = -2*φ1(2) - 2*φ1(3) + (5/4)*φ1(0,2)
+            v1 = -φ1(2) + φ1(3) + 3*φ1(4) + (5/24)*φ1(0,2)
+            
+            u2_2 = -(1/2)*φ2(2,2) + φ2(3,2)
+            u3_2 = (1/2)*φ2(2,2) + φ2(3,2) - 3/16
+            u4_2 = (1/2)*φ2(2) + φ2(3) - (3/8)*φ2(0,2)
+            v2 = (1/6)*φ2(2) - φ2(4) - (1/24)*φ2(0,2)
+            
+            u = [
+                    [   0,    0, 0, 0],
+                    [u2_1, u2_2, 0, 0],
+                    [u3_1, u3_2, 0, 0],
+                    [u4_1, u4_2, 0, 0],
+            ]
+            v = [
+                    [v1, v2, 0, 0,],
+            ]
+
+            a, b = gen_first_col_exp_uv(a,b,ci,u,v,φ)
+
+
+        case "lawson44-gen-mod_3h4s": # GenLawson4 https://ora.ox.ac.uk/objects/uuid:cc001282-4285-4ca2-ad06-31787b540c61/files/m611df1a355ca243beb09824b70e5e774
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci, use_analytic_solution)
+
+            a3_2 = 1/2
+            a4_3 = φ(0,2)
+            
+            b3 = b2 = (1/3) * a4_3
+            b4 = (1/4)*φ(2) + (11/12)*φ(3) + (3/2)*φ(4) + φ(5) - (35/192)*φ(0,2)
+
+            a = [
+                    [0, 0,    0, 0],
+                    [0, 0,    0, 0],
+                    [0, a3_2, 0, 0],
+                    [0, 0, a4_3, 0],
+            ]
+            b = [
+                    [0, b2, b3, b4,],
+            ]
+
+            if extra_options_flag("h_prev_h_h_no_eta", extra_options):
+                φ1 = Phi(h_prev1_no_eta * h/h_no_eta, ci, use_analytic_solution)
+                φ2 = Phi(h_prev2_no_eta * h/h_no_eta, ci, use_analytic_solution)
+                φ3 = Phi(h_prev3_no_eta * h/h_no_eta, ci, use_analytic_solution)
+            elif extra_options_flag("h_only", extra_options):
+                φ1 = Phi(h, ci, use_analytic_solution)
+                φ2 = Phi(h, ci, use_analytic_solution)
+                φ3 = Phi(h, ci, use_analytic_solution)
+            else:
+                φ1 = Phi(h_prev1_no_eta, ci, use_analytic_solution)
+                φ2 = Phi(h_prev2_no_eta, ci, use_analytic_solution)
+                φ3 = Phi(h_prev3_no_eta, ci, use_analytic_solution)
+                
+            u2_1 = -3*φ1(2,2) - 5*φ1(3,2) - 3*φ1(4,2)
+            u3_1 = u2_1 + 35/32
+            u4_1 = -3*φ1(2) - 5*φ1(3) - 3*φ1(4) + (35/16)*φ1(0,2)
+            v1 = -(3/2)*φ1(2) + (1/2)*φ1(3) + 6*φ1(4) + 6*φ1(5) + (35/96)*φ1(0,2)
+            
+            u2_2 = (3/2)*φ2(2,2) + 4*φ2(3,2) + 3*φ2(4,2)
+            u3_2 = u2_2 - 21/32
+            u4_2 = (3/2)*φ2(2) + 4*φ2(3) + 3*φ2(4) - (21/16)*φ2(0,2)
+            v2 = (1/2)*φ2(2) + (1/3)*φ2(3) - 3*φ2(4) - 4*φ2(5) - (7/48)*φ2(0,2)
+            
+            u2_3 = (-1/3)*φ3(2,2) - φ3(3,2) - φ3(4,2)
+            u3_3 = u2_3 + 5/32
+            u4_3 = -(1/3)*φ3(2) - φ3(3) - φ3(4) + (5/16)*φ3(0,2)
+            v3 = -(1/12)*φ3(2) - (1/12)*φ3(3) + (1/2)*φ3(4) + φ3(5) + (5/192)*φ3(0,2)
+            
+            u = [
+                    [   0,    0,    0, 0],
+                    [u2_1, u2_2, u2_3, 0],
+                    [u3_1, u3_2, u3_3, 0],
+                    [u4_1, u4_2, u4_3, 0],
+            ]
+            v = [
+                    [v1, v2, v3, 0,],
+            ]
+
+            a, b = gen_first_col_exp_uv(a,b,ci,u,v,φ)
+
+
+
+        case "lawson45-gen-mod_4h4s": # GenLawson4 https://ora.ox.ac.uk/objects/uuid:cc001282-4285-4ca2-ad06-31787b540c61/files/m611df1a355ca243beb09824b70e5e774
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci, use_analytic_solution)
+
+            a3_2 = 1/2
+            a4_3 = φ(0,2)
+            
+            b2 = (1/3) * φ(0,2)
+            b3 = (1/3) * φ(0,2)
+            b4 = (12/59)*φ(2) + (50/59)*φ(3) + (105/59)*φ(4) + (120/59)*φ(5) - (60/59)*φ(6) - (157/944)*φ(0,2)
+
+            a = [
+                    [0, 0,    0, 0],
+                    [0, 0,    0, 0],
+                    [0, a3_2, 0, 0],
+                    [0, 0, a4_3, 0],
+            ]
+            b = [
+                    [0, b2, b3, b4,],
+            ]
+
+            if extra_options_flag("h_prev_h_h_no_eta", extra_options):
+                φ1 = Phi(h_prev1_no_eta * h/h_no_eta, ci, use_analytic_solution)
+                φ2 = Phi(h_prev2_no_eta * h/h_no_eta, ci, use_analytic_solution)
+                φ3 = Phi(h_prev3_no_eta * h/h_no_eta, ci, use_analytic_solution)
+                φ4 = Phi(h_prev4_no_eta * h/h_no_eta, ci, use_analytic_solution)
+            elif extra_options_flag("h_only", extra_options):
+                φ1 = Phi(h, ci, use_analytic_solution)
+                φ2 = Phi(h, ci, use_analytic_solution)
+                φ3 = Phi(h, ci, use_analytic_solution)
+                φ4 = Phi(h, ci, use_analytic_solution)
+            else:
+                φ1 = Phi(h_prev1_no_eta, ci, use_analytic_solution)
+                φ2 = Phi(h_prev2_no_eta, ci, use_analytic_solution)
+                φ3 = Phi(h_prev3_no_eta, ci, use_analytic_solution)
+                φ4 = Phi(h_prev4_no_eta, ci, use_analytic_solution)
+                
+            u2_1 = -4*φ1(2,2) - (26/3)*φ1(3,2) - 9*φ1(4,2) - 4*φ1(5,2)
+            u3_1 = u2_1 + 105/64
+            u4_1 = -4*φ1(2) - (26/3)*φ1(3) - 9*φ1(4) - 4*φ1(5) + (105/32)*φ1(0,2)
+            v1 = -(116/59)*φ1(2) -  (34/177)*φ1(3) + (519/59)*φ1(4) + (964/59)*φ1(5) - (600/59)*φ1(6) +   (495/944)*φ1(0,2)
+            
+            u2_2 = 3*φ2(2,2) + (19/2)*φ2(3,2) + 12*φ2(4,2) + 6*φ2(5,2)
+            u3_2 = u2_2 - 189/128
+            u4_2 = 3*φ2(2) + (19/2)*φ2(3) + 12*φ2(4) + 6*φ2(5) - (189/64)*φ2(0,2)
+            v2 =  (57/59)*φ2(2) + (121/118)*φ2(3) - (342/59)*φ2(4) - (846/59)*φ2(5) + (600/59)*φ2(6) -  (577/1888)*φ2(0,2)
+            
+            u2_3 = -(4/3)*φ3(2,2) - (14/3)*φ3(3,2) - 7*φ3(4,2) - 4*φ3(5,2)
+            u3_3 = u2_3 + 45/64
+            u4_3 = -(4/3)*φ3(2) - (14/3)*φ3(3) - 7*φ3(4) - 4*φ3(5) +(45/32)*φ3(0,2)
+            v3 = -(56/177)*φ3(2) -  (76/177)*φ3(3) + (112/59)*φ3(4) + (364/59)*φ3(5) - (300/59)*φ3(6) +    (25/236)*φ3(0,2)
+            
+            u2_4 = (1/4)*φ4(2,2) + (88/96)*φ4(3,2) + (3/2)*φ4(4,2) + φ4(5,2)
+            u3_4 = u2_4 - 35/256
+            u4_4 = (1/4)*φ4(2) + (11/12)*φ4(3) + (3/2)*φ4(4) + φ4(5) - (35/128)*φ4(0,2)
+            v4 =  (11/236)*φ4(2) +  (49/708)*φ4(3) - (33/118)*φ4(4) -  (61/59)*φ4(5) + ( 60/59)*φ4(6) - (181/11328)*φ4(0,2)
+
+            u = [
+                    [   0,    0,    0,    0],
+                    [u2_1, u2_2, u2_3, u2_4],
+                    [u3_1, u3_2, u3_3, u3_4],
+                    [u4_1, u4_2, u4_3, u4_4],
+            ]
+            v = [
+                    [v1, v2, v3, v4,],
+            ]
+
+            a, b = gen_first_col_exp_uv(a,b,ci,u,v,φ)
+
+
+
+        case "etdrk2_2s": # https://arxiv.org/pdf/2402.15142v1
+            c1,c2 = 0, 1
+            ci = [c1,c2]
+            φ = Phi(h, ci, use_analytic_solution)   
+            
+            a = [
+                    [0, 0],
+                    [φ(1), 0],
+            ]
+            b = [
+                    [φ(1)-φ(2), φ(2)],
+            ]
+
+        case "etdrk3_a_3s": #non-monotonic # https://arxiv.org/pdf/2402.15142v1
+            c1,c2,c3 = 0, 1, 2/3
+            ci = [c1,c2,c3]
+            φ = Phi(h, ci, use_analytic_solution)   
+            
+            a2_1 = c2*φ(1)
+            a3_2 = (4/9)*φ(2,3)
+            a3_1 = c3*φ(1,3) - a3_2
+            
+            b2 = φ(2) - (1/2)*φ(1)
+            b3 = (3/4) * φ(1)
+            b1 = φ(1) - b2 - b3 
+            
+            a = [
+                    [0, 0, 0],
+                    [a2_1, 0, 0],
+                    [a3_1, a3_2, 0 ]
+            ]
+            b = [
+                    [b1, b2, b3],
+            ]
+
+        case "etdrk3_b_3s": # https://arxiv.org/pdf/2402.15142v1
+            c1,c2,c3 = 0, 4/9, 2/3
+            ci = [c1,c2,c3]
+            φ = Phi(h, ci, use_analytic_solution)   
+            
+            a2_1 = c2*φ(1,2)
+            a3_2 = φ(2,3)
+            a3_1 = c3*φ(1,3) - a3_2
+            
+            b2 = 0
+            b3 = (3/2) * φ(2)
+            b1 = φ(1) - b2 - b3 
+            
+            a = [
+                    [0, 0, 0],
+                    [a2_1, 0, 0],
+                    [a3_1, a3_2, 0 ]
+            ]
+            b = [
+                    [b1, b2, b3],
+            ]
+
+        case "etdrk4_4s": # https://ora.ox.ac.uk/objects/uuid:cc001282-4285-4ca2-ad06-31787b540c61/files/m611df1a355ca243beb09824b70e5e774
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            a3_2 =   φ(1,2)
+            a4_3 = 2*φ(1,2)
+
+            b2 = 2*φ(2) - 4*φ(3)
+            b3 = 2*φ(2) - 4*φ(3)
+            b4 =  -φ(2) + 4*φ(3)
+
+            a = [
+                    [0, 0,0,0],
+                    [0, 0,0,0],
+                    [0, a3_2,0,0],
+                    [0, 0, a4_3,0],
+            ]
+            b = [
+                    [0, b2, b3, b4],
+            ]
+
+            a, b = gen_first_col_exp(a,b,ci,φ)
+
+
+        case "etdrk4_4s_alt": # pg 70 col 1 computed with (4.9) https://ora.ox.ac.uk/objects/uuid:cc001282-4285-4ca2-ad06-31787b540c61/files/m611df1a355ca243beb09824b70e5e774
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            a2_1 = φ(1,2)  #unsure about this, looks bad and is pretty different from col #1 implementations for everything else except the other 4s alt and 5s ostermann??? from the link
+            a3_1 = 0
+            a4_1 = φ(1) - 2*φ(1,2)
+            
+            a3_2 =   φ(1,2)
+            a4_3 = 2*φ(1,2)
+
+            b1 = φ(1) - 3*φ(2) + 4*φ(3)
+            b2 = 2*φ(2) - 4*φ(3)
+            b3 = 2*φ(2) - 4*φ(3)
+            b4 =  -φ(2) + 4*φ(3)
+
+            a = [
+                    [   0, 0,      0,0],
+                    [a2_1, 0,      0,0],
+                    [a3_1, a3_2,   0,0],
+                    [a4_1, 0,   a4_3,0],
+            ]
+            b = [
+                    [0, b2, b3, b4],
+            ]
+
+            #a, b = gen_first_col_exp(a,b,ci,φ)
+
+            
+        case "dpmpp_2s":
+            c2 = float(get_extra_options_kv("c2", str(c2), extra_options))
+            
+            ci = [0,c2]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            b2 = (1/(2*c2)) * φ(1)
+
+            a = [
+                    [0, 0],
+                    [0, 0],
+            ]
+            b = [
+                    [0, b2],
+            ]
+            
+            a, b = gen_first_col_exp(a,b,ci,φ)
+            
+        case "dpmpp_sde_2s":
+            c2 = 1.0 #hardcoded to 1.0 to more closely emulate the configuration for k-diffusion's implementation
+            
+            ci = [0,c2]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            b2 = (1/(2*c2)) * φ(1)
+
+            a = [
+                    [0, 0],
+                    [0, 0],
+            ]
+            b = [
+                    [0, b2],
+            ]
+            
+            a, b = gen_first_col_exp(a,b,ci,φ)
+
+        case "dpmpp_3s":
+            c2 = float(get_extra_options_kv("c2", str(c2), extra_options))
+            c3 = float(get_extra_options_kv("c3", str(c3), extra_options))
+            
+            ci = [0,c2,c3]
+            φ = Phi(h, ci, use_analytic_solution)   
+            
+            a3_2 = (c3**2 / c2) * φ(2,3)
+            b3 = (1/c3) * φ(2)
+
+            a = [
+                    [0, 0, 0],
+                    [0, 0, 0],
+                    [0, a3_2, 0],  
+            ]
+            b = [
+                    [0, 0, b3],
+            ]
+            
+            a, b = gen_first_col_exp(a,b,ci,φ)
+            
+        case "res_5s": #non-monotonic #4th order
+                
+            c1, c2, c3, c4, c5 = 0, 1/2, 1/2, 1, 1/2
+            ci = [c1,c2,c3,c4,c5]
+            φ = Phi(h, ci, use_analytic_solution)   
+
+            a3_2 = φ(2,3)
+            a4_2 = φ(2,4)
+            a5_2 = (1/2)*φ(2,5) - φ(3,4) + (1/4)*φ(2,4) - (1/2)*φ(3,5)
+            
+            a4_3 = a4_2
+            a5_3 = a5_2
+            
+            a5_4 = (1/4)*φ(2,5) - a5_2
+            
+            b4 = -φ(2) + 4*φ(3)
+            b5 = 4*φ(2) - 8*φ(3)
+            
+            a = [
+                    [0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0],
+                    [0, a3_2, 0, 0, 0],
+                    [0, a4_2, a4_3, 0, 0],
+                    [0, a5_2, a5_3, a5_4, 0],
+            ]
+            b = [
+                    [0, 0, 0, b4, b5],
+            ]
+            
+            a, b = gen_first_col_exp(a,b,ci,φ)
+            
+        case "res_5s_hochbruck-ostermann": #non-monotonic #4th order
+                
+            c1, c2, c3, c4, c5 = 0, 1/2, 1/2, 1, 1/2
+            ci = [c1,c2,c3,c4,c5]
+            φ = Phi(h, ci, use_analytic_solution)   
+            
+            a3_2 = 4*φ(2,2)
+            a4_2 = φ(2)
+            a5_2 = (1/4)*φ(2) - φ(3) + 2*φ(2,2) - 4*φ(3,2)
+            
+            a4_3 = φ(2)
+            a5_3 = a5_2
+            
+            a5_4 = φ(2,2) - a5_2
+            
+            b4 =  -φ(2) + 4*φ(3)
+            b5 = 4*φ(2) - 8*φ(3)
+
+            a = [
+                    [0, 0   , 0   , 0   , 0],
+                    [0, 0   , 0   , 0   , 0],
+                    [0, a3_2, 0   , 0   , 0],
+                    [0, a4_2, a4_3, 0   , 0],
+                    [0, a5_2, a5_3, a5_4, 0],
+            ]
+            b = [
+                    [0, 0, 0, b4, b5],
+            ]
+            
+            a, b = gen_first_col_exp(a,b,ci,φ)
+
+            
+        case "res_6s": #non-monotonic #4th order
+                
+            c1, c2, c3, c4, c5, c6 = 0, 1/2, 1/2, 1/3, 1/3, 5/6
+            ci = [c1, c2, c3, c4, c5, c6]
+            φ = Phi(h, ci, use_analytic_solution)
+            
+            a2_1 = c2 * φ(1,2)
+            
+            a3_1 = 0
+            a3_2 = (c3**2 / c2) * φ(2,3)
+            
+            a4_1 = 0
+            a4_2 = (c4**2 / c2) * φ(2,4)
+            a4_3 = (c4**2 * φ(2,4) - a4_2 * c2) / c3
+            
+            a5_1 = 0
+            a5_2 = 0 #zero
+            a5_3 = (-c4 * c5**2 * φ(2,5) + 2*c5**3 * φ(3,5))   /   (c3 * (c3 - c4))
+            a5_4 = (-c3 * c5**2 * φ(2,5) + 2*c5**3 * φ(3,5))   /   (c4 * (c4 - c3))
+            
+            a6_1 = 0
+            a6_2 = 0 #zero
+            a6_3 = (-c4 * c6**2 * φ(2,6) + 2*c6**3 * φ(3,6))   /   (c3 * (c3 - c4))
+            a6_4 = (-c3 * c6**2 * φ(2,6) + 2*c6**3 * φ(3,6))   /   (c4 * (c4 - c3))
+            a6_5 = (c6**2 * φ(2,6) - a6_3*c3 - a6_4*c4)   /   c5
+            #a6_5_alt = (2*c6**3 * φ(3,6) - a6_3*c3**2 - a6_4*c4**2)   /   c5**2
+                    
+            b1 = 0
+            b2 = 0
+            b3 = 0
+            b4 = 0
+            b5 = (-c6*φ(2) + 2*φ(3)) / (c5 * (c5 - c6))
+            b6 = (-c5*φ(2) + 2*φ(3)) / (c6 * (c6 - c5))
+
+            a = [
+                    [0, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0],
+                    [0, a3_2, 0, 0, 0, 0],
+                    [0, a4_2, a4_3, 0, 0, 0],
+                    [0, a5_2, a5_3, a5_4, 0, 0],
+                    [0, a6_2, a6_3, a6_4, a6_5, 0],
+            ]
+            b = [
+                    [0, b2, b3, b4, b5, b6],
+            ]
+            
+            a, b = gen_first_col_exp(a,b,ci,φ)
+
+        case "res_8s": #non-monotonic # this is not EXPRK5S8 https://ora.ox.ac.uk/objects/uuid:cc001282-4285-4ca2-ad06-31787b540c61/files/m611df1a355ca243beb09824b70e5e774
+                
+            c1, c2, c3, c4, c5, c6, c7, c8 = 0, 1/2, 1/2, 1/4,    1/2, 1/5, 2/3, 1
+            ci = [c1, c2, c3, c4, c5, c6, c7, c8]
+            #φ = Phi(h, ci, analytic_solution=use_analytic_solution)
+            
+            ci = [mpf(c_val) for c_val in ci]
+            c1, c2, c3, c4, c5, c6, c7, c8 = [c_val for c_val in ci]
+
+            φ = Phi(mpf(h.item()), ci, analytic_solution=use_analytic_solution)
+            
+            a3_2 = (1/2) * φ(2,3)
+            
+            a4_3 = (1/8) * φ(2,4)
+
+            a5_3 = (-1/2) * φ(2,5) + 2 * φ(3,5)
+            a5_4 =      2 * φ(2,5) - 4 * φ(3,5)
+            
+            a6_4 = (8/25) * φ(2,6) - (32/125) * φ(3,6)
+            a6_5 = (2/25) * φ(2,6) -  (1/2)   * a6_4
+            
+            a7_4 = (-125/162)  * a6_4
+            a7_5 =  (125/1944) * a6_4 -  (16/27) * φ(2,7) + (320/81) * φ(3,7)
+            a7_6 = (3125/3888) * a6_4 + (100/27) * φ(2,7) - (800/81) * φ(3,7)
+            
+            Φ = (5/32)*a6_4 - (1/28)*φ(2,6) + (36/175)*φ(2,7) - (48/25)*φ(3,7) + (6/175)*φ(4,6) + (192/35)*φ(4,7) + 6*φ(4,8)
+            
+            a8_5 =  (208/3)*φ(3,8) -  (16/3) *φ(2,8) -      40*Φ
+            a8_6 = (-250/3)*φ(3,8) + (250/21)*φ(2,8) + (250/7)*Φ
+            a8_7 =      -27*φ(3,8) +  (27/14)*φ(2,8) + (135/7)*Φ
+            
+            b6 = (125/14)*φ(2) - (625/14)*φ(3) + (1125/14)*φ(4)
+            b7 = (-27/14)*φ(2) + (162/7) *φ(3) -  (405/7) *φ(4)
+            b8 =   (1/2) *φ(2) -  (13/2) *φ(3) +   (45/2) *φ(4)
+            
+            b1   =    φ(1)   - b6 - b7 - b8
+            
+            a = [
+                    [0 , 0   , 0   , 0   , 0   , 0   , 0   , 0],
+                    [0 , 0   , 0   , 0   , 0   , 0   , 0   , 0],
+                    
+                    [0 , a3_2, 0   , 0   , 0   , 0   , 0   , 0],
+                    [0 , 0   , a4_3, 0   , 0   , 0   , 0   , 0],
+                    
+                    [0 , 0   , a5_3, a5_4, 0   , 0   , 0   , 0],
+                    [0 , 0   , 0   , a6_4, a6_5, 0   , 0   , 0],
+                    
+                    [0 , 0   , 0   , a7_4, a7_5, a7_6, 0   , 0],
+                    [0 , 0   , 0   , 0   , a8_5, a8_6, a8_7, 0],
+            ]
+            b = [
+                    [0, 0, 0, 0, 0, b6, b7, b8],
+            ]
+            
+            a, b = gen_first_col_exp(a,b,ci,φ)
+
+            a = [[float(val) for val in row] for row in a]
+            b = [[float(val) for val in row] for row in b]
+            ci = [c1, c2, c3, c4, c5, c6, c7, c8]
+
+
+
+        case "res_8s_alt": # this is EXPRK5S8 https://ora.ox.ac.uk/objects/uuid:cc001282-4285-4ca2-ad06-31787b540c61/files/m611df1a355ca243beb09824b70e5e774
+                
+            c1, c2, c3, c4, c5, c6, c7, c8 = 0, 1/2, 1/2, 1/4,    1/2, 1/5, 2/3, 1
+            #ci = [c1, c2, c3, c4, c5, c6, c7, c8]
+            #φ = Phi(h, ci, analytic_solution=use_analytic_solution)
+            
+            ci = [mpf(c_val) for c_val in ci]
+            c1, c2, c3, c4, c5, c6, c7, c8 = [c_val for c_val in ci]
+
+            φ = Phi(mpf(h.item()), ci, analytic_solution=use_analytic_solution)
+            
+            a3_2 = 2*φ(2,2)
+            
+            a4_3 = 2*φ(2,4)
+
+            a5_3 = -2*φ(2,2) + 16*φ(3,2)
+            a5_4 =  8*φ(2,2) - 32*φ(3,2)
+            
+            a6_4 =  8*φ(2,6) - 32*φ(3,6)
+            a6_5 = -2*φ(2,6) + 16*φ(3,6)
+            
+            a7_4 = (-125/162)  * a6_4
+            a7_5 =  (125/1944) * a6_4 -  (4/3) * φ(2,7) +  (40/3)*φ(3,7)
+            a7_6 = (3125/3888) * a6_4 + (25/3) * φ(2,7) - (100/3)*φ(3,7)
+            
+            Φ = (5/32)*a6_4 - (25/28)*φ(2,6) + (81/175)*φ(2,7) - (162/25)*φ(3,7) + (150/7)*φ(4,6) + (972/35)*φ(4,7) + 6*φ(4)
+            
+            a8_5 =  -(16/3)*φ(2) + (208/3)*φ(3) -      40*Φ
+            a8_6 = (250/21)*φ(2) - (250/3)*φ(3) + (250/7)*Φ
+            a8_7 =  (27/14)*φ(2) -      27*φ(3) + (135/7)*Φ
+            
+            b6 = (125/14)*φ(2) - (625/14)*φ(3) + (1125/14)*φ(4)
+            b7 = (-27/14)*φ(2) + (162/7) *φ(3) -  (405/7) *φ(4)
+            b8 =   (1/2) *φ(2) -  (13/2) *φ(3) +   (45/2) *φ(4)
+            
+            a = [
+                    [0 , 0   , 0   , 0   , 0   , 0   , 0   , 0],
+                    [0 , 0   , 0   , 0   , 0   , 0   , 0   , 0],
+                    
+                    [0 , a3_2, 0   , 0   , 0   , 0   , 0   , 0],
+                    [0 , 0   , a4_3, 0   , 0   , 0   , 0   , 0],
+                    
+                    [0 , 0   , a5_3, a5_4, 0   , 0   , 0   , 0],
+                    [0 , 0   , 0   , a6_4, a6_5, 0   , 0   , 0],
+                    
+                    [0 , 0   , 0   , a7_4, a7_5, a7_6, 0   , 0],
+                    [0 , 0   , 0   , 0   , a8_5, a8_6, a8_7, 0],
+            ]
+            b = [
+                    [0, 0, 0, 0, 0, b6, b7, b8],
+            ]
+
+            a, b = gen_first_col_exp(a,b,ci,φ)
+
+            a = [[float(val) for val in row] for row in a]
+            b = [[float(val) for val in row] for row in b]
+            ci = [c1, c2, c3, c4, c5, c6, c7, c8]
+
+
+        case "res_10s":
+                
+            c1, c2, c3, c4, c5, c6, c7, c8, c9, c10 = 0, 1/2, 1/2, 1/3, 1/2,     1/3, 1/4, 3/10, 3/4, 1
+            ci = [c1, c2, c3, c4, c5, c6, c7, c8, c9, c10]
+            #φ = Phi(h, ci, analytic_solution=use_analytic_solution)
+            
+            ci = [mpf(c_val) for c_val in ci]
+            c1, c2, c3, c4, c5, c6, c7, c8, c9, c10 = [c_val for c_val in ci]
+
+            φ = Phi(mpf(h.item()), ci, analytic_solution=use_analytic_solution)
+
+            a3_2 = (c3**2 / c2) * φ(2,3)
+            a4_2 = (c4**2 / c2) * φ(2,4)
+                        
+            b8 =  (c9*c10*φ(2) - 2*(c9+c10)*φ(3) + 6*φ(4))   /   (c8 * (c8-c9) * (c8-c10))
+            b9 =  (c8*c10*φ(2) - 2*(c8+c10)*φ(3) + 6*φ(4))   /   (c9 * (c9-c8) * (c9-c10))
+            
+            b10 = (c8*c9*φ(2)  - 2*(c8+c9) *φ(3) + 6*φ(4))   /   (c10 * (c10-c8) * (c10-c9))
+            
+            a = [
+                    [0, 0, 0, 0, 0,      0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0,      0, 0, 0, 0, 0],
+                    [0, a3_2, 0, 0, 0,      0, 0, 0, 0, 0],
+                    [0, a4_2, 0, 0, 0,      0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0,      0, 0, 0, 0, 0],
+                    
+                    [0, 0, 0, 0, 0,      0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0,      0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0,      0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0,      0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0,      0, 0, 0, 0, 0],
+            ]
+            b = [
+                    [0, 0, 0, 0, 0,      0, 0, b8, b9, b10],
+            ]
+            
+            # a5_3, a5_4
+            # a6_3, a6_4
+            # a7_3, a7_4
+            for i in range(5, 8): # i=5,6,7   j,k ∈ {3, 4}, j != k
+                jk = [(3, 4), (4, 3)]
+                jk = list(permutations([3, 4], 2)) 
+                for j,k in jk:
+                    a[i-1][j-1] = (-ci[i-1]**2 * ci[k-1] * φ(2,i)    +   2*ci[i-1]**3 * φ(3,i))   /   (ci[j-1] * (ci[j-1] - ci[k-1]))
+                
+            for i in range(8, 11): # i=8,9,10   j,k,l ∈ {5, 6, 7}, j != k != l      [    (5, 6, 7), (5, 7, 6),    (6, 5, 7), (6, 7, 5),    (7, 5, 6), (7, 6, 5)]    6 total coeff
+                jkl = list(permutations([5, 6, 7], 3)) 
+                for j,k,l in jkl:
+                    a[i-1][j-1] = (ci[i-1]**2 * ci[k-1] * ci[l-1] * φ(2,i)   -   2*ci[i-1]**3 * (ci[k-1] + ci[l-1]) * φ(3,i)   +   6*ci[i-1]**4 * φ(4,i))    /    (ci[j-1] * (ci[j-1] - ci[k-1]) * (ci[j-1] - ci[l-1]))
+            
+            gen_first_col_exp(a, b, ci, φ)
+            
+            a = [[float(val) for val in row] for row in a]
+            b = [[float(val) for val in row] for row in b]
+            c1, c2, c3, c4, c5, c6, c7, c8, c9, c10 = 0, 1/2, 1/2, 1/3, 1/2,     1/3, 1/4, 3/10, 3/4, 1
+            ci = [c1, c2, c3, c4, c5, c6, c7, c8, c9, c10]
+            
+
+        case "res_15s":
+                
+            c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12,c13,c14,c15 = 0, 1/2, 1/2, 1/3, 1/2,    1/5, 1/4, 18/25, 1/3, 3/10,    1/6, 90/103, 1/3, 3/10, 1/5
+            c1 = 0
+            c2 = c3 = c5 = 1/2
+            c4 = c9 = c13 = 1/3
+            c6 = c15 = 1/5
+            c7 = 1/4
+            c8 = 18/25
+            c10 = c14 = 3/10
+            c11 = 1/6
+            c12 = 90/103
+            c15 = 1/5
+            ci = [c1, c2, c3, c4, c5, c6, c7, c8, c9, c10, c11, c12, c13, c14, c15]
+            ci = [mpf(c_val) for c_val in ci]
+
+            φ = Phi(mpf(h.item()), ci, analytic_solution=use_analytic_solution)
+
+            a = [[mpf(0) for _ in range(15)] for _ in range(15)]
+            b = [[mpf(0) for _ in range(15)]]
+
+            for i in range(3, 5): # i=3,4     j=2
+                j=2
+                a[i-1][j-1] = (ci[i-1]**2 / ci[j-1]) * φ(j,i)
+            
+            
+            for i in range(5, 8): # i=5,6,7   j,k ∈ {3, 4}, j != k
+                jk = list(permutations([3, 4], 2)) 
+                for j,k in jk:
+                    a[i-1][j-1] = (-ci[i-1]**2 * ci[k-1] * φ(2,i)    +   2*ci[i-1]**3 * φ(3,i))   /   prod_diff(ci[j-1], ci[k-1])
+
+            for i in range(8, 12): # i=8,9,10,11  j,k,l ∈ {5, 6, 7}, j != k != l      [    (5, 6, 7), (5, 7, 6),    (6, 5, 7), (6, 7, 5),    (7, 5, 6), (7, 6, 5)]    6 total coeff
+                jkl = list(permutations([5, 6, 7], 3)) 
+                for j,k,l in jkl:
+                    a[i-1][j-1] = (ci[i-1]**2 * ci[k-1] * ci[l-1] * φ(2,i)   -   2*ci[i-1]**3 * (ci[k-1] + ci[l-1]) * φ(3,i)   +   6*ci[i-1]**4 * φ(4,i))    /    (ci[j-1] * (ci[j-1] - ci[k-1]) * (ci[j-1] - ci[l-1]))
+
+            for i in range(12,16): # i=12,13,14,15
+                jkld = list(permutations([8,9,10,11], 4)) 
+                for j,k,l,d in jkld:
+                    numerator = -ci[i-1]**2  *  ci[d-1]*ci[k-1]*ci[l-1]  *  φ(2,i)     +     2*ci[i-1]**3  *  (ci[d-1]*ci[k-1] + ci[d-1]*ci[l-1] + ci[k-1]*ci[l-1])  *  φ(3,i)     -     6*ci[i-1]**4  *  (ci[d-1] + ci[k-1] + ci[l-1])  *  φ(4,i)     +     24*ci[i-1]**5  *  φ(5,i)
+                    a[i-1][j-1] = numerator / prod_diff(ci[j-1], ci[k-1], ci[l-1], ci[d-1])
+
+            """ijkl = list(permutations([12,13,14,15], 4)) 
+            for i,j,k,l in ijkl:
+                #numerator = -ci[j-1]*ci[k-1]*ci[l-1]*φ(2)   +   2*(ci[j-1]*ci[k-1]   +   ci[j-1]*ci[l-1]   +   ci[k-1]*ci[l-1])*φ(3)   -   6*(ci[j-1] + ci[k-1]   +   ci[l-1])*φ(4)   +   24*φ(5)
+                #b[0][i-1] = numerator / prod_diff(ci[i-1], ci[j-1], ci[k-1], ci[l-1])
+                for jjj in range (2, 6): # 2,3,4,5
+                    b[0][i-1] += mu_numerator(jjj, ci[j-1], ci[i-1], ci[k-1], ci[l-1]) * φ(jjj) 
+                b[0][i-1] /= prod_diff(ci[i-1], ci[j-1], ci[k-1], ci[l-1])"""
+                    
+            ijkl = list(permutations([12,13,14,15], 4)) 
+            for i,j,k,l in ijkl:
+                numerator = 0
+                for jjj in range(2, 6):  # 2, 3, 4, 5
+                    numerator += mu_numerator(jjj, ci[j-1], ci[i-1], ci[k-1], ci[l-1]) * φ(jjj)
+                #print(i,j,k,l)
+
+                b[0][i-1] = numerator / prod_diff(ci[i-1], ci[j-1], ci[k-1], ci[l-1])
+            
+            
+            ijkl = list(permutations([12, 13, 14, 15], 4))
+            selected_permutations = {} 
+            sign = 1  
+
+            for i in range(12, 16):
+                results = []
+                for j, k, l, d in ijkl:
+                    if i != j and i != k and i != l and i != d:
+                        numerator = 0
+                        for jjj in range(2, 6):  # 2, 3, 4, 5
+                            numerator += mu_numerator(jjj, ci[j-1], ci[i-1], ci[k-1], ci[l-1]) * φ(jjj)
+                        theta_value = numerator / prod_diff(ci[i-1], ci[j-1], ci[k-1], ci[l-1])
+                        results.append((theta_value, (i, j, k, l, d)))
+
+                results.sort(key=lambda x: abs(x[0]))
+
+                for theta_value, permutation in results:
+                    if sign == 1 and theta_value > 0:
+                        selected_permutations[i] = (theta_value, permutation)
+                        sign *= -1  
+                        break
+                    elif sign == -1 and theta_value < 0:  
+                        selected_permutations[i] = (theta_value, permutation)
+                        sign *= -1 
+                        break
+
+            for i in range(12, 16):
+                if i in selected_permutations:
+                    theta_value, (i, j, k, l, d) = selected_permutations[i]
+                    b[0][i-1] = theta_value  
+                    
+            for i in selected_permutations:
+                theta_value, permutation = selected_permutations[i]
+                print(f"i={i}")
+                print(f"  Selected Theta: {theta_value:.6f}, Permutation: {permutation}")
+            
+            
+            gen_first_col_exp(a, b, ci, φ)
+
+            a = [[float(val) for val in row] for row in a]
+            b = [[float(val) for val in row] for row in b]
+            ci = [c1, c2, c3, c4, c5, c6, c7, c8, c9, c10, c11, c12, c13, c14, c15]
+            
+
+        case "res_16s": # 6th order without weakened order conditions
+                
+            c1 = 0
+            c2 = c3 = c5 = c8 = c12 = 1/2
+            c4 = c11 = c15 = 1/3
+            c6 = c9 = c13 = 1/5
+            c7 = c10 = c14 = 1/4
+            c16 = 1
+            ci = [c1, c2, c3, c4, c5, c6, c7, c8, c9, c10, c11, c12, c13, c14, c15, c16]
+            ci = [mpf(c_val) for c_val in ci]
+            φ = Phi(mpf(h.item()), ci, analytic_solution=use_analytic_solution)
+            
+            a3_2 = (1/2) * φ(2,3)
+
+            a = [[mpf(0) for _ in range(16)] for _ in range(16)]
+            b = [[mpf(0) for _ in range(16)]]
+
+            for i in range(3, 5): # i=3,4     j=2
+                j=2
+                a[i-1][j-1] = (ci[i-1]**2 / ci[j-1]) * φ(j,i)
+            
+            for i in range(5, 8): # i=5,6,7   j,k ∈ {3, 4}, j != k
+                jk = list(permutations([3, 4], 2)) 
+                for j,k in jk:
+                    a[i-1][j-1] = (-ci[i-1]**2 * ci[k-1] * φ(2,i)    +   2*ci[i-1]**3 * φ(3,i))   /   prod_diff(ci[j-1], ci[k-1])
+                    
+            for i in range(8, 12): # i=8,9,10,11  j,k,l ∈ {5, 6, 7}, j != k != l      [    (5, 6, 7), (5, 7, 6),    (6, 5, 7), (6, 7, 5),    (7, 5, 6), (7, 6, 5)]    6 total coeff
+                jkl = list(permutations([5, 6, 7], 3)) 
+                for j,k,l in jkl:
+                    a[i-1][j-1] = (ci[i-1]**2 * ci[k-1] * ci[l-1] * φ(2,i)   -   2*ci[i-1]**3 * (ci[k-1] + ci[l-1]) * φ(3,i)   +   6*ci[i-1]**4 * φ(4,i))    /    (ci[j-1] * (ci[j-1] - ci[k-1]) * (ci[j-1] - ci[l-1]))
+
+            for i in range(12,17): # i=12,13,14,15,16
+                jkld = list(permutations([8,9,10,11], 4)) 
+                for j,k,l,d in jkld:
+                    numerator = -ci[i-1]**2  *  ci[d-1]*ci[k-1]*ci[l-1]  *  φ(2,i)     +     2*ci[i-1]**3  *  (ci[d-1]*ci[k-1] + ci[d-1]*ci[l-1] + ci[k-1]*ci[l-1])  *  φ(3,i)     -     6*ci[i-1]**4  *  (ci[d-1] + ci[k-1] + ci[l-1])  *  φ(4,i)     +     24*ci[i-1]**5  *  φ(5,i)
+                    a[i-1][j-1] = numerator / prod_diff(ci[j-1], ci[k-1], ci[l-1], ci[d-1])
+            
+            """ijdkl = list(permutations([12,13,14,15,16], 5)) 
+            for i,j,d,k,l in ijdkl:
+                #numerator = -ci[j-1]*ci[k-1]*ci[l-1]*φ(2)   +   2*(ci[j-1]*ci[k-1]   +   ci[j-1]*ci[l-1]   +   ci[k-1]*ci[l-1])*φ(3)   -   6*(ci[j-1] + ci[k-1]   +   ci[l-1])*φ(4)   +   24*φ(5)
+                b[0][i-1] = theta(2, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1]) * φ(2)   +  theta(3, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1])*φ(3)   +   theta(4, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1])*φ(4)   +   theta(5, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1])*φ(5)    +    theta(6, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1]) * φ(6)
+                #b[0][i-1] = numerator / prod_diff(ci[i-1], ci[j-1], ci[k-1], ci[l-1])"""
+                    
+                
+            ijdkl = list(permutations([12,13,14,15,16], 5)) 
+            for i,j,d,k,l in ijdkl:
+                #numerator = -ci[j-1]*ci[k-1]*ci[l-1]*φ(2)   +   2*(ci[j-1]*ci[k-1]   +   ci[j-1]*ci[l-1]   +   ci[k-1]*ci[l-1])*φ(3)   -   6*(ci[j-1] + ci[k-1]   +   ci[l-1])*φ(4)   +   24*φ(5)
+                #numerator = theta_numerator(2, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1]) * φ(2)   +  theta_numerator(3, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1])*φ(3)   +   theta_numerator(4, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1])*φ(4)   +   theta_numerator(5, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1])*φ(5)    +    theta_numerator(6, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1]) * φ(6)
+                #b[0][i-1] = numerator / (ci[i-1] *, ci[d-1], ci[j-1], ci[k-1], ci[l-1])
+                #b[0][i-1] = numerator / denominator(ci[i-1], ci[d-1], ci[j-1], ci[k-1], ci[l-1])
+                b[0][i-1] = theta(2, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1]) * φ(2)   +  theta(3, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1])*φ(3)   +   theta(4, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1])*φ(4)   +   theta(5, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1])*φ(5)    +    theta(6, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1]) * φ(6)
+
+            
+            ijdkl = list(permutations([12,13,14,15,16], 5)) 
+            for i,j,d,k,l in ijdkl:
+                numerator = 0
+                for jjj in range(2, 7):  # 2, 3, 4, 5, 6
+                    numerator += theta_numerator(jjj, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1]) * φ(jjj)
+                #print(i,j,d,k,l)
+                b[0][i-1] = numerator / (ci[i-1] *   (ci[i-1] - ci[k-1])   *   (ci[i-1] - ci[j-1]   *   (ci[i-1] - ci[d-1])   *   (ci[i-1] - ci[l-1])))
+
+            gen_first_col_exp(a, b, ci, φ)
+
+            a = [[float(val) for val in row] for row in a]
+            b = [[float(val) for val in row] for row in b]
+            ci = [c1, c2, c3, c4, c5, c6, c7, c8, c9, c10, c11, c12, c13, c14, c15, c16]
+            
+        case "irk_exp_diag_2s":
+            c1 = 1/3
+            c2 = 2/3
+            c1 = float(get_extra_options_kv("c1", str(c1), extra_options))
+            c2 = float(get_extra_options_kv("c2", str(c2), extra_options))
+            
+            lam = (1 - torch.exp(-c1 * h)) / h
+            a2_1 = ( torch.exp(c2*h) - torch.exp(c1*h))    /    (h * torch.exp(2*c1*h))
+            b1 =  (1 + c2*h + torch.exp(h) * (-1 + h - c2*h)) / ((c1-c2) * h**2 * torch.exp(c1*h))
+            b2 = -(1 + c1*h - torch.exp(h) * ( 1 - h + c1*h)) / ((c1-c2) * h**2 * torch.exp(c2*h))
+
+            a = [
+                    [lam, 0],
+                    [a2_1, lam],
+            ]
+            b = [
+                    [b1, b2],
+            ]
+            ci = [c1, c2]
+
+    ci = ci[:]
+    #if rk_type.startswith("lob") == False:
+    ci.append(1)
+    
+    if EO("exp2lin_override_coeff") and is_exponential(rk_type):
+        a = scale_all(a, -sigma.item())
+        b = scale_all(b, -sigma.item())
+    
+    return a, b, u, v, ci, multistep_stages, hybrid_stages, FSAL
+
+
+def scale_all(data, scalar):
+    if isinstance(data, torch.Tensor):
+        return data * scalar
+    elif isinstance(data, list):
+        return [scale_all(x, scalar) for x in data]
+    elif isinstance(data, (float, int)):
+        return data * scalar
+    else:
+        return data  # passthrough unscaled if unknown type... or None, etc
+
+
+def gen_first_col_exp(a, b, c, φ):
+    for i in range(len(c)): 
+        a[i][0] = c[i] * φ(1,i+1) - sum(a[i])
+    for i in range(len(b)): 
+        b[i][0] =        φ(1)     - sum(b[i])
+    return a, b
+
+def gen_first_col_exp_uv(a, b, c, u, v, φ):
+    for i in range(len(c)): 
+        a[i][0] = c[i] * φ(1,i+1) - sum(a[i]) - sum(u[i])
+    for i in range(len(b)): 
+        b[i][0] =        φ(1)     - sum(b[i]) - sum(v[i])
+    return a, b
+
+def rho(j, ci, ck, cl):
+    if j == 2:
+        numerator = ck*cl
+    if j == 3:
+        numerator = (-2 * (ck + cl))
+    if j == 4:
+        numerator = 6
+    return numerator / denominator(ci, ck, cl)
+    
+    
+def mu(j, cd, ci, ck, cl):
+    if j == 2:
+        numerator = -cd * ck * cl
+    if j == 3:
+        numerator = 2 * (cd * ck + cd * cl + ck * cl)
+    if j == 4:
+        numerator = -6 * (cd + ck + cl)
+    if j == 5:
+        numerator = 24
+    return numerator / denominator(ci, cd, ck, cl)
+
+def mu_numerator(j, cd, ci, ck, cl):
+    if j == 2:
+        numerator = -cd * ck * cl
+    if j == 3:
+        numerator = 2 * (cd * ck + cd * cl + ck * cl)
+    if j == 4:
+        numerator = -6 * (cd + ck + cl)
+    if j == 5:
+        numerator = 24
+    return numerator #/ denominator(ci, cd, ck, cl)
+
+
+
+def theta_numerator(j, cd, ci, ck, cj, cl):
+    if j == 2:
+        numerator = -cj * cd * ck * cl
+    if j == 3:
+        numerator = 2 * (cj * ck * cd + cj*ck*cl + ck*cd*cl + cd*cl*cj)
+    if j == 4:
+        numerator = -6*(cj*ck + cj*cd + cj*cl + ck*cd + ck*cl + cd*cl)
+    if j == 5:
+        numerator = 24 * (cj + ck + cl + cd)
+    if j == 6:
+        numerator = -120
+    return numerator # / denominator(ci, cj, ck, cl, cd)
+
+
+def theta(j, cd, ci, ck, cj, cl):
+    if j == 2:
+        numerator = -cj * cd * ck * cl
+    if j == 3:
+        numerator = 2 * (cj * ck * cd + cj*ck*cl + ck*cd*cl + cd*cl*cj)
+    if j == 4:
+        numerator = -6*(cj*ck + cj*cd + cj*cl + ck*cd + ck*cl + cd*cl)
+    if j == 5:
+        numerator = 24 * (cj + ck + cl + cd)
+    if j == 6:
+        numerator = -120
+    return numerator / ( ci * (ci - cj) * (ci - ck) * (ci - cl) * (ci - cd))
+    return numerator / denominator(ci, cj, ck, cl, cd)
+
+
+def prod_diff(cj, ck, cl=None, cd=None):
+    if cl is None and cd is None:
+        return cj * (cj - ck)
+    if cd is None:
+        return cj * (cj - ck) * (cj - cl)
+    else:
+        return cj * (cj - ck) * (cj - cl) * (cj - cd)
+
+def denominator(ci, *args):
+    result = ci 
+    for arg in args:
+        result *= (ci - arg)
+    return result
+
+
+
+def check_condition_4_2(nodes):
+
+    c12, c13, c14, c15 = nodes
+
+    term_1 = (1 / 5) * (c12 + c13 + c14 + c15)
+    term_2 = (1 / 4) * (c12 * c13 + c12 * c14 + c12 * c15 + c13 * c14 + c13 * c15 + c14 * c15)
+    term_3 = (1 / 3) * (c12 * c13 * c14 + c12 * c13 * c15 + c12 * c14 * c15 + c13 * c14 * c15)
+    term_4 = (1 / 2) * (c12 * c13 * c14 * c15)
+
+    result = term_1 - term_2 + term_3 - term_4
+
+    return abs(result - (1 / 6)) < 1e-6  
+
diff --git a/custom_nodes/RES4LYF/beta/rk_guide_func_beta.py b/custom_nodes/RES4LYF/beta/rk_guide_func_beta.py
new file mode 100644
index 0000000000000000000000000000000000000000..573af28811ad503202597a5e84662dc12d2b0a9e
--- /dev/null
+++ b/custom_nodes/RES4LYF/beta/rk_guide_func_beta.py
@@ -0,0 +1,2352 @@
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+
+import itertools
+import copy
+
+from typing          import Optional, Callable, Tuple, Dict, Any, Union, TYPE_CHECKING, TypeVar
+
+if TYPE_CHECKING:
+    from .noise_classes import NoiseGenerator
+    NoiseGeneratorSubclass = TypeVar("NoiseGeneratorSubclass", bound="NoiseGenerator") 
+
+from einops          import rearrange
+
+from ..sigmas        import get_sigmas
+from ..helper        import ExtraOptions, FrameWeightsManager, initialize_or_scale, is_video_model
+from ..latents       import normalize_zscore, get_collinear, get_orthogonal, get_cosine_similarity, get_pearson_similarity, \
+                            get_slerp_weight_for_cossim, normalize_latent, hard_light_blend, slerp_tensor, get_orthogonal_noise_from_channelwise, get_edge_mask
+
+from .rk_method_beta import RK_Method_Beta
+from .constants      import MAX_STEPS
+
+from ..models import PRED
+
+
+#from ..latents import hard_light_blend, normalize_latent
+
+
+
+class LatentGuide:
+    def __init__(self,
+                model,
+                sigmas               : Tensor,
+                UNSAMPLE             : bool,
+                VE_MODEL             : bool,
+                LGW_MASK_RESCALE_MIN : bool,
+                extra_options        : str,
+                device               : str = 'cpu',
+                dtype                : torch.dtype = torch.float64,
+                frame_weights_mgr    : FrameWeightsManager = None,
+                ):
+        
+        self.dtype                    = dtype
+        self.device                   = device
+        self.model                    = model
+
+        if hasattr(model, "model"):
+            model_sampling = model.model.model_sampling
+        elif hasattr(model, "inner_model"):
+            model_sampling = model.inner_model.inner_model.model_sampling
+        
+        self.sigma_min                 = model_sampling.sigma_min.to(dtype=dtype, device=device)
+        self.sigma_max                 = model_sampling.sigma_max.to(dtype=dtype, device=device)
+        self.sigmas                    = sigmas                  .to(dtype=dtype, device=device)
+        self.UNSAMPLE                  = UNSAMPLE
+        self.VE_MODEL                  = VE_MODEL
+        self.VIDEO                     = is_video_model(model)
+        self.SAMPLE                    = (sigmas[0] > sigmas[1])    # type torch.bool
+        self.y0                        = None
+        self.y0_inv                    = None
+        self.y0_mean                   = None
+        self.y0_adain                  = None
+        self.y0_attninj                = None
+        self.y0_style_pos              = None
+        self.y0_style_neg              = None
+
+        self.guide_mode                = ""
+        self.max_steps                 = MAX_STEPS
+        self.mask                      = None
+        self.mask_inv                  = None
+        self.mask_sync                 = None
+        self.mask_drift_x              = None
+        self.mask_drift_y              = None
+        self.mask_lure_x               = None
+        self.mask_lure_y               = None
+        self.mask_mean                 = None
+        self.mask_adain                = None
+        self.mask_attninj              = None
+        self.mask_style_pos            = None
+        self.mask_style_neg            = None
+        self.x_lying_                  = None
+        self.s_lying_                  = None
+        
+        self.LGW_MASK_RESCALE_MIN      = LGW_MASK_RESCALE_MIN
+        self.HAS_LATENT_GUIDE          = False
+        self.HAS_LATENT_GUIDE_INV      = False
+        self.HAS_LATENT_GUIDE_MEAN     = False
+        self.HAS_LATENT_GUIDE_ADAIN    = False
+        self.HAS_LATENT_GUIDE_ATTNINJ  = False
+        self.HAS_LATENT_GUIDE_STYLE_POS= False
+        self.HAS_LATENT_GUIDE_STYLE_NEG= False
+        
+        self.lgw                       = torch.full_like(sigmas, 0., dtype=dtype) 
+        self.lgw_inv                   = torch.full_like(sigmas, 0., dtype=dtype)
+        self.lgw_mean                  = torch.full_like(sigmas, 0., dtype=dtype)
+        self.lgw_adain                 = torch.full_like(sigmas, 0., dtype=dtype)
+        self.lgw_attninj               = torch.full_like(sigmas, 0., dtype=dtype)
+        self.lgw_style_pos             = torch.full_like(sigmas, 0., dtype=dtype)
+        self.lgw_style_neg             = torch.full_like(sigmas, 0., dtype=dtype)
+        
+        self.cossim_tgt                = torch.full_like(sigmas, 0., dtype=dtype) 
+        self.cossim_tgt_inv            = torch.full_like(sigmas, 0., dtype=dtype) 
+        
+        self.guide_cossim_cutoff_          = 1.0
+        self.guide_bkg_cossim_cutoff_      = 1.0
+        self.guide_mean_cossim_cutoff_     = 1.0
+        self.guide_adain_cossim_cutoff_    = 1.0
+        self.guide_attninj_cossim_cutoff_  = 1.0
+        self.guide_style_pos_cossim_cutoff_= 1.0
+        self.guide_style_neg_cossim_cutoff_= 1.0
+
+        self.frame_weights_mgr        = frame_weights_mgr
+        self.frame_weights            = None
+        self.frame_weights_inv        = None
+        
+        #self.freqsep_lowpass_method   = "none"
+        #self.freqsep_sigma            = 0.
+        #self.freqsep_kernel_size      = 0 
+        
+        self.extra_options            = extra_options
+        self.EO                       = ExtraOptions(extra_options)
+
+
+    def init_guides(self,
+            x             : Tensor,
+            RK_IMPLICIT   : bool,
+            guides        : Optional[Tensor]                   = None,
+            noise_sampler : Optional["NoiseGeneratorSubclass"] = None,
+            batch_num     : int                                = 0,
+            sigma_init                                         = None,
+            guide_inversion_y0                                 = None,
+            guide_inversion_y0_inv                             = None,
+        ) -> Tensor:
+        
+        latent_guide_weight              = 0.0
+        latent_guide_weight_inv          = 0.0
+        latent_guide_weight_sync         = 0.0
+        latent_guide_weight_sync_inv     = 0.0
+        latent_guide_weight_drift_x      = 0.0
+        latent_guide_weight_drift_x_inv  = 0.0
+        latent_guide_weight_drift_y      = 0.0
+        latent_guide_weight_drift_y_inv  = 0.0
+        latent_guide_weight_lure_x       = 0.0
+        latent_guide_weight_lure_x_inv   = 0.0
+        latent_guide_weight_lure_y       = 0.0
+        latent_guide_weight_lure_y_inv   = 0.0
+        
+        latent_guide_weight_mean         = 0.0
+        latent_guide_weight_adain        = 0.0
+        latent_guide_weight_attninj      = 0.0
+        latent_guide_weight_style_pos    = 0.0
+        latent_guide_weight_style_neg    = 0.0
+
+        latent_guide_weights             = torch.zeros_like(self.sigmas, dtype=self.dtype, device=self.device)
+        latent_guide_weights_inv         = torch.zeros_like(self.sigmas, dtype=self.dtype, device=self.device)
+        latent_guide_weights_sync        = torch.zeros_like(self.sigmas, dtype=self.dtype, device=self.device)
+        latent_guide_weights_sync_inv    = torch.zeros_like(self.sigmas, dtype=self.dtype, device=self.device)
+        latent_guide_weights_drift_x     = torch.zeros_like(self.sigmas, dtype=self.dtype, device=self.device)
+        latent_guide_weights_drift_x_inv = torch.zeros_like(self.sigmas, dtype=self.dtype, device=self.device)
+        latent_guide_weights_drift_y     = torch.zeros_like(self.sigmas, dtype=self.dtype, device=self.device)
+        latent_guide_weights_drift_y_inv = torch.zeros_like(self.sigmas, dtype=self.dtype, device=self.device)
+        latent_guide_weights_lure_x      = torch.zeros_like(self.sigmas, dtype=self.dtype, device=self.device)
+        latent_guide_weights_lure_x_inv  = torch.zeros_like(self.sigmas, dtype=self.dtype, device=self.device)
+        latent_guide_weights_lure_y      = torch.zeros_like(self.sigmas, dtype=self.dtype, device=self.device)
+        latent_guide_weights_lure_y_inv  = torch.zeros_like(self.sigmas, dtype=self.dtype, device=self.device)
+        latent_guide_weights_mean        = torch.zeros_like(self.sigmas, dtype=self.dtype, device=self.device)
+        latent_guide_weights_adain       = torch.zeros_like(self.sigmas, dtype=self.dtype, device=self.device)
+        latent_guide_weights_attninj     = torch.zeros_like(self.sigmas, dtype=self.dtype, device=self.device)
+        latent_guide_weights_style_pos   = torch.zeros_like(self.sigmas, dtype=self.dtype, device=self.device)
+        latent_guide_weights_style_neg   = torch.zeros_like(self.sigmas, dtype=self.dtype, device=self.device)
+
+        latent_guide           = None
+        latent_guide_inv       = None
+        latent_guide_mean      = None
+        latent_guide_adain     = None
+        latent_guide_attninj   = None
+        latent_guide_style_pos = None
+        latent_guide_style_neg = None
+        
+        self.drift_x_data  = 0.0
+        self.drift_x_sync  = 0.0
+        self.drift_y_data  = 0.0
+        self.drift_y_sync  = 0.0
+        self.drift_y_guide = 0.0
+        
+        if guides is not None:
+            self.guide_mode                 = guides.get("guide_mode", "none")
+            
+            if self.guide_mode.startswith("inversion"):
+                self.guide_mode = self.guide_mode.replace("inversion", "epsilon", 1)
+            else:
+                self.SAMPLE   = True
+                self.UNSAMPLE = False
+            
+            latent_guide_weight              = guides.get("weight_masked",           0.)
+            latent_guide_weight_inv          = guides.get("weight_unmasked",         0.)
+            latent_guide_weight_sync         = guides.get("weight_masked_sync",      0.)
+            latent_guide_weight_sync_inv     = guides.get("weight_unmasked_sync",    0.)
+            latent_guide_weight_drift_x      = guides.get("weight_masked_drift_x",   0.)
+            latent_guide_weight_drift_x_inv  = guides.get("weight_unmasked_drift_x", 0.)
+            latent_guide_weight_drift_y      = guides.get("weight_masked_drift_y",   0.)
+            latent_guide_weight_drift_y_inv  = guides.get("weight_unmasked_drift_y", 0.)
+            latent_guide_weight_lure_x       = guides.get("weight_masked_lure_x",    0.)
+            latent_guide_weight_lure_x_inv   = guides.get("weight_unmasked_lure_x",  0.)
+            latent_guide_weight_lure_y       = guides.get("weight_masked_lure_y",    0.)
+            latent_guide_weight_lure_y_inv   = guides.get("weight_unmasked_lure_y",  0.)
+            latent_guide_weight_mean         = guides.get("weight_mean",             0.)
+            latent_guide_weight_adain        = guides.get("weight_adain",            0.)
+            latent_guide_weight_attninj      = guides.get("weight_attninj",          0.)
+            latent_guide_weight_style_pos    = guides.get("weight_style_pos",        0.)
+            latent_guide_weight_style_neg    = guides.get("weight_style_neg",        0.)
+            #latent_guide_synweight_style_pos = guides.get("synweight_style_pos", 0.)
+            #latent_guide_synweight_style_neg = guides.get("synweight_style_neg", 0.)
+            
+            self.drift_x_data                = guides.get("drift_x_data", 0.)
+            self.drift_x_sync                = guides.get("drift_x_sync", 0.)
+            self.drift_y_data                = guides.get("drift_y_data", 0.)
+            self.drift_y_sync                = guides.get("drift_y_sync", 0.)
+            self.drift_y_guide               = guides.get("drift_y_guide", 0.)
+
+            latent_guide_weights             = guides.get("weights_masked")
+            latent_guide_weights_inv         = guides.get("weights_unmasked")
+            latent_guide_weights_sync        = guides.get("weights_masked_sync")
+            latent_guide_weights_sync_inv    = guides.get("weights_unmasked_sync")
+            latent_guide_weights_drift_x     = guides.get("weights_masked_drift_x")
+            latent_guide_weights_drift_x_inv = guides.get("weights_unmasked_drift_x")
+            latent_guide_weights_drift_y     = guides.get("weights_masked_drift_y")
+            latent_guide_weights_drift_y_inv = guides.get("weights_unmasked_drift_y")
+            latent_guide_weights_lure_x      = guides.get("weights_masked_lure_x")
+            latent_guide_weights_lure_x_inv  = guides.get("weights_unmasked_lure_x")
+            latent_guide_weights_lure_y      = guides.get("weights_masked_lure_y")
+            latent_guide_weights_lure_y_inv  = guides.get("weights_unmasked_lure_y")
+            latent_guide_weights_mean        = guides.get("weights_mean")
+            latent_guide_weights_adain       = guides.get("weights_adain")
+            latent_guide_weights_attninj     = guides.get("weights_attninj")
+            latent_guide_weights_style_pos   = guides.get("weights_style_pos")
+            latent_guide_weights_style_neg   = guides.get("weights_style_neg")
+            #latent_guide_synweights_style_p os = guides.get("synweights_style_pos")
+            #latent_guide_synweights_style_neg = guides.get("synweights_style_neg")
+
+            latent_guide                     = guides.get("guide_masked")
+            latent_guide_inv                 = guides.get("guide_unmasked")
+            latent_guide_mean                = guides.get("guide_mean")
+            latent_guide_adain               = guides.get("guide_adain")
+            latent_guide_attninj             = guides.get("guide_attninj")
+            latent_guide_style_pos           = guides.get("guide_style_pos")
+            latent_guide_style_neg           = guides.get("guide_style_neg")
+
+            self.mask                        = guides.get("mask")
+            self.mask_inv                    = guides.get("unmask")
+            self.mask_sync                   = guides.get("mask_sync")
+            self.mask_drift_x                = guides.get("mask_drift_x")
+            self.mask_drift_y                = guides.get("mask_drift_y")
+            self.mask_lure_x                 = guides.get("mask_lure_x")
+            self.mask_lure_y                 = guides.get("mask_lure_y")
+            self.mask_mean                   = guides.get("mask_mean")
+            self.mask_adain                  = guides.get("mask_adain")
+            self.mask_attninj                = guides.get("mask_attninj")
+            self.mask_style_pos              = guides.get("mask_style_pos")
+            self.mask_style_neg              = guides.get("mask_style_neg")
+
+            scheduler_                       = guides.get("weight_scheduler_masked")
+            scheduler_inv_                   = guides.get("weight_scheduler_unmasked")
+            scheduler_sync_                  = guides.get("weight_scheduler_masked_sync")
+            scheduler_sync_inv_              = guides.get("weight_scheduler_unmasked_sync")
+            scheduler_drift_x_               = guides.get("weight_scheduler_masked_drift_x")
+            scheduler_drift_x_inv_           = guides.get("weight_scheduler_unmasked_drift_x")
+            scheduler_drift_y_               = guides.get("weight_scheduler_masked_drift_y")
+            scheduler_drift_y_inv_           = guides.get("weight_scheduler_unmasked_drift_y")
+            scheduler_lure_x_                = guides.get("weight_scheduler_masked_lure_x")
+            scheduler_lure_x_inv_            = guides.get("weight_scheduler_unmasked_lure_x")
+            scheduler_lure_y_                = guides.get("weight_scheduler_masked_lure_y")
+            scheduler_lure_y_inv_            = guides.get("weight_scheduler_unmasked_lure_y")
+            scheduler_mean_                  = guides.get("weight_scheduler_mean")
+            scheduler_adain_                 = guides.get("weight_scheduler_adain")
+            scheduler_attninj_               = guides.get("weight_scheduler_attninj")
+            scheduler_style_pos_             = guides.get("weight_scheduler_style_pos")
+            scheduler_style_neg_             = guides.get("weight_scheduler_style_neg")
+
+            start_steps_                     = guides.get("start_step_masked",   0)
+            start_steps_inv_                 = guides.get("start_step_unmasked", 0)
+            start_steps_sync_                = guides.get("start_step_masked_sync",   0)
+            start_steps_sync_inv_            = guides.get("start_step_unmasked_sync", 0)
+            start_steps_drift_x_             = guides.get("start_step_masked_drift_x",   0)
+            start_steps_drift_x_inv_         = guides.get("start_step_unmasked_drift_x", 0)
+            start_steps_drift_y_             = guides.get("start_step_masked_drift_y",   0)
+            start_steps_drift_y_inv_         = guides.get("start_step_unmasked_drift_y", 0)
+            start_steps_lure_x_              = guides.get("start_step_masked_lure_x",   0)
+            start_steps_lure_x_inv_          = guides.get("start_step_unmasked_lure_x", 0)
+            start_steps_lure_y_              = guides.get("start_step_masked_lure_y",   0)
+            start_steps_lure_y_inv_          = guides.get("start_step_unmasked_lure_y", 0)
+            start_steps_mean_                = guides.get("start_step_mean",     0)
+            start_steps_adain_               = guides.get("start_step_adain",    0)
+            start_steps_attninj_             = guides.get("start_step_attninj",  0)
+            start_steps_style_pos_           = guides.get("start_step_style_pos", 0)
+            start_steps_style_neg_           = guides.get("start_step_style_neg", 0)
+
+            steps_                           = guides.get("end_step_masked",     1)
+            steps_inv_                       = guides.get("end_step_unmasked",   1)
+            steps_sync_                      = guides.get("end_step_masked_sync",     1)
+            steps_sync_inv_                  = guides.get("end_step_unmasked_sync",   1)
+            steps_drift_x_                   = guides.get("end_step_masked_drift_x",     1)
+            steps_drift_x_inv_               = guides.get("end_step_unmasked_drift_x",   1)
+            steps_drift_y_                   = guides.get("end_step_masked_drift_y",     1)
+            steps_drift_y_inv_               = guides.get("end_step_unmasked_drift_y",   1)
+            steps_lure_x_                    = guides.get("end_step_masked_lure_x",     1)
+            steps_lure_x_inv_                = guides.get("end_step_unmasked_lure_x",   1)
+            steps_lure_y_                    = guides.get("end_step_masked_lure_y",     1)
+            steps_lure_y_inv_                = guides.get("end_step_unmasked_lure_y",   1)
+            
+            steps_mean_                      = guides.get("end_step_mean",       1)
+            steps_adain_                     = guides.get("end_step_adain",      1)
+            steps_attninj_                   = guides.get("end_step_attninj",    1)
+            steps_style_pos_                 = guides.get("end_step_style_pos",  1)
+            steps_style_neg_                 = guides.get("end_step_style_neg",  1)
+
+            self.guide_cossim_cutoff_           = guides.get("cutoff_masked",       1.)
+            self.guide_bkg_cossim_cutoff_       = guides.get("cutoff_unmasked",     1.)
+            self.guide_mean_cossim_cutoff_      = guides.get("cutoff_mean",         1.)
+            self.guide_adain_cossim_cutoff_     = guides.get("cutoff_adain",        1.)
+            self.guide_attninj_cossim_cutoff_   = guides.get("cutoff_attninj",      1.)
+            self.guide_style_pos_cossim_cutoff_ = guides.get("cutoff_style_pos",  1.)
+            self.guide_style_neg_cossim_cutoff_ = guides.get("cutoff_style_neg",  1.)
+            
+            self.sync_lure_iter                 = guides.get("sync_lure_iter",  0)
+            self.sync_lure_sequence             = guides.get("sync_lure_sequence")
+            
+            #self.SYNC_SEPARATE = False
+            #if scheduler_sync_ is not None:
+            #    self.SYNC_SEPARATE = True
+            self.SYNC_SEPARATE = True
+            if scheduler_sync_ is None and scheduler_ is not None:
+
+                latent_guide_weight_sync      = latent_guide_weight
+                latent_guide_weight_sync_inv  = latent_guide_weight_inv
+                latent_guide_weights_sync     = latent_guide_weights
+                latent_guide_weights_sync_inv = latent_guide_weights_inv
+                
+                scheduler_sync_               = scheduler_
+                scheduler_sync_inv_           = scheduler_inv_
+                
+                start_steps_sync_             = start_steps_
+                start_steps_sync_inv_         = start_steps_inv_
+                
+                steps_sync_                   = steps_
+                steps_sync_inv_               = steps_inv_
+            
+            self.SYNC_drift_X = True
+            if scheduler_drift_x_ is None and scheduler_ is not None:
+                self.SYNC_drift_X = False
+
+                latent_guide_weight_drift_x      = latent_guide_weight
+                latent_guide_weight_drift_x_inv  = latent_guide_weight_inv
+                latent_guide_weights_drift_x     = latent_guide_weights
+                latent_guide_weights_drift_x_inv = latent_guide_weights_inv
+                
+                scheduler_drift_x_               = scheduler_
+                scheduler_drift_x_inv_           = scheduler_inv_
+                
+                start_steps_drift_x_             = start_steps_
+                start_steps_drift_x_inv_         = start_steps_inv_
+                
+                steps_drift_x_                   = steps_
+                steps_drift_x_inv_               = steps_inv_
+                
+            self.SYNC_drift_Y = True
+            if scheduler_drift_y_ is None and scheduler_ is not None:
+                self.SYNC_drift_Y = False
+
+                latent_guide_weight_drift_y      = latent_guide_weight
+                latent_guide_weight_drift_y_inv  = latent_guide_weight_inv
+                latent_guide_weights_drift_y     = latent_guide_weights
+                latent_guide_weights_drift_y_inv = latent_guide_weights_inv
+                
+                scheduler_drift_y_               = scheduler_
+                scheduler_drift_y_inv_           = scheduler_inv_
+                
+                start_steps_drift_y_             = start_steps_
+                start_steps_drift_y_inv_         = start_steps_inv_
+                
+                steps_drift_y_                   = steps_
+                steps_drift_y_inv_               = steps_inv_
+            
+            self.SYNC_LURE_X = True
+            if scheduler_lure_x_ is None and scheduler_ is not None:
+                self.SYNC_LURE_X = False
+
+                latent_guide_weight_lure_x      = latent_guide_weight
+                latent_guide_weight_lure_x_inv  = latent_guide_weight_inv
+                latent_guide_weights_lure_x     = latent_guide_weights
+                latent_guide_weights_lure_x_inv = latent_guide_weights_inv
+                
+                scheduler_lure_x_               = scheduler_
+                scheduler_lure_x_inv_           = scheduler_inv_
+                
+                start_steps_lure_x_             = start_steps_
+                start_steps_lure_x_inv_         = start_steps_inv_
+                
+                steps_lure_x_                   = steps_
+                steps_lure_x_inv_               = steps_inv_
+                
+            self.SYNC_LURE_Y = True
+            if scheduler_lure_y_ is None and scheduler_ is not None:
+                self.SYNC_LURE_Y = False
+
+                latent_guide_weight_lure_y      = latent_guide_weight
+                latent_guide_weight_lure_y_inv  = latent_guide_weight_inv
+                latent_guide_weights_lure_y     = latent_guide_weights
+                latent_guide_weights_lure_y_inv = latent_guide_weights_inv
+                
+                scheduler_lure_y_               = scheduler_
+                scheduler_lure_y_inv_           = scheduler_inv_
+                
+                start_steps_lure_y_             = start_steps_
+                start_steps_lure_y_inv_         = start_steps_inv_
+                
+                steps_lure_y_                   = steps_
+                steps_lure_y_inv_               = steps_inv_
+
+            if self.mask         is not None and self.mask.shape    [0] > 1 and self.VIDEO     is False:
+                self.mask         = self.mask    [batch_num].unsqueeze(0)
+            if self.mask_inv     is not None and self.mask_inv.shape[0] > 1 and self.VIDEO     is False:
+                self.mask_inv     = self.mask_inv[batch_num].unsqueeze(0)
+            if self.mask_sync    is not None and self.mask_sync.shape[0] > 1 and self.VIDEO    is False:
+                self.mask_sync    = self.mask_sync[batch_num].unsqueeze(0)
+            if self.mask_drift_x is not None and self.mask_drift_x.shape[0] > 1 and self.VIDEO is False:
+                self.mask_drift_x = self.mask_drift_x[batch_num].unsqueeze(0)
+            if self.mask_drift_y is not None and self.mask_drift_y.shape[0] > 1 and self.VIDEO is False:
+                self.mask_drift_y = self.mask_drift_y[batch_num].unsqueeze(0)
+            if self.mask_lure_x  is not None and self.mask_lure_x.shape[0] > 1 and self.VIDEO  is False:
+                self.mask_lure_x  = self.mask_lure_x[batch_num].unsqueeze(0)
+            if self.mask_lure_y  is not None and self.mask_lure_y.shape[0] > 1 and self.VIDEO  is False:
+                self.mask_lure_y  = self.mask_lure_y[batch_num].unsqueeze(0)
+                
+            if self.guide_mode.startswith("fully_") and not RK_IMPLICIT:
+                self.guide_mode = self.guide_mode[6:]   # fully_pseudoimplicit is only supported for implicit samplers, default back to pseudoimplicit
+
+            guide_sigma_shift = self.EO("guide_sigma_shift", 0.0)                                                                         # effectively hardcoding shift to 0 !!!!!!
+            
+            if latent_guide_weights is None and scheduler_ is not None:
+                total_steps                     = steps_ - start_steps_
+                latent_guide_weights            = get_sigmas(self.model, scheduler_, total_steps, 1.0, shift=guide_sigma_shift).to(dtype=self.dtype, device=self.device) / self.sigma_max
+                prepend                         = torch.zeros(start_steps_,                               dtype=self.dtype, device=self.device)
+                latent_guide_weights            = torch.cat((prepend, latent_guide_weights.to(self.device)), dim=0)
+                
+            if latent_guide_weights_inv is None and scheduler_inv_ is not None:
+                total_steps                     = steps_inv_ - start_steps_inv_
+                latent_guide_weights_inv        = get_sigmas(self.model, scheduler_inv_, total_steps, 1.0, shift=guide_sigma_shift).to(dtype=self.dtype, device=self.device) / self.sigma_max
+                prepend                         = torch.zeros(start_steps_inv_,                               dtype=self.dtype, device=self.device) 
+                latent_guide_weights_inv        = torch.cat((prepend, latent_guide_weights_inv.to(self.device)), dim=0)
+
+            if latent_guide_weights_sync is None and scheduler_sync_ is not None:
+                total_steps                     = steps_sync_ - start_steps_sync_
+                latent_guide_weights_sync       = get_sigmas(self.model, scheduler_sync_, total_steps, 1.0, shift=guide_sigma_shift).to(dtype=self.dtype, device=self.device) / self.sigma_max
+                prepend                         = torch.zeros(start_steps_sync_,                               dtype=self.dtype, device=self.device)
+                latent_guide_weights_sync       = torch.cat((prepend, latent_guide_weights_sync.to(self.device)), dim=0)
+                
+            if latent_guide_weights_sync_inv is None and scheduler_sync_inv_ is not None:
+                total_steps                     = steps_sync_inv_ - start_steps_sync_inv_
+                latent_guide_weights_sync_inv   = get_sigmas(self.model, scheduler_sync_inv_, total_steps, 1.0, shift=guide_sigma_shift).to(dtype=self.dtype, device=self.device) / self.sigma_max
+                prepend                         = torch.zeros(start_steps_sync_inv_,                               dtype=self.dtype, device=self.device) 
+                latent_guide_weights_sync_inv   = torch.cat((prepend, latent_guide_weights_sync_inv.to(self.device)), dim=0)
+                
+            if latent_guide_weights_drift_x is None and scheduler_drift_x_ is not None:
+                total_steps                     = steps_drift_x_ - start_steps_drift_x_
+                latent_guide_weights_drift_x    = get_sigmas(self.model, scheduler_drift_x_, total_steps, 1.0, shift=guide_sigma_shift).to(dtype=self.dtype, device=self.device) / self.sigma_max
+                prepend                         = torch.zeros(start_steps_drift_x_,                               dtype=self.dtype, device=self.device)
+                latent_guide_weights_drift_x    = torch.cat((prepend, latent_guide_weights_drift_x.to(self.device)), dim=0)
+                
+            if latent_guide_weights_drift_x_inv is None and scheduler_drift_x_inv_ is not None:
+                total_steps                      = steps_drift_x_inv_ - start_steps_drift_x_inv_
+                latent_guide_weights_drift_x_inv = get_sigmas(self.model, scheduler_drift_x_inv_, total_steps, 1.0, shift=guide_sigma_shift).to(dtype=self.dtype, device=self.device) / self.sigma_max
+                prepend                          = torch.zeros(start_steps_drift_x_inv_,                               dtype=self.dtype, device=self.device) 
+                latent_guide_weights_drift_x_inv = torch.cat((prepend, latent_guide_weights_drift_x_inv.to(self.device)), dim=0)
+                
+            if latent_guide_weights_drift_y is None and scheduler_drift_y_ is not None:
+                total_steps                     = steps_drift_y_ - start_steps_drift_y_
+                latent_guide_weights_drift_y    = get_sigmas(self.model, scheduler_drift_y_, total_steps, 1.0, shift=guide_sigma_shift).to(dtype=self.dtype, device=self.device) / self.sigma_max
+                prepend                         = torch.zeros(start_steps_drift_y_,                               dtype=self.dtype, device=self.device)
+                latent_guide_weights_drift_y    = torch.cat((prepend, latent_guide_weights_drift_y.to(self.device)), dim=0)
+                
+            if latent_guide_weights_drift_y_inv is None and scheduler_drift_y_inv_ is not None:
+                total_steps                      = steps_drift_y_inv_ - start_steps_drift_y_inv_
+                latent_guide_weights_drift_y_inv = get_sigmas(self.model, scheduler_drift_y_inv_, total_steps, 1.0, shift=guide_sigma_shift).to(dtype=self.dtype, device=self.device) / self.sigma_max
+                prepend                          = torch.zeros(start_steps_drift_y_inv_,                               dtype=self.dtype, device=self.device) 
+                latent_guide_weights_drift_y_inv = torch.cat((prepend, latent_guide_weights_drift_y_inv.to(self.device)), dim=0)
+                
+            if latent_guide_weights_lure_x is None and scheduler_lure_x_ is not None:
+                total_steps                     = steps_lure_x_ - start_steps_lure_x_
+                latent_guide_weights_lure_x     = get_sigmas(self.model, scheduler_lure_x_, total_steps, 1.0, shift=guide_sigma_shift).to(dtype=self.dtype, device=self.device) / self.sigma_max
+                prepend                         = torch.zeros(start_steps_lure_x_,                               dtype=self.dtype, device=self.device)
+                latent_guide_weights_lure_x     = torch.cat((prepend, latent_guide_weights_lure_x.to(self.device)), dim=0)
+                
+            if latent_guide_weights_lure_x_inv is None and scheduler_lure_x_inv_ is not None:
+                total_steps                     = steps_lure_x_inv_ - start_steps_lure_x_inv_
+                latent_guide_weights_lure_x_inv = get_sigmas(self.model, scheduler_lure_x_inv_, total_steps, 1.0, shift=guide_sigma_shift).to(dtype=self.dtype, device=self.device) / self.sigma_max
+                prepend                         = torch.zeros(start_steps_lure_x_inv_,                               dtype=self.dtype, device=self.device) 
+                latent_guide_weights_lure_x_inv = torch.cat((prepend, latent_guide_weights_lure_x_inv.to(self.device)), dim=0)
+                
+            if latent_guide_weights_lure_y is None and scheduler_lure_y_ is not None:
+                total_steps                     = steps_lure_y_ - start_steps_lure_y_
+                latent_guide_weights_lure_y     = get_sigmas(self.model, scheduler_lure_y_, total_steps, 1.0, shift=guide_sigma_shift).to(dtype=self.dtype, device=self.device) / self.sigma_max
+                prepend                         = torch.zeros(start_steps_lure_y_,                               dtype=self.dtype, device=self.device)
+                latent_guide_weights_lure_y     = torch.cat((prepend, latent_guide_weights_lure_y.to(self.device)), dim=0)
+                
+            if latent_guide_weights_lure_y_inv is None and scheduler_lure_y_inv_ is not None:
+                total_steps                     = steps_lure_y_inv_ - start_steps_lure_y_inv_
+                latent_guide_weights_lure_y_inv = get_sigmas(self.model, scheduler_lure_y_inv_, total_steps, 1.0, shift=guide_sigma_shift).to(dtype=self.dtype, device=self.device) / self.sigma_max
+                prepend                         = torch.zeros(start_steps_lure_y_inv_,                               dtype=self.dtype, device=self.device) 
+                latent_guide_weights_lure_y_inv = torch.cat((prepend, latent_guide_weights_lure_y_inv.to(self.device)), dim=0)
+                
+
+            if latent_guide_weights_mean is None and scheduler_mean_ is not None:
+                total_steps                     = steps_mean_ - start_steps_mean_
+                latent_guide_weights_mean       = get_sigmas(self.model, scheduler_mean_, total_steps, 1.0, shift=guide_sigma_shift).to(dtype=self.dtype, device=self.device) / self.sigma_max
+                prepend                         = torch.zeros(start_steps_mean_,                                                        dtype=self.dtype, device=self.device) 
+                latent_guide_weights_mean       = torch.cat((prepend, latent_guide_weights_mean.to(self.device)), dim=0)
+            
+            if latent_guide_weights_adain is None and scheduler_adain_ is not None:
+                total_steps                     = steps_adain_ - start_steps_adain_
+                latent_guide_weights_adain      = get_sigmas(self.model, scheduler_adain_, total_steps, 1.0, shift=guide_sigma_shift).to(dtype=self.dtype, device=self.device) / self.sigma_max
+                prepend                         = torch.zeros(start_steps_adain_,                                                         dtype=self.dtype, device=self.device) 
+                latent_guide_weights_adain      = torch.cat((prepend, latent_guide_weights_adain.to(self.device)), dim=0)
+            
+            if latent_guide_weights_attninj is None and scheduler_attninj_ is not None:
+                total_steps                     = steps_attninj_ - start_steps_attninj_
+                latent_guide_weights_attninj    = get_sigmas(self.model, scheduler_attninj_, total_steps, 1.0, shift=guide_sigma_shift).to(dtype=self.dtype, device=self.device) / self.sigma_max
+                prepend                         = torch.zeros(start_steps_attninj_,                                                         dtype=self.dtype, device=self.device) 
+                latent_guide_weights_attninj    = torch.cat((prepend, latent_guide_weights_attninj.to(self.device)), dim=0)
+            
+            if latent_guide_weights_style_pos is None and scheduler_style_pos_ is not None:
+                total_steps                     = steps_style_pos_ - start_steps_style_pos_
+                latent_guide_weights_style_pos  = get_sigmas(self.model, scheduler_style_pos_, total_steps, 1.0, shift=guide_sigma_shift).to(dtype=self.dtype, device=self.device) / self.sigma_max
+                prepend                         = torch.zeros(start_steps_style_pos_,                                                         dtype=self.dtype, device=self.device) 
+                latent_guide_weights_style_pos  = torch.cat((prepend, latent_guide_weights_style_pos.to(self.device)), dim=0)
+            
+            if latent_guide_weights_style_neg is None and scheduler_style_neg_ is not None:
+                total_steps                     = steps_style_neg_ - start_steps_style_neg_
+                latent_guide_weights_style_neg  = get_sigmas(self.model, scheduler_style_neg_, total_steps, 1.0, shift=guide_sigma_shift).to(dtype=self.dtype, device=self.device) / self.sigma_max
+                prepend                         = torch.zeros(start_steps_style_neg_,                                                         dtype=self.dtype, device=self.device) 
+                latent_guide_weights_style_neg  = torch.cat((prepend, latent_guide_weights_style_neg.to(self.device)), dim=0)
+            
+            if scheduler_ != "constant":
+                latent_guide_weights            = initialize_or_scale(latent_guide_weights,      latent_guide_weight,      self.max_steps)
+            if scheduler_inv_ != "constant":
+                latent_guide_weights_inv        = initialize_or_scale(latent_guide_weights_inv,  latent_guide_weight_inv,  self.max_steps)
+            if scheduler_sync_ != "constant":
+                latent_guide_weights_sync       = initialize_or_scale(latent_guide_weights_sync,      latent_guide_weight_sync,      self.max_steps)
+            if scheduler_sync_inv_ != "constant": 
+                latent_guide_weights_sync_inv   = initialize_or_scale(latent_guide_weights_sync_inv,  latent_guide_weight_sync_inv,  self.max_steps)
+                
+            latent_guide_weights_sync     = 1 - latent_guide_weights_sync     if latent_guide_weights_sync     is not None else latent_guide_weights
+            latent_guide_weights_sync_inv = 1 - latent_guide_weights_sync_inv if latent_guide_weights_sync_inv is not None else latent_guide_weights_inv
+            latent_guide_weight_sync      = 1 - latent_guide_weight_sync
+            latent_guide_weight_sync_inv  = 1 - latent_guide_weight_sync_inv# these are more intuitive to use if these are reversed... so that sync weight = 1.0 means "maximum guide strength"
+            
+            
+            if scheduler_drift_x_ != "constant": 
+                latent_guide_weights_drift_x     = initialize_or_scale(latent_guide_weights_drift_x,      latent_guide_weight_drift_x,      self.max_steps)
+            if scheduler_drift_x_inv_ != "constant": 
+                latent_guide_weights_drift_x_inv = initialize_or_scale(latent_guide_weights_drift_x_inv,  latent_guide_weight_drift_x_inv,  self.max_steps)
+            if scheduler_drift_y_ != "constant": 
+                latent_guide_weights_drift_y     = initialize_or_scale(latent_guide_weights_drift_y,      latent_guide_weight_drift_y,      self.max_steps)
+            if scheduler_drift_y_inv_ != "constant": 
+                latent_guide_weights_drift_y_inv = initialize_or_scale(latent_guide_weights_drift_y_inv,  latent_guide_weight_drift_y_inv,  self.max_steps)
+            if scheduler_lure_x_ != "constant": 
+                latent_guide_weights_lure_x      = initialize_or_scale(latent_guide_weights_lure_x,      latent_guide_weight_lure_x,      self.max_steps)
+            if scheduler_lure_x_inv_ != "constant": 
+                latent_guide_weights_lure_x_inv  = initialize_or_scale(latent_guide_weights_lure_x_inv,  latent_guide_weight_lure_x_inv,  self.max_steps)
+            if scheduler_lure_y_ != "constant": 
+                latent_guide_weights_lure_y      = initialize_or_scale(latent_guide_weights_lure_y,      latent_guide_weight_lure_y,      self.max_steps)
+            if scheduler_lure_y_inv_ != "constant": 
+                latent_guide_weights_lure_y_inv  = initialize_or_scale(latent_guide_weights_lure_y_inv,  latent_guide_weight_lure_y_inv,  self.max_steps)
+            if scheduler_mean_ != "constant": 
+                latent_guide_weights_mean        = initialize_or_scale(latent_guide_weights_mean, latent_guide_weight_mean, self.max_steps)
+            if scheduler_adain_ != "constant": 
+                latent_guide_weights_adain       = initialize_or_scale(latent_guide_weights_adain, latent_guide_weight_adain, self.max_steps)
+            if scheduler_attninj_ != "constant": 
+                latent_guide_weights_attninj     = initialize_or_scale(latent_guide_weights_attninj, latent_guide_weight_attninj, self.max_steps)
+            if scheduler_style_pos_ != "constant": 
+                latent_guide_weights_style_pos   = initialize_or_scale(latent_guide_weights_style_pos, latent_guide_weight_style_pos, self.max_steps)
+            if scheduler_style_neg_ != "constant": 
+                latent_guide_weights_style_neg   = initialize_or_scale(latent_guide_weights_style_neg, latent_guide_weight_style_neg, self.max_steps)
+
+            latent_guide_weights            [steps_            :] = 0
+            latent_guide_weights_inv        [steps_inv_        :] = 0
+            latent_guide_weights_sync       [steps_sync_       :] = 1 #one
+            latent_guide_weights_sync_inv   [steps_sync_inv_   :] = 1 #one
+            latent_guide_weights_drift_x    [steps_drift_x_    :] = 0
+            latent_guide_weights_drift_x_inv[steps_drift_x_inv_:] = 0
+            latent_guide_weights_drift_y    [steps_drift_y_    :] = 0
+            latent_guide_weights_drift_y_inv[steps_drift_y_inv_:] = 0
+            latent_guide_weights_lure_x     [steps_lure_x_     :] = 0
+            latent_guide_weights_lure_x_inv [steps_lure_x_inv_ :] = 0
+            latent_guide_weights_lure_y     [steps_lure_y_     :] = 0
+            latent_guide_weights_lure_y_inv [steps_lure_y_inv_ :] = 0
+            latent_guide_weights_mean       [steps_mean_       :] = 0
+            latent_guide_weights_adain      [steps_adain_      :] = 0
+            latent_guide_weights_attninj    [steps_attninj_    :] = 0
+            latent_guide_weights_style_pos  [steps_style_pos_  :] = 0
+            latent_guide_weights_style_neg  [steps_style_neg_  :] = 0
+        
+        self.lgw             = F.pad(latent_guide_weights,             (0, self.max_steps), value=0.0)
+        self.lgw_inv         = F.pad(latent_guide_weights_inv,         (0, self.max_steps), value=0.0)
+        self.lgw_sync        = F.pad(latent_guide_weights_sync,        (0, self.max_steps), value=1.0) #one
+        self.lgw_sync_inv    = F.pad(latent_guide_weights_sync_inv,    (0, self.max_steps), value=1.0) #one
+        self.lgw_drift_x     = F.pad(latent_guide_weights_drift_x,     (0, self.max_steps), value=0.0)
+        self.lgw_drift_x_inv = F.pad(latent_guide_weights_drift_x_inv, (0, self.max_steps), value=0.0)
+        self.lgw_drift_y     = F.pad(latent_guide_weights_drift_y,     (0, self.max_steps), value=0.0)
+        self.lgw_drift_y_inv = F.pad(latent_guide_weights_drift_y_inv, (0, self.max_steps), value=0.0)
+        self.lgw_lure_x      = F.pad(latent_guide_weights_lure_x,      (0, self.max_steps), value=0.0)
+        self.lgw_lure_x_inv  = F.pad(latent_guide_weights_lure_x_inv,  (0, self.max_steps), value=0.0)
+        self.lgw_lure_y      = F.pad(latent_guide_weights_lure_y,      (0, self.max_steps), value=0.0)
+        self.lgw_lure_y_inv  = F.pad(latent_guide_weights_lure_y_inv,  (0, self.max_steps), value=0.0)
+        self.lgw_mean        = F.pad(latent_guide_weights_mean,        (0, self.max_steps), value=0.0)
+        self.lgw_adain       = F.pad(latent_guide_weights_adain,       (0, self.max_steps), value=0.0)
+        self.lgw_attninj     = F.pad(latent_guide_weights_attninj,     (0, self.max_steps), value=0.0)
+        self.lgw_style_pos   = F.pad(latent_guide_weights_style_pos,   (0, self.max_steps), value=0.0)
+        self.lgw_style_neg   = F.pad(latent_guide_weights_style_neg,   (0, self.max_steps), value=0.0)
+        
+        mask, self.LGW_MASK_RESCALE_MIN = prepare_mask(x, self.mask, self.LGW_MASK_RESCALE_MIN)
+        self.mask = mask.to(dtype=self.dtype, device=self.device)
+
+        if self.mask_inv is not None:
+            mask_inv, self.LGW_MASK_RESCALE_MIN = prepare_mask(x, self.mask_inv, self.LGW_MASK_RESCALE_MIN)
+            self.mask_inv = mask_inv.to(dtype=self.dtype, device=self.device)
+        else:
+            self.mask_inv = (1-self.mask)
+            
+        if self.mask_sync is not None:
+            mask_sync, self.LGW_MASK_RESCALE_MIN = prepare_mask(x, self.mask_sync, self.LGW_MASK_RESCALE_MIN)
+            self.mask_sync = mask_sync.to(dtype=self.dtype, device=self.device)
+        else:
+            self.mask_sync = self.mask
+            
+        if self.mask_drift_x is not None:
+            mask_drift_x, self.LGW_MASK_RESCALE_MIN = prepare_mask(x, self.mask_drift_x, self.LGW_MASK_RESCALE_MIN)
+            self.mask_drift_x = mask_drift_x.to(dtype=self.dtype, device=self.device)
+        else:
+            self.mask_drift_x = self.mask
+            
+        if self.mask_drift_y is not None:
+            mask_drift_y, self.LGW_MASK_RESCALE_MIN = prepare_mask(x, self.mask_drift_y, self.LGW_MASK_RESCALE_MIN)
+            self.mask_drift_y = mask_drift_y.to(dtype=self.dtype, device=self.device)
+        else:
+            self.mask_drift_y = self.mask
+            
+        if self.mask_lure_x is not None:
+            mask_lure_x, self.LGW_MASK_RESCALE_MIN = prepare_mask(x, self.mask_lure_x, self.LGW_MASK_RESCALE_MIN)
+            self.mask_lure_x = mask_lure_x.to(dtype=self.dtype, device=self.device)
+        else:
+            self.mask_lure_x = self.mask
+            
+        if self.mask_lure_y is not None:
+            mask_lure_y, self.LGW_MASK_RESCALE_MIN = prepare_mask(x, self.mask_lure_y, self.LGW_MASK_RESCALE_MIN)
+            self.mask_lure_y = mask_lure_y.to(dtype=self.dtype, device=self.device)
+        else:
+            self.mask_lure_y = self.mask
+            
+        mask_style_pos, self.LGW_MASK_RESCALE_MIN = prepare_mask(x, self.mask_style_pos, self.LGW_MASK_RESCALE_MIN)
+        self.mask_style_pos = mask_style_pos.to(dtype=self.dtype, device=self.device)
+
+        
+        mask_style_neg, self.LGW_MASK_RESCALE_MIN = prepare_mask(x, self.mask_style_neg, self.LGW_MASK_RESCALE_MIN)
+        self.mask_style_neg = mask_style_neg.to(dtype=self.dtype, device=self.device)
+    
+        if latent_guide is not None:
+            self.HAS_LATENT_GUIDE = True
+            if type(latent_guide) is dict:
+                if latent_guide    ['samples'].shape[0] > 1:
+                    latent_guide['samples']     = latent_guide    ['samples'][batch_num].unsqueeze(0)
+                latent_guide_samples = self.model.inner_model.inner_model.process_latent_in(latent_guide['samples']).clone().to(dtype=self.dtype, device=self.device)
+            elif type(latent_guide) is torch.Tensor:
+                latent_guide_samples = latent_guide.to(dtype=self.dtype, device=self.device)
+            else:
+                raise ValueError(f"Invalid latent type: {type(latent_guide)}")
+
+            if self.VIDEO and latent_guide_samples.shape[2] == 1:
+                latent_guide_samples = latent_guide_samples.repeat(1, 1, x.shape[2], 1, 1)
+
+            if self.SAMPLE:
+                self.y0 = latent_guide_samples
+            elif sigma_init != 0.0:
+                pass
+            elif self.UNSAMPLE: # and self.mask is not None:
+                mask = self.mask.to(x.device)
+                x = (1-mask) * x + mask * latent_guide_samples.to(x.device)
+            else:
+                x = latent_guide_samples.to(x.device)
+        else:
+            self.y0 = torch.zeros_like(x, dtype=self.dtype, device=self.device)
+
+        if latent_guide_inv is not None:
+            self.HAS_LATENT_GUIDE_INV = True
+            if type(latent_guide_inv) is dict:
+                if latent_guide_inv['samples'].shape[0] > 1:
+                    latent_guide_inv['samples'] = latent_guide_inv['samples'][batch_num].unsqueeze(0)
+                latent_guide_inv_samples = self.model.inner_model.inner_model.process_latent_in(latent_guide_inv['samples']).clone().to(dtype=self.dtype, device=self.device)
+            elif type(latent_guide_inv) is torch.Tensor:
+                latent_guide_inv_samples = latent_guide_inv.to(dtype=self.dtype, device=self.device)
+            else:
+                raise ValueError(f"Invalid latent type: {type(latent_guide_inv)}")
+
+            if self.VIDEO and latent_guide_inv_samples.shape[2] == 1:
+                latent_guide_inv_samples = latent_guide_inv_samples.repeat(1, 1, x.shape[2], 1, 1)
+
+            if self.SAMPLE:
+                self.y0_inv = latent_guide_inv_samples
+            elif sigma_init != 0.0:
+                pass
+            elif self.UNSAMPLE: # and self.mask is not None:
+                mask_inv = self.mask_inv.to(x.device)
+                x = (1-mask_inv) * x + mask_inv * latent_guide_inv_samples.to(x.device) #fixed old approach, which was mask, (1-mask)
+            else:
+                x = latent_guide_inv_samples.to(x.device)   #THIS COULD LEAD TO WEIRD BEHAVIOR! OVERWRITING X WITH LG_INV AFTER SETTING TO LG above!
+        else:
+            self.y0_inv = torch.zeros_like(x, dtype=self.dtype, device=self.device)
+
+        if latent_guide_mean is not None:
+            self.HAS_LATENT_GUIDE_MEAN = True
+            if type(latent_guide_mean) is dict:
+                if latent_guide_mean['samples'].shape[0] > 1:
+                    latent_guide_mean['samples'] = latent_guide_mean['samples'][batch_num].unsqueeze(0)
+                latent_guide_mean_samples = self.model.inner_model.inner_model.process_latent_in(latent_guide_mean['samples']).clone().to(dtype=self.dtype, device=self.device)
+            elif type(latent_guide_mean) is torch.Tensor:
+                latent_guide_mean_samples = latent_guide_mean.to(dtype=self.dtype, device=self.device)
+            else:
+                raise ValueError(f"Invalid latent type: {type(latent_guide_mean)}")
+
+            if self.VIDEO and latent_guide_mean_samples.shape[2] == 1:
+                latent_guide_mean_samples = latent_guide_mean_samples.repeat(1, 1, x.shape[2], 1, 1)
+
+            self.y0_mean = latent_guide_mean_samples
+            """if self.SAMPLE:
+                self.y0_mean = latent_guide_mean_samples
+            elif self.UNSAMPLE: # and self.mask is not None:
+                mask_mean = self.mask_mean.to(x.device)
+                x = (1-mask_mean) * x + mask_mean * latent_guide_mean_samples.to(x.device) #fixed old approach, which was mask, (1-mask)     # NECESSARY?
+            else:
+                x = latent_guide_mean_samples.to(x.device)   #THIS COULD LEAD TO WEIRD BEHAVIOR! OVERWRITING X WITH LG_MEAN AFTER SETTING TO LG above!"""
+        else:
+            self.y0_mean = torch.zeros_like(x, dtype=self.dtype, device=self.device)
+
+        if latent_guide_adain is not None:
+            self.HAS_LATENT_GUIDE_ADAIN = True
+            if type(latent_guide_adain) is dict:
+                if latent_guide_adain['samples'].shape[0] > 1:
+                    latent_guide_adain['samples'] = latent_guide_adain['samples'][batch_num].unsqueeze(0)
+                latent_guide_adain_samples = self.model.inner_model.inner_model.process_latent_in(latent_guide_adain['samples']).clone().to(dtype=self.dtype, device=self.device)
+            elif type(latent_guide_adain) is torch.Tensor:
+                latent_guide_adain_samples = latent_guide_adain.to(dtype=self.dtype, device=self.device)
+            else:
+                raise ValueError(f"Invalid latent type: {type(latent_guide_adain)}")
+
+            if self.VIDEO and latent_guide_adain_samples.shape[2] == 1:
+                latent_guide_adain_samples = latent_guide_adain_samples.repeat(1, 1, x.shape[2], 1, 1)
+
+            self.y0_adain = latent_guide_adain_samples
+            """if self.SAMPLE:
+                self.y0_adain = latent_guide_adain_samples
+            elif self.UNSAMPLE: # and self.mask is not None:
+                if self.mask_adain is not None:
+                    mask_adain = self.mask_adain.to(x.device)
+                    x = (1-mask_adain) * x + mask_adain * latent_guide_adain_samples.to(x.device) #fixed old approach, which was mask, (1-mask)     # NECESSARY?
+                else:
+                    x = latent_guide_adain_samples.to(x.device)
+            else:
+                x = latent_guide_adain_samples.to(x.device)   #THIS COULD LEAD TO WEIRD BEHAVIOR! OVERWRITING X WITH LG_ADAIN AFTER SETTING TO LG above!"""
+        else:
+            self.y0_adain = torch.zeros_like(x, dtype=self.dtype, device=self.device)
+
+        if latent_guide_attninj is not None:
+            self.HAS_LATENT_GUIDE_ATTNINJ = True
+            if type(latent_guide_attninj) is dict:
+                if latent_guide_attninj['samples'].shape[0] > 1:
+                    latent_guide_attninj['samples'] = latent_guide_attninj['samples'][batch_num].unsqueeze(0)
+                latent_guide_attninj_samples = self.model.inner_model.inner_model.process_latent_in(latent_guide_attninj['samples']).clone().to(dtype=self.dtype, device=self.device)
+            elif type(latent_guide_attninj) is torch.Tensor:
+                latent_guide_attninj_samples = latent_guide_attninj.to(dtype=self.dtype, device=self.device)
+            else:
+                raise ValueError(f"Invalid latent type: {type(latent_guide_attninj)}")
+
+            if self.VIDEO and latent_guide_attninj_samples.shape[2] == 1:
+                latent_guide_attninj_samples = latent_guide_attninj_samples.repeat(1, 1, x.shape[2], 1, 1)
+
+            self.y0_attninj = latent_guide_attninj_samples
+            """if self.SAMPLE:
+                self.y0_attninj = latent_guide_attninj_samples
+            elif self.UNSAMPLE: # and self.mask is not None:
+                if self.mask_attninj is not None:
+                    mask_attninj = self.mask_attninj.to(x.device)
+                    x = (1-mask_attninj) * x + mask_attninj * latent_guide_attninj_samples.to(x.device) #fixed old approach, which was mask, (1-mask)     # NECESSARY?
+                else:
+                    x = latent_guide_attninj_samples.to(x.device)  
+            else:
+                x = latent_guide_attninj_samples.to(x.device)   #THIS COULD LEAD TO WEIRD BEHAVIOR! OVERWRITING X WITH LG_ADAIN AFTER SETTING TO LG above!"""
+        else:
+            self.y0_attninj = torch.zeros_like(x, dtype=self.dtype, device=self.device)
+
+
+        if latent_guide_style_pos is not None:
+            self.HAS_LATENT_GUIDE_STYLE_POS = True
+            if type(latent_guide_style_pos) is dict:
+                if latent_guide_style_pos['samples'].shape[0] > 1:
+                    latent_guide_style_pos['samples'] = latent_guide_style_pos['samples'][batch_num].unsqueeze(0)
+                latent_guide_style_pos_samples = self.model.inner_model.inner_model.process_latent_in(latent_guide_style_pos['samples']).clone().to(dtype=self.dtype, device=self.device)
+            elif type(latent_guide_style_pos) is torch.Tensor:
+                latent_guide_style_pos_samples = latent_guide_style_pos.to(dtype=self.dtype, device=self.device)
+            else:
+                raise ValueError(f"Invalid latent type: {type(latent_guide_style_pos)}")
+
+            if self.VIDEO and latent_guide_style_pos_samples.shape[2] == 1:
+                latent_guide_style_pos_samples = latent_guide_style_pos_samples.repeat(1, 1, x.shape[2], 1, 1)
+
+            self.y0_style_pos = latent_guide_style_pos_samples
+            """if self.SAMPLE:
+                self.y0_style_pos = latent_guide_style_pos_samples
+            elif self.UNSAMPLE: # and self.mask is not None:
+                if self.mask_style_pos is not None:
+                    mask_style_pos = self.mask_style_pos.to(x.device)
+                    x = (1-mask_style_pos) * x + mask_style_pos * latent_guide_style_pos_samples.to(x.device) #fixed old approach, which was mask, (1-mask)     # NECESSARY?
+                else:
+                    x = latent_guide_style_pos_samples.to(x.device)  
+            else:
+                x = latent_guide_style_pos_samples.to(x.device)   #THIS COULD LEAD TO WEIRD BEHAVIOR! OVERWRITING X WITH LG_ADAIN AFTER SETTING TO LG above!"""
+        else:
+            self.y0_style_pos = torch.zeros_like(x, dtype=self.dtype, device=self.device)
+
+
+        if latent_guide_style_neg is not None:
+            self.HAS_LATENT_GUIDE_STYLE_NEG = True
+            if type(latent_guide_style_neg) is dict:
+                if latent_guide_style_neg['samples'].shape[0] > 1:
+                    latent_guide_style_neg['samples'] = latent_guide_style_neg['samples'][batch_num].unsqueeze(0)
+                latent_guide_style_neg_samples = self.model.inner_model.inner_model.process_latent_in(latent_guide_style_neg['samples']).clone().to(dtype=self.dtype, device=self.device)
+            elif type(latent_guide_style_neg) is torch.Tensor:
+                latent_guide_style_neg_samples = latent_guide_style_neg.to(dtype=self.dtype, device=self.device)
+            else:
+                raise ValueError(f"Invalid latent type: {type(latent_guide_style_neg)}")
+
+            if self.VIDEO and latent_guide_style_neg_samples.shape[2] == 1:
+                latent_guide_style_neg_samples = latent_guide_style_neg_samples.repeat(1, 1, x.shape[2], 1, 1)
+
+            self.y0_style_neg = latent_guide_style_neg_samples
+            """if self.SAMPLE:
+                self.y0_style_neg = latent_guide_style_neg_samples
+            elif self.UNSAMPLE: # and self.mask is not None:
+                if self.mask_style_neg is not None:
+                    mask_style_neg = self.mask_style_neg.to(x.device)
+                    x = (1-mask_style_neg) * x + mask_style_neg * latent_guide_style_neg_samples.to(x.device) #fixed old approach, which was mask, (1-mask)     # NECESSARY?
+                else:
+                    x = latent_guide_style_neg_samples.to(x.device)  
+            else:
+                x = latent_guide_style_neg_samples.to(x.device)   #THIS COULD LEAD TO WEIRD BEHAVIOR! OVERWRITING X WITH LG_ADAIN AFTER SETTING TO LG above!"""
+        else:
+            self.y0_style_neg = torch.zeros_like(x, dtype=self.dtype, device=self.device)
+
+        if self.UNSAMPLE and not self.SAMPLE: #sigma_next > sigma:   # TODO: VERIFY APPROACH FOR INVERSION
+            if guide_inversion_y0 is not None:
+                self.y0 = guide_inversion_y0
+            else:
+                self.y0     = noise_sampler(sigma=self.sigma_max, sigma_next=self.sigma_min).to(dtype=self.dtype, device=self.device)
+                self.y0     = normalize_zscore(self.y0,     channelwise=True, inplace=True)
+                self.y0    *= self.sigma_max
+                
+            if guide_inversion_y0_inv is not None:
+                self.y0_inv = guide_inversion_y0_inv
+            else:
+                self.y0_inv = noise_sampler(sigma=self.sigma_max, sigma_next=self.sigma_min).to(dtype=self.dtype, device=self.device)
+                self.y0_inv = normalize_zscore(self.y0_inv, channelwise=True, inplace=True)
+                self.y0_inv*= self.sigma_max
+
+            
+        if self.VIDEO and self.frame_weights_mgr is not None:
+            num_frames = x.shape[2]
+            self.frame_weights     = self.frame_weights_mgr.get_frame_weights_by_name('frame_weights', num_frames)
+            self.frame_weights_inv = self.frame_weights_mgr.get_frame_weights_by_name('frame_weights_inv', num_frames)
+            
+        x, self.y0, self.y0_inv = self.normalize_inputs(x, self.y0, self.y0_inv)       # ???
+
+        return x
+
+    def prepare_weighted_masks(self, step:int, lgw_type="default") -> Tuple[Tensor, Tensor]:
+        if lgw_type == "sync":
+            lgw_     = self.lgw_sync    [step]
+            lgw_inv_ = self.lgw_sync_inv[step]
+            mask     = torch.ones_like (self.y0) if self.mask_sync is None else   self.mask_sync
+            mask_inv = torch.zeros_like(self.y0) if self.mask_sync is None else 1-self.mask_sync
+        elif lgw_type == "drift_x":
+            lgw_     = self.lgw_drift_x    [step]
+            lgw_inv_ = self.lgw_drift_x_inv[step]
+            mask     = torch.ones_like (self.y0) if self.mask_drift_x is None else   self.mask_drift_x
+            mask_inv = torch.zeros_like(self.y0) if self.mask_drift_x is None else 1-self.mask_drift_x
+        elif lgw_type == "drift_y":
+            lgw_     = self.lgw_drift_y    [step]
+            lgw_inv_ = self.lgw_drift_y_inv[step]
+            mask     = torch.ones_like (self.y0) if self.mask_drift_y is None else   self.mask_drift_y
+            mask_inv = torch.zeros_like(self.y0) if self.mask_drift_y is None else 1-self.mask_drift_y
+        elif lgw_type == "lure_x":
+            lgw_     = self.lgw_lure_x    [step]
+            lgw_inv_ = self.lgw_lure_x_inv[step]
+            mask     = torch.ones_like (self.y0) if self.mask_lure_x is None else   self.mask_lure_x
+            mask_inv = torch.zeros_like(self.y0) if self.mask_lure_x is None else 1-self.mask_lure_x
+        elif lgw_type == "lure_y":
+            lgw_     = self.lgw_lure_y    [step]
+            lgw_inv_ = self.lgw_lure_y_inv[step]
+            mask     = torch.ones_like (self.y0) if self.mask_lure_y is None else   self.mask_lure_y
+            mask_inv = torch.zeros_like(self.y0) if self.mask_lure_y is None else 1-self.mask_lure_y
+        else:
+            lgw_     = self.lgw    [step]
+            lgw_inv_ = self.lgw_inv[step]
+            mask     = torch.ones_like (self.y0) if self.mask     is None else self.mask
+            mask_inv = torch.zeros_like(self.y0) if self.mask_inv is None else self.mask_inv
+
+        if self.LGW_MASK_RESCALE_MIN: 
+            lgw_mask     =    mask  * (1-lgw_)     + lgw_
+            lgw_mask_inv = (1-mask) * (1-lgw_inv_) + lgw_inv_
+        else:
+            if self.HAS_LATENT_GUIDE:
+                lgw_mask = mask * lgw_
+            else:
+                lgw_mask = torch.zeros_like(mask)
+            
+            if self.HAS_LATENT_GUIDE_INV:
+                if mask_inv is not None:
+                    lgw_mask_inv = torch.minimum(mask_inv, (1-mask) * lgw_inv_)
+                    #lgw_mask_inv = torch.minimum(1-mask_inv, (1-mask) * lgw_inv_)
+                else:
+                    lgw_mask_inv = (1-mask) * lgw_inv_
+            else:
+                lgw_mask_inv = torch.zeros_like(mask)
+
+        return lgw_mask, lgw_mask_inv
+
+
+    def get_masks_for_step(self, step:int, lgw_type="default") -> Tuple[Tensor, Tensor]:
+        lgw_mask, lgw_mask_inv = self.prepare_weighted_masks(step, lgw_type=lgw_type)
+        normalize_frame_weights_per_step = self.EO("normalize_frame_weights_per_step")
+        normalize_frame_weights_per_step_inv = self.EO("normalize_frame_weights_per_step_inv")
+
+        if self.VIDEO and self.frame_weights_mgr:
+            num_frames = lgw_mask.shape[2]
+            if self.HAS_LATENT_GUIDE:
+                frame_weights = self.frame_weights_mgr.get_frame_weights_by_name('frame_weights', num_frames, step)
+                apply_frame_weights(lgw_mask, frame_weights, normalize_frame_weights_per_step)
+            if self.HAS_LATENT_GUIDE_INV:
+                frame_weights_inv = self.frame_weights_mgr.get_frame_weights_by_name('frame_weights_inv', num_frames, step)
+                apply_frame_weights(lgw_mask_inv, frame_weights_inv, normalize_frame_weights_per_step_inv)
+
+        return lgw_mask.to(self.device), lgw_mask_inv.to(self.device)
+
+
+
+    def get_cossim_adjusted_lgw_masks(self, data:Tensor, step:int) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
+        
+        if self.HAS_LATENT_GUIDE:
+            y0     = self.y0.clone()
+        else:
+            y0     = torch.zeros_like(data)
+            
+        if self.HAS_LATENT_GUIDE_INV:
+            y0_inv = self.y0_inv.clone()
+        else:
+            y0_inv = torch.zeros_like(data)
+
+        if y0.shape[0] > 1:                                    # this is for changing the guide on a per-step basis
+            y0 = y0[min(step, y0.shape[0]-1)].unsqueeze(0)
+        
+        lgw_mask, lgw_mask_inv = self.get_masks_for_step(step)
+        
+        y0_cossim, y0_cossim_inv  = 1.0, 1.0
+        if self.HAS_LATENT_GUIDE:
+            y0_cossim     = get_pearson_similarity(data, y0,     mask=lgw_mask)
+        if self.HAS_LATENT_GUIDE_INV:
+            y0_cossim_inv = get_pearson_similarity(data, y0_inv, mask=lgw_mask_inv)
+        
+        #if y0_cossim < self.guide_cossim_cutoff_ or y0_cossim_inv < self.guide_bkg_cossim_cutoff_:
+        if y0_cossim     >= self.guide_cossim_cutoff_:
+            lgw_mask     *= 0
+        if y0_cossim_inv >= self.guide_bkg_cossim_cutoff_:
+            lgw_mask_inv *= 0
+        
+        return y0, y0_inv, lgw_mask, lgw_mask_inv
+
+
+
+
+
+
+
+
+
+
+
+
+    @torch.no_grad
+    def process_pseudoimplicit_guides_substep(self,
+                                            x_0                         : Tensor,
+                                            x_                          : Tensor,
+                                            eps_                        : Tensor,
+                                            eps_prev_                   : Tensor,
+                                            data_                       : Tensor,
+                                            denoised_prev               : Tensor,
+                                            row                         : int,
+                                            step                        : int,
+                                            step_sched                  : int,
+                                            sigmas                      : Tensor,
+                                            NS                                  ,
+                                            RK                                  ,
+                                            pseudoimplicit_row_weights  : Tensor,
+                                            pseudoimplicit_step_weights : Tensor,
+                                            full_iter                   : int,
+                                            BONGMATH                    : bool,
+                                            ):
+        
+        if "pseudoimplicit" not in self.guide_mode or (self.lgw[step_sched] == 0 and self.lgw_inv[step_sched] == 0):
+            return x_0, x_, eps_, None, None
+        
+        sigma = sigmas[step]
+
+        if self.s_lying_ is not None:
+            if row >= len(self.s_lying_):
+                return x_0, x_, eps_, None, None
+        
+        if self.guide_mode.startswith("fully_"):
+            data_cossim_test = denoised_prev
+        else:
+            data_cossim_test = data_[row]
+            
+        y0, y0_inv, lgw_mask, lgw_mask_inv = self.get_cossim_adjusted_lgw_masks(data_cossim_test, step_sched)
+        
+        if not (lgw_mask.any() != 0 or lgw_mask_inv.any() != 0):  # cossim score too similar! deactivate guide for this step
+            return x_0, x_, eps_, None, None
+
+
+        if "fully_pseudoimplicit" in self.guide_mode:
+            if self.x_lying_ is None:
+                return x_0, x_, eps_, None, None        
+            else:
+                x_row_pseudoimplicit     = self.x_lying_[row]
+                sub_sigma_pseudoimplicit = self.s_lying_[row]
+        
+        
+        
+        if RK.IMPLICIT:
+            x_ = RK.update_substep(x_0,
+                                    x_,
+                                    eps_,
+                                    eps_prev_,
+                                    row,
+                                    RK.row_offset,
+                                    NS.h_new,
+                                    NS.h_new_orig,
+                                    )
+            
+            x_[row] = NS.rebound_overshoot_substep(x_0, x_[row])
+            
+            if row > 0:
+                x_[row] = NS.swap_noise_substep(x_0, x_[row])
+                if BONGMATH and step < sigmas.shape[0]-1 and not self.EO("disable_pseudoimplicit_bongmath"):
+                    x_0, x_, eps_ = RK.bong_iter(x_0,
+                                                x_,
+                                                eps_,
+                                                eps_prev_,
+                                                data_,
+                                                sigma,
+                                                NS.s_,
+                                                row,
+                                                RK.row_offset,
+                                                NS.h,
+                                                step,
+                                                step_sched,
+                                                )
+        else:
+            eps_[row] = RK.get_epsilon(x_0, x_[row], denoised_prev, sigma, NS.s_[row])
+            
+        if self.EO("pseudoimplicit_denoised_prev"):
+            eps_[row] = RK.get_epsilon(x_0, x_[row], denoised_prev, sigma, NS.s_[row])
+
+        eps_substep_guide     = torch.zeros_like(x_0)
+        eps_substep_guide_inv = torch.zeros_like(x_0)
+        
+        if self.HAS_LATENT_GUIDE:
+            eps_substep_guide     = RK.get_guide_epsilon(x_0, x_[row], y0,     sigma, NS.s_[row], NS.sigma_down, None)  
+        if self.HAS_LATENT_GUIDE_INV:
+            eps_substep_guide_inv = RK.get_guide_epsilon(x_0, x_[row], y0_inv, sigma, NS.s_[row], NS.sigma_down, None)  
+
+
+
+        if self.guide_mode in {"pseudoimplicit", "pseudoimplicit_cw", "pseudoimplicit_projection", "pseudoimplicit_projection_cw"}:
+            maxmin_ratio = (NS.sub_sigma - RK.sigma_min) / NS.sub_sigma
+            
+            if   self.EO("guide_pseudoimplicit_power_substep_flip_maxmin_scaling"):
+                maxmin_ratio *= (RK.rows-row) / RK.rows
+            elif self.EO("guide_pseudoimplicit_power_substep_maxmin_scaling"):
+                maxmin_ratio *= row / RK.rows
+            
+            sub_sigma_2 = NS.sub_sigma - maxmin_ratio * (NS.sub_sigma * pseudoimplicit_row_weights[row] * pseudoimplicit_step_weights[full_iter] * self.lgw[step_sched])
+
+            eps_tmp_ = eps_.clone()
+
+            eps_ = self.process_channelwise(x_0,
+                                            eps_,
+                                            data_,
+                                            row,
+                                            eps_substep_guide,
+                                            eps_substep_guide_inv,
+                                            y0,
+                                            y0_inv,
+                                            lgw_mask,
+                                            lgw_mask_inv,
+                                            use_projection = self.guide_mode in {"pseudoimplicit_projection", "pseudoimplicit_projection_cw"},
+                                            channelwise    = self.guide_mode in {"pseudoimplicit_cw",         "pseudoimplicit_projection_cw"},
+                                            )
+
+            x_row_tmp = x_[row] + RK.h_fn(sub_sigma_2, NS.sub_sigma) * eps_[row]
+            
+            eps_                     = eps_tmp_
+            x_row_pseudoimplicit     = x_row_tmp
+            sub_sigma_pseudoimplicit = sub_sigma_2
+
+
+        if RK.IMPLICIT and BONGMATH and step < sigmas.shape[0]-1 and not self.EO("disable_pseudobongmath"):
+            x_[row] = NS.sigma_from_to(x_0, x_row_pseudoimplicit, sigma, sub_sigma_pseudoimplicit, NS.s_[row])
+            
+            x_0, x_, eps_ = RK.bong_iter(x_0,
+                                        x_,
+                                        eps_,
+                                        eps_prev_,
+                                        data_,
+                                        sigma,
+                                        NS.s_,
+                                        row,
+                                        RK.row_offset,
+                                        NS.h,
+                                        step,
+                                        step_sched,
+                                        ) 
+            
+        return x_0, x_, eps_, x_row_pseudoimplicit, sub_sigma_pseudoimplicit
+
+
+
+    @torch.no_grad
+    def prepare_fully_pseudoimplicit_guides_substep(self,
+                                                    x_0,
+                                                    x_,
+                                                    eps_,
+                                                    eps_prev_,
+                                                    data_,
+                                                    denoised_prev,
+                                                    row,
+                                                    step,
+                                                    step_sched,
+                                                    sigmas,
+                                                    eta_substep,
+                                                    overshoot_substep,
+                                                    s_noise_substep,
+                                                    NS,
+                                                    RK,
+                                                    pseudoimplicit_row_weights,
+                                                    pseudoimplicit_step_weights,
+                                                    full_iter,
+                                                    BONGMATH,
+                                                    ):
+        
+        if "fully_pseudoimplicit" not in self.guide_mode or (self.lgw[step_sched] == 0 and self.lgw_inv[step_sched] == 0):
+            return x_0, x_, eps_ 
+        
+        sigma = sigmas[step]
+        
+        y0, y0_inv, lgw_mask, lgw_mask_inv = self.get_cossim_adjusted_lgw_masks(denoised_prev, step_sched)
+        
+        if not (lgw_mask.any() != 0 or lgw_mask_inv.any() != 0):  # cossim score too similar! deactivate guide for this step
+            return x_0, x_, eps_
+        
+
+        # PREPARE FULLY PSEUDOIMPLICIT GUIDES
+        if self.guide_mode in {"fully_pseudoimplicit", "fully_pseudoimplicit_cw", "fully_pseudoimplicit_projection", "fully_pseudoimplicit_projection_cw"} and (self.lgw[step_sched] > 0 or self.lgw_inv[step_sched] > 0):
+            x_lying_   = x_.clone()
+            eps_lying_ = eps_.clone()
+            s_lying_   = []
+            
+            for r in range(RK.rows):
+                
+                NS.set_sde_substep(r, RK.multistep_stages, eta_substep, overshoot_substep, s_noise_substep)
+
+                maxmin_ratio      = (NS.sub_sigma - RK.sigma_min) / NS.sub_sigma
+                fully_sub_sigma_2 =  NS.sub_sigma - maxmin_ratio * (NS.sub_sigma * pseudoimplicit_row_weights[r] * pseudoimplicit_step_weights[full_iter] * self.lgw[step_sched])
+                
+                s_lying_.append(fully_sub_sigma_2)
+
+                if RK.IMPLICIT:
+                    x_ = RK.update_substep(x_0,
+                                            x_,
+                                            eps_,
+                                            eps_prev_,
+                                            r,
+                                            RK.row_offset,
+                                            NS.h_new,
+                                            NS.h_new_orig,
+                                            ) 
+                    
+                    x_[r] = NS.rebound_overshoot_substep(x_0, x_[r])
+
+                    if r > 0:
+                        x_[r] = NS.swap_noise_substep(x_0, x_[r])
+                        if BONGMATH and step < sigmas.shape[0]-1 and not self.EO("disable_fully_pseudoimplicit_bongmath"):
+                            x_0, x_, eps_ = RK.bong_iter(x_0,
+                                                        x_,
+                                                        eps_,
+                                                        eps_prev_,
+                                                        data_,
+                                                        sigma,
+                                                        NS.s_,
+                                                        r,
+                                                        RK.row_offset,
+                                                        NS.h,
+                                                        step,
+                                                        step_sched,
+                                                        )
+                            
+                if self.EO("fully_pseudoimplicit_denoised_prev"):
+                    eps_[r] = RK.get_epsilon(x_0, x_[r], denoised_prev, sigma, NS.s_[r])
+                
+                eps_substep_guide     = torch.zeros_like(x_0)
+                eps_substep_guide_inv = torch.zeros_like(x_0)
+                
+                if self.HAS_LATENT_GUIDE:
+                    eps_substep_guide     = RK.get_guide_epsilon(x_0, x_[r], y0,     sigma, NS.s_[r], NS.sigma_down, None)  
+                if self.HAS_LATENT_GUIDE_INV:
+                    eps_substep_guide_inv = RK.get_guide_epsilon(x_0, x_[r], y0_inv, sigma, NS.s_[r], NS.sigma_down, None)  
+                
+                eps_ = self.process_channelwise(x_0,
+                                                eps_,
+                                                data_,
+                                                row,
+                                                eps_substep_guide,
+                                                eps_substep_guide_inv,
+                                                y0,
+                                                y0_inv,
+                                                lgw_mask,
+                                                lgw_mask_inv,
+                                                use_projection = self.guide_mode in {"fully_pseudoimplicit_projection", "fully_pseudoimplicit_projection_cw"},
+                                                channelwise    = self.guide_mode in {"fully_pseudoimplicit_cw",         "fully_pseudoimplicit_projection_cw"},
+                                                )
+
+                x_lying_[r]   = x_[r] + RK.h_fn(fully_sub_sigma_2, NS.sub_sigma) * eps_[r]
+                data_lying    = x_[r] + RK.h_fn(0,                 NS.s_[r])     * eps_[r] 
+                
+                eps_lying_[r] = RK.get_epsilon(x_0, x_[r], data_lying, sigma, NS.s_[r])
+                
+            if not self.EO("pseudoimplicit_disable_eps_lying"):
+                eps_ = eps_lying_
+            
+            if not self.EO("pseudoimplicit_disable_newton_iter"):
+                x_, eps_ = RK.newton_iter(x_0,
+                                        x_,
+                                        eps_,
+                                        eps_prev_,
+                                        data_,
+                                        NS.s_,
+                                        0,
+                                        NS.h,
+                                        sigmas,
+                                        step,
+                                        "lying",
+                                        )
+            
+            self.x_lying_ = x_lying_
+            self.s_lying_ = s_lying_
+
+        return x_0, x_, eps_ 
+
+
+
+    @torch.no_grad
+    def process_guides_data_substep(self,
+                                x_row         : Tensor,
+                                data_row      : Tensor,
+                                step          : int,
+                                sigma_row     : Tensor,
+                                frame_targets : Optional[Tensor] = None,
+                                ):
+        if not self.HAS_LATENT_GUIDE and not self.HAS_LATENT_GUIDE_INV:
+            return x_row
+
+        y0, y0_inv, lgw_mask, lgw_mask_inv = self.get_cossim_adjusted_lgw_masks(data_row, step)
+        
+        if not (lgw_mask.any() != 0 or lgw_mask_inv.any() != 0):  # cossim score too similar! deactivate guide for this step
+            return x_row
+
+        if self.VIDEO and self.frame_weights_mgr is not None and frame_targets is None:
+            num_frames = data_row.shape[2]
+            frame_targets = self.frame_weights_mgr.get_frame_weights_by_name('frame_targets', num_frames, step)
+            if frame_targets is None:
+                frame_targets = torch.tensor(self.EO("frame_targets", [1.0]))
+            frame_targets = torch.clamp(frame_targets, 0.0, 1.0).to(self.device)
+
+        if self.guide_mode in {"data", "data_projection", "lure", "lure_projection"}:
+            if frame_targets is None:
+                x_row = self.get_data_substep(x_row, data_row, y0, y0_inv, lgw_mask, lgw_mask_inv, step, sigma_row)
+            else:
+                t_dim = x_row.shape[-3]
+                for t in range(t_dim): #temporal dimension
+                    frame_target = float(frame_targets[t] if len(frame_targets) > t else frame_targets[-1])
+                    x_row[...,t:t+1,:,:] = self.get_data_substep(
+                                                                x_row       [...,t:t+1,:,:], 
+                                                                data_row    [...,t:t+1,:,:],
+                                                                y0          [...,t:t+1,:,:], 
+                                                                y0_inv      [...,t:t+1,:,:], 
+                                                                lgw_mask    [...,t:t+1,:,:], 
+                                                                lgw_mask_inv[...,t:t+1,:,:], 
+                                                                step, 
+                                                                sigma_row, 
+                                                                frame_target)
+        
+        return x_row
+
+
+
+
+    @torch.no_grad
+    def get_data_substep(self,
+                        x_row         : Tensor,
+                        data_row      : Tensor,
+                        y0            : Tensor,
+                        y0_inv        : Tensor,
+                        lgw_mask      : Tensor,
+                        lgw_mask_inv  : Tensor,
+                        step          : int,
+                        sigma_row     : Tensor,
+                        frame_target  : float = 1.0,
+                        ):
+
+        if not self.HAS_LATENT_GUIDE and not self.HAS_LATENT_GUIDE_INV:
+            return x_row
+
+        if self.guide_mode in {"data", "data_projection", "lure", "lure_projection"}:
+            data_targets = self.EO("data_targets", [1.0])
+            step_target = step if len(data_targets) > step else len(data_targets)-1
+            
+            cossim_target = frame_target * data_targets[step_target]
+            
+            if self.HAS_LATENT_GUIDE:
+                if self.guide_mode.endswith("projection"):
+                    d_collinear_d_lerp = get_collinear(data_row, y0)  
+                    d_lerp_ortho_d     = get_orthogonal(y0, data_row)  
+                    y0                 = d_collinear_d_lerp + d_lerp_ortho_d
+                    
+                if   cossim_target == 1.0:
+                    d_slerped = y0
+                elif cossim_target == 0.0:
+                    d_slerped = data_row
+                else:
+                    y0_pearsim    = get_pearson_similarity(data_row, y0,     mask=self.mask)
+                    slerp_weight  = get_slerp_weight_for_cossim(y0_pearsim.item(), cossim_target)
+                    d_slerped     = slerp_tensor(slerp_weight, data_row, y0) # lgw_mask * slerp_weight same as using mask below
+                    
+                """if self.guide_mode == "data_projection":
+                    d_collinear_d_lerp = get_collinear(data_row, d_slerped)  
+                    d_lerp_ortho_d     = get_orthogonal(d_slerped, data_row)  
+                    d_slerped          = d_collinear_d_lerp + d_lerp_ortho_d"""
+                    
+                if self.VE_MODEL:
+                    x_row = x_row + lgw_mask * (d_slerped - data_row) 
+                else:
+                    x_row = x_row + lgw_mask * (self.sigma_max - sigma_row) * (d_slerped - data_row) 
+
+                
+            if self.HAS_LATENT_GUIDE_INV:
+                if self.guide_mode.endswith("projection"):
+                    d_collinear_d_lerp = get_collinear(data_row, y0_inv)  
+                    d_lerp_ortho_d     = get_orthogonal(y0_inv, data_row)  
+                    y0_inv             = d_collinear_d_lerp + d_lerp_ortho_d
+                
+                if   cossim_target == 1.0:
+                    d_slerped_inv = y0_inv
+                elif cossim_target == 0.0:
+                    d_slerped_inv = data_row
+                else:
+                    y0_pearsim    = get_pearson_similarity(data_row, y0_inv, mask=self.mask_inv)
+                    slerp_weight  = get_slerp_weight_for_cossim(y0_pearsim.item(), cossim_target)
+                    d_slerped_inv = slerp_tensor(slerp_weight, data_row, y0_inv)
+                    
+                """if self.guide_mode == "data_projection":
+                    d_collinear_d_lerp = get_collinear(data_row, d_slerped_inv)  
+                    d_lerp_ortho_d     = get_orthogonal(d_slerped_inv, data_row)  
+                    d_slerped_inv      = d_collinear_d_lerp + d_lerp_ortho_d"""
+                    
+                if self.VE_MODEL:
+                    x_row = x_row + lgw_mask_inv * (d_slerped_inv - data_row) 
+                else:
+                    x_row = x_row + lgw_mask_inv * (self.sigma_max - sigma_row) * (d_slerped_inv - data_row) 
+
+                    
+        return x_row
+
+    @torch.no_grad
+    def swap_data(self,
+        x     : Tensor,
+        data  : Tensor,
+        y     : Tensor,
+        sigma : Tensor,
+        mask  : Optional[Tensor] = None,
+    ):
+        mask = 1.0 if mask is None else mask
+        if self.VE_MODEL:
+            return x + mask * (y - data)
+        else:
+            return x + mask * (self.sigma_max - sigma) * (y - data)
+
+    @torch.no_grad
+    def process_guides_eps_substep(self,
+                                x_0           : Tensor,
+                                x_row         : Tensor,
+                                data_row      : Tensor,
+                                eps_row       : Tensor,
+                                step          : int,
+                                sigma         : Tensor,
+                                sigma_down    : Tensor,
+                                sigma_row     : Tensor,
+                                frame_targets : Optional[Tensor] = None,
+                                RK=None,
+                                ):
+        
+        if not self.HAS_LATENT_GUIDE and not self.HAS_LATENT_GUIDE_INV:
+            return eps_row
+
+        y0, y0_inv, lgw_mask, lgw_mask_inv = self.get_cossim_adjusted_lgw_masks(data_row, step)
+        
+        if not (lgw_mask.any() != 0 or lgw_mask_inv.any() != 0):  # cossim score too similar! deactivate guide for this step
+            return eps_row
+
+        if self.VIDEO and data_row.ndim == 5 and frame_targets is None:
+            num_frames = data_row.shape[2]
+            frame_targets = self.frame_weights_mgr.get_frame_weights_by_name('frame_targets', num_frames, step)
+            if frame_targets is None:
+                frame_targets = self.EO("frame_targets", [1.0])
+            frame_targets = torch.clamp(frame_targets, 0.0, 1.0)
+            
+        eps_y0     = torch.zeros_like(x_0)
+        eps_y0_inv = torch.zeros_like(x_0)
+        
+        if self.HAS_LATENT_GUIDE:
+            eps_y0     = RK.get_guide_epsilon(x_0, x_row, y0,     sigma, sigma_row, sigma_down, None)  
+            
+        if self.HAS_LATENT_GUIDE_INV:
+            eps_y0_inv = RK.get_guide_epsilon(x_0, x_row, y0_inv, sigma, sigma_row, sigma_down, None)  
+
+        if self.guide_mode in {"epsilon", "epsilon_projection"}:
+            if frame_targets is None:
+                eps_row = self.get_eps_substep(eps_row, eps_y0, eps_y0_inv, lgw_mask, lgw_mask_inv, step, sigma_row)
+            else:
+                t_dim = x_row.shape[-3]
+                for t in range(t_dim): #temporal dimension
+                    frame_target = float(frame_targets[t] if len(frame_targets) > t else frame_targets[-1])
+                    eps_row[...,t:t+1,:,:] = self.get_eps_substep(
+                                                                eps_row     [...,t:t+1,:,:],
+                                                                eps_y0      [...,t:t+1,:,:], 
+                                                                eps_y0_inv  [...,t:t+1,:,:], 
+                                                                lgw_mask    [...,t:t+1,:,:], 
+                                                                lgw_mask_inv[...,t:t+1,:,:], 
+                                                                step, 
+                                                                sigma_row, 
+                                                                frame_target)
+                    
+        return eps_row
+
+
+
+    @torch.no_grad
+    def get_eps_substep(self,
+                        eps_row       : Tensor,
+                        eps_y0        : Tensor,
+                        eps_y0_inv    : Tensor,
+                        lgw_mask      : Tensor,
+                        lgw_mask_inv  : Tensor,
+                        step          : int,
+                        sigma_row     : Tensor,
+                        frame_target  : float = 1.0,
+                        ):
+        
+        if not self.HAS_LATENT_GUIDE and not self.HAS_LATENT_GUIDE_INV:
+            return eps_row
+
+        if self.guide_mode in {"epsilon", "epsilon_projection"}:
+            eps_targets = self.EO("eps_targets", [1.0])
+            step_target = step if len(eps_targets) > step else len(eps_targets)-1
+            
+            cossim_target = frame_target * eps_targets[step_target]
+            
+            if self.HAS_LATENT_GUIDE:
+                if self.guide_mode == "epsilon_projection":
+                    d_collinear_d_lerp = get_collinear(eps_row, eps_y0)  
+                    d_lerp_ortho_d     = get_orthogonal(eps_y0, eps_row)  
+                    eps_y0             = d_collinear_d_lerp + d_lerp_ortho_d
+                    
+                if   cossim_target == 1.0:
+                    d_slerped = eps_y0
+                elif cossim_target == 0.0:
+                    d_slerped = eps_row
+                else:
+                    y0_pearsim    = get_pearson_similarity(eps_row, eps_y0,     mask=self.mask)
+                    slerp_weight  = get_slerp_weight_for_cossim(y0_pearsim.item(), cossim_target)
+                    d_slerped     = slerp_tensor(slerp_weight, eps_row, eps_y0) # lgw_mask * slerp_weight same as using mask below
+                    
+                """if self.guide_mode == "data_projection":
+                    d_collinear_d_lerp = get_collinear(data_row, d_slerped)  
+                    d_lerp_ortho_d     = get_orthogonal(d_slerped, data_row)  
+                    d_slerped          = d_collinear_d_lerp + d_lerp_ortho_d"""
+                    
+                eps_row = eps_row + lgw_mask * (d_slerped - eps_row) 
+
+                
+            if self.HAS_LATENT_GUIDE_INV:
+                if self.guide_mode == "epsilon_projection":
+                    d_collinear_d_lerp = get_collinear(eps_row, eps_y0_inv)  
+                    d_lerp_ortho_d     = get_orthogonal(eps_y0_inv, eps_row)  
+                    eps_y0_inv             = d_collinear_d_lerp + d_lerp_ortho_d
+                
+                if   cossim_target == 1.0:
+                    d_slerped_inv = eps_y0_inv
+                elif cossim_target == 0.0:
+                    d_slerped_inv = eps_row
+                else:
+                    y0_pearsim    = get_pearson_similarity(eps_row, eps_y0_inv, mask=self.mask_inv)
+                    slerp_weight  = get_slerp_weight_for_cossim(y0_pearsim.item(), cossim_target)
+                    d_slerped_inv = slerp_tensor(slerp_weight, eps_row, eps_y0_inv)
+                    
+                """if self.guide_mode == "data_projection":
+                    d_collinear_d_lerp = get_collinear(data_row, d_slerped_inv)  
+                    d_lerp_ortho_d     = get_orthogonal(d_slerped_inv, data_row)  
+                    d_slerped_inv      = d_collinear_d_lerp + d_lerp_ortho_d"""
+                    
+                eps_row = eps_row + lgw_mask_inv * (d_slerped_inv - eps_row) 
+
+        return eps_row
+
+
+
+
+
+    @torch.no_grad
+    def process_guides_substep(self,
+                                x_0           : Tensor,
+                                x_            : Tensor,
+                                eps_          : Tensor,
+                                data_         : Tensor,
+                                row           :  int,
+                                step_sched    :  int,
+                                sigma         : Tensor,
+                                sigma_next    : Tensor,
+                                sigma_down    : Tensor,
+                                s_            : Tensor,
+                                epsilon_scale :  float,
+                                RK,
+                                ):
+        
+        if not self.HAS_LATENT_GUIDE and not self.HAS_LATENT_GUIDE_INV:
+            return eps_, x_
+
+        y0, y0_inv, lgw_mask, lgw_mask_inv = self.get_cossim_adjusted_lgw_masks(data_[row], step_sched)
+        
+        if not (lgw_mask.any() != 0 or lgw_mask_inv.any() != 0):  # cossim score too similar! deactivate guide for this step
+            return eps_, x_ 
+
+        if self.EO(["substep_eps_ch_mean_std", "substep_eps_ch_mean", "substep_eps_ch_std", "substep_eps_mean_std", "substep_eps_mean", "substep_eps_std"]):
+            eps_orig = eps_.clone()
+        
+        if self.EO("dynamic_guides_mean_std"):
+            y_shift, y_inv_shift = normalize_latent([y0, y0_inv], [data_, data_])
+            y0 = y_shift
+            if self.EO("dynamic_guides_inv"):
+                y0_inv = y_inv_shift
+
+        if self.EO("dynamic_guides_mean"):
+            y_shift, y_inv_shift = normalize_latent([y0, y0_inv], [data_, data_], std=False)
+            y0 = y_shift
+            if self.EO("dynamic_guides_inv"):
+                y0_inv = y_inv_shift
+
+
+
+        if "data_old" == self.guide_mode:
+            y0_tmp = y0.clone()
+            if self.HAS_LATENT_GUIDE:
+                y0_tmp = (1-lgw_mask) * data_[row] + lgw_mask * y0
+                y0_tmp = (1-lgw_mask_inv) * y0_tmp + lgw_mask_inv * y0_inv
+            x_[row+1] = y0_tmp + eps_[row]
+            
+        if self.guide_mode == "data_old_projection":
+
+            d_lerp             = data_[row]   +   lgw_mask * (y0-data_[row])   +   lgw_mask_inv * (y0_inv-data_[row])
+            
+            d_collinear_d_lerp = get_collinear(data_[row], d_lerp)  
+            d_lerp_ortho_d     = get_orthogonal(d_lerp, data_[row])  
+            
+            data_[row]         = d_collinear_d_lerp + d_lerp_ortho_d
+            
+            x_[row+1]          = data_[row] + eps_[row] * sigma
+            
+
+
+            #elif (self.UNSAMPLE or self.guide_mode in {"epsilon", "epsilon_cw", "epsilon_projection", "epsilon_projection_cw"}) and (self.lgw[step] > 0 or self.lgw_inv[step] > 0):
+        elif self.guide_mode in {"epsilon", "epsilon_cw", "epsilon_projection", "epsilon_projection_cw"} and (self.lgw[step_sched] > 0 or self.lgw_inv[step_sched] > 0):
+            if sigma_down < sigma   or   s_[row] < RK.sigma_max:
+                                
+                eps_substep_guide     = torch.zeros_like(x_0)
+                eps_substep_guide_inv = torch.zeros_like(x_0)
+                
+                if self.HAS_LATENT_GUIDE:
+                    eps_substep_guide     = RK.get_guide_epsilon(x_0, x_[row], y0,     sigma, s_[row], sigma_down, epsilon_scale)  
+                    
+                if self.HAS_LATENT_GUIDE_INV:
+                    eps_substep_guide_inv = RK.get_guide_epsilon(x_0, x_[row], y0_inv, sigma, s_[row], sigma_down, epsilon_scale)  
+
+                tol_value = self.EO("tol", -1.0)
+                if tol_value >= 0:
+                    for b, c in itertools.product(range(x_0.shape[0]), range(x_0.shape[1])):
+                        current_diff       = torch.norm(data_[row][b][c] - y0    [b][c]) 
+                        current_diff_inv   = torch.norm(data_[row][b][c] - y0_inv[b][c]) 
+                        
+                        lgw_scaled         = torch.nan_to_num(1-(tol_value/current_diff),     0)
+                        lgw_scaled_inv     = torch.nan_to_num(1-(tol_value/current_diff_inv), 0)
+                        
+                        lgw_tmp            = min(self.lgw[step_sched]    , lgw_scaled)
+                        lgw_tmp_inv        = min(self.lgw_inv[step_sched], lgw_scaled_inv)
+
+                        lgw_mask_clamp     = torch.clamp(lgw_mask,     max=lgw_tmp)
+                        lgw_mask_clamp_inv = torch.clamp(lgw_mask_inv, max=lgw_tmp_inv)
+
+                        eps_[row][b][c]    = eps_[row][b][c] + lgw_mask_clamp[b][0] * (eps_substep_guide[b][c] - eps_[row][b][c]) + lgw_mask_clamp_inv[b][0] * (eps_substep_guide_inv[b][c] - eps_[row][b][c])
+                
+                elif self.guide_mode in {"epsilon"}: 
+                    #eps_[row] = slerp(lgw_mask.mean().item(), eps_[row], eps_substep_guide)
+                    if self.EO("slerp_epsilon_guide"):
+                        if eps_substep_guide.sum() != 0:
+                            eps_[row] = slerp_tensor(lgw_mask, eps_[row], eps_substep_guide)
+                        if eps_substep_guide_inv.sum() != 0:
+                            eps_[row] = slerp_tensor(lgw_mask_inv, eps_[row], eps_substep_guide_inv)
+                    else:
+                        eps_[row] = eps_[row] + lgw_mask * (eps_substep_guide - eps_[row]) + lgw_mask_inv * (eps_substep_guide_inv - eps_[row])
+                    
+                    #eps_[row] = slerp_barycentric(eps_[row].norm(), eps_substep_guide.norm(), eps_substep_guide_inv.norm(), 1-lgw_mask-lgw_mask_inv, lgw_mask, lgw_mask_inv)
+                    
+                elif self.guide_mode in {"epsilon_projection"}:
+                    if self.EO("slerp_epsilon_guide"):
+                        if eps_substep_guide.sum() != 0:
+                            eps_row_slerp = slerp_tensor(self.mask, eps_[row], eps_substep_guide)
+                        if eps_substep_guide_inv.sum() != 0:
+                            eps_row_slerp = slerp_tensor((1-self.mask), eps_row_slerp, eps_substep_guide_inv)
+
+                        eps_collinear_eps_slerp = get_collinear(eps_[row], eps_row_slerp)
+                        eps_slerp_ortho_eps     = get_orthogonal(eps_row_slerp, eps_[row])
+
+                        eps_sum                = eps_collinear_eps_slerp + eps_slerp_ortho_eps
+
+                        eps_[row] = slerp_tensor(lgw_mask, eps_[row] , eps_sum)
+                        eps_[row] = slerp_tensor(lgw_mask_inv, eps_[row], eps_sum)
+                    else:
+                        eps_row_lerp           = eps_[row]   +   self.mask * (eps_substep_guide-eps_[row])   +   (1-self.mask) * (eps_substep_guide_inv-eps_[row])
+
+                        eps_collinear_eps_lerp = get_collinear(eps_[row], eps_row_lerp)
+                        eps_lerp_ortho_eps     = get_orthogonal(eps_row_lerp, eps_[row])
+
+                        eps_sum                = eps_collinear_eps_lerp + eps_lerp_ortho_eps
+
+                        eps_[row]              = eps_[row] + lgw_mask * (eps_sum - eps_[row]) + lgw_mask_inv * (eps_sum - eps_[row])
+                    
+                    
+                    #eps_row_slerp          = eps_[row]   +   self.mask * (eps_substep_guide-eps_[row])   +   (1-self.mask) * (eps_substep_guide_inv-eps_[row])
+
+                    
+                elif self.guide_mode in {"epsilon_cw", "epsilon_projection_cw"}:
+                    eps_ = self.process_channelwise(x_0,
+                                                    eps_,
+                                                    data_,
+                                                    row,
+                                                    eps_substep_guide,
+                                                    eps_substep_guide_inv,
+                                                    y0,
+                                                    y0_inv,
+                                                    lgw_mask,
+                                                    lgw_mask_inv,
+                                                    use_projection = self.guide_mode == "epsilon_projection_cw",
+                                                    channelwise    = True
+                                                    )
+
+        temporal_smoothing = self.EO("temporal_smoothing", 0.0)
+        if temporal_smoothing > 0:
+            eps_[row] = apply_temporal_smoothing(eps_[row], temporal_smoothing)
+            
+        if self.EO("substep_eps_ch_mean_std"):
+            eps_[row] = normalize_latent(eps_[row], eps_orig[row])
+        if self.EO("substep_eps_ch_mean"):
+            eps_[row] = normalize_latent(eps_[row], eps_orig[row], std=False)
+        if self.EO("substep_eps_ch_std"):
+            eps_[row] = normalize_latent(eps_[row], eps_orig[row], mean=False)
+        if self.EO("substep_eps_mean_std"):
+            eps_[row] = normalize_latent(eps_[row], eps_orig[row], channelwise=False)
+        if self.EO("substep_eps_mean"):
+            eps_[row] = normalize_latent(eps_[row], eps_orig[row], std=False, channelwise=False)
+        if self.EO("substep_eps_std"):
+            eps_[row] = normalize_latent(eps_[row], eps_orig[row], mean=False, channelwise=False)
+        return eps_, x_
+    
+
+    def process_channelwise(self,
+                            x_0                   : Tensor,
+                            eps_                  : Tensor,
+                            data_                 : Tensor,
+                            row                   : int,
+                            eps_substep_guide     : Tensor,
+                            eps_substep_guide_inv : Tensor,
+                            y0                    : Tensor,
+                            y0_inv                : Tensor,
+                            lgw_mask              : Tensor,
+                            lgw_mask_inv          : Tensor,
+                            use_projection        : bool    = False,
+                            channelwise           : bool    = False
+                            ):
+        
+        avg, avg_inv = 0, 0
+        for b, c in itertools.product(range(x_0.shape[0]), range(x_0.shape[1])):
+            avg     += torch.norm(lgw_mask    [b][0] * data_[row][b][c]   -   lgw_mask    [b][0] * y0    [b][c])
+            avg_inv += torch.norm(lgw_mask_inv[b][0] * data_[row][b][c]   -   lgw_mask_inv[b][0] * y0_inv[b][c])
+            
+        avg     /= x_0.shape[1]
+        avg_inv /= x_0.shape[1]
+        
+        for b, c in itertools.product(range(x_0.shape[0]), range(x_0.shape[1])):
+            if channelwise:
+                ratio     = torch.nan_to_num(torch.norm(lgw_mask    [b][0] * data_[row][b][c] - lgw_mask    [b][0] * y0    [b][c])   /   avg,     0)
+                ratio_inv = torch.nan_to_num(torch.norm(lgw_mask_inv[b][0] * data_[row][b][c] - lgw_mask_inv[b][0] * y0_inv[b][c])   /   avg_inv, 0)
+            else:
+                ratio     = 1.
+                ratio_inv = 1.
+                    
+            if self.EO("slerp_epsilon_guide"):
+                if eps_substep_guide[b][c].sum() != 0:
+                    eps_[row][b][c] = slerp_tensor(ratio * lgw_mask[b][0], eps_[row][b][c], eps_substep_guide[b][c])
+                if eps_substep_guide_inv[b][c].sum() != 0:
+                    eps_[row][b][c] = slerp_tensor(ratio_inv * lgw_mask_inv[b][0], eps_[row][b][c], eps_substep_guide_inv[b][c])
+            else:
+                eps_[row][b][c]            = eps_[row][b][c]   +   ratio * lgw_mask[b][0] * (eps_substep_guide[b][c] - eps_[row][b][c])   +   ratio_inv * lgw_mask_inv[b][0] * (eps_substep_guide_inv[b][c] - eps_[row][b][c])
+            
+            if use_projection:
+                if self.EO("slerp_epsilon_guide"):
+                    if eps_substep_guide[b][c].sum() != 0:
+                        eps_row_lerp = slerp_tensor(self.mask[b][0], eps_[row][b][c], eps_substep_guide[b][c])
+                    if eps_substep_guide_inv[b][c].sum() != 0:
+                        eps_row_lerp = slerp_tensor((1-self.mask[b][0]), eps_[row][b][c], eps_substep_guide_inv[b][c])
+                else:
+                    eps_row_lerp           = eps_[row][b][c]   +          self.mask[b][0] * (eps_substep_guide[b][c] - eps_[row][b][c])   +              (1-self.mask[b][0]) * (eps_substep_guide_inv[b][c] - eps_[row][b][c]) # should this ever be self.mask_inv?
+
+                eps_collinear_eps_lerp = get_collinear (eps_[row][b][c], eps_row_lerp)
+                eps_lerp_ortho_eps     = get_orthogonal(eps_row_lerp   , eps_[row][b][c])
+
+                eps_sum                = eps_collinear_eps_lerp + eps_lerp_ortho_eps
+
+
+                if self.EO("slerp_epsilon_guide"):
+                    if eps_substep_guide[b][c].sum() != 0:
+                        eps_[row][b][c] = slerp_tensor(ratio * lgw_mask[b][0], eps_[row][b][c], eps_sum)
+                    if eps_substep_guide_inv[b][c].sum() != 0:
+                        eps_[row][b][c] = slerp_tensor(ratio_inv * lgw_mask_inv[b][0], eps_[row][b][c], eps_sum)
+                else:
+                    eps_[row][b][c]        = eps_[row][b][c]   +   ratio * lgw_mask[b][0] * (eps_sum                 - eps_[row][b][c])   +   ratio_inv * lgw_mask_inv[b][0] * (eps_sum                     - eps_[row][b][c])
+            else:
+                if self.EO("slerp_epsilon_guide"):
+                    if eps_substep_guide[b][c].sum() != 0:
+                        eps_[row][b][c] = slerp_tensor(ratio * lgw_mask[b][0], eps_[row][b][c], eps_substep_guide[b][c])
+                    if eps_substep_guide_inv[b][c].sum() != 0:
+                        eps_[row][b][c] = slerp_tensor(ratio_inv * lgw_mask_inv[b][0], eps_[row][b][c], eps_substep_guide_inv[b][c])
+                else:
+                    eps_[row][b][c]        = eps_[row][b][c]   +   ratio * lgw_mask[b][0] * (eps_substep_guide[b][c] - eps_[row][b][c])   +   ratio_inv * lgw_mask_inv[b][0] * (eps_substep_guide_inv[b][c] - eps_[row][b][c])
+                
+        return eps_
+
+    
+    def normalize_inputs(self, x:Tensor, y0:Tensor, y0_inv:Tensor):
+        """
+        Modifies and returns 'x' by matching its mean and/or std to y0 and/or y0_inv.
+        Controlled by extra_options.
+
+        Returns:
+            - x      (modified)
+            - y0     (may be modified to match mean and std from y0_inv)
+            - y0_inv (unchanged)
+        """
+        if self.guide_mode == "epsilon_guide_mean_std_from_bkg":
+            y0 = normalize_latent(y0, y0_inv)
+
+        input_norm = self.EO("input_norm", "")
+        input_std  = self.EO("input_std", 1.0)
+                
+        if input_norm == "input_ch_mean_set_std_to":
+            x = normalize_latent(x, set_std=input_std)
+
+        if input_norm == "input_ch_set_std_to":
+            x = normalize_latent(x, set_std=input_std, mean=False)
+                
+        if input_norm == "input_mean_set_std_to":
+            x = normalize_latent(x, set_std=input_std,             channelwise=False)
+            
+        if input_norm == "input_std_set_std_to":
+            x = normalize_latent(x, set_std=input_std, mean=False, channelwise=False)
+        
+        return x, y0, y0_inv
+
+
+
+def apply_frame_weights(mask, frame_weights, normalize=False):
+    original_mask_mean = mask.mean()
+    if frame_weights is not None:
+        for f in range(mask.shape[2]):
+            frame_weight = frame_weights[f]
+            mask[..., f:f+1, :, :] *= frame_weight
+        if normalize:
+            mask_mean = mask.mean()
+            mask *= (original_mask_mean / mask_mean)
+
+
+
+def prepare_mask(x, mask, LGW_MASK_RESCALE_MIN) -> tuple[torch.Tensor, bool]:
+    if mask is None:
+        mask = torch.ones_like(x[:,0:1,...])
+        LGW_MASK_RESCALE_MIN = False
+        return mask, LGW_MASK_RESCALE_MIN
+    
+    target_height = x.shape[-2]
+    target_width  = x.shape[-1]
+
+    spatial_mask = None
+    if x.ndim == 5 and mask.shape[0] > 1 and mask.ndim < 4:
+        target_frames = x.shape[-3]
+        spatial_mask = mask.unsqueeze(0).unsqueeze(0)  # [B, H, W] -> [1, 1, B, H, W]
+        spatial_mask = F.interpolate(spatial_mask, 
+                                    size=(target_frames, target_height, target_width), 
+                                    mode='trilinear', 
+                                    align_corners=False)  # [1, 1, F, H, W]
+        repeat_shape = [1]  # batch
+        for i in range(1, x.ndim - 3):
+            repeat_shape.append(x.shape[i])
+        repeat_shape.extend([1, 1, 1])  # frames, height, width
+    elif mask.ndim == 4: #temporal mask batch
+        mask = F.interpolate(mask, size=(target_height, target_width), mode='bilinear', align_corners=False)
+        mask = mask.repeat(x.shape[-4],1,1,1)
+        mask.unsqueeze_(0)
+
+    else:
+        spatial_mask = mask.unsqueeze(1)
+        spatial_mask = F.interpolate(spatial_mask, size=(target_height, target_width), mode='bilinear', align_corners=False)
+
+        while spatial_mask.ndim < x.ndim:
+            spatial_mask = spatial_mask.unsqueeze(2)
+        
+        repeat_shape = [1]  # batch
+        for i in range(1, x.ndim - 2):
+            repeat_shape.append(x.shape[i])
+        repeat_shape.extend([1, 1])  # height and width
+        repeat_shape[1] = 1                                   # only need one channel for masks
+
+    if spatial_mask is not None:
+        mask = spatial_mask.repeat(*repeat_shape).to(x.dtype)
+        
+        del spatial_mask
+    return mask, LGW_MASK_RESCALE_MIN
+    
+def apply_temporal_smoothing(tensor, temporal_smoothing):
+    if temporal_smoothing <= 0 or tensor.ndim != 5:
+        return tensor
+
+    kernel_size = 5
+    padding = kernel_size // 2
+    temporal_kernel = torch.tensor(
+        [0.1, 0.2, 0.4, 0.2, 0.1],
+        device=tensor.device, dtype=tensor.dtype
+    ) * temporal_smoothing
+    temporal_kernel[kernel_size//2] += (1 - temporal_smoothing)
+    temporal_kernel = temporal_kernel / temporal_kernel.sum()
+
+    # resahpe for conv1d
+    b, c, f, h, w = tensor.shape
+    data_flat = tensor.permute(0, 1, 3, 4, 2).reshape(-1, f)
+
+    # apply smoohting
+    data_smooth = F.conv1d(
+        data_flat.unsqueeze(1),
+        temporal_kernel.view(1, 1, -1),
+        padding=padding
+    ).squeeze(1)
+
+    return data_smooth.view(b, c, h, w, f).permute(0, 1, 4, 2, 3)
+
+def get_guide_epsilon_substep(x_0, x_, y0, y0_inv, s_, row, row_offset, rk_type, b=None, c=None):
+    s_in = x_0.new_ones([x_0.shape[0]])
+    
+    if b is not None and c is not None:  
+        index = (b, c)
+    elif b is not None: 
+        index = (b,)
+    else: 
+        index = ()
+
+    if RK_Method_Beta.is_exponential(rk_type):
+        eps_row     =  y0    [index] -  x_0[index]
+        eps_row_inv =  y0_inv[index] -  x_0[index]
+    else:
+        eps_row     = (x_[row][index] - y0    [index]) / (s_[row] * s_in) # was row+row_offset before for x_!!   not right...     also? potential issues here with x_[row+1] being RK.rows+2 with gauss-legendre_2s 1 imp step 1 imp substep
+        eps_row_inv = (x_[row][index] - y0_inv[index]) / (s_[row] * s_in)
+    
+    return eps_row, eps_row_inv
+
+def get_guide_epsilon(x_0, x_, y0, sigma, rk_type, b=None, c=None):
+    s_in = x_0.new_ones([x_0.shape[0]])
+    
+    if b is not None and c is not None:  
+        index = (b, c)
+    elif b is not None: 
+        index = (b,)
+    else: 
+        index = ()
+
+    if RK_Method_Beta.is_exponential(rk_type):
+        eps     = y0    [index] - x_0[index]
+    else:
+        eps     = (x_[index] - y0    [index]) / (sigma * s_in)
+    
+    return eps
+
+
+
+@torch.no_grad
+def noise_cossim_guide_tiled(x_list, guide, cossim_mode="forward", tile_size=2, step=0):
+
+    guide_tiled = rearrange(guide, "b c (h t1) (w t2) -> b (t1 t2) c h w", t1=tile_size, t2=tile_size)
+
+    x_tiled_list = [
+        rearrange(x, "b c (h t1) (w t2) -> b (t1 t2) c h w", t1=tile_size, t2=tile_size)
+        for x in x_list
+    ]
+    x_tiled_stack = torch.stack([x_tiled[0] for x_tiled in x_tiled_list])  # [n_x, n_tiles, c, h, w]
+
+    guide_flat = guide_tiled[0].view(guide_tiled.shape[1], -1).unsqueeze(0)  # [1, n_tiles, c*h*w]
+    x_flat = x_tiled_stack.view(x_tiled_stack.size(0), x_tiled_stack.size(1), -1)  # [n_x, n_tiles, c*h*w]
+
+    cossim_tmp_all = F.cosine_similarity(x_flat, guide_flat, dim=-1)  # [n_x, n_tiles]
+
+    if cossim_mode == "forward":
+        indices = cossim_tmp_all.argmax(dim=0) 
+    elif cossim_mode == "reverse":
+        indices = cossim_tmp_all.argmin(dim=0) 
+    elif cossim_mode == "orthogonal":
+        indices = torch.abs(cossim_tmp_all).argmin(dim=0) 
+    elif cossim_mode == "forward_reverse":
+        if step % 2 == 0:
+            indices = cossim_tmp_all.argmax(dim=0)
+        else:
+            indices = cossim_tmp_all.argmin(dim=0)
+    elif cossim_mode == "reverse_forward":
+        if step % 2 == 1:
+            indices = cossim_tmp_all.argmax(dim=0)
+        else:
+            indices = cossim_tmp_all.argmin(dim=0)
+    elif cossim_mode == "orthogonal_reverse":
+        if step % 2 == 0:
+            indices = torch.abs(cossim_tmp_all).argmin(dim=0)
+        else:
+            indices = cossim_tmp_all.argmin(dim=0)
+    elif cossim_mode == "reverse_orthogonal":
+        if step % 2 == 1:
+            indices = torch.abs(cossim_tmp_all).argmin(dim=0)
+        else:
+            indices = cossim_tmp_all.argmin(dim=0)
+    else:
+        target_value = float(cossim_mode)
+        indices = torch.abs(cossim_tmp_all - target_value).argmin(dim=0)  
+
+    x_tiled_out = x_tiled_stack[indices, torch.arange(indices.size(0))]  # [n_tiles, c, h, w]
+
+    x_tiled_out = x_tiled_out.unsqueeze(0) 
+    x_detiled = rearrange(x_tiled_out, "b (t1 t2) c h w -> b c (h t1) (w t2)", t1=tile_size, t2=tile_size)
+
+    return x_detiled
+
+
+@torch.no_grad
+def noise_cossim_eps_tiled(x_list, eps, noise_list, cossim_mode="forward", tile_size=2, step=0):
+
+    eps_tiled = rearrange(eps, "b c (h t1) (w t2) -> b (t1 t2) c h w", t1=tile_size, t2=tile_size)
+    x_tiled_list = [
+        rearrange(x, "b c (h t1) (w t2) -> b (t1 t2) c h w", t1=tile_size, t2=tile_size)
+        for x in x_list
+    ]
+    noise_tiled_list = [
+        rearrange(noise, "b c (h t1) (w t2) -> b (t1 t2) c h w", t1=tile_size, t2=tile_size)
+        for noise in noise_list
+    ]
+
+    noise_tiled_stack = torch.stack([noise_tiled[0] for noise_tiled in noise_tiled_list])  # [n_x, n_tiles, c, h, w]
+    eps_expanded = eps_tiled[0].view(eps_tiled.shape[1], -1).unsqueeze(0)  # [1, n_tiles, c*h*w]
+    noise_flat = noise_tiled_stack.view(noise_tiled_stack.size(0), noise_tiled_stack.size(1), -1)  # [n_x, n_tiles, c*h*w]
+    cossim_tmp_all = F.cosine_similarity(noise_flat, eps_expanded, dim=-1)  # [n_x, n_tiles]
+
+    if cossim_mode == "forward":
+        indices = cossim_tmp_all.argmax(dim=0)  
+    elif cossim_mode == "reverse":
+        indices = cossim_tmp_all.argmin(dim=0) 
+    elif cossim_mode == "orthogonal":
+        indices = torch.abs(cossim_tmp_all).argmin(dim=0) 
+    elif cossim_mode == "orthogonal_pos":
+        positive_mask = cossim_tmp_all > 0
+        positive_tmp = torch.where(positive_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('inf')))
+        indices = positive_tmp.argmin(dim=0)
+    elif cossim_mode == "orthogonal_neg":
+        negative_mask = cossim_tmp_all < 0
+        negative_tmp = torch.where(negative_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('-inf')))
+        indices = negative_tmp.argmax(dim=0)
+    elif cossim_mode == "orthogonal_posneg":
+        if step % 2 == 0:
+            positive_mask = cossim_tmp_all > 0
+            positive_tmp = torch.where(positive_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('inf')))
+            indices = positive_tmp.argmin(dim=0)
+        else:
+            negative_mask = cossim_tmp_all < 0
+            negative_tmp = torch.where(negative_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('-inf')))
+            indices = negative_tmp.argmax(dim=0)
+    elif cossim_mode == "orthogonal_negpos":
+        if step % 2 == 1:
+            positive_mask = cossim_tmp_all > 0
+            positive_tmp = torch.where(positive_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('inf')))
+            indices = positive_tmp.argmin(dim=0)
+        else:
+            negative_mask = cossim_tmp_all < 0
+            negative_tmp = torch.where(negative_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('-inf')))
+            indices = negative_tmp.argmax(dim=0)
+    elif cossim_mode == "forward_reverse":
+        if step % 2 == 0:
+            indices = cossim_tmp_all.argmax(dim=0)
+        else:
+            indices = cossim_tmp_all.argmin(dim=0)
+    elif cossim_mode == "reverse_forward":
+        if step % 2 == 1:
+            indices = cossim_tmp_all.argmax(dim=0)
+        else:
+            indices = cossim_tmp_all.argmin(dim=0)
+    elif cossim_mode == "orthogonal_reverse":
+        if step % 2 == 0:
+            indices = torch.abs(cossim_tmp_all).argmin(dim=0)
+        else:
+            indices = cossim_tmp_all.argmin(dim=0)
+    elif cossim_mode == "reverse_orthogonal":
+        if step % 2 == 1:
+            indices = torch.abs(cossim_tmp_all).argmin(dim=0)
+        else:
+            indices = cossim_tmp_all.argmin(dim=0)
+    else:
+        target_value = float(cossim_mode)
+        indices = torch.abs(cossim_tmp_all - target_value).argmin(dim=0)
+    #else:
+    #    raise ValueError(f"Unknown cossim_mode: {cossim_mode}")
+
+    x_tiled_stack = torch.stack([x_tiled[0] for x_tiled in x_tiled_list])  # [n_x, n_tiles, c, h, w]
+    x_tiled_out = x_tiled_stack[indices, torch.arange(indices.size(0))]  # [n_tiles, c, h, w]
+
+    x_tiled_out = x_tiled_out.unsqueeze(0)  # restore batch dim
+    x_detiled = rearrange(x_tiled_out, "b (t1 t2) c h w -> b c (h t1) (w t2)", t1=tile_size, t2=tile_size)
+    return x_detiled
+
+
+
+@torch.no_grad
+def noise_cossim_guide_eps_tiled(x_0, x_list, y0, noise_list, cossim_mode="forward", tile_size=2, step=0, sigma=None, rk_type=None):
+
+    x_tiled_stack = torch.stack([
+        rearrange(x, "b c (h t1) (w t2) -> b (t1 t2) c h w", t1=tile_size, t2=tile_size)[0]
+        for x in x_list
+    ])  # [n_x, n_tiles, c, h, w]
+    eps_guide_stack = torch.stack([
+        rearrange(x - y0, "b c (h t1) (w t2) -> b (t1 t2) c h w", t1=tile_size, t2=tile_size)[0]
+        for x in x_list
+    ])  # [n_x, n_tiles, c, h, w]
+    del x_list
+
+    noise_tiled_stack = torch.stack([
+        rearrange(noise, "b c (h t1) (w t2) -> b (t1 t2) c h w", t1=tile_size, t2=tile_size)[0]
+        for noise in noise_list
+    ])  # [n_x, n_tiles, c, h, w]
+    del noise_list
+
+    noise_flat = noise_tiled_stack.view(noise_tiled_stack.size(0), noise_tiled_stack.size(1), -1)  # [n_x, n_tiles, c*h*w]
+    eps_guide_flat = eps_guide_stack.view(eps_guide_stack.size(0), eps_guide_stack.size(1), -1)  # [n_x, n_tiles, c*h*w]
+
+    cossim_tmp_all = F.cosine_similarity(noise_flat, eps_guide_flat, dim=-1)  # [n_x, n_tiles]
+    del noise_tiled_stack, noise_flat, eps_guide_stack, eps_guide_flat
+
+    if cossim_mode == "forward":
+        indices = cossim_tmp_all.argmax(dim=0) 
+    elif cossim_mode == "reverse":
+        indices = cossim_tmp_all.argmin(dim=0) 
+    elif cossim_mode == "orthogonal":
+        indices = torch.abs(cossim_tmp_all).argmin(dim=0) 
+    elif cossim_mode == "orthogonal_pos":
+        positive_mask = cossim_tmp_all > 0
+        positive_tmp = torch.where(positive_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('inf')))
+        indices = positive_tmp.argmin(dim=0)
+    elif cossim_mode == "orthogonal_neg":
+        negative_mask = cossim_tmp_all < 0
+        negative_tmp = torch.where(negative_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('-inf')))
+        indices = negative_tmp.argmax(dim=0)
+    elif cossim_mode == "orthogonal_posneg":
+        if step % 2 == 0:
+            positive_mask = cossim_tmp_all > 0
+            positive_tmp = torch.where(positive_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('inf')))
+            indices = positive_tmp.argmin(dim=0)
+        else:
+            negative_mask = cossim_tmp_all < 0
+            negative_tmp = torch.where(negative_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('-inf')))
+            indices = negative_tmp.argmax(dim=0)
+    elif cossim_mode == "orthogonal_negpos":
+        if step % 2 == 1:
+            positive_mask = cossim_tmp_all > 0
+            positive_tmp = torch.where(positive_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('inf')))
+            indices = positive_tmp.argmin(dim=0)
+        else:
+            negative_mask = cossim_tmp_all < 0
+            negative_tmp = torch.where(negative_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('-inf')))
+            indices = negative_tmp.argmax(dim=0)
+    elif cossim_mode == "forward_reverse":
+        if step % 2 == 0:
+            indices = cossim_tmp_all.argmax(dim=0)
+        else:
+            indices = cossim_tmp_all.argmin(dim=0)
+    elif cossim_mode == "reverse_forward":
+        if step % 2 == 1:
+            indices = cossim_tmp_all.argmax(dim=0)
+        else:
+            indices = cossim_tmp_all.argmin(dim=0)
+    elif cossim_mode == "orthogonal_reverse":
+        if step % 2 == 0:
+            indices = torch.abs(cossim_tmp_all).argmin(dim=0)
+        else:
+            indices = cossim_tmp_all.argmin(dim=0)
+    elif cossim_mode == "reverse_orthogonal":
+        if step % 2 == 1:
+            indices = torch.abs(cossim_tmp_all).argmin(dim=0)
+        else:
+            indices = cossim_tmp_all.argmin(dim=0)
+    else:
+        target_value = float(cossim_mode)
+        indices = torch.abs(cossim_tmp_all - target_value).argmin(dim=0)  
+
+    x_tiled_out = x_tiled_stack[indices, torch.arange(indices.size(0))]  # [n_tiles, c, h, w]
+    del x_tiled_stack
+
+    x_tiled_out = x_tiled_out.unsqueeze(0)  
+    x_detiled = rearrange(x_tiled_out, "b (t1 t2) c h w -> b c (h t1) (w t2)", t1=tile_size, t2=tile_size)
+
+    return x_detiled
+
+
+
+
+
+
+
+class NoiseStepHandlerOSDE:
+    def __init__(self, x, eps=None, data=None, x_init=None, guide=None, guide_bkg=None):
+        self.noise = None
+        self.x = x
+        self.eps = eps
+        self.data = data
+        self.x_init = x_init
+        self.guide = guide
+        self.guide_bkg = guide_bkg
+        
+        self.eps_list = None
+
+        self.noise_cossim_map = {
+            "eps_orthogonal":              [self.noise, self.eps],
+            "eps_data_orthogonal":         [self.noise, self.eps, self.data],
+
+            "data_orthogonal":             [self.noise, self.data],
+            "xinit_orthogonal":            [self.noise, self.x_init],
+            
+            "x_orthogonal":                [self.noise, self.x],
+            "x_data_orthogonal":           [self.noise, self.x, self.data],
+            "x_eps_orthogonal":            [self.noise, self.x, self.eps],
+
+            "x_eps_data_orthogonal":       [self.noise, self.x, self.eps, self.data],
+            "x_eps_data_xinit_orthogonal": [self.noise, self.x, self.eps, self.data, self.x_init],
+            
+            "x_eps_guide_orthogonal":      [self.noise, self.x, self.eps, self.guide],
+            "x_eps_guide_bkg_orthogonal":  [self.noise, self.x, self.eps, self.guide_bkg],
+            
+            "noise_orthogonal":            [self.noise, self.x_init],
+            
+            "guide_orthogonal":            [self.noise, self.guide],
+            "guide_bkg_orthogonal":        [self.noise, self.guide_bkg],
+        }
+
+    def check_cossim_source(self, source):
+        return source in self.noise_cossim_map
+
+    def get_ortho_noise(self, noise, prev_noises=None, max_iter=100, max_score=1e-7, NOISE_COSSIM_SOURCE="eps_orthogonal"):
+        
+        if NOISE_COSSIM_SOURCE not in self.noise_cossim_map:
+            raise ValueError(f"Invalid NOISE_COSSIM_SOURCE: {NOISE_COSSIM_SOURCE}")
+        
+        self.noise_cossim_map[NOISE_COSSIM_SOURCE][0] = noise
+
+        params = self.noise_cossim_map[NOISE_COSSIM_SOURCE]
+        
+        noise = get_orthogonal_noise_from_channelwise(*params, max_iter=max_iter, max_score=max_score)
+        
+        return noise
+
+
+
+
+
+# NOTE: NS AND SUBSTEP ADDED!
+def handle_tiled_etc_noise_steps(
+                                x_0,
+                                x,
+                                x_prenoise,
+                                x_init,
+                                eps,
+                                denoised,
+                                y0,
+                                y0_inv,
+                                step,
+                                rk_type,
+                                RK,
+                                NS,
+                                SUBSTEP,
+                                sigma_up,
+                                sigma,
+                                sigma_next,
+                                alpha_ratio,
+                                s_noise,
+                                noise_mode,
+                                SDE_NOISE_EXTERNAL,
+                                sde_noise_t,
+                                NOISE_COSSIM_SOURCE,
+                                NOISE_COSSIM_MODE,
+                                noise_cossim_tile_size,
+                                noise_cossim_iterations,
+                                extra_options):
+    
+    EO = ExtraOptions(extra_options)
+    
+    x_tmp          = []
+    cossim_tmp     = []
+    noise_tmp_list = []
+    
+    if step > EO("noise_cossim_end_step", MAX_STEPS):
+        NOISE_COSSIM_SOURCE       = EO("noise_cossim_takeover_source"    , "eps")
+        NOISE_COSSIM_MODE         = EO("noise_cossim_takeover_mode"      , "forward"              )
+        noise_cossim_tile_size    = EO("noise_cossim_takeover_tile"      , noise_cossim_tile_size )
+        noise_cossim_iterations   = EO("noise_cossim_takeover_iterations", noise_cossim_iterations)
+        
+    for i in range(noise_cossim_iterations):
+        #x_tmp.append(NS.swap_noise(x_0, x, sigma, sigma, sigma_next, ))
+        x_tmp.append(NS.add_noise_post(x, sigma_up, sigma, sigma_next, alpha_ratio, s_noise, noise_mode, SDE_NOISE_EXTERNAL, sde_noise_t)    )#y0, lgw, sigma_down are currently unused
+        noise_tmp = x_tmp[i] - x
+        if EO("noise_noise_zscore_norm"):
+            noise_tmp = normalize_zscore(noise_tmp, channelwise=False, inplace=True)
+        if EO("noise_noise_zscore_norm_cw"):
+            noise_tmp = normalize_zscore(noise_tmp, channelwise=True,  inplace=True)
+        if EO("noise_eps_zscore_norm"):
+            eps       = normalize_zscore(eps,       channelwise=False, inplace=True)
+        if EO("noise_eps_zscore_norm_cw"):
+            eps       = normalize_zscore(eps,       channelwise=True,  inplace=True)
+            
+        if   NOISE_COSSIM_SOURCE in ("eps_tiled", "guide_epsilon_tiled", "guide_bkg_epsilon_tiled", "iig_tiled"):
+            noise_tmp_list.append(noise_tmp)
+        if   NOISE_COSSIM_SOURCE == "eps":
+            cossim_tmp.append(get_cosine_similarity(eps, noise_tmp))
+        if   NOISE_COSSIM_SOURCE == "eps_ch":
+            cossim_total = torch.zeros_like(eps[0][0][0][0])
+            for ch in range(eps.shape[1]):
+                cossim_total += get_cosine_similarity(eps[0][ch], noise_tmp[0][ch])
+            cossim_tmp.append(cossim_total)
+        elif NOISE_COSSIM_SOURCE == "data":
+            cossim_tmp.append(get_cosine_similarity(denoised, noise_tmp))
+        elif NOISE_COSSIM_SOURCE == "latent":
+            cossim_tmp.append(get_cosine_similarity(x_prenoise, noise_tmp))
+        elif NOISE_COSSIM_SOURCE == "x_prenoise":
+            cossim_tmp.append(get_cosine_similarity(x_prenoise, x_tmp[i]))
+        elif NOISE_COSSIM_SOURCE == "x":
+            cossim_tmp.append(get_cosine_similarity(x, x_tmp[i]))
+        elif NOISE_COSSIM_SOURCE == "x_data":
+            cossim_tmp.append(get_cosine_similarity(denoised, x_tmp[i]))
+        elif NOISE_COSSIM_SOURCE == "x_init_vs_noise":
+            cossim_tmp.append(get_cosine_similarity(x_init, noise_tmp))
+        elif NOISE_COSSIM_SOURCE == "mom":
+            cossim_tmp.append(get_cosine_similarity(denoised, x + sigma_next*noise_tmp))
+        elif NOISE_COSSIM_SOURCE == "guide":
+            cossim_tmp.append(get_cosine_similarity(y0, x_tmp[i]))
+        elif NOISE_COSSIM_SOURCE == "guide_bkg":
+            cossim_tmp.append(get_cosine_similarity(y0_inv, x_tmp[i]))
+            
+    if step < EO("noise_cossim_start_step", 0):
+        x = x_tmp[0]
+
+    elif (NOISE_COSSIM_SOURCE == "eps_tiled"):
+        x = noise_cossim_eps_tiled(x_tmp, eps, noise_tmp_list, cossim_mode=NOISE_COSSIM_MODE, tile_size=noise_cossim_tile_size, step=step)
+    elif (NOISE_COSSIM_SOURCE == "guide_epsilon_tiled"):
+        x = noise_cossim_guide_eps_tiled(x_0, x_tmp, y0, noise_tmp_list, cossim_mode=NOISE_COSSIM_MODE, tile_size=noise_cossim_tile_size, step=step, sigma=sigma, rk_type=rk_type)
+    elif (NOISE_COSSIM_SOURCE == "guide_bkg_epsilon_tiled"):
+        x = noise_cossim_guide_eps_tiled(x_0, x_tmp, y0_inv, noise_tmp_list, cossim_mode=NOISE_COSSIM_MODE, tile_size=noise_cossim_tile_size, step=step, sigma=sigma, rk_type=rk_type)
+    elif (NOISE_COSSIM_SOURCE == "guide_tiled"):
+        x = noise_cossim_guide_tiled(x_tmp, y0, cossim_mode=NOISE_COSSIM_MODE, tile_size=noise_cossim_tile_size, step=step)
+    elif (NOISE_COSSIM_SOURCE == "guide_bkg_tiled"):
+        x = noise_cossim_guide_tiled(x_tmp, y0_inv, cossim_mode=NOISE_COSSIM_MODE, tile_size=noise_cossim_tile_size)
+    else:
+        for i in range(len(x_tmp)):
+            if   (NOISE_COSSIM_MODE == "forward") and (cossim_tmp[i] == max(cossim_tmp)):
+                x = x_tmp[i]
+                break
+            elif (NOISE_COSSIM_MODE == "reverse") and (cossim_tmp[i] == min(cossim_tmp)):
+                x = x_tmp[i]
+                break
+            elif (NOISE_COSSIM_MODE == "orthogonal") and (abs(cossim_tmp[i]) == min(abs(val) for val in cossim_tmp)):
+                x = x_tmp[i]
+                break
+            elif (NOISE_COSSIM_MODE != "forward") and (NOISE_COSSIM_MODE != "reverse") and (NOISE_COSSIM_MODE != "orthogonal"):
+                x = x_tmp[0]
+                break
+    return x
+
+
+
+
+
+def get_masked_epsilon_projection(x_0, x_, eps_, y0, y0_inv, s_, row, row_offset, rk_type, LG, step):
+    
+    eps_row, eps_row_inv = get_guide_epsilon_substep(x_0, x_, y0, y0_inv, s_, row, row_offset, rk_type)
+    eps_row_lerp = eps_[row]   +   LG.mask * (eps_row-eps_[row])   +   (1-LG.mask) * (eps_row_inv-eps_[row])
+    eps_collinear_eps_lerp = get_collinear(eps_[row], eps_row_lerp)
+    eps_lerp_ortho_eps     = get_orthogonal(eps_row_lerp, eps_[row])
+    eps_sum = eps_collinear_eps_lerp + eps_lerp_ortho_eps
+    lgw_mask, lgw_mask_inv = LG.get_masks_for_step(step)
+    eps_substep_guide = eps_[row] + lgw_mask * (eps_sum - eps_[row]) + lgw_mask_inv * (eps_sum - eps_[row])
+    return eps_substep_guide
+
+
+
diff --git a/custom_nodes/RES4LYF/beta/rk_method_beta.py b/custom_nodes/RES4LYF/beta/rk_method_beta.py
new file mode 100644
index 0000000000000000000000000000000000000000..89746d8142c77ae233980c78481ebf6c3eda6fdb
--- /dev/null
+++ b/custom_nodes/RES4LYF/beta/rk_method_beta.py
@@ -0,0 +1,1170 @@
+import torch
+from torch import Tensor
+from typing import Optional, Callable, Tuple, List, Dict, Any, Union
+
+import comfy.model_patcher
+import comfy.supported_models
+
+import itertools 
+
+from .phi_functions        import Phi
+from .rk_coefficients_beta import get_implicit_sampler_name_list, get_rk_methods_beta
+from ..helper              import ExtraOptions
+from ..latents             import get_orthogonal, get_collinear, get_cosine_similarity, tile_latent, untile_latent
+
+from ..res4lyf             import RESplain
+
+MAX_STEPS = 10000
+
+
+def get_data_from_step   (x:Tensor, x_next:Tensor, sigma:Tensor, sigma_next:Tensor) -> Tensor:
+    h = sigma_next - sigma
+    return (sigma_next * x - sigma * x_next) / h
+
+def get_epsilon_from_step(x:Tensor, x_next:Tensor, sigma:Tensor, sigma_next:Tensor) -> Tensor:
+    h = sigma_next - sigma
+    return (x - x_next) / h
+
+
+
+class RK_Method_Beta:
+    def __init__(self,
+                model,
+                rk_type               : str,
+                VE_MODEL              : bool,
+                noise_anchor          : float,
+                noise_boost_normalize : bool        = True,
+                model_device          : str         = 'cuda',
+                work_device           : str         = 'cpu',
+                dtype                 : torch.dtype = torch.float64,
+                extra_options         : str         = ""
+                ):
+        
+        self.work_device                 = work_device
+        self.model_device                = model_device
+        self.dtype                       : torch.dtype = dtype
+
+        self.model                       = model
+
+        if hasattr(model, "model"):
+            model_sampling = model.model.model_sampling
+        elif hasattr(model, "inner_model"):
+            model_sampling = model.inner_model.inner_model.model_sampling
+        
+        self.sigma_min                   : Tensor                   = model_sampling.sigma_min.to(dtype=dtype, device=work_device)
+        self.sigma_max                   : Tensor                   = model_sampling.sigma_max.to(dtype=dtype, device=work_device)
+
+        self.rk_type                     : str                      = rk_type
+
+        self.IMPLICIT                    : str                      = rk_type in get_implicit_sampler_name_list(nameOnly=True)
+        self.EXPONENTIAL                 : bool                     = RK_Method_Beta.is_exponential(rk_type)
+        self.VE_MODEL                    : bool                     = VE_MODEL
+
+        self.SYNC_SUBSTEP_MEAN_CW        : bool                     = noise_boost_normalize
+
+        self.A                           : Optional[Tensor]         = None
+        self.B                           : Optional[Tensor]         = None
+        self.U                           : Optional[Tensor]         = None
+        self.V                           : Optional[Tensor]         = None
+
+        self.rows                        : int                      = 0
+        self.cols                        : int                      = 0
+
+        self.denoised                    : Optional[Tensor]         = None
+        self.uncond                      : Optional[Tensor]         = None
+
+        self.y0                          : Optional[Tensor]         = None
+        self.y0_inv                      : Optional[Tensor]         = None
+
+        self.multistep_stages            : int                      = 0
+        self.row_offset                  : Optional[int]            = None
+
+        self.cfg_cw                      : float                    = 1.0
+        self.extra_args                  : Optional[Dict[str, Any]] = None
+
+        self.extra_options               : str                      = extra_options
+        self.EO                          : ExtraOptions             = ExtraOptions(extra_options)
+
+        self.reorder_tableau_indices     : list[int]                = self.EO("reorder_tableau_indices", [-1])
+
+        self.LINEAR_ANCHOR_X_0           : float                    = noise_anchor
+        
+        self.tile_sizes                  : Optional[List[Tuple[int,int]]] = None
+        self.tile_cnt                    : int                      = 0
+        self.latent_compression_ratio    : int                      = 8
+
+    @staticmethod
+    def is_exponential(rk_type:str) -> bool:
+        if rk_type.startswith(( "res", 
+                                "dpmpp", 
+                                "ddim", 
+                                "pec", 
+                                "etdrk", 
+                                "lawson", 
+                                "abnorsett",
+                                )): 
+            return True
+        else:
+            return False
+
+    @staticmethod
+    def create(model,
+            rk_type       : str,
+            VE_MODEL      : bool,
+            noise_anchor  : float       = 1.0,
+            noise_boost_normalize  : bool = True,
+            model_device  : str         = 'cuda',
+            work_device   : str         = 'cpu',
+            dtype         : torch.dtype = torch.float64,
+            extra_options : str         = ""
+            ) -> "Union[RK_Method_Exponential, RK_Method_Linear]":
+        
+        if RK_Method_Beta.is_exponential(rk_type):
+            return RK_Method_Exponential(model, rk_type, VE_MODEL, noise_anchor, noise_boost_normalize, model_device, work_device, dtype, extra_options)
+        else:
+            return RK_Method_Linear     (model, rk_type, VE_MODEL, noise_anchor, noise_boost_normalize, model_device, work_device, dtype, extra_options)
+                
+    def __call__(self):
+        raise NotImplementedError("This method got clownsharked!")
+    
+    def model_epsilon(self, x:Tensor, sigma:Tensor, **extra_args) -> Tuple[Tensor, Tensor]:
+        s_in     = x.new_ones([x.shape[0]])
+        denoised = self.model(x, sigma * s_in, **extra_args)
+        denoised = self.calc_cfg_channelwise(denoised)
+        eps      = (x - denoised) / (sigma * s_in).view(x.shape[0], 1, 1, 1)       #return x0 ###################################THIS WORKS ONLY WITH THE MODEL SAMPLING PATCH
+        return eps, denoised
+    
+    def model_denoised(self, x:Tensor, sigma:Tensor, **extra_args) -> Tensor:
+        s_in     = x.new_ones([x.shape[0]])
+        control_tiles = None
+        y0_style_pos = self.extra_args['model_options']['transformer_options'].get("y0_style_pos")
+        y0_style_neg = self.extra_args['model_options']['transformer_options'].get("y0_style_neg")
+        y0_style_pos_tile, sy0_style_neg_tiles = None, None
+        
+        if self.EO("tile_model_calls"):
+            tile_h = self.EO("tile_h", 128)
+            tile_w = self.EO("tile_w", 128)
+            
+            denoised_tiles = []
+            
+            tiles, orig_shape, grid, strides = tile_latent(x, tile_size=(tile_h,tile_w))
+            
+            for i in range(tiles.shape[0]):
+                tile = tiles[i].unsqueeze(0)
+                
+                denoised_tile = self.model(tile, sigma * s_in, **extra_args)
+                
+                denoised_tiles.append(denoised_tile)
+                
+            denoised_tiles = torch.cat(denoised_tiles, dim=0)
+            
+            denoised = untile_latent(denoised_tiles, orig_shape, grid, strides)
+            
+        elif self.tile_sizes is not None:
+            tile_h_full = self.tile_sizes[self.tile_cnt % len(self.tile_sizes)][0]
+            tile_w_full = self.tile_sizes[self.tile_cnt % len(self.tile_sizes)][1] 
+            
+            if tile_h_full == -1:
+                tile_h      = x.shape[-2]
+                tile_h_full = tile_h * self.latent_compression_ratio
+            else:
+                tile_h = tile_h_full // self.latent_compression_ratio
+                
+            if tile_w_full == -1:
+                tile_w      = x.shape[-1]
+                tile_w_full = tile_w * self.latent_compression_ratio
+            else:
+                tile_w = tile_w_full // self.latent_compression_ratio
+            
+            #tile_h = tile_h_full // self.latent_compression_ratio
+            #tile_w = tile_w_full // self.latent_compression_ratio
+            
+            self.tile_cnt += 1
+            
+            #if len(self.tile_sizes) == 1 and self.tile_cnt % 2 == 1:
+            #    tile_h, tile_w = tile_w, tile_h
+            #    tile_h_full, tile_w_full = tile_w_full, tile_h_full
+            
+            if (self.tile_cnt // len(self.tile_sizes)) % 2 == 1 and self.EO("tiles_autorotate"):
+                tile_h, tile_w = tile_w, tile_h
+                tile_h_full, tile_w_full = tile_w_full, tile_h_full
+            
+            xt_negative = self.model.inner_model.conds.get('xt_negative', self.model.inner_model.conds.get('negative'))
+            negative_control = xt_negative[0].get('control')
+            
+            if negative_control is not None and hasattr(negative_control, 'cond_hint_original'):
+                negative_cond_hint_init = negative_control.cond_hint.clone() if negative_control.cond_hint is not None else None
+            
+            xt_positive = self.model.inner_model.conds.get('xt_positive', self.model.inner_model.conds.get('positive'))
+            positive_control = xt_positive[0].get('control')
+            
+            if positive_control is not None and hasattr(positive_control, 'cond_hint_original'):
+                positive_cond_hint_init = positive_control.cond_hint.clone() if positive_control.cond_hint is not None else None
+                if positive_control.cond_hint_original.shape[-1] != x.shape[-2] * self.latent_compression_ratio or positive_control.cond_hint_original.shape[-2] != x.shape[-1] * self.latent_compression_ratio:
+                    positive_control_pretile = comfy.utils.bislerp(positive_control.cond_hint_original.clone().to(torch.float16).to('cuda'), x.shape[-1] * self.latent_compression_ratio, x.shape[-2] * self.latent_compression_ratio)
+                    positive_control.cond_hint_original = positive_control_pretile.to(positive_control.cond_hint_original)
+                positive_control_pretile = positive_control.cond_hint_original.clone().to(torch.float16).to('cuda')
+                control_tiles, control_orig_shape, control_grid, control_strides = tile_latent(positive_control_pretile, tile_size=(tile_h_full,tile_w_full))
+                control_tiles = control_tiles
+            
+            denoised_tiles = []
+            
+            tiles, orig_shape, grid, strides = tile_latent(x, tile_size=(tile_h,tile_w))
+            
+            if y0_style_pos is not None:
+                y0_style_pos_tiles, _, _, _ = tile_latent(y0_style_pos, tile_size=(tile_h,tile_w))
+            if y0_style_neg is not None:
+                y0_style_neg_tiles, _, _, _ = tile_latent(y0_style_neg, tile_size=(tile_h,tile_w))
+            
+            for i in range(tiles.shape[0]):
+                tile = tiles[i].unsqueeze(0)
+                self.extra_args['model_options']['transformer_options']['x_tmp'] = tile
+                if control_tiles is not None:
+                    positive_control.cond_hint = control_tiles[i].unsqueeze(0).to(positive_control.cond_hint)
+                    if negative_control is not None:
+                        negative_control.cond_hint = control_tiles[i].unsqueeze(0).to(positive_control.cond_hint)
+                
+                if y0_style_pos is not None:
+                    self.extra_args['model_options']['transformer_options']['y0_style_pos'] = y0_style_pos_tiles[i].unsqueeze(0)
+                if y0_style_neg is not None:
+                    self.extra_args['model_options']['transformer_options']['y0_style_neg'] = y0_style_neg_tiles[i].unsqueeze(0)
+                
+                denoised_tile = self.model(tile, sigma * s_in, **extra_args)
+                
+                denoised_tiles.append(denoised_tile)
+                
+            denoised_tiles = torch.cat(denoised_tiles, dim=0)
+            
+            denoised = untile_latent(denoised_tiles, orig_shape, grid, strides)
+            
+        else:
+            denoised = self.model(x, sigma * s_in, **extra_args)
+        
+        if control_tiles is not None:
+            positive_control.cond_hint = positive_cond_hint_init
+            if negative_control is not None:
+                negative_control.cond_hint = negative_cond_hint_init
+                
+        if y0_style_pos is not None:
+            self.extra_args['model_options']['transformer_options']['y0_style_pos'] = y0_style_pos
+        if y0_style_neg is not None:
+            self.extra_args['model_options']['transformer_options']['y0_style_neg'] = y0_style_neg
+        
+        denoised = self.calc_cfg_channelwise(denoised)
+        return denoised
+
+    def update_transformer_options(self,
+                transformer_options : Optional[dict] = None,
+                ):
+
+        self.extra_args.setdefault("model_options", {}).setdefault("transformer_options", {}).update(transformer_options)
+        return
+
+    def set_coeff(self,
+                rk_type    : str,
+                h          : Tensor,
+                c1         : float  = 0.0,
+                c2         : float  = 0.5,
+                c3         : float  = 1.0,
+                step       : int    = 0,
+                sigmas     : Optional[Tensor] = None,
+                sigma_down : Optional[Tensor] = None,
+                ) -> None:
+
+        self.rk_type     = rk_type
+        self.IMPLICIT    = rk_type in get_implicit_sampler_name_list(nameOnly=True)
+        self.EXPONENTIAL = RK_Method_Beta.is_exponential(rk_type) 
+
+        sigma            = sigmas[step]
+        sigma_next       = sigmas[step+1]
+        
+        h_prev = []
+        a, b, u, v, ci, multistep_stages, hybrid_stages, FSAL = get_rk_methods_beta(rk_type,
+                                                                                    h,
+                                                                                    c1,
+                                                                                    c2,
+                                                                                    c3,
+                                                                                    h_prev,
+                                                                                    step,
+                                                                                    sigmas,
+                                                                                    sigma,
+                                                                                    sigma_next,
+                                                                                    sigma_down,
+                                                                                    self.extra_options,
+                                                                                    )
+        
+        self.multistep_stages = multistep_stages
+        self.hybrid_stages    = hybrid_stages
+        
+        self.A = torch.tensor(a,  dtype=h.dtype, device=h.device)
+        self.B = torch.tensor(b,  dtype=h.dtype, device=h.device)
+        self.C = torch.tensor(ci, dtype=h.dtype, device=h.device)
+
+        self.U = torch.tensor(u,  dtype=h.dtype, device=h.device) if u is not None else None
+        self.V = torch.tensor(v,  dtype=h.dtype, device=h.device) if v is not None else None
+        
+        self.rows = self.A.shape[0]
+        self.cols = self.A.shape[1]
+        
+        self.row_offset = 1 if not self.IMPLICIT and self.A[0].sum() == 0 else 0  
+        
+        if self.IMPLICIT and self.reorder_tableau_indices[0] != -1:
+            self.reorder_tableau(self.reorder_tableau_indices)
+
+
+
+    def reorder_tableau(self, indices:list[int]) -> None:
+        #if indices[0]:
+        self.A    = self.A   [indices]
+        self.B[0] = self.B[0][indices]
+        self.C    = self.C   [indices]
+        self.C = torch.cat((self.C, self.C[-1:])) 
+        return
+
+
+
+    def update_substep(self,
+                        x_0        : Tensor,
+                        x_         : Tensor,
+                        eps_       : Tensor,
+                        eps_prev_  : Tensor,
+                        row        : int,
+                        row_offset : int,
+                        h_new      : Tensor,
+                        h_new_orig : Tensor,
+                        lying_eps_row_factor : float = 1.0,
+                        sigma      : Optional[Tensor] = None,
+                        ) -> Tensor:
+        
+        if row < self.rows - row_offset   and   self.multistep_stages == 0:
+            row_tmp_offset = row + row_offset
+
+        else:
+            row_tmp_offset = row + 1
+                
+        #zr_base   = self.zum(row+row_offset+self.multistep_stages, eps_, eps_prev_)  # TODO: why unused?
+        
+        if self.SYNC_SUBSTEP_MEAN_CW and lying_eps_row_factor != 1.0:
+            zr_orig = self.zum(row+row_offset+self.multistep_stages, eps_, eps_prev_)
+            x_orig_row = x_0 + h_new * zr_orig
+        
+        #eps_row      = eps_     [row].clone()
+        #eps_prev_row = eps_prev_[row].clone()
+        
+        eps_     [row] *= lying_eps_row_factor
+        eps_prev_[row] *= lying_eps_row_factor
+        
+        if self.EO("exp2lin_override"):
+            zr = self.zum2(row+row_offset+self.multistep_stages, eps_, eps_prev_, h_new, sigma)
+            x_[row_tmp_offset] = x_0 + zr
+        else:
+            zr = self.zum(row+row_offset+self.multistep_stages, eps_, eps_prev_)
+
+            x_[row_tmp_offset] = x_0 + h_new * zr
+        
+        if self.SYNC_SUBSTEP_MEAN_CW and lying_eps_row_factor != 1.0:
+            x_[row_tmp_offset] = x_[row_tmp_offset] - x_[row_tmp_offset].mean(dim=(-2,-1), keepdim=True) + x_orig_row.mean(dim=(-2,-1), keepdim=True)
+        
+        #eps_     [row] = eps_row
+        #eps_prev_[row] = eps_prev_row
+        
+        if (self.SYNC_SUBSTEP_MEAN_CW and h_new != h_new_orig) or self.EO("sync_mean_noise"):
+            if not self.EO("disable_sync_mean_noise"):
+                x_row_down = x_0 + h_new_orig * zr
+                x_[row_tmp_offset] = x_[row_tmp_offset] - x_[row_tmp_offset].mean(dim=(-2,-1), keepdim=True) + x_row_down.mean(dim=(-2,-1), keepdim=True)
+        
+        return x_
+
+
+
+    def zum2(self, row:int, k:Tensor, k_prev:Tensor=None, h_new:Tensor=None, sigma:Tensor=None) -> Tensor:
+        if row < self.rows:
+            return self.a_k_einsum2(row, k, h_new, sigma)
+        else:
+            row = row - self.rows
+            return self.b_k_einsum2(row, k, h_new, sigma)
+
+    def a_k_einsum2(self, row:int, k:Tensor, h:Tensor, sigma:Tensor) -> Tensor:
+        return torch.einsum('i,j,k,i... -> ...', self.A[row], h.unsqueeze(0), -sigma.unsqueeze(0), k[:self.cols])
+    
+    def b_k_einsum2(self, row:int, k:Tensor, h:Tensor, sigma:Tensor) -> Tensor:
+        return torch.einsum('i,j,k,i... -> ...', self.B[row], h.unsqueeze(0), -sigma.unsqueeze(0), k[:self.cols])
+
+    
+    def a_k_einsum(self, row:int, k     :Tensor) -> Tensor:
+        return torch.einsum('i, i... -> ...', self.A[row], k[:self.cols])
+    
+    def b_k_einsum(self, row:int, k     :Tensor) -> Tensor:
+        return torch.einsum('i, i... -> ...', self.B[row], k[:self.cols])
+    
+    def u_k_einsum(self, row:int, k_prev:Tensor) -> Tensor:
+        return torch.einsum('i, i... -> ...', self.U[row], k_prev[:self.cols]) if (self.U is not None and k_prev is not None) else 0
+    
+    def v_k_einsum(self, row:int, k_prev:Tensor) -> Tensor:
+        return torch.einsum('i, i... -> ...', self.V[row], k_prev[:self.cols]) if (self.V is not None and k_prev is not None) else 0
+    
+    
+    
+    def zum(self, row:int, k:Tensor, k_prev:Tensor=None,) -> Tensor:
+        if row < self.rows:
+            return self.a_k_einsum(row, k) + self.u_k_einsum(row, k_prev)
+        else:
+            row = row - self.rows
+            return self.b_k_einsum(row, k) + self.v_k_einsum(row, k_prev)
+        
+    def zum_tableau(self,  k:Tensor, k_prev:Tensor=None,) -> Tensor:
+        a_k_sum = torch.einsum('ij, j... -> i...', self.A, k[:self.cols])
+        u_k_sum = torch.einsum('ij, j... -> i...', self.U, k_prev[:self.cols]) if (self.U is not None and k_prev is not None) else 0
+        return a_k_sum + u_k_sum
+        
+    def get_x(self, data:Tensor, noise:Tensor, sigma:Tensor):
+        if self.VE_MODEL:
+            return data + sigma * noise
+        else:
+            return (self.sigma_max - sigma) * data + sigma * noise
+
+    def init_cfg_channelwise(self, x:Tensor, cfg_cw:float=1.0, **extra_args) -> Dict[str, Any]:
+        self.uncond = [torch.full_like(x, 0.0)]
+        self.cfg_cw = cfg_cw
+        if cfg_cw != 1.0:
+            def post_cfg_function(args):
+                self.uncond[0] = args["uncond_denoised"]
+                return args["denoised"]
+            model_options = extra_args.get("model_options", {}).copy()
+            extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+        return extra_args
+            
+            
+    def calc_cfg_channelwise(self, denoised:Tensor) -> Tensor:
+        if self.cfg_cw != 1.0:            
+            avg = 0
+            for b, c in itertools.product(range(denoised.shape[0]), range(denoised.shape[1])):
+                avg     += torch.norm(denoised[b][c] - self.uncond[0][b][c])
+            avg  /= denoised.shape[1]
+            
+            for b, c in itertools.product(range(denoised.shape[0]), range(denoised.shape[1])):
+                ratio     = torch.nan_to_num(torch.norm(denoised[b][c] - self.uncond[0][b][c])   /   avg,     0)
+                denoised_new = self.uncond[0] + ratio * self.cfg_cw * (denoised - self.uncond[0])
+            return denoised_new
+        else:
+            return denoised
+        
+
+    @staticmethod
+    def calculate_res_2m_step(
+                            x_0        : Tensor,
+                            denoised_  : Tensor,
+                            sigma_down : Tensor,
+                            sigmas     : Tensor,
+                            step       : int,
+                            ) -> Tuple[Tensor, Tensor]:
+        
+        if denoised_[2].sum() == 0:
+            return None, None
+        
+        sigma      = sigmas[step]
+        sigma_prev = sigmas[step-1]
+        
+        h_prev = -torch.log(sigma/sigma_prev)
+        h      = -torch.log(sigma_down/sigma)
+
+        c1 = 0
+        c2 = (-h_prev / h).item()
+
+        ci = [c1,c2]
+        φ = Phi(h, ci, analytic_solution=True)
+
+        b2 = φ(2)/c2
+        b1 = φ(1) - b2
+        
+        eps_2 = denoised_[1] - x_0
+        eps_1 = denoised_[0] - x_0
+
+        h_a_k_sum = h * (b1 * eps_1 + b2 * eps_2)
+        
+        x = torch.exp(-h) * x_0 + h_a_k_sum
+        
+        denoised = x_0 + (sigma / (sigma - sigma_down)) * h_a_k_sum
+
+        return x, denoised
+
+
+    @staticmethod
+    def calculate_res_3m_step(
+                            x_0        : Tensor,
+                            denoised_  : Tensor,
+                            sigma_down : Tensor,
+                            sigmas     : Tensor,
+                            step       : int,
+                            ) -> Tuple[Tensor, Tensor]:
+        
+        if denoised_[3].sum() == 0:
+            return None, None
+        
+        sigma       = sigmas[step]
+        sigma_prev  = sigmas[step-1]
+        sigma_prev2 = sigmas[step-2]
+
+        h       = -torch.log(sigma_down/sigma)
+        h_prev  = -torch.log(sigma/sigma_prev)
+        h_prev2 = -torch.log(sigma/sigma_prev2)
+
+        c1 = 0
+        c2 = (-h_prev  / h).item()
+        c3 = (-h_prev2 / h).item()
+
+        ci = [c1,c2,c3]
+        φ = Phi(h, ci, analytic_solution=True)
+        
+        gamma = (3*(c3**3) - 2*c3) / (c2*(2 - 3*c2))
+
+        b3 = (1 / (gamma * c2 + c3)) * φ(2, -h)      
+        b2 = gamma * b3 
+        b1 = φ(1, -h) - b2 - b3    
+        
+        eps_3 = denoised_[2] - x_0
+        eps_2 = denoised_[1] - x_0
+        eps_1 = denoised_[0] - x_0
+
+        h_a_k_sum = h * (b1 * eps_1 + b2 * eps_2 + b3 * eps_3)
+        
+        x = torch.exp(-h) * x_0 + h_a_k_sum
+        
+        denoised = x_0 + (sigma / (sigma - sigma_down)) * h_a_k_sum
+
+        return x, denoised
+
+    def swap_rk_type_at_step_or_threshold(self,
+                                            x_0               : Tensor,
+                                            data_prev_        : Tensor,
+                                            NS,
+                                            sigmas            : Tensor,
+                                            step              : Tensor,
+                                            rk_swap_step      : int,
+                                            rk_swap_threshold : float,
+                                            rk_swap_type      : str,
+                                            rk_swap_print     : bool,
+                                            ) -> str:
+        if rk_swap_type == "":
+            if self.EXPONENTIAL:
+                rk_swap_type = "res_3m" 
+            else:
+                rk_swap_type = "deis_3m"
+            
+        if step > rk_swap_step and self.rk_type != rk_swap_type:
+            RESplain("Switching rk_type to:", rk_swap_type)
+            self.rk_type = rk_swap_type
+            
+            if RK_Method_Beta.is_exponential(rk_swap_type):
+                self.__class__ = RK_Method_Exponential
+            else:
+                self.__class__ = RK_Method_Linear
+                
+            if rk_swap_type in get_implicit_sampler_name_list(nameOnly=True):
+                self.IMPLICIT   = True
+                self.row_offset = 0
+                NS.row_offset   = 0
+            else:
+                self.IMPLICIT   = False
+                self.row_offset = 1
+                NS.row_offset   = 1
+            NS.h_fn     = self.h_fn
+            NS.t_fn     = self.t_fn
+            NS.sigma_fn = self.sigma_fn
+            
+            
+            
+        if step > 2 and sigmas[step+1] > 0 and self.rk_type != rk_swap_type and rk_swap_threshold > 0:
+            x_res_2m, denoised_res_2m = self.calculate_res_2m_step(x_0, data_prev_, NS.sigma_down, sigmas, step)
+            x_res_3m, denoised_res_3m = self.calculate_res_3m_step(x_0, data_prev_, NS.sigma_down, sigmas, step)
+            if denoised_res_2m is not None:
+                if rk_swap_print:
+                    RESplain("res_3m - res_2m:", torch.norm(denoised_res_3m - denoised_res_2m).item())
+                if rk_swap_threshold > torch.norm(denoised_res_2m - denoised_res_3m):
+                    RESplain("Switching rk_type to:", rk_swap_type, "at step:", step)
+                    self.rk_type = rk_swap_type
+            
+                    if RK_Method_Beta.is_exponential(rk_swap_type):
+                        self.__class__ = RK_Method_Exponential
+                    else:
+                        self.__class__ = RK_Method_Linear
+                
+                    if rk_swap_type in get_implicit_sampler_name_list(nameOnly=True):
+                        self.IMPLICIT   = True
+                        self.row_offset = 0
+                        NS.row_offset   = 0
+                    else:
+                        self.IMPLICIT   = False
+                        self.row_offset = 1
+                        NS.row_offset   = 1
+                    NS.h_fn     = self.h_fn
+                    NS.t_fn     = self.t_fn
+                    NS.sigma_fn = self.sigma_fn
+            
+        return self.rk_type
+
+
+    def bong_iter(self,
+                    x_0       : Tensor,
+                    x_        : Tensor,
+                    eps_      : Tensor,
+                    eps_prev_ : Tensor,
+                    data_     : Tensor,
+                    sigma     : Tensor,
+                    s_        : Tensor,
+                    row       : int,
+                    row_offset: int,
+                    h         : Tensor,
+                    step      : int,
+                    step_sched: int,
+                    BONGMATH_Y : bool = False,
+                    y0_bongflow : Optional[Tensor] = None,
+                    noise_sync: Optional[Tensor] = None,
+                    eps_x_    : Optional[Tensor] = None,
+                    eps_y_    : Optional[Tensor] = None,
+                    #eps_x2y_  : Optional[Tensor] = None,
+                    data_x_   : Optional[Tensor] = None,
+                    data_y_   : Optional[Tensor] = None,
+                    #yt_       : Optional[Tensor] = None,
+                    #yt_0      : Optional[Tensor] = None,
+                    LG = None,
+                    ) -> Tuple[Tensor, Tensor, Tensor]:
+        
+        if x_0.ndim == 4:
+            norm_dim = (-2,-1)
+        elif x_0.ndim == 5:
+            norm_dim = (-4,-2,-1)
+        
+        if BONGMATH_Y:
+            lgw_mask_,      lgw_mask_inv_      = LG.get_masks_for_step(step_sched)
+            lgw_mask_sync_, lgw_mask_sync_inv_ = LG.get_masks_for_step(step_sched, lgw_type="sync")
+
+            weight_mask = lgw_mask_+lgw_mask_inv_
+            if LG.SYNC_SEPARATE:
+                sync_mask = lgw_mask_sync_+lgw_mask_sync_inv_
+            else:
+                sync_mask = 1.
+            
+        
+        if self.EO("bong_start_step", 0) > step or step > self.EO("bong_stop_step", 10000) or (self.unsample_bongmath == False and s_[-1] > s_[0]):
+            return x_0, x_, eps_
+        
+        bong_iter_max_row = self.rows - row_offset
+        if self.EO("bong_iter_max_row_full"):
+            bong_iter_max_row = self.rows
+            
+        if self.EO("bong_iter_lock_x_0_ch_means"):
+            x_0_ch_means = x_0.mean(dim=norm_dim, keepdim=True)
+            
+        if self.EO("bong_iter_lock_x_row_ch_means"):
+            x_row_means = []
+            for rr in range(row+row_offset):
+                x_row_mean = x_[rr].mean(dim=norm_dim, keepdim=True)
+                x_row_means.append(x_row_mean)
+        
+        if row < bong_iter_max_row   and   self.multistep_stages == 0:
+            bong_strength = self.EO("bong_strength", 1.0)
+            
+            if bong_strength != 1.0:
+                x_0_tmp  = x_0 .clone()
+                x_tmp_   = x_  .clone()
+                eps_tmp_ = eps_.clone()
+
+            for i in range(100):     #bongmath for eps_prev_ not implemented?
+                x_0 = x_[row+row_offset] - h * self.zum(row+row_offset, eps_, eps_prev_)
+                
+                if self.EO("bong_iter_lock_x_0_ch_means"):
+                    x_0 = x_0 - x_0.mean(dim=norm_dim, keepdim=True) + x_0_ch_means
+                
+                for rr in range(row+row_offset):
+                    x_[rr] = x_0 + h * self.zum(rr, eps_, eps_prev_)
+                
+                if self.EO("bong_iter_lock_x_row_ch_means"):
+                    for rr in range(row+row_offset):
+                        x_[rr] = x_[rr] - x_[rr].mean(dim=norm_dim, keepdim=True) + x_row_means[rr]
+                
+                for rr in range(row+row_offset):
+                    if self.EO("zonkytar"):
+                        #eps_[rr] = self.get_unsample_epsilon(x_[rr], x_0, data_[rr], sigma, s_[rr])
+                        eps_[rr] = self.get_epsilon(x_[rr], x_0, data_[rr], sigma, s_[rr])
+                    else:
+                        if BONGMATH_Y and not self.EO("disable_bongmath_y"):
+                            if self.EXPONENTIAL:
+                                eps_x_ = data_x_ - x_0
+                                eps_x2y_ = data_y_ - x_0
+                                if self.VE_MODEL:
+                                    eps_ = sync_mask * eps_x_   +   (1-sync_mask) * eps_x2y_   +   weight_mask * (-eps_y_+sigma*(-noise_sync))
+                                    if self.EO("sync_x2y"):
+                                        eps_ = sync_mask * eps_x_   +   (1-sync_mask) * eps_x2y_   +   weight_mask * (-eps_x2y_+sigma*(-noise_sync))
+                                else:
+                                    eps_ = sync_mask * eps_x_   +   (1-sync_mask) * eps_x2y_   +   weight_mask * (-eps_y_+sigma*(y0_bongflow-noise_sync))
+                                    if self.EO("sync_x2y"):
+                                        eps_ = sync_mask * eps_x_   +   (1-sync_mask) * eps_x2y_   +   weight_mask * (-eps_x2y_+sigma*(y0_bongflow-noise_sync))
+                            else:
+                                eps_x_  [:s_.shape[0]] = (x_[:s_.shape[0]] - data_x_[:s_.shape[0]]) / s_.view(-1,1,1,1,1)   # or should it be vs x_0???
+                                eps_x2y_ = torch.zeros_like(eps_x_)
+                                eps_x2y_[:s_.shape[0]] = (x_[:s_.shape[0]] - data_y_[:s_.shape[0]]) / s_.view(-1,1,1,1,1)   # or should it be vs x_0???
+
+                                if self.VE_MODEL:
+                                    eps_ = sync_mask * eps_x_   +   (1-sync_mask) * eps_x2y_   +   weight_mask * (noise_sync-eps_y_)
+                                    if self.EO("sync_x2y"):
+                                        eps_ = sync_mask * eps_x_   +   (1-sync_mask) * eps_x2y_   +   weight_mask * (noise_sync-eps_x2y_)
+                                else: 
+                                    eps_ = sync_mask * eps_x_   +   (1-sync_mask) * eps_x2y_   +   weight_mask * (noise_sync-eps_y_-y0_bongflow)
+                                    if self.EO("sync_x2y"):
+                                        eps_ = sync_mask * eps_x_   +   (1-sync_mask) * eps_x2y_   +   weight_mask * (noise_sync-eps_x2y_-y0_bongflow)
+
+                        else:
+                            eps_[rr] = self.get_epsilon(x_0, x_[rr], data_[rr], sigma, s_[rr])
+                    
+            if bong_strength != 1.0:
+                x_0  = x_0_tmp  + bong_strength * (x_0  - x_0_tmp)
+                x_   = x_tmp_   + bong_strength * (x_   - x_tmp_)
+                eps_ = eps_tmp_ + bong_strength * (eps_ - eps_tmp_)
+        
+        return x_0, x_, eps_ #,   yt_0, yt_
+
+
+    def newton_iter(self,
+                    x_0        : Tensor,
+                    x_         : Tensor,
+                    eps_       : Tensor,
+                    eps_prev_  : Tensor,
+                    data_      : Tensor,
+                    s_         : Tensor,
+                    row        : int,
+                    h          : Tensor,
+                    sigmas     : Tensor,
+                    step       : int,
+                    newton_name: str,
+                    SYNC_GUIDE_ACTIVE: bool,
+                    ) -> Tuple[Tensor, Tensor]:
+        if SYNC_GUIDE_ACTIVE:
+            return x_, eps_
+        newton_iter_name = "newton_iter_" + newton_name
+        
+        default_anchor_x_all = False
+        if newton_name == "lying":
+            default_anchor_x_all = True
+        
+        newton_iter                 = self.EO(newton_iter_name,                      100)
+        newton_iter_skip_last_steps = self.EO(newton_iter_name + "_skip_last_steps",   0)
+        newton_iter_mixing_rate     = self.EO(newton_iter_name + "_mixing_rate",     1.0)
+        
+        newton_iter_anchor          = self.EO(newton_iter_name + "_anchor",            0)
+        newton_iter_anchor_x_all    = self.EO(newton_iter_name + "_anchor_x_all",    default_anchor_x_all)
+        newton_iter_type            = self.EO(newton_iter_name + "_type",           "from_epsilon")
+        newton_iter_sequence        = self.EO(newton_iter_name + "_sequence",       "double")
+        
+        row_b_offset = 0
+        if self.EO(newton_iter_name + "_include_row_b"):
+            row_b_offset = 1
+        
+        if step >= len(sigmas)-1-newton_iter_skip_last_steps   or   sigmas[step+1] == 0   or   not self.IMPLICIT:
+            return x_, eps_
+        
+        sigma = sigmas[step]
+        
+        start, stop = 0, self.rows+row_b_offset
+        if newton_name   == "pre":
+            start = row
+        elif newton_name == "post":
+            start = row + 1
+            
+        if newton_iter_anchor >= 0:
+            eps_anchor = eps_[newton_iter_anchor].clone()
+            
+        if newton_iter_anchor_x_all:
+            x_orig_ = x_.clone()
+            
+        for n_iter in range(newton_iter):
+            for r in range(start, stop):
+                if newton_iter_anchor >= 0:
+                    eps_[newton_iter_anchor] = eps_anchor.clone()
+                if newton_iter_anchor_x_all:
+                    x_ = x_orig_.clone()
+                x_tmp, eps_tmp = x_[r].clone(), eps_[r].clone()
+                
+                seq_start, seq_stop = r, r+1
+                
+                if newton_iter_sequence == "double":
+                    seq_start, seq_stop = start, stop
+                    
+                for r_ in range(seq_start, seq_stop):
+                    x_[r_] = x_0 + h * self.zum(r_, eps_, eps_prev_)
+
+                for r_ in range(seq_start, seq_stop):
+                    if newton_iter_type == "from_data":
+                        data_[r_] = get_data_from_step(x_0, x_[r_], sigma, s_[r_])  
+                        eps_ [r_] = self.get_epsilon(x_0, x_[r_], data_[r_], sigma, s_[r_])
+                    elif newton_iter_type == "from_step":
+                        eps_ [r_] = get_epsilon_from_step(x_0, x_[r_], sigma, s_[r_])
+                    elif newton_iter_type == "from_alt":
+                        eps_ [r_] = x_0/sigma - x_[r_]/s_[r_]
+                    elif newton_iter_type == "from_epsilon":
+                        eps_ [r_] = self.get_epsilon(x_0, x_[r_], data_[r_], sigma, s_[r_])
+                    
+                    if self.EO(newton_iter_name + "_opt"):
+                        opt_timing, opt_type, opt_subtype = self.EO(newton_iter_name+"_opt", [str])
+                        
+                        opt_start, opt_stop = 0, self.rows+row_b_offset
+                        if    opt_timing == "early":
+                            opt_stop  = row + 1
+                        elif  opt_timing == "late":
+                            opt_start = row + 1
+
+                        for r2 in range(opt_start, opt_stop): 
+                            if r_ != r2:
+                                if   opt_subtype == "a":
+                                    eps_a = eps_[r2]
+                                    eps_b = eps_[r_]
+                                elif opt_subtype == "b":
+                                    eps_a = eps_[r_]
+                                    eps_b = eps_[r2]
+                                
+                                if   opt_type == "ortho":
+                                    eps_ [r_] = get_orthogonal(eps_a, eps_b)
+                                elif opt_type == "collin":
+                                    eps_ [r_] = get_collinear (eps_a, eps_b)
+                                elif opt_type == "proj":
+                                    eps_ [r_] = get_collinear (eps_a, eps_b) + get_orthogonal(eps_b, eps_a)
+                                    
+                    x_  [r_] =   x_tmp + newton_iter_mixing_rate * (x_  [r_] -   x_tmp)
+                    eps_[r_] = eps_tmp + newton_iter_mixing_rate * (eps_[r_] - eps_tmp)
+                    
+                if newton_iter_sequence == "double":
+                    break
+        
+        return x_, eps_
+
+
+
+
+class RK_Method_Exponential(RK_Method_Beta):
+    def __init__(self,
+                model,
+                rk_type       : str,
+                VE_MODEL      : bool,
+                noise_anchor  : float,
+                noise_boost_normalize  : bool,
+
+                model_device  : str         = 'cuda',
+                work_device   : str         = 'cpu',
+                dtype         : torch.dtype = torch.float64,
+                extra_options : str         = "",
+                ):
+        
+        super().__init__(model,
+                        rk_type,
+                        VE_MODEL,
+                        noise_anchor,
+                        noise_boost_normalize,
+                        model_device  = model_device,
+                        work_device   = work_device,
+                        dtype         = dtype,
+                        extra_options = extra_options,
+                        ) 
+        
+    @staticmethod
+    def alpha_fn(neg_h:Tensor) -> Tensor:
+        return torch.exp(neg_h)
+
+    @staticmethod
+    def sigma_fn(t:Tensor) -> Tensor:
+        #return 1/(torch.exp(-t)+1)
+        return t.neg().exp()
+
+    @staticmethod
+    def t_fn(sigma:Tensor) -> Tensor:
+        #return -torch.log((1.-sigma)/sigma)
+        return sigma.log().neg()
+    
+    @staticmethod
+    def h_fn(sigma_down:Tensor, sigma:Tensor) -> Tensor:
+        #return (-torch.log((1.-sigma_down)/sigma_down)) - (-torch.log((1.-sigma)/sigma))
+        return -torch.log(sigma_down/sigma)
+
+    def __call__(self,
+                x         : Tensor,
+                sub_sigma : Tensor,
+                x_0       : Optional[Tensor] = None,
+                sigma     : Optional[Tensor] = None,
+                transformer_options : Optional[dict] = None,
+                ) -> Tuple[Tensor, Tensor]:
+        
+        x_0   = x         if x_0   is None else x_0
+        sigma = sub_sigma if sigma is None else sigma
+        
+        if transformer_options is not None:
+            self.extra_args.setdefault("model_options", {}).setdefault("transformer_options", {}).update(transformer_options)
+
+        denoised = self.model_denoised(x.to(self.model_device), sub_sigma.to(self.model_device), **self.extra_args).to(sigma.device)
+        
+        eps_anchored = (x_0 - denoised) / sigma
+        eps_unmoored = (x   - denoised) / sub_sigma
+        
+        eps      = eps_unmoored + self.LINEAR_ANCHOR_X_0 * (eps_anchored - eps_unmoored)
+        
+        denoised = x_0 - sigma * eps
+        
+        epsilon  = denoised - x_0
+        
+        #epsilon = denoised - x
+        
+        if self.EO("exp2lin_override"):
+            epsilon = (x_0 - denoised) / sigma
+        
+        return epsilon, denoised
+    
+    def get_eps(self, *args):
+        if   len(args) == 3:
+            x, denoised, sigma = args
+            return denoised - x
+        elif len(args) == 5:
+            x_0, x, denoised, sigma, sub_sigma = args
+            eps_anchored = (x_0 - denoised) / sigma
+            eps_unmoored = (x   - denoised) / sub_sigma
+            eps      = eps_unmoored + self.LINEAR_ANCHOR_X_0 * (eps_anchored - eps_unmoored)
+            denoised = x_0 - sigma * eps
+            eps_out = denoised - x_0
+            if self.EO("exp2lin_override"):
+                eps_out = (x_0 - denoised) / sigma
+            return eps_out
+            
+        else:
+            raise ValueError(f"get_eps expected 3 or 5 arguments, got {len(args)}")
+    
+    def get_epsilon(self,
+                    x_0       : Tensor,
+                    x         : Tensor,
+                    denoised  : Tensor,
+                    sigma     : Tensor,
+                    sub_sigma : Tensor,
+                    ) -> Tensor:
+        
+        eps_anchored = (x_0 - denoised) / sigma
+        eps_unmoored = (x   - denoised) / sub_sigma
+        
+        eps      = eps_unmoored + self.LINEAR_ANCHOR_X_0 * (eps_anchored - eps_unmoored)
+        
+        denoised = x_0 - sigma * eps
+        if self.EO("exp2lin_override"):
+            return (x_0 - denoised) / sigma
+        else:
+            return denoised - x_0
+    
+    
+    
+    def get_epsilon_anchored(self, x_0:Tensor, denoised:Tensor, sigma:Tensor) -> Tensor:
+        return denoised - x_0
+    
+    
+    
+    def get_guide_epsilon(self,
+                            x_0           : Tensor,
+                            x             : Tensor,
+                            y             : Tensor,
+                            sigma         : Tensor,
+                            sigma_cur     : Tensor,
+                            sigma_down    : Optional[Tensor] = None,
+                            epsilon_scale : Optional[Tensor] = None,
+                            ) -> Tensor:
+
+        sigma_cur = epsilon_scale if epsilon_scale is not None else sigma_cur
+
+        if sigma_down > sigma:
+            eps_unmoored = (sigma_cur/(self.sigma_max - sigma_cur)) * (x   - y)
+        else:
+            eps_unmoored = y - x 
+        
+        if self.EO("manually_anchor_unsampler"):
+            if sigma_down > sigma:
+                eps_anchored = (sigma    /(self.sigma_max - sigma)) * (x_0 - y)
+            else:
+                eps_anchored = y - x_0
+            eps_guide = eps_unmoored + self.LINEAR_ANCHOR_X_0 * (eps_anchored - eps_unmoored)
+        else:
+            eps_guide = eps_unmoored
+        
+        return eps_guide
+
+
+
+class RK_Method_Linear(RK_Method_Beta):
+    def __init__(self,
+                model,
+                rk_type       : str,
+                VE_MODEL      : bool,
+                noise_anchor  : float,
+                noise_boost_normalize  : bool,
+                model_device  : str         = 'cuda',
+                work_device   : str         = 'cpu',
+                dtype         : torch.dtype = torch.float64,
+                extra_options : str         = "",
+                ):
+        
+        super().__init__(model,
+                        rk_type,
+                        VE_MODEL,
+                        noise_anchor,
+                        noise_boost_normalize,
+                        model_device  = model_device,
+                        work_device   = work_device,
+                        dtype         = dtype,
+                        extra_options = extra_options,
+                        ) 
+        
+    @staticmethod
+    def alpha_fn(neg_h:Tensor) -> Tensor:
+        return torch.ones_like(neg_h)
+
+    @staticmethod
+    def sigma_fn(t:Tensor) -> Tensor:
+        return t
+
+    @staticmethod
+    def t_fn(sigma:Tensor) -> Tensor:
+        return sigma
+    
+    @staticmethod
+    def h_fn(sigma_down:Tensor, sigma:Tensor) -> Tensor:
+        return sigma_down - sigma
+    
+    def __call__(self,
+                x         : Tensor,
+                sub_sigma : Tensor,
+                x_0       : Optional[Tensor] = None,
+                sigma     : Optional[Tensor] = None,
+                transformer_options : Optional[dict] = None,
+                ) -> Tuple[Tensor, Tensor]:
+        
+        x_0   = x         if x_0   is None else x_0
+        sigma = sub_sigma if sigma is None else sigma
+        
+        if transformer_options is not None:
+            self.extra_args.setdefault("model_options", {}).setdefault("transformer_options", {}).update(transformer_options)     
+        
+        denoised = self.model_denoised(x.to(self.model_device), sub_sigma.to(self.model_device), **self.extra_args).to(sigma.device)
+
+        epsilon_anchor   = (x_0 - denoised) / sigma
+        epsilon_unmoored =   (x - denoised) / sub_sigma
+        
+        epsilon = epsilon_unmoored + self.LINEAR_ANCHOR_X_0 * (epsilon_anchor - epsilon_unmoored)
+
+        return epsilon, denoised
+
+    def get_eps(self, *args):
+        if   len(args) == 3:
+            x, denoised, sigma = args
+            return (x - denoised) / sigma
+        elif len(args == 5):
+            x_0, x, denoised, sigma, sub_sigma = args
+            eps_anchor   = (x_0 - denoised) / sigma
+            eps_unmoored =   (x - denoised) / sub_sigma
+            return eps_unmoored + self.LINEAR_ANCHOR_X_0 * (eps_anchor - eps_unmoored)
+        else:
+            raise ValueError(f"get_eps expected 3 or 5 arguments, got {len(args)}")
+
+    def get_epsilon(self,
+                    x_0       : Tensor,
+                    x         : Tensor,
+                    denoised  : Tensor,
+                    sigma     : Tensor,
+                    sub_sigma : Tensor,
+                    ) -> Tensor:
+        
+        eps_anchor   = (x_0 - denoised) / sigma
+        eps_unmoored =   (x - denoised) / sub_sigma
+        
+        return eps_unmoored + self.LINEAR_ANCHOR_X_0 * (eps_anchor - eps_unmoored)
+    
+    
+    
+    def get_epsilon_anchored(self, x_0:Tensor, denoised:Tensor, sigma:Tensor) -> Tensor:
+        return (x_0 - denoised) / sigma
+    
+    
+    
+    def get_guide_epsilon(self, 
+                            x_0           : Tensor, 
+                            x             : Tensor, 
+                            y             : Tensor, 
+                            sigma         : Tensor, 
+                            sigma_cur     : Tensor, 
+                            sigma_down    : Optional[Tensor] = None, 
+                            epsilon_scale : Optional[Tensor] = None, 
+                            ) -> Tensor:
+
+        if sigma_down > sigma:
+            sigma_ratio = self.sigma_max - sigma_cur.clone()
+        else:
+            sigma_ratio = sigma_cur.clone()
+        sigma_ratio = epsilon_scale if epsilon_scale is not None else sigma_ratio
+
+        if sigma_down is None:
+            return (x - y) / sigma_ratio
+        else:
+            if sigma_down > sigma:
+                return (y - x) / sigma_ratio
+            else:
+                return (x - y) / sigma_ratio
+
+
+
+
+"""
+
+
+
+
+if EO("bong2m") and RK.multistep_stages > 0 and step < len(sigmas)-4:
+    h_no_eta       = -torch.log(sigmas[step+1]/sigmas[step])
+    h_prev1_no_eta = -torch.log(sigmas[step]  /sigmas[step-1])
+    c2_prev = (-h_prev1_no_eta / h_no_eta).item()
+    eps_prev = denoised_data_prev - x_0
+    
+    φ = Phi(h_prev, [0.,c2_prev])
+    a2_1 = c2_prev * φ(1,2)
+    for i in range(100):
+        x_prev = x_0 - h_prev * (a2_1 * eps_prev)
+        eps_prev = denoised_data_prev - x_prev
+        
+    eps_[1] = eps_prev
+    
+if EO("bong3m") and RK.multistep_stages > 0 and step < len(sigmas)-10:
+    h_no_eta       = -torch.log(sigmas[step+1]/sigmas[step])
+    h_prev1_no_eta = -torch.log(sigmas[step]  /sigmas[step-1])
+    h_prev2_no_eta = -torch.log(sigmas[step]  /sigmas[step-2])
+    c2_prev        = (-h_prev1_no_eta / h_no_eta).item()
+    c3_prev        = (-h_prev2_no_eta / h_no_eta).item()      
+    
+    eps_prev2 = denoised_data_prev2 - x_0
+    eps_prev  = denoised_data_prev  - x_0
+    
+    φ = Phi(h_prev1_no_eta, [0.,c2_prev, c3_prev])
+    a2_1 = c2_prev * φ(1,2)
+    for i in range(100):
+        x_prev = x_0 - h_prev1_no_eta * (a2_1 * eps_prev)
+        eps_prev = denoised_data_prev2 - x_prev
+        
+    eps_[1] = eps_prev
+    
+    φ = Phi(h_prev2_no_eta, [0.,c3_prev, c3_prev])
+    
+    def calculate_gamma(c2_prev, c3_prev):
+        return (3*(c3_prev**3) - 2*c3_prev) / (c2_prev*(2 - 3*c2_prev))
+    gamma = calculate_gamma(c2_prev, c3_prev)
+    
+    a2_1 = c2_prev * φ(1,2)
+    a3_2 = gamma * c2_prev * φ(2,2) + (c3_prev ** 2 / c2_prev) * φ(2, 3)
+    a3_1 = c3_prev * φ(1,3) - a3_2
+    
+    for i in range(100):
+        x_prev2 = x_0     - h_prev2_no_eta * (a3_1 * eps_prev + a3_2 * eps_prev2)
+        x_prev  = x_prev2 + h_prev2_no_eta * (a2_1 * eps_prev)
+        
+        eps_prev2 = denoised_data_prev - x_prev2
+        eps_prev  = denoised_data_prev2 - x_prev
+        
+    eps_[2] = eps_prev2
+"""
\ No newline at end of file
diff --git a/custom_nodes/RES4LYF/beta/rk_noise_sampler_beta.py b/custom_nodes/RES4LYF/beta/rk_noise_sampler_beta.py
new file mode 100644
index 0000000000000000000000000000000000000000..43c52efb2af879196218f9bbd4f25ec415dcb2ae
--- /dev/null
+++ b/custom_nodes/RES4LYF/beta/rk_noise_sampler_beta.py
@@ -0,0 +1,849 @@
+import torch
+
+from torch  import Tensor
+from typing import Optional, Callable, Tuple, Dict, Any, Union, TYPE_CHECKING, TypeVar
+
+if TYPE_CHECKING:
+    from .rk_method_beta import RK_Method_Exponential, RK_Method_Linear
+
+import comfy.model_patcher
+import comfy.supported_models
+
+from .noise_classes import NOISE_GENERATOR_CLASSES, NOISE_GENERATOR_CLASSES_SIMPLE
+from .constants     import MAX_STEPS
+
+from ..helper       import ExtraOptions, has_nested_attr 
+from ..latents      import normalize_zscore, get_orthogonal, get_collinear
+from ..res4lyf      import RESplain
+
+
+
+
+NOISE_MODE_NAMES = ["none",
+                    #"hard_sq",
+                    "hard",
+                    "lorentzian", 
+                    "soft", 
+                    "soft-linear",
+                    "softer",
+                    "eps",
+                    "sinusoidal",
+                    "exp", 
+                    "vpsde",
+                    "er4",
+                    "hard_var", 
+                    ]
+
+
+
+def get_data_from_step(x, x_next, sigma, sigma_next): # assumes 100% linear trajectory
+    h = sigma_next - sigma
+    return (sigma_next * x - sigma * x_next) / h
+
+def get_epsilon_from_step(x, x_next, sigma, sigma_next):
+    h = sigma_next - sigma
+    return (x - x_next) / h
+
+
+
+class RK_NoiseSampler:
+    def __init__(self,
+                RK            : Union["RK_Method_Exponential", "RK_Method_Linear"],
+                model,
+                step          : int=0,
+                device        : str='cuda',
+                dtype         : torch.dtype=torch.float64,
+                extra_options : str=""
+                ):
+        
+        self.device                 = device
+        self.dtype                  = dtype
+        
+        self.model                  = model
+
+        if has_nested_attr(model, "inner_model.inner_model.model_sampling"):
+            model_sampling = model.inner_model.inner_model.model_sampling
+        elif has_nested_attr(model, "model.model_sampling"):
+            model_sampling = model.model.model_sampling
+            
+        self.sigma_max              = model_sampling.sigma_max.to(dtype=self.dtype, device=self.device)
+        self.sigma_min              = model_sampling.sigma_min.to(dtype=self.dtype, device=self.device)
+        
+                        
+        self.sigma_fn               = RK.sigma_fn
+        self.t_fn                   = RK.t_fn
+        self.h_fn                   = RK.h_fn
+
+        self.row_offset             = 1 if not RK.IMPLICIT else 0
+        
+        self.step                   = step
+        
+        self.noise_sampler          = None
+        self.noise_sampler2         = None
+        
+        self.noise_mode_sde         = None
+        self.noise_mode_sde_substep = None
+        
+        self.LOCK_H_SCALE           = True
+        
+        self.CONST                  = isinstance(model_sampling, comfy.model_sampling.CONST)
+        self.VARIANCE_PRESERVING    = isinstance(model_sampling, comfy.model_sampling.CONST)
+        
+        self.extra_options          = extra_options
+        self.EO                     = ExtraOptions(extra_options)
+        
+        self.DOWN_SUBSTEP           = self.EO("down_substep")
+        self.DOWN_STEP              = self.EO("down_step")
+        
+        self.init_noise             = None
+
+
+
+
+    def init_noise_samplers(self,
+                            x                      : Tensor,              
+                            noise_seed             : int,
+                            noise_seed_substep     : int,
+                            noise_sampler_type     : str,
+                            noise_sampler_type2    : str,
+                            noise_mode_sde         : str,
+                            noise_mode_sde_substep : str,
+                            overshoot_mode         : str,
+                            overshoot_mode_substep : str,
+                            noise_boost_step       : float,
+                            noise_boost_substep    : float,
+                            alpha                  : float,
+                            alpha2                 : float,
+                            k                      : float = 1.0,
+                            k2                     : float = 1.0,
+                            scale                  : float = 0.1,
+                            scale2                 : float = 0.1,
+                            last_rng                       = None,
+                            last_rng_substep               = None,
+                            ) -> None:
+        
+        self.noise_sampler_type     = noise_sampler_type
+        self.noise_sampler_type2    = noise_sampler_type2
+        self.noise_mode_sde         = noise_mode_sde
+        self.noise_mode_sde_substep = noise_mode_sde_substep
+        self.overshoot_mode         = overshoot_mode
+        self.overshoot_mode_substep = overshoot_mode_substep
+        self.noise_boost_step       = noise_boost_step
+        self.noise_boost_substep    = noise_boost_substep
+        self.s_in                   = x.new_ones([1], dtype=self.dtype, device=self.device)
+        
+        if noise_seed < 0 and last_rng is None:
+            seed = torch.initial_seed()+1 
+            RESplain("SDE noise seed: ", seed, " (set via torch.initial_seed()+1)", debug=True)
+        if noise_seed < 0 and last_rng is not None:
+            seed = torch.initial_seed() 
+            RESplain("SDE noise seed: ", seed, " (set via torch.initial_seed())", debug=True)
+        else:
+            seed = noise_seed
+            RESplain("SDE noise seed: ", seed, debug=True)
+
+            
+        #seed2 = seed + MAX_STEPS #for substep noise generation. offset needed to ensure seeds are not reused
+            
+        if noise_sampler_type == "fractal":
+            self.noise_sampler        = NOISE_GENERATOR_CLASSES.get(noise_sampler_type )(x=x, seed=seed,               sigma_min=self.sigma_min, sigma_max=self.sigma_max)
+            self.noise_sampler.alpha  = alpha
+            self.noise_sampler.k      = k
+            self.noise_sampler.scale  = scale
+        if noise_sampler_type2 == "fractal":
+            self.noise_sampler2       = NOISE_GENERATOR_CLASSES.get(noise_sampler_type2)(x=x, seed=noise_seed_substep, sigma_min=self.sigma_min, sigma_max=self.sigma_max)
+            self.noise_sampler2.alpha = alpha2
+            self.noise_sampler2.k     = k2
+            self.noise_sampler2.scale = scale2
+        else:
+            self.noise_sampler  = NOISE_GENERATOR_CLASSES_SIMPLE.get(noise_sampler_type )(x=x, seed=seed,               sigma_min=self.sigma_min, sigma_max=self.sigma_max)
+            self.noise_sampler2 = NOISE_GENERATOR_CLASSES_SIMPLE.get(noise_sampler_type2)(x=x, seed=noise_seed_substep, sigma_min=self.sigma_min, sigma_max=self.sigma_max)
+            
+        if last_rng is not None:
+            self.noise_sampler .generator.set_state(last_rng)
+            self.noise_sampler2.generator.set_state(last_rng_substep)
+            
+            
+    def set_substep_list(self, RK:Union["RK_Method_Exponential", "RK_Method_Linear"]) -> None:
+        
+        self.multistep_stages = RK.multistep_stages
+        self.rows = RK.rows
+        self.C    = RK.C
+        self.s_ = self.sigma_fn(self.t_fn(self.sigma) + self.h * self.C)
+    
+    
+    def get_substep_list(self, RK:Union["RK_Method_Exponential", "RK_Method_Linear"], sigma, h) -> None:
+        s_ = RK.sigma_fn(RK.t_fn(sigma) + h * RK.C)
+        return s_
+    
+    
+    def get_sde_coeff(self, sigma_next:Tensor, sigma_down:Tensor=None, sigma_up:Tensor=None, eta:float=0.0, VP_OVERRIDE=None) -> Tuple[Tensor,Tensor,Tensor]:
+        VARIANCE_PRESERVING = VP_OVERRIDE if VP_OVERRIDE is not None else self.VARIANCE_PRESERVING
+
+        if VARIANCE_PRESERVING:
+            if sigma_down is not None:
+                alpha_ratio = (1 - sigma_next) / (1 - sigma_down)
+                sigma_up = (sigma_next ** 2 - sigma_down ** 2 * alpha_ratio ** 2) ** 0.5 
+                
+            elif sigma_up is not None:
+                if sigma_up >= sigma_next:
+                    RESplain("Maximum VPSDE noise level exceeded: falling back to hard noise mode.", debug=True)
+                    if eta >= 1:
+                        sigma_up = sigma_next * 0.9999 #avoid sqrt(neg_num) later 
+                    else:
+                        sigma_up = sigma_next * eta 
+                    
+                if VP_OVERRIDE is not None:
+                    sigma_signal   =              1 - sigma_next
+                else:
+                    sigma_signal   = self.sigma_max - sigma_next
+                sigma_residual = (sigma_next ** 2 - sigma_up ** 2) ** .5
+                alpha_ratio    = sigma_signal + sigma_residual
+                sigma_down     = sigma_residual / alpha_ratio     
+        
+        else:
+            alpha_ratio = torch.ones_like(sigma_next)
+            
+            if sigma_down is not None:
+                sigma_up   = (sigma_next ** 2 - sigma_down ** 2) ** .5   # not sure this is correct           #TODO: CHECK THIS
+            elif sigma_up is not None:
+                sigma_down = (sigma_next ** 2 - sigma_up   ** 2) ** .5    
+        
+        return alpha_ratio, sigma_down, sigma_up
+
+
+
+    def set_sde_step(self, sigma:Tensor, sigma_next:Tensor, eta:float, overshoot:float, s_noise:float) -> None:
+        self.sigma_0    = sigma
+        self.sigma_next = sigma_next
+        
+        self.s_noise    = s_noise
+        self.eta        = eta
+        self.overshoot  = overshoot
+        
+        self.sigma_up_eta, self.sigma_eta, self.sigma_down_eta, self.alpha_ratio_eta \
+            = self.get_sde_step(sigma, sigma_next, eta, self.noise_mode_sde, self.DOWN_STEP, SUBSTEP=False)
+            
+        self.sigma_up, self.sigma, self.sigma_down, self.alpha_ratio \
+            = self.get_sde_step(sigma, sigma_next, overshoot, self.overshoot_mode, self.DOWN_STEP, SUBSTEP=False)
+        
+        self.h          = self.h_fn(self.sigma_down, self.sigma)
+        self.h_no_eta   = self.h_fn(self.sigma_next, self.sigma)
+        self.h          = self.h + self.noise_boost_step * (self.h_no_eta - self.h)
+        
+
+        
+        
+        
+    def set_sde_substep(self,
+                        row                 : int,
+                        multistep_stages    : int,
+                        eta_substep         : float,
+                        overshoot_substep   : float,
+                        s_noise_substep     : float,
+                        full_iter           : int = 0,
+                        diag_iter           : int = 0,
+                        implicit_steps_full : int = 0,
+                        implicit_steps_diag : int = 0
+                        ) -> None:    
+        
+        # start with stepsizes for no overshoot/noise addition/noise swapping
+        self.sub_sigma_up_eta    = self.sub_sigma_up                          = 0.0
+        self.sub_sigma_eta       = self.sub_sigma                             = self.s_[row]
+        self.sub_sigma_down_eta  = self.sub_sigma_down  = self.sub_sigma_next = self.s_[row+self.row_offset+multistep_stages]
+        self.sub_alpha_ratio_eta = self.sub_alpha_ratio                       = 1.0
+        
+        self.s_noise_substep     = s_noise_substep
+        self.eta_substep         = eta_substep
+        self.overshoot_substep   = overshoot_substep
+
+
+        if row < self.rows   and   self.s_[row+self.row_offset+multistep_stages] > 0:
+            if   diag_iter > 0 and diag_iter == implicit_steps_diag and self.EO("implicit_substep_skip_final_eta"):
+                pass
+            elif diag_iter > 0 and                                      self.EO("implicit_substep_only_first_eta"):
+                pass
+            elif full_iter > 0 and full_iter == implicit_steps_full and self.EO("implicit_step_skip_final_eta"):
+                pass
+            elif full_iter > 0 and                                      self.EO("implicit_step_only_first_eta"):
+                pass
+            elif (full_iter > 0 or diag_iter > 0)                   and self.noise_sampler_type2 == "brownian":
+                pass # brownian noise does not increment its seed when generated, deactivate on implicit repeats to avoid burn
+            elif full_iter > 0 and                                      self.EO("implicit_step_only_first_all_eta"):
+                self.sigma_down_eta   = self.sigma_next
+                self.sigma_up_eta    *= 0
+                self.alpha_ratio_eta /= self.alpha_ratio_eta
+                
+                self.sigma_down       = self.sigma_next
+                self.sigma_up        *= 0
+                self.alpha_ratio     /= self.alpha_ratio
+                
+                self.h_new = self.h = self.h_no_eta
+            
+            elif (row < self.rows-self.row_offset-multistep_stages   or   diag_iter < implicit_steps_diag)   or   self.EO("substep_eta_use_final"):
+                self.sub_sigma_up,     self.sub_sigma,     self.sub_sigma_down,     self.sub_alpha_ratio     = self.get_sde_substep(sigma               = self.s_[row],
+                                                                                                                                    sigma_next          = self.s_[row+self.row_offset+multistep_stages],
+                                                                                                                                    eta                 = overshoot_substep,
+                                                                                                                                    noise_mode_override = self.overshoot_mode_substep,
+                                                                                                                                    DOWN                = self.DOWN_SUBSTEP)
+                
+                self.sub_sigma_up_eta, self.sub_sigma_eta, self.sub_sigma_down_eta, self.sub_alpha_ratio_eta = self.get_sde_substep(sigma               = self.s_[row],
+                                                                                                                                    sigma_next          = self.s_[row+self.row_offset+multistep_stages],
+                                                                                                                                    eta                 = eta_substep,
+                                                                                                                                    noise_mode_override = self.noise_mode_sde_substep,
+                                                                                                                                    DOWN                = self.DOWN_SUBSTEP)
+
+        if self.h_fn(self.sub_sigma_next, self.sigma) != 0:
+            self.h_new      = self.h * self.h_fn(self.sub_sigma_down,     self.sigma) / self.h_fn(self.sub_sigma_next, self.sigma) 
+            self.h_eta      = self.h * self.h_fn(self.sub_sigma_down_eta, self.sigma) / self.h_fn(self.sub_sigma_next, self.sigma) 
+            self.h_new_orig = self.h_new.clone()
+            self.h_new      = self.h_new + self.noise_boost_substep * (self.h - self.h_eta)
+        else:
+            self.h_new = self.h_eta = self.h
+            self.h_new_orig = self.h_new.clone()
+        
+        
+        
+
+    def get_sde_substep(self,
+                        sigma               :Tensor,
+                        sigma_next          :Tensor,
+                        eta                 :float         = 0.0  ,
+                        noise_mode_override :Optional[str] = None ,
+                        DOWN                :bool          = False,
+                        ) -> Tuple[Tensor,Tensor,Tensor,Tensor]:
+        
+        return self.get_sde_step(sigma=sigma, sigma_next=sigma_next, eta=eta, noise_mode_override=noise_mode_override, DOWN=DOWN, SUBSTEP=True,)
+
+    def get_sde_step(self,
+                        sigma               :Tensor,
+                        sigma_next          :Tensor,
+                        eta                 :float         = 0.0  ,
+                        noise_mode_override :Optional[str] = None ,
+                        DOWN                :bool          = False,
+                        SUBSTEP             :bool          = False,
+                        VP_OVERRIDE                        = None,
+                        ) -> Tuple[Tensor,Tensor,Tensor,Tensor]:
+        
+        VARIANCE_PRESERVING = VP_OVERRIDE if VP_OVERRIDE is not None else self.VARIANCE_PRESERVING
+            
+        if noise_mode_override is not None:
+            noise_mode = noise_mode_override
+        elif SUBSTEP:
+            noise_mode = self.noise_mode_sde_substep
+        else:
+            noise_mode = self.noise_mode_sde
+        
+        if DOWN: #calculates noise level by first scaling sigma_down from sigma_next, instead of sigma_up from sigma_next
+            eta_fn = lambda eta_scale: 1-eta_scale
+            sud_fn = lambda sd: (sd, None)
+        else:
+            eta_fn = lambda eta_scale:   eta_scale
+            sud_fn = lambda su: (None, su)
+        
+        su, sd, sud = None, None, None
+        eta_ratio   = None
+        sigma_base  = sigma_next
+        
+        sigmax      = self.sigma_max if VP_OVERRIDE is None else 1
+        
+        match noise_mode:
+            case "hard":
+                eta_ratio = eta
+            case "exp": 
+                h = -(sigma_next/sigma).log()
+                eta_ratio = (1 - (-2*eta*h).exp())**.5
+            case "soft":
+                eta_ratio = 1-(1 - eta) + eta * ((sigma_next) / sigma)
+            case "softer":
+                eta_ratio = 1-torch.sqrt(1 - (eta**2 * (sigma**2 - sigma_next**2)) / sigma**2)
+            case "soft-linear":
+                eta_ratio = 1-eta * (sigma_next - sigma)
+            case "sinusoidal":
+                eta_ratio = eta * torch.sin(torch.pi * (sigma_next / sigmax)) ** 2
+            case "eps":
+                eta_ratio = eta * torch.sqrt((sigma_next/sigma) ** 2 * (sigma ** 2 - sigma_next ** 2) ) 
+                
+            case "lorentzian":
+                eta_ratio  = eta
+                alpha      = 1 / ((sigma_next.to(sigma.dtype))**2 + 1)
+                sigma_base = ((1 - alpha) ** 0.5).to(sigma.dtype)
+                
+            case "hard_var":
+                sigma_var = (-1 + torch.sqrt(1 + 4 * sigma)) / 2
+                if sigma_next > sigma_var:
+                    eta_ratio  = 0
+                    sigma_base = sigma_next
+                else:
+                    eta_ratio  = eta
+                    sigma_base = torch.sqrt((sigma - sigma_next).abs() + 1e-10)
+            
+            case "hard_sq":
+                sigma_hat = sigma * (1 + eta)
+                su        = (sigma_hat ** 2 - sigma ** 2) ** .5    #su
+                
+                if VARIANCE_PRESERVING:
+                    alpha_ratio, sd, su = self.get_sde_coeff(sigma_next, None, su, eta, VARIANCE_PRESERVING)
+                else:
+                    sd          = sigma_next
+                    sigma       = sigma_hat
+                    alpha_ratio = torch.ones_like(sigma)
+                    
+            case "vpsde":
+                alpha_ratio, sd, su = self.get_vpsde_step_RF(sigma, sigma_next, eta)
+                
+            case "er4":
+                #def noise_scaler(sigma):
+                #    return sigma * ((sigma ** 0.3).exp() + 10.0)
+                noise_scaler = lambda sigma: sigma * ((sigma ** eta).exp() + 10.0)
+                alpha_ratio = noise_scaler(sigma_next) / noise_scaler(sigma)
+                sigma_up    = (sigma_next ** 2 - sigma ** 2 * alpha_ratio ** 2) ** 0.5
+                eta_ratio = sigma_up / sigma_next
+
+                
+        if eta_ratio is not None:
+            sud = sigma_base * eta_fn(eta_ratio)
+            alpha_ratio, sd, su = self.get_sde_coeff(sigma_next, *sud_fn(sud), eta, VARIANCE_PRESERVING)
+        
+        su          = torch.nan_to_num(su,          0.0)
+        sd          = torch.nan_to_num(sd,    float(sigma_next))
+        alpha_ratio = torch.nan_to_num(alpha_ratio, 1.0)
+
+        return su, sigma, sd, alpha_ratio
+    
+    def get_vpsde_step_RF(self, sigma:Tensor, sigma_next:Tensor, eta:float) -> Tuple[Tensor,Tensor,Tensor]:
+        dt          = sigma - sigma_next
+        sigma_up    = eta * sigma * dt**0.5
+        alpha_ratio = 1 - dt * (eta**2/4) * (1 + sigma)
+        sigma_down  = sigma_next - (eta/4)*sigma*(1-sigma)*(sigma - sigma_next)
+        return sigma_up, sigma_down, alpha_ratio
+    
+    def linear_noise_init(self, y:Tensor, sigma_curr:Tensor, x_base:Optional[Tensor]=None, x_curr:Optional[Tensor]=None, mask:Optional[Tensor]=None) -> Tensor: 
+
+        y_noised = (self.sigma_max - sigma_curr) * y + sigma_curr * self.init_noise
+
+        if x_curr is not None:
+            x_curr = x_curr + sigma_curr * (self.init_noise - y)
+            x_base = x_base + self.sigma * (self.init_noise - y)
+            return y_noised, x_base, x_curr
+
+        if mask is not None:
+            y_noised = mask * y_noised + (1-mask) * y
+        
+        return y_noised
+
+    def linear_noise_step(self, y:Tensor, sigma_curr:Optional[Tensor]=None, x_base:Optional[Tensor]=None, x_curr:Optional[Tensor]=None, brownian_sigma:Optional[Tensor]=None, brownian_sigma_next:Optional[Tensor]=None, mask:Optional[Tensor]=None) -> Tensor:
+        if self.sigma_up_eta == 0   or   self.sigma_next == 0:
+            return y, x_base, x_curr
+        
+        sigma_curr = self.sub_sigma if sigma_curr is None else sigma_curr
+
+        brownian_sigma      = sigma_curr              if brownian_sigma      is None else brownian_sigma
+        brownian_sigma_next = self.sigma_next.clone() if brownian_sigma_next is None else brownian_sigma_next
+        
+        if brownian_sigma == brownian_sigma_next:
+            brownian_sigma_next *= 0.999
+            
+        if brownian_sigma_next > brownian_sigma and not self.EO("disable_brownian_swap"): # should this really be done?
+            brownian_sigma, brownian_sigma_next = brownian_sigma_next, brownian_sigma
+        
+        noise = self.noise_sampler(sigma=brownian_sigma, sigma_next=brownian_sigma_next)
+        noise = normalize_zscore(noise, channelwise=True, inplace=True)
+
+        y_noised = (self.sigma_max - sigma_curr) * y + sigma_curr * noise
+        
+        if x_curr is not None:
+            x_curr = x_curr + sigma_curr * (noise - y)
+            x_base = x_base + self.sigma * (noise - y)
+            return y_noised, x_base, x_curr
+        
+        if mask is not None:
+            y_noised = mask * y_noised + (1-mask) * y
+        
+        return y_noised
+
+
+    def linear_noise_substep(self, y:Tensor, sigma_curr:Optional[Tensor]=None, x_base:Optional[Tensor]=None, x_curr:Optional[Tensor]=None, brownian_sigma:Optional[Tensor]=None, brownian_sigma_next:Optional[Tensor]=None, mask:Optional[Tensor]=None) -> Tensor:
+        if self.sub_sigma_up_eta == 0   or   self.sub_sigma_next == 0:
+            return y, x_base, x_curr
+        
+        sigma_curr = self.sub_sigma if sigma_curr is None else sigma_curr
+
+        brownian_sigma      = sigma_curr                  if brownian_sigma      is None else brownian_sigma
+        brownian_sigma_next = self.sub_sigma_next.clone() if brownian_sigma_next is None else brownian_sigma_next
+
+        if brownian_sigma == brownian_sigma_next:
+            brownian_sigma_next *= 0.999
+
+        if brownian_sigma_next > brownian_sigma and not self.EO("disable_brownian_swap"): # should this really be done?
+            brownian_sigma, brownian_sigma_next = brownian_sigma_next, brownian_sigma
+        
+        noise = self.noise_sampler2(sigma=brownian_sigma, sigma_next=brownian_sigma_next)
+        noise = normalize_zscore(noise, channelwise=True, inplace=True)
+
+        y_noised = (self.sigma_max - sigma_curr) * y + sigma_curr * noise
+        
+        if x_curr is not None:
+            x_curr = x_curr + sigma_curr * (noise - y)
+            x_base = x_base + self.sigma * (noise - y)
+            return y_noised, x_base, x_curr
+        
+        if mask is not None:
+            y_noised = mask * y_noised + (1-mask) * y
+        
+        return y_noised
+
+
+    def swap_noise_step(self, x_0:Tensor, x_next:Tensor, brownian_sigma:Optional[Tensor]=None, brownian_sigma_next:Optional[Tensor]=None, mask:Optional[Tensor]=None) -> Tensor:
+        if self.sigma_up_eta == 0   or   self.sigma_next == 0:
+            return x_next
+
+        brownian_sigma      = self.sigma.clone()      if brownian_sigma      is None else brownian_sigma
+        brownian_sigma_next = self.sigma_next.clone() if brownian_sigma_next is None else brownian_sigma_next
+        
+        if brownian_sigma == brownian_sigma_next:
+            brownian_sigma_next *= 0.999
+            
+        eps_next      = (x_0 - x_next) / (self.sigma - self.sigma_next)
+        denoised_next = x_0 - self.sigma * eps_next
+        
+        if brownian_sigma_next > brownian_sigma and not self.EO("disable_brownian_swap"): # should this really be done?
+            brownian_sigma, brownian_sigma_next = brownian_sigma_next, brownian_sigma
+        
+        noise = self.noise_sampler(sigma=brownian_sigma, sigma_next=brownian_sigma_next)
+        noise = normalize_zscore(noise, channelwise=True, inplace=True)
+
+        x_noised = self.alpha_ratio_eta * (denoised_next + self.sigma_down_eta * eps_next) + self.sigma_up_eta * noise * self.s_noise
+
+        if mask is not None:
+            x = mask * x_noised + (1-mask) * x_next
+        else:
+            x = x_noised
+        
+        return x
+
+
+    def swap_noise_substep(self, x_0:Tensor, x_next:Tensor, brownian_sigma:Optional[Tensor]=None, brownian_sigma_next:Optional[Tensor]=None, mask:Optional[Tensor]=None, guide:Optional[Tensor]=None) -> Tensor:
+        if self.sub_sigma_up_eta == 0   or   self.sub_sigma_next == 0:
+            return x_next
+        
+        brownian_sigma      = self.sub_sigma.clone()      if brownian_sigma      is None else brownian_sigma
+        brownian_sigma_next = self.sub_sigma_next.clone() if brownian_sigma_next is None else brownian_sigma_next
+
+        if brownian_sigma == brownian_sigma_next:
+            brownian_sigma_next *= 0.999
+            
+        eps_next      = (x_0 - x_next) / (self.sigma - self.sub_sigma_next)
+        denoised_next = x_0 - self.sigma * eps_next
+        
+        if brownian_sigma_next > brownian_sigma and not self.EO("disable_brownian_swap"): # should this really be done?
+            brownian_sigma, brownian_sigma_next = brownian_sigma_next, brownian_sigma
+        
+        noise = self.noise_sampler2(sigma=brownian_sigma, sigma_next=brownian_sigma_next)
+        noise = normalize_zscore(noise, channelwise=True, inplace=True)
+
+        x_noised = self.sub_alpha_ratio_eta * (denoised_next + self.sub_sigma_down_eta * eps_next) + self.sub_sigma_up_eta * noise * self.s_noise_substep
+
+        if mask is not None:
+            x = mask * x_noised + (1-mask) * x_next
+        else:
+            x = x_noised
+        
+        return x
+
+
+
+
+    def swap_noise_inv_substep(self, x_0:Tensor, x_next:Tensor, eta_substep:float, row:int, row_offset_multistep_stages:int, brownian_sigma:Optional[Tensor]=None, brownian_sigma_next:Optional[Tensor]=None, mask:Optional[Tensor]=None, guide:Optional[Tensor]=None) -> Tensor:
+        if self.sub_sigma_up_eta == 0   or   self.sub_sigma_next == 0:
+            return x_next
+        
+        brownian_sigma      = self.sub_sigma.clone()      if brownian_sigma      is None else brownian_sigma
+        brownian_sigma_next = self.sub_sigma_next.clone() if brownian_sigma_next is None else brownian_sigma_next
+
+        if brownian_sigma == brownian_sigma_next:
+            brownian_sigma_next *= 0.999
+            
+        eps_next      = (x_0 - x_next) / ((1-self.sigma) - (1-self.sub_sigma_next))
+        denoised_next = x_0 - (1-self.sigma) * eps_next
+        
+        if brownian_sigma_next > brownian_sigma and not self.EO("disable_brownian_swap"): # should this really be done?
+            brownian_sigma, brownian_sigma_next = brownian_sigma_next, brownian_sigma
+        
+        noise = self.noise_sampler2(sigma=brownian_sigma, sigma_next=brownian_sigma_next)
+        noise = normalize_zscore(noise, channelwise=True, inplace=True)
+        
+        sub_sigma_up,     sub_sigma,     sub_sigma_down,     sub_alpha_ratio     = self.get_sde_substep(sigma               = 1-self.s_[row],
+                                                                                                                            sigma_next          = 1-self.s_[row_offset_multistep_stages],
+                                                                                                                            eta                 = eta_substep,
+                                                                                                                            noise_mode_override = self.noise_mode_sde_substep,
+                                                                                                                            DOWN                = self.DOWN_SUBSTEP)
+
+        x_noised = sub_alpha_ratio * (denoised_next + sub_sigma_down * eps_next) + sub_sigma_up * noise * self.s_noise_substep
+
+        if mask is not None:
+            x = mask * x_noised + (1-mask) * x_next
+        else:
+            x = x_noised
+        
+        return x
+
+
+    def swap_noise(self,
+                    x_0                 :Tensor,
+                    x_next              :Tensor,
+                    sigma_0             :Tensor,
+                    sigma               :Tensor,
+                    sigma_next          :Tensor,
+                    sigma_down          :Tensor,
+                    sigma_up            :Tensor,
+                    alpha_ratio         :Tensor,
+                    s_noise             :float,
+                    SUBSTEP             :bool             = False,
+                    brownian_sigma      :Optional[Tensor] = None,
+                    brownian_sigma_next :Optional[Tensor] = None,
+                    ) -> Tensor:
+        
+        if sigma_up == 0:
+            return x_next
+        
+        if brownian_sigma is None:
+            brownian_sigma = sigma.clone()
+        if brownian_sigma_next is None:
+            brownian_sigma_next = sigma_next.clone()
+        if sigma_next == 0:
+            return x_next
+        if brownian_sigma == brownian_sigma_next:
+            brownian_sigma_next *= 0.999
+        eps_next      = (x_0 - x_next) / (sigma_0 - sigma_next)
+        denoised_next = x_0 - sigma_0 * eps_next
+        
+        if brownian_sigma_next > brownian_sigma:
+            s_tmp               = brownian_sigma
+            brownian_sigma      = brownian_sigma_next
+            brownian_sigma_next = s_tmp
+        
+        if not SUBSTEP:
+            noise = self.noise_sampler(sigma=brownian_sigma, sigma_next=brownian_sigma_next)
+        else:
+            noise = self.noise_sampler2(sigma=brownian_sigma, sigma_next=brownian_sigma_next)
+            
+        noise = normalize_zscore(noise, channelwise=True, inplace=True)
+
+        x = alpha_ratio * (denoised_next + sigma_down * eps_next) + sigma_up * noise * s_noise
+        return x
+
+    # not used. WARNING: some parameters have a different order than swap_noise!
+    def add_noise_pre(self,
+                        x_0                :Tensor,
+                        x                  :Tensor,
+                        sigma_up           :Tensor,
+                        sigma_0            :Tensor,
+                        sigma              :Tensor,
+                        sigma_next         :Tensor,
+                        real_sigma_down    :Tensor,
+                        alpha_ratio        :Tensor,
+                        s_noise            :float,
+                        noise_mode         :str,
+                        SDE_NOISE_EXTERNAL :bool             = False,
+                        sde_noise_t        :Optional[Tensor] = None,
+                        SUBSTEP            :bool             = False,
+                        ) -> Tensor:
+        
+        if not self.CONST and noise_mode == "hard_sq": 
+            if self.LOCK_H_SCALE:
+                x = self.swap_noise(x_0             = x_0,
+                                    x               = x,
+                                    sigma           = sigma,
+                                    sigma_0         = sigma_0,
+                                    sigma_next      = sigma_next,
+                                    real_sigma_down = real_sigma_down,
+                                    sigma_up        = sigma_up,
+                                    alpha_ratio     = alpha_ratio,
+                                    s_noise         = s_noise,
+                                    SUBSTEP         = SUBSTEP,
+                                    )
+            else:
+                x = self.add_noise( x                  = x,
+                                    sigma_up           = sigma_up,
+                                    sigma              = sigma,
+                                    sigma_next         = sigma_next,
+                                    alpha_ratio        = alpha_ratio,
+                                    s_noise            = s_noise,
+                                    SDE_NOISE_EXTERNAL = SDE_NOISE_EXTERNAL,
+                                    sde_noise_t        = sde_noise_t,
+                                    SUBSTEP            = SUBSTEP,
+                                    )
+                
+        return x
+        
+    # only used for handle_tiled_etc_noise_steps() in rk_guide_func_beta.py
+    def add_noise_post(self,
+                        x_0                :Tensor,
+                        x                  :Tensor,
+                        sigma_up           :Tensor,
+                        sigma_0            :Tensor,
+                        sigma              :Tensor,
+                        sigma_next         :Tensor,
+                        real_sigma_down    :Tensor,
+                        alpha_ratio        :Tensor,
+                        s_noise            :float,
+                        noise_mode         :str,
+                        SDE_NOISE_EXTERNAL :bool             = False,
+                        sde_noise_t        :Optional[Tensor] = None,
+                        SUBSTEP            :bool             = False,
+                        ) -> Tensor:
+        
+        if self.CONST   or   (not self.CONST and noise_mode != "hard_sq"):
+            if self.LOCK_H_SCALE:
+                x = self.swap_noise(x_0             = x_0,
+                                    x               = x,
+                                    sigma           = sigma,
+                                    sigma_0         = sigma_0,
+                                    sigma_next      = sigma_next,
+                                    real_sigma_down = real_sigma_down,
+                                    sigma_up        = sigma_up,
+                                    alpha_ratio     = alpha_ratio,
+                                    s_noise         = s_noise,
+                                    SUBSTEP         = SUBSTEP,
+                                    )
+            else:
+                x = self.add_noise( x                  = x,
+                                    sigma_up           = sigma_up,
+                                    sigma              = sigma,
+                                    sigma_next         = sigma_next,
+                                    alpha_ratio        = alpha_ratio,
+                                    s_noise            = s_noise,
+                                    SDE_NOISE_EXTERNAL = SDE_NOISE_EXTERNAL,
+                                    sde_noise_t        = sde_noise_t,
+                                    SUBSTEP            = SUBSTEP,
+                                    )
+        return x
+
+    def add_noise(self,
+                    x                 :Tensor,
+                    sigma_up          :Tensor,
+                    sigma             :Tensor,
+                    sigma_next        :Tensor,
+                    alpha_ratio       :Tensor,
+                    s_noise           :float,
+                    SDE_NOISE_EXTERNAL :bool             = False,
+                    sde_noise_t        :Optional[Tensor] = None,
+                    SUBSTEP            :bool             = False,
+                    ) -> Tensor:
+
+        if sigma_next > 0.0 and sigma_up > 0.0:
+            if sigma_next > sigma:
+                sigma, sigma_next = sigma_next, sigma
+            
+            if sigma == sigma_next:
+                sigma_next = sigma * 0.9999
+            if not SUBSTEP:
+                noise = self.noise_sampler (sigma=sigma, sigma_next=sigma_next)
+            else:
+                noise = self.noise_sampler2(sigma=sigma, sigma_next=sigma_next)
+
+            #noise_ortho = get_orthogonal(noise, x)
+            #noise_ortho = noise_ortho / noise_ortho.std()model,
+            noise = normalize_zscore(noise, channelwise=True, inplace=True)
+
+            if SDE_NOISE_EXTERNAL:
+                noise = (1-s_noise) * noise + s_noise * sde_noise_t
+            
+            x_next = alpha_ratio * x + noise * sigma_up * s_noise
+            
+            return x_next
+        
+        else:
+            return x
+    
+    def sigma_from_to(self,                           
+                        x_0        : Tensor,
+                        x_down     : Tensor,
+                        sigma      : Tensor,
+                        sigma_down : Tensor,
+                        sigma_next : Tensor) -> Tensor:   #sigma, sigma_from, sigma_to
+        
+        eps      = (x_0 - x_down) / (sigma - sigma_down)
+        denoised =  x_0 - sigma * eps
+        x_next   = denoised + sigma_next * eps              # VESDE vs VPSDE equiv.?
+        return x_next
+
+    def rebound_overshoot_step(self, x_0:Tensor, x:Tensor) -> Tensor:
+        eps      = (x_0 - x) / (self.sigma - self.sigma_down)
+        denoised =  x_0 - self.sigma * eps
+        x        = denoised + self.sigma_next * eps
+        return x
+    
+    def rebound_overshoot_substep(self, x_0:Tensor, x:Tensor) -> Tensor:
+        if self.sigma - self.sub_sigma_down > 0:
+            sub_eps      = (x_0 - x) / (self.sigma - self.sub_sigma_down)
+            sub_denoised =  x_0 - self.sigma * sub_eps
+            x            = sub_denoised + self.sub_sigma_next * sub_eps
+        return x
+
+    def prepare_sigmas(self,
+                        sigmas             : Tensor,
+                        sigmas_override    : Tensor,
+                        d_noise            : float,
+                        d_noise_start_step : int,
+                        sampler_mode       : str) -> Tuple[Tensor,bool]:
+        #SIGMA_MIN = torch.full_like(self.sigma_min, 0.00227896) if self.sigma_min < 0.00227896 else self.sigma_min        # prevent black image with unsampling flux, which has a sigma_min of 0.0002
+        SIGMA_MIN = self.sigma_min #torch.full_like(self.sigma_min, max(0.01, self.sigma_min.item()))
+        if sigmas_override is not None:
+            sigmas = sigmas_override.clone().to(sigmas.device).to(sigmas.dtype)
+            
+        if d_noise_start_step == 0:
+            sigmas = sigmas.clone() * d_noise
+        
+        UNSAMPLE_FROM_ZERO = False
+        if sigmas[0] == 0.0:      #remove padding used to prevent comfy from adding noise to the latent (for unsampling, etc.)
+            UNSAMPLE = True
+            if sigmas[-1] == 0.0:
+                UNSAMPLE_FROM_ZERO = True
+            #sigmas   = sigmas[1:-1]   # was cleaving off 1.0 at the end when restart looping
+            sigmas   = sigmas[1:]
+            if sigmas[-1] == 0.0:
+                sigmas = sigmas[:-1]
+        else:
+            UNSAMPLE = False
+        
+        if hasattr(self.model, "sigmas"):
+            self.model.sigmas = sigmas
+            
+        if sampler_mode == "standard":
+            UNSAMPLE = False
+        
+        consecutive_duplicate_mask = torch.cat((torch.tensor([True], device=sigmas.device), torch.diff(sigmas) != 0))
+        sigmas = sigmas[consecutive_duplicate_mask]
+                
+        if sigmas[-1] == 0:
+            if sigmas[-2] < SIGMA_MIN:
+                sigmas[-2] = SIGMA_MIN
+            elif (sigmas[-2] - SIGMA_MIN).abs() > 1e-4:
+                sigmas = torch.cat((sigmas[:-1], SIGMA_MIN.unsqueeze(0), sigmas[-1:]))
+                
+        elif UNSAMPLE_FROM_ZERO and not torch.isclose(sigmas[0], SIGMA_MIN):
+            sigmas = torch.cat([SIGMA_MIN.unsqueeze(0), sigmas])
+        
+        self.sigmas       = sigmas
+        self.UNSAMPLE     = UNSAMPLE
+        self.d_noise      = d_noise
+        self.sampler_mode = sampler_mode
+        
+        return sigmas, UNSAMPLE
+    
+    
+    
+
+
+
+def extract_latent_swap_noise(self, x:Tensor, x_noise_swapped:Tensor, sigma:Tensor, old_noise:Tensor) -> Tensor:
+    return (x - x_noise_swapped) / sigma + old_noise
+
+def update_latent_swap_noise(self, x:Tensor, sigma:Tensor, old_noise:Tensor, new_noise:Tensor) -> Tensor:
+    return x + sigma * (new_noise - old_noise)
+
+
+
+
diff --git a/custom_nodes/RES4LYF/beta/rk_sampler_beta.py b/custom_nodes/RES4LYF/beta/rk_sampler_beta.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec73f0b754db6332585fbcfaf993919f7964fa23
--- /dev/null
+++ b/custom_nodes/RES4LYF/beta/rk_sampler_beta.py
@@ -0,0 +1,2242 @@
+import torch
+from torch import Tensor
+import torch.nn.functional as F
+from tqdm.auto import trange
+import gc
+from typing import Optional, Callable, Tuple, List, Dict, Any, Union
+import math
+import copy
+
+from comfy.model_sampling import EPS
+import comfy
+
+from ..res4lyf              import RESplain
+from ..helper               import ExtraOptions, FrameWeightsManager
+from ..latents              import lagrange_interpolation, get_collinear, get_orthogonal, get_cosine_similarity, get_pearson_similarity, get_slerp_weight_for_cossim, get_slerp_ratio, slerp_tensor, get_edge_mask, normalize_zscore, compute_slerp_ratio_for_target, find_slerp_ratio_grid
+from ..style_transfer       import apply_scattersort_spatial, apply_adain_spatial
+
+from .rk_method_beta        import RK_Method_Beta
+from .rk_noise_sampler_beta import RK_NoiseSampler
+from .rk_guide_func_beta    import LatentGuide
+from .phi_functions         import Phi
+from .constants             import MAX_STEPS, GUIDE_MODE_NAMES_PSEUDOIMPLICIT
+
+def init_implicit_sampling(
+        RK             : RK_Method_Beta,
+        x_0            : Tensor,
+        x_             : Tensor,
+        eps_           : Tensor,
+        eps_prev_      : Tensor,
+        data_          : Tensor,
+        eps            : Tensor,
+        denoised       : Tensor,
+        denoised_prev2 : Tensor,
+        step           : int,
+        sigmas         : Tensor,
+        h              : Tensor,
+        s_             : Tensor,
+        EO             : ExtraOptions,
+        SYNC_GUIDE_ACTIVE,
+        ):
+    
+    sigma = sigmas[step]
+    if EO("implicit_skip_model_call_at_start") and denoised.sum() + eps.sum() != 0:
+        if denoised_prev2.sum() == 0:
+            eps_ [0] = eps.clone()
+            data_[0] = denoised.clone()
+            eps_ [0] = RK.get_epsilon_anchored(x_0, denoised, sigma)
+        else:
+            sratio = sigma - s_[0]
+            data_[0] = denoised + sratio * (denoised - denoised_prev2)
+            
+    elif EO("implicit_full_skip_model_call_at_start") and denoised.sum() + eps.sum() != 0:
+        if denoised_prev2.sum() == 0:
+            eps_ [0] = eps.clone()
+            data_[0] = denoised.clone()
+            eps_ [0] = RK.get_epsilon_anchored(x_0, denoised, sigma)
+        else:
+            for r in range(RK.rows):
+                sratio = sigma - s_[r]
+                data_[r] = denoised + sratio * (denoised - denoised_prev2)
+                eps_ [r] = RK.get_epsilon_anchored(x_0, data_[r], s_[r])
+
+    elif EO("implicit_lagrange_skip_model_call_at_start") and denoised.sum() + eps.sum() != 0:
+        if denoised_prev2.sum() == 0:
+            eps_ [0] = eps.clone()
+            data_[0] = denoised.clone()
+            eps_ [0] = RK.get_epsilon_anchored(x_0, denoised, sigma)   
+        else:
+            sigma_prev    = sigmas[step-1]
+            h_prev        = sigma - sigma_prev
+            w             = h / h_prev
+            substeps_prev = len(RK.C[:-1])
+            
+            for r in range(RK.rows):
+                sratio = sigma - s_[r]
+                data_[r] = lagrange_interpolation([0,1], [denoised_prev2, denoised], 1 + w*RK.C[r]).squeeze(0) + denoised_prev2 - denoised
+                eps_ [r] = RK.get_epsilon_anchored(x_0, data_[r], s_[r])      
+                
+            if EO("implicit_lagrange_skip_model_call_at_start_0_only"):
+                for r in range(RK.rows):
+                    eps_ [r] = eps_ [0].clone() * s_[0] / s_[r]
+                    data_[r] = denoised.clone()
+
+
+    elif EO("implicit_lagrange_init") and denoised.sum() + eps.sum() != 0:
+        sigma_prev    = sigmas[step-1]
+        h_prev        = sigma - sigma_prev
+        w             = h / h_prev
+        substeps_prev = len(RK.C[:-1])
+
+        z_prev_ = eps_.clone()
+        for r in range (substeps_prev):
+            z_prev_[r] = h * RK.zum(r, eps_) # u,v not implemented for lagrange guess for implicit
+        zi_1  = lagrange_interpolation(RK.C[:-1], z_prev_[:substeps_prev], RK.C[0]).squeeze(0) # + x_prev - x_0"""
+        x_[0] = x_0 + zi_1
+        
+    else:
+        
+        eps_[0], data_[0] = RK(x_[0], sigma, x_0, sigma)
+
+    if not EO(("implicit_lagrange_init", "radaucycle", "implicit_full_skip_model_call_at_start", "implicit_lagrange_skip_model_call_at_start")):
+        for r in range(RK.rows):
+            eps_ [r] = eps_ [0].clone() * sigma / s_[r]
+            data_[r] = data_[0].clone()
+
+    x_, eps_ = RK.newton_iter(x_0, x_, eps_, eps_prev_, data_, s_, 0, h, sigmas, step, "init", SYNC_GUIDE_ACTIVE)
+    return x_, eps_, data_
+
+
+@torch.no_grad()
+def sample_rk_beta(
+        model,
+        x                             : Tensor,
+        sigmas                        : Tensor,
+        sigmas_override               : Optional[Tensor]   = None,
+        
+        extra_args                    : Optional[Tensor]   = None,
+        callback                      : Optional[Callable] = None,
+        disable                       : bool               = None,
+        
+        sampler_mode                  : str                = "standard",
+
+        rk_type                       : str                = "res_2m",
+        implicit_sampler_name         : str                = "use_explicit",
+
+        c1                            : float              =  0.0,
+        c2                            : float              =  0.5,
+        c3                            : float              =  1.0,
+
+        noise_sampler_type            : str                = "gaussian",
+        noise_sampler_type_substep    : str                = "gaussian",
+        noise_mode_sde                : str                = "hard",
+        noise_mode_sde_substep        : str                = "hard",
+
+        eta                           : float              =  0.5,
+        eta_substep                   : float              =  0.5,
+
+
+
+
+        noise_scaling_weight          : float              = 0.0,
+        noise_scaling_type            : str                = "sampler",
+        noise_scaling_mode            : str                = "linear",
+        noise_scaling_eta             : float              = 0.0,
+        noise_scaling_cycles          : int                = 1,
+        
+        noise_scaling_weights         : Optional[Tensor]   = None,
+        noise_scaling_etas            : Optional[Tensor]   = None,
+        
+        noise_boost_step              : float              = 0.0,
+        noise_boost_substep           : float              = 0.0,
+        noise_boost_normalize         : bool               = True,
+        noise_anchor                  : float              = 1.0,
+        
+        s_noise                       : float              = 1.0,
+        s_noise_substep               : float              = 1.0,
+        d_noise                       : float              = 1.0,
+        d_noise_start_step            : int                = 0,
+        d_noise_inv                   : float              = 1.0,
+        d_noise_inv_start_step        : int                = 0,
+        
+        
+        
+        alpha                         : float              = -1.0,
+        alpha_substep                 : float              = -1.0,
+        k                             : float              =  1.0,
+        k_substep                     : float              =  1.0,
+        
+        momentum                      : float              =  0.0,
+
+
+        overshoot_mode                : str                = "hard",
+        overshoot_mode_substep        : str                = "hard",
+        overshoot                     : float              =  0.0,
+        overshoot_substep             : float              =  0.0,
+
+        implicit_type                 : str                = "predictor-corrector",
+        implicit_type_substeps        : str                = "predictor-corrector",
+        
+        implicit_steps_diag           : int                =  0,
+        implicit_steps_full           : int                =  0,
+
+        etas                          : Optional[Tensor]   = None,
+        etas_substep                  : Optional[Tensor]   = None,
+        s_noises                      : Optional[Tensor]   = None,
+        s_noises_substep              : Optional[Tensor]   = None,
+        
+        momentums                     : Optional[Tensor]   = None,
+
+        regional_conditioning_weights : Optional[Tensor]   = None,
+        regional_conditioning_floors  : Optional[Tensor]   = None,
+        narcissism_start_step         : int                = 0,
+        narcissism_end_step           : int                = 5,
+                
+        LGW_MASK_RESCALE_MIN          : bool                          = True,
+        guides                        : Optional[Tuple[Any, ...]]     = None,
+        epsilon_scales                : Optional[Tensor]              = None,
+        frame_weights_mgr             : Optional[FrameWeightsManager] = None,
+
+        sde_noise                     : list    [Tensor]   = [],
+
+        noise_seed                    : int                = -1,
+        noise_initial                 : Optional[Tensor]   = None,
+        image_initial                 : Optional[Tensor]   = None,
+
+        cfgpp                         : float              = 0.0,
+        cfg_cw                        : float              = 1.0,
+
+        BONGMATH                      : bool               = True,
+        unsample_bongmath             = None,
+
+        state_info                    : Optional[dict[str, Any]] = None,
+        state_info_out                : Optional[dict[str, Any]] = None,
+        
+        rk_swap_type                  : str                = "",
+        rk_swap_step                  : int                = MAX_STEPS,
+        rk_swap_threshold             : float              = 0.0,
+        rk_swap_print                 : bool               = False,
+        
+        steps_to_run                  : int                = -1,
+        start_at_step                 : int                = -1,
+        tile_sizes                    : Optional[List[Tuple[int,int]]] = None,
+        
+        flow_sync_eps                 : float              = 0.0,
+        
+        sde_mask                      : Optional[Tensor]   = None,
+        
+        batch_num                     : int                = 0,
+        
+        extra_options                 : str                = "",
+        
+        AttnMask   = None,
+        RegContext = None,
+        RegParam   = None,
+        
+        AttnMask_neg   = None,
+        RegContext_neg = None,
+        RegParam_neg   = None,
+        ):
+    
+    if sampler_mode == "NULL":
+        return x
+    
+    EO             = ExtraOptions(extra_options)
+    default_dtype  = EO("default_dtype", torch.float64)
+    
+    extra_args     = {} if extra_args     is None else extra_args
+    model_device   = model.inner_model.inner_model.device #x.device
+    work_device    = 'cpu' if EO("work_device_cpu") else model_device
+
+    state_info     = {} if state_info     is None else state_info
+    state_info_out = {} if state_info_out is None else state_info_out
+    
+    VE_MODEL = isinstance(model.inner_model.inner_model.model_sampling, EPS)
+    
+    RENOISE = False
+    if 'raw_x' in state_info and sampler_mode in {"resample", "unsample"}:
+        if x.shape == state_info['raw_x'].shape:
+            x = state_info['raw_x'].to(work_device) #clone()
+        else:
+            denoised = comfy.utils.bislerp(state_info['denoised'], x.shape[-1], x.shape[-2])
+            x = denoised.to(x)
+            RENOISE = True
+        RESplain("Continuing from raw latent from previous sampler.", debug=False)
+    
+    
+    
+    start_step = 0
+    if 'end_step' in state_info and (sampler_mode == "resample" or sampler_mode == "unsample"):
+
+        if state_info['completed'] != True and state_info['end_step'] != 0 and state_info['end_step'] != -1 and state_info['end_step'] < len(state_info['sigmas'])-1 :   #incomplete run in previous sampler node
+            
+            if state_info['sampler_mode'] in {"standard","resample"} and sampler_mode == "unsample" and sigmas[2] < sigmas[1]:
+                sigmas = torch.flip(state_info['sigmas'], dims=[0])
+                start_step = (len(sigmas)-1) - (state_info['end_step']) #-1) #removed -1 at the end here. correct?
+                
+            if state_info['sampler_mode'] == "unsample"              and sampler_mode == "resample" and sigmas[2] > sigmas[1]:
+                sigmas = torch.flip(state_info['sigmas'], dims=[0])
+                start_step = (len(sigmas)-1) - state_info['end_step'] #-1)
+        elif state_info['sampler_mode'] == "unsample" and sampler_mode == "resample":
+            start_step = 0
+        
+        if state_info['sampler_mode'] in {"standard", "resample"}    and sampler_mode == "resample":
+            start_step = state_info['end_step'] if state_info['end_step'] != -1 else 0
+            if start_step > 0:
+                sigmas = state_info['sigmas'].clone()
+
+            
+            
+    if sde_mask is not None:
+        from .rk_guide_func_beta import prepare_mask
+        sde_mask, _ = prepare_mask(x, sde_mask, LGW_MASK_RESCALE_MIN)
+        sde_mask = sde_mask.to(x.device).to(x.dtype)
+    
+
+
+    x      = x     .to(dtype=default_dtype, device=work_device)
+    sigmas = sigmas.to(dtype=default_dtype, device=work_device)
+    
+    c1                          = EO("c1"                         , c1)
+    c2                          = EO("c2"                         , c2)
+    c3                          = EO("c3"                         , c3)
+    
+    cfg_cw                      = EO("cfg_cw"                     , cfg_cw)
+    
+    noise_seed                  = EO("noise_seed"                 , noise_seed)
+    noise_seed_substep          = EO("noise_seed_substep"         , noise_seed + MAX_STEPS)
+    
+    pseudoimplicit_row_weights  = EO("pseudoimplicit_row_weights" , [1. for _ in range(100)])
+    pseudoimplicit_step_weights = EO("pseudoimplicit_step_weights", [1. for _ in range(max(implicit_steps_diag, implicit_steps_full)+1)])
+
+    noise_scaling_cycles = EO("noise_scaling_cycles", 1)
+    noise_boost_step     = EO("noise_boost_step",     0.0)
+    noise_boost_substep  = EO("noise_boost_substep",  0.0)
+    
+    # SETUP SAMPLER
+    if implicit_sampler_name not in ("use_explicit", "none"):
+        rk_type = implicit_sampler_name
+    RESplain("rk_type:", rk_type)
+    if implicit_sampler_name == "none":
+        implicit_steps_diag = implicit_steps_full = 0
+        
+    RK            = RK_Method_Beta.create(model, rk_type, VE_MODEL, noise_anchor, noise_boost_normalize, model_device=model_device, work_device=work_device, dtype=default_dtype, extra_options=extra_options)
+    RK.extra_args = RK.init_cfg_channelwise(x, cfg_cw, **extra_args)
+    RK.tile_sizes = tile_sizes
+    RK.extra_args['model_options']['transformer_options']['regional_conditioning_weight'] = 0.0
+    RK.extra_args['model_options']['transformer_options']['regional_conditioning_floor']  = 0.0
+    
+    RK.unsample_bongmath = BONGMATH if unsample_bongmath is None else unsample_bongmath # allow turning off bongmath for unsampling with cycles
+
+
+    # SETUP SIGMAS
+    sigmas_orig = sigmas.clone()
+    NS               = RK_NoiseSampler(RK, model, device=work_device, dtype=default_dtype, extra_options=extra_options)
+    sigmas, UNSAMPLE = NS.prepare_sigmas(sigmas, sigmas_override, d_noise, d_noise_start_step, sampler_mode)
+    if UNSAMPLE and sigmas_orig[0] == 0.0 and sigmas_orig[0] != sigmas[0] and sigmas[1] < sigmas[2]:
+        sigmas = torch.cat([torch.full_like(sigmas[0], 0.0).unsqueeze(0), sigmas])
+        if start_step == 0:
+            start_step  = 1
+        else:
+            start_step -= 1
+    
+    if sampler_mode in {"resample", "unsample"}:
+        state_info_sigma_next = state_info.get('sigma_next', -1)
+        state_info_start_step = (sigmas == state_info_sigma_next).nonzero().flatten()
+        if state_info_start_step.shape[0] > 0:
+            start_step = state_info_start_step.item()
+            
+    start_step = start_at_step if start_at_step >= 0 else start_step
+
+    
+    SDE_NOISE_EXTERNAL = False
+    if sde_noise is not None:
+        if len(sde_noise) > 0 and sigmas[1] > sigmas[2]:
+            SDE_NOISE_EXTERNAL = True
+            sigma_up_total = torch.zeros_like(sigmas[0])
+            for i in range(len(sde_noise)-1):
+                sigma_up_total += sigmas[i+1]
+            etas = torch.full_like(sigmas, eta / sigma_up_total)
+    
+    if 'last_rng' in state_info and sampler_mode in {"resample", "unsample"}:
+        last_rng         = state_info['last_rng'].clone()
+        last_rng_substep = state_info['last_rng_substep'].clone()
+    else:
+        last_rng         = None
+        last_rng_substep = None
+    
+    NS.init_noise_samplers(x, noise_seed, noise_seed_substep, noise_sampler_type, noise_sampler_type_substep, noise_mode_sde, noise_mode_sde_substep, \
+                            overshoot_mode, overshoot_mode_substep, noise_boost_step, noise_boost_substep, alpha, alpha_substep, k, k_substep, \
+                            last_rng=last_rng, last_rng_substep=last_rng_substep,)
+
+    data_               = None
+    eps_                = None
+    eps                 = torch.zeros_like(x, dtype=default_dtype, device=work_device)
+    denoised            = torch.zeros_like(x, dtype=default_dtype, device=work_device)
+    denoised_prev       = torch.zeros_like(x, dtype=default_dtype, device=work_device)
+    denoised_prev2      = torch.zeros_like(x, dtype=default_dtype, device=work_device)
+    x_                  = None
+    eps_prev_           = None
+    denoised_data_prev  = None
+    denoised_data_prev2 = None
+    h_prev              = None
+    eps_y2x_            = None
+    eps_x2y_            = None
+    eps_y_              = None
+    eps_prev_y_         = None
+    data_y_             = None
+    yt_                 = None
+    yt_0                = None
+    eps_yt_             = None
+    eps_x_              = None 
+    data_y_             = None
+    data_x_             = None
+    z_                  = None # for tracking residual noise for model scattersort/synchronized diffusion
+    
+    y0_bongflow         = state_info.get('y0_bongflow')
+    y0_bongflow_orig    = state_info.get('y0_bongflow_orig')
+    noise_bongflow      = state_info.get('noise_bongflow')
+    y0_standard_guide   = state_info.get('y0_standard_guide')
+    y0_inv_standard_guide = state_info.get('y0_inv_standard_guide')
+    
+    data_prev_y_        = state_info.get('data_prev_y_')
+    data_prev_x_        = state_info.get('data_prev_x_')
+    data_prev_x2y_      = state_info.get('data_prev_x2y_')
+
+    # BEGIN SAMPLING LOOP    
+    num_steps = len(sigmas[start_step:])-2 if sigmas[-1] == 0 else len(sigmas[start_step:])-1
+    
+    if steps_to_run >= 0:
+        current_steps =              min(num_steps, steps_to_run)
+        num_steps     = start_step + min(num_steps, steps_to_run)
+    else:
+        current_steps =              num_steps
+        num_steps     = start_step + num_steps
+    #current_steps = current_steps + 1 if sigmas[-1] == 0 and steps_to_run < 0 and UNSAMPLE else current_steps
+    
+    INIT_SAMPLE_LOOP = True
+    step = start_step
+    sigma, sigma_next, data_prev_ = None, None, None
+    
+    if (num_steps-1) == len(sigmas)-2 and sigmas[-1] == 0 and sigmas[-2] == NS.sigma_min:
+        progress_bar = trange(current_steps+1, disable=disable)
+    else:
+        progress_bar = trange(current_steps, disable=disable)
+    
+
+    # SETUP GUIDES
+    LG = LatentGuide(model, sigmas, UNSAMPLE, VE_MODEL, LGW_MASK_RESCALE_MIN, extra_options, device=work_device, dtype=default_dtype, frame_weights_mgr=frame_weights_mgr)
+
+    guide_inversion_y0     = state_info.get('guide_inversion_y0')
+    guide_inversion_y0_inv = state_info.get('guide_inversion_y0_inv')
+
+    x = LG.init_guides(x, RK.IMPLICIT, guides, NS.noise_sampler, batch_num, sigmas[step], guide_inversion_y0, guide_inversion_y0_inv)
+    LG.y0     = y0_standard_guide     if y0_standard_guide     is not None else LG.y0
+    LG.y0_inv = y0_inv_standard_guide if y0_inv_standard_guide is not None else LG.y0_inv
+    if (LG.mask != 1.0).any()   and  ((LG.y0 == 0).all() or (LG.y0_inv == 0).all()) : #  and   not LG.guide_mode.startswith("flow"):  # (LG.y0.sum() == 0 or LG.y0_inv.sum() == 0):
+        SKIP_PSEUDO = True
+        RESplain("skipping pseudo...")
+        if   LG.y0    .sum() == 0:
+            SKIP_PSEUDO_Y = "y0"
+        elif LG.y0_inv.sum() == 0:
+            SKIP_PSEUDO_Y = "y0_inv"
+    else:
+        SKIP_PSEUDO = False
+    if guides is not None and guides.get('guide_mode', '') != "inversion" or sampler_mode != "unsample":  #do not set denoised_prev to noise guide with inversion!
+        if   LG.y0.sum()     != 0 and LG.y0_inv.sum() != 0:
+            denoised_prev = LG.mask * LG.y0 + (1-LG.mask) * LG.y0_inv         
+        elif LG.y0.sum()     != 0:
+            denoised_prev = LG.y0
+        elif LG.y0_inv.sum() != 0:
+            denoised_prev = LG.y0_inv
+    data_cached = None
+        
+    if EO("pseudo_mix_strength"):
+        orig_y0     = LG.y0.clone()
+        orig_y0_inv = LG.y0_inv.clone()
+    
+    #gc.collect()
+    BASE_STARTED = False
+    INV_STARTED  = False
+    FLOW_STARTED = False
+    FLOW_STOPPED = False
+    noise_xt, noise_yt = None, None
+    FLOW_RESUMED = False
+    if state_info.get('FLOW_STARTED', False) and not state_info.get('FLOW_STOPPED', False):
+        FLOW_RESUMED = True
+        y0 = state_info['y0'].to(work_device) 
+        data_cached = state_info['data_cached'].to(work_device) 
+        data_x_prev_ = state_info['data_x_prev_'].to(work_device) 
+
+    if noise_initial is not None:
+        x_init = noise_initial.to(x)
+        RK.update_transformer_options({'x_init': x_init._copy() if hasattr(x_init, 'is_nested') and x_init.is_nested else x_init.clone()})
+
+    #progress_bar = trange(len(sigmas)-1-start_step, disable=disable)
+    
+    #if EO("eps_adain") or EO("x_init_to_model"):
+    
+    if AttnMask is not None:
+        RK.update_transformer_options({'AttnMask'  : AttnMask})
+        RK.update_transformer_options({'RegContext': RegContext})
+
+    if AttnMask_neg is not None:
+        RK.update_transformer_options({'AttnMask_neg'  : AttnMask_neg})
+        RK.update_transformer_options({'RegContext_neg': RegContext_neg})
+        
+    if EO("y0_to_transformer_options"):
+        RK.update_transformer_options({'y0':  LG.y0.clone()})
+    
+    if EO("y0_inv_to_transformer_options"):
+        RK.update_transformer_options({'y0_inv':  LG.y0_inv.clone()})
+        for block in model.inner_model.inner_model.diffusion_model.double_stream_blocks:
+            for attr in ["txt_q_cache", "txt_k_cache", "txt_v_cache", "img_q_cache", "img_k_cache", "img_v_cache"]:
+                if hasattr(block.block.attn1, attr):
+                    delattr(block.block.attn1, attr)
+
+        for block in model.inner_model.inner_model.diffusion_model.single_stream_blocks:
+            block.block.attn1.EO = EO 
+            for attr in ["txt_q_cache", "txt_k_cache", "txt_v_cache", "img_q_cache", "img_k_cache", "img_v_cache"]:
+                if hasattr(block.block.attn1, attr):
+                    delattr(block.block.attn1, attr)
+
+    RK.update_transformer_options({'ExtraOptions': copy.deepcopy(EO)})
+    if EO("update_cross_attn"):
+        update_cross_attn = {
+            'src_llama_start': EO('src_llama_start', 0),
+            'src_llama_end':   EO('src_llama_end', 0),
+            'src_t5_start':    EO('src_t5_start', 0),
+            'src_t5_end':      EO('src_t5_end', 0),
+
+            'tgt_llama_start': EO('tgt_llama_start', 0),
+            'tgt_llama_end':   EO('tgt_llama_end', 0),
+            'tgt_t5_start':    EO('tgt_t5_start', 0),
+            'tgt_t5_end':      EO('tgt_t5_end', 0),
+            'skip_cross_attn': EO('skip_cross_attn', False),
+            
+            'update_q':        EO('update_q', False),
+            'update_k':        EO('update_k', True),
+            'update_v':        EO('update_v', True),
+            
+            
+            'lamb':  EO('lamb', 0.01),
+            'erase': EO('erase', 10.0),
+        }
+        RK.update_transformer_options({'update_cross_attn':  update_cross_attn})
+    else:
+        RK.update_transformer_options({'update_cross_attn':  None})
+
+    if LG.HAS_LATENT_GUIDE_ADAIN:
+        RK.update_transformer_options({'blocks_adain_cache': []})
+    if LG.HAS_LATENT_GUIDE_ATTNINJ:
+        RK.update_transformer_options({'blocks_attninj_cache': []})
+    if LG.HAS_LATENT_GUIDE_STYLE_POS:
+        if LG.HAS_LATENT_GUIDE and y0_standard_guide is None:
+            y0_cache = LG.y0.clone().cpu()
+            RK.update_transformer_options({'y0_standard_guide': LG.y0})
+        
+    sigmas_scheduled = sigmas.clone() # store for return in state_info_out
+    
+    if EO("sigma_restarts"):
+        sigma_restarts = 1 + EO("sigma_restarts", 0)
+        sigmas = sigmas[step:num_steps+1].repeat(sigma_restarts)
+        step = 0
+        num_steps = 2 * sigma_restarts - 1
+        
+    if RENOISE:      # TODO: adapt for noise inversion somehow
+        if VE_MODEL:
+            x = x + sigmas[step] * NS.noise_sampler(sigma=sigmas[step], sigma_next=sigmas[step+1])
+        else:
+            x = (1 - sigmas[step]) * x + sigmas[step] * NS.noise_sampler(sigma=sigmas[step], sigma_next=sigmas[step+1])
+    LG.ADAIN_NOISE_MODE = ""
+    StyleMMDiT = None
+    if guides is not None:
+        RK.update_transformer_options({"freqsep_lowpass_method": guides.get("freqsep_lowpass_method")})
+        RK.update_transformer_options({"freqsep_sigma":          guides.get("freqsep_sigma")})
+        RK.update_transformer_options({"freqsep_kernel_size":    guides.get("freqsep_kernel_size")})
+        RK.update_transformer_options({"freqsep_inner_kernel_size":    guides.get("freqsep_inner_kernel_size")})
+        RK.update_transformer_options({"freqsep_stride":    guides.get("freqsep_stride")})
+
+        
+        RK.update_transformer_options({"freqsep_lowpass_weight": guides.get("freqsep_lowpass_weight")})
+        RK.update_transformer_options({"freqsep_highpass_weight":guides.get("freqsep_highpass_weight")})
+        RK.update_transformer_options({"freqsep_mask":           guides.get("freqsep_mask")})
+
+        StyleMMDiT = guides.get('StyleMMDiT')
+        if StyleMMDiT is not None:
+            StyleMMDiT.init_guides(model)
+            LG.ADAIN_NOISE_MODE = StyleMMDiT.noise_mode
+            
+            if EO("mycoshock"):
+                StyleMMDiT.Retrojector = model.inner_model.inner_model.diffusion_model.Retrojector
+                image_initial_shock = StyleMMDiT.apply_data_shock(image_initial.to(x))
+                if VE_MODEL:
+                    x = image_initial_shock.to(x) + sigmas[0] * noise_initial.to(x)
+                else:
+                    x = (1 - sigmas[0]) * image_initial_shock.to(x) + sigmas[0] * noise_initial.to(x)
+
+    RK.update_transformer_options({"model_sampling": model.inner_model.inner_model.model_sampling})
+    # BEGIN SAMPLING LOOP
+    
+    while step < num_steps:
+        sigma, sigma_next = sigmas[step], sigmas[step+1]
+        if sigma_next > sigma:
+            step_sched = torch.where(torch.flip(sigmas, dims=[0]) == sigma)[0][0].item()
+        else:
+            step_sched = step
+        
+        SYNC_GUIDE_ACTIVE = LG.guide_mode.startswith("sync") and (LG.lgw[step_sched] != 0 or LG.lgw_inv[step_sched] != 0 or LG.lgw_sync[step_sched] != 0 or LG.lgw_sync_inv[step_sched] != 0)
+        
+        if StyleMMDiT is not None:
+            RK.update_transformer_options({'StyleMMDiT': StyleMMDiT})
+        else:
+            if LG.HAS_LATENT_GUIDE_ADAIN:
+                if LG.lgw_adain[step_sched] == 0.0:
+                    RK.update_transformer_options({'y0_adain': None})
+                    RK.update_transformer_options({'blocks_adain': {}})
+                    RK.update_transformer_options({'sort_and_scatter': {}})
+                else:
+                    RK.update_transformer_options({'y0_adain': LG.y0_adain.clone()})
+                    if 'blocks_adain_mmdit' in guides:
+                        blocks_adain = {
+                            "double_weights": [val * LG.lgw_adain[step_sched] for val in guides['blocks_adain_mmdit']['double_weights']],
+                            "single_weights": [val * LG.lgw_adain[step_sched] for val in guides['blocks_adain_mmdit']['single_weights']],
+                            "double_blocks" : guides['blocks_adain_mmdit']['double_blocks'],
+                            "single_blocks" : guides['blocks_adain_mmdit']['single_blocks'],
+                        }
+                    RK.update_transformer_options({'blocks_adain': blocks_adain})
+                    RK.update_transformer_options({'sort_and_scatter': guides['sort_and_scatter']})
+                    RK.update_transformer_options({'noise_mode_adain': guides['sort_and_scatter']['noise_mode']})
+                        
+            
+            if LG.HAS_LATENT_GUIDE_ATTNINJ:
+                if LG.lgw_attninj[step_sched] == 0.0:
+                    RK.update_transformer_options({'y0_attninj': None})
+                    RK.update_transformer_options({'blocks_attninj'    : {}})
+                    RK.update_transformer_options({'blocks_attninj_qkv': {}})
+                else:
+                    RK.update_transformer_options({'y0_attninj': LG.y0_attninj.clone()})
+                    if 'blocks_attninj_mmdit' in guides:
+                        blocks_attninj = {
+                            "double_weights": [val * LG.lgw_attninj[step_sched] for val in guides['blocks_attninj_mmdit']['double_weights']],
+                            "single_weights": [val * LG.lgw_attninj[step_sched] for val in guides['blocks_attninj_mmdit']['single_weights']],
+                            "double_blocks" : guides['blocks_attninj_mmdit']['double_blocks'],
+                            "single_blocks" : guides['blocks_attninj_mmdit']['single_blocks'],
+                        }
+                    RK.update_transformer_options({'blocks_attninj'    : blocks_attninj})
+                    RK.update_transformer_options({'blocks_attninj_qkv': guides['blocks_attninj_qkv']})
+        
+        if LG.HAS_LATENT_GUIDE_STYLE_POS:
+            if LG.lgw_style_pos[step_sched] == 0.0:
+                RK.update_transformer_options({'y0_style_pos':        None})
+                RK.update_transformer_options({'y0_style_pos_weight': 0.0})
+                RK.update_transformer_options({'y0_style_pos_synweight': 0.0})
+                RK.update_transformer_options({'y0_style_pos_mask': None})
+            else:
+                RK.update_transformer_options({'y0_style_pos':        LG.y0_style_pos.clone()})
+                RK.update_transformer_options({'y0_style_pos_weight': LG.lgw_style_pos[step_sched]})
+                RK.update_transformer_options({'y0_style_pos_synweight': guides['synweight_style_pos']})
+                RK.update_transformer_options({'y0_style_pos_mask': LG.mask_style_pos})
+                RK.update_transformer_options({'y0_style_pos_mask_edge': guides.get('mask_edge_style_pos')})
+                RK.update_transformer_options({'y0_style_method': guides['style_method']})
+                RK.update_transformer_options({'y0_style_tile_height': guides.get('style_tile_height')})
+                RK.update_transformer_options({'y0_style_tile_width': guides.get('style_tile_width')})
+                RK.update_transformer_options({'y0_style_tile_padding': guides.get('style_tile_padding')})
+                
+                if EO("style_edge_width"):
+                    RK.update_transformer
+                
+                #if LG.HAS_LATENT_GUIDE:
+                #    y0_cache = LG.y0.clone().cpu()
+                #    RK.update_transformer_options({'y0_standard_guide': LG.y0})
+                    
+                if LG.HAS_LATENT_GUIDE_INV and y0_inv_standard_guide is None:
+                    y0_inv_cache = LG.y0_inv.clone().cpu()
+                    RK.update_transformer_options({'y0_inv_standard_guide': LG.y0_inv})
+                    
+
+        if LG.HAS_LATENT_GUIDE_STYLE_NEG:
+            if LG.lgw_style_neg[step_sched] == 0.0:
+                RK.update_transformer_options({'y0_style_neg':        None})
+                RK.update_transformer_options({'y0_style_neg_weight': 0.0})
+                RK.update_transformer_options({'y0_style_neg_synweight': 0.0})
+                RK.update_transformer_options({'y0_style_neg_mask': None})
+            else:
+                RK.update_transformer_options({'y0_style_neg':        LG.y0_style_neg.clone()})
+                RK.update_transformer_options({'y0_style_neg_weight': LG.lgw_style_neg[step_sched]})
+                RK.update_transformer_options({'y0_style_neg_synweight': guides['synweight_style_neg']})
+                RK.update_transformer_options({'y0_style_neg_mask': LG.mask_style_neg})
+                RK.update_transformer_options({'y0_style_neg_mask_edge': guides.get('mask_edge_style_neg')})
+                RK.update_transformer_options({'y0_style_method': guides['style_method']})
+                RK.update_transformer_options({'y0_style_tile_height': guides.get('style_tile_height')})
+                RK.update_transformer_options({'y0_style_tile_width': guides.get('style_tile_width')})
+                RK.update_transformer_options({'y0_style_tile_padding': guides.get('style_tile_padding')})
+
+        if AttnMask_neg is not None:
+            RK.update_transformer_options({'regional_conditioning_weight_neg': RegParam_neg.weights[step_sched]})
+            RK.update_transformer_options({'regional_conditioning_floor_neg':  RegParam_neg.floors[step_sched]})
+
+        if AttnMask is not None:
+            RK.update_transformer_options({'regional_conditioning_weight': RegParam.weights[step_sched]})
+            RK.update_transformer_options({'regional_conditioning_floor':  RegParam.floors[step_sched]})
+
+        elif regional_conditioning_weights is not None:
+            RK.extra_args['model_options']['transformer_options']['regional_conditioning_weight'] = regional_conditioning_weights[step_sched]
+            RK.extra_args['model_options']['transformer_options']['regional_conditioning_floor']  = regional_conditioning_floors [step_sched]
+        
+        epsilon_scale        = float(epsilon_scales [step_sched]) if epsilon_scales        is not None else None
+        eta                  = etas                 [step_sched].to(x)  if etas                  is not None else eta
+        eta_substep          = etas_substep         [step_sched].to(x)  if etas_substep          is not None else eta_substep
+        s_noise              = s_noises             [step_sched].to(x)  if s_noises              is not None else s_noise
+        s_noise_substep      = s_noises_substep     [step_sched].to(x)  if s_noises_substep      is not None else s_noise_substep
+        noise_scaling_eta    = noise_scaling_etas   [step_sched].to(x)  if noise_scaling_etas    is not None else noise_scaling_eta
+        noise_scaling_weight = noise_scaling_weights[step_sched].to(x)  if noise_scaling_weights is not None else noise_scaling_weight
+        
+        NS.set_sde_step(sigma, sigma_next, eta, overshoot, s_noise)
+        RK.set_coeff(rk_type, NS.h, c1, c2, c3, step, sigmas, NS.sigma_down)
+        NS.set_substep_list(RK)
+
+        if (noise_scaling_eta > 0 or noise_scaling_weight != 0) and noise_scaling_type != "model_d":
+            if noise_scaling_type == "model_alpha":
+                VP_OVERRIDE=True
+            else:
+                VP_OVERRIDE=None
+            if noise_scaling_type in {"sampler", "model", "model_alpha"}:
+                if noise_scaling_type == "model_alpha":
+                    sigma_divisor = NS.sigma_max
+                else:
+                    sigma_divisor = 1.0
+                
+                if RK.multistep_stages > 0:                                              # hardcoded s_[1] for multistep samplers, which are never multistage
+                    lying_su, lying_sigma, lying_sd, lying_alpha_ratio = NS.get_sde_step(NS.s_[1]/sigma_divisor, NS.s_[0]/sigma_divisor, noise_scaling_eta, noise_scaling_mode, VP_OVERRIDE=VP_OVERRIDE)
+                    
+                else:
+                    lying_su, lying_sigma, lying_sd, lying_alpha_ratio = NS.get_sde_step(sigma/sigma_divisor, NS.sigma_down/sigma_divisor, noise_scaling_eta, noise_scaling_mode, VP_OVERRIDE=VP_OVERRIDE)
+                    for _ in range(noise_scaling_cycles-1):
+                        lying_su, lying_sigma, lying_sd, lying_alpha_ratio = NS.get_sde_step(sigma/sigma_divisor, lying_sd/sigma_divisor, noise_scaling_eta, noise_scaling_mode, VP_OVERRIDE=VP_OVERRIDE)
+                lying_s_ = NS.get_substep_list(RK, sigma, RK.h_fn(lying_sd, lying_sigma))
+                lying_s_ = NS.s_ + noise_scaling_weight * (lying_s_ - NS.s_)
+            else:
+                lying_s_ = NS.s_.clone()
+        
+
+        rk_swap_stages = 3 if rk_swap_type != "" else 0
+        data_prev_len = len(data_prev_)-1 if data_prev_ is not None else 3
+        recycled_stages = max(rk_swap_stages, RK.multistep_stages, RK.hybrid_stages, data_prev_len)
+        
+        if INIT_SAMPLE_LOOP:
+            INIT_SAMPLE_LOOP = False
+            x_, data_, eps_, eps_prev_ = (torch.zeros(RK.rows+2, *x.shape, dtype=default_dtype, device=work_device) for _ in range(4))
+            if LG.ADAIN_NOISE_MODE == "smart":
+                z_ = torch.zeros(RK.rows+2, *x.shape, dtype=default_dtype, device=work_device)
+                z_[0] = noise_initial.clone()
+                RK.update_transformer_options({'z_' : z_})
+            
+            if sampler_mode in {"unsample", "resample"}:
+                data_prev_ = state_info.get('data_prev_')
+                if data_prev_ is not None:
+                    if x.shape == state_info['raw_x'].shape:
+                        data_prev_ = state_info['data_prev_'].clone().to(dtype=default_dtype, device=work_device)
+                    else:
+                        data_prev_ = torch.stack([comfy.utils.bislerp(data_prev_item, x.shape[-1], x.shape[-2]) for data_prev_item in state_info['data_prev_']])
+                        data_prev_ = data_prev_.to(x)
+                else:
+                    data_prev_ =  torch.zeros(4, *x.shape, dtype=default_dtype, device=work_device) # multistep max is 4m... so 4 needed
+            else:
+                data_prev_ =  torch.zeros(4, *x.shape, dtype=default_dtype, device=work_device) # multistep max is 4m... so 4 needed
+            
+            recycled_stages = len(data_prev_)-1
+        
+        if RK.rows+2 > x_.shape[0]:
+            row_gap = RK.rows+2 - x_.shape[0]
+            x_gap_, data_gap_, eps_gap_, eps_prev_gap_ = (torch.zeros(row_gap, *x.shape, dtype=default_dtype, device=work_device) for _ in range(4))
+            x_        = torch.cat((x_       ,x_gap_)       , dim=0)
+            data_     = torch.cat((data_    ,data_gap_)    , dim=0)
+            eps_      = torch.cat((eps_     ,eps_gap_)     , dim=0)
+            eps_prev_ = torch.cat((eps_prev_,eps_prev_gap_), dim=0)
+            
+            if LG.ADAIN_NOISE_MODE == "smart":
+                z_gap_ = torch.zeros(row_gap, *x.shape, dtype=default_dtype, device=work_device)
+                z_    = torch.cat((z_       ,z_gap_)       , dim=0)
+                RK.update_transformer_options({'z_' : z_})
+
+        sde_noise_t = None
+        if SDE_NOISE_EXTERNAL:
+            if step >= len(sde_noise):
+                SDE_NOISE_EXTERNAL=False
+            else:
+                sde_noise_t = sde_noise[step]
+        
+        x_[0] = x.clone()
+        # PRENOISE METHOD HERE!
+        x_0   = x_[0].clone()
+        if EO("guide_step_cutoff") or EO("guide_step_min"):
+            x_0_orig = x_0.clone()
+        
+        # RECYCLE STAGES FOR MULTISTEP
+        if RK.multistep_stages > 0 or RK.hybrid_stages > 0:
+            if SYNC_GUIDE_ACTIVE:
+                lgw_mask_, lgw_mask_inv_ = LG.get_masks_for_step(step)
+                lgw_mask_sync_, lgw_mask_sync_inv_ = LG.get_masks_for_step(step, lgw_type="sync")
+
+                weight_mask = lgw_mask_+lgw_mask_inv_
+                if LG.SYNC_SEPARATE:
+                    sync_mask = lgw_mask_sync_+lgw_mask_sync_inv_
+                else:
+                    sync_mask = 1.
+                            
+                if VE_MODEL:
+                    yt_0 = y0_bongflow + sigma * noise_bongflow
+                else:
+                    yt_0 = (1-sigma) * y0_bongflow  + sigma * noise_bongflow
+                for ms in range(min(len(data_prev_), len(eps_))):
+                    eps_x   = RK.get_epsilon_anchored(x_0,  data_prev_x_[ms], sigma)
+                    eps_y   = RK.get_epsilon_anchored(yt_0, data_prev_y_[ms], sigma)
+                    eps_x2y = RK.get_epsilon_anchored(yt_0, data_prev_y_[ms], sigma)
+
+                    if RK.EXPONENTIAL:
+                        if VE_MODEL:
+                            eps_[ms] = sync_mask * eps_x  +  (1-sync_mask) * eps_x2y  +  weight_mask * (-eps_y + sigma*(-noise_bongflow))
+                            if EO("sync_x2y"):
+                                eps_[ms] = sync_mask * eps_x  +  (1-sync_mask) * eps_x2y  +  weight_mask * (-eps_x2y + sigma*(-noise_bongflow))
+                        else:
+                            eps_[ms] = sync_mask * eps_x  +  (1-sync_mask) * eps_x2y  +  weight_mask * (-eps_y + sigma*(y0_bongflow-noise_bongflow))
+                            if EO("sync_x2y"):
+                                eps_[ms] = sync_mask * eps_x  +  (1-sync_mask) * eps_x2y  +  weight_mask * (-eps_x2y + sigma*(y0_bongflow-noise_bongflow))
+                    else:
+                        if VE_MODEL:
+                            eps_[ms] = sync_mask * eps_x  +  (1-sync_mask) * eps_x2y  +  weight_mask * (-eps_y + (noise_bongflow))
+                            if EO("sync_x2y"):
+                                eps_[ms] = sync_mask * eps_x  +  (1-sync_mask) * eps_x2y  +  weight_mask * (-eps_x2y + (noise_bongflow))
+                        else:
+                            eps_[ms] = sync_mask * eps_x  +  (1-sync_mask) * eps_x2y  +  weight_mask * (-eps_y + (noise_bongflow-y0_bongflow))
+                            if EO("sync_x2y"):
+                                eps_[ms] = sync_mask * eps_x  +  (1-sync_mask) * eps_x2y  +  weight_mask * (-eps_x2y + (noise_bongflow-y0_bongflow))
+
+                    #if RK.EXPONENTIAL:
+                    #    if VE_MODEL:
+                    #        eps_[ms] = sync_mask * weight_mask_inv * (eps_x - weight_mask * eps_y) +  weight_mask * sigma*(-noise_bongflow)
+                    #    else:
+                    #        #eps_[ms] = (lgw_mask_sync_+lgw_mask_sync_inv_) * (1-(lgw_mask_+lgw_mask_inv_)) * (eps_x - (lgw_mask_+lgw_mask_inv_) * eps_y) +  (lgw_mask_+lgw_mask_inv_) * sigma*(y0_bongflow-noise_bongflow)
+                    #        eps_[ms] = sync_mask * weight_mask_inv * (eps_x - weight_mask * eps_y) +  weight_mask * sigma*(y0_bongflow-noise_bongflow)
+                    #else:
+                    #    if VE_MODEL:
+                    #        eps_[ms] = sync_mask * weight_mask_inv * (eps_x - weight_mask * eps_y) +  weight_mask *       (noise_bongflow)
+                    #    else:
+                    #        #eps_[ms] = (lgw_mask_sync_+lgw_mask_sync_inv_) * (1-(lgw_mask_+lgw_mask_inv_)) * (eps_x - (lgw_mask_+lgw_mask_inv_) * eps_y) +  (lgw_mask_+lgw_mask_inv_) *       (noise_bongflow-y0_bongflow)
+                    #        eps_[ms] = sync_mask * weight_mask_inv * (eps_x - weight_mask * eps_y) +  weight_mask *       (noise_bongflow-y0_bongflow)
+                eps_prev_ = eps_.clone()
+            
+            else:
+                for ms in range(min(len(data_prev_), len(eps_))):
+                    eps_[ms] = RK.get_epsilon_anchored(x_0, data_prev_[ms], sigma)
+                eps_prev_ = eps_.clone()
+
+
+
+        # INITIALIZE IMPLICIT SAMPLING
+        if RK.IMPLICIT:
+            x_, eps_, data_ = init_implicit_sampling(RK, x_0, x_, eps_, eps_prev_, data_, eps, denoised, denoised_prev2, step, sigmas, NS.h, NS.s_, EO, SYNC_GUIDE_ACTIVE)
+
+        implicit_steps_total = (implicit_steps_full + 1) * (implicit_steps_diag + 1)
+
+        # BEGIN FULLY IMPLICIT LOOP
+        cossim_counter = 0
+        adaptive_lgw = LG.lgw.clone()
+        full_iter = 0
+        while full_iter < implicit_steps_full+1:
+
+            if RK.IMPLICIT:
+                x_, eps_ = RK.newton_iter(x_0, x_, eps_, eps_prev_, data_, NS.s_, 0, NS.h, sigmas, step, "init", SYNC_GUIDE_ACTIVE)
+
+            # PREPARE FULLY PSEUDOIMPLICIT GUIDES
+            if step > 0 or not SKIP_PSEUDO:
+                if full_iter > 0 and EO("fully_implicit_reupdate_x"):
+                    x_[0] = NS.sigma_from_to(x_0, x, sigma, sigma_next, NS.s_[0])
+                    x_0   = NS.sigma_from_to(x_0, x, sigma, sigma_next, sigma)
+                
+                if EO("fully_pseudo_init") and full_iter == 0:
+                    guide_mode_tmp = LG.guide_mode
+                    LG.guide_mode = "fully_" + LG.guide_mode
+                x_0, x_, eps_ = LG.prepare_fully_pseudoimplicit_guides_substep(x_0, x_, eps_, eps_prev_, data_, denoised_prev, 0, step, step_sched, sigmas, eta_substep, overshoot_substep, s_noise_substep, \
+                                                                                NS, RK, pseudoimplicit_row_weights, pseudoimplicit_step_weights, full_iter, BONGMATH)
+                if EO("fully_pseudo_init") and full_iter == 0:
+                    LG.guide_mode = guide_mode_tmp
+
+            # TABLEAU LOOP
+            for row in range(RK.rows - RK.multistep_stages - RK.row_offset + 1):
+                diag_iter = 0
+                while diag_iter < implicit_steps_diag+1:
+                    
+
+                    if noise_sampler_type_substep == "brownian" and (full_iter > 0 or diag_iter > 0):
+                        eta_substep = 0.
+                    
+                    NS.set_sde_substep(row, RK.multistep_stages, eta_substep, overshoot_substep, s_noise_substep, full_iter, diag_iter, implicit_steps_full, implicit_steps_diag)
+
+                    # PRENOISE METHOD HERE!
+                    
+                    # A-TABLEAU
+                    if row < RK.rows:
+
+                        # PREPARE PSEUDOIMPLICIT GUIDES
+                        if step > 0 or not SKIP_PSEUDO:
+                            x_0, x_, eps_, x_row_pseudoimplicit, sub_sigma_pseudoimplicit = LG.process_pseudoimplicit_guides_substep(x_0, x_, eps_, eps_prev_, data_, denoised_prev, row, step, step_sched, sigmas, NS, RK, \
+                                                                                                                        pseudoimplicit_row_weights, pseudoimplicit_step_weights, full_iter, BONGMATH)
+                        
+                        # PREPARE MODEL CALL
+                        if LG.guide_mode in GUIDE_MODE_NAMES_PSEUDOIMPLICIT and (step > 0 or not SKIP_PSEUDO) and (LG.lgw[step_sched] > 0 or LG.lgw_inv[step_sched] > 0) and x_row_pseudoimplicit is not None:
+
+                            x_tmp =     x_row_pseudoimplicit 
+                            s_tmp = sub_sigma_pseudoimplicit 
+
+                        # Fully implicit iteration (explicit only)                   # or... Fully implicit iteration (implicit only... not standard) 
+                        elif (full_iter > 0 and RK.row_offset == 1 and row == 0)   or   (full_iter > 0 and RK.row_offset == 0 and row == 0 and EO("fully_implicit_update_x")):
+                            if EO("fully_explicit_pogostick_eta"): 
+                                super_alpha_ratio, super_sigma_down, super_sigma_up = NS.get_sde_coeff(sigma, sigma_next, None, eta)
+                                x = super_alpha_ratio * x + super_sigma_up * NS.noise_sampler(sigma=sigma_next, sigma_next=sigma)
+                                
+                                x_tmp = x
+                                s_tmp = sigma
+                            elif EO("enable_fully_explicit_lagrange_rebound1"):
+                                substeps_prev = len(RK.C[:-1]) 
+                                x_tmp = lagrange_interpolation(RK.C[1:-1], x_[1:substeps_prev], RK.C[0]).squeeze(0)
+                                
+                            elif EO("enable_fully_explicit_lagrange_rebound2"):
+                                substeps_prev = len(RK.C[:-1]) 
+                                x_tmp = lagrange_interpolation(RK.C[1:], x_[1:substeps_prev+1], RK.C[0]).squeeze(0)
+
+                            elif EO("enable_fully_explicit_rebound1"):  # 17630, faded dots, just crap
+                                eps_tmp, denoised_tmp = RK(x, sigma_next, x, sigma_next)
+                                eps_tmp = (x - denoised_tmp) / sigma_next
+                                x_[0] = denoised_tmp + sigma * eps_tmp
+                                
+                                x_0 =   x_[0]
+                                x_tmp = x_[0]
+                                s_tmp = sigma
+                                
+                            elif implicit_type == "rebound":          # TODO: ADAPT REBOUND IMPLICIT TO WORK WITH FLOW GUIDE MODE
+                                eps_tmp, denoised_tmp = RK(x, sigma_next, x_0, sigma)
+                                eps_tmp = (x - denoised_tmp) / sigma_next
+                                x = denoised_tmp + sigma * eps_tmp
+                                
+                                x_tmp = x
+                                s_tmp = sigma
+                                
+                            elif implicit_type == "retro-eta" and (NS.sub_sigma_up > 0 or NS.sub_sigma_up_eta > 0): 
+                                x_tmp = NS.sigma_from_to(x_0, x, sigma, sigma_next, sigma)
+                                s_tmp = sigma
+                                
+                            elif implicit_type == "bongmath" and (NS.sub_sigma_up > 0 or NS.sub_sigma_up_eta > 0): 
+                                if BONGMATH:
+                                    x_tmp =    x_[row]
+                                    s_tmp = NS.s_[row]
+                                else:
+                                    x_tmp = NS.sigma_from_to(x_0, x, sigma, sigma_next, sigma)
+                                    s_tmp = sigma
+                                
+                            else:
+                                x_tmp = x
+                                s_tmp = sigma_next
+
+
+
+                        # All others
+                        else:
+                            # three potential toggle options: force rebound/model call, force PC style, force pogostick style
+                            if diag_iter > 0: # Diagonally implicit iteration (explicit or implicit)
+                                if EO("diag_explicit_pogostick_eta"): 
+                                    super_alpha_ratio, super_sigma_down, super_sigma_up = NS.get_sde_coeff(NS.s_[row], NS.s_[row+RK.row_offset+RK.multistep_stages], None, eta)
+                                    x_[row+RK.row_offset] = super_alpha_ratio * x_[row+RK.row_offset] + super_sigma_up * NS.noise_sampler(sigma=NS.s_[row+RK.row_offset+RK.multistep_stages], sigma_next=NS.s_[row])
+                                    
+                                    x_tmp = x_[row+RK.row_offset]
+                                    s_tmp = sigma
+                                
+                                elif implicit_type_substeps == "rebound":
+                                    eps_[row], data_[row] = RK(x_[row+RK.row_offset], NS.s_[row+RK.row_offset+RK.multistep_stages], x_0, sigma)
+                                    
+                                    x_ = RK.update_substep(x_0, x_, eps_, eps_prev_, row, RK.row_offset, NS.h_new, NS.h_new_orig)
+                                    x_[row+RK.row_offset] = NS.rebound_overshoot_substep(x_0, x_[row+RK.row_offset])
+
+                                    x_[row+RK.row_offset] = NS.sigma_from_to(x_0,    x_[row+RK.row_offset],    sigma,    NS.s_[row+RK.row_offset+RK.multistep_stages],    NS.s_[row])
+                                    x_tmp = x_[row+RK.row_offset]
+                                    s_tmp = NS.s_[row]
+                                    
+                                elif implicit_type_substeps == "retro-eta" and (NS.sub_sigma_up > 0 or NS.sub_sigma_up_eta > 0):
+                                    x_tmp = NS.sigma_from_to(x_0,   x_[row+RK.row_offset],    sigma,    NS.s_[row+RK.row_offset+RK.multistep_stages],    NS.s_[row])
+                                    s_tmp = NS.s_[row]
+                                    
+                                elif implicit_type_substeps == "bongmath" and (NS.sub_sigma_up > 0 or NS.sub_sigma_up_eta > 0) and not EO("disable_diag_explicit_bongmath_rebound"): 
+                                    if BONGMATH:
+                                        x_tmp =    x_[row]
+                                        s_tmp = NS.s_[row]
+                                    else:
+                                        x_tmp = NS.sigma_from_to(x_0, x_[row+RK.row_offset], sigma, NS.s_[row+RK.row_offset+RK.multistep_stages], NS.s_[row])
+                                        s_tmp = NS.s_[row]
+                                    
+                                else:
+                                    x_tmp =    x_[row+RK.row_offset]
+                                    s_tmp = NS.s_[row+RK.row_offset+RK.multistep_stages]
+                            else:
+                                x_tmp = x_[row]
+                                s_tmp = NS.sub_sigma 
+
+
+
+                            if RK.IMPLICIT: 
+                                if not EO("disable_implicit_guide_preproc"):
+                                    eps_, x_      = LG.process_guides_substep(x_0, x_, eps_,      data_, row, step_sched, sigma, sigma_next, NS.sigma_down, NS.s_, epsilon_scale, RK)
+                                    eps_prev_, x_ = LG.process_guides_substep(x_0, x_, eps_prev_, data_, row, step_sched, sigma, sigma_next, NS.sigma_down, NS.s_, epsilon_scale, RK)
+                                if row == 0 and (EO("implicit_lagrange_init")  or   EO("radaucycle")):
+                                    pass
+                                else:
+                                    x_[row+RK.row_offset] = x_0 + NS.h_new * RK.zum(row+RK.row_offset, eps_, eps_prev_)
+                                    x_[row+RK.row_offset] = NS.rebound_overshoot_substep(x_0, x_[row+RK.row_offset])
+                                    if row > 0:
+                                        if not LG.guide_mode.startswith("flow") or (LG.lgw[step_sched] == 0 and LG.lgw[step+1] == 0   and   LG.lgw_inv[step_sched] == 0 and LG.lgw_inv[step+1] == 0):
+                                            x_row_tmp = NS.swap_noise_substep(x_0, x_[row+RK.row_offset], mask=sde_mask, guide=LG.y0)
+                                            
+                                            if LG.ADAIN_NOISE_MODE == "smart": #_smartnoise_implicit"):
+                                                data_next = denoised + NS.h_new * RK.zum(row+RK.row_offset+RK.multistep_stages, data_, data_prev_) 
+                                                if VE_MODEL:
+                                                    z_[row+RK.row_offset] = (x_row_tmp - data_next) / s_tmp
+                                                else:
+                                                    z_[row+RK.row_offset] = (x_row_tmp - (NS.sigma_max-s_tmp)*data_next) / s_tmp
+                                                RK.update_transformer_options({'z_' : z_})
+                                            
+                                            if SYNC_GUIDE_ACTIVE:
+                                                noise_bongflow_new = (x_row_tmp - x_[row+RK.row_offset]) / s_tmp + noise_bongflow
+                                                yt_[row+RK.row_offset] += s_tmp * (noise_bongflow_new - noise_bongflow)
+                                                x_0 += sigma * (noise_bongflow_new - noise_bongflow)
+                                                if not EO("disable_i_bong"):
+                                                    for i_bong in range(len(NS.s_)):
+                                                        x_[i_bong] += NS.s_[i_bong] * (noise_bongflow_new - noise_bongflow)
+                                                noise_bongflow = noise_bongflow_new
+                                            
+                                            x_[row+RK.row_offset] = x_row_tmp
+                                        
+                                        if SYNC_GUIDE_ACTIVE:
+                                            if VE_MODEL:
+                                                yt_[:NS.s_.shape[0], 0] = y0_bongflow + NS.s_.view(-1, *[1]*(x.ndim-1)) * (noise_bongflow)
+                                                yt_0   = y0_bongflow + sigma * (noise_bongflow)
+                                            else:
+                                                yt_[:NS.s_.shape[0], 0] = y0_bongflow + NS.s_.view(-1, *[1]*(x.ndim-1)) * (noise_bongflow - y0_bongflow)
+                                                yt_0   = y0_bongflow + sigma * (noise_bongflow - y0_bongflow)
+                                            
+                                            if RK.EXPONENTIAL:
+                                                eps_y_ = data_y_ - yt_0 # yt_        # watch out for fuckery with size of tableau being smaller later in a chained sampler
+                                            else:
+                                                if BONGMATH:
+                                                    eps_y_[:NS.s_.shape[0]] = (yt_[:NS.s_.shape[0]] - data_y_[:NS.s_.shape[0]]) / NS.s_.view(-1,*[1]*(x_.ndim-1)) 
+                                                else:
+                                                    eps_y_[:NS.s_.shape[0]] = (yt_0.repeat(NS.s_.shape[0], *[1]*(x_.ndim-1)) - data_y_[:NS.s_.shape[0]]) / sigma    # calc exact to c0 node
+                                            if not BONGMATH:
+                                                if RK.EXPONENTIAL:
+                                                    eps_x_ = data_x_ - x_0 
+                                                else:
+                                                    eps_x_ = (x_0 - data_x_) / sigma
+                                            
+                                            weight_mask = lgw_mask_+lgw_mask_inv_
+                                            if LG.SYNC_SEPARATE:
+                                                sync_mask = lgw_mask_sync_+lgw_mask_sync_inv_
+                                            else:
+                                                sync_mask = 1.
+                                            
+                                            for ms in range(len(eps_)):
+                                                if RK.EXPONENTIAL:
+                                                    if VE_MODEL:         # ZERO IS THIS                      # ONE IS THIS
+                                                        eps_[ms] = sync_mask * eps_x_[ms]  +  (1-sync_mask) * eps_x2y_[ms]  +  weight_mask * (-eps_y_[ms] + sigma*(-noise_bongflow))
+                                                        if EO("sync_x2y"):
+                                                            eps_[ms] = sync_mask * eps_x_[ms]  +  (1-sync_mask) * eps_x2y_[ms]  +  weight_mask * (-eps_x2y_[ms] + sigma*(-noise_bongflow))
+                                                    else:
+                                                        eps_[ms] = sync_mask * eps_x_[ms]  +  (1-sync_mask) * eps_x2y_[ms]  +  weight_mask * (-eps_y_[ms] + sigma*(y0_bongflow-noise_bongflow))
+                                                        if EO("sync_x2y"):
+                                                            eps_[ms] = sync_mask * eps_x_[ms]  +  (1-sync_mask) * eps_x2y_[ms]  +  weight_mask * (-eps_x2y_[ms] + sigma*(y0_bongflow-noise_bongflow))
+                                                else:
+                                                    if VE_MODEL:
+                                                        eps_[ms] = sync_mask * eps_x_[ms]  +  (1-sync_mask) * eps_x2y_[ms]  +  weight_mask * (-eps_y_[ms] + (noise_bongflow))
+                                                        if EO("sync_x2y"):
+                                                            eps_[ms] = sync_mask * eps_x_[ms]  +  (1-sync_mask) * eps_x2y_[ms]  +  weight_mask * (-eps_x2y_[ms] + (noise_bongflow))
+                                                    else:
+                                                        eps_[ms] = sync_mask * eps_x_[ms]  +  (1-sync_mask) * eps_x2y_[ms]  +  weight_mask * (-eps_y_[ms] + (noise_bongflow-y0_bongflow))
+                                                        if EO("sync_x2y"):
+                                                            eps_[ms] = sync_mask * eps_x_[ms]  +  (1-sync_mask) * eps_x2y_[ms]  +  weight_mask * (-eps_x2y_[ms] + (noise_bongflow-y0_bongflow))
+
+                                            
+                                        if BONGMATH and step < sigmas.shape[0]-1 and sigma > 0.03 and not EO("disable_implicit_prebong"):
+                                            BONGMATH_Y = SYNC_GUIDE_ACTIVE
+                                            
+                                            x_0, x_, eps_ = RK.bong_iter(x_0, x_, eps_, eps_prev_, data_, sigma, NS.s_, row, RK.row_offset, NS.h, step, step_sched,
+                                                                        BONGMATH_Y, y0_bongflow, noise_bongflow, eps_x_, eps_y_, data_x_, data_y_, LG)     # TRY WITH h_new ??
+                                            #                            BONGMATH_Y, y0_bongflow, noise_bongflow, eps_x_, eps_y_, eps_x2y_, data_x_, LG)     # TRY WITH h_new ??
+                                            
+                                            #if EO("eps_adain_smartnoise_bongmath"):
+                                            if LG.ADAIN_NOISE_MODE == "smart":
+                                                if VE_MODEL:
+                                                    z_[:NS.s_.shape[0], ...] = (x_ - data_)[:NS.s_.shape[0], ...] / NS.s_.view(-1,*[1]*(x_.ndim-1))
+                                                else:
+                                                    z_[:NS.s_.shape[0], ...] = (x_[:NS.s_.shape[0], ...] - (NS.sigma_max - NS.s_.view(-1,*[1]*(x_.ndim-1)))*data_[:NS.s_.shape[0], ...])[:NS.s_.shape[0], ...] / NS.s_.view(-1,*[1]*(x_.ndim-1))
+                                                RK.update_transformer_options({'z_' : z_})
+                                            
+                                    x_tmp = x_[row+RK.row_offset]
+
+                        lying_eps_row_factor = 1.0
+                        # MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL
+                        if RK.IMPLICIT   and   row == 0   and   (EO("implicit_lazy_recycle_first_model_call_at_start")   or   EO("radaucycle")  or RK.C[0] == 0.0):
+                            pass
+                        else: 
+                            if s_tmp == 0:
+                                break
+                            x_, eps_ = RK.newton_iter(x_0, x_, eps_, eps_prev_, data_, NS.s_, row, NS.h, sigmas, step, "pre", SYNC_GUIDE_ACTIVE) # will this do anything? not x_tmp
+
+                            # DETAIL BOOST
+                            if noise_scaling_type == "model_alpha" and noise_scaling_weight != 0 and noise_scaling_eta > 0:
+                                s_tmp = s_tmp + noise_scaling_weight * (s_tmp * lying_alpha_ratio   -   s_tmp)
+                            if noise_scaling_type == "model"       and noise_scaling_weight != 0 and noise_scaling_eta > 0:
+                                s_tmp = lying_s_[row]
+                                if RK.multistep_stages > 0:
+                                    s_tmp = lying_sd
+
+                            # SYNC GUIDE ---------------------------
+                            if LG.guide_mode.startswith("sync") and (LG.lgw[step_sched] == 0 and LG.lgw_inv[step_sched] == 0 and LG.lgw_sync[step_sched] == 0 and LG.lgw_sync_inv[step_sched] == 0):
+                                data_cached = None
+                            elif SYNC_GUIDE_ACTIVE:
+                                lgw_mask_,         lgw_mask_inv_         = LG.get_masks_for_step(step_sched)
+                                lgw_mask_sync_,    lgw_mask_sync_inv_    = LG.get_masks_for_step(step_sched, lgw_type="sync")
+                                lgw_mask_drift_x_, lgw_mask_drift_x_inv_ = LG.get_masks_for_step(step_sched, lgw_type="drift_x")
+                                lgw_mask_drift_y_, lgw_mask_drift_y_inv_ = LG.get_masks_for_step(step_sched, lgw_type="drift_y")
+                                lgw_mask_lure_x_,  lgw_mask_lure_x_inv_  = LG.get_masks_for_step(step_sched, lgw_type="lure_x")
+                                lgw_mask_lure_y_,  lgw_mask_lure_y_inv_  = LG.get_masks_for_step(step_sched, lgw_type="lure_y")
+                                
+                                weight_mask  = lgw_mask_         + lgw_mask_inv_
+                                sync_mask    = lgw_mask_sync_    + lgw_mask_sync_inv_
+
+
+                                drift_x_mask = lgw_mask_drift_x_ + lgw_mask_drift_x_inv_
+                                drift_y_mask = lgw_mask_drift_y_ + lgw_mask_drift_y_inv_
+                                lure_x_mask  = lgw_mask_lure_x_  + lgw_mask_lure_x_inv_
+                                lure_y_mask  = lgw_mask_lure_y_  + lgw_mask_lure_y_inv_
+                                
+                                if eps_x_ is None:
+                                    eps_x_       = torch.zeros(RK.rows+2, *x.shape, dtype=default_dtype, device=work_device)
+                                    data_x_      = torch.zeros(RK.rows+2, *x.shape, dtype=default_dtype, device=work_device)
+                                    eps_y2x_     = torch.zeros(RK.rows+2, *x.shape, dtype=default_dtype, device=work_device)
+                                    eps_x2y_     = torch.zeros(RK.rows+2, *x.shape, dtype=default_dtype, device=work_device)
+                                    eps_yt_      = torch.zeros(RK.rows+2, *x.shape, dtype=default_dtype, device=work_device)
+                                    eps_y_       = torch.zeros(RK.rows+2, *x.shape, dtype=default_dtype, device=work_device)
+                                    eps_prev_y_  = torch.zeros(RK.rows+2, *x.shape, dtype=default_dtype, device=work_device)
+                                    data_y_      = torch.zeros(RK.rows+2, *x.shape, dtype=default_dtype, device=work_device)
+                                    yt_          = torch.zeros(RK.rows+2, *x.shape, dtype=default_dtype, device=work_device)
+                                    
+                                    RUN_X_0_COPY = False
+                                    if noise_bongflow is None:
+                                        RUN_X_0_COPY = True
+                                        data_prev_x_ = torch.zeros(4, *x.shape, dtype=default_dtype, device=work_device)
+                                        data_prev_y_ = torch.zeros(4, *x.shape, dtype=default_dtype, device=work_device)
+                                        
+                                        noise_bongflow = normalize_zscore(NS.noise_sampler(sigma=sigma, sigma_next=NS.sigma_min), channelwise=True, inplace=True)
+
+                                        _, _ = RK(noise_bongflow, s_tmp/s_tmp, noise_bongflow, sigma/sigma, transformer_options={'latent_type': 'xt'})
+
+                                        if RK.extra_args['model_options']['transformer_options'].get('y0_standard_guide') is not None:
+                                            if hasattr(model.inner_model.inner_model.diffusion_model, "y0_standard_guide"):
+                                                LG.y0 = y0_standard_guide = model.inner_model.inner_model.diffusion_model.y0_standard_guide.clone()
+                                                del model.inner_model.inner_model.diffusion_model.y0_standard_guide
+                                                RK.extra_args['model_options']['transformer_options']['y0_standard_guide'] = None
+                                        
+                                        if RK.extra_args['model_options']['transformer_options'].get('y0_inv_standard_guide') is not None:
+                                            if hasattr(model.inner_model.inner_model.diffusion_model, "y0_inv_standard_guide"):
+                                                LG.y0_inv = y0_inv_standard_guide = model.inner_model.inner_model.diffusion_model.y0_inv_standard_guide.clone() # RK.extra_args['model_options']['transformer_options'].get('y0_standard_guide')
+                                                del model.inner_model.inner_model.diffusion_model.y0_inv_standard_guide
+                                                RK.extra_args['model_options']['transformer_options']['y0_inv_standard_guide'] = None
+
+                                        y0_bongflow = LG.HAS_LATENT_GUIDE * LG.mask * LG.y0   +   LG.HAS_LATENT_GUIDE_INV * LG.mask_inv * LG.y0_inv  #LG.y0.clone()
+                                    
+                                    if VE_MODEL:
+                                        yt_0 = y0_bongflow + sigma * noise_bongflow
+                                        yt   = y0_bongflow + s_tmp * noise_bongflow
+                                    else:
+                                        yt_0 = (1-sigma) * y0_bongflow  + sigma * noise_bongflow
+                                        yt   = (1-s_tmp) * y0_bongflow  + s_tmp * noise_bongflow
+                                        
+                                    yt_[row] = yt
+                                    
+                                    if RUN_X_0_COPY:
+                                        x_0 = yt_0.clone()
+                                        x_tmp = x_[row] = yt.clone()
+                                else:
+                                    y0_bongflow_orig = y0_bongflow.clone() if y0_bongflow_orig is None else y0_bongflow_orig
+                                    y0_bongflow = y0_bongflow + LG.drift_x_data  * drift_x_mask * (data_x           - y0_bongflow) \
+                                                              + LG.drift_x_sync  * drift_x_mask * (data_barf        - y0_bongflow) \
+                                                              + LG.drift_y_data  * drift_y_mask * (data_y           - y0_bongflow) \
+                                                              + LG.drift_y_sync  * drift_y_mask * (data_barf_y      - y0_bongflow) \
+                                                              + LG.drift_y_guide * drift_y_mask * (y0_bongflow_orig - y0_bongflow)
+                                    
+                                    if torch.norm(y0_bongflow_orig - y0_bongflow) != 0 and EO("enable_y0_bongflow_update"):
+                                        RK.update_transformer_options({'y0_style_pos': y0_bongflow.clone()})
+                                    
+                                    if not EO("skip_yt"):
+                                        yt_0 = RK.get_x(y0_bongflow, noise_bongflow, sigma)
+                                        yt   = RK.get_x(y0_bongflow, noise_bongflow, s_tmp)
+                                        
+                                        yt_[row] = yt
+
+                                if ((LG.lgw[step_sched].item() in {1,0} and LG.lgw_inv[step_sched].item() in {1,0} and LG.lgw[step_sched] == 1-LG.lgw_sync[step_sched] and LG.lgw_inv[step_sched] == 1-LG.lgw_sync_inv[step_sched]) or EO("sync_speed_mode")) and not EO("disable_sync_speed_mode"):
+                                    data_y = y0_bongflow.clone()
+                                    eps_y  = RK.get_eps(yt_0, yt_[row], data_y, sigma, s_tmp)
+
+                                else:
+                                    eps_y, data_y = RK(yt_[row], s_tmp, yt_0,  sigma, transformer_options={'latent_type': 'yt'})
+                                    
+                                eps_x, data_x = RK(x_tmp, s_tmp, x_0, sigma, transformer_options={'latent_type': 'xt', 'row': row, "x_tmp": x_tmp})
+                                #if hasattr(model.inner_model.inner_model.diffusion_model, "eps_out"):
+                                    
+                                
+                                for sync_lure_iter in range(LG.sync_lure_iter):
+                                    if LG.sync_lure_sequence == "x -> y":
+
+                                        if lure_x_mask.abs().sum() > 0:
+                                            x_tmp = LG.swap_data(x_tmp, data_x, data_y, s_tmp, lure_x_mask)
+                                            eps_x_lure, data_x_lure = RK(x_tmp, s_tmp, x_0, sigma, transformer_options={'latent_type': 'xt'})
+                                            eps_x  = eps_x  + lure_x_mask * (eps_x_lure  - eps_x)
+                                            data_x = data_x + lure_x_mask * (data_x_lure - data_x)
+                                        
+                                        if lure_y_mask.abs().sum() > 0:                      
+                                            y_tmp = yt_[row].clone()
+                                            y_tmp = LG.swap_data(y_tmp, data_y, data_x, s_tmp, lure_y_mask) 
+                                            eps_y_lure, data_y_lure = RK(y_tmp, s_tmp, yt_0, sigma, transformer_options={'latent_type': 'yt'})
+                                            eps_y  = eps_y  + lure_y_mask * (eps_y_lure  - eps_y)
+                                            data_y = data_y + lure_y_mask * (data_y_lure - data_y)
+                                        
+                                    elif LG.sync_lure_sequence == "y -> x":
+                                            
+                                        if lure_y_mask.abs().sum() > 0:                      
+                                            y_tmp = yt_[row].clone()
+                                            y_tmp = LG.swap_data(y_tmp, data_y, data_x, s_tmp, lure_y_mask) 
+                                            eps_y_lure, data_y_lure = RK(y_tmp, s_tmp, yt_0, sigma, transformer_options={'latent_type': 'yt'})
+                                            eps_y  = eps_y  + lure_y_mask * (eps_y_lure  - eps_y)
+                                            data_y = data_y + lure_y_mask * (data_y_lure - data_y)
+                                        
+                                        if lure_x_mask.abs().sum() > 0:
+                                            x_tmp = LG.swap_data(x_tmp, data_x, data_y, s_tmp, lure_x_mask)
+                                            eps_x_lure, data_x_lure = RK(x_tmp, s_tmp, x_0, sigma, transformer_options={'latent_type': 'xt'})
+                                            eps_x  = eps_x  + lure_x_mask * (eps_x_lure  - eps_x)
+                                            data_x = data_x + lure_x_mask * (data_x_lure - data_x)
+
+                                    elif LG.sync_lure_sequence == "xy -> xy":
+                                        data_x_orig, data_y_orig = data_x.clone(), data_y.clone()
+                                        
+                                        if lure_x_mask.abs().sum() > 0:
+                                            x_tmp = LG.swap_data(x_tmp, data_x_orig, data_y_orig, s_tmp, lure_x_mask)
+                                            eps_x_lure, data_x_lure = RK(x_tmp, s_tmp, x_0, sigma, transformer_options={'latent_type': 'xt'})
+                                            eps_x  = eps_x  + lure_x_mask * (eps_x_lure  - eps_x)
+                                            data_x = data_x + lure_x_mask * (data_x_lure - data_x)
+                                        
+                                        if lure_y_mask.abs().sum() > 0:                      
+                                            y_tmp = yt_[row].clone()
+                                            y_tmp = LG.swap_data(y_tmp, data_y_orig, data_x_orig, s_tmp, lure_y_mask) 
+                                            eps_y_lure, data_y_lure = RK(y_tmp, s_tmp, yt_0, sigma, transformer_options={'latent_type': 'yt'})
+                                            eps_y  = eps_y  + lure_y_mask * (eps_y_lure  - eps_y)
+                                            data_y = data_y + lure_y_mask * (data_y_lure - data_y)
+                                
+                                if EO("sync_proj_y"):
+                                    d_collinear_d_lerp = get_collinear(eps_x, eps_y)  
+                                    d_lerp_ortho_d     = get_orthogonal(eps_y, eps_x)  
+                                    eps_y             = d_collinear_d_lerp + d_lerp_ortho_d
+                                    
+                                if EO("sync_proj_y2"):
+                                    d_collinear_d_lerp = get_collinear(eps_y, eps_x)  
+                                    d_lerp_ortho_d     = get_orthogonal(eps_x, eps_y)  
+                                    eps_y             = d_collinear_d_lerp + d_lerp_ortho_d
+                                    
+                                if EO("sync_proj_x"):
+                                    d_collinear_d_lerp = get_collinear(eps_y, eps_x)  
+                                    d_lerp_ortho_d     = get_orthogonal(eps_x, eps_y)  
+                                    eps_x             = d_collinear_d_lerp + d_lerp_ortho_d
+
+                                if EO("sync_proj_x2"):
+                                    d_collinear_d_lerp = get_collinear(eps_x, eps_y)  
+                                    d_lerp_ortho_d     = get_orthogonal(eps_y, eps_x)  
+                                    eps_x             = d_collinear_d_lerp + d_lerp_ortho_d
+
+                                eps_x2y = RK.get_eps(x_0, x_[row], data_y, sigma, s_tmp)
+                                eps_x2y_[row] = eps_x2y
+                                
+                                eps_y2x = RK.get_eps(x_0, x_[row], data_y, sigma, s_tmp)
+                                eps_y2x_[row] = eps_y2x
+                                
+                                if RK.EXPONENTIAL:
+                                    if VE_MODEL:         # ZERO IS THIS                      # ONE IS THIS 
+                                        eps_[row]  = sync_mask * eps_x   +   (1-sync_mask) * eps_x2y   +   weight_mask * (-eps_y + sigma*(-noise_bongflow)) 
+                                        if EO("sync_x2y"):
+                                            eps_[row]  = sync_mask * eps_x   +   (1-sync_mask) * eps_x2y   +   weight_mask * (-eps_x2y + sigma*(-noise_bongflow)) 
+                                    else:
+                                        eps_[row]  = sync_mask * eps_x   +   (1-sync_mask) * eps_x2y   +   weight_mask * (-eps_y + sigma*(y0_bongflow-noise_bongflow))   #+   lure_x_mask * sigma*(data_y - data_x) 
+                                        if EO("sync_x2y"):
+                                            eps_[row]  = sync_mask * eps_x   -   (1-sync_mask) * eps_x2y   +   weight_mask * (-eps_x2y + sigma*(y0_bongflow-noise_bongflow)) 
+                                        eps_yt_[row]  = sync_mask * eps_y   +   (1-sync_mask) * eps_y2x   +   weight_mask * (-eps_x + sigma*(y0_bongflow-noise_bongflow))         # differentiate guide as well toward the x pred?
+                                else:
+                                    if VE_MODEL:
+                                        eps_[row]  = sync_mask * eps_x   +   (1-sync_mask) * eps_x2y   +   weight_mask * (noise_bongflow - eps_y)
+                                        if EO("sync_x2y"):
+                                            eps_[row]  = sync_mask * eps_x   +   (1-sync_mask) * eps_x2y   +   weight_mask * (noise_bongflow - eps_x2y)
+                                    else:
+                                        eps_[row]  = sync_mask * eps_x   +   (1-sync_mask) * eps_x2y   +   weight_mask * (noise_bongflow - eps_y - y0_bongflow)
+                                        if EO("sync_x2y"):
+                                            eps_[row]  = sync_mask * eps_x   +   (1-sync_mask) * eps_x2y   +   weight_mask * (noise_bongflow - eps_x2y - y0_bongflow)
+                                        eps_yt_[row]  = sync_mask * eps_y   +   (1-sync_mask) * eps_y2x   +   weight_mask * (noise_bongflow - eps_x - y0_bongflow)         # differentiate guide as well toward the x pred?
+
+                                if VE_MODEL:
+                                    data_[row] = x_0   +   sync_mask * NS.h * eps_x   +   (1-sync_mask) * NS.h * eps_x2y   -   weight_mask * (sigma*(eps_y + noise_bongflow))   # -   lure_x_mask * (sigma*(eps_y + eps_x)) 
+                                    data_barf_y = yt_0   +   sync_mask * NS.h * eps_y   +   (1-sync_mask) * NS.h * eps_y2x   -   weight_mask * (sigma*(eps_x + noise_bongflow))
+                                    if EO("sync_x2y"):
+                                        data_[row] = x_0   +   sync_mask * NS.h * eps_x   +   (1-sync_mask) * NS.h * eps_x2y   -   weight_mask * (sigma*(eps_x2y + noise_bongflow)) 
+
+                                else:
+
+                                    data_[row] = x_0   +   sync_mask * NS.h * eps_x   +   (1-sync_mask) * NS.h * eps_x2y   -   weight_mask * (NS.h * eps_y + sigma*(noise_bongflow-y0_bongflow)) 
+                                    data_barf_y = yt_0   +   sync_mask * NS.h * eps_y   +   (1-sync_mask) * NS.h * eps_y2x   -   weight_mask * (NS.h * eps_x + sigma*(noise_bongflow-y0_bongflow)) 
+                                    if EO("sync_x2y"):
+                                        data_[row] = x_0   +   sync_mask * NS.h * eps_x   +   (1-sync_mask) * NS.h * eps_x2y   -   weight_mask * (NS.h * eps_x2y + sigma*(noise_bongflow-y0_bongflow)) 
+
+                                if EO("data_is_y0_with_lure_x_mask"):
+                                    data_[row] = data_[row] + lure_x_mask * (y0_bongflow - data_[row])
+
+                                if EO("eps_is_y0_with_lure_x_mask"):
+                                    if RK.EXPONENTIAL:
+                                        eps_[row] = eps_[row] + lure_x_mask * ((y0_bongflow - x_0) - eps_[row])
+                                    else:
+                                        eps_[row] = eps_[row] + lure_x_mask * (((x_0 - y0_bongflow) / sigma) - eps_[row])
+                                data_barf   = data_[row]
+                                data_cached = data_x
+                                
+                                eps_x_ [row] = eps_x
+                                data_x_[row] = data_x
+                                
+                                eps_y_ [row] = eps_y
+                                data_y_[row] = data_y
+
+                                if EO("sync_use_fake_eps_y"):
+                                    if RK.EXPONENTIAL:
+                                        if VE_MODEL:
+                                            eps_y_ [row] = sigma * ( - noise_bongflow)
+                                        else:
+                                            eps_y_ [row] = sigma * (y0_bongflow - noise_bongflow)
+                                    else:
+                                        if VE_MODEL:
+                                            eps_y_ [row] = noise_bongflow
+                                        else:
+                                            eps_y_ [row] = noise_bongflow - y0_bongflow
+                                if EO("sync_use_fake_data_y"):
+                                    data_y_[row] = y0_bongflow
+                                    
+
+                            
+                            
+                            elif LG.guide_mode.startswith("flow") and (LG.lgw[step_sched] > 0 or LG.lgw_inv[step_sched] > 0) and not FLOW_STOPPED and not EO("flow_sync") :
+                                lgw_mask_, lgw_mask_inv_ = LG.get_masks_for_step(step)
+                                if not FLOW_STARTED and not FLOW_RESUMED:
+                                    FLOW_STARTED = True
+                                    data_x_prev_ = torch.zeros_like(data_prev_)
+
+                                    y0 = LG.HAS_LATENT_GUIDE * LG.mask * LG.y0   +   LG.HAS_LATENT_GUIDE_INV * LG.mask_inv * LG.y0_inv 
+                                    
+                                    yx0 = y0.clone()
+                                    
+                                    if EO("flow_slerp"):
+                                        y0_inv                 = LG.HAS_LATENT_GUIDE * LG.mask * LG.y0_inv   +   LG.HAS_LATENT_GUIDE_INV * LG.mask_inv * LG.y0 
+                                        y0 = LG.y0.clone()
+                                        y0_inv = LG.y0_inv.clone()
+                                        flow_slerp_guide_ratio = EO("flow_slerp_guide_ratio", 0.5)
+                                        y_slerp                = slerp_tensor(flow_slerp_guide_ratio, y0, y0_inv)
+                                        yx0                    = y_slerp.clone()
+                                    
+                                    x_[row], x_0 =  yx0.clone(), yx0.clone()
+                                    if EO("guide_step_cutoff") or EO("guide_step_min"):
+                                        x_0_orig = yx0.clone()
+                                    
+                                    if EO("flow_yx0_init_y0_inv"):
+                                        yx0 = LG.HAS_LATENT_GUIDE * LG.mask * LG.y0_inv   +   LG.HAS_LATENT_GUIDE_INV * LG.mask_inv * LG.y0
+                                    
+                                    if step > 0:
+                                        if EO("flow_manual_masks"):
+                                            y0  = (1 - (LG.HAS_LATENT_GUIDE * LG.lgw[step_sched] * LG.mask + LG.HAS_LATENT_GUIDE_INV * LG.lgw_inv[step_sched] * LG.mask_inv)) * denoised   +   LG.HAS_LATENT_GUIDE * LG.lgw[step_sched] * LG.mask * LG.y0   +   LG.HAS_LATENT_GUIDE_INV * LG.lgw_inv[step_sched] * LG.mask_inv * LG.y0_inv
+                                        else:
+                                            y0  = (1 - (lgw_mask_ + lgw_mask_inv_)) * denoised   +   lgw_mask_ * LG.y0   +   lgw_mask_inv_ * LG.y0_inv
+                                        yx0 = y0.clone()
+                                        
+                                        if EO("flow_slerp"):
+                                            if EO("flow_manual_masks"):
+                                                y0_inv                 = (1 - (LG.HAS_LATENT_GUIDE * LG.lgw[step_sched] * LG.mask + LG.HAS_LATENT_GUIDE_INV * LG.lgw_inv[step_sched] * LG.mask_inv)) * denoised   +   LG.HAS_LATENT_GUIDE * LG.lgw[step_sched] * LG.mask * LG.y0_inv   +   LG.HAS_LATENT_GUIDE_INV * LG.lgw_inv[step_sched] * LG.mask_inv * LG.y0
+                                            else:
+                                                y0_inv  = (1 - (lgw_mask_ + lgw_mask_inv_)) * denoised   +   lgw_mask_ * LG.y0_inv   +   lgw_mask_inv_ * LG.y0
+                                            flow_slerp_guide_ratio = EO("flow_slerp_guide_ratio", 0.5)
+                                            y_slerp                = slerp_tensor(flow_slerp_guide_ratio, y0, y0_inv)
+                                            yx0                    = y_slerp.clone()
+                                
+                                else:
+                                    yx0_prev = data_cached
+                                    if EO("flow_manual_masks"):
+                                        yx0 = (1 - (LG.HAS_LATENT_GUIDE * LG.lgw[step_sched] * LG.mask + LG.HAS_LATENT_GUIDE_INV * LG.lgw_inv[step_sched] * LG.mask_inv)) * yx0_prev   +   LG.HAS_LATENT_GUIDE * LG.lgw[step_sched] * LG.mask * x_tmp   +   LG.HAS_LATENT_GUIDE_INV * LG.lgw_inv[step_sched] * LG.mask_inv * x_tmp
+                                    else:
+                                        yx0 = (1 - (lgw_mask_ + lgw_mask_inv_)) * yx0_prev   +   (lgw_mask_ + lgw_mask_inv_) * x_tmp
+
+                                    if not EO("flow_static_guides"):
+                                        if EO("flow_manual_masks"):
+                                            y0 = (1 - (LG.HAS_LATENT_GUIDE * LG.lgw[step_sched] * LG.mask + LG.HAS_LATENT_GUIDE_INV * LG.lgw_inv[step_sched] * LG.mask_inv)) * yx0_prev   +   LG.HAS_LATENT_GUIDE * LG.lgw[step_sched] * LG.mask * LG.y0   +   LG.HAS_LATENT_GUIDE_INV * LG.lgw_inv[step_sched] * LG.mask_inv * LG.y0_inv
+                                        else:
+                                            y0 = (1 - (lgw_mask_ + lgw_mask_inv_)) * yx0_prev   +   lgw_mask_ * LG.y0   +   lgw_mask_inv_ * LG.y0_inv
+                                        
+                                        if EO("flow_slerp"):
+                                            if EO("flow_manual_masks"):
+                                                y0_inv = (1 - (LG.HAS_LATENT_GUIDE * LG.lgw[step_sched] * LG.mask + LG.HAS_LATENT_GUIDE_INV * LG.lgw_inv[step_sched] * LG.mask_inv)) * yx0_prev   +   LG.HAS_LATENT_GUIDE * LG.lgw[step_sched] * LG.mask * LG.y0_inv   +   LG.HAS_LATENT_GUIDE_INV * LG.lgw_inv[step_sched] * LG.mask_inv * LG.y0
+                                            else:
+                                                y0_inv = (1 - (lgw_mask_ + lgw_mask_inv_)) * yx0_prev   +   lgw_mask_ * LG.y0_inv   +   lgw_mask_inv_ * LG.y0
+
+                                y0_orig = y0.clone()
+                                if EO("flow_proj_xy"):
+                                    d_collinear_d_lerp = get_collinear(yx0, y0_orig)  
+                                    d_lerp_ortho_d     = get_orthogonal(y0_orig, yx0)  
+                                    y0                 = d_collinear_d_lerp + d_lerp_ortho_d
+                                
+                                if EO("flow_proj_yx"):
+                                    d_collinear_d_lerp = get_collinear(y0_orig, yx0)  
+                                    d_lerp_ortho_d     = get_orthogonal(yx0, y0_orig)  
+                                    yx0                = d_collinear_d_lerp + d_lerp_ortho_d
+                                
+                                y0_inv_orig = None
+                                if EO("flow_proj_xy_inv"):
+                                    y0_inv_orig = y0_inv.clone()
+                                    d_collinear_d_lerp = get_collinear(yx0, y0_inv)  
+                                    d_lerp_ortho_d     = get_orthogonal(y0_inv, yx0)  
+                                    y0_inv             = d_collinear_d_lerp + d_lerp_ortho_d
+                                    
+                                if EO("flow_proj_yx_inv"):
+                                    y0_inv_orig = y0_inv if y0_inv_orig is None else y0_inv_orig
+                                    d_collinear_d_lerp = get_collinear(y0_inv_orig, yx0)  
+                                    d_lerp_ortho_d     = get_orthogonal(yx0, y0_inv_orig)  
+                                    yx0                = d_collinear_d_lerp + d_lerp_ortho_d
+                                del y0_orig
+
+                                flow_cossim_iter = EO("flow_cossim_iter", 1)
+
+                                if step == 0:
+                                    noise_yt = noise_fn(y0, sigma, sigma_next, NS.noise_sampler, flow_cossim_iter) # normalize_zscore(NS.noise_sampler(sigma=sigma, sigma_next=sigma_next), channelwise=True, inplace=True)
+                                if not EO("flow_disable_renoise_y0"):
+                                    if noise_yt is None:
+                                        noise_yt = noise_fn(x_0, sigma, sigma_next, NS.noise_sampler, flow_cossim_iter)
+                                    else:
+                                        noise_yt = (1-eta) * noise_yt + eta * noise_fn(x_0, sigma, sigma_next, NS.noise_sampler, flow_cossim_iter)
+
+                                if VE_MODEL:
+                                    yt        = y0 + s_tmp * noise_yt
+                                else:
+                                    yt        = (NS.sigma_max-s_tmp) * y0 + (s_tmp/NS.sigma_max) * noise_yt
+                                if not EO("flow_disable_doublenoise_y0"):
+                                    if noise_yt is None:
+                                        noise_yt = noise_fn(x_0, sigma, sigma_next, NS.noise_sampler, flow_cossim_iter)
+                                    else:
+                                        noise_yt = (1-eta) * noise_yt + eta * noise_fn(x_0, sigma, sigma_next, NS.noise_sampler, flow_cossim_iter)
+
+                                if VE_MODEL:
+                                    y0_noised = y0 + sigma * noise_yt
+                                else:
+                                    y0_noised = (NS.sigma_max-sigma) * y0 + sigma * noise_yt
+                                
+                                if EO("flow_slerp"):
+                                    noise = noise_fn(y0_inv, sigma, sigma_next, NS.noise_sampler, flow_cossim_iter) 
+                                    yt_inv        = (NS.sigma_max-s_tmp) * y0_inv + (s_tmp/NS.sigma_max) * noise
+                                    if not EO("flow_disable_doublenoise_y0_inv"):
+                                        noise = noise_fn(y0_inv, sigma, sigma_next, NS.noise_sampler, flow_cossim_iter) 
+                                    y0_noised_inv = (NS.sigma_max-sigma) * y0_inv + sigma * noise
+                                
+                                if step == 0:
+                                    noise_xt = noise_fn(yx0, sigma, sigma_next, NS.noise_sampler, flow_cossim_iter) 
+                                if EO("flow_slerp"):
+                                    xt         = yx0 + (s_tmp/NS.sigma_max) * (noise - y_slerp)
+                                    if not EO("flow_disable_doublenoise_x_0"):
+                                        noise = noise_fn(x_0, sigma, sigma_next, NS.noise_sampler, flow_cossim_iter) 
+                                    x_0_noised = x_0 + sigma * (noise - y_slerp)
+                                else:
+                                    if not EO("flow_disable_renoise_x_0"):
+                                        if noise_xt is None:
+                                            noise_xt = noise_fn(x_0, sigma, sigma_next, NS.noise_sampler, flow_cossim_iter)
+                                        else:
+                                            noise_xt = (1-eta_substep) * noise_xt + eta_substep * noise_fn(x_0, sigma, sigma_next, NS.noise_sampler, flow_cossim_iter)
+
+                                    if VE_MODEL:
+                                        xt         = yx0 + (s_tmp) * yx0 + (s_tmp) * (noise_xt - y0)
+                                    else:
+                                        xt         = yx0 + (s_tmp/NS.sigma_max) * (noise_xt - y0)
+                                    if not EO("flow_disable_doublenoise_x_0"):
+                                        if noise_xt is None:
+                                            noise_xt = noise_fn(x_0, sigma, sigma_next, NS.noise_sampler, flow_cossim_iter)
+                                        else:
+                                            noise_xt = (1-eta_substep) * noise_xt + eta_substep * noise_fn(x_0, sigma, sigma_next, NS.noise_sampler, flow_cossim_iter)
+                                    if VE_MODEL:
+                                        x_0_noised = x_0 + (sigma) * x_0 + (sigma) * (noise_xt - y0)
+                                    else:
+                                        x_0_noised = x_0 + (sigma/NS.sigma_max) * (noise_xt - y0)    # just lerp noise add, (1-sigma)*y0 + sigma*noise assuming x_0 == y0, which is true initially...
+                                
+                                eps_y, data_y = RK(yt, s_tmp, y0_noised,  sigma, transformer_options={'latent_type': 'yt'})
+                                eps_x, data_x = RK(xt, s_tmp, x_0_noised, sigma, transformer_options={'latent_type': 'xt'})
+                    
+                                if EO("flow_slerp"):
+                                    eps_y_inv, data_y_inv = RK(yt_inv, s_tmp, y0_noised_inv, sigma, transformer_options={'latent_type': 'yt_inv'})
+                                
+                                if LG.lgw[step+1] == 0 and LG.lgw_inv[step+1] == 0:    # break out of differentiating x0 and return to differentiating eps/velocity field
+                                    if EO("flow_shit_out_yx0"):
+                                        eps_ [row]       = eps_x - eps_y
+                                        data_[row]       = yx0
+                                        if row == 0:
+                                            x_[row] = x_0 = xt 
+                                        else:
+                                            x_[row] = xt
+                                    if not EO("flow_shit_out_new"):
+                                        eps_ [row]       = eps_x
+                                        data_[row]       = data_x
+                                        if row == 0:
+                                            x_[row] = x_0 = xt 
+                                        else:
+                                            x_[row] = xt
+                                    
+                                    else:
+                                        eps_ [row]        = (1 - (lgw_mask_ + lgw_mask_inv_)) * eps_x   +   (lgw_mask_ + lgw_mask_inv_) * eps_y
+                                        data_[row]        = (1 - (lgw_mask_ + lgw_mask_inv_)) * data_x   +   (lgw_mask_ + lgw_mask_inv_) * data_y
+                                        if row == 0:
+                                            x_[row] = x_0 = (1 - (lgw_mask_ + lgw_mask_inv_)) * xt   +   (lgw_mask_ + lgw_mask_inv_) * yt 
+                                        else:
+                                            x_[row] = (1 - (lgw_mask_ + lgw_mask_inv_)) * xt   +   (lgw_mask_ + lgw_mask_inv_) * yt
+                                
+                                    FLOW_STOPPED = True
+                                else:
+                                    if not EO("flow_slerp"):
+                                        if RK.EXPONENTIAL:
+                                            eps_y_alt = data_y - x_0
+                                            eps_x_alt = data_x - x_0
+                                        else:
+                                            eps_y_alt = (x_0 - data_y) / sigma
+                                            eps_x_alt = (x_0 - data_x) / sigma
+                                            
+                                        if EO("flow_y_zero"):
+                                            eps_y_alt *= LG.mask
+                                        
+                                        eps_[row]  = eps_yx = (eps_y_alt - eps_x_alt)
+                                        eps_y_lin           = (x_0 - data_y) / sigma
+                                        if EO("flow_y_zero"):
+                                            eps_y_lin *= LG.mask
+                                        eps_x_lin           = (x_0 - data_x) / sigma
+                                        eps_yx_lin          = (eps_y_lin - eps_x_lin)
+                                        
+                                        data_[row] = (1 - (lgw_mask_ + lgw_mask_inv_)) * data_x   +   (lgw_mask_ + lgw_mask_inv_) * data_y
+                                        
+                                        if EO("flow_reverse_data_masks"):
+                                            data_[row] = (1 - (lgw_mask_ + lgw_mask_inv_)) * data_y   +   (lgw_mask_ + lgw_mask_inv_) * data_x
+
+                                        if flow_sync_eps != 0.0:
+                                            if RK.EXPONENTIAL:
+                                                eps_[row] = (1-flow_sync_eps) * eps_[row] + flow_sync_eps * (data_[row] - x_0)
+                                            else:
+                                                eps_[row] = (1-flow_sync_eps) * eps_[row] + flow_sync_eps * (x_0 - data_[row]) / sigma
+                                        
+                                        if EO("flow_sync_eps_mask"): 
+                                            flow_sync_eps = EO("flow_sync_eps_mask", 1.0)
+                                            if RK.EXPONENTIAL:
+                                                eps_[row] = (lgw_mask_ + lgw_mask_inv_) * (1-flow_sync_eps) * eps_[row] + (1 - (lgw_mask_ + lgw_mask_inv_)) * flow_sync_eps * (data_[row] - x_0) 
+                                            else:
+                                                eps_[row] = (lgw_mask_ + lgw_mask_inv_) * (1-flow_sync_eps) * eps_[row] + (1 - (lgw_mask_ + lgw_mask_inv_)) * flow_sync_eps * (x_0 - data_[row]) / sigma
+
+                                        if EO("flow_sync_eps_revmask"): 
+                                            flow_sync_eps = EO("flow_sync_eps_revmask", 1.0)
+                                            if RK.EXPONENTIAL:
+                                                eps_[row] = (1 - (lgw_mask_ + lgw_mask_inv_)) * (1-flow_sync_eps) * eps_[row] + (lgw_mask_ + lgw_mask_inv_) * flow_sync_eps * (data_[row] - x_0) 
+                                            else:
+                                                eps_[row] = (1 - (lgw_mask_ + lgw_mask_inv_)) * (1-flow_sync_eps) * eps_[row] + (lgw_mask_ + lgw_mask_inv_) * flow_sync_eps * (x_0 - data_[row]) / sigma
+
+                                        if EO("flow_sync_eps_maskonly"):
+                                            flow_sync_eps = EO("flow_sync_eps_maskonly", 1.0)
+                                            if RK.EXPONENTIAL:
+                                                eps_[row] = (lgw_mask_ + lgw_mask_inv_) * eps_[row] + (1 - (lgw_mask_ + lgw_mask_inv_)) * (data_[row] - x_0) 
+                                            else:
+                                                eps_[row] = (lgw_mask_ + lgw_mask_inv_) * eps_[row] + (1 - (lgw_mask_ + lgw_mask_inv_)) * (x_0 - data_[row]) / sigma
+
+                                        if EO("flow_sync_eps_revmaskonly"): 
+                                            flow_sync_eps = EO("flow_sync_eps_revmaskonly", 1.0)
+                                            if RK.EXPONENTIAL:
+                                                eps_[row] = (1 - (lgw_mask_ + lgw_mask_inv_)) * eps_[row] + (lgw_mask_ + lgw_mask_inv_) * (data_[row] - x_0) 
+                                            else:
+                                                eps_[row] = (1 - (lgw_mask_ + lgw_mask_inv_)) * eps_[row] + (lgw_mask_ + lgw_mask_inv_) * (x_0 - data_[row]) / sigma
+
+                                    if EO("flow_slerp"):
+                                        if RK.EXPONENTIAL:
+                                            eps_y_alt     = data_y     - x_0
+                                            eps_y_alt_inv = data_y_inv - x_0
+                                            eps_x_alt     = data_x     - x_0
+                                        else:
+                                            eps_y_alt     = (x_0 - data_y)     / sigma
+                                            eps_y_alt_inv = (x_0 - data_y_inv) / sigma
+                                            eps_x_alt     = (x_0 - data_x)     / sigma
+                                        
+                                        flow_slerp_ratio2 = EO("flow_slerp_ratio2", 0.5)
+
+                                        eps_yx     = (eps_y_alt - eps_x_alt)
+                                        eps_y_lin  = (x_0 - data_y) / sigma
+                                        eps_x_lin  = (x_0 - data_x) / sigma
+                                        eps_yx_lin = (eps_y_lin - eps_x_lin)
+                                        
+                                        eps_yx_inv     = (eps_y_alt_inv - eps_x_alt)
+                                        eps_y_lin_inv  = (x_0 - data_y_inv) / sigma
+                                        eps_x_lin      = (x_0 - data_x)     / sigma
+                                        eps_yx_lin_inv = (eps_y_lin_inv - eps_x_lin)
+                                        
+                                        data_row     = x_0 - sigma * eps_yx_lin
+                                        data_row_inv = x_0 - sigma * eps_yx_lin_inv
+                                        
+                                        if EO("flow_slerp_similarity_ratio"):
+                                            flow_slerp_similarity_ratio = EO("flow_slerp_similarity_ratio", 1.0)
+                                            flow_slerp_ratio2           = find_slerp_ratio_grid(data_row, data_row_inv, LG.y0.clone(), LG.y0_inv.clone(), flow_slerp_similarity_ratio)
+                                        
+                                        eps_ [row] = slerp_tensor(flow_slerp_ratio2, eps_yx,   eps_yx_inv)
+                                        data_[row] = slerp_tensor(flow_slerp_ratio2, data_row, data_row_inv)
+                                        
+                                        if EO("flow_slerp_autoalter"):
+                                            data_row_slerp = slerp_tensor(0.5, data_row, data_row_inv)
+                                            y0_pearsim     = get_pearson_similarity(data_row_slerp, y0)
+                                            y0_pearsim_inv = get_pearson_similarity(data_row_slerp, y0_inv)
+                                            
+                                            if y0_pearsim > y0_pearsim_inv:
+                                                data_[row] = data_row_inv 
+                                                eps_ [row] = (eps_y_alt_inv - eps_x_alt)
+                                            else:
+                                                data_[row] = data_row
+                                                eps_ [row] = (eps_y_alt     - eps_x_alt)
+                                            
+                                        if EO("flow_slerp_recalc_eps_row"):
+                                            if RK.EXPONENTIAL:
+                                                eps_[row]  = data_[row] - x_0
+                                            else:
+                                                eps_[row]  = (x_0 - data_[row]) / sigma
+                                        
+                                        if EO("flow_slerp_recalc_data_row"):
+                                            if RK.EXPONENTIAL:
+                                                data_[row] = x_0 + eps_[row]
+                                            else:
+                                                data_[row] = x_0 - sigma * eps_[row]
+
+                                    data_cached = data_x 
+
+                            if step < EO("direct_pre_pseudo_guide", 0) and step > 0:
+                                for i_pseudo in range(EO("direct_pre_pseudo_guide_iter", 1)):
+                                    x_tmp += LG.lgw[step_sched] * LG.mask * (NS.sigma_max - s_tmp) * (LG.y0 - denoised)     +     LG.lgw_inv[step_sched] * LG.mask_inv * (NS.sigma_max - s_tmp) * (LG.y0_inv - denoised)
+                                    eps_[row], data_[row] = RK(x_tmp, s_tmp, x_0, sigma)
+                            
+                            # MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL MODEL CALL
+                            
+                            if    SYNC_GUIDE_ACTIVE:
+                                pass
+                            elif not ((not LG.guide_mode.startswith("flow"))   or   FLOW_STOPPED   or    (LG.guide_mode.startswith("flow") and LG.lgw[step_sched] == 0 and LG.lgw_inv[step_sched] == 0)): #(LG.guide_mode.startswith("flow") and (LG.lgw[step_sched] != 0 or LG.lgw_inv[step_sched] != 0)) or FLOW_STOPPED:
+                                pass
+                            elif LG.guide_mode.startswith("lure") and (LG.lgw[step_sched] > 0 or LG.lgw_inv[step_sched] > 0):
+                                eps_[row], data_[row] = RK(x_tmp, s_tmp, x_0, sigma, transformer_options={'latent_type': 'yt'})
+                                
+                            else:
+                                if EO("protoshock") and StyleMMDiT is not None and StyleMMDiT.data_shock_start_step <= step_sched < StyleMMDiT.data_shock_end_step:
+                                    eps_[row], data_[row] = RK(x_tmp, s_tmp, x_0, sigma, transformer_options={'row': row, 'x_tmp': x_tmp, 'sigma_next': sigma_next})
+                                    data_wct = StyleMMDiT.apply_data_shock(data_[row])
+                                    if VE_MODEL:
+                                        x_tmp = x_tmp + (data_wct - data_[row])
+                                    else:
+                                        x_tmp = x_tmp + (NS.sigma_max-NS.s_[row]) * (data_wct - data_[row])
+                                    #x_[row+RK.row_offset] = x_tmp
+                                    x_[row] = x_tmp
+                                    if row == 0:
+                                        x_0 = x_tmp
+                                        
+                                if EO("preshock"):
+                                    eps_[row], data_[row] = RK(x_tmp, s_tmp, x_0, sigma, transformer_options={'row': row, 'x_tmp': x_tmp, 'sigma_next': sigma_next})
+                                    if VE_MODEL:
+                                        x_tmp = x_tmp + (data_wct - data_[row])
+                                    else:
+                                        x_tmp = x_tmp + (NS.sigma_max-NS.s_[row]) * (data_wct - data_[row])
+                                    x_[row] = x_tmp
+                                    if row == 0:
+                                        x_0 = x_tmp
+                                
+                                eps_[row], data_[row] = RK(x_tmp, s_tmp, x_0, sigma, transformer_options={'row': row, 'x_tmp': x_tmp, 'sigma_next': sigma_next})
+                                
+                                #if EO("yoloshock") and StyleMMDiT is not None and StyleMMDiT.data_shock_start_step <= step_sched < StyleMMDiT.data_shock_end_step:
+                                if not EO("disable_yoloshock") and StyleMMDiT is not None and StyleMMDiT.data_shock_start_step <= step_sched < StyleMMDiT.data_shock_end_step:
+                                    data_wct = StyleMMDiT.apply_data_shock(data_[row])
+                                    if VE_MODEL:
+                                        x_tmp = x_tmp + (data_wct - data_[row])
+                                    else:
+                                        x_tmp = x_tmp + (NS.sigma_max-NS.s_[row]) * (data_wct - data_[row])
+                                    #x_[row+RK.row_offset] = x_tmp
+                                    x_[row] = x_tmp
+                                    if row == 0:
+                                        x_0 = x_tmp
+                                    data_[row] = data_wct
+                                    if RK.EXPONENTIAL:
+                                        eps_[row] = data_[row] - x_0
+                                    else:
+                                        eps_[row] = (x_0 - data_[row]) / sigma
+                                    
+                                
+                                if hasattr(model.inner_model.inner_model.diffusion_model, "eps_out"):  # fp64 model out override, for testing only
+                                    eps_out = model.inner_model.inner_model.diffusion_model.eps_out
+                                    del model.inner_model.inner_model.diffusion_model.eps_out
+                                    if eps_out.shape[0] == 2:
+                                        data_cond   = x_0 - sigma * eps_out[1]
+                                        data_uncond = x_0 - sigma * eps_out[0]
+                                        data_row = data_uncond + model.inner_model.cfg * (data_cond - data_uncond)
+                                        eps_row = (x_0 - data_row) / sigma
+                                    else:
+                                        data_row = x_0 - sigma * eps_out
+                                    if RK.EXPONENTIAL:
+                                        eps_row = data_row - x_0
+                                    else:
+                                        eps_row = eps_out
+                                    if torch.norm(eps_row - eps_[row]) < 0.01 and torch.norm(data_row - data_[row]) < 0.01:  # if some other cfg/post-cfg func was used, detect and ignore this
+                                        eps_[row] = eps_row
+                                        data_[row] = data_row  
+                                
+
+                            if RK.extra_args['model_options']['transformer_options'].get('y0_standard_guide') is not None:
+                                if hasattr(model.inner_model.inner_model.diffusion_model, "y0_standard_guide"):
+                                    LG.y0 = model.inner_model.inner_model.diffusion_model.y0_standard_guide.clone()
+                                    del model.inner_model.inner_model.diffusion_model.y0_standard_guide
+                                    RK.extra_args['model_options']['transformer_options']['y0_standard_guide'] = None
+                                
+                            if RK.extra_args['model_options']['transformer_options'].get('y0_inv_standard_guide') is not None:
+                                if hasattr(model.inner_model.inner_model.diffusion_model, "y0_inv_standard_guide"):
+                                    LG.y0_inv = model.inner_model.inner_model.diffusion_model.y0_inv_standard_guide.clone() # RK.extra_args['model_options']['transformer_options'].get('y0_standard_guide')
+                                    del model.inner_model.inner_model.diffusion_model.y0_inv_standard_guide
+                                    RK.extra_args['model_options']['transformer_options']['y0_inv_standard_guide'] = None
+
+                            if LG.guide_mode.startswith("lure") and (LG.lgw[step_sched] > 0 or LG.lgw_inv[step_sched] > 0):
+                                x_tmp = LG.process_guides_data_substep(x_tmp, data_[row], step_sched, s_tmp)
+                                eps_[row], data_[row] = RK(x_tmp, s_tmp, x_0, sigma, transformer_options={'latent_type': 'xt'})
+
+                            if momentum != 0.0:
+                                data_[row] = data_[row] - momentum * (data_prev_[0] - data_[row])  #negative!
+                                eps_[row]  = RK.get_epsilon(x_0, x_tmp, data_[row], sigma, s_tmp)    # ... why was this here??? for momentum maybe?
+
+                            if row < RK.rows and noise_scaling_weight != 0 and noise_scaling_type in {"sampler", "sampler_substep"}:
+                                if noise_scaling_type == "sampler_substep":
+                                    sub_lying_su, sub_lying_sigma, sub_lying_sd, sub_lying_alpha_ratio = NS.get_sde_substep(NS.s_[row], NS.s_[row+RK.row_offset+RK.multistep_stages], noise_scaling_eta, noise_scaling_mode)
+                                    for _ in range(noise_scaling_cycles-1):
+                                        sub_lying_su, sub_lying_sigma, sub_lying_sd, sub_lying_alpha_ratio = NS.get_sde_substep(NS.s_[row], sub_lying_sd, noise_scaling_eta, noise_scaling_mode)
+                                    lying_s_[row+1] = sub_lying_sd
+                                substep_noise_scaling_ratio = NS.s_[row+1]/lying_s_[row+1]
+                                if RK.multistep_stages > 0:
+                                    substep_noise_scaling_ratio = sigma_next/lying_sd                   #fails with resample?
+                                
+                                lying_eps_row_factor = (1 - noise_scaling_weight*(substep_noise_scaling_ratio-1))
+
+                        # GUIDE 
+                        if not EO("disable_guides_eps_substep"):
+                            eps_, x_      = LG.process_guides_substep(x_0, x_, eps_,      data_, row, step_sched, NS.sigma, NS.sigma_next, NS.sigma_down, NS.s_, epsilon_scale, RK)
+                        if not EO("disable_guides_eps_prev_substep"):
+                            eps_prev_, x_ = LG.process_guides_substep(x_0, x_, eps_prev_, data_, row, step_sched, NS.sigma, NS.sigma_next, NS.sigma_down, NS.s_, epsilon_scale, RK)
+                        
+                        if LG.y0_mean is not None and LG.y0_mean.sum() != 0.0:
+
+                            if EO("guide_mean_scattersort"):
+                                data_row_mean = apply_scattersort_spatial(data_[row], LG.y0_mean)
+                                eps_row_mean  = RK.get_eps(x_0, data_row_mean, s_tmp)
+                            else:
+                                eps_row_mean = eps_[row] - eps_[row].mean(dim=(-2,-1), keepdim=True) + (LG.y0_mean - x_0).mean(dim=(-2,-1), keepdim=True)
+                            
+                            if LG.mask_mean is not None:
+                                eps_row_mean = LG.mask_mean * eps_row_mean + (1-LG.mask_mean) * eps_[row]
+                            
+                            eps_[row] = eps_[row] + LG.lgw_mean[step_sched] * (eps_row_mean - eps_[row])
+                            
+                        if (full_iter == 0 and diag_iter == 0)   or   EO("newton_iter_post_use_on_implicit_steps"):
+                            x_, eps_ = RK.newton_iter(x_0, x_, eps_, eps_prev_, data_, NS.s_, row, NS.h, sigmas, step, "post", SYNC_GUIDE_ACTIVE)
+
+                    # UPDATE   #for row in range(RK.rows - RK.multistep_stages - RK.row_offset + 1):
+                    if EO("exp2lin_override") and RK.EXPONENTIAL:
+                        x_ = RK.update_substep(x_0, x_, eps_, eps_prev_, row, RK.row_offset, NS.h_new, NS.h_new_orig, lying_eps_row_factor=lying_eps_row_factor, sigma=sigma)   #modifies eps_[row] if lying_eps_row_factor != 1.0
+                        #x_ = RK.update_substep(x_0, x_, eps_, eps_prev_, row, RK.row_offset, -sigma*NS.h_new, -sigma*NS.h_new_orig, lying_eps_row_factor=lying_eps_row_factor)   #modifies eps_[row] if lying_eps_row_factor != 1.0
+                    else:
+                        x_ = RK.update_substep(x_0, x_, eps_, eps_prev_, row, RK.row_offset, NS.h_new, NS.h_new_orig, lying_eps_row_factor=lying_eps_row_factor)   #modifies eps_[row] if lying_eps_row_factor != 1.0
+                    
+                    x_[row+RK.row_offset] = NS.rebound_overshoot_substep(x_0, x_[row+RK.row_offset])
+                    
+                    if SYNC_GUIDE_ACTIVE: #yt_ is not None:
+                        #yt_ = RK.update_substep(yt_0, yt_, eps_y_, eps_prev_y_, row, RK.row_offset, NS.h_new, NS.h_new_orig, lying_eps_row_factor=lying_eps_row_factor)   #modifies eps_[row] if lying_eps_row_factor != 1.0
+                        yt_ = RK.update_substep(yt_0, yt_, eps_yt_, eps_prev_y_, row, RK.row_offset, NS.h_new, NS.h_new_orig, lying_eps_row_factor=lying_eps_row_factor, sigma=sigma)   #modifies eps_[row] if lying_eps_row_factor != 1.0
+                        yt_[row+RK.row_offset] = NS.rebound_overshoot_substep(yt_0, yt_[row+RK.row_offset])
+                    
+                    if not RK.IMPLICIT and NS.noise_mode_sde_substep != "hard_sq":
+
+                        x_means_per_substep = x_[row+RK.row_offset].mean(dim=(-2,-1), keepdim=True)
+
+                        if not LG.guide_mode.startswith("flow") or (LG.lgw[step_sched] == 0 and LG.lgw[step+1] == 0   and   LG.lgw_inv[step_sched] == 0 and LG.lgw_inv[step+1] == 0):
+                            #if LG.guide_mode.startswith("sync") and (LG.lgw[step_sched] != 0.0 or LG.lgw_inv[step_sched] != 0.0):
+                            #    x_row_tmp = x_[row+RK.row_offset].clone()
+                                
+                            #x_[row+RK.row_offset] = NS.swap_noise_substep(x_0, x_[row+RK.row_offset], mask=sde_mask, guide=LG.y0)
+                            x_row_tmp = NS.swap_noise_substep(x_0, x_[row+RK.row_offset], mask=sde_mask, guide=LG.y0)
+                            
+                            #if EO("eps_adain_smartnoise_substep"):
+                            if LG.ADAIN_NOISE_MODE == "smart":
+                                #eps_row_next = (x_0 - x_[row+RK.row_offset]) / (sigma - NS.s_[row+RK.row_offset])
+                                #denoised_row_next = x_0 - sigma * eps_row_next
+                                #
+                                #eps_swapped = (x_row_tmp - denoised_row_next) / NS.s_[row+RK.row_offset]
+                                #
+                                #noise_row_next = eps_swapped + denoised_row_next
+                                #z_[row+RK.row_offset] = noise_row_next
+                                #RK.update_transformer_options({'z_' : z_})
+                                data_next = denoised + NS.h_new * RK.zum(row+RK.row_offset+RK.multistep_stages, data_, data_prev_) 
+                                if VE_MODEL:
+                                    z_[row+RK.row_offset] = (x_row_tmp - data_next) / NS.s_[row+RK.row_offset]
+                                else:
+                                    z_[row+RK.row_offset] = (x_row_tmp - (NS.sigma_max-NS.s_[row+RK.row_offset])*data_next) / NS.s_[row+RK.row_offset]
+                                RK.update_transformer_options({'z_' : z_})
+                            
+                            elif LG.ADAIN_NOISE_MODE == "update": #EO("eps_adain"):
+                                x_init_new = (x_row_tmp - x_[row+RK.row_offset]) / s_tmp + x_init
+                                x_0 += sigma * (x_init_new - x_init)
+                                x_init = x_init_new
+                                RK.update_transformer_options({'x_init' : x_init.clone()})
+                            
+                            if SYNC_GUIDE_ACTIVE:
+                                noise_bongflow_new = (x_row_tmp - x_[row+RK.row_offset]) / s_tmp + noise_bongflow
+                                yt_[row+RK.row_offset] += s_tmp * (noise_bongflow_new - noise_bongflow)
+                                x_0 += sigma * (noise_bongflow_new - noise_bongflow)
+                                noise_bongflow = noise_bongflow_new
+                            
+                            x_[row+RK.row_offset] = x_row_tmp
+                            
+                        elif LG.guide_mode.startswith("flow"):
+                            pass
+
+                    if not LG.guide_mode.startswith("lure"):
+                        x_[row+RK.row_offset] = LG.process_guides_data_substep(x_[row+RK.row_offset], data_[row], step_sched, NS.s_[row])
+                    
+                    if ((not EO("protoshock") and not EO("yoloshock"))  or EO("fuckitshock")) and StyleMMDiT is not None and StyleMMDiT.data_shock_start_step <= step_sched < StyleMMDiT.data_shock_end_step:
+                        data_wct = StyleMMDiT.apply_data_shock(data_[row])
+                        if VE_MODEL:
+                            x_[row+RK.row_offset] = x_[row+RK.row_offset] + (data_wct - data_[row])
+                        else:
+                            x_[row+RK.row_offset] = x_[row+RK.row_offset] + (NS.sigma_max-NS.s_[row]) * (data_wct - data_[row])
+                    
+
+                    if SYNC_GUIDE_ACTIVE: # # # # ## # # ## # YIIIIKES ---------------------------------------------------------------------------------------------------------
+                        if VE_MODEL:
+                            yt_[:NS.s_.shape[0], 0] = y0_bongflow + NS.s_.view(-1, *[1]*(x.ndim-1)) * (noise_bongflow)
+                            yt_0   = y0_bongflow + sigma * (noise_bongflow)
+                        else:
+                            yt_[:NS.s_.shape[0], 0] = y0_bongflow + NS.s_.view(-1, *[1]*(x.ndim-1)) * (noise_bongflow - y0_bongflow)
+                            yt_0   = y0_bongflow + sigma * (noise_bongflow - y0_bongflow)
+                        if RK.EXPONENTIAL:
+                            eps_y_ = data_y_ - yt_0 # yt_        # watch out for fuckery with size of tableau being smaller later in a chained sampler
+                        else:
+                            if BONGMATH:
+                                eps_y_[:NS.s_.shape[0]] = (yt_[:NS.s_.shape[0]] - data_y_[:NS.s_.shape[0]]) / NS.s_.view(-1,*[1]*(x_.ndim-1)) 
+                            else:
+                                eps_y_[:NS.s_.shape[0]] = (yt_0.repeat(NS.s_.shape[0], *[1]*(x_.ndim-1)) - data_y_[:NS.s_.shape[0]]) / sigma    # calc exact to c0 node
+                        if not BONGMATH and (eta != 0 or eta_substep != 0):
+                            if RK.EXPONENTIAL:
+                                eps_x_ = data_x_ - x_0 
+                            else:
+                                eps_x_ = (x_0 - data_x_) / sigma
+
+                            weight_mask = lgw_mask_+lgw_mask_inv_
+                            if LG.SYNC_SEPARATE:
+                                sync_mask = lgw_mask_sync_+lgw_mask_sync_inv_
+                            else:
+                                sync_mask = 1.
+                            
+                            for ms in range(len(eps_)):
+                                if RK.EXPONENTIAL:
+                                    if VE_MODEL:
+                                        eps_[ms] = sync_mask * eps_x_[ms]  +  (1-sync_mask) * eps_x2y_[ms]  +  weight_mask * (-eps_y_[ms] + sigma*(-noise_bongflow))
+                                        if EO("sync_x2y"):
+                                            eps_[ms] = sync_mask * eps_x_[ms]  +  (1-sync_mask) * eps_x2y_[ms]  +  weight_mask * (-eps_x2y_[ms] + sigma*(-noise_bongflow))
+                                    else:
+                                        eps_[ms] = sync_mask * eps_x_[ms]  +  (1-sync_mask) * eps_x2y_[ms]  +  weight_mask * (-eps_y_[ms] + sigma*(y0_bongflow-noise_bongflow))
+                                        if EO("sync_x2y"):
+                                            eps_[ms] = sync_mask * eps_x_[ms]  +  (1-sync_mask) * eps_x2y_[ms]  +  weight_mask * (-eps_x2y_[ms] + sigma*(y0_bongflow-noise_bongflow))
+                                else:
+                                    if VE_MODEL:
+                                        eps_[ms] = sync_mask * eps_x_[ms]  +  (1-sync_mask) * eps_x2y_[ms]  +  weight_mask * (-eps_y_[ms] + (noise_bongflow))
+                                        if EO("sync_x2y"):
+                                            eps_[ms] = sync_mask * eps_x_[ms]  +  (1-sync_mask) * eps_x2y_[ms]  +  weight_mask * (-eps_x2y_[ms] + (noise_bongflow))
+                                    else:
+                                        eps_[ms] = sync_mask * eps_x_[ms]  +  (1-sync_mask) * eps_x2y_[ms]  +  weight_mask * (-eps_y_[ms] + (noise_bongflow-y0_bongflow))
+                                        if EO("sync_x2y"):
+                                            eps_[ms] = sync_mask * eps_x_[ms]  +  (1-sync_mask) * eps_x2y_[ms]  +  weight_mask * (-eps_x2y_[ms] + (noise_bongflow-y0_bongflow))
+
+                    if BONGMATH and NS.s_[row] > RK.sigma_min and NS.h < RK.sigma_max/2   and   (diag_iter == implicit_steps_diag or EO("enable_diag_explicit_bongmath_all"))   and not EO("disable_terminal_bongmath"):
+                        if step == 0 and UNSAMPLE:
+                            pass
+                        elif full_iter == implicit_steps_full or not EO("disable_fully_explicit_bongmath_except_final"):
+                            if sigma > 0.03:
+                                BONGMATH_Y = SYNC_GUIDE_ACTIVE
+                                x_0, x_, eps_ = RK.bong_iter(x_0, x_, eps_, eps_prev_, data_, sigma, NS.s_, row, RK.row_offset, NS.h, step, step_sched,
+                                                            BONGMATH_Y, y0_bongflow, noise_bongflow, eps_x_, eps_y_, data_x_, data_y_, LG)
+                                #                            BONGMATH_Y, y0_bongflow, noise_bongflow, eps_x_, eps_y_, eps_x2y_, data_x_, LG)
+                                #if EO("eps_adain_smartnoise_bongmath"):
+                                if LG.ADAIN_NOISE_MODE == "smart":
+                                    if VE_MODEL:
+                                        z_[:NS.s_.shape[0], ...] = (x_ - data_)[:NS.s_.shape[0], ...] / NS.s_.view(-1,*[1]*(x_.ndim-1))
+                                    else:
+                                        z_[:NS.s_.shape[0], ...] = (x_[:NS.s_.shape[0], ...] - (NS.sigma_max - NS.s_.view(-1,*[1]*(x_.ndim-1)))*data_[:NS.s_.shape[0], ...])[:NS.s_.shape[0], ...] / NS.s_.view(-1,*[1]*(x_.ndim-1))
+                                    RK.update_transformer_options({'z_' : z_})
+                    diag_iter += 1
+
+                    #progress_bar.update( round(1 / implicit_steps_total, 2) )
+                    
+                    #step_update = round(1 / implicit_steps_total, 2)
+                    #progress_bar.update(float(f"{step_update:.2f}")) 
+
+            x_next = x_[RK.rows - RK.multistep_stages - RK.row_offset + 1]
+            x_next = NS.rebound_overshoot_step(x_0, x_next)
+            
+            if SYNC_GUIDE_ACTIVE:           # YT_NEXT UPDATE STEP --------------------------------------
+                yt_next = yt_[RK.rows - RK.multistep_stages - RK.row_offset + 1]
+                yt_next = NS.rebound_overshoot_step(yt_0, yt_next)
+            
+            eps = (x_0 - x_next) / (sigma - sigma_next)
+            denoised = x_0 - sigma * eps
+            
+            if EO("postshock") and step < EO("postshock", 10):
+                eps_row, data_row = RK(x_next, sigma_next, x_next, sigma_next, transformer_options={'row': row, 'x_tmp': x_next, 'sigma_next': sigma_next})
+                if VE_MODEL:
+                    x_next = x_next + (data_row - denoised)
+                else:
+                    x_next = x_next + (NS.sigma_max-sigma_next) * (data_row - denoised)
+                eps = (x_0 - x_next) / (sigma - sigma_next)
+                denoised = x_0 - sigma * eps
+
+            if EO("data_sampler") and step > EO("data_sampler_start_step", 0) and step < EO("data_sampler_end_step", 5):
+                data_sampler_weight = EO("data_sampler_weight", 1.0)
+                denoised_step = RK.zum(row+RK.row_offset+RK.multistep_stages, data_, data_prev_) 
+                x_next = LG.swap_data(x_next, denoised, denoised_step, data_sampler_weight * sigma_next)
+                eps = (x_0 - x_next) / (sigma - sigma_next)
+                denoised = x_0 - sigma * eps
+            
+            x_0_prev = x_0.clone()
+
+            x_means_per_step = x_next.mean(dim=(-2,-1), keepdim=True)
+
+            if eta == 0.0:
+                x = x_next
+                if SYNC_GUIDE_ACTIVE:
+                    yt_0 = yt_[0] = yt_next
+                #elif LG.guide_mode.startswith("sync") and (LG.lgw[step_sched] != 0.0 or LG.lgw_inv[step_sched] != 0.0):
+                #    noise_sync_new = NS.noise_sampler(sigma=sigma, sigma_next=sigma_next)
+                #    x = x_next + sigma * eta * (noise_sync_new - noise_bongflow)
+                #    noise_bongflow += eta * (noise_sync_new - noise_bongflow)
+            elif not LG.guide_mode.startswith("flow") or (LG.lgw[step_sched] == 0 and LG.lgw[step+1] == 0   and   LG.lgw_inv[step_sched] == 0 and LG.lgw_inv[step+1] == 0):
+                x = NS.swap_noise_step(x_0, x_next, mask=sde_mask)
+                
+                #if EO("eps_adain_smartnoise"):
+                if LG.ADAIN_NOISE_MODE == "smart":
+                    #noise_next = eps + denoised
+                    #eps_swapped = (x - denoised) / sigma_next
+                    #
+                    #noise_next = eps_swapped + denoised
+                    #z_[0] = noise_next
+                    #RK.update_transformer_options({'z_' : z_})
+                    if full_iter+1 < implicit_steps_full+1: # are we to loop for full iter after this?
+                        if VE_MODEL:
+                            #z_[row+RK.row_offset] = (x - denoised) / sigma_next
+                            z_[0] = (x_0 - denoised) / sigma
+                        else:
+                            #z_[row+RK.row_offset] = (x - (NS.sigma_max-sigma_next) * denoised) / sigma_next
+                            z_[0] = (x_0 - (NS.sigma_max-sigma) * denoised) / sigma
+                    else: #we're advancing to next step, x is x_next
+                        if VE_MODEL:
+                            #z_[row+RK.row_offset] = (x - denoised) / sigma_next
+                            z_[0] = (x - denoised) / sigma_next
+                        else:
+                            #z_[row+RK.row_offset] = (x - (NS.sigma_max-sigma_next) * denoised) / sigma_next
+                            z_[0] = (x - (NS.sigma_max-sigma_next) * denoised) / sigma_next
+                    RK.update_transformer_options({'z_' : z_})
+
+                elif LG.ADAIN_NOISE_MODE == "update": #EO("eps_adain"):
+                    x_init_new = (x - x_next) / sigma_next + x_init
+                    x_0 += sigma * (x_init_new - x_init)
+                    x_init = x_init_new
+                    RK.update_transformer_options({'x_init' : x_init.clone()})
+                
+                if SYNC_GUIDE_ACTIVE:
+                    noise_bongflow_new = (x - x_next) / sigma_next + noise_bongflow
+                    yt_next += sigma_next * (noise_bongflow_new - noise_bongflow)
+                    x_0 += sigma * (noise_bongflow_new - noise_bongflow)
+                    if not EO("disable_i_bong"):
+                        for i_bong in range(len(NS.s_)):
+                            x_[i_bong] += NS.s_[i_bong] * (noise_bongflow_new - noise_bongflow)
+                    #x_[0] += sigma * (noise_bongflow_new - noise_bongflow)
+                    yt_0 = yt_[0] = yt_next
+                    noise_bongflow = noise_bongflow_new
+            else:
+                x = x_next
+            
+            if EO("keep_step_means"):
+                x = x - x.mean(dim=(-2,-1), keepdim=True) + x_means_per_step
+
+            
+            callback_step = len(sigmas)-1 - step if sampler_mode == "unsample" else step
+            preview_callback(x, eps, denoised, x_, eps_, data_, callback_step, sigma, sigma_next, callback, EO, preview_override=data_cached, FLOW_STOPPED=FLOW_STOPPED)
+            
+            h_prev = NS.h
+            x_prev = x_0
+            
+            denoised_prev2 = denoised_prev
+            denoised_prev  = denoised
+            
+            full_iter += 1
+            
+            if LG.lgw[step_sched] > 0 and step >= EO("guide_cutoff_start_step", 0) and cossim_counter < EO("guide_cutoff_max_iter", 10) and (EO("guide_cutoff") or EO("guide_min")):
+                guide_cutoff = EO("guide_cutoff", 1.0)
+                denoised_norm = data_[0] - data_[0].mean(dim=(-2,-1), keepdim=True)
+                y0_norm       = LG.y0    - LG.y0   .mean(dim=(-2,-1), keepdim=True)
+                y0_cossim     = get_cosine_similarity(denoised_norm, y0_norm)
+                if y0_cossim > guide_cutoff and LG.lgw[step_sched] > EO("guide_cutoff_floor", 0.0):
+                    if not EO("guide_cutoff_fast"):
+                        LG.lgw[step_sched] *= EO("guide_cutoff_factor", 0.9)
+                    else:
+                        LG.lgw *= EO("guide_cutoff_factor", 0.9)
+                    full_iter -= 1
+                if y0_cossim < EO("guide_min", 0.0) and LG.lgw[step_sched] < EO("guide_min_ceiling", 1.0):
+                    if not EO("guide_cutoff_fast"):
+                        LG.lgw[step_sched] *= EO("guide_min_factor", 1.1)
+                    else:
+                        LG.lgw *= EO("guide_min_factor", 1.1)
+                    full_iter -= 1
+        
+        #if EO("smartnoise"): #TODO: determine if this was useful
+        #    z_[0] = z_next
+        
+        if FLOW_STARTED and FLOW_STOPPED:
+            data_prev_ = data_x_prev_
+        if FLOW_STARTED and not FLOW_STOPPED:
+            data_x_prev_[0] = data_cached       # data_cached is data_x from flow mode. this allows multistep to resume seamlessly.
+            for ms in range(recycled_stages):
+                data_x_prev_[recycled_stages - ms] = data_x_prev_[recycled_stages - ms - 1]
+
+        #if LG.guide_mode.startswith("sync") and (LG.lgw[step_sched] != 0.0 or LG.lgw_inv[step_sched] != 0.0):
+        #    data_prev_[0] = x_0 - sigma * eps_[0]
+        #else:
+        data_prev_[0] = data_[0]                # with flow mode, this will be the differentiated guide/"denoised"
+        for ms in range(recycled_stages):
+            data_prev_[recycled_stages - ms] = data_prev_[recycled_stages - ms - 1]   # TODO: verify that this does not run on every substep...
+
+        if SYNC_GUIDE_ACTIVE:
+            data_prev_x_[0] = data_x      
+            for ms in range(recycled_stages):
+                data_prev_x_[recycled_stages - ms] = data_prev_x_[recycled_stages - ms - 1] 
+
+            data_prev_y_[0] = data_y     
+            for ms in range(recycled_stages):
+                data_prev_y_[recycled_stages - ms] = data_prev_y_[recycled_stages - ms - 1] 
+        
+        rk_type = RK.swap_rk_type_at_step_or_threshold(x_0, data_prev_, NS, sigmas, step, rk_swap_step, rk_swap_threshold, rk_swap_type, rk_swap_print)
+        if step > rk_swap_step:
+            implicit_steps_full = 0
+            implicit_steps_diag = 0
+
+        if EO("bong2m") or EO("bong3m"):
+            denoised_data_prev2 = denoised_data_prev
+            denoised_data_prev = data_[0]
+        
+        if SKIP_PSEUDO and not LG.guide_mode.startswith("flow"):
+            if SKIP_PSEUDO_Y == "y0":
+                LG.y0 = denoised
+                LG.HAS_LATENT_GUIDE = True
+            else:
+                LG.y0_inv = denoised
+                LG.HAS_LATENT_GUIDE_INV = True
+                
+        if EO("pseudo_mix_strength"):
+            pseudo_mix_strength = EO("pseudo_mix_strength", 0.0)
+            LG.y0     = orig_y0     + pseudo_mix_strength * (denoised - orig_y0)
+            LG.y0_inv = orig_y0_inv + pseudo_mix_strength * (denoised - orig_y0_inv)
+            
+        #if sampler_mode == "unsample":
+        #    progress_bar.n -= 1 
+        #    progress_bar.refresh() 
+        #else:
+        #    progress_bar.update(1)
+        progress_bar.update(1)  #THIS WAS HERE
+        step += 1
+        
+        if EO("skip_step", -1) == step:
+            step += 1
+
+        if d_noise_start_step     == step:
+            sigmas = sigmas.clone() * d_noise
+            if sigmas.max() > NS.sigma_max:
+                sigmas = sigmas / NS.sigma_max
+        if d_noise_inv_start_step == step:
+            sigmas = sigmas.clone() / d_noise_inv
+            if sigmas.max() > NS.sigma_max:
+                sigmas = sigmas / NS.sigma_max
+        
+        if LG.lgw[step_sched] > 0 and step >= EO("guide_step_cutoff_start_step", 0) and cossim_counter < EO("guide_step_cutoff_max_iter", 10) and (EO("guide_step_cutoff") or EO("guide_step_min")):
+            guide_cutoff = EO("guide_step_cutoff", 1.0)
+            eps_trash, data_trash = RK(x, sigma_next, x_0, sigma)
+            denoised_norm = data_trash - data_trash.mean(dim=(-2,-1), keepdim=True)
+            y0_norm       = LG.y0    - LG.y0   .mean(dim=(-2,-1), keepdim=True)
+            y0_cossim     = get_cosine_similarity(denoised_norm, y0_norm)
+            if y0_cossim > guide_cutoff and LG.lgw[step_sched] > EO("guide_step_cutoff_floor", 0.0):
+                if not EO("guide_step_cutoff_fast"):
+                    LG.lgw[step_sched] *= EO("guide_step_cutoff_factor", 0.9)
+                else:
+                    LG.lgw *= EO("guide_step_cutoff_factor", 0.9)
+                step -= 1
+                x_0 = x = x_[0] = x_0_orig.clone()
+            if y0_cossim < EO("guide_step_min", 0.0) and LG.lgw[step_sched] < EO("guide_step_min_ceiling", 1.0):
+                if not EO("guide_step_cutoff_fast"):
+                    LG.lgw[step_sched] *= EO("guide_step_min_factor", 1.1)
+                else:
+                    LG.lgw *= EO("guide_step_min_factor", 1.1)
+                step -= 1
+                x_0 = x = x_[0] = x_0_orig.clone()
+        # END SAMPLING LOOP ---------------------------------------------------------------------------------------------------
+
+    #progress_bar.close()
+    RK.update_transformer_options({'update_cross_attn':  None})
+    if step == len(sigmas)-2 and sigmas[-1] == 0 and sigmas[-2] == NS.sigma_min and not INIT_SAMPLE_LOOP:
+        if EO("skip_final_model_call"):
+            sigma_min = NS.sigma_min.view((1,) * x.ndim).to(x)
+            denoised  = model.inner_model.inner_model.model_sampling.calculate_denoised(sigma_min, eps, x)
+            x = denoised
+        else:
+            eps, denoised = RK(x, NS.sigma_min, x, NS.sigma_min)
+            x = denoised
+            #progress_bar.update(1)
+
+    eps      = eps     .to(model_device)
+    denoised = denoised.to(model_device)
+    x        = x       .to(model_device)
+    
+    progress_bar.close()
+
+    if not (UNSAMPLE and sigmas[1] > sigmas[0]) and not EO("preview_last_step_always") and sigma is not None   and   not (FLOW_STARTED and not FLOW_STOPPED):
+        callback_step = len(sigmas)-1 - step if sampler_mode == "unsample" else step
+        preview_callback(x, eps, denoised, x_, eps_, data_, callback_step, sigma, sigma_next, callback, EO, preview_override=data_cached, FLOW_STOPPED=FLOW_STOPPED)
+
+    if INIT_SAMPLE_LOOP:
+        state_info_out = state_info
+    else:
+        if guides is not None and guides.get('guide_mode', "") == 'inversion':
+            guide_inversion_y0     = state_info.get('guide_inversion_y0')
+            guide_inversion_y0_inv = state_info.get('guide_inversion_y0_inv')
+            
+            if sampler_mode == "unsample" and guide_inversion_y0 is None:
+                guide_inversion_y0     = LG.y0.clone()
+            if sampler_mode == "unsample" and guide_inversion_y0_inv is None:
+                guide_inversion_y0_inv = LG.y0_inv.clone()
+                
+            if sampler_mode in {"standard", "resample"} and guide_inversion_y0 is None:
+                guide_inversion_y0 = NS.noise_sampler(sigma=NS.sigma_max, sigma_next=NS.sigma_min).to(x)
+                guide_inversion_y0 = normalize_zscore(guide_inversion_y0, channelwise=True, inplace=True)
+            if sampler_mode in {"standard", "resample"} and guide_inversion_y0_inv is None:
+                guide_inversion_y0_inv = NS.noise_sampler(sigma=NS.sigma_max, sigma_next=NS.sigma_min).to(x)
+                guide_inversion_y0_inv = normalize_zscore(guide_inversion_y0_inv, channelwise=True, inplace=True)
+                
+            state_info_out['guide_inversion_y0']     = guide_inversion_y0
+            state_info_out['guide_inversion_y0_inv'] = guide_inversion_y0_inv
+
+        state_info_out['raw_x']             = x.to('cpu')
+        state_info_out['denoised']          = denoised.to('cpu')
+        state_info_out['data_prev_']        = data_prev_.to('cpu')
+        state_info_out['end_step']          = step
+        state_info_out['sigma_next']        = sigma_next.clone()
+        state_info_out['sigmas']            = sigmas_scheduled.clone()
+        state_info_out['sampler_mode']      = sampler_mode
+        state_info_out['last_rng']          = NS.noise_sampler .generator.get_state().clone()
+        state_info_out['last_rng_substep']  = NS.noise_sampler2.generator.get_state().clone()
+        state_info_out['completed']         = step == len(sigmas)-2 and sigmas[-1] == 0 and sigmas[-2] == NS.sigma_min
+        state_info_out['FLOW_STARTED']      = FLOW_STARTED
+        state_info_out['FLOW_STOPPED']      = FLOW_STOPPED
+        state_info_out['noise_bongflow']    = noise_bongflow
+        state_info_out['y0_bongflow']       = y0_bongflow
+        state_info_out['y0_bongflow_orig']  = y0_bongflow_orig
+        state_info_out['y0_standard_guide']       = y0_standard_guide
+        state_info_out['y0_inv_standard_guide']  = y0_inv_standard_guide
+        state_info_out['data_prev_y_']      = data_prev_y_
+        state_info_out['data_prev_x_']      = data_prev_x_
+
+        if noise_initial is not None:
+            state_info_out['noise_initial'] = noise_initial.to('cpu')
+        if image_initial is not None:
+            state_info_out['image_initial'] = image_initial.to('cpu')
+
+        if FLOW_STARTED and not FLOW_STOPPED:
+            state_info_out['y0']           = y0.to('cpu') 
+            #state_info_out['y0_inv']       = y0_inv.to('cpu')       # TODO: implement this?
+            state_info_out['data_cached']  = data_cached.to('cpu')
+            state_info_out['data_x_prev_'] = data_x_prev_.to('cpu')
+
+    return x
+
+def noise_fn(x, sigma, sigma_next, noise_sampler, cossim_iter=1):
+    
+    noise  = normalize_zscore(noise_sampler(sigma=sigma, sigma_next=sigma_next), channelwise=True, inplace=True)
+    cossim = get_pearson_similarity(x, noise)
+    
+    for i in range(cossim_iter):
+        noise_new  = normalize_zscore(noise_sampler(sigma=sigma, sigma_next=sigma_next), channelwise=True, inplace=True)
+        cossim_new = get_pearson_similarity(x, noise_new)
+        
+        if cossim_new > cossim:
+            noise  = noise_new
+            cossim = cossim_new
+    
+    return noise
+
+
+def preview_callback(
+                    x          : Tensor,
+                    eps        : Tensor,
+                    denoised   : Tensor,
+                    x_         : Tensor,
+                    eps_       : Tensor,
+                    data_      : Tensor,
+                    step       : int,
+                    sigma      : Tensor,
+                    sigma_next : Tensor,
+                    callback   : Callable,
+                    EO         : ExtraOptions,
+                    preview_override : Optional[Tensor] = None,
+                    FLOW_STOPPED : bool = False):
+
+    if EO("eps_substep_preview"):
+        row_callback = EO("eps_substep_preview", 0)
+        denoised_callback = eps_[row_callback]
+        
+    elif EO("denoised_substep_preview"):
+        row_callback = EO("denoised_substep_preview", 0)
+        denoised_callback = data_[row_callback]
+        
+    elif EO("x_substep_preview"):
+        row_callback = EO("x_substep_preview", 0)
+        denoised_callback = x_[row_callback]
+        
+    elif EO("eps_preview"):
+        denoised_callback = eps
+        
+    elif EO("denoised_preview"):
+        denoised_callback = denoised
+        
+    elif EO("x_preview"):
+        denoised_callback = x
+        
+    elif preview_override is not None and FLOW_STOPPED == False:
+        denoised_callback = preview_override
+        
+    else:
+        denoised_callback = data_[0]
+        
+    callback({'x': x, 'i': step, 'sigma': sigma, 'sigma_next': sigma_next, 'denoised': denoised_callback.to(torch.float32)}) if callback is not None else None
+    
+    return
+
diff --git a/custom_nodes/RES4LYF/beta/samplers.py b/custom_nodes/RES4LYF/beta/samplers.py
new file mode 100644
index 0000000000000000000000000000000000000000..f810e6ebef1b30ae100edb378445e50179077d15
--- /dev/null
+++ b/custom_nodes/RES4LYF/beta/samplers.py
@@ -0,0 +1,2744 @@
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+
+from typing import Optional, Callable, Tuple, Dict, Any, Union
+import copy
+import gc
+
+import comfy.samplers
+import comfy.sample
+import comfy.sampler_helpers
+import comfy.model_sampling
+import comfy.latent_formats
+import comfy.sd
+import comfy.supported_models
+import comfy.utils
+import comfy.nested_tensor
+from comfy.samplers import CFGGuider, sampling_function
+
+import latent_preview
+
+from ..helper               import initialize_or_scale, get_res4lyf_scheduler_list, OptionsManager, ExtraOptions
+from ..res4lyf              import RESplain
+from ..latents              import normalize_zscore, get_orthogonal
+from ..sigmas               import get_sigmas
+#import ..models              # import ReFluxPatcher
+
+from .constants             import MAX_STEPS, IMPLICIT_TYPE_NAMES
+from .noise_classes         import NOISE_GENERATOR_CLASSES_SIMPLE, NOISE_GENERATOR_NAMES_SIMPLE, NOISE_GENERATOR_NAMES
+from .rk_noise_sampler_beta import NOISE_MODE_NAMES
+from .rk_coefficients_beta  import get_default_sampler_name, get_sampler_name_list, process_sampler_name
+
+
+def copy_cond(conditioning):
+    new_conditioning = []
+    if type(conditioning[0][0]) == list:
+        for i in range(len(conditioning)):
+            new_conditioning_i = []
+            for embedding, cond in conditioning[i]:
+                cond_copy = {}
+                for k, v in cond.items():
+                    if isinstance(v, torch.Tensor):
+                        cond_copy[k] = v.clone()
+                    else:
+                        cond_copy[k] = v  # ensure we're not copying huge shit like controlnets
+                new_conditioning_i.append([embedding.clone(), cond_copy])
+            new_conditioning.append(new_conditioning_i)
+    else:
+        for embedding, cond in conditioning:
+            cond_copy = {}
+            for k, v in cond.items():
+                if isinstance(v, torch.Tensor):
+                    cond_copy[k] = v.clone()
+                else:
+                    cond_copy[k] = v  # ensure we're not copying huge shit like controlnets
+            new_conditioning.append([embedding.clone(), cond_copy])
+            
+    return new_conditioning
+
+
+def generate_init_noise(x, seed, noise_type_init, noise_stdev, noise_mean, noise_normalize,
+                        sigma_max, sigma_min, alpha_init=None, k_init=None, EO=None):
+    if noise_type_init == "none" or noise_stdev == 0.0:
+        return torch.zeros_like(x)
+
+    noise_sampler_init = NOISE_GENERATOR_CLASSES_SIMPLE.get(noise_type_init)(
+        x=x, seed=seed, sigma_max=sigma_max, sigma_min=sigma_min
+    )
+
+    if noise_type_init == "fractal":
+        noise_sampler_init.alpha = alpha_init
+        noise_sampler_init.k = k_init
+        noise_sampler_init.scale = 0.1
+
+    noise = noise_sampler_init(sigma=sigma_max * noise_stdev, sigma_next=sigma_min)
+
+    if noise_normalize and noise.std() > 0:
+        channelwise = EO("init_noise_normalize_channelwise", "true") if EO else "true"
+        channelwise = True if channelwise == "true" else False
+        noise = normalize_zscore(noise, channelwise=channelwise, inplace=True)
+
+    noise *= noise_stdev
+    noise = (noise - noise.mean()) + noise_mean
+    return noise
+
+
+class SharkGuider(CFGGuider):
+    def __init__(self, model_patcher):
+        super().__init__(model_patcher)
+        self.cfgs = {}
+
+    def set_conds(self, **kwargs):
+        self.inner_set_conds(kwargs)
+
+    def set_cfgs(self, **kwargs):
+        self.cfgs = {**kwargs}
+        self.cfg  = self.cfgs.get('xt', self.cfg)
+
+    def predict_noise(self, x, timestep, model_options={}, seed=None):
+        latent_type = model_options['transformer_options'].get('latent_type', 'xt')
+        positive = self.conds.get(f'{latent_type}_positive', self.conds.get('xt_positive'))
+        negative = self.conds.get(f'{latent_type}_negative', self.conds.get('xt_negative'))
+        positive = self.conds.get('xt_positive') if positive is None else positive
+        negative = self.conds.get('xt_negative') if negative is None else negative
+        cfg      = self.cfgs.get(latent_type, self.cfg)
+        
+        model_options['transformer_options']['yt_positive'] = self.conds.get('yt_positive')
+        model_options['transformer_options']['yt_negative'] = self.conds.get('yt_negative')
+        
+        return sampling_function(self.inner_model, x, timestep, negative, positive, cfg, model_options=model_options, seed=seed)
+
+
+
+class SharkSampler:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "noise_type_init": (NOISE_GENERATOR_NAMES_SIMPLE, {"default": "gaussian"}),
+                "noise_stdev":     ("FLOAT",                      {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":0.01, "round": False, }),
+                "noise_seed":      ("INT",                        {"default": 0,   "min": -1,       "max": 0xffffffffffffffff}),
+                "sampler_mode":    (['unsample', 'standard', 'resample'], {"default": "standard"}),
+                "scheduler":       (get_res4lyf_scheduler_list(), {"default": "beta57"},),
+                "steps":           ("INT",                        {"default": 30,  "min": 1,        "max": 10000.0}),
+                "denoise":         ("FLOAT",                      {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":0.01}),
+                "denoise_alt":     ("FLOAT",                      {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":0.01}),
+                "cfg":             ("FLOAT",                      {"default": 5.5, "min": -10000.0, "max": 10000.0, "step":0.01, "round": False, "tooltip": "Negative values use channelwise CFG." }),
+                },
+            "optional": {
+                "model":           ("MODEL",),
+                "positive":        ("CONDITIONING", ),
+                "negative":        ("CONDITIONING", ),
+                "sampler":         ("SAMPLER", ),
+                "sigmas":          ("SIGMAS", ),
+                "latent_image":    ("LATENT", ),     
+                "extra_options":   ("STRING",                     {"default": "", "multiline": True}),   
+                "options":         ("OPTIONS", ),   
+                }
+            }
+
+    RETURN_TYPES = ("LATENT", 
+                    "LATENT",  
+                    "LATENT",)
+    
+    RETURN_NAMES = ("output", 
+                    "denoised",
+                    "sde_noise",) 
+    
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/samplers"
+    EXPERIMENTAL = True
+    
+    def main(self, 
+            model                                       = None,
+            cfg                : float                  =  5.5, 
+            scheduler          : str                    = "beta57", 
+            steps              : int                    = 30, 
+            steps_to_run       : int                    = -1,
+            sampler_mode       : str                    = "standard",
+            denoise            : float                  =  1.0, 
+            denoise_alt        : float                  =  1.0,
+            noise_type_init    : str                    = "gaussian",
+            latent_image       : Optional[dict[Tensor]] = None,
+            
+            positive                                    = None,
+            negative                                    = None,
+            sampler                                     = None,
+            sigmas             : Optional[Tensor]       = None,
+            noise_stdev        : float                  =  1.0,
+            noise_mean         : float                  =  0.0,
+            noise_normalize    : bool                   = True,
+            
+            d_noise            : float                  =  1.0,
+            alpha_init         : float                  = -1.0,
+            k_init             : float                  =  1.0,
+            cfgpp              : float                  =  0.0,
+            noise_seed         : int                    = -1,
+            options                                     = None,
+            sde_noise                                   = None,
+            sde_noise_steps    : int                    =  1,
+            
+            rebounds           : int                    =  0,
+            unsample_cfg       : float                  = 1.0,
+            unsample_eta       : float                  = 0.5,
+            unsampler_name     : str                    = "none",
+            unsample_steps_to_run : int                 = -1,
+            eta_decay_scale   : float                  = 1.0,
+            
+            #ultracascade_stage : str = "stage_UP",
+            ultracascade_latent_image : Optional[dict[str,Any]] = None,
+            ultracascade_guide_weights: Optional[Tuple] = None,
+            
+            ultracascade_latent_width : int = 0,
+            ultracascade_latent_height: int = 0,
+
+            extra_options      : str = "", 
+            **kwargs,
+            ): 
+        
+            
+            disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
+
+
+
+            # INIT EXTENDABLE OPTIONS INPUTS
+            
+            options_mgr     = OptionsManager(options, **kwargs)
+                        
+            extra_options  += "\n" + options_mgr.get('extra_options', "")
+            EO              = ExtraOptions(extra_options)
+            default_dtype   = EO("default_dtype", torch.float64)
+            default_device  = EO("work_device", "cuda" if torch.cuda.is_available() else "cpu")
+            
+            noise_stdev     = options_mgr.get('noise_init_stdev', noise_stdev)
+            noise_mean      = options_mgr.get('noise_init_mean',  noise_mean)
+            noise_type_init = options_mgr.get('noise_type_init',  noise_type_init)
+            d_noise         = options_mgr.get('d_noise',          d_noise)
+            alpha_init      = options_mgr.get('alpha_init',       alpha_init)
+            k_init          = options_mgr.get('k_init',           k_init)
+            sde_noise       = options_mgr.get('sde_noise',        sde_noise)
+            sde_noise_steps = options_mgr.get('sde_noise_steps',  sde_noise_steps)
+            rebounds        = options_mgr.get('rebounds',         rebounds)
+            unsample_cfg    = options_mgr.get('unsample_cfg',     unsample_cfg)
+            unsample_eta    = options_mgr.get('unsample_eta',     unsample_eta)
+            unsampler_name  = options_mgr.get('unsampler_name',   unsampler_name)
+            unsample_steps_to_run = options_mgr.get('unsample_steps_to_run',   unsample_steps_to_run)
+            
+            eta_decay_scale = options_mgr.get('eta_decay_scale',  eta_decay_scale)
+            start_at_step   = options_mgr.get('start_at_step',    -1)
+            tile_sizes      = options_mgr.get('tile_sizes',       None)
+            flow_sync_eps   = options_mgr.get('flow_sync_eps',    0.0)
+            
+            unsampler_name, _ = process_sampler_name(unsampler_name)
+
+            
+            #ultracascade_stage        = options_mgr.get('ultracascade_stage',         ultracascade_stage)
+            ultracascade_latent_image  = options_mgr.get('ultracascade_latent_image',  ultracascade_latent_image)
+            ultracascade_latent_width  = options_mgr.get('ultracascade_latent_width',  ultracascade_latent_width)
+            ultracascade_latent_height = options_mgr.get('ultracascade_latent_height', ultracascade_latent_height)
+            
+            
+            if 'BONGMATH' in sampler.extra_options:
+                sampler.extra_options['start_at_step'] = start_at_step
+                sampler.extra_options['tile_sizes']    = tile_sizes
+                
+                sampler.extra_options['unsample_bongmath'] = options_mgr.get('unsample_bongmath', sampler.extra_options['BONGMATH'])   # allow turning off bongmath for unsampling with cycles
+                sampler.extra_options['flow_sync_eps'] = flow_sync_eps
+                
+            is_chained = False
+            if latent_image is not None:
+                if 'positive' in latent_image and positive is None:
+                    positive = copy_cond(latent_image['positive'])
+                    if positive is not None and 'control' in positive[0][1]:
+                        for i in range(len(positive)):
+                            positive[i][1]['control']      = latent_image['positive'][i][1]['control']
+                            if hasattr(latent_image['positive'][i][1]['control'], 'base'):
+                                positive[i][1]['control'].base = latent_image['positive'][i][1]['control'].base
+                    is_chained = True
+                if 'negative' in latent_image and negative is None:
+                    negative = copy_cond(latent_image['negative'])
+                    if negative is not None and 'control' in negative[0][1]:
+                        for i in range(len(negative)):
+                            negative[i][1]['control']      = latent_image['negative'][i][1]['control']
+                            if hasattr(latent_image['negative'][i][1]['control'], 'base'):
+                                negative[i][1]['control'].base = latent_image['negative'][i][1]['control'].base
+                    is_chained = True
+                if 'sampler' in latent_image and sampler is None:
+                    sampler = copy_cond(latent_image['sampler'])  #.clone()
+                    is_chained = True
+            
+            if 'steps_to_run' in sampler.extra_options:
+                sampler.extra_options['steps_to_run'] = steps_to_run
+
+            guider_input = options_mgr.get('guider', None)
+            if guider_input is not None and is_chained is False:
+                guider = guider_input
+                work_model = guider.model_patcher
+                RESplain("Shark: Using model from ClownOptions_GuiderInput: ", guider.model_patcher.model.diffusion_model.__class__.__name__)
+                RESplain("SharkWarning: \"flow\" guide mode does not work with ClownOptions_GuiderInput")
+                if hasattr(guider, 'cfg') and guider.cfg is not None:
+                    cfg = guider.cfg
+                    RESplain("Shark: Using cfg from ClownOptions_GuiderInput: ", cfg)
+                if hasattr(guider, 'original_conds') and guider.original_conds is not None:
+                    if 'positive' in guider.original_conds:
+                        first_ = guider.original_conds['positive'][0]['cross_attn']
+                        second_ = {k: v for k, v in guider.original_conds['positive'][0].items() if k != 'cross_attn'}
+                        positive = [[first_, second_],]
+                        RESplain("Shark: Using positive cond from ClownOptions_GuiderInput")
+                    if 'negative' in guider.original_conds:
+                        first_ = guider.original_conds['negative'][0]['cross_attn']
+                        second_ = {k: v for k, v in guider.original_conds['negative'][0].items() if k != 'cross_attn'}
+                        negative = [[first_, second_],]
+                        RESplain("Shark: Using negative cond from ClownOptions_GuiderInput")
+            else:
+                guider = None
+                work_model   = model#.clone()
+            
+            if latent_image is not None:
+                latent_image['samples'] = comfy.sample.fix_empty_latent_channels(work_model, latent_image['samples'])
+                
+            if positive is None or negative is None:
+                from ..conditioning import EmptyConditioningGenerator
+                EmptyCondGen       = EmptyConditioningGenerator(work_model)
+                positive, negative = EmptyCondGen.zero_none_conditionings_([positive, negative])
+
+            if cfg < 0:
+                sampler.extra_options['cfg_cw'] = -cfg
+                cfg = 1.0
+            else:
+                sampler.extra_options.pop("cfg_cw", None) 
+
+            
+            is_nested_input = latent_image is not None and 'samples' in latent_image and isinstance(latent_image['samples'], comfy.nested_tensor.NestedTensor)
+            if not EO("disable_dummy_sampler_init") and not is_nested_input:
+                sampler_null = comfy.samplers.ksampler("rk_beta",
+                    {
+                        "sampler_mode": "NULL",
+                    })
+                if latent_image is not None and 'samples' in latent_image:
+                    latent_vram_factor = EO("latent_vram_factor", 3)
+                    x_null = torch.zeros_like(latent_image['samples']).repeat_interleave(latent_vram_factor, dim=-1)
+                elif ultracascade_latent_height * ultracascade_latent_width > 0:
+                    x_null = comfy.sample.fix_empty_latent_channels(model, torch.zeros((1,16,ultracascade_latent_height,ultracascade_latent_width)))
+                else:
+                    print("Fallback: spawning dummy 1,16,256,256 latent.")
+                    x_null = comfy.sample.fix_empty_latent_channels(model, torch.zeros((1,16,256,256)))
+                _ = comfy.sample.sample_custom(work_model, x_null, cfg, sampler_null, torch.linspace(1, 0, 10).to(x_null.dtype).to(x_null.device), negative, negative, x_null, noise_mask=None, callback=None, disable_pbar=disable_pbar, seed=noise_seed)
+
+            sigma_min = work_model.get_model_object('model_sampling').sigma_min
+            sigma_max = work_model.get_model_object('model_sampling').sigma_max
+            
+            if sampler is None:
+                raise ValueError("sampler is required")
+            else:
+                sampler = copy.deepcopy(sampler)
+        
+        
+        
+            # INIT SIGMAS
+            if sigmas is not None:
+                sigmas = sigmas.clone().to(dtype=default_dtype, device=default_device) # does this type carry into clown after passing through comfy?
+                sigmas *= denoise   # ... otherwise we have to interpolate and that might not be ideal for tiny custom schedules...
+            else: 
+                sigmas = get_sigmas(work_model, scheduler, steps, abs(denoise)).to(dtype=default_dtype, device=default_device)
+            sigmas *= denoise_alt
+
+            # USE NULL FLOATS AS "FLAGS" TO PREVENT COMFY NOISE ADDITION
+            if sampler_mode.startswith("unsample"): 
+                null   = torch.tensor([0.0], device=sigmas.device, dtype=sigmas.dtype)
+                sigmas = torch.flip(sigmas, dims=[0])
+                sigmas = torch.cat([sigmas, null])
+                
+            elif sampler_mode.startswith("resample"):
+                null   = torch.tensor([0.0], device=sigmas.device, dtype=sigmas.dtype)
+                sigmas = torch.cat([null, sigmas])
+                sigmas = torch.cat([sigmas, null])
+
+
+
+            latent_x = {}
+            # INIT STATE INFO FOR CONTINUING GENERATION ACROSS MULTIPLE SAMPLER NODES
+            if latent_image is not None:
+                samples = latent_image['samples']
+                latent_x['samples'] = samples._copy() if isinstance(samples, comfy.nested_tensor.NestedTensor) else samples.clone()
+                if 'noise_mask' in latent_image:
+                    noise_mask = latent_image['noise_mask']
+                    latent_x['noise_mask'] = noise_mask._copy() if isinstance(noise_mask, comfy.nested_tensor.NestedTensor) else noise_mask.clone()
+                state_info = copy.deepcopy(latent_image['state_info']) if 'state_info' in latent_image else {}
+            else:
+                state_info = {}
+            state_info_out = {}
+            
+            
+            
+            # SETUP CONDITIONING EMBEDS
+            
+            pos_cond = copy_cond(positive)
+            neg_cond = copy_cond(negative)
+            
+            
+            
+            # SETUP FOR ULTRACASCADE IF DETECTED
+            if work_model.model.model_config.unet_config.get('stable_cascade_stage') == 'up':
+                
+                ultracascade_guide_weight = EO("ultracascade_guide_weight", 0.0)
+                ultracascade_guide_type   = EO("ultracascade_guide_type", "residual")
+                
+                x_lr = None
+                if ultracascade_latent_height * ultracascade_latent_width > 0:
+                    x_lr        = latent_image['samples'].clone() if latent_image is not None else None
+                    x_lr_bs     = 1                               if x_lr         is     None else x_lr.shape[-4]
+                    x_lr_dtype  = default_dtype                   if x_lr         is     None else x_lr.dtype
+                    x_lr_device = 'cuda'                          if x_lr         is     None else x_lr.device
+                    
+                    ultracascade_stage_up_upscale_align_corners = EO("ultracascade_stage_up_upscale_align_corners", False)
+                    ultracascade_stage_up_upscale_mode          = EO("ultracascade_stage_up_upscale_mode",         "bicubic")
+                    latent_x['samples'] = torch.zeros([x_lr_bs, 16, ultracascade_latent_height, ultracascade_latent_width], dtype=x_lr_dtype, device=x_lr_device)
+                
+                    data_prev_ = state_info.get('data_prev_')
+                    if EO("ultracascade_stage_up_preserve_data_prev") and data_prev_ is not None:
+                        data_prev_ = data_prev_.squeeze(1) 
+
+                        if data_prev_.dim() == 4: 
+                            data_prev_ = F.interpolate(
+                                data_prev_,
+                                size=latent_x['samples'].shape[-2:],
+                                mode=ultracascade_stage_up_upscale_mode,
+                                align_corners=ultracascade_stage_up_upscale_align_corners
+                                )
+                        else:
+                            print("data_prev_ upscale failed.")
+                        state_info['data_prev_'] = data_prev_.unsqueeze(1)
+                    
+                    else:
+                        state_info['data_prev_'] = data_prev_ #None   # = None was leading to errors even with sampler_mode=standard due to below with = state_info['data_prev_'][batch_num]
+                
+                if x_lr is not None:
+                    if x_lr.shape[-2:] != latent_image['samples'].shape[-2:]:
+                        x_height, x_width = latent_image['samples'].shape[-2:]
+                        ultracascade_stage_up_upscale_align_corners = EO("ultracascade_stage_up_upscale_align_corners", False)
+                        ultracascade_stage_up_upscale_mode          = EO("ultracascade_stage_up_upscale_mode",         "bicubic")
+
+                        x_lr = F.interpolate(x_lr, size=(x_height, x_width), mode=ultracascade_stage_up_upscale_mode, align_corners=ultracascade_stage_up_upscale_align_corners)
+                        
+                ultracascade_guide_weights = initialize_or_scale(ultracascade_guide_weights, ultracascade_guide_weight, MAX_STEPS)
+
+                patch = work_model.model_options.get("transformer_options", {}).get("patches_replace", {}).get("ultracascade", {}).get("main")
+                if patch is not None:
+                    patch.update(x_lr=x_lr, guide_weights=ultracascade_guide_weights, guide_type=ultracascade_guide_type)
+                else:
+                    work_model.model.diffusion_model.set_sigmas_schedule(sigmas_schedule = sigmas)
+                    work_model.model.diffusion_model.set_sigmas_prev    (sigmas_prev     = sigmas[:1])
+                    work_model.model.diffusion_model.set_guide_weights  (guide_weights   = ultracascade_guide_weights)
+                    work_model.model.diffusion_model.set_guide_type     (guide_type      = ultracascade_guide_type)
+                    work_model.model.diffusion_model.set_x_lr           (x_lr            = x_lr)
+                
+            elif work_model.model.model_config.unet_config.get('stable_cascade_stage') == 'b':
+                #if sampler_mode != "resample":
+                #    state_info['data_prev_'] = None    #commented out as it was throwing an error below with = state_info['data_prev_'][batch_num]
+                
+                c_pos, c_neg = [], []
+                for t in pos_cond:
+                    d_pos = t[1].copy()
+                    d_neg = t[1].copy()
+                    
+                    x_lr = None
+                    if ultracascade_latent_height * ultracascade_latent_width > 0:
+                        x_lr = latent_image['samples'].clone()
+                        latent_x['samples'] = torch.zeros([x_lr.shape[-4], 4, ultracascade_latent_height // 4, ultracascade_latent_width // 4], dtype=x_lr.dtype, device=x_lr.device)
+                    
+                    d_pos['stable_cascade_prior'] = x_lr
+
+                    pooled_output = d_neg.get("pooled_output", None)
+                    if pooled_output is not None:
+                        d_neg["pooled_output"] = torch.zeros_like(pooled_output)
+                    
+                    c_pos.append(                 [t[0],  d_pos])            
+                    c_neg.append([torch.zeros_like(t[0]), d_neg])
+                pos_cond = c_pos
+                neg_cond = c_neg
+                
+            elif ultracascade_latent_height * ultracascade_latent_width > 0:
+                latent_x['samples'] = torch.zeros([1, 16, ultracascade_latent_height, ultracascade_latent_width], dtype=default_dtype, device=sigmas.device)
+            
+            
+            
+            # NOISE, ORTHOGONALIZE, OR ZERO EMBEDS
+            
+            if pos_cond is None or neg_cond is None:
+                from ..conditioning import EmptyConditioningGenerator
+                EmptyCondGen       = EmptyConditioningGenerator(work_model)
+                pos_cond, neg_cond = EmptyCondGen.zero_none_conditionings_([pos_cond, neg_cond])
+
+
+
+            if EO(("cond_noise", "uncond_noise")):
+                if noise_seed == -1:
+                    cond_seed = torch.initial_seed() + 1
+                else:
+                    cond_seed = noise_seed
+                
+                t5_seed              = EO("t5_seed"             , cond_seed)
+                clip_seed            = EO("clip_seed"           , cond_seed+1)
+                t5_noise_type        = EO("t5_noise_type"       , "gaussian")
+                clip_noise_type      = EO("clip_noise_type"     , "gaussian")
+                t5_noise_sigma_max   = EO("t5_noise_sigma_max"  , "gaussian")
+                t5_noise_sigma_min   = EO("t5_noise_sigma_min"  , "gaussian")
+                clip_noise_sigma_max = EO("clip_noise_sigma_max", "gaussian")
+                clip_noise_sigma_min = EO("clip_noise_sigma_min", "gaussian")
+                
+                noise_sampler_t5     = NOISE_GENERATOR_CLASSES_SIMPLE.get(  t5_noise_type)(x=pos_cond[0][0],                  seed=  t5_seed, sigma_max=  t5_noise_sigma_max, sigma_min=  t5_noise_sigma_min, )
+                noise_sampler_clip   = NOISE_GENERATOR_CLASSES_SIMPLE.get(clip_noise_type)(x=pos_cond[0][1]['pooled_output'], seed=clip_seed, sigma_max=clip_noise_sigma_max, sigma_min=clip_noise_sigma_min, )
+                
+                t5_noise_scale   = EO("t5_noise_scale",   1.0)
+                clip_noise_scale = EO("clip_noise_scale", 1.0)
+                
+                if EO("cond_noise"):
+                    t5_noise   = noise_sampler_t5  (sigma=  t5_noise_sigma_max, sigma_next=  t5_noise_sigma_min)
+                    clip_noise = noise_sampler_clip(sigma=clip_noise_sigma_max, sigma_next=clip_noise_sigma_min)
+                    
+                    pos_cond[0][0]                  = pos_cond[0][0]                  + t5_noise_scale   * (t5_noise   - pos_cond[0][0])
+                    pos_cond[0][1]['pooled_output'] = pos_cond[0][1]['pooled_output'] + clip_noise_scale * (clip_noise - pos_cond[0][1]['pooled_output'])
+                    
+                if EO("uncond_noise"):
+                    t5_noise   = noise_sampler_t5  (sigma=  t5_noise_sigma_max, sigma_next=  t5_noise_sigma_min)
+                    clip_noise = noise_sampler_clip(sigma=clip_noise_sigma_max, sigma_next=clip_noise_sigma_min)
+                    
+                    neg_cond[0][0]                  = neg_cond[0][0]                  + t5_noise_scale   * (t5_noise   - neg_cond[0][0])
+                    neg_cond[0][1]['pooled_output'] = neg_cond[0][1]['pooled_output'] + clip_noise_scale * (clip_noise - neg_cond[0][1]['pooled_output'])
+
+            if EO("uncond_ortho"):
+                neg_cond[0][0]                  = get_orthogonal(neg_cond[0][0],                  pos_cond[0][0])
+                neg_cond[0][1]['pooled_output'] = get_orthogonal(neg_cond[0][1]['pooled_output'], pos_cond[0][1]['pooled_output'])
+            
+
+            if "noise_seed" in sampler.extra_options:
+                if sampler.extra_options['noise_seed'] == -1 and noise_seed != -1:
+                    sampler.extra_options['noise_seed'] = noise_seed + 1
+                    RESplain("Shark: setting clown noise seed to: ", sampler.extra_options['noise_seed'], debug=True)
+
+            if "sampler_mode" in sampler.extra_options:
+                sampler.extra_options['sampler_mode'] = sampler_mode
+
+            if "extra_options" in sampler.extra_options:
+                extra_options += "\n"
+                extra_options += sampler.extra_options['extra_options']
+                sampler.extra_options['extra_options'] = extra_options
+
+            samples = latent_x['samples']
+            latent_image_batch = {"samples": samples._copy() if isinstance(samples, comfy.nested_tensor.NestedTensor) else samples.clone()}
+            if 'noise_mask' in latent_x and latent_x['noise_mask'] is not None:
+                noise_mask = latent_x['noise_mask']
+                latent_image_batch['noise_mask'] = noise_mask._copy() if isinstance(noise_mask, comfy.nested_tensor.NestedTensor) else noise_mask.clone()
+
+            if EO("no_batch_loop"):
+                x = latent_image_batch['samples'].to(default_dtype)
+
+                if isinstance(x, comfy.nested_tensor.NestedTensor):
+                    noise = comfy.nested_tensor.NestedTensor([
+                        generate_init_noise(
+                            x=t.clone(), seed=noise_seed + idx,
+                            noise_type_init=noise_type_init, noise_stdev=noise_stdev,
+                            noise_mean=noise_mean, noise_normalize=noise_normalize,
+                            sigma_max=sigma_max, sigma_min=sigma_min,
+                            alpha_init=alpha_init, k_init=k_init, EO=EO
+                        )
+                        for idx, t in enumerate(x.unbind())
+                    ])
+                else:
+                    noise = generate_init_noise(
+                        x=x.clone(), seed=noise_seed,
+                        noise_type_init=noise_type_init, noise_stdev=noise_stdev,
+                        noise_mean=noise_mean, noise_normalize=noise_normalize,
+                        sigma_max=sigma_max, sigma_min=sigma_min,
+                        alpha_init=alpha_init, k_init=k_init, EO=EO
+                    )
+
+                if guider is None:
+                    guider = SharkGuider(work_model)
+                    flow_cond = options_mgr.get('flow_cond', {})
+                    if flow_cond and 'yt_positive' in flow_cond:
+                        if 'yt_inv_positive' not in flow_cond:
+                            guider.set_conds(yt_positive=flow_cond.get('yt_positive'),
+                                             yt_negative=flow_cond.get('yt_negative'))
+                            guider.set_cfgs(yt=flow_cond.get('yt_cfg'), xt=cfg)
+                        else:
+                            guider.set_conds(yt_positive=flow_cond.get('yt_positive'),
+                                             yt_negative=flow_cond.get('yt_negative'),
+                                             yt_inv_positive=flow_cond.get('yt_inv_positive'),
+                                             yt_inv_negative=flow_cond.get('yt_inv_negative'))
+                            guider.set_cfgs(yt=flow_cond.get('yt_cfg'),
+                                           yt_inv=flow_cond.get('yt_inv_cfg'), xt=cfg)
+                    else:
+                        guider.set_cfgs(xt=cfg)
+                    guider.set_conds(xt_positive=pos_cond, xt_negative=neg_cond)
+                elif type(guider) == SharkGuider:
+                    guider.set_cfgs(xt=cfg)
+                    guider.set_conds(xt_positive=pos_cond, xt_negative=neg_cond)
+                else:
+                    try:
+                        guider.set_cfg(cfg)
+                        guider.set_conds(pos_cond, neg_cond)
+                    except:
+                        pass
+
+                if latent_image is not None and 'state_info' in latent_image and 'sigmas' in latent_image['state_info']:
+                    steps_len = max(sigmas.shape[-1] - 1, latent_image['state_info']['sigmas'].shape[-1] - 1)
+                else:
+                    steps_len = sigmas.shape[-1] - 1
+
+                x0_output = {}
+                try:
+                    callback = latent_preview.prepare_callback(work_model, steps_len, x0_output,
+                        shape=x.shape if hasattr(x, 'is_nested') and x.is_nested else None)
+                except TypeError:
+                    callback = latent_preview.prepare_callback(work_model, steps_len, x0_output)
+
+                noise_mask = latent_image_batch.get("noise_mask", None)
+
+                if noise_mask is not None:
+                    stored_image = state_info.get('image_initial')
+                    x_initial = stored_image if stored_image is not None else x
+                    stored_noise = state_info.get('noise_initial')
+                    noise_initial = stored_noise if stored_noise is not None else noise
+                else:
+                    x_initial = x
+                    noise_initial = noise
+
+                state_info_out = {}
+                if 'BONGMATH' in sampler.extra_options:
+                    sampler.extra_options['state_info'] = state_info
+                    sampler.extra_options['state_info_out'] = state_info_out
+                    sampler.extra_options['image_initial'] = x_initial
+                    sampler.extra_options['noise_initial'] = noise_initial
+
+                if rebounds > 0:
+                    cfgs_cached = guider.cfgs
+                    steps_to_run_cached = sampler.extra_options['steps_to_run']
+                    eta_cached         = sampler.extra_options['eta']
+                    eta_substep_cached = sampler.extra_options['eta_substep']
+
+                    etas_cached         = sampler.extra_options['etas'].clone()
+                    etas_substep_cached = sampler.extra_options['etas_substep'].clone()
+
+                    unsample_etas = torch.full_like(etas_cached, unsample_eta)
+                    rk_type_cached = sampler.extra_options['rk_type']
+
+                    if sampler.extra_options['sampler_mode'] == "unsample":
+                        guider.cfgs = {
+                            'xt': unsample_cfg,
+                            'yt': unsample_cfg,
+                        }
+                        if unsample_eta != -1.0:
+                            sampler.extra_options['eta_substep']  = unsample_eta
+                            sampler.extra_options['eta']          = unsample_eta
+                            sampler.extra_options['etas_substep'] = unsample_etas
+                            sampler.extra_options['etas']         = unsample_etas
+                        if unsampler_name != "none":
+                            sampler.extra_options['rk_type']      = unsampler_name
+                        if unsample_steps_to_run > -1:
+                            sampler.extra_options['steps_to_run'] = unsample_steps_to_run
+                    else:
+                        guider.cfgs = cfgs_cached
+
+                    guider.cfgs = cfgs_cached
+                    sampler.extra_options['steps_to_run'] = steps_to_run_cached
+
+                    eta_decay           = eta_cached
+                    eta_substep_decay   = eta_substep_cached
+                    unsample_eta_decay  = unsample_eta
+
+                    etas_decay          = etas_cached
+                    etas_substep_decay  = etas_substep_cached
+                    unsample_etas_decay = unsample_etas
+
+                if isinstance(x, comfy.nested_tensor.NestedTensor):
+                    samples = guider.sample(noise, x._copy(), sampler, sigmas, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
+                else:
+                    samples = guider.sample(noise, x.clone(), sampler, sigmas, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
+
+                if rebounds > 0:
+                    noise_seed_cached   = sampler.extra_options['noise_seed']
+                    cfgs_cached         = guider.cfgs
+                    sampler_mode_cached = sampler.extra_options['sampler_mode']
+
+                    for restarts_iter in range(rebounds):
+                        sampler.extra_options['state_info'] = sampler.extra_options['state_info_out']
+
+                        sigmas = sampler.extra_options['state_info_out']['sigmas'] if sigmas is None else sigmas
+
+                        if   sampler.extra_options['sampler_mode'] == "standard":
+                            sampler.extra_options['sampler_mode'] = "unsample"
+                        elif sampler.extra_options['sampler_mode'] == "unsample":
+                            sampler.extra_options['sampler_mode'] = "resample"
+                        elif sampler.extra_options['sampler_mode'] == "resample":
+                            sampler.extra_options['sampler_mode'] = "unsample"
+
+                        sampler.extra_options['noise_seed'] = -1
+
+                        if sampler.extra_options['sampler_mode'] == "unsample":
+                            guider.cfgs = {
+                                'xt': unsample_cfg,
+                                'yt': unsample_cfg,
+                            }
+                            if unsample_eta != -1.0:
+                                sampler.extra_options['eta_substep']  = unsample_eta_decay
+                                sampler.extra_options['eta']          = unsample_eta_decay
+                                sampler.extra_options['etas_substep'] = unsample_etas
+                                sampler.extra_options['etas']         = unsample_etas
+                            else:
+                                sampler.extra_options['eta_substep']  = eta_substep_decay
+                                sampler.extra_options['eta']          = eta_decay
+                                sampler.extra_options['etas_substep'] = etas_substep_decay
+                                sampler.extra_options['etas']         = etas_decay
+                            if unsampler_name != "none":
+                                sampler.extra_options['rk_type']  = unsampler_name
+                            if unsample_steps_to_run > -1:
+                                sampler.extra_options['steps_to_run'] = unsample_steps_to_run
+                        else:
+                            guider.cfgs = cfgs_cached
+                            sampler.extra_options['eta_substep']  = eta_substep_decay
+                            sampler.extra_options['eta']          = eta_decay
+                            sampler.extra_options['etas_substep'] = etas_substep_decay
+                            sampler.extra_options['etas']         = etas_decay
+                            sampler.extra_options['rk_type']      = rk_type_cached
+                            sampler.extra_options['steps_to_run'] = steps_to_run_cached
+
+                        samples = guider.sample(noise, samples.clone(), sampler, sigmas, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=-1)
+
+                        eta_substep_decay   *= eta_decay_scale
+                        eta_decay           *= eta_decay_scale
+                        unsample_eta_decay  *= eta_decay_scale
+
+                        etas_substep_decay  *= eta_decay_scale
+                        etas_decay          *= eta_decay_scale
+                        unsample_etas_decay *= eta_decay_scale
+
+                    sampler.extra_options['noise_seed'] = noise_seed_cached
+                    guider.cfgs = cfgs_cached
+                    sampler.extra_options['sampler_mode'] = sampler_mode_cached
+                    sampler.extra_options['eta_substep']  = eta_substep_cached
+                    sampler.extra_options['eta']          = eta_cached
+                    sampler.extra_options['etas_substep'] = etas_substep_cached
+                    sampler.extra_options['etas']         = etas_cached
+
+                if noise_mask is not None:
+                    if hasattr(samples, 'is_nested') and samples.is_nested:
+                        blended = []
+                        x_initial_list = x_initial.unbind() if hasattr(x_initial, 'is_nested') and x_initial.is_nested else [x_initial]
+                        if hasattr(noise_mask, 'is_nested') and noise_mask.is_nested:
+                            mask_list = noise_mask.unbind()
+                        else:
+                            mask_list = [noise_mask]
+                        for idx, s in enumerate(samples.unbind()):
+                            xi = x_initial_list[idx] if idx < len(x_initial_list) else x_initial_list[0]
+                            m = mask_list[idx] if idx < len(mask_list) else mask_list[0]
+                            if s.ndim == m.ndim:
+                                reshaped_mask = comfy.utils.reshape_mask(m, s.shape).to(s.device)
+                                blended.append(s * reshaped_mask + xi.to(s.device) * (1.0 - reshaped_mask))
+                            else:
+                                blended.append(s)
+                        samples = comfy.nested_tensor.NestedTensor(blended)
+                    else:
+                        if hasattr(noise_mask, 'is_nested') and noise_mask.is_nested:
+                            noise_mask = noise_mask.unbind()[0]
+                        reshaped_mask = comfy.utils.reshape_mask(noise_mask, samples.shape).to(samples.device)
+                        samples = samples * reshaped_mask + x_initial.to(samples.device) * (1.0 - reshaped_mask)
+
+                samples = samples.to(comfy.model_management.intermediate_device())
+
+                out = latent_x.copy()
+                out["samples"] = samples
+
+                if "x0" in x0_output:
+                    x0_out = work_model.model.process_latent_out(x0_output["x0"].cpu())
+                    if hasattr(samples, 'is_nested') and samples.is_nested:
+                        latent_shapes = [t.shape for t in samples.unbind()]
+                        x0_out = comfy.nested_tensor.NestedTensor(
+                            comfy.utils.unpack_latents(x0_out, latent_shapes)
+                        )
+                    out_denoised = latent_x.copy()
+                    out_denoised["samples"] = x0_out
+                else:
+                    out_denoised = out
+
+                out['positive'] = positive
+                out['negative'] = negative
+                out['model'] = work_model
+                out['sampler'] = sampler
+
+                if noise_mask is not None:
+                    state_info_out['image_initial'] = x_initial
+                    state_info_out['noise_initial'] = noise_initial
+
+                out['state_info'] = state_info_out
+
+                return (out, out_denoised, None)
+
+            out_samples          = []
+            out_denoised_samples = []
+            out_state_info       = []
+            
+            for batch_num in range(latent_image_batch['samples'].shape[0]):
+                latent_unbatch            = copy.deepcopy(latent_x)
+                if isinstance(latent_image_batch['samples'][batch_num], comfy.nested_tensor.NestedTensor):
+                    latent_unbatch['samples'] = latent_image_batch['samples'][batch_num]._copy()
+                else:
+                    latent_unbatch['samples'] = latent_image_batch['samples'][batch_num].clone().unsqueeze(0)
+                
+                if 'BONGMATH' in sampler.extra_options:
+                    sampler.extra_options['batch_num'] = batch_num
+
+
+                if noise_seed == -1 and sampler_mode in {"unsample", "resample"}:
+                    if latent_image.get('state_info', {}).get('last_rng', None) is not None:
+                        seed = torch.initial_seed() + batch_num
+                    else:
+                        seed = torch.initial_seed() + 1 + batch_num
+                else:
+                    if EO("lock_batch_seed"):
+                        seed = noise_seed
+                    else:
+                        seed = noise_seed + batch_num
+                    torch     .manual_seed(seed)
+                    torch.cuda.manual_seed(seed)
+
+                if hasattr(latent_unbatch["samples"], 'is_nested') and latent_unbatch["samples"].is_nested:
+                    x = latent_unbatch["samples"]._copy().to(default_dtype)
+                else:
+                    x = latent_unbatch["samples"].clone().to(default_dtype) # does this type carry into clown after passing through comfy?
+
+
+
+                if sde_noise is None and sampler_mode.startswith("unsample"):
+                    sde_noise = []
+                else:
+                    sde_noise_steps = 1
+
+                for total_steps_iter in range (sde_noise_steps):
+
+                    if noise_type_init != "none" and noise_stdev != 0.0:
+                        RESplain("Initial latent noise seed: ", seed, debug=True)
+
+                    noise = generate_init_noise(
+                        x=x, seed=seed,
+                        noise_type_init=noise_type_init, noise_stdev=noise_stdev,
+                        noise_mean=noise_mean, noise_normalize=noise_normalize,
+                        sigma_max=sigma_max, sigma_min=sigma_min,
+                        alpha_init=alpha_init, k_init=k_init, EO=EO
+                    )
+
+                    noise_mask = latent_unbatch["noise_mask"] if "noise_mask" in latent_unbatch else None
+
+                    x_input = x
+                    if noise_mask is not None and 'noise_initial' in state_info:
+                        stored_noise = state_info.get('noise_initial')
+                        if stored_noise is not None:
+                            if stored_noise.dim() > 3 and stored_noise.shape[0] > batch_num:
+                                stored_noise = stored_noise[batch_num]
+                            if stored_noise.shape == noise.shape:
+                                noise = stored_noise.to(noise.device, dtype=noise.dtype)
+                                RESplain("Using stored noise_initial from previous sampler", debug=True)
+
+                        stored_image = state_info.get('image_initial')
+                        if stored_image is not None:
+                            if stored_image.dim() > 3 and stored_image.shape[0] > batch_num:
+                                stored_image = stored_image[batch_num]
+                            if stored_image.shape == x.shape:
+                                x_input = stored_image.to(x.device, dtype=x.dtype)
+                                RESplain("Using stored image_initial from previous sampler", debug=True)
+
+                    if 'BONGMATH' in sampler.extra_options:
+                        sampler.extra_options['noise_initial'] = noise
+                        sampler.extra_options['image_initial'] = x_input
+
+                    x0_output = {}
+
+                    if latent_image is not None and 'state_info' in latent_image and 'sigmas' in latent_image['state_info']:
+                        steps_len = max(sigmas.shape[-1] - 1,    latent_image['state_info']['sigmas'].shape[-1]-1)
+                    else:
+                        steps_len = sigmas.shape[-1]-1
+                    callback     = latent_preview.prepare_callback(work_model, steps_len, x0_output)
+
+                    if 'BONGMATH' in sampler.extra_options: # verify the sampler is rk_sampler_beta()
+                        sampler.extra_options['state_info']     = copy.deepcopy(state_info)         ##############################
+                        if state_info != {} and state_info != {'data_prev_': None}:  #second condition is for ultracascade
+                            sampler.extra_options['state_info']['raw_x']            = state_info['raw_x']           [batch_num]
+                            sampler.extra_options['state_info']['data_prev_']       = state_info['data_prev_']      [batch_num]
+                            sampler.extra_options['state_info']['last_rng']         = state_info['last_rng']        [batch_num]
+                            sampler.extra_options['state_info']['last_rng_substep'] = state_info['last_rng_substep'][batch_num]
+                            if 'image_initial' in state_info and state_info['image_initial'].dim() > 3:
+                                sampler.extra_options['state_info']['image_initial'] = state_info['image_initial'][batch_num]
+                            if 'noise_initial' in state_info and state_info['noise_initial'].dim() > 3:
+                                sampler.extra_options['state_info']['noise_initial'] = state_info['noise_initial'][batch_num]
+                        #state_info     = copy.deepcopy(latent_image['state_info']) if 'state_info' in latent_image else {}
+                        state_info_out = {}
+                        sampler.extra_options['state_info_out'] = state_info_out
+                        
+                    if type(pos_cond[0][0]) == list:
+                        pos_cond_tmp = pos_cond[batch_num]
+                        positive_tmp = positive[batch_num]
+                    else:
+                        pos_cond_tmp = pos_cond
+                        positive_tmp = positive
+                    
+                    for i in range(len(neg_cond)): # crude fix for copy.deepcopy converting superclass into real object
+                        if 'control' in neg_cond[i][1]:
+                            neg_cond[i][1]['control']          = negative[i][1]['control']
+                            if hasattr(negative[i][1]['control'], 'base'):
+                                neg_cond[i][1]['control'].base     = negative[i][1]['control'].base
+                    for i in range(len(pos_cond_tmp)): # crude fix for copy.deepcopy converting superclass into real object
+                        if 'control' in pos_cond_tmp[i][1]:
+                            pos_cond_tmp[i][1]['control']      = positive_tmp[i][1]['control']
+                            if hasattr(positive[i][1]['control'], 'base'):
+                                pos_cond_tmp[i][1]['control'].base = positive_tmp[i][1]['control'].base
+                    
+                    # SETUP REGIONAL COND
+                    
+                    if pos_cond_tmp[0][1] is not None: 
+                        if 'callback_regional' in pos_cond_tmp[0][1]:
+                            pos_cond_tmp = pos_cond_tmp[0][1]['callback_regional'](work_model)
+                        
+                        if 'AttnMask' in pos_cond_tmp[0][1]:
+                            sampler.extra_options['AttnMask']   = pos_cond_tmp[0][1]['AttnMask']
+                            sampler.extra_options['RegContext'] = pos_cond_tmp[0][1]['RegContext']
+                            sampler.extra_options['RegParam']   = pos_cond_tmp[0][1]['RegParam']
+                            
+                            if isinstance(model.model.model_config, (comfy.supported_models.SDXL, comfy.supported_models.SD15)):
+                                latent_up_dummy = F.interpolate(latent_image['samples'].to(torch.float16), size=(latent_image['samples'].shape[-2] * 2, latent_image['samples'].shape[-1] * 2), mode="nearest")
+                                sampler.extra_options['AttnMask'].set_latent(latent_up_dummy)
+                                sampler.extra_options['AttnMask'].generate()
+                                sampler.extra_options['AttnMask'].mask_up   = sampler.extra_options['AttnMask'].attn_mask.mask
+                                
+                                latent_down_dummy = F.interpolate(latent_image['samples'].to(torch.float16), size=(latent_image['samples'].shape[-2] // 2, latent_image['samples'].shape[-1] // 2), mode="nearest")
+                                sampler.extra_options['AttnMask'].set_latent(latent_down_dummy)
+                                sampler.extra_options['AttnMask'].generate()
+                                sampler.extra_options['AttnMask'].mask_down = sampler.extra_options['AttnMask'].attn_mask.mask
+                                
+                                if isinstance(model.model.model_config, comfy.supported_models.SD15):
+                                    latent_down_dummy = F.interpolate(latent_image['samples'].to(torch.float16), size=(latent_image['samples'].shape[-2] // 4, latent_image['samples'].shape[-1] // 4), mode="nearest")
+                                    sampler.extra_options['AttnMask'].set_latent(latent_down_dummy)
+                                    sampler.extra_options['AttnMask'].generate()
+                                    sampler.extra_options['AttnMask'].mask_down2 = sampler.extra_options['AttnMask'].attn_mask.mask
+                                    
+                            if isinstance(model.model.model_config, (comfy.supported_models.Stable_Cascade_C)):
+                                latent_up_dummy = F.interpolate(latent_image['samples'].to(torch.float16), size=(latent_image['samples'].shape[-2] * 2, latent_image['samples'].shape[-1] * 2), mode="nearest")
+                                sampler.extra_options['AttnMask'].set_latent(latent_up_dummy)
+                                # cascade concats 4 + 4 tokens (clip_text_pooled, clip_img)
+                                sampler.extra_options['AttnMask'].context_lens = [context_len + 8 for context_len in sampler.extra_options['AttnMask'].context_lens] 
+                                sampler.extra_options['AttnMask'].text_len = sum(sampler.extra_options['AttnMask'].context_lens)
+                            else:
+                                sampler.extra_options['AttnMask'].set_latent(latent_image['samples'])
+                            sampler.extra_options['AttnMask'].generate()
+                            
+                    if neg_cond[0][1] is not None: 
+                        if 'callback_regional' in neg_cond[0][1]:
+                            neg_cond = neg_cond[0][1]['callback_regional'](work_model)
+                        
+                        if 'AttnMask' in neg_cond[0][1]:
+                            sampler.extra_options['AttnMask_neg']   = neg_cond[0][1]['AttnMask']
+                            sampler.extra_options['RegContext_neg'] = neg_cond[0][1]['RegContext']
+                            sampler.extra_options['RegParam_neg']   = neg_cond[0][1]['RegParam']
+                            
+                            if isinstance(model.model.model_config, (comfy.supported_models.SDXL, comfy.supported_models.SD15)):
+                                latent_up_dummy = F.interpolate(latent_image['samples'].to(torch.float16), size=(latent_image['samples'].shape[-2] * 2, latent_image['samples'].shape[-1] * 2), mode="nearest")
+                                sampler.extra_options['AttnMask_neg'].set_latent(latent_up_dummy)
+                                sampler.extra_options['AttnMask_neg'].generate()
+                                sampler.extra_options['AttnMask_neg'].mask_up   = sampler.extra_options['AttnMask_neg'].attn_mask.mask
+                                
+                                latent_down_dummy = F.interpolate(latent_image['samples'].to(torch.float16), size=(latent_image['samples'].shape[-2] // 2, latent_image['samples'].shape[-1] // 2), mode="nearest")
+                                sampler.extra_options['AttnMask_neg'].set_latent(latent_down_dummy)
+                                sampler.extra_options['AttnMask_neg'].generate()
+                                sampler.extra_options['AttnMask_neg'].mask_down = sampler.extra_options['AttnMask_neg'].attn_mask.mask
+                                
+                                if isinstance(model.model.model_config, comfy.supported_models.SD15):
+                                    latent_down_dummy = F.interpolate(latent_image['samples'].to(torch.float16), size=(latent_image['samples'].shape[-2] // 4, latent_image['samples'].shape[-1] // 4), mode="nearest")
+                                    sampler.extra_options['AttnMask_neg'].set_latent(latent_down_dummy)
+                                    sampler.extra_options['AttnMask_neg'].generate()
+                                    sampler.extra_options['AttnMask_neg'].mask_down2 = sampler.extra_options['AttnMask_neg'].attn_mask.mask
+                            
+                            if isinstance(model.model.model_config, (comfy.supported_models.Stable_Cascade_C)):
+                                latent_up_dummy = F.interpolate(latent_image['samples'].to(torch.float16), size=(latent_image['samples'].shape[-2] * 2, latent_image['samples'].shape[-1] * 2), mode="nearest")
+                                sampler.extra_options['AttnMask'].set_latent(latent_up_dummy)
+                                # cascade concats 4 + 4 tokens (clip_text_pooled, clip_img)
+                                sampler.extra_options['AttnMask'].context_lens = [context_len + 8 for context_len in sampler.extra_options['AttnMask'].context_lens] 
+                                sampler.extra_options['AttnMask'].text_len = sum(sampler.extra_options['AttnMask'].context_lens)
+                            else:
+                                sampler.extra_options['AttnMask_neg'].set_latent(latent_image['samples'])
+                            sampler.extra_options['AttnMask_neg'].generate()
+                    
+                    
+                    
+                    
+                    
+                    if guider is None:
+                        guider = SharkGuider(work_model)
+                        flow_cond = options_mgr.get('flow_cond', {})
+                        if flow_cond != {} and 'yt_positive' in flow_cond and not 'yt_inv_positive' in flow_cond:   #and not 'yt_inv;_positive' in flow_cond:   # typo???
+                            guider.set_conds(yt_positive=flow_cond.get('yt_positive'), yt_negative=flow_cond.get('yt_negative'),)
+                            guider.set_cfgs(yt=flow_cond.get('yt_cfg'), xt=cfg)
+                        elif flow_cond != {} and 'yt_positive' in flow_cond and 'yt_inv_positive' in flow_cond:
+                            guider.set_conds(yt_positive=flow_cond.get('yt_positive'), yt_negative=flow_cond.get('yt_negative'), yt_inv_positive=flow_cond.get('yt_inv_positive'), yt_inv_negative=flow_cond.get('yt_inv_negative'),)
+                            guider.set_cfgs(yt=flow_cond.get('yt_cfg'), yt_inv=flow_cond.get('yt_inv_cfg'), xt=cfg)
+                        else:
+                            guider.set_cfgs(xt=cfg)
+                        
+                        guider.set_conds(xt_positive=pos_cond_tmp, xt_negative=neg_cond)
+                        
+                    elif type(guider) == SharkGuider:
+                        guider.set_cfgs(xt=cfg)
+                        guider.set_conds(xt_positive=pos_cond_tmp, xt_negative=neg_cond)
+                    else:
+                        try:
+                            guider.set_cfg(cfg)
+                        except:
+                            RESplain("SharkWarning: guider.set_cfg failed but assuming cfg already set correctly.")
+                        try:
+                            guider.set_conds(pos_cond_tmp, neg_cond)
+                        except:
+                            RESplain("SharkWarning: guider.set_conds failed but assuming conds already set correctly.")
+                    
+                    if rebounds > 0:
+                        cfgs_cached = guider.cfgs
+                        steps_to_run_cached = sampler.extra_options['steps_to_run']
+                        eta_cached         = sampler.extra_options['eta']
+                        eta_substep_cached = sampler.extra_options['eta_substep']
+                        
+                        etas_cached         = sampler.extra_options['etas'].clone()
+                        etas_substep_cached = sampler.extra_options['etas_substep'].clone()
+                        
+                        unsample_etas = torch.full_like(etas_cached, unsample_eta)
+                        rk_type_cached = sampler.extra_options['rk_type']
+                        
+                        if sampler.extra_options['sampler_mode'] == "unsample":
+                            guider.cfgs = {
+                                'xt': unsample_cfg,
+                                'yt': unsample_cfg,
+                            }
+                            if unsample_eta != -1.0:
+                                sampler.extra_options['eta_substep']  = unsample_eta
+                                sampler.extra_options['eta']          = unsample_eta
+                                sampler.extra_options['etas_substep'] = unsample_etas
+                                sampler.extra_options['etas']         = unsample_etas
+                            if unsampler_name != "none":
+                                sampler.extra_options['rk_type']      = unsampler_name
+                            if unsample_steps_to_run > -1:
+                                sampler.extra_options['steps_to_run'] = unsample_steps_to_run
+                                
+                        else:
+                            guider.cfgs = cfgs_cached
+                        
+                        guider.cfgs = cfgs_cached
+                        sampler.extra_options['steps_to_run'] = steps_to_run_cached
+                        
+                        eta_decay           = eta_cached
+                        eta_substep_decay   = eta_substep_cached
+                        unsample_eta_decay  = unsample_eta
+                        
+                        etas_decay          = etas_cached
+                        etas_substep_decay  = etas_substep_cached
+                        unsample_etas_decay = unsample_etas
+                    if isinstance(x_input, comfy.nested_tensor.NestedTensor):
+                        samples = guider.sample(noise, x_input._copy(), sampler, sigmas, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
+                    else:
+                        samples = guider.sample(noise, x_input.clone(), sampler, sigmas, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
+                    
+                    if rebounds > 0: 
+                        noise_seed_cached   = sampler.extra_options['noise_seed']
+                        cfgs_cached         = guider.cfgs
+                        sampler_mode_cached = sampler.extra_options['sampler_mode']
+                        
+                        for restarts_iter in range(rebounds):
+                            sampler.extra_options['state_info'] = sampler.extra_options['state_info_out']
+                            
+                            #steps = sampler.extra_options['state_info_out']['sigmas'].shape[-1] - 3
+                            sigmas = sampler.extra_options['state_info_out']['sigmas'] if sigmas is None else sigmas
+                            #if len(sigmas) > 2 and sigmas[1] < sigmas[2] and sampler.extra_options['state_info_out']['sampler_mode'] == "unsample": # and sampler_mode == "resample":
+                            #    sigmas = torch.flip(sigmas, dims=[0])
+                                
+                            if   sampler.extra_options['sampler_mode'] == "standard":
+                                sampler.extra_options['sampler_mode'] = "unsample"
+                            elif sampler.extra_options['sampler_mode'] == "unsample":
+                                sampler.extra_options['sampler_mode'] = "resample"
+                            elif sampler.extra_options['sampler_mode'] == "resample":
+                                sampler.extra_options['sampler_mode'] = "unsample"
+                            
+                            sampler.extra_options['noise_seed'] = -1
+                            
+                            if sampler.extra_options['sampler_mode'] == "unsample":
+                                guider.cfgs = {
+                                    'xt': unsample_cfg,
+                                    'yt': unsample_cfg,
+                                }
+                                if unsample_eta != -1.0:
+                                    sampler.extra_options['eta_substep']  = unsample_eta_decay
+                                    sampler.extra_options['eta']          = unsample_eta_decay
+                                    sampler.extra_options['etas_substep'] = unsample_etas
+                                    sampler.extra_options['etas']         = unsample_etas
+                                else:
+                                    sampler.extra_options['eta_substep']  = eta_substep_decay
+                                    sampler.extra_options['eta']          = eta_decay
+                                    sampler.extra_options['etas_substep'] = etas_substep_decay
+                                    sampler.extra_options['etas']         = etas_decay
+                                if unsampler_name != "none":
+                                    sampler.extra_options['rk_type']  = unsampler_name
+                                if unsample_steps_to_run > -1:
+                                    sampler.extra_options['steps_to_run'] = unsample_steps_to_run
+                            else:
+                                guider.cfgs = cfgs_cached
+                                sampler.extra_options['eta_substep']  = eta_substep_decay
+                                sampler.extra_options['eta']          = eta_decay
+                                sampler.extra_options['etas_substep'] = etas_substep_decay
+                                sampler.extra_options['etas']         = etas_decay
+                                sampler.extra_options['rk_type']      = rk_type_cached
+                                sampler.extra_options['steps_to_run'] = steps_to_run_cached
+
+                                
+                            samples = guider.sample(noise, samples.clone(), sampler, sigmas, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=-1)
+
+                            eta_substep_decay   *= eta_decay_scale
+                            eta_decay           *= eta_decay_scale
+                            unsample_eta_decay  *= eta_decay_scale
+                            
+                            etas_substep_decay  *= eta_decay_scale
+                            etas_decay          *= eta_decay_scale
+                            unsample_etas_decay *= eta_decay_scale                        
+                        
+                        sampler.extra_options['noise_seed'] = noise_seed_cached
+                        guider.cfgs = cfgs_cached
+                        sampler.extra_options['sampler_mode'] = sampler_mode_cached
+                        sampler.extra_options['eta_substep']  = eta_substep_cached
+                        sampler.extra_options['eta']          = eta_cached
+                        sampler.extra_options['etas_substep'] = etas_substep_cached
+                        sampler.extra_options['etas']         = etas_cached
+                        sampler.extra_options['rk_type']      = rk_type_cached
+                        sampler.extra_options['steps_to_run'] = steps_to_run_cached   # TODO: verify this is carried on
+
+                    if noise_mask is not None:
+                        if 'BONGMATH' in sampler.extra_options:
+                            batch_state_info = sampler.extra_options.get('state_info', {})
+                            latent_for_mask = batch_state_info.get('image_initial', x)
+                        else:
+                            stored_image = state_info.get('image_initial')
+                            if stored_image is not None and stored_image.dim() > 3:
+                                latent_for_mask = stored_image[batch_num]
+                            elif stored_image is not None:
+                                latent_for_mask = stored_image
+                            else:
+                                latent_for_mask = x
+                        reshaped_mask = comfy.utils.reshape_mask(noise_mask, samples.shape).to(samples.device)
+                        samples = samples * reshaped_mask + latent_for_mask.to(samples.device) * (1.0 - reshaped_mask)
+
+                    out = latent_unbatch.copy()
+                    out["samples"] = samples
+                    
+                    if "x0" in x0_output:
+                        out_denoised            = latent_unbatch.copy()
+                        out_denoised["samples"] = work_model.model.process_latent_out(x0_output["x0"].cpu())
+                    else:
+                        out_denoised            = out
+
+                    out_samples         .append(out         ["samples"])
+                    out_denoised_samples.append(out_denoised["samples"])
+                    
+                    
+                    
+                    # ACCUMULATE UNSAMPLED SDE NOISE
+                    if total_steps_iter > 1: 
+                        if 'raw_x' in state_info_out:
+                            sde_noise_out = state_info_out['raw_x']
+                        else:
+                            sde_noise_out = out["samples"]  
+                        sde_noise.append(normalize_zscore(sde_noise_out, channelwise=True, inplace=True))    
+                    
+                    out_state_info.append(state_info_out)
+                    
+                    # INCREMENT BATCH LOOP
+                    if not EO("lock_batch_seed"):
+                        seed += 1
+                    if latent_image is not None: #needed for ultracascade, where latent_image input is not really used for stage C/first stage
+                        if latent_image.get('state_info', {}).get('last_rng', None) is None:
+                            torch.manual_seed(seed)
+
+
+            gc.collect()
+
+            # STACK SDE NOISES, SAVE STATE INFO
+            state_info_out = out_state_info[0]
+            if 'raw_x' in out_state_info[0]:
+                state_info_out['raw_x']            = torch.stack([out_state_info[_]['raw_x']            for _ in range(len(out_state_info))])
+                state_info_out['data_prev_']       = torch.stack([out_state_info[_]['data_prev_']       for _ in range(len(out_state_info))])
+                state_info_out['last_rng']         = torch.stack([out_state_info[_]['last_rng']         for _ in range(len(out_state_info))])
+                state_info_out['last_rng_substep'] = torch.stack([out_state_info[_]['last_rng_substep'] for _ in range(len(out_state_info))])
+                if 'image_initial' in out_state_info[0]:
+                    state_info_out['image_initial'] = torch.stack([out_state_info[_]['image_initial'] for _ in range(len(out_state_info))])
+                if 'noise_initial' in out_state_info[0]:
+                    state_info_out['noise_initial'] = torch.stack([out_state_info[_]['noise_initial'] for _ in range(len(out_state_info))])
+            elif 'raw_x' in state_info:
+                state_info_out = state_info
+
+            out_samples             = [tensor.squeeze(0) for tensor in out_samples]
+            out_denoised_samples    = [tensor.squeeze(0) for tensor in out_denoised_samples]
+
+            out         ['samples'] = torch.stack(out_samples,          dim=0)
+            out_denoised['samples'] = torch.stack(out_denoised_samples, dim=0)
+
+            out['state_info']       = copy.deepcopy(state_info_out)
+            state_info              = {}
+            
+            out['positive'] = positive
+            out['negative'] = negative
+            out['model']    = work_model#.clone()
+            out['sampler']  = sampler
+            
+
+            return (out, out_denoised, sde_noise,)
+
+
+
+
+class SharkSampler_Beta:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "scheduler":       (get_res4lyf_scheduler_list(), {"default": "beta57"},),
+                "steps":           ("INT",                        {"default": 30,  "min": 1,        "max": 10000.0}),
+                "steps_to_run":    ("INT",                        {"default": -1,  "min": -1,       "max": MAX_STEPS}),
+                "denoise":         ("FLOAT",                      {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":0.01}),
+                "cfg":             ("FLOAT",                      {"default": 5.5, "min": -10000.0, "max": 10000.0, "step":0.01, "round": False, "tooltip": "Negative values use channelwise CFG." }),
+                "seed":            ("INT",                        {"default": 0,   "min": -1,       "max": 0xffffffffffffffff}),
+                "sampler_mode": (['unsample', 'standard', 'resample'], {"default": "standard"}),
+                },
+            "optional": {
+                "model":           ("MODEL",),
+                "positive":        ("CONDITIONING", ),
+                "negative":        ("CONDITIONING", ),
+                "sampler":         ("SAMPLER", ),
+                "sigmas":          ("SIGMAS", ),
+                "latent_image":    ("LATENT", ),     
+                "options":         ("OPTIONS", ),   
+                }
+            }
+
+    RETURN_TYPES = ("LATENT", 
+                    "LATENT", 
+                    "OPTIONS",)
+    
+    RETURN_NAMES = ("output", 
+                    "denoised",
+                    "options",) 
+    
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/samplers"
+    
+    def main(self, 
+            model                                    = None,
+            cfg             : float                  =  5.5, 
+            scheduler       : str                    = "beta57", 
+            steps           : int                    = 30, 
+            steps_to_run    : int                    = -1,
+            sampler_mode    : str                    = "standard",
+            denoise         : float                  =  1.0, 
+            denoise_alt     : float                  =  1.0,
+            noise_type_init : str                    = "gaussian",
+            latent_image    : Optional[dict[Tensor]] = None,
+            
+            positive                                 = None,
+            negative                                 = None,
+            sampler                                  = None,
+            sigmas          : Optional[Tensor]       = None,
+            noise_stdev     : float                  =  1.0,
+            noise_mean      : float                  =  0.0,
+            noise_normalize : bool                   = True,
+            
+            d_noise         : float                  =  1.0,
+            alpha_init      : float                  = -1.0,
+            k_init          : float                  =  1.0,
+            cfgpp           : float                  =  0.0,
+            seed            : int                    = -1,
+            options                                  = None,
+            sde_noise                                = None,
+            sde_noise_steps : int                    =  1,
+        
+            extra_options   : str                    = "", 
+            **kwargs,
+            ): 
+        
+
+        options_mgr = OptionsManager(options, **kwargs)
+        
+        if denoise < 0:
+            denoise_alt = -denoise
+            denoise = 1.0
+        
+        #if 'steps_to_run' in sampler.extra_options:
+        #    sampler.extra_options['steps_to_run'] = steps_to_run
+        if 'positive' in latent_image and positive is None:
+            positive = latent_image['positive']
+        if 'negative' in latent_image and negative is None:
+            negative = latent_image['negative']
+        if 'sampler'  in latent_image and sampler  is None:
+            sampler  = latent_image['sampler']
+        if 'model'    in latent_image and model    is None:
+            model    = latent_image['model']
+            
+        #if model.model.model_config.unet_config.get('stable_cascade_stage') == 'b':
+        #    if 'noise_type_sde' in sampler.extra_options:
+        #        noise_type_sde         = "pyramid-cascade_B"
+        #        noise_type_sde_substep = "pyramid-cascade_B"
+        
+        output, denoised, sde_noise = SharkSampler().main(
+            model           = model, 
+            cfg             = cfg, 
+            scheduler       = scheduler,
+            steps           = steps, 
+            steps_to_run    = steps_to_run,
+            denoise         = denoise,
+            latent_image    = latent_image, 
+            positive        = positive,
+            negative        = negative, 
+            sampler         = sampler, 
+            cfgpp           = cfgpp, 
+            noise_seed      = seed, 
+            options         = options, 
+            sde_noise       = sde_noise, 
+            sde_noise_steps = sde_noise_steps, 
+            noise_type_init = noise_type_init,
+            noise_stdev     = noise_stdev,
+            sampler_mode    = sampler_mode,
+            denoise_alt     = denoise_alt,
+            sigmas          = sigmas,
+
+            extra_options   = extra_options)
+        
+        return (output, denoised,options_mgr.as_dict())
+
+
+
+
+
+class SharkChainsampler_Beta(SharkSampler_Beta):  
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "steps_to_run":    ("INT",                        {"default": -1,  "min": -1,       "max": MAX_STEPS}),
+                "cfg":             ("FLOAT",                      {"default": 5.5, "min": -10000.0, "max": 10000.0, "step":0.01, "round": False, "tooltip": "Negative values use channelwise CFG." }),
+                "sampler_mode": (['unsample', 'resample'], {"default": "resample"}),
+                },
+            "optional": {
+                "model":           ("MODEL",),
+                "positive":        ("CONDITIONING", ),
+                "negative":        ("CONDITIONING", ),
+                "sampler":         ("SAMPLER", ),
+                "sigmas":          ("SIGMAS", ),
+                "latent_image":    ("LATENT", ),     
+                "options":         ("OPTIONS", ),   
+                }
+            }
+
+    def main(self, 
+            model                 = None,
+            steps_to_run          = -1, 
+            cfg                   = 5.5, 
+            latent_image          = None,
+            sigmas                = None,
+            sampler_mode          = "",
+            seed            : int = -1, 
+             **kwargs):  
+        
+        steps = latent_image['state_info']['sigmas'].shape[-1] - 3
+        sigmas = latent_image['state_info']['sigmas'] if sigmas is None else sigmas
+        if len(sigmas) > 2 and sigmas[1] < sigmas[2] and latent_image['state_info']['sampler_mode'] == "unsample" and sampler_mode == "resample":
+            sigmas = torch.flip(sigmas, dims=[0])
+        
+        return super().main(model=model, sampler_mode=sampler_mode, steps_to_run=steps_to_run, sigmas=sigmas, steps=steps, cfg=cfg, seed=seed, latent_image=latent_image, **kwargs)
+
+
+
+
+
+class ClownSamplerAdvanced_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "noise_type_sde":         (NOISE_GENERATOR_NAMES_SIMPLE, {"default": "gaussian"}),
+                    "noise_type_sde_substep": (NOISE_GENERATOR_NAMES_SIMPLE, {"default": "gaussian"}),
+                    "noise_mode_sde":         (NOISE_MODE_NAMES,             {"default": 'hard',                                                        "tooltip": "How noise scales with the sigma schedule. Hard is the most aggressive, the others start strong and drop rapidly."}),
+                    "noise_mode_sde_substep": (NOISE_MODE_NAMES,             {"default": 'hard',                                                        "tooltip": "How noise scales with the sigma schedule. Hard is the most aggressive, the others start strong and drop rapidly."}),
+                    "overshoot_mode":         (NOISE_MODE_NAMES,             {"default": 'hard',                                                        "tooltip": "How step size overshoot scales with the sigma schedule. Hard is the most aggressive, the others start strong and drop rapidly."}),
+                    "overshoot_mode_substep": (NOISE_MODE_NAMES,             {"default": 'hard',                                                        "tooltip": "How substep size overshoot scales with the sigma schedule. Hard is the most aggressive, the others start strong and drop rapidly."}),
+                    "eta":                    ("FLOAT",                      {"default": 0.5, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Calculated noise amount to be added, then removed, after each step."}),
+                    "eta_substep":            ("FLOAT",                      {"default": 0.5, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Calculated noise amount to be added, then removed, after each step."}),
+                    "overshoot":              ("FLOAT",                      {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Boost the size of each denoising step, then rescale to match the original. Has a softening effect."}),
+                    "overshoot_substep":      ("FLOAT",                      {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Boost the size of each denoising substep, then rescale to match the original. Has a softening effect."}),
+                    "noise_scaling_weight":   ("FLOAT",                      {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set to positive values to create a sharper, grittier, more detailed image. Set to negative values to soften and deepen the colors."}),
+                    "noise_boost_step":       ("FLOAT",                      {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set to positive values to create a sharper, grittier, more detailed image. Set to negative values to soften and deepen the colors."}),
+                    "noise_boost_substep":    ("FLOAT",                      {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set to positive values to create a sharper, grittier, more detailed image. Set to negative values to soften and deepen the colors."}),
+                    "noise_anchor":           ("FLOAT",                      {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Typically set to between 1.0 and 0.0. Lower values cerate a grittier, more detailed image."}),
+                    "s_noise":                ("FLOAT",                      {"default": 1.0, "min": -10000, "max": 10000, "step":0.01,                 "tooltip": "Adds extra SDE noise. Values around 1.03-1.07 can lead to a moderate boost in detail and paint textures."}),
+                    "s_noise_substep":        ("FLOAT",                      {"default": 1.0, "min": -10000, "max": 10000, "step":0.01,                 "tooltip": "Adds extra SDE noise. Values around 1.03-1.07 can lead to a moderate boost in detail and paint textures."}),
+                    "d_noise":                ("FLOAT",                      {"default": 1.0, "min": -10000, "max": 10000, "step":0.01,                 "tooltip": "Downscales the sigma schedule. Values around 0.98-0.95 can lead to a large boost in detail and paint textures."}),
+                    "momentum":               ("FLOAT",                      {"default": 1.0, "min": -10000, "max": 10000, "step":0.01,                 "tooltip": "Accelerate convergence with positive values when sampling, negative values when unsampling."}),
+                    "noise_seed_sde":         ("INT",                        {"default": -1, "min": -1, "max": 0xffffffffffffffff}),
+                    "sampler_name":           (get_sampler_name_list(),      {"default": get_default_sampler_name()}), 
+
+                    "implicit_type":          (IMPLICIT_TYPE_NAMES,          {"default": "predictor-corrector"}), 
+                    "implicit_type_substeps": (IMPLICIT_TYPE_NAMES,          {"default": "predictor-corrector"}), 
+                    "implicit_steps":         ("INT",                        {"default": 0, "min": 0, "max": 10000}),
+                    "implicit_substeps":      ("INT",                        {"default": 0, "min": 0, "max": 10000}),
+                    "bongmath":               ("BOOLEAN",                    {"default": True}),
+                    },
+                "optional": 
+                    {
+                    "guides":                 ("GUIDES", ),     
+                    "automation":             ("AUTOMATION", ),
+                    "extra_options":          ("STRING",                     {"default": "", "multiline": True}),   
+                    "options":                ("OPTIONS", ),   
+                    }
+                }
+
+    RETURN_TYPES = ("SAMPLER",)
+    RETURN_NAMES = ("sampler", ) 
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/samplers"
+    EXPERIMENTAL = True
+    
+    def main(self, 
+            noise_type_sde                : str = "gaussian",
+            noise_type_sde_substep        : str = "gaussian",
+            noise_mode_sde                : str = "hard",
+            overshoot_mode                : str = "hard",
+            overshoot_mode_substep        : str = "hard",
+            
+            eta                           : float = 0.5,
+            eta_substep                   : float = 0.5,
+            momentum                      : float = 0.0,
+
+
+
+            noise_scaling_weight          : float = 0.0,
+            noise_scaling_type            : str   = "sampler",
+            noise_scaling_mode            : str   = "linear",
+            noise_scaling_eta             : float = 0.0,
+            noise_scaling_cycles          : int   = 1,
+            
+            noise_scaling_weights         : Optional[Tensor]       = None,
+            noise_scaling_etas            : Optional[Tensor]       = None,
+            
+            noise_boost_step              : float = 0.0,
+            noise_boost_substep           : float = 0.0,
+            noise_boost_normalize         : bool  = True,
+            noise_anchor                  : float = 1.0,
+            
+            s_noise                       : float = 1.0,
+            s_noise_substep               : float = 1.0,
+            d_noise                       : float = 1.0,
+            d_noise_start_step            : int   = 0,
+            d_noise_inv                   : float = 1.0,
+            d_noise_inv_start_step        : int   = 0,
+            
+            
+            
+            alpha_sde                     : float = -1.0,
+            k_sde                         : float = 1.0,
+            cfgpp                         : float = 0.0,
+            c1                            : float = 0.0,
+            c2                            : float = 0.5,
+            c3                            : float = 1.0,
+            noise_seed_sde                : int = -1,
+            sampler_name                  : str = "res_2m",
+            implicit_sampler_name         : str = "gauss-legendre_2s",
+            
+            implicit_substeps             : int = 0,
+            implicit_steps                : int = 0,
+            
+            rescale_floor                 : bool = True,
+            sigmas_override               : Optional[Tensor] = None,
+            
+            guides                        = None,
+            options                       = None,
+            sde_noise                     = None,
+            sde_noise_steps               : int = 1,
+            
+            extra_options                 : str = "",
+            automation                    = None,
+            etas                          : Optional[Tensor] = None,
+            etas_substep                  : Optional[Tensor] = None,
+            s_noises                      : Optional[Tensor] = None,
+            s_noises_substep              : Optional[Tensor] = None,
+            epsilon_scales                : Optional[Tensor] = None,
+            regional_conditioning_weights : Optional[Tensor] = None,
+            frame_weights_mgr             = None,
+            noise_mode_sde_substep        : str = "hard",
+            
+            overshoot                     : float = 0.0,
+            overshoot_substep             : float = 0.0,
+
+            bongmath                      : bool = True,
+            
+            implicit_type                 : str = "predictor-corrector",
+            implicit_type_substeps        : str = "predictor-corrector",
+            
+            rk_swap_step                  : int = MAX_STEPS,
+            rk_swap_print                 : bool = False,
+            rk_swap_threshold             : float = 0.0,
+            rk_swap_type                  : str = "",
+            
+            steps_to_run                  : int = -1,
+            
+            sde_mask                      : Optional[Tensor] = None,
+            
+            **kwargs,
+            ): 
+        
+        
+        
+            options_mgr = OptionsManager(options, **kwargs)
+            extra_options    += "\n" + options_mgr.get('extra_options', "")
+            EO = ExtraOptions(extra_options)
+            default_dtype = EO("default_dtype", torch.float64)
+
+    
+    
+            sampler_name, implicit_sampler_name = process_sampler_name(sampler_name)
+
+            implicit_steps_diag = implicit_substeps
+            implicit_steps_full = implicit_steps
+
+            if noise_mode_sde == "none":
+                eta = 0.0
+                noise_mode_sde = "hard"
+
+            noise_type_sde    = options_mgr.get('noise_type_sde'   , noise_type_sde)
+            noise_mode_sde    = options_mgr.get('noise_mode_sde'   , noise_mode_sde)
+            eta               = options_mgr.get('eta'              , eta)
+            eta_substep       = options_mgr.get('eta_substep'      , eta_substep)
+            
+            
+            
+            noise_scaling_weight   = options_mgr.get('noise_scaling_weight'  , noise_scaling_weight)
+            noise_scaling_type     = options_mgr.get('noise_scaling_type'    , noise_scaling_type)
+            noise_scaling_mode     = options_mgr.get('noise_scaling_mode'    , noise_scaling_mode)
+            noise_scaling_eta      = options_mgr.get('noise_scaling_eta'     , noise_scaling_eta)
+            noise_scaling_cycles   = options_mgr.get('noise_scaling_cycles'  , noise_scaling_cycles)
+            
+            noise_scaling_weights  = options_mgr.get('noise_scaling_weights' , noise_scaling_weights)
+            noise_scaling_etas     = options_mgr.get('noise_scaling_etas'    , noise_scaling_etas)
+            
+            noise_boost_step       = options_mgr.get('noise_boost_step'      , noise_boost_step)
+            noise_boost_substep    = options_mgr.get('noise_boost_substep'   , noise_boost_substep)
+            noise_boost_normalize  = options_mgr.get('noise_boost_normalize' , noise_boost_normalize)
+            noise_anchor           = options_mgr.get('noise_anchor'          , noise_anchor)
+            
+            s_noise                = options_mgr.get('s_noise'               , s_noise)
+            s_noise_substep        = options_mgr.get('s_noise_substep'       , s_noise_substep)
+            d_noise                = options_mgr.get('d_noise'               , d_noise)
+            d_noise_start_step     = options_mgr.get('d_noise_start_step'    , d_noise_start_step)
+            d_noise_inv            = options_mgr.get('d_noise_inv'           , d_noise_inv)
+            d_noise_inv_start_step = options_mgr.get('d_noise_inv_start_step', d_noise_inv_start_step)
+            
+            
+            
+            alpha_sde         = options_mgr.get('alpha_sde'        , alpha_sde)
+            k_sde             = options_mgr.get('k_sde'            , k_sde)
+            c1                = options_mgr.get('c1'               , c1)
+            c2                = options_mgr.get('c2'               , c2)
+            c3                = options_mgr.get('c3'               , c3)
+
+            frame_weights_mgr = options_mgr.get('frame_weights_mgr', frame_weights_mgr)
+            sde_noise         = options_mgr.get('sde_noise'        , sde_noise)
+            sde_noise_steps   = options_mgr.get('sde_noise_steps'  , sde_noise_steps)
+            
+            rk_swap_step      = options_mgr.get('rk_swap_step'     , rk_swap_step)
+            rk_swap_print     = options_mgr.get('rk_swap_print'    , rk_swap_print)
+            rk_swap_threshold = options_mgr.get('rk_swap_threshold', rk_swap_threshold)
+            rk_swap_type      = options_mgr.get('rk_swap_type'     , rk_swap_type)
+
+            steps_to_run      = options_mgr.get('steps_to_run'     , steps_to_run)
+            
+            noise_seed_sde    = options_mgr.get('noise_seed_sde'   , noise_seed_sde)
+            momentum          = options_mgr.get('momentum'         , momentum)
+
+            sde_mask          = options_mgr.get('sde_mask'         , sde_mask)
+
+
+            rescale_floor = EO("rescale_floor")
+
+            if automation is not None:
+                etas              = automation['etas']              if 'etas'              in automation else None
+                etas_substep      = automation['etas_substep']      if 'etas_substep'      in automation else None
+                s_noises          = automation['s_noises']          if 's_noises'          in automation else None
+                s_noises_substep  = automation['s_noises_substep']  if 's_noises_substep'  in automation else None
+                epsilon_scales    = automation['epsilon_scales']    if 'epsilon_scales'    in automation else None
+                frame_weights_mgr = automation['frame_weights_mgr'] if 'frame_weights_mgr' in automation else None
+                
+            etas             = options_mgr.get('etas',         etas)
+            etas_substep     = options_mgr.get('etas_substep', etas_substep)
+            
+            s_noises         = options_mgr.get('s_noises',         s_noises)
+            s_noises_substep = options_mgr.get('s_noises_substep', s_noises_substep)
+
+            etas             = initialize_or_scale(etas,             eta,             MAX_STEPS).to(default_dtype)
+            etas_substep     = initialize_or_scale(etas_substep,     eta_substep,     MAX_STEPS).to(default_dtype)
+            s_noises         = initialize_or_scale(s_noises,         s_noise,         MAX_STEPS).to(default_dtype)
+            s_noises_substep = initialize_or_scale(s_noises_substep, s_noise_substep, MAX_STEPS).to(default_dtype)
+
+            etas             = F.pad(etas,             (0, MAX_STEPS), value=0.0)
+            etas_substep     = F.pad(etas_substep,     (0, MAX_STEPS), value=0.0)
+            s_noises         = F.pad(s_noises,         (0, MAX_STEPS), value=1.0)
+            s_noises_substep = F.pad(s_noises_substep, (0, MAX_STEPS), value=1.0)
+
+            if sde_noise is None:
+                sde_noise = []
+            else:
+                sde_noise = copy.deepcopy(sde_noise)
+                sde_noise = normalize_zscore(sde_noise, channelwise=True, inplace=True)
+
+
+            sampler = comfy.samplers.ksampler("rk_beta", 
+                {
+                    "eta"                           : eta,
+                    "eta_substep"                   : eta_substep,
+
+                    "alpha"                         : alpha_sde,
+                    "k"                             : k_sde,
+                    "c1"                            : c1,
+                    "c2"                            : c2,
+                    "c3"                            : c3,
+                    "cfgpp"                         : cfgpp,
+
+                    "noise_sampler_type"            : noise_type_sde,
+                    "noise_sampler_type_substep"    : noise_type_sde_substep,
+                    "noise_mode_sde"                : noise_mode_sde,
+                    "noise_seed"                    : noise_seed_sde,
+                    "rk_type"                       : sampler_name,
+                    "implicit_sampler_name"         : implicit_sampler_name,
+
+                    "implicit_steps_diag"           : implicit_steps_diag,
+                    "implicit_steps_full"           : implicit_steps_full,
+
+                    "LGW_MASK_RESCALE_MIN"          : rescale_floor,
+                    "sigmas_override"               : sigmas_override,
+                    "sde_noise"                     : sde_noise,
+
+                    "extra_options"                 : extra_options,
+                    "sampler_mode"                  : "standard",
+
+                    "etas"                          : etas,
+                    "etas_substep"                  : etas_substep,
+                    
+                    
+                    
+                    "s_noises"                      : s_noises,
+                    "s_noises_substep"              : s_noises_substep,
+                    "epsilon_scales"                : epsilon_scales,
+                    "regional_conditioning_weights" : regional_conditioning_weights,
+
+                    "guides"                        : guides,
+                    "frame_weights_mgr"             : frame_weights_mgr,
+                    "eta_substep"                   : eta_substep,
+                    "noise_mode_sde_substep"        : noise_mode_sde_substep,
+                    
+                    
+                    
+                    "noise_scaling_weight"          : noise_scaling_weight,
+                    "noise_scaling_type"            : noise_scaling_type,
+                    "noise_scaling_mode"            : noise_scaling_mode,
+                    "noise_scaling_eta"             : noise_scaling_eta,
+                    "noise_scaling_cycles"          : noise_scaling_cycles,
+                    
+                    "noise_scaling_weights"         : noise_scaling_weights,
+                    "noise_scaling_etas"            : noise_scaling_etas,
+                    
+                    "noise_boost_step"              : noise_boost_step,
+                    "noise_boost_substep"           : noise_boost_substep,
+                    "noise_boost_normalize"         : noise_boost_normalize,
+                    "noise_anchor"                  : noise_anchor,
+                    
+                    "s_noise"                       : s_noise,
+                    "s_noise_substep"               : s_noise_substep,
+                    "d_noise"                       : d_noise,
+                    "d_noise_start_step"            : d_noise_start_step,
+                    "d_noise_inv"                   : d_noise_inv,
+                    "d_noise_inv_start_step"        : d_noise_inv_start_step,
+
+
+
+                    "overshoot_mode"                : overshoot_mode,
+                    "overshoot_mode_substep"        : overshoot_mode_substep,
+                    "overshoot"                     : overshoot,
+                    "overshoot_substep"             : overshoot_substep,
+                    "BONGMATH"                      : bongmath,
+
+                    "implicit_type"                 : implicit_type,
+                    "implicit_type_substeps"        : implicit_type_substeps,
+                    
+                    "rk_swap_step"                  : rk_swap_step,
+                    "rk_swap_print"                 : rk_swap_print,
+                    "rk_swap_threshold"             : rk_swap_threshold,
+                    "rk_swap_type"                  : rk_swap_type,
+                    
+                    "steps_to_run"                  : steps_to_run,
+                    
+                    "sde_mask"                      : sde_mask,
+                    
+                    "momentum"                      : momentum,
+                })
+
+
+            return (sampler, )
+
+
+
+
+
+
+
+class ClownsharKSampler_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        inputs = {"required":
+                    {
+                    "eta":          ("FLOAT",                      {"default": 0.5, "min": -100.0, "max": 100.0,     "step":0.01, "round": False, "tooltip": "Calculated noise amount to be added, then removed, after each step."}),
+                    "sampler_name": (get_sampler_name_list     (), {"default": get_default_sampler_name()}), 
+                    "scheduler":    (get_res4lyf_scheduler_list(), {"default": "beta57"},),
+                    "steps":        ("INT",                        {"default": 30,  "min":  1,     "max": MAX_STEPS}),
+                    "steps_to_run": ("INT",                        {"default": -1,  "min": -1,     "max": MAX_STEPS}),
+                    "denoise":      ("FLOAT",                      {"default": 1.0, "min": -10000, "max": MAX_STEPS, "step":0.01}),
+                    "cfg":          ("FLOAT",                      {"default": 5.5, "min": -100.0, "max": 100.0,     "step":0.01, "round": False, }),
+                    "seed":         ("INT",                        {"default": 0,   "min": -1,     "max": 0xffffffffffffffff}),
+                    "sampler_mode": (['unsample', 'standard', 'resample'], {"default": "standard"}),
+                    "bongmath":     ("BOOLEAN",                    {"default": True}),
+                    },
+                "optional": 
+                    {
+                    "model":        ("MODEL",),
+                    "positive":     ("CONDITIONING",),
+                    "negative":     ("CONDITIONING",),
+                    "latent_image": ("LATENT",),
+                    "sigmas":       ("SIGMAS",), 
+                    "guides":       ("GUIDES",), 
+                    "options":      ("OPTIONS", {}),   
+                    }
+                }
+        
+        return inputs
+
+    RETURN_TYPES = ("LATENT", 
+                    "LATENT",
+                    "OPTIONS",
+                    )
+    
+    RETURN_NAMES = ("output", 
+                    "denoised",
+                    "options",
+                    ) 
+    
+    FUNCTION = "main"
+    CATEGORY = "RES4LYF/samplers"
+    
+    def main(self, 
+            model                                                  = None,
+            denoise                       : float                  = 1.0, 
+            scheduler                     : str                    = "beta57", 
+            cfg                           : float                  = 1.0, 
+            seed                          : int                    = -1, 
+            positive                                               = None, 
+            negative                                               = None, 
+            latent_image                  : Optional[dict[Tensor]] = None, 
+            steps                         : int                    = 30,
+            steps_to_run                  : int                    = -1,
+            bongmath                      : bool                   = True,
+            sampler_mode                  : str                    = "standard",
+            
+            noise_type_sde                : str                    = "gaussian", 
+            noise_type_sde_substep        : str                    = "gaussian", 
+            noise_mode_sde                : str                    = "hard",
+            noise_mode_sde_substep        : str                    = "hard",
+
+            
+            overshoot_mode                : str                    = "hard", 
+            overshoot_mode_substep        : str                    = "hard",
+            overshoot                     : float                  = 0.0, 
+            overshoot_substep             : float                  = 0.0,
+            
+            eta                           : float                  = 0.5, 
+            eta_substep                   : float                  = 0.5,
+            momentum                      : float                  = 0.0,
+            
+            
+            
+            noise_scaling_weight         : float                  = 0.0,
+            noise_scaling_type            : str                    = "sampler",
+            noise_scaling_mode            : str                    = "linear",
+            noise_scaling_eta             : float                  = 0.0,
+            noise_scaling_cycles          : int                    = 1,
+            
+            noise_scaling_weights         : Optional[Tensor]       = None,
+            noise_scaling_etas            : Optional[Tensor]       = None,
+            
+            noise_boost_step              : float                  = 0.0,
+            noise_boost_substep           : float                  = 0.0,
+            noise_boost_normalize         : bool                   = True,
+            noise_anchor                  : float                  = 1.0,
+            
+            s_noise                       : float                  = 1.0,
+            s_noise_substep               : float                  = 1.0,
+            d_noise                       : float                  = 1.0,
+            d_noise_start_step            : int                    = 0,
+            d_noise_inv                   : float                  = 1.0,
+            d_noise_inv_start_step        : int                    = 0,
+            
+            
+            
+            alpha_sde                     : float                  = -1.0, 
+            k_sde                         : float                  = 1.0,
+            cfgpp                         : float                  = 0.0,
+            c1                            : float                  = 0.0, 
+            c2                            : float                  = 0.5, 
+            c3                            : float                  = 1.0,
+            noise_seed_sde                : int                    = -1,
+            sampler_name                  : str                    = "res_2m", 
+            implicit_sampler_name         : str                    = "use_explicit",
+            
+            implicit_type                 : str                    = "bongmath",
+            implicit_type_substeps        : str                    = "bongmath",
+            implicit_steps                : int                    = 0,
+            implicit_substeps             : int                    = 0, 
+
+            sigmas                        : Optional[Tensor]       = None,
+            sigmas_override               : Optional[Tensor]       = None, 
+            guides                                                 = None, 
+            options                                                = None, 
+            sde_noise                                              = None,
+            sde_noise_steps               : int                    = 1, 
+            extra_options                 : str                    = "", 
+            automation                                             = None, 
+
+            epsilon_scales                : Optional[Tensor]       = None, 
+            regional_conditioning_weights : Optional[Tensor]       = None,
+            frame_weights_mgr                                      = None, 
+
+
+            rescale_floor                 : bool                   = True, 
+            
+            rk_swap_step                  : int                    = MAX_STEPS,
+            rk_swap_print                 : bool                   = False,
+            rk_swap_threshold             : float                  = 0.0,
+            rk_swap_type                  : str                    = "",
+            
+            sde_mask                      : Optional[Tensor]       = None,
+
+            #start_at_step                 : int                    = 0,
+            #stop_at_step                  : int                    = MAX_STEPS,
+                        
+            **kwargs
+            ): 
+        
+        options_mgr = OptionsManager(options, **kwargs)
+        extra_options    += "\n" + options_mgr.get('extra_options', "")
+
+        #if model is None:
+        #    model = latent_image['model']
+        
+        
+        # defaults for ClownSampler
+        eta_substep = eta
+        
+        # defaults for SharkSampler
+        noise_type_init = "gaussian"
+        noise_stdev     = 1.0
+        denoise_alt     = 1.0
+        channelwise_cfg = False
+        
+        if denoise < 0:
+            denoise_alt = -denoise
+            denoise = 1.0
+        
+        is_chained = False
+
+        if latent_image is not None and 'positive' in latent_image and positive is None:
+            positive = latent_image['positive']
+            is_chained = True
+        if latent_image is not None and 'negative' in latent_image and negative is None:
+            negative = latent_image['negative']
+            is_chained = True
+        if latent_image is not None and 'model' in latent_image and model is None:
+            model = latent_image['model']
+            is_chained = True
+        
+        guider = options_mgr.get('guider', None)
+        if is_chained is False and guider is not None:
+            model = guider.model_patcher
+
+        if model.model.model_config.unet_config.get('stable_cascade_stage') == 'b':
+            noise_type_sde         = "pyramid-cascade_B"
+            noise_type_sde_substep = "pyramid-cascade_B"
+        
+        
+        #if options is not None:
+        #options_mgr = OptionsManager(options_inputs)
+        noise_seed_sde         = options_mgr.get('noise_seed_sde'        , noise_seed_sde)
+        
+        
+        noise_type_sde         = options_mgr.get('noise_type_sde'        , noise_type_sde)
+        noise_type_sde_substep = options_mgr.get('noise_type_sde_substep', noise_type_sde_substep)
+        
+        options_mgr.update('noise_type_sde',         noise_type_sde)
+        options_mgr.update('noise_type_sde_substep', noise_type_sde_substep)
+        
+        noise_mode_sde         = options_mgr.get('noise_mode_sde'        , noise_mode_sde)
+        noise_mode_sde_substep = options_mgr.get('noise_mode_sde_substep', noise_mode_sde_substep)
+        
+        overshoot_mode         = options_mgr.get('overshoot_mode'        , overshoot_mode)
+        overshoot_mode_substep = options_mgr.get('overshoot_mode_substep', overshoot_mode_substep)
+
+        eta                    = options_mgr.get('eta'                   , eta)
+        eta_substep            = options_mgr.get('eta_substep'           , eta_substep)
+        
+        options_mgr.update('eta',         eta)
+        options_mgr.update('eta_substep', eta_substep)
+
+        overshoot              = options_mgr.get('overshoot'             , overshoot)
+        overshoot_substep      = options_mgr.get('overshoot_substep'     , overshoot_substep)
+        
+        
+    
+        noise_scaling_weight   = options_mgr.get('noise_scaling_weight' , noise_scaling_weight)
+        noise_scaling_type     = options_mgr.get('noise_scaling_type'    , noise_scaling_type)
+        noise_scaling_mode     = options_mgr.get('noise_scaling_mode'    , noise_scaling_mode)
+        noise_scaling_eta      = options_mgr.get('noise_scaling_eta'     , noise_scaling_eta)
+        noise_scaling_cycles   = options_mgr.get('noise_scaling_cycles'  , noise_scaling_cycles)
+        
+        noise_scaling_weights  = options_mgr.get('noise_scaling_weights' , noise_scaling_weights)
+        noise_scaling_etas     = options_mgr.get('noise_scaling_etas'    , noise_scaling_etas)
+        
+        noise_boost_step       = options_mgr.get('noise_boost_step'      , noise_boost_step)
+        noise_boost_substep    = options_mgr.get('noise_boost_substep'   , noise_boost_substep)
+        noise_boost_normalize  = options_mgr.get('noise_boost_normalize' , noise_boost_normalize)
+        noise_anchor           = options_mgr.get('noise_anchor'          , noise_anchor)
+        
+        s_noise                = options_mgr.get('s_noise'               , s_noise)
+        s_noise_substep        = options_mgr.get('s_noise_substep'       , s_noise_substep)
+        d_noise                = options_mgr.get('d_noise'               , d_noise)
+        d_noise_start_step     = options_mgr.get('d_noise_start_step'          , d_noise_start_step)
+        d_noise_inv            = options_mgr.get('d_noise_inv'           , d_noise_inv)
+        d_noise_inv_start_step = options_mgr.get('d_noise_inv_start_step', d_noise_inv_start_step)
+        
+        
+        momentum               = options_mgr.get('momentum'              , momentum)
+
+        
+        implicit_type          = options_mgr.get('implicit_type'         , implicit_type)
+        implicit_type_substeps = options_mgr.get('implicit_type_substeps', implicit_type_substeps)
+        implicit_steps         = options_mgr.get('implicit_steps'        , implicit_steps)
+        implicit_substeps      = options_mgr.get('implicit_substeps'     , implicit_substeps)
+        
+        alpha_sde              = options_mgr.get('alpha_sde'             , alpha_sde)
+        k_sde                  = options_mgr.get('k_sde'                 , k_sde)
+        c1                     = options_mgr.get('c1'                    , c1)
+        c2                     = options_mgr.get('c2'                    , c2)
+        c3                     = options_mgr.get('c3'                    , c3)
+
+        frame_weights_mgr      = options_mgr.get('frame_weights_mgr'     , frame_weights_mgr)
+        
+        sde_noise              = options_mgr.get('sde_noise'             , sde_noise)
+        sde_noise_steps        = options_mgr.get('sde_noise_steps'       , sde_noise_steps)
+        
+        extra_options          = options_mgr.get('extra_options'         , extra_options)
+        
+        automation             = options_mgr.get('automation'            , automation)
+        
+        # SharkSampler Options
+        noise_type_init        = options_mgr.get('noise_type_init'       , noise_type_init)
+        noise_stdev            = options_mgr.get('noise_stdev'           , noise_stdev)
+        sampler_mode           = options_mgr.get('sampler_mode'          , sampler_mode)
+        denoise_alt            = options_mgr.get('denoise_alt'           , denoise_alt)
+        
+        channelwise_cfg        = options_mgr.get('channelwise_cfg'       , channelwise_cfg)
+        
+        options_mgr.update('noise_type_init', noise_type_init)
+        options_mgr.update('noise_stdev',     noise_stdev)
+        options_mgr.update('denoise_alt',     denoise_alt)
+        #options_mgr.update('channelwise_cfg', channelwise_cfg)
+        
+        sigmas                 = options_mgr.get('sigmas'                , sigmas)
+        
+        rk_swap_type           = options_mgr.get('rk_swap_type'          , rk_swap_type)
+        rk_swap_step           = options_mgr.get('rk_swap_step'          , rk_swap_step)
+        rk_swap_threshold      = options_mgr.get('rk_swap_threshold'     , rk_swap_threshold)
+        rk_swap_print          = options_mgr.get('rk_swap_print'         , rk_swap_print)
+        
+        sde_mask               = options_mgr.get('sde_mask'              , sde_mask)
+
+        
+        #start_at_step          = options_mgr.get('start_at_step'         , start_at_step)
+        #stop_at_ste            = options_mgr.get('stop_at_step'          , stop_at_step)
+                
+        if channelwise_cfg: # != 1.0:
+            cfg = -abs(cfg)  # set cfg negative for shark, to flag as cfg_cw
+
+
+
+
+        sampler, = ClownSamplerAdvanced_Beta().main(
+            noise_type_sde                = noise_type_sde,
+            noise_type_sde_substep        = noise_type_sde_substep,
+            noise_mode_sde                = noise_mode_sde,
+            noise_mode_sde_substep        = noise_mode_sde_substep,
+            
+            eta                           = eta,
+            eta_substep                   = eta_substep,
+            
+
+            
+            overshoot                     = overshoot,
+            overshoot_substep             = overshoot_substep,
+            
+            overshoot_mode                = overshoot_mode,
+            overshoot_mode_substep        = overshoot_mode_substep,
+            
+            
+            momentum                      = momentum,
+
+            alpha_sde                     = alpha_sde,
+            k_sde                         = k_sde,
+            cfgpp                         = cfgpp,
+            c1                            = c1,
+            c2                            = c2,
+            c3                            = c3,
+            sampler_name                  = sampler_name,
+            implicit_sampler_name         = implicit_sampler_name,
+
+            implicit_type                 = implicit_type,
+            implicit_type_substeps        = implicit_type_substeps,
+            implicit_steps                = implicit_steps,
+            implicit_substeps             = implicit_substeps,
+
+            rescale_floor                 = rescale_floor,
+            sigmas_override               = sigmas_override,
+            
+            noise_seed_sde                = noise_seed_sde,
+            
+            guides                        = guides,
+            options                       = options_mgr.as_dict(),
+
+            extra_options                 = extra_options,
+            automation                    = automation,
+
+
+
+            noise_scaling_weight          = noise_scaling_weight,
+            noise_scaling_type            = noise_scaling_type,
+            noise_scaling_mode            = noise_scaling_mode,
+            noise_scaling_eta             = noise_scaling_eta,
+            noise_scaling_cycles          = noise_scaling_cycles,
+            
+            noise_scaling_weights         = noise_scaling_weights,
+            noise_scaling_etas            = noise_scaling_etas,
+
+            noise_boost_step              = noise_boost_step,
+            noise_boost_substep           = noise_boost_substep,
+            noise_boost_normalize         = noise_boost_normalize,
+            noise_anchor                  = noise_anchor,
+            
+            s_noise                       = s_noise,
+            s_noise_substep               = s_noise_substep,
+            d_noise                       = d_noise,
+            d_noise_start_step            = d_noise_start_step,
+            d_noise_inv                   = d_noise_inv,
+            d_noise_inv_start_step        = d_noise_inv_start_step,
+            
+            
+            epsilon_scales                = epsilon_scales,
+            regional_conditioning_weights = regional_conditioning_weights,
+            frame_weights_mgr             = frame_weights_mgr,
+            
+            sde_noise                     = sde_noise,
+            sde_noise_steps               = sde_noise_steps,
+            
+            rk_swap_step                  = rk_swap_step,
+            rk_swap_print                 = rk_swap_print,
+            rk_swap_threshold             = rk_swap_threshold,
+            rk_swap_type                  = rk_swap_type,
+            
+            steps_to_run                  = steps_to_run,
+            
+            sde_mask                      = sde_mask,
+            
+            bongmath                      = bongmath,
+            )
+            
+        
+        output, denoised, sde_noise = SharkSampler().main(
+            model           = model, 
+            cfg             = cfg, 
+            scheduler       = scheduler,
+            steps           = steps, 
+            steps_to_run    = steps_to_run,
+            denoise         = denoise,
+            latent_image    = latent_image, 
+            positive        = positive,
+            negative        = negative, 
+            sampler         = sampler, 
+            cfgpp           = cfgpp, 
+            noise_seed      = seed, 
+            options         = options_mgr.as_dict(), 
+            sde_noise       = sde_noise, 
+            sde_noise_steps = sde_noise_steps, 
+            noise_type_init = noise_type_init,
+            noise_stdev     = noise_stdev,
+            sampler_mode    = sampler_mode,
+            denoise_alt     = denoise_alt,
+            sigmas          = sigmas,
+
+            extra_options   = extra_options)
+        
+        return (output, denoised, options_mgr.as_dict(),) # {'model':model,},)
+
+
+
+
+
+
+
+
+
+
+
+class ClownsharkChainsampler_Beta(ClownsharKSampler_Beta):  
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "eta":          ("FLOAT",                 {"default": 0.5, "min": -100.0, "max": 100.0,     "step":0.01, "round": False, "tooltip": "Calculated noise amount to be added, then removed, after each step."}),
+                "sampler_name": (get_sampler_name_list(), {"default": get_default_sampler_name()}), 
+                "steps_to_run": ("INT",                   {"default": -1,  "min": -1,       "max": MAX_STEPS}),
+                "cfg":          ("FLOAT",                 {"default": 5.5, "min": -10000.0, "max": 10000.0, "step":0.01, "round": False, "tooltip": "Negative values use channelwise CFG." }),
+                "sampler_mode": (['unsample', 'resample'],{"default": "resample"}),
+                "bongmath":     ("BOOLEAN",               {"default": True}),
+                },
+            "optional": {
+                "model":        ("MODEL",),
+                "positive":     ("CONDITIONING", ),
+                "negative":     ("CONDITIONING", ),
+                #"sampler":      ("SAMPLER", ),
+                "sigmas":       ("SIGMAS", ),
+                "latent_image": ("LATENT", ),     
+                "guides":       ("GUIDES", ),   
+                "options":      ("OPTIONS", ),   
+                }
+            }
+
+    def main(self, 
+            eta                   =  0.5,
+            sampler_name          = "res_2m",
+            steps_to_run          = -1, 
+            cfg                   =  5.5, 
+            bongmath              = True,
+            seed            : int = -1, 
+            latent_image          = None,
+            sigmas                = None,
+            sampler_mode          = "",
+            
+             **kwargs):  
+        
+        steps = latent_image['state_info']['sigmas'].shape[-1] - 3
+        sigmas = latent_image['state_info']['sigmas'] if sigmas is None else sigmas
+        if len(sigmas) > 2 and sigmas[1] < sigmas[2] and latent_image['state_info']['sampler_mode'] == "unsample" and sampler_mode == "resample":
+            sigmas = torch.flip(sigmas, dims=[0])
+        
+        return super().main(eta=eta, sampler_name=sampler_name, sampler_mode=sampler_mode, sigmas=sigmas, steps_to_run=steps_to_run, steps=steps, cfg=cfg, bongmath=bongmath, seed=seed, latent_image=latent_image, **kwargs)
+
+
+
+
+
+
+class ClownSampler_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        inputs = {"required":
+                    {
+                    "eta":          ("FLOAT",                      {"default": 0.5, "min": -100.0, "max": 100.0,     "step":0.01, "round": False, "tooltip": "Calculated noise amount to be added, then removed, after each step."}),
+                    "sampler_name": (get_sampler_name_list     (), {"default": get_default_sampler_name()}), 
+                    "seed":         ("INT",                        {"default": -1,   "min": -1,     "max": 0xffffffffffffffff}),
+                    "bongmath":     ("BOOLEAN",                    {"default": True}),
+                    },
+                "optional": 
+                    {
+                    "guides":       ("GUIDES",), 
+                    "options":      ("OPTIONS", {}),   
+                    }
+                }
+        
+        return inputs
+
+    RETURN_TYPES = ("SAMPLER",)
+    
+    RETURN_NAMES = ("sampler",) 
+    
+    FUNCTION = "main"
+    CATEGORY = "RES4LYF/samplers"
+    
+    def main(self, 
+            model                                                  = None,
+            denoise                       : float                  = 1.0, 
+            scheduler                     : str                    = "beta57", 
+            cfg                           : float                  = 1.0, 
+            seed                          : int                    = -1, 
+            positive                                               = None, 
+            negative                                               = None, 
+            latent_image                  : Optional[dict[Tensor]] = None, 
+            steps                         : int                    = 30,
+            steps_to_run                  : int                    = -1,
+            bongmath                      : bool                   = True,
+            sampler_mode                  : str                    = "standard",
+            
+            noise_type_sde                : str                    = "gaussian", 
+            noise_type_sde_substep        : str                    = "gaussian", 
+            noise_mode_sde                : str                    = "hard",
+            noise_mode_sde_substep        : str                    = "hard",
+
+            
+            overshoot_mode                : str                    = "hard", 
+            overshoot_mode_substep        : str                    = "hard",
+            overshoot                     : float                  = 0.0, 
+            overshoot_substep             : float                  = 0.0,
+            
+            eta                           : float                  = 0.5, 
+            eta_substep                   : float                  = 0.5,
+            
+            noise_scaling_weight          : float                  = 0.0,
+            noise_boost_step              : float                  = 0.0, 
+            noise_boost_substep           : float                  = 0.0, 
+            noise_anchor                  : float                  = 1.0,
+            
+            s_noise                       : float                  = 1.0, 
+            s_noise_substep               : float                  = 1.0, 
+            d_noise                       : float                  = 1.0, 
+            d_noise_start_step            : int                    = 0,
+            d_noise_inv                   : float                  = 1.0,
+            d_noise_inv_start_step        : int                    = 0,
+
+            
+            alpha_sde                     : float                  = -1.0, 
+            k_sde                         : float                  = 1.0,
+            cfgpp                         : float                  = 0.0,
+            c1                            : float                  = 0.0, 
+            c2                            : float                  = 0.5, 
+            c3                            : float                  = 1.0,
+            noise_seed_sde                : int                    = -1,
+            sampler_name                  : str                    = "res_2m", 
+            implicit_sampler_name         : str                    = "use_explicit",
+            
+            implicit_type                 : str                    = "bongmath",
+            implicit_type_substeps        : str                    = "bongmath",
+            implicit_steps                : int                    = 0,
+            implicit_substeps             : int                    = 0, 
+
+            sigmas                        : Optional[Tensor]       = None,
+            sigmas_override               : Optional[Tensor]       = None, 
+            guides                                                 = None, 
+            options                                                = None, 
+            sde_noise                                              = None,
+            sde_noise_steps               : int                    = 1, 
+            extra_options                 : str                    = "", 
+            automation                                             = None, 
+
+            epsilon_scales                : Optional[Tensor]       = None, 
+            regional_conditioning_weights : Optional[Tensor]       = None,
+            frame_weights_mgr                                      = None, 
+
+
+            rescale_floor                 : bool                   = True, 
+            
+            rk_swap_step                  : int                    = MAX_STEPS,
+            rk_swap_print                 : bool                   = False,
+            rk_swap_threshold             : float                  = 0.0,
+            rk_swap_type                  : str                    = "",
+            
+            sde_mask                      : Optional[Tensor]       = None,
+
+            
+            #start_at_step                 : int                    = 0,
+            #stop_at_step                  : int                    = MAX_STEPS,
+                        
+            **kwargs
+            ): 
+        
+        options_mgr = OptionsManager(options, **kwargs)
+        extra_options    += "\n" + options_mgr.get('extra_options', "")
+        
+        
+        # defaults for ClownSampler
+        eta_substep = eta
+        
+        # defaults for SharkSampler
+        noise_type_init = "gaussian"
+        noise_stdev     = 1.0
+        denoise_alt     = 1.0
+        channelwise_cfg = False #1.0
+        
+        
+        #if options is not None:
+        #options_mgr = OptionsManager(options_inputs)
+        noise_type_sde         = options_mgr.get('noise_type_sde'        , noise_type_sde)
+        noise_type_sde_substep = options_mgr.get('noise_type_sde_substep', noise_type_sde_substep)
+        
+        noise_mode_sde         = options_mgr.get('noise_mode_sde'        , noise_mode_sde)
+        noise_mode_sde_substep = options_mgr.get('noise_mode_sde_substep', noise_mode_sde_substep)
+        
+        overshoot_mode         = options_mgr.get('overshoot_mode'        , overshoot_mode)
+        overshoot_mode_substep = options_mgr.get('overshoot_mode_substep', overshoot_mode_substep)
+
+        eta                    = options_mgr.get('eta'                   , eta)
+        eta_substep            = options_mgr.get('eta_substep'           , eta_substep)
+
+        overshoot              = options_mgr.get('overshoot'             , overshoot)
+        overshoot_substep      = options_mgr.get('overshoot_substep'     , overshoot_substep)
+        
+        noise_scaling_weight   = options_mgr.get('noise_scaling_weight' , noise_scaling_weight)
+        noise_boost_step       = options_mgr.get('noise_boost_step'      , noise_boost_step)
+        noise_boost_substep    = options_mgr.get('noise_boost_substep'   , noise_boost_substep)
+        
+        noise_anchor           = options_mgr.get('noise_anchor'          , noise_anchor)
+
+        s_noise                = options_mgr.get('s_noise'               , s_noise)
+        s_noise_substep        = options_mgr.get('s_noise_substep'       , s_noise_substep)
+
+        d_noise                = options_mgr.get('d_noise'               , d_noise)
+        d_noise_start_step     = options_mgr.get('d_noise_start_step'    , d_noise_start_step)
+        d_noise_inv            = options_mgr.get('d_noise_inv'           , d_noise_inv)
+        d_noise_inv_start_step = options_mgr.get('d_noise_inv_start_step', d_noise_inv_start_step)
+        
+        implicit_type          = options_mgr.get('implicit_type'         , implicit_type)
+        implicit_type_substeps = options_mgr.get('implicit_type_substeps', implicit_type_substeps)
+        implicit_steps         = options_mgr.get('implicit_steps'        , implicit_steps)
+        implicit_substeps      = options_mgr.get('implicit_substeps'     , implicit_substeps)
+        
+        alpha_sde              = options_mgr.get('alpha_sde'             , alpha_sde)
+        k_sde                  = options_mgr.get('k_sde'                 , k_sde)
+        c1                     = options_mgr.get('c1'                    , c1)
+        c2                     = options_mgr.get('c2'                    , c2)
+        c3                     = options_mgr.get('c3'                    , c3)
+
+        frame_weights_mgr      = options_mgr.get('frame_weights_mgr'     , frame_weights_mgr)
+        
+        sde_noise              = options_mgr.get('sde_noise'             , sde_noise)
+        sde_noise_steps        = options_mgr.get('sde_noise_steps'       , sde_noise_steps)
+        
+        extra_options          = options_mgr.get('extra_options'         , extra_options)
+        
+        automation             = options_mgr.get('automation'            , automation)
+        
+        # SharkSampler Options
+        noise_type_init        = options_mgr.get('noise_type_init'       , noise_type_init)
+        noise_stdev            = options_mgr.get('noise_stdev'           , noise_stdev)
+        sampler_mode           = options_mgr.get('sampler_mode'          , sampler_mode)
+        denoise_alt            = options_mgr.get('denoise_alt'           , denoise_alt)
+        
+        channelwise_cfg        = options_mgr.get('channelwise_cfg'       , channelwise_cfg)
+        
+        sigmas                 = options_mgr.get('sigmas'                , sigmas)
+        
+        rk_swap_type           = options_mgr.get('rk_swap_type'          , rk_swap_type)
+        rk_swap_step           = options_mgr.get('rk_swap_step'          , rk_swap_step)
+        rk_swap_threshold      = options_mgr.get('rk_swap_threshold'     , rk_swap_threshold)
+        rk_swap_print          = options_mgr.get('rk_swap_print'         , rk_swap_print)
+        
+        sde_mask               = options_mgr.get('sde_mask'              , sde_mask)
+
+        
+        #start_at_step          = options_mgr.get('start_at_step'         , start_at_step)
+        #stop_at_ste            = options_mgr.get('stop_at_step'          , stop_at_step)
+                
+        if channelwise_cfg: # != 1.0:
+            cfg = -abs(cfg)  # set cfg negative for shark, to flag as cfg_cw
+
+        noise_seed_sde = seed
+
+
+        sampler, = ClownSamplerAdvanced_Beta().main(
+            noise_type_sde                = noise_type_sde,
+            noise_type_sde_substep        = noise_type_sde_substep,
+            noise_mode_sde                = noise_mode_sde,
+            noise_mode_sde_substep        = noise_mode_sde_substep,
+            
+            eta                           = eta,
+            eta_substep                   = eta_substep,
+            
+            s_noise                       = s_noise,
+            s_noise_substep               = s_noise_substep,
+            
+            overshoot                     = overshoot,
+            overshoot_substep             = overshoot_substep,
+            
+            overshoot_mode                = overshoot_mode,
+            overshoot_mode_substep        = overshoot_mode_substep,
+            
+            d_noise                       = d_noise,
+            d_noise_start_step            = d_noise_start_step,
+            d_noise_inv                   = d_noise_inv,
+            d_noise_inv_start_step        = d_noise_inv_start_step,
+
+            alpha_sde                     = alpha_sde,
+            k_sde                         = k_sde,
+            cfgpp                         = cfgpp,
+            c1                            = c1,
+            c2                            = c2,
+            c3                            = c3,
+            sampler_name                  = sampler_name,
+            implicit_sampler_name         = implicit_sampler_name,
+
+            implicit_type                 = implicit_type,
+            implicit_type_substeps        = implicit_type_substeps,
+            implicit_steps                = implicit_steps,
+            implicit_substeps             = implicit_substeps,
+
+            rescale_floor                 = rescale_floor,
+            sigmas_override               = sigmas_override,
+            
+            noise_seed_sde                = noise_seed_sde,
+            
+            guides                        = guides,
+            options                       = options_mgr.as_dict(),
+
+            extra_options                 = extra_options,
+            automation                    = automation,
+
+            noise_scaling_weight          = noise_scaling_weight,
+            noise_boost_step              = noise_boost_step,
+            noise_boost_substep           = noise_boost_substep,
+            
+            epsilon_scales                = epsilon_scales,
+            regional_conditioning_weights = regional_conditioning_weights,
+            frame_weights_mgr             = frame_weights_mgr,
+            
+            sde_noise                     = sde_noise,
+            sde_noise_steps               = sde_noise_steps,
+            
+            rk_swap_step                  = rk_swap_step,
+            rk_swap_print                 = rk_swap_print,
+            rk_swap_threshold             = rk_swap_threshold,
+            rk_swap_type                  = rk_swap_type,
+            
+            steps_to_run                  = steps_to_run,
+            
+            sde_mask                      = sde_mask,
+            
+            bongmath                      = bongmath,
+            )
+            
+        return (sampler,)
+    
+
+
+
+
+
+
+
+
+class BongSampler:
+    @classmethod
+    def INPUT_TYPES(cls):
+        inputs = {"required":
+                    {
+                    "model":        ("MODEL",),
+                    "seed":         ("INT",                        {"default": 0,   "min": -1,     "max": 0xffffffffffffffff}),
+                    "steps":        ("INT",                        {"default": 30,  "min":  1,     "max": MAX_STEPS}),
+                    "cfg":          ("FLOAT",                      {"default": 5.5, "min": -100.0, "max": 100.0,     "step":0.01, "round": False, }),
+                    "sampler_name": (["res_2m", "res_3m", "res_2s", "res_3s","res_2m_sde", "res_3m_sde", "res_2s_sde", "res_3s_sde"], {"default": "res_2s_sde"}), 
+                    "scheduler":    (get_res4lyf_scheduler_list(), {"default": "beta57"},),
+                    "denoise":      ("FLOAT",                      {"default": 1.0, "min": -10000, "max": MAX_STEPS, "step":0.01}),
+                    },
+                "optional": 
+                    {
+                    "positive":     ("CONDITIONING",),
+                    "negative":     ("CONDITIONING",),
+                    "latent_image": ("LATENT",),
+                    }
+                }
+        
+        return inputs
+
+    RETURN_TYPES = ("LATENT", )
+    
+    RETURN_NAMES = ("output", )
+    
+    FUNCTION = "main"
+    CATEGORY = "RES4LYF/samplers"
+    
+    def main(self, 
+            model                                                  = None,
+            denoise                       : float                  = 1.0, 
+            scheduler                     : str                    = "beta57", 
+            cfg                           : float                  = 1.0, 
+            seed                          : int                    = 42, 
+            positive                                               = None, 
+            negative                                               = None, 
+            latent_image                  : Optional[dict[Tensor]] = None, 
+            steps                         : int                    = 30,
+            steps_to_run                  : int                    = -1,
+            bongmath                      : bool                   = True,
+            sampler_mode                  : str                    = "standard",
+            
+            noise_type_sde                : str                    = "brownian", 
+            noise_type_sde_substep        : str                    = "brownian", 
+            noise_mode_sde                : str                    = "hard",
+            noise_mode_sde_substep        : str                    = "hard",
+
+            
+            overshoot_mode                : str                    = "hard", 
+            overshoot_mode_substep        : str                    = "hard",
+            overshoot                     : float                  = 0.0, 
+            overshoot_substep             : float                  = 0.0,
+            
+            eta                           : float                  = 0.5, 
+            eta_substep                   : float                  = 0.5,
+            d_noise                       : float                  = 1.0, 
+            s_noise                       : float                  = 1.0, 
+            s_noise_substep               : float                  = 1.0, 
+            
+            alpha_sde                     : float                  = -1.0, 
+            k_sde                         : float                  = 1.0,
+            cfgpp                         : float                  = 0.0,
+            c1                            : float                  = 0.0, 
+            c2                            : float                  = 0.5, 
+            c3                            : float                  = 1.0,
+            noise_seed_sde                : int                    = -1,
+            sampler_name                  : str                    = "res_2m", 
+            implicit_sampler_name         : str                    = "use_explicit",
+            
+            implicit_type                 : str                    = "bongmath",
+            implicit_type_substeps        : str                    = "bongmath",
+            implicit_steps                : int                    = 0,
+            implicit_substeps             : int                    = 0, 
+
+            sigmas                        : Optional[Tensor]       = None,
+            sigmas_override               : Optional[Tensor]       = None, 
+            guides                                                 = None, 
+            options                                                = None, 
+            sde_noise                                              = None,
+            sde_noise_steps               : int                    = 1, 
+            extra_options                 : str                    = "", 
+            automation                                             = None, 
+
+            epsilon_scales                : Optional[Tensor]       = None, 
+            regional_conditioning_weights : Optional[Tensor]       = None,
+            frame_weights_mgr                                      = None, 
+            noise_scaling_weight         : float                  = 0.0, 
+            noise_boost_step              : float                  = 0.0, 
+            noise_boost_substep           : float                  = 0.0, 
+            noise_anchor                  : float                  = 1.0,
+
+            rescale_floor                 : bool                   = True, 
+            
+            rk_swap_step                  : int                    = MAX_STEPS,
+            rk_swap_print                 : bool                   = False,
+            rk_swap_threshold             : float                  = 0.0,
+            rk_swap_type                  : str                    = "",
+            
+            #start_at_step                 : int                    = 0,
+            #stop_at_step                  : int                    = MAX_STEPS,
+                        
+            **kwargs
+            ): 
+        
+        options_mgr = OptionsManager(options, **kwargs)
+        extra_options    += "\n" + options_mgr.get('extra_options', "")
+        
+        if model.model.model_config.unet_config.get('stable_cascade_stage') == 'b':
+            noise_type_sde         = "pyramid-cascade_B"
+            noise_type_sde_substep = "pyramid-cascade_B"
+        
+        if sampler_name.endswith("_sde"):
+            sampler_name = sampler_name[:-4]
+            eta = 0.5
+        else:
+            eta = 0.0
+        
+        # defaults for ClownSampler
+        eta_substep = eta
+        
+        # defaults for SharkSampler
+        noise_type_init = "gaussian"
+        noise_stdev     = 1.0
+        denoise_alt     = 1.0
+        channelwise_cfg = False #1.0
+        
+        
+        #if options is not None:
+        #options_mgr = OptionsManager(options_inputs)
+        noise_type_sde         = options_mgr.get('noise_type_sde'        , noise_type_sde)
+        noise_type_sde_substep = options_mgr.get('noise_type_sde_substep', noise_type_sde_substep)
+        
+        noise_mode_sde         = options_mgr.get('noise_mode_sde'        , noise_mode_sde)
+        noise_mode_sde_substep = options_mgr.get('noise_mode_sde_substep', noise_mode_sde_substep)
+        
+        overshoot_mode         = options_mgr.get('overshoot_mode'        , overshoot_mode)
+        overshoot_mode_substep = options_mgr.get('overshoot_mode_substep', overshoot_mode_substep)
+
+        eta                    = options_mgr.get('eta'                   , eta)
+        eta_substep            = options_mgr.get('eta_substep'           , eta_substep)
+
+        overshoot              = options_mgr.get('overshoot'             , overshoot)
+        overshoot_substep      = options_mgr.get('overshoot_substep'     , overshoot_substep)
+        
+        noise_scaling_weight  = options_mgr.get('noise_scaling_weight' , noise_scaling_weight)
+
+        noise_boost_step       = options_mgr.get('noise_boost_step'      , noise_boost_step)
+        noise_boost_substep    = options_mgr.get('noise_boost_substep'   , noise_boost_substep)
+        
+        noise_anchor           = options_mgr.get('noise_anchor'          , noise_anchor)
+
+        s_noise                = options_mgr.get('s_noise'               , s_noise)
+        s_noise_substep        = options_mgr.get('s_noise_substep'       , s_noise_substep)
+
+        d_noise                = options_mgr.get('d_noise'               , d_noise)
+        
+        implicit_type          = options_mgr.get('implicit_type'         , implicit_type)
+        implicit_type_substeps = options_mgr.get('implicit_type_substeps', implicit_type_substeps)
+        implicit_steps         = options_mgr.get('implicit_steps'        , implicit_steps)
+        implicit_substeps      = options_mgr.get('implicit_substeps'     , implicit_substeps)
+        
+        alpha_sde              = options_mgr.get('alpha_sde'             , alpha_sde)
+        k_sde                  = options_mgr.get('k_sde'                 , k_sde)
+        c1                     = options_mgr.get('c1'                    , c1)
+        c2                     = options_mgr.get('c2'                    , c2)
+        c3                     = options_mgr.get('c3'                    , c3)
+
+        frame_weights_mgr      = options_mgr.get('frame_weights_mgr'     , frame_weights_mgr)
+        
+        sde_noise              = options_mgr.get('sde_noise'             , sde_noise)
+        sde_noise_steps        = options_mgr.get('sde_noise_steps'       , sde_noise_steps)
+        
+        extra_options          = options_mgr.get('extra_options'         , extra_options)
+        
+        automation             = options_mgr.get('automation'            , automation)
+        
+        # SharkSampler Options
+        noise_type_init        = options_mgr.get('noise_type_init'       , noise_type_init)
+        noise_stdev            = options_mgr.get('noise_stdev'           , noise_stdev)
+        sampler_mode           = options_mgr.get('sampler_mode'          , sampler_mode)
+        denoise_alt            = options_mgr.get('denoise_alt'           , denoise_alt)
+        
+        channelwise_cfg        = options_mgr.get('channelwise_cfg'       , channelwise_cfg)
+        
+        sigmas                 = options_mgr.get('sigmas'                , sigmas)
+        
+        rk_swap_type           = options_mgr.get('rk_swap_type'          , rk_swap_type)
+        rk_swap_step           = options_mgr.get('rk_swap_step'          , rk_swap_step)
+        rk_swap_threshold      = options_mgr.get('rk_swap_threshold'     , rk_swap_threshold)
+        rk_swap_print          = options_mgr.get('rk_swap_print'         , rk_swap_print)
+        
+        #start_at_step          = options_mgr.get('start_at_step'         , start_at_step)
+        #stop_at_ste            = options_mgr.get('stop_at_step'          , stop_at_step)
+                
+        if channelwise_cfg: # != 1.0:
+            cfg = -abs(cfg)  # set cfg negative for shark, to flag as cfg_cw
+
+
+
+
+        sampler, = ClownSamplerAdvanced_Beta().main(
+            noise_type_sde                = noise_type_sde,
+            noise_type_sde_substep        = noise_type_sde_substep,
+            noise_mode_sde                = noise_mode_sde,
+            noise_mode_sde_substep        = noise_mode_sde_substep,
+            
+            eta                           = eta,
+            eta_substep                   = eta_substep,
+            
+            s_noise                       = s_noise,
+            s_noise_substep               = s_noise_substep,
+            
+            overshoot                     = overshoot,
+            overshoot_substep             = overshoot_substep,
+            
+            overshoot_mode                = overshoot_mode,
+            overshoot_mode_substep        = overshoot_mode_substep,
+            
+            d_noise                       = d_noise,
+            #d_noise_start_step            = d_noise_start_step,
+            #d_noise_inv                   = d_noise_inv,
+            #d_noise_inv_start_step        = d_noise_inv_start_step,
+
+            alpha_sde                     = alpha_sde,
+            k_sde                         = k_sde,
+            cfgpp                         = cfgpp,
+            c1                            = c1,
+            c2                            = c2,
+            c3                            = c3,
+            sampler_name                  = sampler_name,
+            implicit_sampler_name         = implicit_sampler_name,
+
+            implicit_type                 = implicit_type,
+            implicit_type_substeps        = implicit_type_substeps,
+            implicit_steps                = implicit_steps,
+            implicit_substeps             = implicit_substeps,
+
+            rescale_floor                 = rescale_floor,
+            sigmas_override               = sigmas_override,
+            
+            noise_seed_sde                = noise_seed_sde,
+            
+            guides                        = guides,
+            options                       = options_mgr.as_dict(),
+
+            extra_options                 = extra_options,
+            automation                    = automation,
+
+            noise_scaling_weight         = noise_scaling_weight,
+            noise_boost_step              = noise_boost_step,
+            noise_boost_substep           = noise_boost_substep,
+            
+            epsilon_scales                = epsilon_scales,
+            regional_conditioning_weights = regional_conditioning_weights,
+            frame_weights_mgr             = frame_weights_mgr,
+            
+            sde_noise                     = sde_noise,
+            sde_noise_steps               = sde_noise_steps,
+            
+            rk_swap_step                  = rk_swap_step,
+            rk_swap_print                 = rk_swap_print,
+            rk_swap_threshold             = rk_swap_threshold,
+            rk_swap_type                  = rk_swap_type,
+            
+            steps_to_run                  = steps_to_run,
+            
+            bongmath                      = bongmath,
+            )
+            
+        
+        output, denoised, sde_noise = SharkSampler().main(
+            model           = model, 
+            cfg             = cfg, 
+            scheduler       = scheduler,
+            steps           = steps, 
+            steps_to_run    = steps_to_run,
+            denoise         = denoise,
+            latent_image    = latent_image, 
+            positive        = positive,
+            negative        = negative, 
+            sampler         = sampler, 
+            cfgpp           = cfgpp, 
+            noise_seed      = seed, 
+            options         = options_mgr.as_dict(), 
+            sde_noise       = sde_noise, 
+            sde_noise_steps = sde_noise_steps, 
+            noise_type_init = noise_type_init,
+            noise_stdev     = noise_stdev,
+            sampler_mode    = sampler_mode,
+            denoise_alt     = denoise_alt,
+            sigmas          = sigmas,
+
+            extra_options   = extra_options)
+        
+        return (output, )
\ No newline at end of file
diff --git a/custom_nodes/RES4LYF/beta/samplers_extensions.py b/custom_nodes/RES4LYF/beta/samplers_extensions.py
new file mode 100644
index 0000000000000000000000000000000000000000..0bfcdb811c47c0f6924cdb5524d2e8ed62bbea6d
--- /dev/null
+++ b/custom_nodes/RES4LYF/beta/samplers_extensions.py
@@ -0,0 +1,4638 @@
+import torch
+from torch import Tensor
+import torch.nn.functional as F
+
+from dataclasses import dataclass, asdict
+from typing import Optional, Callable, Tuple, Dict, Any, Union
+import copy
+
+from nodes import MAX_RESOLUTION
+
+from ..latents               import get_edge_mask
+from ..helper                import OptionsManager, FrameWeightsManager, initialize_or_scale, get_res4lyf_scheduler_list, parse_range_string, parse_tile_sizes, parse_range_string_int
+
+from .rk_coefficients_beta   import RK_SAMPLER_NAMES_BETA_FOLDERS, get_default_sampler_name, get_sampler_name_list, process_sampler_name
+
+from .noise_classes          import NOISE_GENERATOR_NAMES_SIMPLE
+from .rk_noise_sampler_beta  import NOISE_MODE_NAMES
+from .constants              import IMPLICIT_TYPE_NAMES, GUIDE_MODE_NAMES_BETA_SIMPLE, MAX_STEPS, FRAME_WEIGHTS_CONFIG_NAMES, FRAME_WEIGHTS_DYNAMICS_NAMES, FRAME_WEIGHTS_SCHEDULE_NAMES
+
+
+class ClownSamplerSelector_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "sampler_name": (get_sampler_name_list(),  {"default": get_default_sampler_name()}), 
+                    },
+                "optional": 
+                    {
+                    }
+                }
+
+    RETURN_TYPES = (RK_SAMPLER_NAMES_BETA_FOLDERS,)
+    RETURN_NAMES = ("sampler_name",) 
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_options"
+    
+    def main(self,
+            sampler_name = "res_2m",
+            ):
+        
+        sampler_name, implicit_sampler_name = process_sampler_name(sampler_name)
+        
+        sampler_name = sampler_name if implicit_sampler_name == "use_explicit" else implicit_sampler_name
+        
+        return (sampler_name,)
+
+
+
+class ClownOptions_SDE_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "noise_type_sde":         (NOISE_GENERATOR_NAMES_SIMPLE, {"default": "gaussian"}),
+                    "noise_type_sde_substep": (NOISE_GENERATOR_NAMES_SIMPLE, {"default": "gaussian"}),
+                    "noise_mode_sde":         (NOISE_MODE_NAMES,             {"default": 'hard',                                                        "tooltip": "How noise scales with the sigma schedule. Hard is the most aggressive, the others start strong and drop rapidly."}),
+                    "noise_mode_sde_substep": (NOISE_MODE_NAMES,             {"default": 'hard',                                                        "tooltip": "How noise scales with the sigma schedule. Hard is the most aggressive, the others start strong and drop rapidly."}),
+                    "eta":                    ("FLOAT",                      {"default": 0.5, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Calculated noise amount to be added, then removed, after each step."}),
+                    "eta_substep":            ("FLOAT",                      {"default": 0.5, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Calculated noise amount to be added, then removed, after each step."}),
+                    "seed":                   ("INT",                        {"default": -1, "min": -1, "max": 0xffffffffffffffff}),
+                    },
+                "optional": 
+                    {
+                    "etas":                   ("SIGMAS", ),
+                    "etas_substep":           ("SIGMAS", ),
+                    "options":                ("OPTIONS", ),   
+                    }
+                }
+
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_options"
+    
+    def main(self,
+            noise_type_sde         = "gaussian",
+            noise_type_sde_substep = "gaussian",
+            noise_mode_sde         = "hard",
+            noise_mode_sde_substep = "hard",
+            eta                    = 0.5,
+            eta_substep            = 0.5,
+            seed             : int = -1,
+            etas             : Optional[Tensor] = None,
+            etas_substep     : Optional[Tensor] = None,
+            options                = None,
+            ): 
+        
+        options = options if options is not None else {}
+        
+        if noise_mode_sde == "none":
+            noise_mode_sde = "hard"
+            eta = 0.0
+            
+        if noise_mode_sde_substep == "none":
+            noise_mode_sde_substep = "hard"
+            eta_substep = 0.0
+            
+        if noise_type_sde == "none":
+            noise_type_sde = "gaussian"
+            eta = 0.0
+            
+        if noise_type_sde_substep == "none":
+            noise_type_sde_substep = "gaussian"
+            eta_substep = 0.0
+            
+        options['noise_type_sde']         = noise_type_sde
+        options['noise_type_sde_substep'] = noise_type_sde_substep
+        options['noise_mode_sde']         = noise_mode_sde
+        options['noise_mode_sde_substep'] = noise_mode_sde_substep
+        options['eta']                    = eta
+        options['eta_substep']            = eta_substep
+        options['noise_seed_sde']         = seed
+        
+        options['etas']                   = etas
+        options['etas_substep']           = etas_substep
+
+        return (options,)
+
+
+
+class ClownOptions_StepSize_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "overshoot_mode":         (NOISE_MODE_NAMES, {"default": 'hard',                                                        "tooltip": "How step size overshoot scales with the sigma schedule. Hard is the most aggressive, the others start strong and drop rapidly."}),
+                    "overshoot_mode_substep": (NOISE_MODE_NAMES, {"default": 'hard',                                                        "tooltip": "How substep size overshoot scales with the sigma schedule. Hard is the most aggressive, the others start strong and drop rapidly."}),
+                    "overshoot":              ("FLOAT",          {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Boost the size of each denoising step, then rescale to match the original. Has a softening effect."}),
+                    "overshoot_substep":      ("FLOAT",          {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Boost the size of each denoising substep, then rescale to match the original. Has a softening effect."}),
+                    },
+                "optional": 
+                    {
+                    "options":                ("OPTIONS", ),   
+                    }
+                }
+
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_options"
+    
+    def main(self,
+            overshoot_mode         = "hard",
+            overshoot_mode_substep = "hard",
+            overshoot              = 0.0,
+            overshoot_substep      = 0.0,
+            options                = None,
+            ): 
+        
+        options = options if options is not None else {}
+            
+        options['overshoot_mode']         = overshoot_mode
+        options['overshoot_mode_substep'] = overshoot_mode_substep
+        options['overshoot']              = overshoot
+        options['overshoot_substep']      = overshoot_substep
+
+        return (options,
+            )
+
+
+@dataclass
+class DetailBoostOptions:
+    noise_scaling_weight : float = 0.0
+    noise_boost_step     : float = 0.0
+    noise_boost_substep  : float = 0.0
+    noise_anchor         : float = 1.0
+    s_noise              : float = 1.0
+    s_noise_substep      : float = 1.0
+    d_noise              : float = 1.0
+
+DETAIL_BOOST_METHODS = [
+    'sampler',
+    'sampler_normal',
+    'sampler_substep',
+    'sampler_substep_normal',
+    'model',
+    'model_alpha',
+    ]
+
+class ClownOptions_DetailBoost_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "weight":     ("FLOAT",              {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set to positive values to create a sharper, grittier, more detailed image. Set to negative values to soften and deepen the colors."}),
+                    "method":     (DETAIL_BOOST_METHODS, {"default": "model",                                                       "tooltip": "Determines whether the sampler or the model underestimates the noise level."}),
+                    #"noise_scaling_mode":    (['linear'] + NOISE_MODE_NAMES,  {"default": 'hard',                                          "tooltip": "Changes the steps where the effect is greatest. Most affect early steps, sinusoidal affects middle steps."}),
+                    "mode":       (NOISE_MODE_NAMES,     {"default": 'hard',                                                        "tooltip": "Changes the steps where the effect is greatest. Most affect early steps, sinusoidal affects middle steps."}),
+                    "eta":        ("FLOAT",              {"default": 0.5, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "The strength of the effect of the noise_scaling_mode. Linear ignores this parameter."}),
+                    "start_step": ("INT",                {"default": 3,   "min": 0,      "max": MAX_STEPS}),
+                    "end_step":   ("INT",                {"default": 10,  "min": -1,     "max": MAX_STEPS}),
+
+                    #"noise_scaling_cycles":  ("INT",              {"default": 1, "min": 1, "max": MAX_STEPS}),
+
+                    #"noise_boost_step":      ("FLOAT",            {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set to positive values to create a sharper, grittier, more detailed image. Set to negative values to soften and deepen the colors."}),
+                    #"noise_boost_substep":   ("FLOAT",            {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set to positive values to create a sharper, grittier, more detailed image. Set to negative values to soften and deepen the colors."}),
+                    #"sampler_scaling_normalize":("BOOLEAN",          {"default": False,                                                          "tooltip": "Limit saturation and luminosity drift."}),
+                    },
+                "optional": 
+                    {
+                    "weights": ("SIGMAS", ),
+                    "etas":    ("SIGMAS", ),
+                    "options":               ("OPTIONS", ),   
+                    }
+                }
+
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_options"
+    
+    def main(self,
+            weight      : float = 0.0,
+            method      : str   = "sampler",
+            mode        : str   = "linear",
+            eta         : float = 0.5,
+            start_step  : int   = 0,
+            end_step    : int   = -1,
+
+
+            noise_scaling_cycles      : int   = 1,
+
+            noise_boost_step          : float = 0.0,
+            noise_boost_substep       : float = 0.0,
+            sampler_scaling_normalize : bool  = False,
+
+            weights     : Optional[Tensor] = None,
+            etas        : Optional[Tensor] = None,
+            
+            options                        = None
+            ):
+        
+        noise_scaling_weight     = weight
+        noise_scaling_type       = method
+        noise_scaling_mode       = mode
+        noise_scaling_eta        = eta
+        noise_scaling_start_step = start_step
+        noise_scaling_end_step   = end_step
+        
+        noise_scaling_weights = weights
+        noise_scaling_etas    = etas
+        
+        
+        options = options if options is not None else {}
+        
+        default_dtype = torch.float64
+        default_device = torch.device('cuda')
+        
+        if noise_scaling_type.endswith("_normal"):
+            sampler_scaling_normalize = True
+            noise_scaling_type = noise_scaling_type[:-7]
+        
+        if noise_scaling_end_step == -1:
+            noise_scaling_end_step = MAX_STEPS
+        
+        if noise_scaling_weights == None: 
+            noise_scaling_weights = initialize_or_scale(None, noise_scaling_weight, MAX_STEPS).to(default_dtype).to(default_device)
+        
+        if noise_scaling_etas == None: 
+            noise_scaling_etas = initialize_or_scale(None, noise_scaling_eta, MAX_STEPS).to(default_dtype).to(default_device)
+        
+        noise_scaling_prepend = torch.zeros((noise_scaling_start_step,), dtype=default_dtype, device=default_device)
+        
+        noise_scaling_weights = torch.cat((noise_scaling_prepend, noise_scaling_weights), dim=0)
+        noise_scaling_etas    = torch.cat((noise_scaling_prepend, noise_scaling_etas),    dim=0)
+
+        if noise_scaling_weights.shape[-1] > noise_scaling_end_step:
+            noise_scaling_weights = noise_scaling_weights[:noise_scaling_end_step]
+            
+        if noise_scaling_etas.shape[-1] > noise_scaling_end_step:
+            noise_scaling_etas = noise_scaling_etas[:noise_scaling_end_step]
+        
+        noise_scaling_weights = F.pad(noise_scaling_weights, (0, MAX_STEPS), value=0.0)
+        noise_scaling_etas = F.pad(noise_scaling_etas, (0, MAX_STEPS), value=0.0)
+        
+        options['noise_scaling_weight']  = noise_scaling_weight
+        options['noise_scaling_type']    = noise_scaling_type
+        options['noise_scaling_mode']    = noise_scaling_mode
+        options['noise_scaling_eta']     = noise_scaling_eta
+        options['noise_scaling_cycles']  = noise_scaling_cycles
+        
+        options['noise_scaling_weights'] = noise_scaling_weights
+        options['noise_scaling_etas']    = noise_scaling_etas
+        
+        options['noise_boost_step']      = noise_boost_step
+        options['noise_boost_substep']   = noise_boost_substep
+        options['noise_boost_normalize'] = sampler_scaling_normalize
+
+        """options['DetailBoostOptions'] = DetailBoostOptions(
+            noise_scaling_weight = noise_scaling_weight,
+            noise_scaling_type    = noise_scaling_type,
+            noise_scaling_mode    = noise_scaling_mode,
+            noise_scaling_eta     = noise_scaling_eta,
+            
+            noise_boost_step      = noise_boost_step,
+            noise_boost_substep   = noise_boost_substep,
+            noise_boost_normalize = noise_boost_normalize,
+            
+            noise_anchor          = noise_anchor,
+            s_noise               = s_noise,
+            s_noise_substep       = s_noise_substep,
+            d_noise               = d_noise
+            d_noise_start_step    = d_noise_start_step
+        )"""
+
+        return (options,)
+
+
+
+
+class ClownOptions_SigmaScaling_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "s_noise":              ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01,                 "tooltip": "Adds extra SDE noise. Values around 1.03-1.07 can lead to a moderate boost in detail and paint textures."}),
+                    "s_noise_substep":      ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01,                 "tooltip": "Adds extra SDE noise. Values around 1.03-1.07 can lead to a moderate boost in detail and paint textures."}),
+                    "noise_anchor_sde":     ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Typically set to between 1.0 and 0.0. Lower values cerate a grittier, more detailed image."}),
+                    
+                    "lying":                ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01,                 "tooltip": "Downscales the sigma schedule. Values around 0.98-0.95 can lead to a large boost in detail and paint textures."}),
+                    "lying_inv":            ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01,                 "tooltip": "Upscales the sigma schedule. Will soften the image and deepen colors. Use after d_noise to counteract desaturation."}),
+                    "lying_start_step":     ("INT",   {"default": 0, "min": 0, "max": MAX_STEPS}),
+                    "lying_inv_start_step": ("INT",   {"default": 1, "min": 0, "max": MAX_STEPS}),
+
+                    },
+                "optional": 
+                    {
+                    "s_noises":             ("SIGMAS", ),
+                    "s_noises_substep":     ("SIGMAS", ),
+                    "options":              ("OPTIONS", ),
+                    }
+                }
+
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_options"
+    
+    def main(self,
+            noise_anchor_sde        : float = 1.0,
+            
+            s_noise                 : float = 1.0,
+            s_noise_substep         : float = 1.0,
+            lying                   : float = 1.0,
+            lying_start_step        : int   = 0,
+            
+            lying_inv               : float = 1.0,
+            lying_inv_start_step    : int   = 1,
+            
+            s_noises                : Optional[Tensor] = None,
+            s_noises_substep        : Optional[Tensor] = None,
+            options                         = None
+            ):
+        
+        options = options if options is not None else {}
+        
+        default_dtype = torch.float64
+        default_device = torch.device('cuda')
+        
+        
+        
+        options['noise_anchor']           = noise_anchor_sde
+        options['s_noise']                = s_noise
+        options['s_noise_substep']        = s_noise_substep
+        options['d_noise']                = lying
+        options['d_noise_start_step']     = lying_start_step
+        options['d_noise_inv']            = lying_inv
+        options['d_noise_inv_start_step'] = lying_inv_start_step
+
+        options['s_noises']                = s_noises
+        options['s_noises_substep']        = s_noises_substep
+
+        return (options,)
+
+
+
+class ClownOptions_FlowGuide:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "sync_eps": ("FLOAT", {"default": 0.75, "min": -10000.0, "max": 10000.0, "step":0.01, "round": False, "tooltip": "Accelerate convergence with positive values when sampling, negative values when unsampling."}),
+                    },
+                "optional": 
+                    {
+                    "options":               ("OPTIONS", ),   
+                    }
+                }
+
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_options"
+    
+    def main(self,
+            sync_eps = 0.75,
+            options  = None
+            ):
+        
+        options = options if options is not None else {}
+            
+        options['flow_sync_eps'] = sync_eps
+
+        return (options,)
+
+
+
+class ClownOptions_Momentum_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "momentum": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step":0.01, "round": False, "tooltip": "Accelerate convergence with positive values when sampling, negative values when unsampling."}),
+                    },
+                "optional": 
+                    {
+                    "options":               ("OPTIONS", ),   
+                    }
+                }
+
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_options"
+    
+    def main(self,
+            momentum = 0.0,
+            options  = None
+            ):
+        
+        options = options if options is not None else {}
+            
+        options['momentum'] = momentum
+
+        return (options,)
+
+
+
+class ClownOptions_ImplicitSteps_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "implicit_type":          (IMPLICIT_TYPE_NAMES, {"default": "bongmath"}), 
+                    "implicit_type_substeps": (IMPLICIT_TYPE_NAMES, {"default": "bongmath"}), 
+                    "implicit_steps":         ("INT",               {"default": 0, "min": 0, "max": 10000}),
+                    "implicit_substeps":      ("INT",               {"default": 0, "min": 0, "max": 10000}),
+                    },
+                "optional": 
+                    {
+                    "options":                ("OPTIONS", ),   
+                    }
+                }
+
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_options"
+    
+    def main(self,
+            implicit_type          = "bongmath",
+            implicit_type_substeps = "bongmath",
+            implicit_steps         = 0,
+            implicit_substeps      = 0,
+            options                = None
+            ): 
+        
+        options = options if options is not None else {}
+            
+        options['implicit_type']          = implicit_type
+        options['implicit_type_substeps'] = implicit_type_substeps
+        options['implicit_steps']         = implicit_steps
+        options['implicit_substeps']      = implicit_substeps
+
+        return (options,)
+
+
+
+class ClownOptions_Cycles_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "cycles"          : ("FLOAT", {"default": 0.0, "min":  0.0,   "max": 10000, "step":0.5,  "round": 0.5}),
+                    "eta_decay_scale" : ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01, "tooltip": "Multiplies etas by this number after every cycle. May help drive convergence." }),
+                    "unsample_eta"    : ("FLOAT", {"default": 0.5, "min": -10000, "max": 10000, "step":0.01}),
+                    "unsampler_override"  : (get_sampler_name_list(), {"default": "none"}),
+                    "unsample_steps_to_run"  : ("INT", {"default": -1, "min":  -1,   "max": 10000, "step":1,  "round": 1}),
+                    "unsample_cfg"    : ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01}),
+                    "unsample_bongmath" : ("BOOLEAN", {"default": False}),
+                    },
+                "optional": 
+                    {
+                    "options":    ("OPTIONS", ),   
+                    }
+                }
+
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_options"
+    
+    def main(self,
+            cycles          = 0,
+            unsample_eta    = 0.5,
+            eta_decay_scale = 1.0,
+            unsample_cfg    = 1.0,
+            unsampler_override  = "none",
+            unsample_steps_to_run = -1,
+            unsample_bongmath = False,
+            options         = None
+            ): 
+        
+        options = options if options is not None else {}
+            
+        options['rebounds']        = int(cycles * 2)
+        options['unsample_eta']    = unsample_eta
+        options['unsampler_name']  = unsampler_override
+        options['eta_decay_scale'] = eta_decay_scale
+        options['unsample_steps_to_run'] = unsample_steps_to_run
+        options['unsample_cfg']    = unsample_cfg
+        options['unsample_bongmath'] = unsample_bongmath
+
+        return (options,)
+
+
+
+
+class SharkOptions_StartStep_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "start_at_step": ("INT", {"default": 0, "min": -1, "max": 10000, "step":1,}),
+
+                    },
+                "optional": 
+                    {
+                    "options":    ("OPTIONS", ),   
+                    }
+                }
+
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_options"
+    
+    def main(self,
+            start_at_step = 0,
+            options       = None
+            ): 
+        
+        options = options if options is not None else {}
+            
+        options['start_at_step'] = start_at_step
+
+        return (options,)
+
+
+
+
+class ClownOptions_Tile_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "tile_width" : ("INT", {"default": 1024, "min": -1, "max": 10000, "step":1,}),
+                    "tile_height": ("INT", {"default": 1024, "min": -1, "max": 10000, "step":1,}),
+                    },
+                "optional": 
+                    {
+                    "options":    ("OPTIONS", ),   
+                    }
+                }
+
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_options"
+    
+    def main(self,
+            tile_height = 1024,
+            tile_width  = 1024,
+            options     = None
+            ): 
+        
+        options = options if options is not None else {}
+        
+        tile_sizes = options.get('tile_sizes', [])
+        tile_sizes.append((tile_height, tile_width))
+        options['tile_sizes'] = tile_sizes
+
+        return (options,)
+
+
+
+class ClownOptions_Tile_Advanced_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "tile_sizes": ("STRING", {"default": "1024,1024", "multiline": True}),   
+                    },
+                "optional": 
+                    {
+                    "options":    ("OPTIONS", ),   
+                    }
+                }
+
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_options"
+    
+    def main(self,
+            tile_sizes = "1024,1024",
+            options    = None
+            ): 
+        
+        options = options if options is not None else {}
+            
+        tiles_height_width = parse_tile_sizes(tile_sizes)
+        options['tile_sizes'] = [(tile[-1], tile[-2]) for tile in tiles_height_width]  # swap height and width to be consistent... width, height
+
+        return (options,)
+
+
+
+
+class ClownOptions_ExtraOptions_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "extra_options": ("STRING", {"default": "", "multiline": True}),   
+                    },
+                "optional": 
+                    {
+                    "options": ("OPTIONS", ),   
+                    }  
+            }
+        
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_options"
+
+    def main(self,
+            extra_options = "",
+            options       = None
+            ):
+
+        options = options if options is not None else {}
+        
+        if 'extra_options' in options:
+            options['extra_options'] += '\n' + extra_options
+        else:
+            options['extra_options']  = extra_options
+
+        return (options, )
+
+
+
+
+class ClownOptions_DenoisedSampling_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "cycles"          : ("FLOAT", {"default": 0.0, "min":  0.0,   "max": 10000, "step":0.5,  "round": 0.5}),
+                    "eta_decay_scale" : ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01, "tooltip": "Multiplies etas by this number after every cycle. May help drive convergence." }),
+                    "unsample_eta"    : ("FLOAT", {"default": 0.5, "min": -10000, "max": 10000, "step":0.01}),
+                    "unsampler_override"  : (get_sampler_name_list(), {"default": "none"}),
+                    "unsample_steps_to_run"  : ("INT", {"default": -1, "min":  -1,   "max": 10000, "step":1,  "round": 1}),
+                    "unsample_cfg"    : ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01}),
+                    "unsample_bongmath" : ("BOOLEAN", {"default": False}),                    
+                    },
+                "optional": 
+                    {
+                    "options": ("OPTIONS", ),   
+                    }  
+            }
+        
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_options"
+
+    def main(self,
+            extra_options = "",
+            options       = None
+            ):
+
+        options = options if options is not None else {}
+        
+        if 'extra_options' in options:
+            options['extra_options'] += '\n' + extra_options
+        else:
+            options['extra_options']  = extra_options
+
+        return (options, )
+
+
+
+
+class ClownOptions_Automation_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {},
+                "optional": {
+                    "etas":             ("SIGMAS", ),
+                    "etas_substep":     ("SIGMAS", ),
+                    "s_noises":         ("SIGMAS", ),
+                    "s_noises_substep": ("SIGMAS", ),
+                    "epsilon_scales":   ("SIGMAS", ),
+                    "frame_weights":    ("SIGMAS", ),
+                    "options":          ("OPTIONS",),  
+                    }  
+                }
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_options"
+
+    def main(self,
+            etas             = None,
+            etas_substep     = None,
+            s_noises         = None,
+            s_noises_substep = None,
+            epsilon_scales   = None,
+            frame_weights    = None,
+            options          = None
+            ):
+                
+        options = options if options is not None else {}
+            
+        options_mgr = OptionsManager(options)
+
+        frame_weights_mgr = options_mgr.get("frame_weights_mgr")
+        if frame_weights_mgr is None and frame_weights is not None:
+            frame_weights_mgr = FrameWeightsManager()
+            frame_weights_mgr.set_custom_weights("frame_weights", frame_weights)
+            
+        automation = {
+            "etas"              : etas,
+            "etas_substep"      : etas_substep,
+            "s_noises"          : s_noises,
+            "s_noises_substep"  : s_noises_substep,
+            "epsilon_scales"    : epsilon_scales,
+            "frame_weights_mgr" : frame_weights_mgr,
+        }
+        
+        options["automation"] = automation
+
+        return (options, )
+
+
+
+
+
+class SharkOptions_GuideCond_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {},
+                "optional": {
+                    "positive" : ("CONDITIONING", ),
+                    "negative" : ("CONDITIONING", ),
+                    "cfg"      : ("FLOAT",   {"default": 1.0, "min": -10000, "max": 10000, "step":0.01}),
+                    "options"  : ("OPTIONS",),  
+                    }  
+                }
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_options"
+
+    def main(self,
+            positive = None,
+            negative = None,
+            cfg      = 1.0,
+            options  = None,
+            ):
+                
+        options = options if options is not None else {}
+
+        flow_cond = {
+            "yt_positive" : positive,
+            "yt_negative" : negative,
+            "yt_cfg"      : cfg,
+        }
+        
+        options["flow_cond"] = flow_cond
+
+        return (options, )
+
+
+
+
+class SharkOptions_GuideConds_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {},
+                "optional": {
+                    "positive_masked"   : ("CONDITIONING", ),
+                    "positive_unmasked" : ("CONDITIONING", ),
+                    "negative_masked"   : ("CONDITIONING", ),
+                    "negative_unmasked" : ("CONDITIONING", ),
+                    "cfg_masked"        : ("FLOAT",   {"default": 1.0, "min": -10000, "max": 10000, "step":0.01}),
+                    "cfg_unmasked"      : ("FLOAT",   {"default": 1.0, "min": -10000, "max": 10000, "step":0.01}),
+                    "options"           : ("OPTIONS",),  
+                    }  
+                }
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_options"
+
+    def main(self,
+            positive_masked   = None,
+            negative_masked   = None,
+            cfg_masked        = 1.0,
+            positive_unmasked = None,
+            negative_unmasked = None,
+            cfg_unmasked      = 1.0,
+            options  = None,
+            ):
+                
+        options = options if options is not None else {}
+
+        flow_cond = {
+            "yt_positive"     : positive_masked,
+            "yt_negative"     : negative_masked,
+            "yt_cfg"          : cfg_masked,
+            "yt_inv_positive" : positive_unmasked,
+            "yt_inv_negative" : negative_unmasked,
+            "yt_inv_cfg"      : cfg_unmasked,
+        }
+        
+        options["flow_cond"] = flow_cond
+
+        return (options, )
+
+
+
+
+
+class SharkOptions_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "noise_type_init": (NOISE_GENERATOR_NAMES_SIMPLE, {"default": "gaussian"}),
+                "s_noise_init":    ("FLOAT",                      {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":0.01, "round": False, }),
+                "denoise_alt":     ("FLOAT",                      {"default": 1.0, "min": -10000,   "max": 10000,   "step":0.01}),
+                "channelwise_cfg": ("BOOLEAN",                    {"default": False}),
+                },
+            "optional": {
+                "options":         ("OPTIONS", ),   
+                }
+            }
+
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_options"
+    
+    def main(self,
+            noise_type_init = "gaussian",
+            s_noise_init    = 1.0,
+            denoise_alt     = 1.0,
+            channelwise_cfg = False,
+            options         = None
+            ): 
+        
+        options = options if options is not None else {}
+            
+        options['noise_type_init']  = noise_type_init
+        options['noise_init_stdev'] = s_noise_init
+        options['denoise_alt']      = denoise_alt
+        options['channelwise_cfg']  = channelwise_cfg
+
+        return (options,)
+    
+
+
+
+class SharkOptions_UltraCascade_Latent_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "width":   ("INT", {"default": 60, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
+                "height":  ("INT", {"default": 36, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
+                },
+            "optional": {
+                "options": ("OPTIONS",),   
+                }
+            }
+
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_options"
+    
+    def main(self,
+            width  : int = 60,
+            height : int = 36,
+            options       = None,
+            ): 
+        
+        options = options if options is not None else {}
+            
+        options['ultracascade_latent_width']  = width
+        options['ultracascade_latent_height'] = height
+
+        return (options,)
+
+
+
+
+class ClownOptions_SwapSampler_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "sampler_name":       (get_sampler_name_list(), {"default": get_default_sampler_name()}), 
+                "swap_below_err":     ("FLOAT",                 {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Swap samplers if the error per step falls below this threshold."}),
+                "swap_at_step":       ("INT",                   {"default": 30,  "min": 1,      "max": 10000}),
+                "log_err_to_console": ("BOOLEAN",               {"default": False}),
+                },
+            "optional": {
+                "options":            ("OPTIONS", ),   
+                }
+            }
+
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_options"
+    
+    def main(self,
+            sampler_name       = "res_3m",
+            swap_below_err     = 0.0,
+            swap_at_step       = 30,
+            log_err_to_console = False,
+            options            = None,
+            ): 
+        
+        sampler_name, implicit_sampler_name = process_sampler_name(sampler_name)
+        
+        sampler_name = sampler_name if implicit_sampler_name == "use_explicit" else implicit_sampler_name
+                
+        options = options if options is not None else {}
+            
+        options['rk_swap_type']      = sampler_name
+        options['rk_swap_threshold'] = swap_below_err
+        options['rk_swap_step']      = swap_at_step
+        options['rk_swap_print']     = log_err_to_console
+
+        return (options,)
+    
+    
+
+    
+    
+class ClownOptions_SDE_Mask_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "max":               ("FLOAT",                                     {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Clamp the max value for the mask."}),
+                "min":               ("FLOAT",                                     {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Clamp the min value for the mask."}),
+                "invert_mask":       ("BOOLEAN",                                   {"default": False}),
+                },
+            "optional": {
+                "mask":              ("MASK", ),
+                "options":           ("OPTIONS", ),   
+                }
+            }
+
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_options"
+    
+    def main(self,
+            max = 1.0,
+            min = 0.0,
+            invert_mask = False,
+            mask     = None,
+            options      = None,
+            ): 
+        
+        options = copy.deepcopy(options) if options is not None else {}
+        
+        if invert_mask:
+            mask = 1-mask
+        
+        mask = ((mask - mask.min()) * (max - min)) / (mask.max() - mask.min()) + min    
+        
+        options['sde_mask'] = mask
+
+        return (options,)
+
+
+
+
+class ClownGuide_Mean_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "weight":               ("FLOAT",                                     {"default": 0.75, "min":  -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
+                    "cutoff":               ("FLOAT",                                     {"default": 1.0,  "min":  0.0,    "max": 1.0,   "step":0.01, "round": False, "tooltip": "Disables the guide for the next step when the denoised image is similar to the guide. Higher values will strengthen the effect."}),
+                    "weight_scheduler":     (["constant"] + get_res4lyf_scheduler_list(), {"default": "beta57"},),
+                    "start_step":           ("INT",                                       {"default": 0,    "min":  0,      "max": 10000}),
+                    "end_step":             ("INT",                                       {"default": 15,   "min": -1,      "max": 10000}),
+                    "invert_mask":          ("BOOLEAN",                                   {"default": False}),
+                    },
+                "optional": 
+                    {
+                    "guide":                ("LATENT", ),
+                    "mask":                 ("MASK", ),
+                    "weights":              ("SIGMAS", ),
+                    "guides":               ("GUIDES", ),
+                    }  
+                }
+        
+    RETURN_TYPES = ("GUIDES",)
+    RETURN_NAMES = ("guides",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_extensions"
+
+    def main(self,
+            weight_scheduler          = "constant",
+            start_step                = 0,
+            end_step                  = 30,
+            cutoff                    = 1.0,
+            guide                     = None,
+            weight                    = 0.0,
+
+            channelwise_mode          = False,
+            projection_mode           = False,
+            weights                   = None,
+            mask                      = None,
+            invert_mask               = False,
+            
+            guides                    = None,
+            ):
+        
+        default_dtype = torch.float64
+        
+        mask = 1-mask if mask is not None else None
+        
+        if end_step == -1:
+            end_step = MAX_STEPS
+        
+        if guide is not None:
+            raw_x = guide.get('state_info', {}).get('raw_x', None)
+            if raw_x is not None:
+                guide          = {'samples': guide['state_info']['raw_x'].clone()}
+            else:
+                guide          = {'samples': guide['samples'].clone()}
+        
+        if weight_scheduler == "constant": # and weights == None: 
+            weights = initialize_or_scale(None, weight, end_step).to(default_dtype)
+            weights = F.pad(weights, (0, MAX_STEPS), value=0.0)
+            
+        guides = copy.deepcopy(guides) if guides is not None else {}
+        
+        guides['weight_mean']           = weight
+        guides['weights_mean']          = weights
+        guides['guide_mean']            = guide
+        guides['mask_mean']             = mask
+        
+        guides['weight_scheduler_mean'] = weight_scheduler
+        guides['start_step_mean']       = start_step
+        guides['end_step_mean']         = end_step
+        
+        guides['cutoff_mean']           = cutoff
+        
+        return (guides, )
+
+
+
+class ClownGuide_FrequencySeparation:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "apply_to"       : (["AdaIN"], {"default": "AdaIN"}),
+                    "method"         : (["gaussian", "gaussian_pw", "median", "median_pw",], {"default": "median"}),
+                    "sigma":             ("FLOAT", {"default": 3.0, "min":  -10000.0, "max": 10000.0, "step":0.01, "round": False, "tooltip": "Low values produce results closer to the guide image. No effect with median."}),
+                    "kernel_size":       ("INT",   {"default": 8,    "min":  1,      "max": 11111, "step": 1, "tooltip": "Primary control with median. Set the Re___Patcher node to float32 or lower precision if you have OOMs. You may have them regardless at higher kernel sizes with median."}),
+                    "inner_kernel_size": ("INT",   {"default": 2,    "min":  1,      "max": 11111, "step": 1, "tooltip": "Should be equal to, or less than, kernel_size."}),
+                    "stride":            ("INT",   {"default": 2,    "min":  1,      "max": 11111, "step": 1, "tooltip": "Should be equal to, or less than, inner_kernel_size."}),
+
+
+                    "lowpass_weight":    ("FLOAT", {"default": 1.0, "min":  -10000.0, "max": 10000.0, "step":0.01, "round": False, "tooltip": "Typically should be set to 1.0. Lower values may sharpen the image, higher values may blur the image."}),
+                    "highpass_weight":   ("FLOAT", {"default": 1.0, "min":  -10000.0, "max": 10000.0, "step":0.01, "round": False, "tooltip": "Typically should be set to 1.0. Higher values may sharpen the image, lower values may blur the image."}),
+
+                    "guides":            ("GUIDES", ),
+                    },
+                "optional": 
+                    {
+                    "mask"           : ("MASK",),
+                    }  
+                }
+    
+    RETURN_TYPES = ("GUIDES",)
+    RETURN_NAMES = ("guides",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_extensions"
+    EXPERIMENTAL = True
+
+    def main(self,
+            apply_to       = "AdaIN",
+            method         = "median",
+            sigma          = 3.0,
+            kernel_size       = 9,
+            inner_kernel_size = 2,
+            stride            = 2,
+            lowpass_weight    = 1.0,
+            highpass_weight   = 1.0,
+            guides            = None,
+            mask              = None,
+            ):
+        
+        guides = copy.deepcopy(guides) if guides is not None else {}
+        
+        guides['freqsep_apply_to']       = apply_to
+        guides['freqsep_lowpass_method'] = method
+        guides['freqsep_sigma']          = sigma
+        guides['freqsep_kernel_size']    = kernel_size
+        guides['freqsep_inner_kernel_size']    = inner_kernel_size
+        guides['freqsep_stride']         = stride
+
+        guides['freqsep_lowpass_weight'] = lowpass_weight
+        guides['freqsep_highpass_weight']= highpass_weight
+        guides['freqsep_mask']           = mask
+
+        return (guides, )
+
+
+
+
+class ClownGuide_Style_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "apply_to":         (["positive", "negative", "denoised"],           {"default": "positive", "tooltip": "When using CFG, decides whether to apply the guide to the positive or negative conditioning."}),
+                    "method":           (["AdaIN", "WCT", "WCT2", "scattersort","none"], {"default": "WCT"}),
+                    "weight":           ("FLOAT",                                        {"default": 1.0, "min":  -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide by multiplying all other weights by this value."}),
+                    "synweight":        ("FLOAT",                                        {"default": 1.0, "min":  -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the relative strength of the guide on the opposite conditioning to what was selected: i.e., negative if positive in apply_to. Recommended to avoid CFG burn."}),
+                    "weight_scheduler": (["constant"] + get_res4lyf_scheduler_list(),    {"default": "constant", "tooltip": "Selecting any scheduler except constant will cause the strength to gradually decay to zero. Try beta57 vs. linear quadratic."},),
+                    "start_step":       ("INT",                                          {"default": 0,    "min":  0,      "max": 10000}),
+                    "end_step":         ("INT",                                          {"default": -1,   "min": -1,      "max": 10000}),
+                    "invert_mask":      ("BOOLEAN",                                      {"default": False}),
+                    },
+                "optional": 
+                    {
+                    "guide":            ("LATENT", ),
+                    "mask":             ("MASK", ),
+                    "weights":          ("SIGMAS", ),
+                    "guides":           ("GUIDES", ),
+                    }  
+                }
+    
+    RETURN_TYPES = ("GUIDES",)
+    RETURN_NAMES = ("guides",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_extensions"
+    DESCRIPTION  = "Transfer some visual aspects of style from a guide (reference) image. If nothing about style is specified in the prompt, it may just transfer the lighting and color scheme." + \
+                "If using CFG results in burn, or a very dark/bright image in the preview followed by a bad output, try duplicating and chaining this node, so that the guide may be applied to both positive and negative conditioning." + \
+                "Currently supported models: SD1.5, SDXL, Stable Cascade, SD3.5, AuraFlow, Flux, HiDream, WAN, and LTXV."
+
+    def main(self,
+            apply_to         = "all",
+            method           = "WCT",
+            weight           = 1.0,
+            synweight        = 1.0,
+            weight_scheduler = "constant",
+            start_step       = 0,
+            end_step         = 15,
+            invert_mask      = False,
+            
+            guide            = None,
+            mask             = None,
+            weights          = None,
+            guides           = None,
+            ):
+        
+        default_dtype = torch.float64
+        
+        mask = 1-mask if mask is not None else None
+        
+        if end_step == -1:
+            end_step = MAX_STEPS
+        
+        if guide is not None:
+            raw_x = guide.get('state_info', {}).get('raw_x', None)
+            if raw_x is not None:
+                guide          = {'samples': guide['state_info']['raw_x'].clone()}
+            else:
+                guide          = {'samples': guide['samples'].clone()}
+        
+        if weight_scheduler == "constant": # and weights == None: 
+            weights = initialize_or_scale(None, weight, end_step).to(default_dtype)
+            prepend = torch.zeros(start_step).to(weights)
+            weights = torch.cat([prepend, weights])
+            weights = F.pad(weights, (0, MAX_STEPS), value=0.0)
+            
+        guides = copy.deepcopy(guides) if guides is not None else {}
+        
+        guides['style_method'] = method
+        
+        if apply_to in {"positive", "all"}:
+        
+            guides['weight_style_pos']           = weight
+            guides['weights_style_pos']          = weights
+
+            guides['synweight_style_pos']        = synweight
+
+            guides['guide_style_pos']            = guide
+            guides['mask_style_pos']             = mask
+
+            guides['weight_scheduler_style_pos'] = weight_scheduler
+            guides['start_step_style_pos']       = start_step
+            guides['end_step_style_pos']         = end_step
+            
+        if apply_to in {"negative", "all"}:
+            guides['weight_style_neg']           = weight
+            guides['weights_style_neg']          = weights
+
+            guides['synweight_style_neg']        = synweight
+
+            guides['guide_style_neg']            = guide
+            guides['mask_style_neg']             = mask
+
+            guides['weight_scheduler_style_neg'] = weight_scheduler
+            guides['start_step_style_neg']       = start_step
+            guides['end_step_style_neg']         = end_step
+        
+        if apply_to in {"denoised", "all"}:
+        
+            guides['weight_style_denoised']           = weight
+            guides['weights_style_denoised']          = weights
+
+            guides['synweight_style_denoised']        = synweight
+
+            guides['guide_style_denoised']            = guide
+            guides['mask_style_denoised']             = mask
+
+            guides['weight_scheduler_style_denoised'] = weight_scheduler
+            guides['start_step_style_denoised']       = start_step
+            guides['end_step_style_denoised']         = end_step
+            
+        return (guides, )
+
+
+
+
+
+
+class ClownGuide_Style_EdgeWidth:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "edge_width":       ("INT",     {"default": 20,  "min":  1,   "max": 10000}),
+                    },
+                "optional": 
+                    {
+                    "guides":           ("GUIDES", ),
+                    }  
+                }
+    
+    RETURN_TYPES = ("GUIDES",)
+    RETURN_NAMES = ("guides",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_extensions"
+    DESCRIPTION  = "Set an edge mask for some style guide types such as scattersort. Can help mitigate seams."
+
+    def main(self,
+            edge_width = 20,
+            guides     = None,
+            ):
+        
+        guides = copy.deepcopy(guides) if guides is not None else {}
+        
+        if guides.get('mask_style_pos') is not None:
+            guides['mask_edge_style_pos'] = get_edge_mask(guides.get('mask_style_pos'), edge_width)
+            
+        if guides.get('mask_style_neg') is not None:
+            guides['mask_edge_style_neg'] = get_edge_mask(guides.get('mask_style_neg'), edge_width)
+        
+        return (guides, )
+
+
+
+
+
+class ClownGuide_Style_TileSize:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "height": ("INT",     {"default": 128,  "min":  16,   "max": 10000, "step": 16}),
+                    "width" : ("INT",     {"default": 128,  "min":  16,   "max": 10000, "step": 16}),
+                    "padding" : ("INT",     {"default": 64,  "min":  0,   "max": 10000, "step": 16}),
+                    },
+                "optional": 
+                    {
+                    "guides":           ("GUIDES", ),
+                    }  
+                }
+    
+    RETURN_TYPES = ("GUIDES",)
+    RETURN_NAMES = ("guides",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_extensions"
+    DESCRIPTION  = "Set a tile size for some style guide types such as scattersort. Can improve adherence to the input image."
+
+    def main(self,
+            height = 128,
+            width  = 128,
+            padding = 64,
+            guides = None,
+            ):
+        
+        guides = copy.deepcopy(guides) if guides is not None else {}
+        
+        guides['style_tile_height']  = height  // 16
+        guides['style_tile_width']   = width   // 16
+        guides['style_tile_padding'] = padding // 16
+
+        return (guides, )
+
+
+
+
+
+
+
+
+
+class ClownGuides_Sync:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "weight_masked":               ("FLOAT",                                     {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
+                    "weight_unmasked":             ("FLOAT",                                     {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide_bkg."}),
+                    "weight_scheduler_masked":     (["constant"] + get_res4lyf_scheduler_list(), {"default": "beta57"},),
+                    "weight_scheduler_unmasked":   (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
+                    "weight_start_step_masked":           ("INT",                                       {"default": 0,    "min":  0,      "max": 10000}),
+                    "weight_start_step_unmasked":         ("INT",                                       {"default": 0,    "min":  0,      "max": 10000}),
+                    "weight_end_step_masked":             ("INT",                                       {"default": 15,   "min": -1,      "max": 10000}),
+                    "weight_end_step_unmasked":           ("INT",                                       {"default": 15,   "min": -1,      "max": 10000}),
+                    
+                    "sync_masked":               ("FLOAT",                                     {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
+                    "sync_unmasked":             ("FLOAT",                                     {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide_bkg."}),
+                    "sync_scheduler_masked":     (["constant"] + get_res4lyf_scheduler_list(), {"default": "beta57"},),
+                    "sync_scheduler_unmasked":   (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
+                    "sync_start_step_masked":           ("INT",                                       {"default": 0,    "min":  0,      "max": 10000}),
+                    "sync_start_step_unmasked":         ("INT",                                       {"default": 0,    "min":  0,      "max": 10000}),
+                    "sync_end_step_masked":             ("INT",                                       {"default": 15,   "min": -1,      "max": 10000}),
+                    "sync_end_step_unmasked":           ("INT",                                       {"default": 15,   "min": -1,      "max": 10000}),
+                    "invert_mask":                 ("BOOLEAN",                                   {"default": False}),
+                    },
+                "optional": 
+                    {
+                    "guide_masked":                ("LATENT", ),
+                    "guide_unmasked":              ("LATENT", ),
+                    "mask":                        ("MASK", ),
+                    "weights_masked":              ("SIGMAS", ),
+                    "weights_unmasked":            ("SIGMAS", ),
+                    "syncs_masked":              ("SIGMAS", ),
+                    "syncs_unmasked":            ("SIGMAS", ),
+                    }  
+                }
+        
+    RETURN_TYPES = ("GUIDES",)
+    RETURN_NAMES = ("guides",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_extensions"
+    EXPERIMENTAL = True
+
+    def main(self,
+            weight_masked              = 0.0,
+            weight_unmasked            = 0.0,
+            weight_scheduler_masked    = "constant",
+            weight_scheduler_unmasked  = "constant",
+            weight_start_step_masked   = 0,
+            weight_start_step_unmasked = 0,
+            weight_end_step_masked     = 30,
+            weight_end_step_unmasked   = 30,
+
+            sync_masked                = 0.0,
+            sync_unmasked              = 0.0,
+            sync_scheduler_masked      = "constant",
+            sync_scheduler_unmasked    = "constant",
+            sync_start_step_masked     = 0,
+            sync_start_step_unmasked   = 0,
+            sync_end_step_masked       = 30,
+            sync_end_step_unmasked     = 30,
+
+            guide_masked               = None,
+            guide_unmasked             = None,
+            
+            weights_masked             = None,
+            weights_unmasked           = None,
+            syncs_masked               = None,
+            syncs_unmasked             = None,
+            mask                       = None,
+            unmask                     = None,
+            invert_mask                = False,
+            
+            guide_mode                 = "sync",
+            channelwise_mode           = False,
+            projection_mode            = False,
+            
+            cutoff_masked              = 1.0,
+            cutoff_unmasked            = 1.0,
+            ):
+
+        default_dtype = torch.float64
+        
+        if weight_end_step_masked   == -1:
+            weight_end_step_masked   = MAX_STEPS
+        if weight_end_step_unmasked == -1:
+            weight_end_step_unmasked = MAX_STEPS
+            
+        if sync_end_step_masked   == -1:
+            sync_end_step_masked   = MAX_STEPS
+        if sync_end_step_unmasked == -1:
+            sync_end_step_unmasked = MAX_STEPS
+        
+        if guide_masked is None:
+            weight_scheduler_masked = "constant"
+            weight_start_step_masked       = 0
+            weight_end_step_masked         = 30
+            weight_masked           = 0.0
+            weights_masked          = None
+            
+            sync_scheduler_masked = "constant"
+            sync_start_step_masked       = 0
+            sync_end_step_masked         = 30
+            sync_masked           = 0.0
+            syncs_masked          = None
+        
+        if guide_unmasked is None:
+            weight_scheduler_unmasked = "constant"
+            weight_start_step_unmasked       = 0
+            weight_end_step_unmasked         = 30
+            weight_unmasked           = 0.0
+            weights_unmasked          = None
+        
+            sync_scheduler_unmasked = "constant"
+            sync_start_step_unmasked       = 0
+            sync_end_step_unmasked         = 30
+            sync_unmasked           = 0.0
+            syncs_unmasked          = None
+        
+        if guide_masked is not None:
+            raw_x = guide_masked.get('state_info', {}).get('raw_x', None)
+            if False: #raw_x is not None:
+                guide_masked   = {'samples': guide_masked['state_info']['raw_x'].clone()}
+            else:
+                guide_masked   = {'samples': guide_masked['samples'].clone()}
+        
+        if guide_unmasked is not None:
+            raw_x = guide_unmasked.get('state_info', {}).get('raw_x', None)
+            if False: #raw_x is not None:
+                guide_unmasked = {'samples': guide_unmasked['state_info']['raw_x'].clone()}
+            else:
+                guide_unmasked = {'samples': guide_unmasked['samples'].clone()}
+        
+        if invert_mask and mask is not None:
+            mask = 1-mask
+                
+        if projection_mode:
+            guide_mode = guide_mode + "_projection"
+        
+        if channelwise_mode:
+            guide_mode = guide_mode + "_cw"
+            
+        if guide_mode == "unsample_cw":
+            guide_mode = "unsample"
+        if guide_mode == "resample_cw":
+            guide_mode = "resample"
+        
+        if weight_scheduler_masked == "constant" and weights_masked == None: 
+            weights_masked = initialize_or_scale(None, weight_masked, weight_end_step_masked).to(default_dtype)
+            prepend      = torch.zeros(weight_start_step_masked, dtype=default_dtype, device=weights_masked.device)
+            weights_masked = torch.cat((prepend, weights_masked), dim=0)
+            weights_masked = F.pad(weights_masked, (0, MAX_STEPS), value=0.0)
+        
+        if weight_scheduler_unmasked == "constant" and weights_unmasked == None: 
+            weights_unmasked = initialize_or_scale(None, weight_unmasked, weight_end_step_unmasked).to(default_dtype)
+            prepend      = torch.zeros(weight_start_step_unmasked, dtype=default_dtype, device=weights_unmasked.device)
+            weights_unmasked = torch.cat((prepend, weights_unmasked), dim=0)
+            weights_unmasked = F.pad(weights_unmasked, (0, MAX_STEPS), value=0.0)
+        
+        # Values for the sync scheduler will be inverted in rk_guide_func_beta.py as it's easier to understand:
+        # makes it so that a sync weight of 1.0 = full guide strength (which previously was 0.0)
+        if sync_scheduler_masked == "constant" and syncs_masked == None: 
+            syncs_masked = initialize_or_scale(None, sync_masked, sync_end_step_masked).to(default_dtype)
+            prepend      = torch.zeros(sync_start_step_masked, dtype=default_dtype, device=syncs_masked.device)
+            syncs_masked = torch.cat((prepend, syncs_masked), dim=0)
+            syncs_masked = F.pad(syncs_masked, (0, MAX_STEPS), value=0.0)
+        
+        if sync_scheduler_unmasked == "constant" and syncs_unmasked == None: 
+            syncs_unmasked = initialize_or_scale(None, sync_unmasked, sync_end_step_unmasked).to(default_dtype)
+            prepend      = torch.zeros(sync_start_step_unmasked, dtype=default_dtype, device=syncs_unmasked.device)
+            syncs_unmasked = torch.cat((prepend, syncs_unmasked), dim=0)
+            syncs_unmasked = F.pad(syncs_unmasked, (0, MAX_STEPS), value=0.0)
+        
+        guides = {
+            "guide_mode"                : guide_mode,
+
+            "guide_masked"              : guide_masked,
+            "guide_unmasked"            : guide_unmasked,
+            "mask"                      : mask,
+            "unmask"                    : unmask,
+
+            "weight_masked"             : weight_masked,
+            "weight_unmasked"           : weight_unmasked,
+            "weight_scheduler_masked"   : weight_scheduler_masked,
+            "weight_scheduler_unmasked" : weight_scheduler_unmasked,
+            "start_step_masked"         : weight_start_step_masked,
+            "start_step_unmasked"       : weight_start_step_unmasked,
+            "end_step_masked"           : weight_end_step_masked,
+            "end_step_unmasked"         : weight_end_step_unmasked,
+            
+            "weights_masked"            : weights_masked,
+            "weights_unmasked"          : weights_unmasked,
+            
+            "weight_masked_sync"             : sync_masked,
+            "weight_unmasked_sync"           : sync_unmasked,
+            "weight_scheduler_masked_sync"   : sync_scheduler_masked,
+            "weight_scheduler_unmasked_sync" : sync_scheduler_unmasked,
+            "start_step_masked_sync"         : sync_start_step_masked,
+            "start_step_unmasked_sync"       : sync_start_step_unmasked,
+            "end_step_masked_sync"           : sync_end_step_masked,
+            "end_step_unmasked_sync"         : sync_end_step_unmasked,
+            
+            "weights_masked_sync"            : syncs_masked,
+            "weights_unmasked_sync"          : syncs_unmasked,
+            
+            "cutoff_masked"             : cutoff_masked,
+            "cutoff_unmasked"           : cutoff_unmasked
+        }
+        
+        
+        return (guides, )
+
+
+
+
+
+
+class ClownGuides_Sync_Advanced:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "weight_masked":               ("FLOAT",                                     {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
+                    "weight_unmasked":             ("FLOAT",                                     {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide_bkg."}),
+                    "weight_scheduler_masked":     (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
+                    "weight_scheduler_unmasked":   (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
+                    "weight_start_step_masked":    ("INT",                                       {"default": 0,    "min":  0,      "max": 10000}),
+                    "weight_start_step_unmasked":  ("INT",                                       {"default": 0,    "min":  0,      "max": 10000}),
+                    "weight_end_step_masked":      ("INT",                                       {"default": 30,   "min": -1,      "max": 10000}),
+                    "weight_end_step_unmasked":    ("INT",                                       {"default": -1,   "min": -1,      "max": 10000}),
+                    
+                    "sync_masked":                 ("FLOAT",                                     {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
+                    "sync_unmasked":               ("FLOAT",                                     {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide_bkg."}),
+                    "sync_scheduler_masked":       (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
+                    "sync_scheduler_unmasked":     (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
+                    "sync_start_step_masked":      ("INT",                                       {"default": 0,    "min":  0,      "max": 10000}),
+                    "sync_start_step_unmasked":    ("INT",                                       {"default": 0,    "min":  0,      "max": 10000}),
+                    "sync_end_step_masked":        ("INT",                                       {"default": -1,   "min": -1,      "max": 10000}),
+                    "sync_end_step_unmasked":      ("INT",                                       {"default": -1,   "min": -1,      "max": 10000}),
+                    
+                    "drift_x_data":                ("FLOAT",                                     {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
+                    "drift_x_sync":                ("FLOAT",                                     {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
+                    "drift_x_masked":              ("FLOAT",                                     {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
+                    "drift_x_unmasked":            ("FLOAT",                                     {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide_bkg."}),
+                    "drift_x_scheduler_masked":    (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
+                    "drift_x_scheduler_unmasked":  (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
+                    "drift_x_start_step_masked":   ("INT",                                       {"default": 0,    "min":  0,      "max": 10000}),
+                    "drift_x_start_step_unmasked": ("INT",                                       {"default": 0,    "min":  0,      "max": 10000}),
+                    "drift_x_end_step_masked":     ("INT",                                       {"default": -1,   "min": -1,      "max": 10000}),
+                    "drift_x_end_step_unmasked":   ("INT",                                       {"default": -1,   "min": -1,      "max": 10000}),
+                    
+                    "drift_y_data":                ("FLOAT",                                     {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
+                    "drift_y_sync":                ("FLOAT",                                     {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
+                    "drift_y_guide":               ("FLOAT",                                     {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
+                    "drift_y_masked":              ("FLOAT",                                     {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
+                    "drift_y_unmasked":            ("FLOAT",                                     {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide_bkg."}),
+                    "drift_y_scheduler_masked":    (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
+                    "drift_y_scheduler_unmasked":  (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
+                    "drift_y_start_step_masked":   ("INT",                                       {"default": 0,    "min":  0,      "max": 10000}),
+                    "drift_y_start_step_unmasked": ("INT",                                       {"default": 0,    "min":  0,      "max": 10000}),
+                    "drift_y_end_step_masked":     ("INT",                                       {"default": -1,   "min": -1,      "max": 10000}),
+                    "drift_y_end_step_unmasked":   ("INT",                                       {"default": -1,   "min": -1,      "max": 10000}),
+                    
+                    "lure_x_masked":               ("FLOAT",                                     {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
+                    "lure_x_unmasked":             ("FLOAT",                                     {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide_bkg."}),
+                    "lure_x_scheduler_masked":     (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
+                    "lure_x_scheduler_unmasked":   (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
+                    "lure_x_start_step_masked":    ("INT",                                       {"default": 0,    "min":  0,      "max": 10000}),
+                    "lure_x_start_step_unmasked":  ("INT",                                       {"default": 0,    "min":  0,      "max": 10000}),
+                    "lure_x_end_step_masked":      ("INT",                                       {"default": -1,   "min": -1,      "max": 10000}),
+                    "lure_x_end_step_unmasked":    ("INT",                                       {"default": -1,   "min": -1,      "max": 10000}),
+                    
+                    "lure_y_masked":               ("FLOAT",                                     {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
+                    "lure_y_unmasked":             ("FLOAT",                                     {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide_bkg."}),
+                    "lure_y_scheduler_masked":     (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
+                    "lure_y_scheduler_unmasked":   (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
+                    "lure_y_start_step_masked":    ("INT",                                       {"default": 0,    "min":  0,      "max": 10000}),
+                    "lure_y_start_step_unmasked":  ("INT",                                       {"default": 0,    "min":  0,      "max": 10000}),
+                    "lure_y_end_step_masked":      ("INT",                                       {"default": -1,   "min": -1,      "max": 10000}),
+                    "lure_y_end_step_unmasked":    ("INT",                                       {"default": -1,   "min": -1,      "max": 10000}),
+                    
+                    "lure_iter":          ("INT",                                       {"default": 0,   "min": 0,      "max": 10000}),
+                    "lure_sequence":      (["x -> y", "y -> x", "xy -> xy"],                                   {"default": "y -> x"}),
+                    
+                    "invert_mask":        ("BOOLEAN",                                   {"default": False}),
+                    "invert_mask_sync":   ("BOOLEAN",                                   {"default": False}),
+                    "invert_mask_drift_x": ("BOOLEAN",                                  {"default": False}),
+                    "invert_mask_drift_y": ("BOOLEAN",                                  {"default": False}),
+                    "invert_mask_lure_x": ("BOOLEAN",                                   {"default": False}),
+                    "invert_mask_lure_y": ("BOOLEAN",                                   {"default": False}),
+
+                    },
+                "optional": 
+                    {
+                    "guide_masked":       ("LATENT", ),
+                    "guide_unmasked":     ("LATENT", ),
+                    "mask":               ("MASK", ),
+                    "mask_sync":          ("MASK", ),
+                    "mask_drift_x":        ("MASK", ),
+                    "mask_drift_y":        ("MASK", ),
+                    "mask_lure_x":        ("MASK", ),
+                    "mask_lure_y":        ("MASK", ),
+                    "weights_masked":     ("SIGMAS", ),
+                    "weights_unmasked":   ("SIGMAS", ),
+                    "syncs_masked":       ("SIGMAS", ),
+                    "syncs_unmasked":     ("SIGMAS", ),
+                    "drift_xs_masked":     ("SIGMAS", ),
+                    "drift_xs_unmasked":   ("SIGMAS", ),
+                    "drift_ys_masked":     ("SIGMAS", ),
+                    "drift_ys_unmasked":   ("SIGMAS", ),
+                    "lure_xs_masked":     ("SIGMAS", ),
+                    "lure_xs_unmasked":   ("SIGMAS", ),
+                    "lure_ys_masked":     ("SIGMAS", ),
+                    "lure_ys_unmasked":   ("SIGMAS", ),
+                    }  
+                }
+        
+    RETURN_TYPES = ("GUIDES",)
+    RETURN_NAMES = ("guides",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_extensions"
+    EXPERIMENTAL = True
+
+    def main(self,
+            weight_masked              = 0.0,
+            weight_unmasked            = 0.0,
+            weight_scheduler_masked    = "constant",
+            weight_scheduler_unmasked  = "constant",
+            weight_start_step_masked   = 0,
+            weight_start_step_unmasked = 0,
+            weight_end_step_masked     = 30,
+            weight_end_step_unmasked   = 30,
+
+            sync_masked                = 0.0,
+            sync_unmasked              = 0.0,
+            sync_scheduler_masked      = "constant",
+            sync_scheduler_unmasked    = "constant",
+            sync_start_step_masked     = 0,
+            sync_start_step_unmasked   = 0,
+            sync_end_step_masked       = 30,
+            sync_end_step_unmasked     = 30,
+            
+            drift_x_data = 0.0,
+            drift_x_sync = 0.0,
+            drift_y_data = 0.0,
+            drift_y_sync = 0.0,
+            drift_y_guide = 0.0,
+
+            drift_x_masked                = 0.0,
+            drift_x_unmasked              = 0.0,
+            drift_x_scheduler_masked      = "constant",
+            drift_x_scheduler_unmasked    = "constant",
+            drift_x_start_step_masked     = 0,
+            drift_x_start_step_unmasked   = 0,
+            drift_x_end_step_masked       = 30,
+            drift_x_end_step_unmasked     = 30,
+            
+            drift_y_masked                = 0.0,
+            drift_y_unmasked              = 0.0,
+            drift_y_scheduler_masked      = "constant",
+            drift_y_scheduler_unmasked    = "constant",
+            drift_y_start_step_masked     = 0,
+            drift_y_start_step_unmasked   = 0,
+            drift_y_end_step_masked       = 30,
+            drift_y_end_step_unmasked     = 30,
+
+            lure_x_masked                = 0.0,
+            lure_x_unmasked              = 0.0,
+            lure_x_scheduler_masked      = "constant",
+            lure_x_scheduler_unmasked    = "constant",
+            lure_x_start_step_masked     = 0,
+            lure_x_start_step_unmasked   = 0,
+            lure_x_end_step_masked       = 30,
+            lure_x_end_step_unmasked     = 30,
+            
+            lure_y_masked                = 0.0,
+            lure_y_unmasked              = 0.0,
+            lure_y_scheduler_masked      = "constant",
+            lure_y_scheduler_unmasked    = "constant",
+            lure_y_start_step_masked     = 0,
+            lure_y_start_step_unmasked   = 0,
+            lure_y_end_step_masked       = 30,
+            lure_y_end_step_unmasked     = 30,
+
+            guide_masked               = None,
+            guide_unmasked             = None,
+            
+            weights_masked             = None,
+            weights_unmasked           = None,
+            syncs_masked               = None,
+            syncs_unmasked             = None,
+            drift_xs_masked             = None,
+            drift_xs_unmasked           = None,
+            drift_ys_masked             = None,
+            drift_ys_unmasked           = None,
+            lure_xs_masked             = None,
+            lure_xs_unmasked           = None,
+            lure_ys_masked             = None,
+            lure_ys_unmasked           = None,
+            
+            lure_iter                  = 0,
+            lure_sequence              = "x -> y",
+            
+            mask                       = None,
+            unmask                     = None,
+            mask_sync                  = None,
+            mask_drift_x                = None,
+            mask_drift_y                = None,
+            mask_lure_x                = None,
+            mask_lure_y                = None,
+
+            invert_mask                = False,
+            invert_mask_sync           = False,
+            invert_mask_drift_x         = False,
+            invert_mask_drift_y         = False,
+            invert_mask_lure_x         = False,
+            invert_mask_lure_y         = False,
+            
+            guide_mode                 = "sync",
+            channelwise_mode           = False,
+            projection_mode            = False,
+            
+            cutoff_masked              = 1.0,
+            cutoff_unmasked            = 1.0,
+            ):
+
+        default_dtype = torch.float64
+        
+        if weight_end_step_masked   == -1:
+            weight_end_step_masked   = MAX_STEPS
+        if weight_end_step_unmasked == -1:
+            weight_end_step_unmasked = MAX_STEPS
+        
+        if sync_end_step_masked   == -1:
+            sync_end_step_masked   = MAX_STEPS
+        if sync_end_step_unmasked == -1:
+            sync_end_step_unmasked = MAX_STEPS
+        
+        if drift_x_end_step_masked   == -1:
+            drift_x_end_step_masked   = MAX_STEPS
+        if drift_x_end_step_unmasked == -1:
+            drift_x_end_step_unmasked = MAX_STEPS
+        if drift_y_end_step_masked   == -1:
+            drift_y_end_step_masked   = MAX_STEPS
+        if drift_y_end_step_unmasked == -1:
+            drift_y_end_step_unmasked = MAX_STEPS
+        
+        if lure_x_end_step_masked   == -1:
+            lure_x_end_step_masked   = MAX_STEPS
+        if lure_x_end_step_unmasked == -1:
+            lure_x_end_step_unmasked = MAX_STEPS
+        if lure_y_end_step_masked   == -1:
+            lure_y_end_step_masked   = MAX_STEPS
+        if lure_y_end_step_unmasked == -1:
+            lure_y_end_step_unmasked = MAX_STEPS
+        
+        
+        
+        if guide_masked is None:
+            weight_scheduler_masked = "constant"
+            weight_start_step_masked       = 0
+            weight_end_step_masked         = 30
+            weight_masked           = 0.0
+            weights_masked          = None
+            
+            sync_scheduler_masked = "constant"
+            sync_start_step_masked       = 0
+            sync_end_step_masked         = 30
+            sync_masked           = 0.0
+            syncs_masked          = None
+        
+            drift_x_scheduler_masked = "constant"
+            drift_x_start_step_masked       = 0
+            drift_x_end_step_masked         = 30
+            drift_x_masked           = 0.0
+            drift_xs_masked          = None
+        
+            drift_y_scheduler_masked = "constant"
+            drift_y_start_step_masked       = 0
+            drift_y_end_step_masked         = 30
+            drift_y_masked           = 0.0
+            drift_ys_masked          = None
+        
+            lure_x_scheduler_masked = "constant"
+            lure_x_start_step_masked       = 0
+            lure_x_end_step_masked         = 30
+            lure_x_masked           = 0.0
+            lure_xs_masked          = None
+        
+            lure_y_scheduler_masked = "constant"
+            lure_y_start_step_masked       = 0
+            lure_y_end_step_masked         = 30
+            lure_y_masked           = 0.0
+            lure_ys_masked          = None
+        
+        if guide_unmasked is None:
+            weight_scheduler_unmasked = "constant"
+            weight_start_step_unmasked       = 0
+            weight_end_step_unmasked         = 30
+            weight_unmasked           = 0.0
+            weights_unmasked          = None
+        
+            sync_scheduler_unmasked = "constant"
+            sync_start_step_unmasked       = 0
+            sync_end_step_unmasked         = 30
+            sync_unmasked           = 0.0
+            syncs_unmasked          = None
+        
+            drift_x_scheduler_unmasked = "constant"
+            drift_x_start_step_unmasked       = 0
+            drift_x_end_step_unmasked         = 30
+            drift_x_unmasked           = 0.0
+            drift_xs_unmasked          = None
+        
+            drift_y_scheduler_unmasked = "constant"
+            drift_y_start_step_unmasked       = 0
+            drift_y_end_step_unmasked         = 30
+            drift_y_unmasked           = 0.0
+            drift_ys_unmasked          = None
+        
+            lure_x_scheduler_unmasked = "constant"
+            lure_x_start_step_unmasked       = 0
+            lure_x_end_step_unmasked         = 30
+            lure_x_unmasked           = 0.0
+            lure_xs_unmasked          = None
+        
+            lure_y_scheduler_unmasked = "constant"
+            lure_y_start_step_unmasked       = 0
+            lure_y_end_step_unmasked         = 30
+            lure_y_unmasked           = 0.0
+            lure_ys_unmasked          = None
+        
+        
+        if guide_masked is not None:
+            raw_x = guide_masked.get('state_info', {}).get('raw_x', None)
+            if False: #raw_x is not None:
+                guide_masked   = {'samples': guide_masked['state_info']['raw_x'].clone()}
+            else:
+                guide_masked   = {'samples': guide_masked['samples'].clone()}
+        
+        if guide_unmasked is not None:
+            raw_x = guide_unmasked.get('state_info', {}).get('raw_x', None)
+            if False: #raw_x is not None:
+                guide_unmasked = {'samples': guide_unmasked['state_info']['raw_x'].clone()}
+            else:
+                guide_unmasked = {'samples': guide_unmasked['samples'].clone()}
+        
+        if invert_mask and mask is not None:
+            mask = 1-mask
+        if invert_mask_sync and mask_sync is not None:
+            mask_sync = 1-mask_sync
+        if invert_mask_drift_x and mask_drift_x is not None:
+            mask_drift_x = 1-mask_drift_x
+        if invert_mask_drift_y and mask_drift_y is not None:
+            mask_drift_y = 1-mask_drift_y
+        if invert_mask_lure_x and mask_lure_x is not None:
+            mask_lure_x = 1-mask_lure_x
+        if invert_mask_lure_y and mask_lure_y is not None:
+            mask_lure_y = 1-mask_lure_y
+        
+        if projection_mode:
+            guide_mode = guide_mode + "_projection"
+        
+        if channelwise_mode:
+            guide_mode = guide_mode + "_cw"
+            
+        if guide_mode == "unsample_cw":
+            guide_mode = "unsample"
+        if guide_mode == "resample_cw":
+            guide_mode = "resample"
+        
+        if weight_scheduler_masked == "constant" and weights_masked == None: 
+            weights_masked = initialize_or_scale(None, weight_masked, weight_end_step_masked).to(default_dtype)
+            prepend      = torch.zeros(weight_start_step_masked, dtype=default_dtype, device=weights_masked.device)
+            weights_masked = torch.cat((prepend, weights_masked), dim=0)
+            weights_masked = F.pad(weights_masked, (0, MAX_STEPS), value=0.0)
+        
+        if weight_scheduler_unmasked == "constant" and weights_unmasked == None: 
+            weights_unmasked = initialize_or_scale(None, weight_unmasked, weight_end_step_unmasked).to(default_dtype)
+            prepend      = torch.zeros(weight_start_step_unmasked, dtype=default_dtype, device=weights_unmasked.device)
+            weights_unmasked = torch.cat((prepend, weights_unmasked), dim=0)
+            weights_unmasked = F.pad(weights_unmasked, (0, MAX_STEPS), value=0.0)
+        
+        # Values for the sync scheduler will be inverted in rk_guide_func_beta.py as it's easier to understand:
+        # makes it so that a sync weight of 1.0 = full guide strength (which previously was 0.0)
+        if sync_scheduler_masked == "constant" and syncs_masked == None: 
+            syncs_masked = initialize_or_scale(None, sync_masked, sync_end_step_masked).to(default_dtype)
+            prepend      = torch.zeros(sync_start_step_masked, dtype=default_dtype, device=syncs_masked.device)
+            syncs_masked = torch.cat((prepend, syncs_masked), dim=0)
+            syncs_masked = F.pad(syncs_masked, (0, MAX_STEPS), value=0.0)
+        
+        if sync_scheduler_unmasked == "constant" and syncs_unmasked == None: 
+            syncs_unmasked = initialize_or_scale(None, sync_unmasked, sync_end_step_unmasked).to(default_dtype)
+            prepend      = torch.zeros(sync_start_step_unmasked, dtype=default_dtype, device=syncs_unmasked.device)
+            syncs_unmasked = torch.cat((prepend, syncs_unmasked), dim=0)
+            syncs_unmasked = F.pad(syncs_unmasked, (0, MAX_STEPS), value=0.0)
+        
+        if drift_x_scheduler_masked == "constant" and drift_xs_masked == None: 
+            drift_xs_masked = initialize_or_scale(None, drift_x_masked, drift_x_end_step_masked).to(default_dtype)
+            prepend      = torch.zeros(drift_x_start_step_masked, dtype=default_dtype, device=drift_xs_masked.device)
+            drift_xs_masked = torch.cat((prepend, drift_xs_masked), dim=0)
+            drift_xs_masked = F.pad(drift_xs_masked, (0, MAX_STEPS), value=0.0)
+        
+        if drift_x_scheduler_unmasked == "constant" and drift_xs_unmasked == None: 
+            drift_xs_unmasked = initialize_or_scale(None, drift_x_unmasked, drift_x_end_step_unmasked).to(default_dtype)
+            prepend      = torch.zeros(drift_x_start_step_unmasked, dtype=default_dtype, device=drift_xs_unmasked.device)
+            drift_xs_unmasked = torch.cat((prepend, drift_xs_unmasked), dim=0)
+            drift_xs_unmasked = F.pad(drift_xs_unmasked, (0, MAX_STEPS), value=0.0)
+        
+        if drift_y_scheduler_masked == "constant" and drift_ys_masked == None: 
+            drift_ys_masked = initialize_or_scale(None, drift_y_masked, drift_y_end_step_masked).to(default_dtype)
+            prepend      = torch.zeros(drift_y_start_step_masked, dtype=default_dtype, device=drift_ys_masked.device)
+            drift_ys_masked = torch.cat((prepend, drift_ys_masked), dim=0)
+            drift_ys_masked = F.pad(drift_ys_masked, (0, MAX_STEPS), value=0.0)
+        
+        if drift_y_scheduler_unmasked == "constant" and drift_ys_unmasked == None: 
+            drift_ys_unmasked = initialize_or_scale(None, drift_y_unmasked, drift_y_end_step_unmasked).to(default_dtype)
+            prepend      = torch.zeros(drift_y_start_step_unmasked, dtype=default_dtype, device=drift_ys_unmasked.device)
+            drift_ys_unmasked = torch.cat((prepend, drift_ys_unmasked), dim=0)
+            drift_ys_unmasked = F.pad(drift_ys_unmasked, (0, MAX_STEPS), value=0.0)
+        
+        if lure_x_scheduler_masked == "constant" and lure_xs_masked == None: 
+            lure_xs_masked = initialize_or_scale(None, lure_x_masked, lure_x_end_step_masked).to(default_dtype)
+            prepend      = torch.zeros(lure_x_start_step_masked, dtype=default_dtype, device=lure_xs_masked.device)
+            lure_xs_masked = torch.cat((prepend, lure_xs_masked), dim=0)
+            lure_xs_masked = F.pad(lure_xs_masked, (0, MAX_STEPS), value=0.0)
+        
+        if lure_x_scheduler_unmasked == "constant" and lure_xs_unmasked == None: 
+            lure_xs_unmasked = initialize_or_scale(None, lure_x_unmasked, lure_x_end_step_unmasked).to(default_dtype)
+            prepend      = torch.zeros(lure_x_start_step_unmasked, dtype=default_dtype, device=lure_xs_unmasked.device)
+            lure_xs_unmasked = torch.cat((prepend, lure_xs_unmasked), dim=0)
+            lure_xs_unmasked = F.pad(lure_xs_unmasked, (0, MAX_STEPS), value=0.0)
+        
+        if lure_y_scheduler_masked == "constant" and lure_ys_masked == None: 
+            lure_ys_masked = initialize_or_scale(None, lure_y_masked, lure_y_end_step_masked).to(default_dtype)
+            prepend      = torch.zeros(lure_y_start_step_masked, dtype=default_dtype, device=lure_ys_masked.device)
+            lure_ys_masked = torch.cat((prepend, lure_ys_masked), dim=0)
+            lure_ys_masked = F.pad(lure_ys_masked, (0, MAX_STEPS), value=0.0)
+        
+        if lure_y_scheduler_unmasked == "constant" and lure_ys_unmasked == None: 
+            lure_ys_unmasked = initialize_or_scale(None, lure_y_unmasked, lure_y_end_step_unmasked).to(default_dtype)
+            prepend      = torch.zeros(lure_y_start_step_unmasked, dtype=default_dtype, device=lure_ys_unmasked.device)
+            lure_ys_unmasked = torch.cat((prepend, lure_ys_unmasked), dim=0)
+            lure_ys_unmasked = F.pad(lure_ys_unmasked, (0, MAX_STEPS), value=0.0)
+        
+        
+        guides = {
+            "guide_mode"                        : guide_mode,
+        
+            "guide_masked"                      : guide_masked,
+            "guide_unmasked"                    : guide_unmasked,
+            "mask"                              : mask,
+            "unmask"                            : unmask,
+            "mask_sync"                         : mask_sync,
+            "mask_lure_x"                       : mask_lure_x,
+            "mask_lure_y"                       : mask_lure_y,
+        
+            "weight_masked"                     : weight_masked,
+            "weight_unmasked"                   : weight_unmasked,
+            "weight_scheduler_masked"           : weight_scheduler_masked,
+            "weight_scheduler_unmasked"         : weight_scheduler_unmasked,
+            "start_step_masked"                 : weight_start_step_masked,
+            "start_step_unmasked"               : weight_start_step_unmasked,
+            "end_step_masked"                   : weight_end_step_masked,
+            "end_step_unmasked"                 : weight_end_step_unmasked,
+            
+            "weights_masked"                    : weights_masked,
+            "weights_unmasked"                  : weights_unmasked,
+            
+            "weight_masked_sync"                : sync_masked,
+            "weight_unmasked_sync"              : sync_unmasked,
+            "weight_scheduler_masked_sync"      : sync_scheduler_masked,
+            "weight_scheduler_unmasked_sync"    : sync_scheduler_unmasked,
+            "start_step_masked_sync"            : sync_start_step_masked,
+            "start_step_unmasked_sync"          : sync_start_step_unmasked,
+            "end_step_masked_sync"              : sync_end_step_masked,
+            "end_step_unmasked_sync"            : sync_end_step_unmasked,
+            
+            "weights_masked_sync"               : syncs_masked,
+            "weights_unmasked_sync"             : syncs_unmasked,
+            
+            "drift_x_data"                      : drift_x_data,
+            "drift_x_sync"                      : drift_x_sync,
+            "drift_y_data"                      : drift_y_data,
+            "drift_y_sync"                      : drift_y_sync,
+            "drift_y_guide"                     : drift_y_guide,
+            
+            "weight_masked_drift_x"             : drift_x_masked,
+            "weight_unmasked_drift_x"           : drift_x_unmasked,
+            "weight_scheduler_masked_drift_x"   : drift_x_scheduler_masked,
+            "weight_scheduler_unmasked_drift_x" : drift_x_scheduler_unmasked,
+            "start_step_masked_drift_x"         : drift_x_start_step_masked,
+            "start_step_unmasked_drift_x"       : drift_x_start_step_unmasked,
+            "end_step_masked_drift_x"           : drift_x_end_step_masked,
+            "end_step_unmasked_drift_x"         : drift_x_end_step_unmasked,
+            
+            "weights_masked_drift_x"            : drift_xs_masked,
+            "weights_unmasked_drift_x"          : drift_xs_unmasked,
+            
+            
+            "weight_masked_drift_y"             : drift_y_masked,
+            "weight_unmasked_drift_y"           : drift_y_unmasked,
+            "weight_scheduler_masked_drift_y"   : drift_y_scheduler_masked,
+            "weight_scheduler_unmasked_drift_y" : drift_y_scheduler_unmasked,
+            "start_step_masked_drift_y"         : drift_y_start_step_masked,
+            "start_step_unmasked_drift_y"       : drift_y_start_step_unmasked,
+            "end_step_masked_drift_y"           : drift_y_end_step_masked,
+            "end_step_unmasked_drift_y"         : drift_y_end_step_unmasked,
+            
+            "weights_masked_drift_y"            : drift_ys_masked,
+            "weights_unmasked_drift_y"          : drift_ys_unmasked,
+            
+            "weight_masked_lure_x"              : lure_x_masked,
+            "weight_unmasked_lure_x"            : lure_x_unmasked,
+            "weight_scheduler_masked_lure_x"    : lure_x_scheduler_masked,
+            "weight_scheduler_unmasked_lure_x"  : lure_x_scheduler_unmasked,
+            "start_step_masked_lure_x"          : lure_x_start_step_masked,
+            "start_step_unmasked_lure_x"        : lure_x_start_step_unmasked,
+            "end_step_masked_lure_x"            : lure_x_end_step_masked,
+            "end_step_unmasked_lure_x"          : lure_x_end_step_unmasked,
+            
+            "weights_masked_lure_x"             : lure_xs_masked,
+            "weights_unmasked_lure_x"           : lure_xs_unmasked,
+            
+            
+            "weight_masked_lure_y"              : lure_y_masked,
+            "weight_unmasked_lure_y"            : lure_y_unmasked,
+            "weight_scheduler_masked_lure_y"    : lure_y_scheduler_masked,
+            "weight_scheduler_unmasked_lure_y"  : lure_y_scheduler_unmasked,
+            "start_step_masked_lure_y"          : lure_y_start_step_masked,
+            "start_step_unmasked_lure_y"        : lure_y_start_step_unmasked,
+            "end_step_masked_lure_y"            : lure_y_end_step_masked,
+            "end_step_unmasked_lure_y"          : lure_y_end_step_unmasked,
+            
+            "weights_masked_lure_y"             : lure_ys_masked,
+            "weights_unmasked_lure_y"           : lure_ys_unmasked,
+            
+            "sync_lure_iter"                    : lure_iter,
+            "sync_lure_sequence"                : lure_sequence,
+
+            "cutoff_masked"                     : cutoff_masked,
+            "cutoff_unmasked"                   : cutoff_unmasked
+        }
+        
+        
+        return (guides, )
+
+
+
+
+
+
+
+
+
+class ClownGuide_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "guide_mode":           (GUIDE_MODE_NAMES_BETA_SIMPLE,       {"default": 'epsilon',                                                      "tooltip": "Recommended: epsilon or mean/mean_std with sampler_mode = standard, and unsample/resample with sampler_mode = unsample/resample. Epsilon_dynamic_mean, etc. are only used with two latent inputs and a mask. Blend/hard_light/mean/mean_std etc. require low strengths, start with 0.01-0.02."}),
+                    "channelwise_mode":     ("BOOLEAN",                                   {"default": True}),
+                    "projection_mode":      ("BOOLEAN",                                   {"default": True}),
+                    "weight":               ("FLOAT",                                     {"default": 0.75, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
+                    "cutoff":               ("FLOAT",                                     {"default": 1.0,  "min": 0.0,    "max": 1.0,   "step":0.01, "round": False, "tooltip": "Disables the guide for the next step when the denoised image is similar to the guide. Higher values will strengthen the effect."}),
+                    "weight_scheduler":     (["constant"] + get_res4lyf_scheduler_list(), {"default": "beta57"},),
+                    "start_step":           ("INT",                                       {"default": 0,    "min":  0,      "max": 10000}),
+                    "end_step":             ("INT",                                       {"default": 15,   "min": -1,      "max": 10000}),
+                    "invert_mask":          ("BOOLEAN",                                   {"default": False}),
+                    },
+                "optional": 
+                    {
+                    "guide":                ("LATENT", ),
+                    "mask":                 ("MASK", ),
+                    "weights":              ("SIGMAS", ),
+                    }  
+                }
+        
+    RETURN_TYPES = ("GUIDES",)
+    RETURN_NAMES = ("guides",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_extensions"
+
+    def main(self,
+            weight_scheduler          = "constant",
+            weight_scheduler_unmasked = "constant",
+            start_step                = 0,
+            start_step_unmasked       = 0,
+            end_step                  = 30,
+            end_step_unmasked         = 30,
+            cutoff                    = 1.0,
+            cutoff_unmasked           = 1.0,
+            guide                     = None,
+            guide_unmasked            = None,
+            weight                    = 0.0,
+            weight_unmasked           = 0.0,
+
+            guide_mode                = "epsilon",
+            channelwise_mode          = False,
+            projection_mode           = False,
+            weights                   = None,
+            weights_unmasked          = None,
+            mask                      = None,
+            unmask                    = None,
+            invert_mask               = False,
+            ):
+        
+        CG = ClownGuides_Beta()
+        
+        mask = 1-mask if mask is not None else None
+        
+        if end_step == -1:
+            end_step = MAX_STEPS
+        
+        if guide is not None:
+            raw_x = guide.get('state_info', {}).get('raw_x', None)
+            
+            if False: # raw_x is not None:
+                guide          = {'samples': guide['state_info']['raw_x'].clone()}
+            else:
+                guide          = {'samples': guide['samples'].clone()}
+                
+        if guide_unmasked is not None:
+            raw_x = guide_unmasked.get('state_info', {}).get('raw_x', None)
+            if False: #raw_x is not None:
+                guide_unmasked = {'samples': guide_unmasked['state_info']['raw_x'].clone()}
+            else:
+                guide_unmasked = {'samples': guide_unmasked['samples'].clone()}
+        
+        guides, = CG.main(
+            weight_scheduler_masked   = weight_scheduler,
+            weight_scheduler_unmasked = weight_scheduler_unmasked,
+            start_step_masked         = start_step,
+            start_step_unmasked       = start_step_unmasked,
+            end_step_masked           = end_step,
+            end_step_unmasked         = end_step_unmasked,
+            cutoff_masked             = cutoff,
+            cutoff_unmasked           = cutoff_unmasked,
+            guide_masked              = guide,
+            guide_unmasked            = guide_unmasked,
+            weight_masked             = weight,
+            weight_unmasked           = weight_unmasked,
+
+            guide_mode                = guide_mode,
+            channelwise_mode          = channelwise_mode,
+            projection_mode           = projection_mode,
+            weights_masked            = weights,
+            weights_unmasked          = weights_unmasked,
+            mask                      = mask,
+            unmask                    = unmask,
+            invert_mask               = invert_mask
+        )
+
+        return (guides, )
+
+
+        #return (guides[0], )
+
+
+
+
+class ClownGuides_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "guide_mode":                  (GUIDE_MODE_NAMES_BETA_SIMPLE,                {"default": 'epsilon',                                                      "tooltip": "Recommended: epsilon or mean/mean_std with sampler_mode = standard, and unsample/resample with sampler_mode = unsample/resample. Epsilon_dynamic_mean, etc. are only used with two latent inputs and a mask. Blend/hard_light/mean/mean_std etc. require low strengths, start with 0.01-0.02."}),
+                    "channelwise_mode":            ("BOOLEAN",                                   {"default": True}),
+                    "projection_mode":             ("BOOLEAN",                                   {"default": True}),
+                    "weight_masked":               ("FLOAT",                                     {"default": 0.75, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
+                    "weight_unmasked":             ("FLOAT",                                     {"default": 0.75, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide_bkg."}),
+                    "cutoff_masked":               ("FLOAT",                                     {"default": 1.0,  "min": 0.0,    "max": 1.0,   "step":0.01, "round": False, "tooltip": "Disables the guide for the next step when the denoised image is similar to the guide. Higher values will strengthen the effect."}),
+                    "cutoff_unmasked":             ("FLOAT",                                     {"default": 1.0,  "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Disables the guide for the next step when the denoised image is similar to the guide. Higher values will strengthen the effect."}),
+                    "weight_scheduler_masked":     (["constant"] + get_res4lyf_scheduler_list(), {"default": "beta57"},),
+                    "weight_scheduler_unmasked":   (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
+                    "start_step_masked":           ("INT",                                       {"default": 0,    "min":  0,      "max": 10000}),
+                    "start_step_unmasked":         ("INT",                                       {"default": 0,    "min":  0,      "max": 10000}),
+                    "end_step_masked":             ("INT",                                       {"default": 15,   "min": -1,      "max": 10000}),
+                    "end_step_unmasked":           ("INT",                                       {"default": 15,   "min": -1,      "max": 10000}),
+                    "invert_mask":                 ("BOOLEAN",                                   {"default": False}),
+                    },
+                "optional": 
+                    {
+                    "guide_masked":                ("LATENT", ),
+                    "guide_unmasked":              ("LATENT", ),
+                    "mask":                        ("MASK", ),
+                    "weights_masked":              ("SIGMAS", ),
+                    "weights_unmasked":            ("SIGMAS", ),
+                    }  
+                }
+        
+    RETURN_TYPES = ("GUIDES",)
+    RETURN_NAMES = ("guides",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_extensions"
+
+    def main(self,
+            weight_scheduler_masked   = "constant",
+            weight_scheduler_unmasked = "constant",
+            start_step_masked         = 0,
+            start_step_unmasked       = 0,
+            end_step_masked           = 30,
+            end_step_unmasked         = 30,
+            cutoff_masked             = 1.0,
+            cutoff_unmasked           = 1.0,
+            guide_masked              = None,
+            guide_unmasked            = None,
+            weight_masked             = 0.0,
+            weight_unmasked           = 0.0,
+
+            guide_mode                = "epsilon",
+            channelwise_mode          = False,
+            projection_mode           = False,
+            weights_masked            = None,
+            weights_unmasked          = None,
+            mask                      = None,
+            unmask                    = None,
+            invert_mask               = False,
+            ):
+
+        default_dtype = torch.float64
+        
+        if end_step_masked   == -1:
+            end_step_masked   = MAX_STEPS
+        if end_step_unmasked == -1:
+            end_step_unmasked = MAX_STEPS
+        
+        if guide_masked is None:
+            weight_scheduler_masked = "constant"
+            start_step_masked       = 0
+            end_step_masked         = 30
+            cutoff_masked           = 1.0
+            guide_masked            = None
+            weight_masked           = 0.0
+            weights_masked          = None
+            #mask                    = None
+        
+        if guide_unmasked is None:
+            weight_scheduler_unmasked = "constant"
+            start_step_unmasked       = 0
+            end_step_unmasked         = 30
+            cutoff_unmasked           = 1.0
+            guide_unmasked            = None
+            weight_unmasked           = 0.0
+            weights_unmasked          = None
+            #unmask                    = None
+        
+        if guide_masked is not None:
+            raw_x = guide_masked.get('state_info', {}).get('raw_x', None)
+            if False: #raw_x is not None:
+                guide_masked   = {'samples': guide_masked['state_info']['raw_x'].clone()}
+            else:
+                guide_masked   = {'samples': guide_masked['samples'].clone()}
+        
+        if guide_unmasked is not None:
+            raw_x = guide_unmasked.get('state_info', {}).get('raw_x', None)
+            if False: #raw_x is not None:
+                guide_unmasked = {'samples': guide_unmasked['state_info']['raw_x'].clone()}
+            else:
+                guide_unmasked = {'samples': guide_unmasked['samples'].clone()}
+        
+        if invert_mask and mask is not None:
+            mask = 1-mask
+                
+        if projection_mode:
+            guide_mode = guide_mode + "_projection"
+        
+        if channelwise_mode:
+            guide_mode = guide_mode + "_cw"
+            
+        if guide_mode == "unsample_cw":
+            guide_mode = "unsample"
+        if guide_mode == "resample_cw":
+            guide_mode = "resample"
+        
+        if weight_scheduler_masked == "constant" and weights_masked == None: 
+            weights_masked = initialize_or_scale(None, weight_masked, end_step_masked).to(default_dtype)
+            prepend      = torch.zeros(start_step_masked, dtype=default_dtype, device=weights_masked.device)
+            weights_masked = torch.cat((prepend, weights_masked), dim=0)
+            weights_masked = F.pad(weights_masked, (0, MAX_STEPS), value=0.0)
+        
+        if weight_scheduler_unmasked == "constant" and weights_unmasked == None: 
+            weights_unmasked = initialize_or_scale(None, weight_unmasked, end_step_unmasked).to(default_dtype)
+            prepend      = torch.zeros(start_step_unmasked, dtype=default_dtype, device=weights_unmasked.device)
+            weights_unmasked = torch.cat((prepend, weights_unmasked), dim=0)
+            weights_unmasked = F.pad(weights_unmasked, (0, MAX_STEPS), value=0.0)
+        
+        guides = {
+            "guide_mode"                : guide_mode,
+            "weight_masked"             : weight_masked,
+            "weight_unmasked"           : weight_unmasked,
+            "weights_masked"            : weights_masked,
+            "weights_unmasked"          : weights_unmasked,
+            "guide_masked"              : guide_masked,
+            "guide_unmasked"            : guide_unmasked,
+            "mask"                      : mask,
+            "unmask"                    : unmask,
+
+            "weight_scheduler_masked"   : weight_scheduler_masked,
+            "weight_scheduler_unmasked" : weight_scheduler_unmasked,
+            "start_step_masked"         : start_step_masked,
+            "start_step_unmasked"       : start_step_unmasked,
+            "end_step_masked"           : end_step_masked,
+            "end_step_unmasked"         : end_step_unmasked,
+            "cutoff_masked"             : cutoff_masked,
+            "cutoff_unmasked"           : cutoff_unmasked
+        }
+        
+        
+        return (guides, )
+
+
+
+
+class ClownGuidesAB_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "guide_mode":         (GUIDE_MODE_NAMES_BETA_SIMPLE,                {"default": 'epsilon',                                                      "tooltip": "Recommended: epsilon or mean/mean_std with sampler_mode = standard, and unsample/resample with sampler_mode = unsample/resample. Epsilon_dynamic_mean, etc. are only used with two latent inputs and a mask. Blend/hard_light/mean/mean_std etc. require low strengths, start with 0.01-0.02."}),
+                    "channelwise_mode":   ("BOOLEAN",                                   {"default": False}),
+                    "projection_mode":    ("BOOLEAN",                                   {"default": False}),
+                    "weight_A":           ("FLOAT",                                     {"default": 0.75, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
+                    "weight_B":           ("FLOAT",                                     {"default": 0.75, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide_bkg."}),
+                    "cutoff_A":           ("FLOAT",                                     {"default": 1.0,  "min": 0.0,    "max": 1.0,   "step":0.01, "round": False, "tooltip": "Disables the guide for the next step when the denoised image is similar to the guide. Higher values will strengthen the effect."}),
+                    "cutoff_B":           ("FLOAT",                                     {"default": 1.0,  "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Disables the guide for the next step when the denoised image is similar to the guide. Higher values will strengthen the effect."}),
+                    "weight_scheduler_A": (["constant"] + get_res4lyf_scheduler_list(), {"default": "beta57"},),
+                    "weight_scheduler_B": (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
+                    "start_step_A":       ("INT",                                       {"default": 0,    "min":  0,      "max": 10000}),
+                    "start_step_B":       ("INT",                                       {"default": 0,    "min":  0,      "max": 10000}),
+                    "end_step_A":         ("INT",                                       {"default": 15,   "min": -1,      "max": 10000}),
+                    "end_step_B":         ("INT",                                       {"default": 15,   "min": -1,      "max": 10000}),
+                    "invert_masks":       ("BOOLEAN",                                   {"default": False}),
+                    },
+                "optional": 
+                    {
+                    "guide_A":            ("LATENT", ),
+                    "guide_B":            ("LATENT", ),
+                    "mask_A":             ("MASK", ),
+                    "mask_B":             ("MASK", ),
+                    "weights_A":          ("SIGMAS", ),
+                    "weights_B":          ("SIGMAS", ),
+                    }  
+                }
+        
+    RETURN_TYPES = ("GUIDES",)
+    RETURN_NAMES = ("guides",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_extensions"
+
+    def main(self,
+            weight_scheduler_A = "constant",
+            weight_scheduler_B = "constant",
+            start_step_A       = 0,
+            start_step_B       = 0,
+            end_step_A         = 30,
+            end_step_B         = 30,
+            cutoff_A           = 1.0,
+            cutoff_B           = 1.0,
+            guide_A            = None,
+            guide_B            = None,
+            weight_A           = 0.0,
+            weight_B           = 0.0,
+
+            guide_mode         = "epsilon",
+            channelwise_mode   = False,
+            projection_mode    = False,
+            weights_A          = None,
+            weights_B          = None,
+            mask_A             = None,
+            mask_B             = None,
+            invert_masks       : bool = False,
+            ):
+        
+        default_dtype = torch.float64
+        
+        if end_step_A == -1:
+            end_step_A = MAX_STEPS
+        if end_step_B == -1:
+            end_step_B = MAX_STEPS
+        
+        if guide_A is not None:
+            raw_x = guide_A.get('state_info', {}).get('raw_x', None)
+            if False: #raw_x is not None:
+                guide_A          = {'samples': guide_A['state_info']['raw_x'].clone()}
+            else:
+                guide_A          = {'samples': guide_A['samples'].clone()}
+                
+        if guide_B is not None:
+            raw_x = guide_B.get('state_info', {}).get('raw_x', None)
+            if False: #raw_x is not None:
+                guide_B = {'samples': guide_B['state_info']['raw_x'].clone()}
+            else:
+                guide_B = {'samples': guide_B['samples'].clone()}
+        
+        if guide_A is None:
+            guide_A  = guide_B
+            guide_B  = None
+            mask_A   = mask_B
+            mask_B   = None
+            weight_B = 0.0
+            
+        if guide_B is None:
+            weight_B = 0.0
+            
+        if mask_A is None and mask_B is not None:
+            mask_A = 1-mask_B
+                        
+        if projection_mode:
+            guide_mode = guide_mode + "_projection"
+        
+        if channelwise_mode:
+            guide_mode = guide_mode + "_cw"
+            
+        if guide_mode == "unsample_cw":
+            guide_mode = "unsample"
+        if guide_mode == "resample_cw":
+            guide_mode = "resample"
+        
+        if weight_scheduler_A == "constant" and weights_A == None: 
+            weights_A = initialize_or_scale(None, weight_A, end_step_A).to(default_dtype)
+            prepend      = torch.zeros(start_step_A, dtype=default_dtype, device=weights_A.device)
+            weights_A = torch.cat((prepend, weights_A), dim=0)
+            weights_A = F.pad(weights_A, (0, MAX_STEPS), value=0.0)
+        
+        if weight_scheduler_B == "constant" and weights_B == None: 
+            weights_B = initialize_or_scale(None, weight_B, end_step_B).to(default_dtype)
+            prepend      = torch.zeros(start_step_B, dtype=default_dtype, device=weights_B.device)
+            weights_B = torch.cat((prepend, weights_B), dim=0)
+            weights_B = F.pad(weights_B, (0, MAX_STEPS), value=0.0)
+            
+        if invert_masks:
+            mask_A = 1-mask_A if mask_A is not None else None
+            mask_B = 1-mask_B if mask_B is not None else None
+    
+        guides = {
+            "guide_mode"                : guide_mode,
+            "weight_masked"             : weight_A,
+            "weight_unmasked"           : weight_B,
+            "weights_masked"            : weights_A,
+            "weights_unmasked"          : weights_B,
+            "guide_masked"              : guide_A,
+            "guide_unmasked"            : guide_B,
+            "mask"                      : mask_A,
+            "unmask"                    : mask_B,
+
+            "weight_scheduler_masked"   : weight_scheduler_A,
+            "weight_scheduler_unmasked" : weight_scheduler_B,
+            "start_step_masked"         : start_step_A,
+            "start_step_unmasked"       : start_step_B,
+            "end_step_masked"           : end_step_A,
+            "end_step_unmasked"         : end_step_B,
+            "cutoff_masked"             : cutoff_A,
+            "cutoff_unmasked"           : cutoff_B
+        }
+        
+        return (guides, )
+    
+
+
+class ClownOptions_Combine:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "options": ("OPTIONS",),
+            },
+        }
+
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    FUNCTION = "main"
+    CATEGORY = "RES4LYF/sampler_options"
+
+    def main(self, options, **kwargs):
+        options_mgr = OptionsManager(options, **kwargs)
+        return (options_mgr.as_dict(),)
+
+
+
+class ClownOptions_Frameweights:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "config_name": (FRAME_WEIGHTS_CONFIG_NAMES, {"default": "frame_weights", "tooltip": "Apply to specific type of per-frame weights."}),
+                "dynamics": (FRAME_WEIGHTS_DYNAMICS_NAMES, {"default": "ease_out", "tooltip": "The function type used for the dynamic period. constant: no change, linear: steady change, ease_out: starts fast, ease_in: starts slow"}),
+                "schedule": (FRAME_WEIGHTS_SCHEDULE_NAMES, {"default": "moderate_early", "tooltip": "fast_early: fast change starts immediately, slow_late: slow change starts later"}),
+                "scale": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of change over the course of the frame weights. 1.0 means that the guides have no influence by the end."}),
+                "reverse": ("BOOLEAN", {"default": False, "tooltip": "Reverse the frame weights"}),
+            },
+            "optional": {
+                "frame_weights": ("SIGMAS", {"tooltip": "Overrides all other settings EXCEPT reverse."}),
+                "custom_string": ("STRING", {"tooltip": "Overrides all other settings EXCEPT reverse.", "multiline": True}),
+                "options": ("OPTIONS",),
+            },
+        }
+
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    FUNCTION = "main"
+    CATEGORY = "RES4LYF/sampler_options"
+
+    def main(self,
+            config_name,
+            dynamics,
+            schedule,
+            scale,
+            reverse,
+            frame_weights = None,
+            custom_string = None,
+            options       = None,
+            ):
+        
+        options_mgr = OptionsManager(options if options is not None else {})
+
+        frame_weights_mgr = options_mgr.get("frame_weights_mgr")
+        if frame_weights_mgr is None:
+            frame_weights_mgr = FrameWeightsManager()
+
+        if custom_string is not None and custom_string.strip() == "":
+            custom_string = None
+        
+        frame_weights_mgr.add_weight_config(
+            config_name,
+            dynamics=dynamics,
+            schedule=schedule,
+            scale=scale,
+            is_reversed=reverse,
+            frame_weights=frame_weights,
+            custom_string=custom_string
+        )
+        
+        options_mgr.update("frame_weights_mgr", frame_weights_mgr)
+        
+        return (options_mgr.as_dict(),)
+
+
+class SharkOptions_GuiderInput:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"guider": ("GUIDER", ),
+                    },
+                "optional":
+                    {"options": ("OPTIONS", ),
+                    }
+                }
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_options"
+
+    def main(self, guider, options=None):
+        options_mgr = OptionsManager(options if options is not None else {})
+        
+        if isinstance(guider, dict):
+            guider = guider.get('samples', None)
+            
+        if isinstance(guider, torch.Tensor):
+            guider = guider.detach().cpu()
+        
+        if options_mgr is None:
+            options_mgr = OptionsManager()
+            
+        options_mgr.update("guider", guider)
+        
+        return (options_mgr.as_dict(), )
+
+
+
+
+
+class ClownGuide_AdaIN_MMDiT_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "weight":           ("FLOAT",                                     {"default": 1.0, "min":  -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide by multiplying all other weights by this value."}),
+                    "weight_scheduler": (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
+                    "double_blocks"   : ("STRING",                                    {"default": "", "multiline": True}),
+                    "double_weights"  : ("STRING",                                    {"default": "", "multiline": True}),
+                    "single_blocks"   : ("STRING",                                    {"default": "20", "multiline": True}),
+                    "single_weights"  : ("STRING",                                    {"default": "0.5", "multiline": True}),
+                    "start_step":       ("INT",                                       {"default": 0,    "min":  0,      "max": 10000}),
+                    "end_step":         ("INT",                                       {"default": 15,   "min": -1,      "max": 10000}),
+                    "invert_mask":      ("BOOLEAN",                                   {"default": False}),
+                    },
+                "optional": 
+                    {
+                    "guide":            ("LATENT", ),
+                    "mask":             ("MASK", ),
+                    "weights":          ("SIGMAS", ),
+                    "guides":           ("GUIDES", ),
+                    }  
+                }
+    
+    RETURN_TYPES = ("GUIDES",)
+    RETURN_NAMES = ("guides",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_extensions"
+
+    def main(self,
+            weight           = 1.0,
+            weight_scheduler = "constant",
+            double_weights   = "0.1",
+            single_weights   = "0.0", 
+            double_blocks    = "all",
+            single_blocks    = "all", 
+            start_step       = 0,
+            end_step         = 15,
+            invert_mask      = False,
+            
+            guide            = None,
+            mask             = None,
+            weights          = None,
+            guides           = None,
+            ):
+        
+        default_dtype = torch.float64
+        
+        mask = 1-mask if mask is not None else None
+        
+        double_weights = parse_range_string(double_weights)
+        single_weights = parse_range_string(single_weights)
+        
+        if len(double_weights) == 0:
+            double_weights.append(0.0)
+        if len(single_weights) == 0:
+            single_weights.append(0.0)
+            
+        if len(double_weights) == 1:
+            double_weights = double_weights * 100
+        if len(single_weights) == 1:
+            single_weights = single_weights * 100
+            
+        if type(double_weights[0]) == int:
+            double_weights = [float(val) for val in double_weights]
+        if type(single_weights[0]) == int:
+            single_weights = [float(val) for val in single_weights]
+        
+        if double_blocks == "all":
+            double_blocks  = [val for val in range(100)]
+            if len(double_weights) == 1:
+                double_weights = [double_weights[0]] * 100
+        else:
+            double_blocks  = parse_range_string(double_blocks)
+            
+            weights_expanded = [0.0] * 100
+            for b, w in zip(double_blocks, double_weights):
+                weights_expanded[b] = w
+            double_weights = weights_expanded
+            
+        
+        if single_blocks == "all":
+            single_blocks = [val for val in range(100)]
+            if len(single_weights) == 1:
+                single_weights = [single_weights[0]] * 100
+        else:
+            single_blocks  = parse_range_string(single_blocks)
+            
+            weights_expanded = [0.0] * 100
+            for b, w in zip(single_blocks, single_weights):
+                weights_expanded[b] = w
+            single_weights = weights_expanded
+        
+        
+        
+        if end_step == -1:
+            end_step = MAX_STEPS
+        
+        if guide is not None:
+            raw_x = guide.get('state_info', {}).get('raw_x', None)
+            if raw_x is not None:
+                guide          = {'samples': guide['state_info']['raw_x'].clone()}
+            else:
+                guide          = {'samples': guide['samples'].clone()}
+        
+        if weight_scheduler == "constant": # and weights == None: 
+            weights = initialize_or_scale(None, weight, end_step).to(default_dtype)
+            prepend = torch.zeros(start_step).to(weights)
+            weights = torch.cat([prepend, weights])
+            weights = F.pad(weights, (0, MAX_STEPS), value=0.0)
+        
+        guides = copy.deepcopy(guides) if guides is not None else {}
+        
+        guides['weight_adain']           = weight
+        guides['weights_adain']          = weights
+        
+        guides['blocks_adain_mmdit'] = {
+            "double_weights": double_weights,
+            "single_weights": single_weights,
+            "double_blocks" : double_blocks,
+            "single_blocks" : single_blocks,
+        }
+        
+        guides['guide_adain']            = guide
+        guides['mask_adain']             = mask
+
+        guides['weight_scheduler_adain'] = weight_scheduler
+        guides['start_step_adain']       = start_step
+        guides['end_step_adain']         = end_step
+        
+        return (guides, )
+
+
+
+
+
+class ClownGuide_AttnInj_MMDiT_Beta:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "weight":           ("FLOAT",                                     {"default": 1.0, "min":  -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide by multiplying all other weights by this value."}),
+                    "weight_scheduler": (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
+                    "double_blocks"   : ("STRING",                                    {"default": "0,1,3", "multiline": True}),
+                    "double_weights"  : ("STRING",                                    {"default": "1.0", "multiline": True}),
+                    "single_blocks"   : ("STRING",                                    {"default": "20", "multiline": True}),
+                    "single_weights"  : ("STRING",                                    {"default": "0.5", "multiline": True}),
+                    
+                    "img_q":            ("FLOAT",                                     {"default": 0.0, "min":  -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set relative injection strength."}),
+                    "img_k":            ("FLOAT",                                     {"default": 0.0, "min":  -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set relative injection strength."}),
+                    "img_v":            ("FLOAT",                                     {"default": 1.0, "min":  -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set relative injection strength."}),
+
+                    "txt_q":            ("FLOAT",                                     {"default": 0.0, "min":  -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set relative injection strength."}),
+                    "txt_k":            ("FLOAT",                                     {"default": 0.0, "min":  -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set relative injection strength."}),
+                    "txt_v":            ("FLOAT",                                     {"default": 0.0, "min":  -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set relative injection strength."}),
+
+                    "img_q_norm":       ("FLOAT",                                     {"default": 0.0, "min":  -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set relative injection strength."}),
+                    "img_k_norm":       ("FLOAT",                                     {"default": 0.0, "min":  -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set relative injection strength."}),
+                    "img_v_norm":       ("FLOAT",                                     {"default": 0.0, "min":  -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set relative injection strength."}),
+
+                    "txt_q_norm":       ("FLOAT",                                     {"default": 0.0, "min":  -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set relative injection strength."}),
+                    "txt_k_norm":       ("FLOAT",                                     {"default": 0.0, "min":  -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set relative injection strength."}),
+                    "txt_v_norm":       ("FLOAT",                                     {"default": 0.0, "min":  -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set relative injection strength."}),
+
+                    "start_step":       ("INT",                                       {"default": 0,    "min":  0,      "max": 10000}),
+                    "end_step":         ("INT",                                       {"default": 15,   "min": -1,      "max": 10000}),
+                    "invert_mask":      ("BOOLEAN",                                   {"default": False}),
+                    },
+                "optional": 
+                    {
+                    "guide":            ("LATENT", ),
+                    "mask":             ("MASK", ),
+                    "weights":          ("SIGMAS", ),
+                    "guides":           ("GUIDES", ),
+                    }  
+                }
+    
+    RETURN_TYPES = ("GUIDES",)
+    RETURN_NAMES = ("guides",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_extensions"
+
+    def main(self,
+            weight           = 1.0,
+            weight_scheduler = "constant",
+            double_weights   = "0.1",
+            single_weights   = "0.0", 
+            double_blocks    = "all",
+            single_blocks    = "all", 
+            
+            img_q            = 0.0,
+            img_k            = 0.0,
+            img_v            = 0.0,
+            
+            txt_q            = 0.0,
+            txt_k            = 0.0,
+            txt_v            = 0.0,
+            
+            img_q_norm       = 0.0,
+            img_k_norm       = 0.0,
+            img_v_norm       = 0.0,
+            
+            txt_q_norm       = 0.0,
+            txt_k_norm       = 0.0,
+            txt_v_norm       = 0.0,
+            
+            start_step       = 0,
+            end_step         = 15,
+            invert_mask      = False,
+            
+            guide            = None,
+            mask             = None,
+            weights          = None,
+            guides           = None,
+            ):
+        
+        default_dtype = torch.float64
+        
+        mask = 1-mask if mask is not None else None
+        
+        double_weights = parse_range_string(double_weights)
+        single_weights = parse_range_string(single_weights)
+        
+        if len(double_weights) == 0:
+            double_weights.append(0.0)
+        if len(single_weights) == 0:
+            single_weights.append(0.0)
+        
+        if len(double_weights) == 1:
+            double_weights = double_weights * 100
+        if len(single_weights) == 1:
+            single_weights = single_weights * 100
+        
+        if type(double_weights[0]) == int:
+            double_weights = [float(val) for val in double_weights]
+        if type(single_weights[0]) == int:
+            single_weights = [float(val) for val in single_weights]
+        
+        if double_blocks == "all":
+            double_blocks  = [val for val in range(100)]
+            if len(double_weights) == 1:
+                double_weights = [double_weights[0]] * 100
+        else:
+            double_blocks  = parse_range_string(double_blocks)
+            
+            weights_expanded = [0.0] * 100
+            for b, w in zip(double_blocks, double_weights):
+                weights_expanded[b] = w
+            double_weights = weights_expanded
+            
+        
+        if single_blocks == "all":
+            single_blocks = [val for val in range(100)]
+            if len(single_weights) == 1:
+                single_weights = [single_weights[0]] * 100
+        else:
+            single_blocks  = parse_range_string(single_blocks)
+            
+            weights_expanded = [0.0] * 100
+            for b, w in zip(single_blocks, single_weights):
+                weights_expanded[b] = w
+            single_weights = weights_expanded
+        
+        
+        
+        if end_step == -1:
+            end_step = MAX_STEPS
+        
+        if guide is not None:
+            raw_x = guide.get('state_info', {}).get('raw_x', None)
+            if raw_x is not None:
+                guide          = {'samples': guide['state_info']['raw_x'].clone()}
+            else:
+                guide          = {'samples': guide['samples'].clone()}
+        
+        if weight_scheduler == "constant": # and weights == None: 
+            weights = initialize_or_scale(None, weight, end_step).to(default_dtype)
+            prepend = torch.zeros(start_step).to(weights)
+            weights = torch.cat([prepend, weights])
+            weights = F.pad(weights, (0, MAX_STEPS), value=0.0)
+        
+        guides = copy.deepcopy(guides) if guides is not None else {}
+        
+        guides['weight_attninj']           = weight
+        guides['weights_attninj']          = weights
+        
+        guides['blocks_attninj_mmdit'] = {
+            "double_weights": double_weights,
+            "single_weights": single_weights,
+            "double_blocks" : double_blocks,
+            "single_blocks" : single_blocks,
+        }
+        
+        guides['blocks_attninj_qkv'] = {
+            "img_q": img_q,
+            "img_k": img_k,
+            "img_v": img_v,
+            "txt_q": txt_q,
+            "txt_k": txt_k,
+            "txt_v": txt_v,
+            
+            "img_q_norm": img_q_norm,
+            "img_k_norm": img_k_norm,
+            "img_v_norm": img_v_norm,
+            "txt_q_norm": txt_q_norm,
+            "txt_k_norm": txt_k_norm,
+            "txt_v_norm": txt_v_norm,
+        }
+        
+        guides['guide_attninj']            = guide
+        guides['mask_attninj']             = mask
+
+        guides['weight_scheduler_attninj'] = weight_scheduler
+        guides['start_step_attninj']       = start_step
+        guides['end_step_attninj']         = end_step
+        
+        return (guides, )
+
+
+
+
+
+
+class ClownGuide_StyleNorm_Advanced_HiDream:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "weight":           ("FLOAT",                                     {"default": 1.0, "min":  -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide by multiplying all other weights by this value."}),
+                    "weight_scheduler": (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
+                    
+                    "double_blocks"   : ("STRING",                                    {"default": "all", "multiline": True}),
+                    "double_weights"  : ("STRING",                                    {"default": "1.0", "multiline": True}),
+                    "single_blocks"   : ("STRING",                                    {"default": "all", "multiline": True}),
+                    "single_weights"  : ("STRING",                                    {"default": "1.0", "multiline": True}),
+
+                    "mode": (["scattersort", "AdaIN"], {"default": "scattersort"},),
+                    "noise_mode": (["direct", "update", "smart", "recon", "bonanza"], {"default": "smart"},),
+
+                    #"shared_experts":          ("BOOLEAN", {"default": False}),
+                    
+                    "ff_1"                   : ("BOOLEAN", {"default": False}),
+                    "ff_1_silu"              : ("BOOLEAN", {"default": False}),
+                    "ff_3"                   : ("BOOLEAN", {"default": False}),
+                    "ff_13"                  : ("BOOLEAN", {"default": False}),
+                    "ff_2"                   : ("BOOLEAN", {"default": False}),
+                    
+                    "moe_gate"               : ("BOOLEAN", {"default": False}),
+                    "topk_weight"            : ("BOOLEAN", {"default": False}),
+                    "moe_ff_1"               : ("BOOLEAN", {"default": False}),
+                    "moe_ff_1_silu"          : ("BOOLEAN", {"default": False}),
+                    "moe_ff_3"               : ("BOOLEAN", {"default": False}),
+                    "moe_ff_13"              : ("BOOLEAN", {"default": False}),
+                    "moe_ff_2"               : ("BOOLEAN", {"default": False}),
+                    "moe_sum"                : ("BOOLEAN", {"default": False}),
+                    "moe_out"                : ("BOOLEAN", {"default": False}),
+
+                    "double_img_io":           ("BOOLEAN", {"default": False}),
+                    "double_img_norm0":        ("BOOLEAN", {"default": False}),
+                    "double_img_attn":         ("BOOLEAN", {"default": False}),
+                    "double_img_attn_gated":   ("BOOLEAN", {"default": False}),
+                    "double_img":              ("BOOLEAN", {"default": False}),
+                    "double_img_norm1":        ("BOOLEAN", {"default": False}),
+                    "double_img_ff_i":         ("BOOLEAN", {"default": False}),
+
+                    "double_txt_io":           ("BOOLEAN", {"default": False}),
+                    "double_txt_norm0":        ("BOOLEAN", {"default": False}),
+                    "double_txt_attn":         ("BOOLEAN", {"default": False}),
+                    "double_txt_attn_gated":   ("BOOLEAN", {"default": False}),
+                    "double_txt":              ("BOOLEAN", {"default": False}),
+                    "double_txt_norm1":        ("BOOLEAN", {"default": False}),
+                    "double_txt_ff_t":         ("BOOLEAN", {"default": False}),
+
+                    "single_img_io":           ("BOOLEAN", {"default": False}),
+                    "single_img_norm0":        ("BOOLEAN", {"default": False}),
+                    "single_img_attn":         ("BOOLEAN", {"default": False}),
+                    "single_img_attn_gated":   ("BOOLEAN", {"default": False}),
+                    "single_img":              ("BOOLEAN", {"default": False}),
+                    "single_img_norm1":        ("BOOLEAN", {"default": False}),
+                    "single_img_ff_i":         ("BOOLEAN", {"default": False}),
+                    
+                    "attn_img_q_norm"       : ("BOOLEAN", {"default": False}),
+                    "attn_img_k_norm"       : ("BOOLEAN", {"default": False}),
+                    "attn_img_v_norm"       : ("BOOLEAN", {"default": False}),
+                    "attn_txt_q_norm"       : ("BOOLEAN", {"default": False}),
+                    "attn_txt_k_norm"       : ("BOOLEAN", {"default": False}),
+                    "attn_txt_v_norm"       : ("BOOLEAN", {"default": False}),
+                    "attn_img_double"       : ("BOOLEAN", {"default": False}),
+                    "attn_txt_double"       : ("BOOLEAN", {"default": False}),
+                    "attn_img_single"       : ("BOOLEAN", {"default": False}),
+                    
+                    "proj_out"           : ("BOOLEAN", {"default": False}),
+                    
+                    "start_step":       ("INT",      {"default": 0,    "min":  0,      "max": 10000}),
+                    "end_step":         ("INT",      {"default": 15,   "min": -1,      "max": 10000}),
+                    "invert_mask":      ("BOOLEAN",  {"default": False}),
+                    },
+                "optional": 
+                    {
+                    "guide":            ("LATENT", ),
+                    "mask":             ("MASK", ),
+                    "weights":          ("SIGMAS", ),
+                    "guides":           ("GUIDES", ),
+                    }  
+                }
+    
+    RETURN_TYPES = ("GUIDES",)
+    RETURN_NAMES = ("guides",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_extensions"
+    EXPERIMENTAL = True
+
+    def main(self,
+            weight           = 1.0,
+            weight_scheduler = "constant",
+            mode             = "scattersort",
+            noise_mode       = "smart",
+            double_weights   = "0.1",
+            single_weights   = "0.0", 
+            double_blocks    = "all",
+            single_blocks    = "all", 
+            start_step       = 0,
+            end_step         = 15,
+            invert_mask      = False,
+
+            moe_gate                = False,
+            topk_weight             = False,
+            moe_out                 = False,
+            moe_sum                 = False,
+            ff_1                    = False,
+            ff_1_silu               = False,
+            ff_3                    = False,
+            ff_13                   = False,
+            ff_2                    = False,
+            shared_experts          = False,
+            
+            moe_ff_1                = False,
+            moe_ff_1_silu           = False,
+            moe_ff_3                = False,
+            moe_ff_13               = False,
+            moe_ff_2                = False,
+
+            double_img_io           = False,
+            double_img_norm0        = False,
+            double_img_attn         = False,
+            double_img_norm1        = False,
+            double_img_attn_gated   = False,
+            double_img              = False,
+            double_img_ff_i         = False,
+
+            double_txt_io           = False,
+            double_txt_norm0        = False,
+            double_txt_attn         = False,
+            double_txt_attn_gated   = False,
+            double_txt              = False,
+            double_txt_norm1        = False,
+            double_txt_ff_t         = False,
+
+            single_img_io           = False,
+            single_img_norm0        = False,
+            single_img_attn         = False,
+            single_img_attn_gated   = False,
+            single_img              = False,
+            single_img_norm1        = False,
+            single_img_ff_i         = False,
+            
+            attn_img_q_norm         = False,
+            attn_img_k_norm         = False,
+            attn_img_v_norm         = False,
+            attn_txt_q_norm         = False,
+            attn_txt_k_norm         = False,
+            attn_txt_v_norm         = False,
+            attn_img_single         = False,
+            attn_img_double         = False,
+            attn_txt_double         = False,
+            
+            proj_out             = False,
+
+            guide            = None,
+            mask             = None,
+            weights          = None,
+            guides           = None,
+            ):
+        
+        default_dtype = torch.float64
+        
+        mask = 1-mask if mask is not None else None
+        
+        double_weights = parse_range_string(double_weights)
+        single_weights = parse_range_string(single_weights)
+        
+        if len(double_weights) == 0:
+            double_weights.append(0.0)
+        if len(single_weights) == 0:
+            single_weights.append(0.0)
+            
+        if len(double_weights) == 1:
+            double_weights = double_weights * 100
+        if len(single_weights) == 1:
+            single_weights = single_weights * 100
+            
+        if type(double_weights[0]) == int:
+            double_weights = [float(val) for val in double_weights]
+        if type(single_weights[0]) == int:
+            single_weights = [float(val) for val in single_weights]
+        
+        if double_blocks == "all":
+            double_blocks  = [val for val in range(100)]
+            if len(double_weights) == 1:
+                double_weights = [double_weights[0]] * 100
+        else:
+            double_blocks  = parse_range_string(double_blocks)
+            
+            weights_expanded = [0.0] * 100
+            for b, w in zip(double_blocks, double_weights):
+                weights_expanded[b] = w
+            double_weights = weights_expanded
+            
+        
+        if single_blocks == "all":
+            single_blocks = [val for val in range(100)]
+            if len(single_weights) == 1:
+                single_weights = [single_weights[0]] * 100
+        else:
+            single_blocks  = parse_range_string(single_blocks)
+            
+            weights_expanded = [0.0] * 100
+            for b, w in zip(single_blocks, single_weights):
+                weights_expanded[b] = w
+            single_weights = weights_expanded
+        
+        
+        
+        if end_step == -1:
+            end_step = MAX_STEPS
+        
+        if guide is not None:
+            raw_x = guide.get('state_info', {}).get('raw_x', None)
+            if raw_x is not None:
+                guide          = {'samples': guide['state_info']['raw_x'].clone()}
+            else:
+                guide          = {'samples': guide['samples'].clone()}
+        
+        if weight_scheduler == "constant": # and weights == None: 
+            weights = initialize_or_scale(None, weight, end_step).to(default_dtype)
+            prepend = torch.zeros(start_step).to(weights)
+            weights = torch.cat([prepend, weights])
+            weights = F.pad(weights, (0, MAX_STEPS), value=0.0)
+        
+        guides = copy.deepcopy(guides) if guides is not None else {}
+        
+        guides['weight_adain']           = weight
+        guides['weights_adain']          = weights
+        
+        guides['blocks_adain_mmdit'] = {
+            "double_weights": double_weights,
+            "single_weights": single_weights,
+            "double_blocks" : double_blocks,
+            "single_blocks" : single_blocks,
+        }
+        guides['sort_and_scatter'] = {
+            "mode"                  : mode,
+            "noise_mode"            : noise_mode,
+
+            "moe_gate"              : moe_gate,
+            "topk_weight"           : topk_weight,
+            "moe_sum"               : moe_sum,
+            "moe_out"               : moe_out,
+
+            "ff_1"                  : ff_1,
+            "ff_1_silu"             : ff_1_silu,
+            "ff_3"                  : ff_3,
+            "ff_13"                 : ff_13,
+            "ff_2"                  : ff_2,
+            
+            "moe_ff_1"              : moe_ff_1,
+            "moe_ff_1_silu"         : moe_ff_1_silu,
+            "moe_ff_3"              : moe_ff_3,
+            "moe_ff_13"             : moe_ff_13,
+            "moe_ff_2"              : moe_ff_2,
+            
+            "shared_experts"        : shared_experts,
+
+            "double_img_io"         : double_img_io,
+            "double_img_norm0"      : double_img_norm0,
+            "double_img_attn"       : double_img_attn,
+            "double_img_norm1"      : double_img_norm1,
+            "double_img_attn_gated" : double_img_attn_gated,
+            "double_img"            : double_img,
+            "double_img_ff_i"       : double_img_ff_i,
+
+            "double_txt_io"         : double_txt_io,
+            "double_txt_norm0"      : double_txt_norm0,
+            "double_txt_attn"       : double_txt_attn,
+            "double_txt_attn_gated" : double_txt_attn_gated,
+            "double_txt"            : double_txt,
+            "double_txt_norm1"      : double_txt_norm1,
+            "double_txt_ff_t"       : double_txt_ff_t,
+
+            "single_img_io"         : single_img_io,
+            "single_img_norm0"      : single_img_norm0,
+            "single_img_attn"       : single_img_attn,
+            "single_img_attn_gated" : single_img_attn_gated,
+            "single_img"            : single_img,
+            "single_img_norm1"      : single_img_norm1,
+            "single_img_ff_i"       : single_img_ff_i,
+            
+            "attn_img_q_norm"       : attn_img_q_norm,
+            "attn_img_k_norm"       : attn_img_k_norm,
+            "attn_img_v_norm"       : attn_img_v_norm,
+            "attn_txt_q_norm"       : attn_txt_q_norm,
+            "attn_txt_k_norm"       : attn_txt_k_norm,
+            "attn_txt_v_norm"       : attn_txt_v_norm,
+            "attn_img_single"       : attn_img_single,
+            "attn_img_double"       : attn_img_double,
+            
+            "proj_out"           : proj_out,
+        }
+        
+        guides['guide_adain']            = guide
+        guides['mask_adain']             = mask
+
+        guides['weight_scheduler_adain'] = weight_scheduler
+        guides['start_step_adain']       = start_step
+        guides['end_step_adain']         = end_step
+        
+        return (guides, )
+
+
+
+
+from ..style_transfer import StyleMMDiT_Model, StyleUNet_Model, DEFAULT_BLOCK_WEIGHTS_MMDIT, DEFAULT_ATTN_WEIGHTS_MMDIT, DEFAULT_BASE_WEIGHTS_MMDIT
+
+STYLE_MODES = [
+    "none", 
+    #"sinkhornsort",
+    "scattersort_dir", 
+    "scattersort_dir2",
+    "scattersort", 
+    "tiled_scattersort",
+    "AdaIN", 
+    "tiled_AdaIN", 
+    "WCT",
+    "WCT2",
+    "injection",
+]
+
+class ClownStyle_Boost:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "noise_mode":           (["direct", "update", "smart", "recon", "bonanza"], {"default": "update"},),
+                    "recon_lure":           (STYLE_MODES,    {"default": "WCT", "tooltip": "Only used if noise_mode = recon. Can increase the strength of the style."},),
+                    "datashock":            (STYLE_MODES,    {"default": "scattersort", "tooltip": "Will drastically increase the strength at low denoise levels. Use with img2img workflows."},),
+                    "datashock_weight":     ("FLOAT",        {"default": 1.0, "min":  -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide by multiplying all other weights by this value."}),
+                    "datashock_start_step": ("INT",          {"default": 0, "min": 0, "max": 10000, "step": 1, "tooltip": "Start step for data shock."}),
+                    "datashock_end_step"  : ("INT",          {"default": 1, "min": 1, "max": 10000, "step": 1, "tooltip": "End step for data shock."}),
+
+                    "tile_h" :     ("INT",   {"default": 128, "min": 16, "max": 10000, "step": 16, "tooltip": "Tile size for tiled modes. Lower values will transfer composition more effectively. Dimensions of image must be divisible by this value."}),
+                    "tile_w" :     ("INT",   {"default": 128, "min": 16, "max": 10000, "step": 16, "tooltip": "Tile size for tiled modes. Lower values will transfer composition more effectively. Dimensions of image must be divisible by this value."}),
+                    },
+                "optional": 
+                    {
+                    "guides": ("GUIDES", ),
+                    #"datashock_weights": ("SIGMAS",),
+                    }  
+                }
+    
+    RETURN_TYPES = ("GUIDES",)
+    RETURN_NAMES = ("guides",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_extensions"
+
+    def main(self,
+            noise_mode  = "update",
+            recon_lure  = "default",
+            datashock  = None,
+            datashock_weight = 1.0,
+            datashock_start_step  = None,
+            datashock_end_step    = None,
+            tile_h = 0,
+            tile_w = 0,
+            guides      = None,
+            ):
+        guides = copy.deepcopy(guides) if guides is not None else {}
+        
+        StyleMMDiT = guides.get('StyleMMDiT')
+        
+        if StyleMMDiT is None:
+            StyleMMDiT = StyleMMDiT_Model()
+            
+            weights = {
+                "h_tile"  : tile_h // 16,
+                "w_tile"  : tile_w // 16,
+            }
+            StyleMMDiT.set_weights(**weights)
+        
+        StyleMMDiT.noise_mode = noise_mode
+        StyleMMDiT.recon_lure = recon_lure
+        StyleMMDiT.data_shock = datashock
+        StyleMMDiT.data_shock_weight = datashock_weight
+        StyleMMDiT.data_shock_start_step = datashock_start_step
+        StyleMMDiT.data_shock_end_step   = datashock_end_step
+        
+        guides['StyleMMDiT'] = StyleMMDiT
+        return (guides,)
+        
+        #guides['StyleMMDiT'].noise_mode = noise_mode
+        #guides['StyleMMDiT'].recon_lure = recon_lure
+        #guides['StyleMMDiT'].data_shock = datashock
+        #guides['StyleMMDiT'].data_shock_start_step = datashock_start_step
+        #guides['StyleMMDiT'].data_shock_end_step   = datashock_end_step
+        #return (guides, )
+
+
+
+
+
+
+class ClownStyle_MMDiT:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "mode":        (STYLE_MODES, {"default": "scattersort"},),
+
+                    "proj_in":     ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "proj_out":    ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+
+                    "tile_h" :     ("INT",   {"default": 128, "min": 16, "max": 10000, "step": 16, "tooltip": "Tile size for tiled modes. Lower values will transfer composition more effectively. Dimensions of image must be divisible by this value."}),
+                    "tile_w" :     ("INT",   {"default": 128, "min": 16, "max": 10000, "step": 16, "tooltip": "Tile size for tiled modes. Lower values will transfer composition more effectively. Dimensions of image must be divisible by this value."}),
+
+                    #"start_step": ("INT", {"default": 0, "min": 16, "max": 10000, "step": 1, "tooltip": "Start step for data shock."}),
+                    #"end_step"  : ("INT", {"default": 1, "min": 16, "max": 10000, "step": 1, "tooltip": "End step for data shock."}),
+
+                    "invert_mask": ("BOOLEAN", {"default": False}),
+                    },
+                "optional": 
+                    {
+                    "positive" :   ("CONDITIONING", ),
+                    "negative" :   ("CONDITIONING", ),
+                    "guide":       ("LATENT", ),
+                    "mask":        ("MASK", ),
+                    "blocks":      ("BLOCKS", ),
+                    "guides":      ("GUIDES", ),
+                    }  
+                }
+    
+    RETURN_TYPES = ("GUIDES",)
+    RETURN_NAMES = ("guides",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_extensions"
+
+    def main(self,
+            mode        = "scattersort",
+
+            proj_in     = 0.0,
+            proj_out    = 0.0,
+            tile_h      = 128,
+            tile_w      = 128,
+            invert_mask = False,
+            positive    = None,
+            negative    = None,
+            guide       = None,
+            mask        = None,
+            blocks      = None,
+            guides      = None,
+            ):
+        
+        #mask = 1-mask if mask is not None else None
+
+        if guide is not None:
+            raw_x = guide.get('state_info', {}).get('raw_x', None)
+            if raw_x is not None:
+                guide = {'samples': guide['state_info']['raw_x'].clone()}
+            else:
+                guide = {'samples': guide['samples'].clone()}
+        
+        guides = copy.deepcopy(guides) if guides is not None else {}
+        blocks = copy.deepcopy(blocks) if blocks is not None else {}
+
+        StyleMMDiT = blocks.get('StyleMMDiT')
+        
+        if StyleMMDiT is None:
+            StyleMMDiT = StyleMMDiT_Model()
+        
+        weights = {
+            "proj_in" : proj_in,
+            "proj_out": proj_out,
+            
+            "h_tile"  : tile_h // 16,
+            "w_tile"  : tile_w // 16,
+        }
+
+        StyleMMDiT.set_mode(mode)
+        StyleMMDiT.set_weights(**weights)
+        StyleMMDiT.set_conditioning(positive, negative)
+        StyleMMDiT.mask = [mask]
+        StyleMMDiT.guides = [guide]
+        
+        StyleMMDiT_ = guides.get('StyleMMDiT')
+        if StyleMMDiT_ is not None:
+            StyleMMDiT_.merge_weights(StyleMMDiT)
+        else:
+            StyleMMDiT_ = StyleMMDiT
+
+        guides['StyleMMDiT'] = StyleMMDiT_
+
+        return (guides, )
+
+
+
+class ClownStyle_Block_MMDiT:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "mode":          (STYLE_MODES, {"default": "scattersort"},),
+                    "apply_to":      (["img", "img+txt","img,txt", "txt",], {"default": "img+txt"},),
+                    "block_type":    (["double", "double,single", "single"], {"default": "single"},),
+                    "block_list":    ("STRING", {"default": "all", "multiline": True}),
+                    "block_weights": ("STRING", {"default": "1.0", "multiline": True}),
+                    
+                    "attn_norm":     ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "attn_norm_mod": ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "attn":          ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "attn_gated":    ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "attn_res":      ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+
+                    "ff_norm":       ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "ff_norm_mod":   ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "ff":            ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "ff_gated":      ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "ff_res":        ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+
+                    "tile_h":        ("INT",    {"default": 128, "min": 16, "max": 10000, "step": 16, "tooltip": "Tile size for tiled modes. Lower values will transfer composition more effectively. Dimensions of image must be divisible by this value."}),
+                    "tile_w":        ("INT",    {"default": 128, "min": 16, "max": 10000, "step": 16, "tooltip": "Tile size for tiled modes. Lower values will transfer composition more effectively. Dimensions of image must be divisible by this value."}),
+
+                    "invert_mask":   ("BOOLEAN",{"default": False}),
+                    },
+                "optional": 
+                    {
+                    "mask":        ("MASK", ),
+                    "blocks":      ("BLOCKS", ),
+                    }  
+                }
+    
+    RETURN_TYPES = ("BLOCKS",)
+    RETURN_NAMES = ("blocks",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_extensions"
+
+    def main(self,
+            mode        = "scattersort",
+            noise_mode  = "update",
+            apply_to    = "joint",
+            block_type  = "double",
+            block_list    = "all",
+            block_weights = "1.0",
+            
+            attn_norm     = 0.0,
+            attn_norm_mod = 0.0,
+            attn          = 0.0,
+            attn_gated    = 0.0,
+            attn_res      = 0.0,
+            ff_norm       = 0.0,
+            ff_norm_mod   = 0.0,
+            ff            = 0.0,
+            ff_gated      = 0.0,
+            ff_res        = 0.0,
+
+            tile_h      = 128,
+            tile_w      = 128,
+
+            invert_mask = False,
+
+            Attn        = None,
+            MoE         = None,
+            FF          = None,
+
+            mask        = None,
+            blocks      = None,
+            ):
+        
+        #mask = 1-mask if mask is not None else None
+
+        blocks = copy.deepcopy(blocks) if blocks is not None else {}
+        
+        block_weights = parse_range_string(block_weights)
+        
+        if len(block_weights) == 0:
+            block_weights.append(0.0)
+            
+        if len(block_weights) == 1:
+            block_weights = block_weights * 100
+            
+        if type(block_weights[0]) == int:
+            block_weights = [float(val) for val in block_weights]
+        
+        if    "all" in block_list:
+            block_list = [val for val in range(100)]
+            if len(block_weights) == 1:
+                block_weights = [block_weights[0]] * 100
+        elif "even" in block_list:
+            block_list = [val for val in range(0, 100, 2)]
+            if len(block_weights) == 1:
+                block_weights = [block_weights[0]] * 100
+        elif  "odd" in block_list:
+            block_list = [val for val in range(1, 100, 2)]
+            if len(block_weights) == 1:
+                block_weights = [block_weights[0]] * 100
+        else:
+            block_list  = parse_range_string_int(block_list)
+            
+            weights_expanded = [0.0] * 100
+            for b, w in zip(block_list, block_weights):
+                weights_expanded[b] = w
+            block_weights = weights_expanded
+        
+        StyleMMDiT = blocks.get('StyleMMDiT')
+        if StyleMMDiT is None:
+            StyleMMDiT = StyleMMDiT_Model()
+        
+        weights = {
+            "attn_norm"    : attn_norm,
+            "attn_norm_mod": attn_norm_mod,
+            "attn"         : attn,
+            "attn_gated"   : attn_gated,
+            "attn_res"     : attn_res,
+            "ff_norm"      : ff_norm,
+            "ff_norm_mod"  : ff_norm_mod,
+            "ff"           : ff,
+            "ff_gated"     : ff_gated,
+            "ff_res"       : ff_res,
+            
+            "h_tile"       : tile_h // 16,
+            "w_tile"       : tile_w // 16,
+        }
+        
+        block_types = block_type.split(",")
+        
+        for block_type in block_types:
+        
+            if   block_type == "double":
+                style_blocks = StyleMMDiT.double_blocks
+            elif block_type == "single":
+                style_blocks = StyleMMDiT.single_blocks
+                
+            for bid in block_list:
+                block = style_blocks[bid]
+                scaled_weights = {
+                    k: (v * block_weights[bid]) if isinstance(v, float) else v
+                    for k, v in weights.items()
+                }
+
+                if "img" in apply_to  or block_type == "single":
+                    block.img.set_mode(mode)
+                    block.img.set_weights(**scaled_weights)
+                    block.img.apply_to = [apply_to]
+
+                if "txt" in apply_to and block_type == "double":
+                    mode = "scattersort" if mode == "tiled_scattersort" else mode
+                    mode = "AdaIN" if mode == "tiled_AdaIN" else mode
+                    block.txt.set_mode(mode)
+                    block.txt.set_weights(**scaled_weights)
+                    block.txt.apply_to = [apply_to]
+                
+                block.img.apply_to = [apply_to]
+                if hasattr(block, "txt"):
+                    block.txt.apply_to = [apply_to]
+                
+                block.mask = [mask]
+
+        blocks['StyleMMDiT'] = StyleMMDiT
+
+        return (blocks, )
+
+
+
+class ClownStyle_Attn_MMDiT:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "mode":          (STYLE_MODES, {"default": "scattersort"},),
+                    "apply_to":      (["img","img+txt","img,txt","txt"], {"default": "img+txt"},),
+                    "block_type":    (["double", "double,single", "single"], {"default": "single"},),
+                    "block_list":    ("STRING", {"default": "all", "multiline": True}),
+                    "block_weights": ("STRING", {"default": "1.0", "multiline": True}),
+                    
+                    "q_proj": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "k_proj": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "v_proj": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "q_norm": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "k_norm": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "out":    ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+
+                    "tile_h": ("INT",   {"default": 128, "min": 16, "max": 10000, "step": 16, "tooltip": "Tile size for tiled modes. Lower values will transfer composition more effectively. Dimensions of image must be divisible by this value."}),
+                    "tile_w": ("INT",   {"default": 128, "min": 16, "max": 10000, "step": 16, "tooltip": "Tile size for tiled modes. Lower values will transfer composition more effectively. Dimensions of image must be divisible by this value."}),
+
+                    "invert_mask":   ("BOOLEAN", {"default": False}),
+                    },
+                "optional": 
+                    {
+                    "mask":   ("MASK", ),
+                    "blocks": ("BLOCKS", ),
+                    }  
+                }
+    
+    RETURN_TYPES = ("BLOCKS",)
+    RETURN_NAMES = ("blocks",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_extensions"
+
+    def main(self,
+            mode        = "scattersort",
+            noise_mode  = "update",
+            apply_to    = "joint",
+            block_type  = "double",
+            block_list    = "all",
+            block_weights = "1.0",
+            
+            q_proj = 0.0,
+            k_proj = 0.0,
+            v_proj = 0.0,
+            q_norm = 0.0,
+            k_norm = 0.0,
+            out    = 0.0,
+            
+            tile_h = 128,
+            tile_w = 128,
+
+            invert_mask = False,
+
+            mask        = None,
+            blocks      = None,
+            ):
+        
+        #mask = 1-mask if mask is not None else None
+
+        blocks = copy.deepcopy(blocks) if blocks is not None else {}
+        
+        block_weights = parse_range_string(block_weights)
+        
+        if len(block_weights) == 0:
+            block_weights.append(0.0)
+            
+        if len(block_weights) == 1:
+            block_weights = block_weights * 100
+            
+        if type(block_weights[0]) == int:
+            block_weights = [float(val) for val in block_weights]
+        
+        if "all" in block_list:
+            block_list = [val for val in range(100)]
+            if len(block_weights) == 1:
+                block_weights = [block_weights[0]] * 100
+        elif "even" in block_list:
+            block_list = [val for val in range(0, 100, 2)]
+            if len(block_weights) == 1:
+                block_weights = [block_weights[0]] * 100
+        elif "odd" in block_list:
+            block_list = [val for val in range(1, 100, 2)]
+            if len(block_weights) == 1:
+                block_weights = [block_weights[0]] * 100
+        else:
+            block_list  = parse_range_string_int(block_list)
+            
+            weights_expanded = [0.0] * 100
+            for b, w in zip(block_list, block_weights):
+                weights_expanded[b] = w
+            block_weights = weights_expanded
+        
+        StyleMMDiT = blocks.get('StyleMMDiT')
+        if StyleMMDiT is None:
+            StyleMMDiT = StyleMMDiT_Model()
+        
+        weights = {
+            "q_proj": q_proj,
+            "k_proj": k_proj,
+            "v_proj": v_proj,
+            "q_norm": q_norm,
+            "k_norm": k_norm,
+            "out"   : out,
+            
+            "h_tile": tile_h // 16,
+            "w_tile": tile_w // 16,
+        }
+        
+        block_types = block_type.split(",")
+        
+        for block_type in block_types:
+            
+            if   block_type == "double":
+                style_blocks = StyleMMDiT.double_blocks
+            elif block_type == "single":
+                style_blocks = StyleMMDiT.single_blocks
+            
+            for bid in block_list:
+                block = style_blocks[bid]
+                scaled_weights = {
+                    k: (v * block_weights[bid]) if isinstance(v, float) else v
+                    for k, v in weights.items()
+                }
+
+                if "img" in apply_to  or block_type == "single":
+                    block.img.ATTN.set_mode(mode)
+                    block.img.ATTN.set_weights(**scaled_weights)
+                    block.img.ATTN.apply_to = [apply_to]
+
+                if "txt" in apply_to and block_type == "double":
+                    mode = "scattersort" if mode == "tiled_scattersort" else mode
+                    mode = "AdaIN" if mode == "tiled_AdaIN" else mode
+                    block.txt.ATTN.set_mode(mode)
+                    block.txt.ATTN.set_weights(**scaled_weights)
+                    block.txt.ATTN.apply_to = [apply_to]
+                
+                block.img.ATTN.apply_to = [apply_to]
+                if hasattr(block, "txt"):
+                    block.txt.ATTN.apply_to = [apply_to]
+                
+                block.attn_mask = [mask]
+
+        blocks['StyleMMDiT'] = StyleMMDiT
+
+        return (blocks, )
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+class ClownStyle_UNet:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "mode":        (STYLE_MODES, {"default": "scattersort"},),
+
+                    "proj_in":     ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "proj_out":    ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+
+                    "tile_h" :     ("INT",   {"default": 128, "min": 16, "max": 10000, "step": 16, "tooltip": "Tile size for tiled modes. Lower values will transfer composition more effectively. Dimensions of image must be divisible by this value."}),
+                    "tile_w" :     ("INT",   {"default": 128, "min": 16, "max": 10000, "step": 16, "tooltip": "Tile size for tiled modes. Lower values will transfer composition more effectively. Dimensions of image must be divisible by this value."}),
+
+                    #"start_step": ("INT", {"default": 0, "min": 16, "max": 10000, "step": 1, "tooltip": "Start step for data shock."}),
+                    #"end_step"  : ("INT", {"default": 1, "min": 16, "max": 10000, "step": 1, "tooltip": "End step for data shock."}),
+
+                    "invert_mask": ("BOOLEAN", {"default": False}),
+                    },
+                "optional": 
+                    {
+                    "positive" :   ("CONDITIONING", ),
+                    "negative" :   ("CONDITIONING", ),
+                    "guide":       ("LATENT", ),
+                    "mask":        ("MASK", ),
+                    "blocks":      ("BLOCKS", ),
+                    "guides":      ("GUIDES", ),
+                    }  
+                }
+    
+    RETURN_TYPES = ("GUIDES",)
+    RETURN_NAMES = ("guides",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_extensions"
+
+    def main(self,
+            mode        = "scattersort",
+
+            proj_in     = 0.0,
+            proj_out    = 0.0,
+            tile_h      = 128,
+            tile_w      = 128,
+            invert_mask = False,
+            positive    = None,
+            negative    = None,
+            guide       = None,
+            mask        = None,
+            blocks      = None,
+            guides      = None,
+            ):
+        
+        #mask = 1-mask if mask is not None else None
+
+        if guide is not None:
+            raw_x = guide.get('state_info', {}).get('raw_x', None)
+            if raw_x is not None:
+                guide = {'samples': guide['state_info']['raw_x'].clone()}
+            else:
+                guide = {'samples': guide['samples'].clone()}
+        
+        guides = copy.deepcopy(guides) if guides is not None else {}
+        blocks = copy.deepcopy(blocks) if blocks is not None else {}
+
+        StyleMMDiT = blocks.get('StyleMMDiT')
+        
+        if StyleMMDiT is None:
+            StyleMMDiT = StyleUNet_Model()
+        
+        weights = {
+            "proj_in" : proj_in,
+            "proj_out": proj_out,
+            
+            "h_tile"  : tile_h // 8,
+            "w_tile"  : tile_w // 8,
+        }
+
+        StyleMMDiT.set_mode(mode)
+        StyleMMDiT.set_weights(**weights)
+        StyleMMDiT.set_conditioning(positive, negative)
+        StyleMMDiT.mask = [mask]
+        StyleMMDiT.guides = [guide]
+        
+        StyleMMDiT_ = guides.get('StyleMMDiT')
+        if StyleMMDiT_ is not None:
+            StyleMMDiT_.merge_weights(StyleMMDiT)
+        else:
+            StyleMMDiT_ = StyleMMDiT
+
+        guides['StyleMMDiT'] = StyleMMDiT_
+
+        return (guides, )
+
+
+
+
+UNET_BLOCK_TYPES = [
+    "input", 
+    "middle", 
+    "output",
+    "input,middle",
+    "input,output",
+    "middle,output",
+    "input,middle,output",
+]
+
+class ClownStyle_Block_UNet:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "mode":          (STYLE_MODES, {"default": "scattersort"},),
+                    #"apply_to":      (["img", "img+txt","img,txt", "txt",], {"default": "img+txt"},),
+                    "block_type":    (UNET_BLOCK_TYPES, {"default": "input"},),
+                    "block_list":    ("STRING", {"default": "all", "multiline": True}),
+                    "block_weights": ("STRING", {"default": "1.0", "multiline": True}),
+                    
+                    "resample": ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "res":      ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "spatial":  ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+
+                    "tile_h":        ("INT",    {"default": 128, "min": 16, "max": 10000, "step": 16, "tooltip": "Tile size for tiled modes. Lower values will transfer composition more effectively. Dimensions of image must be divisible by this value."}),
+                    "tile_w":        ("INT",    {"default": 128, "min": 16, "max": 10000, "step": 16, "tooltip": "Tile size for tiled modes. Lower values will transfer composition more effectively. Dimensions of image must be divisible by this value."}),
+
+                    "invert_mask":   ("BOOLEAN",{"default": False}),
+                    },
+                "optional": 
+                    {
+                    "mask":        ("MASK", ),
+                    "blocks":      ("BLOCKS", ),
+                    }  
+                }
+    
+    RETURN_TYPES = ("BLOCKS",)
+    RETURN_NAMES = ("blocks",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_extensions"
+
+    def main(self,
+            mode        = "scattersort",
+            noise_mode  = "update",
+            apply_to    = "",
+            block_type  = "input",
+            block_list    = "all",
+            block_weights = "1.0",
+            
+            resample = 0.0,
+            res      = 0.0,
+            spatial  = 0.0,
+
+            tile_h      = 128,
+            tile_w      = 128,
+
+            invert_mask = False,
+
+            mask        = None,
+            blocks      = None,
+            ):
+        
+        #mask = 1-mask if mask is not None else None
+
+        blocks = copy.deepcopy(blocks) if blocks is not None else {}
+        
+        block_weights = parse_range_string(block_weights)
+        
+        if len(block_weights) == 0:
+            block_weights.append(0.0)
+            
+        if len(block_weights) == 1:
+            block_weights = block_weights * 100
+            
+        if type(block_weights[0]) == int:
+            block_weights = [float(val) for val in block_weights]
+        
+        if    "all" in block_list:
+            block_list = [val for val in range(100)]
+            if len(block_weights) == 1:
+                block_weights = [block_weights[0]] * 100
+        elif "even" in block_list:
+            block_list = [val for val in range(0, 100, 2)]
+            if len(block_weights) == 1:
+                block_weights = [block_weights[0]] * 100
+        elif  "odd" in block_list:
+            block_list = [val for val in range(1, 100, 2)]
+            if len(block_weights) == 1:
+                block_weights = [block_weights[0]] * 100
+        else:
+            block_list  = parse_range_string_int(block_list)
+            
+            weights_expanded = [0.0] * 100
+            for b, w in zip(block_list, block_weights):
+                weights_expanded[b] = w
+            block_weights = weights_expanded
+        
+        StyleMMDiT = blocks.get('StyleMMDiT')
+        if StyleMMDiT is None:
+            StyleMMDiT = StyleUNet_Model()
+        
+        weights = {
+            "resample": resample,
+            "res":      res,
+            "spatial":  spatial,
+
+            "h_tile"       : tile_h // 16,
+            "w_tile"       : tile_w // 16,
+        }
+        
+        block_types = block_type.split(",")
+        
+        for block_type in block_types:
+        
+            if   block_type == "input":
+                style_blocks = StyleMMDiT.input_blocks
+            elif block_type == "middle":
+                style_blocks = StyleMMDiT.middle_blocks
+            elif block_type == "output":
+                style_blocks = StyleMMDiT.output_blocks
+                
+            for bid in block_list:
+                block = style_blocks[bid]
+                scaled_weights = {
+                    k: (v * block_weights[bid]) if isinstance(v, float) else v
+                    for k, v in weights.items()
+                }
+
+                block.set_mode(mode)
+                block.set_weights(**scaled_weights)
+                block.apply_to = [apply_to]
+
+                block.mask = [mask]
+
+        blocks['StyleMMDiT'] = StyleMMDiT
+
+        return (blocks, )
+
+
+
+
+class ClownStyle_Attn_UNet:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "mode":          (STYLE_MODES, {"default": "scattersort"},),
+                    "apply_to":      (["self","self,cross","cross"], {"default": "self"},),
+                    "block_type":    (UNET_BLOCK_TYPES, {"default": "input"},),
+                    "block_list":    ("STRING", {"default": "all", "multiline": True}),
+                    "block_weights": ("STRING", {"default": "1.0", "multiline": True}),
+                    
+                    "q_proj": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "k_proj": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "v_proj": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+
+                    "out":    ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+
+                    "tile_h": ("INT",   {"default": 128, "min": 16, "max": 10000, "step": 16, "tooltip": "Tile size for tiled modes. Lower values will transfer composition more effectively. Dimensions of image must be divisible by this value."}),
+                    "tile_w": ("INT",   {"default": 128, "min": 16, "max": 10000, "step": 16, "tooltip": "Tile size for tiled modes. Lower values will transfer composition more effectively. Dimensions of image must be divisible by this value."}),
+
+                    "invert_mask":   ("BOOLEAN", {"default": False}),
+                    },
+                "optional": 
+                    {
+                    "mask":   ("MASK", ),
+                    "blocks": ("BLOCKS", ),
+                    }  
+                }
+    
+    RETURN_TYPES = ("BLOCKS",)
+    RETURN_NAMES = ("blocks",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_extensions"
+
+    def main(self,
+            mode        = "scattersort",
+            noise_mode  = "update",
+            apply_to    = "self",
+            block_type  = "input",
+            block_list    = "all",
+            block_weights = "1.0",
+            
+            q_proj = 0.0,
+            k_proj = 0.0,
+            v_proj = 0.0,
+
+            out    = 0.0,
+            
+            tile_h = 128,
+            tile_w = 128,
+
+            invert_mask = False,
+
+            mask        = None,
+            blocks      = None,
+            ):
+        
+        #mask = 1-mask if mask is not None else None
+
+        blocks = copy.deepcopy(blocks) if blocks is not None else {}
+        
+        block_weights = parse_range_string(block_weights)
+        
+        if len(block_weights) == 0:
+            block_weights.append(0.0)
+            
+        if len(block_weights) == 1:
+            block_weights = block_weights * 100
+            
+        if type(block_weights[0]) == int:
+            block_weights = [float(val) for val in block_weights]
+        
+        if "all" in block_list:
+            block_list = [val for val in range(100)]
+            if len(block_weights) == 1:
+                block_weights = [block_weights[0]] * 100
+        elif "even" in block_list:
+            block_list = [val for val in range(0, 100, 2)]
+            if len(block_weights) == 1:
+                block_weights = [block_weights[0]] * 100
+        elif "odd" in block_list:
+            block_list = [val for val in range(1, 100, 2)]
+            if len(block_weights) == 1:
+                block_weights = [block_weights[0]] * 100
+        else:
+            block_list  = parse_range_string_int(block_list)
+            
+            weights_expanded = [0.0] * 100
+            for b, w in zip(block_list, block_weights):
+                weights_expanded[b] = w
+            block_weights = weights_expanded
+        
+        StyleMMDiT = blocks.get('StyleMMDiT')
+        if StyleMMDiT is None:
+            StyleMMDiT = StyleUNet_Model()
+        
+        weights = {
+            "q_proj": q_proj,
+            "k_proj": k_proj,
+            "v_proj": v_proj,
+            "out"   : out,
+            
+            "h_tile": tile_h // 8,
+            "w_tile": tile_w // 8,
+        }
+        
+        block_types = block_type.split(",")
+        
+        for block_type in block_types:
+            
+            if   block_type == "input":
+                style_blocks = StyleMMDiT.input_blocks
+            elif block_type == "middle":
+                style_blocks = StyleMMDiT.middle_blocks
+            elif block_type == "output":
+                style_blocks = StyleMMDiT.output_blocks
+            
+            for bid in block_list:
+                block = style_blocks[bid]
+                scaled_weights = {
+                    k: (v * block_weights[bid]) if isinstance(v, float) else v
+                    for k, v in weights.items()
+                }
+
+                #for tfmr_block in block.spatial_block.TFMR:
+                tfmr_block = block.spatial_block.TFMR
+                if "self" in apply_to:
+                    tfmr_block.ATTN1.set_mode(mode)
+                    tfmr_block.ATTN1.set_weights(**scaled_weights)
+                    tfmr_block.ATTN1.apply_to = [apply_to]
+
+                if "cross" in apply_to:
+                    tfmr_block.ATTN2.set_mode(mode)
+                    tfmr_block.ATTN2.set_weights(**scaled_weights)
+                    tfmr_block.ATTN2.apply_to = [apply_to]
+                
+                block.attn_mask = [mask]
+
+        blocks['StyleMMDiT'] = StyleMMDiT
+
+        return (blocks, )
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+class ClownStyle_ResBlock_UNet:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "mode":          (STYLE_MODES, {"default": "scattersort"},),
+                    #"apply_to":      (["img", "img+txt","img,txt", "txt",], {"default": "img+txt"},),
+                    "block_type":    (UNET_BLOCK_TYPES, {"default": "input"},),
+                    "block_list":    ("STRING", {"default": "all", "multiline": True}),
+                    "block_weights": ("STRING", {"default": "1.0", "multiline": True}),
+                    
+                    "in_norm": ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "in_silu": ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "in_conv": ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+
+                    "emb_silu": ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "emb_linear": ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "emb_res": ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+
+                    "out_norm": ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "out_silu": ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "out_conv": ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+
+                    "residual": ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+
+                    "tile_h":        ("INT",    {"default": 128, "min": 16, "max": 10000, "step": 16, "tooltip": "Tile size for tiled modes. Lower values will transfer composition more effectively. Dimensions of image must be divisible by this value."}),
+                    "tile_w":        ("INT",    {"default": 128, "min": 16, "max": 10000, "step": 16, "tooltip": "Tile size for tiled modes. Lower values will transfer composition more effectively. Dimensions of image must be divisible by this value."}),
+
+                    "invert_mask":   ("BOOLEAN",{"default": False}),
+                    },
+                "optional": 
+                    {
+                    "mask":        ("MASK", ),
+                    "blocks":      ("BLOCKS", ),
+                    }  
+                }
+    
+    RETURN_TYPES = ("BLOCKS",)
+    RETURN_NAMES = ("blocks",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_extensions"
+
+    def main(self,
+            mode        = "scattersort",
+            noise_mode  = "update",
+            apply_to    = "",
+            block_type  = "input",
+            block_list    = "all",
+            block_weights = "1.0",
+            
+            in_norm  = 0.0,
+            in_silu  = 0.0,
+            in_conv  = 0.0,
+            
+            emb_silu   = 0.0,
+            emb_linear = 0.0,
+            emb_res    = 0.0,
+            
+            out_norm = 0.0,
+            out_silu = 0.0,
+            out_conv = 0.0,
+            
+            residual = 0.0,
+
+            tile_h      = 128,
+            tile_w      = 128,
+
+            invert_mask = False,
+
+            mask        = None,
+            blocks      = None,
+            ):
+        
+        #mask = 1-mask if mask is not None else None
+
+        blocks = copy.deepcopy(blocks) if blocks is not None else {}
+        
+        block_weights = parse_range_string(block_weights)
+        
+        if len(block_weights) == 0:
+            block_weights.append(0.0)
+            
+        if len(block_weights) == 1:
+            block_weights = block_weights * 100
+            
+        if type(block_weights[0]) == int:
+            block_weights = [float(val) for val in block_weights]
+        
+        if    "all" in block_list:
+            block_list = [val for val in range(100)]
+            if len(block_weights) == 1:
+                block_weights = [block_weights[0]] * 100
+        elif "even" in block_list:
+            block_list = [val for val in range(0, 100, 2)]
+            if len(block_weights) == 1:
+                block_weights = [block_weights[0]] * 100
+        elif  "odd" in block_list:
+            block_list = [val for val in range(1, 100, 2)]
+            if len(block_weights) == 1:
+                block_weights = [block_weights[0]] * 100
+        else:
+            block_list  = parse_range_string_int(block_list)
+            
+            weights_expanded = [0.0] * 100
+            for b, w in zip(block_list, block_weights):
+                weights_expanded[b] = w
+            block_weights = weights_expanded
+        
+        StyleMMDiT = blocks.get('StyleMMDiT')
+        if StyleMMDiT is None:
+            StyleMMDiT = StyleUNet_Model()
+        
+        weights = {
+            "in_norm": in_norm,
+            "in_silu": in_silu,
+            "in_conv": in_conv,
+            
+            "emb_silu":   emb_silu,
+            "emb_linear": emb_linear,
+            "emb_res": emb_res,
+            
+            "out_norm": out_norm,
+            "out_silu": out_silu,
+            "out_conv": out_conv,
+            
+            "residual": residual,
+
+            "h_tile": tile_h // 8,
+            "w_tile": tile_w // 8,
+        }
+        
+        block_types = block_type.split(",")
+        
+        for block_type in block_types:
+        
+            if   block_type == "input":
+                style_blocks = StyleMMDiT.input_blocks
+            elif block_type == "middle":
+                style_blocks = StyleMMDiT.middle_blocks
+            elif block_type == "output":
+                style_blocks = StyleMMDiT.output_blocks
+                
+            for bid in block_list:
+                block = style_blocks[bid]
+                scaled_weights = {
+                    k: (v * block_weights[bid]) if isinstance(v, float) else v
+                    for k, v in weights.items()
+                }
+
+                block.res_block.set_mode(mode)
+                block.res_block.set_weights(**scaled_weights)
+                block.res_block.apply_to = [apply_to]
+                block.res_block.mask = [mask]
+
+        blocks['StyleMMDiT'] = StyleMMDiT
+
+        return (blocks, )
+
+
+
+
+
+
+
+
+class ClownStyle_SpatialBlock_UNet:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "mode":          (STYLE_MODES, {"default": "scattersort"},),
+                    #"apply_to":      (["img", "img+txt","img,txt", "txt",], {"default": "img+txt"},),
+                    "block_type":    (UNET_BLOCK_TYPES, {"default": "input"},),
+                    "block_list":    ("STRING", {"default": "all", "multiline": True}),
+                    "block_weights": ("STRING", {"default": "1.0", "multiline": True}),
+                    
+                    "norm_in":     ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "proj_in":     ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "transformer_block": ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "transformer": ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "proj_out":    ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "res":         ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+
+                    "tile_h":      ("INT",    {"default": 128, "min": 16, "max": 10000, "step": 16, "tooltip": "Tile size for tiled modes. Lower values will transfer composition more effectively. Dimensions of image must be divisible by this value."}),
+                    "tile_w":      ("INT",    {"default": 128, "min": 16, "max": 10000, "step": 16, "tooltip": "Tile size for tiled modes. Lower values will transfer composition more effectively. Dimensions of image must be divisible by this value."}),
+
+                    "invert_mask": ("BOOLEAN",{"default": False}),
+                    },
+                "optional": 
+                    {
+                    "mask":        ("MASK", ),
+                    "blocks":      ("BLOCKS", ),
+                    }  
+                }
+    
+    RETURN_TYPES = ("BLOCKS",)
+    RETURN_NAMES = ("blocks",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_extensions"
+
+    def main(self,
+            mode        = "scattersort",
+            noise_mode  = "update",
+            apply_to    = "",
+            block_type  = "input",
+            block_list    = "all",
+            block_weights = "1.0",
+            
+            norm_in     = 0.0,
+            proj_in     = 0.0,
+            transformer_block = 0.0,
+            transformer = 0.0,
+            proj_out    = 0.0,
+            res         = 0.0,
+
+            tile_h      = 128,
+            tile_w      = 128,
+
+            invert_mask = False,
+
+            mask        = None,
+            blocks      = None,
+            ):
+        
+        spatial_norm_in     = norm_in
+        spatial_proj_in     = proj_in
+        spatial_transformer_block = transformer_block
+        spatial_transformer = transformer
+        spatial_proj_out    = proj_out
+        spatial_res         = res
+        
+        #mask = 1-mask if mask is not None else None
+
+        blocks = copy.deepcopy(blocks) if blocks is not None else {}
+        
+        block_weights = parse_range_string(block_weights)
+        
+        if len(block_weights) == 0:
+            block_weights.append(0.0)
+            
+        if len(block_weights) == 1:
+            block_weights = block_weights * 100
+            
+        if type(block_weights[0]) == int:
+            block_weights = [float(val) for val in block_weights]
+        
+        if    "all" in block_list:
+            block_list = [val for val in range(100)]
+            if len(block_weights) == 1:
+                block_weights = [block_weights[0]] * 100
+        elif "even" in block_list:
+            block_list = [val for val in range(0, 100, 2)]
+            if len(block_weights) == 1:
+                block_weights = [block_weights[0]] * 100
+        elif  "odd" in block_list:
+            block_list = [val for val in range(1, 100, 2)]
+            if len(block_weights) == 1:
+                block_weights = [block_weights[0]] * 100
+        else:
+            block_list  = parse_range_string_int(block_list)
+            
+            weights_expanded = [0.0] * 100
+            for b, w in zip(block_list, block_weights):
+                weights_expanded[b] = w
+            block_weights = weights_expanded
+        
+        StyleMMDiT = blocks.get('StyleMMDiT')
+        if StyleMMDiT is None:
+            StyleMMDiT = StyleUNet_Model()
+        
+        weights = {
+            "spatial_norm_in"    : spatial_norm_in,
+            "spatial_proj_in"    : spatial_proj_in,
+            "spatial_transformer_block": spatial_transformer_block,
+            "spatial_transformer": spatial_transformer,
+            "spatial_proj_out"   : spatial_proj_out,
+            "spatial_res"        : spatial_res,
+
+            "h_tile": tile_h // 8,
+            "w_tile": tile_w // 8,
+        }
+        
+        block_types = block_type.split(",")
+        
+        for block_type in block_types:
+        
+            if   block_type == "input":
+                style_blocks = StyleMMDiT.input_blocks
+            elif block_type == "middle":
+                style_blocks = StyleMMDiT.middle_blocks
+            elif block_type == "output":
+                style_blocks = StyleMMDiT.output_blocks
+                
+            for bid in block_list:
+                block = style_blocks[bid]
+                scaled_weights = {
+                    k: (v * block_weights[bid]) if isinstance(v, float) else v
+                    for k, v in weights.items()
+                }
+
+                block.spatial_block.set_mode(mode)
+                block.spatial_block.set_weights(**scaled_weights)
+                block.spatial_block.apply_to = [apply_to]
+
+                block.spatial_block.mask = [mask]
+
+        blocks['StyleMMDiT'] = StyleMMDiT
+
+        return (blocks, )
+
+
+
+
+
+
+
+class ClownStyle_TransformerBlock_UNet:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required":
+                    {
+                    "mode":          (STYLE_MODES, {"default": "scattersort"},),
+                    #"apply_to":      (["img", "img+txt","img,txt", "txt",], {"default": "img+txt"},),
+                    "block_type":    (UNET_BLOCK_TYPES, {"default": "input"},),
+                    "block_list":    ("STRING", {"default": "all", "multiline": True}),
+                    "block_weights": ("STRING", {"default": "1.0", "multiline": True}),
+                    
+                    "norm1": ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "norm2": ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "norm3": ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    
+                    "self_attn":  ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "cross_attn": ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "ff":         ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+
+                    "self_attn_res":  ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "cross_attn_res": ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+                    "ff_res":         ("FLOAT",  {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Strength of effect on layer; skips extra calculation if set to 0.0. Skips interpolation if set to 1.0."}),
+
+                    "tile_h":        ("INT",    {"default": 128, "min": 16, "max": 10000, "step": 16, "tooltip": "Tile size for tiled modes. Lower values will transfer composition more effectively. Dimensions of image must be divisible by this value."}),
+                    "tile_w":        ("INT",    {"default": 128, "min": 16, "max": 10000, "step": 16, "tooltip": "Tile size for tiled modes. Lower values will transfer composition more effectively. Dimensions of image must be divisible by this value."}),
+
+                    "invert_mask":   ("BOOLEAN",{"default": False}),
+                    },
+                "optional": 
+                    {
+                    "mask":        ("MASK", ),
+                    "blocks":      ("BLOCKS", ),
+                    }  
+                }
+    
+    RETURN_TYPES = ("BLOCKS",)
+    RETURN_NAMES = ("blocks",)
+    FUNCTION     = "main"
+    CATEGORY     = "RES4LYF/sampler_extensions"
+
+    def main(self,
+            mode        = "scattersort",
+            noise_mode  = "update",
+            apply_to    = "",
+            block_type  = "input",
+            block_list    = "all",
+            block_weights = "1.0",
+            
+            norm1      = 0.0,
+            norm2      = 0.0,
+            norm3      = 0.0,
+            
+            self_attn  = 0.0,
+            cross_attn = 0.0,
+            ff         = 0.0,
+            
+            self_attn_res  = 0.0,
+            cross_attn_res = 0.0,
+            ff_res         = 0.0,
+
+            tile_h      = 128,
+            tile_w      = 128,
+
+            invert_mask = False,
+
+            mask        = None,
+            blocks      = None,
+            ):
+        
+        #mask = 1-mask if mask is not None else None
+
+        blocks = copy.deepcopy(blocks) if blocks is not None else {}
+        
+        block_weights = parse_range_string(block_weights)
+        
+        if len(block_weights) == 0:
+            block_weights.append(0.0)
+            
+        if len(block_weights) == 1:
+            block_weights = block_weights * 100
+            
+        if type(block_weights[0]) == int:
+            block_weights = [float(val) for val in block_weights]
+        
+        if    "all" in block_list:
+            block_list = [val for val in range(100)]
+            if len(block_weights) == 1:
+                block_weights = [block_weights[0]] * 100
+        elif "even" in block_list:
+            block_list = [val for val in range(0, 100, 2)]
+            if len(block_weights) == 1:
+                block_weights = [block_weights[0]] * 100
+        elif  "odd" in block_list:
+            block_list = [val for val in range(1, 100, 2)]
+            if len(block_weights) == 1:
+                block_weights = [block_weights[0]] * 100
+        else:
+            block_list  = parse_range_string_int(block_list)
+            
+            weights_expanded = [0.0] * 100
+            for b, w in zip(block_list, block_weights):
+                weights_expanded[b] = w
+            block_weights = weights_expanded
+        
+        StyleMMDiT = blocks.get('StyleMMDiT')
+        if StyleMMDiT is None:
+            StyleMMDiT = StyleUNet_Model()
+        
+        weights = {
+            "norm1"     : norm1,
+            "norm2"     : norm2,
+            "norm3"     : norm3,
+            
+            "self_attn" : self_attn,
+            "cross_attn": cross_attn,
+            "ff"        : ff,
+            
+            "self_attn_res" : self_attn_res,
+            "cross_attn_res": cross_attn_res,
+            "ff_res"        : ff_res,
+
+            "h_tile": tile_h // 8,
+            "w_tile": tile_w // 8,
+        }
+        
+        block_types = block_type.split(",")
+        
+        for block_type in block_types:
+        
+            if   block_type == "input":
+                style_blocks = StyleMMDiT.input_blocks
+            elif block_type == "middle":
+                style_blocks = StyleMMDiT.middle_blocks
+            elif block_type == "output":
+                style_blocks = StyleMMDiT.output_blocks
+                
+            for bid in block_list:
+                block = style_blocks[bid]
+                scaled_weights = {
+                    k: (v * block_weights[bid]) if isinstance(v, float) else v
+                    for k, v in weights.items()
+                }
+
+                block.spatial_block.TFMR.set_mode(mode)
+                block.spatial_block.TFMR.set_weights(**scaled_weights)
+                block.spatial_block.TFMR.apply_to = [apply_to]
+
+                block.spatial_block.TFMR.mask = [mask]
+
+        blocks['StyleMMDiT'] = StyleMMDiT
+
+        return (blocks, )
+
+
+
diff --git a/custom_nodes/RES4LYF/chroma/layers.py b/custom_nodes/RES4LYF/chroma/layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..b42d0e92e659c51de28f37573148a7eacf13ad73
--- /dev/null
+++ b/custom_nodes/RES4LYF/chroma/layers.py
@@ -0,0 +1,183 @@
+import torch
+from torch import Tensor, nn
+
+#from comfy.ldm.flux.math import attention
+from comfy.ldm.flux.layers import (
+    MLPEmbedder,
+    RMSNorm,
+    QKNorm,
+    SelfAttention,
+    ModulationOut,
+)
+
+from .math import attention, rope, apply_rope
+
+class ChromaModulationOut(ModulationOut):
+    @classmethod
+    def from_offset(cls, tensor: torch.Tensor, offset: int = 0) -> ModulationOut:
+        return cls(
+            shift=tensor[:, offset : offset + 1, :],
+            scale=tensor[:, offset + 1 : offset + 2, :],
+            gate=tensor[:, offset + 2 : offset + 3, :],
+        )
+
+
+
+
+class Approximator(nn.Module):
+    def __init__(self, in_dim: int, out_dim: int, hidden_dim: int, n_layers = 5, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.in_proj = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device)
+        self.layers = nn.ModuleList([MLPEmbedder(hidden_dim, hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)])
+        self.norms = nn.ModuleList([RMSNorm(hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)])
+        self.out_proj = operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device)
+
+    @property
+    def device(self):
+        # Get the device of the module (assumes all parameters are on the same device)
+        return next(self.parameters()).device
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = self.in_proj(x)
+
+        for layer, norms in zip(self.layers, self.norms):
+            x = x + layer(norms(x))
+
+        x = self.out_proj(x)
+
+        return x
+
+
+class ReChromaDoubleStreamBlock(nn.Module):
+    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        self.num_heads = num_heads
+        self.hidden_size = hidden_size
+        self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
+
+        self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.img_mlp = nn.Sequential(
+            operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
+            nn.GELU(approximate="tanh"),
+            operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
+        )
+
+        self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
+
+        self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.txt_mlp = nn.Sequential(
+            operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
+            nn.GELU(approximate="tanh"),
+            operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
+        )
+        self.flipped_img_txt = flipped_img_txt
+
+    def forward(self, img: Tensor, txt: Tensor, pe: Tensor, vec: Tensor, attn_mask=None):
+        (img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec
+
+        # prepare image for attention
+        img_modulated = self.img_norm1(img)
+        img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
+        img_qkv = self.img_attn.qkv(img_modulated)
+        img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
+
+        # prepare txt for attention
+        txt_modulated = self.txt_norm1(txt)
+        txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
+        txt_qkv = self.txt_attn.qkv(txt_modulated)
+        txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
+
+        # run actual attention
+        attn = attention(torch.cat((txt_q, img_q), dim=2),
+                         torch.cat((txt_k, img_k), dim=2),
+                         torch.cat((txt_v, img_v), dim=2),
+                         pe=pe, mask=attn_mask)
+
+        txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
+
+        # calculate the img bloks
+        img = img + img_mod1.gate * self.img_attn.proj(img_attn)
+        img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
+
+        # calculate the txt bloks
+        txt += txt_mod1.gate * self.txt_attn.proj(txt_attn)
+        txt += txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
+
+        if txt.dtype == torch.float16:
+            txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504)
+
+        return img, txt
+
+
+class ReChromaSingleStreamBlock(nn.Module):
+    """
+    A DiT block with parallel linear layers as described in
+    https://arxiv.org/abs/2302.05442 and adapted modulation interface.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qk_scale: float = None,
+        dtype=None,
+        device=None,
+        operations=None
+    ):
+        super().__init__()
+        self.hidden_dim = hidden_size
+        self.num_heads = num_heads
+        head_dim = hidden_size // num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        # qkv and mlp_in
+        self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, dtype=dtype, device=device)
+        # proj and mlp_out
+        self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, dtype=dtype, device=device)
+
+        self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations)
+
+        self.hidden_size = hidden_size
+        self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+
+        self.mlp_act = nn.GELU(approximate="tanh")
+
+    def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None) -> Tensor:
+        mod = vec
+        x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
+        qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
+
+        q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        q, k = self.norm(q, k, v)
+
+        # compute attention
+        attn = attention(q, k, v, pe=pe, mask=attn_mask)
+        # compute activation in mlp stream, cat again and run second linear layer
+        output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
+        x += mod.gate * output
+        if x.dtype == torch.float16:
+            x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504)
+        return x
+
+
+class LastLayer(nn.Module):
+    def __init__(self, hidden_size: int, patch_size: int, out_channels: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.linear = operations.Linear(hidden_size, out_channels, bias=True, dtype=dtype, device=device)
+
+    def forward(self, x: Tensor, vec: Tensor) -> Tensor:
+        shift, scale = vec
+        shift = shift.squeeze(1)
+        scale = scale.squeeze(1)
+        x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
+        x = self.linear(x)
+        return x
diff --git a/custom_nodes/RES4LYF/chroma/math.py b/custom_nodes/RES4LYF/chroma/math.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2f59be4d0be8f0bfa98c762bdf5867fc76bb44c
--- /dev/null
+++ b/custom_nodes/RES4LYF/chroma/math.py
@@ -0,0 +1,43 @@
+import torch
+from einops import rearrange
+from torch import Tensor
+from comfy.ldm.modules.attention import attention_pytorch
+
+import comfy.model_management
+
+def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None) -> Tensor:
+    q, k = apply_rope(q, k, pe)
+
+    heads = q.shape[1]
+    x = attention_pytorch(q, k, v, heads, skip_reshape=True, mask=mask)
+    #if mask is not None:
+    #    x = attention_pytorch(q, k, v, heads, skip_reshape=True, mask=mask)
+    #else:
+    #    from comfy.ldm.modules.attention import optimized_attention
+    #    x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=None)
+    return x
+
+
+def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
+    assert dim % 2 == 0
+    if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu() or comfy.model_management.is_directml_enabled():
+        device = torch.device("cpu")
+    else:
+        device = pos.device
+
+    scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=device)
+    omega = 1.0 / (theta**scale)
+    out = torch.einsum("...n,d->...nd", pos.to(dtype=torch.float32, device=device), omega)
+    out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
+    out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
+    return out.to(dtype=torch.float32, device=pos.device)
+
+
+def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):
+    xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+    xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+    xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+    xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
+    return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
+
+
diff --git a/custom_nodes/RES4LYF/chroma/model.py b/custom_nodes/RES4LYF/chroma/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf41c7142851541b75579ffcdac23a63b35d33b4
--- /dev/null
+++ b/custom_nodes/RES4LYF/chroma/model.py
@@ -0,0 +1,1501 @@
+#Original code can be found on: https://github.com/black-forest-labs/flux
+
+from dataclasses import dataclass
+
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+from einops import rearrange, repeat
+import comfy.ldm.common_dit
+
+from ..helper import ExtraOptions
+
+from ..latents import tile_latent, untile_latent, gaussian_blur_2d, median_blur_2d
+from ..style_transfer import apply_scattersort_masked, apply_scattersort_tiled, adain_seq_inplace, adain_patchwise_row_batch_med, adain_patchwise_row_batch
+
+from comfy.ldm.flux.layers import (
+    EmbedND,
+    timestep_embedding,
+)
+
+from .layers import (
+    ReChromaDoubleStreamBlock,
+    LastLayer,
+    ReChromaSingleStreamBlock,
+    Approximator,
+    ChromaModulationOut,
+)
+
+
+@dataclass
+class ChromaParams:
+    in_channels        : int
+    out_channels       : int
+    context_in_dim     : int
+    hidden_size        : int
+    mlp_ratio          : float
+    num_heads          : int
+    depth              : int
+    depth_single_blocks: int
+    axes_dim           : list
+    theta              : int
+    patch_size         : int
+    qkv_bias           : bool
+    in_dim             : int
+    out_dim            : int
+    hidden_dim         : int
+    n_layers           : int
+
+
+
+
+class ReChroma(nn.Module):
+    """
+    Transformer model for flow matching on sequences.
+    """
+
+    def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs):
+        super().__init__()
+        self.dtype        = dtype
+        params            = ChromaParams(**kwargs)
+        self.params       = params
+        self.patch_size   = params.patch_size
+        self.in_channels  = params.in_channels
+        self.out_channels = params.out_channels
+        if params.hidden_size % params.num_heads != 0:
+            raise ValueError(
+                f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
+            )
+        pe_dim = params.hidden_size // params.num_heads
+        if sum(params.axes_dim) != pe_dim:
+            raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
+        self.hidden_size = params.hidden_size
+        self.num_heads   = params.num_heads
+        self.in_dim      = params.in_dim
+        self.out_dim     = params.out_dim
+        self.hidden_dim  = params.hidden_dim
+        self.n_layers    = params.n_layers
+        self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
+        self.img_in      = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device)
+        self.txt_in      = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device)
+        # set as nn identity for now, will overwrite it later.
+        self.distilled_guidance_layer = Approximator(
+                    in_dim=self.in_dim,
+                    hidden_dim=self.hidden_dim,
+                    out_dim=self.out_dim,
+                    n_layers=self.n_layers,
+                    dtype=dtype, device=device, operations=operations
+                )
+
+
+        self.double_blocks = nn.ModuleList(
+            [
+                ReChromaDoubleStreamBlock(
+                    self.hidden_size,
+                    self.num_heads,
+                    mlp_ratio=params.mlp_ratio,
+                    qkv_bias=params.qkv_bias,
+                    dtype=dtype, device=device, operations=operations
+                )
+                for _ in range(params.depth)
+            ]
+        )
+
+        self.single_blocks = nn.ModuleList(
+            [
+                ReChromaSingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations)
+                for _ in range(params.depth_single_blocks)
+            ]
+        )
+
+        if final_layer:
+            self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations)
+
+        self.skip_mmdit = []
+        self.skip_dit = []
+        self.lite = False
+
+    def get_modulations(self, tensor: torch.Tensor, block_type: str, *, idx: int = 0):
+        # This function slices up the modulations tensor which has the following layout:
+        #   single     : num_single_blocks * 3 elements
+        #   double_img : num_double_blocks * 6 elements
+        #   double_txt : num_double_blocks * 6 elements
+        #   final      : 2 elements
+        if block_type == "final":
+            return (tensor[:, -2:-1, :], tensor[:, -1:, :])
+        single_block_count = self.params.depth_single_blocks
+        double_block_count = self.params.depth
+        offset = 3 * idx
+        if block_type == "single":
+            return ChromaModulationOut.from_offset(tensor, offset)
+        # Double block modulations are 6 elements so we double 3 * idx.
+        offset *= 2
+        if block_type in {"double_img", "double_txt"}:
+            # Advance past the single block modulations.
+            offset += 3 * single_block_count
+            if block_type == "double_txt":
+                # Advance past the double block img modulations.
+                offset += 6 * double_block_count
+            return (
+                ChromaModulationOut.from_offset(tensor, offset),
+                ChromaModulationOut.from_offset(tensor, offset + 3),
+            )
+        raise ValueError("Bad block_type")
+
+
+    def forward_blocks(
+        self,
+        img       : Tensor,
+        img_ids   : Tensor,
+        txt       : Tensor,
+        txt_ids   : Tensor,
+        timesteps : Tensor,
+        guidance  : Tensor  = None,
+        control             = None,
+        update_cross_attn   = None,
+        transformer_options ={},
+        attn_mask : Tensor  = None,
+        UNCOND : bool = False,
+    ) -> Tensor:
+        patches_replace = transformer_options.get("patches_replace", {})
+        if img.ndim != 3 or txt.ndim != 3:
+            raise ValueError("Input img and txt tensors must have 3 dimensions.")
+
+        # running on sequences img
+        img = self.img_in(img)
+
+        # distilled vector guidance
+        mod_index_length = 344
+        distill_timestep = timestep_embedding(timesteps.detach().clone(), 16).to(img.device, img.dtype)
+        # guidance = guidance *
+        distil_guidance = timestep_embedding(guidance.detach().clone(), 16).to(img.device, img.dtype)
+
+        # get all modulation index
+        modulation_index = timestep_embedding(torch.arange(mod_index_length), 32).to(img.device, img.dtype)
+        # we need to broadcast the modulation index here so each batch has all of the index
+        modulation_index = modulation_index.unsqueeze(0).repeat(img.shape[0], 1, 1).to(img.device, img.dtype)
+        # and we need to broadcast timestep and guidance along too
+        timestep_guidance = torch.cat([distill_timestep, distil_guidance], dim=1).unsqueeze(1).repeat(1, mod_index_length, 1).to(img.dtype).to(img.device, img.dtype)
+        # then and only then we could concatenate it together
+        input_vec = torch.cat([timestep_guidance, modulation_index], dim=-1).to(img.device, img.dtype)
+
+        mod_vectors = self.distilled_guidance_layer(input_vec)
+
+        txt = self.txt_in(txt)
+
+        ids = torch.cat((txt_ids, img_ids), dim=1)
+        pe = self.pe_embedder(ids)
+        
+        weight    = -1 * transformer_options.get("regional_conditioning_weight", 0.0)
+        floor     = -1 * transformer_options.get("regional_conditioning_floor",  0.0)
+        mask_zero = None
+        mask = None
+        
+        text_len = txt.shape[1] # mask_obj[0].text_len
+        
+        if not UNCOND and 'AttnMask' in transformer_options: # and weight != 0:
+            AttnMask = transformer_options['AttnMask']
+            mask = transformer_options['AttnMask'].attn_mask.mask.to('cuda')
+            if mask_zero is None:
+                mask_zero = torch.ones_like(mask)
+                img_len = transformer_options['AttnMask'].img_len
+                #mask_zero[:text_len, :text_len] = mask[:text_len, :text_len]
+                mask_zero[:text_len, :] = mask[:text_len, :]
+                mask_zero[:, :text_len] = mask[:, :text_len]
+            if weight == 0:
+                mask = None
+            
+        if UNCOND and 'AttnMask_neg' in transformer_options: # and weight != 0:
+            AttnMask = transformer_options['AttnMask_neg']
+            if mask_zero is None:
+                mask_zero = torch.ones_like(mask)
+                img_len = transformer_options['AttnMask_neg'].img_len
+                #mask_zero[:text_len, :text_len] = mask[:text_len, :text_len]
+                mask_zero[:text_len, :] = mask[:text_len, :]
+                mask_zero[:, :text_len] = mask[:, :text_len]
+            if weight == 0:
+                mask = None
+            
+        elif UNCOND and 'AttnMask' in transformer_options:
+            AttnMask = transformer_options['AttnMask']
+            mask = transformer_options['AttnMask'].attn_mask.mask.to('cuda')
+            if mask_zero is None:
+                mask_zero = torch.ones_like(mask)
+                img_len = transformer_options['AttnMask'].img_len
+                #mask_zero[:text_len, :text_len] = mask[:text_len, :text_len]
+                mask_zero[:text_len, :] = mask[:text_len, :]
+                mask_zero[:, :text_len] = mask[:, :text_len]
+            if weight == 0:
+                mask = None
+
+        if mask is not None and not type(mask[0][0].item()) == bool:
+            mask = mask.to(img.dtype)
+        if mask_zero is not None and not type(mask_zero[0][0].item()) == bool:
+            mask_zero = mask_zero.to(img.dtype)
+
+        total_layers = len(self.double_blocks) + len(self.single_blocks)
+        
+        attn_mask = mask if attn_mask is None else attn_mask
+        
+        blocks_replace = patches_replace.get("dit", {})
+        for i, block in enumerate(self.double_blocks):
+            if i not in self.skip_mmdit:
+                double_mod = (
+                    self.get_modulations(mod_vectors, "double_img", idx=i),
+                    self.get_modulations(mod_vectors, "double_txt", idx=i),
+                )
+                if ("double_block", i) in blocks_replace:
+                    def block_wrap(args):
+                        out = {}
+                        out["img"], out["txt"] = block( img       = args["img"],
+                                                        txt       = args["txt"],
+                                                        vec       = args["vec"],
+                                                        pe        = args["pe"],
+                                                        attn_mask = args.get("attn_mask"))
+                        return out
+
+                    out = blocks_replace[("double_block", i)]({ "img"             : img,
+                                                                "txt"             : txt,
+                                                                "vec"             : double_mod,
+                                                                "pe"              : pe,
+                                                                "attn_mask"       : attn_mask},
+                                                                {"original_block" : block_wrap})
+                    txt = out["txt"] 
+                    img = out["img"]
+                else:
+
+                    if   weight > 0 and mask is not None and     weight  <=      i/total_layers:
+                        img, txt = block(img=img, txt=txt, vec=double_mod, pe=pe, attn_mask=mask_zero)
+                        
+                    elif (weight < 0 and mask is not None and abs(weight) <= (1 - i/total_layers)):
+                        img_tmpZ, txt_tmpZ = img.clone(), txt.clone()
+
+                        img_tmpZ, txt = block(img=img_tmpZ, txt=txt_tmpZ, vec=double_mod, pe=pe, attn_mask=mask)
+                        img, txt_tmpZ = block(img=img     , txt=txt     , vec=double_mod, pe=pe, attn_mask=mask_zero)
+                        
+                    elif floor > 0 and mask is not None and     floor  >=      i/total_layers:
+                        mask_tmp = mask.clone()
+                        mask_tmp[text_len:, text_len:] = 1.0
+                        img, txt = block(img=img, txt=txt, vec=double_mod, pe=pe, attn_mask=mask_tmp)
+                        
+                    elif floor < 0 and mask is not None and abs(floor) >= (1 - i/total_layers):
+                        mask_tmp = mask.clone()
+                        mask_tmp[text_len:, text_len:] = 1.0
+                        img, txt = block(img=img, txt=txt, vec=double_mod, pe=pe, attn_mask=mask_tmp)
+                        
+                    elif update_cross_attn is not None and update_cross_attn['skip_cross_attn']:
+                        print("update_cross_attn not yet implemented for Chroma.", flush=True)
+                        #img, txt_init = block(img, img_masks, txt, clip, rope, mask, update_cross_attn=update_cross_attn)
+                    
+                    else:
+                        img, txt = block(img=img, txt=txt, vec=double_mod, pe=pe, attn_mask=attn_mask)
+
+                    #img, txt = block(img=img, txt=txt, vec=double_mod, pe=pe, attn_mask=attn_mask)
+
+                if control is not None: # Controlnet
+                    control_i = control.get("input")
+                    if i < len(control_i):
+                        add = control_i[i]
+                        if add is not None:
+                            img += add
+
+        img = torch.cat((txt, img), 1)
+
+        for i, block in enumerate(self.single_blocks):
+            if i not in self.skip_dit:
+                single_mod = self.get_modulations(mod_vectors, "single", idx=i)
+                if ("single_block", i) in blocks_replace:
+                    def block_wrap(args):
+                        out = {}
+                        out["img"] = block( args["img"],
+                                            vec=args["vec"],
+                                            pe=args["pe"],
+                                            attn_mask=args.get("attn_mask"))
+                        return out
+
+                    out = blocks_replace[("single_block", i)]({ "img"             : img,
+                                                                "vec"             : single_mod,
+                                                                "pe"              : pe,
+                                                                "attn_mask"       : attn_mask},
+                                                                {"original_block" : block_wrap})
+                    img = out["img"]
+                else:
+
+                    if   weight > 0 and mask is not None and     weight  <=      (i+len(self.double_blocks))/total_layers:
+                        img = block(img, vec=single_mod, pe=pe, attn_mask=mask_zero)
+                        
+                    elif weight < 0 and mask is not None and abs(weight) <= (1 - (i+len(self.double_blocks))/total_layers):
+                        img = block(img, vec=single_mod, pe=pe, attn_mask=mask_zero)
+                        
+                    elif floor > 0 and mask is not None and     floor  >=      (i+len(self.double_blocks))/total_layers:
+                        mask_tmp = mask.clone()
+                        mask_tmp[text_len:, text_len:] = 1.0
+                        img = block(img, vec=single_mod, pe=pe, attn_mask=mask_tmp)
+                        
+                    elif floor < 0 and mask is not None and abs(floor) >= (1 - (i+len(self.double_blocks))/total_layers):
+                        mask_tmp = mask.clone()
+                        mask_tmp[text_len:, text_len:] = 1.0
+                        img = block(img, vec=single_mod, pe=pe, attn_mask=mask_tmp)
+                        
+                    else:
+                        img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask)
+
+                if control is not None: # Controlnet
+                    control_o = control.get("output")
+                    if i < len(control_o):
+                        add = control_o[i]
+                        if add is not None:
+                            img[:, txt.shape[1] :, ...] += add
+
+        img = img[:, txt.shape[1] :, ...]
+        final_mod = self.get_modulations(mod_vectors, "final")
+        img = self.final_layer(img, vec=final_mod)  # (N, T, patch_size ** 2 * out_channels)
+        return img
+
+    def forward_chroma_depr(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs):
+        bs, c, h, w = x.shape
+        patch_size = 2
+        x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))
+
+        img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
+
+        h_len = ((h + (patch_size // 2)) // patch_size)
+        w_len = ((w + (patch_size // 2)) // patch_size)
+        img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
+        img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
+        img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
+        img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
+
+        txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
+        out = self.forward_orig(img, img_ids, context, txt_ids, timestep, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None))
+        return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h,:w]
+
+    
+    def _get_img_ids(self, x, bs, h_len, w_len, h_start, h_end, w_start, w_end):
+        img_ids          = torch.zeros(  (h_len,   w_len, 3),              device=x.device, dtype=x.dtype)
+        img_ids[..., 1] += torch.linspace(h_start, h_end - 1, steps=h_len, device=x.device, dtype=x.dtype)[:, None]
+        img_ids[..., 2] += torch.linspace(w_start, w_end - 1, steps=w_len, device=x.device, dtype=x.dtype)[None, :]
+        img_ids          = repeat(img_ids, "h w c -> b (h w) c", b=bs)
+        return img_ids
+
+
+
+    def forward(self,
+                x,
+                timestep,
+                context,
+                #y,
+                guidance,
+                control             = None,
+                transformer_options = {},
+                **kwargs
+                ):
+        x_orig = x.clone()
+        SIGMA = timestep[0].unsqueeze(0)
+        update_cross_attn = transformer_options.get("update_cross_attn")
+        EO = transformer_options.get("ExtraOptions", ExtraOptions(""))
+        if EO is not None:
+            EO.mute = True
+
+        y0_style_pos        = transformer_options.get("y0_style_pos")
+        y0_style_neg        = transformer_options.get("y0_style_neg")
+
+        y0_style_pos_weight    = transformer_options.get("y0_style_pos_weight", 0.0)
+        y0_style_pos_synweight = transformer_options.get("y0_style_pos_synweight", 0.0)
+        y0_style_pos_synweight *= y0_style_pos_weight
+
+        y0_style_neg_weight    = transformer_options.get("y0_style_neg_weight", 0.0)
+        y0_style_neg_synweight = transformer_options.get("y0_style_neg_synweight", 0.0)
+        y0_style_neg_synweight *= y0_style_neg_weight
+        
+        weight    = -1 * transformer_options.get("regional_conditioning_weight", 0.0)
+        floor     = -1 * transformer_options.get("regional_conditioning_floor",  0.0)
+        
+        freqsep_lowpass_method = transformer_options.get("freqsep_lowpass_method")
+        freqsep_sigma          = transformer_options.get("freqsep_sigma")
+        freqsep_kernel_size    = transformer_options.get("freqsep_kernel_size")
+        freqsep_inner_kernel_size    = transformer_options.get("freqsep_inner_kernel_size")
+        freqsep_stride    = transformer_options.get("freqsep_stride")
+        
+        freqsep_lowpass_weight = transformer_options.get("freqsep_lowpass_weight")
+        freqsep_highpass_weight= transformer_options.get("freqsep_highpass_weight")
+        freqsep_mask           = transformer_options.get("freqsep_mask")
+        
+        out_list = []
+        for i in range(len(transformer_options['cond_or_uncond'])):
+            UNCOND = transformer_options['cond_or_uncond'][i] == 1
+            
+            if update_cross_attn is not None:
+                update_cross_attn['UNCOND'] = UNCOND
+                
+            img = x
+            bs, c, h, w = x.shape
+            patch_size  = 2
+            img           = comfy.ldm.common_dit.pad_to_patch_size(img, (patch_size, patch_size))    # 1,16,192,192
+            
+            transformer_options['original_shape'] = img.shape
+            transformer_options['patch_size']     = patch_size
+
+            h_len = ((h + (patch_size // 2)) // patch_size) # h_len 96
+            w_len = ((w + (patch_size // 2)) // patch_size) # w_len 96
+
+            img = rearrange(img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) # img 1,9216,64     1,16,128,128 -> 1,4096,64
+
+            context_tmp = None
+            
+            if not UNCOND and 'AttnMask' in transformer_options: # and weight != 0:
+                AttnMask = transformer_options['AttnMask']
+                mask = transformer_options['AttnMask'].attn_mask.mask.to('cuda')
+
+                if weight == 0:
+                    context_tmp = transformer_options['RegContext'].context.to(context.dtype).to(context.device)
+                    mask = None
+                else:
+                    context_tmp = transformer_options['RegContext'].context.to(context.dtype).to(context.device)
+                
+            if UNCOND and 'AttnMask_neg' in transformer_options: # and weight != 0:
+                AttnMask = transformer_options['AttnMask_neg']
+                mask = transformer_options['AttnMask_neg'].attn_mask.mask.to('cuda')
+
+                if weight == 0:
+                    context_tmp = transformer_options['RegContext_neg'].context.to(context.dtype).to(context.device)
+                    mask = None
+                else:
+                    context_tmp = transformer_options['RegContext_neg'].context.to(context.dtype).to(context.device)
+
+            elif UNCOND and 'AttnMask' in transformer_options:
+                AttnMask = transformer_options['AttnMask']
+                mask = transformer_options['AttnMask'].attn_mask.mask.to('cuda')
+                A       = context
+                B       = transformer_options['RegContext'].context
+                context_tmp = A.repeat(1,    (B.shape[1] // A.shape[1]) + 1, 1)[:,   :B.shape[1], :]
+
+            if context_tmp is None:
+                context_tmp = context[i][None,...].clone()
+
+            txt_ids      = torch.zeros((bs, context_tmp.shape[1], 3), device=img.device, dtype=img.dtype)      # txt_ids        1, 256,3
+            img_ids_orig = self._get_img_ids(img, bs, h_len, w_len, 0, h_len, 0, w_len)                  # img_ids_orig = 1,9216,3
+
+
+            out_tmp = self.forward_blocks(img       [i][None,...].clone(), 
+                                        img_ids_orig[i][None,...].clone(), 
+                                        context_tmp,
+                                        txt_ids     [i][None,...].clone(), 
+                                        timestep    [i][None,...].clone(), 
+                                        #y           [i][None,...].clone(),
+                                        guidance    [i][None,...].clone(),
+                                        control, 
+                                        update_cross_attn=update_cross_attn,
+                                        transformer_options=transformer_options,
+                                        UNCOND = UNCOND,
+                                        )  # context 1,256,4096   y 1,768
+            out_list.append(out_tmp)
+            
+        out = torch.stack(out_list, dim=0).squeeze(dim=1)
+        
+        eps = rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h,:w]
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        dtype = eps.dtype if self.style_dtype is None else self.style_dtype
+        
+        if y0_style_pos is not None:
+            y0_style_pos_weight    = transformer_options.get("y0_style_pos_weight")
+            y0_style_pos_synweight = transformer_options.get("y0_style_pos_synweight")
+            y0_style_pos_synweight *= y0_style_pos_weight
+            y0_style_pos_mask = transformer_options.get("y0_style_pos_mask")
+            y0_style_pos_mask_edge = transformer_options.get("y0_style_pos_mask_edge")
+
+            y0_style_pos = y0_style_pos.to(dtype)
+            x   = x_orig.clone().to(dtype)
+            eps = eps.to(dtype)
+            eps_orig = eps.clone()
+            
+            sigma = SIGMA #t_orig[0].to(torch.float32) / 1000
+            denoised = x - sigma * eps
+            
+            denoised_embed = self.Retrojector.embed(denoised)
+            y0_adain_embed = self.Retrojector.embed(y0_style_pos)
+            
+            if transformer_options['y0_style_method'] == "scattersort":
+                tile_h, tile_w = transformer_options.get('y0_style_tile_height'), transformer_options.get('y0_style_tile_width')
+                pad = transformer_options.get('y0_style_tile_padding')
+                if pad is not None and tile_h is not None and tile_w is not None:
+                    
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    if EO("scattersort_median_LP"):
+                        denoised_spatial_LP = median_blur_2d(denoised_spatial, kernel_size=EO("scattersort_median_LP",7))
+                        y0_adain_spatial_LP = median_blur_2d(y0_adain_spatial, kernel_size=EO("scattersort_median_LP",7))
+                        
+                        denoised_spatial_HP = denoised_spatial - denoised_spatial_LP
+                        y0_adain_spatial_HP = y0_adain_spatial - y0_adain_spatial_LP
+                        
+                        denoised_spatial_LP = apply_scattersort_tiled(denoised_spatial_LP, y0_adain_spatial_LP, tile_h, tile_w, pad)
+                        
+                        denoised_spatial = denoised_spatial_LP + denoised_spatial_HP
+                        denoised_embed = rearrange(denoised_spatial, "b c h w -> b (h w) c")
+                    else:
+                        denoised_spatial = apply_scattersort_tiled(denoised_spatial, y0_adain_spatial, tile_h, tile_w, pad)
+                    
+                    denoised_embed = rearrange(denoised_spatial, "b c h w -> b (h w) c")
+                    
+                else:
+                    denoised_embed = apply_scattersort_masked(denoised_embed, y0_adain_embed, y0_style_pos_mask, y0_style_pos_mask_edge, h_len, w_len)
+
+
+
+            elif transformer_options['y0_style_method'] == "AdaIN":
+                if freqsep_mask is not None:
+                    freqsep_mask = freqsep_mask.view(1, 1, *freqsep_mask.shape[-2:]).float()
+                    freqsep_mask = F.interpolate(freqsep_mask.float(), size=(h_len, w_len), mode='nearest-exact')
+                
+                if hasattr(self, "adain_tile"):
+                    tile_h, tile_w = self.adain_tile
+                    
+                    denoised_pretile = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_pretile = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    if self.adain_flag:
+                        h_off = tile_h // 2
+                        w_off = tile_w // 2
+                        denoised_pretile = denoised_pretile[:,:,h_off:-h_off, w_off:-w_off]
+                        self.adain_flag = False
+                    else:
+                        h_off = 0
+                        w_off = 0
+                        self.adain_flag = True
+                    
+                    tiles,    orig_shape, grid, strides = tile_latent(denoised_pretile, tile_size=(tile_h,tile_w))
+                    y0_tiles, orig_shape, grid, strides = tile_latent(y0_adain_pretile, tile_size=(tile_h,tile_w))
+                    
+                    tiles_out = []
+                    for i in range(tiles.shape[0]):
+                        tile = tiles[i].unsqueeze(0)
+                        y0_tile = y0_tiles[i].unsqueeze(0)
+                        
+                        tile    = rearrange(tile,    "b c h w -> b (h w) c", h=tile_h, w=tile_w)
+                        y0_tile = rearrange(y0_tile, "b c h w -> b (h w) c", h=tile_h, w=tile_w)
+                        
+                        tile = adain_seq_inplace(tile, y0_tile)
+                        tiles_out.append(rearrange(tile, "b (h w) c -> b c h w", h=tile_h, w=tile_w))
+                    
+                    tiles_out_tensor = torch.cat(tiles_out, dim=0)
+                    tiles_out_tensor = untile_latent(tiles_out_tensor, orig_shape, grid, strides)
+
+                    if h_off == 0:
+                        denoised_pretile = tiles_out_tensor
+                    else:
+                        denoised_pretile[:,:,h_off:-h_off, w_off:-w_off] = tiles_out_tensor
+                    denoised_embed = rearrange(denoised_pretile, "b c h w -> b (h w) c", h=h_len, w=w_len)
+
+                elif freqsep_lowpass_method is not None and freqsep_lowpass_method.endswith("pw"): #EO("adain_pw"):
+
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+
+                    if   freqsep_lowpass_method == "median_pw":
+                        denoised_spatial_new = adain_patchwise_row_batch_med(denoised_spatial.clone(), y0_adain_spatial.clone().repeat(denoised_spatial.shape[0],1,1,1), sigma=freqsep_sigma, kernel_size=freqsep_kernel_size, use_median_blur=True, lowpass_weight=freqsep_lowpass_weight, highpass_weight=freqsep_highpass_weight)
+                    elif freqsep_lowpass_method == "gaussian_pw": 
+                        denoised_spatial_new = adain_patchwise_row_batch(denoised_spatial.clone(), y0_adain_spatial.clone().repeat(denoised_spatial.shape[0],1,1,1), sigma=freqsep_sigma, kernel_size=freqsep_kernel_size)
+                    
+                    denoised_embed = rearrange(denoised_spatial_new, "b c h w -> b (h w) c", h=h_len, w=w_len)
+
+                elif freqsep_lowpass_method is not None: 
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    if   freqsep_lowpass_method == "median":
+                        denoised_spatial_LP = median_blur_2d(denoised_spatial, kernel_size=freqsep_kernel_size)
+                        y0_adain_spatial_LP = median_blur_2d(y0_adain_spatial, kernel_size=freqsep_kernel_size)
+                    elif freqsep_lowpass_method == "gaussian":
+                        denoised_spatial_LP = gaussian_blur_2d(denoised_spatial, sigma=freqsep_sigma, kernel_size=freqsep_kernel_size)
+                        y0_adain_spatial_LP = gaussian_blur_2d(y0_adain_spatial, sigma=freqsep_sigma, kernel_size=freqsep_kernel_size)
+                    
+                    denoised_spatial_HP = denoised_spatial - denoised_spatial_LP
+                    
+                    if EO("adain_fs_uhp"):
+                        y0_adain_spatial_HP = y0_adain_spatial - y0_adain_spatial_LP
+                        
+                        denoised_spatial_ULP = gaussian_blur_2d(denoised_spatial, sigma=EO("adain_fs_uhp_sigma", 1.0), kernel_size=EO("adain_fs_uhp_kernel_size", 3))
+                        y0_adain_spatial_ULP = gaussian_blur_2d(y0_adain_spatial, sigma=EO("adain_fs_uhp_sigma", 1.0), kernel_size=EO("adain_fs_uhp_kernel_size", 3))
+                        
+                        denoised_spatial_UHP = denoised_spatial_HP  - denoised_spatial_ULP
+                        y0_adain_spatial_UHP = y0_adain_spatial_HP  - y0_adain_spatial_ULP
+                        
+                        #denoised_spatial_HP  = y0_adain_spatial_ULP + denoised_spatial_UHP
+                        denoised_spatial_HP  = denoised_spatial_ULP + y0_adain_spatial_UHP
+                    
+                    denoised_spatial_new = freqsep_lowpass_weight * y0_adain_spatial_LP + freqsep_highpass_weight * denoised_spatial_HP
+                    denoised_embed = rearrange(denoised_spatial_new, "b c h w -> b (h w) c", h=h_len, w=w_len)
+
+                else:
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    
+                for adain_iter in range(EO("style_iter", 0)):
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    denoised_embed = self.Retrojector.embed(self.Retrojector.unembed(denoised_embed))
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    
+            elif transformer_options['y0_style_method'] == "WCT":
+                self.StyleWCT.set(y0_adain_embed)
+                denoised_embed = self.StyleWCT.get(denoised_embed)
+                
+                if transformer_options.get('y0_standard_guide') is not None:
+                    y0_standard_guide = transformer_options.get('y0_standard_guide')
+                    
+                    y0_standard_guide_embed = self.Retrojector.embed(y0_standard_guide)
+                    f_cs = self.StyleWCT.get(y0_standard_guide_embed)
+                    self.y0_standard_guide = self.Retrojector.unembed(f_cs)
+
+                if transformer_options.get('y0_inv_standard_guide') is not None:
+                    y0_inv_standard_guide = transformer_options.get('y0_inv_standard_guide')
+
+                    y0_inv_standard_guide_embed = self.Retrojector.embed(y0_inv_standard_guide)
+                    f_cs = self.StyleWCT.get(y0_inv_standard_guide_embed)
+                    self.y0_inv_standard_guide = self.Retrojector.unembed(f_cs)
+
+            denoised_approx = self.Retrojector.unembed(denoised_embed)
+            
+            eps = (x - denoised_approx) / sigma
+
+            if not UNCOND:
+                if eps.shape[0] == 2:
+                    eps[1] = eps_orig[1] + y0_style_pos_weight * (eps[1] - eps_orig[1])
+                    eps[0] = eps_orig[0] + y0_style_pos_synweight * (eps[0] - eps_orig[0])
+                else:
+                    eps[0] = eps_orig[0] + y0_style_pos_weight * (eps[0] - eps_orig[0])
+            elif eps.shape[0] == 1 and UNCOND:
+                eps[0] = eps_orig[0] + y0_style_pos_synweight * (eps[0] - eps_orig[0])
+            
+            eps = eps.float()
+        
+        if y0_style_neg is not None:
+            y0_style_neg_weight    = transformer_options.get("y0_style_neg_weight")
+            y0_style_neg_synweight = transformer_options.get("y0_style_neg_synweight")
+            y0_style_neg_synweight *= y0_style_neg_weight
+            y0_style_neg_mask = transformer_options.get("y0_style_neg_mask")
+            y0_style_neg_mask_edge = transformer_options.get("y0_style_neg_mask_edge")
+            
+            y0_style_neg = y0_style_neg.to(dtype)
+            x   = x_orig.clone().to(dtype)
+            eps = eps.to(dtype)
+            eps_orig = eps.clone()
+            
+            sigma = SIGMA #t_orig[0].to(torch.float32) / 1000
+            denoised = x - sigma * eps
+
+            denoised_embed = self.Retrojector.embed(denoised)
+            y0_adain_embed = self.Retrojector.embed(y0_style_neg)
+            
+            if transformer_options['y0_style_method'] == "scattersort":
+                tile_h, tile_w = transformer_options.get('y0_style_tile_height'), transformer_options.get('y0_style_tile_width')
+                pad = transformer_options.get('y0_style_tile_padding')
+                if pad is not None and tile_h is not None and tile_w is not None:
+                    
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    denoised_spatial = apply_scattersort_tiled(denoised_spatial, y0_adain_spatial, tile_h, tile_w, pad)
+                    
+                    denoised_embed = rearrange(denoised_spatial, "b c h w -> b (h w) c")
+
+                else:
+                    denoised_embed = apply_scattersort_masked(denoised_embed, y0_adain_embed, y0_style_neg_mask, y0_style_neg_mask_edge, h_len, w_len)
+            
+            
+            elif transformer_options['y0_style_method'] == "AdaIN":
+                denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                for adain_iter in range(EO("style_iter", 0)):
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    denoised_embed = self.Retrojector.embed(self.Retrojector.unembed(denoised_embed))
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    
+            elif transformer_options['y0_style_method'] == "WCT":
+                self.StyleWCT.set(y0_adain_embed)
+                denoised_embed = self.StyleWCT.get(denoised_embed)
+
+            denoised_approx = self.Retrojector.unembed(denoised_embed)
+
+            if UNCOND:
+                eps = (x - denoised_approx) / sigma
+                eps[0] = eps_orig[0] + y0_style_neg_weight * (eps[0] - eps_orig[0])
+                if eps.shape[0] == 2:
+                    eps[1] = eps_orig[1] + y0_style_neg_synweight * (eps[1] - eps_orig[1])
+            elif eps.shape[0] == 1 and not UNCOND:
+                eps[0] = eps_orig[0] + y0_style_neg_synweight * (eps[0] - eps_orig[0])
+            
+            eps = eps.float()
+            
+        return eps
+
+
+
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        dtype = eps.dtype if self.style_dtype is None else self.style_dtype
+        pinv_dtype = torch.float32 if dtype != torch.float64 else dtype
+        W_inv = None
+        
+        
+        #if eps.shape[0] == 2 or (eps.shape[0] == 1): #: and not UNCOND):
+        if y0_style_pos is not None and y0_style_pos_weight != 0.0:
+            y0_style_pos = y0_style_pos.to(dtype)
+            x   = x.to(dtype)
+            eps = eps.to(dtype)
+            eps_orig = eps.clone()
+            
+            sigma = SIGMA #t_orig[0].to(torch.float32) / 1000
+            denoised = x - sigma * eps
+            
+            img = comfy.ldm.common_dit.pad_to_patch_size(denoised, (self.patch_size, self.patch_size))
+
+            img = rearrange(img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) # img 1,9216,64     1,16,128,128 -> 1,4096,64
+
+            img_y0_adain = comfy.ldm.common_dit.pad_to_patch_size(y0_style_pos, (self.patch_size, self.patch_size))
+
+            img_y0_adain = rearrange(img_y0_adain, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) # img 1,9216,64     1,16,128,128 -> 1,4096,64
+
+            W = self.img_in.weight.data.to(dtype)   # shape [2560, 64]
+            b = self.img_in.bias.data.to(dtype)     # shape [2560]
+            
+            denoised_embed = F.linear(img         .to(W), W, b).to(img)
+            y0_adain_embed = F.linear(img_y0_adain.to(W), W, b).to(img_y0_adain)
+
+            if transformer_options['y0_style_method'] == "AdaIN":
+                if freqsep_mask is not None:
+                    freqsep_mask = freqsep_mask.view(1, 1, *freqsep_mask.shape[-2:]).float()
+                    freqsep_mask = F.interpolate(freqsep_mask.float(), size=(h_len, w_len), mode='nearest-exact')
+                
+                if freqsep_lowpass_method is not None and freqsep_lowpass_method.endswith("pw"): #EO("adain_pw"):
+                    
+                    #if self.y0_adain_embed is None or self.y0_adain_embed.shape != y0_adain_embed.shape or torch.norm(self.y0_adain_embed - y0_adain_embed) > 0:
+                    #    self.y0_adain_embed = y0_adain_embed
+                    #    self.adain_pw_cache = None
+                        
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+
+                    if freqsep_lowpass_method == "median_alt": 
+                        denoised_spatial_new = adain_patchwise_row_batch_medblur(denoised_spatial.clone(), y0_adain_spatial.clone().repeat(denoised_spatial.shape[0],1,1,1), sigma=freqsep_sigma, kernel_size=freqsep_kernel_size, use_median_blur=True)
+                    elif freqsep_lowpass_method == "median_pw":
+                        denoised_spatial_new = adain_patchwise_row_batch_realmedblur(denoised_spatial.clone(), y0_adain_spatial.clone().repeat(denoised_spatial.shape[0],1,1,1), sigma=freqsep_sigma, kernel_size=freqsep_kernel_size, use_median_blur=True, lowpass_weight=freqsep_lowpass_weight, highpass_weight=freqsep_highpass_weight)
+                    elif freqsep_lowpass_method == "gaussian_pw": 
+                        denoised_spatial_new = adain_patchwise_row_batch(denoised_spatial.clone(), y0_adain_spatial.clone().repeat(denoised_spatial.shape[0],1,1,1), sigma=freqsep_sigma, kernel_size=freqsep_kernel_size)
+                    
+                    denoised_embed = rearrange(denoised_spatial_new, "b c h w -> b (h w) c", h=h_len, w=w_len)
+
+                elif freqsep_lowpass_method is not None and freqsep_lowpass_method == "distribution": 
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+
+                    denoised_spatial_new = adain_patchwise_strict_sortmatch9(denoised_spatial.clone(), y0_adain_spatial.clone().repeat(denoised_spatial.shape[0],1,1,1), kernel_size=freqsep_kernel_size, inner_kernel_size=freqsep_inner_kernel_size, mask=freqsep_mask, stride=freqsep_stride)
+
+                    denoised_embed = rearrange(denoised_spatial_new, "b c h w -> b (h w) c", h=h_len, w=w_len)
+                
+                elif freqsep_lowpass_method is not None: 
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    if   freqsep_lowpass_method == "median":
+                        denoised_spatial_LP = median_blur_2d(denoised_spatial, kernel_size=freqsep_kernel_size)
+                        y0_adain_spatial_LP = median_blur_2d(y0_adain_spatial, kernel_size=freqsep_kernel_size)
+                    elif freqsep_lowpass_method == "gaussian":
+                        denoised_spatial_LP = gaussian_blur_2d(denoised_spatial, sigma=freqsep_sigma, kernel_size=freqsep_kernel_size)
+                        y0_adain_spatial_LP = gaussian_blur_2d(y0_adain_spatial, sigma=freqsep_sigma, kernel_size=freqsep_kernel_size)
+                    
+                    denoised_spatial_HP = denoised_spatial - denoised_spatial_LP
+                    
+                    if EO("adain_fs_uhp"):
+                        y0_adain_spatial_HP = y0_adain_spatial - y0_adain_spatial_LP
+                        
+                        denoised_spatial_ULP = gaussian_blur_2d(denoised_spatial, sigma=EO("adain_fs_uhp_sigma", 1.0), kernel_size=EO("adain_fs_uhp_kernel_size", 3))
+                        y0_adain_spatial_ULP = gaussian_blur_2d(y0_adain_spatial, sigma=EO("adain_fs_uhp_sigma", 1.0), kernel_size=EO("adain_fs_uhp_kernel_size", 3))
+                        
+                        denoised_spatial_UHP = denoised_spatial_HP  - denoised_spatial_ULP
+                        y0_adain_spatial_UHP = y0_adain_spatial_HP  - y0_adain_spatial_ULP
+                        
+                        #denoised_spatial_HP  = y0_adain_spatial_ULP + denoised_spatial_UHP
+                        denoised_spatial_HP  = denoised_spatial_ULP + y0_adain_spatial_UHP
+                    
+                    denoised_spatial_new = freqsep_lowpass_weight * y0_adain_spatial_LP + freqsep_highpass_weight * denoised_spatial_HP
+                    denoised_embed = rearrange(denoised_spatial_new, "b c h w -> b (h w) c", h=h_len, w=w_len)
+
+                else:
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                
+                #denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                #for adain_iter in range(EO("style_iter", 0)):
+                #    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                #    denoised_embed = (denoised_embed - b) @ torch.linalg.pinv(W.to(pinv_dtype)).T.to(dtype)
+                #    denoised_embed = F.linear(denoised_embed         .to(W), W, b).to(img)
+                #    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    
+            elif transformer_options['y0_style_method'] == "WCT":
+                if self.y0_adain_embed is None or self.y0_adain_embed.shape != y0_adain_embed.shape or torch.norm(self.y0_adain_embed - y0_adain_embed) > 0:
+                    self.y0_adain_embed = y0_adain_embed
+                    
+                    f_s          = y0_adain_embed[0].clone()
+                    self.mu_s    = f_s.mean(dim=0, keepdim=True)
+                    f_s_centered = f_s - self.mu_s
+                    
+                    cov = (f_s_centered.T.double() @ f_s_centered.double()) / (f_s_centered.size(0) - 1)
+
+                    S_eig, U_eig = torch.linalg.eigh(cov + 1e-5 * torch.eye(cov.size(0), dtype=cov.dtype, device=cov.device))
+                    S_eig_sqrt    = S_eig.clamp(min=0).sqrt() # eigenvalues -> singular values
+                    
+                    whiten = U_eig @ torch.diag(S_eig_sqrt) @ U_eig.T
+                    self.y0_color  = whiten.to(f_s_centered)
+
+                for wct_i in range(eps.shape[0]):
+                    f_c          = denoised_embed[wct_i].clone()
+                    mu_c         = f_c.mean(dim=0, keepdim=True)
+                    f_c_centered = f_c - mu_c
+                    
+                    cov = (f_c_centered.T.double() @ f_c_centered.double()) / (f_c_centered.size(0) - 1)
+
+                    S_eig, U_eig  = torch.linalg.eigh(cov + 1e-5 * torch.eye(cov.size(0), dtype=cov.dtype, device=cov.device))
+                    inv_sqrt_eig  = S_eig.clamp(min=0).rsqrt() 
+                    
+                    whiten = U_eig @ torch.diag(inv_sqrt_eig) @ U_eig.T
+                    whiten = whiten.to(f_c_centered)
+
+                    f_c_whitened = f_c_centered @ whiten.T
+                    f_cs         = f_c_whitened @ self.y0_color.T + self.mu_s
+                    
+                    denoised_embed[wct_i] = f_cs
+
+            
+            denoised_approx = (denoised_embed - b.to(denoised_embed)) @ torch.linalg.pinv(W).T.to(denoised_embed)
+            denoised_approx = denoised_approx.to(eps)
+            
+            denoised_approx = rearrange(denoised_approx, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h,:w]
+            
+            eps = (x - denoised_approx) / sigma
+            
+            if not UNCOND:
+                if eps.shape[0] == 2:
+                    eps[1] = eps_orig[1] + y0_style_pos_weight * (eps[1] - eps_orig[1])
+                    eps[0] = eps_orig[0] + y0_style_pos_synweight * (eps[0] - eps_orig[0])
+                else:
+                    eps[0] = eps_orig[0] + y0_style_pos_weight * (eps[0] - eps_orig[0])
+            elif eps.shape[0] == 1 and UNCOND:
+                eps[0] = eps_orig[0] + y0_style_pos_synweight * (eps[0] - eps_orig[0])
+                #if eps.shape[0] == 2:
+                #    eps[1] = eps_orig[1] + y0_style_neg_synweight * (eps[1] - eps_orig[1])
+            
+            eps = eps.float()
+        
+        #if eps.shape[0] == 2 or (eps.shape[0] == 1): # and UNCOND):
+        if y0_style_neg is not None and y0_style_neg_weight != 0.0:
+            y0_style_neg = y0_style_neg.to(dtype)
+            x   = x.to(dtype)
+            eps = eps.to(dtype)
+            eps_orig = eps.clone()
+            
+            sigma = SIGMA #t_orig[0].to(torch.float32) / 1000
+            denoised = x - sigma * eps
+            
+            img = comfy.ldm.common_dit.pad_to_patch_size(denoised, (self.patch_size, self.patch_size))
+
+            h_len = ((h + (patch_size // 2)) // patch_size) # h_len 96
+            w_len = ((w + (patch_size // 2)) // patch_size) # w_len 96
+            img = rearrange(img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) # img 1,9216,64     1,16,128,128 -> 1,4096,64
+            
+            img_y0_adain = comfy.ldm.common_dit.pad_to_patch_size(y0_style_neg, (self.patch_size, self.patch_size))
+
+            img_y0_adain = rearrange(img_y0_adain, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) # img 1,9216,64     1,16,128,128 -> 1,4096,64
+
+            W = self.img_in.weight.data.to(dtype)   # shape [2560, 64]
+            b = self.img_in.bias.data.to(dtype)     # shape [2560]
+            
+            denoised_embed = F.linear(img         .to(W), W, b).to(img)
+            y0_adain_embed = F.linear(img_y0_adain.to(W), W, b).to(img_y0_adain)
+            
+            if transformer_options['y0_style_method'] == "AdaIN":
+                if freqsep_mask is not None:
+                    freqsep_mask = freqsep_mask.view(1, 1, *freqsep_mask.shape[-2:]).float()
+                    freqsep_mask = F.interpolate(freqsep_mask.float(), size=(h_len, w_len), mode='nearest-exact')
+                
+                if freqsep_lowpass_method is not None and freqsep_lowpass_method.endswith("pw"): #EO("adain_pw"):
+                    
+                    #if self.y0_adain_embed is None or self.y0_adain_embed.shape != y0_adain_embed.shape or torch.norm(self.y0_adain_embed - y0_adain_embed) > 0:
+                    #    self.y0_adain_embed = y0_adain_embed
+                    #    self.adain_pw_cache = None
+                        
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+
+                    if freqsep_lowpass_method == "median_alt": 
+                        denoised_spatial_new = adain_patchwise_row_batch_medblur(denoised_spatial.clone(), y0_adain_spatial.clone(), sigma=freqsep_sigma, kernel_size=freqsep_kernel_size, use_median_blur=True)
+                    elif freqsep_lowpass_method == "median_pw":
+                        denoised_spatial_new = adain_patchwise_row_batch_realmedblur(denoised_spatial.clone(), y0_adain_spatial.clone(), sigma=freqsep_sigma, kernel_size=freqsep_kernel_size, use_median_blur=True, lowpass_weight=freqsep_lowpass_weight, highpass_weight=freqsep_highpass_weight)
+                    elif freqsep_lowpass_method == "gaussian_pw": 
+                        denoised_spatial_new = adain_patchwise_row_batch(denoised_spatial.clone(), y0_adain_spatial.clone(), sigma=freqsep_sigma, kernel_size=freqsep_kernel_size)
+                    
+                    denoised_embed = rearrange(denoised_spatial_new, "b c h w -> b (h w) c", h=h_len, w=w_len)
+
+                elif freqsep_lowpass_method is not None and freqsep_lowpass_method == "distribution": 
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+
+                    denoised_spatial_new = adain_patchwise_strict_sortmatch9(denoised_spatial.clone(), y0_adain_spatial.clone(), kernel_size=freqsep_kernel_size, inner_kernel_size=freqsep_inner_kernel_size, mask=freqsep_mask, stride=freqsep_stride)
+
+                    denoised_embed = rearrange(denoised_spatial_new, "b c h w -> b (h w) c", h=h_len, w=w_len)
+                
+                elif freqsep_lowpass_method is not None: 
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    if   freqsep_lowpass_method == "median":
+                        denoised_spatial_LP = median_blur_2d(denoised_spatial, kernel_size=freqsep_kernel_size)
+                        y0_adain_spatial_LP = median_blur_2d(y0_adain_spatial, kernel_size=freqsep_kernel_size)
+                    elif freqsep_lowpass_method == "gaussian":
+                        denoised_spatial_LP = gaussian_blur_2d(denoised_spatial, sigma=freqsep_sigma, kernel_size=freqsep_kernel_size)
+                        y0_adain_spatial_LP = gaussian_blur_2d(y0_adain_spatial, sigma=freqsep_sigma, kernel_size=freqsep_kernel_size)
+                    
+                    denoised_spatial_HP = denoised_spatial - denoised_spatial_LP
+                    
+                    if EO("adain_fs_uhp"):
+                        y0_adain_spatial_HP = y0_adain_spatial - y0_adain_spatial_LP
+                        
+                        denoised_spatial_ULP = gaussian_blur_2d(denoised_spatial, sigma=EO("adain_fs_uhp_sigma", 1.0), kernel_size=EO("adain_fs_uhp_kernel_size", 3))
+                        y0_adain_spatial_ULP = gaussian_blur_2d(y0_adain_spatial, sigma=EO("adain_fs_uhp_sigma", 1.0), kernel_size=EO("adain_fs_uhp_kernel_size", 3))
+                        
+                        denoised_spatial_UHP = denoised_spatial_HP  - denoised_spatial_ULP
+                        y0_adain_spatial_UHP = y0_adain_spatial_HP  - y0_adain_spatial_ULP
+                        
+                        #denoised_spatial_HP  = y0_adain_spatial_ULP + denoised_spatial_UHP
+                        denoised_spatial_HP  = denoised_spatial_ULP + y0_adain_spatial_UHP
+                    
+                    denoised_spatial_new = freqsep_lowpass_weight * y0_adain_spatial_LP + freqsep_highpass_weight * denoised_spatial_HP
+                    denoised_embed = rearrange(denoised_spatial_new, "b c h w -> b (h w) c", h=h_len, w=w_len)
+
+                else:
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                
+                #denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                #for adain_iter in range(EO("style_iter", 0)):
+                #    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                #    denoised_embed = (denoised_embed - b) @ torch.linalg.pinv(W.to(pinv_dtype)).T.to(dtype)
+                #    denoised_embed = F.linear(denoised_embed         .to(W), W, b).to(img)
+                #    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    
+            elif transformer_options['y0_style_method'] == "WCT":
+                if self.y0_adain_embed is None or self.y0_adain_embed.shape != y0_adain_embed.shape or torch.norm(self.y0_adain_embed - y0_adain_embed) > 0:
+                    self.y0_adain_embed = y0_adain_embed
+                    
+                    f_s          = y0_adain_embed[0].clone()
+                    self.mu_s    = f_s.mean(dim=0, keepdim=True)
+                    f_s_centered = f_s - self.mu_s
+                    
+                    cov = (f_s_centered.T.double() @ f_s_centered.double()) / (f_s_centered.size(0) - 1)
+
+                    S_eig, U_eig = torch.linalg.eigh(cov + 1e-5 * torch.eye(cov.size(0), dtype=cov.dtype, device=cov.device))
+                    S_eig_sqrt    = S_eig.clamp(min=0).sqrt() # eigenvalues -> singular values
+                    
+                    whiten = U_eig @ torch.diag(S_eig_sqrt) @ U_eig.T
+                    self.y0_color  = whiten.to(f_s_centered)
+
+                for wct_i in range(eps.shape[0]):
+                    f_c          = denoised_embed[wct_i].clone()
+                    mu_c         = f_c.mean(dim=0, keepdim=True)
+                    f_c_centered = f_c - mu_c
+                    
+                    cov = (f_c_centered.T.double() @ f_c_centered.double()) / (f_c_centered.size(0) - 1)
+
+                    S_eig, U_eig  = torch.linalg.eigh(cov + 1e-5 * torch.eye(cov.size(0), dtype=cov.dtype, device=cov.device))
+                    inv_sqrt_eig  = S_eig.clamp(min=0).rsqrt() 
+                    
+                    whiten = U_eig @ torch.diag(inv_sqrt_eig) @ U_eig.T
+                    whiten = whiten.to(f_c_centered)
+
+                    f_c_whitened = f_c_centered @ whiten.T
+                    f_cs         = f_c_whitened @ self.y0_color.T + self.mu_s
+                    
+                    denoised_embed[wct_i] = f_cs
+
+            denoised_approx = (denoised_embed - b.to(denoised_embed)) @ torch.linalg.pinv(W).T.to(denoised_embed)
+            denoised_approx = denoised_approx.to(eps)
+            
+            denoised_approx = rearrange(denoised_approx, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h,:w]
+            
+            if UNCOND:
+                eps = (x - denoised_approx) / sigma
+                eps[0] = eps_orig[0] + y0_style_neg_weight * (eps[0] - eps_orig[0])
+                if eps.shape[0] == 2:
+                    eps[1] = eps_orig[1] + y0_style_neg_synweight * (eps[1] - eps_orig[1])
+            elif eps.shape[0] == 1 and not UNCOND:
+                eps[0] = eps_orig[0] + y0_style_neg_synweight * (eps[0] - eps_orig[0])
+            
+            eps = eps.float()
+            
+        return eps
+
+
+
+def adain_seq(content: torch.Tensor, style: torch.Tensor, eps: float = 1e-7) -> torch.Tensor:
+    return ((content - content.mean(1, keepdim=True)) / (content.std(1, keepdim=True) + eps)) * (style.std(1, keepdim=True) + eps) + style.mean(1, keepdim=True)
+
+
+
+def adain_seq_inplace(content: torch.Tensor, style: torch.Tensor, eps: float = 1e-7) -> torch.Tensor:
+    mean_c = content.mean(1, keepdim=True)
+    std_c  = content.std (1, keepdim=True).add_(eps)  # in-place add
+    mean_s = style.mean  (1, keepdim=True)
+    std_s  = style.std   (1, keepdim=True).add_(eps)
+
+    content.sub_(mean_c).div_(std_c).mul_(std_s).add_(mean_s)  # in-place chain
+    return content
+
+
+
+
+
+
+
+
+
+def gaussian_blur_2d(img: torch.Tensor, sigma: float, kernel_size: int = None) -> torch.Tensor:
+    B, C, H, W = img.shape
+    dtype = img.dtype
+    device = img.device
+
+    if kernel_size is None:
+        kernel_size = int(2 * math.ceil(3 * sigma) + 1)
+
+    if kernel_size % 2 == 0:
+        kernel_size += 1
+
+    coords = torch.arange(kernel_size, dtype=torch.float64) - kernel_size // 2
+    g = torch.exp(-0.5 * (coords / sigma) ** 2)
+    g = g / g.sum()
+
+    kernel_2d = g[:, None] * g[None, :]
+    kernel_2d = kernel_2d.to(dtype=dtype, device=device)
+
+    kernel = kernel_2d.expand(C, 1, kernel_size, kernel_size)
+
+    pad = kernel_size // 2
+    img_padded = F.pad(img, (pad, pad, pad, pad), mode='reflect')
+
+    return F.conv2d(img_padded, kernel, groups=C)
+
+
+def median_blur_2d(img: torch.Tensor, kernel_size: int = 3) -> torch.Tensor:
+    if kernel_size % 2 == 0:
+        kernel_size += 1
+    pad = kernel_size // 2
+
+    B, C, H, W = img.shape
+    img_padded = F.pad(img, (pad, pad, pad, pad), mode='reflect')
+
+    unfolded = img_padded.unfold(2, kernel_size, 1).unfold(3, kernel_size, 1)
+    # unfolded: [B, C, H, W, kH, kW] → flatten to patches
+    patches = unfolded.contiguous().view(B, C, H, W, -1)
+    median = patches.median(dim=-1).values
+    return median
+
+
+def adain_patchwise(content: torch.Tensor, style: torch.Tensor, sigma: float = 1.0, kernel_size: int = None, eps: float = 1e-5) -> torch.Tensor:
+
+    B, C, H, W = content.shape
+    device     = content.device
+    dtype      = content.dtype
+
+    if kernel_size is None:
+        kernel_size = int(2 * math.ceil(3 * sigma) + 1)
+    if kernel_size % 2 == 0:
+        kernel_size += 1
+
+    pad    = kernel_size // 2
+    coords = torch.arange(kernel_size, dtype=torch.float64, device=device) - pad
+    gauss  = torch.exp(-0.5 * (coords / sigma) ** 2)
+    gauss /= gauss.sum()
+    kernel_2d = (gauss[:, None] * gauss[None, :]).to(dtype=dtype)
+
+    weight = kernel_2d.view(1, 1, kernel_size, kernel_size)
+
+    content_padded = F.pad(content, (pad, pad, pad, pad), mode='reflect')
+    style_padded   = F.pad(style,   (pad, pad, pad, pad), mode='reflect')
+    result = torch.zeros_like(content)
+
+    for i in range(H):
+        for j in range(W):
+            c_patch = content_padded[:, :, i:i + kernel_size, j:j + kernel_size]
+            s_patch =   style_padded[:, :, i:i + kernel_size, j:j + kernel_size]
+            w = weight.expand_as(c_patch)
+
+            c_mean =  (c_patch              * w).sum(dim=(-1, -2), keepdim=True)
+            c_std  = ((c_patch - c_mean)**2 * w).sum(dim=(-1, -2), keepdim=True).sqrt() + eps
+            s_mean =  (s_patch              * w).sum(dim=(-1, -2), keepdim=True)
+            s_std  = ((s_patch - s_mean)**2 * w).sum(dim=(-1, -2), keepdim=True).sqrt() + eps
+
+            normed =  (c_patch[:, :, pad:pad+1, pad:pad+1] - c_mean) / c_std
+            stylized = normed * s_std + s_mean
+            result[:, :, i, j] = stylized.squeeze(-1).squeeze(-1)
+
+    return result
+
+
+
+
+def adain_patchwise_row_batch(content: torch.Tensor, style: torch.Tensor, sigma: float = 1.0, kernel_size: int = None, eps: float = 1e-5) -> torch.Tensor:
+
+    B, C, H, W = content.shape
+    device, dtype = content.device, content.dtype
+
+    if kernel_size is None:
+        kernel_size = int(2 * math.ceil(3 * sigma) + 1)
+    if kernel_size % 2 == 0:
+        kernel_size += 1
+
+    pad = kernel_size // 2
+    coords = torch.arange(kernel_size, dtype=torch.float64, device=device) - pad
+    gauss = torch.exp(-0.5 * (coords / sigma) ** 2)
+    gauss = (gauss / gauss.sum()).to(dtype)
+    kernel_2d = (gauss[:, None] * gauss[None, :])
+
+    weight = kernel_2d.view(1, 1, kernel_size, kernel_size)
+
+    content_padded = F.pad(content, (pad, pad, pad, pad), mode='reflect')
+    style_padded = F.pad(style, (pad, pad, pad, pad), mode='reflect')
+    result = torch.zeros_like(content)
+
+    for i in range(H):
+        c_row_patches = torch.stack([
+            content_padded[:, :, i:i+kernel_size, j:j+kernel_size]
+            for j in range(W)
+        ], dim=0)  # [W, B, C, k, k]
+
+        s_row_patches = torch.stack([
+            style_padded[:, :, i:i+kernel_size, j:j+kernel_size]
+            for j in range(W)
+        ], dim=0)
+
+        w = weight.expand_as(c_row_patches[0])
+
+        c_mean = (c_row_patches * w).sum(dim=(-1, -2), keepdim=True)
+        c_std  = ((c_row_patches - c_mean) ** 2 * w).sum(dim=(-1, -2), keepdim=True).sqrt() + eps
+        s_mean = (s_row_patches * w).sum(dim=(-1, -2), keepdim=True)
+        s_std  = ((s_row_patches - s_mean) ** 2 * w).sum(dim=(-1, -2), keepdim=True).sqrt() + eps
+
+        center = kernel_size // 2
+        central = c_row_patches[:, :, :, center:center+1, center:center+1]
+        normed = (central - c_mean) / c_std
+        stylized = normed * s_std + s_mean
+
+        result[:, :, i, :] = stylized.squeeze(-1).squeeze(-1).permute(1, 2, 0)  # [B,C,W]
+
+    return result
+
+
+
+
+
+
+
+def adain_patchwise_row_batch_medblur(content: torch.Tensor, style: torch.Tensor, sigma: float = 1.0, kernel_size: int = None, eps: float = 1e-5, mask: torch.Tensor = None, use_median_blur: bool = False) -> torch.Tensor:
+    B, C, H, W = content.shape
+    device, dtype = content.device, content.dtype
+
+    if kernel_size is None:
+        kernel_size = int(2 * math.ceil(3 * abs(sigma)) + 1)
+    if kernel_size % 2 == 0:
+        kernel_size += 1
+
+    pad = kernel_size // 2
+
+    content_padded = F.pad(content, (pad, pad, pad, pad), mode='reflect')
+    style_padded = F.pad(style, (pad, pad, pad, pad), mode='reflect')
+    result = torch.zeros_like(content)
+
+    scaling = torch.ones((B, 1, H, W), device=device, dtype=dtype)
+    sigma_scale = torch.ones((H, W), device=device, dtype=torch.float32)
+    if mask is not None:
+        with torch.no_grad():
+            padded_mask = F.pad(mask.float(), (pad, pad, pad, pad), mode="reflect")
+            blurred_mask = F.avg_pool2d(padded_mask, kernel_size=kernel_size, stride=1, padding=pad)
+            blurred_mask = blurred_mask[..., pad:-pad, pad:-pad]
+            edge_proximity = blurred_mask * (1.0 - blurred_mask)
+            scaling = 1.0 - (edge_proximity / 0.25).clamp(0.0, 1.0)
+            sigma_scale = scaling[0, 0]  # assuming single-channel mask broadcasted across B, C
+
+    if not use_median_blur:
+        coords = torch.arange(kernel_size, dtype=torch.float64, device=device) - pad
+        base_gauss = torch.exp(-0.5 * (coords / sigma) ** 2)
+        base_gauss = (base_gauss / base_gauss.sum()).to(dtype)
+        gaussian_table = {}
+        for s in sigma_scale.unique():
+            sig = float((sigma * s + eps).clamp(min=1e-3))
+            gauss_local = torch.exp(-0.5 * (coords / sig) ** 2)
+            gauss_local = (gauss_local / gauss_local.sum()).to(dtype)
+            kernel_2d = gauss_local[:, None] * gauss_local[None, :]
+            gaussian_table[s.item()] = kernel_2d
+
+    for i in range(H):
+        row_result = torch.zeros(B, C, W, dtype=dtype, device=device)
+        for j in range(W):
+            c_patch = content_padded[:, :, i:i+kernel_size, j:j+kernel_size]
+            s_patch = style_padded[:, :, i:i+kernel_size, j:j+kernel_size]
+
+            if use_median_blur:
+                c_flat = c_patch.reshape(B, C, -1)
+                s_flat = s_patch.reshape(B, C, -1)
+
+                c_median = c_flat.median(dim=-1, keepdim=True).values
+                s_median = s_flat.median(dim=-1, keepdim=True).values
+
+                c_std = (c_flat - c_median).abs().mean(dim=-1, keepdim=True) + eps
+                s_std = (s_flat - s_median).abs().mean(dim=-1, keepdim=True) + eps
+
+                center = kernel_size // 2
+                central = c_patch[:, :, center, center].unsqueeze(-1)
+
+                normed = (central - c_median) / c_std
+                stylized = normed * s_std + s_median
+            else:
+                k = gaussian_table[float(sigma_scale[i, j].item())]
+                local_weight = k.view(1, 1, kernel_size, kernel_size).expand(B, C, kernel_size, kernel_size)
+
+                c_mean = (c_patch * local_weight).sum(dim=(-1, -2), keepdim=True)
+                c_std = ((c_patch - c_mean) ** 2 * local_weight).sum(dim=(-1, -2), keepdim=True).sqrt() + eps
+                s_mean = (s_patch * local_weight).sum(dim=(-1, -2), keepdim=True)
+                s_std = ((s_patch - s_mean) ** 2 * local_weight).sum(dim=(-1, -2), keepdim=True).sqrt() + eps
+
+                center = kernel_size // 2
+                central = c_patch[:, :, center:center+1, center:center+1]
+                normed = (central - c_mean) / c_std
+                stylized = normed * s_std + s_mean
+
+            local_scaling = scaling[:, :, i, j].view(B, 1, 1, 1)
+            stylized = central * (1 - local_scaling) + stylized * local_scaling
+
+            row_result[:, :, j] = stylized.squeeze(-1).squeeze(-1)
+        result[:, :, i, :] = row_result
+
+    return result
+
+
+
+
+
+
+
+def adain_patchwise_row_batch_realmedblur(content: torch.Tensor, style: torch.Tensor, sigma: float = 1.0, kernel_size: int = None, eps: float = 1e-5, mask: torch.Tensor = None, use_median_blur: bool = False, lowpass_weight=1.0, highpass_weight=1.0) -> torch.Tensor:
+    B, C, H, W = content.shape
+    device, dtype = content.device, content.dtype
+
+    if kernel_size is None:
+        kernel_size = int(2 * math.ceil(3 * abs(sigma)) + 1)
+    if kernel_size % 2 == 0:
+        kernel_size += 1
+
+    pad = kernel_size // 2
+
+    content_padded = F.pad(content, (pad, pad, pad, pad), mode='reflect')
+    style_padded = F.pad(style, (pad, pad, pad, pad), mode='reflect')
+    result = torch.zeros_like(content)
+
+    scaling = torch.ones((B, 1, H, W), device=device, dtype=dtype)
+    sigma_scale = torch.ones((H, W), device=device, dtype=torch.float32)
+    if mask is not None:
+        with torch.no_grad():
+            padded_mask = F.pad(mask.float(), (pad, pad, pad, pad), mode="reflect")
+            blurred_mask = F.avg_pool2d(padded_mask, kernel_size=kernel_size, stride=1, padding=pad)
+            blurred_mask = blurred_mask[..., pad:-pad, pad:-pad]
+            edge_proximity = blurred_mask * (1.0 - blurred_mask)
+            scaling = 1.0 - (edge_proximity / 0.25).clamp(0.0, 1.0)
+            sigma_scale = scaling[0, 0]  # assuming single-channel mask broadcasted across B, C
+
+    if not use_median_blur:
+        coords = torch.arange(kernel_size, dtype=torch.float64, device=device) - pad
+        base_gauss = torch.exp(-0.5 * (coords / sigma) ** 2)
+        base_gauss = (base_gauss / base_gauss.sum()).to(dtype)
+        gaussian_table = {}
+        for s in sigma_scale.unique():
+            sig = float((sigma * s + eps).clamp(min=1e-3))
+            gauss_local = torch.exp(-0.5 * (coords / sig) ** 2)
+            gauss_local = (gauss_local / gauss_local.sum()).to(dtype)
+            kernel_2d = gauss_local[:, None] * gauss_local[None, :]
+            gaussian_table[s.item()] = kernel_2d
+
+    for i in range(H):
+        row_result = torch.zeros(B, C, W, dtype=dtype, device=device)
+        for j in range(W):
+            c_patch = content_padded[:, :, i:i+kernel_size, j:j+kernel_size]
+            s_patch = style_padded[:, :, i:i+kernel_size, j:j+kernel_size]
+
+            if use_median_blur:
+                # Median blur with residual restoration
+                unfolded_c = c_patch.reshape(B, C, -1)
+                unfolded_s = s_patch.reshape(B, C, -1)
+
+                c_median = unfolded_c.median(dim=-1, keepdim=True).values
+                s_median = unfolded_s.median(dim=-1, keepdim=True).values
+
+                center = kernel_size // 2
+                central = c_patch[:, :, center, center].view(B, C, 1)
+                residual = central - c_median
+                stylized = lowpass_weight * s_median + residual * highpass_weight
+            else:
+                k = gaussian_table[float(sigma_scale[i, j].item())]
+                local_weight = k.view(1, 1, kernel_size, kernel_size).expand(B, C, kernel_size, kernel_size)
+
+                c_mean = (c_patch * local_weight).sum(dim=(-1, -2), keepdim=True)
+                c_std = ((c_patch - c_mean) ** 2 * local_weight).sum(dim=(-1, -2), keepdim=True).sqrt() + eps
+                s_mean = (s_patch * local_weight).sum(dim=(-1, -2), keepdim=True)
+                s_std = ((s_patch - s_mean) ** 2 * local_weight).sum(dim=(-1, -2), keepdim=True).sqrt() + eps
+
+                center = kernel_size // 2
+                central = c_patch[:, :, center:center+1, center:center+1]
+                normed = (central - c_mean) / c_std
+                stylized = normed * s_std + s_mean
+
+            local_scaling = scaling[:, :, i, j].view(B, 1, 1)
+            stylized = central * (1 - local_scaling) + stylized * local_scaling
+
+            row_result[:, :, j] = stylized.squeeze(-1)
+        result[:, :, i, :] = row_result
+
+    return result
+
+
+
+
+
+
+
+
+
+
+
+
+
+def patchwise_sort_transfer9(src: torch.Tensor, ref: torch.Tensor) -> torch.Tensor:
+    """
+    src, ref: [B, C, N] where N = K*K
+    Returns: [B, C, N] with values from ref permuted to match the sort-order of src.
+    """
+    src_sorted, src_idx  = src.sort(dim=-1)
+    ref_sorted, _        = ref.sort(dim=-1)
+    out = torch.zeros_like(src)
+    out.scatter_(dim=-1, index=src_idx, src=ref_sorted)
+    return out
+
+def masked_patchwise_sort_transfer9(
+    src       : torch.Tensor,         # [B, C, N]
+    ref       : torch.Tensor,         # [B, C, N]
+    mask_flat : torch.Tensor          # [B, N]  bool
+) -> torch.Tensor:
+    """
+    Only rearrange N positions where mask_flat[b] is True... to be implemented fully later. 
+    """
+    B,C,N = src.shape
+    out = src.clone()
+    for b in range(B):
+        valid = mask_flat[b]             # [N] boolean
+        if valid.sum() == 0:
+            continue
+        sc = src[b,:,valid]              # [C, M]
+        ss = ref[b,:,valid]              # [C, M]
+        sc_s, idx = sc.sort(dim=-1)      # sort the channelz
+        ss_s, _   = ss.sort(dim=-1)
+        res = torch.zeros_like(sc)
+        res.scatter_(dim=-1, index=idx, src=ss_s)
+        out[b,:,valid] = res
+    return out
+
+def adain_patchwise_strict_sortmatch9(
+    content           : torch.Tensor,        # [B,C,H,W]
+    style             : torch.Tensor,        # [B,C,H,W]
+    kernel_size       : int,
+    inner_kernel_size : int = 1,
+    stride            : int = 1,
+    mask              : torch.Tensor = None  # [B,1,H,W]
+) -> torch.Tensor:
+    B,C,H,W = content.shape
+    assert inner_kernel_size <= kernel_size
+    pad       = kernel_size//2
+    inner_off = (kernel_size - inner_kernel_size)//2
+
+    # reflect-pad
+    cp = F.pad(content, (pad,)*4, mode='reflect')
+    sp = F.pad(style,   (pad,)*4, mode='reflect')
+    out = content.clone()
+
+    if mask is not None:
+        mask = mask[:,0].bool()  # [B,H,W]
+
+    for i in range(0, H, stride):
+        for j in range(0, W, stride):
+            pc = cp[:, :, i:i+kernel_size, j:j+kernel_size]   # [B,C,K,K]
+            ps = sp[:, :, i:i+kernel_size, j:j+kernel_size]
+
+            Bc = pc.reshape(B, C, -1)
+            Bs = ps.reshape(B, C, -1)
+
+            matched_flat = patchwise_sort_transfer9(Bc, Bs)
+            matched = matched_flat.view(B, C, kernel_size, kernel_size)
+
+            y0, x0 = inner_off, inner_off
+            y1, x1 = y0 + inner_kernel_size, x0 + inner_kernel_size
+            inner = matched[:, :, y0:y1, x0:x1]  # [B,C,inner,inner]
+
+            dst_y0 = i + y0 - pad
+            dst_x0 = j + x0 - pad
+            dst_y1 = dst_y0 + inner_kernel_size
+            dst_x1 = dst_x0 + inner_kernel_size
+
+            oy0 = max(dst_y0, 0); ox0 = max(dst_x0, 0)
+            oy1 = min(dst_y1, H); ox1 = min(dst_x1, W)
+
+            iy0 = oy0 - dst_y0; ix0 = ox0 - dst_x0
+            iy1 = iy0 + (oy1 - oy0); ix1 = ix0 + (ox1 - ox0)
+
+            if mask is None:
+                out[:, :, oy0:oy1, ox0:ox1] = inner[:, :, iy0:iy1, ix0:ix1]
+            else:
+                ibm = mask[:, oy0:oy1, ox0:ox1]  # [B,inner,inner]
+                for b in range(B):
+                    sel = ibm[b]  # [inner,inner]   # w/ regard to kernel
+                    if sel.any():
+                        out[b:b+1, :, oy0:oy1, ox0:ox1][:, :,sel]   =   inner[b:b+1, :, iy0:iy1, ix0:ix1][:, :, sel]
+    return out
+
+
diff --git a/custom_nodes/RES4LYF/example_workflows/chroma regional antiblur.json b/custom_nodes/RES4LYF/example_workflows/chroma regional antiblur.json
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diff --git a/custom_nodes/RES4LYF/example_workflows/chroma txt2img.json b/custom_nodes/RES4LYF/example_workflows/chroma txt2img.json
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diff --git a/custom_nodes/RES4LYF/example_workflows/comparison ksampler vs csksampler chain workflows.json b/custom_nodes/RES4LYF/example_workflows/comparison ksampler vs csksampler chain workflows.json
new file mode 100644
index 0000000000000000000000000000000000000000..70668297b22a805abb5100cff89607650226ebb8
--- /dev/null
+++ b/custom_nodes/RES4LYF/example_workflows/comparison ksampler vs csksampler chain workflows.json	
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diff --git a/custom_nodes/RES4LYF/example_workflows/hidream guide epsilon projection.json b/custom_nodes/RES4LYF/example_workflows/hidream guide epsilon projection.json
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diff --git a/custom_nodes/RES4LYF/example_workflows/hidream guide flow.json b/custom_nodes/RES4LYF/example_workflows/hidream guide flow.json
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diff --git a/custom_nodes/RES4LYF/example_workflows/hidream guide fully_pseudoimplicit.json b/custom_nodes/RES4LYF/example_workflows/hidream guide fully_pseudoimplicit.json
new file mode 100644
index 0000000000000000000000000000000000000000..33d96915e34de1c6c24b45dfde96c2202eb946cb
--- /dev/null
+++ b/custom_nodes/RES4LYF/example_workflows/hidream guide fully_pseudoimplicit.json	
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diff --git a/custom_nodes/RES4LYF/example_workflows/hidream guide lure.json b/custom_nodes/RES4LYF/example_workflows/hidream guide lure.json
new file mode 100644
index 0000000000000000000000000000000000000000..2461f5d5347c5924e49ec577ef3bb7b03c5fb42e
--- /dev/null
+++ b/custom_nodes/RES4LYF/example_workflows/hidream guide lure.json	
@@ -0,0 +1 @@
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diff --git a/custom_nodes/RES4LYF/example_workflows/hidream style transfer txt2img.json b/custom_nodes/RES4LYF/example_workflows/hidream style transfer txt2img.json
new file mode 100644
index 0000000000000000000000000000000000000000..a2e83b30d27af16f3c170c9535d44cbf0dd3666c
--- /dev/null
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diff --git a/custom_nodes/RES4LYF/example_workflows/hidream unsampling data.json b/custom_nodes/RES4LYF/example_workflows/hidream unsampling data.json
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diff --git a/custom_nodes/RES4LYF/example_workflows/hidream unsampling pseudoimplicit.json b/custom_nodes/RES4LYF/example_workflows/hidream unsampling pseudoimplicit.json
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diff --git a/custom_nodes/RES4LYF/example_workflows/hidream unsampling.json b/custom_nodes/RES4LYF/example_workflows/hidream unsampling.json
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diff --git a/custom_nodes/RES4LYF/example_workflows/intro to clownsampling.json b/custom_nodes/RES4LYF/example_workflows/intro to clownsampling.json
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The difference between 24x24 and 25x25 is a significant drop in quality and coherence. Therefore, it is best to just set these dimensions directly.\n\nThe best trained resolutions are:\n\n24x24 > 32x32\n30x16 > 40x24 \n\n48x24 also works, but seems to result in more doubling problems than the others.\n\n\n"],"color":"#432","bgcolor":"#653"},{"id":628,"type":"Note","pos":[-1012.45947265625,4084.7783203125],"size":[331.63720703125,415.29815673828125],"flags":{},"order":7,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Stage UP: a patched version of Stable Cascade stage C (\"UltraPixel\"). \n\nThe key with these dimensions is to keep the aspect ratio the same as the stage C latent. Typically, best results are with a 1.5x upscale. 2.0x works, but will result in somewhat more issues with doubling, and can be a lot slower. However, the detail level will also be very high.\n\nSome viable resolutions are listed below. Asterisks signify ones that have been verified to work particularly well.\n\n32x32\n36x36 **\n40x40\n42x42\n48x48 *\n\n40x24\n50x30\n60x36 **\n70x42\n80x48 *\n\n72x36 \n80x40 *\n96x48 (very slow!)\n\n\n\n"],"color":"#432","bgcolor":"#653"},{"id":632,"type":"CheckpointLoaderSimple","pos":[-1073.474609375,2726.673583984375],"size":[452.7829895019531,102.89583587646484],"flags":{},"order":8,"mode":0,"inputs":[],"outputs":[{"name":"MODEL","localized_name":"MODEL","type":"MODEL","links":null},{"name":"CLIP","localized_name":"CLIP","type":"CLIP","links":null},{"name":"VAE","localized_name":"VAE","type":"VAE","links":null}],"properties":{"Node name for S&R":"CheckpointLoaderSimple"},"widgets_values":["cascade_B-lite_refined_CSBW_v1.1.safetensors"]},{"id":633,"type":"Note","pos":[-1075.468994140625,2892.701416015625],"size":[457.5304870605469,94.27093505859375],"flags":{},"order":9,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["This is the stage B lite CSBW finetune (model only).\n\nhttps://huggingface.co/ClownsharkBatwing/Cascade_Stage_B_CSBW_Refined/blob/main/stage_b_lite_CSBW_v1.1.safetensors"],"color":"#432","bgcolor":"#653"},{"id":634,"type":"Note","pos":[-575.989501953125,2895.603271484375],"size":[547.0546875,91.47331237792969],"flags":{},"order":10,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["This is a finetune of stage A. You will get a sharper image, but in images with large white areas, small circular grey halos are sometimes visible.\n\nhttps://huggingface.co/madebyollin/stage-a-ft-hq/blob/main/stage_a_ft_hq.safetensors"],"color":"#432","bgcolor":"#653"},{"id":630,"type":"Note","pos":[-3309.3076171875,3048.958984375],"size":[717.709228515625,165.61032104492188],"flags":{},"order":11,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["I recommend the BF16 version of stage C. There is no visible difference vs. the full precision weights, and it halves the disk space requirements.\n\nhttps://huggingface.co/stabilityai/stable-cascade/blob/main/stage_c_bf16.safetensors\n\nIMPORTANT: The original UltraPixel \"safetensors\" is not a safetensors at all - it is a PICKLE, where they lazily (at best) changed the file extension to \".safetensors\"!\n\nI converted it to a real safetensors file, and it's available below:\n\nhttps://huggingface.co/ClownsharkBatwing/ultrapixel_convert/blob/main/ultrapixel_t2i.safetensors"],"color":"#432","bgcolor":"#653"},{"id":584,"type":"UltraCascade_Loader","pos":[-2564.4580078125,3133.043212890625],"size":[345.5117492675781,82.95540618896484],"flags":{},"order":12,"mode":0,"inputs":[],"outputs":[{"name":"MODEL","localized_name":"MODEL","type":"MODEL","shape":3,"links":[1890],"slot_index":0}],"properties":{"Node name for S&R":"UltraCascade_Loader"},"widgets_values":["stage_c_bf16.safetensors","ultrapixel_t2i.safetensors"]},{"id":635,"type":"Note","pos":[-3307.105712890625,3272.173095703125],"size":[715.61083984375,89.37511444091797],"flags":{},"order":13,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Any clip G will do. The Cascade version is available at:\n\nhttps://huggingface.co/stabilityai/stable-cascade/blob/main/text_encoder/model.bf16.safetensors\n\n"],"color":"#432","bgcolor":"#653"},{"id":636,"type":"Note","pos":[-3306.760009765625,3418.6708984375],"size":[715.61083984375,113.57872772216797],"flags":{},"order":14,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["The LORA was trained with OneTrainer (https://github.com/Nerogar/OneTrainer) on some of my own SDXL generations. It has deep colors and is strong with wacky paint, illustration, and vector art styles. \n\nCascade learns extremely quickly and is very adept with artistic styles (it knows many artist names).\n\nhttps://huggingface.co/ClownsharkBatwing/CSBW_Style/blob/main/csbw_cascade_dark_ema.safetensors\n"],"color":"#432","bgcolor":"#653"},{"id":629,"type":"Note","pos":[-647.965087890625,4084.8818359375],"size":[331.63720703125,415.29815673828125],"flags":{},"order":15,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Stage B: the Stable Cascade superresolution model.\n\nAs with stage UP, the key with these dimensions is to keep the aspect ratio the same as the prior latents. Theoretically, any resolution may be used, though some odd distortions can occur when the ideal upscale ratio is not used. It's not entirely clear what those ratios are, so some experimentation may be necessary. \n\nSome resolutions that work particularly well are:\n\n1536x1536 *\n2048x2048 *\n\n1600x960\n2560x1536 **\n2880x1792 *\n3200x1920\n\nIf you use stage B lite, you can hit 4k resolutions without even using more than 12GB of VRAM.\n\nIt's highly recommended to use the CSBW finetune of stage B, as it fixes many of the severe artifact problems the original release had.\n\nNote: CFG is not needed for this stage!"],"color":"#432","bgcolor":"#653"},{"id":637,"type":"Note","pos":[-1838.5732421875,2922.63671875],"size":[457.5304870605469,94.27093505859375],"flags":{},"order":16,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Perturbed attention guidance (PAG) makes an enormous difference with Stable Cascade stages C and UP. Like CFG, it will double the runtime."],"color":"#432","bgcolor":"#653"},{"id":598,"type":"CLIPTextEncode","pos":[-1811.0350341796875,3205.474853515625],"size":[351.592529296875,173.00360107421875],"flags":{},"order":201,"mode":0,"inputs":[{"name":"clip","localized_name":"clip","type":"CLIP","link":1937}],"outputs":[{"name":"CONDITIONING","localized_name":"CONDITIONING","type":"CONDITIONING","links":[1907,1911,1914],"slot_index":0}],"properties":{"Node name for S&R":"CLIPTextEncode"},"widgets_values":["impasto oil painting by Yayoi Kusama and Lisa Frank, thick paint textures, tunning contrasts at  night with stylish roughly drawn thick black lines, a nuclear explosion destroying a city, its towering wide glowing nuclear mushroom cloud enveloping the entire skyline, the nuclear fireball lighting up the dark sky"]},{"id":601,"type":"UltraCascade_PerturbedAttentionGuidance","pos":[-1808.5911865234375,3084.306884765625],"size":[344.3999938964844,58],"flags":{},"order":200,"mode":0,"inputs":[{"name":"model","localized_name":"model","type":"MODEL","link":1904}],"outputs":[{"name":"MODEL","localized_name":"MODEL","type":"MODEL","links":[1909,1910],"slot_index":0}],"properties":{"Node name for S&R":"UltraCascade_PerturbedAttentionGuidance"},"widgets_values":[3]},{"id":599,"type":"CLIPTextEncode","pos":[-1814.4205322265625,3435.57763671875],"size":[356.2470703125,110.6326904296875],"flags":{},"order":202,"mode":0,"inputs":[{"name":"clip","localized_name":"clip","type":"CLIP","link":1938}],"outputs":[{"name":"CONDITIONING","localized_name":"CONDITIONING","type":"CONDITIONING","links":[1908,1912,1915],"slot_index":0}],"properties":{"Node name for S&R":"CLIPTextEncode"},"widgets_values":["low quality, bad quality, low detail, blurry, unsharp"]},{"id":631,"type":"Note","pos":[-1557.671142578125,2725.4599609375],"size":[457.5304870605469,94.27093505859375],"flags":{},"order":17,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["This is a checkpoint that, for convenience, includes the stage B lite CSBW finetune, clip G, and stage A (the FT_HQ finetune).\n\nhttps://huggingface.co/ClownsharkBatwing/CSBW_Style/blob/main/cascade_B-lite_refined_CSBW_v1.1.safetensors"],"color":"#432","bgcolor":"#653"},{"id":649,"type":"Note","pos":[2011.257080078125,3860],"size":[282.2704772949219,88],"flags":{},"order":18,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Since \"steps_to_run\" is set to -1,\nthis will run all remaining steps."],"color":"#432","bgcolor":"#653"},{"id":648,"type":"Note","pos":[1661.257080078125,3860],"size":[283.8087463378906,88],"flags":{},"order":19,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Runs the next 10 steps (out of 30)."],"color":"#432","bgcolor":"#653"},{"id":657,"type":"ClownsharKSampler_Beta","pos":[1710,3140],"size":[296.93646240234375,418],"flags":{},"order":20,"mode":0,"inputs":[{"name":"model","localized_name":"model","type":"MODEL","shape":7,"link":null},{"name":"positive","localized_name":"positive","type":"CONDITIONING","shape":7,"link":null},{"name":"negative","localized_name":"negative","type":"CONDITIONING","shape":7,"link":null},{"name":"latent_image","localized_name":"latent_image","type":"LATENT","shape":7,"link":null},{"name":"sigmas","localized_name":"sigmas","type":"SIGMAS","shape":7,"link":null},{"name":"guides","localized_name":"guides","type":"GUIDES","shape":7,"link":null},{"name":"options","localized_name":"options","type":"OPTIONS","shape":7,"link":null}],"outputs":[{"name":"output","localized_name":"output","type":"LATENT","links":[],"slot_index":0},{"name":"denoised","localized_name":"denoised","type":"LATENT","links":null},{"name":"options","localized_name":"options","type":"OPTIONS","links":null}],"properties":{"Node name for S&R":"ClownsharKSampler_Beta"},"widgets_values":[0.5,"exponential/res_3s","beta57",30,-1,1,5.5,0,"fixed","standard",true]},{"id":680,"type":"ClownSampler_Beta","pos":[1050,3140],"size":[283.6876220703125,174],"flags":{},"order":21,"mode":0,"inputs":[{"name":"guides","localized_name":"guides","type":"GUIDES","shape":7,"link":null},{"name":"options","localized_name":"options","type":"OPTIONS","shape":7,"link":null}],"outputs":[{"name":"sampler","localized_name":"sampler","type":"SAMPLER","links":[1973]}],"properties":{"Node name for S&R":"ClownSampler_Beta"},"widgets_values":[0.5,"exponential/res_3s",-1,"fixed",true]},{"id":685,"type":"Note","pos":[3440,5450],"size":[280.6243896484375,109.73818969726562],"flags":{},"order":22,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["NOTE: \"epsilon_scales\" is currently unused, but exists as a placeholder. \n\n\"frame_weights\" is for video models such as Hunyuan. This is for use with guides."],"color":"#432","bgcolor":"#653"},{"id":713,"type":"Note","pos":[4574.66552734375,4613.29833984375],"size":[280.0735168457031,88],"flags":{},"order":23,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["INPAINTING TIP: Try using the settings to the right with a feathered mask."],"color":"#432","bgcolor":"#653"},{"id":670,"type":"SigmasSchedulePreview","pos":[3850,5410],"size":[315,270],"flags":{},"order":24,"mode":0,"inputs":[{"name":"model","localized_name":"model","type":"MODEL","link":null},{"name":"sigmas","localized_name":"sigmas","type":"SIGMAS","shape":7,"link":null}],"outputs":[],"properties":{"Node name for S&R":"SigmasSchedulePreview"},"widgets_values":["hard",0.25,1,1,1,"beta57",30,2.1,0]},{"id":654,"type":"BetaSamplingScheduler","pos":[1420,2780],"size":[210,106],"flags":{},"order":25,"mode":0,"inputs":[{"name":"model","localized_name":"model","type":"MODEL","link":null}],"outputs":[{"name":"SIGMAS","localized_name":"SIGMAS","type":"SIGMAS","links":null}],"properties":{"Node name for S&R":"BetaSamplingScheduler"},"widgets_values":[20,0.5,0.7]},{"id":653,"type":"Note","pos":[1390,2940],"size":[252.12789916992188,117.73304748535156],"flags":{},"order":26,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["\"beta57\" is equivalent to the BetaSamplingScheduler node above. I have found the results to be generally superior to the default \"beta\" (where the values are both set to 0.60).\n\n\n"],"color":"#432","bgcolor":"#653"},{"id":643,"type":"Note","pos":[751.2572021484375,4100],"size":[507.688720703125,165.58355712890625],"flags":{},"order":27,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["\"steps_to_run\": When set to -1, it will run all steps per usual. \n\nIf set to a positive value, it will run that number of steps, and then stop and pass the latent off to the next sampler node.\n\nIf the next sampler node's \"sampler_mode\" is set to \"resample\", it will then continue where the first one left off. \n\nThis even works with multistep samplers, as it carries its \"momentum\" from node to the next. This is not the case for \"KSampler (Advanced)\", or any other sampler nodes that I'm aware of."],"color":"#432","bgcolor":"#653"},{"id":724,"type":"CLIPTextEncode","pos":[990,4960],"size":[210,88],"flags":{},"order":28,"mode":0,"inputs":[{"name":"clip","localized_name":"clip","type":"CLIP","link":null}],"outputs":[{"name":"CONDITIONING","localized_name":"CONDITIONING","type":"CONDITIONING","links":[1982,1983],"slot_index":0}],"properties":{"Node name for S&R":"CLIPTextEncode"},"widgets_values":[""]},{"id":722,"type":"ClownGuide_Beta","pos":[1250,5430],"size":[315,290],"flags":{},"order":29,"mode":0,"inputs":[{"name":"guide","localized_name":"guide","type":"LATENT","shape":7,"link":null},{"name":"mask","localized_name":"mask","type":"MASK","shape":7,"link":null},{"name":"weights","localized_name":"weights","type":"SIGMAS","shape":7,"link":null}],"outputs":[{"name":"guides","localized_name":"guides","type":"GUIDES","links":[1984],"slot_index":0}],"properties":{"Node name for S&R":"ClownGuide_Beta"},"widgets_values":["epsilon",false,false,0.5,1,"constant",0,1000,false]},{"id":720,"type":"ClownsharKSampler_Beta","pos":[1260,4940],"size":[315,418],"flags":{},"order":178,"mode":0,"inputs":[{"name":"model","localized_name":"model","type":"MODEL","shape":7,"link":null},{"name":"positive","localized_name":"positive","type":"CONDITIONING","shape":7,"link":1982},{"name":"negative","localized_name":"negative","type":"CONDITIONING","shape":7,"link":1983},{"name":"latent_image","localized_name":"latent_image","type":"LATENT","shape":7,"link":null},{"name":"sigmas","localized_name":"sigmas","type":"SIGMAS","shape":7,"link":null},{"name":"guides","localized_name":"guides","type":"GUIDES","shape":7,"link":1984},{"name":"options","localized_name":"options","type":"OPTIONS","shape":7,"link":null}],"outputs":[{"name":"output","localized_name":"output","type":"LATENT","links":null},{"name":"denoised","localized_name":"denoised","type":"LATENT","links":null},{"name":"options","localized_name":"options","type":"OPTIONS","links":null}],"properties":{"Node name for S&R":"ClownsharKSampler_Beta"},"widgets_values":[0,"multistep/res_2m","beta57",30,-1,1,1,0,"fixed","unsample",true]},{"id":721,"type":"ClownsharKSampler_Beta","pos":[1620,4940],"size":[315,418],"flags":{},"order":183,"mode":0,"inputs":[{"name":"model","localized_name":"model","type":"MODEL","shape":7,"link":null},{"name":"positive","localized_name":"positive","type":"CONDITIONING","shape":7,"link":null},{"name":"negative","localized_name":"negative","type":"CONDITIONING","shape":7,"link":null},{"name":"latent_image","localized_name":"latent_image","type":"LATENT","shape":7,"link":null},{"name":"sigmas","localized_name":"sigmas","type":"SIGMAS","shape":7,"link":null},{"name":"guides","localized_name":"guides","type":"GUIDES","shape":7,"link":1985},{"name":"options","localized_name":"options","type":"OPTIONS","shape":7,"link":null}],"outputs":[{"name":"output","localized_name":"output","type":"LATENT","links":null},{"name":"denoised","localized_name":"denoised","type":"LATENT","links":null},{"name":"options","localized_name":"options","type":"OPTIONS","links":null}],"properties":{"Node name for S&R":"ClownsharKSampler_Beta"},"widgets_values":[0,"multistep/res_2m","beta57",30,-1,1,5.5,-1,"fixed","resample",true]},{"id":727,"type":"Note","pos":[890,5170],"size":[333.3896179199219,108.9758071899414],"flags":{},"order":30,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["UNSAMPLER SETTINGS: \n\nEta should usually be 0.0. \nCFG should be 1.0, and used with an empty prompt.\n\nDenoise < 1.0 can help with adherence to the unsampled image."],"color":"#432","bgcolor":"#653"},{"id":731,"type":"Note","pos":[1980,5160],"size":[364.70263671875,103.89823150634766],"flags":{},"order":31,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Ensure resampler denoise matches the unsampler denoise.\n\nLow eta values can be used here (try 0.1 to 0.25). Sometimes they can actually improve adherence to the unsampled image."],"color":"#432","bgcolor":"#653"},{"id":733,"type":"Note","pos":[880,5530],"size":[339.3138122558594,133.51815795898438],"flags":{},"order":32,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Typical guide settings for unsampling/resampling with a rectified flow model (AuraFlow, SD3.5, Flux) are to the right.\n\nThis will generally NOT work well with UNSAMPLING SD1.5, SDXL, Cascade, etc.! (These guide nodes however work great as regular guides with these models!)"],"color":"#432","bgcolor":"#653"},{"id":659,"type":"Note","pos":[1427.1387939453125,3646.506591796875],"size":[352.2813415527344,88],"flags":{},"order":33,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["All of the configurations above will have the same output (and runtime) as the chained samplers below."],"color":"#432","bgcolor":"#653"},{"id":610,"type":"ClownsharKSampler_Beta","pos":[-1373.449462890625,3188.063232421875],"size":[311.41375732421875,693.9824829101562],"flags":{},"order":213,"mode":0,"inputs":[{"name":"model","localized_name":"model","type":"MODEL","shape":7,"link":1909},{"name":"positive","localized_name":"positive","type":"CONDITIONING","shape":7,"link":1907},{"name":"negative","localized_name":"negative","type":"CONDITIONING","shape":7,"link":1908},{"name":"latent_image","localized_name":"latent_image","type":"LATENT","shape":7,"link":null},{"name":"sigmas","localized_name":"sigmas","type":"SIGMAS","shape":7,"link":null},{"name":"guides","localized_name":"guides","type":"GUIDES","shape":7,"link":null},{"name":"options","localized_name":"options","type":"OPTIONS","shape":7,"link":1951},{"name":"options 2","type":"OPTIONS","link":null}],"outputs":[{"name":"output","localized_name":"output","type":"LATENT","links":[1949],"slot_index":0},{"name":"denoised","localized_name":"denoised","type":"LATENT","links":null},{"name":"options","localized_name":"options","type":"OPTIONS","links":null}],"properties":{"Node name for S&R":"ClownsharKSampler_Beta"},"widgets_values":[0.5,"exponential/res_3s","beta57",30,-1,1,5.5,1,"fixed","standard",true]},{"id":612,"type":"ClownsharKSampler_Beta","pos":[-1014.779296875,3187.209228515625],"size":[314.421142578125,693.9824829101562],"flags":{},"order":221,"mode":0,"inputs":[{"name":"model","localized_name":"model","type":"MODEL","shape":7,"link":1910},{"name":"positive","localized_name":"positive","type":"CONDITIONING","shape":7,"link":1911},{"name":"negative","localized_name":"negative","type":"CONDITIONING","shape":7,"link":1912},{"name":"latent_image","localized_name":"latent_image","type":"LATENT","shape":7,"link":1949},{"name":"sigmas","localized_name":"sigmas","type":"SIGMAS","shape":7,"link":null},{"name":"guides","localized_name":"guides","type":"GUIDES","shape":7,"link":null},{"name":"options","localized_name":"options","type":"OPTIONS","shape":7,"link":1947},{"name":"options 2","type":"OPTIONS","link":null}],"outputs":[{"name":"output","localized_name":"output","type":"LATENT","links":[1950],"slot_index":0},{"name":"denoised","localized_name":"denoised","type":"LATENT","links":null},{"name":"options","localized_name":"options","type":"OPTIONS","links":null}],"properties":{"Node name for S&R":"ClownsharKSampler_Beta"},"widgets_values":[0.5,"exponential/res_3s","beta57",30,-1,1,5.5,-1,"fixed","standard",true]},{"id":613,"type":"ClownsharKSampler_Beta","pos":[-648.0813598632812,3185.39013671875],"size":[309.2452087402344,691.814208984375],"flags":{},"order":226,"mode":0,"inputs":[{"name":"model","localized_name":"model","type":"MODEL","shape":7,"link":1926},{"name":"positive","localized_name":"positive","type":"CONDITIONING","shape":7,"link":1914},{"name":"negative","localized_name":"negative","type":"CONDITIONING","shape":7,"link":1915},{"name":"latent_image","localized_name":"latent_image","type":"LATENT","shape":7,"link":1950},{"name":"sigmas","localized_name":"sigmas","type":"SIGMAS","shape":7,"link":null},{"name":"guides","localized_name":"guides","type":"GUIDES","shape":7,"link":null},{"name":"options","localized_name":"options","type":"OPTIONS","shape":7,"link":1948},{"name":"options 2","type":"OPTIONS","link":null}],"outputs":[{"name":"output","localized_name":"output","type":"LATENT","links":[1923],"slot_index":0},{"name":"denoised","localized_name":"denoised","type":"LATENT","links":null},{"name":"options","localized_name":"options","type":"OPTIONS","links":null}],"properties":{"Node name for S&R":"ClownsharKSampler_Beta"},"widgets_values":[0.5,"exponential/res_3s","beta57",30,-1,1,1,-1,"fixed","standard",true]},{"id":716,"type":"Note","pos":[4574.66552734375,4963.29833984375],"size":[270.65277099609375,108.61186218261719],"flags":{},"order":34,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["INPAINTING TIP: Try using the settings to the right with a feathered mask, and \"end_step\" set to the number of sampling steps (or less). This will allow the entire image to change slightly to help heal any seams that may appear."],"color":"#432","bgcolor":"#653"},{"id":714,"type":"ClownGuide_Beta","pos":[4874.66552734375,4503.29833984375],"size":[257.2991638183594,290],"flags":{},"order":35,"mode":0,"inputs":[{"name":"guide","localized_name":"guide","type":"LATENT","shape":7,"link":null},{"name":"mask","localized_name":"mask","type":"MASK","shape":7,"link":null},{"name":"weights","localized_name":"weights","type":"SIGMAS","shape":7,"link":null}],"outputs":[{"name":"guides","localized_name":"guides","type":"GUIDES","links":null}],"properties":{"Node name for S&R":"ClownGuide_Beta"},"widgets_values":["epsilon",false,false,1,1,"constant",0,1000,false]},{"id":715,"type":"ClownGuide_Beta","pos":[4874.66552734375,4863.29833984375],"size":[254.67617797851562,290],"flags":{},"order":36,"mode":0,"inputs":[{"name":"guide","localized_name":"guide","type":"LATENT","shape":7,"link":null},{"name":"mask","localized_name":"mask","type":"MASK","shape":7,"link":null},{"name":"weights","localized_name":"weights","type":"SIGMAS","shape":7,"link":null}],"outputs":[{"name":"guides","localized_name":"guides","type":"GUIDES","links":null}],"properties":{"Node name for S&R":"ClownGuide_Beta"},"widgets_values":["epsilon",false,true,1,1,"beta57",0,40,false]},{"id":709,"type":"Note","pos":[5570,4480],"size":[280.0735168457031,88],"flags":{},"order":37,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Note: The \"guide_masked\" latent image will control the region that is \"masked out\"! And vice versa with \"guide_unmasked\"."],"color":"#432","bgcolor":"#653"},{"id":688,"type":"ClownGuides_Beta","pos":[5542.884765625,3927.678955078125],"size":[315,450],"flags":{},"order":38,"mode":0,"inputs":[{"name":"guide_masked","localized_name":"guide_masked","type":"LATENT","shape":7,"link":null},{"name":"guide_unmasked","localized_name":"guide_unmasked","type":"LATENT","shape":7,"link":null},{"name":"mask","localized_name":"mask","type":"MASK","shape":7,"link":null},{"name":"weights_masked","localized_name":"weights_masked","type":"SIGMAS","shape":7,"link":null},{"name":"weights_unmasked","localized_name":"weights_unmasked","type":"SIGMAS","shape":7,"link":null}],"outputs":[{"name":"guides","localized_name":"guides","type":"GUIDES","links":[1977],"slot_index":0}],"properties":{"Node name for S&R":"ClownGuides_Beta"},"widgets_values":["epsilon",false,true,0.75,0.75,1,1,"beta57","constant",0,0,15,15,false]},{"id":707,"type":"ClownGuide_Beta","pos":[5206.15283203125,3929.6015625],"size":[315,290],"flags":{},"order":39,"mode":0,"inputs":[{"name":"guide","localized_name":"guide","type":"LATENT","shape":7,"link":null},{"name":"mask","localized_name":"mask","type":"MASK","shape":7,"link":null},{"name":"weights","localized_name":"weights","type":"SIGMAS","shape":7,"link":null}],"outputs":[{"name":"guides","localized_name":"guides","type":"GUIDES","links":null}],"properties":{"Node name for S&R":"ClownGuide_Beta"},"widgets_values":["pseudoimplicit",false,false,0.15,1,"beta57",5,15,false]},{"id":706,"type":"Note","pos":[5228.6552734375,4270.94677734375],"size":[280.0735168457031,88],"flags":{},"order":40,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Tip: Try a delayed start (start_step > 0), like shown above with pseudoimplicit, for wacky results!"],"color":"#432","bgcolor":"#653"},{"id":711,"type":"Note","pos":[5570,4610],"size":[280.0735168457031,88],"flags":{},"order":41,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Tip: I recommend drawing your masks on random load image nodes, for convenience."],"color":"#432","bgcolor":"#653"},{"id":712,"type":"Note","pos":[5546.97216796875,3772.719970703125],"size":[308.80828857421875,88],"flags":{},"order":42,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["INPAINTING TIP: Use ClownRegionalConditioning ClownGuides together with the same mask!"],"color":"#432","bgcolor":"#653"},{"id":744,"type":"Note","pos":[7538.74755859375,3817.72216796875],"size":[337.9170227050781,389.18304443359375],"flags":{},"order":43,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["It can be confusing at first, trying to understand which area is affected by which conditioning or mask. I suggest starting with prompts like \"blue ice\" and \"red fire\" with region_bleed = 0.0 to clear things up.\n\nTO THE LEFT:\n\nThe two nodes to the left will automatically create an unmasked area, based on what areas are not masked by mask, mask_A, or mask_B.\n\nAs an example:\n\nPositive_A will affect the area masked by \"mask_A\".\n\nPositive_B will affect the area masked by \"mask_B\".\n\nPositive_unmasked will affect the area that is not masked by \"mask_A\" or \"mask_B\".\n\nTO THE RIGHT:\n\nThese two nodes give you manual control over the area for each prompt. This is especially useful for temporal attention with video modes like WAN. The risk is if you fail to ensure every part of the image (or frame) is masked by one of the masks, you'll end up with an unconditioned area that will look like pure noise."],"color":"#432","bgcolor":"#653"},{"id":756,"type":"TemporalCrossAttnMask","pos":[7017.90087890625,4790.6005859375],"size":[210,82],"flags":{},"order":44,"mode":0,"inputs":[],"outputs":[{"name":"temporal_mask","localized_name":"temporal_mask","type":"MASK","links":[1988]}],"properties":{"Node name for S&R":"TemporalCrossAttnMask"},"widgets_values":[1,65]},{"id":757,"type":"TemporalCrossAttnMask","pos":[7017.12060546875,4954.5556640625],"size":[210,82],"flags":{},"order":45,"mode":0,"inputs":[],"outputs":[{"name":"temporal_mask","localized_name":"temporal_mask","type":"MASK","links":[1989],"slot_index":0}],"properties":{"Node name for S&R":"TemporalCrossAttnMask"},"widgets_values":[65,133]},{"id":758,"type":"Note","pos":[7644.2734375,4659.04248046875],"size":[275.73828125,88],"flags":{},"order":46,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Sometimes it is beneficial to allow self-attention masks to overlap slightly. This is similar to the \"edge_width\" parameter above, except it overlaps frames, not spatial components (areas of the image)."],"color":"#432","bgcolor":"#653"},{"id":751,"type":"TemporalSplitAttnMask","pos":[7668.654296875,4808.83642578125],"size":[210,130],"flags":{},"order":47,"mode":0,"inputs":[],"outputs":[{"name":"temporal_mask","localized_name":"temporal_mask","type":"MASK","links":[1986],"slot_index":0}],"properties":{"Node name for S&R":"TemporalSplitAttnMask"},"widgets_values":[1,69,1,65]},{"id":753,"type":"TemporalSplitAttnMask","pos":[7668.654296875,4998.83642578125],"size":[210,130],"flags":{},"order":48,"mode":0,"inputs":[],"outputs":[{"name":"temporal_mask","localized_name":"temporal_mask","type":"MASK","links":[1987],"slot_index":0}],"properties":{"Node name for S&R":"TemporalSplitAttnMask"},"widgets_values":[61,133,65,133]},{"id":749,"type":"Note","pos":[6907.34326171875,4623.6328125],"size":[280.0735168457031,88],"flags":{},"order":49,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["The advanced version of the WAN patcher can set a sliding self-attention window. The \"size\" is the number of latent frames (which is 1/4th the number of frames in the final output)."],"color":"#432","bgcolor":"#653"},{"id":689,"type":"Note","pos":[4562.91796875,3811.044189453125],"size":[494.1324462890625,535.6380004882812],"flags":{},"order":50,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Guides are a way of controlling the image generation process without denoising an image directly, but by steering the denoising process itself. This can mimic many of the benefits of unsampling, without the need to spend extra time unsampling the image.\n\nThere are two main guide modes:\n\n\"Epsilon\" can be used in conjunction with unsampling/resampling workflows to dramatically improve results with rectified flow models (AuraFlow, SD3.5, Flux). It can also be used directly. It works by modifying the noise prediction made by the model to align with the guide image.\n\n\"Pseudoimplicit\" works by lying to the model about the state of the denoising process, so that it generates a noise prediction that strongly aligns with the guide image. \"Fully_pseudoimplicit\" is only supported with \"fully_implicit\" and \"diag_implicit\" samplers (all others will default back to pseudoimplicit).\n\nChannelwise and projection modes can have a dramatic effect. I especially recommend trying epsilon with these modes, though they are quite interesting with pseudoimplicit as well. \"projection_mode\" can result in some issues with image details if used for the entire sampling process.\n\nCUTOFF:\n\nFlux has extremely strong self-attention, and has issues with getting \"stuck\" if the guide strength is too high (or used for too many steps), which results in an output that looks nearly identical to the guide. \"cutoff\" does a crude check for how similar the image is to the guide - if it exceeds that value, it will turn off the guide for that step. Try setting to 0.5 or 0.6 when using Flux.\n\nWEIGHT SCHEDULERS:\n\nThese control the weight at each step. For example, with the settings shown:\n\n * the \"unmasked\" region will have a weight of 0.75 for the first 15 steps, then 0.0 for every step after that\n\n * the \"masked\" region will start with a weight of 0.75 for the first step, gradually declining until reaching 0.0 after 15 steps (and remaining at 0.0)\n"],"color":"#432","bgcolor":"#653"},{"id":726,"type":"Note","pos":[5630,5410],"size":[257.97479248046875,159.16941833496094],"flags":{},"order":51,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["VAEEncodeAdvanced is a quality of life node for convenience when using multiple guides.\n\nNote: the mask input is for a black and white image. It is there for convenience with converting any masks you may have saved as black and white images into masks you can use in a workflow."],"color":"#432","bgcolor":"#653"},{"id":691,"type":"ClownsharKSampler_Beta","pos":[5894.66552734375,3923.29833984375],"size":[274.2724609375,418],"flags":{},"order":179,"mode":0,"inputs":[{"name":"model","localized_name":"model","type":"MODEL","shape":7,"link":null},{"name":"positive","localized_name":"positive","type":"CONDITIONING","shape":7,"link":null},{"name":"negative","localized_name":"negative","type":"CONDITIONING","shape":7,"link":null},{"name":"latent_image","localized_name":"latent_image","type":"LATENT","shape":7,"link":null},{"name":"sigmas","localized_name":"sigmas","type":"SIGMAS","shape":7,"link":null},{"name":"guides","localized_name":"guides","type":"GUIDES","shape":7,"link":1977},{"name":"options","localized_name":"options","type":"OPTIONS","shape":7,"link":null}],"outputs":[{"name":"output","localized_name":"output","type":"LATENT","links":null},{"name":"denoised","localized_name":"denoised","type":"LATENT","links":null},{"name":"options","localized_name":"options","type":"OPTIONS","links":null}],"properties":{"Node name for S&R":"ClownsharKSampler_Beta"},"widgets_values":[0.5,"multistep/res_2m","beta57",30,-1,1,5.5,0,"fixed","standard",true]},{"id":695,"type":"ModelSamplingAdvancedResolution","pos":[8110,3210],"size":[260.3999938964844,126],"flags":{},"order":215,"mode":0,"inputs":[{"name":"model","localized_name":"model","type":"MODEL","link":1980},{"name":"latent_image","localized_name":"latent_image","type":"LATENT","link":null}],"outputs":[{"name":"model","localized_name":"model","type":"MODEL","links":null}],"properties":{"Node name for S&R":"ModelSamplingAdvancedResolution"},"widgets_values":["exponential",1.35,0.85]},{"id":701,"type":"Note","pos":[7080,3060],"size":[327.4920959472656,88],"flags":{},"order":52,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Loader nodes are provided for convenience with Flux and SD3.5. The Flux loader can also load the Redux (and ClipVision) models for you."],"color":"#432","bgcolor":"#653"},{"id":694,"type":"FluxGuidanceDisable","pos":[7790,3210],"size":[210,82],"flags":{},"order":208,"mode":0,"inputs":[{"name":"model","localized_name":"model","type":"MODEL","link":1979}],"outputs":[{"name":"model","localized_name":"model","type":"MODEL","links":[1980],"slot_index":0}],"properties":{"Node name for S&R":"FluxGuidanceDisable"},"widgets_values":[true,true]},{"id":699,"type":"Note","pos":[7760,3030],"size":[253.01846313476562,112.91952514648438],"flags":{},"order":53,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["This disables \"Flux Guidance\" (which is actually NOT disabled by setting to 1.0 or 0.0). It can be helpful in many cases where you wish to banish the \"Flux look\" to the bottom of a creepy old well in Transylvania."],"color":"#432","bgcolor":"#653"},{"id":660,"type":"ClownsharKSampler_Beta","pos":[3907.121826171875,3512.491943359375],"size":[293.78173828125,618],"flags":{},"order":205,"mode":0,"inputs":[{"name":"model","localized_name":"model","type":"MODEL","shape":7,"link":null},{"name":"positive","localized_name":"positive","type":"CONDITIONING","shape":7,"link":null},{"name":"negative","localized_name":"negative","type":"CONDITIONING","shape":7,"link":null},{"name":"latent_image","localized_name":"latent_image","type":"LATENT","shape":7,"link":null},{"name":"sigmas","localized_name":"sigmas","type":"SIGMAS","shape":7,"link":null},{"name":"guides","localized_name":"guides","type":"GUIDES","shape":7,"link":null},{"name":"options","localized_name":"options","type":"OPTIONS","shape":7,"link":1962},{"name":"options 2","type":"OPTIONS","link":1963},{"name":"options 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This is the approach traditionally used in KSampler (Advanced)."],"color":"#432","bgcolor":"#653"},{"id":765,"type":"Note","pos":[-3299.93603515625,2699.88427734375],"size":[389.86285400390625,98.29244232177734],"flags":{},"order":64,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["THESE NODES ARE NOT REQUIRED TO USE RES4LYF!!!\n\nThese descriptions are included only out of a desire to consolidate all CSBW node documentation into one location."],"color":"#432","bgcolor":"#653"},{"id":728,"type":"Note","pos":[1311.5924072265625,4784.7666015625],"size":[213.4912109375,88],"flags":{},"order":65,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["        UNSAMPLER NODE"],"color":"#432","bgcolor":"#653"},{"id":723,"type":"ClownGuide_Beta","pos":[1620,5430],"size":[315,290],"flags":{},"order":66,"mode":0,"inputs":[{"name":"guide","localized_name":"guide","type":"LATENT","shape":7,"link":null},{"name":"mask","localized_name":"mask","type":"MASK","shape":7,"link":null},{"name":"weights","localized_name":"weights","type":"SIGMAS","shape":7,"link":null}],"outputs":[{"name":"guides","localized_name":"guides","type":"GUIDES","links":[1985],"slot_index":0}],"properties":{"Node name for S&R":"ClownGuide_Beta"},"widgets_values":["epsilon",false,true,0.75,1,"beta57",0,10,false]},{"id":766,"type":"Note","pos":[1980.62939453125,5502.46337890625],"size":[366.45068359375,97.77838134765625],"flags":{},"order":67,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Tip: \"projection\" and \"channelwise\" modes can increase the intensity of the effect with epsilon and data guide modes. 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It's worth experimenting with."],"color":"#432","bgcolor":"#653"},{"id":732,"type":"Note","pos":[1981.25732421875,5003.00537109375],"size":[361.62445068359375,90.62290954589844],"flags":{},"order":68,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Tip: multistep samplers usually adhere to unsampled images more effectively than others."],"color":"#432","bgcolor":"#653"},{"id":768,"type":"ClownGuides_Beta","pos":[4877.3896484375,5235.57373046875],"size":[333.3587951660156,450],"flags":{},"order":69,"mode":0,"inputs":[{"name":"guide_masked","localized_name":"guide_masked","type":"LATENT","shape":7,"link":null},{"name":"guide_unmasked","localized_name":"guide_unmasked","type":"LATENT","shape":7,"link":null},{"name":"mask","localized_name":"mask","type":"MASK","shape":7,"link":null},{"name":"weights_masked","localized_name":"weights_masked","type":"SIGMAS","shape":7,"link":null},{"name":"weights_unmasked","localized_name":"weights_unmasked","type":"SIGMAS","shape":7,"link":null}],"outputs":[{"name":"guides","localized_name":"guides","type":"GUIDES","links":null}],"properties":{"Node name for S&R":"ClownGuides_Beta"},"widgets_values":["lure",false,false,1,1,1,1,"linear_quadratic","constant",0,0,8,-1,false]},{"id":769,"type":"Note","pos":[4576.1689453125,5281.28271484375],"size":[266.2802734375,135.71385192871094],"flags":{},"order":70,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["INPAINTING TIP: Try using the settings to the right with your input image connected to both the guide_masked and guide_unmasked inputs. Adjust \"end_step_masked\" to change the strength of the inpainting effect (or weight_masked, or eight_scheduler_masked)."],"color":"#432","bgcolor":"#653"},{"id":725,"type":"VAEEncodeAdvanced","pos":[5930,5410],"size":[255.3756103515625,278],"flags":{},"order":71,"mode":0,"inputs":[{"name":"image_1","localized_name":"image_1","type":"IMAGE","shape":7,"link":null},{"name":"image_2","localized_name":"image_2","type":"IMAGE","shape":7,"link":null},{"name":"mask","localized_name":"mask","type":"IMAGE","shape":7,"link":null},{"name":"latent","localized_name":"latent","type":"LATENT","shape":7,"link":null},{"name":"vae","localized_name":"vae","type":"VAE","shape":7,"link":null}],"outputs":[{"name":"latent_1","localized_name":"latent_1","type":"LATENT","links":null},{"name":"latent_2","localized_name":"latent_2","type":"LATENT","links":null},{"name":"mask","localized_name":"mask","type":"MASK","links":null},{"name":"empty_latent","localized_name":"empty_latent","type":"LATENT","links":null},{"name":"width","localized_name":"width","type":"INT","links":null},{"name":"height","localized_name":"height","type":"INT","links":null}],"properties":{"Node name for S&R":"VAEEncodeAdvanced"},"widgets_values":["false",1024,1024,"red",false,"16_channels"]},{"id":735,"type":"ClownGuide_Beta","pos":[5147.18896484375,4861.30419921875],"size":[254.67617797851562,290],"flags":{},"order":72,"mode":0,"inputs":[{"name":"guide","localized_name":"guide","type":"LATENT","shape":7,"link":null},{"name":"mask","localized_name":"mask","type":"MASK","shape":7,"link":null},{"name":"weights","localized_name":"weights","type":"SIGMAS","shape":7,"link":null}],"outputs":[{"name":"guides","localized_name":"guides","type":"GUIDES","links":[1992],"slot_index":0}],"properties":{"Node name for S&R":"ClownGuide_Beta"},"widgets_values":["flow",false,false,1,1,"constant",0,40,false]},{"id":770,"type":"ClownsharKSampler_Beta","pos":[5451.8759765625,4763.7705078125],"size":[315,438],"flags":{},"order":184,"mode":0,"inputs":[{"name":"model","localized_name":"model","type":"MODEL","shape":7,"link":null},{"name":"positive","localized_name":"positive","type":"CONDITIONING","shape":7,"link":null},{"name":"negative","localized_name":"negative","type":"CONDITIONING","shape":7,"link":null},{"name":"latent_image","localized_name":"latent_image","type":"LATENT","shape":7,"link":null},{"name":"sigmas","localized_name":"sigmas","type":"SIGMAS","shape":7,"link":null},{"name":"guides","localized_name":"guides","type":"GUIDES","shape":7,"link":1992},{"name":"options","localized_name":"options","type":"OPTIONS","shape":7,"link":1994},{"name":"options 2","type":"OPTIONS","link":null}],"outputs":[{"name":"output","localized_name":"output","type":"LATENT","links":null},{"name":"denoised","localized_name":"denoised","type":"LATENT","links":null},{"name":"options","localized_name":"options","type":"OPTIONS","links":null}],"properties":{"Node name for S&R":"ClownsharKSampler_Beta"},"widgets_values":[0.5,"multistep/res_2m","beta57",30,-1,1,5.5,0,"randomize","standard",true]},{"id":772,"type":"SharkOptions_GuideCond_Beta","pos":[5230.13525390625,5239.04736328125],"size":[210,98],"flags":{},"order":73,"mode":0,"inputs":[{"name":"positive","localized_name":"positive","type":"CONDITIONING","shape":7,"link":null},{"name":"negative","localized_name":"negative","type":"CONDITIONING","shape":7,"link":null},{"name":"options","localized_name":"options","type":"OPTIONS","shape":7,"link":null}],"outputs":[{"name":"options","localized_name":"options","type":"OPTIONS","links":[1994]}],"properties":{"Node name for S&R":"SharkOptions_GuideCond_Beta"},"widgets_values":[5.5]},{"id":773,"type":"Note","pos":[5229.7958984375,5385.55810546875],"size":[272.242919921875,112.58575439453125],"flags":{},"order":74,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["When using \"flow\" mode a second set of conditionings can be added that will be used to evolve the guide itself to sync up better with your image during sampling. Try describing the guide with some creative liberties to bend things in the desired stylistic direction."],"color":"#432","bgcolor":"#653"},{"id":774,"type":"Note","pos":[4576.4013671875,5501.66796875],"size":[266.2802734375,135.71385192871094],"flags":{},"order":75,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["TIP: ClownGuides allows you to use multiple input images, each with their own separate schedule and strength settings. 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For example, with the settings shown, the weight will begin at 1.70, and gradually decline before reaching 0.0 after 10 steps (and remaining at 0.0).\n\n\"mask_type\" currently only has the \"gradient\" option, but others may be added later. \n\nYes, this does mean you can use masks with gradients (so you can feather and blur them if you wish)!\n\nMASK_TYPE:\n\nThere are options here that are a bit like causal attention in LLMs. For example, \"boolean_masked\" means the masked area can \"see\" the entire image (via self-attention), while the unmasked area cannot \"see\" the masked area. This is very useful with Flux if you wish to generate a character close to the camera but have an unblurred background. Place the character in the masked area, describe only the background in the unmasked area, select \"boolean_masked\" and set region_bleed = 0.0. \n\nEDGE_WIDTH:\n\nThis creates overlapping self-attention at the boundaries between masked and unmasked areas. This helps to conceal seams. Try values like 50 or 150 to start, and watch the preview.\n\n"],"color":"#432","bgcolor":"#653"},{"id":776,"type":"ClownRegionalConditionings","pos":[9220.0224609375,3819.96826171875],"size":[238.2400665283203,266],"flags":{},"order":222,"mode":0,"inputs":[{"name":"cond_regions","localized_name":"cond_regions","type":"COND_REGIONS","shape":7,"link":2000},{"name":"weights","localized_name":"weights","type":"SIGMAS","shape":7,"link":null},{"name":"region_bleeds","localized_name":"region_bleeds","type":"SIGMAS","shape":7,"link":null}],"outputs":[{"name":"conditioning","localized_name":"conditioning","type":"CONDITIONING","links":null}],"properties":{"Node name for S&R":"ClownRegionalConditionings"},"widgets_values":[0.9,0.25,0,"constant",0,-1,"boolean",false]},{"id":782,"type":"Note","pos":[8261.9765625,4016.659423828125],"size":[272.1261291503906,131.35166931152344],"flags":{},"order":81,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["\"Spineless\" mode means that the region \"has no spine\" and is susceptible to influence by other regions (via self-attention). This is comparable to the \"boolean_masked\" etc. modes in the nodes to the left. For example, \"boolean_masked\" sets the masked area to \"spineless\"."],"color":"#432","bgcolor":"#653"},{"id":785,"type":"Note","pos":[8574.7734375,4022.382080078125],"size":[210,97.39286804199219],"flags":{},"order":82,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Unlimited regions may be set using these nodes. Up to 12 regions have been successfully tested in a single workflow."],"color":"#432","bgcolor":"#653"},{"id":786,"type":"Note","pos":[8979.33203125,4020.87939453125],"size":[212.89056396484375,176.87088012695312],"flags":{},"order":83,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["The risk of manual control over all masks is that you miss an area (some part ends up not being covered by any mask) which means it then has no conditioning. \n\nThis is easily avoided by simply not hooking up a mask to the final node. It will use any remaining unmasked area as the final mask."],"color":"#432","bgcolor":"#653"},{"id":755,"type":"Note","pos":[7241.16015625,4666.3251953125],"size":[275.73828125,88],"flags":{},"order":84,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["If sliding self-attention is used, only cross-attention needs to be masked."],"color":"#432","bgcolor":"#653"},{"id":748,"type":"ReWanPatcherAdvanced","pos":[6702.76953125,4816.7041015625],"size":[279.3623352050781,214],"flags":{},"order":85,"mode":0,"inputs":[{"name":"model","localized_name":"model","type":"MODEL","link":null}],"outputs":[{"name":"model","localized_name":"model","type":"MODEL","links":null}],"properties":{"Node name for S&R":"ReWanPatcherAdvanced"},"widgets_values":["all","all",true,"standard",60]},{"id":750,"type":"Note","pos":[6444.103515625,4815.1484375],"size":[225.1619873046875,212.99703979492188],"flags":{},"order":86,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Sliding self-attention is useful for generating sequences where the conditioning changes from one frame to another, or for reducing VRAM requirements and reducing inference time when generating long sequences. At least 601 frames can be generated in one shot on a RTX 4090 with the above settings.\n\nThere are two modes: standard and circular. Circular allows the first frame to \"see\" the last frame, whereas standard does not."],"color":"#432","bgcolor":"#653"},{"id":742,"type":"Note","pos":[6420.9990234375,3806.16064453125],"size":[345.86224365234375,263.46356201171875],"flags":{},"order":87,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Regional conditioning requires for you to patch the model with a \"Re\" (for Regional) patcher (shown below) and to use the beta versions of either ClownSampler + SharkSampler, or ClownSharkSampler.\n\nFully compatible with Flux Redux, CFG, etc.\n\nHiDream notes:\nRegional negative conditioning is currently supported with HiDream and is useful for controlling styles (i.e., \"photo\" in a region that should be a painting, and vice versa). \n\nWith HiDream, weight and region_bleed may also be set to negative values. The effect in terms of strength is the same for -0.9 vs 0.9, but it will change whether it operates on initial or final blocks in the model. The effect can be quite different.\n"],"color":"#432","bgcolor":"#653"},{"id":737,"type":"ReFluxPatcher","pos":[6490,4140],"size":[210,82],"flags":{},"order":88,"mode":0,"inputs":[{"name":"model","localized_name":"model","type":"MODEL","link":null}],"outputs":[{"name":"model","localized_name":"model","type":"MODEL","links":null}],"properties":{"Node name for S&R":"ReFluxPatcher"},"widgets_values":["float32",true]},{"id":778,"type":"ClownRegionalConditioning","pos":[8500,3820],"size":[211.60000610351562,122],"flags":{},"order":185,"mode":0,"inputs":[{"name":"cond_regions","localized_name":"cond_regions","type":"COND_REGIONS","shape":7,"link":1995},{"name":"conditioning","localized_name":"conditioning","type":"CONDITIONING","shape":7,"link":null},{"name":"mask","localized_name":"mask","type":"MASK","shape":7,"link":null}],"outputs":[{"name":"cond_regions","localized_name":"cond_regions","type":"COND_REGIONS","links":[1996],"slot_index":0}],"properties":{"Node name for S&R":"ClownRegionalConditioning"},"widgets_values":[false,128]},{"id":775,"type":"ClownRegionalConditioning","pos":[8260,3820],"size":[211.60000610351562,122],"flags":{},"order":89,"mode":0,"inputs":[{"name":"cond_regions","localized_name":"cond_regions","type":"COND_REGIONS","shape":7,"link":null},{"name":"conditioning","localized_name":"conditioning","type":"CONDITIONING","shape":7,"link":null},{"name":"mask","localized_name":"mask","type":"MASK","shape":7,"link":null}],"outputs":[{"name":"cond_regions","localized_name":"cond_regions","type":"COND_REGIONS","links":[1995],"slot_index":0}],"properties":{"Node name for S&R":"ClownRegionalConditioning"},"widgets_values":[true,128]},{"id":779,"type":"ClownRegionalConditioning","pos":[8740.115234375,3820.114990234375],"size":[211.60000610351562,122],"flags":{},"order":204,"mode":0,"inputs":[{"name":"cond_regions","localized_name":"cond_regions","type":"COND_REGIONS","shape":7,"link":1996},{"name":"conditioning","localized_name":"conditioning","type":"CONDITIONING","shape":7,"link":null},{"name":"mask","localized_name":"mask","type":"MASK","shape":7,"link":null}],"outputs":[{"name":"cond_regions","localized_name":"cond_regions","type":"COND_REGIONS","links":[1999],"slot_index":0}],"properties":{"Node name for S&R":"ClownRegionalConditioning"},"widgets_values":[false,128]},{"id":783,"type":"ClownRegionalConditioning","pos":[8975.990234375,3820.1171875],"size":[211.60000610351562,122],"flags":{},"order":214,"mode":0,"inputs":[{"name":"cond_regions","localized_name":"cond_regions","type":"COND_REGIONS","shape":7,"link":1999},{"name":"conditioning","localized_name":"conditioning","type":"CONDITIONING","shape":7,"link":null},{"name":"mask","localized_name":"mask","type":"MASK","shape":7,"link":null}],"outputs":[{"name":"cond_regions","localized_name":"cond_regions","type":"COND_REGIONS","links":[2000],"slot_index":0}],"properties":{"Node name for S&R":"ClownRegionalConditioning"},"widgets_values":[false,128]},{"id":651,"type":"Note","pos":[1041.0577392578125,2843.751953125],"size":[304.6747741699219,235.28672790527344],"flags":{},"order":90,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["SDE NOISE:\n\n\"eta\" represents how much noise the sampler adds after each step. If set to 0.0, the samplers will be \"ODEs\". If set to > 0.0, they will be \"SDEs\" and/or \"ancestral\". \n\nThe math has been carefully designed for both variance preserving and exploding models: results are particularly good with SD1.5, SDXL, Stable Cascade, Auraflow, SD3.5 Medium, and Flux. \n\nIn most cases, using eta will result in gains in quality and coherence when using at least 20 sampling steps. Best results are with 30 or more. \n"],"color":"#432","bgcolor":"#653"},{"id":638,"type":"Note","pos":[690,2842.6943359375],"size":[321.5638427734375,270.1020202636719],"flags":{},"order":91,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["SPEED:\n\nAll multistep samplers, like Euler, use one model call per step. Therefore, they run at the same speed.\n\nAll others have the number of model calls per step listed at the end of the name in terms of \"stages\" (abbreviated \"s\").\n\nTherefore, \"res_2s\" has 2 stages, and uses 2 model calls per step. Each step will take 2x as long as a Euler step. \"ralston_3s\" will take 3x as long.\n\nImplicit samplers benefit enormously from an extra model call to initialize each step. Therefore, \"gauss-legendre_2s\" will actually use 3 model calls per step.\n\n"],"color":"#432","bgcolor":"#653"},{"id":681,"type":"Note","pos":[691.0578002929688,3171.1572265625],"size":[320.96875,168.8627166748047],"flags":{},"order":92,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["IMPORTANT: the seed here is set to -1! \n\nThis means \"use the next available seed\" (which will be the most recently used seed + 1).\n\nThis setting is irrelevant if eta = 0.0. It is only used for SDE sampling (where noise is added after each step, the amount of which is controlled by \"eta\").\n\n"],"color":"#432","bgcolor":"#653"},{"id":801,"type":"Note","pos":[631.0161743164062,2693.894775390625],"size":[602.4559326171875,93.21308135986328],"flags":{},"order":93,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["MANY COMPLEX WORKFLOWS BECOME MUCH SIMPLER WHEN USING RES4LYF NODES.\n\nA great emphasis has been placed, during the design of these nodes, on usability - ensuring they are not just more powerful than the default KSampler nodes, and don't just provide superior results, but are also ultimately easier to use, encouraging experimentation. "],"color":"#432","bgcolor":"#653"},{"id":655,"type":"Note","pos":[2114.199951171875,2702.9755859375],"size":[321.8917236328125,108.77723693847656],"flags":{},"order":94,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["BONGMath is an algorithm unique to RES4LYF that vastly improves sampling quality and coherence in most cases, with little to no effect on sampling speed.\n\nIt has no effect when eta is set to 0. \n"],"color":"#432","bgcolor":"#653"},{"id":667,"type":"Note","pos":[2630,2720],"size":[242.25900268554688,198.10833740234375],"flags":{},"order":96,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["OPTIONS NODES:\n\nThese can be connected directly to ClownSampler, ClownSharkSampler, and SharkSampler, to control a variety of advanced parameters.\n\nSHARKoptions may be connected to SHARKsampler or clownSHARKsampler.\n\nCLOWNoptions may be connected to CLOWNsampler or CLOWNsharksampler."],"color":"#432","bgcolor":"#653"},{"id":669,"type":"Note","pos":[2894.58544921875,3200.53369140625],"size":[478.7455139160156,399.50189208984375],"flags":{},"order":97,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["ClownOptions_SDE controls the noise that is added after each step (or substep).\n\nNOISE TYPES:\n\nBrownian can give very good results, and is the \"correct\" noise type to use from a mathematical perspective. It does, however, result in a burned image with BONGMath when using many of the higher order samplers (the issue is with \"non-monotonic\" ones, which are typically those \n whose names end with \"5s\" or greater).\n\nNOISE MODES:\n\nThe \"noise mode\" controls how much noise is actually used after each step. The list is roughly sorted in order of strength (hard at the top being the strongest, hard_var at the bottom being the weakest - and the only one that uses \"mathematically correct\" scaling). \n\n\"Sinusoidal\" begins very weak, then becomes strong in the middle of the sampling process before losing strength again.\n\nThe \"soft\" noise types begin very strong, and drop off extremely rapidly.\n\nSUBSTEPS:\n\nAny sampler that is not euler or ddim uses information from multiple model calls (\"stages\") to predict the step. Multistep samplers reuse previous steps as \"stages\", whereas the rest make new model calls. \n\nThe settings for \"substep\" control these intermediate \"substeps\". If eta_substep is set to 0, BONGMath will have no effect."],"color":"#432","bgcolor":"#653"},{"id":661,"type":"ClownOptions_SDE_Beta","pos":[3414.8017578125,3262.863037109375],"size":[315,266],"flags":{},"order":98,"mode":0,"inputs":[{"name":"etas","localized_name":"etas","type":"SIGMAS","shape":7,"link":null},{"name":"etas_substep","localized_name":"etas_substep","type":"SIGMAS","shape":7,"link":null},{"name":"options","localized_name":"options","type":"OPTIONS","shape":7,"link":null}],"outputs":[{"name":"options","localized_name":"options","type":"OPTIONS","links":[1963],"slot_index":0}],"properties":{"Node name for S&R":"ClownOptions_SDE_Beta"},"widgets_values":["gaussian","gaussian","hard","hard",0.5,0.5,-1,"randomize"]},{"id":677,"type":"Note","pos":[2900,3650],"size":[471.3785095214844,160.20542907714844],"flags":{},"order":99,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Overshoot > 0 causes the sampler to \"overshoot\" the step size, and then scale backwards to where it was really supposed to end. This is what all other SDE and ancestral sampler implementations do, though I have found it to adversely affect accuracy, especially with high eta values (> 0.7), resulting in softened, simplified images with little detail.\n\nHowever, careful use can soften images and deepen colors with pleasant results.\n\nTo mimic the behavior of the typical SDE and ancestral sampler implementations, set these settings to match those in ClownOptions_SDE."],"color":"#432","bgcolor":"#653"},{"id":683,"type":"Note","pos":[2900,5230],"size":[481.8527526855469,325.1487731933594],"flags":{},"order":100,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["RES4LYF heavily emphasizes giving you control over the sampling process!\n\nYou will often see these green \"sigmas\" inputs that aren't really for sigmas. These are used to control parameters on a step-by-step basis. \n\nIMPORTANT: The values used in the input here are multiplied by the value in ClownSampler/SharkSampler/ClownsharKSampler!\n\nFor example, the KarrasSchedule connected below creates a list of numbers:\n\n1.0, 1.0, 1.0, 1.0, 1.0\n\n(The rest is automatically filled in with 0.0.)\n\nThese are then multiplied by the value for \"eta\" (0.5) in the connected ClownsharKSampler node:\n\n0.5, 0.5, 0.5, 0.5, 0.5\n\nThe result is the sampler sets \"eta\" to 0.5 for the first 5 steps, and then 0.0 for every step after that. \n\nTry connecting something like the BetaScheduler while using \"beta57\" as your sampling scheduler!"],"color":"#432","bgcolor":"#653"},{"id":676,"type":"ClownOptions_StepSize_Beta","pos":[3420,3660],"size":[316.0789794921875,130],"flags":{},"order":101,"mode":0,"inputs":[{"name":"options","localized_name":"options","type":"OPTIONS","shape":7,"link":null}],"outputs":[{"name":"options","localized_name":"options","type":"OPTIONS","links":[1968],"slot_index":0}],"properties":{"Node name for S&R":"ClownOptions_StepSize_Beta"},"widgets_values":["hard","hard",0,0]},{"id":668,"type":"Note","pos":[2900,2720],"size":[476.7748718261719,425.3497314453125],"flags":{},"order":102,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["SharkOptions controls the initial noise generated before starting the sampling process. \n\nNOISE TYPES:\n\nPerlin is great with Stable Cascade, and with Flux will frequently result in a less blurred image (and a somewhat less saturated look, which can be helpful for realism).\n\nThe \"color\" noise modes have more low frequency information (structure), brown being greater than pink. White is neutral, while blue and especially violet have extra high frequency information (details).\n\nhires-pyramid-bicubic can generate exceptionally sharp images in many cases. The other pyramid noise types, and studentt, are often great for painterly styles.\n\nOTHER OPTIONS:\n\n\"noise_stdev\" increases the \"size\" of the noise. Values around 1.05 to 1.1 can have a wonderful effect with some painterly styles, with a boost in saturation.\n\n\"denoise_alt\" overrides the denoise setting. It has a very different effect that can often be easier to control when doing img2img generations with rectified flow models. (It scales the sigmas schedule, instead of slicing it).\n\n\"channelwise_cfg\" changes the cfg method used to one that can be very good with models that use a 16 channel VAE (SD3.5, Flux). Setting a negative value in the \"cfg\" box in any ClownsharKSampler or SharkSampler node is equivalent to using this toggle (for example, cfg = -2.0 is the same as setting cfg = 2.0, and channelwise_cfg = true)."],"color":"#432","bgcolor":"#653"},{"id":666,"type":"SharkOptions_Beta","pos":[3413.490478515625,2880],"size":[257.98193359375,130],"flags":{},"order":103,"mode":0,"inputs":[{"name":"options","localized_name":"options","type":"OPTIONS","shape":7,"link":null}],"outputs":[{"name":"options","localized_name":"options","type":"OPTIONS","links":[1962]}],"properties":{"Node name for S&R":"SharkOptions_Beta"},"widgets_values":["gaussian",1,1,false]},{"id":684,"type":"KarrasScheduler","pos":[3190,5610],"size":[210,130],"flags":{},"order":104,"mode":0,"inputs":[],"outputs":[{"name":"SIGMAS","localized_name":"SIGMAS","type":"SIGMAS","links":[1975,1976],"slot_index":0}],"properties":{"Node name for S&R":"KarrasScheduler"},"widgets_values":[5,1,1,1]},{"id":687,"type":"Note","pos":[2910,5610],"size":[257.2243957519531,88],"flags":{},"order":105,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Tip: use SigmasPreview very heavily so that you can *see* what's going on!"],"color":"#432","bgcolor":"#653"},{"id":671,"type":"Note","pos":[3820,5080],"size":[363.5062255859375,260.6607971191406],"flags":{},"order":106,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["This node can be used to visualize the effect of different noise parameters on how much noise is actually added (or removed) during the sampling process.\n\nDelta (Δ) signifies change. So for example, Δt = step size.\n\nThe most important thing to look at is the original sigma (σ) schedule vs σup. \n\nThe difference between σ (white line) and σup (red line above) is how much noise is added by the sampler after each step. If the two overlap, you aren't adding noise, and it's in ODE mode (eta = 0.0).\n\nThe most important thing to try here is higher or lower eta values, and different noise_modes. Try comparing \"hard\" vs \"soft\" vs \"hard_var\" with eta = 0.5."],"color":"#432","bgcolor":"#653"},{"id":686,"type":"SigmasPreview","pos":[3430,5610],"size":[289.7076110839844,128.47837829589844],"flags":{},"order":187,"mode":0,"inputs":[{"name":"sigmas","localized_name":"sigmas","type":"SIGMAS","link":1976}],"outputs":[{"name":"IMAGE","localized_name":"IMAGE","type":"IMAGE","links":null}],"properties":{"Node name for S&R":"SigmasPreview"},"widgets_values":[false]},{"id":682,"type":"ClownOptions_Automation_Beta","pos":[3430,5250],"size":[284.9833984375,146],"flags":{},"order":186,"mode":0,"inputs":[{"name":"etas","localized_name":"etas","type":"SIGMAS","shape":7,"link":1975},{"name":"etas_substep","localized_name":"etas_substep","type":"SIGMAS","shape":7,"link":null},{"name":"s_noises","localized_name":"s_noises","type":"SIGMAS","shape":7,"link":null},{"name":"s_noises_substep","localized_name":"s_noises_substep","type":"SIGMAS","shape":7,"link":null},{"name":"epsilon_scales","localized_name":"epsilon_scales","type":"SIGMAS","shape":7,"link":null},{"name":"frame_weights","localized_name":"frame_weights","type":"SIGMAS","shape":7,"link":null},{"name":"options","localized_name":"options","type":"OPTIONS","shape":7,"link":null}],"outputs":[{"name":"options","localized_name":"options","type":"OPTIONS","links":[1974]}],"properties":{"Node name for S&R":"ClownOptions_Automation_Beta"},"widgets_values":[]},{"id":673,"type":"Note","pos":[2900.961181640625,4977.04736328125],"size":[480.85333251953125,190.63368225097656],"flags":{},"order":107,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["This option node can be very useful for SAVING TIME! \n\n\"swap_below_error\" is a tolerance threshold where, if the total error at each step falls below the value in the box, it will switch to the sampler specified here.\n\n\"log_err_to_console\" will print these values at each step to the terminal/console/cmd.exe window where ComfyUI is running. This is essential if you wish to choose a reasonable value for \"swap_below_err\".\n\n\"swap_at_step\" will switch after the step specified, no matter what. This is equivalent to chaining two samplers together as shown to the left - it's just more convenient and compact.\n"],"color":"#432","bgcolor":"#653"},{"id":665,"type":"ClownOptions_SwapSampler_Beta","pos":[3430.416015625,5008.54541015625],"size":[287.92266845703125,130],"flags":{},"order":108,"mode":0,"inputs":[{"name":"options","localized_name":"options","type":"OPTIONS","shape":7,"link":null}],"outputs":[{"name":"options","localized_name":"options","type":"OPTIONS","links":[1972],"slot_index":0}],"properties":{"Node name for S&R":"ClownOptions_SwapSampler_Beta"},"widgets_values":["multistep/res_3m",0,30,false]},{"id":798,"type":"ClownOptions_Momentum_Beta","pos":[3433.12158203125,4837.14990234375],"size":[286.6007995605469,58],"flags":{},"order":109,"mode":0,"inputs":[{"name":"options","localized_name":"options","type":"OPTIONS","shape":7,"link":null}],"outputs":[{"name":"options","localized_name":"options","type":"OPTIONS","links":[2003],"slot_index":0}],"properties":{"Node name for S&R":"ClownOptions_Momentum_Beta"},"widgets_values":[0.5]},{"id":803,"type":"Note","pos":[2904.318359375,4827.84912109375],"size":[481.74639892578125,88],"flags":{},"order":110,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Momentum can be used to accelerate convergence in some cases. Use carefully.\n\nMay be best used with chained workflows, with momentum applied only to some portion of early steps."],"color":"#432","bgcolor":"#653"},{"id":672,"type":"Note","pos":[2904.900634765625,4490.91796875],"size":[483.2145690917969,270.3226623535156],"flags":{},"order":111,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Implicit steps refine the sampling process by feeding the output of each step back into its input and rerunning it. This means setting either to \"1\" will increase the runtime 2x, as you're doubling the number of steps. \n\nThey can drastically increase quality. In some cases, results can actually be improved by cutting the step count in half, and running with implicit_steps=1 or implicit_substeps=1 (which results in an equivalent runtime).\n\nWith the other samplers, rebound will add one extra model call per step. \n\nBongmath and predictor-corrector can have significantly different effects. Rebound can as well (but also adds 1 model call per implicit step or substep).\n\nTRUE IMPLICIT SAMPLERS:\n\nIt is recommended to use \"implicit_steps\" with the \"fully_implicit\" samplers, and \"implicit_substeps\" with the \"diag_implicit\" samplers. Both of these sampler types will ignore the \"implicit_type\" settings.\n"],"color":"#432","bgcolor":"#653"},{"id":675,"type":"Note","pos":[2906.080322265625,4199.57763671875],"size":[474.05108642578125,230.29006958007812],"flags":{},"order":112,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["ClownOptions_DetailBoost aims to BREAK the noise scaling math that was so carefully prepared by ClownOptions_SDE. Most users see this as a replacement for the \"Detail Daemon\" node.\n\nTry experimenting with different methods: ones that end with \"normal\" will attempt to preserve luminosity in the image after the adjustments to the noise are made.\n\nIt is worth trying \"sinusoidal\" mode as well, as this is designed to be strongest at middle steps.\n\nEta will increase the intensity of the effect. \n\nIt seems to be best to not have this start on the first step (step 0), and to have it end no more than halfway (end_step = 1/2 of total steps or less). With method = \"model\", this seems to add a lot of detail without losing saturation, increasing luminosity, or triggering mutations."],"color":"#432","bgcolor":"#653"},{"id":763,"type":"ClownOptions_DetailBoost_Beta","pos":[3436.449462890625,4207.4345703125],"size":[282.9725646972656,218],"flags":{},"order":113,"mode":0,"inputs":[{"name":"weights","localized_name":"weights","type":"SIGMAS","shape":7,"link":null},{"name":"etas","localized_name":"etas","type":"SIGMAS","shape":7,"link":null},{"name":"options","localized_name":"options","type":"OPTIONS","shape":7,"link":null}],"outputs":[{"name":"options","localized_name":"options","type":"OPTIONS","links":[1991],"slot_index":0}],"properties":{"Node name for S&R":"ClownOptions_DetailBoost_Beta"},"widgets_values":[1,"model","hard",0.5,3,10]},{"id":761,"type":"Note","pos":[2905.32470703125,3869.700439453125],"size":[471.6525573730469,266.9491882324219],"flags":{},"order":114,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["ClownOptions_SigmaScaling aims to BREAK the noise scaling math that was so carefully prepared by ClownOptions_SDE.\n\n\"noise_anchor_sde\" can make the image look much dirtier with lower values. Try 0.5 for starters, especially with any non-multistep sampler.\n\n\"s_noise\" increases the \"size\" of the noise added with each step. Values around 1.05-1.1 can considerably boost saturation in painterly images. BONGMath is particularly good when this is set to values > 1.0.\n\n\"s_noise_substep\" is not compatible with BONGMath. You will get a terrible image if you use them together.\n\n\"lying\" is equivalent to the popular \"lying sigmas\" approach. Like \"noise_anchor\", values < 1.0 will increase the \"dirty\" look. Try starting with 0.95.\n\n\"lying_inv\" will do the opposite of \"lying\". If you find your images look \"dried out\" or desaturated when using lying, try setting this to a similar value, and have it start at a later step, as shown below."],"color":"#432","bgcolor":"#653"},{"id":760,"type":"ClownOptions_SigmaScaling_Beta","pos":[3436.542724609375,3886.986328125],"size":[272.21429443359375,454],"flags":{},"order":115,"mode":0,"inputs":[{"name":"s_noises","localized_name":"s_noises","type":"SIGMAS","shape":7,"link":null},{"name":"s_noises_substep","localized_name":"s_noises_substep","type":"SIGMAS","shape":7,"link":null},{"name":"options","localized_name":"options","type":"OPTIONS","shape":7,"link":null}],"outputs":[{"name":"options","localized_name":"options","type":"OPTIONS","links":[1990],"slot_index":0}],"properties":{"Node name for S&R":"ClownOptions_SigmaScaling_Beta"},"widgets_values":[1,1,1,0.9500000000000001,0.9500000000000001,2,8]},{"id":678,"type":"Note","pos":[3783.1923828125,4263.46484375],"size":[363.2837219238281,88],"flags":{},"order":116,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Tip: if your results are too noisy,\ntry setting \"overshoot\" in \"ClownOptions StepSize\" to the same value as \"eta\" used in \"ClownOptions SDE\"! 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Effect is typically considerably less precise."],"color":"#432","bgcolor":"#653"},{"id":866,"type":"Note","pos":[10292.3017578125,4024.706787109375],"size":[284.8223571777344,176.87057495117188],"flags":{},"order":170,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["BLUR KILLER TIP:\n\nWhen generating photography with Flux or HiDream (where blur can be frustratingly difficult to avoid), try using a style guide for the first 1/3rd of steps that is a sharp photograph with similar lighting/hues to what you are aiming for. You might need to try a handful of photos before landing on a \"hit\", but the right one will eliminate blur 100%, even with a close up portrait photograph.\n"],"color":"#432","bgcolor":"#653"},{"id":872,"type":"ClownGuide_AdaIN_MMDiT_Beta","pos":[10680,3810],"size":[246.13087463378906,430],"flags":{},"order":171,"mode":0,"inputs":[{"name":"guide","localized_name":"guide","type":"LATENT","shape":7,"link":null},{"name":"mask","localized_name":"mask","type":"MASK","shape":7,"link":null},{"name":"weights","localized_name":"weights","type":"SIGMAS","shape":7,"link":null},{"name":"guides","localized_name":"guides","type":"GUIDES","shape":7,"link":null}],"outputs":[{"name":"guides","localized_name":"guides","type":"GUIDES","links":null}],"properties":{"Node name for S&R":"ClownGuide_AdaIN_MMDiT_Beta"},"widgets_values":[1,"constant","","","20","0.5",0,15,false]},{"id":874,"type":"ClownGuide_AttnInj_MMDiT_Beta","pos":[10990,3810],"size":[272.0969543457031,718],"flags":{},"order":172,"mode":0,"inputs":[{"name":"guide","localized_name":"guide","type":"LATENT","shape":7,"link":null},{"name":"mask","localized_name":"mask","type":"MASK","shape":7,"link":null},{"name":"weights","localized_name":"weights","type":"SIGMAS","shape":7,"link":null},{"name":"guides","localized_name":"guides","type":"GUIDES","shape":7,"link":null}],"outputs":[{"name":"guides","localized_name":"guides","type":"GUIDES","links":null}],"properties":{"Node name for S&R":"ClownGuide_AttnInj_MMDiT_Beta"},"widgets_values":[1,"constant","0,1,3","1.0","20","0.5",0,0,1,0,0,0,0,0,0,0,0,0,0,15,false]},{"id":873,"type":"Note","pos":[10590,4300],"size":[348.2928771972656,313.42919921875],"flags":{},"order":173,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["ClownGuide AdaIN and AttnInj:\n\nAdvanced experimental nodes for HiDream. Very strong effect and can be used together with all other guide nodes.\n\nBest used like a monkey in a missile silo. Start pushing buttons and you'll win eventually!\n\nList the blocks you wish the effect to be applied to, and the weight of the effect on that block, in the same order. \"all\" will use all blocks of that type, and if only one weight is listed, it will use that for all blocks listed.\n\nThere are 16 double blocks, and 32 single blocks. Each is numbered beginning at 0. For example, the following block numberings are equivalent for double_blocks:\n\nall\n0-15\n0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15\n"],"color":"#432","bgcolor":"#653"},{"id":875,"type":"Note","pos":[10980,4590],"size":[301.1705017089844,233.60943603515625],"flags":{},"order":174,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Injects calculated attention from the guide into the main sampling process. This will carry over some compositional information, as well as lighting. 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\ No newline at end of file
diff --git a/custom_nodes/RES4LYF/example_workflows/sd35 medium unsampling data.json b/custom_nodes/RES4LYF/example_workflows/sd35 medium unsampling data.json
new file mode 100644
index 0000000000000000000000000000000000000000..b1cf5e6204b262220b70b33324769f56b26aa2e9
--- /dev/null
+++ b/custom_nodes/RES4LYF/example_workflows/sd35 medium unsampling data.json	
@@ -0,0 +1 @@
+{"last_node_id":635,"last_link_id":2023,"nodes":[{"id":627,"type":"SD35Loader","pos":[602.6103515625,-123.47957611083984],"size":[315,218],"flags":{},"order":0,"mode":0,"inputs":[],"outputs":[{"name":"model","localized_name":"model","type":"MODEL","links":[2014],"slot_index":0},{"name":"clip","localized_name":"clip","type":"CLIP","links":[2010],"slot_index":1},{"name":"vae","localized_name":"vae","type":"VAE","links":[2011,2012],"slot_index":2}],"properties":{"Node name for S&R":"SD35Loader"},"widgets_values":["sd3.5_medium.safetensors","default","clip_l_sd35.safetensors","clip_g_sd35.safetensors","t5xxl_fp16.safetensors","sd35_vae.safetensors"]},{"id":628,"type":"LoadImage","pos":[599.166015625,156.38429260253906],"size":[315,314],"flags":{},"order":1,"mode":0,"inputs":[],"outputs":[{"name":"IMAGE","localized_name":"IMAGE","type":"IMAGE","links":[2017]},{"name":"MASK","localized_name":"MASK","type":"MASK","links":null}],"properties":{"Node name for 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diff --git a/custom_nodes/RES4LYF/example_workflows/sd35 medium unsampling.json b/custom_nodes/RES4LYF/example_workflows/sd35 medium unsampling.json
new file mode 100644
index 0000000000000000000000000000000000000000..bc3bdfeffda819050bab0b110061e37a296dc1f3
--- /dev/null
+++ b/custom_nodes/RES4LYF/example_workflows/sd35 medium unsampling.json	
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diff --git a/custom_nodes/RES4LYF/example_workflows/sdxl regional antiblur.json b/custom_nodes/RES4LYF/example_workflows/sdxl regional antiblur.json
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diff --git a/custom_nodes/RES4LYF/example_workflows/sdxl style transfer.json b/custom_nodes/RES4LYF/example_workflows/sdxl style transfer.json
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diff --git a/custom_nodes/RES4LYF/example_workflows/ultracascade txt2img.json b/custom_nodes/RES4LYF/example_workflows/ultracascade txt2img.json
new file mode 100644
index 0000000000000000000000000000000000000000..bf8b53b28cc04c8aaf3d645431564b1c22b6a0e9
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+++ b/custom_nodes/RES4LYF/example_workflows/ultracascade txt2img.json	
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diff --git a/custom_nodes/RES4LYF/example_workflows/wan img2vid 720p (fp8 fast).json b/custom_nodes/RES4LYF/example_workflows/wan img2vid 720p (fp8 fast).json
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diff --git a/custom_nodes/RES4LYF/example_workflows/wan txt2img (fp8 fast).json b/custom_nodes/RES4LYF/example_workflows/wan txt2img (fp8 fast).json
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diff --git a/custom_nodes/RES4LYF/example_workflows/wan vid2vid.json b/custom_nodes/RES4LYF/example_workflows/wan vid2vid.json
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S&R":"ClownsharkChainsampler_Beta"},"widgets_values":[0.5,"exponential/res_2s",1,5.5,"resample",true]},{"id":324,"type":"ClownsharKSampler_Beta","pos":[1433.78466796875,314.1369934082031],"size":[337.03857421875,670],"flags":{},"order":15,"mode":0,"inputs":[{"name":"model","localized_name":"model","type":"MODEL","shape":7,"link":1011},{"name":"positive","localized_name":"positive","type":"CONDITIONING","shape":7,"link":997},{"name":"negative","localized_name":"negative","type":"CONDITIONING","shape":7,"link":832},{"name":"latent_image","localized_name":"latent_image","type":"LATENT","shape":7,"link":1026},{"name":"sigmas","localized_name":"sigmas","type":"SIGMAS","shape":7,"link":null},{"name":"guides","localized_name":"guides","type":"GUIDES","shape":7,"link":null},{"name":"options","localized_name":"options","type":"OPTIONS","shape":7,"link":null}],"outputs":[{"name":"output","localized_name":"output","type":"LATENT","links":[1028],"slot_index":0},{"name":"denoised","localized_name":"denoised","type":"LATENT","links":null},{"name":"options","localized_name":"options","type":"OPTIONS","links":null}],"properties":{"Node name for S&R":"ClownsharKSampler_Beta"},"widgets_values":[0,"multistep/res_2m","beta57",20,12,1,1,0,"fixed","unsample",true]},{"id":395,"type":"ClownOptions_Cycles_Beta","pos":[1843.936767578125,125.13945007324219],"size":[210,130],"flags":{},"order":1,"mode":0,"inputs":[{"name":"options","localized_name":"options","type":"OPTIONS","shape":7,"link":null}],"outputs":[{"name":"options","localized_name":"options","type":"OPTIONS","links":[1029],"slot_index":0}],"properties":{"Node name for S&R":"ClownOptions_Cycles_Beta"},"widgets_values":[10,1,0.5,5.5]},{"id":6,"type":"CLIPTextEncode","pos":[966.9983520507812,314.1016540527344],"size":[447.32421875,169.55857849121094],"flags":{},"order":10,"mode":0,"inputs":[{"name":"clip","localized_name":"clip","type":"CLIP","link":1016}],"outputs":[{"name":"CONDITIONING","localized_name":"CONDITIONING","type":"CONDITIONING","links":[997],"slot_index":0}],"properties":{"Node name for S&R":"CLIPTextEncode"},"widgets_values":["A pretty black woman with thick gorgeous hair walks slowly through a tall, modern colonnade of concrete and glass, cradling a sleek silver laptop under her arm. She wears a sand-colored coat with a high collar and sharp tailoring, the buttons neatly fastened, exuding a quiet, focused confidence. Her complexion is porcelain-smooth, lightly touched by the soft overcast light that filters down through the glass canopy. Dark, straight hair is neatly parted and tucked behind one ear, moving ever so slightly as she walks. Her expression is thoughtful, eyes cast downward in introspection, lips gently pressed into a faint, unreadable line.\n\nThe camera begins off-center, panning slowly to align with the corridor’s clean architectural symmetry. Repeating vertical columns frame her movement, creating a visual rhythm that guides the viewer’s eye toward the vanishing point ahead. As she walks, she shifts just slightly to the side, a natural adjustment that causes the fabric of her coat to pull gently at the seams, adding a subtle sense of motion.\n\nReflections drift along the windows beside her — faint, soft, and ghostlike. The ambient light is cool and diffused, lending the scene a contemplative, almost suspended feeling. Her presence is calm, deliberate, as though she’s carrying not just the laptop, but something unspoken — a sense of purpose shaped quietly in her mind."]},{"id":397,"type":"Note","pos":[639.0505981445312,128.31825256347656],"size":[301.3404235839844,112.45540618896484],"flags":{},"order":2,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Sometimes the first frame looks noisy with WAN. You can either throw it away, use more steps, use a more accurate sampler (2s > 2m, 3s > 2s), or ensure you aren't using a \"fast\" mode for the weights, such as fp8_e4m3fn_fast, which results in a significant hit to quality."],"color":"#432","bgcolor":"#653"},{"id":398,"type":"Note","pos":[1451.546142578125,1060.8258056640625],"size":[295.7769470214844,88],"flags":{},"order":3,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["More \"steps_to_run\" will increase the amount of denoise. Values between 12 and 15 are a good place to start.\n"],"color":"#432","bgcolor":"#653"},{"id":403,"type":"Frames Slice Latent","pos":[555.5986938476562,1154.378173828125],"size":[210,82],"flags":{},"order":4,"mode":0,"inputs":[{"name":"frames","localized_name":"frames","type":"LATENT","link":null}],"outputs":[{"name":"latent","localized_name":"latent","type":"LATENT","links":null}],"properties":{"Node name for S&R":"Frames Slice Latent"},"widgets_values":[0,1]},{"id":402,"type":"Frames Slice","pos":[555.5987548828125,990.6537475585938],"size":[210,82],"flags":{},"order":5,"mode":0,"inputs":[{"name":"frames","localized_name":"frames","type":"IMAGE","link":null}],"outputs":[{"name":"image","localized_name":"image","type":"IMAGE","links":null}],"properties":{"Node name for S&R":"Frames Slice"},"widgets_values":[0,1]},{"id":401,"type":"Note","pos":[550.497802734375,712.9439086914062],"size":[239.34762573242188,200.06405639648438],"flags":{},"order":6,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Can use anything that will load a video as a sequence of frames. The core node \"Load Image\" will work in place of this one, if you are loading an animated .webp.\n\nThis node allows you to set the number of frames loaded.\n\nThe nodes below will also allow you to pick and choose ranges of frames. Be sure to use Image Preview to verify you're picking the ones you want!"],"color":"#432","bgcolor":"#653"},{"id":316,"type":"VHS_LoadVideo","pos":[808.8834228515625,711.6345825195312],"size":[319.19403076171875,808.9393920898438],"flags":{},"order":7,"mode":0,"inputs":[{"name":"meta_batch","localized_name":"meta_batch","type":"VHS_BatchManager","shape":7,"link":null},{"name":"vae","localized_name":"vae","type":"VAE","shape":7,"link":null}],"outputs":[{"name":"IMAGE","localized_name":"IMAGE","type":"IMAGE","links":[1014],"slot_index":0},{"name":"frame_count","localized_name":"frame_count","type":"INT","links":null},{"name":"audio","localized_name":"audio","type":"AUDIO","links":null},{"name":"video_info","localized_name":"video_info","type":"VHS_VIDEOINFO","links":null}],"properties":{"Node name for S&R":"VHS_LoadVideo"},"widgets_values":{"video":"3206567-hd_1080_1920_25fps.mp4","force_rate":0,"force_size":"Disabled","custom_width":512,"custom_height":512,"frame_load_cap":35,"skip_first_frames":0,"select_every_nth":1,"choose video to upload":"image","videopreview":{"hidden":false,"paused":false,"params":{"force_rate":0,"frame_load_cap":35,"skip_first_frames":0,"select_every_nth":1,"filename":"3206567-hd_1080_1920_25fps.mp4","type":"input","format":"video/mp4"},"muted":false}}},{"id":392,"type":"VAEEncodeAdvanced","pos":[1157.1488037109375,712.4218139648438],"size":[244.18490600585938,278],"flags":{},"order":12,"mode":0,"inputs":[{"name":"image_1","localized_name":"image_1","type":"IMAGE","shape":7,"link":1014},{"name":"image_2","localized_name":"image_2","type":"IMAGE","shape":7,"link":null},{"name":"mask","localized_name":"mask","type":"IMAGE","shape":7,"link":null},{"name":"latent","localized_name":"latent","type":"LATENT","shape":7,"link":null},{"name":"vae","localized_name":"vae","type":"VAE","shape":7,"link":1013}],"outputs":[{"name":"latent_1","localized_name":"latent_1","type":"LATENT","links":[1026,1027],"slot_index":0},{"name":"latent_2","localized_name":"latent_2","type":"LATENT","links":null},{"name":"mask","localized_name":"mask","type":"MASK","links":null},{"name":"empty_latent","localized_name":"empty_latent","type":"LATENT","links":[],"slot_index":3},{"name":"width","localized_name":"width","type":"INT","links":[],"slot_index":4},{"name":"height","localized_name":"height","type":"INT","links":[],"slot_index":5}],"properties":{"Node name for S&R":"VAEEncodeAdvanced"},"widgets_values":["false",368,640,"red",false,"16_channels"]},{"id":396,"type":"ClownsharkChainsampler_Beta","pos":[2146.74755859375,313.50225830078125],"size":[315,510],"flags":{},"order":17,"mode":0,"inputs":[{"name":"model","localized_name":"model","type":"MODEL","shape":7,"link":null},{"name":"positive","localized_name":"positive","type":"CONDITIONING","shape":7,"link":null},{"name":"negative","localized_name":"negative","type":"CONDITIONING","shape":7,"link":null},{"name":"sigmas","localized_name":"sigmas","type":"SIGMAS","shape":7,"link":null},{"name":"latent_image","localized_name":"latent_image","type":"LATENT","shape":7,"link":1033},{"name":"guides","localized_name":"guides","type":"GUIDES","shape":7,"link":null},{"name":"options","localized_name":"options","type":"OPTIONS","shape":7,"link":null}],"outputs":[{"name":"output","localized_name":"output","type":"LATENT","links":[1035],"slot_index":0},{"name":"denoised","localized_name":"denoised","type":"LATENT","links":null},{"name":"options","localized_name":"options","type":"OPTIONS","links":null}],"properties":{"Node name for S&R":"ClownsharkChainsampler_Beta"},"widgets_values":[0.5,"exponential/res_2s",-1,5.5,"resample",true]},{"id":400,"type":"Note","pos":[2090.21728515625,70.1985855102539],"size":[324.38916015625,177.81007385253906],"flags":{},"order":8,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Each full cycle reruns the node twice:\n\nresample -> unsample -> resample -> ... \n\nHigher values will change the video more.\n\nres_2m and 3m will preserve more of the initial structure. Res_2s and especially 3s will result in more dramatic change.\n\nIf you use more steps_to_run in ClownsharKSampler, you'll need fewer cycles here."],"color":"#432","bgcolor":"#653"},{"id":399,"type":"Note","pos":[2153.567626953125,887.000244140625],"size":[303.7249755859375,88],"flags":{},"order":9,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["Using a sampler such as res_2s instead of res_2m in this node can reduce or eliminate first frame noise. It's not always necessary, mileage may vary."],"color":"#432","bgcolor":"#653"},{"id":325,"type":"VAEDecode","pos":[2496.82080078125,203.3095703125],"size":[210,46],"flags":{},"order":18,"mode":0,"inputs":[{"name":"samples","localized_name":"samples","type":"LATENT","link":1035},{"name":"vae","localized_name":"vae","type":"VAE","link":1012}],"outputs":[{"name":"IMAGE","localized_name":"IMAGE","type":"IMAGE","links":[945],"slot_index":0}],"properties":{"Node name for S&R":"VAEDecode"},"widgets_values":[]}],"links":[[832,7,0,324,2,"CONDITIONING"],[945,325,0,365,0,"IMAGE"],[997,6,0,324,1,"CONDITIONING"],[1010,346,0,391,0,"MODEL"],[1011,346,0,324,0,"MODEL"],[1012,393,2,325,1,"VAE"],[1013,393,2,392,4,"VAE"],[1014,316,0,392,0,"IMAGE"],[1016,393,1,6,0,"CLIP"],[1017,393,1,7,0,"CLIP"],[1018,393,0,346,0,"MODEL"],[1026,392,0,324,3,"LATENT"],[1027,392,0,346,1,"LATENT"],[1028,324,0,394,4,"LATENT"],[1029,395,0,394,6,"OPTIONS"],[1033,394,0,396,4,"LATENT"],[1035,396,0,325,0,"LATENT"]],"groups":[],"config":{},"extra":{"ds":{"scale":1.464100000000001,"offset":[1126.1541105871463,24.96236469373386]},"node_versions":{"comfy-core":"0.3.26","comfyui_controlnet_aux":"1e9eac6377c882da8bb360c7544607036904362c","ComfyUI-VideoHelperSuite":"c36626c6028faca912eafcedbc71f1d342fb4d2a"},"VHS_latentpreview":false,"VHS_latentpreviewrate":0,"VHS_MetadataImage":true,"VHS_KeepIntermediate":true},"version":0.4}
\ No newline at end of file
diff --git a/custom_nodes/RES4LYF/flux/controlnet.py b/custom_nodes/RES4LYF/flux/controlnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..c033dea52f2e677c57797c17677cb9072fbb04e5
--- /dev/null
+++ b/custom_nodes/RES4LYF/flux/controlnet.py
@@ -0,0 +1,205 @@
+#Original code can be found on: https://github.com/XLabs-AI/x-flux/blob/main/src/flux/controlnet.py
+#modified to support different types of flux controlnets
+
+import torch
+import math
+from torch import Tensor, nn
+from einops import rearrange, repeat
+
+from .layers import (DoubleStreamBlock, EmbedND, LastLayer,
+                                 MLPEmbedder, SingleStreamBlock,
+                                 timestep_embedding)
+
+from .model import Flux
+import comfy.ldm.common_dit
+
+class MistolineCondDownsamplBlock(nn.Module):
+    def __init__(self, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.encoder = nn.Sequential(
+            operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device)
+        )
+
+    def forward(self, x):
+        return self.encoder(x)
+
+class MistolineControlnetBlock(nn.Module):
+    def __init__(self, hidden_size, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.linear = operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device)
+        self.act = nn.SiLU()
+
+    def forward(self, x):
+        return self.act(self.linear(x))
+
+
+class ControlNetFlux(Flux):
+    def __init__(self, latent_input=False, num_union_modes=0, mistoline=False, control_latent_channels=None, image_model=None, dtype=None, device=None, operations=None, **kwargs):
+        super().__init__(final_layer=False, dtype=dtype, device=device, operations=operations, **kwargs)
+
+        self.main_model_double = 19
+        self.main_model_single = 38
+
+        self.mistoline = mistoline
+        # add ControlNet blocks
+        if self.mistoline:
+            control_block = lambda : MistolineControlnetBlock(self.hidden_size, dtype=dtype, device=device, operations=operations)
+        else:
+            control_block = lambda : operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)
+
+        self.controlnet_blocks = nn.ModuleList([])
+        for _ in range(self.params.depth):
+            self.controlnet_blocks.append(control_block())
+
+        self.controlnet_single_blocks = nn.ModuleList([])
+        for _ in range(self.params.depth_single_blocks):
+            self.controlnet_single_blocks.append(control_block())
+
+        self.num_union_modes = num_union_modes
+        self.controlnet_mode_embedder = None
+        if self.num_union_modes > 0:
+            self.controlnet_mode_embedder = operations.Embedding(self.num_union_modes, self.hidden_size, dtype=dtype, device=device)
+
+        self.gradient_checkpointing = False
+        self.latent_input = latent_input
+        if control_latent_channels is None:
+            control_latent_channels = self.in_channels
+        else:
+            control_latent_channels *= 2 * 2 #patch size
+
+        self.pos_embed_input = operations.Linear(control_latent_channels, self.hidden_size, bias=True, dtype=dtype, device=device)
+        if not self.latent_input:
+            if self.mistoline:
+                self.input_cond_block = MistolineCondDownsamplBlock(dtype=dtype, device=device, operations=operations)
+            else:
+                self.input_hint_block = nn.Sequential(
+                    operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device),
+                    nn.SiLU(),
+                    operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+                    nn.SiLU(),
+                    operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+                    nn.SiLU(),
+                    operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+                    nn.SiLU(),
+                    operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+                    nn.SiLU(),
+                    operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+                    nn.SiLU(),
+                    operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+                    nn.SiLU(),
+                    operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device)
+                )
+
+    def forward_orig(
+        self,
+        img: Tensor,
+        img_ids: Tensor,
+        controlnet_cond: Tensor,
+        txt: Tensor,
+        txt_ids: Tensor,
+        timesteps: Tensor,
+        y: Tensor,
+        guidance: Tensor = None,
+        control_type: Tensor = None,
+    ) -> Tensor:
+        if img.ndim != 3 or txt.ndim != 3:
+            raise ValueError("Input img and txt tensors must have 3 dimensions.")
+
+        # running on sequences img
+        img = self.img_in(img)
+
+        controlnet_cond = self.pos_embed_input(controlnet_cond)
+        img = img + controlnet_cond
+        vec = self.time_in(timestep_embedding(timesteps, 256))
+        if self.params.guidance_embed:
+            vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
+        vec = vec + self.vector_in(y)
+        txt = self.txt_in(txt)
+
+        if self.controlnet_mode_embedder is not None and len(control_type) > 0:
+            control_cond = self.controlnet_mode_embedder(torch.tensor(control_type, device=img.device), out_dtype=img.dtype).unsqueeze(0).repeat((txt.shape[0], 1, 1))
+            txt = torch.cat([control_cond, txt], dim=1)
+            txt_ids = torch.cat([txt_ids[:,:1], txt_ids], dim=1)
+
+        ids = torch.cat((txt_ids, img_ids), dim=1)
+        pe = self.pe_embedder(ids)
+
+        controlnet_double = ()
+
+        for i in range(len(self.double_blocks)):
+            img, txt = self.double_blocks[i](img=img, txt=txt, vec=vec, pe=pe)
+            controlnet_double = controlnet_double + (self.controlnet_blocks[i](img),)
+
+        img = torch.cat((txt, img), 1)
+
+        controlnet_single = ()
+
+        for i in range(len(self.single_blocks)):
+            img = self.single_blocks[i](img, vec=vec, pe=pe)
+            controlnet_single = controlnet_single + (self.controlnet_single_blocks[i](img[:, txt.shape[1] :, ...]),)
+
+        repeat = math.ceil(self.main_model_double / len(controlnet_double))
+        if self.latent_input:
+            out_input = ()
+            for x in controlnet_double:
+                    out_input += (x,) * repeat
+        else:
+            out_input = (controlnet_double * repeat)
+
+        out = {"input": out_input[:self.main_model_double]}
+        if len(controlnet_single) > 0:
+            repeat = math.ceil(self.main_model_single / len(controlnet_single))
+            out_output = ()
+            if self.latent_input:
+                for x in controlnet_single:
+                        out_output += (x,) * repeat
+            else:
+                out_output = (controlnet_single * repeat)
+            out["output"] = out_output[:self.main_model_single]
+        return out
+
+    def forward(self, x, timesteps, context, y, guidance=None, hint=None, **kwargs):
+        patch_size = 2
+        if self.latent_input:
+            hint = comfy.ldm.common_dit.pad_to_patch_size(hint, (patch_size, patch_size))
+        elif self.mistoline:
+            hint = hint * 2.0 - 1.0
+            hint = self.input_cond_block(hint)
+        else:
+            hint = hint * 2.0 - 1.0
+            hint = self.input_hint_block(hint)
+
+        hint = rearrange(hint, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
+
+        bs, c, h, w = x.shape
+        x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))
+
+        img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
+
+        h_len = ((h + (patch_size // 2)) // patch_size)
+        w_len = ((w + (patch_size // 2)) // patch_size)
+        img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
+        img_ids[..., 1] = img_ids[..., 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype)[:, None]
+        img_ids[..., 2] = img_ids[..., 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype)[None, :]
+        img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
+
+        txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
+        return self.forward_orig(img, img_ids, hint, context, txt_ids, timesteps, y, guidance, control_type=kwargs.get("control_type", []))
diff --git a/custom_nodes/RES4LYF/flux/layers.py b/custom_nodes/RES4LYF/flux/layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..b70084d3e19af1cee1ada05587a01412215c8a70
--- /dev/null
+++ b/custom_nodes/RES4LYF/flux/layers.py
@@ -0,0 +1,438 @@
+# Adapted from: https://github.com/black-forest-labs/flux
+
+import math
+import torch
+from torch import Tensor, nn
+
+from typing import Optional, Callable, Tuple, Dict, Any, Union, TYPE_CHECKING, TypeVar
+
+import torch.nn.functional as F
+import einops
+from einops import rearrange
+from torch import Tensor
+from dataclasses import dataclass
+
+from .math import attention, rope, apply_rope
+import comfy.ldm.common_dit
+
+class EmbedND(nn.Module):
+    def __init__(self, dim: int, theta: int, axes_dim: list):
+        super().__init__()
+        self.dim      = dim
+        self.theta    = theta
+        self.axes_dim = axes_dim
+
+    def forward(self, ids: Tensor) -> Tensor:
+        n_axes = ids.shape[-1]
+        emb = torch.cat(
+            [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
+            dim=-3,
+        )
+        return emb.unsqueeze(1)
+
+def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0):
+    """
+    Create sinusoidal timestep embeddings.
+    :param t: a 1-D Tensor of N indices, one per batch element. 
+                    These may be fractional.
+    :param dim: the dimension of the output.
+    :param max_period: controls the minimum frequency of the embeddings.
+    :return: an (N, D) Tensor of positional embeddings.
+    """
+    t = time_factor * t
+    half = dim // 2
+    freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half)
+
+    args = t[:, None].float() * freqs[None]
+    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+    if dim % 2:
+        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+    if torch.is_floating_point(t):
+        embedding = embedding.to(t)
+    return embedding
+
+class MLPEmbedder(nn.Module):
+    def __init__(self, in_dim: int, hidden_dim: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.in_layer  = operations.Linear(    in_dim, hidden_dim, bias=True, dtype=dtype, device=device)
+        self.silu      = nn.SiLU()
+        self.out_layer = operations.Linear(hidden_dim, hidden_dim, bias=True, dtype=dtype, device=device)
+
+    def forward(self, x: Tensor) -> Tensor:
+        return self.out_layer(self.silu(self.in_layer(x)))
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.scale = nn.Parameter(torch.empty((dim), dtype=dtype, device=device))    # self.scale.shape = 128
+
+    def forward(self, x: Tensor):
+        return comfy.ldm.common_dit.rms_norm(x, self.scale, 1e-6)
+
+
+class QKNorm(torch.nn.Module):
+    def __init__(self, dim: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.query_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations)
+        self.key_norm   = RMSNorm(dim, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple:
+        q = self.query_norm(q)
+        k = self.key_norm(k)
+        return q.to(v), k.to(v)
+
+
+class SelfAttention(nn.Module):
+    def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.num_heads = num_heads    # 24
+        head_dim  = dim // num_heads   # 128 = 3072 / 24
+
+        self.qkv  = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
+        self.norm = QKNorm(head_dim,                               dtype=dtype, device=device, operations=operations)
+        self.proj = operations.Linear(dim, dim,                    dtype=dtype, device=device)    # dim is usually 3072
+
+
+@dataclass
+class ModulationOut:
+    shift: Tensor
+    scale: Tensor
+    gate:  Tensor
+
+class Modulation(nn.Module):
+    def __init__(self, dim: int, double: bool, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.is_double  = double
+        self.multiplier = 6 if double else 3
+        self.lin        = operations.Linear(dim, self.multiplier * dim, bias=True, dtype=dtype, device=device)
+
+    def forward(self, vec: Tensor) -> tuple:
+        out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)
+        return (ModulationOut(*out[:3]),    ModulationOut(*out[3:]) if self.is_double else None,)
+
+
+class DoubleStreamBlock(nn.Module):
+    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, dtype=None, device=None, operations=None, idx=-1):
+        super().__init__()
+
+        self.idx         = idx
+
+        mlp_hidden_dim   = int(hidden_size * mlp_ratio)
+        self.num_heads   = num_heads
+        self.hidden_size = hidden_size
+        
+        self.img_mod     = Modulation(hidden_size, double=True,                                   dtype=dtype, device=device, operations=operations) # in_features=3072, out_features=18432 (3072*6)
+        self.txt_mod     = Modulation(hidden_size, double=True,                                   dtype=dtype, device=device, operations=operations) # in_features=3072, out_features=18432 (3072*6)
+
+        self.img_attn    = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) # .qkv: in_features=3072, out_features=9216   .proj: 3072,3072
+        self.txt_attn    = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) # .qkv: in_features=3072, out_features=9216   .proj: 3072,3072
+
+        self.img_norm1   = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6,  dtype=dtype, device=device)
+        self.txt_norm1   = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6,  dtype=dtype, device=device)
+
+        self.img_norm2   = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6,  dtype=dtype, device=device)
+        self.txt_norm2   = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6,  dtype=dtype, device=device)
+
+        self.img_mlp = nn.Sequential(
+            operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
+            nn.GELU(approximate="tanh"),
+            operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
+        ) # 3072->12288, 12288->3072  (3072*4)
+        
+        self.txt_mlp = nn.Sequential(
+            operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
+            nn.GELU(approximate="tanh"),
+            operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
+        ) # 3072->12288, 12288->3072  (3072*4)
+
+    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, mask=None, idx=0, update_cross_attn=None, style_block=None) -> Tuple[Tensor, Tensor]: # vec 1,3072  # vec 1,3072    #mask.shape 4608,4608  #img_attn.shape 1,4096,3072    txt_attn.shape 1,512,3072
+
+        img_len = img.shape[-2]
+        txt_len = txt.shape[-2]
+
+        img_mod1, img_mod2  = self.img_mod(vec) # -> 3072, 3072
+        txt_mod1, txt_mod2  = self.txt_mod(vec)
+        
+        img_norm = self.img_norm1(img)
+        txt_norm = self.txt_norm1(txt)
+        
+        img_norm = style_block.img(img_norm, "attn_norm")
+        txt_norm = style_block.txt(txt_norm, "attn_norm")
+
+        img_norm = img_norm * (1+img_mod1.scale) + img_mod1.shift
+        txt_norm = txt_norm * (1+txt_mod1.scale) + txt_mod1.shift
+
+        img_norm = style_block.img(img_norm, "attn_norm_mod")
+        txt_norm = style_block.txt(txt_norm, "attn_norm_mod")
+        
+        
+        
+        ### ATTN ###
+        img_qkv             = self.img_attn.qkv(img_norm)
+        img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        
+        img_q = style_block.img.ATTN(img_q, "q_proj")
+        img_k = style_block.img.ATTN(img_k, "k_proj")
+        img_v = style_block.img.ATTN(img_v, "v_proj")
+        
+        img_q, img_k        = self.img_attn.norm(img_q, img_k, img_v)
+        
+        img_q = style_block.img.ATTN(img_q, "q_norm")
+        img_k = style_block.img.ATTN(img_k, "k_norm")
+        
+        txt_qkv             = self.txt_attn.qkv(txt_norm)
+        txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        
+        txt_q = style_block.txt.ATTN(txt_q, "q_proj")
+        txt_k = style_block.txt.ATTN(txt_k, "k_proj")
+        txt_v = style_block.txt.ATTN(txt_v, "v_proj")
+        
+        txt_q, txt_k        = self.txt_attn.norm(txt_q, txt_k, txt_v)
+        
+        txt_q = style_block.txt.ATTN(txt_q, "q_norm")
+        txt_k = style_block.txt.ATTN(txt_k, "k_norm")
+
+        q, k, v = torch.cat((txt_q, img_q), dim=2), torch.cat((txt_k, img_k), dim=2), torch.cat((txt_v, img_v), dim=2)
+        attn = attention(q, k, v, pe=pe, mask=mask)
+        
+        txt_attn = attn[:,:txt_len]                         # 1, 768,3072
+        img_attn = attn[:,txt_len:]  
+        
+        img_attn = style_block.img.ATTN(img_attn, "out")
+        txt_attn = style_block.txt.ATTN(txt_attn, "out")
+        
+        img_attn = self.img_attn.proj(img_attn)    #to_out
+        txt_attn = self.txt_attn.proj(txt_attn)
+        ### ATTN ###
+
+
+
+        img_attn = style_block.img(img_attn, "attn")
+        txt_attn = style_block.txt(txt_attn, "attn")
+        
+        img_attn *= img_mod1.gate
+        txt_attn *= txt_mod1.gate
+        
+        img_attn = style_block.img(img_attn, "attn_gated")
+        txt_attn = style_block.txt(txt_attn, "attn_gated")
+        
+        img += img_attn
+        txt += txt_attn
+        
+        img = style_block.img(img, "attn_res")
+        txt = style_block.txt(txt, "attn_res")
+        
+        
+        
+        img_norm = self.img_norm2(img)
+        txt_norm = self.txt_norm2(txt)
+        
+        img_norm = style_block.img(img_norm, "ff_norm")
+        txt_norm = style_block.txt(txt_norm, "ff_norm")
+        
+        img_norm = img_norm * (1+img_mod2.scale) + img_mod2.shift
+        txt_norm = txt_norm * (1+txt_mod2.scale) + txt_mod2.shift
+        
+        img_norm = style_block.img(img_norm, "ff_norm_mod")
+        txt_norm = style_block.txt(txt_norm, "ff_norm_mod")
+        
+        img_mlp = self.img_mlp(img_norm)
+        txt_mlp = self.txt_mlp(txt_norm)
+        
+        img_mlp = style_block.img(img_mlp, "ff")
+        txt_mlp = style_block.txt(txt_mlp, "ff")
+        
+        img_mlp *= img_mod2.gate
+        txt_mlp *= txt_mod2.gate
+        
+        img_mlp = style_block.img(img_mlp, "ff_gated")
+        txt_mlp = style_block.txt(txt_mlp, "ff_gated")
+        
+        img += img_mlp
+        txt += txt_mlp
+        
+        img = style_block.img(img, "ff_res")
+        txt = style_block.txt(txt, "ff_res")
+
+        if update_cross_attn is not None:
+            if not update_cross_attn['skip_cross_attn']:
+                UNCOND      = update_cross_attn['UNCOND']
+                
+                txt_update = self.txt_norm1(txt.cpu()).float()
+                txt_update = (1 + txt_mod1.scale.to(txt_update)) * txt_update + txt_mod1.shift.to(txt_update)
+                
+                if UNCOND:
+                    t5_start    = update_cross_attn['src_t5_start']
+                    t5_end      = update_cross_attn['src_t5_end']
+                
+                    txt_src    = txt_update[:,t5_start:t5_end,:].cpu() #.float()
+                    self.c_src = txt_src.transpose(-2,-1).squeeze(0)    # shape [C,1]
+                else:
+                    t5_start    = update_cross_attn['tgt_t5_start']
+                    t5_end      = update_cross_attn['tgt_t5_end']
+                    
+                    lamb  = update_cross_attn['lamb']
+                    erase = update_cross_attn['erase']
+
+                    c_guide   = txt_update[:,t5_start:t5_end,:].transpose(-2,-1).squeeze(0)  # [C,1]
+                    
+                    Wv_old       = self.txt_attn.qkv.weight.data.to(c_guide)              # [C,C]
+
+                    v_star       = Wv_old @ c_guide                             # [C,1]
+
+                    c_src        = self.c_src  #.cpu()                                   # [C,1]
+
+                    lamb         = lamb
+                    erase_scale  = erase
+                    d            = c_src.shape[0]
+
+                    C            = c_src @ c_src.T                              # [C,C]
+                    I            = torch.eye(d, device=C.device, dtype=C.dtype)
+
+                    mat1_v       = lamb*Wv_old + erase_scale*(v_star @ c_src.T)     # [C,C]
+                    mat2_v       = lamb*I      + erase_scale*(C)                    # [C,C]
+                    
+                    I       = I.to("cpu")
+                    C       = C.to("cpu")
+                    c_src   = c_src.to("cpu")
+                    self.c_src   = self.c_src.to("cpu")
+                    v_star  = v_star.to("cpu")
+                    Wv_old  = Wv_old.to("cpu")
+                    c_guide = c_guide.to("cpu")
+                    del I, C, c_src, self.c_src, v_star, Wv_old, c_guide
+
+                    #Wv_new       = mat1_v @ torch.inverse(mat2_v.float()).to(mat1_v)                   # [C,C]
+                    Wv_new = torch.linalg.solve(mat2_v.T, mat1_v.T).T
+
+                    mat1_v = mat1_v.to("cpu")
+                    mat2_v = mat2_v.to("cpu")
+                    del mat1_v, mat2_v
+
+                    update_q = update_cross_attn['update_q']
+                    update_k = update_cross_attn['update_k']
+                    update_v = update_cross_attn['update_v']
+                    
+                    if not update_q:
+                        Wv_new[:3072,    :] = self.txt_attn.qkv.weight.data[:3072,    :].to(Wv_new)
+                    if not update_k:
+                        Wv_new[3072:6144,:] = self.txt_attn.qkv.weight.data[3072:6144,:].to(Wv_new)
+                    if not update_v:
+                        Wv_new[6144:    ,:] = self.txt_attn.qkv.weight.data[6144:    ,:].to(Wv_new)
+                    
+                    self.txt_attn.qkv.weight.data.copy_(Wv_new.to(self.txt_attn.qkv.weight.data.dtype))
+                    
+                    Wv_new = Wv_new.to("cpu")
+                    del Wv_new
+                    #torch.cuda.empty_cache()
+                    
+        return img, txt
+        
+
+class SingleStreamBlock(nn.Module):      #attn.shape = 1,4608,3072       mlp.shape = 1,4608,12288     4096*3 = 12288
+    """
+    A DiT block with parallel linear layers as described in
+    https://arxiv.org/abs/2302.05442 and adapted modulation interface.
+    """
+    def __init__(self, hidden_size: int,  num_heads: int, mlp_ratio: float = 4.0, qk_scale: float = None, dtype=None, device=None, operations=None, idx=-1):
+        super().__init__()
+        self.idx            = idx
+        self.hidden_dim     = hidden_size #3072
+        self.num_heads      = num_heads    #24
+        head_dim            = hidden_size // num_heads
+        self.scale          = qk_scale or head_dim**-0.5   #0.08838834764831845
+
+        self.mlp_hidden_dim = int(hidden_size * mlp_ratio)    #12288== 3072 * 4
+        # qkv and mlp_in
+        self.linear1        = operations.Linear(hidden_size, 3*hidden_size + self.mlp_hidden_dim, dtype=dtype, device=device)
+        # proj and mlp_out
+        self.linear2        = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size,     dtype=dtype, device=device)
+
+        self.norm           = QKNorm(head_dim,                                                      dtype=dtype, device=device, operations=operations)
+
+        self.hidden_size    = hidden_size #3072
+        self.pre_norm       = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+
+        self.mlp_act        = nn.GELU(approximate="tanh")
+        self.modulation     = Modulation(hidden_size, double=False,                                 dtype=dtype, device=device, operations=operations)
+
+
+    # vec 1,3072    x 1,9984,3072
+    def forward(self, img: Tensor, vec: Tensor, pe: Tensor, mask=None, idx=0, style_block=None) -> Tensor:   # x 1,9984,3072 if 2 reg embeds, 1,9472,3072 if none    # 9216x4096 = 16x1536x1536
+        mod, _    = self.modulation(vec)
+        
+        img_norm = self.pre_norm(img)
+        img_norm = style_block.img(img_norm, "attn_norm")
+        
+        img_norm  = (1 + mod.scale) * img_norm + mod.shift   # mod => vec
+        img_norm = style_block.img(img_norm, "attn_norm_mod")
+        
+        
+        
+        ### ATTN ###
+        qkv, mlp = torch.split(self.linear1(img_norm), [3*self.hidden_size, self.mlp_hidden_dim], dim=-1)
+
+        q, k, v  = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)     #q, k, v  = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        
+        q = style_block.img.ATTN(q, "q_proj")
+        k = style_block.img.ATTN(k, "k_proj")
+        v = style_block.img.ATTN(v, "v_proj")
+        
+        q, k     = self.norm(q, k, v)
+        
+        q = style_block.img.ATTN(q, "q_norm")
+        k = style_block.img.ATTN(k, "k_norm")
+        
+        attn = attention(q, k, v, pe=pe, mask=mask)
+        attn = style_block.img.ATTN(attn, "out")
+        ### ATTN ###
+
+
+
+        mlp = style_block.img(mlp, "ff_norm")
+
+        mlp_act = self.mlp_act(mlp)
+        mlp_act = style_block.img(mlp_act, "ff_norm_mod")
+
+        img_ff_i  = self.linear2(torch.cat((attn, mlp_act), 2))   # effectively FF smooshed into one line
+        img_ff_i = style_block.img(img_ff_i, "ff")
+
+        img_ff_i *= mod.gate
+        img_ff_i = style_block.img(img_ff_i, "ff_gated")
+
+        img      += img_ff_i
+        img = style_block.img(img, "ff_res")
+        
+        return img
+
+
+
+class LastLayer(nn.Module):
+    def __init__(self, hidden_size: int, patch_size: int, out_channels: int,                                        dtype=None,  device=None, operations=None):
+        super().__init__()
+        self.norm_final       = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6,               dtype=dtype, device=device)
+        self.linear           = operations.Linear(hidden_size, patch_size * patch_size * out_channels,   bias=True, dtype=dtype, device=device)
+        self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device))
+
+    def forward(self, x: Tensor, vec: Tensor) -> Tensor:
+        shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
+        
+        x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
+        x = self.linear(x)
+        return x
+
+    def forward_scale_shift(self, x: Tensor, vec: Tensor) -> Tensor:
+        shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
+        
+        x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
+        return x
+
+    def forward_linear(self, x: Tensor, vec: Tensor) -> Tensor:
+        x = self.linear(x)
+        return x
+
+
+
+
+
+
diff --git a/custom_nodes/RES4LYF/flux/math.py b/custom_nodes/RES4LYF/flux/math.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce9acf32266e292d1351f87a42d50eefd2368a33
--- /dev/null
+++ b/custom_nodes/RES4LYF/flux/math.py
@@ -0,0 +1,48 @@
+import torch
+from einops import rearrange
+from torch import Tensor
+from comfy.ldm.modules.attention import attention_pytorch
+
+import comfy.model_management
+
+import math
+
+
+def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None) -> Tensor:
+
+    q, k = apply_rope(q, k, pe)
+
+    heads = q.shape[1]
+
+    x = attention_pytorch(q, k, v, heads, skip_reshape=True, mask=mask)
+
+    return x
+
+
+
+
+def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
+    assert dim % 2 == 0
+    if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu() or comfy.model_management.is_directml_enabled():
+        device = torch.device("cpu")
+    else:
+        device = pos.device
+
+    scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=device)
+    omega = 1.0 / (theta**scale)
+    out = torch.einsum("...n,d->...nd", pos.to(dtype=torch.float32, device=device), omega)
+    out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
+    out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
+    return out.to(dtype=torch.float32, device=pos.device)
+
+
+def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):
+    xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+    xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+    xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+    xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
+    return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
+
+
+
+
diff --git a/custom_nodes/RES4LYF/flux/model.py b/custom_nodes/RES4LYF/flux/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..3671f5ee513c2677b2d09a59cc983b924f679143
--- /dev/null
+++ b/custom_nodes/RES4LYF/flux/model.py
@@ -0,0 +1,969 @@
+# Adapted from: https://github.com/black-forest-labs/flux
+
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+from typing import Optional, Callable, Tuple, Dict, List, Any, Union
+
+from ..helper import ExtraOptions
+
+from dataclasses import dataclass
+import copy
+
+from .layers import (
+    DoubleStreamBlock,
+    EmbedND,
+    LastLayer,
+    MLPEmbedder,
+    SingleStreamBlock,
+    timestep_embedding,
+)
+
+from . import layers
+
+#from comfy.ldm.flux.layers import timestep_embedding
+from comfy.ldm.flux.model import Flux as Flux
+
+import math
+import einops
+from einops import rearrange, repeat
+import comfy.ldm.common_dit
+
+from ..latents import tile_latent, untile_latent, gaussian_blur_2d, median_blur_2d
+from ..style_transfer import apply_scattersort_masked, apply_scattersort_tiled, adain_seq_inplace, adain_patchwise_row_batch_med, adain_patchwise_row_batch, StyleMMDiT_Model
+#from ..latents import interpolate_spd
+
+@dataclass
+class FluxParams:
+    in_channels        : int
+    out_channels       : int
+    vec_in_dim         : int
+    context_in_dim     : int
+    hidden_size        : int
+    mlp_ratio          : float
+    num_heads          : int
+    depth              : int
+    depth_single_blocks: int
+    axes_dim           : list
+    theta              : int
+    patch_size         : int
+    qkv_bias           : bool
+    guidance_embed     : bool
+
+class ReFlux(Flux):
+    def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs):
+        super().__init__()
+        self.dtype         = dtype
+        self.timestep      = -1.0
+        self.threshold_inv = False
+        params             = FluxParams(**kwargs)
+        
+        self.params        = params #self.params FluxParams(in_channels=16, out_channels=16, vec_in_dim=768, context_in_dim=4096, hidden_size=3072, mlp_ratio=4.0, num_heads=24, depth=19, depth_single_blocks=38, axes_dim=[16, 56, 56], theta=10000, patch_size=2, qkv_bias=True, guidance_embed=False)
+        self.patch_size    = params.patch_size
+        self.in_channels   = params.in_channels  * params.patch_size * params.patch_size    # in_channels 64
+        self.out_channels  = params.out_channels * params.patch_size * params.patch_size    # out_channels 64
+        
+        if params.hidden_size % params.num_heads != 0:
+            raise ValueError(f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}")
+        pe_dim = params.hidden_size // params.num_heads
+        if sum(params.axes_dim) != pe_dim:
+            raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
+        
+        self.hidden_size   = params.hidden_size  # 3072
+        self.num_heads     = params.num_heads    # 24
+        self.pe_embedder   = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
+        
+        self.img_in        = operations.Linear(     self.in_channels, self.hidden_size, bias=True,                                                    dtype=dtype, device=device)   # in_features=  64, out_features=3072
+        self.txt_in        = operations.Linear(params.context_in_dim, self.hidden_size,                                                               dtype=dtype, device=device)   # in_features=4096, out_features=3072, bias=True
+
+        self.time_in       = MLPEmbedder(           in_dim=256, hidden_dim=self.hidden_size,                                                          dtype=dtype, device=device, operations=operations)
+        self.vector_in     = MLPEmbedder(params.vec_in_dim,                self.hidden_size,                                                          dtype=dtype, device=device, operations=operations) # in_features=768, out_features=3072 (first layer) second layer 3072,3072
+        self.guidance_in   =(MLPEmbedder(           in_dim=256, hidden_dim=self.hidden_size,                                                          dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity())
+
+        self.double_blocks = nn.ModuleList([DoubleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, qkv_bias=params.qkv_bias, dtype=dtype, device=device, operations=operations, idx=_) for _ in range(params.depth)])
+        self.single_blocks = nn.ModuleList([SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio,                           dtype=dtype, device=device, operations=operations, idx=_) for _ in range(params.depth_single_blocks)])
+
+        if final_layer:
+            self.final_layer = layers.LastLayer(self.hidden_size, 1, self.out_channels,                                                                      dtype=dtype, device=device, operations=operations)
+
+
+    
+    
+    def forward_blocks(self,
+                        img      : Tensor,
+                        img_ids  : Tensor,
+                        txt      : Tensor,
+                        txt_ids  : Tensor,
+                        timesteps: Tensor,
+                        y        : Tensor,
+                        guidance : Tensor   = None,
+                        control             = None,
+                        update_cross_attn   = None,
+                        transformer_options = {},
+                        UNCOND : bool = False,
+                        SIGMA = None,
+                        StyleMMDiT_Model = None,
+                        RECON_MODE=False,
+                        ) -> Tensor:
+        
+        if img.ndim != 3 or txt.ndim != 3:
+            raise ValueError("Input img and txt tensors must have 3 dimensions.")
+
+        # running on sequences img   img -> 1,4096,3072
+        img = self.img_in(img)    # 1,9216,64  == 768x192       # 1,9216,64   ==   1,16,128,256 + 1,16,64,64    # 1,8192,64 with uncond/cond   #:,:,64 -> :,:,3072
+        vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype)) # 1 -> 1,3072
+        
+        if self.params.guidance_embed:
+            if guidance is None:
+                print("Guidance strength is none, not using distilled guidance.")
+            else:
+                vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
+
+        vec = vec + self.vector_in(y)  #y.shape=1,768  y==all 0s
+        
+        txt = self.txt_in(txt)
+
+        ids = torch.cat((txt_ids, img_ids), dim=1) # img_ids.shape=1,8192,3    txt_ids.shape=1,512,3    #ids.shape=1,8704,3
+        pe  = self.pe_embedder(ids)                 # pe.shape 1,1,8704,64,2,2
+        
+        
+        weight    = -1 * transformer_options.get("regional_conditioning_weight", 0.0)
+        floor     = -1 * transformer_options.get("regional_conditioning_floor",  0.0)
+        mask_zero = None
+        mask = None
+        
+        text_len = txt.shape[1] 
+        
+        if not UNCOND and 'AttnMask' in transformer_options: 
+            AttnMask = transformer_options['AttnMask']
+            mask = transformer_options['AttnMask'].attn_mask.mask.to('cuda')
+            if mask_zero is None:
+                mask_zero = torch.ones_like(mask)
+                img_len = transformer_options['AttnMask'].img_len
+                mask_zero[:text_len, :] = mask[:text_len, :]
+                mask_zero[:, :text_len] = mask[:, :text_len]
+            if weight == 0:
+                mask = None
+            
+        if UNCOND and 'AttnMask_neg' in transformer_options: 
+            AttnMask = transformer_options['AttnMask_neg']
+            mask = transformer_options['AttnMask_neg'].attn_mask.mask.to('cuda')
+            if mask_zero is None:
+                mask_zero = torch.ones_like(mask)
+                img_len = transformer_options['AttnMask_neg'].img_len
+                mask_zero[:text_len, :] = mask[:text_len, :]
+                mask_zero[:, :text_len] = mask[:, :text_len]
+            if weight == 0:
+                mask = None
+            
+        elif UNCOND and 'AttnMask' in transformer_options:
+            AttnMask = transformer_options['AttnMask']
+            mask = transformer_options['AttnMask'].attn_mask.mask.to('cuda')
+            if mask_zero is None:
+                mask_zero = torch.ones_like(mask)
+                img_len = transformer_options['AttnMask'].img_len
+                mask_zero[:text_len, :] = mask[:text_len, :]
+                mask_zero[:, :text_len] = mask[:, :text_len]
+            if weight == 0:
+                mask = None
+
+        if mask is not None and not type(mask[0][0].item()) == bool:
+            mask = mask.to(img.dtype)
+        if mask_zero is not None and not type(mask_zero[0][0].item()) == bool:
+            mask_zero = mask_zero.to(img.dtype)
+
+        total_layers = len(self.double_blocks) + len(self.single_blocks)
+        
+        ca_idx = 0
+        for i, block in enumerate(self.double_blocks):
+
+            if   weight > 0 and mask is not None and     weight  <=      i/total_layers:
+                img, txt = block(img=img, txt=txt, vec=vec, pe=pe, mask=mask_zero, idx=i, update_cross_attn=update_cross_attn)
+                
+            elif (weight < 0 and mask is not None and abs(weight) <= (1 - i/total_layers)):
+                img_tmpZ, txt_tmpZ = img.clone(), txt.clone()
+                img_tmpZ, txt = block(img=img_tmpZ, txt=txt_tmpZ, vec=vec, pe=pe, mask=mask, idx=i, update_cross_attn=update_cross_attn)
+                img, txt_tmpZ = block(img=img     , txt=txt     , vec=vec, pe=pe, mask=mask_zero, idx=i, update_cross_attn=update_cross_attn)
+                
+            elif floor > 0 and mask is not None and     floor  >=      i/total_layers:
+                mask_tmp = mask.clone()
+                mask_tmp[text_len:, text_len:] = 1.0
+                img, txt = block(img=img, txt=txt, vec=vec, pe=pe, mask=mask_tmp, idx=i, update_cross_attn=update_cross_attn)
+                
+            elif floor < 0 and mask is not None and abs(floor) >= (1 - i/total_layers):
+                mask_tmp = mask.clone()
+                mask_tmp[text_len:, text_len:] = 1.0
+                img, txt = block(img=img, txt=txt, vec=vec, pe=pe, mask=mask_tmp, idx=i, update_cross_attn=update_cross_attn)
+
+            else:
+                img, txt = block(img=img, txt=txt, vec=vec, pe=pe, mask=mask, idx=i, update_cross_attn=update_cross_attn)
+
+
+            if control is not None: 
+                control_i = control.get("input")
+                if i < len(control_i):
+                    add = control_i[i]
+                    if add is not None:
+                        img[:1] += add
+                        
+            if hasattr(self, "pulid_data"):
+                if self.pulid_data:
+                    if i % self.pulid_double_interval == 0:
+                        for _, node_data in self.pulid_data.items():
+                            if torch.any((node_data['sigma_start'] >= timesteps) & (timesteps >= node_data['sigma_end'])):
+                                img = img + node_data['weight'] * self.pulid_ca[ca_idx](node_data['embedding'], img)
+                        ca_idx += 1
+
+        img = torch.cat((txt, img), 1)   #first 256 is txt embed
+        for i, block in enumerate(self.single_blocks):
+
+            if   weight > 0 and mask is not None and     weight  <=      (i+len(self.double_blocks))/total_layers:
+                img = block(img, vec=vec, pe=pe, mask=mask_zero)
+                
+            elif weight < 0 and mask is not None and abs(weight) <= (1 - (i+len(self.double_blocks))/total_layers):
+                img = block(img, vec=vec, pe=pe, mask=mask_zero)
+                
+            elif floor > 0 and mask is not None and     floor  >=      (i+len(self.double_blocks))/total_layers:
+                mask_tmp = mask.clone()
+                mask_tmp[text_len:, text_len:] = 1.0
+                img = block(img, vec=vec, pe=pe, mask=mask_tmp)
+                
+            elif floor < 0 and mask is not None and abs(floor) >= (1 - (i+len(self.double_blocks))/total_layers):
+                mask_tmp = mask.clone()
+                mask_tmp[text_len:, text_len:] = 1.0
+                img = block(img, vec=vec, pe=pe, mask=mask_tmp)
+                
+            else:
+                img = block(img, vec=vec, pe=pe, mask=mask)
+
+
+
+            if control is not None: # Controlnet
+                control_o = control.get("output")
+                if i < len(control_o):
+                    add = control_o[i]
+                    if add is not None:
+                        img[:1, txt.shape[1] :, ...] += add
+                        
+            if hasattr(self, "pulid_data"):
+                # PuLID attention
+                if self.pulid_data:
+                    real_img, txt = img[:, txt.shape[1]:, ...], img[:, :txt.shape[1], ...]
+                    if i % self.pulid_single_interval == 0:
+                        # Will calculate influence of all nodes at once
+                        for _, node_data in self.pulid_data.items():
+                            if torch.any((node_data['sigma_start'] >= timesteps) & (timesteps >= node_data['sigma_end'])):
+                                real_img = real_img + node_data['weight'] * self.pulid_ca[ca_idx](node_data['embedding'], real_img)
+                        ca_idx += 1
+                    img = torch.cat((txt, real_img), 1)
+
+        img = img[:, txt.shape[1] :, ...]
+        img = self.final_layer(img, vec)  # (N, T, patch_size ** 2 * out_channels)     1,8192,3072 -> 1,8192,64 
+        return img
+    
+    def process_img(self, x, index=0, h_offset=0, w_offset=0):
+        bs, c, h, w = x.shape
+        patch_size = self.patch_size
+        x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))
+
+        img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
+        h_len = ((h + (patch_size // 2)) // patch_size)
+        w_len = ((w + (patch_size // 2)) // patch_size)
+
+        h_offset = ((h_offset + (patch_size // 2)) // patch_size)
+        w_offset = ((w_offset + (patch_size // 2)) // patch_size)
+
+        img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
+        img_ids[:, :, 0] = img_ids[:, :, 1] + index
+        img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
+        img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
+        return img, repeat(img_ids, "h w c -> b (h w) c", b=bs)
+
+    
+    def _get_img_ids(self, x, bs, h_len, w_len, h_start, h_end, w_start, w_end):
+        img_ids          = torch.zeros(  (h_len,   w_len, 3),              device=x.device, dtype=x.dtype)
+        img_ids[..., 1] += torch.linspace(h_start, h_end - 1, steps=h_len, device=x.device, dtype=x.dtype)[:, None]
+        img_ids[..., 2] += torch.linspace(w_start, w_end - 1, steps=w_len, device=x.device, dtype=x.dtype)[None, :]
+        img_ids          = repeat(img_ids, "h w c -> b (h w) c", b=bs)
+        return img_ids
+
+    def forward(self,
+                x,
+                timestep,
+                context,
+                y,
+                guidance,
+                ref_latents=None, 
+                control             = None,
+                transformer_options = {},
+                mask                = None,
+                **kwargs
+                ):
+        t = timestep
+        self.max_seq = (128 * 128) // (2 * 2)
+        x_orig      = x.clone()
+        b, c, h, w  = x.shape
+        h_len = ((h + (self.patch_size // 2)) // self.patch_size) # h_len 96
+        w_len = ((w + (self.patch_size // 2)) // self.patch_size) # w_len 96
+        img_len = h_len * w_len
+        img_slice = slice(-img_len, None) #slice(None, img_len)
+        txt_slice = slice(None, -img_len)
+        SIGMA = t[0].clone() #/ 1000
+        EO = transformer_options.get("ExtraOptions", ExtraOptions(""))
+        if EO is not None:
+            EO.mute = True
+
+        if EO("zero_heads"):
+            HEADS = 0
+        else:
+            HEADS = 24
+
+        StyleMMDiT = transformer_options.get('StyleMMDiT', StyleMMDiT_Model())        
+        StyleMMDiT.set_len(h_len, w_len, img_slice, txt_slice, HEADS=HEADS)
+        StyleMMDiT.Retrojector = self.Retrojector if hasattr(self, "Retrojector") else None
+        transformer_options['StyleMMDiT'] = None
+        
+        x_tmp = transformer_options.get("x_tmp")
+        if x_tmp is not None:
+            x_tmp = x_tmp.expand(x.shape[0], -1, -1, -1).clone()
+            img = comfy.ldm.common_dit.pad_to_patch_size(x_tmp, (self.patch_size, self.patch_size))
+        else:
+            img = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
+        
+        y0_style, img_y0_style = None, None
+
+        img_orig, t_orig, y_orig, context_orig = clone_inputs(img, t, y, context)
+    
+        weight    = -1 * transformer_options.get("regional_conditioning_weight", 0.0)
+        floor     = -1 * transformer_options.get("regional_conditioning_floor",  0.0)
+        update_cross_attn = transformer_options.get("update_cross_attn")
+    
+        z_ = transformer_options.get("z_")   # initial noise and/or image+noise from start of rk_sampler_beta() 
+        rk_row = transformer_options.get("row") # for "smart noise"
+        if z_ is not None:
+            x_init = z_[rk_row].to(x)
+        elif 'x_init' in transformer_options:
+            x_init = transformer_options.get('x_init').to(x)
+
+        # recon loop to extract exact noise pred for scattersort guide assembly
+        RECON_MODE = StyleMMDiT.noise_mode == "recon"
+        recon_iterations = 2 if StyleMMDiT.noise_mode == "recon" else 1
+        for recon_iter in range(recon_iterations):
+            y0_style = StyleMMDiT.guides
+            y0_style_active = True if type(y0_style) == torch.Tensor else False
+            
+            RECON_MODE = True     if StyleMMDiT.noise_mode == "recon" and recon_iter == 0     else False
+            
+            if StyleMMDiT.noise_mode == "recon" and recon_iter == 1:
+                x_recon = x_tmp if x_tmp is not None else x_orig
+                noise_prediction = x_recon + (1-SIGMA.to(x_recon)) * eps.to(x_recon)
+                denoised = x_recon - SIGMA.to(x_recon) * eps.to(x_recon)
+                
+                denoised = StyleMMDiT.apply_recon_lure(denoised, y0_style)
+
+                new_x = (1-SIGMA.to(denoised)) * denoised + SIGMA.to(denoised) * noise_prediction
+                img_orig = img = comfy.ldm.common_dit.pad_to_patch_size(new_x, (self.patch_size, self.patch_size))
+                
+                x_init = noise_prediction
+            elif StyleMMDiT.noise_mode == "bonanza":
+                x_init = torch.randn_like(x_init)
+
+            if y0_style_active:
+                if y0_style.sum() == 0.0 and y0_style.std() == 0.0:
+                    y0_style = img_orig.clone()
+                else:
+                    SIGMA_ADAIN         = (SIGMA * EO("eps_adain_sigma_factor", 1.0)).to(y0_style)
+                    y0_style_noised     = (1-SIGMA_ADAIN) * y0_style + SIGMA_ADAIN * x_init[0:1].to(y0_style)   #always only use first batch of noise to avoid broadcasting
+                    img_y0_style_orig   = comfy.ldm.common_dit.pad_to_patch_size(y0_style_noised, (self.patch_size, self.patch_size))
+
+            mask_zero = None
+            
+            out_list = []
+            for cond_iter in range(len(transformer_options['cond_or_uncond'])):
+                UNCOND = transformer_options['cond_or_uncond'][cond_iter] == 1
+                
+                if update_cross_attn is not None:
+                    update_cross_attn['UNCOND'] = UNCOND
+
+                bsz_style = y0_style.shape[0] if y0_style_active else 0
+                bsz       = 1 if RECON_MODE else bsz_style + 1
+
+                img, t, y, context = clone_inputs(img_orig, t_orig, y_orig, context_orig, index=cond_iter)
+                
+                mask = None
+                if not UNCOND and 'AttnMask' in transformer_options: # and weight != 0:
+                    AttnMask = transformer_options['AttnMask']
+                    mask = transformer_options['AttnMask'].attn_mask.mask.to('cuda')
+                    if mask_zero is None:
+                        mask_zero = torch.ones_like(mask)
+                        mask_zero[txt_slice, txt_slice] = mask[txt_slice, txt_slice]
+
+                    if weight == 0:
+                        context = transformer_options['RegContext'].context.to(context.dtype).to(context.device)
+                        mask = None
+                    else:
+                        context = transformer_options['RegContext'].context.to(context.dtype).to(context.device)
+
+                if UNCOND and 'AttnMask_neg' in transformer_options: # and weight != 0:
+                    AttnMask = transformer_options['AttnMask_neg']
+                    mask = transformer_options['AttnMask_neg'].attn_mask.mask.to('cuda')
+                    if mask_zero is None:
+                        mask_zero = torch.ones_like(mask)
+                        mask_zero[txt_slice, txt_slice] = mask[txt_slice, txt_slice]
+
+                    if weight == 0:
+                        context = transformer_options['RegContext_neg'].context.to(context.dtype).to(context.device)
+                        mask = None
+                    else:
+                        context = transformer_options['RegContext_neg'].context.to(context.dtype).to(context.device)
+
+                elif UNCOND and 'AttnMask' in transformer_options:
+                    AttnMask = transformer_options['AttnMask']
+                    mask = transformer_options['AttnMask'].attn_mask.mask.to('cuda')
+                    
+                    if mask_zero is None:
+                        mask_zero = torch.ones_like(mask)
+
+                        mask_zero[txt_slice, txt_slice] = mask[txt_slice, txt_slice]
+                    if weight == 0:                                                                             # ADDED 5/23/2025
+                        context = transformer_options['RegContext'].context.to(context.dtype).to(context.device)  # ADDED 5/26/2025 14:53
+                        mask = None
+                    else:
+                        A       = context
+                        B       = transformer_options['RegContext'].context
+                        context = A.repeat(1,    (B.shape[1] // A.shape[1]) + 1, 1)[:,   :B.shape[1], :]
+
+
+                if y0_style_active and not RECON_MODE:
+                    if mask is None:
+                        context, y, _ = StyleMMDiT.apply_style_conditioning(
+                            UNCOND = UNCOND,
+                            base_context       = context,
+                            base_y             = y,
+                            base_llama3        = None,
+                        )
+                    else:
+                        context = context.repeat(bsz_style + 1, 1, 1)
+                        y = y.repeat(bsz_style + 1, 1)                   if y      is not None else None
+                    img_y0_style = img_y0_style_orig.clone()
+
+                if mask is not None and not type(mask[0][0].item()) == bool:
+                    mask = mask.to(x.dtype)
+                if mask_zero is not None and not type(mask_zero[0][0].item()) == bool:
+                    mask_zero = mask_zero.to(x.dtype)
+
+                clip = self.time_in(timestep_embedding(t, 256).to(x.dtype)) # 1 -> 1,3072
+                if self.params.guidance_embed:
+                    if guidance is None:
+                        print("Guidance strength is none, not using distilled guidance.")
+                    else:
+                        clip = clip + self.guidance_in(timestep_embedding(guidance, 256).to(x.dtype))
+                clip = clip + self.vector_in(y[:,:self.params.vec_in_dim])  #y.shape=1,768  y==all 0s
+                clip = clip.to(x)
+        
+                img_in_dtype = self.img_in.weight.data.dtype
+                if img_in_dtype not in {torch.bfloat16, torch.float16, torch.float32, torch.float64}:
+                    img_in_dtype = x.dtype
+                
+                if ref_latents is not None:
+                    h, w = 0, 0
+                    for ref in ref_latents:
+                        h_offset = 0
+                        w_offset = 0
+                        if ref.shape[-2] + h > ref.shape[-1] + w:
+                            w_offset = w
+                        else:
+                            h_offset = h
+
+                        kontext, kontext_ids = self.process_img(ref, index=1, h_offset=h_offset, w_offset=w_offset)
+                        #kontext = self.img_in(kontext.to(img_in_dtype))
+                        img, img_ids = self.process_img(x)
+                        img = torch.cat([img, kontext], dim=1)
+                        img_ids = torch.cat([img_ids, kontext_ids], dim=1)
+                        h = max(h, ref.shape[-2] + h_offset)
+                        w = max(w, ref.shape[-1] + w_offset)
+                    img = self.img_in(img.to(img_in_dtype))
+                    
+                    img_slice = slice(-2*img_len, None)
+                    StyleMMDiT.KONTEXT = 1
+                    for style_block in StyleMMDiT.double_blocks + StyleMMDiT.single_blocks:
+                        style_block.KONTEXT = 1
+                        for style_block_imgtxt in [style_block.img, getattr(style_block, "txt")]:
+                            style_block_imgtxt.KONTEXT = 1
+                            style_block_imgtxt.ATTN.KONTEXT = 1
+                    StyleMMDiT.datashock_ref = ref_latents[0]
+                else:
+                    
+                    img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=self.patch_size, pw=self.patch_size)
+                    img = self.img_in(img.to(img_in_dtype))
+                    img_ids = self._get_img_ids(img, bsz, h_len, w_len, 0, h_len, 0, w_len)
+
+                if y0_style_active and not RECON_MODE:
+                    img_y0_style = rearrange(img_y0_style_orig, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=self.patch_size, pw=self.patch_size)
+                    img_y0_style = self.img_in(img_y0_style.to(img_in_dtype))  # hidden_states 1,4032,2560         for 1024x1024: -> 1,4096,2560      ,64 -> ,2560 (x40)
+                    if ref_latents is not None:
+                        img_kontext  = self.img_in(kontext.to(img_in_dtype))
+                        #img_base = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=self.patch_size, pw=self.patch_size)
+                        #img_base = self.img_in(img_base.to(img_in_dtype))
+                        #img_ids = self._get_img_ids(img, bsz, h_len, w_len, 0, h_len, 0, w_len)
+                        img_ids      = img_ids     .repeat(bsz,1,1)
+                        #img_y0_style = img_y0_style.repeat(1,bsz,1) # torch.cat([img, img_y0_style], dim=0)
+                        img_y0_style = torch.cat([img_y0_style, img_kontext.repeat(bsz-1,1,1)], dim=1)
+                        
+                        StyleMMDiT.KONTEXT = 2
+                        for style_block in StyleMMDiT.double_blocks + StyleMMDiT.single_blocks:
+                            style_block.KONTEXT = 2
+                            for style_block_imgtxt in [style_block.img, getattr(style_block, "txt")]:
+                                style_block_imgtxt.KONTEXT = 2
+                                style_block_imgtxt.ATTN.KONTEXT = 2
+                        StyleMMDiT.datashock_ref = None
+                        
+                    img = torch.cat([img, img_y0_style], dim=0)
+
+
+                # txt_ids -> 1,414,3
+                txt_ids = torch.zeros((bsz, context.shape[-2], 3), device=img.device, dtype=x.dtype) 
+                ids     = torch.cat((txt_ids, img_ids), dim=-2)   # ids -> 1,4446,3       # flipped from hidream
+                rope    = self.pe_embedder(ids)                  # rope -> 1, 4446, 1, 64, 2, 2
+
+                txt_init = self.txt_in(context)
+                txt_init_len = txt_init.shape[-2]                                       # 271
+
+                img = StyleMMDiT(img, "proj_in")
+                
+                img = img.to(x) if img is not None else None
+                
+                total_layers = len(self.double_blocks) + len(self.single_blocks)
+                
+                # DOUBLE STREAM
+                ca_idx = 0
+                for bid, (block, style_block) in enumerate(zip(self.double_blocks, StyleMMDiT.double_blocks)):
+                    txt = txt_init
+                    if   weight > 0 and mask is not None and     weight  <      bid/total_layers:
+                        img, txt_init = block(img, txt, clip, rope, mask_zero, style_block=style_block)
+                        
+                    elif (weight < 0 and mask is not None and abs(weight) < (1 - bid/total_layers)):
+                        img_tmpZ, txt_tmpZ = img.clone(), txt.clone()
+
+                        # more efficient than the commented lines below being used instead in the loop?
+                        img_tmpZ, txt_init = block(img_tmpZ, txt_tmpZ, clip, rope, mask, style_block=style_block)
+                        img     , txt_tmpZ = block(img     , txt     , clip, rope, mask_zero, style_block=style_block)
+                        
+                    elif floor > 0 and mask is not None and     floor  >      bid/total_layers:
+                        mask_tmp = mask.clone()
+                        mask_tmp[img_slice,img_slice] = 1.0
+                        img, txt_init = block(img, txt, clip, rope, mask_tmp, style_block=style_block)
+                        
+                    elif floor < 0 and mask is not None and abs(floor) > (1 - bid/total_layers):
+                        mask_tmp = mask.clone()
+                        mask_tmp[img_slice,img_slice] = 1.0
+                        img, txt_init = block(img, txt, clip, rope, mask_tmp, style_block=style_block)
+                        
+                    elif update_cross_attn is not None and update_cross_attn['skip_cross_attn']:
+                        img, txt_init = block(img, txt, clip, rope, mask, update_cross_attn=update_cross_attn)
+                        
+                    else:
+                        img, txt_init = block(img, txt, clip, rope, mask, update_cross_attn=update_cross_attn, style_block=style_block)
+
+                    if control is not None: 
+                        control_i = control.get("input")
+                        if bid < len(control_i):
+                            add = control_i[bid]
+                            if add is not None:
+                                img[:1] += add
+                    
+                    if hasattr(self, "pulid_data"):
+                        if self.pulid_data:
+                            if bid % self.pulid_double_interval == 0:
+                                for _, node_data in self.pulid_data.items():
+                                    if torch.any((node_data['sigma_start'] >= timestep) & (timestep >= node_data['sigma_end'])):
+                                        img = img + node_data['weight'] * self.pulid_ca[ca_idx](node_data['embedding'], img)
+                                ca_idx += 1
+
+                # END DOUBLE STREAM
+
+                #img = img[0:1]
+                #txt_init = txt_init[0:1]
+                img       = torch.cat([txt_init, img], dim=-2)   # 4032 + 271 -> 4303     # txt embed from double stream block   # flipped from hidream
+
+                double_layers = len(self.double_blocks)
+
+                # SINGLE STREAM
+                for bid, (block, style_block) in enumerate(zip(self.single_blocks, StyleMMDiT.single_blocks)):
+
+                    if   weight > 0 and mask is not None and     weight  <      (bid+double_layers)/total_layers:
+                        img = block(img, clip, rope, mask_zero, style_block=style_block)
+                    
+                    elif weight < 0 and mask is not None and abs(weight) < (1 - (bid+double_layers)/total_layers):
+                        img = block(img, clip, rope, mask_zero, style_block=style_block)
+                    
+                    elif floor > 0 and mask is not None and     floor  >      (bid+double_layers)/total_layers:
+                        mask_tmp = mask.clone()
+                        mask_tmp[img_slice,img_slice] = 1.0
+                        img = block(img, clip, rope, mask_tmp, style_block=style_block)
+                    
+                    elif floor < 0 and mask is not None and abs(floor) > (1 - (bid+double_layers)/total_layers):
+                        mask_tmp = mask.clone()
+                        mask_tmp[img_slice,img_slice] = 1.0
+                        img = block(img, clip, rope, mask_tmp, style_block=style_block)
+                    
+                    else:
+                        img = block(img, clip, rope, mask, style_block=style_block)
+                    
+                    if control is not None: # Controlnet
+                        control_o = control.get("output")
+                        if bid < len(control_o):
+                            add = control_o[bid]
+                            if add is not None:
+                                img[:1, txt_slice, ...] += add
+                                
+                    if hasattr(self, "pulid_data"):
+                        # PuLID attention
+                        if self.pulid_data:
+                            real_img, txt = img[:, img_slice, ...], img[:, txt_slice, ...]
+                            if bid % self.pulid_single_interval == 0:
+                                # Will calculate influence of all nodes at once
+                                for _, node_data in self.pulid_data.items():
+                                    if torch.any((node_data['sigma_start'] >= timestep) & (timestep >= node_data['sigma_end'])):
+                                        real_img = real_img + node_data['weight'] * self.pulid_ca[ca_idx](node_data['embedding'], real_img)
+                                ca_idx += 1
+                            img = torch.cat((txt, real_img), 1)
+                            
+                # END SINGLE STREAM
+                
+                img = img[..., img_slice, :]
+                #img = self.final_layer(img, clip)   # 4096,2560 -> 4096,64
+                shift, scale = self.final_layer.adaLN_modulation(clip).chunk(2,dim=1)
+                img = (1 + scale[:, None, :]) * self.final_layer.norm_final(img) + shift[:, None, :]
+                
+                img = StyleMMDiT(img, "proj_out")
+
+                if y0_style_active and not RECON_MODE:
+                    img = img[0:1]
+                    #img = img[1:2]
+                
+                #img = self.final_layer.linear(img.to(self.final_layer.linear.weight.data))
+                img = self.final_layer.linear(img)
+
+                #img = self.unpatchify(img, img_sizes)
+                img = img[:,:img_len]  # accomodate kontext
+                img = rearrange(img, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=self.patch_size, pw=self.patch_size)
+                out_list.append(img)
+                
+            output = torch.cat(out_list, dim=0)
+            eps = output[:, :, :h, :w]
+            
+            if recon_iter == 1:
+                denoised = new_x - SIGMA.to(new_x) * eps.to(new_x)
+                if x_tmp is not None:
+                    eps = (x_tmp - denoised.to(x_tmp)) / SIGMA.to(x_tmp)
+                else:
+                    eps = (x_orig - denoised.to(x_orig)) / SIGMA.to(x_orig)
+                    
+
+
+
+
+
+
+
+
+
+
+
+
+        freqsep_lowpass_method = transformer_options.get("freqsep_lowpass_method")
+        freqsep_sigma          = transformer_options.get("freqsep_sigma")
+        freqsep_kernel_size    = transformer_options.get("freqsep_kernel_size")
+        freqsep_inner_kernel_size    = transformer_options.get("freqsep_inner_kernel_size")
+        freqsep_stride    = transformer_options.get("freqsep_stride")
+        
+        freqsep_lowpass_weight = transformer_options.get("freqsep_lowpass_weight")
+        freqsep_highpass_weight= transformer_options.get("freqsep_highpass_weight")
+        freqsep_mask           = transformer_options.get("freqsep_mask")
+
+        y0_style_pos = transformer_options.get("y0_style_pos")
+        y0_style_neg = transformer_options.get("y0_style_neg")
+        
+        # end recon loop
+        self.style_dtype = torch.float32 if self.style_dtype is None else self.style_dtype
+        dtype = eps.dtype if self.style_dtype is None else self.style_dtype
+        
+        if y0_style_pos is not None:
+            y0_style_pos_weight    = transformer_options.get("y0_style_pos_weight")
+            y0_style_pos_synweight = transformer_options.get("y0_style_pos_synweight")
+            y0_style_pos_synweight *= y0_style_pos_weight
+            y0_style_pos_mask = transformer_options.get("y0_style_pos_mask")
+            y0_style_pos_mask_edge = transformer_options.get("y0_style_pos_mask_edge")
+
+            y0_style_pos = y0_style_pos.to(dtype)
+            x   = x_orig.to(dtype)
+            eps = eps.to(dtype)
+            eps_orig = eps.clone()
+            
+            sigma = SIGMA #t_orig[0].to(torch.float32) / 1000
+            denoised = x - sigma * eps
+            
+            denoised_embed = self.Retrojector.embed(denoised)
+            y0_adain_embed = self.Retrojector.embed(y0_style_pos)
+            
+            if transformer_options['y0_style_method'] == "scattersort":
+                tile_h, tile_w = transformer_options.get('y0_style_tile_height'), transformer_options.get('y0_style_tile_width')
+                pad = transformer_options.get('y0_style_tile_padding')
+                if pad is not None and tile_h is not None and tile_w is not None:
+                    
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    if EO("scattersort_median_LP"):
+                        denoised_spatial_LP = median_blur_2d(denoised_spatial, kernel_size=EO("scattersort_median_LP",7))
+                        y0_adain_spatial_LP = median_blur_2d(y0_adain_spatial, kernel_size=EO("scattersort_median_LP",7))
+                        
+                        denoised_spatial_HP = denoised_spatial - denoised_spatial_LP
+                        y0_adain_spatial_HP = y0_adain_spatial - y0_adain_spatial_LP
+                        
+                        denoised_spatial_LP = apply_scattersort_tiled(denoised_spatial_LP, y0_adain_spatial_LP, tile_h, tile_w, pad)
+                        
+                        denoised_spatial = denoised_spatial_LP + denoised_spatial_HP
+                        denoised_embed = rearrange(denoised_spatial, "b c h w -> b (h w) c")
+                    else:
+                        denoised_spatial = apply_scattersort_tiled(denoised_spatial, y0_adain_spatial, tile_h, tile_w, pad)
+                    
+                    denoised_embed = rearrange(denoised_spatial, "b c h w -> b (h w) c")
+                    
+                else:
+                    denoised_embed = apply_scattersort_masked(denoised_embed, y0_adain_embed, y0_style_pos_mask, y0_style_pos_mask_edge, h_len, w_len)
+
+
+
+            elif transformer_options['y0_style_method'] == "AdaIN":
+                if freqsep_mask is not None:
+                    freqsep_mask = freqsep_mask.view(1, 1, *freqsep_mask.shape[-2:]).float()
+                    freqsep_mask = F.interpolate(freqsep_mask.float(), size=(h_len, w_len), mode='nearest-exact')
+                
+                if hasattr(self, "adain_tile"):
+                    tile_h, tile_w = self.adain_tile
+                    
+                    denoised_pretile = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_pretile = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    if self.adain_flag:
+                        h_off = tile_h // 2
+                        w_off = tile_w // 2
+                        denoised_pretile = denoised_pretile[:,:,h_off:-h_off, w_off:-w_off]
+                        self.adain_flag = False
+                    else:
+                        h_off = 0
+                        w_off = 0
+                        self.adain_flag = True
+                    
+                    tiles,    orig_shape, grid, strides = tile_latent(denoised_pretile, tile_size=(tile_h,tile_w))
+                    y0_tiles, orig_shape, grid, strides = tile_latent(y0_adain_pretile, tile_size=(tile_h,tile_w))
+                    
+                    tiles_out = []
+                    for i in range(tiles.shape[0]):
+                        tile = tiles[i].unsqueeze(0)
+                        y0_tile = y0_tiles[i].unsqueeze(0)
+                        
+                        tile    = rearrange(tile,    "b c h w -> b (h w) c", h=tile_h, w=tile_w)
+                        y0_tile = rearrange(y0_tile, "b c h w -> b (h w) c", h=tile_h, w=tile_w)
+                        
+                        tile = adain_seq_inplace(tile, y0_tile)
+                        tiles_out.append(rearrange(tile, "b (h w) c -> b c h w", h=tile_h, w=tile_w))
+                    
+                    tiles_out_tensor = torch.cat(tiles_out, dim=0)
+                    tiles_out_tensor = untile_latent(tiles_out_tensor, orig_shape, grid, strides)
+
+                    if h_off == 0:
+                        denoised_pretile = tiles_out_tensor
+                    else:
+                        denoised_pretile[:,:,h_off:-h_off, w_off:-w_off] = tiles_out_tensor
+                    denoised_embed = rearrange(denoised_pretile, "b c h w -> b (h w) c", h=h_len, w=w_len)
+
+                elif freqsep_lowpass_method is not None and freqsep_lowpass_method.endswith("pw"): #EO("adain_pw"):
+
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+
+                    if   freqsep_lowpass_method == "median_pw":
+                        denoised_spatial_new = adain_patchwise_row_batch_med(denoised_spatial.clone(), y0_adain_spatial.clone().repeat(denoised_spatial.shape[0],1,1,1), sigma=freqsep_sigma, kernel_size=freqsep_kernel_size, use_median_blur=True, lowpass_weight=freqsep_lowpass_weight, highpass_weight=freqsep_highpass_weight)
+                    elif freqsep_lowpass_method == "gaussian_pw": 
+                        denoised_spatial_new = adain_patchwise_row_batch(denoised_spatial.clone(), y0_adain_spatial.clone().repeat(denoised_spatial.shape[0],1,1,1), sigma=freqsep_sigma, kernel_size=freqsep_kernel_size)
+                    
+                    denoised_embed = rearrange(denoised_spatial_new, "b c h w -> b (h w) c", h=h_len, w=w_len)
+
+                elif freqsep_lowpass_method is not None: 
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    if   freqsep_lowpass_method == "median":
+                        denoised_spatial_LP = median_blur_2d(denoised_spatial, kernel_size=freqsep_kernel_size)
+                        y0_adain_spatial_LP = median_blur_2d(y0_adain_spatial, kernel_size=freqsep_kernel_size)
+                    elif freqsep_lowpass_method == "gaussian":
+                        denoised_spatial_LP = gaussian_blur_2d(denoised_spatial, sigma=freqsep_sigma, kernel_size=freqsep_kernel_size)
+                        y0_adain_spatial_LP = gaussian_blur_2d(y0_adain_spatial, sigma=freqsep_sigma, kernel_size=freqsep_kernel_size)
+                    
+                    denoised_spatial_HP = denoised_spatial - denoised_spatial_LP
+                    
+                    if EO("adain_fs_uhp"):
+                        y0_adain_spatial_HP = y0_adain_spatial - y0_adain_spatial_LP
+                        
+                        denoised_spatial_ULP = gaussian_blur_2d(denoised_spatial, sigma=EO("adain_fs_uhp_sigma", 1.0), kernel_size=EO("adain_fs_uhp_kernel_size", 3))
+                        y0_adain_spatial_ULP = gaussian_blur_2d(y0_adain_spatial, sigma=EO("adain_fs_uhp_sigma", 1.0), kernel_size=EO("adain_fs_uhp_kernel_size", 3))
+                        
+                        denoised_spatial_UHP = denoised_spatial_HP  - denoised_spatial_ULP
+                        y0_adain_spatial_UHP = y0_adain_spatial_HP  - y0_adain_spatial_ULP
+                        
+                        #denoised_spatial_HP  = y0_adain_spatial_ULP + denoised_spatial_UHP
+                        denoised_spatial_HP  = denoised_spatial_ULP + y0_adain_spatial_UHP
+                    
+                    denoised_spatial_new = freqsep_lowpass_weight * y0_adain_spatial_LP + freqsep_highpass_weight * denoised_spatial_HP
+                    denoised_embed = rearrange(denoised_spatial_new, "b c h w -> b (h w) c", h=h_len, w=w_len)
+
+                else:
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    
+                for adain_iter in range(EO("style_iter", 0)):
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    denoised_embed = self.Retrojector.embed(self.Retrojector.unembed(denoised_embed))
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    
+            elif transformer_options['y0_style_method'] == "WCT":
+                self.StyleWCT.set(y0_adain_embed)
+                denoised_embed = self.StyleWCT.get(denoised_embed)
+                
+                if transformer_options.get('y0_standard_guide') is not None:
+                    y0_standard_guide = transformer_options.get('y0_standard_guide')
+                    
+                    y0_standard_guide_embed = self.Retrojector.embed(y0_standard_guide)
+                    f_cs = self.StyleWCT.get(y0_standard_guide_embed)
+                    self.y0_standard_guide = self.Retrojector.unembed(f_cs)
+
+                if transformer_options.get('y0_inv_standard_guide') is not None:
+                    y0_inv_standard_guide = transformer_options.get('y0_inv_standard_guide')
+
+                    y0_inv_standard_guide_embed = self.Retrojector.embed(y0_inv_standard_guide)
+                    f_cs = self.StyleWCT.get(y0_inv_standard_guide_embed)
+                    self.y0_inv_standard_guide = self.Retrojector.unembed(f_cs)
+
+            elif transformer_options['y0_style_method'] == "WCT2":
+                self.WaveletStyleWCT.set(y0_adain_embed, h_len, w_len)
+                denoised_embed = self.WaveletStyleWCT.get(denoised_embed, h_len, w_len)
+                
+                if transformer_options.get('y0_standard_guide') is not None:
+                    y0_standard_guide = transformer_options.get('y0_standard_guide')
+                    
+                    y0_standard_guide_embed = self.Retrojector.embed(y0_standard_guide)
+                    f_cs = self.WaveletStyleWCT.get(y0_standard_guide_embed, h_len, w_len)
+                    self.y0_standard_guide = self.Retrojector.unembed(f_cs)
+
+                if transformer_options.get('y0_inv_standard_guide') is not None:
+                    y0_inv_standard_guide = transformer_options.get('y0_inv_standard_guide')
+
+                    y0_inv_standard_guide_embed = self.Retrojector.embed(y0_inv_standard_guide)
+                    f_cs = self.WaveletStyleWCT.get(y0_inv_standard_guide_embed, h_len, w_len)
+                    self.y0_inv_standard_guide = self.Retrojector.unembed(f_cs)
+
+            denoised_approx = self.Retrojector.unembed(denoised_embed)
+            
+            eps = (x - denoised_approx) / sigma
+
+            if not UNCOND:
+                if eps.shape[0] == 2:
+                    eps[1] = eps_orig[1] + y0_style_pos_weight * (eps[1] - eps_orig[1])
+                    eps[0] = eps_orig[0] + y0_style_pos_synweight * (eps[0] - eps_orig[0])
+                else:
+                    eps[0] = eps_orig[0] + y0_style_pos_weight * (eps[0] - eps_orig[0])
+            elif eps.shape[0] == 1 and UNCOND:
+                eps[0] = eps_orig[0] + y0_style_pos_synweight * (eps[0] - eps_orig[0])
+            
+            #eps = eps.float()
+        
+        if y0_style_neg is not None:
+            y0_style_neg_weight    = transformer_options.get("y0_style_neg_weight")
+            y0_style_neg_synweight = transformer_options.get("y0_style_neg_synweight")
+            y0_style_neg_synweight *= y0_style_neg_weight
+            y0_style_neg_mask = transformer_options.get("y0_style_neg_mask")
+            y0_style_neg_mask_edge = transformer_options.get("y0_style_neg_mask_edge")
+            
+            y0_style_neg = y0_style_neg.to(dtype)
+            x   = x_orig.to(dtype)
+            eps = eps.to(dtype)
+            eps_orig = eps.clone()
+            
+            sigma = SIGMA #t_orig[0].to(torch.float32) / 1000
+            denoised = x - sigma * eps
+
+            denoised_embed = self.Retrojector.embed(denoised)
+            y0_adain_embed = self.Retrojector.embed(y0_style_neg)
+            
+            if transformer_options['y0_style_method'] == "scattersort":
+                tile_h, tile_w = transformer_options.get('y0_style_tile_height'), transformer_options.get('y0_style_tile_width')
+                pad = transformer_options.get('y0_style_tile_padding')
+                if pad is not None and tile_h is not None and tile_w is not None:
+                    
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    denoised_spatial = apply_scattersort_tiled(denoised_spatial, y0_adain_spatial, tile_h, tile_w, pad)
+                    
+                    denoised_embed = rearrange(denoised_spatial, "b c h w -> b (h w) c")
+
+                else:
+                    denoised_embed = apply_scattersort_masked(denoised_embed, y0_adain_embed, y0_style_neg_mask, y0_style_neg_mask_edge, h_len, w_len)
+            
+            
+            elif transformer_options['y0_style_method'] == "AdaIN":
+                denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                for adain_iter in range(EO("style_iter", 0)):
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    denoised_embed = self.Retrojector.embed(self.Retrojector.unembed(denoised_embed))
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    
+            elif transformer_options['y0_style_method'] == "WCT":
+                self.StyleWCT.set(y0_adain_embed)
+                denoised_embed = self.StyleWCT.get(denoised_embed)
+
+            elif transformer_options['y0_style_method'] == "WCT2":
+                self.WaveletStyleWCT.set(y0_adain_embed, h_len, w_len)
+                denoised_embed = self.WaveletStyleWCT.get(denoised_embed, h_len, w_len)
+
+            denoised_approx = self.Retrojector.unembed(denoised_embed)
+
+            if UNCOND:
+                eps = (x - denoised_approx) / sigma
+                eps[0] = eps_orig[0] + y0_style_neg_weight * (eps[0] - eps_orig[0])
+                if eps.shape[0] == 2:
+                    eps[1] = eps_orig[1] + y0_style_neg_synweight * (eps[1] - eps_orig[1])
+            elif eps.shape[0] == 1 and not UNCOND:
+                eps[0] = eps_orig[0] + y0_style_neg_synweight * (eps[0] - eps_orig[0])
+            
+            #eps = eps.float()
+        if EO("model_eps_out"):
+            self.eps_out = eps.clone()
+        return eps
+    
+
+    def expand_timesteps(self, t, batch_size, device):
+        if not torch.is_tensor(t):
+            is_mps = device.type == "mps"
+            if isinstance(t, float):
+                dtype = torch.float32 if is_mps else torch.float64
+            else:
+                dtype = torch.int32   if is_mps else torch.int64
+            t = Tensor([t], dtype=dtype, device=device)
+        elif len(t.shape) == 0:
+            t = t[None].to(device)
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        t = t.expand(batch_size)
+        return t
+
+def clone_inputs(*args, index: int=None):
+
+    if index is None:
+        return tuple(x.clone() for x in args)
+    else:
+        return tuple(x[index].unsqueeze(0).clone() for x in args)
+
+
+
diff --git a/custom_nodes/RES4LYF/flux/redux.py b/custom_nodes/RES4LYF/flux/redux.py
new file mode 100644
index 0000000000000000000000000000000000000000..593160ea11b7668b4c1d8e6d7b5eca53513ebb09
--- /dev/null
+++ b/custom_nodes/RES4LYF/flux/redux.py
@@ -0,0 +1,125 @@
+import torch
+import comfy.ops
+import torch.nn
+import torch.nn.functional as F
+
+ops = comfy.ops.manual_cast
+
+class ReReduxImageEncoder(torch.nn.Module):
+    def __init__(
+        self,
+        redux_dim: int = 1152,
+        txt_in_features: int = 4096,
+        device=None,
+        dtype=None,
+    ) -> None:
+        super().__init__()
+
+        self.redux_dim = redux_dim
+        self.device = device
+        self.dtype = dtype
+        
+        self.style_dtype = None
+
+        self.redux_up = ops.Linear(redux_dim, txt_in_features * 3, dtype=dtype)
+        self.redux_down = ops.Linear(txt_in_features * 3, txt_in_features, dtype=dtype)
+
+    def forward(self, sigclip_embeds) -> torch.Tensor:
+        projected_x = self.redux_down(torch.nn.functional.silu(self.redux_up(sigclip_embeds)))
+        return projected_x
+    
+    def feature_match(self, cond, clip_vision_output, mode="WCT"):
+        sigclip_embeds = clip_vision_output.last_hidden_state
+        dense_embed = torch.nn.functional.silu(self.redux_up(sigclip_embeds))
+        t_sqrt = int(dense_embed.shape[-2] ** 0.5)
+        dense_embed_sq = dense_embed.view(dense_embed.shape[-3], t_sqrt, t_sqrt, dense_embed.shape[-1])
+        
+        t_cond_sqrt = int(cond[0][0].shape[-2] ** 0.5) 
+        dense_embed256 = F.interpolate(dense_embed_sq.transpose(-3,-1), size=(t_cond_sqrt,t_cond_sqrt), mode="bicubic")
+        dense_embed256 = dense_embed256.flatten(-2,-1).transpose(-2,-1)
+        
+        dtype = self.style_dtype if hasattr(self, "style_dtype") and self.style_dtype is not None else dense_embed.dtype
+        pinv_dtype = torch.float32 if dtype != torch.float64 else dtype
+        
+        W = self.redux_down.weight.data.to(dtype)   # shape [2560, 64]
+        b = self.redux_down.bias.data.to(dtype)     # shape [2560]
+        
+        cond_256 = cond[0][0].clone()
+        
+        if not hasattr(self, "W_pinv"):
+            self.W_pinv = torch.linalg.pinv(W.to(pinv_dtype).cuda()).to(W)
+        
+        #cond_256_embed = (cond_256 - b) @ torch.linalg.pinv(W.to(pinv_dtype)).T.to(dtype)
+        cond_embed256 = (cond_256 - b.to(cond_256)) @ self.W_pinv.T.to(cond_256)
+        
+        
+        
+        
+        
+        if mode == "AdaIN":
+            cond_embed256 = adain_seq_inplace(cond_embed256, dense_embed256)
+            #for adain_iter in range(EO("style_iter", 0)):
+            #    cond_embed256 = adain_seq_inplace(cond_embed256, dense_embed256)
+            #    cond_embed256 = (cond_embed256 - b) @ torch.linalg.pinv(W.to(pinv_dtype)).T.to(dtype)
+            #    cond_embed256 = F.linear(cond_embed256         .to(W), W, b).to(img)
+            #    cond_embed256 = adain_seq_inplace(cond_embed256, dense_embed256)
+
+        elif mode == "WCT":
+            if not hasattr(self, "dense_embed256") or self.dense_embed256 is None or self.dense_embed256.shape != dense_embed256.shape or torch.norm(self.dense_embed256 - dense_embed256) > 0:
+                self.dense_embed256 = dense_embed256
+                
+                f_s          = dense_embed256[0].clone()
+                self.mu_s    = f_s.mean(dim=0, keepdim=True)
+                f_s_centered = f_s - self.mu_s
+                
+                cov = (f_s_centered.T.double() @ f_s_centered.double()) / (f_s_centered.size(0) - 1)
+
+                S_eig, U_eig = torch.linalg.eigh((cov + 1e-5 * torch.eye(cov.size(0), dtype=cov.dtype, device=cov.device)).cuda())
+                S_eig = S_eig.to(cov)
+                U_eig = U_eig.to(cov)
+                
+                S_eig_sqrt    = S_eig.clamp(min=0).sqrt() # eigenvalues -> singular values
+                
+                whiten = U_eig @ torch.diag(S_eig_sqrt) @ U_eig.T
+                self.y0_color  = whiten.to(f_s_centered)
+
+            for wct_i in range(cond_embed256.shape[-3]):
+                f_c          = cond_embed256[wct_i].clone()
+                mu_c         = f_c.mean(dim=0, keepdim=True)
+                f_c_centered = f_c - mu_c
+                
+                cov = (f_c_centered.T.double() @ f_c_centered.double()) / (f_c_centered.size(0) - 1)
+
+                S_eig, U_eig  = torch.linalg.eigh((cov + 1e-5 * torch.eye(cov.size(0), dtype=cov.dtype, device=cov.device)).cuda())
+                S_eig = S_eig.to(cov)
+                U_eig = U_eig.to(cov)
+                
+                inv_sqrt_eig  = S_eig.clamp(min=0).rsqrt() 
+                
+                whiten = U_eig @ torch.diag(inv_sqrt_eig) @ U_eig.T
+                whiten = whiten.to(f_c_centered)
+
+                f_c_whitened = f_c_centered @ whiten.T
+                f_cs         = f_c_whitened @ self.y0_color.T + self.mu_s
+                
+                cond_embed256[wct_i] = f_cs
+    
+        cond[0][0] = self.redux_down(cond_embed256)
+        return (cond,)
+        
+        
+        
+
+def adain_seq_inplace(content: torch.Tensor, style: torch.Tensor, eps: float = 1e-7) -> torch.Tensor:
+    mean_c = content.mean(1, keepdim=True)
+    std_c  = content.std (1, keepdim=True).add_(eps)
+    mean_s = style.mean  (1, keepdim=True)
+    std_s  = style.std   (1, keepdim=True).add_(eps)
+
+    content.sub_(mean_c).div_(std_c).mul_(std_s).add_(mean_s)
+    return content
+
+
+def adain_seq(content: torch.Tensor, style: torch.Tensor, eps: float = 1e-7) -> torch.Tensor:
+    return ((content - content.mean(1, keepdim=True)) / (content.std(1, keepdim=True) + eps)) * (style.std(1, keepdim=True) + eps) + style.mean(1, keepdim=True)
+
diff --git a/custom_nodes/RES4LYF/hidream/model.py b/custom_nodes/RES4LYF/hidream/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a56f56f35e211d9350b3edb5430c441cd8b05e2
--- /dev/null
+++ b/custom_nodes/RES4LYF/hidream/model.py
@@ -0,0 +1,1490 @@
+import torch
+import torch.nn.functional as F
+import math
+
+import torch.nn as nn
+from torch import Tensor, FloatTensor
+from typing import Optional, Callable, Tuple, List, Dict, Any, Union, TYPE_CHECKING, TypeVar
+from dataclasses import dataclass
+
+import einops
+from einops import repeat, rearrange
+
+from comfy.ldm.lightricks.model import TimestepEmbedding, Timesteps
+import torch.nn.functional as F
+
+from comfy.ldm.flux.math import apply_rope, rope
+#from comfy.ldm.flux.layers import LastLayer
+#from ..flux.layers import LastLayer
+
+from comfy.ldm.modules.attention import optimized_attention, attention_pytorch
+import comfy.model_management
+import comfy.ldm.common_dit
+
+from ..helper  import ExtraOptions
+from ..latents import slerp_tensor, interpolate_spd, tile_latent, untile_latent, gaussian_blur_2d, median_blur_2d
+from ..style_transfer import StyleMMDiT_Model, apply_scattersort_masked, apply_scattersort_tiled, adain_seq_inplace, adain_patchwise_row_batch_med, adain_patchwise_row_batch, adain_seq, apply_scattersort
+
+@dataclass
+class ModulationOut:
+    shift: Tensor
+    scale: Tensor
+    gate : Tensor
+    
+
+
+class BlockType:
+    Double = 2
+    Single = 1
+    Zero   = 0
+
+
+#########################################################################################################################################################################
+class HDBlock(nn.Module):
+    def __init__(
+        self,
+        dim                   : int,
+        heads                 : int,
+        head_dim              : int,
+        num_routed_experts    : int = 4,
+        num_activated_experts : int = 2,
+        block_type            : BlockType = BlockType.Zero,
+        dtype=None, device=None, operations=None
+    ):
+        super().__init__()
+        block_classes = {
+            BlockType.Double : HDBlockDouble,
+            BlockType.Single : HDBlockSingle,
+        }
+        self.block = block_classes[block_type](dim, heads, head_dim, num_routed_experts, num_activated_experts, dtype=dtype, device=device, operations=operations)
+
+    def forward(
+        self,
+        img       :          FloatTensor,
+        img_masks : Optional[FloatTensor]  = None,
+        txt       : Optional[FloatTensor]  = None,
+        clip      :          FloatTensor   = None,
+        rope      :          FloatTensor   = None,
+        mask      : Optional[FloatTensor]  = None,
+        update_cross_attn : Optional[Dict] = None,
+        style_block  = None,
+    ) -> FloatTensor:
+        return self.block(img, img_masks, txt, clip, rope, mask, update_cross_attn, style_block=style_block)
+
+
+
+# Copied from https://github.com/black-forest-labs/flux/blob/main/src/flux/modules/layers.py
+class EmbedND(nn.Module):
+    def __init__(self, theta: int, axes_dim: List[int]):
+        super().__init__()
+        self.theta    = theta
+        self.axes_dim = axes_dim
+
+    def forward(self, ids: Tensor) -> Tensor:
+        n_axes = ids.shape[-1]
+        emb = torch.cat([  rope(ids[..., i], self.axes_dim[i], self.theta)   for i in range(n_axes)],    dim=-3,)
+        return emb.unsqueeze(2)
+
+class PatchEmbed(nn.Module):
+    def __init__(
+        self,
+        patch_size   = 2,
+        in_channels  = 4,
+        out_channels = 1024,
+        dtype=None, device=None, operations=None
+    ):
+        super().__init__()
+        self.patch_size   = patch_size
+        self.out_channels = out_channels
+        self.proj         = operations.Linear(in_channels * patch_size * patch_size, out_channels, bias=True, dtype=dtype, device=device)
+
+    def forward(self, latent):
+        latent = self.proj(latent)
+        return latent
+
+class PooledEmbed(nn.Module):
+    def __init__(self, text_emb_dim, hidden_size, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.pooled_embedder = TimestepEmbedding(in_channels=text_emb_dim, time_embed_dim=hidden_size, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, pooled_embed):
+        return self.pooled_embedder(pooled_embed)
+
+class TimestepEmbed(nn.Module):
+    def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.time_proj         = Timesteps       (num_channels=frequency_embedding_size, flip_sin_to_cos=True, downscale_freq_shift=0)
+        self.timestep_embedder = TimestepEmbedding(in_channels=frequency_embedding_size, time_embed_dim=hidden_size, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, t, wdtype):
+        t_emb = self.time_proj(t).to(dtype=wdtype)
+        t_emb = self.timestep_embedder(t_emb)
+        return t_emb
+
+class TextProjection(nn.Module):
+    def __init__(self, in_features, hidden_size, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.linear = operations.Linear(in_features=in_features, out_features=hidden_size, bias=False, dtype=dtype, device=device)
+
+    def forward(self, caption):
+        hidden_states = self.linear(caption)
+        return hidden_states
+
+
+
+
+
+
+class HDFeedForwardSwiGLU(nn.Module):
+    def __init__(
+        self,
+        dim                : int,
+        hidden_dim         : int,
+        multiple_of        : int             = 256,
+        ffn_dim_multiplier : Optional[float] = None,
+        dtype=None, device=None, operations=None
+    ):
+        super().__init__()
+        hidden_dim = int(2 * hidden_dim / 3)
+        
+        if ffn_dim_multiplier is not None:  # custom dim factor multiplier
+            hidden_dim = int(ffn_dim_multiplier * hidden_dim)
+        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+
+        self.w1 = operations.Linear(dim, hidden_dim, bias=False, dtype=dtype, device=device)
+        self.w2 = operations.Linear(hidden_dim, dim, bias=False, dtype=dtype, device=device)
+        self.w3 = operations.Linear(dim, hidden_dim, bias=False, dtype=dtype, device=device)
+
+    def forward(self, x, style_block=None): # 1,4096,2560 -> 
+        if style_block is not None and x.shape[0] > 1 and x.ndim == 3:
+            x1 = self.w1(x)
+            x1 = style_block(x1, "ff_1")
+            
+            x1 = torch.nn.functional.silu(x1)
+            x1 = style_block(x1, "ff_1_silu")
+            
+            x3 = self.w3(x)
+            x3 = style_block(x3, "ff_3")
+            
+            x13 = x1 * x3
+            x13 = style_block(x13, "ff_13")
+            
+            x2 = self.w2(x13)
+            x2 = style_block(x2, "ff_2")
+            
+            return x2
+        else:
+            return self.w2(torch.nn.functional.silu(self.w1(x)) * self.w3(x))
+
+# Modified from https://github.com/deepseek-ai/DeepSeek-V3/blob/main/inference/model.py
+class HDMoEGate(nn.Module):
+    def __init__(self, dim, num_routed_experts=4, num_activated_experts=2, dtype=None, device=None):
+        super().__init__()
+        self.top_k            = num_activated_experts # 2
+        self.n_routed_experts = num_routed_experts    # 4
+        self.gating_dim       = dim                   # 2560
+        self.weight           = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim), dtype=dtype, device=device))
+
+    def forward(self, x):
+        dtype = self.weight.dtype
+        if dtype not in {torch.bfloat16, torch.float16, torch.float32, torch.float64}:
+            dtype = torch.float32
+            self.weight.data = self.weight.data.to(dtype)
+        
+        logits = F.linear(x.to(dtype), self.weight.to(x.device), None)
+        scores = logits.softmax(dim=-1).to(x)       # logits.shape == 4032,4   scores.shape == 4032,4
+        return torch.topk(scores, k=self.top_k, dim=-1, sorted=False)
+
+class HDMOEFeedForwardSwiGLU(nn.Module):
+    def __init__(
+        self,
+        dim                   : int,
+        hidden_dim            : int,
+        num_routed_experts    : int,
+        num_activated_experts : int,
+        dtype=None, device=None, operations=None
+    ):
+        super().__init__()
+        self.shared_experts =                HDFeedForwardSwiGLU(dim, hidden_dim // 2,  dtype=dtype, device=device, operations=operations)
+        self.experts        = nn.ModuleList([HDFeedForwardSwiGLU(dim, hidden_dim     ,  dtype=dtype, device=device, operations=operations) for i in range(num_routed_experts)])
+        self.gate           = HDMoEGate(dim, num_routed_experts, num_activated_experts, dtype=dtype, device=device)
+        self.num_activated_experts = num_activated_experts
+
+    def forward(self, x, style_block=None):
+        y_shared = self.shared_experts(x, style_block.FF_SHARED)   # 1,4096,2560 -> 1,4096,2560 
+        y_shared = style_block(y_shared, "shared")
+
+        topk_weight, topk_idx = self.gate(x) # -> 4096,2   4096,2
+        topk_weight = style_block(topk_weight, "topk_weight")
+                
+        if y_shared.shape[0] > 1 and style_block.gate[0] and not HDModel.RECON_MODE:
+            topk_idx[0] = topk_idx[1]
+        tk_idx_flat = topk_idx.view(topk_idx.shape[0], -1) 
+        
+        x = x.repeat_interleave(self.num_activated_experts, dim=-2)
+        y = torch.empty_like(x)
+        
+        if style_block.gate[0] and not HDModel.RECON_MODE and y_shared.shape[0] > 1:
+            for i, expert in enumerate(self.experts): # TODO: check for empty expert lists and continue if found to avoid CUBLAS errors
+                x_list = []
+                for b in range(x.shape[0]):
+                    x_sel = x[b][tk_idx_flat[b]==i]
+                    x_list.append(x_sel)
+                x_list = torch.stack(x_list, dim=0)
+                x_out = expert(x_list, style_block.FF_SEPARATE).to(x.dtype)
+                for b in range(y.shape[0]):
+                    y[b][tk_idx_flat[b]==i] = x_out[b]
+        else:
+            for i, expert in enumerate(self.experts): 
+                x_sel = x[tk_idx_flat == i, :]
+                if x_sel.shape[0] == 0:
+                    continue 
+                y[tk_idx_flat == i, :] = expert(x_sel).to(x.dtype)
+                
+        y = style_block(y, "separate")
+
+        y_sum = torch.einsum('abk,abkd->abd', topk_weight, y.view(*topk_weight.shape, -1))
+        
+        y_sum = style_block(y_sum, "sum")
+        
+        y_sum = y_sum.view_as(y_shared) + y_shared
+
+        y_sum = style_block(y_sum, "out")
+        
+        return y_sum
+
+
+def apply_passthrough(denoised_embed, *args, **kwargs):
+    return denoised_embed
+
+class AttentionBuffer:
+    buffer = {}
+
+
+def attention(q: Tensor, k: Tensor, v: Tensor, rope: Tensor, mask: Optional[Tensor] = None):
+    q, k = apply_rope(q, k, rope)
+    if mask is not None:
+        AttentionBuffer.buffer = attention_pytorch(
+            q.view(q.shape[0], -1, q.shape[-1] * q.shape[-2]), 
+            k.view(k.shape[0], -1, k.shape[-1] * k.shape[-2]), 
+            v.view(v.shape[0], -1, v.shape[-1] * v.shape[-2]), 
+            q.shape[2],
+            mask=mask,
+            )
+    else:
+        AttentionBuffer.buffer = optimized_attention(
+            q.view(q.shape[0], -1, q.shape[-1] * q.shape[-2]), 
+            k.view(k.shape[0], -1, k.shape[-1] * k.shape[-2]), 
+            v.view(v.shape[0], -1, v.shape[-1] * v.shape[-2]), 
+            q.shape[2],
+            mask=mask,
+            )
+    return AttentionBuffer.buffer
+
+class HDAttention(nn.Module):
+    def __init__(
+        self,
+        query_dim        : int,
+        heads            : int   = 8,
+        dim_head         : int   = 64,
+
+        eps              : float = 1e-5,
+        out_dim          : int   = None,
+        single           : bool  = False,
+        dtype=None, device=None, operations=None
+    ):
+
+        super().__init__()
+        self.inner_dim          = out_dim if out_dim is not None else dim_head * heads
+        self.query_dim          = query_dim
+        self.out_dim            = out_dim if out_dim is not None else query_dim
+
+        self.heads              = out_dim // dim_head if out_dim is not None else heads
+        self.single             = single
+
+        self.to_q               = operations.Linear (self.query_dim, self.inner_dim, dtype=dtype, device=device)
+        self.to_k               = operations.Linear (self.inner_dim, self.inner_dim, dtype=dtype, device=device)
+        self.to_v               = operations.Linear (self.inner_dim, self.inner_dim, dtype=dtype, device=device)
+        self.to_out             = operations.Linear (self.inner_dim, self.out_dim,   dtype=dtype, device=device)
+        self.q_rms_norm         = operations.RMSNorm(self.inner_dim, eps,            dtype=dtype, device=device)
+        self.k_rms_norm         = operations.RMSNorm(self.inner_dim, eps,            dtype=dtype, device=device)
+
+        if not single:
+            self.to_q_t         = operations.Linear (self.query_dim, self.inner_dim, dtype=dtype, device=device)
+            self.to_k_t         = operations.Linear (self.inner_dim, self.inner_dim, dtype=dtype, device=device)
+            self.to_v_t         = operations.Linear (self.inner_dim, self.inner_dim, dtype=dtype, device=device)
+            self.to_out_t       = operations.Linear (self.inner_dim, self.out_dim,   dtype=dtype, device=device)
+            self.q_rms_norm_t   = operations.RMSNorm(self.inner_dim, eps,            dtype=dtype, device=device)
+            self.k_rms_norm_t   = operations.RMSNorm(self.inner_dim, eps,            dtype=dtype, device=device)
+
+    def forward(
+        self,
+        img       :          FloatTensor,
+        img_masks : Optional[FloatTensor] = None,
+        txt       : Optional[FloatTensor] = None,
+        rope      :          FloatTensor  = None,
+        mask      : Optional[FloatTensor] = None,
+        update_cross_attn : Optional[Dict]= None,
+        style_block = None,
+    ) -> Tensor:
+        bsz = img.shape[0]
+        
+        img_q = self.to_q(img)
+        img_k = self.to_k(img)
+        img_v = self.to_v(img)
+        
+        img_q = style_block.img.ATTN(img_q, "q_proj")
+        img_k = style_block.img.ATTN(img_k, "k_proj")
+        img_v = style_block.img.ATTN(img_v, "v_proj")
+        
+        img_q = self.q_rms_norm(img_q)
+        img_k = self.k_rms_norm(img_k)
+        
+        img_q = style_block.img.ATTN(img_q, "q_norm")
+        img_k = style_block.img.ATTN(img_k, "k_norm")
+
+        inner_dim = img_k.shape[-1]
+        head_dim  = inner_dim // self.heads
+
+        img_q = img_q.view(bsz, -1, self.heads, head_dim)
+        img_k = img_k.view(bsz, -1, self.heads, head_dim)
+        img_v = img_v.view(bsz, -1, self.heads, head_dim)
+        
+        if img_masks is not None:
+            img_k = img_k * img_masks.view(bsz, -1, 1, 1)
+
+        if self.single:
+            attn = attention(img_q, img_k, img_v, rope=rope, mask=mask)
+            attn = style_block.img.ATTN(attn, "out")
+            return self.to_out(attn)
+        else:
+            txt_q = self.to_q_t(txt)
+            txt_k = self.to_k_t(txt)
+            txt_v = self.to_v_t(txt)
+            
+            txt_q = style_block.txt.ATTN(txt_q, "q_proj")
+            txt_k = style_block.txt.ATTN(txt_k, "k_proj")
+            txt_v = style_block.txt.ATTN(txt_v, "v_proj")
+            
+            txt_q = self.q_rms_norm_t(txt_q)
+            txt_k = self.k_rms_norm_t(txt_k)
+            
+            txt_q = style_block.txt.ATTN(txt_q, "q_norm")
+            txt_k = style_block.txt.ATTN(txt_k, "k_norm")
+
+            txt_q   = txt_q.view(bsz, -1, self.heads, head_dim)
+            txt_k   = txt_k.view(bsz, -1, self.heads, head_dim)
+            txt_v   = txt_v.view(bsz, -1, self.heads, head_dim)
+            
+            img_len = img_q.shape[1]
+            txt_len = txt_q.shape[1]
+            
+            attn    = attention(torch.cat([img_q, txt_q], dim=1), 
+                                torch.cat([img_k, txt_k], dim=1), 
+                                torch.cat([img_v, txt_v], dim=1), rope=rope, mask=mask)
+            
+            img_attn, txt_attn = torch.split(attn, [img_len, txt_len], dim=1)   #1, 4480, 2560
+            
+            img_attn = style_block.img.ATTN(img_attn, "out")
+            txt_attn = style_block.txt.ATTN(txt_attn, "out")
+
+            if update_cross_attn is not None:
+                if not update_cross_attn['skip_cross_attn']:
+                    UNCOND      = update_cross_attn['UNCOND']
+                    
+                    if UNCOND:
+                        llama_start = update_cross_attn['src_llama_start']
+                        llama_end   = update_cross_attn['src_llama_end']
+                        t5_start    = update_cross_attn['src_t5_start']
+                        t5_end      = update_cross_attn['src_t5_end']
+                    
+                        txt_src    = torch.cat([txt[:,t5_start:t5_end,:], txt[:,128+llama_start:128+llama_end,:], txt[:,256+llama_start:256+llama_end],], dim=-2).float()
+                        self.c_src = txt_src.transpose(-2,-1).squeeze(0)    # shape [C,1]
+                    else:
+                        llama_start = update_cross_attn['tgt_llama_start']
+                        llama_end   = update_cross_attn['tgt_llama_end']
+                        t5_start    = update_cross_attn['tgt_t5_start']
+                        t5_end      = update_cross_attn['tgt_t5_end']
+                        
+                        lamb  = update_cross_attn['lamb']
+                        erase = update_cross_attn['erase']
+                        
+                        txt_guide = torch.cat([txt[:,t5_start:t5_end,:], txt[:,128+llama_start:128+llama_end,:], txt[:,256+llama_start:256+llama_end],], dim=-2).float()
+                        c_guide   = txt_guide.transpose(-2,-1).squeeze(0)  # [C,1]
+                        
+                        Wv_old       = self.to_v_t.weight.data.float()              # [C,C]
+                        Wk_old       = self.to_k_t.weight.data.float()              # [C,C]
+
+                        v_star       = Wv_old @ c_guide                             # [C,1]
+                        k_star       = Wk_old @ c_guide                             # [C,1]
+
+                        c_src        = self.c_src                                   # [C,1]
+
+                        erase_scale  = erase
+                        d            = c_src.shape[0]
+
+                        C            = c_src @ c_src.T                              # [C,C]
+                        I            = torch.eye(d, device=C.device, dtype=C.dtype)
+
+                        mat1_v       = lamb*Wv_old + erase_scale*(v_star @ c_src.T)     # [C,C]
+                        mat2_v       = lamb*I      + erase_scale*(C)                    # [C,C]
+                        Wv_new       = mat1_v @ torch.inverse(mat2_v)                   # [C,C]
+
+                        mat1_k       = lamb*Wk_old + erase_scale*(k_star @ c_src.T)     # [C,C]
+                        mat2_k       = lamb*I      + erase_scale*(C)                    # [C,C]
+                        Wk_new       = mat1_k @ torch.inverse(mat2_k)                   # [C,C]
+
+                        self.to_v_t.weight.data.copy_(Wv_new.to(self.to_v_t.weight.data.dtype))
+                        self.to_k_t.weight.data.copy_(Wk_new.to(self.to_k_t.weight.data.dtype))
+                
+            return self.to_out(img_attn), self.to_out_t(txt_attn)
+
+    
+    
+    
+#########################################################################################################################################################################
+class HDBlockDouble(nn.Module):
+    buffer = {}
+    
+    def __init__(
+        self,
+        dim                   : int,
+        heads                 : int,
+        head_dim              : int,
+        num_routed_experts    : int = 4,
+        num_activated_experts : int = 2,
+        dtype=None, device=None, operations=None
+    ):
+        super().__init__()
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(dim, 12*dim, bias=True,                                               dtype=dtype, device=device)
+        )
+
+        self.norm1_i = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False,           dtype=dtype, device=device)
+        self.norm1_t = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False,           dtype=dtype, device=device)
+        
+        self.attn1   = HDAttention         (dim, heads, head_dim, single=False,                     dtype=dtype, device=device, operations=operations)
+
+        self.norm3_i = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False,           dtype=dtype, device=device)
+        self.ff_i    = HDMOEFeedForwardSwiGLU(dim, 4*dim, num_routed_experts, num_activated_experts,  dtype=dtype, device=device, operations=operations)
+        
+        self.norm3_t = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False,           dtype=dtype, device=device)                                 
+        self.ff_t    =  HDFeedForwardSwiGLU(dim, 4*dim,                                             dtype=dtype, device=device, operations=operations)
+
+    def forward(
+        self,
+        img       :          FloatTensor,
+        img_masks : Optional[FloatTensor] = None,
+        txt       : Optional[FloatTensor] = None,
+        clip      : Optional[FloatTensor] = None,    # clip = t + p_embedder (from pooled)
+        rope      :          FloatTensor  = None,
+        mask      : Optional[FloatTensor] = None,
+        update_cross_attn : Optional[Dict]= None,
+        style_block = None,
+    ) -> FloatTensor:
+        
+        img_msa_shift, img_msa_scale, img_msa_gate, img_mlp_shift, img_mlp_scale, img_mlp_gate, \
+        txt_msa_shift, txt_msa_scale, txt_msa_gate, txt_mlp_shift, txt_mlp_scale, txt_mlp_gate = self.adaLN_modulation(clip)[:,None].chunk(12, dim=-1)      # 1,1,2560           
+
+        img_norm = self.norm1_i(img)
+        txt_norm = self.norm1_t(txt)
+        
+        img_norm = style_block.img(img_norm, "attn_norm")
+        txt_norm = style_block.txt(txt_norm, "attn_norm")
+        
+        img_norm = img_norm * (1+img_msa_scale) + img_msa_shift
+        txt_norm = txt_norm * (1+txt_msa_scale) + txt_msa_shift
+        
+        img_norm = style_block.img(img_norm, "attn_norm_mod")
+        txt_norm = style_block.txt(txt_norm, "attn_norm_mod")
+
+        img_attn, txt_attn = self.attn1(img_norm, img_masks, txt_norm, rope=rope, mask=mask, update_cross_attn=update_cross_attn, style_block=style_block)
+        
+        img_attn = style_block.img(img_attn, "attn")
+        txt_attn = style_block.txt(txt_attn, "attn")
+
+        img_attn *= img_msa_gate
+        txt_attn *= txt_msa_gate
+
+        img_attn = style_block.img(img_attn, "attn_gated")
+        txt_attn = style_block.txt(txt_attn, "attn_gated")
+
+        img += img_attn
+        txt += txt_attn
+
+        img = style_block.img(img, "attn_res")
+        txt = style_block.txt(txt, "attn_res")
+
+        # FEED FORWARD
+
+        img_norm = self.norm3_i(img)
+        txt_norm = self.norm3_t(txt)
+
+        img_norm = style_block.img(img_norm, "ff_norm")
+        txt_norm = style_block.txt(txt_norm, "ff_norm")
+
+        img_norm = img_norm * (1+img_mlp_scale) + img_mlp_shift
+        txt_norm = txt_norm * (1+txt_mlp_scale) + txt_mlp_shift
+
+        img_norm = style_block.img(img_norm, "ff_norm_mod")
+        txt_norm = style_block.txt(txt_norm, "ff_norm_mod")
+
+        img_ff_i = self.ff_i(img_norm, style_block.img.FF)
+        txt_ff_t = self.ff_t(txt_norm, style_block.txt.FF)
+        
+        img_ff_i = style_block.img(img_ff_i, "ff")
+        txt_ff_t = style_block.txt(txt_ff_t, "ff")
+        
+        img_ff_i *= img_mlp_gate
+        txt_ff_t *= txt_mlp_gate
+
+        img_ff_i = style_block.img(img_ff_i, "ff_gated")
+        txt_ff_t = style_block.txt(txt_ff_t, "ff_gated")
+
+        img += img_ff_i
+        txt += txt_ff_t
+        
+        img = style_block.img(img, "ff_res")
+        txt = style_block.txt(txt, "ff_res")
+
+        return img, txt
+
+
+#########################################################################################################################################################################
+class HDBlockSingle(nn.Module):
+    buffer = {}
+    
+    def __init__(
+        self,
+        dim                   : int,
+        heads                 : int,
+        head_dim              : int,
+        num_routed_experts    : int = 4,
+        num_activated_experts : int = 2,
+        dtype=None, device=None, operations=None
+    ):
+        super().__init__()
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(dim, 6 * dim, bias=True,                                               dtype=dtype, device=device)
+        )
+
+        self.norm1_i = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False,            dtype=dtype, device=device)
+        self.attn1   = HDAttention         (dim, heads, head_dim, single=True,                       dtype=dtype, device=device, operations=operations)
+
+        self.norm3_i = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False,            dtype=dtype, device=device)
+        self.ff_i    = HDMOEFeedForwardSwiGLU(dim, 4*dim, num_routed_experts, num_activated_experts, dtype=dtype, device=device, operations=operations)
+
+    def forward(
+        self,
+        img        :          FloatTensor,
+        img_masks  : Optional[FloatTensor]  = None,
+        txt        : Optional[FloatTensor]  = None,
+        clip       : Optional[FloatTensor]  = None,
+        rope       :          FloatTensor   = None,
+        mask       : Optional[FloatTensor]  = None,
+        update_cross_attn : Optional[Dict] = None,
+        style_block = None,
+    ) -> FloatTensor:
+        
+        img_msa_shift, img_msa_scale, img_msa_gate, img_mlp_shift, img_mlp_scale, img_mlp_gate = self.adaLN_modulation(clip)[:,None].chunk(6, dim=-1)
+
+        img_norm = self.norm1_i(img)  
+        img_norm = style_block.img(img_norm, "attn_norm")        #
+        
+        img_norm = img_norm * (1+img_msa_scale) + img_msa_shift
+        img_norm = style_block.img(img_norm, "attn_norm_mod")    #
+
+        img_attn = self.attn1(img_norm, img_masks, rope=rope, mask=mask, style_block=style_block)
+        img_attn = style_block.img(img_attn, "attn")
+
+        img_attn *= img_msa_gate
+        img_attn = style_block.img(img_attn, "attn_gated")
+
+        img += img_attn
+        img = style_block.img(img, "attn_res")
+
+        img_norm = self.norm3_i(img)
+        img_norm = style_block.img(img_norm, "ff_norm")
+        
+        img_norm = img_norm * (1+img_mlp_scale) + img_mlp_shift
+        img_norm = style_block.img(img_norm, "ff_norm_mod")
+
+        img_ff_i = self.ff_i(img_norm, style_block.img.FF)
+        img_ff_i = style_block.img(img_ff_i, "ff")            # fused... "ff" + "attn"
+        
+        img_ff_i *= img_mlp_gate
+        img_ff_i = style_block.img(img_ff_i, "ff_gated")         # 
+
+        img += img_ff_i
+        img = style_block.img(img, "ff_res")       # 
+
+        return img
+
+
+#########################################################################################################################################################################
+class HDModel(nn.Module):
+    CHANNELS   = 2560
+    RECON_MODE = False
+
+    def __init__(
+        self,
+        patch_size            : Optional[int]   = None,
+        in_channels           : int             = 64,
+        out_channels          : Optional[int]   = None,
+        num_layers            : int             = 16,
+        num_single_layers     : int             = 32,
+        attention_head_dim    : int             = 128,
+        num_attention_heads   : int             = 20,
+        caption_channels      : List[int]       = None,
+        text_emb_dim          : int             = 2048,
+        num_routed_experts    : int             = 4,
+        num_activated_experts : int             = 2,
+        axes_dims_rope        : Tuple[int, int] = ( 32,  32),
+        max_resolution        : Tuple[int, int] = (128, 128),
+        llama_layers          : List[int]       = None,
+        image_model                             = None,     # unused, what was this supposed to be??
+        dtype=None, device=None, operations=None
+    ):
+        self.patch_size          = patch_size
+        self.num_attention_heads = num_attention_heads
+        self.attention_head_dim  = attention_head_dim
+        self.num_layers          = num_layers
+        self.num_single_layers   = num_single_layers
+
+        self.gradient_checkpointing = False
+
+        super().__init__()
+        self.dtype        = dtype
+        self.out_channels = out_channels or in_channels
+        self.inner_dim    = self.num_attention_heads * self.attention_head_dim
+        self.llama_layers = llama_layers
+
+        self.t_embedder   = TimestepEmbed(              self.inner_dim, dtype=dtype, device=device, operations=operations)
+        self.p_embedder   =   PooledEmbed(text_emb_dim, self.inner_dim, dtype=dtype, device=device, operations=operations)
+        self.x_embedder   =    PatchEmbed(
+            patch_size   = patch_size,
+            in_channels  = in_channels,
+            out_channels = self.inner_dim,
+            dtype=dtype, device=device, operations=operations
+        )
+        self.pe_embedder = EmbedND(theta=10000, axes_dim=axes_dims_rope)
+
+        self.double_stream_blocks = nn.ModuleList(
+            [
+                HDBlock(
+                    dim                   = self.inner_dim,
+                    heads                 = self.num_attention_heads,
+                    head_dim              = self.attention_head_dim,
+                    num_routed_experts    = num_routed_experts,
+                    num_activated_experts = num_activated_experts,
+                    block_type            = BlockType.Double,
+                    dtype=dtype, device=device, operations=operations
+                )
+                for i in range(self.num_layers)
+            ]
+        )
+
+        self.single_stream_blocks = nn.ModuleList(
+            [
+                HDBlock(
+                    dim                   = self.inner_dim,
+                    heads                 = self.num_attention_heads,
+                    head_dim              = self.attention_head_dim,
+                    num_routed_experts    = num_routed_experts,
+                    num_activated_experts = num_activated_experts,
+                    block_type            = BlockType.Single,
+                    dtype=dtype, device=device, operations=operations
+                )
+                for i in range(self.num_single_layers)
+            ]
+        )
+
+        self.final_layer = HDLastLayer(self.inner_dim, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations)
+
+        caption_channels   = [caption_channels[1], ] * (num_layers + num_single_layers) + [caption_channels[0], ]
+        caption_projection = []
+        for caption_channel in caption_channels:
+            caption_projection.append(TextProjection(in_features=caption_channel, hidden_size=self.inner_dim, dtype=dtype, device=device, operations=operations))
+        self.caption_projection = nn.ModuleList(caption_projection)
+        self.max_seq            = max_resolution[0] * max_resolution[1] // (patch_size * patch_size)
+
+    def prepare_contexts(self, llama3, context, bsz, img_num_fea):
+        contexts = llama3.movedim(1, 0)
+        contexts = [contexts[k] for k in self.llama_layers]    # len == 48..... of tensors that are 1,143,4096
+
+        if self.caption_projection is not None:
+            contexts_list = []
+            for i, cxt in enumerate(contexts):
+                cxt = self.caption_projection[i](cxt)                          # linear in_features=4096, out_features=2560      len(self.caption_projection) == 49
+                cxt = cxt.view(bsz, -1, img_num_fea)
+                contexts_list.append(cxt)
+            contexts = contexts_list
+            context  = self.caption_projection[-1](context)
+            context  = context.view(bsz, -1, img_num_fea)
+            
+            contexts.append(context)                      # len == 49...... of tensors that are 1,143,2560.   last chunk is T5
+
+        return contexts
+
+    ### FORWARD ... FORWARD ... FORWARD ... FORWARD ... FORWARD ... FORWARD ... FORWARD ... FORWARD ... FORWARD ... FORWARD ... FORWARD ... FORWARD ... FORWARD ###
+    def forward(
+        self,
+        x       :          Tensor,
+        t       :          Tensor,
+        y       : Optional[Tensor]   = None,
+        context : Optional[Tensor]   = None,
+        encoder_hidden_states_llama3 = None,  # 1,32,143,4096
+        image_cond                   = None,  # HiDream E1
+        control                      = None,
+        transformer_options          = {},
+        mask    : Optional[Tensor]   = None,
+    ) -> Tensor:
+        x_orig      = x.clone()
+        b, c, h, w  = x.shape
+        if image_cond is not None: # HiDream E1
+            x = torch.cat([x, image_cond], dim=-1)
+        h_len = ((h + (self.patch_size // 2)) // self.patch_size) # h_len 96
+        w_len = ((w + (self.patch_size // 2)) // self.patch_size) # w_len 96
+        img_len = h_len * w_len
+        txt_slice = slice(img_len, None)
+        img_slice = slice(None, img_len)
+        SIGMA = t[0].clone() / 1000
+        EO = transformer_options.get("ExtraOptions", ExtraOptions(""))
+        if EO is not None:
+            EO.mute = True
+
+        if EO("zero_heads"):
+            HEADS = 0
+        else:
+            HEADS = 20
+
+        StyleMMDiT = transformer_options.get('StyleMMDiT', StyleMMDiT_Model())        
+        StyleMMDiT.set_len(h_len, w_len, img_slice, txt_slice, HEADS=HEADS)
+        StyleMMDiT.Retrojector = self.Retrojector if hasattr(self, "Retrojector") else None
+        transformer_options['StyleMMDiT'] = None
+
+        x_tmp = transformer_options.get("x_tmp")
+        if x_tmp is not None:
+            x_tmp = x_tmp.expand(x.shape[0], -1, -1, -1).clone()
+            img = comfy.ldm.common_dit.pad_to_patch_size(x_tmp, (self.patch_size, self.patch_size))
+        else:
+            img = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
+        
+        y0_style, img_y0_style = None, None
+
+        img_orig, t_orig, y_orig, context_orig, llama3_orig = clone_inputs(img, t, y, context, encoder_hidden_states_llama3)
+    
+        weight    = -1 * transformer_options.get("regional_conditioning_weight", 0.0)
+        floor     = -1 * transformer_options.get("regional_conditioning_floor",  0.0)
+        update_cross_attn = transformer_options.get("update_cross_attn")
+    
+        z_ = transformer_options.get("z_")   # initial noise and/or image+noise from start of rk_sampler_beta() 
+        rk_row = transformer_options.get("row") # for "smart noise"
+        if z_ is not None:
+            x_init = z_[rk_row].to(x)
+        elif 'x_init' in transformer_options:
+            x_init = transformer_options.get('x_init').to(x)
+
+        # recon loop to extract exact noise pred for scattersort guide assembly
+        HDModel.RECON_MODE = StyleMMDiT.noise_mode == "recon"
+        recon_iterations = 2 if StyleMMDiT.noise_mode == "recon" else 1
+        for recon_iter in range(recon_iterations):
+            y0_style = StyleMMDiT.guides
+            y0_style_active = True if type(y0_style) == torch.Tensor else False
+            
+            HDModel.RECON_MODE = True     if StyleMMDiT.noise_mode == "recon" and recon_iter == 0     else False
+            
+            if StyleMMDiT.noise_mode == "recon" and recon_iter == 1:
+                x_recon = x_tmp if x_tmp is not None else x_orig
+                noise_prediction = x_recon + (1-SIGMA.to(x_recon)) * eps.to(x_recon)
+                denoised = x_recon - SIGMA.to(x_recon) * eps.to(x_recon)
+                
+                denoised = StyleMMDiT.apply_recon_lure(denoised, y0_style)
+
+                new_x = (1-SIGMA.to(denoised)) * denoised + SIGMA.to(denoised) * noise_prediction
+                img_orig = img = comfy.ldm.common_dit.pad_to_patch_size(new_x, (self.patch_size, self.patch_size))
+                
+                x_init = noise_prediction
+            elif StyleMMDiT.noise_mode == "bonanza":
+                x_init = torch.randn_like(x_init)
+
+            if y0_style_active:
+                SIGMA_ADAIN         = (SIGMA * EO("eps_adain_sigma_factor", 1.0)).to(y0_style)
+                y0_style_noised     = (1-SIGMA_ADAIN) * y0_style + SIGMA_ADAIN * x_init[0:1].to(y0_style)   #always only use first batch of noise to avoid broadcasting
+                img_y0_style_orig   = comfy.ldm.common_dit.pad_to_patch_size(y0_style_noised, (self.patch_size, self.patch_size))
+
+            mask_zero = None
+            
+            out_list = []
+            for cond_iter in range(len(transformer_options['cond_or_uncond'])):
+                UNCOND = transformer_options['cond_or_uncond'][cond_iter] == 1
+                
+                if update_cross_attn is not None:
+                    update_cross_attn['UNCOND'] = UNCOND
+
+                bsz_style = y0_style.shape[0] if y0_style_active else 0
+                bsz       = 1 if HDModel.RECON_MODE else bsz_style + 1
+
+                img, t, y, context, llama3 = clone_inputs(img_orig, t_orig, y_orig, context_orig, llama3_orig, index=cond_iter)
+                
+                mask = None
+                if not UNCOND and 'AttnMask' in transformer_options: # and weight != 0:
+                    AttnMask = transformer_options['AttnMask']
+                    mask = transformer_options['AttnMask'].attn_mask.mask.to('cuda')
+                    if mask_zero is None:
+                        mask_zero = torch.ones_like(mask)
+                        #img_len = transformer_options['AttnMask'].img_len
+                        mask_zero[img_len:, img_len:] = mask[img_len:, img_len:]
+
+                    if weight == 0:
+                        context = transformer_options['RegContext'].context.to(context.dtype).to(context.device)
+                        context = context.view(128, -1, context.shape[-1]).sum(dim=-2)                                    # 128 !!!
+                        llama3  = transformer_options['RegContext'].llama3 .to(llama3 .dtype).to(llama3 .device)
+                        mask = None
+                    else:
+                        context = transformer_options['RegContext'].context.to(context.dtype).to(context.device)
+                        llama3  = transformer_options['RegContext'].llama3 .to(llama3 .dtype).to(llama3 .device)
+
+                if UNCOND and 'AttnMask_neg' in transformer_options: # and weight != 0:
+                    AttnMask = transformer_options['AttnMask_neg']
+                    mask = transformer_options['AttnMask_neg'].attn_mask.mask.to('cuda')
+                    if mask_zero is None:
+                        mask_zero = torch.ones_like(mask)
+                        img_len = transformer_options['AttnMask_neg'].img_len
+                        mask_zero[img_len:, img_len:] = mask[img_len:, img_len:]
+
+                    if weight == 0:
+                        context = transformer_options['RegContext_neg'].context.to(context.dtype).to(context.device)
+                        context = context.view(128, -1, context.shape[-1]).sum(dim=-2)                                    # 128 !!!
+                        llama3  = transformer_options['RegContext_neg'].llama3 .to(llama3 .dtype).to(llama3 .device)
+                        mask = None
+
+                    else:
+                        context = transformer_options['RegContext_neg'].context.to(context.dtype).to(context.device)
+                        llama3  = transformer_options['RegContext_neg'].llama3 .to(llama3 .dtype).to(llama3 .device)
+
+                elif UNCOND and 'AttnMask' in transformer_options:
+                    AttnMask = transformer_options['AttnMask']
+                    mask = transformer_options['AttnMask'].attn_mask.mask.to('cuda')
+                    
+                    if mask_zero is None:
+                        mask_zero = torch.ones_like(mask)
+                        #img_len = transformer_options['AttnMask'].img_len
+                        mask_zero[img_len:, img_len:] = mask[img_len:, img_len:]
+                    if weight == 0:                                                                             # ADDED 5/23/2025
+                        context = transformer_options['RegContext'].context.to(context.dtype).to(context.device)  # ADDED 5/26/2025 14:53
+                        context = context.view(128, -1, context.shape[-1]).sum(dim=-2)                                    # 128 !!!
+                        llama3  = transformer_options['RegContext'].llama3 .to(llama3 .dtype).to(llama3 .device)
+                        mask = None
+                    else:
+                        A       = context
+                        B       = transformer_options['RegContext'].context
+                        context = A.repeat(1,    (B.shape[1] // A.shape[1]) + 1, 1)[:,   :B.shape[1], :]
+
+                        A       = llama3
+                        B       = transformer_options['RegContext'].llama3
+                        llama3  = A.repeat(1, 1, (B.shape[2] // A.shape[2]) + 1, 1)[:,:, :B.shape[2], :]
+
+                if y0_style_active and not HDModel.RECON_MODE:
+                    if mask is None:
+                        context, y, llama3 = StyleMMDiT.apply_style_conditioning(
+                            UNCOND = UNCOND,
+                            base_context       = context,
+                            base_y             = y,
+                            base_llama3        = llama3,
+                        )
+                    else:
+                        context = context.repeat(bsz_style + 1, 1, 1)
+                        y = y.repeat(bsz_style + 1, 1)                   if y      is not None else None
+                        llama3  =  llama3.repeat(bsz_style + 1, 1, 1, 1) if llama3 is not None else None
+                    img_y0_style = img_y0_style_orig.clone()
+
+                if mask is not None and not type(mask[0][0].item()) == bool:
+                    mask = mask.to(x.dtype)
+                if mask_zero is not None and not type(mask_zero[0][0].item()) == bool:
+                    mask_zero = mask_zero.to(x.dtype)
+
+                # prep embeds
+                t    = self.expand_timesteps(t, bsz, x.device)
+                t    = self.t_embedder      (t,      x.dtype)
+                clip = t + self.p_embedder(y)
+                
+        
+                x_embedder_dtype = self.x_embedder.proj.weight.data.dtype
+                if x_embedder_dtype not in {torch.bfloat16, torch.float16, torch.float32, torch.float64}:
+                    x_embedder_dtype = x.dtype
+                
+                img_sizes = None
+                img, img_masks, img_sizes = self.patchify(img, self.max_seq, img_sizes)   # for 1024x1024: output is   1,4096,64   None   [[64,64]]     hidden_states rearranged not shrunk, patch_size 1x1???
+                if img_masks is None:
+                    pH, pW          = img_sizes[0]
+                    img_ids         = torch.zeros(pH, pW, 3, device=img.device)
+                    img_ids[..., 1] = img_ids[..., 1] + torch.arange(pH, device=img.device)[:, None]
+                    img_ids[..., 2] = img_ids[..., 2] + torch.arange(pW, device=img.device)[None, :]
+                    img_ids         = repeat(img_ids, "h w c -> b (h w) c", b=bsz)
+                img = self.x_embedder(img.to(x_embedder_dtype))
+                #img_len = img.shape[-2]
+
+                if y0_style_active and not HDModel.RECON_MODE:
+                    img_y0_style, _, _ = self.patchify(img_y0_style_orig.clone(), self.max_seq, None)   # for 1024x1024: output is   1,4096,64   None   [[64,64]]     hidden_states rearranged not shrunk, patch_size 1x1???
+                    img_y0_style = self.x_embedder(img_y0_style.to(x_embedder_dtype))  # hidden_states 1,4032,2560         for 1024x1024: -> 1,4096,2560      ,64 -> ,2560 (x40)
+                    img = torch.cat([img, img_y0_style], dim=0)
+
+                contexts = self.prepare_contexts(llama3, context, bsz, img.shape[-1])
+
+                # txt_ids -> 1,414,3
+                txt_ids = torch.zeros(bsz,   contexts[-1].shape[1] + contexts[-2].shape[1] + contexts[0].shape[1],     3,    device=img_ids.device, dtype=img_ids.dtype)
+                ids     = torch.cat((img_ids, txt_ids), dim=-2)   # ids -> 1,4446,3
+                rope    = self.pe_embedder(ids)                  # rope -> 1, 4446, 1, 64, 2, 2
+
+                txt_init     = torch.cat([contexts[-1], contexts[-2]], dim=-2)     # shape[1] == 128, 143       then on another step/call it's 128, 128...??? cuz the contexts is now 1,128,2560
+                txt_init_len = txt_init.shape[-2]                                       # 271
+
+                if mask is not None:
+                    txt_init_list = []
+                    
+                    offset_t5_start    = 0
+                    for i in range(transformer_options['AttnMask'].num_regions):
+                        offset_t5_end   = offset_t5_start + transformer_options['AttnMask'].context_lens_list[i][0]
+                        txt_init_list.append(contexts[-1][:,offset_t5_start:offset_t5_end,:])
+                        offset_t5_start = offset_t5_end
+                    
+                    offset_llama_start = 0
+                    for i in range(transformer_options['AttnMask'].num_regions):
+                        offset_llama_end   = offset_llama_start + transformer_options['AttnMask'].context_lens_list[i][1]
+                        txt_init_list.append(contexts[-2][:,offset_llama_start:offset_llama_end,:])
+                        offset_llama_start = offset_llama_end
+                    
+                    txt_init = torch.cat(txt_init_list, dim=-2)  #T5,LLAMA3 (last block)
+                    txt_init_len = txt_init.shape[-2]
+                    
+                img = StyleMMDiT(img, "proj_in")
+                
+                img = img.to(x) if img is not None else None
+                
+                # DOUBLE STREAM
+                for bid, (block, style_block) in enumerate(zip(self.double_stream_blocks, StyleMMDiT.double_blocks)):
+                    txt_llama = contexts[bid]
+                    txt = torch.cat([txt_init, txt_llama], dim=-2)        # 1,384,2560       # cur_contexts = T5, LLAMA3 (last block), LLAMA3 (current block)
+
+                    if   weight > 0 and mask is not None and     weight  <      bid/48:
+                        img, txt_init = block(img, img_masks, txt, clip, rope, mask_zero, style_block=style_block)
+                        
+                    elif (weight < 0 and mask is not None and abs(weight) < (1 - bid/48)):
+                        img_tmpZ, txt_tmpZ = img.clone(), txt.clone()
+
+                        # more efficient than the commented lines below being used instead in the loop?
+                        img_tmpZ, txt_init = block(img_tmpZ, img_masks, txt_tmpZ, clip, rope, mask, style_block=style_block)
+                        img     , txt_tmpZ = block(img     , img_masks, txt     , clip, rope, mask_zero, style_block=style_block)
+                        
+                    elif floor > 0 and mask is not None and     floor  >      bid/48:
+                        mask_tmp = mask.clone()
+                        mask_tmp[:img_len,:img_len] = 1.0
+                        img, txt_init = block(img, img_masks, txt, clip, rope, mask_tmp, style_block=style_block)
+                        
+                    elif floor < 0 and mask is not None and abs(floor) > (1 - bid/48):
+                        mask_tmp = mask.clone()
+                        mask_tmp[:img_len,:img_len] = 1.0
+                        img, txt_init = block(img, img_masks, txt, clip, rope, mask_tmp, style_block=style_block)
+                        
+                    elif update_cross_attn is not None and update_cross_attn['skip_cross_attn']:
+                        img, txt_init = block(img, img_masks, txt, clip, rope, mask, update_cross_attn=update_cross_attn)
+                        
+                    else:
+                        img, txt_init = block(img, img_masks, txt, clip, rope, mask, update_cross_attn=update_cross_attn, style_block=style_block)
+
+                    txt_init = txt_init[..., :txt_init_len, :]
+                # END DOUBLE STREAM
+
+
+
+
+
+                img       = torch.cat([img, txt_init], dim=-2)   # 4032 + 271 -> 4303     # txt embed from double stream block
+                joint_len = img.shape[-2]
+                
+                if img_masks is not None:
+                    img_masks_ones = torch.ones( (bsz, txt_init.shape[-2] + txt_llama.shape[-2]), device=img_masks.device, dtype=img_masks.dtype)   # encoder_attention_mask_ones=   padding for txt embed concatted onto end of img
+                    img_masks      = torch.cat([img_masks, img_masks_ones], dim=-2)
+
+
+
+
+
+                # SINGLE STREAM
+                for bid, (block, style_block) in enumerate(zip(self.single_stream_blocks, StyleMMDiT.single_blocks)):
+                    txt_llama = contexts[bid+16]                        # T5 pre-embedded for single stream blocks
+                    img = torch.cat([img, txt_llama], dim=-2)            # cat img,txt     opposite of flux which is txt,img       4303 + 143 -> 4446
+
+                    if   weight > 0 and mask is not None and     weight  <      (bid+16)/48:
+                        img = block(img, img_masks, None, clip, rope, mask_zero, style_block=style_block)
+                        
+                    elif weight < 0 and mask is not None and abs(weight) < (1 - (bid+16)/48):
+                        img = block(img, img_masks, None, clip, rope, mask_zero, style_block=style_block)
+                    
+                    elif floor > 0 and mask is not None and     floor  >      (bid+16)/48:
+                        mask_tmp = mask.clone()
+                        mask_tmp[:img_len,:img_len] = 1.0
+                        img = block(img, img_masks, None, clip, rope, mask_tmp, style_block=style_block)
+                        
+                    elif floor < 0 and mask is not None and abs(floor) > (1 - (bid+16)/48):
+                        mask_tmp = mask.clone()
+                        mask_tmp[:img_len,:img_len] = 1.0
+                        img = block(img, img_masks, None, clip, rope, mask_tmp, style_block=style_block)
+                        
+                    else:
+                        img = block(img, img_masks, None, clip, rope, mask, style_block=style_block)
+                        
+                    img = img[..., :joint_len, :]   # slice off txt_llama
+                # END SINGLE STREAM
+                    
+                img = img[..., :img_len, :]
+                #img = self.final_layer(img, clip)   # 4096,2560 -> 4096,64
+                shift, scale = self.final_layer.adaLN_modulation(clip).chunk(2,dim=1)
+                img = (1 + scale[:, None, :]) * self.final_layer.norm_final(img) + shift[:, None, :]
+                if not EO("endojector"):
+                    img = StyleMMDiT(img, "proj_out")
+
+                if y0_style_active and not HDModel.RECON_MODE:
+                    img = img[0:1]
+                
+                if EO("endojector"):
+                    if EO("dumb"):
+                        eps_style = x_init[0:1].to(y0_style) - y0_style
+                    else:
+                        eps_style = (x_tmp[0:1].to(y0_style) - y0_style) / SIGMA.to(y0_style)
+                    eps_embed = self.Endojector.embed(eps_style)
+                    img = StyleMMDiT.scattersort_(img.to(eps_embed), eps_embed)
+                
+                img = self.final_layer.linear(img.to(dtype=self.final_layer.linear.weight.dtype))
+
+                img = self.unpatchify(img, img_sizes)
+                out_list.append(img)
+                
+            output = torch.cat(out_list, dim=0)
+            eps = -output[:, :, :h, :w]
+            
+            if recon_iter == 1:
+                denoised = new_x - SIGMA.to(new_x) * eps.to(new_x)
+                if x_tmp is not None:
+                    eps = (x_tmp - denoised.to(x_tmp)) / SIGMA.to(x_tmp)
+                else:
+                    eps = (x_orig - denoised.to(x_orig)) / SIGMA.to(x_orig)
+                    
+
+
+
+
+
+
+
+
+
+
+
+
+        freqsep_lowpass_method = transformer_options.get("freqsep_lowpass_method")
+        freqsep_sigma          = transformer_options.get("freqsep_sigma")
+        freqsep_kernel_size    = transformer_options.get("freqsep_kernel_size")
+        freqsep_inner_kernel_size    = transformer_options.get("freqsep_inner_kernel_size")
+        freqsep_stride    = transformer_options.get("freqsep_stride")
+        
+        freqsep_lowpass_weight = transformer_options.get("freqsep_lowpass_weight")
+        freqsep_highpass_weight= transformer_options.get("freqsep_highpass_weight")
+        freqsep_mask           = transformer_options.get("freqsep_mask")
+
+        y0_style_pos = transformer_options.get("y0_style_pos")
+        y0_style_neg = transformer_options.get("y0_style_neg")
+        
+        # end recon loop
+        self.style_dtype = torch.float32 if self.style_dtype is None else self.style_dtype
+        dtype = eps.dtype if self.style_dtype is None else self.style_dtype
+        
+        if y0_style_pos is not None:
+            y0_style_pos_weight    = transformer_options.get("y0_style_pos_weight")
+            y0_style_pos_synweight = transformer_options.get("y0_style_pos_synweight")
+            y0_style_pos_synweight *= y0_style_pos_weight
+            y0_style_pos_mask = transformer_options.get("y0_style_pos_mask")
+            y0_style_pos_mask_edge = transformer_options.get("y0_style_pos_mask_edge")
+
+            y0_style_pos = y0_style_pos.to(dtype)
+            x   = x_orig.to(dtype)
+            eps = eps.to(dtype)
+            eps_orig = eps.clone()
+            
+            sigma = SIGMA #t_orig[0].to(torch.float32) / 1000
+            denoised = x - sigma * eps
+            
+            denoised_embed = self.Retrojector.embed(denoised)
+            y0_adain_embed = self.Retrojector.embed(y0_style_pos)
+            
+            if transformer_options['y0_style_method'] == "scattersort":
+                tile_h, tile_w = transformer_options.get('y0_style_tile_height'), transformer_options.get('y0_style_tile_width')
+                pad = transformer_options.get('y0_style_tile_padding')
+                if pad is not None and tile_h is not None and tile_w is not None:
+                    
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    if EO("scattersort_median_LP"):
+                        denoised_spatial_LP = median_blur_2d(denoised_spatial, kernel_size=EO("scattersort_median_LP",7))
+                        y0_adain_spatial_LP = median_blur_2d(y0_adain_spatial, kernel_size=EO("scattersort_median_LP",7))
+                        
+                        denoised_spatial_HP = denoised_spatial - denoised_spatial_LP
+                        y0_adain_spatial_HP = y0_adain_spatial - y0_adain_spatial_LP
+                        
+                        denoised_spatial_LP = apply_scattersort_tiled(denoised_spatial_LP, y0_adain_spatial_LP, tile_h, tile_w, pad)
+                        
+                        denoised_spatial = denoised_spatial_LP + denoised_spatial_HP
+                        denoised_embed = rearrange(denoised_spatial, "b c h w -> b (h w) c")
+                    else:
+                        denoised_spatial = apply_scattersort_tiled(denoised_spatial, y0_adain_spatial, tile_h, tile_w, pad)
+                    
+                    denoised_embed = rearrange(denoised_spatial, "b c h w -> b (h w) c")
+                    
+                else:
+                    denoised_embed = apply_scattersort_masked(denoised_embed, y0_adain_embed, y0_style_pos_mask, y0_style_pos_mask_edge, h_len, w_len)
+
+
+
+            elif transformer_options['y0_style_method'] == "AdaIN":
+                if freqsep_mask is not None:
+                    freqsep_mask = freqsep_mask.view(1, 1, *freqsep_mask.shape[-2:]).float()
+                    freqsep_mask = F.interpolate(freqsep_mask.float(), size=(h_len, w_len), mode='nearest-exact')
+                
+                if hasattr(self, "adain_tile"):
+                    tile_h, tile_w = self.adain_tile
+                    
+                    denoised_pretile = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_pretile = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    if self.adain_flag:
+                        h_off = tile_h // 2
+                        w_off = tile_w // 2
+                        denoised_pretile = denoised_pretile[:,:,h_off:-h_off, w_off:-w_off]
+                        self.adain_flag = False
+                    else:
+                        h_off = 0
+                        w_off = 0
+                        self.adain_flag = True
+                    
+                    tiles,    orig_shape, grid, strides = tile_latent(denoised_pretile, tile_size=(tile_h,tile_w))
+                    y0_tiles, orig_shape, grid, strides = tile_latent(y0_adain_pretile, tile_size=(tile_h,tile_w))
+                    
+                    tiles_out = []
+                    for i in range(tiles.shape[0]):
+                        tile = tiles[i].unsqueeze(0)
+                        y0_tile = y0_tiles[i].unsqueeze(0)
+                        
+                        tile    = rearrange(tile,    "b c h w -> b (h w) c", h=tile_h, w=tile_w)
+                        y0_tile = rearrange(y0_tile, "b c h w -> b (h w) c", h=tile_h, w=tile_w)
+                        
+                        tile = adain_seq_inplace(tile, y0_tile)
+                        tiles_out.append(rearrange(tile, "b (h w) c -> b c h w", h=tile_h, w=tile_w))
+                    
+                    tiles_out_tensor = torch.cat(tiles_out, dim=0)
+                    tiles_out_tensor = untile_latent(tiles_out_tensor, orig_shape, grid, strides)
+
+                    if h_off == 0:
+                        denoised_pretile = tiles_out_tensor
+                    else:
+                        denoised_pretile[:,:,h_off:-h_off, w_off:-w_off] = tiles_out_tensor
+                    denoised_embed = rearrange(denoised_pretile, "b c h w -> b (h w) c", h=h_len, w=w_len)
+
+                elif freqsep_lowpass_method is not None and freqsep_lowpass_method.endswith("pw"): #EO("adain_pw"):
+
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+
+                    if   freqsep_lowpass_method == "median_pw":
+                        denoised_spatial_new = adain_patchwise_row_batch_med(denoised_spatial.clone(), y0_adain_spatial.clone().repeat(denoised_spatial.shape[0],1,1,1), sigma=freqsep_sigma, kernel_size=freqsep_kernel_size, use_median_blur=True, lowpass_weight=freqsep_lowpass_weight, highpass_weight=freqsep_highpass_weight)
+                    elif freqsep_lowpass_method == "gaussian_pw": 
+                        denoised_spatial_new = adain_patchwise_row_batch(denoised_spatial.clone(), y0_adain_spatial.clone().repeat(denoised_spatial.shape[0],1,1,1), sigma=freqsep_sigma, kernel_size=freqsep_kernel_size)
+                    
+                    denoised_embed = rearrange(denoised_spatial_new, "b c h w -> b (h w) c", h=h_len, w=w_len)
+
+                elif freqsep_lowpass_method is not None: 
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    if   freqsep_lowpass_method == "median":
+                        denoised_spatial_LP = median_blur_2d(denoised_spatial, kernel_size=freqsep_kernel_size)
+                        y0_adain_spatial_LP = median_blur_2d(y0_adain_spatial, kernel_size=freqsep_kernel_size)
+                    elif freqsep_lowpass_method == "gaussian":
+                        denoised_spatial_LP = gaussian_blur_2d(denoised_spatial, sigma=freqsep_sigma, kernel_size=freqsep_kernel_size)
+                        y0_adain_spatial_LP = gaussian_blur_2d(y0_adain_spatial, sigma=freqsep_sigma, kernel_size=freqsep_kernel_size)
+                    
+                    denoised_spatial_HP = denoised_spatial - denoised_spatial_LP
+                    
+                    if EO("adain_fs_uhp"):
+                        y0_adain_spatial_HP = y0_adain_spatial - y0_adain_spatial_LP
+                        
+                        denoised_spatial_ULP = gaussian_blur_2d(denoised_spatial, sigma=EO("adain_fs_uhp_sigma", 1.0), kernel_size=EO("adain_fs_uhp_kernel_size", 3))
+                        y0_adain_spatial_ULP = gaussian_blur_2d(y0_adain_spatial, sigma=EO("adain_fs_uhp_sigma", 1.0), kernel_size=EO("adain_fs_uhp_kernel_size", 3))
+                        
+                        denoised_spatial_UHP = denoised_spatial_HP  - denoised_spatial_ULP
+                        y0_adain_spatial_UHP = y0_adain_spatial_HP  - y0_adain_spatial_ULP
+                        
+                        #denoised_spatial_HP  = y0_adain_spatial_ULP + denoised_spatial_UHP
+                        denoised_spatial_HP  = denoised_spatial_ULP + y0_adain_spatial_UHP
+                    
+                    denoised_spatial_new = freqsep_lowpass_weight * y0_adain_spatial_LP + freqsep_highpass_weight * denoised_spatial_HP
+                    denoised_embed = rearrange(denoised_spatial_new, "b c h w -> b (h w) c", h=h_len, w=w_len)
+
+                else:
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    
+                for adain_iter in range(EO("style_iter", 0)):
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    denoised_embed = self.Retrojector.embed(self.Retrojector.unembed(denoised_embed))
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    
+            elif transformer_options['y0_style_method'] == "WCT":
+                self.StyleWCT.set(y0_adain_embed)
+                denoised_embed = self.StyleWCT.get(denoised_embed)
+                
+                if transformer_options.get('y0_standard_guide') is not None:
+                    y0_standard_guide = transformer_options.get('y0_standard_guide')
+                    
+                    y0_standard_guide_embed = self.Retrojector.embed(y0_standard_guide)
+                    f_cs = self.StyleWCT.get(y0_standard_guide_embed)
+                    self.y0_standard_guide = self.Retrojector.unembed(f_cs)
+
+                if transformer_options.get('y0_inv_standard_guide') is not None:
+                    y0_inv_standard_guide = transformer_options.get('y0_inv_standard_guide')
+
+                    y0_inv_standard_guide_embed = self.Retrojector.embed(y0_inv_standard_guide)
+                    f_cs = self.StyleWCT.get(y0_inv_standard_guide_embed)
+                    self.y0_inv_standard_guide = self.Retrojector.unembed(f_cs)
+
+            elif transformer_options['y0_style_method'] == "WCT2":
+                self.WaveletStyleWCT.set(y0_adain_embed, h_len, w_len)
+                denoised_embed = self.WaveletStyleWCT.get(denoised_embed, h_len, w_len)
+                
+                if transformer_options.get('y0_standard_guide') is not None:
+                    y0_standard_guide = transformer_options.get('y0_standard_guide')
+                    
+                    y0_standard_guide_embed = self.Retrojector.embed(y0_standard_guide)
+                    f_cs = self.WaveletStyleWCT.get(y0_standard_guide_embed, h_len, w_len)
+                    self.y0_standard_guide = self.Retrojector.unembed(f_cs)
+
+                if transformer_options.get('y0_inv_standard_guide') is not None:
+                    y0_inv_standard_guide = transformer_options.get('y0_inv_standard_guide')
+
+                    y0_inv_standard_guide_embed = self.Retrojector.embed(y0_inv_standard_guide)
+                    f_cs = self.WaveletStyleWCT.get(y0_inv_standard_guide_embed, h_len, w_len)
+                    self.y0_inv_standard_guide = self.Retrojector.unembed(f_cs)
+
+            denoised_approx = self.Retrojector.unembed(denoised_embed)
+            
+            eps = (x - denoised_approx) / sigma
+
+            if not UNCOND:
+                if eps.shape[0] == 2:
+                    eps[1] = eps_orig[1] + y0_style_pos_weight * (eps[1] - eps_orig[1])
+                    eps[0] = eps_orig[0] + y0_style_pos_synweight * (eps[0] - eps_orig[0])
+                else:
+                    eps[0] = eps_orig[0] + y0_style_pos_weight * (eps[0] - eps_orig[0])
+            elif eps.shape[0] == 1 and UNCOND:
+                eps[0] = eps_orig[0] + y0_style_pos_synweight * (eps[0] - eps_orig[0])
+            
+            #eps = eps.float()
+        
+        if y0_style_neg is not None:
+            y0_style_neg_weight    = transformer_options.get("y0_style_neg_weight")
+            y0_style_neg_synweight = transformer_options.get("y0_style_neg_synweight")
+            y0_style_neg_synweight *= y0_style_neg_weight
+            y0_style_neg_mask = transformer_options.get("y0_style_neg_mask")
+            y0_style_neg_mask_edge = transformer_options.get("y0_style_neg_mask_edge")
+            
+            y0_style_neg = y0_style_neg.to(dtype)
+            x   = x_orig.to(dtype)
+            eps = eps.to(dtype)
+            eps_orig = eps.clone()
+            
+            sigma = SIGMA #t_orig[0].to(torch.float32) / 1000
+            denoised = x - sigma * eps
+
+            denoised_embed = self.Retrojector.embed(denoised)
+            y0_adain_embed = self.Retrojector.embed(y0_style_neg)
+            
+            if transformer_options['y0_style_method'] == "scattersort":
+                tile_h, tile_w = transformer_options.get('y0_style_tile_height'), transformer_options.get('y0_style_tile_width')
+                pad = transformer_options.get('y0_style_tile_padding')
+                if pad is not None and tile_h is not None and tile_w is not None:
+                    
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    denoised_spatial = apply_scattersort_tiled(denoised_spatial, y0_adain_spatial, tile_h, tile_w, pad)
+                    
+                    denoised_embed = rearrange(denoised_spatial, "b c h w -> b (h w) c")
+
+                else:
+                    denoised_embed = apply_scattersort_masked(denoised_embed, y0_adain_embed, y0_style_neg_mask, y0_style_neg_mask_edge, h_len, w_len)
+            
+            
+            elif transformer_options['y0_style_method'] == "AdaIN":
+                denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                for adain_iter in range(EO("style_iter", 0)):
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    denoised_embed = self.Retrojector.embed(self.Retrojector.unembed(denoised_embed))
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    
+            elif transformer_options['y0_style_method'] == "WCT":
+                self.StyleWCT.set(y0_adain_embed)
+                denoised_embed = self.StyleWCT.get(denoised_embed)
+
+            elif transformer_options['y0_style_method'] == "WCT2":
+                self.WaveletStyleWCT.set(y0_adain_embed, h_len, w_len)
+                denoised_embed = self.WaveletStyleWCT.get(denoised_embed, h_len, w_len)
+
+            denoised_approx = self.Retrojector.unembed(denoised_embed)
+
+            if UNCOND:
+                eps = (x - denoised_approx) / sigma
+                eps[0] = eps_orig[0] + y0_style_neg_weight * (eps[0] - eps_orig[0])
+                if eps.shape[0] == 2:
+                    eps[1] = eps_orig[1] + y0_style_neg_synweight * (eps[1] - eps_orig[1])
+            elif eps.shape[0] == 1 and not UNCOND:
+                eps[0] = eps_orig[0] + y0_style_neg_synweight * (eps[0] - eps_orig[0])
+            
+            #eps = eps.float()
+        
+        if EO("model_eps_out"):
+            self.eps_out = eps.clone()
+        return eps
+    
+
+
+
+
+    def expand_timesteps(self, t, batch_size, device):
+        if not torch.is_tensor(t):
+            is_mps = device.type == "mps"
+            if isinstance(t, float):
+                dtype = torch.float32 if is_mps else torch.float64
+            else:
+                dtype = torch.int32   if is_mps else torch.int64
+            t = Tensor([t], dtype=dtype, device=device)
+        elif len(t.shape) == 0:
+            t = t[None].to(device)
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        t = t.expand(batch_size)
+        return t
+
+
+    def unpatchify(self, x: Tensor, img_sizes: List[Tuple[int, int]]) -> List[Tensor]:
+        x_arr = []
+        for i, img_size in enumerate(img_sizes):   #  [[64,64]]
+            pH, pW = img_size
+            x_arr.append(
+                einops.rearrange(x[i, :pH*pW].reshape(1, pH, pW, -1), 'B H W (p1 p2 C) -> B C (H p1) (W p2)',
+                    p1=self.patch_size, p2=self.patch_size)
+            )
+        x = torch.cat(x_arr, dim=0)
+        return x
+
+
+    def patchify(self, x, max_seq, img_sizes=None):
+        pz2 = self.patch_size * self.patch_size
+        if isinstance(x, Tensor):
+            B      = x.shape[0]
+            device = x.device
+            dtype  = x.dtype
+        else:
+            B      = len(x)
+            device = x[0].device
+            dtype  = x[0].dtype
+        x_masks = torch.zeros((B, max_seq), dtype=dtype, device=device)
+
+        if img_sizes is not None:
+            for i, img_size in enumerate(img_sizes): #  [[64,64]]
+                x_masks[i, 0:img_size[0] * img_size[1]] = 1
+            x         = einops.rearrange(x, 'B C S p -> B S (p C)', p=pz2)
+        elif isinstance(x, Tensor):
+            pH, pW    = x.shape[-2] // self.patch_size, x.shape[-1] // self.patch_size
+            x         = einops.rearrange(x, 'B C (H p1) (W p2) -> B (H W) (p1 p2 C)', p1=self.patch_size, p2=self.patch_size)
+            img_sizes = [[pH, pW]] * B
+            x_masks   = None
+        else:
+            raise NotImplementedError
+        return x, x_masks, img_sizes
+    
+    
+def clone_inputs(*args, index: int=None):
+
+    if index is None:
+        return tuple(x.clone() for x in args)
+    else:
+        return tuple(x[index].unsqueeze(0).clone() for x in args)
+
+
+
+def attention_rescale(
+    query, 
+    key, 
+    value,
+    attn_mask=None
+) -> torch.Tensor:
+    L, S = query.size(-2), key.size(-2)
+    scale_factor = 1 / math.sqrt(query.size(-1))
+
+
+    attn_weight = query @ key.transpose(-2, -1) * scale_factor
+    if attn_mask is not None:
+        attn_weight *= attn_mask
+
+    attn_weight = torch.softmax(attn_weight, dim=-1)
+
+    return attn_weight @ value
+
+
+
+class HDLastLayer(nn.Module):
+    def __init__(self, hidden_size: int, patch_size: int, out_channels: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.norm_final       = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.linear           = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
+        self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device))
+
+    def forward(self, x: Tensor, vec: Tensor, modulation_dims=None) -> Tensor:
+        x_dtype = x.dtype
+        
+        dtype = self.linear.weight.dtype
+        if dtype not in {torch.bfloat16, torch.float16, torch.float32, torch.float64}:
+            dtype = torch.float32
+            self.linear.weight.data = self.linear.weight.data.to(dtype)
+            self.linear.bias.data = self.linear.bias.data.to(dtype)
+            self.adaLN_modulation[1].weight.data = self.adaLN_modulation[1].weight.data.to(dtype)
+            self.adaLN_modulation[1].bias.data = self.adaLN_modulation[1].bias.data.to(dtype)
+        
+        x = x.to(dtype)
+        vec = vec.to(dtype)
+        if vec.ndim == 2:
+            vec = vec[:, None, :]
+
+        shift, scale = self.adaLN_modulation(vec).chunk(2, dim=-1)
+        x = apply_mod(self.norm_final(x), (1 + scale), shift, modulation_dims)
+        x = self.linear(x)
+        return x #.to(x_dtype)
+
+def apply_mod(tensor, m_mult, m_add=None, modulation_dims=None):
+    if modulation_dims is None:
+        if m_add is not None:
+            return tensor * m_mult + m_add
+        else:
+            return tensor * m_mult
+    else:
+        for d in modulation_dims:
+            tensor[:, d[0]:d[1]] *= m_mult[:, d[2]]
+            if m_add is not None:
+                tensor[:, d[0]:d[1]] += m_add[:, d[2]]
+        return tensor
+
+
+
+
+
diff --git a/custom_nodes/RES4LYF/legacy/__init__.py b/custom_nodes/RES4LYF/legacy/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..87f9360dd18d0e2279824779648590e8cce68e60
--- /dev/null
+++ b/custom_nodes/RES4LYF/legacy/__init__.py
@@ -0,0 +1,100 @@
+from . import legacy_samplers
+from . import legacy_sampler_rk
+
+from . import rk_sampler
+from . import samplers
+from . import samplers_extensions
+from . import samplers_tiled
+
+
+
+def add_legacy(NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS, extra_samplers):
+    
+    NODE_CLASS_MAPPINGS.update({
+        "Legacy_ClownSampler"                 : legacy_samplers.Legacy_SamplerRK,
+        "Legacy_SharkSampler"                 : legacy_samplers.Legacy_SharkSampler,
+        "Legacy_ClownsharKSampler"            : legacy_samplers.Legacy_ClownsharKSampler,
+        "Legacy_ClownsharKSamplerGuides"      : legacy_samplers.Legacy_ClownsharKSamplerGuides,
+        
+        "ClownSampler"                        : samplers.ClownSampler,
+        "ClownSamplerAdvanced"                : samplers.ClownSamplerAdvanced,
+        "ClownsharKSampler"                   : samplers.ClownsharKSampler,
+        
+        
+        "ClownsharKSamplerGuides"             : samplers_extensions.ClownsharKSamplerGuides,
+        "ClownsharKSamplerGuide"              : samplers_extensions.ClownsharKSamplerGuide,
+    
+        "ClownOptions_SDE_Noise"              : samplers_extensions.ClownOptions_SDE_Noise,
+        "ClownOptions_FrameWeights"           : samplers_extensions.ClownOptions_FrameWeights,
+    
+        "ClownInpaint"                        : samplers_extensions.ClownInpaint,
+        "ClownInpaintSimple"                  : samplers_extensions.ClownInpaintSimple,
+
+        "ClownsharKSamplerOptions"            : samplers_extensions.ClownsharKSamplerOptions,
+
+        "ClownsharKSamplerAutomation"         : samplers_extensions.ClownsharKSamplerAutomation,
+        "ClownsharKSamplerAutomation_Advanced": samplers_extensions.ClownsharKSamplerAutomation_Advanced,
+        "SamplerOptions_TimestepScaling"      : samplers_extensions.SamplerOptions_TimestepScaling,
+        "SamplerOptions_GarbageCollection"    : samplers_extensions.SamplerOptions_GarbageCollection,
+
+        "UltraSharkSampler"                   : samplers.UltraSharkSampler,
+
+        "UltraSharkSampler Tiled"             : samplers_tiled.UltraSharkSampler_Tiled,
+    })
+    
+    NODE_DISPLAY_NAME_MAPPINGS.update({
+        "Legacy_SamplerRK"                            : "Legacy_ClownSampler",
+        "Legacy_SharkSampler"                         : "Legacy_SharkSampler",
+        "Legacy_ClownsharKSampler"                    : "Legacy_ClownsharKSampler",
+        "Legacy_ClownsharKSamplerGuides"              : "Legacy_ClownsharKSamplerGuides",
+        
+        "ClownSampler"                                : "Legacy2_ClownSampler",
+        "ClownSamplerAdvanced"                        : "Legacy2_ClownSamplerAdvanced",
+        "ClownsharKSampler"                           : "Legacy2_ClownsharKSampler",
+        
+        "ClownsharKSamplerGuides"                     : "Legacy2_ClownsharKSamplerGuides",
+        "ClownsharKSamplerGuide"                      : "Legacy2_ClownsharKSamplerGuide",
+
+        "ClownOptions_SDE_Noise"                      : "Legacy2_ClownOptions_SDE_Noise",
+        "ClownOptions_FrameWeights"                   : "Legacy2_ClownOptions_FrameWeights",
+
+        "ClownInpaint"                                : "Legacy2_ClownInpaint",
+        "ClownInpaintSimple"                          : "Legacy2_ClownInpaintSimple",
+
+        "ClownsharKSamplerOptions"                    : "Legacy2_ClownsharKSamplerOptions",
+
+        "ClownsharKSamplerAutomation"                 : "Legacy2_ClownsharKSamplerAutomation",
+        "ClownsharKSamplerAutomation_Advanced"        : "Legacy2_ClownsharKSamplerAutomation_Advanced",
+        "SamplerOptions_TimestepScaling"              : "Legacy2_SamplerOptions_TimestepScaling",
+        "SamplerOptions_GarbageCollection"            : "Legacy2_SamplerOptions_GarbageCollection",
+
+        "UltraSharkSampler"                           : "Legacy2_UltraSharkSampler",
+        "UltraSharkSampler_Tiled"                     : "Legacy2_UltraSharkSampler Tiled",
+    })
+
+
+
+    extra_samplers.update({
+        #"res_2m"     : rk_sampler.sample_res_2m,
+        #"res_2s"     : rk_sampler.sample_res_2s,
+        #"res_3s"     : rk_sampler.sample_res_3s,
+        #"res_5s"     : rk_sampler.sample_res_5s,
+        #"res_6s"     : rk_sampler.sample_res_6s,
+        #"res_2m_sde" : rk_sampler.sample_res_2m_sde,
+        #"res_2s_sde" : rk_sampler.sample_res_2s_sde,
+        #"res_3s_sde" : rk_sampler.sample_res_3s_sde,
+        #"res_5s_sde" : rk_sampler.sample_res_5s_sde,
+        #"res_6s_sde" : rk_sampler.sample_res_6s_sde,
+        #"deis_2m"    : rk_sampler.sample_deis_2m,
+        #"deis_3m"    : rk_sampler.sample_deis_3m,
+        #"deis_4m"    : rk_sampler.sample_deis_4m,
+        #"deis_2m_sde": rk_sampler.sample_deis_2m_sde,
+        #"deis_3m_sde": rk_sampler.sample_deis_3m_sde,
+        #"deis_4m_sde": rk_sampler.sample_deis_4m_sde,
+        "rk"         : rk_sampler.sample_rk,
+        
+        "legacy_rk"  : legacy_sampler_rk.legacy_sample_rk,
+    })
+    
+    return NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS, extra_samplers
+
diff --git a/custom_nodes/RES4LYF/legacy/conditioning.py b/custom_nodes/RES4LYF/legacy/conditioning.py
new file mode 100644
index 0000000000000000000000000000000000000000..213d745411c03f122d36f68efcea0a7ee07bd939
--- /dev/null
+++ b/custom_nodes/RES4LYF/legacy/conditioning.py
@@ -0,0 +1,742 @@
+import torch
+
+import comfy.samplers
+import comfy.sample
+import comfy.sampler_helpers
+import node_helpers
+
+import functools
+from .noise_classes import precision_tool
+from copy import deepcopy
+
+from .helper import initialize_or_scale
+import torch.nn.functional as F
+import copy
+
+from .helper import get_orthogonal, get_collinear
+from ..res4lyf import RESplain
+
+
+def multiply_nested_tensors(structure, scalar):
+    if isinstance(structure, torch.Tensor):
+        return structure * scalar
+    elif isinstance(structure, list):
+        return [multiply_nested_tensors(item, scalar) for item in structure]
+    elif isinstance(structure, dict):
+        return {key: multiply_nested_tensors(value, scalar) for key, value in structure.items()}
+    else:
+        return structure
+
+
+
+
+class ConditioningOrthoCollin:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "conditioning_0": ("CONDITIONING", ), 
+            "conditioning_1": ("CONDITIONING", ),
+            "t5_strength": ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01}),
+            "clip_strength": ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01}),
+            }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "combine"
+
+    CATEGORY = "RES4LYF/conditioning"
+
+    def combine(self, conditioning_0, conditioning_1, t5_strength, clip_strength):
+
+        t5_0_1_collin = get_collinear (conditioning_0[0][0], conditioning_1[0][0])
+        t5_1_0_ortho  = get_orthogonal(conditioning_1[0][0], conditioning_0[0][0])
+
+        t5_combined = t5_0_1_collin + t5_1_0_ortho
+        
+        t5_1_0_collin = get_collinear (conditioning_1[0][0], conditioning_0[0][0])
+        t5_0_1_ortho  = get_orthogonal(conditioning_0[0][0], conditioning_1[0][0])
+
+        t5_B_combined = t5_1_0_collin + t5_0_1_ortho
+
+        pooled_0_1_collin = get_collinear (conditioning_0[0][1]['pooled_output'].unsqueeze(0), conditioning_1[0][1]['pooled_output'].unsqueeze(0)).squeeze(0)
+        pooled_1_0_ortho  = get_orthogonal(conditioning_1[0][1]['pooled_output'].unsqueeze(0), conditioning_0[0][1]['pooled_output'].unsqueeze(0)).squeeze(0)
+
+        pooled_combined = pooled_0_1_collin + pooled_1_0_ortho
+        
+        #conditioning_0[0][0] = conditioning_0[0][0] + t5_strength * (t5_combined - conditioning_0[0][0])
+        
+        #conditioning_0[0][0] = t5_strength * t5_combined + (1-t5_strength) * t5_B_combined
+        
+        conditioning_0[0][0] = t5_strength * t5_0_1_collin + (1-t5_strength) * t5_1_0_collin
+        
+        conditioning_0[0][1]['pooled_output'] = conditioning_0[0][1]['pooled_output'] + clip_strength * (pooled_combined - conditioning_0[0][1]['pooled_output'])
+
+        return (conditioning_0, )
+
+
+
+class CLIPTextEncodeFluxUnguided:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "clip": ("CLIP", ),
+            "clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+            "t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+            }}
+    RETURN_NAMES = ("conditioning", "clip_l_end", "t5xxl_end",)
+    RETURN_TYPES = ("CONDITIONING","INT","INT",)
+    FUNCTION = "encode"
+
+    CATEGORY = "RES4LYF/conditioning"
+
+    def encode(self, clip, clip_l, t5xxl):
+        tokens = clip.tokenize(clip_l)
+        tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"]
+
+        clip_l_end=0
+        for i in range(len(tokens['l'][0])):
+            if tokens['l'][0][i][0] == 49407:
+                clip_l_end=i
+                break
+        t5xxl_end=0
+        for i in range(len(tokens['l'][0])):   # bug? should this be t5xxl?
+            if tokens['t5xxl'][0][i][0] == 1:
+                t5xxl_end=i
+                break
+
+        output = clip.encode_from_tokens(tokens, return_pooled=True, return_dict=True)
+        cond = output.pop("cond")
+        conditioning = [[cond, output]]
+        conditioning[0][1]['clip_l_end'] = clip_l_end
+        conditioning[0][1]['t5xxl_end'] = t5xxl_end
+        return (conditioning, clip_l_end, t5xxl_end,)
+
+
+class StyleModelApplyAdvanced: 
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning": ("CONDITIONING", ),
+                             "style_model": ("STYLE_MODEL", ),
+                             "clip_vision_output": ("CLIP_VISION_OUTPUT", ),
+                             "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.001}),
+                             }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "main"
+    CATEGORY = "RES4LYF/conditioning"
+    DESCRIPTION = "Use with Flux Redux."
+
+    def main(self, clip_vision_output, style_model, conditioning, strength=1.0):
+        cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0)
+        cond = strength * cond
+        c = []
+        for t in conditioning:
+            n = [torch.cat((t[0], cond), dim=1), t[1].copy()]
+            c.append(n)
+        return (c, )
+
+
+class ConditioningZeroAndTruncate: 
+    # needs updating to ensure dims are correct for arbitrary models without hardcoding. 
+    # vanilla ConditioningZeroOut node doesn't truncate and SD3.5M degrades badly with large embeddings, even if zeroed out, as the negative conditioning
+    @classmethod
+    def INPUT_TYPES(s):
+        return { "required": {"conditioning": ("CONDITIONING", )}}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "zero_out"
+
+    CATEGORY = "RES4LYF/conditioning"
+    DESCRIPTION = "Use for negative conditioning with SD3.5. ConditioningZeroOut does not truncate the embedding, \
+                   which results in severe degradation of image quality with SD3.5 when the token limit is exceeded."
+
+    def zero_out(self, conditioning):
+        c = []
+        for t in conditioning:
+            d = t[1].copy()
+            pooled_output = d.get("pooled_output", None)
+            if pooled_output is not None:
+                d["pooled_output"] = torch.zeros((1,2048), dtype=t[0].dtype, device=t[0].device)
+                n = [torch.zeros((1,154,4096), dtype=t[0].dtype, device=t[0].device), d]
+            c.append(n)
+        return (c, )
+
+
+class ConditioningTruncate: 
+    # needs updating to ensure dims are correct for arbitrary models without hardcoding. 
+    @classmethod
+    def INPUT_TYPES(s):
+        return { "required": {"conditioning": ("CONDITIONING", )}}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "zero_out"
+
+    CATEGORY = "RES4LYF/conditioning"
+    DESCRIPTION = "Use for positive conditioning with SD3.5. Tokens beyond 77 result in degradation of image quality."
+
+    def zero_out(self, conditioning):
+        c = []
+        for t in conditioning:
+            d = t[1].copy()
+            pooled_output = d.get("pooled_output", None)
+            if pooled_output is not None:
+                d["pooled_output"] = d["pooled_output"][:, :2048]
+                n = [t[0][:, :154, :4096], d]
+            c.append(n)
+        return (c, )
+
+
+class ConditioningMultiply:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning": ("CONDITIONING", ), 
+                              "multiplier": ("FLOAT", {"default": 1.0, "min": -1000000000.0, "max": 1000000000.0, "step": 0.01})
+                             }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "main"
+
+    CATEGORY = "RES4LYF/conditioning"
+
+    def main(self, conditioning, multiplier):
+        c = multiply_nested_tensors(conditioning, multiplier)
+        return (c,)
+
+
+
+class ConditioningAdd:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning_1": ("CONDITIONING", ), 
+                             "conditioning_2": ("CONDITIONING", ), 
+                              "multiplier": ("FLOAT", {"default": 1.0, "min": -1000000000.0, "max": 1000000000.0, "step": 0.01})
+                             }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "main"
+
+    CATEGORY = "RES4LYF/conditioning"
+
+    def main(self, conditioning_1, conditioning_2, multiplier):
+        
+        conditioning_1[0][0] += multiplier * conditioning_2[0][0]
+        conditioning_1[0][1]['pooled_output'] += multiplier * conditioning_2[0][1]['pooled_output'] 
+        
+        return (conditioning_1,)
+
+
+
+
+class ConditioningCombine:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", )}}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "combine"
+
+    CATEGORY = "RES4LYF/conditioning"
+
+    def combine(self, conditioning_1, conditioning_2):
+        return (conditioning_1 + conditioning_2, )
+
+
+
+class ConditioningAverage :
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning_to": ("CONDITIONING", ), "conditioning_from": ("CONDITIONING", ),
+                              "conditioning_to_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+                             }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "addWeighted"
+
+    CATEGORY = "RES4LYF/conditioning"
+
+    def addWeighted(self, conditioning_to, conditioning_from, conditioning_to_strength):
+        out = []
+
+        if len(conditioning_from) > 1:
+            RESplain("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.")
+
+        cond_from = conditioning_from[0][0]
+        pooled_output_from = conditioning_from[0][1].get("pooled_output", None)
+
+        for i in range(len(conditioning_to)):
+            t1 = conditioning_to[i][0]
+            pooled_output_to = conditioning_to[i][1].get("pooled_output", pooled_output_from)
+            t0 = cond_from[:,:t1.shape[1]]
+            if t0.shape[1] < t1.shape[1]:
+                t0 = torch.cat([t0] + [torch.zeros((1, (t1.shape[1] - t0.shape[1]), t1.shape[2]))], dim=1)
+
+            tw = torch.mul(t1, conditioning_to_strength) + torch.mul(t0, (1.0 - conditioning_to_strength))
+            t_to = conditioning_to[i][1].copy()
+            if pooled_output_from is not None and pooled_output_to is not None:
+                t_to["pooled_output"] = torch.mul(pooled_output_to, conditioning_to_strength) + torch.mul(pooled_output_from, (1.0 - conditioning_to_strength))
+            elif pooled_output_from is not None:
+                t_to["pooled_output"] = pooled_output_from
+
+            n = [tw, t_to]
+            out.append(n)
+        return (out, )
+    
+class ConditioningSetTimestepRange:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning": ("CONDITIONING", ),
+                             "start": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                             "end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
+                             }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "set_range"
+
+    CATEGORY = "RES4LYF/conditioning"
+
+    def set_range(self, conditioning, start, end):
+        c = node_helpers.conditioning_set_values(conditioning, {"start_percent": start,
+                                                                "end_percent": end})
+        return (c, )
+
+class ConditioningAverageScheduler: # don't think this is implemented correctly. needs to be reworked
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                    "conditioning_0": ("CONDITIONING", ), 
+                    "conditioning_1": ("CONDITIONING", ),
+                    "ratio": ("SIGMAS", ),
+                    }
+            }
+    
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "main"
+
+    CATEGORY = "RES4LYF/conditioning"
+
+    @staticmethod
+    def addWeighted(conditioning_to, conditioning_from, conditioning_to_strength): #this function borrowed from comfyui
+        out = []
+
+        if len(conditioning_from) > 1:
+            RESplain("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.")
+
+        cond_from = conditioning_from[0][0]
+        pooled_output_from = conditioning_from[0][1].get("pooled_output", None)
+
+        for i in range(len(conditioning_to)):
+            t1 = conditioning_to[i][0]
+            pooled_output_to = conditioning_to[i][1].get("pooled_output", pooled_output_from)
+            t0 = cond_from[:,:t1.shape[1]]
+            if t0.shape[1] < t1.shape[1]:
+                t0 = torch.cat([t0] + [torch.zeros((1, (t1.shape[1] - t0.shape[1]), t1.shape[2]))], dim=1)
+
+            tw = torch.mul(t1, conditioning_to_strength) + torch.mul(t0, (1.0 - conditioning_to_strength))
+            t_to = conditioning_to[i][1].copy()
+            if pooled_output_from is not None and pooled_output_to is not None:
+                t_to["pooled_output"] = torch.mul(pooled_output_to, conditioning_to_strength) + torch.mul(pooled_output_from, (1.0 - conditioning_to_strength))
+            elif pooled_output_from is not None:
+                t_to["pooled_output"] = pooled_output_from
+
+            n = [tw, t_to]
+            out.append(n)
+        return out
+
+    @staticmethod
+    def create_percent_array(steps):
+        step_size = 1.0 / steps
+        return [{"start_percent": i * step_size, "end_percent": (i + 1) * step_size} for i in range(steps)]
+
+    def main(self, conditioning_0, conditioning_1, ratio):
+        steps = len(ratio)
+
+        percents = self.create_percent_array(steps)
+
+        cond = []
+        for i in range(steps):
+            average = self.addWeighted(conditioning_0, conditioning_1, ratio[i].item())
+            cond += node_helpers.conditioning_set_values(average, {"start_percent": percents[i]["start_percent"], "end_percent": percents[i]["end_percent"]})
+
+        return (cond,)
+
+
+
+class StableCascade_StageB_Conditioning64:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "conditioning": ("CONDITIONING",),
+                              "stage_c": ("LATENT",),
+                             }}
+    RETURN_TYPES = ("CONDITIONING",)
+
+    FUNCTION = "set_prior"
+
+    CATEGORY = "RES4LYF/conditioning"
+
+    @precision_tool.cast_tensor
+    def set_prior(self, conditioning, stage_c):
+        c = []
+        for t in conditioning:
+            d = t[1].copy()
+            d['stable_cascade_prior'] = stage_c['samples']
+            n = [t[0], d]
+            c.append(n)
+        return (c, )
+
+
+
+class Conditioning_Recast64:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "cond_0": ("CONDITIONING",),
+                             },
+                "optional": { "cond_1": ("CONDITIONING",),}
+                }
+    RETURN_TYPES = ("CONDITIONING","CONDITIONING",)
+    RETURN_NAMES = ("cond_0_recast","cond_1_recast",)
+
+    FUNCTION = "main"
+
+    CATEGORY = "RES4LYF/precision"
+
+    @precision_tool.cast_tensor
+    def main(self, cond_0, cond_1 = None):
+        cond_0[0][0] = cond_0[0][0].to(torch.float64)
+        cond_0[0][1]["pooled_output"] = cond_0[0][1]["pooled_output"].to(torch.float64)
+        
+        if cond_1 is not None:
+            cond_1[0][0] = cond_1[0][0].to(torch.float64)
+            cond_1[0][1]["pooled_output"] = cond_1[0][1]["pooled_output"].to(torch.float64)
+
+        return (cond_0, cond_1,)
+
+
+class RegionalMask(torch.nn.Module):
+    def __init__(self, mask: torch.Tensor, conditioning: torch.Tensor, conditioning_regional: torch.Tensor, latent:torch.Tensor, start_percent: float, end_percent: float, mask_type: str, img_len: int, text_len: int) -> None:
+        super().__init__()
+        #self.register_buffer('mask', mask)
+        self.mask = mask.clone().to('cuda')
+        self.conditioning = copy.deepcopy(conditioning)
+        self.conditioning_regional = copy.deepcopy(conditioning_regional)
+        self.latent = latent.clone()
+        self.start_percent = start_percent
+        self.end_percent   = end_percent
+        self.mask_type = mask_type
+        self.img_len = img_len
+        self.text_len = text_len
+
+    def __call__(self, transformer_options, weight=0, dtype=torch.bfloat16, *args, **kwargs):
+        sigma = transformer_options['sigmas'][0]
+        if self.start_percent <= 1 - sigma < self.end_percent:
+            if self.mask_type == "gradient":
+                #mask = self.gen_mask(weight)
+                return self.mask.clone().to(sigma.device) * weight
+
+
+    """def gen_mask(self, weight):             #FOR REGENERATION OF SELF-ATTN MASK
+        b, c, h, w = self.latent.shape
+        h //= 2  # 16x16 PE
+        w //= 2
+        img_len = h * w
+
+        cond_r = torch.cat([cond_reg['cond'] for cond_reg in self.conditioning_regional], dim=1)
+        
+        if self.conditioning is not None:
+            text_len = 256 + cond_r.shape[1]  # 256 = main prompt tokens... half of t5, comfy issue
+            conditioning_regional = [
+                {
+                    'mask': torch.ones((1,   h,    w), dtype=torch.bfloat16),
+                    'cond': torch.ones((1, 256, 4096), dtype=torch.bfloat16),
+                },
+                *self.conditioning_regional,
+            ]
+        else:
+            text_len = cond_r.shape[1]  # 256 = main prompt tokens... half of t5, comfy issue
+            conditioning_regional = self.conditioning_regional
+        
+        all_attn_mask       = torch.zeros((text_len+img_len, text_len+img_len), dtype=torch.bfloat16)
+        self_attn_mask     = torch.zeros((          img_len,          img_len), dtype=torch.bfloat16)
+        self_attn_mask_bkg = torch.zeros((          img_len,          img_len), dtype=torch.bfloat16)
+        
+        prev_len = 0
+        for cond_reg_dict in conditioning_regional:         #FOR REGENERATION OF SELF-ATTN MASK
+            cond_reg         = cond_reg_dict['cond']
+            region_mask_ = 1 - cond_reg_dict['mask'][0]
+            
+            region_mask_sq = cond_reg_dict['mask'][0].to(torch.bfloat16)
+
+            
+            img2txt_mask = torch.nn.functional.interpolate(region_mask_sq[None, None, :, :], (h, w), mode='nearest-exact').flatten().unsqueeze(1).repeat(1, cond_reg.size(1))
+            txt2img_mask = img2txt_mask.transpose(-1, -2)
+            
+            img2txt_mask_sq = torch.nn.functional.interpolate(region_mask_sq[None, None, :, :], (h, w), mode='nearest-exact').flatten().unsqueeze(1).repeat(1, self.img_len)
+            #img2txt_mask_sq = img2txt_mask[:, :1].repeat(1, img_len)
+            txt2img_mask_sq = img2txt_mask_sq.transpose(-1, -2)
+
+            curr_len = prev_len + cond_reg.shape[1]         #FOR REGENERATION OF SELF-ATTN MASK
+            
+            all_attn_mask[prev_len:curr_len, prev_len:curr_len] = 1.0           # self             TXT 2 TXT
+            all_attn_mask[prev_len:curr_len, text_len:        ] = txt2img_mask  # cross            TXT 2 regional IMG
+            all_attn_mask[text_len:        , prev_len:curr_len] = img2txt_mask  # cross   regional IMG 2 TXT
+            
+            #all_attn_mask[text_len:, text_len:] = fp_or(all_attn_mask[text_len:, text_len:]    , fp_and(  img2txt_mask_sq,   txt2img_mask_sq))
+            
+            self_attn_mask     = fp_or(self_attn_mask    , fp_and(                      img2txt_mask_sq,                       txt2img_mask_sq))
+            self_attn_mask_bkg = fp_or(self_attn_mask_bkg, fp_and(img2txt_mask_sq.max()-img2txt_mask_sq, txt2img_mask_sq.max()-txt2img_mask_sq))
+            #self_attn_mask_bkg = fp_or(self_attn_mask_bkg, fp_and(1-img2txt_mask_sq, 1-txt2img_mask_sq))
+            
+            prev_len = curr_len
+
+        all_attn_mask[text_len:, text_len:] = fp_or(self_attn_mask, self_attn_mask_bkg) #combine foreground/background self-attn
+
+        return all_attn_mask
+    """
+    
+    
+class RegionalConditioning(torch.nn.Module):
+    def __init__(self, conditioning: torch.Tensor, region_cond: torch.Tensor, start_percent: float, end_percent: float) -> None:
+        super().__init__()
+        #self.register_buffer('region_cond', region_cond)
+        self.conditioning = conditioning
+        self.region_cond = region_cond.clone().to('cuda')
+        self.start_percent = start_percent
+        self.end_percent   = end_percent
+
+    def __call__(self, transformer_options, dtype=torch.bfloat16, *args,  **kwargs):
+        sigma = transformer_options['sigmas'][0]
+        if self.start_percent <= 1 - sigma < self.end_percent:
+            return self.region_cond.clone().to(sigma.device).to(dtype)
+        return None
+    
+    def concat_cond(self, context, transformer_options, dtype=torch.bfloat16, *args,  **kwargs):
+        sigma = transformer_options['sigmas'][0]
+        if self.start_percent <= 1 - sigma < self.end_percent:
+            region_cond = self.region_cond.clone().to(sigma.device).to(dtype)
+            if self.conditioning is None:
+                return self.region_cond.clone().to(sigma.device).to(dtype)
+            else:
+                return torch.cat([context, region_cond.clone().to(torch.bfloat16)], dim=1)
+        return None
+
+
+
+class FluxRegionalPrompt:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+            "cond": ("CONDITIONING",),
+        }, "optional": {
+            "cond_regional": ("CONDITIONING_REGIONAL",),
+            "mask": ("MASK",),
+        }}
+
+    RETURN_TYPES = ("CONDITIONING_REGIONAL","MASK",)
+    RETURN_NAMES = ("cond_regional","mask_inv")
+    FUNCTION = "main"
+
+    CATEGORY = "RES4LYF/conditioning"
+
+    def main(self, cond, mask, cond_regional=[]):
+        cond_regional = [*cond_regional]
+        cond_regional.append({'mask': mask, 'cond': cond[0][0]})
+        mask_inv = 1-mask
+        return (cond_regional,mask_inv,)
+
+def fp_not(tensor):
+    return 1 - tensor
+
+def fp_or(tensor1, tensor2):
+    return torch.maximum(tensor1, tensor2)
+
+def fp_and(tensor1, tensor2):
+    return torch.minimum(tensor1, tensor2)
+
+class RegionalGenerateConditioningsAndMasks:
+    def __init__(self, conditioning, conditioning_regional, weight, start_percent, end_percent, mask_type):
+        self.conditioning          = conditioning
+        self.conditioning_regional = conditioning_regional
+        self.weight                = weight
+        self.start_percent         = start_percent
+        self.end_percent           = end_percent
+        self.mask_type             = mask_type
+
+    def __call__(self, latent):
+        b, c, h, w = latent.shape
+        h //= 2  # 16x16 PE
+        w //= 2
+        img_len = h * w
+
+        cond_r = torch.cat([cond_reg['cond'] for cond_reg in self.conditioning_regional], dim=1)
+        
+        if self.conditioning is not None:
+            text_len = 256 + cond_r.shape[1]  # 256 = main prompt tokens... half of t5, comfy issue
+            conditioning_regional = [
+                {
+                    'mask': torch.ones((1,   h,    w), dtype=torch.bfloat16),
+                    'cond': torch.ones((1, 256, 4096), dtype=torch.bfloat16),
+                },
+                *self.conditioning_regional,
+            ]
+        else:
+            text_len = cond_r.shape[1]  # 256 = main prompt tokens... half of t5, comfy issue
+            conditioning_regional = self.conditioning_regional
+        
+        all_attn_mask      = torch.zeros((text_len+img_len, text_len+img_len), dtype=torch.bfloat16)
+        self_attn_mask     = torch.zeros((         img_len,          img_len), dtype=torch.bfloat16)
+        self_attn_mask_bkg = torch.zeros((         img_len,          img_len), dtype=torch.bfloat16)
+        
+        prev_len = 0
+        for cond_reg_dict in conditioning_regional:
+            cond_reg    = cond_reg_dict['cond']
+            region_mask = cond_reg_dict['mask'][0]
+            
+            img2txt_mask    = torch.nn.functional.interpolate(region_mask[None, None, :, :], (h, w), mode='nearest-exact').flatten().unsqueeze(1).repeat(1, cond_reg.size(1))
+            txt2img_mask    = img2txt_mask   .transpose(-1, -2)
+            
+            img2txt_mask_sq = torch.nn.functional.interpolate(region_mask[None, None, :, :], (h, w), mode='nearest-exact').flatten().unsqueeze(1).repeat(1, img_len)
+            txt2img_mask_sq = img2txt_mask_sq.transpose(-1, -2)
+
+            curr_len = prev_len + cond_reg.shape[1]
+            
+            all_attn_mask[prev_len:curr_len, prev_len:curr_len] = 1.0           # self             TXT 2 TXT
+            all_attn_mask[prev_len:curr_len, text_len:        ] = txt2img_mask  # cross            TXT 2 regional IMG
+            all_attn_mask[text_len:        , prev_len:curr_len] = img2txt_mask  # cross   regional IMG 2 TXT
+            
+            self_attn_mask     = fp_or(self_attn_mask    , fp_and(                      img2txt_mask_sq,                       txt2img_mask_sq))
+            self_attn_mask_bkg = fp_or(self_attn_mask_bkg, fp_and(img2txt_mask_sq.max()-img2txt_mask_sq, txt2img_mask_sq.max()-txt2img_mask_sq))
+            
+            prev_len = curr_len
+
+        all_attn_mask[text_len:, text_len:] = fp_or(self_attn_mask, self_attn_mask_bkg) #combine foreground/background self-attn
+
+        all_attn_mask         = RegionalMask(all_attn_mask, self.conditioning, self.conditioning_regional, latent, self.start_percent, self.end_percent, self.mask_type, img_len, text_len)
+        regional_conditioning = RegionalConditioning(self.conditioning, cond_r, self.start_percent, self.end_percent)
+
+        return regional_conditioning, all_attn_mask
+
+
+class FluxRegionalConditioning:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+            "mask_weight": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+            "self_attn_floor": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+            "start_percent": ("FLOAT", {"default": 0,   "min": 0.0, "max": 1.0, "step": 0.01}),
+            "end_percent":   ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+            "mask_type": (["gradient"], {"default": "gradient"}),
+        }, 
+            "optional": {
+                "conditioning": ("CONDITIONING",),
+                "conditioning_regional": ("CONDITIONING_REGIONAL",),
+                "mask_weights": ("SIGMAS", ),
+                "self_attn_floors": ("SIGMAS", ),
+
+        }}
+
+    RETURN_TYPES = ("CONDITIONING",)
+    RETURN_NAMES = ("conditioning",)
+    FUNCTION = "main"
+
+    CATEGORY = "RES4LYF/conditioning"
+
+    def main(self, conditioning_regional, mask_weight=1.0, start_percent=0.0, end_percent=1.0, start_step=0, end_step=10000, conditioning=None, mask_weights=None, self_attn_floors=None, self_attn_floor=0.0, mask_type="gradient", latent=None):
+        weight, weights = mask_weight, mask_weights
+        floor, floors = self_attn_floor, self_attn_floors
+        default_dtype = torch.float64
+        max_steps = 10000
+        weights = initialize_or_scale(weights, weight, max_steps).to(default_dtype)
+        weights = F.pad(weights, (0, max_steps), value=0.0)
+        
+        floors = initialize_or_scale(floors, floor, max_steps).to(default_dtype)
+        floors = F.pad(floors, (0, max_steps), value=0.0)
+
+        regional_generate_conditionings_and_masks_fn = RegionalGenerateConditioningsAndMasks(conditioning, conditioning_regional, weight, start_percent, end_percent, mask_type)
+
+        if conditioning is None:
+            conditioning = [
+                                [
+                                    torch.zeros_like(conditioning_regional[0]['cond']),
+                                    {'pooled_output':
+                                        torch.zeros((1,768), dtype=conditioning_regional[0]['cond'].dtype, device=conditioning_regional[0]['cond'].device),
+                                    }
+                                ],
+            ]
+
+        conditioning[0][1]['regional_generate_conditionings_and_masks_fn'] = regional_generate_conditionings_and_masks_fn
+        conditioning[0][1]['regional_conditioning_weights'] = weights
+        conditioning[0][1]['regional_conditioning_floors'] = floors
+        return (copy.deepcopy(conditioning),)
+
+
+"""
+from .models import ReFluxPatcher
+
+class ClownRegionalConditioningFlux:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+            "regional_model": (["auto", "deactivate"], {"default": "auto"}),
+            "mask_weight": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+            "region_bleed": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+            "start_percent": ("FLOAT", {"default": 0,   "min": 0.0, "max": 1.0, "step": 0.01}),
+            "end_percent":   ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+            "mask_type": (["gradient"], {"default": "gradient"}),
+            "invert_mask": ("BOOLEAN", {"default": False}),
+        }, 
+            "optional": {
+                "model":             ("MODEL", ),
+                "positive_masked":  ("CONDITIONING", ),
+                "positive_unmasked":      ("CONDITIONING", ),
+                "mask":              ("MASK", ),
+                "mask_weights": ("SIGMAS", ),
+                "region_bleeds": ("SIGMAS", ),
+        }}
+
+    RETURN_TYPES = ("MODEL", "CONDITIONING",)
+    RETURN_NAMES = ("model", "positive",)
+    FUNCTION = "main"
+
+    CATEGORY = "RES4LYF/conditioning"
+
+    def main(self, model, regional_model, mask_weight=1.0, start_percent=0.0, end_percent=1.0, positive_masked=None, positive_unmasked=None, mask_weights=None, region_bleeds=None, region_bleed=0.0, mask_type="gradient", mask=None, invert_mask=False):
+        if regional_model == "auto":
+            reflux_enable = True
+        else:
+            model, = ReFluxPatcher().main(model, enable=False)
+            return (model, positive_masked,)
+            
+        if invert_mask and mask is not None:
+            mask = 1-mask
+
+        weight, weights = mask_weight, mask_weights
+        floor, floors = region_bleed, region_bleeds
+        default_dtype = torch.float64
+        max_steps = 10000
+        weights = initialize_or_scale(weights, weight, max_steps).to(default_dtype)
+        weights = F.pad(weights, (0, max_steps), value=0.0)
+        
+        floors = initialize_or_scale(floors, floor, max_steps).to(default_dtype)
+        floors = F.pad(floors, (0, max_steps), value=0.0)
+
+        if (positive_masked is None) and (positive_unmasked is None):
+            positive = None
+            reflux_enable = False
+        elif mask is not None:
+            if regional_model == "auto":
+                reflux_enable = True
+            else:
+                reflux_enable = False
+            
+            if positive_unmasked is None:
+                if positive_unmasked is None:
+                    positive_unmasked = [[
+                        torch.zeros((1, 256, 4096)),
+                        {'pooled_output': torch.zeros((1, 768))}
+                        ]]
+            cond_regional, mask_inv     = FluxRegionalPrompt().main(cond=positive_masked,                                    mask=mask)
+            cond_regional, mask_inv_inv = FluxRegionalPrompt().main(cond=positive_unmasked    , cond_regional=cond_regional, mask=mask_inv)
+            
+            positive, = FluxRegionalConditioning().main(conditioning_regional=cond_regional, self_attn_floor=floor, self_attn_floors=floors, mask_weight=weight, mask_weights=weights, start_percent=start_percent, end_percent=end_percent, mask_type=mask_type)
+        else:
+            positive = positive_masked
+            reflux_enable = False
+        
+        if not reflux_enable:
+            model, = ReFluxPatcher().main(model, enable=False)
+            return (model, positive_masked,)
+        else:
+            model, = ReFluxPatcher().main(model, enable=True)
+            return (model, positive,)
+
+"""
diff --git a/custom_nodes/RES4LYF/legacy/constants.py b/custom_nodes/RES4LYF/legacy/constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef57f2990653a3da702aa7c6042a312c8d2cf7ec
--- /dev/null
+++ b/custom_nodes/RES4LYF/legacy/constants.py
@@ -0,0 +1,11 @@
+MAX_STEPS = 10000
+
+
+IMPLICIT_TYPE_NAMES = [
+    "predictor-corrector",
+    "rebound",
+    "retro-eta",
+    "bongmath",
+]
+
+
diff --git a/custom_nodes/RES4LYF/legacy/deis_coefficients.py b/custom_nodes/RES4LYF/legacy/deis_coefficients.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e2e4c0053ac639dcb06cbca0c240c1d3b1c472b
--- /dev/null
+++ b/custom_nodes/RES4LYF/legacy/deis_coefficients.py
@@ -0,0 +1,123 @@
+# Adapted from: https://github.com/zju-pi/diff-sampler/blob/main/gits-main/solver_utils.py
+# fixed the calcs for "rhoab" which suffered from an off-by-one error and made some other minor corrections
+
+import torch
+import numpy as np
+
+# A pytorch reimplementation of DEIS (https://github.com/qsh-zh/deis).
+#############################
+### Utils for DEIS solver ###
+#############################
+#----------------------------------------------------------------------------
+# Transfer from the input time (sigma) used in EDM to that (t) used in DEIS.
+
+def edm2t(edm_steps, epsilon_s=1e-3, sigma_min=0.002, sigma_max=80):
+    vp_sigma = lambda beta_d, beta_min: lambda t: (np.e ** (0.5 * beta_d * (t ** 2) + beta_min * t) - 1) ** 0.5
+    vp_sigma_inv = lambda beta_d, beta_min: lambda sigma: ((beta_min ** 2 + 2 * beta_d * (sigma ** 2 + 1).log()).sqrt() - beta_min) / beta_d
+    vp_beta_d = 2 * (np.log(torch.tensor(sigma_min).cpu() ** 2 + 1) / epsilon_s - np.log(torch.tensor(sigma_max).cpu() ** 2 + 1)) / (epsilon_s - 1)
+    vp_beta_min = np.log(torch.tensor(sigma_max).cpu() ** 2 + 1) - 0.5 * vp_beta_d
+    t_steps = vp_sigma_inv(vp_beta_d.clone().detach().cpu(), vp_beta_min.clone().detach().cpu())(edm_steps.clone().detach().cpu())
+    return t_steps, vp_beta_min, vp_beta_d + vp_beta_min
+
+#----------------------------------------------------------------------------
+
+def cal_poly(prev_t, j, taus):
+    poly = 1
+    for k in range(prev_t.shape[0]):
+        if k == j:
+            continue
+        poly *= (taus - prev_t[k]) / (prev_t[j] - prev_t[k])
+    return poly
+
+#----------------------------------------------------------------------------
+# Transfer from t to alpha_t.
+
+def t2alpha_fn(beta_0, beta_1, t):
+    return torch.exp(-0.5 * t ** 2 * (beta_1 - beta_0) - t * beta_0)
+
+#----------------------------------------------------------------------------
+
+def cal_integrand(beta_0, beta_1, taus):
+    with torch.inference_mode(mode=False):
+        taus = taus.clone()
+        beta_0 = beta_0.clone()
+        beta_1 = beta_1.clone()
+        with torch.enable_grad():
+            taus.requires_grad_(True)
+            alpha = t2alpha_fn(beta_0, beta_1, taus)
+            log_alpha = alpha.log()
+            log_alpha.sum().backward()
+            d_log_alpha_dtau = taus.grad
+    integrand = -0.5 * d_log_alpha_dtau / torch.sqrt(alpha * (1 - alpha))
+    return integrand
+
+#----------------------------------------------------------------------------
+
+def get_deis_coeff_list(t_steps, max_order, N=10000, deis_mode='tab'):
+    """
+    Get the coefficient list for DEIS sampling.
+
+    Args:
+        t_steps: A pytorch tensor. The time steps for sampling.
+        max_order: A `int`. Maximum order of the solver. 1 <= max_order <= 4
+        N: A `int`. Use how many points to perform the numerical integration when deis_mode=='tab'.
+        deis_mode: A `str`. Select between 'tab' and 'rhoab'. Type of DEIS.
+    Returns:
+        A pytorch tensor. A batch of generated samples or sampling trajectories if return_inters=True.
+    """
+    if deis_mode == 'tab':
+        t_steps, beta_0, beta_1 = edm2t(t_steps)
+        C = []
+        for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])):
+            order = min(i+1, max_order)
+            if order == 1:
+                C.append([])
+            else:
+                taus = torch.linspace(t_cur, t_next, N)   # split the interval for integral approximation
+                dtau = (t_next - t_cur) / N
+                prev_t = t_steps[[i - k for k in range(order)]]
+                coeff_temp = []
+                integrand = cal_integrand(beta_0, beta_1, taus)
+                for j in range(order):
+                    poly = cal_poly(prev_t, j, taus)
+                    coeff_temp.append(torch.sum(integrand * poly) * dtau)
+                C.append(coeff_temp)
+
+    elif deis_mode == 'rhoab':
+        # Analytical solution, second order
+        def get_def_integral_2(a, b, start, end, c):
+            coeff = (end**3 - start**3) / 3 - (end**2 - start**2) * (a + b) / 2 + (end - start) * a * b
+            return coeff / ((c - a) * (c - b))
+
+        # Analytical solution, third order
+        def get_def_integral_3(a, b, c, start, end, d):
+            coeff = (end**4 - start**4) / 4 - (end**3 - start**3) * (a + b + c) / 3 \
+                    + (end**2 - start**2) * (a*b + a*c + b*c) / 2 - (end - start) * a * b * c
+            return coeff / ((d - a) * (d - b) * (d - c))
+
+        C = []
+        for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])):
+            order = min(i+1, max_order) #fixed order calcs
+            if order == 1:
+                C.append([])
+            else:
+                prev_t = t_steps[[i - k for k in range(order+1)]]
+                if order == 2:
+                    coeff_cur = ((t_next - prev_t[1])**2 - (t_cur - prev_t[1])**2) / (2 * (t_cur - prev_t[1]))
+                    coeff_prev1 = (t_next - t_cur)**2 / (2 * (prev_t[1] - t_cur))
+                    coeff_temp = [coeff_cur, coeff_prev1]
+                elif order == 3:
+                    coeff_cur = get_def_integral_2(prev_t[1], prev_t[2], t_cur, t_next, t_cur)
+                    coeff_prev1 = get_def_integral_2(t_cur, prev_t[2], t_cur, t_next, prev_t[1])
+                    coeff_prev2 = get_def_integral_2(t_cur, prev_t[1], t_cur, t_next, prev_t[2])
+                    coeff_temp = [coeff_cur, coeff_prev1, coeff_prev2]
+                elif order == 4:
+                    coeff_cur = get_def_integral_3(prev_t[1], prev_t[2], prev_t[3], t_cur, t_next, t_cur)
+                    coeff_prev1 = get_def_integral_3(t_cur, prev_t[2], prev_t[3], t_cur, t_next, prev_t[1])
+                    coeff_prev2 = get_def_integral_3(t_cur, prev_t[1], prev_t[3], t_cur, t_next, prev_t[2])
+                    coeff_prev3 = get_def_integral_3(t_cur, prev_t[1], prev_t[2], t_cur, t_next, prev_t[3])
+                    coeff_temp = [coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3]
+                C.append(coeff_temp)
+ 
+    return C
+
diff --git a/custom_nodes/RES4LYF/legacy/helper.py b/custom_nodes/RES4LYF/legacy/helper.py
new file mode 100644
index 0000000000000000000000000000000000000000..113f937dfbc9dd702525bad8171e3d8afb93daa2
--- /dev/null
+++ b/custom_nodes/RES4LYF/legacy/helper.py
@@ -0,0 +1,342 @@
+import re
+import torch
+from comfy.samplers import SCHEDULER_NAMES
+import torch.nn.functional as F
+from ..res4lyf import RESplain
+
+
+def get_extra_options_kv(key, default, extra_options):
+
+    match = re.search(rf"{key}\s*=\s*([a-zA-Z0-9_.+-]+)", extra_options)
+    if match:
+        value = match.group(1)
+    else:
+        value = default
+    return value
+
+def get_extra_options_list(key, default, extra_options):
+
+    match = re.search(rf"{key}\s*=\s*([a-zA-Z0-9_.,+-]+)", extra_options)
+    if match:
+        value = match.group(1)
+    else:
+        value = default
+    return value
+
+def extra_options_flag(flag, extra_options):
+    return bool(re.search(rf"{flag}", extra_options))
+
+def safe_get_nested(d, keys, default=None):
+    for key in keys:
+        if isinstance(d, dict):
+            d = d.get(key, default)
+        else:
+            return default
+    return d
+
+def is_video_model(model):
+    is_video_model = False
+    try :
+        is_video_model = 'video'  in model.inner_model.inner_model.model_config.unet_config['image_model'] or \
+                         'cosmos' in model.inner_model.inner_model.model_config.unet_config['image_model']
+    except:
+        pass
+    return is_video_model
+
+def is_RF_model(model):
+    from comfy import model_sampling
+    modelsampling = model.inner_model.inner_model.model_sampling
+    return isinstance(modelsampling, model_sampling.CONST)
+
+
+
+def lagrange_interpolation(x_values, y_values, x_new):
+
+    if not isinstance(x_values, torch.Tensor):
+        x_values = torch.tensor(x_values, dtype=torch.get_default_dtype())
+    if x_values.ndim != 1:
+        raise ValueError("x_values must be a 1D tensor or a list of scalars.")
+
+    if not isinstance(x_new, torch.Tensor):
+        x_new = torch.tensor(x_new, dtype=x_values.dtype, device=x_values.device)
+    if x_new.ndim == 0:
+        x_new = x_new.unsqueeze(0)
+
+    if isinstance(y_values, list):
+        y_values = torch.stack(y_values, dim=0)
+    if y_values.ndim < 1:
+        raise ValueError("y_values must have at least one dimension (the sample dimension).")
+
+    n = x_values.shape[0]
+    if y_values.shape[0] != n:
+        raise ValueError(f"Mismatch: x_values has length {n} but y_values has {y_values.shape[0]} samples.")
+
+    m = x_new.shape[0]
+    result_shape = (m,) + y_values.shape[1:]
+    result = torch.zeros(result_shape, dtype=y_values.dtype, device=y_values.device)
+
+    for i in range(n):
+        Li = torch.ones_like(x_new, dtype=y_values.dtype, device=y_values.device)
+        xi = x_values[i]
+        for j in range(n):
+            if i == j:
+                continue
+            xj = x_values[j]
+            Li = Li * ((x_new - xj) / (xi - xj))
+        extra_dims = (1,) * (y_values.ndim - 1)
+        Li = Li.view(m, *extra_dims)
+        result = result + Li * y_values[i]
+
+    return result
+
+
+def get_cosine_similarity_manual(a, b):
+    return (a * b).sum() / (torch.norm(a) * torch.norm(b))
+
+
+
+def get_cosine_similarity(a, b):
+    if a.dim() == 5 and b.dim() == 5 and b.shape[2] == 1:
+        b = b.expand(-1, -1, a.shape[2], -1, -1)
+    return F.cosine_similarity(a.flatten(), b.flatten(), dim=0)
+
+
+def get_pearson_similarity(a, b):
+    a = a.mean(dim=(-2,-1))
+    b = b.mean(dim=(-2,-1))
+    if a.dim() == 5 and b.dim() == 5 and b.shape[2] == 1:
+        b = b.expand(-1, -1, a.shape[2], -1, -1)
+    return F.cosine_similarity(a.flatten(), b.flatten(), dim=0)
+
+
+
+def initialize_or_scale(tensor, value, steps):
+    if tensor is None:
+        return torch.full((steps,), value)
+    else:
+        return value * tensor
+
+
+def has_nested_attr(obj, attr_path):
+    attrs = attr_path.split('.')
+    for attr in attrs:
+        if not hasattr(obj, attr):
+            return False
+        obj = getattr(obj, attr)
+    return True
+
+def get_res4lyf_scheduler_list():
+    scheduler_names = SCHEDULER_NAMES.copy()
+    if "beta57" not in scheduler_names:
+        scheduler_names.append("beta57")
+    return scheduler_names
+
+def conditioning_set_values(conditioning, values={}):
+    c = []
+    for t in conditioning:
+        n = [t[0], t[1].copy()]
+        for k in values:
+            n[1][k] = values[k]
+        c.append(n)
+
+    return c
+
+
+def get_collinear_alt(x, y):
+
+    y_flat = y.view(y.size(0), -1).clone()
+    x_flat = x.view(x.size(0), -1).clone()
+
+    y_flat /= y_flat.norm(dim=-1, keepdim=True)
+    x_proj_y = torch.sum(x_flat * y_flat, dim=-1, keepdim=True) * y_flat
+
+    return x_proj_y.view_as(x)
+
+
+def get_collinear(x, y):
+
+    y_flat = y.view(y.size(0), -1).clone()
+    x_flat = x.view(x.size(0), -1).clone()
+
+    y_flat /= y_flat.norm(dim=-1, keepdim=True)
+    x_proj_y = torch.sum(x_flat * y_flat, dim=-1, keepdim=True) * y_flat
+
+    return x_proj_y.view_as(x)
+
+
+def get_orthogonal(x, y):
+
+    y_flat = y.view(y.size(0), -1).clone()
+    x_flat = x.view(x.size(0), -1).clone()
+
+    y_flat /= y_flat.norm(dim=-1, keepdim=True)
+    x_proj_y = torch.sum(x_flat * y_flat, dim=-1, keepdim=True) * y_flat
+    
+    x_ortho_y = x_flat - x_proj_y 
+
+    return x_ortho_y.view_as(x)
+
+
+# pytorch slerp implementation from https://gist.github.com/Birch-san/230ac46f99ec411ed5907b0a3d728efa
+from torch import FloatTensor, LongTensor, Tensor, Size, lerp, zeros_like
+from torch.linalg import norm
+
+# adapted to PyTorch from:
+# https://gist.github.com/dvschultz/3af50c40df002da3b751efab1daddf2c
+# most of the extra complexity is to support:
+# - many-dimensional vectors
+# - v0 or v1 with last dim all zeroes, or v0 ~colinear with v1
+#   - falls back to lerp()
+#   - conditional logic implemented with parallelism rather than Python loops
+# - many-dimensional tensor for t
+#   - you can ask for batches of slerp outputs by making t more-dimensional than the vectors
+#   -   slerp(
+#         v0:   torch.Size([2,3]),
+#         v1:   torch.Size([2,3]),
+#         t:  torch.Size([4,1,1]), 
+#       )
+#   - this makes it interface-compatible with lerp()
+def slerp(v0: FloatTensor, v1: FloatTensor, t: float|FloatTensor, DOT_THRESHOLD=0.9995):
+  '''
+  Spherical linear interpolation
+  Args:
+    v0: Starting vector
+    v1: Final vector
+    t: Float value between 0.0 and 1.0
+    DOT_THRESHOLD: Threshold for considering the two vectors as
+                            colinear. Not recommended to alter this.
+  Returns:
+      Interpolation vector between v0 and v1
+  '''
+  assert v0.shape == v1.shape, "shapes of v0 and v1 must match"
+
+  # Normalize the vectors to get the directions and angles
+  v0_norm: FloatTensor = norm(v0, dim=-1)
+  v1_norm: FloatTensor = norm(v1, dim=-1)
+
+  v0_normed: FloatTensor = v0 / v0_norm.unsqueeze(-1)
+  v1_normed: FloatTensor = v1 / v1_norm.unsqueeze(-1)
+
+  # Dot product with the normalized vectors
+  dot: FloatTensor = (v0_normed * v1_normed).sum(-1)
+  dot_mag: FloatTensor = dot.abs()
+
+  # if dp is NaN, it's because the v0 or v1 row was filled with 0s
+  # If absolute value of dot product is almost 1, vectors are ~colinear, so use lerp
+  gotta_lerp: LongTensor = dot_mag.isnan() | (dot_mag > DOT_THRESHOLD)
+  can_slerp: LongTensor = ~gotta_lerp
+
+  t_batch_dim_count: int = max(0, t.dim()-v0.dim()) if isinstance(t, Tensor) else 0
+  t_batch_dims: Size = t.shape[:t_batch_dim_count] if isinstance(t, Tensor) else Size([])
+  out: FloatTensor = zeros_like(v0.expand(*t_batch_dims, *[-1]*v0.dim()))
+
+  # if no elements are lerpable, our vectors become 0-dimensional, preventing broadcasting
+  if gotta_lerp.any():
+    lerped: FloatTensor = lerp(v0, v1, t)
+
+    out: FloatTensor = lerped.where(gotta_lerp.unsqueeze(-1), out)
+
+  # if no elements are slerpable, our vectors become 0-dimensional, preventing broadcasting
+  if can_slerp.any():
+
+    # Calculate initial angle between v0 and v1
+    theta_0: FloatTensor = dot.arccos().unsqueeze(-1)
+    sin_theta_0: FloatTensor = theta_0.sin()
+    # Angle at timestep t
+    theta_t: FloatTensor = theta_0 * t
+    sin_theta_t: FloatTensor = theta_t.sin()
+    # Finish the slerp algorithm
+    s0: FloatTensor = (theta_0 - theta_t).sin() / sin_theta_0
+    s1: FloatTensor = sin_theta_t / sin_theta_0
+    slerped: FloatTensor = s0 * v0 + s1 * v1
+
+    out: FloatTensor = slerped.where(can_slerp.unsqueeze(-1), out)
+  
+  return out
+
+
+
+
+class OptionsManager:
+    APPEND_OPTIONS = {"extra_options"}
+
+    def __init__(self, options_inputs=None):
+        self.options_list = options_inputs or []
+        self._merged_dict = None
+
+    def add_option(self, option):
+        """Add a single options dictionary"""
+        if option is not None:
+            self.options_list.append(option)
+            self._merged_dict = None # invalidate cached merged options
+
+    @property
+    def merged(self):
+        """Get merged options with proper priority handling"""
+        if self._merged_dict is None:
+            self._merged_dict = {}
+
+            special_string_options = {
+                key: [] for key in self.APPEND_OPTIONS
+            }
+
+            for options_dict in self.options_list:
+                if options_dict is not None:
+                    for key, value in options_dict.items():
+                        if key in self.APPEND_OPTIONS and value:
+                            special_string_options[key].append(value)
+                        elif isinstance(value, dict):
+                            # Deep merge dictionaries
+                            if key not in self._merged_dict:
+                                self._merged_dict[key] = {}
+
+                            if isinstance(self._merged_dict[key], dict):
+                                self._deep_update(self._merged_dict[key], value)
+                            else:
+                                self._merged_dict[key] = value.copy()
+                        else:
+                            self._merged_dict[key] = value
+
+            # append special case string options (e.g. extra_options)
+            for key, value in special_string_options.items():
+                if value:
+                    self._merged_dict[key] = "\n".join(value)
+
+        return self._merged_dict
+
+    def get(self, key, default=None):
+        return self.merged.get(key, default)
+
+    def _deep_update(self, target_dict, source_dict):
+
+        for key, value in source_dict.items():
+            if isinstance(value, dict) and key in target_dict and isinstance(target_dict[key], dict):
+                # recursive dict update
+                self._deep_update(target_dict[key], value)
+            else:
+                target_dict[key] = value
+
+    def __getitem__(self, key):
+        """Allow dictionary-like access to options"""
+        return self.merged[key]
+
+    def __contains__(self, key):
+        """Allow 'in' operator for options"""
+        return key in self.merged
+
+    def as_dict(self):
+        """Return the merged options as a dictionary"""
+        return self.merged.copy()
+
+    def __bool__(self):
+        """Return True if there are any options"""
+        return len(self.options_list) > 0 and any(opt is not None for opt in self.options_list)
+
+    def debug_print_options(self):
+        for i, options_dict in enumerate(self.options_list):
+            RESplain(f"Options {i}:", debug=True)
+            if options_dict is not None:
+                for key, value in options_dict.items():
+                    RESplain(f"  {key}: {value}", debug=True)
+            else:
+                RESplain("  None", "\n", debug=True)
diff --git a/custom_nodes/RES4LYF/legacy/latents.py b/custom_nodes/RES4LYF/legacy/latents.py
new file mode 100644
index 0000000000000000000000000000000000000000..a73fec6e9badfa6f2782e93f19ff9a8024645e5c
--- /dev/null
+++ b/custom_nodes/RES4LYF/legacy/latents.py
@@ -0,0 +1,1739 @@
+
+import torch
+import torch.nn.functional as F
+import math
+import itertools
+
+import comfy.samplers
+import comfy.sample
+import comfy.sampler_helpers
+import comfy.utils
+
+from .noise_classes import NOISE_GENERATOR_NAMES, NOISE_GENERATOR_CLASSES, precision_tool, prepare_noise
+
+
+
+def initialize_or_scale(tensor, value, steps):
+    if tensor is None:
+        return torch.full((steps,), value)
+    else:
+        return value * tensor
+    
+def latent_normalize_channels(x):
+    mean = x.mean(dim=(2, 3), keepdim=True)
+    std  = x.std (dim=(2, 3), keepdim=True)
+    return  (x - mean) / std
+
+def latent_stdize_channels(x):
+    std  = x.std (dim=(2, 3), keepdim=True)
+    return  x / std
+
+def latent_meancenter_channels(x):
+    mean = x.mean(dim=(2, 3), keepdim=True)
+    return  x - mean
+
+
+def initialize_or_scale(tensor, value, steps):
+    if tensor is None:
+        return torch.full((steps,), value)
+    else:
+        return value * tensor
+
+
+def normalize_latent(target, source=None, mean=True, std=True, set_mean=None, set_std=None, channelwise=True):
+    target = target.clone()
+    source = source.clone() if source is not None else None
+    def normalize_single_latent(single_target, single_source=None):
+        y = torch.zeros_like(single_target)
+        for b in range(y.shape[0]):
+            if channelwise:
+                for c in range(y.shape[1]):
+                    single_source_mean = single_source[b][c].mean() if set_mean is None else set_mean
+                    single_source_std  = single_source[b][c].std()  if set_std  is None else set_std
+                    
+                    if mean and std:
+                        y[b][c] = (single_target[b][c] - single_target[b][c].mean()) / single_target[b][c].std()
+                        if single_source is not None:
+                            y[b][c] = y[b][c] * single_source_std + single_source_mean
+                    elif mean:
+                        y[b][c] = single_target[b][c] - single_target[b][c].mean()
+                        if single_source is not None:
+                            y[b][c] = y[b][c] + single_source_mean
+                    elif std:
+                        y[b][c] = single_target[b][c] / single_target[b][c].std()
+                        if single_source is not None:
+                            y[b][c] = y[b][c] * single_source_std
+            else:
+                single_source_mean = single_source[b].mean() if set_mean is None else set_mean
+                single_source_std  = single_source[b].std()  if set_std  is None else set_std
+                
+                if mean and std:
+                    y[b] = (single_target[b] - single_target[b].mean()) / single_target[b].std()
+                    if single_source is not None:
+                        y[b] = y[b] * single_source_std + single_source_mean
+                elif mean:
+                    y[b] = single_target[b] - single_target[b].mean()
+                    if single_source is not None:
+                        y[b] = y[b] + single_source_mean
+                elif std:
+                    y[b] = single_target[b] / single_target[b].std()
+                    if single_source is not None:
+                        y[b] = y[b] * single_source_std
+        return y
+
+    if isinstance(target, (list, tuple)):
+        if source is not None:
+            assert isinstance(source, (list, tuple)) and len(source) == len(target), \
+                "If target is a list/tuple, source must be a list/tuple of the same length."
+            return [normalize_single_latent(t, s) for t, s in zip(target, source)]
+        else:
+            return [normalize_single_latent(t) for t in target]
+    else:
+        return normalize_single_latent(target, source)
+
+
+
+
+class AdvancedNoise:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required":{
+                "alpha": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":0.1, "round": 0.01}),
+                "k": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":2.0, "round": 0.01}),
+                "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                "noise_type": (NOISE_GENERATOR_NAMES, ),
+            },
+        }
+
+    RETURN_TYPES = ("NOISE",)
+    FUNCTION = "get_noise"
+    CATEGORY = "RES4LYF/noise"
+
+    def get_noise(self, noise_seed, noise_type, alpha, k):
+        return (Noise_RandomNoise(noise_seed, noise_type, alpha, k),)
+
+class Noise_RandomNoise:
+    def __init__(self, seed, noise_type, alpha, k):
+        self.seed = seed
+        self.noise_type = noise_type
+        self.alpha = alpha
+        self.k = k
+
+    def generate_noise(self, input_latent):
+        latent_image = input_latent["samples"]
+        batch_inds = input_latent["batch_index"] if "batch_index" in input_latent else None
+        return prepare_noise(latent_image, self.seed, self.noise_type, batch_inds, self.alpha, self.k)
+
+
+class LatentNoised:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {
+                    "add_noise": ("BOOLEAN", {"default": True}),
+                    "noise_is_latent": ("BOOLEAN", {"default": False}),
+                    "noise_type": (NOISE_GENERATOR_NAMES, ),
+                    "alpha": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":0.1, "round": 0.01}),
+                    "k": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":2.0, "round": 0.01}),
+                    "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                    "latent_image": ("LATENT", ),
+                    "noise_strength": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01, "round": 0.01}),
+                    "normalize": (["false", "true"], {"default": "false"}),
+                     },
+                "optional": 
+                    {
+                    "latent_noise": ("LATENT", ),
+                    "mask": ("MASK", ),
+                    }
+                }
+
+    RETURN_TYPES = ("LATENT",)
+    RETURN_NAMES = ("latent_noised",)
+
+    FUNCTION = "main"
+
+    CATEGORY = "RES4LYF/noise"
+    
+    def main(self, add_noise, noise_is_latent, noise_type, noise_seed, alpha, k, latent_image, noise_strength, normalize, latent_noise=None, mask=None):
+            latent_out = latent_image.copy()
+            samples = latent_out["samples"].clone()
+
+            torch.manual_seed(noise_seed)
+
+            if not add_noise:
+                noise = torch.zeros(samples.size(), dtype=samples.dtype, layout=samples.layout, device="cpu")
+            elif latent_noise is None:
+                batch_inds = latent_out["batch_index"] if "batch_index" in latent_out else None
+                noise = prepare_noise(samples, noise_seed, noise_type, batch_inds, alpha, k)
+            else:
+                noise = latent_noise["samples"]
+
+            if normalize == "true":
+                latent_mean = samples.mean()
+                latent_std = samples.std()
+                noise = noise * latent_std + latent_mean
+
+            if noise_is_latent:
+                noise += samples.cpu()
+                noise.sub_(noise.mean()).div_(noise.std())
+            
+            noise = noise * noise_strength
+
+            if mask is not None:
+                mask = F.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), 
+                                    size=(samples.shape[2], samples.shape[3]), 
+                                    mode="bilinear")
+                mask = mask.expand((-1, samples.shape[1], -1, -1)).to(samples.device)
+                if mask.shape[0] < samples.shape[0]:
+                    mask = mask.repeat((samples.shape[0] - 1) // mask.shape[0] + 1, 1, 1, 1)[:samples.shape[0]]
+                elif mask.shape[0] > samples.shape[0]:
+                    mask = mask[:samples.shape[0]]
+                
+                noise = mask * noise + (1 - mask) * torch.zeros_like(noise)
+
+            latent_out["samples"] = samples.cpu() + noise
+
+            return (latent_out,)
+
+
+
+
+class MaskToggle:
+    def __init__(self):
+        pass
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                    "enable": ("BOOLEAN", {"default": True}),    
+                    "mask": ("MASK", ),
+                     },
+                }
+
+    RETURN_TYPES = ("MASK",)
+    RETURN_NAMES = ("mask",)
+    CATEGORY = "RES4LYF/masks"
+
+    FUNCTION = "main"
+
+    def main(self, enable=True, mask=None):
+        if enable == False:
+            mask = None
+        return (mask, )
+
+
+
+class set_precision:
+    def __init__(self):
+        pass
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                    "latent_image": ("LATENT", ),      
+                    "precision": (["16", "32", "64"], ),
+                    "set_default": ("BOOLEAN", {"default": False})
+                     },
+                }
+
+    RETURN_TYPES = ("LATENT",)
+    RETURN_NAMES = ("passthrough",)
+    CATEGORY = "RES4LYF/precision"
+
+    FUNCTION = "main"
+
+    def main(self, precision="32", latent_image=None, set_default=False):
+        match precision:
+            case "16":
+                if set_default is True:
+                    torch.set_default_dtype(torch.float16)
+                x = latent_image["samples"].to(torch.float16)
+            case "32":
+                if set_default is True:
+                    torch.set_default_dtype(torch.float32)
+                x = latent_image["samples"].to(torch.float32)
+            case "64":
+                if set_default is True:
+                    torch.set_default_dtype(torch.float64)
+                x = latent_image["samples"].to(torch.float64)
+        return ({"samples": x}, )
+    
+
+class set_precision_universal:
+    def __init__(self):
+        pass
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                    "precision": (["bf16", "fp16", "fp32", "fp64", "passthrough"], {"default": "fp32"}),
+                    "set_default": ("BOOLEAN", {"default": False})
+                    },
+            "optional": {
+                    "cond_pos": ("CONDITIONING",),
+                    "cond_neg": ("CONDITIONING",),
+                    "sigmas": ("SIGMAS", ),
+                    "latent_image": ("LATENT", ),
+                    },
+                }
+
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "SIGMAS", "LATENT",)
+    RETURN_NAMES = ("cond_pos","cond_neg","sigmas","latent_image",)
+    CATEGORY = "RES4LYF/precision"
+
+    FUNCTION = "main"
+
+    def main(self, precision="fp32", cond_pos=None, cond_neg=None, sigmas=None, latent_image=None, set_default=False):
+        dtype = None
+        match precision:
+            case "bf16":
+                dtype = torch.bfloat16
+            case "fp16":
+                dtype = torch.float16
+            case "fp32":
+                dtype = torch.float32
+            case "fp64":
+                dtype = torch.float64
+            case "passthrough":
+                return (cond_pos, cond_neg, sigmas, latent_image, )
+        
+        if cond_pos is not None:
+            cond_pos[0][0] = cond_pos[0][0].clone().to(dtype)
+            cond_pos[0][1]["pooled_output"] = cond_pos[0][1]["pooled_output"].clone().to(dtype)
+        
+        if cond_neg is not None:
+            cond_neg[0][0] = cond_neg[0][0].clone().to(dtype)
+            cond_neg[0][1]["pooled_output"] = cond_neg[0][1]["pooled_output"].clone().to(dtype)
+            
+        if sigmas is not None:
+            sigmas = sigmas.clone().to(dtype)
+        
+        if latent_image is not None:
+            x = latent_image["samples"].clone().to(dtype)    
+            latent_image = {"samples": x}
+
+        if set_default is True:
+            torch.set_default_dtype(dtype)
+        
+        return (cond_pos, cond_neg, sigmas, latent_image, )
+    
+    
+class set_precision_advanced:
+    def __init__(self):
+        pass
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                    "latent_image": ("LATENT", ),      
+                    "global_precision": (["64", "32", "16"], ),
+                    "shark_precision": (["64", "32", "16"], ),
+                     },
+                }
+
+    RETURN_TYPES = ("LATENT","LATENT","LATENT","LATENT","LATENT",)
+    RETURN_NAMES = ("PASSTHROUGH","LATENT_CAST_TO_GLOBAL","LATENT_16","LATENT_32","LATENT_64",)
+    CATEGORY = "RES4LYF/precision"
+
+    FUNCTION = "main"
+
+    def main(self, global_precision="32", shark_precision="64", latent_image=None):
+        dtype_map = {
+            "16": torch.float16,
+            "32": torch.float32,
+            "64": torch.float64
+        }
+        precision_map = {
+            "16": 'fp16',
+            "32": 'fp32',
+            "64": 'fp64'
+        }
+
+        torch.set_default_dtype(dtype_map[global_precision])
+        precision_tool.set_cast_type(precision_map[shark_precision])
+
+        latent_passthrough = latent_image["samples"]
+
+        latent_out16 = latent_image["samples"].to(torch.float16)
+        latent_out32 = latent_image["samples"].to(torch.float32)
+        latent_out64 = latent_image["samples"].to(torch.float64)
+
+        target_dtype = dtype_map[global_precision]
+        if latent_image["samples"].dtype != target_dtype:
+            latent_image["samples"] = latent_image["samples"].to(target_dtype)
+
+        latent_cast_to_global = latent_image["samples"]
+
+        return ({"samples": latent_passthrough}, {"samples": latent_cast_to_global}, {"samples": latent_out16}, {"samples": latent_out32}, {"samples": latent_out64})
+    
+class latent_to_cuda:
+    def __init__(self):
+        pass
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                    "latent": ("LATENT", ),      
+                    "to_cuda": ("BOOLEAN", {"default": True}),
+                     },
+                }
+
+    RETURN_TYPES = ("LATENT",)
+    RETURN_NAMES = ("passthrough",)
+    CATEGORY = "RES4LYF/latents"
+
+    FUNCTION = "main"
+
+    def main(self, latent, to_cuda):
+        match to_cuda:
+            case "True":
+                latent = latent.to('cuda')
+            case "False":
+                latent = latent.to('cpu')
+        return (latent,)
+
+class latent_batch:
+    def __init__(self):
+        pass
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                    "latent": ("LATENT", ),      
+                    "batch_size": ("INT", {"default": 0, "min": -10000, "max": 10000}),
+                     },
+                }
+
+    RETURN_TYPES = ("LATENT",)
+    RETURN_NAMES = ("latent_batch",)
+    CATEGORY = "RES4LYF/latents"
+
+    FUNCTION = "main"
+
+    def main(self, latent, batch_size):
+        latent = latent["samples"]
+        b, c, h, w = latent.shape
+        batch_latents = torch.zeros([batch_size, 4, h, w], device=latent.device)
+        for i in range(batch_size):
+            batch_latents[i] = latent
+        return ({"samples": batch_latents}, )
+
+class LatentPhaseMagnitude:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "latent_0_batch": ("LATENT",),
+                "latent_1_batch": ("LATENT",),
+
+                "phase_mix_power": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "magnitude_mix_power": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+
+                "phase_luminosity": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "phase_cyan_red": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "phase_lime_purple": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "phase_pattern_structure": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+
+                "magnitude_luminosity": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "magnitude_cyan_red": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "magnitude_lime_purple": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "magnitude_pattern_structure": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+
+                "latent_0_normal": ("BOOLEAN", {"default": True}),
+                "latent_1_normal": ("BOOLEAN", {"default": True}),
+                "latent_out_normal": ("BOOLEAN", {"default": True}),
+                "latent_0_stdize": ("BOOLEAN", {"default": True}),
+                "latent_1_stdize": ("BOOLEAN", {"default": True}),
+                "latent_out_stdize": ("BOOLEAN", {"default": True}),
+                "latent_0_meancenter": ("BOOLEAN", {"default": True}),
+                "latent_1_meancenter": ("BOOLEAN", {"default": True}),
+                "latent_out_meancenter": ("BOOLEAN", {"default": True}),
+            },
+            "optional": {
+                "phase_mix_powers": ("SIGMAS", ),
+                "magnitude_mix_powers": ("SIGMAS", ),
+
+                "phase_luminositys": ("SIGMAS", ),
+                "phase_cyan_reds": ("SIGMAS", ),
+                "phase_lime_purples": ("SIGMAS", ),
+                "phase_pattern_structures": ("SIGMAS", ),
+
+                "magnitude_luminositys": ("SIGMAS", ),
+                "magnitude_cyan_reds": ("SIGMAS", ),
+                "magnitude_lime_purples": ("SIGMAS", ),
+                "magnitude_pattern_structures": ("SIGMAS", ),
+            }
+        }
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "main"
+    CATEGORY = "RES4LYF/latents"
+    
+    @staticmethod
+    def latent_repeat(latent, batch_size):
+        b, c, h, w = latent.shape
+        batch_latents = torch.zeros((batch_size, c, h, w), dtype=latent.dtype, layout=latent.layout, device=latent.device)
+        for i in range(batch_size):
+            batch_latents[i] = latent
+        return batch_latents
+
+    @staticmethod
+    def mix_latent_phase_magnitude(latent_0, latent_1, power_phase, power_magnitude,
+                                    phase_luminosity, phase_cyan_red, phase_lime_purple, phase_pattern_structure,
+                                    magnitude_luminosity, magnitude_cyan_red, magnitude_lime_purple, magnitude_pattern_structure
+                                    ):
+        dtype = torch.promote_types(latent_0.dtype, latent_1.dtype)
+        # big accuracy problems with fp32 FFT! let's avoid that
+        latent_0 = latent_0.double()
+        latent_1 = latent_1.double()
+
+        latent_0_fft = torch.fft.fft2(latent_0)
+        latent_1_fft = torch.fft.fft2(latent_1)
+
+        latent_0_phase = torch.angle(latent_0_fft)
+        latent_1_phase = torch.angle(latent_1_fft)
+        latent_0_magnitude = torch.abs(latent_0_fft)
+        latent_1_magnitude = torch.abs(latent_1_fft)
+
+        # DC corruption...? handle separately??
+        #dc_index = (0, 0)
+        #dc_0 = latent_0_fft[:, :, dc_index[0], dc_index[1]]
+        #dc_1 = latent_1_fft[:, :, dc_index[0], dc_index[1]]
+        #mixed_dc = dc_0 * 0.5 + dc_1 * 0.5
+        #mixed_dc = dc_0 * (1 - phase_weight) + dc_1 * phase_weight
+
+        # create complex FFT using a weighted mix of phases
+        chan_weights_phase     = [w for w in [phase_luminosity,     phase_cyan_red,     phase_lime_purple,     phase_pattern_structure    ]]
+        chan_weights_magnitude = [w for w in [magnitude_luminosity, magnitude_cyan_red, magnitude_lime_purple, magnitude_pattern_structure]]
+        mixed_phase     = torch.zeros_like(latent_0, dtype=latent_0.dtype, layout=latent_0.layout, device=latent_0.device)
+        mixed_magnitude = torch.zeros_like(latent_0, dtype=latent_0.dtype, layout=latent_0.layout, device=latent_0.device)
+
+        for i in range(4):
+            mixed_phase[:, i]     = ( (latent_0_phase[:,i] * (1-chan_weights_phase[i])) ** power_phase + (latent_1_phase[:,i] * chan_weights_phase[i]) ** power_phase) ** (1/power_phase)
+            mixed_magnitude[:, i]     = ( (latent_0_magnitude[:,i] * (1-chan_weights_magnitude[i])) ** power_magnitude + (latent_1_magnitude[:,i] * chan_weights_magnitude[i]) ** power_magnitude) ** (1/power_magnitude)
+
+        new_fft = mixed_magnitude * torch.exp(1j * mixed_phase)
+
+        #new_fft[:, :, dc_index[0], dc_index[1]] = mixed_dc
+
+        # inverse FFT to convert back to spatial domain
+        mixed_phase_magnitude = torch.fft.ifft2(new_fft).real
+
+        return mixed_phase_magnitude.to(dtype)
+    
+    def main(self, #batch_size, latent_1_repeat,
+             latent_0_batch,  latent_1_batch, latent_0_normal, latent_1_normal, latent_out_normal,
+             latent_0_stdize, latent_1_stdize, latent_out_stdize, 
+             latent_0_meancenter, latent_1_meancenter, latent_out_meancenter, 
+             phase_mix_power, magnitude_mix_power, 
+             phase_luminosity,           phase_cyan_red,           phase_lime_purple,           phase_pattern_structure, 
+             magnitude_luminosity,       magnitude_cyan_red,       magnitude_lime_purple,       magnitude_pattern_structure, 
+             phase_mix_powers=None,      magnitude_mix_powers=None,
+             phase_luminositys=None,     phase_cyan_reds=None,     phase_lime_purples=None,     phase_pattern_structures=None,
+             magnitude_luminositys=None, magnitude_cyan_reds=None, magnitude_lime_purples=None, magnitude_pattern_structures=None
+             ):
+        latent_0_batch = latent_0_batch["samples"].double()
+        latent_1_batch = latent_1_batch["samples"].double().to(latent_0_batch.device)
+
+        #if batch_size == 0:
+        batch_size = latent_0_batch.shape[0]
+        if latent_1_batch.shape[0] == 1:
+            latent_1_batch = self.latent_repeat(latent_1_batch, batch_size)
+
+
+        magnitude_mix_powers         = initialize_or_scale(magnitude_mix_powers,         magnitude_mix_power,         batch_size)
+        phase_mix_powers             = initialize_or_scale(phase_mix_powers,             phase_mix_power,            batch_size)
+
+        phase_luminositys            = initialize_or_scale(phase_luminositys,            phase_luminosity,            batch_size)
+        phase_cyan_reds              = initialize_or_scale(phase_cyan_reds,              phase_cyan_red,              batch_size)
+        phase_lime_purples           = initialize_or_scale(phase_lime_purples,           phase_lime_purple,           batch_size)
+        phase_pattern_structures     = initialize_or_scale(phase_pattern_structures,     phase_pattern_structure,     batch_size)
+
+        magnitude_luminositys        = initialize_or_scale(magnitude_luminositys,        magnitude_luminosity,        batch_size)
+        magnitude_cyan_reds          = initialize_or_scale(magnitude_cyan_reds,          magnitude_cyan_red,          batch_size)
+        magnitude_lime_purples       = initialize_or_scale(magnitude_lime_purples,       magnitude_lime_purple,       batch_size)
+        magnitude_pattern_structures = initialize_or_scale(magnitude_pattern_structures, magnitude_pattern_structure, batch_size)    
+
+        mixed_phase_magnitude_batch = torch.zeros(latent_0_batch.shape, device=latent_0_batch.device)
+
+        if latent_0_normal == True:
+            latent_0_batch = latent_normalize_channels(latent_0_batch)
+        if latent_1_normal == True:
+            latent_1_batch = latent_normalize_channels(latent_1_batch)
+        if latent_0_meancenter == True:
+            latent_0_batch = latent_meancenter_channels(latent_0_batch)
+        if latent_1_meancenter == True:
+            latent_1_batch = latent_meancenter_channels(latent_1_batch)
+        if latent_0_stdize == True:
+            latent_0_batch = latent_stdize_channels(latent_0_batch)
+        if latent_1_stdize == True:
+            latent_1_batch = latent_stdize_channels(latent_1_batch)
+ 
+        for i in range(batch_size):
+            mixed_phase_magnitude = self.mix_latent_phase_magnitude(latent_0_batch[i:i+1], latent_1_batch[i:i+1], phase_mix_powers[i].item(), magnitude_mix_powers[i].item(),
+                                                    phase_luminositys[i].item(), phase_cyan_reds[i].item(),phase_lime_purples[i].item(),phase_pattern_structures[i].item(),
+                                                    magnitude_luminositys[i].item(), magnitude_cyan_reds[i].item(),magnitude_lime_purples[i].item(),magnitude_pattern_structures[i].item()
+                                                    )
+            if latent_out_normal == True:
+                mixed_phase_magnitude = latent_normalize_channels(mixed_phase_magnitude)
+            if latent_out_stdize == True:
+                mixed_phase_magnitude = latent_stdize_channels(mixed_phase_magnitude)
+            if latent_out_meancenter == True:
+                mixed_phase_magnitude = latent_meancenter_channels(mixed_phase_magnitude)                                
+
+            mixed_phase_magnitude_batch[i, :, :, :] = mixed_phase_magnitude
+
+        return ({"samples": mixed_phase_magnitude_batch}, )
+
+class LatentPhaseMagnitudeMultiply:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "latent_0_batch": ("LATENT",),
+
+                "phase_luminosity": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "phase_cyan_red": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "phase_lime_purple": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "phase_pattern_structure": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+
+                "magnitude_luminosity": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "magnitude_cyan_red": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "magnitude_lime_purple": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "magnitude_pattern_structure": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+
+                "latent_0_normal": ("BOOLEAN", {"default": False}),
+                "latent_out_normal": ("BOOLEAN", {"default": False}),
+            },
+            "optional": {
+                "phase_luminositys": ("SIGMAS", ),
+                "phase_cyan_reds": ("SIGMAS", ),
+                "phase_lime_purples": ("SIGMAS", ),
+                "phase_pattern_structures": ("SIGMAS", ),
+
+                "magnitude_luminositys": ("SIGMAS", ),
+                "magnitude_cyan_reds": ("SIGMAS", ),
+                "magnitude_lime_purples": ("SIGMAS", ),
+                "magnitude_pattern_structures": ("SIGMAS", ),
+            }
+        }
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "main"
+    CATEGORY = "RES4LYF/latents"
+
+    @staticmethod
+    def latent_repeat(latent, batch_size):
+        b, c, h, w = latent.shape
+        batch_latents = torch.zeros((batch_size, c, h, w), dtype=latent.dtype, layout=latent.layout, device=latent.device)
+        for i in range(batch_size):
+            batch_latents[i] = latent
+        return batch_latents
+
+    @staticmethod
+    def mix_latent_phase_magnitude(latent_0,  
+                                    phase_luminosity, phase_cyan_red, phase_lime_purple, phase_pattern_structure,
+                                    magnitude_luminosity, magnitude_cyan_red, magnitude_lime_purple, magnitude_pattern_structure
+                                    ):
+        dtype = latent_0.dtype
+        # avoid big accuracy problems with fp32 FFT!
+        latent_0 = latent_0.double()
+
+        latent_0_fft = torch.fft.fft2(latent_0)
+
+        latent_0_phase = torch.angle(latent_0_fft)
+        latent_0_magnitude = torch.abs(latent_0_fft)
+
+        # create new complex FFT using weighted mix of phases
+        chan_weights_phase     = [w for w in [phase_luminosity,     phase_cyan_red,     phase_lime_purple,     phase_pattern_structure    ]]
+        chan_weights_magnitude = [ w for w in [magnitude_luminosity, magnitude_cyan_red, magnitude_lime_purple, magnitude_pattern_structure]]
+        mixed_phase     = torch.zeros_like(latent_0, dtype=latent_0.dtype, layout=latent_0.layout, device=latent_0.device)
+        mixed_magnitude = torch.zeros_like(latent_0, dtype=latent_0.dtype, layout=latent_0.layout, device=latent_0.device)
+
+        for i in range(4):
+            mixed_phase[:, i]     = latent_0_phase[:,i]     * chan_weights_phase[i]
+            mixed_magnitude[:, i] = latent_0_magnitude[:,i] * chan_weights_magnitude[i]
+
+        new_fft = mixed_magnitude * torch.exp(1j * mixed_phase)
+        
+        # inverse FFT to convert back to spatial domain
+        mixed_phase_magnitude = torch.fft.ifft2(new_fft).real
+
+        return mixed_phase_magnitude.to(dtype)
+    
+    def main(self,
+             latent_0_batch, latent_0_normal, latent_out_normal,
+             phase_luminosity,           phase_cyan_red,           phase_lime_purple,           phase_pattern_structure, 
+             magnitude_luminosity,       magnitude_cyan_red,       magnitude_lime_purple,       magnitude_pattern_structure, 
+             phase_luminositys=None,     phase_cyan_reds=None,     phase_lime_purples=None,     phase_pattern_structures=None,
+             magnitude_luminositys=None, magnitude_cyan_reds=None, magnitude_lime_purples=None, magnitude_pattern_structures=None
+             ):
+        latent_0_batch = latent_0_batch["samples"].double()
+
+        batch_size = latent_0_batch.shape[0]
+
+        phase_luminositys            = initialize_or_scale(phase_luminositys,            phase_luminosity,            batch_size)
+        phase_cyan_reds              = initialize_or_scale(phase_cyan_reds,              phase_cyan_red,              batch_size)
+        phase_lime_purples           = initialize_or_scale(phase_lime_purples,           phase_lime_purple,           batch_size)
+        phase_pattern_structures     = initialize_or_scale(phase_pattern_structures,     phase_pattern_structure,     batch_size)
+
+        magnitude_luminositys        = initialize_or_scale(magnitude_luminositys,        magnitude_luminosity,        batch_size)
+        magnitude_cyan_reds          = initialize_or_scale(magnitude_cyan_reds,          magnitude_cyan_red,          batch_size)
+        magnitude_lime_purples       = initialize_or_scale(magnitude_lime_purples,       magnitude_lime_purple,       batch_size)
+        magnitude_pattern_structures = initialize_or_scale(magnitude_pattern_structures, magnitude_pattern_structure, batch_size)    
+
+        mixed_phase_magnitude_batch = torch.zeros(latent_0_batch.shape, device=latent_0_batch.device)
+
+        if latent_0_normal == True:
+            latent_0_batch = latent_normalize_channels(latent_0_batch)
+ 
+        for i in range(batch_size):
+            mixed_phase_magnitude = self.mix_latent_phase_magnitude(latent_0_batch[i:i+1],
+                                                    phase_luminositys[i].item(), phase_cyan_reds[i].item(),phase_lime_purples[i].item(),phase_pattern_structures[i].item(),
+                                                    magnitude_luminositys[i].item(), magnitude_cyan_reds[i].item(),magnitude_lime_purples[i].item(),magnitude_pattern_structures[i].item()
+                                                    )
+            if latent_out_normal == True:
+                mixed_phase_magnitude = latent_normalize_channels(mixed_phase_magnitude)
+
+            mixed_phase_magnitude_batch[i, :, :, :] = mixed_phase_magnitude
+
+        return ({"samples": mixed_phase_magnitude_batch}, )
+
+
+
+class LatentPhaseMagnitudeOffset:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "latent_0_batch": ("LATENT",),
+
+                "phase_luminosity": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "phase_cyan_red": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "phase_lime_purple": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "phase_pattern_structure": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+
+                "magnitude_luminosity": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "magnitude_cyan_red": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "magnitude_lime_purple": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "magnitude_pattern_structure": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+
+                "latent_0_normal": ("BOOLEAN", {"default": False}),
+                "latent_out_normal": ("BOOLEAN", {"default": False}),
+            },
+            "optional": {
+                "phase_luminositys": ("SIGMAS", ),
+                "phase_cyan_reds": ("SIGMAS", ),
+                "phase_lime_purples": ("SIGMAS", ),
+                "phase_pattern_structures": ("SIGMAS", ),
+
+                "magnitude_luminositys": ("SIGMAS", ),
+                "magnitude_cyan_reds": ("SIGMAS", ),
+                "magnitude_lime_purples": ("SIGMAS", ),
+                "magnitude_pattern_structures": ("SIGMAS", ),
+            }
+        }
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "main"
+    CATEGORY = "RES4LYF/latents"
+    
+    @staticmethod
+    def latent_repeat(latent, batch_size):
+        b, c, h, w = latent.shape
+        batch_latents = torch.zeros((batch_size, c, h, w), dtype=latent.dtype, layout=latent.layout, device=latent.device)
+        for i in range(batch_size):
+            batch_latents[i] = latent
+        return batch_latents
+
+    @staticmethod
+    def mix_latent_phase_magnitude(latent_0,  
+                                    phase_luminosity, phase_cyan_red, phase_lime_purple, phase_pattern_structure,
+                                    magnitude_luminosity, magnitude_cyan_red, magnitude_lime_purple, magnitude_pattern_structure
+                                    ):
+        dtype = latent_0.dtype
+        # avoid big accuracy problems with fp32 FFT!
+        latent_0 = latent_0.double()
+
+        latent_0_fft = torch.fft.fft2(latent_0)
+
+        latent_0_phase = torch.angle(latent_0_fft)
+        latent_0_magnitude = torch.abs(latent_0_fft)
+
+        # create new complex FFT using a weighted mix of phases
+        chan_weights_phase     = [w for w in [phase_luminosity,     phase_cyan_red,     phase_lime_purple,     phase_pattern_structure    ]]
+        chan_weights_magnitude = [ w for w in [magnitude_luminosity, magnitude_cyan_red, magnitude_lime_purple, magnitude_pattern_structure]]
+        mixed_phase     = torch.zeros_like(latent_0, dtype=latent_0.dtype, layout=latent_0.layout, device=latent_0.device)
+        mixed_magnitude = torch.zeros_like(latent_0, dtype=latent_0.dtype, layout=latent_0.layout, device=latent_0.device)
+
+        for i in range(4):
+            mixed_phase[:, i]     = latent_0_phase[:,i]     + chan_weights_phase[i]
+            mixed_magnitude[:, i] = latent_0_magnitude[:,i] + chan_weights_magnitude[i]
+
+        new_fft = mixed_magnitude * torch.exp(1j * mixed_phase)
+        
+        # inverse FFT to convert back to spatial domain
+        mixed_phase_magnitude = torch.fft.ifft2(new_fft).real
+
+        return mixed_phase_magnitude.to(dtype)
+    
+    def main(self,
+             latent_0_batch, latent_0_normal, latent_out_normal,
+             phase_luminosity,           phase_cyan_red,           phase_lime_purple,           phase_pattern_structure, 
+             magnitude_luminosity,       magnitude_cyan_red,       magnitude_lime_purple,       magnitude_pattern_structure, 
+             phase_luminositys=None,     phase_cyan_reds=None,     phase_lime_purples=None,     phase_pattern_structures=None,
+             magnitude_luminositys=None, magnitude_cyan_reds=None, magnitude_lime_purples=None, magnitude_pattern_structures=None
+             ):
+        latent_0_batch = latent_0_batch["samples"].double()
+
+        batch_size = latent_0_batch.shape[0]
+
+        phase_luminositys            = initialize_or_scale(phase_luminositys,            phase_luminosity,            batch_size)
+        phase_cyan_reds              = initialize_or_scale(phase_cyan_reds,              phase_cyan_red,              batch_size)
+        phase_lime_purples           = initialize_or_scale(phase_lime_purples,           phase_lime_purple,           batch_size)
+        phase_pattern_structures     = initialize_or_scale(phase_pattern_structures,     phase_pattern_structure,     batch_size)
+
+        magnitude_luminositys        = initialize_or_scale(magnitude_luminositys,        magnitude_luminosity,        batch_size)
+        magnitude_cyan_reds          = initialize_or_scale(magnitude_cyan_reds,          magnitude_cyan_red,          batch_size)
+        magnitude_lime_purples       = initialize_or_scale(magnitude_lime_purples,       magnitude_lime_purple,       batch_size)
+        magnitude_pattern_structures = initialize_or_scale(magnitude_pattern_structures, magnitude_pattern_structure, batch_size)    
+
+        mixed_phase_magnitude_batch = torch.zeros(latent_0_batch.shape, device=latent_0_batch.device)
+
+        if latent_0_normal == True:
+            latent_0_batch = latent_normalize_channels(latent_0_batch)
+ 
+        for i in range(batch_size):
+            mixed_phase_magnitude = self.mix_latent_phase_magnitude(latent_0_batch[i:i+1],
+                                                    phase_luminositys[i].item(), phase_cyan_reds[i].item(),phase_lime_purples[i].item(),phase_pattern_structures[i].item(),
+                                                    magnitude_luminositys[i].item(), magnitude_cyan_reds[i].item(),magnitude_lime_purples[i].item(),magnitude_pattern_structures[i].item()
+                                                    )
+            if latent_out_normal == True:
+                mixed_phase_magnitude = latent_normalize_channels(mixed_phase_magnitude)
+
+            mixed_phase_magnitude_batch[i, :, :, :] = mixed_phase_magnitude
+
+        return ({"samples": mixed_phase_magnitude_batch}, )
+
+
+
+class LatentPhaseMagnitudePower:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "latent_0_batch": ("LATENT",),
+
+                "phase_luminosity": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "phase_cyan_red": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "phase_lime_purple": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "phase_pattern_structure": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+
+                "magnitude_luminosity": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "magnitude_cyan_red": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "magnitude_lime_purple": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "magnitude_pattern_structure": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+
+                "latent_0_normal": ("BOOLEAN", {"default": False}),
+                "latent_out_normal": ("BOOLEAN", {"default": False}),
+            },
+            "optional": {
+                "phase_luminositys": ("SIGMAS", ),
+                "phase_cyan_reds": ("SIGMAS", ),
+                "phase_lime_purples": ("SIGMAS", ),
+                "phase_pattern_structures": ("SIGMAS", ),
+
+                "magnitude_luminositys": ("SIGMAS", ),
+                "magnitude_cyan_reds": ("SIGMAS", ),
+                "magnitude_lime_purples": ("SIGMAS", ),
+                "magnitude_pattern_structures": ("SIGMAS", ),
+            }
+        }
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "main"
+    CATEGORY = "RES4LYF/latents"
+    
+    @staticmethod
+    def latent_repeat(latent, batch_size):
+        b, c, h, w = latent.shape
+        batch_latents = torch.zeros((batch_size, c, h, w), dtype=latent.dtype, layout=latent.layout, device=latent.device)
+        for i in range(batch_size):
+            batch_latents[i] = latent
+        return batch_latents
+
+    @staticmethod
+    def mix_latent_phase_magnitude(latent_0,  
+                                    phase_luminosity, phase_cyan_red, phase_lime_purple, phase_pattern_structure,
+                                    magnitude_luminosity, magnitude_cyan_red, magnitude_lime_purple, magnitude_pattern_structure
+                                    ):
+        dtype = latent_0.dtype
+        # avoid big accuracy problems with fp32 FFT!
+        latent_0 = latent_0.double()
+
+        latent_0_fft = torch.fft.fft2(latent_0)
+
+        latent_0_phase = torch.angle(latent_0_fft)
+        latent_0_magnitude = torch.abs(latent_0_fft)
+
+        # create new complex FFT using a weighted mix of phases
+        chan_weights_phase     = [w for w in [phase_luminosity,     phase_cyan_red,     phase_lime_purple,     phase_pattern_structure    ]]
+        chan_weights_magnitude = [ w for w in [magnitude_luminosity, magnitude_cyan_red, magnitude_lime_purple, magnitude_pattern_structure]]
+        mixed_phase     = torch.zeros_like(latent_0, dtype=latent_0.dtype, layout=latent_0.layout, device=latent_0.device)
+        mixed_magnitude = torch.zeros_like(latent_0, dtype=latent_0.dtype, layout=latent_0.layout, device=latent_0.device)
+
+        for i in range(4):
+            mixed_phase[:, i]     = latent_0_phase[:,i]     ** chan_weights_phase[i]
+            mixed_magnitude[:, i] = latent_0_magnitude[:,i] ** chan_weights_magnitude[i]
+
+        new_fft = mixed_magnitude * torch.exp(1j * mixed_phase)
+        
+        # inverse FFT to convert back to spatial domain
+        mixed_phase_magnitude = torch.fft.ifft2(new_fft).real
+
+        return mixed_phase_magnitude.to(dtype)
+    
+    def main(self,
+             latent_0_batch, latent_0_normal, latent_out_normal,
+             phase_luminosity,           phase_cyan_red,           phase_lime_purple,           phase_pattern_structure, 
+             magnitude_luminosity,       magnitude_cyan_red,       magnitude_lime_purple,       magnitude_pattern_structure, 
+             phase_luminositys=None,     phase_cyan_reds=None,     phase_lime_purples=None,     phase_pattern_structures=None,
+             magnitude_luminositys=None, magnitude_cyan_reds=None, magnitude_lime_purples=None, magnitude_pattern_structures=None
+             ):
+        latent_0_batch = latent_0_batch["samples"].double()
+
+        batch_size = latent_0_batch.shape[0]
+
+        phase_luminositys            = initialize_or_scale(phase_luminositys,            phase_luminosity,            batch_size)
+        phase_cyan_reds              = initialize_or_scale(phase_cyan_reds,              phase_cyan_red,              batch_size)
+        phase_lime_purples           = initialize_or_scale(phase_lime_purples,           phase_lime_purple,           batch_size)
+        phase_pattern_structures     = initialize_or_scale(phase_pattern_structures,     phase_pattern_structure,     batch_size)
+
+        magnitude_luminositys        = initialize_or_scale(magnitude_luminositys,        magnitude_luminosity,        batch_size)
+        magnitude_cyan_reds          = initialize_or_scale(magnitude_cyan_reds,          magnitude_cyan_red,          batch_size)
+        magnitude_lime_purples       = initialize_or_scale(magnitude_lime_purples,       magnitude_lime_purple,       batch_size)
+        magnitude_pattern_structures = initialize_or_scale(magnitude_pattern_structures, magnitude_pattern_structure, batch_size)    
+
+        mixed_phase_magnitude_batch = torch.zeros(latent_0_batch.shape, device=latent_0_batch.device)
+
+        if latent_0_normal == True:
+            latent_0_batch = latent_normalize_channels(latent_0_batch)
+ 
+        for i in range(batch_size):
+            mixed_phase_magnitude = self.mix_latent_phase_magnitude(latent_0_batch[i:i+1],
+                                                    phase_luminositys[i].item(), phase_cyan_reds[i].item(),phase_lime_purples[i].item(),phase_pattern_structures[i].item(),
+                                                    magnitude_luminositys[i].item(), magnitude_cyan_reds[i].item(),magnitude_lime_purples[i].item(),magnitude_pattern_structures[i].item()
+                                                    )
+            if latent_out_normal == True:
+                mixed_phase_magnitude = latent_normalize_channels(mixed_phase_magnitude)
+
+            mixed_phase_magnitude_batch[i, :, :, :] = mixed_phase_magnitude
+
+        return ({"samples": mixed_phase_magnitude_batch}, )
+
+
+
+class StableCascade_StageC_VAEEncode_Exact:
+    def __init__(self, device="cpu"):
+        self.device = device
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "image": ("IMAGE",),
+            "vae": ("VAE", ),
+            "width": ("INT", {"default": 24, "min": 1, "max": 1024, "step": 1}),
+            "height": ("INT", {"default": 24, "min": 1, "max": 1024, "step": 1}),
+        }}
+    RETURN_TYPES = ("LATENT",)
+    RETURN_NAMES = ("stage_c",)
+    FUNCTION = "generate"
+
+    CATEGORY = "RES4LYF/vae"
+    
+    def generate(self, image, vae, width, height):
+        out_width = (width) * vae.downscale_ratio #downscale_ratio = 32
+        out_height = (height) * vae.downscale_ratio
+        #movedim(-1,1) goes from 1,1024,1024,3 to 1,3,1024,1024
+        s = comfy.utils.common_upscale(image.movedim(-1,1), out_width, out_height, "lanczos", "center").movedim(1,-1)
+
+        c_latent = vae.encode(s[:,:,:,:3]) #to slice off alpha channel?
+        return ({
+            "samples": c_latent,
+        },)
+        
+
+
+class StableCascade_StageC_VAEEncode_Exact_Tiled:
+    def __init__(self, device="cpu"):
+        self.device = device
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "image": ("IMAGE",),
+            "vae": ("VAE", ),
+            "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}),
+            "overlap": ("INT", {"default": 16, "min": 8, "max": 128, "step": 8}),
+        }}
+    RETURN_TYPES = ("LATENT",)
+    RETURN_NAMES = ("stage_c",)
+    FUNCTION = "generate"
+
+    CATEGORY = "RES4LYF/vae"
+
+    def generate(self, image, vae, tile_size, overlap):
+        img_width = image.shape[-2]
+        img_height = image.shape[-3]
+        upscale_amount = vae.downscale_ratio  # downscale_ratio = 32
+
+        image = image.movedim(-1, 1)  # bhwc -> bchw 
+
+        encode_fn = lambda img: vae.encode(img.to(vae.device)).to("cpu")
+
+        c_latent = tiled_scale_multidim(
+            image, encode_fn,
+            tile=(tile_size // 8, tile_size // 8),
+            overlap=overlap,
+            upscale_amount=upscale_amount,
+            out_channels=16, 
+            output_device=self.device
+        )
+
+        return ({
+            "samples": c_latent,
+        },)
+
+@torch.inference_mode()
+def tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, upscale_amount=4, out_channels=3, output_device="cpu", pbar=None):
+    dims = len(tile)
+    output_shape = [samples.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), samples.shape[2:]))
+    output = torch.zeros(output_shape, device=output_device)
+
+    for b in range(samples.shape[0]):
+        for it in itertools.product(*map(lambda a: range(0, a[0], a[1] - overlap), zip(samples.shape[2:], tile))):
+            s_in = samples[b:b+1]
+            upscaled = []
+
+            for d in range(dims):
+                pos = max(0, min(s_in.shape[d + 2] - overlap, it[d]))
+                l = min(tile[d], s_in.shape[d + 2] - pos)
+                s_in = s_in.narrow(d + 2, pos, l)
+                upscaled.append(round(pos * upscale_amount))
+
+            ps = function(s_in).to(output_device)
+            mask = torch.ones_like(ps)
+            feather = round(overlap * upscale_amount)
+
+            for t in range(feather):
+                for d in range(2, dims + 2):
+                    mask.narrow(d, t, 1).mul_((1.0 / feather) * (t + 1))
+                    mask.narrow(d, mask.shape[d] - 1 - t, 1).mul_((1.0 / feather) * (t + 1))
+
+            o = output[b:b+1]
+            for d in range(dims):
+                o = o.narrow(d + 2, upscaled[d], mask.shape[d + 2])
+
+            o.add_(ps * mask)
+
+            if pbar is not None:
+                pbar.update(1)
+
+    return output
+
+    
+
+
+class EmptyLatentImageCustom:
+    def __init__(self):
+        self.device = comfy.model_management.intermediate_device()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+            "width": ("INT", {"default": 24, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
+            "height": ("INT", {"default": 24, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
+            "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
+
+            "channels": (['4', '16'], {"default": '4'}),
+            "mode": (['sdxl', 'cascade_b', 'cascade_c', 'exact'], {"default": 'default'}),
+            "compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}),
+            "precision": (['fp16', 'fp32', 'fp64'], {"default": 'fp32'}),
+            
+        }}
+    
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "generate"
+
+    CATEGORY = "RES4LYF/latents"
+
+    def generate(self, width, height, batch_size, channels, mode, compression, precision):
+        c = int(channels)
+
+        ratio = 1
+        match mode:
+            case "sdxl":
+                ratio = 8
+            case "cascade_b":
+                ratio = 4
+            case "cascade_c":
+                ratio = compression
+            case "exact":
+                ratio = 1
+
+        dtype=torch.float32
+        match precision:
+            case "fp16":
+                dtype=torch.float16
+            case "fp32":
+                dtype=torch.float32
+            case "fp64":
+                dtype=torch.float64
+
+        latent = torch.zeros([batch_size, c, height // ratio, width // ratio], dtype=dtype, device=self.device)
+        return ({"samples":latent}, )
+
+class EmptyLatentImage64:
+    def __init__(self):
+        self.device = comfy.model_management.intermediate_device()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "width": ("INT", {"default": 1024, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
+                              "height": ("INT", {"default": 1024, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
+                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "generate"
+
+    CATEGORY = "RES4LYF/latents"
+
+    def generate(self, width, height, batch_size=1):
+        latent = torch.zeros([batch_size, 4, height // 8, width // 8], dtype=torch.float64, device=self.device)
+        return ({"samples":latent}, )
+
+"""class CheckpointLoader32:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "config_name": (folder_paths.get_filename_list("configs"), ),
+                              "ckpt_name": (folder_paths.get_filename_list("checkpoints"), )}}
+    RETURN_TYPES = ("MODEL", "CLIP", "VAE")
+    FUNCTION = "load_checkpoint"
+
+    CATEGORY = "advanced/loaders"
+
+    def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True):
+        #torch.set_default_dtype(torch.float64)
+        config_path = folder_paths.get_full_path("configs", config_name)
+        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
+        return comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))"""
+
+MAX_RESOLUTION=8192
+
+class LatentNoiseBatch_perlin:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s): 
+        return {"required": {
+            "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            "width": ("INT", {"default": 1024, "min": 8, "max": MAX_RESOLUTION, "step": 8}),
+            "height": ("INT", {"default": 1024, "min": 8, "max": MAX_RESOLUTION, "step": 8}),
+            "batch_size": ("INT", {"default": 1, "min": 1, "max": 256}),
+            "detail_level": ("FLOAT", {"default": 0, "min": -1, "max": 1.0, "step": 0.1}),
+            },
+            "optional": {
+                "details": ("SIGMAS", ),
+            }
+        }
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "create_noisy_latents_perlin"
+    CATEGORY = "RES4LYF/noise"
+
+    # found at https://gist.github.com/vadimkantorov/ac1b097753f217c5c11bc2ff396e0a57
+    # which was ported from https://github.com/pvigier/perlin-numpy/blob/master/perlin2d.py
+    def rand_perlin_2d(self, shape, res, fade = lambda t: 6*t**5 - 15*t**4 + 10*t**3):
+        delta = (res[0] / shape[0], res[1] / shape[1])
+        d = (shape[0] // res[0], shape[1] // res[1])
+        
+        grid = torch.stack(torch.meshgrid(torch.arange(0, res[0], delta[0]), torch.arange(0, res[1], delta[1])), dim = -1) % 1
+        angles = 2*math.pi*torch.rand(res[0]+1, res[1]+1)
+        gradients = torch.stack((torch.cos(angles), torch.sin(angles)), dim = -1)
+        
+        tile_grads = lambda slice1, slice2: gradients[slice1[0]:slice1[1], slice2[0]:slice2[1]].repeat_interleave(d[0], 0).repeat_interleave(d[1], 1)
+        dot = lambda grad, shift: (torch.stack((grid[:shape[0],:shape[1],0] + shift[0], grid[:shape[0],:shape[1], 1] + shift[1]  ), dim = -1) * grad[:shape[0], :shape[1]]).sum(dim = -1)
+        
+        n00 = dot(tile_grads([0, -1], [0, -1]), [0,  0])
+        n10 = dot(tile_grads([1, None], [0, -1]), [-1, 0])
+        n01 = dot(tile_grads([0, -1],[1, None]), [0, -1])
+        n11 = dot(tile_grads([1, None], [1, None]), [-1,-1])
+        t = fade(grid[:shape[0], :shape[1]])
+        return math.sqrt(2) * torch.lerp(torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1])
+
+    def rand_perlin_2d_octaves(self, shape, res, octaves=1, persistence=0.5):
+        noise = torch.zeros(shape)
+        frequency = 1
+        amplitude = 1
+        for _ in range(octaves):
+            noise += amplitude * self.rand_perlin_2d(shape, (frequency*res[0], frequency*res[1]))
+            frequency *= 2
+            amplitude *= persistence
+        noise = torch.remainder(torch.abs(noise)*1000000,11)/11
+        # noise = (torch.sin(torch.remainder(noise*1000000,83))+1)/2
+        return noise
+    
+    def scale_tensor(self, x):
+        min_value = x.min()
+        max_value = x.max()
+        x = (x - min_value) / (max_value - min_value)
+        return x
+
+    def create_noisy_latents_perlin(self, seed, width, height, batch_size, detail_level, details=None):
+        if details is None:
+             details = torch.full((10000,), detail_level)
+        else:
+            details = detail_level * details
+        torch.manual_seed(seed)
+        noise = torch.zeros((batch_size, 4, height // 8, width // 8), dtype=torch.float32, device="cpu").cpu()
+        for i in range(batch_size):
+            for j in range(4):
+                noise_values = self.rand_perlin_2d_octaves((height // 8, width // 8), (1,1), 1, 1)
+                result = (1+details[i]/10)*torch.erfinv(2 * noise_values - 1) * (2 ** 0.5)
+                result = torch.clamp(result,-5,5)
+                noise[i, j, :, :] = result
+        return ({"samples": noise},)
+
+
+class LatentNoiseBatch_gaussian_channels:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "latent": ("LATENT",),
+                "mean": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "mean_luminosity": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "mean_cyan_red": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "mean_lime_purple": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "mean_pattern_structure": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "std": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "steps": ("INT", {"default": 0, "min": -10000, "max": 10000}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            },
+            "optional": {
+                "means": ("SIGMAS", ),
+                "mean_luminositys": ("SIGMAS", ),
+                "mean_cyan_reds": ("SIGMAS", ),
+                "mean_lime_purples": ("SIGMAS", ),
+                "mean_pattern_structures": ("SIGMAS", ),
+                "stds": ("SIGMAS", ),
+            }
+        }
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "main"
+
+    CATEGORY = "RES4LYF/noise"
+
+    """    @staticmethod
+    def gaussian_noise_channels_like(x, mean=0.0, mean_luminosity = -0.1, mean_cyan_red = 0.0, mean_lime_purple=0.0, mean_pattern_structure=0.0, std_dev=1.0, seed=42):
+        x = x.squeeze(0)
+
+        noise = torch.randn_like(x) * std_dev + mean
+
+        luminosity = noise[0:1] + mean_luminosity
+        cyan_red = noise[1:2] + mean_cyan_red
+        lime_purple = noise[2:3] + mean_lime_purple
+        pattern_structure = noise[3:4] + mean_pattern_structure
+
+        noise = torch.unsqueeze(torch.cat([luminosity, cyan_red, lime_purple, pattern_structure]), 0)
+
+        return noise.to(x.device)"""
+    
+    @staticmethod
+    def gaussian_noise_channels(x, mean_luminosity = -0.1, mean_cyan_red = 0.0, mean_lime_purple=0.0, mean_pattern_structure=0.0):
+        x = x.squeeze(0)
+
+        luminosity = x[0:1] + mean_luminosity
+        cyan_red = x[1:2] + mean_cyan_red
+        lime_purple = x[2:3] + mean_lime_purple
+        pattern_structure = x[3:4] + mean_pattern_structure
+
+        x = torch.unsqueeze(torch.cat([luminosity, cyan_red, lime_purple, pattern_structure]), 0)
+
+        return x
+
+    def main(self, latent, steps, seed, 
+              mean, mean_luminosity, mean_cyan_red, mean_lime_purple, mean_pattern_structure, std,
+              means=None, mean_luminositys=None, mean_cyan_reds=None, mean_lime_purples=None, mean_pattern_structures=None, stds=None):
+        if steps == 0:
+            steps = len(means)
+
+        x = latent["samples"]
+        b, c, h, w = x.shape  
+
+        noise_latents = torch.zeros([steps, 4, h, w], dtype=x.dtype, layout=x.layout, device=x.device)
+
+        noise_sampler = NOISE_GENERATOR_CLASSES.get('gaussian')(x=x, seed = seed)
+
+        means = initialize_or_scale(means, mean, steps)
+        mean_luminositys = initialize_or_scale(mean_luminositys, mean_luminosity, steps)
+        mean_cyan_reds = initialize_or_scale(mean_cyan_reds, mean_cyan_red, steps)
+        mean_lime_purples = initialize_or_scale(mean_lime_purples, mean_lime_purple, steps)
+        mean_pattern_structures = initialize_or_scale(mean_pattern_structures, mean_pattern_structure, steps)
+
+        stds = initialize_or_scale(stds, std, steps)
+
+        for i in range(steps):
+            noise = noise_sampler(mean=means[i].item(), std=stds[i].item())
+            noise = self.gaussian_noise_channels(noise, mean_luminositys[i].item(), mean_cyan_reds[i].item(), mean_lime_purples[i].item(), mean_pattern_structures[i].item())
+            noise_latents[i] = x + noise
+
+        return ({"samples": noise_latents}, )
+
+class LatentNoiseBatch_gaussian:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "latent": ("LATENT",),
+                "mean": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "std": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "steps": ("INT", {"default": 0, "min": -10000, "max": 10000}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            },
+            "optional": {
+                "means": ("SIGMAS", ),
+                "stds": ("SIGMAS", ),
+                "steps_": ("SIGMAS", ),
+            }
+        }
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "main"
+
+    CATEGORY = "RES4LYF/noise"
+
+    def main(self, latent, mean, std, steps, seed, means=None, stds=None, steps_=None):
+        if steps_ is not None:
+            steps = len(steps_)
+
+        means = initialize_or_scale(means, mean, steps)
+        stds = initialize_or_scale(stds, std, steps)    
+
+        latent_samples = latent["samples"]
+        b, c, h, w = latent_samples.shape  
+
+        noise_latents = torch.zeros([steps, c, h, w], dtype=latent_samples.dtype, layout=latent_samples.layout, device=latent_samples.device)
+
+        noise_sampler = NOISE_GENERATOR_CLASSES.get('gaussian')(x=latent_samples, seed = seed)
+
+        for i in range(steps):
+            noise_latents[i] = noise_sampler(mean=means[i].item(), std=stds[i].item())
+        return ({"samples": noise_latents}, )
+
+class LatentNoiseBatch_fractal:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "latent": ("LATENT",),
+                "alpha": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "k_flip": ("BOOLEAN", {"default": False}),
+                "steps": ("INT", {"default": 0, "min": -10000, "max": 10000}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            },
+            "optional": {
+                "alphas": ("SIGMAS", ),
+                "ks": ("SIGMAS", ),
+                "steps_": ("SIGMAS", ),
+            }
+        }
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "main"
+
+    CATEGORY = "RES4LYF/noise"
+
+    def main(self, latent, alpha, k_flip, steps, seed=42, alphas=None, ks=None, sigmas_=None, steps_=None):
+        if steps_ is not None:
+            steps = len(steps_)
+
+        alphas = initialize_or_scale(alphas, alpha, steps)
+        k_flip = -1 if k_flip else 1
+        ks = initialize_or_scale(ks, k_flip, steps)
+
+        latent_samples = latent["samples"]
+        b, c, h, w = latent_samples.shape  
+        noise_latents = torch.zeros([steps, c, h, w], dtype=latent_samples.dtype, layout=latent_samples.layout, device=latent_samples.device)
+
+        noise_sampler = NOISE_GENERATOR_CLASSES.get('fractal')(x=latent_samples, seed = seed)
+
+        for i in range(steps):
+            noise_latents[i] = noise_sampler(alpha=alphas[i].item(), k=ks[i].item(), scale=0.1)
+
+        return ({"samples": noise_latents}, )
+
+class LatentNoiseList:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "latent": ("LATENT",),
+                "alpha": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
+                "k_flip": ("BOOLEAN", {"default": False}),
+                "steps": ("INT", {"default": 0, "min": -10000, "max": 10000}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            },
+            "optional": {
+                "alphas": ("SIGMAS", ),
+                "ks": ("SIGMAS", ),
+            }
+        }
+
+    RETURN_TYPES = ("LATENT",)
+    OUTPUT_IS_LIST = (True,)
+    FUNCTION = "main"
+    
+    CATEGORY = "RES4LYF/noise"
+
+    def main(self, seed, latent, alpha, k_flip, steps, alphas=None, ks=None):
+        alphas = initialize_or_scale(alphas, alpha, steps)
+        k_flip = -1 if k_flip else 1
+        ks = initialize_or_scale(ks, k_flip, steps)    
+
+        latent_samples = latent["samples"]
+        latents = []
+        size = latent_samples.shape
+
+        steps = len(alphas) if steps == 0 else steps
+
+        noise_sampler = NOISE_GENERATOR_CLASSES.get('fractal')(x=latent_samples, seed=seed)
+
+        for i in range(steps):
+            noise = noise_sampler(alpha=alphas[i].item(), k=ks[i].item(), scale=0.1)
+            noisy_latent = latent_samples + noise
+            new_latent = {"samples": noisy_latent}
+            latents.append(new_latent)
+
+        return (latents, )
+    
+class LatentBatch_channels:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "latent": ("LATENT",),
+                "mode": (["offset", "multiply", "power"],),
+                "luminosity": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+                "cyan_red": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+                "lime_purple": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+                "pattern_structure": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+            },
+            "optional": {
+                "luminositys": ("SIGMAS", ),
+                "cyan_reds": ("SIGMAS", ),
+                "lime_purples": ("SIGMAS", ),
+                "pattern_structures": ("SIGMAS", ),
+            }
+        }
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "main"
+
+    CATEGORY = "RES4LYF/latents"
+    
+    @staticmethod
+    def latent_channels_multiply(x, luminosity = -0.1, cyan_red = 0.0, lime_purple=0.0, pattern_structure=0.0):
+        luminosity = x[0:1] * luminosity
+        cyan_red = x[1:2] * cyan_red
+        lime_purple = x[2:3] * lime_purple
+        pattern_structure = x[3:4] * pattern_structure
+
+        x = torch.unsqueeze(torch.cat([luminosity, cyan_red, lime_purple, pattern_structure]), 0)
+        return x
+
+    @staticmethod
+    def latent_channels_offset(x, luminosity = -0.1, cyan_red = 0.0, lime_purple=0.0, pattern_structure=0.0):
+        luminosity = x[0:1] + luminosity
+        cyan_red = x[1:2] + cyan_red
+        lime_purple = x[2:3] + lime_purple
+        pattern_structure = x[3:4] + pattern_structure
+
+        x = torch.unsqueeze(torch.cat([luminosity, cyan_red, lime_purple, pattern_structure]), 0)
+        return x
+    
+    @staticmethod
+    def latent_channels_power(x, luminosity = -0.1, cyan_red = 0.0, lime_purple=0.0, pattern_structure=0.0):
+        luminosity = x[0:1] ** luminosity
+        cyan_red = x[1:2] ** cyan_red
+        lime_purple = x[2:3] ** lime_purple
+        pattern_structure = x[3:4] ** pattern_structure
+
+        x = torch.unsqueeze(torch.cat([luminosity, cyan_red, lime_purple, pattern_structure]), 0)
+        return x
+
+    def main(self, latent, mode,
+              luminosity, cyan_red, lime_purple, pattern_structure, 
+              luminositys=None, cyan_reds=None, lime_purples=None, pattern_structures=None):
+        
+        x = latent["samples"]
+        b, c, h, w = x.shape  
+
+        noise_latents = torch.zeros([b, c, h, w], dtype=x.dtype, layout=x.layout, device=x.device)
+
+        luminositys = initialize_or_scale(luminositys, luminosity, b)
+        cyan_reds = initialize_or_scale(cyan_reds, cyan_red, b)
+        lime_purples = initialize_or_scale(lime_purples, lime_purple, b)
+        pattern_structures = initialize_or_scale(pattern_structures, pattern_structure, b)
+
+        for i in range(b):
+            if mode == "offset":
+                noise = self.latent_channels_offset(x[i], luminositys[i].item(), cyan_reds[i].item(), lime_purples[i].item(), pattern_structures[i].item())
+            elif mode == "multiply":  
+                noise = self.latent_channels_multiply(x[i], luminositys[i].item(), cyan_reds[i].item(), lime_purples[i].item(), pattern_structures[i].item())
+            elif mode == "power":  
+                noise = self.latent_channels_power(x[i], luminositys[i].item(), cyan_reds[i].item(), lime_purples[i].item(), pattern_structures[i].item())
+            noise_latents[i] = noise
+
+        return ({"samples": noise_latents}, )
+    
+
+class LatentBatch_channels_16:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "latent": ("LATENT",),
+                "mode": (["offset", "multiply", "power"],),
+                "chan_1": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+                "chan_2": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+                "chan_3": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+                "chan_4": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+                "chan_5": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+                "chan_6": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+                "chan_7": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+                "chan_8": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+                "chan_9": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+                "chan_10": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+                "chan_11": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+                "chan_12": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+                "chan_13": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+                "chan_14": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+                "chan_15": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+                "chan_16": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
+            },
+            "optional": {
+                "chan_1s": ("SIGMAS", ),
+                "chan_2s": ("SIGMAS", ),
+                "chan_3s": ("SIGMAS", ),
+                "chan_4s": ("SIGMAS", ),
+                "chan_5s": ("SIGMAS", ),
+                "chan_6s": ("SIGMAS", ),
+                "chan_7s": ("SIGMAS", ),
+                "chan_8s": ("SIGMAS", ),
+                "chan_9s": ("SIGMAS", ),
+                "chan_10s": ("SIGMAS", ),
+                "chan_11s": ("SIGMAS", ),
+                "chan_12s": ("SIGMAS", ),
+                "chan_13s": ("SIGMAS", ),
+                "chan_14s": ("SIGMAS", ),
+                "chan_15s": ("SIGMAS", ),
+                "chan_16s": ("SIGMAS", ),
+
+            }
+        }
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "main"
+
+    CATEGORY = "RES4LYF/latents"
+    
+    @staticmethod
+    def latent_channels_multiply(x, chan_1 = 0.0, chan_2 = 0.0, chan_3 = 0.0, chan_4 = 0.0, chan_5 = 0.0, chan_6 = 0.0, chan_7 = 0.0, chan_8 = 0.0, chan_9 = 0.0, chan_10 = 0.0, chan_11 = 0.0, chan_12 = 0.0, chan_13 = 0.0, chan_14 = 0.0, chan_15 = 0.0, chan_16 = 0.0):
+        chan_1 = x[0:1] * chan_1
+        chan_2 = x[1:2] * chan_2
+        chan_3 = x[2:3] * chan_3
+        chan_4 = x[3:4] * chan_4
+        chan_5 = x[4:5] * chan_5
+        chan_6 = x[5:6] * chan_6
+        chan_7 = x[6:7] * chan_7
+        chan_8 = x[7:8] * chan_8
+        chan_9 = x[8:9] * chan_9
+        chan_10 = x[9:10] * chan_10
+        chan_11 = x[10:11] * chan_11
+        chan_12 = x[11:12] * chan_12
+        chan_13 = x[12:13] * chan_13
+        chan_14 = x[13:14] * chan_14
+        chan_15 = x[14:15] * chan_15
+        chan_16 = x[15:16] * chan_16
+
+        x = torch.unsqueeze(torch.cat([chan_1, chan_2, chan_3, chan_4, chan_5, chan_6, chan_7, chan_8, chan_9, chan_10, chan_11, chan_12, chan_13, chan_14, chan_15, chan_16]), 0)
+        return x
+
+    @staticmethod
+    def latent_channels_offset(x, chan_1 = 0.0, chan_2 = 0.0, chan_3 = 0.0, chan_4 = 0.0, chan_5 = 0.0, chan_6 = 0.0, chan_7 = 0.0, chan_8 = 0.0, chan_9 = 0.0, chan_10 = 0.0, chan_11 = 0.0, chan_12 = 0.0, chan_13 = 0.0, chan_14 = 0.0, chan_15 = 0.0, chan_16 = 0.0):
+        chan_1 = x[0:1] + chan_1
+        chan_2 = x[1:2] + chan_2
+        chan_3 = x[2:3] + chan_3
+        chan_4 = x[3:4] + chan_4
+        chan_5 = x[4:5] + chan_5
+        chan_6 = x[5:6] + chan_6
+        chan_7 = x[6:7] + chan_7
+        chan_8 = x[7:8] + chan_8
+        chan_9 = x[8:9] + chan_9
+        chan_10 = x[9:10] + chan_10
+        chan_11 = x[10:11] + chan_11
+        chan_12 = x[11:12] + chan_12
+        chan_13 = x[12:13] + chan_13
+        chan_14 = x[13:14] + chan_14
+        chan_15 = x[14:15] + chan_15
+        chan_16 = x[15:16] + chan_16
+
+        x = torch.unsqueeze(torch.cat([chan_1, chan_2, chan_3, chan_4, chan_5, chan_6, chan_7, chan_8, chan_9, chan_10, chan_11, chan_12, chan_13, chan_14, chan_15, chan_16]), 0)
+        return x
+
+    @staticmethod
+    def latent_channels_power(x, chan_1 = 0.0, chan_2 = 0.0, chan_3 = 0.0, chan_4 = 0.0, chan_5 = 0.0, chan_6 = 0.0, chan_7 = 0.0, chan_8 = 0.0, chan_9 = 0.0, chan_10 = 0.0, chan_11 = 0.0, chan_12 = 0.0, chan_13 = 0.0, chan_14 = 0.0, chan_15 = 0.0, chan_16 = 0.0):
+        chan_1 = x[0:1] ** chan_1
+        chan_2 = x[1:2] ** chan_2
+        chan_3 = x[2:3] ** chan_3
+        chan_4 = x[3:4] ** chan_4
+        chan_5 = x[4:5] ** chan_5
+        chan_6 = x[5:6] ** chan_6
+        chan_7 = x[6:7] ** chan_7
+        chan_8 = x[7:8] ** chan_8
+        chan_9 = x[8:9] ** chan_9
+        chan_10 = x[9:10] ** chan_10
+        chan_11 = x[10:11] ** chan_11
+        chan_12 = x[11:12] ** chan_12
+        chan_13 = x[12:13] ** chan_13
+        chan_14 = x[13:14] ** chan_14
+        chan_15 = x[14:15] ** chan_15
+        chan_16 = x[15:16] ** chan_16
+
+        x = torch.unsqueeze(torch.cat([chan_1, chan_2, chan_3, chan_4, chan_5, chan_6, chan_7, chan_8, chan_9, chan_10, chan_11, chan_12, chan_13, chan_14, chan_15, chan_16]), 0)
+        return x
+
+    def main(self, latent, mode,
+              chan_1, chan_2, chan_3, chan_4, chan_5, chan_6, chan_7, chan_8, chan_9, chan_10, chan_11, chan_12, chan_13, chan_14, chan_15, chan_16,
+              chan_1s=None, chan_2s=None, chan_3s=None, chan_4s=None, chan_5s=None, chan_6s=None, chan_7s=None, chan_8s=None, chan_9s=None, chan_10s=None, chan_11s=None, chan_12s=None, chan_13s=None, chan_14s=None, chan_15s=None, chan_16s=None):
+        
+        x = latent["samples"]
+        b, c, h, w = x.shape  
+
+        noise_latents = torch.zeros([b, c, h, w], dtype=x.dtype, layout=x.layout, device=x.device)
+        chan_1s = initialize_or_scale(chan_1s, chan_1, b)
+        chan_2s = initialize_or_scale(chan_2s, chan_2, b)
+        chan_3s = initialize_or_scale(chan_3s, chan_3, b)
+        chan_4s = initialize_or_scale(chan_4s, chan_4, b)
+        chan_5s = initialize_or_scale(chan_5s, chan_5, b)
+        chan_6s = initialize_or_scale(chan_6s, chan_6, b)
+        chan_7s = initialize_or_scale(chan_7s, chan_7, b)
+        chan_8s = initialize_or_scale(chan_8s, chan_8, b)
+        chan_9s = initialize_or_scale(chan_9s, chan_9, b)
+        chan_10s = initialize_or_scale(chan_10s, chan_10, b)
+        chan_11s = initialize_or_scale(chan_11s, chan_11, b)
+        chan_12s = initialize_or_scale(chan_12s, chan_12, b)
+        chan_13s = initialize_or_scale(chan_13s, chan_13, b)
+        chan_14s = initialize_or_scale(chan_14s, chan_14, b)
+        chan_15s = initialize_or_scale(chan_15s, chan_15, b)
+        chan_16s = initialize_or_scale(chan_16s, chan_16, b)
+
+        for i in range(b):
+            if mode == "offset":
+                noise = self.latent_channels_offset(x[i], chan_1s[i].item(), chan_2s[i].item(), chan_3s[i].item(), chan_4s[i].item(), chan_5s[i].item(), chan_6s[i].item(), chan_7s[i].item(), chan_8s[i].item(), chan_9s[i].item(), chan_10s[i].item(), chan_11s[i].item(), chan_12s[i].item(), chan_13s[i].item(), chan_14s[i].item(), chan_15s[i].item(), chan_16s[i].item())
+            elif mode == "multiply":  
+                noise = self.latent_channels_multiply(x[i], chan_1s[i].item(), chan_2s[i].item(), chan_3s[i].item(), chan_4s[i].item(), chan_5s[i].item(), chan_6s[i].item(), chan_7s[i].item(), chan_8s[i].item(), chan_9s[i].item(), chan_10s[i].item(), chan_11s[i].item(), chan_12s[i].item(), chan_13s[i].item(), chan_14s[i].item(), chan_15s[i].item(), chan_16s[i].item())
+            elif mode == "power":  
+                noise = self.latent_channels_power(x[i], chan_1s[i].item(), chan_2s[i].item(), chan_3s[i].item(), chan_4s[i].item(), chan_5s[i].item(), chan_6s[i].item(), chan_7s[i].item(), chan_8s[i].item(), chan_9s[i].item(), chan_10s[i].item(), chan_11s[i].item(), chan_12s[i].item(), chan_13s[i].item(), chan_14s[i].item(), chan_15s[i].item(), chan_16s[i].item())
+            noise_latents[i] = noise
+
+        return ({"samples": noise_latents}, )
+    
+class latent_normalize_channels:
+    def __init__(self):
+        pass
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                    "latent": ("LATENT", ),     
+                    "mode": (["full", "channels"],), 
+                    "operation": (["normalize", "center", "standardize"],), 
+                     },
+                }
+
+    RETURN_TYPES = ("LATENT",)
+    RETURN_NAMES = ("passthrough",)
+    CATEGORY = "RES4LYF/latents"
+
+    FUNCTION = "main"
+
+    def main(self, latent, mode, operation):
+        x = latent["samples"]
+        b, c, h, w = x.shape
+
+        if mode == "full":
+            if operation == "normalize":
+                x = (x - x.mean()) / x.std()
+            elif operation == "center":
+                x = x - x.mean()
+            elif operation == "standardize":
+                x = x / x.std()
+
+        elif mode == "channels":
+            if operation == "normalize":
+                for i in range(b):
+                    for j in range(c):
+                        x[i, j] = (x[i, j] - x[i, j].mean()) / x[i, j].std()
+            elif operation == "center":
+                for i in range(b):
+                    for j in range(c):
+                        x[i, j] = x[i, j] - x[i, j].mean()
+            elif operation == "standardize":
+                for i in range(b):
+                    for j in range(c):
+                        x[i, j] = x[i, j] / x[i, j].std()
+
+        return ({"samples": x},)
+
+
+
+
+def hard_light_blend(base_latent, blend_latent):
+    if base_latent.sum() == 0 and base_latent.std() == 0:
+        return base_latent
+    
+    blend_latent = (blend_latent - blend_latent.min()) / (blend_latent.max() - blend_latent.min())
+
+    positive_mask = base_latent >= 0
+    negative_mask = base_latent < 0
+    
+    positive_latent = base_latent * positive_mask.float()
+    negative_latent = base_latent * negative_mask.float()
+
+    positive_result = torch.where(blend_latent < 0.5,
+                                  2 * positive_latent * blend_latent,
+                                  1 - 2 * (1 - positive_latent) * (1 - blend_latent))
+
+    negative_result = torch.where(blend_latent < 0.5,
+                                  2 * negative_latent.abs() * blend_latent,
+                                  1 - 2 * (1 - negative_latent.abs()) * (1 - blend_latent))
+    negative_result = -negative_result
+
+    combined_result = positive_result * positive_mask.float() + negative_result * negative_mask.float()
+
+    #combined_result *= base_latent.max()
+    
+    ks = combined_result
+    ks2 = torch.zeros_like(base_latent)
+    for n in range(base_latent.shape[1]):
+        ks2[0][n] = (ks[0][n]) / ks[0][n].std()
+        ks2[0][n] = (ks2[0][n] * base_latent[0][n].std())
+    combined_result = ks2
+    
+    return combined_result
+
+
+
diff --git a/custom_nodes/RES4LYF/legacy/legacy_sampler_rk.py b/custom_nodes/RES4LYF/legacy/legacy_sampler_rk.py
new file mode 100644
index 0000000000000000000000000000000000000000..07e55a91a4b24dff7780e6cdd7e1a64acfb9a757
--- /dev/null
+++ b/custom_nodes/RES4LYF/legacy/legacy_sampler_rk.py
@@ -0,0 +1,936 @@
+import torch
+import torch.nn.functional as F
+
+
+from tqdm.auto import trange
+import math
+import copy
+import gc
+
+import comfy.model_patcher
+
+from .noise_classes import NOISE_GENERATOR_CLASSES_SIMPLE, NOISE_GENERATOR_CLASSES
+from .deis_coefficients import get_deis_coeff_list
+from .latents import hard_light_blend
+
+from .noise_sigmas_timesteps_scaling import get_res4lyf_step_with_model, get_res4lyf_half_step3
+
+
+def get_epsilon(model, x, sigma, **extra_args):
+    s_in = x.new_ones([x.shape[0]])
+    x0 = model(x, sigma * s_in, **extra_args)
+    eps = (x - x0) / (sigma * s_in) 
+    return eps
+
+def get_denoised(model, x, sigma, **extra_args):
+    s_in = x.new_ones([x.shape[0]])
+    x0 = model(x, sigma * s_in, **extra_args)
+    return x0
+
+
+# Remainder solution
+def __phi(j, neg_h):
+  remainder = torch.zeros_like(neg_h)
+  
+  for k in range(j): 
+    remainder += (neg_h)**k / math.factorial(k)
+  phi_j_h = ((neg_h).exp() - remainder) / (neg_h)**j
+  
+  return phi_j_h
+  
+  
+def calculate_gamma(c2, c3):
+    return (3*(c3**3) - 2*c3) / (c2*(2 - 3*c2))
+
+
+
+
+from typing import Optional
+
+
+def _gamma(n: int,) -> int:
+  """
+  https://en.wikipedia.org/wiki/Gamma_function
+  for every positive integer n,
+  Γ(n) = (n-1)!
+  """
+  return math.factorial(n-1)
+
+def _incomplete_gamma(s: int, x: float, gamma_s: Optional[int] = None) -> float:
+  """
+  https://en.wikipedia.org/wiki/Incomplete_gamma_function#Special_values
+  if s is a positive integer,
+  Γ(s, x) = (s-1)!*∑{k=0..s-1}(x^k/k!)
+  """
+  if gamma_s is None:
+    gamma_s = _gamma(s)
+
+  sum_: float = 0
+  # {k=0..s-1} inclusive
+  for k in range(s):
+    numerator: float = x**k
+    denom: int = math.factorial(k)
+    quotient: float = numerator/denom
+    sum_ += quotient
+  incomplete_gamma_: float = sum_ * math.exp(-x) * gamma_s
+  return incomplete_gamma_
+
+
+# Exact analytic solution originally calculated by Clybius. https://github.com/Clybius/ComfyUI-Extra-Samplers/tree/main
+def phi(j: int, neg_h: float, ):
+  """
+  For j={1,2,3}: you could alternatively use Kat's phi_1, phi_2, phi_3 which perform fewer steps
+
+  Lemma 1
+  https://arxiv.org/abs/2308.02157
+  ϕj(-h) = 1/h^j*∫{0..h}(e^(τ-h)*(τ^(j-1))/((j-1)!)dτ)
+
+  https://www.wolframalpha.com/input?i=integrate+e%5E%28%CF%84-h%29*%28%CF%84%5E%28j-1%29%2F%28j-1%29%21%29d%CF%84
+  = 1/h^j*[(e^(-h)*(-τ)^(-j)*τ(j))/((j-1)!)]{0..h}
+  https://www.wolframalpha.com/input?i=integrate+e%5E%28%CF%84-h%29*%28%CF%84%5E%28j-1%29%2F%28j-1%29%21%29d%CF%84+between+0+and+h
+  = 1/h^j*((e^(-h)*(-h)^(-j)*h^j*(Γ(j)-Γ(j,-h)))/(j-1)!)
+  = (e^(-h)*(-h)^(-j)*h^j*(Γ(j)-Γ(j,-h))/((j-1)!*h^j)
+  = (e^(-h)*(-h)^(-j)*(Γ(j)-Γ(j,-h))/(j-1)!
+  = (e^(-h)*(-h)^(-j)*(Γ(j)-Γ(j,-h))/Γ(j)
+  = (e^(-h)*(-h)^(-j)*(1-Γ(j,-h)/Γ(j))
+
+  requires j>0
+  """
+  assert j > 0
+  gamma_: float = _gamma(j)
+  incomp_gamma_: float = _incomplete_gamma(j, neg_h, gamma_s=gamma_)
+  phi_: float = math.exp(neg_h) * neg_h**-j * (1-incomp_gamma_/gamma_)
+  return phi_
+
+
+rk_coeff = {
+    "gauss-legendre_5s": (
+    [
+        [4563950663 / 32115191526, 
+         (310937500000000 / 2597974476091533 + 45156250000 * (739**0.5) / 8747388808389), 
+         (310937500000000 / 2597974476091533 - 45156250000 * (739**0.5) / 8747388808389),
+         (5236016175 / 88357462711 + 709703235 * (739**0.5) / 353429850844),
+         (5236016175 / 88357462711 - 709703235 * (739**0.5) / 353429850844)],
+         
+        [(4563950663 / 32115191526 - 38339103 * (739**0.5) / 6250000000),
+         (310937500000000 / 2597974476091533 + 9557056475401 * (739**0.5) / 3498955523355600000),
+         (310937500000000 / 2597974476091533 - 14074198220719489 * (739**0.5) / 3498955523355600000),
+         (5236016175 / 88357462711 + 5601362553163918341 * (739**0.5) / 2208936567775000000000),
+         (5236016175 / 88357462711 - 5040458465159165409 * (739**0.5) / 2208936567775000000000)],
+         
+        [(4563950663 / 32115191526 + 38339103 * (739**0.5) / 6250000000),
+         (310937500000000 / 2597974476091533 + 14074198220719489 * (739**0.5) / 3498955523355600000),
+         (310937500000000 / 2597974476091533 - 9557056475401 * (739**0.5) / 3498955523355600000),
+         (5236016175 / 88357462711 + 5040458465159165409 * (739**0.5) / 2208936567775000000000),
+         (5236016175 / 88357462711 - 5601362553163918341 * (739**0.5) / 2208936567775000000000)],
+         
+        [(4563950663 / 32115191526 - 38209 * (739**0.5) / 7938810),
+         (310937500000000 / 2597974476091533 - 359369071093750 * (739**0.5) / 70145310854471391),
+         (310937500000000 / 2597974476091533 - 323282178906250 * (739**0.5) / 70145310854471391),
+         (5236016175 / 88357462711 - 470139 * (739**0.5) / 1413719403376),
+         (5236016175 / 88357462711 - 44986764863 * (739**0.5) / 21205791050640)],
+         
+        [(4563950663 / 32115191526 + 38209 * (739**0.5) / 7938810),
+         (310937500000000 / 2597974476091533 + 359369071093750 * (739**0.5) / 70145310854471391),
+         (310937500000000 / 2597974476091533 + 323282178906250 * (739**0.5) / 70145310854471391),
+         (5236016175 / 88357462711 + 44986764863 * (739**0.5) / 21205791050640),
+         (5236016175 / 88357462711 + 470139 * (739**0.5) / 1413719403376)],
+        
+        [4563950663 / 16057595763,
+         621875000000000 / 2597974476091533,
+         621875000000000 / 2597974476091533,
+         10472032350 / 88357462711,
+         10472032350 / 88357462711]
+    ],
+    [
+        1 / 2,
+        1 / 2 - 99 * (739**0.5) / 10000,
+        1 / 2 + 99 * (739**0.5) / 10000,
+        1 / 2 - (739**0.5) / 60,
+        1 / 2 + (739**0.5) / 60
+    ]
+    ),
+    "gauss-legendre_4s": (
+        [
+            [1/4, 1/4 - 15**0.5 / 6, 1/4 + 15**0.5 / 6, 1/4],            
+            [1/4 + 15**0.5 / 6, 1/4, 1/4 - 15**0.5 / 6, 1/4],          
+            [1/4, 1/4 + 15**0.5 / 6, 1/4, 1/4 - 15**0.5 / 6],            
+            [1/4 - 15**0.5 / 6, 1/4, 1/4 + 15**0.5 / 6, 1/4],           
+            [1/8, 3/8, 3/8, 1/8]                                        
+        ],
+        [
+            1/2 - 15**0.5 / 10,                                     
+            1/2 + 15**0.5 / 10,                                         
+            1/2 + 15**0.5 / 10,                                        
+            1/2 - 15**0.5 / 10                                         
+        ]
+    ),
+    "gauss-legendre_3s": (
+        [
+            [5/36, 2/9 - 15**0.5 / 15, 5/36 - 15**0.5 / 30],
+            [5/36 + 15**0.5 / 24, 2/9, 5/36 - 15**0.5 / 24],
+            [5/36 + 15**0.5 / 30, 2/9 + 15**0.5 / 15, 5/36],
+            [5/18, 4/9, 5/18]
+        ],
+        [1/2 - 15**0.5 / 10, 1/2, 1/2 + 15**0.5 / 10]
+    ),
+    "gauss-legendre_2s": (
+        [
+            [1/4, 1/4 - 3**0.5 / 6],
+            [1/4 + 3**0.5 / 6, 1/4],
+            [1/2, 1/2],
+        ],
+        [1/2 - 3**0.5 / 6, 1/2 + 3**0.5 / 6]
+    ),
+    "radau_iia_3s": (
+        [    
+            [11/45 - 7*6**0.5 / 360, 37/225 - 169*6**0.5 / 1800, -2/225 + 6**0.5 / 75],
+            [37/225 + 169*6**0.5 / 1800, 11/45 + 7*6**0.5 / 360, -2/225 - 6**0.5 / 75],
+            [4/9 - 6**0.5 / 36, 4/9 + 6**0.5 / 36, 1/9],
+            [4/9 - 6**0.5 / 36, 4/9 + 6**0.5 / 36, 1/9],
+        ],
+        [2/5 - 6**0.5 / 10, 2/5 + 6**0.5 / 10, 1.]
+    ),
+    "radau_iia_2s": (
+        [    
+            [5/12, -1/12],
+            [3/4, 1/4],
+            [3/4, 1/4],
+        ],
+        [1/3, 1]
+    ),
+    "lobatto_iiic_3s": (
+        [    
+            [1/6, -1/3, 1/6],
+            [1/6, 5/12, -1/12],
+            [1/6, 2/3, 1/6],
+            [1/6, 2/3, 1/6],
+        ],
+        [0, 1/2, 1]
+    ),
+    "lobatto_iiic_2s": (
+        [    
+            [1/2, -1/2],
+            [1/2, 1/2],
+            [1/2, 1/2],
+        ],
+        [0, 1]
+    ),
+    "dormand-prince_13s": (
+        [
+            [1/18, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [1/48, 1/16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [1/32, 0, 3/32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [5/16, 0, -75/64, 75/64, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [3/80, 0, 0, 3/16, 3/20, 0, 0, 0, 0, 0, 0, 0, 0],
+            [29443841/614563906, 0, 0, 77736538/692538347, -28693883/1125000000, 23124283/1800000000, 0, 0, 0, 0, 0, 0, 0],
+            [16016141/946692911, 0, 0, 61564180/158732637, 22789713/633445777, 545815736/2771057229, -180193667/1043307555, 0, 0, 0, 0, 0, 0],
+            [39632708/573591083, 0, 0, -433636366/683701615, -421739975/2616292301, 100302831/723423059, 790204164/839813087, 800635310/3783071287, 0, 0, 0, 0, 0],
+            [246121993/1340847787, 0, 0, -37695042795/15268766246, -309121744/1061227803, -12992083/490766935, 6005943493/2108947869, 393006217/1396673457, 123872331/1001029789, 0, 0, 0, 0],
+            [-1028468189/846180014, 0, 0, 8478235783/508512852, 1311729495/1432422823, -10304129995/1701304382, -48777925059/3047939560, 15336726248/1032824649, -45442868181/3398467696, 3065993473/597172653, 0, 0, 0],
+            [185892177/718116043, 0, 0, -3185094517/667107341, -477755414/1098053517, -703635378/230739211, 5731566787/1027545527, 5232866602/850066563, -4093664535/808688257, 3962137247/1805957418, 65686358/487910083, 0, 0],
+            [403863854/491063109, 0, 0, -5068492393/434740067, -411421997/543043805, 652783627/914296604, 11173962825/925320556, -13158990841/6184727034, 3936647629/1978049680, -160528059/685178525, 248638103/1413531060, 0, 0],
+            [14005451/335480064, 0, 0, 0, 0, -59238493/1068277825, 181606767/758867731, 561292985/797845732, -1041891430/1371343529, 760417239/1151165299, 118820643/751138087, -528747749/2220607170, 1/4]
+        ],
+        [0, 1/18, 1/12, 1/8, 5/16, 3/8, 59/400, 93/200, 5490023248 / 9719169821, 13/20, 1201146811 / 1299019798, 1, 1],
+    ),
+    "dormand-prince_6s": (
+        [
+            [1/5, 0, 0, 0, 0, 0, 0],
+            [3/40, 9/40, 0, 0, 0, 0, 0],
+            [44/45, -56/15, 32/9, 0, 0, 0, 0],
+            [19372/6561, -25360/2187, 64448/6561, -212/729, 0, 0, 0],
+            [9017/3168, -355/33, 46732/5247, 49/176, -5103/18656, 0],
+            [35/384, 0, 500/1113, 125/192, -2187/6784, 11/84, 0],
+        ],
+        [0, 1/5, 3/10, 4/5, 8/9, 1],
+    ),
+    "dormand-prince_6s_alt": (
+        [
+            [1/5, 0, 0, 0, 0, 0, 0],
+            [3/40, 9/40, 0, 0, 0, 0, 0],
+            [44/45, -56/15, 32/9, 0, 0, 0, 0],
+            [19372/6561, -25360/2187, 64448/6561, -212/729, 0, 0, 0],
+            [9017/3168, -355/33, 46732/5247, 49/176, -5103/18656, 0],
+            [35/384, 0, 500/1113, 125/192, -2187/6784, 11/84, 0],
+        ],
+        [0, 1/5, 3/10, 4/5, 8/9, 1],
+    ),
+    "dormand-prince_7s": (
+        [
+            [1/5, 0, 0, 0, 0, 0, 0],
+            [3/40, 9/40, 0, 0, 0, 0, 0],
+            [44/45, -56/15, 32/9, 0, 0, 0, 0],
+            [19372/6561, -25360/2187, 64448/6561, -212/729, 0, 0, 0],
+            [9017/3168, -355/33, 46732/5247, 49/176, -5103/18656, 0],
+            [35/384, 0, 500/1113, 125/192, -2187/6784, 11/84, 0],
+        ],
+        [0, 1/5, 3/10, 4/5, 8/9, 1],
+    ),
+    "bogacki-shampine_7s": ( #5th order
+        [
+            [1/6, 0, 0, 0, 0, 0, 0],
+            [2/27, 4/27, 0, 0, 0, 0, 0],
+            [183/1372, -162/343, 1053/1372, 0, 0, 0, 0],
+            [68/297, -4/11, 42/143, 1960/3861, 0, 0, 0],
+            [597/22528, 81/352, 63099/585728, 58653/366080, 4617/20480, 0, 0],
+            [174197/959244, -30942/79937, 8152137/19744439, 666106/1039181, -29421/29068, 482048/414219, 0],
+            [587/8064, 0, 4440339/15491840, 24353/124800, 387/44800, 2152/5985, 7267/94080]
+        ],
+        [0, 1/6, 2/9, 3/7, 2/3, 3/4, 1] 
+    ),
+    "rk4_4s": (
+        [
+            [1/2, 0, 0, 0],
+            [0, 1/2, 0, 0],
+            [0, 0, 1, 0],
+            [1/6, 1/3, 1/3, 1/6]
+        ],
+        [0, 1/2, 1/2, 1],
+    ),
+    "rk38_4s": (
+        [
+            [1/3, 0, 0, 0],
+            [-1/3, 1, 0, 0],
+            [1, -1, 1, 0],
+            [1/8, 3/8, 3/8, 1/8]
+        ],
+        [0, 1/3, 2/3, 1],
+    ),
+    "ralston_4s": (
+        [
+            [2/5, 0, 0, 0],
+            [(-2889+1428 * 5**0.5)/1024,   (3785-1620 * 5**0.5)/1024,  0, 0],
+            [(-3365+2094 * 5**0.5)/6040,   (-975-3046 * 5**0.5)/2552,  (467040+203968*5**0.5)/240845, 0],
+            [(263+24*5**0.5)/1812, (125-1000*5**0.5)/3828, (3426304+1661952*5**0.5)/5924787, (30-4*5**0.5)/123]
+        ],
+        [0, 2/5, (14-3 * 5**0.5)/16, 1],
+    ),
+    "heun_3s": (
+        [
+            [1/3, 0, 0],
+            [0, 2/3, 0],
+            [1/4, 0, 3/4]
+        ],
+        [0, 1/3, 2/3],
+    ),
+    "kutta_3s": (
+        [
+            [1/2, 0, 0],
+            [-1, 2, 0],
+            [1/6, 2/3, 1/6]
+        ],
+        [0, 1/2, 1],
+    ),
+    "ralston_3s": (
+        [
+            [1/2, 0, 0],
+            [0, 3/4, 0],
+            [2/9, 1/3, 4/9]
+        ],
+        [0, 1/2, 3/4],
+    ),
+    "houwen-wray_3s": (
+        [
+            [8/15, 0, 0],
+            [1/4, 5/12, 0],
+            [1/4, 0, 3/4]
+        ],
+        [0, 8/15, 2/3],
+    ),
+    "ssprk3_3s": (
+        [
+            [1, 0, 0],
+            [1/4, 1/4, 0],
+            [1/6, 1/6, 2/3]
+        ],
+        [0, 1, 1/2],
+    ),
+    "midpoint_2s": (
+        [
+            [1/2, 0],
+            [0, 1]
+        ],
+        [0, 1/2],
+    ),
+    "heun_2s": (
+        [
+            [1, 0],
+            [1/2, 1/2]
+        ],
+        [0, 1],
+    ),
+    "ralston_2s": (
+        [
+            [2/3, 0],
+            [1/4, 3/4]
+        ],
+        [0, 2/3],
+    ),
+    "buehler": (
+        [
+            [1],
+        ],
+        [0],
+    ),
+}
+
+
+def get_rk_methods(rk_type, h, c1=0.0, c2=0.5, c3=1.0, h_prev=None, h_prev2=None, stepcount=0, sigmas=None):
+    FSAL = False
+    multistep_stages = 0
+    
+    if rk_type[:4] == "deis": 
+        order = int(rk_type[-2])
+        if stepcount < order:
+            if order == 4:
+                rk_type = "res_3s"
+                order = 3
+            elif order == 3:
+                rk_type = "res_3s"
+            elif order == 2:
+                rk_type = "res_2s"
+        else:
+            rk_type = "deis"
+            multistep_stages = order-1
+
+    
+    if rk_type[-2:] == "2m": #multistep method
+        if h_prev is not None: 
+            multistep_stages = 1
+            c2 = -h_prev / h
+            rk_type = rk_type[:-2] + "2s"
+        else:
+            rk_type = rk_type[:-2] + "2s"
+            
+    if rk_type[-2:] == "3m": #multistep method
+        if h_prev2 is not None: 
+            multistep_stages = 2
+            c2 = -h_prev2 / h_prev
+            c3 = -h_prev / h
+            rk_type = rk_type[:-2] + "3s"
+        else:
+            rk_type = rk_type[:-2] + "3s"
+    
+    if rk_type in rk_coeff:
+        ab, ci = copy.deepcopy(rk_coeff[rk_type])
+        ci = ci[:]
+        ci.append(1)
+        
+        alpha_fn = lambda h: 1
+        t_fn = lambda sigma: sigma
+        sigma_fn = lambda t: t
+        h_fn = lambda sigma_down, sigma: sigma_down - sigma
+        model_call = get_denoised
+        EPS_PRED = False
+
+    else:
+        alpha_fn = lambda neg_h: torch.exp(neg_h)
+        t_fn = lambda sigma: sigma.log().neg()
+        sigma_fn = lambda t: t.neg().exp()
+        h_fn = lambda sigma_down, sigma: -torch.log(sigma_down/sigma)
+        model_call = get_denoised
+        EPS_PRED = False
+    
+    match rk_type:
+        case "deis": 
+            alpha_fn = lambda neg_h: torch.exp(neg_h)
+            t_fn = lambda sigma: sigma.log().neg()
+            sigma_fn = lambda t: t.neg().exp()
+            h_fn = lambda sigma_down, sigma: -torch.log(sigma_down/sigma)
+            model_call = get_epsilon
+            EPS_PRED = True
+
+            coeff_list = get_deis_coeff_list(sigmas, multistep_stages+1, deis_mode="rhoab")
+            coeff_list = [[elem / h for elem in inner_list] for inner_list in coeff_list]
+            if multistep_stages == 1:
+                b1, b2 = coeff_list[stepcount]
+                ab = [
+                        [0, 0],
+                        [b1, b2],
+                ]
+                ci = [0, 0, 1]
+            if multistep_stages == 2:
+                b1, b2, b3 = coeff_list[stepcount]
+                ab = [
+                        [0, 0, 0],
+                        [0, 0, 0],
+                        [b1, b2, b3],
+                ]
+                ci = [0, 0, 0, 1]
+            if multistep_stages == 3:
+                b1, b2, b3, b4 = coeff_list[stepcount]
+                ab = [
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0],
+                        [b1, b2, b3, b4],
+                ]
+                ci = [0, 0, 0, 0, 1]
+
+        case "dormand-prince_6s":
+            FSAL = True
+
+        case "ddim":
+            b1 = phi(1, -h)
+            ab = [
+                    [b1],
+            ]
+            ci = [0, 1]
+
+        case "res_2s":
+            a2_1 = c2 * phi(1, -h*c2)
+            b1 =        phi(1, -h) - phi(2, -h)/c2
+            b2 =        phi(2, -h)/c2
+            
+            a2_1 /= (1 - torch.exp(-h*c2)) / h
+            b1 /= phi(1, -h)
+            b2 /= phi(1, -h)
+
+            ab = [
+                    [a2_1, 0],
+                    [b1, b2],
+            ]
+            ci = [0, c2, 1]
+
+        case "res_3s":
+            gamma = calculate_gamma(c2, c3)
+            a2_1 = c2 * phi(1, -h*c2)
+            a3_2 = gamma * c2 * phi(2, -h*c2) + (c3 ** 2 / c2) * phi(2, -h*c3) #phi_2_c3_h  # a32 from k2 to k3
+            a3_1 = c3 * phi(1, -h*c3) - a3_2 # a31 from k1 to k3
+            b3 = (1 / (gamma * c2 + c3)) * phi(2, -h)      
+            b2 = gamma * b3  #simplified version of: b2 = (gamma / (gamma * c2 + c3)) * phi_2_h  
+            b1 = phi(1, -h) - b2 - b3     
+            0
+            a3_2 /= (1 - torch.exp(-h*c3)) / h
+            a3_1 /= (1 - torch.exp(-h*c3)) / h
+            b1 /= phi(1, -h)
+            b2 /= phi(1, -h)
+            b3 /= phi(1, -h)
+            
+            ab = [
+                    [a2_1, 0, 0],
+                    [a3_1, a3_2, 0],
+                    [b1, b2, b3],
+            ]
+            ci = [c1, c2, c3, 1]
+            #ci = [0, c2, c3, 1]
+
+        case "dpmpp_2s":
+            #c2 = 0.5
+            a2_1 =         c2   * phi(1, -h*c2)
+            b1 = (1 - 1/(2*c2)) * phi(1, -h)
+            b2 =     (1/(2*c2)) * phi(1, -h)
+            
+            a2_1 /= (1 - torch.exp(-h*c2)) / h
+            b1 /= phi(1, -h)
+            b2 /= phi(1, -h)
+            
+            ab = [
+                    [a2_1, 0],
+                    [b1, b2],
+            ]
+            ci = [0, c2, 1]
+            
+        case "dpmpp_sde_2s":
+            c2 = 1.0 #hardcoded to 1.0 to more closely emulate the configuration for k-diffusion's implementation
+            a2_1 =         c2   * phi(1, -h*c2)
+            b1 = (1 - 1/(2*c2)) * phi(1, -h)
+            b2 =     (1/(2*c2)) * phi(1, -h)
+            
+            a2_1 /= (1 - torch.exp(-h*c2)) / h
+            b1 /= phi(1, -h)
+            b2 /= phi(1, -h)
+            
+            ab = [
+                    [a2_1, 0],
+                    [b1, b2],
+            ]
+            ci = [0, c2, 1]
+
+        case "dpmpp_3s":
+            a2_1 = c2 * phi(1, -h*c2)
+            a3_2 = (c3**2 / c2) * phi(2, -h*c3)
+            a3_1 = c3 * phi(1, -h*c3) - a3_2
+            b2 = 0
+            b3 = (1/c3) * phi(2, -h)
+            b1 = phi(1, -h) - b2 - b3
+            
+            a2_1 /= (1 - torch.exp(-h*c2)) / h
+            a3_2 /= (1 - torch.exp(-h*c3)) / h
+            a3_1 /= (1 - torch.exp(-h*c3)) / h
+            b1 /= phi(1, -h)
+            b2 /= phi(1, -h)
+            b3 /= phi(1, -h)
+            
+            ab = [
+                    [a2_1, 0, 0],
+                    [a3_1, a3_2, 0],
+                    [b1, b2, b3],
+            ]
+            ci = [0, c2, c3, 1]
+            
+        case "rk_exp_5s":
+                
+            c1, c2, c3, c4, c5 = 0., 0.5, 0.5, 1., 0.5
+            
+            a2_1 = 0.5 * phi(1, -h * c2)
+            
+            a3_1 = 0.5 * phi(1, -h * c3) - phi(2, -h * c3)
+            a3_2 = phi(2, -h * c3)
+            
+            a4_1 = phi(1, -h * c4) - 2 * phi(2, -h * c4)
+            a4_2 = a4_3 = phi(2, -h * c4)
+            
+            a5_2 = a5_3 = 0.5 * phi(2, -h * c5) - phi(3, -h * c4) + 0.25 * phi(2, -h * c4) - 0.5 * phi(3, -h * c5)
+            a5_4 = 0.25 * phi(2, -h * c5) - a5_2
+            a5_1 = 0.5 * phi(1, -h * c5) - 2 * a5_2 - a5_4
+                    
+            b1 = phi(1, -h) - 3 * phi(2, -h) + 4 * phi(3, -h)
+            b2 = b3 = 0
+            b4 = -phi(2, -h) + 4*phi(3, -h)
+            b5 = 4 * phi(2, -h) - 8 * phi(3, -h)
+            
+            a2_1 /= (1 - torch.exp(-h*c2)) / h
+            
+            a3_1 /= (1 - torch.exp(-h*c3)) / h
+            a3_2 /= (1 - torch.exp(-h*c3)) / h
+            
+            a4_1 /= (1 - torch.exp(-h*c4)) / h
+            a4_2 /= (1 - torch.exp(-h*c4)) / h
+            a4_3 /= (1 - torch.exp(-h*c4)) / h
+            
+            a5_1 /= (1 - torch.exp(-h*c5)) / h
+            a5_2 /= (1 - torch.exp(-h*c5)) / h
+            a5_3 /= (1 - torch.exp(-h*c5)) / h
+            a5_4 /= (1 - torch.exp(-h*c5)) / h
+            
+            b1 /= phi(1, -h)
+            b2 /= phi(1, -h)
+            b3 /= phi(1, -h)
+            b4 /= phi(1, -h)
+            b5 /= phi(1, -h)
+            
+            ab = [
+                    [a2_1, 0, 0, 0, 0],
+                    [a3_1, a3_2, 0, 0, 0],
+                    [a4_1, a4_2, a4_3, 0, 0],
+                    [a5_1, a5_2, a5_3, a5_4, 0],
+                    [b1, b2, b3, b4, b5],
+            ]
+            ci = [0., 0.5, 0.5, 1., 0.5, 1]
+
+
+    return ab, ci, multistep_stages, model_call, alpha_fn, t_fn, sigma_fn, h_fn, FSAL, EPS_PRED
+
+def get_rk_methods_order(rk_type):
+    ab, ci, multistep_stages, model_call, alpha_fn, t_fn, sigma_fn, h_fn, FSAL, EPS_PRED = get_rk_methods(rk_type, torch.tensor(1.0).to('cuda').to(torch.float64), c1=0.0, c2=0.5, c3=1.0)
+    return len(ci)-1
+
+def get_rk_methods_order_and_fn(rk_type, h=None, c1=None, c2=None, c3=None, h_prev=None, h_prev2=None, stepcount=0, sigmas=None):
+    if h == None:
+        ab, ci, multistep_stages, model_call, alpha_fn, t_fn, sigma_fn, h_fn, FSAL, EPS_PRED = get_rk_methods(rk_type, torch.tensor(1.0).to('cuda').to(torch.float64), c1=0.0, c2=0.5, c3=1.0)
+    else:
+        ab, ci, multistep_stages, model_call, alpha_fn, t_fn, sigma_fn, h_fn, FSAL, EPS_PRED = get_rk_methods(rk_type, h, c1, c2, c3, h_prev, h_prev2, stepcount, sigmas)
+    return len(ci)-1, model_call, alpha_fn, t_fn, sigma_fn, h_fn, FSAL, EPS_PRED
+
+def get_rk_methods_coeff(rk_type, h, c1, c2, c3, h_prev=None, h_prev2=None, stepcount=0, sigmas=None):
+    ab, ci, multistep_stages, model_call, alpha_fn, t_fn, sigma_fn, h_fn, FSAL, EPS_PRED = get_rk_methods(rk_type, h, c1, c2, c3, h_prev, h_prev2, stepcount, sigmas)
+    return ab, ci, multistep_stages, EPS_PRED
+
+
+
+
+
+
+
+
+
+
+@torch.no_grad()
+def legacy_sample_rk(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, noise_sampler_type="brownian", noise_mode="hard", noise_seed=-1, rk_type="res_2m", implicit_sampler_name="default",
+              sigma_fn_formula="", t_fn_formula="",
+                  eta=0.0, eta_var=0.0, s_noise=1., d_noise=1., alpha=-1.0, k=1.0, scale=0.1, c1=0.0, c2=0.5, c3=1.0, MULTISTEP=False, cfgpp=0.0, implicit_steps=0, reverse_weight=0.0, exp_mode=False,
+                  latent_guide=None, latent_guide_inv=None, latent_guide_weight=0.0, latent_guide_weights=None, guide_mode="blend",
+                  GARBAGE_COLLECT=False, mask=None, LGW_MASK_RESCALE_MIN=True, sigmas_override=None, t_is=None,
+                  ):
+    extra_args = {} if extra_args is None else extra_args
+    
+    if sigmas_override is not None:
+        sigmas = sigmas_override.clone()
+    sigmas = sigmas.clone() * d_noise
+    sigmin = model.inner_model.inner_model.model_sampling.sigma_min 
+    sigmax = model.inner_model.inner_model.model_sampling.sigma_max 
+    
+    UNSAMPLE = False
+    if sigmas[0] == 0.0:      #remove padding used to avoid need for model patch with noise inversion
+        UNSAMPLE = True
+        sigmas = sigmas[1:-1]
+    
+    if mask is None:
+        mask = torch.ones_like(x)
+        LGW_MASK_RESCALE_MIN = False
+    else:
+        mask = mask.unsqueeze(1)
+        mask = mask.repeat(1, x.shape[1], 1, 1) 
+        mask = F.interpolate(mask, size=(x.shape[2], x.shape[3]), mode='bilinear', align_corners=False)
+        mask = mask.to(x.dtype).to(x.device)
+        
+    y0, y0_inv = torch.zeros_like(x), torch.zeros_like(x)
+    if latent_guide is not None:
+        if sigmas[0] > sigmas[1]:
+            y0 = latent_guide = model.inner_model.inner_model.process_latent_in(latent_guide['samples']).clone().to(x.device)
+        else:
+            x = model.inner_model.inner_model.process_latent_in(latent_guide['samples']).clone().to(x.device)
+
+    if latent_guide_inv is not None:
+        if sigmas[0] > sigmas[1]:
+            y0_inv = latent_guide_inv = model.inner_model.inner_model.process_latent_in(latent_guide_inv['samples']).clone().to(x.device)
+        elif UNSAMPLE and mask is not None:
+            x = mask * x + (1-mask) * model.inner_model.inner_model.process_latent_in(latent_guide_inv['samples']).clone().to(x.device)
+
+    uncond = [torch.full_like(x, 0.0)]
+    if cfgpp != 0.0:
+        def post_cfg_function(args):
+            uncond[0] = args["uncond_denoised"]
+            return args["denoised"]
+        model_options = extra_args.get("model_options", {}).copy()
+        extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+    if noise_seed == -1:
+        seed = torch.initial_seed() + 1
+    else:
+        seed = noise_seed
+
+    if noise_sampler_type == "fractal":
+        noise_sampler = NOISE_GENERATOR_CLASSES.get(noise_sampler_type)(x=x, seed=seed, sigma_min=sigmin, sigma_max=sigmax)
+        noise_sampler.alpha = alpha
+        noise_sampler.k = k
+        noise_sampler.scale = scale
+    else:
+        noise_sampler = NOISE_GENERATOR_CLASSES_SIMPLE.get(noise_sampler_type)(x=x, seed=seed, sigma_min=sigmin, sigma_max=sigmax)
+
+    if UNSAMPLE and sigmas[0] < sigmas[1]: #sigma_next > sigma:
+        y0 = noise_sampler(sigma=sigmax, sigma_next=sigmin)
+        y0 = (y0 - y0.mean()) / y0.std()
+        y0_inv = noise_sampler(sigma=sigmax, sigma_next=sigmin)
+        y0_inv = (y0_inv - y0_inv.mean()) / y0_inv.std()
+        
+    order, model_call, alpha_fn, t_fn, sigma_fn, h_fn, FSAL, EPS_PRED = get_rk_methods_order_and_fn(rk_type)
+
+    if exp_mode:
+        model_call = get_denoised
+        alpha_fn = lambda neg_h: torch.exp(neg_h)
+        t_fn     = lambda sigma: sigma.log().neg()
+        sigma_fn = lambda t: t.neg().exp() 
+    
+    xi, ki, ki_u = [torch.zeros_like(x)]*(order+2), [torch.zeros_like(x)]*(order+1), [torch.zeros_like(x)]*(order+1)
+    h, h_prev, h_prev2 = None, None, None
+        
+    xi[0] = x
+
+    for _ in trange(len(sigmas)-1, disable=disable):
+        sigma, sigma_next = sigmas[_], sigmas[_+1]
+        
+        if sigma_next == 0.0:
+            rk_type = "buehler"
+            eta, eta_var = 0, 0
+
+        order, model_call, alpha_fn, t_fn, sigma_fn, h_fn, FSAL, EPS_PRED = get_rk_methods_order_and_fn(rk_type)
+        
+        #sigma_up, sigma, sigma_down, alpha_ratio = get_res4lyf_step_with_model(model, sigma, sigma_next, eta, eta_var, noise_mode, h_fn(sigma_next,sigma) )
+        sigma_up, sigma, sigma_down, alpha_ratio = get_res4lyf_step_with_model(model, sigma, sigma_next, eta, noise_mode)
+
+        t_down, t = t_fn(sigma_down), t_fn(sigma)
+        h = h_fn(sigma_down, sigma)
+        
+        c2, c3 = get_res4lyf_half_step3(sigma, sigma_down, c2, c3, t_fn=t_fn, sigma_fn=sigma_fn, t_fn_formula=t_fn_formula, sigma_fn_formula=sigma_fn_formula)
+        ab, ci, multistep_stages, EPS_PRED = get_rk_methods_coeff(rk_type, h, c1, c2, c3, h_prev, h_prev2, _, sigmas)
+        order = len(ci)-1
+        
+        if exp_mode:
+            for i in range(order):
+                for j in range(order):
+                    ab[i][j] = ab[i][j] * phi(1, -h * ci[i+1])
+        
+        if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST) == False and noise_mode == "hard" and sigma_next > 0.0:
+            noise = noise_sampler(sigma=sigmas[_], sigma_next=sigmas[_+1])
+            noise = torch.nan_to_num((noise - noise.mean()) / noise.std(), 0.0)
+            xi[0] = alpha_ratio * xi[0] + noise * s_noise * sigma_up
+
+        xi_0 = xi[0] # needed for implicit sampling
+
+        if (MULTISTEP == False and FSAL == False) or _ == 0:
+            ki[0]   = model_call(model, xi_0, sigma, **extra_args)
+            if EPS_PRED and rk_type.startswith("deis"):
+                ki[0] = (xi_0 - ki[0]) / sigma
+                ki[0] = ki[0] * (sigma_down-sigma)/(sigma_next-sigma)
+            ki_u[0] = uncond[0]
+
+        if cfgpp != 0.0:
+            ki[0] = uncond[0] + cfgpp * (ki[0] - uncond[0])
+        ki_u[0] = uncond[0]
+
+        for iteration in range(implicit_steps+1):
+            for i in range(multistep_stages, order):
+                if implicit_steps > 0 and iteration > 0 and implicit_sampler_name != "default":
+                    ab, ci, multistep_stages, EPS_PRED = get_rk_methods_coeff(implicit_sampler_name, h, c1, c2, c3, h_prev, h_prev2, _, sigmas)
+                    order = len(ci)-1
+                    if len(ki) < order + 1:
+                        last_value_ki = ki[-1]
+                        last_value_ki_u = ki_u[-1]
+                        ki.extend(  [last_value_ki]   * ((order + 1) - len(ki)))
+                        ki_u.extend([last_value_ki_u] * ((order + 1) - len(ki_u)))
+                    if len(xi) < order + 2:
+                        xi.extend([torch.zeros_like(xi[0])] * ((order + 2) - len(xi)))
+                    
+                    ki[0]   = model_call(model, xi_0, sigma, **extra_args)
+                    ki_u[0] = uncond[0]
+                
+                sigma_mid = sigma_fn(t + h*ci[i+1])
+                alpha_t_1 = alpha_t_1_inv = torch.exp(torch.log(sigma_down/sigma) * ci[i+1] )
+                if sigma_next > sigma:
+                    alpha_t_1_inv = torch.nan_to_num(   torch.exp(torch.log((sigmax - sigma_down)/(sigmax - sigma)) * ci[i+1]),    1.)
+                
+                if LGW_MASK_RESCALE_MIN: 
+                    lgw_mask = mask * (1 - latent_guide_weights[_]) + latent_guide_weights[_]
+                    lgw_mask_inv = (1-mask) * (1 - latent_guide_weights[_]) + latent_guide_weights[_]
+                else:
+                    lgw_mask = mask * latent_guide_weights[_]    
+                    lgw_mask_inv = (1-mask) * latent_guide_weights[_]   
+                    
+                ks, ks_u, ys, ys_inv = torch.zeros_like(x), torch.zeros_like(x), torch.zeros_like(x), torch.zeros_like(x)
+                for j in range(order):
+                    ks     += ab[i][j] * ki[j]
+                    ks_u   += ab[i][j] * ki_u[j]
+                    ys     += ab[i][j] * y0
+                    ys_inv += ab[i][j] * y0_inv
+                    
+                if EPS_PRED and rk_type.startswith("deis"):
+                    epsilon = (h * ks) / (sigma_down - sigma)       #xi[(i+1)%order]  = xi_0 + h*ks
+                    ks = xi_0 - epsilon * sigma        # denoised
+                else:
+                    if implicit_sampler_name.startswith("lobatto") == False:
+                        ks /= sum(ab[i])
+                    elif iteration == 0:
+                        ks /= sum(ab[i])
+                
+                if UNSAMPLE == False and latent_guide is not None and latent_guide_weights[_] > 0.0:
+                    if guide_mode == "hard_light":
+                        lg = latent_guide * sum(ab[i])
+                        if EPS_PRED:
+                            lg = (alpha_fn(-h*ci[i+1]) * xi[0] - latent_guide) / (sigma_fn(t + h*ci[i]) + 1e-8)
+                        hard_light_blend_1 = hard_light_blend(lg, ks)
+                        ks = (1 - lgw_mask) * ks   +   lgw_mask * hard_light_blend_1
+                        
+                    elif guide_mode == "mean_std":
+                        ks2 = torch.zeros_like(x)
+                        for n in range(latent_guide.shape[1]):
+                            ks2[0][n] = (ks[0][n] - ks[0][n].mean()) / ks[0][n].std()
+                            ks2[0][n] = (ks2[0][n] * latent_guide[0][n].std()) + latent_guide[0][n].mean()
+                        ks = (1 - lgw_mask) * ks   +   lgw_mask * ks2
+                        
+                    elif guide_mode == "mean":
+                        ks2 = torch.zeros_like(x)
+                        for n in range(latent_guide.shape[1]):
+                            ks2[0][n] = (ks[0][n] - ks[0][n].mean())
+                            ks2[0][n] = (ks2[0][n]) + latent_guide[0][n].mean()
+                        ks3 = torch.zeros_like(x)
+                        
+                        for n in range(latent_guide.shape[1]):
+                            ks3[0][n] = (ks[0][n] - ks[0][n].mean())
+                            ks3[0][n] = (ks3[0][n]) + latent_guide_inv[0][n].mean()
+                        ks = (1 - lgw_mask) * ks   +   lgw_mask * ks2
+                        ks = (1 - lgw_mask_inv) * ks   +   lgw_mask_inv * ks3
+                        
+                    elif guide_mode == "std":
+                        ks2 = torch.zeros_like(x)
+                        for n in range(latent_guide.shape[1]):
+                            ks2[0][n] = (ks[0][n]) / ks[0][n].std()
+                            ks2[0][n] = (ks2[0][n] * latent_guide[0][n].std())
+                        ks = (1 - lgw_mask) * ks   +   lgw_mask * ks2
+                        
+                    elif guide_mode == "blend": 
+                        ks = (1 - lgw_mask)     * ks   +   lgw_mask     * ys   #+   (1-lgw_mask) * latent_guide_inv
+                        ks = (1 - lgw_mask_inv) * ks   +   lgw_mask_inv * ys_inv
+                        
+                    elif guide_mode == "inversion": 
+                        UNSAMPLE = True
+
+                cfgpp_term = cfgpp*h*(ks - ks_u)
+                xi[(i+1)%order]  = (1-UNSAMPLE * lgw_mask) * (alpha_t_1     * (xi_0 + cfgpp_term)    +    (1 - alpha_t_1)     * ks )     \
+                                    + UNSAMPLE * lgw_mask  * (alpha_t_1_inv * (xi_0 + cfgpp_term)    +    (1 - alpha_t_1_inv) * ys )
+                if UNSAMPLE:
+                    xi[(i+1)%order]  = (1-lgw_mask_inv) * xi[(i+1)%order]   + UNSAMPLE * lgw_mask_inv  * (alpha_t_1_inv * (xi_0 + cfgpp_term)    +      (1 - alpha_t_1_inv) * ys_inv )
+
+                if (i+1)%order > 0 and (i+1)%order > multistep_stages-1:
+                    if GARBAGE_COLLECT: gc.collect(); torch.cuda.empty_cache()
+                    ki[i+1]   = model_call(model, xi[i+1], sigma_fn(t + h*ci[i+1]), **extra_args)
+                    if EPS_PRED and rk_type.startswith("deis"):
+                        ki[i+1] = (xi[i+1] - ki[i+1]) / sigma_fn(t + h*ci[i+1])
+                        ki[i+1] = ki[i+1] * (sigma_down-sigma)/(sigma_next-sigma)
+                    ki_u[i+1] = uncond[0]
+
+            if FSAL and _ > 0:
+                ki  [0] = ki[order-1]
+                ki_u[0] = ki_u[order-1]
+            if MULTISTEP and _ > 0:
+                ki  [0] = denoised
+                ki_u[0] = ki_u[order-1]
+            for ms in range(multistep_stages):
+                ki  [multistep_stages - ms] = ki  [multistep_stages - ms - 1]
+                ki_u[multistep_stages - ms] = ki_u[multistep_stages - ms - 1]
+            if iteration < implicit_steps and implicit_sampler_name == "default":
+                ki  [0] = model_call(model, xi[0], sigma_down, **extra_args)
+                ki_u[0] = uncond[0]
+            elif iteration == implicit_steps and implicit_sampler_name != "default" and implicit_steps > 0:
+                ks, ks_u, ys, ys_inv = torch.zeros_like(x), torch.zeros_like(x), torch.zeros_like(x), torch.zeros_like(x)
+                for j in range(order):
+                    ks     += ab[i+1][j] * ki[j]
+                    ks_u   += ab[i+1][j] * ki_u[j]
+                    ys     += ab[i+1][j] * y0
+                    ys_inv += ab[i+1][j] * y0_inv
+                ks /= sum(ab[i+1])
+                
+                cfgpp_term = cfgpp*h*(ks - ks_u)  #GUIDES NOT FULLY IMPLEMENTED HERE WITH IMPLICIT FINAL STEP
+                xi[(i+1)%order]  = (1-UNSAMPLE * lgw_mask) * (alpha_t_1     * (xi_0 + cfgpp_term)    +    (1 - alpha_t_1)     * ks )     \
+                                    + UNSAMPLE * lgw_mask  * (alpha_t_1_inv * (xi_0 + cfgpp_term)    +    (1 - alpha_t_1_inv) * ys )
+                if UNSAMPLE:
+                    xi[(i+1)%order]  = (1-lgw_mask_inv) * xi[(i+1)%order]   + UNSAMPLE * lgw_mask_inv  * (alpha_t_1_inv * (xi_0 + cfgpp_term)    +      (1 - alpha_t_1_inv) * ys_inv )
+                
+
+            if EPS_PRED == True and exp_mode == False and not rk_type.startswith("deis"):
+                denoised = alpha_fn(-h*ci[i+1]) * xi[0] - sigma * ks
+            elif EPS_PRED == True and rk_type.startswith("deis"):
+                epsilon = (h * ks) / (sigma_down - sigma)
+                denoised = xi_0 - epsilon * sigma        # denoised
+            elif iteration == implicit_steps and implicit_sampler_name != "default" and implicit_steps > 0:
+                denoised = ks
+            else:
+                denoised = ks / sum(ab[i])
+            
+            """if iteration < implicit_steps and implicit_sampler_name != "default":
+                for idx in range(len(ki)):
+                        ki[idx] = denoised"""
+
+        if callback is not None:
+            callback({'x': xi[0], 'i': _, 'sigma': sigma, 'sigma_next': sigma_next, 'denoised': denoised})
+            
+        if (isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST) or noise_mode != "hard") and sigma_next > 0.0:
+            noise = noise_sampler(sigma=sigma, sigma_next=sigma_next)
+            noise = (noise - noise.mean()) / noise.std()
+            
+            if guide_mode == "noise_mean":
+                noise2 = torch.zeros_like(x)
+                for n in range(latent_guide.shape[1]):
+                    noise2[0][n] = (noise[0][n] - noise[0][n].mean())
+                    noise2[0][n] = (noise2[0][n]) + latent_guide[0][n].mean()
+                noise = (1 - lgw_mask) * noise   +   lgw_mask * noise2
+            
+            xi[0] = alpha_ratio * xi[0] + noise * s_noise * sigma_up
+            
+        h_prev2 = h_prev
+        h_prev = h
+        
+    return xi[0]
+
diff --git a/custom_nodes/RES4LYF/legacy/legacy_samplers.py b/custom_nodes/RES4LYF/legacy/legacy_samplers.py
new file mode 100644
index 0000000000000000000000000000000000000000..a6fe2a64904db905d48324506d34b37db8608e10
--- /dev/null
+++ b/custom_nodes/RES4LYF/legacy/legacy_samplers.py
@@ -0,0 +1,677 @@
+import torch
+import torch.nn.functional as F
+
+import comfy.samplers
+import comfy.sample
+import comfy.sampler_helpers
+import comfy.model_sampling
+import comfy.latent_formats
+import comfy.sd
+from comfy_extras.nodes_model_advanced import ModelSamplingSD3, ModelSamplingFlux, ModelSamplingAuraFlow, ModelSamplingStableCascade
+import comfy.supported_models
+
+import latent_preview
+
+from .noise_classes import NOISE_GENERATOR_NAMES, NOISE_GENERATOR_NAMES_SIMPLE, NOISE_GENERATOR_CLASSES_SIMPLE, NOISE_GENERATOR_CLASSES
+
+from .sigmas import get_sigmas
+from .helper import get_res4lyf_scheduler_list
+
+
+def initialize_or_scale(tensor, value, steps):
+    if tensor is None:
+        return torch.full((steps,), value)
+    else:
+        return value * tensor
+    
+def move_to_same_device(*tensors):
+    if not tensors:
+        return tensors
+
+    device = tensors[0].device
+    return tuple(tensor.to(device) for tensor in tensors)
+
+
+
+RK_SAMPLER_NAMES = ["res_2m",
+                    "res_3m",
+                    "res_2s", 
+                    "res_3s",
+                    "rk_exp_5s",
+
+                    "deis_2m",
+                    "deis_3m", 
+                    "deis_4m",
+                    
+                    "ralston_2s",
+                    "ralston_3s",
+                    "ralston_4s", 
+                    
+                    "dpmpp_2m",
+                    "dpmpp_3m",
+                    "dpmpp_2s",
+                    "dpmpp_sde_2s",
+                    "dpmpp_3s",
+                    
+                    "midpoint_2s",
+                    "heun_2s", 
+                    "heun_3s", 
+                    
+                    "houwen-wray_3s",
+                    "kutta_3s", 
+                    "ssprk3_3s",
+                    
+                    "rk38_4s",
+                    "rk4_4s", 
+
+                    "dormand-prince_6s", 
+                    "dormand-prince_13s", 
+                    "bogacki-shampine_7s",
+
+                    "ddim",
+                    "buehler",
+                    ]
+
+
+IRK_SAMPLER_NAMES = [
+                    "gauss-legendre_2s",
+                    "gauss-legendre_3s", 
+                    "gauss-legendre_4s",
+                    "gauss-legendre_5s",
+                    
+                    "radau_iia_2s",
+                    "radau_iia_3s",
+                    
+                    "lobatto_iiic_2s",
+                    "lobatto_iiic_3s",
+                    
+                    "crouzeix_2s",
+                    "crouzeix_3s",
+                    
+                    "irk_exp_diag_2s",
+
+                    "use_explicit", 
+                    ]
+
+
+class Legacy_ClownsharKSampler:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"model": ("MODEL",),
+                    #"add_noise": ("BOOLEAN", {"default": True}),
+                    "noise_type_init": (NOISE_GENERATOR_NAMES_SIMPLE, {"default": "gaussian"}),
+                    "noise_type_sde": (NOISE_GENERATOR_NAMES_SIMPLE, {"default": "brownian"}),
+                    "noise_mode_sde": (["hard", "hard_var", "hard_sq", "soft", "softer", "exp"], {"default": 'hard', "tooltip": "How noise scales with the sigma schedule. Hard is the most aggressive, the others start strong and drop rapidly."}),
+                    "eta": ("FLOAT", {"default": 0.25, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Calculated noise amount to be added, then removed, after each step."}),
+                    "noise_seed": ("INT", {"default": 0, "min": -1, "max": 0xffffffffffffffff}),
+                    #"sampler_mode": (['standard', 'unsample', 'resample'],),
+                    "sampler_mode": (['standard', 'unsample', 'resample',],),
+                    "sampler_name": (RK_SAMPLER_NAMES, {"default": "res_2m"}), 
+                    "implicit_sampler_name": (["default", 
+                                               "gauss-legendre_5s",
+                                               "gauss-legendre_4s",
+                                               "gauss-legendre_3s", 
+                                               "gauss-legendre_2s",
+                                               "crouzeix_2s",
+                                               "radau_iia_3s",
+                                               "radau_iia_2s",
+                                               "lobatto_iiic_3s",
+                                               "lobatto_iiic_2s",
+                                               ], {"default": "default"}), 
+                    "scheduler": (get_res4lyf_scheduler_list(), {"default": "beta57"},),
+                    "steps": ("INT", {"default": 30, "min": 1, "max": 10000}),
+                    "implicit_steps": ("INT", {"default": 0, "min": 0, "max": 10000}),
+                    "denoise": ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01}),
+                    "denoise_alt": ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01}),
+                    "cfg": ("FLOAT", {"default": 5.0, "min": -100.0, "max": 100.0, "step":0.1, "round": False, }),
+                    "shift": ("FLOAT", {"default": 3.0, "min": -1.0, "max": 100.0, "step":0.1, "round": False, }),
+                    "base_shift": ("FLOAT", {"default": 0.85, "min": -1.0, "max": 100.0, "step":0.1, "round": False, }),
+                    "truncate_conditioning": (['false', 'true'], {"default": "true"}),
+                     },
+                "optional": 
+                    {
+                    "positive": ("CONDITIONING", ),
+                    "negative": ("CONDITIONING", ),
+                    "sigmas": ("SIGMAS", ),
+                    "latent_image": ("LATENT", ),     
+                    "guides": ("GUIDES", ),     
+                    "options": ("OPTIONS", ),   
+                    }
+                }
+
+    RETURN_TYPES = ("LATENT","LATENT", ) #"LATENT","LATENT")
+    RETURN_NAMES = ("output", "denoised",) # "output_fp64", "denoised_fp64")
+
+    FUNCTION = "main"
+
+    CATEGORY = "RES4LYF/legacy/samplers"
+    DEPRECATED = True
+    
+    def main(self, model, cfg, truncate_conditioning, sampler_mode, scheduler, steps, denoise=1.0, denoise_alt=1.0,
+             noise_type_init="gaussian", noise_type_sde="brownian", noise_mode_sde="hard", latent_image=None, 
+             positive=None, negative=None, sigmas=None, latent_noise=None, latent_noise_match=None,
+             noise_stdev=1.0, noise_mean=0.0, noise_normalize=True, noise_is_latent=False, 
+             eta=0.25, eta_var=0.0, d_noise=1.0, s_noise=1.0, alpha_init=-1.0, k_init=1.0, alpha_sde=-1.0, k_sde=1.0, cfgpp=0.0, c1=0.0, c2=0.5, c3=1.0, multistep=False, noise_seed=-1, sampler_name="res_2m", implicit_sampler_name="default",
+                    exp_mode=False, t_fn_formula=None, sigma_fn_formula=None, implicit_steps=0,
+                    latent_guide=None, latent_guide_inv=None, latent_guide_weight=0.0, guide_mode="blend", latent_guide_weights=None, latent_guide_mask=None, rescale_floor=True, sigmas_override=None, unsampler_type="linear",
+                    shift=3.0, base_shift=0.85, guides=None, options=None,
+                    ): 
+            default_dtype = torch.float64
+            max_steps = 10000
+
+
+            if noise_seed == -1:
+                seed = torch.initial_seed() + 1
+            else:
+                seed = noise_seed
+                torch.manual_seed(noise_seed)
+            noise_seed_sde = seed + 1
+
+            if options is not None:
+                noise_stdev = options.get('noise_init_stdev', noise_stdev)
+                noise_mean = options.get('noise_init_mean', noise_mean)
+                noise_type_init = options.get('noise_type_init', noise_type_init)
+                noise_type_sde = options.get('noise_type_sde', noise_type_sde)
+                noise_mode_sde = options.get('noise_mode_sde', noise_mode_sde)
+                eta = options.get('eta', eta)
+                s_noise = options.get('s_noise', s_noise)
+                d_noise = options.get('d_noise', d_noise)
+                alpha_init = options.get('alpha_init', alpha_init)
+                k_init = options.get('k_init', k_init)
+                alpha_sde = options.get('alpha_sde', alpha_sde)
+                k_sde = options.get('k_sde', k_sde)
+                noise_seed_sde = options.get('noise_seed_sde', noise_seed+1)
+                c1 = options.get('c1', c1)
+                c2 = options.get('c2', c2)
+                c3 = options.get('c3', c3)
+                t_fn_formula = options.get('t_fn_formula', t_fn_formula)
+                sigma_fn_formula = options.get('sigma_fn_formula', sigma_fn_formula)
+                #unsampler_type = options.get('unsampler_type', unsampler_type)
+
+            if guides is not None:
+                guide_mode, rescale_floor, latent_guide_weight, latent_guide_weights, t_is, latent_guide, latent_guide_inv, latent_guide_mask, scheduler_, steps_, denoise_ = guides
+                """if scheduler == "constant": 
+                    latent_guide_weights = initialize_or_scale(latent_guide_weights, latent_guide_weight, max_steps).to(default_dtype)
+                    latent_guide_weights = F.pad(latent_guide_weights, (0, max_steps), value=0.0)"""
+                if scheduler_ != "constant":
+                    latent_guide_weights = get_sigmas(model, scheduler_, steps_, denoise_).to(default_dtype)
+            latent_guide_weights = initialize_or_scale(latent_guide_weights, latent_guide_weight, max_steps).to(default_dtype)
+            latent_guide_weights = F.pad(latent_guide_weights, (0, max_steps), value=0.0)
+            
+            if shift >= 0:
+                if isinstance(model.model.model_config, comfy.supported_models.SD3):
+                    model = ModelSamplingSD3().patch(model, shift)[0] 
+                elif isinstance(model.model.model_config, comfy.supported_models.AuraFlow):
+                    model = ModelSamplingAuraFlow().patch_aura(model, shift)[0] 
+                elif isinstance(model.model.model_config, comfy.supported_models.Stable_Cascade_C):
+                    model = ModelSamplingStableCascade().patch(model, shift)[0] 
+            if shift >= 0 and base_shift >= 0:
+                if isinstance(model.model.model_config, comfy.supported_models.Flux) or isinstance(model.model.model_config, comfy.supported_models.FluxSchnell):
+                    model = ModelSamplingFlux().patch(model, shift, base_shift, latent_image['samples'].shape[3], latent_image['samples'].shape[2])[0] 
+
+            latent = latent_image
+            latent_image_dtype = latent_image['samples'].dtype
+
+            if positive is None:
+                positive = [[
+                    torch.zeros((1, 154, 4096)),
+                    {'pooled_output': torch.zeros((1, 2048))}
+                    ]]
+            
+            if negative is None:
+                negative = [[
+                    torch.zeros((1, 154, 4096)),
+                    {'pooled_output': torch.zeros((1, 2048))}
+                    ]]
+                
+            if denoise_alt < 0:
+                d_noise = denoise_alt = -denoise_alt
+            if options is not None:
+                d_noise = options.get('d_noise', d_noise)
+            
+            if sigmas is not None:
+                sigmas = sigmas.clone().to(default_dtype)
+            else: 
+                sigmas = get_sigmas(model, scheduler, steps, denoise).to(default_dtype)
+            sigmas *= denoise_alt
+            
+        
+            if sampler_mode.startswith("unsample"): 
+                null = torch.tensor([0.0], device=sigmas.device, dtype=sigmas.dtype)
+                sigmas = torch.flip(sigmas, dims=[0])
+                sigmas = torch.cat([sigmas, null])
+            elif sampler_mode.startswith("resample"):
+                null = torch.tensor([0.0], device=sigmas.device, dtype=sigmas.dtype)
+                sigmas = torch.cat([null, sigmas])
+                sigmas = torch.cat([sigmas, null])
+            
+            if sampler_mode.startswith("unsample_"):
+                unsampler_type = sampler_mode.split("_", 1)[1]
+            elif sampler_mode.startswith("resample_"):
+                unsampler_type = sampler_mode.split("_", 1)[1]
+            else:
+                unsampler_type = ""
+                
+            x = latent_image["samples"].clone().to(default_dtype) 
+            if latent_image is not None:
+                if "samples_fp64" in latent_image:
+                    if latent_image['samples'].shape == latent_image['samples_fp64'].shape:
+                        if torch.norm(latent_image['samples'] - latent_image['samples_fp64']) < 0.01:
+                            x = latent_image["samples_fp64"].clone()
+                
+            if latent_noise is not None:
+                latent_noise["samples"] = latent_noise["samples"].clone().to(default_dtype)  
+            if latent_noise_match is not None:
+                latent_noise_match["samples"] = latent_noise_match["samples"].clone().to(default_dtype)
+
+            if truncate_conditioning == "true" or truncate_conditioning == "true_and_zero_neg":
+                if positive is not None:
+                    positive[0][0] = positive[0][0].clone().to(default_dtype)
+                    positive[0][1]["pooled_output"] = positive[0][1]["pooled_output"].clone().to(default_dtype)
+                if negative is not None:
+                    negative[0][0] = negative[0][0].clone().to(default_dtype)
+                    negative[0][1]["pooled_output"] = negative[0][1]["pooled_output"].clone().to(default_dtype)
+                c = []
+                for t in positive:
+                    d = t[1].copy()
+                    pooled_output = d.get("pooled_output", None)
+                    if pooled_output is not None:
+                        d["pooled_output"] = d["pooled_output"][:, :2048]
+                        n = [t[0][:, :154, :4096], d]
+                    c.append(n)
+                positive = c
+                
+                c = []
+                for t in negative:
+                    d = t[1].copy()
+                    pooled_output = d.get("pooled_output", None)
+                    if pooled_output is not None:
+                        if truncate_conditioning == "true_and_zero_neg":
+                            d["pooled_output"] = torch.zeros((1,2048), dtype=t[0].dtype, device=t[0].device)
+                            n = [torch.zeros((1,154,4096), dtype=t[0].dtype, device=t[0].device), d]
+                        else:
+                            d["pooled_output"] = d["pooled_output"][:, :2048]
+                            n = [t[0][:, :154, :4096], d]
+                    c.append(n)
+                negative = c
+            
+            sigmin = model.model.model_sampling.sigma_min
+            sigmax = model.model.model_sampling.sigma_max
+
+            if noise_type_init == "none":
+                noise = torch.zeros_like(x)
+            elif latent_noise is None:
+                noise_sampler_init = NOISE_GENERATOR_CLASSES_SIMPLE.get(noise_type_init)(x=x, seed=seed, sigma_min=sigmin, sigma_max=sigmax)
+            
+                if noise_type_init == "fractal":
+                    noise_sampler_init.alpha = alpha_init
+                    noise_sampler_init.k = k_init
+                    noise_sampler_init.scale = 0.1
+                noise = noise_sampler_init(sigma=sigmax, sigma_next=sigmin)
+            else:
+                noise = latent_noise["samples"]
+
+            if noise_is_latent: #add noise and latent together and normalize --> noise
+                noise += x.cpu()
+                noise.sub_(noise.mean()).div_(noise.std())
+
+            if noise_normalize and noise.std() > 0:
+                noise.sub_(noise.mean()).div_(noise.std())
+            noise *= noise_stdev
+            noise = (noise - noise.mean()) + noise_mean
+            
+            if latent_noise_match:
+                for i in range(latent_noise_match["samples"].shape[1]):
+                    noise[0][i] = (noise[0][i] - noise[0][i].mean())
+                    noise[0][i] = (noise[0][i]) + latent_noise_match["samples"][0][i].mean()
+
+            noise_mask = latent["noise_mask"] if "noise_mask" in latent else None
+
+            x0_output = {}
+
+            callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
+
+            disable_pbar = False
+            
+            if noise_type_sde == "none":
+                eta_var = eta = 0.0
+                noise_type_sde = "gaussian"
+            if noise_mode_sde == "hard_var":
+                eta_var = eta
+                eta = 0.0
+            
+            if cfg < 0:
+                cfgpp = -cfg
+                cfg = 1.0
+                
+            sampler = comfy.samplers.ksampler("legacy_rk", {"eta": eta, "eta_var": eta_var, "s_noise": s_noise, "d_noise": d_noise, "alpha": alpha_sde, "k": k_sde, "c1": c1, "c2": c2, "c3": c3, "cfgpp": cfgpp, "MULTISTEP": multistep, 
+                                                     "noise_sampler_type": noise_type_sde, "noise_mode": noise_mode_sde, "noise_seed": noise_seed_sde, "rk_type": sampler_name, "implicit_sampler_name": implicit_sampler_name,
+                                                            "exp_mode": exp_mode, "t_fn_formula": t_fn_formula, "sigma_fn_formula": sigma_fn_formula, "implicit_steps": implicit_steps,
+                                                            "latent_guide": latent_guide, "latent_guide_inv": latent_guide_inv, "mask": latent_guide_mask, 
+                                                            "latent_guide_weights": latent_guide_weights, "guide_mode": guide_mode, #"unsampler_type": unsampler_type,
+                                                            "LGW_MASK_RESCALE_MIN": rescale_floor, "sigmas_override": sigmas_override})
+
+            samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, x.clone(), 
+                                                 noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
+
+            out = latent.copy()
+            out["samples"] = samples
+            if "x0" in x0_output:
+                out_denoised = latent.copy()
+                out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu())
+            else:
+                out_denoised = out
+            
+            out["samples_fp64"] = out["samples"].clone()
+            out["samples"] = out["samples"].to(latent_image_dtype)
+            
+            out_denoised["samples_fp64"] = out_denoised["samples"].clone()
+            out_denoised["samples"] = out_denoised["samples"].to(latent_image_dtype)
+
+            return ( out, out_denoised, )
+
+
+
+
+
+class Legacy_SamplerRK:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {#"momentum": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False}),
+                     "eta": ("FLOAT", {"default": 0.25, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Calculated noise amount to be added, then removed, after each step."}),
+                     "eta_var": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Calculate variance-corrected noise amount (overrides eta/noise_mode settings). Cannot be used at very low sigma values; reverts to eta/noise_mode for final steps."}),
+                     "s_noise": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Ratio of calculated noise amount actually added after each step. >1.0 will leave extra noise behind, <1.0 will remove more noise than it adds."}),
+                     "d_noise": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Ratio of calculated noise amount actually added after each step. >1.0 will leave extra noise behind, <1.0 will remove more noise than it adds."}),
+
+                     "noise_mode": (["hard", "hard_sq", "soft", "softer", "exp"], {"default": 'hard', "tooltip": "How noise scales with the sigma schedule. Hard is the most aggressive, the others start strong and drop rapidly."}),
+                     "noise_sampler_type": (NOISE_GENERATOR_NAMES, {"default": "brownian"}),
+                     "alpha": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step":0.1, "round": False, "tooltip": "Fractal noise mode: <0 = extra high frequency noise, >0 = extra low frequency noise, 0 = white noise."}),
+                     "k": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":2.0, "round": False, "tooltip": "Fractal noise mode: all that matters is positive vs. negative. Effect unclear."}),
+                     "noise_seed": ("INT", {"default": -1, "min": -1, "max": 0xffffffffffffffff, "tooltip": "Seed for the SDE noise that is added after each step if eta or eta_var are non-zero. If set to -1, it will use the increment the seed most recently used by the workflow."}),
+                     "rk_type": (RK_SAMPLER_NAMES, {"default": "res_2m"}), 
+                     "exp_mode": ("BOOLEAN", {"default": False, "tooltip": "Convert linear RK methods to exponential form."}), 
+                     "multistep": ("BOOLEAN", {"default": False, "tooltip": "For samplers ending in S only. Reduces cost by one model call per step by reusing the previous step as the current predictor step."}),
+                     "implicit_steps": ("INT", {"default": 0, "min": 0, "max": 100, "step":1, "tooltip": "Number of implicit Runge-Kutta refinement steps to run after each explicit step."}),
+                     "cfgpp": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step":0.01, "round": False, "tooltip": "CFG++ scale. Use in place of, or with, CFG. Currently only working with RES, DPMPP, and DDIM samplers."}),
+                     "latent_guide_weight": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False}),
+                     #"guide_mode": (["hard_light", "mean_std", "mean", "std", "noise_mean", "blend", "inversion"], {"default": 'mean', "tooltip": "The mode used. noise_mean and inversion are currently for test purposes only."}),
+                     "guide_mode": (["hard_light", "mean_std", "mean", "std", "blend",], {"default": 'mean', "tooltip": "The mode used. noise_mean and inversion are currently for test purposes only."}),
+                     #"guide_mode": (["hard_light", "blend", "mean_std", "mean", "std"], {"default": 'mean', "tooltip": "The mode used."}),
+                     "rescale_floor": ("BOOLEAN", {"default": True, "tooltip": "Latent_guide_weight(s) control the minimum value for the latent_guide_mask. If false, they control the maximum value."}),
+                    },
+                    "optional":
+                    {
+                        "latent_guide": ("LATENT", ),
+                        "latent_guide_inv": ("LATENT", ),
+
+                        "latent_guide_mask": ("MASK", ),
+                        "latent_guide_weights": ("SIGMAS", ),
+                        "sigmas_override": ("SIGMAS", ),
+                    }  
+                    
+               }
+    RETURN_TYPES = ("SAMPLER",)
+    CATEGORY = "RES4LYF/legacy/samplers"
+
+    FUNCTION = "get_sampler"
+    DEPRECATED = True
+
+    def get_sampler(self, eta=0.25, eta_var=0.0, d_noise=1.0, s_noise=1.0, alpha=-1.0, k=1.0, cfgpp=0.0, multistep=False, noise_sampler_type="brownian", noise_mode="hard", noise_seed=-1, rk_type="dormand-prince", 
+                    exp_mode=False, t_fn_formula=None, sigma_fn_formula=None, implicit_steps=0,
+                    latent_guide=None, latent_guide_inv=None, latent_guide_weight=0.0, guide_mode="hard_light", latent_guide_weights=None, latent_guide_mask=None, rescale_floor=True, sigmas_override=None,
+                    ):
+        sampler_name = "legacy_rk"
+
+        if latent_guide is None and latent_guide_inv is None:
+            latent_guide_weight = 0.0
+
+        steps = 10000
+        latent_guide_weights = initialize_or_scale(latent_guide_weights, latent_guide_weight, steps)
+            
+        latent_guide_weights = F.pad(latent_guide_weights, (0, 10000), value=0.0)
+
+        sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "eta_var": eta_var, "s_noise": s_noise, "d_noise": d_noise, "alpha": alpha, "k": k, "cfgpp": cfgpp, "MULTISTEP": multistep, "noise_sampler_type": noise_sampler_type, "noise_mode": noise_mode, "noise_seed": noise_seed, "rk_type": rk_type, 
+                                                         "exp_mode": exp_mode, "t_fn_formula": t_fn_formula, "sigma_fn_formula": sigma_fn_formula, "implicit_steps": implicit_steps,
+                                                         "latent_guide": latent_guide, "latent_guide_inv": latent_guide_inv, "mask": latent_guide_mask, "latent_guide_weight": latent_guide_weight, "latent_guide_weights": latent_guide_weights, "guide_mode": guide_mode,
+                                                         "LGW_MASK_RESCALE_MIN": rescale_floor, "sigmas_override": sigmas_override})
+        return (sampler, )
+
+
+
+
+class Legacy_ClownsharKSamplerGuides:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"guide_mode": (["hard_light", "mean_std", "mean", "std", "blend"], {"default": 'blend', "tooltip": "The mode used."}),
+                     "latent_guide_weight": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False}),
+                    "scheduler": (["constant"] + get_res4lyf_scheduler_list(), {"default": "beta57"},),
+                    "steps": ("INT", {"default": 30, "min": 1, "max": 10000}),
+                    "denoise": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False}),
+                     "rescale_floor": ("BOOLEAN", {"default": False, "tooltip": "If true, latent_guide_weight(s) primarily affect the masked areas. If false, they control the unmasked areas."}),
+                    },
+                    "optional": 
+                    {
+                        "latent_guide": ("LATENT", ),
+                        "latent_guide_inv": ("LATENT", ),
+                        "latent_guide_mask": ("MASK", ),
+                        "latent_guide_weights": ("SIGMAS", ),
+                    }  
+               }
+    RETURN_TYPES = ("GUIDES",)
+    CATEGORY = "RES4LYF/legacy/samplers"
+
+    FUNCTION = "get_sampler"
+    DEPRECATED = True
+
+    def get_sampler(self, model=None, scheduler="constant", steps=30, denoise=1.0, latent_guide=None, latent_guide_inv=None, latent_guide_weight=0.0, guide_mode="blend", latent_guide_weights=None, latent_guide_mask=None, rescale_floor=True, t_is=None,
+                    ):
+        default_dtype = torch.float64
+        
+        max_steps = 10000
+        
+        #if scheduler != "constant": 
+        #    latent_guide_weights = get_sigmas(model, scheduler, steps, latent_guide_weight).to(default_dtype)
+            
+        if scheduler == "constant": 
+            latent_guide_weights = initialize_or_scale(None, latent_guide_weight, steps).to(default_dtype)
+            latent_guide_weights = F.pad(latent_guide_weights, (0, max_steps), value=0.0)
+        
+        if latent_guide is not None:
+            x = latent_guide["samples"].clone().to(default_dtype) 
+        if latent_guide_inv is not None:
+            x = latent_guide_inv["samples"].clone().to(default_dtype) 
+
+        guides = (guide_mode, rescale_floor, latent_guide_weight, latent_guide_weights, t_is, latent_guide, latent_guide_inv, latent_guide_mask, scheduler, steps, denoise)
+        return (guides, )
+
+
+
+
+
+class Legacy_SharkSampler:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"model": ("MODEL",),
+                    "add_noise": ("BOOLEAN", {"default": True}),
+                    "noise_normalize": ("BOOLEAN", {"default": True}),
+                    "noise_stdev": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":0.01, "round": False, }),
+                    "noise_mean": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step":0.01, "round": False, }),
+                    "noise_is_latent": ("BOOLEAN", {"default": False}),
+                    "noise_type": (NOISE_GENERATOR_NAMES, {"default": "gaussian"}),
+                    "alpha": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":0.1, "round": False, }),
+                    "k": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":2.0, "round": False, }),
+                    "noise_seed": ("INT", {"default": 0, "min": -1, "max": 0xffffffffffffffff}),
+                    "sampler_mode": (['standard', 'unsample', 'resample'],),
+                    #"scheduler": (comfy.samplers.SCHEDULER_NAMES, ),
+                    "scheduler": (get_res4lyf_scheduler_list(),),
+
+                    "steps": ("INT", {"default": 30, "min": 1, "max": 10000}),
+                    "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10000, "step":0.01}),
+                    "cfg": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 100.0, "step":0.5, "round": False, }),
+                    "truncate_conditioning": (['false', 'true', 'true_and_zero_neg'], ),
+                    
+                    "positive": ("CONDITIONING", ),
+                    "negative": ("CONDITIONING", ),
+                    "sampler": ("SAMPLER", ),
+                    "latent_image": ("LATENT", ),               
+                     },
+                "optional": 
+                    {
+                    "sigmas": ("SIGMAS", ),
+                    "latent_noise": ("LATENT", ),
+                    "latent_noise_match": ("LATENT",),
+                    }
+                }
+
+    RETURN_TYPES = ("LATENT","LATENT","LATENT","LATENT")
+    RETURN_NAMES = ("output", "denoised", "output_fp64", "denoised_fp64")
+
+    FUNCTION = "main"
+
+    CATEGORY = "RES4LYF/legacy/samplers"
+    DEPRECATED = True
+    
+    def main(self, model, add_noise, noise_stdev, noise_mean, noise_normalize, noise_is_latent, noise_type, noise_seed, cfg, truncate_conditioning, alpha, k, positive, negative, sampler,
+             latent_image, sampler_mode, scheduler, steps, denoise, sigmas=None, latent_noise=None, latent_noise_match=None,): 
+
+            latent = latent_image
+            latent_image_dtype = latent_image['samples'].dtype
+            
+            default_dtype = torch.float64
+            
+            if positive is None:
+                positive = [[
+                    torch.zeros((1, 154, 4096)),  # blah[0][0], a tensor of shape (1, 154, 4096)
+                    {'pooled_output': torch.zeros((1, 2048))}
+                    ]]
+            if negative is None:
+                negative = [[
+                    torch.zeros((1, 154, 4096)),  # blah[0][0], a tensor of shape (1, 154, 4096)
+                    {'pooled_output': torch.zeros((1, 2048))}
+                    ]]
+                
+            if denoise < 0:
+                sampler.extra_options['d_noise'] = -denoise
+                denoise = 1.0
+            if sigmas is not None:
+                sigmas = sigmas.clone().to(default_dtype)
+            else: 
+                sigmas = get_sigmas(model, scheduler, steps, denoise).to(default_dtype)
+                
+            #sigmas = sigmas.clone().to(torch.float64)
+        
+            if sampler_mode == "unsample": 
+                null = torch.tensor([0.0], device=sigmas.device, dtype=sigmas.dtype)
+                sigmas = torch.flip(sigmas, dims=[0])
+                sigmas = torch.cat([sigmas, null])
+            elif sampler_mode == "resample":
+                null = torch.tensor([0.0], device=sigmas.device, dtype=sigmas.dtype)
+                sigmas = torch.cat([null, sigmas])
+                sigmas = torch.cat([sigmas, null])
+                
+            if latent_image is not None:
+                x = latent_image["samples"].clone().to(default_dtype) 
+                #x = {"samples": x}
+                
+            if latent_noise is not None:
+                latent_noise["samples"] = latent_noise["samples"].clone().to(default_dtype)  
+            if latent_noise_match is not None:
+                latent_noise_match["samples"] = latent_noise_match["samples"].clone().to(default_dtype)
+
+            if truncate_conditioning == "true" or truncate_conditioning == "true_and_zero_neg":
+                if positive is not None:
+                    positive[0][0] = positive[0][0].clone().to(default_dtype)
+                    positive[0][1]["pooled_output"] = positive[0][1]["pooled_output"].clone().to(default_dtype)
+                c = []
+                for t in positive:
+                    d = t[1].copy()
+                    pooled_output = d.get("pooled_output", None)
+                    if pooled_output is not None:
+                        d["pooled_output"] = d["pooled_output"][:, :2048]
+                        n = [t[0][:, :154, :4096], d]
+                    c.append(n)
+                positive = c
+                
+                c = []
+                for t in negative:
+                    if negative is not None:
+                        negative[0][0] = negative[0][0].clone().to(default_dtype)
+                        negative[0][1]["pooled_output"] = negative[0][1]["pooled_output"].clone().to(default_dtype)
+                    d = t[1].copy()
+                    pooled_output = d.get("pooled_output", None)
+                    if pooled_output is not None:
+                        if truncate_conditioning == "true_and_zero_neg":
+                            d["pooled_output"] = torch.zeros((1,2048), dtype=t[0].dtype, device=t[0].device)
+                            n = [torch.zeros((1,154,4096), dtype=t[0].dtype, device=t[0].device), d]
+                        else:
+                            d["pooled_output"] = d["pooled_output"][:, :2048]
+                            n = [t[0][:, :154, :4096], d]
+                    c.append(n)
+                negative = c
+            
+            sigmin = model.model.model_sampling.sigma_min
+            sigmax = model.model.model_sampling.sigma_max
+
+            if noise_seed == -1:
+                seed = torch.initial_seed() + 1
+            else:
+                seed = noise_seed
+                torch.manual_seed(noise_seed)
+            
+            noise_sampler = NOISE_GENERATOR_CLASSES.get(noise_type)(x=x, seed=seed, sigma_min=sigmin, sigma_max=sigmax)
+            
+            if noise_type == "fractal":
+                noise_sampler.alpha = alpha
+                noise_sampler.k = k
+                noise_sampler.scale = 0.1
+        
+            if not add_noise:
+                noise = torch.zeros_like(x)
+            elif latent_noise is None:
+                noise = noise_sampler(sigma=sigmax, sigma_next=sigmin)
+            else:
+                noise = latent_noise["samples"]
+
+            if noise_is_latent: #add noise and latent together and normalize --> noise
+                noise += x.cpu()
+                noise.sub_(noise.mean()).div_(noise.std())
+
+            if noise_normalize:
+                noise.sub_(noise.mean()).div_(noise.std())
+            noise *= noise_stdev
+            noise = (noise - noise.mean()) + noise_mean
+            
+            if latent_noise_match:
+                for i in range(latent_noise_match["samples"].shape[1]):
+                    noise[0][i] = (noise[0][i] - noise[0][i].mean())
+                    noise[0][i] = (noise[0][i]) + latent_noise_match["samples"][0][i].mean()
+
+                
+            noise_mask = latent["noise_mask"] if "noise_mask" in latent else None
+
+            x0_output = {}
+
+            callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
+
+            disable_pbar = False
+ 
+            samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, x, 
+                                                 noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
+
+            out = latent.copy()
+            out["samples"] = samples
+            if "x0" in x0_output:
+                out_denoised = latent.copy()
+                out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu())
+            else:
+                out_denoised = out
+                
+            out_orig_dtype = out['samples'].clone().to(latent_image_dtype)
+            out_denoised_orig_dtype = out_denoised['samples'].clone().to(latent_image_dtype)
+                
+            return ( {'samples': out_orig_dtype}, {'samples': out_denoised_orig_dtype}, out, out_denoised,)
+            
\ No newline at end of file
diff --git a/custom_nodes/RES4LYF/legacy/models.py b/custom_nodes/RES4LYF/legacy/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..81c44ebfc11a85c33165801731c26762aff22714
--- /dev/null
+++ b/custom_nodes/RES4LYF/legacy/models.py
@@ -0,0 +1,512 @@
+# Code adapted from https://github.com/comfyanonymous/ComfyUI/
+
+import comfy.samplers
+import comfy.sample
+import comfy.sampler_helpers
+import comfy.utils
+from comfy.cli_args import args
+from comfy_extras.nodes_model_advanced import ModelSamplingSD3, ModelSamplingFlux, ModelSamplingAuraFlow, ModelSamplingStableCascade
+
+
+import torch
+
+import folder_paths
+import os
+import json
+import math
+
+import comfy.model_management
+    
+from .flux.model  import ReFlux
+from .flux.layers import SingleStreamBlock as ReSingleStreamBlock, DoubleStreamBlock as ReDoubleStreamBlock
+
+from comfy.ldm.flux.model import Flux
+from comfy.ldm.flux.layers import SingleStreamBlock, DoubleStreamBlock
+
+from .helper import get_orthogonal, get_cosine_similarity
+from ..res4lyf import RESplain
+
+
+class ReFluxPatcher:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+            "model": ("MODEL",),
+            "enable": ("BOOLEAN", {"default": True}),
+           }
+        }
+    RETURN_TYPES = ("MODEL",)
+    RETURN_NAMES = ("model",)
+
+    CATEGORY = "RES4LYF/model_patches"
+    FUNCTION = "main"
+
+    def main(self, model, enable=True):
+        m = model #.clone()
+        
+        if enable:
+            m.model.diffusion_model.__class__ = ReFlux
+            m.model.diffusion_model.threshold_inv = False
+            
+            for i, block in enumerate(m.model.diffusion_model.double_blocks):
+                block.__class__ = ReDoubleStreamBlock
+                block.idx = i
+
+            for i, block in enumerate(m.model.diffusion_model.single_blocks):
+                block.__class__ = ReSingleStreamBlock
+                block.idx = i
+        else:
+            m.model.diffusion_model.__class__ = Flux
+            
+            for i, block in enumerate(m.model.diffusion_model.double_blocks):
+                block.__class__ = DoubleStreamBlock
+                block.idx = i
+
+            for i, block in enumerate(m.model.diffusion_model.single_blocks):
+                block.__class__ = SingleStreamBlock
+                block.idx = i
+        
+        return (m,)
+    
+import types
+
+
+class FluxOrthoCFGPatcher:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+            "model": ("MODEL",),
+            "enable": ("BOOLEAN", {"default": True}),
+            "ortho_T5": ("BOOLEAN", {"default": True}),
+            "ortho_clip_L": ("BOOLEAN", {"default": True}),
+            "zero_clip_L": ("BOOLEAN", {"default": True}),
+           }
+        }
+        
+    RETURN_TYPES = ("MODEL",)
+    RETURN_NAMES = ("model",)
+
+    CATEGORY = "RES4LYF/model_patches"
+    FUNCTION = "main"
+    
+    original_forward = Flux.forward
+
+    @staticmethod
+    def new_forward(self, x, timestep, context, y, guidance, control=None, transformer_options={}, **kwargs):
+
+        for _ in range(500):
+            if self.ortho_T5 and get_cosine_similarity(context[0], context[1]) != 0:
+                context[0] = get_orthogonal(context[0], context[1])
+            if self.ortho_clip_L and get_cosine_similarity(y[0], y[1]) != 0:
+                y[0] = get_orthogonal(y[0].unsqueeze(0), y[1].unsqueeze(0)).squeeze(0)
+                
+        RESplain("postcossim1: ", get_cosine_similarity(context[0], context[1]))
+        RESplain("postcossim2: ", get_cosine_similarity(y[0], y[1]))
+        
+        if self.zero_clip_L:
+            y[0] = torch.zeros_like(y[0])
+        
+        return FluxOrthoCFGPatcher.original_forward(self, x, timestep, context, y, guidance, control, transformer_options, **kwargs)
+
+    def main(self, model, enable=True, ortho_T5=True, ortho_clip_L=True, zero_clip_L=True):
+        m = model.clone()
+
+        if enable:
+            m.model.diffusion_model.ortho_T5 = ortho_T5
+            m.model.diffusion_model.ortho_clip_L = ortho_clip_L
+            m.model.diffusion_model.zero_clip_L = zero_clip_L
+            Flux.forward = types.MethodType(FluxOrthoCFGPatcher.new_forward, m.model.diffusion_model)
+        else:
+            Flux.forward = FluxOrthoCFGPatcher.original_forward
+
+        return (m,)
+    
+    
+    
+    
+class FluxGuidanceDisable:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+            "model": ("MODEL",),
+            "disable": ("BOOLEAN", {"default": True}),
+            "zero_clip_L": ("BOOLEAN", {"default": True}),
+            }
+        }
+        
+    RETURN_TYPES = ("MODEL",)
+    RETURN_NAMES = ("model",)
+
+    FUNCTION = "main"
+    CATEGORY = "RES4LYF/model_patches"
+
+    original_forward = Flux.forward
+
+    @staticmethod
+    def new_forward(self, x, timestep, context, y, guidance, control=None, transformer_options={}, **kwargs):
+
+        y = torch.zeros_like(y)
+        
+        return FluxGuidanceDisable.original_forward(self, x, timestep, context, y, guidance, control, transformer_options, **kwargs)
+
+    def main(self, model, disable=True, zero_clip_L=True):
+        m = model.clone()
+        if disable:
+            m.model.diffusion_model.params.guidance_embed = False
+        else:
+            m.model.diffusion_model.params.guidance_embed = True
+            
+        #m.model.diffusion_model.zero_clip_L = zero_clip_L
+        if zero_clip_L:
+            Flux.forward = types.MethodType(FluxGuidanceDisable.new_forward, m.model.diffusion_model)
+
+        return (m,)
+
+
+
+def time_snr_shift_exponential(alpha, t):
+    return math.exp(alpha) / (math.exp(alpha) + (1 / t - 1) ** 1.0)
+
+def time_snr_shift_linear(alpha, t):
+    if alpha == 1.0:
+        return t
+    return alpha * t / (1 + (alpha - 1) * t)
+
+class ModelSamplingAdvanced:
+    # this is used to set the "shift" using either exponential scaling (default for SD3.5M and Flux) or linear scaling (default for SD3.5L and SD3 2B beta)
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "model": ("MODEL",),
+                    "scaling": (["exponential", "linear"], {"default": 'exponential'}), 
+                    "shift": ("FLOAT", {"default": 3.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False}),
+                    #"base_shift": ("FLOAT", {"default": 3.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False}),
+                    }
+               }
+    
+    RETURN_TYPES = ("MODEL",)
+    RETURN_NAMES = ("model",)
+    
+    FUNCTION = "main"
+    CATEGORY = "RES4LYF/model_shift"
+
+    def sigma_exponential(self, timestep):
+        return time_snr_shift_exponential(self.timestep_shift, timestep / self.multiplier)
+
+    def sigma_linear(self, timestep):
+        return time_snr_shift_linear(self.timestep_shift, timestep / self.multiplier)
+
+    def main(self, model, scaling, shift):
+        m = model.clone()
+        
+        self.timestep_shift = shift
+        self.multiplier = 1000
+        timesteps = 1000
+        sampling_base = None
+        
+        if isinstance(m.model.model_config, comfy.supported_models.Flux) or isinstance(m.model.model_config, comfy.supported_models.FluxSchnell):
+            self.multiplier = 1
+            timesteps = 10000
+            sampling_base = comfy.model_sampling.ModelSamplingFlux
+            sampling_type = comfy.model_sampling.CONST
+            
+        elif isinstance(m.model.model_config, comfy.supported_models.AuraFlow):
+            self.multiplier = 1
+            timesteps = 1000
+            sampling_base = comfy.model_sampling.ModelSamplingDiscreteFlow
+            sampling_type = comfy.model_sampling.CONST
+
+        elif isinstance(m.model.model_config, comfy.supported_models.HunyuanVideo):
+            self.multiplier = 1000
+            timesteps = 1000
+            sampling_base = comfy.model_sampling.ModelSamplingDiscreteFlow
+            sampling_type = comfy.model_sampling.CONST
+
+        elif isinstance(m.model.model_config, comfy.supported_models.CosmosT2V) or isinstance(m.model.model_config, comfy.supported_models.CosmosI2V):
+            self.multiplier = 1
+            timesteps = 1000
+            sampling_base = comfy.model_sampling.ModelSamplingContinuousEDM
+            sampling_type = comfy.model_sampling.CONST
+
+        elif isinstance(m.model.model_config, comfy.supported_models.LTXV):
+            self.multiplier = 1000
+            timesteps = 1000
+            sampling_base = comfy.model_sampling.ModelSamplingFlux
+            sampling_type = comfy.model_sampling.CONST
+            
+        elif isinstance(m.model.model_config, comfy.supported_models.SD3):
+            self.multiplier = 1000
+            timesteps = 1000
+            sampling_base = comfy.model_sampling.ModelSamplingDiscreteFlow
+            sampling_type = comfy.model_sampling.CONST
+
+        if sampling_base is None:
+            raise ValueError("Model not supported by ModelSamplingAdvanced")
+
+        class ModelSamplingAdvanced(sampling_base, sampling_type):
+            pass
+
+        m.object_patches['model_sampling'] = m.model.model_sampling = ModelSamplingAdvanced(m.model.model_config)
+
+        m.model.model_sampling.__dict__['shift']      = self.timestep_shift
+        m.model.model_sampling.__dict__['multiplier'] = self.multiplier
+
+        s_range = torch.arange(1, timesteps + 1, 1).to(torch.float64)
+        if scaling == "exponential": 
+            ts = self.sigma_exponential((s_range / timesteps) * self.multiplier)
+        elif scaling == "linear": 
+            ts = self.sigma_linear((s_range / timesteps) * self.multiplier)
+
+        m.model.model_sampling.register_buffer('sigmas', ts)
+        m.object_patches['model_sampling'].sigmas = m.model.model_sampling.sigmas
+        
+        return (m,)
+
+class ModelSamplingAdvancedResolution:
+    # this is used to set the "shift" using either exponential scaling (default for SD3.5M and Flux) or linear scaling (default for SD3.5L and SD3 2B beta)
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "model": ("MODEL",),
+                    "scaling": (["exponential", "linear"], {"default": 'exponential'}), 
+                    "max_shift": ("FLOAT", {"default": 1.35, "min": -100.0, "max": 100.0, "step":0.01, "round": False}),
+                    "base_shift": ("FLOAT", {"default": 0.85, "min": -100.0, "max": 100.0, "step":0.01, "round": False}),
+                    "latent_image": ("LATENT",),
+                }
+               }
+    
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "main"
+    
+    CATEGORY = "RES4LYF/model_shift"
+
+    def sigma_exponential(self, timestep):
+        return time_snr_shift_exponential(self.timestep_shift, timestep / self.multiplier)
+
+    def sigma_linear(self, timestep):
+        return time_snr_shift_linear(self.timestep_shift, timestep / self.multiplier)
+
+    def main(self, model, scaling, max_shift, base_shift, latent_image):
+        m = model.clone()
+        height, width = latent_image['samples'].shape[2:]
+        
+        x1 = 256
+        x2 = 4096
+        mm = (max_shift - base_shift) / (x2 - x1)
+        b = base_shift - mm * x1
+        shift = (width * height / (8 * 8 * 2 * 2)) * mm + b
+        
+        self.timestep_shift = shift
+        self.multiplier = 1000
+        timesteps = 1000
+        
+        if isinstance(m.model.model_config, comfy.supported_models.Flux) or isinstance(m.model.model_config, comfy.supported_models.FluxSchnell):
+            self.multiplier = 1
+            timesteps = 10000
+            sampling_base = comfy.model_sampling.ModelSamplingFlux
+            sampling_type = comfy.model_sampling.CONST
+
+            
+        elif isinstance(m.model.model_config, comfy.supported_models.AuraFlow):
+            self.multiplier = 1
+            timesteps = 1000
+            sampling_base = comfy.model_sampling.ModelSamplingDiscreteFlow
+            sampling_type = comfy.model_sampling.CONST
+            
+        elif isinstance(m.model.model_config, comfy.supported_models.SD3):
+            self.multiplier = 1000
+            timesteps = 1000
+            sampling_base = comfy.model_sampling.ModelSamplingDiscreteFlow
+            sampling_type = comfy.model_sampling.CONST
+
+        class ModelSamplingAdvanced(sampling_base, sampling_type):
+            pass
+
+        m.object_patches['model_sampling'] = m.model.model_sampling = ModelSamplingAdvanced(m.model.model_config)
+
+        m.model.model_sampling.__dict__['shift']      = self.timestep_shift
+        m.model.model_sampling.__dict__['multiplier'] = self.multiplier
+
+        s_range = torch.arange(1, timesteps + 1, 1).to(torch.float64)
+        if scaling == "exponential": 
+            ts = self.sigma_exponential((s_range / timesteps) * self.multiplier)
+        elif scaling == "linear": 
+            ts = self.sigma_linear((s_range / timesteps) * self.multiplier)
+
+        m.model.model_sampling.register_buffer('sigmas', ts)
+        m.object_patches['model_sampling'].sigmas = m.model.model_sampling.sigmas
+        
+        return (m,)
+    
+    
+class UNetSave:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "filename_prefix": ("STRING", {"default": "models/ComfyUI"}),},
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
+    RETURN_TYPES = ()
+    FUNCTION = "save"
+    OUTPUT_NODE = True
+
+    CATEGORY = "RES4LYF/model_merging"
+    DESCRIPTION = "Save a .safetensors containing only the model data."
+
+    def save(self, model, filename_prefix, prompt=None, extra_pnginfo=None):
+        save_checkpoint(model, clip=None, vae=None, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo)
+        return {}
+
+
+def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefix=None, output_dir=None, prompt=None, extra_pnginfo=None):
+    full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, output_dir)
+    prompt_info = ""
+    if prompt is not None:
+        prompt_info = json.dumps(prompt)
+
+    metadata = {}
+
+    enable_modelspec = True
+    if isinstance(model.model, comfy.model_base.SDXL):
+        if isinstance(model.model, comfy.model_base.SDXL_instructpix2pix):
+            metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-edit"
+        else:
+            metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-base"
+    elif isinstance(model.model, comfy.model_base.SDXLRefiner):
+        metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-refiner"
+    elif isinstance(model.model, comfy.model_base.SVD_img2vid):
+        metadata["modelspec.architecture"] = "stable-video-diffusion-img2vid-v1"
+    elif isinstance(model.model, comfy.model_base.SD3):
+        metadata["modelspec.architecture"] = "stable-diffusion-v3-medium" #TODO: other SD3 variants
+    else:
+        enable_modelspec = False
+
+    if enable_modelspec:
+        metadata["modelspec.sai_model_spec"] = "1.0.0"
+        metadata["modelspec.implementation"] = "sgm"
+        metadata["modelspec.title"] = "{} {}".format(filename, counter)
+
+    #TODO:
+    # "stable-diffusion-v1", "stable-diffusion-v1-inpainting", "stable-diffusion-v2-512",
+    # "stable-diffusion-v2-768-v", "stable-diffusion-v2-unclip-l", "stable-diffusion-v2-unclip-h",
+    # "v2-inpainting"
+
+    extra_keys = {}
+    model_sampling = model.get_model_object("model_sampling")
+    if isinstance(model_sampling, comfy.model_sampling.ModelSamplingContinuousEDM):
+        if isinstance(model_sampling, comfy.model_sampling.V_PREDICTION):
+            extra_keys["edm_vpred.sigma_max"] = torch.tensor(model_sampling.sigma_max).float()
+            extra_keys["edm_vpred.sigma_min"] = torch.tensor(model_sampling.sigma_min).float()
+
+    if model.model.model_type == comfy.model_base.ModelType.EPS:
+        metadata["modelspec.predict_key"] = "epsilon"
+    elif model.model.model_type == comfy.model_base.ModelType.V_PREDICTION:
+        metadata["modelspec.predict_key"] = "v"
+
+    if not args.disable_metadata:
+        metadata["prompt"] = prompt_info
+        if extra_pnginfo is not None:
+            for x in extra_pnginfo:
+                metadata[x] = json.dumps(extra_pnginfo[x])
+
+    output_checkpoint = f"{filename}_{counter:05}_.safetensors"
+    output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
+
+    sd_save_checkpoint(output_checkpoint, model, clip, vae, clip_vision, metadata=metadata, extra_keys=extra_keys)
+
+
+def sd_save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, metadata=None, extra_keys={}):
+    clip_sd = None
+    load_models = [model]
+    if clip is not None:
+        load_models.append(clip.load_model())
+        clip_sd = clip.get_sd()
+
+    comfy.model_management.load_models_gpu(load_models, force_patch_weights=True)
+    clip_vision_sd = clip_vision.get_sd() if clip_vision is not None else None
+    vae_sd = vae.get_sd() if vae is not None else None                             #THIS ALLOWS SAVING UNET ONLY
+    sd = model.model.state_dict_for_saving(clip_sd, vae_sd, clip_vision_sd)
+    for k in extra_keys:
+        sd[k] = extra_keys[k]
+
+    for k in sd:
+        t = sd[k]
+        if not t.is_contiguous():
+            sd[k] = t.contiguous()
+
+    comfy.utils.save_torch_file(sd, output_path, metadata=metadata)
+
+
+
+
+
+
+class TorchCompileModelFluxAdvanced: #adapted from https://github.com/kijai/ComfyUI-KJNodes
+    def __init__(self):
+        self._compiled = False
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "model": ("MODEL",),
+                    "backend": (["inductor", "cudagraphs"],),
+                    "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}),
+                    "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}),
+                    "double_blocks": ("STRING", {"default": "0-18", "multiline": True}),
+                    "single_blocks": ("STRING", {"default": "0-37", "multiline": True}),
+                    "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}),
+                }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "RES4LYF/model_patches"
+    EXPERIMENTAL = True
+
+    def parse_blocks(self, blocks_str):
+        blocks = []
+        for part in blocks_str.split(','):
+            part = part.strip()
+            if '-' in part:
+                start, end = map(int, part.split('-'))
+                blocks.extend(range(start, end + 1))
+            else:
+                blocks.append(int(part))
+        return blocks
+
+    def patch(self, model, backend, mode, fullgraph, single_blocks, double_blocks, dynamic):
+        single_block_list = self.parse_blocks(single_blocks)
+        double_block_list = self.parse_blocks(double_blocks)
+        m = model.clone()
+        diffusion_model = m.get_model_object("diffusion_model")
+        
+        if not self._compiled:
+            try:
+                for i, block in enumerate(diffusion_model.double_blocks):
+                    if i in double_block_list:
+                        #print("Compiling double_block", i)
+                        m.add_object_patch(f"diffusion_model.double_blocks.{i}", torch.compile(block, mode=mode, dynamic=dynamic, fullgraph=fullgraph, backend=backend))
+                for i, block in enumerate(diffusion_model.single_blocks):
+                    if i in single_block_list:
+                        #print("Compiling single block", i)
+                        m.add_object_patch(f"diffusion_model.single_blocks.{i}", torch.compile(block, mode=mode, dynamic=dynamic, fullgraph=fullgraph, backend=backend))
+                self._compiled = True
+                compile_settings = {
+                    "backend": backend,
+                    "mode": mode,
+                    "fullgraph": fullgraph,
+                    "dynamic": dynamic,
+                }
+                setattr(m.model, "compile_settings", compile_settings)
+            except:
+                raise RuntimeError("Failed to compile model")
+        
+        return (m, )
+        # rest of the layers that are not patched
+        # diffusion_model.final_layer = torch.compile(diffusion_model.final_layer, mode=mode, fullgraph=fullgraph, backend=backend)
+        # diffusion_model.guidance_in = torch.compile(diffusion_model.guidance_in, mode=mode, fullgraph=fullgraph, backend=backend)
+        # diffusion_model.img_in = torch.compile(diffusion_model.img_in, mode=mode, fullgraph=fullgraph, backend=backend)
+        # diffusion_model.time_in = torch.compile(diffusion_model.time_in, mode=mode, fullgraph=fullgraph, backend=backend)
+        # diffusion_model.txt_in = torch.compile(diffusion_model.txt_in, mode=mode, fullgraph=fullgraph, backend=backend)
+        # diffusion_model.vector_in = torch.compile(diffusion_model.vector_in, mode=mode, fullgraph=fullgraph, backend=backend)
\ No newline at end of file
diff --git a/custom_nodes/RES4LYF/legacy/noise_classes.py b/custom_nodes/RES4LYF/legacy/noise_classes.py
new file mode 100644
index 0000000000000000000000000000000000000000..b47a36a00148b400459c59c9c4eb0e92728d5f51
--- /dev/null
+++ b/custom_nodes/RES4LYF/legacy/noise_classes.py
@@ -0,0 +1,683 @@
+import torch
+from torch import nn, Tensor, Generator, lerp
+from torch.nn.functional import unfold
+import torch.nn.functional as F
+from typing import Callable, Tuple
+from math import pi
+from comfy.k_diffusion.sampling import BrownianTreeNoiseSampler
+from torch.distributions import StudentT, Laplace
+import numpy as np
+import pywt
+import functools
+from ..res4lyf import RESplain
+
+# Set this to "True" if you have installed OpenSimplex. Recommended to install without dependencies due to conflicting packages: pip3 install opensimplex --no-deps 
+OPENSIMPLEX_ENABLE = False
+
+if OPENSIMPLEX_ENABLE:
+    from opensimplex import OpenSimplex
+
+class PrecisionTool:
+    def __init__(self, cast_type='fp64'):
+        self.cast_type = cast_type
+
+    def cast_tensor(self, func):
+        @functools.wraps(func)
+        def wrapper(*args, **kwargs):
+            if self.cast_type not in ['fp64', 'fp32', 'fp16']:
+                return func(*args, **kwargs)
+
+            target_device = None
+            for arg in args:
+                if torch.is_tensor(arg):
+                    target_device = arg.device
+                    break
+            if target_device is None:
+                for v in kwargs.values():
+                    if torch.is_tensor(v):
+                        target_device = v.device
+                        break
+            
+        # recursively zs_recast tensors in nested dictionaries
+            def cast_and_move_to_device(data):
+                if torch.is_tensor(data):
+                    if self.cast_type == 'fp64':
+                        return data.to(torch.float64).to(target_device)
+                    elif self.cast_type == 'fp32':
+                        return data.to(torch.float32).to(target_device)
+                    elif self.cast_type == 'fp16':
+                        return data.to(torch.float16).to(target_device)
+                elif isinstance(data, dict):
+                    return {k: cast_and_move_to_device(v) for k, v in data.items()}
+                return data
+
+            new_args = [cast_and_move_to_device(arg) for arg in args]
+            new_kwargs = {k: cast_and_move_to_device(v) for k, v in kwargs.items()}
+            
+            return func(*new_args, **new_kwargs)
+        return wrapper
+
+    def set_cast_type(self, new_value):
+        if new_value in ['fp64', 'fp32', 'fp16']:
+            self.cast_type = new_value
+        else:
+            self.cast_type = 'fp64'
+
+precision_tool = PrecisionTool(cast_type='fp64')
+
+
+def noise_generator_factory(cls, **fixed_params):
+    def create_instance(**kwargs):
+        params = {**fixed_params, **kwargs}
+        return cls(**params)
+    return create_instance
+
+def like(x):
+    return {'size': x.shape, 'dtype': x.dtype, 'layout': x.layout, 'device': x.device}
+
+def scale_to_range(x, scaled_min = -1.73, scaled_max = 1.73): #1.73 is roughly the square root of 3
+    return scaled_min + (x - x.min()) * (scaled_max - scaled_min) / (x.max() - x.min())
+
+def normalize(x):
+     return (x - x.mean())/ x.std()
+
+class NoiseGenerator:
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None):
+        self.seed = seed
+
+        if x is not None:
+            self.x      = x
+            self.size   = x.shape
+            self.dtype  = x.dtype
+            self.layout = x.layout
+            self.device = x.device
+        else:   
+            self.x      = torch.zeros(size, dtype, layout, device)
+
+        # allow overriding parameters imported from latent 'x' if specified
+        if size is not None:
+            self.size   = size
+        if dtype is not None:
+            self.dtype  = dtype
+        if layout is not None:
+            self.layout = layout
+        if device is not None:
+            self.device = device
+
+        self.sigma_max = sigma_max.to(device) if isinstance(sigma_max, torch.Tensor) else sigma_max
+        self.sigma_min = sigma_min.to(device) if isinstance(sigma_min, torch.Tensor) else sigma_min
+
+        self.last_seed = seed
+        
+        if generator is None:
+            self.generator = torch.Generator(device=self.device).manual_seed(seed)
+        else:
+            self.generator = generator
+
+    def __call__(self):
+        raise NotImplementedError("This method got clownsharked!")
+    
+    def update(self, **kwargs):
+        
+        if not isinstance(self, BrownianNoiseGenerator):
+            self.last_seed += 1
+        
+        updated_values = []
+        for attribute_name, value in kwargs.items():
+            if value is not None:
+                setattr(self, attribute_name, value)
+            updated_values.append(getattr(self, attribute_name))
+        return tuple(updated_values)
+
+
+
+class BrownianNoiseGenerator(NoiseGenerator):
+    def __call__(self, *, sigma=None, sigma_next=None, **kwargs):
+        return BrownianTreeNoiseSampler(self.x, self.sigma_min, self.sigma_max, seed=self.seed, cpu = self.device.type=='cpu')(sigma, sigma_next)
+
+
+
+class FractalNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                alpha=0.0, k=1.0, scale=0.1): 
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.update(alpha=alpha, k=k, scale=scale)
+
+    def __call__(self, *, alpha=None, k=None, scale=None, **kwargs):
+        self.update(alpha=alpha, k=k, scale=scale)
+        if len(self.size) == 5:
+            b, c, t, h, w = self.size
+        else:
+            b, c, h, w = self.size
+        
+        noise = torch.normal(mean=0.0, std=1.0, size=self.size, dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator)
+        
+        y_freq = torch.fft.fftfreq(h, 1/h, device=self.device)
+        x_freq = torch.fft.fftfreq(w, 1/w, device=self.device)
+
+        if len(self.size) == 5:
+            t_freq = torch.fft.fftfreq(t, 1/t, device=self.device)
+            freq = torch.sqrt(y_freq[:, None, None]**2 + x_freq[None, :, None]**2 + t_freq[None, None, :]**2).clamp(min=1e-10)
+        else:
+            freq = torch.sqrt(y_freq[:, None]**2 + x_freq[None, :]**2).clamp(min=1e-10)
+        
+        spectral_density = self.k / torch.pow(freq, self.alpha * self.scale)
+        spectral_density[0, 0] = 0
+
+        noise_fft = torch.fft.fftn(noise)
+        modified_fft = noise_fft * spectral_density
+        noise = torch.fft.ifftn(modified_fft).real
+
+        return noise / torch.std(noise)
+    
+    
+
+class SimplexNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                 scale=0.01):
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.noise = OpenSimplex(seed=seed)
+        self.scale = scale
+        
+    def __call__(self, *, scale=None, **kwargs):
+        self.update(scale=scale)
+        
+        if len(self.size) == 5:
+            b, c, t, h, w = self.size
+        else:
+            b, c, h, w = self.size
+
+        noise_array = self.noise.noise3array(np.arange(w),np.arange(h),np.arange(c))
+        self.noise = OpenSimplex(seed=self.noise.get_seed()+1)
+        
+        noise_tensor = torch.from_numpy(noise_array).to(self.device)
+        noise_tensor = torch.unsqueeze(noise_tensor, dim=0)
+        if len(self.size) == 5:
+            noise_tensor = torch.unsqueeze(noise_tensor, dim=0)
+        
+        return noise_tensor / noise_tensor.std()
+        #return normalize(scale_to_range(noise_tensor))
+
+
+
+class HiresPyramidNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                 discount=0.7, mode='nearest-exact'):
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.update(discount=discount, mode=mode)
+
+    def __call__(self, *, discount=None, mode=None, **kwargs):
+        self.update(discount=discount, mode=mode)
+
+        if len(self.size) == 5:
+            b, c, t, h, w = self.size
+            orig_h, orig_w, orig_t = h, w, t
+            u = nn.Upsample(size=(orig_h, orig_w, orig_t), mode=self.mode).to(self.device)
+        else:
+            b, c, h, w = self.size
+            orig_h, orig_w = h, w
+            orig_t = t = 1
+            u = nn.Upsample(size=(orig_h, orig_w), mode=self.mode).to(self.device)
+
+        noise = ((torch.rand(size=self.size, dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator) - 0.5) * 2 * 1.73)
+
+        for i in range(4):
+            r = torch.rand(1, device=self.device, generator=self.generator).item() * 2 + 2
+            h, w = min(orig_h * 15, int(h * (r ** i))), min(orig_w * 15, int(w * (r ** i)))
+            if len(self.size) == 5:
+                t = min(orig_t * 15, int(t * (r ** i)))
+                new_noise = torch.randn((b, c, t, h, w), dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator)
+            else:
+                new_noise = torch.randn((b, c, h, w), dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator)
+
+            upsampled_noise = u(new_noise)
+            noise += upsampled_noise * self.discount ** i
+            
+            if h >= orig_h * 15 or w >= orig_w * 15 or t >= orig_t * 15:
+                break  # if resolution is too high
+        
+        return noise / noise.std()
+
+
+
+class PyramidNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                 discount=0.8, mode='nearest-exact'):
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.update(discount=discount, mode=mode)
+
+    def __call__(self, *, discount=None, mode=None, **kwargs):
+        self.update(discount=discount, mode=mode)
+
+        x = torch.zeros(self.size, dtype=self.dtype, layout=self.layout, device=self.device)
+
+        if len(self.size) == 5:
+            b, c, t, h, w = self.size
+            orig_h, orig_w, orig_t = h, w, t
+        else:
+            b, c, h, w = self.size
+            orig_h, orig_w = h, w
+
+        r = 1
+        for i in range(5):
+            r *= 2
+
+            if len(self.size) == 5:
+                scaledSize = (b, c, t * r, h * r, w * r)
+                origSize = (orig_h, orig_w, orig_t)
+            else:
+                scaledSize = (b, c, h * r, w * r)
+                origSize = (orig_h, orig_w)
+
+            x += torch.nn.functional.interpolate(
+                torch.normal(mean=0, std=0.5 ** i, size=scaledSize, dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator),
+                size=origSize, mode=self.mode
+            ) * self.discount ** i
+        return x / x.std()
+
+
+
+class InterpolatedPyramidNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                 discount=0.7, mode='nearest-exact'):
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.update(discount=discount, mode=mode)
+
+
+    def __call__(self, *, discount=None, mode=None, **kwargs):
+        self.update(discount=discount, mode=mode)
+
+        if len(self.size) == 5:
+            b, c, t, h, w = self.size
+            orig_t, orig_h, orig_w = t, h, w
+        else:
+            b, c, h, w = self.size
+            orig_h, orig_w = h, w
+            t = orig_t = 1
+
+        noise = ((torch.rand(size=self.size, dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator) - 0.5) * 2 * 1.73)
+        multipliers = [1]
+
+        for i in range(4):
+            r = torch.rand(1, device=self.device, generator=self.generator).item() * 2 + 2
+            h, w = min(orig_h * 15, int(h * (r ** i))), min(orig_w * 15, int(w * (r ** i)))
+            
+            if len(self.size) == 5:
+                t = min(orig_t * 15, int(t * (r ** i)))
+                new_noise = torch.randn((b, c, t, h, w), dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator)
+                upsampled_noise = nn.functional.interpolate(new_noise, size=(orig_t, orig_h, orig_w), mode=self.mode)
+            else:
+                new_noise = torch.randn((b, c, h, w), dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator)
+                upsampled_noise = nn.functional.interpolate(new_noise, size=(orig_h, orig_w), mode=self.mode)
+
+            noise += upsampled_noise * self.discount ** i
+            multipliers.append(self.discount ** i)
+            
+            if h >= orig_h * 15 or w >= orig_w * 15 or (len(self.size) == 5 and t >= orig_t * 15):
+                break  # if resolution is too high
+        
+        noise = noise / sum([m ** 2 for m in multipliers]) ** 0.5 
+        return noise / noise.std()
+
+
+
+class CascadeBPyramidNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                 levels=10, mode='nearest', size_range=[1,16]):
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.update(epsilon=x, levels=levels, mode=mode, size_range=size_range)
+
+    def __call__(self, *, levels=10, mode='nearest', size_range=[1,16], **kwargs):
+        self.update(levels=levels, mode=mode)
+
+        b, c, h, w = self.size
+
+        epsilon = torch.randn(self.size, dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator)
+        multipliers = [1]
+        for i in range(1, levels):
+            m = 0.75 ** i
+
+            h, w = int(epsilon.size(-2) // (2 ** i)), int(epsilon.size(-2) // (2 ** i))
+            if size_range is None or (size_range[0] <= h <= size_range[1] or size_range[0] <= w <= size_range[1]):
+                offset = torch.randn(epsilon.size(0), epsilon.size(1), h, w, device=self.device, generator=self.generator)
+                epsilon = epsilon + torch.nn.functional.interpolate(offset, size=epsilon.shape[-2:], mode=self.mode) * m
+                multipliers.append(m)
+
+            if h <= 1 or w <= 1:
+                break
+        epsilon = epsilon / sum([m ** 2 for m in multipliers]) ** 0.5 #divides the epsilon tensor by the square root of the sum of the squared multipliers.
+
+        return epsilon
+
+
+class UniformNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                 mean=0.0, scale=1.73):
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.update(mean=mean, scale=scale)
+
+    def __call__(self, *, mean=None, scale=None, **kwargs):
+        self.update(mean=mean, scale=scale)
+
+        noise = torch.rand(self.size, dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator)
+
+        return self.scale * 2 * (noise - 0.5) + self.mean
+
+class GaussianNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                 mean=0.0, std=1.0):
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.update(mean=mean, std=std)
+
+    def __call__(self, *, mean=None, std=None, **kwargs):
+        self.update(mean=mean, std=std)
+
+        noise = torch.randn(self.size, dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator)
+
+        return (noise - noise.mean()) / noise.std()
+
+class GaussianBackwardsNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                 mean=0.0, std=1.0):
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.update(mean=mean, std=std)
+
+    def __call__(self, *, mean=None, std=None, **kwargs):
+        self.update(mean=mean, std=std)
+        RESplain("GaussianBackwards last seed:", self.generator.initial_seed())
+        self.generator.manual_seed(self.generator.initial_seed() - 1)
+        noise = torch.randn(self.size, dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator)
+
+        return (noise - noise.mean()) / noise.std()
+
+class LaplacianNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                 loc=0, scale=1.0):
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.update(loc=loc, scale=scale)
+
+    def __call__(self, *, loc=None, scale=None, **kwargs):
+        self.update(loc=loc, scale=scale)
+
+        # b, c, h, w = self.size
+        # orig_h, orig_w = h, w
+
+        noise = torch.randn(self.size, dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator) / 4.0
+
+        rng_state = torch.random.get_rng_state()
+        torch.manual_seed(self.generator.initial_seed())
+        laplacian_noise = Laplace(loc=self.loc, scale=self.scale).rsample(self.size).to(self.device)
+        self.generator.manual_seed(self.generator.initial_seed() + 1)
+        torch.random.set_rng_state(rng_state)
+
+        noise += laplacian_noise
+        return noise / noise.std()
+
+class StudentTNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                 loc=0, scale=0.2, df=1):
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.update(loc=loc, scale=scale, df=df)
+
+    def __call__(self, *, loc=None, scale=None, df=None, **kwargs):
+        self.update(loc=loc, scale=scale, df=df)
+
+        # b, c, h, w = self.size
+        # orig_h, orig_w = h, w
+
+        rng_state = torch.random.get_rng_state()
+        torch.manual_seed(self.generator.initial_seed())
+
+        noise = StudentT(loc=self.loc, scale=self.scale, df=self.df).rsample(self.size)
+
+        s = torch.quantile(noise.flatten(start_dim=1).abs(), 0.75, dim=-1)
+        
+        if len(self.size) == 5:
+            s = s.reshape(*s.shape, 1, 1, 1, 1)
+        else:
+            s = s.reshape(*s.shape, 1, 1, 1)
+
+        noise = noise.clamp(-s, s)
+        noise_latent = torch.copysign(torch.pow(torch.abs(noise), 0.5), noise).to(self.device)
+
+        self.generator.manual_seed(self.generator.initial_seed() + 1)
+        torch.random.set_rng_state(rng_state)
+        return (noise_latent - noise_latent.mean()) / noise_latent.std()
+
+class WaveletNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                 wavelet='haar'):
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.update(wavelet=wavelet)
+
+    def __call__(self, *, wavelet=None, **kwargs):
+        self.update(wavelet=wavelet)
+
+        # b, c, h, w = self.size
+        # orig_h, orig_w = h, w
+
+        # noise for spatial dimensions only
+        coeffs = pywt.wavedecn(torch.randn(self.size, dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator).to(self.device), wavelet=self.wavelet, mode='periodization')
+        noise = pywt.waverecn(coeffs, wavelet=self.wavelet, mode='periodization')
+        noise_tensor = torch.tensor(noise, dtype=self.dtype, device=self.device)
+
+        noise_tensor = (noise_tensor - noise_tensor.mean()) / noise_tensor.std()
+        return noise_tensor
+
+class PerlinNoiseGenerator(NoiseGenerator):
+    def __init__(self, x=None, size=None, dtype=None, layout=None, device=None, seed=42, generator=None, sigma_min=None, sigma_max=None, 
+                 detail=0.0):
+        super().__init__(x, size, dtype, layout, device, seed, generator, sigma_min, sigma_max)
+        self.update(detail=detail)
+
+    @staticmethod
+    def get_positions(block_shape: Tuple[int, int]) -> Tensor:
+        bh, bw = block_shape
+        positions = torch.stack(
+            torch.meshgrid(
+                [(torch.arange(b) + 0.5) / b for b in (bw, bh)],
+                indexing="xy",
+            ),
+            -1,
+        ).view(1, bh, bw, 1, 1, 2)
+        return positions
+
+    @staticmethod
+    def unfold_grid(vectors: Tensor) -> Tensor:
+        batch_size, _, gpy, gpx = vectors.shape
+        return (
+            unfold(vectors, (2, 2))
+            .view(batch_size, 2, 4, -1)
+            .permute(0, 2, 3, 1)
+            .view(batch_size, 4, gpy - 1, gpx - 1, 2)
+        )
+
+    @staticmethod
+    def smooth_step(t: Tensor) -> Tensor:
+        return t * t * (3.0 - 2.0 * t)
+
+    @staticmethod
+    def perlin_noise_tensor(
+        self,
+        vectors: Tensor, positions: Tensor, step: Callable = None
+    ) -> Tensor:
+        if step is None:
+            step = self.smooth_step
+
+        batch_size = vectors.shape[0]
+        # grid height, grid width
+        gh, gw = vectors.shape[2:4]
+        # block height, block width
+        bh, bw = positions.shape[1:3]
+
+        for i in range(2):
+            if positions.shape[i + 3] not in (1, vectors.shape[i + 2]):
+                raise Exception(
+                    f"Blocks shapes do not match: vectors ({vectors.shape[1]}, {vectors.shape[2]}), positions {gh}, {gw})"
+                )
+
+        if positions.shape[0] not in (1, batch_size):
+            raise Exception(
+                f"Batch sizes do not match: vectors ({vectors.shape[0]}), positions ({positions.shape[0]})"
+            )
+
+        vectors = vectors.view(batch_size, 4, 1, gh * gw, 2)
+        positions = positions.view(positions.shape[0], bh * bw, -1, 2)
+
+        step_x = step(positions[..., 0])
+        step_y = step(positions[..., 1])
+
+        row0 = lerp(
+            (vectors[:, 0] * positions).sum(dim=-1),
+            (vectors[:, 1] * (positions - positions.new_tensor((1, 0)))).sum(dim=-1),
+            step_x,
+        )
+        row1 = lerp(
+            (vectors[:, 2] * (positions - positions.new_tensor((0, 1)))).sum(dim=-1),
+            (vectors[:, 3] * (positions - positions.new_tensor((1, 1)))).sum(dim=-1),
+            step_x,
+        )
+        noise = lerp(row0, row1, step_y)
+        return (
+            noise.view(
+                batch_size,
+                bh,
+                bw,
+                gh,
+                gw,
+            )
+            .permute(0, 3, 1, 4, 2)
+            .reshape(batch_size, gh * bh, gw * bw)
+        )
+
+    def perlin_noise(
+        self,
+        grid_shape: Tuple[int, int],
+        out_shape: Tuple[int, int],
+        batch_size: int = 1,
+        generator: Generator = None,
+        *args,
+        **kwargs,
+    ) -> Tensor:
+        gh, gw = grid_shape         # grid height and width
+        oh, ow = out_shape        # output height and width
+        bh, bw = oh // gh, ow // gw        # block height and width
+
+        if oh != bh * gh:
+            raise Exception(f"Output height {oh} must be divisible by grid height {gh}")
+        if ow != bw * gw != 0:
+            raise Exception(f"Output width {ow} must be divisible by grid width {gw}")
+
+        angle = torch.empty(
+            [batch_size] + [s + 1 for s in grid_shape], device=self.device, *args, **kwargs
+        ).uniform_(to=2.0 * pi, generator=self.generator)
+        # random vectors on grid points
+        vectors = self.unfold_grid(torch.stack((torch.cos(angle), torch.sin(angle)), dim=1))
+        # positions inside grid cells [0, 1)
+        positions = self.get_positions((bh, bw)).to(vectors)
+        return self.perlin_noise_tensor(self, vectors, positions).squeeze(0)
+
+    def __call__(self, *, detail=None, **kwargs):
+        self.update(detail=detail) #currently unused
+
+        if len(self.size) == 5:
+            b, c, t, h, w = self.size
+            noise = torch.randn(self.size, dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator) / 2.0
+            
+            for tt in range(t):
+                for i in range(2):
+                    perlin_slice = self.perlin_noise((h, w), (h, w), batch_size=c, generator=self.generator).to(self.device)
+                    perlin_expanded = perlin_slice.unsqueeze(0).unsqueeze(2)
+                    time_slice = noise[:, :, tt:tt+1, :, :]
+                    noise[:, :, tt:tt+1, :, :] += perlin_expanded
+        else:
+            b, c, h, w = self.size
+            #orig_h, orig_w = h, w
+
+            noise = torch.randn(self.size, dtype=self.dtype, layout=self.layout, device=self.device, generator=self.generator) / 2.0
+            for i in range(2):
+                noise += self.perlin_noise((h, w), (h, w), batch_size=c, generator=self.generator).to(self.device)
+                
+        return noise / noise.std()
+    
+from functools import partial
+
+NOISE_GENERATOR_CLASSES = {
+    "fractal": FractalNoiseGenerator,
+
+    "gaussian": GaussianNoiseGenerator,
+    "gaussian_backwards": GaussianBackwardsNoiseGenerator,
+    "uniform": UniformNoiseGenerator,
+    "pyramid-cascade_B": CascadeBPyramidNoiseGenerator,
+    "pyramid-interpolated": InterpolatedPyramidNoiseGenerator,
+    "pyramid-bilinear": noise_generator_factory(PyramidNoiseGenerator, mode='bilinear'),
+    "pyramid-bicubic": noise_generator_factory(PyramidNoiseGenerator, mode='bicubic'),   
+    "pyramid-nearest": noise_generator_factory(PyramidNoiseGenerator, mode='nearest'),  
+    "hires-pyramid-bilinear": noise_generator_factory(HiresPyramidNoiseGenerator, mode='bilinear'),
+    "hires-pyramid-bicubic": noise_generator_factory(HiresPyramidNoiseGenerator, mode='bicubic'),   
+    "hires-pyramid-nearest": noise_generator_factory(HiresPyramidNoiseGenerator, mode='nearest'),  
+    "brownian": BrownianNoiseGenerator,
+    "laplacian": LaplacianNoiseGenerator,
+    "studentt": StudentTNoiseGenerator,
+    "wavelet": WaveletNoiseGenerator,
+    "perlin": PerlinNoiseGenerator,
+}
+
+
+NOISE_GENERATOR_CLASSES_SIMPLE = {
+    "none": GaussianNoiseGenerator,
+    "brownian": BrownianNoiseGenerator,
+    "gaussian": GaussianNoiseGenerator,
+    "gaussian_backwards": GaussianBackwardsNoiseGenerator,
+    "laplacian": LaplacianNoiseGenerator,
+    "perlin": PerlinNoiseGenerator,
+    "studentt": StudentTNoiseGenerator,
+    "uniform": UniformNoiseGenerator,
+    "wavelet": WaveletNoiseGenerator,
+    "brown": noise_generator_factory(FractalNoiseGenerator, alpha=2.0),
+    "pink": noise_generator_factory(FractalNoiseGenerator, alpha=1.0),
+    "white": noise_generator_factory(FractalNoiseGenerator, alpha=0.0),
+    "blue": noise_generator_factory(FractalNoiseGenerator, alpha=-1.0),
+    "violet": noise_generator_factory(FractalNoiseGenerator, alpha=-2.0),
+    "hires-pyramid-bicubic": noise_generator_factory(HiresPyramidNoiseGenerator, mode='bicubic'),   
+    "hires-pyramid-bilinear": noise_generator_factory(HiresPyramidNoiseGenerator, mode='bilinear'),
+    "hires-pyramid-nearest": noise_generator_factory(HiresPyramidNoiseGenerator, mode='nearest'),  
+    "pyramid-bicubic": noise_generator_factory(PyramidNoiseGenerator, mode='bicubic'),   
+    "pyramid-bilinear": noise_generator_factory(PyramidNoiseGenerator, mode='bilinear'),
+    "pyramid-nearest": noise_generator_factory(PyramidNoiseGenerator, mode='nearest'),  
+    "pyramid-interpolated": InterpolatedPyramidNoiseGenerator,
+    "pyramid-cascade_B": CascadeBPyramidNoiseGenerator,
+}
+
+if OPENSIMPLEX_ENABLE:
+    NOISE_GENERATOR_CLASSES.update({
+        "simplex": SimplexNoiseGenerator,
+    })
+
+NOISE_GENERATOR_NAMES = tuple(NOISE_GENERATOR_CLASSES.keys())
+NOISE_GENERATOR_NAMES_SIMPLE = tuple(NOISE_GENERATOR_CLASSES_SIMPLE.keys())
+
+
+@precision_tool.cast_tensor
+def prepare_noise(latent_image, seed, noise_type, noise_inds=None, alpha=1.0, k=1.0): # adapted from comfy/sample.py: https://github.com/comfyanonymous/ComfyUI
+    #optional arg skip can be used to skip and discard x number of noise generations for a given seed
+    noise_func = NOISE_GENERATOR_CLASSES.get(noise_type)(x=latent_image, seed=seed, sigma_min=0.0291675, sigma_max=14.614642)
+
+    if noise_type == "fractal":
+        noise_func.alpha = alpha
+        noise_func.k = k
+
+    # from here until return is very similar to comfy/sample.py 
+    if noise_inds is None:
+        return noise_func(sigma=14.614642, sigma_next=0.0291675)
+
+    unique_inds, inverse = np.unique(noise_inds, return_inverse=True)
+    noises = []
+    for i in range(unique_inds[-1]+1):
+        noise = noise_func(size = [1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, device=latent_image.device)
+        if i in unique_inds:
+            noises.append(noise)
+    noises = [noises[i] for i in inverse]
+    noises = torch.cat(noises, axis=0)
+    return noises
+
diff --git a/custom_nodes/RES4LYF/legacy/noise_sigmas_timesteps_scaling.py b/custom_nodes/RES4LYF/legacy/noise_sigmas_timesteps_scaling.py
new file mode 100644
index 0000000000000000000000000000000000000000..101b868a348d3211679287ab586049120aeb00e5
--- /dev/null
+++ b/custom_nodes/RES4LYF/legacy/noise_sigmas_timesteps_scaling.py
@@ -0,0 +1,225 @@
+import torch
+#from..noise_classes import *
+import comfy.model_patcher
+from .helper import has_nested_attr
+
+def get_alpha_ratio_from_sigma_up(sigma_up, sigma_next, eta, sigma_max=1.0):
+    if sigma_up >= sigma_next and sigma_next > 0:
+        print("Maximum VPSDE noise level exceeded: falling back to hard noise mode.")
+        # Values below are the theoretical max, but break with exponential integrator stepsize calcs:
+        #sigma_up = sigma_next
+        #alpha_ratio = sigma_max - sigma_next
+        #sigma_down = 0 * sigma_next
+        #return alpha_ratio, sigma_up, sigma_down
+        if eta >= 1:
+            sigma_up = sigma_next * 0.9999 #avoid sqrt(neg_num) later 
+        else:
+            sigma_up = sigma_next * eta 
+        
+    sigma_signal = sigma_max - sigma_next
+    sigma_residual = torch.sqrt(sigma_next**2 - sigma_up**2)
+
+    alpha_ratio = sigma_signal + sigma_residual
+    sigma_down = sigma_residual / alpha_ratio
+    return alpha_ratio, sigma_up, sigma_down
+
+def get_alpha_ratio_from_sigma_down(sigma_down, sigma_next, eta, sigma_max=1.0):
+    alpha_ratio = (1 - sigma_next) / (1 - sigma_down) 
+    sigma_up = (sigma_next ** 2 - sigma_down ** 2 * alpha_ratio ** 2) ** 0.5 
+    
+    if sigma_up >= sigma_next: # "clamp" noise level to max if max exceeded
+        alpha_ratio, sigma_up, sigma_down = get_alpha_ratio_from_sigma_up(sigma_up, sigma_next, eta, sigma_max)
+      
+    return alpha_ratio, sigma_up, sigma_down
+  
+def get_ancestral_step_RF_var(sigma, sigma_next, eta, sigma_max=1.0):
+    dtype = sigma.dtype #calculate variance adjusted sigma up... sigma_up = sqrt(dt)
+
+    sigma, sigma_next = sigma.to(torch.float64), sigma_next.to(torch.float64) # float64 is very important to avoid numerical precision issues
+
+    sigma_diff = (sigma - sigma_next).abs() + 1e-10 
+    sigma_up = torch.sqrt(sigma_diff).to(torch.float64) * eta
+
+    sigma_down_num = (sigma_next**2 - sigma_up**2).to(torch.float64)
+    sigma_down = torch.sqrt(sigma_down_num) / ((1 - sigma_next).to(torch.float64) + torch.sqrt(sigma_down_num).to(torch.float64))
+
+    alpha_ratio = (1 - sigma_next).to(torch.float64) / (1 - sigma_down).to(torch.float64)
+    
+    return sigma_up.to(dtype),  sigma_down.to(dtype), alpha_ratio.to(dtype)
+  
+def get_ancestral_step_RF_lorentzian(sigma, sigma_next, eta, sigma_max=1.0):
+    dtype = sigma.dtype
+    alpha = 1 / ((sigma.to(torch.float64))**2 + 1)
+    sigma_up = eta * (1 - alpha) ** 0.5
+    alpha_ratio, sigma_up, sigma_down = get_alpha_ratio_from_sigma_up(sigma_up, sigma_next, eta, sigma_max)
+    return sigma_up.to(dtype),  sigma_down.to(dtype), alpha_ratio.to(dtype)
+
+def get_ancestral_step_EPS(sigma, sigma_next, eta=1.):
+    # Calculates the noise level (sigma_down) to step down to and the amount of noise to add (sigma_up) when doing an ancestral sampling step.
+    alpha_ratio = torch.full_like(sigma, 1.0)
+    
+    if not eta or not sigma_next:
+        return torch.full_like(sigma, 0.0), sigma_next, alpha_ratio
+      
+    sigma_up = min(sigma_next, eta * (sigma_next ** 2 * (sigma ** 2 - sigma_next ** 2) / sigma ** 2) ** 0.5)
+    sigma_down = (sigma_next ** 2 - sigma_up ** 2) ** 0.5
+    
+    return sigma_up, sigma_down, alpha_ratio
+
+def get_ancestral_step_RF_sinusoidal(sigma_next, eta, sigma_max=1.0):
+    sigma_up = eta * sigma_next * torch.sin(torch.pi * sigma_next) ** 2 
+    alpha_ratio, sigma_up, sigma_down = get_alpha_ratio_from_sigma_up(sigma_up, sigma_next, eta, sigma_max)
+    return sigma_up, sigma_down, alpha_ratio
+
+def get_ancestral_step_RF_softer(sigma, sigma_next, eta, sigma_max=1.0):
+    # math adapted from get_ancestral_step_EPS to work with RF
+    sigma_down = sigma_next * torch.sqrt(1 - (eta**2 * (sigma**2 - sigma_next**2)) / sigma**2)
+    alpha_ratio, sigma_up, sigma_down = get_alpha_ratio_from_sigma_down(sigma_down, sigma_next, eta, sigma_max)
+    return sigma_up, sigma_down, alpha_ratio
+
+def get_ancestral_step_RF_soft(sigma, sigma_next, eta, sigma_max=1.0):
+    """Calculates the noise level (sigma_down) to step down to and the amount of noise to add (sigma_up) when doing a rectified flow sampling step, 
+    and a mixing ratio (alpha_ratio) for scaling the latent during noise addition. Scale is to shape the sigma_down curve."""
+    down_ratio = (1 - eta) + eta * ((sigma_next) / sigma)
+    sigma_down = down_ratio * sigma_next
+    alpha_ratio, sigma_up, sigma_down = get_alpha_ratio_from_sigma_down(sigma_down, sigma_next, eta, sigma_max)
+    return sigma_up, sigma_down, alpha_ratio
+
+def get_ancestral_step_RF_soft_linear(sigma, sigma_next, eta, sigma_max=1.0):
+    sigma_down = sigma_next + eta * (sigma_next - sigma)
+    if sigma_down < 0:
+        return torch.full_like(sigma, 0.), sigma_next, torch.full_like(sigma, 1.)
+    alpha_ratio, sigma_up, sigma_down = get_alpha_ratio_from_sigma_down(sigma_down, sigma_next, eta, sigma_max)
+
+    return sigma_up, sigma_down, alpha_ratio
+
+def get_ancestral_step_RF_exp(sigma, sigma_next, eta, sigma_max=1.0): # TODO: fix black image issue with linear RK
+    h = -torch.log(sigma_next/sigma)
+    sigma_up = sigma_next * (1 - (-2*eta*h).exp())**0.5 
+    alpha_ratio, sigma_up, sigma_down = get_alpha_ratio_from_sigma_up(sigma_up, sigma_next, eta, sigma_max)
+    return sigma_up, sigma_down, alpha_ratio
+
+def get_ancestral_step_RF_sqrd(sigma, sigma_next, eta, sigma_max=1.0):
+    sigma_hat = sigma * (1 + eta)
+    sigma_up = (sigma_hat ** 2 - sigma ** 2) ** .5
+    alpha_ratio, sigma_up, sigma_down = get_alpha_ratio_from_sigma_up(sigma_up, sigma_next, eta, sigma_max)
+    return sigma_up, sigma_down, alpha_ratio
+  
+def get_ancestral_step_RF_hard(sigma_next, eta, sigma_max=1.0):
+    sigma_up = sigma_next * eta 
+    alpha_ratio, sigma_up, sigma_down = get_alpha_ratio_from_sigma_up(sigma_up, sigma_next, eta, sigma_max)
+    return sigma_up, sigma_down, alpha_ratio
+
+def get_vpsde_step_RF(sigma, sigma_next, eta, sigma_max=1.0):
+    dt = sigma - sigma_next
+    sigma_up = eta * sigma * dt**0.5
+    alpha_ratio = 1 - dt * (eta**2/4) * (1 + sigma)
+    sigma_down = sigma_next - (eta/4)*sigma*(1-sigma)*(sigma - sigma_next)
+    return sigma_up, sigma_down, alpha_ratio
+  
+def get_fuckery_step_RF(sigma, sigma_next, eta, sigma_max=1.0):
+    sigma_down = (1-eta) * sigma_next
+    sigma_up = torch.sqrt(sigma_next**2 - sigma_down**2)
+    alpha_ratio = torch.ones_like(sigma_next)
+    return sigma_up, sigma_down, alpha_ratio
+
+
+def get_res4lyf_step_with_model(model, sigma, sigma_next, eta=0.0, noise_mode="hard"):
+  
+    su, sd, alpha_ratio = torch.zeros_like(sigma), sigma_next.clone(), torch.ones_like(sigma)
+    
+    if has_nested_attr(model, "inner_model.inner_model.model_sampling"):
+        model_sampling = model.inner_model.inner_model.model_sampling
+    elif has_nested_attr(model, "model.model_sampling"):
+        model_sampling = model.model.model_sampling
+    
+    if isinstance(model_sampling, comfy.model_sampling.CONST):
+        sigma_var = (-1 + torch.sqrt(1 + 4 * sigma)) / 2                      # sigma_var = (torch.sqrt(1 + 4 * sigma) - 1) / 2       sigma_var = ((4*sigma+1)**0.5 - 1) / 2
+        if noise_mode == "hard_var" and eta > 0.0 and sigma_next > sigma_var:
+            su, sd, alpha_ratio = get_ancestral_step_RF_var(sigma, sigma_next, eta)
+        else:
+            if   noise_mode == "soft":
+                su, sd, alpha_ratio = get_ancestral_step_RF_soft(sigma, sigma_next, eta)
+            elif noise_mode == "softer":
+                su, sd, alpha_ratio = get_ancestral_step_RF_softer(sigma, sigma_next, eta)
+            elif noise_mode == "hard_sq": 
+                su, sd, alpha_ratio = get_ancestral_step_RF_sqrd(sigma, sigma_next, eta)
+            elif noise_mode == "sinusoidal":
+                su, sd, alpha_ratio = get_ancestral_step_RF_sinusoidal(sigma_next, eta)
+            elif noise_mode == "exp": 
+                su, sd, alpha_ratio = get_ancestral_step_RF_exp(sigma, sigma_next, eta)
+            elif noise_mode == "soft-linear": 
+                su, sd, alpha_ratio = get_ancestral_step_RF_soft_linear(sigma, sigma_next, eta) 
+            elif noise_mode == "lorentzian":
+                su, sd, alpha_ratio = get_ancestral_step_RF_lorentzian(sigma, sigma_next, eta)
+            elif noise_mode == "vpsde":
+                su, sd, alpha_ratio = get_vpsde_step_RF(sigma, sigma_next, eta)
+            elif noise_mode == "fuckery":
+                su, sd, alpha_ratio = get_fuckery_step_RF(sigma, sigma_next, eta)
+            else: #elif noise_mode == "hard": #fall back to hard noise from hard_var
+                su, sd, alpha_ratio = get_ancestral_step_RF_hard(sigma_next, eta)
+    else:
+        alpha_ratio = torch.full_like(sigma, 1.0)
+        if noise_mode == "hard_sq":
+            sd = sigma_next
+            sigma_hat = sigma * (1 + eta)
+            su = (sigma_hat ** 2 - sigma ** 2) ** .5
+            sigma = sigma_hat
+        elif noise_mode == "hard":
+            su = eta * sigma_next
+            sd = (sigma_next ** 2 - su ** 2) ** 0.5
+        elif noise_mode == "exp":
+            h = -torch.log(sigma_next/sigma)
+            su = sigma_next * (1 - (-2*eta*h).exp())**0.5 
+            sd = (sigma_next ** 2 - su ** 2) ** 0.5
+        else: #if noise_mode == "soft" or noise_mode == "softer": 
+            su = min(sigma_next, eta * (sigma_next ** 2 * (sigma ** 2 - sigma_next ** 2) / sigma ** 2) ** 0.5)
+            #su, sd, alpha_ratio = get_ancestral_step_EPS(sigma, sigma_next, eta)
+            
+    
+    su = torch.nan_to_num(su, 0.0)
+    sd = torch.nan_to_num(sd, float(sigma_next))
+    alpha_ratio = torch.nan_to_num(alpha_ratio, 1.0)
+    
+    return su, sigma, sd, alpha_ratio
+
+
+
+NOISE_MODE_NAMES = ["none",
+                    "hard_sq",
+                    "hard",
+                    #"hard_down",
+                    "lorentzian", 
+                    "soft", 
+                    "soft-linear",
+                    "softer",
+                    "eps",
+                    "sinusoidal",
+                    "exp", 
+                    "vpsde",
+                    #"fuckery",
+                    "hard_var", 
+                    ]
+
+
+
+def get_res4lyf_half_step3(sigma, sigma_next, c2=0.5, c3=1.0, t_fn=None, sigma_fn=None, t_fn_formula="", sigma_fn_formula="", ):
+
+    t_fn_x     = eval(f"lambda sigma: {t_fn_formula}", {"torch": torch}) if t_fn_formula else t_fn
+    sigma_fn_x = eval(f"lambda t: {sigma_fn_formula}", {"torch": torch}) if sigma_fn_formula else sigma_fn
+        
+    t_x, t_next_x = t_fn_x(sigma), t_fn_x(sigma_next)
+    h_x = t_next_x - t_x
+
+    s2 = t_x + h_x * c2
+    s3 = t_x + h_x * c3
+    sigma_2 = sigma_fn_x(s2)
+    sigma_3 = sigma_fn_x(s3)
+
+    h = t_fn(sigma_next) - t_fn(sigma)
+    c2 = (t_fn(sigma_2) - t_fn(sigma)) / h    
+    c3 = (t_fn(sigma_3) - t_fn(sigma)) / h    
+    
+    return c2, c3
+
+
diff --git a/custom_nodes/RES4LYF/legacy/phi_functions.py b/custom_nodes/RES4LYF/legacy/phi_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..85e867f5cc95dfcad48521c34f2b6f3b777c830d
--- /dev/null
+++ b/custom_nodes/RES4LYF/legacy/phi_functions.py
@@ -0,0 +1,103 @@
+import torch
+import math
+from typing import Optional
+
+
+# Remainder solution
+def _phi(j, neg_h):
+    remainder = torch.zeros_like(neg_h)
+
+    for k in range(j): 
+        remainder += (neg_h)**k / math.factorial(k)
+    phi_j_h = ((neg_h).exp() - remainder) / (neg_h)**j
+
+    return phi_j_h
+
+def calculate_gamma(c2, c3):
+    return (3*(c3**3) - 2*c3) / (c2*(2 - 3*c2))
+
+# Exact analytic solution originally calculated by Clybius. https://github.com/Clybius/ComfyUI-Extra-Samplers/tree/main
+def _gamma(n: int,) -> int:
+    """
+    https://en.wikipedia.org/wiki/Gamma_function
+    for every positive integer n,
+    Γ(n) = (n-1)!
+    """
+    return math.factorial(n-1)
+
+def _incomplete_gamma(s: int, x: float, gamma_s: Optional[int] = None) -> float:
+    """
+    https://en.wikipedia.org/wiki/Incomplete_gamma_function#Special_values
+    if s is a positive integer,
+    Γ(s, x) = (s-1)!*∑{k=0..s-1}(x^k/k!)
+    """
+    if gamma_s is None:
+        gamma_s = _gamma(s)
+
+    sum_: float = 0
+    # {k=0..s-1} inclusive
+    for k in range(s):
+        numerator: float = x**k
+        denom: int = math.factorial(k)
+        quotient: float = numerator/denom
+        sum_ += quotient
+    incomplete_gamma_: float = sum_ * math.exp(-x) * gamma_s
+    return incomplete_gamma_
+
+def phi(j: int, neg_h: float, ):
+    """
+    For j={1,2,3}: you could alternatively use Kat's phi_1, phi_2, phi_3 which perform fewer steps
+
+    Lemma 1
+    https://arxiv.org/abs/2308.02157
+    ϕj(-h) = 1/h^j*∫{0..h}(e^(τ-h)*(τ^(j-1))/((j-1)!)dτ)
+
+    https://www.wolframalpha.com/input?i=integrate+e%5E%28%CF%84-h%29*%28%CF%84%5E%28j-1%29%2F%28j-1%29%21%29d%CF%84
+    = 1/h^j*[(e^(-h)*(-τ)^(-j)*τ(j))/((j-1)!)]{0..h}
+    https://www.wolframalpha.com/input?i=integrate+e%5E%28%CF%84-h%29*%28%CF%84%5E%28j-1%29%2F%28j-1%29%21%29d%CF%84+between+0+and+h
+    = 1/h^j*((e^(-h)*(-h)^(-j)*h^j*(Γ(j)-Γ(j,-h)))/(j-1)!)
+    = (e^(-h)*(-h)^(-j)*h^j*(Γ(j)-Γ(j,-h))/((j-1)!*h^j)
+    = (e^(-h)*(-h)^(-j)*(Γ(j)-Γ(j,-h))/(j-1)!
+    = (e^(-h)*(-h)^(-j)*(Γ(j)-Γ(j,-h))/Γ(j)
+    = (e^(-h)*(-h)^(-j)*(1-Γ(j,-h)/Γ(j))
+
+    requires j>0
+    """
+    assert j > 0
+    gamma_: float = _gamma(j)
+    incomp_gamma_: float = _incomplete_gamma(j, neg_h, gamma_s=gamma_)
+    phi_: float = math.exp(neg_h) * neg_h**-j * (1-incomp_gamma_/gamma_)
+    return phi_
+
+
+
+class Phi:
+    def __init__(self, h, c, analytic_solution=False): 
+        self.h = h
+        self.c = c
+        self.cache = {}  
+        if analytic_solution:
+            self.phi_f = phi
+        else:
+            self.phi_f = _phi  # remainder method
+
+    def __call__(self, j, i=-1):
+        if (j, i) in self.cache:
+            return self.cache[(j, i)]
+
+        if i < 0:
+            c = 1
+        else:
+            c = self.c[i - 1]
+            if c == 0:
+                self.cache[(j, i)] = 0
+                return 0
+
+        if j == 0:
+            result = torch.exp(-self.h * c)
+        else:
+            result = self.phi_f(j, -self.h * c)
+
+        self.cache[(j, i)] = result
+
+        return result
diff --git a/custom_nodes/RES4LYF/legacy/rk_coefficients.py b/custom_nodes/RES4LYF/legacy/rk_coefficients.py
new file mode 100644
index 0000000000000000000000000000000000000000..37eff0cd5bf73965c8175fe3679fb238f1fac3f7
--- /dev/null
+++ b/custom_nodes/RES4LYF/legacy/rk_coefficients.py
@@ -0,0 +1,1793 @@
+import torch
+import copy
+import math
+
+from .deis_coefficients import get_deis_coeff_list
+from .phi_functions import *
+from .helper import get_extra_options_kv
+
+
+from itertools import permutations #, combinations
+import random
+
+RK_SAMPLER_NAMES = ["none",
+                    "res_2m",
+                    "res_3m",
+                    "res_2s", 
+                    "res_3s",
+                    "res_3s_alt",
+                    "res_3s_cox_matthews",
+                    "res_3s_lie",
+                    "res_3s_strehmel_weiner",
+                    "res_4s_krogstad",
+                    "res_4s_strehmel_weiner",
+                    "res_4s_cox_matthews",
+                    "res_4s_munthe-kaas",
+                    "res_5s",
+                    "res_6s",
+                    "res_8s",
+
+                    "res_10s",
+                    "res_15s",
+                    "res_16s",
+                    
+                    "etdrk2_2s",
+                    "etdrk3_a_3s",
+                    "etdrk3_b_3s",
+
+                    #"etdrk4_4s"
+
+                    "deis_2m",
+                    "deis_3m", 
+                    "deis_4m",
+                    
+                    "ralston_2s",
+                    "ralston_3s",
+                    "ralston_4s", 
+                    
+                    "dpmpp_2m",
+                    "dpmpp_3m",
+                    "dpmpp_2s",
+                    "dpmpp_sde_2s",
+                    "dpmpp_3s",
+                    
+                    "lawson4_4s",
+                    "genlawson41_4s",
+                    "modgenlawson41_4s",
+
+
+
+                    "midpoint_2s",
+                    "heun_2s", 
+                    "heun_3s", 
+                    
+                    "houwen-wray_3s",
+                    "kutta_3s", 
+                    "ssprk3_3s",
+                    
+                    "rk38_4s",
+                    "rk4_4s", 
+                    "rk5_7s",
+                    "rk6_7s",
+
+                    "bogacki-shampine_4s",
+                    "bogacki-shampine_7s",
+
+                    "dormand-prince_6s", 
+                    "dormand-prince_13s", 
+
+                    "tsi_7s",
+                    #"verner_robust_16s",
+
+                    "ddim",
+                    "buehler",
+                    ]
+
+
+IRK_SAMPLER_NAMES = ["none",
+                    "explicit_diagonal", 
+                    "explicit_full",
+                    
+                    "irk_exp_diag_2s",
+                    
+                    "gauss-legendre_2s",
+                    "gauss-legendre_3s", 
+                    "gauss-legendre_4s",
+                    "gauss-legendre_5s",
+                    
+                    "radau_ia_2s",
+                    "radau_ia_3s",
+                    "radau_iia_2s",
+                    "radau_iia_3s",
+                    
+                    "lobatto_iiia_2s",
+                    "lobatto_iiia_3s",
+                    "lobatto_iiib_2s",
+                    "lobatto_iiib_3s",
+                    "lobatto_iiic_2s",
+                    "lobatto_iiic_3s",
+                    "lobatto_iiic_star_2s",
+                    "lobatto_iiic_star_3s",
+                    "lobatto_iiid_2s",
+                    "lobatto_iiid_3s",
+                    
+                    "kraaijevanger_spijker_2s",
+                    "qin_zhang_2s",
+                    
+                    "pareschi_russo_2s",
+                    "pareschi_russo_alt_2s",
+                    
+                    "crouzeix_2s",
+                    "crouzeix_3s",
+                    ]
+
+alpha_crouzeix = (2/(3**0.5)) * math.cos(math.pi / 18)
+
+rk_coeff = {
+    "gauss-legendre_5s": (
+    [
+        [4563950663 / 32115191526, 
+         (310937500000000 / 2597974476091533 + 45156250000 * (739**0.5) / 8747388808389), 
+         (310937500000000 / 2597974476091533 - 45156250000 * (739**0.5) / 8747388808389),
+         (5236016175 / 88357462711 + 709703235 * (739**0.5) / 353429850844),
+         (5236016175 / 88357462711 - 709703235 * (739**0.5) / 353429850844)],
+         
+        [(4563950663 / 32115191526 - 38339103 * (739**0.5) / 6250000000),
+         (310937500000000 / 2597974476091533 + 9557056475401 * (739**0.5) / 3498955523355600000),
+         (310937500000000 / 2597974476091533 - 14074198220719489 * (739**0.5) / 3498955523355600000),
+         (5236016175 / 88357462711 + 5601362553163918341 * (739**0.5) / 2208936567775000000000),
+         (5236016175 / 88357462711 - 5040458465159165409 * (739**0.5) / 2208936567775000000000)],
+         
+        [(4563950663 / 32115191526 + 38339103 * (739**0.5) / 6250000000),
+         (310937500000000 / 2597974476091533 + 14074198220719489 * (739**0.5) / 3498955523355600000),
+         (310937500000000 / 2597974476091533 - 9557056475401 * (739**0.5) / 3498955523355600000),
+         (5236016175 / 88357462711 + 5040458465159165409 * (739**0.5) / 2208936567775000000000),
+         (5236016175 / 88357462711 - 5601362553163918341 * (739**0.5) / 2208936567775000000000)],
+         
+        [(4563950663 / 32115191526 - 38209 * (739**0.5) / 7938810),
+         (310937500000000 / 2597974476091533 - 359369071093750 * (739**0.5) / 70145310854471391),
+         (310937500000000 / 2597974476091533 - 323282178906250 * (739**0.5) / 70145310854471391),
+         (5236016175 / 88357462711 - 470139 * (739**0.5) / 1413719403376),
+         (5236016175 / 88357462711 - 44986764863 * (739**0.5) / 21205791050640)],
+         
+        [(4563950663 / 32115191526 + 38209 * (739**0.5) / 7938810),
+         (310937500000000 / 2597974476091533 + 359369071093750 * (739**0.5) / 70145310854471391),
+         (310937500000000 / 2597974476091533 + 323282178906250 * (739**0.5) / 70145310854471391),
+         (5236016175 / 88357462711 + 44986764863 * (739**0.5) / 21205791050640),
+         (5236016175 / 88357462711 + 470139 * (739**0.5) / 1413719403376)],
+    ],
+    [
+        
+        [4563950663 / 16057595763,
+         621875000000000 / 2597974476091533,
+         621875000000000 / 2597974476091533,
+         10472032350 / 88357462711,
+         10472032350 / 88357462711]
+    ],
+    [
+        1 / 2,
+        1 / 2 - 99 * (739**0.5) / 10000,
+        1 / 2 + 99 * (739**0.5) / 10000,
+        1 / 2 - (739**0.5) / 60,
+        1 / 2 + (739**0.5) / 60
+    ]
+    ),
+    "gauss-legendre_4s": (
+        [
+            [1/4, 1/4 - 15**0.5 / 6, 1/4 + 15**0.5 / 6, 1/4],            
+            [1/4 + 15**0.5 / 6, 1/4, 1/4 - 15**0.5 / 6, 1/4],          
+            [1/4, 1/4 + 15**0.5 / 6, 1/4, 1/4 - 15**0.5 / 6],            
+            [1/4 - 15**0.5 / 6, 1/4, 1/4 + 15**0.5 / 6, 1/4],       
+        ],
+        [    
+            [1/8, 3/8, 3/8, 1/8]                                        
+        ],
+        [
+            1/2 - 15**0.5 / 10,                                     
+            1/2 + 15**0.5 / 10,                                         
+            1/2 + 15**0.5 / 10,                                        
+            1/2 - 15**0.5 / 10                                         
+        ]
+    ),
+    "gauss-legendre_3s": (
+        [
+            [5/36, 2/9 - 15**0.5 / 15, 5/36 - 15**0.5 / 30],
+            [5/36 + 15**0.5 / 24, 2/9, 5/36 - 15**0.5 / 24],
+            [5/36 + 15**0.5 / 30, 2/9 + 15**0.5 / 15, 5/36],
+        ],
+        [
+            [5/18, 4/9, 5/18]
+        ],
+        [1/2 - 15**0.5 / 10, 1/2, 1/2 + 15**0.5 / 10]
+    ),
+    "gauss-legendre_2s": (
+        [
+            [1/4, 1/4 - 3**0.5 / 6],
+            [1/4 + 3**0.5 / 6, 1/4],
+        ],
+        [
+            [1/2, 1/2],
+        ],
+        [1/2 - 3**0.5 / 6, 1/2 + 3**0.5 / 6]
+    ),
+    
+    
+    "radau_iia_4s": (
+        [    
+            [],
+            [],
+            [],
+            [],
+        ],
+        [
+            [1/4, 1/4, 1/4, 1/4],
+        ],
+        [(1/11)*(4-6**0.5), (1/11)*(4+6**0.5), 1/2, 1]
+    ),
+    
+    
+    "radau_iia_3s": (
+        [    
+            [11/45 - 7*6**0.5 / 360, 37/225 - 169*6**0.5 / 1800, -2/225 + 6**0.5 / 75],
+            [37/225 + 169*6**0.5 / 1800, 11/45 + 7*6**0.5 / 360, -2/225 - 6**0.5 / 75],
+            [4/9 - 6**0.5 / 36, 4/9 + 6**0.5 / 36, 1/9],
+        ],
+        [
+            [4/9 - 6**0.5 / 36, 4/9 + 6**0.5 / 36, 1/9],
+        ],
+        [2/5 - 6**0.5 / 10, 2/5 + 6**0.5 / 10, 1.]
+    ),
+    "radau_iia_2s": (
+        [    
+            [5/12, -1/12],
+            [3/4, 1/4],
+        ],
+        [
+            [3/4, 1/4],
+        ],
+        [1/3, 1]
+    ),
+    "radau_ia_3s": (
+        [    
+            [1/9, (-1-6**0.5)/18, (-1+6**0.5)/18],
+            [1/9, 11/45 + 7*6**0.5/360, 11/45-43*6**0.5/360],
+            [1/9, 11/45-43*6**0.5/360, 11/45 + 7*6**0.5/360],
+        ],
+        [
+            [1/9, 4/9 + 6**0.5/36, 4/9 - 6**0.5/36],
+        ],
+        [0, 3/5-6**0.5/10, 3/5+6**0.5/10]
+    ),
+    "radau_ia_2s": (
+        [    
+            [1/4, -1/4],
+            [1/4, 5/12],
+        ],
+        [
+            [1/4, 3/4],
+        ],
+        [0, 2/3]
+    ),
+    
+    "lobatto_iiia_3s": (
+        [    
+            [0, 0, 0],
+            [5/24, 1/3, -1/24],
+            [1/6, 2/3, 1/6],
+        ],
+        [
+            [1/6, 2/3, 1/6],
+        ],
+        [0, 1/2, 1]
+    ),
+    "lobatto_iiia_2s": (
+        [    
+            [0, 0],
+            [1/2, 1/2],
+        ],
+        [
+            [1/2, 1/2],
+        ],
+        [0, 1]
+    ),
+    
+    
+    
+    "lobatto_iiib_3s": (
+        [    
+            [1/6, -1/6, 0],
+            [1/6, 1/3, 0],
+            [1/6, 5/6, 0],
+        ],
+        [
+            [1/6, 2/3, 1/6],
+        ],
+        [0, 1/2, 1]
+    ),
+    "lobatto_iiib_2s": (
+        [    
+            [1/2, 0],
+            [1/2, 0],
+        ],
+        [
+            [1/2, 1/2],
+        ],
+        [0, 1]
+    ),
+
+    "lobatto_iiic_3s": (
+        [    
+            [1/6, -1/3, 1/6],
+            [1/6, 5/12, -1/12],
+            [1/6, 2/3, 1/6],
+        ],
+        [
+            [1/6, 2/3, 1/6],
+        ],
+        [0, 1/2, 1]
+    ),
+    "lobatto_iiic_2s": (
+        [    
+            [1/2, -1/2],
+            [1/2, 1/2],
+        ],
+        [
+            [1/2, 1/2],
+        ],
+        [0, 1]
+    ),
+    
+
+    "lobatto_iiic_star_3s": (
+        [    
+            [0, 0, 0],
+            [1/4, 1/4, 0],
+            [0, 1, 0],
+        ],
+        [
+            [1/6, 2/3, 1/6],
+        ],
+        [0, 1/2, 1]
+    ),
+    "lobatto_iiic_star_2s": (
+        [    
+            [0, 0],
+            [1, 0],
+        ],
+        [
+            [1/2, 1/2],
+        ],
+        [0, 1]
+    ),
+    
+    "lobatto_iiid_3s": (
+        [    
+            [1/6, 0, -1/6],
+            [1/12, 5/12, 0],
+            [1/2, 1/3, 1/6],
+        ],
+        [
+            [1/6, 2/3, 1/6],
+        ],
+        [0, 1/2, 1]
+    ),
+    "lobatto_iiid_2s": (
+        [    
+            [1/2, 1/2],
+            [-1/2, 1/2],
+        ],
+        [
+            [1/2, 1/2],
+        ],
+        [0, 1]
+    ),
+    
+
+    
+    "kraaijevanger_spijker_2s": (
+        [    
+            [1/2, 0],
+            [-1/2, 2],
+        ],
+        [
+            [-1/2, 3/2],
+        ],
+        [1/2, 3/2]
+    ),
+    
+    "qin_zhang_2s": (
+        [    
+            [1/4, 0],
+            [1/2, 1/4],
+        ],
+        [
+            [1/2, 1/2],
+        ],
+        [1/4, 3/4]
+    ),
+
+    "pareschi_russo_2s": (
+        [    
+            [(1-2**0.5/2), 0],
+            [1-2*(1-2**0.5/2), (1-2**0.5/2)],
+        ],
+        [
+            [1/2, 1/2],
+        ],
+        [(1-2**0.5/2), 1-(1-2**0.5/2)]
+    ),
+
+
+    "pareschi_russo_alt_2s": (
+        [    
+            [(1-2**0.5/2), 0],
+            [1-(1-2**0.5/2), (1-2**0.5/2)],
+        ],
+        [
+            [1-(1-2**0.5/2), (1-2**0.5/2)],
+        ],
+        [(1-2**0.5/2), 1]
+    ),
+
+    "crouzeix_3s": (
+        [
+            [(1+alpha_crouzeix)/2, 0, 0],
+            [-alpha_crouzeix/2, (1+alpha_crouzeix)/2, 0],
+            [1+alpha_crouzeix, -(1+2*alpha_crouzeix), (1+alpha_crouzeix)/2],
+        ],
+        [
+            [1/(6*alpha_crouzeix**2), 1-(1/(3*alpha_crouzeix**2)), 1/(6*alpha_crouzeix**2)],
+        ],
+        [(1+alpha_crouzeix)/2,   1/2,   (1-alpha_crouzeix)/2],
+    ),
+    
+    "crouzeix_2s": (
+        [
+            [1/2 + 3**0.5 / 6, 0],
+            [-(3**0.5 / 3), 1/2 + 3**0.5 / 6]
+        ],
+        [
+            [1/2, 1/2],
+        ],
+        [1/2 + 3**0.5 / 6, 1/2 - 3**0.5 / 6],
+    ),
+    "verner_13s": ( #verner9. some values are missing, need to revise
+        [
+            [],
+        ],
+        [
+            [],
+        ],
+        [
+            0.03462,
+            0.09702435063878045,
+            0.14553652595817068,
+            0.561,
+            0.22900791159048503,
+            0.544992088409515,
+            0.645,
+            0.48375,
+            0.06757,
+            0.25,
+            0.6590650618730999,
+            0.8206,
+            0.9012,
+        ]
+    ),
+    "verner_robust_16s": (
+        [
+            [],
+            [0.04],
+            [-0.01988527319182291, 0.11637263332969652],
+            [0.0361827600517026, 0, 0.10854828015510781],
+            [2.272114264290177, 0, -8.526886447976398, 6.830772183686221],
+            [0.050943855353893744, 0, 0, 0.1755865049809071, 0.007022961270757467],
+            [0.1424783668683285, 0, 0, -0.3541799434668684, 0.07595315450295101, 0.6765157656337123],
+            [0.07111111111111111, 0, 0, 0, 0, 0.3279909287605898, 0.24089796012829906],
+            [0.07125, 0, 0, 0, 0, 0.32688424515752457, 0.11561575484247544, -0.03375],
+            [0.0482267732246581, 0, 0, 0, 0, 0.039485599804954, 0.10588511619346581, -0.021520063204743093, -0.10453742601833482],
+            [-0.026091134357549235, 0, 0, 0, 0, 0.03333333333333333, -0.1652504006638105, 0.03434664118368617, 0.1595758283215209, 0.21408573218281934],
+            [-0.03628423396255658, 0, 0, 0, 0, -1.0961675974272087, 0.1826035504321331, 0.07082254444170683, -0.02313647018482431, 0.2711204726320933, 1.3081337494229808],
+            [-0.5074635056416975, 0, 0, 0, 0, -6.631342198657237, -0.2527480100908801, -0.49526123800360955, 0.2932525545253887, 1.440108693768281, 6.237934498647056, 0.7270192054526988],
+            [0.6130118256955932, 0, 0, 0, 0, 9.088803891640463, -0.40737881562934486, 1.7907333894903747, 0.714927166761755, -1.4385808578417227, -8.26332931206474, -1.537570570808865, 0.34538328275648716],
+            [-1.2116979103438739, 0, 0, 0, 0, -19.055818715595954, 1.263060675389875, -6.913916969178458, -0.6764622665094981, 3.367860445026608, 18.00675164312591, 6.83882892679428, -1.0315164519219504, 0.4129106232130623],
+            [2.1573890074940536, 0, 0, 0, 0, 23.807122198095804, 0.8862779249216555, 13.139130397598764, -2.604415709287715, -5.193859949783872, -20.412340711541507, -12.300856252505723, 1.5215530950085394],
+        ],
+        [
+            0.014588852784055396, 0, 0, 0, 0, 0, 0, 0.0020241978878893325, 0.21780470845697167,
+            0.12748953408543898, 0.2244617745463132, 0.1787254491259903, 0.07594344758096556,
+            0.12948458791975614, 0.029477447612619417, 0
+        ],
+        [
+            0, 0.04, 0.09648736013787361, 0.1447310402068104, 0.576, 0.2272326564618766,
+            0.5407673435381234, 0.64, 0.48, 0.06754, 0.25, 0.6770920153543243, 0.8115,
+            0.906, 1, 1
+        ],
+    ),
+
+    "dormand-prince_13s": (
+        [
+            [],
+            [1/18],
+            [1/48, 1/16],
+            [1/32, 0, 3/32],
+            [5/16, 0, -75/64, 75/64],
+            [3/80, 0, 0, 3/16, 3/20],
+            [29443841/614563906, 0, 0, 77736538/692538347, -28693883/1125000000, 23124283/1800000000],
+            [16016141/946692911, 0, 0, 61564180/158732637, 22789713/633445777, 545815736/2771057229, -180193667/1043307555],
+            [39632708/573591083, 0, 0, -433636366/683701615, -421739975/2616292301, 100302831/723423059, 790204164/839813087, 800635310/3783071287],
+            [246121993/1340847787, 0, 0, -37695042795/15268766246, -309121744/1061227803, -12992083/490766935, 6005943493/2108947869, 393006217/1396673457, 123872331/1001029789],
+            [-1028468189/846180014, 0, 0, 8478235783/508512852, 1311729495/1432422823, -10304129995/1701304382, -48777925059/3047939560, 15336726248/1032824649, -45442868181/3398467696, 3065993473/597172653],
+            [185892177/718116043, 0, 0, -3185094517/667107341, -477755414/1098053517, -703635378/230739211, 5731566787/1027545527, 5232866602/850066563, -4093664535/808688257, 3962137247/1805957418, 65686358/487910083],
+            [403863854/491063109, 0, 0, -5068492393/434740067, -411421997/543043805, 652783627/914296604, 11173962825/925320556, -13158990841/6184727034, 3936647629/1978049680, -160528059/685178525, 248638103/1413531060],
+        ],
+        [
+            [14005451/335480064, 0, 0, 0, 0, -59238493/1068277825, 181606767/758867731, 561292985/797845732, -1041891430/1371343529, 760417239/1151165299, 118820643/751138087, -528747749/2220607170, 1/4],
+        ],
+        [0, 1/18, 1/12, 1/8, 5/16, 3/8, 59/400, 93/200, 5490023248 / 9719169821, 13/20, 1201146811 / 1299019798, 1, 1],
+    ),
+    "dormand-prince_6s": (
+        [
+            [],
+            [1/5],
+            [3/40, 9/40],
+            [44/45, -56/15, 32/9],
+            [19372/6561, -25360/2187, 64448/6561, -212/729],
+            [9017/3168, -355/33, 46732/5247, 49/176, -5103/18656],
+        ],
+        [
+            [35/384, 0, 500/1113, 125/192, -2187/6784, 11/84],
+        ],
+        [0, 1/5, 3/10, 4/5, 8/9, 1],
+    ),
+    "bogacki-shampine_7s": ( #5th order
+        [
+            [],
+            [1/6],
+            [2/27, 4/27],
+            [183/1372, -162/343, 1053/1372],
+            [68/297, -4/11, 42/143, 1960/3861],
+            [597/22528, 81/352, 63099/585728, 58653/366080, 4617/20480],
+            [174197/959244, -30942/79937, 8152137/19744439, 666106/1039181, -29421/29068, 482048/414219],
+        ],
+        [
+            [587/8064, 0, 4440339/15491840, 24353/124800, 387/44800, 2152/5985, 7267/94080],
+        ],
+        [0, 1/6, 2/9, 3/7, 2/3, 3/4, 1] 
+    ),
+    "bogacki-shampine_4s": ( #5th order
+        [
+            [],
+            [1/2],
+            [0, 3/4],
+            [2/9, 1/3, 4/9],
+        ],
+        [
+            [2/9, 1/3, 4/9, 0],
+        ],
+        [0, 1/2, 3/4, 1] 
+    ),
+    "tsi_7s": ( #5th order 
+        [
+            [],
+            [0.161],
+            [-0.008480655492356989, 0.335480655492357],
+            [2.8971530571054935, -6.359448489975075, 4.3622954328695815],
+            [5.325864828439257, -11.748883564062828, 7.4955393428898365, -0.09249506636175525],
+            [5.86145544294642, -12.92096931784711, 8.159367898576159, -0.071584973281401, -0.02826905039406838],
+            [0.09646076681806523, 0.01, 0.4798896504144996, 1.379008574103742, -3.290069515436081, 2.324710524099774],
+        ],
+        [
+            [0.09646076681806523, 0.01, 0.4798896504144996, 1.379008574103742, -3.290069515436081, 2.324710524099774, 0.0],
+        ],
+        [0.0, 0.161, 0.327, 0.9, 0.9800255409045097, 1.0, 1.0],
+    ),
+    "rk6_7s": ( #5th order
+        [
+            [],
+            [1/3],
+            [0, 2/3],
+            [1/12, 1/3, -1/12],
+            [-1/16, 9/8, -3/16, -3/8],
+            [0, 9/8, -3/8, -3/4, 1/2],
+            [9/44, -9/11, 63/44, 18/11, 0, -16/11],
+        ],
+        [
+            [11/120, 0, 27/40, 27/40, -4/15, -4/15, 11/120],
+        ],
+        [0, 1/3, 2/3, 1/3, 1/2, 1/2, 1],
+    ),
+    "rk5_7s": ( #5th order
+        [
+            [],
+            [1/5],
+            [3/40, 9/40],
+            [44/45, -56/15, 32/9],
+            [19372/6561, -25360/2187, 64448/6561, 212/729], #flipped 212 sign
+            [-9017/3168, -355/33, 46732/5247, 49/176, -5103/18656],
+            [35/384, 0, 500/1113, 125/192, -2187/6784, 11/84],
+        ],
+        [
+            [5179/57600, 0, 7571/16695, 393/640, -92097/339200, 187/2100, 1/40],
+        ],
+        [0, 1/5, 3/10, 4/5, 8/9, 1, 1],
+    ),
+    "ssprk_4s": ( #https://link.springer.com/article/10.1007/s41980-022-00731-x
+        [
+            [],
+            [1/2],
+            [1/2, 1/2],
+            [1/6, 1/6, 1/6],
+        ],
+        [
+            [1/6, 1/6, 1/6, 1/2],
+        ],
+        [0, 1/2, 1, 1/2],
+    ),
+
+    "rk4_4s": (
+        [
+            [],
+            [1/2],
+            [0, 1/2],
+            [0, 0, 1],
+        ],
+        [
+            [1/6, 1/3, 1/3, 1/6],
+        ],
+        [0, 1/2, 1/2, 1],
+    ),
+    "rk38_4s": (
+        [
+            [],
+            [1/3],
+            [-1/3, 1],
+            [1, -1, 1],
+        ],
+        [
+            [1/8, 3/8, 3/8, 1/8],
+        ],
+        [0, 1/3, 2/3, 1],
+    ),
+    "ralston_4s": (
+        [
+            [],
+            [2/5],
+            [(-2889+1428 * 5**0.5)/1024,   (3785-1620 * 5**0.5)/1024],
+            [(-3365+2094 * 5**0.5)/6040,   (-975-3046 * 5**0.5)/2552,  (467040+203968*5**0.5)/240845],
+        ],
+        [
+            [(263+24*5**0.5)/1812, (125-1000*5**0.5)/3828, (3426304+1661952*5**0.5)/5924787, (30-4*5**0.5)/123],
+        ],
+        [0, 2/5, (14-3 * 5**0.5)/16, 1],
+    ),
+    "heun_3s": (
+        [
+            [],
+            [1/3],
+            [0, 2/3],
+        ],
+        [
+            [1/4, 0, 3/4],
+        ],
+        [0, 1/3, 2/3],
+    ),
+    "kutta_3s": (
+        [
+            [],
+            [1/2],
+            [-1, 2],
+        ],
+        [
+            [1/6, 2/3, 1/6],
+        ],
+        [0, 1/2, 1],
+    ),
+    "ralston_3s": (
+        [
+            [],
+            [1/2],
+            [0, 3/4],
+        ],
+        [
+            [2/9, 1/3, 4/9],
+        ],
+        [0, 1/2, 3/4],
+    ),
+    "houwen-wray_3s": (
+        [
+            [],
+            [8/15],
+            [1/4, 5/12],
+        ],
+        [
+            [1/4, 0, 3/4],
+        ],
+        [0, 8/15, 2/3],
+    ),
+    "ssprk3_3s": (
+        [
+            [],
+            [1],
+            [1/4, 1/4],
+        ],
+        [
+            [1/6, 1/6, 2/3],
+        ],
+        [0, 1, 1/2],
+    ),
+    "midpoint_2s": (
+        [
+            [],
+            [1/2],
+        ],
+        [
+            [0, 1],
+        ],
+        [0, 1/2],
+    ),
+    "heun_2s": (
+        [
+            [],
+            [1],
+        ],
+        [
+            [1/2, 1/2],
+        ],
+        [0, 1],
+    ),
+    "ralston_2s": (
+        [
+            [],
+            [2/3],
+        ],
+        [
+            [1/4, 3/4],
+        ],
+        [0, 2/3],
+    ),
+    "buehler": (
+        [
+            [],
+        ],
+        [
+            [1],
+        ],
+        [0],
+    ),
+}
+
+
+
+
+def get_rk_methods(rk_type, h, c1=0.0, c2=0.5, c3=1.0, h_prev=None, h_prev2=None, step=0, sigmas=None, sigma=None, sigma_next=None, sigma_down=None, extra_options=None):
+    FSAL = False
+    multistep_stages = 0
+    
+    if rk_type.startswith(("res", "dpmpp", "ddim"   )): 
+        h_no_eta = -torch.log(sigma_next/sigma)
+        h_prev_no_eta  = -torch.log(sigmas[step]  /sigmas[step-1]) if step >= 1 else None
+        h_prev2_no_eta = -torch.log(sigmas[step-1]/sigmas[step-2]) if step >= 2 else None
+    else:
+        h_no_eta = sigma_next - sigma
+        h_prev_no_eta  = sigmas[step]   - sigmas[step-1] if step >= 1 else None
+        h_prev2_no_eta = sigmas[step-1] - sigmas[step-2] if step >= 2 else None
+    
+    if type(c1) == torch.Tensor:
+        c1 = c1.item()
+    if type(c2) == torch.Tensor:
+        c2 = c2.item()
+    if type(c3) == torch.Tensor:
+        c3 = c3.item()
+    
+    if c1 == -1:
+        c1 = random.uniform(0, 1)
+    if c2 == -1:
+        c2 = random.uniform(0, 1)
+    if c3 == -1:
+        c3 = random.uniform(0, 1)
+        
+    if rk_type[:4] == "deis": 
+        order = int(rk_type[-2])
+        if step < order:
+            if order == 4:
+                rk_type = "res_3s"
+                order = 3
+            elif order == 3:
+                rk_type = "res_3s"
+            elif order == 2:
+                rk_type = "res_2s"
+        else:
+            rk_type = "deis"
+            multistep_stages = order-1
+    
+    if rk_type[-2:] == "2m": #multistep method
+        rk_type = rk_type[:-2] + "2s"
+        if h_prev is not None: 
+            multistep_stages = 1
+            c2 = (-h_prev / h).item()
+            #print("c2: ", c2, h_prev, h)
+            
+    if rk_type[-2:] == "3m": #multistep method
+        rk_type = rk_type[:-2] + "3s"
+        if h_prev2 is not None: 
+            multistep_stages = 2
+            #print("3m")
+            #c2 = (-h_prev2 / (h_prev + h)).item()
+            c2 = (-h_prev2 / h).item()
+            #c3 = (-h_prev / h).item()
+            c3 = (-(h_prev2 + h_prev) / h).item()
+            #print(c2, h_prev2, h_prev)
+            #print(c3, h_prev, h)
+    
+    if rk_type in rk_coeff:
+        a, b, ci = copy.deepcopy(rk_coeff[rk_type])
+        a, b, ci = rk_coeff[rk_type]
+        a = [row + [0] * (len(ci) - len(row)) for row in a]
+
+    match rk_type:
+        case "deis": 
+            coeff_list = get_deis_coeff_list(sigmas, multistep_stages+1, deis_mode="rhoab")
+            coeff_list = [[elem / h for elem in inner_list] for inner_list in coeff_list]
+            if multistep_stages == 1:
+                b1, b2 = coeff_list[step]
+                a = [
+                        [0, 0],
+                        [0, 0],
+                ]
+                b = [
+                        [b1, b2],
+                ]
+                ci = [0, 0]
+            if multistep_stages == 2:
+                b1, b2, b3 = coeff_list[step]
+                a = [
+                        [0, 0, 0],
+                        [0, 0, 0],
+                        [0, 0, 0],
+                ]
+                b = [
+                        [b1, b2, b3],
+                ]
+                ci = [0, 0, 0]
+            if multistep_stages == 3:
+                b1, b2, b3, b4 = coeff_list[step]
+                a = [
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0],
+                        [0, 0, 0, 0],
+                ]
+                b = [
+                    [b1, b2, b3, b4],
+                ]
+                ci = [0, 0, 0, 0]
+            if multistep_stages > 0:
+                for i in range(len(b[0])): 
+                    b[0][i] *= ((sigma_down - sigma) / (sigma_next - sigma))
+
+        case "dormand-prince_6s":
+            FSAL = True
+
+        case "ddim":
+            b1 = phi(1, -h)
+            a = [
+                    [0],
+            ]
+            b = [
+                    [b1],
+            ]
+            ci = [0]
+
+        case "res_2s":
+            c2 = float(get_extra_options_kv("c2", str(c2), extra_options))
+
+            ci = [0, c2]
+            φ = Phi(h, ci)
+            
+            a2_1 = c2 * φ(1,2)
+            b2 = φ(2)/c2
+            b1 = φ(1) - b2
+
+            a = [
+                    [0,0],
+                    [a2_1, 0],
+            ]
+            b = [
+                    [b1, b2],
+            ]
+
+            
+        case "res_3s":
+            c2 = float(get_extra_options_kv("c2", str(c2), extra_options))
+            c3 = float(get_extra_options_kv("c3", str(c3), extra_options))
+            
+            gamma = calculate_gamma(c2, c3)
+            a2_1 = c2 * phi(1, -h*c2)
+            a3_2 = gamma * c2 * phi(2, -h*c2) + (c3 ** 2 / c2) * phi(2, -h*c3) #phi_2_c3_h  # a32 from k2 to k3
+            a3_1 = c3 * phi(1, -h*c3) - a3_2 # a31 from k1 to k3
+            b3 = (1 / (gamma * c2 + c3)) * phi(2, -h)      
+            b2 = gamma * b3  #simplified version of: b2 = (gamma / (gamma * c2 + c3)) * phi_2_h  
+            b1 = phi(1, -h) - b2 - b3     
+            
+            a = [
+                    [0, 0, 0],
+                    [a2_1, 0, 0],
+                    [a3_1, a3_2, 0],
+            ]
+            b = [
+                    [b1, b2, b3],
+            ]
+            ci = [c1, c2, c3]
+
+        case "res_3s_alt":
+            c2 = 1/3
+            c2 = float(get_extra_options_kv("c2", str(c2), extra_options))
+            
+            c1,c2,c3 = 0, c2, 2/3
+            ci = [c1,c2,c3]
+            φ = Phi(h, ci)
+            
+            a = [
+                    [0, 0,                   0],
+                    [0, 0,                   0],
+                    [0, (4/(9*c2)) * φ(2,3), 0],
+            ]
+            b = [
+                    [0, 0, (1/c3)*φ(2)],
+            ]
+            
+            a, b = gen_first_col_exp(a,b,ci,φ)
+            
+        case "res_3s_strehmel_weiner": # weak 4th order, Krogstad
+            c2 = 1/2
+            c2 = float(get_extra_options_kv("c2", str(c2), extra_options))
+
+            ci = [0,c2,1]
+            φ = Phi(h, ci)
+            
+            a = [
+                    [0, 0, 0],
+                    [0, 0, 0],
+                    [0, (1/c2) * φ(2,3), 0],
+            ]
+            b = [
+                    [0, 
+                     0,
+                     φ(2)],
+            ]
+            
+            a, b = gen_first_col_exp(a,b,ci,φ)
+            
+            
+        case "res_3s_cox_matthews": # Cox & Matthews; known as ETD3RK
+            c1,c2,c3 = 0,1/2,1
+            ci = [0,c2,1]
+            φ = Phi(h, ci)
+            
+            a = [
+                    [0, 0, 0],
+                    [0, 0, 0],
+                    [0, (1/c2) * φ(1,3), 0],  # paper said 2 * φ(1,3), but this is the same and more consistent with res_3s_strehmel_weiner
+            ]
+            b = [
+                    [0, 
+                     -8*φ(3) + 4*φ(2),
+                     4*φ(3) - φ(2)],
+            ]
+            
+            a, b = gen_first_col_exp(a,b,ci,φ)
+            
+        case "res_3s_lie": # Lie; known as ETD2CF3
+            c1,c2,c3 = 0, 1/3, 2/3
+            ci = [c1,c2,c3]
+            φ = Phi(h, ci)
+            
+            a = [
+                    [0, 0, 0],
+                    [0, 0, 0],
+                    [0, (4/3)*φ(2,3), 0],  # paper said 2 * φ(1,3), but this is the same and more consistent with res_3s_strehmel_weiner
+            ]
+            b = [
+                    [0, 
+                     6*φ(2) - 18*φ(3),
+                     (-3/2)*φ(2) + 9*φ(3)],
+            ]
+            
+            a, b = gen_first_col_exp(a,b,ci,φ)
+            
+        case "res_4s_cox_matthews": # weak 4th order, Cox & Matthews; unresolved issue, see below
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci)
+            
+            a2_1 = c2 * φ(1,2)
+            a3_2 = c3 * φ(1,3)
+            a4_1 = (1/2) * φ(1,3) * (φ(0,3) - 1) # φ(0,3) == torch.exp(-h*c3)
+            a4_3 = φ(1,3)
+            b1 = φ(1) - 3*φ(2) + 4*φ(3)
+            b2 = 2*φ(2) - 4*φ(3)
+            b3 = 2*φ(2) - 4*φ(3)
+            b4 = 4*φ(3) - φ(2)
+
+            a = [
+                    [0,    0,0,0],
+                    [a2_1, 0,0,0],
+                    [0, a3_2,0,0],
+                    [a4_1, 0,a4_3,0],
+            ]
+            b = [
+                    [b1, b2, b3, b4],
+            ]
+
+        case "res_4s_munthe-kaas": # unstable RKMK4t
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci)
+
+            a = [
+                    [0, 0,      0,        0],
+                    [c2*φ(1,2), 0,      0,        0],
+                    [(h/8)*φ(1,2), (1/2)*(1-h/4)*φ(1,2), 0,        0],
+                    [0, 0,      φ(1), 0],
+            ]
+            b = [
+                    [(1/6)*φ(1)*(1+h/2),
+                     (1/3)*φ(1),
+                     (1/3)*φ(1),
+                     (1/6)*φ(1)*(1-h/2)],
+            ]
+
+        case "res_4s_krogstad": # weak 4th order, Krogstad
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci)
+            
+            a = [
+                    [0, 0,      0,        0],
+                    [0, 0,      0,        0],
+                    [0, φ(2,3), 0,        0],
+                    [0, 0,      2*φ(2,4), 0],
+            ]
+            b = [
+                    [0, 
+                     2*φ(2) - 4*φ(3),
+                     2*φ(2) - 4*φ(3),
+                     -φ(2)  + 4*φ(3)],
+            ]
+            
+            #a = [row + [0] * (len(ci) - len(row)) for row in a]
+            a, b = gen_first_col_exp(a,b,ci,φ)
+            
+        case "res_4s_strehmel_weiner": # weak 4th order, Strehmel & Weiner
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci)
+            
+            a = [
+                    [0, 0,         0,        0],
+                    [0, 0,         0,        0],
+                    [0, c3*φ(2,3), 0,        0],
+                    [0, -2*φ(2,4), 4*φ(2,4), 0],
+            ]
+            b = [
+                    [0, 
+                     0,
+                     4*φ(2) - 8*φ(3), 
+                     -φ(2) +  4*φ(3)],
+            ]
+            
+            a, b = gen_first_col_exp(a,b,ci,φ)
+           
+        case "lawson4_4s": 
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci)
+            
+            a2_1 = c2 * φ(0,2)
+            a3_2 = 1/2
+            a4_3 = φ(0,2)
+            
+            b1 = (1/6) * φ(0)
+            b2 = (1/3) * φ(0,2)
+            b3 = (1/3) * φ(0,2)
+            b4 = 1/6
+
+            a = [
+                    [0,    0,    0,    0],
+                    [a2_1, 0,    0,    0],
+                    [0,    a3_2, 0,    0],
+                    [0,    0,    a4_3, 0],
+            ]
+            b = [
+                    [b1,b2,b3,b4],
+            ]
+
+        case "genlawson41_4s": # GenLawson4 https://ora.ox.ac.uk/objects/uuid:cc001282-4285-4ca2-ad06-31787b540c61/files/m611df1a355ca243beb09824b70e5e774
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci)
+
+
+            a3_2 = 1/2
+            a4_3 = φ(0,2)
+            
+            b2 = (1/3) * φ(0,2)
+            b3 = (1/3) * φ(0,2)
+            b4 = 1/6
+
+            a = [
+                    [0, 0,        0, 0],
+                    [0, 0,          0,        0],
+                    [0, a3_2, 0,        0],
+                    [0, 0, a4_3, 0],
+            ]
+            b = [
+                    [0,
+                     b2,
+                     b3,
+                     b4,],
+            ]
+
+            a, b = gen_first_col_exp(a,b,ci,φ)
+
+        case "modgenlawson41_4s": # GenLawson4 https://ora.ox.ac.uk/objects/uuid:cc001282-4285-4ca2-ad06-31787b540c61/files/m611df1a355ca243beb09824b70e5e774
+            c1,c2,c3,c4 = 0, 1/2, 1/2, 1
+            ci = [c1,c2,c3,c4]
+            φ = Phi(h, ci)
+
+
+            a3_2 = 1/2
+            a4_3 = φ(0,2)
+            
+            b2 = (1/3) * φ(0,2)
+            b3 = (1/3) * φ(0,2)
+            b4 = φ(2) - (1/3)*φ(0,2)
+
+            a = [
+                    [0, 0,        0, 0],
+                    [0, 0,          0,        0],
+                    [0, a3_2, 0,        0],
+                    [0, 0, a4_3, 0],
+            ]
+            b = [
+                    [0,
+                     b2,
+                     b3,
+                     b4,],
+            ]
+
+            a, b = gen_first_col_exp(a,b,ci,φ)
+
+
+        case "etdrk2_2s": # https://arxiv.org/pdf/2402.15142v1
+            c1,c2 = 0, 1
+            ci = [c1,c2]
+            φ = Phi(h, ci)   
+                     
+            a = [
+                    [0, 0],
+                    [φ(1), 0],
+            ]
+            b = [
+                    [φ(1)-φ(2), φ(2)],
+            ]
+
+        case "etdrk3_a_3s": # https://arxiv.org/pdf/2402.15142v1
+            c1,c2,c3 = 0, 1, 2/3
+            ci = [c1,c2,c3]
+            φ = Phi(h, ci)   
+            
+            a2_1 = c2*φ(1)
+            a3_2 = (4/9)*φ(2,3)
+            a3_1 = c3*φ(1,3) - a3_2
+            
+            b2 = φ(2) - (1/2)*φ(1)
+            b3 = (3/4) * φ(1)
+            b1 = φ(1) - b2 - b3 
+                     
+            a = [
+                    [0, 0, 0],
+                    [a2_1, 0, 0],
+                    [a3_1, a3_2, 0 ]
+            ]
+            b = [
+                    [b1, b2, b3],
+            ]
+
+        case "etdrk3_b_3s": # https://arxiv.org/pdf/2402.15142v1
+            c1,c2,c3 = 0, 4/9, 2/3
+            ci = [c1,c2,c3]
+            φ = Phi(h, ci)   
+            
+            a2_1 = c2*φ(1,2)
+            a3_2 = φ(2,3)
+            a3_1 = c3*φ(1,3) - a3_2
+            
+            b2 = 0
+            b3 = (3/2) * φ(2)
+            b1 = φ(1) - b2 - b3 
+                     
+            a = [
+                    [0, 0, 0],
+                    [a2_1, 0, 0],
+                    [a3_1, a3_2, 0 ]
+            ]
+            b = [
+                    [b1, b2, b3],
+            ]
+
+
+        case "dpmpp_2s":
+            c2 = float(get_extra_options_kv("c2", str(c2), extra_options))
+            
+            a2_1 =         c2   * phi(1, -h*c2)
+            b1 = (1 - 1/(2*c2)) * phi(1, -h)
+            b2 =     (1/(2*c2)) * phi(1, -h)
+
+            a = [
+                    [0, 0],
+                    [a2_1, 0],
+            ]
+            b = [
+                    [b1, b2],
+            ]
+            ci = [0, c2]
+            
+        case "dpmpp_sde_2s":
+            c2 = 1.0 #hardcoded to 1.0 to more closely emulate the configuration for k-diffusion's implementation
+            a2_1 =         c2   * phi(1, -h*c2)
+            b1 = (1 - 1/(2*c2)) * phi(1, -h)
+            b2 =     (1/(2*c2)) * phi(1, -h)
+
+            a = [
+                    [0, 0],
+                    [a2_1, 0],
+            ]
+            b = [
+                    [b1, b2],
+            ]
+            ci = [0, c2]
+
+        case "dpmpp_3s":
+            c2 = float(get_extra_options_kv("c2", str(c2), extra_options))
+            c3 = float(get_extra_options_kv("c3", str(c3), extra_options))
+            
+            a2_1 = c2 * phi(1, -h*c2)
+            a3_2 = (c3**2 / c2) * phi(2, -h*c3)
+            a3_1 = c3 * phi(1, -h*c3) - a3_2
+            b2 = 0
+            b3 = (1/c3) * phi(2, -h)
+            b1 = phi(1, -h) - b2 - b3
+
+            a = [
+                    [0, 0, 0],
+                    [a2_1, 0, 0],
+                    [a3_1, a3_2, 0],  
+            ]
+            b = [
+                    [b1, b2, b3],
+            ]
+            ci = [0, c2, c3]
+            
+        case "res_5s": #4th order
+                
+            c1, c2, c3, c4, c5 = 0, 1/2, 1/2, 1, 1/2
+            
+            a2_1 = c2 * phi(1, -h * c2)
+            
+            a3_2 = phi(2, -h * c3)
+            a3_1 = c3 * phi(1, -h * c3) - a3_2
+            #a3_1 = c3 * phi(1, -h * c3) - phi(2, -h * c3)
+
+            a4_2 = a4_3 = phi(2, -h * c4)
+            a4_1 = c4 * phi(1, -h * c4) - a4_2 - a4_3
+            #a4_1 = phi(1, -h * c4) - 2 * phi(2, -h * c4)
+            
+            a5_2 = a5_3 = 0.5 * phi(2, -h * c5) - phi(3, -h * c4) + 0.25 * phi(2, -h * c4) - 0.5 * phi(3, -h * c5)
+            a5_4 = 0.25 * phi(2, -h * c5) - a5_2
+            a5_1 = c5 * phi(1, -h * c5) - a5_2 - a5_3 - a5_4
+                    
+            b2 = b3 = 0
+            b4 = -phi(2, -h) + 4*phi(3, -h)
+            b5 = 4 * phi(2, -h) - 8 * phi(3, -h)
+            #b1 = phi(1, -h) - 3 * phi(2, -h) + 4 * phi(3, -h)
+            b1 = phi(1,-h) - b2 - b3 - b4 - b5
+
+            a = [
+                    [0, 0, 0, 0, 0],
+                    [a2_1, 0, 0, 0, 0],
+                    [a3_1, a3_2, 0, 0, 0],
+                    [a4_1, a4_2, a4_3, 0, 0],
+                    [a5_1, a5_2, a5_3, a5_4, 0],
+            ]
+            b = [
+                    [b1, b2, b3, b4, b5],
+            ]
+            ci = [0., 0.5, 0.5, 1., 0.5]
+            
+        case "res_6s": #4th order
+                
+            c1, c2, c3, c4, c5, c6 = 0, 1/2, 1/2, 1/3, 1/3, 5/6
+            ci = [c1, c2, c3, c4, c5, c6]
+            φ = Phi(h, ci)
+            
+            a2_1 = c2 * φ(1,2)
+            
+            a3_1 = 0
+            a3_2 = (c3**2 / c2) * φ(2,3)
+            
+            a4_1 = 0
+            a4_2 = (c4**2 / c2) * φ(2,4)
+            a4_3 = (c4**2 * φ(2,4) - a4_2 * c2) / c3
+            
+            a5_1 = 0
+            a5_2 = 0 #zero
+            a5_3 = (-c4 * c5**2 * φ(2,5) + 2*c5**3 * φ(3,5))   /   (c3 * (c3 - c4))
+            a5_4 = (-c3 * c5**2 * φ(2,5) + 2*c5**3 * φ(3,5))   /   (c4 * (c4 - c3))
+            
+            a6_1 = 0
+            a6_2 = 0 #zero
+            a6_3 = (-c4 * c6**2 * φ(2,6) + 2*c6**3 * φ(3,6))   /   (c3 * (c3 - c4))
+            a6_4 = (-c3 * c6**2 * φ(2,6) + 2*c6**3 * φ(3,6))   /   (c4 * (c4 - c3))
+            a6_5 = (c6**2 * φ(2,6) - a6_3*c3 - a6_4*c4)   /   c5
+            #a6_5_alt = (2*c6**3 * φ(3,6) - a6_3*c3**2 - a6_4*c4**2)   /   c5**2
+                    
+            b1 = 0
+            b2 = 0
+            b3 = 0
+            b4 = 0
+            b5 = (-c6*φ(2) + 2*φ(3)) / (c5 * (c5 - c6))
+            b6 = (-c5*φ(2) + 2*φ(3)) / (c6 * (c6 - c5))
+
+            a = [
+                    [0, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0],
+                    [0, a3_2, 0, 0, 0, 0],
+                    [0, a4_2, a4_3, 0, 0, 0],
+                    [0, a5_2, a5_3, a5_4, 0, 0],
+                    [0, a6_2, a6_3, a6_4, a6_5, 0],
+            ]
+            b = [
+                    [0, b2, b3, b4, b5, b6],
+            ]
+             
+            for i in range(len(ci)): 
+                a[i][0] = ci[i] * φ(1,i+1) - sum(a[i])
+            for i in range(len(b)): 
+                b[i][0] =         φ(1)     - sum(b[i])
+
+        case "res_8s": #todo: add EKPRK5S8
+                
+            c1, c2, c3, c4, c5, c6, c7, c8 = 0, 1/2, 1/2, 1/4,    1/2, 1/5, 2/3, 1
+            ci = [c1, c2, c3, c4, c5, c6, c7, c8]
+            φ = Phi(h, ci, analytic_solution=True)
+            
+            a3_2 = (1/2) * φ(2,3)
+            
+            a4_3 = (1/8) * φ(2,4)
+
+            a5_3 = (-1/2) * φ(2,5) + 2 * φ(3,5)
+            a5_4 =      2 * φ(2,5) - 4 * φ(3,5)
+            
+            a6_4 = (8/25) * φ(2,6) - (32/125) * φ(3,6)
+            a6_5 = (2/25) * φ(2,6) -  (1/2)   * a6_4
+            
+            a7_4 = (-125/162)  * a6_4
+            a7_5 =  (125/1944) * a6_4 -  (16/27) * φ(2,7) + (320/81) * φ(3,7)
+            a7_6 = (3125/3888) * a6_4 + (100/27) * φ(2,7) - (800/81) * φ(3,7)
+            
+            Φ = (5/32)*a6_4 - (1/28)*φ(2,6) + (36/175)*φ(2,7) - (48/25)*φ(3,7) + (6/175)*φ(4,6) + (192/35)*φ(4,7) + 6*φ(4,8)
+            
+            a8_5 =  (208/3)*φ(3,8) -  (16/3) *φ(2,8) -      40*Φ
+            a8_6 = (-250/3)*φ(3,8) + (250/21)*φ(2,8) + (250/7)*Φ
+            a8_7 =      -27*φ(3,8) +  (27/14)*φ(2,8) + (135/7)*Φ
+            
+            b6 = (125/14)*φ(2) - (625/14)*φ(3) + (1125/14)*φ(4)
+            b7 = (-27/14)*φ(2) + (162/7) *φ(3) -  (405/7) *φ(4)
+            b8 =   (1/2) *φ(2) -  (13/2) *φ(3) +   (45/2) *φ(4)
+            
+            a2_1 = c2*φ(1,2) 
+            a3_1 = c3*φ(1,3) - a3_2
+            a4_1 = c4*φ(1,4) - a4_3
+            a5_1 = c5*φ(1,5) - a5_3 - a5_4 
+            a6_1 = c6*φ(1,6) - a6_4 - a6_5
+            a7_1 = c7*φ(1,7) - a7_4 - a7_5 - a7_6
+            a8_1 = c8*φ(1,8) - a8_5 - a8_6 - a8_7 
+            b1   =    φ(1)   - b6 - b7 - b8
+            
+            a = [
+                    [0,    0, 0, 0, 0, 0, 0, 0],
+                    [a2_1, 0, 0, 0, 0, 0, 0, 0],
+                    
+                    [a3_1, a3_2, 0, 0, 0, 0, 0, 0],
+                    [a4_1, 0, a4_3, 0, 0, 0, 0, 0],
+                    
+                    [a5_1, 0, a5_3, a5_4, 0, 0, 0, 0],
+                    [a6_1, 0, 0, a6_4, a6_5, 0, 0, 0],
+                    
+                    [a7_1 , 0, 0, a7_4, a7_5, a7_6, 0,    0],
+                    [a8_1 , 0, 0, 0,    a8_5, a8_6, a8_7, 0],
+            ]
+            b = [
+                    [b1,   0, 0, 0, 0, b6, b7, b8],
+            ]
+             
+            a = [
+                    [0, 0, 0, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0, 0, 0],
+                    
+                    [0, a3_2, 0, 0, 0, 0, 0, 0],
+                    [0, 0, a4_3, 0, 0, 0, 0, 0],
+                    
+                    [0, 0, a5_3, a5_4, 0, 0, 0, 0],
+                    [0, 0, 0, a6_4, a6_5, 0, 0, 0],
+                    
+                    [0 , 0, 0, a7_4, a7_5, a7_6, 0,    0],
+                    [0 , 0, 0, 0,    a8_5, a8_6, a8_7, 0],
+            ]
+            b = [
+                    [0,   0, 0, 0, 0, b6, b7, b8],
+            ]
+             
+            for i in range(len(a)): 
+                a[i][0] = ci[i] * φ(1,i+1) - sum(a[i])
+            for i in range(len(b)): 
+                b[i][0] =         φ(1)     - sum(b[i])
+            
+            
+
+        case "res_10s":
+                
+            c1, c2, c3, c4, c5, c6, c7, c8, c9, c10 = 0, 1/2, 1/2, 1/3, 1/2,     1/3, 1/4, 3/10, 3/4, 1
+            ci = [c1, c2, c3, c4, c5, c6, c7, c8, c9, c10]
+            φ = Phi(h, ci, analytic_solution=True)
+            
+            a3_2 = (c3**2 / c2) * φ(2,3)
+            
+            a4_2 = (c4**2 / c2) * φ(2,4)
+                        
+            b8 =  (c9*c10*φ(2) - 2*(c9+c10)*φ(3) + 6*φ(4))   /   (c8 * (c8-c9) * (c8-c10))
+            b9 =  (c8*c10*φ(2) - 2*(c8+c10)*φ(3) + 6*φ(4))   /   (c9 * (c9-c8) * (c9-c10))
+            
+            b10 = (c8*c9*φ(2)  - 2*(c8+c9) *φ(3) + 6*φ(4))   /   (c10 * (c10-c8) * (c10-c9))
+            
+            a = [
+                    [0, 0, 0, 0, 0,      0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0,      0, 0, 0, 0, 0],
+                    [0, a3_2, 0, 0, 0,      0, 0, 0, 0, 0],
+                    [0, a4_2, 0, 0, 0,      0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0,      0, 0, 0, 0, 0],
+                    
+                    [0, 0, 0, 0, 0,      0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0,      0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0,      0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0,      0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0,      0, 0, 0, 0, 0],
+            ]
+            b = [
+                    [0, 0, 0, 0, 0,      0, 0, b8, b9, b10],
+            ]
+            
+            # a5_3, a5_4
+            # a6_3, a6_4
+            # a7_3, a7_4
+            for i in range(5, 8): # i=5,6,7   j,k ∈ {3, 4}, j != k
+                jk = [(3, 4), (4, 3)]
+                jk = list(permutations([3, 4], 2)) 
+                for j,k in jk:
+                    a[i-1][j-1] = (-ci[i-1]**2 * ci[k-1] * φ(2,i)    +   2*ci[i-1]**3 * φ(3,i))   /   (ci[j-1] * (ci[j-1] - ci[k-1]))
+                
+            for i in range(8, 11): # i=8,9,10   j,k,l ∈ {5, 6, 7}, j != k != l      [    (5, 6, 7), (5, 7, 6),    (6, 5, 7), (6, 7, 5),    (7, 5, 6), (7, 6, 5)]    6 total coeff
+                jkl = list(permutations([5, 6, 7], 3)) 
+                for j,k,l in jkl:
+                    a[i-1][j-1] = (ci[i-1]**2 * ci[k-1] * ci[l-1] * φ(2,i)   -   2*ci[i-1]**3 * (ci[k-1] + ci[l-1]) * φ(3,i)   +   6*ci[i-1]**4 * φ(4,i))    /    (ci[j-1] * (ci[j-1] - ci[k-1]) * (ci[j-1] - ci[l-1]))
+
+            for i in range(len(a)): 
+                a[i][0] = ci[i] * φ(1,i+1) - sum(a[i])
+            for i in range(len(b)): 
+                b[i][0] =         φ(1)     - sum(b[i])
+            
+            
+
+
+        case "res_15s":
+                
+            c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12,c13,c14,c15 = 0, 1/2, 1/2, 1/3, 1/2,    1/5, 1/4, 18/25, 1/3, 3/10,    1/6, 90/103, 1/3, 3/10, 1/5
+            c1 = 0
+            c2 = c3 = c5 = 1/2
+            c4 = c9 = c13 = 1/3
+            c6 = c15 = 1/5
+            c7 = 1/4
+            c8 = 18/25
+            c10 = c14 = 3/10
+            c11 = 1/6
+            c12 = 90/103
+            c15 = 1/5
+            ci = [c1, c2, c3, c4, c5, c6, c7, c8, c9, c10, c11, c12, c13, c14, c15]
+            φ = Phi(h, ci, analytic_solution=True)
+            
+            a = [[0 for _ in range(15)] for _ in range(15)]
+            b = [[0 for _ in range(15)]]
+
+            for i in range(3, 5): # i=3,4     j=2
+                j=2
+                a[i-1][j-1] = (ci[i-1]**2 / ci[j-1]) * φ(j,i)
+            
+            
+            for i in range(5, 8): # i=5,6,7   j,k ∈ {3, 4}, j != k
+                jk = list(permutations([3, 4], 2)) 
+                for j,k in jk:
+                    a[i-1][j-1] = (-ci[i-1]**2 * ci[k-1] * φ(2,i)    +   2*ci[i-1]**3 * φ(3,i))   /   prod_diff(ci[j-1], ci[k-1])
+
+            for i in range(8, 12): # i=8,9,10,11  j,k,l ∈ {5, 6, 7}, j != k != l      [    (5, 6, 7), (5, 7, 6),    (6, 5, 7), (6, 7, 5),    (7, 5, 6), (7, 6, 5)]    6 total coeff
+                jkl = list(permutations([5, 6, 7], 3)) 
+                for j,k,l in jkl:
+                    a[i-1][j-1] = (ci[i-1]**2 * ci[k-1] * ci[l-1] * φ(2,i)   -   2*ci[i-1]**3 * (ci[k-1] + ci[l-1]) * φ(3,i)   +   6*ci[i-1]**4 * φ(4,i))    /    (ci[j-1] * (ci[j-1] - ci[k-1]) * (ci[j-1] - ci[l-1]))
+
+            for i in range(12,16): # i=12,13,14,15
+                jkld = list(permutations([8,9,10,11], 4)) 
+                for j,k,l,d in jkld:
+                    numerator = -ci[i-1]**2  *  ci[d-1]*ci[k-1]*ci[l-1]  *  φ(2,i)     +     2*ci[i-1]**3  *  (ci[d-1]*ci[k-1] + ci[d-1]*ci[l-1] + ci[k-1]*ci[l-1])  *  φ(3,i)     -     6*ci[i-1]**4  *  (ci[d-1] + ci[k-1] + ci[l-1])  *  φ(4,i)     +     24*ci[i-1]**5  *  φ(5,i)
+                    a[i-1][j-1] = numerator / prod_diff(ci[j-1], ci[k-1], ci[l-1], ci[d-1])
+
+            """ijkl = list(permutations([12,13,14,15], 4)) 
+            for i,j,k,l in ijkl:
+                #numerator = -ci[j-1]*ci[k-1]*ci[l-1]*φ(2)   +   2*(ci[j-1]*ci[k-1]   +   ci[j-1]*ci[l-1]   +   ci[k-1]*ci[l-1])*φ(3)   -   6*(ci[j-1] + ci[k-1]   +   ci[l-1])*φ(4)   +   24*φ(5)
+                #b[0][i-1] = numerator / prod_diff(ci[i-1], ci[j-1], ci[k-1], ci[l-1])
+                for jjj in range (2, 6): # 2,3,4,5
+                    b[0][i-1] += mu_numerator(jjj, ci[j-1], ci[i-1], ci[k-1], ci[l-1]) * φ(jjj) 
+                b[0][i-1] /= prod_diff(ci[i-1], ci[j-1], ci[k-1], ci[l-1])"""
+                    
+            ijkl = list(permutations([12,13,14,15], 4)) 
+            for i,j,k,l in ijkl:
+                numerator = 0
+                for jjj in range(2, 6):  # 2, 3, 4, 5
+                    numerator += mu_numerator(jjj, ci[j-1], ci[i-1], ci[k-1], ci[l-1]) * φ(jjj)
+                #print(i,j,k,l)
+
+                b[0][i-1] = numerator / prod_diff(ci[i-1], ci[j-1], ci[k-1], ci[l-1])
+             
+             
+            ijkl = list(permutations([12, 13, 14, 15], 4))
+            selected_permutations = {} 
+            sign = 1  
+
+            for i in range(12, 16):
+                results = []
+                for j, k, l, d in ijkl:
+                    if i != j and i != k and i != l and i != d:
+                        numerator = 0
+                        for jjj in range(2, 6):  # 2, 3, 4, 5
+                            numerator += mu_numerator(jjj, ci[j-1], ci[i-1], ci[k-1], ci[l-1]) * φ(jjj)
+                        theta_value = numerator / prod_diff(ci[i-1], ci[j-1], ci[k-1], ci[l-1])
+                        results.append((theta_value, (i, j, k, l, d)))
+
+                results.sort(key=lambda x: abs(x[0]))
+
+                for theta_value, permutation in results:
+                    if sign == 1 and theta_value > 0:
+                        selected_permutations[i] = (theta_value, permutation)
+                        sign *= -1  
+                        break
+                    elif sign == -1 and theta_value < 0:  
+                        selected_permutations[i] = (theta_value, permutation)
+                        sign *= -1 
+                        break
+
+            for i in range(12, 16):
+                if i in selected_permutations:
+                    theta_value, (i, j, k, l, d) = selected_permutations[i]
+                    b[0][i-1] = theta_value  
+                    
+            for i in selected_permutations:
+                theta_value, permutation = selected_permutations[i]
+                #print(f"i={i}")
+                #print(f"  Selected Theta: {theta_value:.6f}, Permutation: {permutation}")
+             
+             
+             
+            for i in range(len(a)): 
+                a[i][0] = ci[i] * φ(1,i+1) - sum(a[i])
+            for i in range(len(b)): 
+                b[i][0] =         φ(1)     - sum(b[i])
+            
+            
+
+        case "res_16s": # 6th order without weakened order conditions
+                
+            c1 = 0
+            c2 = c3 = c5 = c8 = c12 = 1/2
+            c4 = c11 = c15 = 1/3
+            c6 = c9 = c13 = 1/5
+            c7 = c10 = c14 = 1/4
+            c16 = 1
+            ci = [c1, c2, c3, c4, c5, c6, c7, c8, c9, c10, c11, c12, c13, c14, c15, c16]
+            φ = Phi(h, ci, analytic_solution=True)
+            
+            a3_2 = (1/2) * φ(2,3)
+
+            a = [[0 for _ in range(16)] for _ in range(16)]
+            b = [[0 for _ in range(16)]]
+
+            for i in range(3, 5): # i=3,4     j=2
+                j=2
+                a[i-1][j-1] = (ci[i-1]**2 / ci[j-1]) * φ(j,i)
+            
+            for i in range(5, 8): # i=5,6,7   j,k ∈ {3, 4}, j != k
+                jk = list(permutations([3, 4], 2)) 
+                for j,k in jk:
+                    a[i-1][j-1] = (-ci[i-1]**2 * ci[k-1] * φ(2,i)    +   2*ci[i-1]**3 * φ(3,i))   /   prod_diff(ci[j-1], ci[k-1])
+                    
+            for i in range(8, 12): # i=8,9,10,11  j,k,l ∈ {5, 6, 7}, j != k != l      [    (5, 6, 7), (5, 7, 6),    (6, 5, 7), (6, 7, 5),    (7, 5, 6), (7, 6, 5)]    6 total coeff
+                jkl = list(permutations([5, 6, 7], 3)) 
+                for j,k,l in jkl:
+                    a[i-1][j-1] = (ci[i-1]**2 * ci[k-1] * ci[l-1] * φ(2,i)   -   2*ci[i-1]**3 * (ci[k-1] + ci[l-1]) * φ(3,i)   +   6*ci[i-1]**4 * φ(4,i))    /    (ci[j-1] * (ci[j-1] - ci[k-1]) * (ci[j-1] - ci[l-1]))
+
+            for i in range(12,17): # i=12,13,14,15,16
+                jkld = list(permutations([8,9,10,11], 4)) 
+                for j,k,l,d in jkld:
+                    numerator = -ci[i-1]**2  *  ci[d-1]*ci[k-1]*ci[l-1]  *  φ(2,i)     +     2*ci[i-1]**3  *  (ci[d-1]*ci[k-1] + ci[d-1]*ci[l-1] + ci[k-1]*ci[l-1])  *  φ(3,i)     -     6*ci[i-1]**4  *  (ci[d-1] + ci[k-1] + ci[l-1])  *  φ(4,i)     +     24*ci[i-1]**5  *  φ(5,i)
+                    a[i-1][j-1] = numerator / prod_diff(ci[j-1], ci[k-1], ci[l-1], ci[d-1])
+                     
+            """ijdkl = list(permutations([12,13,14,15,16], 5)) 
+            for i,j,d,k,l in ijdkl:
+                #numerator = -ci[j-1]*ci[k-1]*ci[l-1]*φ(2)   +   2*(ci[j-1]*ci[k-1]   +   ci[j-1]*ci[l-1]   +   ci[k-1]*ci[l-1])*φ(3)   -   6*(ci[j-1] + ci[k-1]   +   ci[l-1])*φ(4)   +   24*φ(5)
+                b[0][i-1] = theta(2, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1]) * φ(2)   +  theta(3, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1])*φ(3)   +   theta(4, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1])*φ(4)   +   theta(5, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1])*φ(5)    +    theta(6, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1]) * φ(6)
+                #b[0][i-1] = numerator / prod_diff(ci[i-1], ci[j-1], ci[k-1], ci[l-1])"""
+                    
+                
+            ijdkl = list(permutations([12,13,14,15,16], 5)) 
+            for i,j,d,k,l in ijdkl:
+                #numerator = -ci[j-1]*ci[k-1]*ci[l-1]*φ(2)   +   2*(ci[j-1]*ci[k-1]   +   ci[j-1]*ci[l-1]   +   ci[k-1]*ci[l-1])*φ(3)   -   6*(ci[j-1] + ci[k-1]   +   ci[l-1])*φ(4)   +   24*φ(5)
+                #numerator = theta_numerator(2, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1]) * φ(2)   +  theta_numerator(3, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1])*φ(3)   +   theta_numerator(4, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1])*φ(4)   +   theta_numerator(5, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1])*φ(5)    +    theta_numerator(6, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1]) * φ(6)
+                #b[0][i-1] = numerator / (ci[i-1] *, ci[d-1], ci[j-1], ci[k-1], ci[l-1])
+                #b[0][i-1] = numerator / denominator(ci[i-1], ci[d-1], ci[j-1], ci[k-1], ci[l-1])
+                b[0][i-1] = theta(2, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1]) * φ(2)   +  theta(3, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1])*φ(3)   +   theta(4, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1])*φ(4)   +   theta(5, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1])*φ(5)    +    theta(6, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1]) * φ(6)
+
+            
+            ijdkl = list(permutations([12,13,14,15,16], 5)) 
+            for i,j,d,k,l in ijdkl:
+                numerator = 0
+                for jjj in range(2, 7):  # 2, 3, 4, 5, 6
+                    numerator += theta_numerator(jjj, ci[d-1], ci[i-1], ci[k-1], ci[j-1], ci[l-1]) * φ(jjj)
+                #print(i,j,d,k,l)
+                b[0][i-1] = numerator / (ci[i-1] *   (ci[i-1] - ci[k-1])   *   (ci[i-1] - ci[j-1]   *   (ci[i-1] - ci[d-1])   *   (ci[i-1] - ci[l-1])))
+
+                
+            for i in range(len(a)): 
+                a[i][0] = ci[i] * φ(1,i+1) - sum(a[i])
+            for i in range(len(b)): 
+                b[i][0] =         φ(1)     - sum(b[i])
+            
+            
+            
+
+            
+        case "irk_exp_diag_2s":
+            c1 = 1/3
+            c2 = 2/3
+            c1 = float(get_extra_options_kv("c1", str(c1), extra_options))
+            c2 = float(get_extra_options_kv("c2", str(c2), extra_options))
+            
+            lam = (1 - torch.exp(-c1 * h)) / h
+            a2_1 = ( torch.exp(c2*h) - torch.exp(c1*h))    /    (h * torch.exp(2*c1*h))
+            b1 =  (1 + c2*h + torch.exp(h) * (-1 + h - c2*h)) / ((c1-c2) * h**2 * torch.exp(c1*h))
+            b2 = -(1 + c1*h - torch.exp(h) * ( 1 - h + c1*h)) / ((c1-c2) * h**2 * torch.exp(c2*h))
+
+            a = [
+                    [lam, 0],
+                    [a2_1, lam],
+            ]
+            b = [
+                    [b1, b2],
+            ]
+            ci = [c1, c2]
+
+    ci = ci[:]
+    if rk_type.startswith("lob") == False:
+        ci.append(1)
+        
+    return a, b, ci, multistep_stages, FSAL
+
+
+
+def gen_first_col_exp(a, b, c, φ):
+    for i in range(len(c)): 
+        a[i][0] = c[i] * φ(1,i+1) - sum(a[i])
+    for i in range(len(b)): 
+        b[i][0] =         φ(1)     - sum(b[i])
+    return a, b
+
+def rho(j, ci, ck, cl):
+    if j == 2:
+        numerator = ck*cl
+    if j == 3:
+        numerator = (-2 * (ck + cl))
+    if j == 4:
+        numerator = 6
+    return numerator / denominator(ci, ck, cl)
+    
+    
+def mu(j, cd, ci, ck, cl):
+    if j == 2:
+        numerator = -cd * ck * cl
+    if j == 3:
+        numerator = 2 * (cd * ck + cd * cl + ck * cl)
+    if j == 4:
+        numerator = -6 * (cd + ck + cl)
+    if j == 5:
+        numerator = 24
+    return numerator / denominator(ci, cd, ck, cl)
+
+def mu_numerator(j, cd, ci, ck, cl):
+    if j == 2:
+        numerator = -cd * ck * cl
+    if j == 3:
+        numerator = 2 * (cd * ck + cd * cl + ck * cl)
+    if j == 4:
+        numerator = -6 * (cd + ck + cl)
+    if j == 5:
+        numerator = 24
+    return numerator #/ denominator(ci, cd, ck, cl)
+
+
+
+def theta_numerator(j, cd, ci, ck, cj, cl):
+    if j == 2:
+        numerator = -cj * cd * ck * cl
+    if j == 3:
+        numerator = 2 * (cj * ck * cd + cj*ck*cl + ck*cd*cl + cd*cl*cj)
+    if j == 4:
+        numerator = -6*(cj*ck + cj*cd + cj*cl + ck*cd + ck*cl + cd*cl)
+    if j == 5:
+        numerator = 24 * (cj + ck + cl + cd)
+    if j == 6:
+        numerator = -120
+    return numerator # / denominator(ci, cj, ck, cl, cd)
+
+
+def theta(j, cd, ci, ck, cj, cl):
+    if j == 2:
+        numerator = -cj * cd * ck * cl
+    if j == 3:
+        numerator = 2 * (cj * ck * cd + cj*ck*cl + ck*cd*cl + cd*cl*cj)
+    if j == 4:
+        numerator = -6*(cj*ck + cj*cd + cj*cl + ck*cd + ck*cl + cd*cl)
+    if j == 5:
+        numerator = 24 * (cj + ck + cl + cd)
+    if j == 6:
+        numerator = -120
+    return numerator / ( ci * (ci - cj) * (ci - ck) * (ci - cl) * (ci - cd))
+    return numerator / denominator(ci, cj, ck, cl, cd)
+
+
+def prod_diff(cj, ck, cl=None, cd=None, cblah=None):
+    if cl is None and cd is None:
+        return cj * (cj - ck)
+    if cd is None:
+        return cj * (cj - ck) * (cj - cl)
+    else:
+        return cj * (cj - ck) * (cj - cl) * (cj - cd)
+
+def denominator(ci, *args):
+    result = ci 
+    for arg in args:
+        result *= (ci - arg)
+    return result
+
+
+
+def check_condition_4_2(nodes):
+
+    c12, c13, c14, c15 = nodes
+
+    term_1 = (1 / 5) * (c12 + c13 + c14 + c15)
+    term_2 = (1 / 4) * (c12 * c13 + c12 * c14 + c12 * c15 + c13 * c14 + c13 * c15 + c14 * c15)
+    term_3 = (1 / 3) * (c12 * c13 * c14 + c12 * c13 * c15 + c12 * c14 * c15 + c13 * c14 * c15)
+    term_4 = (1 / 2) * (c12 * c13 * c14 * c15)
+
+    result = term_1 - term_2 + term_3 - term_4
+
+    return abs(result - (1 / 6)) < 1e-6  
+
diff --git a/custom_nodes/RES4LYF/legacy/rk_guide_func.py b/custom_nodes/RES4LYF/legacy/rk_guide_func.py
new file mode 100644
index 0000000000000000000000000000000000000000..3182e3cc21a75a438af6f8a72ca1f5b04c131d2a
--- /dev/null
+++ b/custom_nodes/RES4LYF/legacy/rk_guide_func.py
@@ -0,0 +1,1193 @@
+import torch
+import torch.nn.functional as F
+from typing import Tuple
+
+from einops import rearrange
+
+from .sigmas import get_sigmas
+from .latents import hard_light_blend, normalize_latent, initialize_or_scale
+from .rk_method import RK_Method
+from .helper import get_extra_options_kv, extra_options_flag, get_cosine_similarity, get_extra_options_list
+
+
+import itertools
+
+
+def normalize_inputs(x, y0, y0_inv, guide_mode,  extra_options):
+    
+    if guide_mode == "epsilon_guide_mean_std_from_bkg":
+        y0 = normalize_latent(y0, y0_inv)
+        
+    input_norm = get_extra_options_kv("input_norm", "", extra_options)
+    input_std = float(get_extra_options_kv("input_std", "1.0", extra_options))
+    
+    if input_norm == "input_ch_mean_set_std_to":
+        x = normalize_latent(x, set_std=input_std)
+
+    if input_norm == "input_ch_set_std_to":
+        x = normalize_latent(x, set_std=input_std, mean=False)
+            
+    if input_norm == "input_mean_set_std_to":
+        x = normalize_latent(x, set_std=input_std, channelwise=False)
+        
+    if input_norm == "input_std_set_std_to":
+        x = normalize_latent(x, set_std=input_std, mean=False, channelwise=False)
+    
+    return x, y0, y0_inv
+
+
+class LatentGuide:
+    def __init__(self, guides, x, model, sigmas, UNSAMPLE, LGW_MASK_RESCALE_MIN, extra_options, device='cuda', dtype=torch.float64, max_steps=10000):
+        self.model    = model
+        self.sigma_min = model.inner_model.inner_model.model_sampling.sigma_min.to(dtype)
+        self.sigma_max = model.inner_model.inner_model.model_sampling.sigma_max.to(dtype)
+        self.sigmas   = sigmas
+        self.UNSAMPLE = UNSAMPLE
+        self.SAMPLE = (sigmas[0] > sigmas[1])
+        self.extra_options = extra_options
+        self.y0     = torch.zeros_like(x)
+        self.y0_inv = torch.zeros_like(x)
+        self.guide_mode = ""
+        self.mask = None
+        self.mask_inv = None
+        
+        self.latent_guide = None
+        self.latent_guide_inv = None
+
+        self.lgw_masks = []
+        self.lgw_masks_inv = []
+        self.lgw, self.lgw_inv = [torch.full_like(sigmas, 0.) for _ in range(2)]
+        
+        self.guide_cossim_cutoff_, self.guide_bkg_cossim_cutoff_ = 1.0, 1.0
+                
+        latent_guide_weight, latent_guide_weight_inv = 0.,0.
+        latent_guide_weights, latent_guide_weights_inv = None, None
+        latent_guide_weights = torch.zeros_like(sigmas)
+        latent_guide_weights_inv = torch.zeros_like(sigmas)
+        if guides is not None:
+            self.guide_mode, latent_guide_weight, latent_guide_weight_inv, latent_guide_weights, latent_guide_weights_inv, self.latent_guide, self.latent_guide_inv, latent_guide_mask, latent_guide_mask_inv, scheduler_, scheduler_inv_, steps_, steps_inv_, denoise_, denoise_inv_ = guides
+            
+            self.mask, self.mask_inv                                 = latent_guide_mask, latent_guide_mask_inv
+            self.guide_cossim_cutoff_, self.guide_bkg_cossim_cutoff_ = denoise_, denoise_inv_
+            
+            if latent_guide_weights == None:
+                latent_guide_weights = get_sigmas(model, scheduler_, steps_, 1.0).to(x.dtype)
+            
+            if latent_guide_weights_inv == None:
+                latent_guide_weights_inv = get_sigmas(model, scheduler_inv_, steps_inv_, 1.0).to(x.dtype)
+                
+            latent_guide_weights     = initialize_or_scale(latent_guide_weights,     latent_guide_weight,     max_steps).to(dtype)
+            latent_guide_weights_inv = initialize_or_scale(latent_guide_weights_inv, latent_guide_weight_inv, max_steps).to(dtype)
+                
+        latent_guide_weights     = F.pad(latent_guide_weights,     (0, max_steps), value=0.0)
+        latent_guide_weights_inv = F.pad(latent_guide_weights_inv, (0, max_steps), value=0.0)
+        
+        
+        if latent_guide_weights is not None:
+            self.lgw = latent_guide_weights.to(x.device)
+        if latent_guide_weights_inv is not None:
+            self.lgw_inv = latent_guide_weights_inv.to(x.device)
+            
+        self.mask, LGW_MASK_RESCALE_MIN = prepare_mask(x, self.mask, LGW_MASK_RESCALE_MIN)
+        if self.mask_inv is not None:
+            self.mask_inv, LGW_MASK_RESCALE_MIN = prepare_mask(x, self.mask_inv, LGW_MASK_RESCALE_MIN)
+        elif not self.SAMPLE:
+            self.mask_inv = (1-self.mask)
+            
+        for step in range(len(self.sigmas)-1):
+            lgw_mask, lgw_mask_inv = prepare_weighted_masks(self.mask, self.mask_inv, self.lgw[step], self.lgw_inv[step], self.latent_guide, self.latent_guide_inv, LGW_MASK_RESCALE_MIN)
+            self.lgw_masks.append(lgw_mask)
+            self.lgw_masks_inv.append(lgw_mask_inv)
+
+
+    def init_guides(self, x, noise_sampler, latent_guide=None, latent_guide_inv=None):
+        self.y0, self.y0_inv = torch.zeros_like(x), torch.zeros_like(x)
+        latent_guide = self.latent_guide if latent_guide is None else latent_guide
+        latent_guide_inv = self.latent_guide_inv if latent_guide_inv is None else latent_guide_inv
+
+
+        if latent_guide is not None:
+            if type(latent_guide) == dict:
+                latent_guide_samples = self.model.inner_model.inner_model.process_latent_in(latent_guide['samples']).clone().to(x.device)
+            else:
+                latent_guide_samples = latent_guide
+            if self.SAMPLE:
+                self.y0 = latent_guide_samples
+            elif self.UNSAMPLE: # and self.mask is not None:
+                x = (1-self.mask) * x + self.mask * latent_guide_samples
+            else:
+                x = latent_guide_samples
+
+        if latent_guide_inv is not None:
+            if type(latent_guide_inv) == dict:
+                latent_guide_inv_samples = self.model.inner_model.inner_model.process_latent_in(latent_guide_inv['samples']).clone().to(x.device)
+            else:
+                latent_guide_inv_samples = latent_guide_inv
+            if self.SAMPLE:
+                self.y0_inv = latent_guide_inv_samples
+            elif self.UNSAMPLE: # and self.mask is not None:
+                x = (1-self.mask_inv) * x + self.mask_inv * latent_guide_inv_samples #fixed old approach, which was mask, (1-mask)
+            else:
+                x = latent_guide_inv_samples   #THIS COULD LEAD TO WEIRD BEHAVIOR! OVERWRITING X WITH LG_INV AFTER SETTING TO LG above!
+                
+        if self.UNSAMPLE and not self.SAMPLE: #sigma_next > sigma:
+            self.y0 = noise_sampler(sigma=self.sigma_max, sigma_next=self.sigma_min)
+            self.y0 = (self.y0 - self.y0.mean()) / self.y0.std()
+            self.y0_inv = noise_sampler(sigma=self.sigma_max, sigma_next=self.sigma_min)
+            self.y0_inv = (self.y0_inv - self.y0_inv.mean()) / self.y0_inv.std()
+            
+        x, self.y0, self.y0_inv = normalize_inputs(x, self.y0, self.y0_inv, self.guide_mode, self.extra_options)
+
+        return x
+    
+
+
+    def process_guides_substep(self, x_0, x_, eps_, data_, row, step, sigma, sigma_next, sigma_down, s_, unsample_resample_scale, rk, rk_type, extra_options, frame_weights_grp=None):
+
+        y0 = self.y0
+        if self.y0.shape[0] > 1:
+            y0 = self.y0[min(step, self.y0.shape[0]-1)].unsqueeze(0)  
+        y0_inv = self.y0_inv
+        
+        lgw_mask = self.lgw_masks[step].clone()
+        lgw_mask_inv = self.lgw_masks_inv[step].clone() if self.lgw_masks_inv is not None else None
+        
+        lgw = self.lgw[step]
+        lgw_inv = self.lgw_inv[step]
+        
+        latent_guide = self.latent_guide
+        latent_guide_inv = self.latent_guide_inv
+        guide_mode = self.guide_mode
+        UNSAMPLE = self.UNSAMPLE
+
+        if x_0.dim() == 5 and frame_weights_grp is not None:
+            apply_frame_weights(lgw_mask, frame_weights_grp[0])
+            apply_frame_weights(lgw_mask_inv, frame_weights_grp[1])
+
+        if self.guide_mode: 
+            data_norm   = data_[row] - data_[row].mean(dim=(-2,-1), keepdim=True)
+            y0_norm     = y0         -         y0.mean(dim=(-2,-1), keepdim=True)
+            y0_inv_norm = y0_inv     -     y0_inv.mean(dim=(-2,-1), keepdim=True)
+
+            y0_cossim     = get_cosine_similarity(data_norm*lgw_mask,     y0_norm    *lgw_mask)
+            y0_cossim_inv = get_cosine_similarity(data_norm*lgw_mask_inv, y0_inv_norm*lgw_mask_inv)
+            
+            if y0_cossim < self.guide_cossim_cutoff_ or y0_cossim_inv < self.guide_bkg_cossim_cutoff_:
+                lgw_mask_cossim, lgw_mask_cossim_inv = lgw_mask, lgw_mask_inv
+                if y0_cossim     >= self.guide_cossim_cutoff_:
+                    lgw_mask_cossim     = torch.zeros_like(lgw_mask)
+                if y0_cossim_inv >= self.guide_bkg_cossim_cutoff_:
+                    lgw_mask_cossim_inv = torch.zeros_like(lgw_mask_inv)
+                lgw_mask = lgw_mask_cossim
+                lgw_mask_inv = lgw_mask_cossim_inv
+            else:
+                return eps_, x_ 
+        else:
+            return eps_, x_ 
+        
+        if self.UNSAMPLE and RK_Method.is_exponential(rk_type):
+            if not (extra_options_flag("disable_power_unsample", extra_options) or extra_options_flag("disable_power_resample", extra_options)):
+                extra_options += "\npower_unsample\npower_resample\n"
+            if not extra_options_flag("disable_lgw_scaling_substep_ch_mean_std", extra_options):
+                extra_options += "\nsubstep_eps_ch_mean_std\n"
+                
+
+
+        s_in = x_0.new_ones([x_0.shape[0]])
+        eps_orig = eps_.clone()
+        
+        if extra_options_flag("dynamic_guides_mean_std", extra_options):
+            y_shift, y_inv_shift = normalize_latent([y0, y0_inv], [data_, data_])
+            y0 = y_shift
+            if extra_options_flag("dynamic_guides_inv", extra_options):
+                y0_inv = y_inv_shift
+
+        if extra_options_flag("dynamic_guides_mean", extra_options):
+            y_shift, y_inv_shift = normalize_latent([y0, y0_inv], [data_, data_], std=False)
+            y0 = y_shift
+            if extra_options_flag("dynamic_guides_inv", extra_options):
+                y0_inv = y_inv_shift
+
+        if "data" == guide_mode:
+            y0_tmp = y0.clone()
+            if latent_guide_inv is not None:
+                y0_tmp = (1-lgw_mask) * data_[row] + lgw_mask * y0
+                y0_tmp = (1-lgw_mask_inv) * y0_tmp + lgw_mask_inv * y0_inv
+            x_[row+1] = y0_tmp + eps_[row]
+            
+        if guide_mode == "data_projection":
+
+            d_lerp = data_[row]   +   lgw_mask * (y0-data_[row])   +   lgw_mask_inv * (y0_inv-data_[row])
+            
+            d_collinear_d_lerp = get_collinear(data_[row], d_lerp)  
+            d_lerp_ortho_d     = get_orthogonal(d_lerp, data_[row])  
+            
+            data_[row] = d_collinear_d_lerp + d_lerp_ortho_d
+            
+            x_[row+1] = data_[row] + eps_[row] * sigma
+            
+
+        elif "epsilon" in guide_mode:
+            if sigma > sigma_next:
+                    
+                tol_value = float(get_extra_options_kv("tol", "-1.0", extra_options))
+                if tol_value >= 0 and (lgw > 0 or lgw_inv > 0):           
+                    for b, c in itertools.product(range(x_0.shape[0]), range(x_0.shape[1])):
+                        current_diff     = torch.norm(data_[row][b][c] - y0    [b][c])
+                        current_diff_inv = torch.norm(data_[row][b][c] - y0_inv[b][c])
+                        
+                        lgw_scaled     = torch.nan_to_num(1-(tol_value/current_diff),     0)
+                        lgw_scaled_inv = torch.nan_to_num(1-(tol_value/current_diff_inv), 0)
+                        
+                        lgw_tmp     = min(lgw    , lgw_scaled)
+                        lgw_tmp_inv = min(lgw_inv, lgw_scaled_inv)
+
+                        lgw_mask_clamp     = torch.clamp(lgw_mask,     max=lgw_tmp)
+                        lgw_mask_clamp_inv = torch.clamp(lgw_mask_inv, max=lgw_tmp_inv)
+                        
+                        eps_row, eps_row_inv = get_guide_epsilon_substep(x_0, x_, y0, y0_inv, s_, row, rk_type, b, c)
+                        eps_[row][b][c] = eps_[row][b][c] + lgw_mask_clamp[b][c] * (eps_row - eps_[row][b][c]) + lgw_mask_clamp_inv[b][c] * (eps_row_inv - eps_[row][b][c])
+
+
+                elif guide_mode == "epsilon_projection":
+                    eps_row, eps_row_inv = get_guide_epsilon_substep(x_0, x_, y0, y0_inv, s_, row, rk_type)
+                    
+                    if extra_options_flag("eps_proj_v2", extra_options):
+                        
+                        eps_row_lerp_fg = eps_[row]   +   lgw_mask * (eps_row-eps_[row])
+                        eps_row_lerp_bg = eps_[row]   +   lgw_mask_inv * (eps_row_inv-eps_[row])
+                        
+                        eps_collinear_eps_lerp_fg = get_collinear(eps_[row], eps_row_lerp_fg)  
+                        eps_lerp_ortho_eps_fg     = get_orthogonal(eps_row_lerp_fg, eps_[row])  
+                        
+                        eps_collinear_eps_lerp_bg = get_collinear(eps_[row], eps_row_lerp_bg)  
+                        eps_lerp_ortho_eps_bg     = get_orthogonal(eps_row_lerp_bg, eps_[row])  
+                        
+                        eps_[row] = eps_[row] + lgw_mask * (eps_collinear_eps_lerp_fg + eps_lerp_ortho_eps_fg - eps_[row]) + lgw_mask_inv * (eps_collinear_eps_lerp_bg + eps_lerp_ortho_eps_bg - eps_[row]) 
+                        
+                    elif extra_options_flag("eps_proj_v3", extra_options):
+
+                        eps_collinear_eps_lerp_fg = get_collinear(eps_[row], eps_row)  
+                        eps_lerp_ortho_eps_fg     = get_orthogonal(eps_row, eps_[row])  
+                        
+                        eps_collinear_eps_lerp_bg = get_collinear(eps_[row], eps_row_inv)  
+                        eps_lerp_ortho_eps_bg     = get_orthogonal(eps_row_inv, eps_[row])  
+                        
+                        eps_[row] = eps_[row] + lgw_mask * (eps_collinear_eps_lerp_fg + eps_lerp_ortho_eps_fg - eps_[row]) + lgw_mask_inv * (eps_collinear_eps_lerp_bg + eps_lerp_ortho_eps_bg - eps_[row]) 
+                       
+                    elif extra_options_flag("eps_proj_v5", extra_options):
+
+                        eps2g_collin = get_collinear(eps_[row], eps_row)  
+                        g2eps_ortho  = get_orthogonal(eps_row, eps_[row])  
+                        
+                        g2eps_collin = get_collinear(eps_row, eps_[row])  
+                        eps2g_ortho  = get_orthogonal(eps_[row], eps_row)  
+                        
+                        eps2i_collin = get_collinear(eps_[row], eps_row_inv)  
+                        i2eps_ortho  = get_orthogonal(eps_row_inv, eps_[row])  
+                        
+                        i2eps_collin = get_collinear(eps_row_inv, eps_[row])  
+                        eps2i_ortho  = get_orthogonal(eps_[row], eps_row_inv)  
+                        
+                        #eps_[row] = (eps2g_collin+g2eps_ortho)   +   (g2eps_collin+eps2g_ortho)       +       (eps2i_collin+i2eps_ortho)   +   (i2eps_collin+eps2i_ortho)
+                        #eps_[row] = eps_[row] + lgw_mask * (eps2g_collin+g2eps_ortho)   +   (1-lgw_mask) * (g2eps_collin+eps2g_ortho)       +      lgw_mask_inv * (eps2i_collin+i2eps_ortho)   +   (1-lgw_mask_inv) * (i2eps_collin+eps2i_ortho)
+
+                        eps_[row] = lgw_mask * (eps2g_collin+g2eps_ortho)   -   lgw_mask * (g2eps_collin+eps2g_ortho)       +      lgw_mask_inv * (eps2i_collin+i2eps_ortho)   -   lgw_mask_inv * (i2eps_collin+eps2i_ortho)
+                        
+                        #eps_[row] = eps_[row] + lgw_mask * (eps_collinear_eps_lerp_fg + eps_lerp_ortho_eps_fg - eps_[row]) + lgw_mask_inv * (eps_collinear_eps_lerp_bg + eps_lerp_ortho_eps_bg - eps_[row]) 
+                       
+                       
+                    elif extra_options_flag("eps_proj_v4a", extra_options):
+                        eps_row_lerp = eps_[row]   +   lgw_mask * (eps_row-eps_[row])   +   lgw_mask_inv * (eps_row_inv-eps_[row])
+
+                        eps_collinear_eps_lerp = get_collinear(eps_[row], eps_row_lerp)
+                        eps_lerp_ortho_eps     = get_orthogonal(eps_row_lerp, eps_[row])
+
+                        eps_[row] = (1 - torch.clamp(lgw_mask + lgw_mask_inv, max=1.0)) * eps_[row]   +   torch.clamp((lgw_mask + lgw_mask_inv), max=1.0) * (eps_collinear_eps_lerp + eps_lerp_ortho_eps)
+
+
+                    elif extra_options_flag("eps_proj_v4b", extra_options):
+                        eps_row_lerp = eps_[row]   +   lgw_mask * (eps_row-eps_[row])   +   lgw_mask_inv * (eps_row_inv-eps_[row])
+
+                        eps_collinear_eps_lerp = get_collinear(eps_[row], eps_row_lerp)
+                        eps_lerp_ortho_eps     = get_orthogonal(eps_row_lerp, eps_[row])
+
+                        eps_[row] = (1 - (lgw_mask + lgw_mask_inv)/2) * eps_[row]   +   ((lgw_mask + lgw_mask_inv)/2) * (eps_collinear_eps_lerp + eps_lerp_ortho_eps)
+
+                    elif extra_options_flag("eps_proj_v4c", extra_options):
+                        eps_row_lerp = eps_[row]   +   lgw_mask * (eps_row-eps_[row])   +   lgw_mask_inv * (eps_row_inv-eps_[row])
+
+                        eps_collinear_eps_lerp = get_collinear(eps_[row], eps_row_lerp)
+                        eps_lerp_ortho_eps     = get_orthogonal(eps_row_lerp, eps_[row])
+
+                        lgw_mask_sum = (lgw_mask + lgw_mask_inv)
+
+
+                        eps_[row] = (1 - (lgw_mask + lgw_mask_inv)/2) * eps_[row]   +   ((lgw_mask + lgw_mask_inv)/2) * (eps_collinear_eps_lerp + eps_lerp_ortho_eps)
+
+                    elif extra_options_flag("eps_proj_v4e", extra_options):
+                        eps_row_lerp = eps_[row]   +   lgw_mask * (eps_row-eps_[row])   +   lgw_mask_inv * (eps_row_inv-eps_[row])
+
+                        eps_collinear_eps_lerp = get_collinear(eps_[row], eps_row_lerp)
+                        eps_lerp_ortho_eps     = get_orthogonal(eps_row_lerp, eps_[row])
+
+                        eps_sum = eps_collinear_eps_lerp + eps_lerp_ortho_eps
+
+                        eps_[row] = eps_[row] + self.mask * (eps_sum - eps_[row]) + self.mask_inv * (eps_sum - eps_[row])
+
+                    elif extra_options_flag("eps_proj_self1", extra_options):
+                        eps_row_lerp = eps_[row]   +   self.mask * (eps_row-eps_[row])   +   self.mask_inv * (eps_row_inv-eps_[row])
+
+                        eps_collinear_eps_lerp = get_collinear(eps_[row], eps_[row])
+                        eps_lerp_ortho_eps     = get_orthogonal(eps_[row], eps_[row])
+
+                        eps_[row] = eps_collinear_eps_lerp + eps_lerp_ortho_eps
+
+                    elif extra_options_flag("eps_proj_v4z", extra_options):
+                        eps_row_lerp = eps_[row]   +   self.mask * (eps_row-eps_[row])   +   self.mask_inv * (eps_row_inv-eps_[row])
+
+                        eps_collinear_eps_lerp = get_collinear(eps_[row], eps_row_lerp)
+                        eps_lerp_ortho_eps     = get_orthogonal(eps_row_lerp, eps_[row])
+
+                        peak = max(lgw, lgw_inv)
+                        lgw_mask_sum = (lgw_mask + lgw_mask_inv)
+
+                        eps_sum = eps_collinear_eps_lerp + eps_lerp_ortho_eps
+                        #NOT FINISHED!!!
+                        #eps_[row] = eps_[row] + lgw_mask * (eps_sum - eps_[row]) + lgw_mask_inv * (eps_sum - eps_[row])
+
+                    elif extra_options_flag("eps_proj_v5", extra_options):
+                        eps_row_lerp = eps_[row]   +   lgw_mask * (eps_row-eps_[row])   +   lgw_mask_inv * (eps_row_inv-eps_[row])
+
+                        eps_collinear_eps_lerp = get_collinear(eps_[row], eps_row_lerp)
+                        eps_lerp_ortho_eps     = get_orthogonal(eps_row_lerp, eps_[row])
+
+                        eps_[row] = ((lgw_mask + lgw_mask_inv)==0) * eps_[row]   +   ((lgw_mask + lgw_mask_inv)>0) * (eps_collinear_eps_lerp + eps_lerp_ortho_eps)
+
+                    elif extra_options_flag("eps_proj_v6", extra_options):
+                        eps_row_lerp = eps_[row]   +   lgw_mask * (eps_row-eps_[row])   +   lgw_mask_inv * (eps_row_inv-eps_[row])
+
+                        eps_collinear_eps_lerp = get_collinear(eps_[row], eps_row_lerp)
+                        eps_lerp_ortho_eps     = get_orthogonal(eps_row_lerp, eps_[row])
+
+                        eps_[row] = ((lgw_mask * lgw_mask_inv)==0) * eps_[row]   +   ((lgw_mask * lgw_mask_inv)>0) * (eps_collinear_eps_lerp + eps_lerp_ortho_eps)
+
+
+                    elif extra_options_flag("eps_proj_old_default", extra_options):
+                        eps_row_lerp = eps_[row]   +   lgw_mask * (eps_row-eps_[row])   +   lgw_mask_inv * (eps_row_inv-eps_[row])
+                        #eps_row_lerp = eps_[row]   +   lgw_mask * (eps_row-eps_[row])   +   (1-lgw_mask) * (eps_row_inv-eps_[row])
+                        
+                        eps_collinear_eps_lerp = get_collinear(eps_[row], eps_row_lerp)  
+                        eps_lerp_ortho_eps     = get_orthogonal(eps_row_lerp, eps_[row])  
+                        
+                        eps_[row] = eps_collinear_eps_lerp + eps_lerp_ortho_eps
+                        
+                    else: #elif extra_options_flag("eps_proj_v4d", extra_options):
+                        #if row > 0:
+                            #lgw_mask_factor = float(get_extra_options_kv("substep_lgw_mask_factor", "1.0", extra_options))
+                            #lgw_mask_inv_factor = float(get_extra_options_kv("substep_lgw_mask_inv_factor", "1.0", extra_options))
+                        lgw_mask_factor = 1
+                        if extra_options_flag("substep_eps_proj_scaling", extra_options):
+                            lgw_mask_factor = 1/(row+1)
+                            
+                        if extra_options_flag("substep_eps_proj_factors", extra_options):
+                            #value_str = get_extra_options_list("substep_eps_proj_factors", "", extra_options)
+                            #float_list = [float(item.strip()) for item in value_str.split(',') if item.strip()]
+                            float_list = get_extra_options_list("substep_eps_proj_factors", "", extra_options, ret_type=float)
+                            lgw_mask_factor = float_list[row]
+                        
+                        eps_row_lerp = eps_[row]   +   self.mask * (eps_row-eps_[row])   +   (1-self.mask) * (eps_row_inv-eps_[row])
+
+                        eps_collinear_eps_lerp = get_collinear(eps_[row], eps_row_lerp)
+                        eps_lerp_ortho_eps     = get_orthogonal(eps_row_lerp, eps_[row])
+
+                        eps_sum = eps_collinear_eps_lerp + eps_lerp_ortho_eps
+
+                        eps_[row] = eps_[row] + lgw_mask_factor*lgw_mask * (eps_sum - eps_[row]) + lgw_mask_factor*lgw_mask_inv * (eps_sum - eps_[row])
+
+
+
+                elif extra_options_flag("disable_lgw_scaling", extra_options):
+                    eps_row, eps_row_inv = get_guide_epsilon_substep(x_0, x_, y0, y0_inv, s_, row, rk_type)
+                    eps_[row] = eps_[row]      +     lgw_mask * (eps_row - eps_[row])    +    lgw_mask_inv * (eps_row_inv - eps_[row])
+                    
+
+                elif (lgw > 0 or lgw_inv > 0): # default old channelwise epsilon
+                    avg, avg_inv = 0, 0
+                    for b, c in itertools.product(range(x_0.shape[0]), range(x_0.shape[1])):
+                        avg     += torch.norm(data_[row][b][c] - y0    [b][c])
+                        avg_inv += torch.norm(data_[row][b][c] - y0_inv[b][c])
+                    avg     /= x_0.shape[1]
+                    avg_inv /= x_0.shape[1]
+                    
+                    for b, c in itertools.product(range(x_0.shape[0]), range(x_0.shape[1])):
+                        ratio     = torch.nan_to_num(torch.norm(data_[row][b][c] - y0    [b][c])   /   avg,     0)
+                        ratio_inv = torch.nan_to_num(torch.norm(data_[row][b][c] - y0_inv[b][c])   /   avg_inv, 0)
+                        
+                        eps_row, eps_row_inv = get_guide_epsilon_substep(x_0, x_, y0, y0_inv, s_, row, rk_type, b, c)
+                        eps_[row][b][c] = eps_[row][b][c]      +     ratio * lgw_mask[b][c] * (eps_row - eps_[row][b][c])    +    ratio_inv * lgw_mask_inv[b][c] * (eps_row_inv - eps_[row][b][c])
+                        
+                temporal_smoothing = float(get_extra_options_kv("temporal_smoothing", "0.0", extra_options))
+                if temporal_smoothing > 0:
+                    eps_[row] = apply_temporal_smoothing(eps_[row], temporal_smoothing)
+                
+
+
+
+        elif (UNSAMPLE or guide_mode in {"resample", "unsample"}) and (lgw > 0 or lgw_inv > 0):
+                
+            cvf = rk.get_epsilon(x_0, x_[row+1], y0, sigma, s_[row], sigma_down, unsample_resample_scale, extra_options)
+            if UNSAMPLE and sigma > sigma_next and latent_guide_inv is not None:
+                cvf_inv = rk.get_epsilon(x_0, x_[row+1], y0_inv, sigma, s_[row], sigma_down, unsample_resample_scale, extra_options)      
+            else:
+                cvf_inv = torch.zeros_like(cvf)
+
+            tol_value = float(get_extra_options_kv("tol", "-1.0", extra_options))
+            if tol_value >= 0:
+                for b, c in itertools.product(range(x_0.shape[0]), range(x_0.shape[1])):
+                    current_diff     = torch.norm(data_[row][b][c] - y0    [b][c]) 
+                    current_diff_inv = torch.norm(data_[row][b][c] - y0_inv[b][c]) 
+                    
+                    lgw_scaled     = torch.nan_to_num(1-(tol_value/current_diff),     0)
+                    lgw_scaled_inv = torch.nan_to_num(1-(tol_value/current_diff_inv), 0)
+                    
+                    lgw_tmp     = min(lgw    , lgw_scaled)
+                    lgw_tmp_inv = min(lgw_inv, lgw_scaled_inv)
+
+                    lgw_mask_clamp     = torch.clamp(lgw_mask,     max=lgw_tmp)
+                    lgw_mask_clamp_inv = torch.clamp(lgw_mask_inv, max=lgw_tmp_inv)
+
+                    eps_[row][b][c] = eps_[row][b][c] + lgw_mask_clamp[b][c] * (cvf[b][c] - eps_[row][b][c]) + lgw_mask_clamp_inv[b][c] * (cvf_inv[b][c] - eps_[row][b][c])
+                    
+            elif extra_options_flag("disable_lgw_scaling", extra_options):
+                eps_[row] = eps_[row] + lgw_mask * (cvf - eps_[row]) + lgw_mask_inv * (cvf_inv - eps_[row])
+                
+            else:
+                avg, avg_inv = 0, 0
+                for b, c in itertools.product(range(x_0.shape[0]), range(x_0.shape[1])):
+                    avg     += torch.norm(lgw_mask    [b][c] * data_[row][b][c]   -   lgw_mask    [b][c] * y0    [b][c])
+                    avg_inv += torch.norm(lgw_mask_inv[b][c] * data_[row][b][c]   -   lgw_mask_inv[b][c] * y0_inv[b][c])
+                avg     /= x_0.shape[1]
+                avg_inv /= x_0.shape[1]
+                
+                for b, c in itertools.product(range(x_0.shape[0]), range(x_0.shape[1])):
+                    ratio     = torch.nan_to_num(torch.norm(lgw_mask    [b][c] * data_[row][b][c] - lgw_mask    [b][c] * y0    [b][c])   /   avg,     0)
+                    ratio_inv = torch.nan_to_num(torch.norm(lgw_mask_inv[b][c] * data_[row][b][c] - lgw_mask_inv[b][c] * y0_inv[b][c])   /   avg_inv, 0)
+                            
+                    eps_[row][b][c] = eps_[row][b][c]      +     ratio * lgw_mask[b][c] * (cvf[b][c] - eps_[row][b][c])    +    ratio_inv * lgw_mask_inv[b][c] * (cvf_inv[b][c] - eps_[row][b][c])
+                    
+        if extra_options_flag("substep_eps_ch_mean_std", extra_options):
+            eps_[row] = normalize_latent(eps_[row], eps_orig[row])
+        if extra_options_flag("substep_eps_ch_mean", extra_options):
+            eps_[row] = normalize_latent(eps_[row], eps_orig[row], std=False)
+        if extra_options_flag("substep_eps_ch_std", extra_options):
+            eps_[row] = normalize_latent(eps_[row], eps_orig[row], mean=False)
+        if extra_options_flag("substep_eps_mean_std", extra_options):
+            eps_[row] = normalize_latent(eps_[row], eps_orig[row], channelwise=False)
+        if extra_options_flag("substep_eps_mean", extra_options):
+            eps_[row] = normalize_latent(eps_[row], eps_orig[row], std=False, channelwise=False)
+        if extra_options_flag("substep_eps_std", extra_options):
+            eps_[row] = normalize_latent(eps_[row], eps_orig[row], mean=False, channelwise=False)
+        return eps_, x_
+
+
+
+    @torch.no_grad
+    def process_guides_poststep(self, x, denoised, eps, step, extra_options):
+        x_orig = x.clone()
+        mean_weight = float(get_extra_options_kv("mean_weight", "0.01", extra_options))
+        
+        y0 = self.y0
+        if self.y0.shape[0] > 1:
+            y0 = self.y0[min(step, self.y0.shape[0]-1)].unsqueeze(0)  
+        y0_inv = self.y0_inv
+        
+        lgw_mask = self.lgw_masks[step].clone()
+        lgw_mask_inv = self.lgw_masks_inv[step].clone() if self.lgw_masks_inv is not None else None
+        mask = self.mask #needed for bitwise mask below
+        
+        lgw = self.lgw[step]
+        lgw_inv = self.lgw_inv[step]
+        
+        latent_guide = self.latent_guide
+        latent_guide_inv = self.latent_guide_inv
+        guide_mode = self.guide_mode
+        UNSAMPLE = self.UNSAMPLE
+        
+        if self.guide_mode: 
+            data_norm   = denoised - denoised.mean(dim=(-2,-1), keepdim=True)
+            y0_norm     = y0         -         y0.mean(dim=(-2,-1), keepdim=True)
+            y0_inv_norm = y0_inv     -     y0_inv.mean(dim=(-2,-1), keepdim=True)
+
+            y0_cossim     = get_cosine_similarity(data_norm*lgw_mask,     y0_norm    *lgw_mask)
+            y0_cossim_inv = get_cosine_similarity(data_norm*lgw_mask_inv, y0_inv_norm*lgw_mask_inv)
+            
+            if y0_cossim < self.guide_cossim_cutoff_ or y0_cossim_inv < self.guide_bkg_cossim_cutoff_:
+                lgw_mask_cossim, lgw_mask_cossim_inv = lgw_mask, lgw_mask_inv
+                if y0_cossim     >= self.guide_cossim_cutoff_:
+                    lgw_mask_cossim     = torch.zeros_like(lgw_mask)
+                if y0_cossim_inv >= self.guide_bkg_cossim_cutoff_:
+                    lgw_mask_cossim_inv = torch.zeros_like(lgw_mask_inv)
+                lgw_mask = lgw_mask_cossim
+                lgw_mask_inv = lgw_mask_cossim_inv
+            else:
+                return x
+        
+        if guide_mode in {"epsilon_dynamic_mean_std", "epsilon_dynamic_mean", "epsilon_dynamic_std", "epsilon_dynamic_mean_from_bkg"}:
+        
+            denoised_masked     = denoised * ((mask==1)*mask)
+            denoised_masked_inv = denoised * ((mask==0)*(1-mask))
+            
+            
+            d_shift, d_shift_inv = torch.zeros_like(x), torch.zeros_like(x)
+            
+            for b, c in itertools.product(range(x.shape[0]), range(x.shape[1])):
+                denoised_mask     = denoised[b][c][mask[b][c] == 1]
+                denoised_mask_inv = denoised[b][c][mask[b][c] == 0]
+                
+                if guide_mode == "epsilon_dynamic_mean_std":
+                    d_shift[b][c] = (denoised_masked[b][c] - denoised_mask.mean()) / denoised_mask.std()
+                    d_shift[b][c] = (d_shift[b][c] * denoised_mask_inv.std()) + denoised_mask_inv.mean()
+                    
+                elif guide_mode == "epsilon_dynamic_mean":
+                    d_shift[b][c]     = denoised_masked[b][c]     - denoised_mask.mean()     + denoised_mask_inv.mean()
+                    d_shift_inv[b][c] = denoised_masked_inv[b][c] - denoised_mask_inv.mean() + denoised_mask.mean()
+
+                elif guide_mode == "epsilon_dynamic_mean_from_bkg":
+                    d_shift[b][c] = denoised_masked[b][c] - denoised_mask.mean() + denoised_mask_inv.mean()
+
+            if guide_mode in {"epsilon_dynamic_mean_std", "epsilon_dynamic_mean_from_bkg"}:
+                denoised_shifted = denoised   +   mean_weight * lgw_mask * (d_shift - denoised_masked) 
+            elif guide_mode == "epsilon_dynamic_mean":
+                denoised_shifted = denoised   +   mean_weight * lgw_mask * (d_shift - denoised_masked)   +   mean_weight * lgw_mask_inv * (d_shift_inv - denoised_masked_inv)
+                
+            x = denoised_shifted + eps
+        
+        
+        if UNSAMPLE == False and (latent_guide is not None or latent_guide_inv is not None) and guide_mode in ("hard_light", "blend", "blend_projection", "mean_std", "mean", "mean_tiled", "std"):
+            if guide_mode == "hard_light":
+                d_shift, d_shift_inv = hard_light_blend(y0, denoised), hard_light_blend(y0_inv, denoised)
+            elif guide_mode == "blend":
+                d_shift, d_shift_inv = y0, y0_inv
+                
+            elif guide_mode == "blend_projection":
+                #d_shift     = get_collinear(denoised, y0) 
+                #d_shift_inv = get_collinear(denoised, y0_inv) 
+                
+                d_lerp = denoised   +   lgw_mask * (y0-denoised)   +   lgw_mask_inv * (y0_inv-denoised)
+                
+                d_collinear_d_lerp = get_collinear(denoised, d_lerp)  
+                d_lerp_ortho_d     = get_orthogonal(d_lerp, denoised)  
+                
+                denoised_shifted = d_collinear_d_lerp + d_lerp_ortho_d
+                x = denoised_shifted + eps
+                return x
+
+
+            elif guide_mode == "mean_std":
+                d_shift, d_shift_inv = normalize_latent([denoised, denoised], [y0, y0_inv])
+            elif guide_mode == "mean":
+                d_shift, d_shift_inv = normalize_latent([denoised, denoised], [y0, y0_inv], std=False)
+            elif guide_mode == "std":
+                d_shift, d_shift_inv = normalize_latent([denoised, denoised], [y0, y0_inv], mean=False)
+            elif guide_mode == "mean_tiled":
+                mean_tile_size = int(get_extra_options_kv("mean_tile", "8", extra_options))
+                y0_tiled       = rearrange(y0,       "b c (h t1) (w t2) -> (t1 t2) b c h w", t1=mean_tile_size, t2=mean_tile_size)
+                y0_inv_tiled   = rearrange(y0_inv,   "b c (h t1) (w t2) -> (t1 t2) b c h w", t1=mean_tile_size, t2=mean_tile_size)
+                denoised_tiled = rearrange(denoised, "b c (h t1) (w t2) -> (t1 t2) b c h w", t1=mean_tile_size, t2=mean_tile_size)
+                d_shift_tiled, d_shift_inv_tiled = torch.zeros_like(y0_tiled), torch.zeros_like(y0_tiled)
+                for i in range(y0_tiled.shape[0]):
+                    d_shift_tiled[i], d_shift_inv_tiled[i] = normalize_latent([denoised_tiled[i], denoised_tiled[i]], [y0_tiled[i], y0_inv_tiled[i]], std=False)
+                d_shift     = rearrange(d_shift_tiled,     "(t1 t2) b c h w -> b c (h t1) (w t2)", t1=mean_tile_size, t2=mean_tile_size)
+                d_shift_inv = rearrange(d_shift_inv_tiled, "(t1 t2) b c h w -> b c (h t1) (w t2)", t1=mean_tile_size, t2=mean_tile_size)
+
+
+            if guide_mode in ("hard_light", "blend", "mean_std", "mean", "mean_tiled", "std"):
+                if latent_guide_inv is None:
+                    denoised_shifted = denoised   +   lgw_mask * (d_shift - denoised)
+                else:
+                    denoised_shifted = denoised   +   lgw_mask * (d_shift - denoised)   +   lgw_mask_inv * (d_shift_inv - denoised)
+            
+                if extra_options_flag("poststep_denoised_ch_mean_std", extra_options):
+                    denoised_shifted = normalize_latent(denoised_shifted, denoised)
+                if extra_options_flag("poststep_denoised_ch_mean", extra_options):
+                    denoised_shifted = normalize_latent(denoised_shifted, denoised, std=False)
+                if extra_options_flag("poststep_denoised_ch_std", extra_options):
+                    denoised_shifted = normalize_latent(denoised_shifted, denoised, mean=False)
+                if extra_options_flag("poststep_denoised_mean_std", extra_options):
+                    denoised_shifted = normalize_latent(denoised_shifted, denoised, channelwise=False)
+                if extra_options_flag("poststep_denoised_mean", extra_options):
+                    denoised_shifted = normalize_latent(denoised_shifted, denoised, std=False, channelwise=False)
+                if extra_options_flag("poststep_denoised_std", extra_options):
+                    denoised_shifted = normalize_latent(denoised_shifted, denoised, mean=False, channelwise=False)
+
+                x = denoised_shifted + eps
+
+        if extra_options_flag("poststep_x_ch_mean_std", extra_options):
+            x = normalize_latent(x, x_orig)
+        if extra_options_flag("poststep_x_ch_mean", extra_options):
+            x = normalize_latent(x, x_orig, std=False)
+        if extra_options_flag("poststep_x_ch_std", extra_options):
+            x = normalize_latent(x, x_orig, mean=False)
+        if extra_options_flag("poststep_x_mean_std", extra_options):
+            x = normalize_latent(x, x_orig, channelwise=False)
+        if extra_options_flag("poststep_x_mean", extra_options):
+            x = normalize_latent(x, x_orig, std=False, channelwise=False)
+        if extra_options_flag("poststep_x_std", extra_options):
+            x = normalize_latent(x, x_orig, mean=False, channelwise=False)
+        return x
+
+
+
+def apply_frame_weights(mask, frame_weights):
+    if frame_weights is not None:
+        for f in range(mask.shape[2]):
+            frame_weight = frame_weights[f]
+            mask[..., f:f+1, :, :] *= frame_weight
+
+
+
+def prepare_mask(x, mask, LGW_MASK_RESCALE_MIN) -> Tuple[torch.Tensor, bool]:
+    if mask is None:
+        mask = torch.ones_like(x)
+        LGW_MASK_RESCALE_MIN = False
+        return mask, LGW_MASK_RESCALE_MIN
+    
+    spatial_mask = mask.unsqueeze(1)
+    target_height = x.shape[-2]
+    target_width = x.shape[-1]
+    spatial_mask = F.interpolate(spatial_mask, size=(target_height, target_width), mode='bilinear', align_corners=False)
+
+    while spatial_mask.dim() < x.dim():
+        spatial_mask = spatial_mask.unsqueeze(2)
+    
+    repeat_shape = [1] #batch
+    for i in range(1, x.dim() - 2):
+        repeat_shape.append(x.shape[i])
+    repeat_shape.extend([1, 1]) #height and width
+
+    mask = spatial_mask.repeat(*repeat_shape).to(x.dtype).to(x.device)
+    
+    del spatial_mask
+    return mask, LGW_MASK_RESCALE_MIN
+    
+def prepare_weighted_masks(mask, mask_inv, lgw_, lgw_inv_, latent_guide, latent_guide_inv, LGW_MASK_RESCALE_MIN):
+    if LGW_MASK_RESCALE_MIN: 
+        lgw_mask     =    mask  * (1-lgw_) + lgw_
+        lgw_mask_inv = (1-mask) * (1-lgw_inv_) + lgw_inv_
+    else:
+        if latent_guide is not None:
+            lgw_mask = mask * lgw_
+        else:
+            lgw_mask = torch.zeros_like(mask)
+        if latent_guide_inv is not None:
+            if mask_inv is not None:
+                lgw_mask_inv = torch.minimum(1-mask_inv, (1-mask) * lgw_inv_)
+            else:
+                lgw_mask_inv = (1-mask) * lgw_inv_
+        else:
+            lgw_mask_inv = torch.zeros_like(mask)
+    return lgw_mask, lgw_mask_inv
+
+def apply_temporal_smoothing(tensor, temporal_smoothing):
+    if temporal_smoothing <= 0 or tensor.dim() != 5:
+        return tensor
+
+    kernel_size = 5
+    padding = kernel_size // 2
+    temporal_kernel = torch.tensor(
+        [0.1, 0.2, 0.4, 0.2, 0.1],
+        device=tensor.device, dtype=tensor.dtype
+    ) * temporal_smoothing
+    temporal_kernel[kernel_size//2] += (1 - temporal_smoothing)
+    temporal_kernel = temporal_kernel / temporal_kernel.sum()
+
+    # resahpe for conv1d
+    b, c, f, h, w = tensor.shape
+    data_flat = tensor.permute(0, 1, 3, 4, 2).reshape(-1, f)
+
+    # apply smoohting
+    data_smooth = F.conv1d(
+        data_flat.unsqueeze(1),
+        temporal_kernel.view(1, 1, -1),
+        padding=padding
+    ).squeeze(1)
+
+    return data_smooth.view(b, c, h, w, f).permute(0, 1, 4, 2, 3)
+
+def get_guide_epsilon_substep(x_0, x_, y0, y0_inv, s_, row, rk_type, b=None, c=None):
+    s_in = x_0.new_ones([x_0.shape[0]])
+    
+    if b is not None and c is not None:  
+        index = (b, c)
+    elif b is not None: 
+        index = (b,)
+    else: 
+        index = ()
+
+    if RK_Method.is_exponential(rk_type):
+        eps_row     = y0    [index] - x_0[index]
+        eps_row_inv = y0_inv[index] - x_0[index]
+    else:
+        eps_row     = (x_[row+1][index] - y0    [index]) / (s_[row] * s_in)
+        eps_row_inv = (x_[row+1][index] - y0_inv[index]) / (s_[row] * s_in)
+    
+    return eps_row, eps_row_inv
+
+def get_guide_epsilon(x_0, x_, y0, sigma, rk_type, b=None, c=None):
+    s_in = x_0.new_ones([x_0.shape[0]])
+    
+    if b is not None and c is not None:  
+        index = (b, c)
+    elif b is not None: 
+        index = (b,)
+    else: 
+        index = ()
+
+    if RK_Method.is_exponential(rk_type):
+        eps     = y0    [index] - x_0[index]
+    else:
+        eps     = (x_[index] - y0    [index]) / (sigma * s_in)
+    
+    return eps
+
+
+
+
+@torch.no_grad
+def noise_cossim_guide_tiled(x_list, guide, cossim_mode="forward", tile_size=2, step=0):
+
+    guide_tiled = rearrange(guide, "b c (h t1) (w t2) -> b (t1 t2) c h w", t1=tile_size, t2=tile_size)
+
+    x_tiled_list = [
+        rearrange(x, "b c (h t1) (w t2) -> b (t1 t2) c h w", t1=tile_size, t2=tile_size)
+        for x in x_list
+    ]
+    x_tiled_stack = torch.stack([x_tiled[0] for x_tiled in x_tiled_list])  # [n_x, n_tiles, c, h, w]
+
+    guide_flat = guide_tiled[0].view(guide_tiled.shape[1], -1).unsqueeze(0)  # [1, n_tiles, c*h*w]
+    x_flat = x_tiled_stack.view(x_tiled_stack.size(0), x_tiled_stack.size(1), -1)  # [n_x, n_tiles, c*h*w]
+
+    cossim_tmp_all = F.cosine_similarity(x_flat, guide_flat, dim=-1)  # [n_x, n_tiles]
+
+    if cossim_mode == "forward":
+        indices = cossim_tmp_all.argmax(dim=0) 
+    elif cossim_mode == "reverse":
+        indices = cossim_tmp_all.argmin(dim=0) 
+    elif cossim_mode == "orthogonal":
+        indices = torch.abs(cossim_tmp_all).argmin(dim=0) 
+    elif cossim_mode == "forward_reverse":
+        if step % 2 == 0:
+            indices = cossim_tmp_all.argmax(dim=0)
+        else:
+            indices = cossim_tmp_all.argmin(dim=0)
+    elif cossim_mode == "reverse_forward":
+        if step % 2 == 1:
+            indices = cossim_tmp_all.argmax(dim=0)
+        else:
+            indices = cossim_tmp_all.argmin(dim=0)
+    elif cossim_mode == "orthogonal_reverse":
+        if step % 2 == 0:
+            indices = torch.abs(cossim_tmp_all).argmin(dim=0)
+        else:
+            indices = cossim_tmp_all.argmin(dim=0)
+    elif cossim_mode == "reverse_orthogonal":
+        if step % 2 == 1:
+            indices = torch.abs(cossim_tmp_all).argmin(dim=0)
+        else:
+            indices = cossim_tmp_all.argmin(dim=0)
+    else:
+        target_value = float(cossim_mode)
+        indices = torch.abs(cossim_tmp_all - target_value).argmin(dim=0)  
+
+    x_tiled_out = x_tiled_stack[indices, torch.arange(indices.size(0))]  # [n_tiles, c, h, w]
+
+    x_tiled_out = x_tiled_out.unsqueeze(0) 
+    x_detiled = rearrange(x_tiled_out, "b (t1 t2) c h w -> b c (h t1) (w t2)", t1=tile_size, t2=tile_size)
+
+    return x_detiled
+
+
+@torch.no_grad
+def noise_cossim_eps_tiled(x_list, eps, noise_list, cossim_mode="forward", tile_size=2, step=0):
+
+    eps_tiled = rearrange(eps, "b c (h t1) (w t2) -> b (t1 t2) c h w", t1=tile_size, t2=tile_size)
+    x_tiled_list = [
+        rearrange(x, "b c (h t1) (w t2) -> b (t1 t2) c h w", t1=tile_size, t2=tile_size)
+        for x in x_list
+    ]
+    noise_tiled_list = [
+        rearrange(noise, "b c (h t1) (w t2) -> b (t1 t2) c h w", t1=tile_size, t2=tile_size)
+        for noise in noise_list
+    ]
+
+    noise_tiled_stack = torch.stack([noise_tiled[0] for noise_tiled in noise_tiled_list])  # [n_x, n_tiles, c, h, w]
+    eps_expanded = eps_tiled[0].view(eps_tiled.shape[1], -1).unsqueeze(0)  # [1, n_tiles, c*h*w]
+    noise_flat = noise_tiled_stack.view(noise_tiled_stack.size(0), noise_tiled_stack.size(1), -1)  # [n_x, n_tiles, c*h*w]
+    cossim_tmp_all = F.cosine_similarity(noise_flat, eps_expanded, dim=-1)  # [n_x, n_tiles]
+
+    if cossim_mode == "forward":
+        indices = cossim_tmp_all.argmax(dim=0)  
+    elif cossim_mode == "reverse":
+        indices = cossim_tmp_all.argmin(dim=0) 
+    elif cossim_mode == "orthogonal":
+        indices = torch.abs(cossim_tmp_all).argmin(dim=0) 
+    elif cossim_mode == "orthogonal_pos":
+        positive_mask = cossim_tmp_all > 0
+        positive_tmp = torch.where(positive_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('inf')))
+        indices = positive_tmp.argmin(dim=0)
+    elif cossim_mode == "orthogonal_neg":
+        negative_mask = cossim_tmp_all < 0
+        negative_tmp = torch.where(negative_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('-inf')))
+        indices = negative_tmp.argmax(dim=0)
+    elif cossim_mode == "orthogonal_posneg":
+        if step % 2 == 0:
+            positive_mask = cossim_tmp_all > 0
+            positive_tmp = torch.where(positive_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('inf')))
+            indices = positive_tmp.argmin(dim=0)
+        else:
+            negative_mask = cossim_tmp_all < 0
+            negative_tmp = torch.where(negative_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('-inf')))
+            indices = negative_tmp.argmax(dim=0)
+    elif cossim_mode == "orthogonal_negpos":
+        if step % 2 == 1:
+            positive_mask = cossim_tmp_all > 0
+            positive_tmp = torch.where(positive_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('inf')))
+            indices = positive_tmp.argmin(dim=0)
+        else:
+            negative_mask = cossim_tmp_all < 0
+            negative_tmp = torch.where(negative_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('-inf')))
+            indices = negative_tmp.argmax(dim=0)
+    elif cossim_mode == "forward_reverse":
+        if step % 2 == 0:
+            indices = cossim_tmp_all.argmax(dim=0)
+        else:
+            indices = cossim_tmp_all.argmin(dim=0)
+    elif cossim_mode == "reverse_forward":
+        if step % 2 == 1:
+            indices = cossim_tmp_all.argmax(dim=0)
+        else:
+            indices = cossim_tmp_all.argmin(dim=0)
+    elif cossim_mode == "orthogonal_reverse":
+        if step % 2 == 0:
+            indices = torch.abs(cossim_tmp_all).argmin(dim=0)
+        else:
+            indices = cossim_tmp_all.argmin(dim=0)
+    elif cossim_mode == "reverse_orthogonal":
+        if step % 2 == 1:
+            indices = torch.abs(cossim_tmp_all).argmin(dim=0)
+        else:
+            indices = cossim_tmp_all.argmin(dim=0)
+    else:
+        target_value = float(cossim_mode)
+        indices = torch.abs(cossim_tmp_all - target_value).argmin(dim=0)
+    #else:
+    #    raise ValueError(f"Unknown cossim_mode: {cossim_mode}")
+
+    x_tiled_stack = torch.stack([x_tiled[0] for x_tiled in x_tiled_list])  # [n_x, n_tiles, c, h, w]
+    x_tiled_out = x_tiled_stack[indices, torch.arange(indices.size(0))]  # [n_tiles, c, h, w]
+
+    x_tiled_out = x_tiled_out.unsqueeze(0)  # restore batch dim
+    x_detiled = rearrange(x_tiled_out, "b (t1 t2) c h w -> b c (h t1) (w t2)", t1=tile_size, t2=tile_size)
+    return x_detiled
+
+
+
+@torch.no_grad
+def noise_cossim_guide_eps_tiled(x_0, x_list, y0, noise_list, cossim_mode="forward", tile_size=2, step=0, sigma=None, rk_type=None):
+
+    x_tiled_stack = torch.stack([
+        rearrange(x, "b c (h t1) (w t2) -> b (t1 t2) c h w", t1=tile_size, t2=tile_size)[0]
+        for x in x_list
+    ])  # [n_x, n_tiles, c, h, w]
+    eps_guide_stack = torch.stack([
+        rearrange(x - y0, "b c (h t1) (w t2) -> b (t1 t2) c h w", t1=tile_size, t2=tile_size)[0]
+        for x in x_list
+    ])  # [n_x, n_tiles, c, h, w]
+    del x_list
+
+    noise_tiled_stack = torch.stack([
+        rearrange(noise, "b c (h t1) (w t2) -> b (t1 t2) c h w", t1=tile_size, t2=tile_size)[0]
+        for noise in noise_list
+    ])  # [n_x, n_tiles, c, h, w]
+    del noise_list
+
+    noise_flat = noise_tiled_stack.view(noise_tiled_stack.size(0), noise_tiled_stack.size(1), -1)  # [n_x, n_tiles, c*h*w]
+    eps_guide_flat = eps_guide_stack.view(eps_guide_stack.size(0), eps_guide_stack.size(1), -1)  # [n_x, n_tiles, c*h*w]
+
+    cossim_tmp_all = F.cosine_similarity(noise_flat, eps_guide_flat, dim=-1)  # [n_x, n_tiles]
+    del noise_tiled_stack, noise_flat, eps_guide_stack, eps_guide_flat
+
+    if cossim_mode == "forward":
+        indices = cossim_tmp_all.argmax(dim=0) 
+    elif cossim_mode == "reverse":
+        indices = cossim_tmp_all.argmin(dim=0) 
+    elif cossim_mode == "orthogonal":
+        indices = torch.abs(cossim_tmp_all).argmin(dim=0) 
+    elif cossim_mode == "orthogonal_pos":
+        positive_mask = cossim_tmp_all > 0
+        positive_tmp = torch.where(positive_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('inf')))
+        indices = positive_tmp.argmin(dim=0)
+    elif cossim_mode == "orthogonal_neg":
+        negative_mask = cossim_tmp_all < 0
+        negative_tmp = torch.where(negative_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('-inf')))
+        indices = negative_tmp.argmax(dim=0)
+    elif cossim_mode == "orthogonal_posneg":
+        if step % 2 == 0:
+            positive_mask = cossim_tmp_all > 0
+            positive_tmp = torch.where(positive_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('inf')))
+            indices = positive_tmp.argmin(dim=0)
+        else:
+            negative_mask = cossim_tmp_all < 0
+            negative_tmp = torch.where(negative_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('-inf')))
+            indices = negative_tmp.argmax(dim=0)
+    elif cossim_mode == "orthogonal_negpos":
+        if step % 2 == 1:
+            positive_mask = cossim_tmp_all > 0
+            positive_tmp = torch.where(positive_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('inf')))
+            indices = positive_tmp.argmin(dim=0)
+        else:
+            negative_mask = cossim_tmp_all < 0
+            negative_tmp = torch.where(negative_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('-inf')))
+            indices = negative_tmp.argmax(dim=0)
+    elif cossim_mode == "forward_reverse":
+        if step % 2 == 0:
+            indices = cossim_tmp_all.argmax(dim=0)
+        else:
+            indices = cossim_tmp_all.argmin(dim=0)
+    elif cossim_mode == "reverse_forward":
+        if step % 2 == 1:
+            indices = cossim_tmp_all.argmax(dim=0)
+        else:
+            indices = cossim_tmp_all.argmin(dim=0)
+    elif cossim_mode == "orthogonal_reverse":
+        if step % 2 == 0:
+            indices = torch.abs(cossim_tmp_all).argmin(dim=0)
+        else:
+            indices = cossim_tmp_all.argmin(dim=0)
+    elif cossim_mode == "reverse_orthogonal":
+        if step % 2 == 1:
+            indices = torch.abs(cossim_tmp_all).argmin(dim=0)
+        else:
+            indices = cossim_tmp_all.argmin(dim=0)
+    else:
+        target_value = float(cossim_mode)
+        indices = torch.abs(cossim_tmp_all - target_value).argmin(dim=0)  
+
+    x_tiled_out = x_tiled_stack[indices, torch.arange(indices.size(0))]  # [n_tiles, c, h, w]
+    del x_tiled_stack
+
+    x_tiled_out = x_tiled_out.unsqueeze(0)  
+    x_detiled = rearrange(x_tiled_out, "b (t1 t2) c h w -> b c (h t1) (w t2)", t1=tile_size, t2=tile_size)
+
+    return x_detiled
+
+
+
+
+
+
+def get_collinear(x, y):
+
+    y_flat = y.view(y.size(0), -1).clone()
+    x_flat = x.view(x.size(0), -1).clone()
+
+    y_flat /= y_flat.norm(dim=-1, keepdim=True)
+    x_proj_y = torch.sum(x_flat * y_flat, dim=-1, keepdim=True) * y_flat
+
+    return x_proj_y.view_as(x)
+
+
+def get_orthogonal(x, y):
+
+    y_flat = y.view(y.size(0), -1).clone()
+    x_flat = x.view(x.size(0), -1).clone()
+
+    y_flat /= y_flat.norm(dim=-1, keepdim=True)
+    x_proj_y = torch.sum(x_flat * y_flat, dim=-1, keepdim=True) * y_flat
+    
+    x_ortho_y = x_flat - x_proj_y 
+
+    return x_ortho_y.view_as(x)
+
+
+
+def get_orthogonal_noise_from_channelwise(*refs, max_iter=500, max_score=1e-15):
+    noise, *refs = refs
+    noise_tmp = noise.clone()
+    #b,c,h,w = noise.shape
+    if (noise.dim() == 4):
+        b,ch,h,w = noise.shape
+    elif (noise.dim() == 5):
+        b,ch,t,h,w = noise.shape
+    
+    for i in range(max_iter):
+        noise_tmp = gram_schmidt_channels_optimized(noise_tmp, *refs)
+        
+        cossim_scores = []
+        for ref in refs:
+            #for c in range(noise.shape[-3]):
+            for c in range(ch):
+                cossim_scores.append(get_cosine_similarity(noise_tmp[0][c], ref[0][c]).abs())
+            cossim_scores.append(get_cosine_similarity(noise_tmp[0], ref[0]).abs())
+            
+        if max(cossim_scores) < max_score:
+            break
+    
+    return noise_tmp
+
+
+
+def gram_schmidt_channels_optimized(A, *refs):
+    if (A.dim() == 4):
+        b,c,h,w = A.shape
+    elif (A.dim() == 5):
+        b,c,t,h,w = A.shape
+
+    A_flat = A.view(b, c, -1)  
+    
+    for ref in refs:
+        ref_flat = ref.view(b, c, -1).clone()  
+
+        ref_flat /= ref_flat.norm(dim=-1, keepdim=True) 
+
+        proj_coeff = torch.sum(A_flat * ref_flat, dim=-1, keepdim=True)  
+        projection = proj_coeff * ref_flat 
+
+        A_flat -= projection
+
+    return A_flat.view_as(A)
+
+
+
+class NoiseStepHandlerOSDE:
+    def __init__(self, x, eps=None, data=None, x_init=None, guide=None, guide_bkg=None):
+        self.noise = None
+        self.x = x
+        self.eps = eps
+        self.data = data
+        self.x_init = x_init
+        self.guide = guide
+        self.guide_bkg = guide_bkg
+        
+        self.eps_list = None
+
+        self.noise_cossim_map = {
+            "eps_orthogonal":              [self.noise, self.eps],
+            "eps_data_orthogonal":         [self.noise, self.eps, self.data],
+
+            "data_orthogonal":             [self.noise, self.data],
+            "xinit_orthogonal":            [self.noise, self.x_init],
+            
+            "x_orthogonal":                [self.noise, self.x],
+            "x_data_orthogonal":           [self.noise, self.x, self.data],
+            "x_eps_orthogonal":            [self.noise, self.x, self.eps],
+
+            "x_eps_data_orthogonal":       [self.noise, self.x, self.eps, self.data],
+            "x_eps_data_xinit_orthogonal": [self.noise, self.x, self.eps, self.data, self.x_init],
+            
+            "x_eps_guide_orthogonal":      [self.noise, self.x, self.eps, self.guide],
+            "x_eps_guide_bkg_orthogonal":  [self.noise, self.x, self.eps, self.guide_bkg],
+            
+            "noise_orthogonal":            [self.noise, self.x_init],
+            
+            "guide_orthogonal":            [self.noise, self.guide],
+            "guide_bkg_orthogonal":        [self.noise, self.guide_bkg],
+        }
+
+    def check_cossim_source(self, source):
+        return source in self.noise_cossim_map
+
+    def get_ortho_noise(self, noise, prev_noises=None, max_iter=100, max_score=1e-7, NOISE_COSSIM_SOURCE="eps_orthogonal"):
+        
+        if NOISE_COSSIM_SOURCE not in self.noise_cossim_map:
+            raise ValueError(f"Invalid NOISE_COSSIM_SOURCE: {NOISE_COSSIM_SOURCE}")
+        
+        self.noise_cossim_map[NOISE_COSSIM_SOURCE][0] = noise
+
+        params = self.noise_cossim_map[NOISE_COSSIM_SOURCE]
+        
+        noise = get_orthogonal_noise_from_channelwise(*params, max_iter=max_iter, max_score=max_score)
+        
+        return noise
+
+
+
+def handle_tiled_etc_noise_steps(x_0, x, x_prenoise, x_init, eps, denoised, y0, y0_inv, step, 
+                                 rk_type, rk, sigma_up, sigma, sigma_next, alpha_ratio, s_noise, noise_mode, SDE_NOISE_EXTERNAL, sde_noise_t,
+                                 NOISE_COSSIM_SOURCE, NOISE_COSSIM_MODE, noise_cossim_tile_size, noise_cossim_iterations,
+                                 extra_options):
+    
+    x_tmp, cossim_tmp, noise_tmp_list = [], [], []
+    if step > int(get_extra_options_kv("noise_cossim_end_step", "10000", extra_options)):
+        NOISE_COSSIM_SOURCE = get_extra_options_kv("noise_cossim_takeover_source", "eps", extra_options)
+        NOISE_COSSIM_MODE   = get_extra_options_kv("noise_cossim_takeover_mode", "forward", extra_options)
+        noise_cossim_tile_size   = int(get_extra_options_kv("noise_cossim_takeover_tile", str(noise_cossim_tile_size), extra_options))
+        noise_cossim_iterations   = int(get_extra_options_kv("noise_cossim_takeover_iterations", str(noise_cossim_iterations), extra_options))
+        
+    for i in range(noise_cossim_iterations):
+        x_tmp.append(rk.add_noise_post(x, sigma_up, sigma, sigma_next, alpha_ratio, s_noise, noise_mode, SDE_NOISE_EXTERNAL, sde_noise_t)    )#y0, lgw, sigma_down are currently unused
+        noise_tmp = x_tmp[i] - x
+        if extra_options_flag("noise_noise_zscore_norm", extra_options):
+            noise_tmp = (noise_tmp - noise_tmp.mean()) / noise_tmp.std()
+        if extra_options_flag("noise_eps_zscore_norm", extra_options):
+            eps = (eps - eps.mean()) / eps.std()
+        if   NOISE_COSSIM_SOURCE in ("eps_tiled", "guide_epsilon_tiled", "guide_bkg_epsilon_tiled", "iig_tiled"):
+            noise_tmp_list.append(noise_tmp)
+        if   NOISE_COSSIM_SOURCE == "eps":
+            cossim_tmp.append(get_cosine_similarity(eps, noise_tmp))
+        if   NOISE_COSSIM_SOURCE == "eps_ch":
+            cossim_total = torch.zeros_like(eps[0][0][0][0])
+            for ch in range(eps.shape[1]):
+                cossim_total += get_cosine_similarity(eps[0][ch], noise_tmp[0][ch])
+            cossim_tmp.append(cossim_total)
+        elif NOISE_COSSIM_SOURCE == "data":
+            cossim_tmp.append(get_cosine_similarity(denoised, noise_tmp))
+        elif NOISE_COSSIM_SOURCE == "latent":
+            cossim_tmp.append(get_cosine_similarity(x_prenoise, noise_tmp))
+        elif NOISE_COSSIM_SOURCE == "x_prenoise":
+            cossim_tmp.append(get_cosine_similarity(x_prenoise, x_tmp[i]))
+        elif NOISE_COSSIM_SOURCE == "x":
+            cossim_tmp.append(get_cosine_similarity(x, x_tmp[i]))
+        elif NOISE_COSSIM_SOURCE == "x_data":
+            cossim_tmp.append(get_cosine_similarity(denoised, x_tmp[i]))
+        elif NOISE_COSSIM_SOURCE == "x_init_vs_noise":
+            cossim_tmp.append(get_cosine_similarity(x_init, noise_tmp))
+        elif NOISE_COSSIM_SOURCE == "mom":
+            cossim_tmp.append(get_cosine_similarity(denoised, x + sigma_next*noise_tmp))
+        elif NOISE_COSSIM_SOURCE == "guide":
+            cossim_tmp.append(get_cosine_similarity(y0, x_tmp[i]))
+        elif NOISE_COSSIM_SOURCE == "guide_bkg":
+            cossim_tmp.append(get_cosine_similarity(y0_inv, x_tmp[i]))
+            
+    if step < int(get_extra_options_kv("noise_cossim_start_step", "0", extra_options)):
+        x = x_tmp[0]
+
+    elif (NOISE_COSSIM_SOURCE == "eps_tiled"):
+        x = noise_cossim_eps_tiled(x_tmp, eps, noise_tmp_list, cossim_mode=NOISE_COSSIM_MODE, tile_size=noise_cossim_tile_size, step=step)
+    elif (NOISE_COSSIM_SOURCE == "guide_epsilon_tiled"):
+        x = noise_cossim_guide_eps_tiled(x_0, x_tmp, y0, noise_tmp_list, cossim_mode=NOISE_COSSIM_MODE, tile_size=noise_cossim_tile_size, step=step, sigma=sigma, rk_type=rk_type)
+    elif (NOISE_COSSIM_SOURCE == "guide_bkg_epsilon_tiled"):
+        x = noise_cossim_guide_eps_tiled(x_0, x_tmp, y0_inv, noise_tmp_list, cossim_mode=NOISE_COSSIM_MODE, tile_size=noise_cossim_tile_size, step=step, sigma=sigma, rk_type=rk_type)
+    elif (NOISE_COSSIM_SOURCE == "guide_tiled"):
+        x = noise_cossim_guide_tiled(x_tmp, y0, cossim_mode=NOISE_COSSIM_MODE, tile_size=noise_cossim_tile_size, step=step)
+    elif (NOISE_COSSIM_SOURCE == "guide_bkg_tiled"):
+        x = noise_cossim_guide_tiled(x_tmp, y0_inv, cossim_mode=NOISE_COSSIM_MODE, tile_size=noise_cossim_tile_size)
+    else:
+        for i in range(len(x_tmp)):
+            if   (NOISE_COSSIM_MODE == "forward") and (cossim_tmp[i] == max(cossim_tmp)):
+                x = x_tmp[i]
+                break
+            elif (NOISE_COSSIM_MODE == "reverse") and (cossim_tmp[i] == min(cossim_tmp)):
+                x = x_tmp[i]
+                break
+            elif (NOISE_COSSIM_MODE == "orthogonal") and (abs(cossim_tmp[i]) == min(abs(val) for val in cossim_tmp)):
+                x = x_tmp[i]
+                break
+            elif (NOISE_COSSIM_MODE != "forward") and (NOISE_COSSIM_MODE != "reverse") and (NOISE_COSSIM_MODE != "orthogonal"):
+                x = x_tmp[0]
+                break
+    return x
+
+
+
diff --git a/custom_nodes/RES4LYF/legacy/rk_method.py b/custom_nodes/RES4LYF/legacy/rk_method.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7b616eb55a09a9398198cecac95c246567f6cf0
--- /dev/null
+++ b/custom_nodes/RES4LYF/legacy/rk_method.py
@@ -0,0 +1,334 @@
+import torch
+import re
+
+import torch.nn.functional as F
+import torchvision.transforms as T
+
+from .noise_classes import *
+
+import comfy.model_patcher
+import comfy.supported_models
+
+import itertools 
+
+from .rk_coefficients import *
+from .phi_functions import *
+
+
+
+class RK_Method:
+    def __init__(self, model, name="", method="explicit", dynamic_method=False, device='cuda', dtype=torch.float64):
+        self.model = model
+        self.model_sampling = model.inner_model.inner_model.model_sampling
+        self.device = device
+        self.dtype = dtype
+        
+        self.method = method
+        self.dynamic_method = dynamic_method
+        
+        self.stages = 0
+        self.name = name
+        self.ab = None
+        self.a = None
+        self.b = None
+        self.c = None
+        self.denoised = None
+        self.uncond = None
+        
+        self.rows = 0
+        self.cols = 0
+        
+        self.y0 = None
+        self.y0_inv = None
+        
+        self.sigma_min = model.inner_model.inner_model.model_sampling.sigma_min.to(dtype)
+        self.sigma_max = model.inner_model.inner_model.model_sampling.sigma_max.to(dtype)
+        
+        self.noise_sampler = None
+        
+        self.h_prev = None
+        self.h_prev2 = None
+        self.multistep_stages = 0
+        
+        self.cfg_cw = 1.0
+
+        
+    @staticmethod
+    def is_exponential(rk_type):
+        #if rk_type.startswith(("res", "dpmpp", "ddim", "irk_exp_diag_2s"   )): 
+        if rk_type.startswith(("res", "dpmpp", "ddim", "lawson", "genlawson")): 
+            return True
+        else:
+            return False
+
+    @staticmethod
+    def create(model, rk_type, device='cuda', dtype=torch.float64, name="", method="explicit"):
+        if RK_Method.is_exponential(rk_type):
+            return RK_Method_Exponential(model, name, method, device, dtype)
+        else:
+            return RK_Method_Linear(model, name, method, device, dtype)
+                
+    def __call__(self):
+        raise NotImplementedError("This method got clownsharked!")
+    
+    def model_epsilon(self, x, sigma, **extra_args):
+        s_in = x.new_ones([x.shape[0]])
+        denoised = self.model(x, sigma * s_in, **extra_args)
+        denoised = self.calc_cfg_channelwise(denoised)
+
+        #return x0 ###################################THIS WORKS ONLY WITH THE MODEL SAMPLING PATCH
+        eps = (x - denoised) / (sigma * s_in).view(x.shape[0], 1, 1, 1)
+        return eps, denoised
+    
+    def model_denoised(self, x, sigma, **extra_args):
+        s_in = x.new_ones([x.shape[0]])
+        denoised = self.model(x, sigma * s_in, **extra_args)
+        denoised = self.calc_cfg_channelwise(denoised)
+        return denoised
+    
+
+
+    def init_noise_sampler(self, x, noise_seed, noise_sampler_type, alpha, k=1., scale=0.1):
+        seed = torch.initial_seed()+1 if noise_seed == -1 else noise_seed
+        if noise_sampler_type == "fractal":
+            self.noise_sampler = NOISE_GENERATOR_CLASSES.get(noise_sampler_type)(x=x, seed=seed, sigma_min=self.sigma_min, sigma_max=self.sigma_max)
+            self.noise_sampler.alpha = alpha
+            self.noise_sampler.k = k
+            self.noise_sampler.scale = scale
+        else:
+            self.noise_sampler = NOISE_GENERATOR_CLASSES_SIMPLE.get(noise_sampler_type)(x=x, seed=seed, sigma_min=self.sigma_min, sigma_max=self.sigma_max)
+            
+    def add_noise_pre(self, x, sigma_up, sigma, sigma_next, alpha_ratio, s_noise, noise_mode, SDE_NOISE_EXTERNAL=False, sde_noise_t=None):
+        if isinstance(self.model_sampling, comfy.model_sampling.CONST) == False and noise_mode == "hard": 
+            return self.add_noise(x, sigma_up, sigma, sigma_next, alpha_ratio, s_noise, SDE_NOISE_EXTERNAL, sde_noise_t)
+        else:
+            return x
+        
+    def add_noise_post(self, x, sigma_up, sigma, sigma_next, alpha_ratio, s_noise, noise_mode, SDE_NOISE_EXTERNAL=False, sde_noise_t=None):
+        if isinstance(self.model_sampling, comfy.model_sampling.CONST) == True   or   (isinstance(self.model_sampling, comfy.model_sampling.CONST) == False and noise_mode != "hard"):
+            return self.add_noise(x, sigma_up, sigma, sigma_next, alpha_ratio, s_noise, SDE_NOISE_EXTERNAL, sde_noise_t)
+        else:
+            return x
+    
+    def add_noise(self, x, sigma_up, sigma, sigma_next, alpha_ratio, s_noise, SDE_NOISE_EXTERNAL, sde_noise_t):
+
+        if sigma_next > 0.0:
+            noise = self.noise_sampler(sigma=sigma, sigma_next=sigma_next)
+            noise = torch.nan_to_num((noise - noise.mean()) / noise.std(), 0.0)
+
+            if SDE_NOISE_EXTERNAL:
+                noise = (1-s_noise) * noise + s_noise * sde_noise_t
+            
+            return alpha_ratio * x + noise * sigma_up * s_noise
+        
+        else:
+            return x
+
+
+    def set_coeff(self, rk_type, h, c1=0.0, c2=0.5, c3=1.0, stepcount=0, sigmas=None, sigma=None, sigma_down=None, extra_options=None):
+        if rk_type == "default": 
+            return
+
+        sigma = sigmas[stepcount]
+        sigma_next = sigmas[stepcount+1]
+        
+        a, b, ci, multistep_stages, FSAL = get_rk_methods(rk_type, h, c1, c2, c3, self.h_prev, self.h_prev2, stepcount, sigmas, sigma, sigma_next, sigma_down, extra_options)
+        
+        self.multistep_stages = multistep_stages
+        
+        self.a = torch.tensor(a, dtype=h.dtype, device=h.device)
+        self.a = self.a.view(*self.a.shape, 1, 1, 1, 1, 1)
+        
+        
+        self.b = torch.tensor(b, dtype=h.dtype, device=h.device)
+        self.b = self.b.view(*self.b.shape, 1, 1, 1, 1, 1)
+        
+        self.c = torch.tensor(ci, dtype=h.dtype, device=h.device)
+        self.rows = self.a.shape[0]
+        self.cols = self.a.shape[1]
+
+
+    def a_k_sum(self, k, row):
+        if len(k.shape) == 4:
+            a_coeff = self.a[row].squeeze(-1)
+            ks = k * a_coeff.sum(dim=0)
+        elif len(k.shape) == 5:
+            a_coeff = self.a[row].squeeze(-1)
+            ks = (k[0:self.cols] * a_coeff).sum(dim=0)
+        elif len(k.shape) == 6:
+            a_coeff = self.a[row]
+            ks = (k[0:self.cols] * a_coeff).sum(dim=0)
+        else:
+            raise ValueError(f"Unexpected k shape: {k.shape}")
+        return ks
+
+    def b_k_sum(self, k, row):
+        if len(k.shape) == 4:
+            b_coeff = self.b[row].squeeze(-1)
+            ks = k * b_coeff.sum(dim=0)
+        elif len(k.shape) == 5:
+            b_coeff = self.b[row].squeeze(-1)
+            ks = (k[0:self.cols] * b_coeff).sum(dim=0)
+        elif len(k.shape) == 6:
+            b_coeff = self.b[row]
+            ks = (k[0:self.cols] * b_coeff).sum(dim=0)
+        else:
+            raise ValueError(f"Unexpected k shape: {k.shape}")
+        return ks
+
+
+    def init_cfg_channelwise(self, x, cfg_cw=1.0, **extra_args):
+        self.uncond = [torch.full_like(x, 0.0)]
+        self.cfg_cw = cfg_cw
+        if cfg_cw != 1.0:
+            def post_cfg_function(args):
+                self.uncond[0] = args["uncond_denoised"]
+                return args["denoised"]
+            model_options = extra_args.get("model_options", {}).copy()
+            extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+        return extra_args
+            
+            
+    def calc_cfg_channelwise(self, denoised):
+        if self.cfg_cw != 1.0:            
+            avg = 0
+            for b, c in itertools.product(range(denoised.shape[0]), range(denoised.shape[1])):
+                avg     += torch.norm(denoised[b][c] - self.uncond[0][b][c])
+            avg  /= denoised.shape[1]
+            
+            for b, c in itertools.product(range(denoised.shape[0]), range(denoised.shape[1])):
+                ratio     = torch.nan_to_num(torch.norm(denoised[b][c] - self.uncond[0][b][c])   /   avg,     0)
+                denoised_new = self.uncond[0] + ratio * self.cfg_cw * (denoised - self.uncond[0])
+            return denoised_new
+        else:
+            return denoised
+        
+        
+
+class RK_Method_Exponential(RK_Method):
+    def __init__(self, model, name="", method="explicit", device='cuda', dtype=torch.float64):
+        super().__init__(model, name, method, device, dtype) 
+        self.exponential = True
+        self.eps_pred = True
+        
+    @staticmethod
+    def alpha_fn(neg_h):
+        return torch.exp(neg_h)
+
+    @staticmethod
+    def sigma_fn(t):
+        return t.neg().exp()
+
+    @staticmethod
+    def t_fn(sigma):
+        return sigma.log().neg()
+    
+    @staticmethod
+    def h_fn(sigma_down, sigma):
+        return -torch.log(sigma_down/sigma)
+
+    def __call__(self, x_0, x, sigma, h, **extra_args):
+
+        denoised = self.model_denoised(x, sigma, **extra_args)
+        epsilon = denoised - x_0
+        
+        """if self.uncond == None:
+            self.uncond = [torch.zeros_like(x)]
+        denoised_u = self.uncond[0].clone()
+        if torch.all(denoised_u == 0):
+            epsilon_u = [torch.zeros_like(x_0)]
+        else:
+            epsilon_u = denoised_u[0] - x_0"""
+        if h is not None:
+            self.h_prev2 = self.h_prev
+            self.h_prev = h
+        #print("MODEL SIGMA: ", round(float(sigma),3))
+        return epsilon, denoised
+    
+    def data_to_vel(self, x, data, sigma):
+        return data - x
+    
+    def get_epsilon(self, x_0, x, y, sigma, sigma_cur, sigma_down=None, unsample_resample_scale=None, extra_options=None):
+        if sigma_down > sigma:
+            sigma_cur = self.sigma_max - sigma_cur.clone()
+        sigma_cur = unsample_resample_scale if unsample_resample_scale is not None else sigma_cur
+
+        if extra_options is not None:
+            if re.search(r"\bpower_unsample\b", extra_options) or re.search(r"\bpower_resample\b", extra_options):
+                if sigma_down is None:
+                    return y - x_0
+                else:
+                    if sigma_down > sigma:
+                        return (x_0 - y) * sigma_cur
+                    else:
+                        return (y - x_0) * sigma_cur
+            else:
+                if sigma_down is None:
+                    return (y - x_0) / sigma_cur
+                else:
+                    if sigma_down > sigma:
+                        return (x_0 - y) / sigma_cur
+                    else:
+                        return (y - x_0) / sigma_cur
+
+
+
+class RK_Method_Linear(RK_Method):
+    def __init__(self, model, name="", method="explicit", device='cuda', dtype=torch.float64):
+        super().__init__(model, name, method, device, dtype) 
+        self.expanential = False
+        self.eps_pred = True
+        
+    @staticmethod
+    def alpha_fn(neg_h):
+        return torch.ones_like(neg_h)
+
+    @staticmethod
+    def sigma_fn(t):
+        return t
+
+    @staticmethod
+    def t_fn(sigma):
+        return sigma
+    
+    @staticmethod
+    def h_fn(sigma_down, sigma):
+        return sigma_down - sigma
+    
+    def __call__(self, x_0, x, sigma, h, **extra_args):
+        #s_in = x.new_ones([x.shape[0]])
+        
+        epsilon, denoised = self.model_epsilon(x, sigma, **extra_args)
+        
+        """if self.uncond == None:
+            self.uncond = [torch.zeros_like(x)]
+        denoised_u = self.uncond[0].clone()
+        if torch.all(denoised_u[0] == 0):
+            epsilon_u = [torch.zeros_like(x_0)]
+        else:
+            epsilon_u  = (x_0 - denoised_u[0]) / (sigma * s_in).view(x.shape[0], 1, 1, 1)"""
+        if h is not None:
+            self.h_prev2 = self.h_prev
+            self.h_prev = h
+        #print("MODEL SIGMA: ", round(float(sigma),3))
+
+        return epsilon, denoised
+
+    def data_to_vel(self, x, data, sigma):
+        return (data - x) / sigma
+    
+    def get_epsilon(self, x_0, x, y, sigma, sigma_cur, sigma_down=None, unsample_resample_scale=None, extra_options=None):
+        if sigma_down > sigma:
+            sigma_cur = self.sigma_max - sigma_cur.clone()
+        sigma_cur = unsample_resample_scale if unsample_resample_scale is not None else sigma_cur
+
+        if sigma_down is None:
+            return (x - y) / sigma_cur
+        else:
+            if sigma_down > sigma:
+                return (y - x) / sigma_cur
+            else:
+                return (x - y) / sigma_cur
+
+
+
diff --git a/custom_nodes/RES4LYF/legacy/rk_sampler.py b/custom_nodes/RES4LYF/legacy/rk_sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb434db771e03634e42138199dea82ae3a7c3552
--- /dev/null
+++ b/custom_nodes/RES4LYF/legacy/rk_sampler.py
@@ -0,0 +1,683 @@
+import torch
+import torch.nn.functional as F
+
+from tqdm.auto import trange
+
+
+from .noise_classes import *
+from .noise_sigmas_timesteps_scaling import get_res4lyf_step_with_model, get_res4lyf_half_step3
+
+from .rk_method import RK_Method
+from .rk_guide_func import *
+
+from .latents import normalize_latent, initialize_or_scale, latent_normalize_channels
+from .helper import get_extra_options_kv, extra_options_flag, get_cosine_similarity, is_RF_model
+from .sigmas import get_sigmas
+
+PRINT_DEBUG=False
+
+
+def prepare_sigmas(model, sigmas):
+    if sigmas[0] == 0.0:      #remove padding used to prevent comfy from adding noise to the latent (for unsampling, etc.)
+        UNSAMPLE = True
+        sigmas = sigmas[1:-1]
+    else: 
+        UNSAMPLE = False
+        
+    if hasattr(model, "sigmas"):
+        model.sigmas = sigmas
+        
+    return sigmas, UNSAMPLE
+
+
+def prepare_step_to_sigma_zero(rk, irk, rk_type, irk_type, model, x, extra_options, alpha, k, noise_sampler_type, cfg_cw=1.0, **extra_args):
+    rk_type_final_step = f"ralston_{rk_type[-2:]}" if rk_type[-2:] in {"2s", "3s"} else "ralston_3s"
+    rk_type_final_step = f"deis_2m" if rk_type[-2:] in {"2m", "3m", "4m"} else rk_type_final_step
+    rk_type_final_step = f"buehler" if rk_type in {"ddim"} else rk_type_final_step
+    rk_type_final_step = get_extra_options_kv("rk_type_final_step", rk_type_final_step, extra_options)
+    rk = RK_Method.create(model, rk_type_final_step, x.device)
+    rk.init_noise_sampler(x, torch.initial_seed() + 1, noise_sampler_type, alpha=alpha, k=k)
+    extra_args =  rk.init_cfg_channelwise(x, cfg_cw, **extra_args)
+
+    if any(element >= 1 for element in irk.c):
+        irk_type_final_step = f"gauss-legendre_{rk_type[-2:]}" if rk_type[-2:] in {"2s", "3s", "4s", "5s"} else "gauss-legendre_2s"
+        irk_type_final_step = f"deis_2m" if rk_type[-2:] in {"2m", "3m", "4m"} else irk_type_final_step
+        irk_type_final_step = get_extra_options_kv("irk_type_final_step", irk_type_final_step, extra_options)
+        irk = RK_Method.create(model, irk_type_final_step, x.device)
+        irk.init_noise_sampler(x, torch.initial_seed() + 100, noise_sampler_type, alpha=alpha, k=k)
+        extra_args =  irk.init_cfg_channelwise(x, cfg_cw, **extra_args)
+    else:
+        irk_type_final_step = irk_type
+
+    eta, eta_var = 0, 0
+    return rk, irk, rk_type_final_step, irk_type_final_step, eta, eta_var, extra_args
+
+
+
+@torch.no_grad()
+def sample_rk(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler_type="gaussian", noise_mode="hard", noise_seed=-1, rk_type="res_2m", implicit_sampler_name="explicit_full",
+              sigma_fn_formula="", t_fn_formula="",
+                  eta=0.0, eta_var=0.0, s_noise=1., d_noise=1., alpha=-1.0, k=1.0, scale=0.1, c1=0.0, c2=0.5, c3=1.0, implicit_steps=0, reverse_weight=0.0,
+                  latent_guide=None, latent_guide_inv=None, latent_guide_weight=0.0, latent_guide_weight_inv=0.0, latent_guide_weights=None, latent_guide_weights_inv=None, guide_mode="", 
+                  GARBAGE_COLLECT=False, mask=None, mask_inv=None, LGW_MASK_RESCALE_MIN=True, sigmas_override=None, unsample_resample_scales=None,regional_conditioning_weights=None, sde_noise=[],
+                  extra_options="",
+                  etas=None, s_noises=None, momentums=None, guides=None, cfgpp=0.0, cfg_cw = 1.0,regional_conditioning_floors=None, frame_weights_grp=None, eta_substep=0.0, noise_mode_sde_substep="hard", guide_cossim_cutoff_=1.0, guide_bkg_cossim_cutoff_=1.0,
+                  ):
+    extra_args = {} if extra_args is None else extra_args
+
+    noise_cossim_iterations         = int(get_extra_options_kv("noise_cossim_iterations",         "1",          extra_options))
+    noise_substep_cossim_iterations = int(get_extra_options_kv("noise_substep_cossim_iterations", "1",          extra_options))
+    NOISE_COSSIM_MODE               =     get_extra_options_kv("noise_cossim_mode",               "orthogonal", extra_options)
+    NOISE_COSSIM_SOURCE             =     get_extra_options_kv("noise_cossim_source",             "x_eps_data_xinit_orthogonal",       extra_options)
+    NOISE_SUBSTEP_COSSIM_MODE       =     get_extra_options_kv("noise_substep_cossim_mode",       "orthogonal", extra_options)
+    NOISE_SUBSTEP_COSSIM_SOURCE     =     get_extra_options_kv("noise_substep_cossim_source",     "x_eps_data_xinit_orthogonal",       extra_options)
+    SUBSTEP_SKIP_LAST               =     get_extra_options_kv("substep_skip_last",               "false",      extra_options) == "true" 
+    noise_cossim_tile_size          = int(get_extra_options_kv("noise_cossim_tile",               "2",          extra_options))
+    noise_substep_cossim_tile_size  = int(get_extra_options_kv("noise_substep_cossim_tile",       "2",          extra_options))
+    
+    substep_eta           = float(get_extra_options_kv("substep_eta",           str(eta_substep),  extra_options))
+    substep_noise_scaling = float(get_extra_options_kv("substep_noise_scaling", "0.0",  extra_options))
+    substep_noise_mode    =       get_extra_options_kv("substep_noise_mode",    noise_mode_sde_substep, extra_options)
+    
+    substep_eta_start_step = int(get_extra_options_kv("substep_noise_start_step",  "-1", extra_options))
+    substep_eta_final_step = int(get_extra_options_kv("substep_noise_final_step", "-1", extra_options))
+    
+    noise_substep_cossim_max_iter  =   int(get_extra_options_kv("noise_substep_cossim_max_iter",  "5",   extra_options))
+    noise_cossim_max_iter          =   int(get_extra_options_kv("noise_cossim_max_iter",          "5",   extra_options))
+    noise_substep_cossim_max_score = float(get_extra_options_kv("noise_substep_cossim_max_score", "1e-7", extra_options))
+    noise_cossim_max_score         = float(get_extra_options_kv("noise_cossim_max_score",         "1e-7", extra_options))
+    
+    c1 = c1_ = float(get_extra_options_kv("c1", str(c1), extra_options))
+    c2 = c2_ = float(get_extra_options_kv("c2", str(c2), extra_options))
+    c3 = c3_ = float(get_extra_options_kv("c3", str(c3), extra_options))
+    
+    guide_skip_steps = int(get_extra_options_kv("guide_skip_steps", 0, extra_options))        
+
+    cfg_cw = float(get_extra_options_kv("cfg_cw", str(cfg_cw), extra_options))
+    
+    MODEL_SAMPLING = model.inner_model.inner_model.model_sampling
+    
+    s_in, s_one = x.new_ones([x.shape[0]]), x.new_ones([1])
+    default_dtype = getattr(torch, get_extra_options_kv("default_dtype", "float64", extra_options), torch.float64)   
+    max_steps=10000
+    
+    
+    
+    if sigmas_override is not None:
+        sigmas = sigmas_override.clone()
+    sigmas = sigmas.clone() * d_noise
+    sigmas, UNSAMPLE = prepare_sigmas(model, sigmas)
+    
+    SDE_NOISE_EXTERNAL = False
+    if sde_noise is not None:
+        if len(sde_noise) > 0 and sigmas[1] > sigmas[2]:
+            SDE_NOISE_EXTERNAL = True
+            sigma_up_total = torch.zeros_like(sigmas[0])
+            for i in range(len(sde_noise)-1):
+                sigma_up_total += sigmas[i+1]
+            eta = eta / sigma_up_total
+
+    irk_type = implicit_sampler_name
+    if implicit_sampler_name in ("explicit_full", "explicit_diagonal", "none"):
+        irk_type = rk_type
+    
+    rk_type = "buehler" if implicit_steps > 0 and implicit_sampler_name == "explicit_full" else rk_type
+    rk_type = get_extra_options_kv("rk_type", rk_type, extra_options)
+    print("rk_type: ", rk_type)
+
+    rk       = RK_Method.create(model,  rk_type, x.device)
+    irk      = RK_Method.create(model, irk_type, x.device)
+
+    extra_args = irk.init_cfg_channelwise(x, cfg_cw, **extra_args)
+    extra_args =  rk.init_cfg_channelwise(x, cfg_cw, **extra_args)
+
+    rk. init_noise_sampler(x, noise_seed,     noise_sampler_type, alpha=alpha, k=k)
+    irk.init_noise_sampler(x, noise_seed+100, noise_sampler_type, alpha=alpha, k=k)
+
+
+
+    frame_weights, frame_weights_inv = None, None
+    if frame_weights_grp is not None and frame_weights_grp[0] is not None:
+        frame_weights = initialize_or_scale(frame_weights_grp[0], 1.0, max_steps).to(default_dtype)
+        frame_weights = F.pad(frame_weights, (0, max_steps), value=0.0)
+    if frame_weights_grp is not None and frame_weights_grp[1] is not None:
+        frame_weights_inv = initialize_or_scale(frame_weights_grp[1], 1.0, max_steps).to(default_dtype)
+        frame_weights_inv = F.pad(frame_weights_inv, (0, max_steps), value=0.0)
+    frame_weights_grp = (frame_weights, frame_weights_inv)
+
+    LG = LatentGuide(guides, x, model, sigmas, UNSAMPLE, LGW_MASK_RESCALE_MIN, extra_options)
+    x = LG.init_guides(x, rk.noise_sampler)
+    
+    y0, y0_inv = LG.y0, LG.y0_inv
+    lgw, lgw_inv = LG.lgw, LG.lgw_inv
+    guide_mode = LG.guide_mode
+
+
+
+    denoised, denoised_prev, eps, eps_prev = [torch.zeros_like(x) for _ in range(4)]
+    prev_noises = []
+    x_init = x.clone()
+    
+    
+    
+    for step in trange(len(sigmas)-1, disable=disable):
+
+        sigma, sigma_next = sigmas[step], sigmas[step+1]
+        unsample_resample_scale = float(unsample_resample_scales[step]) if unsample_resample_scales is not None else None
+        if regional_conditioning_weights is not None:
+            extra_args['model_options']['transformer_options']['regional_conditioning_weight'] = regional_conditioning_weights[step]
+            extra_args['model_options']['transformer_options']['regional_conditioning_floor']  = regional_conditioning_floors [step]
+        else:
+            extra_args['model_options']['transformer_options']['regional_conditioning_weight'] = 0.0
+            extra_args['model_options']['transformer_options']['regional_conditioning_floor']  = 0.0
+        
+        eta = eta_var = etas[step] if etas is not None else eta
+        s_noise = s_noises[step] if s_noises is not None else s_noise
+        
+  
+        if sigma_next == 0:
+            rk, irk, rk_type, irk_type, eta, eta_var, extra_args = prepare_step_to_sigma_zero(rk, irk, rk_type, irk_type, model, x, extra_options, alpha, k, noise_sampler_type, cfg_cw=cfg_cw, **extra_args)
+
+        sigma_up, sigma, sigma_down, alpha_ratio = get_res4lyf_step_with_model(model, sigma, sigma_next, eta, noise_mode)
+        h     =  rk.h_fn(sigma_down, sigma)
+        h_irk = irk.h_fn(sigma_down, sigma)
+        
+        c2, c3 = get_res4lyf_half_step3(sigma, sigma_down, c2_, c3_, t_fn=rk.t_fn, sigma_fn=rk.sigma_fn, t_fn_formula=t_fn_formula, sigma_fn_formula=sigma_fn_formula)
+        
+        rk. set_coeff(rk_type,  h,     c1, c2, c3, step, sigmas, sigma, sigma_down, extra_options)
+        irk.set_coeff(irk_type, h_irk, c1, c2, c3, step, sigmas, sigma, sigma_down, extra_options)
+        
+        s_       = [(  rk.sigma_fn( rk.t_fn(sigma) +     h*c_)) * s_one for c_ in   rk.c]
+        s_irk_rk = [(  rk.sigma_fn( rk.t_fn(sigma) +     h*c_)) * s_one for c_ in  irk.c]
+        s_irk    = [( irk.sigma_fn(irk.t_fn(sigma) + h_irk*c_)) * s_one for c_ in  irk.c]
+        
+        if step == 0 or step == guide_skip_steps:
+            x_, data_, data_u, eps_ = (torch.zeros(max(rk.rows, irk.rows) + 2, *x.shape, dtype=x.dtype, device=x.device) for step in range(4))
+
+        
+        sde_noise_t = None
+        if SDE_NOISE_EXTERNAL:
+            if step >= len(sde_noise):
+                SDE_NOISE_EXTERNAL=False
+            else:
+                sde_noise_t = sde_noise[step]
+                
+
+        x_prenoise = x.clone()
+        x_[0] = x
+        if sigma_up > 0:
+            x_[0] = rk.add_noise_pre(x, sigma_up, sigma, sigma_next, alpha_ratio, s_noise, noise_mode, SDE_NOISE_EXTERNAL, sde_noise_t) #y0, lgw, sigma_down are currently unused
+        
+        x_0 = x_[0].clone()
+        
+        for ms in range(rk.multistep_stages):
+            if RK_Method.is_exponential(rk_type):
+                eps_ [rk.multistep_stages - ms] = -(x_0 - data_ [rk.multistep_stages - ms])
+            else:
+                eps_ [rk.multistep_stages - ms] =  (x_0 - data_ [rk.multistep_stages - ms]) / sigma
+
+        if implicit_steps == 0 or implicit_sampler_name == "explicit_diagonal": 
+            for row in range(rk.rows - rk.multistep_stages):
+                for exim_iter in range(implicit_steps+1):
+                    sub_sigma_up, sub_sigma, sub_sigma_next, sub_sigma_down, sub_alpha_ratio = 0, s_[row], s_[row+1], s_[row+1], 1
+                        
+                    if (substep_eta_final_step < 0 and step == len(sigmas)-1+substep_eta_final_step)   or   (substep_eta_final_step > 0 and step > substep_eta_final_step):
+                        sub_sigma_up, sub_sigma, sub_sigma_down, sub_alpha_ratio = 0, s_[row], s_[row+1], 1
+                        
+                    edsef=1
+                    if extra_options_flag("explicit_diagonal_eta_substep_factors", extra_options):
+                        #value_str = get_extra_options_list("explicit_diagonal_eta_substep_factors", "", extra_options)
+                        #float_list = [float(item.strip()) for item in value_str.split(',') if item.strip()]
+                        float_list = get_extra_options_list("explicit_diagonal_eta_substep_factors", "", extra_options, ret_type=float)
+                        edsef = float_list[exim_iter]
+                    nsef = 1
+                    if extra_options_flag("noise_eta_substep_factors", extra_options):
+                        #value_str = get_extra_options_list("noise_eta_substep_factors", "", extra_options)
+                        #nsef_list = [float(item.strip()) for item in value_str.split(',') if item.strip()]
+                        nsef_list = get_extra_options_list("noise_eta_substep_factors", "", extra_options, ret_type=float)
+                        nsef = nsef_list[row]
+                    if exim_iter > 0 and rk_type.endswith("m") and step >= int(rk_type[-2]): 
+                        sub_sigma_up, sub_sigma, sub_sigma_down, sub_alpha_ratio = get_res4lyf_step_with_model(model, sigma, sigma_next, substep_eta*edsef*nsef, substep_noise_mode)
+                        sub_sigma_next = sigma_next
+                    if (row > 0 and not extra_options_flag("disable_rough_noise", extra_options)): # and s_[row-1] >= s_[row]:
+                        sub_sigma_up, sub_sigma, sub_sigma_down, sub_alpha_ratio = get_res4lyf_step_with_model(model, s_[row-1], s_[row], substep_eta*edsef*nsef, substep_noise_mode)
+                        sub_sigma_next = s_[row]
+                        
+                    if row > 0 and substep_eta*edsef*nsef > 0 and row < rk.rows and ((SUBSTEP_SKIP_LAST == False) or (row < rk.rows - rk.multistep_stages - 1))   and   (sub_sigma_down > 0) and sigma_next > 0:
+                        substep_noise_scaling_ratio = s_[row+1]/sub_sigma_down
+                        eps_[row-1] *= 1 + substep_noise_scaling*(substep_noise_scaling_ratio-1)
+
+                    h_new = h.clone()
+                    if (rk_type.endswith("m") and step >= int(rk_type[-2]) and sub_sigma_up > 0) or (row > 0 and sub_sigma_up > 0):
+                        if extra_options_flag("substep_eta_c_row_plus_one", extra_options):
+                            h_new = (rk.h_fn(sub_sigma_down, sigma) / rk.c[row+1])[0]  
+                        else:
+                            if exim_iter > 0 and rk_type.endswith("m") and step >= int(rk_type[-2]): 
+                                c_val = -rk.h_prev/h
+                                h_new = (rk.h_fn(sub_sigma_down, sigma)) / c_val
+                            else:   
+                                h_new = (rk.h_fn(sub_sigma_down, sigma) / rk.c[row])[0]   #used to be rk.c[row+1]
+
+                        s_new_       = [(  rk.sigma_fn( rk.t_fn(sigma) +     h_new*c_)) * s_one for c_ in   rk.c]
+                        """print("step, row: ", step, row)
+                        print("h, h_new: ", h.item(), h_new.item())
+                        print("s_: ", s_)
+                        print("s_new_: ", s_new_)
+                        print("sub_sigma_up, sub_sigma, sub_sigma_next, sub_sigma_down, sub_alpha_ratio: ", sub_sigma_up.item(), sub_sigma.item(), sub_sigma_next.item(), sub_sigma_down.item(), sub_alpha_ratio.item())"""
+                    # UPDATE
+                    #print("UPDATE: step,row,h_new: ", step, row, h_new.item())
+                    x_[row+1] = x_0 + h_new * rk.a_k_sum(eps_, row)
+                    if row > 0:
+                        if PRINT_DEBUG:
+                            print("A: step,row,h,h_new: \n", step, row, round(float(h.item()),3), round(float(h_new.item()),3))
+
+                    #print("step, row, exim_iter: ", step, row, exim_iter)
+
+
+                    # NOISE ADD
+                    if is_RF_model(model) == True   or   (is_RF_model(model) == False and noise_mode != "hard"):
+                        if (exim_iter < implicit_steps and sub_sigma_up > 0) or ((row > 0) and (sub_sigma_up > 0) and ((SUBSTEP_SKIP_LAST == False) or (row < rk.rows - rk.multistep_stages - 1))):
+                            if PRINT_DEBUG:
+                                print("A: sub_sigma_up, sub_sigma, sub_sigma_next, sub_sigma_down, sub_alpha_ratio: \n", round(float(sub_sigma_up),3), round(float(sub_sigma),3), round(float(sub_sigma_next),3), round(float(sub_sigma_down),3), round(float(sub_alpha_ratio),3))
+
+                            data_tmp = denoised_prev if data_[row-1].sum() == 0 else data_[row-1]
+                            eps_tmp  = eps_prev      if  eps_[row-1].sum() == 0 else eps_ [row-1]
+                            Osde = NoiseStepHandlerOSDE(x_[row+1], eps_tmp, data_tmp, x_init, y0, y0_inv)
+                            if Osde.check_cossim_source(NOISE_SUBSTEP_COSSIM_SOURCE):
+                                noise = rk.noise_sampler(sigma=sub_sigma, sigma_next=sub_sigma_next) 
+                                noise_osde = Osde.get_ortho_noise(noise, prev_noises, max_iter=noise_substep_cossim_max_iter, max_score=noise_substep_cossim_max_score, NOISE_COSSIM_SOURCE=NOISE_SUBSTEP_COSSIM_SOURCE)
+                                x_[row+1] = sub_alpha_ratio * x_[row+1] + sub_sigma_up * noise_osde * s_noise
+                            elif extra_options_flag("noise_substep_cossim", extra_options):
+                                x_[row+1] = handle_tiled_etc_noise_steps(x_0, x_[row+1], x_prenoise, x_init, eps_tmp, data_tmp, y0, y0_inv, row, rk_type, rk, sub_sigma_up, s_[row-1], s_[row], sub_alpha_ratio, s_noise, substep_noise_mode, SDE_NOISE_EXTERNAL, sde_noise_t,
+                                    NOISE_SUBSTEP_COSSIM_SOURCE, NOISE_SUBSTEP_COSSIM_MODE, noise_substep_cossim_tile_size, noise_substep_cossim_iterations, extra_options)
+                            else:
+                                x_[row+1] = rk.add_noise_post(x_[row+1], sub_sigma_up, sub_sigma, sub_sigma_next, sub_alpha_ratio, s_noise, substep_noise_mode, SDE_NOISE_EXTERNAL, sde_noise_t)
+
+
+                    # MODEL CALL
+                    if step < guide_skip_steps:
+                        eps_row, eps_row_inv = get_guide_epsilon_substep(x_0, x_, y0, y0_inv, s_, row, rk_type)
+                        eps_[row] = LG.mask * eps_row   +   (1-LG.mask) * eps_row_inv
+                    else:
+                        if implicit_steps == 0 or row > 0 or (row == 0 and not extra_options_flag("explicit_diagonal_implicit_predictor", extra_options)):
+                            eps_[row], data_[row] = rk(x_0, x_[row+1], s_[row], h, **extra_args)   
+                            #print("exim: ", step, row, exim_iter)
+                        else:
+                            if extra_options_flag("explicit_diagonal_implicit_predictor_disable_noise", extra_options):
+                                sub_sigma_up, sub_sigma_down, sub_alpha_ratio = sub_sigma_up*0, sub_sigma_next, sub_alpha_ratio/sub_alpha_ratio
+                            eps_[row], data_[row] = rk(x_0, x_[row+1], s_[row], h, **extra_args)
+                            eps_, x_ = LG.process_guides_substep(x_0, x_, eps_, data_, row, step, sigma, sigma_next, sigma_down, s_, unsample_resample_scale, rk, rk_type, extra_options, frame_weights_grp)
+                            h_mini = rk.h_fn(sub_sigma_down, sub_sigma)
+                            x_[row+1] = x_0 + h_mini * eps_[row]
+                            
+                            Osde = NoiseStepHandlerOSDE(x_[row+1], eps_[row], data_[row], x_init, y0, y0_inv)
+                            if Osde.check_cossim_source(NOISE_SUBSTEP_COSSIM_SOURCE):
+                                noise = rk.noise_sampler(sigma=sub_sigma, sigma_next=sub_sigma_next)
+                                noise_osde = Osde.get_ortho_noise(noise, prev_noises, max_iter=noise_substep_cossim_max_iter, max_score=noise_substep_cossim_max_score, NOISE_COSSIM_SOURCE=NOISE_SUBSTEP_COSSIM_SOURCE)
+                                x_[row+1] = sub_alpha_ratio * x_[row+1] + sub_sigma_up * noise_osde * s_noise
+                            else:
+                                x_[row+1] = rk.add_noise_post(x_[row+1], sub_sigma_up, sub_sigma, sub_sigma_next, sub_alpha_ratio, s_noise, substep_noise_mode, SDE_NOISE_EXTERNAL, sde_noise_t)
+                                
+                            for inner_exim_iter in range(implicit_steps): # implicit buehler update to find Yn+1
+                                #print("inner_exim: ", step, row, inner_exim_iter)
+                                eps_[row], data_[row] = rk(x_0, x_[row+1], s_[row+1], h, **extra_args)
+                                eps_, x_ = LG.process_guides_substep(x_0, x_, eps_, data_, row, step, sigma, sigma_next, sigma_down, s_, unsample_resample_scale, rk, rk_type, extra_options, frame_weights_grp)
+                                x_[row+1] = x_0 + h_mini * eps_[row]
+                                
+                                Osde = NoiseStepHandlerOSDE(x_[row+1], eps_[row], data_[row], x_init, y0, y0_inv)
+                                if Osde.check_cossim_source(NOISE_SUBSTEP_COSSIM_SOURCE):
+                                    noise = rk.noise_sampler(sigma=sub_sigma, sigma_next=sub_sigma_next)
+                                    noise_osde = Osde.get_ortho_noise(noise, prev_noises, max_iter=noise_substep_cossim_max_iter, max_score=noise_substep_cossim_max_score, NOISE_COSSIM_SOURCE=NOISE_SUBSTEP_COSSIM_SOURCE)
+                                    x_[row+1] = sub_alpha_ratio * x_[row+1] + sub_sigma_up * noise_osde * s_noise
+                                else:
+                                    x_[row+1] = rk.add_noise_post(x_[row+1], sub_sigma_up, sub_sigma, sub_sigma_next, sub_alpha_ratio, s_noise, substep_noise_mode, SDE_NOISE_EXTERNAL, sde_noise_t)
+
+
+
+                        if extra_options_flag("rk_linear_straight", extra_options):
+                            eps_[row] = (x_0 - data_[row]) / sigma
+                        if sub_sigma_up > 0 and not RK_Method.is_exponential(rk_type):
+                            eps_[row] = (x_0 - data_[row]) / sigma
+
+
+                    # GUIDES 
+                    eps_row_tmp, x_row_tmp = eps_[row].clone(), x_[row+1].clone()
+                    eps_, x_ = LG.process_guides_substep(x_0, x_, eps_, data_, row, step, sigma, sigma_next, sigma_down, s_, unsample_resample_scale, rk, rk_type, extra_options, frame_weights_grp)
+
+                    if extra_options_flag("explicit_diagonal_eps_proj_factors", extra_options):
+                        #value_str = get_extra_options_list("explicit_diagonal_eps_proj_factors", "", extra_options)
+                        #float_list = [float(item.strip()) for item in value_str.split(',') if item.strip()]
+                        value_str = get_extra_options_list("explicit_diagonal_eps_proj_factors", "", extra_options, ret_type=float)
+                        eps_[row] = (float_list[exim_iter]) * eps_[row]   +   (1-float_list[exim_iter]) * eps_row_tmp
+                        x_[row+1] = (float_list[exim_iter]) * x_[row+1]   +   (1-float_list[exim_iter]) * x_row_tmp
+
+                    if row > 0 and exim_iter <= implicit_steps and implicit_steps > 0:
+                        eps_[row-1] = eps_[row]
+                    
+                    if implicit_steps > 0 and row == 0:
+                        break
+
+            if PRINT_DEBUG:
+                print("B: step,h,h_new: \n", step, round(float(h.item()),3), round(float(h_new.item()),3))
+                print("B: sub_sigma_up, sub_sigma, sub_sigma_next, sub_sigma_down, sub_alpha_ratio: \n", round(float(sub_sigma_up),3), round(float(sub_sigma),3), round(float(sub_sigma_next),3), round(float(sub_sigma_down),3), round(float(sub_alpha_ratio),3))
+
+            x = x_0 + h * rk.b_k_sum(eps_, 0)
+                    
+            denoised = x_0 + ((sigma / (sigma - sigma_down)) *  h) * rk.b_k_sum(eps_, 0) 
+            eps = x - denoised
+            x = LG.process_guides_poststep(x, denoised, eps, step, extra_options)
+            
+
+
+
+        # DIAGONALLY IMPLICIT
+        elif implicit_sampler_name=="explicit_diagonal_alt" or any(irk_type.startswith(prefix) for prefix in {"crouzeix", "irk_exp_diag", "pareschi_russo", "kraaijevanger_spijker", "qin_zhang",}):
+            s_irk = [torch.full_like(s_irk[0], sigma.item())] + s_irk
+
+            for row in range(irk.rows - irk.multistep_stages):
+                
+                sub_sigma_up, sub_sigma, sub_sigma_next, sub_sigma_down, sub_alpha_ratio = 0.0, s_irk[row], s_irk[row+1], s_irk[row+1], 1.0
+                if irk.c[row] > 0:
+                    sub_sigma_up, sub_sigma, sub_sigma_down, sub_alpha_ratio = get_res4lyf_step_with_model(model, s_irk[row], s_irk[row+1], substep_eta, substep_noise_mode)
+                
+                if not extra_options_flag("diagonal_implicit_skip_initial", extra_options):
+                    # MODEL CALL
+                    eps_[row], data_[row] = irk(x_0, x_[row], s_irk[row], h_irk, **extra_args) 
+                    
+                    # GUIDES 
+                    eps_, x_ = LG.process_guides_substep(x_0, x_, eps_, data_, row, step, sigma, sigma_next, sigma_down, s_irk, unsample_resample_scale, irk, irk_type, extra_options, frame_weights_grp)
+                
+                for diag_iter in range(implicit_steps):
+                    h_new_irk = h.clone()
+                    if irk.c[row] > 0:
+                        h_new_irk = (irk.h_fn(sub_sigma_down, sigma) / irk.c[row])[0]
+                    
+                    # UPDATE
+                    x_[row+1] = x_0 + h_new_irk * irk.a_k_sum(eps_, row)
+                    
+                    # NOISE ADD              
+                    if is_RF_model(model) == True   or   (is_RF_model(model) == False and noise_mode != "hard"):      
+                        if (row > 0) and (sub_sigma_up > 0) and ((SUBSTEP_SKIP_LAST == False) or (row < irk.rows - irk.multistep_stages - 1)):
+                            data_tmp = denoised_prev if data_[row-1].sum() == 0 else data_[row-1]
+                            eps_tmp  = eps_prev      if  eps_[row-1].sum() == 0 else eps_ [row-1]
+                            Osde = NoiseStepHandlerOSDE(x_[row+1], eps_tmp, data_tmp, x_init, y0, y0_inv)
+                            if Osde.check_cossim_source(NOISE_SUBSTEP_COSSIM_SOURCE):
+                                noise = irk.noise_sampler(sigma=sub_sigma, sigma_next=sub_sigma_next) 
+                                noise_osde = Osde.get_ortho_noise(noise, prev_noises, max_iter=noise_substep_cossim_max_iter, max_score=noise_substep_cossim_max_score, NOISE_COSSIM_SOURCE=NOISE_SUBSTEP_COSSIM_SOURCE)
+                                x_[row+1] = sub_alpha_ratio * x_[row+1] + sub_sigma_up * noise_osde * s_noise
+                            elif extra_options_flag("noise_substep_cossim", extra_options):
+                                x_[row+1] = handle_tiled_etc_noise_steps(x_0, x_[row+1], x_prenoise, x_init, eps_tmp, data_tmp, y0, y0_inv, row, 
+                                    irk_type, irk, sub_sigma_up, s_irk[row-1], s_irk[row], sub_alpha_ratio, s_noise, substep_noise_mode, SDE_NOISE_EXTERNAL, sde_noise_t,
+                                    NOISE_SUBSTEP_COSSIM_SOURCE, NOISE_SUBSTEP_COSSIM_MODE, noise_substep_cossim_tile_size, noise_substep_cossim_iterations,
+                                    extra_options)
+                    else:
+                        x_[row+1] = irk.add_noise_post(x_[row+1], sub_sigma_up, sub_sigma, sub_sigma_next, sub_alpha_ratio, s_noise, substep_noise_mode, SDE_NOISE_EXTERNAL, sde_noise_t)
+                    
+                    # MODEL CALL
+                    eps_[row], data_[row] = irk(x_0, x_[row+1], s_irk[row+1], h_irk, **extra_args)    
+                    if sub_sigma_up > 0 and not RK_Method.is_exponential(irk_type):
+                        eps_[row] = (x_0 - data_[row]) / sigma
+                    
+                    # GUIDES 
+                    eps_, x_ = LG.process_guides_substep(x_0, x_, eps_, data_, row, step, sigma, sigma_next, sigma_down, s_irk, unsample_resample_scale, irk, irk_type, extra_options, frame_weights_grp)
+                    
+                    
+            x = x_0 + h_irk * irk.b_k_sum(eps_, 0) 
+            
+            denoised = x_0 + (sigma / (sigma - sigma_down)) *  h_irk * irk.b_k_sum(eps_, 0) 
+            eps = x - denoised
+            x = LG.process_guides_poststep(x, denoised, eps, step, extra_options)
+
+
+        # FULLY IMPLICIT
+        else:
+            s2 = s_irk_rk[:]
+            s2.append(sigma.unsqueeze(dim=0))
+            s_all = torch.sort(torch.stack(s2, dim=0).squeeze(dim=1).unique(), descending=True)[0]
+            sigmas_and = torch.cat( (sigmas[0:step], s_all), dim=0)
+            
+            data_[0].zero_()
+            eps_ [0].zero_()
+            eps_list = []
+            
+            if extra_options_flag("fast_implicit_guess",  extra_options):
+                if denoised.sum() == 0:
+                    if extra_options_flag("fast_implicit_guess_use_guide",  extra_options):
+                        data_s = y0
+                        eps_s = x_0 - data_s
+                    else:
+                        eps_s, data_s = rk(x_0, x_0, sigma, h, **extra_args)
+                else:
+                    eps_s, data_s = eps, denoised
+                for i in range(len(s_all)-1):
+                    eps_list.append(eps_s * s_all[i]/sigma)
+                if torch.allclose(s_all[-1], sigma_down, atol=1e-8):
+                    eps_list.append(eps_s * sigma_down/sigma)
+            else:
+                # EXPLICIT GUESS
+                x_mid = x
+                for i in range(len(s_all)-1):
+                    x_mid, eps_, data_ = get_explicit_rk_step(rk, rk_type, x_mid, LG, step, s_all[i], s_all[i+1], eta, eta_var, s_noise, noise_mode, c2, c3, step+i, sigmas_and, x_, eps_, data_, unsample_resample_scale, extra_options, frame_weights_grp, 
+                                                              x_init, x_prenoise, NOISE_COSSIM_SOURCE, NOISE_COSSIM_MODE, noise_cossim_max_iter, noise_cossim_max_score, noise_cossim_tile_size, noise_cossim_iterations,SDE_NOISE_EXTERNAL,sde_noise_t,MODEL_SAMPLING,
+                                                              **extra_args)
+
+                    eps_list.append(eps_[0])
+                    data_[0].zero_()
+                    eps_ [0].zero_()
+                    
+                if torch.allclose(s_all[-1], sigma_down, atol=1e-8):
+                    eps_down, data_down = rk(x_0, x_mid, sigma_down, h, **extra_args) #should h_irk = h? going to change it for now.
+                    eps_list.append(eps_down)
+
+            s_all = [s for s in s_all if s in s_irk_rk]
+
+            eps_list = [eps_list[s_all.index(s)].clone() for s in s_irk_rk]
+            eps2_ = torch.stack(eps_list, dim=0)
+
+            # FULLY IMPLICIT LOOP
+            for implicit_iter in range(implicit_steps):
+                for row in range(irk.rows):
+                    x_[row+1] = x_0 + h_irk * irk.a_k_sum(eps2_, row)
+                    eps2_[row], data_[row] = irk(x_0, x_[row+1], s_irk[row], h_irk, **extra_args)
+                    if not extra_options_flag("implicit_loop_skip_guide", extra_options):
+                        eps2_, x_ = LG.process_guides_substep(x_0, x_, eps2_, data_, row, step, sigma, sigma_next, sigma_down, s_irk, unsample_resample_scale, irk, irk_type, extra_options, frame_weights_grp)
+                x = x_0 + h_irk * irk.b_k_sum(eps2_, 0)
+                denoised = x_0 + (sigma / (sigma - sigma_down)) *  h_irk * irk.b_k_sum(eps2_, 0) 
+                eps = x - denoised
+                x = LG.process_guides_poststep(x, denoised, eps, step, extra_options)
+
+
+        preview_callback(x, eps, denoised, x_, eps_, data_, step, sigma, sigma_next, callback, extra_options)
+
+        sde_noise_t = None
+        if SDE_NOISE_EXTERNAL:
+            if step >= len(sde_noise):
+                SDE_NOISE_EXTERNAL=False
+            else:
+                sde_noise_t = sde_noise[step]
+                
+        if is_RF_model(model) == True   or   (is_RF_model(model) == False and noise_mode != "hard"):
+            if sigma_up > 0:
+                #print("NOISE_FULL: sigma_up, sigma, sigma_next, sigma_down, alpha_ratio: ", sigma_up.item(), sigma.item(), sigma_next.item(), sigma_down.item(), alpha_ratio.item())
+                if implicit_steps==0:
+                    rk_or_irk = rk
+                    rk_or_irk_type = rk_type
+                else:
+                    rk_or_irk = irk
+                    rk_or_irk_type = irk_type
+                Osde = NoiseStepHandlerOSDE(x, eps, denoised, x_init, y0, y0_inv)
+                if Osde.check_cossim_source(NOISE_COSSIM_SOURCE):
+                    noise = rk_or_irk.noise_sampler(sigma=sigma, sigma_next=sigma_next)
+                    noise_osde = Osde.get_ortho_noise(noise, prev_noises, max_iter=noise_cossim_max_iter, max_score=noise_cossim_max_score, NOISE_COSSIM_SOURCE=NOISE_COSSIM_SOURCE)
+                    x = alpha_ratio * x + sigma_up * noise_osde * s_noise
+                elif extra_options_flag("noise_cossim", extra_options):
+                    x = handle_tiled_etc_noise_steps(x_0, x, x_prenoise, x_init, eps, denoised, y0, y0_inv, step, 
+                                    rk_or_irk_type, rk_or_irk, sigma_up, sigma, sigma_next, alpha_ratio, s_noise, noise_mode, SDE_NOISE_EXTERNAL, sde_noise_t,
+                                    NOISE_COSSIM_SOURCE, NOISE_COSSIM_MODE, noise_cossim_tile_size, noise_cossim_iterations,
+                                    extra_options)
+                else:
+                    x = rk_or_irk.add_noise_post(x, sigma_up, sigma, sigma_next, alpha_ratio, s_noise, noise_mode, SDE_NOISE_EXTERNAL, sde_noise_t)
+
+        if PRINT_DEBUG:
+            print("Data vs. y0 cossim score: ", get_cosine_similarity(data_[0], y0).item())
+
+        for ms in range(rk.multistep_stages):
+            if RK_Method.is_exponential(rk_type):
+                eps_[rk.multistep_stages - ms] = data_[rk.multistep_stages - ms - 1] - x
+            else:
+                eps_[rk.multistep_stages - ms] = (x - data_[rk.multistep_stages - ms - 1]) / sigma
+                
+            #eps_ [rk.multistep_stages - ms] = eps_ [rk.multistep_stages - ms - 1]
+            data_[rk.multistep_stages - ms] = data_[rk.multistep_stages - ms - 1]
+                
+        eps_ [0] = torch.zeros_like(eps_ [0])
+        data_[0] = torch.zeros_like(data_[0])
+        
+        denoised_prev = denoised
+        eps_prev = eps
+        
+    preview_callback(x, eps, denoised, x_, eps_, data_, step, sigma, sigma_next, callback, extra_options, FINAL_STEP=True)
+    return x
+
+
+
+def get_explicit_rk_step(rk, rk_type, x, LG, step, sigma, sigma_next, eta, eta_var, s_noise, noise_mode, c2, c3, stepcount, sigmas, x_, eps_, data_, unsample_resample_scale, extra_options, frame_weights_grp, 
+                         x_init, x_prenoise, NOISE_COSSIM_SOURCE, NOISE_COSSIM_MODE, noise_cossim_max_iter, noise_cossim_max_score, noise_cossim_tile_size, noise_cossim_iterations,SDE_NOISE_EXTERNAL,sde_noise_t,MODEL_SAMPLING,
+                         **extra_args):
+
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    
+    eta = float(get_extra_options_kv("implicit_substep_eta", eta, extra_options))
+
+    sigma_up, sigma, sigma_down, alpha_ratio = get_res4lyf_step_with_model(rk.model, sigma, sigma_next, eta, noise_mode)
+    h = rk.h_fn(sigma_down, sigma)
+    c2, c3 = get_res4lyf_half_step3(sigma, sigma_down, c2, c3, t_fn=rk.t_fn, sigma_fn=rk.sigma_fn)
+    
+    rk.set_coeff(rk_type, h, c2=c2, c3=c3, stepcount=stepcount, sigmas=sigmas, sigma_down=sigma_down, extra_options=extra_options)
+
+    s_ = [(sigma + h * c_) * s_in for c_ in rk.c]
+    x_[0] = rk.add_noise_pre(x, sigma_up, sigma, sigma_next, alpha_ratio, s_noise, noise_mode)
+    
+    x_0 = x_[0].clone()
+    
+    for ms in range(rk.multistep_stages):
+        if RK_Method.is_exponential(rk_type):
+            eps_ [rk.multistep_stages - ms] = data_ [rk.multistep_stages - ms] - x_0
+        else:
+            eps_ [rk.multistep_stages - ms] = (x_0 - data_ [rk.multistep_stages - ms]) / sigma
+        
+    for row in range(rk.rows - rk.multistep_stages):
+        x_[row+1] = x_0 + h * rk.a_k_sum(eps_, row)
+        eps_[row], data_[row] = rk(x_0, x_[row+1], s_[row], h, **extra_args)
+        
+        eps_, x_ = LG.process_guides_substep(x_0, x_, eps_, data_, row, step, sigma, sigma_next, sigma_down, s_, unsample_resample_scale, rk, rk_type, extra_options, frame_weights_grp)        
+        
+    x = x_0 + h * rk.b_k_sum(eps_, 0)
+    
+    denoised = x_0 + (sigma / (sigma - sigma_down)) *  h * rk.b_k_sum(eps_, 0) 
+    eps = x - denoised
+    
+    y0 = LG.y0
+    if LG.y0.shape[0] > 1:
+        y0 = LG.y0[min(step, LG.y0.shape[0]-1)].unsqueeze(0)  
+        
+    x = LG.process_guides_poststep(x, denoised, eps, step, extra_options)
+
+    #x = rk.add_noise_post(x, sigma_up, sigma, sigma_next, alpha_ratio, s_noise, noise_mode)
+    
+    if is_RF_model(rk.model) == True   or   (is_RF_model(rk.model) == False and noise_mode != "hard"):
+        if sigma_up > 0:
+            Osde = NoiseStepHandlerOSDE(x, eps, denoised, x_init, y0, LG.y0_inv)
+            if Osde.check_cossim_source(NOISE_COSSIM_SOURCE):
+                noise = rk.noise_sampler(sigma=sigma, sigma_next=sigma_next)
+                noise_osde = Osde.get_ortho_noise(noise, [], max_iter=noise_cossim_max_iter, max_score=noise_cossim_max_score, NOISE_COSSIM_SOURCE=NOISE_COSSIM_SOURCE)
+                x = alpha_ratio * x + sigma_up * noise_osde * s_noise
+            elif extra_options_flag("noise_cossim", extra_options):
+                x = handle_tiled_etc_noise_steps(x_0, x, x_prenoise, x_init, eps, denoised, y0, LG.y0_inv, step, 
+                                    rk_type, rk, sigma_up, sigma, sigma_next, alpha_ratio, s_noise, noise_mode, SDE_NOISE_EXTERNAL, sde_noise_t,
+                                    NOISE_COSSIM_SOURCE, NOISE_COSSIM_MODE, noise_cossim_tile_size, noise_cossim_iterations,
+                                    extra_options)
+            else:
+                x = rk.add_noise_post(x, sigma_up, sigma, sigma_next, alpha_ratio, s_noise, noise_mode, SDE_NOISE_EXTERNAL, sde_noise_t)
+
+    for ms in range(rk.multistep_stages): # NEEDS ADJUSTING?
+        eps_ [rk.multistep_stages - ms] = eps_ [rk.multistep_stages - ms - 1]
+        data_[rk.multistep_stages - ms] = data_[rk.multistep_stages - ms - 1]
+
+    return x, eps_, data_
+
+
+
+def preview_callback(x, eps, denoised, x_, eps_, data_, step, sigma, sigma_next, callback, extra_options, FINAL_STEP=False):
+    
+    if FINAL_STEP:
+        denoised_callback = denoised
+        
+    elif extra_options_flag("eps_substep_preview", extra_options):
+        row_callback = int(get_extra_options_kv("eps_substep_preview", "0", extra_options))
+        denoised_callback = eps_[row_callback]
+        
+    elif extra_options_flag("denoised_substep_preview", extra_options):
+        row_callback = int(get_extra_options_kv("denoised_substep_preview", "0", extra_options))
+        denoised_callback = data_[row_callback]
+        
+    elif extra_options_flag("x_substep_preview", extra_options):
+        row_callback = int(get_extra_options_kv("x_substep_preview", "0", extra_options))
+        denoised_callback = x_[row_callback]
+        
+    elif extra_options_flag("eps_preview", extra_options):
+        denoised_callback = eps
+        
+    elif extra_options_flag("denoised_preview", extra_options):
+        denoised_callback = denoised
+        
+    elif extra_options_flag("x_preview", extra_options):
+        denoised_callback = x
+        
+    else:
+        denoised_callback = data_[0]
+        
+    callback({'x': x, 'i': step, 'sigma': sigma, 'sigma_next': sigma_next, 'denoised': denoised_callback.to(torch.float32)}) if callback is not None else None
+    
+    return
+
+
+
+
+def sample_res_2m(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return sample_rk(model, x, sigmas, extra_args, callback, disable, noise_sampler_type="gaussian", noise_mode="hard", noise_seed=-1, rk_type="res_2m", eta=0.0, )
+def sample_res_2s(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return sample_rk(model, x, sigmas, extra_args, callback, disable, noise_sampler_type="gaussian", noise_mode="hard", noise_seed=-1, rk_type="res_2s", eta=0.0, )
+def sample_res_3s(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return sample_rk(model, x, sigmas, extra_args, callback, disable, noise_sampler_type="gaussian", noise_mode="hard", noise_seed=-1, rk_type="res_3s", eta=0.0, )
+def sample_res_5s(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return sample_rk(model, x, sigmas, extra_args, callback, disable, noise_sampler_type="gaussian", noise_mode="hard", noise_seed=-1, rk_type="res_5s", eta=0.0, )
+def sample_res_6s(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return sample_rk(model, x, sigmas, extra_args, callback, disable, noise_sampler_type="gaussian", noise_mode="hard", noise_seed=-1, rk_type="res_6s", eta=0.0, )
+
+def sample_res_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return sample_rk(model, x, sigmas, extra_args, callback, disable, noise_sampler_type="gaussian", noise_mode="hard", noise_seed=-1, rk_type="res_2m", eta=0.5, eta_substep=0.5, )
+def sample_res_2s_sde(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return sample_rk(model, x, sigmas, extra_args, callback, disable, noise_sampler_type="gaussian", noise_mode="hard", noise_seed=-1, rk_type="res_2s", eta=0.5, eta_substep=0.5, )
+def sample_res_3s_sde(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return sample_rk(model, x, sigmas, extra_args, callback, disable, noise_sampler_type="gaussian", noise_mode="hard", noise_seed=-1, rk_type="res_3s", eta=0.5, eta_substep=0.5, )
+def sample_res_5s_sde(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return sample_rk(model, x, sigmas, extra_args, callback, disable, noise_sampler_type="gaussian", noise_mode="hard", noise_seed=-1, rk_type="res_5s", eta=0.5, eta_substep=0.5, )
+def sample_res_6s_sde(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return sample_rk(model, x, sigmas, extra_args, callback, disable, noise_sampler_type="gaussian", noise_mode="hard", noise_seed=-1, rk_type="res_6s", eta=0.5, eta_substep=0.5, )
+
+def sample_deis_2m(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return sample_rk(model, x, sigmas, extra_args, callback, disable, noise_sampler_type="gaussian", noise_mode="hard", noise_seed=-1, rk_type="deis_2m", eta=0.0, )
+def sample_deis_3m(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return sample_rk(model, x, sigmas, extra_args, callback, disable, noise_sampler_type="gaussian", noise_mode="hard", noise_seed=-1, rk_type="deis_3m", eta=0.0, )
+def sample_deis_4m(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return sample_rk(model, x, sigmas, extra_args, callback, disable, noise_sampler_type="gaussian", noise_mode="hard", noise_seed=-1, rk_type="deis_4m", eta=0.0, )
+
+def sample_deis_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return sample_rk(model, x, sigmas, extra_args, callback, disable, noise_sampler_type="gaussian", noise_mode="hard", noise_seed=-1, rk_type="deis_2m", eta=0.5, eta_substep=0.5, )
+def sample_deis_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return sample_rk(model, x, sigmas, extra_args, callback, disable, noise_sampler_type="gaussian", noise_mode="hard", noise_seed=-1, rk_type="deis_3m", eta=0.5, eta_substep=0.5, )
+def sample_deis_4m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    return sample_rk(model, x, sigmas, extra_args, callback, disable, noise_sampler_type="gaussian", noise_mode="hard", noise_seed=-1, rk_type="deis_4m", eta=0.5, eta_substep=0.5, )
+
diff --git a/custom_nodes/RES4LYF/legacy/samplers.py b/custom_nodes/RES4LYF/legacy/samplers.py
new file mode 100644
index 0000000000000000000000000000000000000000..4655a17be3d93498db6eae73b7438409386e103b
--- /dev/null
+++ b/custom_nodes/RES4LYF/legacy/samplers.py
@@ -0,0 +1,804 @@
+from .noise_classes import prepare_noise, NOISE_GENERATOR_CLASSES_SIMPLE, NOISE_GENERATOR_NAMES_SIMPLE, NOISE_GENERATOR_NAMES
+from .sigmas import get_sigmas
+
+
+from .constants import MAX_STEPS
+
+import comfy.samplers
+import comfy.sample
+import comfy.sampler_helpers
+import comfy.model_sampling
+import comfy.latent_formats
+import comfy.sd
+import comfy.supported_models
+
+import latent_preview
+import torch
+import torch.nn.functional as F
+
+import math
+import copy
+
+from .helper import get_extra_options_kv, extra_options_flag, get_res4lyf_scheduler_list
+from .latents import initialize_or_scale
+
+from .noise_classes import prepare_noise, NOISE_GENERATOR_CLASSES_SIMPLE, NOISE_GENERATOR_NAMES_SIMPLE, NOISE_GENERATOR_NAMES
+from .sigmas import get_sigmas
+
+
+from .rk_sampler import sample_rk
+from .rk_coefficients import RK_SAMPLER_NAMES, IRK_SAMPLER_NAMES
+from .rk_guide_func import get_orthogonal
+from .noise_sigmas_timesteps_scaling import NOISE_MODE_NAMES
+
+
+def move_to_same_device(*tensors):
+    if not tensors:
+        return tensors
+
+    device = tensors[0].device
+    return tuple(tensor.to(device) for tensor in tensors)
+
+
+#SCHEDULER_NAMES = comfy.samplers.SCHEDULER_NAMES + ["beta57"]
+
+
+
+class ClownSamplerAdvanced:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {
+                    "noise_type_sde": (NOISE_GENERATOR_NAMES_SIMPLE, {"default": "gaussian"}),
+                    "noise_type_sde_substep": (NOISE_GENERATOR_NAMES_SIMPLE, {"default": "gaussian"}),
+                    "noise_mode_sde": (NOISE_MODE_NAMES, {"default": 'hard', "tooltip": "How noise scales with the sigma schedule. Hard is the most aggressive, the others start strong and drop rapidly."}),
+                    "noise_mode_sde_substep": (NOISE_MODE_NAMES, {"default": 'hard', "tooltip": "How noise scales with the sigma schedule. Hard is the most aggressive, the others start strong and drop rapidly."}),
+                    "eta": ("FLOAT", {"default": 0.5, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Calculated noise amount to be added, then removed, after each step."}),
+                    "eta_substep": ("FLOAT", {"default": 0.5, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Calculated noise amount to be added, then removed, after each step."}),
+                    "s_noise": ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01, "tooltip": "Adds extra SDE noise. Values around 1.03-1.07 can lead to a moderate boost in detail and paint textures."}),
+                    "d_noise": ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01, "tooltip": "Downscales the sigma schedule. Values around 0.98-0.95 can lead to a large boost in detail and paint textures."}),
+                    "noise_seed_sde": ("INT", {"default": -1, "min": -1, "max": 0xffffffffffffffff}),
+                    "sampler_name": (RK_SAMPLER_NAMES, {"default": "res_2m"}), 
+                    "implicit_sampler_name": (IRK_SAMPLER_NAMES, {"default": "explicit_diagonal"}), 
+                    "implicit_steps": ("INT", {"default": 0, "min": 0, "max": 10000}),
+                     },
+                "optional": 
+                    {
+                    "guides": ("GUIDES", ),     
+                    "options": ("OPTIONS", ),   
+                    "automation": ("AUTOMATION", ),
+                    "extra_options": ("STRING", {"default": "", "multiline": True}),   
+                    }
+                }
+
+    RETURN_TYPES = ("SAMPLER",)
+    RETURN_NAMES = ("sampler", ) 
+
+    FUNCTION = "main"
+
+    CATEGORY = "RES4LYF/legacy/samplers"
+    DEPRECATED = True
+    
+    def main(self, 
+             noise_type_sde="gaussian", noise_type_sde_substep="gaussian", noise_mode_sde="hard",
+             eta=0.25, eta_var=0.0, d_noise=1.0, s_noise=1.0, alpha_sde=-1.0, k_sde=1.0, cfgpp=0.0, c1=0.0, c2=0.5, c3=1.0, noise_seed_sde=-1, sampler_name="res_2m", implicit_sampler_name="gauss-legendre_2s",
+                    t_fn_formula=None, sigma_fn_formula=None, implicit_steps=0,
+                    latent_guide=None, latent_guide_inv=None, guide_mode="", latent_guide_weights=None, latent_guide_weights_inv=None, latent_guide_mask=None, latent_guide_mask_inv=None, rescale_floor=True, sigmas_override=None, 
+                    guides=None, options=None, sde_noise=None,sde_noise_steps=1, 
+                    extra_options="", automation=None, etas=None, s_noises=None,unsample_resample_scales=None, regional_conditioning_weights=None,frame_weights_grp=None, eta_substep=0.5, noise_mode_sde_substep="hard",
+                    ): 
+            if implicit_sampler_name == "none":
+                implicit_steps = 0 
+                implicit_sampler_name = "gauss-legendre_2s"
+
+            if noise_mode_sde == "none":
+                eta, eta_var = 0.0, 0.0
+                noise_mode_sde = "hard"
+        
+            default_dtype = getattr(torch, get_extra_options_kv("default_dtype", "float64", extra_options), torch.float64)
+
+            unsample_resample_scales_override = unsample_resample_scales
+
+            if options is not None:
+                noise_type_sde = options.get('noise_type_sde', noise_type_sde)
+                noise_mode_sde = options.get('noise_mode_sde', noise_mode_sde)
+                eta = options.get('eta', eta)
+                s_noise = options.get('s_noise', s_noise)
+                d_noise = options.get('d_noise', d_noise)
+                alpha_sde = options.get('alpha_sde', alpha_sde)
+                k_sde = options.get('k_sde', k_sde)
+                c1 = options.get('c1', c1)
+                c2 = options.get('c2', c2)
+                c3 = options.get('c3', c3)
+                t_fn_formula = options.get('t_fn_formula', t_fn_formula)
+                sigma_fn_formula = options.get('sigma_fn_formula', sigma_fn_formula)
+                frame_weights_grp = options.get('frame_weights_grp', frame_weights_grp)
+                sde_noise = options.get('sde_noise', sde_noise)
+                sde_noise_steps = options.get('sde_noise_steps', sde_noise_steps)
+
+            #noise_seed_sde = torch.initial_seed()+1 if noise_seed_sde < 0 else noise_seed_sde 
+
+            rescale_floor = extra_options_flag("rescale_floor", extra_options)
+
+            if automation is not None:
+                etas = automation['etas'] if 'etas' in automation else None
+                s_noises = automation['s_noises'] if 's_noises' in automation else None
+                unsample_resample_scales = automation['unsample_resample_scales'] if 'unsample_resample_scales' in automation else None
+                frame_weights_grp = automation['frame_weights_grp'] if 'frame_weights_grp' in automation else None
+
+            etas = initialize_or_scale(etas, eta, MAX_STEPS).to(default_dtype)
+            etas = F.pad(etas, (0, MAX_STEPS), value=0.0)
+            s_noises = initialize_or_scale(s_noises, s_noise, MAX_STEPS).to(default_dtype)
+            s_noises = F.pad(s_noises, (0, MAX_STEPS), value=0.0)
+        
+            if sde_noise is None:
+                sde_noise = []
+            else:
+                sde_noise = copy.deepcopy(sde_noise)
+                for i in range(len(sde_noise)):
+                    sde_noise[i] = sde_noise[i]
+                    for j in range(sde_noise[i].shape[1]):
+                        sde_noise[i][0][j] = ((sde_noise[i][0][j] - sde_noise[i][0][j].mean()) / sde_noise[i][0][j].std())
+                        
+            if unsample_resample_scales_override is not None:
+                unsample_resample_scales = unsample_resample_scales_override
+
+            sampler = comfy.samplers.ksampler("rk", {"eta": eta, "eta_var": eta_var, "s_noise": s_noise, "d_noise": d_noise, "alpha": alpha_sde, "k": k_sde, "c1": c1, "c2": c2, "c3": c3, "cfgpp": cfgpp, 
+                                                    "noise_sampler_type": noise_type_sde, "noise_mode": noise_mode_sde, "noise_seed": noise_seed_sde, "rk_type": sampler_name, "implicit_sampler_name": implicit_sampler_name,
+                                                            "t_fn_formula": t_fn_formula, "sigma_fn_formula": sigma_fn_formula, "implicit_steps": implicit_steps,
+                                                            "latent_guide": latent_guide, "latent_guide_inv": latent_guide_inv, "mask": latent_guide_mask, "mask_inv": latent_guide_mask_inv,
+                                                            "latent_guide_weights": latent_guide_weights, "latent_guide_weights_inv": latent_guide_weights_inv, "guide_mode": guide_mode,
+                                                            "LGW_MASK_RESCALE_MIN": rescale_floor, "sigmas_override": sigmas_override, "sde_noise": sde_noise,
+                                                            "extra_options": extra_options,
+                                                            "etas": etas, "s_noises": s_noises, "unsample_resample_scales": unsample_resample_scales, "regional_conditioning_weights": regional_conditioning_weights,
+                                                            "guides": guides, "frame_weights_grp": frame_weights_grp, "eta_substep": eta_substep, "noise_mode_sde_substep": noise_mode_sde_substep,
+                                                            })
+
+            return (sampler, )
+
+
+
+
+class ClownSampler:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {
+                    "noise_type_sde": (NOISE_GENERATOR_NAMES_SIMPLE, {"default": "gaussian"}),
+                    "noise_mode_sde": (NOISE_MODE_NAMES, {"default": 'hard', "tooltip": "How noise scales with the sigma schedule. Hard is the most aggressive, the others start strong and drop rapidly."}),
+                    "eta": ("FLOAT", {"default": 0.5, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Calculated noise amount to be added, then removed, after each step."}),
+                    "s_noise": ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01}),
+                    "d_noise": ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01}),
+                    "noise_seed_sde": ("INT", {"default": -1, "min": -1, "max": 0xffffffffffffffff}),
+                    "sampler_name": (RK_SAMPLER_NAMES, {"default": "res_2m"}), 
+                    "implicit_sampler_name": (IRK_SAMPLER_NAMES, {"default": "explicit_diagonal"}), 
+                    "implicit_steps": ("INT", {"default": 0, "min": 0, "max": 10000}),
+                     },
+                "optional": 
+                    {
+                    "guides": ("GUIDES", ),     
+                    "options": ("OPTIONS", ),   
+                    "automation": ("AUTOMATION", ),
+                    "extra_options": ("STRING", {"default": "", "multiline": True}),   
+                    }
+                }
+
+    RETURN_TYPES = ("SAMPLER",)
+    RETURN_NAMES = ("sampler", ) 
+
+    FUNCTION = "main"
+
+    CATEGORY = "RES4LYF/legacy/samplers"
+    DEPRECATED = True
+    
+    def main(self, 
+             noise_type_sde="gaussian", noise_type_sde_substep="gaussian", noise_mode_sde="hard",
+             eta=0.25, eta_var=0.0, d_noise=1.0, s_noise=1.0, alpha_sde=-1.0, k_sde=1.0, cfgpp=0.0, c1=0.0, c2=0.5, c3=1.0, noise_seed_sde=-1, sampler_name="res_2m", implicit_sampler_name="gauss-legendre_2s",
+                    t_fn_formula=None, sigma_fn_formula=None, implicit_steps=0,
+                    latent_guide=None, latent_guide_inv=None, guide_mode="", latent_guide_weights=None, latent_guide_weights_inv=None, latent_guide_mask=None, latent_guide_mask_inv=None, rescale_floor=True, sigmas_override=None,
+                    guides=None, options=None, sde_noise=None,sde_noise_steps=1, 
+                    extra_options="", automation=None, etas=None, s_noises=None,unsample_resample_scales=None, regional_conditioning_weights=None,frame_weights_grp=None,eta_substep=0.0, noise_mode_sde_substep="hard",
+                    ): 
+
+        eta_substep = eta
+        noise_mode_sde_substep = noise_mode_sde
+        noise_type_sde_substep = noise_type_sde
+
+        sampler = ClownSamplerAdvanced().main(
+                noise_type_sde=noise_type_sde, noise_type_sde_substep=noise_type_sde_substep, noise_mode_sde=noise_mode_sde,
+             eta=eta, eta_var=eta_var, d_noise=d_noise, s_noise=s_noise, alpha_sde=alpha_sde, k_sde=k_sde, cfgpp=cfgpp, c1=c1, c2=c2, c3=c3, noise_seed_sde=noise_seed_sde, sampler_name=sampler_name, implicit_sampler_name=implicit_sampler_name,
+                    t_fn_formula=t_fn_formula, sigma_fn_formula=sigma_fn_formula, implicit_steps=implicit_steps,
+                    latent_guide=latent_guide, latent_guide_inv=latent_guide_inv, guide_mode=guide_mode, latent_guide_weights=latent_guide_weights, latent_guide_weights_inv=latent_guide_weights_inv, latent_guide_mask=latent_guide_mask, latent_guide_mask_inv=latent_guide_mask_inv, rescale_floor=rescale_floor, sigmas_override=sigmas_override,
+                    guides=guides, options=options, sde_noise=sde_noise,sde_noise_steps=sde_noise_steps, 
+                    extra_options=extra_options, automation=automation, etas=etas, s_noises=s_noises,unsample_resample_scales=unsample_resample_scales, regional_conditioning_weights=regional_conditioning_weights,frame_weights_grp=frame_weights_grp, eta_substep=eta_substep, noise_mode_sde_substep=noise_mode_sde_substep,
+                    )
+        
+        return sampler
+
+
+
+def process_sampler_name(selected_value):
+    processed_name = selected_value.split("/")[-1]
+    
+    if selected_value.startswith("fully_implicit") or selected_value.startswith("diag_implicit"):
+        implicit_sampler_name = processed_name
+        sampler_name = "buehler"
+    else:
+        sampler_name = processed_name
+        implicit_sampler_name = "use_explicit"
+    
+    return sampler_name, implicit_sampler_name
+
+
+def copy_cond(positive):
+    new_positive = []
+    for embedding, cond in positive:
+        cond_copy = {}
+        for k, v in cond.items():
+            if isinstance(v, torch.Tensor):
+                cond_copy[k] = v.clone()
+            else:
+                cond_copy[k] = v  # ensure we're not copying huge shit like controlnets
+        new_positive.append([embedding.clone(), cond_copy])
+    return new_positive
+
+
+
+class SharkSamplerAlpha:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"model": ("MODEL",),
+                    "noise_type_init": (NOISE_GENERATOR_NAMES_SIMPLE, {"default": "gaussian"}),
+                    "noise_stdev": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":0.01, "round": False, }),
+                    "noise_seed": ("INT", {"default": 0, "min": -1, "max": 0xffffffffffffffff}),
+                    "sampler_mode": (['standard', 'unsample', 'resample'],),
+                    "scheduler": (get_res4lyf_scheduler_list(), {"default": "beta57"},),
+                    "steps": ("INT", {"default": 30, "min": 1, "max": 10000}),
+                    "denoise": ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01}),
+                    "denoise_alt": ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01}),
+                    "cfg": ("FLOAT", {"default": 3.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Negative values use channelwise CFG." }),
+                     },
+                "optional": 
+                    {
+                    "positive": ("CONDITIONING", ),
+                    "negative": ("CONDITIONING", ),
+                    "sampler": ("SAMPLER", ),
+                    "sigmas": ("SIGMAS", ),
+                    "latent_image": ("LATENT", ),     
+                    "options": ("OPTIONS", ),   
+                    "extra_options": ("STRING", {"default": "", "multiline": True}),   
+                    }
+                }
+
+    RETURN_TYPES = ("LATENT","LATENT", "LATENT",)
+    RETURN_NAMES = ("output", "denoised","sde_noise",) 
+
+    FUNCTION = "main"
+
+    CATEGORY = "RES4LYF/legacy/samplers"
+    DEPRECATED = True
+    
+    def main(self, model, cfg, scheduler, steps, sampler_mode="standard",denoise=1.0, denoise_alt=1.0,
+             noise_type_init="gaussian", latent_image=None, 
+             positive=None, negative=None, sampler=None, sigmas=None, latent_noise=None, latent_noise_match=None,
+             noise_stdev=1.0, noise_mean=0.0, noise_normalize=True, 
+             d_noise=1.0, alpha_init=-1.0, k_init=1.0, cfgpp=0.0, noise_seed=-1,
+                    options=None, sde_noise=None,sde_noise_steps=1, 
+                    extra_options="", 
+                    ): 
+            # blame comfy here
+            raw_x = latent_image['raw_x'] if 'raw_x' in latent_image else None
+            last_seed = latent_image['last_seed'] if 'last_seed' in latent_image else None
+            
+            pos_cond = copy_cond(positive)
+            neg_cond = copy_cond(negative)
+
+            if sampler is None:
+                raise ValueError("sampler is required")
+            else:
+                sampler = copy.deepcopy(sampler)
+
+            default_dtype = getattr(torch, get_extra_options_kv("default_dtype", "float64", extra_options), torch.float64)
+                     
+            model = model.clone()
+            if pos_cond[0][1] is not None: 
+                if "regional_conditioning_weights" in pos_cond[0][1]:
+                    sampler.extra_options['regional_conditioning_weights'] = pos_cond[0][1]['regional_conditioning_weights']
+                    sampler.extra_options['regional_conditioning_floors']  = pos_cond[0][1]['regional_conditioning_floors']
+                    regional_generate_conditionings_and_masks_fn = pos_cond[0][1]['regional_generate_conditionings_and_masks_fn']
+                    regional_conditioning, regional_mask = regional_generate_conditionings_and_masks_fn(latent_image['samples'])
+                    regional_conditioning = copy.deepcopy(regional_conditioning)
+                    regional_mask = copy.deepcopy(regional_mask)
+                    model.set_model_patch(regional_conditioning, 'regional_conditioning_positive')
+                    model.set_model_patch(regional_mask,         'regional_conditioning_mask')
+                    
+            if "noise_seed" in sampler.extra_options:
+                if sampler.extra_options['noise_seed'] == -1 and noise_seed != -1:
+                    sampler.extra_options['noise_seed'] = noise_seed + 1
+                    #print("Shark: setting clown noise seed to: ", sampler.extra_options['noise_seed'])
+
+            if "sampler_mode" in sampler.extra_options:
+                sampler.extra_options['sampler_mode'] = sampler_mode
+
+            if "extra_options" in sampler.extra_options:
+                extra_options += " "
+                extra_options += sampler.extra_options['extra_options']
+                sampler.extra_options['extra_options'] = extra_options
+
+            batch_size = int(get_extra_options_kv("batch_size", "1", extra_options))
+            if batch_size > 1:
+                latent_image['samples'] = latent_image['samples'].repeat(batch_size, 1, 1, 1) 
+            
+            latent_image_batch = {"samples": latent_image['samples']}
+            out_samples, out_samples_fp64, out_denoised_samples, out_denoised_samples_fp64 = [], [], [], []
+            for batch_num in range(latent_image_batch['samples'].shape[0]):
+                latent_unbatch = copy.deepcopy(latent_image)
+                latent_unbatch['samples'] = latent_image_batch['samples'][batch_num].clone().unsqueeze(0)
+                
+
+
+                if noise_seed == -1:
+                    seed = torch.initial_seed() + 1 + batch_num
+                else:
+                    seed = noise_seed + batch_num
+                    torch.manual_seed(seed)
+                    torch.cuda.manual_seed(seed)
+                    #torch.cuda.manual_seed_all(seed)
+
+
+                if options is not None:
+                    noise_stdev     = options.get('noise_init_stdev', noise_stdev)
+                    noise_mean      = options.get('noise_init_mean',  noise_mean)
+                    noise_type_init = options.get('noise_type_init',  noise_type_init)
+                    d_noise         = options.get('d_noise',          d_noise)
+                    alpha_init      = options.get('alpha_init',       alpha_init)
+                    k_init          = options.get('k_init',           k_init)
+                    sde_noise       = options.get('sde_noise',        sde_noise)
+                    sde_noise_steps = options.get('sde_noise_steps',  sde_noise_steps)
+
+                latent_image_dtype = latent_unbatch['samples'].dtype
+
+                if isinstance(model.model.model_config, comfy.supported_models.Flux) or isinstance(model.model.model_config, comfy.supported_models.FluxSchnell):
+                    if pos_cond is None:
+                        pos_cond = [[
+                            torch.zeros((1, 256, 4096)),
+                            {'pooled_output': torch.zeros((1, 768))}
+                            ]]
+
+                    if extra_options_flag("uncond_ortho_flux", extra_options):
+                        if neg_cond is None:
+                            print("uncond_ortho_flux: using random negative conditioning...")
+                            neg_cond = [[
+                                torch.randn((1, 256, 4096)),
+                                {'pooled_output': torch.randn((1, 768))}
+                                ]]
+                        #neg_cond[0][0] = get_orthogonal(neg_cond[0][0].to(torch.bfloat16), pos_cond[0][0].to(torch.bfloat16))
+                        #neg_cond[0][1]['pooled_output'] = get_orthogonal(neg_cond[0][1]['pooled_output'].to(torch.bfloat16), pos_cond[0][1]['pooled_output'].to(torch.bfloat16))
+                        neg_cond[0][0] = get_orthogonal(neg_cond[0][0], pos_cond[0][0])
+                        neg_cond[0][1]['pooled_output'] = get_orthogonal(neg_cond[0][1]['pooled_output'], pos_cond[0][1]['pooled_output'])
+                        
+                    if neg_cond is None:
+                        neg_cond = [[
+                            torch.zeros((1, 256, 4096)),
+                            {'pooled_output': torch.zeros((1, 768))}
+                            ]]
+                else:
+                    if pos_cond is None:
+                        pos_cond = [[
+                            torch.zeros((1, 154, 4096)),
+                            {'pooled_output': torch.zeros((1, 2048))}
+                            ]]
+
+                    if extra_options_flag("uncond_ortho_sd35", extra_options):
+                        if neg_cond is None:
+                            neg_cond = [[
+                                torch.randn((1, 154, 4096)),
+                                {'pooled_output': torch.randn((1, 2048))}
+                                ]]
+                        
+                        neg_cond[0][0] = get_orthogonal(neg_cond[0][0], pos_cond[0][0])
+                        neg_cond[0][1]['pooled_output'] = get_orthogonal(neg_cond[0][1]['pooled_output'], pos_cond[0][1]['pooled_output'])
+                        
+
+                    if neg_cond is None:
+                        neg_cond = [[
+                            torch.zeros((1, 154, 4096)),
+                            {'pooled_output': torch.zeros((1, 2048))}
+                            ]]
+                        
+                        
+                if extra_options_flag("zero_uncond_t5", extra_options):
+                    neg_cond[0][0] = torch.zeros_like(neg_cond[0][0])
+                    
+                if extra_options_flag("zero_uncond_pooled_output", extra_options):
+                    neg_cond[0][1]['pooled_output'] = torch.zeros_like(neg_cond[0][1]['pooled_output'])
+                        
+                if extra_options_flag("zero_pooled_output", extra_options):
+                    pos_cond[0][1]['pooled_output'] = torch.zeros_like(pos_cond[0][1]['pooled_output'])
+                    neg_cond[0][1]['pooled_output'] = torch.zeros_like(neg_cond[0][1]['pooled_output'])
+
+                if denoise_alt < 0:
+                    d_noise = denoise_alt = -denoise_alt
+                if options is not None:
+                    d_noise = options.get('d_noise', d_noise)
+
+                if sigmas is not None:
+                    sigmas = sigmas.clone().to(default_dtype)
+                else: 
+                    sigmas = get_sigmas(model, scheduler, steps, denoise).to(default_dtype)
+                sigmas *= denoise_alt
+
+                if sampler_mode.startswith("unsample"): 
+                    null = torch.tensor([0.0], device=sigmas.device, dtype=sigmas.dtype)
+                    sigmas = torch.flip(sigmas, dims=[0])
+                    sigmas = torch.cat([sigmas, null])
+                elif sampler_mode.startswith("resample"):
+                    null = torch.tensor([0.0], device=sigmas.device, dtype=sigmas.dtype)
+                    sigmas = torch.cat([null, sigmas])
+                    sigmas = torch.cat([sigmas, null])
+
+                x = latent_unbatch["samples"].clone().to(default_dtype) 
+                if latent_unbatch is not None:
+                    if "samples_fp64" in latent_unbatch:
+                        if latent_unbatch['samples'].shape == latent_unbatch['samples_fp64'].shape:
+                            if torch.norm(latent_unbatch['samples'] - latent_unbatch['samples_fp64']) < 0.01:
+                                x = latent_unbatch["samples_fp64"].clone()
+
+                if latent_noise is not None:
+                    latent_noise_samples = latent_noise["samples"].clone().to(default_dtype)  
+                if latent_noise_match is not None:
+                    latent_noise_match_samples = latent_noise_match["samples"].clone().to(default_dtype)
+
+                truncate_conditioning = extra_options_flag("truncate_conditioning", extra_options)
+                if truncate_conditioning == "true" or truncate_conditioning == "true_and_zero_neg":
+                    if pos_cond is not None:
+                        pos_cond[0][0] = pos_cond[0][0].clone().to(default_dtype)
+                        pos_cond[0][1]["pooled_output"] = pos_cond[0][1]["pooled_output"].clone().to(default_dtype)
+                    if neg_cond is not None:
+                        neg_cond[0][0] = neg_cond[0][0].clone().to(default_dtype)
+                        neg_cond[0][1]["pooled_output"] = neg_cond[0][1]["pooled_output"].clone().to(default_dtype)
+                    c = []
+                    for t in pos_cond:
+                        d = t[1].copy()
+                        pooled_output = d.get("pooled_output", None)
+
+                    for t in neg_cond:
+                        d = t[1].copy()
+                        pooled_output = d.get("pooled_output", None)
+                        if pooled_output is not None:
+                            if truncate_conditioning == "true_and_zero_neg":
+                                d["pooled_output"] = torch.zeros((1,2048), dtype=t[0].dtype, device=t[0].device)
+                                n = [torch.zeros((1,154,4096), dtype=t[0].dtype, device=t[0].device), d]
+                            else:
+                                d["pooled_output"] = d["pooled_output"][:, :2048]
+                                n = [t[0][:, :154, :4096], d]
+                        c.append(n)
+                    neg_cond = c
+                
+                sigmin = model.model.model_sampling.sigma_min
+                sigmax = model.model.model_sampling.sigma_max
+
+                if sde_noise is None and sampler_mode.startswith("unsample"):
+                    total_steps = len(sigmas)+1
+                    sde_noise = []
+                else:
+                    total_steps = 1
+
+                for total_steps_iter in range (sde_noise_steps):
+                        
+                    if noise_type_init == "none":
+                        noise = torch.zeros_like(x)
+                    elif latent_noise is None:
+                        print("Initial latent noise seed: ", seed)
+                        noise_sampler_init = NOISE_GENERATOR_CLASSES_SIMPLE.get(noise_type_init)(x=x, seed=seed, sigma_min=sigmin, sigma_max=sigmax)
+                    
+                        if noise_type_init == "fractal":
+                            noise_sampler_init.alpha = alpha_init
+                            noise_sampler_init.k = k_init
+                            noise_sampler_init.scale = 0.1
+                        noise = noise_sampler_init(sigma=sigmax, sigma_next=sigmin)
+                    else:
+                        noise = latent_noise_samples
+
+                    if noise_normalize and noise.std() > 0:
+                        noise = (noise - noise.mean(dim=(-2, -1), keepdim=True)) / noise.std(dim=(-2, -1), keepdim=True)
+                        #noise.sub_(noise.mean()).div_(noise.std())
+                    noise *= noise_stdev
+                    noise = (noise - noise.mean()) + noise_mean
+                    
+                    if latent_noise_match is not None:
+                        for i in range(latent_noise_match_samples.shape[1]):
+                            noise[0][i] = (noise[0][i] - noise[0][i].mean())
+                            noise[0][i] = (noise[0][i]) + latent_noise_match_samples[0][i].mean()
+
+                    noise_mask = latent_unbatch["noise_mask"] if "noise_mask" in latent_unbatch else None
+
+                    x0_output = {}
+
+
+                    if cfg < 0:
+                        sampler.extra_options['cfg_cw'] = -cfg
+                        cfg = 1.0
+                    else:
+                        sampler.extra_options.pop("cfg_cw", None) 
+                        
+                    
+                    if sde_noise is None:
+                        sde_noise = []
+                    else:
+                        sde_noise = copy.deepcopy(sde_noise)
+                        for i in range(len(sde_noise)):
+                            sde_noise[i] = sde_noise[i]
+                            for j in range(sde_noise[i].shape[1]):
+                                sde_noise[i][0][j] = ((sde_noise[i][0][j] - sde_noise[i][0][j].mean()) / sde_noise[i][0][j].std())
+                                
+                    callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
+
+                    disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
+                    
+                    model.model.diffusion_model.raw_x = raw_x
+                    model.model.diffusion_model.last_seed = last_seed
+                    samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, pos_cond, neg_cond, x.clone(), noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
+
+                    out = latent_unbatch.copy()
+                    out["samples"] = samples
+                    if "x0" in x0_output:
+                        out_denoised = latent_unbatch.copy()
+                        out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu())
+                    else:
+                        out_denoised = out
+                    
+                    out["samples_fp64"] = out["samples"].clone()
+                    out["samples"]      = out["samples"].to(latent_image_dtype)
+                    
+                    out_denoised["samples_fp64"] = out_denoised["samples"].clone()
+                    out_denoised["samples"]      = out_denoised["samples"].to(latent_image_dtype)
+                    
+                    out_samples.     append(out["samples"])
+                    out_samples_fp64.append(out["samples_fp64"])
+                    
+                    out_denoised_samples.     append(out_denoised["samples"])
+                    out_denoised_samples_fp64.append(out_denoised["samples_fp64"])
+                    
+                    seed += 1
+                    torch.manual_seed(seed)
+                    if total_steps_iter > 1: 
+                        sde_noise.append(out["samples_fp64"])
+                        
+            out_samples               = [tensor.squeeze(0) for tensor in out_samples]
+            out_samples_fp64          = [tensor.squeeze(0) for tensor in out_samples_fp64]
+            out_denoised_samples      = [tensor.squeeze(0) for tensor in out_denoised_samples]
+            out_denoised_samples_fp64 = [tensor.squeeze(0) for tensor in out_denoised_samples_fp64]
+
+            out['samples']      = torch.stack(out_samples,     dim=0)
+            out['samples_fp64'] = torch.stack(out_samples_fp64, dim=0)
+            
+            out_denoised['samples']      = torch.stack(out_denoised_samples,     dim=0)
+            out_denoised['samples_fp64'] = torch.stack(out_denoised_samples_fp64, dim=0)
+
+            out['raw_x'] = None
+            if hasattr(model.model.diffusion_model, "raw_x"):
+                if model.model.diffusion_model.raw_x is not None:
+                    out['raw_x'] = model.model.diffusion_model.raw_x.clone()
+                    del model.model.diffusion_model.raw_x
+
+            out['last_seed'] = None
+            if hasattr(model.model.diffusion_model, "last_seed"):
+                if model.model.diffusion_model.last_seed is not None:
+                    out['last_seed'] = model.model.diffusion_model.last_seed
+                    del model.model.diffusion_model.last_seed
+
+            return ( out, out_denoised, sde_noise,)
+
+
+
+class ClownsharKSampler:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"model": ("MODEL",),
+                    "noise_type_init": (NOISE_GENERATOR_NAMES_SIMPLE, {"default": "gaussian"}),
+                    "noise_type_sde": (NOISE_GENERATOR_NAMES_SIMPLE, {"default": "gaussian"}),
+                    "noise_mode_sde": (NOISE_MODE_NAMES, {"default": 'hard', "tooltip": "How noise scales with the sigma schedule. Hard is the most aggressive, the others start strong and drop rapidly."}),
+                    "eta": ("FLOAT", {"default": 0.5, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Calculated noise amount to be added, then removed, after each step."}),
+                    "noise_seed": ("INT", {"default": 0, "min": -1, "max": 0xffffffffffffffff}),
+                    "sampler_mode": (['standard', 'unsample', 'resample'],),
+                    "sampler_name": (RK_SAMPLER_NAMES, {"default": "res_2m"}), 
+                    "implicit_sampler_name": (IRK_SAMPLER_NAMES, {"default": "explicit_diagonal"}), 
+                    "scheduler": (get_res4lyf_scheduler_list(), {"default": "beta57"},),
+                    "steps": ("INT", {"default": 30, "min": 1, "max": 10000}),
+                    "implicit_steps": ("INT", {"default": 0, "min": 0, "max": 10000}),
+                    "denoise": ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01}),
+                    "denoise_alt": ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01}),
+                    "cfg": ("FLOAT", {"default": 3.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, }),
+                    "extra_options": ("STRING", {"default": "", "multiline": True}),   
+                     },
+                "optional": 
+                    {
+                    "positive": ("CONDITIONING", ),
+                    "negative": ("CONDITIONING", ),
+                    "sigmas": ("SIGMAS", ),
+                    "latent_image": ("LATENT", ),     
+                    "guides": ("GUIDES", ),     
+                    "options": ("OPTIONS", ),   
+                    "automation": ("AUTOMATION", ),
+                    }
+                }
+
+    RETURN_TYPES = ("LATENT","LATENT", "LATENT",)
+    RETURN_NAMES = ("output", "denoised","sde_noise",) 
+
+    FUNCTION = "main"
+
+    CATEGORY = "RES4LYF/legacy/samplers"
+    DEPRECATED = True
+    
+    def main(self, model, cfg, sampler_mode, scheduler, steps, denoise=1.0, denoise_alt=1.0,
+             noise_type_init="gaussian", noise_type_sde="brownian", noise_mode_sde="hard", latent_image=None, 
+             positive=None, negative=None, sigmas=None, latent_noise=None, latent_noise_match=None,
+             noise_stdev=1.0, noise_mean=0.0, noise_normalize=True, noise_is_latent=False, 
+             eta=0.25, eta_var=0.0, d_noise=1.0, s_noise=1.0, alpha_init=-1.0, k_init=1.0, alpha_sde=-1.0, k_sde=1.0, cfgpp=0.0, c1=0.0, c2=0.5, c3=1.0, noise_seed=-1, sampler_name="res_2m", implicit_sampler_name="default",
+                    t_fn_formula=None, sigma_fn_formula=None, implicit_steps=0,
+                    latent_guide=None, latent_guide_inv=None, guide_mode="blend", latent_guide_weights=None, latent_guide_weights_inv=None, latent_guide_mask=None, latent_guide_mask_inv=None, rescale_floor=True, sigmas_override=None, 
+                    shift=3.0, base_shift=0.85, guides=None, options=None, sde_noise=None,sde_noise_steps=1, shift_scaling="exponential",
+                    extra_options="", automation=None, etas=None, s_noises=None,unsample_resample_scales=None, regional_conditioning_weights=None,frame_weights_grp=None,
+                    ): 
+
+        if noise_seed >= 0:
+            noise_seed_sde = noise_seed + 1
+        else:
+            noise_seed_sde = -1
+        
+        eta_substep = eta
+        noise_mode_sde_substep = noise_mode_sde
+        noise_type_sde_substep = noise_type_sde 
+
+        sampler = ClownSamplerAdvanced().main(
+                noise_type_sde=noise_type_sde, noise_type_sde_substep=noise_type_sde_substep, noise_mode_sde=noise_mode_sde,
+             eta=eta, eta_var=eta_var, d_noise=d_noise, s_noise=s_noise, alpha_sde=alpha_sde, k_sde=k_sde, cfgpp=cfgpp, c1=c1, c2=c2, c3=c3, noise_seed_sde=noise_seed_sde, sampler_name=sampler_name, implicit_sampler_name=implicit_sampler_name,
+                    t_fn_formula=t_fn_formula, sigma_fn_formula=sigma_fn_formula, implicit_steps=implicit_steps,
+                    latent_guide=latent_guide, latent_guide_inv=latent_guide_inv, guide_mode=guide_mode, latent_guide_weights=latent_guide_weights, latent_guide_weights_inv=latent_guide_weights_inv, latent_guide_mask=latent_guide_mask, latent_guide_mask_inv=latent_guide_mask_inv, rescale_floor=rescale_floor, sigmas_override=sigmas_override, 
+                    guides=guides, options=options, sde_noise=sde_noise,sde_noise_steps=sde_noise_steps, 
+                    extra_options=extra_options, automation=automation, etas=etas, s_noises=s_noises,unsample_resample_scales=unsample_resample_scales, regional_conditioning_weights=regional_conditioning_weights,frame_weights_grp=frame_weights_grp, eta_substep=eta_substep, noise_mode_sde_substep=noise_mode_sde_substep,
+                    )
+
+        return SharkSamplerAlpha().main(
+            model=model, cfg=cfg, sampler_mode=sampler_mode, scheduler=scheduler, steps=steps, 
+            denoise=denoise, denoise_alt=denoise_alt, noise_type_init=noise_type_init, 
+            latent_image=latent_image, positive=positive, negative=negative, sampler=sampler[0], 
+            sigmas=sigmas, latent_noise=latent_noise, latent_noise_match=latent_noise_match, 
+            noise_stdev=noise_stdev, noise_mean=noise_mean, noise_normalize=noise_normalize, 
+            d_noise=d_noise, alpha_init=alpha_init, k_init=k_init, cfgpp=cfgpp, noise_seed=noise_seed, 
+            options=options, sde_noise=sde_noise, sde_noise_steps=sde_noise_steps, 
+            extra_options=extra_options
+        )
+
+
+
+
+
+class UltraSharkSampler:  
+    # for use with https://github.com/ClownsharkBatwing/UltraCascade
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "add_noise": ("BOOLEAN", {"default": True}),
+                "normalize_noise": ("BOOLEAN", {"default": False}),
+                "noise_type": (NOISE_GENERATOR_NAMES, ),
+                "alpha": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":0.1, "round": 0.01}),
+                "k": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":2.0, "round": 0.01}),
+                "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                "cfg": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
+                "positive": ("CONDITIONING", ),
+                "negative": ("CONDITIONING", ),
+                "sampler": ("SAMPLER", ),
+                "sigmas": ("SIGMAS", ),
+                "latent_image": ("LATENT", ),               
+                "guide_type": (['residual', 'weighted'], ),
+                "guide_weight": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": 0.01}),
+            },
+            "optional": {
+                #"latent_noise": ("LATENT", ),
+                "guide": ("LATENT",),
+                "guide_weights": ("SIGMAS",),
+                #"style": ("CONDITIONING", ),
+                #"img_style": ("CONDITIONING", ),
+            }
+        }
+
+    RETURN_TYPES = ("LATENT","LATENT","LATENT")
+    RETURN_NAMES = ("output", "denoised_output", "latent_batch")
+
+    FUNCTION = "main"
+
+    CATEGORY = "RES4LYF/legacy/samplers/UltraCascade"
+    DESCRIPTION = "For use with Stable Cascade and UltraCascade."
+    DEPRECATED = True
+    
+    def main(self, model, add_noise, normalize_noise, noise_type, noise_seed, cfg, alpha, k, positive, negative, sampler, 
+               sigmas, guide_type, guide_weight, latent_image, latent_noise=None, guide=None, guide_weights=None, style=None, img_style=None): 
+
+            if model.model.model_config.unet_config.get('stable_cascade_stage') == 'up':
+                model = model.clone()
+                x_lr = guide['samples'] if guide is not None else None
+                guide_weights = initialize_or_scale(guide_weights, guide_weight, 10000)#("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01}),
+                #model.model.diffusion_model.set_guide_weights(guide_weights=guide_weights)
+                #model.model.diffusion_model.set_guide_type(guide_type=guide_type)
+                #model.model.diffusion_model.set_x_lr(x_lr=x_lr)
+                patch = model.model_options.get("transformer_options", {}).get("patches_replace", {}).get("ultracascade", {}).get("main")
+                if patch is not None:
+                    patch.update(x_lr=x_lr, guide_weights=guide_weights, guide_type=guide_type)
+                else:
+                    model.model.diffusion_model.set_sigmas_schedule(sigmas_schedule=sigmas)
+                    model.model.diffusion_model.set_sigmas_prev(sigmas_prev=sigmas[:1])
+                    model.model.diffusion_model.set_guide_weights(guide_weights=guide_weights)
+                    model.model.diffusion_model.set_guide_type(guide_type=guide_type)
+                    model.model.diffusion_model.set_x_lr(x_lr=x_lr)
+                
+            elif model.model.model_config.unet_config['stable_cascade_stage'] == 'b':
+                c_pos, c_neg = [], []
+                for t in positive:
+                    d_pos = t[1].copy()
+                    d_neg = t[1].copy()
+                    
+                    d_pos['stable_cascade_prior'] = guide['samples']
+
+                    pooled_output = d_neg.get("pooled_output", None)
+                    if pooled_output is not None:
+                        d_neg["pooled_output"] = torch.zeros_like(pooled_output)
+                    
+                    c_pos.append([t[0], d_pos])            
+                    c_neg.append([torch.zeros_like(t[0]), d_neg])
+                positive = c_pos
+                negative = c_neg
+                
+            if style is not None:
+                model.set_model_patch(style, 'style_cond')
+            if img_style is not None:
+                model.set_model_patch(img_style,'img_style_cond')
+        
+            # 1, 768      clip_style[0][0][1]['unclip_conditioning'][0]['clip_vision_output'].image_embeds.shape
+            # 1, 1280     clip_style[0][0][1]['pooled_output'].shape 
+            # 1, 77, 1280 clip_style[0][0][0].shape
+        
+            latent = latent_image
+            latent_image = latent["samples"]
+            torch.manual_seed(noise_seed)
+
+            if not add_noise:
+                noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
+            elif latent_noise is None:
+                batch_inds = latent["batch_index"] if "batch_index" in latent else None
+                noise = prepare_noise(latent_image, noise_seed, noise_type, batch_inds, alpha, k)
+            else:
+                noise = latent_noise["samples"]#.to(torch.float64)
+
+            if normalize_noise and noise.std() > 0:
+                noise = (noise - noise.mean(dim=(-2, -1), keepdim=True)) / noise.std(dim=(-2, -1), keepdim=True)
+
+            noise_mask = None
+            if "noise_mask" in latent:
+                noise_mask = latent["noise_mask"]
+
+            x0_output = {}
+            callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
+            disable_pbar = False
+
+            samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, 
+                                                 noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, 
+                                                 seed=noise_seed)
+
+            out = latent.copy()
+            out["samples"] = samples
+            if "x0" in x0_output:
+                out_denoised = latent.copy()
+                out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu())
+            else:
+                out_denoised = out
+                
+            return (out, out_denoised)
+
+
diff --git a/custom_nodes/RES4LYF/legacy/samplers_extensions.py b/custom_nodes/RES4LYF/legacy/samplers_extensions.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3463a8a45b98ceb6f62959df8ee174285a0b094
--- /dev/null
+++ b/custom_nodes/RES4LYF/legacy/samplers_extensions.py
@@ -0,0 +1,592 @@
+from .noise_classes import NOISE_GENERATOR_CLASSES, NOISE_GENERATOR_CLASSES_SIMPLE, NOISE_GENERATOR_NAMES, NOISE_GENERATOR_NAMES_SIMPLE
+
+import comfy.sample
+import comfy.sampler_helpers
+import comfy.model_sampling
+import comfy.latent_formats
+import comfy.sd
+import comfy.supported_models
+from .conditioning import FluxRegionalPrompt, FluxRegionalConditioning
+from .models import ReFluxPatcher
+
+
+import torch
+import torch.nn.functional as F
+
+import copy
+
+from .helper import initialize_or_scale, get_res4lyf_scheduler_list
+
+
+def move_to_same_device(*tensors):
+    if not tensors:
+        return tensors
+
+    device = tensors[0].device
+    return tuple(tensor.to(device) for tensor in tensors)
+
+
+
+
+
+    
+class SamplerOptions_TimestepScaling:
+    # for patching the t_fn and sigma_fn (sigma <-> timestep) formulas to allow picking Runge-Kutta Ci values ("midpoints") with different scaling.
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {
+                     "sampler": ("SAMPLER", ),
+                     "t_fn_formula": ("STRING", {"default": "1/((sigma).exp()+1)", "multiline": True}),
+                     "sigma_fn_formula": ("STRING", {"default": "((1-t)/t).log()", "multiline": True}),
+                    },
+                     "optional": 
+                    {
+                    }  
+               }
+    RETURN_TYPES = ("SAMPLER",)
+    RETURN_NAMES = ("sampler",)
+    FUNCTION = "set_sampler_extra_options"
+    
+    CATEGORY = "RES4LYF/legacy/sampler_extensions"
+    DESCRIPTION = "Patches ClownSampler's t_fn and sigma_fn (sigma <-> timestep) formulas to allow picking Runge-Kutta Ci values (midpoints) with different scaling."
+    DEPRECATED = True
+
+    def set_sampler_extra_options(self, sampler, t_fn_formula=None, sigma_fn_formula=None, ):
+
+        sampler = copy.deepcopy(sampler)
+
+        sampler.extra_options['t_fn_formula']     = t_fn_formula
+        sampler.extra_options['sigma_fn_formula'] = sigma_fn_formula
+
+        return (sampler, )
+
+
+
+class SamplerOptions_GarbageCollection:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {
+                     "sampler": ("SAMPLER", ),
+                     "garbage_collection": ("BOOLEAN", {"default": True}),
+                    },
+                     "optional": 
+                    {
+                    }  
+               }
+    RETURN_TYPES = ("SAMPLER",)
+    RETURN_NAMES = ("sampler",)
+    FUNCTION = "set_sampler_extra_options"
+    CATEGORY = "RES4LYF/legacy/sampler_extensions"
+    DESCRIPTION = "Patches ClownSampler to use garbage collection after every step. This can help with OOM issues during inference for large models like Flux. The tradeoff is slower sampling."
+    DEPRECATED = True
+
+    def set_sampler_extra_options(self, sampler, garbage_collection):
+        sampler = copy.deepcopy(sampler)
+        sampler.extra_options['GARBAGE_COLLECT'] = garbage_collection
+        return (sampler, )
+
+
+
+GUIDE_MODE_NAMES = ["unsample", 
+                    "resample", 
+                    "epsilon",
+                    "epsilon_projection",
+                    "epsilon_dynamic_mean",
+                    "epsilon_dynamic_mean_std", 
+                    "epsilon_dynamic_mean_from_bkg", 
+                    "epsilon_guide_mean_std_from_bkg",
+                    "hard_light", 
+                    "blend", 
+                    "blend_projection",
+                    "mean_std", 
+                    "mean", 
+                    "mean_tiled",
+                    "std", 
+                    "data",
+                    #"data_projection",
+                    "none",
+]
+
+
+
+
+class ClownInpaint: ##################################################################################################################################
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {#"guide_mode": (GUIDE_MODE_NAMES, {"default": 'epsilon', "tooltip": "Recommended: epsilon or mean/mean_std with sampler_mode = standard, and unsample/resample with sampler_mode = unsample/resample. Epsilon_dynamic_mean, etc. are only used with two latent inputs and a mask. Blend/hard_light/mean/mean_std etc. require low strengths, start with 0.01-0.02."}),
+                     "guide_weight":               ("FLOAT", {"default": 0.10, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
+                     "guide_weight_bkg":           ("FLOAT", {"default": 1.00, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide_bkg."}),
+                    "guide_weight_scheduler":     (["constant"] + get_res4lyf_scheduler_list(), {"default": "beta57"},),
+                    "guide_weight_scheduler_bkg": (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
+                    "guide_end_step":              ("INT", {"default": 15, "min": 1, "max": 10000}),
+                    "guide_bkg_end_step":          ("INT", {"default": 10000, "min": 1, "max": 10000}),
+                    },
+                    "optional": 
+                    {
+                        "model":             ("MODEL", ),
+                        "positive_inpaint":  ("CONDITIONING", ),
+                        "positive_bkg":      ("CONDITIONING", ),
+                        "negative":          ("CONDITIONING", ),
+                        "latent_image":      ("LATENT", ),
+                        "mask":              ("MASK", ),
+                        "guide_weights":     ("SIGMAS", ),
+                        "guide_weights_bkg": ("SIGMAS", ),
+                    }  
+               }
+    RETURN_TYPES = ("MODEL","CONDITIONING","CONDITIONING","LATENT","GUIDES",)
+    RETURN_NAMES = ("model","positive"    ,"negative"    ,"latent","guides",)
+    CATEGORY = "RES4LYF/legacy/sampler_extensions"
+
+    FUNCTION = "main"
+    DEPRECATED = True
+
+    def main(self, guide_weight_scheduler="constant", guide_weight_scheduler_bkg="constant", guide_end_step=10000, guide_bkg_end_step=30, guide_weight_scale=1.0, guide_weight_bkg_scale=1.0, guide=None, guide_bkg=None, guide_weight=1.0, guide_weight_bkg=1.0, 
+                    guide_mode="epsilon", guide_weights=None, guide_weights_bkg=None, guide_mask_bkg=None,
+                    model=None, positive_inpaint=None, positive_bkg=None, negative=None, latent_image=None, mask=None, 
+                    ):
+        default_dtype = torch.float64
+        guide = latent_image
+        guide_bkg = {'samples': latent_image['samples'].clone()}
+        
+        max_steps = 10000
+        
+        denoise, denoise_bkg = guide_weight_scale, guide_weight_bkg_scale
+        
+        if guide_mode.startswith("epsilon_") and not guide_mode.startswith("epsilon_projection") and guide_bkg == None:
+            print("Warning: need two latent inputs for guide_mode=",guide_mode," to work. Falling back to epsilon.")
+            guide_mode = "epsilon"
+        
+        if guide_weight_scheduler == "constant": 
+            guide_weights = initialize_or_scale(None, guide_weight, guide_end_step).to(default_dtype)
+            guide_weights = F.pad(guide_weights, (0, max_steps), value=0.0)
+        
+        if guide_weight_scheduler_bkg == "constant": 
+            guide_weights_bkg = initialize_or_scale(None, guide_weight_bkg, guide_bkg_end_step).to(default_dtype)
+            guide_weights_bkg = F.pad(guide_weights_bkg, (0, max_steps), value=0.0)
+            
+        guides = (guide_mode, guide_weight, guide_weight_bkg, guide_weights, guide_weights_bkg, guide, guide_bkg, mask, guide_mask_bkg,
+                  guide_weight_scheduler, guide_weight_scheduler_bkg, guide_end_step, guide_bkg_end_step, denoise, denoise_bkg)
+        
+        latent = {'samples': torch.zeros_like(latent_image['samples'])}
+        if (positive_inpaint is None) and (positive_bkg is None):
+            positive = None
+        else:
+            if positive_bkg is None:
+                if positive_bkg is None:
+                    positive_bkg = [[
+                        torch.zeros((1, 256, 4096)),
+                        {'pooled_output': torch.zeros((1, 768))}
+                        ]]
+            cond_regional, mask_inv     = FluxRegionalPrompt().main(cond=positive_inpaint,                              mask=mask)
+            cond_regional, mask_inv_inv = FluxRegionalPrompt().main(cond=positive_bkg    , cond_regional=cond_regional, mask=mask_inv)
+            
+            positive, = FluxRegionalConditioning().main(conditioning_regional=cond_regional, self_attn_floor=0.0)
+            
+        model, = ReFluxPatcher().main(model, enable=True)
+        
+        return (model, positive, negative, latent, guides, )
+    
+    
+class ClownInpaintSimple: ##################################################################################################################################
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {#"guide_mode": (GUIDE_MODE_NAMES, {"default": 'epsilon', "tooltip": "Recommended: epsilon or mean/mean_std with sampler_mode = standard, and unsample/resample with sampler_mode = unsample/resample. Epsilon_dynamic_mean, etc. are only used with two latent inputs and a mask. Blend/hard_light/mean/mean_std etc. require low strengths, start with 0.01-0.02."}),
+                     "guide_weight":               ("FLOAT", {"default": 0.10, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
+                    "guide_weight_scheduler":     (["constant"] + get_res4lyf_scheduler_list(), {"default": "beta57"},),
+                    "guide_end_step":              ("INT", {"default": 15, "min": 1, "max": 10000}),
+                    },
+                    "optional": 
+                    {
+                        "model":             ("MODEL", ),
+                        "positive_inpaint":  ("CONDITIONING", ),
+                        "negative":          ("CONDITIONING", ),
+                        "latent_image":      ("LATENT", ),
+                        "mask":              ("MASK", ),
+                    }
+               }
+    RETURN_TYPES = ("MODEL","CONDITIONING","CONDITIONING","LATENT","GUIDES",)
+    RETURN_NAMES = ("model","positive"    ,"negative"    ,"latent","guides",)
+    CATEGORY = "RES4LYF/legacy/sampler_extensions"
+
+    FUNCTION = "main"
+    DEPRECATED = True
+
+    def main(self, guide_weight_scheduler="constant", guide_weight_scheduler_bkg="constant", guide_end_step=10000, guide_bkg_end_step=30, guide_weight_scale=1.0, guide_weight_bkg_scale=1.0, guide=None, guide_bkg=None, guide_weight=1.0, guide_weight_bkg=1.0, 
+                    guide_mode="epsilon", guide_weights=None, guide_weights_bkg=None, guide_mask_bkg=None,
+                    model=None, positive_inpaint=None, positive_bkg=None, negative=None, latent_image=None, mask=None, 
+                    ):
+        default_dtype = torch.float64
+        guide = latent_image
+        guide_bkg = {'samples': latent_image['samples'].clone()}
+        
+        max_steps = 10000
+        
+        denoise, denoise_bkg = guide_weight_scale, guide_weight_bkg_scale
+        
+        if guide_mode.startswith("epsilon_") and not guide_mode.startswith("epsilon_projection") and guide_bkg == None:
+            print("Warning: need two latent inputs for guide_mode=",guide_mode," to work. Falling back to epsilon.")
+            guide_mode = "epsilon"
+        
+        if guide_weight_scheduler == "constant": 
+            guide_weights = initialize_or_scale(None, guide_weight, guide_end_step).to(default_dtype)
+            guide_weights = F.pad(guide_weights, (0, max_steps), value=0.0)
+        
+        if guide_weight_scheduler_bkg == "constant": 
+            guide_weights_bkg = initialize_or_scale(None, guide_weight_bkg, guide_bkg_end_step).to(default_dtype)
+            guide_weights_bkg = F.pad(guide_weights_bkg, (0, max_steps), value=0.0)
+            
+        guides = (guide_mode, guide_weight, guide_weight_bkg, guide_weights, guide_weights_bkg, guide, guide_bkg, mask, guide_mask_bkg,
+                  guide_weight_scheduler, guide_weight_scheduler_bkg, guide_end_step, guide_bkg_end_step, denoise, denoise_bkg)
+        
+        latent = {'samples': torch.zeros_like(latent_image['samples'])}
+        if (positive_inpaint is None) and (positive_bkg is None):
+            positive = None
+        else:
+            if positive_bkg is None:
+                if positive_bkg is None:
+                    positive_bkg = [[
+                        torch.zeros((1, 256, 4096)),
+                        {'pooled_output': torch.zeros((1, 768))}
+                        ]]
+            cond_regional, mask_inv     = FluxRegionalPrompt().main(cond=positive_inpaint,                              mask=mask)
+            cond_regional, mask_inv_inv = FluxRegionalPrompt().main(cond=positive_bkg    , cond_regional=cond_regional, mask=mask_inv)
+            
+            positive, = FluxRegionalConditioning().main(conditioning_regional=cond_regional, self_attn_floor=1.0)
+            
+        model, = ReFluxPatcher().main(model, enable=True)
+        
+        return (model, positive, negative, latent, guides, )
+    
+
+##################################################################################################################################
+
+
+class ClownsharKSamplerGuide:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"guide_mode": (GUIDE_MODE_NAMES, {"default": 'epsilon_projection', "tooltip": "Recommended: epsilon or mean/mean_std with sampler_mode = standard, and unsample/resample with sampler_mode = unsample/resample. Epsilon_dynamic_mean, etc. are only used with two latent inputs and a mask. Blend/hard_light/mean/mean_std etc. require low strengths, start with 0.01-0.02."}),
+                     "guide_weight": ("FLOAT", {"default": 0.75, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
+                     #"guide_weight_bkg": ("FLOAT", {"default": 0.75, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide_bkg."}),
+                     "guide_weight_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step":0.01, "round": False, "tooltip": "Disables the guide for the next step when the denoised image is similar to the guide. Higher values will strengthen the effect."}),
+                     #"guide_weight_bkg_scale": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Disables the guide for the next step when the denoised image is similar to the guide. Higher values will strengthen the effect."}),
+                    "guide_weight_scheduler":     (["constant"] + get_res4lyf_scheduler_list(), {"default": "beta57"},),
+                    #"guide_weight_scheduler_bkg": (["constant"] + comfy.samplers.SCHEDULER_NAMES + ["beta57"], {"default": "beta57"},),
+                    "guide_end_step": ("INT", {"default": 15, "min": 1, "max": 10000}),
+                    #"guide_bkg_end_step": ("INT", {"default": 15, "min": 1, "max": 10000}),
+                    },
+                    "optional": 
+                    {
+                        "guide": ("LATENT", ),
+                        #"guide_bkg": ("LATENT", ),
+                        "guide_mask": ("MASK", ),
+                        #"guide_mask_bkg": ("MASK", ),
+                        "guide_weights": ("SIGMAS", ),
+                        #"guide_weights_bkg": ("SIGMAS", ),
+                    }  
+               }
+    RETURN_TYPES = ("GUIDES",)
+    RETURN_NAMES = ("guides",)
+    CATEGORY     = "RES4LYF/legacy/sampler_extensions"
+
+    FUNCTION     = "main"
+    DEPRECATED = True
+
+    def main(self, guide_weight_scheduler="constant", guide_weight_scheduler_bkg="constant", guide_end_step=30, guide_bkg_end_step=30, guide_weight_scale=1.0, guide_weight_bkg_scale=1.0, guide=None, guide_bkg=None, guide_weight=0.0, guide_weight_bkg=0.0, 
+                    guide_mode="blend", guide_weights=None, guide_weights_bkg=None, guide_mask=None, guide_mask_bkg=None,
+                    ):
+        default_dtype = torch.float64
+        
+        max_steps = 10000
+        
+        denoise, denoise_bkg = guide_weight_scale, guide_weight_bkg_scale
+        
+        if guide_mode.startswith("epsilon_") and not guide_mode.startswith("epsilon_projection") and guide_bkg == None:
+            print("Warning: need two latent inputs for guide_mode=",guide_mode," to work. Falling back to epsilon.")
+            guide_mode = "epsilon"
+      
+        if guide_weight_scheduler == "constant" and guide_weights == None: 
+            guide_weights = initialize_or_scale(None, 1.0, guide_end_step).to(default_dtype)
+            #guide_weights = initialize_or_scale(None, guide_weight, guide_end_step).to(default_dtype)
+            guide_weights = F.pad(guide_weights, (0, max_steps), value=0.0)
+        
+        if guide_weight_scheduler_bkg == "constant": 
+            guide_weights_bkg = initialize_or_scale(None, 0.0, guide_bkg_end_step).to(default_dtype)
+            #guide_weights_bkg = initialize_or_scale(None, guide_weight_bkg, guide_bkg_end_step).to(default_dtype)
+            guide_weights_bkg = F.pad(guide_weights_bkg, (0, max_steps), value=0.0)
+            
+        guides = (guide_mode, guide_weight, guide_weight_bkg, guide_weights, guide_weights_bkg, guide, guide_bkg, guide_mask, guide_mask_bkg,
+                  guide_weight_scheduler, guide_weight_scheduler_bkg, guide_end_step, guide_bkg_end_step, denoise, denoise_bkg)
+        return (guides, )
+
+
+
+
+class ClownsharKSamplerGuides:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"guide_mode": (GUIDE_MODE_NAMES, {"default": 'epsilon_projection', "tooltip": "Recommended: epsilon or mean/mean_std with sampler_mode = standard, and unsample/resample with sampler_mode = unsample/resample. Epsilon_dynamic_mean, etc. are only used with two latent inputs and a mask. Blend/hard_light/mean/mean_std etc. require low strengths, start with 0.01-0.02."}),
+                     "guide_weight": ("FLOAT", {"default": 0.75, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide."}),
+                     "guide_weight_bkg": ("FLOAT", {"default": 0.75, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Set the strength of the guide_bkg."}),
+                     "guide_weight_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step":0.01, "round": False, "tooltip": "Disables the guide for the next step when the denoised image is similar to the guide. Higher values will strengthen the effect."}),
+                     "guide_weight_bkg_scale": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step":0.01, "round": False, "tooltip": "Disables the guide for the next step when the denoised image is similar to the guide. Higher values will strengthen the effect."}),
+                    "guide_weight_scheduler":     (["constant"] + get_res4lyf_scheduler_list(), {"default": "beta57"},),
+                    "guide_weight_scheduler_bkg": (["constant"] + get_res4lyf_scheduler_list(), {"default": "constant"},),
+                    "guide_end_step": ("INT", {"default": 15, "min": 1, "max": 10000}),
+                    "guide_bkg_end_step": ("INT", {"default": 15, "min": 1, "max": 10000}),
+                    },
+                    "optional": 
+                    {
+                        "guide": ("LATENT", ),
+                        "guide_bkg": ("LATENT", ),
+                        "guide_mask": ("MASK", ),
+                        "guide_mask_bkg": ("MASK", ),
+                        "guide_weights": ("SIGMAS", ),
+                        "guide_weights_bkg": ("SIGMAS", ),
+                    }  
+               }
+    RETURN_TYPES = ("GUIDES",)
+    RETURN_NAMES = ("guides",)
+    CATEGORY     = "RES4LYF/legacy/sampler_extensions"
+
+    FUNCTION     = "main"
+    DEPRECATED = True
+
+    def main(self, guide_weight_scheduler="constant", guide_weight_scheduler_bkg="constant", guide_end_step=30, guide_bkg_end_step=30, guide_weight_scale=1.0, guide_weight_bkg_scale=1.0, guide=None, guide_bkg=None, guide_weight=0.0, guide_weight_bkg=0.0, 
+                    guide_mode="blend", guide_weights=None, guide_weights_bkg=None, guide_mask=None, guide_mask_bkg=None,
+                    ):
+        default_dtype = torch.float64
+        
+        max_steps = 10000
+        
+        denoise, denoise_bkg = guide_weight_scale, guide_weight_bkg_scale
+        
+        if guide_mode.startswith("epsilon_") and not guide_mode.startswith("epsilon_projection") and guide_bkg == None:
+            print("Warning: need two latent inputs for guide_mode=",guide_mode," to work. Falling back to epsilon.")
+            guide_mode = "epsilon"
+        
+        if guide_weight_scheduler == "constant" and guide_weights == None: 
+            guide_weights = initialize_or_scale(None, 1.0, guide_end_step).to(default_dtype)
+            guide_weights = F.pad(guide_weights, (0, max_steps), value=0.0)
+        
+        if guide_weight_scheduler_bkg == "constant" and guide_weights_bkg == None: 
+            guide_weights_bkg = initialize_or_scale(None, 1.0, guide_bkg_end_step).to(default_dtype)
+            guide_weights_bkg = F.pad(guide_weights_bkg, (0, max_steps), value=0.0)
+    
+        guides = (guide_mode, guide_weight, guide_weight_bkg, guide_weights, guide_weights_bkg, guide, guide_bkg, guide_mask, guide_mask_bkg,
+                  guide_weight_scheduler, guide_weight_scheduler_bkg, guide_end_step, guide_bkg_end_step, denoise, denoise_bkg)
+        return (guides, )
+
+
+
+
+
+
+
+
+class ClownsharKSamplerAutomation:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {
+                    },
+                    "optional": 
+                    {
+                        "etas": ("SIGMAS", ),
+                        "s_noises": ("SIGMAS", ),
+                        "unsample_resample_scales": ("SIGMAS", ),
+
+                    }  
+               }
+    RETURN_TYPES = ("AUTOMATION",)
+    RETURN_NAMES = ("automation",)
+    CATEGORY = "RES4LYF/legacy/sampler_extensions"
+    
+    FUNCTION = "main"
+    DEPRECATED = True
+
+    def main(self, etas=None, s_noises=None, unsample_resample_scales=None,):
+        automation = (etas, s_noises, unsample_resample_scales)
+        return (automation, )
+
+
+
+
+class ClownsharKSamplerAutomation_Advanced:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {
+                    },
+                    "optional": 
+                    {
+                        "automation": ("AUTOMATION", ),
+                        "etas": ("SIGMAS", ),
+                        "etas_substep": ("SIGMAS", ),
+                        "s_noises": ("SIGMAS", ),
+                        "unsample_resample_scales": ("SIGMAS", ),
+                        "frame_weights": ("SIGMAS", ),
+                        "frame_weights_bkg": ("SIGMAS", ),
+                    }  
+               }
+    RETURN_TYPES = ("AUTOMATION",)
+    RETURN_NAMES = ("automation",)
+    CATEGORY = "RES4LYF/legacy/sampler_extensions"
+    
+    FUNCTION = "main"
+    DEPRECATED = True
+
+    def main(self, automation=None, etas=None, etas_substep=None, s_noises=None, unsample_resample_scales=None, frame_weights=None, frame_weights_bkg=None):
+        
+        if automation is None:
+            automation = {}
+        
+        frame_weights_grp = (frame_weights, frame_weights_bkg)
+
+        automation['etas'] = etas
+        automation['etas_substep'] = etas_substep
+        automation['s_noises'] = s_noises
+        automation['unsample_resample_scales'] = unsample_resample_scales
+        automation['frame_weights_grp'] = frame_weights_grp
+
+        return (automation, )
+
+
+
+class ClownsharKSamplerOptions:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "noise_init_stdev": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":0.01, "round": False, }),
+                "noise_init_mean": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step":0.01, "round": False, }),
+                "noise_type_init": (NOISE_GENERATOR_NAMES, {"default": "gaussian"}),
+                "noise_type_sde": (NOISE_GENERATOR_NAMES, {"default": "brownian"}),
+                "noise_mode_sde": (["hard", "hard_var", "hard_sq", "soft", "softer", "exp"], {"default": 'hard', "tooltip": "How noise scales with the sigma schedule. Hard is the most aggressive, the others start strong and drop rapidly."}),
+                "eta": ("FLOAT", {"default": 0.25, "min": -100.0, "max": 100.0, "step":0.01, "round": False}),
+                "s_noise": ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01, "round": False}),
+                "d_noise": ("FLOAT", {"default": 1.0, "min": -10000, "max": 10000, "step":0.01}),
+                "alpha_init": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.1}),
+                "k_init": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 2}),      
+                "alpha_sde": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.1}),
+                "k_sde": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 2}),      
+                "noise_seed": ("INT", {"default": -1, "min": -1, "max": 0xffffffffffffffff, "tooltip": "Seed for the SDE noise that is added after each step if eta or eta_var are non-zero. If set to -1, it will use the increment the seed most recently used by the workflow."}),
+                "c1": ("FLOAT", {"default": 0.0, "min": -1.0, "max": 10000.0, "step": 0.01}),
+                "c2": ("FLOAT", {"default": 0.5, "min": -1.0, "max": 10000.0, "step": 0.01}),
+                "c3": ("FLOAT", {"default": 1.0, "min": -1.0, "max": 10000.0, "step": 0.01}),
+                "t_fn_formula": ("STRING", {"default": "", "multiline": True}),
+                "sigma_fn_formula": ("STRING", {"default": "", "multiline": True}),   
+                #"unsampler_type": (['linear', 'exponential', 'constant'],),
+            },
+            "optional": {
+                "options": ("OPTIONS",),
+            }
+        }
+    
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    CATEGORY = "RES4LYF/legacy/sampler_extensions"
+
+    FUNCTION = "main"
+    DEPRECATED = True
+
+    def main(self, noise_init_stdev, noise_init_mean, c1, c2, c3, eta, s_noise, d_noise, noise_type_init, noise_type_sde, noise_mode_sde, noise_seed,
+                    alpha_init, k_init, alpha_sde, k_sde, t_fn_formula=None, sigma_fn_formula=None, unsampler_type="linear",
+                    alphas=None, etas=None, s_noises=None, d_noises=None, c2s=None, c3s=None,
+                    options=None,
+                    ):
+    
+        if options is None:
+            options = {}
+
+        options['noise_init_stdev'] = noise_init_stdev
+        options['noise_init_mean'] = noise_init_mean
+        options['noise_type_init'] = noise_type_init
+        options['noise_type_sde'] = noise_type_sde
+        options['noise_mode_sde'] = noise_mode_sde
+        options['eta'] = eta
+        options['s_noise'] = s_noise
+        options['d_noise'] = d_noise
+        options['alpha_init'] = alpha_init
+        options['k_init'] = k_init
+        options['alpha_sde'] = alpha_sde
+        options['k_sde'] = k_sde
+        options['noise_seed_sde'] = noise_seed
+        options['c1'] = c1
+        options['c2'] = c2
+        options['c3'] = c3
+        options['t_fn_formula'] = t_fn_formula
+        options['sigma_fn_formula'] = sigma_fn_formula
+        options['unsampler_type'] = unsampler_type
+        
+        return (options,)
+    
+
+
+class ClownOptions_SDE_Noise:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sde_noise_steps": ("INT", {"default": 1, "min": 1, "max": 10000}),
+            },
+            "optional": {
+                "sde_noise": ("LATENT",),
+                "options"  : ("OPTIONS",),
+            }
+        }
+    
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    CATEGORY     = "RES4LYF/legacy/sampler_options"
+    FUNCTION     = "main"
+    DEPRECATED = True
+
+    def main(self, sde_noise_steps, sde_noise, options=None,):
+    
+        if options is None:
+            options = {}
+
+        options['sde_noise_steps'] = sde_noise_steps
+        options['sde_noise'] = sde_noise
+        
+        return (options,)
+
+
+
+class ClownOptions_FrameWeights:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "frame_weights": ("SIGMAS", ),
+            },
+            "optional": {
+                "options": ("OPTIONS",),
+            }
+        }
+    
+    DEPRECATED = True
+    RETURN_TYPES = ("OPTIONS",)
+    RETURN_NAMES = ("options",)
+    CATEGORY = "RES4LYF/legacy/sampler_options"
+
+    FUNCTION = "main"
+    DEPRECATED = True
+
+    def main(self, frame_weights, options=None,):
+
+        if options is None:
+            options = {}
+
+        frame_weights_grp = (frame_weights, frame_weights)
+        options['frame_weights_grp'] = frame_weights_grp
+
+        return (options,)
+
+
diff --git a/custom_nodes/RES4LYF/legacy/samplers_tiled.py b/custom_nodes/RES4LYF/legacy/samplers_tiled.py
new file mode 100644
index 0000000000000000000000000000000000000000..d77aaa56f33510c6da34b7adeccbd449d1f6bfd0
--- /dev/null
+++ b/custom_nodes/RES4LYF/legacy/samplers_tiled.py
@@ -0,0 +1,568 @@
+# tiled sampler code adapted from https://github.com/BlenderNeko/ComfyUI_TiledKSampler 
+# and heavily modified for use with https://github.com/ClownsharkBatwing/UltraCascade
+
+import sys
+import os
+import copy
+from functools import partial
+
+from tqdm.auto import tqdm
+
+import torch
+
+sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
+import comfy.sd
+import comfy.controlnet
+import comfy.model_management
+import comfy.sample
+import comfy.sampler_helpers
+import latent_preview
+
+from nodes import MAX_RESOLUTION
+#MAX_RESOLUTION=8192
+
+import comfy.clip_vision
+import folder_paths
+
+from . import tiling
+from .noise_classes import *
+
+def initialize_or_scale(tensor, value, steps):
+    if tensor is None:
+        return torch.full((steps,), value)
+    else:
+        return value * tensor
+
+def cv_cond(cv_out, conditioning, strength, noise_augmentation): 
+
+    c = []
+    for t in conditioning:
+        o = t[1].copy()
+        x = {"clip_vision_output": cv_out, "strength": strength, "noise_augmentation": noise_augmentation}
+        if "unclip_conditioning" in o:
+            o["unclip_conditioning"] = o["unclip_conditioning"][:] + [x]
+        else:
+            o["unclip_conditioning"] = [x]
+        n = [t[0], o]
+        c.append(n)
+    
+    return c
+
+
+def recursion_to_list(obj, attr):
+    current = obj
+    yield current
+    while True:
+        current = getattr(current, attr, None)
+        if current is not None:
+            yield current
+        else:
+            return
+
+def copy_cond(cond):
+    return [[c1,c2.copy()] for c1,c2 in cond]
+
+def slice_cond(tile_h, tile_h_len, tile_w, tile_w_len, cond, area):
+    tile_h_end = tile_h + tile_h_len
+    tile_w_end = tile_w + tile_w_len
+    coords = area[0] #h_len, w_len, h, w,
+    mask = area[1]
+    if coords is not None:
+        h_len, w_len, h, w = coords
+        h_end = h + h_len
+        w_end = w + w_len
+        if h < tile_h_end and h_end > tile_h and w < tile_w_end and w_end > tile_w:
+            new_h = max(0, h - tile_h)
+            new_w = max(0, w - tile_w)
+            new_h_end = min(tile_h_end, h_end - tile_h)
+            new_w_end = min(tile_w_end, w_end - tile_w)
+            cond[1]['area'] = (new_h_end - new_h, new_w_end - new_w, new_h, new_w)
+        else:
+            return (cond, True)
+    if mask is not None:
+        new_mask = tiling.get_slice(mask, tile_h,tile_h_len,tile_w,tile_w_len)
+        if new_mask.sum().cpu() == 0.0 and 'mask' in cond[1]:
+            return (cond, True)
+        else:
+            cond[1]['mask'] = new_mask
+    return (cond, False)
+
+def slice_gligen(tile_h, tile_h_len, tile_w, tile_w_len, cond, gligen):
+    tile_h_end = tile_h + tile_h_len
+    tile_w_end = tile_w + tile_w_len
+    if gligen is None:
+        return
+    gligen_type = gligen[0]
+    gligen_model = gligen[1]
+    gligen_areas = gligen[2]
+    
+    gligen_areas_new = []
+    for emb, h_len, w_len, h, w in gligen_areas:
+        h_end = h + h_len
+        w_end = w + w_len
+        if h < tile_h_end and h_end > tile_h and w < tile_w_end and w_end > tile_w:
+            new_h = max(0, h - tile_h)
+            new_w = max(0, w - tile_w)
+            new_h_end = min(tile_h_end, h_end - tile_h)
+            new_w_end = min(tile_w_end, w_end - tile_w)
+            gligen_areas_new.append((emb, new_h_end - new_h, new_w_end - new_w, new_h, new_w))
+
+    if len(gligen_areas_new) == 0:
+        del cond['gligen']
+    else:
+        cond['gligen'] = (gligen_type, gligen_model, gligen_areas_new)
+
+def slice_cnet(h, h_len, w, w_len, model:comfy.controlnet.ControlBase, img):
+    if img is None:
+        img = model.cond_hint_original
+    hint = tiling.get_slice(img, h*8, h_len*8, w*8, w_len*8)
+    if isinstance(model, comfy.controlnet.ControlLora):
+        model.cond_hint = hint.float().to(model.device)
+    else:
+        model.cond_hint = hint.to(model.control_model.dtype).to(model.device)
+
+def slices_T2I(h, h_len, w, w_len, model:comfy.controlnet.ControlBase, img):
+    model.control_input = None
+    if img is None:
+        img = model.cond_hint_original
+    model.cond_hint = tiling.get_slice(img, h*8, h_len*8, w*8, w_len*8).float().to(model.device)
+
+# TODO: refactor some of the mess
+
+
+def cnets_and_cnet_imgs(positive, negative, shape): 
+    # cnets
+    cnets =  [c['control'] for (_, c) in positive + negative if 'control' in c]
+    # unroll recursion
+    cnets = list(set([x for m in cnets for x in recursion_to_list(m, "previous_controlnet")]))
+    # filter down to only cnets
+    cnets = [x for x in cnets if isinstance(x, comfy.controlnet.ControlNet)]
+    cnet_imgs = [
+        torch.nn.functional.interpolate(m.cond_hint_original, (shape[-2] * 8, shape[-1] * 8), mode='nearest-exact').to('cpu')
+        if m.cond_hint_original.shape[-2] != shape[-2] * 8 or m.cond_hint_original.shape[-1] != shape[-1] * 8 else None
+        for m in cnets]
+    return cnets, cnet_imgs
+
+def T2Is_and_T2I_imgs(positive, negative, shape): 
+    # T2I
+    T2Is =  [c['control'] for (_, c) in positive + negative if 'control' in c]
+    # unroll recursion
+    T2Is = [x for m in T2Is for x in recursion_to_list(m, "previous_controlnet")]
+    # filter down to only T2I
+    T2Is = [x for x in T2Is if isinstance(x, comfy.controlnet.T2IAdapter)]
+    T2I_imgs = [
+        torch.nn.functional.interpolate(m.cond_hint_original, (shape[-2] * 8, shape[-1] * 8), mode='nearest-exact').to('cpu')
+        if m.cond_hint_original.shape[-2] != shape[-2] * 8 or m.cond_hint_original.shape[-1] != shape[-1] * 8 or (m.channels_in == 1 and m.cond_hint_original.shape[1] != 1) else None
+        for m in T2Is
+    ]
+    T2I_imgs = [
+        torch.mean(img, 1, keepdim=True) if img is not None and m.channels_in == 1 and m.cond_hint_original.shape[1] else img
+        for m, img in zip(T2Is, T2I_imgs)
+    ]
+    return T2Is, T2I_imgs
+
+def spatial_conds_posneg(positive, negative, shape, device): #cond area and mask
+    spatial_conds_pos = [
+        (c[1]['area'] if 'area' in c[1] else None, 
+            comfy.sample.prepare_mask(c[1]['mask'], shape, device) if 'mask' in c[1] else None)
+        for c in positive
+    ]
+    spatial_conds_neg = [
+        (c[1]['area'] if 'area' in c[1] else None, 
+            comfy.sample.prepare_mask(c[1]['mask'], shape, device) if 'mask' in c[1] else None)
+        for c in negative
+    ]
+    return spatial_conds_pos, spatial_conds_neg 
+
+def gligen_posneg(positive, negative):
+    #gligen
+    gligen_pos = [
+        c[1]['gligen'] if 'gligen' in c[1] else None
+        for c in positive
+    ]
+    gligen_neg = [
+        c[1]['gligen'] if 'gligen' in c[1] else None
+        for c in negative
+    ]
+    return gligen_pos, gligen_neg
+
+
+def cascade_tiles(x, input_x, tile_h, tile_w, tile_h_len, tile_w_len):
+    h_cascade = input_x.shape[-2]
+    w_cascade = input_x.shape[-1]
+    
+    h_samples = x.shape[-2]
+    w_samples = x.shape[-1]
+    
+    tile_h_cascade = (h_cascade * tile_h) // h_samples
+    tile_w_cascade = (w_cascade * tile_w) // w_samples
+    
+    tile_h_len_cascade = (h_cascade * tile_h_len) // h_samples
+    tile_w_len_cascade = (w_cascade * tile_w_len) // w_samples
+    
+    return tile_h_cascade, tile_w_cascade, tile_h_len_cascade, tile_w_len_cascade
+
+
+
+def sample_common(model, x, noise, noise_mask, noise_seed, tile_width, tile_height, tiling_strategy, cfg, positive, negative,
+                  preview=False, sampler=None, sigmas=None,
+                  clip_name=None, strength=1.0, noise_augment=1.0, image_cv=None, max_tile_batch_size=3,
+                  guide=None, guide_type='residual', guide_weight=1.0, guide_weights=None,
+                  ):
+
+    device = comfy.model_management.get_torch_device()
+    steps = len(sigmas)-1
+    
+    conds0 = \
+        {"positive": comfy.sampler_helpers.convert_cond(positive),
+         "negative": comfy.sampler_helpers.convert_cond(negative)}
+
+    conds = {}
+    for k in conds0:
+        conds[k] = list(map(lambda a: a.copy(), conds0[k]))
+
+    modelPatches, inference_memory = comfy.sampler_helpers.get_additional_models(conds, model.model_dtype())
+    comfy.model_management.load_models_gpu([model] + modelPatches, model.memory_required(noise.shape) + inference_memory)
+    
+
+    if model.model.model_config.unet_config['stable_cascade_stage'] == 'up':
+        compression = 1
+        guide_weight = 1.0 if guide_weight is None else guide_weight
+        guide_type = 'residual' if guide_type is None else guide_type
+        guide = guide['samples'] if guide is not None else None
+        guide_weights = initialize_or_scale(guide_weights, guide_weight, 10000)
+
+        patch = model.model_options.get("transformer_options", {}).get("patches_replace", {}).get("ultracascade", {}).get("main")  #CHANGED HERE
+        if patch is not None:
+            patch.update(x_lr=guide, guide_weights=guide_weights, guide_type=guide_type)
+        else:
+            model = model.clone()
+            model.model.diffusion_model.set_sigmas_prev(sigmas_prev=sigmas[:1])
+            model.model.diffusion_model.set_guide_weights(guide_weights=guide_weights)
+            model.model.diffusion_model.set_guide_type(guide_type=guide_type)
+        
+    elif model.model.model_config.unet_config['stable_cascade_stage'] == 'c':
+        compression = 1
+        
+    elif model.model.model_config.unet_config['stable_cascade_stage'] == 'b':
+        compression = 4
+        
+        c_pos, c_neg = [], []
+        for t in positive:
+            d_pos = t[1].copy()
+            d_neg = t[1].copy()
+            
+            d_pos['stable_cascade_prior'] = guide['samples']
+
+            pooled_output = d_neg.get("pooled_output", None)
+            if pooled_output is not None:
+                d_neg["pooled_output"] = torch.zeros_like(pooled_output)
+            
+            c_pos.append([t[0], d_pos])            
+            c_neg.append([torch.zeros_like(t[0]), d_neg])
+        positive = c_pos
+        negative = c_neg
+        effnet_samples = positive[0][1]['stable_cascade_prior'].clone()
+        effnet_interpolated = nn.functional.interpolate(effnet_samples.clone().to(torch.float16).to(device), size=torch.Size((x.shape[-2] // 2, x.shape[-1] // 2,)), mode='bilinear', align_corners=True)
+        effnet_full_map = model.model.diffusion_model.effnet_mapper(effnet_interpolated)
+    else:
+        compression = 8 #sd1.5, sdxl, sd3, flux, etc
+        
+    
+    if image_cv is not None: #CLIP VISION LOAD
+        clip_path = folder_paths.get_full_path("clip_vision", clip_name)
+        clip_vision = comfy.clip_vision.load(clip_path)
+        
+
+
+
+    cnets,             cnet_imgs         = cnets_and_cnet_imgs (positive, negative, x.shape)
+    T2Is,              T2I_imgs          = T2Is_and_T2I_imgs   (positive, negative, x.shape)
+    spatial_conds_pos, spatial_conds_neg = spatial_conds_posneg(positive, negative, x.shape, device)
+    gligen_pos,        gligen_neg        = gligen_posneg       (positive, negative)
+    
+    
+    
+    tile_width  = min(x.shape[-1] * compression, tile_width) 
+    tile_height = min(x.shape[2]  * compression, tile_height)
+    
+    if tiling_strategy != 'padded':
+        if noise_mask is not None:
+            x += sigmas[0] * noise_mask * model.model.process_latent_out(noise)
+        else:
+            x += sigmas[0] * model.model.process_latent_out(noise)
+    
+
+
+    if tiling_strategy == 'random' or tiling_strategy == 'random strict':
+        tiles = tiling.get_tiles_and_masks_rgrid(steps, x.shape, tile_height, tile_width, torch.manual_seed(noise_seed), compression=compression)
+    elif tiling_strategy == 'padded':
+        tiles = tiling.get_tiles_and_masks_padded(steps, x.shape, tile_height, tile_width, compression=compression)
+    else:
+        tiles = tiling.get_tiles_and_masks_simple(steps, x.shape, tile_height, tile_width, compression=compression)
+
+
+
+    total_steps = sum([num_steps for img_pass in tiles for steps_list in img_pass for _,_,_,_,num_steps,_ in steps_list])
+    current_step = [0]
+    with tqdm(total=total_steps) as pbar_tqdm:
+        pbar = comfy.utils.ProgressBar(total_steps)
+        def callback(step, x0, x, total_steps, step_inc=1):
+            current_step[0] += step_inc
+            preview_bytes = None
+            if preview == True:
+                previewer = latent_preview.get_previewer(device, model.model.latent_format)
+                preview_bytes = previewer.decode_latent_to_preview_image("JPEG", x0)
+            pbar.update_absolute(current_step[0], preview=preview_bytes)
+            pbar_tqdm.update(step_inc)
+            
+            
+            
+        if tiling_strategy == "random strict":
+            x_next = x.clone()
+            
+        for img_pass in tiles: # img_pass is a set of non-intersecting tiles
+            effnet_slices, effnet_map_slices, tiled_noise_list, tiled_latent_list, tiled_mask_list, tile_h_list, tile_w_list, tile_h_len_list, tile_w_len_list = [],[],[],[],[],[],[],[],[]
+
+            for i in range(len(img_pass)):     
+                for iteration, (tile_h, tile_h_len, tile_w, tile_w_len, tile_steps, tile_mask) in enumerate(img_pass[i]):
+                    tiled_mask = None
+                    if noise_mask is not None:
+                        tiled_mask = tiling.get_slice(noise_mask, tile_h, tile_h_len, tile_w, tile_w_len).to(device)
+                    if tile_mask is not None:
+                        if tiled_mask is not None:
+                            tiled_mask *= tile_mask.to(device)
+                        else:
+                            tiled_mask  = tile_mask.to(device)
+                    
+                    if tiling_strategy == 'padded' or tiling_strategy == 'random strict':
+                        tile_h, tile_h_len, tile_w, tile_w_len, tiled_mask = tiling.mask_at_boundary(   tile_h, tile_h_len, tile_w, tile_w_len, 
+                                                                                                        tile_height, tile_width, x.shape[-2], x.shape[-1],
+                                                                                                        tiled_mask, device, compression=compression)
+                        
+                    if tiled_mask is not None and tiled_mask.sum().cpu() == 0.0:
+                            continue
+                            
+                    tiled_latent = tiling.get_slice(x, tile_h, tile_h_len, tile_w, tile_w_len).to(device)
+                    
+                    if tiling_strategy == 'padded':
+                        tiled_noise = tiling.get_slice(noise, tile_h, tile_h_len, tile_w, tile_w_len).to(device)
+                    else:
+                        if tiled_mask is None or noise_mask is None:
+                            tiled_noise = torch.zeros_like(tiled_latent)
+                        else:
+                            tiled_noise = tiling.get_slice(noise, tile_h, tile_h_len, tile_w, tile_w_len).to(device) * (1 - tiled_mask)
+                    
+                    #TODO: all other condition based stuff like area sets and GLIGEN should also happen here
+
+                    #cnets
+                    for m, img in zip(cnets, cnet_imgs):
+                        slice_cnet(tile_h, tile_h_len, tile_w, tile_w_len, m, img)
+                    
+                    #T2I
+                    for m, img in zip(T2Is, T2I_imgs):
+                        slices_T2I(tile_h, tile_h_len, tile_w, tile_w_len, m, img)
+
+                    pos = copy.deepcopy(positive)
+                    neg = copy.deepcopy(negative)
+
+                    #cond areas
+                    pos = [slice_cond(tile_h, tile_h_len, tile_w, tile_w_len, c, area) for c, area in zip(pos, spatial_conds_pos)]
+                    pos = [c for c, ignore in pos if not ignore]
+                    neg = [slice_cond(tile_h, tile_h_len, tile_w, tile_w_len, c, area) for c, area in zip(neg, spatial_conds_neg)]
+                    neg = [c for c, ignore in neg if not ignore]
+
+                    #gligen
+                    for cond, gligen in zip(pos, gligen_pos):
+                        slice_gligen(tile_h, tile_h_len, tile_w, tile_w_len, cond, gligen)
+                    for cond, gligen in zip(neg, gligen_neg):
+                        slice_gligen(tile_h, tile_h_len, tile_w, tile_w_len, cond, gligen)
+                    
+                    start_step = i * tile_steps
+                    last_step  = i * tile_steps + tile_steps
+
+                    if last_step is not None and last_step < (len(sigmas) - 1):
+                        sigmas = sigmas[:last_step + 1]
+
+                    if start_step is not None:
+                        if start_step < (len(sigmas) - 1):
+                            sigmas = sigmas[start_step:]
+                        else:
+                            if tiled_latent is not None:
+                                return tiled_latent
+                            else:
+                                return torch.zeros_like(noise)
+                
+                    # SLICE, DICE, AND DENOISE
+                    if image_cv is not None: #slice and dice ClipVision for tiling
+                        image_cv    = image_cv.   permute(0,3,1,2)
+                        tile_h_cascade, tile_w_cascade, tile_h_len_cascade, tile_w_len_cascade = cascade_tiles(x, image_cv, tile_h, tile_w, tile_h_len, tile_w_len)
+
+                        image_slice = copy.deepcopy(image_cv)
+                        image_slice = tiling.get_slice(image_slice, tile_h_cascade, tile_h_len_cascade, tile_w_cascade, tile_w_len_cascade).to(device)
+                        image_slice = image_slice.permute(0,2,3,1)
+                        image_cv    = image_cv.   permute(0,2,3,1)
+                        
+                        cv_out_slice = clip_vision.encode_image(image_slice)
+                        pos = cv_cond(cv_out_slice, pos, strength, noise_augment) 
+                    
+                    if model.model.model_config.unet_config['stable_cascade_stage'] == 'up': #slice and dice stage UP guide
+                        tile_h_cascade, tile_w_cascade, tile_h_len_cascade, tile_w_len_cascade = cascade_tiles(x, guide, tile_h, tile_w, tile_h_len, tile_w_len)
+
+                        guide_slice = copy.deepcopy(guide)
+                        guide_slice = tiling.get_slice(guide_slice.clone(), tile_h_cascade, tile_h_len_cascade, tile_w_cascade, tile_w_len_cascade).to(device)
+                        model.model.diffusion_model.set_x_lr(x_lr=guide_slice)
+                        
+                        tile_result = comfy.sample.sample_custom(model, tiled_noise, cfg, sampler, sigmas, pos, neg, tiled_latent, noise_mask=tiled_mask, callback=callback, disable_pbar=True, seed=noise_seed)  
+
+                    elif model.model.model_config.unet_config['stable_cascade_stage'] == 'b':  #slice and dice stage B conditioning
+                        tile_h_cascade, tile_w_cascade, tile_h_len_cascade, tile_w_len_cascade = cascade_tiles(x, effnet_samples.clone(), tile_h, tile_w, tile_h_len, tile_w_len)
+                        effnet_slice = tiling.get_slice(effnet_samples.clone(), tile_h_cascade, tile_h_len_cascade, tile_w_cascade, tile_w_len_cascade).to(device)
+                        effnet_slices.append(effnet_slice)
+                        
+                        tile_h_cascade, tile_w_cascade, tile_h_len_cascade, tile_w_len_cascade = cascade_tiles(x, effnet_full_map.clone(), tile_h, tile_w, tile_h_len, tile_w_len)
+                        effnet_map_slice = tiling.get_slice(effnet_full_map.clone(), tile_h_cascade, tile_h_len_cascade, tile_w_cascade, tile_w_len_cascade).to(device)
+                        effnet_map_slices.append(effnet_map_slice)
+
+                    else: # not stage UP or stage B, default
+                        tile_result = comfy.sample.sample_custom(model, tiled_noise, cfg, sampler, sigmas, pos, neg, tiled_latent, noise_mask=tiled_mask, callback=callback, disable_pbar=True, seed=noise_seed)  
+
+                    if model.model.model_config.unet_config['stable_cascade_stage'] != 'b':
+                        tile_result = tile_result.cpu()
+                        if tiled_mask is not None:
+                            tiled_mask = tiled_mask.cpu()
+                        if tiling_strategy == "random strict":
+                            tiling.set_slice(x_next, tile_result, tile_h, tile_h_len, tile_w, tile_w_len, tiled_mask)
+                        else:
+                            tiling.set_slice(x, tile_result, tile_h, tile_h_len, tile_w, tile_w_len, tiled_mask)
+                        
+
+                    tiled_noise_list .append(tiled_noise)
+                    tiled_latent_list.append(tiled_latent)
+                    tiled_mask_list  .append(tiled_mask)
+                    tile_h_list      .append(tile_h)
+                    tile_w_list      .append(tile_w)
+                    tile_h_len_list  .append(tile_h_len)
+                    tile_w_len_list  .append(tile_w_len)
+                    
+                    #END OF NON-INTERSECTING SET OF TILES
+                    
+                if tiling_strategy == "random strict":   # IS THIS ONE LEVEL OVER??
+                    x = x_next.clone()
+            
+            if model.model.model_config.unet_config['stable_cascade_stage'] == 'b':
+
+                for start_idx in range(0, len(tiled_latent_list), max_tile_batch_size):
+                    
+                    end_idx = start_idx + max_tile_batch_size
+                    
+                    #print("Tiled batch size: ", min(max_tile_batch_size, len(tiled_latent_list))) #end_idx - start_idx)
+                    
+                    tiled_noise_batch  = torch.cat(tiled_noise_list [start_idx:end_idx])
+                    tiled_latent_batch = torch.cat(tiled_latent_list[start_idx:end_idx])
+                    tiled_mask_batch   = torch.cat(tiled_mask_list  [start_idx:end_idx])
+                    
+                    print("Tiled batch size: ", tiled_latent_batch.shape[0])
+
+                    pos[0][1]['stable_cascade_prior'] = torch.cat(effnet_slices[start_idx:end_idx])
+                    neg[0][1]['stable_cascade_prior'] = torch.cat(effnet_slices[start_idx:end_idx])
+                    
+                    tile_result = comfy.sample.sample_custom(model, tiled_noise_batch, cfg, sampler, sigmas, pos, neg, tiled_latent_batch, noise_mask=tiled_mask_batch, callback=partial(callback, step_inc=tiled_latent_batch.shape[0]), disable_pbar=True, seed=noise_seed)
+                    
+                    for i in range(tile_result.shape[0]):
+                        idx = start_idx + i
+                        
+                        single_tile = tile_result[i].unsqueeze(dim=0)
+                        single_mask = tiled_mask_batch[i].unsqueeze(dim=0)
+                        
+                        tiling.set_slice(x, single_tile, tile_h_list[idx], tile_h_len_list[idx], tile_w_list[idx], tile_w_len_list[idx], single_mask.cpu())
+
+                x = x.to('cpu') 
+
+    comfy.sampler_helpers.cleanup_additional_models(modelPatches)
+
+    return x.cpu()
+
+
+
+class UltraSharkSampler_Tiled: #this is for use with https://github.com/ClownsharkBatwing/UltraCascade
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {
+                    "add_noise": ("BOOLEAN", {"default": True}),
+                    "noise_is_latent": ("BOOLEAN", {"default": False}),
+                    "noise_type": (NOISE_GENERATOR_NAMES, ),
+                    "alpha": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":0.1, "round": 0.01}),
+                    "k": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":2.0, "round": 0.01}),
+                    "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                    "cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0}),
+                    "guide_type": (['residual', 'weighted'], ),
+                    "guide_weight": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0, "step":0.01, "round": 0.01}),
+                    
+                    "tile_width": ("INT", {"default": 1024, "min": 2, "max": MAX_RESOLUTION, "step": 1}),
+                    "tile_height": ("INT", {"default": 1024, "min": 2, "max": MAX_RESOLUTION, "step": 1}),
+                    "tiling_strategy": (["padded", "random", "random strict",  'simple'], ),
+                    "max_tile_batch_size": ("INT", {"default": 64, "min": 1, "max": 256, "step": 1}),
+
+                    "model": ("MODEL",),
+                    "positive": ("CONDITIONING", ),
+                    "negative": ("CONDITIONING", ),
+                    "sampler": ("SAMPLER",),
+                    "sigmas": ("SIGMAS",),
+                    "latent_image": ("LATENT", ),
+                    
+                    "clip_name":            (folder_paths.get_filename_list("clip_vision"), {'default': "clip-vit-large-patch14.safetensors"}),
+                    "strength":           ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+                    "noise_augment":      ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+
+                    },
+                    "optional": {
+                        "latent_noise": ("LATENT", ),
+                        "guide": ("LATENT", ),
+                        "guide_weights": ("SIGMAS",),
+                        "image_cv": ("IMAGE",),
+
+                    },
+                    
+                    }
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "sample"
+
+    CATEGORY = "RES4LYF/legacy/samplers/ultracascade"
+    DESCRIPTION = "For use with UltraCascade."
+    DEPRECATED = True
+
+    def sample(self, model, noise_seed, add_noise, noise_is_latent, noise_type, alpha, k, tile_width, tile_height, tiling_strategy, cfg, positive, negative, latent_image, latent_noise=None, sampler=None, sigmas=None, guide=None,
+               clip_name=None, strength=1.0, noise_augment=1.0, image_cv=None, max_tile_batch_size=3,
+               guide_type='residual', guide_weight=1.0, guide_weights=None,
+               ):
+        
+        x = latent_image["samples"].clone()
+
+        torch.manual_seed(noise_seed)
+
+        if not add_noise:
+            noise = torch.zeros(x.size(), dtype=x.dtype, layout=x.layout, device="cpu")
+        elif latent_noise is None:
+            skip = latent_image["batch_index"] if "batch_index" in latent_image else None
+            noise = prepare_noise(x, noise_seed, noise_type, skip, alpha, k)
+        else:
+            noise = latent_noise["samples"]
+
+        if noise_is_latent: #add noise and latent together and normalize --> noise
+            noise += x.cpu()
+            noise.sub_(noise.mean()).div_(noise.std())
+
+        noise_mask = latent_image["noise_mask"].clone() if "noise_mask" in latent_image else None
+
+        latent_out = latent_image.copy()
+        latent_out['samples'] = sample_common(model, x=x, noise=noise, noise_mask=noise_mask, noise_seed=noise_seed, tile_width=tile_width, tile_height=tile_height, tiling_strategy=tiling_strategy, cfg=cfg, positive=positive, negative=negative, 
+                             preview=True, sampler=sampler, sigmas=sigmas,
+                             clip_name=clip_name, strength=strength, noise_augment=noise_augment, image_cv=image_cv, max_tile_batch_size=max_tile_batch_size,
+                             guide=guide, guide_type=guide_type, guide_weight=guide_weight, guide_weights=guide_weights,
+                             )
+        return (latent_out,)
+
+
diff --git a/custom_nodes/RES4LYF/legacy/sigmas.py b/custom_nodes/RES4LYF/legacy/sigmas.py
new file mode 100644
index 0000000000000000000000000000000000000000..dfece719141480556904a84082e1048d6d282f07
--- /dev/null
+++ b/custom_nodes/RES4LYF/legacy/sigmas.py
@@ -0,0 +1,1266 @@
+import torch
+
+import numpy as np
+from math import *
+import builtins
+from scipy.interpolate import CubicSpline
+import torch.nn.functional as F
+import torch.nn as nn
+import torch.optim as optim
+
+from comfy.k_diffusion.sampling import get_sigmas_polyexponential, get_sigmas_karras
+import comfy.samplers
+
+def rescale_linear(input, input_min, input_max, output_min, output_max):
+    output = ((input - input_min) / (input_max - input_min)) * (output_max - output_min) + output_min;
+    return output
+
+class set_precision_sigmas:
+    def __init__(self):
+        pass
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                    "sigmas": ("SIGMAS", ),   
+                    "precision": (["16", "32", "64"], ),
+                    "set_default": ("BOOLEAN", {"default": False})
+                     },
+                }
+
+    RETURN_TYPES = ("SIGMAS",)
+    RETURN_NAMES = ("passthrough",)
+    CATEGORY = "RES4LYF/precision"
+
+    FUNCTION = "main"
+
+    def main(self, precision="32", sigmas=None, set_default=False):
+        match precision:
+            case "16":
+                if set_default is True:
+                    torch.set_default_dtype(torch.float16)
+                sigmas = sigmas.to(torch.float16)
+            case "32":
+                if set_default is True:
+                    torch.set_default_dtype(torch.float32)
+                sigmas = sigmas.to(torch.float32)
+            case "64":
+                if set_default is True:
+                    torch.set_default_dtype(torch.float64)
+                sigmas = sigmas.to(torch.float64)
+        return (sigmas, )
+
+
+class SimpleInterpolator(nn.Module):
+    def __init__(self):
+        super(SimpleInterpolator, self).__init__()
+        self.net = nn.Sequential(
+            nn.Linear(1, 16),
+            nn.ReLU(),
+            nn.Linear(16, 32),
+            nn.ReLU(),
+            nn.Linear(32, 1)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+def train_interpolator(model, sigma_schedule, steps, epochs=5000, lr=0.01):
+    with torch.inference_mode(False):
+        model = SimpleInterpolator()
+        sigma_schedule = sigma_schedule.clone()
+
+        criterion = nn.MSELoss()
+        optimizer = optim.Adam(model.parameters(), lr=lr)
+        
+        x_train = torch.linspace(0, 1, steps=steps).unsqueeze(1)
+        y_train = sigma_schedule.unsqueeze(1)
+
+        # disable inference mode for training
+        model.train()
+        for epoch in range(epochs):
+            optimizer.zero_grad()
+
+            # fwd pass
+            outputs = model(x_train)
+            loss = criterion(outputs, y_train)
+            loss.backward()
+            optimizer.step()
+
+    return model
+
+def interpolate_sigma_schedule_model(sigma_schedule, target_steps):
+    model = SimpleInterpolator()
+    sigma_schedule = sigma_schedule.float().detach()
+
+    # train on original sigma schedule
+    trained_model = train_interpolator(model, sigma_schedule, len(sigma_schedule))
+
+    # generate target steps for interpolation
+    x_interpolated = torch.linspace(0, 1, target_steps).unsqueeze(1)
+
+    # inference w/o gradients
+    trained_model.eval()
+    with torch.no_grad():
+        interpolated_sigma = trained_model(x_interpolated).squeeze()
+
+    return interpolated_sigma
+
+
+
+
+class sigmas_interpolate:
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sigmas_0": ("SIGMAS", {"forceInput": True}),
+                "sigmas_1": ("SIGMAS", {"forceInput": True}),
+                "mode": (["linear", "nearest", "polynomial", "exponential", "power", "model"],),
+                "order": ("INT", {"default": 8, "min": 1,"max": 64,"step": 1}),
+            }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS","SIGMAS",)
+    RETURN_NAMES = ("sigmas_0", "sigmas_1")
+    CATEGORY = "RES4LYF/sigmas"
+    
+
+
+
+    def interpolate_sigma_schedule_poly(self, sigma_schedule, target_steps):
+        order = self.order
+        sigma_schedule_np = sigma_schedule.cpu().numpy()
+
+        # orig steps (assuming even spacing)
+        original_steps = np.linspace(0, 1, len(sigma_schedule_np))
+
+        # fit polynomial of the given order
+        coefficients = np.polyfit(original_steps, sigma_schedule_np, deg=order)
+
+        # generate new steps where we want to interpolate the data
+        target_steps_np = np.linspace(0, 1, target_steps)
+
+        # eval polynomial at new steps
+        interpolated_sigma_np = np.polyval(coefficients, target_steps_np)
+
+        interpolated_sigma = torch.tensor(interpolated_sigma_np, device=sigma_schedule.device, dtype=sigma_schedule.dtype)
+        return interpolated_sigma
+
+    def interpolate_sigma_schedule_constrained(self, sigma_schedule, target_steps):
+        sigma_schedule_np = sigma_schedule.cpu().numpy()
+
+        # orig steps
+        original_steps = np.linspace(0, 1, len(sigma_schedule_np))
+
+        # target steps for interpolation
+        target_steps_np = np.linspace(0, 1, target_steps)
+
+        # fit cubic spline with fixed start and end values
+        cs = CubicSpline(original_steps, sigma_schedule_np, bc_type=((1, 0.0), (1, 0.0)))
+
+        # eval spline at the target steps
+        interpolated_sigma_np = cs(target_steps_np)
+
+        interpolated_sigma = torch.tensor(interpolated_sigma_np, device=sigma_schedule.device, dtype=sigma_schedule.dtype)
+
+        return interpolated_sigma
+    
+    def interpolate_sigma_schedule_exp(self, sigma_schedule, target_steps):
+        # transform to log space
+        log_sigma_schedule = torch.log(sigma_schedule)
+
+        # define the original and target step ranges
+        original_steps = torch.linspace(0, 1, steps=len(sigma_schedule))
+        target_steps = torch.linspace(0, 1, steps=target_steps)
+
+        # interpolate in log space
+        interpolated_log_sigma = F.interpolate(
+            log_sigma_schedule.unsqueeze(0).unsqueeze(0),  # Add fake batch and channel dimensions
+            size=target_steps.shape[0],
+            mode='linear',
+            align_corners=True
+        ).squeeze()
+
+        # transform back to exponential space
+        interpolated_sigma_schedule = torch.exp(interpolated_log_sigma)
+
+        return interpolated_sigma_schedule
+    
+    def interpolate_sigma_schedule_power(self, sigma_schedule, target_steps):
+        sigma_schedule_np = sigma_schedule.cpu().numpy()
+        original_steps = np.linspace(1, len(sigma_schedule_np), len(sigma_schedule_np))
+
+        # power regression using a log-log transformation
+        log_x = np.log(original_steps)
+        log_y = np.log(sigma_schedule_np)
+
+        # linear regression on log-log data
+        coefficients = np.polyfit(log_x, log_y, deg=1)  # degree 1 for linear fit in log-log space
+        a = np.exp(coefficients[1])  # a = "b" = intercept (exp because of the log transform)
+        b = coefficients[0]  # b = "m" = slope
+
+        target_steps_np = np.linspace(1, len(sigma_schedule_np), target_steps)
+
+        # power law prediction: y = a * x^b
+        interpolated_sigma_np = a * (target_steps_np ** b)
+
+        interpolated_sigma = torch.tensor(interpolated_sigma_np, device=sigma_schedule.device, dtype=sigma_schedule.dtype)
+
+        return interpolated_sigma
+            
+    def interpolate_sigma_schedule_linear(self, sigma_schedule, target_steps):
+        return F.interpolate(sigma_schedule.unsqueeze(0).unsqueeze(0), target_steps, mode='linear').squeeze(0).squeeze(0)
+
+    def interpolate_sigma_schedule_nearest(self, sigma_schedule, target_steps):
+        return F.interpolate(sigma_schedule.unsqueeze(0).unsqueeze(0), target_steps, mode='nearest').squeeze(0).squeeze(0)    
+    
+    def interpolate_nearest_neighbor(self, sigma_schedule, target_steps):
+        original_steps = torch.linspace(0, 1, steps=len(sigma_schedule))
+        target_steps = torch.linspace(0, 1, steps=target_steps)
+
+        # interpolate original -> target steps using nearest neighbor
+        indices = torch.searchsorted(original_steps, target_steps)
+        indices = torch.clamp(indices, 0, len(sigma_schedule) - 1)  # clamp indices to valid range
+
+        # set nearest neighbor via indices
+        interpolated_sigma = sigma_schedule[indices]
+
+        return interpolated_sigma
+
+
+    def main(self, sigmas_0, sigmas_1, mode, order):
+
+        self.order = order
+
+        if   mode == "linear": 
+            interpolate = self.interpolate_sigma_schedule_linear
+        if   mode == "nearest": 
+            interpolate = self.interpolate_nearest_neighbor
+        elif mode == "polynomial":
+            interpolate = self.interpolate_sigma_schedule_poly
+        elif mode == "exponential":
+            interpolate = self.interpolate_sigma_schedule_exp
+        elif mode == "power":
+            interpolate = self.interpolate_sigma_schedule_power
+        elif mode == "model":
+            with torch.inference_mode(False):
+                interpolate = interpolate_sigma_schedule_model
+        
+        sigmas_0 = interpolate(sigmas_0, len(sigmas_1))
+        return (sigmas_0, sigmas_1,)
+    
+class sigmas_noise_inversion:
+    # flip sigmas for unsampling, and pad both fwd/rev directions with null bytes to disable noise scaling, etc from the model.
+    # will cause model to return epsilon prediction instead of calculated denoised latent image.
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sigmas": ("SIGMAS", {"forceInput": True}),
+            }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS","SIGMAS",)
+    RETURN_NAMES = ("sigmas_fwd","sigmas_rev",)
+    CATEGORY = "RES4LYF/sigmas"
+    DESCRIPTION = "For use with unsampling. Connect sigmas_fwd to the unsampling (first) node, and sigmas_rev to the sampling (second) node."
+    
+    def main(self, sigmas):
+        sigmas = sigmas.clone().to(torch.float64)
+        
+        null = torch.tensor([0.0], device=sigmas.device, dtype=sigmas.dtype)
+        sigmas_fwd = torch.flip(sigmas, dims=[0])
+        sigmas_fwd = torch.cat([sigmas_fwd, null])
+        
+        sigmas_rev = torch.cat([null, sigmas])
+        sigmas_rev = torch.cat([sigmas_rev, null])
+        
+        return (sigmas_fwd, sigmas_rev,)
+
+
+def compute_sigma_next_variance_floor(sigma):
+    return (-1 + torch.sqrt(1 + 4 * sigma)) / 2
+
+class sigmas_variance_floor:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sigmas": ("SIGMAS", {"forceInput": True}),
+            }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "RES4LYF/sigmas"
+    
+    DESCRIPTION = ("Process a sigma schedule so that any steps that are too large for variance-locked SDE sampling are replaced with the maximum permissible value."
+        "Will be very difficult to approach sigma = 0 due to the nature of the math, as steps become very small much below approximately sigma = 0.15 to 0.2.")
+    
+    def main(self, sigmas):
+        dtype = sigmas.dtype
+        sigmas = sigmas.clone().to(torch.float64)
+        for i in range(len(sigmas) - 1):
+            sigma_next = (-1 + torch.sqrt(1 + 4 * sigmas[i])) / 2
+            
+            if sigmas[i+1] < sigma_next and sigmas[i+1] > 0.0:
+                print("swapped i+1 with sigma_next+0.001: ", sigmas[i+1], sigma_next + 0.001)
+                sigmas[i+1] = sigma_next + 0.001
+        return (sigmas.to(dtype),)
+
+
+class sigmas_from_text:
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "text": ("STRING", {"default": "", "multiline": True}),
+            }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS",)
+    RETURN_NAMES = ("sigmas",)
+    CATEGORY = "RES4LYF/sigmas"
+    
+    def main(self, text):
+        text_list = [float(val) for val in text.replace(",", " ").split()]
+        #text_list = [float(val.strip()) for val in text.split(",")]
+
+        sigmas = torch.tensor(text_list).to('cuda').to(torch.float64)
+        
+        return (sigmas,)
+
+
+
+class sigmas_concatenate:
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sigmas_1": ("SIGMAS", {"forceInput": True}),
+                "sigmas_2": ("SIGMAS", {"forceInput": True}),
+            }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "RES4LYF/sigmas"
+    
+    def main(self, sigmas_1, sigmas_2):
+        return (torch.cat((sigmas_1, sigmas_2)),)
+
+class sigmas_truncate:
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sigmas": ("SIGMAS", {"forceInput": True}),
+                "sigmas_until": ("INT", {"default": 10, "min": 0,"max": 1000,"step": 1}),
+            }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "RES4LYF/sigmas"
+    
+    def main(self, sigmas, sigmas_until):
+        return (sigmas[:sigmas_until],)
+
+class sigmas_start:
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sigmas": ("SIGMAS", {"forceInput": True}),
+                "sigmas_until": ("INT", {"default": 10, "min": 0,"max": 1000,"step": 1}),
+            }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "RES4LYF/sigmas"
+    
+    def main(self, sigmas, sigmas_until):
+        return (sigmas[sigmas_until:],)
+        
+class sigmas_split:
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sigmas": ("SIGMAS", {"forceInput": True}),
+                "sigmas_start": ("INT", {"default": 0, "min": 0,"max": 1000,"step": 1}),
+                "sigmas_end": ("INT", {"default": 1000, "min": 0,"max": 1000,"step": 1}),
+            }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "RES4LYF/sigmas"
+    
+    def main(self, sigmas, sigmas_start, sigmas_end):
+        return (sigmas[sigmas_start:sigmas_end],)
+
+        sigmas_stop_step = sigmas_end - sigmas_start
+        return (sigmas[sigmas_start:][:sigmas_stop_step],)
+    
+class sigmas_pad:
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sigmas": ("SIGMAS", {"forceInput": True}),
+                "value": ("FLOAT", {"default": 0.0, "min": -10000,"max": 10000,"step": 0.01})
+            }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "RES4LYF/sigmas"
+    
+    def main(self, sigmas, value):
+        return (torch.cat((sigmas, torch.tensor([value], dtype=sigmas.dtype))),)
+    
+class sigmas_unpad:
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sigmas": ("SIGMAS", {"forceInput": True}),
+            }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "RES4LYF/sigmas"
+    
+    def main(self, sigmas):
+        return (sigmas[:-1],)
+
+class sigmas_set_floor:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sigmas": ("SIGMAS", {"forceInput": True}),
+                "floor": ("FLOAT", {"default": 0.0291675, "min": -10000,"max": 10000,"step": 0.01}),
+                "new_floor": ("FLOAT", {"default": 0.0291675, "min": -10000,"max": 10000,"step": 0.01})
+            }
+        }
+
+    RETURN_TYPES = ("SIGMAS",)
+    FUNCTION = "set_floor"
+
+    CATEGORY = "RES4LYF/sigmas"
+
+    def set_floor(self, sigmas, floor, new_floor):
+        sigmas[sigmas <= floor] = new_floor
+        return (sigmas,)    
+    
+class sigmas_delete_below_floor:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sigmas": ("SIGMAS", {"forceInput": True}),
+                "floor": ("FLOAT", {"default": 0.0291675, "min": -10000,"max": 10000,"step": 0.01})
+            }
+        }
+
+    RETURN_TYPES = ("SIGMAS",)
+    FUNCTION = "delete_below_floor"
+
+    CATEGORY = "RES4LYF/sigmas"
+
+    def delete_below_floor(self, sigmas, floor):
+        return (sigmas[sigmas >= floor],)    
+
+class sigmas_delete_value:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sigmas": ("SIGMAS", {"forceInput": True}),
+                "value": ("FLOAT", {"default": 0.0, "min": -1000,"max": 1000,"step": 0.01})
+            }
+        }
+
+    RETURN_TYPES = ("SIGMAS",)
+    FUNCTION = "delete_value"
+
+    CATEGORY = "RES4LYF/sigmas"
+
+    def delete_value(self, sigmas, value):
+        return (sigmas[sigmas != value],) 
+
+class sigmas_delete_consecutive_duplicates:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sigmas_1": ("SIGMAS", {"forceInput": True})
+            }
+        }
+
+    RETURN_TYPES = ("SIGMAS",)
+    FUNCTION = "delete_consecutive_duplicates"
+
+    CATEGORY = "RES4LYF/sigmas"
+
+    def delete_consecutive_duplicates(self, sigmas_1):
+        mask = sigmas_1[:-1] != sigmas_1[1:]
+        mask = torch.cat((mask, torch.tensor([True])))
+        return (sigmas_1[mask],) 
+
+class sigmas_cleanup:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sigmas": ("SIGMAS", {"forceInput": True}),
+                "sigmin": ("FLOAT", {"default": 0.0291675, "min": 0,"max": 1000,"step": 0.01})
+            }
+        }
+
+    RETURN_TYPES = ("SIGMAS",)
+    FUNCTION = "cleanup"
+
+    CATEGORY = "RES4LYF/sigmas"
+
+    def cleanup(self, sigmas, sigmin):
+        sigmas_culled = sigmas[sigmas >= sigmin]
+    
+        mask = sigmas_culled[:-1] != sigmas_culled[1:]
+        mask = torch.cat((mask, torch.tensor([True])))
+        filtered_sigmas = sigmas_culled[mask]
+        return (torch.cat((filtered_sigmas,torch.tensor([0]))),)
+
+class sigmas_mult:
+    def __init__(self):
+        pass   
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sigmas": ("SIGMAS", {"forceInput": True}),
+                "multiplier": ("FLOAT", {"default": 1, "min": -10000,"max": 10000,"step": 0.01})
+            },
+            "optional": {
+                "sigmas2": ("SIGMAS", {"forceInput": False})
+            }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "RES4LYF/sigmas"
+    
+    def main(self, sigmas, multiplier, sigmas2=None):
+        if sigmas2 is not None:
+            return (sigmas * sigmas2 * multiplier,)
+        else:
+            return (sigmas * multiplier,)    
+
+class sigmas_modulus:
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sigmas": ("SIGMAS", {"forceInput": True}),
+                "divisor": ("FLOAT", {"default": 1, "min": -1000,"max": 1000,"step": 0.01})
+            }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "RES4LYF/sigmas"
+    
+    def main(self, sigmas, divisor):
+        return (sigmas % divisor,)
+        
+class sigmas_quotient:
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sigmas": ("SIGMAS", {"forceInput": True}),
+                "divisor": ("FLOAT", {"default": 1, "min": -1000,"max": 1000,"step": 0.01})
+            }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "RES4LYF/sigmas"
+    
+    def main(self, sigmas, divisor):
+        return (sigmas // divisor,)
+
+class sigmas_add:
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sigmas": ("SIGMAS", {"forceInput": True}),
+                "addend": ("FLOAT", {"default": 1, "min": -1000,"max": 1000,"step": 0.01})
+            }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "RES4LYF/sigmas"
+    
+    def main(self, sigmas, addend):
+        return (sigmas + addend,)
+
+class sigmas_power:
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sigmas": ("SIGMAS", {"forceInput": True}),
+                "power": ("FLOAT", {"default": 1, "min": -100,"max": 100,"step": 0.01})
+            }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "RES4LYF/sigmas"
+    
+    def main(self, sigmas, power):
+        return (sigmas ** power,)
+
+class sigmas_abs:
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sigmas": ("SIGMAS", {"forceInput": True})
+            }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "RES4LYF/sigmas"
+    
+    def main(self, sigmas):
+        return (abs(sigmas),)
+
+class sigmas2_mult:
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sigmas_1": ("SIGMAS", {"forceInput": True}),
+                "sigmas_2": ("SIGMAS", {"forceInput": True}),
+            }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "RES4LYF/sigmas"
+    
+    def main(self, sigmas_1, sigmas_2):
+        return (sigmas_1 * sigmas_2,)
+
+class sigmas2_add:
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sigmas_1": ("SIGMAS", {"forceInput": True}),
+                "sigmas_2": ("SIGMAS", {"forceInput": True}),
+            }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "RES4LYF/sigmas"
+    
+    def main(self, sigmas_1, sigmas_2):
+        return (sigmas_1 + sigmas_2,)
+
+class sigmas_rescale:
+    def __init__(self):
+        pass
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "start": ("FLOAT", {"default": 1.0, "min": -10000,"max": 10000,"step": 0.01}),
+                "end": ("FLOAT", {"default": 0.0, "min": -10000,"max": 10000,"step": 0.01}),
+                "sigmas": ("SIGMAS", ),
+            },
+            "optional": {
+            }
+        }
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS",)
+    RETURN_NAMES = ("sigmas_rescaled",)
+    CATEGORY = "RES4LYF/sigmas"
+    DESCRIPTION = ("Can be used to set denoise. Results are generally better than with the approach used by KSampler and most nodes with denoise values "
+                   "(which slice the sigmas schedule according to step count, not the noise level). Will also flip the sigma schedule if the start and end values are reversed." 
+                   )
+      
+    def main(self, start=0, end=-1, sigmas=None):
+
+        s_out_1 = ((sigmas - sigmas.min()) * (start - end)) / (sigmas.max() - sigmas.min()) + end     
+        
+        return (s_out_1,)
+
+
+class sigmas_math1:
+    def __init__(self):
+        pass
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "start": ("INT", {"default": 0, "min": 0,"max": 10000,"step": 1}),
+                "stop": ("INT", {"default": 0, "min": 0,"max": 10000,"step": 1}),
+                "trim": ("INT", {"default": 0, "min": -10000,"max": 0,"step": 1}),
+                "x": ("FLOAT", {"default": 1, "min": -10000,"max": 10000,"step": 0.01}),
+                "y": ("FLOAT", {"default": 1, "min": -10000,"max": 10000,"step": 0.01}),
+                "z": ("FLOAT", {"default": 1, "min": -10000,"max": 10000,"step": 0.01}),
+                "f1": ("STRING", {"default": "s", "multiline": True}),
+                "rescale" : ("BOOLEAN", {"default": False}),
+                "max1": ("FLOAT", {"default": 14.614642, "min": -10000,"max": 10000,"step": 0.01}),
+                "min1": ("FLOAT", {"default": 0.0291675, "min": -10000,"max": 10000,"step": 0.01}),
+            },
+            "optional": {
+                "a": ("SIGMAS", {"forceInput": False}),
+                "b": ("SIGMAS", {"forceInput": False}),               
+                "c": ("SIGMAS", {"forceInput": False}),
+            }
+        }
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "RES4LYF/sigmas"
+    def main(self, start=0, stop=0, trim=0, a=None, b=None, c=None, x=1.0, y=1.0, z=1.0, f1="s", rescale=False, min1=1.0, max1=1.0):
+        if stop == 0:
+            t_lens = [len(tensor) for tensor in [a, b, c] if tensor is not None]
+            t_len = stop = min(t_lens) if t_lens else 0
+        else:
+            stop = stop + 1
+            t_len = stop - start 
+            
+        stop = stop + trim
+        t_len = t_len + trim
+        
+        t_a = t_b = t_c = None
+        if a is not None:
+            t_a = a[start:stop]
+        if b is not None:
+            t_b = b[start:stop]
+        if c is not None:
+            t_c = c[start:stop]               
+            
+        t_s = torch.arange(0.0, t_len)
+    
+        t_x = torch.full((t_len,), x)
+        t_y = torch.full((t_len,), y)
+        t_z = torch.full((t_len,), z)
+        eval_namespace = {"__builtins__": None, "round": builtins.round, "np": np, "a": t_a, "b": t_b, "c": t_c, "x": t_x, "y": t_y, "z": t_z, "s": t_s, "torch": torch}
+        eval_namespace.update(np.__dict__)
+        
+        s_out_1 = eval(f1, eval_namespace)
+        
+        if rescale == True:
+            s_out_1 = ((s_out_1 - min(s_out_1)) * (max1 - min1)) / (max(s_out_1) - min(s_out_1)) + min1     
+        
+        return (s_out_1,)
+
+class sigmas_math3:
+    def __init__(self):
+        pass
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "start": ("INT", {"default": 0, "min": 0,"max": 10000,"step": 1}),
+                "stop": ("INT", {"default": 0, "min": 0,"max": 10000,"step": 1}),
+                "trim": ("INT", {"default": 0, "min": -10000,"max": 0,"step": 1}),
+            },
+            "optional": {
+                "a": ("SIGMAS", {"forceInput": False}),
+                "b": ("SIGMAS", {"forceInput": False}),               
+                "c": ("SIGMAS", {"forceInput": False}),
+                "x": ("FLOAT", {"default": 1, "min": -10000,"max": 10000,"step": 0.01}),
+                "y": ("FLOAT", {"default": 1, "min": -10000,"max": 10000,"step": 0.01}),
+                "z": ("FLOAT", {"default": 1, "min": -10000,"max": 10000,"step": 0.01}),
+                "f1": ("STRING", {"default": "s", "multiline": True}),
+                "rescale1" : ("BOOLEAN", {"default": False}),
+                "max1": ("FLOAT", {"default": 14.614642, "min": -10000,"max": 10000,"step": 0.01}),
+                "min1": ("FLOAT", {"default": 0.0291675, "min": -10000,"max": 10000,"step": 0.01}),
+                "f2": ("STRING", {"default": "s", "multiline": True}),
+                "rescale2" : ("BOOLEAN", {"default": False}),
+                "max2": ("FLOAT", {"default": 14.614642, "min": -10000,"max": 10000,"step": 0.01}),
+                "min2": ("FLOAT", {"default": 0.0291675, "min": -10000,"max": 10000,"step": 0.01}),
+                "f3": ("STRING", {"default": "s", "multiline": True}),
+                "rescale3" : ("BOOLEAN", {"default": False}),
+                "max3": ("FLOAT", {"default": 14.614642, "min": -10000,"max": 10000,"step": 0.01}),
+                "min3": ("FLOAT", {"default": 0.0291675, "min": -10000,"max": 10000,"step": 0.01}),
+            }
+        }
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS","SIGMAS","SIGMAS")
+    CATEGORY = "RES4LYF/sigmas"
+    def main(self, start=0, stop=0, trim=0, a=None, b=None, c=None, x=1.0, y=1.0, z=1.0, f1="s", f2="s", f3="s", rescale1=False, rescale2=False, rescale3=False, min1=1.0, max1=1.0, min2=1.0, max2=1.0, min3=1.0, max3=1.0):
+        if stop == 0:
+            t_lens = [len(tensor) for tensor in [a, b, c] if tensor is not None]
+            t_len = stop = min(t_lens) if t_lens else 0
+        else:
+            stop = stop + 1
+            t_len = stop - start 
+            
+        stop = stop + trim
+        t_len = t_len + trim
+        
+        t_a = t_b = t_c = None
+        if a is not None:
+            t_a = a[start:stop]
+        if b is not None:
+            t_b = b[start:stop]
+        if c is not None:
+            t_c = c[start:stop]               
+            
+        t_s = torch.arange(0.0, t_len)
+    
+        t_x = torch.full((t_len,), x)
+        t_y = torch.full((t_len,), y)
+        t_z = torch.full((t_len,), z)
+        eval_namespace = {"__builtins__": None, "np": np, "a": t_a, "b": t_b, "c": t_c, "x": t_x, "y": t_y, "z": t_z, "s": t_s, "torch": torch}
+        eval_namespace.update(np.__dict__)
+        
+        s_out_1 = eval(f1, eval_namespace)
+        s_out_2 = eval(f2, eval_namespace)
+        s_out_3 = eval(f3, eval_namespace)
+        
+        if rescale1 == True:
+            s_out_1 = ((s_out_1 - min(s_out_1)) * (max1 - min1)) / (max(s_out_1) - min(s_out_1)) + min1
+        if rescale2 == True:
+            s_out_2 = ((s_out_2 - min(s_out_2)) * (max2 - min2)) / (max(s_out_2) - min(s_out_2)) + min2
+        if rescale3 == True:
+            s_out_3 = ((s_out_3 - min(s_out_3)) * (max3 - min3)) / (max(s_out_3) - min(s_out_3)) + min3        
+        
+        return s_out_1, s_out_2, s_out_3
+
+class sigmas_iteration_karras:
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "steps_up": ("INT", {"default": 30, "min": 0,"max": 10000,"step": 1}),
+                "steps_down": ("INT", {"default": 30, "min": 0,"max": 10000,"step": 1}),
+                "rho_up": ("FLOAT", {"default": 3, "min": -10000,"max": 10000,"step": 0.01}),
+                "rho_down": ("FLOAT", {"default": 4, "min": -10000,"max": 10000,"step": 0.01}),
+                "s_min_start": ("FLOAT", {"default":0.0291675, "min": -10000,"max": 10000,"step": 0.01}),
+                "s_max": ("FLOAT", {"default": 2, "min": -10000,"max": 10000,"step": 0.01}),
+                "s_min_end": ("FLOAT", {"default": 0.0291675, "min": -10000,"max": 10000,"step": 0.01}),
+            },
+            "optional": {
+                "momentums": ("SIGMAS", {"forceInput": False}),
+                "sigmas": ("SIGMAS", {"forceInput": False}),             
+            }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS","SIGMAS")
+    RETURN_NAMES = ("momentums","sigmas")
+    CATEGORY = "RES4LYF/schedulers"
+    
+    def main(self, steps_up, steps_down, rho_up, rho_down, s_min_start, s_max, s_min_end, sigmas=None, momentums=None):
+        s_up = get_sigmas_karras(steps_up, s_min_start, s_max, rho_up)
+        s_down = get_sigmas_karras(steps_down, s_min_end, s_max, rho_down) 
+        s_up = s_up[:-1]
+        s_down = s_down[:-1]  
+        s_up = torch.flip(s_up, dims=[0])
+        sigmas_new = torch.cat((s_up, s_down), dim=0)
+        momentums_new = torch.cat((s_up, -1*s_down), dim=0)
+        
+        if sigmas is not None:
+            sigmas = torch.cat([sigmas, sigmas_new])
+        else:
+            sigmas = sigmas_new
+            
+        if momentums is not None:
+            momentums = torch.cat([momentums, momentums_new])
+        else:
+            momentums = momentums_new
+        
+        return (momentums,sigmas) 
+ 
+class sigmas_iteration_polyexp:
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "steps_up": ("INT", {"default": 30, "min": 0,"max": 10000,"step": 1}),
+                "steps_down": ("INT", {"default": 30, "min": 0,"max": 10000,"step": 1}),
+                "rho_up": ("FLOAT", {"default": 0.6, "min": -10000,"max": 10000,"step": 0.01}),
+                "rho_down": ("FLOAT", {"default": 0.8, "min": -10000,"max": 10000,"step": 0.01}),
+                "s_min_start": ("FLOAT", {"default":0.0291675, "min": -10000,"max": 10000,"step": 0.01}),
+                "s_max": ("FLOAT", {"default": 2, "min": -10000,"max": 10000,"step": 0.01}),
+                "s_min_end": ("FLOAT", {"default": 0.0291675, "min": -10000,"max": 10000,"step": 0.01}),
+            },
+            "optional": {
+                "momentums": ("SIGMAS", {"forceInput": False}),
+                "sigmas": ("SIGMAS", {"forceInput": False}),             
+            }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS","SIGMAS")
+    RETURN_NAMES = ("momentums","sigmas")
+    CATEGORY = "RES4LYF/schedulers"
+    
+    def main(self, steps_up, steps_down, rho_up, rho_down, s_min_start, s_max, s_min_end, sigmas=None, momentums=None):
+        s_up = get_sigmas_polyexponential(steps_up, s_min_start, s_max, rho_up)
+        s_down = get_sigmas_polyexponential(steps_down, s_min_end, s_max, rho_down) 
+        s_up = s_up[:-1]
+        s_down = s_down[:-1]
+        s_up = torch.flip(s_up, dims=[0])
+        sigmas_new = torch.cat((s_up, s_down), dim=0)
+        momentums_new = torch.cat((s_up, -1*s_down), dim=0)
+
+        if sigmas is not None:
+            sigmas = torch.cat([sigmas, sigmas_new])
+        else:
+            sigmas = sigmas_new
+
+        if momentums is not None:
+            momentums = torch.cat([momentums, momentums_new])
+        else:
+            momentums = momentums_new
+
+        return (momentums,sigmas) 
+
+class tan_scheduler:
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "steps": ("INT", {"default": 20, "min": 0,"max": 100000,"step": 1}),
+                "offset": ("FLOAT", {"default": 20, "min": 0,"max": 100000,"step": 0.1}),
+                "slope": ("FLOAT", {"default": 20, "min": -100000,"max": 100000,"step": 0.1}),
+                "start": ("FLOAT", {"default": 20, "min": -100000,"max": 100000,"step": 0.1}),
+                "end": ("FLOAT", {"default": 20, "min": -100000,"max": 100000,"step": 0.1}),
+                "sgm" : ("BOOLEAN", {"default": False}),
+                "pad" : ("BOOLEAN", {"default": False}),
+            }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "RES4LYF/schedulers"
+    
+    def main(self, steps, slope, offset, start, end, sgm, pad):
+        smax = ((2/pi)*atan(-slope*(0-offset))+1)/2
+        smin = ((2/pi)*atan(-slope*((steps-1)-offset))+1)/2
+
+        srange = smax-smin
+        sscale = start - end
+        
+        if sgm:
+            steps+=1
+
+        sigmas = [  ( (((2/pi)*atan(-slope*(x-offset))+1)/2) - smin) * (1/srange) * sscale + end    for x in range(steps)]
+        
+        if sgm:
+            sigmas = sigmas[:-1]
+        if pad:
+            sigmas = torch.tensor(sigmas+[0])
+        else:
+            sigmas = torch.tensor(sigmas)
+        return (sigmas,)
+
+class tan_scheduler_2stage:
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "steps": ("INT", {"default": 40, "min": 0,"max": 100000,"step": 1}),
+                "midpoint": ("INT", {"default": 20, "min": 0,"max": 100000,"step": 1}),
+                "pivot_1": ("INT", {"default": 10, "min": 0,"max": 100000,"step": 1}),
+                "pivot_2": ("INT", {"default": 30, "min": 0,"max": 100000,"step": 1}),
+                "slope_1": ("FLOAT", {"default": 1, "min": -100000,"max": 100000,"step": 0.1}),
+                "slope_2": ("FLOAT", {"default": 1, "min": -100000,"max": 100000,"step": 0.1}),
+                "start": ("FLOAT", {"default": 1.0, "min": -100000,"max": 100000,"step": 0.1}),
+                "middle": ("FLOAT", {"default": 0.5, "min": -100000,"max": 100000,"step": 0.1}),
+                "end": ("FLOAT", {"default": 0.0, "min": -100000,"max": 100000,"step": 0.1}),
+                "pad" : ("BOOLEAN", {"default": False}),
+            }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS",)
+    RETURN_NAMES = ("sigmas",)
+    CATEGORY = "RES4LYF/schedulers"
+
+    def get_tan_sigmas(self, steps, slope, pivot, start, end):
+        smax = ((2/pi)*atan(-slope*(0-pivot))+1)/2
+        smin = ((2/pi)*atan(-slope*((steps-1)-pivot))+1)/2
+
+        srange = smax-smin
+        sscale = start - end
+
+        sigmas = [  ( (((2/pi)*atan(-slope*(x-pivot))+1)/2) - smin) * (1/srange) * sscale + end    for x in range(steps)]
+        
+        return sigmas
+
+    def main(self, steps, midpoint, start, middle, end, pivot_1, pivot_2, slope_1, slope_2, pad):
+        steps += 2
+        stage_2_len = steps - midpoint
+        stage_1_len = steps - stage_2_len
+
+        tan_sigmas_1 = self.get_tan_sigmas(stage_1_len, slope_1, pivot_1, start, middle)
+        tan_sigmas_2 = self.get_tan_sigmas(stage_2_len, slope_2, pivot_2 - stage_1_len, middle, end)
+        
+        tan_sigmas_1 = tan_sigmas_1[:-1]
+        if pad:
+            tan_sigmas_2 = tan_sigmas_2+[0]
+
+        tan_sigmas = torch.tensor(tan_sigmas_1 + tan_sigmas_2)
+
+        return (tan_sigmas,)
+
+class tan_scheduler_2stage_simple:
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "steps": ("INT", {"default": 40, "min": 0,"max": 100000,"step": 1}),
+                "pivot_1": ("FLOAT", {"default": 1, "min": -100000,"max": 100000,"step": 0.01}),
+                "pivot_2": ("FLOAT", {"default": 1, "min": -100000,"max": 100000,"step": 0.01}),
+                "slope_1": ("FLOAT", {"default": 1, "min": -100000,"max": 100000,"step": 0.01}),
+                "slope_2": ("FLOAT", {"default": 1, "min": -100000,"max": 100000,"step": 0.01}),
+                "start": ("FLOAT", {"default": 1.0, "min": -100000,"max": 100000,"step": 0.01}),
+                "middle": ("FLOAT", {"default": 0.5, "min": -100000,"max": 100000,"step": 0.01}),
+                "end": ("FLOAT", {"default": 0.0, "min": -100000,"max": 100000,"step": 0.01}),
+                "pad" : ("BOOLEAN", {"default": False}),
+            }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS",)
+    RETURN_NAMES = ("sigmas",)
+    CATEGORY = "RES4LYF/schedulers"
+
+    def get_tan_sigmas(self, steps, slope, pivot, start, end):
+        smax = ((2/pi)*atan(-slope*(0-pivot))+1)/2
+        smin = ((2/pi)*atan(-slope*((steps-1)-pivot))+1)/2
+
+        srange = smax-smin
+        sscale = start - end
+
+        sigmas = [  ( (((2/pi)*atan(-slope*(x-pivot))+1)/2) - smin) * (1/srange) * sscale + end    for x in range(steps)]
+        
+        return sigmas
+
+    def main(self, steps, start, middle, end, pivot_1, pivot_2, slope_1, slope_2, pad):
+        steps += 2
+
+        midpoint = int( (steps*pivot_1 + steps*pivot_2) / 2 )
+        pivot_1 = int(steps * pivot_1)
+        pivot_2 = int(steps * pivot_2)
+
+        slope_1 = slope_1 / (steps/40)
+        slope_2 = slope_2 / (steps/40)
+
+        stage_2_len = steps - midpoint
+        stage_1_len = steps - stage_2_len
+
+        tan_sigmas_1 = self.get_tan_sigmas(stage_1_len, slope_1, pivot_1, start, middle)
+        tan_sigmas_2 = self.get_tan_sigmas(stage_2_len, slope_2, pivot_2 - stage_1_len, middle, end)
+        
+        tan_sigmas_1 = tan_sigmas_1[:-1]
+        if pad:
+            tan_sigmas_2 = tan_sigmas_2+[0]
+
+        tan_sigmas = torch.tensor(tan_sigmas_1 + tan_sigmas_2)
+
+        return (tan_sigmas,)
+    
+class linear_quadratic_advanced:
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "steps": ("INT", {"default": 40, "min": 0,"max": 100000,"step": 1}),
+                "denoise": ("FLOAT", {"default": 1.0, "min": -100000,"max": 100000,"step": 0.01}),
+                "inflection_percent": ("FLOAT", {"default": 0.5, "min": 0,"max": 1,"step": 0.01}),
+            },
+            # "optional": {
+            # }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS",)
+    RETURN_NAMES = ("sigmas",)
+    CATEGORY = "RES4LYF/schedulers"
+
+    def main(self, steps, denoise, inflection_percent, model=None):
+        sigmas = get_sigmas(model, "linear_quadratic", steps, denoise, inflection_percent)
+
+        return (sigmas, )
+
+
+class constant_scheduler:
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "steps": ("INT", {"default": 40, "min": 0,"max": 100000,"step": 1}),
+                "value_start": ("FLOAT", {"default": 1.0, "min": -100000,"max": 100000,"step": 0.01}),
+                "value_end": ("FLOAT", {"default": 0.0, "min": -100000,"max": 100000,"step": 0.01}),
+                "cutoff_percent": ("FLOAT", {"default": 1.0, "min": 0,"max": 1,"step": 0.01}),
+            }
+        }
+
+    FUNCTION = "main"
+    RETURN_TYPES = ("SIGMAS",)
+    RETURN_NAMES = ("sigmas",)
+    CATEGORY = "RES4LYF/schedulers"
+
+    def main(self, steps, value_start, value_end, cutoff_percent):
+        sigmas = torch.ones(steps + 1) * value_start
+        cutoff_step = int(round(steps * cutoff_percent)) + 1
+        sigmas = torch.concat((sigmas[:cutoff_step], torch.ones(steps + 1 - cutoff_step) * value_end), dim=0)
+
+        return (sigmas,)
+
+
+def get_sigmas_simple_exponential(model, steps):
+    s = model.model_sampling
+    sigs = []
+    ss = len(s.sigmas) / steps
+    for x in range(steps):
+        sigs += [float(s.sigmas[-(1 + int(x * ss))])]
+    sigs += [0.0]
+    sigs = torch.FloatTensor(sigs)
+    exp = torch.exp(torch.log(torch.linspace(1, 0, steps + 1)))
+    return sigs * exp
+
+extra_schedulers = {
+    "simple_exponential": get_sigmas_simple_exponential
+}
+
+
+
+
+def get_sigmas(model, scheduler, steps, denoise, lq_inflection_percent=0.5): #adapted from comfyui
+    total_steps = steps
+    if denoise < 1.0:
+        if denoise <= 0.0:
+            return (torch.FloatTensor([]),)
+        total_steps = int(steps/denoise)
+
+    #model_sampling = model.get_model_object("model_sampling")
+    if hasattr(model, "model"):
+        model_sampling = model.model.model_sampling
+    elif hasattr(model, "inner_model"):
+        model_sampling = model.inner_model.inner_model.model_sampling
+    if scheduler == "beta57":
+        sigmas = comfy.samplers.beta_scheduler(model_sampling, total_steps, alpha=0.5, beta=0.7)
+    elif scheduler == "linear_quadratic":
+        linear_steps = int(total_steps * lq_inflection_percent)
+        sigmas = comfy.samplers.linear_quadratic_schedule(model_sampling, total_steps, threshold_noise=0.025, linear_steps=linear_steps)
+    else:
+        sigmas = comfy.samplers.calculate_sigmas(model_sampling, scheduler, total_steps).cpu()
+    
+    sigmas = sigmas[-(steps + 1):]
+    return sigmas
+
+
+
diff --git a/custom_nodes/RES4LYF/legacy/tiling.py b/custom_nodes/RES4LYF/legacy/tiling.py
new file mode 100644
index 0000000000000000000000000000000000000000..207dac3828f64fe0f086aa9e9b1b1dce100916dd
--- /dev/null
+++ b/custom_nodes/RES4LYF/legacy/tiling.py
@@ -0,0 +1,180 @@
+import torch
+import itertools
+import numpy as np
+
+# tiled sampler code adapted from https://github.com/BlenderNeko/ComfyUI_TiledKSampler 
+# for use with https://github.com/ClownsharkBatwing/UltraCascade
+
+def grouper(n, iterable):
+    it = iter(iterable)
+    while True:
+        chunk = list(itertools.islice(it, n))
+        if not chunk:
+            return
+        yield chunk
+
+def create_batches(n, iterable):
+    groups = itertools.groupby(iterable, key= lambda x: (x[1], x[3]))
+    for _, x in groups:
+        for y in grouper(n, x):
+            yield y
+
+
+def get_slice(tensor, h, h_len, w, w_len):
+    t = tensor.narrow(-2, h, h_len)
+    t = t.narrow(-1, w, w_len)
+    return t
+
+def set_slice(tensor1,tensor2,  h, h_len, w, w_len, mask=None):
+    if mask is not None:
+        tensor1[:,:,h:h+h_len,w:w+w_len] = tensor1[:,:,h:h+h_len,w:w+w_len] * (1 - mask) +  tensor2 * mask
+    else:
+        tensor1[:,:,h:h+h_len,w:w+w_len] = tensor2
+
+def get_tiles_and_masks_simple(steps, latent_shape, tile_height, tile_width, compression=4):
+    latent_size_h = latent_shape[-2]
+    latent_size_w = latent_shape[-1]
+    tile_size_h = int(tile_height // compression)   #CHANGED FROM 8
+    tile_size_w = int(tile_width // compression)   #CHANGED FROM 8
+
+    h = np.arange(0,latent_size_h, tile_size_h)
+    w = np.arange(0,latent_size_w, tile_size_w)
+
+    def create_tile(hs, ws, i, j):
+        h = int(hs[i])
+        w = int(ws[j])
+        h_len = min(tile_size_h, latent_size_h - h)
+        w_len = min(tile_size_w, latent_size_w - w)
+        return (h, h_len, w, w_len, steps, None)
+
+    passes = [
+        [[create_tile(h, w, i, j) for i in range(len(h)) for j in range(len(w))]],
+    ]
+    return passes
+
+def get_tiles_and_masks_padded(steps, latent_shape, tile_height, tile_width, compression=4):
+    batch_size = latent_shape[0]
+    latent_size_h = latent_shape[-2]
+    latent_size_w = latent_shape[-1]
+
+    tile_size_h = int(tile_height // compression)   #CHANGED FROM 8
+    tile_size_w = int(tile_width // compression)    #CHANGED FROM 8
+    #if compression > 1:
+    tile_size_h = int((tile_size_h // 4) * 4)       #MIGHT BE A PROBLEM WITH STAGE C?
+    tile_size_w = int((tile_size_w // 4) * 4)
+
+    #masks
+    mask_h = [0,tile_size_h // 4, tile_size_h - tile_size_h // 4, tile_size_h]
+    mask_w = [0,tile_size_w // 4, tile_size_w - tile_size_w // 4, tile_size_w]
+    masks = [[] for _ in range(3)]
+    for i in range(3):
+        for j in range(3):
+            mask = torch.zeros((batch_size,1,tile_size_h, tile_size_w), dtype=torch.float32, device='cpu')
+            mask[:,:, mask_h[i]:mask_h[i+1], 
+                      mask_w[j]:mask_w[j+1]] = 1.0
+            masks[i].append(mask)
+    
+    def create_mask(h_ind, w_ind, h_ind_max, w_ind_max, mask_h, mask_w, h_len, w_len):
+        mask = masks[1][1]
+        if not (h_ind == 0 or h_ind == h_ind_max or w_ind == 0 or w_ind == w_ind_max):
+            return get_slice(mask, 0, h_len, 0, w_len)
+        mask = mask.clone()
+        if h_ind == 0 and mask_h:
+            mask += masks[0][1]
+        if h_ind == h_ind_max and mask_h:
+            mask += masks[2][1]
+        if w_ind == 0 and mask_w:
+            mask += masks[1][0]
+        if w_ind == w_ind_max and mask_w:
+            mask += masks[1][2]
+        if h_ind == 0 and w_ind == 0 and mask_h and mask_w:
+            mask += masks[0][0]
+        if h_ind == 0 and w_ind == w_ind_max and mask_h and mask_w:
+            mask += masks[0][2]
+        if h_ind == h_ind_max and w_ind == 0 and mask_h and mask_w:
+            mask += masks[2][0]
+        if h_ind == h_ind_max and w_ind == w_ind_max and mask_h and mask_w:
+            mask += masks[2][2]
+        return get_slice(mask, 0, h_len, 0, w_len)
+
+    h = np.arange(0,latent_size_h, tile_size_h)
+    h_shift = np.arange(tile_size_h // 2, latent_size_h - tile_size_h // 2, tile_size_h)
+    w = np.arange(0,latent_size_w, tile_size_w)
+    w_shift = np.arange(tile_size_w // 2, latent_size_w - tile_size_h // 2, tile_size_w)
+    
+
+    def create_tile(hs, ws, mask_h, mask_w, i, j):
+        h = int(hs[i])
+        w = int(ws[j])
+        h_len = min(tile_size_h, latent_size_h - h)
+        w_len = min(tile_size_w, latent_size_w - w)
+        mask = create_mask(i,j,len(hs)-1, len(ws)-1, mask_h, mask_w, h_len, w_len)
+        return (h, h_len, w, w_len, steps, mask)
+    
+    passes = [
+        [[create_tile(h,       w,       True,  True,  i, j) for i in range(len(h))       for j in range(len(w))]],
+        [[create_tile(h_shift, w,       False, True,  i, j) for i in range(len(h_shift)) for j in range(len(w))]],
+        [[create_tile(h,       w_shift, True,  False, i, j) for i in range(len(h))       for j in range(len(w_shift))]],
+        [[create_tile(h_shift, w_shift, False, False, i,j) for i in range(len(h_shift)) for j in range(len(w_shift))]],
+    ]
+    
+    return passes
+
+def mask_at_boundary(h, h_len, w, w_len, tile_size_h, tile_size_w, latent_size_h, latent_size_w, mask, device='cpu', compression=4):
+    tile_size_h = int(tile_size_h // compression)   #CHANGED FROM 8
+    tile_size_w = int(tile_size_w // compression)   #CHANGED FROM 8
+    
+    if (h_len == tile_size_h or h_len == latent_size_h) and (w_len == tile_size_w or w_len == latent_size_w):
+        return h, h_len, w, w_len, mask
+    h_offset = min(0, latent_size_h - (h + tile_size_h))
+    w_offset = min(0, latent_size_w - (w + tile_size_w))
+    new_mask = torch.zeros((1,1,tile_size_h, tile_size_w), dtype=torch.float32, device=device)
+    new_mask[:,:,-h_offset:h_len if h_offset == 0 else tile_size_h, -w_offset:w_len if w_offset == 0 else tile_size_w] =  1.0 if mask is None else mask
+    return h + h_offset, tile_size_h, w + w_offset, tile_size_w, new_mask
+
+def get_tiles_and_masks_rgrid(steps, latent_shape, tile_height, tile_width, generator, compression=4):
+
+    def calc_coords(latent_size, tile_size, jitter):
+        tile_coords = int((latent_size + jitter - 1) // tile_size + 1)
+        tile_coords = [np.clip(tile_size * c - jitter, 0, latent_size) for c in range(tile_coords + 1)]
+        tile_coords = [(c1, c2-c1) for c1, c2 in zip(tile_coords, tile_coords[1:])]
+        return tile_coords
+    
+    #calc stuff
+    batch_size = latent_shape[0]
+    latent_size_h = latent_shape[-2]
+    latent_size_w = latent_shape[-1]
+    tile_size_h = int(tile_height // compression)   #CHANGED FROM 8
+    tile_size_w = int(tile_width // compression)   #CHANGED FROM 8
+
+    tiles_all = []
+
+    for s in range(steps):
+        rands = torch.rand((2,), dtype=torch.float32, generator=generator, device='cpu').numpy()
+
+        jitter_w1 = int(rands[0] * tile_size_w)
+        jitter_w2 = int(((rands[0] + .5) % 1.0) * tile_size_w)
+        jitter_h1 = int(rands[1] * tile_size_h)
+        jitter_h2 = int(((rands[1] + .5) % 1.0) * tile_size_h)
+
+        #calc number of tiles
+        tiles_h = [
+            calc_coords(latent_size_h, tile_size_h, jitter_h1),
+            calc_coords(latent_size_h, tile_size_h, jitter_h2)
+        ]
+        tiles_w = [
+            calc_coords(latent_size_w, tile_size_w, jitter_w1),
+            calc_coords(latent_size_w, tile_size_w, jitter_w2)
+        ]
+
+        tiles = []
+        if s % 2 == 0:
+            for i, h in enumerate(tiles_h[0]):
+                for w in tiles_w[i%2]:
+                    tiles.append((int(h[0]), int(h[1]), int(w[0]), int(w[1]), 1, None))
+        else:
+            for i, w in enumerate(tiles_w[0]):
+                for h in tiles_h[i%2]:
+                    tiles.append((int(h[0]), int(h[1]), int(w[0]), int(w[1]), 1, None))
+        tiles_all.append(tiles)
+    return [tiles_all]
diff --git a/custom_nodes/RES4LYF/lightricks/model.py b/custom_nodes/RES4LYF/lightricks/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ac8c02738c7e46ff402125db2d7b2d1ab25dd36
--- /dev/null
+++ b/custom_nodes/RES4LYF/lightricks/model.py
@@ -0,0 +1,734 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+import comfy.ldm.modules.attention
+import comfy.ldm.common_dit
+from einops import rearrange
+import math
+from typing import Dict, Optional, Tuple, List
+
+from .symmetric_patchifier import SymmetricPatchifier, latent_to_pixel_coords
+from ..helper  import ExtraOptions
+
+
+def get_timestep_embedding(
+    timesteps: torch.Tensor,
+    embedding_dim: int,
+    flip_sin_to_cos: bool = False,
+    downscale_freq_shift: float = 1,
+    scale: float = 1,
+    max_period: int = 10000,
+):
+    """
+    This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
+
+    Args
+        timesteps (torch.Tensor):
+            a 1-D Tensor of N indices, one per batch element. These may be fractional.
+        embedding_dim (int):
+            the dimension of the output.
+        flip_sin_to_cos (bool):
+            Whether the embedding order should be `cos, sin` (if True) or `sin, cos` (if False)
+        downscale_freq_shift (float):
+            Controls the delta between frequencies between dimensions
+        scale (float):
+            Scaling factor applied to the embeddings.
+        max_period (int):
+            Controls the maximum frequency of the embeddings
+    Returns
+        torch.Tensor: an [N x dim] Tensor of positional embeddings.
+    """
+    assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
+
+    half_dim = embedding_dim // 2
+    exponent = -math.log(max_period) * torch.arange(
+        start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
+    )
+    exponent = exponent / (half_dim - downscale_freq_shift)
+
+    emb = torch.exp(exponent)
+    emb = timesteps[:, None].float() * emb[None, :]
+
+    # scale embeddings
+    emb = scale * emb
+
+    # concat sine and cosine embeddings
+    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
+
+    # flip sine and cosine embeddings
+    if flip_sin_to_cos:
+        emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
+
+    # zero pad
+    if embedding_dim % 2 == 1:
+        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
+    return emb
+
+
+class TimestepEmbedding(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        time_embed_dim: int,
+        act_fn: str = "silu",
+        out_dim: int = None,
+        post_act_fn: Optional[str] = None,
+        cond_proj_dim=None,
+        sample_proj_bias=True,
+        dtype=None, device=None, operations=None,
+    ):
+        super().__init__()
+
+        self.linear_1 = operations.Linear(in_channels, time_embed_dim, sample_proj_bias, dtype=dtype, device=device)
+
+        if cond_proj_dim is not None:
+            self.cond_proj = operations.Linear(cond_proj_dim, in_channels, bias=False, dtype=dtype, device=device)
+        else:
+            self.cond_proj = None
+
+        self.act = nn.SiLU()
+
+        if out_dim is not None:
+            time_embed_dim_out = out_dim
+        else:
+            time_embed_dim_out = time_embed_dim
+        self.linear_2 = operations.Linear(time_embed_dim, time_embed_dim_out, sample_proj_bias, dtype=dtype, device=device)
+
+        if post_act_fn is None:
+            self.post_act = None
+        # else:
+        #     self.post_act = get_activation(post_act_fn)
+
+    def forward(self, sample, condition=None):
+        if condition is not None:
+            sample = sample + self.cond_proj(condition)
+        sample = self.linear_1(sample)
+
+        if self.act is not None:
+            sample = self.act(sample)
+
+        sample = self.linear_2(sample)
+
+        if self.post_act is not None:
+            sample = self.post_act(sample)
+        return sample
+
+
+class Timesteps(nn.Module):
+    def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, scale: int = 1):
+        super().__init__()
+        self.num_channels = num_channels
+        self.flip_sin_to_cos = flip_sin_to_cos
+        self.downscale_freq_shift = downscale_freq_shift
+        self.scale = scale
+
+    def forward(self, timesteps):
+        t_emb = get_timestep_embedding(
+            timesteps,
+            self.num_channels,
+            flip_sin_to_cos=self.flip_sin_to_cos,
+            downscale_freq_shift=self.downscale_freq_shift,
+            scale=self.scale,
+        )
+        return t_emb
+
+
+class PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module):
+    """
+    For PixArt-Alpha.
+
+    Reference:
+    https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L164C9-L168C29
+    """
+
+    def __init__(self, embedding_dim, size_emb_dim, use_additional_conditions: bool = False, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.outdim = size_emb_dim
+        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
+        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, timestep, resolution, aspect_ratio, batch_size, hidden_dtype):
+        timesteps_proj = self.time_proj(timestep)
+        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype))  # (N, D)
+        return timesteps_emb
+
+
+class AdaLayerNormSingle(nn.Module):
+    r"""
+    Norm layer adaptive layer norm single (adaLN-single).
+
+    As proposed in PixArt-Alpha (see: https://arxiv.org/abs/2310.00426; Section 2.3).
+
+    Parameters:
+        embedding_dim (`int`): The size of each embedding vector.
+        use_additional_conditions (`bool`): To use additional conditions for normalization or not.
+    """
+
+    def __init__(self, embedding_dim: int, use_additional_conditions: bool = False, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.emb = PixArtAlphaCombinedTimestepSizeEmbeddings(
+            embedding_dim, size_emb_dim=embedding_dim // 3, use_additional_conditions=use_additional_conditions, dtype=dtype, device=device, operations=operations
+        )
+
+        self.silu = nn.SiLU()
+        self.linear = operations.Linear(embedding_dim, 6 * embedding_dim, bias=True, dtype=dtype, device=device)
+
+    def forward(
+        self,
+        timestep: torch.Tensor,
+        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
+        batch_size: Optional[int] = None,
+        hidden_dtype: Optional[torch.dtype] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        # No modulation happening here.
+        added_cond_kwargs = added_cond_kwargs or {"resolution": None, "aspect_ratio": None}
+        embedded_timestep = self.emb(timestep, **added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_dtype)
+        return self.linear(self.silu(embedded_timestep)), embedded_timestep
+
+class PixArtAlphaTextProjection(nn.Module):
+    """
+    Projects caption embeddings. Also handles dropout for classifier-free guidance.
+
+    Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
+    """
+
+    def __init__(self, in_features, hidden_size, out_features=None, act_fn="gelu_tanh", dtype=None, device=None, operations=None):
+        super().__init__()
+        if out_features is None:
+            out_features = hidden_size
+        self.linear_1 = operations.Linear(in_features=in_features, out_features=hidden_size, bias=True, dtype=dtype, device=device)
+        if act_fn == "gelu_tanh":
+            self.act_1 = nn.GELU(approximate="tanh")
+        elif act_fn == "silu":
+            self.act_1 = nn.SiLU()
+        else:
+            raise ValueError(f"Unknown activation function: {act_fn}")
+        self.linear_2 = operations.Linear(in_features=hidden_size, out_features=out_features, bias=True, dtype=dtype, device=device)
+
+    def forward(self, caption):
+        hidden_states = self.linear_1(caption)
+        hidden_states = self.act_1(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+        return hidden_states
+
+
+class GELU_approx(nn.Module):
+    def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.proj = operations.Linear(dim_in, dim_out, dtype=dtype, device=device)
+
+    def forward(self, x):
+        return torch.nn.functional.gelu(self.proj(x), approximate="tanh")
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, dim_out, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=None):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        project_in = GELU_approx(dim, inner_dim, dtype=dtype, device=device, operations=operations)
+
+        self.net = nn.Sequential(
+            project_in,
+            nn.Dropout(dropout),
+            operations.Linear(inner_dim, dim_out, dtype=dtype, device=device)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+def apply_rotary_emb(input_tensor, freqs_cis): #TODO: remove duplicate funcs and pick the best/fastest one
+    cos_freqs = freqs_cis[0]
+    sin_freqs = freqs_cis[1]
+
+    t_dup = rearrange(input_tensor, "... (d r) -> ... d r", r=2)
+    t1, t2 = t_dup.unbind(dim=-1)
+    t_dup = torch.stack((-t2, t1), dim=-1)
+    input_tensor_rot = rearrange(t_dup, "... d r -> ... (d r)")
+
+    out = input_tensor * cos_freqs + input_tensor_rot * sin_freqs
+
+    return out
+
+
+class CrossAttention(nn.Module):
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., attn_precision=None, dtype=None, device=None, operations=None):
+        super().__init__()
+        inner_dim = dim_head * heads
+        context_dim = query_dim if context_dim is None else context_dim
+        self.attn_precision = attn_precision
+
+        self.heads = heads
+        self.dim_head = dim_head
+
+        self.q_norm = operations.RMSNorm(inner_dim, dtype=dtype, device=device)
+        self.k_norm = operations.RMSNorm(inner_dim, dtype=dtype, device=device)
+
+        self.to_q = operations.Linear(query_dim, inner_dim, bias=True, dtype=dtype, device=device)
+        self.to_k = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device)
+        self.to_v = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device)
+
+        self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
+
+    def forward(self, x, context=None, mask=None, pe=None):
+        q = self.to_q(x)
+        context = x if context is None else context
+        k = self.to_k(context)
+        v = self.to_v(context)
+
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+
+        if pe is not None:
+            q = apply_rotary_emb(q, pe)
+            k = apply_rotary_emb(k, pe)
+
+        if mask is None:
+            out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision)
+        else:
+            out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision)
+        return self.to_out(out)
+
+
+class BasicTransformerBlock(nn.Module):
+    def __init__(self, dim, n_heads, d_head, context_dim=None, attn_precision=None, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.attn_precision = attn_precision
+        self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, context_dim=None, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations)
+        self.ff = FeedForward(dim, dim_out=dim, glu=True, dtype=dtype, device=device, operations=operations)
+
+        self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations)
+
+        self.scale_shift_table = nn.Parameter(torch.empty(6, dim, device=device, dtype=dtype))
+
+    def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None):
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)).unbind(dim=2)
+
+        x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe) * gate_msa
+
+        x += self.attn2(x, context=context, mask=attention_mask)
+
+        y = comfy.ldm.common_dit.rms_norm(x) * (1 + scale_mlp) + shift_mlp
+        x += self.ff(y) * gate_mlp
+
+        return x
+
+def get_fractional_positions(indices_grid, max_pos):
+    fractional_positions = torch.stack(
+        [
+            indices_grid[:, i] / max_pos[i]
+            for i in range(3)
+        ],
+        dim=-1,
+    )
+    return fractional_positions
+
+
+def precompute_freqs_cis(indices_grid, dim, out_dtype, theta=10000.0, max_pos=[20, 2048, 2048]):
+    dtype = torch.float32 #self.dtype
+
+    fractional_positions = get_fractional_positions(indices_grid, max_pos)
+
+    start = 1
+    end = theta
+    device = fractional_positions.device
+
+    indices = theta ** (
+        torch.linspace(
+            math.log(start, theta),
+            math.log(end, theta),
+            dim // 6,
+            device=device,
+            dtype=dtype,
+        )
+    )
+    indices = indices.to(dtype=dtype)
+
+    indices = indices * math.pi / 2
+
+    freqs = (
+        (indices * (fractional_positions.unsqueeze(-1) * 2 - 1))
+        .transpose(-1, -2)
+        .flatten(2)
+    )
+
+    cos_freq = freqs.cos().repeat_interleave(2, dim=-1)
+    sin_freq = freqs.sin().repeat_interleave(2, dim=-1)
+    if dim % 6 != 0:
+        cos_padding = torch.ones_like(cos_freq[:, :, : dim % 6])
+        sin_padding = torch.zeros_like(cos_freq[:, :, : dim % 6])
+        cos_freq = torch.cat([cos_padding, cos_freq], dim=-1)
+        sin_freq = torch.cat([sin_padding, sin_freq], dim=-1)
+    return cos_freq.to(out_dtype), sin_freq.to(out_dtype)
+
+
+class ReLTXVModel(torch.nn.Module):
+    def __init__(self,
+                 in_channels=128,
+                 cross_attention_dim=2048,
+                 attention_head_dim=64,
+                 num_attention_heads=32,
+
+                 caption_channels=4096,
+                 num_layers=28,
+
+
+                 positional_embedding_theta=10000.0,
+                 positional_embedding_max_pos=[20, 2048, 2048],
+                 causal_temporal_positioning=False,
+                 vae_scale_factors=(8, 32, 32),
+                 dtype=None, device=None, operations=None, **kwargs):
+        super().__init__()
+        self.generator = None
+        self.vae_scale_factors = vae_scale_factors
+        self.dtype = dtype
+        self.out_channels = in_channels
+        self.inner_dim = num_attention_heads * attention_head_dim
+        self.causal_temporal_positioning = causal_temporal_positioning
+
+        self.patchify_proj = operations.Linear(in_channels, self.inner_dim, bias=True, dtype=dtype, device=device)
+
+        self.adaln_single = AdaLayerNormSingle(
+            self.inner_dim, use_additional_conditions=False, dtype=dtype, device=device, operations=operations
+        )
+
+        # self.adaln_single.linear = operations.Linear(self.inner_dim, 4 * self.inner_dim, bias=True, dtype=dtype, device=device)
+
+        self.caption_projection = PixArtAlphaTextProjection(
+            in_features=caption_channels, hidden_size=self.inner_dim, dtype=dtype, device=device, operations=operations
+        )
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock(
+                    self.inner_dim,
+                    num_attention_heads,
+                    attention_head_dim,
+                    context_dim=cross_attention_dim,
+                    # attn_precision=attn_precision,
+                    dtype=dtype, device=device, operations=operations
+                )
+                for d in range(num_layers)
+            ]
+        )
+
+        self.scale_shift_table = nn.Parameter(torch.empty(2, self.inner_dim, dtype=dtype, device=device))
+        self.norm_out = operations.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.proj_out = operations.Linear(self.inner_dim, self.out_channels, dtype=dtype, device=device)
+
+        self.patchifier = SymmetricPatchifier(1)
+
+    def forward(self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, **kwargs):
+        patches_replace = transformer_options.get("patches_replace", {})
+        
+        SIGMA = timestep[0].unsqueeze(0) #/ 1000
+        EO = transformer_options.get("ExtraOptions", ExtraOptions(""))
+        
+        y0_style_pos        = transformer_options.get("y0_style_pos")
+        y0_style_neg        = transformer_options.get("y0_style_neg")
+
+        y0_style_pos_weight    = transformer_options.get("y0_style_pos_weight", 0.0)
+        y0_style_pos_synweight = transformer_options.get("y0_style_pos_synweight", 0.0)
+        y0_style_pos_synweight *= y0_style_pos_weight
+
+        y0_style_neg_weight    = transformer_options.get("y0_style_neg_weight", 0.0)
+        y0_style_neg_synweight = transformer_options.get("y0_style_neg_synweight", 0.0)
+        y0_style_neg_synweight *= y0_style_neg_weight
+        
+        x_orig = x.clone()
+
+        orig_shape = list(x.shape)
+
+        x, latent_coords = self.patchifier.patchify(x)
+        pixel_coords = latent_to_pixel_coords(
+            latent_coords=latent_coords,
+            scale_factors=self.vae_scale_factors,
+            causal_fix=self.causal_temporal_positioning,
+        )
+
+        if keyframe_idxs is not None:
+            pixel_coords[:, :, -keyframe_idxs.shape[2]:] = keyframe_idxs
+
+        fractional_coords = pixel_coords.to(torch.float32)
+        fractional_coords[:, 0] = fractional_coords[:, 0] * (1.0 / frame_rate)
+
+        x = self.patchify_proj(x)
+        timestep = timestep * 1000.0
+
+        if attention_mask is not None and not torch.is_floating_point(attention_mask):
+            attention_mask = (attention_mask - 1).to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])) * torch.finfo(x.dtype).max
+
+        pe = precompute_freqs_cis(fractional_coords, dim=self.inner_dim, out_dtype=x.dtype)
+
+        batch_size = x.shape[0]
+        timestep, embedded_timestep = self.adaln_single(
+            timestep.flatten(),
+            {"resolution": None, "aspect_ratio": None},
+            batch_size=batch_size,
+            hidden_dtype=x.dtype,
+        )
+        # Second dimension is 1 or number of tokens (if timestep_per_token)
+        timestep = timestep.view(batch_size, -1, timestep.shape[-1])
+        embedded_timestep = embedded_timestep.view(
+            batch_size, -1, embedded_timestep.shape[-1]
+        )
+
+        # 2. Blocks
+        if self.caption_projection is not None:
+            batch_size = x.shape[0]
+            context = self.caption_projection(context)
+            context = context.view(
+                batch_size, -1, x.shape[-1]
+            )
+
+        blocks_replace = patches_replace.get("dit", {})
+        for i, block in enumerate(self.transformer_blocks):
+            if ("double_block", i) in blocks_replace:
+                def block_wrap(args):
+                    out = {}
+                    out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"])
+                    return out
+
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe}, {"original_block": block_wrap})
+                x = out["img"]
+            else:
+                x = block(
+                    x,
+                    context=context,
+                    attention_mask=attention_mask,
+                    timestep=timestep,
+                    pe=pe
+                )
+
+        # 3. Output
+        scale_shift_values = (
+            self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + embedded_timestep[:, :, None]
+        )
+        shift, scale = scale_shift_values[:, :, 0], scale_shift_values[:, :, 1]
+        x = self.norm_out(x)
+        # Modulation
+        x = x * (1 + scale) + shift
+        x = self.proj_out(x)
+
+        x = self.patchifier.unpatchify(
+            latents=x,
+            output_height=orig_shape[3],
+            output_width=orig_shape[4],
+            output_num_frames=orig_shape[2],
+            out_channels=orig_shape[1] // math.prod(self.patchifier.patch_size),
+        )
+
+        eps = x
+
+
+
+
+
+        
+        dtype = eps.dtype if self.style_dtype is None else self.style_dtype
+        pinv_dtype = torch.float32 if dtype != torch.float64 else dtype
+        W_inv = None
+        
+        
+        #if eps.shape[0] == 2 or (eps.shape[0] == 1): #: and not UNCOND):
+        if y0_style_pos is not None and y0_style_pos_weight != 0.0:
+            y0_style_pos = y0_style_pos.to(torch.float32)
+            x   = x_orig.clone().to(torch.float32)
+            eps = eps.to(torch.float32)
+            eps_orig = eps.clone()
+            
+            sigma = SIGMA #t_orig[0].to(torch.float32) / 1000
+            denoised = x - sigma * eps
+            
+            img,          img_latent_coords          = self.patchifier.patchify(denoised)
+            img_y0_adain, img_y0_adain_latent_coords = self.patchifier.patchify(y0_style_pos)
+
+            W = self.patchify_proj.weight.data.to(torch.float32)   # shape [2560, 64]
+            b = self.patchify_proj.bias  .data.to(torch.float32)     # shape [2560]
+            
+            denoised_embed = F.linear(img         .to(W), W, b).to(img)
+            y0_adain_embed = F.linear(img_y0_adain.to(W), W, b).to(img_y0_adain)
+
+            if transformer_options['y0_style_method'] == "AdaIN":
+                denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                for adain_iter in range(EO("style_iter", 0)):
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    denoised_embed = (denoised_embed - b) @ torch.linalg.pinv(W.to(pinv_dtype)).T.to(dtype)
+                    denoised_embed = F.linear(denoised_embed.to(W), W, b).to(img)
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    
+            elif transformer_options['y0_style_method'] == "WCT":
+                if self.y0_adain_embed is None or self.y0_adain_embed.shape != y0_adain_embed.shape or torch.norm(self.y0_adain_embed - y0_adain_embed) > 0:
+                    self.y0_adain_embed = y0_adain_embed
+                    
+                    f_s          = y0_adain_embed[0].clone()
+                    self.mu_s    = f_s.mean(dim=0, keepdim=True)
+                    f_s_centered = f_s - self.mu_s
+                    
+                    cov = (f_s_centered.T.double() @ f_s_centered.double()) / (f_s_centered.size(0) - 1)
+
+                    S_eig, U_eig = torch.linalg.eigh(cov + 1e-5 * torch.eye(cov.size(0), dtype=cov.dtype, device=cov.device))
+                    S_eig_sqrt    = S_eig.clamp(min=0).sqrt() # eigenvalues -> singular values
+                    
+                    whiten = U_eig @ torch.diag(S_eig_sqrt) @ U_eig.T
+                    self.y0_color  = whiten.to(f_s_centered)
+
+                for wct_i in range(eps.shape[0]):
+                    f_c          = denoised_embed[wct_i].clone()
+                    mu_c         = f_c.mean(dim=0, keepdim=True)
+                    f_c_centered = f_c - mu_c
+                    
+                    cov = (f_c_centered.T.double() @ f_c_centered.double()) / (f_c_centered.size(0) - 1)
+
+                    S_eig, U_eig  = torch.linalg.eigh(cov + 1e-5 * torch.eye(cov.size(0), dtype=cov.dtype, device=cov.device))
+                    inv_sqrt_eig  = S_eig.clamp(min=0).rsqrt() 
+                    
+                    whiten = U_eig @ torch.diag(inv_sqrt_eig) @ U_eig.T
+                    whiten = whiten.to(f_c_centered)
+
+                    f_c_whitened = f_c_centered @ whiten.T
+                    f_cs         = f_c_whitened @ self.y0_color.T + self.mu_s
+                    
+                    denoised_embed[wct_i] = f_cs
+
+            
+            denoised_approx = (denoised_embed - b.to(denoised_embed)) @ torch.linalg.pinv(W).T.to(denoised_embed)
+            denoised_approx = denoised_approx.to(eps)
+            
+
+            denoised_approx = self.patchifier.unpatchify(
+                latents=denoised_approx,
+                output_height=orig_shape[3],
+                output_width=orig_shape[4],
+                output_num_frames=orig_shape[2],
+                out_channels=orig_shape[1] // math.prod(self.patchifier.patch_size),
+            )
+            
+            eps = (x - denoised_approx) / sigma
+            
+            #UNCOND = transformer_options['cond_or_uncond'][cond_iter] == 1
+
+            if eps.shape[0] == 1 and transformer_options['cond_or_uncond'][0] == 1:
+                eps[0] = eps_orig[0] + y0_style_pos_synweight * (eps[0] - eps_orig[0])
+                #if eps.shape[0] == 2:
+                #    eps[1] = eps_orig[1] + y0_style_neg_synweight * (eps[1] - eps_orig[1])
+            else: #if not UNCOND:
+                if eps.shape[0] == 2:
+                    eps[1] = eps_orig[1] + y0_style_pos_weight * (eps[1] - eps_orig[1])
+                    eps[0] = eps_orig[0] + y0_style_pos_synweight * (eps[0] - eps_orig[0])
+                else:
+                    eps[0] = eps_orig[0] + y0_style_pos_weight * (eps[0] - eps_orig[0])
+            
+            eps = eps.float()
+        
+        #if eps.shape[0] == 2 or (eps.shape[0] == 1): # and UNCOND):
+        if y0_style_neg is not None and y0_style_neg_weight != 0.0:
+            y0_style_neg = y0_style_neg.to(torch.float32)
+            x   = x_orig.clone().to(torch.float32)
+            eps = eps.to(torch.float32)
+            eps_orig = eps.clone()
+            
+            sigma = SIGMA #t_orig[0].to(torch.float32) / 1000
+            denoised = x - sigma * eps
+            
+            img,          img_latent_coords          = self.patchifier.patchify(denoised)
+            img_y0_adain, img_y0_adain_latent_coords = self.patchifier.patchify(y0_style_neg)
+
+            W = self.patchify_proj.weight.data.to(torch.float32)   # shape [2560, 64]
+            b = self.patchify_proj.bias  .data.to(torch.float32)     # shape [2560]
+            
+            denoised_embed = F.linear(img         .to(W), W, b).to(img)
+            y0_adain_embed = F.linear(img_y0_adain.to(W), W, b).to(img_y0_adain)
+            
+            if transformer_options['y0_style_method'] == "AdaIN":
+                denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                for adain_iter in range(EO("style_iter", 0)):
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    denoised_embed = (denoised_embed - b) @ torch.linalg.pinv(W.to(pinv_dtype)).T.to(dtype)
+                    denoised_embed = F.linear(denoised_embed.to(W), W, b).to(img)
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    
+            elif transformer_options['y0_style_method'] == "WCT":
+                if self.y0_adain_embed is None or self.y0_adain_embed.shape != y0_adain_embed.shape or torch.norm(self.y0_adain_embed - y0_adain_embed) > 0:
+                    self.y0_adain_embed = y0_adain_embed
+                    
+                    f_s          = y0_adain_embed[0].clone()
+                    self.mu_s    = f_s.mean(dim=0, keepdim=True)
+                    f_s_centered = f_s - self.mu_s
+                    
+                    cov = (f_s_centered.T.double() @ f_s_centered.double()) / (f_s_centered.size(0) - 1)
+
+                    S_eig, U_eig = torch.linalg.eigh(cov + 1e-5 * torch.eye(cov.size(0), dtype=cov.dtype, device=cov.device))
+                    S_eig_sqrt    = S_eig.clamp(min=0).sqrt() # eigenvalues -> singular values
+                    
+                    whiten = U_eig @ torch.diag(S_eig_sqrt) @ U_eig.T
+                    self.y0_color  = whiten.to(f_s_centered)
+
+                for wct_i in range(eps.shape[0]):
+                    f_c          = denoised_embed[wct_i].clone()
+                    mu_c         = f_c.mean(dim=0, keepdim=True)
+                    f_c_centered = f_c - mu_c
+                    
+                    cov = (f_c_centered.T.double() @ f_c_centered.double()) / (f_c_centered.size(0) - 1)
+
+                    S_eig, U_eig  = torch.linalg.eigh(cov + 1e-5 * torch.eye(cov.size(0), dtype=cov.dtype, device=cov.device))
+                    inv_sqrt_eig  = S_eig.clamp(min=0).rsqrt() 
+                    
+                    whiten = U_eig @ torch.diag(inv_sqrt_eig) @ U_eig.T
+                    whiten = whiten.to(f_c_centered)
+
+                    f_c_whitened = f_c_centered @ whiten.T
+                    f_cs         = f_c_whitened @ self.y0_color.T + self.mu_s
+                    
+                    denoised_embed[wct_i] = f_cs
+
+            denoised_approx = (denoised_embed - b.to(denoised_embed)) @ torch.linalg.pinv(W).T.to(denoised_embed)
+            denoised_approx = denoised_approx.to(eps)
+            
+            #denoised_approx = rearrange(denoised_approx, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h,:w]
+            
+            #denoised_approx = self.unpatchify(denoised_approx, (h + 1) // self.patch_size, (w + 1) // self.patch_size)[:,:,:h,:w]
+            
+            denoised_approx = self.patchifier.unpatchify(
+                latents=denoised_approx,
+                output_height=orig_shape[3],
+                output_width=orig_shape[4],
+                output_num_frames=orig_shape[2],
+                out_channels=orig_shape[1] // math.prod(self.patchifier.patch_size),
+            )
+            
+            if eps.shape[0] == 1 and not transformer_options['cond_or_uncond'][0] == 1:
+                eps[0] = eps_orig[0] + y0_style_neg_synweight * (eps[0] - eps_orig[0])
+            else:
+                eps = (x - denoised_approx) / sigma
+                eps[0] = eps_orig[0] + y0_style_neg_weight * (eps[0] - eps_orig[0])
+                if eps.shape[0] == 2:
+                    eps[1] = eps_orig[1] + y0_style_neg_synweight * (eps[1] - eps_orig[1])
+            
+            eps = eps.float()
+        
+        return eps
+
+
+
+
+
+def adain_seq_inplace(content: torch.Tensor, style: torch.Tensor, eps: float = 1e-7) -> torch.Tensor:
+    mean_c = content.mean(1, keepdim=True)
+    std_c  = content.std (1, keepdim=True).add_(eps)  # in-place add
+    mean_s = style.mean  (1, keepdim=True)
+    std_s  = style.std   (1, keepdim=True).add_(eps)
+
+    content.sub_(mean_c).div_(std_c).mul_(std_s).add_(mean_s)  # in-place chain
+    return content
+
+
+def adain_seq(content: torch.Tensor, style: torch.Tensor, eps: float = 1e-7) -> torch.Tensor:
+    return ((content - content.mean(1, keepdim=True)) / (content.std(1, keepdim=True) + eps)) * (style.std(1, keepdim=True) + eps) + style.mean(1, keepdim=True)
+
+
+
diff --git a/custom_nodes/RES4LYF/lightricks/symmetric_patchifier.py b/custom_nodes/RES4LYF/lightricks/symmetric_patchifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b9972b9fb589bb5516c64dd557c0914dda45116
--- /dev/null
+++ b/custom_nodes/RES4LYF/lightricks/symmetric_patchifier.py
@@ -0,0 +1,117 @@
+from abc import ABC, abstractmethod
+from typing import Tuple
+
+import torch
+from einops import rearrange
+from torch import Tensor
+
+
+def latent_to_pixel_coords(
+    latent_coords: Tensor, scale_factors: Tuple[int, int, int], causal_fix: bool = False
+) -> Tensor:
+    """
+    Converts latent coordinates to pixel coordinates by scaling them according to the VAE's
+    configuration.
+    Args:
+        latent_coords (Tensor): A tensor of shape [batch_size, 3, num_latents]
+        containing the latent corner coordinates of each token.
+        scale_factors (Tuple[int, int, int]): The scale factors of the VAE's latent space.
+        causal_fix (bool): Whether to take into account the different temporal scale
+            of the first frame. Default = False for backwards compatibility.
+    Returns:
+        Tensor: A tensor of pixel coordinates corresponding to the input latent coordinates.
+    """
+    pixel_coords = (
+        latent_coords
+        * torch.tensor(scale_factors, device=latent_coords.device)[None, :, None]
+    )
+    if causal_fix:
+        # Fix temporal scale for first frame to 1 due to causality
+        pixel_coords[:, 0] = (pixel_coords[:, 0] + 1 - scale_factors[0]).clamp(min=0)
+    return pixel_coords
+
+
+class Patchifier(ABC):
+    def __init__(self, patch_size: int):
+        super().__init__()
+        self._patch_size = (1, patch_size, patch_size)
+
+    @abstractmethod
+    def patchify(
+        self, latents: Tensor, frame_rates: Tensor, scale_grid: bool
+    ) -> Tuple[Tensor, Tensor]:
+        pass
+
+    @abstractmethod
+    def unpatchify(
+        self,
+        latents: Tensor,
+        output_height: int,
+        output_width: int,
+        output_num_frames: int,
+        out_channels: int,
+    ) -> Tuple[Tensor, Tensor]:
+        pass
+
+    @property
+    def patch_size(self):
+        return self._patch_size
+
+    def get_latent_coords(
+        self, latent_num_frames, latent_height, latent_width, batch_size, device
+    ):
+        """
+        Return a tensor of shape [batch_size, 3, num_patches] containing the
+            top-left corner latent coordinates of each latent patch.
+        The tensor is repeated for each batch element.
+        """
+        latent_sample_coords = torch.meshgrid(
+            torch.arange(0, latent_num_frames, self._patch_size[0], device=device),
+            torch.arange(0, latent_height, self._patch_size[1], device=device),
+            torch.arange(0, latent_width, self._patch_size[2], device=device),
+            indexing="ij",
+        )
+        latent_sample_coords = torch.stack(latent_sample_coords, dim=0)
+        latent_coords = latent_sample_coords.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1)
+        latent_coords = rearrange(
+            latent_coords, "b c f h w -> b c (f h w)", b=batch_size
+        )
+        return latent_coords
+
+
+class SymmetricPatchifier(Patchifier):
+    def patchify(
+        self,
+        latents: Tensor,
+    ) -> Tuple[Tensor, Tensor]:
+        b, _, f, h, w = latents.shape
+        latent_coords = self.get_latent_coords(f, h, w, b, latents.device)
+        latents = rearrange(
+            latents,
+            "b c (f p1) (h p2) (w p3) -> b (f h w) (c p1 p2 p3)",
+            p1=self._patch_size[0],
+            p2=self._patch_size[1],
+            p3=self._patch_size[2],
+        )
+        return latents, latent_coords
+
+    def unpatchify(
+        self,
+        latents: Tensor,
+        output_height: int,
+        output_width: int,
+        output_num_frames: int,
+        out_channels: int,
+    ) -> Tuple[Tensor, Tensor]:
+        output_height = output_height // self._patch_size[1]
+        output_width = output_width // self._patch_size[2]
+        latents = rearrange(
+            latents,
+            "b (f h w) (c p q) -> b c f (h p) (w q) ",
+            f=output_num_frames,
+            h=output_height,
+            w=output_width,
+            p=self._patch_size[1],
+            q=self._patch_size[2],
+        )
+        return latents
diff --git a/custom_nodes/RES4LYF/misc_scripts/replace_metadata.py b/custom_nodes/RES4LYF/misc_scripts/replace_metadata.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ff177ac270b739f6d315f29ee35da91517d231f
--- /dev/null
+++ b/custom_nodes/RES4LYF/misc_scripts/replace_metadata.py
@@ -0,0 +1,39 @@
+#!/usr/bin/env python3
+
+import argparse
+from PIL import Image
+from PIL.PngImagePlugin import PngInfo
+
+def extract_metadata(image_path):
+    image = Image.open(image_path)
+    metadata = image.info
+    return metadata
+
+def replace_metadata(source_image_path, target_image_path, output_image_path):
+    metadata = extract_metadata(source_image_path)
+
+    target_image = Image.open(target_image_path)
+    
+    png_info = PngInfo()
+    for key, value in metadata.items():
+        png_info.add_text(key, str(value))
+    
+    target_image.save(output_image_path, pnginfo=png_info)
+
+def main():
+    parser = argparse.ArgumentParser(description="Copy metadata from one PNG image to another.")
+    parser.add_argument('source', type=str, help="Path to the source PNG image with the metadata.")
+    parser.add_argument('target', type=str, help="Path to the target PNG image to replace metadata.")
+    parser.add_argument('output', type=str, help="Path for the output PNG image with replaced metadata.")
+
+    args = parser.parse_args()
+
+    replace_metadata(args.source, args.target, args.output)
+
+    print(f"Metadata from '{args.source}' has been copied to '{args.output}'.")
+
+if __name__ == "__main__":
+    main()
+
+
+
diff --git a/custom_nodes/RES4LYF/sd/attention.py b/custom_nodes/RES4LYF/sd/attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee16e2082cb12657f5fba35c54de0e6c75a8bc51
--- /dev/null
+++ b/custom_nodes/RES4LYF/sd/attention.py
@@ -0,0 +1,1088 @@
+import math
+import sys
+
+import torch
+import torch.nn.functional as F
+from torch import nn, einsum
+from einops import rearrange, repeat
+from typing import Optional
+import logging
+
+from comfy.ldm.modules.diffusionmodules.util import AlphaBlender, timestep_embedding
+from comfy.ldm.modules.sub_quadratic_attention import efficient_dot_product_attention
+
+from comfy import model_management
+
+if model_management.xformers_enabled():
+    import xformers
+    import xformers.ops
+
+if model_management.sage_attention_enabled():
+    try:
+        from sageattention import sageattn
+    except ModuleNotFoundError:
+        logging.error(f"\n\nTo use the `--use-sage-attention` feature, the `sageattention` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install sageattention")
+        exit(-1)
+
+if model_management.flash_attention_enabled():
+    try:
+        from flash_attn import flash_attn_func
+    except ModuleNotFoundError:
+        logging.error(f"\n\nTo use the `--use-flash-attention` feature, the `flash-attn` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install flash-attn")
+        exit(-1)
+
+from comfy.cli_args import args
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+from ..style_transfer import apply_scattersort, apply_scattersort_spatial
+
+FORCE_UPCAST_ATTENTION_DTYPE = model_management.force_upcast_attention_dtype()
+
+def get_attn_precision(attn_precision, current_dtype):
+    if args.dont_upcast_attention:
+        return None
+
+    if FORCE_UPCAST_ATTENTION_DTYPE is not None and current_dtype in FORCE_UPCAST_ATTENTION_DTYPE:
+        return FORCE_UPCAST_ATTENTION_DTYPE[current_dtype]
+    return attn_precision
+
+def exists(val):
+    return val is not None
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d
+
+
+# feedforward
+class GEGLU(nn.Module):
+    def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=ops):
+        super().__init__()
+        self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device)
+
+    def forward(self, x):
+        x, gate = self.proj(x).chunk(2, dim=-1)
+        return x * F.gelu(gate)
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=ops):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = default(dim_out, dim)
+        project_in = nn.Sequential(
+            operations.Linear(dim, inner_dim, dtype=dtype, device=device),
+            nn.GELU()
+        ) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device, operations=operations)
+
+        self.net = nn.Sequential(
+            project_in,
+            nn.Dropout(dropout),
+            operations.Linear(inner_dim, dim_out, dtype=dtype, device=device)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+def Normalize(in_channels, dtype=None, device=None):
+    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
+
+def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
+    attn_precision = get_attn_precision(attn_precision, q.dtype)
+
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+
+    scale = dim_head ** -0.5
+
+    h = heads
+    if skip_reshape:
+         q, k, v = map(
+            lambda t: t.reshape(b * heads, -1, dim_head),
+            (q, k, v),
+        )
+    else:
+        q, k, v = map(
+            lambda t: t.unsqueeze(3)
+            .reshape(b, -1, heads, dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b * heads, -1, dim_head)
+            .contiguous(),
+            (q, k, v),
+        )
+
+    # force cast to fp32 to avoid overflowing
+    if attn_precision == torch.float32:
+        sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale
+    else:
+        sim = einsum('b i d, b j d -> b i j', q, k) * scale
+
+    del q, k
+
+    if exists(mask):
+        if mask.dtype == torch.bool:
+            mask = rearrange(mask, 'b ... -> b (...)') #TODO: check if this bool part matches pytorch attention
+            max_neg_value = -torch.finfo(sim.dtype).max
+            mask = repeat(mask, 'b j -> (b h) () j', h=h)
+            sim.masked_fill_(~mask, max_neg_value)
+        else:
+            if len(mask.shape) == 2:
+                bs = 1
+            else:
+                bs = mask.shape[0]
+            mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])
+            sim.add_(mask)
+
+    # attention, what we cannot get enough of
+    sim = sim.softmax(dim=-1)
+
+    out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v)
+
+    if skip_output_reshape:
+        out = (
+            out.unsqueeze(0)
+            .reshape(b, heads, -1, dim_head)
+        )
+    else:
+        out = (
+            out.unsqueeze(0)
+            .reshape(b, heads, -1, dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b, -1, heads * dim_head)
+        )
+    return out
+
+
+def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
+    attn_precision = get_attn_precision(attn_precision, query.dtype)
+
+    if skip_reshape:
+        b, _, _, dim_head = query.shape
+    else:
+        b, _, dim_head = query.shape
+        dim_head //= heads
+
+    if skip_reshape:
+        query = query.reshape(b * heads, -1, dim_head)
+        value = value.reshape(b * heads, -1, dim_head)
+        key = key.reshape(b * heads, -1, dim_head).movedim(1, 2)
+    else:
+        query = query.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
+        value = value.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
+        key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1)
+
+
+    dtype = query.dtype
+    upcast_attention = attn_precision == torch.float32 and query.dtype != torch.float32
+    if upcast_attention:
+        bytes_per_token = torch.finfo(torch.float32).bits//8
+    else:
+        bytes_per_token = torch.finfo(query.dtype).bits//8
+    batch_x_heads, q_tokens, _ = query.shape
+    _, _, k_tokens = key.shape
+
+    mem_free_total, _ = model_management.get_free_memory(query.device, True)
+
+    kv_chunk_size_min = None
+    kv_chunk_size = None
+    query_chunk_size = None
+
+    for x in [4096, 2048, 1024, 512, 256]:
+        count = mem_free_total / (batch_x_heads * bytes_per_token * x * 4.0)
+        if count >= k_tokens:
+            kv_chunk_size = k_tokens
+            query_chunk_size = x
+            break
+
+    if query_chunk_size is None:
+        query_chunk_size = 512
+
+    if mask is not None:
+        if len(mask.shape) == 2:
+            bs = 1
+        else:
+            bs = mask.shape[0]
+        mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])
+
+    hidden_states = efficient_dot_product_attention(
+        query,
+        key,
+        value,
+        query_chunk_size=query_chunk_size,
+        kv_chunk_size=kv_chunk_size,
+        kv_chunk_size_min=kv_chunk_size_min,
+        use_checkpoint=False,
+        upcast_attention=upcast_attention,
+        mask=mask,
+    )
+
+    hidden_states = hidden_states.to(dtype)
+    if skip_output_reshape:
+        hidden_states = hidden_states.unflatten(0, (-1, heads))
+    else:
+        hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2)
+    return hidden_states
+
+def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
+    attn_precision = get_attn_precision(attn_precision, q.dtype)
+
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+
+    scale = dim_head ** -0.5
+
+    if skip_reshape:
+         q, k, v = map(
+            lambda t: t.reshape(b * heads, -1, dim_head),
+            (q, k, v),
+        )
+    else:
+        q, k, v = map(
+            lambda t: t.unsqueeze(3)
+            .reshape(b, -1, heads, dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b * heads, -1, dim_head)
+            .contiguous(),
+            (q, k, v),
+        )
+
+    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+
+    mem_free_total = model_management.get_free_memory(q.device)
+
+    if attn_precision == torch.float32:
+        element_size = 4
+        upcast = True
+    else:
+        element_size = q.element_size()
+        upcast = False
+
+    gb = 1024 ** 3
+    tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * element_size
+    modifier = 3
+    mem_required = tensor_size * modifier
+    steps = 1
+
+
+    if mem_required > mem_free_total:
+        steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
+        # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
+        #      f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
+
+    if steps > 64:
+        max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
+        raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
+                            f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free')
+
+    if mask is not None:
+        if len(mask.shape) == 2:
+            bs = 1
+        else:
+            bs = mask.shape[0]
+        mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])
+
+    # print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size)
+    first_op_done = False
+    cleared_cache = False
+    while True:
+        try:
+            slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+            for i in range(0, q.shape[1], slice_size):
+                end = i + slice_size
+                if upcast:
+                    with torch.autocast(enabled=False, device_type = 'cuda'):
+                        s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * scale
+                else:
+                    s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * scale
+
+                if mask is not None:
+                    if len(mask.shape) == 2:
+                        s1 += mask[i:end]
+                    else:
+                        if mask.shape[1] == 1:
+                            s1 += mask
+                        else:
+                            s1 += mask[:, i:end]
+
+                s2 = s1.softmax(dim=-1).to(v.dtype)
+                del s1
+                first_op_done = True
+
+                r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
+                del s2
+            break
+        except model_management.OOM_EXCEPTION as e:
+            if first_op_done == False:
+                model_management.soft_empty_cache(True)
+                if cleared_cache == False:
+                    cleared_cache = True
+                    logging.warning("out of memory error, emptying cache and trying again")
+                    continue
+                steps *= 2
+                if steps > 64:
+                    raise e
+                logging.warning("out of memory error, increasing steps and trying again {}".format(steps))
+            else:
+                raise e
+
+    del q, k, v
+
+    if skip_output_reshape:
+        r1 = (
+            r1.unsqueeze(0)
+            .reshape(b, heads, -1, dim_head)
+        )
+    else:
+        r1 = (
+            r1.unsqueeze(0)
+            .reshape(b, heads, -1, dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b, -1, heads * dim_head)
+        )
+    return r1
+
+BROKEN_XFORMERS = False
+try:
+    x_vers = xformers.__version__
+    # XFormers bug confirmed on all versions from 0.0.21 to 0.0.26 (q with bs bigger than 65535 gives CUDA error)
+    BROKEN_XFORMERS = x_vers.startswith("0.0.2") and not x_vers.startswith("0.0.20")
+except:
+    pass
+
+def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
+    b = q.shape[0]
+    dim_head = q.shape[-1]
+    # check to make sure xformers isn't broken
+    disabled_xformers = False
+
+    if BROKEN_XFORMERS:
+        if b * heads > 65535:
+            disabled_xformers = True
+
+    if not disabled_xformers:
+        if torch.jit.is_tracing() or torch.jit.is_scripting():
+            disabled_xformers = True
+
+    if disabled_xformers:
+        return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape)
+
+    if skip_reshape:
+        # b h k d -> b k h d
+        q, k, v = map(
+            lambda t: t.permute(0, 2, 1, 3),
+            (q, k, v),
+        )
+    # actually do the reshaping
+    else:
+        dim_head //= heads
+        q, k, v = map(
+            lambda t: t.reshape(b, -1, heads, dim_head),
+            (q, k, v),
+        )
+
+    if mask is not None:
+        # add a singleton batch dimension
+        if mask.ndim == 2:
+            mask = mask.unsqueeze(0)
+        # add a singleton heads dimension
+        if mask.ndim == 3:
+            mask = mask.unsqueeze(1)
+        # pad to a multiple of 8
+        pad = 8 - mask.shape[-1] % 8
+        # the xformers docs says that it's allowed to have a mask of shape (1, Nq, Nk)
+        # but when using separated heads, the shape has to be (B, H, Nq, Nk)
+        # in flux, this matrix ends up being over 1GB
+        # here, we create a mask with the same batch/head size as the input mask (potentially singleton or full)
+        mask_out = torch.empty([mask.shape[0], mask.shape[1], q.shape[1], mask.shape[-1] + pad], dtype=q.dtype, device=q.device)
+
+        mask_out[..., :mask.shape[-1]] = mask
+        # doesn't this remove the padding again??
+        mask = mask_out[..., :mask.shape[-1]]
+        mask = mask.expand(b, heads, -1, -1)
+
+    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask)
+
+    if skip_output_reshape:
+        out = out.permute(0, 2, 1, 3)
+    else:
+        out = (
+            out.reshape(b, -1, heads * dim_head)
+        )
+
+    return out
+
+if model_management.is_nvidia(): #pytorch 2.3 and up seem to have this issue.
+    SDP_BATCH_LIMIT = 2**15
+else:
+    #TODO: other GPUs ?
+    SDP_BATCH_LIMIT = 2**31
+
+
+def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+        q, k, v = map(
+            lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
+            (q, k, v),
+        )
+
+    if mask is not None:
+        # add a batch dimension if there isn't already one
+        if mask.ndim == 2:
+            mask = mask.unsqueeze(0)
+        # add a heads dimension if there isn't already one
+        if mask.ndim == 3:
+            mask = mask.unsqueeze(1)
+
+    if SDP_BATCH_LIMIT >= b:
+        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
+        if not skip_output_reshape:
+            out = (
+                out.transpose(1, 2).reshape(b, -1, heads * dim_head)
+            )
+    else:
+        out = torch.empty((b, q.shape[2], heads * dim_head), dtype=q.dtype, layout=q.layout, device=q.device)
+        for i in range(0, b, SDP_BATCH_LIMIT):
+            m = mask
+            if mask is not None:
+                if mask.shape[0] > 1:
+                    m = mask[i : i + SDP_BATCH_LIMIT]
+
+            out[i : i + SDP_BATCH_LIMIT] = torch.nn.functional.scaled_dot_product_attention(
+                q[i : i + SDP_BATCH_LIMIT],
+                k[i : i + SDP_BATCH_LIMIT],
+                v[i : i + SDP_BATCH_LIMIT],
+                attn_mask=m,
+                dropout_p=0.0, is_causal=False
+            ).transpose(1, 2).reshape(-1, q.shape[2], heads * dim_head)
+    return out
+
+
+def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+        tensor_layout = "HND"
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+        q, k, v = map(
+            lambda t: t.view(b, -1, heads, dim_head),
+            (q, k, v),
+        )
+        tensor_layout = "NHD"
+
+    if mask is not None:
+        # add a batch dimension if there isn't already one
+        if mask.ndim == 2:
+            mask = mask.unsqueeze(0)
+        # add a heads dimension if there isn't already one
+        if mask.ndim == 3:
+            mask = mask.unsqueeze(1)
+
+    try:
+        out = sageattn(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout)
+    except Exception as e:
+        logging.error("Error running sage attention: {}, using pytorch attention instead.".format(e))
+        if tensor_layout == "NHD":
+            q, k, v = map(
+                lambda t: t.transpose(1, 2),
+                (q, k, v),
+            )
+        return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape)
+
+    if tensor_layout == "HND":
+        if not skip_output_reshape:
+            out = (
+                out.transpose(1, 2).reshape(b, -1, heads * dim_head)
+            )
+    else:
+        if skip_output_reshape:
+            out = out.transpose(1, 2)
+        else:
+            out = out.reshape(b, -1, heads * dim_head)
+    return out
+
+
+try:
+    @torch.library.custom_op("flash_attention::flash_attn", mutates_args=())
+    def flash_attn_wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,
+                    dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor:
+        return flash_attn_func(q, k, v, dropout_p=dropout_p, causal=causal)
+
+
+    @flash_attn_wrapper.register_fake
+    def flash_attn_fake(q, k, v, dropout_p=0.0, causal=False):
+        # Output shape is the same as q
+        return q.new_empty(q.shape)
+except AttributeError as error:
+    FLASH_ATTN_ERROR = error
+
+    def flash_attn_wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,
+                    dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor:
+        assert False, f"Could not define flash_attn_wrapper: {FLASH_ATTN_ERROR}"
+
+
+def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+        q, k, v = map(
+            lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
+            (q, k, v),
+        )
+
+    if mask is not None:
+        # add a batch dimension if there isn't already one
+        if mask.ndim == 2:
+            mask = mask.unsqueeze(0)
+        # add a heads dimension if there isn't already one
+        if mask.ndim == 3:
+            mask = mask.unsqueeze(1)
+
+    try:
+        assert mask is None
+        out = flash_attn_wrapper(
+            q.transpose(1, 2),
+            k.transpose(1, 2),
+            v.transpose(1, 2),
+            dropout_p=0.0,
+            causal=False,
+        ).transpose(1, 2)
+    except Exception as e:
+        logging.warning(f"Flash Attention failed, using default SDPA: {e}")
+        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
+    if not skip_output_reshape:
+        out = (
+            out.transpose(1, 2).reshape(b, -1, heads * dim_head)
+        )
+    return out
+
+
+optimized_attention = attention_basic
+
+if model_management.sage_attention_enabled():
+    logging.info("Using sage attention")
+    optimized_attention = attention_sage
+elif model_management.xformers_enabled():
+    logging.info("Using xformers attention")
+    optimized_attention = attention_xformers
+elif model_management.flash_attention_enabled():
+    logging.info("Using Flash Attention")
+    optimized_attention = attention_flash
+elif model_management.pytorch_attention_enabled():
+    logging.info("Using pytorch attention")
+    optimized_attention = attention_pytorch
+else:
+    if args.use_split_cross_attention:
+        logging.info("Using split optimization for attention")
+        optimized_attention = attention_split
+    else:
+        logging.info("Using sub quadratic optimization for attention, if you have memory or speed issues try using: --use-split-cross-attention")
+        optimized_attention = attention_sub_quad
+
+optimized_attention_masked = optimized_attention
+
+def optimized_attention_for_device(device, mask=False, small_input=False):
+    if small_input:
+        if model_management.pytorch_attention_enabled():
+            return attention_pytorch #TODO: need to confirm but this is probably slightly faster for small inputs in all cases
+        else:
+            return attention_basic
+
+    if device == torch.device("cpu"):
+        return attention_sub_quad
+
+    if mask:
+        return optimized_attention_masked
+
+    return optimized_attention
+
+
+class ReCrossAttention(nn.Module):
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., attn_precision=None, dtype=None, device=None, operations=ops):
+        super().__init__()
+        inner_dim = dim_head * heads
+        context_dim = default(context_dim, query_dim)
+        self.attn_precision = attn_precision
+
+        self.heads = heads
+        self.dim_head = dim_head
+
+        self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
+
+        self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
+
+    def forward(self, x, context=None, value=None, mask=None, style_block=None):
+        q = self.to_q(x)
+        q = style_block(q, "q_proj")
+        #SELF_ATTN = True if context is None else False
+        context = default(context, x)    # if context is None, return x
+        k = self.to_k(context)
+        k = style_block(k, "k_proj")
+        if value is not None:
+            v = self.to_v(value)
+            del value
+        else:
+            v = self.to_v(context)
+        v = style_block(v, "v_proj")
+
+        if mask is None:
+            out = optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision)
+        else:
+            #if SELF_ATTN and mask.shape[-2] != q.shape[-2]:
+            #    mask = F.interpolate(mask[None, None].float(), size=(q.shape[-2], q.shape[-2]),    mode='nearest')[0,0].to(mask)
+            #elif mask.shape[-2] != q.shape[-2]: # cross attn
+            #    mask = F.interpolate(mask[None, None].float(), size=(q.shape[-2], mask.shape[-1]), mode='nearest')[0,0].to(mask)
+            out = attention_pytorch(q, k, v, self.heads, mask=mask)
+            #out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision)
+        out = style_block(out, "out")
+        return self.to_out(out)
+
+
+class ReBasicTransformerBlock(nn.Module):
+    def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, ff_in=False, inner_dim=None,
+                 disable_self_attn=False, disable_temporal_crossattention=False, switch_temporal_ca_to_sa=False, attn_precision=None, dtype=None, device=None, operations=ops):
+        super().__init__()
+
+        self.ff_in = ff_in or inner_dim is not None
+        if inner_dim is None:
+            inner_dim = dim
+
+        self.is_res = inner_dim == dim
+        self.attn_precision = attn_precision
+
+        if self.ff_in:
+            self.norm_in = operations.LayerNorm(dim, dtype=dtype, device=device)
+            self.ff_in = FeedForward(dim, dim_out=inner_dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations)
+
+        self.disable_self_attn = disable_self_attn
+        self.attn1 = ReCrossAttention(query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout,
+                              context_dim=context_dim if self.disable_self_attn else None, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations)  # is a self-attention if not self.disable_self_attn
+        self.ff = FeedForward(inner_dim, dim_out=dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations)
+
+        if disable_temporal_crossattention:
+            if switch_temporal_ca_to_sa:
+                raise ValueError
+            else:
+                self.attn2 = None
+        else:
+            context_dim_attn2 = None
+            if not switch_temporal_ca_to_sa:
+                context_dim_attn2 = context_dim
+
+            self.attn2 = ReCrossAttention(query_dim=inner_dim, context_dim=context_dim_attn2,
+                                heads=n_heads, dim_head=d_head, dropout=dropout, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations)  # is self-attn if context is none
+            self.norm2 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
+
+        self.norm1 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
+        self.norm3 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
+        self.n_heads = n_heads
+        self.d_head = d_head
+        self.switch_temporal_ca_to_sa = switch_temporal_ca_to_sa
+
+    def forward(self, x, context=None, transformer_options={}, style_block=None):
+        extra_options = {}
+        block = transformer_options.get("block", None)
+        block_index = transformer_options.get("block_index", 0)
+        transformer_patches = {}
+        transformer_patches_replace = {}
+        
+        self_mask  = transformer_options.get('self_mask')
+        cross_mask = transformer_options.get('cross_mask')
+        
+        if self_mask is not None and cross_mask is not None:
+            if self_mask.shape[-2] == x.shape[-2]:
+                pass
+            elif self_mask.shape[-2] < x.shape[-2]:
+                self_mask  = transformer_options.get('self_mask_up')
+                cross_mask = transformer_options.get('cross_mask_up')
+            else:
+                self_mask  = transformer_options.get('self_mask_down')
+                cross_mask = transformer_options.get('cross_mask_down')
+                
+                if self_mask.shape[-2] > x.shape[-2]:
+                    self_mask  = transformer_options.get('self_mask_down2')
+                    cross_mask = transformer_options.get('cross_mask_down2')
+
+        for k in transformer_options:
+            if k == "patches":
+                transformer_patches = transformer_options[k]
+            elif k == "patches_replace":
+                transformer_patches_replace = transformer_options[k]
+            else:
+                extra_options[k] = transformer_options[k]
+
+        extra_options["n_heads"] = self.n_heads
+        extra_options["dim_head"] = self.d_head
+        extra_options["attn_precision"] = self.attn_precision
+
+        if self.ff_in: # never true for sdxl?
+            x_skip = x
+            x = self.ff_in(self.norm_in(x))
+            if self.is_res:
+                x += x_skip
+                
+        n = self.norm1(x)
+        n = style_block(n, "norm1")
+        if self.disable_self_attn:
+            context_attn1 = context
+        else:
+            context_attn1 = None
+        value_attn1 = None
+
+        if "attn1_patch" in transformer_patches:
+            patch = transformer_patches["attn1_patch"]
+            if context_attn1 is None:
+                context_attn1 = n
+            value_attn1 = context_attn1
+            for p in patch:
+                n, context_attn1, value_attn1 = p(n, context_attn1, value_attn1, extra_options)
+
+        if block is not None:
+            transformer_block = (block[0], block[1], block_index)
+        else:
+            transformer_block = None
+        attn1_replace_patch = transformer_patches_replace.get("attn1", {})
+        block_attn1 = transformer_block
+        if block_attn1 not in attn1_replace_patch:
+            block_attn1 = block
+
+        if block_attn1 in attn1_replace_patch:
+            if context_attn1 is None:
+                context_attn1 = n
+                value_attn1 = n
+            n = self.attn1.to_q(n)
+            context_attn1 = self.attn1.to_k(context_attn1)
+            value_attn1 = self.attn1.to_v(value_attn1)
+            n = attn1_replace_patch[block_attn1](n, context_attn1, value_attn1, extra_options)
+            n = self.attn1.to_out(n)
+        else:
+            n = self.attn1(n, context=context_attn1, value=value_attn1, mask=self_mask, style_block=style_block.ATTN1)         # self attention                                             #####
+            n = style_block(n, "self_attn")
+
+        if "attn1_output_patch" in transformer_patches:
+            patch = transformer_patches["attn1_output_patch"]
+            for p in patch:
+                n = p(n, extra_options)
+
+        x += n ###########
+        x = style_block(x, "self_attn_res")
+        if "middle_patch" in transformer_patches:
+            patch = transformer_patches["middle_patch"]
+            for p in patch:
+                x = p(x, extra_options)
+
+        if self.attn2 is not None:
+            n = self.norm2(x)
+            n = style_block(n, "norm2")
+            
+            if self.switch_temporal_ca_to_sa:
+                context_attn2 = n
+            else:
+                context_attn2 = context
+            value_attn2 = None
+            if "attn2_patch" in transformer_patches:
+                patch = transformer_patches["attn2_patch"]
+                value_attn2 = context_attn2
+                for p in patch:
+                    n, context_attn2, value_attn2 = p(n, context_attn2, value_attn2, extra_options)
+
+            attn2_replace_patch = transformer_patches_replace.get("attn2", {})
+            block_attn2 = transformer_block
+            if block_attn2 not in attn2_replace_patch:
+                block_attn2 = block
+
+            if block_attn2 in attn2_replace_patch:
+                if value_attn2 is None:
+                    value_attn2 = context_attn2
+                n = self.attn2.to_q(n)
+                context_attn2 = self.attn2.to_k(context_attn2)
+                value_attn2 = self.attn2.to_v(value_attn2)
+                n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options)
+                n = self.attn2.to_out(n)
+            else:
+                n = self.attn2(n, context=context_attn2, value=value_attn2, mask=cross_mask, style_block=style_block.ATTN2)       # real cross attention                                ##### b (h w) c
+                n = style_block(n, "cross_attn")
+
+
+        if "attn2_output_patch" in transformer_patches:
+            patch = transformer_patches["attn2_output_patch"]
+            for p in patch:
+                n = p(n, extra_options)
+
+        x += n ###########
+        x = style_block(x, "cross_attn_res")
+
+        if self.is_res: # always true with sdxl?
+            x_skip = x
+            
+        if not self.is_res:
+            pass
+        
+        x = self.norm3(x)
+        x = style_block(x, "norm3")
+        x = self.ff(x)
+        x = style_block(x, "ff")
+
+        if self.is_res:
+            x += x_skip
+            
+        x = style_block(x, "ff_res")
+
+        return x
+
+
+class ReSpatialTransformer(nn.Module):
+    """
+    Transformer block for image-like data.
+    First, project the input (aka embedding)
+    and reshape to b, t, d.
+    Then apply standard transformer action.
+    Finally, reshape to image
+    NEW: use_linear for more efficiency instead of the 1x1 convs
+    """
+    def __init__(self, in_channels, n_heads, d_head,
+                 depth=1, dropout=0., context_dim=None,
+                 disable_self_attn=False, use_linear=False,
+                 use_checkpoint=True, attn_precision=None, dtype=None, device=None, operations=ops):
+        super().__init__()
+        if exists(context_dim) and not isinstance(context_dim, list):
+            context_dim = [context_dim] * depth
+        self.in_channels = in_channels
+        inner_dim = n_heads * d_head
+        self.norm = operations.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
+        if not use_linear:
+            self.proj_in = operations.Conv2d(in_channels,
+                                     inner_dim,
+                                     kernel_size=1,
+                                     stride=1,
+                                     padding=0, dtype=dtype, device=device)
+        else:
+            self.proj_in = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device)
+
+        self.transformer_blocks = nn.ModuleList(
+            [ReBasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
+                                   disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations)
+                for d in range(depth)]
+        )
+        if not use_linear:
+            self.proj_out = operations.Conv2d(inner_dim,in_channels,
+                                                  kernel_size=1,
+                                                  stride=1,
+                                                  padding=0, dtype=dtype, device=device)
+        else:
+            self.proj_out = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device)
+        self.use_linear = use_linear
+
+    def forward(self, x, context=None, style_block=None, transformer_options={}):
+        # note: if no context is given, cross-attention defaults to self-attention
+        if not isinstance(context, list):
+            context = [context] * len(self.transformer_blocks)
+        b, c, h, w = x.shape
+        transformer_options["activations_shape"] = list(x.shape)
+        x_in = x
+        x = self.norm(x)
+        x = style_block(x, "spatial_norm_in")
+        if not self.use_linear:
+            x = self.proj_in(x)
+            x = style_block(x, "spatial_proj_in")
+        x = x.movedim(1, 3).flatten(1, 2).contiguous()
+        if self.use_linear:
+            x = self.proj_in(x)
+            x = style_block(x, "spatial_proj_in")
+        for i, block in enumerate(self.transformer_blocks):
+            transformer_options["block_index"] = i
+            x = block(x, context=context[i], style_block=style_block.TFMR, transformer_options=transformer_options)
+            x = style_block(x, "spatial_transformer_block")
+        x = style_block(x, "spatial_transformer")
+        if self.use_linear:
+            x = self.proj_out(x)
+        x = x.reshape(x.shape[0], h, w, x.shape[-1]).movedim(3, 1).contiguous()
+        if not self.use_linear:
+            x = self.proj_out(x)
+        x = style_block(x, "spatial_proj_out")
+        x = x + x_in
+        x = style_block(x, "spatial_res")
+        return x
+
+
+class SpatialVideoTransformer(ReSpatialTransformer):
+    def __init__(
+        self,
+        in_channels,
+        n_heads,
+        d_head,
+        depth=1,
+        dropout=0.0,
+        use_linear=False,
+        context_dim=None,
+        use_spatial_context=False,
+        timesteps=None,
+        merge_strategy: str = "fixed",
+        merge_factor: float = 0.5,
+        time_context_dim=None,
+        ff_in=False,
+        checkpoint=False,
+        time_depth=1,
+        disable_self_attn=False,
+        disable_temporal_crossattention=False,
+        max_time_embed_period: int = 10000,
+        attn_precision=None,
+        dtype=None, device=None, operations=ops
+    ):
+        super().__init__(
+            in_channels,
+            n_heads,
+            d_head,
+            depth=depth,
+            dropout=dropout,
+            use_checkpoint=checkpoint,
+            context_dim=context_dim,
+            use_linear=use_linear,
+            disable_self_attn=disable_self_attn,
+            attn_precision=attn_precision,
+            dtype=dtype, device=device, operations=operations
+        )
+        self.time_depth = time_depth
+        self.depth = depth
+        self.max_time_embed_period = max_time_embed_period
+
+        time_mix_d_head = d_head
+        n_time_mix_heads = n_heads
+
+        time_mix_inner_dim = int(time_mix_d_head * n_time_mix_heads)
+
+        inner_dim = n_heads * d_head
+        if use_spatial_context:
+            time_context_dim = context_dim
+
+        self.time_stack = nn.ModuleList(
+            [
+                BasicTransformerBlock(
+                    inner_dim,
+                    n_time_mix_heads,
+                    time_mix_d_head,
+                    dropout=dropout,
+                    context_dim=time_context_dim,
+                    # timesteps=timesteps,
+                    checkpoint=checkpoint,
+                    ff_in=ff_in,
+                    inner_dim=time_mix_inner_dim,
+                    disable_self_attn=disable_self_attn,
+                    disable_temporal_crossattention=disable_temporal_crossattention,
+                    attn_precision=attn_precision,
+                    dtype=dtype, device=device, operations=operations
+                )
+                for _ in range(self.depth)
+            ]
+        )
+
+        assert len(self.time_stack) == len(self.transformer_blocks)
+
+        self.use_spatial_context = use_spatial_context
+        self.in_channels = in_channels
+
+        time_embed_dim = self.in_channels * 4
+        self.time_pos_embed = nn.Sequential(
+            operations.Linear(self.in_channels, time_embed_dim, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(time_embed_dim, self.in_channels, dtype=dtype, device=device),
+        )
+
+        self.time_mixer = AlphaBlender(
+            alpha=merge_factor, merge_strategy=merge_strategy
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        context: Optional[torch.Tensor] = None,
+        time_context: Optional[torch.Tensor] = None,
+        timesteps: Optional[int] = None,
+        image_only_indicator: Optional[torch.Tensor] = None,
+        transformer_options={}
+    ) -> torch.Tensor:
+        _, _, h, w = x.shape
+        transformer_options["activations_shape"] = list(x.shape)
+        x_in = x
+        spatial_context = None
+        if exists(context):
+            spatial_context = context
+
+        if self.use_spatial_context:
+            assert (
+                context.ndim == 3
+            ), f"n dims of spatial context should be 3 but are {context.ndim}"
+
+            if time_context is None:
+                time_context = context
+            time_context_first_timestep = time_context[::timesteps]
+            time_context = repeat(
+                time_context_first_timestep, "b ... -> (b n) ...", n=h * w
+            )
+        elif time_context is not None and not self.use_spatial_context:
+            time_context = repeat(time_context, "b ... -> (b n) ...", n=h * w)
+            if time_context.ndim == 2:
+                time_context = rearrange(time_context, "b c -> b 1 c")
+
+        x = self.norm(x)
+        if not self.use_linear:
+            x = self.proj_in(x)
+        x = rearrange(x, "b c h w -> b (h w) c")
+        if self.use_linear:
+            x = self.proj_in(x)
+
+        num_frames = torch.arange(timesteps, device=x.device)
+        num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps)
+        num_frames = rearrange(num_frames, "b t -> (b t)")
+        t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False, max_period=self.max_time_embed_period).to(x.dtype)
+        emb = self.time_pos_embed(t_emb)
+        emb = emb[:, None, :]
+
+        for it_, (block, mix_block) in enumerate(
+            zip(self.transformer_blocks, self.time_stack)
+        ):
+            transformer_options["block_index"] = it_
+            x = block(
+                x,
+                context=spatial_context,
+                transformer_options=transformer_options,
+            )
+
+            x_mix = x
+            x_mix = x_mix + emb
+
+            B, S, C = x_mix.shape
+            x_mix = rearrange(x_mix, "(b t) s c -> (b s) t c", t=timesteps)
+            x_mix = mix_block(x_mix, context=time_context) #TODO: transformer_options
+            x_mix = rearrange(
+                x_mix, "(b s) t c -> (b t) s c", s=S, b=B // timesteps, c=C, t=timesteps
+            )
+
+            x = self.time_mixer(x_spatial=x, x_temporal=x_mix, image_only_indicator=image_only_indicator)
+
+        if self.use_linear:
+            x = self.proj_out(x)
+        x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
+        if not self.use_linear:
+            x = self.proj_out(x)
+        out = x + x_in
+        return out
+
+
diff --git a/custom_nodes/RES4LYF/sd/openaimodel.py b/custom_nodes/RES4LYF/sd/openaimodel.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ce4e110f5d7df8586ddf35723ff03b5b5b51588
--- /dev/null
+++ b/custom_nodes/RES4LYF/sd/openaimodel.py
@@ -0,0 +1,1458 @@
+from abc import abstractmethod
+import torch
+import torch as th
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+import logging
+import copy
+
+from ..helper import ExtraOptions
+
+from comfy.ldm.modules.diffusionmodules.util import (
+    checkpoint,
+    avg_pool_nd,
+    timestep_embedding,
+    AlphaBlender,
+)
+from comfy.ldm.modules.attention import SpatialTransformer, SpatialVideoTransformer, default
+from .attention import ReSpatialTransformer, ReBasicTransformerBlock
+from comfy.ldm.util import exists
+import comfy.patcher_extension
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+from comfy.ldm.modules.diffusionmodules.openaimodel import TimestepBlock, TimestepEmbedSequential, Upsample, Downsample, ResBlock, VideoResBlock
+from ..latents import slerp_tensor, interpolate_spd, tile_latent, untile_latent, gaussian_blur_2d, median_blur_2d
+
+from ..style_transfer import apply_scattersort_masked, apply_scattersort_tiled, adain_seq_inplace, adain_patchwise_row_batch_med, adain_patchwise_row_batch, apply_scattersort, apply_scattersort_spatial, StyleMMDiT_Model, StyleUNet_Model
+
+#This is needed because accelerate makes a copy of transformer_options which breaks "transformer_index"
+def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None, time_context=None, num_video_frames=None, image_only_indicator=None, style_block=None):
+    for layer in ts:
+        if isinstance(layer, VideoResBlock): # UNUSED
+            x = layer(x, emb, num_video_frames, image_only_indicator)
+        elif isinstance(layer, TimestepBlock):  # ResBlock(TimestepBlock)
+            x = layer(x, emb, style_block.res_block)
+            x = style_block(x, "res")
+        elif isinstance(layer, SpatialVideoTransformer):   # UNUSED
+            x = layer(x, context, time_context, num_video_frames, image_only_indicator, transformer_options)
+            if "transformer_index" in transformer_options:
+                transformer_options["transformer_index"] += 1
+        elif isinstance(layer, ReSpatialTransformer):          # USED
+            x = layer(x, context, style_block.spatial_block, transformer_options,)
+            x = style_block(x, "spatial")
+            if "transformer_index" in transformer_options:
+                transformer_options["transformer_index"] += 1
+        elif isinstance(layer, Upsample):
+            x = layer(x, output_shape=output_shape)
+            x = style_block(x, "resample")
+        elif isinstance(layer, Downsample):
+            x = layer(x)
+            x = style_block(x, "resample")
+        else:
+            if "patches" in transformer_options and "forward_timestep_embed_patch" in transformer_options["patches"]:
+                found_patched = False
+                for class_type, handler in transformer_options["patches"]["forward_timestep_embed_patch"]:
+                    if isinstance(layer, class_type):
+                        x = handler(layer, x, emb, context, transformer_options, output_shape, time_context, num_video_frames, image_only_indicator)
+                        found_patched = True
+                        break
+                if found_patched:
+                    continue
+            x = layer(x)
+    return x
+
+
+
+
+class ReResBlock(TimestepBlock):
+    """
+    A residual block that can optionally change the number of channels.
+    :param channels: the number of input channels.
+    :param emb_channels: the number of timestep embedding channels.
+    :param dropout: the rate of dropout.
+    :param out_channels: if specified, the number of out channels.
+    :param use_conv: if True and out_channels is specified, use a spatial
+        convolution instead of a smaller 1x1 convolution to change the
+        channels in the skip connection.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param use_checkpoint: if True, use gradient checkpointing on this module.
+    :param up: if True, use this block for upsampling.
+    :param down: if True, use this block for downsampling.
+    """
+
+    def __init__(
+        self,
+        channels,
+        emb_channels,
+        dropout,
+        out_channels=None,
+        use_conv=False,
+        use_scale_shift_norm=False,
+        dims=2,
+        use_checkpoint=False,
+        up=False,
+        down=False,
+        kernel_size=3,
+        exchange_temb_dims=False,
+        skip_t_emb=False,
+        dtype=None,
+        device=None,
+        operations=ops
+    ):
+        super().__init__()
+        self.channels = channels
+        self.emb_channels = emb_channels
+        self.dropout = dropout
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.use_checkpoint = use_checkpoint
+        self.use_scale_shift_norm = use_scale_shift_norm
+        self.exchange_temb_dims = exchange_temb_dims
+
+        if isinstance(kernel_size, list):
+            padding = [k // 2 for k in kernel_size]
+        else:
+            padding = kernel_size // 2
+
+        self.in_layers = nn.Sequential(
+            operations.GroupNorm(32, channels, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.conv_nd(dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device),
+        )
+
+        self.updown = up or down
+
+        if up:
+            self.h_upd = Upsample(channels, False, dims, dtype=dtype, device=device)
+            self.x_upd = Upsample(channels, False, dims, dtype=dtype, device=device)
+        elif down:
+            self.h_upd = Downsample(channels, False, dims, dtype=dtype, device=device)
+            self.x_upd = Downsample(channels, False, dims, dtype=dtype, device=device)
+        else:
+            self.h_upd = self.x_upd = nn.Identity()
+
+        self.skip_t_emb = skip_t_emb
+        if self.skip_t_emb:
+            self.emb_layers = None
+            self.exchange_temb_dims = False
+        else:
+            self.emb_layers = nn.Sequential(
+                nn.SiLU(),
+                operations.Linear(
+                    emb_channels,
+                    2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype, device=device
+                ),
+            )
+        self.out_layers = nn.Sequential(
+            operations.GroupNorm(32, self.out_channels, dtype=dtype, device=device),
+            nn.SiLU(),
+            nn.Dropout(p=dropout),
+            operations.conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device)
+            ,
+        )
+
+        if self.out_channels == channels:
+            self.skip_connection = nn.Identity()
+        elif use_conv:
+            self.skip_connection = operations.conv_nd(
+                dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device
+            )
+        else:
+            self.skip_connection = operations.conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device)
+
+    def forward(self, x, emb, style_block=None):
+        """
+        Apply the block to a Tensor, conditioned on a timestep embedding.
+        :param x: an [N x C x ...] Tensor of features.
+        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        return checkpoint(
+            self._forward, (x, emb, style_block), self.parameters(), self.use_checkpoint
+        )
+
+
+    def _forward(self, x, emb, style_block=None):
+        #if self.updown: # not used with sdxl?
+        #    in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+        #    h = in_rest(x)
+        #    h = self.h_upd(h)
+        #    x = self.x_upd(x)
+        #    h = in_conv(h)
+        #else:
+        #    h = self.in_layers(x)
+        
+        h = self.in_layers[0](x)
+        h = style_block(h, "in_norm")
+        
+        h = self.in_layers[1](h)
+        h = style_block(h, "in_silu")
+        
+        h = self.in_layers[2](h)
+        h = style_block(h, "in_conv")
+        
+
+        emb_out = None
+        if not self.skip_t_emb:
+            #emb_out = self.emb_layers(emb).type(h.dtype)
+            emb_out = self.emb_layers[0](emb).type(h.dtype)
+            emb_out = style_block(emb_out, "emb_silu")
+            
+            emb_out = self.emb_layers[1](emb_out)
+            emb_out = style_block(emb_out, "emb_linear")
+            
+            while len(emb_out.shape) < len(h.shape):
+                emb_out = emb_out[..., None]
+                
+        if self.use_scale_shift_norm: # not used with sdxl?
+            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+            h = out_norm(h)
+            if emb_out is not None:
+                scale, shift = th.chunk(emb_out, 2, dim=1)
+                h *= (1 + scale)
+                h += shift
+            h = out_rest(h)
+        else:
+            if emb_out is not None:
+                if self.exchange_temb_dims:
+                    emb_out = emb_out.movedim(1, 2)
+                h = h + emb_out
+                h = style_block(h, "emb_res")
+            #h = self.out_layers(h)
+            h = self.out_layers[0](h)
+            h = style_block(h, "out_norm")
+            
+            h = self.out_layers[1](h)
+            h = style_block(h, "out_silu")
+            
+            h = self.out_layers[3](h) # [2] is dropout
+            h = style_block(h, "out_conv")
+            
+        res_out = self.skip_connection(x) + h
+        res_out = style_block(res_out, "residual")
+        return res_out   
+        #return self.skip_connection(x) + h
+
+
+
+
+class Timestep(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, t):
+        return timestep_embedding(t, self.dim)
+
+def apply_control(h, control, name):
+    if control is not None and name in control and len(control[name]) > 0:
+        ctrl = control[name].pop()
+        if ctrl is not None:
+            try:
+                h += ctrl
+            except:
+                logging.warning("warning control could not be applied {} {}".format(h.shape, ctrl.shape))
+    return h
+
+
+class ReUNetModel(nn.Module):
+    """
+    The full UNet model with attention and timestep embedding.
+    :param in_channels: channels in the input Tensor.
+    :param model_channels: base channel count for the model.
+    :param out_channels: channels in the output Tensor.
+    :param num_res_blocks: number of residual blocks per downsample.
+    :param dropout: the dropout probability.
+    :param channel_mult: channel multiplier for each level of the UNet.
+    :param conv_resample: if True, use learned convolutions for upsampling and
+        downsampling.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param num_classes: if specified (as an int), then this model will be
+        class-conditional with `num_classes` classes.
+    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
+    :param num_heads: the number of attention heads in each attention layer.
+    :param num_heads_channels: if specified, ignore num_heads and instead use
+                               a fixed channel width per attention head.
+    :param num_heads_upsample: works with num_heads to set a different number
+                               of heads for upsampling. Deprecated.
+    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
+    :param resblock_updown: use residual blocks for up/downsampling.
+    :param use_new_attention_order: use a different attention pattern for potentially
+                                    increased efficiency.
+    """
+
+    def __init__(
+        self,
+        image_size,
+        in_channels,
+        model_channels,
+        out_channels,
+        num_res_blocks,
+        dropout                         = 0,
+        channel_mult                    = (1, 2, 4, 8),
+        conv_resample                   = True,
+        dims                            = 2,
+        num_classes                     = None,
+        use_checkpoint                  = False,
+        dtype                           = th.float32,
+        num_heads                       = -1,
+        num_head_channels               = -1,
+        num_heads_upsample              = -1,
+        use_scale_shift_norm            = False,
+        resblock_updown                 = False,
+        use_new_attention_order         = False,
+        use_spatial_transformer         = False,    # custom transformer support
+        transformer_depth               = 1,              # custom transformer support
+        context_dim                     = None,                 # custom transformer support
+        n_embed                         = None,                     # custom support for prediction of discrete ids into codebook of first stage vq model
+        legacy                          = True,
+        disable_self_attentions         = None,
+        num_attention_blocks            = None,
+        disable_middle_self_attn        = False,
+        use_linear_in_transformer       = False,
+        adm_in_channels                 = None,
+        transformer_depth_middle        = None,
+        transformer_depth_output        = None,
+        use_temporal_resblock           = False,
+        use_temporal_attention          = False,
+        time_context_dim                = None,
+        extra_ff_mix_layer              = False,
+        use_spatial_context             = False,
+        merge_strategy                  = None,
+        merge_factor                    = 0.0,
+        video_kernel_size               = None,
+        disable_temporal_crossattention = False,
+        max_ddpm_temb_period            = 10000,
+        attn_precision                  = None,
+        device                          = None,
+        operations                      = ops,
+    ):
+        super().__init__()
+
+        if context_dim is not None:
+            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
+            # from omegaconf.listconfig import ListConfig
+            # if type(context_dim) == ListConfig:
+            #     context_dim = list(context_dim)
+
+        if num_heads_upsample == -1:
+            num_heads_upsample = num_heads
+
+        if num_heads == -1:
+            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
+
+        if num_head_channels == -1:
+            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
+
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+
+        if isinstance(num_res_blocks, int):
+            self.num_res_blocks = len(channel_mult) * [num_res_blocks]
+        else:
+            if len(num_res_blocks) != len(channel_mult):
+                raise ValueError("provide num_res_blocks either as an int (globally constant) or "
+                                 "as a list/tuple (per-level) with the same length as channel_mult")
+            self.num_res_blocks = num_res_blocks
+
+        if disable_self_attentions is not None:
+            # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
+            assert len(disable_self_attentions) == len(channel_mult)
+        if num_attention_blocks is not None:
+            assert len(num_attention_blocks) == len(self.num_res_blocks)
+
+        transformer_depth = transformer_depth[:]
+        transformer_depth_output = transformer_depth_output[:]
+
+        self.dropout                = dropout
+        self.channel_mult           = channel_mult
+        self.conv_resample          = conv_resample
+        self.num_classes            = num_classes
+        self.use_checkpoint         = use_checkpoint
+        self.dtype                  = dtype
+        self.num_heads              = num_heads
+        self.num_head_channels      = num_head_channels
+        self.num_heads_upsample     = num_heads_upsample
+        self.use_temporal_resblocks = use_temporal_resblock
+        self.predict_codebook_ids   = n_embed is not None
+
+        self.default_num_video_frames = None
+
+        time_embed_dim = model_channels * 4
+        self.time_embed = nn.Sequential(
+            operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
+        )
+
+        if self.num_classes is not None:
+            if isinstance(self.num_classes, int):
+                self.label_emb = nn.Embedding(num_classes, time_embed_dim, dtype=self.dtype, device=device)
+            elif self.num_classes == "continuous":
+                logging.debug("setting up linear c_adm embedding layer")
+                self.label_emb = nn.Linear(1, time_embed_dim)
+            elif self.num_classes == "sequential":
+                assert adm_in_channels is not None
+                self.label_emb = nn.Sequential(
+                    nn.Sequential(
+                        operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),
+                        nn.SiLU(),
+                        operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
+                    )
+                )
+            else:
+                raise ValueError()
+
+        self.input_blocks = nn.ModuleList(
+            [
+                TimestepEmbedSequential(
+                    operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)
+                )
+            ]
+        )
+        self._feature_size = model_channels
+        input_block_chans = [model_channels]
+        ch = model_channels
+        ds = 1
+
+        def get_attention_layer(
+            ch,
+            num_heads,
+            dim_head,
+            depth=1,
+            context_dim=None,
+            use_checkpoint=False,
+            disable_self_attn=False,
+        ):
+            if use_temporal_attention:
+                return SpatialVideoTransformer(
+                    ch,
+                    num_heads,
+                    dim_head,
+                    depth                           = depth,
+                    context_dim                     = context_dim,
+                    time_context_dim                = time_context_dim,
+                    dropout                         = dropout,
+                    ff_in                           = extra_ff_mix_layer,
+                    use_spatial_context             = use_spatial_context,
+                    merge_strategy                  = merge_strategy,
+                    merge_factor                    = merge_factor,
+                    checkpoint                      = use_checkpoint,
+                    use_linear                      = use_linear_in_transformer,
+                    disable_self_attn               = disable_self_attn,
+                    disable_temporal_crossattention = disable_temporal_crossattention,
+                    max_time_embed_period           = max_ddpm_temb_period,
+                    attn_precision                  = attn_precision,
+                    dtype=self.dtype, device=device, operations=operations,
+                )
+            else:
+                return SpatialTransformer(
+                                ch, num_heads, dim_head, depth=depth, context_dim=context_dim,
+                                disable_self_attn=disable_self_attn, use_linear=use_linear_in_transformer,
+                                use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations
+                            )
+
+        def get_resblock(
+            merge_factor,
+            merge_strategy,
+            video_kernel_size,
+            ch,
+            time_embed_dim,
+            dropout,
+            out_channels,
+            dims,
+            use_checkpoint,
+            use_scale_shift_norm,
+            down       = False,
+            up         = False,
+            dtype      = None,
+            device     = None,
+            operations = ops
+        ):
+            if self.use_temporal_resblocks:
+                return VideoResBlock(
+                    merge_factor         = merge_factor,
+                    merge_strategy       = merge_strategy,
+                    video_kernel_size    = video_kernel_size,
+                    channels             = ch,
+                    emb_channels         = time_embed_dim,
+                    dropout              = dropout,
+                    out_channels         = out_channels,
+                    dims                 = dims,
+                    use_checkpoint       = use_checkpoint,
+                    use_scale_shift_norm = use_scale_shift_norm,
+                    down                 = down,
+                    up                   = up,
+                    dtype=dtype, device=device, operations=operations,
+                )
+            else:
+                return ResBlock(
+                    channels             = ch,
+                    emb_channels         = time_embed_dim,
+                    dropout              = dropout,
+                    out_channels         = out_channels,
+                    use_checkpoint       = use_checkpoint,
+                    dims                 = dims,
+                    use_scale_shift_norm = use_scale_shift_norm,
+                    down                 = down,
+                    up                   = up,
+                    dtype=dtype, device=device, operations=operations,
+                )
+
+        for level, mult in enumerate(channel_mult):
+            for nr in range(self.num_res_blocks[level]):
+                layers = [
+                    get_resblock(
+                        merge_factor         = merge_factor,
+                        merge_strategy       = merge_strategy,
+                        video_kernel_size    = video_kernel_size,
+                        ch                   = ch,
+                        time_embed_dim       = time_embed_dim,
+                        dropout              = dropout,
+                        out_channels         = mult * model_channels,
+                        dims                 = dims,
+                        use_checkpoint       = use_checkpoint,
+                        use_scale_shift_norm = use_scale_shift_norm,
+                        dtype=self.dtype, device=device, operations=operations,
+                    )
+                ]
+                ch = mult * model_channels
+                num_transformers = transformer_depth.pop(0)
+                if num_transformers > 0:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+                    if legacy:
+                        #num_heads = 1
+                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+                    if exists(disable_self_attentions):
+                        disabled_sa = disable_self_attentions[level]
+                    else:
+                        disabled_sa = False
+
+                    if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
+                        layers.append(get_attention_layer(
+                                ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim,
+                                disable_self_attn=disabled_sa, use_checkpoint=use_checkpoint)
+                        )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        get_resblock(
+                            merge_factor         = merge_factor,
+                            merge_strategy       = merge_strategy,
+                            video_kernel_size    = video_kernel_size,
+                            ch                   = ch,
+                            time_embed_dim       = time_embed_dim,
+                            dropout              = dropout,
+                            out_channels         = out_ch,
+                            dims                 = dims,
+                            use_checkpoint       = use_checkpoint,
+                            use_scale_shift_norm = use_scale_shift_norm,
+                            down                 = True,
+                            dtype=self.dtype, device=device, operations=operations,
+                        )
+                        if resblock_updown
+                        else Downsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations)
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                ds *= 2
+                self._feature_size += ch
+
+        if num_head_channels == -1:
+            dim_head = ch // num_heads
+        else:
+            num_heads = ch // num_head_channels
+            dim_head = num_head_channels
+        if legacy:
+            #num_heads = 1
+            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+        mid_block = [
+            get_resblock(
+                merge_factor         = merge_factor,
+                merge_strategy       = merge_strategy,
+                video_kernel_size    = video_kernel_size,
+                ch                   = ch,
+                time_embed_dim       = time_embed_dim,
+                dropout              = dropout,
+                out_channels         = None,
+                dims                 = dims,
+                use_checkpoint       = use_checkpoint,
+                use_scale_shift_norm = use_scale_shift_norm,
+                dtype=self.dtype, device=device, operations=operations,
+            )]
+
+        self.middle_block = None
+        if transformer_depth_middle >= -1:
+            if transformer_depth_middle >= 0:
+                mid_block += [get_attention_layer(  # always uses a self-attn
+                                ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim,
+                                disable_self_attn=disable_middle_self_attn, use_checkpoint=use_checkpoint
+                            ),
+                get_resblock(
+                    merge_factor         = merge_factor,
+                    merge_strategy       = merge_strategy,
+                    video_kernel_size    = video_kernel_size,
+                    ch                   = ch,
+                    time_embed_dim       = time_embed_dim,
+                    dropout              = dropout,
+                    out_channels         = None,
+                    dims                 = dims,
+                    use_checkpoint       = use_checkpoint,
+                    use_scale_shift_norm = use_scale_shift_norm,
+                    dtype=self.dtype, device=device, operations=operations,
+                )]
+            self.middle_block = TimestepEmbedSequential(*mid_block)
+        self._feature_size += ch
+
+        self.output_blocks = nn.ModuleList([])
+        for level, mult in list(enumerate(channel_mult))[::-1]:
+            for i in range(self.num_res_blocks[level] + 1):
+                ich = input_block_chans.pop()
+                layers = [
+                    get_resblock(
+                        merge_factor         = merge_factor,
+                        merge_strategy       = merge_strategy,
+                        video_kernel_size    = video_kernel_size,
+                        ch                   = ch + ich,
+                        time_embed_dim       = time_embed_dim,
+                        dropout              = dropout,
+                        out_channels         = model_channels * mult,
+                        dims                 = dims,
+                        use_checkpoint       = use_checkpoint,
+                        use_scale_shift_norm = use_scale_shift_norm,
+                        dtype=self.dtype, device=device, operations=operations,
+                    )
+                ]
+                ch = model_channels * mult
+                num_transformers = transformer_depth_output.pop()
+                if num_transformers > 0:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+                    if legacy:
+                        #num_heads = 1
+                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+                    if exists(disable_self_attentions):
+                        disabled_sa = disable_self_attentions[level]
+                    else:
+                        disabled_sa = False
+
+                    if not exists(num_attention_blocks) or i < num_attention_blocks[level]:
+                        layers.append(
+                            get_attention_layer(
+                                ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim,
+                                disable_self_attn=disabled_sa, use_checkpoint=use_checkpoint
+                            )
+                        )
+                if level and i == self.num_res_blocks[level]:
+                    out_ch = ch
+                    layers.append(
+                        get_resblock(
+                            merge_factor         = merge_factor,
+                            merge_strategy       = merge_strategy,
+                            video_kernel_size    = video_kernel_size,
+                            ch                   = ch,
+                            time_embed_dim       = time_embed_dim,
+                            dropout              = dropout,
+                            out_channels         = out_ch,
+                            dims                 = dims,
+                            use_checkpoint       = use_checkpoint,
+                            use_scale_shift_norm = use_scale_shift_norm,
+                            up                   = True,
+                            dtype=self.dtype, device=device, operations=operations,
+                        )
+                        if resblock_updown
+                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations)
+                    )
+                    ds //= 2
+                self.output_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+
+        self.out = nn.Sequential(
+            operations.GroupNorm(32, ch, dtype=self.dtype, device=device),
+            nn.SiLU(),
+            operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device),
+        )
+        if self.predict_codebook_ids:
+            self.id_predictor = nn.Sequential(
+            operations.GroupNorm(32, ch, dtype=self.dtype, device=device),
+            operations.conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device),
+            #nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
+        )
+
+
+
+    def forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):
+        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
+            self._forward,
+            self,
+            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
+        ).execute(x, timesteps, context, y, control, transformer_options, **kwargs)
+
+    def _forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):
+        """
+        Apply the model to an input batch.
+        :param x: an [N x C x ...] Tensor of inputs.
+        :param timesteps: a 1-D batch of timesteps.
+        :param context: conditioning plugged in via crossattn
+        :param y: an [N] Tensor of labels, if class-conditional.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        h_len, w_len = x.shape[-2:]
+        img_len = h_len * w_len
+        transformer_options["original_shape"] = list(x.shape)
+        transformer_options["transformer_index"] = 0
+        transformer_patches = transformer_options.get("patches", {})
+        SIGMA = transformer_options['sigmas'].to(x) # timestep[0].unsqueeze(0) #/ 1000
+
+        img_slice = slice(None, -1) #slice(None, img_len)   # for the sake of cross attn... :-1
+        txt_slice = slice(None, -1)
+        
+        EO = transformer_options.get("ExtraOptions", ExtraOptions(""))
+        if EO is not None:
+            EO.mute = True
+
+        if EO("zero_heads"):
+            HEADS = 0
+        else:
+            HEADS = 10 # self.input_blocks[4][1].transformer_blocks[0].attn2.heads # HEADS = 10
+
+        StyleMMDiT = transformer_options.get('StyleMMDiT', StyleUNet_Model())        
+        StyleMMDiT.set_len(h_len, w_len, img_slice, txt_slice, HEADS=HEADS)
+        StyleMMDiT.Retrojector = self.Retrojector if hasattr(self, "Retrojector") else None
+        transformer_options['StyleMMDiT'] = None
+        
+        x_tmp = transformer_options.get("x_tmp")
+        if x_tmp is not None:
+            x_tmp = x_tmp.clone() / ((SIGMA ** 2 + 1) ** 0.5)
+            x_tmp = x_tmp.expand_as(x) # (x.shape[0], -1, -1, -1) # .clone().to(x)
+        
+        y0_style, img_y0_style = None, None
+
+        
+        x_orig, timesteps_orig, y_orig, context_orig = clone_inputs(x, timesteps, y, context)
+        h_orig = x_orig.clone()
+
+        weight    = -1 * transformer_options.get("regional_conditioning_weight", 0.0)
+        floor     = -1 * transformer_options.get("regional_conditioning_floor",  0.0)
+        
+        #floor     = min(floor, weight)
+        mask_zero, mask_up_zero, mask_down_zero, mask_down2_zero = None, None, None, None
+        txt_len = context.shape[1] # mask_obj[0].text_len
+        
+
+        z_ = transformer_options.get("z_")   # initial noise and/or image+noise from start of rk_sampler_beta() 
+        rk_row = transformer_options.get("row") # for "smart noise"
+        if z_ is not None:
+            x_init = z_[rk_row].to(x)
+        elif 'x_init' in transformer_options:
+            x_init = transformer_options.get('x_init').to(x)
+
+        # recon loop to extract exact noise pred for scattersort guide assembly
+        RECON_MODE = StyleMMDiT.noise_mode == "recon"
+        recon_iterations = 2 if StyleMMDiT.noise_mode == "recon" else 1
+        for recon_iter in range(recon_iterations):
+            y0_style = StyleMMDiT.guides
+            y0_style_active = True if type(y0_style) == torch.Tensor else False
+            
+            RECON_MODE = True     if StyleMMDiT.noise_mode == "recon" and recon_iter == 0     else False
+            
+            ISIGMA = SIGMA
+            if StyleMMDiT.noise_mode == "recon" and recon_iter == 1: 
+                ISIGMA = SIGMA * EO("ISIGMA_FACTOR", 1.0)
+                
+                model_sampling = transformer_options.get('model_sampling')     
+                timesteps_orig = model_sampling.timestep(ISIGMA).expand_as(timesteps_orig)
+                
+                x_recon = x_tmp if x_tmp is not None else x_orig
+                #noise_prediction = x_recon + (1-SIGMA.to(x_recon)) * eps.to(x_recon)
+                noise_prediction = eps.to(x_recon)
+                denoised = x_recon * ((SIGMA.to(x_recon) ** 2 + 1) ** 0.5)   -   SIGMA.to(x_recon) * eps.to(x_recon)
+                
+                denoised = StyleMMDiT.apply_recon_lure(denoised, y0_style.to(x_recon))   # .to(denoised)
+
+                new_x = (denoised + ISIGMA.to(x_recon) * noise_prediction) / ((ISIGMA.to(x_recon) ** 2 + 1) ** 0.5)
+                h_orig = new_x.clone().to(x)
+                x_init = noise_prediction
+            elif StyleMMDiT.noise_mode == "bonanza":
+                x_init = torch.randn_like(x_init)
+
+            if y0_style_active:
+                if y0_style.sum() == 0.0 and y0_style.std() == 0.0:
+                    y0_style_noised = x.clone()
+                else:
+                    y0_style_noised = (y0_style + ISIGMA.to(y0_style) * x_init.expand_as(x).to(y0_style)) / ((ISIGMA.to(y0_style) ** 2 + 1) ** 0.5)    #x_init.expand(x.shape[0],-1,-1,-1).to(y0_style)) 
+
+            out_list = []
+            for cond_iter in range(len(transformer_options['cond_or_uncond'])):
+                UNCOND = transformer_options['cond_or_uncond'][cond_iter] == 1
+                
+                bsz_style = y0_style.shape[0] if y0_style_active else 0
+                bsz       = 1 if RECON_MODE else bsz_style + 1
+                
+                h, timesteps, context = clone_inputs(h_orig[cond_iter].unsqueeze(0), timesteps_orig[cond_iter].unsqueeze(0), context_orig[cond_iter].unsqueeze(0))
+                y = y_orig[cond_iter].unsqueeze(0).clone() if y_orig is not None else None
+                
+
+                mask, mask_up, mask_down, mask_down2 = None, None, None, None
+                if not UNCOND and 'AttnMask' in transformer_options: # and weight != 0:
+                    AttnMask = transformer_options['AttnMask']
+                    mask = transformer_options['AttnMask'].attn_mask.mask.to('cuda')
+                    mask_up   = transformer_options['AttnMask'].mask_up.to('cuda')
+                    mask_down = transformer_options['AttnMask'].mask_down.to('cuda')
+                    if hasattr(transformer_options['AttnMask'], "mask_down2"):
+                        mask_down2 = transformer_options['AttnMask'].mask_down2.to('cuda')
+                    if weight == 0:
+                        context = transformer_options['RegContext'].context.to(context.dtype).to(context.device)
+                        mask, mask_up, mask_down, mask_down2 = None, None, None, None
+                    else:
+                        context = transformer_options['RegContext'].context.to(context.dtype).to(context.device)
+                    
+                    txt_len = context.shape[1]
+                    if mask_zero is None:
+                        mask_zero = torch.ones_like(mask)
+                        mask_zero[:, :txt_len] = mask[:, :txt_len]
+                    if mask_up_zero is None:
+                        mask_up_zero = torch.ones_like(mask_up)
+                        mask_up_zero[:, :txt_len] = mask_up[:, :txt_len]
+                    if mask_down_zero is None:
+                        mask_down_zero = torch.ones_like(mask_down)
+                        mask_down_zero[:, :txt_len] = mask_down[:, :txt_len]
+                    if mask_down2_zero is None and mask_down2 is not None:
+                        mask_down2_zero = torch.ones_like(mask_down2)
+                        mask_down2_zero[:, :txt_len] = mask_down2[:, :txt_len]
+
+
+                if UNCOND and 'AttnMask_neg' in transformer_options: # and weight != 0:
+                    AttnMask = transformer_options['AttnMask_neg']
+                    mask = transformer_options['AttnMask_neg'].attn_mask.mask.to('cuda')
+                    mask_up   = transformer_options['AttnMask_neg'].mask_up.to('cuda')
+                    mask_down = transformer_options['AttnMask_neg'].mask_down.to('cuda')
+                    if hasattr(transformer_options['AttnMask_neg'], "mask_down2"):
+                        mask_down2 = transformer_options['AttnMask_neg'].mask_down2.to('cuda')
+                    if weight == 0:
+                        context = transformer_options['RegContext_neg'].context.to(context.dtype).to(context.device)
+                        mask, mask_up, mask_down, mask_down2 = None, None, None, None
+                    else:
+                        context = transformer_options['RegContext_neg'].context.to(context.dtype).to(context.device)
+                        
+                    txt_len = context.shape[1]
+                    if mask_zero is None:
+                        mask_zero = torch.ones_like(mask)
+                        mask_zero[:, :txt_len] = mask[:, :txt_len]
+                    if mask_up_zero is None:
+                        mask_up_zero = torch.ones_like(mask_up)
+                        mask_up_zero[:, :txt_len] = mask_up[:, :txt_len]
+                    if mask_down_zero is None:
+                        mask_down_zero = torch.ones_like(mask_down)
+                        mask_down_zero[:, :txt_len] = mask_down[:, :txt_len]
+                    if mask_down2_zero is None and mask_down2 is not None:
+                        mask_down2_zero = torch.ones_like(mask_down2)
+                        mask_down2_zero[:, :txt_len] = mask_down2[:, :txt_len]
+
+                elif UNCOND and 'AttnMask' in transformer_options:
+                    AttnMask = transformer_options['AttnMask']
+                    mask = transformer_options['AttnMask'].attn_mask.mask.to('cuda')
+                    mask_up   = transformer_options['AttnMask'].mask_up.to('cuda')
+                    mask_down = transformer_options['AttnMask'].mask_down.to('cuda')
+                    if hasattr(transformer_options['AttnMask'], "mask_down2"):
+                        mask_down2 = transformer_options['AttnMask'].mask_down2.to('cuda')
+                    A       = context
+                    B       = transformer_options['RegContext'].context
+                    context = A.repeat(1,    (B.shape[1] // A.shape[1]) + 1, 1)[:,   :B.shape[1], :]
+                    
+                    txt_len = context.shape[1]
+                    if mask_zero is None:
+                        mask_zero = torch.ones_like(mask)
+                        mask_zero[:, :txt_len] = mask[:, :txt_len]
+                    if mask_up_zero is None:
+                        mask_up_zero = torch.ones_like(mask_up)
+                        mask_up_zero[:, :txt_len] = mask_up[:, :txt_len]
+                    if mask_down_zero is None:
+                        mask_down_zero = torch.ones_like(mask_down)
+                        mask_down_zero[:, :txt_len] = mask_down[:, :txt_len]
+                    if mask_down2_zero is None and mask_down2 is not None:
+                        mask_down2_zero = torch.ones_like(mask_down2)
+                        mask_down2_zero[:, :txt_len] = mask_down2[:, :txt_len]
+                    if weight == 0:                                                                             # ADDED 5/23/2025
+                        mask, mask_up, mask_down, mask_down2 = None, None, None, None
+
+
+                if mask is not None:
+                    if mask            is not None and not type(mask[0][0]           .item()) == bool:
+                        mask            = mask           .to(x.dtype)
+                    if mask_up         is not None and not type(mask_up[0][0]        .item()) == bool:
+                        mask_up         = mask_up        .to(x.dtype)
+                    if mask_down       is not None and not type(mask_down[0][0]      .item()) == bool:
+                        mask_down       = mask_down      .to(x.dtype)
+                    if mask_down2      is not None and not type(mask_down2[0][0]     .item()) == bool:
+                        mask_down2      = mask_down2     .to(x.dtype)
+                        
+                    if mask_zero       is not None and not type(mask_zero[0][0]      .item()) == bool:
+                        mask_zero       = mask_zero      .to(x.dtype)
+                    if mask_up_zero    is not None and not type(mask_up_zero[0][0]   .item()) == bool:
+                        mask_up_zero    = mask_up_zero   .to(x.dtype)
+                    if mask_down_zero  is not None and not type(mask_down_zero[0][0] .item()) == bool:
+                        mask_down_zero  = mask_down_zero .to(x.dtype)
+                    if mask_down2_zero is not None and not type(mask_down2_zero[0][0].item()) == bool:
+                        mask_down2_zero = mask_down2_zero.to(x.dtype)
+                        
+                    transformer_options['cross_mask']       = mask      [:,:txt_len]
+                    transformer_options['self_mask']        = mask      [:,txt_len:]
+                    transformer_options['cross_mask_up']    = mask_up   [:,:txt_len]
+                    transformer_options['self_mask_up']     = mask_up   [:,txt_len:]
+                    transformer_options['cross_mask_down']  = mask_down [:,:txt_len]
+                    transformer_options['self_mask_down']   = mask_down [:,txt_len:]
+                    transformer_options['cross_mask_down2'] = mask_down2[:,:txt_len] if mask_down2 is not None else None
+                    transformer_options['self_mask_down2']  = mask_down2[:,txt_len:] if mask_down2 is not None else None
+                
+                #h = x
+                if y0_style_active and not RECON_MODE:
+                    if mask is None:
+                        context, y, _ = StyleMMDiT.apply_style_conditioning(
+                            UNCOND       = UNCOND,
+                            base_context = context,
+                            base_y       = y,
+                            base_llama3  = None,
+                        )
+                    else:
+                        context = context.repeat(bsz_style + 1, 1, 1)
+                        y = y.repeat(bsz_style + 1, 1)                   if y      is not None else None
+                    h = torch.cat([h, y0_style_noised[cond_iter:cond_iter+1]], dim=0).to(h)
+
+
+
+                total_layers = len(self.input_blocks) + len(self.middle_block) + len(self.output_blocks)
+
+                num_video_frames = kwargs.get("num_video_frames", self.default_num_video_frames)
+                image_only_indicator = kwargs.get("image_only_indicator", None)
+                time_context = kwargs.get("time_context", None)
+
+                assert (y is not None) == (
+                    self.num_classes is not None
+                ), "must specify y if and only if the model is class-conditional"
+                hs, hs_adain = [], []
+                t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
+                emb = self.time_embed(t_emb)
+
+                if "emb_patch" in transformer_patches:
+                    patch = transformer_patches["emb_patch"]
+                    for p in patch:
+                        emb = p(emb, self.model_channels, transformer_options)
+
+                if self.num_classes is not None:
+                    assert y.shape[0] == h.shape[0]
+                    emb = emb + self.label_emb(y)
+
+                #for id, module in enumerate(self.input_blocks):
+                for id, (module, style_block) in enumerate(zip(self.input_blocks, StyleMMDiT.input_blocks)):
+                    transformer_options["block"] = ("input", id)
+
+                    if mask is not None:
+                        transformer_options['cross_mask']       = mask      [:,:txt_len]
+                        transformer_options['self_mask']        = mask      [:,txt_len:]
+                        transformer_options['cross_mask_up']    = mask_up   [:,:txt_len]
+                        transformer_options['self_mask_up']     = mask_up   [:,txt_len:]
+                        transformer_options['cross_mask_down']  = mask_down [:,:txt_len]
+                        transformer_options['self_mask_down']   = mask_down [:,txt_len:]
+                        transformer_options['cross_mask_down2'] = mask_down2[:,:txt_len] if mask_down2 is not None else None
+                        transformer_options['self_mask_down2']  = mask_down2[:,txt_len:] if mask_down2 is not None else None
+                        
+                    if   weight > 0 and mask is not None and     weight  <      id/total_layers:
+                        transformer_options['cross_mask'] = None
+                        transformer_options['self_mask']  = None
+                    
+                    elif weight < 0 and mask is not None and abs(weight) < (1 - id/total_layers):
+                        transformer_options['cross_mask'] = None
+                        transformer_options['self_mask']  = None
+                        
+                    elif floor > 0 and mask is not None and       floor  >      id/total_layers:
+                        transformer_options['cross_mask']       = mask_zero      [:,:txt_len]
+                        transformer_options['self_mask']        = mask_zero      [:,txt_len:]
+                        transformer_options['cross_mask_up']    = mask_up_zero   [:,:txt_len]
+                        transformer_options['self_mask_up']     = mask_up_zero   [:,txt_len:]
+                        transformer_options['cross_mask_down']  = mask_down_zero [:,:txt_len]
+                        transformer_options['self_mask_down']   = mask_down_zero [:,txt_len:]
+                        transformer_options['cross_mask_down2'] = mask_down2_zero[:,:txt_len] if mask_down2_zero is not None else None
+                        transformer_options['self_mask_down2']  = mask_down2_zero[:,txt_len:] if mask_down2_zero is not None else None
+                    
+                    elif floor < 0 and mask is not None and   abs(floor) > (1 - id/total_layers):
+                        transformer_options['cross_mask']       = mask_zero      [:,:txt_len]
+                        transformer_options['self_mask']        = mask_zero      [:,txt_len:]
+                        transformer_options['cross_mask_up']    = mask_up_zero   [:,:txt_len]
+                        transformer_options['self_mask_up']     = mask_up_zero   [:,txt_len:]
+                        transformer_options['cross_mask_down']  = mask_down_zero [:,:txt_len]
+                        transformer_options['self_mask_down']   = mask_down_zero [:,txt_len:]
+                        transformer_options['cross_mask_down2'] = mask_down2_zero[:,:txt_len] if mask_down2_zero is not None else None
+                        transformer_options['self_mask_down2']  = mask_down2_zero[:,txt_len:] if mask_down2_zero is not None else None
+
+                    h = forward_timestep_embed(module, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator, style_block=style_block)
+                    if id == 0:
+                        h = StyleMMDiT(h, "proj_in")
+                    h = apply_control(h, control, 'input')
+                    if "input_block_patch" in transformer_patches:
+                        patch = transformer_patches["input_block_patch"]
+                        for p in patch:
+                            h = p(h, transformer_options)
+                    
+                    hs.append(h)
+                    
+                    if "input_block_patch_after_skip" in transformer_patches:
+                        patch = transformer_patches["input_block_patch_after_skip"]
+                        for p in patch:
+                            h = p(h, transformer_options)
+
+                transformer_options["block"] = ("middle", 0)
+                if self.middle_block is not None:
+                    style_block = StyleMMDiT.middle_blocks[0]
+
+                    if mask is not None:
+                        transformer_options['cross_mask']       = mask      [:,:txt_len]
+                        transformer_options['self_mask']        = mask      [:,txt_len:]
+                        transformer_options['cross_mask_up']    = mask_up   [:,:txt_len]
+                        transformer_options['self_mask_up']     = mask_up   [:,txt_len:]
+                        transformer_options['cross_mask_down']  = mask_down [:,:txt_len]
+                        transformer_options['self_mask_down']   = mask_down [:,txt_len:]
+                        transformer_options['cross_mask_down2'] = mask_down2[:,:txt_len] if mask_down2 is not None else None
+                        transformer_options['self_mask_down2']  = mask_down2[:,txt_len:] if mask_down2 is not None else None
+                        
+                    if   weight > 0 and mask is not None and     weight  <      (len(self.input_blocks) + 1)/total_layers:
+                        transformer_options['cross_mask'] = None
+                        transformer_options['self_mask']  = None
+                    
+                    elif weight < 0 and mask is not None and abs(weight) < (1 - (len(self.input_blocks) + 1)/total_layers):
+                        transformer_options['cross_mask'] = None
+                        transformer_options['self_mask']  = None
+                        
+                    elif floor > 0 and mask is not None and       floor  >      (len(self.input_blocks) + 1)/total_layers:
+                        transformer_options['cross_mask']       = mask_zero      [:,:txt_len]
+                        transformer_options['self_mask']        = mask_zero      [:,txt_len:]
+                        transformer_options['cross_mask_up']    = mask_up_zero   [:,:txt_len]
+                        transformer_options['self_mask_up']     = mask_up_zero   [:,txt_len:]
+                        transformer_options['cross_mask_down']  = mask_down_zero [:,:txt_len]
+                        transformer_options['self_mask_down']   = mask_down_zero [:,txt_len:]
+                        transformer_options['cross_mask_down2'] = mask_down2_zero[:,:txt_len] if mask_down2_zero is not None else None
+                        transformer_options['self_mask_down2']  = mask_down2_zero[:,txt_len:] if mask_down2_zero is not None else None
+                    
+                    elif floor < 0 and mask is not None and   abs(floor) > (1 - (len(self.input_blocks) + 1)/total_layers):
+                        transformer_options['cross_mask']       = mask_zero      [:,:txt_len]
+                        transformer_options['self_mask']        = mask_zero      [:,txt_len:]
+                        transformer_options['cross_mask_up']    = mask_up_zero   [:,:txt_len]
+                        transformer_options['self_mask_up']     = mask_up_zero   [:,txt_len:]
+                        transformer_options['cross_mask_down']  = mask_down_zero [:,:txt_len]
+                        transformer_options['self_mask_down']   = mask_down_zero [:,txt_len:]
+                        transformer_options['cross_mask_down2'] = mask_down2_zero[:,:txt_len] if mask_down2_zero is not None else None
+                        transformer_options['self_mask_down2']  = mask_down2_zero[:,txt_len:] if mask_down2_zero is not None else None
+
+                    h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator, style_block=style_block)
+                
+                h = apply_control(h, control, 'middle')
+
+                #for id, module in enumerate(self.output_blocks):
+                for id, (module, style_block) in enumerate(zip(self.output_blocks, StyleMMDiT.output_blocks)):
+                    transformer_options["block"] = ("output", id)
+                    
+                    hsp = hs.pop()
+                    hsp = apply_control(hsp, control, 'output')
+
+                    if "output_block_patch" in transformer_patches:
+                        patch = transformer_patches["output_block_patch"]
+                        for p in patch:
+                            h, hsp = p(h, hsp, transformer_options)
+
+                    h = th.cat([h, hsp], dim=1)
+                    del hsp
+                    if len(hs) > 0:
+                        output_shape = hs[-1].shape
+                    else:
+                        output_shape = None
+                        
+
+                    
+                    if mask is not None:
+                        transformer_options['cross_mask']       = mask      [:,:txt_len]
+                        transformer_options['self_mask']        = mask      [:,txt_len:]
+                        transformer_options['cross_mask_up']    = mask_up   [:,:txt_len]
+                        transformer_options['self_mask_up']     = mask_up   [:,txt_len:]
+                        transformer_options['cross_mask_down']  = mask_down [:,:txt_len]
+                        transformer_options['self_mask_down']   = mask_down [:,txt_len:]
+                        transformer_options['cross_mask_down2'] = mask_down2[:,:txt_len] if mask_down2 is not None else None
+                        transformer_options['self_mask_down2']  = mask_down2[:,txt_len:] if mask_down2 is not None else None
+                        
+                    if   weight > 0 and mask is not None and     weight  <      (len(self.input_blocks) + 1 + id)/total_layers:
+                        transformer_options['cross_mask'] = None
+                        transformer_options['self_mask']  = None
+                    
+                    elif weight < 0 and mask is not None and abs(weight) < (1 - (len(self.input_blocks) + 1 + id)/total_layers):
+                        transformer_options['cross_mask'] = None
+                        transformer_options['self_mask']  = None
+                        
+                    elif floor > 0 and mask is not None and       floor  >      (len(self.input_blocks) + 1 + id)/total_layers:
+                        transformer_options['cross_mask']       = mask_zero      [:,:txt_len]
+                        transformer_options['self_mask']        = mask_zero      [:,txt_len:]
+                        transformer_options['cross_mask_up']    = mask_up_zero   [:,:txt_len]
+                        transformer_options['self_mask_up']     = mask_up_zero   [:,txt_len:]
+                        transformer_options['cross_mask_down']  = mask_down_zero [:,:txt_len]
+                        transformer_options['self_mask_down']   = mask_down_zero [:,txt_len:]
+                        transformer_options['cross_mask_down2'] = mask_down2_zero[:,:txt_len] if mask_down2_zero is not None else None
+                        transformer_options['self_mask_down2']  = mask_down2_zero[:,txt_len:] if mask_down2_zero is not None else None
+                    
+                    elif floor < 0 and mask is not None and   abs(floor) > (1 - (len(self.input_blocks) + 1 + id)/total_layers):
+                        transformer_options['cross_mask']       = mask_zero      [:,:txt_len]
+                        transformer_options['self_mask']        = mask_zero      [:,txt_len:]
+                        transformer_options['cross_mask_up']    = mask_up_zero   [:,:txt_len]
+                        transformer_options['self_mask_up']     = mask_up_zero   [:,txt_len:]
+                        transformer_options['cross_mask_down']  = mask_down_zero [:,:txt_len]
+                        transformer_options['self_mask_down']   = mask_down_zero [:,txt_len:]
+                        transformer_options['cross_mask_down2'] = mask_down2_zero[:,:txt_len] if mask_down2_zero is not None else None
+                        transformer_options['self_mask_down2']  = mask_down2_zero[:,txt_len:] if mask_down2_zero is not None else None
+
+                    h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator, style_block=style_block)
+                
+                h = h.type(x.dtype)
+                
+                if self.predict_codebook_ids:
+                    eps = self.id_predictor(h)
+                else:
+                    eps = self.out(h)
+                    eps = StyleMMDiT(eps, "proj_out")
+                    
+                out_list.append(eps[0:1])
+                
+            eps = torch.stack(out_list, dim=0).squeeze(dim=1)
+
+            if recon_iter == 1:
+                denoised = new_x * ((ISIGMA ** 2 + 1) ** 0.5)  - ISIGMA.to(new_x) * eps.to(new_x)
+                if x_tmp is not None:
+                    eps = (x_tmp * ((SIGMA ** 2 + 1) ** 0.5) - denoised.to(x_tmp)) / SIGMA.to(x_tmp)
+                else:
+                    eps = (x_orig * ((SIGMA ** 2 + 1) ** 0.5) - denoised.to(x_orig)) / SIGMA.to(x_orig)
+            
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+        y0_style_pos        = transformer_options.get("y0_style_pos")
+        y0_style_neg        = transformer_options.get("y0_style_neg")
+
+        y0_style_pos_weight    = transformer_options.get("y0_style_pos_weight", 0.0)
+        y0_style_pos_synweight = transformer_options.get("y0_style_pos_synweight", 0.0)
+        y0_style_pos_synweight *= y0_style_pos_weight
+
+        y0_style_neg_weight    = transformer_options.get("y0_style_neg_weight", 0.0)
+        y0_style_neg_synweight = transformer_options.get("y0_style_neg_synweight", 0.0)
+        y0_style_neg_synweight *= y0_style_neg_weight
+        
+
+        freqsep_lowpass_method = transformer_options.get("freqsep_lowpass_method")
+        freqsep_sigma          = transformer_options.get("freqsep_sigma")
+        freqsep_kernel_size    = transformer_options.get("freqsep_kernel_size")
+        freqsep_inner_kernel_size    = transformer_options.get("freqsep_inner_kernel_size")
+        freqsep_stride    = transformer_options.get("freqsep_stride")
+        
+        freqsep_lowpass_weight = transformer_options.get("freqsep_lowpass_weight")
+        freqsep_highpass_weight= transformer_options.get("freqsep_highpass_weight")
+        freqsep_mask           = transformer_options.get("freqsep_mask")
+        
+
+
+        dtype = eps.dtype if self.style_dtype is None else self.style_dtype
+        h_len //= self.Retrojector.patch_size
+        w_len //= self.Retrojector.patch_size
+        
+        if y0_style_pos is not None:
+            y0_style_pos_weight    = transformer_options.get("y0_style_pos_weight")
+            y0_style_pos_synweight = transformer_options.get("y0_style_pos_synweight")
+            y0_style_pos_synweight *= y0_style_pos_weight
+            y0_style_pos_mask = transformer_options.get("y0_style_pos_mask")
+            y0_style_pos_mask_edge = transformer_options.get("y0_style_pos_mask_edge")
+
+            y0_style_pos = y0_style_pos.to(dtype)
+            #x   = x.to(dtype)
+            x   = x_orig.clone().to(torch.float64) * ((SIGMA ** 2 + 1) ** 0.5)
+            eps = eps.to(dtype)
+            eps_orig = eps.clone()
+            
+            sigma = SIGMA #t_orig[0].to(torch.float32) / 1000
+            denoised = x - sigma * eps
+            
+            denoised_embed = self.Retrojector.embed(denoised)     # 2,4,96,168 -> 2,16128,320
+            y0_adain_embed = self.Retrojector.embed(y0_style_pos)
+            
+            if transformer_options['y0_style_method'] == "scattersort":
+                tile_h, tile_w = transformer_options.get('y0_style_tile_height'), transformer_options.get('y0_style_tile_width')
+                pad = transformer_options.get('y0_style_tile_padding')
+                if pad is not None and tile_h is not None and tile_w is not None:
+                    
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    if EO("scattersort_median_LP"):
+                        denoised_spatial_LP = median_blur_2d(denoised_spatial, kernel_size=EO("scattersort_median_LP",7))
+                        y0_adain_spatial_LP = median_blur_2d(y0_adain_spatial, kernel_size=EO("scattersort_median_LP",7))
+                        
+                        denoised_spatial_HP = denoised_spatial - denoised_spatial_LP
+                        y0_adain_spatial_HP = y0_adain_spatial - y0_adain_spatial_LP
+                        
+                        denoised_spatial_LP = apply_scattersort_tiled(denoised_spatial_LP, y0_adain_spatial_LP, tile_h, tile_w, pad)
+                        
+                        denoised_spatial = denoised_spatial_LP + denoised_spatial_HP
+                        denoised_embed = rearrange(denoised_spatial, "b c h w -> b (h w) c")
+                    else:
+                        denoised_spatial = apply_scattersort_tiled(denoised_spatial, y0_adain_spatial, tile_h, tile_w, pad)
+                    
+                    denoised_embed = rearrange(denoised_spatial, "b c h w -> b (h w) c")
+                    
+                else:
+                    denoised_embed = apply_scattersort_masked(denoised_embed, y0_adain_embed, y0_style_pos_mask, y0_style_pos_mask_edge, h_len, w_len)
+
+
+
+            elif transformer_options['y0_style_method'] == "AdaIN":
+                if freqsep_mask is not None:
+                    freqsep_mask = freqsep_mask.view(1, 1, *freqsep_mask.shape[-2:]).float()
+                    freqsep_mask = F.interpolate(freqsep_mask.float(), size=(h_len, w_len), mode='nearest-exact')
+                
+                if hasattr(self, "adain_tile"):
+                    tile_h, tile_w = self.adain_tile
+                    
+                    denoised_pretile = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_pretile = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    if self.adain_flag:
+                        h_off = tile_h // 2
+                        w_off = tile_w // 2
+                        denoised_pretile = denoised_pretile[:,:,h_off:-h_off, w_off:-w_off]
+                        self.adain_flag = False
+                    else:
+                        h_off = 0
+                        w_off = 0
+                        self.adain_flag = True
+                    
+                    tiles,    orig_shape, grid, strides = tile_latent(denoised_pretile, tile_size=(tile_h,tile_w))
+                    y0_tiles, orig_shape, grid, strides = tile_latent(y0_adain_pretile, tile_size=(tile_h,tile_w))
+                    
+                    tiles_out = []
+                    for i in range(tiles.shape[0]):
+                        tile = tiles[i].unsqueeze(0)
+                        y0_tile = y0_tiles[i].unsqueeze(0)
+                        
+                        tile    = rearrange(tile,    "b c h w -> b (h w) c", h=tile_h, w=tile_w)
+                        y0_tile = rearrange(y0_tile, "b c h w -> b (h w) c", h=tile_h, w=tile_w)
+                        
+                        tile = adain_seq_inplace(tile, y0_tile)
+                        tiles_out.append(rearrange(tile, "b (h w) c -> b c h w", h=tile_h, w=tile_w))
+                    
+                    tiles_out_tensor = torch.cat(tiles_out, dim=0)
+                    tiles_out_tensor = untile_latent(tiles_out_tensor, orig_shape, grid, strides)
+
+                    if h_off == 0:
+                        denoised_pretile = tiles_out_tensor
+                    else:
+                        denoised_pretile[:,:,h_off:-h_off, w_off:-w_off] = tiles_out_tensor
+                    denoised_embed = rearrange(denoised_pretile, "b c h w -> b (h w) c", h=h_len, w=w_len)
+
+                elif freqsep_lowpass_method is not None and freqsep_lowpass_method.endswith("pw"): #EO("adain_pw"):
+
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+
+                    if   freqsep_lowpass_method == "median_pw":
+                        denoised_spatial_new = adain_patchwise_row_batch_med(denoised_spatial.clone(), y0_adain_spatial.clone().repeat(denoised_spatial.shape[0],1,1,1), sigma=freqsep_sigma, kernel_size=freqsep_kernel_size, use_median_blur=True, lowpass_weight=freqsep_lowpass_weight, highpass_weight=freqsep_highpass_weight)
+                    elif freqsep_lowpass_method == "gaussian_pw": 
+                        denoised_spatial_new = adain_patchwise_row_batch(denoised_spatial.clone(), y0_adain_spatial.clone().repeat(denoised_spatial.shape[0],1,1,1), sigma=freqsep_sigma, kernel_size=freqsep_kernel_size)
+                    
+                    denoised_embed = rearrange(denoised_spatial_new, "b c h w -> b (h w) c", h=h_len, w=w_len)
+
+                elif freqsep_lowpass_method is not None: 
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    if   freqsep_lowpass_method == "median":
+                        denoised_spatial_LP = median_blur_2d(denoised_spatial, kernel_size=freqsep_kernel_size)
+                        y0_adain_spatial_LP = median_blur_2d(y0_adain_spatial, kernel_size=freqsep_kernel_size)
+                    elif freqsep_lowpass_method == "gaussian":
+                        denoised_spatial_LP = gaussian_blur_2d(denoised_spatial, sigma=freqsep_sigma, kernel_size=freqsep_kernel_size)
+                        y0_adain_spatial_LP = gaussian_blur_2d(y0_adain_spatial, sigma=freqsep_sigma, kernel_size=freqsep_kernel_size)
+                    
+                    denoised_spatial_HP = denoised_spatial - denoised_spatial_LP
+                    
+                    if EO("adain_fs_uhp"):
+                        y0_adain_spatial_HP = y0_adain_spatial - y0_adain_spatial_LP
+                        
+                        denoised_spatial_ULP = gaussian_blur_2d(denoised_spatial, sigma=EO("adain_fs_uhp_sigma", 1.0), kernel_size=EO("adain_fs_uhp_kernel_size", 3))
+                        y0_adain_spatial_ULP = gaussian_blur_2d(y0_adain_spatial, sigma=EO("adain_fs_uhp_sigma", 1.0), kernel_size=EO("adain_fs_uhp_kernel_size", 3))
+                        
+                        denoised_spatial_UHP = denoised_spatial_HP  - denoised_spatial_ULP
+                        y0_adain_spatial_UHP = y0_adain_spatial_HP  - y0_adain_spatial_ULP
+                        
+                        #denoised_spatial_HP  = y0_adain_spatial_ULP + denoised_spatial_UHP
+                        denoised_spatial_HP  = denoised_spatial_ULP + y0_adain_spatial_UHP
+                    
+                    denoised_spatial_new = freqsep_lowpass_weight * y0_adain_spatial_LP + freqsep_highpass_weight * denoised_spatial_HP
+                    denoised_embed = rearrange(denoised_spatial_new, "b c h w -> b (h w) c", h=h_len, w=w_len)
+
+                else:
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    
+                for adain_iter in range(EO("style_iter", 0)):
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    denoised_embed = self.Retrojector.embed(self.Retrojector.unembed(denoised_embed))
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    
+            elif transformer_options['y0_style_method'] == "WCT":
+                self.StyleWCT.set(y0_adain_embed)
+                denoised_embed = self.StyleWCT.get(denoised_embed)
+                
+                if transformer_options.get('y0_standard_guide') is not None:
+                    y0_standard_guide = transformer_options.get('y0_standard_guide')
+                    
+                    y0_standard_guide_embed = self.Retrojector.embed(y0_standard_guide)
+                    f_cs = self.StyleWCT.get(y0_standard_guide_embed)
+                    self.y0_standard_guide = self.Retrojector.unembed(f_cs)
+
+                if transformer_options.get('y0_inv_standard_guide') is not None:
+                    y0_inv_standard_guide = transformer_options.get('y0_inv_standard_guide')
+
+                    y0_inv_standard_guide_embed = self.Retrojector.embed(y0_inv_standard_guide)
+                    f_cs = self.StyleWCT.get(y0_inv_standard_guide_embed)
+                    self.y0_inv_standard_guide = self.Retrojector.unembed(f_cs)
+
+            denoised_approx = self.Retrojector.unembed(denoised_embed)
+            
+            eps = (x - denoised_approx) / sigma
+
+            if not UNCOND:
+                if eps.shape[0] == 2:
+                    eps[1] = eps_orig[1] + y0_style_pos_weight * (eps[1] - eps_orig[1])
+                    eps[0] = eps_orig[0] + y0_style_pos_synweight * (eps[0] - eps_orig[0])
+                else:
+                    eps[0] = eps_orig[0] + y0_style_pos_weight * (eps[0] - eps_orig[0])
+            elif eps.shape[0] == 1 and UNCOND:
+                eps[0] = eps_orig[0] + y0_style_pos_synweight * (eps[0] - eps_orig[0])
+            
+            eps = eps.float()
+        
+        if y0_style_neg is not None:
+            y0_style_neg_weight    = transformer_options.get("y0_style_neg_weight")
+            y0_style_neg_synweight = transformer_options.get("y0_style_neg_synweight")
+            y0_style_neg_synweight *= y0_style_neg_weight
+            y0_style_neg_mask = transformer_options.get("y0_style_neg_mask")
+            y0_style_neg_mask_edge = transformer_options.get("y0_style_neg_mask_edge")
+            
+            y0_style_neg = y0_style_neg.to(dtype)
+            #x   = x.to(dtype)
+            x   = x_orig.clone().to(torch.float64) * ((SIGMA ** 2 + 1) ** 0.5)
+            eps = eps.to(dtype)
+            eps_orig = eps.clone()
+            
+            sigma = SIGMA #t_orig[0].to(torch.float32) / 1000
+            denoised = x - sigma * eps
+
+            denoised_embed = self.Retrojector.embed(denoised)
+            y0_adain_embed = self.Retrojector.embed(y0_style_neg)
+            
+            if transformer_options['y0_style_method'] == "scattersort":
+                tile_h, tile_w = transformer_options.get('y0_style_tile_height'), transformer_options.get('y0_style_tile_width')
+                pad = transformer_options.get('y0_style_tile_padding')
+                if pad is not None and tile_h is not None and tile_w is not None:
+                    
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    denoised_spatial = apply_scattersort_tiled(denoised_spatial, y0_adain_spatial, tile_h, tile_w, pad)
+                    
+                    denoised_embed = rearrange(denoised_spatial, "b c h w -> b (h w) c")
+
+                else:
+                    denoised_embed = apply_scattersort_masked(denoised_embed, y0_adain_embed, y0_style_neg_mask, y0_style_neg_mask_edge, h_len, w_len)
+            
+            
+            elif transformer_options['y0_style_method'] == "AdaIN":
+                denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                for adain_iter in range(EO("style_iter", 0)):
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    denoised_embed = self.Retrojector.embed(self.Retrojector.unembed(denoised_embed))
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    
+            elif transformer_options['y0_style_method'] == "WCT":
+                self.StyleWCT.set(y0_adain_embed)
+                denoised_embed = self.StyleWCT.get(denoised_embed)
+
+            denoised_approx = self.Retrojector.unembed(denoised_embed)
+
+            if UNCOND:
+                eps = (x - denoised_approx) / sigma
+                eps[0] = eps_orig[0] + y0_style_neg_weight * (eps[0] - eps_orig[0])
+                if eps.shape[0] == 2:
+                    eps[1] = eps_orig[1] + y0_style_neg_synweight * (eps[1] - eps_orig[1])
+            elif eps.shape[0] == 1 and not UNCOND:
+                eps[0] = eps_orig[0] + y0_style_neg_synweight * (eps[0] - eps_orig[0])
+            
+            eps = eps.float()
+            
+        return eps
+
+
+
+
+
+
+
+
+
+def clone_inputs_unsafe(*args, index: int=None):
+
+    if index is None:
+        return tuple(x.clone() for x in args)
+    else:
+        return tuple(x[index].unsqueeze(0).clone() for x in args)
+    
+    
+def clone_inputs(*args, index: int = None):
+    if index is None:
+        return tuple(x.clone() if x is not None else None for x in args)
+    else:
+        return tuple(x[index].unsqueeze(0).clone() if x is not None else None for x in args)
+    
+    
+
diff --git a/custom_nodes/RES4LYF/sd35/mmdit.py b/custom_nodes/RES4LYF/sd35/mmdit.py
new file mode 100644
index 0000000000000000000000000000000000000000..24d3edd49ae815698fc6f1d9016a9e06505d2634
--- /dev/null
+++ b/custom_nodes/RES4LYF/sd35/mmdit.py
@@ -0,0 +1,1739 @@
+from functools import partial
+from typing import Dict, Optional, List
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import copy
+
+from comfy.ldm.modules.attention import optimized_attention
+from comfy.ldm.modules.attention import attention_pytorch #as optimized_attention
+from einops import rearrange, repeat
+from comfy.ldm.modules.diffusionmodules.util import timestep_embedding
+import comfy.ops
+import comfy.ldm.common_dit
+
+from ..helper import ExtraOptions
+
+from ..latents import tile_latent, untile_latent, gaussian_blur_2d, median_blur_2d
+from ..style_transfer import apply_scattersort_masked, apply_scattersort_tiled, adain_seq_inplace, adain_patchwise_row_batch_med, adain_patchwise_row_batch
+
+#from .attention import optimized_attention
+#from .util import timestep_embedding
+#import ops
+#import common_dit
+
+
+def default(x, y):
+    if x is not None:
+        return x
+    return y
+
+class Mlp(nn.Module):
+    """ MLP as used in Vision Transformer, MLP-Mixer and related networks
+    """
+    def __init__(
+            self,
+            in_features,
+            hidden_features = None,
+            out_features    = None,
+            act_layer       = nn.GELU,
+            norm_layer      = None,
+            bias            = True,
+            drop            = 0.,
+            use_conv        = False,
+            dtype           = None,
+            device          = None,
+            operations      = None,
+    ):
+        super().__init__()
+        out_features    = out_features or in_features
+        hidden_features = hidden_features or in_features
+        drop_probs      = drop
+        linear_layer    = partial(operations.Conv2d, kernel_size=1) if use_conv else operations.Linear
+
+        self.fc1        = linear_layer(in_features, hidden_features, bias =bias, dtype=dtype, device=device)
+        self.act        = act_layer()
+        self.drop1      = nn.Dropout(drop_probs)
+        self.norm       = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
+        self.fc2        = linear_layer(hidden_features, out_features, bias=bias, dtype=dtype, device=device)
+        self.drop2      = nn.Dropout(drop_probs)
+
+    def forward(self, x):
+        x = self.fc1  (x)
+        x = self.act  (x)
+        x = self.drop1(x)
+        x = self.norm (x)
+        x = self.fc2  (x)
+        x = self.drop2(x)
+        return x
+
+class PatchEmbed(nn.Module):
+    """ 2D Image to Patch Embedding
+    """
+    dynamic_img_pad: torch.jit.Final[bool]
+
+    def __init__(
+            self,
+            img_size        : Optional[int] = 224,
+            patch_size      : int           = 16,
+            in_chans        : int           = 3,
+            embed_dim       : int           = 768,
+            norm_layer                      = None,
+            flatten         : bool          = True,
+            bias            : bool          = True,
+            strict_img_size : bool          = True,
+            dynamic_img_pad : bool          = True,
+            padding_mode                    ='circular',
+            conv3d                          = False,
+            dtype                           = None,
+            device                          = None,
+            operations                      = None,
+    ):
+        super().__init__()
+        try:
+            len(patch_size)
+            self.patch_size = patch_size
+        except:
+            if conv3d:
+                self.patch_size = (patch_size, patch_size, patch_size)
+            else:
+                self.patch_size = (patch_size, patch_size)
+        self.padding_mode = padding_mode
+
+        # flatten spatial dim and transpose to channels last, kept for bwd compat
+        self.flatten         = flatten
+        self.strict_img_size = strict_img_size
+        self.dynamic_img_pad = dynamic_img_pad
+        if conv3d:
+            self.proj = operations.Conv3d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device)
+        else:
+            self.proj = operations.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device)
+        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
+
+    def forward(self, x):
+        if self.dynamic_img_pad:
+            x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size, padding_mode=self.padding_mode)
+        x = self.proj(x)
+        if self.flatten:
+            x = x.flatten(2).transpose(1, 2)  # NCHW -> NLC
+        x = self.norm(x)
+        return x
+
+def modulate(x, shift, scale):
+    if shift is None:
+        shift = torch.zeros_like(scale)
+    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+
+#################################################################################
+#                   Sine/Cosine Positional Embedding Functions                  #
+#################################################################################
+
+
+def get_2d_sincos_pos_embed(
+    embed_dim,
+    grid_size,
+    cls_token      = False,
+    extra_tokens   = 0,
+    scaling_factor = None,
+    offset         = None,
+):
+    """
+    grid_size: int of the grid height and width
+    return:
+    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
+    """
+    grid_h = np.arange(grid_size, dtype=np.float32)
+    grid_w = np.arange(grid_size, dtype=np.float32)
+    grid   = np.meshgrid(grid_w, grid_h)  # here w goes first
+    grid   = np.stack(grid, axis=0)
+    if scaling_factor is not None:
+        grid = grid / scaling_factor
+    if offset is not None:
+        grid = grid - offset
+
+    grid = grid.reshape([2, 1, grid_size, grid_size])
+    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
+    if cls_token and extra_tokens > 0:
+        pos_embed = np.concatenate(
+            [np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0
+        )
+    return pos_embed
+
+
+def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
+    assert embed_dim % 2 == 0
+
+    # use half of dimensions to encode grid_h
+    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
+    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)
+
+    emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
+    return emb
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+    """
+    embed_dim: output dimension for each position
+    pos: a list of positions to be encoded: size (M,)
+    out: (M, D)
+    """
+    assert embed_dim % 2 == 0
+    omega  = np.arange(embed_dim // 2, dtype=np.float64)
+    omega /= embed_dim / 2.0
+    omega  = 1.0 / 10000**omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = np.einsum("m,d->md", pos, omega)  # (M, D/2), outer product
+
+    emb_sin = np.sin(out)  # (M, D/2)
+    emb_cos = np.cos(out)  # (M, D/2)
+
+    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+    return emb
+
+def get_1d_sincos_pos_embed_from_grid_torch(embed_dim, pos, device=None, dtype=torch.float32):
+    omega   = torch.arange(embed_dim // 2, device=device, dtype=dtype)
+    omega  /= embed_dim / 2.0
+    omega   = 1.0 / 10000**omega  # (D/2,)
+    pos     = pos.reshape(-1)  # (M,)
+    out     = torch.einsum("m,d->md", pos, omega)  # (M, D/2), outer product
+    emb_sin = torch.sin(out)  # (M, D/2)
+    emb_cos = torch.cos(out)  # (M, D/2)
+    emb     = torch.cat([emb_sin, emb_cos], dim=1)  # (M, D)
+    return emb
+
+def get_2d_sincos_pos_embed_torch(embed_dim, w, h, val_center=7.5, val_magnitude=7.5, device=None, dtype=torch.float32):
+    small          = min(h, w)
+    val_h          = (h / small) * val_magnitude
+    val_w          = (w / small) * val_magnitude
+    grid_h, grid_w = torch.meshgrid(torch.linspace(-val_h + val_center, val_h + val_center, h, device=device, dtype=dtype), torch.linspace(-val_w + val_center, val_w + val_center, w, device=device, dtype=dtype), indexing='ij')
+    emb_h          = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype)
+    emb_w          = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype)
+    emb            = torch.cat([emb_w, emb_h], dim=1)  # (H*W, D)
+    return emb
+
+
+#################################################################################
+#               Embedding Layers for Timesteps and Class Labels                 #
+#################################################################################
+
+
+class TimestepEmbedder(nn.Module):
+    """
+    Embeds scalar timesteps into vector representations.
+    """
+
+    def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(hidden_size,              hidden_size, bias=True, dtype=dtype, device=device),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+
+    def forward(self, t, dtype, **kwargs):
+        t_freq = timestep_embedding(t, self.frequency_embedding_size).to(dtype)
+        t_emb = self.mlp(t_freq)
+        return t_emb
+
+
+class VectorEmbedder(nn.Module):
+    """
+    Embeds a flat vector of dimension input_dim
+    """
+
+    def __init__(self, input_dim: int, hidden_size: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            operations.Linear(input_dim,   hidden_size, bias=True, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        emb = self.mlp(x)
+        return emb
+
+
+#################################################################################
+#                                 Core DiT Model                                #
+#################################################################################
+
+
+def split_qkv(qkv, head_dim):
+    qkv = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, -1, head_dim).movedim(2, 0)
+    return qkv[0], qkv[1], qkv[2]
+
+
+class SelfAttention(nn.Module):
+    ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug")
+
+    def __init__(
+        self,
+        dim       : int,
+        num_heads : int             = 8,
+        qkv_bias  : bool            = False,
+        qk_scale  : Optional[float] = None,
+        proj_drop : float           = 0.0,
+        attn_mode : str             = "xformers",
+        pre_only  : bool            = False,
+        qk_norm   : Optional[str]   = None,
+        rmsnorm   : bool            = False,
+        dtype                       = None,
+        device                      = None,
+        operations                  = None,
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim  = dim // num_heads
+
+        self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
+        if not pre_only:
+            self.proj      = operations.Linear(dim, dim, dtype=dtype, device=device)
+            self.proj_drop = nn.Dropout(proj_drop)
+        assert attn_mode in self.ATTENTION_MODES
+        self.attn_mode = attn_mode
+        self.pre_only  = pre_only
+
+        if qk_norm == "rms":
+            self.ln_q =              RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
+            self.ln_k =              RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
+        elif qk_norm == "ln":
+            self.ln_q = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
+            self.ln_k = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
+        elif qk_norm is None:
+            self.ln_q = nn.Identity()
+            self.ln_k = nn.Identity()
+        else:
+            raise ValueError(qk_norm)
+
+    def pre_attention(self, x: torch.Tensor) -> torch.Tensor:
+        B, L, C = x.shape
+        qkv     = self.qkv(x)
+        q, k, v = split_qkv(qkv, self.head_dim)
+        q       = self.ln_q(q).reshape(q.shape[0], q.shape[1], -1)
+        k       = self.ln_k(k).reshape(q.shape[0], q.shape[1], -1)
+        return (q, k, v)
+
+    def post_attention(self, x: torch.Tensor) -> torch.Tensor:
+        assert not self.pre_only
+        x = self.proj     (x)
+        x = self.proj_drop(x)
+        return x
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        q, k, v = self.pre_attention(x)
+        x       = optimized_attention(
+            q, k, v, heads=self.num_heads
+        )
+        x       = self.post_attention(x)
+        return x
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(
+        self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None
+    ):
+        """
+        Initialize the RMSNorm normalization layer.
+        Args:
+            dim (int): The dimension of the input tensor.
+            eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
+        Attributes:
+            eps (float): A small value added to the denominator for numerical stability.
+            weight (nn.Parameter): Learnable scaling parameter.
+        """
+        super().__init__()
+        self.eps             = eps
+        self.learnable_scale = elementwise_affine
+        if self.learnable_scale:
+            self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype))
+        else:
+            self.register_parameter("weight", None)
+
+    def forward(self, x):
+        return comfy.ldm.common_dit.rms_norm(x, self.weight, self.eps)
+
+
+
+class SwiGLUFeedForward(nn.Module):
+    def __init__(
+        self,
+        dim                : int,
+        hidden_dim         : int,
+        multiple_of        : int,
+        ffn_dim_multiplier : Optional[float] = None,
+    ):
+        """
+        Initialize the FeedForward module.
+
+        Args:
+            dim (int): Input dimension.
+            hidden_dim (int): Hidden dimension of the feedforward layer.
+            multiple_of (int): Value to ensure hidden dimension is a multiple of this value.
+            ffn_dim_multiplier (float, optional): Custom multiplier for hidden dimension. Defaults to None.
+
+        Attributes:
+            w1 (ColumnParallelLinear): Linear transformation for the first layer.
+            w2 (RowParallelLinear): Linear transformation for the second layer.
+            w3 (ColumnParallelLinear): Linear transformation for the third layer.
+
+        """
+        super().__init__()
+        hidden_dim     = int(2 * hidden_dim / 3)
+        # custom dim factor multiplier
+        if ffn_dim_multiplier is not None:
+            hidden_dim = int(ffn_dim_multiplier * hidden_dim)
+        hidden_dim     = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+
+        self.w1 = nn.Linear(dim, hidden_dim, bias=False)
+        self.w2 = nn.Linear(hidden_dim, dim, bias=False)
+        self.w3 = nn.Linear(dim, hidden_dim, bias=False)
+
+    def forward(self, x):
+        return self.w2(nn.functional.silu(self.w1(x)) * self.w3(x))
+
+
+class DismantledBlock(nn.Module):
+    """
+    A DiT block with gated adaptive layer norm (adaLN) conditioning.
+    """
+
+    ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug")
+
+    def __init__(
+        self,
+        hidden_size       : int,
+        num_heads         : int,
+        mlp_ratio         : float         = 4.0,
+        attn_mode         : str           = "xformers",
+        qkv_bias          : bool          = False,
+        pre_only          : bool          = False,
+        rmsnorm           : bool          = False,
+        scale_mod_only    : bool          = False,
+        swiglu            : bool          = False,
+        qk_norm           : Optional[str] = None,
+        x_block_self_attn : bool          = False,
+        dtype                             = None,
+        device                            = None,
+        operations                        = None,
+        **block_kwargs,
+    ):
+        super().__init__()
+        assert attn_mode in self.ATTENTION_MODES
+        if not rmsnorm:
+            self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        else:
+            self.norm1 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.attn = SelfAttention(
+            dim        = hidden_size,
+            num_heads  = num_heads,
+            qkv_bias   = qkv_bias,
+            attn_mode  = attn_mode,
+            pre_only   = pre_only,
+            qk_norm    = qk_norm,
+            rmsnorm    = rmsnorm,
+            dtype      = dtype,
+            device     = device,
+            operations = operations
+        )
+        if x_block_self_attn:
+            assert not pre_only
+            assert not scale_mod_only
+            self.x_block_self_attn = True
+            self.attn2 = SelfAttention(
+                dim        = hidden_size,
+                num_heads  = num_heads,
+                qkv_bias   = qkv_bias,
+                attn_mode  = attn_mode,
+                pre_only   = False,
+                qk_norm    = qk_norm,
+                rmsnorm    = rmsnorm,
+                dtype      = dtype,
+                device     = device,
+                operations = operations
+            )
+        else:
+            self.x_block_self_attn = False
+        if not pre_only:
+            if not rmsnorm:
+                self.norm2 = operations.LayerNorm(
+                    hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device
+                )
+            else:
+                self.norm2 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        if not pre_only:
+            if not swiglu:
+                self.mlp = Mlp(
+                    in_features     = hidden_size,
+                    hidden_features = mlp_hidden_dim,
+                    act_layer       = lambda: nn.GELU(approximate = "tanh"),
+                    drop            = 0,
+                    dtype           = dtype,
+                    device          = device,
+                    operations      = operations
+                )
+            else:
+                self.mlp = SwiGLUFeedForward(
+                    dim         = hidden_size,
+                    hidden_dim  = mlp_hidden_dim,
+                    multiple_of = 256,
+                )
+        self.scale_mod_only = scale_mod_only
+        if x_block_self_attn:
+            assert not pre_only
+            assert not scale_mod_only
+            n_mods = 9
+        elif not scale_mod_only:
+            n_mods = 6 if not pre_only else 2
+        else:
+            n_mods = 4 if not pre_only else 1
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(), operations.Linear(hidden_size, n_mods * hidden_size, bias=True, dtype=dtype, device=device)
+        )
+        self.pre_only = pre_only
+
+    def pre_attention(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
+        if not self.pre_only:
+            if not self.scale_mod_only:
+                (
+                    shift_msa,
+                    scale_msa,
+                    gate_msa,
+                    shift_mlp,
+                    scale_mlp,
+                    gate_mlp,
+                ) = self.adaLN_modulation(c).chunk(6, dim=1)
+            else:
+                shift_msa = None
+                shift_mlp = None
+                (
+                    scale_msa,
+                    gate_msa,
+                    scale_mlp,
+                    gate_mlp,
+                ) = self.adaLN_modulation(
+                    c
+                ).chunk(4, dim=1)
+            qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa))
+            return qkv, (
+                x,
+                gate_msa,
+                shift_mlp,
+                scale_mlp,
+                gate_mlp,
+            )
+        else:
+            if not self.scale_mod_only:
+                (
+                    shift_msa,
+                    scale_msa,
+                ) = self.adaLN_modulation(
+                    c
+                ).chunk(2, dim=1)
+            else:
+                shift_msa = None
+                scale_msa = self.adaLN_modulation(c)
+            qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa))
+            return qkv, None
+
+    def post_attention(self, attn, x, gate_msa, shift_mlp, scale_mlp, gate_mlp):
+        assert not self.pre_only
+        x = x + gate_msa.unsqueeze(1) * self.attn.post_attention(attn)
+        x = x + gate_mlp.unsqueeze(1) * self.mlp(
+            modulate(self.norm2(x), shift_mlp, scale_mlp)
+        )
+        return x
+
+    def pre_attention_x(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
+        assert self.x_block_self_attn
+        (
+            shift_msa,
+            scale_msa,
+            gate_msa,
+            shift_mlp,
+            scale_mlp,
+            gate_mlp,
+            shift_msa2,
+            scale_msa2,
+            gate_msa2,
+        ) = self.adaLN_modulation(c).chunk(9, dim=1)
+        x_norm = self.norm1(x)
+        qkv  = self.attn .pre_attention(modulate(x_norm, shift_msa,  scale_msa ))
+        qkv2 = self.attn2.pre_attention(modulate(x_norm, shift_msa2, scale_msa2))
+        return qkv, qkv2, (
+            x,
+            gate_msa,
+            shift_mlp,
+            scale_mlp,
+            gate_mlp,
+            gate_msa2,
+        )
+
+    def post_attention_x(self, attn, attn2, x, gate_msa, shift_mlp, scale_mlp, gate_mlp, gate_msa2):
+        assert not self.pre_only
+        attn1 = self.attn .post_attention(attn)
+        attn2 = self.attn2.post_attention(attn2)
+        out1  = gate_msa .unsqueeze(1)  * attn1
+        out2  = gate_msa2.unsqueeze(1)  * attn2
+        x     = x + out1
+        x     = x + out2
+        x     = x + gate_mlp.unsqueeze(1) * self.mlp(
+            modulate(self.norm2(x), shift_mlp, scale_mlp)
+        )
+        return x
+
+    def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
+        assert not self.pre_only
+        if self.x_block_self_attn:
+            qkv, qkv2, intermediates = self.pre_attention_x(x, c)
+            attn, _ = optimized_attention(
+                qkv[0], qkv[1], qkv[2],
+                num_heads=self.attn.num_heads,
+            )
+            attn2, _ = optimized_attention(
+                qkv2[0], qkv2[1], qkv2[2],
+                num_heads=self.attn2.num_heads,
+            )
+            return self.post_attention_x(attn, attn2, *intermediates)
+        else:
+            qkv,       intermediates = self.pre_attention  (x, c)
+            attn = optimized_attention(
+                qkv[0], qkv[1], qkv[2],
+                heads=self.attn.num_heads,
+            )
+            return self.post_attention(attn, *intermediates)
+
+
+def block_mixing(*args, use_checkpoint=True, **kwargs):
+    if use_checkpoint:
+        return torch.utils.checkpoint.checkpoint(
+            _block_mixing, *args, use_reentrant=False, **kwargs
+        )
+    else:
+        return _block_mixing(*args, **kwargs)
+
+# context_qkv = Tuple[Tensor,Tensor,Tensor] 2,154,1536 2,154,1536 2,154,24,64             x_qkv 2,4096,1536, ..., 2,4096,24,64
+def _block_mixing(context, x, context_block, x_block, c, mask=None):
+    context_qkv, context_intermediates = context_block.pre_attention(context, c)
+
+    if x_block.x_block_self_attn:  # x_qkv2 = self-attn?
+        x_qkv, x_qkv2, x_intermediates = x_block.pre_attention_x(x, c)
+    else:
+        x_qkv,         x_intermediates = x_block.pre_attention  (x, c)
+
+    o = []
+    for t in range(3):
+        o.append(torch.cat((context_qkv[t], x_qkv[t]), dim=1))
+    qkv = tuple(o)
+
+    if mask is not None:
+        attn = attention_pytorch(      #1,4186,1536    
+            qkv[0], qkv[1], qkv[2],
+            heads = x_block.attn.num_heads,
+            mask  = mask #> 0 if mask is not None else None,
+        )
+    else:
+        attn = optimized_attention(      #1,4186,1536    
+            qkv[0], qkv[1], qkv[2],
+            heads = x_block.attn.num_heads,
+            mask  = None #> 0 if mask is not None else None,
+        )
+    
+    context_attn, x_attn = (
+        attn[:, : context_qkv[0].shape[1]   ],
+        attn[:,   context_qkv[0].shape[1] : ],
+    )
+
+    if not context_block.pre_only:
+        context = context_block.post_attention(context_attn, *context_intermediates)
+
+    else:
+        context = None
+    if x_block.x_block_self_attn:
+        attn2 = optimized_attention(      # x_qkv2  2,4096,1536
+                x_qkv2[0], x_qkv2[1], x_qkv2[2],
+                heads = x_block.attn2.num_heads,
+            )
+        x = x_block.post_attention_x(x_attn, attn2, *x_intermediates)
+    else:
+        x = x_block.post_attention  (x_attn, *x_intermediates)
+    return context, x
+
+
+class ReJointBlock(nn.Module):
+    """just a small wrapper to serve as a fsdp unit"""
+
+    def __init__(
+        self,
+        *args,
+        **kwargs,
+    ):
+        super().__init__()
+        pre_only           = kwargs.pop("pre_only")
+        qk_norm            = kwargs.pop("qk_norm",           None )
+        x_block_self_attn  = kwargs.pop("x_block_self_attn", False)
+        self.context_block = DismantledBlock(*args, pre_only=pre_only, qk_norm=qk_norm,                                      **kwargs)
+        self.x_block       = DismantledBlock(*args, pre_only=False,    qk_norm=qk_norm, x_block_self_attn=x_block_self_attn, **kwargs)
+
+    def forward(self, *args, **kwargs):  # context_block, x_block are DismantledBlock
+        return block_mixing(                      # args = Tuple[Tensor,Tensor]   2,154,1536   2,4096,1536
+            *args, context_block=self.context_block, x_block=self.x_block, **kwargs
+        )
+
+
+class FinalLayer(nn.Module):
+    """
+    The final layer of DiT.
+    """
+
+    def __init__(
+        self,
+        hidden_size        : int,
+        patch_size         : int,
+        out_channels       : int,
+        total_out_channels : Optional[int] = None,
+        dtype                              = None,
+        device                             = None,
+        operations                         = None,
+    ):
+        super().__init__()
+        self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.linear = (
+            operations.Linear(hidden_size, patch_size * patch_size * out_channels,   bias=True, dtype=dtype, device=device)
+            if (total_out_channels is None)
+            else operations.Linear(hidden_size, total_out_channels,                  bias=True, dtype=dtype, device=device)
+        )
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size,               bias=True, dtype=dtype, device=device)
+        )
+
+    def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
+        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
+        x            = modulate(self.norm_final(x), shift, scale)
+        x            = self.linear(x)
+        return x
+
+class SelfAttentionContext(nn.Module):
+    def __init__(self, dim, heads=8, dim_head=64, dtype=None, device=None, operations=None):
+        super().__init__()
+        dim_head      = dim // heads
+        inner_dim     = dim
+
+        self.heads    = heads
+        self.dim_head = dim_head
+
+        self.qkv      = operations.Linear(dim, dim * 3, bias=True, dtype=dtype, device=device)
+
+        self.proj     = operations.Linear(inner_dim, dim,          dtype=dtype, device=device)
+
+    def forward(self, x):
+        qkv     = self.qkv(x)
+        q, k, v = split_qkv(qkv, self.dim_head)
+        x       = optimized_attention(q.reshape(q.shape[0], q.shape[1], -1), k, v, heads=self.heads)
+        return self.proj(x)
+
+class ContextProcessorBlock(nn.Module):
+    def __init__(self, context_size, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.norm1 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6,                                                   dtype=dtype, device=device)
+        self.attn  = SelfAttentionContext(context_size,                                                                                       dtype=dtype, device=device, operations=operations)
+        self.norm2 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6,                                                   dtype=dtype, device=device)
+        self.mlp   = Mlp(in_features=context_size, hidden_features=(context_size * 4), act_layer=lambda: nn.GELU(approximate="tanh"), drop=0, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, x):
+        x += self.attn(self.norm1(x))
+        x += self.mlp (self.norm2(x))
+        return x
+
+class ContextProcessor(nn.Module):
+    def __init__(self, context_size, num_layers, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.layers = torch.nn.ModuleList([ContextProcessorBlock(context_size,                                     dtype=dtype, device=device, operations=operations) for i in range(num_layers)])
+        self.norm   =                       operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+
+    def forward(self, x):
+        for i, l in enumerate(self.layers):
+            x = l(x)
+        return self.norm(x)
+
+class MMDiT(nn.Module):
+    """
+    Diffusion model with a Transformer backbone.
+    """
+
+    def __init__(
+        self,
+        input_size               :  int                 = 32,
+        patch_size               :  int                 = 2,
+        in_channels              :  int                 = 4,
+        depth                    :  int                 = 28,
+        # hidden_size            :  Optional[int]       = None,
+        # num_heads              :  Optional[int]       = None,
+        mlp_ratio                :  float               = 4.0,
+        learn_sigma              :  bool                = False,
+        adm_in_channels          :  Optional[int]       = None,
+        context_embedder_config  :  Optional[Dict]      = None,
+        compile_core             :  bool                = False,
+        use_checkpoint           :  bool                = False,
+        register_length          :  int                 = 0,
+        attn_mode                :  str                 = "torch",
+        rmsnorm                  :  bool                = False,
+        scale_mod_only           :  bool                = False,
+        swiglu                   :  bool                = False,
+        out_channels             :  Optional[int]       = None,
+        pos_embed_scaling_factor :  Optional[float]     = None,
+        pos_embed_offset         :  Optional[float]     = None,
+        pos_embed_max_size       :  Optional[int]       = None,
+        num_patches                                     = None,
+        qk_norm                  :  Optional[str]       = None,
+        qkv_bias                 :  bool                = True,
+        context_processor_layers                        = None,
+        x_block_self_attn        :  bool                = False,
+        x_block_self_attn_layers :  Optional[List[int]] = [],
+        context_size                                    = 4096,
+        num_blocks                                      = None,
+        final_layer                                     = True,
+        skip_blocks                                     = False,
+        dtype                                           = None, #TODO
+        device                                          = None,
+        operations                                      = None,
+    ):
+        super().__init__()
+        self.dtype                    = dtype
+        self.learn_sigma              = learn_sigma
+        self.in_channels              = in_channels
+        default_out_channels          = in_channels * 2 if learn_sigma else in_channels
+        self.out_channels             = default(out_channels, default_out_channels)
+        self.patch_size               = patch_size
+        self.pos_embed_scaling_factor = pos_embed_scaling_factor
+        self.pos_embed_offset         = pos_embed_offset
+        self.pos_embed_max_size       = pos_embed_max_size
+        self.x_block_self_attn_layers = x_block_self_attn_layers
+
+        # hidden_size = default(hidden_size, 64 * depth)
+        # num_heads = default(num_heads, hidden_size // 64)
+
+        # apply magic --> this defines a head_size of 64
+        self.hidden_size = 64 * depth
+        num_heads        = depth
+        if num_blocks is None:
+            num_blocks   = depth
+
+        self.depth       = depth
+        self.num_heads   = num_heads
+
+        self.x_embedder  = PatchEmbed(
+            input_size,
+            patch_size,
+            in_channels,
+            self.hidden_size,
+            bias            = True,
+            strict_img_size = self.pos_embed_max_size is None,
+            dtype           = dtype,
+            device          = device,
+            operations      = operations
+        )
+        self.t_embedder = TimestepEmbedder(self.hidden_size,                                                    dtype=dtype, device=device, operations=operations)
+
+        self.y_embedder = None
+        if adm_in_channels is not None:
+            assert isinstance(adm_in_channels, int)
+            self.y_embedder = VectorEmbedder(adm_in_channels,                                 self.hidden_size, dtype=dtype, device=device, operations=operations)
+
+        if context_processor_layers is not None:
+            self.context_processor = ContextProcessor(context_size, context_processor_layers,                   dtype=dtype, device=device, operations=operations)
+        else:
+            self.context_processor = None
+
+        self.context_embedder = nn.Identity()
+        if context_embedder_config is not None:
+            if context_embedder_config["target"] == "torch.nn.Linear":
+                self.context_embedder = operations.Linear(**context_embedder_config["params"],                  dtype=dtype, device=device)
+
+        self.register_length = register_length
+        if self.register_length > 0:
+            self.register = nn.Parameter(torch.randn(1, register_length,                      self.hidden_size, dtype=dtype, device=device))
+
+        # num_patches = self.x_embedder.num_patches
+        # Will use fixed sin-cos embedding:
+        # just use a buffer already
+        if num_patches is not None:
+            self.register_buffer(
+                "pos_embed",
+                torch.empty(1, num_patches,                                                   self.hidden_size, dtype=dtype, device=device),
+            )
+        else:
+            self.pos_embed = None
+
+        self.use_checkpoint = use_checkpoint
+        if not skip_blocks:
+            self.joint_blocks = nn.ModuleList(
+                [
+                    ReJointBlock(
+                        self.hidden_size,
+                        num_heads,
+                        mlp_ratio         = mlp_ratio,
+                        qkv_bias          = qkv_bias,
+                        attn_mode         = attn_mode,
+                        pre_only          = (i == num_blocks - 1) and final_layer,
+                        rmsnorm           = rmsnorm,
+                        scale_mod_only    = scale_mod_only,
+                        swiglu            = swiglu,
+                        qk_norm           = qk_norm,
+                        x_block_self_attn = (i in self.x_block_self_attn_layers) or x_block_self_attn,
+                        dtype             = dtype,
+                        device            = device,
+                        operations        = operations,
+                    )
+                    for i in range(num_blocks)
+                ]
+            )
+
+        if final_layer:
+            self.final_layer = FinalLayer(self.hidden_size, patch_size, self.out_channels,                      dtype=dtype, device=device, operations=operations)
+
+        if compile_core:
+            assert False
+            self.forward_core_with_concat = torch.compile(self.forward_core_with_concat)
+
+    def cropped_pos_embed(self, hw, device=None):
+        p    = self.x_embedder.patch_size[0]
+        h, w = hw
+        # patched size
+        h    = (h + 1) // p
+        w    = (w + 1) // p
+        if self.pos_embed is None:
+            return get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, device=device)
+        assert self.pos_embed_max_size is not None
+        assert h <= self.pos_embed_max_size, (h, self.pos_embed_max_size)
+        assert w <= self.pos_embed_max_size, (w, self.pos_embed_max_size)
+        top  = (self.pos_embed_max_size - h) // 2
+        left = (self.pos_embed_max_size - w) // 2
+        spatial_pos_embed = rearrange(
+            self.pos_embed,
+            "1 (h w) c -> 1 h w c",
+            h = self.pos_embed_max_size,
+            w = self.pos_embed_max_size,
+        )
+        spatial_pos_embed = spatial_pos_embed[:, top : top + h, left : left + w, :]
+        spatial_pos_embed = rearrange(spatial_pos_embed, "1 h w c -> 1 (h w) c")
+        # print(spatial_pos_embed, top, left, h, w)
+        # # t = get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, 7.875, 7.875, device=device) #matches exactly for 1024 res
+        # t = get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, 7.5, 7.5, device=device) #scales better
+        # # print(t)
+        # return t
+        return spatial_pos_embed
+
+    def unpatchify(self, x, hw=None):
+        """
+        x: (N, T, patch_size**2 * C)
+        imgs: (N, H, W, C)
+        """
+        c = self.out_channels
+        p = self.x_embedder.patch_size[0]
+        if hw is None:
+            h = w = int(x.shape[1] ** 0.5)
+        else:
+            h, w = hw
+            h    = (h + 1) // p
+            w    = (w + 1) // p
+        assert h * w == x.shape[1]
+
+        x    = x.reshape(shape=(x.shape[0], h, w, p, p, c))
+        x    = torch.einsum("nhwpqc->nchpwq", x)
+        imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
+        return imgs
+
+
+
+
+    def forward_core_with_concat(
+        self,
+        x       : torch.Tensor,
+        c_mod   : torch.Tensor,
+        c_mod_base : torch.Tensor,
+        context : Optional[torch.Tensor] = None,
+        context_base : Optional[torch.Tensor] = None,
+        control                          = None,
+        transformer_options              = {},
+    ) -> torch.Tensor:
+        patches_replace = transformer_options.get("patches_replace", {})
+        if self.register_length > 0:
+            context = torch.cat(
+                (
+                    repeat(self.register, "1 ... -> b ...", b=x.shape[0]),
+                    default(context, torch.Tensor([]).type_as(x)),
+                ),
+                1,
+            )
+
+        weight    = transformer_options['reg_cond_weight'] if 'reg_cond_weight' in transformer_options else 0.0
+        floor     = transformer_options['reg_cond_floor']  if 'reg_cond_floor'  in transformer_options else 0.0
+        floor     = min(floor, weight)
+                
+        if type(weight) == float or type(weight) == int:
+            pass
+        else:
+            weight = weight.item()
+
+        AttnMask = transformer_options.get('AttnMask')
+        mask     = None
+        if AttnMask is not None and weight > 0:
+            mask                      = AttnMask.get(weight=weight) #mask_obj[0](transformer_options, weight.item())
+            
+            mask_type_bool = type(mask[0][0].item()) == bool if mask is not None else False
+            if not mask_type_bool:
+                mask = mask.to(x.dtype)
+            
+            text_len                  = context.shape[1] # mask_obj[0].text_len
+            
+            mask[text_len:,text_len:] = torch.clamp(mask[text_len:,text_len:], min=floor.to(mask.device))   #ORIGINAL SELF-ATTN REGION BLEED
+            #reg_cond_mask = reg_cond_mask_expanded.unsqueeze(0).clone() if reg_cond_mask_expanded is not None else None
+        mask_type_bool = type(mask[0][0].item()) == bool if mask is not None else False
+        if weight <= 0.0:
+            mask = None
+            context = context_base
+            c_mod = c_mod_base
+
+        # context is B, L', D
+        # x is B, L, D
+        blocks_replace = patches_replace.get("dit", {})
+        blocks         = len(self.joint_blocks)
+        for i in range(blocks):
+            if mask_type_bool and weight < (i / (blocks-1)) and mask is not None:
+                mask = mask.to(x.dtype) # torch.ones((*mask.shape,), dtype=mask.dtype, device=mask.device) #(mask == mask) #set all to false
+                
+            if ("double_block", i) in blocks_replace:
+                def block_wrap(args):
+                    out                    = {}
+                    out["txt"], out["img"] = self.joint_blocks[i](args["txt"], args["img"], c=args["vec"])
+                    return out
+
+                out     = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": c_mod}, {"original_block": block_wrap})
+                context = out["txt"]
+                x       = out["img"]
+            else:
+                context, x = self.joint_blocks[i](
+                    context,
+                    x,
+                    c              = c_mod,
+                    use_checkpoint = self.use_checkpoint,
+                    mask           = mask,
+                )
+            if control is not None:
+                control_o = control.get("output")
+                if i < len(control_o):
+                    add = control_o[i]
+                    if add is not None:
+                        x += add
+
+        x = self.final_layer(x, c_mod)  # (N, T, patch_size ** 2 * out_channels)
+        return x
+
+    def forward(
+        self,
+        x      : torch.Tensor,
+        t      : torch.Tensor,
+        y      : Optional[torch.Tensor] = None,
+        context: Optional[torch.Tensor] = None,
+        control                         =   None,
+        transformer_options             = {},
+    ) -> torch.Tensor:
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+        SIGMA = t[0].clone() / 1000
+        EO = transformer_options.get("ExtraOptions", ExtraOptions(""))
+        if EO is not None:
+            EO.mute = True
+            
+        y0_style_pos        = transformer_options.get("y0_style_pos")
+        y0_style_neg        = transformer_options.get("y0_style_neg")
+
+        y0_style_pos_weight    = transformer_options.get("y0_style_pos_weight", 0.0)
+        y0_style_pos_synweight = transformer_options.get("y0_style_pos_synweight", 0.0)
+        y0_style_pos_synweight *= y0_style_pos_weight
+
+        y0_style_neg_weight    = transformer_options.get("y0_style_neg_weight", 0.0)
+        y0_style_neg_synweight = transformer_options.get("y0_style_neg_synweight", 0.0)
+        y0_style_neg_synweight *= y0_style_neg_weight
+        
+        weight    = -1 * transformer_options.get("regional_conditioning_weight", 0.0)
+        floor     = -1 * transformer_options.get("regional_conditioning_floor",  0.0)
+        
+        freqsep_lowpass_method = transformer_options.get("freqsep_lowpass_method")
+        freqsep_sigma          = transformer_options.get("freqsep_sigma")
+        freqsep_kernel_size    = transformer_options.get("freqsep_kernel_size")
+        freqsep_inner_kernel_size    = transformer_options.get("freqsep_inner_kernel_size")
+        freqsep_stride    = transformer_options.get("freqsep_stride")
+        
+        freqsep_lowpass_weight = transformer_options.get("freqsep_lowpass_weight")
+        freqsep_highpass_weight= transformer_options.get("freqsep_highpass_weight")
+        freqsep_mask           = transformer_options.get("freqsep_mask")
+        
+
+        x_orig = x.clone()
+        y_orig = y.clone()
+        
+        h,w = x.shape[-2:]
+        h_len = ((h + (self.patch_size // 2)) // self.patch_size) # h_len 96
+        w_len = ((w + (self.patch_size // 2)) // self.patch_size) # w_len 96
+
+        out_list = []
+        for i in range(len(transformer_options['cond_or_uncond'])):
+            UNCOND = transformer_options['cond_or_uncond'][i] == 1
+
+            x = x_orig.clone()
+            y = y_orig.clone()
+            
+            context_base = context[i][None,...].clone()
+
+            if UNCOND:
+                #transformer_options['reg_cond_weight'] = -1
+                #context_tmp = context[i][None,...].clone()
+                
+                transformer_options['reg_cond_weight'] = transformer_options.get("regional_conditioning_weight", 0.0) #transformer_options['regional_conditioning_weight']
+                transformer_options['reg_cond_floor']  = transformer_options.get("regional_conditioning_floor",  0.0) #transformer_options['regional_conditioning_floor'] #if "regional_conditioning_floor" in transformer_options else 0.0
+                transformer_options['reg_cond_mask_orig'] = transformer_options.get('regional_conditioning_mask_orig')
+                
+                AttnMask   = transformer_options.get('AttnMask',   None)                    
+                RegContext = transformer_options.get('RegContext', None)
+                
+                if AttnMask is not None and transformer_options['reg_cond_weight'] > 0.0:
+                    AttnMask.attn_mask_recast(x.dtype)
+                    context_tmp = RegContext.get().to(context.dtype)
+                    #context_tmp = 0 * context_tmp.clone()
+                    
+                    A = context[i][None,...].clone()
+                    B = context_tmp
+                    context_tmp = A.repeat(1, (B.shape[1] // A.shape[1]) + 1, 1)[:, :B.shape[1], :]
+
+                else:
+                    context_tmp = context[i][None,...].clone()
+            
+            elif UNCOND == False:
+                transformer_options['reg_cond_weight'] = transformer_options.get("regional_conditioning_weight", 0.0) #transformer_options['regional_conditioning_weight']
+                transformer_options['reg_cond_floor']  = transformer_options.get("regional_conditioning_floor",  0.0) #transformer_options['regional_conditioning_floor'] #if "regional_conditioning_floor" in transformer_options else 0.0
+                transformer_options['reg_cond_mask_orig'] = transformer_options.get('regional_conditioning_mask_orig')
+                
+                AttnMask   = transformer_options.get('AttnMask',   None)                    
+                RegContext = transformer_options.get('RegContext', None)
+                
+                if AttnMask is not None and transformer_options['reg_cond_weight'] > 0.0:
+                    AttnMask.attn_mask_recast(x.dtype)
+                    context_tmp = RegContext.get().to(context.dtype)
+                else:
+                    context_tmp = context[i][None,...].clone()
+
+            
+            if context_tmp is None:
+                context_tmp = context[i][None,...].clone()
+                
+            #context = context_tmp
+
+
+            if self.context_processor is not None:
+                context_tmp = self.context_processor(context_tmp)
+
+            hw = x.shape[-2:]
+            x  = self.x_embedder(x) + comfy.ops.cast_to_input(self.cropped_pos_embed(hw, device=x.device), x)
+            c  = self.t_embedder(t, dtype=x.dtype)  # (N, D)      # c is like vec...
+            if y is not None and self.y_embedder is not None:
+                y = self.y_embedder(y_orig.clone())  # (N, D)
+                c = c + y  # (N, D)                              # vec = vec + y     (y = pooled_output    1,2048)
+
+            if context_tmp is not None:
+                context_tmp = self.context_embedder(context_tmp)
+
+
+
+            if self.context_processor is not None:
+                context_base = self.context_processor(context_base)
+
+            #hw = x.shape[-2:]
+            #x  = self.x_embedder(x) + comfy.ops.cast_to_input(self.cropped_pos_embed(hw, device=x.device), x)
+            c_base  = self.t_embedder(t, dtype=x.dtype)  # (N, D)      # c is like vec...
+            if y is not None and self.y_embedder is not None:
+                y = self.y_embedder(y_orig.clone())  # (N, D)
+                c_base = c_base + y  # (N, D)                              # vec = vec + y     (y = pooled_output    1,2048)
+
+            if context_base is not None:
+                context_base = self.context_embedder(context_base)
+
+
+            x = self.forward_core_with_concat(
+                                                x[i][None,...],
+                                                c[i][None,...],
+                                                c_base[i][None,...],
+                                                context_tmp,
+                                                context_base, #context[i][None,...].clone(),
+                                                control, 
+                                                transformer_options,
+                                                )
+
+            x = self.unpatchify(x, hw=hw)  # (N, out_channels, H, W)
+            
+            out_list.append(x)
+        
+        
+        x = torch.stack(out_list, dim=0).squeeze(dim=1)
+        eps = x[:,:,:hw[-2],:hw[-1]]
+        
+        
+        
+        
+        
+        
+        
+        
+        dtype = eps.dtype if self.style_dtype is None else self.style_dtype
+        
+        if y0_style_pos is not None:
+            y0_style_pos_weight    = transformer_options.get("y0_style_pos_weight")
+            y0_style_pos_synweight = transformer_options.get("y0_style_pos_synweight")
+            y0_style_pos_synweight *= y0_style_pos_weight
+            y0_style_pos_mask = transformer_options.get("y0_style_pos_mask")
+            y0_style_pos_mask_edge = transformer_options.get("y0_style_pos_mask_edge")
+
+            y0_style_pos = y0_style_pos.to(dtype)
+            x   = x_orig.clone().to(dtype)
+            eps = eps.to(dtype)
+            eps_orig = eps.clone()
+            
+            sigma = SIGMA #t_orig[0].to(torch.float32) / 1000
+            denoised = x - sigma * eps
+            
+            denoised_embed = self.Retrojector.embed(denoised)
+            y0_adain_embed = self.Retrojector.embed(y0_style_pos)
+            
+            if transformer_options['y0_style_method'] == "scattersort":
+                tile_h, tile_w = transformer_options.get('y0_style_tile_height'), transformer_options.get('y0_style_tile_width')
+                pad = transformer_options.get('y0_style_tile_padding')
+                if pad is not None and tile_h is not None and tile_w is not None:
+                    
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    if EO("scattersort_median_LP"):
+                        denoised_spatial_LP = median_blur_2d(denoised_spatial, kernel_size=EO("scattersort_median_LP",7))
+                        y0_adain_spatial_LP = median_blur_2d(y0_adain_spatial, kernel_size=EO("scattersort_median_LP",7))
+                        
+                        denoised_spatial_HP = denoised_spatial - denoised_spatial_LP
+                        y0_adain_spatial_HP = y0_adain_spatial - y0_adain_spatial_LP
+                        
+                        denoised_spatial_LP = apply_scattersort_tiled(denoised_spatial_LP, y0_adain_spatial_LP, tile_h, tile_w, pad)
+                        
+                        denoised_spatial = denoised_spatial_LP + denoised_spatial_HP
+                        denoised_embed = rearrange(denoised_spatial, "b c h w -> b (h w) c")
+                    else:
+                        denoised_spatial = apply_scattersort_tiled(denoised_spatial, y0_adain_spatial, tile_h, tile_w, pad)
+                    
+                    denoised_embed = rearrange(denoised_spatial, "b c h w -> b (h w) c")
+                    
+                else:
+                    denoised_embed = apply_scattersort_masked(denoised_embed, y0_adain_embed, y0_style_pos_mask, y0_style_pos_mask_edge, h_len, w_len)
+
+
+
+            elif transformer_options['y0_style_method'] == "AdaIN":
+                if freqsep_mask is not None:
+                    freqsep_mask = freqsep_mask.view(1, 1, *freqsep_mask.shape[-2:]).float()
+                    freqsep_mask = F.interpolate(freqsep_mask.float(), size=(h_len, w_len), mode='nearest-exact')
+                
+                if hasattr(self, "adain_tile"):
+                    tile_h, tile_w = self.adain_tile
+                    
+                    denoised_pretile = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_pretile = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    if self.adain_flag:
+                        h_off = tile_h // 2
+                        w_off = tile_w // 2
+                        denoised_pretile = denoised_pretile[:,:,h_off:-h_off, w_off:-w_off]
+                        self.adain_flag = False
+                    else:
+                        h_off = 0
+                        w_off = 0
+                        self.adain_flag = True
+                    
+                    tiles,    orig_shape, grid, strides = tile_latent(denoised_pretile, tile_size=(tile_h,tile_w))
+                    y0_tiles, orig_shape, grid, strides = tile_latent(y0_adain_pretile, tile_size=(tile_h,tile_w))
+                    
+                    tiles_out = []
+                    for i in range(tiles.shape[0]):
+                        tile = tiles[i].unsqueeze(0)
+                        y0_tile = y0_tiles[i].unsqueeze(0)
+                        
+                        tile    = rearrange(tile,    "b c h w -> b (h w) c", h=tile_h, w=tile_w)
+                        y0_tile = rearrange(y0_tile, "b c h w -> b (h w) c", h=tile_h, w=tile_w)
+                        
+                        tile = adain_seq_inplace(tile, y0_tile)
+                        tiles_out.append(rearrange(tile, "b (h w) c -> b c h w", h=tile_h, w=tile_w))
+                    
+                    tiles_out_tensor = torch.cat(tiles_out, dim=0)
+                    tiles_out_tensor = untile_latent(tiles_out_tensor, orig_shape, grid, strides)
+
+                    if h_off == 0:
+                        denoised_pretile = tiles_out_tensor
+                    else:
+                        denoised_pretile[:,:,h_off:-h_off, w_off:-w_off] = tiles_out_tensor
+                    denoised_embed = rearrange(denoised_pretile, "b c h w -> b (h w) c", h=h_len, w=w_len)
+
+                elif freqsep_lowpass_method is not None and freqsep_lowpass_method.endswith("pw"): #EO("adain_pw"):
+
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+
+                    if   freqsep_lowpass_method == "median_pw":
+                        denoised_spatial_new = adain_patchwise_row_batch_med(denoised_spatial.clone(), y0_adain_spatial.clone().repeat(denoised_spatial.shape[0],1,1,1), sigma=freqsep_sigma, kernel_size=freqsep_kernel_size, use_median_blur=True, lowpass_weight=freqsep_lowpass_weight, highpass_weight=freqsep_highpass_weight)
+                    elif freqsep_lowpass_method == "gaussian_pw": 
+                        denoised_spatial_new = adain_patchwise_row_batch(denoised_spatial.clone(), y0_adain_spatial.clone().repeat(denoised_spatial.shape[0],1,1,1), sigma=freqsep_sigma, kernel_size=freqsep_kernel_size)
+                    
+                    denoised_embed = rearrange(denoised_spatial_new, "b c h w -> b (h w) c", h=h_len, w=w_len)
+
+                elif freqsep_lowpass_method is not None: 
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    if   freqsep_lowpass_method == "median":
+                        denoised_spatial_LP = median_blur_2d(denoised_spatial, kernel_size=freqsep_kernel_size)
+                        y0_adain_spatial_LP = median_blur_2d(y0_adain_spatial, kernel_size=freqsep_kernel_size)
+                    elif freqsep_lowpass_method == "gaussian":
+                        denoised_spatial_LP = gaussian_blur_2d(denoised_spatial, sigma=freqsep_sigma, kernel_size=freqsep_kernel_size)
+                        y0_adain_spatial_LP = gaussian_blur_2d(y0_adain_spatial, sigma=freqsep_sigma, kernel_size=freqsep_kernel_size)
+                    
+                    denoised_spatial_HP = denoised_spatial - denoised_spatial_LP
+                    
+                    if EO("adain_fs_uhp"):
+                        y0_adain_spatial_HP = y0_adain_spatial - y0_adain_spatial_LP
+                        
+                        denoised_spatial_ULP = gaussian_blur_2d(denoised_spatial, sigma=EO("adain_fs_uhp_sigma", 1.0), kernel_size=EO("adain_fs_uhp_kernel_size", 3))
+                        y0_adain_spatial_ULP = gaussian_blur_2d(y0_adain_spatial, sigma=EO("adain_fs_uhp_sigma", 1.0), kernel_size=EO("adain_fs_uhp_kernel_size", 3))
+                        
+                        denoised_spatial_UHP = denoised_spatial_HP  - denoised_spatial_ULP
+                        y0_adain_spatial_UHP = y0_adain_spatial_HP  - y0_adain_spatial_ULP
+                        
+                        #denoised_spatial_HP  = y0_adain_spatial_ULP + denoised_spatial_UHP
+                        denoised_spatial_HP  = denoised_spatial_ULP + y0_adain_spatial_UHP
+                    
+                    denoised_spatial_new = freqsep_lowpass_weight * y0_adain_spatial_LP + freqsep_highpass_weight * denoised_spatial_HP
+                    denoised_embed = rearrange(denoised_spatial_new, "b c h w -> b (h w) c", h=h_len, w=w_len)
+
+                else:
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    
+                for adain_iter in range(EO("style_iter", 0)):
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    denoised_embed = self.Retrojector.embed(self.Retrojector.unembed(denoised_embed))
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    
+            elif transformer_options['y0_style_method'] == "WCT":
+                self.StyleWCT.set(y0_adain_embed)
+                denoised_embed = self.StyleWCT.get(denoised_embed)
+                
+                if transformer_options.get('y0_standard_guide') is not None:
+                    y0_standard_guide = transformer_options.get('y0_standard_guide')
+                    
+                    y0_standard_guide_embed = self.Retrojector.embed(y0_standard_guide)
+                    f_cs = self.StyleWCT.get(y0_standard_guide_embed)
+                    self.y0_standard_guide = self.Retrojector.unembed(f_cs)
+
+                if transformer_options.get('y0_inv_standard_guide') is not None:
+                    y0_inv_standard_guide = transformer_options.get('y0_inv_standard_guide')
+
+                    y0_inv_standard_guide_embed = self.Retrojector.embed(y0_inv_standard_guide)
+                    f_cs = self.StyleWCT.get(y0_inv_standard_guide_embed)
+                    self.y0_inv_standard_guide = self.Retrojector.unembed(f_cs)
+
+            denoised_approx = self.Retrojector.unembed(denoised_embed)
+            
+            eps = (x - denoised_approx) / sigma
+
+            if not UNCOND:
+                if eps.shape[0] == 2:
+                    eps[1] = eps_orig[1] + y0_style_pos_weight * (eps[1] - eps_orig[1])
+                    eps[0] = eps_orig[0] + y0_style_pos_synweight * (eps[0] - eps_orig[0])
+                else:
+                    eps[0] = eps_orig[0] + y0_style_pos_weight * (eps[0] - eps_orig[0])
+            elif eps.shape[0] == 1 and UNCOND:
+                eps[0] = eps_orig[0] + y0_style_pos_synweight * (eps[0] - eps_orig[0])
+            
+            eps = eps.float()
+        
+        if y0_style_neg is not None:
+            y0_style_neg_weight    = transformer_options.get("y0_style_neg_weight")
+            y0_style_neg_synweight = transformer_options.get("y0_style_neg_synweight")
+            y0_style_neg_synweight *= y0_style_neg_weight
+            y0_style_neg_mask = transformer_options.get("y0_style_neg_mask")
+            y0_style_neg_mask_edge = transformer_options.get("y0_style_neg_mask_edge")
+            
+            y0_style_neg = y0_style_neg.to(dtype)
+            x   = x_orig.clone().to(dtype)
+            eps = eps.to(dtype)
+            eps_orig = eps.clone()
+            
+            sigma = SIGMA #t_orig[0].to(torch.float32) / 1000
+            denoised = x - sigma * eps
+
+            denoised_embed = self.Retrojector.embed(denoised)
+            y0_adain_embed = self.Retrojector.embed(y0_style_neg)
+            
+            if transformer_options['y0_style_method'] == "scattersort":
+                tile_h, tile_w = transformer_options.get('y0_style_tile_height'), transformer_options.get('y0_style_tile_width')
+                pad = transformer_options.get('y0_style_tile_padding')
+                if pad is not None and tile_h is not None and tile_w is not None:
+                    
+                    denoised_spatial = rearrange(denoised_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    y0_adain_spatial = rearrange(y0_adain_embed, "b (h w) c -> b c h w", h=h_len, w=w_len)
+                    
+                    denoised_spatial = apply_scattersort_tiled(denoised_spatial, y0_adain_spatial, tile_h, tile_w, pad)
+                    
+                    denoised_embed = rearrange(denoised_spatial, "b c h w -> b (h w) c")
+
+                else:
+                    denoised_embed = apply_scattersort_masked(denoised_embed, y0_adain_embed, y0_style_neg_mask, y0_style_neg_mask_edge, h_len, w_len)
+            
+            
+            elif transformer_options['y0_style_method'] == "AdaIN":
+                denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                for adain_iter in range(EO("style_iter", 0)):
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    denoised_embed = self.Retrojector.embed(self.Retrojector.unembed(denoised_embed))
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    
+            elif transformer_options['y0_style_method'] == "WCT":
+                self.StyleWCT.set(y0_adain_embed)
+                denoised_embed = self.StyleWCT.get(denoised_embed)
+
+            denoised_approx = self.Retrojector.unembed(denoised_embed)
+
+            if UNCOND:
+                eps = (x - denoised_approx) / sigma
+                eps[0] = eps_orig[0] + y0_style_neg_weight * (eps[0] - eps_orig[0])
+                if eps.shape[0] == 2:
+                    eps[1] = eps_orig[1] + y0_style_neg_synweight * (eps[1] - eps_orig[1])
+            elif eps.shape[0] == 1 and not UNCOND:
+                eps[0] = eps_orig[0] + y0_style_neg_synweight * (eps[0] - eps_orig[0])
+            
+            eps = eps.float()
+            
+        return eps
+
+
+
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        dtype = eps.dtype if self.style_dtype is None else self.style_dtype
+        pinv_dtype = torch.float32 if dtype != torch.float64 else dtype
+        W_inv = None
+
+        #if eps.shape[0] == 2 or (eps.shape[0] == 1 and not UNCOND):
+        if y0_style_pos is not None:
+            y0_style_pos_weight    = transformer_options.get("y0_style_pos_weight")
+            y0_style_pos_synweight = transformer_options.get("y0_style_pos_synweight")
+            y0_style_pos_synweight *= y0_style_pos_weight
+            
+            y0_style_pos = y0_style_pos.to(torch.float64)
+            x   = x_orig.to(torch.float64)
+            eps = eps.to(torch.float64)
+            eps_orig = eps.clone()
+            
+            sigma = SIGMA# t_orig[0].to(torch.float64) / 1000
+            denoised = x - sigma * eps
+
+            hw = denoised.shape[-2:]
+            
+            features = 1536# denoised_embed.shape[-1] # should be 1536
+            
+            W_conv = self.x_embedder.proj.weight.to(torch.float64)  # [1536, 16, 2, 2]
+            W_flat = W_conv.view(features, -1).to(torch.float64)    # [1536, 64]
+            W_pinv = torch.linalg.pinv(W_flat)            # [64, 1536]
+
+            x_embedder64 = copy.deepcopy(self.x_embedder.proj).to(denoised)
+
+            #y = self.x_embedder.proj(denoised.to(torch.float16)).float()
+            y = x_embedder64(denoised)
+            B, C_out, H_out, W_out = y.shape
+            y_flat = y.view(B, C_out, -1)                   # [B, 1536, N]
+            y_flat = y_flat.permute(0, 2, 1)                # [B, N, 1536]
+
+            bias = self.x_embedder.proj.bias.to(torch.float64)               # [1536]
+            denoised_embed = y_flat - bias.view(1, 1, -1)
+
+
+
+
+            #y = self.x_embedder.proj(y0_style_pos.to(torch.float16)).float()
+            y = x_embedder64(y0_style_pos)
+            
+            B, C_out, H_out, W_out = y.shape
+            y_flat = y.view(B, C_out, -1)                   # [B, 1536,    N]
+            y_flat = y_flat.permute(0, 2, 1)                # [B, N   , 1536]
+
+            bias = self.x_embedder.proj.bias.to(torch.float64)              # [1536]
+            y0_adain_embed = y_flat - bias.view(1, 1, -1)
+
+
+            #denoised_embed = adain_seq(denoised_embed, y0_adain_embed)
+            
+
+            
+            if transformer_options['y0_style_method'] == "AdaIN":
+                denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                """for adain_iter in range(EO("style_iter", 0)):
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    denoised_embed = (denoised_embed - b) @ torch.linalg.pinv(W.to(pinv_dtype)).T.to(dtype)       #  not going to work! needs 
+                    denoised_embed = F.linear(denoised_embed         .to(W), W, b).to(img)
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)"""
+
+            elif transformer_options['y0_style_method'] == "WCT":
+                if self.y0_adain_embed is None or self.y0_adain_embed.shape != y0_adain_embed.shape or torch.norm(self.y0_adain_embed - y0_adain_embed) > 0:
+                    self.y0_adain_embed = y0_adain_embed
+                    
+                    f_s          = y0_adain_embed[0].clone()
+                    self.mu_s    = f_s.mean(dim=0, keepdim=True)
+                    f_s_centered = f_s - self.mu_s
+                    
+                    cov = (f_s_centered.T.double() @ f_s_centered.double()) / (f_s_centered.size(0) - 1)
+
+                    S_eig, U_eig = torch.linalg.eigh(cov + 1e-5 * torch.eye(cov.size(0), dtype=cov.dtype, device=cov.device))
+                    S_eig_sqrt    = S_eig.clamp(min=0).sqrt() # eigenvalues -> singular values
+                    
+                    whiten = U_eig @ torch.diag(S_eig_sqrt) @ U_eig.T
+                    self.y0_color  = whiten.to(f_s_centered)
+
+                for wct_i in range(eps.shape[0]):
+                    f_c          = denoised_embed[wct_i].clone()
+                    mu_c         = f_c.mean(dim=0, keepdim=True)
+                    f_c_centered = f_c - mu_c
+                    
+                    cov = (f_c_centered.T.double() @ f_c_centered.double()) / (f_c_centered.size(0) - 1)
+
+                    S_eig, U_eig  = torch.linalg.eigh(cov + 1e-5 * torch.eye(cov.size(0), dtype=cov.dtype, device=cov.device))
+                    inv_sqrt_eig  = S_eig.clamp(min=0).rsqrt() 
+                    
+                    whiten = U_eig @ torch.diag(inv_sqrt_eig) @ U_eig.T
+                    whiten = whiten.to(f_c_centered)
+
+                    f_c_whitened = f_c_centered @ whiten.T
+                    f_cs         = f_c_whitened @ self.y0_color.T + self.mu_s
+                    
+                    denoised_embed[wct_i] = f_cs
+
+            
+            x_patches = denoised_embed @ W_pinv.T                  # [B,N,64]
+
+            x_patches = x_patches.permute(0, 2, 1)                 # [B,64,N]
+
+            x_reconstructed = torch.nn.functional.fold(
+                x_patches,                                         # [B, 64, N]
+                output_size=(H_out * 2, W_out * 2),        # restore original input shape
+                kernel_size=2,
+                stride=2
+            )
+
+            denoised_approx = x_reconstructed #.view(B, 16, H_out * 2, W_out * 2)
+            
+            
+            
+            eps = (x - denoised_approx) / sigma
+            #if eps.shape[0] == 2:
+            #    eps[1] = eps_orig[1] + y0_style_pos_weight * (eps[1] - eps_orig[1])
+            #    eps[0] = eps_orig[0] + y0_style_pos_synweight * (eps[0] - eps_orig[0])
+            #else:
+            #    eps[0] = eps_orig[0] + y0_style_pos_weight * (eps[0] - eps_orig[0])
+                
+            if not UNCOND:
+                if eps.shape[0] == 2:
+                    eps[1] = eps_orig[1] + y0_style_pos_weight * (eps[1] - eps_orig[1])
+                    eps[0] = eps_orig[0] + y0_style_pos_synweight * (eps[0] - eps_orig[0])
+                else:
+                    eps[0] = eps_orig[0] + y0_style_pos_weight * (eps[0] - eps_orig[0])
+            elif eps.shape[0] == 1 and UNCOND:
+                eps[0] = eps_orig[0] + y0_style_pos_synweight * (eps[0] - eps_orig[0])
+                
+            eps = eps.float()
+
+        #if eps.shape[0] == 2 or (eps.shape[0] == 1 and UNCOND):
+        if y0_style_neg is not None:
+            y0_style_neg_weight    = transformer_options.get("y0_style_neg_weight")
+            y0_style_neg_synweight = transformer_options.get("y0_style_neg_synweight")
+            y0_style_neg_synweight *= y0_style_neg_weight
+            
+            y0_style_neg = y0_style_neg.to(torch.float64)
+            x   = x_orig.to(torch.float64)
+            eps = eps.to(torch.float64)
+            eps_orig = eps.clone()
+            
+            sigma = SIGMA# t_orig[0].to(torch.float64) / 1000
+            denoised = x - sigma * eps
+
+            hw = denoised.shape[-2:]
+            
+            features = 1536# denoised_embed.shape[-1] # should be 1536
+            
+            W_conv = self.x_embedder.proj.weight.float()  # [1536, 16, 2, 2]
+            W_flat = W_conv.view(features, -1).float()    # [1536, 64]
+            W_pinv = torch.linalg.pinv(W_flat)            # [64, 1536]
+
+
+
+            y = self.x_embedder.proj(denoised.to(torch.float16)).float()
+            B, C_out, H_out, W_out = y.shape
+            y_flat = y.view(B, C_out, -1)                   # [B, 1536, N]
+            y_flat = y_flat.permute(0, 2, 1)                # [B, N, 1536]
+
+            bias = self.x_embedder.proj.bias.float()               # [1536]
+            denoised_embed = y_flat - bias.view(1, 1, -1)
+
+
+
+            y = self.x_embedder.proj(y0_style_neg.to(torch.float16)).float()
+            B, C_out, H_out, W_out = y.shape
+            y_flat = y.view(B, C_out, -1)                   # [B, 1536,    N]
+            y_flat = y_flat.permute(0, 2, 1)                # [B, N   , 1536]
+
+            bias = self.x_embedder.proj.bias.float()               # [1536]
+            y0_adain_embed = y_flat - bias.view(1, 1, -1)
+
+
+            #denoised_embed = adain_seq(denoised_embed, y0_adain_embed)
+            
+            if transformer_options['y0_style_method'] == "AdaIN":
+                denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                """for adain_iter in range(EO("style_iter", 0)):
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    denoised_embed = (denoised_embed - b) @ torch.linalg.pinv(W.to(pinv_dtype)).T.to(dtype)
+                    denoised_embed = F.linear(denoised_embed         .to(W), W, b).to(img)
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)"""
+
+            elif transformer_options['y0_style_method'] == "WCT":
+                if self.y0_adain_embed is None or self.y0_adain_embed.shape != y0_adain_embed.shape or torch.norm(self.y0_adain_embed - y0_adain_embed) > 0:
+                    self.y0_adain_embed = y0_adain_embed
+                    
+                    f_s          = y0_adain_embed[0].clone()
+                    self.mu_s    = f_s.mean(dim=0, keepdim=True)
+                    f_s_centered = f_s - self.mu_s
+                    
+                    cov = (f_s_centered.T.double() @ f_s_centered.double()) / (f_s_centered.size(0) - 1)
+
+                    S_eig, U_eig = torch.linalg.eigh(cov + 1e-5 * torch.eye(cov.size(0), dtype=cov.dtype, device=cov.device))
+                    S_eig_sqrt    = S_eig.clamp(min=0).sqrt() # eigenvalues -> singular values
+                    
+                    whiten = U_eig @ torch.diag(S_eig_sqrt) @ U_eig.T
+                    self.y0_color  = whiten.to(f_s_centered)
+
+                for wct_i in range(eps.shape[0]):
+                    f_c          = denoised_embed[wct_i].clone()
+                    mu_c         = f_c.mean(dim=0, keepdim=True)
+                    f_c_centered = f_c - mu_c
+                    
+                    cov = (f_c_centered.T.double() @ f_c_centered.double()) / (f_c_centered.size(0) - 1)
+
+                    S_eig, U_eig  = torch.linalg.eigh(cov + 1e-5 * torch.eye(cov.size(0), dtype=cov.dtype, device=cov.device))
+                    inv_sqrt_eig  = S_eig.clamp(min=0).rsqrt() 
+                    
+                    whiten = U_eig @ torch.diag(inv_sqrt_eig) @ U_eig.T
+                    whiten = whiten.to(f_c_centered)
+
+                    f_c_whitened = f_c_centered @ whiten.T
+                    f_cs         = f_c_whitened @ self.y0_color.T + self.mu_s
+                    
+                    denoised_embed[wct_i] = f_cs
+            
+            
+            x_patches = denoised_embed @ W_pinv.T                  # [B,N,64]
+
+            x_patches = x_patches.permute(0, 2, 1)                 # [B,64,N]
+
+            x_reconstructed = torch.nn.functional.fold(
+                x_patches,                                         # [B, 64, N]
+                output_size=(H_out * 2, W_out * 2),        # restore original input shape
+                kernel_size=2,
+                stride=2
+            )
+
+            denoised_approx = x_reconstructed #.view(B, 16, H_out * 2, W_out * 2)
+            
+            
+            
+            #eps = (x - denoised_approx) / sigma
+            #eps[0] = eps_orig[0] + y0_style_neg_weight * (eps[0] - eps_orig[0])
+            #if eps.shape[0] == 2:
+            #    eps[1] = eps_orig[1] + y0_style_neg_synweight * (eps[1] - eps_orig[1])
+                
+            if UNCOND:
+                eps = (x - denoised_approx) / sigma
+                eps[0] = eps_orig[0] + y0_style_neg_weight * (eps[0] - eps_orig[0])
+                if eps.shape[0] == 2:
+                    eps[1] = eps_orig[1] + y0_style_neg_synweight * (eps[1] - eps_orig[1])
+            elif eps.shape[0] == 1 and not UNCOND:
+                eps[0] = eps_orig[0] + y0_style_neg_synweight * (eps[0] - eps_orig[0])
+                
+            eps = eps.float()
+
+
+        
+        return eps
+
+
+
+
+
+class ReOpenAISignatureMMDITWrapper(MMDiT):
+    def forward(
+        self,
+        x         : torch.Tensor,
+        timesteps : torch.Tensor,
+        context   : Optional[torch.Tensor] = None,
+        y         : Optional[torch.Tensor] = None,
+        control                            = None,
+        transformer_options                = {},
+        **kwargs,
+    ) -> torch.Tensor:
+        return super().forward(x, timesteps, context=context, y=y, control=control, transformer_options=transformer_options)
+
+
+
+def adain_seq_inplace(content: torch.Tensor, style: torch.Tensor, eps: float = 1e-7) -> torch.Tensor:
+    mean_c = content.mean(1, keepdim=True)
+    std_c  = content.std (1, keepdim=True).add_(eps)  # in-place add
+    mean_s = style.mean  (1, keepdim=True)
+    std_s  = style.std   (1, keepdim=True).add_(eps)
+
+    content.sub_(mean_c).div_(std_c).mul_(std_s).add_(mean_s)  # in-place chain
+    return content
+
+
+
+def adain_seq(content: torch.Tensor, style: torch.Tensor, eps: float = 1e-7) -> torch.Tensor:
+    return ((content - content.mean(1, keepdim=True)) / (content.std(1, keepdim=True) + eps)) * (style.std(1, keepdim=True) + eps) + style.mean(1, keepdim=True)
+
+
+
+
diff --git a/custom_nodes/RES4LYF/wan/model.py b/custom_nodes/RES4LYF/wan/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..00ad31a85b40e9a168ac49057bdcead1da8ea7cd
--- /dev/null
+++ b/custom_nodes/RES4LYF/wan/model.py
@@ -0,0 +1,1346 @@
+# original version: https://github.com/Wan-Video/Wan2.1/blob/main/wan/modules/model.py
+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+import math
+from typing import Optional, Callable, Tuple, Dict, Any, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import repeat
+
+from comfy.ldm.modules.attention import optimized_attention, attention_pytorch
+from comfy.ldm.flux.layers import EmbedND
+from comfy.ldm.flux.math import apply_rope
+from comfy.ldm.modules.diffusionmodules.mmdit import RMSNorm
+import comfy.ldm.common_dit
+import comfy.model_management
+
+from ..latents import interpolate_spd
+from ..helper  import ExtraOptions
+
+
+def sinusoidal_embedding_1d(dim, position):
+    # preprocess
+    assert dim % 2 == 0
+    half     = dim // 2
+    position = position.type(torch.float32)
+
+    # calculation
+    sinusoid = torch.outer(
+        position, torch.pow(10000, -torch.arange(half).to(position).div(half)))
+    x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
+    return x
+
+
+
+class ReWanRawSelfAttention(nn.Module):
+
+    def __init__(self,
+                dim,
+                num_heads,
+                window_size        = (-1, -1),
+                qk_norm            = True,
+                eps                = 1e-6, 
+                operation_settings = {}):
+        assert dim % num_heads == 0
+        super().__init__()
+        self.dim         = dim
+        self.num_heads   = num_heads
+        self.head_dim    = dim // num_heads
+        self.window_size = window_size
+        self.qk_norm     = qk_norm
+        self.eps         = eps
+
+        # layers
+        self.q = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.k = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.v = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.o = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.norm_q = RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
+        self.norm_k = RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
+
+    def forward(self, x, freqs, mask=None):
+        r"""
+        Args:
+            x(Tensor): Shape [B, L, num_heads, C / num_heads]
+            freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
+        """
+        b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
+
+        # query, key, value function
+        def qkv_fn(x):
+            q = self.norm_q(self.q(x)).view(b, s, n, d)
+            k = self.norm_k(self.k(x)).view(b, s, n, d)
+            v = self.v(x).view(b, s, n * d)
+            return q, k, v
+
+        q, k, v = qkv_fn(x)
+        q, k    = apply_rope(q, k, freqs)
+        # q,k.shape = 2,14040,12,128      v.shape = 2,14040,1536
+
+        x = optimized_attention(
+            q.view(b, s, n * d),
+            k.view(b, s, n * d),
+            v,
+            heads=self.num_heads,
+        )
+
+        x = self.o(x)
+        return x
+
+
+def attention_weights(q, k):
+    # implementation of in-place softmax to reduce memory req
+    scores = torch.matmul(q, k.transpose(-2, -1))
+    scores.div_(math.sqrt(q.size(-1)))
+    torch.exp(scores, out=scores)
+    summed = torch.sum(scores, dim=-1, keepdim=True)
+    scores /= summed
+    return scores.nan_to_num_(0.0, 65504., -65504.)
+
+
+
+
+
+class ReWanSlidingSelfAttention(nn.Module):
+
+    def __init__(self,
+                dim,
+                num_heads,
+                window_size        = (-1, -1),
+                qk_norm            = True,
+                eps                = 1e-6, 
+                operation_settings = {}):
+        assert dim % num_heads == 0
+        super().__init__()
+        self.dim         = dim
+        self.num_heads   = num_heads
+        self.head_dim    = dim // num_heads
+        self.window_size = window_size
+        self.qk_norm     = qk_norm
+        self.eps         = eps
+        self.winderz     = 15
+        self.winderz_type= "standard"
+
+        # layers
+        self.q = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.k = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.v = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.o = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.norm_q = RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
+        self.norm_k = RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
+        
+
+    def forward(self, x, freqs, mask=None, grid_sizes=None):
+        r"""
+        Args:
+            x(Tensor): Shape [B, L, num_heads, C / num_heads]
+            freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
+        """
+        b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
+
+        # query, key, value function
+        def qkv_fn(x):
+            q = self.norm_q(self.q(x)).view(b, s, n, d)
+            k = self.norm_k(self.k(x)).view(b, s, n, d)
+            v = self.v(x).view(b, s, n * d)
+            return q, k, v
+
+        q, k, v = qkv_fn(x)
+        q, k    = apply_rope(q, k, freqs)
+        # q,k.shape = 2,14040,12,128      v.shape = 2,14040,1536
+    
+        img_len = grid_sizes[1] * grid_sizes[2]
+        total_frames = int(q.shape[1] // img_len)
+
+        window_size = self.winderz
+        half_window = window_size // 2
+
+        q_ = q.view(b, s, n * d)
+        k_ = k.view(b, s, n * d)
+        x_list = []
+
+        for i in range(total_frames):
+            q_start =  i      * img_len
+            q_end   = (i + 1) * img_len
+
+            # circular frame indices for key/value window
+            center = i
+            #window_indices = [(center + offset) % total_frames for offset in range(-half_window, half_window + 1)]
+            if self.winderz_type == "standard":
+                start = max(0, center - half_window)
+                end   = min(total_frames, center + half_window + 1)
+                # Shift window if it would be too short
+                if end - start < window_size:
+                    if start == 0:
+                        end = min(total_frames, start + window_size)
+                    elif end == total_frames:
+                        start = max(0, end - window_size)
+
+                window_indices = list(range(start, end))
+            elif self.winderz_type == "circular":
+                window_indices = [(center + offset) % total_frames for offset in range(-half_window, half_window + 1)]
+            
+            # frame indices to token indices
+            token_indices = []
+            for frame in window_indices:
+                start = frame * img_len
+                token_indices.extend(range(start, start + img_len))
+
+            token_indices = torch.tensor(token_indices, device=q.device)
+
+            x = optimized_attention(
+                q_[:, q_start:q_end, :],           # [B, img_len, C]
+                k_.index_select(1, token_indices), # [B, window_size * img_len, C]
+                v .index_select(1, token_indices),
+                heads=self.num_heads,
+            )
+
+            x_list.append(x)
+
+        x = torch.cat(x_list, dim=1)
+        del x_list, q, k, v, q_, k_
+
+        x = self.o(x)
+        return x
+
+
+
+
+class ReWanT2VSlidingCrossAttention(ReWanSlidingSelfAttention):
+
+    def forward(self, x, context, context_clip=None, mask=None, grid_sizes=None):
+        r"""
+        Args:
+            x(Tensor): Shape [B, L1, C]
+            context(Tensor): Shape [B, L2, C]
+        """
+        # compute query, key, value
+        q = self.norm_q(self.q(x))
+        k = self.norm_k(self.k(context))
+        v =             self.v(context)
+
+        img_len = grid_sizes[1] * grid_sizes[2]
+        total_frames = int(q.shape[1] // img_len)
+
+        window_size = self.winderz
+        half_window = window_size // 2
+        
+        b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
+        q_, k_ = q, k
+        #q_ = q.view(b, s, n * d)
+        #k_ = k.view(b, s, n * d)
+        x_list = []
+
+        for i in range(total_frames):
+            q_start =  i      * img_len
+            q_end   = (i + 1) * img_len
+
+            # circular frame indices for key/value window
+            center = i
+            #window_indices = [(center + offset) % total_frames for offset in range(-half_window, half_window + 1)]
+            if self.winderz_type == "standard":
+                start = max(0, center - half_window)
+                end   = min(total_frames, center + half_window + 1)
+                # Shift window if it would be too short
+                if end - start < window_size:
+                    if start == 0:
+                        end = min(total_frames, start + window_size)
+                    elif end == total_frames:
+                        start = max(0, end - window_size)
+
+                window_indices = list(range(start, end))
+            elif self.winderz_type == "circular":
+                window_indices = [(center + offset) % total_frames for offset in range(-half_window, half_window + 1)]
+            
+            # frame indices to token indices
+            token_indices = []
+            for frame in window_indices:
+                start = frame * img_len
+                token_indices.extend(range(start, start + img_len))
+
+            token_indices = torch.tensor(token_indices, device=q.device)
+
+            x = optimized_attention(
+                q_[:, q_start:q_end, :],           # [B, img_len, C]
+                k_, #.index_select(1, token_indices), # [B, window_size * img_len, C]
+                v , #.index_select(1, token_indices),
+                heads=self.num_heads,
+            )
+
+            x_list.append(x)
+
+        x = torch.cat(x_list, dim=1)
+        del x_list, q, k, v, q_, k_
+
+        x = self.o(x)
+        return x
+
+
+
+
+class ReWanSelfAttention(nn.Module):
+
+    def __init__(self,
+                dim,
+                num_heads,
+                window_size        = (-1, -1),
+                qk_norm            = True,
+                eps                = 1e-6, 
+                operation_settings = {}):
+        assert dim % num_heads == 0
+        super().__init__()
+        self.dim         = dim
+        self.num_heads   = num_heads
+        self.head_dim    = dim // num_heads
+        self.window_size = window_size
+        self.qk_norm     = qk_norm
+        self.eps         = eps
+
+        # layers
+        self.q = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.k = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.v = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.o = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.norm_q = RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
+        self.norm_k = RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
+        
+
+    def forward(self, x, freqs, mask=None, grid_sizes=None):
+        r"""
+        Args:
+            x(Tensor): Shape [B, L, num_heads, C / num_heads]
+            freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
+        """
+        b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
+
+        # query, key, value function
+        def qkv_fn(x):
+            q = self.norm_q(self.q(x)).view(b, s, n, d)
+            k = self.norm_k(self.k(x)).view(b, s, n, d)
+            v = self.v(x).view(b, s, n * d)
+            return q, k, v
+
+        q, k, v = qkv_fn(x)
+        q, k    = apply_rope(q, k, freqs)
+        # q,k.shape = 2,14040,12,128      v.shape = 2,14040,1536
+
+        if mask is not None and mask.shape[-1] > 0:
+            #dtype = mask.dtype if mask.dtype == torch.bool else q.dtype
+            #txt_len = mask.shape[1] - mask.shape[0]
+            x = attention_pytorch(
+                q.view(b, s, n * d),
+                k.view(b, s, n * d),
+                v,
+                heads=self.num_heads,
+                mask=mask#[:,txt_len:].to(dtype)
+            )
+        else:
+            x = optimized_attention(
+                q.view(b, s, n * d),
+                k.view(b, s, n * d),
+                v,
+                heads=self.num_heads,
+            )
+
+        x = self.o(x)
+        return x
+
+
+class ReWanT2VRawCrossAttention(ReWanSelfAttention):
+
+    def forward(self, x, context, context_clip=None, mask=None, grid_sizes=None):
+        r"""
+        Args:
+            x(Tensor): Shape [B, L1, C]
+            context(Tensor): Shape [B, L2, C]
+        """
+        # compute query, key, value
+        q = self.norm_q(self.q(x))
+        k = self.norm_k(self.k(context))
+        v =             self.v(context)
+
+        x = optimized_attention(q, k, v, heads=self.num_heads, mask=None)
+
+        x = self.o(x)
+        return x
+
+
+class ReWanT2VCrossAttention(ReWanSelfAttention):
+
+    def forward(self, x, context, context_clip=None, mask=None, grid_sizes=None):
+        r"""
+        Args:
+            x(Tensor): Shape [B, L1, C]
+            context(Tensor): Shape [B, L2, C]
+        """
+        # compute query, key, value
+        q = self.norm_q(self.q(x))
+        k = self.norm_k(self.k(context))
+        v =             self.v(context)
+        #if mask is not None:
+        #    num_repeats = q.shape[1] // mask.shape[0]
+        #    mask = mask.repeat(num_repeats, 1)
+        # compute attention    # x.shape 2,14040,1536     q.shape 2,14040,1536     k,v.shape = 2,512,1536       mask = 14040,512   num_heads=12
+        if mask is not None: # and (mask.shape[-1] - mask.shape[-2]) == k.shape[-2]:  # need mask shape 11664,5120
+            #dtype = mask.dtype if mask.dtype == torch.bool else q.dtype
+            dtype = torch.bool
+            x = attention_pytorch(q, k, v, heads=self.num_heads, mask=mask.to(q.device).bool())
+
+            #x = attention_pytorch(q, k, v, heads=self.num_heads, mask=mask[:,:k.shape[-2]].to(q.device).bool())
+        else:
+            x = optimized_attention(q, k, v, heads=self.num_heads, mask=None)
+
+        x = self.o(x)
+        return x
+
+
+class ReWanI2VCrossAttention(ReWanSelfAttention):   # image2video only
+
+    def __init__(self,
+                dim,
+                num_heads,
+                window_size=(-1, -1),
+                qk_norm=True,
+                eps=1e-6, operation_settings={}, ):
+        super().__init__(dim, num_heads, window_size, qk_norm, eps, operation_settings=operation_settings)
+
+        self.k_img = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.v_img = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        # self.alpha = nn.Parameter(torch.zeros((1, )))
+        self.norm_k_img = RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
+
+    def forward(self, x, context, context_clip=None, mask=None, grid_sizes=None):
+        r"""
+        Args:
+            x(Tensor): Shape [B, L1, C]
+            context(Tensor): Shape [B, L2, C]
+        """
+        """context_img = context[:, :257]
+        context     = context[:, 257:]
+        mask_clip = None"""
+        
+        context_img = context_clip
+        
+        mask_clip = None
+        if mask is not None:
+            mask_clip = F.interpolate(mask[None, None, ...].to(torch.float16), (mask.shape[0], 257 * mask.shape[1]//512), mode='nearest-exact').squeeze().to(mask.dtype)
+            """mask_clip = []
+            for i in range(mask.shape[-1]//512):
+                mask_clip.append(mask[:,i*512:i*512 + 257])
+            mask_clip = torch.cat(mask_clip, dim=-1)"""
+
+        # compute query, key, value
+        q = self.norm_q(self.q(x))
+        k = self.norm_k(self.k(context))
+        v = self.v(context)
+        k_img = self.norm_k_img(self.k_img(context_img))
+        v_img = self.v_img(context_img)
+        img_x = optimized_attention(q, k_img, v_img, heads=self.num_heads, mask=mask_clip)
+        # compute attention
+        x = optimized_attention(q, k, v, heads=self.num_heads, mask=mask)
+
+        # output
+        x = x + img_x
+        x = self.o(x)
+        return x
+
+
+WAN_CROSSATTENTION_CLASSES = {
+    't2v_cross_attn': ReWanT2VCrossAttention,
+    'i2v_cross_attn': ReWanI2VCrossAttention,
+}
+
+
+class ReWanAttentionBlock(nn.Module):
+
+    def __init__(self,
+                cross_attn_type,
+                dim,
+                ffn_dim,
+                num_heads,
+                window_size        =  (-1, -1),
+                qk_norm            =  True,
+                cross_attn_norm    =  False,
+                eps                =  1e-6, 
+                operation_settings = {}):
+        super().__init__()
+        self.dim             = dim
+        self.ffn_dim         = ffn_dim
+        self.num_heads       = num_heads
+        self.window_size     = window_size
+        self.qk_norm         = qk_norm
+        self.cross_attn_norm = cross_attn_norm
+        self.eps             = eps
+
+        # layers
+        self.norm1     = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.self_attn = ReWanSelfAttention(  dim, num_heads, window_size, qk_norm,
+                                            eps, operation_settings=operation_settings)
+        self.norm3     = operation_settings.get("operations").LayerNorm(
+            dim, eps,
+            elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if cross_attn_norm else nn.Identity()
+        
+        self.cross_attn = WAN_CROSSATTENTION_CLASSES[cross_attn_type](
+                                                                        dim,
+                                                                        num_heads,
+                                                                        (-1, -1),
+                                                                        qk_norm,
+                                                                        eps, 
+                                                                        operation_settings=operation_settings)
+        
+        self.norm2 = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.ffn = nn.Sequential(
+            operation_settings.get("operations").Linear(dim, ffn_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), nn.GELU(approximate='tanh'),
+            operation_settings.get("operations").Linear(ffn_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
+
+        # modulation
+        self.modulation = nn.Parameter(torch.empty(1, 6, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
+
+    def forward(
+        self,
+        x,
+        e,
+        freqs,
+        context,
+        context_clip=None,
+        self_mask=None,
+        cross_mask=None,
+        grid_sizes = None,
+        #mask=None,
+    ):
+        r"""
+        Args:
+            x(Tensor): Shape [B, L, C]
+            e(Tensor): Shape [B, 6, C]
+            freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
+        """
+        # assert e.dtype == torch.float32
+        
+        e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e).chunk(6, dim=1)
+        # assert e[0].dtype == torch.float32
+        # e = tuple with 6 elem, shape = 2,1,1536    # with length = 33 so 9 frames
+        # self-attention
+
+        y = self.self_attn(
+            self.norm1(x) * (1 + e[1]) + e[0],
+            freqs,
+            grid_sizes=grid_sizes,
+            mask=self_mask) # mask[:,txt_len:])
+
+        x = x + y * e[2]
+
+        # cross-attention & ffn   # x,y.shape 2,14040,1536   
+        x = x + self.cross_attn(self.norm3(x), context, context_clip=context_clip, mask=cross_mask, grid_sizes=grid_sizes,) #mask[:,:txt_len])
+        #print("before norm2 ", torch.cuda.memory_allocated() / 1024**3)
+        y = self.ffn(self.norm2(x) * (1 + e[4]) + e[3])
+        #print("after norm2 ", torch.cuda.memory_allocated() / 1024**3)
+        x = x + y * e[5]
+        return x
+
+
+class Head(nn.Module):
+
+    def __init__(self, dim, out_dim, patch_size, eps=1e-6, operation_settings={}):
+        super().__init__()
+        self.dim        = dim
+        self.out_dim    = out_dim
+        self.patch_size = patch_size
+        self.eps        = eps
+
+        # layers
+        out_dim = math.prod(patch_size) * out_dim
+        self.norm = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.head = operation_settings.get("operations").Linear   (dim, out_dim,                       device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+
+        # modulation
+        self.modulation = nn.Parameter(torch.empty(1, 2, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
+
+    def forward(self, x, e):
+        r"""
+        Args:
+            x(Tensor): Shape [B, L1, C]
+            e(Tensor): Shape [B, C]
+        """
+        # assert e.dtype == torch.float32
+        e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e.unsqueeze(1)).chunk(2, dim=1)
+        x = (self.head(self.norm(x) * (1 + e[1]) + e[0]))
+        return x
+
+
+class MLPProj(torch.nn.Module):
+
+    def __init__(self, in_dim, out_dim, operation_settings={}):
+        super().__init__()
+
+        self.proj = torch.nn.Sequential(
+            operation_settings                 .get("operations").LayerNorm(in_dim,          device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), operation_settings.get("operations").Linear(in_dim, in_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")),
+            torch.nn.GELU(), operation_settings.get("operations").Linear   (in_dim, out_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")),
+            operation_settings                 .get("operations").LayerNorm(out_dim,         device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
+
+    def forward(self, image_embeds):
+        clip_extra_context_tokens = self.proj(image_embeds)
+        return clip_extra_context_tokens
+
+
+class ReWanModel(torch.nn.Module):
+    r"""
+    Wan diffusion backbone supporting both text-to-video and image-to-video.
+    """
+
+    def __init__(self,
+                model_type      = 't2v',
+                patch_size      = (1, 2, 2),
+                text_len        = 512,
+                in_dim          = 16,
+                dim             = 2048,
+                ffn_dim         = 8192,
+                freq_dim        = 256,
+                text_dim        = 4096,
+                out_dim         = 16,
+                num_heads       = 16,
+                num_layers      = 32,
+                window_size     = (-1, -1),
+                qk_norm         = True,
+                cross_attn_norm = True,
+                eps             = 1e-6,
+                image_model     = None,
+                device          = None,
+                dtype           = None,
+                operations      = None,
+                ):
+        r"""
+        Initialize the diffusion model backbone.
+
+        Args:
+            model_type (`str`, *optional*, defaults to 't2v'):
+                Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video)
+            patch_size (`tuple`, *optional*, defaults to (1, 2, 2)):
+                3D patch dimensions for video embedding (t_patch, h_patch, w_patch)
+            text_len (`int`, *optional*, defaults to 512):
+                Fixed length for text embeddings
+            in_dim (`int`, *optional*, defaults to 16):
+                Input video channels (C_in)
+            dim (`int`, *optional*, defaults to 2048):
+                Hidden dimension of the transformer
+            ffn_dim (`int`, *optional*, defaults to 8192):
+                Intermediate dimension in feed-forward network
+            freq_dim (`int`, *optional*, defaults to 256):
+                Dimension for sinusoidal time embeddings
+            text_dim (`int`, *optional*, defaults to 4096):
+                Input dimension for text embeddings
+            out_dim (`int`, *optional*, defaults to 16):
+                Output video channels (C_out)
+            num_heads (`int`, *optional*, defaults to 16):
+                Number of attention heads
+            num_layers (`int`, *optional*, defaults to 32):
+                Number of transformer blocks
+            window_size (`tuple`, *optional*, defaults to (-1, -1)):
+                Window size for local attention (-1 indicates global attention)
+            qk_norm (`bool`, *optional*, defaults to True):
+                Enable query/key normalization
+            cross_attn_norm (`bool`, *optional*, defaults to False):
+                Enable cross-attention normalization
+            eps (`float`, *optional*, defaults to 1e-6):
+                Epsilon value for normalization layers
+        """
+
+        super().__init__()
+        self.dtype           = dtype
+        operation_settings   = {"operations": operations, "device": device, "dtype": dtype}
+
+        assert model_type in ['t2v', 'i2v']
+        self.model_type      = model_type
+
+        self.patch_size      = patch_size
+        self.text_len        = text_len
+        self.in_dim          = in_dim
+        self.dim             = dim
+        self.ffn_dim         = ffn_dim
+        self.freq_dim        = freq_dim
+        self.text_dim        = text_dim
+        self.out_dim         = out_dim
+        self.num_heads       = num_heads
+        self.num_layers      = num_layers
+        self.window_size     = window_size
+        self.qk_norm         = qk_norm
+        self.cross_attn_norm = cross_attn_norm
+        self.eps             = eps
+
+        # embeddings
+        self.patch_embedding = operations.Conv3d(
+            in_dim, dim, kernel_size=patch_size, stride=patch_size, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) #dtype=torch.float32)
+        
+        
+        self.text_embedding = nn.Sequential(
+            operations.Linear(text_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), nn.GELU(approximate='tanh'),
+            operations.Linear(dim, dim,      device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
+
+        self.time_embedding = nn.Sequential(
+            operations.Linear(freq_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), nn.SiLU(), operations.Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
+        self.time_projection = nn.Sequential(nn.SiLU(), operations.Linear(dim, dim * 6, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
+
+        # blocks
+        cross_attn_type = 't2v_cross_attn' if model_type == 't2v' else 'i2v_cross_attn'
+        
+        self.blocks = nn.ModuleList([
+            ReWanAttentionBlock(
+                                cross_attn_type,
+                                dim, 
+                                ffn_dim, num_heads,
+                                window_size,
+                                qk_norm,
+                                cross_attn_norm,
+                                eps, 
+                                operation_settings=operation_settings)
+            
+            for _ in range(num_layers)
+        ])
+
+        # head
+        self.head = Head(dim, out_dim, patch_size, eps, operation_settings=operation_settings)
+
+        d = dim // num_heads
+        self.rope_embedder = EmbedND(dim=d, theta=10000.0, axes_dim=[d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)])
+
+        if model_type == 'i2v':
+            self.img_emb = MLPProj(1280, dim, operation_settings=operation_settings)
+        else:
+            self.img_emb = None
+
+
+    def invert_patch_embedding(self, z: torch.Tensor, original_shape: torch.Size, grid_sizes: Optional[Tuple[int,int,int]] = None) -> torch.Tensor:
+
+        import torch.nn.functional as F
+        B, C_in, D, H, W = original_shape
+        pD, pH, pW = self.patch_size
+        sD, sH, sW = pD, pH, pW
+
+        if z.ndim == 3:
+            # [B, S, C_out] -> reshape to [B, C_out, D', H', W']
+            S = z.shape[1]
+            if grid_sizes is None:
+                Dp = D // pD
+                Hp = H // pH
+                Wp = W // pW
+            else:
+                Dp, Hp, Wp = grid_sizes
+            C_out = z.shape[2]
+            z = z.transpose(1, 2).reshape(B, C_out, Dp, Hp, Wp)
+        else:
+            B2, C_out, Dp, Hp, Wp = z.shape
+            assert B2 == B, "Batch size mismatch... ya sharked it."
+
+        # kncokout bias
+        b = self.patch_embedding.bias.view(1, C_out, 1, 1, 1)
+        z_nobias = z - b
+
+        # 2D filter -> pinv
+        w3 = self.patch_embedding.weight         # [C_out, C_in, 1, pH, pW]
+        w2 = w3.squeeze(2)                       # [C_out, C_in, pH, pW]
+        out_ch, in_ch, kH, kW = w2.shape
+        W_flat = w2.view(out_ch, -1)            # [C_out, in_ch*pH*pW]
+        W_pinv = torch.linalg.pinv(W_flat)      # [in_ch*pH*pW, C_out]
+
+        # merge depth for 2D unfold wackiness
+        z2 = z_nobias.permute(0,2,1,3,4).reshape(B*Dp, C_out, Hp, Wp)
+
+        # apply pinv ... get patch vectors
+        z_flat    = z2.reshape(B*Dp, C_out, -1)  # [B*Dp, C_out, L]
+        x_patches = W_pinv @ z_flat              # [B*Dp, in_ch*pH*pW, L]
+
+        # fold -> spatial frames
+        x2 = F.fold(
+            x_patches,
+            output_size=(H, W),
+            kernel_size=(pH, pW),
+            stride=(sH, sW)
+        )  # → [B*Dp, C_in, H, W]
+
+        # un-merge depth
+        x2 = x2.reshape(B, Dp, in_ch, H, W)           # [B, Dp,  C_in, H, W]
+        x_recon = x2.permute(0,2,1,3,4).contiguous()  # [B, C_in,   D, H, W]
+        return x_recon
+
+
+    def forward_orig(
+        self,
+        x,
+        t,
+        context,
+        clip_fea = None,
+        freqs    = None,
+        transformer_options = {},
+        UNCOND = False,
+    ):
+        r"""
+        Forward pass through the diffusion model
+
+        Args:
+            x (Tensor):
+                List of input video tensors with shape [B, C_in, F, H, W]
+            t (Tensor):
+                Diffusion timesteps tensor of shape [B]
+            context (List[Tensor]):
+                List of text embeddings each with shape [B, L, C]
+            seq_len (`int`):
+                Maximum sequence length for positional encoding
+            clip_fea (Tensor, *optional*):
+                CLIP image features for image-to-video mode
+            y (List[Tensor], *optional*):
+                Conditional video inputs for image-to-video mode, same shape as x
+
+        Returns:
+            List[Tensor]:
+                List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8]
+        """
+        
+        
+        """trash = x[:,16:,...]
+        x_slice_flip = torch.cat([x[:,:16,...], torch.flip(trash, dims=[2])], dim=1)
+        x_slice_flip = self.patch_embedding(x_slice_flip.float()).to(x.dtype) 
+        x          = self.patch_embedding(x.float()).to(x.dtype)  
+        x = torch.cat([x[:,:,:9,...], x_slice_flip[:,:,9:,...]], dim=2)"""
+        
+        """x1 = self.patch_embedding(x[:,:,:8,...].float()).to(x.dtype)
+        
+        x_slice = torch.cat([x[:,:16,8:,...], trash[:,:,0:9, ...]], dim=1)
+        
+        x2          = self.patch_embedding(x_slice.float()).to(x.dtype) 
+        
+        x = torch.cat([x1, x2], dim=2)"""
+        
+
+        y0_style_pos        = transformer_options.get("y0_style_pos")
+        y0_style_neg        = transformer_options.get("y0_style_neg")
+        SIGMA = t[0].clone() / 1000
+        EO = transformer_options.get("ExtraOptions", ExtraOptions(""))
+        
+        # embeddings
+        #self.patch_embedding.to(self.time_embedding[0].weight.dtype)
+        x_orig     = x.clone()
+        #x          = self.patch_embedding(x.float()).to(self.time_embedding[0].weight.dtype)     #next line to torch.Size([1, 5120, 17, 30, 30]) from 1,36,17,30,30
+        x          = self.patch_embedding(x.float()).to(x.dtype)         # vram jumped from ~16-16.5 up to 17.98     gained 300mb with weights at torch.float8_e4m3fn
+        grid_sizes = x.shape[2:]
+        x          = x.flatten(2).transpose(1, 2)      # x.shape 1,32400,5120  bfloat16   316.4 MB
+
+        # time embeddings
+        e = self.time_embedding(
+            sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype))
+        e0 = self.time_projection(e).unflatten(1, (6, self.dim))              # e0.shape = 2,6,1536       tiny ( < 0.1 MB)
+
+        # context
+        context = self.text_embedding(context)
+
+        context_clip = None
+        if clip_fea is not None and self.img_emb is not None:
+            context_clip = self.img_emb(clip_fea)  # bs x 257 x dim
+            #context      = torch.concat([context_clip, context], dim=1)
+
+        # arguments
+        kwargs = dict(
+            e       = e0,
+            freqs   = freqs,              # 1,32400,1,64,2,2 bfloat16 15.8 MB
+            context = context,            # 1,1536,5120      bfloat16 15.0 MB
+            context_clip = context_clip,
+            grid_sizes = grid_sizes)
+
+
+
+
+
+        weight    = transformer_options['reg_cond_weight'] if 'reg_cond_weight' in transformer_options else 0.0
+        floor     = transformer_options['reg_cond_floor']  if 'reg_cond_floor'  in transformer_options else 0.0
+        
+        floor     = min(floor, weight)
+        
+        if type(weight) == float or type(weight) == int:
+            pass
+        else:
+            weight = weight.item()
+        
+        AttnMask = transformer_options.get('AttnMask')    # somewhere around here, jumped to 20.6GB
+        mask     = None
+        if AttnMask is not None and weight > 0:
+            mask                      = AttnMask.get(weight=weight) #mask_obj[0](transformer_options, weight.item())         # 32400,33936  bool   1048.6 MB
+            
+            mask_type_bool = type(mask[0][0].item()) == bool if mask is not None else False
+            if not mask_type_bool:
+                mask = mask.to(x.dtype)
+            
+            #text_len                  = context.shape[1] # mask_obj[0].text_len
+            
+            #mask[text_len:,text_len:] = torch.clamp(mask[text_len:,text_len:], min=floor.to(mask.device))   #ORIGINAL SELF-ATTN REGION BLEED
+            #reg_cond_mask = reg_cond_mask_expanded.unsqueeze(0).clone() if reg_cond_mask_expanded is not None else None
+        
+        mask_type_bool = type(mask[0][0].item()) == bool if mask is not None else False
+
+
+
+
+        txt_len = context.shape[1] # mask_obj[0].text_len
+        #txt_len = mask.shape[-1] - mask.shape[-2] if mask is not None else "Unlogic Condition"          #what's the point of this?
+        
+        #self_attn_mask  = mask[:, txt_len:]
+        #cross_attn_mask = mask[:,:txt_len ].bool()
+        #i = 0
+        #for block in self.blocks:
+        for i, block in enumerate(self.blocks):
+            if mask_type_bool and weight < (i / (len(self.blocks)-1)) and mask is not None:
+                mask = mask.to(x.dtype)
+            
+            #if mask_type_bool and weight < (i / (len(self.blocks)-1)) and mask is not None:
+            #    mask = mask.to(x.dtype)
+            
+            if mask is not None:
+                #if True:
+                #    x = block(x, self_mask=None, cross_mask=mask.bool(), **kwargs)
+                if mask_type_bool and floor < 0 and          (i / (len(self.blocks)-1)) < (-floor):    # use self-attn mask until block number
+                    x = block(x, self_mask=mask[:,txt_len:], cross_mask=mask[:,:txt_len].bool(), **kwargs)
+                elif mask_type_bool and floor > 0 and  floor < (i / (len(self.blocks)-1)):               # use self-attn mask after block number
+                    x = block(x, self_mask=mask[:,txt_len:], cross_mask=mask[:,:txt_len].bool(), **kwargs)
+                    #x = block(x, self_mask=None, cross_mask=mask[:,:txt_len].bool(), **kwargs)
+                elif floor == 0:
+                    x = block(x, self_mask=mask[:,txt_len:], cross_mask=mask[:,:txt_len].bool(), **kwargs)
+                else:
+                    #x = block(x, self_mask=mask[:,txt_len:], cross_mask=mask[:,:txt_len].bool(), **kwargs)
+                    x = block(x, self_mask=None, cross_mask=mask[:,:txt_len].bool(), **kwargs)
+            
+            else:
+                x = block(x, **kwargs)
+            #x = block(x, mask=mask, **kwargs)
+            
+            #i += 1
+
+        # head
+        x = self.head(x, e)
+
+        # unpatchify
+        eps = self.unpatchify(x, grid_sizes)
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        dtype = eps.dtype if self.style_dtype is None else self.style_dtype
+        pinv_dtype = torch.float32 if dtype != torch.float64 else dtype
+        W_inv = None
+        
+        
+        #if eps.shape[0] == 2 or (eps.shape[0] == 1 and not UNCOND):
+        if y0_style_pos is not None:
+            y0_style_pos_weight    = transformer_options.get("y0_style_pos_weight")
+            y0_style_pos_synweight = transformer_options.get("y0_style_pos_synweight")
+            y0_style_pos_synweight *= y0_style_pos_weight
+            
+            y0_style_pos = y0_style_pos.to(torch.float32)
+            x   = x_orig.clone().to(torch.float32)
+            eps = eps.to(torch.float32)
+            eps_orig = eps.clone()
+            
+            sigma = SIGMA #t_orig[0].to(torch.float32) / 1000
+            denoised = x - sigma * eps
+
+
+            img = comfy.ldm.common_dit.pad_to_patch_size(denoised, self.patch_size)
+            patch_size = self.patch_size
+
+            denoised_embed          = self.patch_embedding(img.float()) #.to(x.dtype)         # vram jumped from ~16-16.5 up to 17.98     gained 300mb with weights at torch.float8_e4m3fn
+            grid_sizes = denoised_embed.shape[2:]
+            denoised_embed          = denoised_embed.flatten(2).transpose(1, 2) 
+
+
+            img_y0_adain = comfy.ldm.common_dit.pad_to_patch_size(y0_style_pos, self.patch_size)
+            patch_size = self.patch_size
+
+            y0_adain_embed          = self.patch_embedding(img_y0_adain.float()) #.to(x.dtype)         # vram jumped from ~16-16.5 up to 17.98     gained 300mb with weights at torch.float8_e4m3fn
+            grid_sizes = y0_adain_embed.shape[2:]
+            y0_adain_embed          = y0_adain_embed.flatten(2).transpose(1, 2) 
+
+
+            if transformer_options['y0_style_method'] == "AdaIN":
+                denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                for adain_iter in range(EO("style_iter", 0)):
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    #denoised_embed = (denoised_embed - b) @ torch.linalg.pinv(W.to(pinv_dtype)).T.to(dtype)
+                    denoised_embed = self.invert_patch_embedding(denoised_embed, x_orig.shape, grid_sizes)
+                    denoised_embed = self.patch_embedding(denoised_embed.float()) #.to(x.dtype)         # vram jumped from ~16-16.5 up to 17.98     gained 300mb with weights at torch.float8_e4m3fn
+                    grid_sizes     = denoised_embed.shape[2:]
+                    denoised_embed = denoised_embed.flatten(2).transpose(1, 2) 
+                    
+                    #denoised_embed = F.linear(denoised_embed         .to(W), W, b).to(img)
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    
+                    
+                    
+                    
+            elif transformer_options['y0_style_method'] == "WCT":
+                if self.y0_adain_embed is None or self.y0_adain_embed.shape != y0_adain_embed.shape or torch.norm(self.y0_adain_embed - y0_adain_embed) > 0:
+                    self.y0_adain_embed = y0_adain_embed
+                    
+                    f_s          = y0_adain_embed[0].clone()
+                    self.mu_s    = f_s.mean(dim=0, keepdim=True)
+                    f_s_centered = f_s - self.mu_s
+                    
+                    cov = (f_s_centered.T.double() @ f_s_centered.double()) / (f_s_centered.size(0) - 1)
+
+                    S_eig, U_eig = torch.linalg.eigh(cov + 1e-5 * torch.eye(cov.size(0), dtype=cov.dtype, device=cov.device))
+                    S_eig_sqrt    = S_eig.clamp(min=0).sqrt() # eigenvalues -> singular values
+                    
+                    whiten = U_eig @ torch.diag(S_eig_sqrt) @ U_eig.T
+                    self.y0_color  = whiten.to(f_s_centered)
+
+                for wct_i in range(eps.shape[0]):
+                    f_c          = denoised_embed[wct_i].clone()
+                    mu_c         = f_c.mean(dim=0, keepdim=True)
+                    f_c_centered = f_c - mu_c
+                    
+                    cov = (f_c_centered.T.double() @ f_c_centered.double()) / (f_c_centered.size(0) - 1)
+
+                    S_eig, U_eig  = torch.linalg.eigh(cov + 1e-5 * torch.eye(cov.size(0), dtype=cov.dtype, device=cov.device))
+                    inv_sqrt_eig  = S_eig.clamp(min=0).rsqrt() 
+                    
+                    whiten = U_eig @ torch.diag(inv_sqrt_eig) @ U_eig.T
+                    whiten = whiten.to(f_c_centered)
+
+                    f_c_whitened = f_c_centered @ whiten.T
+                    f_cs         = f_c_whitened @ self.y0_color.T + self.mu_s
+                    
+                    denoised_embed[wct_i] = f_cs
+
+            denoised_approx = self.invert_patch_embedding(denoised_embed, x_orig.shape, grid_sizes)
+            
+            denoised_approx = denoised_approx.to(eps)
+
+            eps = (x - denoised_approx) / sigma
+            #if eps.shape[0] == 2:
+            #    eps[1] = eps_orig[1] + y0_style_pos_weight * (eps[1] - eps_orig[1])
+            #    eps[0] = eps_orig[0] + y0_style_pos_synweight * (eps[0] - eps_orig[0])
+            #else:
+            #    eps[0] = eps_orig[0] + y0_style_pos_weight * (eps[0] - eps_orig[0])
+            
+            if not UNCOND:
+                if eps.shape[0] == 2:
+                    eps[1] = eps_orig[1] + y0_style_pos_weight * (eps[1] - eps_orig[1])
+                    eps[0] = eps_orig[0] + y0_style_pos_synweight * (eps[0] - eps_orig[0])
+                else:
+                    eps[0] = eps_orig[0] + y0_style_pos_weight * (eps[0] - eps_orig[0])
+            elif eps.shape[0] == 1 and UNCOND:
+                eps[0] = eps_orig[0] + y0_style_pos_synweight * (eps[0] - eps_orig[0])
+            
+            eps = eps.float()
+        
+        
+        #if eps.shape[0] == 2 or (eps.shape[0] == 1 and UNCOND):
+        if y0_style_neg is not None:
+            y0_style_neg_weight    = transformer_options.get("y0_style_neg_weight")
+            y0_style_neg_synweight = transformer_options.get("y0_style_neg_synweight")
+            y0_style_neg_synweight *= y0_style_neg_weight
+            
+            y0_style_neg = y0_style_neg.to(torch.float32)
+            x   = x_orig.clone().to(torch.float32)
+            eps = eps.to(torch.float32)
+            eps_orig = eps.clone()
+            
+            sigma = SIGMA #t_orig[0].to(torch.float32) / 1000
+            denoised = x - sigma * eps
+
+
+            img = comfy.ldm.common_dit.pad_to_patch_size(denoised, self.patch_size)
+            patch_size = self.patch_size
+
+            denoised_embed          = self.patch_embedding(img.float()) #.to(x.dtype)         # vram jumped from ~16-16.5 up to 17.98     gained 300mb with weights at torch.float8_e4m3fn
+            grid_sizes = denoised_embed.shape[2:]
+            denoised_embed          = denoised_embed.flatten(2).transpose(1, 2) 
+
+
+            img_y0_adain = comfy.ldm.common_dit.pad_to_patch_size(y0_style_neg, self.patch_size)
+            patch_size = self.patch_size
+
+            y0_adain_embed          = self.patch_embedding(img_y0_adain.float()) #.to(x.dtype)         # vram jumped from ~16-16.5 up to 17.98     gained 300mb with weights at torch.float8_e4m3fn
+            grid_sizes = y0_adain_embed.shape[2:]
+            y0_adain_embed          = y0_adain_embed.flatten(2).transpose(1, 2) 
+
+
+            if transformer_options['y0_style_method'] == "AdaIN":
+                denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                for adain_iter in range(EO("style_iter", 0)):
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    #denoised_embed = (denoised_embed - b) @ torch.linalg.pinv(W.to(pinv_dtype)).T.to(dtype)
+                    denoised_embed = self.invert_patch_embedding(denoised_embed, x_orig.shape, grid_sizes)
+                    denoised_embed = self.patch_embedding(denoised_embed.float()) #.to(x.dtype)         # vram jumped from ~16-16.5 up to 17.98     gained 300mb with weights at torch.float8_e4m3fn
+                    grid_sizes = denoised_embed.shape[2:]
+                    denoised_embed = denoised_embed.flatten(2).transpose(1, 2)                         
+                    
+                    #denoised_embed = F.linear(denoised_embed         .to(W), W, b).to(img)
+                    denoised_embed = adain_seq_inplace(denoised_embed, y0_adain_embed)
+                    
+                    
+                    
+                    
+            elif transformer_options['y0_style_method'] == "WCT":
+                if self.y0_adain_embed is None or self.y0_adain_embed.shape != y0_adain_embed.shape or torch.norm(self.y0_adain_embed - y0_adain_embed) > 0:
+                    self.y0_adain_embed = y0_adain_embed
+                    
+                    f_s          = y0_adain_embed[0].clone()
+                    self.mu_s    = f_s.mean(dim=0, keepdim=True)
+                    f_s_centered = f_s - self.mu_s
+                    
+                    cov = (f_s_centered.T.double() @ f_s_centered.double()) / (f_s_centered.size(0) - 1)
+
+                    S_eig, U_eig = torch.linalg.eigh(cov + 1e-5 * torch.eye(cov.size(0), dtype=cov.dtype, device=cov.device))
+                    S_eig_sqrt    = S_eig.clamp(min=0).sqrt() # eigenvalues -> singular values
+                    
+                    whiten = U_eig @ torch.diag(S_eig_sqrt) @ U_eig.T
+                    self.y0_color  = whiten.to(f_s_centered)
+
+                for wct_i in range(eps.shape[0]):
+                    f_c          = denoised_embed[wct_i].clone()
+                    mu_c         = f_c.mean(dim=0, keepdim=True)
+                    f_c_centered = f_c - mu_c
+                    
+                    cov = (f_c_centered.T.double() @ f_c_centered.double()) / (f_c_centered.size(0) - 1)
+
+                    S_eig, U_eig  = torch.linalg.eigh(cov + 1e-5 * torch.eye(cov.size(0), dtype=cov.dtype, device=cov.device))
+                    inv_sqrt_eig  = S_eig.clamp(min=0).rsqrt() 
+                    
+                    whiten = U_eig @ torch.diag(inv_sqrt_eig) @ U_eig.T
+                    whiten = whiten.to(f_c_centered)
+
+                    f_c_whitened = f_c_centered @ whiten.T
+                    f_cs         = f_c_whitened @ self.y0_color.T + self.mu_s
+                    
+                    denoised_embed[wct_i] = f_cs
+
+            denoised_approx = self.invert_patch_embedding(denoised_embed, x_orig.shape, grid_sizes)
+            
+            denoised_approx = denoised_approx.to(eps)
+
+            #eps = (x - denoised_approx) / sigma
+            #eps[0] = eps_orig[0] + y0_style_neg_weight * (eps[0] - eps_orig[0])
+            #if eps.shape[0] == 2:
+            #    eps[1] = eps_orig[1] + y0_style_neg_synweight * (eps[1] - eps_orig[1])
+                
+            if UNCOND:
+                eps = (x - denoised_approx) / sigma
+                eps[0] = eps_orig[0] + y0_style_neg_weight * (eps[0] - eps_orig[0])
+                if eps.shape[0] == 2:
+                    eps[1] = eps_orig[1] + y0_style_neg_synweight * (eps[1] - eps_orig[1])
+            elif eps.shape[0] == 1 and not UNCOND:
+                eps[0] = eps_orig[0] + y0_style_neg_synweight * (eps[0] - eps_orig[0])
+            
+            eps = eps.float()
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        return eps
+    
+    
+    
+    
+    
+    
+    # context.shape = 2,512,1536     x.shape = 2,14040,1536      timestep.shape      h_len=30, w_len=52    30 * 52 = 1560
+    def forward(self, x, timestep, context, clip_fea=None, transformer_options={}, **kwargs):
+        
+        """if False: #clip_fea is not None:
+            bs, c, t, h, w = x.shape
+            x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)
+            patch_size = self.patch_size    # tuple = 1,2,2,
+            
+            t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
+            h_len = ((h + (patch_size[1] // 2)) // patch_size[1])
+            w_len = ((w + (patch_size[2] // 2)) // patch_size[2])
+            
+            img_ids = torch.zeros((t_len, h_len, w_len, 3), device=x.device, dtype=x.dtype)
+            
+            img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1)
+            img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1)
+            img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).reshape(1, 1, -1)
+            
+            img_ids = repeat(img_ids, "t h w c -> b (t h w) c", b=bs)
+            # 14040 = 9 * 1560       1560 = 1536 + 24  1560/24 = 65
+            freqs = self.rope_embedder(img_ids).movedim(1, 2)
+            return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs)[:, :, :t, :h, :w]"""
+            
+        
+        
+        
+        #x = torch.cat([x[:,:,:8,...],   torch.flip(x[:,:,8:,...], dims=[2])], dim=2)
+        
+        x_orig = x.clone()      # 1,16,36,60,60   bfloat16
+        timestep_orig = timestep.clone() # 1000    float32
+        context_orig = context.clone() # 1,512,4096 bfloat16
+        
+        
+        out_list = []
+        for i in range(len(transformer_options['cond_or_uncond'])):
+            UNCOND = transformer_options['cond_or_uncond'][i] == 1
+
+            x = x_orig.clone()
+            timestep = timestep_orig.clone()
+            context = context_orig.clone()
+
+
+            bs, c, t, h, w = x.shape
+            x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)
+            patch_size = self.patch_size
+            
+
+
+            transformer_options['original_shape'] = x.shape
+            transformer_options['patch_size']     = patch_size
+            
+
+            """if UNCOND:
+                transformer_options['reg_cond_weight'] = 0.0 # -1
+                context_tmp = context[i][None,...].clone()"""
+                
+            if UNCOND:
+                #transformer_options['reg_cond_weight'] = -1
+                #context_tmp = context[i][None,...].clone()
+                
+                transformer_options['reg_cond_weight'] = transformer_options.get("regional_conditioning_weight", 0.0) #transformer_options['regional_conditioning_weight']
+                transformer_options['reg_cond_floor']  = transformer_options.get("regional_conditioning_floor",  0.0) #transformer_options['regional_conditioning_floor'] #if "regional_conditioning_floor" in transformer_options else 0.0
+                transformer_options['reg_cond_mask_orig'] = transformer_options.get('regional_conditioning_mask_orig')
+                
+                AttnMask   = transformer_options.get('AttnMask',   None)                    
+                RegContext = transformer_options.get('RegContext', None)
+                
+                if AttnMask is not None and transformer_options['reg_cond_weight'] != 0.0:
+                    AttnMask.attn_mask_recast(x.dtype)
+                    context_tmp = RegContext.get().to(context.dtype)
+                    clip_fea    = RegContext.get_clip_fea()
+                    clip_fea     = clip_fea.to(x.dtype) if clip_fea else None
+                    
+                    A = context[i][None,...].clone()
+                    B = context_tmp
+                    context_tmp = A.repeat(1, (B.shape[1] // A.shape[1]) + 1, 1)[:, :B.shape[1], :]
+
+                else:
+                    context_tmp = context[i][None,...].clone()
+            
+            elif UNCOND == False:
+                transformer_options['reg_cond_weight'] = transformer_options.get("regional_conditioning_weight", 0.0) #transformer_options['regional_conditioning_weight']
+                transformer_options['reg_cond_floor']  = transformer_options.get("regional_conditioning_floor", 0.0) #transformer_options['regional_conditioning_floor'] #if "regional_conditioning_floor" in transformer_options else 0.0
+                transformer_options['reg_cond_mask_orig'] = transformer_options.get('regional_conditioning_mask_orig')
+                
+                AttnMask   = transformer_options.get('AttnMask',   None)
+                RegContext = transformer_options.get('RegContext', None)
+                
+                if AttnMask is not None and transformer_options['reg_cond_weight'] != 0.0:
+                    AttnMask.attn_mask_recast(x.dtype)
+                    context_tmp  = RegContext.get()
+                    clip_fea     = RegContext.get_clip_fea()
+                    clip_fea     = clip_fea.to(x.dtype) if clip_fea else None
+                else:
+                    context_tmp = context[i][None,...].clone()
+
+            if context_tmp is None:
+                context_tmp = context[i][None,...].clone()
+            context_tmp = context_tmp.to(context.dtype)
+            
+            
+            
+            t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
+            h_len = ((h + (patch_size[1] // 2)) // patch_size[1])
+            w_len = ((w + (patch_size[2] // 2)) // patch_size[2])
+            
+            img_ids = torch.zeros((t_len, h_len, w_len, 3), device=x.device, dtype=x.dtype)
+            
+            img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1)
+            img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1)
+            img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).reshape(1, 1, -1)
+            
+            img_ids = repeat(img_ids, "t h w c -> b (t h w) c", b=bs)
+            # 14040 = 9 * 1560       1560 = 1536 + 24  1560/24 = 65
+            freqs = self.rope_embedder(img_ids).movedim(1, 2).to(x.dtype)
+            
+            
+            
+            
+            out_x = self.forward_orig(
+                                        x          [i][None,...], 
+                                        timestep   [i][None,...], 
+                                        context_tmp,
+                                        clip_fea            = clip_fea,
+                                        freqs               = freqs[i][None,...],
+                                        transformer_options = transformer_options,
+                                        UNCOND              = UNCOND,
+                                        )[:, :, :t, :h, :w]
+        
+            #out_x = torch.cat([out_x[:,:,:8,...],   torch.flip(out_x[:,:,8:,...], dims=[2])], dim=2)
+            out_list.append(out_x)
+        
+        out_stack = torch.stack(out_list, dim=0).squeeze(dim=1)
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+
+        return out_stack
+        
+
+    def unpatchify(self, x, grid_sizes):
+        r"""
+        Reconstruct video tensors from patch embeddings.
+
+        Args:
+            x (List[Tensor]):
+                List of patchified features, each with shape [L, C_out * prod(patch_size)]
+            grid_sizes (Tensor):
+                Original spatial-temporal grid dimensions before patching,
+                    shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches)
+
+        Returns:
+            List[Tensor]:
+                Reconstructed video tensors with shape [L, C_out, F, H / 8, W / 8]
+        """
+
+        c = self.out_dim
+        u = x
+        b = u.shape[0]
+        u = u[:, :math.prod(grid_sizes)].view(b, *grid_sizes, *self.patch_size, c)
+        u = torch.einsum('bfhwpqrc->bcfphqwr', u)
+        u = u.reshape(b, c, *[i * j for i, j in zip(grid_sizes, self.patch_size)])
+    
+        
+        
+        return u
+
+
+
+
+def adain_seq_inplace(content: torch.Tensor, style: torch.Tensor, eps: float = 1e-7) -> torch.Tensor:
+    mean_c = content.mean(1, keepdim=True)
+    std_c  = content.std (1, keepdim=True).add_(eps)
+    mean_s = style.mean  (1, keepdim=True)
+    std_s  = style.std   (1, keepdim=True).add_(eps)
+
+    content.sub_(mean_c).div_(std_c).mul_(std_s).add_(mean_s)
+    return content
diff --git a/custom_nodes/RES4LYF/wan/vae.py b/custom_nodes/RES4LYF/wan/vae.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8ebc5ec6c4ee2cbce802837c052004eb8cc362c
--- /dev/null
+++ b/custom_nodes/RES4LYF/wan/vae.py
@@ -0,0 +1,567 @@
+# original version: https://github.com/Wan-Video/Wan2.1/blob/main/wan/modules/vae.py
+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from comfy.ldm.modules.diffusionmodules.model import vae_attention
+
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+CACHE_T = 2
+
+
+class CausalConv3d(ops.Conv3d):
+    """
+    Causal 3d convolusion.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._padding = (self.padding[2], self.padding[2], self.padding[1],
+                         self.padding[1], 2 * self.padding[0], 0)
+        self.padding = (0, 0, 0)
+
+    def forward(self, x, cache_x=None):
+        padding = list(self._padding)
+        if cache_x is not None and self._padding[4] > 0:
+            cache_x = cache_x.to(x.device)
+            x = torch.cat([cache_x, x], dim=2)
+            padding[4] -= cache_x.shape[2]
+        x = F.pad(x, padding)
+
+        return super().forward(x)
+
+
+class RMS_norm(nn.Module):
+
+    def __init__(self, dim, channel_first=True, images=True, bias=False):
+        super().__init__()
+        broadcastable_dims = (1, 1, 1) if not images else (1, 1)
+        shape = (dim, *broadcastable_dims) if channel_first else (dim,)
+
+        self.channel_first = channel_first
+        self.scale = dim**0.5
+        self.gamma = nn.Parameter(torch.ones(shape))
+        self.bias = nn.Parameter(torch.zeros(shape)) if bias else None
+
+    def forward(self, x):
+        return F.normalize(
+            x, dim=(1 if self.channel_first else -1)) * self.scale * self.gamma.to(x) + (self.bias.to(x) if self.bias is not None else 0)
+
+
+class Upsample(nn.Upsample):
+
+    def forward(self, x):
+        """
+        Fix bfloat16 support for nearest neighbor interpolation.
+        """
+        return super().forward(x.float()).type_as(x)
+
+
+class Resample(nn.Module):
+
+    def __init__(self, dim, mode):
+        assert mode in ('none', 'upsample2d', 'upsample3d', 'downsample2d',
+                        'downsample3d')
+        super().__init__()
+        self.dim = dim
+        self.mode = mode
+
+        # layers
+        if mode == 'upsample2d':
+            self.resample = nn.Sequential(
+                Upsample(scale_factor=(2., 2.), mode='nearest-exact'),
+                ops.Conv2d(dim, dim // 2, 3, padding=1))
+        elif mode == 'upsample3d':
+            self.resample = nn.Sequential(
+                Upsample(scale_factor=(2., 2.), mode='nearest-exact'),
+                ops.Conv2d(dim, dim // 2, 3, padding=1))
+            self.time_conv = CausalConv3d(
+                dim, dim * 2, (3, 1, 1), padding=(1, 0, 0))
+
+        elif mode == 'downsample2d':
+            self.resample = nn.Sequential(
+                nn.ZeroPad2d((0, 1, 0, 1)),
+                ops.Conv2d(dim, dim, 3, stride=(2, 2)))
+        elif mode == 'downsample3d':
+            self.resample = nn.Sequential(
+                nn.ZeroPad2d((0, 1, 0, 1)),
+                ops.Conv2d(dim, dim, 3, stride=(2, 2)))
+            self.time_conv = CausalConv3d(
+                dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0))
+
+        else:
+            self.resample = nn.Identity()
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        b, c, t, h, w = x.size()
+        if self.mode == 'upsample3d':
+            if feat_cache is not None:
+                idx = feat_idx[0]
+                if feat_cache[idx] is None:
+                    feat_cache[idx] = 'Rep'
+                    feat_idx[0] += 1
+                else:
+
+                    cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                    if cache_x.shape[2] < 2 and feat_cache[
+                            idx] is not None and feat_cache[idx] != 'Rep':
+                        # cache last frame of last two chunk
+                        cache_x = torch.cat([
+                            feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                                cache_x.device), cache_x
+                        ],
+                                            dim=2)
+                    if cache_x.shape[2] < 2 and feat_cache[
+                            idx] is not None and feat_cache[idx] == 'Rep':
+                        cache_x = torch.cat([
+                            torch.zeros_like(cache_x).to(cache_x.device),
+                            cache_x
+                        ],
+                                            dim=2)
+                    if feat_cache[idx] == 'Rep':
+                        x = self.time_conv(x)
+                    else:
+                        x = self.time_conv(x, feat_cache[idx])
+                    feat_cache[idx] = cache_x
+                    feat_idx[0] += 1
+
+                    x = x.reshape(b, 2, c, t, h, w)
+                    x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]),
+                                    3)
+                    x = x.reshape(b, c, t * 2, h, w)
+        t = x.shape[2]
+        x = rearrange(x, 'b c t h w -> (b t) c h w')
+        x = self.resample(x)
+        x = rearrange(x, '(b t) c h w -> b c t h w', t=t)
+
+        if self.mode == 'downsample3d':
+            if feat_cache is not None:
+                idx = feat_idx[0]
+                if feat_cache[idx] is None:
+                    feat_cache[idx] = x.clone()
+                    feat_idx[0] += 1
+                else:
+
+                    cache_x = x[:, :, -1:, :, :].clone()
+                    # if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx]!='Rep':
+                    #     # cache last frame of last two chunk
+                    #     cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
+
+                    x = self.time_conv(
+                        torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2))
+                    feat_cache[idx] = cache_x
+                    feat_idx[0] += 1
+        return x
+
+    def init_weight(self, conv):
+        conv_weight = conv.weight
+        nn.init.zeros_(conv_weight)
+        c1, c2, t, h, w = conv_weight.size()
+        one_matrix = torch.eye(c1, c2)
+        init_matrix = one_matrix
+        nn.init.zeros_(conv_weight)
+        #conv_weight.data[:,:,-1,1,1] = init_matrix * 0.5
+        conv_weight.data[:, :, 1, 0, 0] = init_matrix  #* 0.5
+        conv.weight.data.copy_(conv_weight)
+        nn.init.zeros_(conv.bias.data)
+
+    def init_weight2(self, conv):
+        conv_weight = conv.weight.data
+        nn.init.zeros_(conv_weight)
+        c1, c2, t, h, w = conv_weight.size()
+        init_matrix = torch.eye(c1 // 2, c2)
+        #init_matrix = repeat(init_matrix, 'o ... -> (o 2) ...').permute(1,0,2).contiguous().reshape(c1,c2)
+        conv_weight[:c1 // 2, :, -1, 0, 0] = init_matrix
+        conv_weight[c1 // 2:, :, -1, 0, 0] = init_matrix
+        conv.weight.data.copy_(conv_weight)
+        nn.init.zeros_(conv.bias.data)
+
+
+class ResidualBlock(nn.Module):
+
+    def __init__(self, in_dim, out_dim, dropout=0.0):
+        super().__init__()
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+
+        # layers
+        self.residual = nn.Sequential(
+            RMS_norm(in_dim, images=False), nn.SiLU(),
+            CausalConv3d(in_dim, out_dim, 3, padding=1),
+            RMS_norm(out_dim, images=False), nn.SiLU(), nn.Dropout(dropout),
+            CausalConv3d(out_dim, out_dim, 3, padding=1))
+        self.shortcut = CausalConv3d(in_dim, out_dim, 1) \
+            if in_dim != out_dim else nn.Identity()
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        h = self.shortcut(x)
+        for layer in self.residual:
+            if isinstance(layer, CausalConv3d) and feat_cache is not None:
+                idx = feat_idx[0]
+                cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                    # cache last frame of last two chunk
+                    cache_x = torch.cat([
+                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                            cache_x.device), cache_x
+                    ],
+                                        dim=2)
+                x = layer(x, feat_cache[idx])
+                feat_cache[idx] = cache_x
+                feat_idx[0] += 1
+            else:
+                x = layer(x)
+        return x + h
+
+
+class AttentionBlock(nn.Module):
+    """
+    Causal self-attention with a single head.
+    """
+
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+
+        # layers
+        self.norm = RMS_norm(dim)
+        self.to_qkv = ops.Conv2d(dim, dim * 3, 1)
+        self.proj = ops.Conv2d(dim, dim, 1)
+        self.optimized_attention = vae_attention()
+
+    def forward(self, x):
+        identity = x
+        b, c, t, h, w = x.size()
+        x = rearrange(x, 'b c t h w -> (b t) c h w')
+        x = self.norm(x)
+        # compute query, key, value
+
+        q, k, v = self.to_qkv(x).chunk(3, dim=1)
+        x = self.optimized_attention(q, k, v)
+
+        # output
+        x = self.proj(x)
+        x = rearrange(x, '(b t) c h w-> b c t h w', t=t)
+        return x + identity
+
+
+class Encoder3d(nn.Module):
+
+    def __init__(self,
+                 dim=128,
+                 z_dim=4,
+                 dim_mult=[1, 2, 4, 4],
+                 num_res_blocks=2,
+                 attn_scales=[],
+                 temperal_downsample=[True, True, False],
+                 dropout=0.0):
+        super().__init__()
+        self.dim = dim
+        self.z_dim = z_dim
+        self.dim_mult = dim_mult
+        self.num_res_blocks = num_res_blocks
+        self.attn_scales = attn_scales
+        self.temperal_downsample = temperal_downsample
+
+        # dimensions
+        dims = [dim * u for u in [1] + dim_mult]
+        scale = 1.0
+
+        # init block
+        self.conv1 = CausalConv3d(3, dims[0], 3, padding=1)
+
+        # downsample blocks
+        downsamples = []
+        for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
+            # residual (+attention) blocks
+            for _ in range(num_res_blocks):
+                downsamples.append(ResidualBlock(in_dim, out_dim, dropout))
+                if scale in attn_scales:
+                    downsamples.append(AttentionBlock(out_dim))
+                in_dim = out_dim
+
+            # downsample block
+            if i != len(dim_mult) - 1:
+                mode = 'downsample3d' if temperal_downsample[
+                    i] else 'downsample2d'
+                downsamples.append(Resample(out_dim, mode=mode))
+                scale /= 2.0
+        self.downsamples = nn.Sequential(*downsamples)
+
+        # middle blocks
+        self.middle = nn.Sequential(
+            ResidualBlock(out_dim, out_dim, dropout), AttentionBlock(out_dim),
+            ResidualBlock(out_dim, out_dim, dropout))
+
+        # output blocks
+        self.head = nn.Sequential(
+            RMS_norm(out_dim, images=False), nn.SiLU(),
+            CausalConv3d(out_dim, z_dim, 3, padding=1))
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        if feat_cache is not None:
+            idx = feat_idx[0]
+            cache_x = x[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                # cache last frame of last two chunk
+                cache_x = torch.cat([
+                    feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                        cache_x.device), cache_x
+                ],
+                                    dim=2)
+            x = self.conv1(x, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            x = self.conv1(x)
+
+        ## downsamples
+        for layer in self.downsamples:
+            if feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## middle
+        for layer in self.middle:
+            if isinstance(layer, ResidualBlock) and feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## head
+        for layer in self.head:
+            if isinstance(layer, CausalConv3d) and feat_cache is not None:
+                idx = feat_idx[0]
+                cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                    # cache last frame of last two chunk
+                    cache_x = torch.cat([
+                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                            cache_x.device), cache_x
+                    ],
+                                        dim=2)
+                x = layer(x, feat_cache[idx])
+                feat_cache[idx] = cache_x
+                feat_idx[0] += 1
+            else:
+                x = layer(x)
+        return x
+
+
+class Decoder3d(nn.Module):
+
+    def __init__(self,
+                 dim=128,
+                 z_dim=4,
+                 dim_mult=[1, 2, 4, 4],
+                 num_res_blocks=2,
+                 attn_scales=[],
+                 temperal_upsample=[False, True, True],
+                 dropout=0.0):
+        super().__init__()
+        self.dim = dim
+        self.z_dim = z_dim
+        self.dim_mult = dim_mult
+        self.num_res_blocks = num_res_blocks
+        self.attn_scales = attn_scales
+        self.temperal_upsample = temperal_upsample
+
+        # dimensions
+        dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]
+        scale = 1.0 / 2**(len(dim_mult) - 2)
+
+        # init block
+        self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1)
+
+        # middle blocks
+        self.middle = nn.Sequential(
+            ResidualBlock(dims[0], dims[0], dropout), AttentionBlock(dims[0]),
+            ResidualBlock(dims[0], dims[0], dropout))
+
+        # upsample blocks
+        upsamples = []
+        for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
+            # residual (+attention) blocks
+            if i == 1 or i == 2 or i == 3:
+                in_dim = in_dim // 2
+            for _ in range(num_res_blocks + 1):
+                upsamples.append(ResidualBlock(in_dim, out_dim, dropout))
+                if scale in attn_scales:
+                    upsamples.append(AttentionBlock(out_dim))
+                in_dim = out_dim
+
+            # upsample block
+            if i != len(dim_mult) - 1:
+                mode = 'upsample3d' if temperal_upsample[i] else 'upsample2d'
+                upsamples.append(Resample(out_dim, mode=mode))
+                scale *= 2.0
+        self.upsamples = nn.Sequential(*upsamples)
+
+        # output blocks
+        self.head = nn.Sequential(
+            RMS_norm(out_dim, images=False), nn.SiLU(),
+            CausalConv3d(out_dim, 3, 3, padding=1))
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        ## conv1
+        if feat_cache is not None:
+            idx = feat_idx[0]
+            cache_x = x[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                # cache last frame of last two chunk
+                cache_x = torch.cat([
+                    feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                        cache_x.device), cache_x
+                ],
+                                    dim=2)
+            x = self.conv1(x, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            x = self.conv1(x)
+
+        ## middle
+        for layer in self.middle:
+            if isinstance(layer, ResidualBlock) and feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## upsamples
+        for layer in self.upsamples:
+            if feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## head
+        for layer in self.head:
+            if isinstance(layer, CausalConv3d) and feat_cache is not None:
+                idx = feat_idx[0]
+                cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                    # cache last frame of last two chunk
+                    cache_x = torch.cat([
+                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                            cache_x.device), cache_x
+                    ],
+                                        dim=2)
+                x = layer(x, feat_cache[idx])
+                feat_cache[idx] = cache_x
+                feat_idx[0] += 1
+            else:
+                x = layer(x)
+        return x
+
+
+def count_conv3d(model):
+    count = 0
+    for m in model.modules():
+        if isinstance(m, CausalConv3d):
+            count += 1
+    return count
+
+
+class WanVAE(nn.Module):
+
+    def __init__(self,
+                 dim=128,
+                 z_dim=4,
+                 dim_mult=[1, 2, 4, 4],
+                 num_res_blocks=2,
+                 attn_scales=[],
+                 temperal_downsample=[True, True, False],
+                 dropout=0.0):
+        super().__init__()
+        self.dim = dim
+        self.z_dim = z_dim
+        self.dim_mult = dim_mult
+        self.num_res_blocks = num_res_blocks
+        self.attn_scales = attn_scales
+        self.temperal_downsample = temperal_downsample
+        self.temperal_upsample = temperal_downsample[::-1]
+
+        # modules
+        self.encoder = Encoder3d(dim, z_dim * 2, dim_mult, num_res_blocks,
+                                 attn_scales, self.temperal_downsample, dropout)
+        self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1)
+        self.conv2 = CausalConv3d(z_dim, z_dim, 1)
+        self.decoder = Decoder3d(dim, z_dim, dim_mult, num_res_blocks,
+                                 attn_scales, self.temperal_upsample, dropout)
+
+    def forward(self, x):
+        mu, log_var = self.encode(x)
+        z = self.reparameterize(mu, log_var)
+        x_recon = self.decode(z)
+        return x_recon, mu, log_var
+
+    def encode(self, x):
+        self.clear_cache()
+        ## cache
+        t = x.shape[2]
+        iter_ = 1 + (t - 1) // 4
+        ## 对encode输入的x，按时间拆分为1、4、4、4....
+        for i in range(iter_):
+            self._enc_conv_idx = [0]
+            if i == 0:
+                out = self.encoder(
+                    x[:, :, :1, :, :],
+                    feat_cache=self._enc_feat_map,
+                    feat_idx=self._enc_conv_idx)
+            else:
+                out_ = self.encoder(
+                    x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
+                    feat_cache=self._enc_feat_map,
+                    feat_idx=self._enc_conv_idx)
+                out = torch.cat([out, out_], 2)
+        mu, log_var = self.conv1(out).chunk(2, dim=1)
+        self.clear_cache()
+        return mu
+
+    def decode(self, z):
+        self.clear_cache()
+        # z: [b,c,t,h,w]
+
+        iter_ = z.shape[2]
+        x = self.conv2(z)
+        for i in range(iter_):
+            self._conv_idx = [0]
+            if i == 0:
+                out = self.decoder(
+                    x[:, :, i:i + 1, :, :],
+                    feat_cache=self._feat_map,
+                    feat_idx=self._conv_idx)
+            else:
+                out_ = self.decoder(
+                    x[:, :, i:i + 1, :, :],
+                    feat_cache=self._feat_map,
+                    feat_idx=self._conv_idx)
+                out = torch.cat([out, out_], 2)
+        self.clear_cache()
+        return out
+
+    def reparameterize(self, mu, log_var):
+        std = torch.exp(0.5 * log_var)
+        eps = torch.randn_like(std)
+        return eps * std + mu
+
+    def sample(self, imgs, deterministic=False):
+        mu, log_var = self.encode(imgs)
+        if deterministic:
+            return mu
+        std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0))
+        return mu + std * torch.randn_like(std)
+
+    def clear_cache(self):
+        self._conv_num = count_conv3d(self.decoder)
+        self._conv_idx = [0]
+        self._feat_map = [None] * self._conv_num
+        #cache encode
+        self._enc_conv_num = count_conv3d(self.encoder)
+        self._enc_conv_idx = [0]
+        self._enc_feat_map = [None] * self._enc_conv_num
diff --git a/custom_nodes/autodownloadmodels/.github/workflows/publish.yml b/custom_nodes/autodownloadmodels/.github/workflows/publish.yml
new file mode 100644
index 0000000000000000000000000000000000000000..79601651e56d41e090e78f4a68f7f99b83eb366d
--- /dev/null
+++ b/custom_nodes/autodownloadmodels/.github/workflows/publish.yml
@@ -0,0 +1,28 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+      - master
+    paths:
+      - "pyproject.toml"
+
+permissions:
+  issues: write
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'AIExplorer25' }}
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+        with:
+          submodules: true
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@v1
+        with:
+          ## Add your own personal access token to your Github Repository secrets and reference it here.
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
diff --git a/custom_nodes/comfyui-custom-scripts/.github/workflows/publish.yml b/custom_nodes/comfyui-custom-scripts/.github/workflows/publish.yml
new file mode 100644
index 0000000000000000000000000000000000000000..c9c70bf7f47b9e294ac2436b8fe2a2b8878bc106
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/.github/workflows/publish.yml
@@ -0,0 +1,25 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+    paths:
+      - "pyproject.toml"
+
+permissions:
+  issues: write
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'pythongosssss' }}
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@v1
+        with:
+          ## Add your own personal access token to your Github Repository secrets and reference it here.
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
diff --git a/custom_nodes/comfyui-custom-scripts/py/autocomplete.py b/custom_nodes/comfyui-custom-scripts/py/autocomplete.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ac6a0524823d13849b6cbe02efedc7bffea8f38
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/py/autocomplete.py
@@ -0,0 +1,29 @@
+from server import PromptServer
+from aiohttp import web
+import os
+import folder_paths
+
+dir = os.path.abspath(os.path.join(__file__, "../../user"))
+if not os.path.exists(dir):
+    os.mkdir(dir)
+file = os.path.join(dir, "autocomplete.txt")
+
+
+@PromptServer.instance.routes.get("/pysssss/autocomplete")
+async def get_autocomplete(request):
+    if os.path.isfile(file):
+        return web.FileResponse(file)
+    return web.Response(status=404)
+
+
+@PromptServer.instance.routes.post("/pysssss/autocomplete")
+async def update_autocomplete(request):
+    with open(file, "w", encoding="utf-8") as f:
+        f.write(await request.text())
+    return web.Response(status=200)
+
+
+@PromptServer.instance.routes.get("/pysssss/loras")
+async def get_loras(request):
+    loras = folder_paths.get_filename_list("loras")
+    return web.json_response(list(map(lambda a: os.path.splitext(a)[0], loras)))
diff --git a/custom_nodes/comfyui-custom-scripts/py/better_combos.py b/custom_nodes/comfyui-custom-scripts/py/better_combos.py
new file mode 100644
index 0000000000000000000000000000000000000000..d31043b0afdb8943409f3d97adbdde2db9dc38ee
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/py/better_combos.py
@@ -0,0 +1,172 @@
+import glob
+import os
+from nodes import LoraLoader, CheckpointLoaderSimple
+import folder_paths
+from server import PromptServer
+from folder_paths import get_directory_by_type
+from aiohttp import web
+import shutil
+
+
+@PromptServer.instance.routes.get("/pysssss/view/{name}")
+async def view(request):
+    name = request.match_info["name"]
+    pos = name.index("/")
+    type = name[0:pos]
+    name = name[pos+1:]
+
+    image_path = folder_paths.get_full_path(
+        type, name)
+    if not image_path:
+        return web.Response(status=404)
+
+    filename = os.path.basename(image_path)
+    return web.FileResponse(image_path, headers={"Content-Disposition": f"filename=\"{filename}\""})
+
+
+@PromptServer.instance.routes.post("/pysssss/save/{name}")
+async def save_preview(request):
+    name = request.match_info["name"]
+    pos = name.index("/")
+    type = name[0:pos]
+    name = name[pos+1:]
+
+    body = await request.json()
+
+    dir = get_directory_by_type(body.get("type", "output"))
+    subfolder = body.get("subfolder", "")
+    full_output_folder = os.path.join(dir, os.path.normpath(subfolder))
+
+    filepath = os.path.join(full_output_folder, body.get("filename", ""))
+
+    if os.path.commonpath((dir, os.path.abspath(filepath))) != dir:
+        return web.Response(status=400)
+
+    image_path = folder_paths.get_full_path(type, name)
+    image_path = os.path.splitext(
+        image_path)[0] + os.path.splitext(filepath)[1]
+
+    shutil.copyfile(filepath, image_path)
+
+    return web.json_response({
+        "image":  type + "/" + os.path.basename(image_path)
+    })
+
+
+@PromptServer.instance.routes.get("/pysssss/examples/{name}")
+async def get_examples(request):
+    name = request.match_info["name"]
+    pos = name.index("/")
+    type = name[0:pos]
+    name = name[pos+1:]
+
+    file_path = folder_paths.get_full_path(
+        type, name)
+    if not file_path:
+        return web.Response(status=404)
+
+    file_path_no_ext = os.path.splitext(file_path)[0]
+    examples = []
+
+    if os.path.isdir(file_path_no_ext):
+        examples += sorted(map(lambda t: os.path.relpath(t, file_path_no_ext),
+                               glob.glob(file_path_no_ext + "/*.txt")))
+
+    if os.path.isfile(file_path_no_ext + ".txt"):
+        examples += ["notes"]
+
+    return web.json_response(examples)
+
+
+@PromptServer.instance.routes.post("/pysssss/examples/{name}")
+async def save_example(request):
+    name = request.match_info["name"]
+    pos = name.index("/")
+    type = name[0:pos]
+    name = name[pos+1:]
+    body = await request.json()
+    example_name = body["name"]
+    example = body["example"]
+
+    file_path = folder_paths.get_full_path(
+        type, name)
+    if not file_path:
+        return web.Response(status=404)
+
+    if not example_name.endswith(".txt"):
+        example_name += ".txt"
+
+    file_path_no_ext = os.path.splitext(file_path)[0]
+    example_file = os.path.join(file_path_no_ext, example_name)
+    if not os.path.exists(file_path_no_ext):
+        os.mkdir(file_path_no_ext)
+    with open(example_file, 'w', encoding='utf8') as f:
+        f.write(example)
+
+    return web.Response(status=201)
+
+
+@PromptServer.instance.routes.get("/pysssss/images/{type}")
+async def get_images(request):
+    type = request.match_info["type"]
+    names = folder_paths.get_filename_list(type)
+
+    images = {}
+    for item_name in names:
+        file_name = os.path.splitext(item_name)[0]
+        file_path = folder_paths.get_full_path(type, item_name)
+
+        if file_path is None:
+            continue
+
+        file_path_no_ext = os.path.splitext(file_path)[0]
+
+        for ext in ["png", "jpg", "jpeg", "preview.png", "preview.jpeg"]:
+            if os.path.isfile(file_path_no_ext + "." + ext):
+                images[item_name] = f"{type}/{file_name}.{ext}"
+                break
+
+    return web.json_response(images)
+
+
+class LoraLoaderWithImages(LoraLoader):
+    RETURN_TYPES = (*LoraLoader.RETURN_TYPES, "STRING",)
+    RETURN_NAMES = (*getattr(LoraLoader, "RETURN_NAMES",
+                    LoraLoader.RETURN_TYPES), "example")
+
+    @classmethod
+    def INPUT_TYPES(s):
+        types = super().INPUT_TYPES()
+        types["optional"] = {"prompt": ("STRING", {"hidden": True})}
+        return types
+
+    def load_lora(self, **kwargs):
+        prompt = kwargs.pop("prompt", "")
+        return (*super().load_lora(**kwargs), prompt)
+
+
+class CheckpointLoaderSimpleWithImages(CheckpointLoaderSimple):
+    RETURN_TYPES = (*CheckpointLoaderSimple.RETURN_TYPES, "STRING",)
+    RETURN_NAMES = (*getattr(CheckpointLoaderSimple, "RETURN_NAMES",
+                    CheckpointLoaderSimple.RETURN_TYPES), "example")
+
+    @classmethod
+    def INPUT_TYPES(s):
+        types = super().INPUT_TYPES()
+        types["optional"] = {"prompt": ("STRING", {"hidden": True})}
+        return types
+
+    def load_checkpoint(self, **kwargs):
+        prompt = kwargs.pop("prompt", "")
+        return (*super().load_checkpoint(**kwargs), prompt)
+
+
+NODE_CLASS_MAPPINGS = {
+    "LoraLoader|pysssss": LoraLoaderWithImages,
+    "CheckpointLoader|pysssss": CheckpointLoaderSimpleWithImages,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LoraLoader|pysssss": "Lora Loader 🐍",
+    "CheckpointLoader|pysssss": "Checkpoint Loader 🐍",
+}
diff --git a/custom_nodes/comfyui-custom-scripts/py/constrain_image.py b/custom_nodes/comfyui-custom-scripts/py/constrain_image.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c768893c5ed5c1cb1247bca77414dc8762d3fde
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/py/constrain_image.py
@@ -0,0 +1,71 @@
+import torch
+import numpy as np
+from PIL import Image
+
+class ConstrainImage:
+    """
+    A node that constrains an image to a maximum and minimum size while maintaining aspect ratio.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "max_width": ("INT", {"default": 1024, "min": 0}),
+                "max_height": ("INT", {"default": 1024, "min": 0}),
+                "min_width": ("INT", {"default": 0, "min": 0}),
+                "min_height": ("INT", {"default": 0, "min": 0}),
+                "crop_if_required": (["yes", "no"], {"default": "no"}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "constrain_image"
+    CATEGORY = "image"
+    OUTPUT_IS_LIST = (True,)
+
+    def constrain_image(self, images, max_width, max_height, min_width, min_height, crop_if_required):
+        crop_if_required = crop_if_required == "yes"
+        results = []
+        for image in images:
+            i = 255. * image.cpu().numpy()
+            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)).convert("RGB")
+
+            current_width, current_height = img.size
+            aspect_ratio = current_width / current_height
+
+            constrained_width = min(max(current_width, min_width), max_width)
+            constrained_height = min(max(current_height, min_height), max_height)
+
+            if constrained_width / constrained_height > aspect_ratio:
+                constrained_width = max(int(constrained_height * aspect_ratio), min_width)
+                if crop_if_required:
+                    constrained_height = int(current_height / (current_width / constrained_width))
+            else:
+                constrained_height = max(int(constrained_width / aspect_ratio), min_height)
+                if crop_if_required:
+                    constrained_width = int(current_width / (current_height / constrained_height))
+
+            resized_image = img.resize((constrained_width, constrained_height), Image.LANCZOS)
+
+            if crop_if_required and (constrained_width > max_width or constrained_height > max_height):
+                left = max((constrained_width - max_width) // 2, 0)
+                top = max((constrained_height - max_height) // 2, 0)
+                right = min(constrained_width, max_width) + left
+                bottom = min(constrained_height, max_height) + top
+                resized_image = resized_image.crop((left, top, right, bottom))
+
+            resized_image = np.array(resized_image).astype(np.float32) / 255.0
+            resized_image = torch.from_numpy(resized_image)[None,]
+            results.append(resized_image)
+                
+        return (results,)
+
+NODE_CLASS_MAPPINGS = {
+    "ConstrainImage|pysssss": ConstrainImage,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ConstrainImage|pysssss": "Constrain Image 🐍",
+}
diff --git a/custom_nodes/comfyui-custom-scripts/py/constrain_image_for_video.py b/custom_nodes/comfyui-custom-scripts/py/constrain_image_for_video.py
new file mode 100644
index 0000000000000000000000000000000000000000..91c0c502b8d8f6202d96a26b045e393b4552fc8e
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/py/constrain_image_for_video.py
@@ -0,0 +1,72 @@
+import torch
+import numpy as np
+from PIL import Image
+
+class ConstrainImageforVideo:
+    """
+    A node that constrains an image to a maximum and minimum size while maintaining aspect ratio.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "max_width": ("INT", {"default": 1024, "min": 0}),
+                "max_height": ("INT", {"default": 1024, "min": 0}),
+                "min_width": ("INT", {"default": 0, "min": 0}),
+                "min_height": ("INT", {"default": 0, "min": 0}),
+                "crop_if_required": (["yes", "no"], {"default": "no"}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("IMAGE",)
+    FUNCTION = "constrain_image_for_video"
+    CATEGORY = "image"
+
+    def constrain_image_for_video(self, images, max_width, max_height, min_width, min_height, crop_if_required):
+        crop_if_required = crop_if_required == "yes"
+        results = []
+        for image in images:
+            i = 255. * image.cpu().numpy()
+            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)).convert("RGB")
+
+            current_width, current_height = img.size
+            aspect_ratio = current_width / current_height
+
+            constrained_width = max(min(current_width, min_width), max_width)
+            constrained_height = max(min(current_height, min_height), max_height)
+
+            if constrained_width / constrained_height > aspect_ratio:
+                constrained_width = max(int(constrained_height * aspect_ratio), min_width)
+                if crop_if_required:
+                    constrained_height = int(current_height / (current_width / constrained_width))
+            else:
+                constrained_height = max(int(constrained_width / aspect_ratio), min_height)
+                if crop_if_required:
+                    constrained_width = int(current_width / (current_height / constrained_height))
+
+            resized_image = img.resize((constrained_width, constrained_height), Image.LANCZOS)
+
+            if crop_if_required and (constrained_width > max_width or constrained_height > max_height):
+                left = max((constrained_width - max_width) // 2, 0)
+                top = max((constrained_height - max_height) // 2, 0)
+                right = min(constrained_width, max_width) + left
+                bottom = min(constrained_height, max_height) + top
+                resized_image = resized_image.crop((left, top, right, bottom))
+
+            resized_image = np.array(resized_image).astype(np.float32) / 255.0
+            resized_image = torch.from_numpy(resized_image)[None,]
+            results.append(resized_image)
+            all_images = torch.cat(results, dim=0)
+                
+        return (all_images, all_images.size(0),)
+
+NODE_CLASS_MAPPINGS = {
+    "ConstrainImageforVideo|pysssss": ConstrainImageforVideo,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ConstrainImageforVideo|pysssss": "Constrain Image for Video 🐍",
+}
diff --git a/custom_nodes/comfyui-custom-scripts/py/math_expression.py b/custom_nodes/comfyui-custom-scripts/py/math_expression.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a41655b6876a077338040b0099c4d4e8c9dbcd2
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/py/math_expression.py
@@ -0,0 +1,252 @@
+import ast
+import math
+import random
+import operator as op
+
+# Hack: string type that is always equal in not equal comparisons
+class AnyType(str):
+    def __ne__(self, __value: object) -> bool:
+        return False
+
+
+# Our any instance wants to be a wildcard string
+any = AnyType("*")
+
+operators = {
+    ast.Add: op.add,
+    ast.Sub: op.sub,
+    ast.Mult: op.mul,
+    ast.Div: op.truediv,
+    ast.FloorDiv: op.floordiv,
+    ast.Pow: op.pow,
+    ast.BitXor: op.xor,
+    ast.USub: op.neg,
+    ast.Mod: op.mod,
+    ast.BitAnd: op.and_,
+    ast.BitOr: op.or_,
+    ast.Invert: op.invert,
+    ast.And: lambda a, b: 1 if a and b else 0,
+    ast.Or: lambda a, b: 1 if a or b else 0,
+    ast.Not: lambda a: 0 if a else 1,
+    ast.RShift: op.rshift,
+    ast.LShift: op.lshift
+}
+
+# TODO: restructure args to provide more info, generate hint based on args to save duplication
+functions = {
+    "round": {
+        "args": (1, 2),
+        "call": lambda a, b = None: round(a, b),
+        "hint": "number, dp? = 0"
+    },
+    "ceil": {
+        "args": (1, 1),
+        "call": lambda a: math.ceil(a),
+        "hint": "number"
+    },
+    "floor": {
+        "args": (1, 1),
+        "call": lambda a: math.floor(a),
+        "hint": "number"
+    },
+    "min": {
+        "args": (2, None),
+        "call": lambda *args: min(*args),
+        "hint": "...numbers"
+    },
+    "max": {
+        "args": (2, None),
+        "call": lambda *args: max(*args),
+        "hint": "...numbers"
+    },
+    "randomint": {
+        "args": (2, 2),
+        "call": lambda a, b: random.randint(a, b),
+        "hint": "min, max"
+    },
+    "randomchoice": {
+        "args": (2, None),
+        "call": lambda *args: random.choice(args),
+        "hint": "...numbers"
+    },
+    "sqrt": {
+        "args": (1, 1),
+        "call": lambda a: math.sqrt(a),
+        "hint": "number"
+    },
+    "int": {
+        "args": (1, 1),
+        "call": lambda a = None: int(a),
+        "hint": "number"
+    },
+    "iif": {
+        "args": (3, 3),
+        "call": lambda a, b, c = None: b if a else c,
+        "hint": "value, truepart, falsepart"
+    },
+}
+
+autocompleteWords = list({
+    "text": x,
+    "value": f"{x}()",
+    "showValue": False,
+    "hint": f"{functions[x]['hint']}",
+    "caretOffset": -1
+} for x in functions.keys())
+
+
+class MathExpression:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "expression": ("STRING", {"multiline": True, "dynamicPrompts": False, "pysssss.autocomplete": {
+                    "words": autocompleteWords,
+                    "separator": ""
+                }}),
+            },
+            "optional": {
+                "a": (any, ),
+                "b": (any,),
+                "c": (any, ),
+            },
+            "hidden": {"extra_pnginfo": "EXTRA_PNGINFO",
+                       "prompt": "PROMPT"},
+        }
+
+    RETURN_TYPES = ("INT", "FLOAT", )
+    FUNCTION = "evaluate"
+    CATEGORY = "utils"
+    OUTPUT_NODE = True
+
+    @classmethod
+    def IS_CHANGED(s, expression, **kwargs):
+        if "random" in expression:
+            return float("nan")
+        return expression
+
+    def get_widget_value(self, extra_pnginfo, prompt, node_name, widget_name):
+        workflow = extra_pnginfo["workflow"] if "workflow" in extra_pnginfo else { "nodes": [] }
+        node_id = None
+        for node in workflow["nodes"]:
+            name = node["type"]
+            if "properties" in node:
+                if "Node name for S&R" in node["properties"]:
+                    name = node["properties"]["Node name for S&R"]
+            if name == node_name:
+                node_id = node["id"]
+                break
+            if "title" in node:
+                name = node["title"]
+            if name == node_name:
+                node_id = node["id"]
+                break
+        if node_id is not None:
+            values = prompt[str(node_id)]
+            if "inputs" in values:
+                if widget_name in values["inputs"]:
+                    value = values["inputs"][widget_name]
+                    if isinstance(value, list):
+                        raise ValueError("Converted widgets are not supported via named reference, use the inputs instead.")
+                    return value
+            raise NameError(f"Widget not found: {node_name}.{widget_name}")
+        raise NameError(f"Node not found: {node_name}.{widget_name}")
+
+    def get_size(self, target, property):
+        if isinstance(target, dict) and "samples" in target:
+            # Latent
+            if property == "width":
+                return target["samples"].shape[3] * 8
+            return target["samples"].shape[2] * 8
+        else:
+            # Image
+            if property == "width":
+                return target.shape[2]
+            return target.shape[1]
+
+    def evaluate(self, expression, prompt, extra_pnginfo={}, a=None, b=None, c=None):
+        expression = expression.replace('\n', ' ').replace('\r', '')
+        node = ast.parse(expression, mode='eval').body
+
+        lookup = {"a": a, "b": b, "c": c}
+
+        def eval_op(node, l, r):
+            l = eval_expr(l)
+            r = eval_expr(r)
+            l = l if isinstance(l, int) else float(l)
+            r = r if isinstance(r, int) else float(r)
+            return operators[type(node.op)](l, r)
+
+        def eval_expr(node):
+            if isinstance(node, ast.Constant) or isinstance(node, ast.Num):
+                return node.n
+            elif isinstance(node, ast.BinOp):
+                return eval_op(node, node.left, node.right)
+            elif isinstance(node, ast.BoolOp):
+                return eval_op(node, node.values[0], node.values[1])
+            elif isinstance(node, ast.UnaryOp):
+                return operators[type(node.op)](eval_expr(node.operand))
+            elif isinstance(node, ast.Attribute):
+                if node.value.id in lookup:
+                    if node.attr == "width" or node.attr == "height":
+                        return self.get_size(lookup[node.value.id], node.attr)
+
+                return self.get_widget_value(extra_pnginfo, prompt, node.value.id, node.attr)
+            elif isinstance(node, ast.Name):
+                if node.id in lookup:
+                    val = lookup[node.id]
+                    if isinstance(val, (int, float, complex)):
+                        return val
+                    else:
+                        raise TypeError(
+                            f"Compex types (LATENT/IMAGE) need to reference their width/height, e.g. {node.id}.width")
+                raise NameError(f"Name not found: {node.id}")
+            elif isinstance(node, ast.Call):
+                if node.func.id in functions:
+                    fn = functions[node.func.id]
+                    l = len(node.args)
+                    if l < fn["args"][0] or (fn["args"][1] is not None and l > fn["args"][1]):
+                        if fn["args"][1] is None:
+                            toErr = " or more"
+                        else:
+                            toErr = f" to {fn['args'][1]}"
+                        raise SyntaxError(
+                            f"Invalid function call: {node.func.id} requires {fn['args'][0]}{toErr} arguments")
+                    args = []
+                    for arg in node.args:
+                        args.append(eval_expr(arg))
+                    return fn["call"](*args)
+                raise NameError(f"Invalid function call: {node.func.id}")
+            elif isinstance(node, ast.Compare):
+                l = eval_expr(node.left)
+                r = eval_expr(node.comparators[0])
+                if isinstance(node.ops[0], ast.Eq):
+                    return 1 if l == r else 0
+                if isinstance(node.ops[0], ast.NotEq):
+                    return 1 if l != r else 0
+                if isinstance(node.ops[0], ast.Gt):
+                    return 1 if l > r else 0
+                if isinstance(node.ops[0], ast.GtE):
+                    return 1 if l >= r else 0
+                if isinstance(node.ops[0], ast.Lt):
+                    return 1 if l < r else 0
+                if isinstance(node.ops[0], ast.LtE):
+                    return 1 if l <= r else 0
+                raise NotImplementedError(
+                    "Operator " + node.ops[0].__class__.__name__ + " not supported.")
+            else:
+                raise TypeError(node)
+
+        r = eval_expr(node)
+        return {"ui": {"value": [r]}, "result": (int(r), float(r),)}
+
+
+NODE_CLASS_MAPPINGS = {
+    "MathExpression|pysssss": MathExpression,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "MathExpression|pysssss": "Math Expression 🐍",
+}
+
diff --git a/custom_nodes/comfyui-custom-scripts/py/model_info.py b/custom_nodes/comfyui-custom-scripts/py/model_info.py
new file mode 100644
index 0000000000000000000000000000000000000000..b360bafb35fdfc25dd94902076fab6baa39ee23c
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/py/model_info.py
@@ -0,0 +1,115 @@
+import hashlib
+import json
+from aiohttp import web
+from server import PromptServer
+import folder_paths
+import os
+
+
+def get_metadata(filepath):
+    with open(filepath, "rb") as file:
+        # https://github.com/huggingface/safetensors#format
+        # 8 bytes: N, an unsigned little-endian 64-bit integer, containing the size of the header
+        header_size = int.from_bytes(file.read(8), "little", signed=False)
+
+        if header_size <= 0:
+            raise BufferError("Invalid header size")
+
+        header = file.read(header_size)
+        if header_size <= 0:
+            raise BufferError("Invalid header")
+
+        header_json = json.loads(header)
+        return header_json["__metadata__"] if "__metadata__" in header_json else None
+
+
+@PromptServer.instance.routes.post("/pysssss/metadata/notes/{name}")
+async def save_notes(request):
+    name = request.match_info["name"]
+    pos = name.index("/")
+    type = name[0:pos]
+    name = name[pos+1:]
+
+    file_path = None
+    if type == "embeddings" or type == "loras":
+        name = name.lower()
+        files = folder_paths.get_filename_list(type)
+        for f in files:
+            lower_f = f.lower()
+            if lower_f == name:
+                file_path = folder_paths.get_full_path(type, f)
+            else:
+                n = os.path.splitext(f)[0].lower()
+                if n == name:
+                    file_path = folder_paths.get_full_path(type, f)
+
+            if file_path is not None:
+                break
+    else:
+        file_path = folder_paths.get_full_path(
+            type, name)
+    if not file_path:
+        return web.Response(status=404)
+
+    file_no_ext = os.path.splitext(file_path)[0]
+    info_file = file_no_ext + ".txt"
+    with open(info_file, "w") as f:
+        f.write(await request.text())
+
+    return web.Response(status=200)
+
+
+@PromptServer.instance.routes.get("/pysssss/metadata/{name}")
+async def load_metadata(request):
+    name = request.match_info["name"]
+    pos = name.index("/")
+    type = name[0:pos]
+    name = name[pos+1:]
+
+    file_path = None
+    if type == "embeddings" or type == "loras":
+        name = name.lower()
+        files = folder_paths.get_filename_list(type)
+        for f in files:
+            lower_f = f.lower()
+            if lower_f == name:
+                file_path = folder_paths.get_full_path(type, f)
+            else:
+                n = os.path.splitext(f)[0].lower()
+                if n == name:
+                    file_path = folder_paths.get_full_path(type, f)
+
+            if file_path is not None:
+                break
+    else:
+        file_path = folder_paths.get_full_path(
+            type, name)
+    if not file_path:
+        return web.Response(status=404)
+
+    try:
+        meta = get_metadata(file_path)
+    except:
+        meta = None
+
+    if meta is None:
+        meta = {}
+
+    file_no_ext = os.path.splitext(file_path)[0]
+
+    info_file = file_no_ext + ".txt"
+    if os.path.isfile(info_file):
+        with open(info_file, "r") as f:
+            meta["pysssss.notes"] = f.read()
+
+    hash_file = file_no_ext + ".sha256"
+    if os.path.isfile(hash_file):
+        with open(hash_file, "rt") as f:
+            meta["pysssss.sha256"] = f.read()
+    else:
+        with open(file_path, "rb") as f:
+            meta["pysssss.sha256"] = hashlib.sha256(f.read()).hexdigest()
+        with open(hash_file, "wt") as f:
+            f.write(meta["pysssss.sha256"])
+
+    return web.json_response(meta)
diff --git a/custom_nodes/comfyui-custom-scripts/py/play_sound.py b/custom_nodes/comfyui-custom-scripts/py/play_sound.py
new file mode 100644
index 0000000000000000000000000000000000000000..65f599d2680553a20031d81d1fdf161d73edefef
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/py/play_sound.py
@@ -0,0 +1,42 @@
+# Hack: string type that is always equal in not equal comparisons
+class AnyType(str):
+    def __ne__(self, __value: object) -> bool:
+        return False
+
+
+# Our any instance wants to be a wildcard string
+any = AnyType("*")
+
+
+class PlaySound:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "any": (any, {}),
+            "mode": (["always", "on empty queue"], {}),
+            "volume": ("FLOAT", {"min": 0, "max": 1, "step": 0.1, "default": 0.5}),
+            "file": ("STRING", { "default": "notify.mp3" })
+        }}
+
+    FUNCTION = "nop"
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+    OUTPUT_NODE = True
+    RETURN_TYPES = (any,)
+
+    CATEGORY = "utils"
+
+    def IS_CHANGED(self, **kwargs):
+        return float("NaN")
+
+    def nop(self, any, mode, volume, file):
+        return {"ui": {"a": []}, "result": (any,)}
+
+
+NODE_CLASS_MAPPINGS = {
+    "PlaySound|pysssss": PlaySound,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "PlaySound|pysssss": "PlaySound 🐍",
+}
diff --git a/custom_nodes/comfyui-custom-scripts/py/repeater.py b/custom_nodes/comfyui-custom-scripts/py/repeater.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d83c67e2132b9db708ef087ad788d22c58c80c2
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/py/repeater.py
@@ -0,0 +1,46 @@
+# Hack: string type that is always equal in not equal comparisons
+class AnyType(str):
+    def __ne__(self, __value: object) -> bool:
+        return False
+
+
+# Our any instance wants to be a wildcard string
+any = AnyType("*")
+
+
+class Repeater:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "source": (any, {}),
+            "repeats": ("INT", {"min": 0, "max": 5000, "default": 2}),
+            "output": (["single", "multi"], {}),
+            "node_mode": (["reuse", "create"], {}),
+        }}
+
+    RETURN_TYPES = (any,)
+    FUNCTION = "repeat"
+    OUTPUT_NODE = False
+    OUTPUT_IS_LIST = (True,)
+
+    CATEGORY = "utils"
+
+    def repeat(self, repeats, output, node_mode, **kwargs):
+        if output == "multi":
+            # Multi outputs are split to indiviual nodes on the frontend when serializing
+            return ([kwargs["source"]],)
+        elif node_mode == "reuse":
+            # When reusing we have a single input node, repeat that N times
+            return ([kwargs["source"]] * repeats,)
+        else:
+            # When creating new nodes, they'll be added dynamically when the graph is serialized
+            return ((list(kwargs.values())),)
+
+
+NODE_CLASS_MAPPINGS = {
+    "Repeater|pysssss": Repeater,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "Repeater|pysssss": "Repeater 🐍",
+}
diff --git a/custom_nodes/comfyui-custom-scripts/py/reroute_primitive.py b/custom_nodes/comfyui-custom-scripts/py/reroute_primitive.py
new file mode 100644
index 0000000000000000000000000000000000000000..c22a7d1ef00eddfbde6185e702b6c3462521ccda
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/py/reroute_primitive.py
@@ -0,0 +1,59 @@
+# Hack: string type that is always equal in not equal comparisons
+class AnyType(str):
+    def __ne__(self, __value: object) -> bool:
+        return False
+
+
+# Our any instance wants to be a wildcard string
+any = AnyType("*")
+
+
+class ReroutePrimitive:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {"value": (any, )},
+        }
+
+    @classmethod
+    def VALIDATE_INPUTS(s, **kwargs):
+        return True
+
+    RETURN_TYPES = (any,)
+    FUNCTION = "route"
+    CATEGORY = "__hidden__"
+
+    def route(self, value):
+        return (value,)
+
+
+class MultiPrimitive:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {},
+            "optional": {"value": (any, )},
+        }
+
+    @classmethod
+    def VALIDATE_INPUTS(s, **kwargs):
+        return True
+
+    RETURN_TYPES = (any,)
+    FUNCTION = "listify"
+    CATEGORY = "utils"
+    OUTPUT_IS_LIST = (True,)
+
+    def listify(self, **kwargs):
+        return (list(kwargs.values()),)
+
+
+NODE_CLASS_MAPPINGS = {
+    "ReroutePrimitive|pysssss": ReroutePrimitive,
+    # "MultiPrimitive|pysssss": MultiPrimitive,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ReroutePrimitive|pysssss": "Reroute Primitive 🐍",
+    # "MultiPrimitive|pysssss": "Multi Primitive 🐍",
+}
diff --git a/custom_nodes/comfyui-custom-scripts/py/show_text.py b/custom_nodes/comfyui-custom-scripts/py/show_text.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a013a5bc7a82e29f38f35ff85dab827ba42a254
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/py/show_text.py
@@ -0,0 +1,49 @@
+class ShowText:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "text": ("STRING", {"forceInput": True}),
+            },
+            "hidden": {
+                "unique_id": "UNIQUE_ID",
+                "extra_pnginfo": "EXTRA_PNGINFO",
+            },
+        }
+
+    INPUT_IS_LIST = True
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "notify"
+    OUTPUT_NODE = True
+    OUTPUT_IS_LIST = (True,)
+
+    CATEGORY = "utils"
+
+    def notify(self, text, unique_id=None, extra_pnginfo=None):
+        if unique_id is not None and extra_pnginfo is not None:
+            if not isinstance(extra_pnginfo, list):
+                print("Error: extra_pnginfo is not a list")
+            elif (
+                not isinstance(extra_pnginfo[0], dict)
+                or "workflow" not in extra_pnginfo[0]
+            ):
+                print("Error: extra_pnginfo[0] is not a dict or missing 'workflow' key")
+            else:
+                workflow = extra_pnginfo[0]["workflow"]
+                node = next(
+                    (x for x in workflow["nodes"] if str(x["id"]) == str(unique_id[0])),
+                    None,
+                )
+                if node:
+                    node["widgets_values"] = [text]
+
+        return {"ui": {"text": text}, "result": (text,)}
+
+
+NODE_CLASS_MAPPINGS = {
+    "ShowText|pysssss": ShowText,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ShowText|pysssss": "Show Text 🐍",
+}
diff --git a/custom_nodes/comfyui-custom-scripts/py/string_function.py b/custom_nodes/comfyui-custom-scripts/py/string_function.py
new file mode 100644
index 0000000000000000000000000000000000000000..f502ba31f52720f019c9f1d155d17a9cb9e335f9
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/py/string_function.py
@@ -0,0 +1,49 @@
+import re
+
+class StringFunction:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "action": (["append", "replace"], {}),
+                "tidy_tags": (["yes", "no"], {}),
+            },
+            "optional": {
+                "text_a": ("STRING", {"multiline": True, "dynamicPrompts": False}),
+                "text_b": ("STRING", {"multiline": True, "dynamicPrompts": False}),
+                "text_c": ("STRING", {"multiline": True, "dynamicPrompts": False})
+            }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "exec"
+    CATEGORY = "utils"
+    OUTPUT_NODE = True
+
+    def exec(self, action, tidy_tags, text_a="", text_b="", text_c=""):     
+        tidy_tags = tidy_tags == "yes"
+        out = ""
+        if action == "append":
+            out = (", " if tidy_tags else "").join(filter(None, [text_a, text_b, text_c]))
+        else:
+           if text_c is None:
+               text_c = ""
+           if text_b.startswith("/") and text_b.endswith("/"):
+               regex = text_b[1:-1]
+               out = re.sub(regex, text_c, text_a)
+           else:
+               out = text_a.replace(text_b, text_c)
+        if tidy_tags:
+            out = re.sub(r"\s{2,}", " ", out)
+            out = out.replace(" ,", ",")
+            out = re.sub(r",{2,}", ",", out)
+            out = out.strip()
+        return {"ui": {"text": (out,)}, "result": (out,)}
+            
+NODE_CLASS_MAPPINGS = {
+    "StringFunction|pysssss": StringFunction,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "StringFunction|pysssss": "String Function 🐍",
+}
diff --git a/custom_nodes/comfyui-custom-scripts/py/system_notification.py b/custom_nodes/comfyui-custom-scripts/py/system_notification.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5a52c14773ff86bbb3b29c6405a05238d8b46eb
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/py/system_notification.py
@@ -0,0 +1,41 @@
+# Hack: string type that is always equal in not equal comparisons
+class AnyType(str):
+    def __ne__(self, __value: object) -> bool:
+        return False
+
+
+# Our any instance wants to be a wildcard string
+any = AnyType("*")
+
+
+class SystemNotification:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "message": ("STRING", {"default": "Your notification has triggered."}),
+            "any": (any, {}),
+            "mode": (["always", "on empty queue"], {}),
+        }}
+
+    FUNCTION = "nop"
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+    OUTPUT_NODE = True
+    RETURN_TYPES = (any,)
+
+    CATEGORY = "utils"
+
+    def IS_CHANGED(self, **kwargs):
+        return float("NaN")
+
+    def nop(self, any, message, mode):
+        return {"ui": {"message": message, "mode": mode}, "result": (any,)}
+
+
+NODE_CLASS_MAPPINGS = {
+    "SystemNotification|pysssss": SystemNotification,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "SystemNotification|pysssss": "SystemNotification 🐍",
+}
diff --git a/custom_nodes/comfyui-custom-scripts/py/text_files.py b/custom_nodes/comfyui-custom-scripts/py/text_files.py
new file mode 100644
index 0000000000000000000000000000000000000000..253de9b6bde24b8daa15df627516801faf68e9a0
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/py/text_files.py
@@ -0,0 +1,200 @@
+import os
+import folder_paths
+import json
+from server import PromptServer
+import glob
+from aiohttp import web
+
+
+def get_allowed_dirs():
+    dir = os.path.abspath(os.path.join(__file__, "../../user"))
+    file = os.path.join(dir, "text_file_dirs.json")
+    with open(file, "r") as f:
+        return json.loads(f.read())
+
+
+def get_valid_dirs():
+    return get_allowed_dirs().keys()
+
+
+def get_dir_from_name(name):
+    dirs = get_allowed_dirs()
+    if name not in dirs:
+        raise KeyError(name + " dir not found")
+
+    path = dirs[name]
+    path = path.replace("$input", folder_paths.get_input_directory())
+    path = path.replace("$output", folder_paths.get_output_directory())
+    path = path.replace("$temp", folder_paths.get_temp_directory())
+    return path
+
+
+def is_child_dir(parent_path, child_path):
+    parent_path = os.path.abspath(parent_path)
+    child_path = os.path.abspath(child_path)
+    return os.path.commonpath([parent_path]) == os.path.commonpath([parent_path, child_path])
+
+
+def get_real_path(dir):
+    dir = dir.replace("/**/", "/")
+    dir = os.path.abspath(dir)
+    dir = os.path.split(dir)[0]
+    return dir
+
+
+@PromptServer.instance.routes.get("/pysssss/text-file/{name}")
+async def get_files(request):
+    name = request.match_info["name"]
+    dir = get_dir_from_name(name)
+    recursive = "/**/" in dir
+    # Ugh cant use root_path on glob... lazy hack..
+    pre = get_real_path(dir)
+
+    files = list(map(lambda t: os.path.relpath(t, pre),
+                     glob.glob(dir, recursive=recursive)))
+
+    if len(files) == 0:
+        files = ["[none]"]
+    return web.json_response(files)
+
+
+def get_file(root_dir, file):
+    if file == "[none]" or not file or not file.strip():
+        raise ValueError("No file")
+
+    root_dir = get_dir_from_name(root_dir)
+    root_dir = get_real_path(root_dir)
+    if not os.path.exists(root_dir):
+        os.mkdir(root_dir)
+    full_path = os.path.join(root_dir, file)
+
+    if not is_child_dir(root_dir, full_path):
+        raise ReferenceError()
+
+    return full_path
+
+
+class TextFileNode:
+    RETURN_TYPES = ("STRING",)
+    CATEGORY = "utils"
+
+    @classmethod
+    def VALIDATE_INPUTS(self, root_dir, file, **kwargs):
+        if file == "[none]" or not file or not file.strip():
+            return True
+        get_file(root_dir, file)
+        return True
+
+    def load_text(self, **kwargs):
+        self.file = get_file(kwargs["root_dir"], kwargs["file"])
+        with open(self.file, "r") as f:
+            return (f.read(), )
+
+
+class LoadText(TextFileNode):
+    @classmethod
+    def IS_CHANGED(self, **kwargs):
+        return os.path.getmtime(self.file)
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "root_dir": (list(get_valid_dirs()), {}),
+                "file": (["[none]"], {
+                    "pysssss.binding": [{
+                        "source": "root_dir",
+                        "callback": [{
+                            "type": "set",
+                            "target": "$this.disabled",
+                            "value": True
+                        }, {
+                            "type": "fetch",
+                            "url": "/pysssss/text-file/{$source.value}",
+                            "then": [{
+                                "type": "set",
+                                "target": "$this.options.values",
+                                "value": "$result"
+                            }, {
+                                "type": "validate-combo"
+                            }, {
+                                "type": "set",
+                                "target": "$this.disabled",
+                                "value": False
+                            }]
+                        }],
+                    }]
+                })
+            },
+        }
+
+    FUNCTION = "load_text"
+
+
+class SaveText(TextFileNode):
+    OUTPUT_NODE = True
+    
+    @classmethod
+    def IS_CHANGED(self, **kwargs):
+        return float("nan")
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "root_dir": (list(get_valid_dirs()), {}),
+                "file": ("STRING", {"default": "file.txt"}),
+                "append": (["append", "overwrite", "new only"], {}),
+                "insert": ("BOOLEAN", {
+                    "default": True, "label_on": "new line", "label_off": "none",
+                    "pysssss.binding": [{
+                        "source": "append",
+                        "callback": [{
+                            "type": "if",
+                            "condition": [{
+                                "left": "$source.value",
+                                "op": "eq",
+                                "right": '"append"'
+                            }],
+                            "true": [{
+                                "type": "set",
+                                "target": "$this.disabled",
+                                "value": False
+                            }],
+                            "false": [{
+                                "type": "set",
+                                "target": "$this.disabled",
+                                "value": True
+                            }],
+                        }]
+                    }]
+                }),
+                "text": ("STRING", {"forceInput": True, "multiline": True})
+            },
+        }
+
+    FUNCTION = "write_text"
+
+    def write_text(self, **kwargs):
+        self.file = get_file(kwargs["root_dir"], kwargs["file"])
+        if kwargs["append"] == "new only" and os.path.exists(self.file):
+            raise FileExistsError(
+                self.file + " already exists and 'new only' is selected.")
+        with open(self.file, "a+" if kwargs["append"] == "append" else "w") as f:
+            is_append = f.tell() != 0
+            if is_append and kwargs["insert"]:
+                f.write("\n")
+            f.write(kwargs["text"])
+
+        return super().load_text(**kwargs)
+
+
+NODE_CLASS_MAPPINGS = {
+    "LoadText|pysssss": LoadText,
+    "SaveText|pysssss": SaveText,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LoadText|pysssss": "Load Text 🐍",
+    "SaveText|pysssss": "Save Text 🐍",
+}
diff --git a/custom_nodes/comfyui-custom-scripts/py/workflows.py b/custom_nodes/comfyui-custom-scripts/py/workflows.py
new file mode 100644
index 0000000000000000000000000000000000000000..233e8c84ce490a1fd2a51e603b9836d869dc0262
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/py/workflows.py
@@ -0,0 +1,61 @@
+from server import PromptServer
+from aiohttp import web
+import os
+import inspect
+import json
+import importlib
+import sys
+sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
+import pysssss
+
+root_directory = os.path.dirname(inspect.getfile(PromptServer))
+workflows_directory = os.path.join(root_directory, "pysssss-workflows")
+workflows_directory = pysssss.get_config_value(
+    "workflows.directory", workflows_directory)
+if not os.path.isabs(workflows_directory):
+    workflows_directory = os.path.abspath(os.path.join(root_directory, workflows_directory))
+
+NODE_CLASS_MAPPINGS = {}
+NODE_DISPLAY_NAME_MAPPINGS = {}
+
+
+@PromptServer.instance.routes.get("/pysssss/workflows")
+async def get_workflows(request):
+    files = []
+    for dirpath, directories, file in os.walk(workflows_directory):
+        for file in file:
+            if (file.endswith(".json")):
+                files.append(os.path.relpath(os.path.join(
+                    dirpath, file), workflows_directory))
+    return web.json_response(list(map(lambda f: os.path.splitext(f)[0].replace("\\", "/"), files)))
+
+
+@PromptServer.instance.routes.get("/pysssss/workflows/{name:.+}")
+async def get_workflow(request):
+    file = os.path.abspath(os.path.join(
+        workflows_directory, request.match_info["name"] + ".json"))
+    if os.path.commonpath([file, workflows_directory]) != workflows_directory:
+        return web.Response(status=403)
+
+    return web.FileResponse(file)
+
+
+@PromptServer.instance.routes.post("/pysssss/workflows")
+async def save_workflow(request):
+    json_data = await request.json()
+    file = os.path.abspath(os.path.join(
+        workflows_directory, json_data["name"] + ".json"))
+    if os.path.commonpath([file, workflows_directory]) != workflows_directory:
+        return web.Response(status=403)
+
+    if os.path.exists(file) and ("overwrite" not in json_data or json_data["overwrite"] == False):
+        return web.Response(status=409)
+
+    sub_path = os.path.dirname(file)
+    if not os.path.exists(sub_path):
+        os.makedirs(sub_path)
+
+    with open(file, "w") as f:
+        f.write(json.dumps(json_data["workflow"]))
+
+    return web.Response(status=201)
diff --git a/custom_nodes/comfyui-custom-scripts/user/text_file_dirs.json b/custom_nodes/comfyui-custom-scripts/user/text_file_dirs.json
new file mode 100644
index 0000000000000000000000000000000000000000..15adc2255e0109be7b860d591f54187520c673aa
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/user/text_file_dirs.json
@@ -0,0 +1,5 @@
+{
+	"input": "$input/**/*.txt",
+	"output": "$output/**/*.txt",
+	"temp": "$temp/**/*.txt"
+}
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/assets/canvas2svg.js b/custom_nodes/comfyui-custom-scripts/web/js/assets/canvas2svg.js
new file mode 100644
index 0000000000000000000000000000000000000000..03dc3923759ccbe8cf70eaab387b792cc0ba32ca
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/assets/canvas2svg.js
@@ -0,0 +1,1192 @@
+/*!!
+ *  Canvas 2 Svg v1.0.19
+ *  A low level canvas to SVG converter. Uses a mock canvas context to build an SVG document.
+ *
+ *  Licensed under the MIT license:
+ *  http://www.opensource.org/licenses/mit-license.php
+ *
+ *  Author:
+ *  Kerry Liu
+ *
+ *  Copyright (c) 2014 Gliffy Inc.
+ */
+
+;(function() {
+    "use strict";
+
+    var STYLES, ctx, CanvasGradient, CanvasPattern, namedEntities;
+
+    //helper function to format a string
+    function format(str, args) {
+        var keys = Object.keys(args), i;
+        for (i=0; i<keys.length; i++) {
+            str = str.replace(new RegExp("\\{" + keys[i] + "\\}", "gi"), args[keys[i]]);
+        }
+        return str;
+    }
+
+    //helper function that generates a random string
+    function randomString(holder) {
+        var chars, randomstring, i;
+        if (!holder) {
+            throw new Error("cannot create a random attribute name for an undefined object");
+        }
+        chars = "ABCDEFGHIJKLMNOPQRSTUVWXTZabcdefghiklmnopqrstuvwxyz";
+        randomstring = "";
+        do {
+            randomstring = "";
+            for (i = 0; i < 12; i++) {
+                randomstring += chars[Math.floor(Math.random() * chars.length)];
+            }
+        } while (holder[randomstring]);
+        return randomstring;
+    }
+
+    //helper function to map named to numbered entities
+    function createNamedToNumberedLookup(items, radix) {
+        var i, entity, lookup = {}, base10, base16;
+        items = items.split(',');
+        radix = radix || 10;
+        // Map from named to numbered entities.
+        for (i = 0; i < items.length; i += 2) {
+            entity = '&' + items[i + 1] + ';';
+            base10 = parseInt(items[i], radix);
+            lookup[entity] = '&#'+base10+';';
+        }
+        //FF and IE need to create a regex from hex values ie &nbsp; == \xa0
+        lookup["\\xa0"] = '&#160;';
+        return lookup;
+    }
+
+    //helper function to map canvas-textAlign to svg-textAnchor
+    function getTextAnchor(textAlign) {
+        //TODO: support rtl languages
+        var mapping = {"left":"start", "right":"end", "center":"middle", "start":"start", "end":"end"};
+        return mapping[textAlign] || mapping.start;
+    }
+
+    //helper function to map canvas-textBaseline to svg-dominantBaseline
+    function getDominantBaseline(textBaseline) {
+        //INFO: not supported in all browsers
+        var mapping = {"alphabetic": "alphabetic", "hanging": "hanging", "top":"text-before-edge", "bottom":"text-after-edge", "middle":"central"};
+        return mapping[textBaseline] || mapping.alphabetic;
+    }
+
+    // Unpack entities lookup where the numbers are in radix 32 to reduce the size
+    // entity mapping courtesy of tinymce
+    namedEntities = createNamedToNumberedLookup(
+        '50,nbsp,51,iexcl,52,cent,53,pound,54,curren,55,yen,56,brvbar,57,sect,58,uml,59,copy,' +
+            '5a,ordf,5b,laquo,5c,not,5d,shy,5e,reg,5f,macr,5g,deg,5h,plusmn,5i,sup2,5j,sup3,5k,acute,' +
+            '5l,micro,5m,para,5n,middot,5o,cedil,5p,sup1,5q,ordm,5r,raquo,5s,frac14,5t,frac12,5u,frac34,' +
+            '5v,iquest,60,Agrave,61,Aacute,62,Acirc,63,Atilde,64,Auml,65,Aring,66,AElig,67,Ccedil,' +
+            '68,Egrave,69,Eacute,6a,Ecirc,6b,Euml,6c,Igrave,6d,Iacute,6e,Icirc,6f,Iuml,6g,ETH,6h,Ntilde,' +
+            '6i,Ograve,6j,Oacute,6k,Ocirc,6l,Otilde,6m,Ouml,6n,times,6o,Oslash,6p,Ugrave,6q,Uacute,' +
+            '6r,Ucirc,6s,Uuml,6t,Yacute,6u,THORN,6v,szlig,70,agrave,71,aacute,72,acirc,73,atilde,74,auml,' +
+            '75,aring,76,aelig,77,ccedil,78,egrave,79,eacute,7a,ecirc,7b,euml,7c,igrave,7d,iacute,7e,icirc,' +
+            '7f,iuml,7g,eth,7h,ntilde,7i,ograve,7j,oacute,7k,ocirc,7l,otilde,7m,ouml,7n,divide,7o,oslash,' +
+            '7p,ugrave,7q,uacute,7r,ucirc,7s,uuml,7t,yacute,7u,thorn,7v,yuml,ci,fnof,sh,Alpha,si,Beta,' +
+            'sj,Gamma,sk,Delta,sl,Epsilon,sm,Zeta,sn,Eta,so,Theta,sp,Iota,sq,Kappa,sr,Lambda,ss,Mu,' +
+            'st,Nu,su,Xi,sv,Omicron,t0,Pi,t1,Rho,t3,Sigma,t4,Tau,t5,Upsilon,t6,Phi,t7,Chi,t8,Psi,' +
+            't9,Omega,th,alpha,ti,beta,tj,gamma,tk,delta,tl,epsilon,tm,zeta,tn,eta,to,theta,tp,iota,' +
+            'tq,kappa,tr,lambda,ts,mu,tt,nu,tu,xi,tv,omicron,u0,pi,u1,rho,u2,sigmaf,u3,sigma,u4,tau,' +
+            'u5,upsilon,u6,phi,u7,chi,u8,psi,u9,omega,uh,thetasym,ui,upsih,um,piv,812,bull,816,hellip,' +
+            '81i,prime,81j,Prime,81u,oline,824,frasl,88o,weierp,88h,image,88s,real,892,trade,89l,alefsym,' +
+            '8cg,larr,8ch,uarr,8ci,rarr,8cj,darr,8ck,harr,8dl,crarr,8eg,lArr,8eh,uArr,8ei,rArr,8ej,dArr,' +
+            '8ek,hArr,8g0,forall,8g2,part,8g3,exist,8g5,empty,8g7,nabla,8g8,isin,8g9,notin,8gb,ni,8gf,prod,' +
+            '8gh,sum,8gi,minus,8gn,lowast,8gq,radic,8gt,prop,8gu,infin,8h0,ang,8h7,and,8h8,or,8h9,cap,8ha,cup,' +
+            '8hb,int,8hk,there4,8hs,sim,8i5,cong,8i8,asymp,8j0,ne,8j1,equiv,8j4,le,8j5,ge,8k2,sub,8k3,sup,8k4,' +
+            'nsub,8k6,sube,8k7,supe,8kl,oplus,8kn,otimes,8l5,perp,8m5,sdot,8o8,lceil,8o9,rceil,8oa,lfloor,8ob,' +
+            'rfloor,8p9,lang,8pa,rang,9ea,loz,9j0,spades,9j3,clubs,9j5,hearts,9j6,diams,ai,OElig,aj,oelig,b0,' +
+            'Scaron,b1,scaron,bo,Yuml,m6,circ,ms,tilde,802,ensp,803,emsp,809,thinsp,80c,zwnj,80d,zwj,80e,lrm,' +
+            '80f,rlm,80j,ndash,80k,mdash,80o,lsquo,80p,rsquo,80q,sbquo,80s,ldquo,80t,rdquo,80u,bdquo,810,dagger,' +
+            '811,Dagger,81g,permil,81p,lsaquo,81q,rsaquo,85c,euro', 32);
+
+
+    //Some basic mappings for attributes and default values.
+    STYLES = {
+        "strokeStyle":{
+            svgAttr : "stroke", //corresponding svg attribute
+            canvas : "#000000", //canvas default
+            svg : "none",       //svg default
+            apply : "stroke"    //apply on stroke() or fill()
+        },
+        "fillStyle":{
+            svgAttr : "fill",
+            canvas : "#000000",
+            svg : null, //svg default is black, but we need to special case this to handle canvas stroke without fill
+            apply : "fill"
+        },
+        "lineCap":{
+            svgAttr : "stroke-linecap",
+            canvas : "butt",
+            svg : "butt",
+            apply : "stroke"
+        },
+        "lineJoin":{
+            svgAttr : "stroke-linejoin",
+            canvas : "miter",
+            svg : "miter",
+            apply : "stroke"
+        },
+        "miterLimit":{
+            svgAttr : "stroke-miterlimit",
+            canvas : 10,
+            svg : 4,
+            apply : "stroke"
+        },
+        "lineWidth":{
+            svgAttr : "stroke-width",
+            canvas : 1,
+            svg : 1,
+            apply : "stroke"
+        },
+        "globalAlpha": {
+            svgAttr : "opacity",
+            canvas : 1,
+            svg : 1,
+            apply :  "fill stroke"
+        },
+        "font":{
+            //font converts to multiple svg attributes, there is custom logic for this
+            canvas : "10px sans-serif"
+        },
+        "shadowColor":{
+            canvas : "#000000"
+        },
+        "shadowOffsetX":{
+            canvas : 0
+        },
+        "shadowOffsetY":{
+            canvas : 0
+        },
+        "shadowBlur":{
+            canvas : 0
+        },
+        "textAlign":{
+            canvas : "start"
+        },
+        "textBaseline":{
+            canvas : "alphabetic"
+        },
+        "lineDash" : {
+            svgAttr : "stroke-dasharray",
+            canvas : [],
+            svg : null,
+            apply : "stroke"
+        }
+    };
+
+    /**
+     *
+     * @param gradientNode - reference to the gradient
+     * @constructor
+     */
+    CanvasGradient = function(gradientNode, ctx) {
+        this.__root = gradientNode;
+        this.__ctx = ctx;
+    };
+
+    /**
+     * Adds a color stop to the gradient root
+     */
+    CanvasGradient.prototype.addColorStop = function(offset, color) {
+        var stop = this.__ctx.__createElement("stop"), regex, matches;
+        stop.setAttribute("offset", offset);
+        if(color.indexOf("rgba") !== -1) {
+            //separate alpha value, since webkit can't handle it
+            regex = /rgba\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d?\.?\d*)\s*\)/gi;
+            matches = regex.exec(color);
+            stop.setAttribute("stop-color", format("rgb({r},{g},{b})", {r:matches[1], g:matches[2], b:matches[3]}));
+            stop.setAttribute("stop-opacity", matches[4]);
+        } else {
+            stop.setAttribute("stop-color", color);
+        }
+        this.__root.appendChild(stop);
+    };
+
+    CanvasPattern = function(pattern, ctx) {
+        this.__root = pattern;
+        this.__ctx = ctx;
+    };
+
+    /**
+     * The mock canvas context
+     * @param o - options include:
+     * width - width of your canvas (defaults to 500)
+     * height - height of your canvas (defaults to 500)
+     * enableMirroring - enables canvas mirroring (get image data) (defaults to false)
+     * document - the document object (defaults to the current document)
+     */
+    ctx = function(o) {
+
+        var defaultOptions = { width:500, height:500, enableMirroring : false}, options;
+
+        //keep support for this way of calling C2S: new C2S(width,height)
+        if(arguments.length > 1) {
+            options = defaultOptions;
+            options.width = arguments[0];
+            options.height = arguments[1];
+        } else if( !o ) {
+            options = defaultOptions;
+        } else {
+            options = o;
+        }
+
+        if(!(this instanceof ctx)) {
+            //did someone call this without new?
+            return new ctx(options);
+        }
+
+        //setup options
+        this.width = options.width || defaultOptions.width;
+        this.height = options.height || defaultOptions.height;
+        this.enableMirroring = options.enableMirroring !== undefined ? options.enableMirroring : defaultOptions.enableMirroring;
+
+        this.canvas = this;   ///point back to this instance!
+        this.__document = options.document || document;
+        this.__canvas = this.__document.createElement("canvas");
+        this.__ctx = this.__canvas.getContext("2d");
+
+        this.__setDefaultStyles();
+        this.__stack = [this.__getStyleState()];
+        this.__groupStack = [];
+
+        //the root svg element
+        this.__root = this.__document.createElementNS("http://www.w3.org/2000/svg", "svg");
+        this.__root.setAttribute("version", 1.1);
+        this.__root.setAttribute("xmlns", "http://www.w3.org/2000/svg");
+        this.__root.setAttributeNS("http://www.w3.org/2000/xmlns/", "xmlns:xlink", "http://www.w3.org/1999/xlink");
+        this.__root.setAttribute("width", this.width);
+        this.__root.setAttribute("height", this.height);
+
+        //make sure we don't generate the same ids in defs
+        this.__ids = {};
+
+        //defs tag
+        this.__defs = this.__document.createElementNS("http://www.w3.org/2000/svg", "defs");
+        this.__root.appendChild(this.__defs);
+
+        //also add a group child. the svg element can't use the transform attribute
+        this.__currentElement = this.__document.createElementNS("http://www.w3.org/2000/svg", "g");
+        this.__root.appendChild(this.__currentElement);
+    };
+
+
+    /**
+     * Creates the specified svg element
+     * @private
+     */
+    ctx.prototype.__createElement = function (elementName, properties, resetFill) {
+        if (typeof properties === "undefined") {
+            properties = {};
+        }
+
+        var element = this.__document.createElementNS("http://www.w3.org/2000/svg", elementName),
+            keys = Object.keys(properties), i, key;
+        if(resetFill) {
+            //if fill or stroke is not specified, the svg element should not display. By default SVG's fill is black.
+            element.setAttribute("fill", "none");
+            element.setAttribute("stroke", "none");
+        }
+        for(i=0; i<keys.length; i++) {
+            key = keys[i];
+            element.setAttribute(key, properties[key]);
+        }
+        return element;
+    };
+
+    /**
+     * Applies default canvas styles to the context
+     * @private
+     */
+    ctx.prototype.__setDefaultStyles = function() {
+        //default 2d canvas context properties see:http://www.w3.org/TR/2dcontext/
+        var keys = Object.keys(STYLES), i, key;
+        for(i=0; i<keys.length; i++) {
+            key = keys[i];
+            this[key] = STYLES[key].canvas;
+        }
+    };
+
+    /**
+     * Applies styles on restore
+     * @param styleState
+     * @private
+     */
+    ctx.prototype.__applyStyleState = function(styleState) {
+        var keys = Object.keys(styleState), i, key;
+        for(i=0; i<keys.length; i++) {
+            key = keys[i];
+            this[key] = styleState[key];
+        }
+    };
+
+    /**
+     * Gets the current style state
+     * @return {Object}
+     * @private
+     */
+    ctx.prototype.__getStyleState = function() {
+        var i, styleState = {}, keys = Object.keys(STYLES), key;
+        for(i=0; i<keys.length; i++) {
+            key = keys[i];
+            styleState[key] = this[key];
+        }
+        return styleState;
+    };
+
+    /**
+     * Apples the current styles to the current SVG element. On "ctx.fill" or "ctx.stroke"
+     * @param type
+     * @private
+     */
+    ctx.prototype.__applyStyleToCurrentElement = function(type) {
+        var keys = Object.keys(STYLES), i, style, value, id, regex, matches;
+        for(i=0; i<keys.length; i++) {
+            style = STYLES[keys[i]];
+            value = this[keys[i]];
+            if(style.apply) {
+                //is this a gradient or pattern?
+                if(style.apply.indexOf("fill")!==-1 && value instanceof CanvasPattern) {
+                    //pattern
+                    if(value.__ctx) {
+                        //copy over defs
+                        while(value.__ctx.__defs.childNodes.length) {
+                            id = value.__ctx.__defs.childNodes[0].getAttribute("id");
+                            this.__ids[id] = id;
+                            this.__defs.appendChild(value.__ctx.__defs.childNodes[0]);
+                        }
+                    }
+                    this.__currentElement.setAttribute("fill", format("url(#{id})", {id:value.__root.getAttribute("id")}));
+                }
+                else if(style.apply.indexOf("fill")!==-1 && value instanceof CanvasGradient) {
+                    //gradient
+                    this.__currentElement.setAttribute("fill", format("url(#{id})", {id:value.__root.getAttribute("id")}));
+                } else if(style.apply.indexOf(type)!==-1 && style.svg !== value) {
+                    if((style.svgAttr === "stroke" || style.svgAttr === "fill") && value.indexOf("rgba") !== -1) {
+                        //separate alpha value, since illustrator can't handle it
+                        regex = /rgba\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d?\.?\d*)\s*\)/gi;
+                        matches = regex.exec(value);
+                        this.__currentElement.setAttribute(style.svgAttr, format("rgb({r},{g},{b})", {r:matches[1], g:matches[2], b:matches[3]}));
+                        //should take globalAlpha here
+                        var opacity = matches[4];
+                        var globalAlpha = this.globalAlpha;
+                        if (globalAlpha != null) {
+                            opacity *= globalAlpha;
+                        }
+                        this.__currentElement.setAttribute(style.svgAttr+"-opacity", opacity);
+                    } else {
+                        var attr = style.svgAttr;
+                        if (keys[i] === 'globalAlpha') {
+                            attr = type+'-'+style.svgAttr;
+                            if (this.__currentElement.getAttribute(attr)) {
+                                 //fill-opacity or stroke-opacity has already been set by stroke or fill.
+                                continue;
+                            }
+                        }
+                        //otherwise only update attribute if right type, and not svg default
+                        this.__currentElement.setAttribute(attr, value);
+
+
+                    }
+                }
+            }
+        }
+
+    };
+
+    /**
+     * Will return the closest group or svg node. May return the current element.
+     * @private
+     */
+    ctx.prototype.__closestGroupOrSvg = function(node) {
+        node = node || this.__currentElement;
+        if(node.nodeName === "g" || node.nodeName === "svg") {
+            return node;
+        } else {
+            return this.__closestGroupOrSvg(node.parentNode);
+        }
+    };
+
+    /**
+     * Returns the serialized value of the svg so far
+     * @param fixNamedEntities - Standalone SVG doesn't support named entities, which document.createTextNode encodes.
+     *                           If true, we attempt to find all named entities and encode it as a numeric entity.
+     * @return serialized svg
+     */
+    ctx.prototype.getSerializedSvg = function(fixNamedEntities) {
+        var serialized = new XMLSerializer().serializeToString(this.__root),
+            keys, i, key, value, regexp, xmlns;
+
+        //IE search for a duplicate xmnls because they didn't implement setAttributeNS correctly
+        xmlns = /xmlns="http:\/\/www\.w3\.org\/2000\/svg".+xmlns="http:\/\/www\.w3\.org\/2000\/svg/gi;
+        if(xmlns.test(serialized)) {
+            serialized = serialized.replace('xmlns="http://www.w3.org/2000/svg','xmlns:xlink="http://www.w3.org/1999/xlink');
+        }
+
+        if(fixNamedEntities) {
+            keys = Object.keys(namedEntities);
+            //loop over each named entity and replace with the proper equivalent.
+            for(i=0; i<keys.length; i++) {
+                key = keys[i];
+                value = namedEntities[key];
+                regexp = new RegExp(key, "gi");
+                if(regexp.test(serialized)) {
+                    serialized = serialized.replace(regexp, value);
+                }
+            }
+        }
+
+        return serialized;
+    };
+
+
+    /**
+     * Returns the root svg
+     * @return
+     */
+    ctx.prototype.getSvg = function() {
+        return this.__root;
+    };
+    /**
+     * Will generate a group tag.
+     */
+    ctx.prototype.save = function() {
+        var group = this.__createElement("g"), parent = this.__closestGroupOrSvg();
+        this.__groupStack.push(parent);
+        parent.appendChild(group);
+        this.__currentElement = group;
+        this.__stack.push(this.__getStyleState());
+    };
+    /**
+     * Sets current element to parent, or just root if already root
+     */
+    ctx.prototype.restore = function(){
+        this.__currentElement = this.__groupStack.pop();
+        //Clearing canvas will make the poped group invalid, currentElement is set to the root group node.
+        if (!this.__currentElement) {
+            this.__currentElement = this.__root.childNodes[1];
+        }
+        var state = this.__stack.pop();
+        this.__applyStyleState(state);
+
+    };
+
+    /**
+     * Helper method to add transform
+     * @private
+     */
+    ctx.prototype.__addTransform = function(t) {
+
+        //if the current element has siblings, add another group
+        var parent = this.__closestGroupOrSvg();
+        if(parent.childNodes.length > 0) {
+            var group = this.__createElement("g");
+            parent.appendChild(group);
+            this.__currentElement = group;
+        }
+
+        var transform = this.__currentElement.getAttribute("transform");
+        if(transform) {
+            transform += " ";
+        } else {
+            transform = "";
+        }
+        transform += t;
+        this.__currentElement.setAttribute("transform", transform);
+    };
+
+    /**
+     *  scales the current element
+     */
+    ctx.prototype.scale = function(x, y) {
+        if(y === undefined) {
+            y = x;
+        }
+        this.__addTransform(format("scale({x},{y})", {x:x, y:y}));
+    };
+
+    /**
+     * rotates the current element
+     */
+    ctx.prototype.rotate = function(angle){
+        var degrees = (angle * 180 / Math.PI);
+        this.__addTransform(format("rotate({angle},{cx},{cy})", {angle:degrees, cx:0, cy:0}));
+    };
+
+    /**
+     * translates the current element
+     */
+    ctx.prototype.translate = function(x, y){
+        this.__addTransform(format("translate({x},{y})", {x:x,y:y}));
+    };
+
+    /**
+     * applies a transform to the current element
+     */
+    ctx.prototype.transform = function(a, b, c, d, e, f){
+        this.__addTransform(format("matrix({a},{b},{c},{d},{e},{f})", {a:a, b:b, c:c, d:d, e:e, f:f}));
+    };
+
+    /**
+     * Create a new Path Element
+     */
+    ctx.prototype.beginPath = function(){
+        var path, parent;
+
+        // Note that there is only one current default path, it is not part of the drawing state.
+        // See also: https://html.spec.whatwg.org/multipage/scripting.html#current-default-path
+        this.__currentDefaultPath = "";
+        this.__currentPosition = {};
+
+        path = this.__createElement("path", {}, true);
+        parent = this.__closestGroupOrSvg();
+        parent.appendChild(path);
+        this.__currentElement = path;
+    };
+
+    /**
+     * Helper function to apply currentDefaultPath to current path element
+     * @private
+     */
+    ctx.prototype.__applyCurrentDefaultPath = function() {
+        if(this.__currentElement.nodeName === "path") {
+            var d = this.__currentDefaultPath;
+            this.__currentElement.setAttribute("d", d);
+        } else {
+            throw new Error("Attempted to apply path command to node " + this.__currentElement.nodeName);
+        }
+    };
+
+    /**
+     * Helper function to add path command
+     * @private
+     */
+    ctx.prototype.__addPathCommand = function(command){
+        this.__currentDefaultPath += " ";
+        this.__currentDefaultPath += command;
+    };
+
+    /**
+     * Adds the move command to the current path element,
+     * if the currentPathElement is not empty create a new path element
+     */
+    ctx.prototype.moveTo = function(x,y){
+        if(this.__currentElement.nodeName !== "path") {
+            this.beginPath();
+        }
+
+        // creates a new subpath with the given point
+        this.__currentPosition = {x: x, y: y};
+        this.__addPathCommand(format("M {x} {y}", {x:x, y:y}));
+    };
+
+    /**
+     * Closes the current path
+     */
+    ctx.prototype.closePath = function(){
+        this.__addPathCommand("Z");
+    };
+
+    /**
+     * Adds a line to command
+     */
+    ctx.prototype.lineTo = function(x, y){
+        this.__currentPosition = {x: x, y: y};
+        if (this.__currentDefaultPath.indexOf('M') > -1) {
+            this.__addPathCommand(format("L {x} {y}", {x:x, y:y}));
+        } else {
+            this.__addPathCommand(format("M {x} {y}", {x:x, y:y}));
+        }
+    };
+
+    /**
+     * Add a bezier command
+     */
+    ctx.prototype.bezierCurveTo = function(cp1x, cp1y, cp2x, cp2y, x, y) {
+        this.__currentPosition = {x: x, y: y};
+        this.__addPathCommand(format("C {cp1x} {cp1y} {cp2x} {cp2y} {x} {y}",
+            {cp1x:cp1x, cp1y:cp1y, cp2x:cp2x, cp2y:cp2y, x:x, y:y}));
+    };
+
+    /**
+     * Adds a quadratic curve to command
+     */
+    ctx.prototype.quadraticCurveTo = function(cpx, cpy, x, y){
+        this.__currentPosition = {x: x, y: y};
+        this.__addPathCommand(format("Q {cpx} {cpy} {x} {y}", {cpx:cpx, cpy:cpy, x:x, y:y}));
+    };
+
+
+    /**
+     * Return a new normalized vector of given vector
+     */
+    var normalize = function(vector) {
+        var len = Math.sqrt(vector[0] * vector[0] + vector[1] * vector[1]);
+        return [vector[0] / len, vector[1] / len];
+    };
+
+    /**
+     * Adds the arcTo to the current path
+     *
+     * @see http://www.w3.org/TR/2015/WD-2dcontext-20150514/#dom-context-2d-arcto
+     */
+    ctx.prototype.arcTo = function(x1, y1, x2, y2, radius) {
+        // Let the point (x0, y0) be the last point in the subpath.
+        var x0 = this.__currentPosition && this.__currentPosition.x;
+        var y0 = this.__currentPosition && this.__currentPosition.y;
+
+        // First ensure there is a subpath for (x1, y1).
+        if (typeof x0 == "undefined" || typeof y0 == "undefined") {
+            return;
+        }
+
+        // Negative values for radius must cause the implementation to throw an IndexSizeError exception.
+        if (radius < 0) {
+            throw new Error("IndexSizeError: The radius provided (" + radius + ") is negative.");
+        }
+
+        // If the point (x0, y0) is equal to the point (x1, y1),
+        // or if the point (x1, y1) is equal to the point (x2, y2),
+        // or if the radius radius is zero,
+        // then the method must add the point (x1, y1) to the subpath,
+        // and connect that point to the previous point (x0, y0) by a straight line.
+        if (((x0 === x1) && (y0 === y1))
+            || ((x1 === x2) && (y1 === y2))
+            || (radius === 0)) {
+            this.lineTo(x1, y1);
+            return;
+        }
+
+        // Otherwise, if the points (x0, y0), (x1, y1), and (x2, y2) all lie on a single straight line,
+        // then the method must add the point (x1, y1) to the subpath,
+        // and connect that point to the previous point (x0, y0) by a straight line.
+        var unit_vec_p1_p0 = normalize([x0 - x1, y0 - y1]);
+        var unit_vec_p1_p2 = normalize([x2 - x1, y2 - y1]);
+        if (unit_vec_p1_p0[0] * unit_vec_p1_p2[1] === unit_vec_p1_p0[1] * unit_vec_p1_p2[0]) {
+            this.lineTo(x1, y1);
+            return;
+        }
+
+        // Otherwise, let The Arc be the shortest arc given by circumference of the circle that has radius radius,
+        // and that has one point tangent to the half-infinite line that crosses the point (x0, y0) and ends at the point (x1, y1),
+        // and that has a different point tangent to the half-infinite line that ends at the point (x1, y1), and crosses the point (x2, y2).
+        // The points at which this circle touches these two lines are called the start and end tangent points respectively.
+
+        // note that both vectors are unit vectors, so the length is 1
+        var cos = (unit_vec_p1_p0[0] * unit_vec_p1_p2[0] + unit_vec_p1_p0[1] * unit_vec_p1_p2[1]);
+        var theta = Math.acos(Math.abs(cos));
+
+        // Calculate origin
+        var unit_vec_p1_origin = normalize([
+            unit_vec_p1_p0[0] + unit_vec_p1_p2[0],
+            unit_vec_p1_p0[1] + unit_vec_p1_p2[1]
+        ]);
+        var len_p1_origin = radius / Math.sin(theta / 2);
+        var x = x1 + len_p1_origin * unit_vec_p1_origin[0];
+        var y = y1 + len_p1_origin * unit_vec_p1_origin[1];
+
+        // Calculate start angle and end angle
+        // rotate 90deg clockwise (note that y axis points to its down)
+        var unit_vec_origin_start_tangent = [
+            -unit_vec_p1_p0[1],
+            unit_vec_p1_p0[0]
+        ];
+        // rotate 90deg counter clockwise (note that y axis points to its down)
+        var unit_vec_origin_end_tangent = [
+            unit_vec_p1_p2[1],
+            -unit_vec_p1_p2[0]
+        ];
+        var getAngle = function(vector) {
+            // get angle (clockwise) between vector and (1, 0)
+            var x = vector[0];
+            var y = vector[1];
+            if (y >= 0) { // note that y axis points to its down
+                return Math.acos(x);
+            } else {
+                return -Math.acos(x);
+            }
+        };
+        var startAngle = getAngle(unit_vec_origin_start_tangent);
+        var endAngle = getAngle(unit_vec_origin_end_tangent);
+
+        // Connect the point (x0, y0) to the start tangent point by a straight line
+        this.lineTo(x + unit_vec_origin_start_tangent[0] * radius,
+                    y + unit_vec_origin_start_tangent[1] * radius);
+
+        // Connect the start tangent point to the end tangent point by arc
+        // and adding the end tangent point to the subpath.
+        this.arc(x, y, radius, startAngle, endAngle);
+    };
+
+    /**
+     * Sets the stroke property on the current element
+     */
+    ctx.prototype.stroke = function(){
+        if(this.__currentElement.nodeName === "path") {
+            this.__currentElement.setAttribute("paint-order", "fill stroke markers");
+        }
+        this.__applyCurrentDefaultPath();
+        this.__applyStyleToCurrentElement("stroke");
+    };
+
+    /**
+     * Sets fill properties on the current element
+     */
+    ctx.prototype.fill = function(){
+        if(this.__currentElement.nodeName === "path") {
+            this.__currentElement.setAttribute("paint-order", "stroke fill markers");
+        }
+        this.__applyCurrentDefaultPath();
+        this.__applyStyleToCurrentElement("fill");
+    };
+
+    /**
+     *  Adds a rectangle to the path.
+     */
+    ctx.prototype.rect = function(x, y, width, height){
+        if(this.__currentElement.nodeName !== "path") {
+            this.beginPath();
+        }
+        this.moveTo(x, y);
+        this.lineTo(x+width, y);
+        this.lineTo(x+width, y+height);
+        this.lineTo(x, y+height);
+        this.lineTo(x, y);
+        this.closePath();
+    };
+
+
+    /**
+     * adds a rectangle element
+     */
+    ctx.prototype.fillRect = function(x, y, width, height){
+        var rect, parent;
+        rect = this.__createElement("rect", {
+            x : x,
+            y : y,
+            width : width,
+            height : height
+        }, true);
+        parent = this.__closestGroupOrSvg();
+        parent.appendChild(rect);
+        this.__currentElement = rect;
+        this.__applyStyleToCurrentElement("fill");
+    };
+
+    /**
+     * Draws a rectangle with no fill
+     * @param x
+     * @param y
+     * @param width
+     * @param height
+     */
+    ctx.prototype.strokeRect = function(x, y, width, height){
+        var rect, parent;
+        rect = this.__createElement("rect", {
+            x : x,
+            y : y,
+            width : width,
+            height : height
+        }, true);
+        parent = this.__closestGroupOrSvg();
+        parent.appendChild(rect);
+        this.__currentElement = rect;
+        this.__applyStyleToCurrentElement("stroke");
+    };
+
+
+    /**
+     * Clear entire canvas:
+     * 1. save current transforms
+     * 2. remove all the childNodes of the root g element
+     */
+    ctx.prototype.__clearCanvas = function() {
+        var current = this.__closestGroupOrSvg(),
+            transform = current.getAttribute("transform");
+        var rootGroup = this.__root.childNodes[1];
+        var childNodes = rootGroup.childNodes;
+        for (var i = childNodes.length - 1; i >= 0; i--) {
+            if (childNodes[i]) {
+                rootGroup.removeChild(childNodes[i]);
+            }
+        }
+        this.__currentElement = rootGroup;
+        //reset __groupStack as all the child group nodes are all removed.
+        this.__groupStack = [];
+        if (transform) {
+            this.__addTransform(transform);
+        }
+    };
+
+    /**
+     * "Clears" a canvas by just drawing a white rectangle in the current group.
+     */
+    ctx.prototype.clearRect = function(x, y, width, height) {
+        //clear entire canvas
+        if (x === 0 && y === 0 && width === this.width && height === this.height) {
+            this.__clearCanvas();
+            return;
+        }
+        var rect, parent = this.__closestGroupOrSvg();
+        rect = this.__createElement("rect", {
+            x : x,
+            y : y,
+            width : width,
+            height : height,
+            fill : "#FFFFFF"
+        }, true);
+        parent.appendChild(rect);
+    };
+
+    /**
+     * Adds a linear gradient to a defs tag.
+     * Returns a canvas gradient object that has a reference to it's parent def
+     */
+    ctx.prototype.createLinearGradient = function(x1, y1, x2, y2){
+        var grad = this.__createElement("linearGradient", {
+            id : randomString(this.__ids),
+            x1 : x1+"px",
+            x2 : x2+"px",
+            y1 : y1+"px",
+            y2 : y2+"px",
+            "gradientUnits" : "userSpaceOnUse"
+        }, false);
+        this.__defs.appendChild(grad);
+        return new CanvasGradient(grad, this);
+    };
+
+    /**
+     * Adds a radial gradient to a defs tag.
+     * Returns a canvas gradient object that has a reference to it's parent def
+     */
+    ctx.prototype.createRadialGradient = function(x0, y0, r0, x1, y1, r1){
+        var grad = this.__createElement("radialGradient", {
+            id : randomString(this.__ids),
+            cx : x1+"px",
+            cy : y1+"px",
+            r  : r1+"px",
+            fx : x0+"px",
+            fy : y0+"px",
+            "gradientUnits" : "userSpaceOnUse"
+        }, false);
+        this.__defs.appendChild(grad);
+        return new CanvasGradient(grad, this);
+
+    };
+
+    /**
+     * Parses the font string and returns svg mapping
+     * @private
+     */
+    ctx.prototype.__parseFont = function() {
+        var regex = /^\s*(?=(?:(?:[-a-z]+\s*){0,2}(italic|oblique))?)(?=(?:(?:[-a-z]+\s*){0,2}(small-caps))?)(?=(?:(?:[-a-z]+\s*){0,2}(bold(?:er)?|lighter|[1-9]00))?)(?:(?:normal|\1|\2|\3)\s*){0,3}((?:xx?-)?(?:small|large)|medium|smaller|larger|[.\d]+(?:\%|in|[cem]m|ex|p[ctx]))(?:\s*\/\s*(normal|[.\d]+(?:\%|in|[cem]m|ex|p[ctx])))?\s*([-,\'\"\sa-z]+?)\s*$/i;
+        var fontPart = regex.exec( this.font );
+        var data = {
+            style : fontPart[1] || 'normal',
+            size : fontPart[4] || '10px',
+            family : fontPart[6] || 'sans-serif',
+            weight: fontPart[3] || 'normal',
+            decoration : fontPart[2] || 'normal',
+            href : null
+        };
+
+        //canvas doesn't support underline natively, but we can pass this attribute
+        if(this.__fontUnderline === "underline") {
+            data.decoration = "underline";
+        }
+
+        //canvas also doesn't support linking, but we can pass this as well
+        if(this.__fontHref) {
+            data.href = this.__fontHref;
+        }
+
+        return data;
+    };
+
+    /**
+     * Helper to link text fragments
+     * @param font
+     * @param element
+     * @return {*}
+     * @private
+     */
+    ctx.prototype.__wrapTextLink = function(font, element) {
+        if(font.href) {
+            var a = this.__createElement("a");
+            a.setAttributeNS("http://www.w3.org/1999/xlink", "xlink:href", font.href);
+            a.appendChild(element);
+            return a;
+        }
+        return element;
+    };
+
+    /**
+     * Fills or strokes text
+     * @param text
+     * @param x
+     * @param y
+     * @param action - stroke or fill
+     * @private
+     */
+    ctx.prototype.__applyText = function(text, x, y, action) {
+        var font = this.__parseFont(),
+            parent = this.__closestGroupOrSvg(),
+            textElement = this.__createElement("text", {
+                "font-family" : font.family,
+                "font-size" : font.size,
+                "font-style" : font.style,
+                "font-weight" : font.weight,
+                "text-decoration" : font.decoration,
+                "x" : x,
+                "y" : y,
+                "text-anchor": getTextAnchor(this.textAlign),
+                "dominant-baseline": getDominantBaseline(this.textBaseline)
+            }, true);
+
+        textElement.appendChild(this.__document.createTextNode(text));
+        this.__currentElement = textElement;
+        this.__applyStyleToCurrentElement(action);
+        parent.appendChild(this.__wrapTextLink(font,textElement));
+    };
+
+    /**
+     * Creates a text element
+     * @param text
+     * @param x
+     * @param y
+     */
+    ctx.prototype.fillText = function(text, x, y){
+        this.__applyText(text, x, y, "fill");
+    };
+
+    /**
+     * Strokes text
+     * @param text
+     * @param x
+     * @param y
+     */
+    ctx.prototype.strokeText = function(text, x, y){
+        this.__applyText(text, x, y, "stroke");
+    };
+
+    /**
+     * No need to implement this for svg.
+     * @param text
+     * @return {TextMetrics}
+     */
+    ctx.prototype.measureText = function(text){
+        this.__ctx.font = this.font;
+        return this.__ctx.measureText(text);
+    };
+
+    /**
+     *  Arc command!
+     */
+    ctx.prototype.arc = function(x, y, radius, startAngle, endAngle, counterClockwise) {
+        // in canvas no circle is drawn if no angle is provided.
+        if (startAngle === endAngle) {
+            return;
+        }
+        startAngle = startAngle % (2*Math.PI);
+        endAngle = endAngle % (2*Math.PI);
+        if(startAngle === endAngle) {
+            //circle time! subtract some of the angle so svg is happy (svg elliptical arc can't draw a full circle)
+            endAngle = ((endAngle + (2*Math.PI)) - 0.001 * (counterClockwise ? -1 : 1)) % (2*Math.PI);
+        }
+        var endX = x+radius*Math.cos(endAngle),
+            endY = y+radius*Math.sin(endAngle),
+            startX = x+radius*Math.cos(startAngle),
+            startY = y+radius*Math.sin(startAngle),
+            sweepFlag = counterClockwise ? 0 : 1,
+            largeArcFlag = 0,
+            diff = endAngle - startAngle;
+
+        // https://github.com/gliffy/canvas2svg/issues/4
+        if(diff < 0) {
+            diff += 2*Math.PI;
+        }
+
+        if(counterClockwise) {
+            largeArcFlag = diff > Math.PI ? 0 : 1;
+        } else {
+            largeArcFlag = diff > Math.PI ? 1 : 0;
+        }
+
+        this.lineTo(startX, startY);
+        this.__addPathCommand(format("A {rx} {ry} {xAxisRotation} {largeArcFlag} {sweepFlag} {endX} {endY}",
+            {rx:radius, ry:radius, xAxisRotation:0, largeArcFlag:largeArcFlag, sweepFlag:sweepFlag, endX:endX, endY:endY}));
+
+        this.__currentPosition = {x: endX, y: endY};
+    };
+
+    /**
+     * Generates a ClipPath from the clip command.
+     */
+    ctx.prototype.clip = function(){
+        var group = this.__closestGroupOrSvg(),
+            clipPath = this.__createElement("clipPath"),
+            id =  randomString(this.__ids),
+            newGroup = this.__createElement("g");
+
+        this.__applyCurrentDefaultPath();
+        group.removeChild(this.__currentElement);
+        clipPath.setAttribute("id", id);
+        clipPath.appendChild(this.__currentElement);
+
+        this.__defs.appendChild(clipPath);
+
+        //set the clip path to this group
+        group.setAttribute("clip-path", format("url(#{id})", {id:id}));
+
+        //clip paths can be scaled and transformed, we need to add another wrapper group to avoid later transformations
+        // to this path
+        group.appendChild(newGroup);
+
+        this.__currentElement = newGroup;
+
+    };
+
+    /**
+     * Draws a canvas, image or mock context to this canvas.
+     * Note that all svg dom manipulation uses node.childNodes rather than node.children for IE support.
+     * http://www.whatwg.org/specs/web-apps/current-work/multipage/the-canvas-element.html#dom-context-2d-drawimage
+     */
+    ctx.prototype.drawImage = function(){
+        //convert arguments to a real array
+        var args = Array.prototype.slice.call(arguments),
+            image=args[0],
+            dx, dy, dw, dh, sx=0, sy=0, sw, sh, parent, svg, defs, group,
+            currentElement, svgImage, canvas, context, id;
+
+        if(args.length === 3) {
+            dx = args[1];
+            dy = args[2];
+            sw = image.width;
+            sh = image.height;
+            dw = sw;
+            dh = sh;
+        } else if(args.length === 5) {
+            dx = args[1];
+            dy = args[2];
+            dw = args[3];
+            dh = args[4];
+            sw = image.width;
+            sh = image.height;
+        } else if(args.length === 9) {
+            sx = args[1];
+            sy = args[2];
+            sw = args[3];
+            sh = args[4];
+            dx = args[5];
+            dy = args[6];
+            dw = args[7];
+            dh = args[8];
+        } else {
+            throw new Error("Inavlid number of arguments passed to drawImage: " + arguments.length);
+        }
+
+        parent = this.__closestGroupOrSvg();
+        currentElement = this.__currentElement;
+        var translateDirective = "translate(" + dx + ", " + dy + ")";
+        if(image instanceof ctx) {
+            //canvas2svg mock canvas context. In the future we may want to clone nodes instead.
+            //also I'm currently ignoring dw, dh, sw, sh, sx, sy for a mock context.
+            svg = image.getSvg().cloneNode(true);
+            if (svg.childNodes && svg.childNodes.length > 1) {
+                defs = svg.childNodes[0];
+                while(defs.childNodes.length) {
+                    id = defs.childNodes[0].getAttribute("id");
+                    this.__ids[id] = id;
+                    this.__defs.appendChild(defs.childNodes[0]);
+                }
+                group = svg.childNodes[1];
+                if (group) {
+                    //save original transform
+                    var originTransform = group.getAttribute("transform");
+                    var transformDirective;
+                    if (originTransform) {
+                        transformDirective = originTransform+" "+translateDirective;
+                    } else {
+                        transformDirective = translateDirective;
+                    }
+                    group.setAttribute("transform", transformDirective);
+                    parent.appendChild(group);
+                }
+            }
+        } else if(image.nodeName === "CANVAS" || image.nodeName === "IMG") {
+            //canvas or image
+            svgImage = this.__createElement("image");
+            svgImage.setAttribute("width", dw);
+            svgImage.setAttribute("height", dh);
+            svgImage.setAttribute("preserveAspectRatio", "none");
+
+            if(sx || sy || sw !== image.width || sh !== image.height) {
+                //crop the image using a temporary canvas
+                canvas = this.__document.createElement("canvas");
+                canvas.width = dw;
+                canvas.height = dh;
+                context = canvas.getContext("2d");
+                context.drawImage(image, sx, sy, sw, sh, 0, 0, dw, dh);
+                image = canvas;
+            }
+            svgImage.setAttribute("transform", translateDirective);
+            svgImage.setAttributeNS("http://www.w3.org/1999/xlink", "xlink:href",
+                image.nodeName === "CANVAS" ? image.toDataURL() : image.getAttribute("src"));
+            parent.appendChild(svgImage);
+        }
+    };
+
+    /**
+     * Generates a pattern tag
+     */
+    ctx.prototype.createPattern = function(image, repetition){
+        var pattern = this.__document.createElementNS("http://www.w3.org/2000/svg", "pattern"), id = randomString(this.__ids),
+            img;
+        pattern.setAttribute("id", id);
+        pattern.setAttribute("width", image.width);
+        pattern.setAttribute("height", image.height);
+        if(image.nodeName === "CANVAS" || image.nodeName === "IMG") {
+            img = this.__document.createElementNS("http://www.w3.org/2000/svg", "image");
+            img.setAttribute("width", image.width);
+            img.setAttribute("height", image.height);
+            img.setAttributeNS("http://www.w3.org/1999/xlink", "xlink:href",
+                image.nodeName === "CANVAS" ? image.toDataURL() : image.getAttribute("src"));
+            pattern.appendChild(img);
+            this.__defs.appendChild(pattern);
+        } else if(image instanceof ctx) {
+            pattern.appendChild(image.__root.childNodes[1]);
+            this.__defs.appendChild(pattern);
+        }
+        return new CanvasPattern(pattern, this);
+    };
+
+    ctx.prototype.setLineDash = function(dashArray) {
+        if (dashArray && dashArray.length > 0) {
+            this.lineDash = dashArray.join(",");
+        } else {
+            this.lineDash = null;
+        }
+    };
+
+    /**
+     * Not yet implemented
+     */
+    ctx.prototype.drawFocusRing = function(){};
+    ctx.prototype.createImageData = function(){};
+    ctx.prototype.getImageData = function(){};
+    ctx.prototype.putImageData = function(){};
+    ctx.prototype.globalCompositeOperation = function(){};
+    ctx.prototype.setTransform = function(){};
+
+    //add options for alternative namespace
+    if (typeof window === "object") {
+        window.C2S = ctx;
+    }
+
+    // CommonJS/Browserify
+    if (typeof module === "object" && typeof module.exports === "object") {
+        module.exports = ctx;
+    }
+
+}());
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/assets/favicon-active.ico b/custom_nodes/comfyui-custom-scripts/web/js/assets/favicon-active.ico
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diff --git a/custom_nodes/comfyui-custom-scripts/web/js/assets/notify.mp3 b/custom_nodes/comfyui-custom-scripts/web/js/assets/notify.mp3
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diff --git a/custom_nodes/comfyui-custom-scripts/web/js/autocompleter.js b/custom_nodes/comfyui-custom-scripts/web/js/autocompleter.js
new file mode 100644
index 0000000000000000000000000000000000000000..d0fb2ecae710d291d0d42845a6e0395f61c74e4d
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/autocompleter.js
@@ -0,0 +1,602 @@
+import { app } from "../../../scripts/app.js";
+import { ComfyWidgets } from "../../../scripts/widgets.js";
+import { api } from "../../../scripts/api.js";
+import { $el, ComfyDialog } from "../../../scripts/ui.js";
+import { TextAreaAutoComplete } from "./common/autocomplete.js";
+import { ModelInfoDialog } from "./common/modelInfoDialog.js";
+import { LoraInfoDialog } from "./modelInfo.js";
+
+function parseCSV(csvText) {
+	const rows = [];
+	const delimiter = ",";
+	const quote = '"';
+	let currentField = "";
+	let inQuotedField = false;
+
+	function pushField() {
+		rows[rows.length - 1].push(currentField);
+		currentField = "";
+		inQuotedField = false;
+	}
+
+	rows.push([]); // Initialize the first row
+
+	for (let i = 0; i < csvText.length; i++) {
+		const char = csvText[i];
+		const nextChar = csvText[i + 1];
+
+		// Special handling for backslash escaped quotes
+		if (char === "\\" && nextChar === quote) {
+			currentField += quote;
+			i++;
+		}
+
+		if (!inQuotedField) {
+			if (char === quote) {
+				inQuotedField = true;
+			} else if (char === delimiter) {
+				pushField();
+			} else if (char === "\r" || char === "\n" || i === csvText.length - 1) {
+				pushField();
+				if (nextChar === "\n") {
+					i++; // Handle Windows line endings (\r\n)
+				}
+				rows.push([]); // Start a new row
+			} else {
+				currentField += char;
+			}
+		} else {
+			if (char === quote && nextChar === quote) {
+				currentField += quote;
+				i++; // Skip the next quote
+			} else if (char === quote) {
+				inQuotedField = false;
+			} else if (char === "\r" || char === "\n" || i === csvText.length - 1) {
+				// Dont allow new lines in quoted text, assume its wrong
+				const parsed = parseCSV(currentField);
+				rows.pop();
+				rows.push(...parsed);
+				inQuotedField = false;
+				currentField = "";
+				rows.push([]);
+			} else {
+				currentField += char;
+			}
+		}
+	}
+
+	if (currentField || csvText[csvText.length - 1] === ",") {
+		pushField();
+	}
+
+	// Remove the last row if it's empty
+	if (rows[rows.length - 1].length === 0) {
+		rows.pop();
+	}
+
+	return rows;
+}
+
+async function getCustomWords() {
+	const resp = await api.fetchApi("/pysssss/autocomplete", { cache: "no-store" });
+	if (resp.status === 200) {
+		return await resp.text();
+	}
+	return undefined;
+}
+
+async function addCustomWords(text) {
+	if (!text) {
+		text = await getCustomWords();
+	}
+	if (text) {
+		TextAreaAutoComplete.updateWords(
+			"pysssss.customwords",
+			parseCSV(text).reduce((p, n) => {
+				let text;
+				let priority;
+				let value;
+				let num;
+				switch (n.length) {
+					case 0:
+						return;
+					case 1:
+						// Single word
+						text = n[0];
+						break;
+					case 2:
+						// Word,[priority|alias]
+						num = +n[1];
+						if (isNaN(num)) {
+							text = n[0] + "🔄️" + n[1];
+							value = n[0];
+						} else {
+							text = n[0];
+							priority = num;
+						}
+						break;
+					case 4:
+						// a1111 csv format?
+						value = n[0];
+						priority = +n[2];
+						const aliases = n[3]?.trim();
+						if (aliases && aliases !== "null") { // Weird null in an example csv, maybe they are JSON.parsing the last column?
+							const split = aliases.split(",");
+							for (const text of split) {
+								p[text] = { text, priority, value };
+							}
+						}
+						text = value;
+						break;
+					default:
+						// Word,alias,priority
+						text = n[1];
+						value = n[0];
+						priority = +n[2];
+						break;
+				}
+				p[text] = { text, priority, value };
+				return p;
+			}, {})
+		);
+	}
+}
+
+function toggleLoras() {
+	[TextAreaAutoComplete.globalWords, TextAreaAutoComplete.globalWordsExclLoras] = [
+		TextAreaAutoComplete.globalWordsExclLoras,
+		TextAreaAutoComplete.globalWords,
+	];
+}
+
+class EmbeddingInfoDialog extends ModelInfoDialog {
+	async addInfo() {
+		super.addInfo();
+		const info = await this.addCivitaiInfo();
+		if (info) {
+			$el("div", {
+				parent: this.content,
+				innerHTML: info.description,
+				style: {
+					maxHeight: "250px",
+					overflow: "auto",
+				},
+			});
+		}
+	}
+}
+
+class CustomWordsDialog extends ComfyDialog {
+	async show() {
+		const text = await getCustomWords();
+		this.words = $el("textarea", {
+			textContent: text,
+			style: {
+				width: "70vw",
+				height: "70vh",
+			},
+		});
+
+		const input = $el("input", {
+			style: {
+				flex: "auto",
+			},
+			value:
+				"https://gist.githubusercontent.com/pythongosssss/1d3efa6050356a08cea975183088159a/raw/a18fb2f94f9156cf4476b0c24a09544d6c0baec6/danbooru-tags.txt",
+		});
+
+		super.show(
+			$el(
+				"div",
+				{
+					style: {
+						display: "flex",
+						flexDirection: "column",
+						overflow: "hidden",
+						maxHeight: "100%",
+					},
+				},
+				[
+					$el("h2", {
+						textContent: "Custom Autocomplete Words",
+						style: {
+							color: "#fff",
+							marginTop: 0,
+							textAlign: "center",
+							fontFamily: "sans-serif",
+						},
+					}),
+					$el(
+						"div",
+						{
+							style: {
+								color: "#fff",
+								fontFamily: "sans-serif",
+								display: "flex",
+								alignItems: "center",
+								gap: "5px",
+							},
+						},
+						[
+							$el("label", { textContent: "Load Custom List: " }),
+							input,
+							$el("button", {
+								textContent: "Load",
+								onclick: async () => {
+									try {
+										const res = await fetch(input.value);
+										if (res.status !== 200) {
+											throw new Error("Error loading: " + res.status + " " + res.statusText);
+										}
+										this.words.value = await res.text();
+									} catch (error) {
+										alert("Error loading custom list, try manually copy + pasting the list");
+									}
+								},
+							}),
+						]
+					),
+					this.words,
+				]
+			)
+		);
+	}
+
+	createButtons() {
+		const btns = super.createButtons();
+		const save = $el("button", {
+			type: "button",
+			textContent: "Save",
+			onclick: async (e) => {
+				try {
+					const res = await api.fetchApi("/pysssss/autocomplete", { method: "POST", body: this.words.value });
+					if (res.status !== 200) {
+						throw new Error("Error saving: " + res.status + " " + res.statusText);
+					}
+					save.textContent = "Saved!";
+					addCustomWords(this.words.value);
+					setTimeout(() => {
+						save.textContent = "Save";
+					}, 500);
+				} catch (error) {
+					alert("Error saving word list!");
+					console.error(error);
+				}
+			},
+		});
+
+		btns.unshift(save);
+		return btns;
+	}
+}
+
+const id = "pysssss.AutoCompleter";
+
+app.registerExtension({
+	name: id,
+	init() {
+		const STRING = ComfyWidgets.STRING;
+		const SKIP_WIDGETS = new Set(["ttN xyPlot.x_values", "ttN xyPlot.y_values"]);
+		ComfyWidgets.STRING = function (node, inputName, inputData) {
+			const r = STRING.apply(this, arguments);
+
+			if (inputData[1]?.multiline) {
+				// Disabled on this input
+				const config = inputData[1]?.["pysssss.autocomplete"];
+				if (config === false) return r;
+
+				// In list of widgets to skip
+				const id = `${node.comfyClass}.${inputName}`;
+				if (SKIP_WIDGETS.has(id)) return r;
+
+				let words;
+				let separator;
+				if (typeof config === "object") {
+					separator = config.separator;
+					words = {};
+					if (config.words) {
+						// Custom wordlist, this will have been registered on setup
+						Object.assign(words, TextAreaAutoComplete.groups[node.comfyClass + "." + inputName] ?? {});
+					}
+
+					for (const item of config.groups ?? []) {
+						if (item === "*") {
+							// This widget wants all global words included
+							Object.assign(words, TextAreaAutoComplete.globalWords);
+						} else {
+							// This widget wants a specific group included
+							Object.assign(words, TextAreaAutoComplete.groups[item] ?? {});
+						}
+					}
+				}
+
+				new TextAreaAutoComplete(r.widget.inputEl, words, separator);
+			}
+
+			return r;
+		};
+
+		TextAreaAutoComplete.globalSeparator = localStorage.getItem(id + ".AutoSeparate") ?? ", ";
+		const enabledSetting = app.ui.settings.addSetting({
+			id,
+			name: "🐍 Text Autocomplete",
+			defaultValue: true,
+			type: (name, setter, value) => {
+				return $el("tr", [
+					$el("td", [
+						$el("label", {
+							for: id.replaceAll(".", "-"),
+							textContent: name,
+						}),
+					]),
+					$el("td", [
+						$el(
+							"label",
+							{
+								textContent: "Enabled ",
+								style: {
+									display: "block",
+								},
+							},
+							[
+								$el("input", {
+									id: id.replaceAll(".", "-"),
+									type: "checkbox",
+									checked: value,
+									onchange: (event) => {
+										const checked = !!event.target.checked;
+										TextAreaAutoComplete.enabled = checked;
+										setter(checked);
+									},
+								}),
+							]
+						),
+						$el(
+							"label.comfy-tooltip-indicator",
+							{
+								title: "This requires other ComfyUI nodes/extensions that support using LoRAs in the prompt.",
+								textContent: "Loras enabled ",
+								style: {
+									display: "block",
+								},
+							},
+							[
+								$el("input", {
+									type: "checkbox",
+									checked: !!TextAreaAutoComplete.lorasEnabled,
+									onchange: (event) => {
+										const checked = !!event.target.checked;
+										TextAreaAutoComplete.lorasEnabled = checked;
+										toggleLoras();
+										localStorage.setItem(id + ".ShowLoras", TextAreaAutoComplete.lorasEnabled);
+									},
+								}),
+							]
+						),
+						$el(
+							"label",
+							{
+								textContent: "Auto-insert comma ",
+								style: {
+									display: "block",
+								},
+							},
+							[
+								$el("input", {
+									type: "checkbox",
+									checked: !!TextAreaAutoComplete.globalSeparator,
+									onchange: (event) => {
+										const checked = !!event.target.checked;
+										TextAreaAutoComplete.globalSeparator = checked ? ", " : "";
+										localStorage.setItem(id + ".AutoSeparate", TextAreaAutoComplete.globalSeparator);
+									},
+								}),
+							]
+						),
+						$el(
+							"label",
+							{
+								textContent: "Replace _ with space ",
+								style: {
+									display: "block",
+								},
+							},
+							[
+								$el("input", {
+									type: "checkbox",
+									checked: !!TextAreaAutoComplete.replacer,
+									onchange: (event) => {
+										const checked = !!event.target.checked;
+										TextAreaAutoComplete.replacer = checked ? (v) => v.replaceAll("_", " ") : undefined;
+										localStorage.setItem(id + ".ReplaceUnderscore", checked);
+									},
+								}),
+							]
+						),
+						$el(
+							"label",
+							{
+								textContent: "Insert suggestion on: ",
+								style: {
+									display: "block",
+								},
+							},
+							[
+								$el(
+									"label",
+									{
+										textContent: "Tab",
+										style: {
+											display: "block",
+											marginLeft: "20px",
+										},
+									},
+									[
+										$el("input", {
+											type: "checkbox",
+											checked: !!TextAreaAutoComplete.insertOnTab,
+											onchange: (event) => {
+												const checked = !!event.target.checked;
+												TextAreaAutoComplete.insertOnTab = checked;
+												localStorage.setItem(id + ".InsertOnTab", checked);
+											},
+										}),
+									]
+								),
+								$el(
+									"label",
+									{
+										textContent: "Enter",
+										style: {
+											display: "block",
+											marginLeft: "20px",
+										},
+									},
+									[
+										$el("input", {
+											type: "checkbox",
+											checked: !!TextAreaAutoComplete.insertOnEnter,
+											onchange: (event) => {
+												const checked = !!event.target.checked;
+												TextAreaAutoComplete.insertOnEnter = checked;
+												localStorage.setItem(id + ".InsertOnEnter", checked);
+											},
+										}),
+									]
+								),
+							]
+						),
+						$el(
+							"label",
+							{
+								textContent: "Max suggestions: ",
+								style: {
+									display: "block",
+								},
+							},
+							[
+								$el("input", {
+									type: "number",
+									value: +TextAreaAutoComplete.suggestionCount,
+									style: {
+										width: "80px"
+									},
+									onchange: (event) => {
+										const value = +event.target.value;
+										TextAreaAutoComplete.suggestionCount = value;;
+										localStorage.setItem(id + ".SuggestionCount", TextAreaAutoComplete.suggestionCount);
+									},
+								}),
+							]
+						),
+						$el("button", {
+							textContent: "Manage Custom Words",
+							onclick: () => {
+								try {
+									// Try closing old settings window
+									if (typeof app.ui.settings.element?.close === "function") { 
+										app.ui.settings.element.close();
+									}	
+								} catch (error) {
+								}
+								try {
+									// Try closing new vue dialog
+									document.querySelector(".p-dialog-close-button").click();
+								} catch (error) {
+									// Fallback to just hiding the element
+									app.ui.settings.element.style.display = "none";
+								}
+								
+								new CustomWordsDialog().show();
+							},
+							style: {
+								fontSize: "14px",
+								display: "block",
+								marginTop: "5px",
+							},
+						}),
+					]),
+				]);
+			},
+		});
+
+		TextAreaAutoComplete.enabled = enabledSetting.value;
+		TextAreaAutoComplete.replacer = localStorage.getItem(id + ".ReplaceUnderscore") === "true" ? (v) => v.replaceAll("_", " ") : undefined;
+		TextAreaAutoComplete.insertOnTab = localStorage.getItem(id + ".InsertOnTab") !== "false";
+		TextAreaAutoComplete.insertOnEnter = localStorage.getItem(id + ".InsertOnEnter") !== "false";
+		TextAreaAutoComplete.lorasEnabled = localStorage.getItem(id + ".ShowLoras") === "true";
+		TextAreaAutoComplete.suggestionCount = +localStorage.getItem(id + ".SuggestionCount") || 20;
+	},
+	setup() {
+		async function addEmbeddings() {
+			const embeddings = await api.getEmbeddings();
+			const words = {};
+			words["embedding:"] = { text: "embedding:" };
+
+			for (const emb of embeddings) {
+				const v = `embedding:${emb}`;
+				words[v] = {
+					text: v,
+					info: () => new EmbeddingInfoDialog(emb).show("embeddings", emb),
+					use_replacer: false,
+				};
+			}
+
+			TextAreaAutoComplete.updateWords("pysssss.embeddings", words);
+		}
+
+		async function addLoras() {
+			let loras;
+			try {
+				loras = LiteGraph.registered_node_types["LoraLoader"]?.nodeData.input.required.lora_name[0];
+			} catch (error) {}
+
+			if (!loras?.length) {
+				loras = await api.fetchApi("/pysssss/loras", { cache: "no-store" }).then((res) => res.json());
+			}
+
+			const words = {};
+			words["lora:"] = { text: "lora:" };
+
+			for (const lora of loras) {
+				const v = `<lora:${lora}:1.0>`;
+				words[v] = {
+					text: v,
+					info: () => new LoraInfoDialog(lora).show("loras", lora),
+					use_replacer: false,
+				};
+			}
+
+			TextAreaAutoComplete.updateWords("pysssss.loras", words);
+		}
+
+		// store global words with/without loras
+		Promise.all([addEmbeddings(), addCustomWords()])
+			.then(() => {
+				TextAreaAutoComplete.globalWordsExclLoras = Object.assign({}, TextAreaAutoComplete.globalWords);
+			})
+			.then(addLoras)
+			.then(() => {
+				if (!TextAreaAutoComplete.lorasEnabled) {
+					toggleLoras(); // off by default
+				}
+			});
+	},
+	beforeRegisterNodeDef(_, def) {
+		// Process each input to see if there is a custom word list for
+		// { input: { required: { something: ["STRING", { "pysssss.autocomplete": ["groupid", ["custom", "words"] ] }] } } }
+		const inputs = { ...def.input?.required, ...def.input?.optional };
+		for (const input in inputs) {
+			const config = inputs[input][1]?.["pysssss.autocomplete"];
+			if (!config) continue;
+			if (typeof config === "object" && config.words) {
+				const words = {};
+				for (const text of config.words || []) {
+					const obj = typeof text === "string" ? { text } : text;
+					words[obj.text] = obj;
+				}
+				TextAreaAutoComplete.updateWords(def.name + "." + input, words, false);
+			}
+		}
+	},
+});
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/betterCombos.js b/custom_nodes/comfyui-custom-scripts/web/js/betterCombos.js
new file mode 100644
index 0000000000000000000000000000000000000000..cbf0a0740471035dc1d1d4f14101c4888dd91c2a
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/betterCombos.js
@@ -0,0 +1,534 @@
+import { app } from "../../../scripts/app.js";
+import { ComfyWidgets } from "../../../scripts/widgets.js";
+import { $el } from "../../../scripts/ui.js";
+import { api } from "../../../scripts/api.js";
+
+const CHECKPOINT_LOADER = "CheckpointLoader|pysssss";
+const LORA_LOADER = "LoraLoader|pysssss";
+const IMAGE_WIDTH = 384;
+const IMAGE_HEIGHT = 384;
+
+function getType(node) {
+	if (node.comfyClass === CHECKPOINT_LOADER) {
+		return "checkpoints";
+	}
+	return "loras";
+}
+
+function getWidgetName(type) {
+	return type === "checkpoints" ? "ckpt_name" : "lora_name";
+}
+
+function encodeRFC3986URIComponent(str) {
+	return encodeURIComponent(str).replace(/[!'()*]/g, (c) => `%${c.charCodeAt(0).toString(16).toUpperCase()}`);
+}
+
+const calculateImagePosition = (el, bodyRect) => {
+	let { top, left, right } = el.getBoundingClientRect();
+	const { width: bodyWidth, height: bodyHeight } = bodyRect;
+
+	const isSpaceRight = right + IMAGE_WIDTH <= bodyWidth;
+	if (isSpaceRight) {
+		left = right;
+	} else {
+		left -= IMAGE_WIDTH;
+	}
+
+	top = top - IMAGE_HEIGHT / 2;
+	if (top + IMAGE_HEIGHT > bodyHeight) {
+		top = bodyHeight - IMAGE_HEIGHT;
+	}
+	if (top < 0) {
+		top = 0;
+	}
+
+	return { left: Math.round(left), top: Math.round(top), isLeft: !isSpaceRight };
+};
+
+function showImage(relativeToEl, imageEl) {
+	const bodyRect = document.body.getBoundingClientRect();
+	if (!bodyRect) return;
+
+	const { left, top, isLeft } = calculateImagePosition(relativeToEl, bodyRect);
+
+	imageEl.style.left = `${left}px`;
+	imageEl.style.top = `${top}px`;
+
+	if (isLeft) {
+		imageEl.classList.add("left");
+	} else {
+		imageEl.classList.remove("left");
+	}
+
+	document.body.appendChild(imageEl);
+}
+
+let imagesByType = {};
+const loadImageList = async (type) => {
+	imagesByType[type] = await (await api.fetchApi(`/pysssss/images/${type}`)).json();
+};
+
+app.registerExtension({
+	name: "pysssss.Combo++",
+	init() {
+		const displayOptions = { "List (normal)": 0, "Tree (subfolders)": 1, "Thumbnails (grid)": 2 };
+		const displaySetting = app.ui.settings.addSetting({
+			id: "pysssss.Combo++.Submenu",
+			name: "🐍 Lora & Checkpoint loader display mode",
+			defaultValue: 1,
+			type: "combo",
+			options: (value) => {
+				value = +value;
+
+				return Object.entries(displayOptions).map(([k, v]) => ({
+					value: v,
+					text: k,
+					selected: k === value,
+				}));
+			},
+		});
+
+		$el("style", {
+			textContent: `
+				.pysssss-combo-image {
+					position: absolute;
+					left: 0;
+					top: 0;
+					width: ${IMAGE_WIDTH}px;
+					height: ${IMAGE_HEIGHT}px;
+					object-fit: contain;
+					object-position: top left;
+					z-index: 9999;
+				}
+				.pysssss-combo-image.left {
+					object-position: top right;
+				}
+				.pysssss-combo-folder { opacity: 0.7 }
+				.pysssss-combo-folder-arrow { display: inline-block; width: 15px; }
+				.pysssss-combo-folder:hover { background-color: rgba(255, 255, 255, 0.1); }
+				.pysssss-combo-prefix { display: none }
+
+				/* Special handling for when the filter input is populated to revert to normal */
+				.litecontextmenu:has(input:not(:placeholder-shown)) .pysssss-combo-folder-contents {
+					display: block !important;
+				}
+				.litecontextmenu:has(input:not(:placeholder-shown)) .pysssss-combo-folder { 
+					display: none;
+				}
+				.litecontextmenu:has(input:not(:placeholder-shown)) .pysssss-combo-prefix { 
+					display: inline;
+				}
+				.litecontextmenu:has(input:not(:placeholder-shown)) .litemenu-entry { 
+					padding-left: 2px !important;
+				}
+
+				/* Grid mode */
+				.pysssss-combo-grid {
+					display: grid;
+					grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
+					gap: 10px;
+					overflow-x: hidden;
+					max-width: 60vw;
+				}
+				.pysssss-combo-grid .comfy-context-menu-filter {
+					grid-column: 1 / -1;
+					position: sticky;
+					top: 0;
+				}
+				.pysssss-combo-grid .litemenu-entry {
+					word-break: break-word;
+					display: flex;
+					flex-direction: column;
+					justify-content: space-between;
+					align-items: center;
+				}
+				.pysssss-combo-grid .litemenu-entry:before {
+					content: "";
+					display: block;
+					width: 100%;
+					height: 250px;
+					background-size: contain;
+					background-position: center;
+					background-repeat: no-repeat;
+					/* No-image image attribution: Picture icons created by Pixel perfect - Flaticon */
+					background-image: var(--background-image, url(extensions/ComfyUI-Custom-Scripts/js/assets/no-image.png));
+				}
+
+			`,
+			parent: document.body,
+		});
+		const p1 = loadImageList("checkpoints");
+		const p2 = loadImageList("loras");
+
+		const refreshComboInNodes = app.refreshComboInNodes;
+		app.refreshComboInNodes = async function () {
+			const r = await Promise.all([
+				refreshComboInNodes.apply(this, arguments),
+				loadImageList("checkpoints").catch(() => {}),
+				loadImageList("loras").catch(() => {}),
+			]);
+			return r[0];
+		};
+
+		const imageHost = $el("img.pysssss-combo-image");
+
+		const positionMenu = (menu, fillWidth) => {
+			// compute best position
+			let left = app.canvas.last_mouse[0] - 10;
+			let top = app.canvas.last_mouse[1] - 10;
+
+			const body_rect = document.body.getBoundingClientRect();
+			const root_rect = menu.getBoundingClientRect();
+
+			if (body_rect.width && left > body_rect.width - root_rect.width - 10) left = body_rect.width - root_rect.width - 10;
+			if (body_rect.height && top > body_rect.height - root_rect.height - 10) top = body_rect.height - root_rect.height - 10;
+
+			menu.style.left = `${left}px`;
+			menu.style.top = `${top}px`;
+			if (fillWidth) {
+				menu.style.right = "10px";
+			}
+		};
+
+		const updateMenu = async (menu, type) => {
+			try {
+				await p1;
+				await p2;
+			} catch (error) {
+				console.error(error);
+				console.error("Error loading pysssss.betterCombos data");
+			}
+
+			// Clamp max height so it doesn't overflow the screen
+			const position = menu.getBoundingClientRect();
+			const maxHeight = window.innerHeight - position.top - 20;
+			menu.style.maxHeight = `${maxHeight}px`;
+
+			const images = imagesByType[type];
+			const items = menu.querySelectorAll(".litemenu-entry");
+
+			// Add image handler to items
+			const addImageHandler = (item) => {
+				const text = item.getAttribute("data-value").trim();
+				if (images[text]) {
+					const textNode = document.createTextNode("*");
+					item.appendChild(textNode);
+
+					item.addEventListener(
+						"mouseover",
+						() => {
+							imageHost.src = `/pysssss/view/${encodeRFC3986URIComponent(images[text])}?${+new Date()}`;
+							document.body.appendChild(imageHost);
+							showImage(item, imageHost);
+						},
+						{ passive: true }
+					);
+					item.addEventListener(
+						"mouseout",
+						() => {
+							imageHost.remove();
+						},
+						{ passive: true }
+					);
+					item.addEventListener(
+						"click",
+						() => {
+							imageHost.remove();
+						},
+						{ passive: true }
+					);
+				}
+			};
+
+			const createTree = () => {
+				// Create a map to store folder structures
+				const folderMap = new Map();
+				const rootItems = [];
+				const splitBy = (navigator.platform || navigator.userAgent).includes("Win") ? /\/|\\/ : /\//;
+				const itemsSymbol = Symbol("items");
+
+				// First pass - organize items into folder structure
+				for (const item of items) {
+					const path = item.getAttribute("data-value").split(splitBy);
+
+					// Remove path from visible text
+					item.textContent = path[path.length - 1];
+					if (path.length > 1) {
+						// Add the prefix path back in so it can be filtered on
+						const prefix = $el("span.pysssss-combo-prefix", {
+							textContent: path.slice(0, -1).join("/") + "/",
+						});
+						item.prepend(prefix);
+					}
+
+					addImageHandler(item);
+
+					if (path.length === 1) {
+						rootItems.push(item);
+						continue;
+					}
+
+					// Temporarily remove the item from current position
+					item.remove();
+
+					// Create folder hierarchy
+					let currentLevel = folderMap;
+					for (let i = 0; i < path.length - 1; i++) {
+						const folder = path[i];
+						if (!currentLevel.has(folder)) {
+							currentLevel.set(folder, new Map());
+						}
+						currentLevel = currentLevel.get(folder);
+					}
+
+					// Store the actual item in the deepest folder
+					if (!currentLevel.has(itemsSymbol)) {
+						currentLevel.set(itemsSymbol, []);
+					}
+					currentLevel.get(itemsSymbol).push(item);
+				}
+
+				const createFolderElement = (name) => {
+					const folder = $el("div.litemenu-entry.pysssss-combo-folder", {
+						innerHTML: `<span class="pysssss-combo-folder-arrow">▶</span> ${name}`,
+						style: { paddingLeft: "5px" },
+					});
+					return folder;
+				};
+
+				const insertFolderStructure = (parentElement, map, level = 0) => {
+					for (const [folderName, content] of map.entries()) {
+						if (folderName === itemsSymbol) continue;
+
+						const folderElement = createFolderElement(folderName);
+						folderElement.style.paddingLeft = `${level * 10 + 5}px`;
+						parentElement.appendChild(folderElement);
+
+						const childContainer = $el("div.pysssss-combo-folder-contents", {
+							style: { display: "none" },
+						});
+
+						// Add items in this folder
+						const items = content.get(itemsSymbol) || [];
+						for (const item of items) {
+							item.style.paddingLeft = `${(level + 1) * 10 + 14}px`;
+							childContainer.appendChild(item);
+						}
+
+						// Recursively add subfolders
+						insertFolderStructure(childContainer, content, level + 1);
+						parentElement.appendChild(childContainer);
+
+						// Add click handler for folder
+						folderElement.addEventListener("click", (e) => {
+							e.stopPropagation();
+							const arrow = folderElement.querySelector(".pysssss-combo-folder-arrow");
+							const contents = folderElement.nextElementSibling;
+							if (contents.style.display === "none") {
+								contents.style.display = "block";
+								arrow.textContent = "▼";
+							} else {
+								contents.style.display = "none";
+								arrow.textContent = "▶";
+							}
+						});
+					}
+				};
+
+				insertFolderStructure(items[0]?.parentElement || menu, folderMap);
+				positionMenu(menu);
+			};
+
+			const addImageData = (item) => {
+				const text = item.getAttribute("data-value").trim();
+				if (images[text]) {
+					item.style.setProperty("--background-image", `url(/pysssss/view/${encodeRFC3986URIComponent(images[text])})`);
+				}
+			};
+
+			if (displaySetting.value === 1 || displaySetting.value === true) {
+				createTree();
+			} else if (displaySetting.value === 2) {
+				menu.classList.add("pysssss-combo-grid");
+
+				for (const item of items) {
+					addImageData(item);
+				}
+				positionMenu(menu, true);
+			} else {
+				for (const item of items) {
+					addImageHandler(item);
+				}
+			}
+		};
+
+		const mutationObserver = new MutationObserver((mutations) => {
+			const node = app.canvas.current_node;
+
+			if (!node || (node.comfyClass !== LORA_LOADER && node.comfyClass !== CHECKPOINT_LOADER)) {
+				return;
+			}
+
+			for (const mutation of mutations) {
+				for (const removed of mutation.removedNodes) {
+					if (removed.classList?.contains("litecontextmenu")) {
+						imageHost.remove();
+					}
+				}
+
+				for (const added of mutation.addedNodes) {
+					if (added.classList?.contains("litecontextmenu")) {
+						const overWidget = app.canvas.getWidgetAtCursor();
+						const type = getType(node);
+						if (overWidget?.name === getWidgetName(type)) {
+							requestAnimationFrame(() => {
+								// Bad hack to prevent showing on right click menu by checking for the filter input
+								if (!added.querySelector(".comfy-context-menu-filter")) return;
+								updateMenu(added, type);
+							});
+						}
+						return;
+					}
+				}
+			}
+		});
+		mutationObserver.observe(document.body, { childList: true, subtree: false });
+	},
+	async beforeRegisterNodeDef(nodeType, nodeData, app) {
+		const isCkpt = nodeData.name === CHECKPOINT_LOADER;
+		const isLora = nodeData.name === LORA_LOADER;
+		if (isCkpt || isLora) {
+			const onAdded = nodeType.prototype.onAdded;
+			nodeType.prototype.onAdded = function () {
+				onAdded?.apply(this, arguments);
+				const { widget: exampleList } = ComfyWidgets["COMBO"](this, "example", [[""], {}], app);
+				this.widgets.find((w) => w.name === "prompt").computeSize = () => [0, -4];
+				let exampleWidget;
+
+				const get = async (route, suffix) => {
+					const url = encodeRFC3986URIComponent(`${getType(nodeType)}${suffix || ""}`);
+					return await api.fetchApi(`/pysssss/${route}/${url}`);
+				};
+
+				const getExample = async () => {
+					if (exampleList.value === "[none]") {
+						if (exampleWidget) {
+							exampleWidget.inputEl.remove();
+							exampleWidget = null;
+							this.widgets.length -= 1;
+						}
+						return;
+					}
+
+					const v = this.widgets[0].value;
+					const pos = v.lastIndexOf(".");
+					const name = v.substr(0, pos);
+					let exampleName = exampleList.value;
+					let viewPath = `/${name}`;
+					if (exampleName === "notes") {
+						viewPath += ".txt";
+					} else {
+						viewPath += `/${exampleName}`;
+					}
+					const example = await (await get("view", viewPath)).text();
+					if (!exampleWidget) {
+						exampleWidget = ComfyWidgets["STRING"](this, "prompt", ["STRING", { multiline: true }], app).widget;
+						exampleWidget.inputEl.readOnly = true;
+						exampleWidget.inputEl.style.opacity = 0.6;
+					}
+					exampleWidget.value = example;
+				};
+
+				const exampleCb = exampleList.callback;
+				exampleList.callback = function () {
+					getExample();
+					return exampleCb?.apply(this, arguments) ?? exampleList.value;
+				};
+
+				const listExamples = async () => {
+					exampleList.disabled = true;
+					exampleList.options.values = ["[none]"];
+					exampleList.value = "[none]";
+					let examples = [];
+					if (this.widgets[0].value) {
+						try {
+							examples = await (await get("examples", `/${this.widgets[0].value}`)).json();
+						} catch (error) {}
+					}
+					exampleList.options.values = ["[none]", ...examples];
+					exampleList.value = exampleList.options.values[+!!examples.length];
+					exampleList.callback();
+					exampleList.disabled = !examples.length;
+					app.graph.setDirtyCanvas(true, true);
+				};
+
+				// Expose function to update examples
+				nodeType.prototype["pysssss.updateExamples"] = listExamples;
+
+				const modelWidget = this.widgets[0];
+				const modelCb = modelWidget.callback;
+				let prev = undefined;
+				modelWidget.callback = function () {
+					let ret = modelCb?.apply(this, arguments) ?? modelWidget.value;
+					if (typeof ret === "object" && "content" in ret) {
+						ret = ret.content;
+						modelWidget.value = ret;
+					}
+					let v = ret;
+					if (prev !== v) {
+						listExamples();
+						prev = v;
+					}
+					return ret;
+				};
+				setTimeout(() => {
+					modelWidget.callback();
+				}, 30);
+			};
+		}
+
+		const getExtraMenuOptions = nodeType.prototype.getExtraMenuOptions;
+		nodeType.prototype.getExtraMenuOptions = function (_, options) {
+			if (this.imgs) {
+				// If this node has images then we add an open in new tab item
+				let img;
+				if (this.imageIndex != null) {
+					// An image is selected so select that
+					img = this.imgs[this.imageIndex];
+				} else if (this.overIndex != null) {
+					// No image is selected but one is hovered
+					img = this.imgs[this.overIndex];
+				}
+				if (img) {
+					const nodes = app.graph._nodes.filter((n) => n.comfyClass === LORA_LOADER || n.comfyClass === CHECKPOINT_LOADER);
+					if (nodes.length) {
+						options.unshift({
+							content: "Save as Preview",
+							submenu: {
+								options: nodes.map((n) => ({
+									content: n.widgets[0].value,
+									callback: async () => {
+										const url = new URL(img.src);
+										await api.fetchApi("/pysssss/save/" + encodeRFC3986URIComponent(`${getType(n)}/${n.widgets[0].value}`), {
+											method: "POST",
+											body: JSON.stringify({
+												filename: url.searchParams.get("filename"),
+												subfolder: url.searchParams.get("subfolder"),
+												type: url.searchParams.get("type"),
+											}),
+											headers: {
+												"content-type": "application/json",
+											},
+										});
+										loadImageList(getType(n));
+									},
+								})),
+							},
+						});
+					}
+				}
+			}
+			return getExtraMenuOptions?.apply(this, arguments);
+		};
+	},
+});
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/common/autocomplete.css b/custom_nodes/comfyui-custom-scripts/web/js/common/autocomplete.css
new file mode 100644
index 0000000000000000000000000000000000000000..1f371d6203a94202c48c851a3321c7e423de477b
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/common/autocomplete.css
@@ -0,0 +1,62 @@
+.pysssss-autocomplete {
+	color: var(--descrip-text);
+	background-color: var(--comfy-menu-bg);
+	position: absolute;
+	font-family: sans-serif;
+	box-shadow: 3px 3px 8px rgba(0, 0, 0, 0.4);
+	z-index: 9999;
+	overflow: auto;
+}
+
+.pysssss-autocomplete-item {
+	cursor: pointer;
+	padding: 3px 7px;
+	display: flex;
+	border-left: 3px solid transparent;
+	align-items: center;
+}
+
+.pysssss-autocomplete-item--selected {
+	border-left-color: dodgerblue;
+}
+
+.pysssss-autocomplete-highlight {
+	font-weight: bold;
+	text-decoration: underline;
+	text-decoration-color: dodgerblue;
+}
+
+.pysssss-autocomplete-pill {
+	margin-left: auto;
+	font-size: 10px;
+	color: #fff;
+	padding: 2px 4px 2px 14px;
+	position: relative;
+}
+
+.pysssss-autocomplete-pill::after {
+	content: "";
+	display: block;
+	background: rgba(255, 255, 255, 0.25);
+	width: calc(100% - 10px);
+	height: 100%;
+	position: absolute;
+	left: 10px;
+	top: 0;
+	border-radius: 5px;
+}
+
+.pysssss-autocomplete-pill + .pysssss-autocomplete-pill {
+	margin-left: 0;
+}
+
+.pysssss-autocomplete-item-info {
+	margin-left: auto;
+	transition: filter 0.2s;
+	will-change: filter;
+	text-decoration: none;
+	padding-left: 10px;
+}
+.pysssss-autocomplete-item-info:hover {
+	filter: invert(1);
+}
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/common/autocomplete.js b/custom_nodes/comfyui-custom-scripts/web/js/common/autocomplete.js
new file mode 100644
index 0000000000000000000000000000000000000000..ac5fb1d006bad80c67870655ccfc82a120ddc4a7
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/common/autocomplete.js
@@ -0,0 +1,692 @@
+import { $el } from "../../../../scripts/ui.js";
+import { addStylesheet } from "./utils.js";
+
+addStylesheet(import.meta.url);
+
+/*
+    https://github.com/component/textarea-caret-position
+    The MIT License (MIT)
+
+    Copyright (c) 2015 Jonathan Ong me@jongleberry.com
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+*/
+const getCaretCoordinates = (function () {
+	// We'll copy the properties below into the mirror div.
+	// Note that some browsers, such as Firefox, do not concatenate properties
+	// into their shorthand (e.g. padding-top, padding-bottom etc. -> padding),
+	// so we have to list every single property explicitly.
+	var properties = [
+		"direction", // RTL support
+		"boxSizing",
+		"width", // on Chrome and IE, exclude the scrollbar, so the mirror div wraps exactly as the textarea does
+		"height",
+		"overflowX",
+		"overflowY", // copy the scrollbar for IE
+
+		"borderTopWidth",
+		"borderRightWidth",
+		"borderBottomWidth",
+		"borderLeftWidth",
+		"borderStyle",
+
+		"paddingTop",
+		"paddingRight",
+		"paddingBottom",
+		"paddingLeft",
+
+		// https://developer.mozilla.org/en-US/docs/Web/CSS/font
+		"fontStyle",
+		"fontVariant",
+		"fontWeight",
+		"fontStretch",
+		"fontSize",
+		"fontSizeAdjust",
+		"lineHeight",
+		"fontFamily",
+
+		"textAlign",
+		"textTransform",
+		"textIndent",
+		"textDecoration", // might not make a difference, but better be safe
+
+		"letterSpacing",
+		"wordSpacing",
+
+		"tabSize",
+		"MozTabSize",
+	];
+
+	var isBrowser = typeof window !== "undefined";
+	var isFirefox = isBrowser && window.mozInnerScreenX != null;
+
+	return function getCaretCoordinates(element, position, options) {
+		if (!isBrowser) {
+			throw new Error("textarea-caret-position#getCaretCoordinates should only be called in a browser");
+		}
+
+		var debug = (options && options.debug) || false;
+		if (debug) {
+			var el = document.querySelector("#input-textarea-caret-position-mirror-div");
+			if (el) el.parentNode.removeChild(el);
+		}
+
+		// The mirror div will replicate the textarea's style
+		var div = document.createElement("div");
+		div.id = "input-textarea-caret-position-mirror-div";
+		document.body.appendChild(div);
+
+		var style = div.style;
+		var computed = window.getComputedStyle ? window.getComputedStyle(element) : element.currentStyle; // currentStyle for IE < 9
+		var isInput = element.nodeName === "INPUT";
+
+		// Default textarea styles
+		style.whiteSpace = "pre-wrap";
+		if (!isInput) style.wordWrap = "break-word"; // only for textarea-s
+
+		// Position off-screen
+		style.position = "absolute"; // required to return coordinates properly
+		if (!debug) style.visibility = "hidden"; // not 'display: none' because we want rendering
+
+		// Transfer the element's properties to the div
+		properties.forEach(function (prop) {
+			if (isInput && prop === "lineHeight") {
+				// Special case for <input>s because text is rendered centered and line height may be != height
+				if (computed.boxSizing === "border-box") {
+					var height = parseInt(computed.height);
+					var outerHeight =
+						parseInt(computed.paddingTop) +
+						parseInt(computed.paddingBottom) +
+						parseInt(computed.borderTopWidth) +
+						parseInt(computed.borderBottomWidth);
+					var targetHeight = outerHeight + parseInt(computed.lineHeight);
+					if (height > targetHeight) {
+						style.lineHeight = height - outerHeight + "px";
+					} else if (height === targetHeight) {
+						style.lineHeight = computed.lineHeight;
+					} else {
+						style.lineHeight = 0;
+					}
+				} else {
+					style.lineHeight = computed.height;
+				}
+			} else {
+				style[prop] = computed[prop];
+			}
+		});
+
+		if (isFirefox) {
+			// Firefox lies about the overflow property for textareas: https://bugzilla.mozilla.org/show_bug.cgi?id=984275
+			if (element.scrollHeight > parseInt(computed.height)) style.overflowY = "scroll";
+		} else {
+			style.overflow = "hidden"; // for Chrome to not render a scrollbar; IE keeps overflowY = 'scroll'
+		}
+
+		div.textContent = element.value.substring(0, position);
+		// The second special handling for input type="text" vs textarea:
+		// spaces need to be replaced with non-breaking spaces - http://stackoverflow.com/a/13402035/1269037
+		if (isInput) div.textContent = div.textContent.replace(/\s/g, "\u00a0");
+
+		var span = document.createElement("span");
+		// Wrapping must be replicated *exactly*, including when a long word gets
+		// onto the next line, with whitespace at the end of the line before (#7).
+		// The  *only* reliable way to do that is to copy the *entire* rest of the
+		// textarea's content into the <span> created at the caret position.
+		// For inputs, just '.' would be enough, but no need to bother.
+		span.textContent = element.value.substring(position) || "."; // || because a completely empty faux span doesn't render at all
+		div.appendChild(span);
+
+		var coordinates = {
+			top: span.offsetTop + parseInt(computed["borderTopWidth"]),
+			left: span.offsetLeft + parseInt(computed["borderLeftWidth"]),
+			height: parseInt(computed["lineHeight"]),
+		};
+
+		if (debug) {
+			span.style.backgroundColor = "#aaa";
+		} else {
+			document.body.removeChild(div);
+		}
+
+		return coordinates;
+	};
+})();
+
+/*
+    Key functions from:
+    https://github.com/yuku/textcomplete
+    © Yuku Takahashi - This software is licensed under the MIT license.
+
+    The MIT License (MIT)
+
+    Copyright (c) 2015 Jonathan Ong me@jongleberry.com
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+*/
+const CHAR_CODE_ZERO = "0".charCodeAt(0);
+const CHAR_CODE_NINE = "9".charCodeAt(0);
+
+class TextAreaCaretHelper {
+	constructor(el, getScale) {
+		this.el = el;
+		this.getScale = getScale;
+	}
+
+	#calculateElementOffset() {
+		const rect = this.el.getBoundingClientRect();
+		const owner = this.el.ownerDocument;
+		if (owner == null) {
+			throw new Error("Given element does not belong to document");
+		}
+		const { defaultView, documentElement } = owner;
+		if (defaultView == null) {
+			throw new Error("Given element does not belong to window");
+		}
+		const offset = {
+			top: rect.top + defaultView.pageYOffset,
+			left: rect.left + defaultView.pageXOffset,
+		};
+		if (documentElement) {
+			offset.top -= documentElement.clientTop;
+			offset.left -= documentElement.clientLeft;
+		}
+		return offset;
+	}
+
+	#isDigit(charCode) {
+		return CHAR_CODE_ZERO <= charCode && charCode <= CHAR_CODE_NINE;
+	}
+
+	#getLineHeightPx() {
+		const computedStyle = getComputedStyle(this.el);
+		const lineHeight = computedStyle.lineHeight;
+		// If the char code starts with a digit, it is either a value in pixels,
+		// or unitless, as per:
+		// https://drafts.csswg.org/css2/visudet.html#propdef-line-height
+		// https://drafts.csswg.org/css2/cascade.html#computed-value
+		if (this.#isDigit(lineHeight.charCodeAt(0))) {
+			const floatLineHeight = parseFloat(lineHeight);
+			// In real browsers the value is *always* in pixels, even for unit-less
+			// line-heights. However, we still check as per the spec.
+			return this.#isDigit(lineHeight.charCodeAt(lineHeight.length - 1))
+				? floatLineHeight * parseFloat(computedStyle.fontSize)
+				: floatLineHeight;
+		}
+		// Otherwise, the value is "normal".
+		// If the line-height is "normal", calculate by font-size
+		return this.#calculateLineHeightPx(this.el.nodeName, computedStyle);
+	}
+
+	/**
+	 * Returns calculated line-height of the given node in pixels.
+	 */
+	#calculateLineHeightPx(nodeName, computedStyle) {
+		const body = document.body;
+		if (!body) return 0;
+
+		const tempNode = document.createElement(nodeName);
+		tempNode.innerHTML = "&nbsp;";
+		Object.assign(tempNode.style, {
+			fontSize: computedStyle.fontSize,
+			fontFamily: computedStyle.fontFamily,
+			padding: "0",
+			position: "absolute",
+		});
+		body.appendChild(tempNode);
+
+		// Make sure textarea has only 1 row
+		if (tempNode instanceof HTMLTextAreaElement) {
+			tempNode.rows = 1;
+		}
+
+		// Assume the height of the element is the line-height
+		const height = tempNode.offsetHeight;
+		body.removeChild(tempNode);
+
+		return height;
+	}
+
+	getCursorOffset() {
+		const scale = this.getScale();
+		const elOffset = this.#calculateElementOffset();
+		const elScroll = this.#getElScroll();
+		const cursorPosition = this.#getCursorPosition();
+		const lineHeight = this.#getLineHeightPx();
+		const top = elOffset.top - (elScroll.top * scale) + (cursorPosition.top + lineHeight) * scale;
+		const left = elOffset.left - elScroll.left + cursorPosition.left;
+		const clientTop = this.el.getBoundingClientRect().top;
+		if (this.el.dir !== "rtl") {
+			return { top, left, lineHeight, clientTop };
+		} else {
+			const right = document.documentElement ? document.documentElement.clientWidth - left : 0;
+			return { top, right, lineHeight, clientTop };
+		}
+	}
+
+	#getElScroll() {
+		return { top: this.el.scrollTop, left: this.el.scrollLeft };
+	}
+
+	#getCursorPosition() {
+		return getCaretCoordinates(this.el, this.el.selectionEnd);
+	}
+
+	getBeforeCursor() {
+		return this.el.selectionStart !== this.el.selectionEnd ? null : this.el.value.substring(0, this.el.selectionEnd);
+	}
+
+	getAfterCursor() {
+		return this.el.value.substring(this.el.selectionEnd);
+	}
+
+	insertAtCursor(value, offset, finalOffset) {
+		if (this.el.selectionStart != null) {
+			const startPos = this.el.selectionStart;
+			const endPos = this.el.selectionEnd;
+
+			// Move selection to beginning of offset
+			this.el.selectionStart = this.el.selectionStart + offset;
+
+			// Using execCommand to support undo, but since it's officially 
+			// 'deprecated' we need a backup solution, but it won't support undo :(
+			let pasted = true;
+			try {
+				if (!document.execCommand("insertText", false, value)) {
+					pasted = false;
+				}
+			} catch (e) {
+				console.error("Error caught during execCommand:", e);
+				pasted = false;
+			}
+
+			if (!pasted) {
+				console.error(
+					"execCommand unsuccessful; not supported. Adding text manually, no undo support.");
+				textarea.setRangeText(modifiedText, this.el.selectionStart, this.el.selectionEnd, 'end');
+			}
+
+			this.el.selectionEnd = this.el.selectionStart = startPos + value.length + offset + (finalOffset ?? 0);
+		} else {
+			// Using execCommand to support undo, but since it's officially 
+			// 'deprecated' we need a backup solution, but it won't support undo :(
+			let pasted = true;
+			try {
+				if (!document.execCommand("insertText", false, value)) {
+					pasted = false;
+				}
+			} catch (e) {
+				console.error("Error caught during execCommand:", e);
+				pasted = false;
+			}
+
+			if (!pasted) {
+				console.error(
+					"execCommand unsuccessful; not supported. Adding text manually, no undo support.");
+				this.el.value += value;
+			}
+		}
+	}
+}
+
+/*********************/
+
+/**
+ * @typedef {{
+ * 	text: string,
+ * 	priority?: number,
+ * 	info?: Function,
+ * 	hint?: string,
+ *  showValue?: boolean,
+ *  caretOffset?: number
+ * }} AutoCompleteEntry
+ */
+export class TextAreaAutoComplete {
+	static globalSeparator = "";
+	static enabled = true;
+	static insertOnTab = true;
+	static insertOnEnter = true;
+	static replacer = undefined;
+	static lorasEnabled = false;
+	static suggestionCount = 20;
+
+	/** @type {Record<string, Record<string, AutoCompleteEntry>>} */
+	static groups = {};
+	/** @type {Set<string>} */
+	static globalGroups = new Set();
+	/** @type {Record<string, AutoCompleteEntry>} */
+	static globalWords = {};
+	/** @type {Record<string, AutoCompleteEntry>} */
+	static globalWordsExclLoras = {};
+
+	/** @type {HTMLTextAreaElement} */
+	el;
+
+	/** @type {Record<string, AutoCompleteEntry>} */
+	overrideWords;
+	overrideSeparator = "";
+
+	get words() {
+		return this.overrideWords ?? TextAreaAutoComplete.globalWords;
+	}
+
+	get separator() {
+		return this.overrideSeparator ?? TextAreaAutoComplete.globalSeparator;
+	}
+
+	/**
+	 * @param {HTMLTextAreaElement} el
+	 */
+	constructor(el, words = null, separator = null) {
+		this.el = el;
+		this.helper = new TextAreaCaretHelper(el, () => app.canvas.ds.scale);
+		this.dropdown = $el("div.pysssss-autocomplete");
+		this.overrideWords = words;
+		this.overrideSeparator = separator;
+
+		this.#setup();
+	}
+
+	#setup() {
+		this.el.addEventListener("keydown", this.#keyDown.bind(this));
+		this.el.addEventListener("keypress", this.#keyPress.bind(this));
+		this.el.addEventListener("keyup", this.#keyUp.bind(this));
+		this.el.addEventListener("click", this.#hide.bind(this));
+		this.el.addEventListener("blur", () => setTimeout(() => this.#hide(), 150));
+	}
+
+	/**
+	 * @param {KeyboardEvent} e
+	 */
+	#keyDown(e) {
+		if (!TextAreaAutoComplete.enabled) return;
+
+		if (this.dropdown.parentElement) {
+			// We are visible
+			switch (e.key) {
+				case "ArrowUp":
+					e.preventDefault();
+					if (this.selected.index) {
+						this.#setSelected(this.currentWords[this.selected.index - 1].wordInfo);
+					} else {
+						this.#setSelected(this.currentWords[this.currentWords.length - 1].wordInfo);
+					}
+					break;
+				case "ArrowDown":
+					e.preventDefault();
+					if (this.selected.index === this.currentWords.length - 1) {
+						this.#setSelected(this.currentWords[0].wordInfo);
+					} else {
+						this.#setSelected(this.currentWords[this.selected.index + 1].wordInfo);
+					}
+					break;
+				case "Tab":
+					if (TextAreaAutoComplete.insertOnTab) {
+						this.#insertItem();
+						e.preventDefault();
+					}
+					break;
+			}
+		}
+	}
+
+	/**
+	 * @param {KeyboardEvent} e
+	 */
+	#keyPress(e) {
+		if (!TextAreaAutoComplete.enabled) return;
+		if (this.dropdown.parentElement) {
+			// We are visible
+			switch (e.key) {
+				case "Enter":
+					if (!e.ctrlKey) {
+						if (TextAreaAutoComplete.insertOnEnter) {
+							this.#insertItem();
+							e.preventDefault();
+						}
+					}
+					break;
+			}
+		}
+
+		if (!e.defaultPrevented) {
+			this.#update();
+		}
+	}
+
+	#keyUp(e) {
+		if (!TextAreaAutoComplete.enabled) return;
+		if (this.dropdown.parentElement) {
+			// We are visible
+			switch (e.key) {
+				case "Escape":
+					e.preventDefault();
+					this.#hide();
+					break;
+			}
+		} else if (e.key.length > 1 && e.key != "Delete" && e.key != "Backspace") {
+			return;
+		}
+		if (!e.defaultPrevented) {
+			this.#update();
+		}
+	}
+
+	#setSelected(item) {
+		if (this.selected) {
+			this.selected.el.classList.remove("pysssss-autocomplete-item--selected");
+		}
+
+		this.selected = item;
+		this.selected.el.classList.add("pysssss-autocomplete-item--selected");
+	}
+
+	#insertItem() {
+		if (!this.selected) return;
+		this.selected.el.click();
+	}
+
+	#getFilteredWords(term) {
+		term = term.toLocaleLowerCase();
+
+		const priorityMatches = [];
+		const prefixMatches = [];
+		const includesMatches = [];
+		for (const word of Object.keys(this.words)) {
+			const lowerWord = word.toLocaleLowerCase();
+			if (lowerWord === term) {
+				// Dont include exact matches
+				continue;
+			}
+
+			const pos = lowerWord.indexOf(term);
+			if (pos === -1) {
+				// No match
+				continue;
+			}
+
+			const wordInfo = this.words[word];
+			if (wordInfo.priority) {
+				priorityMatches.push({ pos, wordInfo });
+			} else if (pos) {
+				includesMatches.push({ pos, wordInfo });
+			} else {
+				prefixMatches.push({ pos, wordInfo });
+			}
+		}
+
+		priorityMatches.sort(
+			(a, b) =>
+				b.wordInfo.priority - a.wordInfo.priority ||
+				a.wordInfo.text.length - b.wordInfo.text.length ||
+				a.wordInfo.text.localeCompare(b.wordInfo.text)
+		);
+
+		const top = priorityMatches.length * 0.2;
+		return priorityMatches.slice(0, top).concat(prefixMatches, priorityMatches.slice(top), includesMatches).slice(0, TextAreaAutoComplete.suggestionCount);
+	}
+
+	#update() {
+		let before = this.helper.getBeforeCursor();
+		if (before?.length) {
+			const m = before.match(/([^,;"|{}()\n]+)$/);
+			if (m) {
+				before = m[0]
+					.replace(/^\s+/, "")
+					.replace(/\s/g, "_") || null;
+			} else {
+				before = null;
+			}
+		}
+
+		if (!before) {
+			this.#hide();
+			return;
+		}
+
+		this.currentWords = this.#getFilteredWords(before);
+		if (!this.currentWords.length) {
+			this.#hide();
+			return;
+		}
+
+		this.dropdown.style.display = "";
+
+		let hasSelected = false;
+		const items = this.currentWords.map(({ wordInfo, pos }, i) => {
+			const parts = [
+				$el("span", {
+					textContent: wordInfo.text.substr(0, pos),
+				}),
+				$el("span.pysssss-autocomplete-highlight", {
+					textContent: wordInfo.text.substr(pos, before.length),
+				}),
+				$el("span", {
+					textContent: wordInfo.text.substr(pos + before.length),
+				}),
+			];
+
+			if (wordInfo.hint) {
+				parts.push(
+					$el("span.pysssss-autocomplete-pill", {
+						textContent: wordInfo.hint,
+					})
+				);
+			}
+
+			if (wordInfo.priority) {
+				parts.push(
+					$el("span.pysssss-autocomplete-pill", {
+						textContent: wordInfo.priority,
+					})
+				);
+			}
+
+			if (wordInfo.value && wordInfo.text !== wordInfo.value && wordInfo.showValue !== false) {
+				parts.push(
+					$el("span.pysssss-autocomplete-pill", {
+						textContent: wordInfo.value,
+					})
+				);
+			}
+
+			if (wordInfo.info) {
+				parts.push(
+					$el("a.pysssss-autocomplete-item-info", {
+						textContent: "ℹ️",
+						title: "View info...",
+						onclick: (e) => {
+							e.stopPropagation();
+							wordInfo.info();
+							e.preventDefault();
+						},
+					})
+				);
+			}
+			const item = $el(
+				"div.pysssss-autocomplete-item",
+				{
+				  onclick: () => {
+					this.el.focus();
+					let value = wordInfo.value ?? wordInfo.text;
+					const use_replacer = wordInfo.use_replacer ?? true;
+					if (TextAreaAutoComplete.replacer && use_replacer) {
+					  value = TextAreaAutoComplete.replacer(value);
+					}
+					value = this.#escapeParentheses(value);
+					
+					const afterCursor = this.helper.getAfterCursor();
+					const shouldAddSeparator = !afterCursor.trim().startsWith(this.separator.trim());
+					this.helper.insertAtCursor(
+					  value + (shouldAddSeparator ? this.separator : ''),
+					  -before.length,
+					  wordInfo.caretOffset
+					);			
+					setTimeout(() => {
+					  this.#update();
+					}, 150);
+				  },
+				},
+				parts
+			  );
+
+			if (wordInfo === this.selected) {
+				hasSelected = true;
+			}
+
+			wordInfo.index = i;
+			wordInfo.el = item;
+
+			return item;
+		});
+
+		this.#setSelected(hasSelected ? this.selected : this.currentWords[0].wordInfo);
+		this.dropdown.replaceChildren(...items);
+
+		if (!this.dropdown.parentElement) {
+			document.body.append(this.dropdown);
+		}
+
+		const position = this.helper.getCursorOffset();
+		this.dropdown.style.left = (position.left ?? 0) + "px";
+		this.dropdown.style.top = (position.top ?? 0) + "px";
+		this.dropdown.style.maxHeight = (window.innerHeight - position.top) + "px";
+	}
+
+	#escapeParentheses(text) {
+		return text.replace(/\(/g, '\\(').replace(/\)/g, '\\)');
+	  }
+
+	#hide() {
+		this.selected = null;
+		this.dropdown.remove();
+	}
+
+	static updateWords(id, words, addGlobal = true) {
+		const isUpdate = id in TextAreaAutoComplete.groups;
+		TextAreaAutoComplete.groups[id] = words;
+		if (addGlobal) {
+			TextAreaAutoComplete.globalGroups.add(id);
+		}
+
+		if (isUpdate) {
+			// Remerge all words
+			TextAreaAutoComplete.globalWords = Object.assign(
+				{},
+				...Object.keys(TextAreaAutoComplete.groups)
+					.filter((k) => TextAreaAutoComplete.globalGroups.has(k))
+					.map((k) => TextAreaAutoComplete.groups[k])
+			);
+		} else if (addGlobal) {
+			// Just insert the new words
+			Object.assign(TextAreaAutoComplete.globalWords, words);
+		}
+	}
+}
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/common/binding.js b/custom_nodes/comfyui-custom-scripts/web/js/common/binding.js
new file mode 100644
index 0000000000000000000000000000000000000000..7533e7e5e93beee798123a8ed84d36a50d2a4702
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/common/binding.js
@@ -0,0 +1,244 @@
+// @ts-check
+// @ts-ignore
+import { ComfyWidgets } from "../../../../scripts/widgets.js";
+// @ts-ignore
+import { api } from "../../../../scripts/api.js";
+// @ts-ignore
+import { app } from "../../../../scripts/app.js";
+
+const PathHelper = {
+	get(obj, path) {
+		if (typeof path !== "string") {
+			// Hardcoded value
+			return path;
+		}
+
+		if (path[0] === '"' && path[path.length - 1] === '"') {
+			// Hardcoded string
+			return JSON.parse(path);
+		}
+
+		// Evaluate the path
+		path = path.split(".").filter(Boolean);
+		for (const p of path) {
+			const k = isNaN(+p) ? p : +p;
+			obj = obj[k];
+		}
+
+		return obj;
+	},
+	set(obj, path, value) {
+		// https://stackoverflow.com/a/54733755
+		if (Object(obj) !== obj) return obj; // When obj is not an object
+		// If not yet an array, get the keys from the string-path
+		if (!Array.isArray(path)) path = path.toString().match(/[^.[\]]+/g) || [];
+		path.slice(0, -1).reduce(
+			(
+				a,
+				c,
+				i // Iterate all of them except the last one
+			) =>
+				Object(a[c]) === a[c] // Does the key exist and is its value an object?
+					? // Yes: then follow that path
+					  a[c]
+					: // No: create the key. Is the next key a potential array-index?
+					  (a[c] =
+							Math.abs(path[i + 1]) >> 0 === +path[i + 1]
+								? [] // Yes: assign a new array object
+								: {}), // No: assign a new plain object
+			obj
+		)[path[path.length - 1]] = value; // Finally assign the value to the last key
+		return obj; // Return the top-level object to allow chaining
+	},
+};
+
+/***
+	@typedef { {
+		left: string;
+		op: "eq" | "ne",
+		right: string
+	} } IfCondition 
+
+	@typedef { {
+		type: "if",
+		condition: Array<IfCondition>,
+		true?: Array<BindingCallback>,
+		false?: Array<BindingCallback>
+	} } IfCallback
+
+	@typedef { {
+		type: "fetch",
+		url: string,
+		then: Array<BindingCallback>
+	} } FetchCallback 
+
+	@typedef { {
+		type: "set",
+		target: string,
+		value: string
+	} } SetCallback 
+
+	@typedef { {
+		type: "validate-combo",
+	} } ValidateComboCallback 
+
+	@typedef { IfCallback | FetchCallback | SetCallback | ValidateComboCallback } BindingCallback 
+
+	@typedef { {
+		source: string,
+		callback: Array<BindingCallback>
+	} } Binding 
+***/
+
+/**
+ * @param {IfCondition} condition
+ */
+function evaluateCondition(condition, state) {
+	const left = PathHelper.get(state, condition.left);
+	const right = PathHelper.get(state, condition.right);
+
+	let r;
+	if (condition.op === "eq") {
+		r = left === right;
+	} else {
+		r = left !== right;
+	}
+
+	return r;
+}
+
+/**
+ * @type { Record<BindingCallback["type"], (cb: any, state: Record<string, any>) => Promise<void>> }
+ */
+const callbacks = {
+	/**
+	 * @param {IfCallback} cb
+	 */
+	async if(cb, state) {
+		// For now only support ANDs
+		let success = true;
+		for (const condition of cb.condition) {
+			const r = evaluateCondition(condition, state);
+			if (!r) {
+				success = false;
+				break;
+			}
+		}
+
+		for (const m of cb[success + ""] ?? []) {
+			await invokeCallback(m, state);
+		}
+	},
+	/**
+	 * @param {FetchCallback} cb
+	 */
+	async fetch(cb, state) {
+		const url = cb.url.replace(/\{([^\}]+)\}/g, (m, v) => {
+			return PathHelper.get(state, v);
+		});
+		const res = await (await api.fetchApi(url)).json();
+		state["$result"] = res;
+		for (const m of cb.then) {
+			await invokeCallback(m, state);
+		}
+	},
+	/**
+	 * @param {SetCallback} cb
+	 */
+	async set(cb, state) {
+		const value = PathHelper.get(state, cb.value);
+		PathHelper.set(state, cb.target, value);
+	},
+	async "validate-combo"(cb, state) {
+		const w = state["$this"];
+		const valid = w.options.values.includes(w.value);
+		if (!valid) {
+			w.value = w.options.values[0];
+		}
+	},
+};
+
+async function invokeCallback(callback, state) {
+	if (callback.type in callbacks) {
+		// @ts-ignore
+		await callbacks[callback.type](callback, state);
+	} else {
+		console.warn(
+			"%c[🐍 pysssss]",
+			"color: limegreen",
+			`[binding ${state.$node.comfyClass}.${state.$this.name}]`,
+			"unsupported binding callback type:",
+			callback.type
+		);
+	}
+}
+
+app.registerExtension({
+	name: "pysssss.Binding",
+	beforeRegisterNodeDef(node, nodeData) {
+		const hasBinding = (v) => {
+			if (!v) return false;
+			return Object.values(v).find((c) => c[1]?.["pysssss.binding"]);
+		};
+		const inputs = { ...nodeData.input?.required, ...nodeData.input?.optional };
+		if (hasBinding(inputs)) {
+			const onAdded = node.prototype.onAdded;
+			node.prototype.onAdded = function () {
+				const r = onAdded?.apply(this, arguments);
+
+				for (const widget of this.widgets || []) {
+					const bindings = inputs[widget.name][1]?.["pysssss.binding"];
+					if (!bindings) continue;
+
+					for (const binding of bindings) {
+						/**
+						 * @type {import("../../../../../web/types/litegraph.d.ts").IWidget}
+						 */
+						const source = this.widgets.find((w) => w.name === binding.source);
+						if (!source) {
+							console.warn(
+								"%c[🐍 pysssss]",
+								"color: limegreen",
+								`[binding ${node.comfyClass}.${widget.name}]`,
+								"unable to find source binding widget:",
+								binding.source,
+								binding
+							);
+							continue;
+						}
+
+						let lastValue;
+						async function valueChanged() {
+							const state = {
+								$this: widget,
+								$source: source,
+								$node: node,
+							};
+
+							for (const callback of binding.callback) {
+								await invokeCallback(callback, state);
+							}
+
+							app.graph.setDirtyCanvas(true, false);
+						}
+
+						const cb = source.callback;
+						source.callback = function () {
+							const v = cb?.apply(this, arguments) ?? source.value;
+							if (v !== lastValue) {
+								lastValue = v;
+								valueChanged();
+							}
+							return v;
+						};
+
+						lastValue = source.value;
+						valueChanged();
+					}
+				}
+
+				return r;
+			};
+		}
+	},
+});
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/common/lightbox.css b/custom_nodes/comfyui-custom-scripts/web/js/common/lightbox.css
new file mode 100644
index 0000000000000000000000000000000000000000..c4307fd0a201f6ae396e0cb9e2b5e00dba8fc285
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/common/lightbox.css
@@ -0,0 +1,102 @@
+.pysssss-lightbox {
+	width: 100vw;
+	height: 100vh;
+	position: fixed;
+	top: 0;
+	left: 0;
+	z-index: 1001;
+	background: rgba(0, 0, 0, 0.6);
+	display: flex;
+	align-items: center;
+	transition: opacity 0.2s;
+}
+
+.pysssss-lightbox-prev,
+.pysssss-lightbox-next {
+	height: 60px;
+	display: flex;
+	align-items: center;
+}
+
+.pysssss-lightbox-prev:after,
+.pysssss-lightbox-next:after {
+	border-style: solid;
+	border-width: 0.25em 0.25em 0 0;
+	display: inline-block;
+	height: 0.45em;
+	left: 0.15em;
+	position: relative;
+	top: 0.15em;
+	transform: rotate(-135deg) scale(0.75);
+	vertical-align: top;
+	width: 0.45em;
+	padding: 10px;
+	font-size: 20px;
+	margin: 0 10px 0 20px;
+	transition: color 0.2s;
+	flex-shrink: 0;
+	content: "";
+}
+
+.pysssss-lightbox-next:after {
+	transform: rotate(45deg) scale(0.75);
+	margin: 0 20px 0 0px;
+}
+
+.pysssss-lightbox-main {
+	display: grid;
+	flex: auto;
+	place-content: center;
+	text-align: center;
+}
+
+.pysssss-lightbox-link {
+	display: flex;
+	justify-content: center;
+	align-items: center;
+	position: relative;
+}
+
+.pysssss-lightbox .lds-ring {
+	position: absolute;
+	left: 50%;
+	top: 50%;
+	transform: translate(-50%, -50%);
+}
+
+.pysssss-lightbox-img {
+	max-height: 90vh;
+	max-width: calc(100vw - 130px);
+	height: auto;
+	object-fit: contain;
+	border: 3px solid white;
+	border-radius: 4px;
+	transition: opacity 0.2s;
+	user-select: none;
+}
+
+.pysssss-lightbox-img:hover {
+	border-color: dodgerblue;
+}
+
+.pysssss-lightbox-close {
+	font-size: 80px;
+	line-height: 1ch;
+	height: 1ch;
+	width: 1ch;
+	position: absolute;
+	right: 10px;
+	top: 10px;
+	padding: 5px;
+}
+
+.pysssss-lightbox-close:after {
+	content: "\00d7";
+}
+
+.pysssss-lightbox-close:hover,
+.pysssss-lightbox-prev:hover,
+.pysssss-lightbox-next:hover {
+	color: dodgerblue;
+	cursor: pointer;
+}
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/common/lightbox.js b/custom_nodes/comfyui-custom-scripts/web/js/common/lightbox.js
new file mode 100644
index 0000000000000000000000000000000000000000..5c9079067f6fd26ab8e392e55add76d52a885858
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/common/lightbox.js
@@ -0,0 +1,149 @@
+import { $el } from "../../../../scripts/ui.js";
+import { addStylesheet, getUrl, loadImage } from "./utils.js";
+import { createSpinner } from "./spinner.js";
+
+addStylesheet(getUrl("lightbox.css", import.meta.url));
+
+const $$el = (tag, name, ...args) => {
+	if (name) name = "-" + name;
+	return $el(tag + ".pysssss-lightbox" + name, ...args);
+};
+
+const ani = async (a, t, b) => {
+	a();
+	await new Promise((r) => setTimeout(r, t));
+	b();
+};
+
+export class Lightbox {
+	constructor() {
+		this.el = $$el("div", "", {
+			parent: document.body,
+			onclick: (e) => {
+				e.stopImmediatePropagation();
+				this.close();
+			},
+			style: {
+				display: "none",
+				opacity: 0,
+			},
+		});
+		this.closeBtn = $$el("div", "close", {
+			parent: this.el,
+		});
+		this.prev = $$el("div", "prev", {
+			parent: this.el,
+			onclick: (e) => {
+				this.update(-1);
+				e.stopImmediatePropagation();
+			},
+		});
+		this.main = $$el("div", "main", {
+			parent: this.el,
+		});
+		this.next = $$el("div", "next", {
+			parent: this.el,
+			onclick: (e) => {
+				this.update(1);
+				e.stopImmediatePropagation();
+			},
+		});
+		this.link = $$el("a", "link", {
+			parent: this.main,
+			target: "_blank",
+		});
+		this.spinner = createSpinner();
+		this.link.appendChild(this.spinner);
+		this.img = $$el("img", "img", {
+			style: {
+				opacity: 0,
+			},
+			parent: this.link,
+			onclick: (e) => {
+				e.stopImmediatePropagation();
+			},
+			onwheel: (e) => {
+				if (!(e instanceof WheelEvent) || e.ctrlKey) {
+					return;
+				}
+				const direction = Math.sign(e.deltaY);
+				this.update(direction);
+			},
+		});
+	}
+
+	close() {
+		ani(
+			() => (this.el.style.opacity = 0),
+			200,
+			() => (this.el.style.display = "none")
+		);
+	}
+
+	async show(images, index) {
+		this.images = images;
+		this.index = index || 0;
+		await this.update(0);
+	}
+
+	async update(shift) {
+		if (shift < 0 && this.index <= 0) {
+			return;
+		}
+		if (shift > 0 && this.index >= this.images.length - 1) {
+			return;
+		}
+		this.index += shift;
+
+		this.prev.style.visibility = this.index ? "unset" : "hidden";
+		this.next.style.visibility = this.index === this.images.length - 1 ? "hidden" : "unset";
+
+		const img = this.images[this.index];
+		this.el.style.display = "flex";
+		this.el.clientWidth; // Force a reflow
+		this.el.style.opacity = 1;
+		this.img.style.opacity = 0;
+		this.spinner.style.display = "inline-block";
+		try {
+			await loadImage(img);
+		} catch (err) {
+			console.error('failed to load image', img, err);
+		}
+		this.spinner.style.display = "none";
+		this.link.href = img;
+		this.img.src = img;
+		this.img.style.opacity = 1;
+	}
+
+	async updateWithNewImage(img, feedDirection) {
+		// No-op if lightbox is not open
+		if (this.el.style.display === "none" || this.el.style.opacity === "0") return;
+
+		// Ensure currently shown image does not change
+		const [method, shift] = feedDirection === "newest first" ? ["unshift", 1] : ["push", 0];
+		this.images[method](img);
+		await this.update(shift);
+	}
+}
+
+export const lightbox = new Lightbox();
+
+addEventListener('keydown', (event) => {
+	if (lightbox.el.style.display === 'none') {
+		return;
+	}
+	const { key } = event;
+	switch (key) {
+		case 'ArrowLeft':
+		case 'a':
+			lightbox.update(-1);
+			break;
+		case 'ArrowRight':
+		case 'd':
+			lightbox.update(1);
+			break;
+		case 'Escape':
+			lightbox.close();
+			break;
+	}
+});
\ No newline at end of file
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/common/modelInfoDialog.css b/custom_nodes/comfyui-custom-scripts/web/js/common/modelInfoDialog.css
new file mode 100644
index 0000000000000000000000000000000000000000..a5b9f627fc348ca17db78e5b28bc43a86e46560a
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/common/modelInfoDialog.css
@@ -0,0 +1,119 @@
+.pysssss-model-info {
+	color: white;
+	font-family: sans-serif;
+	max-width: 90vw;
+}
+.pysssss-model-content {
+	display: flex;
+	flex-direction: column;
+	overflow: hidden;
+}
+.pysssss-model-info h2 {
+	text-align: center;
+	margin: 0 0 10px 0;
+}
+.pysssss-model-info p {
+	margin: 5px 0;
+}
+.pysssss-model-info a {
+	color: dodgerblue;
+}
+.pysssss-model-info a:hover {
+	text-decoration: underline;
+}
+.pysssss-model-tags-list {
+	display: flex;
+	flex-wrap: wrap;
+	list-style: none;
+	gap: 10px;
+	max-height: 200px;
+	overflow: auto;
+	margin: 10px 0;
+	padding: 0;
+}
+.pysssss-model-tag {
+	background-color: rgb(128, 213, 247);
+	color: #000;
+	display: flex;
+	align-items: center;
+	gap: 5px;
+	border-radius: 5px;
+	padding: 2px 5px;
+	cursor: pointer;
+}
+.pysssss-model-tag--selected span::before {
+	content: "✅";
+	position: absolute;
+	background-color: dodgerblue;
+	left: 0;
+	top: 0;
+	right: 0;
+	bottom: 0;
+	text-align: center;
+}
+.pysssss-model-tag:hover {
+	outline: 2px solid dodgerblue;
+}
+.pysssss-model-tag p {
+	margin: 0;
+}
+.pysssss-model-tag span {
+	text-align: center;
+	border-radius: 5px;
+	background-color: dodgerblue;
+	color: #fff;
+	padding: 2px;
+	position: relative;
+	min-width: 20px;
+	overflow: hidden;
+}
+
+.pysssss-model-metadata .comfy-modal-content {
+	max-width: 100%;
+}
+.pysssss-model-metadata label {
+	margin-right: 1ch;
+	color: #ccc;
+}
+
+.pysssss-model-metadata span {
+	color: dodgerblue;
+}
+
+.pysssss-preview {
+	max-width: 50%;
+	margin-left: 10px;
+	position: relative;
+}
+.pysssss-preview img {
+	max-height: 300px;
+}
+.pysssss-preview button {
+	position: absolute;
+	font-size: 12px;
+	bottom: 10px;
+	right: 10px;
+}
+.pysssss-preview button+button {
+	bottom: 34px;
+}
+
+.pysssss-preview button.pysssss-preview-nav {
+	bottom: unset;
+	right: 30px;
+	top: 10px;
+	font-size: 14px;
+	line-height: 14px;
+}
+
+.pysssss-preview button.pysssss-preview-nav+.pysssss-preview-nav {
+	right: 10px;
+}
+.pysssss-model-notes {
+	background-color: rgba(0, 0, 0, 0.25);
+	padding: 5px;
+	margin-top: 5px;
+}
+.pysssss-model-notes:empty {
+	display: none;
+}
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/common/modelInfoDialog.js b/custom_nodes/comfyui-custom-scripts/web/js/common/modelInfoDialog.js
new file mode 100644
index 0000000000000000000000000000000000000000..032767da17bfc12f60ac8c6f926d8366a3b436e6
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/common/modelInfoDialog.js
@@ -0,0 +1,358 @@
+import { $el, ComfyDialog } from "../../../../scripts/ui.js";
+import { api } from "../../../../scripts/api.js";
+import { addStylesheet } from "./utils.js";
+
+addStylesheet(import.meta.url);
+
+class MetadataDialog extends ComfyDialog {
+	constructor() {
+		super();
+
+		this.element.classList.add("pysssss-model-metadata");
+	}
+	show(metadata) {
+		super.show(
+			$el(
+				"div",
+				Object.keys(metadata).map((k) =>
+					$el("div", [
+						$el("label", { textContent: k }),
+						$el("span", { textContent: typeof metadata[k] === "object" ? JSON.stringify(metadata[k]) : metadata[k] }),
+					])
+				)
+			)
+		);
+	}
+}
+
+export class ModelInfoDialog extends ComfyDialog {
+	constructor(name, node) {
+		super();
+		this.name = name;
+		this.node = node;
+		this.element.classList.add("pysssss-model-info");
+	}
+
+	get customNotes() {
+		return this.metadata["pysssss.notes"];
+	}
+
+	set customNotes(v) {
+		this.metadata["pysssss.notes"] = v;
+	}
+
+	get hash() {
+		return this.metadata["pysssss.sha256"];
+	}
+
+	async show(type, value) {
+		this.type = type;
+
+		const req = api.fetchApi("/pysssss/metadata/" + encodeURIComponent(`${type}/${value}`));
+		this.info = $el("div", { style: { flex: "auto" } });
+		this.img = $el("img", { style: { display: "none" } });
+		this.imgWrapper = $el("div.pysssss-preview", [this.img]);
+		this.main = $el("main", { style: { display: "flex" } }, [this.info, this.imgWrapper]);
+		this.content = $el("div.pysssss-model-content", [$el("h2", { textContent: this.name }), this.main]);
+
+		const loading = $el("div", { textContent: "ℹ️ Loading...", parent: this.content });
+
+		super.show(this.content);
+
+		this.metadata = await (await req).json();
+		this.viewMetadata.style.cursor = this.viewMetadata.style.opacity = "";
+		this.viewMetadata.removeAttribute("disabled");
+
+		loading.remove();
+		this.addInfo();
+	}
+
+	createButtons() {
+		const btns = super.createButtons();
+		this.viewMetadata = $el("button", {
+			type: "button",
+			textContent: "View raw metadata",
+			disabled: "disabled",
+			style: {
+				opacity: 0.5,
+				cursor: "not-allowed",
+			},
+			onclick: (e) => {
+				if (this.metadata) {
+					new MetadataDialog().show(this.metadata);
+				}
+			},
+		});
+
+		btns.unshift(this.viewMetadata);
+		return btns;
+	}
+
+	getNoteInfo() {
+		function parseNote() {
+			if (!this.customNotes) return [];
+
+			let notes = [];
+			// Extract links from notes
+			const r = new RegExp("(\\bhttps?:\\/\\/[^\\s]+)", "g");
+			let end = 0;
+			let m;
+			do {
+				m = r.exec(this.customNotes);
+				let pos;
+				let fin = 0;
+				if (m) {
+					pos = m.index;
+					fin = m.index + m[0].length;
+				} else {
+					pos = this.customNotes.length;
+				}
+
+				let pre = this.customNotes.substring(end, pos);
+				if (pre) {
+					pre = pre.replaceAll("\n", "<br>");
+					notes.push(
+						$el("span", {
+							innerHTML: pre,
+						})
+					);
+				}
+				if (m) {
+					notes.push(
+						$el("a", {
+							href: m[0],
+							textContent: m[0],
+							target: "_blank",
+						})
+					);
+				}
+
+				end = fin;
+			} while (m);
+			return notes;
+		}
+
+		let textarea;
+		let notesContainer;
+		const editText = "✏️ Edit";
+		const edit = $el("a", {
+			textContent: editText,
+			href: "#",
+			style: {
+				float: "right",
+				color: "greenyellow",
+				textDecoration: "none",
+			},
+			onclick: async (e) => {
+				e.preventDefault();
+
+				if (textarea) {
+					this.customNotes = textarea.value;
+
+					const resp = await api.fetchApi("/pysssss/metadata/notes/" + encodeURIComponent(`${this.type}/${this.name}`), {
+						method: "POST",
+						body: this.customNotes,
+					});
+
+					if (resp.status !== 200) {
+						console.error(resp);
+						alert(`Error saving notes (${req.status}) ${req.statusText}`);
+						return;
+					}
+
+					e.target.textContent = editText;
+					textarea.remove();
+					textarea = null;
+
+					notesContainer.replaceChildren(...parseNote.call(this));
+					this.node?.["pysssss.updateExamples"]?.();
+				} else {
+					e.target.textContent = "💾 Save";
+					textarea = $el("textarea", {
+						style: {
+							width: "100%",
+							minWidth: "200px",
+							minHeight: "50px",
+						},
+						textContent: this.customNotes,
+					});
+					e.target.after(textarea);
+					notesContainer.replaceChildren();
+					textarea.style.height = Math.min(textarea.scrollHeight, 300) + "px";
+				}
+			},
+		});
+
+		notesContainer = $el("div.pysssss-model-notes", parseNote.call(this));
+		return $el(
+			"div",
+			{
+				style: { display: "contents" },
+			},
+			[edit, notesContainer]
+		);
+	}
+
+	addInfo() {
+		const usageHint = this.metadata["modelspec.usage_hint"];
+		if (usageHint) {
+			this.addInfoEntry("Usage Hint", usageHint);
+		}
+		this.addInfoEntry("Notes", this.getNoteInfo());
+	}
+
+	addInfoEntry(name, value) {
+		return $el(
+			"p",
+			{
+				parent: this.info,
+			},
+			[
+				typeof name === "string" ? $el("label", { textContent: name + ": " }) : name,
+				typeof value === "string" ? $el("span", { textContent: value }) : value,
+			]
+		);
+	}
+
+	async getCivitaiDetails() {
+		const req = await fetch("https://civitai.com/api/v1/model-versions/by-hash/" + this.hash);
+		if (req.status === 200) {
+			return await req.json();
+		} else if (req.status === 404) {
+			throw new Error("Model not found");
+		} else {
+			throw new Error(`Error loading info (${req.status}) ${req.statusText}`);
+		}
+	}
+
+	addCivitaiInfo() {
+		const promise = this.getCivitaiDetails();
+		const content = $el("span", { textContent: "ℹ️ Loading..." });
+
+		this.addInfoEntry(
+			$el("label", [
+				$el("img", {
+					style: {
+						width: "18px",
+						position: "relative",
+						top: "3px",
+						margin: "0 5px 0 0",
+					},
+					src: "https://civitai.com/favicon.ico",
+				}),
+				$el("span", { textContent: "Civitai: " }),
+			]),
+			content
+		);
+
+		return promise
+			.then((info) => {
+				content.replaceChildren(
+					$el("a", {
+						href: "https://civitai.com/models/" + info.modelId,
+						textContent: "View " + info.model.name,
+						target: "_blank",
+					})
+				);
+
+				const allPreviews = info.images?.filter((i) => i.type === "image");
+				const previews = allPreviews?.filter((i) => i.nsfwLevel <= ModelInfoDialog.nsfwLevel);
+				if (previews?.length) {
+					let previewIndex = 0;
+					let preview;
+					const updatePreview = () => {
+						preview = previews[previewIndex];
+						this.img.src = preview.url;
+					};
+
+					updatePreview();
+					this.img.style.display = "";
+
+					this.img.title = `${previews.length} previews.`;
+					if (allPreviews.length !== previews.length) {
+						this.img.title += ` ${allPreviews.length - previews.length} images hidden due to NSFW level.`;
+					}
+
+					this.imgSave = $el("button", {
+						textContent: "Use as preview",
+						parent: this.imgWrapper,
+						onclick: async () => {
+							// Convert the preview to a blob
+							const blob = await (await fetch(this.img.src)).blob();
+
+							// Store it in temp
+							const name = "temp_preview." + new URL(this.img.src).pathname.split(".")[1];
+							const body = new FormData();
+							body.append("image", new File([blob], name));
+							body.append("overwrite", "true");
+							body.append("type", "temp");
+
+							const resp = await api.fetchApi("/upload/image", {
+								method: "POST",
+								body,
+							});
+
+							if (resp.status !== 200) {
+								console.error(resp);
+								alert(`Error saving preview (${req.status}) ${req.statusText}`);
+								return;
+							}
+
+							// Use as preview
+							await api.fetchApi("/pysssss/save/" + encodeURIComponent(`${this.type}/${this.name}`), {
+								method: "POST",
+								body: JSON.stringify({
+									filename: name,
+									type: "temp",
+								}),
+								headers: {
+									"content-type": "application/json",
+								},
+							});
+							app.refreshComboInNodes();
+						},
+					});
+
+					$el("button", {
+						textContent: "Show metadata",
+						parent: this.imgWrapper,
+						onclick: async () => {
+							if (preview.meta && Object.keys(preview.meta).length) {
+								new MetadataDialog().show(preview.meta);
+							} else {
+								alert("No image metadata found");
+							}
+						},
+					});
+
+					const addNavButton = (icon, direction) => {
+						$el("button.pysssss-preview-nav", {
+							textContent: icon,
+							parent: this.imgWrapper,
+							onclick: async () => {
+								previewIndex += direction;
+								if (previewIndex < 0) {
+									previewIndex = previews.length - 1;
+								} else if (previewIndex >= previews.length) {
+									previewIndex = 0;
+								}
+								updatePreview();
+							},
+						});
+					};
+
+					if (previews.length > 1) {
+						addNavButton("‹", -1);
+						addNavButton("›", 1);
+					}
+				} else if (info.images?.length) {
+					$el("span", { style: { opacity: 0.6 }, textContent: "⚠️ All images hidden due to NSFW level setting.", parent: this.imgWrapper });
+				}
+
+				return info;
+			})
+			.catch((err) => {
+				content.textContent = "⚠️ " + err.message;
+			});
+	}
+}
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/common/spinner.css b/custom_nodes/comfyui-custom-scripts/web/js/common/spinner.css
new file mode 100644
index 0000000000000000000000000000000000000000..cfb7a0d961de791e721f5351476a0d014285736e
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/common/spinner.css
@@ -0,0 +1,35 @@
+.pysssss-lds-ring {
+	display: inline-block;
+	position: absolute;
+	width: 80px;
+	height: 80px;
+}
+.pysssss-lds-ring div {
+	box-sizing: border-box;
+	display: block;
+	position: absolute;
+	width: 64px;
+	height: 64px;
+	margin: 8px;
+	border: 5px solid #fff;
+	border-radius: 50%;
+	animation: lds-ring 1.2s cubic-bezier(0.5, 0, 0.5, 1) infinite;
+	border-color: #fff transparent transparent transparent;
+}
+.pysssss-lds-ring div:nth-child(1) {
+	animation-delay: -0.45s;
+}
+.pysssss-lds-ring div:nth-child(2) {
+	animation-delay: -0.3s;
+}
+.pysssss-lds-ring div:nth-child(3) {
+	animation-delay: -0.15s;
+}
+@keyframes lds-ring {
+	0% {
+		transform: rotate(0deg);
+	}
+	100% {
+		transform: rotate(360deg);
+	}
+}
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/common/spinner.js b/custom_nodes/comfyui-custom-scripts/web/js/common/spinner.js
new file mode 100644
index 0000000000000000000000000000000000000000..407599017aab85cddf6edab18afe91c3013c3cf0
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/common/spinner.js
@@ -0,0 +1,9 @@
+import { addStylesheet } from "./utils.js";
+
+addStylesheet(import.meta.url);
+
+export function createSpinner() {
+	const div = document.createElement("div");
+	div.innerHTML = `<div class="pysssss-lds-ring"><div></div><div></div><div></div><div></div></div>`;
+	return div.firstElementChild;
+}
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/common/utils.js b/custom_nodes/comfyui-custom-scripts/web/js/common/utils.js
new file mode 100644
index 0000000000000000000000000000000000000000..cd7539d449a2ac85672fb36da4be725f352b86f8
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/common/utils.js
@@ -0,0 +1,30 @@
+import { $el } from "../../../../scripts/ui.js";
+
+export function addStylesheet(url) {
+	if (url.endsWith(".js")) {
+		url = url.substr(0, url.length - 2) + "css";
+	}
+	$el("link", {
+		parent: document.head,
+		rel: "stylesheet",
+		type: "text/css",
+		href: url.startsWith("http") ? url : getUrl(url),
+	});
+}
+
+export function getUrl(path, baseUrl) {
+	if (baseUrl) {
+		return new URL(path, baseUrl).toString();
+	} else {
+		return new URL("../" + path, import.meta.url).toString();
+	}
+}
+
+export async function loadImage(url) {
+	return new Promise((res, rej) => {
+		const img = new Image();
+		img.onload = res;
+		img.onerror = rej;
+		img.src = url;
+	});
+}
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/contextMenuHook.js b/custom_nodes/comfyui-custom-scripts/web/js/contextMenuHook.js
new file mode 100644
index 0000000000000000000000000000000000000000..0fd35b41a4a4f6b7110c417d76235e07b22e7ce4
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/contextMenuHook.js
@@ -0,0 +1,90 @@
+import { app } from "../../../scripts/app.js";
+app.registerExtension({
+	name: "pysssss.ContextMenuHook",
+	init() {
+		const getOrSet = (target, name, create) => {
+			if (name in target) return target[name];
+			return (target[name] = create());
+		};
+		const symbol = getOrSet(window, "__pysssss__", () => Symbol("__pysssss__"));
+		const store = getOrSet(window, symbol, () => ({}));
+		const contextMenuHook = getOrSet(store, "contextMenuHook", () => ({}));
+		for (const e of ["ctor", "preAddItem", "addItem"]) {
+			if (!contextMenuHook[e]) {
+				contextMenuHook[e] = [];
+			}
+		}
+
+		// Big ol' hack to get allow customizing the context menu
+		// Replace the addItem function with our own that wraps the context of "this" with a proxy
+		// That proxy then replaces the constructor with another proxy
+		// That proxy then calls the custom ContextMenu that supports filters
+		const ctorProxy = new Proxy(LiteGraph.ContextMenu, {
+			construct(target, args) {
+				return new LiteGraph.ContextMenu(...args);
+			},
+		});
+
+		function triggerCallbacks(name, getArgs, handler) {
+			const callbacks = contextMenuHook[name];
+			if (callbacks && callbacks instanceof Array) {
+				for (const cb of callbacks) {
+					const r = cb(...getArgs());
+					handler?.call(this, r);
+				}
+			} else {
+				console.warn("[pysssss 🐍]", `invalid ${name} callbacks`, callbacks, name in contextMenuHook);
+			}
+		}
+
+		const addItem = LiteGraph.ContextMenu.prototype.addItem;
+		LiteGraph.ContextMenu.prototype.addItem = function () {
+			const proxy = new Proxy(this, {
+				get(target, prop) {
+					if (prop === "constructor") {
+						return ctorProxy;
+					}
+					return target[prop];
+				},
+			});
+			proxy.__target__ = this;
+
+			let el;
+			let args = arguments;
+			triggerCallbacks(
+				"preAddItem",
+				() => [el, this, args],
+				(r) => {
+					if (r !== undefined) el = r;
+				}
+			);
+
+			if (el === undefined) {
+				el = addItem.apply(proxy, arguments);
+			}
+
+			triggerCallbacks(
+				"addItem",
+				() => [el, this, args],
+				(r) => {
+					if (r !== undefined) el = r;
+				}
+			);
+			return el;
+		};
+
+		// We also need to patch the ContextMenu constructor to unwrap the parent else it fails a LiteGraph type check
+		const ctxMenu = LiteGraph.ContextMenu;
+		LiteGraph.ContextMenu = function (values, options) {
+			if (options?.parentMenu) {
+				if (options.parentMenu.__target__) {
+					options.parentMenu = options.parentMenu.__target__;
+				}
+			}
+
+			triggerCallbacks("ctor", () => [values, options]);
+			return ctxMenu.call(this, values, options);
+		};
+		LiteGraph.ContextMenu.prototype = ctxMenu.prototype;
+	},
+});
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/customColors.js b/custom_nodes/comfyui-custom-scripts/web/js/customColors.js
new file mode 100644
index 0000000000000000000000000000000000000000..2b0fefca7aa86b48d4e81126a6549e4b5fe16505
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/customColors.js
@@ -0,0 +1,98 @@
+import { app } from "../../../scripts/app.js";
+import { $el } from "../../../scripts/ui.js";
+
+const colorShade = (col, amt) => {
+	col = col.replace(/^#/, "");
+	if (col.length === 3) col = col[0] + col[0] + col[1] + col[1] + col[2] + col[2];
+
+	let [r, g, b] = col.match(/.{2}/g);
+	[r, g, b] = [parseInt(r, 16) + amt, parseInt(g, 16) + amt, parseInt(b, 16) + amt];
+
+	r = Math.max(Math.min(255, r), 0).toString(16);
+	g = Math.max(Math.min(255, g), 0).toString(16);
+	b = Math.max(Math.min(255, b), 0).toString(16);
+
+	const rr = (r.length < 2 ? "0" : "") + r;
+	const gg = (g.length < 2 ? "0" : "") + g;
+	const bb = (b.length < 2 ? "0" : "") + b;
+
+	return `#${rr}${gg}${bb}`;
+};
+
+app.registerExtension({
+	name: "pysssss.CustomColors",
+	setup() {
+		let picker;
+		let activeNode;
+		const onMenuNodeColors = LGraphCanvas.onMenuNodeColors;
+		LGraphCanvas.onMenuNodeColors = function (value, options, e, menu, node) {
+			const r = onMenuNodeColors.apply(this, arguments);
+			requestAnimationFrame(() => {
+				const menus = document.querySelectorAll(".litecontextmenu");
+				for (let i = menus.length - 1; i >= 0; i--) {
+					if (menus[i].firstElementChild.textContent.includes("No color") || menus[i].firstElementChild.value?.content?.includes("No color")) {
+						$el(
+							"div.litemenu-entry.submenu",
+							{
+								parent: menus[i],
+								$: (el) => {
+									el.onclick = () => {
+										LiteGraph.closeAllContextMenus();
+										if (!picker) {
+											picker = $el("input", {
+												type: "color",
+												parent: document.body,
+												style: {
+													display: "none",
+												},
+											});
+											picker.onchange = () => {
+												if (activeNode) {
+													const fApplyColor = function(node){
+														if (picker.value) {
+															if (node.constructor === LiteGraph.LGraphGroup) {
+																node.color = picker.value;
+															} else {
+																node.color = colorShade(picker.value, 20);
+																node.bgcolor = picker.value;
+															}
+														}
+													}
+													const graphcanvas = LGraphCanvas.active_canvas;
+													if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1){
+														fApplyColor(activeNode);
+													} else {
+														for (let i in graphcanvas.selected_nodes) {
+															fApplyColor(graphcanvas.selected_nodes[i]);
+														}
+													}
+
+													activeNode.setDirtyCanvas(true, true);
+												}
+											};
+										}
+										activeNode = null;
+										picker.value = node.bgcolor;
+										activeNode = node;
+										picker.click();
+									};
+								},
+							},
+							[
+								$el("span", {
+									style: {
+										paddingLeft: "4px",
+										display: "block",
+									},
+									textContent: "🎨 Custom",
+								}),
+							]
+						);
+						break;
+					}
+				}
+			});
+			return r;
+		};
+	},
+});
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/faviconStatus.js b/custom_nodes/comfyui-custom-scripts/web/js/faviconStatus.js
new file mode 100644
index 0000000000000000000000000000000000000000..3dcd196bcbc83082144faef0d247ef5eac3d668c
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/faviconStatus.js
@@ -0,0 +1,58 @@
+import { api } from "../../../scripts/api.js";
+import { app } from "../../../scripts/app.js";
+
+// Simple script that adds the current queue size to the window title
+// Adds a favicon that changes color while active
+
+app.registerExtension({
+	name: "pysssss.FaviconStatus",
+	async setup() {
+		let link = document.querySelector("link[rel~='icon']");
+		if (!link) {
+			link = document.createElement("link");
+			link.rel = "icon";
+			document.head.appendChild(link);
+		}
+
+		const getUrl = (active, user) => new URL(`assets/favicon${active ? "-active" : ""}${user ? ".user" : ""}.ico`, import.meta.url);
+		const testUrl = async (active) => {
+			const url = getUrl(active, true);
+			const r = await fetch(url, {
+				method: "HEAD",
+			});
+			if (r.status === 200) {
+				return url;
+			}
+			return getUrl(active, false);
+		};
+		const activeUrl = await testUrl(true);
+		const idleUrl = await testUrl(false);
+
+		let executing = false;
+		const update = () => (link.href = executing ? activeUrl : idleUrl);
+
+		for (const e of ["execution_start", "progress"]) {
+			api.addEventListener(e, () => {
+				executing = true;
+				update();
+			});
+		}
+
+		api.addEventListener("executing", ({ detail }) => {
+			// null will be sent when it's finished
+			executing = !!detail;
+			update();
+		});
+
+		api.addEventListener("status", ({ detail }) => {
+			let title = "ComfyUI";
+			if (detail && detail.exec_info.queue_remaining) {
+				title = `(${detail.exec_info.queue_remaining}) ${title}`;
+			}
+			document.title = title;
+			update();
+			executing = false;
+		});
+		update();
+	},
+});
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/graphArrange.js b/custom_nodes/comfyui-custom-scripts/web/js/graphArrange.js
new file mode 100644
index 0000000000000000000000000000000000000000..8f25e241a2b91a01ca95a376385bdbe572994752
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/graphArrange.js
@@ -0,0 +1,91 @@
+import { app } from "../../../scripts/app.js";
+
+app.registerExtension({
+	name: "pysssss.GraphArrange",
+	setup(app) {
+		const orig = LGraphCanvas.prototype.getCanvasMenuOptions;
+		LGraphCanvas.prototype.getCanvasMenuOptions = function () {
+			const options = orig.apply(this, arguments);
+			options.push({ content: "Arrange (float left)", callback: () => graph.arrange() });
+			options.push({
+				content: "Arrange (float right)",
+				callback: () => {
+					(function () {
+						var margin = 50;
+						var layout;
+
+						const nodes = this.computeExecutionOrder(false, true);
+						const columns = [];
+
+						// Find node first use
+						for (let i = nodes.length - 1; i >= 0; i--) {
+							const node = nodes[i];
+							let max = null;
+							for (const out of node.outputs || []) {
+								if (out.links) {
+									for (const link of out.links) {
+										const outNode = app.graph.getNodeById(app.graph.links[link].target_id);
+										if (!outNode) continue;
+										var l = outNode._level - 1;
+										if (max === null) max = l;
+										else if (l < max) max = l;
+									}
+								}
+							}
+							if (max != null) node._level = max;
+						}
+
+						for (let i = 0; i < nodes.length; ++i) {
+							const node = nodes[i];
+							const col = node._level || 1;
+							if (!columns[col]) {
+								columns[col] = [];
+							}
+							columns[col].push(node);
+						}
+
+						let x = margin;
+
+						for (let i = 0; i < columns.length; ++i) {
+							const column = columns[i];
+							if (!column) {
+								continue;
+							}
+							column.sort((a, b) => {
+								var as = !(a.type === "SaveImage" || a.type === "PreviewImage");
+								var bs = !(b.type === "SaveImage" || b.type === "PreviewImage");
+								var r = as - bs;
+								if (r === 0) r = (a.inputs?.length || 0) - (b.inputs?.length || 0);
+								if (r === 0) r = (a.outputs?.length || 0) - (b.outputs?.length || 0);
+								return r;
+							});
+							let max_size = 100;
+							let y = margin + LiteGraph.NODE_TITLE_HEIGHT;
+							for (let j = 0; j < column.length; ++j) {
+								const node = column[j];
+								node.pos[0] = layout == LiteGraph.VERTICAL_LAYOUT ? y : x;
+								node.pos[1] = layout == LiteGraph.VERTICAL_LAYOUT ? x : y;
+								const max_size_index = layout == LiteGraph.VERTICAL_LAYOUT ? 1 : 0;
+								if (node.size[max_size_index] > max_size) {
+									max_size = node.size[max_size_index];
+								}
+								const node_size_index = layout == LiteGraph.VERTICAL_LAYOUT ? 0 : 1;
+								y += node.size[node_size_index] + margin + LiteGraph.NODE_TITLE_HEIGHT + j;
+							}
+
+							// Right align in column
+							for (let j = 0; j < column.length; ++j) {
+								const node = column[j];
+								node.pos[0] += max_size - node.size[0];
+							}
+							x += max_size + margin;
+						}
+
+						this.setDirtyCanvas(true, true);
+					}).apply(app.graph);
+				},
+			});
+			return options;
+		};
+	},
+});
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/imageFeed.js b/custom_nodes/comfyui-custom-scripts/web/js/imageFeed.js
new file mode 100644
index 0000000000000000000000000000000000000000..4afdc066b023a43dccaf91884c8910b1fc470961
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/imageFeed.js
@@ -0,0 +1,604 @@
+import { api } from "../../../scripts/api.js";
+import { app } from "../../../scripts/app.js";
+import { $el } from "../../../scripts/ui.js";
+import { lightbox } from "./common/lightbox.js";
+
+$el("style", {
+	textContent: `
+	.pysssss-image-feed {
+		position: absolute;
+		background: var(--comfy-menu-bg);
+		color: var(--fg-color);
+		z-index: 99;
+		font-family: sans-serif;
+		font-size: 12px;
+		display: flex;
+		flex-direction: column;
+	}
+	div > .pysssss-image-feed {
+		position: static;
+	}
+	.pysssss-image-feed--top, .pysssss-image-feed--bottom {
+		width: 100vw;
+		min-height: 30px;
+		max-height: calc(var(--max-size, 20) * 1vh);
+	}
+	.pysssss-image-feed--top {
+		top: 0;
+	}
+	.pysssss-image-feed--bottom {
+		bottom: 0;
+		flex-direction: column-reverse;
+		padding-top: 5px;
+	}
+	.pysssss-image-feed--left, .pysssss-image-feed--right {
+		top: 0;
+		height: 100vh;
+		min-width: 200px;
+		max-width: calc(var(--max-size, 10) * 1vw);
+	}
+	.comfyui-body-left .pysssss-image-feed--left, .comfyui-body-right .pysssss-image-feed--right {
+		height: 100%;
+	}
+	.pysssss-image-feed--left {
+		left: 0;
+	}
+	.pysssss-image-feed--right {
+		right: 0;
+	}
+
+	.pysssss-image-feed--left .pysssss-image-feed-menu, .pysssss-image-feed--right .pysssss-image-feed-menu {
+		flex-direction: column;
+	}
+
+	.pysssss-image-feed-menu {
+		position: relative;
+		flex: 0 1 min-content;
+		display: flex;
+		gap: 5px;
+		padding: 5px;
+		justify-content: space-between;
+	}
+	.pysssss-image-feed-btn-group {
+		align-items: stretch;
+		display: flex;
+		gap: .5rem;
+		flex: 0 1 fit-content;
+		justify-content: flex-end;
+	}
+	.pysssss-image-feed-btn {
+		background-color:var(--comfy-input-bg);
+		border-radius:5px;
+		border:2px solid var(--border-color);
+		color: var(--fg-color);
+		cursor:pointer;
+		display:inline-block;
+		flex: 0 1 fit-content;
+		text-decoration:none;
+	}
+	.pysssss-image-feed-btn.sizing-btn:checked {
+		filter: invert();
+	}
+	.pysssss-image-feed-btn.clear-btn {
+		padding: 5px 20px;
+	}
+	.pysssss-image-feed-btn.hide-btn {
+		padding: 5px;
+		aspect-ratio: 1 / 1;
+	}
+	.pysssss-image-feed-btn:hover {
+		filter: brightness(1.2);
+	}
+	.pysssss-image-feed-btn:active {
+		position:relative;
+		top:1px;
+	}
+	
+	.pysssss-image-feed-menu section {
+		border-radius: 5px;
+		background: rgba(0,0,0,0.6);
+		padding: 0 5px;
+		display: flex;
+		gap: 5px;
+		align-items: center;
+		position: relative;
+	}
+	.pysssss-image-feed-menu section span {
+		white-space: nowrap;
+	}
+	.pysssss-image-feed-menu section input {
+		flex: 1 1 100%;
+		background: rgba(0,0,0,0.6);
+		border-radius: 5px;
+		overflow: hidden;
+		z-index: 100;
+	}
+
+	.sizing-menu {
+		position: relative;
+	}
+
+	.size-controls-flyout {
+		position: absolute;
+		transform: scaleX(0%);
+		transition: 200ms ease-out;
+		transition-delay: 500ms;
+		z-index: 101;
+		width: 300px;
+	}
+
+	.sizing-menu:hover .size-controls-flyout {
+		transform: scale(1, 1);
+		transition: 200ms linear;
+		transition-delay: 0;
+	}
+	.pysssss-image-feed--bottom .size-controls-flyout  {
+		transform: scale(1,0);
+		transform-origin: bottom;
+		bottom: 0;
+		left: 0;
+	}
+	.pysssss-image-feed--top .size-controls-flyout  {
+		transform: scale(1,0);
+		transform-origin: top;
+		top: 0;
+		left: 0;
+	}
+	.pysssss-image-feed--left .size-controls-flyout  {
+		transform: scale(0, 1);
+		transform-origin: left;
+		top: 0;
+		left: 0;
+	}
+	.pysssss-image-feed--right .size-controls-flyout  {
+		transform: scale(0, 1);
+		transform-origin: right;
+		top: 0;
+		right: 0;
+	}
+	
+	.pysssss-image-feed-menu > * {
+		min-height: 24px;
+	}
+	.pysssss-image-feed-list {
+		flex: 1 1 auto;
+		overflow-y: auto;
+		display: grid;
+		align-items: center;
+		justify-content: center;
+		gap: 4px;
+		grid-auto-rows: min-content;
+		grid-template-columns: repeat(var(--img-sz, 3), 1fr);
+		transition: 100ms linear;
+		scrollbar-gutter: stable both-edges;
+		padding: 5px;
+		background: var(--comfy-input-bg);
+		border-radius: 5px;
+		margin: 5px;
+		margin-top: 0px;
+	}
+	.pysssss-image-feed-list:empty {
+		display: none;
+	}
+	.pysssss-image-feed-list div {
+		height: 100%;
+		text-align: center;
+	}
+	.pysssss-image-feed-list::-webkit-scrollbar {
+		background: var(--comfy-input-bg);
+		border-radius: 5px;
+	}
+	.pysssss-image-feed-list::-webkit-scrollbar-thumb {
+		background:var(--comfy-menu-bg);
+		border: 5px solid transparent;
+		border-radius: 8px;
+		background-clip: content-box;
+	}
+	.pysssss-image-feed-list::-webkit-scrollbar-thumb:hover {
+		background: var(--border-color);
+		background-clip: content-box;
+	}
+	.pysssss-image-feed-list img {
+		object-fit: var(--img-fit, contain);
+		max-width: 100%;
+		max-height: calc(var(--max-size) * 1vh);
+		border-radius: 4px;
+	}
+	.pysssss-image-feed-list img:hover {
+		filter: brightness(1.2);
+	}`,
+	parent: document.body,
+});
+
+app.registerExtension({
+	name: "pysssss.ImageFeed",
+	async setup() {
+		let visible = true;
+		const seenImages = new Map();
+		const showButton = $el("button.comfy-settings-btn", {
+			textContent: "🖼️",
+			style: {
+				right: "16px",
+				cursor: "pointer",
+				display: "none",
+			},
+		});
+		let showMenuButton;
+		if (!app.menu?.element.style.display && app.menu?.settingsGroup) {
+			showMenuButton = new (await import("../../../scripts/ui/components/button.js")).ComfyButton({
+				icon: "image-multiple",
+				action: () => showButton.click(),
+				tooltip: "Show Image Feed 🐍",
+				content: "Show Image Feed 🐍",
+			});
+			showMenuButton.enabled = false;
+			showMenuButton.element.style.display = "none";
+			app.menu.settingsGroup.append(showMenuButton);
+		}
+
+		const getVal = (n, d) => {
+			const v = localStorage.getItem("pysssss.ImageFeed." + n);
+			if (v && !isNaN(+v)) {
+				return v;
+			}
+			return d;
+		};
+
+		const saveVal = (n, v) => {
+			localStorage.setItem("pysssss.ImageFeed." + n, v);
+		};
+
+		const imageFeed = $el("div.pysssss-image-feed");
+		const imageList = $el("div.pysssss-image-feed-list");
+
+		function updateMenuParent(location) {
+			if (showMenuButton) {
+				const el = document.querySelector(".comfyui-body-" + location);
+				if (!el) return;
+				el.append(imageFeed);
+			} else {
+				if (!imageFeed.parent) {
+					document.body.append(imageFeed);
+				}
+			}
+		}
+
+		const feedLocation = app.ui.settings.addSetting({
+			id: "pysssss.ImageFeed.Location",
+			name: "🐍 Image Feed Location",
+			defaultValue: "bottom",
+			type: () => {
+				return $el("tr", [
+					$el("td", [
+						$el("label", {
+							textContent: "🐍 Image Feed Location:",
+						}),
+					]),
+					$el("td", [
+						$el(
+							"select",
+							{
+								style: {
+									fontSize: "14px",
+								},
+								oninput: (e) => {
+									feedLocation.value = e.target.value;
+									imageFeed.className = `pysssss-image-feed pysssss-image-feed--${feedLocation.value}`;
+									updateMenuParent(feedLocation.value);
+									saveVal("Location", feedLocation.value);
+									window.dispatchEvent(new Event("resize"));
+								},
+							},
+							["left", "top", "right", "bottom", "hidden"].map((m) =>
+								$el("option", {
+									value: m,
+									textContent: m,
+									selected: feedLocation.value === m,
+								})
+							)
+						),
+					]),
+				]);
+			},
+			onChange(value) {
+				if (value === "hidden") {
+					imageFeed.remove();
+					if (showMenuButton) {
+						requestAnimationFrame(() => {
+							showMenuButton.element.style.display = "none";
+						});
+					}
+					showButton.style.display = "none";
+				} else {
+					showMenuButton.element.style.display = "unset";
+					showButton.style.display = visible ? "none" : "unset";
+					imageFeed.className = `pysssss-image-feed pysssss-image-feed--${value}`;
+					updateMenuParent(value);
+				}
+			},
+		});
+
+		const feedDirection = app.ui.settings.addSetting({
+			id: "pysssss.ImageFeed.Direction",
+			name: "🐍 Image Feed Direction",
+			defaultValue: "newest first",
+			type: () => {
+				return $el("tr", [
+					$el("td", [
+						$el("label", {
+							textContent: "🐍 Image Feed Direction:",
+						}),
+					]),
+					$el("td", [
+						$el(
+							"select",
+							{
+								style: {
+									fontSize: "14px",
+								},
+								oninput: (e) => {
+									feedDirection.value = e.target.value;
+									imageList.replaceChildren(...[...imageList.childNodes].reverse());
+								},
+							},
+							["newest first", "oldest first"].map((m) =>
+								$el("option", {
+									value: m,
+									textContent: m,
+									selected: feedDirection.value === m,
+								})
+							)
+						),
+					]),
+				]);
+			},
+		});
+
+		const deduplicateFeed = app.ui.settings.addSetting({
+			id: "pysssss.ImageFeed.Deduplication",
+			name: "🐍 Image Feed Deduplication",
+			tooltip: `Ensures unique images in the image feed but at the cost of CPU-bound performance impact \
+(from hundreds of milliseconds to seconds per image, depending on byte size). For workflows that produce duplicate images, turning this setting on may yield overall client-side performance improvements \
+by reducing the number of images in the feed.
+
+Recommended: "enabled (max performance)" uness images are erroneously deduplicated.`,
+			defaultValue: 0,
+			type: "combo",
+			options: (value) => {
+				let dedupeOptions = { disabled: 0, "enabled (slow)": 1, "enabled (performance)": 0.5, "enabled (max performance)": 0.25 };
+				return Object.entries(dedupeOptions).map(([k, v]) => ({
+					value: v,
+					text: k,
+					selected: k === value,
+				}));
+			},
+		});
+
+		const maxImages = app.ui.settings.addSetting({
+			id: "pysssss.ImageFeed.MaxImages",
+			name: "🐍 Image Feed Max Images",
+			tooltip: `Limits the number of images in the feed to a maximum, removing the oldest images as new ones are added.`,
+			defaultValue: 0,
+			type: "number",
+		});
+
+		const saveNodeOnly = app.ui.settings.addSetting({
+			id: "pysssss.ImageFeed.SaveNodeOnly",
+			name: "🐍 Image Feed Display 'SaveImage' Only",
+			tooltip: `Only show images from 'SaveImage' nodes. This prevents 'PreviewImage' node outputs from appearing in the feed.`,
+			defaultValue: false,
+			type: "boolean",
+		});
+
+		const clearButton = $el("button.pysssss-image-feed-btn.clear-btn", {
+			textContent: "Clear",
+			onclick: () => {
+				imageList.replaceChildren();
+				window.dispatchEvent(new Event("resize"));
+			},
+		});
+
+		const hideButton = $el("button.pysssss-image-feed-btn.hide-btn", {
+			textContent: "❌",
+			onclick: () => {
+				imageFeed.style.display = "none";
+				showButton.style.display = feedLocation.value === "hidden" ? "none" : "unset";
+				if (showMenuButton) {
+					showMenuButton.enabled = true;
+					showMenuButton.element.style.display = "";
+				}
+				saveVal("Visible", 0);
+				visible = false;
+				window.dispatchEvent(new Event("resize"));
+			},
+		});
+
+		let columnInput;
+		function updateColumnCount(v) {
+			columnInput.parentElement.title = `Controls the number of columns in the feed (${v} columns).\nClick label to set custom value.`;
+			imageFeed.style.setProperty("--img-sz", v);
+			saveVal("ImageSize", v);
+			columnInput.max = Math.max(10, v, columnInput.max);
+			columnInput.value = v;
+			window.dispatchEvent(new Event("resize"));
+		}
+
+		function addImageToFeed(href) {
+			const method = feedDirection.value === "newest first" ? "prepend" : "append";
+
+			if (maxImages.value > 0 && imageList.children.length >= maxImages.value) {
+				imageList.children[method === "prepend" ? imageList.children.length - 1 : 0].remove();
+			}
+
+			imageList[method](
+				$el("div", [
+					$el(
+						"a",
+						{
+							target: "_blank",
+							href,
+							onclick: (e) => {
+								const imgs = [...imageList.querySelectorAll("img")].map((img) => img.getAttribute("src"));
+								lightbox.show(imgs, imgs.indexOf(href));
+								e.preventDefault();
+							},
+						},
+						[$el("img", { src: href })]
+					),
+				])
+			);
+			// If lightbox is open, update it with new image
+			lightbox.updateWithNewImage(href, feedDirection.value);
+		}
+
+		imageFeed.append(
+			$el("div.pysssss-image-feed-menu", [
+				$el("section.sizing-menu", {}, [
+					$el("label.size-control-handle", { textContent: "↹ Resize Feed" }),
+					$el("div.size-controls-flyout", {}, [
+						$el("section.size-control.feed-size-control", {}, [
+							$el("span", {
+								textContent: "Feed Size...",
+							}),
+							$el("input", {
+								type: "range",
+								min: 10,
+								max: 80,
+								oninput: (e) => {
+									e.target.parentElement.title = `Controls the maximum size of the image feed panel (${e.target.value}vh)`;
+									imageFeed.style.setProperty("--max-size", e.target.value);
+									saveVal("FeedSize", e.target.value);
+									window.dispatchEvent(new Event("resize"));
+								},
+								$: (el) => {
+									requestAnimationFrame(() => {
+										el.value = getVal("FeedSize", 25);
+										el.oninput({ target: el });
+									});
+								},
+							}),
+						]),
+						$el("section.size-control.image-size-control", {}, [
+							$el("a", {
+								textContent: "Column count...",
+								style: {
+									cursor: "pointer",
+									textDecoration: "underline",
+								},
+								onclick: () => {
+									const v = +prompt("Enter custom column count", 20);
+									if (!isNaN(v)) {
+										updateColumnCount(v);
+									}
+								},
+							}),
+							$el("input", {
+								type: "range",
+								min: 1,
+								max: 10,
+								step: 1,
+								oninput: (e) => {
+									updateColumnCount(e.target.value);
+								},
+								$: (el) => {
+									columnInput = el;
+									requestAnimationFrame(() => {
+										updateColumnCount(getVal("ImageSize", 4));
+									});
+								},
+							}),
+						]),
+					]),
+				]),
+				$el("div.pysssss-image-feed-btn-group", {}, [clearButton, hideButton]),
+			]),
+			imageList
+		);
+		showButton.onclick = () => {
+			imageFeed.style.display = "flex";
+			showButton.style.display = "none";
+			if (showMenuButton) {
+				showMenuButton.enabled = false;
+				showMenuButton.element.style.display = "none";
+			}
+
+			saveVal("Visible", 1);
+			visible = true;
+			window.dispatchEvent(new Event("resize"));
+		};
+		document.querySelector(".comfy-settings-btn").after(showButton);
+		window.dispatchEvent(new Event("resize"));
+
+		if (!+getVal("Visible", 1)) {
+			hideButton.onclick();
+		}
+
+		api.addEventListener("executed", ({ detail }) => {
+			if (visible && detail?.output?.images) {
+				if (detail.node?.includes?.(":")) {
+					// Ignore group nodes
+					const n = app.graph.getNodeById(detail.node.split(":")[0]);
+					if (n?.getInnerNodes) return;
+				}
+
+				// Apply "Display Save Image Node Only" filter if setting is enabled
+				const nodeName = detail.node?.split(":")?.[0];
+				if (nodeName) {
+					const node = app.graph.getNodeById(nodeName);
+
+					if (saveNodeOnly.value && node?.type !== "SaveImage") return;
+				}
+
+				for (const src of detail.output.images) {
+					const href = `./view?filename=${encodeURIComponent(src.filename)}&type=${src.type}&
+					subfolder=${encodeURIComponent(src.subfolder)}&t=${+new Date()}`;
+
+					// deduplicateFeed.value is essentially the scaling factor used for image hashing
+					// but when deduplication is disabled, this value is "0"
+					if (deduplicateFeed.value > 0) {
+						// deduplicate by ignoring images with the same filename/type/subfolder
+						const fingerprint = JSON.stringify({ filename: src.filename, type: src.type, subfolder: src.subfolder });
+						if (seenImages.has(fingerprint)) {
+							// NOOP: image is a duplicate
+						} else {
+							seenImages.set(fingerprint, true);
+							let img = $el("img", { src: href });
+							img.onerror = () => {
+								// fall back to default behavior
+								addImageToFeed(href);
+							};
+							img.onload = () => {
+								// redraw the image onto a canvas to strip metadata (resize if performance mode)
+								let imgCanvas = document.createElement("canvas");
+								let imgScalar = deduplicateFeed.value;
+								imgCanvas.width = imgScalar * img.width;
+								imgCanvas.height = imgScalar * img.height;
+
+								let imgContext = imgCanvas.getContext("2d");
+								imgContext.drawImage(img, 0, 0, imgCanvas.width, imgCanvas.height);
+								const data = imgContext.getImageData(0, 0, imgCanvas.width, imgCanvas.height);
+
+								// calculate fast hash of the image data
+								let hash = 0;
+								for (const b of data.data) {
+									hash = (hash << 5) - hash + b;
+								}
+
+								// add image to feed if we've never seen the hash before
+								if (seenImages.has(hash)) {
+									// NOOP: image is a duplicate
+								} else {
+									// if we got to here, then the image is unique--so add to feed
+									seenImages.set(hash, true);
+									addImageToFeed(href);
+								}
+							};
+						}
+					} else {
+						addImageToFeed(href);
+					}
+				}
+			}
+		});
+	},
+});
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/kSamplerAdvDenoise.js b/custom_nodes/comfyui-custom-scripts/web/js/kSamplerAdvDenoise.js
new file mode 100644
index 0000000000000000000000000000000000000000..7b92d4f1d6596a6d9679d3ef4c7dffe9ea297327
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/kSamplerAdvDenoise.js
@@ -0,0 +1,54 @@
+import { app } from "../../../scripts/app.js";
+app.registerExtension({
+	name: "pysssss.KSamplerAdvDenoise",
+	async beforeRegisterNodeDef(nodeType) {
+		// Add menu options to conver to/from widgets
+		const origGetExtraMenuOptions = nodeType.prototype.getExtraMenuOptions;
+		nodeType.prototype.getExtraMenuOptions = function (_, options) {
+			const r = origGetExtraMenuOptions?.apply?.(this, arguments);
+
+			let stepsWidget = null;
+			let startAtWidget = null;
+			let endAtWidget = null;
+			for (const w of this.widgets || []) {
+				if (w.name === "steps") {
+					stepsWidget = w;
+				} else if (w.name === "start_at_step") {
+					startAtWidget = w;
+				} else if (w.name === "end_at_step") {
+					endAtWidget = w;
+				}
+			}
+
+			if (stepsWidget && startAtWidget && endAtWidget) {
+				options.push(
+					{
+						content: "Set Denoise",
+						callback: () => {
+							const steps = +prompt("How many steps do you want?", 15);
+							if (isNaN(steps)) {
+								return;
+							}
+							const denoise = +prompt("How much denoise? (0-1)", 0.5);
+							if (isNaN(denoise)) {
+								return;
+							}
+
+							stepsWidget.value = Math.floor(steps / Math.max(0, Math.min(1, denoise)));
+							stepsWidget.callback?.(stepsWidget.value);
+
+							startAtWidget.value = stepsWidget.value - steps;
+							startAtWidget.callback?.(startAtWidget.value);
+
+							endAtWidget.value = stepsWidget.value;
+							endAtWidget.callback?.(endAtWidget.value);
+						},
+					},
+					null
+				);
+			}
+
+			return r;
+		};
+	},
+});
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/linkRenderMode.js b/custom_nodes/comfyui-custom-scripts/web/js/linkRenderMode.js
new file mode 100644
index 0000000000000000000000000000000000000000..010de6ee1085e3fbb4e5b3d3a381e38e261b53c7
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/linkRenderMode.js
@@ -0,0 +1,57 @@
+import { app } from "../../../scripts/app.js";
+import { $el } from "../../../scripts/ui.js";
+
+const id = "pysssss.LinkRenderMode";
+const ext = {
+	name: id,
+	async setup(app) {
+		if (app.extensions.find((ext) => ext.name === "Comfy.LinkRenderMode")) {
+			console.log("%c[🐍 pysssss]", "color: limegreen", "Skipping LinkRenderMode as core extension found");
+			return;
+		}
+		const setting = app.ui.settings.addSetting({
+			id,
+			name: "🐍 Link Render Mode",
+			defaultValue: 2,
+			type: () => {
+				return $el("tr", [
+					$el("td", [
+						$el("label", {
+							for: id.replaceAll(".", "-"),
+							textContent: "🐍 Link Render Mode:",
+						}),
+					]),
+					$el("td", [
+						$el(
+							"select",
+							{
+								textContent: "Manage",
+								style: {
+									fontSize: "14px",
+								},
+								oninput: (e) => {
+									setting.value = e.target.value;
+									app.canvas.links_render_mode = +e.target.value;
+									app.graph.setDirtyCanvas(true, true);
+								},
+							},
+							LiteGraph.LINK_RENDER_MODES.map((m, i) =>
+								$el("option", {
+									value: i,
+									textContent: m,
+									selected: i == app.canvas.links_render_mode,
+								})
+							)
+						),
+					]),
+				]);
+			},
+			onChange(value) {
+				app.canvas.links_render_mode = +value;
+				app.graph.setDirtyCanvas(true);
+			},
+		});
+	},
+};
+
+app.registerExtension(ext);
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/mathExpression.js b/custom_nodes/comfyui-custom-scripts/web/js/mathExpression.js
new file mode 100644
index 0000000000000000000000000000000000000000..d0dde50ec2fa00a556b56baa17ec2be340035ef3
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/mathExpression.js
@@ -0,0 +1,44 @@
+import { app } from "../../../scripts/app.js";
+import { ComfyWidgets } from "../../../scripts/widgets.js";
+
+app.registerExtension({
+	name: "pysssss.MathExpression",
+	init() {
+		const STRING = ComfyWidgets.STRING;
+		ComfyWidgets.STRING = function (node, inputName, inputData) {
+			const r = STRING.apply(this, arguments);
+			r.widget.dynamicPrompts = inputData?.[1].dynamicPrompts;
+			return r;
+		};
+	},
+	beforeRegisterNodeDef(nodeType) {
+		if (nodeType.comfyClass === "MathExpression|pysssss") {
+			const onDrawForeground = nodeType.prototype.onDrawForeground;
+
+			nodeType.prototype.onNodeCreated = function() {
+				// These are typed as any to bypass backend validation
+				// update frontend to restrict types
+				for(const input of this.inputs) {
+					input.type = "INT,FLOAT,IMAGE,LATENT";
+				}
+			}
+
+			nodeType.prototype.onDrawForeground = function (ctx) {
+				const r = onDrawForeground?.apply?.(this, arguments);
+
+				const v = app.nodeOutputs?.[this.id + ""];
+				if (!this.flags.collapsed && v) {
+					const text = v.value[0] + "";
+					ctx.save();
+					ctx.font = "bold 12px sans-serif";
+					ctx.fillStyle = "dodgerblue";
+					const sz = ctx.measureText(text);
+					ctx.fillText(text, this.size[0] - sz.width - 5, LiteGraph.NODE_SLOT_HEIGHT * 3);
+					ctx.restore();
+				}
+
+				return r;
+			};
+		}
+	},
+});
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/middleClickAddDefaultNode.js b/custom_nodes/comfyui-custom-scripts/web/js/middleClickAddDefaultNode.js
new file mode 100644
index 0000000000000000000000000000000000000000..43f944f2835f56357b7701c2ef987261ceff9a8b
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/middleClickAddDefaultNode.js
@@ -0,0 +1,49 @@
+import { app } from "../../../scripts/app.js";
+
+const id = "pysssss.MiddleClickAddDefaultNode";
+const ext = {
+	name: id,
+	async setup(app) {
+		app.ui.settings.addSetting({
+			id,
+			name: "🐍 Middle click slot to add",
+			defaultValue: "Reroute",
+			type: "combo",
+			options: (value) =>
+				[
+					...Object.keys(LiteGraph.registered_node_types)
+						.filter((k) => k.includes("Reroute"))
+						.sort((a, b) => {
+							if (a === "Reroute") return -1;
+							if (b === "Reroute") return 1;
+							return a.localeCompare(b);
+						}),
+					"[None]",
+				].map((m) => ({
+					value: m,
+					text: m,
+					selected: !value ? m === "[None]" : m === value,
+				})),
+			onChange(value) {
+				const enable = value && value !== "[None]";
+				if (value === true) {
+					value = "Reroute";
+				}
+				LiteGraph.middle_click_slot_add_default_node = enable;
+				if (enable) {
+					for (const arr of Object.values(LiteGraph.slot_types_default_in).concat(
+						Object.values(LiteGraph.slot_types_default_out)
+					)) {
+						const idx = arr.indexOf(value);
+						if (idx !== 0) {
+							arr.splice(idx, 1);
+						}
+						arr.unshift(value);
+					}
+				}
+			},
+		});
+	},
+};
+
+app.registerExtension(ext);
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/modelInfo.js b/custom_nodes/comfyui-custom-scripts/web/js/modelInfo.js
new file mode 100644
index 0000000000000000000000000000000000000000..b5db20a113fdb8f75b820306985fbf743e5f6bcc
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/modelInfo.js
@@ -0,0 +1,412 @@
+import { app } from "../../../scripts/app.js";
+import { api } from "../../../scripts/api.js";
+import { $el } from "../../../scripts/ui.js";
+import { ModelInfoDialog } from "./common/modelInfoDialog.js";
+
+const MAX_TAGS = 500;
+const NsfwLevel = {
+	PG: 1,
+	PG13: 2,
+	R: 4,
+	X: 8,
+	XXX: 16,
+	Blocked: 32,
+};
+
+export class LoraInfoDialog extends ModelInfoDialog {
+	getTagFrequency() {
+		if (!this.metadata.ss_tag_frequency) return [];
+
+		const datasets = JSON.parse(this.metadata.ss_tag_frequency);
+		const tags = {};
+		for (const setName in datasets) {
+			const set = datasets[setName];
+			for (const t in set) {
+				if (t in tags) {
+					tags[t] += set[t];
+				} else {
+					tags[t] = set[t];
+				}
+			}
+		}
+
+		return Object.entries(tags).sort((a, b) => b[1] - a[1]);
+	}
+
+	getResolutions() {
+		let res = [];
+		if (this.metadata.ss_bucket_info) {
+			const parsed = JSON.parse(this.metadata.ss_bucket_info);
+			if (parsed?.buckets) {
+				for (const { resolution, count } of Object.values(parsed.buckets)) {
+					res.push([count, `${resolution.join("x")} * ${count}`]);
+				}
+			}
+		}
+		res = res.sort((a, b) => b[0] - a[0]).map((a) => a[1]);
+		let r = this.metadata.ss_resolution;
+		if (r) {
+			const s = r.split(",");
+			const w = s[0].replace("(", "");
+			const h = s[1].replace(")", "");
+			res.push(`${w.trim()}x${h.trim()} (Base res)`);
+		} else if ((r = this.metadata["modelspec.resolution"])) {
+			res.push(r + " (Base res");
+		}
+		if (!res.length) {
+			res.push("⚠️ Unknown");
+		}
+		return res;
+	}
+
+	getTagList(tags) {
+		return tags.map((t) =>
+			$el(
+				"li.pysssss-model-tag",
+				{
+					dataset: {
+						tag: t[0],
+					},
+					$: (el) => {
+						el.onclick = () => {
+							el.classList.toggle("pysssss-model-tag--selected");
+						};
+					},
+				},
+				[
+					$el("p", {
+						textContent: t[0],
+					}),
+					$el("span", {
+						textContent: t[1],
+					}),
+				]
+			)
+		);
+	}
+
+	addTags() {
+		let tags = this.getTagFrequency();
+		if (!tags?.length) {
+			tags = this.metadata["modelspec.tags"]?.split(",").map((t) => [t.trim(), 1]);
+		}
+		let hasMore;
+		if (tags?.length) {
+			const c = tags.length;
+			let list;
+			if (c > MAX_TAGS) {
+				tags = tags.slice(0, MAX_TAGS);
+				hasMore = $el("p", [
+					$el("span", { textContent: `⚠️ Only showing first ${MAX_TAGS} tags ` }),
+					$el("a", {
+						href: "#",
+						textContent: `Show all ${c}`,
+						onclick: () => {
+							list.replaceChildren(...this.getTagList(this.getTagFrequency()));
+							hasMore.remove();
+						},
+					}),
+				]);
+			}
+			list = $el("ol.pysssss-model-tags-list", this.getTagList(tags));
+			this.tags = $el("div", [list]);
+		} else {
+			this.tags = $el("p", { textContent: "⚠️ No tag frequency metadata found" });
+		}
+
+		this.content.append(this.tags);
+
+		if (hasMore) {
+			this.content.append(hasMore);
+		}
+	}
+
+	addExample(title, value, name) {
+		const textArea = $el("textarea", {
+			textContent: value,
+			style: {
+				whiteSpace: "pre-wrap",
+				margin: "10px 0",
+				color: "#fff",
+				background: "#222",
+				padding: "5px",
+				borderRadius: "5px",
+				maxHeight: "250px",
+				overflow: "auto",
+				display: "block",
+				border: "none",
+				width: "calc(100% - 10px)",
+			},
+		});
+		$el(
+			"p",
+			{
+				parent: this.content,
+				textContent: `${title}: `,
+			},
+			[
+				textArea,
+				$el("button", {
+					onclick: async () => {
+						await this.saveAsExample(textArea.value, `${name}.txt`);
+					},
+					textContent: "Save as Example",
+					style: {
+						fontSize: "14px",
+					},
+				}),
+				$el("hr"),
+			]
+		);
+	}
+
+	async addInfo() {
+		this.addInfoEntry("Name", this.metadata.ss_output_name || "⚠️ Unknown");
+		this.addInfoEntry("Base Model", this.metadata.ss_sd_model_name || "⚠️ Unknown");
+		this.addInfoEntry("Clip Skip", this.metadata.ss_clip_skip || "⚠️ Unknown");
+
+		this.addInfoEntry(
+			"Resolution",
+			$el(
+				"select",
+				this.getResolutions().map((r) => $el("option", { textContent: r }))
+			)
+		);
+
+		super.addInfo();
+		const p = this.addCivitaiInfo();
+		this.addTags();
+
+		const info = await p;
+		this.addExample("Trained Words", info?.trainedWords?.join(", ") ?? "", "trainedwords");
+
+		const triggerPhrase = this.metadata["modelspec.trigger_phrase"];
+		if (triggerPhrase) {
+			this.addExample("Trigger Phrase", triggerPhrase, "triggerphrase");
+		}
+
+		$el("div", {
+			parent: this.content,
+			innerHTML: info?.description ?? this.metadata["modelspec.description"] ?? "[No description provided]",
+			style: {
+				maxHeight: "250px",
+				overflow: "auto",
+			},
+		});
+	}
+
+	async saveAsExample(example, name = "example.txt") {
+		if (!example.length) {
+			return;
+		}
+		try {
+			name = prompt("Enter example name", name);
+			if (!name) return;
+
+			await api.fetchApi("/pysssss/examples/" + encodeURIComponent(`${this.type}/${this.name}`), {
+				method: "POST",
+				body: JSON.stringify({
+					name,
+					example,
+				}),
+				headers: {
+					"content-type": "application/json",
+				},
+			});
+			this.node?.["pysssss.updateExamples"]?.();
+			alert("Saved!");
+		} catch (error) {
+			console.error(error);
+			alert("Error saving: " + error);
+		}
+	}
+
+	createButtons() {
+		const btns = super.createButtons();
+		function tagsToCsv(tags) {
+			return tags.map((el) => el.dataset.tag).join(", ");
+		}
+		function copyTags(e, tags) {
+			const textarea = $el("textarea", {
+				parent: document.body,
+				style: {
+					position: "fixed",
+				},
+				textContent: tagsToCsv(tags),
+			});
+			textarea.select();
+			try {
+				document.execCommand("copy");
+				if (!e.target.dataset.text) {
+					e.target.dataset.text = e.target.textContent;
+				}
+				e.target.textContent = "Copied " + tags.length + " tags";
+				setTimeout(() => {
+					e.target.textContent = e.target.dataset.text;
+				}, 1000);
+			} catch (ex) {
+				prompt("Copy to clipboard: Ctrl+C, Enter", text);
+			} finally {
+				document.body.removeChild(textarea);
+			}
+		}
+
+		btns.unshift(
+			$el("button", {
+				type: "button",
+				textContent: "Save Selected as Example",
+				onclick: async (e) => {
+					const tags = tagsToCsv([...this.tags.querySelectorAll(".pysssss-model-tag--selected")]);
+					await this.saveAsExample(tags);
+				},
+			}),
+			$el("button", {
+				type: "button",
+				textContent: "Copy Selected",
+				onclick: (e) => {
+					copyTags(e, [...this.tags.querySelectorAll(".pysssss-model-tag--selected")]);
+				},
+			}),
+			$el("button", {
+				type: "button",
+				textContent: "Copy All",
+				onclick: (e) => {
+					copyTags(e, [...this.tags.querySelectorAll(".pysssss-model-tag")]);
+				},
+			})
+		);
+
+		return btns;
+	}
+}
+
+class CheckpointInfoDialog extends ModelInfoDialog {
+	async addInfo() {
+		super.addInfo();
+		const info = await this.addCivitaiInfo();
+		if (info) {
+			this.addInfoEntry("Base Model", info.baseModel || "⚠️ Unknown");
+
+			$el("div", {
+				parent: this.content,
+				innerHTML: info.description,
+				style: {
+					maxHeight: "250px",
+					overflow: "auto",
+				},
+			});
+		}
+	}
+}
+
+const lookups = {};
+
+function addInfoOption(node, type, infoClass, widgetNamePattern, opts) {
+	const widgets = widgetNamePattern
+		? node.widgets.filter((w) => w.name === widgetNamePattern || w.name.match(`^${widgetNamePattern}$`))
+		: [node.widgets[0]];
+	for (const widget of widgets) {
+		let value = widget.value;
+		if (value?.content) {
+			value = value.content;
+		}
+		if (!value || value === "None") {
+			return;
+		}
+		let optName;
+		const split = value.split(/[.\\/]/);
+		optName = split[split.length - 2];
+		opts.push({
+			content: optName,
+			callback: async () => {
+				new infoClass(value, node).show(type, value);
+			},
+		});
+	}
+}
+
+function addTypeOptions(node, typeName, options) {
+	const type = typeName.toLowerCase() + "s";
+	const values = lookups[typeName][node.type];
+	if (!values) return;
+
+	const widgets = Object.keys(values);
+	const cls = type === "loras" ? LoraInfoDialog : CheckpointInfoDialog;
+
+	const opts = [];
+	for (const w of widgets) {
+		addInfoOption(node, type, cls, w, opts);
+	}
+
+	if (!opts.length) return;
+
+	if (opts.length === 1) {
+		opts[0].content = `View ${typeName} info...`;
+		options.unshift(opts[0]);
+	} else {
+		options.unshift({
+			title: `View ${typeName} info...`,
+			has_submenu: true,
+			submenu: {
+				options: opts,
+			},
+		});
+	}
+}
+
+app.registerExtension({
+	name: "pysssss.ModelInfo",
+	setup() {
+		const addSetting = (type, defaultValue) => {
+			app.ui.settings.addSetting({
+				id: `pysssss.ModelInfo.${type}Nodes`,
+				name: `🐍 Model Info - ${type} Nodes/Widgets`,
+				type: "text",
+				defaultValue,
+				tooltip: `Comma separated list of NodeTypeName or NodeTypeName.WidgetName that contain ${type} node names that should have the View Info option available.\nIf no widget name is specifed the first widget will be used. Regex matches (e.g. NodeName..*lora_\\d+) are supported in the widget name.`,
+				onChange(value) {
+					lookups[type] = value.split(",").reduce((p, n) => {
+						n = n.trim();
+						const pos = n.indexOf(".");
+						const split = pos === -1 ? [n] : [n.substring(0, pos), n.substring(pos + 1)];
+						p[split[0]] ??= {};
+						p[split[0]][split[1] ?? ""] = true;
+						return p;
+					}, {});
+				},
+			});
+		};
+		addSetting(
+			"Lora",
+			["LoraLoader.lora_name", "LoraLoader|pysssss", "LoraLoaderModelOnly.lora_name", "LoRA Stacker.lora_name.*"].join(",")
+		);
+		addSetting(
+			"Checkpoint",
+			["CheckpointLoader.ckpt_name", "CheckpointLoaderSimple", "CheckpointLoader|pysssss", "Efficient Loader", "Eff. Loader SDXL"].join(",")
+		);
+
+		app.ui.settings.addSetting({
+			id: `pysssss.ModelInfo.NsfwLevel`,
+			name: `🐍 Model Info - Image Preview Max NSFW Level`,
+			type: "combo",
+			defaultValue: "PG13",
+			options: Object.keys(NsfwLevel),
+			tooltip: `Hides preview images that are tagged as a higher NSFW level`,
+			onChange(value) {
+				ModelInfoDialog.nsfwLevel = NsfwLevel[value] ?? NsfwLevel.PG;
+			},
+		});
+	},
+	beforeRegisterNodeDef(nodeType) {
+		const getExtraMenuOptions = nodeType.prototype.getExtraMenuOptions;
+		nodeType.prototype.getExtraMenuOptions = function (_, options) {
+			if (this.widgets) {
+				for (const type in lookups) {
+					addTypeOptions(this, type, options);
+				}
+			}
+
+			return getExtraMenuOptions?.apply(this, arguments);
+		};
+	},
+});
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/nodeFinder.js b/custom_nodes/comfyui-custom-scripts/web/js/nodeFinder.js
new file mode 100644
index 0000000000000000000000000000000000000000..d3727724b4ad09454aa77736115d81d6426be9ef
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/nodeFinder.js
@@ -0,0 +1,82 @@
+import { app } from "../../../scripts/app.js";
+import { api } from "../../../scripts/api.js";
+
+// Adds a menu option to toggle follow the executing node
+// Adds a menu option to go to the currently executing node
+// Adds a menu option to go to a node by type
+
+app.registerExtension({
+	name: "pysssss.NodeFinder",
+	setup() {
+		let followExecution = false;
+
+		const centerNode = (id) => {
+			if (!followExecution || !id) return;
+			const node = app.graph.getNodeById(id);
+			if (!node) return;
+			app.canvas.centerOnNode(node);
+		};
+
+		api.addEventListener("executing", ({ detail }) => centerNode(detail));
+
+		// Add canvas menu options
+		const orig = LGraphCanvas.prototype.getCanvasMenuOptions;
+		LGraphCanvas.prototype.getCanvasMenuOptions = function () {
+			const options = orig.apply(this, arguments);
+			options.push(null, {
+				content: followExecution ? "Stop following execution" : "Follow execution",
+				callback: () => {
+					if ((followExecution = !followExecution)) {
+						centerNode(app.runningNodeId);
+					}
+				},
+			});
+			if (app.runningNodeId) {
+				options.push({
+					content: "Show executing node",
+					callback: () => {
+						const node = app.graph.getNodeById(app.runningNodeId);
+						if (!node) return;
+						app.canvas.centerOnNode(node);
+					},
+				});
+			}
+
+			const nodes = app.graph._nodes;
+			const types = nodes.reduce((p, n) => {
+				if (n.type in p) {
+					p[n.type].push(n);
+				} else {
+					p[n.type] = [n];
+				}
+				return p;
+			}, {});
+			options.push({
+				content: "Go to node",
+				has_submenu: true,
+				submenu: {
+					options: Object.keys(types)
+						.sort()
+						.map((t) => ({
+							content: t,
+							has_submenu: true,
+							submenu: {
+								options: types[t]
+									.sort((a, b) => {
+										return a.pos[0] - b.pos[0];
+									})
+									.map((n) => ({
+										content: `${n.getTitle()} - #${n.id} (${n.pos[0]}, ${n.pos[1]})`,
+										callback: () => {
+											app.canvas.centerOnNode(n);
+										},
+									})),
+							},
+						})),
+				},
+			});
+
+			return options;
+		};
+	},
+});
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/playSound.js b/custom_nodes/comfyui-custom-scripts/web/js/playSound.js
new file mode 100644
index 0000000000000000000000000000000000000000..de71058989079786807ad4b66ca6853c4b0b0ac7
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/playSound.js
@@ -0,0 +1,36 @@
+import { app } from "../../../scripts/app.js";
+
+app.registerExtension({
+	name: "pysssss.PlaySound",
+	async beforeRegisterNodeDef(nodeType, nodeData, app) {
+		if (nodeData.name === "PlaySound|pysssss") {
+			const onExecuted = nodeType.prototype.onExecuted;
+			nodeType.prototype.onExecuted = async function () {
+				onExecuted?.apply(this, arguments);
+				if (this.widgets[0].value === "on empty queue") {
+					if (app.ui.lastQueueSize !== 0) {
+						await new Promise((r) => setTimeout(r, 500));
+					}
+					if (app.ui.lastQueueSize !== 0) {
+						return;
+					}
+				}
+				let file = this.widgets[2].value;
+				if (!file) {
+					file = "notify.mp3";
+				}
+				if (!file.startsWith("http")) {
+					if (!file.includes("/")) {
+						file = "assets/" + file;
+					}
+					file = new URL(file, import.meta.url)
+				}
+				
+				const url = new URL(file);
+				const audio = new Audio(url);
+				audio.volume = this.widgets[1].value;
+				audio.play();
+			};
+		}
+	},
+});
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/presetText.js b/custom_nodes/comfyui-custom-scripts/web/js/presetText.js
new file mode 100644
index 0000000000000000000000000000000000000000..472f11bef9caa382703f6144c32d48c647b2443d
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/presetText.js
@@ -0,0 +1,257 @@
+import { app } from "../../../scripts/app.js";
+
+// Allows you to manage preset tags for e.g. common negative prompt
+// Also performs replacements on any text field e.g. allowing you to use preset text in CLIP Text encode fields
+
+let replaceRegex;
+const id = "pysssss.PresetText.Presets";
+const MISSING = Symbol();
+
+const getPresets = () => {
+	let items;
+	try {
+		items = JSON.parse(localStorage.getItem(id));
+	} catch (error) {}
+	if (!items || !items.length) {
+		items = [{ name: "default negative", value: "worst quality" }];
+	}
+	return items;
+};
+
+let presets = getPresets();
+
+app.registerExtension({
+	name: "pysssss.PresetText",
+	setup() {
+		app.ui.settings.addSetting({
+			id: "pysssss.PresetText.ReplacementRegex",
+			name: "🐍 Preset Text Replacement Regex",
+			type: "text",
+			defaultValue: "(?:^|[^\\w])(?<replace>@(?<id>[\\w-]+))",
+			tooltip:
+				"The regex should return two named capture groups: id (the name of the preset text to use), replace (the matched text to replace)",
+			attrs: {
+				style: {
+					fontFamily: "monospace",
+				},
+			},
+			onChange(value) {
+				if (!value) {
+					replaceRegex = null;
+					return;
+				}
+				try {
+					replaceRegex = new RegExp(value, "g");
+				} catch (error) {
+					alert("Error creating regex for preset text replacement, no replacements will be performed.");
+					replaceRegex = null;
+				}
+			},
+		});
+
+		const drawNodeWidgets = LGraphCanvas.prototype.drawNodeWidgets
+		LGraphCanvas.prototype.drawNodeWidgets = function(node) {
+			const c = LiteGraph.WIDGET_BGCOLOR;
+			try {
+				if(node[MISSING]) {
+					LiteGraph.WIDGET_BGCOLOR = "red"
+				}
+				return drawNodeWidgets.apply(this, arguments);
+			} finally {
+				LiteGraph.WIDGET_BGCOLOR = c;
+			}
+		}
+	},
+	registerCustomNodes() {
+		class PresetTextNode extends LiteGraph.LGraphNode {
+			constructor() {
+				super();
+				this.title = "Preset Text 🐍";
+				this.isVirtualNode = true;
+				this.serialize_widgets = true;
+				this.addOutput("text", "STRING");
+
+				const widget = this.addWidget("combo", "value", presets[0].name, () => {}, {
+					values: presets.map((p) => p.name),
+				});
+				this.addWidget("button", "Manage", "Manage", () => {
+					const container = document.createElement("div");
+					Object.assign(container.style, {
+						display: "grid",
+						gridTemplateColumns: "1fr 1fr",
+						gap: "10px",
+					});
+
+					const addNew = document.createElement("button");
+					addNew.textContent = "Add New";
+					addNew.classList.add("pysssss-presettext-addnew");
+					Object.assign(addNew.style, {
+						fontSize: "13px",
+						gridColumn: "1 / 3",
+						color: "dodgerblue",
+						width: "auto",
+						textAlign: "center",
+					});
+					addNew.onclick = () => {
+						addRow({ name: "", value: "" });
+					};
+					container.append(addNew);
+
+					function addRow(p) {
+						const name = document.createElement("input");
+						const nameLbl = document.createElement("label");
+						name.value = p.name;
+						nameLbl.textContent = "Name:";
+						nameLbl.append(name);
+
+						const value = document.createElement("input");
+						const valueLbl = document.createElement("label");
+						value.value = p.value;
+						valueLbl.textContent = "Value:";
+						valueLbl.append(value);
+
+						addNew.before(nameLbl, valueLbl);
+					}
+					for (const p of presets) {
+						addRow(p);
+					}
+
+					const help = document.createElement("span");
+					help.textContent = "To remove a preset set the name or value to blank";
+					help.style.gridColumn = "1 / 3";
+					container.append(help);
+
+					dialog.show("");
+					dialog.textElement.append(container);
+				});
+
+				const dialog = new app.ui.dialog.constructor();
+				dialog.element.classList.add("comfy-settings");
+
+				const closeButton = dialog.element.querySelector("button");
+				closeButton.textContent = "CANCEL";
+				const saveButton = document.createElement("button");
+				saveButton.textContent = "SAVE";
+				saveButton.onclick = function () {
+					const inputs = dialog.element.querySelectorAll("input");
+					const p = [];
+					for (let i = 0; i < inputs.length; i += 2) {
+						const n = inputs[i];
+						const v = inputs[i + 1];
+						if (!n.value.trim() || !v.value.trim()) {
+							continue;
+						}
+						p.push({ name: n.value, value: v.value });
+					}
+
+					widget.options.values = p.map((p) => p.name);
+					if (!widget.options.values.includes(widget.value)) {
+						widget.value = widget.options.values[0];
+					}
+
+					presets = p;
+					localStorage.setItem(id, JSON.stringify(presets));
+
+					dialog.close();
+				};
+
+				closeButton.before(saveButton);
+
+				this.applyToGraph = function (workflow) {
+					// For each output link copy our value over the original widget value
+					if (this.outputs[0].links && this.outputs[0].links.length) {
+						for (const l of this.outputs[0].links) {
+							const link_info = app.graph.links[l];
+							const outNode = app.graph.getNodeById(link_info.target_id);
+							const outIn = outNode && outNode.inputs && outNode.inputs[link_info.target_slot];
+							if (outIn.widget) {
+								const w = outNode.widgets.find((w) => w.name === outIn.widget.name);
+								if (!w) continue;
+								const preset = presets.find((p) => p.name === widget.value);
+								if (!preset) {
+									this[MISSING] = true;
+									app.graph.setDirtyCanvas(true, true);
+									const msg = `Preset text '${widget.value}' not found. Please fix this and queue again.`;
+									throw new Error(msg);
+								}
+								delete this[MISSING];
+								w.value = preset.value;
+							}
+						}
+					}
+				};
+			}
+		}
+
+		LiteGraph.registerNodeType(
+			"PresetText|pysssss",
+			Object.assign(PresetTextNode, {
+				title: "Preset Text 🐍",
+			})
+		);
+
+		PresetTextNode.category = "utils";
+	},
+	nodeCreated(node) {
+		if (node.widgets) {
+			// Locate dynamic prompt text widgets
+			const widgets = node.widgets.filter((n) => n.type === "customtext" || n.type === "text");
+			for (const widget of widgets) {
+				const callbacks = [
+					() => {
+						let prompt = widget.value;
+						if (replaceRegex && typeof prompt.replace !== 'undefined') {
+							prompt = prompt.replace(replaceRegex, (match, p1, p2, index, text, groups) => {
+								if (!groups.replace || !groups.id) return match; // No match, bad regex?
+
+								const preset = presets.find((p) => p.name.replaceAll(/\s/g, "-") === groups.id);
+								if (!preset) return match; // Invalid name
+
+								const pos = match.indexOf(groups.replace);
+								return match.substring(0, pos) + preset.value;
+							});
+						}
+						return prompt;
+					},
+				];
+				let inheritedSerializeValue = widget.serializeValue || null;
+
+				let called = false;
+				const serializeValue = async (workflowNode, widgetIndex) => {
+					const origWidgetValue = widget.value;
+					if (called) return origWidgetValue;
+					called = true;
+
+					let allCallbacks = [...callbacks];
+					if (inheritedSerializeValue) {
+						allCallbacks.push(inheritedSerializeValue)
+					}
+					let valueIsUndefined = false;
+
+					for (const cb of allCallbacks) {
+						let value = await cb(workflowNode, widgetIndex);
+						// Need to check the callback return value before it is set on widget.value as it coerces it to a string (even for undefined)
+						if (value === undefined) valueIsUndefined = true;
+						widget.value = value;
+					}
+
+					const prompt = valueIsUndefined ? undefined : widget.value;
+					widget.value = origWidgetValue;
+
+					called = false;
+
+					return prompt;
+				};
+
+				Object.defineProperty(widget, "serializeValue", {
+					get() {
+						return serializeValue;
+					},
+					set(cb) {
+						inheritedSerializeValue = cb;
+					},
+				});
+			}
+		}
+	},
+});
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/quickNodes.js b/custom_nodes/comfyui-custom-scripts/web/js/quickNodes.js
new file mode 100644
index 0000000000000000000000000000000000000000..a52581b32ed9cc6af01f016774518d17693b2132
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/quickNodes.js
@@ -0,0 +1,196 @@
+import { app } from "../../../scripts/app.js";
+
+// Adds a bunch of context menu entries for quickly adding common steps
+
+function addMenuHandler(nodeType, cb) {
+	const getOpts = nodeType.prototype.getExtraMenuOptions;
+	nodeType.prototype.getExtraMenuOptions = function () {
+		const r = getOpts.apply(this, arguments);
+		cb.apply(this, arguments);
+		return r;
+	};
+}
+
+function getOrAddVAELoader(node) {
+	let vaeNode = app.graph._nodes.find((n) => n.type === "VAELoader");
+	if (!vaeNode) {
+		vaeNode = addNode("VAELoader", node);
+	}
+	return vaeNode;
+}
+
+function addNode(name, nextTo, options) {
+	options = { select: true, shiftY: 0, before: false, ...(options || {}) };
+	const node = LiteGraph.createNode(name);
+	app.graph.add(node);
+	node.pos = [
+		options.before ? nextTo.pos[0] - node.size[0] - 30 : nextTo.pos[0] + nextTo.size[0] + 30,
+		nextTo.pos[1] + options.shiftY,
+	];
+	if (options.select) {
+		app.canvas.selectNode(node, false);
+	}
+	return node;
+}
+
+app.registerExtension({
+	name: "pysssss.QuickNodes",
+	async beforeRegisterNodeDef(nodeType, nodeData, app) {
+		if (nodeData.input && nodeData.input.required) {
+			const keys = Object.keys(nodeData.input.required);
+			for (let i = 0; i < keys.length; i++) {
+				if (nodeData.input.required[keys[i]][0] === "VAE") {
+					addMenuHandler(nodeType, function (_, options) {
+						options.unshift({
+							content: "Use VAE",
+							callback: () => {
+								getOrAddVAELoader(this).connect(0, this, i);
+							},
+						});
+					});
+					break;
+				}
+			}
+		}
+
+		if (nodeData.name === "KSampler") {
+			addMenuHandler(nodeType, function (_, options) {
+				options.unshift(
+					{
+						content: "Add Blank Input",
+						callback: () => {
+							const imageNode = addNode("EmptyLatentImage", this, { before: true });
+							imageNode.connect(0, this, 3);
+						},
+					},
+					{
+						content: "Add Hi-res Fix",
+						callback: () => {
+							const upscaleNode = addNode("LatentUpscale", this);
+							this.connect(0, upscaleNode, 0);
+
+							const sampleNode = addNode("KSampler", upscaleNode);
+
+							for (let i = 0; i < 3; i++) {
+								const l = this.getInputLink(i);
+								if (l) {
+									app.graph.getNodeById(l.origin_id).connect(l.origin_slot, sampleNode, i);
+								}
+							}
+
+							upscaleNode.connect(0, sampleNode, 3);
+						},
+					},
+					{
+						content: "Add 2nd Pass",
+						callback: () => {
+							const upscaleNode = addNode("LatentUpscale", this);
+							this.connect(0, upscaleNode, 0);
+
+							const ckptNode = addNode("CheckpointLoaderSimple", this);
+							const sampleNode = addNode("KSampler", ckptNode);
+
+							const positiveLink = this.getInputLink(1);
+							const negativeLink = this.getInputLink(2);
+							const positiveNode = positiveLink
+								? app.graph.add(app.graph.getNodeById(positiveLink.origin_id).clone())
+								: addNode("CLIPTextEncode");
+							const negativeNode = negativeLink
+								? app.graph.add(app.graph.getNodeById(negativeLink.origin_id).clone())
+								: addNode("CLIPTextEncode");
+
+							ckptNode.connect(0, sampleNode, 0);
+							ckptNode.connect(1, positiveNode, 0);
+							ckptNode.connect(1, negativeNode, 0);
+							positiveNode.connect(0, sampleNode, 1);
+							negativeNode.connect(0, sampleNode, 2);
+							upscaleNode.connect(0, sampleNode, 3);
+						},
+					},
+					{
+						content: "Add Save Image",
+						callback: () => {
+							const decodeNode = addNode("VAEDecode", this);
+							this.connect(0, decodeNode, 0);
+
+							getOrAddVAELoader(decodeNode).connect(0, decodeNode, 1);
+
+							const saveNode = addNode("SaveImage", decodeNode);
+							decodeNode.connect(0, saveNode, 0);
+						},
+					}
+				);
+			});
+		}
+
+		if (nodeData.name === "CheckpointLoaderSimple") {
+			addMenuHandler(nodeType, function (_, options) {
+				options.unshift({
+					content: "Add Clip Skip",
+					callback: () => {
+						const clipSkipNode = addNode("CLIPSetLastLayer", this);
+						const clipLinks = this.outputs[1].links ? this.outputs[1].links.map((l) => ({ ...graph.links[l] })) : [];
+
+						this.disconnectOutput(1);
+						this.connect(1, clipSkipNode, 0);
+
+						for (const clipLink of clipLinks) {
+							clipSkipNode.connect(0, clipLink.target_id, clipLink.target_slot);
+						}
+					},
+				});
+			});
+		}
+
+		if (
+			nodeData.name === "CheckpointLoaderSimple" ||
+			nodeData.name === "CheckpointLoader" ||
+			nodeData.name === "CheckpointLoader|pysssss" ||
+			nodeData.name === "LoraLoader" ||
+			nodeData.name === "LoraLoader|pysssss"
+		) {
+			addMenuHandler(nodeType, function (_, options) {
+				function addLora(type) {
+					const loraNode = addNode(type, this);
+
+					const modelLinks = this.outputs[0].links ? this.outputs[0].links.map((l) => ({ ...graph.links[l] })) : [];
+					const clipLinks = this.outputs[1].links ? this.outputs[1].links.map((l) => ({ ...graph.links[l] })) : [];
+
+					this.disconnectOutput(0);
+					this.disconnectOutput(1);
+
+					this.connect(0, loraNode, 0);
+					this.connect(1, loraNode, 1);
+
+					for (const modelLink of modelLinks) {
+						loraNode.connect(0, modelLink.target_id, modelLink.target_slot);
+					}
+
+					for (const clipLink of clipLinks) {
+						loraNode.connect(1, clipLink.target_id, clipLink.target_slot);
+					}
+				}
+				options.unshift(
+					{
+						content: "Add LoRA",
+						callback: () => addLora.call(this, "LoraLoader"),
+					},
+					{
+						content: "Add 🐍 LoRA",
+						callback: () => addLora.call(this, "LoraLoader|pysssss"),
+					},
+					{
+						content: "Add Prompts",
+						callback: () => {
+							const positiveNode = addNode("CLIPTextEncode", this);
+							const negativeNode = addNode("CLIPTextEncode", this, { shiftY: positiveNode.size[1] + 30 });
+
+							this.connect(1, positiveNode, 0);
+							this.connect(1, negativeNode, 0);
+						},
+					}
+				);
+			});
+		}
+	},
+});
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/repeater.js b/custom_nodes/comfyui-custom-scripts/web/js/repeater.js
new file mode 100644
index 0000000000000000000000000000000000000000..23432c4d4fa89f899312a7fa4bd83a04c635a6b5
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/repeater.js
@@ -0,0 +1,123 @@
+import { app } from "../../../scripts/app.js";
+
+const REPEATER = "Repeater|pysssss";
+
+app.registerExtension({
+	name: "pysssss.Repeater",
+	init() {
+		const graphToPrompt = app.graphToPrompt;
+		app.graphToPrompt = async function () {
+			const res = await graphToPrompt.apply(this, arguments);
+
+			const id = Date.now() + "_";
+			let u = 0;
+
+			let newNodes = {};
+			const newRepeaters = {};
+			for (const nodeId in res.output) {
+				let output = res.output[nodeId];
+				if (output.class_type === REPEATER) {
+					const isMulti = output.inputs.output === "multi";
+					if (output.inputs.node_mode === "create") {
+						// We need to clone the input for every repeat
+						const orig = res.output[output.inputs.source[0]];
+						if (isMulti) {
+							if (!newRepeaters[nodeId]) {
+								newRepeaters[nodeId] = [];
+								newRepeaters[nodeId][output.inputs.repeats - 1] = nodeId;
+							}
+						}
+						for (let i = 0; i < output.inputs.repeats - 1; i++) {
+							const clonedInputId = id + ++u;
+
+							if (isMulti) {
+								// If multi create we need to clone the repeater too
+								newNodes[clonedInputId] = structuredClone(orig);
+
+								output = structuredClone(output);
+
+								const clonedRepeaterId = id + ++u;
+								newNodes[clonedRepeaterId] = output;
+								output.inputs["source"][0] = clonedInputId;
+
+								newRepeaters[nodeId][i] = clonedRepeaterId;
+							} else {
+								newNodes[clonedInputId] = orig;
+							}
+							output.inputs[clonedInputId] = [clonedInputId, output.inputs.source[1]];
+						}
+					} else if (isMulti) {
+						newRepeaters[nodeId] = Array(output.inputs.repeats).fill(nodeId);
+					}
+				}
+			}
+
+			Object.assign(res.output, newNodes);
+			newNodes = {};
+
+			for (const nodeId in res.output) {
+				const output = res.output[nodeId];
+				for (const k in output.inputs) {
+					const v = output.inputs[k];
+					if (v instanceof Array) {
+						const repeaterId = v[0];
+						const source = newRepeaters[repeaterId];
+						if (source) {
+							v[0] = source.pop();
+							v[1] = 0;
+						}
+					}
+				}
+			}
+
+			// Object.assign(res.output, newNodes);
+
+			return res;
+		};
+	},
+	beforeRegisterNodeDef(nodeType, nodeData, app) {
+		if (nodeData.name === REPEATER) {
+			const SETUP_OUTPUTS = Symbol();
+			nodeType.prototype[SETUP_OUTPUTS] = function (repeats) {
+				if (repeats == null) {
+					repeats = this.widgets[0].value;
+				}
+				while (this.outputs.length > repeats) {
+					this.removeOutput(repeats);
+				}
+				const id = Date.now() + "_";
+				let u = 0;
+				while (this.outputs.length < repeats) {
+					this.addOutput(id + ++u, "*", { label: "*" });
+				}
+			};
+
+			const onAdded = nodeType.prototype.onAdded;
+			nodeType.prototype.onAdded = function () {
+				const self = this;
+				const repeatsCb = this.widgets[0].callback;
+				this.widgets[0].callback = async function () {
+					const v = (await repeatsCb?.apply(this, arguments)) ?? this.value;
+					if (self.widgets[1].value === "multi") {
+						self[SETUP_OUTPUTS](v);
+					}
+					return v;
+				};
+
+				const outputCb = this.widgets[1].callback;
+				this.widgets[1].callback = async function () {
+					const v = (await outputCb?.apply(this, arguments)) ?? this.value;
+					if (v === "single") {
+						self.outputs[0].shape = 6;
+						self[SETUP_OUTPUTS](1);
+					} else {
+						delete self.outputs[0].shape;
+						self[SETUP_OUTPUTS]();
+					}
+					return v;
+				};
+				return onAdded?.apply(this, arguments);
+			};
+		}
+	},
+});
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/reroutePrimitive.js b/custom_nodes/comfyui-custom-scripts/web/js/reroutePrimitive.js
new file mode 100644
index 0000000000000000000000000000000000000000..7b934d717073b3b4dc210a48356580c615802375
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/reroutePrimitive.js
@@ -0,0 +1,348 @@
+import { app } from "../../../scripts/app.js";
+import { ComfyWidgets } from "../../../scripts/widgets.js";
+
+const REROUTE_PRIMITIVE = "ReroutePrimitive|pysssss";
+const MULTI_PRIMITIVE = "MultiPrimitive|pysssss";
+const LAST_TYPE = Symbol("LastType");
+
+app.registerExtension({
+	name: "pysssss.ReroutePrimitive",
+	init() {
+		// On graph configure, fire onGraphConfigured to create widgets
+		const graphConfigure = LGraph.prototype.configure;
+		LGraph.prototype.configure = function () {
+			const r = graphConfigure.apply(this, arguments);
+			for (const n of app.graph._nodes) {
+				if (n.type === REROUTE_PRIMITIVE) {
+					n.onGraphConfigured();
+				}
+			}
+
+			return r;
+		};
+
+		// Hide this node as it is no longer supported
+		const getNodeTypesCategories = LiteGraph.getNodeTypesCategories;
+		LiteGraph.getNodeTypesCategories = function() {
+			return getNodeTypesCategories.apply(this, arguments).filter(c => !c.startsWith("__hidden__"));
+		}
+
+		const graphToPrompt = app.graphToPrompt;
+		app.graphToPrompt = async function () {
+			const res = await graphToPrompt.apply(this, arguments);
+
+			const multiOutputs = [];
+			for (const nodeId in res.output) {
+				const output = res.output[nodeId];
+				if (output.class_type === MULTI_PRIMITIVE) {
+					multiOutputs.push({ id: nodeId, inputs: output.inputs });
+				}
+			}
+
+			function permute(outputs) {
+				function generatePermutations(inputs, currentIndex, currentPermutation, result) {
+					if (currentIndex === inputs.length) {
+						result.push({ ...currentPermutation });
+						return;
+					}
+
+					const input = inputs[currentIndex];
+
+					for (const k in input) {
+						currentPermutation[currentIndex] = input[k];
+						generatePermutations(inputs, currentIndex + 1, currentPermutation, result);
+					}
+				}
+
+				const inputs = outputs.map((output) => output.inputs);
+				const result = [];
+				const current = new Array(inputs.length);
+
+				generatePermutations(inputs, 0, current, result);
+
+				return outputs.map((output, index) => ({
+					...output,
+					inputs: result.reduce((p, permutation) => {
+						const count = Object.keys(p).length;
+						p["value" + (count || "")] = permutation[index];
+						return p;
+					}, {}),
+				}));
+			}
+
+			const permutations = permute(multiOutputs);
+			for (let i = 0; i < permutations.length; i++) {
+				res.output[multiOutputs[i].id].inputs = permutations[i].inputs;
+			}
+
+			return res;
+		};
+	},
+	async beforeRegisterNodeDef(nodeType, nodeData, app) {
+		function addOutputHandler() {
+			// Finds the first non reroute output node down the chain
+			nodeType.prototype.getFirstReroutedOutput = function (slot) {
+				if (nodeData.name === MULTI_PRIMITIVE) {
+					slot = 0;
+				}
+				const links = this.outputs[slot].links;
+				if (!links) return null;
+
+				const search = [];
+				for (const l of links) {
+					const link = app.graph.links[l];
+					if (!link) continue;
+
+					const node = app.graph.getNodeById(link.target_id);
+					if (node.type !== REROUTE_PRIMITIVE && node.type !== MULTI_PRIMITIVE) {
+						return { node, link };
+					}
+					search.push({ node, link });
+				}
+
+				for (const { link, node } of search) {
+					const r = node.getFirstReroutedOutput(link.target_slot);
+					if (r) {
+						return r;
+					}
+				}
+			};
+		}
+
+		if (nodeData.name === REROUTE_PRIMITIVE) {
+			const configure = nodeType.prototype.configure || LGraphNode.prototype.configure;
+			const onConnectionsChange = nodeType.prototype.onConnectionsChange;
+			const onAdded = nodeType.prototype.onAdded;
+
+			nodeType.title_mode = LiteGraph.NO_TITLE;
+
+			function hasAnyInput(node) {
+				for (const input of node.inputs) {
+					if (input.link) {
+						return true;
+					}
+				}
+				return false;
+			}
+
+			// Remove input text
+			nodeType.prototype.onAdded = function () {
+				onAdded?.apply(this, arguments);
+				this.inputs[0].label = "";
+				this.outputs[0].label = "value";
+				this.setSize(this.computeSize());
+			};
+
+			// Restore any widgets
+			nodeType.prototype.onGraphConfigured = function () {
+				if (hasAnyInput(this)) return;
+
+				const outputNode = this.getFirstReroutedOutput(0);
+				if (outputNode) {
+					this.checkPrimitiveWidget(outputNode);
+				}
+			};
+
+			// Check if we need to create (or remove) a widget on the node
+			nodeType.prototype.checkPrimitiveWidget = function ({ node, link }) {
+				let widgetType = link.type;
+				let targetLabel = widgetType;
+				const input = node.inputs[link.target_slot];
+				if (input.widget?.config?.[0] instanceof Array) {
+					targetLabel = input.widget.name;
+					widgetType = "COMBO";
+				}
+
+				if (widgetType in ComfyWidgets) {
+					if (!this.widgets?.length) {
+						let v;
+						if (this.widgets_values?.length) {
+							v = this.widgets_values[0];
+						}
+						let config = [link.type, {}];
+						if (input.widget?.config) {
+							config = input.widget.config;
+						}
+						const { widget } = ComfyWidgets[widgetType](this, "value", config, app);
+						if (v !== undefined && (!this[LAST_TYPE] || this[LAST_TYPE] === widgetType)) {
+							widget.value = v;
+						}
+						this[LAST_TYPE] = widgetType;
+					}
+				} else if (this.widgets) {
+					this.widgets.length = 0;
+				}
+
+				return targetLabel;
+			};
+
+			// Finds all input nodes from the current reroute
+			nodeType.prototype.getReroutedInputs = function (slot) {
+				let nodes = [{ node: this }];
+				let node = this;
+				while (node?.type === REROUTE_PRIMITIVE) {
+					const input = node.inputs[slot];
+					if (input.link) {
+						const link = app.graph.links[input.link];
+						node = app.graph.getNodeById(link.origin_id);
+						slot = link.origin_slot;
+						nodes.push({
+							node,
+							link,
+						});
+					} else {
+						node = null;
+					}
+				}
+
+				return nodes;
+			};
+
+			addOutputHandler();
+
+			// Update the type of all reroutes in a chain
+			nodeType.prototype.changeRerouteType = function (slot, type, label) {
+				const color = LGraphCanvas.link_type_colors[type];
+				const output = this.outputs[slot];
+				this.inputs[slot].label = " ";
+				output.label = label || (type === "*" ? "value" : type);
+				output.type = type;
+
+				// Process all linked outputs
+				for (const linkId of output.links || []) {
+					const link = app.graph.links[linkId];
+					if (!link) continue;
+					link.color = color;
+					const node = app.graph.getNodeById(link.target_id);
+					if (node.changeRerouteType) {
+						// Recursively update reroutes
+						node.changeRerouteType(link.target_slot, type, label);
+					} else {
+						// Validate links to 'real' nodes
+						const theirType = node.inputs[link.target_slot].type;
+						if (theirType !== type && theirType !== "*") {
+							node.disconnectInput(link.target_slot);
+						}
+					}
+				}
+
+				if (this.inputs[slot].link) {
+					const link = app.graph.links[this.inputs[slot].link];
+					if (link) link.color = color;
+				}
+			};
+
+			// Override configure so we can flag that we are configuring to avoid link validation breaking
+			let configuring = false;
+			nodeType.prototype.configure = function () {
+				configuring = true;
+				const r = configure?.apply(this, arguments);
+				configuring = false;
+
+				return r;
+			};
+
+			Object.defineProperty(nodeType, "title_mode", {
+				get() {
+					return app.canvas.current_node?.widgets?.length ? LiteGraph.NORMAL_TITLE : LiteGraph.NO_TITLE;
+				},
+			});
+
+			nodeType.prototype.onConnectionsChange = function (type, _, connected, link_info) {
+				// If configuring treat everything as OK as links may not be set by litegraph yet
+				if (configuring) return;
+
+				const isInput = type === LiteGraph.INPUT;
+				const slot = isInput ? link_info.target_slot : link_info.origin_slot;
+
+				let targetLabel = null;
+				let targetNode = null;
+				let targetType = "*";
+				let targetSlot = slot;
+
+				const inputPath = this.getReroutedInputs(slot);
+				const rootInput = inputPath[inputPath.length - 1];
+				const outputNode = this.getFirstReroutedOutput(slot);
+				if (rootInput.node.type === REROUTE_PRIMITIVE) {
+					// Our input node is a reroute, so see if we have an output
+					if (outputNode) {
+						targetType = outputNode.link.type;
+					} else if (rootInput.node.widgets) {
+						rootInput.node.widgets.length = 0;
+					}
+					targetNode = rootInput;
+					targetSlot = rootInput.link?.target_slot ?? slot;
+				} else {
+					// We have a real input, so we want to use that type
+					targetNode = inputPath[inputPath.length - 2];
+					targetType = rootInput.node.outputs[rootInput.link.origin_slot].type;
+					targetSlot = rootInput.link.target_slot;
+				}
+
+				if (this.widgets && inputPath.length > 1) {
+					// We have an input node so remove our widget
+					this.widgets.length = 0;
+				}
+
+				if (outputNode && rootInput.node.checkPrimitiveWidget) {
+					// We have an output, check if we need to create a widget
+					targetLabel = rootInput.node.checkPrimitiveWidget(outputNode);
+				}
+
+				// Trigger an update of the type to all child nodes
+				targetNode.node.changeRerouteType(targetSlot, targetType, targetLabel);
+
+				return onConnectionsChange?.apply(this, arguments);
+			};
+
+			// When collapsed fix the size to just the dot
+			const computeSize = nodeType.prototype.computeSize || LGraphNode.prototype.computeSize;
+			nodeType.prototype.computeSize = function () {
+				const r = computeSize.apply(this, arguments);
+				if (this.flags?.collapsed) {
+					return [1, 25];
+				} else if (this.widgets?.length) {
+					return r;
+				} else {
+					let w = 75;
+					if (this.outputs?.[0]?.label) {
+						const t = LiteGraph.NODE_TEXT_SIZE * this.outputs[0].label.length * 0.6 + 30;
+						if (t > w) {
+							w = t;
+						}
+					}
+					return [w, r[1]];
+				}
+			};
+
+			// On collapse shrink the node to just a dot
+			const collapse = nodeType.prototype.collapse || LGraphNode.prototype.collapse;
+			nodeType.prototype.collapse = function () {
+				collapse.apply(this, arguments);
+				this.setSize(this.computeSize());
+				requestAnimationFrame(() => {
+					this.setDirtyCanvas(true, true);
+				});
+			};
+
+			// Shift the bounding area up slightly as LiteGraph miscalculates it for collapsed nodes
+			nodeType.prototype.onBounding = function (area) {
+				if (this.flags?.collapsed) {
+					area[1] -= 15;
+				}
+			};
+		} else if (nodeData.name === MULTI_PRIMITIVE) {
+			addOutputHandler();
+			nodeType.prototype.onConnectionsChange = function (type, _, connected, link_info) {
+				for (let i = 0; i < this.inputs.length - 1; i++) {
+					if (!this.inputs[i].link) {
+						this.removeInput(i--);
+					}
+				}
+				if (this.inputs[this.inputs.length - 1].link) {
+					this.addInput("v" + +new Date(), this.inputs[0].type).label = "value";
+				}
+			};
+		}
+	},
+});
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/showImageOnMenu.js b/custom_nodes/comfyui-custom-scripts/web/js/showImageOnMenu.js
new file mode 100644
index 0000000000000000000000000000000000000000..4fed9de92e333270f1c2066e7667e34fd40e539b
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/showImageOnMenu.js
@@ -0,0 +1,81 @@
+import { app } from "../../../scripts/app.js";
+import { api } from "../../../scripts/api.js";
+import { $el } from "../../../scripts/ui.js";
+
+const id = "pysssss.ShowImageOnMenu";
+const ext = {
+	name: id,
+	async setup(app) {
+		let enabled = true;
+		let nodeId = null;
+		const img = $el("img", {
+			style: {
+				width: "100%",
+				height: "150px",
+				objectFit: "contain",
+			},
+		});
+		const link = $el(
+			"a",
+			{
+				style: {
+					width: "100%",
+					height: "150px",
+					marginTop: "10px",
+					order: 100, // Place this item last (until someone else has a higher order)
+					display: "none",
+				},
+				href: "#",
+				onclick: (e) => {
+					e.stopPropagation();
+					e.preventDefault();
+					const node = app.graph.getNodeById(nodeId);
+					if (!node) return;
+					app.canvas.centerOnNode(node);
+					app.canvas.setZoom(1);
+				},
+			},
+			[img]
+		);
+
+		app.ui.menuContainer.append(link);
+
+		const show = (src, node) => {
+			img.src = src;
+			nodeId = Number(node);
+			link.style.display = "unset";
+		};
+
+		api.addEventListener("executed", ({ detail }) => {
+			if (!enabled) return;
+			const images = detail?.output?.images;
+			if (!images || !images.length) return;
+			const format = app.getPreviewFormatParam();
+			const src = [
+				`./view?filename=${encodeURIComponent(images[0].filename)}`,
+				`type=${images[0].type}`,
+				`subfolder=${encodeURIComponent(images[0].subfolder)}`,
+				`t=${+new Date()}${format}`,].join('&');
+			show(src, detail.node);
+		});
+
+		api.addEventListener("b_preview", ({ detail }) => {
+			if (!enabled) return;
+			show(URL.createObjectURL(detail), app.runningNodeId);
+		});
+
+		app.ui.settings.addSetting({
+			id,
+			name: "🐍 Show Image On Menu",
+			defaultValue: true,
+			type: "boolean",
+			onChange(value) {
+				enabled = value;
+
+				if (!enabled) link.style.display = "none";
+			},
+		});
+	},
+};
+
+app.registerExtension(ext);
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/showText.js b/custom_nodes/comfyui-custom-scripts/web/js/showText.js
new file mode 100644
index 0000000000000000000000000000000000000000..464fd404632397e26c2af1d2297970da6c726920
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/showText.js
@@ -0,0 +1,78 @@
+import { app } from "../../../scripts/app.js";
+import { ComfyWidgets } from "../../../scripts/widgets.js";
+
+// Displays input text on a node
+
+// TODO: This should need to be so complicated. Refactor at some point.
+
+app.registerExtension({
+	name: "pysssss.ShowText",
+	async beforeRegisterNodeDef(nodeType, nodeData, app) {
+		if (nodeData.name === "ShowText|pysssss") {
+			function populate(text) {
+				if (this.widgets) {
+					// On older frontend versions there is a hidden converted-widget
+					const isConvertedWidget = +!!this.inputs?.[0].widget;
+					for (let i = isConvertedWidget; i < this.widgets.length; i++) {
+						this.widgets[i].onRemove?.();
+					}
+					this.widgets.length = isConvertedWidget;
+				}
+
+				const v = [...text];
+				if (!v[0]) {
+					v.shift();
+				}
+				for (let list of v) {
+					// Force list to be an array, not sure why sometimes it is/isn't
+					if (!(list instanceof Array)) list = [list];
+					for (const l of list) {
+						const w = ComfyWidgets["STRING"](this, "text_" + this.widgets?.length ?? 0, ["STRING", { multiline: true }], app).widget;
+						w.inputEl.readOnly = true;
+						w.inputEl.style.opacity = 0.6;
+						w.value = l;
+					}
+				}
+
+				requestAnimationFrame(() => {
+					const sz = this.computeSize();
+					if (sz[0] < this.size[0]) {
+						sz[0] = this.size[0];
+					}
+					if (sz[1] < this.size[1]) {
+						sz[1] = this.size[1];
+					}
+					this.onResize?.(sz);
+					app.graph.setDirtyCanvas(true, false);
+				});
+			}
+
+			// When the node is executed we will be sent the input text, display this in the widget
+			const onExecuted = nodeType.prototype.onExecuted;
+			nodeType.prototype.onExecuted = function (message) {
+				onExecuted?.apply(this, arguments);
+				populate.call(this, message.text);
+			};
+
+			const VALUES = Symbol();
+			const configure = nodeType.prototype.configure;
+			nodeType.prototype.configure = function () {
+				// Store unmodified widget values as they get removed on configure by new frontend
+				this[VALUES] = arguments[0]?.widgets_values;
+				return configure?.apply(this, arguments);
+			};
+
+			const onConfigure = nodeType.prototype.onConfigure;
+			nodeType.prototype.onConfigure = function () {
+				onConfigure?.apply(this, arguments);
+				const widgets_values = this[VALUES];
+				if (widgets_values?.length) {
+					// In newer frontend there seems to be a delay in creating the initial widget
+					requestAnimationFrame(() => {
+						populate.call(this, widgets_values.slice(+(widgets_values.length > 1 && this.inputs?.[0].widget)));
+					});
+				}
+			};
+		}
+	},
+});
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/snapToGrid.js b/custom_nodes/comfyui-custom-scripts/web/js/snapToGrid.js
new file mode 100644
index 0000000000000000000000000000000000000000..07d5aa8c7bec97e2e14a8470fc8df2e82c14c39d
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/snapToGrid.js
@@ -0,0 +1,73 @@
+import { app } from "../../../scripts/app.js";
+
+let setting;
+const id = "pysssss.SnapToGrid";
+
+/** Wraps the provided function call to set/reset shiftDown when setting is enabled. */
+function wrapCallInSettingCheck(fn) {
+	if (setting?.value) {
+		const shift = app.shiftDown;
+		app.shiftDown = true;
+		const r = fn();
+		app.shiftDown = shift;
+		return r;
+	}
+	return fn();
+}
+
+const ext = {
+	name: id,
+	init() {
+		setting = app.ui.settings.addSetting({
+			id,
+			name: "🐍 Always snap to grid",
+			defaultValue: false,
+			type: "boolean",
+			onChange(value) {
+				app.canvas.align_to_grid = value;
+			},
+		});
+
+		// We need to register our hooks after the core snap to grid extension runs
+		// Do this from the graph configure function so we still get onNodeAdded calls
+		const configure = LGraph.prototype.configure;
+		LGraph.prototype.configure = function () {
+			// Override drawNode to draw the drop position
+			const drawNode = LGraphCanvas.prototype.drawNode;
+			LGraphCanvas.prototype.drawNode = function () {
+				wrapCallInSettingCheck(() => drawNode.apply(this, arguments));
+			};
+
+			// Override node added to add a resize handler to force grid alignment
+			const onNodeAdded = app.graph.onNodeAdded;
+			app.graph.onNodeAdded = function (node) {
+				const r = onNodeAdded?.apply(this, arguments);
+				const onResize = node.onResize;
+				node.onResize = function () {
+					wrapCallInSettingCheck(() => onResize?.apply(this, arguments));
+				};
+				return r;
+			};
+
+
+			const groupMove = LGraphGroup.prototype.move;
+			LGraphGroup.prototype.move = function(deltax, deltay, ignore_nodes) {
+				wrapCallInSettingCheck(() => groupMove.apply(this, arguments));
+			}
+
+			const canvasDrawGroups = LGraphCanvas.prototype.drawGroups;
+			LGraphCanvas.prototype.drawGroups = function (canvas, ctx) {
+				wrapCallInSettingCheck(() => canvasDrawGroups.apply(this, arguments));
+			}
+
+			const canvasOnGroupAdd = LGraphCanvas.onGroupAdd;
+			LGraphCanvas.onGroupAdd = function() {
+				wrapCallInSettingCheck(() => canvasOnGroupAdd.apply(this, arguments));
+			}
+
+			return configure.apply(this, arguments);
+		};
+	},
+};
+
+app.registerExtension(ext);
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/snapToGridGuide.js b/custom_nodes/comfyui-custom-scripts/web/js/snapToGridGuide.js
new file mode 100644
index 0000000000000000000000000000000000000000..54a8a0c70b2705620be0c63c2cecdb3ea032420f
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/snapToGridGuide.js
@@ -0,0 +1,163 @@
+import { app } from "../../../scripts/app.js";
+import { $el } from "../../../scripts/ui.js";
+
+let guide_config;
+const id = "pysssss.SnapToGrid.Guide";
+const guide_config_default = {
+	lines: {
+		enabled: false,
+		fillStyle: "rgba(255, 0, 0, 0.5)",
+	},
+	block: {
+		enabled: false,
+		fillStyle: "rgba(0, 0, 255, 0.5)",
+	},
+}
+
+const ext = {
+	name: id,
+	init() {
+		if (localStorage.getItem(id) === null) {
+			localStorage.setItem(id, JSON.stringify(guide_config_default));
+		}
+		guide_config = JSON.parse(localStorage.getItem(id));
+
+		app.ui.settings.addSetting({
+			id,
+			name: "🐍 Display drag-and-drop guides",
+			type: (name, setter, value) => {
+				return $el("tr", [
+					$el("td", [
+						$el("label", {
+							for: id.replaceAll(".", "-"),
+							textContent: name,
+						}),
+					]),
+					$el("td", [
+						$el(
+							"label",
+							{
+								textContent: "Lines: ",
+								style: {
+									display: "inline-block",
+								},
+							},
+							[
+								$el("input", {
+									id: id.replaceAll(".", "-") + "-line-text",
+									type: "text",
+									value: guide_config.lines.fillStyle,
+									onchange: (event) => {
+										guide_config.lines.fillStyle = event.target.value;
+										localStorage.setItem(id, JSON.stringify(guide_config));
+									}
+								}),
+								$el("input", {
+									id: id.replaceAll(".", "-") + "-line-checkbox",
+									type: "checkbox",
+									checked: guide_config.lines.enabled,
+									onchange: (event) => {
+										guide_config.lines.enabled = !!event.target.checked;
+										localStorage.setItem(id, JSON.stringify(guide_config));
+									},
+								}),
+							]
+						),
+						$el(
+							"label",
+							{
+								textContent: "Block: ",
+								style: {
+									display: "inline-block",
+								},
+							},
+							[
+								$el("input", {
+									id: id.replaceAll(".", "-") + "-block-text",
+									type: "text",
+									value: guide_config.block.fillStyle,
+									onchange: (event) => {
+										guide_config.block.fillStyle = event.target.value;
+										localStorage.setItem(id, JSON.stringify(guide_config));
+									}
+								}),
+								$el("input", {
+									id: id.replaceAll(".", "-") + '-block-checkbox',
+									type: "checkbox",
+									checked: guide_config.block.enabled,
+									onchange: (event) => {
+										guide_config.block.enabled = !!event.target.checked;
+										localStorage.setItem(id, JSON.stringify(guide_config));
+									},
+								}),
+							]
+						),
+					]),
+				]);
+			}
+		});
+
+		const alwaysSnapToGrid = () =>
+			app.ui.settings.getSettingValue("pysssss.SnapToGrid", /* default=*/ false);
+		const snapToGridEnabled = () =>
+			app.shiftDown || alwaysSnapToGrid();
+
+		// Override drag-and-drop behavior to show orthogonal guide lines around selected node(s) and preview of where the node(s) will be placed
+		const origDrawNode = LGraphCanvas.prototype.drawNode;
+		LGraphCanvas.prototype.drawNode = function (node, ctx) {
+			const enabled = guide_config.lines.enabled || guide_config.block.enabled;
+			if (enabled && this.node_dragged && node.id in this.selected_nodes && snapToGridEnabled()) {
+				// discretize the canvas into grid
+				let x = LiteGraph.CANVAS_GRID_SIZE * Math.round(node.pos[0] / LiteGraph.CANVAS_GRID_SIZE);
+				let y = LiteGraph.CANVAS_GRID_SIZE * Math.round(node.pos[1] / LiteGraph.CANVAS_GRID_SIZE);
+
+				// calculate the width and height of the node
+				// (also need to shift the y position of the node, depending on whether the title is visible)
+				x -= node.pos[0];
+				y -= node.pos[1];
+				let w, h;
+				if (node.flags.collapsed) {
+					w = node._collapsed_width;
+					h = LiteGraph.NODE_TITLE_HEIGHT;
+					y -= LiteGraph.NODE_TITLE_HEIGHT;
+				} else {
+					w = node.size[0];
+					h = node.size[1];
+					let titleMode = node.constructor.title_mode;
+					if (titleMode !== LiteGraph.TRANSPARENT_TITLE && titleMode !== LiteGraph.NO_TITLE) {
+						h += LiteGraph.NODE_TITLE_HEIGHT;
+						y -= LiteGraph.NODE_TITLE_HEIGHT;
+					}
+				}
+
+				// save the original fill style
+				const f = ctx.fillStyle;
+
+				// draw preview for drag-and-drop (rectangle to show where the node will be placed)
+				if (guide_config.block.enabled) {
+					ctx.fillStyle = guide_config.block.fillStyle;
+					ctx.fillRect(x, y, w, h);
+				}
+
+				// add guide lines around node (arbitrarily long enough to span most workflows)
+				if (guide_config.lines.enabled) {
+					const xd = 10000;
+					const yd = 10000;
+					const thickness = 3;
+					ctx.fillStyle = guide_config.lines.fillStyle;
+					ctx.fillRect(x - xd, y, 2*xd, thickness);
+					ctx.fillRect(x, y - yd, thickness, 2*yd);
+					ctx.fillRect(x - xd, y + h, 2*xd, thickness);
+					ctx.fillRect(x + w, y - yd, thickness, 2*yd);
+				}
+
+				// restore the original fill style
+				ctx.fillStyle = f;
+			}
+
+			return origDrawNode.apply(this, arguments);
+		};
+	},
+};
+
+app.registerExtension(ext);
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/stringFunction.js b/custom_nodes/comfyui-custom-scripts/web/js/stringFunction.js
new file mode 100644
index 0000000000000000000000000000000000000000..cc150dff04a2e94b9bc768f6331964c13a96ddbe
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/stringFunction.js
@@ -0,0 +1,33 @@
+import { app } from "../../../scripts/app.js";
+import { ComfyWidgets } from "../../../scripts/widgets.js";
+
+// Displays input text on a node
+
+app.registerExtension({
+	name: "pysssss.StringFunction",
+	async beforeRegisterNodeDef(nodeType, nodeData, app) {
+		if (nodeData.name === "StringFunction|pysssss") {
+			const onExecuted = nodeType.prototype.onExecuted;
+			nodeType.prototype.onExecuted = function (message) {
+				onExecuted?.apply(this, arguments);
+
+				if (this.widgets) {
+					const pos = this.widgets.findIndex((w) => w.name === "result");
+					if (pos !== -1) {
+						for (let i = pos; i < this.widgets.length; i++) {
+							this.widgets[i].onRemove?.();
+						}
+						this.widgets.length = pos;
+					}
+				}
+
+				const w = ComfyWidgets["STRING"](this, "result", ["STRING", { multiline: true }], app).widget;
+				w.inputEl.readOnly = true;
+				w.inputEl.style.opacity = 0.6;
+				w.value = message.text;
+
+				this.onResize?.(this.size);
+			};
+		}
+	},
+});
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/swapResolution.js b/custom_nodes/comfyui-custom-scripts/web/js/swapResolution.js
new file mode 100644
index 0000000000000000000000000000000000000000..40f6dcc851780077c79e7e142fd0f97104e85a09
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/swapResolution.js
@@ -0,0 +1,30 @@
+import { app } from "../../../scripts/app.js";
+app.registerExtension({
+	name: "pysssss.SwapResolution",
+	async beforeRegisterNodeDef(nodeType, nodeData) {
+		const inputs = { ...nodeData.input?.required, ...nodeData.input?.optional };
+		if (inputs.width && inputs.height) {
+			const origGetExtraMenuOptions = nodeType.prototype.getExtraMenuOptions;
+			nodeType.prototype.getExtraMenuOptions = function (_, options) {
+				const r = origGetExtraMenuOptions?.apply?.(this, arguments);
+
+				options.push(
+					{
+						content: "Swap width/height",
+						callback: () => {
+							const w = this.widgets.find((w) => w.name === "width");
+							const h = this.widgets.find((w) => w.name === "height");
+							const a = w.value;
+							w.value = h.value;
+							h.value = a;
+							app.graph.setDirtyCanvas(true);
+						},
+					},
+					null
+				);
+
+				return r;
+			};
+		}
+	},
+});
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/systemNotification.js b/custom_nodes/comfyui-custom-scripts/web/js/systemNotification.js
new file mode 100644
index 0000000000000000000000000000000000000000..803b8c90629f6722efd090e0bf5839e395c4402b
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/systemNotification.js
@@ -0,0 +1,47 @@
+import { app } from "../../../scripts/app.js";
+
+const notificationSetup = () => {
+	if (!("Notification" in window)) {
+		console.log("This browser does not support notifications.");
+		alert("This browser does not support notifications.");
+		return;
+	}
+	if (Notification.permission === "denied") {
+		console.log("Notifications are blocked. Please enable them in your browser settings.");
+		alert("Notifications are blocked. Please enable them in your browser settings.");
+		return;
+	}
+	if (Notification.permission !== "granted") {
+		Notification.requestPermission();
+	}
+	return true;
+};
+
+app.registerExtension({
+	name: "pysssss.SystemNotification",
+	async beforeRegisterNodeDef(nodeType, nodeData, app) {
+		if (nodeData.name === "SystemNotification|pysssss") {
+			const onExecuted = nodeType.prototype.onExecuted;
+			nodeType.prototype.onExecuted = async function ({ message, mode }) {
+				onExecuted?.apply(this, arguments);
+
+				if (mode === "on empty queue") {
+					if (app.ui.lastQueueSize !== 0) {
+						await new Promise((r) => setTimeout(r, 500));
+					}
+					if (app.ui.lastQueueSize !== 0) {
+						return;
+					}
+				}
+				if (!notificationSetup()) return;
+				const notification = new Notification("ComfyUI", { body: message ?? "Your notification has triggered." });
+			};
+
+			const onNodeCreated = nodeType.prototype.onNodeCreated;
+			nodeType.prototype.onNodeCreated = function () {
+				onNodeCreated?.apply(this, arguments);
+				notificationSetup();
+			};
+		}
+	},
+});
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/useNumberInputPrompt.js b/custom_nodes/comfyui-custom-scripts/web/js/useNumberInputPrompt.js
new file mode 100644
index 0000000000000000000000000000000000000000..a92aa51095bc474b3cf80697aa238ad8814b4c3d
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/useNumberInputPrompt.js
@@ -0,0 +1,36 @@
+import { app } from "../../../scripts/app.js";
+
+const id = "pysssss.UseNumberInputPrompt";
+const ext = {
+	name: id,
+	async setup(app) {
+		const prompt = LGraphCanvas.prototype.prompt;
+
+		const setting = app.ui.settings.addSetting({
+			id,
+			name: "🐍 Use number input on value entry",
+			defaultValue: false,
+			type: "boolean",
+		});
+
+		LGraphCanvas.prototype.prompt = function () {
+			const dialog = prompt.apply(this, arguments);
+			if (setting.value && typeof arguments[1] === "number") {
+				// If this should be a number then update the imput
+				const input = dialog.querySelector("input");
+				input.type = "number";
+
+				// Add constraints
+				const widget = app.canvas.node_widget?.[1];
+				if (widget?.options) {
+					for (const prop of ["min", "max", "step"]) {
+						if (widget.options[prop]) input[prop] = widget.options[prop];
+					}
+				}
+			}
+			return dialog;
+		};
+	},
+};
+
+app.registerExtension(ext);
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/widgetDefaults.js b/custom_nodes/comfyui-custom-scripts/web/js/widgetDefaults.js
new file mode 100644
index 0000000000000000000000000000000000000000..859749d16fda455bcdfb4a6fba89a7340c839183
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/widgetDefaults.js
@@ -0,0 +1,297 @@
+import { app } from "../../../scripts/app.js";
+import { $el, ComfyDialog } from "../../../scripts/ui.js";
+
+// Allows you to specify custom default values for any widget on any node
+
+const id = "pysssss.WidgetDefaults";
+const nodeDataKey = Symbol();
+
+app.registerExtension({
+	name: id,
+	beforeRegisterNodeDef(nodeType, nodeData) {
+		nodeType[nodeDataKey] = nodeData;
+	},
+	setup() {
+		let defaults;
+		let regexDefaults;
+		let setting;
+
+		const getNodeDefaults = (node, defaults) => {
+			const nodeDefaults = defaults[node.type] ?? {};
+			const propSetBy = {};
+
+			Object.keys(regexDefaults)
+				.filter((r) => new RegExp(r).test(node.type))
+				.reduce((p, n) => {
+					const props = regexDefaults[n];
+					for (const k in props) {
+						// Use the longest matching key as its probably the most specific
+						if (!(k in nodeDefaults) || (k in propSetBy && n.length > propSetBy[k].length)) {
+							propSetBy[k] = n;
+							nodeDefaults[k] = props[k];
+						}
+					}
+					return p;
+				}, nodeDefaults);
+
+			return nodeDefaults;
+		};
+
+		const applyDefaults = (defaults) => {
+			for (const node of Object.values(LiteGraph.registered_node_types)) {
+				const nodeData = node[nodeDataKey];
+				if (!nodeData) continue;
+				const nodeDefaults = getNodeDefaults(node, defaults);
+				if (!nodeDefaults) continue;
+				const inputs = { ...(nodeData.input?.required || {}), ...(nodeData.input?.optional || {}) };
+
+				for (const w in nodeDefaults) {
+					const widgetDef = inputs[w];
+					if (widgetDef) {
+						let v = nodeDefaults[w];
+						if (widgetDef[0] === "INT" || widgetDef[0] === "FLOAT") {
+							v = +v;
+						}
+						if (widgetDef[1]) {
+							widgetDef[1].default = v;
+						} else {
+							widgetDef[1] = { default: v };
+						}
+					}
+				}
+			}
+		};
+
+		const getDefaults = () => {
+			let items;
+			regexDefaults = {};
+			try {
+				items = JSON.parse(setting.value);
+				items = items.reduce((p, n) => {
+					if (n.node.startsWith("/") && n.node.endsWith("/")) {
+						const name = n.node.substring(1, n.node.length - 1);
+						try {
+							// Validate regex
+							new RegExp(name);
+
+							if (!regexDefaults[name]) regexDefaults[name] = {};
+							regexDefaults[name][n.widget] = n.value;
+						} catch (error) {}
+					}
+
+					if (!p[n.node]) p[n.node] = {};
+					p[n.node][n.widget] = n.value;
+					return p;
+				}, {});
+			} catch (error) {}
+			if (!items) {
+				items = {};
+			}
+			applyDefaults(items);
+			return items;
+		};
+
+		const onNodeAdded = app.graph.onNodeAdded;
+		app.graph.onNodeAdded = function (node) {
+			onNodeAdded?.apply?.(this, arguments);
+
+			// See if we have any defaults for this type of node
+			const nodeDefaults = getNodeDefaults(node.constructor, defaults);
+			if (!nodeDefaults) return;
+
+			// Dont run if they are pre-configured nodes from load/pastes
+			const stack = new Error().stack;
+			if (stack.includes("pasteFromClipboard") || stack.includes("loadGraphData")) {
+				return;
+			}
+
+			for (const k in nodeDefaults) {
+				if (k.startsWith("property.")) {
+					const name = k.substring(9);
+					let v = nodeDefaults[k];
+					// Special handling for some built in values
+					if (name in node || ["color", "bgcolor", "title"].includes(name)) {
+						node[name] = v;
+					} else {
+						// Try using the correct type
+						if (!node.properties) node.properties = {};
+						if (typeof node.properties[name] === "number") v = +v;
+						else if (typeof node.properties[name] === "boolean") v = v === "true";
+						else if (v === "true") v = true;
+
+						node.properties[name] = v;
+					}
+				}
+			}
+		};
+
+		class WidgetDefaultsDialog extends ComfyDialog {
+			constructor() {
+				super();
+				this.element.classList.add("comfy-manage-templates");
+				this.grid = $el(
+					"div",
+					{
+						style: {
+							display: "grid",
+							gridTemplateColumns: "1fr auto auto auto",
+							gap: "5px",
+						},
+						className: "pysssss-widget-defaults",
+					},
+					[
+						$el("label", {
+							textContent: "Node Class",
+						}),
+						$el("label", {
+							textContent: "Widget Name",
+						}),
+						$el("label", {
+							textContent: "Default Value",
+						}),
+						$el("label"),
+						(this.rows = $el("div", {
+							style: {
+								display: "contents",
+							},
+						})),
+					]
+				);
+			}
+
+			createButtons() {
+				const btns = super.createButtons();
+				btns[0].textContent = "Cancel";
+				btns.unshift(
+					$el("button", {
+						type: "button",
+						textContent: "Add New",
+						onclick: () => this.addRow(),
+					}),
+					$el("button", {
+						type: "button",
+						textContent: "Save",
+						onclick: () => this.save(),
+					})
+				);
+				return btns;
+			}
+
+			addRow(node = "", widget = "", value = "") {
+				let nameInput;
+				this.rows.append(
+					$el(
+						"div",
+						{
+							style: {
+								display: "contents",
+							},
+							className: "pysssss-widget-defaults-row",
+						},
+						[
+							$el("input", {
+								placeholder: "e.g. CheckpointLoaderSimple",
+								value: node,
+							}),
+							$el("input", {
+								placeholder: "e.g. ckpt_name",
+								value: widget,
+								$: (el) => (nameInput = el),
+							}),
+							$el("input", {
+								placeholder: "e.g. myBestModel.safetensors",
+								value,
+							}),
+							$el("button", {
+								textContent: "Delete",
+								style: {
+									fontSize: "12px",
+									color: "red",
+									fontWeight: "normal",
+								},
+								onclick: (e) => {
+									nameInput.value = "";
+									e.target.parentElement.style.display = "none";
+								},
+							}),
+						]
+					)
+				);
+			}
+
+			save() {
+				const rows = this.rows.children;
+				const items = [];
+
+				for (const row of rows) {
+					const inputs = row.querySelectorAll("input");
+					const node = inputs[0].value.trim();
+					const widget = inputs[1].value.trim();
+					const value = inputs[2].value;
+					if (node && widget) {
+						items.push({ node, widget, value });
+					}
+				}
+
+				setting.value = JSON.stringify(items);
+				defaults = getDefaults();
+
+				this.close();
+			}
+
+			show() {
+				this.rows.replaceChildren();
+				for (const nodeName in defaults) {
+					const node = defaults[nodeName];
+					for (const widgetName in node) {
+						this.addRow(nodeName, widgetName, node[widgetName]);
+					}
+				}
+
+				this.addRow();
+				super.show(this.grid);
+			}
+		}
+
+		setting = app.ui.settings.addSetting({
+			id,
+			name: "🐍 Widget Defaults",
+			type: () => {
+				return $el("tr", [
+					$el("td", [
+						$el("label", {
+							for: id.replaceAll(".", "-"),
+							textContent: "🐍 Widget & Property Defaults:",
+						}),
+					]),
+					$el("td", [
+						$el("button", {
+							textContent: "Manage",
+							onclick: () => {
+								try {
+									// Try closing old settings window
+									if (typeof app.ui.settings.element?.close === "function") {
+										app.ui.settings.element.close();
+									}
+								} catch (error) {}
+								try {
+									// Try closing new vue dialog
+									document.querySelector(".p-dialog-close-button").click();
+								} catch (error) {
+									// Fallback to just hiding the element
+									app.ui.settings.element.style.display = "none";
+								}
+								const dialog = new WidgetDefaultsDialog();
+								dialog.show();
+							},
+							style: {
+								fontSize: "14px",
+							},
+						}),
+					]),
+				]);
+			},
+		});
+		defaults = getDefaults();
+	},
+});
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/workflowImage.js b/custom_nodes/comfyui-custom-scripts/web/js/workflowImage.js
new file mode 100644
index 0000000000000000000000000000000000000000..37c3ad06c6272c1a8b1cab264515285412cec0d2
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/workflowImage.js
@@ -0,0 +1,640 @@
+import { app } from "../../../scripts/app.js";
+import { importA1111 } from "../../../scripts/pnginfo.js";
+import { ComfyWidgets } from "../../../scripts/widgets.js";
+
+let getDrawTextConfig = null;
+let fileInput;
+
+class WorkflowImage {
+	static accept = "";
+
+	getBounds() {
+		// Calculate the min max bounds for the nodes on the graph
+		const bounds = app.graph._nodes.reduce(
+			(p, n) => {
+				if (n.pos[0] < p[0]) p[0] = n.pos[0];
+				if (n.pos[1] < p[1]) p[1] = n.pos[1];
+				const bounds = n.getBounding();
+				const r = n.pos[0] + bounds[2];
+				const b = n.pos[1] + bounds[3];
+				if (r > p[2]) p[2] = r;
+				if (b > p[3]) p[3] = b;
+				return p;
+			},
+			[99999, 99999, -99999, -99999]
+		);
+
+		bounds[0] -= 100;
+		bounds[1] -= 100;
+		bounds[2] += 100;
+		bounds[3] += 100;
+		return bounds;
+	}
+
+	saveState() {
+		this.state = {
+			scale: app.canvas.ds.scale,
+			width: app.canvas.canvas.width,
+			height: app.canvas.canvas.height,
+			offset: app.canvas.ds.offset,
+			transform: app.canvas.canvas.getContext("2d").getTransform(), // Save the original transformation matrix
+		};
+	}
+
+	restoreState() {
+		app.canvas.ds.scale = this.state.scale;
+		app.canvas.canvas.width = this.state.width;
+		app.canvas.canvas.height = this.state.height;
+		app.canvas.ds.offset = this.state.offset;
+		app.canvas.canvas.getContext("2d").setTransform(this.state.transform); // Reapply the original transformation matrix
+	}
+
+	updateView(bounds) {
+		const scale = window.devicePixelRatio || 1;
+		app.canvas.ds.scale = 1;
+		app.canvas.canvas.width = (bounds[2] - bounds[0]) * scale;
+		app.canvas.canvas.height = (bounds[3] - bounds[1]) * scale;
+		app.canvas.ds.offset = [-bounds[0], -bounds[1]];
+		app.canvas.canvas.getContext("2d").setTransform(scale, 0, 0, scale, 0, 0);
+	}
+
+	getDrawTextConfig(_, widget) {
+		return {
+			x: 10,
+			y: widget.last_y + 10,
+			resetTransform: false,
+		};
+	}
+
+	async export(includeWorkflow) {
+		// Save the current state of the canvas
+		this.saveState();
+		// Update to render the whole workflow
+		this.updateView(this.getBounds());
+
+		// Flag that we are saving and render the canvas
+		getDrawTextConfig = this.getDrawTextConfig;
+		app.canvas.draw(true, true);
+		getDrawTextConfig = null;
+
+		// Generate a blob of the image containing the workflow
+		const blob = await this.getBlob(includeWorkflow ? JSON.stringify(app.graph.serialize()) : undefined);
+
+		// Restore initial state and redraw
+		this.restoreState();
+		app.canvas.draw(true, true);
+
+		// Download the generated image
+		this.download(blob);
+	}
+
+	download(blob) {
+		const url = URL.createObjectURL(blob);
+		const a = document.createElement("a");
+		Object.assign(a, {
+			href: url,
+			download: "workflow." + this.extension,
+			style: "display: none",
+		});
+		document.body.append(a);
+		a.click();
+		setTimeout(function () {
+			a.remove();
+			window.URL.revokeObjectURL(url);
+		}, 0);
+	}
+
+	static import() {
+		if (!fileInput) {
+			fileInput = document.createElement("input");
+			Object.assign(fileInput, {
+				type: "file",
+				style: "display: none",
+				onchange: () => {
+					app.handleFile(fileInput.files[0]);
+				},
+			});
+			document.body.append(fileInput);
+		}
+		fileInput.accept = WorkflowImage.accept;
+		fileInput.click();
+	}
+}
+
+class PngWorkflowImage extends WorkflowImage {
+	static accept = ".png,image/png";
+	extension = "png";
+
+	n2b(n) {
+		return new Uint8Array([(n >> 24) & 0xff, (n >> 16) & 0xff, (n >> 8) & 0xff, n & 0xff]);
+	}
+
+	joinArrayBuffer(...bufs) {
+		const result = new Uint8Array(bufs.reduce((totalSize, buf) => totalSize + buf.byteLength, 0));
+		bufs.reduce((offset, buf) => {
+			result.set(buf, offset);
+			return offset + buf.byteLength;
+		}, 0);
+		return result;
+	}
+
+	crc32(data) {
+		const crcTable =
+			PngWorkflowImage.crcTable ||
+			(PngWorkflowImage.crcTable = (() => {
+				let c;
+				const crcTable = [];
+				for (let n = 0; n < 256; n++) {
+					c = n;
+					for (let k = 0; k < 8; k++) {
+						c = c & 1 ? 0xedb88320 ^ (c >>> 1) : c >>> 1;
+					}
+					crcTable[n] = c;
+				}
+				return crcTable;
+			})());
+		let crc = 0 ^ -1;
+		for (let i = 0; i < data.byteLength; i++) {
+			crc = (crc >>> 8) ^ crcTable[(crc ^ data[i]) & 0xff];
+		}
+		return (crc ^ -1) >>> 0;
+	}
+
+	async getBlob(workflow) {
+		return new Promise((r) => {
+			app.canvasEl.toBlob(async (blob) => {
+				if (workflow) {
+					// If we have a workflow embed it in the PNG
+					const buffer = await blob.arrayBuffer();
+					const typedArr = new Uint8Array(buffer);
+					const view = new DataView(buffer);
+
+					const data = new TextEncoder().encode(`tEXtworkflow\0${workflow}`);
+					const chunk = this.joinArrayBuffer(this.n2b(data.byteLength - 4), data, this.n2b(this.crc32(data)));
+
+					const sz = view.getUint32(8) + 20;
+					const result = this.joinArrayBuffer(typedArr.subarray(0, sz), chunk, typedArr.subarray(sz));
+
+					blob = new Blob([result], { type: "image/png" });
+				}
+
+				r(blob);
+			});
+		});
+	}
+}
+
+class DataReader {
+	/**	@type {DataView} */
+	view;
+	/** @type {boolean | undefined} */
+	littleEndian;
+	offset = 0;
+
+	/**
+	 * @param {DataView} view
+	 */
+	constructor(view) {
+		this.view = view;
+	}
+
+	/**
+	 * Reads N bytes and increments the offset
+	 * @param {1 | 2 | 4 | 8} size
+	 */
+	read(size, signed = false, littleEndian = undefined) {
+		const v = this.peek(size, signed, littleEndian);
+		this.offset += size;
+		return v;
+	}
+
+	/**
+	 * Reads N bytes
+	 * @param {1 | 2 | 4 | 8} size
+	 */
+	peek(size, signed = false, littleEndian = undefined) {
+		this.view.getBigInt64;
+		let m = "";
+		if (size === 8) m += "Big";
+		m += signed ? "Int" : "Uint";
+		m += size * 8;
+		m = "get" + m;
+		if (!this.view[m]) {
+			throw new Error("Method not found: " + m);
+		}
+
+		return this.view[m](this.offset, littleEndian == null ? this.littleEndian : littleEndian);
+	}
+
+	/**
+	 * Seeks to the specified position or by the number of bytes specified relative to the current offset
+	 * @param {number} pos
+	 * @param {boolean} relative
+	 */
+	seek(pos, relative = true) {
+		if (relative) {
+			this.offset += pos;
+		} else {
+			this.offset = pos;
+		}
+	}
+}
+
+class Tiff {
+	/** @type {DataReader} */
+	#reader;
+	#start;
+
+	readExif(reader) {
+		const TIFF_MARKER = 0x2a;
+		const EXIF_IFD = 0x8769;
+
+		this.#reader = reader;
+		this.#start = this.#reader.offset;
+		this.#readEndianness();
+
+		if (!this.#reader.read(2) === TIFF_MARKER) {
+			throw new Error("Invalid TIFF: Marker not found.");
+		}
+
+		const dirOffset = this.#reader.read(4);
+		this.#reader.seek(this.#start + dirOffset, false);
+
+		for (const t of this.#readTags()) {
+			if (t.id === EXIF_IFD) {
+				return this.#readExifTag(t);
+			}
+		}
+		throw new Error("No EXIF: TIFF Exif IFD tag not found");
+	}
+
+	#readUserComment(tag) {
+		this.#reader.seek(this.#start + tag.offset, false);
+		const encoding = this.#reader.read(8);
+		if (encoding !== 0x45444f43494e55n) {
+			throw new Error("Unable to read non-Unicode data");
+		}
+		const decoder = new TextDecoder("utf-16be");
+		return decoder.decode(new DataView(this.#reader.view.buffer, this.#reader.offset, tag.count - 8));
+	}
+
+	#readExifTag(exifTag) {
+		const EXIF_USER_COMMENT = 0x9286;
+
+		this.#reader.seek(this.#start + exifTag.offset, false);
+		for (const t of this.#readTags()) {
+			if (t.id === EXIF_USER_COMMENT) {
+				return this.#readUserComment(t);
+			}
+		}
+		throw new Error("No embedded data: UserComment Exif tag not found");
+	}
+
+	*#readTags() {
+		const count = this.#reader.read(2);
+		for (let i = 0; i < count; i++) {
+			yield {
+				id: this.#reader.read(2),
+				type: this.#reader.read(2),
+				count: this.#reader.read(4),
+				offset: this.#reader.read(4),
+			};
+		}
+	}
+
+	#readEndianness() {
+		const II = 0x4949;
+		const MM = 0x4d4d;
+		const endianness = this.#reader.read(2);
+		if (endianness === II) {
+			this.#reader.littleEndian = true;
+		} else if (endianness === MM) {
+			this.#reader.littleEndian = false;
+		} else {
+			throw new Error("Invalid JPEG: Endianness marker not found.");
+		}
+	}
+}
+
+class Jpeg {
+	/** @type {DataReader} */
+	#reader;
+
+	/**
+	 * @param {ArrayBuffer} buffer
+	 */
+	readExif(buffer) {
+		const JPEG_MARKER = 0xffd8;
+		const EXIF_SIG = 0x45786966;
+
+		this.#reader = new DataReader(new DataView(buffer));
+		if (!this.#reader.read(2) === JPEG_MARKER) {
+			throw new Error("Invalid JPEG: SOI not found.");
+		}
+
+		const app0 = this.#readAppMarkerId();
+		if (app0 !== 0) {
+			throw new Error(`Invalid JPEG: APP0 not found [found: ${app0}].`);
+		}
+
+		this.#consumeAppSegment();
+		const app1 = this.#readAppMarkerId();
+		if (app1 !== 1) {
+			throw new Error(`No EXIF: APP1 not found [found: ${app0}].`);
+		}
+
+		// Skip size
+		this.#reader.seek(2);
+
+		if (this.#reader.read(4) !== EXIF_SIG) {
+			throw new Error(`No EXIF: Invalid EXIF header signature.`);
+		}
+		if (this.#reader.read(2) !== 0) {
+			throw new Error(`No EXIF: Invalid EXIF header.`);
+		}
+
+		return new Tiff().readExif(this.#reader);
+	}
+
+	#readAppMarkerId() {
+		const APP0_MARKER = 0xffe0;
+		return this.#reader.read(2) - APP0_MARKER;
+	}
+
+	#consumeAppSegment() {
+		this.#reader.seek(this.#reader.read(2) - 2);
+	}
+}
+
+class SvgWorkflowImage extends WorkflowImage {
+	static accept = ".svg,image/svg+xml";
+	extension = "svg";
+
+	static init() {
+		// Override file handling to allow drag & drop of SVG
+		const handleFile = app.handleFile;
+		app.handleFile = async function (file) {
+			if (file && (file.type === "image/svg+xml" || file.name?.endsWith(".svg"))) {
+				const reader = new FileReader();
+				reader.onload = () => {
+					// Extract embedded workflow from desc tags
+					const descEnd = reader.result.lastIndexOf("</desc>");
+					if (descEnd !== -1) {
+						const descStart = reader.result.lastIndexOf("<desc>", descEnd);
+						if (descStart !== -1) {
+							const json = reader.result.substring(descStart + 6, descEnd);
+							this.loadGraphData(JSON.parse(SvgWorkflowImage.unescapeXml(json)));
+						}
+					}
+				};
+				reader.readAsText(file);
+				return;
+			} else if (file && (file.type === "image/jpeg" || file.name?.endsWith(".jpg") || file.name?.endsWith(".jpeg"))) {
+				if (
+					await new Promise((resolve) => {
+						try {
+							// This shouldnt go in here but it's easier than refactoring handleFile
+							const reader = new FileReader();
+							reader.onload = async () => {
+								try {
+									const value = new Jpeg().readExif(reader.result);
+									importA1111(app.graph, value);
+									resolve(true);
+								} catch (error) {
+									resolve(false);
+								}
+							};
+							reader.onerror = () => resolve(false);
+							reader.readAsArrayBuffer(file);
+						} catch (error) {
+							resolve(false);
+						}
+					})
+				) {
+					return;
+				}
+			}
+			return handleFile.apply(this, arguments);
+		};
+	}
+
+	static escapeXml(unsafe) {
+		return unsafe.replaceAll("&", "&amp;").replaceAll("<", "&lt;").replaceAll(">", "&gt;");
+	}
+
+	static unescapeXml(safe) {
+		return safe.replaceAll("&amp;", "&").replaceAll("&lt;", "<").replaceAll("&gt;", ">");
+	}
+
+	getDrawTextConfig(_, widget) {
+		const domWrapper = widget.inputEl.closest(".dom-widget") ?? widget.inputEl;
+		return {
+			x: parseInt(domWrapper.style.left),
+			y: parseInt(domWrapper.style.top),
+			resetTransform: true,
+		};
+	}
+
+	saveState() {
+		super.saveState();
+		this.state.ctx = app.canvas.ctx;
+	}
+
+	restoreState() {
+		super.restoreState();
+		app.canvas.ctx = this.state.ctx;
+	}
+
+	updateView(bounds) {
+		super.updateView(bounds);
+		this.createSvgCtx(bounds);
+	}
+
+	createSvgCtx(bounds) {
+		const ctx = this.state.ctx;
+		const svgCtx = (this.svgCtx = new C2S(bounds[2] - bounds[0], bounds[3] - bounds[1]));
+		svgCtx.canvas.getBoundingClientRect = function () {
+			return { width: svgCtx.width, height: svgCtx.height };
+		};
+
+		// Override the c2s handling of images to draw images as canvases
+		const drawImage = svgCtx.drawImage;
+		svgCtx.drawImage = function (...args) {
+			const image = args[0];
+			// If we are an image node and not a datauri then we need to replace with a canvas
+			// we cant convert to data uri here as it is an async process
+			if (image.nodeName === "IMG" && !image.src.startsWith("data:image/")) {
+				const canvas = document.createElement("canvas");
+				canvas.width = image.width;
+				canvas.height = image.height;
+				const imgCtx = canvas.getContext("2d");
+				imgCtx.drawImage(image, 0, 0);
+				args[0] = canvas;
+			}
+
+			return drawImage.apply(this, args);
+		};
+
+		// Implement missing required functions
+		svgCtx.getTransform = function () {
+			return ctx.getTransform();
+		};
+		svgCtx.resetTransform = function () {
+			return ctx.resetTransform();
+		};
+		svgCtx.roundRect = svgCtx.rect;
+		app.canvas.ctx = svgCtx;
+	}
+
+	getBlob(workflow) {
+		let svg = this.svgCtx.getSerializedSvg(true).replace("<svg ", `<svg style="background: ${app.canvas.clear_background_color}" `);
+
+		if (workflow) {
+			svg = svg.replace("</svg>", `<desc>${SvgWorkflowImage.escapeXml(workflow)}</desc></svg>`);
+		}
+
+		return new Blob([svg], { type: "image/svg+xml" });
+	}
+}
+
+app.registerExtension({
+	name: "pysssss.WorkflowImage",
+	init() {
+		// https://codepen.io/peterhry/pen/nbMaYg
+		function wrapText(context, text, x, y, maxWidth, lineHeight) {
+			var words = text.split(" "),
+				line = "",
+				i,
+				test,
+				metrics;
+
+			for (i = 0; i < words.length; i++) {
+				test = words[i];
+				metrics = context.measureText(test);
+				while (metrics.width > maxWidth) {
+					// Determine how much of the word will fit
+					test = test.substring(0, test.length - 1);
+					metrics = context.measureText(test);
+				}
+				if (words[i] != test) {
+					words.splice(i + 1, 0, words[i].substr(test.length));
+					words[i] = test;
+				}
+
+				test = line + words[i] + " ";
+				metrics = context.measureText(test);
+
+				if (metrics.width > maxWidth && i > 0) {
+					context.fillText(line, x, y);
+					line = words[i] + " ";
+					y += lineHeight;
+				} else {
+					line = test;
+				}
+			}
+
+			context.fillText(line, x, y);
+		}
+
+		const stringWidget = ComfyWidgets.STRING;
+		// Override multiline string widgets to draw text using canvas while saving as svg
+		ComfyWidgets.STRING = function () {
+			const w = stringWidget.apply(this, arguments);
+			if (w.widget && w.widget.type === "customtext") {
+				const draw = w.widget.draw;
+				w.widget.draw = function (ctx) {
+					draw.apply(this, arguments);
+					if (this.inputEl.hidden) return;
+
+					if (getDrawTextConfig) {
+						const config = getDrawTextConfig(ctx, this);
+						const t = ctx.getTransform();
+						ctx.save();
+						if (config.resetTransform) {
+							ctx.resetTransform();
+						}
+
+						const style = document.defaultView.getComputedStyle(this.inputEl, null);
+						const x = config.x;
+						const y = config.y;
+						const domWrapper = this.inputEl.closest(".dom-widget") ?? widget.inputEl;
+						let w = parseInt(domWrapper.style.width);
+						if (w === 0) {
+							w = this.node.size[0] - 20;
+						}
+						const h = parseInt(domWrapper.style.height);
+						ctx.fillStyle = style.getPropertyValue("background-color");
+						ctx.fillRect(x, y, w, h);
+
+						ctx.fillStyle = style.getPropertyValue("color");
+						ctx.font = style.getPropertyValue("font");
+
+						const line = t.d * 12;
+						const split = this.inputEl.value.split("\n");
+						let start = y;
+						for (const l of split) {
+							start += line;
+							wrapText(ctx, l, x + 4, start, w, line);
+						}
+
+						ctx.restore();
+					}
+				};
+			}
+			return w;
+		};
+	},
+	setup() {
+		const script = document.createElement("script");
+		script.onload = function () {
+			const formats = [SvgWorkflowImage, PngWorkflowImage];
+			for (const f of formats) {
+				f.init?.call();
+				WorkflowImage.accept += (WorkflowImage.accept ? "," : "") + f.accept;
+			}
+
+			// Add canvas menu options
+			const orig = LGraphCanvas.prototype.getCanvasMenuOptions;
+			LGraphCanvas.prototype.getCanvasMenuOptions = function () {
+				const options = orig.apply(this, arguments);
+
+				options.push(null, {
+					content: "Workflow Image",
+					submenu: {
+						options: [
+							{
+								content: "Import",
+								callback: () => {
+									WorkflowImage.import();
+								},
+							},
+							{
+								content: "Export",
+								submenu: {
+									options: formats.flatMap((f) => [
+										{
+											content: f.name.replace("WorkflowImage", "").toLocaleLowerCase(),
+											callback: () => {
+												new f().export(true);
+											},
+										},
+										{
+											content: f.name.replace("WorkflowImage", "").toLocaleLowerCase() + " (no embedded workflow)",
+											callback: () => {
+												new f().export();
+											},
+										},
+									]),
+								},
+							},
+						],
+					},
+				});
+				return options;
+			};
+		};
+
+		script.src = new URL(`assets/canvas2svg.js`, import.meta.url);
+		document.body.append(script);
+	},
+});
diff --git a/custom_nodes/comfyui-custom-scripts/web/js/workflows.js b/custom_nodes/comfyui-custom-scripts/web/js/workflows.js
new file mode 100644
index 0000000000000000000000000000000000000000..0225260ae7cc6b420c272ab3f54e389910a93815
--- /dev/null
+++ b/custom_nodes/comfyui-custom-scripts/web/js/workflows.js
@@ -0,0 +1,343 @@
+import { app } from "../../../scripts/app.js";
+import { api } from "../../../scripts/api.js";
+import { $el } from "../../../scripts/ui.js";
+
+// Adds workflow management
+// Original implementation by https://github.com/i-h4x
+// Thanks for permission to reimplement as an extension
+
+const style = `
+#comfy-save-button, #comfy-load-button {
+   position: relative;
+   overflow: hidden;
+}
+.pysssss-workflow-arrow {
+   position: absolute;
+   top: 0;
+   bottom: 0;
+   right: 0;
+   font-size: 12px;
+   display: flex;
+   align-items: center;
+   width: 24px;
+   justify-content: center;
+   background: rgba(255,255,255,0.1);
+}
+.pysssss-workflow-arrow:after {
+   content: "▼";
+}
+.pysssss-workflow-arrow:hover {
+   filter: brightness(1.6);
+   background-color: var(--comfy-menu-bg);
+}
+.pysssss-workflow-load .litemenu-entry:not(.has_submenu):before,
+.pysssss-workflow-load ~ .litecontextmenu .litemenu-entry:not(.has_submenu):before {
+	content: "🎛️";
+	padding-right: 5px;
+}
+.pysssss-workflow-load .litemenu-entry.has_submenu:before,
+.pysssss-workflow-load ~ .litecontextmenu .litemenu-entry.has_submenu:before {
+	content: "📂";
+	padding-right: 5px;
+	position: relative;
+	top: -1px;
+}
+.pysssss-workflow-popup ~ .litecontextmenu {
+	transform: scale(1.3);
+}
+`;
+
+async function getWorkflows() {
+	const response = await api.fetchApi("/pysssss/workflows", { cache: "no-store" });
+	return await response.json();
+}
+
+async function getWorkflow(name) {
+	const response = await api.fetchApi(`/pysssss/workflows/${encodeURIComponent(name)}`, { cache: "no-store" });
+	return await response.json();
+}
+
+async function saveWorkflow(name, workflow, overwrite) {
+	try {
+		const response = await api.fetchApi("/pysssss/workflows", {
+			method: "POST",
+			headers: {
+				"Content-Type": "application/json",
+			},
+			body: JSON.stringify({ name, workflow, overwrite }),
+		});
+		if (response.status === 201) {
+			return true;
+		}
+		if (response.status === 409) {
+			return false;
+		}
+		throw new Error(response.statusText);
+	} catch (error) {
+		console.error(error);
+	}
+}
+
+class PysssssWorkflows {
+	async load() {
+		this.workflows = await getWorkflows();
+		if(this.workflows.length) {
+			this.workflows.sort();
+		}
+		this.loadMenu.style.display = this.workflows.length ? "flex" : "none";
+	}
+
+	getMenuOptions(callback) {
+		const menu = [];
+		const directories = new Map();
+		for (const workflow of this.workflows || []) {
+			const path = workflow.split("/");
+			let parent = menu;
+			let currentPath = "";
+			for (let i = 0; i < path.length - 1; i++) {
+				currentPath += "/" + path[i];
+				let newParent = directories.get(currentPath);
+				if (!newParent) {
+					newParent = {
+						title: path[i],
+						has_submenu: true,
+						submenu: {
+							options: [],
+						},
+					};
+					parent.push(newParent);
+					newParent = newParent.submenu.options;
+					directories.set(currentPath, newParent);
+				}
+				parent = newParent;
+			}
+			parent.push({
+				title: path[path.length - 1],
+				callback: () => callback(workflow),
+			});
+		}
+		return menu;
+	}
+
+	constructor() {
+		function addWorkflowMenu(type, getOptions) {
+			return $el("div.pysssss-workflow-arrow", {
+				parent: document.getElementById(`comfy-${type}-button`),
+				onclick: (e) => {
+					e.preventDefault();
+					e.stopPropagation();
+
+					LiteGraph.closeAllContextMenus();
+					const menu = new LiteGraph.ContextMenu(
+						getOptions(),
+						{
+							event: e,
+							scale: 1.3,
+						},
+						window
+					);
+					menu.root.classList.add("pysssss-workflow-popup");
+					menu.root.classList.add(`pysssss-workflow-${type}`);
+				},
+			});
+		}
+
+		this.loadMenu = addWorkflowMenu("load", () =>
+			this.getMenuOptions(async (workflow) => {
+				const json = await getWorkflow(workflow);
+				app.loadGraphData(json);
+			})
+		);
+		addWorkflowMenu("save", () => {
+			return [
+				{
+					title: "Save as",
+					callback: () => {
+						let filename = prompt("Enter filename", this.workflowName || "workflow");
+						if (filename) {
+							if (!filename.toLowerCase().endsWith(".json")) {
+								filename += ".json";
+							}
+
+							this.workflowName = filename;
+
+							const json = JSON.stringify(app.graph.serialize(), null, 2); // convert the data to a JSON string
+							const blob = new Blob([json], { type: "application/json" });
+							const url = URL.createObjectURL(blob);
+							const a = $el("a", {
+								href: url,
+								download: filename,
+								style: { display: "none" },
+								parent: document.body,
+							});
+							a.click();
+							setTimeout(function () {
+								a.remove();
+								window.URL.revokeObjectURL(url);
+							}, 0);
+						}
+					},
+				},
+				{
+					title: "Save to workflows",
+					callback: async () => {
+						const name = prompt("Enter filename", this.workflowName || "workflow");
+						if (name) {
+							this.workflowName = name;
+
+							const data = app.graph.serialize();
+							if (!(await saveWorkflow(name, data))) {
+								if (confirm("A workspace with this name already exists, do you want to overwrite it?")) {
+									await saveWorkflow(name, app.graph.serialize(), true);
+								} else {
+									return;
+								}
+							}
+							await this.load();
+						}
+					},
+				},
+			];
+		});
+		this.load();
+
+		const handleFile = app.handleFile;
+		const self = this;
+		app.handleFile = function (file) {
+			if (file?.name?.endsWith(".json")) {
+				self.workflowName = file.name;
+			} else {
+				self.workflowName = null;
+			}
+			return handleFile.apply(this, arguments);
+		};
+	}
+}
+
+const refreshComboInNodes = app.refreshComboInNodes;
+let workflows;
+
+async function sendToWorkflow(img, workflow) {
+	const graph = !workflow ? app.graph.serialize() : await getWorkflow(workflow);
+	const nodes = graph.nodes.filter((n) => n.type === "LoadImage");
+	let targetNode;
+	if (nodes.length === 0) {
+		alert("To send the image to another workflow, that workflow must have a LoadImage node.");
+		return;
+	} else if (nodes.length > 1) {
+		targetNode = nodes.find((n) => n.title?.toLowerCase().includes("input"));
+		if (!targetNode) {
+			targetNode = nodes[0];
+			alert(
+				"The target workflow has multiple LoadImage nodes, include 'input' in the name of the one you want to use. The first one will be used here."
+			);
+		}
+	} else {
+		targetNode = nodes[0];
+	}
+
+	const blob = await (await fetch(img.src)).blob();
+	const name =
+		(workflow || "sendtoworkflow").replace(/\//g, "_") +
+		"-" +
+		+new Date() +
+		new URLSearchParams(img.src.split("?")[1]).get("filename");
+	const body = new FormData();
+	body.append("image", new File([blob], name));
+
+	const resp = await api.fetchApi("/upload/image", {
+		method: "POST",
+		body,
+	});
+
+	if (resp.status === 200) {
+		await refreshComboInNodes.call(app);
+		targetNode.widgets_values[0] = name;
+		app.loadGraphData(graph);
+		app.graph.getNodeById(targetNode.id);
+	} else {
+		alert(resp.status + " - " + resp.statusText);
+	}
+}
+
+app.registerExtension({
+	name: "pysssss.Workflows",
+	init() {
+		$el("style", {
+			textContent: style,
+			parent: document.head,
+		});
+	},
+
+	async refreshComboInNodes() {
+		workflows.load()
+	},
+	
+	async setup() {
+		workflows = new PysssssWorkflows();
+
+		const comfyDefault = "[ComfyUI Default]";
+		const defaultWorkflow = app.ui.settings.addSetting({
+			id: "pysssss.Workflows.Default",
+			name: "🐍 Default Workflow",
+			defaultValue: comfyDefault,
+			type: "combo",
+			options: (value) =>
+				[comfyDefault, ...workflows.workflows].map((m) => ({
+					value: m,
+					text: m,
+					selected: m === value,
+				})),
+		});
+
+		document.getElementById("comfy-load-default-button").onclick = async function () {
+			if (
+				localStorage["Comfy.Settings.Comfy.ConfirmClear"] === "false" ||
+				confirm(`Load default workflow (${defaultWorkflow.value})?`)
+			) {
+				if (defaultWorkflow.value === comfyDefault) {
+					app.loadGraphData();
+				} else {
+					const json = await getWorkflow(defaultWorkflow.value);
+					app.loadGraphData(json);
+				}
+			}
+		};
+	},
+	async beforeRegisterNodeDef(nodeType, nodeData, app) {
+		const getExtraMenuOptions = nodeType.prototype.getExtraMenuOptions;
+		nodeType.prototype.getExtraMenuOptions = function (_, options) {
+			const r = getExtraMenuOptions?.apply?.(this, arguments);
+			let img;
+			if (this.imageIndex != null) {
+				// An image is selected so select that
+				img = this.imgs[this.imageIndex];
+			} else if (this.overIndex != null) {
+				// No image is selected but one is hovered
+				img = this.imgs[this.overIndex];
+			}
+
+			if (img) {
+				let pos = options.findIndex((o) => o.content === "Save Image");
+				if (pos === -1) {
+					pos = 0;
+				} else {
+					pos++;
+				}
+
+				options.splice(pos, 0, {
+					content: "Send to workflow",
+					has_submenu: true,
+					submenu: {
+						options: [
+							{ callback: () => sendToWorkflow(img), title: "[Current workflow]" },
+							...workflows.getMenuOptions(sendToWorkflow.bind(null, img)),
+						],
+					},
+				});
+			}
+
+			return r;
+		};
+	},
+});
diff --git a/custom_nodes/comfyui-ideogram-autoprompter/.github/workflows/publish.yml b/custom_nodes/comfyui-ideogram-autoprompter/.github/workflows/publish.yml
new file mode 100644
index 0000000000000000000000000000000000000000..9013093f8b8d9df536ce809074522a76f3515074
--- /dev/null
+++ b/custom_nodes/comfyui-ideogram-autoprompter/.github/workflows/publish.yml
@@ -0,0 +1,22 @@
+name: Publish to Comfy Registry
+
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+    paths:
+      - "pyproject.toml"
+
+jobs:
+  publish-node:
+    name: Publish custom node to registry
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@main
+        with:
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
diff --git a/custom_nodes/comfyui-ideogram-autoprompter/nodes/autoprompter_node.py b/custom_nodes/comfyui-ideogram-autoprompter/nodes/autoprompter_node.py
new file mode 100644
index 0000000000000000000000000000000000000000..84f6321f54bd3d1f938548a6be32d72f9c90f893
--- /dev/null
+++ b/custom_nodes/comfyui-ideogram-autoprompter/nodes/autoprompter_node.py
@@ -0,0 +1,257 @@
+"""Ideogram 4 Autoprompter node.
+
+Same runtime contract as the KJ Ideogram 4 Prompt Builder: the editor state is
+serialized into hidden widgets and assembled into a strict-key-order caption JSON at
+graph-run time (plus a rendered preview). The AI layer (idea/image -> caption) runs
+entirely from the node UI through the HTTP routes; it never touches execute().
+"""
+
+import json
+import os
+
+import numpy as np
+import torch
+from PIL import Image, ImageDraw, ImageFont
+
+from comfy_api.latest import io
+
+_FONT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "fonts", "FreeMono.ttf")
+
+
+def _hex_rgb(h):
+    h = h.lstrip("#")
+    return (int(h[0:2], 16), int(h[2:4], 16), int(h[4:6], 16)) if len(h) == 6 else (255, 255, 255)
+
+
+def _font(size):
+    try:
+        return ImageFont.truetype(_FONT_PATH, size)
+    except Exception:
+        try:
+            return ImageFont.load_default(size)
+        except Exception:
+            return ImageFont.load_default()
+
+
+def _wrap(draw, text, font, max_w):
+    lines = []
+    for para in text.split("\n"):
+        line = ""
+        for word in para.split():
+            test = word if not line else line + " " + word
+            if line and draw.textlength(test, font=font) > max_w:
+                lines.append(line)
+                line = word
+            else:
+                line = test
+        lines.append(line)
+    return lines
+
+
+def _render_preview(boxes, width, height):
+    # Render the regions + prompts on a black canvas, mirroring the JS editor.
+    long_edge = max(width, height)
+    scale = min(1.0, 1024 / long_edge) if long_edge > 0 else 1.0
+    rw = max(1, round(width * scale))
+    rh = max(1, round(height * scale))
+
+    img = Image.new("RGBA", (rw, rh), (0, 0, 0, 255))
+    overlay = Image.new("RGBA", (rw, rh), (0, 0, 0, 0))
+    draw = ImageDraw.Draw(overlay)
+    fs = max(10, round(rh / 64))
+    font = _font(fs)
+    tag_font = _font(max(9, fs - 2))
+    lh = fs + 2
+
+    for i, box in enumerate(boxes):
+        if not isinstance(box, dict) or box.get("nobbox"):
+            continue
+        palette = [c for c in (box.get("palette") or []) if c]
+        r, g, b = _hex_rgb(palette[0]) if palette else (140, 140, 140)
+        x1 = max(0, min(rw, round(box.get("x", 0) * rw)))
+        y1 = max(0, min(rh, round(box.get("y", 0) * rh)))
+        x2 = max(0, min(rw, round((box.get("x", 0) + box.get("w", 0)) * rw)))
+        y2 = max(0, min(rh, round((box.get("y", 0) + box.get("h", 0)) * rh)))
+        if x2 < x1:
+            x1, x2 = x2, x1
+        if y2 < y1:
+            y1, y2 = y2, y1
+
+        draw.rectangle([x1, y1, x2, y2], outline=(r, g, b, 255), width=2)
+
+        pal5 = palette[:5]
+        if pal5 and (x2 - x1) > 2:
+            sh = max(5, fs // 2)
+            seg = (x2 - x1) / len(pal5)
+            for p, hexc in enumerate(pal5):
+                sx = x1 + round(p * seg)
+                draw.rectangle([sx, y1, x1 + round((p + 1) * seg), y1 + sh], fill=_hex_rgb(hexc))
+
+        etype = "text" if box.get("type") == "text" else "obj"
+        tag = str(i + 1).zfill(2)
+        tw = draw.textlength(tag, font=tag_font)
+        draw.rectangle([x1, y1, x1 + tw + 6, y1 + fs + 2], fill=(r, g, b, 255))
+        tagfill = (0, 0, 0, 255) if (0.299 * r + 0.587 * g + 0.114 * b) > 140 else (255, 255, 255, 255)
+        draw.text((x1 + 3, y1 + 1), tag, fill=tagfill, font=tag_font)
+
+        body = box.get("desc", "") or ""
+        if etype == "text" and box.get("text"):
+            body = '"%s"%s' % (box["text"], " — " + body if body else "")
+        if body and (x2 - x1) > 8:
+            ty = y1 + fs + 5
+            for line in _wrap(draw, body, font, x2 - x1 - 8):
+                if ty > y2:
+                    break
+                draw.text((x1 + 4, ty), line, fill=(212, 212, 212, 255), font=font)
+                ty += lh
+
+    img = Image.alpha_composite(img, overlay).convert("RGB")
+    arr = np.asarray(img, dtype=np.float32) / 255.0
+    return torch.from_numpy(arr).unsqueeze(0)
+
+
+def _norm_bbox(box):
+    def c(v):
+        return max(0, min(1000, round(v * 1000)))
+    x, y, w, h = box.get("x", 0.0), box.get("y", 0.0), box.get("w", 0.0), box.get("h", 0.0)
+    ymin, xmin, ymax, xmax = c(y), c(x), c(y + h), c(x + w)
+    if ymin > ymax:
+        ymin, ymax = ymax, ymin
+    if xmin > xmax:
+        xmin, xmax = xmax, xmin
+    return [ymin, xmin, ymax, xmax]
+
+
+def _palette(colors):
+    if isinstance(colors, dict):
+        colors = colors.values()
+    return [c.upper() for c in colors if c]
+
+
+def _dumps(v, lvl=0):
+    # Like json.dumps(ensure_ascii=False, indent=4), but scalar arrays stay on one line.
+    pad, end = "    " * (lvl + 1), "    " * lvl
+    if isinstance(v, str):
+        return json.dumps(v, ensure_ascii=False)
+    if isinstance(v, list):
+        if not v:
+            return "[]"
+        if all(not isinstance(x, (dict, list)) for x in v):
+            return "[" + ", ".join(_dumps(x, lvl) for x in v) + "]"
+        return "[\n" + ",\n".join(pad + _dumps(x, lvl + 1) for x in v) + "\n" + end + "]"
+    if isinstance(v, dict):
+        if not v:
+            return "{}"
+        items = [pad + json.dumps(k, ensure_ascii=False) + ": " + _dumps(val, lvl + 1) for k, val in v.items()]
+        return "{\n" + ",\n".join(items) + "\n" + end + "}"
+    return json.dumps(v, ensure_ascii=False)
+
+
+def _parse_json_list(s):
+    if s:
+        try:
+            v = json.loads(s)
+            if isinstance(v, list):
+                return v
+        except json.JSONDecodeError:
+            pass
+    return []
+
+
+class Ideogram4Autoprompter(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="Ideogram4Autoprompter",
+            display_name="Ideogram 4 Autoprompter",
+            category="Ideogram/text",
+            search_aliases=["ideogram", "autoprompter", "caption", "bbox", "llm", "qwen", "gemini"],
+            is_experimental=True,
+            description="""
+AI autoprompter for Ideogram 4's structured JSON caption format.
+
+Describe an idea (and/or drop a reference image) in the node UI and let a local
+vision LLM (Qwen3-VL, default) or Gemini build the full caption — background,
+placed elements with bboxes, descriptions, text, and color palettes. Everything
+remains editable on the canvas afterward. At run time the node only outputs the
+assembled caption JSON string (plus a preview).""",
+            inputs=[
+                io.Int.Input("width", default=1024, min=64, max=16384, step=8,
+                             tooltip="Canvas aspect width (also the pixel grid the bbox is measured in)."),
+                io.Int.Input("height", default=1024, min=64, max=16384, step=8,
+                             tooltip="Canvas aspect height (also the pixel grid the bbox is measured in)."),
+                io.String.Input("high_level_description", multiline=True, default="",
+                                tooltip="Optional one-line overview of the whole image (blank = omitted)."),
+                io.String.Input("background", multiline=True, default="",
+                                tooltip="Required scene background description."),
+                io.DynamicCombo.Input("style", options=[
+                    io.DynamicCombo.Option("none", []),
+                    io.DynamicCombo.Option("photo", [
+                        io.String.Input("photo", default=""),
+                    ]),
+                    io.DynamicCombo.Option("art_style", [
+                        io.String.Input("art_style", default=""),
+                    ]),
+                ]),
+                io.String.Input("aesthetics", default="", tooltip="Style descriptor (blank = omitted)."),
+                io.String.Input("lighting", default="", tooltip="Style descriptor (blank = omitted)."),
+                io.String.Input("medium", default="", tooltip="Style descriptor (blank = omitted)."),
+                io.String.Input("style_palette_data", default="", socketless=True, advanced=True,
+                                tooltip="Serialized style color palette from the editor (managed by the node UI)."),
+                io.String.Input("elements_data", default="", socketless=True, advanced=True,
+                                tooltip="Serialized regions from the editor (managed by the node UI)."),
+                io.String.Input("ai_state", default="", socketless=True, advanced=True,
+                                tooltip="Serialized autoprompter UI state (managed by the node UI; never the API key)."),
+            ],
+            outputs=[
+                io.String.Output(display_name="prompt"),
+                io.Image.Output(display_name="preview"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, width, height, background, style,
+                high_level_description="", aesthetics="", lighting="", medium="",
+                style_palette_data="", elements_data="", ai_state="") -> io.NodeOutput:
+        boxes = _parse_json_list(elements_data)
+
+        caption = {}
+        if high_level_description.strip():
+            caption["high_level_description"] = high_level_description
+
+        kind = style["style"]                               # "none" | "photo" | "art_style"
+        if kind != "none":
+            sd = {"aesthetics": aesthetics, "lighting": lighting}
+            if kind == "photo":
+                sd["photo"] = style.get("photo", "")
+                sd["medium"] = medium
+            else:
+                sd["medium"] = medium
+                sd["art_style"] = style.get("art_style", "")
+            palette = _palette(_parse_json_list(style_palette_data))
+            if palette:
+                sd["color_palette"] = palette
+            caption["style_description"] = sd
+
+        elements = []
+        for box in boxes:
+            if not isinstance(box, dict):
+                continue
+            etype = "text" if box.get("type") == "text" else "obj"
+            elem = {"type": etype}                          # key order matters
+            if not box.get("nobbox"):
+                elem["bbox"] = _norm_bbox(box)
+            if etype == "text":
+                elem["text"] = box.get("text", "")
+            elem["desc"] = box.get("desc", "")
+            palette = _palette(box.get("palette", []))
+            if palette:
+                elem["color_palette"] = palette[:5]
+            elements.append(elem)
+
+        caption["compositional_deconstruction"] = {
+            "background": background,
+            "elements": elements,
+        }
+        preview = _render_preview(boxes, width, height)
+        return io.NodeOutput(_dumps(caption), preview)
diff --git a/custom_nodes/comfyui-ideogram-autoprompter/nodes/caption_schema.py b/custom_nodes/comfyui-ideogram-autoprompter/nodes/caption_schema.py
new file mode 100644
index 0000000000000000000000000000000000000000..92f44e42a2432b595a60c45d96384d451b2a23d4
--- /dev/null
+++ b/custom_nodes/comfyui-ideogram-autoprompter/nodes/caption_schema.py
@@ -0,0 +1,249 @@
+"""Ideogram 4 caption schema: the LLM instruction prompt + a tolerant parser.
+
+The model is asked to emit a single JSON object matching Ideogram 4's structured
+caption format. Real models add prose, code fences, or trailing commentary, so
+`parse_caption` extracts the first balanced object and coerces it into the exact
+shape the editor expects (key order is normalized on export by the node, not here).
+"""
+
+import json
+import re
+
+# ── The schema instruction handed to the LLM as a system / leading prompt ──
+SYSTEM_PROMPT = """You are an expert prompt engineer for Ideogram 4, an image model trained on \
+structured JSON captions. Convert the user's idea (and the reference image, if one is given) \
+into ONE Ideogram 4 caption JSON object. Output ONLY the JSON — no markdown fences, no commentary.
+
+SCHEMA (key order matters):
+{
+  "high_level_description": "<one or two sentence overview of the whole image>",
+  "style_description": {
+    // include this block only when style is meaningful. Pick EXACTLY ONE of photo / art_style.
+    // Photo order:    aesthetics, lighting, photo, medium, color_palette
+    // Non-photo order: aesthetics, lighting, medium, art_style, color_palette
+    "aesthetics": "<visual mood keywords>",
+    "lighting": "<lighting description>",
+    "photo": "<camera/lens specs, e.g. '35mm, f/1.4, shallow depth of field'>",   // photos only
+    "art_style": "<style description>",                                            // non-photos only
+    "medium": "<photograph | illustration | 3d_render | painting | ...>",
+    "color_palette": ["#RRGGBB", ...]   // up to 16 UPPERCASE hex codes, optional
+  },
+  "compositional_deconstruction": {
+    "background": "<description of the scene/environment>",
+    "elements": [
+      {
+        "type": "obj",
+        "bbox": [ymin, xmin, ymax, xmax],   // integers 0-1000, origin top-left, optional
+        "desc": "<detailed description of this object>",
+        "color_palette": ["#RRGGBB", ...]   // up to 5 UPPERCASE hex codes, optional
+      },
+      {
+        "type": "text",
+        "bbox": [ymin, xmin, ymax, xmax],
+        "text": "<the literal text to render>",
+        "desc": "<how the text looks: font feel, weight, treatment>",
+        "color_palette": ["#RRGGBB", ...]
+      }
+    ]
+  }
+}
+
+RULES:
+- high_level_description and compositional_deconstruction.background are required; always fill them.
+- Break the scene into concrete elements. Give each a bbox on the 0-1000 grid; place them where they
+  belong (rough placement in hundreds is fine). The grid is [ymin, xmin, ymax, xmax], top-left origin.
+- Use type "text" for any rendered words/letters and put the exact words in "text".
+- All hex colors UPPERCASE #RRGGBB. <=16 colors in the style palette, <=5 per element.
+- Choose photo vs art_style from the idea; if it reads photographic use "photo", otherwise "art_style".
+- Return a single valid JSON object and nothing else."""
+
+# Appended to the system prompt when the user asks for high element density. The model
+# should decompose the scene far more granularly — one element (and bbox) per distinct
+# object instead of grouping co-located things together.
+DENSITY_HIGH = """
+
+ELEMENT DENSITY: HIGH.
+- Decompose the scene as granularly as possible. Give EVERY distinct object its own element
+  with its own bbox — never merge several objects into one region.
+- Example: a table holding a vase becomes TWO elements (one bbox for the table, one for the
+  vase), not one. Split groups, sets, and clusters into individual elements as well.
+- Include small and secondary objects (props, accents, background details) as their own
+  elements with tight bboxes. Aim for a thorough, exhaustive breakdown of the composition."""
+
+
+def get_system_prompt(density="normal"):
+    """Return the system prompt for the requested element density ('normal' | 'high')."""
+    if density == "high":
+        return SYSTEM_PROMPT + DENSITY_HIGH
+    return SYSTEM_PROMPT
+
+
+def build_user_prompt(idea, has_image):
+    idea = (idea or "").strip()
+    parts = []
+    if has_image:
+        parts.append("A reference image is provided. Describe and deconstruct what it depicts.")
+    if idea:
+        parts.append("User idea: " + idea)
+    if not parts:
+        parts.append("Invent a compelling, well-composed image and deconstruct it.")
+    parts.append("Produce the Ideogram 4 caption JSON now.")
+    return "\n".join(parts)
+
+
+def _extract_json(text):
+    """Return the first balanced {...} block in text, or None.
+
+    Tolerates ```json fences and TRUNCATED output: if the model ran out of tokens
+    mid-object the braces never balance, so we close any still-open strings/arrays/
+    objects at the point the text ends and return that repaired block. This is the
+    common cause of "No JSON object found" on slow local models that hit the token cap.
+    """
+    if not text:
+        return None
+    # strip ```json ... ``` fences if present (closing fence optional when truncated)
+    fence = re.search(r"```(?:json)?\s*(.*?)```", text, re.DOTALL)
+    if fence:
+        text = fence.group(1)
+    else:
+        fence_open = re.search(r"```(?:json)?\s*(.*)", text, re.DOTALL)
+        if fence_open:
+            text = fence_open.group(1)
+    start = text.find("{")
+    if start == -1:
+        return None
+    depth, in_str, esc = 0, False, False
+    stack = []                                 # track '{' / '[' for truncation repair
+    for i in range(start, len(text)):
+        c = text[i]
+        if in_str:
+            if esc:
+                esc = False
+            elif c == "\\":
+                esc = True
+            elif c == '"':
+                in_str = False
+        else:
+            if c == '"':
+                in_str = True
+            elif c in "{[":
+                stack.append(c)
+                if c == "{":
+                    depth += 1
+            elif c in "}]":
+                if stack:
+                    stack.pop()
+                if c == "}":
+                    depth -= 1
+                    if depth == 0:
+                        return text[start:i + 1]
+    # Truncated: rebuild from the start, closing whatever is still open.
+    frag = text[start:]
+    if in_str:
+        frag += '"'
+    frag = frag.rstrip().rstrip(",")           # drop a dangling trailing comma
+    # drop a dangling "key": with no value yet, and any comma it left behind
+    frag = re.sub(r',?\s*"[^"]*"\s*:\s*$', "", frag).rstrip().rstrip(",")
+    for opener in reversed(stack):
+        frag += "}" if opener == "{" else "]"
+    return frag
+
+
+def _clean_hex_list(v, cap):
+    out = []
+    if isinstance(v, str):
+        v = [v]
+    if isinstance(v, list):
+        for c in v:
+            if isinstance(c, str) and re.fullmatch(r"#?[0-9a-fA-F]{6}", c.strip()):
+                h = c.strip().lstrip("#").upper()
+                out.append("#" + h)
+    return out[:cap]
+
+
+def _clamp_bbox(v):
+    if not isinstance(v, list) or len(v) != 4:
+        return None
+    try:
+        b = [max(0, min(1000, int(round(float(x))))) for x in v]
+    except (TypeError, ValueError):
+        return None
+    ymin, xmin, ymax, xmax = b
+    if ymin > ymax:
+        ymin, ymax = ymax, ymin
+    if xmin > xmax:
+        xmin, xmax = xmax, xmin
+    return [ymin, xmin, ymax, xmax]
+
+
+def _snippet(text, limit=400):
+    s = (text or "").strip().replace("\n", " ")
+    return s[:limit] + ("…" if len(s) > limit else "")
+
+
+def parse_caption(text):
+    """Parse model output into a normalized caption dict, or raise ValueError."""
+    raw = _extract_json(text)
+    if raw is None:
+        raise ValueError(
+            "No JSON object found in model output. The model replied with prose instead "
+            "of JSON (try a stronger/instruct vision model). Raw output: %s" % _snippet(text)
+        )
+    try:
+        data = json.loads(raw)
+    except json.JSONDecodeError as e:
+        raise ValueError(
+            "Model output was not valid JSON (%s). Raw output: %s" % (e, _snippet(text))
+        )
+    if not isinstance(data, dict):
+        raise ValueError("Model output was not a JSON object. Raw output: %s" % _snippet(text))
+
+    cap = {}
+    hld = data.get("high_level_description")
+    if isinstance(hld, str) and hld.strip():
+        cap["high_level_description"] = hld.strip()
+
+    sd = data.get("style_description")
+    if isinstance(sd, dict):
+        style = {}
+        for k in ("aesthetics", "lighting", "medium"):
+            if isinstance(sd.get(k), str):
+                style[k] = sd[k]
+        if isinstance(sd.get("photo"), str):
+            style["photo"] = sd["photo"]
+        elif isinstance(sd.get("art_style"), str):
+            style["art_style"] = sd["art_style"]
+        pal = _clean_hex_list(sd.get("color_palette"), 16)
+        if pal:
+            style["color_palette"] = pal
+        if style:
+            cap["style_description"] = style
+
+    cd = data.get("compositional_deconstruction")
+    if not isinstance(cd, dict):
+        cd = {}
+    background = cd.get("background")
+    elements_in = cd.get("elements") if isinstance(cd.get("elements"), list) else []
+
+    elements = []
+    for el in elements_in:
+        if not isinstance(el, dict):
+            continue
+        etype = "text" if el.get("type") == "text" else "obj"
+        out = {"type": etype}
+        bb = _clamp_bbox(el.get("bbox"))
+        if bb is not None:
+            out["bbox"] = bb
+        if etype == "text":
+            out["text"] = el.get("text", "") if isinstance(el.get("text"), str) else ""
+        out["desc"] = el.get("desc", "") if isinstance(el.get("desc"), str) else ""
+        pal = _clean_hex_list(el.get("color_palette"), 5)
+        if pal:
+            out["color_palette"] = pal
+        elements.append(out)
+
+    cap["compositional_deconstruction"] = {
+        "background": background if isinstance(background, str) else "",
+        "elements": elements,
+    }
+    return cap
diff --git a/custom_nodes/comfyui-ideogram-autoprompter/nodes/llm_gemini.py b/custom_nodes/comfyui-ideogram-autoprompter/nodes/llm_gemini.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb4ca67ad5133d61f19badb6e368ef20d2104601
--- /dev/null
+++ b/custom_nodes/comfyui-ideogram-autoprompter/nodes/llm_gemini.py
@@ -0,0 +1,82 @@
+"""Gemini backend over the public Generative Language REST API.
+
+Kept to plain `requests` so no extra SDK is required. The user supplies a free API
+key in the node UI, fetches the available models, then generates. The key is never
+written into the workflow.
+"""
+
+import base64
+import io as _io
+
+import requests
+
+from .caption_schema import build_user_prompt, get_system_prompt, parse_caption
+
+API_ROOT = "https://generativelanguage.googleapis.com/v1beta"
+
+
+def list_models(api_key):
+    """Return [{id, display_name}] for models that support generateContent."""
+    if not api_key:
+        raise ValueError("No API key provided.")
+    r = requests.get("%s/models" % API_ROOT, params={"key": api_key}, timeout=30)
+    if r.status_code != 200:
+        raise ValueError(_err(r))
+    out = []
+    for m in r.json().get("models", []):
+        if "generateContent" in (m.get("supportedGenerationMethods") or []):
+            name = m.get("name", "")              # "models/gemini-2.0-flash"
+            mid = name.split("/", 1)[-1] if "/" in name else name
+            out.append({"id": mid, "display_name": m.get("displayName", mid)})
+    out.sort(key=lambda x: x["id"])
+    return out
+
+
+def _err(resp):
+    try:
+        e = resp.json().get("error", {})
+        return "Gemini API %s: %s" % (resp.status_code, e.get("message", resp.text[:200]))
+    except Exception:
+        return "Gemini API %s: %s" % (resp.status_code, resp.text[:200])
+
+
+def generate(api_key, model_id, idea, pil_image=None, density="normal"):
+    """Call generateContent and return a normalized caption dict."""
+    if not api_key:
+        raise ValueError("No API key provided.")
+    if not model_id:
+        raise ValueError("No model selected.")
+
+    parts = [{"text": build_user_prompt(idea, pil_image is not None)}]
+    if pil_image is not None:
+        buf = _io.BytesIO()
+        pil_image.convert("RGB").save(buf, format="PNG")
+        parts.append({
+            "inline_data": {
+                "mime_type": "image/png",
+                "data": base64.b64encode(buf.getvalue()).decode("ascii"),
+            }
+        })
+
+    body = {
+        "systemInstruction": {"parts": [{"text": get_system_prompt(density)}]},
+        "contents": [{"role": "user", "parts": parts}],
+        "generationConfig": {
+            "temperature": 0.7,
+            "responseMimeType": "application/json",
+        },
+    }
+    url = "%s/models/%s:generateContent" % (API_ROOT, model_id)
+    r = requests.post(url, params={"key": api_key}, json=body, timeout=120)
+    if r.status_code != 200:
+        raise ValueError(_err(r))
+
+    data = r.json()
+    candidates = data.get("candidates") or []
+    if not candidates:
+        fb = data.get("promptFeedback", {})
+        raise ValueError("Gemini returned no candidates (%s)." % fb.get("blockReason", "unknown"))
+    text = "".join(
+        p.get("text", "") for p in candidates[0].get("content", {}).get("parts", [])
+    )
+    return parse_caption(text)
diff --git a/custom_nodes/comfyui-ideogram-autoprompter/nodes/llm_local.py b/custom_nodes/comfyui-ideogram-autoprompter/nodes/llm_local.py
new file mode 100644
index 0000000000000000000000000000000000000000..35df7f3775163755aefb69c89e56c85348fbf3bf
--- /dev/null
+++ b/custom_nodes/comfyui-ideogram-autoprompter/nodes/llm_local.py
@@ -0,0 +1,317 @@
+"""Local vision-LLM backend: Huihui-Qwen3-VL-4B-Instruct-abliterated via transformers.
+
+Loaded on demand, optionally 4-bit, and unloaded after each generation by default so
+it doesn't hold VRAM during the rest of the workflow. Download progress (first run only)
+is reported through a module-level status dict polled by the frontend.
+"""
+
+import gc
+import re
+import threading
+
+from .caption_schema import build_user_prompt, get_system_prompt, parse_caption
+
+DEFAULT_MODEL = "huihui-ai/Huihui-Qwen3-VL-4B-Instruct-abliterated"
+
+_LOCK = threading.Lock()
+_STATE = {"model": None, "processor": None, "model_id": None, "four_bit": None, "attn": None}
+
+# Polled by GET /ideogram_autoprompter/local/status
+STATUS = {"stage": "idle", "detail": "", "model_id": DEFAULT_MODEL}
+
+
+def _set_status(stage, detail=""):
+    STATUS["stage"] = stage
+    STATUS["detail"] = detail
+
+
+def is_loaded():
+    return _STATE["model"] is not None
+
+
+def unload():
+    with _LOCK:
+        _STATE["model"] = None
+        _STATE["processor"] = None
+        _STATE["model_id"] = None
+        _STATE["four_bit"] = None
+        _STATE["attn"] = None
+    gc.collect()
+    try:
+        import torch
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+    except Exception:
+        pass
+    _set_status("idle")
+
+
+def _pick_device_dtype(four_bit):
+    import torch
+    if torch.cuda.is_available():
+        dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
+        return "auto", dtype
+    return "cpu", torch.float32
+
+
+def _flash_available():
+    try:
+        import importlib.util
+        return importlib.util.find_spec("flash_attn") is not None
+    except Exception:
+        return False
+
+
+def _resolve_attn(mode, force_sdpa):
+    """Map the UI's attention choice to a transformers attn_implementation string.
+
+    Flash-Attention 2 is the fastest but only works in fp16/bf16 and must be installed;
+    when it can't be used we fall back to SDPA (PyTorch's built-in fused attention, which
+    is still much faster than the 'eager' path). 4-bit/8-bit models force SDPA.
+    """
+    if force_sdpa:
+        return "sdpa"
+    if mode == "eager":
+        return "eager"
+    if mode == "sdpa":
+        return "sdpa"
+    if mode == "flash_attention_2":
+        return "flash_attention_2" if _flash_available() else "sdpa"
+    # auto: prefer flash if present, else sdpa
+    return "flash_attention_2" if _flash_available() else "sdpa"
+
+
+def _load_error_message(model_id, e):
+    """Turn a from_pretrained failure into a message that names the real cause.
+
+    A download/connection failure must NOT be reported as "not a vision model" —
+    that was misleading users whose model was fine but whose HF endpoint was down.
+    """
+    import os
+
+    msg = str(e)
+    low = msg.lower()
+
+    network_signs = (
+        "couldn't connect", "could not connect", "connection error", "max retries",
+        "failed to establish", "name resolution", "timed out", "offline mode",
+        "localentrynotfound", "we couldn't connect", "hf-mirror",
+    )
+    if any(s in low for s in network_signs):
+        endpoint = os.environ.get("HF_ENDPOINT")
+        hint = ""
+        if endpoint and "huggingface.co" not in endpoint:
+            hint = (
+                " Your HF_ENDPOINT is set to %s — that mirror looks unreachable. "
+                "Run `unset HF_ENDPOINT` in the terminal you launch ComfyUI from and "
+                "restart, then retry." % endpoint
+            )
+        else:
+            hint = (
+                " Check your internet connection (or HF_ENDPOINT) and retry; the model "
+                "downloads several GB on first run."
+            )
+        return (
+            "Could not download '%s' — network/endpoint problem, not a model problem.%s "
+            "Underlying error: %s" % (model_id, hint, msg)
+        )
+
+    arch_signs = (
+        "unrecognized", "does not recognize", "not a vision", "no module named",
+        "keyerror", "architecture", "imagetexttotext", "vision",
+        "got an unexpected keyword", "object has no attribute",
+    )
+    if any(s in low for s in arch_signs):
+        return (
+            "Could not load '%s' as a vision model. This node needs a multimodal / "
+            "vision LLM — e.g. huihui-ai/Huihui-Qwen3-VL-8B-Instruct-abliterated, "
+            "huihui-ai/Huihui-Qwen3-VL-4B-Instruct-abliterated, or "
+            "Qwen/Qwen3-VL-4B-Instruct. Plain text LLMs (Qwen3 / Qwen3.5) are not "
+            "supported. Underlying error: %s" % (model_id, msg)
+        )
+
+    return "Could not load '%s'. Underlying error: %s" % (model_id, msg)
+
+
+def _load(model_id, four_bit, attn):
+    """Load model+processor into _STATE. Caller holds _LOCK."""
+    if (_STATE["model"] is not None and _STATE["model_id"] == model_id
+            and _STATE["four_bit"] == four_bit and _STATE["attn"] == attn):
+        return
+    if _STATE["model"] is not None:
+        _STATE["model"] = None
+        _STATE["processor"] = None
+        gc.collect()
+
+    import torch
+    from transformers import AutoProcessor
+
+    _set_status("downloading", "Fetching %s (first run downloads several GB)…" % model_id)
+    device_map, dtype = _pick_device_dtype(four_bit)
+
+    quant = None
+    if four_bit and torch.cuda.is_available():
+        try:
+            from transformers import BitsAndBytesConfig
+            quant = BitsAndBytesConfig(
+                load_in_4bit=True,
+                bnb_4bit_compute_dtype=dtype,
+                bnb_4bit_quant_type="nf4",
+                bnb_4bit_use_double_quant=True,
+            )
+        except Exception:
+            quant = None
+
+    # Architecture-agnostic: let transformers dispatch to the right class from the model's
+    # own config (Qwen3-VL, Gemma 3/3n, LLaVA, etc.). The dedicated Qwen3-VL class is only a
+    # fallback for older transformers where the Auto map didn't include Qwen3-VL yet.
+    try:
+        from transformers import AutoModelForImageTextToText as _VLModel
+    except Exception:
+        from transformers import Qwen3VLForConditionalGeneration as _VLModel
+
+    # Flash-Attn needs fp16/bf16 weights, so quantized loads force SDPA.
+    attn_impl = _resolve_attn(attn, force_sdpa=(quant is not None))
+
+    kwargs = {
+        "device_map": device_map,
+        "dtype": dtype,
+        "trust_remote_code": True,
+        "attn_implementation": attn_impl,
+    }
+    if quant is not None:
+        kwargs["quantization_config"] = quant
+        kwargs.pop("dtype", None)
+
+    _set_status("loading", "Loading model (%s attention)…" % attn_impl)
+    try:
+        model = _VLModel.from_pretrained(model_id, **kwargs)
+    except Exception as e:
+        # Flash-Attn often installs but fails to load (kernel/CUDA mismatch); retry on SDPA
+        # before giving up so a bad flash build never blocks generation.
+        if attn_impl == "flash_attention_2":
+            kwargs["attn_implementation"] = "sdpa"
+            try:
+                model = _VLModel.from_pretrained(model_id, **kwargs)
+                attn_impl = "sdpa"
+            except Exception as e2:
+                raise RuntimeError(_load_error_message(model_id, e2)) from e2
+        else:
+            raise RuntimeError(_load_error_message(model_id, e)) from e
+    model.eval()
+    processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+
+    _STATE["model"] = model
+    _STATE["processor"] = processor
+    _STATE["model_id"] = model_id
+    _STATE["four_bit"] = four_bit
+    _STATE["attn"] = attn
+    _set_status("ready")
+
+
+def _apply_template(processor, messages, think):
+    """Tokenize a message list. When think is False, ask the chat template to disable
+    reasoning (Qwen3-style `enable_thinking`); processors that don't accept the kwarg
+    are retried without it."""
+    base = dict(tokenize=True, add_generation_prompt=True,
+                return_dict=True, return_tensors="pt")
+    if not think:
+        try:
+            return processor.apply_chat_template(messages, enable_thinking=False, **base)
+        except Exception:
+            pass
+    return processor.apply_chat_template(messages, **base)
+
+
+def _build_inputs(processor, model, sys_text, user_text, pil_image, think):
+    """Tokenize one VLM turn, working across model families.
+
+    Most chat templates accept a separate ``system`` role, but some popular vision
+    models (e.g. Gemma 3 / 3n) reject it. When that happens we fold the system text
+    into the user turn so any vision model can still be driven the same way.
+    """
+    user_content = []
+    if pil_image is not None:
+        user_content.append({"type": "image", "image": pil_image})
+    user_content.append({"type": "text", "text": user_text})
+
+    with_system = [
+        {"role": "system", "content": [{"type": "text", "text": sys_text}]},
+        {"role": "user", "content": user_content},
+    ]
+    try:
+        inputs = _apply_template(processor, with_system, think)
+    except Exception:
+        # Fold the system prompt into the user turn for models without a system role.
+        merged = [{"type": "text", "text": sys_text + "\n\n"}]
+        if pil_image is not None:
+            merged.append({"type": "image", "image": pil_image})
+        merged.append({"type": "text", "text": user_text})
+        inputs = _apply_template(processor, [{"role": "user", "content": merged}], think)
+
+    return inputs.to(model.device)
+
+
+def _make_streamer(processor, model_id):
+    """A TextStreamer that prints generated tokens to the ComfyUI console live."""
+    try:
+        import sys
+        from transformers import TextStreamer
+        tok = getattr(processor, "tokenizer", None) or processor
+        sys.stdout.write("\n[ideogram-autoprompter] local '%s' streaming:\n" % model_id)
+        sys.stdout.flush()
+        return TextStreamer(tok, skip_prompt=True, skip_special_tokens=True)
+    except Exception:
+        return None
+
+
+def generate(idea, pil_image=None, model_id=None, four_bit=False, unload_after=True,
+             density="normal", attn="auto", think=False, stream_to_console=True,
+             max_new_tokens=2048):
+    """Run the local VLM and return a normalized caption dict."""
+    import torch
+
+    model_id = model_id or DEFAULT_MODEL
+    STATUS["model_id"] = model_id
+    with _LOCK:
+        try:
+            _load(model_id, four_bit, attn)
+            model, processor = _STATE["model"], _STATE["processor"]
+
+            _set_status("generating", "Generating caption…")
+            sys_text = get_system_prompt(density)
+            user_text = build_user_prompt(idea, pil_image is not None)
+            inputs = _build_inputs(processor, model, sys_text, user_text, pil_image, think)
+
+            streamer = _make_streamer(processor, model_id) if stream_to_console else None
+            with torch.no_grad():
+                generated = model.generate(
+                    **inputs,
+                    max_new_tokens=max_new_tokens,
+                    do_sample=False,
+                    streamer=streamer,
+                )
+            trimmed = generated[:, inputs["input_ids"].shape[1]:]
+            text = processor.batch_decode(
+                trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+            )[0]
+            # Drop any reasoning the model emitted before its JSON answer.
+            text = re.sub(r"<think>.*?</think>", "", text, flags=re.DOTALL)
+
+            caption = parse_caption(text)
+            _set_status("ready")
+            return caption
+        finally:
+            if unload_after:
+                _STATE["model"] = None
+                _STATE["processor"] = None
+                _STATE["model_id"] = None
+                _STATE["four_bit"] = None
+                _STATE["attn"] = None
+                gc.collect()
+                try:
+                    if torch.cuda.is_available():
+                        torch.cuda.empty_cache()
+                except Exception:
+                    pass
+                _set_status("idle")
diff --git a/custom_nodes/comfyui-ideogram-autoprompter/nodes/llm_ollama.py b/custom_nodes/comfyui-ideogram-autoprompter/nodes/llm_ollama.py
new file mode 100644
index 0000000000000000000000000000000000000000..c055829350c321d6396e1e5508021f2c8c4d701e
--- /dev/null
+++ b/custom_nodes/comfyui-ideogram-autoprompter/nodes/llm_ollama.py
@@ -0,0 +1,127 @@
+"""Ollama backend — llama.cpp / GGUF inference, far faster than transformers on most GPUs.
+
+Talks to a running Ollama server over HTTP (no extra Python deps beyond requests). Tokens
+are streamed to the ComfyUI console so you can watch generation live, and `format:json`
+plus a disabled "think" mode keep the output clean. Thinking is off by default.
+"""
+
+import base64
+import io as _io
+import json
+import sys
+
+import requests
+
+from .caption_schema import build_user_prompt, get_system_prompt, parse_caption
+
+DEFAULT_HOST = "http://localhost:11434"
+
+
+def _host(host):
+    h = (host or "").strip() or DEFAULT_HOST
+    if not h.startswith("http://") and not h.startswith("https://"):
+        h = "http://" + h
+    return h.rstrip("/")
+
+
+def list_models(host=None):
+    """Return [{id, display_name}] for models pulled into the local Ollama server."""
+    url = _host(host) + "/api/tags"
+    try:
+        r = requests.get(url, timeout=15)
+    except requests.RequestException as e:
+        raise ValueError(
+            "Could not reach Ollama at %s. Is `ollama serve` running? (%s)" % (_host(host), e)
+        )
+    if r.status_code != 200:
+        raise ValueError("Ollama %s: %s" % (r.status_code, r.text[:200]))
+    out = []
+    for m in r.json().get("models", []):
+        name = m.get("name") or m.get("model")
+        if name:
+            out.append({"id": name, "display_name": name})
+    out.sort(key=lambda x: x["id"])
+    if not out:
+        raise ValueError(
+            "Ollama is running but has no models. Pull a vision model first, "
+            "e.g. `ollama pull qwen2.5vl` or `ollama pull gemma3`."
+        )
+    return out
+
+
+def _post_chat(url, body):
+    return requests.post(url, json=body, stream=True, timeout=600)
+
+
+def generate(host, model_id, idea, pil_image=None, density="normal", think=False,
+             unload_after=True, stream_to_console=True):
+    """Stream a caption from Ollama and return a normalized caption dict."""
+    if not model_id:
+        raise ValueError("No Ollama model selected. Click Fetch and pick one.")
+
+    url = _host(host) + "/api/chat"
+    user_msg = {"role": "user", "content": build_user_prompt(idea, pil_image is not None)}
+    if pil_image is not None:
+        buf = _io.BytesIO()
+        pil_image.convert("RGB").save(buf, format="PNG")
+        user_msg["images"] = [base64.b64encode(buf.getvalue()).decode("ascii")]
+
+    body = {
+        "model": model_id,
+        "messages": [
+            {"role": "system", "content": get_system_prompt(density)},
+            user_msg,
+        ],
+        "stream": True,
+        "think": bool(think),
+        "format": "json",                      # constrain output to valid JSON
+        "options": {"temperature": 0.7},
+        # keep_alive 0 unloads the model from VRAM the moment this response ends, instead
+        # of Ollama's default 5-minute idle window. Negative would keep it resident forever.
+        "keep_alive": 0 if unload_after else "5m",
+    }
+
+    try:
+        r = _post_chat(url, body)
+    except requests.RequestException as e:
+        raise ValueError("Could not reach Ollama at %s (%s)." % (_host(host), e))
+
+    # Some models/versions reject an explicit `think` field — retry once without it.
+    if r.status_code != 200 and "think" in (r.text or "").lower():
+        r.close()
+        body.pop("think", None)
+        r = _post_chat(url, body)
+
+    pieces = []
+    with r:
+        if r.status_code != 200:
+            raise ValueError("Ollama %s: %s" % (r.status_code, r.text[:300]))
+        if stream_to_console:
+            sys.stdout.write("\n[ideogram-autoprompter] Ollama '%s' streaming:\n" % model_id)
+            sys.stdout.flush()
+        for line in r.iter_lines():
+            if not line:
+                continue
+            try:
+                obj = json.loads(line.decode("utf-8"))
+            except Exception:
+                continue
+            if obj.get("error"):
+                raise ValueError("Ollama error: %s" % obj["error"])
+            msg = obj.get("message") or {}
+            # surface reasoning too (when think is on) so the console shows the full picture
+            for key in ("thinking", "content"):
+                chunk = msg.get(key)
+                if chunk:
+                    if key == "content":
+                        pieces.append(chunk)
+                    if stream_to_console:
+                        sys.stdout.write(chunk)
+                        sys.stdout.flush()
+            if obj.get("done"):
+                break
+        if stream_to_console:
+            sys.stdout.write("\n")
+            sys.stdout.flush()
+
+    return parse_caption("".join(pieces))
diff --git a/custom_nodes/comfyui-ideogram-autoprompter/nodes/llm_openrouter.py b/custom_nodes/comfyui-ideogram-autoprompter/nodes/llm_openrouter.py
new file mode 100644
index 0000000000000000000000000000000000000000..bfb97c78b81ad15c8b4b0c8417173f56a33b0993
--- /dev/null
+++ b/custom_nodes/comfyui-ideogram-autoprompter/nodes/llm_openrouter.py
@@ -0,0 +1,95 @@
+"""OpenRouter backend over the OpenAI-compatible chat completions API.
+
+Kept to plain `requests` so no extra SDK is required. The user supplies an
+OpenRouter API key in the node UI, fetches the available vision models, then
+generates. The key is never written into the workflow.
+
+Unlike Gemini we do not force a JSON response_format: OpenRouter spans many
+providers and some reject that field, so — like the local/ollama backends — we
+rely on the system prompt plus parse_caption()'s tolerant JSON extraction.
+"""
+
+import base64
+import io as _io
+
+import requests
+
+from .caption_schema import build_user_prompt, get_system_prompt, parse_caption
+
+API_ROOT = "https://openrouter.ai/api/v1"
+
+# Optional attribution headers OpenRouter surfaces on its dashboard; harmless if ignored.
+_BASE_HEADERS = {
+    "HTTP-Referer": "https://github.com/collbroGTR/comfyui-ideogram-autoprompter",
+    "X-Title": "Ideogram 4 Autoprompter",
+}
+
+
+def _headers(api_key):
+    h = dict(_BASE_HEADERS)
+    if api_key:
+        h["Authorization"] = "Bearer %s" % api_key
+    return h
+
+
+def _err(resp):
+    try:
+        e = resp.json().get("error", {})
+        msg = e.get("message") if isinstance(e, dict) else e
+        return "OpenRouter API %s: %s" % (resp.status_code, msg or resp.text[:200])
+    except Exception:
+        return "OpenRouter API %s: %s" % (resp.status_code, resp.text[:200])
+
+
+def list_models(api_key):
+    """Return [{id, display_name}] for models that accept image input."""
+    r = requests.get("%s/models" % API_ROOT, headers=_headers(api_key), timeout=30)
+    if r.status_code != 200:
+        raise ValueError(_err(r))
+    out = []
+    for m in r.json().get("data", []):
+        arch = m.get("architecture") or {}
+        mods = arch.get("input_modalities") or []
+        if "image" not in mods and "image" not in (arch.get("modality") or ""):
+            continue
+        mid = m.get("id", "")
+        out.append({"id": mid, "display_name": m.get("name", mid)})
+    out.sort(key=lambda x: x["id"])
+    return out
+
+
+def generate(api_key, model_id, idea, pil_image=None, density="normal"):
+    """Call chat/completions and return a normalized caption dict."""
+    if not api_key:
+        raise ValueError("No API key provided.")
+    if not model_id:
+        raise ValueError("No model selected.")
+
+    content = [{"type": "text", "text": build_user_prompt(idea, pil_image is not None)}]
+    if pil_image is not None:
+        buf = _io.BytesIO()
+        pil_image.convert("RGB").save(buf, format="PNG")
+        b64 = base64.b64encode(buf.getvalue()).decode("ascii")
+        content.append({
+            "type": "image_url",
+            "image_url": {"url": "data:image/png;base64,%s" % b64},
+        })
+
+    body = {
+        "model": model_id,
+        "messages": [
+            {"role": "system", "content": get_system_prompt(density)},
+            {"role": "user", "content": content},
+        ],
+        "temperature": 0.7,
+    }
+    r = requests.post("%s/chat/completions" % API_ROOT, headers=_headers(api_key), json=body, timeout=120)
+    if r.status_code != 200:
+        raise ValueError(_err(r))
+
+    data = r.json()
+    choices = data.get("choices") or []
+    if not choices:
+        raise ValueError("OpenRouter returned no choices.")
+    text = (choices[0].get("message") or {}).get("content") or ""
+    return parse_caption(text)
diff --git a/custom_nodes/comfyui-ideogram-autoprompter/nodes/routes.py b/custom_nodes/comfyui-ideogram-autoprompter/nodes/routes.py
new file mode 100644
index 0000000000000000000000000000000000000000..6309767cc2366f145aa8b06621ecdbc5a94f49c5
--- /dev/null
+++ b/custom_nodes/comfyui-ideogram-autoprompter/nodes/routes.py
@@ -0,0 +1,140 @@
+"""HTTP routes that drive on-demand caption generation from the node UI.
+
+Generation is a UI action, not graph execution, so the frontend POSTs here and the
+heavy work runs in a thread executor to keep ComfyUI's event loop responsive.
+"""
+
+import asyncio
+import base64
+import io
+import traceback
+
+from aiohttp import web
+from PIL import Image
+from server import PromptServer
+
+from . import llm_gemini, llm_local, llm_ollama, llm_openrouter
+
+routes = PromptServer.instance.routes
+
+
+def _decode_image(image_b64):
+    if not image_b64:
+        return None
+    if "," in image_b64:                       # strip a data URL prefix if present
+        image_b64 = image_b64.split(",", 1)[1]
+    try:
+        raw = base64.b64decode(image_b64)
+        return Image.open(io.BytesIO(raw)).convert("RGB")
+    except Exception:
+        return None
+
+
+@routes.post("/ideogram_autoprompter/gemini/models")
+async def gemini_models(request):
+    try:
+        data = await request.json()
+        loop = asyncio.get_event_loop()
+        models = await loop.run_in_executor(
+            None, llm_gemini.list_models, (data.get("api_key") or "").strip()
+        )
+        return web.json_response({"models": models})
+    except Exception as e:
+        return web.json_response({"error": str(e)}, status=400)
+
+
+@routes.post("/ideogram_autoprompter/ollama/models")
+async def ollama_models(request):
+    try:
+        data = await request.json()
+        loop = asyncio.get_event_loop()
+        models = await loop.run_in_executor(
+            None, llm_ollama.list_models, (data.get("host") or "").strip()
+        )
+        return web.json_response({"models": models})
+    except Exception as e:
+        return web.json_response({"error": str(e)}, status=400)
+
+
+@routes.post("/ideogram_autoprompter/openrouter/models")
+async def openrouter_models(request):
+    try:
+        data = await request.json()
+        loop = asyncio.get_event_loop()
+        models = await loop.run_in_executor(
+            None, llm_openrouter.list_models, (data.get("api_key") or "").strip()
+        )
+        return web.json_response({"models": models})
+    except Exception as e:
+        return web.json_response({"error": str(e)}, status=400)
+
+
+@routes.post("/ideogram_autoprompter/generate")
+async def generate(request):
+    try:
+        data = await request.json()
+    except Exception:
+        return web.json_response({"error": "Invalid request body."}, status=400)
+
+    backend = data.get("backend", "local")
+    idea = data.get("idea", "")
+    density = "high" if data.get("density") == "high" else "normal"
+    think = bool(data.get("think", False))
+    pil_image = _decode_image(data.get("image_b64"))
+    loop = asyncio.get_event_loop()
+
+    try:
+        if backend == "gemini":
+            caption = await loop.run_in_executor(
+                None, lambda: llm_gemini.generate(
+                    (data.get("api_key") or "").strip(),
+                    data.get("model") or "",
+                    idea, pil_image, density=density,
+                ),
+            )
+        elif backend == "ollama":
+            caption = await loop.run_in_executor(
+                None, lambda: llm_ollama.generate(
+                    (data.get("host") or "").strip(),
+                    data.get("model") or "",
+                    idea, pil_image, density=density, think=think,
+                    unload_after=bool(data.get("unload_after", True)),
+                ),
+            )
+        elif backend == "openrouter":
+            caption = await loop.run_in_executor(
+                None, lambda: llm_openrouter.generate(
+                    (data.get("api_key") or "").strip(),
+                    data.get("model") or "",
+                    idea, pil_image, density=density,
+                ),
+            )
+        else:
+            attn = data.get("attn") or "auto"
+            caption = await loop.run_in_executor(
+                None, lambda: llm_local.generate(
+                    idea,
+                    pil_image=pil_image,
+                    model_id=(data.get("model") or "").strip() or None,
+                    four_bit=bool(data.get("four_bit", False)),
+                    unload_after=bool(data.get("unload_after", True)),
+                    density=density,
+                    attn=attn,
+                    think=think,
+                ),
+            )
+        return web.json_response({"caption": caption})
+    except Exception as e:
+        traceback.print_exc()
+        return web.json_response({"error": str(e)}, status=400)
+
+
+@routes.get("/ideogram_autoprompter/local/status")
+async def local_status(request):
+    return web.json_response(dict(llm_local.STATUS, loaded=llm_local.is_loaded()))
+
+
+@routes.post("/ideogram_autoprompter/local/unload")
+async def local_unload(request):
+    llm_local.unload()
+    return web.json_response({"ok": True})
diff --git a/custom_nodes/comfyui-ideogram-autoprompter/web/js/autoprompter.js b/custom_nodes/comfyui-ideogram-autoprompter/web/js/autoprompter.js
new file mode 100644
index 0000000000000000000000000000000000000000..384e717412966fbc7e45d029de552ee203b2dda0
--- /dev/null
+++ b/custom_nodes/comfyui-ideogram-autoprompter/web/js/autoprompter.js
@@ -0,0 +1,1291 @@
+import { chainCallback, addMiddleClickPan, addWheelPassthrough, rectHitTest, cursorForBboxMode } from './utility.js';
+const { app } = window.comfyAPI.app;
+
+const HANDLE = 8;            // hit radius (canvas px) for corners/edges
+const MAX_ELEM_COLORS = 5;   // Ideogram 4 per-element palette cap
+const MAX_STYLE_COLORS = 16; // Ideogram 4 style palette cap
+const DEFAULT_LOCAL_MODEL = "huihui-ai/Huihui-Qwen3-VL-4B-Instruct-abliterated";
+const DEFAULT_OLLAMA_HOST = "http://localhost:11434";
+// Popular local vision LLMs offered as a dropdown. The field still accepts ANY Hugging
+// Face vision model id — these are just convenient, known-loadable starting points.
+const LOCAL_MODEL_PRESETS = [
+  "huihui-ai/Huihui-Qwen3-VL-4B-Instruct-abliterated",
+  "huihui-ai/Huihui-Qwen3-VL-8B-Instruct-abliterated",
+  "Qwen/Qwen3-VL-2B-Instruct",
+  "Qwen/Qwen3-VL-4B-Instruct",
+  "Qwen/Qwen3-VL-8B-Instruct",
+  "Qwen/Qwen2.5-VL-3B-Instruct",
+  "Qwen/Qwen2.5-VL-7B-Instruct",
+  "google/gemma-3-4b-it",
+  "google/gemma-3-12b-it",
+  "OpenGVLab/InternVL3-2B-hf",
+  "OpenGVLab/InternVL3-8B-hf",
+  "mistralai/Mistral-Small-3.1-24B-Instruct-2503",
+];
+let copiedBox = null;        // internal clipboard for copy/paste of regions (shared across nodes)
+
+// Black or white, whichever contrasts better with the given hex background.
+function textOn(hex) {
+  const h = (hex || "").replace("#", "");
+  if (h.length < 6) return "#000";
+  const r = parseInt(h.slice(0, 2), 16), g = parseInt(h.slice(2, 4), 16), b = parseInt(h.slice(4, 6), 16);
+  return (0.299 * r + 0.587 * g + 0.114 * b) > 140 ? "#000" : "#fff";
+}
+
+// Premium monochrome theme: pure-black surfaces, white text, white/outlined controls.
+function injectStyle() {
+  if (document.getElementById("ideoap-style")) return;
+  const s = document.createElement("style");
+  s.id = "ideoap-style";
+  s.textContent = `
+    .ideoap-wrap { display:flex; flex-direction:column; overflow:hidden; position:relative; pointer-events:auto; gap:6px; background:#000; padding:2px; border-radius:6px; }
+    .ideoap-canvas { cursor:crosshair; display:block; width:100%; height:auto; flex:0 0 auto; background:#000; border:1px solid #1c1c1c; border-radius:6px; outline:none; }
+    .ideoap-ai { display:flex; flex-direction:column; gap:7px; padding:9px; background:#050505; border:1px solid #1f1f1f; border-radius:6px; }
+    .ideoap-bar { display:flex; align-items:center; gap:7px; font:11px ui-sans-serif,system-ui,sans-serif; color:#cfcfcf; user-select:none; padding:0 2px; flex:0 0 auto; flex-wrap:wrap; }
+    .ideoap-panel { display:flex; flex-direction:column; gap:6px; padding:8px; background:#070707; border:1px solid #1f1f1f; border-radius:6px; font:11px ui-sans-serif,system-ui,sans-serif; color:#dcdcdc; flex:0 0 auto; }
+    .ideoap-row { display:flex; align-items:center; gap:7px; flex-wrap:wrap; }
+    .ideoap-lbl { color:#8a8a8a; font:10px ui-sans-serif,system-ui,sans-serif; letter-spacing:.04em; text-transform:uppercase; }
+    .ideoap-btn { background:transparent; border:1px solid #3a3a3a; border-radius:5px; color:#e6e6e6; font:11px ui-sans-serif,system-ui,sans-serif; cursor:pointer; padding:3px 10px; line-height:16px; white-space:nowrap; flex-shrink:0; transition:background .12s,color .12s,border-color .12s; }
+    .ideoap-btn:hover { background:#fff; color:#000; border-color:#fff; }
+    .ideoap-btn.active { background:#fff; color:#000; border-color:#fff; }
+    .ideoap-btn:disabled { opacity:.4; cursor:default; background:transparent; color:#888; border-color:#2a2a2a; }
+    .ideoap-btn.primary { background:#fff; color:#000; border-color:#fff; font-weight:600; }
+    .ideoap-btn.primary:hover { background:#dcdcdc; border-color:#dcdcdc; }
+    .ideoap-area, .ideoap-input { width:100%; box-sizing:border-box; background:#000; border:1px solid #2c2c2c; border-radius:5px; color:#f0f0f0; font:12px ui-monospace,monospace; padding:6px 8px; resize:vertical; }
+    .ideoap-area { min-height:34px; }
+    .ideoap-area:focus, .ideoap-input:focus { border-color:#fff; outline:none; }
+    .ideoap-input { resize:none; }
+    .ideoap-select { background:#000; border:1px solid #2c2c2c; border-radius:5px; color:#f0f0f0; font:11px ui-sans-serif,system-ui,sans-serif; padding:3px 6px; flex:1; min-width:80px; }
+    .ideoap-drop { flex:1; min-width:120px; display:flex; align-items:center; justify-content:center; gap:8px; min-height:40px; border:1px dashed #333; border-radius:5px; color:#7a7a7a; font:11px ui-sans-serif,system-ui,sans-serif; cursor:pointer; padding:4px; text-align:center; transition:border-color .12s,color .12s; }
+    .ideoap-drop:hover, .ideoap-drop.drag { border-color:#fff; color:#ccc; }
+    .ideoap-thumb { height:40px; width:auto; max-width:80px; border-radius:4px; border:1px solid #2c2c2c; object-fit:cover; }
+    .ideoap-status { font:11px ui-sans-serif,system-ui,sans-serif; color:#8a8a8a; min-height:14px; flex:1; }
+    .ideoap-status.err { color:#ff6b6b; }
+    .ideoap-sw { width:20px; height:20px; border:1px solid #444; border-radius:4px; cursor:pointer; flex-shrink:0; position:relative; }
+    .ideoap-sw input { position:absolute; opacity:0; width:0; height:0; pointer-events:none; }
+    .ideoap-inline { position:absolute; box-sizing:border-box; background:rgba(0,0,0,0.94); border:1px solid #fff; border-radius:4px; color:#fff; font:13px ui-monospace,monospace; padding:4px 5px; resize:none; outline:none; z-index:10; }
+    .ideoap-toggle { display:flex; align-items:center; gap:5px; color:#9a9a9a; font:10px ui-sans-serif,system-ui,sans-serif; cursor:pointer; user-select:none; }
+    .ideoap-spin { width:11px; height:11px; border:2px solid #444; border-top-color:#fff; border-radius:50%; display:inline-block; animation:ideoap-rot .7s linear infinite; vertical-align:-1px; }
+    @keyframes ideoap-rot { to { transform:rotate(360deg); } }
+  `;
+  document.head.appendChild(s);
+}
+
+app.registerExtension({
+  name: "Ideogram.Autoprompter",
+
+  async beforeRegisterNodeDef(nodeType, nodeData) {
+    if (nodeData?.name !== "Ideogram4Autoprompter") return;
+    injectStyle();
+
+    chainCallback(nodeType.prototype, "onNodeCreated", function () {
+      const node = this;
+      const findW = (n) => node.widgets?.find((w) => w.name === n);
+      const elementsWidget = findW("elements_data");
+      const stylePaletteWidget = findW("style_palette_data");
+      const aiStateWidget = findW("ai_state");
+      const wWidget = findW("width"), hWidget = findW("height");
+
+      // Hide the data widgets while keeping them serializable.
+      function hideDataWidgets() {
+        for (const w of [elementsWidget, stylePaletteWidget, aiStateWidget]) {
+          if (!w) continue;
+          w.hidden = true;
+          w.computeSize = () => [0, -4];
+        }
+        for (const name of ["elements_data", "style_palette_data", "ai_state"]) {
+          const i = node.inputs?.findIndex((inp) => inp.name === name);
+          if (i != null && i !== -1) node.removeInput(i);
+        }
+      }
+      hideDataWidgets();
+
+      node._boxes = [];        // {x,y,w,h normalized 0-1, type, text, desc, palette[]}
+      node._stylePalette = []; // global style color palette (hex[])
+      node._activeIdx = -1;
+      node._drawing = false;
+      node._dragMode = null;
+      node._dragStartN = null;
+      node._boxAtStart = null;
+      node._hoverTitle = null;
+      node._hoverBox = null;
+      node._focused = false;
+      node._selected = false;
+      node._areaH = node._areaH || {};
+      node._areaObservers = [];
+      // AI state (serialized to ai_state, minus the API key) + transient image/key.
+      node._ai = { backend: "local", idea: "", local_model: DEFAULT_LOCAL_MODEL,
+        gemini_model: "", unload_after: true, four_bit: false, density: "normal", attn: "auto",
+        ollama_host: DEFAULT_OLLAMA_HOST, ollama_model: "", openrouter_model: "", think: false };
+      node._apiKey = "";       // session only — never serialized
+      node._orKey = "";        // OpenRouter key — session only, never serialized
+      node._image = null;      // { b64, url } — session only
+      node._gening = false;
+
+      // ── DOM ──
+      const wrap = document.createElement("div");
+      wrap.className = "ideoap-wrap";
+
+      // AI bar (top)
+      const aiBar = document.createElement("div");
+      aiBar.className = "ideoap-ai";
+
+      // backend toggle
+      const backendRow = document.createElement("div");
+      backendRow.className = "ideoap-row";
+      const backendLbl = document.createElement("span"); backendLbl.className = "ideoap-lbl"; backendLbl.textContent = "Engine";
+      backendRow.appendChild(backendLbl);
+      const localBtn = document.createElement("button"); localBtn.className = "ideoap-btn"; localBtn.textContent = "Local";
+      localBtn.title = "Transformers (HF auto-download). Most compatible, but slower.";
+      const ollamaBtn = document.createElement("button"); ollamaBtn.className = "ideoap-btn"; ollamaBtn.textContent = "Ollama";
+      ollamaBtn.title = "Ollama (llama.cpp / GGUF) — fastest on most GPUs. Needs `ollama serve` running.";
+      const gemBtn = document.createElement("button"); gemBtn.className = "ideoap-btn"; gemBtn.textContent = "Gemini";
+      const orBtn = document.createElement("button"); orBtn.className = "ideoap-btn"; orBtn.textContent = "OpenRouter";
+      orBtn.title = "OpenRouter — one key, hundreds of hosted vision models (GPT-4o, Claude, Gemini, Qwen-VL…).";
+      backendRow.appendChild(localBtn); backendRow.appendChild(ollamaBtn); backendRow.appendChild(gemBtn); backendRow.appendChild(orBtn);
+      // detail / object-density segmented control
+      const densSpacer = document.createElement("span"); densSpacer.style.flex = "1"; backendRow.appendChild(densSpacer);
+      const densLbl = document.createElement("span"); densLbl.className = "ideoap-lbl"; densLbl.textContent = "Detail";
+      backendRow.appendChild(densLbl);
+      const densNormalBtn = document.createElement("button"); densNormalBtn.className = "ideoap-btn"; densNormalBtn.textContent = "Normal";
+      densNormalBtn.title = "Default decomposition (system prompt as-is).";
+      const densHighBtn = document.createElement("button"); densHighBtn.className = "ideoap-btn"; densHighBtn.textContent = "High";
+      densHighBtn.title = "Aggressive decomposition: one bbox per distinct object (e.g. table and vase become two).";
+      backendRow.appendChild(densNormalBtn); backendRow.appendChild(densHighBtn);
+      aiBar.appendChild(backendRow);
+
+      // idea
+      const ideaTa = document.createElement("textarea");
+      ideaTa.className = "ideoap-area";
+      ideaTa.placeholder = "Describe the image you want… (or drop a reference image)";
+      ideaTa.style.minHeight = "46px";
+      aiBar.appendChild(ideaTa);
+
+      // image drop + generate row
+      const genRow = document.createElement("div");
+      genRow.className = "ideoap-row";
+      const drop = document.createElement("div");
+      drop.className = "ideoap-drop";
+      drop.textContent = "+ reference image (optional)";
+      const fileInput = document.createElement("input");
+      fileInput.type = "file"; fileInput.accept = "image/*"; fileInput.style.display = "none";
+      const generateBtn = document.createElement("button");
+      generateBtn.className = "ideoap-btn primary"; generateBtn.textContent = "Generate";
+      genRow.appendChild(drop); genRow.appendChild(fileInput); genRow.appendChild(generateBtn);
+      aiBar.appendChild(genRow);
+
+      // gemini-only row (api key + fetch + model)
+      const gemRow = document.createElement("div");
+      gemRow.className = "ideoap-row";
+      const keyInput = document.createElement("input");
+      keyInput.className = "ideoap-input"; keyInput.type = "password";
+      keyInput.placeholder = "Gemini API key"; keyInput.style.flex = "1"; keyInput.style.minWidth = "120px";
+      const fetchBtn = document.createElement("button"); fetchBtn.className = "ideoap-btn"; fetchBtn.textContent = "Fetch models";
+      const modelSelect = document.createElement("select"); modelSelect.className = "ideoap-select";
+      const ph = document.createElement("option"); ph.value = ""; ph.textContent = "— models —"; modelSelect.appendChild(ph);
+      gemRow.appendChild(keyInput); gemRow.appendChild(fetchBtn); gemRow.appendChild(modelSelect);
+      aiBar.appendChild(gemRow);
+
+      // key hint — shown only while the Gemini key box is empty
+      const keyHint = document.createElement("div");
+      keyHint.className = "ideoap-status";
+      keyHint.style.cssText = "color:#8a8a8a; font:11px ui-sans-serif,system-ui,sans-serif;";
+      keyHint.innerHTML = 'Get a free API key from <a href="https://aistudio.google.com/" target="_blank" rel="noopener" style="color:#fff; text-decoration:underline;">aistudio.google.com</a>';
+      aiBar.appendChild(keyHint);
+
+      // ollama row (host + fetch + model select)
+      const ollamaRow = document.createElement("div");
+      ollamaRow.className = "ideoap-row";
+      const hostInput = document.createElement("input");
+      hostInput.className = "ideoap-input";
+      hostInput.placeholder = DEFAULT_OLLAMA_HOST; hostInput.value = DEFAULT_OLLAMA_HOST;
+      hostInput.style.flex = "1"; hostInput.style.minWidth = "120px";
+      hostInput.title = "Ollama server URL.";
+      const ollamaFetchBtn = document.createElement("button"); ollamaFetchBtn.className = "ideoap-btn"; ollamaFetchBtn.textContent = "Fetch models";
+      const ollamaSelect = document.createElement("select"); ollamaSelect.className = "ideoap-select";
+      const oph = document.createElement("option"); oph.value = ""; oph.textContent = "— models —"; ollamaSelect.appendChild(oph);
+      ollamaRow.appendChild(hostInput); ollamaRow.appendChild(ollamaFetchBtn); ollamaRow.appendChild(ollamaSelect);
+      aiBar.appendChild(ollamaRow);
+
+      const ollamaHint = document.createElement("div");
+      ollamaHint.className = "ideoap-status";
+      ollamaHint.style.cssText = "color:#8a8a8a; font:11px ui-sans-serif,system-ui,sans-serif;";
+      ollamaHint.innerHTML = 'Fastest engine. Start it with <span style="color:#fff;">ollama serve</span> and pull a vision model, e.g. <span style="color:#fff;">ollama pull qwen2.5vl</span>.';
+      aiBar.appendChild(ollamaHint);
+
+      // openrouter row (api key + fetch + model select)
+      const orRow = document.createElement("div");
+      orRow.className = "ideoap-row";
+      const orKeyInput = document.createElement("input");
+      orKeyInput.className = "ideoap-input"; orKeyInput.type = "password";
+      orKeyInput.placeholder = "OpenRouter API key"; orKeyInput.style.flex = "1"; orKeyInput.style.minWidth = "120px";
+      const orFetchBtn = document.createElement("button"); orFetchBtn.className = "ideoap-btn"; orFetchBtn.textContent = "Fetch models";
+      const orSelect = document.createElement("select"); orSelect.className = "ideoap-select";
+      const orph = document.createElement("option"); orph.value = ""; orph.textContent = "— models —"; orSelect.appendChild(orph);
+      orRow.appendChild(orKeyInput); orRow.appendChild(orFetchBtn); orRow.appendChild(orSelect);
+      aiBar.appendChild(orRow);
+
+      const orHint = document.createElement("div");
+      orHint.className = "ideoap-status";
+      orHint.style.cssText = "color:#8a8a8a; font:11px ui-sans-serif,system-ui,sans-serif;";
+      orHint.innerHTML = 'Get a key from <a href="https://openrouter.ai/keys" target="_blank" rel="noopener" style="color:#fff; text-decoration:underline;">openrouter.ai/keys</a>. Fetch lists vision-capable models only.';
+      aiBar.appendChild(orHint);
+
+      // local-only row (model id + 4-bit)
+      const localRow = document.createElement("div");
+      localRow.className = "ideoap-row";
+      const localModelInput = document.createElement("input");
+      localModelInput.className = "ideoap-input"; localModelInput.value = DEFAULT_LOCAL_MODEL;
+      localModelInput.style.flex = "1"; localModelInput.style.minWidth = "140px";
+      localModelInput.title = "Hugging Face id of any local vision LLM (Qwen-VL, Gemma 3, InternVL, …). Pick a preset or type your own.";
+      const localModelList = document.createElement("datalist");
+      localModelList.id = "ideoap-local-models-" + (node.id != null ? node.id : Math.random().toString(36).slice(2));
+      for (const m of LOCAL_MODEL_PRESETS) {
+        const opt = document.createElement("option"); opt.value = m; localModelList.appendChild(opt);
+      }
+      localModelInput.setAttribute("list", localModelList.id);
+      const attnSelect = document.createElement("select");
+      attnSelect.className = "ideoap-select";
+      attnSelect.style.flex = "0 0 auto"; attnSelect.style.minWidth = "92px";
+      attnSelect.title = "Attention kernel. Auto picks Flash-Attention 2 if installed, else SDPA (fast PyTorch fused attention). SDPA forces SDPA; Eager is the slow reference path.";
+      for (const [val, label] of [["auto", "Attn: Auto"], ["flash_attention_2", "Attn: Flash"], ["sdpa", "Attn: SDPA"], ["eager", "Attn: Eager"]]) {
+        const opt = document.createElement("option"); opt.value = val; opt.textContent = label; attnSelect.appendChild(opt);
+      }
+      const fourBitWrap = document.createElement("label"); fourBitWrap.className = "ideoap-toggle";
+      const fourBit = document.createElement("input"); fourBit.type = "checkbox";
+      fourBitWrap.appendChild(fourBit); fourBitWrap.appendChild(document.createTextNode("4-bit"));
+      localRow.appendChild(localModelInput); localRow.appendChild(localModelList);
+      localRow.appendChild(attnSelect); localRow.appendChild(fourBitWrap);
+      aiBar.appendChild(localRow);
+
+      // status + settings row
+      const statusRow = document.createElement("div");
+      statusRow.className = "ideoap-row";
+      const status = document.createElement("div"); status.className = "ideoap-status";
+      const unloadWrap = document.createElement("label"); unloadWrap.className = "ideoap-toggle";
+      unloadWrap.title = "Free the model right after generating. Local: drops it from VRAM. Ollama: sends keep_alive 0 so it unloads instantly instead of idling for 5 minutes.";
+      const unloadCb = document.createElement("input"); unloadCb.type = "checkbox"; unloadCb.checked = true;
+      unloadWrap.appendChild(unloadCb); unloadWrap.appendChild(document.createTextNode("unload after"));
+      const thinkWrap = document.createElement("label"); thinkWrap.className = "ideoap-toggle";
+      thinkWrap.title = "Let the model reason before answering. Off is faster and more reliable for JSON; turn on only for models that need it.";
+      const thinkCb = document.createElement("input"); thinkCb.type = "checkbox"; thinkCb.checked = false;
+      thinkWrap.appendChild(thinkCb); thinkWrap.appendChild(document.createTextNode("think"));
+      statusRow.appendChild(status); statusRow.appendChild(thinkWrap); statusRow.appendChild(unloadWrap);
+      aiBar.appendChild(statusRow);
+
+      // editor toolbar
+      const bar = document.createElement("div");
+      bar.className = "ideoap-bar";
+      const hint = document.createElement("span");
+      hint.style.flex = "1";
+      const copyBtn = document.createElement("button");
+      copyBtn.className = "ideoap-btn"; copyBtn.textContent = "Copy";
+      copyBtn.title = "Copy the current caption JSON to the clipboard";
+      const importBtn = document.createElement("button");
+      importBtn.className = "ideoap-btn"; importBtn.textContent = "Paste";
+      importBtn.title = "Parse a caption JSON (clipboard, else prompt) and populate the node";
+      const clearBtn = document.createElement("button");
+      clearBtn.className = "ideoap-btn"; clearBtn.textContent = "Clear all";
+      const tokenSpan = document.createElement("span");
+      tokenSpan.style.cssText = "color:#666; white-space:nowrap;";
+      tokenSpan.title = "Rough token estimate (~chars/4) of the caption prompt — not exact";
+      bar.appendChild(hint); bar.appendChild(tokenSpan); bar.appendChild(copyBtn); bar.appendChild(importBtn); bar.appendChild(clearBtn);
+
+      // style palette row
+      const styleBar = document.createElement("div");
+      styleBar.className = "ideoap-bar";
+      const styleLbl = document.createElement("span"); styleLbl.className = "ideoap-lbl"; styleLbl.textContent = "Style colors";
+      styleBar.appendChild(styleLbl);
+
+      const canvasEl = document.createElement("canvas");
+      canvasEl.className = "ideoap-canvas";
+      canvasEl.tabIndex = 0;
+      canvasEl.title = "Drag to draw · click to select · alt-click overlap · dbl-click edit · " +
+        "Del remove · Ctrl/Cmd+C/V/D copy/paste/duplicate";
+      const ctx = canvasEl.getContext("2d");
+      addWheelPassthrough(wrap);
+      addMiddleClickPan(canvasEl);
+
+      const panel = document.createElement("div");
+      panel.className = "ideoap-panel";
+
+      wrap.appendChild(aiBar);
+      wrap.appendChild(bar); wrap.appendChild(styleBar); wrap.appendChild(canvasEl); wrap.appendChild(panel);
+
+      const TOOLBAR_H = 22;
+      node._widgetHeight = 480;
+      node.ideoEditor = node.addDOMWidget("ideo_editor", "Ideogram4Autoprompter", wrap, {
+        serialize: false, hideOnZoom: false,
+        getMinHeight: () => node._widgetHeight,
+      });
+      node.resizable = true;
+
+      // ── canvas sizing ──
+      function setCanvasSize(w, h) {
+        canvasEl.style.aspectRatio = `${w} / ${h}`;
+        if (node.graph) node.graph.setDirtyCanvas(true, true);
+      }
+      function syncCanvasToDims() {
+        const w = wWidget ? wWidget.value : 1024, h = hWidget ? hWidget.value : 1024;
+        setCanvasSize(Math.max(1, w), Math.max(1, h));
+        drawCanvas();
+      }
+      function recalcWidgetHeight() {
+        const contentH = panel.offsetTop + panel.offsetHeight;
+        if (contentH > 0) {
+          node._widgetHeight = contentH + 10;
+        } else {
+          const ratio = (hWidget?.value || 1) / (wWidget?.value || 1);
+          node._widgetHeight = Math.round(Math.max(100, node.size[0] - 30) * ratio) + TOOLBAR_H + 220;
+        }
+      }
+      function fitNode() {
+        recalcWidgetHeight();
+        const minH = node.computeSize()[1];
+        if (node.size[1] < minH) node.setSize([node.size[0], minH]);
+      }
+
+      // ── geometry helpers ──
+      function logW() { return canvasEl.offsetWidth || 1; }
+      function logH() { return canvasEl.offsetHeight || 1; }
+      function toPx(b) {
+        const W = logW(), H = logH();
+        return { x1: b.x * W, y1: b.y * H, x2: (b.x + b.w) * W, y2: (b.y + b.h) * H };
+      }
+      function mouseN(e) {
+        const r = canvasEl.getBoundingClientRect();
+        return { x: (e.clientX - r.left) / r.width, y: (e.clientY - r.top) / r.height };
+      }
+      function clamp01(v) { return Math.max(0, Math.min(1, v)); }
+      function wrapLines(text, maxW) {
+        const lines = [];
+        for (const para of text.split("\n")) {
+          let line = "";
+          for (const word of para.split(/\s+/)) {
+            if (!word) continue;
+            const test = line ? line + " " + word : word;
+            if (line && ctx.measureText(test).width > maxW) { lines.push(line); line = word; }
+            else line = test;
+          }
+          lines.push(line);
+        }
+        return lines;
+      }
+      function normalizeBox(b) {
+        let x = b.x, y = b.y, w = b.w, h = b.h;
+        if (w < 0) { x += w; w = -w; }
+        if (h < 0) { y += h; h = -h; }
+        x = clamp01(x); y = clamp01(y);
+        w = Math.min(w, 1 - x); h = Math.min(h, 1 - y);
+        return { ...b, x, y, w: Math.max(0, w), h: Math.max(0, h) };
+      }
+      function boxesAt(mN) {
+        const rx = HANDLE / logW(), ry = HANDLE / logH();
+        const res = [];
+        for (let i = node._boxes.length - 1; i >= 0; i--) {
+          const b = node._boxes[i];
+          const mode = rectHitTestN(mN.x, mN.y, b.x, b.y, b.x + b.w, b.y + b.h, rx, ry);
+          if (mode) res.push({ index: i, mode });
+        }
+        const ai = res.findIndex((c) => c.index === node._activeIdx);
+        if (ai > 0) res.unshift(res.splice(ai, 1)[0]);
+        return res;
+      }
+      function hitTest(mN) {
+        const cands = boxesAt(mN);
+        if (!cands.length) return null;
+        return cands.find((c) => c.index === node._activeIdx && c.mode !== "move") || cands[0];
+      }
+      function tagRects() {
+        ctx.font = "bold 11px ui-monospace,monospace";
+        const W = logW(), H = logH(), h = 14;
+        const placed = [], rects = [];
+        const hits = (a, b) => a.x < b.x + b.w && a.x + a.w > b.x && a.y < b.y + b.h && a.y + a.h > b.y;
+        for (let i = 0; i < node._boxes.length; i++) {
+          const b = node._boxes[i];
+          const x1 = b.x * W, y1 = b.y * H, x2 = (b.x + b.w) * W, y2 = (b.y + b.h) * H;
+          const tag = String(i + 1).padStart(2, "0");
+          const w = ctx.measureText(tag).width + 8;
+          let pick = [x1, y1];
+          for (const [cx, cy] of [[x1, y1], [x2 - w, y1], [x2 - w, y2 - h], [x1, y2 - h]]) {
+            if (!placed.some((p) => hits({ x: cx, y: cy, w, h }, p))) { pick = [cx, cy]; break; }
+          }
+          const r = { x: pick[0], y: pick[1], w, h, tag };
+          placed.push(r); rects[i] = r;
+        }
+        return rects;
+      }
+      function titleAt(mN) {
+        const px = mN.x * logW(), py = mN.y * logH();
+        const rects = tagRects();
+        for (let i = node._boxes.length - 1; i >= 0; i--) {
+          const r = rects[i];
+          if (r && px >= r.x && px <= r.x + r.w && py >= r.y && py <= r.y + r.h) return i;
+        }
+        return null;
+      }
+      function pickForSelection(mN, cycle) {
+        const cands = boxesAt(mN);
+        if (!cands.length) return null;
+        const ah = cands.find((c) => c.index === node._activeIdx && c.mode !== "move");
+        if (ah && !cycle) return ah;
+        const ti = titleAt(mN);
+        if (ti !== null && !cycle) return { index: ti, mode: "move" };
+        if (cycle && cands.length > 1) {
+          const pos = cands.findIndex((c) => c.index === node._activeIdx);
+          return cands[(pos + 1) % cands.length];
+        }
+        return cands.find((c) => c.index === node._activeIdx && c.mode !== "move") || cands[0];
+      }
+      function rectHitTestN(mx, my, x1, y1, x2, y2, rx, ry) {
+        const h = (cx, cy) => Math.abs(mx - cx) < rx && Math.abs(my - cy) < ry;
+        if (h(x1, y1)) return "resize-tl";
+        if (h(x2, y1)) return "resize-tr";
+        if (h(x1, y2)) return "resize-bl";
+        if (h(x2, y2)) return "resize-br";
+        if (mx >= x1 && mx <= x2 && Math.abs(my - y1) < ry) return "resize-t";
+        if (mx >= x1 && mx <= x2 && Math.abs(my - y2) < ry) return "resize-b";
+        if (my >= y1 && my <= y2 && Math.abs(mx - x1) < rx) return "resize-l";
+        if (my >= y1 && my <= y2 && Math.abs(mx - x2) < rx) return "resize-r";
+        if (mx >= x1 && mx <= x2 && my >= y1 && my <= y2) return "move";
+        return null;
+      }
+      function applyDrag(mode, start, dN) {
+        let { x, y, w, h } = start;
+        const dx = dN.x, dy = dN.y;
+        switch (mode) {
+          case "move": x += dx; y += dy; x = clamp01(Math.min(x, 1 - w)); y = clamp01(Math.min(y, 1 - h)); break;
+          case "draw":
+          case "resize-br": w += dx; h += dy; break;
+          case "resize-tl": x += dx; y += dy; w -= dx; h -= dy; break;
+          case "resize-tr": y += dy; w += dx; h -= dy; break;
+          case "resize-bl": x += dx; w -= dx; h += dy; break;
+          case "resize-t": y += dy; h -= dy; break;
+          case "resize-b": h += dy; break;
+          case "resize-l": x += dx; w -= dx; break;
+          case "resize-r": w += dx; break;
+        }
+        return mode === "move" ? { ...start, x, y } : normalizeBox({ ...start, x, y, w, h });
+      }
+
+      // ── drawing ──
+      let _rafPending = false;
+      function drawCanvas() {
+        if (_rafPending) return;
+        _rafPending = true;
+        requestAnimationFrame(() => { _rafPending = false; _draw(); });
+      }
+      function _draw() {
+        const W = logW(), H = logH(), d = window.devicePixelRatio || 1;
+        const bw = Math.round(W * d), bh = Math.round(H * d);
+        if (canvasEl.width !== bw || canvasEl.height !== bh) { canvasEl.width = bw; canvasEl.height = bh; }
+        ctx.setTransform(d, 0, 0, d, 0, 0);
+        ctx.clearRect(0, 0, W, H);
+        ctx.fillStyle = "#000"; ctx.fillRect(0, 0, W, H);
+        const aIdx = (node._focused || node._selected) ? node._activeIdx : -1;
+        const order = node._boxes.map((_, i) => i).filter((i) => i !== aIdx);
+        if (aIdx >= 0 && aIdx < node._boxes.length) order.push(aIdx);
+        const tagR = tagRects();
+        for (const i of order) {
+          const b = node._boxes[i], active = i === aIdx;
+          const pal = (b.palette || []).filter(Boolean);
+          const col = pal.length ? pal[0] : "#9a9a9a";       // box accent = first palette color, else grey
+          const { x1, y1, x2, y2 } = toPx(b);
+          const w = x2 - x1, h = y2 - y1;
+          const hovered = i === node._hoverBox || active;
+          if (active) { ctx.fillStyle = "rgba(0,0,0,0.9)"; ctx.fillRect(x1, y1, w, h); }
+          ctx.fillStyle = col + (hovered ? "33" : "1f");
+          ctx.fillRect(x1, y1, w, h);
+          if (b.nobbox) ctx.setLineDash([6, 4]);
+          const lw = active ? 2 : (hovered ? 1.5 : 1);
+          ctx.strokeStyle = col; ctx.lineWidth = lw;
+          ctx.strokeRect(x1 + lw / 2, y1 + lw / 2, w - lw, h - lw);
+          ctx.setLineDash([]);
+          if (pal.length) {
+            const sw = w / pal.length, n = pal.length, sh = 7;
+            for (let p = 0; p < n; p++) {
+              const sx = x1 + Math.round(p * sw);
+              ctx.fillStyle = pal[p];
+              ctx.fillRect(sx, y1, x1 + Math.round((p + 1) * sw) - sx, sh);
+            }
+          }
+          ctx.save();
+          ctx.beginPath(); ctx.rect(x1, y1, w, h); ctx.clip();
+          let body = b.desc || "";
+          if (b.type === "text" && b.text) body = `"${b.text}"` + (body ? " — " + body : "");
+          if (body) {
+            ctx.font = "12px ui-monospace,monospace";
+            ctx.fillStyle = "#e6e6e6";
+            const pad = 4, lh = 14;
+            let ty = y1 + 15 + 12;
+            for (const line of wrapLines(body, w - pad * 2)) {
+              if (ty > y1 + h) break;
+              ctx.fillText(line, x1 + pad, ty);
+              ty += lh;
+            }
+          }
+          const tr = tagR[i];
+          const tagBg = col;
+          ctx.font = "bold 11px ui-monospace,monospace";
+          ctx.fillStyle = tagBg;
+          ctx.fillRect(tr.x, tr.y, tr.w, 14);
+          if (i === node._hoverTitle) {
+            ctx.fillStyle = "rgba(255,255,255,0.3)"; ctx.fillRect(tr.x, tr.y, tr.w, 14);
+            ctx.strokeStyle = "#fff"; ctx.lineWidth = 1; ctx.strokeRect(tr.x + 0.5, tr.y + 0.5, tr.w - 1, 13);
+          }
+          ctx.fillStyle = textOn(tagBg);
+          ctx.fillText(tr.tag, tr.x + 4, tr.y + 11);
+          ctx.restore();
+        }
+      }
+
+      // ── serialization ──
+      function serialize() {
+        if (elementsWidget) elementsWidget.value = node._boxes.length ? JSON.stringify(node._boxes) : "";
+        if (stylePaletteWidget) stylePaletteWidget.value = node._stylePalette.length ? JSON.stringify(node._stylePalette) : "";
+      }
+      function serializeAi() {
+        if (aiStateWidget) aiStateWidget.value = JSON.stringify(node._ai);   // never includes the API key
+      }
+      function commit() { serialize(); renderPanel(); drawCanvas(); updateTokens(); }
+
+      function removeBox(i) {
+        node._boxes.splice(i, 1);
+        if (node._boxes.length === 0) node._activeIdx = -1;
+        else if (i <= node._activeIdx) node._activeIdx = Math.max(0, node._activeIdx - 1);
+      }
+
+      // ── pointer interaction ──
+      canvasEl.addEventListener("mousedown", (e) => {
+        if (e.button === 2) {
+          e.preventDefault();
+          const hit = hitTest(mouseN(e));
+          if (hit) removeBox(hit.index);
+          else if (node._activeIdx >= 0) removeBox(node._activeIdx);
+          commit(); fitNode();
+          return;
+        }
+        if (e.button !== 0) return;
+        canvasEl.focus();
+        node._hoverTitle = null; node._hoverBox = null;
+        const mN = mouseN(e);
+        const hit = pickForSelection(mN, e.altKey);
+        if (hit) {
+          node._activeIdx = hit.index;
+          node._dragMode = hit.mode;
+          node._boxAtStart = { ...node._boxes[hit.index] };
+        } else {
+          node._dragMode = "draw";
+          const nb = { x: mN.x, y: mN.y, w: 0, h: 0, type: "obj", text: "", desc: "", palette: [] };
+          node._boxes.push(nb);
+          node._activeIdx = node._boxes.length - 1;
+          node._boxAtStart = { ...nb };
+        }
+        node._drawing = true;
+        node._dragStartN = mN;
+        document.addEventListener("mousemove", onMove);
+        document.addEventListener("mouseup", onUp);
+        e.preventDefault(); e.stopPropagation();
+        drawCanvas();
+      });
+      canvasEl.addEventListener("mousemove", (e) => {
+        if (node._drawing) return;
+        const mN = mouseN(e);
+        const ti = titleAt(mN);
+        const hit = hitTest(mN);
+        const hb = ti != null ? ti : (hit ? hit.index : null);
+        if (ti !== node._hoverTitle || hb !== node._hoverBox) {
+          node._hoverTitle = ti; node._hoverBox = hb; drawCanvas();
+        }
+        canvasEl.style.cursor = ti != null ? "pointer" : (hit ? (cursorForBboxMode(hit.mode) || "crosshair") : "crosshair");
+      });
+      canvasEl.addEventListener("mouseleave", () => {
+        if (node._hoverTitle !== null || node._hoverBox !== null) {
+          node._hoverTitle = null; node._hoverBox = null; drawCanvas();
+        }
+      });
+
+      // ── inline description editing ──
+      let inlineTa = null;
+      function closeInlineEditor() { if (inlineTa) { inlineTa.remove(); inlineTa = null; } }
+      function openInlineEditor(idx) {
+        closeInlineEditor();
+        const b = node._boxes[idx];
+        if (!b) return;
+        node._activeIdx = idx;
+        const dw = canvasEl.offsetWidth, dh = canvasEl.offsetHeight;
+        const ox = canvasEl.offsetLeft, oy = canvasEl.offsetTop;
+        const w = Math.min(dw, Math.max(70, b.w * dw));
+        const h = Math.min(dh, Math.max(42, b.h * dh));
+        const left = Math.max(ox, Math.min(ox + b.x * dw, ox + dw - w));
+        const top = Math.max(oy, Math.min(oy + b.y * dh, oy + dh - h));
+        const ta = document.createElement("textarea");
+        ta.className = "ideoap-inline";
+        ta.value = b.desc || "";
+        ta.style.left = left + "px"; ta.style.top = top + "px";
+        ta.style.width = w + "px"; ta.style.height = h + "px";
+        ta.style.borderColor = (b.palette || []).find(Boolean) || "#fff";
+        stopProp(ta);
+        wrap.appendChild(ta);
+        inlineTa = ta;
+        ta.focus(); ta.select();
+        const orig = b.desc || "";
+        let cancelled = false;
+        ta.addEventListener("input", () => { b.desc = ta.value; drawCanvas(); updateTokens(); });
+        ta.addEventListener("keydown", (e) => {
+          e.stopPropagation();
+          if (e.key === "Escape") { cancelled = true; b.desc = orig; ta.blur(); }
+          else if (e.key === "Enter" && (e.ctrlKey || e.metaKey)) ta.blur();
+        });
+        ta.addEventListener("blur", () => {
+          if (!cancelled) b.desc = ta.value;
+          closeInlineEditor(); commit();
+        });
+      }
+      canvasEl.addEventListener("dblclick", (e) => {
+        e.preventDefault(); e.stopPropagation();
+        const cands = boxesAt(mouseN(e));
+        const target = cands.find((c) => c.index === node._activeIdx) || cands[0];
+        if (target) openInlineEditor(target.index);
+      });
+
+      function pasteBox() {
+        if (!copiedBox) return;
+        const nb = JSON.parse(JSON.stringify(copiedBox));
+        nb.x = Math.max(0, Math.min(clamp01(nb.x + 0.03), 1 - nb.w));
+        nb.y = Math.max(0, Math.min(clamp01(nb.y + 0.03), 1 - nb.h));
+        delete nb.nobbox;
+        node._boxes.push(nb);
+        node._activeIdx = node._boxes.length - 1;
+        commit(); fitNode();
+      }
+      canvasEl.addEventListener("keydown", (e) => {
+        if (node._drawing) return;
+        const ctrl = e.ctrlKey || e.metaKey;
+        if ((e.key === "Delete" || e.key === "Backspace") && node._activeIdx >= 0) {
+          e.preventDefault(); e.stopPropagation();
+          removeBox(node._activeIdx); commit(); fitNode();
+        } else if (ctrl && e.key === "c" && node._activeIdx >= 0) {
+          e.preventDefault(); e.stopPropagation();
+          copiedBox = JSON.parse(JSON.stringify(node._boxes[node._activeIdx]));
+        } else if (ctrl && e.key === "v" && copiedBox) {
+          e.preventDefault(); e.stopPropagation();
+          pasteBox();
+        } else if (ctrl && e.key === "d" && node._activeIdx >= 0) {
+          e.preventDefault(); e.stopPropagation();
+          copiedBox = JSON.parse(JSON.stringify(node._boxes[node._activeIdx]));
+          pasteBox();
+        }
+      });
+
+      function onMove(e) {
+        if (!node._drawing) return;
+        const mN = mouseN(e);
+        const dN = { x: mN.x - node._dragStartN.x, y: mN.y - node._dragStartN.y };
+        const nb = applyDrag(node._dragMode, node._boxAtStart, dN);
+        delete nb.nobbox;
+        node._boxes[node._activeIdx] = nb;
+        drawCanvas();
+      }
+      function onUp() {
+        if (!node._drawing) return;
+        node._drawing = false;
+        document.removeEventListener("mousemove", onMove);
+        document.removeEventListener("mouseup", onUp);
+        const b = node._boxes[node._activeIdx];
+        if (b && (b.w < 0.005 || b.h < 0.005) && node._dragMode === "draw") removeBox(node._activeIdx);
+        commit();
+      }
+      canvasEl.addEventListener("contextmenu", (e) => e.preventDefault());
+      clearBtn.addEventListener("mousedown", (e) => e.stopPropagation());
+      clearBtn.addEventListener("click", () => {
+        closeInlineEditor();
+        node._boxes = []; node._activeIdx = -1; node._stylePalette = [];
+        commit(); rebuildStylePalette(); fitNode();
+      });
+
+      // ── build caption JSON (mirrors Python key order) ──
+      function pyJson(v, lvl = 0) {
+        if (v === null) return "null";
+        if (typeof v === "number" || typeof v === "boolean") return String(v);
+        if (typeof v === "string") return JSON.stringify(v);
+        const pad = "    ".repeat(lvl + 1), end = "    ".repeat(lvl);
+        if (Array.isArray(v)) {
+          if (!v.length) return "[]";
+          if (v.every((x) => x === null || typeof x !== "object"))
+            return "[" + v.map((x) => pyJson(x, lvl)).join(", ") + "]";
+          return "[\n" + v.map((x) => pad + pyJson(x, lvl + 1)).join(",\n") + "\n" + end + "]";
+        }
+        const keys = Object.keys(v);
+        if (!keys.length) return "{}";
+        return "{\n" + keys.map((k) => pad + JSON.stringify(k) + ": " + pyJson(v[k], lvl + 1)).join(",\n") + "\n" + end + "}";
+      }
+      function getW(name) { const w = findW(name); return w ? w.value : ""; }
+      function cleanPalette(arr) { return (arr || []).filter((c) => c).map((c) => c.toUpperCase()); }
+      function normBboxJS(b) {
+        const c = (v) => Math.max(0, Math.min(1000, Math.round(v * 1000)));
+        let ymin = c(b.y), xmin = c(b.x), ymax = c(b.y + b.h), xmax = c(b.x + b.w);
+        if (ymin > ymax) [ymin, ymax] = [ymax, ymin];
+        if (xmin > xmax) [xmin, xmax] = [xmax, xmin];
+        return [ymin, xmin, ymax, xmax];
+      }
+      function buildCaption() {
+        const cap = {};
+        if ((getW("high_level_description") || "").trim()) cap.high_level_description = getW("high_level_description");
+        const styleW = findW("style");
+        const kind = styleW ? styleW.value : "none";
+        if (kind !== "none") {
+          const sd = { aesthetics: getW("aesthetics"), lighting: getW("lighting") };
+          if (kind === "photo") { sd.photo = getW("style.photo") || ""; sd.medium = getW("medium"); }
+          else { sd.medium = getW("medium"); sd.art_style = getW("style.art_style") || ""; }
+          const pal = cleanPalette(node._stylePalette);
+          if (pal.length) sd.color_palette = pal;
+          cap.style_description = sd;
+        }
+        const elements = node._boxes.map((b) => {
+          const etype = b.type === "text" ? "text" : "obj";
+          const el = { type: etype };
+          if (!b.nobbox) el.bbox = normBboxJS(b);
+          if (etype === "text") el.text = b.text || "";
+          el.desc = b.desc || "";
+          const pal = cleanPalette(b.palette).slice(0, MAX_ELEM_COLORS);
+          if (pal.length) el.color_palette = pal;
+          return el;
+        });
+        cap.compositional_deconstruction = { background: getW("background"), elements };
+        return pyJson(cap);
+      }
+      function updateTokens() {
+        tokenSpan.textContent = "~" + Math.ceil(buildCaption().length / 4) + " tok";
+      }
+      async function doCopy() {
+        const txt = buildCaption();
+        try { await navigator.clipboard.writeText(txt); copyBtn.textContent = "Copied"; setTimeout(() => (copyBtn.textContent = "Copy"), 900); }
+        catch (e) { window.prompt("Copy the caption JSON:", txt); }
+      }
+      copyBtn.addEventListener("mousedown", (e) => e.stopPropagation());
+      copyBtn.addEventListener("click", doCopy);
+
+      // ── import a caption JSON and populate the node ──
+      function setWidgetVal(name, val) {
+        const w = findW(name);
+        if (w) { w.value = val; w.callback?.(val); }
+      }
+      function bboxElemToBox(el, idx) {
+        if (!el || typeof el !== "object") return null;
+        const box = { type: el.type === "text" ? "text" : "obj",
+          text: el.text || "", desc: el.desc || "",
+          palette: Array.isArray(el.color_palette) ? el.color_palette.slice() : [] };
+        const bb = el.bbox;
+        if (Array.isArray(bb) && bb.length === 4) {
+          const [ymin, xmin, ymax, xmax] = bb;
+          box.x = xmin / 1000; box.y = ymin / 1000; box.w = (xmax - xmin) / 1000; box.h = (ymax - ymin) / 1000;
+        } else {
+          const k = (idx || 0) % 6;
+          box.x = 0.03 + k * 0.035; box.y = 0.03 + k * 0.035; box.w = 0.22; box.h = 0.14;
+          box.nobbox = true;
+        }
+        return box;
+      }
+      function applyCaption(cap) {
+        const cd = (cap && cap.compositional_deconstruction) || {};
+        const els = Array.isArray(cd.elements) ? cd.elements : [];
+        node._boxes = els.map((el, i) => bboxElemToBox(el, i)).filter(Boolean);
+        node._activeIdx = node._boxes.length ? 0 : -1;
+        setWidgetVal("high_level_description", cap.high_level_description || "");
+        setWidgetVal("background", cd.background || "");
+        const sd = cap.style_description || {};
+        let kind = "none";
+        if (typeof sd.photo === "string") kind = "photo";
+        else if (typeof sd.art_style === "string") kind = "art_style";
+        const styleW = findW("style");
+        if (styleW) styleW.value = kind;
+        if (kind === "photo") setWidgetVal("style.photo", sd.photo || "");
+        else if (kind === "art_style") setWidgetVal("style.art_style", sd.art_style || "");
+        setWidgetVal("aesthetics", sd.aesthetics || "");
+        setWidgetVal("lighting", sd.lighting || "");
+        setWidgetVal("medium", sd.medium || "");
+        node._stylePalette = Array.isArray(sd.color_palette) ? sd.color_palette.slice() : [];
+      }
+      function tryParseCaption(t) {
+        if (!t) return null;
+        try { const o = JSON.parse(t); return (o && typeof o === "object" && o.compositional_deconstruction) ? o : null; }
+        catch (e) { return null; }
+      }
+      async function doImport() {
+        let cap = null, txt = "";
+        try { txt = (await navigator.clipboard.readText() || "").trim(); cap = tryParseCaption(txt); } catch (e) {}
+        if (!cap) { txt = (window.prompt("Paste Ideogram 4 caption JSON:", "") || "").trim(); cap = tryParseCaption(txt); }
+        if (!cap) { if (txt) alert("Not a valid Ideogram 4 caption JSON (needs 'compositional_deconstruction')."); return; }
+        closeInlineEditor();
+        applyCaption(cap);
+        syncCanvasToDims(); commit(); rebuildStylePalette(); fitNode();
+      }
+      importBtn.addEventListener("mousedown", (e) => e.stopPropagation());
+      importBtn.addEventListener("click", doImport);
+
+      // ── AI: backend toggle, image, generation ──
+      function refreshKeyHint() {
+        // Show the "get a free key" line only in Gemini mode while the key box is empty.
+        keyHint.style.display = (node._ai.backend === "gemini" && !node._apiKey) ? "" : "none";
+      }
+      function refreshBackendUI() {
+        const b = node._ai.backend;
+        const isGem = b === "gemini", isOllama = b === "ollama", isLocal = b === "local", isOr = b === "openrouter";
+        localBtn.classList.toggle("active", isLocal);
+        ollamaBtn.classList.toggle("active", isOllama);
+        gemBtn.classList.toggle("active", isGem);
+        orBtn.classList.toggle("active", isOr);
+        gemRow.style.display = isGem ? "" : "none";
+        ollamaRow.style.display = isOllama ? "" : "none";
+        ollamaHint.style.display = isOllama ? "" : "none";
+        orRow.style.display = isOr ? "" : "none";
+        orHint.style.display = isOr ? "" : "none";
+        localRow.style.display = isLocal ? "" : "none";
+        // unload-after applies to local (free VRAM) and ollama (keep_alive:0); think to both
+        unloadWrap.style.display = (isLocal || isOllama) ? "" : "none";
+        thinkWrap.style.display = (isGem || isOr) ? "none" : "";
+        refreshKeyHint();
+        requestAnimationFrame(fitNode);
+      }
+      function setBackend(b) { node._ai.backend = b; serializeAi(); refreshBackendUI(); }
+      localBtn.addEventListener("mousedown", (e) => e.stopPropagation());
+      ollamaBtn.addEventListener("mousedown", (e) => e.stopPropagation());
+      gemBtn.addEventListener("mousedown", (e) => e.stopPropagation());
+      orBtn.addEventListener("mousedown", (e) => e.stopPropagation());
+      localBtn.addEventListener("click", () => setBackend("local"));
+      ollamaBtn.addEventListener("click", () => setBackend("ollama"));
+      gemBtn.addEventListener("click", () => setBackend("gemini"));
+      orBtn.addEventListener("click", () => setBackend("openrouter"));
+
+      function refreshDensityUI() {
+        const high = node._ai.density === "high";
+        densHighBtn.classList.toggle("active", high);
+        densNormalBtn.classList.toggle("active", !high);
+      }
+      function setDensity(d) { node._ai.density = d; serializeAi(); refreshDensityUI(); }
+      densNormalBtn.addEventListener("mousedown", (e) => e.stopPropagation());
+      densHighBtn.addEventListener("mousedown", (e) => e.stopPropagation());
+      densNormalBtn.addEventListener("click", () => setDensity("normal"));
+      densHighBtn.addEventListener("click", () => setDensity("high"));
+
+      stopProp(ideaTa);
+      ideaTa.addEventListener("input", () => { node._ai.idea = ideaTa.value; serializeAi(); });
+      stopProp(keyInput);
+      keyInput.addEventListener("input", () => { node._apiKey = keyInput.value; refreshKeyHint(); });   // not serialized
+      stopProp(localModelInput);
+      localModelInput.addEventListener("input", () => { node._ai.local_model = localModelInput.value; serializeAi(); });
+      stopProp(modelSelect);
+      modelSelect.addEventListener("change", () => { node._ai.gemini_model = modelSelect.value; serializeAi(); });
+      stopProp(attnSelect);
+      attnSelect.addEventListener("change", () => { node._ai.attn = attnSelect.value; serializeAi(); });
+      stopProp(fourBit);
+      fourBit.addEventListener("change", () => { node._ai.four_bit = fourBit.checked; serializeAi(); });
+      stopProp(unloadCb);
+      unloadCb.addEventListener("change", () => { node._ai.unload_after = unloadCb.checked; serializeAi(); });
+      stopProp(hostInput);
+      hostInput.addEventListener("input", () => { node._ai.ollama_host = hostInput.value; serializeAi(); });
+      stopProp(ollamaSelect);
+      ollamaSelect.addEventListener("change", () => { node._ai.ollama_model = ollamaSelect.value; serializeAi(); });
+      stopProp(orKeyInput);
+      orKeyInput.addEventListener("input", () => { node._orKey = orKeyInput.value; });   // not serialized
+      stopProp(orSelect);
+      orSelect.addEventListener("change", () => { node._ai.openrouter_model = orSelect.value; serializeAi(); });
+      stopProp(thinkCb);
+      thinkCb.addEventListener("change", () => { node._ai.think = thinkCb.checked; serializeAi(); });
+
+      function setImageFromFile(file) {
+        if (!file || !file.type.startsWith("image/")) return;
+        const reader = new FileReader();
+        reader.onload = () => {
+          node._image = { b64: reader.result, url: reader.result };
+          drop.innerHTML = "";
+          const img = document.createElement("img");
+          img.className = "ideoap-thumb"; img.src = reader.result;
+          const rm = document.createElement("button");
+          rm.className = "ideoap-btn"; rm.textContent = "✕"; rm.title = "Remove image";
+          rm.addEventListener("click", (e) => { e.stopPropagation(); clearImage(); });
+          drop.appendChild(img); drop.appendChild(rm);
+          fitNode();
+        };
+        reader.readAsDataURL(file);
+      }
+      function clearImage() {
+        node._image = null;
+        drop.innerHTML = ""; drop.textContent = "+ reference image (optional)";
+        fileInput.value = "";
+        fitNode();
+      }
+      stopProp(drop);
+      drop.addEventListener("click", () => fileInput.click());
+      fileInput.addEventListener("change", () => setImageFromFile(fileInput.files?.[0]));
+      drop.addEventListener("dragover", (e) => { e.preventDefault(); drop.classList.add("drag"); });
+      drop.addEventListener("dragleave", () => drop.classList.remove("drag"));
+      drop.addEventListener("drop", (e) => {
+        e.preventDefault(); e.stopPropagation(); drop.classList.remove("drag");
+        setImageFromFile(e.dataTransfer?.files?.[0]);
+      });
+
+      function setStatus(msg, isErr, busy) {
+        status.classList.toggle("err", !!isErr);
+        status.innerHTML = (busy ? '<span class="ideoap-spin"></span> ' : "") + (msg || "");
+      }
+      function setGenBusy(busy) {
+        node._gening = busy;
+        generateBtn.disabled = busy;
+        generateBtn.textContent = busy ? "Generating…" : "Generate";
+      }
+      async function pollStatus() {
+        try {
+          const r = await fetch("/ideogram_autoprompter/local/status");
+          if (!r.ok) return;
+          const s = await r.json();
+          const label = { downloading: "Downloading model…", loading: "Loading model…",
+            generating: "Generating caption…", ready: "Generating caption…", idle: "" }[s.stage] || s.stage;
+          if (node._gening && label) setStatus(label + (s.detail ? " " + s.detail : ""), false, true);
+        } catch (e) {}
+      }
+
+      stopProp(fetchBtn);
+      fetchBtn.addEventListener("mousedown", (e) => e.stopPropagation());
+      fetchBtn.addEventListener("click", async () => {
+        if (!node._apiKey) { setStatus("Enter a Gemini API key first.", true); return; }
+        fetchBtn.disabled = true; setStatus("Fetching models…", false, true);
+        try {
+          const r = await fetch("/ideogram_autoprompter/gemini/models", {
+            method: "POST", headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({ api_key: node._apiKey }),
+          });
+          const data = await r.json();
+          if (!r.ok || data.error) throw new Error(data.error || ("HTTP " + r.status));
+          modelSelect.innerHTML = "";
+          for (const m of data.models) {
+            const o = document.createElement("option");
+            o.value = m.id; o.textContent = m.display_name || m.id;
+            modelSelect.appendChild(o);
+          }
+          if (node._ai.gemini_model) modelSelect.value = node._ai.gemini_model;
+          if (!modelSelect.value && modelSelect.options.length) {
+            modelSelect.selectedIndex = 0; node._ai.gemini_model = modelSelect.value; serializeAi();
+          }
+          setStatus(data.models.length + " models loaded.");
+        } catch (e) { setStatus("Error: " + e.message, true); }
+        finally { fetchBtn.disabled = false; }
+      });
+
+      stopProp(ollamaFetchBtn);
+      ollamaFetchBtn.addEventListener("mousedown", (e) => e.stopPropagation());
+      ollamaFetchBtn.addEventListener("click", async () => {
+        ollamaFetchBtn.disabled = true; setStatus("Fetching Ollama models…", false, true);
+        try {
+          const r = await fetch("/ideogram_autoprompter/ollama/models", {
+            method: "POST", headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({ host: node._ai.ollama_host || DEFAULT_OLLAMA_HOST }),
+          });
+          const data = await r.json();
+          if (!r.ok || data.error) throw new Error(data.error || ("HTTP " + r.status));
+          ollamaSelect.innerHTML = "";
+          for (const m of data.models) {
+            const o = document.createElement("option");
+            o.value = m.id; o.textContent = m.display_name || m.id;
+            ollamaSelect.appendChild(o);
+          }
+          if (node._ai.ollama_model) ollamaSelect.value = node._ai.ollama_model;
+          if (!ollamaSelect.value && ollamaSelect.options.length) {
+            ollamaSelect.selectedIndex = 0; node._ai.ollama_model = ollamaSelect.value; serializeAi();
+          }
+          setStatus(data.models.length + " models loaded.");
+        } catch (e) { setStatus("Error: " + e.message, true); }
+        finally { ollamaFetchBtn.disabled = false; }
+      });
+
+      stopProp(orFetchBtn);
+      orFetchBtn.addEventListener("mousedown", (e) => e.stopPropagation());
+      orFetchBtn.addEventListener("click", async () => {
+        if (!node._orKey) { setStatus("Enter an OpenRouter API key first.", true); return; }
+        orFetchBtn.disabled = true; setStatus("Fetching OpenRouter models…", false, true);
+        try {
+          const r = await fetch("/ideogram_autoprompter/openrouter/models", {
+            method: "POST", headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({ api_key: node._orKey }),
+          });
+          const data = await r.json();
+          if (!r.ok || data.error) throw new Error(data.error || ("HTTP " + r.status));
+          orSelect.innerHTML = "";
+          for (const m of data.models) {
+            const o = document.createElement("option");
+            o.value = m.id; o.textContent = m.display_name || m.id;
+            orSelect.appendChild(o);
+          }
+          if (node._ai.openrouter_model) orSelect.value = node._ai.openrouter_model;
+          if (!orSelect.value && orSelect.options.length) {
+            orSelect.selectedIndex = 0; node._ai.openrouter_model = orSelect.value; serializeAi();
+          }
+          setStatus(data.models.length + " vision models loaded.");
+        } catch (e) { setStatus("Error: " + e.message, true); }
+        finally { orFetchBtn.disabled = false; }
+      });
+
+      generateBtn.addEventListener("mousedown", (e) => e.stopPropagation());
+      generateBtn.addEventListener("click", async () => {
+        if (node._gening) return;
+        if (!ideaTa.value.trim() && !node._image) { setStatus("Enter an idea or add an image.", true); return; }
+        const backend = node._ai.backend;
+        if (backend === "gemini" && (!node._apiKey || !modelSelect.value)) {
+          setStatus("Set an API key and fetch/select a model.", true); return;
+        }
+        if (backend === "ollama" && !ollamaSelect.value) {
+          setStatus("Fetch and select an Ollama model.", true); return;
+        }
+        if (backend === "openrouter" && (!node._orKey || !orSelect.value)) {
+          setStatus("Set an OpenRouter key and fetch/select a model.", true); return;
+        }
+        setGenBusy(true); setStatus("Starting…", false, true);
+        let poll = backend === "local" ? setInterval(pollStatus, 1200) : null;
+        try {
+          const model = backend === "gemini" ? modelSelect.value
+            : backend === "ollama" ? ollamaSelect.value
+            : backend === "openrouter" ? orSelect.value
+            : localModelInput.value.trim();
+          const payload = {
+            backend, idea: ideaTa.value,
+            image_b64: node._image ? node._image.b64 : null,
+            model,
+            api_key: backend === "openrouter" ? (node._orKey || "") : (node._apiKey || ""),
+            host: node._ai.ollama_host || DEFAULT_OLLAMA_HOST,
+            four_bit: node._ai.four_bit, unload_after: node._ai.unload_after,
+            density: node._ai.density, attn: node._ai.attn, think: node._ai.think,
+          };
+          const r = await fetch("/ideogram_autoprompter/generate", {
+            method: "POST", headers: { "Content-Type": "application/json" },
+            body: JSON.stringify(payload),
+          });
+          const data = await r.json();
+          if (!r.ok || data.error) throw new Error(data.error || ("HTTP " + r.status));
+          closeInlineEditor();
+          applyCaption(data.caption);
+          syncCanvasToDims(); commit(); rebuildStylePalette(); fitNode();
+          setStatus("Caption applied — edit anything on the canvas.");
+        } catch (e) {
+          setStatus("Error: " + e.message, true);
+        } finally {
+          if (poll) clearInterval(poll);
+          setGenBusy(false);
+        }
+      });
+
+      // ── property panel ──
+      function stopProp(el) {
+        for (const ev of ["mousedown", "pointerdown", "wheel"]) el.addEventListener(ev, (e) => e.stopPropagation());
+      }
+      function buildSwatchRow(container, arr, max, onEdit, onStruct) {
+        arr.forEach((hex, i) => {
+          const sw = document.createElement("div");
+          sw.className = "ideoap-sw";
+          sw.style.background = hex;
+          sw.title = "Click to edit · right-click to remove";
+          const inp = document.createElement("input");
+          inp.type = "color"; inp.value = hex;
+          stopProp(sw);
+          sw.addEventListener("click", () => inp.click());
+          sw.addEventListener("contextmenu", (e) => { e.preventDefault(); e.stopPropagation(); arr.splice(i, 1); onStruct(); });
+          inp.addEventListener("input", () => { arr[i] = inp.value; sw.style.background = inp.value; onEdit(); });
+          sw.appendChild(inp);
+          container.appendChild(sw);
+        });
+        if (arr.length < max) {
+          const add = document.createElement("button");
+          add.className = "ideoap-btn"; add.textContent = "+";
+          stopProp(add);
+          add.addEventListener("click", () => { arr.push("#ffffff"); onStruct(); });
+          container.appendChild(add);
+        }
+      }
+      function rebuildStylePalette() {
+        while (styleBar.children.length > 1) styleBar.removeChild(styleBar.lastChild);
+        buildSwatchRow(styleBar, node._stylePalette, MAX_STYLE_COLORS,
+          () => { serialize(); drawCanvas(); },
+          () => { serialize(); drawCanvas(); rebuildStylePalette(); fitNode(); });
+      }
+      function makeArea(field, value, placeholder, onInput, defaultH) {
+        const ta = document.createElement("textarea");
+        ta.className = "ideoap-area";
+        ta.placeholder = placeholder;
+        ta.value = value || "";
+        const h = node._areaH[field] || defaultH;
+        if (h) ta.style.height = h + "px";
+        stopProp(ta);
+        ta.addEventListener("input", onInput);
+        const ro = new ResizeObserver(() => {
+          if (ta.offsetHeight > 0) { node._areaH[field] = ta.offsetHeight; fitNode(); }
+        });
+        ro.observe(ta);
+        node._areaObservers.push(ro);
+        return ta;
+      }
+      function renderPanel() {
+        for (const ro of node._areaObservers) ro.disconnect();
+        node._areaObservers = [];
+        panel.innerHTML = "";
+        const b = node._boxes[node._activeIdx];
+        if (!b) {
+          hint.textContent = "Drag on the canvas to add a region";
+          const p = document.createElement("div");
+          p.style.color = "#777";
+          p.textContent = node._boxes.length ? "Click a region to edit it." : "No regions yet — generate or draw one.";
+          panel.appendChild(p);
+          requestAnimationFrame(fitNode);
+          return;
+        }
+        const col = (b.palette || []).find(Boolean) || "#ddd";
+        hint.innerHTML = `Editing <b style="color:${col}">region ${node._activeIdx + 1}</b> · dbl-click to edit · alt-click overlap · right-click/del to remove`;
+        const typeRow = document.createElement("div");
+        typeRow.className = "ideoap-row";
+        const lbl = document.createElement("span"); lbl.className = "ideoap-lbl"; lbl.textContent = "type"; typeRow.appendChild(lbl);
+        for (const t of ["obj", "text"]) {
+          const btn = document.createElement("button");
+          btn.className = "ideoap-btn" + (b.type === t ? " active" : "");
+          btn.textContent = t;
+          stopProp(btn);
+          btn.addEventListener("click", () => { b.type = t; commit(); });
+          typeRow.appendChild(btn);
+        }
+        panel.appendChild(typeRow);
+        if (b.type === "text") {
+          panel.appendChild(makeArea("text", b.text, "text to render (verbatim)",
+            function () { b.text = this.value; serialize(); drawCanvas(); updateTokens(); }));
+        }
+        panel.appendChild(makeArea("desc", b.desc, "description of this region",
+          function () { b.desc = this.value; serialize(); drawCanvas(); updateTokens(); }, 110));
+        const palRow = document.createElement("div");
+        palRow.className = "ideoap-row";
+        const pl = document.createElement("span"); pl.className = "ideoap-lbl"; pl.textContent = "colors"; palRow.appendChild(pl);
+        b.palette = b.palette || [];
+        buildSwatchRow(palRow, b.palette, MAX_ELEM_COLORS,
+          () => { serialize(); drawCanvas(); }, commit);
+        panel.appendChild(palRow);
+        requestAnimationFrame(fitNode);
+      }
+
+      // ── widget callbacks ──
+      for (const w of [wWidget, hWidget]) {
+        if (!w) continue;
+        chainCallback(w, "callback", () => { syncCanvasToDims(); drawCanvas(); fitNode(); });
+      }
+      for (const name of ["background", "high_level_description", "aesthetics", "lighting", "medium", "style"]) {
+        const w = findW(name);
+        if (w) chainCallback(w, "callback", () => updateTokens());
+      }
+
+      // ── keep canvas + getMinHeight in sync while resized ──
+      let _resizing = false;
+      chainCallback(node, "onResize", function () {
+        if (_resizing) return;
+        _resizing = true;
+        recalcWidgetHeight();
+        const minH = node.computeSize()[1];
+        if (node.size[1] < minH) node.size[1] = minH;
+        drawCanvas();
+        _resizing = false;
+      });
+
+      wrap.addEventListener("focusin", () => { if (!node._focused) { node._focused = true; drawCanvas(); } });
+      wrap.addEventListener("focusout", (e) => {
+        if (!wrap.contains(e.relatedTarget)) { node._focused = false; drawCanvas(); }
+      });
+      chainCallback(node, "onSelected", function () { node._selected = true; drawCanvas(); });
+      chainCallback(node, "onDeselected", function () { node._selected = false; drawCanvas(); });
+      chainCallback(node, "onRemoved", function () {
+        closeInlineEditor();
+        for (const ro of node._areaObservers) ro.disconnect();
+        node._areaObservers = [];
+      });
+
+      // Sync the AI DOM controls from node._ai (after load or init).
+      function syncAiUI() {
+        ideaTa.value = node._ai.idea || "";
+        localModelInput.value = node._ai.local_model || DEFAULT_LOCAL_MODEL;
+        attnSelect.value = node._ai.attn || "auto";
+        fourBit.checked = !!node._ai.four_bit;
+        unloadCb.checked = node._ai.unload_after !== false;
+        hostInput.value = node._ai.ollama_host || DEFAULT_OLLAMA_HOST;
+        thinkCb.checked = !!node._ai.think;
+        if (node._ai.gemini_model) {
+          const exists = Array.from(modelSelect.options).some((o) => o.value === node._ai.gemini_model);
+          if (!exists) {
+            const o = document.createElement("option");
+            o.value = node._ai.gemini_model; o.textContent = node._ai.gemini_model;
+            modelSelect.appendChild(o);
+          }
+          modelSelect.value = node._ai.gemini_model;
+        }
+        if (node._ai.ollama_model) {
+          const exists = Array.from(ollamaSelect.options).some((o) => o.value === node._ai.ollama_model);
+          if (!exists) {
+            const o = document.createElement("option");
+            o.value = node._ai.ollama_model; o.textContent = node._ai.ollama_model;
+            ollamaSelect.appendChild(o);
+          }
+          ollamaSelect.value = node._ai.ollama_model;
+        }
+        if (node._ai.openrouter_model) {
+          const exists = Array.from(orSelect.options).some((o) => o.value === node._ai.openrouter_model);
+          if (!exists) {
+            const o = document.createElement("option");
+            o.value = node._ai.openrouter_model; o.textContent = node._ai.openrouter_model;
+            orSelect.appendChild(o);
+          }
+          orSelect.value = node._ai.openrouter_model;
+        }
+        refreshDensityUI();
+        refreshBackendUI();
+      }
+
+      // ── restore on load ──
+      chainCallback(node, "onConfigure", function () {
+        if (elementsWidget?.value) {
+          try {
+            const parsed = JSON.parse(elementsWidget.value);
+            if (Array.isArray(parsed)) {
+              node._boxes = parsed.filter((b) => b && typeof b.x === "number");
+              node._activeIdx = node._boxes.length ? 0 : -1;
+            }
+          } catch (e) {}
+        }
+        if (stylePaletteWidget?.value) {
+          try {
+            const sp = JSON.parse(stylePaletteWidget.value);
+            if (Array.isArray(sp)) node._stylePalette = sp.filter((c) => typeof c === "string");
+          } catch (e) {}
+        }
+        if (aiStateWidget?.value) {
+          try {
+            const a = JSON.parse(aiStateWidget.value);
+            if (a && typeof a === "object") node._ai = Object.assign(node._ai, a);
+          } catch (e) {}
+        }
+        hideDataWidgets();
+        syncCanvasToDims();
+        rebuildStylePalette();
+        syncAiUI();
+        renderPanel();
+        drawCanvas();
+        updateTokens();
+        requestAnimationFrame(fitNode);
+      });
+
+      // initial layout
+      setTimeout(() => {
+        hideDataWidgets();
+        if (node.size[0] < 400) node.setSize([400, node.size[1]]);
+        syncCanvasToDims();
+        rebuildStylePalette();
+        syncAiUI();
+        renderPanel();
+        drawCanvas();
+        updateTokens();
+        fitNode();
+      }, 0);
+    });
+  },
+});
diff --git a/custom_nodes/comfyui-ideogram-autoprompter/web/js/utility.js b/custom_nodes/comfyui-ideogram-autoprompter/web/js/utility.js
new file mode 100644
index 0000000000000000000000000000000000000000..60e588fcbfeb2b7fd3a534f2957fbe55628db0da
--- /dev/null
+++ b/custom_nodes/comfyui-ideogram-autoprompter/web/js/utility.js
@@ -0,0 +1,81 @@
+const { app } = window.comfyAPI.app;
+
+// ─── General-purpose helpers (adapted from ComfyUI-KJNodes) ───
+
+export function chainCallback(object, property, callback) {
+  if (object == undefined) {
+    console.error("Tried to add callback to non-existant object");
+    return;
+  }
+  if (property in object) {
+    const callback_orig = object[property];
+    object[property] = function () {
+      const r = callback_orig.apply(this, arguments);
+      callback.apply(this, arguments);
+      return r;
+    };
+  } else {
+    object[property] = callback;
+  }
+}
+
+// ─── Middle-click pan passthrough for DOM widgets ───
+export function addMiddleClickPan(element) {
+  const onMouseDown = (e) => {
+    if (e.button !== 1) return;
+    e.preventDefault();
+    const ds = app.canvas?.ds;
+    if (!ds) return;
+    const startX = e.clientX, startY = e.clientY;
+    const startOffsetX = ds.offset[0], startOffsetY = ds.offset[1];
+    const onMove = (me) => {
+      ds.offset[0] = startOffsetX + (me.clientX - startX);
+      ds.offset[1] = startOffsetY + (me.clientY - startY);
+      app.canvas.setDirty(true, true);
+    };
+    const onUp = () => {
+      document.removeEventListener('mousemove', onMove);
+      document.removeEventListener('mouseup', onUp);
+    };
+    document.addEventListener('mousemove', onMove);
+    document.addEventListener('mouseup', onUp);
+  };
+  element.addEventListener('mousedown', onMouseDown);
+  return () => element.removeEventListener('mousedown', onMouseDown);
+}
+
+// ─── Wheel zoom passthrough for DOM widgets ───
+export function addWheelPassthrough(element) {
+  element.addEventListener("wheel", (e) => {
+    const gc = document.getElementById("graph-canvas");
+    if (gc) {
+      gc.dispatchEvent(new WheelEvent(e.type, e));
+      e.preventDefault();
+    }
+  }, { passive: false });
+}
+
+// Bounding-box hit test: corners, then edges, then interior.
+export function rectHitTest(mx, my, x1, y1, x2, y2, radius) {
+  const hit = (cx, cy) => Math.abs(mx - cx) < radius && Math.abs(my - cy) < radius;
+  if (hit(x1, y1)) return "resize-tl";
+  if (hit(x2, y1)) return "resize-tr";
+  if (hit(x1, y2)) return "resize-bl";
+  if (hit(x2, y2)) return "resize-br";
+  if (mx >= x1 && mx <= x2 && Math.abs(my - y1) < radius) return "resize-t";
+  if (mx >= x1 && mx <= x2 && Math.abs(my - y2) < radius) return "resize-b";
+  if (my >= y1 && my <= y2 && Math.abs(mx - x1) < radius) return "resize-l";
+  if (my >= y1 && my <= y2 && Math.abs(mx - x2) < radius) return "resize-r";
+  if (mx >= x1 && mx <= x2 && my >= y1 && my <= y2) return "move";
+  return null;
+}
+
+// CSS cursor for a bbox hit mode string.
+export function cursorForBboxMode(mode) {
+  if (mode === "move") return "move";
+  if (mode === "resize-tl" || mode === "resize-br") return "nwse-resize";
+  if (mode === "resize-tr" || mode === "resize-bl") return "nesw-resize";
+  if (mode === "resize-t" || mode === "resize-b") return "ns-resize";
+  if (mode === "resize-l" || mode === "resize-r") return "ew-resize";
+  return null;
+}
diff --git a/custom_nodes/hf-model-downloader/.github/workflows/publish_action.yml b/custom_nodes/hf-model-downloader/.github/workflows/publish_action.yml
new file mode 100644
index 0000000000000000000000000000000000000000..43cbd0ffc287d755daf9a513a944175a0bc0fbf5
--- /dev/null
+++ b/custom_nodes/hf-model-downloader/.github/workflows/publish_action.yml
@@ -0,0 +1,24 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+    paths:
+      - "pyproject.toml"
+
+permissions:
+  issues: write
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'michaelgold' }}
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@v1
+        with:
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
\ No newline at end of file
diff --git a/custom_nodes/hf-model-downloader/js/model_downloader.css b/custom_nodes/hf-model-downloader/js/model_downloader.css
new file mode 100644
index 0000000000000000000000000000000000000000..f1207b0500fbc9e73bf7ce3e63ce31f4eb41923f
--- /dev/null
+++ b/custom_nodes/hf-model-downloader/js/model_downloader.css
@@ -0,0 +1,265 @@
+.hal-fun-top-menu {
+    box-sizing: border-box;
+    white-space: nowrap;
+    background: var(--content-bg);
+    color: var(--content-fg);
+    display: flex;
+    flex-direction: column;
+    min-width: 300px;
+    max-width: 500px;
+    border: 1px solid var(--border-color);
+    border-radius: 12px;
+    box-shadow: 0 4px 6px -1px rgb(0 0 0 / 0.1), 
+                0 2px 4px -2px rgb(0 0 0 / 0.1);
+    max-height: 80vh;
+    overflow-y: auto;
+    backdrop-filter: blur(8px);
+    transition: all 0.3s ease;
+}
+
+.hal-fun-top-menu * {
+    box-sizing: inherit;
+}
+
+.hal-fun-downloader-container {
+    display: flex;
+    flex-direction: column;
+    gap: 8px;
+}
+
+.hal-fun-downloader-container h2 {
+    margin: 0;
+    padding: 16px 20px;
+    color: var(--fg-color);
+    font-size: 1.25rem;
+    font-weight: 600;
+    border-bottom: 1px solid var(--border-color);
+    letter-spacing: -0.025em;
+}
+
+.model-list {
+    list-style: none;
+    padding: 0;
+    margin: 0;
+    display: flex;
+    flex-direction: column;
+    gap: 4px;
+}
+
+.model-item {
+    list-style: none;
+    padding: 0;
+    margin: 0;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    padding: 8px 16px;
+    border-radius: 8px;
+    transition: all 0.2s ease;
+}
+
+.model-item:last-child {
+    border-bottom: none;
+}
+
+.model-item:hover {
+    background-color: var(--comfy-input-bg);
+    transform: translateX(4px);
+}
+
+.model-item label {
+    display: flex;
+    align-items: center;
+    gap: 0.8em;
+    cursor: pointer;
+    user-select: none;
+    flex-grow: 1;
+    width: 100%;
+    text-align: start;
+    padding: 8px 12px 8px 8px;
+    font-size: 0.95rem;
+    color: var(--fg-color);
+    transition: color 0.2s ease;
+}
+
+.model-item input[type="checkbox"] {
+    width: 18px;
+    height: 18px;
+    accent-color: var(--accent-color);
+    cursor: pointer;
+    border-radius: 4px;
+    border: 2px solid var(--border-color);
+    transition: all 0.2s ease;
+}
+
+.model-item input[type="checkbox"]:checked {
+    background-color: var(--accent-color);
+    border-color: var(--accent-color);
+}
+
+.download-btn {
+    background: var(--accent-color);
+    color: white;
+    border: none;
+    padding: 6px 12px;
+    border-radius: 6px;
+    cursor: pointer;
+    transition: all 0.2s ease;
+    font-size: 0.9rem;
+    font-weight: 500;
+    margin-left: 8px;
+    box-shadow: 0 1px 2px 0 rgb(0 0 0 / 0.05);
+}
+
+.download-btn:hover {
+    background-color: var(--accent-color);
+    opacity: 0.9;
+    transform: translateY(-1px);
+    box-shadow: 0 4px 6px -1px rgb(0 0 0 / 0.1);
+}
+
+.download-btn:active {
+    transform: translateY(0);
+}
+
+.download-status {
+    padding: 12px 20px;
+    border-top: 1px solid var(--border-color);
+    color: var(--fg-color);
+    font-size: 0.9rem;
+    text-align: center;
+    background-color: var(--comfy-input-bg);
+    border-radius: 0 0 12px 12px;
+}
+
+/* Animation */
+.comfyui-popup.left {
+    transform-origin: top right;
+    animation: popup-left 0.3s cubic-bezier(0.4, 0, 0.2, 1);
+}
+
+@keyframes popup-left {
+    from {
+        opacity: 0;
+        transform: translateY(-12px);
+    }
+    to {
+        opacity: 1;
+        transform: translateY(0);
+    }
+}
+
+.login-section {
+    padding: 16px 20px;
+    border-bottom: 1px solid var(--border-color);
+    display: flex;
+    flex-direction: column;
+    gap: 12px;
+    background-color: var(--comfy-input-bg);
+    border-radius: 8px;
+    margin: 8px 16px;
+}
+
+.login-section .input-group {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    gap: 8px;
+}
+
+.login-section .button-group {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    gap: 8px;
+}
+
+.login-section input {
+    flex-grow: 1;
+    padding: 8px 12px;
+    border: 1px solid var(--border-color);
+    border-radius: 6px;
+    background: var(--content-bg);
+    color: var(--fg-color);
+    font-size: 0.9rem;
+    transition: all 0.2s ease;
+}
+
+.login-section input:focus {
+    outline: none;
+    border-color: var(--accent-color);
+    box-shadow: 0 0 0 2px rgba(var(--accent-color-rgb), 0.1);
+}
+
+.login-section input:disabled {
+    opacity: 0.7;
+    cursor: not-allowed;
+}
+
+.login-section .token-link {
+    color: var(--accent-color);
+    text-decoration: none;
+    font-size: 0.85rem;
+    text-align: center;
+    transition: all 0.2s ease;
+    padding: 4px 8px;
+    border-radius: 4px;
+}
+
+.login-section .token-link:hover {
+    background-color: rgba(var(--accent-color-rgb), 0.1);
+}
+
+.login-section .token-link.hidden {
+    display: none;
+}
+
+.login-section button {
+    background: var(--accent-color);
+    color: white;
+    border: none;
+    padding: 8px 16px;
+    border-radius: 6px;
+    cursor: pointer;
+    transition: all 0.2s ease;
+    font-size: 0.9rem;
+    font-weight: 500;
+    box-shadow: 0 1px 2px 0 rgb(0 0 0 / 0.05);
+}
+
+.login-section button:hover {
+    opacity: 0.9;
+    transform: translateY(-1px);
+    box-shadow: 0 4px 6px -1px rgb(0 0 0 / 0.1);
+}
+
+.login-section button:active {
+    transform: translateY(0);
+}
+
+.login-section button.logged-in {
+    background: var(--success-color);
+}
+
+.login-section button.logout-btn {
+    background: var(--error-color);
+}
+
+.login-section button.logout-btn:hover {
+    opacity: 0.9;
+}
+
+.login-section .user-info {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    padding: 8px 12px;
+    background: var(--content-bg);
+    border-radius: 6px;
+    font-size: 0.9rem;
+    color: var(--fg-color);
+}
+
+.login-section .user-info.hidden {
+    display: none;
+} 
\ No newline at end of file
diff --git a/custom_nodes/hf-model-downloader/js/model_downloader.js b/custom_nodes/hf-model-downloader/js/model_downloader.js
new file mode 100644
index 0000000000000000000000000000000000000000..1c3db29895ba9ca3c8a55927d391fd82a1608a39
--- /dev/null
+++ b/custom_nodes/hf-model-downloader/js/model_downloader.js
@@ -0,0 +1,528 @@
+import { app } from "/scripts/app.js";
+import { api } from "/scripts/api.js";
+import { $el } from "/scripts/ui.js";
+
+// Create a new extension
+app.registerExtension({
+  name: "hal.fun.model.downloader",
+  
+  async setup() {
+    // Register a dockable panel
+    const panelId = "hal-fun-model-downloader";
+    
+    // Create panel content function
+    const createPanelContent = () => {
+      const container = $el("div.hal-fun-downloader-container", {
+        style: {
+          display: "flex",
+          flexDirection: "column",
+          height: "100%",
+          overflow: "hidden"
+        }
+      }, [
+        $el("h2", {
+          style: {
+            margin: "0",
+            padding: "16px 20px",
+            fontSize: "1.25rem",
+            fontWeight: "600",
+            borderBottom: "1px solid var(--border-color)"
+          }
+        }, "🦾 Model Downloader"),
+        
+        // Login section
+        $el("div.login-section", {
+          style: {
+            padding: "16px 20px",
+            borderBottom: "1px solid var(--border-color)",
+            display: "flex",
+            flexDirection: "column",
+            gap: "12px"
+          }
+        }, [
+          $el("div.input-group", {
+            style: {
+              display: "flex",
+              gap: "8px",
+              alignItems: "center"
+            }
+          }, [
+            $el("input.hf-token-input", {
+              type: "password",
+              placeholder: "Hugging Face Token",
+              style: {
+                flex: "1",
+                padding: "8px 12px",
+                border: "1px solid var(--border-color)",
+                borderRadius: "6px",
+                background: "var(--comfy-input-bg)",
+                color: "var(--fg-color)"
+              }
+            }),
+            $el("button.login-btn", {
+              textContent: "Login",
+              style: {
+                padding: "8px 16px",
+                background: "var(--accent-color)",
+                color: "white",
+                border: "none",
+                borderRadius: "6px",
+                cursor: "pointer"
+              }
+            })
+          ]),
+          $el("div.token-note", {
+            style: {
+              fontSize: "0.85rem",
+              color: "var(--fg-color)",
+              textAlign: "center"
+            }
+          }, [
+            "You need a Hugging Face token to download gated models. ",
+            $el("a", {
+              href: "https://huggingface.co/settings/tokens/new?tokenType=read",
+              target: "_blank",
+              textContent: "Get a read token",
+              style: {
+                color: "var(--accent-color)"
+              }
+            })
+          ]),
+          $el("div.user-info", {
+            style: {
+              display: "none",
+              alignItems: "center",
+              gap: "8px"
+            }
+          }, [
+            $el("span.user-name", { textContent: "Logged in to Hugging Face" }),
+            $el("button.logout-btn", {
+              textContent: "Logout",
+              style: {
+                padding: "4px 12px",
+                background: "var(--error-color)",
+                color: "white",
+                border: "none",
+                borderRadius: "4px",
+                cursor: "pointer"
+              }
+            })
+          ])
+        ]),
+        
+        // Model controls
+        $el("div.model-controls", {
+          style: {
+            padding: "12px 20px",
+            borderBottom: "1px solid var(--border-color)",
+            display: "flex",
+            gap: "8px"
+          }
+        }, [
+          $el("button.select-all-btn", {
+            textContent: "Select All",
+            style: {
+              flex: "1",
+              padding: "6px 12px",
+              border: "1px solid var(--border-color)",
+              borderRadius: "4px",
+              background: "var(--comfy-input-bg)",
+              cursor: "pointer"
+            }
+          }),
+          $el("button.download-selected-btn", {
+            textContent: "Download Selected",
+            style: {
+              flex: "1",
+              padding: "6px 12px",
+              border: "1px solid var(--border-color)",
+              borderRadius: "4px",
+              background: "var(--comfy-input-bg)",
+              cursor: "pointer"
+            }
+          })
+        ]),
+        
+        // Model list
+        $el("div", {
+          style: {
+            flex: "1",
+            overflow: "auto"
+          }
+        }, [
+          $el("ul.model-list", {
+            style: {
+              listStyle: "none",
+              padding: "0",
+              margin: "0"
+            }
+          })
+        ]),
+        
+        // Status
+        $el("div.download-status", {
+          style: {
+            padding: "12px 20px",
+            borderTop: "1px solid var(--border-color)",
+            fontSize: "0.9rem",
+            textAlign: "center"
+          }
+        })
+      ]);
+
+      // Initialize functionality
+      setupPanelHandlers(container);
+      
+      return container;
+    };
+
+    // Create the panel once
+    const panel = createPanelContent();
+    panel.style.display = "none";
+    panel.style.position = "fixed";
+    panel.style.top = "60px";
+    panel.style.right = "10px";
+    panel.style.width = "700px";
+    panel.style.maxHeight = "80vh";
+    panel.style.background = "var(--comfy-menu-bg)";
+    panel.style.border = "1px solid var(--border-color)";
+    panel.style.borderRadius = "8px";
+    panel.style.boxShadow = "0 4px 6px rgba(0, 0, 0, 0.1)";
+    panel.style.zIndex = "1000";
+    document.body.appendChild(panel);
+
+    // Add button to the menu using the new ComfyUI button API
+    try {
+      const { ComfyButton } = await import("../../scripts/ui/components/button.js");
+      const { ComfyButtonGroup } = await import("../../scripts/ui/components/buttonGroup.js");
+      
+      const modelDownloaderButton = new ComfyButton({
+        icon: "download",
+        action: () => {
+          panel.style.display = panel.style.display === "none" ? "flex" : "none";
+        },
+        tooltip: "Model Downloader - Download and manage Hugging Face models",
+        content: "Models",
+        classList: "comfyui-button comfyui-menu-mobile-collapse"
+      });
+
+      // Add to menu if settingsGroup exists (new style menu)
+      if (app.menu?.settingsGroup) {
+        const buttonGroup = new ComfyButtonGroup(modelDownloaderButton.element);
+        app.menu.settingsGroup.element.before(buttonGroup.element);
+        console.log('Model Downloader button added to menu');
+        
+        // Close panel when clicking outside
+        document.addEventListener("click", (e) => {
+          if (!panel.contains(e.target) && !modelDownloaderButton.element.contains(e.target) && panel.style.display === "flex") {
+            panel.style.display = "none";
+          }
+        });
+      } else {
+        console.warn('app.menu.settingsGroup not found, using legacy button');
+        createLegacyMenuButton(panel);
+      }
+    } catch (exception) {
+      console.error('Error creating Model Downloader button:', exception);
+      createLegacyMenuButton(panel);
+    }
+  }
+});
+
+// Handler functions
+function setupPanelHandlers(container) {
+  const loginBtn = container.querySelector(".login-btn");
+  const logoutBtn = container.querySelector(".logout-btn");
+  const tokenInput = container.querySelector(".hf-token-input");
+  const userInfo = container.querySelector(".user-info");
+  const inputGroup = container.querySelector(".input-group");
+  const tokenNote = container.querySelector(".token-note");
+  const selectAllBtn = container.querySelector(".select-all-btn");
+  const downloadSelectedBtn = container.querySelector(".download-selected-btn");
+  const modelList = container.querySelector(".model-list");
+  const status = container.querySelector(".download-status");
+
+  // Check login status
+  async function checkLoginStatus() {
+    try {
+      const response = await api.fetchApi("/hal-fun-downloader/login-status");
+      if (!response.ok) throw new Error(`Login status error: ${response.status}`);
+      const data = await response.json();
+      updateLoginUI(data.logged_in);
+    } catch (error) {
+      console.error("Error checking login status:", error);
+    }
+  }
+
+  function updateLoginUI(isLoggedIn) {
+    if (isLoggedIn) {
+      loginBtn.textContent = "Logged In";
+      loginBtn.style.background = "var(--success-color)";
+      tokenInput.disabled = true;
+      inputGroup.style.display = "none";
+      userInfo.style.display = "flex";
+      tokenNote.style.display = "none";
+    } else {
+      loginBtn.textContent = "Login";
+      loginBtn.style.background = "var(--accent-color)";
+      tokenInput.disabled = false;
+      inputGroup.style.display = "flex";
+      userInfo.style.display = "none";
+      tokenNote.style.display = "block";
+    }
+  }
+
+  // Login handler
+  loginBtn.onclick = async () => {
+    const token = tokenInput.value.trim();
+    if (!token) {
+      status.textContent = "Please enter a Hugging Face token";
+      return;
+    }
+
+    try {
+      status.textContent = "Logging in...";
+      const response = await api.fetchApi("/hal-fun-downloader/login", {
+        method: "POST",
+        body: JSON.stringify({ token }),
+      });
+
+      if (!response.ok) {
+        const error = await response.json();
+        throw new Error(error.error || "Login failed");
+      }
+
+      updateLoginUI(true);
+      status.textContent = "Successfully logged in";
+      await loadModels(container);
+    } catch (error) {
+      console.error("Login error:", error);
+      status.textContent = `Login error: ${error.message}`;
+    }
+  };
+
+  // Logout handler
+  logoutBtn.onclick = async () => {
+    try {
+      status.textContent = "Logging out...";
+      const response = await api.fetchApi("/hal-fun-downloader/logout", {
+        method: "POST",
+      });
+
+      if (!response.ok) {
+        const error = await response.json();
+        throw new Error(error.error || "Logout failed");
+      }
+
+      updateLoginUI(false);
+      status.textContent = "Successfully logged out";
+      await loadModels(container);
+    } catch (error) {
+      console.error("Logout error:", error);
+      status.textContent = `Logout error: ${error.message}`;
+    }
+  };
+
+  // Select all handler
+  selectAllBtn.onclick = () => {
+    const checkboxes = modelList.querySelectorAll('input[type="checkbox"]');
+    const allChecked = Array.from(checkboxes).every((cb) => cb.checked);
+    checkboxes.forEach((cb) => {
+      cb.checked = !allChecked;
+      cb.dispatchEvent(new Event("change"));
+    });
+    selectAllBtn.textContent = allChecked ? "Select All" : "Deselect All";
+  };
+
+  // Download selected handler
+  downloadSelectedBtn.onclick = async () => {
+    const selectedModels = Array.from(
+      modelList.querySelectorAll('input[type="checkbox"]:checked')
+    ).map((cb) => cb.dataset.modelName);
+
+    if (selectedModels.length === 0) {
+      status.textContent = "Please select models to download";
+      return;
+    }
+
+    status.textContent = "Downloading selected models...";
+    downloadSelectedBtn.disabled = true;
+
+    try {
+      const response = await api.fetchApi("/hal-fun-downloader/download", {
+        method: "POST",
+        body: JSON.stringify({ model_names: selectedModels }),
+      });
+      
+      if (!response.ok) {
+        throw new Error(`Failed to download models: ${response.status}`);
+      }
+      
+      const result = await response.json();
+      status.textContent = result.status;
+      await loadModels(container);
+    } catch (error) {
+      console.error("Download error:", error);
+      status.textContent = `Error: ${error.message}`;
+    } finally {
+      downloadSelectedBtn.disabled = false;
+    }
+  };
+
+  // Load models initially
+  checkLoginStatus();
+  loadModels(container);
+}
+
+async function loadModels(container) {
+  const modelList = container.querySelector(".model-list");
+  const status = container.querySelector(".download-status");
+
+  try {
+    const [configResponse, activeResponse] = await Promise.all([
+      api.fetchApi("/hal-fun-downloader/config"),
+      api.fetchApi("/hal-fun-downloader/active")
+    ]);
+
+    if (!configResponse.ok || !activeResponse.ok) {
+      throw new Error("Failed to load configuration");
+    }
+
+    const config = await configResponse.json();
+    const activeConfig = await activeResponse.json();
+
+    modelList.innerHTML = "";
+
+    if (!Array.isArray(config) || config.length === 0) {
+      modelList.innerHTML = '<li style="padding: 20px; text-align: center;">No models found</li>';
+      return;
+    }
+
+    for (const model of config) {
+      if (!model || !model.local_path) continue;
+
+      // Determine display name: use filename if present, otherwise use repo name
+      let modelName;
+      if (model.filename) {
+        // Single file download: use filename
+        modelName = model.filename;
+      } else {
+        // Full repo download: use repo name (last part of repo_id)
+        modelName = model.repo_id.split("/").pop();
+      }
+      
+      const isEnabled = activeConfig?.enabled_models?.includes(modelName);
+      const isDownloaded = activeConfig.model_status?.[modelName]?.downloaded;
+
+      const modelItem = $el("li.model-item", {
+        style: {
+          display: "flex",
+          alignItems: "center",
+          justifyContent: "space-between",
+          padding: "12px 20px",
+          borderBottom: "1px solid var(--border-color)"
+        }
+      }, [
+        $el("label", {
+          style: {
+            display: "flex",
+            alignItems: "center",
+            gap: "12px",
+            flex: "1",
+            cursor: "pointer"
+          }
+        }, [
+          $el("input", {
+            type: "checkbox",
+            checked: isEnabled,
+            dataset: { modelName },
+            onchange: async (e) => {
+              try {
+                if (e.target.checked) {
+                  activeConfig.enabled_models.push(modelName);
+                } else {
+                  activeConfig.enabled_models = activeConfig.enabled_models.filter(
+                    (m) => m !== modelName
+                  );
+                }
+                const updateResponse = await api.fetchApi("/hal-fun-downloader/active", {
+                  method: "POST",
+                  body: JSON.stringify(activeConfig),
+                });
+                if (!updateResponse.ok) throw new Error("Failed to update config");
+              } catch (error) {
+                console.error("Error updating model status:", error);
+                e.target.checked = !e.target.checked;
+                status.textContent = `Error: ${error.message}`;
+              }
+            }
+          }),
+          $el("span", {
+            textContent: isDownloaded ? "✓ " : model.license?.required ? "🔒 " : ""
+          }),
+          $el("span", { textContent: modelName })
+        ]),
+        $el("button.download-btn", {
+          textContent: isDownloaded ? "Downloaded" : "Download",
+          disabled: isDownloaded,
+          style: {
+            padding: "4px 12px",
+            border: "1px solid var(--border-color)",
+            borderRadius: "4px",
+            background: "var(--comfy-input-bg)",
+            cursor: isDownloaded ? "not-allowed" : "pointer",
+            opacity: isDownloaded ? "0.5" : "1"
+          },
+          onclick: async () => {
+            status.textContent = "Starting download...";
+            
+            try {
+              const response = await api.fetchApi("/hal-fun-downloader/download", {
+                method: "POST",
+                body: JSON.stringify({ model_name: modelName }),
+              });
+              
+              if (!response.ok) throw new Error(`Download failed: ${response.status}`);
+              
+              const result = await response.json();
+              status.textContent = result.status;
+              await loadModels(container);
+            } catch (error) {
+              console.error("Download error:", error);
+              status.textContent = `Error: ${error.message}`;
+            }
+          }
+        })
+      ]);
+
+      modelList.appendChild(modelItem);
+    }
+  } catch (error) {
+    console.error("Error loading models:", error);
+    modelList.innerHTML = `<li style="padding: 20px; text-align: center; color: var(--error-color);">Error: ${error.message}</li>`;
+    status.textContent = `Error: ${error.message}`;
+  }
+}
+
+// Legacy fallback for older ComfyUI versions
+function createLegacyMenuButton(panel) {
+  const menuRight = document.querySelector(".comfyui-menu-right .comfyui-button-group");
+  if (!menuRight) return;
+
+  const button = document.createElement("button");
+  button.className = "comfyui-button";
+  button.title = "Model Downloader";
+  button.innerHTML = '<i class="mdi mdi-download"></i><span>Models</span>';
+
+  button.onclick = () => {
+    panel.style.display = panel.style.display === "none" ? "flex" : "none";
+  };
+
+  document.addEventListener("click", (e) => {
+    if (!panel.contains(e.target) && !button.contains(e.target) && panel.style.display === "flex") {
+      panel.style.display = "none";
+    }
+  });
+
+  menuRight.appendChild(button);
+}
diff --git a/custom_nodes/hf-model-downloader/js/model_downloader.js.backup b/custom_nodes/hf-model-downloader/js/model_downloader.js.backup
new file mode 100644
index 0000000000000000000000000000000000000000..58d4a07249f4247bb87b5d1d26596d6a4acf6a7b
--- /dev/null
+++ b/custom_nodes/hf-model-downloader/js/model_downloader.js.backup
@@ -0,0 +1,1385 @@
+import { app } from "/scripts/app.js";
+import { api } from "/scripts/api.js";
+import { $el } from "/scripts/ui.js";
+
+// Create a new extension
+app.registerExtension({
+  name: "hal.fun.model.downloader",
+  
+  async setup() {
+    // Register a dockable panel
+    const panelId = "hal-fun-model-downloader";
+    
+    // Create panel content function
+    const createPanelContent = () => {
+      const container = $el("div.hal-fun-downloader-container", {
+        style: {
+          display: "flex",
+          flexDirection: "column",
+          height: "100%",
+          overflow: "hidden"
+        }
+      }, [
+        $el("h2", {
+          style: {
+            margin: "0",
+            padding: "16px 20px",
+            fontSize: "1.25rem",
+            fontWeight: "600",
+            borderBottom: "1px solid var(--border-color)"
+          }
+        }, "🦾 Model Downloader"),
+        
+        // Login section
+        $el("div.login-section", {
+          style: {
+            padding: "16px 20px",
+            borderBottom: "1px solid var(--border-color)",
+            display: "flex",
+            flexDirection: "column",
+            gap: "12px"
+          }
+        }, [
+          $el("div.input-group", {
+            style: {
+              display: "flex",
+              gap: "8px",
+              alignItems: "center"
+            }
+          }, [
+            $el("input.hf-token-input", {
+              type: "password",
+              placeholder: "Hugging Face Token",
+              style: {
+                flex: "1",
+                padding: "8px 12px",
+                border: "1px solid var(--border-color)",
+                borderRadius: "6px",
+                background: "var(--comfy-input-bg)",
+                color: "var(--fg-color)"
+              }
+            }),
+            $el("button.login-btn", {
+              textContent: "Login",
+              style: {
+                padding: "8px 16px",
+                background: "var(--accent-color)",
+                color: "white",
+                border: "none",
+                borderRadius: "6px",
+                cursor: "pointer"
+              }
+            })
+          ]),
+          $el("div.token-note", {
+            style: {
+              fontSize: "0.85rem",
+              color: "var(--fg-color)",
+              textAlign: "center"
+            }
+          }, [
+            "You need a Hugging Face token to download gated models. ",
+            $el("a", {
+              href: "https://huggingface.co/settings/tokens/new?tokenType=read",
+              target: "_blank",
+              textContent: "Get a read token",
+              style: {
+                color: "var(--accent-color)"
+              }
+            })
+          ]),
+          $el("div.user-info", {
+            style: {
+              display: "none",
+              alignItems: "center",
+              gap: "8px"
+            }
+          }, [
+            $el("span.user-name", { textContent: "Logged in to Hugging Face" }),
+            $el("button.logout-btn", {
+              textContent: "Logout",
+              style: {
+                padding: "4px 12px",
+                background: "var(--error-color)",
+                color: "white",
+                border: "none",
+                borderRadius: "4px",
+                cursor: "pointer"
+              }
+            })
+          ])
+        ]),
+        
+        // Model controls
+        $el("div.model-controls", {
+          style: {
+            padding: "12px 20px",
+            borderBottom: "1px solid var(--border-color)",
+            display: "flex",
+            gap: "8px"
+          }
+        }, [
+          $el("button.select-all-btn", {
+            textContent: "Select All",
+            style: {
+              flex: "1",
+              padding: "6px 12px",
+              border: "1px solid var(--border-color)",
+              borderRadius: "4px",
+              background: "var(--comfy-input-bg)",
+              cursor: "pointer"
+            }
+          }),
+          $el("button.download-selected-btn", {
+            textContent: "Download Selected",
+            style: {
+              flex: "1",
+              padding: "6px 12px",
+              border: "1px solid var(--border-color)",
+              borderRadius: "4px",
+              background: "var(--comfy-input-bg)",
+              cursor: "pointer"
+            }
+          })
+        ]),
+        
+        // Model list
+        $el("div", {
+          style: {
+            flex: "1",
+            overflow: "auto"
+          }
+        }, [
+          $el("ul.model-list", {
+            style: {
+              listStyle: "none",
+              padding: "0",
+              margin: "0"
+            }
+          })
+        ]),
+        
+        // Status
+        $el("div.download-status", {
+          style: {
+            padding: "12px 20px",
+            borderTop: "1px solid var(--border-color)",
+            fontSize: "0.9rem",
+            textAlign: "center"
+          }
+        })
+      ]);
+
+      // Initialize functionality
+      setupPanelHandlers(container);
+      
+      return container;
+    };
+
+    // Register the panel with ComfyUI's panel system
+    app.ui.registerPanel?.({
+      id: panelId,
+      title: "Model Downloader",
+      icon: "download",
+      location: "right",
+      size: { width: 400 },
+      render: createPanelContent
+    });
+
+    // Fallback for older ComfyUI versions
+    if (!app.ui.registerPanel) {
+      console.warn("Panel API not available, creating menu button fallback");
+      createLegacyMenuButton(createPanelContent);
+    }
+  }
+});
+
+// Handler functions
+function setupPanelHandlers(container) {
+  const loginBtn = container.querySelector(".login-btn");
+  const logoutBtn = container.querySelector(".logout-btn");
+  const tokenInput = container.querySelector(".hf-token-input");
+  const userInfo = container.querySelector(".user-info");
+  const inputGroup = container.querySelector(".input-group");
+  const tokenNote = container.querySelector(".token-note");
+  const selectAllBtn = container.querySelector(".select-all-btn");
+  const downloadSelectedBtn = container.querySelector(".download-selected-btn");
+  const modelList = container.querySelector(".model-list");
+  const status = container.querySelector(".download-status");
+
+  // Check login status
+  async function checkLoginStatus() {
+    try {
+      const response = await api.fetchApi("/hal-fun-downloader/login-status");
+      if (!response.ok) throw new Error(`Login status error: ${response.status}`);
+      const data = await response.json();
+      updateLoginUI(data.logged_in);
+    } catch (error) {
+      console.error("Error checking login status:", error);
+    }
+  }
+
+  function updateLoginUI(isLoggedIn) {
+    if (isLoggedIn) {
+      loginBtn.textContent = "Logged In";
+      loginBtn.style.background = "var(--success-color)";
+      tokenInput.disabled = true;
+      inputGroup.style.display = "none";
+      userInfo.style.display = "flex";
+      tokenNote.style.display = "none";
+    } else {
+      loginBtn.textContent = "Login";
+      loginBtn.style.background = "var(--accent-color)";
+      tokenInput.disabled = false;
+      inputGroup.style.display = "flex";
+      userInfo.style.display = "none";
+      tokenNote.style.display = "block";
+    }
+  }
+
+  // Login handler
+  loginBtn.onclick = async () => {
+    const token = tokenInput.value.trim();
+    if (!token) {
+      status.textContent = "Please enter a Hugging Face token";
+      return;
+    }
+
+    try {
+      status.textContent = "Logging in...";
+      const response = await api.fetchApi("/hal-fun-downloader/login", {
+        method: "POST",
+        body: JSON.stringify({ token }),
+      });
+
+      if (!response.ok) {
+        const error = await response.json();
+        throw new Error(error.error || "Login failed");
+      }
+
+      updateLoginUI(true);
+      status.textContent = "Successfully logged in";
+      await loadModels(container);
+    } catch (error) {
+      console.error("Login error:", error);
+      status.textContent = `Login error: ${error.message}`;
+    }
+  };
+
+  // Logout handler
+  logoutBtn.onclick = async () => {
+    try {
+      status.textContent = "Logging out...";
+      const response = await api.fetchApi("/hal-fun-downloader/logout", {
+        method: "POST",
+      });
+
+      if (!response.ok) {
+        const error = await response.json();
+        throw new Error(error.error || "Logout failed");
+      }
+
+      updateLoginUI(false);
+      status.textContent = "Successfully logged out";
+      await loadModels(container);
+    } catch (error) {
+      console.error("Logout error:", error);
+      status.textContent = `Logout error: ${error.message}`;
+    }
+  };
+
+  // Select all handler
+  selectAllBtn.onclick = () => {
+    const checkboxes = modelList.querySelectorAll('input[type="checkbox"]');
+    const allChecked = Array.from(checkboxes).every((cb) => cb.checked);
+    checkboxes.forEach((cb) => {
+      cb.checked = !allChecked;
+      cb.dispatchEvent(new Event("change"));
+    });
+    selectAllBtn.textContent = allChecked ? "Select All" : "Deselect All";
+  };
+
+  // Download selected handler
+  downloadSelectedBtn.onclick = async () => {
+    const selectedModels = Array.from(
+      modelList.querySelectorAll('input[type="checkbox"]:checked')
+    ).map((cb) => cb.dataset.modelName);
+
+    if (selectedModels.length === 0) {
+      status.textContent = "Please select models to download";
+      return;
+    }
+
+    status.textContent = "Downloading selected models...";
+    downloadSelectedBtn.disabled = true;
+
+    try {
+      const response = await api.fetchApi("/hal-fun-downloader/download", {
+        method: "POST",
+        body: JSON.stringify({ model_names: selectedModels }),
+      });
+      
+      if (!response.ok) {
+        throw new Error(`Failed to download models: ${response.status}`);
+      }
+      
+      const result = await response.json();
+      status.textContent = result.status;
+      await loadModels(container);
+    } catch (error) {
+      console.error("Download error:", error);
+      status.textContent = `Error: ${error.message}`;
+    } finally {
+      downloadSelectedBtn.disabled = false;
+    }
+  };
+
+  // Load models initially
+  checkLoginStatus();
+  loadModels(container);
+}
+
+async function loadModels(container) {
+  const modelList = container.querySelector(".model-list");
+  const status = container.querySelector(".download-status");
+
+  try {
+    const [configResponse, activeResponse] = await Promise.all([
+      api.fetchApi("/hal-fun-downloader/config"),
+      api.fetchApi("/hal-fun-downloader/active")
+    ]);
+
+    if (!configResponse.ok || !activeResponse.ok) {
+      throw new Error("Failed to load configuration");
+    }
+
+    const config = await configResponse.json();
+    const activeConfig = await activeResponse.json();
+
+    modelList.innerHTML = "";
+
+    if (!Array.isArray(config) || config.length === 0) {
+      modelList.innerHTML = '<li style="padding: 20px; text-align: center;">No models found</li>';
+      return;
+    }
+
+    for (const model of config) {
+      if (!model || !model.local_path) continue;
+
+      const modelName = model.local_path.split("/").pop();
+      const isEnabled = activeConfig?.enabled_models?.includes(modelName);
+      const isDownloaded = activeConfig.model_status?.[modelName]?.downloaded;
+
+      const modelItem = $el("li.model-item", {
+        style: {
+          display: "flex",
+          alignItems: "center",
+          justifyContent: "space-between",
+          padding: "12px 20px",
+          borderBottom: "1px solid var(--border-color)"
+        }
+      }, [
+        $el("label", {
+          style: {
+            display: "flex",
+            alignItems: "center",
+            gap: "12px",
+            flex: "1",
+            cursor: "pointer"
+          }
+        }, [
+          $el("input", {
+            type: "checkbox",
+            checked: isEnabled,
+            dataset: { modelName },
+            onchange: async (e) => {
+              try {
+                if (e.target.checked) {
+                  activeConfig.enabled_models.push(modelName);
+                } else {
+                  activeConfig.enabled_models = activeConfig.enabled_models.filter(
+                    (m) => m !== modelName
+                  );
+                }
+                const updateResponse = await api.fetchApi("/hal-fun-downloader/active", {
+                  method: "POST",
+                  body: JSON.stringify(activeConfig),
+                });
+                if (!updateResponse.ok) throw new Error("Failed to update config");
+              } catch (error) {
+                console.error("Error updating model status:", error);
+                e.target.checked = !e.target.checked;
+                status.textContent = `Error: ${error.message}`;
+              }
+            }
+          }),
+          $el("span", {
+            textContent: isDownloaded ? "✓ " : model.license?.required ? "🔒 " : ""
+          }),
+          $el("span", { textContent: modelName })
+        ]),
+        $el("button.download-btn", {
+          textContent: isDownloaded ? "Downloaded" : "Download",
+          disabled: isDownloaded,
+          style: {
+            padding: "4px 12px",
+            border: "1px solid var(--border-color)",
+            borderRadius: "4px",
+            background: "var(--comfy-input-bg)",
+            cursor: isDownloaded ? "not-allowed" : "pointer",
+            opacity: isDownloaded ? "0.5" : "1"
+          },
+          onclick: async () => {
+            status.textContent = "Starting download...";
+            
+            try {
+              const response = await api.fetchApi("/hal-fun-downloader/download", {
+                method: "POST",
+                body: JSON.stringify({ model_name: modelName }),
+              });
+              
+              if (!response.ok) throw new Error(`Download failed: ${response.status}`);
+              
+              const result = await response.json();
+              status.textContent = result.status;
+              await loadModels(container);
+            } catch (error) {
+              console.error("Download error:", error);
+              status.textContent = `Error: ${error.message}`;
+            }
+          }
+        })
+      ]);
+
+      modelList.appendChild(modelItem);
+    }
+  } catch (error) {
+    console.error("Error loading models:", error);
+    modelList.innerHTML = `<li style="padding: 20px; text-align: center; color: var(--error-color);">Error: ${error.message}</li>`;
+    status.textContent = `Error: ${error.message}`;
+  }
+}
+
+// Legacy fallback for older ComfyUI versions
+function createLegacyMenuButton(createPanelContent) {
+  const menuRight = document.querySelector(".comfyui-menu-right .comfyui-button-group");
+  if (!menuRight) return;
+
+  const button = document.createElement("button");
+  button.className = "comfyui-button";
+  button.title = "Model Downloader";
+  button.innerHTML = '<i class="mdi mdi-download"></i><span>Models</span>';
+
+  const panel = createPanelContent();
+  panel.style.display = "none";
+  panel.style.position = "fixed";
+  panel.style.top = "60px";
+  panel.style.right = "10px";
+  panel.style.width = "400px";
+  panel.style.maxHeight = "80vh";
+  panel.style.background = "var(--comfy-menu-bg)";
+  panel.style.border = "1px solid var(--border-color)";
+  panel.style.borderRadius = "8px";
+  panel.style.boxShadow = "0 4px 6px rgba(0, 0, 0, 0.1)";
+  panel.style.zIndex = "1000";
+
+  document.body.appendChild(panel);
+
+  button.onclick = () => {
+    panel.style.display = panel.style.display === "none" ? "flex" : "none";
+  };
+
+  document.addEventListener("click", (e) => {
+    if (!panel.contains(e.target) && !button.contains(e.target) && panel.style.display === "flex") {
+      panel.style.display = "none";
+    }
+  });
+
+  menuRight.appendChild(button);
+}
+
+.hal-fun-top-menu {
+    box-sizing: border-box;
+    white-space: nowrap;
+    background: var(--content-bg);
+    color: var(--content-fg);
+    display: flex;
+    flex-direction: column;
+    min-width: 400px;
+    max-width: 800px;
+    border: 1px solid var(--border-color);
+    border-radius: 0.5rem;
+    box-shadow: rgb(0 0 0 / 20%) 0px 5px 5px -3px, 
+                rgb(0 0 0 / 14%) 0px 8px 10px 1px, 
+                rgb(0 0 0 / 12%) 0px 3px 14px 2px;
+    max-height: 80vh;
+    overflow-y: auto;
+}
+
+.hal-fun-top-menu * {
+    box-sizing: inherit;
+}
+
+.hal-fun-downloader-container {
+    display: flex;
+    flex-direction: column;
+}
+
+.hal-fun-downloader-container h2 {
+    margin: 0;
+    padding: 8px 12px;
+    color: var(--fg-color);
+    font-size: 1.2rem;
+    font-weight: 600;
+    border-bottom: 1px solid var(--border-color);
+    text-shadow: 4px 4px 4px rgba(0, 0, 0, 0.8);
+}
+
+.model-list {
+    list-style: none;
+    padding: 0;
+    margin: 0;
+}
+
+.model-item {
+    list-style: none;
+    padding: 0;
+    margin: 0;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    padding: 8px 12px;
+    border-bottom: 1px solid var(--border-color);
+}
+
+.model-item:last-child {
+    border-bottom: none;
+}
+
+.model-item:hover {
+    background-color: var(--comfy-input-bg);
+}
+
+.model-item label {
+    display: flex;
+    align-items: center;
+    gap: 0.6em;
+    cursor: pointer;
+    user-select: none;
+    flex-grow: 1;
+    width: 100%;
+    text-align: start;
+    padding: 8px 12px 8px 8px;
+}
+
+.model-item input[type="checkbox"] {
+    width: 16px;
+    height: 16px;
+    accent-color: var(--accent-color);
+}
+
+.download-btn {
+    background: none;
+    color: var(--fg-color);
+    border: 1px solid var(--border-color);
+    padding: 4px 8px;
+    border-radius: 4px;
+    cursor: pointer;
+    transition: background 0.2s;
+    font-size: 0.9rem;
+    margin-left: 8px;
+}
+
+.download-btn:hover {
+    background-color: var(--comfy-input-bg);
+}
+
+.download-status {
+    padding: 8px 12px;
+    border-top: 1px solid var(--border-color);
+    color: var(--fg-color);
+    font-size: 0.9rem;
+    font-style: italic;
+    text-align: center;
+}
+
+/* Animation */
+.comfyui-popup.left {
+    transform-origin: top right;
+    animation: popup-left 0.2s ease-out;
+}
+
+@keyframes popup-left {
+    from {
+        opacity: 0;
+        transform: translateY(-8px);
+    }
+    to {
+        opacity: 1;
+        transform: translateY(0);
+    }
+}
+
+.model-controls {
+    padding: 8px 12px;
+    border-bottom: 1px solid var(--border-color);
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+}
+
+.model-controls button {
+    background: none;
+    color: var(--fg-color);
+    border: 1px solid var(--border-color);
+    padding: 4px 8px;
+    border-radius: 4px;
+    cursor: pointer;
+    transition: background 0.2s;
+    font-size: 0.9rem;
+}
+
+.model-controls button:hover {
+    background-color: var(--comfy-input-bg);
+}
+
+.model-controls button:disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+}
+
+.model-item .status-icon {
+    margin-right: 8px;
+    color: var(--error-color);
+}
+
+.model-item .status-icon.downloaded {
+    color: var(--success-color);
+}
+
+.download-btn:disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+}
+
+.login-section {
+    padding: 8px 12px;
+    border-bottom: 1px solid var(--border-color);
+    display: flex;
+    flex-direction: column;
+    gap: 8px;
+}
+
+.login-section .input-group {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+}
+
+.login-section input {
+    flex-grow: 1;
+    margin-right: 8px;
+    padding: 4px 8px;
+    border: 1px solid var(--border-color);
+    border-radius: 4px;
+    background: var(--comfy-input-bg);
+    color: var(--fg-color);
+}
+
+.login-section .token-link {
+    color: var(--accent-color);
+    text-decoration: underline;
+    font-size: 0.8rem;
+    text-align: center;
+}
+
+.login-section .token-link:hover {
+    opacity: 0.8;
+}
+
+.login-section .token-note {
+    font-size: 0.8rem;
+    color: var(--fg-color);
+    font-style: italic;
+    text-align: center;
+    margin-top: 4px;
+}
+
+.login-section button {
+    background: none;
+    color: var(--fg-color);
+    border: 1px solid var(--border-color);
+    padding: 4px 8px;
+    border-radius: 4px;
+    cursor: pointer;
+    transition: background 0.2s;
+    font-size: 0.9rem;
+}
+
+.login-section button:hover {
+    background-color: var(--comfy-input-bg);
+}
+
+.login-section button.logged-in {
+    color: var(--success-color);
+}
+
+.license-dialog {
+    position: fixed;
+    top: 50%;
+    left: 50%;
+    transform: translate(-50%, -50%);
+    background: var(--content-bg);
+    border: 1px solid var(--border-color);
+    border-radius: 0.5rem;
+    padding: 20px;
+    z-index: 1000;
+    max-width: 600px;
+    width: 90%;
+    max-height: 80vh;
+    overflow-y: auto;
+    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+}
+
+.license-dialog-overlay {
+    position: fixed;
+    top: 0;
+    left: 0;
+    right: 0;
+    bottom: 0;
+    background: rgba(0, 0, 0, 0.5);
+    z-index: 999;
+}
+
+.license-dialog h3 {
+    margin: 0 0 16px 0;
+    color: var(--fg-color);
+}
+
+.license-dialog p {
+    margin: 0 0 16px 0;
+    color: var(--fg-color);
+    line-height: 1.5;
+}
+
+.license-dialog .buttons {
+    display: flex;
+    justify-content: flex-end;
+    gap: 8px;
+}
+
+.license-dialog button {
+    padding: 8px 16px;
+    border-radius: 4px;
+    cursor: pointer;
+    font-size: 0.9rem;
+}
+
+.license-dialog .cancel-btn {
+    background: none;
+    border: 1px solid var(--border-color);
+    color: var(--fg-color);
+}
+
+.license-dialog .accept-btn {
+    background: var(--accent-color);
+    border: none;
+    color: white;
+}
+
+.model-item .status-icon.gated {
+    color: var(--warning-color);
+}
+
+.model-item .license-info {
+    font-size: 0.8rem;
+    color: var(--warning-color);
+    margin-left: 8px;
+    text-decoration: none;
+}
+
+.model-item .license-info:hover {
+    text-decoration: underline;
+}
+
+.model-item .license-info.granted {
+    color: var(--success-color);
+}
+`;
+
+document.head.appendChild(style);
+
+// Create a new extension
+app.registerExtension({
+  name: "hal.fun.model.downloader",
+  async setup() {
+    // Find the right menu group or create a new one
+    const menuRight = document.querySelector(
+      ".comfyui-menu-right .comfyui-button-group"
+    );
+    if (!menuRight) return;
+
+    // Create the button
+    const button = document.createElement("button");
+    button.className = "comfyui-button";
+    button.title = "Model Downloader";
+    button.innerHTML = '<i class="mdi mdi-download"></i><span>Models</span>';
+
+    // Create the panel
+    const panel = document.createElement("div");
+    panel.style.display = "none";
+    panel.className = "comfy-menu-panel comfyui-popup hal-fun-top-menu";
+    panel.style.position = "fixed";
+    panel.style.top = "40px";
+    panel.style.right = "10px";
+    panel.innerHTML = `
+            <div class="hal-fun-downloader-container">
+                <h2>🦾 Hal.fun Model Downloader</h2>
+                <div class="login-section">
+                    <div class="input-group">
+                        <input type="password" class="hf-token-input" placeholder="Hugging Face Token">
+                        <button class="login-btn">Login</button>
+                        
+                    </div>
+                    <div class="token-note">
+                        You need a Hugging Face token to download gated models.&nbsp;
+                        <a href="https://huggingface.co/settings/tokens/new?tokenType=read" target="_blank" class="token-link">Get a read token from Hugging Face</a>
+                    </div>
+                    
+                    
+                    <div class="user-info hidden">
+                        <span class="user-name"></span>
+                        <button class="logout-btn">Logout</button>
+                    </div>
+                </div>
+                <div class="model-controls">
+                    <button class="select-all-btn">Select All</button>
+                    <button class="download-selected-btn">Download Selected</button>
+                </div>
+                <menu class="model-list"></menu>
+                <div class="download-status"></div>
+            </div>
+            <div class="license-dialog-overlay" style="display: none;"></div>
+            <div class="license-dialog" style="display: none;">
+                <h3>License Agreement Required</h3>
+                <p>This model requires you to accept the following license agreement:</p>
+                <p class="license-name"></p>
+                <p>Please visit the model page to review and accept the license:</p>
+                <p><a href="#" class="license-url" target="_blank">View License Agreement</a></p>
+                <div class="buttons">
+                    <button class="cancel-btn">Cancel</button>
+                    <button class="accept-btn">I Accept</button>
+                </div>
+            </div>
+        `;
+    document.body.appendChild(panel);
+
+    // Toggle panel when clicking the button
+    button.onclick = () => {
+      if (panel.style.display === "none") {
+        panel.style.display = "block";
+        panel.classList.add("left", "open");
+        button.classList.add("primary");
+      } else {
+        panel.style.display = "none";
+        panel.classList.remove("left", "open");
+        button.classList.remove("primary");
+      }
+    };
+
+    // Close panel when clicking outside
+    document.addEventListener("click", (e) => {
+      if (
+        !panel.contains(e.target) &&
+        !button.contains(e.target) &&
+        panel.style.display === "block"
+      ) {
+        panel.style.display = "none";
+        panel.classList.remove("left", "open");
+        button.classList.remove("primary");
+      }
+    });
+
+    // Add button to menu
+    menuRight.appendChild(button);
+
+    // Load model configurations
+    async function loadModels() {
+      try {
+        const response = await api.fetchApi("/hal-fun-downloader/config");
+        if (!response.ok) {
+          throw new Error(
+            `Config API error: ${response.status} ${response.statusText}`
+          );
+        }
+        const config = await response.json();
+        console.log("Loaded config:", config);
+
+        const activeResponse = await api.fetchApi("/hal-fun-downloader/active");
+        if (!activeResponse.ok) {
+          throw new Error(
+            `Active config API error: ${activeResponse.status} ${activeResponse.statusText}`
+          );
+        }
+        const activeConfig = await activeResponse.json();
+        console.log("Loaded active config:", activeConfig);
+
+        const modelList = panel.querySelector(".model-list");
+        if (!modelList) {
+          throw new Error("Could not find .model-list element");
+        }
+        modelList.innerHTML = "";
+
+        if (!Array.isArray(config) || config.length === 0) {
+          modelList.innerHTML =
+            '<li class="model-item">No models found in configuration</li>';
+          return;
+        }
+
+        // Add event listeners for control buttons
+        const selectAllBtn = panel.querySelector(".select-all-btn");
+        const downloadSelectedBtn = panel.querySelector(
+          ".download-selected-btn"
+        );
+
+        selectAllBtn.onclick = () => {
+          const checkboxes = modelList.querySelectorAll(
+            'input[type="checkbox"]'
+          );
+          const allChecked = Array.from(checkboxes).every((cb) => cb.checked);
+          checkboxes.forEach((cb) => {
+            cb.checked = !allChecked;
+            cb.dispatchEvent(new Event("change"));
+          });
+          selectAllBtn.textContent = allChecked ? "Select All" : "Deselect All";
+        };
+
+        downloadSelectedBtn.onclick = async () => {
+          const selectedModels = Array.from(
+            modelList.querySelectorAll('input[type="checkbox"]:checked')
+          ).map((cb) => cb.dataset.modelName);
+
+          if (selectedModels.length === 0) {
+            const status = panel.querySelector(".download-status");
+            status.textContent = "Please select models to download";
+            return;
+          }
+
+          const status = panel.querySelector(".download-status");
+          status.textContent = "Downloading selected models...";
+          downloadSelectedBtn.disabled = true;
+
+          try {
+            const response = await api.fetchApi(
+              "/hal-fun-downloader/download",
+              {
+                method: "POST",
+                body: JSON.stringify({ model_names: selectedModels }),
+              }
+            );
+            if (!response.ok) {
+              throw new Error(`Failed to download models: ${response.status}`);
+            }
+            const result = await response.json();
+            status.textContent = result.status;
+            await loadModels(); // Refresh the list
+          } catch (error) {
+            console.error("Download error:", error);
+            status.textContent = `Error: ${error.message}`;
+          } finally {
+            downloadSelectedBtn.disabled = false;
+          }
+        };
+
+        // Create model items asynchronously
+        for (const model of config) {
+          if (!model || !model.local_path) {
+            console.warn("Invalid model config:", model);
+            continue;
+          }
+
+          const modelName = model.local_path.split("/").pop();
+          const isEnabled =
+            Array.isArray(activeConfig?.enabled_models) &&
+            activeConfig.enabled_models.includes(modelName);
+          const isDownloaded =
+            activeConfig.model_status?.[modelName]?.downloaded;
+
+          const modelItem = document.createElement("li");
+          modelItem.className = "model-item";
+          modelItem.innerHTML = `
+                <label>
+                    <input type="checkbox" data-model-name="${modelName}" ${
+            isEnabled ? "checked" : ""
+          }>
+                    ${
+                      isDownloaded
+                        ? '<span class="status-icon downloaded">✓</span>'
+                        : model.license?.required
+                        ? '<span class="status-icon gated">🔒</span>'
+                        : ""
+                    }
+                    ${modelName}
+                    ${
+                      model.license?.required
+                        ? `<a href="${model.license.url}" target="_blank" class="license-info">(License Required)</a>`
+                        : ""
+                    }
+                </label>
+                <button class="download-btn" ${isDownloaded ? "disabled" : ""}>
+                    ${isDownloaded ? "Downloaded" : "Download"}
+                </button>
+            `;
+
+          // Check license status for models that require it
+          if (model.license?.required) {
+            const licenseInfo = modelItem.querySelector(".license-info");
+            if (licenseInfo) {
+              try {
+                const response = await api.fetchApi(
+                  "/hal-fun-downloader/check-license",
+                  {
+                    method: "POST",
+                    body: JSON.stringify({
+                      repo_id: model.repo_id,
+                      filename: model.filename,
+                    }),
+                  }
+                );
+
+                if (!response.ok) {
+                  throw new Error(`License check failed: ${response.status}`);
+                }
+
+                const result = await response.json();
+                if (result.accepted) {
+                  licenseInfo.textContent = "(License Granted)";
+                  licenseInfo.classList.add("granted");
+                } else {
+                  licenseInfo.textContent = "(License Required)";
+                  licenseInfo.classList.remove("granted");
+                }
+              } catch (error) {
+                console.error("License check error:", error);
+                if (
+                  error.message.includes("401") ||
+                  error.message.includes("403")
+                ) {
+                  licenseInfo.textContent = "(Login Required)";
+                  licenseInfo.classList.remove("granted");
+                } else {
+                  licenseInfo.textContent = "(License Required)";
+                  licenseInfo.classList.remove("granted");
+                }
+              }
+            }
+          }
+
+          const checkbox = modelItem.querySelector("input");
+          checkbox.onchange = async () => {
+            try {
+              if (checkbox.checked) {
+                activeConfig.enabled_models.push(modelName);
+              } else {
+                activeConfig.enabled_models =
+                  activeConfig.enabled_models.filter((m) => m !== modelName);
+              }
+              const updateResponse = await api.fetchApi(
+                "/hal-fun-downloader/active",
+                {
+                  method: "POST",
+                  body: JSON.stringify(activeConfig),
+                }
+              );
+              if (!updateResponse.ok) {
+                throw new Error(
+                  `Failed to update active config: ${updateResponse.status}`
+                );
+              }
+            } catch (error) {
+              console.error("Error updating model status:", error);
+              checkbox.checked = !checkbox.checked; // Revert the checkbox
+              const status = panel.querySelector(".download-status");
+              status.textContent = `Error: ${error.message}`;
+            }
+          };
+
+          const downloadBtn = modelItem.querySelector(".download-btn");
+          downloadBtn.onclick = async () => {
+            const status = panel.querySelector(".download-status");
+            if (model.license?.required) {
+              try {
+                const response = await api.fetchApi(
+                  "/hal-fun-downloader/check-license",
+                  {
+                    method: "POST",
+                    body: JSON.stringify({
+                      repo_id: model.repo_id,
+                      filename: model.filename,
+                    }),
+                  }
+                );
+
+                if (!response.ok) {
+                  throw new Error(`License check failed: ${response.status}`);
+                }
+
+                const result = await response.json();
+                if (!result.accepted) {
+                  showLicenseDialog(model);
+                  return;
+                }
+              } catch (error) {
+                console.error("License check error:", error);
+                status.textContent = `Error checking license: ${error.message}`;
+                return;
+              }
+            }
+
+            status.textContent = "Starting download...";
+            downloadBtn.disabled = true;
+
+            try {
+              const response = await api.fetchApi(
+                "/hal-fun-downloader/download",
+                {
+                  method: "POST",
+                  body: JSON.stringify({ model_name: modelName }),
+                }
+              );
+              if (!response.ok) {
+                throw new Error(`Download failed: ${response.status}`);
+              }
+              const result = await response.json();
+              status.textContent = result.status;
+              await loadModels(); // Refresh the list
+            } catch (error) {
+              console.error("Download error:", error);
+              status.textContent = `Error: ${error.message}`;
+            } finally {
+              downloadBtn.disabled = false;
+            }
+          };
+
+          modelList.appendChild(modelItem);
+        }
+      } catch (error) {
+        console.error("Error loading models:", error);
+        const modelList = panel.querySelector(".model-list");
+        if (modelList) {
+          modelList.innerHTML = `<li class="model-item">Error loading models: ${error.message}</li>`;
+        }
+        const status = panel.querySelector(".download-status");
+        if (status) {
+          status.textContent = `Error: ${error.message}`;
+        }
+      }
+    }
+
+    // Load models when the extension is initialized and when the panel is opened
+    checkLoginStatus(); // Check login status on initialization
+    loadModels();
+    button.addEventListener("click", () => {
+      if (panel.style.display === "none") {
+        checkLoginStatus();
+        loadModels(); // Refresh the list when opening the panel
+      }
+    });
+
+    // Add login functionality
+    async function checkLoginStatus() {
+      try {
+        const response = await api.fetchApi("/hal-fun-downloader/login-status");
+        if (!response.ok)
+          throw new Error(`Login status error: ${response.status}`);
+        const data = await response.json();
+        updateLoginUI(data.logged_in);
+      } catch (error) {
+        console.error("Error checking login status:", error);
+      }
+    }
+
+    function updateLoginUI(isLoggedIn) {
+      const loginBtn = panel.querySelector(".login-btn");
+      const tokenInput = panel.querySelector(".hf-token-input");
+      const tokenLink = panel.querySelector(".token-link");
+      const inputGroup = panel.querySelector(".input-group");
+      const userInfo = panel.querySelector(".user-info");
+      const userName = panel.querySelector(".user-name");
+      const logoutBtn = panel.querySelector(".logout-btn");
+      const tokenNote = panel.querySelector(".token-note");
+
+      if (isLoggedIn) {
+        loginBtn.textContent = "Logged In";
+        loginBtn.classList.add("logged-in");
+        tokenInput.value = "";
+        tokenInput.disabled = true;
+        tokenLink.classList.add("hidden");
+        inputGroup.style.display = "none";
+        userInfo.classList.remove("hidden");
+        tokenNote.classList.add("hidden");
+        userName.textContent = "Logged in to Hugging Face";
+      } else {
+        loginBtn.textContent = "Login";
+        loginBtn.classList.remove("logged-in");
+        tokenInput.disabled = false;
+        tokenLink.classList.remove("hidden");
+        inputGroup.style.display = "flex";
+        userInfo.classList.add("hidden");
+        tokenNote.classList.remove("hidden");
+      }
+    }
+
+    // Add login button handler
+    const loginBtn = panel.querySelector(".login-btn");
+    loginBtn.onclick = async () => {
+      const tokenInput = panel.querySelector(".hf-token-input");
+      const token = tokenInput.value.trim();
+      const status = panel.querySelector(".download-status");
+
+      // If already logged in, handle logout
+      if (loginBtn.classList.contains("logged-in")) {
+        try {
+          status.textContent = "Logging out...";
+          const response = await api.fetchApi("/hal-fun-downloader/logout", {
+            method: "POST",
+          });
+
+          if (!response.ok) {
+            const error = await response.json();
+            throw new Error(error.error || "Logout failed");
+          }
+
+          updateLoginUI(false);
+          status.textContent = "Successfully logged out";
+
+          // Refresh the model list to update download and license status
+          await loadModels();
+        } catch (error) {
+          console.error("Logout error:", error);
+          status.textContent = `Logout error: ${error.message}`;
+        }
+        return;
+      }
+
+      // Handle login
+      if (!token) {
+        status.textContent =
+          "Please create a new read token at https://huggingface.co/settings/tokens/new?tokenType=read (required for gated models)";
+        return;
+      }
+
+      try {
+        status.textContent = "Logging in...";
+        const response = await api.fetchApi("/hal-fun-downloader/login", {
+          method: "POST",
+          body: JSON.stringify({ token }),
+        });
+
+        if (!response.ok) {
+          const error = await response.json();
+          throw new Error(error.error || "Login failed");
+        }
+
+        updateLoginUI(true);
+        status.textContent = "Successfully logged in";
+
+        // Refresh the model list to update download and license status
+        await loadModels();
+      } catch (error) {
+        console.error("Login error:", error);
+        status.textContent = `Login error: ${error.message}`;
+      }
+    };
+
+    // Add logout button handler
+    const logoutBtn = panel.querySelector(".logout-btn");
+    logoutBtn.onclick = async () => {
+      const status = panel.querySelector(".download-status");
+      try {
+        status.textContent = "Logging out...";
+        const response = await api.fetchApi("/hal-fun-downloader/logout", {
+          method: "POST",
+        });
+
+        if (!response.ok) {
+          const error = await response.json();
+          throw new Error(error.error || "Logout failed");
+        }
+
+        updateLoginUI(false);
+        status.textContent = "Successfully logged out";
+
+        // Refresh the model list to update download and license status
+        await loadModels();
+      } catch (error) {
+        console.error("Logout error:", error);
+        status.textContent = `Logout error: ${error.message}`;
+      }
+    };
+
+    // Add license dialog handling
+    const licenseDialog = panel.querySelector(".license-dialog");
+    const licenseOverlay = panel.querySelector(".license-dialog-overlay");
+    const licenseUrl = panel.querySelector(".license-url");
+    const licenseName = panel.querySelector(".license-name");
+    const cancelBtn = panel.querySelector(".cancel-btn");
+    const acceptBtn = panel.querySelector(".accept-btn");
+
+    function showLicenseDialog(model) {
+      licenseName.textContent = model.license.name;
+      licenseUrl.href = model.license.url;
+      licenseDialog.style.display = "block";
+      licenseOverlay.style.display = "block";
+
+      // Update accept button to check license status
+      acceptBtn.onclick = async () => {
+        try {
+          const status = panel.querySelector(".download-status");
+          status.textContent = "Checking license acceptance...";
+
+          const response = await api.fetchApi(
+            "/hal-fun-downloader/check-license",
+            {
+              method: "POST",
+              body: JSON.stringify({
+                repo_id: model.repo_id,
+                filename: model.filename,
+              }),
+            }
+          );
+
+          if (!response.ok) {
+            throw new Error(`License check failed: ${response.status}`);
+          }
+
+          const result = await response.json();
+          if (result.accepted) {
+            hideLicenseDialog();
+            // Proceed with download
+            downloadBtn.click();
+          } else {
+            status.textContent =
+              "Please accept the license on Hugging Face first";
+            window.open(licenseUrl.href, "_blank");
+          }
+        } catch (error) {
+          console.error("License check error:", error);
+          status.textContent = `Error checking license: ${error.message}`;
+        }
+      };
+    }
+
+    function hideLicenseDialog() {
+      licenseDialog.style.display = "none";
+      licenseOverlay.style.display = "none";
+    }
+
+    cancelBtn.onclick = hideLicenseDialog;
+    acceptBtn.onclick = () => {
+      hideLicenseDialog();
+      // Open the license URL in a new tab
+      window.open(licenseUrl.href, "_blank");
+    };
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/.github/workflows/publish_comfy_registry_action.yml b/custom_nodes/rgthree-comfy/.github/workflows/publish_comfy_registry_action.yml
new file mode 100644
index 0000000000000000000000000000000000000000..d52a3bba8f75308fa0d74dfc211f443c8ebb8bb2
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/.github/workflows/publish_comfy_registry_action.yml
@@ -0,0 +1,20 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+    paths:
+      - "pyproject.toml"
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@main
+        with:
+          personal_access_token: ${{ secrets.COMFY_REGISTRY_ACCESS_TOKEN }}
diff --git a/custom_nodes/rgthree-comfy/docs/rgthree_router.png b/custom_nodes/rgthree-comfy/docs/rgthree_router.png
new file mode 100644
index 0000000000000000000000000000000000000000..f3581d708f98c6352dfcc414ac0bd81f2eb2fc59
Binary files /dev/null and b/custom_nodes/rgthree-comfy/docs/rgthree_router.png differ
diff --git a/custom_nodes/rgthree-comfy/docs/rgthree_seed.png b/custom_nodes/rgthree-comfy/docs/rgthree_seed.png
new file mode 100644
index 0000000000000000000000000000000000000000..32c392fbccf38f7e0d150cbeed0cc9066f8ac610
Binary files /dev/null and b/custom_nodes/rgthree-comfy/docs/rgthree_seed.png differ
diff --git a/custom_nodes/rgthree-comfy/py/__init__.py b/custom_nodes/rgthree-comfy/py/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/custom_nodes/rgthree-comfy/py/any_switch.py b/custom_nodes/rgthree-comfy/py/any_switch.py
new file mode 100644
index 0000000000000000000000000000000000000000..99713fafd3db1812b384689b7ed70b6e7f96f4d9
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/any_switch.py
@@ -0,0 +1,38 @@
+from .context_utils import is_context_empty
+from .constants import get_category, get_name
+from .utils import FlexibleOptionalInputType, any_type
+
+
+def is_none(value):
+  """Checks if a value is none. Pulled out in case we want to expand what 'None' means."""
+  if value is not None:
+    if isinstance(value, dict) and 'model' in value and 'clip' in value:
+      return is_context_empty(value)
+  return value is None
+
+
+class RgthreeAnySwitch:
+  """The dynamic Any Switch. """
+
+  NAME = get_name("Any Switch")
+  CATEGORY = get_category()
+
+  @classmethod
+  def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+    return {
+      "required": {},
+      "optional": FlexibleOptionalInputType(any_type),
+    }
+
+  RETURN_TYPES = (any_type,)
+  RETURN_NAMES = ('*',)
+  FUNCTION = "switch"
+
+  def switch(self, **kwargs):
+    """Chooses the first non-empty item to output."""
+    any_value = None
+    for key, value in kwargs.items():
+      if key.startswith('any_') and not is_none(value):
+        any_value = value
+        break
+    return (any_value,)
diff --git a/custom_nodes/rgthree-comfy/py/config.py b/custom_nodes/rgthree-comfy/py/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..a50c49293e3a574881351a0f04d58b72d638561b
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/config.py
@@ -0,0 +1,111 @@
+import os
+import json
+
+from .utils import get_dict_value, set_dict_value, dict_has_key, load_json_file
+from .pyproject import VERSION
+
+
+def get_config_value(key, default=None):
+  return get_dict_value(RGTHREE_CONFIG, key, default)
+
+
+def extend_config(default_config, user_config):
+  """ Returns a new config dict combining user_config into defined keys for default_config."""
+  cfg = {}
+  for key, value in default_config.items():
+    if key not in user_config:
+      cfg[key] = value
+    elif isinstance(value, dict):
+      cfg[key] = extend_config(value, user_config[key])
+    else:
+      cfg[key] = user_config[key] if key in user_config else value
+  return cfg
+
+
+def set_user_config(data: dict):
+  """ Sets the user configuration."""
+  count = 0
+  for key, value in data.items():
+    if dict_has_key(DEFAULT_CONFIG, key):
+      set_dict_value(USER_CONFIG, key, value)
+      set_dict_value(RGTHREE_CONFIG, key, value)
+      count += 1
+  if count > 0:
+    write_user_config()
+
+
+def get_rgthree_default_config():
+  """ Gets the default configuration."""
+  return load_json_file(DEFAULT_CONFIG_FILE, default={})
+
+
+def get_rgthree_user_config():
+  """ Gets the user configuration."""
+  return load_json_file(USER_CONFIG_FILE, default={})
+
+
+def write_user_config():
+  """ Writes the user configuration."""
+  with open(USER_CONFIG_FILE, 'w+', encoding='UTF-8') as file:
+    json.dump(USER_CONFIG, file, sort_keys=True, indent=2, separators=(",", ": "))
+
+
+THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+DEFAULT_CONFIG_FILE = os.path.join(THIS_DIR, '..', 'rgthree_config.json.default')
+USER_CONFIG_FILE = os.path.join(THIS_DIR, '..', 'rgthree_config.json')
+
+DEFAULT_CONFIG = {}
+USER_CONFIG = {}
+RGTHREE_CONFIG = {}
+
+
+def refresh_config():
+  """Refreshes the config."""
+  global DEFAULT_CONFIG, USER_CONFIG, RGTHREE_CONFIG
+  DEFAULT_CONFIG = get_rgthree_default_config()
+  USER_CONFIG = get_rgthree_user_config()
+
+  # Migrate old config options into "features"
+  needs_to_write_user_config = False
+  if 'patch_recursive_execution' in USER_CONFIG:
+    del USER_CONFIG['patch_recursive_execution']
+    needs_to_write_user_config = True
+
+  if 'features' in USER_CONFIG and 'patch_recursive_execution' in USER_CONFIG['features']:
+    del USER_CONFIG['features']['patch_recursive_execution']
+    needs_to_write_user_config = True
+
+  if 'show_alerts_for_corrupt_workflows' in USER_CONFIG:
+    if 'features' not in USER_CONFIG:
+      USER_CONFIG['features'] = {}
+    USER_CONFIG['features']['show_alerts_for_corrupt_workflows'] = USER_CONFIG[
+      'show_alerts_for_corrupt_workflows']
+    del USER_CONFIG['show_alerts_for_corrupt_workflows']
+    needs_to_write_user_config = True
+
+  if 'monitor_for_corrupt_links' in USER_CONFIG:
+    if 'features' not in USER_CONFIG:
+      USER_CONFIG['features'] = {}
+    USER_CONFIG['features']['monitor_for_corrupt_links'] = USER_CONFIG['monitor_for_corrupt_links']
+    del USER_CONFIG['monitor_for_corrupt_links']
+    needs_to_write_user_config = True
+
+  if needs_to_write_user_config is True:
+    print('writing new user config.')
+    write_user_config()
+
+  RGTHREE_CONFIG = {"version": VERSION} | extend_config(DEFAULT_CONFIG, USER_CONFIG)
+
+  if "unreleased" in USER_CONFIG and "unreleased" not in RGTHREE_CONFIG:
+    RGTHREE_CONFIG["unreleased"] = USER_CONFIG["unreleased"]
+
+  if "debug" in USER_CONFIG and "debug" not in RGTHREE_CONFIG:
+    RGTHREE_CONFIG["debug"] = USER_CONFIG["debug"]
+
+
+def get_config():
+  """Returns the congfig."""
+  return RGTHREE_CONFIG
+
+
+refresh_config()
diff --git a/custom_nodes/rgthree-comfy/py/constants.py b/custom_nodes/rgthree-comfy/py/constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..73b7d687ad24999c3b441f2c2ef39e4e8b3f3d54
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/constants.py
@@ -0,0 +1,11 @@
+
+NAMESPACE='rgthree'
+
+def get_name(name):
+    return '{} ({})'.format(name, NAMESPACE)
+
+def get_category(sub_dirs = None):
+    if sub_dirs is None:
+        return NAMESPACE
+    else:
+        return "{}/utils".format(NAMESPACE)
diff --git a/custom_nodes/rgthree-comfy/py/context.py b/custom_nodes/rgthree-comfy/py/context.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2c94f6a687606708cd1399e228c6b6b7d451318
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/context.py
@@ -0,0 +1,33 @@
+"""The Context node."""
+from .context_utils import (ORIG_CTX_OPTIONAL_INPUTS, ORIG_CTX_RETURN_NAMES, ORIG_CTX_RETURN_TYPES,
+                            get_orig_context_return_tuple, new_context)
+from .constants import get_category, get_name
+
+
+class RgthreeContext:
+  """The initial Context node.
+
+  For now, this nodes' outputs will remain as-is, as they are perfect for most 1.5 application, but
+  is also backwards compatible with other Context nodes.
+  """
+
+  NAME = get_name("Context")
+  CATEGORY = get_category()
+
+  @classmethod
+  def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+    return {
+      "required": {},
+      "optional": ORIG_CTX_OPTIONAL_INPUTS,
+      "hidden": {
+        "version": "FLOAT"
+      },
+    }
+
+  RETURN_TYPES = ORIG_CTX_RETURN_TYPES
+  RETURN_NAMES = ORIG_CTX_RETURN_NAMES
+  FUNCTION = "convert"
+
+  def convert(self, base_ctx=None, **kwargs):  # pylint: disable = missing-function-docstring
+    ctx = new_context(base_ctx, **kwargs)
+    return get_orig_context_return_tuple(ctx)
diff --git a/custom_nodes/rgthree-comfy/py/context_big.py b/custom_nodes/rgthree-comfy/py/context_big.py
new file mode 100644
index 0000000000000000000000000000000000000000..411cdc8fee86118c0a4201ff7441eab0f5c792d1
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/context_big.py
@@ -0,0 +1,31 @@
+"""The Conmtext big node."""
+from .constants import get_category, get_name
+from .context_utils import (ALL_CTX_OPTIONAL_INPUTS, ALL_CTX_RETURN_NAMES, ALL_CTX_RETURN_TYPES,
+                            new_context, get_context_return_tuple)
+
+
+class RgthreeBigContext:
+  """The Context Big node.
+
+  This context node will expose all context fields as inputs and outputs. It is backwards compatible
+  with other context nodes and can be intertwined with them.
+  """
+
+  NAME = get_name("Context Big")
+  CATEGORY = get_category()
+
+  @classmethod
+  def INPUT_TYPES(cls):  # pylint: disable = invalid-name,missing-function-docstring
+    return {
+      "required": {},
+      "optional": ALL_CTX_OPTIONAL_INPUTS,
+      "hidden": {},
+    }
+
+  RETURN_TYPES = ALL_CTX_RETURN_TYPES
+  RETURN_NAMES = ALL_CTX_RETURN_NAMES
+  FUNCTION = "convert"
+
+  def convert(self, base_ctx=None, **kwargs):  # pylint: disable = missing-function-docstring
+    ctx = new_context(base_ctx, **kwargs)
+    return get_context_return_tuple(ctx)
diff --git a/custom_nodes/rgthree-comfy/py/context_merge.py b/custom_nodes/rgthree-comfy/py/context_merge.py
new file mode 100644
index 0000000000000000000000000000000000000000..02d0afe68554f16e99d99ff969afbd98a646ff8c
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/context_merge.py
@@ -0,0 +1,37 @@
+"""The Context Switch (Big)."""
+from .constants import get_category, get_name
+from .context_utils import (ORIG_CTX_RETURN_TYPES, ORIG_CTX_RETURN_NAMES, merge_new_context,
+                            get_orig_context_return_tuple, is_context_empty)
+from .utils import FlexibleOptionalInputType
+
+
+class RgthreeContextMerge:
+  """The Context Merge node."""
+
+  NAME = get_name("Context Merge")
+  CATEGORY = get_category()
+
+  @classmethod
+  def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+    return {
+      "required": {},
+      "optional": FlexibleOptionalInputType("RGTHREE_CONTEXT"),
+    }
+
+  RETURN_TYPES = ORIG_CTX_RETURN_TYPES
+  RETURN_NAMES = ORIG_CTX_RETURN_NAMES
+  FUNCTION = "merge"
+
+  def get_return_tuple(self, ctx):
+    """Returns the context data. Separated so it can be overridden."""
+    return get_orig_context_return_tuple(ctx)
+
+  def merge(self, **kwargs):
+    """Merges any non-null passed contexts; later ones overriding earlier."""
+    ctxs = [
+      value for key, value in kwargs.items()
+      if key.startswith('ctx_') and not is_context_empty(value)
+    ]
+    ctx = merge_new_context(*ctxs)
+
+    return self.get_return_tuple(ctx)
diff --git a/custom_nodes/rgthree-comfy/py/context_merge_big.py b/custom_nodes/rgthree-comfy/py/context_merge_big.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4039e4eb87a3e6fedae2a871f13bb4537cc12a9
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/context_merge_big.py
@@ -0,0 +1,16 @@
+"""The Context Switch (Big)."""
+from .constants import get_category, get_name
+from .context_utils import (ALL_CTX_RETURN_TYPES, ALL_CTX_RETURN_NAMES, get_context_return_tuple)
+from .context_merge import RgthreeContextMerge
+
+
+class RgthreeContextMergeBig(RgthreeContextMerge):
+  """The Context Merge Big node."""
+
+  NAME = get_name("Context Merge Big")
+  RETURN_TYPES = ALL_CTX_RETURN_TYPES
+  RETURN_NAMES = ALL_CTX_RETURN_NAMES
+
+  def get_return_tuple(self, ctx):
+    """Returns the context data. Separated so it can be overridden."""
+    return get_context_return_tuple(ctx)
diff --git a/custom_nodes/rgthree-comfy/py/context_switch.py b/custom_nodes/rgthree-comfy/py/context_switch.py
new file mode 100644
index 0000000000000000000000000000000000000000..67e02e1452da99b4df57f0d00a3fd6963a5d48ce
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/context_switch.py
@@ -0,0 +1,36 @@
+"""The original Context Switch."""
+from .constants import get_category, get_name
+from .context_utils import (ORIG_CTX_RETURN_TYPES, ORIG_CTX_RETURN_NAMES, is_context_empty,
+                            get_orig_context_return_tuple)
+from .utils import FlexibleOptionalInputType
+
+
+class RgthreeContextSwitch:
+  """The (original) Context Switch node."""
+
+  NAME = get_name("Context Switch")
+  CATEGORY = get_category()
+
+  @classmethod
+  def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+    return {
+      "required": {},
+      "optional": FlexibleOptionalInputType("RGTHREE_CONTEXT"),
+    }
+
+  RETURN_TYPES = ORIG_CTX_RETURN_TYPES
+  RETURN_NAMES = ORIG_CTX_RETURN_NAMES
+  FUNCTION = "switch"
+
+  def get_return_tuple(self, ctx):
+    """Returns the context data. Separated so it can be overridden."""
+    return get_orig_context_return_tuple(ctx)
+
+  def switch(self, **kwargs):
+    """Chooses the first non-empty Context to output."""
+    ctx = None
+    for key, value in kwargs.items():
+      if key.startswith('ctx_') and not is_context_empty(value):
+        ctx = value
+        break
+    return self.get_return_tuple(ctx)
diff --git a/custom_nodes/rgthree-comfy/py/context_switch_big.py b/custom_nodes/rgthree-comfy/py/context_switch_big.py
new file mode 100644
index 0000000000000000000000000000000000000000..4382e3027641d8a546ef509be824e18533376551
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/context_switch_big.py
@@ -0,0 +1,16 @@
+"""The Context Switch (Big)."""
+from .constants import get_category, get_name
+from .context_utils import (ALL_CTX_RETURN_TYPES, ALL_CTX_RETURN_NAMES, get_context_return_tuple)
+from .context_switch import RgthreeContextSwitch
+
+
+class RgthreeContextSwitchBig(RgthreeContextSwitch):
+  """The Context Switch Big node."""
+
+  NAME = get_name("Context Switch Big")
+  RETURN_TYPES = ALL_CTX_RETURN_TYPES
+  RETURN_NAMES = ALL_CTX_RETURN_NAMES
+
+  def get_return_tuple(self, ctx):
+    """Overrides the RgthreeContextSwitch `get_return_tuple` to return big context data."""
+    return get_context_return_tuple(ctx)
diff --git a/custom_nodes/rgthree-comfy/py/context_utils.py b/custom_nodes/rgthree-comfy/py/context_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c2e68478fce2cd97042af8a9aed2ea84ef78b3e
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/context_utils.py
@@ -0,0 +1,118 @@
+"""A set of constants and utilities for handling contexts.
+
+Sets up the inputs and outputs for the Context going forward, with additional functions for
+creating and exporting context objects.
+"""
+import comfy.samplers
+import folder_paths
+
+_all_context_input_output_data = {
+  "base_ctx": ("base_ctx", "RGTHREE_CONTEXT", "CONTEXT"),
+  "model": ("model", "MODEL", "MODEL"),
+  "clip": ("clip", "CLIP", "CLIP"),
+  "vae": ("vae", "VAE", "VAE"),
+  "positive": ("positive", "CONDITIONING", "POSITIVE"),
+  "negative": ("negative", "CONDITIONING", "NEGATIVE"),
+  "latent": ("latent", "LATENT", "LATENT"),
+  "images": ("images", "IMAGE", "IMAGE"),
+  "seed": ("seed", "INT", "SEED"),
+  "steps": ("steps", "INT", "STEPS"),
+  "step_refiner": ("step_refiner", "INT", "STEP_REFINER"),
+  "cfg": ("cfg", "FLOAT", "CFG"),
+  "ckpt_name": ("ckpt_name", folder_paths.get_filename_list("checkpoints"), "CKPT_NAME"),
+  "sampler": ("sampler", comfy.samplers.KSampler.SAMPLERS, "SAMPLER"),
+  "scheduler": ("scheduler", comfy.samplers.KSampler.SCHEDULERS, "SCHEDULER"),
+  "clip_width": ("clip_width", "INT", "CLIP_WIDTH"),
+  "clip_height": ("clip_height", "INT", "CLIP_HEIGHT"),
+  "text_pos_g": ("text_pos_g", "STRING", "TEXT_POS_G"),
+  "text_pos_l": ("text_pos_l", "STRING", "TEXT_POS_L"),
+  "text_neg_g": ("text_neg_g", "STRING", "TEXT_NEG_G"),
+  "text_neg_l": ("text_neg_l", "STRING", "TEXT_NEG_L"),
+  "mask": ("mask", "MASK", "MASK"),
+  "control_net": ("control_net", "CONTROL_NET", "CONTROL_NET"),
+}
+
+force_input_types = ["INT", "STRING", "FLOAT"]
+force_input_names = ["sampler", "scheduler", "ckpt_name"]
+
+
+def _create_context_data(input_list=None):
+  """Returns a tuple of context inputs, return types, and return names to use in a node"s def"""
+  if input_list is None:
+    input_list = _all_context_input_output_data.keys()
+  list_ctx_return_types = []
+  list_ctx_return_names = []
+  ctx_optional_inputs = {}
+  for inp in input_list:
+    data = _all_context_input_output_data[inp]
+    list_ctx_return_types.append(data[1])
+    list_ctx_return_names.append(data[2])
+    ctx_optional_inputs[data[0]] = tuple([data[1]] + ([{
+      "forceInput": True
+    }] if data[1] in force_input_types or data[0] in force_input_names else []))
+
+  ctx_return_types = tuple(list_ctx_return_types)
+  ctx_return_names = tuple(list_ctx_return_names)
+  return (ctx_optional_inputs, ctx_return_types, ctx_return_names)
+
+
+ALL_CTX_OPTIONAL_INPUTS, ALL_CTX_RETURN_TYPES, ALL_CTX_RETURN_NAMES = _create_context_data()
+
+_original_ctx_inputs_list = [
+  "base_ctx", "model", "clip", "vae", "positive", "negative", "latent", "images", "seed"
+]
+ORIG_CTX_OPTIONAL_INPUTS, ORIG_CTX_RETURN_TYPES, ORIG_CTX_RETURN_NAMES = _create_context_data(
+  _original_ctx_inputs_list)
+
+
+def new_context(base_ctx, **kwargs):
+  """Creates a new context from the provided data, with an optional base ctx to start."""
+  context = base_ctx if base_ctx is not None else None
+  new_ctx = {}
+  for key in _all_context_input_output_data:
+    if key == "base_ctx":
+      continue
+    v = kwargs[key] if key in kwargs else None
+    new_ctx[key] = v if v is not None else context[
+      key] if context is not None and key in context else None
+  return new_ctx
+
+
+def merge_new_context(*args):
+  """Creates a new context by merging provided contexts with the latter overriding same fields."""
+  new_ctx = {}
+  for key in _all_context_input_output_data:
+    if key == "base_ctx":
+      continue
+    v = None
+    # Move backwards through the passed contexts until we find a value and use it.
+    for ctx in reversed(args):
+      v = ctx[key] if not is_context_empty(ctx) and key in ctx else None
+      if v is not None:
+        break
+    new_ctx[key] = v
+  return new_ctx
+
+
+def get_context_return_tuple(ctx, inputs_list=None):
+  """Returns a tuple for returning in the order of the inputs list."""
+  if inputs_list is None:
+    inputs_list = _all_context_input_output_data.keys()
+  tup_list = [
+    ctx,
+  ]
+  for key in inputs_list:
+    if key == "base_ctx":
+      continue
+    tup_list.append(ctx[key] if ctx is not None and key in ctx else None)
+  return tuple(tup_list)
+
+
+def get_orig_context_return_tuple(ctx):
+  """Returns a tuple for returning from a node with only the original context keys."""
+  return get_context_return_tuple(ctx, _original_ctx_inputs_list)
+
+
+def is_context_empty(ctx):
+  """Checks if the provided ctx is None or contains just None values."""
+  return not ctx or all(v is None for v in ctx.values())
diff --git a/custom_nodes/rgthree-comfy/py/display_any.py b/custom_nodes/rgthree-comfy/py/display_any.py
new file mode 100644
index 0000000000000000000000000000000000000000..19238dbab0cc886cbde4d0106724cd3bc95ed085
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/display_any.py
@@ -0,0 +1,77 @@
+import json
+from .constants import get_category, get_name
+from .utils import any_type, get_dict_value
+
+
+class RgthreeDisplayAny:
+  """Display any data node."""
+
+  NAME = get_name('Display Any')
+  CATEGORY = get_category()
+
+  @classmethod
+  def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+    return {
+      "required": {
+        "source": (any_type, {}),
+      },
+      "hidden": {
+        "unique_id": "UNIQUE_ID",
+        "extra_pnginfo": "EXTRA_PNGINFO",
+      },
+    }
+
+  RETURN_TYPES = ()
+  FUNCTION = "main"
+  OUTPUT_NODE = True
+
+  def main(self, source=None, unique_id=None, extra_pnginfo=None):
+    value = 'None'
+    if isinstance(source, str):
+      value = source
+    elif isinstance(source, (int, float, bool)):
+      value = str(source)
+    elif source is not None:
+      try:
+        value = json.dumps(source)
+      except Exception:
+        try:
+          value = str(source)
+        except Exception:
+          value = 'source exists, but could not be serialized.'
+
+    # Save the output to the pnginfo so it's pre-filled when loading the data.
+    if extra_pnginfo and unique_id:
+      for node in get_dict_value(extra_pnginfo, 'workflow.nodes', []):
+        if str(node['id']) == str(unique_id):
+          node['widgets_values'] = [value]
+          break
+
+    return {"ui": {"text": (value,)}}
+
+
+class RgthreeDisplayInt:
+  """Old DisplayInt node.
+
+  Can be ported over to DisplayAny if https://github.com/comfyanonymous/ComfyUI/issues/1527 fixed.
+  """
+
+  NAME = get_name('Display Int')
+  CATEGORY = get_category()
+
+  @classmethod
+  def INPUT_TYPES(s):
+    return {
+      "required": {
+        "input": ("INT", {
+          "forceInput": True
+        }),
+      },
+    }
+
+  RETURN_TYPES = ()
+  FUNCTION = "main"
+  OUTPUT_NODE = True
+
+  def main(self, input=None):
+    return {"ui": {"text": (input,)}}
diff --git a/custom_nodes/rgthree-comfy/py/dynamic_context.py b/custom_nodes/rgthree-comfy/py/dynamic_context.py
new file mode 100644
index 0000000000000000000000000000000000000000..411210d0e0cbc3c038b8b9c0822b69658fc63c7d
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/dynamic_context.py
@@ -0,0 +1,56 @@
+"""The Dynamic Context node."""
+from mimetypes import add_type
+from .constants import get_category, get_name
+from .utils import ByPassTypeTuple, FlexibleOptionalInputType
+
+
+class RgthreeDynamicContext:
+  """The Dynamic Context node.
+
+  Similar to the static Context and Context Big nodes, this allows users to add any number and
+  variety of inputs to a Dynamic Context node, and return the outputs by key name.
+  """
+
+  NAME = get_name("Dynamic Context")
+  CATEGORY = get_category()
+
+  @classmethod
+  def INPUT_TYPES(cls):  # pylint: disable = invalid-name,missing-function-docstring
+    return {
+      "required": {},
+      "optional": FlexibleOptionalInputType(add_type),
+      "hidden": {},
+    }
+
+  RETURN_TYPES = ByPassTypeTuple(("RGTHREE_DYNAMIC_CONTEXT",))
+  RETURN_NAMES = ByPassTypeTuple(("CONTEXT",))
+  FUNCTION = "main"
+
+  def main(self, **kwargs):
+    """Creates a new context from the provided data, with an optional base ctx to start.
+
+    This node takes a list of named inputs that are the named keys (with an optional "+ " prefix)
+    which are to be stored within the ctx dict as well as a list of keys contained in `output_keys`
+    to determine the list of output data.
+    """
+
+    base_ctx = kwargs.get('base_ctx', None)
+    output_keys = kwargs.get('output_keys', None)
+
+    new_ctx = base_ctx.copy() if base_ctx is not None else {}
+
+    for key_raw, value in kwargs.items():
+      if key_raw in ['base_ctx', 'output_keys']:
+        continue
+      key = key_raw.upper()
+      if key.startswith('+ '):
+        key = key[2:]
+      new_ctx[key] = value
+
+    print(new_ctx)
+
+    res = [new_ctx]
+    output_keys = output_keys.split(',') if output_keys is not None else []
+    for key in output_keys:
+      res.append(new_ctx[key] if key in new_ctx else None)
+    return tuple(res)
diff --git a/custom_nodes/rgthree-comfy/py/dynamic_context_switch.py b/custom_nodes/rgthree-comfy/py/dynamic_context_switch.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d1b9e6a7eb1a592db3941298e57ff0c38dfb5e0
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/dynamic_context_switch.py
@@ -0,0 +1,39 @@
+"""The original Context Switch."""
+from .constants import get_category, get_name
+from .context_utils import is_context_empty
+from .utils import ByPassTypeTuple, FlexibleOptionalInputType
+
+
+class RgthreeDynamicContextSwitch:
+  """The initial Context Switch node."""
+
+  NAME = get_name("Dynamic Context Switch")
+  CATEGORY = get_category()
+
+  @classmethod
+  def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+    return {
+      "required": {},
+      "optional": FlexibleOptionalInputType("RGTHREE_DYNAMIC_CONTEXT"),
+    }
+
+  RETURN_TYPES = ByPassTypeTuple(("RGTHREE_DYNAMIC_CONTEXT",))
+  RETURN_NAMES = ByPassTypeTuple(("CONTEXT",))
+  FUNCTION = "switch"
+
+  def switch(self, **kwargs):
+    """Chooses the first non-empty Context to output."""
+
+    output_keys = kwargs.get('output_keys', None)
+
+    ctx = None
+    for key, value in kwargs.items():
+      if key.startswith('ctx_') and not is_context_empty(value):
+        ctx = value
+        break
+
+    res = [ctx]
+    output_keys = output_keys.split(',') if output_keys is not None else []
+    for key in output_keys:
+      res.append(ctx[key] if ctx is not None and key in ctx else None)
+    return tuple(res)
diff --git a/custom_nodes/rgthree-comfy/py/image_comparer.py b/custom_nodes/rgthree-comfy/py/image_comparer.py
new file mode 100644
index 0000000000000000000000000000000000000000..079ebdbf6051fa86cf89f75f55f8502904d0c93f
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/image_comparer.py
@@ -0,0 +1,42 @@
+from nodes import PreviewImage
+
+from .constants import get_category, get_name
+
+
+class RgthreeImageComparer(PreviewImage):
+  """A node that compares two images in the UI."""
+
+  NAME = get_name('Image Comparer')
+  CATEGORY = get_category()
+  FUNCTION = "compare_images"
+  DESCRIPTION = "Compares two images with a hover slider, or click from properties."
+
+  @classmethod
+  def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+    return {
+      "required": {},
+      "optional": {
+        "image_a": ("IMAGE",),
+        "image_b": ("IMAGE",),
+      },
+      "hidden": {
+        "prompt": "PROMPT",
+        "extra_pnginfo": "EXTRA_PNGINFO"
+      },
+    }
+
+  def compare_images(self,
+                     image_a=None,
+                     image_b=None,
+                     filename_prefix="rgthree.compare.",
+                     prompt=None,
+                     extra_pnginfo=None):
+
+    result = { "ui": { "a_images":[], "b_images": [] } }
+    if image_a is not None and len(image_a) > 0:
+      result['ui']['a_images'] = self.save_images(image_a, filename_prefix, prompt, extra_pnginfo)['ui']['images']
+
+    if image_b is not None and len(image_b) > 0:
+      result['ui']['b_images'] = self.save_images(image_b, filename_prefix, prompt, extra_pnginfo)['ui']['images']
+
+    return result
\ No newline at end of file
diff --git a/custom_nodes/rgthree-comfy/py/image_inset_crop.py b/custom_nodes/rgthree-comfy/py/image_inset_crop.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f5f29adf825494dc91dd00519d8d2af287cce19
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/image_inset_crop.py
@@ -0,0 +1,93 @@
+"""Image Inset Crop, with percentages."""
+from .log import log_node_info
+from .constants import get_category, get_name
+from nodes import MAX_RESOLUTION
+
+
+def get_new_bounds(width, height, left, right, top, bottom):
+  """Returns the new bounds for an image with inset crop data."""
+  left = 0 + left
+  right = width - right
+  top = 0 + top
+  bottom = height - bottom
+  return (left, right, top, bottom)
+
+
+class RgthreeImageInsetCrop:
+  """Image Inset Crop, with percentages."""
+
+  NAME = get_name('Image Inset Crop')
+  CATEGORY = get_category()
+
+  @classmethod
+  def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+    return {
+      "required": {
+        "image": ("IMAGE",),
+        "measurement": (['Pixels', 'Percentage'],),
+        "left": ("INT", {
+          "default": 0,
+          "min": 0,
+          "max": MAX_RESOLUTION,
+          "step": 8
+        }),
+        "right": ("INT", {
+          "default": 0,
+          "min": 0,
+          "max": MAX_RESOLUTION,
+          "step": 8
+        }),
+        "top": ("INT", {
+          "default": 0,
+          "min": 0,
+          "max": MAX_RESOLUTION,
+          "step": 8
+        }),
+        "bottom": ("INT", {
+          "default": 0,
+          "min": 0,
+          "max": MAX_RESOLUTION,
+          "step": 8
+        }),
+      },
+    }
+
+  RETURN_TYPES = ("IMAGE",)
+  FUNCTION = "crop"
+
+  # pylint: disable = too-many-arguments
+  def crop(self, measurement, left, right, top, bottom, image=None):
+    """Does the crop."""
+
+    _, height, width, _ = image.shape
+
+    if measurement == 'Percentage':
+      left = int(width - (width * (100 - left) / 100))
+      right = int(width - (width * (100 - right) / 100))
+      top = int(height - (height * (100 - top) / 100))
+      bottom = int(height - (height * (100 - bottom) / 100))
+
+    # Snap to 8 pixels
+    left = left // 8 * 8
+    right = right // 8 * 8
+    top = top // 8 * 8
+    bottom = bottom // 8 * 8
+
+    if left == 0 and right == 0 and bottom == 0 and top == 0:
+      return (image,)
+
+    inset_left, inset_right, inset_top, inset_bottom = get_new_bounds(width, height, left, right,
+                                                                      top, bottom)
+    if inset_top > inset_bottom:
+      raise ValueError(
+        f"Invalid cropping dimensions top ({inset_top}) exceeds bottom ({inset_bottom})")
+    if inset_left > inset_right:
+      raise ValueError(
+        f"Invalid cropping dimensions left ({inset_left}) exceeds right ({inset_right})")
+
+    log_node_info(
+      self.NAME, f'Cropping image {width}x{height} width inset by {inset_left},{inset_right}, ' +
+      f'and height inset by {inset_top}, {inset_bottom}')
+    image = image[:, inset_top:inset_bottom, inset_left:inset_right, :]
+
+    return (image,)
diff --git a/custom_nodes/rgthree-comfy/py/image_or_latent_size.py b/custom_nodes/rgthree-comfy/py/image_or_latent_size.py
new file mode 100644
index 0000000000000000000000000000000000000000..65f62f86fcfdfc5caedfddf773cb808f796bbef1
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/image_or_latent_size.py
@@ -0,0 +1,31 @@
+from .utils import FlexibleOptionalInputType, any_type
+from .constants import get_category, get_name
+
+
+class RgthreeImageOrLatentSize:
+  """The ImageOrLatentSize Node."""
+
+  NAME = get_name('Image or Latent Size')
+  CATEGORY = get_category()
+
+  @classmethod
+  def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+    return {
+      "required": {},
+      "optional": FlexibleOptionalInputType(any_type),
+    }
+
+  RETURN_TYPES = ("INT", "INT")
+  RETURN_NAMES = ('WIDTH', 'HEIGHT')
+  FUNCTION = "main"
+
+  def main(self, **kwargs):
+    """Does the node's work."""
+    image_or_latent_or_mask = kwargs.get('input', None)
+
+    if isinstance(image_or_latent_or_mask, dict) and 'samples' in image_or_latent_or_mask:
+      count, _, height, width = image_or_latent_or_mask['samples'].shape
+      return (width * 8, height * 8)
+
+    batch, height, width, channel = image_or_latent_or_mask.shape
+    return (width, height)
diff --git a/custom_nodes/rgthree-comfy/py/image_resize.py b/custom_nodes/rgthree-comfy/py/image_resize.py
new file mode 100644
index 0000000000000000000000000000000000000000..a21001b36ac8f6de616d68c2a9827d791acff2ee
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/image_resize.py
@@ -0,0 +1,117 @@
+import torch
+import comfy.utils
+import nodes
+
+from .constants import get_category, get_name
+
+
+class RgthreeImageResize:
+  """Image Resize."""
+
+  NAME = get_name("Image Resize")
+  CATEGORY = get_category()
+
+  @classmethod
+  def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+    return {
+      "required": {
+        "image": ("IMAGE",),
+        "measurement": (["pixels", "percentage"],),
+        "width": (
+          "INT", {
+            "default": 0,
+            "min": 0,
+            "max": nodes.MAX_RESOLUTION,
+            "step": 1,
+            "tooltip": (
+              "The width of the desired resize. A pixel value if measurement is 'pixels' or a"
+              " 100% scale percentage value if measurement is 'percentage'. Passing '0' will"
+              " calculate the dimension based on the height."
+            ),
+          },
+        ),
+        "height": ("INT", {
+          "default": 0,
+          "min": 0,
+          "max": nodes.MAX_RESOLUTION,
+          "step": 1
+        }),
+        "fit": (["crop", "pad", "contain"], {
+          "tooltip": (
+            "'crop' resizes so the image covers the desired width and height, and center-crops the"
+            " excess, returning exactly the desired width and height."
+            "\n'pad' resizes so the image fits inside the desired width and height, and fills the"
+            " empty space returning exactly the desired width and height."
+            "\n'contain' resizes so the image fits inside the desired width and height, and"
+            " returns the image with it's new size, with one side liekly smaller than the desired."
+            "\n\nNote, if either width or height is '0', the effective fit is 'contain'."
+          )
+        },
+               ),
+        "method": (nodes.ImageScale.upscale_methods,),
+      },
+    }
+
+  RETURN_TYPES = ("IMAGE", "INT", "INT",)
+  RETURN_NAMES = ("IMAGE", "WIDTH", "HEIGHT",)
+  FUNCTION = "main"
+  DESCRIPTION = """Resize the image."""
+
+  def main(self, image, measurement, width, height, method, fit):
+    """Resizes the image."""
+    _, H, W, _ = image.shape
+
+    if measurement == "percentage":
+      width = round(width * W / 100)
+      height = round(height * H / 100)
+
+    if (width == 0 and height == 0) or (width == W and height == H):
+      return (image, W, H)
+
+    # If one dimension is 0, then calculate the desired value from the ratio of the set dimension.
+    # This also implies a 'contain' fit since the width and height will be scaled with a locked
+    # aspect ratio.
+    if width == 0 or height == 0:
+      width = round(height / H * W) if width == 0 else width
+      height = round(width / W * H) if height == 0 else height
+      fit = "contain"
+
+    # At this point, width and height are our output height, but our resize sizes will be different.
+    resized_width = width
+    resized_height = height
+    if fit == "crop":
+      # If we resize against the opposite ratio, then choose the ratio that has the overhang.
+      if (height / H * W) > width:
+        resized_width = round(height / H * W)
+      elif (width / W * H) > height:
+        resized_height = round(width / W * H)
+    elif fit == "contain" or fit == "pad":
+      # If we resize against the opposite ratio, then choose the ratio that has the overhang.
+      if (height / H * W) > width:
+        resized_height = round(width / W * H)
+      elif (width / W * H) > height:
+        resized_width = round(height / H * W)
+
+    out_image = comfy.utils.common_upscale(
+      image.clone().movedim(-1, 1), resized_width, resized_height, method, crop="disabled"
+    ).movedim(1, -1)
+    OB, OH, OW, OC = out_image.shape
+
+    if fit != "contain":
+      # First, we crop, then we pad; no need to check fit (other than not 'contain') since the size
+      # should already be correct.
+      if OW > width:
+        out_image = out_image.narrow(-2, (OW - width) // 2, width)
+      if OH > height:
+        out_image = out_image.narrow(-3, (OH - height) // 2, height)
+
+      OB, OH, OW, OC = out_image.shape
+      if width != OW or height != OH:
+        padded_image = torch.zeros((OB, height, width, OC), dtype=image.dtype, device=image.device)
+        x = (width - OW) // 2
+        y = (height - OH) // 2
+        for b in range(OB):
+          padded_image[b, y:y + OH, x:x + OW, :] = out_image[b]
+        out_image = padded_image
+
+    return (out_image, out_image.shape[2], out_image.shape[1])
diff --git a/custom_nodes/rgthree-comfy/py/ksampler_config.py b/custom_nodes/rgthree-comfy/py/ksampler_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..043ea49e4d09fe1b8fdae92d2d95bb6ee324c717
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/ksampler_config.py
@@ -0,0 +1,56 @@
+"""Some basic config stuff I use for SDXL."""
+
+from .constants import get_category, get_name
+from nodes import MAX_RESOLUTION
+import comfy.samplers
+
+
+class RgthreeKSamplerConfig:
+  """Some basic config stuff I started using for SDXL, but useful in other spots too."""
+
+  NAME = get_name('KSampler Config')
+  CATEGORY = get_category()
+
+  @classmethod
+  def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+    return {
+      "required": {
+        "steps_total": ("INT", {
+          "default": 30,
+          "min": 1,
+          "max": MAX_RESOLUTION,
+          "step": 1,
+        }),
+        "refiner_step": ("INT", {
+          "default": 24,
+          "min": 1,
+          "max": MAX_RESOLUTION,
+          "step": 1,
+        }),
+        "cfg": ("FLOAT", {
+          "default": 8.0,
+          "min": 0.0,
+          "max": 100.0,
+          "step": 0.5,
+        }),
+        "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
+        "scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
+        #"refiner_ascore_pos": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
+        #"refiner_ascore_neg": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
+      },
+    }
+
+  RETURN_TYPES = ("INT", "INT", "FLOAT", comfy.samplers.KSampler.SAMPLERS,
+                  comfy.samplers.KSampler.SCHEDULERS)
+  RETURN_NAMES = ("STEPS", "REFINER_STEP", "CFG", "SAMPLER", "SCHEDULER")
+  FUNCTION = "main"
+
+  def main(self, steps_total, refiner_step, cfg, sampler_name, scheduler):
+    """main"""
+    return (
+      steps_total,
+      refiner_step,
+      cfg,
+      sampler_name,
+      scheduler,
+    )
diff --git a/custom_nodes/rgthree-comfy/py/log.py b/custom_nodes/rgthree-comfy/py/log.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f41675ef6ccdb610821f147cb860c7b720f3eab
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/log.py
@@ -0,0 +1,100 @@
+import datetime
+import time
+from .pyproject import NAME
+
+# https://stackoverflow.com/questions/4842424/list-of-ansi-color-escape-sequences
+# https://en.wikipedia.org/wiki/ANSI_escape_code#3-bit_and_4-bit
+COLORS = {
+  'BLACK': '\33[30m',
+  'RED': '\33[31m',
+  'GREEN': '\33[32m',
+  'YELLOW': '\33[33m',
+  'BLUE': '\33[34m',
+  'MAGENTA': '\33[35m',
+  'CYAN': '\33[36m',
+  'WHITE': '\33[37m',
+  'GREY': '\33[90m',
+  'BRIGHT_RED': '\33[91m',
+  'BRIGHT_GREEN': '\33[92m',
+  'BRIGHT_YELLOW': '\33[93m',
+  'BRIGHT_BLUE': '\33[94m',
+  'BRIGHT_MAGENTA': '\33[95m',
+  'BRIGHT_CYAN': '\33[96m',
+  'BRIGHT_WHITE': '\33[97m',
+  # Styles.
+  'RESET': '\33[0m',  # Note, Portainer doesn't like 00 here, so we'll use 0. Should be fine...
+  'BOLD': '\33[01m',
+  'NORMAL': '\33[22m',
+  'ITALIC': '\33[03m',
+  'UNDERLINE': '\33[04m',
+  'BLINK': '\33[05m',
+  'BLINK2': '\33[06m',
+  'SELECTED': '\33[07m',
+  # Backgrounds
+  'BG_BLACK': '\33[40m',
+  'BG_RED': '\33[41m',
+  'BG_GREEN': '\33[42m',
+  'BG_YELLOW': '\33[43m',
+  'BG_BLUE': '\33[44m',
+  'BG_MAGENTA': '\33[45m',
+  'BG_CYAN': '\33[46m',
+  'BG_WHITE': '\33[47m',
+  'BG_GREY': '\33[100m',
+  'BG_BRIGHT_RED': '\33[101m',
+  'BG_BRIGHT_GREEN': '\33[102m',
+  'BG_BRIGHT_YELLOW': '\33[103m',
+  'BG_BRIGHT_BLUE': '\33[104m',
+  'BG_BRIGHT_MAGENTA': '\33[105m',
+  'BG_BRIGHT_CYAN': '\33[106m',
+  'BG_BRIGHT_WHITE': '\33[107m',
+}
+
+
+def log_node_success(node_name, message, msg_color='RESET'):
+  """Logs a success message."""
+  _log_node("BRIGHT_GREEN", node_name, message, msg_color=msg_color)
+
+
+def log_node_info(node_name, message, msg_color='RESET'):
+  """Logs an info message."""
+  _log_node("CYAN", node_name, message, msg_color=msg_color)
+
+
+def log_node_error(node_name, message, msg_color='RESET'):
+  """Logs an info message."""
+  _log_node("RED", node_name, message, msg_color=msg_color)
+
+
+def log_node_warn(node_name, message, msg_color='RESET'):
+  """Logs an warn message."""
+  _log_node("YELLOW", node_name, message, msg_color=msg_color)
+
+
+def log_node(node_name, message, msg_color='RESET'):
+  """Logs a message."""
+  _log_node("CYAN", node_name, message, msg_color=msg_color)
+
+
+def _log_node(color, node_name, message, msg_color='RESET'):
+  """Logs for a node message."""
+  log(message, color=color, prefix=node_name.replace(" (rgthree)", ""), msg_color=msg_color)
+
+LOGGED = {}
+
+def log(message, color=None, msg_color=None, prefix=None, id=None, at_most_secs=None):
+  """Basic logging."""
+  now = int(time.time())
+  if id:
+    if at_most_secs is None:
+      raise ValueError('at_most_secs should be set if an id is set.')
+    if id in LOGGED:
+      last_logged = LOGGED[id]
+      if now < last_logged + at_most_secs:
+        return
+    LOGGED[id] = now
+  color = COLORS[color] if color is not None and color in COLORS else COLORS["BRIGHT_GREEN"]
+  msg_color = COLORS[msg_color] if msg_color is not None and msg_color in COLORS else ''
+  prefix = f'[{prefix}]' if prefix is not None else ''
+  msg = f'{color}[{NAME}]{prefix}'
+  msg += f'{msg_color} {message}{COLORS["RESET"]}'
+  print(msg)
diff --git a/custom_nodes/rgthree-comfy/py/lora_stack.py b/custom_nodes/rgthree-comfy/py/lora_stack.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6d947aadcd7fcd3a49564695275041c8ac9e223
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/lora_stack.py
@@ -0,0 +1,46 @@
+from .constants import get_category, get_name
+from nodes import LoraLoader
+import folder_paths
+
+
+class RgthreeLoraLoaderStack:
+
+    NAME = get_name('Lora Loader Stack')
+    CATEGORY = get_category()
+
+    @classmethod
+    def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "clip": ("CLIP", ),
+
+                "lora_01": (['None'] + folder_paths.get_filename_list("loras"), ),
+                "strength_01":("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+
+                "lora_02": (['None'] + folder_paths.get_filename_list("loras"), ),
+                "strength_02":("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+
+                "lora_03": (['None'] + folder_paths.get_filename_list("loras"), ),
+                "strength_03":("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+
+                "lora_04": (['None'] + folder_paths.get_filename_list("loras"), ),
+                "strength_04":("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL", "CLIP")
+    FUNCTION = "load_lora"
+
+    def load_lora(self, model, clip, lora_01, strength_01, lora_02, strength_02, lora_03, strength_03, lora_04, strength_04):
+        if lora_01 != "None" and strength_01 != 0:
+            model, clip = LoraLoader().load_lora(model, clip, lora_01, strength_01, strength_01)
+        if lora_02 != "None" and strength_02 != 0:
+            model, clip = LoraLoader().load_lora(model, clip, lora_02, strength_02, strength_02)
+        if lora_03 != "None" and strength_03 != 0:
+            model, clip = LoraLoader().load_lora(model, clip, lora_03, strength_03, strength_03)
+        if lora_04 != "None" and strength_04 != 0:
+            model, clip = LoraLoader().load_lora(model, clip, lora_04, strength_04, strength_04)
+
+        return (model, clip)
+
diff --git a/custom_nodes/rgthree-comfy/py/power_lora_loader.py b/custom_nodes/rgthree-comfy/py/power_lora_loader.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d11ac547205a95aa6b100cc90816954146abd44
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/power_lora_loader.py
@@ -0,0 +1,101 @@
+import folder_paths
+
+from typing import Union
+
+from nodes import LoraLoader
+from .constants import get_category, get_name
+from .power_prompt_utils import get_lora_by_filename
+from .utils import FlexibleOptionalInputType, any_type
+from .server.utils_info import get_model_info_file_data
+from .log import log_node_warn
+
+NODE_NAME = get_name('Power Lora Loader')
+
+
+class RgthreePowerLoraLoader:
+  """ The Power Lora Loader is a powerful, flexible node to add multiple loras to a model/clip."""
+
+  NAME = NODE_NAME
+  CATEGORY = get_category()
+
+  @classmethod
+  def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+    return {
+      "required": {
+      },
+      # Since we will pass any number of loras in from the UI, this needs to always allow an
+      "optional": FlexibleOptionalInputType(type=any_type, data={
+        "model": ("MODEL",),
+        "clip": ("CLIP",),
+      }),
+      "hidden": {},
+    }
+
+  RETURN_TYPES = ("MODEL", "CLIP")
+  RETURN_NAMES = ("MODEL", "CLIP")
+  FUNCTION = "load_loras"
+
+  def load_loras(self, model=None, clip=None, **kwargs):
+    """Loops over the provided loras in kwargs and applies valid ones."""
+    for key, value in kwargs.items():
+      key = key.upper()
+      if key.startswith('LORA_') and 'on' in value and 'lora' in value and 'strength' in value:
+        strength_model = value['strength']
+        # If we just passed one strength value, then use it for both, if we passed a strengthTwo
+        # as well, then our `strength` will be for the model, and `strengthTwo` for clip.
+        strength_clip = value['strengthTwo'] if 'strengthTwo' in value else None
+        if clip is None:
+          if strength_clip is not None and strength_clip != 0:
+            log_node_warn(NODE_NAME, 'Recieved clip strength eventhough no clip supplied!')
+          strength_clip = 0
+        else:
+          strength_clip = strength_clip if strength_clip is not None else strength_model
+        if value['on'] and (strength_model != 0 or strength_clip != 0):
+          lora = get_lora_by_filename(value['lora'], log_node=self.NAME)
+          if model is not None and lora is not None:
+            model, clip = LoraLoader().load_lora(model, clip, lora, strength_model, strength_clip)
+
+    return (model, clip)
+
+  @classmethod
+  def get_enabled_loras_from_prompt_node(cls,
+                                         prompt_node: dict) -> list[dict[str, Union[str, float]]]:
+    """Gets enabled loras of a node within a server prompt."""
+    result = []
+    for name, lora in prompt_node['inputs'].items():
+      if name.startswith('lora_') and lora['on']:
+        lora_file = get_lora_by_filename(lora['lora'], log_node=cls.NAME)
+        if lora_file is not None:  # Add the same safety check
+          lora_dict = {
+            'name': lora['lora'],
+            'strength': lora['strength'],
+            'path': folder_paths.get_full_path("loras", lora_file)
+          }
+          if 'strengthTwo' in lora:
+            lora_dict['strength_clip'] = lora['strengthTwo']
+          result.append(lora_dict)
+    return result
+
+  @classmethod
+  def get_enabled_triggers_from_prompt_node(cls, prompt_node: dict, max_each: int = 1):
+    """Gets trigger words up to the max for enabled loras of a node within a server prompt."""
+    loras = [l['name'] for l in cls.get_enabled_loras_from_prompt_node(prompt_node)]
+    trained_words = []
+    for lora in loras:
+      info = get_model_info_file_data(lora, 'loras', default={})
+      if not info or not info.keys():
+        log_node_warn(
+          NODE_NAME,
+          f'No info found for lora {lora} when grabbing triggers. Have you generated an info file'
+          ' from the Power Lora Loader "Show Info" dialog?'
+        )
+        continue
+      if 'trainedWords' not in info or not info['trainedWords']:
+        log_node_warn(
+          NODE_NAME,
+          f'No trained words for lora {lora} when grabbing triggers. Have you fetched data from'
+          'civitai or manually added words?'
+        )
+        continue
+      trained_words += [w for wi in info['trainedWords'][:max_each] if (wi and (w := wi['word']))]
+    return trained_words
diff --git a/custom_nodes/rgthree-comfy/py/power_primitive.py b/custom_nodes/rgthree-comfy/py/power_primitive.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8deea1c8bbe4d0e48c16b9b535f0478f3cbae04
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/power_primitive.py
@@ -0,0 +1,83 @@
+import re
+
+from .utils import FlexibleOptionalInputType, any_type
+from .constants import get_category, get_name
+
+
+def cast_to_str(x):
+  """Handles our cast to a string."""
+  if x is None:
+    return ''
+  try:
+    return str(x)
+  except (ValueError, TypeError):
+    return ''
+
+
+def cast_to_float(x):
+  """Handles our cast to a float."""
+  try:
+    return float(x)
+  except (ValueError, TypeError):
+    return 0.0
+
+
+def cast_to_bool(x):
+  """Handles our cast to a bool."""
+  try:
+    return bool(float(x))
+  except (ValueError, TypeError):
+    return str(x).lower() not in ['0', 'false', 'null', 'none', '']
+
+
+output_to_type = {
+  'STRING': {
+    'cast': cast_to_str,
+    'null': '',
+  },
+  'FLOAT': {
+    'cast': cast_to_float,
+    'null': 0.0,
+  },
+  'INT': {
+    'cast': lambda x: int(cast_to_float(x)),
+    'null': 0,
+  },
+  'BOOLEAN': {
+    'cast': cast_to_bool,
+    'null': False,
+  },
+  # This can be removed soon, there was a bug where this should have been BOOLEAN
+  'BOOL': {
+    'cast': cast_to_bool,
+    'null': False,
+  },
+}
+
+
+class RgthreePowerPrimitive:
+  """The Power Primitive Node."""
+
+  NAME = get_name('Power Primitive')
+  CATEGORY = get_category()
+
+  @classmethod
+  def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+    return {
+      "required": {},
+      "optional": FlexibleOptionalInputType(any_type),
+    }
+
+  RETURN_TYPES = (any_type,)
+  RETURN_NAMES = ('*',)
+  FUNCTION = "main"
+
+  def main(self, **kwargs):
+    """Outputs the expected type."""
+    output = kwargs.get('value', None)
+    output_type = re.sub(r'\s*\([^\)]*\)\s*$', '', kwargs.get('type', ''))
+    output_type = output_to_type[output_type]
+    cast = output_type['cast']
+    output = cast(output)
+
+    return (output,)
diff --git a/custom_nodes/rgthree-comfy/py/power_prompt.py b/custom_nodes/rgthree-comfy/py/power_prompt.py
new file mode 100644
index 0000000000000000000000000000000000000000..5fa75bf084f6a6b089adcec549886d5942004af7
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/power_prompt.py
@@ -0,0 +1,95 @@
+import os
+
+from .log import log_node_warn, log_node_info, log_node_success
+
+from .constants import get_category, get_name
+from .power_prompt_utils import get_and_strip_loras
+from nodes import LoraLoader, CLIPTextEncode
+import folder_paths
+
+NODE_NAME = get_name('Power Prompt')
+
+
+class RgthreePowerPrompt:
+
+  NAME = NODE_NAME
+  CATEGORY = get_category()
+
+  @classmethod
+  def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+    # Removed Saved Prompts feature; No sure it worked any longer. UI should fail gracefully,
+    # TODO: Rip out saved prompt input data
+    SAVED_PROMPTS_FILES=[]
+    SAVED_PROMPTS_CONTENT=[]
+    return {
+      'required': {
+        'prompt': ('STRING', {
+          'multiline': True,
+          'dynamicPrompts': True
+        }),
+      },
+      'optional': {
+        "opt_model": ("MODEL",),
+        "opt_clip": ("CLIP",),
+        'insert_lora': (['CHOOSE', 'DISABLE LORAS'] +
+                        [os.path.splitext(x)[0] for x in folder_paths.get_filename_list('loras')],),
+        'insert_embedding': ([
+          'CHOOSE',
+        ] + [os.path.splitext(x)[0] for x in folder_paths.get_filename_list('embeddings')],),
+        'insert_saved': ([
+          'CHOOSE',
+        ] + SAVED_PROMPTS_FILES,),
+      },
+      'hidden': {
+        'values_insert_saved': (['CHOOSE'] + SAVED_PROMPTS_CONTENT,),
+      }
+    }
+
+  RETURN_TYPES = (
+    'CONDITIONING',
+    'MODEL',
+    'CLIP',
+    'STRING',
+  )
+  RETURN_NAMES = (
+    'CONDITIONING',
+    'MODEL',
+    'CLIP',
+    'TEXT',
+  )
+  FUNCTION = 'main'
+
+  def main(self,
+           prompt,
+           opt_model=None,
+           opt_clip=None,
+           insert_lora=None,
+           insert_embedding=None,
+           insert_saved=None,
+           values_insert_saved=None):
+    if insert_lora == 'DISABLE LORAS':
+      prompt, loras, skipped, unfound = get_and_strip_loras(prompt, log_node=NODE_NAME, silent=True)
+      log_node_info(
+        NODE_NAME,
+        f'Disabling all found loras ({len(loras)}) and stripping lora tags for TEXT output.')
+    elif opt_model is not None and opt_clip is not None:
+      prompt, loras, skipped, unfound = get_and_strip_loras(prompt, log_node=NODE_NAME)
+      if len(loras) > 0:
+        for lora in loras:
+          opt_model, opt_clip = LoraLoader().load_lora(opt_model, opt_clip, lora['lora'],
+                                                       lora['strength'], lora['strength'])
+          log_node_success(NODE_NAME, f'Loaded "{lora["lora"]}" from prompt')
+        log_node_info(NODE_NAME, f'{len(loras)} Loras processed; stripping tags for TEXT output.')
+    elif '<lora:' in prompt:
+      prompt, loras, skipped, unfound = get_and_strip_loras(prompt, log_node=NODE_NAME, silent=True)
+      total_loras = len(loras) + len(skipped) + len(unfound)
+      if total_loras:
+        log_node_warn(
+          NODE_NAME, f'Found {len(loras)} lora tags in prompt but model & clip were not supplied!')
+        log_node_info(NODE_NAME, 'Loras not processed, keeping for TEXT output.')
+
+    conditioning = None
+    if opt_clip is not None:
+      conditioning = CLIPTextEncode().encode(opt_clip, prompt)[0]
+
+    return (conditioning, opt_model, opt_clip, prompt)
diff --git a/custom_nodes/rgthree-comfy/py/power_prompt_simple.py b/custom_nodes/rgthree-comfy/py/power_prompt_simple.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a94cab5554d7ad9cb1b8238398eca3749414023
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/power_prompt_simple.py
@@ -0,0 +1,42 @@
+import os
+import folder_paths
+from nodes import CLIPTextEncode
+from .constants import get_category, get_name
+from .power_prompt import RgthreePowerPrompt
+
+class RgthreePowerPromptSimple(RgthreePowerPrompt):
+
+    NAME=get_name('Power Prompt - Simple')
+    CATEGORY = get_category()
+
+    @classmethod
+    def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+        # Removed Saved Prompts feature; No sure it worked any longer. UI should fail gracefully,
+        # TODO: Rip out saved prompt input data
+        SAVED_PROMPTS_FILES=[]
+        SAVED_PROMPTS_CONTENT=[]
+        return {
+            'required': {
+                'prompt': ('STRING', {'multiline': True, 'dynamicPrompts': True}),
+            },
+            'optional': {
+                "opt_clip": ("CLIP", ),
+                'insert_embedding': (['CHOOSE',] + [os.path.splitext(x)[0] for x in folder_paths.get_filename_list('embeddings')],),
+                'insert_saved': (['CHOOSE',] + SAVED_PROMPTS_FILES,),
+            },
+            'hidden': {
+                'values_insert_saved': (['CHOOSE'] + SAVED_PROMPTS_CONTENT,),
+            }
+        }
+
+    RETURN_TYPES = ('CONDITIONING', 'STRING',)
+    RETURN_NAMES = ('CONDITIONING', 'TEXT',)
+    FUNCTION = 'main'
+
+    def main(self, prompt, opt_clip=None, insert_embedding=None, insert_saved=None, values_insert_saved=None):
+        conditioning=None
+        if opt_clip != None:
+            conditioning = CLIPTextEncode().encode(opt_clip, prompt)[0]
+
+        return (conditioning, prompt)
+
diff --git a/custom_nodes/rgthree-comfy/py/power_prompt_utils.py b/custom_nodes/rgthree-comfy/py/power_prompt_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea167f4bd8db0db352afe63e5aa752b7467e4dd7
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/power_prompt_utils.py
@@ -0,0 +1,104 @@
+"""Utilities for Power Prompt nodes."""
+import re
+import os
+import folder_paths
+
+from .log import log_node_warn, log_node_info
+
+
+def get_and_strip_loras(prompt, silent=False, log_node="Power Prompt"):
+  """Collects and strips lora tags from a prompt."""
+  pattern = r'<lora:([^:>]*?)(?::(-?\d*(?:\.\d*)?))?>'
+  lora_paths = folder_paths.get_filename_list('loras')
+
+  matches = re.findall(pattern, prompt)
+
+  loras = []
+  unfound_loras = []
+  skipped_loras = []
+  for match in matches:
+    tag_path = match[0]
+
+    strength = float(match[1] if len(match) > 1 and len(match[1]) else 1.0)
+    if strength == 0:
+      if not silent:
+        log_node_info(log_node, f'Skipping "{tag_path}" with strength of zero')
+      skipped_loras.append({'lora': tag_path, 'strength': strength})
+      continue
+
+    lora_path = get_lora_by_filename(tag_path, lora_paths, log_node=None if silent else log_node)
+    if lora_path is None:
+      unfound_loras.append({'lora': tag_path, 'strength': strength})
+      continue
+
+    loras.append({'lora': lora_path, 'strength': strength})
+
+  return (re.sub(pattern, '', prompt), loras, skipped_loras, unfound_loras)
+
+
+# pylint: disable = too-many-return-statements, too-many-branches
+def get_lora_by_filename(file_path, lora_paths=None, log_node=None):
+  """Returns a lora by filename, looking for exactl paths and then fuzzier matching."""
+  lora_paths = lora_paths if lora_paths is not None else folder_paths.get_filename_list('loras')
+
+  if file_path in lora_paths:
+    return file_path
+
+  lora_paths_no_ext = [os.path.splitext(x)[0] for x in lora_paths]
+
+  # See if we've entered the exact path, but without the extension
+  if file_path in lora_paths_no_ext:
+    found = lora_paths[lora_paths_no_ext.index(file_path)]
+    return found
+
+  # Same check, but ensure file_path is without extension.
+  file_path_force_no_ext = os.path.splitext(file_path)[0]
+  if file_path_force_no_ext in lora_paths_no_ext:
+    found = lora_paths[lora_paths_no_ext.index(file_path_force_no_ext)]
+    return found
+
+  # See if we passed just the name, without paths.
+  lora_filenames_only = [os.path.basename(x) for x in lora_paths]
+  if file_path in lora_filenames_only:
+    found = lora_paths[lora_filenames_only.index(file_path)]
+    if log_node is not None:
+      log_node_info(log_node, f'Matched Lora input "{file_path}" to "{found}".')
+    return found
+
+  # Same, but force the input to be without paths
+  file_path_force_filename = os.path.basename(file_path)
+  lora_filenames_only = [os.path.basename(x) for x in lora_paths]
+  if file_path_force_filename in lora_filenames_only:
+    found = lora_paths[lora_filenames_only.index(file_path_force_filename)]
+    if log_node is not None:
+      log_node_info(log_node, f'Matched Lora input "{file_path}" to "{found}".')
+    return found
+
+  # Check the filenames and without extension.
+  lora_filenames_and_no_ext = [os.path.splitext(os.path.basename(x))[0] for x in lora_paths]
+  if file_path in lora_filenames_and_no_ext:
+    found = lora_paths[lora_filenames_and_no_ext.index(file_path)]
+    if log_node is not None:
+      log_node_info(log_node, f'Matched Lora input "{file_path}" to "{found}".')
+    return found
+
+  # And, one last forcing the input to be the same
+  file_path_force_filename_and_no_ext = os.path.splitext(os.path.basename(file_path))[0]
+  if file_path_force_filename_and_no_ext in lora_filenames_and_no_ext:
+    found = lora_paths[lora_filenames_and_no_ext.index(file_path_force_filename_and_no_ext)]
+    if log_node is not None:
+      log_node_info(log_node, f'Matched Lora input "{file_path}" to "{found}".')
+    return found
+
+  # Finally, super fuzzy, we'll just check if the input exists in the path at all.
+  for index, lora_path in enumerate(lora_paths):
+    if file_path in lora_path:
+      found = lora_paths[index]
+      if log_node is not None:
+        log_node_warn(log_node, f'Fuzzy-matched Lora input "{file_path}" to "{found}".')
+      return found
+
+  if log_node is not None:
+    log_node_warn(log_node, f'Lora "{file_path}" not found, skipping.')
+
+  return None
diff --git a/custom_nodes/rgthree-comfy/py/power_puter.py b/custom_nodes/rgthree-comfy/py/power_puter.py
new file mode 100644
index 0000000000000000000000000000000000000000..80cd7b016f4265d1784e961eaa2b9cf7477572cf
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/power_puter.py
@@ -0,0 +1,848 @@
+"""The Power Puter is a powerful node that can compute and evaluate Python-like code safely allowing
+for complex operations for primitives and workflow items for output. From string concatenation, to
+math operations, list comprehension, and node value output.
+
+Originally based off https://github.com/pythongosssss/ComfyUI-Custom-Scripts/blob/aac13aa7ce35b07d43633c3bbe654a38c00d74f5/py/math_expression.py
+under an MIT License https://github.com/pythongosssss/ComfyUI-Custom-Scripts/blob/aac13aa7ce35b07d43633c3bbe654a38c00d74f5/LICENSE
+"""
+
+import math
+import ast
+import json
+import random
+import dataclasses
+import re
+import time
+import operator as op
+import datetime
+import numpy as np
+import hashlib
+
+from typing import Any, Callable, Iterable, Optional, Union
+from types import MappingProxyType
+
+from .constants import get_category, get_name
+from .utils import ByPassTypeTuple, FlexibleOptionalInputType, any_type, get_dict_value
+from .log import log_node_error, log_node_warn, log_node_info
+
+from .power_lora_loader import RgthreePowerLoraLoader
+
+from nodes import ImageBatch
+from comfy_extras.nodes_latent import LatentBatch
+
+
+class LoopBreak(Exception):
+  """A special error type that is caught in a loop for correct breaking behavior."""
+
+  def __init__(self):
+    super().__init__('Cannot use "break" outside of a loop.')
+
+
+class LoopContinue(Exception):
+  """A special error type that is caught in a loop for correct continue behavior."""
+
+  def __init__(self):
+    super().__init__('Cannot use "continue" outside of a loop.')
+
+
+@dataclasses.dataclass(frozen=True)  # Note, kw_only=True is only python 3.10+
+class Function():
+  """Function data.
+
+  Attributes:
+    name: The name of the function as called from the node.
+    call: The callable (reference, lambda, etc), or a string if on _Puter instance.
+    args: A tuple that represents the minimum and maximum number of args (or arg for no limit).
+  """
+
+  name: str
+  call: Union[Callable, str]
+  args: tuple[int, Optional[int]]
+
+
+def purge_vram(purge_models=True):
+  """Purges vram and, optionally, unloads models."""
+  import gc
+  import torch
+  gc.collect()
+  if torch.cuda.is_available():
+    torch.cuda.empty_cache()
+    torch.cuda.ipc_collect()
+  if purge_models:
+    import comfy
+    comfy.model_management.unload_all_models()
+    comfy.model_management.soft_empty_cache()
+
+
+def batch(*args):
+  """Batches multiple image or latents together."""
+
+  def check_is_latent(item) -> bool:
+    return isinstance(item, dict) and 'samples' in item
+
+  args = list(args)
+  result = args.pop(0)
+  is_latent = check_is_latent(result)
+  node = LatentBatch() if is_latent else ImageBatch()
+
+  for arg in args:
+    if is_latent != check_is_latent(arg):
+      raise ValueError(
+        f'batch() error: Expecting "{"LATENT" if is_latent else "IMAGE"}"'
+        f' but got "{"IMAGE" if is_latent else "LATENT"}".'
+      )
+    result = node.batch(result, arg)[0]
+  return result
+
+
+_BUILTIN_FN_PREFIX = '__rgthreefn.'
+
+
+def _get_built_in_fn_key(fn: Function) -> str:
+  """Returns a key for a built-in function."""
+  return f'{_BUILTIN_FN_PREFIX}{hash(fn.name)}'
+
+
+def _get_built_in_fn_by_key(fn_key: str):
+  """Returns the `Function` for the provided key (purposefully, not name)."""
+  if not fn_key.startswith(_BUILTIN_FN_PREFIX) or fn_key not in _BUILT_INS_BY_NAME_AND_KEY:
+    raise ValueError('No built in function found.')
+  return _BUILT_INS_BY_NAME_AND_KEY[fn_key]
+
+def sha264(message: str):
+  if isinstance(message, str):
+    return hashlib.sha256(message.encode()).hexdigest()
+  return None
+
+_BUILT_IN_FNS_LIST = [
+  Function(name="round", call=round, args=(1, 2)),
+  Function(name="ceil", call=math.ceil, args=(1, 1)),
+  Function(name="floor", call=math.floor, args=(1, 1)),
+  Function(name="sqrt", call=math.sqrt, args=(1, 1)),
+  Function(name="min", call=min, args=(2, None)),
+  Function(name="max", call=max, args=(2, None)),
+  Function(name=".random_int", call=random.randint, args=(2, 2)),
+  Function(name=".random_choice", call=random.choice, args=(1, 1)),
+  Function(name=".random_seed", call=random.seed, args=(1, 1)),
+  Function(name="re", call=re.compile, args=(1, 1)),
+  Function(name="len", call=len, args=(1, 1)),
+  Function(name="enumerate", call=enumerate, args=(1, 1)),
+  Function(name="range", call=range, args=(1, 3)),
+  # Casts
+  Function(name="int", call=int, args=(1, 1)),
+  Function(name="float", call=float, args=(1, 1)),
+  Function(name="str", call=str, args=(1, 1)),
+  Function(name="bool", call=bool, args=(1, 1)),
+  Function(name="list", call=list, args=(1, 1)),
+  Function(name="tuple", call=tuple, args=(1, 1)),
+  # Special
+  Function(name="dir", call=dir, args=(1, 1)),
+  Function(name="type", call=type, args=(1, 1)),
+  Function(name="print", call=print, args=(0, None)),
+  Function(name="sha264", call=sha264, args=(1, 1)),
+  # Comfy Specials
+  Function(name="node", call='_get_node', args=(0, 1)),
+  Function(name="nodes", call='_get_nodes', args=(0, 1)),
+  Function(name="input_node", call='_get_input_node', args=(0, 1)),
+  Function(name="purge_vram", call=purge_vram, args=(0, 1)),
+  Function(name="batch", call=batch, args=(2, None)),
+]
+
+_BUILT_INS_BY_NAME_AND_KEY = {
+  fn.name: fn for fn in _BUILT_IN_FNS_LIST
+} | {
+  key: fn for fn in _BUILT_IN_FNS_LIST if (key := _get_built_in_fn_key(fn))
+}
+
+_BUILT_INS = MappingProxyType(
+  {fn.name: key for fn in _BUILT_IN_FNS_LIST if (key := _get_built_in_fn_key(fn))} | {
+    'random':
+      MappingProxyType({
+        'int': _get_built_in_fn_key(_BUILT_INS_BY_NAME_AND_KEY['.random_int']),
+        'choice': _get_built_in_fn_key(_BUILT_INS_BY_NAME_AND_KEY['.random_choice']),
+        'seed': _get_built_in_fn_key(_BUILT_INS_BY_NAME_AND_KEY['.random_seed']),
+      }),
+  }
+)
+
+# A dict of types to blocked attributes/methods. Used to disallow file system access or other
+# invocations we may want to block. Necessary for any instance type that is possible to create from
+# the code or standard ComfyUI inputs.
+#
+# For instance, a user does not have access to the numpy module directly, so they cannot invoke
+# `numpy.save`. However, a user can access a numpy.ndarray instance from a tensor and, from there,
+# an attempt to call `tofile` or `dump` etc. would need to be blocked.
+_BLOCKED_METHODS_OR_ATTRS = MappingProxyType({np.ndarray: ['tofile', 'dump']})
+
+# Special functions by class type (called from the Attrs.)
+_SPECIAL_FUNCTIONS = {
+  RgthreePowerLoraLoader.NAME: {
+    # Get a list of the enabled loras from a power lora loader.
+    "loras": RgthreePowerLoraLoader.get_enabled_loras_from_prompt_node,
+    "triggers": RgthreePowerLoraLoader.get_enabled_triggers_from_prompt_node,
+  }
+}
+
+# Series of regex checks for usage of a non-deterministic function. Using these is fine, but means
+# the output can't be cached because it's either random, or is associated with another node that is
+# not connected to ours (like looking up a node in the prompt). Using these means downstream nodes
+# would always be run; that is fine for something like a final JSON output, but less so for a prompt
+# text.
+_NON_DETERMINISTIC_FUNCTION_CHECKS = [r'(?<!input_)(nodes?)\(',]
+
+_OPERATORS = {
+  # operator
+  ast.Add: op.add,
+  ast.Sub: op.sub,
+  ast.Mult: op.mul,
+  ast.MatMult: op.matmul,
+  ast.Div: op.truediv,
+  ast.Mod: op.mod,
+  ast.Pow: op.pow,
+  ast.RShift: op.rshift,
+  ast.LShift: op.lshift,
+  ast.BitOr: op.or_,
+  ast.BitXor: op.xor,
+  ast.BitAnd: op.and_,
+  ast.FloorDiv: op.floordiv,
+  # boolop
+  ast.And: lambda a, b: a and b,
+  ast.Or: lambda a, b: a or b,
+  # unaryop
+  ast.Invert: op.invert,
+  ast.Not: lambda a: 0 if a else 1,
+  ast.USub: op.neg,
+  # cmpop
+  ast.Eq: op.eq,
+  ast.NotEq: op.ne,
+  ast.Lt: op.lt,
+  ast.LtE: op.le,
+  ast.Gt: op.gt,
+  ast.GtE: op.ge,
+  ast.Is: op.is_,
+  ast.IsNot: op.is_not,
+  ast.In: lambda a, b: a in b,
+  ast.NotIn: lambda a, b: a not in b,
+
+}
+
+_NODE_NAME = get_name("Power Puter")
+
+
+def _update_code(code: str, unique_id: str, log=False):
+  """Updates the code to either newer syntax or general cleaning."""
+
+  # Change usage of `input_node` so the passed variable is a string, if it isn't. So, instead of
+  # `input_node(a)` it needs to be `input_node('a')`
+  code = re.sub(r'input_node\(([^\'"].*?)\)', r'input_node("\1")', code)
+
+  # Update use of `random_int` to `random.int`
+  srch = re.compile(r'random_int\(')
+  if re.search(srch, code):
+    if log:
+      log_node_warn(
+        _NODE_NAME, f"Power Puter node #{unique_id} should update to use the `random.int`"
+        " built-in instead of `random_int`."
+      )
+    code = re.sub(srch, 'random.int(', code)
+
+  # Update use of `random_choice` to `random.choice`
+  srch = re.compile(r'random_choice\(')
+  if re.search(srch, code):
+    if log:
+      log_node_warn(
+        _NODE_NAME, f"Power Puter node #{unique_id} should update to use the `random.choice`"
+        " built-in instead of `random_choice`."
+      )
+    code = re.sub(srch, 'random.choice(', code)
+  return code
+
+
+class RgthreePowerPuter:
+  """A powerful node that can compute and evaluate expressions and output as various types."""
+
+  NAME = _NODE_NAME
+  CATEGORY = get_category()
+
+  @classmethod
+  def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+    return {
+      "required": {},
+      "optional": FlexibleOptionalInputType(any_type),
+      "hidden": {
+        "unique_id": "UNIQUE_ID",
+        "extra_pnginfo": "EXTRA_PNGINFO",
+        "prompt": "PROMPT",
+        "dynprompt": "DYNPROMPT"
+      },
+    }
+
+  RETURN_TYPES = ByPassTypeTuple((any_type,))
+  RETURN_NAMES = ByPassTypeTuple(("*",))
+  FUNCTION = "main"
+
+  @classmethod
+  def IS_CHANGED(cls, **kwargs):
+    """Forces a changed state if we could be unaware of data changes (like using `node()`)."""
+
+    code = _update_code(kwargs['code'], unique_id=kwargs['unique_id'])
+    # Strip string literals and comments.
+    code = re.sub(r"'[^']+?'", "''", code)
+    code = re.sub(r'"[^"]+?"', '""', code)
+    code = re.sub(r'#.*\n', '\n', code)
+
+    # If we have a non-deterministic function, then we'll always consider ourself changed since we
+    # cannot be sure that the data would be the same (random, another unconnected node, etc).
+    for check in _NON_DETERMINISTIC_FUNCTION_CHECKS:
+      matches = re.search(check, code)
+      if matches:
+        log_node_warn(
+          _NODE_NAME,
+          f"Note, Power Puter (node #{kwargs['unique_id']}) cannot be cached b/c it's using a"
+          f" non-deterministic function call. Matches function call for '{matches.group(1)}'."
+        )
+        return time.time()
+
+    # Advanced checks.
+    has_rand_seed = re.search(r'random\.seed\(', code)
+    has_rand_int_or_choice = re.search(r'(?<!\.)(random\.(int|choice))\(', code)
+    if has_rand_int_or_choice:
+      if not has_rand_seed or has_rand_seed.span()[0] > has_rand_int_or_choice.span()[0]:
+        log_node_warn(
+          _NODE_NAME,
+          f"Note, Power Puter (node #{kwargs['unique_id']}) cannot be cached b/c it's using a"
+          " non-deterministic function call. Matches function call for"
+          f" `{has_rand_int_or_choice.group(1)}`."
+        )
+        return time.time()
+      if has_rand_seed:
+        log_node_info(
+          _NODE_NAME,
+          f"Power Puter node #{kwargs['unique_id']} WILL be cached eventhough it's using"
+          f" a non-deterministic random call `{has_rand_int_or_choice.group(1)}` because it also"
+          f" calls `random.seed` first. NOTE: Please ensure that the seed value is deterministic."
+        )
+
+    return 42
+
+  def main(self, **kwargs):
+    """Does the nodes' work."""
+    code = kwargs['code']
+    unique_id = kwargs['unique_id']
+    pnginfo = kwargs['extra_pnginfo']
+    workflow = pnginfo["workflow"] if "workflow" in pnginfo else {"nodes": []}
+    prompt = kwargs['prompt']
+    dynprompt = kwargs['dynprompt']
+
+    outputs = get_dict_value(kwargs, 'outputs.outputs', None)
+    if not outputs:
+      output = kwargs.get('output', None)
+      if not output:
+        output = 'STRING'
+      outputs = [output]
+
+    ctx = {}
+    # Set variable names, defaulting to None instead of KeyErrors
+    for c in list('abcdefghijklmnopqrstuvwxyz'):
+      ctx[c] = kwargs[c] if c in kwargs else None
+
+    code = _update_code(kwargs['code'], unique_id=kwargs['unique_id'], log=True)
+
+    eva = _Puter(
+      code=code,
+      ctx=ctx,
+      workflow=workflow,
+      prompt=prompt,
+      dynprompt=dynprompt,
+      unique_id=unique_id
+    )
+    values = eva.execute()
+
+    # Check if we have multiple outputs that the returned value is a tuple and raise if not.
+    if len(outputs) > 1 and not isinstance(values, tuple):
+      t = re.sub(r'^<[a-z]*\s(.*?)>$', r'\1', str(type(values)))
+      msg = (
+        f"When using multiple node outputs, the value from the code should be a 'tuple' with the"
+        f" number of items equal to the number of outputs. But value from code was of type {t}."
+      )
+      log_node_error(_NODE_NAME, f'{msg}\n')
+      raise ValueError(msg)
+
+    if len(outputs) == 1:
+      values = (values,)
+
+    if len(values) > len(outputs):
+      log_node_warn(
+        _NODE_NAME,
+        f"Expected value from code to be tuple with {len(outputs)} items, but value from code had"
+        f" {len(values)} items. Extra values will be dropped."
+      )
+    elif len(values) < len(outputs):
+      log_node_warn(
+        _NODE_NAME,
+        f"Expected value from code to be tuple with {len(outputs)} items, but value from code had"
+        f" {len(values)} items. Extra outputs will be null."
+      )
+
+    # Now, we'll go over out return tuple, and cast as the output types.
+    response = []
+    for i, output in enumerate(outputs):
+      value = values[i] if len(values) > i else None
+      if value is not None:
+        if output == 'INT':
+          value = int(value)
+        elif output == 'FLOAT':
+          value = float(value)
+        # Accidentally defined "BOOL" when should have been "BOOLEAN."
+        # TODO: Can prob get rid of BOOl after a bit when UIs would be updated from sending
+        # BOOL incorrectly.
+        elif output in ('BOOL', 'BOOLEAN'):
+          value = bool(value)
+        elif output == 'STRING':
+          if isinstance(value, (dict, list)):
+            value = json.dumps(value, indent=2)
+          else:
+            value = str(value)
+        elif output == '*':
+          # Do nothing, the output will be passed as-is. This could be anything and it's up to the
+          # user to control the intended output, like passing through an input value, etc.
+          pass
+      response.append(value)
+    return tuple(response)
+
+
+class _Puter:
+  """The main computation evaluator, using ast.parse the code.
+
+  See https://www.basicexamples.com/example/python/ast for examples.
+  """
+
+  def __init__(self, *, code: str, ctx: dict[str, Any], workflow, prompt, dynprompt, unique_id):
+    ctx = ctx or {}
+    self._ctx = {**ctx}
+    self._code = code
+    self._workflow = workflow
+    self._prompt = prompt
+    self._unique_id = unique_id
+    self._dynprompt = dynprompt
+    # These are now expanded lazily when needed.
+    self._prompt_nodes = None
+    self._prompt_node = None
+
+  def execute(self, code=Optional[str]) -> Any:
+    """Evaluates a the code block."""
+
+    # Always store random state and initialize a new seed. We'll restore the state later.
+    initial_random_state = random.getstate()
+    random.seed(datetime.datetime.now().timestamp())
+    last_value = None
+    try:
+      code = code or self._code
+      node = ast.parse(self._code)
+      ctx = {**self._ctx}
+      for body in node.body:
+        last_value = self._eval_statement(body, ctx)
+        # If we got a return, then that's it folks.
+        if isinstance(body, ast.Return):
+          break
+    except:
+      random.setstate(initial_random_state)
+      raise
+    random.setstate(initial_random_state)
+    return last_value
+
+  def _get_prompt_nodes(self):
+    """Expands the prompt nodes lazily from the dynamic prompt.
+
+    https://github.com/comfyanonymous/ComfyUI/blob/fc657f471a29d07696ca16b566000e8e555d67d1/comfy_execution/graph.py#L22
+    """
+    if self._prompt_nodes is None:
+      self._prompt_nodes = []
+      if self._dynprompt:
+        all_ids = self._dynprompt.all_node_ids()
+        self._prompt_nodes = [{'id': k} | {**self._dynprompt.get_node(k)} for k in all_ids]
+    return self._prompt_nodes
+
+  def _get_prompt_node(self):
+    if self._prompt_nodes is None:
+      self._prompt_node = [n for n in self._get_prompt_nodes() if n['id'] == self._unique_id][0]
+    return self._prompt_node
+
+  def _get_nodes(self, node_id: Union[int, str, re.Pattern, None] = None) -> list[Any]:
+    """Get a list of the nodes that match the node_id, or all the nodes in the prompt."""
+    nodes = self._get_prompt_nodes().copy()
+    if not node_id:
+      return nodes
+
+    if isinstance(node_id, re.Pattern):
+      found = [n for n in nodes if re.search(node_id, get_dict_value(n, '_meta.title', ''))]
+    else:
+      node_id = str(node_id)
+      found = None
+      if re.match(r'\d+$', node_id):
+        found = [n for n in nodes if node_id == n['id']]
+      if not found:
+        found = [n for n in nodes if node_id == get_dict_value(n, '_meta.title', '')]
+    return found
+
+  def _get_node(self, node_id: Union[int, str, re.Pattern, None] = None) -> Union[Any, None]:
+    """Returns a prompt-node from the hidden prompt."""
+    if node_id is None:
+      return self._get_prompt_node()
+    nodes = self._get_nodes(node_id)
+    if nodes and len(nodes) > 1:
+      log_node_warn(_NODE_NAME, f"More than one node found for '{node_id}'. Returning first.")
+    return nodes[0] if nodes else None
+
+  def _get_input_node(self, input_name, node=None):
+    """Gets the (non-muted) node of an input connection from a node (default to the power puter)."""
+    node = node if node else self._get_prompt_node()
+    try:
+      connected_node_id = node['inputs'][input_name][0]
+      return [n for n in self._get_prompt_nodes() if n['id'] == connected_node_id][0]
+    except (TypeError, IndexError, KeyError):
+      log_node_warn(_NODE_NAME, f'No input node found for "{input_name}". ')
+    return None
+
+  def _eval_statement(self, stmt: ast.AST, ctx: dict, prev_stmt: Union[ast.AST, None] = None):
+    """Evaluates an ast.stmt."""
+
+    if '__returned__' in ctx:
+      return ctx['__returned__']
+
+    # print('\n\n----: _eval_statement')
+    # print(type(stmt))
+    # print(ctx)
+
+    if isinstance(stmt, (ast.FormattedValue, ast.Expr)):
+      return self._eval_statement(stmt.value, ctx=ctx)
+
+    if isinstance(stmt, (ast.Constant, ast.Num)):
+      return stmt.n
+
+    if isinstance(stmt, ast.BinOp):
+      left = self._eval_statement(stmt.left, ctx=ctx)
+      right = self._eval_statement(stmt.right, ctx=ctx)
+      return _OPERATORS[type(stmt.op)](left, right)
+
+    if isinstance(stmt, ast.BoolOp):
+      is_and = isinstance(stmt.op, ast.And)
+      is_or = isinstance(stmt.op, ast.Or)
+      stmt_value_eval = None
+      for stmt_value in stmt.values:
+        stmt_value_eval = self._eval_statement(stmt_value, ctx=ctx)
+        # If we're an and operator and have a falsyt value, then we stop and return. Likewise, if
+        # we're an or operator and have a truthy value, we can stop and return.
+        if (is_and and not stmt_value_eval) or (is_or and stmt_value_eval):
+          return stmt_value_eval
+      # Always return the last if we made it here w/o success.
+      return stmt_value_eval
+
+    if isinstance(stmt, ast.UnaryOp):
+      return _OPERATORS[type(stmt.op)](self._eval_statement(stmt.operand, ctx=ctx))
+
+    if isinstance(stmt, (ast.Attribute, ast.Subscript)):
+      # Like: node(14).inputs.sampler_name (Attribute)
+      # Like: node(14)['inputs']['sampler_name'] (Subscript)
+      item = self._eval_statement(stmt.value, ctx=ctx)
+      attr = None
+      # if hasattr(stmt, 'attr'):
+      if isinstance(stmt, ast.Attribute):
+        attr = stmt.attr
+      else:
+        # Slice could be a name or a constant; evaluate it
+        attr = self._eval_statement(stmt.slice, ctx=ctx)
+      # Check if we're blocking access to this attribute/method on this item type.
+      for typ, names in _BLOCKED_METHODS_OR_ATTRS.items():
+        if isinstance(item, typ) and isinstance(attr, str) and attr in names:
+          raise ValueError(f'Disallowed access to "{attr}" for type {typ}.')
+      try:
+        val = item[attr]
+      except (TypeError, IndexError, KeyError):
+        try:
+          val = getattr(item, attr)
+        except AttributeError:
+          # If we're a dict, then just return None instead of error; saves time.
+          if isinstance(item, dict):
+            # Any special cases in the _SPECIAL_FUNCTIONS
+            class_type = get_dict_value(item, "class_type")
+            if class_type in _SPECIAL_FUNCTIONS and attr in _SPECIAL_FUNCTIONS[class_type]:
+              val = _SPECIAL_FUNCTIONS[class_type][attr]
+              # If our previous statment was a Call, then send back a tuple of the callable and
+              # the evaluated item, and it will make the call; perhaps also adding other arguments
+              # only it knows about.
+              if isinstance(prev_stmt, ast.Call):
+                return (val, item)
+              val = val(item)
+            else:
+              val = None
+          else:
+            raise
+      return val
+
+    if isinstance(stmt, (ast.List, ast.Tuple)):
+      value = []
+      for elt in stmt.elts:
+        value.append(self._eval_statement(elt, ctx=ctx))
+      return tuple(value) if isinstance(stmt, ast.Tuple) else value
+
+    if isinstance(stmt, ast.Dict):
+      the_dict = {}
+      if stmt.keys:
+        if len(stmt.keys) != len(stmt.values):
+          raise ValueError('Expected same number of keys as values for dict.')
+        for i, k in enumerate(stmt.keys):
+          item_key = self._eval_statement(k, ctx=ctx)
+          item_value = self._eval_statement(stmt.values[i], ctx=ctx)
+          the_dict[item_key] = item_value
+      return the_dict
+
+    # f-strings: https://www.basicexamples.com/example/python/ast-JoinedStr
+    # Note, this will str() all evaluated items in the fstrings, and doesn't handle f-string
+    # directives, like padding, etc.
+    if isinstance(stmt, ast.JoinedStr):
+      vals = [str(self._eval_statement(v, ctx=ctx)) for v in stmt.values]
+      val = ''.join(vals)
+      return val
+
+    if isinstance(stmt, ast.Slice):
+      if not stmt.lower or not stmt.upper:
+        raise ValueError('Unhandled Slice w/o lower or upper.')
+      slice_lower = self._eval_statement(stmt.lower, ctx=ctx)
+      slice_upper = self._eval_statement(stmt.upper, ctx=ctx)
+      if stmt.step:
+        slice_step = self._eval_statement(stmt.step, ctx=ctx)
+        return slice(slice_lower, slice_upper, slice_step)
+      return slice(slice_lower, slice_upper)
+
+    if isinstance(stmt, ast.Name):
+      if stmt.id in ctx:
+        val = ctx[stmt.id]
+        return val
+      if stmt.id in _BUILT_INS:
+        val = _BUILT_INS[stmt.id]
+        return val
+      raise NameError(f"Name not found: {stmt.id}")
+
+    if isinstance(stmt, ast.For):
+      for_iter = self._eval_statement(stmt.iter, ctx=ctx)
+      for item in for_iter:
+        # Set the for var(s)
+        if isinstance(stmt.target, ast.Name):
+          ctx[stmt.target.id] = item
+        elif isinstance(stmt.target, ast.Tuple):  # dict, like `for k, v in d.entries()`
+          for i, elt in enumerate(stmt.target.elts):
+            ctx[elt.id] = item[i]
+        bodies = stmt.body if isinstance(stmt.body, list) else [stmt.body]
+        breaked = False
+        for body in bodies:
+          # Catch any breaks or continues and handle inside the loop normally.
+          try:
+            value = self._eval_statement(body, ctx=ctx)
+          except (LoopBreak, LoopContinue) as e:
+            breaked = isinstance(e, LoopBreak)
+            break
+        if breaked:
+          break
+      return None
+
+    if isinstance(stmt, ast.While):
+      while self._eval_statement(stmt.test, ctx=ctx):
+        bodies = stmt.body if isinstance(stmt.body, list) else [stmt.body]
+        breaked = False
+        for body in bodies:
+          # Catch any breaks or continues and handle inside the loop normally.
+          try:
+            value = self._eval_statement(body, ctx=ctx)
+          except (LoopBreak, LoopContinue) as e:
+            breaked = isinstance(e, LoopBreak)
+            break
+        if breaked:
+          break
+      return None
+
+    if isinstance(stmt, ast.ListComp):
+      # Like: [v.lora for name, v in node(19).inputs.items() if name.startswith('lora_')]
+      # Like: [v.lower() for v in lora_list]
+      # Like: [v for v in l if v.startswith('B')]
+      # Like: [v.lower() for v in l if v.startswith('B') or v.startswith('F')]
+      # ---
+      # Like: [l for n in nodes(re('Loras')).values() if (l := n.loras)]
+      final_list = []
+
+      gen_ctx = {**ctx}
+
+      generators = [*stmt.generators]
+
+      def handle_gen(generators: list[ast.comprehension]):
+        gen = generators.pop(0)
+        if isinstance(gen.target, ast.Name):
+          gen_ctx[gen.target.id] = None
+        elif isinstance(gen.target, ast.Tuple):  # dict, like `for k, v in d.entries()`
+          for elt in gen.target.elts:
+            gen_ctx[elt.id] = None
+        else:
+          raise ValueError('Na')
+
+        gen_iters = None
+        # A call, like my_dct.items(), or a named ctx list
+        if isinstance(gen.iter, ast.Call):
+          gen_iters = self._eval_statement(gen.iter, ctx=gen_ctx)
+        elif isinstance(gen.iter, (ast.Name, ast.Attribute, ast.List, ast.Tuple)):
+          gen_iters = self._eval_statement(gen.iter, ctx=gen_ctx)
+
+        if not isinstance(gen_iters, Iterable):
+          raise ValueError('No iteraors found for list comprehension')
+
+        for gen_iter in gen_iters:
+          if_ctx = {**gen_ctx}
+          if isinstance(gen.target, ast.Tuple):  # dict, like `for k, v in d.entries()`
+            for i, elt in enumerate(gen.target.elts):
+              if_ctx[elt.id] = gen_iter[i]
+          else:
+            if_ctx[gen.target.id] = gen_iter
+          good = True
+          for ifcall in gen.ifs:
+            if not self._eval_statement(ifcall, ctx=if_ctx):
+              good = False
+              break
+          if not good:
+            continue
+          gen_ctx.update(if_ctx)
+          if len(generators):
+            handle_gen(generators)
+          else:
+            final_list.append(self._eval_statement(stmt.elt, gen_ctx))
+        generators.insert(0, gen)
+
+      handle_gen(generators)
+      return final_list
+
+    if isinstance(stmt, ast.Call):
+      call = None
+      args = []
+      kwargs = {}
+      if isinstance(stmt.func, ast.Attribute):
+        call = self._eval_statement(stmt.func, prev_stmt=stmt, ctx=ctx)
+        if isinstance(call, tuple):
+          args.append(call[1])
+          call = call[0]
+        if not call:
+          raise ValueError(f'No call for ast.Call {stmt.func}')
+
+      name = ''
+      if isinstance(stmt.func, ast.Name):
+        name = stmt.func.id
+        if name in _BUILT_INS:
+          call = _BUILT_INS[name]
+
+      if isinstance(call, str) and call.startswith(_BUILTIN_FN_PREFIX):
+        fn = _get_built_in_fn_by_key(call)
+        call = fn.call
+        if isinstance(call, str):
+          call = getattr(self, call)
+        num_args = len(stmt.args)
+        if num_args < fn.args[0] or (fn.args[1] is not None and num_args > fn.args[1]):
+          toErr = " or more" if fn.args[1] is None else f" to {fn.args[1]}"
+          raise SyntaxError(f"Invalid function call: {fn.name} requires {fn.args[0]}{toErr} args")
+
+      if not call:
+        raise ValueError(f'No call for ast.Call {name}')
+
+      for arg in stmt.args:
+        args.append(self._eval_statement(arg, ctx=ctx))
+      for kwarg in stmt.keywords:
+        kwargs[kwarg.arg] = self._eval_statement(kwarg.value, ctx=ctx)
+      return call(*args, **kwargs)
+
+    if isinstance(stmt, ast.Compare):
+      l = self._eval_statement(stmt.left, ctx=ctx)
+      r = self._eval_statement(stmt.comparators[0], ctx=ctx)
+      if isinstance(stmt.ops[0], ast.Eq):
+        return 1 if l == r else 0
+      if isinstance(stmt.ops[0], ast.NotEq):
+        return 1 if l != r else 0
+      if isinstance(stmt.ops[0], ast.Gt):
+        return 1 if l > r else 0
+      if isinstance(stmt.ops[0], ast.GtE):
+        return 1 if l >= r else 0
+      if isinstance(stmt.ops[0], ast.Lt):
+        return 1 if l < r else 0
+      if isinstance(stmt.ops[0], ast.LtE):
+        return 1 if l <= r else 0
+      if isinstance(stmt.ops[0], ast.In):
+        return 1 if l in r else 0
+      if isinstance(stmt.ops[0], ast.Is):
+        return 1 if l is r else 0
+      if isinstance(stmt.ops[0], ast.IsNot):
+        return 1 if l is not r else 0
+      raise NotImplementedError("Operator " + stmt.ops[0].__class__.__name__ + " not supported.")
+
+    if isinstance(stmt, (ast.If, ast.IfExp)):
+      value = self._eval_statement(stmt.test, ctx=ctx)
+      if value:
+        # ast.If is a list, ast.IfExp is an object.
+        bodies = stmt.body if isinstance(stmt.body, list) else [stmt.body]
+        for body in bodies:
+          value = self._eval_statement(body, ctx=ctx)
+      elif stmt.orelse:
+        # ast.If is a list, ast.IfExp is an object. TBH, I don't know why the If is a list, it's
+        # only ever one item AFAICT.
+        orelses = stmt.orelse if isinstance(stmt.orelse, list) else [stmt.orelse]
+        for orelse in orelses:
+          value = self._eval_statement(orelse, ctx=ctx)
+      return value
+
+    # Assign a variable and add it to our ctx.
+    if isinstance(stmt, (ast.Assign, ast.AugAssign)):
+      if isinstance(stmt, ast.AugAssign):
+        left = self._eval_statement(stmt.target, ctx=ctx)
+        right = self._eval_statement(stmt.value, ctx=ctx)
+        value = _OPERATORS[type(stmt.op)](left, right)
+        target = stmt.target
+      else:
+        value = self._eval_statement(stmt.value, ctx=ctx)
+        if len(stmt.targets) != 1:
+          raise ValueError('Expected length of assign targets to be 1')
+        target = stmt.targets[0]
+
+      if isinstance(target, ast.Tuple):  # like `a, z = (1,2)` (ast.Assign only)
+        for i, elt in enumerate(target.elts):
+          ctx[elt.id] = value[i]
+      elif isinstance(target, ast.Name):  # like `a = 1``
+        ctx[target.id] = value
+      elif isinstance(target, ast.Subscript) and isinstance(target.value, ast.Name):  # `a[0] = 1`
+        ctx[target.value.id][self._eval_statement(target.slice, ctx=ctx)] = value
+      else:
+        raise ValueError('Unhandled target type for Assign.')
+      return value
+
+    # For assigning a var in a list comprehension.
+    # Like [name for node in node_list if (name := node.name)]
+    if isinstance(stmt, ast.NamedExpr):
+      value = self._eval_statement(stmt.value, ctx=ctx)
+      ctx[stmt.target.id] = value
+      return value
+
+    if isinstance(stmt, ast.Return):
+      if stmt.value is None:
+        value = None
+      else:
+        value = self._eval_statement(stmt.value, ctx=ctx)
+      # Mark that we have a return value, as we may be deeper in evaluation, like going through an
+      # if condition's body.
+      ctx['__returned__'] = value
+      return value
+
+    # Raise an error for break or continue, which should be caught and handled inside of loops,
+    # otherwise the error will be raised (which is desired when used outside of a loop).
+    if isinstance(stmt, ast.Break):
+      raise LoopBreak()
+    if isinstance(stmt, ast.Continue):
+      raise LoopContinue()
+
+    # Literally nothing.
+    if isinstance(stmt, ast.Pass):
+      return None
+
+    raise TypeError(stmt)
diff --git a/custom_nodes/rgthree-comfy/py/pyproject.py b/custom_nodes/rgthree-comfy/py/pyproject.py
new file mode 100644
index 0000000000000000000000000000000000000000..26eab343a142079580af83d990c8e0d2cd28057f
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/pyproject.py
@@ -0,0 +1,70 @@
+import os
+import re
+import json
+
+from .utils import set_dict_value
+
+_THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+_FILE_PY_PROJECT = os.path.join(_THIS_DIR, '..', 'pyproject.toml')
+
+
+def read_pyproject():
+  """Reads the pyproject.toml file"""
+  data = {}
+  last_key = ''
+  lines = []
+  # I'd like to use tomllib/tomli, but I'd much rather not introduce dependencies since I've yet to
+  # need to and not everyone may have 3.11. We've got a controlled config file anyway.
+  with open(_FILE_PY_PROJECT, "r", encoding='utf-8') as f:
+    lines = f.readlines()
+  for line in lines:
+    line = line.strip()
+    if re.match(r'\[([^\]]+)\]$', line):
+      last_key = line[1:-1]
+      set_dict_value(data, last_key, data[last_key] if last_key in data else {})
+      continue
+    value_matches = re.match(r'^([^\s\=]+)\s*=\s*(.*)$', line)
+    if value_matches:
+      try:
+        set_dict_value(data, f'{last_key}.{value_matches[1]}', json.loads(value_matches[2]))
+      except json.decoder.JSONDecodeError:
+        # We don't handle multiline arrays or curly brackets; that's ok, we know the file.
+        pass
+
+  return data
+
+
+_DATA = read_pyproject()
+
+# We would want these to fail if they don't exist, so assume they do.
+VERSION: str = _DATA['project']['version']
+NAME: str = _DATA['project']['name']
+LOGO_URL: str = _DATA['tool']['comfy']['Icon']
+
+if not LOGO_URL.endswith('.svg'):
+  raise ValueError('Bad logo url.')
+
+LOGO_SVG = None
+async def get_logo_svg():
+  import aiohttp
+  global LOGO_SVG
+  if LOGO_SVG is not None:
+    return LOGO_SVG
+  # Fetch the logo so we have any updated markup.
+  try:
+    async with aiohttp.ClientSession(
+      trust_env=True, connector=aiohttp.TCPConnector(verify_ssl=True)
+    ) as session:
+      headers = {
+        "user-agent": f"rgthree-comfy/{VERSION}",
+        'Cache-Control': 'no-cache',
+        'Pragma': 'no-cache',
+        'Expires': '0'
+      }
+      async with session.get(LOGO_URL, headers=headers) as resp:
+        LOGO_SVG = await resp.text()
+    LOGO_SVG = re.sub(r'(id="bg".*fill=)"[^\"]+"', r'\1"{bg}"', LOGO_SVG)
+    LOGO_SVG = re.sub(r'(id="fg".*fill=)"[^\"]+"', r'\1"{fg}"', LOGO_SVG)
+  except Exception:
+    LOGO_SVG = '<svg></svg>'
+  return LOGO_SVG
diff --git a/custom_nodes/rgthree-comfy/py/sdxl_empty_latent_image.py b/custom_nodes/rgthree-comfy/py/sdxl_empty_latent_image.py
new file mode 100644
index 0000000000000000000000000000000000000000..c367aaa6931b32060be2032c19a668e02469c941
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/sdxl_empty_latent_image.py
@@ -0,0 +1,63 @@
+from nodes import EmptyLatentImage
+from .constants import get_category, get_name
+
+
+class RgthreeSDXLEmptyLatentImage:
+
+  NAME = get_name('SDXL Empty Latent Image')
+  CATEGORY = get_category()
+
+  @classmethod
+  def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+    return {
+      "required": {
+        "dimensions": (
+          [
+            # 'Custom',
+            '1536 x 640   (landscape)',
+            '1344 x 768   (landscape)',
+            '1216 x 832   (landscape)',
+            '1152 x 896   (landscape)',
+            '1024 x 1024  (square)',
+            ' 896 x 1152  (portrait)',
+            ' 832 x 1216  (portrait)',
+            ' 768 x 1344  (portrait)',
+            ' 640 x 1536  (portrait)',
+          ],
+          {
+            "default": '1024 x 1024  (square)'
+          }),
+        "clip_scale": ("FLOAT", {
+          "default": 2.0,
+          "min": 1.0,
+          "max": 10.0,
+          "step": .5
+        }),
+        "batch_size": ("INT", {
+          "default": 1,
+          "min": 1,
+          "max": 64
+        }),
+      },
+      # "optional": {
+      #   "custom_width": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 64}),
+      #   "custom_height": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 64}),
+      # }
+    }
+
+  RETURN_TYPES = ("LATENT", "INT", "INT")
+  RETURN_NAMES = ("LATENT", "CLIP_WIDTH", "CLIP_HEIGHT")
+  FUNCTION = "generate"
+
+  def generate(self, dimensions, clip_scale, batch_size):
+    """Generates the latent and exposes the clip_width and clip_height"""
+    if True:
+      result = [x.strip() for x in dimensions.split('x')]
+      width = int(result[0])
+      height = int(result[1].split(' ')[0])
+    latent = EmptyLatentImage().generate(width, height, batch_size)[0]
+    return (
+      latent,
+      int(width * clip_scale),
+      int(height * clip_scale),
+    )
diff --git a/custom_nodes/rgthree-comfy/py/sdxl_power_prompt_postive.py b/custom_nodes/rgthree-comfy/py/sdxl_power_prompt_postive.py
new file mode 100644
index 0000000000000000000000000000000000000000..d070da03be21354517cc283eca199f0510b0e6f8
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/sdxl_power_prompt_postive.py
@@ -0,0 +1,178 @@
+import os
+import re
+from nodes import MAX_RESOLUTION
+from comfy_extras.nodes_clip_sdxl import CLIPTextEncodeSDXL
+
+from .log import log_node_warn, log_node_info, log_node_success
+from .constants import get_category, get_name
+from .power_prompt_utils import get_and_strip_loras
+from nodes import LoraLoader, CLIPTextEncode
+import folder_paths
+
+NODE_NAME = get_name('SDXL Power Prompt - Positive')
+
+
+class RgthreeSDXLPowerPromptPositive:
+  """The Power Prompt for positive conditioning."""
+
+  NAME = NODE_NAME
+  CATEGORY = get_category()
+
+  @classmethod
+  def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+    # Removed Saved Prompts feature; No sure it worked any longer. UI should fail gracefully,
+    # TODO: Rip out saved prompt input data
+    SAVED_PROMPTS_FILES=[]
+    SAVED_PROMPTS_CONTENT=[]
+    return {
+      'required': {
+        'prompt_g': ('STRING', {
+          'multiline': True,
+          'dynamicPrompts': True
+        }),
+        'prompt_l': ('STRING', {
+          'multiline': True,
+          'dynamicPrompts': True
+        }),
+      },
+      'optional': {
+        "opt_model": ("MODEL",),
+        "opt_clip": ("CLIP",),
+        "opt_clip_width": ("INT", {
+          "forceInput": True,
+          "default": 1024.0,
+          "min": 0,
+          "max": MAX_RESOLUTION
+        }),
+        "opt_clip_height": ("INT", {
+          "forceInput": True,
+          "default": 1024.0,
+          "min": 0,
+          "max": MAX_RESOLUTION
+        }),
+        'insert_lora': (['CHOOSE', 'DISABLE LORAS'] +
+                        [os.path.splitext(x)[0] for x in folder_paths.get_filename_list('loras')],),
+        'insert_embedding': ([
+          'CHOOSE',
+        ] + [os.path.splitext(x)[0] for x in folder_paths.get_filename_list('embeddings')],),
+        'insert_saved': ([
+          'CHOOSE',
+        ] + SAVED_PROMPTS_FILES,),
+        # We'll hide these in the UI for now.
+        "target_width": ("INT", {
+          "default": -1,
+          "min": -1,
+          "max": MAX_RESOLUTION
+        }),
+        "target_height": ("INT", {
+          "default": -1,
+          "min": -1,
+          "max": MAX_RESOLUTION
+        }),
+        "crop_width": ("INT", {
+          "default": -1,
+          "min": -1,
+          "max": MAX_RESOLUTION
+        }),
+        "crop_height": ("INT", {
+          "default": -1,
+          "min": -1,
+          "max": MAX_RESOLUTION
+        }),
+      },
+      'hidden': {
+        'values_insert_saved': (['CHOOSE'] + SAVED_PROMPTS_CONTENT,),
+      }
+    }
+
+  RETURN_TYPES = ('CONDITIONING', 'MODEL', 'CLIP', 'STRING', 'STRING')
+  RETURN_NAMES = ('CONDITIONING', 'MODEL', 'CLIP', 'TEXT_G', 'TEXT_L')
+  FUNCTION = 'main'
+
+  def main(self,
+           prompt_g,
+           prompt_l,
+           opt_model=None,
+           opt_clip=None,
+           opt_clip_width=None,
+           opt_clip_height=None,
+           insert_lora=None,
+           insert_embedding=None,
+           insert_saved=None,
+           target_width=-1,
+           target_height=-1,
+           crop_width=-1,
+           crop_height=-1,
+           values_insert_saved=None):
+
+    if insert_lora == 'DISABLE LORAS':
+      prompt_g, loras_g, _skipped, _unfound = get_and_strip_loras(prompt_g,
+                                                                  True,
+                                                                  log_node=self.NAME)
+      prompt_l, loras_l, _skipped, _unfound = get_and_strip_loras(prompt_l,
+                                                                  True,
+                                                                  log_node=self.NAME)
+      loras = loras_g + loras_l
+      log_node_info(
+        NODE_NAME,
+        f'Disabling all found loras ({len(loras)}) and stripping lora tags for TEXT output.')
+    elif opt_model is not None and opt_clip is not None:
+      prompt_g, loras_g, _skipped, _unfound = get_and_strip_loras(prompt_g, log_node=self.NAME)
+      prompt_l, loras_l, _skipped, _unfound = get_and_strip_loras(prompt_l, log_node=self.NAME)
+      loras = loras_g + loras_l
+      if len(loras) > 0:
+        for lora in loras:
+          opt_model, opt_clip = LoraLoader().load_lora(opt_model, opt_clip, lora['lora'],
+                                                       lora['strength'], lora['strength'])
+          log_node_success(NODE_NAME, f'Loaded "{lora["lora"]}" from prompt')
+        log_node_info(NODE_NAME, f'{len(loras)} Loras processed; stripping tags for TEXT output.')
+    elif '<lora:' in prompt_g or '<lora:' in prompt_l:
+      _prompt_g, loras_g, _skipped, _unfound = get_and_strip_loras(prompt_g,
+                                                                   True,
+                                                                   log_node=self.NAME)
+      _prompt_l, loras_l, _skipped, _unfound = get_and_strip_loras(prompt_l,
+                                                                   True,
+                                                                   log_node=self.NAME)
+      loras = loras_g + loras_l
+      if len(loras):
+        log_node_warn(
+          NODE_NAME, f'Found {len(loras)} lora tags in prompt but model & clip were not supplied!')
+        log_node_info(NODE_NAME, 'Loras not processed, keeping for TEXT output.')
+
+    conditioning = self.get_conditioning(prompt_g, prompt_l, opt_clip, opt_clip_width,
+                                         opt_clip_height, target_width, target_height, crop_width,
+                                         crop_height)
+
+    return (conditioning, opt_model, opt_clip, prompt_g, prompt_l)
+
+  def get_conditioning(self, prompt_g, prompt_l, opt_clip, opt_clip_width, opt_clip_height,
+                       target_width, target_height, crop_width, crop_height):
+    """Checks the inputs and gets the conditioning."""
+    conditioning = None
+    if opt_clip is not None:
+      do_regular_clip_text_encode = opt_clip_width and opt_clip_height
+      if do_regular_clip_text_encode:
+        target_width = target_width if target_width and target_width > 0 else opt_clip_width
+        target_height = target_height if target_height and target_height > 0 else opt_clip_height
+        crop_width = crop_width if crop_width and crop_width > 0 else 0
+        crop_height = crop_height if crop_height and crop_height > 0 else 0
+        try:
+          conditioning = CLIPTextEncodeSDXL().encode(opt_clip, opt_clip_width, opt_clip_height,
+                                                     crop_width, crop_height, target_width,
+                                                     target_height, prompt_g, prompt_l)[0]
+        except Exception:
+          do_regular_clip_text_encode = True
+          log_node_info(
+            self.NAME,
+            'Exception while attempting to CLIPTextEncodeSDXL, will fall back to standard encoding.'
+          )
+      else:
+        log_node_info(
+          self.NAME,
+          'CLIP supplied, but not CLIP_WIDTH and CLIP_HEIGHT. Text encoding will use standard ' +
+          'encoding with prompt_g and prompt_l concatenated.')
+
+      if not do_regular_clip_text_encode:
+        conditioning = CLIPTextEncode().encode(
+          opt_clip, f'{prompt_g if prompt_g else ""}\n{prompt_l if prompt_l else ""}')[0]
+    return conditioning
diff --git a/custom_nodes/rgthree-comfy/py/sdxl_power_prompt_simple.py b/custom_nodes/rgthree-comfy/py/sdxl_power_prompt_simple.py
new file mode 100644
index 0000000000000000000000000000000000000000..9774d878359a52a313d7ec5f42a5c795989cc7cd
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/sdxl_power_prompt_simple.py
@@ -0,0 +1,106 @@
+"""A simpler SDXL Power Prompt that doesn't load Loras, like for negative."""
+import os
+import re
+import folder_paths
+from nodes import MAX_RESOLUTION, LoraLoader
+from comfy_extras.nodes_clip_sdxl import CLIPTextEncodeSDXL
+from .sdxl_power_prompt_postive import RgthreeSDXLPowerPromptPositive
+
+from .log import log_node_warn, log_node_info, log_node_success
+
+from .constants import get_category, get_name
+
+NODE_NAME = get_name('SDXL Power Prompt - Simple / Negative')
+
+
+class RgthreeSDXLPowerPromptSimple(RgthreeSDXLPowerPromptPositive):
+  """A simpler SDXL Power Prompt that doesn't handle Loras."""
+
+  NAME = NODE_NAME
+  CATEGORY = get_category()
+
+  @classmethod
+  def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+    # Removed Saved Prompts feature; No sure it worked any longer. UI should fail gracefully,
+    # TODO: Rip out saved prompt input data
+    SAVED_PROMPTS_FILES=[]
+    SAVED_PROMPTS_CONTENT=[]
+    return {
+      'required': {
+        'prompt_g': ('STRING', {
+          'multiline': True,
+          'dynamicPrompts': True
+        }),
+        'prompt_l': ('STRING', {
+          'multiline': True,
+          'dynamicPrompts': True
+        }),
+      },
+      'optional': {
+        "opt_clip": ("CLIP",),
+        "opt_clip_width": ("INT", {
+          "forceInput": True,
+          "default": 1024.0,
+          "min": 0,
+          "max": MAX_RESOLUTION
+        }),
+        "opt_clip_height": ("INT", {
+          "forceInput": True,
+          "default": 1024.0,
+          "min": 0,
+          "max": MAX_RESOLUTION
+        }),
+        'insert_embedding': ([
+          'CHOOSE',
+        ] + [os.path.splitext(x)[0] for x in folder_paths.get_filename_list('embeddings')],),
+        'insert_saved': ([
+          'CHOOSE',
+        ] + SAVED_PROMPTS_FILES,),
+        # We'll hide these in the UI for now.
+        "target_width": ("INT", {
+          "default": -1,
+          "min": -1,
+          "max": MAX_RESOLUTION
+        }),
+        "target_height": ("INT", {
+          "default": -1,
+          "min": -1,
+          "max": MAX_RESOLUTION
+        }),
+        "crop_width": ("INT", {
+          "default": -1,
+          "min": -1,
+          "max": MAX_RESOLUTION
+        }),
+        "crop_height": ("INT", {
+          "default": -1,
+          "min": -1,
+          "max": MAX_RESOLUTION
+        }),
+      },
+      'hidden': {
+        'values_insert_saved': (['CHOOSE'] + SAVED_PROMPTS_CONTENT,),
+      }
+    }
+
+  RETURN_TYPES = ('CONDITIONING', 'STRING', 'STRING')
+  RETURN_NAMES = ('CONDITIONING', 'TEXT_G', 'TEXT_L')
+  FUNCTION = 'main'
+
+  def main(self,
+           prompt_g,
+           prompt_l,
+           opt_clip=None,
+           opt_clip_width=None,
+           opt_clip_height=None,
+           insert_embedding=None,
+           insert_saved=None,
+           target_width=-1,
+           target_height=-1,
+           crop_width=-1,
+           crop_height=-1,
+           values_insert_saved=None):
+
+    conditioning = self.get_conditioning(prompt_g, prompt_l, opt_clip, opt_clip_width,
+                                         opt_clip_height, target_width, target_height, crop_width, crop_height)
+    return (conditioning, prompt_g, prompt_l)
diff --git a/custom_nodes/rgthree-comfy/py/seed.py b/custom_nodes/rgthree-comfy/py/seed.py
new file mode 100644
index 0000000000000000000000000000000000000000..b470f213357e12567296c9d9d191174944c0451f
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/seed.py
@@ -0,0 +1,135 @@
+"""See node."""
+import random
+from datetime import datetime
+
+from .utils_graph import get_worflow_node
+
+from .constants import get_category, get_name
+from .log import log_node_warn, log_node_info
+
+# Some extension must be setting a seed as server-generated seeds were not random. We'll set a new
+# seed and use that state going forward.
+initial_random_state = random.getstate()
+random.seed(datetime.now().timestamp())
+rgthree_seed_random_state = random.getstate()
+random.setstate(initial_random_state)
+
+
+def new_random_seed():
+  """ Gets a new random seed from the rgthree_seed_random_state and resetting the previous state."""
+  global rgthree_seed_random_state
+  prev_random_state = random.getstate()
+  random.setstate(rgthree_seed_random_state)
+  seed = random.randint(1, 1125899906842624)
+  rgthree_seed_random_state = random.getstate()
+  random.setstate(prev_random_state)
+  return seed
+
+
+class RgthreeSeed:
+  """Seed node."""
+
+  NAME = get_name('Seed')
+  CATEGORY = get_category()
+
+  @classmethod
+  def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+    return {
+      "required": {
+        "seed": ("INT", {
+          "default": 0,
+          "min": -1125899906842624,
+          "max": 1125899906842624
+        }),
+      },
+      "hidden": {
+        "prompt": "PROMPT",
+        "extra_pnginfo": "EXTRA_PNGINFO",
+        "unique_id": "UNIQUE_ID",
+      },
+    }
+
+  RETURN_TYPES = ("INT",)
+  RETURN_NAMES = ("SEED",)
+  FUNCTION = "main"
+
+  @classmethod
+  def IS_CHANGED(cls, seed, prompt=None, extra_pnginfo=None, unique_id=None):
+    """Forces a changed state if we happen to get a special seed, as if from the API directly."""
+    if seed in (-1, -2, -3):
+      # This isn't used, but a different value than previous will force it to be "changed"
+      return new_random_seed()
+    return seed
+
+  def main(self, seed=0, prompt=None, extra_pnginfo=None, unique_id=None):
+    """Returns the passed seed on execution."""
+
+    # We generate random seeds on the frontend in the seed node before sending the workflow in for
+    # many reasons. However, if we want to use this in an API call without changing the seed before
+    # sending, then users _could_ pass in "-1" and get a random seed used and added to the metadata.
+    # Though, this should likely be discouraged for several reasons (thus, a lot of logging).
+    if seed in (-1, -2, -3):
+      log_node_warn(
+        self.NAME,
+        f'Got "{seed}" as passed seed. ' +
+        'This shouldn\'t happen when queueing from the ComfyUI frontend.',
+        msg_color="YELLOW"
+      )
+      if seed in (-2, -3):
+        log_node_warn(
+          self.NAME,
+          f'Cannot {"increment" if seed == -2 else "decrement"} seed from ' +
+          'server, but will generate a new random seed.',
+          msg_color="YELLOW"
+        )
+
+      original_seed = seed
+      seed = new_random_seed()
+      log_node_info(self.NAME, f'Server-generated random seed {seed} and saving to workflow.')
+      log_node_warn(
+        self.NAME,
+        f'NOTE: Re-queues passing in "{seed}" and server-generated random seed won\'t be cached.',
+        msg_color="YELLOW"
+      )
+
+      if unique_id is None:
+        log_node_warn(
+          self.NAME, 'Cannot save server-generated seed to image metadata because ' +
+          'the node\'s id was not provided.'
+        )
+      else:
+        if extra_pnginfo is None:
+          log_node_warn(
+            self.NAME, 'Cannot save server-generated seed to image workflow ' +
+            'metadata because workflow was not provided.'
+          )
+        else:
+          log_node_info(self.NAME, f'Looking for Seed node with id "{unique_id}"')
+          workflow_node = get_worflow_node(extra_pnginfo, str(unique_id))
+          if workflow_node is None or 'widgets_values' not in workflow_node:
+            log_node_warn(
+              self.NAME, 'Cannot save server-generated seed to image workflow ' +
+              'metadata because node was not found in the provided workflow.'
+            )
+          else:
+            for index, widget_value in enumerate(workflow_node['widgets_values']):
+              if widget_value == original_seed:
+                workflow_node['widgets_values'][index] = seed
+
+        if prompt is None:
+          log_node_warn(
+            self.NAME, 'Cannot save server-generated seed to image API prompt ' +
+            'metadata because prompt was not provided.'
+          )
+        else:
+          prompt_node = prompt[str(unique_id)]
+          if prompt_node is None or 'inputs' not in prompt_node or 'seed' not in prompt_node[
+            'inputs']:
+            log_node_warn(
+              self.NAME, 'Cannot save server-generated seed to image workflow ' +
+              'metadata because node was not found in the provided workflow.'
+            )
+          else:
+            prompt_node['inputs']['seed'] = seed
+
+    return (seed,)
diff --git a/custom_nodes/rgthree-comfy/py/server/rgthree_server.py b/custom_nodes/rgthree-comfy/py/server/rgthree_server.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe42165e463429cd34a898b7a118ec7ebad03ded
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/server/rgthree_server.py
@@ -0,0 +1,48 @@
+import os
+from aiohttp import web
+from server import PromptServer
+
+from ..config import get_config_value
+from ..log import log
+from .utils_server import set_default_page_resources, set_default_page_routes, get_param
+from .routes_config import *
+from .routes_model_info import *
+
+THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+DIR_WEB = os.path.abspath(f'{THIS_DIR}/../../web/')
+
+routes = PromptServer.instance.routes
+
+# Sometimes other pages (link_fixer, etc.) may want to import JS from the comfyui
+# directory. To allows TS to resolve like '../comfyui/file.js', we'll also resolve any module HTTP
+# to these routes.
+set_default_page_resources("comfyui", routes)
+set_default_page_resources("common", routes)
+set_default_page_resources("lib", routes)
+
+set_default_page_routes("link_fixer", routes)
+if get_config_value('unreleased.models_page.enabled') is True:
+  set_default_page_routes("models", routes)
+
+
+@routes.get('/rgthree/api/print')
+async def api_print(request):
+  """Logs a user message to the terminal."""
+
+  message_type = get_param(request, 'type')
+  if message_type == 'PRIMITIVE_REROUTE':
+    log(
+      "You are using rgthree-comfy reroutes with a ComfyUI Primitive node. Unfortunately, ComfyUI "
+      "has removed support for this. While rgthree-comfy has a best-effort support fallback for "
+      "now, it may no longer work as expected and is strongly recommended you either replace the "
+      "Reroute node using ComfyUI's reroute node, or refrain from using the Primitive node "
+      "(you can always use the rgthree-comfy \"Power Primitive\" for non-combo primitives).",
+      prefix="Reroute",
+      color="YELLOW",
+      id=message_type,
+      at_most_secs=20
+    )
+  else:
+    log("Unknown log type from api", prefix="rgthree-comfy",color ="YELLOW")
+
+  return web.json_response({})
diff --git a/custom_nodes/rgthree-comfy/py/server/routes_config.py b/custom_nodes/rgthree-comfy/py/server/routes_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..74e7e1e29688ad52c018906321af8e7dc1e67321
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/server/routes_config.py
@@ -0,0 +1,67 @@
+import json
+import re
+from aiohttp import web
+
+from server import PromptServer
+
+from ..pyproject import get_logo_svg
+from .utils_server import is_param_truthy, get_param
+from ..config import get_config, set_user_config, refresh_config
+
+routes = PromptServer.instance.routes
+
+
+@routes.get('/rgthree/config.js')
+def api_get_user_config_file(request):
+  """ Returns the user configuration as a javascript file. """
+  data_str = json.dumps(get_config(), sort_keys=True, indent=2, separators=(",", ": "))
+  text = f'export const rgthreeConfig = {data_str}'
+  return web.Response(text=text, content_type='application/javascript')
+
+
+@routes.get('/rgthree/api/config')
+def api_get_user_config(request):
+  """ Returns the user configuration. """
+  if is_param_truthy(request, 'refresh'):
+    refresh_config()
+  return web.json_response(get_config())
+
+
+@routes.post('/rgthree/api/config')
+async def api_set_user_config(request):
+  """ Returns the user configuration. """
+  post = await request.post()
+  data = json.loads(post.get("json"))
+  set_user_config(data)
+  return web.json_response({"status": "ok"})
+
+
+@routes.get('/rgthree/logo.svg')
+async def get_logo(request, as_markup=False):
+  """ Returns the rgthree logo with color config. """
+  bg = get_param(request, 'bg', 'transparent')
+  fg = get_param(request, 'fg', '#111111')
+  w = get_param(request, 'w')
+  h = get_param(request, 'h')
+  css_class = get_param(request, 'cssClass')
+  svg = await get_logo_svg()
+  resp = svg.format(bg=bg, fg=fg)
+  if w is not None:
+    resp = re.sub(r'(<svg[^\>]*?)width="[^\"]+"', r'\1', resp)
+    if str(w).isnumeric():
+      resp = re.sub(r'<svg', f'<svg width="{w}"', resp)
+  if h is not None:
+    resp = re.sub(r'(<svg[^\>]*?)height="[^\"]+"', r'\1', resp)
+    if str(h).isnumeric():
+      resp = re.sub(r'<svg', f'<svg height="{h}"', resp)
+  if css_class is not None:
+    resp = re.sub(r'<svg', f'<svg class="{css_class}"', resp)
+  if as_markup:
+    resp = re.sub(r'^.*?<svg', r'<svg', resp, flags=re.DOTALL)
+  return web.Response(text=resp, content_type='image/svg+xml')
+
+
+@routes.get('/rgthree/logo_markup.svg')
+async def get_logo_markup(request):
+  """ Returns the rgthree logo svg markup (no doctype) with options. """
+  return await get_logo(request, as_markup=True)
diff --git a/custom_nodes/rgthree-comfy/py/server/routes_model_info.py b/custom_nodes/rgthree-comfy/py/server/routes_model_info.py
new file mode 100644
index 0000000000000000000000000000000000000000..61feb4de339132d22d82fc74cbdac89aa43d8ecd
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/server/routes_model_info.py
@@ -0,0 +1,199 @@
+import os
+import json
+from aiohttp import web
+
+from ..log import log
+from server import PromptServer
+import folder_paths
+
+from ..utils import abspath, path_exists
+from .utils_server import get_param, is_param_falsy
+from .utils_info import delete_model_info, get_model_info, set_model_info_partial, get_file_info
+
+routes = PromptServer.instance.routes
+
+
+def _check_valid_model_type(request):
+  model_type = request.match_info['type']
+  if model_type not in ['loras', 'checkpoints']:
+    return web.json_response({'status': 404, 'error': f'Invalid model type: {model_type}'})
+  return None
+
+
+@routes.get('/rgthree/api/{type}')
+async def api_get_models_list(request):
+  """Returns a list of model types from user configuration.
+
+  By default, a list of filenames are provided. If `format=details` is specified, a list of objects
+  with additional _file info_ is provided. This includes modigied time, hasInfoFile, and imageLocal
+  among others.
+  """
+  if _check_valid_model_type(request):
+    return _check_valid_model_type(request)
+
+  model_type = request.match_info['type']
+  files = folder_paths.get_filename_list(model_type)
+  format_param = get_param(request, 'format')
+  if format_param == 'details':
+    response = []
+    bad_files_first = None
+    bad_files_num = 0
+    for file in files:
+      file_info = get_file_info(file, model_type)
+      # Some folks were seeing null in this list, which is odd since it's coming from ComfyUI files.
+      # See https://github.com/rgthree/rgthree-comfy/issues/574#issuecomment-3494629132 We'll check
+      # and log if we haven't found, maybe someone will have more info.
+      if file_info is not None:
+        response.append(file_info)
+      else:
+        bad_files_num += 1
+        if not bad_files_first:
+          bad_files_first = file
+    if bad_files_first:
+      log(
+        f"Couldn't get file info for {bad_files_first}"
+        f"{f' and {bad_files_num} other {model_type}.' if bad_files_num > 1 else '.'} "
+        "ComfyUI thinks they exist, but they were not found on the filesystem.",
+        prefix="Power Lora Loader",
+        color="YELLOW",
+        id=f'no_file_details_{model_type}',
+        at_most_secs=30
+      )
+    return web.json_response(response)
+
+  return web.json_response(list(files))
+
+
+@routes.get('/rgthree/api/{type}/info')
+async def api_get_models_info(request):
+  """Returns a list model info; either all or a specific ones if provided a 'files' param.
+
+  If a `light` param is specified and not falsy, no metadata will be fetched.
+  """
+  if _check_valid_model_type(request):
+    return _check_valid_model_type(request)
+
+  model_type = request.match_info['type']
+  files_param = get_param(request, 'files')
+  maybe_fetch_metadata = files_param is not None
+  if not is_param_falsy(request, 'light'):
+    maybe_fetch_metadata = False
+  api_response = await models_info_response(
+    request, model_type, maybe_fetch_metadata=maybe_fetch_metadata
+  )
+  return web.json_response(api_response)
+
+
+@routes.get('/rgthree/api/{type}/info/refresh')
+async def api_get_refresh_get_models_info(request):
+  """Refreshes model info; either all or specific ones if provided a 'files' param. """
+  if _check_valid_model_type(request):
+    return _check_valid_model_type(request)
+
+  model_type = request.match_info['type']
+  api_response = await models_info_response(
+    request, model_type, maybe_fetch_civitai=True, maybe_fetch_metadata=True
+  )
+  return web.json_response(api_response)
+
+
+@routes.get('/rgthree/api/{type}/info/clear')
+async def api_get_delete_model_info(request):
+  """Clears model info from the filesystem for the provided file."""
+  if _check_valid_model_type(request):
+    return _check_valid_model_type(request)
+
+  api_response = {'status': 200}
+  model_type = request.match_info['type']
+  files_param = get_param(request, 'files')
+  if files_param is not None:
+    files_param = files_param.split(',')
+  del_info = not is_param_falsy(request, 'del_info')
+  del_metadata = not is_param_falsy(request, 'del_metadata')
+  del_civitai = not is_param_falsy(request, 'del_civitai')
+  if not files_param:
+    api_response['status'] = '404'
+    api_response['error'] = f'No file provided. Please pass files=ALL to clear {model_type} info.'
+  else:
+    if len(files_param) == 1 and files_param[
+      0] == "ALL":  # Force the user to supply files=ALL to trigger all clearing.
+      files_param = folder_paths.get_filename_list(model_type)
+    for file_param in files_param:
+      await delete_model_info(
+        file_param,
+        model_type,
+        del_info=del_info,
+        del_metadata=del_metadata,
+        del_civitai=del_civitai
+      )
+  return web.json_response(api_response)
+
+
+@routes.post('/rgthree/api/{type}/info')
+async def api_post_save_model_data(request):
+  """Saves data to a model by name. """
+  if _check_valid_model_type(request):
+    return _check_valid_model_type(request)
+
+  model_type = request.match_info['type']
+  api_response = {'status': 200}
+  file_param = get_param(request, 'file')
+  if file_param is None:
+    api_response['status'] = '404'
+    api_response['error'] = 'No model found at path'
+  else:
+    post = await request.post()
+    await set_model_info_partial(file_param, model_type, json.loads(post.get("json")))
+    info_data = await get_model_info(file_param, model_type)
+    api_response['data'] = info_data
+  return web.json_response(api_response)
+
+
+@routes.get('/rgthree/api/{type}/img')
+async def api_get_models_info_img(request):
+  """ Returns an image response if one exists for the model. """
+  if _check_valid_model_type(request):
+    return _check_valid_model_type(request)
+
+  model_type = request.match_info['type']
+  file_param = get_param(request, 'file')
+  file_path = folder_paths.get_full_path(model_type, file_param)
+  if not path_exists(file_path):
+    file_path = abspath(file_path)
+  img_path = None
+  for ext in ['jpg', 'png', 'jpeg']:
+    try_path = f'{os.path.splitext(file_path)[0]}.{ext}'
+    if path_exists(try_path):
+      img_path = try_path
+      break
+
+  if not path_exists(img_path):
+    api_response = {}
+    api_response['status'] = '404'
+    api_response['error'] = 'No model found at path'
+    return web.json_response(api_response)
+
+  return web.FileResponse(img_path)
+
+
+async def models_info_response(
+  request, model_type, maybe_fetch_civitai=False, maybe_fetch_metadata=False
+):
+  """Gets model info for all or a single model type."""
+  api_response = {'status': 200, 'data': []}
+  light = not is_param_falsy(request, 'light')
+  files_param = get_param(request, 'files')
+  if files_param is not None:
+    files_param = files_param.split(',')
+  else:
+    files_param = folder_paths.get_filename_list(model_type)
+  for file_param in files_param:
+    info_data = await get_model_info(
+      file_param,
+      model_type,
+      maybe_fetch_civitai=maybe_fetch_civitai,
+      maybe_fetch_metadata=maybe_fetch_metadata,
+      light=light
+    )
+    api_response['data'].append(info_data)
+  return api_response
diff --git a/custom_nodes/rgthree-comfy/py/server/utils_info.py b/custom_nodes/rgthree-comfy/py/server/utils_info.py
new file mode 100644
index 0000000000000000000000000000000000000000..4723a664540235fa04f18c1460c2eef6f8fc88f5
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/server/utils_info.py
@@ -0,0 +1,452 @@
+import hashlib
+import json
+import os
+import re
+from datetime import datetime
+
+import requests
+
+from server import PromptServer
+import folder_paths
+
+from ..utils import abspath, get_dict_value, load_json_file, file_exists, remove_path, save_json_file
+from ..utils_userdata import read_userdata_json, save_userdata_json, delete_userdata_file
+
+
+def _get_info_cache_file(data_type: str, file_hash: str):
+  return f'info/{file_hash}.{data_type}.json'
+
+
+async def delete_model_info(
+  file: str, model_type, del_info=True, del_metadata=True, del_civitai=True
+):
+  """Delete the info json, and the civitai & metadata caches."""
+  file_path = get_folder_path(file, model_type)
+  if file_path is None:
+    return
+  if del_info:
+    remove_path(get_info_file(file_path))
+  if del_civitai or del_metadata:
+    file_hash = _get_sha256_hash(file_path)
+    if del_civitai:
+      json_file_path = _get_info_cache_file(file_hash, 'civitai')
+      delete_userdata_file(json_file_path)
+    if del_metadata:
+      json_file_path = _get_info_cache_file(file_hash, 'metadata')
+      delete_userdata_file(json_file_path)
+
+
+def get_file_info(file: str, model_type):
+  """Gets basic file info, like created or modified date."""
+  file_path = get_folder_path(file, model_type)
+  if file_path is None:
+    return None
+  return {
+    'file': file,
+    'path': file_path,
+    'modified': os.path.getmtime(file_path) * 1000,  # millis
+    'imageLocal': f'/rgthree/api/{model_type}/img?file={file}' if get_img_file(file_path) else None,
+    'hasInfoFile': get_info_file(file_path) is not None,
+  }
+
+
+def get_info_file(file_path: str, force=False):
+  # Try to load a rgthree-info.json file next to the file.
+  info_path = f'{file_path}.rgthree-info.json'
+  return info_path if file_exists(info_path) or force else None
+
+
+def get_img_file(file_path: str, force=False):
+  for ext in ['jpg', 'png', 'jpeg', 'webp']:
+    try_path = f'{os.path.splitext(file_path)[0]}.{ext}'
+    if file_exists(try_path):
+      return try_path
+
+
+def get_model_info_file_data(file: str, model_type, default=None):
+  """Returns the data from the info file, or a default value if it doesn't exist."""
+  file_path = get_folder_path(file, model_type)
+  if file_path is None:
+    return default
+  return load_json_file(get_info_file(file_path), default=default)
+
+
+async def get_model_info(
+  file: str,
+  model_type,
+  default=None,
+  maybe_fetch_civitai=False,
+  force_fetch_civitai=False,
+  maybe_fetch_metadata=False,
+  force_fetch_metadata=False,
+  light=False
+):
+  """Compiles a model info given a stored file next to the model, and/or metadata/civitai."""
+
+  file_path = get_folder_path(file, model_type)
+  if file_path is None:
+    return default
+
+  should_save = False
+  # basic data
+  basic_data = get_file_info(file, model_type)
+  # Try to load a rgthree-info.json file next to the file.
+  info_data = get_model_info_file_data(file, model_type, default={})
+
+  for key in ['file', 'path', 'modified', 'imageLocal', 'hasInfoFile']:
+    if key in basic_data and basic_data[key] and (
+      key not in info_data or info_data[key] != basic_data[key]
+    ):
+      info_data[key] = basic_data[key]
+      should_save = True
+
+  # Check if we have an image next to the file and, if so, add it to the front of the images
+  # (if it isn't already).
+  img_next_to_file = basic_data['imageLocal']
+
+  if 'images' not in info_data:
+    info_data['images'] = []
+    should_save = True
+
+  if img_next_to_file:
+    if len(info_data['images']) == 0 or info_data['images'][0]['url'] != img_next_to_file:
+      info_data['images'].insert(0, {'url': img_next_to_file})
+      should_save = True
+
+  # If we just want light data then bail now with just existing data, plus file, path and img if
+  # next to the file.
+  if light and not maybe_fetch_metadata and not force_fetch_metadata and not maybe_fetch_civitai and not force_fetch_civitai:
+    return info_data
+
+  if 'raw' not in info_data:
+    info_data['raw'] = {}
+    should_save = True
+
+  should_save = _update_data(info_data) or should_save
+
+  should_fetch_civitai = force_fetch_civitai is True or (
+    maybe_fetch_civitai is True and 'civitai' not in info_data['raw']
+  )
+  should_fetch_metadata = force_fetch_metadata is True or (
+    maybe_fetch_metadata is True and 'metadata' not in info_data['raw']
+  )
+
+  if should_fetch_metadata:
+    data_meta = _get_model_metadata(file, model_type, default={}, refresh=force_fetch_metadata)
+    should_save = _merge_metadata(info_data, data_meta) or should_save
+
+  if should_fetch_civitai:
+    data_civitai = _get_model_civitai_data(
+      file, model_type, default={}, refresh=force_fetch_civitai
+    )
+    should_save = _merge_civitai_data(info_data, data_civitai) or should_save
+
+  if 'sha256' not in info_data:
+    file_hash = _get_sha256_hash(file_path)
+    if file_hash is not None:
+      info_data['sha256'] = file_hash
+      should_save = True
+
+  if should_save:
+    if 'trainedWords' in info_data:
+      # Sort by count; if it doesn't exist, then assume it's a top item from civitai or elsewhere.
+      info_data['trainedWords'] = sorted(
+        info_data['trainedWords'],
+        key=lambda w: w['count'] if 'count' in w else 99999,
+        reverse=True
+      )
+    save_model_info(file, info_data, model_type)
+
+    # If we're saving, then the UI is likely waiting to see if the refreshed data is coming in.
+    await PromptServer.instance.send(f"rgthree-refreshed-{model_type}-info", {"data": info_data})
+
+  return info_data
+
+
+def _update_data(info_data: dict) -> bool:
+  """Ports old data to new data if necessary."""
+  should_save = False
+  # If we have "triggerWords" then move them over to "trainedWords"
+  if 'triggerWords' in info_data and len(info_data['triggerWords']) > 0:
+    civitai_words = ','.join((
+      get_dict_value(info_data, 'raw.civitai.triggerWords', default=[]) +
+      get_dict_value(info_data, 'raw.civitai.trainedWords', default=[])
+    ))
+    if 'trainedWords' not in info_data:
+      info_data['trainedWords'] = []
+    for trigger_word in info_data['triggerWords']:
+      word_data = next((data for data in info_data['trainedWords'] if data['word'] == trigger_word),
+                       None)
+      if word_data is None:
+        word_data = {'word': trigger_word}
+        info_data['trainedWords'].append(word_data)
+      if trigger_word in civitai_words:
+        word_data['civitai'] = True
+      else:
+        word_data['user'] = True
+
+    del info_data['triggerWords']
+    should_save = True
+  return should_save
+
+
+def _merge_metadata(info_data: dict, data_meta: dict) -> bool:
+  """Returns true if data was saved."""
+  should_save = False
+
+  base_model_file = get_dict_value(data_meta, 'ss_sd_model_name', None)
+  if base_model_file:
+    info_data['baseModelFile'] = base_model_file
+
+  # Loop over metadata tags
+  trained_words = {}
+  if 'ss_tag_frequency' in data_meta and isinstance(data_meta['ss_tag_frequency'], dict):
+    for bucket_value in data_meta['ss_tag_frequency'].values():
+      if isinstance(bucket_value, dict):
+        for tag, count in bucket_value.items():
+          if tag not in trained_words:
+            trained_words[tag] = {'word': tag, 'count': 0, 'metadata': True}
+          trained_words[tag]['count'] = trained_words[tag]['count'] + count
+
+  if 'trainedWords' not in info_data:
+    info_data['trainedWords'] = list(trained_words.values())
+    should_save = True
+  else:
+    # We can't merge, because the list may have other data, like it's part of civitaidata.
+    merged_dict = {}
+    for existing_word_data in info_data['trainedWords']:
+      merged_dict[existing_word_data['word']] = existing_word_data
+    for new_key, new_word_data in trained_words.items():
+      if new_key not in merged_dict:
+        merged_dict[new_key] = {}
+      merged_dict[new_key] = {**merged_dict[new_key], **new_word_data}
+    info_data['trainedWords'] = list(merged_dict.values())
+    should_save = True
+
+  # trained_words = list(trained_words.values())
+  # info_data['meta_trained_words'] = trained_words
+  info_data['raw']['metadata'] = data_meta
+  should_save = True
+
+  if 'sha256' not in info_data and '_sha256' in data_meta:
+    info_data['sha256'] = data_meta['_sha256']
+    should_save = True
+
+  return should_save
+
+
+def _merge_civitai_data(info_data: dict, data_civitai: dict) -> bool:
+  """Returns true if data was saved."""
+  should_save = False
+
+  if 'name' not in info_data:
+    info_data['name'] = get_dict_value(data_civitai, 'model.name', '')
+    should_save = True
+    version_name = get_dict_value(data_civitai, 'name')
+    if version_name is not None:
+      info_data['name'] += f' - {version_name}'
+
+  if 'type' not in info_data:
+    info_data['type'] = get_dict_value(data_civitai, 'model.type')
+    should_save = True
+  if 'baseModel' not in info_data:
+    info_data['baseModel'] = get_dict_value(data_civitai, 'baseModel')
+    should_save = True
+
+  # We always want to merge triggerword.
+  civitai_trigger = get_dict_value(data_civitai, 'triggerWords', default=[])
+  civitai_trained = get_dict_value(data_civitai, 'trainedWords', default=[])
+  civitai_words = ','.join(civitai_trigger + civitai_trained)
+  if civitai_words:
+    civitai_words = re.sub(r"\s*,\s*", ",", civitai_words)
+    civitai_words = re.sub(r",+", ",", civitai_words)
+    civitai_words = re.sub(r"^,", "", civitai_words)
+    civitai_words = re.sub(r",$", "", civitai_words)
+    if civitai_words:
+      civitai_words = civitai_words.split(',')
+      if 'trainedWords' not in info_data:
+        info_data['trainedWords'] = []
+      for trigger_word in civitai_words:
+        word_data = next(
+          (data for data in info_data['trainedWords'] if data['word'] == trigger_word), None
+        )
+        if word_data is None:
+          word_data = {'word': trigger_word}
+          info_data['trainedWords'].append(word_data)
+        word_data['civitai'] = True
+
+  if 'sha256' not in info_data:
+    info_data['sha256'] = data_civitai['_sha256']
+    should_save = True
+
+  if 'modelId' in data_civitai:
+    info_data['links'] = info_data['links'] if 'links' in info_data else []
+    civitai_link = f'https://civitai.com/models/{get_dict_value(data_civitai, "modelId")}'
+    if get_dict_value(data_civitai, "id"):
+      civitai_link += f'?modelVersionId={get_dict_value(data_civitai, "id")}'
+    info_data['links'].append(civitai_link)
+    info_data['links'].append(data_civitai['_civitai_api'])
+    should_save = True
+
+  # Take images from civitai
+  if 'images' in data_civitai:
+    info_data_image_urls = list(
+      map(lambda i: i['url'] if 'url' in i else None, info_data['images'])
+    )
+    for img in data_civitai['images']:
+      img_url = get_dict_value(img, 'url')
+      if img_url is not None and img_url not in info_data_image_urls:
+        img_id = os.path.splitext(os.path.basename(img_url))[0] if img_url is not None else None
+        img_data = {
+          'url': img_url,
+          'civitaiUrl': f'https://civitai.com/images/{img_id}' if img_id is not None else None,
+          'width': get_dict_value(img, 'width'),
+          'height': get_dict_value(img, 'height'),
+          'type': get_dict_value(img, 'type'),
+          'nsfwLevel': get_dict_value(img, 'nsfwLevel'),
+          'seed': get_dict_value(img, 'meta.seed'),
+          'positive': get_dict_value(img, 'meta.prompt'),
+          'negative': get_dict_value(img, 'meta.negativePrompt'),
+          'steps': get_dict_value(img, 'meta.steps'),
+          'sampler': get_dict_value(img, 'meta.sampler'),
+          'cfg': get_dict_value(img, 'meta.cfgScale'),
+          'model': get_dict_value(img, 'meta.Model'),
+          'resources': get_dict_value(img, 'meta.resources'),
+        }
+        info_data['images'].append(img_data)
+        should_save = True
+
+  # The raw data
+  if 'civitai' not in info_data['raw']:
+    info_data['raw']['civitai'] = data_civitai
+    should_save = True
+
+  return should_save
+
+
+def _get_model_civitai_data(file: str, model_type, default=None, refresh=False):
+  """Gets the civitai data, either cached from the user directory, or from civitai api."""
+  file_hash = _get_sha256_hash(get_folder_path(file, model_type))
+  if file_hash is None:
+    return None
+
+  json_file_path = _get_info_cache_file(file_hash, 'civitai')
+
+  api_url = f'https://civitai.com/api/v1/model-versions/by-hash/{file_hash}'
+  file_data = read_userdata_json(json_file_path)
+  if file_data is None or refresh is True:
+    try:
+      response = requests.get(api_url, timeout=5000)
+      data = response.json()
+      save_userdata_json(
+        json_file_path, {
+          'url': api_url,
+          'timestamp': datetime.now().timestamp(),
+          'response': data
+        }
+      )
+      file_data = read_userdata_json(json_file_path)
+    except requests.exceptions.RequestException as e:  # This is the correct syntax
+      print(e)
+  response = file_data['response'] if file_data is not None and 'response' in file_data else None
+  if response is not None:
+    response['_sha256'] = file_hash
+    response['_civitai_api'] = api_url
+  return response if response is not None else default
+
+
+def _get_model_metadata(file: str, model_type, default=None, refresh=False):
+  """Gets the metadata from the file itself."""
+  file_path = get_folder_path(file, model_type)
+  file_hash = _get_sha256_hash(file_path)
+  if file_hash is None:
+    return default
+
+  json_file_path = _get_info_cache_file(file_hash, 'metadata')
+
+  file_data = read_userdata_json(json_file_path)
+  if file_data is None or refresh is True:
+    data = _read_file_metadata_from_header(file_path)
+    if data is not None:
+      file_data = {'url': file, 'timestamp': datetime.now().timestamp(), 'response': data}
+      save_userdata_json(json_file_path, file_data)
+  response = file_data['response'] if file_data is not None and 'response' in file_data else None
+  if response is not None:
+    response['_sha256'] = file_hash
+  return response if response is not None else default
+
+
+def _read_file_metadata_from_header(file_path: str) -> dict:
+  """Reads the file's header and returns a JSON dict metdata if available."""
+  data = None
+  try:
+    if file_path.endswith('.safetensors'):
+      with open(file_path, "rb") as file:
+        # https://github.com/huggingface/safetensors#format
+        # 8 bytes: N, an unsigned little-endian 64-bit integer, containing the size of the header
+        header_size = int.from_bytes(file.read(8), "little", signed=False)
+
+        if header_size <= 0:
+          raise BufferError("Invalid header size")
+
+        header = file.read(header_size)
+        if header is None:
+          raise BufferError("Invalid header")
+
+        header_json = json.loads(header)
+        data = header_json["__metadata__"] if "__metadata__" in header_json else None
+
+        if data is not None:
+          for key, value in data.items():
+            if isinstance(value, str) and value.startswith('{') and value.endswith('}'):
+              try:
+                value_as_json = json.loads(value)
+                data[key] = value_as_json
+              except Exception:
+                print(f'metdata for field {key} did not parse as json')
+  except requests.exceptions.RequestException as e:
+    print(e)
+    data = None
+
+  return data
+
+
+def get_folder_path(file: str, model_type) -> str | None:
+  """Gets the file path ensuring it exists."""
+  file_path = folder_paths.get_full_path(model_type, file)
+  if not file_exists(file_path):
+    file_path = abspath(file_path)
+  if not file_exists(file_path):
+    file_path = None
+  return file_path
+
+
+def _get_sha256_hash(file_path: str | None):
+  """Returns the hash for the file."""
+  if not file_path or not file_exists(file_path):
+    return None
+  BUF_SIZE = 1024 * 128  # lets read stuff in 64kb chunks!
+  file_hash = None
+  sha256_hash = hashlib.sha256()
+  with open(file_path, "rb") as f:
+    # Read and update hash string value in blocks of BUF_SIZE
+    for byte_block in iter(lambda: f.read(BUF_SIZE), b""):
+      sha256_hash.update(byte_block)
+    file_hash = sha256_hash.hexdigest()
+  return file_hash
+
+
+async def set_model_info_partial(file: str, model_type: str, info_data_partial):
+  """Sets partial data into the existing model info data."""
+  info_data = await get_model_info(file, model_type, default={})
+  info_data = {**info_data, **info_data_partial}
+  save_model_info(file, info_data, model_type)
+
+
+def save_model_info(file: str, info_data, model_type):
+  """Saves the model info alongside the model itself."""
+  file_path = get_folder_path(file, model_type)
+  if file_path is None:
+    return
+  info_path = get_info_file(file_path, force=True)
+  save_json_file(info_path, info_data)
diff --git a/custom_nodes/rgthree-comfy/py/server/utils_server.py b/custom_nodes/rgthree-comfy/py/server/utils_server.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2fa5d741964a767daf45ed7918c7b371348d222
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/server/utils_server.py
@@ -0,0 +1,65 @@
+import os
+from aiohttp import web
+
+from ..utils import sub_abspath
+
+THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+DIR_WEB = os.path.abspath(f'{THIS_DIR}/../../web/')
+
+
+def get_param(request, param, default=None):
+  """Gets a param from a request."""
+  return request.rel_url.query[param] if param in request.rel_url.query else default
+
+
+def is_param_falsy(request, param):
+  """Determines if a param is explicitly 0 or false."""
+  val = get_param(request, param)
+  return val is not None and (val == "0" or val.upper() == "FALSE")
+
+
+def is_param_truthy(request, param):
+  """Determines if a param is explicitly 0 or false."""
+  val = get_param(request, param)
+  return val is not None and not is_param_falsy(request, param)
+
+
+def set_default_page_resources(path, routes):
+  """Sets up routes for handling static files under a path."""
+
+  @routes.get(f'/rgthree/{path}/{{file}}')
+  async def get_resource(request):
+    """Returns a resource file."""
+    filepath = request.match_info['file']
+    abspath = sub_abspath(os.path.join(DIR_WEB, path), filepath)
+    if abspath is None:
+      return web.HTTPNotFound()
+    return web.FileResponse(abspath)
+
+  @routes.get(f'/rgthree/{path}/{{subdir}}/{{file}}')
+  async def get_resource_subdir(request):
+    """Returns a resource file."""
+    filepath = os.path.join(request.match_info['subdir'], request.match_info['file'])
+    abspath = sub_abspath(os.path.join(DIR_WEB, path), filepath)
+    if abspath is None:
+      return web.HTTPNotFound()
+    return web.FileResponse(abspath)
+
+
+def set_default_page_routes(path, routes):
+  """ Sets default path handling for a hosted rgthree page. """
+
+  @routes.get(f'/rgthree/{path}')
+  async def get_path_redir(request):
+    """ Redirects to the path adding a trailing slash. """
+    raise web.HTTPFound(f'{request.path}/')
+
+  @routes.get(f'/rgthree/{path}/')
+  async def get_path_index(request):
+    """ Handles the page's index loading. """
+    html = ''
+    with open(os.path.join(DIR_WEB, path, 'index.html'), 'r', encoding='UTF-8') as file:
+      html = file.read()
+    return web.Response(text=html, content_type='text/html')
+
+  set_default_page_resources(path, routes)
diff --git a/custom_nodes/rgthree-comfy/py/utils.py b/custom_nodes/rgthree-comfy/py/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e86949e904e12c53bb1c02750f16776028edce82
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/utils.py
@@ -0,0 +1,177 @@
+import json
+import os
+import re
+
+from typing import Union
+
+
+class AnyType(str):
+  """A special class that is always equal in not equal comparisons. Credit to pythongosssss"""
+
+  def __ne__(self, __value: object) -> bool:
+    return False
+
+
+class FlexibleOptionalInputType(dict):
+  """A special class to make flexible nodes that pass data to our python handlers.
+
+  Enables both flexible/dynamic input types (like for Any Switch) or a dynamic number of inputs
+  (like for Any Switch, Context Switch, Context Merge, Power Lora Loader, etc).
+
+  Initially, ComfyUI only needed to return True for `__contains__` below, which told ComfyUI that
+  our node will handle the input, regardless of what it is.
+
+  However, after https://github.com/comfyanonymous/ComfyUI/pull/2666 ComdyUI's execution changed
+  also checking the data for the key; specifcially, the type which is the first tuple entry. This
+  type is supplied to our FlexibleOptionalInputType and returned for any non-data key. This can be a
+  real type, or use the AnyType for additional flexibility.
+  """
+
+  def __init__(self, type, data: Union[dict, None] = None):
+    """Initializes the FlexibleOptionalInputType.
+
+    Args:
+      type: The flexible type to use when ComfyUI retrieves an unknown key (via `__getitem__`).
+      data: An optional dict to use as the basis. This is stored both in a `data` attribute, so we
+        can look it up without hitting our overrides, as well as iterated over and adding its key
+        and values to our `self` keys. This way, when looked at, we will appear to represent this
+        data. When used in an "optional" INPUT_TYPES, these are the starting optional node types.
+    """
+    self.type = type
+    self.data = data
+    if self.data is not None:
+      for k, v in self.data.items():
+        self[k] = v
+
+  def __getitem__(self, key):
+    # If we have this key in the initial data, then return it. Otherwise return the tuple with our
+    # flexible type.
+    if self.data is not None and key in self.data:
+      val = self.data[key]
+      return val
+    return (self.type,)
+
+  def __contains__(self, key):
+    """Always contain a key, and we'll always return the tuple above when asked for it."""
+    return True
+
+
+any_type = AnyType("*")
+
+
+def is_dict_value_falsy(data: dict, dict_key: str):
+  """Checks if a dict value is falsy."""
+  val = get_dict_value(data, dict_key)
+  return not val
+
+
+def get_dict_value(data: dict, dict_key: str, default=None):
+  """Gets a deeply nested value given a dot-delimited key."""
+  keys = dict_key.split('.')
+  key = keys.pop(0) if len(keys) > 0 else None
+  found = data[key] if key in data else None
+  if found is not None and len(keys) > 0:
+    return get_dict_value(found, '.'.join(keys), default)
+  return found if found is not None else default
+
+
+def set_dict_value(data: dict, dict_key: str, value, create_missing_objects=True):
+  """Sets a deeply nested value given a dot-delimited key."""
+  keys = dict_key.split('.')
+  key = keys.pop(0) if len(keys) > 0 else None
+  if key not in data:
+    if create_missing_objects is False:
+      return data
+    data[key] = {}
+  if len(keys) == 0:
+    data[key] = value
+  else:
+    set_dict_value(data[key], '.'.join(keys), value, create_missing_objects)
+
+  return data
+
+
+def dict_has_key(data: dict, dict_key):
+  """Checks if a dict has a deeply nested dot-delimited key."""
+  keys = dict_key.split('.')
+  key = keys.pop(0) if len(keys) > 0 else None
+  if key is None or key not in data:
+    return False
+  if len(keys) == 0:
+    return True
+  return dict_has_key(data[key], '.'.join(keys))
+
+
+def load_json_file(file: str, default=None):
+  """Reads a json file and returns the json dict, stripping out "//" comments first."""
+  if path_exists(file):
+    with open(file, 'r', encoding='UTF-8') as file:
+      config = file.read()
+      try:
+        return json.loads(config)
+      except json.decoder.JSONDecodeError:
+        try:
+          config = re.sub(r"^\s*//\s.*", "", config, flags=re.MULTILINE)
+          return json.loads(config)
+        except json.decoder.JSONDecodeError:
+          try:
+            config = re.sub(r"(?:^|\s)//.*", "", config, flags=re.MULTILINE)
+            return json.loads(config)
+          except json.decoder.JSONDecodeError:
+            pass
+  return default
+
+
+def save_json_file(file_path: str, data: dict):
+  """Saves a json file."""
+  os.makedirs(os.path.dirname(file_path), exist_ok=True)
+  with open(file_path, 'w+', encoding='UTF-8') as file:
+    json.dump(data, file, sort_keys=False, indent=2, separators=(",", ": "))
+
+
+def path_exists(path):
+  """Checks if a path exists, accepting None type."""
+  if path is not None:
+    return os.path.exists(path)
+  return False
+
+
+def file_exists(path):
+  """Checks if a file exists, accepting None type."""
+  if path is not None:
+    return os.path.isfile(path)
+  return False
+
+
+def remove_path(path):
+  """Removes a path, if it exists."""
+  if path_exists(path):
+    os.remove(path)
+    return True
+  return False
+
+def abspath(file_path: str):
+  """Resolves the abspath of a file, resolving symlinks and user dirs."""
+  abs_path = os.path.abspath(file_path) if file_path else file_path
+  if abs_path and not path_exists(abs_path):
+    maybe_path = os.path.abspath(os.path.realpath(os.path.expanduser(file_path)))
+    abs_path = maybe_path if path_exists(maybe_path) else abs_path
+  return abs_path
+
+def sub_abspath(parent_dir: str, rel_path: str):
+  """Resolves the abspath under a parent directory ensuring it exists and is contained within."""
+  rel_path = os.path.join(parent_dir, rel_path)
+  abs_path = abspath(rel_path)
+  if not path_exists(abs_path) or not abs_path.startswith(parent_dir):
+    return None
+  return abs_path
+
+class ByPassTypeTuple(tuple):
+  """A special class that will return additional "AnyType" strings beyond defined values.
+  Credit to Trung0246
+  """
+
+  def __getitem__(self, index):
+    if index > len(self) - 1:
+      return AnyType("*")
+    return super().__getitem__(index)
diff --git a/custom_nodes/rgthree-comfy/py/utils_graph.py b/custom_nodes/rgthree-comfy/py/utils_graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..0982a55cbc105b297c970770227d7da9be1f7abf
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/utils_graph.py
@@ -0,0 +1,20 @@
+from .utils import get_dict_value
+
+
+def get_worflow_node(extra_pnginfo, node_id: str, default=None):
+  # First, break out of any subgraphs
+  node_ids = str(node_id).split(':')
+  workflow_nodes = get_dict_value(extra_pnginfo, 'workflow.nodes', default=[])
+  workflow_subgraphs = get_dict_value(extra_pnginfo, 'workflow.definitions.subgraphs', default=[])
+  nodes_list = workflow_nodes
+  found = None
+  for individual_node_id in node_ids:
+    found = next((n for n in nodes_list if str(n['id']) == individual_node_id), None)
+    if isinstance(found, dict) and 'type' in found:
+      # Are we a subgraph? Right now, subgraph types are a UUID that exists as an id in the
+      # aubgraphs list. But, rather than check if we're a UUID, let's just check if it exists
+      # anyway, that when if (when) Comfy changes the id structure we'll keep working.
+      subgraph = next((n for n in workflow_subgraphs if str(n['id']) == found['type']), None)
+      if isinstance(subgraph, dict) and 'nodes' in subgraph:
+        nodes_list = subgraph['nodes']
+  return found if found is not None else default
diff --git a/custom_nodes/rgthree-comfy/py/utils_userdata.py b/custom_nodes/rgthree-comfy/py/utils_userdata.py
new file mode 100644
index 0000000000000000000000000000000000000000..b10b757ab503908fb03ae6df97ef775809a3f40d
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/py/utils_userdata.py
@@ -0,0 +1,50 @@
+import os
+
+from .utils import load_json_file, path_exists, save_json_file
+
+THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+USERDATA = os.path.join(THIS_DIR, '..', 'userdata')
+
+
+def read_userdata_file(rel_path: str):
+  """Reads a file from the userdata directory."""
+  file_path = clean_path(rel_path)
+  if path_exists(file_path):
+    with open(file_path, 'r', encoding='UTF-8') as file:
+      return file.read()
+  return None
+
+
+def save_userdata_file(rel_path: str, content: str):
+  """Saves a file from the userdata directory."""
+  file_path = clean_path(rel_path)
+  with open(file_path, 'w+', encoding='UTF-8') as file:
+    file.write(content)
+
+
+def delete_userdata_file(rel_path: str):
+  """Deletes a file from the userdata directory."""
+  file_path = clean_path(rel_path)
+  if os.path.isfile(file_path):
+    os.remove(file_path)
+
+
+def read_userdata_json(rel_path: str):
+  """Reads a json file from the userdata directory."""
+  file_path = clean_path(rel_path)
+  return load_json_file(file_path)
+
+
+def save_userdata_json(rel_path: str, data: dict):
+  """Saves a json file from the userdata directory."""
+  file_path = clean_path(rel_path)
+  return save_json_file(file_path, data)
+
+
+def clean_path(rel_path: str):
+  """Cleans a relative path by splitting on forward slash and os.path.joining."""
+  cleaned = USERDATA
+  paths = rel_path.split('/')
+  for path in paths:
+    cleaned = os.path.join(cleaned, path)
+  return cleaned
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/any_switch.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/any_switch.ts
new file mode 100644
index 0000000000000000000000000000000000000000..9e72642b60436236eccd3177cb8e2573f00ac33c
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/any_switch.ts
@@ -0,0 +1,103 @@
+import type {
+  ComfyApp,
+  INodeInputSlot,
+  INodeOutputSlot,
+  LGraphNode,
+  LLink,
+} from "@comfyorg/frontend";
+import type {ComfyNodeDef} from "typings/comfy.js";
+
+import {app} from "scripts/app.js";
+import {IoDirection, addConnectionLayoutSupport, followConnectionUntilType} from "./utils.js";
+import {RgthreeBaseServerNode} from "./base_node.js";
+import {NodeTypesString} from "./constants.js";
+import {removeUnusedInputsFromEnd} from "./utils_inputs_outputs.js";
+import {debounce} from "rgthree/common/shared_utils.js";
+
+class RgthreeAnySwitch extends RgthreeBaseServerNode {
+  static override title = NodeTypesString.ANY_SWITCH;
+  static override type = NodeTypesString.ANY_SWITCH;
+  static comfyClass = NodeTypesString.ANY_SWITCH;
+
+  private stabilizeBound = this.stabilize.bind(this);
+  private nodeType: string | string[] | null = null;
+
+  constructor(title = RgthreeAnySwitch.title) {
+    super(title);
+    // Adding five. Note, configure will add as many as was in the stored workflow automatically.
+    this.addAnyInput(5);
+  }
+
+  override onConnectionsChange(
+    type: number,
+    slotIndex: number,
+    isConnected: boolean,
+    linkInfo: LLink,
+    ioSlot: INodeOutputSlot | INodeInputSlot,
+  ) {
+    super.onConnectionsChange?.(type, slotIndex, isConnected, linkInfo, ioSlot);
+    this.scheduleStabilize();
+  }
+
+  onConnectionsChainChange() {
+    this.scheduleStabilize();
+  }
+
+  scheduleStabilize(ms = 64) {
+    return debounce(this.stabilizeBound, ms);
+  }
+
+  private addAnyInput(num = 1) {
+    for (let i = 0; i < num; i++) {
+      this.addInput(
+        `any_${String(this.inputs.length + 1).padStart(2, "0")}`,
+        (this.nodeType || "*") as string,
+      );
+    }
+  }
+
+  stabilize() {
+    // First, clean up the dynamic number of inputs.
+    removeUnusedInputsFromEnd(this, 4);
+    this.addAnyInput();
+
+    // We prefer the inputs, then the output.
+    let connectedType = followConnectionUntilType(this, IoDirection.INPUT, undefined, true);
+    if (!connectedType) {
+      connectedType = followConnectionUntilType(this, IoDirection.OUTPUT, undefined, true);
+    }
+    // TODO: What this doesn't do is broadcast to other nodes when its type changes. Reroute node
+    // does, but, for now, if this was connected to another Any Switch, say, the second one wouldn't
+    // change its type when the first does. The user would need to change the connections.
+    this.nodeType = connectedType?.type || "*";
+    for (const input of this.inputs) {
+      input.type = this.nodeType as string; // So, types can indeed be arrays,,
+    }
+    for (const output of this.outputs) {
+      output.type = this.nodeType as string; // So, types can indeed be arrays,,
+      output.label =
+        output.type === "RGTHREE_CONTEXT"
+          ? "CONTEXT"
+          : Array.isArray(this.nodeType) || this.nodeType.includes(",")
+            ? connectedType?.label || String(this.nodeType)
+            : String(this.nodeType);
+    }
+  }
+
+  static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, RgthreeAnySwitch);
+    addConnectionLayoutSupport(RgthreeAnySwitch, app, [
+      ["Left", "Right"],
+      ["Right", "Left"],
+    ]);
+  }
+}
+
+app.registerExtension({
+  name: "rgthree.AnySwitch",
+  async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: any, app: ComfyApp) {
+    if (nodeData.name === "Any Switch (rgthree)") {
+      RgthreeAnySwitch.setUp(nodeType, nodeData);
+    }
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/base_any_input_connected_node.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/base_any_input_connected_node.ts
new file mode 100644
index 0000000000000000000000000000000000000000..d54279a949528162111c57c0c732943cb53f30fc
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/base_any_input_connected_node.ts
@@ -0,0 +1,354 @@
+import type {
+  Vector2,
+  LLink,
+  INodeInputSlot,
+  INodeOutputSlot,
+  LGraphNode as TLGraphNode,
+  ISlotType,
+  ConnectByTypeOptions,
+  TWidgetType,
+  IWidgetOptions,
+  IWidget,
+  IBaseWidget,
+  WidgetTypeMap,
+} from "@comfyorg/frontend";
+
+import {app} from "scripts/app.js";
+import {RgthreeBaseVirtualNode} from "./base_node.js";
+import {rgthree} from "./rgthree.js";
+import {
+  PassThroughFollowing,
+  addConnectionLayoutSupport,
+  addMenuItem,
+  getConnectedInputNodes,
+  getConnectedInputNodesAndFilterPassThroughs,
+  getConnectedOutputNodes,
+  getConnectedOutputNodesAndFilterPassThroughs,
+} from "./utils.js";
+
+/**
+ * A Virtual Node that allows any node's output to connect to it.
+ */
+export class BaseAnyInputConnectedNode extends RgthreeBaseVirtualNode {
+  override isVirtualNode = true;
+
+  /**
+   * Whether inputs show the immediate nodes, or follow and show connected nodes through
+   * passthrough nodes.
+   */
+  readonly inputsPassThroughFollowing: PassThroughFollowing = PassThroughFollowing.NONE;
+
+  debouncerTempWidth: number = 0;
+  schedulePromise: Promise<void> | null = null;
+
+  constructor(title = BaseAnyInputConnectedNode.title) {
+    super(title);
+  }
+
+  override onConstructed() {
+    this.addInput("", "*");
+    return super.onConstructed();
+  }
+
+  override clone() {
+    const cloned = super.clone()!;
+    // Copying to clipboard (and also, creating node templates) work by cloning nodes and, for some
+    // reason, it manually manipulates the cloned data. So, we want to keep the present input slots
+    // so if it's pasted/templatized the data is correct. Otherwise, clear the inputs and so the new
+    // node is ready to go, fresh.
+    if (!rgthree.canvasCurrentlyCopyingToClipboardWithMultipleNodes) {
+      while (cloned.inputs.length > 1) {
+        cloned.removeInput(cloned.inputs.length - 1);
+      }
+      if (cloned.inputs[0]) {
+        cloned.inputs[0].label = "";
+      }
+    }
+    return cloned;
+  }
+
+  /**
+   * Schedules a promise to run a stabilization, debouncing duplicate requests.
+   */
+  scheduleStabilizeWidgets(ms = 100) {
+    if (!this.schedulePromise) {
+      this.schedulePromise = new Promise((resolve) => {
+        setTimeout(() => {
+          this.schedulePromise = null;
+          this.doStablization();
+          resolve();
+        }, ms);
+      });
+    }
+    return this.schedulePromise;
+  }
+
+  /**
+   * Ensures we have at least one empty input at the end, returns true if changes were made, or false
+   * if no changes were needed.
+   */
+  private stabilizeInputsOutputs(): boolean {
+    let changed = false;
+    const hasEmptyInput = !this.inputs[this.inputs.length - 1]?.link;
+    if (!hasEmptyInput) {
+      this.addInput("", "*");
+      changed = true;
+    }
+    for (let index = this.inputs.length - 2; index >= 0; index--) {
+      const input = this.inputs[index]!;
+      if (!input.link) {
+        this.removeInput(index);
+        changed = true;
+      } else {
+        const node = getConnectedInputNodesAndFilterPassThroughs(
+          this,
+          this,
+          index,
+          this.inputsPassThroughFollowing,
+        )[0];
+        const newName = node?.title || "";
+        if (input.name !== newName) {
+          input.name = node?.title || "";
+          changed = true;
+        }
+      }
+    }
+    return changed;
+  }
+
+  /**
+   * Stabilizes the node's inputs and widgets.
+   */
+  private doStablization() {
+    if (!this.graph) {
+      return;
+    }
+    let dirty = false;
+
+    // When we add/remove widgets, litegraph is going to mess up the size, so we
+    // store it so we can retrieve it in computeSize. Hacky..
+    (this as any)._tempWidth = this.size[0];
+
+    dirty = this.stabilizeInputsOutputs();
+    const linkedNodes = getConnectedInputNodesAndFilterPassThroughs(this);
+    dirty = this.handleLinkedNodesStabilization(linkedNodes) || dirty;
+
+    // Only mark dirty if something's changed.
+    if (dirty) {
+      this.graph.setDirtyCanvas(true, true);
+    }
+
+    // Schedule another stabilization in the future.
+    this.scheduleStabilizeWidgets(500);
+  }
+
+  /**
+   * Handles stabilization of linked nodes. To be overridden. Should return true if changes were
+   * made, or false if no changes were needed.
+   */
+  handleLinkedNodesStabilization(linkedNodes: TLGraphNode[]): boolean {
+    linkedNodes; // No-op, but makes overridding in VSCode cleaner.
+    throw new Error("handleLinkedNodesStabilization should be overridden.");
+  }
+
+  onConnectionsChainChange() {
+    this.scheduleStabilizeWidgets();
+  }
+
+  override onConnectionsChange(
+    type: number,
+    index: number,
+    connected: boolean,
+    linkInfo: LLink,
+    ioSlot: INodeOutputSlot | INodeInputSlot,
+  ) {
+    super.onConnectionsChange &&
+      super.onConnectionsChange(type, index, connected, linkInfo, ioSlot);
+    if (!linkInfo) return;
+    // Follow outputs to see if we need to trigger an onConnectionChange.
+    const connectedNodes = getConnectedOutputNodesAndFilterPassThroughs(this);
+    for (const node of connectedNodes) {
+      if ((node as BaseAnyInputConnectedNode).onConnectionsChainChange) {
+        (node as BaseAnyInputConnectedNode).onConnectionsChainChange();
+      }
+    }
+    this.scheduleStabilizeWidgets();
+  }
+
+  override removeInput(slot: number) {
+    (this as any)._tempWidth = this.size[0];
+    return super.removeInput(slot);
+  }
+
+  override addInput<TProperties extends Partial<INodeInputSlot>>(
+    name: string,
+    type: ISlotType,
+    extra_info?: TProperties | undefined,
+  ): INodeInputSlot & TProperties {
+    (this as any)._tempWidth = this.size[0];
+    return super.addInput(name, type, extra_info);
+  }
+
+  override addWidget<Type extends TWidgetType, TValue extends WidgetTypeMap[Type]["value"]>(
+    type: Type,
+    name: string,
+    value: TValue,
+    callback: IBaseWidget["callback"] | string | null,
+    options?: IWidgetOptions | string,
+  ):
+    | IBaseWidget<string | number | boolean | object | undefined, string, IWidgetOptions<unknown>>
+    | WidgetTypeMap[Type] {
+    (this as any)._tempWidth = this.size[0];
+    return super.addWidget(type, name, value, callback, options);
+  }
+
+  override removeWidget(widget: IBaseWidget | IWidget | number | undefined): void {
+    (this as any)._tempWidth = this.size[0];
+    super.removeWidget(widget);
+  }
+
+  override computeSize(out: Vector2) {
+    let size = super.computeSize(out);
+    if ((this as any)._tempWidth) {
+      size[0] = (this as any)._tempWidth;
+      // We sometimes get repeated calls to compute size, so debounce before clearing.
+      this.debouncerTempWidth && clearTimeout(this.debouncerTempWidth);
+      this.debouncerTempWidth = setTimeout(() => {
+        (this as any)._tempWidth = null;
+      }, 32);
+    }
+    // If we're collapsed, then subtract the total calculated height of the other input slots.
+    if (this.properties["collapse_connections"]) {
+      const rows = Math.max(this.inputs?.length || 0, this.outputs?.length || 0, 1) - 1;
+      size[1] = size[1] - rows * LiteGraph.NODE_SLOT_HEIGHT;
+    }
+    setTimeout(() => {
+      this.graph?.setDirtyCanvas(true, true);
+    }, 16);
+    return size;
+  }
+
+  /**
+   * When we connect our output, check our inputs and make sure we're not trying to connect a loop.
+   */
+  override onConnectOutput(
+    outputIndex: number,
+    inputType: string | -1,
+    inputSlot: INodeInputSlot,
+    inputNode: TLGraphNode,
+    inputIndex: number,
+  ): boolean {
+    let canConnect = true;
+    if (super.onConnectOutput) {
+      canConnect = super.onConnectOutput(outputIndex, inputType, inputSlot, inputNode, inputIndex);
+    }
+    if (canConnect) {
+      const nodes = getConnectedInputNodes(this); // We want passthrough nodes, since they will loop.
+      if (nodes.includes(inputNode)) {
+        alert(
+          `Whoa, whoa, whoa. You've just tried to create a connection that loops back on itself, ` +
+            `a situation that could create a time paradox, the results of which could cause a ` +
+            `chain reaction that would unravel the very fabric of the space time continuum, ` +
+            `and destroy the entire universe!`,
+        );
+        canConnect = false;
+      }
+    }
+    return canConnect;
+  }
+
+  override onConnectInput(
+    inputIndex: number,
+    outputType: string | -1,
+    outputSlot: INodeOutputSlot,
+    outputNode: TLGraphNode,
+    outputIndex: number,
+  ): boolean {
+    let canConnect = true;
+    if (super.onConnectInput) {
+      canConnect = super.onConnectInput(
+        inputIndex,
+        outputType,
+        outputSlot,
+        outputNode,
+        outputIndex,
+      );
+    }
+    if (canConnect) {
+      const nodes = getConnectedOutputNodes(this); // We want passthrough nodes, since they will loop.
+      if (nodes.includes(outputNode)) {
+        alert(
+          `Whoa, whoa, whoa. You've just tried to create a connection that loops back on itself, ` +
+            `a situation that could create a time paradox, the results of which could cause a ` +
+            `chain reaction that would unravel the very fabric of the space time continuum, ` +
+            `and destroy the entire universe!`,
+        );
+        canConnect = false;
+      }
+    }
+    return canConnect;
+  }
+
+  /**
+   * If something is dropped on us, just add it to the bottom. onConnectInput should already cancel
+   * if it's disallowed.
+   */
+  override connectByTypeOutput(
+    slot: number | string,
+    sourceNode: TLGraphNode,
+    sourceSlotType: ISlotType,
+    optsIn?: ConnectByTypeOptions,
+  ): LLink | null {
+    const lastInput = this.inputs[this.inputs.length - 1];
+    if (!lastInput?.link && lastInput?.type === "*") {
+      var sourceSlot = sourceNode.findOutputSlotByType(sourceSlotType, false, true);
+      return sourceNode.connect(sourceSlot, this, slot);
+    }
+    return super.connectByTypeOutput(slot, sourceNode, sourceSlotType, optsIn);
+  }
+
+  static override setUp() {
+    super.setUp();
+    addConnectionLayoutSupport(this, app, [
+      ["Left", "Right"],
+      ["Right", "Left"],
+    ]);
+    addMenuItem(this, app, {
+      name: (node) =>
+        `${node.properties?.["collapse_connections"] ? "Show" : "Collapse"} Connections`,
+      property: "collapse_connections",
+      prepareValue: (_value, node) => !node.properties?.["collapse_connections"],
+      callback: (_node) => {
+        app.canvas.getCurrentGraph()?.setDirtyCanvas(true, true);
+      },
+    });
+  }
+}
+
+// Ok, hack time! LGraphNode's connectByType is powerful, but for our nodes, that have multiple "*"
+// input types, it seems it just takes the first one, and disconnects it. I'd rather we don't do
+// that and instead take the next free one. If that doesn't work, then we'll give it to the old
+// method.
+const oldLGraphNodeConnectByType = LGraphNode.prototype.connectByType;
+LGraphNode.prototype.connectByType = function connectByType(
+  slot: number | string,
+  targetNode: TLGraphNode,
+  targetSlotType: ISlotType,
+  optsIn?: ConnectByTypeOptions,
+): LLink | null {
+  // If we're dropping on a node, and the last input is free and an "*" type, then connect there
+  // first...
+  if (targetNode.inputs) {
+    for (const [index, input] of targetNode.inputs.entries()) {
+      if (!input.link && input.type === "*") {
+        this.connect(slot, targetNode, index);
+        return null;
+      }
+    }
+  }
+  return (
+    (oldLGraphNodeConnectByType &&
+      oldLGraphNodeConnectByType.call(this, slot, targetNode, targetSlotType, optsIn)) ||
+    null
+  );
+};
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/base_node.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/base_node.ts
new file mode 100644
index 0000000000000000000000000000000000000000..be4e1be533355e24b47acb73cb5d03b988532150
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/base_node.ts
@@ -0,0 +1,504 @@
+import type {
+  IWidget,
+  LGraphCanvas,
+  IContextMenuValue,
+  IFoundSlot,
+  LGraphEventMode,
+  LGraphNodeConstructor,
+  ISerialisedNode,
+  IBaseWidget,
+} from "@comfyorg/frontend";
+import type {ComfyNodeDef} from "typings/comfy.js";
+import type {RgthreeBaseServerNodeConstructor} from "typings/rgthree.js";
+
+import {app} from "scripts/app.js";
+import {ComfyWidgets} from "scripts/widgets.js";
+import {SERVICE as KEY_EVENT_SERVICE} from "./services/key_events_services.js";
+import {LogLevel, rgthree} from "./rgthree.js";
+import {addHelpMenuItem} from "./utils.js";
+import {RgthreeHelpDialog} from "rgthree/common/dialog.js";
+import {
+  importIndividualNodesInnerOnDragDrop,
+  importIndividualNodesInnerOnDragOver,
+} from "./feature_import_individual_nodes.js";
+import {defineProperty, moveArrayItem} from "rgthree/common/shared_utils.js";
+
+/**
+ * A base node with standard methods, directly extending the LGraphNode.
+ * This can be used for ui-nodes and a further base for server nodes.
+ */
+export abstract class RgthreeBaseNode extends LGraphNode {
+  /**
+   * Action strings that can be exposed and triggered from other nodes, like Fast Actions Button.
+   */
+  static exposedActions: string[] = [];
+
+  static override title: string = "__NEED_CLASS_TITLE__";
+  static override type: string = "__NEED_CLASS_TYPE__";
+  static override category = "rgthree";
+  static _category = "rgthree"; // `category` seems to get reset by comfy, so reset to this after.
+
+  /** Our constructor ensures there's a widget array, so we get rid of the nullability. */
+  override widgets!: IWidget[];
+
+  /**
+   * The comfyClass is property ComfyUI and extensions may care about, even through it is only for
+   * server nodes. RgthreeBaseServerNode below overrides this with the expected value and we just
+   * set it here so extensions that are none the wiser don't break on some unchecked string method
+   * call on an undefined calue.
+   */
+  override comfyClass: string = "__NEED_COMFY_CLASS__";
+
+  /** Used by the ComfyUI-Manager badge. */
+  readonly nickname = "rgthree";
+  /** Are we a virtual node? */
+  override readonly isVirtualNode: boolean = false;
+  /** Are we able to be dropped on (if config is enabled too). */
+  isDropEnabled = false;
+  /** A state member determining if we're currently removed. */
+  removed = false;
+  /** A state member determining if we're currently "configuring."" */
+  configuring = false;
+  /** A temporary width value that can be used to ensure compute size operates correctly. */
+  _tempWidth = 0;
+
+  /** Private Mode member so we can override the setter/getter and call an `onModeChange`. */
+  private rgthree_mode?: LGraphEventMode;
+
+  /** An internal bool set when `onConstructed` is run. */
+  private __constructed__ = false;
+  /** The help dialog. */
+  private helpDialog: RgthreeHelpDialog | null = null;
+
+  constructor(title = RgthreeBaseNode.title, skipOnConstructedCall = true) {
+    super(title);
+    if (title == "__NEED_CLASS_TITLE__") {
+      throw new Error("RgthreeBaseNode needs overrides.");
+    }
+    // Ensure these exist since some other extensions will break in their onNodeCreated.
+    this.widgets = this.widgets || [];
+    this.properties = this.properties || {};
+
+    // Some checks we want to do after we're constructed, looking that data is set correctly and
+    // that our base's `onConstructed` was called (if not, set a DEV warning).
+    setTimeout(() => {
+      // Check we have a comfyClass defined.
+      if (this.comfyClass == "__NEED_COMFY_CLASS__") {
+        throw new Error("RgthreeBaseNode needs a comfy class override.");
+      }
+      if (this.constructor.type == "__NEED_CLASS_TYPE__") {
+        throw new Error("RgthreeBaseNode needs overrides.");
+      }
+      // Ensure we've called onConstructed before we got here.
+      this.checkAndRunOnConstructed();
+    });
+
+    defineProperty(this, "mode", {
+      get: () => {
+        return this.rgthree_mode;
+      },
+      set: (mode: LGraphEventMode) => {
+        if (this.rgthree_mode != mode) {
+          const oldMode = this.rgthree_mode;
+          this.rgthree_mode = mode;
+          this.onModeChange(oldMode, mode);
+        }
+      },
+    });
+  }
+
+  private checkAndRunOnConstructed() {
+    if (!this.__constructed__) {
+      this.onConstructed();
+      const [n, v] = rgthree.logger.logParts(
+        LogLevel.DEV,
+        `[RgthreeBaseNode] Child class did not call onConstructed for "${this.type}.`,
+      );
+      console[n]?.(...v);
+    }
+    return this.__constructed__;
+  }
+
+  override onDragOver(e: DragEvent): boolean {
+    if (!this.isDropEnabled) return false;
+    return importIndividualNodesInnerOnDragOver(this, e);
+  }
+
+  override async onDragDrop(e: DragEvent): Promise<boolean> {
+    if (!this.isDropEnabled) return false;
+    return importIndividualNodesInnerOnDragDrop(this, e);
+  }
+
+  /**
+   * When a node is finished with construction, we must call this. Failure to do so will result in
+   * an error message from the timeout in this base class. This is broken out and becomes the
+   * responsibility of the child class because
+   */
+  onConstructed() {
+    if (this.__constructed__) return false;
+    // This is kinda a hack, but if this.type is still null, then set it to undefined to match.
+    this.type = this.type ?? undefined;
+    this.__constructed__ = true;
+    rgthree.invokeExtensionsAsync("nodeCreated", this);
+    return this.__constructed__;
+  }
+
+  override configure(info: ISerialisedNode): void {
+    this.configuring = true;
+    super.configure(info);
+    // Fix https://github.com/comfyanonymous/ComfyUI/issues/1448 locally.
+    // Can removed when fixed and adopted.
+    for (const w of this.widgets || []) {
+      w.last_y = w.last_y || 0;
+    }
+    this.configuring = false;
+  }
+
+  /**
+   * Override clone for, at the least, deep-copying properties.
+   */
+  override clone() {
+    const cloned = super.clone()!;
+    // This is wild, but LiteGraph doesn't deep clone data, so we will. We'll use structured clone,
+    // which most browsers in 2022 support, but but we'll check.
+    if (cloned?.properties && !!window.structuredClone) {
+      cloned.properties = structuredClone(cloned.properties);
+    }
+    // [🤮] https://github.com/Comfy-Org/ComfyUI_frontend/issues/5037
+    // ComfyUI started throwing errors when some of our nodes wanted to remove inputs when cloning
+    // (like our dynamic inputs) because the disconnect method that's automatically called assumes
+    // there should be a graph. For now, I _think_ we can simply assign the current graph to avoid
+    // the error, which would then be overwritten when placed...
+    cloned.graph = this.graph;
+    return cloned;
+  }
+
+  /** When a mode change, we want all connected nodes to match. */
+  onModeChange(from: LGraphEventMode | undefined, to: LGraphEventMode) {
+    // Override
+  }
+
+  /**
+   * Given a string, do something. At the least, handle any `exposedActions` that may be called and
+   * passed into from other nodes, like Fast Actions Button
+   */
+  async handleAction(action: string) {
+    action; // No-op. Should be overridden but OK if not.
+  }
+
+  /**
+   * This didn't exist in LiteGraph/Comfy, but now it's added. Ours was a bit more flexible, though.
+   */
+  override removeWidget(widget: IBaseWidget | IWidget | number | undefined): void {
+    if (typeof widget === "number") {
+      widget = this.widgets[widget];
+    }
+    if (!widget) return;
+
+    // Comfy added their own removeWidget, but it's not fully rolled out to stable, so keep our
+    // original implementation.
+    // TODO: Actually, scratch that. The ComfyUI impl doesn't call widtget.onRemove?.() and so
+    // we shouldn't switch to it yet. See: https://github.com/Comfy-Org/ComfyUI_frontend/issues/5090
+    const canUseComfyUIRemoveWidget = false;
+    if (canUseComfyUIRemoveWidget && typeof super.removeWidget === 'function') {
+      super.removeWidget(widget as IBaseWidget);
+    } else {
+      const index = this.widgets.indexOf(widget as IWidget);
+      if (index > -1) {
+        this.widgets.splice(index, 1);
+      }
+      widget.onRemove?.();
+    }
+  }
+
+  /**
+   * Replaces an existing widget.
+   */
+  replaceWidget(widgetOrSlot: IWidget | number | undefined, newWidget: IWidget) {
+    let index = null;
+    if (widgetOrSlot) {
+      index = typeof widgetOrSlot === "number" ? widgetOrSlot : this.widgets.indexOf(widgetOrSlot);
+      this.removeWidget(this.widgets[index]!);
+    }
+    index = index != null ? index : this.widgets.length - 1;
+    if (this.widgets.includes(newWidget)) {
+      moveArrayItem(this.widgets, newWidget, index);
+    } else {
+      this.widgets.splice(index, 0, newWidget);
+    }
+  }
+
+  /**
+   * A default version of the logive when a node does not set `getSlotMenuOptions`. This is
+   * necessary because child nodes may want to define getSlotMenuOptions but LiteGraph then won't do
+   * it's default logic. This bakes it so child nodes can call this instead (and this doesn't set
+   * getSlotMenuOptions for all child nodes in case it doesn't exist).
+   */
+  defaultGetSlotMenuOptions(slot: IFoundSlot): IContextMenuValue[] {
+    const menu_info: IContextMenuValue[] = [];
+    if (slot?.output?.links?.length) {
+      menu_info.push({content: "Disconnect Links", slot});
+    }
+    let inputOrOutput = slot.input || slot.output;
+    if (inputOrOutput) {
+      if (inputOrOutput.removable) {
+        menu_info.push(
+          inputOrOutput.locked ? {content: "Cannot remove"} : {content: "Remove Slot", slot},
+        );
+      }
+      if (!inputOrOutput.nameLocked) {
+        menu_info.push({content: "Rename Slot", slot});
+      }
+    }
+    return menu_info;
+  }
+
+  override onRemoved(): void {
+    super.onRemoved?.();
+    this.removed = true;
+  }
+
+  static setUp<T extends RgthreeBaseNode>(...args: any[]) {
+    // No-op.
+  }
+
+  /**
+   * A function to provide help text to be overridden.
+   */
+  getHelp() {
+    return "";
+  }
+
+  showHelp() {
+    const help = this.getHelp() || (this.constructor as any).help;
+    if (help) {
+      this.helpDialog = new RgthreeHelpDialog(this, help).show();
+      this.helpDialog.addEventListener("close", (e) => {
+        this.helpDialog = null;
+      });
+    }
+  }
+
+  override onKeyDown(event: KeyboardEvent): void {
+    KEY_EVENT_SERVICE.handleKeyDownOrUp(event);
+    if (event.key == "?" && !this.helpDialog) {
+      this.showHelp();
+    }
+  }
+
+  override onKeyUp(event: KeyboardEvent): void {
+    KEY_EVENT_SERVICE.handleKeyDownOrUp(event);
+  }
+
+  override getExtraMenuOptions(
+    canvas: LGraphCanvas,
+    options: (IContextMenuValue<unknown> | null)[],
+  ): (IContextMenuValue<unknown> | null)[] {
+    // Some other extensions override getExtraMenuOptions on the nodeType as it comes through from
+    // the server, so we can call out to that if we don't have our own.
+    if (super.getExtraMenuOptions) {
+      super.getExtraMenuOptions?.apply(this, [canvas, options]);
+    } else if (this.constructor.nodeType?.prototype?.getExtraMenuOptions) {
+      this.constructor.nodeType?.prototype?.getExtraMenuOptions?.apply(this, [canvas, options]);
+    }
+    // If we have help content, then add a menu item.
+    const help = this.getHelp() || (this.constructor as any).help;
+    if (help) {
+      addHelpMenuItem(this, help, options);
+    }
+    return [];
+  }
+}
+
+/**
+ * A virtual node. Right now, this is just a wrapper for RgthreeBaseNode (which was the initial
+ * base virtual node).
+ */
+export class RgthreeBaseVirtualNode extends RgthreeBaseNode {
+  override isVirtualNode = true;
+
+  constructor(title = RgthreeBaseNode.title) {
+    super(title, false);
+  }
+
+  static override setUp() {
+    if (!this.type) {
+      throw new Error(`Missing type for RgthreeBaseVirtualNode: ${this.title}`);
+    }
+    LiteGraph.registerNodeType(this.type, this);
+    if (this._category) {
+      this.category = this._category;
+    }
+  }
+}
+
+/**
+ * A base node with standard methods, extending the LGraphNode.
+ * This is somewhat experimental, but if comfyui is going to keep breaking widgets and inputs, it
+ * seems safer than NOT overriding.
+ */
+export class RgthreeBaseServerNode extends RgthreeBaseNode {
+  static nodeType: LGraphNodeConstructor | null = null;
+  static nodeData: ComfyNodeDef | null = null;
+
+  // Drop is enabled by default for server nodes.
+  override isDropEnabled = true;
+
+  constructor(title: string) {
+    super(title, true);
+    this.serialize_widgets = true;
+    this.setupFromServerNodeData();
+    this.onConstructed();
+  }
+
+  getWidgets() {
+    return ComfyWidgets;
+  }
+
+  /**
+   * This takes the server data and builds out the inputs, outputs and widgets. It's similar to the
+   * ComfyNode constructor in registerNodes in ComfyUI's app.js, but is more stable and thus
+   * shouldn't break as often when it modifyies widgets and types.
+   */
+  async setupFromServerNodeData() {
+    const nodeData = (this.constructor as any).nodeData;
+    if (!nodeData) {
+      throw Error("No node data");
+    }
+
+    // Necessary for serialization so Comfy backend can check types.
+    // Serialized as `class_type`. See app.js#graphToPrompt
+    this.comfyClass = nodeData.name;
+
+    let inputs = nodeData["input"]["required"];
+    if (nodeData["input"]["optional"] != undefined) {
+      inputs = Object.assign({}, inputs, nodeData["input"]["optional"]);
+    }
+
+    const WIDGETS = this.getWidgets();
+
+    const config: {minWidth: number; minHeight: number; widget?: null | {options: any}} = {
+      minWidth: 1,
+      minHeight: 1,
+      widget: null,
+    };
+    for (const inputName in inputs) {
+      const inputData = inputs[inputName];
+      const type = inputData[0];
+      // If we're forcing the input, just do it now and forget all that widget stuff.
+      // This is one of the differences from ComfyNode and provides smoother experience for inputs
+      // that are going to remain inputs anyway.
+      // Also, it fixes https://github.com/comfyanonymous/ComfyUI/issues/1404 (for rgthree nodes)
+      if (inputData[1]?.forceInput) {
+        this.addInput(inputName, type);
+      } else {
+        let widgetCreated = true;
+        if (Array.isArray(type)) {
+          // Enums
+          Object.assign(config, WIDGETS.COMBO(this, inputName, inputData, app) || {});
+        } else if (`${type}:${inputName}` in WIDGETS) {
+          // Support custom widgets by Type:Name
+          Object.assign(
+            config,
+            WIDGETS[`${type}:${inputName}`]!(this, inputName, inputData, app) || {},
+          );
+        } else if (type in WIDGETS) {
+          // Standard type widgets
+          Object.assign(config, WIDGETS[type]!(this, inputName, inputData, app) || {});
+        } else {
+          // Node connection inputs
+          this.addInput(inputName, type);
+          widgetCreated = false;
+        }
+
+        // Don't actually need this right now, but ported it over from ComfyWidget.
+        if (widgetCreated && inputData[1]?.forceInput && config?.widget) {
+          if (!config.widget.options) config.widget.options = {};
+          config.widget.options.forceInput = inputData[1].forceInput;
+        }
+        if (widgetCreated && inputData[1]?.defaultInput && config?.widget) {
+          if (!config.widget.options) config.widget.options = {};
+          config.widget.options.defaultInput = inputData[1].defaultInput;
+        }
+      }
+    }
+
+    for (const o in nodeData["output"]) {
+      let output = nodeData["output"][o];
+      if (output instanceof Array) output = "COMBO";
+      const outputName = nodeData["output_name"][o] || output;
+      const outputShape = nodeData["output_is_list"][o]
+        ? LiteGraph.GRID_SHAPE
+        : LiteGraph.CIRCLE_SHAPE;
+      this.addOutput(outputName, output, {shape: outputShape});
+    }
+
+    const s = this.computeSize();
+    // Sometime around v1.12.6 this broke as `minWidth` and `minHeight` were being explicitly set
+    // to `undefined` in the above Object.assign call (specifically for `WIDGETS[INT]`. We can avoid
+    // that by ensureing we're at a number in that case.
+    // See https://github.com/Comfy-Org/ComfyUI_frontend/issues/3045
+    s[0] = Math.max(config.minWidth ?? 1, s[0] * 1.5);
+    s[1] = Math.max(config.minHeight ?? 1, s[1]);
+    this.size = s;
+    this.serialize_widgets = true;
+  }
+
+  static __registeredForOverride__: boolean = false;
+  static registerForOverride(
+    comfyClass: typeof LGraphNode,
+    nodeData: ComfyNodeDef,
+    rgthreeClass: RgthreeBaseServerNodeConstructor,
+  ) {
+    if (OVERRIDDEN_SERVER_NODES.has(comfyClass)) {
+      throw Error(
+        `Already have a class to override ${
+          comfyClass.type || comfyClass.name || comfyClass.title
+        }`,
+      );
+    }
+    OVERRIDDEN_SERVER_NODES.set(comfyClass, rgthreeClass);
+    // Mark the rgthreeClass as `__registeredForOverride__` because ComfyUI will repeatedly call
+    // this and certain setups will only want to setup once (like adding context menus, etc).
+    if (!rgthreeClass.__registeredForOverride__) {
+      rgthreeClass.__registeredForOverride__ = true;
+      rgthreeClass.nodeType = comfyClass;
+      rgthreeClass.nodeData = nodeData;
+      rgthreeClass.onRegisteredForOverride(comfyClass, rgthreeClass);
+    }
+  }
+
+  static onRegisteredForOverride(comfyClass: any, rgthreeClass: any) {
+    // To be overridden
+  }
+}
+
+/**
+ * Keeps track of the rgthree-comfy nodes that come from the server (and want to be ComfyNodes) that
+ * we override into a own, more flexible and cleaner nodes.
+ */
+const OVERRIDDEN_SERVER_NODES = new Map<any, any>();
+
+const oldregisterNodeType = LiteGraph.registerNodeType;
+/**
+ * ComfyUI calls registerNodeType with its ComfyNode, but we don't trust that will remain stable, so
+ * we need to identify it, intercept it, and supply our own class for the node.
+ */
+LiteGraph.registerNodeType = async function (nodeId: string, baseClass: any) {
+  const clazz = OVERRIDDEN_SERVER_NODES.get(baseClass) || baseClass;
+  if (clazz !== baseClass) {
+    const classLabel = clazz.type || clazz.name || clazz.title;
+    const [n, v] = rgthree.logger.logParts(
+      LogLevel.DEBUG,
+      `${nodeId}: replacing default ComfyNode implementation with custom ${classLabel} class.`,
+    );
+    console[n]?.(...v);
+    // Note, we don't currently call our rgthree.invokeExtensionsAsync w/ beforeRegisterNodeDef as
+    // this runs right after that. However, this does mean that extensions cannot actually change
+    // anything about overriden server rgthree nodes in their beforeRegisterNodeDef (as when comfy
+    // calls it, it's for the wrong ComfyNode class). Calling it here, however, would re-run
+    // everything causing more issues than not. If we wanted to support beforeRegisterNodeDef then
+    // it would mean rewriting ComfyUI's registerNodeDef which, frankly, is not worth it.
+  }
+  return oldregisterNodeType.call(LiteGraph, nodeId, clazz);
+};
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/base_node_collector.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/base_node_collector.ts
new file mode 100644
index 0000000000000000000000000000000000000000..30076301e30ee179ebd566bfd6e6b4f3ac54c122
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/base_node_collector.ts
@@ -0,0 +1,99 @@
+import type {INodeOutputSlot, LGraphNode} from "@comfyorg/frontend";
+
+import {rgthree} from "./rgthree.js";
+import {BaseAnyInputConnectedNode} from "./base_any_input_connected_node.js";
+import {
+  PassThroughFollowing,
+  getConnectedInputNodes,
+  getConnectedInputNodesAndFilterPassThroughs,
+  shouldPassThrough,
+} from "./utils.js";
+
+/**
+ * Base collector node that monitors changing inputs and outputs.
+ */
+export class BaseCollectorNode extends BaseAnyInputConnectedNode {
+  /**
+   * We only want to show nodes through re_route nodes, other pass through nodes show each input.
+   */
+  override readonly inputsPassThroughFollowing: PassThroughFollowing =
+    PassThroughFollowing.REROUTE_ONLY;
+
+  readonly logger = rgthree.newLogSession("[BaseCollectorNode]");
+
+  constructor(title?: string) {
+    super(title);
+  }
+
+  override clone() {
+    const cloned = super.clone()!;
+    return cloned;
+  }
+
+  override handleLinkedNodesStabilization(linkedNodes: LGraphNode[]) {
+    return false; // No-op, no widgets.
+  }
+
+  /**
+   * When we connect an input, check to see if it's already connected and cancel it.
+   */
+  override onConnectInput(
+    inputIndex: number,
+    outputType: string | -1,
+    outputSlot: INodeOutputSlot,
+    outputNode: LGraphNode,
+    outputIndex: number,
+  ): boolean {
+    let canConnect = super.onConnectInput(
+      inputIndex,
+      outputType,
+      outputSlot,
+      outputNode,
+      outputIndex,
+    );
+    if (canConnect) {
+      const allConnectedNodes = getConnectedInputNodes(this); // We want passthrough nodes, since they will loop.
+      const nodesAlreadyInSlot = getConnectedInputNodes(this, undefined, inputIndex);
+      if (allConnectedNodes.includes(outputNode)) {
+        // If we're connecting to the same slot, then allow it by replacing the one we have.
+        // const slotsOriginNode = getOriginNodeByLink(this.inputs[inputIndex]?.link);
+        const [n, v] = this.logger.debugParts(
+          `${outputNode.title} is already connected to ${this.title}.`,
+        );
+        console[n]?.(...v);
+        if (nodesAlreadyInSlot.includes(outputNode)) {
+          const [n, v] = this.logger.debugParts(
+            `... but letting it slide since it's for the same slot.`,
+          );
+          console[n]?.(...v);
+        } else {
+          canConnect = false;
+        }
+      }
+      if (canConnect && shouldPassThrough(outputNode, PassThroughFollowing.REROUTE_ONLY)) {
+        const connectedNode = getConnectedInputNodesAndFilterPassThroughs(
+          outputNode,
+          undefined,
+          undefined,
+          PassThroughFollowing.REROUTE_ONLY,
+        )[0];
+        if (connectedNode && allConnectedNodes.includes(connectedNode)) {
+          // If we're connecting to the same slot, then allow it by replacing the one we have.
+          const [n, v] = this.logger.debugParts(
+            `${connectedNode.title} is already connected to ${this.title}.`,
+          );
+          console[n]?.(...v);
+          if (nodesAlreadyInSlot.includes(connectedNode)) {
+            const [n, v] = this.logger.debugParts(
+              `... but letting it slide since it's for the same slot.`,
+            );
+            console[n]?.(...v);
+          } else {
+            canConnect = false;
+          }
+        }
+      }
+    }
+    return canConnect;
+  }
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/base_node_mode_changer.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/base_node_mode_changer.ts
new file mode 100644
index 0000000000000000000000000000000000000000..ae1afeafd900c6143a696c5612fc63edf59327d3
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/base_node_mode_changer.ts
@@ -0,0 +1,106 @@
+import type {LGraphNode, IWidget} from "@comfyorg/frontend";
+
+import {BaseAnyInputConnectedNode} from "./base_any_input_connected_node.js";
+import {changeModeOfNodes, PassThroughFollowing} from "./utils.js";
+import {wait} from "rgthree/common/shared_utils.js";
+
+export class BaseNodeModeChanger extends BaseAnyInputConnectedNode {
+  override readonly inputsPassThroughFollowing: PassThroughFollowing = PassThroughFollowing.ALL;
+
+  static collapsible = false;
+  override isVirtualNode = true;
+
+  // These Must be overriden
+  readonly modeOn: number = -1;
+  readonly modeOff: number = -1;
+
+  static "@toggleRestriction" = {
+    type: "combo",
+    values: ["default", "max one", "always one"],
+  };
+
+  constructor(title?: string) {
+    super(title);
+    this.properties["toggleRestriction"] = "default";
+  }
+
+  override onConstructed(): boolean {
+    wait(10).then(() => {
+      if (this.modeOn < 0 || this.modeOff < 0) {
+        throw new Error("modeOn and modeOff must be overridden.");
+      }
+    });
+    this.addOutput("OPT_CONNECTION", "*");
+    return super.onConstructed();
+  }
+
+  override handleLinkedNodesStabilization(linkedNodes: LGraphNode[]) {
+    let changed = false;
+    for (const [index, node] of linkedNodes.entries()) {
+      let widget: IWidget | undefined = this.widgets && this.widgets[index];
+      if (!widget) {
+        // When we add a widget, litegraph is going to mess up the size, so we
+        // store it so we can retrieve it in computeSize. Hacky..
+        (this as any)._tempWidth = this.size[0];
+        widget = this.addWidget("toggle", "", false, "", {on: "yes", off: "no"}) as IWidget;
+        changed = true;
+      }
+      if (node) {
+        changed = this.setWidget(widget, node) || changed;
+      }
+    }
+    if (this.widgets && this.widgets.length > linkedNodes.length) {
+      this.widgets.length = linkedNodes.length;
+      changed = true;
+    }
+    return changed;
+  }
+
+  private setWidget(widget: IWidget, linkedNode: LGraphNode, forceValue?: boolean) {
+    let changed = false;
+    const value = forceValue == null ? linkedNode.mode === this.modeOn : forceValue;
+    let name = `Enable ${linkedNode.title}`;
+    // Need to set initally
+    if (widget.name !== name) {
+      widget.name = `Enable ${linkedNode.title}`;
+      widget.options = {on: "yes", off: "no"};
+      widget.value = value;
+      (widget as any).doModeChange = (forceValue?: boolean, skipOtherNodeCheck?: boolean) => {
+        let newValue = forceValue == null ? linkedNode.mode === this.modeOff : forceValue;
+        if (skipOtherNodeCheck !== true) {
+          if (newValue && (this.properties?.["toggleRestriction"] as string)?.includes(" one")) {
+            for (const widget of this.widgets) {
+              (widget as any).doModeChange(false, true);
+            }
+          } else if (!newValue && this.properties?.["toggleRestriction"] === "always one") {
+            newValue = this.widgets.every((w) => !w.value || w === widget);
+          }
+        }
+        changeModeOfNodes(linkedNode, (newValue ? this.modeOn : this.modeOff))
+        widget.value = newValue;
+      };
+      widget.callback = () => {
+        (widget as any).doModeChange();
+      };
+      changed = true;
+    }
+    if (forceValue != null) {
+      const newMode = (forceValue ? this.modeOn : this.modeOff) as 1 | 2 | 3 | 4;
+      if (linkedNode.mode !== newMode) {
+        changeModeOfNodes(linkedNode, newMode);
+        changed = true;
+      }
+    }
+    return changed;
+  }
+
+  forceWidgetOff(widget: IWidget, skipOtherNodeCheck?: boolean) {
+    (widget as any).doModeChange(false, skipOtherNodeCheck);
+  }
+  forceWidgetOn(widget: IWidget, skipOtherNodeCheck?: boolean) {
+    (widget as any).doModeChange(true, skipOtherNodeCheck);
+  }
+  forceWidgetToggle(widget: IWidget, skipOtherNodeCheck?: boolean) {
+    (widget as any).doModeChange(!widget.value, skipOtherNodeCheck);
+  }
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/base_power_prompt.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/base_power_prompt.ts
new file mode 100644
index 0000000000000000000000000000000000000000..02a161b7e88be4f6184578578b14d0f0c869373d
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/base_power_prompt.ts
@@ -0,0 +1,366 @@
+import type {
+  LLink,
+  LGraphNode,
+  INodeOutputSlot,
+  INodeInputSlot,
+  ISerialisedNode,
+  IComboWidget,
+  IBaseWidget,
+} from "@comfyorg/frontend";
+import type {ComfyNodeDef} from "typings/comfy.js";
+
+import {api} from "scripts/api.js";
+import {wait} from "rgthree/common/shared_utils.js";
+import {rgthree} from "./rgthree.js";
+
+/** Wraps a node instance keeping closure without mucking the finicky types. */
+export class PowerPrompt {
+  readonly isSimple: boolean;
+  readonly node: LGraphNode;
+  readonly promptEl: HTMLTextAreaElement;
+  nodeData: ComfyNodeDef;
+  readonly combos: {[key: string]: IComboWidget} = {};
+  readonly combosValues: {[key: string]: string[]} = {};
+  boundOnFreshNodeDefs!: (event: CustomEvent) => void;
+
+  private configuring = false;
+
+  constructor(node: LGraphNode, nodeData: ComfyNodeDef) {
+    this.node = node;
+    this.node.properties = this.node.properties || {};
+
+    this.node.properties["combos_filter"] = "";
+
+    this.nodeData = nodeData;
+    this.isSimple = this.nodeData.name.includes("Simple");
+
+    this.promptEl = (node.widgets![0]! as any).inputEl;
+    this.addAndHandleKeyboardLoraEditWeight();
+
+    this.patchNodeRefresh();
+
+    const oldConfigure = this.node.configure;
+    this.node.configure = (info: ISerialisedNode) => {
+      this.configuring = true;
+      oldConfigure?.apply(this.node, [info]);
+      this.configuring = false;
+    };
+
+    const oldOnConnectionsChange = this.node.onConnectionsChange;
+    this.node.onConnectionsChange = (
+      type: number,
+      slotIndex: number,
+      isConnected: boolean,
+      link_info: LLink,
+      _ioSlot: INodeOutputSlot | INodeInputSlot,
+    ) => {
+      oldOnConnectionsChange?.apply(this.node, [type, slotIndex, isConnected, link_info, _ioSlot]);
+      this.onNodeConnectionsChange(type, slotIndex, isConnected, link_info, _ioSlot);
+    };
+
+    const oldOnConnectInput = this.node.onConnectInput;
+    this.node.onConnectInput = (
+      inputIndex: number,
+      outputType: INodeOutputSlot["type"],
+      outputSlot: INodeOutputSlot,
+      outputNode: LGraphNode,
+      outputIndex: number,
+    ) => {
+      let canConnect = true;
+      if (oldOnConnectInput) {
+        canConnect = oldOnConnectInput.apply(this.node, [
+          inputIndex,
+          outputType,
+          outputSlot,
+          outputNode,
+          outputIndex,
+        ]);
+      }
+      return (
+        this.configuring ||
+        !!rgthree.loadingApiJson ||
+        (canConnect && !this.node.inputs[inputIndex]!.disabled)
+      );
+    };
+
+    const oldOnConnectOutput = this.node.onConnectOutput;
+    this.node.onConnectOutput = (
+      outputIndex: number,
+      inputType: INodeInputSlot["type"],
+      inputSlot: INodeInputSlot,
+      inputNode: LGraphNode,
+      inputIndex: number,
+    ) => {
+      let canConnect = true;
+      if (oldOnConnectOutput) {
+        canConnect = oldOnConnectOutput?.apply(this.node, [
+          outputIndex,
+          inputType,
+          inputSlot,
+          inputNode,
+          inputIndex,
+        ]);
+      }
+      return (
+        this.configuring ||
+        !!rgthree.loadingApiJson ||
+        (canConnect && !this.node.outputs[outputIndex]!.disabled)
+      );
+    };
+
+    const onPropertyChanged = this.node.onPropertyChanged;
+    this.node.onPropertyChanged = (property: string, value: any, prevValue: any) => {
+      const v = onPropertyChanged && onPropertyChanged.call(this.node, property, value, prevValue);
+      if (property === "combos_filter") {
+        this.refreshCombos(this.nodeData);
+      }
+      return v ?? true;
+    };
+
+    // Strip all widgets but prompt (we'll re-add them in refreshCombos)
+    // this.node.widgets.splice(1);
+    for (let i = this.node.widgets!.length - 1; i >= 0; i--) {
+      if (this.shouldRemoveServerWidget(this.node.widgets![i]!)) {
+        this.node.widgets!.splice(i, 1);
+      }
+    }
+
+    this.refreshCombos(nodeData);
+    setTimeout(() => {
+      this.stabilizeInputsOutputs();
+    }, 32);
+  }
+
+  /**
+   * Cleans up optional out puts when we don't have the optional input. Purely a vanity function.
+   */
+  onNodeConnectionsChange(
+    _type: number,
+    _slotIndex: number,
+    _isConnected: boolean,
+    _linkInfo: LLink,
+    _ioSlot: INodeOutputSlot | INodeInputSlot,
+  ) {
+    this.stabilizeInputsOutputs();
+  }
+
+  private stabilizeInputsOutputs() {
+    // If we are currently "configuring" then skip this stabilization. The connected nodes may
+    // not yet be configured.
+    if (this.configuring || rgthree.loadingApiJson) {
+      return;
+    }
+    // If our first input is connected, then we can show the proper output.
+    const clipLinked = this.node.inputs.some((i) => i.name.includes("clip") && !!i.link);
+    const modelLinked = this.node.inputs.some((i) => i.name.includes("model") && !!i.link);
+    for (const output of this.node.outputs) {
+      const type = (output.type as string).toLowerCase();
+      if (type.includes("model")) {
+        output.disabled = !modelLinked;
+      } else if (type.includes("conditioning")) {
+        output.disabled = !clipLinked;
+      } else if (type.includes("clip")) {
+        output.disabled = !clipLinked;
+      } else if (type.includes("string")) {
+        // Our text prompt is always enabled, but let's color it so it stands out
+        // if the others are disabled. #7F7 is Litegraph's default.
+        output.color_off = "#7F7";
+        output.color_on = "#7F7";
+      }
+      if (output.disabled) {
+        // this.node.disconnectOutput(index);
+      }
+    }
+  }
+
+  onFreshNodeDefs(event: CustomEvent) {
+    this.refreshCombos(event.detail[this.nodeData.name]);
+  }
+
+  shouldRemoveServerWidget(widget: IBaseWidget) {
+    return (
+      widget.name?.startsWith("insert_") ||
+      widget.name?.startsWith("target_") ||
+      widget.name?.startsWith("crop_") ||
+      widget.name?.startsWith("values_")
+    );
+  }
+
+  refreshCombos(nodeData: ComfyNodeDef) {
+    this.nodeData = nodeData;
+    let filter: RegExp | null = null;
+    if ((this.node.properties["combos_filter"] as string)?.trim()) {
+      try {
+        filter = new RegExp((this.node.properties["combos_filter"] as string).trim(), "i");
+      } catch (e) {
+        console.error(`Could not parse "${filter}" for Regular Expression`, e);
+        filter = null;
+      }
+    }
+
+    // Add the combo for hidden inputs of nodeData
+    let data = Object.assign(
+      {},
+      this.nodeData.input?.optional || {},
+      this.nodeData.input?.hidden || {},
+    );
+
+    for (const [key, value] of Object.entries(data)) {
+      //Object.entries(this.nodeData.input?.hidden || {})) {
+      if (Array.isArray(value[0])) {
+        let values = value[0] as string[];
+        if (key.startsWith("insert")) {
+          values = filter
+            ? values.filter(
+                (v, i) => i < 1 || (i == 1 && v.match(/^disable\s[a-z]/i)) || filter?.test(v),
+              )
+            : values;
+          const shouldShow =
+            values.length > 2 || (values.length > 1 && !values[1]!.match(/^disable\s[a-z]/i));
+          if (shouldShow) {
+            if (!this.combos[key]) {
+              this.combos[key] = this.node.addWidget(
+                "combo",
+                key,
+                values[0]!,
+                (selected) => {
+                  if (selected !== values[0] && !selected.match(/^disable\s[a-z]/i)) {
+                    // We wait a frame because if we use a keydown event to call, it'll wipe out
+                    // the selection.
+                    wait().then(() => {
+                      if (key.includes("embedding")) {
+                        this.insertSelectionText(`embedding:${selected}`);
+                      } else if (key.includes("saved")) {
+                        this.insertSelectionText(
+                          this.combosValues[`values_${key}`]![values.indexOf(selected)]!,
+                        );
+                      } else if (key.includes("lora")) {
+                        this.insertSelectionText(`<lora:${selected}:1.0>`);
+                      }
+                      this.combos[key]!.value = values[0]!;
+                    });
+                  }
+                },
+                {
+                  values,
+                  serialize: true, // Don't include this in prompt.
+                },
+              ) as IComboWidget;
+              (this.combos[key]! as any).oldComputeSize = this.combos[key]!.computeSize;
+              let node = this.node;
+              this.combos[key]!.computeSize = function (width: number) {
+                const size = (this as any).oldComputeSize?.(width) || [
+                  width,
+                  LiteGraph.NODE_WIDGET_HEIGHT,
+                ];
+                if (this === node.widgets![node.widgets!.length - 1]) {
+                  size[1] += 10;
+                }
+                return size;
+              };
+            }
+            this.combos[key]!.options!.values = values;
+            this.combos[key]!.value = values[0]!;
+          } else if (!shouldShow && this.combos[key]) {
+            this.node.widgets!.splice(this.node.widgets!.indexOf(this.combos[key]!), 1);
+            delete this.combos[key];
+          }
+        } else if (key.startsWith("values")) {
+          this.combosValues[key] = values;
+        }
+      }
+    }
+  }
+
+  insertSelectionText(text: string) {
+    if (!this.promptEl) {
+      console.error("Asked to insert text, but no textbox found.");
+      return;
+    }
+    let prompt = this.promptEl.value;
+    // Use selectionEnd as the split; if we have highlighted text, then we likely don't want to
+    // overwrite it (we could have just deleted it more easily).
+    let first = prompt.substring(0, this.promptEl.selectionEnd).replace(/ +$/, "");
+    first = first + (["\n"].includes(first[first.length - 1]!) ? "" : first.length ? " " : "");
+    let second = prompt.substring(this.promptEl.selectionEnd).replace(/^ +/, "");
+    second = (["\n"].includes(second[0]!) ? "" : second.length ? " " : "") + second;
+    this.promptEl.value = first + text + second;
+    this.promptEl.focus();
+    this.promptEl.selectionStart = first.length;
+    this.promptEl.selectionEnd = first.length + text.length;
+  }
+
+  /**
+   * Adds a keydown event listener to our prompt so we can see if we're using the
+   * ctrl/cmd + up/down arrows shortcut. This kind of competes with the core extension
+   * "Comfy.EditAttention" but since that only handles parenthesis and listens on window, we should
+   * be able to intercept and cancel the bubble if we're doing the same action within the lora tag.
+   */
+  addAndHandleKeyboardLoraEditWeight() {
+    this.promptEl.addEventListener("keydown", (event: KeyboardEvent) => {
+      // If we're not doing a ctrl/cmd + arrow key, then bail.
+      if (!(event.key === "ArrowUp" || event.key === "ArrowDown")) return;
+      if (!event.ctrlKey && !event.metaKey) return;
+      // Unfortunately, we can't see Comfy.EditAttention delta in settings, so we hardcode to 0.01.
+      // We can acutally do better too, let's make it .1 by default, and .01 if also holding shift.
+      const delta = event.shiftKey ? 0.01 : 0.1;
+
+      let start = this.promptEl.selectionStart;
+      let end = this.promptEl.selectionEnd;
+      let fullText = this.promptEl.value;
+      let selectedText = fullText.substring(start, end);
+
+      // We don't care about fully rewriting Comfy.EditAttention, we just want to see if our
+      // selected text is a lora, which will always start with "<lora:". So work backwards until we
+      // find something that we know can't be a lora, or a "<".
+      if (!selectedText) {
+        const stopOn = "<>()\r\n\t"; // Allow spaces, since they can be in the filename
+        if (fullText[start] == ">") {
+          start -= 2;
+          end -= 2;
+        }
+        if (fullText[end - 1] == "<") {
+          start += 2;
+          end += 2;
+        }
+        while (!stopOn.includes(fullText[start]!) && start > 0) {
+          start--;
+        }
+        while (!stopOn.includes(fullText[end - 1]!) && end < fullText.length) {
+          end++;
+        }
+        selectedText = fullText.substring(start, end);
+      }
+
+      // Bail if this isn't a lora.
+      if (!selectedText.startsWith("<lora:") || !selectedText.endsWith(">")) {
+        return;
+      }
+
+      let weight = Number(selectedText.match(/:(-?\d*(\.\d*)?)>$/)?.[1]) ?? 1;
+      weight += event.key === "ArrowUp" ? delta : -delta;
+      const updatedText = selectedText.replace(/(:-?\d*(\.\d*)?)?>$/, `:${weight.toFixed(2)}>`);
+
+      // Handle the new value and cancel the bubble so Comfy.EditAttention doesn't also try.
+      this.promptEl.setRangeText(updatedText, start, end, "select");
+      event.preventDefault();
+      event.stopPropagation();
+    });
+  }
+
+  /**
+   * Patches over api.getNodeDefs in comfy's api.js to fire a custom event that we can listen to
+   * here and manually refresh our combos when a request comes in to fetch the node data; which
+   * only happens once at startup (but before custom nodes js runs), and then after clicking
+   * the "Refresh" button in the floating menu, which is what we care about.
+   */
+  patchNodeRefresh() {
+    this.boundOnFreshNodeDefs = this.onFreshNodeDefs.bind(this);
+    api.addEventListener("fresh-node-defs", this.boundOnFreshNodeDefs as EventListener);
+    const oldNodeRemoved = this.node.onRemoved;
+    this.node.onRemoved = () => {
+      oldNodeRemoved?.call(this.node);
+      api.removeEventListener("fresh-node-defs", this.boundOnFreshNodeDefs as EventListener);
+    };
+  }
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/bookmark.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/bookmark.ts
new file mode 100644
index 0000000000000000000000000000000000000000..8eb742302b56d958b35e0c3e30c2e48b9c1f5188
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/bookmark.ts
@@ -0,0 +1,163 @@
+import type {
+  LGraph,
+  LGraphCanvas,
+  LGraphNode,
+  Point,
+  CanvasMouseEvent,
+  Subgraph,
+} from "@comfyorg/frontend";
+
+import {app} from "scripts/app.js";
+import {RgthreeBaseVirtualNode} from "./base_node.js";
+import {SERVICE as KEY_EVENT_SERVICE} from "./services/key_events_services.js";
+import {SERVICE as BOOKMARKS_SERVICE} from "./services/bookmarks_services.js";
+import {NodeTypesString} from "./constants.js";
+import {getClosestOrSelf, query} from "rgthree/common/utils_dom.js";
+import {wait} from "rgthree/common/shared_utils.js";
+import {findFromNodeForSubgraph} from "./utils.js";
+
+/**
+ * A bookmark node. Can be placed anywhere in the workflow, and given a shortcut key that will
+ * navigate to that node, with it in the top-left corner.
+ */
+export class Bookmark extends RgthreeBaseVirtualNode {
+  static override type = NodeTypesString.BOOKMARK;
+  static override title = NodeTypesString.BOOKMARK;
+  override comfyClass = NodeTypesString.BOOKMARK;
+
+  // Really silly, but Litegraph assumes we have at least one input/output... so we need to
+  // counteract it's computeSize calculation by offsetting the start.
+  static slot_start_y = -20;
+
+  // LiteGraph adds mroe spacing than we want when calculating a nodes' `_collapsed_width`, so we'll
+  // override it with a setter and re-set it measured exactly as we want.
+  ___collapsed_width: number = 0;
+
+  override isVirtualNode = true;
+  override serialize_widgets = true;
+
+  //@ts-ignore - TS Doesn't like us overriding a property with accessors but, too bad.
+  override get _collapsed_width() {
+    return this.___collapsed_width;
+  }
+
+  override set _collapsed_width(width: number) {
+    const canvas = app.canvas as LGraphCanvas;
+    const ctx = canvas.canvas.getContext("2d")!;
+    const oldFont = ctx.font;
+    ctx.font = canvas.title_text_font;
+    this.___collapsed_width = 40 + ctx.measureText(this.title).width;
+    ctx.font = oldFont;
+  }
+
+  readonly keypressBound;
+
+  constructor(title = Bookmark.title) {
+    super(title);
+    const nextShortcutChar = BOOKMARKS_SERVICE.getNextShortcut();
+    this.addWidget(
+      "text",
+      "shortcut_key",
+      nextShortcutChar,
+      (value: string, ...args) => {
+        value = value.trim()[0] || "1";
+      },
+      {
+        y: 8,
+      },
+    );
+    this.addWidget("number", "zoom", 1, (value: number) => {}, {
+      y: 8 + LiteGraph.NODE_WIDGET_HEIGHT + 4,
+      max: 2,
+      min: 0.5,
+      precision: 2,
+    });
+    this.keypressBound = this.onKeypress.bind(this);
+    this.title = "🔖";
+    this.onConstructed();
+  }
+
+  // override computeSize(out?: Vector2 | undefined): Vector2 {
+  //   super.computeSize(out);
+  //   const minHeight = (this.widgets?.length || 0) * (LiteGraph.NODE_WIDGET_HEIGHT + 4) + 16;
+  //   this.size[1] = Math.max(minHeight, this.size[1]);
+  // }
+
+  get shortcutKey(): string {
+    return (this.widgets[0]?.value as string)?.toLocaleLowerCase() ?? "";
+  }
+
+  override onAdded(graph: LGraph): void {
+    KEY_EVENT_SERVICE.addEventListener("keydown", this.keypressBound as EventListener);
+  }
+
+  override onRemoved(): void {
+    KEY_EVENT_SERVICE.removeEventListener("keydown", this.keypressBound as EventListener);
+  }
+
+  onKeypress(event: CustomEvent<{originalEvent: KeyboardEvent}>) {
+    const originalEvent = event.detail.originalEvent;
+    const target = (originalEvent.target as HTMLElement)!;
+    if (getClosestOrSelf(target, 'input,textarea,[contenteditable="true"]')) {
+      return;
+    }
+
+    // Only the shortcut keys are held down, optionally including "shift".
+    if (KEY_EVENT_SERVICE.areOnlyKeysDown(this.widgets[0]!.value as string, true)) {
+      this.canvasToBookmark();
+      originalEvent.preventDefault();
+      originalEvent.stopPropagation();
+    }
+  }
+
+  /**
+   * Called from LiteGraph's `processMouseDown` after it would invoke the input box for the
+   * shortcut_key, so we check if it exists and then add our own event listener so we can track the
+   * keys down for the user. Note, blocks drag if the return is truthy.
+   */
+  override onMouseDown(event: CanvasMouseEvent, pos: Point, graphCanvas: LGraphCanvas): boolean {
+    const input = query<HTMLInputElement>(".graphdialog > input.value");
+    if (input && input.value === this.widgets[0]?.value) {
+      input.addEventListener("keydown", (e) => {
+        // ComfyUI swallows keydown on inputs, so we need to call out to rgthree to use downkeys.
+        KEY_EVENT_SERVICE.handleKeyDownOrUp(e);
+        e.preventDefault();
+        e.stopPropagation();
+        input.value = Object.keys(KEY_EVENT_SERVICE.downKeys).join(" + ");
+      });
+    }
+    return false;
+  }
+
+  async canvasToBookmark() {
+    const canvas = app.canvas as LGraphCanvas;
+    if (this.graph !== app.canvas.getCurrentGraph()) {
+      const subgraph = this.graph as Subgraph;
+      // At some point, ComfyUI made a second param for openSubgraph which appears to be the node
+      // that id double-clicked on to open the subgraph. We don't have that in the bookmark, so
+      // we'll look for it. Note, that when opening the root graph, this will be null (since there's
+      // no such node). It seems to still navigate fine, though there's a console error about
+      // proxyWidgets or something..
+      const fromNode = findFromNodeForSubgraph(subgraph.id);
+      canvas.openSubgraph(subgraph, fromNode!);
+      await wait(16);
+    }
+    // ComfyUI seemed to break us again, but couldn't repro. No reason to not check, I guess.
+    // https://github.com/rgthree/rgthree-comfy/issues/71
+    if (canvas?.ds?.offset) {
+      canvas.ds.offset[0] = -this.pos[0] + 16;
+      canvas.ds.offset[1] = -this.pos[1] + 40;
+    }
+    if (canvas?.ds?.scale != null) {
+      canvas.ds.scale = Number(this.widgets[1]!.value || 1);
+    }
+    canvas.setDirty(true, true);
+  }
+}
+
+app.registerExtension({
+  name: "rgthree.Bookmark",
+  registerCustomNodes() {
+    Bookmark.setUp();
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/bypasser.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/bypasser.ts
new file mode 100644
index 0000000000000000000000000000000000000000..0cfb51f83a0ed7f221cc9e66392d9b58f2c6e331
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/bypasser.ts
@@ -0,0 +1,52 @@
+import type {LGraphNode} from "@comfyorg/frontend";
+
+import {app} from "scripts/app.js";
+import {BaseNodeModeChanger} from "./base_node_mode_changer.js";
+import {NodeTypesString} from "./constants.js";
+
+const MODE_BYPASS = 4;
+const MODE_ALWAYS = 0;
+
+class BypasserNode extends BaseNodeModeChanger {
+  static override exposedActions = ["Bypass all", "Enable all", "Toggle all"];
+
+  static override type = NodeTypesString.FAST_BYPASSER;
+  static override title = NodeTypesString.FAST_BYPASSER;
+  override comfyClass = NodeTypesString.FAST_BYPASSER;
+
+  override readonly modeOn = MODE_ALWAYS;
+  override readonly modeOff = MODE_BYPASS;
+
+  constructor(title = BypasserNode.title) {
+    super(title);
+    this.onConstructed();
+  }
+
+  override async handleAction(action: string) {
+    if (action === "Bypass all") {
+      for (const widget of this.widgets || []) {
+        this.forceWidgetOff(widget, true);
+      }
+    } else if (action === "Enable all") {
+      for (const widget of this.widgets || []) {
+        this.forceWidgetOn(widget, true);
+      }
+    } else if (action === "Toggle all") {
+      for (const widget of this.widgets || []) {
+        this.forceWidgetToggle(widget, true);
+      }
+    }
+  }
+}
+
+app.registerExtension({
+  name: "rgthree.Bypasser",
+  registerCustomNodes() {
+    BypasserNode.setUp();
+  },
+  loadedGraphNode(node: LGraphNode) {
+    if (node.type == BypasserNode.title) {
+      (node as any)._tempWidth = node.size[0];
+    }
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/comfy_ui_bar.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/comfy_ui_bar.ts
new file mode 100644
index 0000000000000000000000000000000000000000..af79cc9571286a4601d5db77e6f28ef9e2df3071
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/comfy_ui_bar.ts
@@ -0,0 +1,264 @@
+import {app} from "scripts/app.js";
+import {iconGear, iconStarFilled, logoRgthreeAsync} from "rgthree/common/media/svgs.js";
+import {$el, empty} from "rgthree/common/utils_dom.js";
+import {SERVICE as BOOKMARKS_SERVICE} from "./services/bookmarks_services.js";
+import {SERVICE as CONFIG_SERVICE} from "./services/config_service.js";
+import {RgthreeConfigDialog} from "./config.js";
+import {wait} from "rgthree/common/shared_utils.js";
+
+let rgthreeButtonGroup: RgthreeComfyButtonGroup | null = null;
+
+function addRgthreeTopBarButtons() {
+  if (!CONFIG_SERVICE.getFeatureValue("comfy_top_bar_menu.enabled")) {
+    if (rgthreeButtonGroup?.element?.parentElement) {
+      rgthreeButtonGroup.element.parentElement.removeChild(rgthreeButtonGroup.element);
+    }
+    return;
+  } else if (rgthreeButtonGroup) {
+    app.menu?.settingsGroup.element.before(rgthreeButtonGroup.element);
+    return;
+  }
+
+  const buttons = [];
+
+  const rgthreeButton = new RgthreeComfyButton({
+    icon: "<svg></svg>",
+    tooltip: "rgthree-comfy",
+    primary: true,
+    // content: 'rgthree-comfy',
+    // app,
+    enabled: true,
+    classList: "comfyui-button comfyui-menu-mobile-collapse primary",
+  });
+  buttons.push(rgthreeButton);
+  logoRgthreeAsync().then((t) => {
+    rgthreeButton.setIcon(t);
+  });
+
+  rgthreeButton.withPopup(
+    new RgthreeComfyPopup(
+      {target: rgthreeButton.element},
+      $el("menu.rgthree-menu.rgthree-top-menu", {
+        children: [
+          $el("li", {
+            child: $el("button.rgthree-button-reset", {
+              html: iconGear + "Settings (rgthree-comfy)",
+              onclick: () => new RgthreeConfigDialog().show(),
+            }),
+          }),
+          $el("li", {
+            child: $el("button.rgthree-button-reset", {
+              html: iconStarFilled + "Star on Github",
+              onclick: () => window.open("https://github.com/rgthree/rgthree-comfy", "_blank"),
+            }),
+          }),
+        ],
+      }),
+    ),
+    "click",
+  );
+
+  if (CONFIG_SERVICE.getFeatureValue("comfy_top_bar_menu.button_bookmarks.enabled")) {
+    const bookmarksListEl = $el("menu.rgthree-menu.rgthree-top-menu");
+    bookmarksListEl.appendChild(
+      $el("li.rgthree-message", {
+        child: $el("span", {text: "No bookmarks in current workflow."}),
+      }),
+    );
+    const bookmarksButton = new RgthreeComfyButton({
+      icon: "bookmark",
+      tooltip: "Workflow Bookmarks (rgthree-comfy)",
+      // app,
+    });
+    const bookmarksPopup = new RgthreeComfyPopup(
+      {target: bookmarksButton.element, modal: false},
+      bookmarksListEl,
+    );
+    bookmarksPopup.onOpen(() => {
+      const bookmarks = BOOKMARKS_SERVICE.getCurrentBookmarks();
+      empty(bookmarksListEl);
+      if (bookmarks.length) {
+        for (const b of bookmarks) {
+          bookmarksListEl.appendChild(
+            $el("li", {
+              child: $el("button.rgthree-button-reset", {
+                text: `[${b.shortcutKey}] ${b.title}`,
+                onclick: () => {
+                  b.canvasToBookmark();
+                },
+              }),
+            }),
+          );
+        }
+      } else {
+        bookmarksListEl.appendChild(
+          $el("li.rgthree-message", {
+            child: $el("span", {text: "No bookmarks in current workflow."}),
+          }),
+        );
+      }
+      // bookmarksPopup.update();
+    });
+    bookmarksButton.withPopup(bookmarksPopup, "hover");
+    buttons.push(bookmarksButton);
+  }
+
+  rgthreeButtonGroup = new RgthreeComfyButtonGroup(...buttons);
+  app.menu?.settingsGroup.element.before(rgthreeButtonGroup.element);
+}
+
+app.registerExtension({
+  name: "rgthree.TopMenu",
+  async setup() {
+    addRgthreeTopBarButtons();
+
+    CONFIG_SERVICE.addEventListener("config-change", ((e: CustomEvent) => {
+      if (e.detail?.key?.includes("features.comfy_top_bar_menu")) {
+        addRgthreeTopBarButtons();
+      }
+    }) as EventListener);
+  },
+});
+
+// The following are rough hacks since ComfyUI took away their button/buttongroup/popup
+// functionality. TODO: Find a better spot to add rgthree controls to the UI, I suppose.
+
+class RgthreeComfyButtonGroup {
+  element = $el("div.rgthree-comfybar-top-button-group");
+  buttons: RgthreeComfyButton[];
+
+  constructor(...buttons: RgthreeComfyButton[]) {
+    this.buttons = buttons;
+    this.update();
+  }
+
+  insert(button: RgthreeComfyButton, index: number) {
+    this.buttons.splice(index, 0, button);
+    this.update();
+  }
+
+  append(button: RgthreeComfyButton) {
+    this.buttons.push(button);
+    this.update();
+  }
+
+  remove(indexOrButton: RgthreeComfyButton | number) {
+    if (typeof indexOrButton !== "number") {
+      indexOrButton = this.buttons.indexOf(indexOrButton);
+    }
+    if (indexOrButton > -1) {
+      const btn = this.buttons.splice(indexOrButton, 1);
+      this.update();
+      return btn;
+    }
+    return null;
+  }
+
+  update() {
+    this.element.replaceChildren(...this.buttons.map((b) => b["element"] ?? b));
+  }
+}
+
+interface RgthreeComfyButtonOptions {
+  icon?: string;
+  primary?: boolean;
+  overIcon?: string;
+  iconSize?: number;
+  content?: string | HTMLElement;
+  tooltip?: string;
+  enabled?: boolean;
+  action?: (e: Event, btn: RgthreeComfyButton) => void;
+  classList?: string;
+  visibilitySetting?: {id: string; showValue: any};
+  // app?: ComfyApp;
+}
+
+class RgthreeComfyButton {
+  element = $el("button.rgthree-comfybar-top-button.rgthree-button-reset.rgthree-button");
+  iconElement = $el("span.rgthree-button-icon");
+  constructor(opts: RgthreeComfyButtonOptions) {
+    opts.icon && this.setIcon(opts.icon);
+    opts.tooltip && this.element.setAttribute("title", opts.tooltip);
+    opts.primary && this.element.classList.add("-primary");
+  }
+
+  setIcon(iconOrMarkup: string) {
+    const markup = iconOrMarkup.startsWith("<")
+      ? iconOrMarkup
+      : `<i class="mdi mdi-${iconOrMarkup}"></i>`;
+    this.iconElement.innerHTML = markup;
+    if (!this.iconElement.parentElement) {
+      this.element.appendChild(this.iconElement);
+    }
+  }
+
+  withPopup(popup: RgthreeComfyPopup, trigger: "click" | "hover") {
+    if (trigger === "click") {
+      this.element.addEventListener("click", () => {
+        popup.open();
+      });
+    }
+    if (trigger === "hover") {
+      this.element.addEventListener("pointerenter", () => {
+        popup.open();
+      });
+    }
+  }
+}
+
+interface RgthreeComfyPopupOptions {
+  target: HTMLElement;
+  classList?: string;
+  modal?: boolean;
+}
+
+class RgthreeComfyPopup {
+  element: HTMLElement;
+  target?: HTMLElement;
+  onOpenFn: (() => Promise<void> | void) | null = null;
+  opts: RgthreeComfyPopupOptions;
+
+  onWindowClickBound = this.onWindowClick.bind(this);
+
+  constructor(opts: RgthreeComfyPopupOptions, element: HTMLElement) {
+    this.element = element;
+    this.opts = opts;
+    opts.target && (this.target = opts.target);
+    opts.modal && this.element.classList.add("-modal");
+  }
+
+  async open() {
+    if (!this.target) {
+      throw new Error("No target for RgthreeComfyPopup");
+    }
+    if (this.onOpenFn) {
+      await this.onOpenFn();
+    }
+    await wait(16);
+    const rect = this.target.getBoundingClientRect();
+    this.element.setAttribute("state", "measuring");
+    document.body.appendChild(this.element);
+    this.element.style.position = "fixed";
+    this.element.style.left = `${rect.left}px`;
+    this.element.style.top = `${rect.top + rect.height}px`;
+    this.element.setAttribute("state", "open");
+    if (this.opts.modal) {
+      document.body.classList.add("rgthree-modal-menu-open");
+    }
+    window.addEventListener("click", this.onWindowClickBound);
+  }
+
+  close() {
+    this.element.remove();
+    document.body.classList.remove("rgthree-modal-menu-open");
+    window.removeEventListener("click", this.onWindowClickBound);
+  }
+
+  onOpen(fn: (() => void) | null) {
+    this.onOpenFn = fn;
+  }
+
+  onWindowClick() {
+    this.close();
+  }
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/config.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/config.ts
new file mode 100644
index 0000000000000000000000000000000000000000..6593d0fe85bdc675217a62e55b8dd82f6988af04
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/config.ts
@@ -0,0 +1,416 @@
+import {app} from "scripts/app.js";
+import { RgthreeDialog, RgthreeDialogOptions } from "rgthree/common/dialog.js";
+import { createElement as $el, queryAll as $$ } from "rgthree/common/utils_dom.js";
+import { checkmark, logoRgthree } from "rgthree/common/media/svgs.js";
+import { LogLevel, rgthree } from "./rgthree.js";
+import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js";
+
+/** Types of config used as a hint for the form handling. */
+enum ConfigType {
+  UNKNOWN,
+  BOOLEAN,
+  STRING,
+  NUMBER,
+  ARRAY,
+}
+
+enum ConfigInputType {
+  UNKNOWN,
+  CHECKLIST, // Which is a multiselect array.
+}
+
+const TYPE_TO_STRING = {
+  [ConfigType.UNKNOWN]: "unknown",
+  [ConfigType.BOOLEAN]: "boolean",
+  [ConfigType.STRING]: "string",
+  [ConfigType.NUMBER]: "number",
+  [ConfigType.ARRAY]: "array",
+};
+
+type ConfigurationSchema = {
+  key: string;
+  type: ConfigType;
+  label: string;
+  inputType?: ConfigInputType,
+  options?: string[] | number[] | ConfigurationSchemaOption[];
+  description?: string;
+  subconfig?: ConfigurationSchema[];
+  isDevOnly?: boolean;
+  onSave?: (value: any) => void;
+};
+
+type ConfigurationSchemaOption = { value: any; label: string };
+
+/**
+ * A static schema of sorts to layout options found in the config.
+ */
+const CONFIGURABLE: { [key: string]: ConfigurationSchema[] } = {
+  features: [
+    {
+      key: "features.progress_bar.enabled",
+      type: ConfigType.BOOLEAN,
+      label: "Prompt Progress Bar",
+      description: `Shows a minimal progress bar for nodes and steps at the top of the app.`,
+      subconfig: [
+        {
+          key: "features.progress_bar.height",
+          type: ConfigType.NUMBER,
+          label: "Height of the bar",
+        },
+        {
+          key: "features.progress_bar.position",
+          type: ConfigType.STRING,
+          label: "Position at top or bottom of window",
+          options: ["top", "bottom"],
+        },
+      ],
+    },
+    {
+      key: "features.import_individual_nodes.enabled",
+      type: ConfigType.BOOLEAN,
+      label: "Import Individual Nodes Widgets",
+      description:
+        "Dragging & Dropping a similar image/JSON workflow onto (most) current workflow nodes" +
+        "will allow you to import that workflow's node's widgets when it has the same " +
+        "id and type. This is useful when you have several images and you'd like to import just " +
+        "one part of a previous iteration, like a seed, or prompt.",
+    },
+  ],
+  menus: [
+    {
+      key: "features.comfy_top_bar_menu.enabled",
+      type: ConfigType.BOOLEAN,
+      label: "Enable Top Bar Menu",
+      description:
+        "Have quick access from ComfyUI's new top bar to rgthree-comfy bookmarks, settings " +
+        "(and more to come).",
+    },
+    {
+      key: "features.menu_queue_selected_nodes",
+      type: ConfigType.BOOLEAN,
+      label: "Show 'Queue Selected Output Nodes'",
+      description:
+        "Will show a menu item in the right-click context menus to queue (only) the selected " +
+        "output nodes.",
+    },
+    {
+      key: "features.menu_auto_nest.subdirs",
+      type: ConfigType.BOOLEAN,
+      label: "Auto Nest Subdirectories in Menus",
+      description:
+        "When a large, flat list of values contain sub-directories, auto nest them. (Like, for " +
+        "a large list of checkpoints).",
+      subconfig: [
+        {
+          key: "features.menu_auto_nest.threshold",
+          type: ConfigType.NUMBER,
+          label: "Number of items needed to trigger nesting.",
+        },
+      ],
+    },
+    {
+      key: "features.menu_bookmarks.enabled",
+      type: ConfigType.BOOLEAN,
+      label: "Show Bookmarks in context menu",
+      description: "Will list bookmarks in the rgthree-comfy right-click context menu.",
+    },
+  ],
+  groups: [
+    {
+      key: "features.group_header_fast_toggle.enabled",
+      type: ConfigType.BOOLEAN,
+      label: "Show fast toggles in Group Headers",
+      description: "Show quick toggles in Groups' Headers to quickly mute, bypass or queue.",
+      subconfig: [
+        {
+          key: "features.group_header_fast_toggle.toggles",
+          type: ConfigType.ARRAY,
+          label: "Which toggles to show.",
+          inputType: ConfigInputType.CHECKLIST,
+          options: [
+            { value: "queue", label: "queue" },
+            { value: "bypass", label: "bypass" },
+            { value: "mute", label: "mute" },
+          ],
+        },
+        {
+          key: "features.group_header_fast_toggle.show",
+          type: ConfigType.STRING,
+          label: "When to show them.",
+          options: [
+            { value: "hover", label: "on hover" },
+            { value: "always", label: "always" },
+          ],
+        },
+      ],
+    },
+  ],
+  power_lora_loader: [
+    {
+      key: "nodes.power_lora_loader.show_info_badge",
+      type: ConfigType.BOOLEAN,
+      label: "Show info badge/button",
+      description: "Show an info badge/button on each lora row to signal and open lora details.",
+    },
+  ],
+  advanced: [
+    {
+      key: "features.show_alerts_for_corrupt_workflows",
+      type: ConfigType.BOOLEAN,
+      label: "Detect Corrupt Workflows",
+      description:
+        "Will show a message at the top of the screen when loading a workflow that has " +
+        "corrupt linking data.",
+    },
+    {
+      key: "log_level",
+      type: ConfigType.STRING,
+      label: "Log level for browser dev console.",
+      description:
+        "Further down the list, the more verbose logs to the console will be. For instance, " +
+        "selecting 'IMPORTANT' means only important message will be logged to the browser " +
+        "console, while selecting 'WARN' will log all messages at or higher than WARN, including " +
+        "'ERROR' and 'IMPORTANT' etc.",
+      options: ["IMPORTANT", "ERROR", "WARN", "INFO", "DEBUG", "DEV"],
+      isDevOnly: true,
+      onSave: function (value: LogLevel) {
+        rgthree.setLogLevel(value);
+      },
+    },
+    {
+      key: "features.invoke_extensions_async.node_created",
+      type: ConfigType.BOOLEAN,
+      label: "Allow other extensions to call nodeCreated on rgthree-nodes.",
+      isDevOnly: true,
+      description:
+        "Do not disable unless you are having trouble (and then file an issue at rgthree-comfy)." +
+        "Prior to Apr 2024 it was not possible for other extensions to invoke their nodeCreated " +
+        "event on some rgthree-comfy nodes. Now it's possible and this option is only here in " +
+        "for easy if something is wrong.",
+    },
+  ],
+};
+
+/**
+ * Creates a new fieldrow for main or sub configuration items.
+ */
+function fieldrow(item: ConfigurationSchema) {
+  const initialValue = CONFIG_SERVICE.getConfigValue(item.key);
+  const container = $el(`div.fieldrow.-type-${TYPE_TO_STRING[item.type]}`, {
+    dataset: {
+      name: item.key,
+      initial: initialValue,
+      type: item.type,
+    },
+  });
+
+  $el(`label[for="${item.key}"]`, {
+    children: [
+      $el(`span[text="${item.label}"]`),
+      item.description ? $el("small", { html: item.description }) : null,
+    ],
+    parent: container,
+  });
+
+  let input;
+  if (item.options?.length) {
+    if (item.inputType === ConfigInputType.CHECKLIST) {
+      const initialValueList = initialValue || [];
+      input = $el<HTMLSelectElement>(`fieldset.rgthree-checklist-group[id="${item.key}"]`, {
+        parent: container,
+        children: item.options.map((o) => {
+          const label = (o as ConfigurationSchemaOption).label || String(o);
+          const value = (o as ConfigurationSchemaOption).value || o;
+          const id = `${item.key}_${value}`;
+          return $el<HTMLSpanElement>(`span.rgthree-checklist-item`, {
+            children: [
+              $el<HTMLInputElement>(`input[type="checkbox"][value="${value}"]`, {
+                id,
+                checked: initialValueList.includes(value),
+              }),
+              $el<HTMLInputElement>(`label`, {
+                for: id,
+                text: label,
+              })
+            ]
+          });
+        }),
+      });
+    } else {
+      input = $el<HTMLSelectElement>(`select[id="${item.key}"]`, {
+        parent: container,
+        children: item.options.map((o) => {
+          const label = (o as ConfigurationSchemaOption).label || String(o);
+          const value = (o as ConfigurationSchemaOption).value || o;
+          const valueSerialized = JSON.stringify({ value: value });
+          return $el<HTMLOptionElement>(`option[value="${valueSerialized}"]`, {
+            text: label,
+            selected: valueSerialized === JSON.stringify({ value: initialValue }),
+          });
+        }),
+      });
+    }
+  } else if (item.type === ConfigType.BOOLEAN) {
+    container.classList.toggle("-checked", !!initialValue);
+    input = $el<HTMLInputElement>(`input[type="checkbox"][id="${item.key}"]`, {
+      parent: container,
+      checked: initialValue,
+    });
+  } else {
+    input = $el(`input[id="${item.key}"]`, {
+      parent: container,
+      value: initialValue,
+    });
+  }
+  $el("div.fieldrow-value", { children: [input], parent: container });
+  return container;
+}
+
+/**
+ * A dialog to edit rgthree-comfy settings and config.
+ */
+export class RgthreeConfigDialog extends RgthreeDialog {
+  constructor() {
+    const content = $el("div");
+
+    content.appendChild(RgthreeConfigDialog.buildFieldset(CONFIGURABLE["features"]!, "Features"));
+    content.appendChild(RgthreeConfigDialog.buildFieldset(CONFIGURABLE["menus"]!, "Menus"));
+    content.appendChild(RgthreeConfigDialog.buildFieldset(CONFIGURABLE["groups"]!, "Groups"));
+    content.appendChild(RgthreeConfigDialog.buildFieldset(CONFIGURABLE["power_lora_loader"]!, "Power Lora Loader"));
+    content.appendChild(RgthreeConfigDialog.buildFieldset(CONFIGURABLE["advanced"]!, "Advanced"));
+
+    content.addEventListener("input", (e) => {
+      const changed = this.getChangedFormData();
+      ($$(".save-button", this.element)[0] as HTMLButtonElement).disabled =
+        !Object.keys(changed).length;
+    });
+    content.addEventListener("change", (e) => {
+      const changed = this.getChangedFormData();
+      ($$(".save-button", this.element)[0] as HTMLButtonElement).disabled =
+        !Object.keys(changed).length;
+    });
+
+    const dialogOptions: RgthreeDialogOptions = {
+      class: "-iconed -settings",
+      title: logoRgthree + `<h2>Settings - rgthree-comfy</h2>`,
+      content,
+      onBeforeClose: () => {
+        const changed = this.getChangedFormData();
+        if (Object.keys(changed).length) {
+          return confirm("Looks like there are unsaved changes. Are you sure you want close?");
+        }
+        return true;
+      },
+      buttons: [
+        {
+          label: "Save",
+          disabled: true,
+          className: "rgthree-button save-button -blue",
+          callback: async (e) => {
+            const changed = this.getChangedFormData();
+            if (!Object.keys(changed).length) {
+              this.close();
+              return;
+            }
+            const success = await CONFIG_SERVICE.setConfigValues(changed);
+            if (success) {
+              for (const key of Object.keys(changed)) {
+                Object.values(CONFIGURABLE)
+                  .flat()
+                  .find((f) => f.key === key)
+                  ?.onSave?.(changed[key]);
+              }
+              this.close();
+              rgthree.showMessage({
+                id: "config-success",
+                message: `${checkmark} Successfully saved rgthree-comfy settings!`,
+                timeout: 4000,
+              });
+              ($$(".save-button", this.element)[0] as HTMLButtonElement).disabled = true;
+            } else {
+              alert("There was an error saving rgthree-comfy configuration.");
+            }
+          },
+        },
+      ],
+    };
+    super(dialogOptions);
+  }
+
+  private static buildFieldset(datas: ConfigurationSchema[], label: string) {
+    const fieldset = $el(`fieldset`, { children: [$el(`legend[text="${label}"]`)] });
+    for (const data of datas) {
+      if (data.isDevOnly && !rgthree.isDevMode()) {
+        continue;
+      }
+      const container = $el("div.formrow");
+      container.appendChild(fieldrow(data));
+
+      if (data.subconfig) {
+        for (const subfeature of data.subconfig) {
+          container.appendChild(fieldrow(subfeature));
+        }
+      }
+      fieldset.appendChild(container);
+    }
+    return fieldset;
+  }
+
+  getChangedFormData() {
+    return $$("[data-name]", this.contentElement).reduce((acc: { [key: string]: any }, el) => {
+      const name = el.dataset["name"]!;
+      const type = el.dataset["type"]!;
+      const initialValue = CONFIG_SERVICE.getConfigValue(name);
+      let currentValueEl = $$("fieldset.rgthree-checklist-group, input, textarea, select", el)[0] as HTMLInputElement;
+      let currentValue: any = null;
+      if (type === String(ConfigType.BOOLEAN)) {
+        currentValue = currentValueEl.checked;
+        // Not sure I like this side effect in here, but it's easy to just do it now.
+        el.classList.toggle("-checked", currentValue);
+      } else {
+        currentValue = currentValueEl?.value;
+        if (currentValueEl.nodeName === "SELECT") {
+          currentValue = JSON.parse(currentValue).value;
+        } else if (currentValueEl.classList.contains('rgthree-checklist-group')) {
+          currentValue = [];
+          for (const check of $$<HTMLInputElement>('input[type="checkbox"]', currentValueEl)) {
+            if (check.checked) {
+              currentValue.push(check.value);
+            }
+          }
+        } else if (type === String(ConfigType.NUMBER)) {
+          currentValue = Number(currentValue) || initialValue;
+        }
+      }
+      if (JSON.stringify(currentValue) !== JSON.stringify(initialValue)) {
+        acc[name] = currentValue;
+      }
+      return acc;
+    }, {});
+  }
+}
+
+app.ui.settings.addSetting({
+  id: "rgthree.config",
+  defaultValue: null,
+  name: "Open rgthree-comfy config",
+  type: () => {
+    // Adds a row to open the dialog from the ComfyUI settings.
+    return $el("tr.rgthree-comfyui-settings-row", {
+      children: [
+        $el("td", {
+          child: `<div>${logoRgthree} [rgthree-comfy] configuration / settings</div>`,
+        }),
+        $el("td", {
+          child: $el('button.rgthree-button.-blue[text="rgthree-comfy settings"]', {
+            events: {
+              click: (e: PointerEvent) => {
+                new RgthreeConfigDialog().show();
+              },
+            },
+          }),
+        }),
+      ],
+    });
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/constants.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/constants.ts
new file mode 100644
index 0000000000000000000000000000000000000000..4e6a13f8ed895fdee59da922bd91ed90245741f7
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/constants.ts
@@ -0,0 +1,72 @@
+import {SERVICE as CONFIG_SERVICE} from "./services/config_service.js";
+
+export function addRgthree(str: string) {
+  return str + " (rgthree)";
+}
+
+export function stripRgthree(str: string) {
+  return str.replace(/\s*\(rgthree\)$/, "");
+}
+
+export const NodeTypesString = {
+  ANY_SWITCH: addRgthree("Any Switch"),
+  CONTEXT: addRgthree("Context"),
+  CONTEXT_BIG: addRgthree("Context Big"),
+  CONTEXT_SWITCH: addRgthree("Context Switch"),
+  CONTEXT_SWITCH_BIG: addRgthree("Context Switch Big"),
+  CONTEXT_MERGE: addRgthree("Context Merge"),
+  CONTEXT_MERGE_BIG: addRgthree("Context Merge Big"),
+  DYNAMIC_CONTEXT: addRgthree("Dynamic Context"),
+  DYNAMIC_CONTEXT_SWITCH: addRgthree("Dynamic Context Switch"),
+  DISPLAY_ANY: addRgthree("Display Any"),
+  IMAGE_OR_LATENT_SIZE: addRgthree("Image or Latent Size"),
+
+  NODE_MODE_RELAY: addRgthree("Mute / Bypass Relay"),
+  NODE_MODE_REPEATER: addRgthree("Mute / Bypass Repeater"),
+  FAST_MUTER: addRgthree("Fast Muter"),
+  FAST_BYPASSER: addRgthree("Fast Bypasser"),
+  FAST_GROUPS_MUTER: addRgthree("Fast Groups Muter"),
+  FAST_GROUPS_BYPASSER: addRgthree("Fast Groups Bypasser"),
+  FAST_ACTIONS_BUTTON: addRgthree("Fast Actions Button"),
+  LABEL: addRgthree("Label"),
+  POWER_PRIMITIVE: addRgthree("Power Primitive"),
+  POWER_PROMPT: addRgthree("Power Prompt"),
+  POWER_PROMPT_SIMPLE: addRgthree("Power Prompt - Simple"),
+  POWER_PUTER: addRgthree("Power Puter"),
+  POWER_CONDUCTOR: addRgthree("Power Conductor"),
+  SDXL_EMPTY_LATENT_IMAGE: addRgthree("SDXL Empty Latent Image"),
+  SDXL_POWER_PROMPT_POSITIVE: addRgthree("SDXL Power Prompt - Positive"),
+  SDXL_POWER_PROMPT_NEGATIVE: addRgthree("SDXL Power Prompt - Simple / Negative"),
+  POWER_LORA_LOADER: addRgthree("Power Lora Loader"),
+  KSAMPLER_CONFIG: addRgthree("KSampler Config"),
+  NODE_COLLECTOR: addRgthree("Node Collector"),
+  REROUTE: addRgthree("Reroute"),
+  RANDOM_UNMUTER: addRgthree("Random Unmuter"),
+  SEED: addRgthree("Seed"),
+  BOOKMARK: addRgthree("Bookmark"),
+  IMAGE_COMPARER: addRgthree("Image Comparer"),
+  IMAGE_INSET_CROP: addRgthree("Image Inset Crop"),
+};
+
+const UNRELEASED_KEYS = {
+  [NodeTypesString.DYNAMIC_CONTEXT]: "dynamic_context",
+  [NodeTypesString.DYNAMIC_CONTEXT_SWITCH]: "dynamic_context",
+  [NodeTypesString.POWER_CONDUCTOR]: "power_conductor",
+};
+
+
+/**
+ * Gets the list of nodes from NoteTypeString above, filtering any that are not applicable.
+ */
+export function getNodeTypeStrings() {
+  const unreleasedKeys = Object.keys(UNRELEASED_KEYS);
+  return Object.values(NodeTypesString)
+    .map((i) => stripRgthree(i))
+    .filter((i) => {
+      if (unreleasedKeys.includes(i)) {
+        return !!CONFIG_SERVICE.getConfigValue(`unreleased.${UNRELEASED_KEYS[i]}.enabled`)
+      }
+      return true;
+    })
+    .sort();
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/context.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/context.ts
new file mode 100644
index 0000000000000000000000000000000000000000..a49b7a9ed6bd779b2e2a9b8368659441731b2911
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/context.ts
@@ -0,0 +1,488 @@
+import type {
+  INodeInputSlot,
+  INodeOutputSlot,
+  LGraphCanvas as TLGraphCanvas,
+  LGraphNode as TLGraphNode,
+  LLink,
+  ISlotType,
+  ConnectByTypeOptions,
+} from "@comfyorg/frontend";
+import type {ComfyNodeDef} from "typings/comfy.js";
+
+import {app} from "scripts/app.js";
+import {
+  IoDirection,
+  addConnectionLayoutSupport,
+  addMenuItem,
+  matchLocalSlotsToServer,
+  replaceNode,
+} from "./utils.js";
+import {RgthreeBaseServerNode} from "./base_node.js";
+import {SERVICE as KEY_EVENT_SERVICE} from "./services/key_events_services.js";
+import {RgthreeBaseServerNodeConstructor} from "typings/rgthree.js";
+import {debounce, wait} from "rgthree/common/shared_utils.js";
+import {removeUnusedInputsFromEnd} from "./utils_inputs_outputs.js";
+import {NodeTypesString} from "./constants.js";
+
+/**
+ * Takes a non-context node and determins for its input or output slot, if there is a valid
+ * connection for an opposite context output or input slot.
+ */
+function findMatchingIndexByTypeOrName(
+  otherNode: TLGraphNode,
+  otherSlot: INodeInputSlot | INodeOutputSlot,
+  ctxSlots: INodeInputSlot[] | INodeOutputSlot[],
+) {
+  const otherNodeType = (otherNode.type || "").toUpperCase();
+  const otherNodeName = (otherNode.title || "").toUpperCase();
+  let otherSlotType = otherSlot.type as string;
+  if (Array.isArray(otherSlotType) || otherSlotType.includes(",")) {
+    otherSlotType = "COMBO";
+  }
+  const otherSlotName = otherSlot.name.toUpperCase().replace("OPT_", "").replace("_NAME", "");
+  let ctxSlotIndex = -1;
+  if (["CONDITIONING", "INT", "STRING", "FLOAT", "COMBO"].includes(otherSlotType)) {
+    ctxSlotIndex = ctxSlots.findIndex((ctxSlot) => {
+      const ctxSlotName = ctxSlot.name.toUpperCase().replace("OPT_", "").replace("_NAME", "");
+      let ctxSlotType = ctxSlot.type as string;
+      if (Array.isArray(ctxSlotType) || ctxSlotType.includes(",")) {
+        ctxSlotType = "COMBO";
+      }
+      if (ctxSlotType !== otherSlotType) {
+        return false;
+      }
+      // Straightforward matches.
+      if (
+        ctxSlotName === otherSlotName ||
+        (ctxSlotName === "SEED" && otherSlotName.includes("SEED")) ||
+        (ctxSlotName === "STEP_REFINER" && otherSlotName.includes("AT_STEP")) ||
+        (ctxSlotName === "STEP_REFINER" && otherSlotName.includes("REFINER_STEP"))
+      ) {
+        return true;
+      }
+      // If postive other node, try to match conditining and text.
+      if (
+        (otherNodeType.includes("POSITIVE") || otherNodeName.includes("POSITIVE")) &&
+        ((ctxSlotName === "POSITIVE" && otherSlotType === "CONDITIONING") ||
+          (ctxSlotName === "TEXT_POS_G" && otherSlotName.includes("TEXT_G")) ||
+          (ctxSlotName === "TEXT_POS_L" && otherSlotName.includes("TEXT_L")))
+      ) {
+        return true;
+      }
+      if (
+        (otherNodeType.includes("NEGATIVE") || otherNodeName.includes("NEGATIVE")) &&
+        ((ctxSlotName === "NEGATIVE" && otherSlotType === "CONDITIONING") ||
+          (ctxSlotName === "TEXT_NEG_G" && otherSlotName.includes("TEXT_G")) ||
+          (ctxSlotName === "TEXT_NEG_L" && otherSlotName.includes("TEXT_L")))
+      ) {
+        return true;
+      }
+      return false;
+    });
+  } else {
+    ctxSlotIndex = ctxSlots.map((s) => s.type).indexOf(otherSlotType);
+  }
+  return ctxSlotIndex;
+}
+
+/**
+ * A Base Context node for other context based nodes to extend.
+ */
+export class BaseContextNode extends RgthreeBaseServerNode {
+  constructor(title: string) {
+    super(title);
+  }
+
+  // LiteGraph adds more spacing than we want when calculating a nodes' `_collapsed_width`, so we'll
+  // override it with a setter and re-set it measured exactly as we want.
+  ___collapsed_width: number = 0;
+
+  //@ts-ignore - TS Doesn't like us overriding a property with accessors but, too bad.
+  override get _collapsed_width() {
+    return this.___collapsed_width;
+  }
+
+  override set _collapsed_width(width: number) {
+    const canvas = app.canvas as TLGraphCanvas;
+    const ctx = canvas.canvas.getContext("2d")!;
+    const oldFont = ctx.font;
+    ctx.font = canvas.title_text_font;
+    let title = this.title.trim();
+    this.___collapsed_width = 30 + (title ? 10 + ctx.measureText(title).width : 0);
+    ctx.font = oldFont;
+  }
+
+  override connectByType(
+    slot: number | string,
+    targetNode: TLGraphNode,
+    targetSlotType: ISlotType,
+    optsIn?: ConnectByTypeOptions,
+  ): LLink | null {
+    let canConnect = super.connectByType?.call(this, slot, targetNode, targetSlotType, optsIn);
+    if (!super.connectByType) {
+      canConnect = LGraphNode.prototype.connectByType.call(
+        this,
+        slot,
+        targetNode,
+        targetSlotType,
+        optsIn,
+      );
+    }
+    if (!canConnect && slot === 0) {
+      const ctrlKey = KEY_EVENT_SERVICE.ctrlKey;
+      // Okay, we've dragged a context and it can't connect.. let's connect all the other nodes.
+      // Unfortunately, we don't know which are null now, so we'll just connect any that are
+      // not already connected.
+      for (const [index, input] of (targetNode.inputs || []).entries()) {
+        if (input.link && !ctrlKey) {
+          continue;
+        }
+        const thisOutputSlot = findMatchingIndexByTypeOrName(targetNode, input, this.outputs);
+        if (thisOutputSlot > -1) {
+          this.connect(thisOutputSlot, targetNode, index);
+        }
+      }
+    }
+    return null;
+  }
+
+  override connectByTypeOutput(
+    slot: number | string,
+    sourceNode: TLGraphNode,
+    sourceSlotType: ISlotType,
+    optsIn?: ConnectByTypeOptions,
+  ): LLink | null {
+    let canConnect = super.connectByTypeOutput?.call(
+      this,
+      slot,
+      sourceNode,
+      sourceSlotType,
+      optsIn,
+    );
+    if (!super.connectByType) {
+      canConnect = LGraphNode.prototype.connectByTypeOutput.call(
+        this,
+        slot,
+        sourceNode,
+        sourceSlotType,
+        optsIn,
+      );
+    }
+    if (!canConnect && slot === 0) {
+      const ctrlKey = KEY_EVENT_SERVICE.ctrlKey;
+      // Okay, we've dragged a context and it can't connect.. let's connect all the other nodes.
+      // Unfortunately, we don't know which are null now, so we'll just connect any that are
+      // not already connected.
+      for (const [index, output] of (sourceNode.outputs || []).entries()) {
+        if (output.links?.length && !ctrlKey) {
+          continue;
+        }
+        const thisInputSlot = findMatchingIndexByTypeOrName(sourceNode, output, this.inputs);
+        if (thisInputSlot > -1) {
+          sourceNode.connect(index, this, thisInputSlot);
+        }
+      }
+    }
+    return null;
+  }
+
+  static override setUp(
+    comfyClass: typeof LGraphNode,
+    nodeData: ComfyNodeDef,
+    ctxClass: RgthreeBaseServerNodeConstructor,
+  ) {
+    RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, ctxClass);
+    // [🤮] ComfyUI only adds "required" inputs to the outputs list when dragging an output to
+    // empty space, but since RGTHREE_CONTEXT is optional, it doesn't get added to the menu because
+    // ...of course. So, we'll manually add it. Of course, we also have to do this in a timeout
+    // because ComfyUI clears out `LiteGraph.slot_types_default_out` in its own 'Comfy.SlotDefaults'
+    // extension and we need to wait for that to happen.
+    wait(500).then(() => {
+      LiteGraph.slot_types_default_out["RGTHREE_CONTEXT"] =
+        LiteGraph.slot_types_default_out["RGTHREE_CONTEXT"] || [];
+      LiteGraph.slot_types_default_out["RGTHREE_CONTEXT"].push((comfyClass as any).comfyClass);
+    });
+  }
+
+  static override onRegisteredForOverride(comfyClass: any, ctxClass: any) {
+    addConnectionLayoutSupport(ctxClass, app, [
+      ["Left", "Right"],
+      ["Right", "Left"],
+    ]);
+    setTimeout(() => {
+      ctxClass.category = comfyClass.category;
+    });
+  }
+}
+
+/**
+ * The original Context node.
+ */
+class ContextNode extends BaseContextNode {
+  static override title = NodeTypesString.CONTEXT;
+  static override type = NodeTypesString.CONTEXT;
+  static comfyClass = NodeTypesString.CONTEXT;
+
+  constructor(title = ContextNode.title) {
+    super(title);
+  }
+
+  static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    BaseContextNode.setUp(comfyClass, nodeData, ContextNode);
+  }
+
+  static override onRegisteredForOverride(comfyClass: any, ctxClass: any) {
+    BaseContextNode.onRegisteredForOverride(comfyClass, ctxClass);
+    addMenuItem(ContextNode, app, {
+      name: "Convert To Context Big",
+      callback: (node) => {
+        replaceNode(node, ContextBigNode.type);
+      },
+    });
+  }
+}
+
+/**
+ * The Context Big node.
+ */
+class ContextBigNode extends BaseContextNode {
+  static override title = NodeTypesString.CONTEXT_BIG;
+  static override type = NodeTypesString.CONTEXT_BIG;
+  static comfyClass = NodeTypesString.CONTEXT_BIG;
+
+  constructor(title = ContextBigNode.title) {
+    super(title);
+  }
+
+  static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    BaseContextNode.setUp(comfyClass, nodeData, ContextBigNode);
+  }
+
+  static override onRegisteredForOverride(comfyClass: any, ctxClass: any) {
+    BaseContextNode.onRegisteredForOverride(comfyClass, ctxClass);
+    addMenuItem(ContextBigNode, app, {
+      name: "Convert To Context (Original)",
+      callback: (node) => {
+        replaceNode(node, ContextNode.type);
+      },
+    });
+  }
+}
+
+/**
+ * A base node for Context Switche nodes and Context Merges nodes that will always add another empty
+ * ctx input, no less than five.
+ */
+class BaseContextMultiCtxInputNode extends BaseContextNode {
+  private stabilizeBound = this.stabilize.bind(this);
+
+  constructor(title: string) {
+    super(title);
+    // Adding five. Note, configure will add as many as was in the stored workflow automatically.
+    this.addContextInput(5);
+  }
+
+  private addContextInput(num = 1) {
+    for (let i = 0; i < num; i++) {
+      this.addInput(`ctx_${String(this.inputs.length + 1).padStart(2, "0")}`, "RGTHREE_CONTEXT");
+    }
+  }
+
+  override onConnectionsChange(
+    type: number,
+    slotIndex: number,
+    isConnected: boolean,
+    link: LLink,
+    ioSlot: INodeInputSlot | INodeOutputSlot,
+  ): void {
+    super.onConnectionsChange?.apply(this, [...arguments] as any);
+    if (type === LiteGraph.INPUT) {
+      this.scheduleStabilize();
+    }
+  }
+
+  private scheduleStabilize(ms = 64) {
+    return debounce(this.stabilizeBound, 64);
+  }
+
+  /**
+   * Stabilizes the inputs; removing any disconnected ones from the bottom, then adding an empty
+   * one to the end so we always have one empty one to expand.
+   */
+  private stabilize() {
+    removeUnusedInputsFromEnd(this, 4);
+    this.addContextInput();
+  }
+}
+
+/**
+ * The Context Switch (original) node.
+ */
+class ContextSwitchNode extends BaseContextMultiCtxInputNode {
+  static override title = NodeTypesString.CONTEXT_SWITCH;
+  static override type = NodeTypesString.CONTEXT_SWITCH;
+  static comfyClass = NodeTypesString.CONTEXT_SWITCH;
+
+  constructor(title = ContextSwitchNode.title) {
+    super(title);
+  }
+
+  static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    BaseContextNode.setUp(comfyClass, nodeData, ContextSwitchNode);
+  }
+
+  static override onRegisteredForOverride(comfyClass: any, ctxClass: any) {
+    BaseContextNode.onRegisteredForOverride(comfyClass, ctxClass);
+    addMenuItem(ContextSwitchNode, app, {
+      name: "Convert To Context Switch Big",
+      callback: (node) => {
+        replaceNode(node, ContextSwitchBigNode.type);
+      },
+    });
+  }
+}
+
+/**
+ * The Context Switch Big node.
+ */
+class ContextSwitchBigNode extends BaseContextMultiCtxInputNode {
+  static override title = NodeTypesString.CONTEXT_SWITCH_BIG;
+  static override type = NodeTypesString.CONTEXT_SWITCH_BIG;
+  static comfyClass = NodeTypesString.CONTEXT_SWITCH_BIG;
+
+  constructor(title = ContextSwitchBigNode.title) {
+    super(title);
+  }
+
+  static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    BaseContextNode.setUp(comfyClass, nodeData, ContextSwitchBigNode);
+  }
+
+  static override onRegisteredForOverride(comfyClass: any, ctxClass: any) {
+    BaseContextNode.onRegisteredForOverride(comfyClass, ctxClass);
+    addMenuItem(ContextSwitchBigNode, app, {
+      name: "Convert To Context Switch",
+      callback: (node) => {
+        replaceNode(node, ContextSwitchNode.type);
+      },
+    });
+  }
+}
+
+/**
+ * The Context Merge (original) node.
+ */
+class ContextMergeNode extends BaseContextMultiCtxInputNode {
+  static override title = NodeTypesString.CONTEXT_MERGE;
+  static override type = NodeTypesString.CONTEXT_MERGE;
+  static comfyClass = NodeTypesString.CONTEXT_MERGE;
+
+  constructor(title = ContextMergeNode.title) {
+    super(title);
+  }
+
+  static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    BaseContextNode.setUp(comfyClass, nodeData, ContextMergeNode);
+  }
+
+  static override onRegisteredForOverride(comfyClass: any, ctxClass: any) {
+    BaseContextNode.onRegisteredForOverride(comfyClass, ctxClass);
+    addMenuItem(ContextMergeNode, app, {
+      name: "Convert To Context Merge Big",
+      callback: (node) => {
+        replaceNode(node, ContextMergeBigNode.type);
+      },
+    });
+  }
+}
+
+/**
+ * The Context Switch Big node.
+ */
+class ContextMergeBigNode extends BaseContextMultiCtxInputNode {
+  static override title = NodeTypesString.CONTEXT_MERGE_BIG;
+  static override type = NodeTypesString.CONTEXT_MERGE_BIG;
+  static comfyClass = NodeTypesString.CONTEXT_MERGE_BIG;
+
+  constructor(title = ContextMergeBigNode.title) {
+    super(title);
+  }
+
+  static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    BaseContextNode.setUp(comfyClass, nodeData, ContextMergeBigNode);
+  }
+
+  static override onRegisteredForOverride(comfyClass: any, ctxClass: any) {
+    BaseContextNode.onRegisteredForOverride(comfyClass, ctxClass);
+    addMenuItem(ContextMergeBigNode, app, {
+      name: "Convert To Context Switch",
+      callback: (node) => {
+        replaceNode(node, ContextMergeNode.type);
+      },
+    });
+  }
+}
+
+const contextNodes = [
+  ContextNode,
+  ContextBigNode,
+  ContextSwitchNode,
+  ContextSwitchBigNode,
+  ContextMergeNode,
+  ContextMergeBigNode,
+];
+const contextTypeToServerDef: {[type: string]: ComfyNodeDef} = {};
+
+function fixBadConfigs(node: ContextNode) {
+  // Dumb mistake, but let's fix our mispelling. This will probably need to stay in perpetuity to
+  // keep any old workflows operating.
+  const wrongName = node.outputs.find((o, i) => o.name === "CLIP_HEIGTH");
+  if (wrongName) {
+    wrongName.name = "CLIP_HEIGHT";
+  }
+}
+
+app.registerExtension({
+  name: "rgthree.Context",
+  async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    // Loop over out context nodes and see if any match the server data.
+    for (const ctxClass of contextNodes) {
+      if (nodeData.name === ctxClass.type) {
+        contextTypeToServerDef[ctxClass.type] = nodeData;
+        ctxClass.setUp(nodeType, nodeData);
+        break;
+      }
+    }
+  },
+
+  async nodeCreated(node: TLGraphNode) {
+    const type = node.type || (node.constructor as any).type;
+    const serverDef = type && contextTypeToServerDef[type];
+    if (serverDef) {
+      fixBadConfigs(node as ContextNode);
+      matchLocalSlotsToServer(node, IoDirection.OUTPUT, serverDef);
+      // Switches don't need to change inputs, only context outputs
+      if (!type!.includes("Switch") && !type!.includes("Merge")) {
+        matchLocalSlotsToServer(node, IoDirection.INPUT, serverDef);
+      }
+      // }, 100);
+    }
+  },
+
+  /**
+   * When we're loaded from the server, check if we're using an out of date version and update our
+   * inputs / outputs to match.
+   */
+  async loadedGraphNode(node: TLGraphNode) {
+    const type = node.type || (node.constructor as any).type;
+    const serverDef = type && contextTypeToServerDef[type];
+    if (serverDef) {
+      fixBadConfigs(node as ContextNode);
+      matchLocalSlotsToServer(node, IoDirection.OUTPUT, serverDef);
+      // Switches don't need to change inputs, only context outputs
+      if (!type!.includes("Switch") && !type!.includes("Merge")) {
+        matchLocalSlotsToServer(node, IoDirection.INPUT, serverDef);
+      }
+    }
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/dialog_info.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/dialog_info.ts
new file mode 100644
index 0000000000000000000000000000000000000000..6c8b02a4e02bab68e44aac56f7789df44a804bc1
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/dialog_info.ts
@@ -0,0 +1,436 @@
+import {RgthreeDialog, RgthreeDialogOptions} from "rgthree/common/dialog.js";
+import {
+  createElement as $el,
+  empty,
+  appendChildren,
+  getClosestOrSelf,
+  query,
+  queryAll,
+  setAttributes,
+} from "rgthree/common/utils_dom.js";
+import {
+  logoCivitai,
+  link,
+  pencilColored,
+  diskColored,
+  dotdotdot,
+} from "rgthree/common/media/svgs.js";
+import {RgthreeModelInfo} from "typings/rgthree.js";
+import {CHECKPOINT_INFO_SERVICE, LORA_INFO_SERVICE} from "rgthree/common/model_info_service.js";
+import {rgthree} from "./rgthree.js";
+import {MenuButton} from "rgthree/common/menu.js";
+import {generateId, injectCss} from "rgthree/common/shared_utils.js";
+import {rgthreeApi} from "rgthree/common/rgthree_api.js";
+
+/**
+ * A dialog that displays information about a model/lora/etc.
+ */
+abstract class RgthreeInfoDialog extends RgthreeDialog {
+  private modifiedModelData = false;
+  private modelInfo: RgthreeModelInfo | null = null;
+
+  constructor(file: string) {
+    const dialogOptions: RgthreeDialogOptions = {
+      class: "rgthree-info-dialog",
+      title: `<h2>Loading...</h2>`,
+      content: "<center>Loading..</center>",
+      onBeforeClose: () => {
+        return true;
+      },
+    };
+    super(dialogOptions);
+    this.init(file);
+  }
+
+  abstract getModelInfo(file: string): Promise<RgthreeModelInfo | null>;
+  abstract refreshModelInfo(file: string): Promise<RgthreeModelInfo | null>;
+  abstract clearModelInfo(file: string): Promise<RgthreeModelInfo | null>;
+
+  private async init(file: string) {
+    const cssPromise = injectCss("rgthree/common/css/dialog_model_info.css");
+    this.modelInfo = await this.getModelInfo(file);
+    await cssPromise;
+    this.setContent(this.getInfoContent());
+    this.setTitle(this.modelInfo?.["name"] || this.modelInfo?.["file"] || "Unknown");
+    this.attachEvents();
+  }
+
+  protected override getCloseEventDetail(): {detail: any} {
+    const detail = {
+      dirty: this.modifiedModelData,
+    };
+    return {detail};
+  }
+
+  private attachEvents() {
+    this.contentElement.addEventListener("click", async (e: MouseEvent) => {
+      const target = getClosestOrSelf(e.target as HTMLElement, "[data-action]");
+      const action = target?.getAttribute("data-action");
+      if (!target || !action) {
+        return;
+      }
+      await this.handleEventAction(action, target, e);
+    });
+  }
+
+  private async handleEventAction(action: string, target: HTMLElement, e?: Event) {
+    const info = this.modelInfo!;
+    if (!info?.file) {
+      return;
+    }
+    if (action === "fetch-civitai") {
+      this.modelInfo = await this.refreshModelInfo(info.file);
+      this.setContent(this.getInfoContent());
+      this.setTitle(this.modelInfo?.["name"] || this.modelInfo?.["file"] || "Unknown");
+    } else if (action === "copy-trained-words") {
+      const selected = queryAll(".-rgthree-is-selected", target.closest("tr")!);
+      const text = selected.map((el) => el.getAttribute("data-word")).join(", ");
+      await navigator.clipboard.writeText(text);
+      rgthree.showMessage({
+        id: "copy-trained-words-" + generateId(4),
+        type: "success",
+        message: `Successfully copied ${selected.length} key word${
+          selected.length === 1 ? "" : "s"
+        }.`,
+        timeout: 4000,
+      });
+    } else if (action === "toggle-trained-word") {
+      target?.classList.toggle("-rgthree-is-selected");
+      const tr = target.closest("tr");
+      if (tr) {
+        const span = query("td:first-child > *", tr)!;
+        let small = query("small", span);
+        if (!small) {
+          small = $el("small", {parent: span});
+        }
+        const num = queryAll(".-rgthree-is-selected", tr).length;
+        small.innerHTML = num
+          ? `${num} selected | <span role="button" data-action="copy-trained-words">Copy</span>`
+          : "";
+        // this.handleEventAction('copy-trained-words', target, e);
+      }
+    } else if (action === "edit-row") {
+      const tr = target!.closest("tr")!;
+      const td = query("td:nth-child(2)", tr)!;
+      const input = td.querySelector("input,textarea");
+      if (!input) {
+        const fieldName = tr.dataset["fieldName"] as string;
+        tr.classList.add("-rgthree-editing");
+        const isTextarea = fieldName === "userNote";
+        const input = $el(`${isTextarea ? "textarea" : 'input[type="text"]'}`, {
+          value: td.textContent,
+        });
+        input.addEventListener("keydown", (e) => {
+          if (!isTextarea && e.key === "Enter") {
+            const modified = saveEditableRow(info!, tr, true);
+            this.modifiedModelData = this.modifiedModelData || modified;
+            e.stopPropagation();
+            e.preventDefault();
+          } else if (e.key === "Escape") {
+            const modified = saveEditableRow(info!, tr, false);
+            this.modifiedModelData = this.modifiedModelData || modified;
+            e.stopPropagation();
+            e.preventDefault();
+          }
+        });
+        appendChildren(empty(td), [input]);
+        input.focus();
+      } else if (target!.nodeName.toLowerCase() === "button") {
+        const modified = saveEditableRow(info!, tr, true);
+        this.modifiedModelData = this.modifiedModelData || modified;
+      }
+      e?.preventDefault();
+      e?.stopPropagation();
+    }
+  }
+
+  private getInfoContent() {
+    const info = this.modelInfo || {};
+    const civitaiLink = info.links?.find((i) => i.includes("civitai.com/models"));
+    const html = `
+      <ul class="rgthree-info-area">
+        <li title="Type" class="rgthree-info-tag -type -type-${(
+          info.type || ""
+        ).toLowerCase()}"><span>${info.type || ""}</span></li>
+        <li title="Base Model" class="rgthree-info-tag -basemodel -basemodel-${(
+          info.baseModel || ""
+        ).toLowerCase()}"><span>${info.baseModel || ""}</span></li>
+        <li class="rgthree-info-menu" stub="menu"></li>
+        ${
+          ""
+          //   !civitaiLink
+          //     ? ""
+          //     : `
+          //   <li title="Visit on Civitai" class="-link -civitai"><a href="${civitaiLink}" target="_blank">Civitai ${link}</a></li>
+          // `
+        }
+      </ul>
+
+      <table class="rgthree-info-table">
+        ${infoTableRow("File", info.file || "")}
+        ${infoTableRow("Hash (sha256)", info.sha256 || "")}
+        ${
+          civitaiLink
+            ? infoTableRow(
+                "Civitai",
+                `<a href="${civitaiLink}" target="_blank">${logoCivitai}View on Civitai</a>`,
+              )
+            : info.raw?.civitai?.error === "Model not found"
+              ? infoTableRow(
+                  "Civitai",
+                  '<i>Model not found</i> <span class="-help" title="The model was not found on civitai with the sha256 hash. It\'s possible the model was removed, re-uploaded, or was never on civitai to begin with."></span>',
+                )
+              : info.raw?.civitai?.error
+                ? infoTableRow("Civitai", info.raw?.civitai?.error)
+                : !info.raw?.civitai
+                  ? infoTableRow(
+                      "Civitai",
+                      `<button class="rgthree-button" data-action="fetch-civitai">Fetch info from civitai</button>`,
+                    )
+                  : ""
+        }
+
+        ${infoTableRow(
+          "Name",
+          info.name || info.raw?.metadata?.ss_output_name || "",
+          "The name for display.",
+          "name",
+        )}
+
+        ${
+          !info.baseModelFile && !info.baseModelFile
+            ? ""
+            : infoTableRow(
+                "Base Model",
+                (info.baseModel || "") + (info.baseModelFile ? ` (${info.baseModelFile})` : ""),
+              )
+        }
+
+
+        ${
+          !info.trainedWords?.length
+            ? ""
+            : infoTableRow(
+                "Trained Words",
+                getTrainedWordsMarkup(info.trainedWords) ?? "",
+                "Trained words from the metadata and/or civitai. Click to select for copy.",
+              )
+        }
+
+        ${
+          !info.raw?.metadata?.ss_clip_skip || info.raw?.metadata?.ss_clip_skip == "None"
+            ? ""
+            : infoTableRow("Clip Skip", info.raw?.metadata?.ss_clip_skip)
+        }
+        ${infoTableRow(
+          "Strength Min",
+          info.strengthMin ?? "",
+          "The recommended minimum strength, In the Power Lora Loader node, strength will signal when it is below this threshold.",
+          "strengthMin",
+        )}
+        ${infoTableRow(
+          "Strength Max",
+          info.strengthMax ?? "",
+          "The recommended maximum strength. In the Power Lora Loader node, strength will signal when it is above this threshold.",
+          "strengthMax",
+        )}
+        ${
+          "" /*infoTableRow(
+          "User Tags",
+          info.userTags?.join(", ") ?? "",
+          "A list of tags to make filtering easier  in the Power Lora Chooser.",
+          "userTags",
+        )*/
+        }
+        ${infoTableRow(
+          "Additional Notes",
+          info.userNote ?? "",
+          "Additional notes you'd like to keep and reference in the info dialog.",
+          "userNote",
+        )}
+
+      </table>
+
+      <ul class="rgthree-info-images">${
+        info.images
+          ?.map(
+            (img) => `
+        <li>
+          <figure>${
+            img.type === 'video'
+              ? `<video src="${img.url}" autoplay loop></video>`
+              : `<img src="${img.url}" />`
+            }
+            <figcaption><!--
+              -->${imgInfoField(
+                "",
+                img.civitaiUrl
+                  ? `<a href="${img.civitaiUrl}" target="_blank">civitai${link}</a>`
+                  : undefined,
+              )}<!--
+              -->${imgInfoField("seed", img.seed)}<!--
+              -->${imgInfoField("steps", img.steps)}<!--
+              -->${imgInfoField("cfg", img.cfg)}<!--
+              -->${imgInfoField("sampler", img.sampler)}<!--
+              -->${imgInfoField("model", img.model)}<!--
+              -->${imgInfoField("positive", img.positive)}<!--
+              -->${imgInfoField("negative", img.negative)}<!--
+            --><!--${
+              ""
+              //   img.resources?.length
+              //     ? `
+              //   <tr><td>Resources</td><td><ul>
+              //   ${(img.resources || [])
+              //     .map(
+              //       (r) => `
+              //     <li>[${r.type || ""}] ${r.name || ""} ${
+              //       r.weight != null ? `@ ${r.weight}` : ""
+              //     }</li>
+              //   `,
+              //     )
+              //     .join("")}
+              //   </ul></td></tr>
+              // `
+              //     : ""
+            }--></figcaption>
+          </figure>
+        </li>`,
+          )
+          .join("") ?? ""
+      }</ul>
+    `;
+
+    const div = $el("div", {html});
+
+    if (rgthree.isDevMode()) {
+      setAttributes(query('[stub="menu"]', div)!, {
+        children: [
+          new MenuButton({
+            icon: dotdotdot,
+            options: [
+              {label: "More Actions", type: "title"},
+              {
+                label: "Open API JSON",
+                callback: async (e: PointerEvent) => {
+                  if (this.modelInfo?.file) {
+                    window.open(
+                      `rgthree/api/loras/info?file=${encodeURIComponent(this.modelInfo.file)}`,
+                    );
+                  }
+                },
+              },
+              {
+                label: "Clear all local info",
+                callback: async (e: PointerEvent) => {
+                  if (this.modelInfo?.file) {
+                    this.modelInfo = await LORA_INFO_SERVICE.clearFetchedInfo(this.modelInfo.file);
+                    this.setContent(this.getInfoContent());
+                    this.setTitle(
+                      this.modelInfo?.["name"] || this.modelInfo?.["file"] || "Unknown",
+                    );
+                  }
+                },
+              },
+            ],
+          }),
+        ],
+      });
+    }
+
+    return div;
+  }
+}
+
+export class RgthreeLoraInfoDialog extends RgthreeInfoDialog {
+  override async getModelInfo(file: string) {
+    return LORA_INFO_SERVICE.getInfo(file, false, false);
+  }
+  override async refreshModelInfo(file: string) {
+    return LORA_INFO_SERVICE.refreshInfo(file);
+  }
+  override async clearModelInfo(file: string) {
+    return LORA_INFO_SERVICE.clearFetchedInfo(file);
+  }
+}
+
+export class RgthreeCheckpointInfoDialog extends RgthreeInfoDialog {
+  override async getModelInfo(file: string) {
+    return CHECKPOINT_INFO_SERVICE.getInfo(file, false, false);
+  }
+  override async refreshModelInfo(file: string) {
+    return CHECKPOINT_INFO_SERVICE.refreshInfo(file);
+  }
+  override async clearModelInfo(file: string) {
+    return CHECKPOINT_INFO_SERVICE.clearFetchedInfo(file);
+  }
+}
+
+/**
+ * Generates a uniform markup string for a table row.
+ */
+function infoTableRow(
+  name: string,
+  value: string | number,
+  help: string = "",
+  editableFieldName = "",
+) {
+  return `
+    <tr class="${editableFieldName ? "editable" : ""}" ${
+      editableFieldName ? `data-field-name="${editableFieldName}"` : ""
+    }>
+      <td><span>${name} ${help ? `<span class="-help" title="${help}"></span>` : ""}<span></td>
+      <td ${editableFieldName ? "" : 'colspan="2"'}>${
+        String(value).startsWith("<") ? value : `<span>${value}<span>`
+      }</td>
+      ${
+        editableFieldName
+          ? `<td style="width: 24px;"><button class="rgthree-button-reset rgthree-button-edit" data-action="edit-row">${pencilColored}${diskColored}</button></td>`
+          : ""
+      }
+    </tr>`;
+}
+
+function getTrainedWordsMarkup(words: RgthreeModelInfo["trainedWords"]) {
+  let markup = `<ul class="rgthree-info-trained-words-list">`;
+  for (const wordData of words || []) {
+    markup += `<li title="${wordData.word}" data-word="${
+      wordData.word
+    }" class="rgthree-info-trained-words-list-item" data-action="toggle-trained-word">
+      <span>${wordData.word}</span>
+      ${wordData.civitai ? logoCivitai : ""}
+      ${wordData.count != null ? `<small>${wordData.count}</small>` : ""}
+    </li>`;
+  }
+  markup += `</ul>`;
+  return markup;
+}
+
+/**
+ * Saves / cancels an editable row. Returns a boolean if the data was modified.
+ */
+function saveEditableRow(info: RgthreeModelInfo, tr: HTMLElement, saving = true): boolean {
+  const fieldName = tr.dataset["fieldName"] as "file";
+  const input = query<HTMLInputElement>("input,textarea", tr)!;
+  let newValue = info[fieldName] ?? "";
+  let modified = false;
+  if (saving) {
+    newValue = input!.value;
+    if (fieldName.startsWith("strength")) {
+      if (Number.isNaN(Number(newValue))) {
+        alert(`You must enter a number into the ${fieldName} field.`);
+        return false;
+      }
+      newValue = (Math.round(Number(newValue) * 100) / 100).toFixed(2);
+    }
+    LORA_INFO_SERVICE.savePartialInfo(info.file!, {[fieldName]: newValue});
+    modified = true;
+  }
+  tr.classList.remove("-rgthree-editing");
+  const td = query("td:nth-child(2)", tr)!;
+  appendChildren(empty(td), [$el("span", {text: newValue})]);
+  return modified;
+}
+
+function imgInfoField(label: string, value?: string | number) {
+  return value != null ? `<span>${label ? `<label>${label} </label>` : ""}${value}</span>` : "";
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/display_any.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/display_any.ts
new file mode 100644
index 0000000000000000000000000000000000000000..f2c725a5eef7db3faacf58ceb63ed01c3a341760
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/display_any.ts
@@ -0,0 +1,71 @@
+import type {LGraphNodeConstructor, LGraphNode as TLGraphNode} from "@comfyorg/frontend";
+import type {ComfyNodeDef} from "typings/comfy.js";
+import type {ComfyApp} from "@comfyorg/frontend";
+
+import {app} from "scripts/app.js";
+import {ComfyWidgets} from "scripts/widgets.js";
+import {addConnectionLayoutSupport} from "./utils.js";
+import {rgthree} from "./rgthree.js";
+
+let hasShownAlertForUpdatingInt = false;
+
+app.registerExtension({
+  name: "rgthree.DisplayAny",
+  async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef, app: ComfyApp) {
+    if (nodeData.name === "Display Any (rgthree)" || nodeData.name === "Display Int (rgthree)") {
+      const onNodeCreated = nodeType.prototype.onNodeCreated;
+      nodeType.prototype.onNodeCreated = function () {
+        onNodeCreated ? onNodeCreated.apply(this, []) : undefined;
+
+        (this as any).showValueWidget = ComfyWidgets["STRING"](
+          this,
+          "output",
+          ["STRING", {multiline: true}],
+          app,
+        ).widget;
+        (this as any).showValueWidget.inputEl!.readOnly = true;
+        (this as any).showValueWidget.serializeValue = async (node: TLGraphNode, index: number) => {
+          const n =
+            rgthree.getNodeFromInitialGraphToPromptSerializedWorkflowBecauseComfyUIBrokeStuff(node);
+          if (n) {
+            // Since we need a round trip to get the value, the serizalized value means nothing, and
+            // saving it to the metadata would just be confusing. So, we clear it here.
+            n.widgets_values![index] = "";
+          } else {
+            console.warn(
+              "No serialized node found in workflow. May be attributed to " +
+                "https://github.com/comfyanonymous/ComfyUI/issues/2193",
+            );
+          }
+          return "";
+        };
+      };
+
+      addConnectionLayoutSupport(nodeType as LGraphNodeConstructor, app, [["Left"], ["Right"]]);
+
+      const onExecuted = nodeType.prototype.onExecuted;
+      nodeType.prototype.onExecuted = function (message: any) {
+        onExecuted?.apply(this, [message]);
+        (this as any).showValueWidget.value = message.text[0];
+      };
+    }
+  },
+
+  // This ports Display Int to DisplayAny, but ComfyUI still shows an error.
+  // If https://github.com/comfyanonymous/ComfyUI/issues/1527 is fixed, this could work.
+  // async loadedGraphNode(node: TLGraphNode) {
+  //   if (node.type === "Display Int (rgthree)") {
+  //     replaceNode(node, "Display Any (rgthree)", new Map([["input", "source"]]));
+  //     if (!hasShownAlertForUpdatingInt) {
+  //       hasShownAlertForUpdatingInt = true;
+  //       setTimeout(() => {
+  //         alert(
+  //           "Don't worry, your 'Display Int' nodes have been updated to the new " +
+  //             "'Display Any' nodes! You can ignore the error message underneath (for that node)." +
+  //             "\n\nThanks.\n- rgthree",
+  //         );
+  //       }, 128);
+  //     }
+  //   }
+  // },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/dynamic_context.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/dynamic_context.ts
new file mode 100644
index 0000000000000000000000000000000000000000..a964d6209159e0a7f9e2c112415dabc001a41a70
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/dynamic_context.ts
@@ -0,0 +1,302 @@
+import type {
+  IContextMenuValue,
+  IFoundSlot,
+  INodeInputSlot,
+  INodeOutputSlot,
+  ISlotType,
+  LGraphNode,
+  LLink,
+} from "@comfyorg/frontend";
+import type {ComfyNodeDef} from "typings/comfy.js";
+
+import {app} from "scripts/app.js";
+import {
+  IoDirection,
+  followConnectionUntilType,
+  getConnectedInputInfosAndFilterPassThroughs,
+} from "./utils.js";
+import {rgthree} from "./rgthree.js";
+import {
+  SERVICE as CONTEXT_SERVICE,
+  InputMutation,
+  InputMutationOperation,
+} from "./services/context_service.js";
+import {NodeTypesString} from "./constants.js";
+import {removeUnusedInputsFromEnd} from "./utils_inputs_outputs.js";
+import {DynamicContextNodeBase} from "./dynamic_context_base.js";
+import {SERVICE as CONFIG_SERVICE} from "./services/config_service.js";
+
+const OWNED_PREFIX = "+";
+const REGEX_OWNED_PREFIX = /^\+\s*/;
+const REGEX_EMPTY_INPUT = /^\+\s*$/;
+
+/**
+ * The Dynamic Context node.
+ */
+export class DynamicContextNode extends DynamicContextNodeBase {
+  static override title = NodeTypesString.DYNAMIC_CONTEXT;
+  static override type = NodeTypesString.DYNAMIC_CONTEXT;
+  static comfyClass = NodeTypesString.DYNAMIC_CONTEXT;
+
+  constructor(title = DynamicContextNode.title) {
+    super(title);
+  }
+
+  override onNodeCreated() {
+    this.addInput("base_ctx", "RGTHREE_DYNAMIC_CONTEXT");
+    this.ensureOneRemainingNewInputSlot();
+    super.onNodeCreated();
+  }
+
+  override onConnectionsChange(
+    type: ISlotType,
+    slotIndex: number,
+    isConnected: boolean,
+    link: LLink | null | undefined,
+    ioSlot: INodeInputSlot | INodeOutputSlot,
+  ): void {
+    super.onConnectionsChange?.call(this, type, slotIndex, isConnected, link, ioSlot);
+    if (this.configuring) {
+      return;
+    }
+    if (type === LiteGraph.INPUT) {
+      if (isConnected) {
+        this.handleInputConnected(slotIndex);
+      } else {
+        this.handleInputDisconnected(slotIndex);
+      }
+    }
+  }
+
+  override onConnectInput(
+    inputIndex: number,
+    outputType: INodeOutputSlot["type"],
+    outputSlot: INodeOutputSlot,
+    outputNode: LGraphNode,
+    outputIndex: number,
+  ): boolean {
+    let canConnect = true;
+    if (super.onConnectInput) {
+      canConnect = super.onConnectInput.apply(this, [...arguments] as any);
+    }
+    if (
+      canConnect &&
+      outputNode instanceof DynamicContextNode &&
+      outputIndex === 0 &&
+      inputIndex !== 0
+    ) {
+      const [n, v] = rgthree.logger.warnParts(
+        "Currently, you can only connect a context node in the first slot.",
+      );
+      console[n]?.call(console, ...v);
+      canConnect = false;
+    }
+    return canConnect;
+  }
+
+  handleInputConnected(slotIndex: number) {
+    const ioSlot = this.inputs[slotIndex];
+    const connectedIndexes = [];
+    if (slotIndex === 0) {
+      let baseNodeInfos = getConnectedInputInfosAndFilterPassThroughs(this, this, 0);
+      const baseNodes = baseNodeInfos.map((n) => n.node)!;
+      const baseNodesDynamicCtx = baseNodes[0] as DynamicContextNodeBase;
+      if (baseNodesDynamicCtx?.provideInputsData) {
+        const inputsData = CONTEXT_SERVICE.getDynamicContextInputsData(baseNodesDynamicCtx);
+        console.log("inputsData", inputsData);
+        for (const input of baseNodesDynamicCtx.provideInputsData()) {
+          if (input.name === "base_ctx" || input.name === "+") {
+            continue;
+          }
+          this.addContextInput(input.name, input.type, input.index);
+          this.stabilizeNames();
+        }
+      }
+    } else if (this.isInputSlotForNewInput(slotIndex)) {
+      this.handleNewInputConnected(slotIndex);
+    }
+  }
+
+  isInputSlotForNewInput(slotIndex: number) {
+    const ioSlot = this.inputs[slotIndex];
+    return ioSlot && ioSlot.name === "+" && ioSlot.type === "*";
+  }
+
+  handleNewInputConnected(slotIndex: number) {
+    if (!this.isInputSlotForNewInput(slotIndex)) {
+      throw new Error('Expected the incoming slot index to be the "new input" input.');
+    }
+    const ioSlot = this.inputs[slotIndex]!;
+    let cxn = null;
+    if (ioSlot.link != null) {
+      cxn = followConnectionUntilType(this, IoDirection.INPUT, slotIndex, true);
+    }
+    if (cxn?.type && cxn?.name) {
+      let name = this.addOwnedPrefix(this.getNextUniqueNameForThisNode(cxn.name));
+      if (name.match(/^\+\s*[A-Z_]+(\.\d+)?$/)) {
+        name = name.toLowerCase();
+      }
+      ioSlot.name = name;
+      ioSlot.type = cxn.type as string;
+      ioSlot.removable = true;
+      while (!this.outputs[slotIndex]) {
+        this.addOutput("*", "*");
+      }
+      this.outputs[slotIndex]!.type = cxn.type as string;
+      this.outputs[slotIndex]!.name = this.stripOwnedPrefix(name).toLocaleUpperCase();
+      // This is a dumb override for ComfyUI's widgetinputs issues.
+      if (cxn.type === "COMBO" || cxn.type.includes(",") || Array.isArray(cxn.type)) {
+        (this.outputs[slotIndex] as any).widget = true;
+      }
+      this.inputsMutated({
+        operation: InputMutationOperation.ADDED,
+        node: this,
+        slotIndex,
+        slot: ioSlot,
+      });
+      this.stabilizeNames();
+      this.ensureOneRemainingNewInputSlot();
+    }
+  }
+
+  handleInputDisconnected(slotIndex: number) {
+    const inputs = this.getContextInputsList();
+    if (slotIndex === 0) {
+      for (let index = inputs.length - 1; index > 0; index--) {
+        if (index === 0 || index === inputs.length - 1) {
+          continue;
+        }
+        const input = inputs[index]!;
+        if (!this.isOwnedInput(input.name)) {
+          if (input.link || this.outputs[index]?.links?.length) {
+            this.renameContextInput(index, input.name, true);
+          } else {
+            this.removeContextInput(index);
+          }
+        }
+      }
+      this.setSize(this.computeSize());
+      this.setDirtyCanvas(true, true);
+    }
+  }
+
+  ensureOneRemainingNewInputSlot() {
+    removeUnusedInputsFromEnd(this, 1, REGEX_EMPTY_INPUT);
+    this.addInput(OWNED_PREFIX, "*");
+  }
+
+  getNextUniqueNameForThisNode(desiredName: string) {
+    const inputs = this.getContextInputsList();
+    const allExistingKeys = inputs.map((i) => this.stripOwnedPrefix(i.name).toLocaleUpperCase());
+    desiredName = this.stripOwnedPrefix(desiredName);
+    let newName = desiredName;
+    let n = 0;
+    while (allExistingKeys.includes(newName.toLocaleUpperCase())) {
+      newName = `${desiredName}.${++n}`;
+    }
+    return newName;
+  }
+
+  override removeInput(slotIndex: number) {
+    const slot = this.inputs[slotIndex]!;
+    super.removeInput(slotIndex);
+    if (this.outputs[slotIndex]) {
+      this.removeOutput(slotIndex);
+    }
+    this.inputsMutated({operation: InputMutationOperation.REMOVED, node: this, slotIndex, slot});
+    this.stabilizeNames();
+  }
+
+  stabilizeNames() {
+    const inputs = this.getContextInputsList();
+    const names: string[] = [];
+    for (const [index, input] of inputs.entries()) {
+      if (index === 0 || index === inputs.length - 1) {
+        continue;
+      }
+      input.label = undefined;
+      this.outputs[index]!.label = undefined;
+      let origName = this.stripOwnedPrefix(input.name).replace(/\.\d+$/, "");
+      let name = input.name;
+      if (!this.isOwnedInput(name)) {
+        names.push(name.toLocaleUpperCase());
+      } else {
+        let n = 0;
+        name = this.addOwnedPrefix(origName);
+        while (names.includes(this.stripOwnedPrefix(name).toLocaleUpperCase())) {
+          name = `${this.addOwnedPrefix(origName)}.${++n}`;
+        }
+        names.push(this.stripOwnedPrefix(name).toLocaleUpperCase());
+        if (input.name !== name) {
+          this.renameContextInput(index, name);
+        }
+      }
+    }
+  }
+
+  override getSlotMenuOptions(slot: IFoundSlot): IContextMenuValue[] {
+    const editable = this.isOwnedInput(slot.input!.name) && this.type !== "*";
+    return [
+      {
+        content: "✏️ Rename Input",
+        disabled: !editable,
+        callback: () => {
+          var dialog = app.canvas.createDialog(
+            "<span class='name'>Name</span><input autofocus type='text'/><button>OK</button>",
+            {},
+          );
+          var dialogInput = dialog.querySelector("input")!;
+          if (dialogInput) {
+            dialogInput.value = this.stripOwnedPrefix(slot.input!.name || "");
+          }
+          var inner = () => {
+            this.handleContextMenuRenameInputDialog(slot.slot, dialogInput.value);
+            dialog.close();
+          };
+          dialog.querySelector("button")!.addEventListener("click", inner);
+          dialogInput.addEventListener("keydown", (e) => {
+            dialog.is_modified = true;
+            if (e.keyCode == 27) {
+              dialog.close();
+            } else if (e.keyCode == 13) {
+              inner();
+            } else if (e.keyCode != 13 && (e.target as HTMLElement)?.localName != "textarea") {
+              return;
+            }
+            e.preventDefault();
+            e.stopPropagation();
+          });
+          dialogInput.focus();
+        },
+      },
+      {
+        content: "🗑️ Delete Input",
+        disabled: !editable,
+        callback: () => {
+          this.removeInput(slot.slot);
+        },
+      },
+    ];
+  }
+
+  handleContextMenuRenameInputDialog(slotIndex: number, value: string) {
+    app.graph.beforeChange();
+    this.renameContextInput(slotIndex, value);
+    this.stabilizeNames();
+    this.setDirtyCanvas(true, true);
+    app.graph.afterChange();
+  }
+}
+
+const contextDynamicNodes = [DynamicContextNode];
+app.registerExtension({
+  name: "rgthree.DynamicContext",
+  async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    if (!CONFIG_SERVICE.getConfigValue("unreleased.dynamic_context.enabled")) {
+      return;
+    }
+    if (nodeData.name === DynamicContextNode.type) {
+      DynamicContextNode.setUp(nodeType, nodeData);
+    }
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/dynamic_context_base.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/dynamic_context_base.ts
new file mode 100644
index 0000000000000000000000000000000000000000..a983ac30c9766bfe8ed0fd347f794bdfd8d8a86e
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/dynamic_context_base.ts
@@ -0,0 +1,241 @@
+import type {INodeInputSlot, LGraphNodeConstructor} from "@comfyorg/frontend";
+import type {ComfyNodeDef} from "typings/comfy.js";
+
+import {app} from "scripts/app.js";
+import {BaseContextNode} from "./context.js";
+import {RgthreeBaseServerNode} from "./base_node.js";
+import {moveArrayItem, wait} from "rgthree/common/shared_utils.js";
+import {RgthreeInvisibleWidget} from "./utils_widgets.js";
+import {
+  getContextOutputName,
+  InputMutation,
+  InputMutationOperation,
+} from "./services/context_service.js";
+import {SERVICE as CONTEXT_SERVICE} from "./services/context_service.js";
+
+const OWNED_PREFIX = "+";
+const REGEX_OWNED_PREFIX = /^\+\s*/;
+const REGEX_EMPTY_INPUT = /^\+\s*$/;
+
+export type InputLike = {
+  name: string;
+  type: number | string;
+  label?: string;
+  link: number | null;
+  removable?: boolean;
+  boundingRect: any;
+};
+
+/**
+ * The base context node that contains some shared between DynamicContext nodes. Not labels
+ * `abstract` so we can reference `this` in static methods.
+ */
+export class DynamicContextNodeBase extends BaseContextNode {
+  protected readonly hasShadowInputs: boolean = false;
+
+  getContextInputsList(): InputLike[] {
+    return this.inputs;
+  }
+
+  provideInputsData() {
+    const inputs = this.getContextInputsList();
+    return inputs
+      .map((input, index) => ({
+        name: this.stripOwnedPrefix(input.name),
+        type: String(input.type),
+        index,
+      }))
+      .filter((i) => i.type !== "*");
+  }
+
+  addOwnedPrefix(name: string) {
+    return `+ ${this.stripOwnedPrefix(name)}`;
+  }
+
+  isOwnedInput(inputOrName: string | null | INodeInputSlot) {
+    const name = typeof inputOrName == "string" ? inputOrName : inputOrName?.name || "";
+    return REGEX_OWNED_PREFIX.test(name);
+  }
+
+  stripOwnedPrefix(name: string) {
+    return name.replace(REGEX_OWNED_PREFIX, "");
+  }
+
+  // handleUpstreamMutation(mutation: InputMutation) {
+  //   throw new Error('handleUpstreamMutation not overridden!')
+  // }
+
+  handleUpstreamMutation(mutation: InputMutation) {
+    console.log(`[node ${this.id}] handleUpstreamMutation`, mutation);
+    if (mutation.operation === InputMutationOperation.ADDED) {
+      const slot = mutation.slot;
+      if (!slot) {
+        throw new Error("Cannot have an ADDED mutation without a provided slot data.");
+      }
+      this.addContextInput(
+        this.stripOwnedPrefix(slot.name),
+        slot.type as string,
+        mutation.slotIndex,
+      );
+      return;
+    }
+    if (mutation.operation === InputMutationOperation.REMOVED) {
+      const slot = mutation.slot;
+      if (!slot) {
+        throw new Error("Cannot have an REMOVED mutation without a provided slot data.");
+      }
+      this.removeContextInput(mutation.slotIndex);
+      return;
+    }
+    if (mutation.operation === InputMutationOperation.RENAMED) {
+      const slot = mutation.slot;
+      if (!slot) {
+        throw new Error("Cannot have an RENAMED mutation without a provided slot data.");
+      }
+      this.renameContextInput(mutation.slotIndex, slot.name);
+      return;
+    }
+  }
+  override clone() {
+    const cloned = super.clone()! as DynamicContextNodeBase;
+    while (cloned.inputs.length > 1) {
+      cloned.removeInput(cloned.inputs.length - 1);
+    }
+    while (cloned.widgets.length > 1) {
+      cloned.removeWidget(cloned.widgets.length - 1);
+    }
+    while (cloned.outputs.length > 1) {
+      cloned.removeOutput(cloned.outputs.length - 1);
+    }
+    return cloned;
+  }
+
+  /**
+   * Adds the basic output_keys widget. Should be called _after_ specific nodes setup their inputs
+   * or widgets.
+   */
+  override onNodeCreated() {
+    const node = this;
+    this.addCustomWidget(
+      new RgthreeInvisibleWidget("output_keys", "RGTHREE_DYNAMIC_CONTEXT_OUTPUTS", "", () => {
+        return (node.outputs || [])
+          .map((o, i) => i > 0 && o.name)
+          .filter((n) => n !== false)
+          .join(",");
+      }),
+    );
+  }
+
+  addContextInput(name: string, type: string, slot = -1) {
+    const inputs = this.getContextInputsList();
+    if (this.hasShadowInputs) {
+      inputs.push({name, type, link: null, boundingRect: null});
+    } else {
+      this.addInput(name, type);
+    }
+    if (slot > -1) {
+      moveArrayItem(inputs, inputs.length - 1, slot);
+    } else {
+      slot = inputs.length - 1;
+    }
+    if (type !== "*") {
+      const output = this.addOutput(getContextOutputName(name), type);
+      if (type === "COMBO" || String(type).includes(",") || Array.isArray(type)) {
+        (output as any).widget = true;
+      }
+      if (slot > -1) {
+        moveArrayItem(this.outputs, this.outputs.length - 1, slot);
+      }
+    }
+    this.fixInputsOutputsLinkSlots();
+    this.inputsMutated({
+      operation: InputMutationOperation.ADDED,
+      node: this,
+      slotIndex: slot,
+      slot: inputs[slot]!,
+    });
+  }
+
+  removeContextInput(slotIndex: number) {
+    if (this.hasShadowInputs) {
+      const inputs = this.getContextInputsList();
+      const input = inputs.splice(slotIndex, 1)[0];
+      if (this.outputs[slotIndex]) {
+        this.removeOutput(slotIndex);
+      }
+    } else {
+      this.removeInput(slotIndex);
+    }
+  }
+
+  renameContextInput(index: number, newName: string, forceOwnBool: boolean | null = null) {
+    const inputs = this.getContextInputsList();
+    const input = inputs[index]!;
+    const oldName = input.name;
+    newName = this.stripOwnedPrefix(newName.trim() || this.getSlotDefaultInputLabel(index));
+    if (forceOwnBool === true || (this.isOwnedInput(oldName) && forceOwnBool !== false)) {
+      newName = this.addOwnedPrefix(newName);
+    }
+    if (oldName !== newName) {
+      input.name = newName;
+      input.removable = this.isOwnedInput(newName);
+      this.outputs[index]!.name = getContextOutputName(inputs[index]!.name);
+      this.inputsMutated({
+        node: this,
+        operation: InputMutationOperation.RENAMED,
+        slotIndex: index,
+        slot: input,
+      });
+    }
+  }
+
+  getSlotDefaultInputLabel(slotIndex: number) {
+    const inputs = this.getContextInputsList();
+    const input = inputs[slotIndex]!;
+    let defaultLabel = this.stripOwnedPrefix(input.name).toLowerCase();
+    return defaultLabel.toLocaleLowerCase();
+  }
+
+  inputsMutated(mutation: InputMutation) {
+    CONTEXT_SERVICE.onInputChanges(this, mutation);
+  }
+
+  fixInputsOutputsLinkSlots() {
+    if (!this.hasShadowInputs) {
+      const inputs = this.getContextInputsList();
+      for (let index = inputs.length - 1; index > 0; index--) {
+        const input = inputs[index]!;
+        if ((input === null || input === void 0 ? void 0 : input.link) != null) {
+          app.graph.links[input.link!]!.target_slot = index;
+        }
+      }
+    }
+    const outputs = this.outputs;
+    for (let index = outputs.length - 1; index > 0; index--) {
+      const output = outputs[index];
+      if (output) {
+        output.nameLocked = true;
+        for (const link of output.links || []) {
+          app.graph.links[link!]!.origin_slot = index;
+        }
+      }
+    }
+  }
+
+  static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, this);
+    // [🤮] ComfyUI only adds "required" inputs to the outputs list when dragging an output to
+    // empty space, but since RGTHREE_CONTEXT is optional, it doesn't get added to the menu because
+    // ...of course. So, we'll manually add it. Of course, we also have to do this in a timeout
+    // because ComfyUI clears out `LiteGraph.slot_types_default_out` in its own 'Comfy.SlotDefaults'
+    // extension and we need to wait for that to happen.
+    wait(500).then(() => {
+      LiteGraph.slot_types_default_out["RGTHREE_DYNAMIC_CONTEXT"] =
+        LiteGraph.slot_types_default_out["RGTHREE_DYNAMIC_CONTEXT"] || [];
+      const comfyClassStr = (comfyClass as LGraphNodeConstructor).comfyClass;
+      if (comfyClassStr) {
+        LiteGraph.slot_types_default_out["RGTHREE_DYNAMIC_CONTEXT"].push(comfyClassStr);
+      }
+    });
+  }
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/dynamic_context_switch.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/dynamic_context_switch.ts
new file mode 100644
index 0000000000000000000000000000000000000000..b2f090ba56d7b4319c90d3243e7aa8aa59e4b3df
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/dynamic_context_switch.ts
@@ -0,0 +1,214 @@
+import type {
+  LGraphNode,
+  LLink,
+  LGraphCanvas,
+  INodeInputSlot,
+  INodeOutputSlot,
+  ISlotType,
+} from "@comfyorg/frontend";
+import type {ComfyNodeDef} from "typings/comfy.js";
+
+import {app} from "scripts/app.js";
+import {DynamicContextNodeBase, InputLike} from "./dynamic_context_base.js";
+import {NodeTypesString} from "./constants.js";
+import {
+  InputMutation,
+  SERVICE as CONTEXT_SERVICE,
+  getContextOutputName,
+} from "./services/context_service.js";
+import {getConnectedInputNodesAndFilterPassThroughs} from "./utils.js";
+import {debounce, moveArrayItem} from "rgthree/common/shared_utils.js";
+import {measureText} from "./utils_canvas.js";
+import {SERVICE as CONFIG_SERVICE} from "./services/config_service.js";
+
+type ShadowInputData = {
+  node: LGraphNode;
+  slot: number;
+  shadowIndex: number;
+  shadowIndexIfShownSingularly: number;
+  shadowIndexFull: number;
+  nodeIndex: number;
+  type: string | -1;
+  name: string;
+  key: string;
+  // isDuplicatedBefore: boolean,
+  duplicatesBefore: number[];
+  duplicatesAfter: number[];
+};
+
+/**
+ * The Context Switch  node.
+ */
+class DynamicContextSwitchNode extends DynamicContextNodeBase {
+  static override title = NodeTypesString.DYNAMIC_CONTEXT_SWITCH;
+  static override type = NodeTypesString.DYNAMIC_CONTEXT_SWITCH;
+  static comfyClass = NodeTypesString.DYNAMIC_CONTEXT_SWITCH;
+
+  protected override readonly hasShadowInputs = true;
+
+  // override hasShadowInputs = true;
+
+  /**
+   * We should be able to assume that `lastInputsList` is the input list after the last, major
+   * synchronous change. Which should mean, if we're handling a change that is currently live, but
+   * not represented in our node (like, an upstream node has already removed an input), then we
+   * should be able to compar the current InputList to this `lastInputsList`.
+   */
+  lastInputsList: ShadowInputData[] = [];
+
+  private shadowInputs: (InputLike & {count: number})[] = [
+    {name: "base_ctx", type: "RGTHREE_DYNAMIC_CONTEXT", link: null, count: 0, boundingRect: null},
+  ];
+
+  constructor(title = DynamicContextSwitchNode.title) {
+    super(title);
+  }
+
+  override getContextInputsList() {
+    return this.shadowInputs;
+  }
+  override handleUpstreamMutation(mutation: InputMutation) {
+    this.scheduleHardRefresh();
+  }
+
+  override onConnectionsChange(
+    type: ISlotType,
+    slotIndex: number,
+    isConnected: boolean,
+    link: LLink | null | undefined,
+    inputOrOutput: INodeInputSlot | INodeOutputSlot,
+  ): void {
+    super.onConnectionsChange?.call(this, type, slotIndex, isConnected, link, inputOrOutput);
+    if (this.configuring) {
+      return;
+    }
+    if (type === LiteGraph.INPUT) {
+      this.scheduleHardRefresh();
+    }
+  }
+
+  scheduleHardRefresh(ms = 64) {
+    return debounce(() => {
+      this.refreshInputsAndOutputs();
+    }, ms);
+  }
+
+  override onNodeCreated() {
+    this.addInput("ctx_1", "RGTHREE_DYNAMIC_CONTEXT");
+    this.addInput("ctx_2", "RGTHREE_DYNAMIC_CONTEXT");
+    this.addInput("ctx_3", "RGTHREE_DYNAMIC_CONTEXT");
+    this.addInput("ctx_4", "RGTHREE_DYNAMIC_CONTEXT");
+    this.addInput("ctx_5", "RGTHREE_DYNAMIC_CONTEXT");
+    super.onNodeCreated();
+  }
+
+  override addContextInput(name: string, type: string, slot?: number): void {}
+
+  /**
+   * This is a "hard" refresh of the list, but looping over the actual context inputs, and
+   * recompiling the shadowInputs and outputs.
+   */
+  private refreshInputsAndOutputs() {
+    const inputs: (InputLike & {count: number})[] = [
+      {name: "base_ctx", type: "RGTHREE_DYNAMIC_CONTEXT", link: null, count: 0, boundingRect: null},
+    ];
+    let numConnected = 0;
+    for (let i = 0; i < this.inputs.length; i++) {
+      const childCtxs = getConnectedInputNodesAndFilterPassThroughs(
+        this,
+        this,
+        i,
+      ) as DynamicContextNodeBase[];
+      if (childCtxs.length > 1) {
+        throw new Error("How is there more than one input?");
+      }
+      const ctx = childCtxs[0];
+      if (!ctx) continue;
+      numConnected++;
+      const slotsData = CONTEXT_SERVICE.getDynamicContextInputsData(ctx);
+      console.log(slotsData);
+      for (const slotData of slotsData) {
+        const found = inputs.find(
+          (n) => getContextOutputName(slotData.name) === getContextOutputName(n.name),
+        );
+        if (found) {
+          found.count += 1;
+          continue;
+        }
+        inputs.push({
+          name: slotData.name,
+          type: slotData.type,
+          link: null,
+          count: 1,
+          boundingRect: null,
+        });
+      }
+    }
+    this.shadowInputs = inputs;
+    // First output is always CONTEXT, so "p" is the offset.
+    let i = 0;
+    for (i; i < this.shadowInputs.length; i++) {
+      const data = this.shadowInputs[i]!;
+      let existing = this.outputs.find(
+        (o) => getContextOutputName(o.name) === getContextOutputName(data.name),
+      );
+      if (!existing) {
+        existing = this.addOutput(getContextOutputName(data.name), data.type);
+      }
+      moveArrayItem(this.outputs, existing, i);
+      delete existing.rgthree_status;
+      if (data.count !== numConnected) {
+        existing.rgthree_status = "WARN";
+      }
+    }
+    while (this.outputs[i]) {
+      const output = this.outputs[i];
+      if (output?.links?.length) {
+        output.rgthree_status = "ERROR";
+        i++;
+      } else {
+        this.removeOutput(i);
+      }
+    }
+    this.fixInputsOutputsLinkSlots();
+  }
+
+  override onDrawForeground(ctx: CanvasRenderingContext2D, canvas: LGraphCanvas): void {
+    const low_quality = (canvas?.ds?.scale ?? 1) < 0.6;
+    if (low_quality || this.size[0] <= 10) {
+      return;
+    }
+    let y = LiteGraph.NODE_SLOT_HEIGHT - 1;
+    const w = this.size[0];
+    ctx.save();
+    ctx.font = "normal " + LiteGraph.NODE_SUBTEXT_SIZE + "px Arial";
+    ctx.textAlign = "right";
+
+    for (const output of this.outputs) {
+      if (!output.rgthree_status) {
+        y += LiteGraph.NODE_SLOT_HEIGHT;
+        continue;
+      }
+      const x = w - 20 - measureText(ctx, output.name);
+      if (output.rgthree_status === "ERROR") {
+        ctx.fillText("🛑", x, y);
+      } else if (output.rgthree_status === "WARN") {
+        ctx.fillText("⚠️", x, y);
+      }
+      y += LiteGraph.NODE_SLOT_HEIGHT;
+    }
+    ctx.restore();
+  }
+}
+
+app.registerExtension({
+  name: "rgthree.DynamicContextSwitch",
+  async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    if (!CONFIG_SERVICE.getConfigValue("unreleased.dynamic_context.enabled")) {
+      return;
+    }
+    if (nodeData.name === DynamicContextSwitchNode.type) {
+      DynamicContextSwitchNode.setUp(nodeType, nodeData);
+    }
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/fast_actions_button.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/fast_actions_button.ts
new file mode 100644
index 0000000000000000000000000000000000000000..890c05aca78783062cc767902afe42f7d9540312
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/fast_actions_button.ts
@@ -0,0 +1,359 @@
+import type {
+  LGraph,
+  LGraphNode,
+  ISerialisedNode,
+  IButtonWidget,
+  IComboWidget,
+  IWidget,
+  IBaseWidget,
+} from "@comfyorg/frontend";
+import type {ComfyApp} from "@comfyorg/frontend";
+import type {RgthreeBaseVirtualNode} from "./base_node.js";
+
+import {app} from "scripts/app.js";
+import {BaseAnyInputConnectedNode} from "./base_any_input_connected_node.js";
+import {NodeTypesString} from "./constants.js";
+import {addMenuItem, changeModeOfNodes} from "./utils.js";
+import {rgthree} from "./rgthree.js";
+
+const MODE_ALWAYS = 0;
+const MODE_MUTE = 2;
+const MODE_BYPASS = 4;
+
+/**
+ * The Fast Actions Button.
+ *
+ * This adds a button that the user can connect any node to and then choose an action to take on
+ * that node when the button is pressed. Default actions are "Mute," "Bypass," and "Enable," but
+ * Nodes can expose actions additional actions that can then be called back.
+ */
+class FastActionsButton extends BaseAnyInputConnectedNode {
+  static override type = NodeTypesString.FAST_ACTIONS_BUTTON;
+  static override title = NodeTypesString.FAST_ACTIONS_BUTTON;
+  override comfyClass = NodeTypesString.FAST_ACTIONS_BUTTON;
+
+  readonly logger = rgthree.newLogSession("[FastActionsButton]");
+
+  static "@buttonText" = {type: "string"};
+  static "@shortcutModifier" = {
+    type: "combo",
+    values: ["ctrl", "alt", "shift"],
+  };
+  static "@shortcutKey" = {type: "string"};
+
+  static collapsible = false;
+
+  override readonly isVirtualNode = true;
+
+  override serialize_widgets = true;
+
+  readonly buttonWidget: IButtonWidget;
+
+  readonly widgetToData = new Map<IWidget, {comfy?: ComfyApp; node?: LGraphNode}>();
+  readonly nodeIdtoFunctionCache = new Map<number, string>();
+
+  readonly keypressBound;
+  readonly keyupBound;
+
+  private executingFromShortcut = false;
+
+  override properties!: BaseAnyInputConnectedNode["properties"] & {
+    buttonText: string;
+    shortcutModifier: string;
+    shortcutKey: string;
+  };
+
+  constructor(title?: string) {
+    super(title);
+    this.properties["buttonText"] = "🎬 Action!";
+    this.properties["shortcutModifier"] = "alt";
+    this.properties["shortcutKey"] = "";
+    this.buttonWidget = this.addWidget(
+      "button",
+      this.properties["buttonText"],
+      "",
+      () => {
+        this.executeConnectedNodes();
+      },
+      {serialize: false},
+    ) as IButtonWidget;
+
+    this.keypressBound = this.onKeypress.bind(this);
+    this.keyupBound = this.onKeyup.bind(this);
+    this.onConstructed();
+  }
+
+  /** When we're given data to configure, like from a PNG or JSON. */
+  override configure(info: ISerialisedNode): void {
+    super.configure(info);
+    // Since we add the widgets dynamically, we need to wait to set their values
+    // with a short timeout.
+    setTimeout(() => {
+      if (info.widgets_values) {
+        for (let [index, value] of info.widgets_values.entries()) {
+          if (index > 0) {
+            if (typeof value === "string" && value.startsWith("comfy_action:")) {
+              value = value.replace("comfy_action:", "");
+              this.addComfyActionWidget(index, value);
+            }
+            if (this.widgets[index]) {
+              this.widgets[index]!.value = value;
+            }
+          }
+        }
+      }
+    }, 100);
+  }
+
+  override clone() {
+    const cloned = super.clone()!;
+    cloned.properties["buttonText"] = "🎬 Action!";
+    cloned.properties["shortcutKey"] = "";
+    return cloned;
+  }
+
+  override onAdded(graph: LGraph): void {
+    window.addEventListener("keydown", this.keypressBound);
+    window.addEventListener("keyup", this.keyupBound);
+  }
+
+  override onRemoved(): void {
+    window.removeEventListener("keydown", this.keypressBound);
+    window.removeEventListener("keyup", this.keyupBound);
+  }
+
+  async onKeypress(event: KeyboardEvent) {
+    const target = (event.target as HTMLElement)!;
+    if (
+      this.executingFromShortcut ||
+      target.localName == "input" ||
+      target.localName == "textarea"
+    ) {
+      return;
+    }
+    if (
+      this.properties["shortcutKey"].trim() &&
+      this.properties["shortcutKey"].toLowerCase() === event.key.toLowerCase()
+    ) {
+      const shortcutModifier = this.properties["shortcutModifier"];
+      let good = shortcutModifier === "ctrl" && event.ctrlKey;
+      good = good || (shortcutModifier === "alt" && event.altKey);
+      good = good || (shortcutModifier === "shift" && event.shiftKey);
+      good = good || (shortcutModifier === "meta" && event.metaKey);
+      if (good) {
+        setTimeout(() => {
+          this.executeConnectedNodes();
+        }, 20);
+        this.executingFromShortcut = true;
+        event.preventDefault();
+        event.stopImmediatePropagation();
+        app.canvas.dirty_canvas = true;
+        return false;
+      }
+    }
+    return;
+  }
+
+  onKeyup(event: KeyboardEvent) {
+    const target = (event.target as HTMLElement)!;
+    if (target.localName == "input" || target.localName == "textarea") {
+      return;
+    }
+    this.executingFromShortcut = false;
+  }
+
+  override onPropertyChanged(property: string, value: unknown, prevValue?: unknown) {
+    if (property == "buttonText" && typeof value === "string") {
+      this.buttonWidget.name = value;
+    }
+    if (property == "shortcutKey" && typeof value === "string") {
+      this.properties["shortcutKey"] = value.trim()[0]?.toLowerCase() ?? "";
+    }
+    return true;
+  }
+
+  override handleLinkedNodesStabilization(linkedNodes: LGraphNode[]) {
+    let changed = false;
+    // Remove any widgets and data for widgets that are no longer linked.
+    for (const [widget, data] of this.widgetToData.entries()) {
+      if (!data.node) {
+        continue;
+      }
+      if (!linkedNodes.includes(data.node)) {
+        const index = this.widgets.indexOf(widget);
+        if (index > -1) {
+          this.widgetToData.delete(widget);
+          this.removeWidget(widget);
+          changed = true;
+        } else {
+          const [m, a] = this.logger.debugParts("Connected widget is not in widgets... weird.");
+          console[m]?.(...a);
+        }
+      }
+    }
+
+    const badNodes: LGraphNode[] = []; // Nodes that are deleted elsewhere may not exist in linkedNodes.
+    let indexOffset = 1; // Start with button, increment when we hit a non-node widget (like comfy)
+    for (const [index, node] of linkedNodes.entries()) {
+      // Sometimes linkedNodes is stale.
+      if (!node) {
+        const [m, a] = this.logger.debugParts("linkedNode provided that does not exist. ");
+        console[m]?.(...a);
+        badNodes.push(node);
+        continue;
+      }
+      let widgetAtSlot = this.widgets[index + indexOffset];
+      if (widgetAtSlot && this.widgetToData.get(widgetAtSlot)?.comfy) {
+        indexOffset++;
+        widgetAtSlot = this.widgets[index + indexOffset];
+      }
+
+      if (!widgetAtSlot || this.widgetToData.get(widgetAtSlot)?.node?.id !== node.id) {
+        // Find the next widget that matches the node.
+        let widget: IWidget | null = null;
+        for (let i = index + indexOffset; i < this.widgets.length; i++) {
+          if (this.widgetToData.get(this.widgets[i]!)?.node?.id === node.id) {
+            widget = this.widgets.splice(i, 1)[0]!;
+            this.widgets.splice(index + indexOffset, 0, widget);
+            changed = true;
+            break;
+          }
+        }
+        if (!widget) {
+          // Add a widget at this spot.
+          const exposedActions: string[] = (node.constructor as any).exposedActions || [];
+          widget = this.addWidget("combo", node.title, "None", "", {
+            values: ["None", "Mute", "Bypass", "Enable", ...exposedActions],
+          }) as IWidget;
+          widget.serializeValue = async (_node: LGraphNode, _index: number) => {
+            return widget?.value;
+          };
+          this.widgetToData.set(widget, {node});
+          changed = true;
+        }
+      }
+    }
+
+    // Go backwards through widgets, and remove any that are not in out widgetToData
+    for (let i = this.widgets.length - 1; i > linkedNodes.length + indexOffset - 1; i--) {
+      const widgetAtSlot = this.widgets[i];
+      if (widgetAtSlot && this.widgetToData.get(widgetAtSlot)?.comfy) {
+        continue;
+      }
+      this.removeWidget(widgetAtSlot);
+      changed = true;
+    }
+    return changed;
+  }
+
+  override removeWidget(widget: IBaseWidget | IWidget | number | undefined): void {
+    widget = typeof widget === "number" ? this.widgets[widget] : widget;
+    if (widget && this.widgetToData.has(widget as IWidget)) {
+      this.widgetToData.delete(widget as IWidget);
+    }
+    super.removeWidget(widget);
+  }
+
+  /**
+   * Runs through the widgets, and executes the actions.
+   */
+  async executeConnectedNodes() {
+    for (const widget of this.widgets) {
+      if (widget == this.buttonWidget) {
+        continue;
+      }
+      const action = widget.value;
+      const {comfy, node} = this.widgetToData.get(widget) ?? {};
+      if (comfy) {
+        if (action === "Queue Prompt") {
+          await comfy.queuePrompt(0);
+        }
+        continue;
+      }
+      if (node) {
+        if (action === "Mute") {
+          changeModeOfNodes(node, MODE_MUTE);
+        } else if (action === "Bypass") {
+          changeModeOfNodes(node, MODE_BYPASS);
+        } else if (action === "Enable") {
+          changeModeOfNodes(node, MODE_ALWAYS);
+        }
+        // If there's a handleAction, always call it.
+        if ((node as RgthreeBaseVirtualNode).handleAction) {
+          if (typeof action !== "string") {
+            throw new Error("Fast Actions Button action should be a string: " + action);
+          }
+          await (node as RgthreeBaseVirtualNode).handleAction(action);
+        }
+        this.graph?.change();
+        continue;
+      }
+      console.warn("Fast Actions Button has a widget without correct data.");
+    }
+  }
+
+  /**
+   * Adds a ComfyActionWidget at the provided slot (or end).
+   */
+  addComfyActionWidget(slot?: number, value?: string) {
+    let widget = this.addWidget(
+      "combo",
+      "Comfy Action",
+      "None",
+      () => {
+        if (String(widget.value).startsWith("MOVE ")) {
+          this.widgets.push(this.widgets.splice(this.widgets.indexOf(widget), 1)[0]!);
+          widget.value = String(widget.rgthree_lastValue);
+        } else if (String(widget.value).startsWith("REMOVE ")) {
+          this.removeWidget(widget);
+        }
+        widget.rgthree_lastValue = widget.value;
+      },
+      {
+        values: ["None", "Queue Prompt", "REMOVE Comfy Action", "MOVE to end"],
+      },
+    ) as IComboWidget;
+    widget.rgthree_lastValue = value;
+
+    widget.serializeValue = async (_node: LGraphNode, _index: number) => {
+      return `comfy_app:${widget?.value}`;
+    };
+    this.widgetToData.set(widget, {comfy: app});
+
+    if (slot != null) {
+      this.widgets.splice(slot, 0, this.widgets.splice(this.widgets.indexOf(widget), 1)[0]!);
+    }
+    return widget;
+  }
+
+  override onSerialize(serialised: ISerialisedNode) {
+    super.onSerialize?.(serialised);
+    for (let [index, value] of (serialised.widgets_values || []).entries()) {
+      if (this.widgets[index]?.name === "Comfy Action") {
+        serialised.widgets_values![index] = `comfy_action:${value}`;
+      }
+    }
+  }
+
+  static override setUp() {
+    super.setUp();
+    addMenuItem(this, app, {
+      name: "➕ Append a Comfy Action",
+      callback: (nodeArg: LGraphNode) => {
+        (nodeArg as FastActionsButton).addComfyActionWidget();
+      },
+    });
+  }
+}
+
+app.registerExtension({
+  name: "rgthree.FastActionsButton",
+  registerCustomNodes() {
+    FastActionsButton.setUp();
+  },
+  loadedGraphNode(node: LGraphNode) {
+    if (node.type == FastActionsButton.title) {
+      (node as FastActionsButton)._tempWidth = node.size[0];
+    }
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/fast_groups_bypasser.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/fast_groups_bypasser.ts
new file mode 100644
index 0000000000000000000000000000000000000000..4e18b07f71c96284356fb454db66e6a1d0a50825
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/fast_groups_bypasser.ts
@@ -0,0 +1,38 @@
+import type {Size} from "@comfyorg/frontend";
+
+import {app} from "scripts/app.js";
+import {NodeTypesString} from "./constants.js";
+import {BaseFastGroupsModeChanger} from "./fast_groups_muter.js";
+
+/**
+ * Fast Bypasser implementation that looks for groups in the workflow and adds toggles to mute them.
+ */
+export class FastGroupsBypasser extends BaseFastGroupsModeChanger {
+  static override type = NodeTypesString.FAST_GROUPS_BYPASSER;
+  static override title = NodeTypesString.FAST_GROUPS_BYPASSER;
+  override comfyClass = NodeTypesString.FAST_GROUPS_BYPASSER;
+
+  static override exposedActions = ["Bypass all", "Enable all", "Toggle all"];
+
+  protected override helpActions = "bypass and enable";
+
+  override readonly modeOn = LiteGraph.ALWAYS;
+  override readonly modeOff = 4; // Used by Comfy for "bypass"
+
+  constructor(title = FastGroupsBypasser.title) {
+    super(title);
+    this.onConstructed();
+  }
+}
+
+app.registerExtension({
+  name: "rgthree.FastGroupsBypasser",
+  registerCustomNodes() {
+    FastGroupsBypasser.setUp();
+  },
+  loadedGraphNode(node: FastGroupsBypasser) {
+    if (node.type == FastGroupsBypasser.title) {
+      node.tempSize = [...node.size] as Size;
+    }
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/fast_groups_muter.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/fast_groups_muter.ts
new file mode 100644
index 0000000000000000000000000000000000000000..4eee7f8c9e7d8898072a81655e26a87f75f23ead
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/fast_groups_muter.ts
@@ -0,0 +1,542 @@
+import type {
+  LGraphNode,
+  LGraph as TLGraph,
+  LGraphCanvas as TLGraphCanvas,
+  Vector2,
+  Size,
+  LGraphGroup,
+  CanvasMouseEvent,
+  Point,
+} from "@comfyorg/frontend";
+
+import {app} from "scripts/app.js";
+import {RgthreeBaseVirtualNode} from "./base_node.js";
+import {NodeTypesString} from "./constants.js";
+import {SERVICE as FAST_GROUPS_SERVICE} from "./services/fast_groups_service.js";
+import {drawNodeWidget, fitString} from "./utils_canvas.js";
+import {RgthreeBaseWidget} from "./utils_widgets.js";
+import { changeModeOfNodes, getGroupNodes } from "./utils.js";
+
+const PROPERTY_SORT = "sort";
+const PROPERTY_SORT_CUSTOM_ALPHA = "customSortAlphabet";
+const PROPERTY_MATCH_COLORS = "matchColors";
+const PROPERTY_MATCH_TITLE = "matchTitle";
+const PROPERTY_SHOW_NAV = "showNav";
+const PROPERTY_SHOW_ALL_GRAPHS = "showAllGraphs";
+const PROPERTY_RESTRICTION = "toggleRestriction";
+
+/**
+ * Fast Muter implementation that looks for groups in the workflow and adds toggles to mute them.
+ */
+export abstract class BaseFastGroupsModeChanger extends RgthreeBaseVirtualNode {
+  static override type = NodeTypesString.FAST_GROUPS_MUTER;
+  static override title = NodeTypesString.FAST_GROUPS_MUTER;
+
+  static override exposedActions = ["Mute all", "Enable all", "Toggle all"];
+
+  readonly modeOn: number = LiteGraph.ALWAYS;
+  readonly modeOff: number = LiteGraph.NEVER;
+
+  private debouncerTempWidth: number = 0;
+  tempSize: Vector2 | null = null;
+
+  // We don't need to serizalize since we'll just be checking group data on startup anyway
+  override serialize_widgets = false;
+
+  protected helpActions = "mute and unmute";
+
+  static "@matchColors" = {type: "string"};
+  static "@matchTitle" = {type: "string"};
+  static "@showNav" = {type: "boolean"};
+  static "@showAllGraphs" = {type: "boolean"};
+  static "@sort" = {
+    type: "combo",
+    values: ["position", "alphanumeric", "custom alphabet"],
+  };
+  static "@customSortAlphabet" = {type: "string"};
+
+  override properties!: RgthreeBaseVirtualNode["properties"] & {
+    [PROPERTY_MATCH_COLORS]: string;
+    [PROPERTY_MATCH_TITLE]: string;
+    [PROPERTY_SHOW_NAV]: boolean;
+    [PROPERTY_SHOW_ALL_GRAPHS]: boolean;
+    [PROPERTY_SORT]: string;
+    [PROPERTY_SORT_CUSTOM_ALPHA]: string;
+    [PROPERTY_RESTRICTION]: string;
+  };
+
+  static "@toggleRestriction" = {
+    type: "combo",
+    values: ["default", "max one", "always one"],
+  };
+
+  constructor(title = FastGroupsMuter.title) {
+    super(title);
+    this.properties[PROPERTY_MATCH_COLORS] = "";
+    this.properties[PROPERTY_MATCH_TITLE] = "";
+    this.properties[PROPERTY_SHOW_NAV] = true;
+    this.properties[PROPERTY_SHOW_ALL_GRAPHS] = true;
+    this.properties[PROPERTY_SORT] = "position";
+    this.properties[PROPERTY_SORT_CUSTOM_ALPHA] = "";
+    this.properties[PROPERTY_RESTRICTION] = "default";
+  }
+
+  override onConstructed(): boolean {
+    this.addOutput("OPT_CONNECTION", "*");
+    return super.onConstructed();
+  }
+
+  override onAdded(graph: TLGraph): void {
+    FAST_GROUPS_SERVICE.addFastGroupNode(this);
+  }
+
+  override onRemoved(): void {
+    FAST_GROUPS_SERVICE.removeFastGroupNode(this);
+  }
+
+  refreshWidgets() {
+    const canvas = app.canvas as TLGraphCanvas;
+    let sort = this.properties?.[PROPERTY_SORT] || "position";
+    let customAlphabet: string[] | null = null;
+    if (sort === "custom alphabet") {
+      const customAlphaStr = this.properties?.[PROPERTY_SORT_CUSTOM_ALPHA]?.replace(/\n/g, "");
+      if (customAlphaStr && customAlphaStr.trim()) {
+        customAlphabet = customAlphaStr.includes(",")
+          ? customAlphaStr.toLocaleLowerCase().split(",")
+          : customAlphaStr.toLocaleLowerCase().trim().split("");
+      }
+      if (!customAlphabet?.length) {
+        sort = "alphanumeric";
+        customAlphabet = null;
+      }
+    }
+
+    const groups = [...FAST_GROUPS_SERVICE.getGroups(sort)];
+    // The service will return pre-sorted groups for alphanumeric and position. If this node has a
+    // custom sort, then we need to sort it manually.
+    if (customAlphabet?.length) {
+      groups.sort((a, b) => {
+        let aIndex = -1;
+        let bIndex = -1;
+        // Loop and find indexes. As we're finding multiple, a single for loop is more efficient.
+        for (const [index, alpha] of customAlphabet!.entries()) {
+          aIndex =
+            aIndex < 0 ? (a.title.toLocaleLowerCase().startsWith(alpha) ? index : -1) : aIndex;
+          bIndex =
+            bIndex < 0 ? (b.title.toLocaleLowerCase().startsWith(alpha) ? index : -1) : bIndex;
+          if (aIndex > -1 && bIndex > -1) {
+            break;
+          }
+        }
+        // Now compare.
+        if (aIndex > -1 && bIndex > -1) {
+          const ret = aIndex - bIndex;
+          if (ret === 0) {
+            return a.title.localeCompare(b.title);
+          }
+          return ret;
+        } else if (aIndex > -1) {
+          return -1;
+        } else if (bIndex > -1) {
+          return 1;
+        }
+        return a.title.localeCompare(b.title);
+      });
+    }
+
+    // See if we're filtering by colors, and match against the built-in keywords and actuial hex
+    // values.
+    let filterColors = (
+      (this.properties?.[PROPERTY_MATCH_COLORS] as string)?.split(",") || []
+    ).filter((c) => c.trim());
+    if (filterColors.length) {
+      filterColors = filterColors.map((color) => {
+        color = color.trim().toLocaleLowerCase();
+        if (LGraphCanvas.node_colors[color]) {
+          color = LGraphCanvas.node_colors[color]!.groupcolor;
+        }
+        color = color.replace("#", "").toLocaleLowerCase();
+        if (color.length === 3) {
+          color = color.replace(/(.)(.)(.)/, "$1$1$2$2$3$3");
+        }
+        return `#${color}`;
+      });
+    }
+
+    // Go over the groups
+    let index = 0;
+    for (const group of groups) {
+      if (filterColors.length) {
+        let groupColor = group.color?.replace("#", "").trim().toLocaleLowerCase();
+        if (!groupColor) {
+          continue;
+        }
+        if (groupColor.length === 3) {
+          groupColor = groupColor.replace(/(.)(.)(.)/, "$1$1$2$2$3$3");
+        }
+        groupColor = `#${groupColor}`;
+        if (!filterColors.includes(groupColor)) {
+          continue;
+        }
+      }
+      if (this.properties?.[PROPERTY_MATCH_TITLE]?.trim()) {
+        try {
+          if (!new RegExp(this.properties[PROPERTY_MATCH_TITLE], "i").exec(group.title)) {
+            continue;
+          }
+        } catch (e) {
+          console.error(e);
+          continue;
+        }
+      }
+      const showAllGraphs = this.properties?.[PROPERTY_SHOW_ALL_GRAPHS];
+      if (!showAllGraphs && group.graph !== app.canvas.getCurrentGraph()) {
+        continue;
+      }
+      let isDirty = false;
+      const widgetLabel = `Enable ${group.title}`;
+      let widget = this.widgets.find((w) => w.label === widgetLabel) as FastGroupsToggleRowWidget;
+      if (!widget) {
+        // When we add a widget, litegraph is going to mess up the size, so we
+        // store it so we can retrieve it in computeSize. Hacky..
+        this.tempSize = [...this.size] as Size;
+        widget = this.addCustomWidget(
+          new FastGroupsToggleRowWidget(group, this),
+        ) as FastGroupsToggleRowWidget;
+        this.setSize(this.computeSize());
+        isDirty = true;
+      }
+      if (widget.label != widgetLabel) {
+        widget.label = widgetLabel;
+        isDirty = true;
+      }
+      if (
+        group.rgthree_hasAnyActiveNode != null &&
+        widget.toggled != group.rgthree_hasAnyActiveNode
+      ) {
+        widget.toggled = group.rgthree_hasAnyActiveNode;
+        isDirty = true;
+      }
+      if (this.widgets[index] !== widget) {
+        const oldIndex = this.widgets.findIndex((w) => w === widget);
+        this.widgets.splice(index, 0, this.widgets.splice(oldIndex, 1)[0]!);
+        isDirty = true;
+      }
+      if (isDirty) {
+        this.setDirtyCanvas(true, false);
+      }
+      index++;
+    }
+
+    // Everything should now be in order, so let's remove all remaining widgets.
+    while ((this.widgets || [])[index]) {
+      this.removeWidget(index++);
+    }
+  }
+
+  override computeSize(out?: Vector2) {
+    let size = super.computeSize(out);
+    if (this.tempSize) {
+      size[0] = Math.max(this.tempSize[0], size[0]);
+      size[1] = Math.max(this.tempSize[1], size[1]);
+      // We sometimes get repeated calls to compute size, so debounce before clearing.
+      this.debouncerTempWidth && clearTimeout(this.debouncerTempWidth);
+      this.debouncerTempWidth = setTimeout(() => {
+        this.tempSize = null;
+      }, 32);
+    }
+    setTimeout(() => {
+      this.graph?.setDirtyCanvas(true, true);
+    }, 16);
+    return size;
+  }
+
+  override async handleAction(action: string) {
+    if (action === "Mute all" || action === "Bypass all") {
+      const alwaysOne = this.properties?.[PROPERTY_RESTRICTION] === "always one";
+      for (const [index, widget] of this.widgets.entries()) {
+        (widget as any)?.doModeChange(alwaysOne && !index ? true : false, true);
+      }
+    } else if (action === "Enable all") {
+      const onlyOne = this.properties?.[PROPERTY_RESTRICTION].includes(" one");
+      for (const [index, widget] of this.widgets.entries()) {
+        (widget as any)?.doModeChange(onlyOne && index > 0 ? false : true, true);
+      }
+    } else if (action === "Toggle all") {
+      const onlyOne = this.properties?.[PROPERTY_RESTRICTION].includes(" one");
+      let foundOne = false;
+      for (const [index, widget] of this.widgets.entries()) {
+        // If you have only one, then we'll stop at the first.
+        let newValue: boolean = onlyOne && foundOne ? false : !widget.value;
+        foundOne = foundOne || newValue;
+        (widget as any)?.doModeChange(newValue, true);
+      }
+      // And if you have always one, then we'll flip the last
+      if (!foundOne && this.properties?.[PROPERTY_RESTRICTION] === "always one") {
+        (this.widgets[this.widgets.length - 1] as any)?.doModeChange(true, true);
+      }
+    }
+  }
+
+  override getHelp() {
+    return `
+      <p>The ${this.type!.replace(
+        "(rgthree)",
+        "",
+      )} is an input-less node that automatically collects all groups in your current
+      workflow and allows you to quickly ${this.helpActions} all nodes within the group.</p>
+      <ul>
+        <li>
+          <p>
+            <strong>Properties.</strong> You can change the following properties (by right-clicking
+            on the node, and select "Properties" or "Properties Panel" from the menu):
+          </p>
+          <ul>
+            <li><p>
+              <code>${PROPERTY_MATCH_COLORS}</code> - Only add groups that match the provided
+              colors. Can be ComfyUI colors (red, pale_blue) or hex codes (#a4d399). Multiple can be
+              added, comma delimited.
+            </p></li>
+            <li><p>
+              <code>${PROPERTY_MATCH_TITLE}</code> - Filter the list of toggles by title match
+              (string match, or regular expression).
+            </p></li>
+            <li><p>
+              <code>${PROPERTY_SHOW_NAV}</code> - Add / remove a quick navigation arrow to take you
+              to the group. <i>(default: true)</i>
+            </p></li>
+            <li><p>
+              <code>${PROPERTY_SHOW_ALL_GRAPHS}</code> - Show groups from all [sub]graphs in the
+              workflow. <i>(default: true)</i>
+            </p></li>
+            <li><p>
+              <code>${PROPERTY_SORT}</code> - Sort the toggles' order by "alphanumeric", graph
+              "position", or "custom alphabet". <i>(default: "position")</i>
+            </p></li>
+            <li>
+              <p>
+                <code>${PROPERTY_SORT_CUSTOM_ALPHA}</code> - When the
+                <code>${PROPERTY_SORT}</code> property is "custom alphabet" you can define the
+                alphabet to use here, which will match the <i>beginning</i> of each group name and
+                sort against it. If group titles do not match any custom alphabet entry, then they
+                will be put after groups that do, ordered alphanumerically.
+              </p>
+              <p>
+                This can be a list of single characters, like "zyxw..." or comma delimited strings
+                for more control, like "sdxl,pro,sd,n,p".
+              </p>
+              <p>
+                Note, when two group title match the same custom alphabet entry, the <i>normal
+                alphanumeric alphabet</i> breaks the tie. For instance, a custom alphabet of
+                "e,s,d" will order groups names like "SDXL, SEGS, Detailer" eventhough the custom
+                alphabet has an "e" before "d" (where one may expect "SE" to be before "SD").
+              </p>
+              <p>
+                To have "SEGS" appear before "SDXL" you can use longer strings. For instance, the
+                custom alphabet value of "se,s,f" would work here.
+              </p>
+            </li>
+            <li><p>
+              <code>${PROPERTY_RESTRICTION}</code> - Optionally, attempt to restrict the number of
+              widgets that can be enabled to a maximum of one, or always one.
+              </p>
+              <p><em><strong>Note:</strong> If using "max one" or "always one" then this is only
+              enforced when clicking a toggle on this node; if nodes within groups are changed
+              outside of the initial toggle click, then these restriction will not be enforced, and
+              could result in a state where more than one toggle is enabled. This could also happen
+              if nodes are overlapped with multiple groups.
+            </p></li>
+
+          </ul>
+        </li>
+      </ul>`;
+  }
+}
+
+/**
+ * Fast Bypasser implementation that looks for groups in the workflow and adds toggles to mute them.
+ */
+export class FastGroupsMuter extends BaseFastGroupsModeChanger {
+  static override type = NodeTypesString.FAST_GROUPS_MUTER;
+  static override title = NodeTypesString.FAST_GROUPS_MUTER;
+  override comfyClass = NodeTypesString.FAST_GROUPS_MUTER;
+
+  static override exposedActions = ["Bypass all", "Enable all", "Toggle all"];
+
+  protected override helpActions = "mute and unmute";
+
+  override readonly modeOn: number = LiteGraph.ALWAYS;
+  override readonly modeOff: number = LiteGraph.NEVER;
+
+  constructor(title = FastGroupsMuter.title) {
+    super(title);
+    this.onConstructed();
+  }
+}
+
+/**
+ * The PowerLoraLoaderHeaderWidget that renders a toggle all switch, as well as some title info
+ * (more necessary for the double model & clip strengths to label them).
+ */
+class FastGroupsToggleRowWidget extends RgthreeBaseWidget<{toggled: boolean}> {
+  override value = {toggled: false};
+  override options = {on: "yes", off: "no"};
+  override readonly type = "custom";
+
+  label: string = "";
+  group: LGraphGroup;
+  node: BaseFastGroupsModeChanger;
+
+  constructor(group: LGraphGroup, node: BaseFastGroupsModeChanger) {
+    super("RGTHREE_TOGGLE_AND_NAV");
+    this.group = group;
+    this.node = node;
+  }
+
+  doModeChange(force?: boolean, skipOtherNodeCheck?: boolean) {
+    this.group.recomputeInsideNodes();
+    const hasAnyActiveNodes = getGroupNodes(this.group).some((n) => n.mode === LiteGraph.ALWAYS);
+    let newValue = force != null ? force : !hasAnyActiveNodes;
+    if (skipOtherNodeCheck !== true) {
+      // TODO: This work should probably live in BaseFastGroupsModeChanger instead of the widgets.
+      if (newValue && this.node.properties?.[PROPERTY_RESTRICTION]?.includes(" one")) {
+        for (const widget of this.node.widgets) {
+          if (widget instanceof FastGroupsToggleRowWidget) {
+            widget.doModeChange(false, true);
+          }
+        }
+      } else if (!newValue && this.node.properties?.[PROPERTY_RESTRICTION] === "always one") {
+        newValue = this.node.widgets.every((w) => !w.value || w === this);
+      }
+    }
+    changeModeOfNodes(getGroupNodes(this.group), (newValue ? this.node.modeOn : this.node.modeOff));
+    this.group.rgthree_hasAnyActiveNode = newValue;
+    this.toggled = newValue;
+    this.group.graph?.setDirtyCanvas(true, false);
+  }
+
+  get toggled() {
+    return this.value.toggled;
+  }
+  set toggled(value: boolean) {
+    this.value.toggled = value;
+  }
+
+  toggle(value?: boolean) {
+    value = value == null ? !this.toggled : value;
+    if (value !== this.toggled) {
+      this.value.toggled = value;
+      this.doModeChange();
+    }
+  }
+
+  draw(
+    ctx: CanvasRenderingContext2D,
+    node: FastGroupsMuter,
+    width: number,
+    posY: number,
+    height: number,
+  ) {
+    const widgetData = drawNodeWidget(ctx, {size: [width, height], pos: [15, posY]});
+
+    const showNav = node.properties?.[PROPERTY_SHOW_NAV] !== false;
+
+    // Render from right to left, since the text on left will take available space.
+    // `currentX` markes the current x position moving backwards.
+    let currentX = widgetData.width - widgetData.margin;
+
+    // The nav arrow
+    if (!widgetData.lowQuality && showNav) {
+      currentX -= 7; // Arrow space margin
+      const midY = widgetData.posY + widgetData.height * 0.5;
+      ctx.fillStyle = ctx.strokeStyle = "#89A";
+      ctx.lineJoin = "round";
+      ctx.lineCap = "round";
+      const arrow = new Path2D(`M${currentX} ${midY} l -7 6 v -3 h -7 v -6 h 7 v -3 z`);
+      ctx.fill(arrow);
+      ctx.stroke(arrow);
+      currentX -= 14;
+
+      currentX -= 7;
+      ctx.strokeStyle = widgetData.colorOutline;
+      ctx.stroke(new Path2D(`M ${currentX} ${widgetData.posY} v ${widgetData.height}`));
+    } else if (widgetData.lowQuality && showNav) {
+      currentX -= 28;
+    }
+
+    // The toggle itself.
+    currentX -= 7;
+    ctx.fillStyle = this.toggled ? "#89A" : "#333";
+    ctx.beginPath();
+    const toggleRadius = height * 0.36;
+    ctx.arc(currentX - toggleRadius, posY + height * 0.5, toggleRadius, 0, Math.PI * 2);
+    ctx.fill();
+    currentX -= toggleRadius * 2;
+
+    if (!widgetData.lowQuality) {
+      currentX -= 4;
+      ctx.textAlign = "right";
+      ctx.fillStyle = this.toggled ? widgetData.colorText : widgetData.colorTextSecondary;
+      const label = this.label;
+      const toggleLabelOn = this.options.on || "true";
+      const toggleLabelOff = this.options.off || "false";
+      ctx.fillText(this.toggled ? toggleLabelOn : toggleLabelOff, currentX, posY + height * 0.7);
+      currentX -= Math.max(
+        ctx.measureText(toggleLabelOn).width,
+        ctx.measureText(toggleLabelOff).width,
+      );
+
+      currentX -= 7;
+      ctx.textAlign = "left";
+      let maxLabelWidth = widgetData.width - widgetData.margin - 10 - (widgetData.width - currentX);
+      if (label != null) {
+        ctx.fillText(
+          fitString(ctx, label, maxLabelWidth),
+          widgetData.margin + 10,
+          posY + height * 0.7,
+        );
+      }
+    }
+  }
+
+  override serializeValue(node: LGraphNode, index: number) {
+    return this.value;
+  }
+
+  override mouse(event: CanvasMouseEvent, pos: Vector2, node: LGraphNode): boolean {
+    if (event.type == "pointerdown") {
+      if (node.properties?.[PROPERTY_SHOW_NAV] !== false && pos[0] >= node.size[0] - 15 - 28 - 1) {
+        const canvas = app.canvas as TLGraphCanvas;
+        const lowQuality = (canvas.ds?.scale || 1) <= 0.5;
+        if (!lowQuality) {
+          // Clicked on right half with nav arrow, go to the group, center on group and set
+          // zoom to see it all.
+          canvas.centerOnNode(this.group);
+          const zoomCurrent = canvas.ds?.scale || 1;
+          const zoomX = canvas.canvas.width / this.group._size[0] - 0.02;
+          const zoomY = canvas.canvas.height / this.group._size[1] - 0.02;
+          canvas.setZoom(Math.min(zoomCurrent, zoomX, zoomY), [
+            canvas.canvas.width / 2,
+            canvas.canvas.height / 2,
+          ]);
+          canvas.setDirty(true, true);
+        }
+      } else {
+        this.toggle();
+      }
+    }
+    return true;
+  }
+}
+
+app.registerExtension({
+  name: "rgthree.FastGroupsMuter",
+  registerCustomNodes() {
+    FastGroupsMuter.setUp();
+  },
+  loadedGraphNode(node: LGraphNode) {
+    if (node.type == FastGroupsMuter.title) {
+      (node as FastGroupsMuter).tempSize = [...node.size] as Point;
+    }
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/feature_group_fast_toggle.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/feature_group_fast_toggle.ts
new file mode 100644
index 0000000000000000000000000000000000000000..260d7cba41f5b1f30c2f6f83e294dd2b410e9948
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/feature_group_fast_toggle.ts
@@ -0,0 +1,305 @@
+import type {
+  LGraphCanvas as TLGraphCanvas,
+  LGraphGroup as TLGraphGroup,
+  LGraph as TLGraph,
+  Vector2,
+  CanvasMouseEvent,
+} from "@comfyorg/frontend";
+import type {AdjustedMouseCustomEvent} from "typings/rgthree.js";
+
+import {app} from "scripts/app.js";
+import {rgthree} from "./rgthree.js";
+import {changeModeOfNodes, getGroupNodes, getOutputNodes} from "./utils.js";
+import {SERVICE as CONFIG_SERVICE} from "./services/config_service.js";
+
+const BTN_SIZE = 20;
+const BTN_MARGIN: Vector2 = [6, 6];
+const BTN_SPACING = 8;
+const BTN_GRID = BTN_SIZE / 8;
+
+const TOGGLE_TO_MODE = new Map([
+  ["MUTE", LiteGraph.NEVER],
+  ["BYPASS", 4],
+]);
+
+function getToggles() {
+  return [...CONFIG_SERVICE.getFeatureValue("group_header_fast_toggle.toggles", [])].reverse();
+}
+
+/**
+ * Determines if the user clicked on an fast header icon.
+ */
+function clickedOnToggleButton(e: CanvasMouseEvent, group: TLGraphGroup): string | null {
+  const toggles = getToggles();
+  const pos = group.pos;
+  const size = group.size;
+  for (let i = 0; i < toggles.length; i++) {
+    const toggle = toggles[i];
+    if (
+      LiteGraph.isInsideRectangle(
+        e.canvasX,
+        e.canvasY,
+        pos[0] + size[0] - (BTN_SIZE + BTN_MARGIN[0]) * (i + 1),
+        pos[1] + BTN_MARGIN[1],
+        BTN_SIZE,
+        BTN_SIZE,
+      )
+    ) {
+      return toggle;
+    }
+  }
+  return null;
+}
+
+/**
+ * Registers the GroupHeaderToggles which places a mute and/or bypass icons in groups headers for
+ * quick, single-click ability to mute/bypass.
+ */
+app.registerExtension({
+  name: "rgthree.GroupHeaderToggles",
+  async setup() {
+    /**
+     * LiteGraph won't call `drawGroups` unless the canvas is dirty. Other nodes will do this, but
+     * in small workflows, we'll want to trigger it dirty so we can be drawn if we're in hover mode.
+     */
+    setInterval(() => {
+      if (
+        CONFIG_SERVICE.getFeatureValue("group_header_fast_toggle.enabled") &&
+        CONFIG_SERVICE.getFeatureValue("group_header_fast_toggle.show") !== "always"
+      ) {
+        app.canvas.setDirty(true, true);
+      }
+    }, 250);
+
+    /**
+     * Handles a click on the icon area if the user has the extension enable from settings.
+     * Hooks into the already overriden mouse down processor from rgthree.
+     */
+    rgthree.addEventListener("on-process-mouse-down", ((e: AdjustedMouseCustomEvent) => {
+      if (!CONFIG_SERVICE.getFeatureValue("group_header_fast_toggle.enabled")) return;
+
+      const canvas = app.canvas as TLGraphCanvas;
+      if (canvas.selected_group) {
+        const originalEvent = e.detail.originalEvent;
+        const group = canvas.selected_group;
+        const clickedOnToggle = clickedOnToggleButton(originalEvent, group) || "";
+        const toggleAction = clickedOnToggle?.toLocaleUpperCase();
+        if (toggleAction) {
+          console.log(toggleAction);
+          const nodes = getGroupNodes(group);
+          if (toggleAction === "QUEUE") {
+            const outputNodes = getOutputNodes(nodes);
+            if (!outputNodes?.length) {
+              rgthree.showMessage({
+                id: "no-output-in-group",
+                type: "warn",
+                timeout: 4000,
+                message: "No output nodes for group!",
+              });
+            } else {
+              rgthree.queueOutputNodes(outputNodes);
+            }
+          } else {
+            const toggleMode = TOGGLE_TO_MODE.get(toggleAction);
+            if (toggleMode) {
+              group.recomputeInsideNodes();
+              const hasAnyActiveNodes = nodes.some((n) => n.mode === LiteGraph.ALWAYS);
+              const isAllMuted =
+                !hasAnyActiveNodes && nodes.every((n) => n.mode === LiteGraph.NEVER);
+              const isAllBypassed =
+                !hasAnyActiveNodes && !isAllMuted && nodes.every((n) => n.mode === 4);
+
+              let newMode: 0 | 1 | 2 | 3 | 4 = LiteGraph.ALWAYS;
+              if (toggleMode === LiteGraph.NEVER) {
+                newMode = isAllMuted ? LiteGraph.ALWAYS : LiteGraph.NEVER;
+              } else {
+                newMode = isAllBypassed ? LiteGraph.ALWAYS : 4;
+              }
+              changeModeOfNodes(nodes, newMode);
+            }
+          }
+          // Make it such that we're not then moving the group on drag.
+          canvas.selected_group = null;
+          canvas.dragging_canvas = false;
+        }
+      }
+    }) as EventListener);
+
+    /**
+     * Overrides LiteGraph's Canvas method for drawingGroups and, after calling the original, checks
+     * that the user has enabled fast toggles and draws them on the top-right of the app..
+     */
+    const drawGroups = LGraphCanvas.prototype.drawGroups;
+    LGraphCanvas.prototype.drawGroups = function (
+      canvasEl: HTMLCanvasElement,
+      ctx: CanvasRenderingContext2D,
+    ) {
+      drawGroups.apply(this, [...arguments] as any);
+
+      if (
+        !CONFIG_SERVICE.getFeatureValue("group_header_fast_toggle.enabled") ||
+        !rgthree.lastCanvasMouseEvent
+      ) {
+        return;
+      }
+
+      const graph = app.canvas.graph as TLGraph;
+
+      let groups: TLGraphGroup[];
+      // Default to hover if not always.
+      if (CONFIG_SERVICE.getFeatureValue("group_header_fast_toggle.show") !== "always") {
+        const hoverGroup = graph.getGroupOnPos(
+          rgthree.lastCanvasMouseEvent.canvasX,
+          rgthree.lastCanvasMouseEvent.canvasY,
+        );
+        groups = hoverGroup ? [hoverGroup] : [];
+      } else {
+        groups = graph._groups || [];
+      }
+
+      if (!groups.length) {
+        return;
+      }
+
+      const toggles = getToggles();
+
+      ctx.save();
+      for (const group of groups || []) {
+        const nodes = getGroupNodes(group);
+        let anyActive = false;
+        let allMuted = !!nodes.length;
+        let allBypassed = allMuted;
+
+        // Find the current state of the group's nodes.
+        for (const node of nodes) {
+          if (!(node instanceof LGraphNode)) continue;
+          anyActive = anyActive || node.mode === LiteGraph.ALWAYS;
+          allMuted = allMuted && node.mode === LiteGraph.NEVER;
+          allBypassed = allBypassed && node.mode === 4;
+          if (anyActive || (!allMuted && !allBypassed)) {
+            break;
+          }
+        }
+
+        // Display each toggle.
+        for (let i = 0; i < toggles.length; i++) {
+          const toggle = toggles[i];
+          const pos = group._pos;
+          const size = group._size;
+          ctx.fillStyle = ctx.strokeStyle = group.color || "#335";
+          const x = pos[0] + size[0] - BTN_MARGIN[0] - BTN_SIZE - (BTN_SPACING + BTN_SIZE) * i;
+          const y = pos[1] + BTN_MARGIN[1];
+          const midX = x + BTN_SIZE / 2;
+          const midY = y + BTN_SIZE / 2;
+          if (toggle === "queue") {
+            const outputNodes = getOutputNodes(nodes);
+            const oldGlobalAlpha = ctx.globalAlpha;
+            if (!outputNodes?.length) {
+              ctx.globalAlpha = 0.5;
+            }
+            ctx.lineJoin = "round";
+            ctx.lineCap = "round";
+            const arrowSizeX = BTN_SIZE * 0.6;
+            const arrowSizeY = BTN_SIZE * 0.7;
+            const arrow = new Path2D(
+              `M ${x + arrowSizeX / 2} ${midY} l 0 -${arrowSizeY / 2} l ${arrowSizeX} ${arrowSizeY / 2} l -${arrowSizeX} ${arrowSizeY / 2} z`,
+            );
+            ctx.stroke(arrow);
+            if (outputNodes?.length) {
+              ctx.fill(arrow);
+            }
+            ctx.globalAlpha = oldGlobalAlpha;
+          } else {
+            const on = toggle === "bypass" ? allBypassed : allMuted;
+
+            ctx.beginPath();
+            ctx.lineJoin = "round";
+            ctx.rect(x, y, BTN_SIZE, BTN_SIZE);
+
+            ctx.lineWidth = 2;
+            if (toggle === "mute") {
+              ctx.lineJoin = "round";
+              ctx.lineCap = "round";
+
+              if (on) {
+                ctx.stroke(
+                  new Path2D(`
+                    ${eyeFrame(midX, midY)}
+                    ${eyeLashes(midX, midY)}
+                `),
+                );
+              } else {
+                const radius = BTN_GRID * 1.5;
+
+                // Eyeball fill
+                ctx.fill(
+                  new Path2D(`
+                    ${eyeFrame(midX, midY)}
+                    ${eyeFrame(midX, midY, -1)}
+                    ${circlePath(midX, midY, radius)}
+                    ${circlePath(midX + BTN_GRID / 2, midY - BTN_GRID / 2, BTN_GRID * 0.375)}
+                  `),
+                  "evenodd",
+                );
+
+                // Eye Outline Stroke
+                ctx.stroke(new Path2D(`${eyeFrame(midX, midY)} ${eyeFrame(midX, midY, -1)}`));
+
+                // Eye lashes (faded)
+                ctx.globalAlpha = this.editor_alpha * 0.5;
+                ctx.stroke(new Path2D(`${eyeLashes(midX, midY)} ${eyeLashes(midX, midY, -1)}`));
+                ctx.globalAlpha = this.editor_alpha;
+              }
+            } else {
+              const lineChanges = on
+                ? `a ${BTN_GRID * 3}, ${BTN_GRID * 3} 0 1, 1 ${BTN_GRID * 3 * 2},0
+                  l ${BTN_GRID * 2.0} 0`
+                : `l ${BTN_GRID * 8} 0`;
+
+              ctx.stroke(
+                new Path2D(`
+                  M ${x} ${midY}
+                  ${lineChanges}
+                  M ${x + BTN_SIZE} ${midY} l -2  2
+                  M ${x + BTN_SIZE} ${midY} l -2 -2
+                `),
+              );
+              ctx.fill(new Path2D(`${circlePath(x + BTN_GRID * 3, midY, BTN_GRID * 1.8)}`));
+            }
+          }
+        }
+      }
+      ctx.restore();
+    };
+  },
+});
+
+function eyeFrame(midX: number, midY: number, yFlip = 1) {
+  return `
+      M ${midX - BTN_SIZE / 2} ${midY}
+      c ${BTN_GRID * 1.5} ${yFlip * BTN_GRID * 2.5}, ${BTN_GRID * (8 - 1.5)} ${
+        yFlip * BTN_GRID * 2.5
+      }, ${BTN_GRID * 8} 0
+  `;
+}
+
+function eyeLashes(midX: number, midY: number, yFlip = 1) {
+  return `
+    M ${midX - BTN_GRID * 3.46} ${midY + yFlip * BTN_GRID * 0.9} l -1.15  ${1.25 * yFlip}
+    M ${midX - BTN_GRID * 2.38} ${midY + yFlip * BTN_GRID * 1.6} l -0.90  ${1.5 * yFlip}
+    M ${midX - BTN_GRID * 1.15} ${midY + yFlip * BTN_GRID * 1.95} l -0.50  ${1.75 * yFlip}
+    M ${midX + BTN_GRID * 0.0} ${midY + yFlip * BTN_GRID * 2.0} l  0.00  ${2.0 * yFlip}
+    M ${midX + BTN_GRID * 1.15} ${midY + yFlip * BTN_GRID * 1.95} l  0.50  ${1.75 * yFlip}
+    M ${midX + BTN_GRID * 2.38} ${midY + yFlip * BTN_GRID * 1.6} l  0.90  ${1.5 * yFlip}
+    M ${midX + BTN_GRID * 3.46} ${midY + yFlip * BTN_GRID * 0.9} l  1.15  ${1.25 * yFlip}
+`;
+}
+
+function circlePath(cx: number, cy: number, radius: number) {
+  return `
+      M ${cx} ${cy}
+      m ${radius}, 0
+      a ${radius},${radius} 0 1, 1 -${radius * 2},0
+      a ${radius},${radius} 0 1, 1  ${radius * 2},0
+  `;
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/feature_import_individual_nodes.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/feature_import_individual_nodes.ts
new file mode 100644
index 0000000000000000000000000000000000000000..f01aa87d402c8d3c9111db2e7823af20050ecf76
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/feature_import_individual_nodes.ts
@@ -0,0 +1,85 @@
+import type {INodeSlot, LGraphNode, LGraphNodeConstructor} from "@comfyorg/frontend";
+import type {ComfyNodeDef} from "typings/comfy.js";
+
+import {app} from "scripts/app.js";
+import {tryToGetWorkflowDataFromEvent} from "rgthree/common/utils_workflow.js";
+import {SERVICE as CONFIG_SERVICE} from "./services/config_service.js";
+import {NodeTypesString} from "./constants.js";
+
+/**
+ * Registers the GroupHeaderToggles which places a mute and/or bypass icons in groups headers for
+ * quick, single-click ability to mute/bypass.
+ */
+app.registerExtension({
+  name: "rgthree.ImportIndividualNodes",
+  async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    const onDragOver = nodeType.prototype.onDragOver;
+    nodeType.prototype.onDragOver = function (e: DragEvent) {
+      let handled = onDragOver?.apply?.(this, [...arguments] as any);
+      if (handled != null) {
+        return handled;
+      }
+      return importIndividualNodesInnerOnDragOver(this, e);
+    };
+
+    const onDragDrop = nodeType.prototype.onDragDrop;
+    nodeType.prototype.onDragDrop = async function (e: DragEvent) {
+      const alreadyHandled = await onDragDrop?.apply?.(this, [...arguments] as any);
+      if (alreadyHandled) {
+        return alreadyHandled;
+      }
+      return importIndividualNodesInnerOnDragDrop(this, e);
+    };
+  },
+});
+
+export function importIndividualNodesInnerOnDragOver(node: LGraphNode, e: DragEvent): boolean {
+  return (
+    (node.widgets?.length && !!CONFIG_SERVICE.getFeatureValue("import_individual_nodes.enabled")) ||
+    false
+  );
+}
+
+export async function importIndividualNodesInnerOnDragDrop(node: LGraphNode, e: DragEvent) {
+  if (!node.widgets?.length || !CONFIG_SERVICE.getFeatureValue("import_individual_nodes.enabled")) {
+    return false;
+  }
+
+  const dynamicWidgetLengthNodes = [NodeTypesString.POWER_LORA_LOADER];
+
+  let handled = false;
+  const {workflow, prompt} = await tryToGetWorkflowDataFromEvent(e);
+  const exact = (workflow?.nodes || []).find(
+    (n: any) =>
+      n.id === node.id &&
+      n.type === node.type &&
+      (dynamicWidgetLengthNodes.includes(node.type) ||
+        n.widgets_values?.length === node.widgets_values?.length),
+  );
+  if (!exact) {
+    // If we tried, but didn't find an exact match, then allow user to stop the default behavior.
+    handled = !confirm(
+      "[rgthree-comfy] Could not find a matching node (same id & type) in the dropped workflow." +
+        " Would you like to continue with the default drop behaviour instead?",
+    );
+  } else if (!exact.widgets_values?.length) {
+    handled = !confirm(
+      "[rgthree-comfy] Matching node found (same id & type) but there's no widgets to set." +
+        " Would you like to continue with the default drop behaviour instead?",
+    );
+  } else if (
+    confirm(
+      "[rgthree-comfy] Found a matching node (same id & type) in the dropped workflow." +
+        " Would you like to set the widget values?",
+    )
+  ) {
+    node.configure({
+      // Title is overridden if it's not supplied; set it to the current then.
+      title: node.title,
+      widgets_values: [...(exact?.widgets_values || [])],
+      mode: exact.mode,
+    } as any);
+    handled = true;
+  }
+  return handled;
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/image_comparer.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/image_comparer.ts
new file mode 100644
index 0000000000000000000000000000000000000000..3c648f8f8a36040cd6122ebc510dcc90bfb865fe
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/image_comparer.ts
@@ -0,0 +1,480 @@
+import {
+  LGraphCanvas,
+  LGraphNode,
+  Vector2,
+  LGraphNodeConstructor,
+  CanvasMouseEvent,
+  ISerialisedNode,
+  Point,
+  CanvasPointerEvent,
+} from "@comfyorg/frontend";
+import type {ComfyNodeDef} from "typings/comfy.js";
+
+import {app} from "scripts/app.js";
+import {api} from "scripts/api.js";
+import {RgthreeBaseServerNode} from "./base_node.js";
+import {NodeTypesString} from "./constants.js";
+import {addConnectionLayoutSupport} from "./utils.js";
+import {RgthreeBaseHitAreas, RgthreeBaseWidget, RgthreeBaseWidgetBounds} from "./utils_widgets.js";
+import {measureText} from "./utils_canvas.js";
+
+type ComfyImageServerData = {filename: string; type: string; subfolder: string};
+type ComfyImageData = {name: string; selected: boolean; url: string; img?: HTMLImageElement};
+type OldExecutedPayload = {
+  images: ComfyImageServerData[];
+};
+type ExecutedPayload = {
+  a_images?: ComfyImageServerData[];
+  b_images?: ComfyImageServerData[];
+};
+
+function imageDataToUrl(data: ComfyImageServerData) {
+  return api.apiURL(
+    `/view?filename=${encodeURIComponent(data.filename)}&type=${data.type}&subfolder=${
+      data.subfolder
+    }${app.getPreviewFormatParam()}${app.getRandParam()}`,
+  );
+}
+
+/**
+ * Compares two images in one canvas node.
+ */
+export class RgthreeImageComparer extends RgthreeBaseServerNode {
+  static override title = NodeTypesString.IMAGE_COMPARER;
+  static override type = NodeTypesString.IMAGE_COMPARER;
+  static comfyClass = NodeTypesString.IMAGE_COMPARER;
+
+  // These is what the core preview image node uses to show the context menu. May not be that helpful
+  // since it likely will always be "0" when a context menu is invoked without manually changing
+  // something.
+  override imageIndex: number = 0;
+  override imgs: InstanceType<typeof Image>[] = [];
+
+  override serialize_widgets = true;
+
+  isPointerDown = false;
+  isPointerOver = false;
+  pointerOverPos: Vector2 = [0, 0];
+
+  private canvasWidget: RgthreeImageComparerWidget | null = null;
+
+  static "@comparer_mode" = {
+    type: "combo",
+    values: ["Slide", "Click"],
+  };
+
+  constructor(title = RgthreeImageComparer.title) {
+    super(title);
+    this.properties["comparer_mode"] = "Slide";
+  }
+
+  override onExecuted(output: ExecutedPayload | OldExecutedPayload) {
+    super.onExecuted?.(output);
+    if ("images" in output) {
+      this.canvasWidget!.value = {
+        images: (output.images || []).map((d, i) => {
+          return {
+            name: i === 0 ? "A" : "B",
+            selected: true,
+            url: imageDataToUrl(d),
+          };
+        }),
+      };
+    } else {
+      output.a_images = output.a_images || [];
+      output.b_images = output.b_images || [];
+      const imagesToChoose: ComfyImageData[] = [];
+      const multiple = output.a_images.length + output.b_images.length > 2;
+      for (const [i, d] of output.a_images.entries()) {
+        imagesToChoose.push({
+          name: output.a_images.length > 1 || multiple ? `A${i + 1}` : "A",
+          selected: i === 0,
+          url: imageDataToUrl(d),
+        });
+      }
+      for (const [i, d] of output.b_images.entries()) {
+        imagesToChoose.push({
+          name: output.b_images.length > 1 || multiple ? `B${i + 1}` : "B",
+          selected: i === 0,
+          url: imageDataToUrl(d),
+        });
+      }
+      this.canvasWidget!.value = {images: imagesToChoose};
+    }
+  }
+
+  override onSerialize(serialised: ISerialisedNode) {
+    super.onSerialize && super.onSerialize(serialised);
+    for (let [index, widget_value] of (serialised.widgets_values || []).entries()) {
+      if (this.widgets[index]?.name === "rgthree_comparer") {
+        serialised.widgets_values![index] = (
+          this.widgets[index] as unknown as RgthreeImageComparerWidget
+        ).value.images.map((d) => {
+          d = {...d};
+          delete d.img;
+          return d;
+        });
+      }
+    }
+  }
+
+  override onNodeCreated() {
+    this.canvasWidget = this.addCustomWidget(
+      new RgthreeImageComparerWidget("rgthree_comparer", this),
+    ) as RgthreeImageComparerWidget;
+    this.setSize(this.computeSize());
+    this.setDirtyCanvas(true, true);
+  }
+
+  /**
+   * Sets mouse as down or up based on param. If it's down, we also loop to check pointer is still
+   * down. This is because LiteGraph doesn't fire `onMouseUp` every time there's a mouse up, so we
+   * need to manually monitor `pointer_is_down` and, when it's no longer true, set mouse as up here.
+   */
+  private setIsPointerDown(down: boolean = this.isPointerDown) {
+    const newIsDown = down && !!app.canvas.pointer_is_down;
+    if (this.isPointerDown !== newIsDown) {
+      this.isPointerDown = newIsDown;
+      this.setDirtyCanvas(true, false);
+    }
+    this.imageIndex = this.isPointerDown ? 1 : 0;
+    if (this.isPointerDown) {
+      requestAnimationFrame(() => {
+        this.setIsPointerDown();
+      });
+    }
+  }
+
+  override onMouseDown(event: CanvasPointerEvent, pos: Point, canvas: LGraphCanvas): boolean {
+    super.onMouseDown?.(event, pos, canvas);
+    this.setIsPointerDown(true);
+    return false;
+  }
+
+  override onMouseEnter(event: CanvasPointerEvent): void {
+    super.onMouseEnter?.(event);
+    this.setIsPointerDown(!!app.canvas.pointer_is_down);
+    this.isPointerOver = true;
+  }
+
+  override onMouseLeave(event: CanvasPointerEvent): void {
+    super.onMouseLeave?.(event);
+    this.setIsPointerDown(false);
+    this.isPointerOver = false;
+  }
+
+  override onMouseMove(event: CanvasPointerEvent, pos: Point, canvas: LGraphCanvas): void {
+    super.onMouseMove?.(event, pos, canvas);
+    this.pointerOverPos = [...pos] as Point;
+    this.imageIndex = this.pointerOverPos[0] > this.size[0] / 2 ? 1 : 0;
+  }
+
+  override getHelp(): string {
+    return `
+      <p>
+        The ${this.type!.replace("(rgthree)", "")} node compares two images on top of each other.
+      </p>
+      <ul>
+        <li>
+          <p>
+            <strong>Notes</strong>
+          </p>
+          <ul>
+            <li><p>
+              The right-click menu may show image options (Open Image, Save Image, etc.) which will
+              correspond to the first image (image_a) if clicked on the left-half of the node, or
+              the second image if on the right half of the node.
+            </p></li>
+          </ul>
+        </li>
+        <li>
+          <p>
+            <strong>Inputs</strong>
+          </p>
+          <ul>
+            <li><p>
+              <code>image_a</code> <i>Optional.</i> The first image to use to compare.
+              image_a.
+            </p></li>
+            <li><p>
+              <code>image_b</code> <i>Optional.</i> The second image to use to compare.
+            </p></li>
+            <li><p>
+              <b>Note</b> <code>image_a</code> and <code>image_b</code> work best when a single
+              image is provided. However, if each/either are a batch, you can choose which item
+              from each batch are chosen to be compared. If either <code>image_a</code> or
+              <code>image_b</code> are not provided, the node will choose the first two from the
+              provided input if it's a batch, otherwise only show the single image (just as
+              Preview Image would).
+            </p></li>
+          </ul>
+        </li>
+        <li>
+          <p>
+            <strong>Properties.</strong> You can change the following properties (by right-clicking
+            on the node, and select "Properties" or "Properties Panel" from the menu):
+          </p>
+          <ul>
+            <li><p>
+              <code>comparer_mode</code> - Choose between "Slide" and "Click". Defaults to "Slide".
+            </p></li>
+          </ul>
+        </li>
+      </ul>`;
+  }
+
+  static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, RgthreeImageComparer);
+  }
+
+  static override onRegisteredForOverride(comfyClass: any) {
+    addConnectionLayoutSupport(RgthreeImageComparer, app, [
+      ["Left", "Right"],
+      ["Right", "Left"],
+    ]);
+    setTimeout(() => {
+      RgthreeImageComparer.category = comfyClass.category;
+    });
+  }
+}
+
+type RgthreeImageComparerWidgetValue = {
+  images: ComfyImageData[];
+};
+
+class RgthreeImageComparerWidget extends RgthreeBaseWidget<RgthreeImageComparerWidgetValue> {
+  override readonly type = "custom";
+
+  private node: RgthreeImageComparer;
+
+  protected override hitAreas: RgthreeBaseHitAreas<any> = {
+    // We dynamically set this when/if we draw the labels.
+  };
+
+  private selected: [ComfyImageData?, ComfyImageData?] = [];
+
+  constructor(name: string, node: RgthreeImageComparer) {
+    super(name);
+    this.node = node;
+  }
+
+  private _value: RgthreeImageComparerWidgetValue = {images: []};
+
+  set value(v: RgthreeImageComparerWidgetValue) {
+    // Despite `v` typed as RgthreeImageComparerWidgetValue, we may have gotten an array of strings
+    // from previous versions. We can handle that gracefully.
+    let cleanedVal;
+    if (Array.isArray(v)) {
+      cleanedVal = v.map((d, i) => {
+        if (!d || typeof d === "string") {
+          // We usually only have two here, so they're selected.
+          d = {url: d, name: i == 0 ? "A" : "B", selected: true};
+        }
+        return d;
+      });
+    } else {
+      cleanedVal = v.images || [];
+    }
+
+    // If we have multiple items in our sent value but we don't have both an "A" and a "B" then
+    // just simplify it down to the first two in the list.
+    if (cleanedVal.length > 2) {
+      const hasAAndB =
+        cleanedVal.some((i) => i.name.startsWith("A")) &&
+        cleanedVal.some((i) => i.name.startsWith("B"));
+      if (!hasAAndB) {
+        cleanedVal = [cleanedVal[0], cleanedVal[1]];
+      }
+    }
+
+    let selected = cleanedVal.filter((d) => d.selected);
+    // None are selected.
+    if (!selected.length && cleanedVal.length) {
+      cleanedVal[0]!.selected = true;
+    }
+
+    selected = cleanedVal.filter((d) => d.selected);
+    if (selected.length === 1 && cleanedVal.length > 1) {
+      cleanedVal.find((d) => !d.selected)!.selected = true;
+    }
+
+    this._value.images = cleanedVal;
+
+    selected = cleanedVal.filter((d) => d.selected);
+    this.setSelected(selected as [ComfyImageData, ComfyImageData]);
+  }
+
+  get value() {
+    return this._value;
+  }
+
+  setSelected(selected: [ComfyImageData, ComfyImageData]) {
+    this._value.images.forEach((d) => (d.selected = false));
+    this.node.imgs.length = 0;
+    for (const sel of selected) {
+      if (!sel.img) {
+        sel.img = new Image();
+        sel.img.src = sel.url;
+        this.node.imgs.push(sel.img);
+      }
+      sel.selected = true;
+    }
+    this.selected = selected;
+  }
+
+  draw(ctx: CanvasRenderingContext2D, node: RgthreeImageComparer, width: number, y: number) {
+    this.hitAreas = {};
+    if (this.value.images.length > 2) {
+      ctx.textAlign = "left";
+      ctx.textBaseline = "top";
+      ctx.font = `14px Arial`;
+      // Let's calculate the widths of all the labels.
+      const drawData: any = [];
+      const spacing = 5;
+      let x = 0;
+      for (const img of this.value.images) {
+        const width = measureText(ctx, img.name);
+        drawData.push({
+          img,
+          text: img.name,
+          x,
+          width: measureText(ctx, img.name),
+        });
+        x += width + spacing;
+      }
+      x = (node.size[0] - (x - spacing)) / 2;
+      for (const d of drawData) {
+        ctx.fillStyle = d.img.selected ? "rgba(180, 180, 180, 1)" : "rgba(180, 180, 180, 0.5)";
+        ctx.fillText(d.text, x, y);
+        this.hitAreas[d.text] = {
+          bounds: [x, y, d.width, 14],
+          data: d.img,
+          onDown: this.onSelectionDown,
+        };
+        x += d.width + spacing;
+      }
+      y += 20;
+    }
+
+    if (node.properties?.["comparer_mode"] === "Click") {
+      this.drawImage(ctx, this.selected[this.node.isPointerDown ? 1 : 0], y);
+    } else {
+      this.drawImage(ctx, this.selected[0], y);
+      if (node.isPointerOver) {
+        this.drawImage(ctx, this.selected[1], y, this.node.pointerOverPos[0]);
+      }
+    }
+  }
+
+  private onSelectionDown(
+    event: CanvasMouseEvent,
+    pos: Vector2,
+    node: LGraphNode,
+    bounds?: RgthreeBaseWidgetBounds,
+  ) {
+    const selected = [...this.selected];
+    if (bounds?.data.name.startsWith("A")) {
+      selected[0] = bounds.data;
+    } else if (bounds?.data.name.startsWith("B")) {
+      selected[1] = bounds.data;
+    }
+    this.setSelected(selected as [ComfyImageData, ComfyImageData]);
+  }
+
+  private drawImage(
+    ctx: CanvasRenderingContext2D,
+    image: ComfyImageData | undefined,
+    y: number,
+    cropX?: number,
+  ) {
+    if (!image?.img?.naturalWidth || !image?.img?.naturalHeight) {
+      return;
+    }
+    let [nodeWidth, nodeHeight] = this.node.size as [number, number];
+    const imageAspect = image?.img.naturalWidth / image?.img.naturalHeight;
+    let height = nodeHeight - y;
+    const widgetAspect = nodeWidth / height;
+    let targetWidth, targetHeight;
+    let offsetX = 0;
+    if (imageAspect > widgetAspect) {
+      targetWidth = nodeWidth;
+      targetHeight = nodeWidth / imageAspect;
+    } else {
+      targetHeight = height;
+      targetWidth = height * imageAspect;
+      offsetX = (nodeWidth - targetWidth) / 2;
+    }
+    const widthMultiplier = image?.img.naturalWidth / targetWidth;
+
+    const sourceX = 0;
+    const sourceY = 0;
+    const sourceWidth =
+      cropX != null ? (cropX - offsetX) * widthMultiplier : image?.img.naturalWidth;
+    const sourceHeight = image?.img.naturalHeight;
+    const destX = (nodeWidth - targetWidth) / 2;
+    const destY = y + (height - targetHeight) / 2;
+    const destWidth = cropX != null ? cropX - offsetX : targetWidth;
+    const destHeight = targetHeight;
+    ctx.save();
+    ctx.beginPath();
+    let globalCompositeOperation = ctx.globalCompositeOperation;
+    if (cropX) {
+      ctx.rect(destX, destY, destWidth, destHeight);
+      ctx.clip();
+    }
+    ctx.drawImage(
+      image?.img,
+      sourceX,
+      sourceY,
+      sourceWidth,
+      sourceHeight,
+      destX,
+      destY,
+      destWidth,
+      destHeight,
+    );
+    // Shows a label overlayed on the image. Not perfect, keeping commented out.
+    // ctx.globalCompositeOperation = "difference";
+    // ctx.fillStyle = "rgba(180, 180, 180, 1)";
+    // ctx.textAlign = "center";
+    // ctx.font = `32px Arial`;
+    // ctx.fillText(image.name, nodeWidth / 2, y + 32);
+    if (cropX != null && cropX >= (nodeWidth - targetWidth) / 2 && cropX <= targetWidth + offsetX) {
+      ctx.beginPath();
+      ctx.moveTo(cropX, destY);
+      ctx.lineTo(cropX, destY + destHeight);
+      ctx.globalCompositeOperation = "difference";
+      ctx.strokeStyle = "rgba(255,255,255, 1)";
+      ctx.stroke();
+    }
+    ctx.globalCompositeOperation = globalCompositeOperation;
+    ctx.restore();
+  }
+
+  computeSize(width: number): Vector2 {
+    return [width, 20];
+  }
+
+  override serializeValue(
+    node: LGraphNode,
+    index: number,
+  ): RgthreeImageComparerWidgetValue | Promise<RgthreeImageComparerWidgetValue> {
+    const v = [];
+    for (const data of this._value.images) {
+      // Remove the img since it can't serialize.
+      const d = {...data};
+      delete d.img;
+      v.push(d);
+    }
+    return {images: v};
+  }
+}
+
+app.registerExtension({
+  name: "rgthree.ImageComparer",
+  async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    if (nodeData.name === RgthreeImageComparer.type) {
+      RgthreeImageComparer.setUp(nodeType, nodeData);
+    }
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/image_inset_crop.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/image_inset_crop.ts
new file mode 100644
index 0000000000000000000000000000000000000000..631ceaf0cf155649d89fa721cfa2b738b1f4bfc7
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/image_inset_crop.ts
@@ -0,0 +1,70 @@
+import type {LGraph, LGraphNodeConstructor, ISerialisedNode} from "@comfyorg/frontend";
+import type {ComfyNodeDef} from "typings/comfy.js";
+
+import {app} from "scripts/app.js";
+import {RgthreeBaseServerNode} from "./base_node.js";
+import {NodeTypesString} from "./constants.js";
+
+class ImageInsetCrop extends RgthreeBaseServerNode {
+  static override title = NodeTypesString.IMAGE_INSET_CROP;
+  static override type = NodeTypesString.IMAGE_INSET_CROP;
+  static comfyClass = NodeTypesString.IMAGE_INSET_CROP;
+
+  static override exposedActions = ["Reset Crop"];
+  static maxResolution = 8192;
+
+  constructor(title = ImageInsetCrop.title) {
+    super(title);
+  }
+
+  override onAdded(graph: LGraph): void {
+    const measurementWidget = this.widgets[0]!;
+    let callback = measurementWidget.callback;
+    measurementWidget.callback = (...args) => {
+      this.setWidgetStep();
+      callback && callback.apply(measurementWidget, [...args]);
+    };
+    this.setWidgetStep();
+  }
+
+  override configure(info: ISerialisedNode): void {
+    super.configure(info);
+    this.setWidgetStep();
+  }
+
+  private setWidgetStep() {
+    const measurementWidget = this.widgets[0]!;
+    for (let i = 1; i <= 4; i++) {
+      if (measurementWidget.value === "Pixels") {
+        this.widgets[i]!.options.step = 80;
+        this.widgets[i]!.options.max = ImageInsetCrop.maxResolution;
+      } else {
+        this.widgets[i]!.options.step = 10;
+        this.widgets[i]!.options.max = 99;
+      }
+    }
+  }
+
+  override async handleAction(action: string): Promise<void> {
+    if (action === "Reset Crop") {
+      for (const widget of this.widgets) {
+        if (["left", "right", "top", "bottom"].includes(widget.name!)) {
+          widget.value = 0;
+        }
+      }
+    }
+  }
+
+  static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, ImageInsetCrop);
+  }
+}
+
+app.registerExtension({
+  name: "rgthree.ImageInsetCrop",
+  async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    if (nodeData.name === NodeTypesString.IMAGE_INSET_CROP) {
+      ImageInsetCrop.setUp(nodeType, nodeData);
+    }
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/image_or_latent_size.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/image_or_latent_size.ts
new file mode 100644
index 0000000000000000000000000000000000000000..d52a1e49cc918f83b38ebaaccca1279398308d7e
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/image_or_latent_size.ts
@@ -0,0 +1,51 @@
+import type {ISerialisedNode} from "@comfyorg/frontend";
+import type {ComfyNodeDef} from "typings/comfy";
+
+import {app} from "scripts/app.js";
+import {RgthreeBaseServerNode} from "./base_node.js";
+import {NodeTypesString} from "./constants.js";
+
+class RgthreeImageOrLatentSize extends RgthreeBaseServerNode {
+  static override title = NodeTypesString.IMAGE_OR_LATENT_SIZE;
+  static override type = NodeTypesString.IMAGE_OR_LATENT_SIZE;
+  static comfyClass = NodeTypesString.IMAGE_OR_LATENT_SIZE;
+
+  static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, NODE_CLASS);
+  }
+
+  constructor(title = NODE_CLASS.title) {
+    super(title);
+  }
+
+  override onNodeCreated() {
+    super.onNodeCreated?.();
+
+    // Litegraph uses an array of acceptable input types, even though ComfyUI's types don't type
+    // it out that way.
+    this.addInput("input", ["IMAGE", "LATENT", "MASK"] as any);
+  }
+
+  override configure(info: ISerialisedNode): void {
+    super.configure(info);
+
+    if (this.inputs?.length) {
+      // Litegraph uses an array of acceptable input types, even though ComfyUI's types don't type
+      // it out that way.
+      this.inputs[0]!.type = ["IMAGE", "LATENT", "MASK"] as any;
+    }
+  }
+}
+
+/** An uniformed name reference to the node class. */
+const NODE_CLASS = RgthreeImageOrLatentSize;
+
+/** Register the node. */
+app.registerExtension({
+  name: "rgthree.ImageOrLatentSize",
+  async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    if (nodeData.name === NODE_CLASS.type) {
+      NODE_CLASS.setUp(nodeType, nodeData);
+    }
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/label.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/label.ts
new file mode 100644
index 0000000000000000000000000000000000000000..4d240e08650760c69e47353cc9a8ee66d6d437fc
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/label.ts
@@ -0,0 +1,229 @@
+import type {
+  LGraphCanvas as TLGraphCanvas,
+  LGraphNode,
+  Vector2,
+  CanvasMouseEvent,
+} from "@comfyorg/frontend";
+
+import {app} from "scripts/app.js";
+import {RgthreeBaseVirtualNode} from "./base_node.js";
+import {NodeTypesString} from "./constants.js";
+import {rgthree} from "./rgthree.js";
+
+/**
+ * A label node that allows you to put floating text anywhere on the graph. The text is the `Title`
+ * and the font size, family, color, alignment as well as a background color, padding, and
+ * background border radius can all be adjusted in the properties. Multiline text can be added from
+ * the properties panel (because ComfyUI let's you shift + enter there, only).
+ */
+export class Label extends RgthreeBaseVirtualNode {
+  static override type = NodeTypesString.LABEL;
+  static override title = NodeTypesString.LABEL;
+  override comfyClass = NodeTypesString.LABEL;
+
+  static readonly title_mode = LiteGraph.NO_TITLE;
+  static collapsable = false;
+
+  static "@fontSize" = {type: "number"};
+  static "@fontFamily" = {type: "string"};
+  static "@fontColor" = {type: "string"};
+  static "@textAlign" = {type: "combo", values: ["left", "center", "right"]};
+  static "@backgroundColor" = {type: "string"};
+  static "@padding" = {type: "number"};
+  static "@borderRadius" = {type: "number"};
+  static "@angle" = {type: "number"};
+
+  override properties!: RgthreeBaseVirtualNode["properties"] & {
+    fontSize: number;
+    fontFamily: string;
+    fontColor: string;
+    textAlign: string;
+    backgroundColor: string;
+    padding: number;
+    borderRadius: number;
+    angle: number;
+  };
+
+  override resizable = false;
+
+  constructor(title = Label.title) {
+    super(title);
+    this.properties["fontSize"] = 12;
+    this.properties["fontFamily"] = "Arial";
+    this.properties["fontColor"] = "#ffffff";
+    this.properties["textAlign"] = "left";
+    this.properties["backgroundColor"] = "transparent";
+    this.properties["padding"] = 0;
+    this.properties["borderRadius"] = 0;
+    this.properties["angle"] = 0;
+    this.color = "#fff0";
+    this.bgcolor = "#fff0";
+
+    this.onConstructed();
+  }
+
+  draw(ctx: CanvasRenderingContext2D) {
+    this.flags = this.flags || {};
+    this.flags.allow_interaction = !this.flags.pinned;
+    ctx.save();
+    this.color = "#fff0";
+    this.bgcolor = "#fff0";
+    const fontColor = this.properties["fontColor"] || "#ffffff";
+    const backgroundColor = this.properties["backgroundColor"] || "";
+    ctx.font = `${Math.max(this.properties["fontSize"] || 0, 1)}px ${
+      this.properties["fontFamily"] ?? "Arial"
+    }`;
+    const padding = Number(this.properties["padding"]) ?? 0;
+
+    // Support literal "\\n" sequences as newlines and trim trailing newlines
+    const processedTitle = (this.title ?? "").replace(/\\n/g, "\n").replace(/\n*$/, "");
+    const lines = processedTitle.split("\n");
+
+    const maxWidth = Math.max(...lines.map((s) => ctx.measureText(s).width));
+    this.size[0] = maxWidth + padding * 2;
+    this.size[1] = this.properties["fontSize"] * lines.length + padding * 2;
+
+    // Apply rotation around the center, if angle provided
+    const angleDeg = parseInt(String(this.properties["angle"] ?? 0)) || 0;
+    if (angleDeg) {
+      const cx = this.size[0] / 2;
+      const cy = this.size[1] / 2;
+      ctx.translate(cx, cy);
+      ctx.rotate((angleDeg * Math.PI) / 180);
+      ctx.translate(-cx, -cy);
+    }
+
+    if (backgroundColor) {
+      ctx.beginPath();
+      const borderRadius = Number(this.properties["borderRadius"]) || 0;
+      ctx.roundRect(0, 0, this.size[0], this.size[1], [borderRadius]);
+      ctx.fillStyle = backgroundColor;
+      ctx.fill();
+    }
+    ctx.textAlign = "left";
+    let textX = padding;
+    if (this.properties["textAlign"] === "center") {
+      ctx.textAlign = "center";
+      textX = this.size[0] / 2;
+    } else if (this.properties["textAlign"] === "right") {
+      ctx.textAlign = "right";
+      textX = this.size[0] - padding;
+    }
+    ctx.textBaseline = "top";
+    ctx.fillStyle = fontColor;
+    let currentY = padding;
+    for (let i = 0; i < lines.length; i++) {
+      ctx.fillText(lines[i] || " ", textX, currentY);
+      currentY += this.properties["fontSize"];
+    }
+    ctx.restore();
+  }
+
+  override onDblClick(event: CanvasMouseEvent, pos: Vector2, canvas: TLGraphCanvas) {
+    // Since everything we can do here is in the properties, let's pop open the properties panel.
+    LGraphCanvas.active_canvas.showShowNodePanel(this);
+  }
+
+  override onShowCustomPanelInfo(panel: HTMLElement) {
+    panel.querySelector('div.property[data-property="Mode"]')?.remove();
+    panel.querySelector('div.property[data-property="Color"]')?.remove();
+  }
+
+  override inResizeCorner(x: number, y: number) {
+    // A little ridiculous there's both a resizable property and this method separately to draw the
+    // resize icon...
+    return this.resizable;
+  }
+
+  override getHelp() {
+    return `
+      <p>
+        The rgthree-comfy ${this.type!.replace("(rgthree)", "")} node allows you to add a floating
+        label to your workflow.
+      </p>
+      <p>
+        The text shown is the "Title" of the node and you can adjust the the font size, font family,
+        font color, text alignment as well as a background color, padding, and background border
+        radius from the node's properties. You can double-click the node to open the properties
+        panel.
+      <p>
+      <ul>
+        <li>
+          <p>
+            <strong>Pro tip #1:</strong> You can add multiline text from the properties panel
+            <i>(because ComfyUI let's you shift + enter there, only)</i>.
+          </p>
+        </li>
+        <li>
+          <p>
+            <strong>Pro tip #2:</strong> You can use ComfyUI's native "pin" option in the
+            right-click menu to make the label stick to the workflow and clicks to "go through".
+            You can right-click at any time to unpin.
+          </p>
+        </li>
+        <li>
+          <p>
+            <strong>Pro tip #3:</strong> Color values are hexidecimal strings, like "#FFFFFF" for
+            white, or "#660000" for dark red. You can supply a 7th & 8th value (or 5th if using
+            shorthand) to create a transluscent color. For instance, "#FFFFFF88" is semi-transparent
+            white.
+          </p>
+        </li>
+      </ul>`;
+  }
+}
+
+/**
+ * We override the drawNode to see if we're drawing our label and, if so, hijack it so we can draw
+ * it like we want. We also do call out to oldDrawNode, which takes care of very minimal things,
+ * like a select box.
+ */
+const oldDrawNode = LGraphCanvas.prototype.drawNode;
+LGraphCanvas.prototype.drawNode = function (node: LGraphNode, ctx: CanvasRenderingContext2D) {
+  if (node.constructor === Label.prototype.constructor) {
+    // These get set very aggressively; maybe an extension is doing it. We'll just clear them out
+    // each time.
+    (node as Label).bgcolor = "transparent";
+    (node as Label).color = "transparent";
+    const v = oldDrawNode.apply(this, arguments as any);
+    (node as Label).draw(ctx);
+    return v;
+  }
+
+  const v = oldDrawNode.apply(this, arguments as any);
+  return v;
+};
+
+/**
+ * We override LGraph getNodeOnPos to see if we're being called while also processing a mouse down
+ * and, if so, filter out any label nodes on labels that are pinned. This makes the click go
+ * "through" the label. We still allow right clicking (so you can unpin) and double click for the
+ * properties panel, though that takes two double clicks (one to select, one to actually double
+ * click).
+ */
+const oldGetNodeOnPos = LGraph.prototype.getNodeOnPos;
+LGraph.prototype.getNodeOnPos = function (x: number, y: number, nodes_list?: LGraphNode[]) {
+  if (
+    // processMouseDown always passes in the nodes_list
+    nodes_list &&
+    rgthree.processingMouseDown &&
+    rgthree.lastCanvasMouseEvent?.type.includes("down") &&
+    rgthree.lastCanvasMouseEvent?.which === 1
+  ) {
+    // Using the same logic from LGraphCanvas processMouseDown, let's see if we consider this a
+    // double click.
+    let isDoubleClick = LiteGraph.getTime() - LGraphCanvas.active_canvas.last_mouseclick < 300;
+    if (!isDoubleClick) {
+      nodes_list = [...nodes_list].filter((n) => !(n instanceof Label) || !n.flags?.pinned);
+    }
+  }
+  return oldGetNodeOnPos.apply(this, [x, y, nodes_list]);
+};
+
+// Register the extension.
+app.registerExtension({
+  name: "rgthree.Label",
+  registerCustomNodes() {
+    Label.setUp();
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/menu_auto_nest.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/menu_auto_nest.ts
new file mode 100644
index 0000000000000000000000000000000000000000..79d0d2897caa3dd59dcb43150381b7990fb1dd4e
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/menu_auto_nest.ts
@@ -0,0 +1,156 @@
+import type {
+  LGraphNode,
+  ContextMenu,
+  IContextMenuOptions,
+  IContextMenuValue,
+} from "@comfyorg/frontend";
+
+import {app} from "scripts/app.js";
+import {rgthree} from "./rgthree.js";
+import {SERVICE as CONFIG_SERVICE} from "./services/config_service.js";
+
+const SPECIAL_ENTRIES = [/^(CHOOSE|NONE|DISABLE|OPEN)(\s|$)/i, /^\p{Extended_Pictographic}/gu];
+
+/**
+ * Handles a large, flat list of string values given ContextMenu and breaks it up into subfolder, if
+ * they exist. This is experimental and initially built to work for CheckpointLoaderSimple.
+ */
+app.registerExtension({
+  name: "rgthree.ContextMenuAutoNest",
+  async setup() {
+    const logger = rgthree.newLogSession("[ContextMenuAutoNest]");
+
+    const existingContextMenu = LiteGraph.ContextMenu;
+
+    // @ts-ignore: TypeScript doesn't like this override.
+    LiteGraph.ContextMenu = function (
+      values: IContextMenuValue[],
+      options: IContextMenuOptions<string, {rgthree_doNotNest: boolean}>,
+    ) {
+      const threshold = CONFIG_SERVICE.getConfigValue("features.menu_auto_nest.threshold", 20);
+      const enabled = CONFIG_SERVICE.getConfigValue("features.menu_auto_nest.subdirs", false);
+
+      // If we're not enabled, or are incompatible, then just call out safely.
+      let incompatible: string | boolean = !enabled || !!options?.extra?.rgthree_doNotNest;
+      if (!incompatible) {
+        if (values.length <= threshold) {
+          incompatible = `Skipping context menu auto nesting b/c threshold is not met (${threshold})`;
+        }
+        // If there's a rgthree_originalCallback, then we're nested and don't need to check things
+        // we only expect on the first nesting.
+        if (!(options.parentMenu?.options as any)?.rgthree_originalCallback) {
+          // On first context menu, we require a callback and a flat list of options as strings.
+          if (!options?.callback) {
+            incompatible = `Skipping context menu auto nesting b/c a callback was expected.`;
+          } else if (values.some((i) => typeof i !== "string")) {
+            incompatible = `Skipping context menu auto nesting b/c not all values were strings.`;
+          }
+        }
+      }
+      if (incompatible) {
+        if (enabled) {
+          const [n, v] = logger.infoParts(
+            "Skipping context menu auto nesting for incompatible menu.",
+          );
+          console[n]?.(...v);
+        }
+        return existingContextMenu.apply(this as any, [...arguments] as any);
+      }
+
+      const folders: {[key: string]: IContextMenuValue[]} = {};
+      const specialOps: IContextMenuValue[] = [];
+      const folderless: IContextMenuValue[] = [];
+      for (const value of values) {
+        if (!value) {
+          folderless.push(value);
+          continue;
+        }
+        const newValue = typeof value === "string" ? {content: value} : Object.assign({}, value);
+        (newValue as any).rgthree_originalValue = (value as any).rgthree_originalValue || value;
+        const valueContent = newValue.content || "";
+        const splitBy = valueContent.indexOf("/") > -1 ? "/" : "\\";
+        const valueSplit = valueContent.split(splitBy);
+        if (valueSplit.length > 1) {
+          const key = valueSplit.shift()!;
+          newValue.content = valueSplit.join(splitBy);
+          folders[key] = folders[key] || [];
+          folders[key]!.push(newValue);
+        } else if (SPECIAL_ENTRIES.some((r) => r.test(valueContent))) {
+          specialOps.push(newValue);
+        } else {
+          folderless.push(newValue);
+        }
+      }
+
+      const foldersCount = Object.values(folders).length;
+      if (foldersCount > 0) {
+        // Propogate the original callback down through the options.
+        (options as any).rgthree_originalCallback =
+          (options as any).rgthree_originalCallback ||
+          (options.parentMenu?.options as any)?.rgthree_originalCallback ||
+          options.callback;
+        const oldCallback = (options as any)?.rgthree_originalCallback;
+        options.callback = undefined;
+        const newCallback = (
+          item: IContextMenuValue,
+          options: IContextMenuOptions,
+          event: MouseEvent,
+          parentMenu: ContextMenu | undefined,
+          node: LGraphNode,
+        ) => {
+          oldCallback?.((item as any)?.rgthree_originalValue!, options, event, undefined, node);
+        };
+        const [n, v] = logger.infoParts(`Nested folders found (${foldersCount}).`);
+        console[n]?.(...v);
+        const newValues: IContextMenuValue[] = [];
+        for (const [folderName, folderValues] of Object.entries(folders)) {
+          newValues.push({
+            content: `📁 ${folderName}`,
+            has_submenu: true,
+            callback: () => {
+              /* no-op, use the item callback. */
+            },
+            submenu: {
+              options: folderValues.map((value) => {
+                value!.callback = newCallback;
+                return value;
+              }),
+            },
+          });
+        }
+        values = ([] as IContextMenuValue[]).concat(
+          specialOps.map((f) => {
+            if (typeof f === "string") {
+              f = {content: f};
+            }
+            f!.callback = newCallback;
+            return f;
+          }),
+          newValues,
+          folderless.map((f) => {
+            if (typeof f === "string") {
+              f = {content: f};
+            }
+            f!.callback = newCallback;
+            return f;
+          }),
+        );
+      }
+      if (options.scale == null) {
+        options.scale = Math.max(app.canvas.ds?.scale || 1, 1);
+      }
+
+      const oldCtrResponse = existingContextMenu.call(this as any, values, options as any);
+      // For some reason, LiteGraph calls submenus with "this.constructor" which no longer allows
+      // us to continue building deep nesting, as well as skips many other extensions (even
+      // ComfyUI's core extensions like translations) from working on submenus. It also removes
+      // search, etc. While this is a recent-ish issue, I can't seem to find the culpit as it looks
+      // like old litegraph always did this. Perhaps changing it to a Class? Anyway, this fixes it;
+      // Hopefully without issues.
+      if ((oldCtrResponse as any)?.constructor) {
+        (oldCtrResponse as any).constructor = LiteGraph.ContextMenu;
+      }
+      return this;
+    };
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/menu_copy_image.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/menu_copy_image.ts
new file mode 100644
index 0000000000000000000000000000000000000000..b4c5dac8bb0ebce2deb57cdbb39f95f43f7fd453
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/menu_copy_image.ts
@@ -0,0 +1,75 @@
+import type {IContextMenuValue, LGraphCanvas} from "@comfyorg/frontend";
+import type {ComfyNodeDef} from "typings/comfy.js";
+
+import {app} from "scripts/app.js";
+
+const clipboardSupportedPromise = new Promise<boolean>(async (resolve) => {
+  try {
+    // MDN says to check this, but it doesn't work in Mozilla... however, in secure contexts
+    // (localhost included), it's given by default if the user has it flagged.. so we should be
+    // able to check in the latter ClipboardItem too.
+    const result = await navigator.permissions.query({name: "clipboard-write"} as any);
+    resolve(result.state === "granted");
+    return;
+  } catch (e) {
+    try {
+      if (!navigator.clipboard.write) {
+        throw new Error();
+      }
+      new ClipboardItem({"image/png": new Blob([], {type: "image/png"})});
+      resolve(true);
+      return;
+    } catch (e) {
+      resolve(false);
+    }
+  }
+});
+
+/**
+ * Adds a "Copy Image" to images in similar fashion to the "native" Open Image and Save Image
+ * options.
+ */
+app.registerExtension({
+  name: "rgthree.CopyImageToClipboard",
+  async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    if (nodeData.name.toLowerCase().includes("image")) {
+      if (await clipboardSupportedPromise) {
+        const getExtraMenuOptions = nodeType.prototype.getExtraMenuOptions;
+        nodeType.prototype.getExtraMenuOptions = function (
+          canvas: LGraphCanvas,
+          options: (IContextMenuValue<unknown> | null)[],
+        ): (IContextMenuValue<unknown> | null)[] {
+          options = getExtraMenuOptions?.call(this, canvas, options) ?? options;
+          // If we already have a copy image somehow, then let's skip ours.
+          if (this.imgs?.length) {
+            let img =
+              this.imgs[this.imageIndex || 0] || this.imgs[this.overIndex || 0] || this.imgs[0];
+            const foundIdx = options.findIndex((option) => option?.content?.includes("Copy Image"));
+            if (img && foundIdx === -1) {
+              const menuItem: IContextMenuValue = {
+                content: "Copy Image (rgthree)",
+                callback: () => {
+                  const canvas = document.createElement("canvas");
+                  const ctx = canvas.getContext("2d")!;
+                  canvas.width = img.naturalWidth;
+                  canvas.height = img.naturalHeight;
+                  ctx.drawImage(img, 0, 0, img.naturalWidth, img.naturalHeight);
+                  canvas.toBlob((blob) => {
+                    navigator.clipboard.write([new ClipboardItem({"image/png": blob!})]);
+                  });
+                },
+              };
+              let idx = options.findIndex((option) => option?.content?.includes("Open Image")) + 1;
+              if (idx != null) {
+                options.splice(idx, 0, menuItem);
+              } else {
+                options.unshift(menuItem);
+              }
+            }
+          }
+          return [];
+        };
+      }
+    }
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/menu_queue_node.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/menu_queue_node.ts
new file mode 100644
index 0000000000000000000000000000000000000000..c8e5507c631cb3a195c37c6f23ee0500cca85a95
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/menu_queue_node.ts
@@ -0,0 +1,93 @@
+import type {
+  IContextMenuValue,
+  LGraphCanvas as TLGraphCanvas,
+  LGraphNode,
+} from "@comfyorg/frontend";
+import type {ComfyNodeDef} from "typings/comfy.js";
+
+import {app} from "scripts/app.js";
+import {rgthree} from "./rgthree.js";
+import {getGroupNodes, getOutputNodes} from "./utils.js";
+import {SERVICE as CONFIG_SERVICE} from "./services/config_service.js";
+
+function showQueueNodesMenuIfOutputNodesAreSelected(
+  existingOptions: (IContextMenuValue<unknown> | null)[],
+) {
+  if (CONFIG_SERVICE.getConfigValue("features.menu_queue_selected_nodes") === false) {
+    return;
+  }
+  const outputNodes = getOutputNodes(Object.values(app.canvas.selected_nodes));
+  const menuItem = {
+    content: `Queue Selected Output Nodes (rgthree) &nbsp;`,
+    className: "rgthree-contextmenu-item",
+    callback: () => {
+      rgthree.queueOutputNodes(outputNodes);
+    },
+    disabled: !outputNodes.length,
+  };
+
+  let idx = existingOptions.findIndex((o) => o?.content === "Outputs") + 1;
+  idx = idx || existingOptions.findIndex((o) => o?.content === "Align") + 1;
+  idx = idx || 3;
+  existingOptions.splice(idx, 0, menuItem);
+}
+
+function showQueueGroupNodesMenuIfGroupIsSelected(
+  existingOptions: (IContextMenuValue<unknown> | null)[],
+) {
+  if (CONFIG_SERVICE.getConfigValue("features.menu_queue_selected_nodes") === false) {
+    return;
+  }
+  const group =
+    rgthree.lastCanvasMouseEvent &&
+    (app.canvas.getCurrentGraph() || app.graph).getGroupOnPos(
+      rgthree.lastCanvasMouseEvent.canvasX,
+      rgthree.lastCanvasMouseEvent.canvasY,
+    );
+
+  const outputNodes: LGraphNode[] | null = (group && getOutputNodes(getGroupNodes(group))) || null;
+  const menuItem = {
+    content: `Queue Group Output Nodes (rgthree) &nbsp;`,
+    className: "rgthree-contextmenu-item",
+    callback: () => {
+      outputNodes && rgthree.queueOutputNodes(outputNodes);
+    },
+    disabled: !outputNodes?.length,
+  };
+
+  let idx = existingOptions.findIndex((o) => o?.content?.startsWith("Queue Selected ")) + 1;
+  idx = idx || existingOptions.findIndex((o) => o?.content === "Outputs") + 1;
+  idx = idx || existingOptions.findIndex((o) => o?.content === "Align") + 1;
+  idx = idx || 3;
+  existingOptions.splice(idx, 0, menuItem);
+}
+
+/**
+ * Adds a "Queue Node" menu item to all output nodes, working with `rgthree.queueOutputNode` to
+ * execute only a single node's path.
+ */
+app.registerExtension({
+  name: "rgthree.QueueNode",
+  async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    const getExtraMenuOptions = nodeType.prototype.getExtraMenuOptions;
+    nodeType.prototype.getExtraMenuOptions = function (
+      canvas: TLGraphCanvas,
+      options: (IContextMenuValue<unknown> | null)[],
+    ): (IContextMenuValue<unknown> | null)[] {
+      const extraOptions = getExtraMenuOptions?.call(this, canvas, options) ?? [];
+      showQueueNodesMenuIfOutputNodesAreSelected(options);
+      showQueueGroupNodesMenuIfGroupIsSelected(options);
+      return extraOptions;
+    };
+  },
+
+  async setup() {
+    const getCanvasMenuOptions = LGraphCanvas.prototype.getCanvasMenuOptions;
+    LGraphCanvas.prototype.getCanvasMenuOptions = function (...args: any[]) {
+      const options = getCanvasMenuOptions.apply(this, [...args] as any);
+      showQueueNodesMenuIfOutputNodesAreSelected(options);
+      showQueueGroupNodesMenuIfGroupIsSelected(options);
+      return options;
+    };
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/muter.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/muter.ts
new file mode 100644
index 0000000000000000000000000000000000000000..89fe00f22d604d28a04c118c74b7768ae076598a
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/muter.ts
@@ -0,0 +1,51 @@
+import type {LGraphNode} from "@comfyorg/frontend";
+
+import {app} from "scripts/app.js";
+import {BaseNodeModeChanger} from "./base_node_mode_changer.js";
+import {NodeTypesString} from "./constants.js";
+
+const MODE_MUTE = 2;
+const MODE_ALWAYS = 0;
+
+class MuterNode extends BaseNodeModeChanger {
+  static override exposedActions = ["Mute all", "Enable all", "Toggle all"];
+
+  static override type = NodeTypesString.FAST_MUTER;
+  static override title = NodeTypesString.FAST_MUTER;
+  override comfyClass = NodeTypesString.FAST_MUTER;
+  override readonly modeOn = MODE_ALWAYS;
+  override readonly modeOff = MODE_MUTE;
+
+  constructor(title = MuterNode.title) {
+    super(title);
+    this.onConstructed();
+  }
+
+  override async handleAction(action: string) {
+    if (action === "Mute all") {
+      for (const widget of this.widgets) {
+        this.forceWidgetOff(widget, true);
+      }
+    } else if (action === "Enable all") {
+      for (const widget of this.widgets) {
+        this.forceWidgetOn(widget, true);
+      }
+    } else if (action === "Toggle all") {
+      for (const widget of this.widgets) {
+        this.forceWidgetToggle(widget, true);
+      }
+    }
+  }
+}
+
+app.registerExtension({
+  name: "rgthree.Muter",
+  registerCustomNodes() {
+    MuterNode.setUp();
+  },
+  loadedGraphNode(node: LGraphNode) {
+    if (node.type == MuterNode.title) {
+      (node as any)._tempWidth = node.size[0];
+    }
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/node_collector.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/node_collector.ts
new file mode 100644
index 0000000000000000000000000000000000000000..87bb6ba8151f7d68417a6db0221b9083531d4312
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/node_collector.ts
@@ -0,0 +1,168 @@
+import type {
+  LLink,
+  LGraph,
+  LGraphCanvas,
+  LGraphNode as TLGraphNode,
+  IContextMenuOptions,
+  ContextMenu,
+  IContextMenuValue,
+  Size,
+  ISerialisedNode,
+  Point,
+} from "@comfyorg/frontend";
+
+import {app} from "scripts/app.js";
+import {addConnectionLayoutSupport} from "./utils.js";
+import {wait} from "rgthree/common/shared_utils.js";
+import {ComfyWidgets} from "scripts/widgets.js";
+import {BaseCollectorNode} from "./base_node_collector.js";
+import {NodeTypesString} from "./constants.js";
+
+/**
+ * The Collector Node. Takes any number of inputs as connections for nodes and collects them into
+ * one outputs. The next node will decide what to do with them.
+ *
+ * Currently only works with the Fast Muter, Fast Bypasser, and Fast Actions Button.
+ */
+class CollectorNode extends BaseCollectorNode {
+  static override type = NodeTypesString.NODE_COLLECTOR;
+  static override title = NodeTypesString.NODE_COLLECTOR;
+  override comfyClass = NodeTypesString.NODE_COLLECTOR;
+
+  constructor(title = CollectorNode.title) {
+    super(title);
+    this.onConstructed();
+  }
+
+  override onConstructed(): boolean {
+    this.addOutput("Output", "*");
+    return super.onConstructed();
+  }
+}
+
+/** Legacy "Combiner" */
+class CombinerNode extends CollectorNode {
+  static legacyType = "Node Combiner (rgthree)";
+  static override title = "‼️ Node Combiner [DEPRECATED]";
+
+  constructor(title = CombinerNode.title) {
+    super(title);
+
+    const note = ComfyWidgets["STRING"](
+      this,
+      "last_seed",
+      ["STRING", {multiline: true}],
+      app,
+    ).widget;
+    note.inputEl!.value =
+      'The Node Combiner has been renamed to Node Collector. You can right-click and select "Update to Node Collector" to attempt to automatically update.';
+    note.inputEl!.readOnly = true;
+    note.inputEl!.style.backgroundColor = "#332222";
+    note.inputEl!.style.fontWeight = "bold";
+    note.inputEl!.style.fontStyle = "italic";
+    note.inputEl!.style.opacity = "0.8";
+
+    this.getExtraMenuOptions = (
+      canvas: LGraphCanvas,
+      options: (IContextMenuValue<unknown> | null)[],
+    ): (IContextMenuValue<unknown> | null)[] => {
+      options.splice(options.length - 1, 0, {
+        content: "‼️ Update to Node Collector",
+        callback: (
+          _value: IContextMenuValue,
+          _options: IContextMenuOptions,
+          _event: MouseEvent,
+          _parentMenu: ContextMenu | undefined,
+          _node: TLGraphNode,
+        ) => {
+          updateCombinerToCollector(this);
+        },
+      });
+      return [];
+    };
+  }
+
+  override configure(info: ISerialisedNode) {
+    super.configure(info);
+    if (this.title != CombinerNode.title && !this.title.startsWith("‼️")) {
+      this.title = "‼️ " + this.title;
+    }
+  }
+}
+
+/**
+ * Updates a Node Combiner to a Node Collector.
+ */
+async function updateCombinerToCollector(node: TLGraphNode) {
+  if (node.type === CombinerNode.legacyType) {
+    // Create a new CollectorNode.
+    const newNode = new CollectorNode();
+    if (node.title != CombinerNode.title) {
+      newNode.title = node.title.replace("‼️ ", "");
+    }
+    // Port the position, size, and properties from the old node.
+    newNode.pos = [...node.pos] as Point;
+    newNode.size = [...node.size] as Size;
+    newNode.properties = {...node.properties};
+    // We now collect the links data, inputs and outputs, of the old node since these will be
+    // lost when we remove it.
+    const links: any[] = [];
+    const graph = (node.graph || app.graph);
+    for (const [index, output] of node.outputs.entries()) {
+      for (const linkId of output.links || []) {
+        const link: LLink = graph.links[linkId]!;
+        if (!link) continue;
+        const targetNode = graph.getNodeById(link.target_id);
+        links.push({node: newNode, slot: index, targetNode, targetSlot: link.target_slot});
+      }
+    }
+    for (const [index, input] of node.inputs.entries()) {
+      const linkId = input.link;
+      if (linkId) {
+        const link: LLink = graph.links[linkId]!;
+        const originNode = graph.getNodeById(link.origin_id);
+        links.push({
+          node: originNode,
+          slot: link.origin_slot,
+          targetNode: newNode,
+          targetSlot: index,
+        });
+      }
+    }
+    // Add the new node, remove the old node.
+    graph.add(newNode);
+    await wait();
+    // Now go through and connect the other nodes up as they were.
+    for (const link of links) {
+      link.node.connect(link.slot, link.targetNode, link.targetSlot);
+    }
+    await wait();
+    graph.remove(node);
+  }
+}
+
+app.registerExtension({
+  name: "rgthree.NodeCollector",
+  registerCustomNodes() {
+    addConnectionLayoutSupport(CollectorNode, app, [
+      ["Left", "Right"],
+      ["Right", "Left"],
+    ]);
+
+    LiteGraph.registerNodeType(CollectorNode.title, CollectorNode);
+    CollectorNode.category = CollectorNode._category;
+  },
+});
+
+app.registerExtension({
+  name: "rgthree.NodeCombiner",
+  registerCustomNodes() {
+    addConnectionLayoutSupport(CombinerNode, app, [
+      ["Left", "Right"],
+      ["Right", "Left"],
+    ]);
+
+    LiteGraph.registerNodeType(CombinerNode.legacyType, CombinerNode);
+    CombinerNode.category = CombinerNode._category;
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/node_mode_relay.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/node_mode_relay.ts
new file mode 100644
index 0000000000000000000000000000000000000000..aac47b4c6f62efac5c70a78344258a2ace950de1
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/node_mode_relay.ts
@@ -0,0 +1,280 @@
+import type {
+  INodeInputSlot,
+  INodeOutputSlot,
+  LGraphCanvas,
+  LGraphEventMode,
+  LGraphNode,
+  LLink,
+  Vector2,
+  ISerialisedNode,
+} from "@comfyorg/frontend";
+
+import {app} from "scripts/app.js";
+import {
+  PassThroughFollowing,
+  addConnectionLayoutSupport,
+  changeModeOfNodes,
+  getConnectedInputNodesAndFilterPassThroughs,
+  getConnectedOutputNodesAndFilterPassThroughs,
+} from "./utils.js";
+import {wait} from "rgthree/common/shared_utils.js";
+import {BaseCollectorNode} from "./base_node_collector.js";
+import {NodeTypesString, stripRgthree} from "./constants.js";
+import {fitString} from "./utils_canvas.js";
+import {rgthree} from "./rgthree.js";
+
+const MODE_ALWAYS = 0;
+const MODE_MUTE = 2;
+const MODE_BYPASS = 4;
+const MODE_REPEATS = [MODE_MUTE, MODE_BYPASS];
+const MODE_NOTHING = -99; // MADE THIS UP.
+
+const MODE_TO_OPTION = new Map([
+  [MODE_ALWAYS, "ACTIVE"],
+  [MODE_MUTE, "MUTE"],
+  [MODE_BYPASS, "BYPASS"],
+  [MODE_NOTHING, "NOTHING"],
+]);
+
+const OPTION_TO_MODE = new Map([
+  ["ACTIVE", MODE_ALWAYS],
+  ["MUTE", MODE_MUTE],
+  ["BYPASS", MODE_BYPASS],
+  ["NOTHING", MODE_NOTHING],
+]);
+
+const MODE_TO_PROPERTY = new Map([
+  [MODE_MUTE, "on_muted_inputs"],
+  [MODE_BYPASS, "on_bypassed_inputs"],
+  [MODE_ALWAYS, "on_any_active_inputs"],
+]);
+
+const logger = rgthree.newLogSession("[NodeModeRelay]");
+
+/**
+ * Like a BaseCollectorNode, this relay node connects to a Repeater node and _relays_ mode changes
+ * changes to the repeater (so it can go on to modify its connections).
+ */
+class NodeModeRelay extends BaseCollectorNode {
+  override readonly inputsPassThroughFollowing: PassThroughFollowing = PassThroughFollowing.ALL;
+
+  static override type = NodeTypesString.NODE_MODE_RELAY;
+  static override title = NodeTypesString.NODE_MODE_RELAY;
+  override comfyClass = NodeTypesString.NODE_MODE_RELAY;
+
+  static "@on_muted_inputs" = {
+    type: "combo",
+    values: ["MUTE", "ACTIVE", "BYPASS", "NOTHING"],
+  };
+
+  static "@on_bypassed_inputs" = {
+    type: "combo",
+    values: ["BYPASS", "ACTIVE", "MUTE", "NOTHING"],
+  };
+
+  static "@on_any_active_inputs" = {
+    type: "combo",
+    values: ["BYPASS", "ACTIVE", "MUTE", "NOTHING"],
+  };
+
+  constructor(title?: string) {
+    super(title);
+    this.properties["on_muted_inputs"] = "MUTE";
+    this.properties["on_bypassed_inputs"] = "BYPASS";
+    this.properties["on_any_active_inputs"] = "ACTIVE";
+
+    this.onConstructed();
+  }
+
+  override onConstructed() {
+    this.addOutput("REPEATER", "_NODE_REPEATER_", {
+      color_on: "#Fc0",
+      color_off: "#a80",
+      shape: LiteGraph.ARROW_SHAPE,
+    });
+
+    setTimeout(() => {
+      this.stabilize();
+    }, 500);
+    return super.onConstructed();
+  }
+
+  override onModeChange(from: LGraphEventMode | undefined, to: LGraphEventMode) {
+    super.onModeChange(from, to);
+    // If we aren't connected to anything, then we'll use our mode to relay when it changes.
+    if (this.inputs.length <= 1 && !this.isInputConnected(0) && this.isAnyOutputConnected()) {
+      const [n, v] = logger.infoParts(`Mode change without any inputs; relaying our mode.`);
+      console[n]?.(...v);
+      // Pass "to" since there may be other getters in the way to access this.mode directly.
+      this.dispatchModeToRepeater(to);
+    }
+  }
+
+  override onDrawForeground(ctx: CanvasRenderingContext2D, canvas: LGraphCanvas): void {
+    if (this.flags?.collapsed) {
+      return;
+    }
+    if (
+      this.properties["on_muted_inputs"] !== "MUTE" ||
+      this.properties["on_bypassed_inputs"] !== "BYPASS" ||
+      this.properties["on_any_active_inputs"] != "ACTIVE"
+    ) {
+      let margin = 15;
+      ctx.textAlign = "left";
+      let label = `*(MUTE > ${this.properties["on_muted_inputs"]},  `;
+      label += `BYPASS > ${this.properties["on_bypassed_inputs"]},  `;
+      label += `ACTIVE > ${this.properties["on_any_active_inputs"]})`;
+      ctx.fillStyle = LiteGraph.WIDGET_SECONDARY_TEXT_COLOR;
+      const oldFont = ctx.font;
+      ctx.font = "italic " + (LiteGraph.NODE_SUBTEXT_SIZE - 2) + "px Arial";
+      ctx.fillText(fitString(ctx, label, this.size[0] - 20), 15, this.size[1] - 6);
+      ctx.font = oldFont;
+    }
+  }
+
+  override computeSize(out: Vector2) {
+    let size = super.computeSize(out);
+    if (
+      this.properties["on_muted_inputs"] !== "MUTE" ||
+      this.properties["on_bypassed_inputs"] !== "BYPASS" ||
+      this.properties["on_any_active_inputs"] != "ACTIVE"
+    ) {
+      size[1] += 17;
+    }
+    return size;
+  }
+  override onConnectOutput(
+    outputIndex: number,
+    inputType: string | -1,
+    inputSlot: INodeInputSlot,
+    inputNode: LGraphNode,
+    inputIndex: number,
+  ): boolean {
+    let canConnect = super.onConnectOutput?.(
+      outputIndex,
+      inputType,
+      inputSlot,
+      inputNode,
+      inputIndex,
+    );
+    let nextNode = getConnectedOutputNodesAndFilterPassThroughs(this, inputNode)[0] ?? inputNode;
+    return canConnect && nextNode.type === NodeTypesString.NODE_MODE_REPEATER;
+  }
+
+  override onConnectionsChange(
+    type: number,
+    slotIndex: number,
+    isConnected: boolean,
+    link_info: LLink,
+    ioSlot: INodeOutputSlot | INodeInputSlot,
+  ): void {
+    super.onConnectionsChange(type, slotIndex, isConnected, link_info, ioSlot);
+    setTimeout(() => {
+      this.stabilize();
+    }, 500);
+  }
+
+  stabilize() {
+    // If we aren't connected to a repeater, then theres no sense in checking. And if we are, but
+    // have no inputs, then we're also not ready.
+    if (!this.graph || !this.isAnyOutputConnected() || !this.isInputConnected(0)) {
+      return;
+    }
+    const inputNodes = getConnectedInputNodesAndFilterPassThroughs(
+      this,
+      this,
+      -1,
+      this.inputsPassThroughFollowing,
+    );
+    let mode: LGraphEventMode | -99 | undefined = undefined;
+    for (const inputNode of inputNodes) {
+      // If we haven't set our mode to be, then let's set it. Otherwise, mode will stick if it
+      // remains constant, otherwise, if we hit an ALWAYS, then we'll unmute all repeaters and
+      // if not then we won't do anything.
+      if (mode === undefined) {
+        mode = inputNode.mode;
+      } else if (mode === inputNode.mode && MODE_REPEATS.includes(mode)) {
+        continue;
+      } else if (inputNode.mode === MODE_ALWAYS || mode === MODE_ALWAYS) {
+        mode = MODE_ALWAYS;
+      } else {
+        mode = undefined;
+      }
+    }
+
+    this.dispatchModeToRepeater(mode);
+    setTimeout(() => {
+      this.stabilize();
+    }, 500);
+  }
+
+  /**
+   * Sends the mode to the repeater, checking to see if we're modifying our mode.
+   */
+  private dispatchModeToRepeater(mode?: LGraphEventMode | -99 | null) {
+    if (mode != null) {
+      const propertyVal = this.properties?.[MODE_TO_PROPERTY.get(mode) || ""];
+      const newMode = OPTION_TO_MODE.get(propertyVal as string);
+      mode = (newMode !== null ? newMode : mode) as LGraphEventMode | -99;
+      if (mode !== null && mode !== MODE_NOTHING) {
+        if (this.outputs?.length) {
+          const outputNodes = getConnectedOutputNodesAndFilterPassThroughs(this);
+          for (const outputNode of outputNodes) {
+            changeModeOfNodes(outputNode, mode);
+            wait(16).then(() => {
+              outputNode.setDirtyCanvas(true, true);
+            });
+          }
+        }
+      }
+    }
+  }
+
+  override getHelp() {
+    return `
+      <p>
+        This node will relay its input nodes' modes (Mute, Bypass, or Active) to a connected
+        ${stripRgthree(NodeTypesString.NODE_MODE_REPEATER)} (which would then repeat that mode
+        change to all of its inputs).
+      </p>
+      <ul>
+          <li><p>
+            When all connected input nodes are muted, the relay will set a connected repeater to
+            mute (by default).
+          </p></li>
+          <li><p>
+            When all connected input nodes are bypassed, the relay will set a connected repeater to
+            bypass (by default).
+          </p></li>
+          <li><p>
+            When any connected input nodes are active, the relay will set a connected repeater to
+            active (by default).
+          </p></li>
+          <li><p>
+            If no inputs are connected, the relay will set a connected repeater to its mode <i>when
+            its own mode is changed</i>. <b>Note</b>, if any inputs are connected, then the above
+            will occur and the Relay's mode does not matter.
+          </p></li>
+      </ul>
+      <p>
+        Note, you can change which signals get sent on the above in the <code>Properties</code>.
+        For instance, you could configure an inverse relay which will send a MUTE when any of its
+        inputs are active (instead of sending an ACTIVE signal), and send an ACTIVE signal when all
+        of its inputs are muted (instead of sending a MUTE signal), etc.
+      </p>
+    `;
+  }
+}
+
+app.registerExtension({
+  name: "rgthree.NodeModeRepeaterHelper",
+  registerCustomNodes() {
+    addConnectionLayoutSupport(NodeModeRelay, app, [
+      ["Left", "Right"],
+      ["Right", "Left"],
+    ]);
+
+    LiteGraph.registerNodeType(NodeModeRelay.type, NodeModeRelay);
+    NodeModeRelay.category = NodeModeRelay._category;
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/node_mode_repeater.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/node_mode_repeater.ts
new file mode 100644
index 0000000000000000000000000000000000000000..452b8d75a52370bc4571211946c5c05c72f1883e
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/node_mode_repeater.ts
@@ -0,0 +1,216 @@
+import type {
+  INodeInputSlot,
+  INodeOutputSlot,
+  LGraphEventMode,
+  LGraphGroup,
+  LGraphNode,
+  LLink,
+} from "@comfyorg/frontend";
+
+import {app} from "scripts/app.js";
+import {BaseCollectorNode} from "./base_node_collector.js";
+import {NodeTypesString, stripRgthree} from "./constants.js";
+import {
+  PassThroughFollowing,
+  addConnectionLayoutSupport,
+  changeModeOfNodes,
+  getConnectedInputNodesAndFilterPassThroughs,
+  getConnectedOutputNodesAndFilterPassThroughs,
+  getGroupNodes,
+} from "./utils.js";
+
+class NodeModeRepeater extends BaseCollectorNode {
+  override readonly inputsPassThroughFollowing: PassThroughFollowing = PassThroughFollowing.ALL;
+
+  static override type = NodeTypesString.NODE_MODE_REPEATER;
+  static override title = NodeTypesString.NODE_MODE_REPEATER;
+  override comfyClass = NodeTypesString.NODE_MODE_REPEATER;
+
+  private hasRelayInput = false;
+  private hasTogglerOutput = false;
+
+  constructor(title?: string) {
+    super(title);
+    this.onConstructed();
+  }
+
+  override onConstructed(): boolean {
+    this.addOutput("OPT_CONNECTION", "*", {
+      color_on: "#Fc0",
+      color_off: "#a80",
+    });
+
+    return super.onConstructed();
+  }
+
+  override onConnectOutput(
+    outputIndex: number,
+    inputType: string | -1,
+    inputSlot: INodeInputSlot,
+    inputNode: LGraphNode,
+    inputIndex: number,
+  ): boolean {
+    // We can only connect to a a FAST_MUTER or FAST_BYPASSER if we aren't connectged to a relay, since the relay wins.
+    let canConnect = !this.hasRelayInput;
+    canConnect =
+      canConnect && super.onConnectOutput(outputIndex, inputType, inputSlot, inputNode, inputIndex);
+    // Output can only connect to a FAST MUTER, FAST BYPASSER, NODE_COLLECTOR OR ACTION BUTTON
+    let nextNode = getConnectedOutputNodesAndFilterPassThroughs(this, inputNode)[0] || inputNode;
+    return (
+      canConnect &&
+      [
+        NodeTypesString.FAST_MUTER,
+        NodeTypesString.FAST_BYPASSER,
+        NodeTypesString.NODE_COLLECTOR,
+        NodeTypesString.FAST_ACTIONS_BUTTON,
+        NodeTypesString.REROUTE,
+        NodeTypesString.RANDOM_UNMUTER,
+      ].includes(nextNode.type || "")
+    );
+  }
+
+  override onConnectInput(
+    inputIndex: number,
+    outputType: string | -1,
+    outputSlot: INodeOutputSlot,
+    outputNode: LGraphNode,
+    outputIndex: number,
+  ): boolean {
+    // We can only connect to a a FAST_MUTER or FAST_BYPASSER if we aren't connectged to a relay, since the relay wins.
+    let canConnect = super.onConnectInput?.(
+      inputIndex,
+      outputType,
+      outputSlot,
+      outputNode,
+      outputIndex,
+    );
+    // Output can only connect to a FAST MUTER or FAST BYPASSER
+    let nextNode = getConnectedOutputNodesAndFilterPassThroughs(this, outputNode)[0] || outputNode;
+    const isNextNodeRelay = nextNode.type === NodeTypesString.NODE_MODE_RELAY;
+    return canConnect && (!isNextNodeRelay || !this.hasTogglerOutput);
+  }
+
+  override onConnectionsChange(
+    type: number,
+    slotIndex: number,
+    isConnected: boolean,
+    linkInfo: LLink,
+    ioSlot: INodeOutputSlot | INodeInputSlot,
+  ): void {
+    super.onConnectionsChange(type, slotIndex, isConnected, linkInfo, ioSlot);
+
+    let hasTogglerOutput = false;
+    let hasRelayInput = false;
+
+    const outputNodes = getConnectedOutputNodesAndFilterPassThroughs(this);
+    for (const outputNode of outputNodes) {
+      if (
+        outputNode?.type === NodeTypesString.FAST_MUTER ||
+        outputNode?.type === NodeTypesString.FAST_BYPASSER
+      ) {
+        hasTogglerOutput = true;
+        break;
+      }
+    }
+
+    const inputNodes = getConnectedInputNodesAndFilterPassThroughs(this);
+    for (const [index, inputNode] of inputNodes.entries()) {
+      if (inputNode?.type === NodeTypesString.NODE_MODE_RELAY) {
+        // We can't be connected to a relay if we're connected to a toggler. Something has gone wrong.
+        if (hasTogglerOutput) {
+          console.log(`Can't be connected to a Relay if also output to a toggler.`);
+          this.disconnectInput(index);
+        } else {
+          hasRelayInput = true;
+          if (this.inputs[index]) {
+            this.inputs[index]!.color_on = "#FC0";
+            this.inputs[index]!.color_off = "#a80";
+          }
+        }
+      } else {
+        changeModeOfNodes(inputNode, this.mode);
+      }
+    }
+
+    this.hasTogglerOutput = hasTogglerOutput;
+    this.hasRelayInput = hasRelayInput;
+
+    // If we have a relay input, then we should remove the toggler output, or add it if not.
+    if (this.hasRelayInput) {
+      if (this.outputs[0]) {
+        this.disconnectOutput(0);
+        this.removeOutput(0);
+      }
+    } else if (!this.outputs[0]) {
+      this.addOutput("OPT_CONNECTION", "*", {
+        color_on: "#Fc0",
+        color_off: "#a80",
+      });
+    }
+  }
+
+  /** When a mode change, we want all connected nodes to match except for connected relays. */
+  override onModeChange(from: LGraphEventMode | undefined, to: LGraphEventMode) {
+    super.onModeChange(from, to);
+    const linkedNodes = getConnectedInputNodesAndFilterPassThroughs(this).filter(
+      (node) => node.type !== NodeTypesString.NODE_MODE_RELAY,
+    );
+    if (linkedNodes.length) {
+      for (const node of linkedNodes) {
+        if (node.type !== NodeTypesString.NODE_MODE_RELAY) {
+          // Use "to" as there may be other getters in the way to access this.mode directly.
+          changeModeOfNodes(node, to);
+        }
+      }
+    } else if (this.graph?._groups?.length) {
+      // No linked nodes.. check if we're in a group.
+      for (const group of this.graph._groups as LGraphGroup[]) {
+        group.recomputeInsideNodes();
+        const groupNodes = getGroupNodes(group);
+        if (groupNodes?.includes(this)) {
+          for (const node of groupNodes) {
+            if (node !== this) {
+              // Use "to" as there may be other getters in the way to access this.mode directly.
+              changeModeOfNodes(node, to);
+            }
+          }
+        }
+      }
+    }
+  }
+
+  override getHelp(): string {
+    return `
+      <p>
+        When this node's mode (Mute, Bypass, Active) changes, it will "repeat" that mode to all
+        connected input nodes, or, if there are no connected nodes AND it is overlapping a group,
+        "repeat" it's mode to all nodes in that group.
+      </p>
+      <ul>
+        <li><p>
+          Optionally, connect this mode's output to a ${stripRgthree(NodeTypesString.FAST_MUTER)}
+          or ${stripRgthree(NodeTypesString.FAST_BYPASSER)} for a single toggle to quickly
+          mute/bypass all its connected nodes.
+        </p></li>
+        <li><p>
+          Optionally, connect a ${stripRgthree(NodeTypesString.NODE_MODE_RELAY)} to this nodes
+          inputs to have it automatically toggle its mode. If connected, this will always take
+          precedence (and disconnect any connected fast togglers).
+        </p></li>
+      </ul>
+    `;
+  }
+}
+
+app.registerExtension({
+  name: "rgthree.NodeModeRepeater",
+  registerCustomNodes() {
+    addConnectionLayoutSupport(NodeModeRepeater, app, [
+      ["Left", "Right"],
+      ["Right", "Left"],
+    ]);
+
+    LiteGraph.registerNodeType(NodeModeRepeater.type, NodeModeRepeater);
+    NodeModeRepeater.category = NodeModeRepeater._category;
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/power_conductor.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/power_conductor.ts
new file mode 100644
index 0000000000000000000000000000000000000000..9fb57cfe3682c7939c45b0333d9fcd6188debc46
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/power_conductor.ts
@@ -0,0 +1,137 @@
+import type {Parser, Node, Tree} from "web-tree-sitter";
+import type {IStringWidget, IWidget} from "@comfyorg/frontend";
+
+import {app} from "scripts/app.js";
+import {Exposed, execute, PyTuple} from "rgthree/common/py_parser.js";
+import {RgthreeBaseVirtualNode} from "./base_node.js";
+import {RgthreeBetterButtonWidget} from "./utils_widgets.js";
+import {NodeTypesString} from "./constants.js";
+import {ComfyWidgets} from "scripts/widgets.js";
+import {SERVICE as CONFIG_SERVICE} from "./services/config_service.js";
+import { changeModeOfNodes, getNodeById } from "./utils.js";
+
+const BUILT_INS = {
+  node: {
+    fn: (query: string | number) => {
+      if (typeof query === "number" || /^\d+(\.\d+)?/.exec(query)) {
+        return new ComfyNodeWrapper(Number(query));
+      }
+      return null;
+    },
+  },
+};
+
+class RgthreePowerConductor extends RgthreeBaseVirtualNode {
+  static override title = NodeTypesString.POWER_CONDUCTOR;
+  static override type = NodeTypesString.POWER_CONDUCTOR;
+  override comfyClass = NodeTypesString.POWER_CONDUCTOR;
+
+  override serialize_widgets = true;
+
+  private codeWidget: IStringWidget;
+  private buttonWidget: RgthreeBetterButtonWidget;
+
+  constructor(title = RgthreePowerConductor.title) {
+    super(title);
+
+    this.codeWidget = ComfyWidgets.STRING(this, "", ["STRING", {multiline: true}], app).widget;
+    this.addCustomWidget(this.codeWidget);
+
+    (this.buttonWidget = new RgthreeBetterButtonWidget("Run", (...args: any[]) => {
+      this.execute();
+    })),
+      this.addCustomWidget(this.buttonWidget);
+
+    this.onConstructed();
+  }
+
+  private execute() {
+    execute(this.codeWidget.value, {}, BUILT_INS);
+  }
+}
+
+const NODE_CLASS = RgthreePowerConductor;
+
+/**
+ * A wrapper around nodes to add helpers and control the exposure of properties and methods.
+ */
+class ComfyNodeWrapper {
+  #id: number;
+
+  constructor(id: number) {
+    this.#id = id;
+  }
+
+  private getNode() {
+    return getNodeById(this.#id)!;
+  }
+
+  @Exposed get id() {
+    return this.getNode().id;
+  }
+
+  @Exposed get title() {
+    return this.getNode().title;
+  }
+  set title(value: string) {
+    this.getNode().title = value;
+  }
+
+  @Exposed get widgets() {
+    return new PyTuple(this.getNode().widgets?.map((w) => new ComfyWidgetWrapper(w as IWidget)));
+  }
+
+  @Exposed get mode() {
+    return this.getNode().mode;
+  }
+
+  @Exposed mute() {
+    changeModeOfNodes(this.getNode(), 2);
+  }
+
+  @Exposed bypass() {
+    changeModeOfNodes(this.getNode(), 4);
+  }
+
+  @Exposed enable() {
+    changeModeOfNodes(this.getNode(), 0);
+  }
+}
+
+/**
+ * A wrapper around widgets to add helpers and control the exposure of properties and methods.
+ */
+class ComfyWidgetWrapper {
+  #widget: IWidget;
+
+  constructor(widget: IWidget) {
+    this.#widget = widget;
+  }
+
+  @Exposed get value() {
+    return this.#widget.value;
+  }
+
+  @Exposed get label() {
+    return this.#widget.label;
+  }
+
+  @Exposed toggle(value?: boolean) {
+    // IF the widget has a "toggle" method, then call it.
+    if (typeof (this.#widget as any)["toggle"] === "function") {
+      (this.#widget as any)["toggle"](value);
+    } else {
+      // Error.
+    }
+  }
+}
+
+/** Register the node. */
+app.registerExtension({
+  name: "rgthree.PowerConductor",
+  registerCustomNodes() {
+    if (CONFIG_SERVICE.getConfigValue("unreleased.power_conductor.enabled")) {
+      NODE_CLASS.setUp();
+    }
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/power_lora_loader.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/power_lora_loader.ts
new file mode 100644
index 0000000000000000000000000000000000000000..557bd5e08d98e061084026ca3dc64677d0bdc302
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/power_lora_loader.ts
@@ -0,0 +1,890 @@
+import type {
+  LGraphNode as TLGraphNode,
+  LGraphCanvas,
+  Vector2,
+  IContextMenuValue,
+  IFoundSlot,
+  CanvasMouseEvent,
+  ISerialisedNode,
+  ICustomWidget,
+  CanvasPointerEvent,
+} from "@comfyorg/frontend";
+import type {ComfyApiFormat, ComfyNodeDef} from "typings/comfy.js";
+import type {RgthreeModelInfo} from "typings/rgthree.js";
+
+import {app} from "scripts/app.js";
+import {RgthreeBaseServerNode} from "./base_node.js";
+import {rgthree} from "./rgthree.js";
+import {addConnectionLayoutSupport} from "./utils.js";
+import {NodeTypesString} from "./constants.js";
+import {
+  drawInfoIcon,
+  drawNumberWidgetPart,
+  drawRoundedRectangle,
+  drawTogglePart,
+  fitString,
+  isLowQuality,
+} from "./utils_canvas.js";
+import {
+  RgthreeBaseHitAreas,
+  RgthreeBaseWidget,
+  RgthreeBetterButtonWidget,
+  RgthreeDividerWidget,
+} from "./utils_widgets.js";
+import {rgthreeApi} from "rgthree/common/rgthree_api.js";
+import {SERVICE as CONFIG_SERVICE} from "./services/config_service.js";
+import {showLoraChooser} from "./utils_menu.js";
+import {moveArrayItem, removeArrayItem} from "rgthree/common/shared_utils.js";
+import {RgthreeLoraInfoDialog} from "./dialog_info.js";
+import {LORA_INFO_SERVICE} from "rgthree/common/model_info_service.js";
+// import { RgthreePowerLoraChooserDialog } from "./dialog_power_lora_chooser.js";
+
+const PROP_LABEL_SHOW_STRENGTHS = "Show Strengths";
+const PROP_LABEL_SHOW_STRENGTHS_STATIC = `@${PROP_LABEL_SHOW_STRENGTHS}`;
+const PROP_LABEL_LORA_MATCH = "Match";
+const PROP_LABEL_LORA_MATCH_STATIC = `@${PROP_LABEL_LORA_MATCH}`;
+const PROP_VALUE_SHOW_STRENGTHS_SINGLE = "Single Strength";
+const PROP_VALUE_SHOW_STRENGTHS_SEPARATE = "Separate Model & Clip";
+
+/**
+ * The Power Lora Loader is a super-simply Lora Loader node that can load multiple Loras at once
+ * in an ultra-condensed node allowing fast toggling, and advanced strength setting.
+ */
+class RgthreePowerLoraLoader extends RgthreeBaseServerNode {
+  static override title = NodeTypesString.POWER_LORA_LOADER;
+  static override type = NodeTypesString.POWER_LORA_LOADER;
+  static comfyClass = NodeTypesString.POWER_LORA_LOADER;
+
+  override serialize_widgets = true;
+
+  private logger = rgthree.newLogSession(`[Power Lora Stack]`);
+
+  static [PROP_LABEL_SHOW_STRENGTHS_STATIC] = {
+    type: "combo",
+    values: [PROP_VALUE_SHOW_STRENGTHS_SINGLE, PROP_VALUE_SHOW_STRENGTHS_SEPARATE],
+  };
+
+  static [PROP_LABEL_LORA_MATCH_STATIC] = {
+    type: "string",
+  };
+
+  /** Counts the number of lora widgets. This is used to give unique names.  */
+  private loraWidgetsCounter = 0;
+
+  /** Keep track of the spacer, new lora widgets will go before it when it exists. */
+  private widgetButtonSpacer: ICustomWidget | null = null;
+
+  constructor(title = NODE_CLASS.title) {
+    super(title);
+
+    this.properties[PROP_LABEL_SHOW_STRENGTHS] = PROP_VALUE_SHOW_STRENGTHS_SINGLE;
+    this.properties[PROP_LABEL_LORA_MATCH] = "";
+
+    // Prefetch loras list.
+    rgthreeApi.getLoras();
+
+    // [🤮] If ComfyUI is loading from API JSON it doesn't pass us the actual information at all
+    // (like, in a `configure` call) and tries to set the widget data on its own. Unfortunately,
+    // since Power Lora Loader has dynamic widgets, this fails on ComfyUI's side. We can do so after
+    // the fact but, unfortuntely, we need to do it after a timeout since we don't have any
+    // information at this point to be able to tell what data we need (like, even the node id, let
+    // alone the actual data).
+    if (rgthree.loadingApiJson) {
+      const fullApiJson = rgthree.loadingApiJson;
+      setTimeout(() => {
+        this.configureFromApiJson(fullApiJson);
+      }, 16);
+    }
+  }
+
+  private configureFromApiJson(fullApiJson: ComfyApiFormat) {
+    if (this.id == null) {
+      const [n, v] = this.logger.errorParts("Cannot load from API JSON without node id.");
+      console[n]?.(...v);
+      return;
+    }
+    const nodeData =
+      fullApiJson[this.id] || fullApiJson[String(this.id)] || fullApiJson[Number(this.id)];
+    if (nodeData == null) {
+      const [n, v] = this.logger.errorParts(`No node found in API JSON for node id ${this.id}.`);
+      console[n]?.(...v);
+      return;
+    }
+    this.configure({
+      widgets_values: Object.values(nodeData.inputs).filter(
+        (input) => typeof (input as any)?.["lora"] === "string",
+      ),
+    });
+  }
+
+  /**
+   * Handles configuration from a saved workflow by first removing our default widgets that were
+   * added in `onNodeCreated`, letting `super.configure` and do nothing, then create our lora
+   * widgets and, finally, add back in our default widgets.
+   */
+  override configure(
+    info: ISerialisedNode | {widgets_values: ISerialisedNode["widgets_values"]},
+  ): void {
+    while (this.widgets?.length) this.removeWidget(0);
+    this.widgetButtonSpacer = null;
+    // Since we may be calling into configure manually for just widgets_values setting (like, from
+    // API JSON) we want to only call the parent class's configure with a real ISerialisedNode data.
+    if ((info as ISerialisedNode).id != null) {
+      super.configure(info as ISerialisedNode);
+    }
+
+    (this as any)._tempWidth = this.size[0];
+    (this as any)._tempHeight = this.size[1];
+    for (const widgetValue of info.widgets_values || []) {
+      if ((widgetValue as PowerLoraLoaderWidgetValue)?.lora !== undefined) {
+        const widget = this.addNewLoraWidget();
+        widget.value = {...(widgetValue as PowerLoraLoaderWidgetValue)};
+      }
+    }
+    this.addNonLoraWidgets();
+    this.size[0] = (this as any)._tempWidth;
+    this.size[1] = Math.max((this as any)._tempHeight, this.computeSize()[1]);
+  }
+
+  /**
+   * Adds the non-lora widgets. If we'll be configured then we remove them and add them back, so
+   * this is really only for newly created nodes in the current session.
+   */
+  override onNodeCreated() {
+    super.onNodeCreated?.();
+    this.addNonLoraWidgets();
+    const computed = this.computeSize();
+    this.size = this.size || [0, 0];
+    this.size[0] = Math.max(this.size[0], computed[0]);
+    this.size[1] = Math.max(this.size[1], computed[1]);
+    this.setDirtyCanvas(true, true);
+  }
+
+  /** Adds a new lora widget in the proper slot. */
+  private addNewLoraWidget(lora?: string) {
+    this.loraWidgetsCounter++;
+    const widget = this.addCustomWidget(
+      new PowerLoraLoaderWidget("lora_" + this.loraWidgetsCounter),
+    ) as PowerLoraLoaderWidget;
+    if (lora) widget.setLora(lora);
+    if (this.widgetButtonSpacer) {
+      moveArrayItem(this.widgets, widget, this.widgets.indexOf(this.widgetButtonSpacer));
+    }
+
+    return widget;
+  }
+
+  /** Adds the non-lora widgets around any lora ones that may be there from configuration. */
+  private addNonLoraWidgets() {
+    moveArrayItem(
+      this.widgets,
+      this.addCustomWidget(new RgthreeDividerWidget({marginTop: 4, marginBottom: 0, thickness: 0})),
+      0,
+    );
+    moveArrayItem(this.widgets, this.addCustomWidget(new PowerLoraLoaderHeaderWidget()), 1);
+
+    this.widgetButtonSpacer = this.addCustomWidget(
+      new RgthreeDividerWidget({marginTop: 4, marginBottom: 0, thickness: 0}),
+    ) as RgthreeDividerWidget;
+
+    this.addCustomWidget(
+      new RgthreeBetterButtonWidget(
+        "➕ Add Lora",
+        (event: CanvasMouseEvent, pos: Vector2, node: TLGraphNode) => {
+          this.showLoraChooser(event, (value: string) => {
+            if (value.includes("Power Lora Chooser")) {
+              // new RgthreePowerLoraChooserDialog().show();
+            } else if (value !== "NONE") {
+              this.addNewLoraWidget(value);
+              const computed = this.computeSize();
+              const tempHeight = (this as any)._tempHeight ?? 15;
+              this.size[1] = Math.max(tempHeight, computed[1]);
+              this.setDirtyCanvas(true, true);
+            }
+            // }, null, ["⚡️ Power Lora Chooser", ...loras]);
+          });
+          return true;
+        },
+      ),
+    );
+  }
+
+  async showLoraChooser(event: CanvasMouseEvent, onChoose: (value: string) => void) {
+    const lorasDetails = await rgthreeApi.getLoras();
+    let loras = lorasDetails.map((l) => l.file);
+    let prefix = "";
+    if (this.properties[PROP_LABEL_LORA_MATCH]) {
+      const rgx = new RegExp(this.properties[PROP_LABEL_LORA_MATCH] as string);
+      loras = loras.filter((l) => l.match(rgx));
+      if (loras[0]) {
+        prefix = loras[0];
+        for (const lora of loras) {
+          let similar = "";
+          let i = 0;
+          while (prefix[i] && prefix[i] === lora[i]) {
+            similar += prefix[i++];
+          }
+          prefix = similar;
+          if (!prefix) break;
+        }
+        if (prefix) {
+          loras = loras.map((l) => l.replace(prefix!, ""));
+        }
+      }
+    }
+    showLoraChooser(
+      event as MouseEvent,
+      (value: IContextMenuValue | string) => {
+        if (typeof value === "string") {
+          onChoose(prefix + value);
+        }
+        this.setDirtyCanvas(true, true);
+      },
+      null,
+      [...loras],
+    );
+  }
+
+  /**
+   * Hacks the `getSlotInPosition` call made from LiteGraph so we can show a custom context menu
+   * for widgets.
+   *
+   * Normally this method, called from LiteGraph's processContextMenu, will only get Inputs or
+   * Outputs. But that's not good enough because we we also want to provide a custom menu when
+   * clicking a widget for this node... so we are left to HACK once again!
+   *
+   * To achieve this:
+   *  - Here, in LiteGraph's processContextMenu it asks the clicked node to tell it which input or
+   *    output the user clicked on in `getSlotInPosition`
+   *  - We check, and if we didn't, then we see if we clicked a widget and, if so, pass back some
+   *    data that looks like we clicked an output to fool LiteGraph like a silly child.
+   *  - As LiteGraph continues in its `processContextMenu`, it will then immediately call
+   *    the clicked node's `getSlotMenuOptions` when `getSlotInPosition` returns data.
+   *  - So, just below, we can then give LiteGraph the ContextMenu options we have.
+   *
+   * The only issue is that LiteGraph also checks `input/output.type` to set the ContextMenu title,
+   * so we need to supply that property (and set it to what we want our title). Otherwise, this
+   * should be pretty clean.
+   */
+  override getSlotInPosition(canvasX: number, canvasY: number): any {
+    const slot = super.getSlotInPosition(canvasX, canvasY);
+    // No slot, let's see if it's a widget.
+    if (!slot) {
+      let lastWidget = null;
+      for (const widget of this.widgets) {
+        // If last_y isn't set, something is wrong. Bail.
+        if (!widget.last_y) return;
+        if (canvasY > this.pos[1] + widget.last_y) {
+          lastWidget = widget;
+          continue;
+        }
+        break;
+      }
+      // Only care about lora widget clicks.
+      if (lastWidget?.name?.startsWith("lora_")) {
+        return {widget: lastWidget, output: {type: "LORA WIDGET"}};
+      }
+    }
+    return slot;
+  }
+
+  /**
+   * Working with the overridden `getSlotInPosition` above, this method checks if the passed in
+   * option is actually a widget from it and then hijacks the context menu all together.
+   */
+  override getSlotMenuOptions(slot: IFoundSlot) {
+    // Oddly, LiteGraph doesn't call back into our node with a custom menu (even though it let's us
+    // define a custom menu to begin with... wtf?). So, we'll return null so the default is not
+    // triggered and then we'll just show one ourselves because.. yea.
+    if (slot?.widget?.name?.startsWith("lora_")) {
+      const widget = slot.widget as PowerLoraLoaderWidget;
+      const index = this.widgets.indexOf(widget);
+      const canMoveUp = !!this.widgets[index - 1]?.name?.startsWith("lora_");
+      const canMoveDown = !!this.widgets[index + 1]?.name?.startsWith("lora_");
+      const menuItems: (IContextMenuValue | null)[] = [
+        {
+          content: `ℹ️ Show Info`,
+          callback: () => {
+            widget.showLoraInfoDialog();
+          },
+        },
+        null, // Divider
+        {
+          content: `${widget.value.on ? "⚫" : "🟢"} Toggle ${widget.value.on ? "Off" : "On"}`,
+          callback: () => {
+            widget.value.on = !widget.value.on;
+          },
+        },
+        {
+          content: `⬆️ Move Up`,
+          disabled: !canMoveUp,
+          callback: () => {
+            moveArrayItem(this.widgets, widget, index - 1);
+          },
+        },
+        {
+          content: `⬇️ Move Down`,
+          disabled: !canMoveDown,
+          callback: () => {
+            moveArrayItem(this.widgets, widget, index + 1);
+          },
+        },
+        {
+          content: `🗑️ Remove`,
+          callback: () => {
+            removeArrayItem(this.widgets, widget);
+          },
+        },
+      ];
+      new LiteGraph.ContextMenu(menuItems, {
+        title: "LORA WIDGET",
+        event: rgthree.lastCanvasMouseEvent!,
+      });
+
+      // [🤮] ComfyUI doesn't have a possible return type as falsy, even though the impl skips the
+      // menu when the return is falsy. Casting as any.
+      return undefined as any;
+    }
+    return this.defaultGetSlotMenuOptions(slot);
+  }
+
+  /**
+   * When `refreshComboInNode` is called from ComfyUI, then we'll kick off a fresh loras fetch.
+   */
+  override refreshComboInNode(defs: any) {
+    rgthreeApi.getLoras(true);
+  }
+
+  /**
+   * Returns true if there are any Lora Widgets. Useful for widgets to ask as they render.
+   */
+  hasLoraWidgets() {
+    return !!this.widgets?.find((w) => w.name?.startsWith("lora_"));
+  }
+
+  /**
+   * This will return true when all lora widgets are on, false when all are off, or null if it's
+   * mixed.
+   */
+  allLorasState() {
+    let allOn = true;
+    let allOff = true;
+    for (const widget of this.widgets) {
+      if (widget.name?.startsWith("lora_")) {
+        const on = (widget.value as any)?.on;
+        allOn = allOn && on === true;
+        allOff = allOff && on === false;
+        if (!allOn && !allOff) {
+          return null;
+        }
+      }
+    }
+    return allOn && this.widgets?.length ? true : false;
+  }
+
+  /**
+   * Toggles all the loras on or off.
+   */
+  toggleAllLoras() {
+    const allOn = this.allLorasState();
+    const toggledTo = !allOn ? true : false;
+    for (const widget of this.widgets) {
+      if (widget.name?.startsWith("lora_") && (widget.value as any)?.on != null) {
+        (widget.value as any).on = toggledTo;
+      }
+    }
+  }
+
+  static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, NODE_CLASS);
+  }
+
+  static override onRegisteredForOverride(comfyClass: any, ctxClass: any) {
+    addConnectionLayoutSupport(NODE_CLASS, app, [
+      ["Left", "Right"],
+      ["Right", "Left"],
+    ]);
+    setTimeout(() => {
+      NODE_CLASS.category = comfyClass.category;
+    });
+  }
+
+  override getHelp() {
+    return `
+      <p>
+        The ${this.type!.replace("(rgthree)", "")} is a powerful node that condenses 100s of pixels
+        of functionality in a single, dynamic node that allows you to add loras, change strengths,
+        and quickly toggle on/off all without taking up half your screen.
+      </p>
+      <ul>
+        <li><p>
+          Add as many Lora's as you would like by clicking the "+ Add Lora" button.
+          There's no real limit!
+        </p></li>
+        <li><p>
+          Right-click on a Lora widget for special options to move the lora up or down
+          (no image affect, only presentational), toggle it on/off, or delete the row all together.
+        </p></li>
+        <li>
+          <p>
+            <strong>Properties.</strong> You can change the following properties (by right-clicking
+            on the node, and select "Properties" or "Properties Panel" from the menu):
+          </p>
+          <ul>
+            <li><p>
+              <code>${PROP_LABEL_SHOW_STRENGTHS}</code> - Change between showing a single, simple
+              strength (which will be used for both model and clip), or a more advanced view with
+              both model and clip strengths being modifiable.
+            </p></li>
+          </ul>
+        </li>
+      </ul>`;
+  }
+}
+
+/**
+ * The PowerLoraLoaderHeaderWidget that renders a toggle all switch, as well as some title info
+ * (more necessary for the double model & clip strengths to label them).
+ */
+class PowerLoraLoaderHeaderWidget extends RgthreeBaseWidget<{type: string}> {
+  override value = {type: "PowerLoraLoaderHeaderWidget"};
+  override readonly type = "custom";
+
+  protected override hitAreas: RgthreeBaseHitAreas<"toggle"> = {
+    toggle: {bounds: [0, 0] as Vector2, onDown: this.onToggleDown},
+  };
+
+  private showModelAndClip: boolean | null = null;
+
+  constructor(name: string = "PowerLoraLoaderHeaderWidget") {
+    super(name);
+  }
+
+  draw(
+    ctx: CanvasRenderingContext2D,
+    node: RgthreePowerLoraLoader,
+    w: number,
+    posY: number,
+    height: number,
+  ) {
+    if (!node.hasLoraWidgets()) {
+      return;
+    }
+    // Since draw is the loop that runs, this is where we'll check the property state (rather than
+    // expect the node to tell us it's state etc).
+    this.showModelAndClip =
+      node.properties[PROP_LABEL_SHOW_STRENGTHS] === PROP_VALUE_SHOW_STRENGTHS_SEPARATE;
+    const margin = 10;
+    const innerMargin = margin * 0.33;
+    const lowQuality = isLowQuality();
+    const allLoraState = node.allLorasState();
+
+    // Move slightly down. We don't have a border and this feels a bit nicer.
+    posY += 2;
+    const midY = posY + height * 0.5;
+    let posX = 10;
+    ctx.save();
+    this.hitAreas.toggle.bounds = drawTogglePart(ctx, {posX, posY, height, value: allLoraState});
+
+    if (!lowQuality) {
+      posX += this.hitAreas.toggle.bounds[1] + innerMargin;
+
+      ctx.globalAlpha = app.canvas.editor_alpha * 0.55;
+      ctx.fillStyle = LiteGraph.WIDGET_TEXT_COLOR;
+      ctx.textAlign = "left";
+      ctx.textBaseline = "middle";
+      ctx.fillText("Toggle All", posX, midY);
+
+      let rposX = node.size[0] - margin - innerMargin - innerMargin;
+      ctx.textAlign = "center";
+      ctx.fillText(
+        this.showModelAndClip ? "Clip" : "Strength",
+        rposX - drawNumberWidgetPart.WIDTH_TOTAL / 2,
+        midY,
+      );
+      if (this.showModelAndClip) {
+        rposX = rposX - drawNumberWidgetPart.WIDTH_TOTAL - innerMargin * 2;
+        ctx.fillText("Model", rposX - drawNumberWidgetPart.WIDTH_TOTAL / 2, midY);
+      }
+    }
+    ctx.restore();
+  }
+
+  /**
+   * Handles a pointer down on the toggle's defined hit area.
+   */
+  onToggleDown(event: CanvasMouseEvent, pos: Vector2, node: TLGraphNode) {
+    (node as RgthreePowerLoraLoader).toggleAllLoras();
+    this.cancelMouseDown();
+    return true;
+  }
+}
+
+const DEFAULT_LORA_WIDGET_DATA: PowerLoraLoaderWidgetValue = {
+  on: true,
+  lora: null as string | null,
+  strength: 1,
+  strengthTwo: null as number | null,
+};
+
+type PowerLoraLoaderWidgetValue = {
+  on: boolean;
+  lora: string | null;
+  strength: number;
+  strengthTwo: number | null;
+};
+
+/**
+ * The PowerLoaderWidget that combines several custom drawing and functionality in a single row.
+ */
+class PowerLoraLoaderWidget extends RgthreeBaseWidget<PowerLoraLoaderWidgetValue> {
+  override readonly type = "custom";
+
+  /** Whether the strength has changed with mouse move (to cancel mouse up). */
+  private haveMouseMovedStrength = false;
+  private loraInfoPromise: Promise<RgthreeModelInfo | null> | null = null;
+  private loraInfo: RgthreeModelInfo | null = null;
+
+  private showModelAndClip: boolean | null = null;
+
+  protected override hitAreas: RgthreeBaseHitAreas<
+    | "toggle"
+    | "lora"
+    | "info"
+    | "strengthDec"
+    | "strengthVal"
+    | "strengthInc"
+    | "strengthAny"
+    | "strengthTwoDec"
+    | "strengthTwoVal"
+    | "strengthTwoInc"
+    | "strengthTwoAny"
+  > = {
+    toggle: {bounds: [0, 0] as Vector2, onDown: this.onToggleDown},
+    lora: {bounds: [0, 0] as Vector2, onClick: this.onLoraClick},
+    info: {bounds: [0, 0] as Vector2, onDown: this.onInfoDown},
+
+    strengthDec: {bounds: [0, 0] as Vector2, onClick: this.onStrengthDecDown},
+    strengthVal: {bounds: [0, 0] as Vector2, onClick: this.onStrengthValUp},
+    strengthInc: {bounds: [0, 0] as Vector2, onClick: this.onStrengthIncDown},
+    strengthAny: {bounds: [0, 0] as Vector2, onMove: this.onStrengthAnyMove},
+
+    strengthTwoDec: {bounds: [0, 0] as Vector2, onClick: this.onStrengthTwoDecDown},
+    strengthTwoVal: {bounds: [0, 0] as Vector2, onClick: this.onStrengthTwoValUp},
+    strengthTwoInc: {bounds: [0, 0] as Vector2, onClick: this.onStrengthTwoIncDown},
+    strengthTwoAny: {bounds: [0, 0] as Vector2, onMove: this.onStrengthTwoAnyMove},
+  };
+
+  constructor(name: string) {
+    super(name);
+  }
+
+  private _value = {
+    on: true,
+    lora: null as string | null,
+    strength: 1,
+    strengthTwo: null as number | null,
+  };
+
+  set value(v) {
+    this._value = v;
+    // In case widgets are messed up, we can correct course here.
+    if (typeof this._value !== "object") {
+      this._value = {...DEFAULT_LORA_WIDGET_DATA};
+      if (this.showModelAndClip) {
+        this._value.strengthTwo = this._value.strength;
+      }
+    }
+    this.getLoraInfo();
+  }
+
+  get value() {
+    return this._value;
+  }
+
+  setLora(lora: string) {
+    this._value.lora = lora;
+    this.getLoraInfo();
+  }
+
+  /** Draws our widget with a toggle, lora selector, and number selector all in a single row. */
+  draw(ctx: CanvasRenderingContext2D, node: TLGraphNode, w: number, posY: number, height: number) {
+    // Since draw is the loop that runs, this is where we'll check the property state (rather than
+    // expect the node to tell us it's state etc).
+    let currentShowModelAndClip =
+      node.properties[PROP_LABEL_SHOW_STRENGTHS] === PROP_VALUE_SHOW_STRENGTHS_SEPARATE;
+    if (this.showModelAndClip !== currentShowModelAndClip) {
+      let oldShowModelAndClip = this.showModelAndClip;
+      this.showModelAndClip = currentShowModelAndClip;
+      if (this.showModelAndClip) {
+        // If we're setting show both AND we're not null, then re-set to the current strength.
+        if (oldShowModelAndClip != null) {
+          this.value.strengthTwo = this.value.strength ?? 1;
+        }
+      } else {
+        this.value.strengthTwo = null;
+        this.hitAreas.strengthTwoDec.bounds = [0, -1];
+        this.hitAreas.strengthTwoVal.bounds = [0, -1];
+        this.hitAreas.strengthTwoInc.bounds = [0, -1];
+        this.hitAreas.strengthTwoAny.bounds = [0, -1];
+      }
+    }
+
+    ctx.save();
+    const margin = 10;
+    const innerMargin = margin * 0.33;
+    const lowQuality = isLowQuality();
+    const midY = posY + height * 0.5;
+
+    // We'll move posX along as we draw things.
+    let posX = margin;
+
+    // Draw the background.
+    drawRoundedRectangle(ctx, {pos: [posX, posY], size: [node.size[0] - margin * 2, height]});
+
+    // Draw the toggle
+    this.hitAreas.toggle.bounds = drawTogglePart(ctx, {posX, posY, height, value: this.value.on});
+    posX += this.hitAreas.toggle.bounds[1] + innerMargin;
+
+    // If low quality, then we're done rendering.
+    if (lowQuality) {
+      ctx.restore();
+      return;
+    }
+
+    // If we're not toggled on, then make everything after faded.
+    if (!this.value.on) {
+      ctx.globalAlpha = app.canvas.editor_alpha * 0.4;
+    }
+
+    ctx.fillStyle = LiteGraph.WIDGET_TEXT_COLOR;
+
+    // Now, we draw the strength number part on the right, so we know the width of it to draw the
+    // lora label as flexible.
+    let rposX = node.size[0] - margin - innerMargin - innerMargin;
+
+    const strengthValue = this.showModelAndClip
+      ? (this.value.strengthTwo ?? 1)
+      : (this.value.strength ?? 1);
+
+    let textColor: string | undefined = undefined;
+    if (this.loraInfo?.strengthMax != null && strengthValue > this.loraInfo?.strengthMax) {
+      textColor = "#c66";
+    } else if (this.loraInfo?.strengthMin != null && strengthValue < this.loraInfo?.strengthMin) {
+      textColor = "#c66";
+    }
+
+    const [leftArrow, text, rightArrow] = drawNumberWidgetPart(ctx, {
+      posX: node.size[0] - margin - innerMargin - innerMargin,
+      posY,
+      height,
+      value: strengthValue,
+      direction: -1,
+      textColor,
+    });
+
+    this.hitAreas.strengthDec.bounds = leftArrow;
+    this.hitAreas.strengthVal.bounds = text;
+    this.hitAreas.strengthInc.bounds = rightArrow;
+    this.hitAreas.strengthAny.bounds = [leftArrow[0], rightArrow[0] + rightArrow[1] - leftArrow[0]];
+
+    rposX = leftArrow[0] - innerMargin;
+
+    if (this.showModelAndClip) {
+      rposX -= innerMargin;
+      // If we're showing both, then the rightmost we just drew is our "strengthTwo", so reset and
+      // then draw our model ("strength" one) to the left.
+      this.hitAreas.strengthTwoDec.bounds = this.hitAreas.strengthDec.bounds;
+      this.hitAreas.strengthTwoVal.bounds = this.hitAreas.strengthVal.bounds;
+      this.hitAreas.strengthTwoInc.bounds = this.hitAreas.strengthInc.bounds;
+      this.hitAreas.strengthTwoAny.bounds = this.hitAreas.strengthAny.bounds;
+
+      let textColor: string | undefined = undefined;
+      if (this.loraInfo?.strengthMax != null && this.value.strength > this.loraInfo?.strengthMax) {
+        textColor = "#c66";
+      } else if (
+        this.loraInfo?.strengthMin != null &&
+        this.value.strength < this.loraInfo?.strengthMin
+      ) {
+        textColor = "#c66";
+      }
+      const [leftArrow, text, rightArrow] = drawNumberWidgetPart(ctx, {
+        posX: rposX,
+        posY,
+        height,
+        value: this.value.strength ?? 1,
+        direction: -1,
+        textColor,
+      });
+      this.hitAreas.strengthDec.bounds = leftArrow;
+      this.hitAreas.strengthVal.bounds = text;
+      this.hitAreas.strengthInc.bounds = rightArrow;
+      this.hitAreas.strengthAny.bounds = [
+        leftArrow[0],
+        rightArrow[0] + rightArrow[1] - leftArrow[0],
+      ];
+      rposX = leftArrow[0] - innerMargin;
+    }
+
+    const infoIconSize = height * 0.66;
+    const infoWidth = infoIconSize + innerMargin + innerMargin;
+    // Draw an info emoji; if checks if it's enabled (to quickly turn it on or off)
+    if (CONFIG_SERVICE.getConfigValue("nodes.power_lora_loader.show_info_badge")) {
+      rposX -= innerMargin;
+      drawInfoIcon(
+        ctx,
+        rposX - infoIconSize,
+        posY + (height - infoIconSize) / 2,
+        infoIconSize,
+        this.loraInfo?.raw?.civitai ? "FILLED" : this.loraInfo?.hasInfoFile ? "OUTLINED" : "GRAYED",
+      );
+      // ctx.fillText('ℹ', posX, midY);
+      (this.hitAreas as any).info.bounds = [rposX - infoIconSize, infoWidth];
+      rposX = rposX - infoIconSize - innerMargin;
+    } else {
+      (this.hitAreas as any).info.bounds = [0, 0];
+    }
+
+    // Draw lora label
+    const loraWidth = rposX - posX;
+    ctx.textAlign = "left";
+    ctx.textBaseline = "middle";
+    const loraLabel = String(this.value?.lora || "None");
+    ctx.fillText(fitString(ctx, loraLabel, loraWidth), posX, midY);
+
+    this.hitAreas.lora.bounds = [posX, loraWidth];
+    posX += loraWidth + innerMargin;
+
+    ctx.globalAlpha = app.canvas.editor_alpha;
+    ctx.restore();
+  }
+
+  override serializeValue(
+    node: TLGraphNode,
+    index: number,
+  ): PowerLoraLoaderWidgetValue | Promise<PowerLoraLoaderWidgetValue> {
+    const v = {...this.value};
+    // Never send the second value to the backend if we're not showing it, otherwise, let's just
+    // make sure it's not null.
+    if (!this.showModelAndClip) {
+      delete (v as any).strengthTwo;
+    } else {
+      this.value.strengthTwo = this.value.strengthTwo ?? 1;
+      v.strengthTwo = this.value.strengthTwo;
+    }
+    return v;
+  }
+
+  onToggleDown(event: CanvasMouseEvent, pos: Vector2, node: TLGraphNode) {
+    this.value.on = !this.value.on;
+    this.cancelMouseDown(); // Clear the down since we handle it.
+    return true;
+  }
+
+  onInfoDown(event: CanvasMouseEvent, pos: Vector2, node: TLGraphNode) {
+    this.showLoraInfoDialog();
+  }
+
+  onLoraClick(event: CanvasMouseEvent, pos: Vector2, node: RgthreePowerLoraLoader) {
+    node.showLoraChooser(event, (value: string) => {
+      this.value.lora = value;
+      this.loraInfo = null;
+      this.getLoraInfo();
+    });
+    this.cancelMouseDown();
+  }
+
+  onStrengthDecDown(event: CanvasMouseEvent, pos: Vector2, node: TLGraphNode) {
+    this.stepStrength(-1, false);
+  }
+  onStrengthIncDown(event: CanvasMouseEvent, pos: Vector2, node: TLGraphNode) {
+    this.stepStrength(1, false);
+  }
+  onStrengthTwoDecDown(event: CanvasMouseEvent, pos: Vector2, node: TLGraphNode) {
+    this.stepStrength(-1, true);
+  }
+  onStrengthTwoIncDown(event: CanvasMouseEvent, pos: Vector2, node: TLGraphNode) {
+    this.stepStrength(1, true);
+  }
+
+  onStrengthAnyMove(event: CanvasMouseEvent, pos: Vector2, node: TLGraphNode) {
+    this.doOnStrengthAnyMove(event, false);
+  }
+
+  onStrengthTwoAnyMove(event: CanvasMouseEvent, pos: Vector2, node: TLGraphNode) {
+    this.doOnStrengthAnyMove(event, true);
+  }
+
+  private doOnStrengthAnyMove(event: CanvasMouseEvent, isTwo = false) {
+    if (event.deltaX) {
+      let prop: "strengthTwo" | "strength" = isTwo ? "strengthTwo" : "strength";
+      this.haveMouseMovedStrength = true;
+      this.value[prop] = (this.value[prop] ?? 1) + event.deltaX * 0.05;
+    }
+  }
+
+  onStrengthValUp(event: CanvasPointerEvent, pos: Vector2, node: TLGraphNode) {
+    this.doOnStrengthValUp(event, false);
+  }
+
+  onStrengthTwoValUp(event: CanvasPointerEvent, pos: Vector2, node: TLGraphNode) {
+    this.doOnStrengthValUp(event, true);
+  }
+
+  private doOnStrengthValUp(event: CanvasPointerEvent, isTwo = false) {
+    if (this.haveMouseMovedStrength) return;
+    let prop: "strengthTwo" | "strength" = isTwo ? "strengthTwo" : "strength";
+    const canvas = app.canvas as LGraphCanvas;
+    canvas.prompt("Value", this.value[prop], (v: string) => (this.value[prop] = Number(v)), event);
+  }
+
+  override onMouseUp(event: CanvasPointerEvent, pos: Vector2, node: TLGraphNode): boolean | void {
+    super.onMouseUp(event, pos, node);
+    this.haveMouseMovedStrength = false;
+  }
+
+  showLoraInfoDialog() {
+    if (!this.value.lora || this.value.lora === "None") {
+      return;
+    }
+    const infoDialog = new RgthreeLoraInfoDialog(this.value.lora).show();
+    infoDialog.addEventListener("close", ((e: CustomEvent<{dirty: boolean}>) => {
+      if (e.detail.dirty) {
+        this.getLoraInfo(true);
+      }
+    }) as EventListener);
+  }
+
+  private stepStrength(direction: -1 | 1, isTwo = false) {
+    let step = 0.05;
+    let prop: "strengthTwo" | "strength" = isTwo ? "strengthTwo" : "strength";
+    let strength = (this.value[prop] ?? 1) + step * direction;
+    this.value[prop] = Math.round(strength * 100) / 100;
+  }
+
+  private getLoraInfo(force = false) {
+    if (!this.loraInfoPromise || force == true) {
+      let promise;
+      if (this.value.lora && this.value.lora != "None") {
+        promise = LORA_INFO_SERVICE.getInfo(this.value.lora, force, true);
+      } else {
+        promise = Promise.resolve(null);
+      }
+      this.loraInfoPromise = promise.then((v) => (this.loraInfo = v));
+    }
+    return this.loraInfoPromise;
+  }
+}
+
+/** An uniformed name reference to the node class. */
+const NODE_CLASS = RgthreePowerLoraLoader;
+
+/** Register the node. */
+app.registerExtension({
+  name: "rgthree.PowerLoraLoader",
+  async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    if (nodeData.name === NODE_CLASS.type) {
+      NODE_CLASS.setUp(nodeType, nodeData);
+    }
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/power_primitive.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/power_primitive.ts
new file mode 100644
index 0000000000000000000000000000000000000000..a4e4847057ca713ea3704787f9f9c6c5ff5344d3
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/power_primitive.ts
@@ -0,0 +1,254 @@
+import type {
+  IWidget,
+  INodeInputSlot,
+  LGraphCanvas as TLGraphCanvas,
+  LGraphNodeConstructor,
+  IContextMenuValue,
+  INodeOutputSlot,
+  ISlotType,
+  ISerialisedNode,
+  LLink,
+  IBaseWidget,
+} from "@comfyorg/frontend";
+import type {ComfyNodeDef} from "typings/comfy.js";
+
+import {app} from "scripts/app.js";
+import {RgthreeBaseServerNode} from "./base_node.js";
+import {NodeTypesString} from "./constants.js";
+import {ComfyWidgets} from "scripts/widgets.js";
+import {moveArrayItem} from "rgthree/common/shared_utils.js";
+
+const PROPERTY_HIDE_TYPE_SELECTOR = "hideTypeSelector";
+const PRIMITIVES = {
+  STRING: "STRING",
+  // "STRING (multiline)": "STRING",
+  INT: "INT",
+  FLOAT: "FLOAT",
+  BOOLEAN: "BOOLEAN",
+};
+
+class RgthreePowerPrimitive extends RgthreeBaseServerNode {
+  static override title = NodeTypesString.POWER_PRIMITIVE;
+  static override type = NodeTypesString.POWER_PRIMITIVE;
+  static comfyClass = NodeTypesString.POWER_PRIMITIVE;
+
+  private outputTypeWidget!: IWidget;
+  private valueWidget!: IWidget;
+  private typeState: string = '';
+
+  static "@hideTypeSelector" = {type: "boolean"};
+
+  override properties!: RgthreeBaseServerNode["properties"] & {
+    [PROPERTY_HIDE_TYPE_SELECTOR]: boolean;
+  };
+
+  constructor(title = NODE_CLASS.title) {
+    super(title);
+    this.properties[PROPERTY_HIDE_TYPE_SELECTOR] = false;
+  }
+
+  static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, NODE_CLASS);
+  }
+
+  /**
+   * Adds the non-lora widgets. If we'll be configured then we remove them and add them back, so
+   * this is really only for newly created nodes in the current session.
+   */
+  override onNodeCreated() {
+    super.onNodeCreated?.();
+    this.addInitialWidgets();
+  }
+
+  /**
+   * Ensures to set the type widget whenever we configure.
+   */
+  override configure(info: ISerialisedNode): void {
+    super.configure(info);
+    // Update BOOL to BOOLEAN due to a bug using BOOL instead of BOOLEAN.
+    if (this.outputTypeWidget.value === 'BOOL') {
+      this.outputTypeWidget.value = 'BOOLEAN';
+    }
+    setTimeout(() => {
+      this.setTypedData();
+    });
+  }
+
+  /**
+   * Adds menu options for the node: quick toggle to show/hide the first widget, and a menu-option
+   * to change the type (for easier changing when hiding the first widget).
+   */
+  override getExtraMenuOptions(
+    canvas: TLGraphCanvas,
+    options: (IContextMenuValue<unknown> | null)[],
+  ) {
+    const that = this;
+    super.getExtraMenuOptions(canvas, options);
+    const isHidden = !!this.properties[PROPERTY_HIDE_TYPE_SELECTOR];
+
+    const menuItems = [
+      {
+        content: `${isHidden ? "Show" : "Hide"} Type Selector Widget`,
+        callback: (...args: any[]) => {
+          this.setProperty(
+            PROPERTY_HIDE_TYPE_SELECTOR,
+            !this.properties[PROPERTY_HIDE_TYPE_SELECTOR],
+          );
+        },
+      },
+      {
+        content: `Set type`,
+        submenu: {
+          options: Object.keys(PRIMITIVES),
+          callback(value: any, ...args: any[]) {
+            that.outputTypeWidget.value = value;
+            that.setTypedData();
+          },
+        },
+      },
+    ];
+
+    options.splice(0, 0, ...menuItems, null);
+    return [];
+  }
+
+  private addInitialWidgets() {
+    if (!this.outputTypeWidget) {
+      this.outputTypeWidget = this.addWidget(
+        "combo",
+        "type",
+        "STRING",
+        (...args) => {
+          this.setTypedData();
+        },
+        {
+          values: Object.keys(PRIMITIVES),
+        },
+      ) as IWidget;
+      this.outputTypeWidget.hidden = this.properties[PROPERTY_HIDE_TYPE_SELECTOR];
+    }
+    this.setTypedData();
+  }
+
+  /**
+   * Sets the correct inputs, outputs, and widgets for the designated type (with the
+   * `outputTypeWidget`) being the source of truth.
+   */
+  private setTypedData() {
+    const name = "value";
+    const type = this.outputTypeWidget.value as string;
+    const linked = !!this.inputs?.[0]?.link;
+    const newTypeState = `${type}|${linked}`;
+    if (this.typeState == newTypeState) return;
+    this.typeState = newTypeState;
+
+    let value = this.valueWidget?.value ?? null;
+    let newWidget: IWidget | null= null;
+    // If we're linked, then set the UI to an empty string widget input, since the ComfyUI is rather
+    // confusing by showing a value that is not the actual value used (from the input).
+    if (linked) {
+      newWidget = ComfyWidgets["STRING"](this, name, ["STRING"], app).widget;
+      newWidget.value = "";
+    } else if (type == "STRING") {
+      newWidget = ComfyWidgets["STRING"](this, name, ["STRING", {multiline: true}], app).widget;
+      newWidget.value = value ? "" : String(value);
+    } else if (type === "INT" || type === "FLOAT") {
+      const isFloat = type === "FLOAT";
+      newWidget = this.addWidget("number", name, value ?? 1 as any, undefined, {
+        precision: isFloat ? 1 : 0,
+        step2: isFloat ? 0.1 : 0,
+      }) as IWidget;
+      value = Number(value);
+      value = value == null || isNaN(value) ? 0 : value;
+      newWidget.value = value;
+    } else if (type === "BOOLEAN") {
+      newWidget = this.addWidget("toggle", name, !!(value ?? true), undefined, {
+        on: "true",
+        off: "false",
+      }) as IWidget;
+      if (typeof value === "string") {
+        value = !["false", "null", "None", "", "0"].includes(value.toLowerCase());
+      }
+      newWidget.value = !!value;
+    }
+    if (newWidget == null) {
+      throw new Error(`Unsupported type "${type}".`);
+    }
+
+    if (this.valueWidget) {
+      this.replaceWidget(this.valueWidget, newWidget);
+    } else {
+      if (!this.widgets.includes(newWidget)) {
+        this.addCustomWidget(newWidget);
+      }
+      moveArrayItem(this.widgets, newWidget, 1);
+    }
+    this.valueWidget = newWidget;
+
+    // Set the input data.
+    if (!this.inputs?.length) {
+      this.addInput("value", "*", {widget: this.valueWidget as any});
+    } else {
+      this.inputs[0]!.widget = this.valueWidget as any;
+    }
+
+    // Set the output data.
+    const output = this.outputs[0]!;
+    const outputLabel = output.label === "*" || output.label === output.type ? null : output.label;
+    output.type = type;
+    output.label = outputLabel || output.type;
+  }
+
+  /**
+   * Sets the correct typed data when we change any connections (really care about
+   * onnecting/disconnecting the value input.)
+   */
+  override onConnectionsChange(
+    type: ISlotType,
+    index: number,
+    isConnected: boolean,
+    link_info: LLink | null | undefined,
+    inputOrOutput: INodeInputSlot | INodeOutputSlot,
+  ): void {
+    super.onConnectionsChange?.apply(this, [...arguments] as any);
+    if (this.inputs.includes(inputOrOutput as INodeInputSlot)) {
+      this.setTypedData();
+    }
+  }
+
+  /**
+   * Sets the correct output type widget state when our `PROPERTY_HIDE_TYPE_SELECTOR` changes.
+   */
+  override onPropertyChanged(name: string, value: unknown, prev_value?: unknown): boolean {
+    if (name === PROPERTY_HIDE_TYPE_SELECTOR) {
+      if (!this.outputTypeWidget) {
+        return true;
+      }
+      this.outputTypeWidget.hidden = this.properties[PROPERTY_HIDE_TYPE_SELECTOR];
+      if (this.outputTypeWidget.hidden) {
+        this.outputTypeWidget.computeLayoutSize = () => ({
+          minHeight: 0,
+          minWidth: 0,
+          maxHeight: 0,
+          maxWidth: 0,
+        });
+      } else {
+        this.outputTypeWidget.computeLayoutSize = undefined;
+      }
+    }
+    return true;
+  }
+}
+
+/** An uniformed name reference to the node class. */
+const NODE_CLASS = RgthreePowerPrimitive;
+
+/** Register the node. */
+app.registerExtension({
+  name: "rgthree.PowerPrimitive",
+  async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    if (nodeData.name === NODE_CLASS.type) {
+      NODE_CLASS.setUp(nodeType, nodeData);
+    }
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/power_prompt.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/power_prompt.ts
new file mode 100644
index 0000000000000000000000000000000000000000..6eac6d22fea50c21cda72cf03424836ce8dd2496
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/power_prompt.ts
@@ -0,0 +1,48 @@
+import type {LGraphNode, LGraphNodeConstructor} from "@comfyorg/frontend";
+import type {ComfyNodeDef} from "typings/comfy.js";
+
+import {app} from "scripts/app.js";
+import {addConnectionLayoutSupport} from "./utils.js";
+import {PowerPrompt} from "./base_power_prompt.js";
+import {NodeTypesString} from "./constants.js";
+
+let nodeData: ComfyNodeDef | null = null;
+app.registerExtension({
+  name: "rgthree.PowerPrompt",
+  async beforeRegisterNodeDef(nodeType: typeof LGraphNode, passedNodeData: ComfyNodeDef) {
+    if (passedNodeData.name.includes("Power Prompt") && passedNodeData.name.includes("rgthree")) {
+      nodeData = passedNodeData;
+      const onNodeCreated = nodeType.prototype.onNodeCreated;
+      nodeType.prototype.onNodeCreated = function () {
+        onNodeCreated ? onNodeCreated.apply(this, []) : undefined;
+        (this as any).powerPrompt = new PowerPrompt(this as LGraphNode, passedNodeData);
+      };
+      addConnectionLayoutSupport(nodeType as LGraphNodeConstructor, app, [
+        ["Left", "Right"],
+        ["Right", "Left"],
+      ]);
+    }
+  },
+  async loadedGraphNode(node: LGraphNode) {
+    if (node.type === NodeTypesString.POWER_PROMPT) {
+      setTimeout(() => {
+        // If the first output is STRING, then it's the text output from the initial launch.
+        // Let's port it to the new
+        if (node.outputs[0]!.type === "STRING") {
+          if (node.outputs[0]!.links) {
+            node.outputs[3]!.links = node.outputs[3]!.links || [];
+            for (const link of node.outputs[0]!.links) {
+              node.outputs[3]!.links.push(link);
+              (node.graph || app.graph).links[link]!.origin_slot = 3;
+            }
+            node.outputs[0]!.links = null;
+          }
+          node.outputs[0]!.type = nodeData!.output![0] as string;
+          node.outputs[0]!.name = nodeData!.output_name![0] || (node.outputs[0]!.type as string);
+          node.outputs[0]!.color_on = undefined;
+          node.outputs[0]!.color_off = undefined;
+        }
+      }, 50);
+    }
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/power_puter.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/power_puter.ts
new file mode 100644
index 0000000000000000000000000000000000000000..74fc76869f4d64893fc075a840bc622106ec2814
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/power_puter.ts
@@ -0,0 +1,461 @@
+import type {
+  LGraphNode,
+  IWidget,
+  Vector2,
+  CanvasMouseEvent,
+} from "@comfyorg/frontend";
+import type {ComfyNodeDef} from "typings/comfy.js";
+
+import {app} from "scripts/app.js";
+import {RgthreeBaseServerNode} from "./base_node.js";
+import {NodeTypesString} from "./constants.js";
+import {removeUnusedInputsFromEnd} from "./utils_inputs_outputs.js";
+import {debounce} from "rgthree/common/shared_utils.js";
+import {ComfyWidgets} from "scripts/widgets.js";
+import {RgthreeBaseHitAreas, RgthreeBaseWidget, RgthreeBaseWidgetBounds} from "./utils_widgets.js";
+import {
+  drawPlusIcon,
+  drawRoundedRectangle,
+  drawWidgetButton,
+  isLowQuality,
+  measureText,
+} from "./utils_canvas.js";
+import {rgthree} from "./rgthree.js";
+
+type Vector4 = [number, number, number, number];
+
+const ALPHABET = "abcdefghijklmnopqrstuv".split("");
+
+const OUTPUT_TYPES = ["STRING", "INT", "FLOAT", "BOOLEAN", "*"];
+
+class RgthreePowerPuter extends RgthreeBaseServerNode {
+  static override title = NodeTypesString.POWER_PUTER;
+  static override type = NodeTypesString.POWER_PUTER;
+  static comfyClass = NodeTypesString.POWER_PUTER;
+
+  private outputTypeWidget!: OutputsWidget;
+  private expressionWidget!: IWidget;
+  private stabilizeBound = this.stabilize.bind(this);
+
+  constructor(title = NODE_CLASS.title) {
+    super(title);
+    // Note, configure will add as many as was in the stored workflow automatically.
+    this.addAnyInput(2);
+    this.addInitialWidgets();
+  }
+
+  // /**
+  //  * We need to patch in the configure to fix a bug where Power Puter was using BOOL instead of
+  //  * BOOLEAN.
+  //  */
+  // override configure(info: ISerialisedNode): void {
+  //   super.configure(info);
+  //   // Update BOOL to BOOLEAN due to a bug using BOOL instead of BOOLEAN.
+  //   this.outputTypeWidget
+  // }
+
+  static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, NODE_CLASS);
+  }
+
+  override onConnectionsChange(...args: any[]): void {
+    super.onConnectionsChange?.apply(this, [...arguments] as any);
+    this.scheduleStabilize();
+  }
+
+  scheduleStabilize(ms = 64) {
+    return debounce(this.stabilizeBound, ms);
+  }
+
+  stabilize() {
+    removeUnusedInputsFromEnd(this, 1);
+    this.addAnyInput();
+    this.setOutputs();
+  }
+
+  private addInitialWidgets() {
+    if (!this.outputTypeWidget) {
+      this.outputTypeWidget = this.addCustomWidget(
+        new OutputsWidget("outputs", this),
+      ) as OutputsWidget;
+      this.expressionWidget = ComfyWidgets["STRING"](
+        this,
+        "code",
+        ["STRING", {multiline: true}],
+        app,
+      ).widget;
+    }
+  }
+
+  private addAnyInput(num = 1) {
+    for (let i = 0; i < num; i++) {
+      this.addInput(ALPHABET[this.inputs.length]!, "*" as string);
+    }
+  }
+
+  private setOutputs() {
+    const desiredOutputs = this.outputTypeWidget.value.outputs;
+    for (let i = 0; i < Math.max(this.outputs.length, desiredOutputs.length); i++) {
+      const desired = desiredOutputs[i];
+      let output = this.outputs[i];
+      if (!desired && output) {
+        this.disconnectOutput(i);
+        this.removeOutput(i);
+        continue;
+      }
+      output = output || this.addOutput("", "");
+      const outputLabel =
+        output.label === "*" || output.label === output.type ? null : output.label;
+      output.type = String(desired);
+      output.label = outputLabel || output.type;
+    }
+  }
+
+  override getHelp() {
+    return `
+      <p>
+        The ${this.type!.replace("(rgthree)", "")} is a powerful and versatile node that opens the
+        door for a wide range of utility by offering mult-line code parsing for output. This node
+        can be used for simple string concatenation, or math operations; to an image dimension or a
+        node's widgets with advanced list comprehension.
+        If you want to output something in your workflow, this is the node to do it.
+      </p>
+
+      <ul>
+        <li><p>
+          Evaluate almost any kind of input and more, and choose your output from INT, FLOAT,
+          STRING, or BOOLEAN.
+        </p></li>
+        <li><p>
+          Connect some nodes and do simply math operations like <code>a + b</code> or
+          <code>ceil(1 / 2)</code>.
+        </p></li>
+        <li><p>
+          Or do more advanced things, like input an image, and get the width like
+          <code>a.shape[2]</code>.
+        </p></li>
+        <li><p>
+          Even more powerful, you can target nodes in the prompt that's sent to the backend. For
+          instance; if you have a Power Lora Loader node at id #5, and want to get a comma-delimited
+          list of the enabled loras, you could enter
+          <code>', '.join([v.lora for v in node(5).inputs.values() if 'lora' in v and v.on])</code>.
+        </p></li>
+        <li><p>
+          See more at the <a target="_blank"
+          href="https://github.com/rgthree/rgthree-comfy/wiki/Node:-Power-Puter">rgthree-comfy
+          wiki</a>.
+        </p></li>
+      </ul>`;
+  }
+}
+
+/** An uniformed name reference to the node class. */
+const NODE_CLASS = RgthreePowerPuter;
+
+type OutputsWidgetValue = {
+  outputs: string[];
+};
+
+const OUTPUTS_WIDGET_CHIP_HEIGHT = LiteGraph.NODE_WIDGET_HEIGHT - 4;
+const OUTPUTS_WIDGET_CHIP_SPACE = 4;
+
+const OUTPUTS_WIDGET_CHIP_ARROW_WIDTH = 5.5;
+const OUTPUTS_WIDGET_CHIP_ARROW_HEIGHT = 4;
+
+/**
+ * The OutputsWidget is an advanced widget that has a background similar to others, but then a
+ * series of "chips" that correspond to the outputs of the node. The chips are dynamic and wrap to
+ * additional rows as space is needed. Additionally, there is a "+" chip to add more.
+ */
+class OutputsWidget extends RgthreeBaseWidget<OutputsWidgetValue> {
+  override readonly type = "custom";
+  private _value: OutputsWidgetValue = {outputs: ["STRING"]};
+
+  private rows = 1;
+  private neededHeight = LiteGraph.NODE_WIDGET_HEIGHT + 8;
+  private node!: RgthreePowerPuter;
+
+  protected override hitAreas: RgthreeBaseHitAreas<
+    | "add"
+    | "output0"
+    | "output1"
+    | "output2"
+    | "output3"
+    | "output4"
+    | "output5"
+    | "output6"
+    | "output7"
+    | "output8"
+    | "output9"
+  > = {
+    add: {bounds: [0, 0] as Vector2, onClick: this.onAddChipDown},
+    output0: {bounds: [0, 0] as Vector2, onClick: this.onOutputChipDown, data: {index: 0}},
+    output1: {bounds: [0, 0] as Vector2, onClick: this.onOutputChipDown, data: {index: 1}},
+    output2: {bounds: [0, 0] as Vector2, onClick: this.onOutputChipDown, data: {index: 2}},
+    output3: {bounds: [0, 0] as Vector2, onClick: this.onOutputChipDown, data: {index: 3}},
+    output4: {bounds: [0, 0] as Vector2, onClick: this.onOutputChipDown, data: {index: 4}},
+    output5: {bounds: [0, 0] as Vector2, onClick: this.onOutputChipDown, data: {index: 5}},
+    output6: {bounds: [0, 0] as Vector2, onClick: this.onOutputChipDown, data: {index: 6}},
+    output7: {bounds: [0, 0] as Vector2, onClick: this.onOutputChipDown, data: {index: 7}},
+    output8: {bounds: [0, 0] as Vector2, onClick: this.onOutputChipDown, data: {index: 8}},
+    output9: {bounds: [0, 0] as Vector2, onClick: this.onOutputChipDown, data: {index: 9}},
+  };
+
+  constructor(name: string, node: RgthreePowerPuter) {
+    super(name);
+    this.node = node;
+  }
+
+  set value(v: OutputsWidgetValue) {
+    // Handle a string being passed in, as the original Power Puter output widget was a string.
+    let outputs = typeof v === "string" ? [v] : [...v.outputs];
+    // Handle a case where the initial version used "BOOL" instead of "BOOLEAN" incorrectly.
+    outputs = outputs.map((o) => (o === "BOOL" ? "BOOLEAN" : o));
+    this._value.outputs = outputs;
+  }
+
+  get value(): OutputsWidgetValue {
+    return this._value;
+  }
+
+  /** Displays the menu to choose a new output type. */
+  onAddChipDown(
+    event: CanvasMouseEvent,
+    pos: Vector2,
+    node: LGraphNode,
+    bounds: RgthreeBaseWidgetBounds,
+  ) {
+    new LiteGraph.ContextMenu(OUTPUT_TYPES, {
+      event: event,
+      title: "Add an output",
+      className: "rgthree-dark",
+      callback: (value) => {
+        if (isLowQuality()) return;
+        if (typeof value === "string" && OUTPUT_TYPES.includes(value)) {
+          this._value.outputs.push(value);
+          this.node.scheduleStabilize();
+        }
+      },
+    });
+
+    this.cancelMouseDown();
+    return true;
+  }
+
+  /** Displays a context menu tied to an output chip within our widget. */
+  onOutputChipDown(
+    event: CanvasMouseEvent,
+    pos: Vector2,
+    node: LGraphNode,
+    bounds: RgthreeBaseWidgetBounds,
+  ) {
+    const options: Array<null | string> = [...OUTPUT_TYPES];
+    if (this.value.outputs.length > 1) {
+      options.push(null, "🗑️ Delete");
+    }
+
+    new LiteGraph.ContextMenu(options, {
+      event: event,
+      title: `Edit output #${bounds.data.index + 1}`,
+      className: "rgthree-dark",
+      callback: (value) => {
+        const index = bounds.data.index;
+        if (typeof value !== "string" || value === this._value.outputs[index] || isLowQuality()) {
+          return;
+        }
+        const output = this.node.outputs[index]!;
+        if (value.toLocaleLowerCase().includes("delete")) {
+          if (output.links?.length) {
+            rgthree.showMessage({
+              id: "puter-remove-linked-output",
+              type: "warn",
+              message: "[Power Puter] Removed and disconnected output from that was connected!",
+              timeout: 3000,
+            });
+            this.node.disconnectOutput(index);
+          }
+          this.node.removeOutput(index);
+          this._value.outputs.splice(index, 1);
+          this.node.scheduleStabilize();
+          return;
+        }
+        if (output.links?.length && value !== "*") {
+          rgthree.showMessage({
+            id: "puter-remove-linked-output",
+            type: "warn",
+            message:
+              "[Power Puter] Changing output type of linked output! You should check for" +
+              " compatibility.",
+            timeout: 3000,
+          });
+        }
+        this._value.outputs[index] = value;
+        this.node.scheduleStabilize();
+      },
+    });
+
+    this.cancelMouseDown();
+    return true;
+  }
+
+  /**
+   * Computes the layout size to ensure the height is what we need to accomodate all the chips;
+   * specifically, SPACE on the top, plus the CHIP_HEIGHT + SPACE underneath multiplied by the
+   * number of rows necessary.
+   */
+  computeLayoutSize(node: LGraphNode) {
+    this.neededHeight =
+      OUTPUTS_WIDGET_CHIP_SPACE +
+      (OUTPUTS_WIDGET_CHIP_HEIGHT + OUTPUTS_WIDGET_CHIP_SPACE) * this.rows;
+    return {
+      minHeight: this.neededHeight,
+      maxHeight: this.neededHeight,
+      minWidth: 0, // Need just zero here to be flexible with the width.
+    };
+  }
+
+  /**
+   * Draws our nifty, advanced widget keeping track of the space and wrapping to multiple lines when
+   * more chips than can fit are shown.
+   */
+  draw(ctx: CanvasRenderingContext2D, node: LGraphNode, w: number, posY: number, height: number) {
+    ctx.save();
+    // Despite what `height` was passed in, which is often not our actual height, we'll use oun
+    //  calculated needed height.
+    height = this.neededHeight;
+    const margin = 10;
+    const innerMargin = margin * 0.33;
+    const width = node.size[0] - margin * 2;
+    let borderRadius = LiteGraph.NODE_WIDGET_HEIGHT * 0.5;
+    let midY = posY + height * 0.5;
+    let posX = margin;
+    let rposX = node.size[0] - margin;
+
+    // Draw the background encompassing everything, and move our current posX's to create space from
+    // the border.
+
+    drawRoundedRectangle(ctx, {pos: [posX, posY], size: [width, height], borderRadius});
+    posX += innerMargin * 2;
+    rposX -= innerMargin * 2;
+
+    // If low quality, then we're done.
+    if (isLowQuality()) {
+      ctx.restore();
+      return;
+    }
+
+    // Add put our "outputs" label, and a divider line.
+    ctx.fillStyle = LiteGraph.WIDGET_SECONDARY_TEXT_COLOR;
+    ctx.textAlign = "left";
+    ctx.textBaseline = "middle";
+    ctx.fillText("outputs", posX, midY);
+    posX += measureText(ctx, "outputs") + innerMargin * 2;
+    ctx.stroke(new Path2D(`M ${posX} ${posY} v ${height}`));
+    posX += 1 + innerMargin * 2;
+
+    // Now, prepare our values for the chips; adjust the posY to be within the space, the height to
+    // be that of the chips, and the new midY for the chips.
+    const inititalPosX = posX;
+    posY += OUTPUTS_WIDGET_CHIP_SPACE;
+    height = OUTPUTS_WIDGET_CHIP_HEIGHT;
+    borderRadius = height * 0.5;
+    midY = posY + height / 2;
+    ctx.textAlign = "center";
+    ctx.lineJoin = ctx.lineCap = "round";
+    ctx.fillStyle = ctx.strokeStyle = LiteGraph.WIDGET_TEXT_COLOR;
+    let rows = 1;
+    const values = this.value?.outputs ?? [];
+    const fontSize = ctx.font.match(/(\d+)px/);
+    if (fontSize?.[1]) {
+      ctx.font = ctx.font.replace(fontSize[1], `${Number(fontSize[1]) - 2}`);
+    }
+
+    // Loop over our values, and add them from left to right, measuring the width before placing to
+    // see if we need to wrap the the next line, and updating the hitAreas of the chips.
+    let i = 0;
+    for (i; i < values.length; i++) {
+      const hitArea = this.hitAreas[`output${i}` as "output1"];
+      const isClicking = !!hitArea.wasMouseClickedAndIsOver;
+      hitArea.data.index = i;
+
+      const text = values[i]!;
+      const textWidth = measureText(ctx, text) + innerMargin * 2;
+      const width = textWidth + OUTPUTS_WIDGET_CHIP_ARROW_WIDTH + innerMargin * 5;
+
+      // If our width is too long, then wrap the values and increment our rows.
+      if (posX + width >= rposX) {
+        posX = inititalPosX;
+        posY = posY + height + 4;
+        midY = posY + height / 2;
+        rows++;
+      }
+
+      drawWidgetButton(
+        ctx,
+        {pos: [posX, posY], size: [width, height], borderRadius},
+        null,
+        isClicking,
+      );
+      const startX = posX;
+      posX += innerMargin * 2;
+      const newMidY = midY + (isClicking ? 1 : 0);
+      ctx.fillText(text, posX + textWidth / 2, newMidY);
+      posX += textWidth + innerMargin;
+      const arrow = new Path2D(
+        `M${posX} ${newMidY - OUTPUTS_WIDGET_CHIP_ARROW_HEIGHT / 2}
+         h${OUTPUTS_WIDGET_CHIP_ARROW_WIDTH}
+         l-${OUTPUTS_WIDGET_CHIP_ARROW_WIDTH / 2} ${OUTPUTS_WIDGET_CHIP_ARROW_HEIGHT} z`,
+      );
+      ctx.fill(arrow);
+      ctx.stroke(arrow);
+      posX += OUTPUTS_WIDGET_CHIP_ARROW_WIDTH + innerMargin * 2;
+      hitArea.bounds = [startX, posY, width, height] as Vector4;
+      posX += OUTPUTS_WIDGET_CHIP_SPACE; // Space Between
+    }
+    // Zero out and following hitAreas.
+    for (i; i < 9; i++) {
+      const hitArea = this.hitAreas[`output${i}` as "output1"];
+      if (hitArea.bounds[0] > 0) {
+        hitArea.bounds = [0, 0, 0, 0] as Vector4;
+      }
+    }
+
+    // Draw the add arrow, if we're not at the max.
+    const addHitArea = this.hitAreas["add"];
+    if (this.value.outputs.length < 10) {
+      const isClicking = !!addHitArea.wasMouseClickedAndIsOver;
+      const plusSize = 10;
+      let plusWidth = innerMargin * 2 + plusSize + innerMargin * 2;
+      if (posX + plusWidth >= rposX) {
+        posX = inititalPosX;
+        posY = posY + height + 4;
+        midY = posY + height / 2;
+        rows++;
+      }
+      drawWidgetButton(
+        ctx,
+        {size: [plusWidth, height], pos: [posX, posY], borderRadius},
+        null,
+        isClicking,
+      );
+      drawPlusIcon(ctx, posX + innerMargin * 2, midY + (isClicking ? 1 : 0), plusSize);
+      addHitArea.bounds = [posX, posY, plusWidth, height] as Vector4;
+    } else {
+      addHitArea.bounds = [0, 0, 0, 0] as Vector4;
+    }
+
+    // Set the rows now that we're drawn.
+    this.rows = rows;
+    ctx.restore();
+  }
+}
+
+/** Register the node. */
+app.registerExtension({
+  name: "rgthree.PowerPuter",
+  async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    if (nodeData.name === NODE_CLASS.type) {
+      NODE_CLASS.setUp(nodeType, nodeData);
+    }
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/random_unmuter.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/random_unmuter.ts
new file mode 100644
index 0000000000000000000000000000000000000000..1f951a94e8a2614878045fd6a72f8725bc0dbfde
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/random_unmuter.ts
@@ -0,0 +1,117 @@
+import type {LGraphNode} from "@comfyorg/frontend";
+
+import {app} from "scripts/app.js";
+import {BaseAnyInputConnectedNode} from "./base_any_input_connected_node.js";
+import {NodeTypesString} from "./constants.js";
+import {rgthree} from "./rgthree.js";
+import {changeModeOfNodes, getConnectedInputNodesAndFilterPassThroughs} from "./utils.js";
+
+const MODE_MUTE = 2;
+const MODE_ALWAYS = 0;
+
+class RandomUnmuterNode extends BaseAnyInputConnectedNode {
+  static override exposedActions = ["Mute all", "Enable all"];
+
+  static override type = NodeTypesString.RANDOM_UNMUTER;
+  override comfyClass = NodeTypesString.RANDOM_UNMUTER;
+  static override title = RandomUnmuterNode.type;
+  readonly modeOn = MODE_ALWAYS;
+  readonly modeOff = MODE_MUTE;
+
+  tempEnabledNode: LGraphNode | null = null;
+  processingQueue: boolean = false;
+
+  onQueueBound = this.onQueue.bind(this);
+  onQueueEndBound = this.onQueueEnd.bind(this);
+  onGraphtoPromptBound = this.onGraphtoPrompt.bind(this);
+  onGraphtoPromptEndBound = this.onGraphtoPromptEnd.bind(this);
+
+  constructor(title = RandomUnmuterNode.title) {
+    super(title);
+
+    rgthree.addEventListener("queue", this.onQueueBound);
+    rgthree.addEventListener("queue-end", this.onQueueEndBound);
+    rgthree.addEventListener("graph-to-prompt", this.onGraphtoPromptBound);
+    rgthree.addEventListener("graph-to-prompt-end", this.onGraphtoPromptEndBound);
+    this.onConstructed();
+  }
+
+  override onRemoved() {
+    rgthree.removeEventListener("queue", this.onQueueBound);
+    rgthree.removeEventListener("queue-end", this.onQueueEndBound);
+    rgthree.removeEventListener("graph-to-prompt", this.onGraphtoPromptBound);
+    rgthree.removeEventListener("graph-to-prompt-end", this.onGraphtoPromptEndBound);
+  }
+
+  onQueue(event: Event) {
+    this.processingQueue = true;
+  }
+  onQueueEnd(event: Event) {
+    this.processingQueue = false;
+  }
+  onGraphtoPrompt(event: Event) {
+    if (!this.processingQueue) {
+      return;
+    }
+    this.tempEnabledNode = null;
+    // Check that all are muted and, if so, choose one to unmute.
+    const linkedNodes = getConnectedInputNodesAndFilterPassThroughs(this);
+    let allMuted = true;
+    if (linkedNodes.length) {
+      for (const node of linkedNodes) {
+        if (node.mode !== this.modeOff) {
+          allMuted = false;
+          break;
+        }
+      }
+      if (allMuted) {
+        this.tempEnabledNode = linkedNodes[Math.floor(Math.random() * linkedNodes.length)] || null;
+        if (this.tempEnabledNode) {
+          changeModeOfNodes(this.tempEnabledNode, this.modeOn);
+        }
+      }
+    }
+  }
+  onGraphtoPromptEnd(event: Event) {
+    if (this.tempEnabledNode) {
+      changeModeOfNodes(this.tempEnabledNode, this.modeOff);
+      this.tempEnabledNode = null;
+    }
+  }
+
+  override handleLinkedNodesStabilization(linkedNodes: LGraphNode[]) {
+    return false; // No-op, no widgets.
+  }
+
+  override getHelp(): string {
+    return `
+      <p>
+        Use this node to unmute on of its inputs randomly when the graph is queued (and, immediately
+        mute it back).
+      </p>
+      <ul>
+        <li><p>
+          NOTE: All input nodes MUST be muted to start; if not this node will not randomly unmute
+          another. (This is powerful, as the generated image can be dragged in and the chosen input
+          will already by unmuted and work w/o any further action.)
+        </p></li>
+        <li><p>
+          TIP: Connect a Repeater's output to this nodes input and place that Repeater on a group
+          without any other inputs, and it will mute/unmute the entire group.
+        </p></li>
+      </ul>
+    `;
+  }
+}
+
+app.registerExtension({
+  name: "rgthree.RandomUnmuter",
+  registerCustomNodes() {
+    RandomUnmuterNode.setUp();
+  },
+  loadedGraphNode(node: LGraphNode) {
+    if (node.type == RandomUnmuterNode.title) {
+      (node as any)._tempWidth = node.size[0];
+    }
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/reroute.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/reroute.ts
new file mode 100644
index 0000000000000000000000000000000000000000..de0155ae5c644ccc938ca8ce7b7ade726b7c39b3
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/reroute.ts
@@ -0,0 +1,1285 @@
+import type {
+  Vector2,
+  LLink,
+  LGraphCanvas as TLGraphCanvas,
+  LGraph as TLGraph,
+  INodeInputSlot,
+  INodeOutputSlot,
+  LGraphNode as TLGraphNode,
+  LinkDirection,
+  ISerialisedNode,
+  Point,
+  Size,
+} from "@comfyorg/frontend";
+
+import {app} from "scripts/app.js";
+// @ts-ignore
+import {rgthreeConfig} from "rgthree/config.js";
+import {rgthree} from "./rgthree.js";
+import {
+  IoDirection,
+  LAYOUT_CLOCKWISE,
+  LAYOUT_LABEL_OPPOSITES,
+  LAYOUT_LABEL_TO_DATA,
+  addConnectionLayoutSupport,
+  addMenuItem,
+  getSlotLinks,
+  isValidConnection,
+  setConnectionsLayout,
+  waitForCanvas,
+} from "./utils.js";
+import {SERVICE as KEY_EVENT_SERVICE} from "./services/key_events_services.js";
+import {wait} from "rgthree/common/shared_utils.js";
+import {RgthreeBaseVirtualNode} from "./base_node.js";
+import {NodeTypesString} from "./constants.js";
+import {rgthreeApi} from "rgthree/common/rgthree_api.js";
+import {getWidgetConfig, mergeIfValid, setWidgetConfig} from "./utils_deprecated_comfyui.js";
+
+const CONFIG_REROUTE = rgthreeConfig?.["nodes"]?.["reroute"] || {};
+
+const CONFIG_FAST_REROUTE = CONFIG_REROUTE["fast_reroute"];
+const CONFIG_FAST_REROUTE_ENABLED = CONFIG_FAST_REROUTE["enabled"] ?? false;
+const CONFIG_KEY_CREATE_WHILE_LINKING = CONFIG_FAST_REROUTE["key_create_while_dragging_link"];
+const CONFIG_KEY_ROTATE = CONFIG_FAST_REROUTE["key_rotate"];
+const CONFIG_KEY_RESIZE = CONFIG_FAST_REROUTE["key_resize"];
+const CONFIG_KEY_MOVE = CONFIG_FAST_REROUTE["key_move"];
+const CONFIG_KEY_CXN_INPUT = CONFIG_FAST_REROUTE["key_connections_input"];
+const CONFIG_KEY_CXN_OUTPUT = CONFIG_FAST_REROUTE["key_connections_output"];
+
+let configWidth = Math.max(
+  Math.round((Number(CONFIG_REROUTE["default_width"]) || 40) / 10) * 10,
+  10,
+);
+let configHeight = Math.max(
+  Math.round((Number(CONFIG_REROUTE["default_height"]) || 30) / 10) * 10,
+  10,
+);
+// Don't allow too small sizes. Granted, 400 is too small, but at least you can right click and
+// resize... 10x10 you cannot.
+while (configWidth * configHeight < 400) {
+  configWidth += 10;
+  configHeight += 10;
+}
+const configDefaultSize = [configWidth, configHeight] as Vector2;
+const configResizable = !!CONFIG_REROUTE["default_resizable"];
+let configLayout: [string, string] = CONFIG_REROUTE["default_layout"];
+if (!Array.isArray(configLayout)) {
+  configLayout = ["Left", "Right"];
+}
+if (!LAYOUT_LABEL_TO_DATA[configLayout[0]]) {
+  configLayout[0] = "Left";
+}
+if (!LAYOUT_LABEL_TO_DATA[configLayout[1]] || configLayout[0] == configLayout[1]) {
+  configLayout[1] = LAYOUT_LABEL_OPPOSITES[configLayout[0]]!;
+}
+
+type FastRerouteEntryCtx = {
+  node: TLGraphNode;
+  input?: INodeInputSlot;
+  output?: INodeOutputSlot;
+  slot: number;
+  pos: Vector2;
+  direction?: LinkDirection;
+};
+
+type FastRerouteEntry = {
+  node: RerouteNode;
+  previous: FastRerouteEntryCtx;
+  current?: FastRerouteEntryCtx;
+};
+
+/**
+ * RerouteService handles any coordination between reroute nodes and the system. Mostly, it's for
+ * fast-rerouting that can create a new reroute nodes while dragging a link.
+ */
+class RerouteService {
+  private isFastLinking = false;
+  private handledNewRerouteKeypress = false;
+  private connectingData: FastRerouteEntryCtx | null = null;
+  private fastReroutesHistory: FastRerouteEntry[] = [];
+
+  private handleLinkingKeydownBound = this.handleLinkingKeydown.bind(this);
+  private handleLinkingKeyupBound = this.handleLinkingKeyup.bind(this);
+
+  constructor() {
+    if (CONFIG_FAST_REROUTE_ENABLED && CONFIG_KEY_CREATE_WHILE_LINKING?.trim()) {
+      this.onCanvasSetUpListenerForLinking();
+    }
+  }
+
+  /**
+   * Waits for canvas to be available, then sets up a property accessor for `connecting_node` so
+   * we can start/stop monitoring for shortcut keys.
+   */
+  async onCanvasSetUpListenerForLinking() {
+    const canvas = await waitForCanvas();
+
+    // With the new UI released in August 2024, ComfyUI changed LiteGraph's code, removing
+    // connecting_node, connecting_node, connecting_node, and connecting_node properties and instead
+    // using an array of connecting_links. We can try to accomodate both for a while.
+    const canvasProperty = true ? "connecting_links" : "connecting_node";
+    (canvas as any)[`_${canvasProperty}`];
+    const thisService = this;
+    Object.defineProperty(canvas, canvasProperty, {
+      get: function () {
+        return this[`_${canvasProperty}`];
+      },
+      set: function (value) {
+        const isValNull = !value || !value?.length;
+        const isPropNull = !this[`_${canvasProperty}`] || !this[`_${canvasProperty}`]?.length;
+        const isStartingLinking = !isValNull && isPropNull;
+        const isStoppingLinking = !isPropNull && isValNull;
+        this[`_${canvasProperty}`] = value;
+        if (isStartingLinking) {
+          thisService.startingLinking();
+        }
+        if (isStoppingLinking) {
+          thisService.stoppingLinking();
+          thisService.connectingData = null;
+        }
+      },
+    });
+  }
+
+  /**
+   * When the user is actively dragging a link, listens for keydown events so we can enable
+   * shortcuts.
+   *
+   * Is only accessible if both `CONFIG_FAST_REROUTE_ENABLED` is true, and
+   * CONFIG_KEY_CREATE_WHILE_LINKING is not falsy/empty.
+   */
+  private startingLinking() {
+    this.isFastLinking = true;
+    KEY_EVENT_SERVICE.addEventListener("keydown", this.handleLinkingKeydownBound as EventListener);
+    KEY_EVENT_SERVICE.addEventListener("keyup", this.handleLinkingKeyupBound as EventListener);
+  }
+
+  /**
+   * When the user stops actively dragging a link, cleans up motnioring data and events.
+   *
+   * Is only accessible if both `CONFIG_FAST_REROUTE_ENABLED` is true, and
+   * CONFIG_KEY_CREATE_WHILE_LINKING is not falsy/empty.
+   */
+  private stoppingLinking() {
+    this.isFastLinking = false;
+    this.fastReroutesHistory = [];
+    KEY_EVENT_SERVICE.removeEventListener(
+      "keydown",
+      this.handleLinkingKeydownBound as EventListener,
+    );
+    KEY_EVENT_SERVICE.removeEventListener("keyup", this.handleLinkingKeyupBound as EventListener);
+  }
+
+  /**
+   * Handles the keydown event.
+   *
+   * Is only accessible if both `CONFIG_FAST_REROUTE_ENABLED` is true, and
+   * CONFIG_KEY_CREATE_WHILE_LINKING is not falsy/empty.
+   */
+  private handleLinkingKeydown(event: KeyboardEvent) {
+    if (
+      !this.handledNewRerouteKeypress &&
+      KEY_EVENT_SERVICE.areOnlyKeysDown(CONFIG_KEY_CREATE_WHILE_LINKING)
+    ) {
+      this.handledNewRerouteKeypress = true;
+      this.insertNewRerouteWhileLinking();
+    }
+  }
+
+  /**
+   * Handles the keyup event.
+   *
+   * Is only accessible if both `CONFIG_FAST_REROUTE_ENABLED` is true, and
+   * CONFIG_KEY_CREATE_WHILE_LINKING is not falsy/empty.
+   */
+  private handleLinkingKeyup(event: KeyboardEvent) {
+    if (
+      this.handledNewRerouteKeypress &&
+      !KEY_EVENT_SERVICE.areOnlyKeysDown(CONFIG_KEY_CREATE_WHILE_LINKING)
+    ) {
+      this.handledNewRerouteKeypress = false;
+    }
+  }
+
+  private getConnectingData(): FastRerouteEntryCtx {
+    const oldCanvas = app.canvas as any;
+    if (
+      oldCanvas.connecting_node &&
+      oldCanvas.connecting_slot != null &&
+      oldCanvas.connecting_pos?.length
+    ) {
+      return {
+        node: oldCanvas.connecting_node,
+        input: oldCanvas.connecting_input,
+        output: oldCanvas.connecting_output,
+        slot: oldCanvas.connecting_slot,
+        pos: [...oldCanvas.connecting_pos] as Vector2,
+      };
+    }
+    const canvas = app.canvas;
+    if (canvas.connecting_links?.length) {
+      // Assume just the first.
+      const link = canvas.connecting_links[0]!;
+      return {
+        node: link.node,
+        input: link.input ?? undefined,
+        output: link.output ?? undefined,
+        slot: link.slot,
+        pos: [...link.pos] as Point,
+      };
+    }
+    throw new Error("Error, handling linking keydown, but there's no link.");
+  }
+
+  private setCanvasConnectingData(ctx: FastRerouteEntryCtx) {
+    const oldCanvas = app.canvas as any;
+    if (
+      oldCanvas.connecting_node &&
+      oldCanvas.connecting_slot != null &&
+      oldCanvas.connecting_pos?.length
+    ) {
+      oldCanvas.connecting_node = ctx.node;
+      oldCanvas.connecting_input = ctx.input;
+      oldCanvas.connecting_output = ctx.output;
+      oldCanvas.connecting_slot = ctx.slot;
+      oldCanvas.connecting_pos = ctx.pos;
+    }
+    const canvas = app.canvas;
+    if (canvas.connecting_links?.length) {
+      // Assume just the first.
+      const link = canvas.connecting_links[0]!;
+      link.node = ctx.node;
+      link.input = ctx.input;
+      link.output = ctx.output;
+      link.slot = ctx.slot;
+      link.pos = ctx.pos;
+    }
+    // const newCanvas = app.canvas as unknown as TypedLGraphCanvas;
+    // if (newCanvas.linkConnector.renderLinks?.length) {
+    //   newCanvas.linkConnector.reset();
+    //   newCanvas.linkConnector.dragNewFromOutput(app.graph as any, ctx.node as any, ctx.output as any);
+    // }
+
+    //   const link = newCanvas.linkConnector.renderLinks[0]! as any;
+    //   link.node = ctx.node;
+    //   link.fromSlot = ctx.output || ctx.input;
+    //   link.fromSlotIndex = ctx.slot;
+    //   link.fromPos = ctx.pos;
+    //   link.fromDirection = ctx.direction || 4;
+    // }
+  }
+
+  /**
+   * Inserts a new reroute (while linking) as called from key down handler.
+   *
+   * Is only accessible if both `CONFIG_FAST_REROUTE_ENABLED` is true, and
+   * CONFIG_KEY_CREATE_WHILE_LINKING is not falsy/empty.
+   */
+  private insertNewRerouteWhileLinking() {
+    const canvas = app.canvas;
+    this.connectingData = this.getConnectingData();
+    if (!this.connectingData) {
+      throw new Error("Error, handling linking keydown, but there's no link.");
+    }
+
+    const data = this.connectingData;
+    const node = LiteGraph.createNode("Reroute (rgthree)") as RerouteNode;
+    const entry: FastRerouteEntry = {
+      node,
+      previous: {...this.connectingData},
+      current: undefined,
+    };
+    this.fastReroutesHistory.push(entry);
+
+    let connectingDir = (data.input || data.output)?.dir;
+    if (!connectingDir) {
+      connectingDir = data.input ? LiteGraph.LEFT : LiteGraph.RIGHT;
+    }
+
+    let newPos = canvas.convertEventToCanvasOffset({
+      clientX: Math.round(canvas.last_mouse_position[0] / 10) * 10,
+      clientY: Math.round(canvas.last_mouse_position[1] / 10) * 10,
+    } as MouseEvent);
+    entry.node.pos = newPos;
+    canvas.graph!.add(entry.node);
+    canvas.selectNode(entry.node);
+
+    // Find out which direction we're generally moving.
+    const distX = entry.node.pos[0] - data.pos[0];
+    const distY = entry.node.pos[1] - data.pos[1];
+
+    const layout: [string, string] = ["Left", "Right"];
+    if (distX > 0 && Math.abs(distX) > Math.abs(distY)) {
+      // To the right, and further right than up or down.
+      layout[0] = data.output ? "Left" : "Right";
+      layout[1] = LAYOUT_LABEL_OPPOSITES[layout[0]]!;
+      node.pos[0] -= node.size[0] + 10;
+      node.pos[1] -= Math.round(node.size[1] / 2 / 10) * 10;
+    } else if (distX < 0 && Math.abs(distX) > Math.abs(distY)) {
+      // To the left, and further right than up or down.
+      layout[0] = data.output ? "Right" : "Left";
+      layout[1] = LAYOUT_LABEL_OPPOSITES[layout[0]]!;
+      node.pos[1] -= Math.round(node.size[1] / 2 / 10) * 10;
+    } else if (distY < 0 && Math.abs(distY) > Math.abs(distX)) {
+      // Above and further above than left or right.
+      layout[0] = data.output ? "Bottom" : "Top";
+      layout[1] = LAYOUT_LABEL_OPPOSITES[layout[0]]!;
+      node.pos[0] -= Math.round(node.size[0] / 2 / 10) * 10;
+    } else if (distY > 0 && Math.abs(distY) > Math.abs(distX)) {
+      // Below and further below than left or right.
+      layout[0] = data.output ? "Top" : "Bottom";
+      layout[1] = LAYOUT_LABEL_OPPOSITES[layout[0]]!;
+      node.pos[0] -= Math.round(node.size[0] / 2 / 10) * 10;
+      node.pos[1] -= node.size[1] + 10;
+    }
+    setConnectionsLayout(entry.node, layout);
+
+    if (data.output) {
+      data.node.connect(data.slot, entry.node, 0);
+      data.node = entry.node;
+      data.output = entry.node.outputs[0]!;
+      data.slot = 0;
+      data.pos = entry.node.getConnectionPos(false, 0);
+      data.direction =
+        layout[0] === "Top" ? 2 : layout[0] === "Bottom" ? 1 : layout[0] === "Left" ? 4 : 3;
+    } else {
+      entry.node.connect(0, data.node, data.slot);
+      data.node = entry.node;
+      data.input = entry.node.inputs[0]!;
+      data.slot = 0;
+      data.pos = entry.node.getConnectionPos(true, 0);
+      data.direction =
+        layout[1] === "Top" ? 2 : layout[1] === "Bottom" ? 1 : layout[1] === "Left" ? 4 : 3;
+    }
+    this.setCanvasConnectingData(data);
+    entry.current = {...this.connectingData};
+
+    app.graph.setDirtyCanvas(true, true);
+  }
+
+  /**
+   * Is called from a reroute node when it is resized or moved so the service can check if we're
+   * actively linking to it, and it can update the linking data so the connection moves too, by
+   * updating `connecting_pos`.
+   */
+  handleMoveOrResizeNodeMaybeWhileDragging(node: RerouteNode) {
+    const data = this.connectingData!;
+    if (this.isFastLinking && node === data?.node) {
+      const entry = this.fastReroutesHistory[this.fastReroutesHistory.length - 1];
+      if (entry) {
+        data.pos = entry.node.getConnectionPos(!!data.input, 0);
+        this.setCanvasConnectingData(data);
+      }
+    }
+  }
+
+  /**
+   * Is called from a reroute node when it is deleted so the service can check if we're actively
+   * linking to it and go "back" in history to the previous node.
+   */
+  handleRemovedNodeMaybeWhileDragging(node: RerouteNode) {
+    const currentEntry = this.fastReroutesHistory[this.fastReroutesHistory.length - 1];
+    if (currentEntry?.node === node) {
+      this.setCanvasConnectingData(currentEntry.previous);
+      this.fastReroutesHistory.splice(this.fastReroutesHistory.length - 1, 1);
+      if (currentEntry.previous.node) {
+        app.canvas.selectNode(currentEntry.previous.node);
+      }
+    }
+  }
+}
+
+const SERVICE = new RerouteService();
+
+/**
+ * The famous ReroutNode, that has true multidirectional, expansive sizes, etc.
+ */
+class RerouteNode extends RgthreeBaseVirtualNode {
+  static override title = NodeTypesString.REROUTE;
+  static override type = NodeTypesString.REROUTE;
+  override comfyClass = NodeTypesString.REROUTE;
+
+  static readonly title_mode = LiteGraph.NO_TITLE;
+
+  static collapsable = false;
+  static layout_slot_offset = 5;
+  static size: Vector2 = configDefaultSize; // Starting size, read from within litegraph.core
+
+  override isVirtualNode = true;
+  readonly hideSlotLabels = true;
+
+  private schedulePromise: Promise<void> | null = null;
+
+  defaultConnectionsLayout = Array.from(configLayout);
+
+  /** Shortcuts defined in the config. */
+  private shortcuts = {
+    rotate: {keys: CONFIG_KEY_ROTATE, state: false},
+    connection_input: {keys: CONFIG_KEY_CXN_INPUT, state: false},
+    connection_output: {keys: CONFIG_KEY_CXN_OUTPUT, state: false},
+    resize: {
+      keys: CONFIG_KEY_RESIZE,
+      state: false,
+      initialMousePos: [-1, -1] as Vector2,
+      initialNodeSize: [-1, -1] as Vector2,
+      initialNodePos: [-1, -1] as Vector2,
+      resizeOnSide: [-1, -1] as Vector2,
+    },
+    move: {
+      keys: CONFIG_KEY_MOVE,
+      state: false,
+      initialMousePos: [-1, -1] as Vector2,
+      initialNodePos: [-1, -1] as Vector2,
+    },
+  };
+
+  constructor(title = RerouteNode.title) {
+    super(title);
+    this.onConstructed();
+  }
+
+  override onConstructed(): boolean {
+    this.setResizable(!!(this.properties["resizable"] ?? configResizable));
+    this.size = RerouteNode.size; // Starting size.
+    this.addInput("", "*");
+    this.addOutput("", "*");
+    setTimeout(() => this.applyNodeSize(), 20);
+    return super.onConstructed();
+  }
+
+  override configure(info: ISerialisedNode): void {
+    if (info.inputs?.length) {
+      info.inputs.length = 1;
+    }
+    super.configure(info);
+    this.configuring = true;
+    this.setResizable(!!(this.properties["resizable"] ?? configResizable));
+    this.applyNodeSize();
+    this.configuring = false;
+  }
+
+  setResizable(resizable: boolean) {
+    this.properties["resizable"] = !!resizable;
+    this.resizable = this.properties["resizable"];
+  }
+
+  override clone() {
+    const cloned = super.clone()!;
+    cloned.inputs[0]!.type = "*";
+    cloned.outputs[0]!.type = "*";
+    return cloned;
+  }
+
+  /**
+   * Copied a good bunch of this from the original reroute included with comfy.
+   */
+  override onConnectionsChange(
+    type: number,
+    _slotIndex: number,
+    connected: boolean,
+    _link_info: LLink,
+    _ioSlot: INodeOutputSlot | INodeInputSlot,
+  ) {
+    // Prevent multiple connections to different types when we have no input
+    if (connected && type === LiteGraph.OUTPUT) {
+      // Ignore wildcard nodes as these will be updated to real types
+      const types = new Set(
+        this.outputs[0]!.links!.map((l) => app.graph.links[l]?.type).filter((t) => t && t !== "*"),
+      );
+      if (types.size > 1) {
+        const linksToDisconnect = [];
+        for (let i = 0; i < this.outputs[0]!.links!.length - 1; i++) {
+          const linkId = this.outputs[0]!.links![i]!;
+          const link = app.graph.links[linkId];
+          linksToDisconnect.push(link);
+        }
+        for (const link of linksToDisconnect) {
+          const node = app.graph.getNodeById(link!.target_id)!;
+          node.disconnectInput(link!.target_slot);
+        }
+      }
+    }
+    this.scheduleStabilize();
+  }
+
+  override onDrawForeground(ctx: CanvasRenderingContext2D, canvas: TLGraphCanvas): void {
+    if (this.properties?.["showLabel"]) {
+      // ComfyUI seemed to break us again, but couldn't repro. No reason to not check, I guess.
+      // https://github.com/rgthree/rgthree-comfy/issues/71
+      const low_quality = canvas?.ds?.scale && canvas.ds.scale < 0.6;
+      if (low_quality || this.size[0] <= 10) {
+        return;
+      }
+      const fontSize = Math.min(14, (this.size[1] * 0.65) | 0);
+      ctx.save();
+      ctx.fillStyle = "#888";
+      ctx.font = `${fontSize}px Arial`;
+      ctx.textAlign = "center";
+      ctx.textBaseline = "middle";
+      ctx.fillText(
+        String(
+          this.title && this.title !== RerouteNode.title
+            ? this.title
+            : this.outputs?.[0]?.type || "",
+        ),
+        this.size[0] / 2,
+        this.size[1] / 2,
+        this.size[0] - 30,
+      );
+      ctx.restore();
+    }
+  }
+
+  /** Finds the input slot; since we only ever have one, this is always 0. */
+  override findInputSlot<TReturn extends false>(name: string, returnObj?: TReturn): number;
+  override findInputSlot<TReturn extends true>(name: string, returnObj?: TReturn): INodeInputSlot;
+  override findInputSlot(name: string, returnObj: boolean = false): number | INodeInputSlot {
+    return returnObj ? this.inputs[0]! : 0;
+  }
+
+  /** Finds the output slot; since we only ever have one, this is always 0. */
+  override findOutputSlot<TReturn extends false>(name: string, returnObj?: TReturn): number;
+  override findOutputSlot<TReturn extends true>(name: string, returnObj?: TReturn): INodeOutputSlot;
+  override findOutputSlot(name: unknown, returnObj?: unknown): number | INodeOutputSlot {
+    return returnObj ? this.outputs[0]! : 0;
+  }
+
+  override disconnectOutput(slot: string | number, targetNode?: TLGraphNode | undefined): boolean {
+    return super.disconnectOutput(slot, targetNode);
+  }
+
+  override disconnectInput(slot: string | number): boolean {
+    // [🤮] ComfyUI's reroute nodes will disconnect our input if it doesn't yet match (ours being
+    // "*" and it's being a type. This mostly happens if we're converting reroutes to rgthree
+    // reroutes, the old reroute does a check and calls disconnectInput. Luckily, we can be smarter
+    // and check if we're being asked to disconnect from an old reroute while we're replacing
+    // reroute nodes (via rgthree.replacingReroute state).
+    if (rgthree.replacingReroute != null && this.inputs[0]?.link) {
+      const graph = app.graph as TLGraph;
+      const link = graph.links[this.inputs[0].link];
+      const node = link?.origin_id != null ? graph.getNodeById(link.origin_id) : null;
+      // We'll also be asked to disconnect when the old one is removed, so we only want to stop a
+      // disconnect when the connected node is NOT the one being removed/replaced.
+      if (rgthree.replacingReroute !== node?.id) {
+        return false;
+      }
+    }
+    return super.disconnectInput(slot);
+  }
+
+  scheduleStabilize(ms = 64) {
+    if (!this.schedulePromise) {
+      this.schedulePromise = new Promise((resolve) => {
+        setTimeout(() => {
+          this.schedulePromise = null;
+          this.stabilize();
+          resolve();
+        }, ms);
+      });
+    }
+    return this.schedulePromise;
+  }
+
+  stabilize() {
+    // If we are currently "configuring" then skip this stabilization. The connected nodes may
+    // not yet be configured.
+    if (this.configuring) {
+      return;
+    }
+    // Find root input
+    let currentNode: TLGraphNode | null = this;
+    let updateNodes = [];
+    let input = null;
+    let inputType = null;
+    let inputNode = null;
+    let inputNodeOutputSlot = null;
+    while (currentNode) {
+      updateNodes.unshift(currentNode);
+      const linkId: number | null = currentNode.inputs[0]!.link;
+      if (linkId !== null) {
+        const link: LLink = (app.graph as TLGraph).links[linkId]!;
+        const node: TLGraphNode = (app.graph as TLGraph).getNodeById(link.origin_id)!;
+        if (!node) {
+          // Bummer, somthing happened.. should we cleanup?
+          app.graph.removeLink(linkId);
+          currentNode = null;
+          break;
+        }
+        const type = (node.constructor as typeof TLGraphNode).type;
+        if (type?.includes("Reroute")) {
+          if (node === this) {
+            // We've found a circle
+            currentNode.disconnectInput(link.target_slot);
+            currentNode = null;
+          } else {
+            // Move the previous node
+            currentNode = node;
+          }
+        } else {
+          // We've found the end
+          inputNode = node;
+          inputNodeOutputSlot = link.origin_slot;
+          input = node.outputs[inputNodeOutputSlot] ?? null;
+          inputType = input?.type ?? null;
+          break;
+        }
+      } else {
+        // This path has no input node
+        currentNode = null;
+        break;
+      }
+    }
+
+    // Find all outputs
+    const nodes: TLGraphNode[] = [this];
+    let outputNode = null;
+    let outputType = null;
+    // For primitive nodes, which look at the widget to dsplay themselves.
+    let outputWidgetConfig = null;
+    let outputWidget = null;
+    while (nodes.length) {
+      currentNode = nodes.pop()!;
+      const outputs = (currentNode.outputs ? currentNode.outputs[0]!.links : []) || [];
+      if (outputs.length) {
+        for (const linkId of outputs) {
+          const link = app.graph.links[linkId];
+
+          // When disconnecting sometimes the link is still registered
+          if (!link) continue;
+
+          const node = app.graph.getNodeById(link.target_id) as TLGraphNode;
+          // Don't know why this ever happens.. but it did around the repeater..
+          if (!node) continue;
+          const type = (node.constructor as any).type;
+          if (type?.includes("Reroute")) {
+            // Follow reroute nodes
+            nodes.push(node);
+            updateNodes.push(node);
+          } else {
+            // We've found an output
+            const output = node.inputs?.[link.target_slot] ?? null;
+            const nodeOutType = output?.type;
+            if (nodeOutType == null) {
+              console.warn(
+                `[rgthree] Reroute - Connected node ${node.id} does not have type information for ` +
+                  `slot ${link.target_slot}. Skipping connection enforcement, but something is odd ` +
+                  `with that node.`,
+              );
+            } else if (
+              inputType &&
+              inputType !== "*" &&
+              nodeOutType !== "*" &&
+              !isValidConnection(input, output)
+            ) {
+              // The output doesnt match our input so disconnect it
+              console.warn(
+                `[rgthree] Reroute - Disconnecting connected node's input (${node.id}.${
+                  link.target_slot
+                }) (${node.type}) because its type (${String(
+                  nodeOutType,
+                )}) does not match the reroute type (${String(inputType)})`,
+              );
+              node.disconnectInput(link.target_slot);
+            } else {
+              outputType = nodeOutType;
+              outputNode = node;
+              outputWidgetConfig = null;
+              outputWidget = null;
+              // For primitive nodes, which look at the widget to dsplay themselves.
+              if (output?.widget) {
+                rgthreeApi.print("PRIMITIVE_REROUTE");
+                try {
+                  const config = getWidgetConfig(output);
+                  if (!outputWidgetConfig && config) {
+                    outputWidgetConfig = config[1] ?? {};
+                    outputType = config[0];
+                    if (!outputWidget) {
+                      outputWidget = outputNode.widgets?.find(
+                        (w) => w.name === output?.widget?.name,
+                      );
+                    }
+                    const merged = mergeIfValid(output, [config[0], outputWidgetConfig]);
+                    if (merged?.customConfig) {
+                      outputWidgetConfig = merged.customConfig;
+                    }
+                  }
+                } catch (e) {
+                  // Something happened, probably because comfyUI changes their methods.
+                  console.error(
+                    "[rgthree] Could not propagate widget infor for reroute; maybe ComfyUI updated?",
+                  );
+                  outputWidgetConfig = null;
+                  outputWidget = null;
+                }
+              }
+            }
+          }
+        }
+      } else {
+        // No more outputs for this path
+      }
+    }
+
+    const displayType = inputType || outputType || "*";
+    const color = LGraphCanvas.link_type_colors[displayType];
+
+    // Update the types of each node
+    for (const node of updateNodes) {
+      // If we dont have an input type we are always wildcard but we'll show the output type
+      // This lets you change the output link to a different type and all nodes will update
+      node.outputs[0]!.type = inputType || "*";
+      (node as any).__outputType = displayType;
+      node.outputs[0]!.name = input?.name || "";
+      node.size = node.computeSize();
+      (node as any).applyNodeSize?.();
+
+      for (const l of node.outputs[0]!.links || []) {
+        const link = app.graph.links[l];
+        if (link && color) {
+          link.color = color;
+        }
+      }
+
+      try {
+        // For primitive nodes, which look at the widget to dsplay themselves; we get by with just
+        // an object with 'name'.
+        if (outputWidgetConfig && outputWidget && outputType) {
+          rgthreeApi.print("PRIMITIVE_REROUTE");
+          node.inputs[0]!.widget = {name: "value"} as any;
+          setWidgetConfig(
+            node.inputs[0],
+            [outputType ?? displayType, outputWidgetConfig],
+            // outputWidget, // This never existed?
+          );
+        } else {
+          setWidgetConfig(node.inputs[0], null);
+        }
+      } catch (e) {
+        // Something happened, probably because comfyUI changes their methods.
+        console.error("[rgthree] Could not set widget config for reroute; maybe ComfyUI updated?");
+        outputWidgetConfig = null;
+        outputWidget = null;
+        if (node.inputs[0]?.widget) {
+          delete node.inputs[0].widget;
+        }
+      }
+    }
+
+    if (inputNode && inputNodeOutputSlot != null) {
+      const links = inputNode.outputs[inputNodeOutputSlot]!.links;
+      for (const l of links || []) {
+        const link = app.graph.links[l];
+        if (link && color) {
+          link.color = color;
+        }
+      }
+    }
+    (inputNode as any)?.onConnectionsChainChange?.();
+    (outputNode as any)?.onConnectionsChainChange?.();
+    app.graph.setDirtyCanvas(true, true);
+  }
+
+  /**
+   * When called, sets the node size, and the properties size, and calls out to `stabilizeLayout`.
+   */
+  override setSize(size: Vector2): void {
+    const oldSize = [...this.size] as Size;
+    const newSize = [...size] as Size;
+    super.setSize(newSize);
+    this.properties["size"] = [...this.size];
+    this.stabilizeLayout(oldSize, newSize);
+  }
+
+  /**
+   * Looks at the current layout and determins if we also need to set a `connections_dir` based on
+   * the size of the node (and what that connections_dir should be).
+   */
+  private stabilizeLayout(oldSize: Vector2, newSize: Vector2) {
+    if (newSize[0] === 10 || newSize[1] === 10) {
+      const props = this.properties;
+      props["connections_layout"] = props["connections_layout"] || ["Left", "Right"];
+      props["connections_dir"] = props["connections_dir"] || [-1, -1];
+
+      const layout = props["connections_layout"] as [string, string];
+      const dir = props["connections_dir"] as [number, number];
+
+      if (oldSize[0] > 10 && newSize[0] === 10) {
+        dir[0] = LiteGraph.DOWN;
+        dir[1] = LiteGraph.UP;
+        if (layout[0] === "Bottom") {
+          layout[1] = "Top";
+        } else if (layout[1] === "Top") {
+          layout[0] = "Bottom";
+        } else {
+          layout[0] = "Top";
+          layout[1] = "Bottom";
+          dir[0] = LiteGraph.UP;
+          dir[1] = LiteGraph.DOWN;
+        }
+        this.setDirtyCanvas(true, true);
+      } else if (oldSize[1] > 10 && newSize[1] === 10) {
+        dir[0] = LiteGraph.RIGHT;
+        dir[1] = LiteGraph.LEFT;
+        if (layout[0] === "Right") {
+          layout[1] = "Left";
+        } else if (layout[1] === "Left") {
+          layout[0] = "Right";
+        } else {
+          layout[0] = "Left";
+          layout[1] = "Right";
+          dir[0] = LiteGraph.LEFT;
+          dir[1] = LiteGraph.RIGHT;
+        }
+        this.setDirtyCanvas(true, true);
+      }
+    }
+    SERVICE.handleMoveOrResizeNodeMaybeWhileDragging(this);
+  }
+
+  applyNodeSize() {
+    this.properties["size"] = this.properties["size"] || RerouteNode.size;
+    this.properties["size"] = [
+      Number((this.properties["size"] as Size)[0]),
+      Number((this.properties["size"] as Size)[1]),
+    ];
+    this.size = this.properties["size"] as Size;
+    app.graph.setDirtyCanvas(true, true);
+  }
+
+  /**
+   * Rotates the node, including changing size and moving input's and output's layouts.
+   */
+  rotate(degrees: 90 | -90 | 180) {
+    const w = this.size[0];
+    const h = this.size[1];
+    this.properties["connections_layout"] =
+      this.properties["connections_layout"] || (this as RerouteNode).defaultConnectionsLayout;
+
+    const connections_layout = this.properties["connections_layout"] as [string, string];
+    const inputDirIndex = LAYOUT_CLOCKWISE.indexOf(connections_layout[0]);
+    const outputDirIndex = LAYOUT_CLOCKWISE.indexOf(connections_layout[1]);
+    if (degrees == 90 || degrees === -90) {
+      if (degrees === -90) {
+        connections_layout[0] = LAYOUT_CLOCKWISE[(((inputDirIndex - 1) % 4) + 4) % 4]!;
+        connections_layout[1] = LAYOUT_CLOCKWISE[(((outputDirIndex - 1) % 4) + 4) % 4]!;
+      } else {
+        connections_layout[0] = LAYOUT_CLOCKWISE[(((inputDirIndex + 1) % 4) + 4) % 4]!;
+        connections_layout[1] = LAYOUT_CLOCKWISE[(((outputDirIndex + 1) % 4) + 4) % 4]!;
+      }
+    } else if (degrees === 180) {
+      connections_layout[0] = LAYOUT_CLOCKWISE[(((inputDirIndex + 2) % 4) + 4) % 4]!;
+      connections_layout[1] = LAYOUT_CLOCKWISE[(((outputDirIndex + 2) % 4) + 4) % 4]!;
+    }
+    this.setSize([h, w]);
+  }
+
+  /**
+   * Manually handles a move called from `onMouseMove` while the resize shortcut is active.
+   */
+  private manuallyHandleMove(event: PointerEvent) {
+    const shortcut = this.shortcuts.move;
+    if (shortcut.state) {
+      const diffX = Math.round((event.clientX - shortcut.initialMousePos[0]) / 10) * 10;
+      const diffY = Math.round((event.clientY - shortcut.initialMousePos[1]) / 10) * 10;
+      this.pos[0] = shortcut.initialNodePos[0] + diffX;
+      this.pos[1] = shortcut.initialNodePos[1] + diffY;
+      this.setDirtyCanvas(true, true);
+      SERVICE.handleMoveOrResizeNodeMaybeWhileDragging(this);
+    }
+  }
+
+  /**
+   * Manually handles a resize called from `onMouseMove` while the resize shortcut is active.
+   */
+  private manuallyHandleResize(event: PointerEvent) {
+    const shortcut = this.shortcuts.resize;
+    if (shortcut.state) {
+      let diffX = Math.round((event.clientX - shortcut.initialMousePos[0]) / 10) * 10;
+      let diffY = Math.round((event.clientY - shortcut.initialMousePos[1]) / 10) * 10;
+      diffX *= shortcut.resizeOnSide[0] === LiteGraph.LEFT ? -1 : 1;
+      diffY *= shortcut.resizeOnSide[1] === LiteGraph.UP ? -1 : 1;
+      const oldSize = [...this.size] as Size;
+      this.setSize([
+        Math.max(10, shortcut.initialNodeSize[0] + diffX),
+        Math.max(10, shortcut.initialNodeSize[1] + diffY),
+      ]);
+      if (shortcut.resizeOnSide[0] === LiteGraph.LEFT && oldSize[0] > 10) {
+        this.pos[0] = shortcut.initialNodePos[0] - diffX;
+      }
+      if (shortcut.resizeOnSide[1] === LiteGraph.UP && oldSize[1] > 10) {
+        this.pos[1] = shortcut.initialNodePos[1] - diffY;
+      }
+      this.setDirtyCanvas(true, true);
+    }
+  }
+
+  /**
+   * Cycles the connection (input or output) to the next available layout. Note, when the width or
+   * height is only 10px, then layout sticks to the ends of the longer size, and we move a
+   * `connections_dir` property which is only paid attention to in `utils` when size of one axis
+   * is equal to 10.
+   * `manuallyHandleResize` handles the reset of `connections_dir` when a node is resized.
+   */
+  private cycleConnection(ioDir: IoDirection) {
+    const props = this.properties;
+    props["connections_layout"] = props["connections_layout"] || ["Left", "Right"];
+    const connections_layout = this.properties["connections_layout"] as [string, string];
+
+    const propIdx = ioDir == IoDirection.INPUT ? 0 : 1;
+    const oppositeIdx = propIdx ? 0 : 1;
+    let currentLayout = connections_layout[propIdx];
+    let oppositeLayout = connections_layout[oppositeIdx];
+
+    if (this.size[0] === 10 || this.size[1] === 10) {
+      props["connections_dir"] = props["connections_dir"] || [-1, -1];
+      const connections_dir = this.properties["connections_dir"] as [number, number];
+      let currentDir = connections_dir[propIdx] as number;
+      // let oppositeDir = connections_dir[oppositeIdx];
+      const options: number[] =
+        this.size[0] === 10
+          ? currentLayout === "Bottom"
+            ? [LiteGraph.DOWN, LiteGraph.RIGHT, LiteGraph.LEFT]
+            : [LiteGraph.UP, LiteGraph.LEFT, LiteGraph.RIGHT]
+          : currentLayout === "Right"
+            ? [LiteGraph.RIGHT, LiteGraph.DOWN, LiteGraph.UP]
+            : [LiteGraph.LEFT, LiteGraph.UP, LiteGraph.DOWN];
+      let idx = options.indexOf(currentDir);
+      let next = options[idx + 1] ?? options[0]!;
+      connections_dir[propIdx] = next;
+      return;
+    }
+
+    let next = currentLayout;
+    do {
+      let idx = LAYOUT_CLOCKWISE.indexOf(next);
+      next = LAYOUT_CLOCKWISE[idx + 1] ?? LAYOUT_CLOCKWISE[0]!;
+    } while (next === oppositeLayout);
+    connections_layout[propIdx] = next;
+    this.setDirtyCanvas(true, true);
+  }
+
+  /**
+   * Handles a mouse move while this node is selected. Note, though, that the actual work here is
+   * processed bycause the move and resize shortcuts set `canvas.node_capturing_input` to this node
+   * when they start (otherwise onMouseMove only fires when the mouse moves within the node's
+   * bounds).
+   */
+  override onMouseMove(event: PointerEvent): void {
+    if (this.shortcuts.move.state) {
+      const shortcut = this.shortcuts.move;
+      if (shortcut.initialMousePos[0] === -1) {
+        shortcut.initialMousePos[0] = event.clientX;
+        shortcut.initialMousePos[1] = event.clientY;
+        shortcut.initialNodePos[0] = this.pos[0];
+        shortcut.initialNodePos[1] = this.pos[1];
+      }
+      this.manuallyHandleMove(event);
+    } else if (this.shortcuts.resize.state) {
+      const shortcut = this.shortcuts.resize;
+      if (shortcut.initialMousePos[0] === -1) {
+        shortcut.initialMousePos[0] = event.clientX;
+        shortcut.initialMousePos[1] = event.clientY;
+        shortcut.initialNodeSize[0] = this.size[0];
+        shortcut.initialNodeSize[1] = this.size[1];
+        shortcut.initialNodePos[0] = this.pos[0];
+        shortcut.initialNodePos[1] = this.pos[1];
+        const canvas = app.canvas as TLGraphCanvas;
+        const offset = canvas.convertEventToCanvasOffset(event);
+        shortcut.resizeOnSide[0] = this.pos[0] > offset[0] ? LiteGraph.LEFT : LiteGraph.RIGHT;
+        shortcut.resizeOnSide[1] = this.pos[1] > offset[1] ? LiteGraph.UP : LiteGraph.DOWN;
+      }
+      this.manuallyHandleResize(event);
+    }
+  }
+
+  /**
+   * Handles a key down while this node is selected, starting a shortcut if the keys are newly
+   * pressed.
+   */
+  override onKeyDown(event: KeyboardEvent) {
+    super.onKeyDown(event);
+    const canvas = app.canvas as TLGraphCanvas;
+
+    // Only handle shortcuts while we're enabled in the config.
+    if (CONFIG_FAST_REROUTE_ENABLED) {
+      for (const [key, shortcut] of Object.entries(this.shortcuts)) {
+        if (!shortcut.state) {
+          const keys = KEY_EVENT_SERVICE.areOnlyKeysDown(shortcut.keys);
+          if (keys) {
+            shortcut.state = true;
+            if (key === "rotate") {
+              this.rotate(90);
+            } else if (key.includes("connection")) {
+              this.cycleConnection(key.includes("input") ? IoDirection.INPUT : IoDirection.OUTPUT);
+            }
+            if ((shortcut as any).initialMousePos) {
+              canvas.node_capturing_input = this;
+            }
+          }
+        }
+      }
+    }
+  }
+
+  /**
+   * Handles a key up while this node is selected, canceling any current shortcut.
+   */
+  override onKeyUp(event: KeyboardEvent) {
+    super.onKeyUp(event);
+    const canvas = app.canvas as TLGraphCanvas;
+
+    // Only handle shortcuts while we're enabled in the config.
+    if (CONFIG_FAST_REROUTE_ENABLED) {
+      for (const [key, shortcut] of Object.entries(this.shortcuts)) {
+        if (shortcut.state) {
+          const keys = KEY_EVENT_SERVICE.areOnlyKeysDown(shortcut.keys);
+          if (!keys) {
+            shortcut.state = false;
+            if ((shortcut as any).initialMousePos) {
+              (shortcut as any).initialMousePos = [-1, -1];
+              if ((canvas.node_capturing_input = this)) {
+                canvas.node_capturing_input = null;
+              }
+              this.setDirtyCanvas(true, true);
+            }
+          }
+        }
+      }
+    }
+  }
+
+  /**
+   * Handles a deselection of the node, canceling any current shortcut.
+   */
+  override onDeselected(): void {
+    super.onDeselected?.();
+    const canvas = app.canvas as TLGraphCanvas;
+    for (const [key, shortcut] of Object.entries(this.shortcuts)) {
+      shortcut.state = false;
+      if ((shortcut as any).initialMousePos) {
+        (shortcut as any).initialMousePos = [-1, -1];
+        if ((canvas.node_capturing_input = this)) {
+          canvas.node_capturing_input = null;
+        }
+        this.setDirtyCanvas(true, true);
+      }
+    }
+  }
+
+  override onRemoved(): void {
+    super.onRemoved?.();
+    // If we're removed, let's call out to the link dragging above. In a settimeout because this is
+    // called as we're removing with further cleanup Litegraph does, and we want the handler to
+    // cleanup further, afterwards
+    setTimeout(() => {
+      SERVICE.handleRemovedNodeMaybeWhileDragging(this);
+    }, 32);
+  }
+
+  override getHelp() {
+    return `
+      <p>
+        Finally, a comfortable, powerful reroute node with true multi-direction and powerful
+        shortcuts to bring your workflow to the next level.
+      </p>
+
+      ${
+        !CONFIG_FAST_REROUTE_ENABLED
+          ? `<p><i>Fast Shortcuts are currently disabled.</b>`
+          : `
+        <ul>
+          <li><p>
+            <code>${CONFIG_KEY_CREATE_WHILE_LINKING}</code> Create a new reroute node while dragging
+            a link, connecting it to the link in the place and continuing the link.
+          </p></li>
+          <li><p>
+            <code>${CONFIG_KEY_ROTATE}</code> Rotate the selected reroute node counter clockwise 90
+            degrees.
+          </p></li>
+          <li><p>
+            <code>${CONFIG_KEY_RESIZE}</code> Resize the selected reroute node from the nearest
+            corner by holding down and moving your mouse.
+          </p></li>
+          <li><p>
+            <code>${CONFIG_KEY_MOVE}</code> Move the selected reroute node by holding down and
+            moving your mouse.
+          </p></li>
+          <li><p>
+            <code>${CONFIG_KEY_CXN_INPUT}</code> Change the input layout/direction of the selected
+            reroute node.
+          </p></li>
+          <li><p>
+            <code>${CONFIG_KEY_CXN_OUTPUT}</code> Change the output layout/direction of the selected
+            reroute node.
+          </p></li>
+        </ul>
+      `
+      }
+      <p><small>
+        To change, ${!CONFIG_FAST_REROUTE_ENABLED ? "enable" : "disable"} or configure sohrtcuts,
+        make a copy of
+        <code>/custom_nodes/rgthree-comfy/rgthree_config.json.default</code> to
+        <code>/custom_nodes/rgthree-comfy/rgthree_config.json</code> and configure under
+        <code>nodes > reroute > fast_reroute</code>.
+      </small></p>
+    `;
+  }
+}
+
+addMenuItem(RerouteNode, app, {
+  name: (node) => `${node.properties?.["showLabel"] ? "Hide" : "Show"} Label/Title`,
+  property: "showLabel",
+  callback: async (node, value) => {
+    app.graph.setDirtyCanvas(true, true);
+  },
+});
+
+addMenuItem(RerouteNode, app, {
+  name: (node) => `${node.resizable ? "No" : "Allow"} Resizing`,
+  callback: (node) => {
+    (node as RerouteNode).setResizable(!node.resizable);
+    node.size[0] = Math.max(40, node.size[0]);
+    node.size[1] = Math.max(30, node.size[1]);
+    (node as RerouteNode).applyNodeSize();
+  },
+});
+
+addMenuItem(RerouteNode, app, {
+  name: "Static Width",
+  property: "size",
+  subMenuOptions: (() => {
+    const options = [];
+    for (let w = 8; w > 0; w--) {
+      options.push(`${w * 10}`);
+    }
+    return options;
+  })(),
+  prepareValue: (value, node) => [Number(value), node.size[1]],
+  callback: (node) => {
+    (node as RerouteNode).setResizable(false);
+    (node as RerouteNode).applyNodeSize();
+  },
+});
+
+addMenuItem(RerouteNode, app, {
+  name: "Static Height",
+  property: "size",
+  subMenuOptions: (() => {
+    const options = [];
+    for (let w = 8; w > 0; w--) {
+      options.push(`${w * 10}`);
+    }
+    return options;
+  })(),
+  prepareValue: (value, node) => [node.size[0], Number(value)],
+  callback: (node) => {
+    (node as RerouteNode).setResizable(false);
+    (node as RerouteNode).applyNodeSize();
+  },
+});
+
+addConnectionLayoutSupport(
+  RerouteNode,
+  app,
+  [
+    ["Left", "Right"],
+    ["Left", "Top"],
+    ["Left", "Bottom"],
+    ["Right", "Left"],
+    ["Right", "Top"],
+    ["Right", "Bottom"],
+    ["Top", "Left"],
+    ["Top", "Right"],
+    ["Top", "Bottom"],
+    ["Bottom", "Left"],
+    ["Bottom", "Right"],
+    ["Bottom", "Top"],
+  ],
+  (node) => {
+    (node as RerouteNode).applyNodeSize();
+  },
+);
+
+addMenuItem(RerouteNode, app, {
+  name: "Rotate",
+  subMenuOptions: [
+    "Rotate 90° Clockwise",
+    "Rotate 90° Counter-Clockwise",
+    "Rotate 180°",
+    null,
+    "Flip Horizontally",
+    "Flip Vertically",
+  ],
+  callback: (node_: TLGraphNode, value) => {
+    const node = node_ as RerouteNode;
+    if (value?.startsWith("Rotate 90° Clockwise")) {
+      node.rotate(90);
+    } else if (value?.startsWith("Rotate 90° Counter-Clockwise")) {
+      node.rotate(-90);
+    } else if (value?.startsWith("Rotate 180°")) {
+      node.rotate(180);
+    } else {
+      const connections_layout = node.properties["connections_layout"] as [string, string];
+      const inputDirIndex = LAYOUT_CLOCKWISE.indexOf(connections_layout[0]);
+      const outputDirIndex = LAYOUT_CLOCKWISE.indexOf(connections_layout[1]);
+      if (value?.startsWith("Flip Horizontally")) {
+        if (["Left", "Right"].includes(connections_layout[0])) {
+          connections_layout[0] = LAYOUT_CLOCKWISE[(((inputDirIndex + 2) % 4) + 4) % 4]!;
+        }
+        if (["Left", "Right"].includes(connections_layout[1])) {
+          connections_layout[1] = LAYOUT_CLOCKWISE[(((outputDirIndex + 2) % 4) + 4) % 4]!;
+        }
+      } else if (value?.startsWith("Flip Vertically")) {
+        if (["Top", "Bottom"].includes(connections_layout[0])) {
+          connections_layout[0] = LAYOUT_CLOCKWISE[(((inputDirIndex + 2) % 4) + 4) % 4]!;
+        }
+        if (["Top", "Bottom"].includes(connections_layout[1])) {
+          connections_layout[1] = LAYOUT_CLOCKWISE[(((outputDirIndex + 2) % 4) + 4) % 4]!;
+        }
+      }
+    }
+  },
+});
+
+addMenuItem(RerouteNode, app, {
+  name: "Clone New Reroute...",
+  subMenuOptions: ["Before", "After"],
+  callback: async (node, value) => {
+    const clone = node.clone()!;
+    const pos = [...node.pos];
+    if (value === "Before") {
+      clone.pos = [pos[0]! - 20, pos[1]! - 20];
+      app.graph.add(clone);
+      await wait();
+      const inputLinks = getSlotLinks(node.inputs[0]);
+      for (const inputLink of inputLinks) {
+        const link = inputLink.link;
+        const linkedNode = app.graph.getNodeById(link.origin_id) as TLGraphNode;
+        if (linkedNode) {
+          linkedNode.connect(0, clone, 0);
+        }
+      }
+      clone.connect(0, node, 0);
+    } else {
+      clone.pos = [pos[0]! + 20, pos[1]! + 20];
+      app.graph.add(clone);
+      await wait();
+      const outputLinks = getSlotLinks(node.outputs[0]);
+      node.connect(0, clone, 0);
+      for (const outputLink of outputLinks) {
+        const link = outputLink.link;
+        const linkedNode = app.graph.getNodeById(link.target_id) as TLGraphNode;
+        if (linkedNode) {
+          clone.connect(0, linkedNode, link.target_slot);
+        }
+      }
+    }
+  },
+});
+
+app.registerExtension({
+  name: "rgthree.Reroute",
+  registerCustomNodes() {
+    RerouteNode.setUp();
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/rgthree.scss b/custom_nodes/rgthree-comfy/src_web/comfyui/rgthree.scss
new file mode 100644
index 0000000000000000000000000000000000000000..4df9c3a9cf890133029fb8fb8dca2e4b615ff80f
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/rgthree.scss
@@ -0,0 +1,328 @@
+.rgthree-top-messages-container {
+  position: fixed;
+  z-index: 9999;
+  top: 0;
+  left: 0;
+  width: 100%;
+  height: 0;
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: start;
+}
+
+.rgthree-top-messages-container > div {
+  position: relative;
+  height: fit-content;
+  padding: 4px;
+  margin-top: -100px; /* re-set by JS */
+  opacity: 0;
+  transition: all 0.33s ease-in-out;
+  z-index: 3;
+}
+.rgthree-top-messages-container > div:last-child {
+  z-index: 2;
+}
+.rgthree-top-messages-container > div:not(.-show) {
+  z-index: 1;
+}
+
+.rgthree-top-messages-container > div.-show {
+  opacity: 1;
+  margin-top: 0px !important;
+}
+
+.rgthree-top-messages-container > div.-show {
+  opacity: 1;
+  transform: translateY(0%);
+}
+
+.rgthree-top-messages-container > div > div {
+  position: relative;
+  background: #353535;
+  color: #fff;
+  display: flex;
+  flex-direction: row;
+  align-items: center;
+  justify-content: center;
+  height: fit-content;
+  box-shadow: 0 0 10px rgba(0, 0, 0, 0.88);
+  padding: 6px 12px;
+  border-radius: 4px;
+  font-family: Arial, sans-serif;
+  font-size: 14px;
+}
+.rgthree-top-messages-container > div > div > span {
+  display: flex;
+  flex-direction: row;
+  align-items: center;
+  justify-content: center;
+}
+.rgthree-top-messages-container > div > div > span svg {
+  width: 20px;
+  height: auto;
+  margin-right: 8px;
+}
+.rgthree-top-messages-container > div > div > span svg.icon-checkmark {
+  fill: #2e9720;
+}
+
+.rgthree-top-messages-container [type="warn"]::before,
+.rgthree-top-messages-container [type="success"]::before {
+  content: '⚠️';
+  display: inline-block;
+  flex: 0 0 auto;
+  font-size: 18px;
+  margin-right: 4px;
+  line-height: 1;
+}
+.rgthree-top-messages-container [type="success"]::before {
+  content: '🎉';
+}
+
+.rgthree-top-messages-container a {
+  cursor: pointer;
+  text-decoration: underline;
+  color: #fc0;
+  margin-left: 4px;
+  display: inline-block;
+  line-height: 1;
+}
+
+.rgthree-top-messages-container a:hover {
+  color: #fc0;
+  text-decoration: none;
+}
+
+/* Fix node selector being crazy long b/c of array types. */
+.litegraph.litesearchbox input,
+.litegraph.litesearchbox select {
+  max-width: 250px;
+}
+
+/* There's no reason for this z-index to be so high. It layers on top of things it shouldn't,
+  (like pythongssss' image gallery, the properties panel, etc.) */
+.comfy-multiline-input {
+  z-index: 1 !important;
+}
+.comfy-multiline-input:focus {
+  z-index: 2 !important;
+}
+.litegraph .dialog {
+  z-index: 3 !important; /* This is set to 1, but goes under the multi-line inputs, so bump it. */
+}
+
+
+@import '../common/css/buttons.scss';
+@import '../common/css/dialog.scss';
+@import '../common/css/menu.scss';
+
+.rgthree-dialog.-settings {
+  width: 100%;
+}
+.rgthree-dialog.-settings fieldset {
+  border: 1px solid rgba(255, 255, 255, 0.25);
+  padding: 0 12px 8px;
+  margin-bottom: 16px;
+}
+.rgthree-dialog.-settings fieldset > legend {
+  margin-left: 8px;
+  padding: 0 8px;
+  opacity: 0.5;
+}
+.rgthree-dialog.-settings .formrow {
+  display: flex;
+  flex-direction: column;
+}
+.rgthree-dialog.-settings .formrow + .formrow {
+  border-top: 1px solid rgba(255, 255, 255, 0.25);
+}
+.rgthree-dialog.-settings .fieldrow {
+  display: flex;
+  flex-direction: row;
+}
+.rgthree-dialog.-settings .fieldrow > label {
+  flex: 1 1 auto;
+  user-select: none;
+  padding: 8px 12px 12px;
+}
+.rgthree-dialog.-settings .fieldrow > label span {
+  font-weight: bold;
+}
+.rgthree-dialog.-settings .fieldrow > label small {
+  display: block;
+  margin-top: 4px;
+  font-size: calc(11rem / 16);
+  opacity: 0.75;
+  padding-left: 16px;
+}
+.rgthree-dialog.-settings .fieldrow ~ .fieldrow {
+  font-size: 0.9rem;
+  border-top: 1px dotted rgba(255, 255, 255, 0.25);
+}
+.rgthree-dialog.-settings .fieldrow ~ .fieldrow label {
+  padding-left: 28px;
+}
+.rgthree-dialog.-settings .fieldrow:first-child:not(.-checked) ~ .fieldrow {
+  display: none;
+}
+.rgthree-dialog.-settings .fieldrow:hover {
+  background: rgba(255,255,255,0.1);
+}
+.rgthree-dialog.-settings .fieldrow ~ .fieldrow span {
+  font-weight: normal;
+}
+
+.rgthree-dialog.-settings .fieldrow > .fieldrow-value {
+  display: flex;
+  align-items: center;
+  justify-content: end;
+  flex: 0 0 auto;
+  width: 50%;
+  max-width: 230px;
+}
+.rgthree-dialog.-settings .fieldrow.-type-boolean > .fieldrow-value {
+  max-width: 64px;
+}
+.rgthree-dialog.-settings .fieldrow.-type-number input {
+  width: 48px;
+  text-align: right;
+}
+
+.rgthree-dialog.-settings .fieldrow input[type="checkbox"] {
+  width: 24px;
+  height: 24px;
+  cursor: pointer;
+}
+
+.rgthree-dialog.-settings .fieldrow fieldset.rgthree-checklist-group {
+  padding: 0;
+  border: 0;
+  margin: 0;
+
+  > span.rgthree-checklist-item {
+    display: inline-block;
+    white-space: nowrap;
+    padding-right: 6px;
+    vertical-align: middle;
+
+    input[type="checkbox"] {
+      width: 16px;
+      height: 16px;
+    }
+
+    label {
+      padding-left: 4px;
+      text-align: left;
+      cursor: pointer;
+    }
+  }
+}
+
+.rgthree-comfyui-settings-row div {
+  display: flex;
+  flex-direction: row;
+  align-items: center;
+  justify-content: end;
+}
+.rgthree-comfyui-settings-row div svg {
+  width: 36px;
+  height: 36px;
+  margin-right: 16px;
+}
+
+
+.litegraph.litecontextmenu .litemenu-title .rgthree-contextmenu-title-rgthree-comfy,
+.litegraph.litecontextmenu .litemenu-entry.rgthree-contextmenu-item {
+  display: flex;
+  flex-direction: row;
+  align-items: center;
+  justify-content: start;
+}
+
+.litegraph.litecontextmenu .litemenu-title .rgthree-contextmenu-title-rgthree-comfy svg,
+.litegraph.litecontextmenu .litemenu-entry.rgthree-contextmenu-item svg {
+  fill: currentColor;
+  width: auto;
+  height: 16px;
+  margin-right: 6px;
+}
+.litegraph.litecontextmenu .litemenu-entry.rgthree-contextmenu-item svg.github-star {
+  fill: rgb(227, 179, 65);
+}
+
+.litegraph.litecontextmenu .litemenu-title .rgthree-contextmenu-title-rgthree-comfy,
+.litegraph.litecontextmenu .litemenu-entry.rgthree-contextmenu-label {
+  color: #dde;
+  background-color: #212121 !important;
+  margin: 0;
+  padding: 2px;
+  cursor: default;
+  opacity: 1;
+  padding: 4px;
+  font-weight: bold;
+}
+.litegraph.litecontextmenu .litemenu-title .rgthree-contextmenu-title-rgthree-comfy {
+  font-size: 1.1em;
+  color: #fff;
+  background-color: #090909 !important;
+  justify-content: center;
+  padding: 4px 8px;
+}
+
+rgthree-progress-bar {
+  display: block;
+  position: relative;
+  z-index: 999;
+  top: 0;
+  left: 0;
+  height: 14px;
+  font-size: 10px;
+  width: 100%;
+  overflow: hidden;
+  box-shadow: 0px 0px 3px rgba(0, 0, 0, 0.25);
+  box-shadow:
+    inset 0px -1px 0px rgba(0, 0, 0, 0.25),
+    0px 1px 0px rgba(255, 255, 255, 0.125);
+
+}
+
+* ~ rgthree-progress-bar,
+.comfyui-body-bottom rgthree-progress-bar {
+  box-shadow:
+    0px -1px 0px rgba(0, 0, 0, 1),
+    inset 0px 1px 0px rgba(255, 255, 255, 0.15), inset 0px -1px 0px rgba(0, 0, 0, 0.25), 0px 1px 0px rgba(255, 255, 255, 0.125);
+}
+
+body:not([style*=grid]):not([class*=grid]) {
+  rgthree-progress-bar {
+    position: fixed;
+    top: 0px;
+    bottom: auto;
+  }
+  rgthree-progress-bar.rgthree-pos-bottom {
+    top: auto;
+    bottom: 0px;
+  }
+}
+
+
+.rgthree-debug-keydowns {
+  display: block;
+  position: fixed;
+  z-index: 1050;
+  top: 3px;
+  right: 8px;
+  font-size: 10px;
+  color: #fff;
+  font-family: sans-serif;
+  pointer-events: none;
+}
+
+
+.rgthree-comfy-about-badge-logo {
+  width: 20px;
+  height: 20px;
+  background: url(/rgthree/logo.svg?bg=transparent&fg=%2393c5fd);
+  background-size: 100% 100%;
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/rgthree.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/rgthree.ts
new file mode 100644
index 0000000000000000000000000000000000000000..206cb938be881d121361fc1c35581c47367b7a46
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/rgthree.ts
@@ -0,0 +1,1062 @@
+import type {
+  LGraphCanvas as TLGraphCanvas,
+  LGraphNode,
+  IContextMenuValue,
+  LGraph as TLGraph,
+  IContextMenuOptions,
+  ISerialisedGraph,
+  CanvasMouseEvent,
+  CanvasPointerExtensions,
+  NodeId,
+  ISerialisedNode,
+  Positionable,
+  ComfyApp,
+} from "@comfyorg/frontend";
+import type {ComfyApiFormat, ComfyApiPrompt} from "typings/comfy.js";
+
+import {app} from "scripts/app.js";
+import {api} from "scripts/api.js";
+import {SERVICE as CONFIG_SERVICE} from "./services/config_service.js";
+import {SERVICE as BOOKMARKS_SERVICE} from "./services/bookmarks_services.js";
+import {SERVICE as KEY_EVENT_SERVICE} from "./services/key_events_services.js";
+import {WorkflowLinkFixer} from "rgthree/common/link_fixer.js";
+import {injectCss, wait} from "rgthree/common/shared_utils.js";
+import {replaceNode, waitForCanvas, waitForGraph} from "./utils.js";
+import {NodeTypesString, addRgthree, getNodeTypeStrings} from "./constants.js";
+import {RgthreeProgressBar} from "rgthree/common/progress_bar.js";
+import {RgthreeConfigDialog} from "./config.js";
+import {
+  iconGear,
+  iconNode,
+  iconReplace,
+  iconStarFilled,
+  logoRgthree,
+} from "rgthree/common/media/svgs.js";
+import {createElement, queryAll, query} from "rgthree/common/utils_dom.js";
+
+export enum LogLevel {
+  IMPORTANT = 1,
+  ERROR,
+  WARN,
+  INFO,
+  DEBUG,
+  DEV,
+}
+
+const LogLevelKeyToLogLevel: {[key: string]: LogLevel} = {
+  IMPORTANT: LogLevel.IMPORTANT,
+  ERROR: LogLevel.ERROR,
+  WARN: LogLevel.WARN,
+  INFO: LogLevel.INFO,
+  DEBUG: LogLevel.DEBUG,
+  DEV: LogLevel.DEV,
+};
+
+type ConsoleLogFns = "log" | "error" | "warn" | "debug" | "info";
+const LogLevelToMethod: {[key in LogLevel]: ConsoleLogFns} = {
+  [LogLevel.IMPORTANT]: "log",
+  [LogLevel.ERROR]: "error",
+  [LogLevel.WARN]: "warn",
+  [LogLevel.INFO]: "info",
+  [LogLevel.DEBUG]: "log",
+  [LogLevel.DEV]: "log",
+};
+const LogLevelToCSS: {[key in LogLevel]: string} = {
+  [LogLevel.IMPORTANT]: "font-weight: bold; color: blue;",
+  [LogLevel.ERROR]: "",
+  [LogLevel.WARN]: "",
+  [LogLevel.INFO]: "font-style: italic; color: blue;",
+  [LogLevel.DEBUG]: "font-style: italic; color: #444;",
+  [LogLevel.DEV]: "color: #004b68;",
+};
+
+let GLOBAL_LOG_LEVEL = LogLevel.ERROR;
+
+/**
+ * At some point in Summer of 2024 ComfyUI broke third-party api calls by assuming api paths follow
+ * a certain structure. However, rgthree-comfy wants an `/rgthree/` prefix for that same reason, so
+ * we overwrite the apiUrl method to fix.
+ */
+const apiURL = api.apiURL;
+api.apiURL = function (route: string): string {
+  if (route.includes("rgthree/")) {
+    return (this.api_base + "/" + route).replace(/\/\//g, "/");
+  }
+  return apiURL.apply(this, arguments as any);
+};
+
+/**
+ * A blocklist of extensions to disallow hooking into rgthree's base classes when calling the
+ * `rgthree.invokeExtensionsAsync` method (which runs outside of ComfyNode's
+ * `app.invokeExtensionsAsync` which is private).
+ *
+ * In Apr 2024 the base rgthree node class added support for other extensions using `nodeCreated`
+ * and `beforeRegisterNodeDef` which allows other extensions to modify the class. However, since it
+ * had been months since divorcing the ComfyNode in rgthree-comfy due to instability and
+ * inflexibility, this was a bit risky as other extensions hadn't ever run with this ability. This
+ * list attempts to block extensions from being able to call into rgthree-comfy nodes via the
+ * `nodeCreated` and `beforeRegisterNodeDef` callbacks now that rgthree-comfy is utilizing them
+ * because they do not work. Oddly, it's ComfyUI's own extension that is broken.
+ */
+const INVOKE_EXTENSIONS_BLOCKLIST = [
+  {
+    name: "Comfy.WidgetInputs",
+    reason:
+      "Major conflict with rgthree-comfy nodes' inputs causing instability and " +
+      "repeated link disconnections.",
+  },
+  {
+    name: "efficiency.widgethider",
+    reason:
+      "Overrides value getter before widget getter is prepared. Can be lifted if/when " +
+      "https://github.com/jags111/efficiency-nodes-comfyui/pull/203 is pulled.",
+  },
+];
+
+/** A basic wrapper around logger. */
+class Logger {
+  /** Logs a message to the console if it meets the current log level. */
+  log(level: LogLevel, message: string, ...args: any[]) {
+    const [n, v] = this.logParts(level, message, ...args);
+    console[n]?.(...v);
+  }
+
+  /**
+   * Returns a tuple of the console function and its arguments. Useful for callers to make the
+   * actual console.<fn> call to gain benefits of DevTools knowing the source line.
+   *
+   * If the input is invalid or the level doesn't meet the configuration level, then the return
+   * value is an unknown function and empty set of values. Callers can use optionla chaining
+   * successfully:
+   *
+   *     const [fn, values] = logger.logPars(LogLevel.INFO, 'my message');
+   *     console[fn]?.(...values); // Will work even if INFO won't be logged.
+   *
+   */
+  logParts(level: LogLevel, message: string, ...args: any[]): [ConsoleLogFns, any[]] {
+    if (level <= GLOBAL_LOG_LEVEL) {
+      const css = LogLevelToCSS[level] || "";
+      if (level === LogLevel.DEV) {
+        message = `🔧 ${message}`;
+      }
+      return [LogLevelToMethod[level], [`%c${message}`, css, ...args]];
+    }
+    return ["none" as "info", []];
+  }
+}
+
+/**
+ * A log session, with the name as the prefix. A new session will stack prefixes.
+ */
+class LogSession {
+  readonly logger = new Logger();
+  readonly logsCache: {[key: string]: {lastShownTime: number}} = {};
+
+  constructor(readonly name?: string) {}
+
+  /**
+   * Returns the console log method to use and the arguments to pass so the call site can log from
+   * there. This extra work at the call site allows for easier debugging in the dev console.
+   *
+   *     const [logMethod, logArgs] = logger.logParts(LogLevel.DEBUG, message, ...args);
+   *     console[logMethod]?.(...logArgs);
+   */
+  logParts(level: LogLevel, message?: string, ...args: any[]): [ConsoleLogFns, any[]] {
+    message = `${this.name || ""}${message ? " " + message : ""}`;
+    return this.logger.logParts(level, message, ...args);
+  }
+
+  logPartsOnceForTime(
+    level: LogLevel,
+    time: number,
+    message?: string,
+    ...args: any[]
+  ): [ConsoleLogFns, any[]] {
+    message = `${this.name || ""}${message ? " " + message : ""}`;
+    const cacheKey = `${level}:${message}`;
+    const cacheEntry = this.logsCache[cacheKey];
+    const now = +new Date();
+    if (cacheEntry && cacheEntry.lastShownTime + time > now) {
+      return ["none" as "info", []];
+    }
+    const parts = this.logger.logParts(level, message, ...args);
+    if (console[parts[0]]) {
+      this.logsCache[cacheKey] = this.logsCache[cacheKey] || ({} as {lastShownTime: number});
+      this.logsCache[cacheKey]!.lastShownTime = now;
+    }
+    return parts;
+  }
+
+  debugParts(message?: string, ...args: any[]) {
+    return this.logParts(LogLevel.DEBUG, message, ...args);
+  }
+
+  infoParts(message?: string, ...args: any[]) {
+    return this.logParts(LogLevel.INFO, message, ...args);
+  }
+
+  warnParts(message?: string, ...args: any[]) {
+    return this.logParts(LogLevel.WARN, message, ...args);
+  }
+
+  errorParts(message?: string, ...args: any[]) {
+    return this.logParts(LogLevel.ERROR, message, ...args);
+  }
+
+  newSession(name?: string) {
+    return new LogSession(`${this.name}${name}`);
+  }
+}
+
+export type RgthreeUiMessage = {
+  id: string;
+  message: string;
+  type?: "warn" | "info" | "success" | null;
+  timeout?: number;
+  // closeable?: boolean; // TODO
+  actions?: Array<{
+    label: string;
+    href?: string;
+    callback?: (event: MouseEvent) => void;
+  }>;
+};
+
+/**
+ * A global class as 'rgthree'; exposed on wiindow. Lots can go in here.
+ */
+class Rgthree extends EventTarget {
+  /** Exposes the ComfyUI api instance on rgthree. */
+  readonly api = api;
+  private settingsDialog: RgthreeConfigDialog | null = null;
+  private progressBarEl: RgthreeProgressBar | null = null;
+  private rgthreeCssPromise: Promise<void>;
+
+  /** Stores a node id that we will use to queu only that output node (with `queueOutputNode`). */
+  private queueNodeIds: NodeId[] | null = null;
+
+  readonly version = CONFIG_SERVICE.getConfigValue("version");
+
+  logger = new LogSession("[rgthree]");
+
+  monitorBadLinksAlerted = false;
+  monitorLinkTimeout: number | null = null;
+
+  processingQueue = false;
+  /**
+   * The API Json currently being loaded, or null. Can be used as a falsy boolean to determine if
+   * `app.loadApiJson` is currently executing.
+   */
+  loadingApiJson: ComfyApiFormat | null = null;
+  replacingReroute: NodeId | null = null;
+  processingMouseDown = false;
+  processingMouseUp = false;
+  processingMouseMove = false;
+  lastCanvasMouseEvent: CanvasMouseEvent | null = null;
+
+  // Comfy/LiteGraph states so nodes and tell what the hell is going on.
+  canvasCurrentlyCopyingToClipboard = false;
+  canvasCurrentlyCopyingToClipboardWithMultipleNodes = false;
+  canvasCurrentlyPastingFromClipboard = false;
+  canvasCurrentlyPastingFromClipboardWithMultipleNodes = false;
+  initialGraphToPromptSerializedWorkflowBecauseComfyUIBrokeStuff: any = null;
+
+  private readonly isMac: boolean = !!(
+    navigator.platform?.toLocaleUpperCase().startsWith("MAC") ||
+    (navigator as any).userAgentData?.platform?.toLocaleUpperCase().startsWith("MAC")
+  );
+
+  constructor() {
+    super();
+
+    const logLevel =
+      LogLevelKeyToLogLevel[CONFIG_SERVICE.getConfigValue("log_level")] ?? GLOBAL_LOG_LEVEL;
+    this.setLogLevel(logLevel);
+
+    this.initializeGraphAndCanvasHooks();
+    this.initializeComfyUIHooks();
+    this.initializeContextMenu();
+
+    this.rgthreeCssPromise = injectCss("extensions/rgthree-comfy/rgthree.css");
+
+    this.initializeProgressBar();
+
+    CONFIG_SERVICE.addEventListener("config-change", ((e: CustomEvent) => {
+      if (e.detail?.key?.includes("features.progress_bar")) {
+        this.initializeProgressBar();
+      }
+    }) as EventListener);
+
+    if (CONFIG_SERVICE.getConfigValue("debug.keys_down.enabled")) {
+      const elDebugKeydowns = createElement<HTMLDivElement>("div.rgthree-debug-keydowns", {
+        parent: document.body,
+      });
+      const updateDebugKeyDown = () => {
+        elDebugKeydowns.innerText = Object.keys(KEY_EVENT_SERVICE.downKeys).join(" ");
+      };
+      KEY_EVENT_SERVICE.addEventListener("keydown", updateDebugKeyDown);
+      KEY_EVENT_SERVICE.addEventListener("keyup", updateDebugKeyDown);
+    }
+  }
+
+  /**
+   * Initializes the top progress bar, if it's configured.
+   */
+  async initializeProgressBar() {
+    if (CONFIG_SERVICE.getConfigValue("features.progress_bar.enabled")) {
+      await this.rgthreeCssPromise;
+      if (!this.progressBarEl) {
+        this.progressBarEl = RgthreeProgressBar.create();
+        this.progressBarEl.setAttribute(
+          "title",
+          "Progress Bar by rgthree. right-click for rgthree menu.",
+        );
+
+        this.progressBarEl.addEventListener("contextmenu", async (e) => {
+          e.stopPropagation();
+          e.preventDefault();
+        });
+
+        this.progressBarEl.addEventListener("pointerdown", async (e) => {
+          LiteGraph.closeAllContextMenus();
+          if (e.button == 2) {
+            const canvas = await waitForCanvas();
+            new LiteGraph.ContextMenu(this.getRgthreeIContextMenuValues(), {
+              title: `<div class="rgthree-contextmenu-item rgthree-contextmenu-title-rgthree-comfy">${logoRgthree} rgthree-comfy</div>`,
+              left: e.clientX,
+              top: 5,
+            });
+            return;
+          }
+          if (e.button == 0) {
+            const nodeId = this.progressBarEl?.currentNodeId;
+            if (nodeId) {
+              const [canvas, graph] = await Promise.all([waitForCanvas(), waitForGraph()]);
+              const node = graph.getNodeById(Number(nodeId));
+              if (node) {
+                canvas.centerOnNode(node);
+                e.stopPropagation();
+                e.preventDefault();
+              }
+            }
+            return;
+          }
+        });
+      }
+      // Handle both cases in case someone hasn't updated. Can probably just assume
+      // `isUpdatedComfyBodyClasses` is true in the near future.
+      const isUpdatedComfyBodyClasses = !!query(".comfyui-body-top");
+      const position = CONFIG_SERVICE.getConfigValue("features.progress_bar.position");
+      this.progressBarEl.classList.toggle("rgthree-pos-bottom", position === "bottom");
+      // If ComfyUI is updated with the body segments, then use that.
+      if (isUpdatedComfyBodyClasses) {
+        if (position === "bottom") {
+          query(".comfyui-body-bottom")!.appendChild(this.progressBarEl);
+        } else {
+          query(".comfyui-body-top")!.appendChild(this.progressBarEl);
+        }
+      } else {
+        document.body.appendChild(this.progressBarEl);
+      }
+      const height = CONFIG_SERVICE.getConfigValue("features.progress_bar.height") || 14;
+      this.progressBarEl.style.height = `${height}px`;
+      const fontSize = Math.max(10, Number(height) - 10);
+      this.progressBarEl.style.fontSize = `${fontSize}px`;
+      this.progressBarEl.style.fontWeight = fontSize <= 12 ? "bold" : "normal";
+    } else {
+      this.progressBarEl?.remove();
+    }
+  }
+
+  /**
+   * Initialize a bunch of hooks into LiteGraph itself so we can either keep state or context on
+   * what's happening so nodes can respond appropriately. This is usually to fix broken assumptions
+   * in the unowned code [🤮], but sometimes to add features or enhancements too [⭐].
+   */
+  private async initializeGraphAndCanvasHooks() {
+    const rgthree = this;
+
+    // [🤮] To mitigate changes from https://github.com/rgthree/rgthree-comfy/issues/69
+    // and https://github.com/comfyanonymous/ComfyUI/issues/2193 we can try to store the workflow
+    // node so our nodes can find the seralized node. Works with method
+    // `getNodeFromInitialGraphToPromptSerializedWorkflowBecauseComfyUIBrokeStuff` to find a node
+    // while serializing. What a way to work around...
+    const graphSerialize = LGraph.prototype.serialize;
+    LGraph.prototype.serialize = function () {
+      const response = graphSerialize.apply(this, [...arguments] as any) as any;
+      rgthree.initialGraphToPromptSerializedWorkflowBecauseComfyUIBrokeStuff = response;
+      return response;
+    };
+
+    // Overrides LiteGraphs' processMouseDown to both keep state as well as dispatch a custom event.
+    const processMouseDown = LGraphCanvas.prototype.processMouseDown;
+    LGraphCanvas.prototype.processMouseDown = function (e: PointerEvent) {
+      rgthree.processingMouseDown = true;
+      const returnVal = processMouseDown.apply(this, [...arguments] as any);
+      rgthree.dispatchCustomEvent("on-process-mouse-down", {originalEvent: e});
+      rgthree.processingMouseDown = false;
+      return returnVal;
+    };
+
+    // Overrides LiteGraph's `adjustMouseEvent` to capture the last even coming in and out. Useful
+    // to capture the last `canvasX` and `canvasY` properties, which are not the same as LiteGraph's
+    // `canvas.last_mouse_position`, unfortunately.
+    const adjustMouseEvent = LGraphCanvas.prototype.adjustMouseEvent;
+    LGraphCanvas.prototype.adjustMouseEvent = function <T extends MouseEvent>(
+      e: T & Partial<CanvasPointerExtensions>,
+    ): asserts e is T & CanvasMouseEvent {
+      adjustMouseEvent.apply(this, [...arguments] as any);
+      rgthree.lastCanvasMouseEvent = e as CanvasMouseEvent;
+    };
+
+    // [🤮] Copying to clipboard clones nodes and then manipulates the linking data manually which
+    // does not allow a node to handle connections. This harms nodes that manually handle inputs,
+    // like our any-input nodes that may start with one input, and manually add new ones when one is
+    // attached.
+    const copyToClipboard = LGraphCanvas.prototype.copyToClipboard;
+    LGraphCanvas.prototype.copyToClipboard = function (items?: Iterable<Positionable>) {
+      rgthree.canvasCurrentlyCopyingToClipboard = true;
+      rgthree.canvasCurrentlyCopyingToClipboardWithMultipleNodes =
+        Object.values(items || this.selected_nodes || []).length > 1;
+      const value = copyToClipboard.apply(this, [...arguments] as any);
+      rgthree.canvasCurrentlyCopyingToClipboard = false;
+      rgthree.canvasCurrentlyCopyingToClipboardWithMultipleNodes = false;
+      return value;
+    };
+
+    // [🤮] Pasting from clipboard.
+    const pasteFromClipboard = LGraphCanvas.prototype.pasteFromClipboard;
+    LGraphCanvas.prototype.pasteFromClipboard = function (...args: any[]) {
+      rgthree.canvasCurrentlyPastingFromClipboard = true;
+      pasteFromClipboard.apply(this, [...arguments] as any);
+      rgthree.canvasCurrentlyPastingFromClipboard = false;
+    };
+
+    // [⭐] Make it so when we add a group, we get to name it immediately.
+    const onGroupAdd = LGraphCanvas.onGroupAdd;
+    LGraphCanvas.onGroupAdd = function (...args: any[]) {
+      const graph = app.canvas.getCurrentGraph()!;
+      onGroupAdd.apply(this, [...args] as any);
+      // [🤮] Bad typing here.. especially the last arg; it is LGraphNode but can really be anything
+      // with pos or size... pity. See more in our litegraph.d.ts.
+      LGraphCanvas.onShowPropertyEditor(
+        {} as any,
+        null as any,
+        null as any,
+        null as any,
+        graph._groups[graph._groups.length - 1]! as unknown as LGraphNode,
+      );
+    };
+  }
+
+  /**
+   * [🤮] Handles the same exact thing as ComfyApp's `invokeExtensionsAsync`, but done here since
+   * it is #private in ComfyApp because... of course it us. This is necessary since we purposefully
+   * avoid using the ComfyNode due to historical instability and inflexibility for all the advanced
+   * ui stuff rgthree-comfy nodes do, but we can still have other custom nodes know what's happening
+   * with rgthree-comfy; specifically, for `nodeCreated` as of now.
+   */
+  async invokeExtensionsAsync(method: "nodeCreated", ...args: any[]) {
+    const comfyapp = app as ComfyApp;
+    if (CONFIG_SERVICE.getConfigValue("features.invoke_extensions_async.node_created") === false) {
+      const [m, a] = this.logParts(
+        LogLevel.INFO,
+        `Skipping invokeExtensionsAsync for applicable rgthree-comfy nodes`,
+      );
+      console[m]?.(...a);
+      return Promise.resolve();
+    }
+    return await Promise.all(
+      comfyapp.extensions.map(async (ext) => {
+        if (ext?.[method]) {
+          try {
+            const blocked = INVOKE_EXTENSIONS_BLOCKLIST.find((block) =>
+              ext.name.toLowerCase().startsWith(block.name.toLowerCase()),
+            );
+            if (blocked) {
+              const [n, v] = this.logger.logPartsOnceForTime(
+                LogLevel.WARN,
+                5000,
+                `Blocked extension '${ext.name}' method '${method}' for rgthree-nodes because: ${blocked.reason}`,
+              );
+              console[n]?.(...v);
+              return Promise.resolve();
+            }
+            return await (ext[method] as Function)(...args, comfyapp);
+          } catch (error) {
+            const [n, v] = this.logParts(
+              LogLevel.ERROR,
+              `Error calling extension '${ext.name}' method '${method}' for rgthree-node.`,
+              {error},
+              {extension: ext},
+              {args},
+            );
+            console[n]?.(...v);
+          }
+        }
+      }),
+    );
+  }
+
+  /**
+   * Wraps `dispatchEvent` for easier CustomEvent dispatching.
+   */
+  private dispatchCustomEvent(event: string, detail?: any) {
+    if (detail != null) {
+      return this.dispatchEvent(new CustomEvent(event, {detail}));
+    }
+    return this.dispatchEvent(new CustomEvent(event));
+  }
+
+  /**
+   * Initializes hooks specific to an rgthree-comfy context menu on the root menu.
+   */
+  private async initializeContextMenu() {
+    const that = this;
+    setTimeout(async () => {
+      const getCanvasMenuOptions = LGraphCanvas.prototype.getCanvasMenuOptions;
+      LGraphCanvas.prototype.getCanvasMenuOptions = function (...args: any[]) {
+        let existingOptions = getCanvasMenuOptions.apply(this, [...args] as any);
+
+        const options: (IContextMenuValue | null)[] = [];
+        options.push(null); // Divider
+        options.push(null); // Divider
+        options.push(null); // Divider
+        options.push({
+          content: logoRgthree + `rgthree-comfy`,
+          className: "rgthree-contextmenu-item rgthree-contextmenu-main-item-rgthree-comfy",
+          submenu: {
+            options: that.getRgthreeIContextMenuValues(),
+          },
+        });
+        options.push(null); // Divider
+        options.push(null); // Divider
+
+        let idx = null;
+        idx = idx || existingOptions.findIndex((o) => o?.content?.startsWith?.("Queue Group")) + 1;
+        idx =
+          idx || existingOptions.findIndex((o) => o?.content?.startsWith?.("Queue Selected")) + 1;
+        idx = idx || existingOptions.findIndex((o) => o?.content?.startsWith?.("Convert to Group"));
+        idx = idx || existingOptions.findIndex((o) => o?.content?.startsWith?.("Arrange ("));
+        idx = idx || existingOptions.findIndex((o) => !o) + 1;
+        idx = idx || 3;
+        // [🤮] existingOptions is typed as IContextMenuValue<string> even though it need not be
+        // a string due to the crazy typing from the original litegraph. oh well.
+        (existingOptions as (IContextMenuValue | null)[]).splice(idx, 0, ...options);
+        for (let i = existingOptions.length; i > 0; i--) {
+          if (existingOptions[i] === null && existingOptions[i + 1] === null) {
+            existingOptions.splice(i, 1);
+          }
+        }
+
+        return existingOptions;
+      };
+    }, 1016);
+  }
+
+  /**
+   * Returns the standard menu items for an rgthree-comfy context menu.
+   */
+  private getRgthreeIContextMenuValues(): IContextMenuValue[] {
+    const [canvas, graph] = [app.canvas as TLGraphCanvas, app.canvas.getCurrentGraph()!];
+    const selectedNodes = Object.values(canvas.selected_nodes || {});
+    let rerouteNodes: LGraphNode[] = [];
+    if (selectedNodes.length) {
+      rerouteNodes = selectedNodes.filter((n) => n.type === "Reroute");
+    } else {
+      rerouteNodes = graph._nodes.filter((n) => n.type == "Reroute");
+    }
+    const rerouteLabel = selectedNodes.length ? "selected" : "all";
+
+    const showBookmarks = CONFIG_SERVICE.getFeatureValue("menu_bookmarks.enabled");
+    const bookmarkMenuItems = showBookmarks ? getBookmarks() : [];
+
+    return [
+      {
+        content: "Nodes",
+        disabled: true,
+        className: "rgthree-contextmenu-item rgthree-contextmenu-label",
+      },
+      {
+        content: iconNode + "All",
+        className: "rgthree-contextmenu-item",
+        has_submenu: true,
+        submenu: {
+          options: getNodeTypeStrings() as unknown as IContextMenuValue[],
+          callback: (
+            value: string | IContextMenuValue,
+            options: IContextMenuOptions,
+            event: MouseEvent,
+          ) => {
+            const node = LiteGraph.createNode(addRgthree(value as string));
+            if (node) {
+              node.pos = [
+                rgthree.lastCanvasMouseEvent!.canvasX,
+                rgthree.lastCanvasMouseEvent!.canvasY,
+              ];
+              canvas.graph!.add(node);
+              canvas.selectNode(node);
+              graph.setDirtyCanvas(true, true);
+            }
+          },
+          extra: {rgthree_doNotNest: true},
+        },
+      },
+
+      {
+        content: "Actions",
+        disabled: true,
+        className: "rgthree-contextmenu-item rgthree-contextmenu-label",
+      },
+      {
+        content: iconGear + "Settings (rgthree-comfy)",
+        disabled: !!this.settingsDialog,
+        className: "rgthree-contextmenu-item",
+        callback: (...args: any[]) => {
+          this.settingsDialog = new RgthreeConfigDialog().show();
+          this.settingsDialog.addEventListener("close", (e) => {
+            this.settingsDialog = null;
+          });
+        },
+      },
+      {
+        content: iconReplace + ` Convert ${rerouteLabel} Reroutes`,
+        disabled: !rerouteNodes.length,
+        className: "rgthree-contextmenu-item",
+        callback: (...args: any[]) => {
+          const msg =
+            `Convert ${rerouteLabel} ComfyUI Reroutes to Reroute (rgthree) nodes? \n` +
+            `(First save a copy of your workflow & check reroute connections afterwards)`;
+          if (!window.confirm(msg)) {
+            return;
+          }
+          (async () => {
+            for (const node of [...rerouteNodes]) {
+              if (node.type == "Reroute") {
+                this.replacingReroute = node.id;
+                await replaceNode(node, NodeTypesString.REROUTE);
+                this.replacingReroute = null;
+              }
+            }
+          })();
+        },
+      },
+      ...bookmarkMenuItems,
+      {
+        content: "More...",
+        disabled: true,
+        className: "rgthree-contextmenu-item rgthree-contextmenu-label",
+      },
+      {
+        content: iconStarFilled + "Star on Github",
+        className: "rgthree-contextmenu-item rgthree-contextmenu-github",
+        callback: (...args: any[]) => {
+          window.open("https://github.com/rgthree/rgthree-comfy", "_blank");
+        },
+      },
+    ];
+  }
+
+  /**
+   * Wraps an `app.queuePrompt` call setting a specific node id that we will inspect and change the
+   * serialized graph right before being sent (below, in our `api.queuePrompt` override).
+   */
+  async queueOutputNodes(nodes: LGraphNode[]) {
+    // We can use just these next two lines when
+    // https://github.com/ltdrdata/ComfyUI-Inspire-Pack/pull/258 is pulled. Until then, we'll keep
+    // our custom logic as ComfyUI-Inspire-Pack would cause it not to work.
+    // app.canvas.selectItems(nodes);
+    // app.extensionManager.command.execute('Comfy.QueueSelectedOutputNodes');
+    const nodeIds = nodes.map((n) => n.id);
+    try {
+      this.queueNodeIds = nodeIds;
+      await app.queuePrompt(0);
+    } catch (e) {
+      const [n, v] = this.logParts(
+        LogLevel.ERROR,
+        `There was an error queuing nodes ${nodeIds}`,
+        e,
+      );
+      console[n]?.(...v);
+    } finally {
+      this.queueNodeIds = null;
+    }
+  }
+
+  /**
+   * Recusively walks backwards from a node adding its inputs to the `newOutput` from `oldOutput`.
+   */
+  private recursiveAddNodes(nodeId: string, oldOutput: ComfyApiFormat, newOutput: ComfyApiFormat) {
+    let currentId = nodeId;
+    let currentNode = oldOutput[currentId]!;
+    if (newOutput[currentId] == null) {
+      newOutput[currentId] = currentNode;
+      for (const inputValue of Object.values(currentNode.inputs || [])) {
+        if (Array.isArray(inputValue)) {
+          this.recursiveAddNodes(inputValue[0], oldOutput, newOutput);
+        }
+      }
+    }
+    return newOutput;
+  }
+
+  /**
+   * Initialize a bunch of hooks into ComfyUI and/or LiteGraph itself so we can either keep state or
+   * context on what's happening so nodes can respond appropriately. This is usually to fix broken
+   * assumptions in the unowned code [🤮], but sometimes to add features or enhancements too [⭐].
+   */
+  private initializeComfyUIHooks() {
+    const rgthree = this;
+
+    // Keep state for when the app is queuing the prompt. For instance, this is used for seed to
+    // understand if we're serializing because we're queueing (and return the random seed to use) or
+    // for saving the workflow (and keep -1, etc.).
+    const queuePrompt = app.queuePrompt as Function;
+    app.queuePrompt = async function (number: number, batchCount?: number) {
+      rgthree.processingQueue = true;
+      rgthree.dispatchCustomEvent("queue");
+      try {
+        return await queuePrompt.apply(app, [...arguments]);
+      } finally {
+        rgthree.processingQueue = false;
+        rgthree.dispatchCustomEvent("queue-end");
+      }
+    };
+
+    // Keep state for when the app is in the middle of loading from an api JSON file.
+    const loadApiJson = app.loadApiJson;
+    app.loadApiJson = async function (apiData: any, fileName: string) {
+      rgthree.loadingApiJson = apiData as ComfyApiFormat;
+      try {
+        loadApiJson.apply(app, [...arguments] as any);
+      } finally {
+        rgthree.loadingApiJson = null;
+      }
+    };
+
+    // Keep state for when the app is serizalizing the graph to prompt.
+    const graphToPrompt = app.graphToPrompt;
+    app.graphToPrompt = async function () {
+      rgthree.dispatchCustomEvent("graph-to-prompt");
+      let promise = graphToPrompt.apply(app, [...arguments] as any);
+      await promise;
+      rgthree.dispatchCustomEvent("graph-to-prompt-end");
+      return promise;
+    };
+
+    // Override the queuePrompt for api to intercept the prompt output and, if queueNodeIds is set,
+    // then we only want to queue those nodes, by rewriting the api format (prompt 'output' field)
+    // so only those are evaluated.
+    const apiQueuePrompt = api.queuePrompt as Function;
+    api.queuePrompt = async function (index: number, prompt: ComfyApiPrompt, ...args: any[]) {
+      if (rgthree.queueNodeIds?.length && prompt.output) {
+        const oldOutput = prompt.output;
+        let newOutput = {};
+        for (const queueNodeId of rgthree.queueNodeIds) {
+          rgthree.recursiveAddNodes(String(queueNodeId), oldOutput, newOutput);
+        }
+        prompt.output = newOutput;
+      }
+      rgthree.dispatchCustomEvent("comfy-api-queue-prompt-before", {
+        workflow: prompt.workflow,
+        output: prompt.output,
+      });
+      const response = apiQueuePrompt.apply(app, [index, prompt, ...args]);
+      rgthree.dispatchCustomEvent("comfy-api-queue-prompt-end");
+      return response;
+    };
+
+    // Hook into a clean call; allow us to clear and rgthree messages.
+    const clean = app.clean;
+    app.clean = function () {
+      rgthree.clearAllMessages();
+      clean && clean.apply(app, [...arguments] as any);
+    };
+
+    // Hook into a data load, like from an image or JSON drop-in. This is (currently) used to
+    // monitor for bad linking data.
+    const loadGraphData = app.loadGraphData;
+    // NOTE: This was "serializedLGraph" in pre-litegraph types, which maps to `ISerialisedGraph`
+    // now; though, @comfyorg/comfyui-frontend-types have the signature as `ComfyWorkflowJSON` which
+    // is not exported and a zod type. Looks like there's mostly an overlap with ISerialisedGraph.
+    app.loadGraphData = function (graph: ISerialisedGraph) {
+      if (rgthree.monitorLinkTimeout) {
+        clearTimeout(rgthree.monitorLinkTimeout);
+        rgthree.monitorLinkTimeout = null;
+      }
+      rgthree.clearAllMessages();
+      // Try to make a copy to use, because ComfyUI's loadGraphData will modify it.
+      let graphCopy: ISerialisedGraph | null;
+      try {
+        graphCopy = JSON.parse(JSON.stringify(graph));
+      } catch (e) {
+        graphCopy = null;
+      }
+      setTimeout(() => {
+        const wasLoadingAborted = document
+          .querySelector(".comfy-modal-content")
+          ?.textContent?.includes("Loading aborted due");
+        const graphToUse = wasLoadingAborted ? graphCopy || graph : app.graph;
+        const fixer = WorkflowLinkFixer.create(graphToUse as unknown as TLGraph);
+        const fixBadLinksResult = fixer.check();
+        if (fixBadLinksResult.hasBadLinks) {
+          const [n, v] = rgthree.logParts(
+            LogLevel.WARN,
+            `The workflow you've loaded has corrupt linking data. Open ${
+              new URL(location.href).origin
+            }/rgthree/link_fixer to try to fix.`,
+          );
+          console[n]?.(...v);
+          if (CONFIG_SERVICE.getConfigValue("features.show_alerts_for_corrupt_workflows")) {
+            rgthree.showMessage({
+              id: "bad-links",
+              type: "warn",
+              message:
+                "The workflow you've loaded has corrupt linking data that may be able to be fixed.",
+              actions: [
+                {
+                  label: "Open fixer",
+                  href: "/rgthree/link_fixer",
+                },
+                {
+                  label: "Fix in place",
+                  href: "/rgthree/link_fixer",
+                  callback: (event) => {
+                    event.stopPropagation();
+                    event.preventDefault();
+                    if (
+                      confirm(
+                        "This will attempt to fix in place. Please make sure to have a saved copy of your workflow.",
+                      )
+                    ) {
+                      try {
+                        const fixBadLinksResult = fixer.fix();
+                        if (!fixBadLinksResult.hasBadLinks) {
+                          rgthree.hideMessage("bad-links");
+                          alert(
+                            "Success! It's possible some valid links may have been affected. Please check and verify your workflow.",
+                          );
+                          wasLoadingAborted && app.loadGraphData(fixBadLinksResult.graph);
+                          if (
+                            CONFIG_SERVICE.getConfigValue("features.monitor_for_corrupt_links") ||
+                            CONFIG_SERVICE.getConfigValue("features.monitor_bad_links")
+                          ) {
+                            rgthree.monitorLinkTimeout = setTimeout(() => {
+                              rgthree.monitorBadLinks();
+                            }, 5000);
+                          }
+                        }
+                      } catch (e) {
+                        console.error(e);
+                        alert("Unsuccessful at fixing corrupt data. :(");
+                        rgthree.hideMessage("bad-links");
+                      }
+                    }
+                  },
+                },
+              ],
+            });
+          }
+        } else if (
+          CONFIG_SERVICE.getConfigValue("features.monitor_for_corrupt_links") ||
+          CONFIG_SERVICE.getConfigValue("features.monitor_bad_links")
+        ) {
+          rgthree.monitorLinkTimeout = setTimeout(() => {
+            rgthree.monitorBadLinks();
+          }, 5000);
+        }
+      }, 100);
+      return loadGraphData && loadGraphData.apply(app, [...arguments] as any);
+    };
+  }
+
+  /**
+   * [🤮] Finds a node in the currently serializing workflow from the hook setup above. This is to
+   * mitigate breakages from https://github.com/comfyanonymous/ComfyUI/issues/2193 we can try to
+   * store the workflow node so our nodes can find the seralized node.
+   */
+  getNodeFromInitialGraphToPromptSerializedWorkflowBecauseComfyUIBrokeStuff(
+    node: LGraphNode,
+  ): ISerialisedNode | null {
+    return (
+      this.initialGraphToPromptSerializedWorkflowBecauseComfyUIBrokeStuff?.nodes?.find(
+        (n: ISerialisedNode) => n.id === node.id,
+      ) ?? null
+    );
+  }
+
+  /**
+   * Shows a message in the UI.
+   */
+  async showMessage(data: RgthreeUiMessage) {
+    let container = document.querySelector(".rgthree-top-messages-container");
+    if (!container) {
+      container = document.createElement("div");
+      container.classList.add("rgthree-top-messages-container");
+      document.body.appendChild(container);
+    }
+    // If we have a dialog open then we want to append the message to the dialog so they show over
+    // the modal.
+    const dialogs = queryAll<HTMLDialogElement>("dialog[open]");
+    if (dialogs.length) {
+      let dialog = dialogs[dialogs.length - 1]!;
+      dialog.appendChild(container);
+      dialog.addEventListener("close", (e) => {
+        document.body.appendChild(container!);
+      });
+    }
+    // Hide if we exist.
+    await this.hideMessage(data.id);
+
+    const messageContainer = document.createElement("div");
+    messageContainer.setAttribute("type", data.type || "info");
+
+    const message = document.createElement("span");
+    message.innerHTML = data.message;
+    messageContainer.appendChild(message);
+
+    for (let a = 0; a < (data.actions || []).length; a++) {
+      const action = data.actions![a]!;
+      if (a > 0) {
+        const sep = document.createElement("span");
+        sep.innerHTML = "&nbsp;|&nbsp;";
+        messageContainer.appendChild(sep);
+      }
+
+      const actionEl = document.createElement("a");
+      actionEl.innerText = action.label;
+      if (action.href) {
+        actionEl.target = "_blank";
+        actionEl.href = action.href;
+      }
+      if (action.callback) {
+        actionEl.onclick = (e) => {
+          return action.callback!(e);
+        };
+      }
+      messageContainer.appendChild(actionEl);
+    }
+
+    const messageAnimContainer = document.createElement("div");
+    messageAnimContainer.setAttribute("msg-id", data.id);
+    messageAnimContainer.appendChild(messageContainer);
+    container.appendChild(messageAnimContainer);
+
+    // Add. Wait. Measure. Wait. Anim.
+    await wait(64);
+    messageAnimContainer.style.marginTop = `-${messageAnimContainer.offsetHeight}px`;
+    await wait(64);
+    messageAnimContainer.classList.add("-show");
+
+    if (data.timeout) {
+      await wait(data.timeout);
+      this.hideMessage(data.id);
+    }
+  }
+
+  /**
+   * Hides a message in the UI.
+   */
+  async hideMessage(id: string) {
+    const msg = document.querySelector(`.rgthree-top-messages-container > [msg-id="${id}"]`);
+    if (msg?.classList.contains("-show")) {
+      msg.classList.remove("-show");
+      await wait(750);
+    }
+    msg && msg.remove();
+  }
+
+  /**
+   * Clears all messages in the UI.
+   */
+  async clearAllMessages() {
+    let container = document.querySelector(".rgthree-top-messages-container");
+    container && (container.innerHTML = "");
+  }
+
+  setLogLevel(level?: LogLevel | string) {
+    if (typeof level === "string") {
+      level = LogLevelKeyToLogLevel[CONFIG_SERVICE.getConfigValue("log_level")];
+    }
+    if (level != null) {
+      GLOBAL_LOG_LEVEL = level;
+    }
+  }
+
+  logParts(level: LogLevel, message?: string, ...args: any[]) {
+    return this.logger.logParts(level, message, ...args);
+  }
+
+  newLogSession(name?: string) {
+    return this.logger.newSession(name);
+  }
+
+  isDebugMode() {
+    if (window.location.href.includes("rgthree-debug=false")) {
+      return false;
+    }
+    return GLOBAL_LOG_LEVEL >= LogLevel.DEBUG || window.location.href.includes("rgthree-debug");
+  }
+
+  isDevMode() {
+    if (window.location.href.includes("rgthree-dev=false")) {
+      return false;
+    }
+    return GLOBAL_LOG_LEVEL >= LogLevel.DEV || window.location.href.includes("rgthree-dev");
+  }
+
+  monitorBadLinks() {
+    const badLinksFound = WorkflowLinkFixer.create(app.graph).check();
+    if (badLinksFound.hasBadLinks && !this.monitorBadLinksAlerted) {
+      this.monitorBadLinksAlerted = true;
+      alert(
+        `Problematic links just found in live data. Can you save your workflow and file a bug with ` +
+          `the last few steps you took to trigger this at ` +
+          `https://github.com/rgthree/rgthree-comfy/issues. Thank you!`,
+      );
+    } else if (!badLinksFound.hasBadLinks) {
+      // Clear the alert once fixed so we can alert again.
+      this.monitorBadLinksAlerted = false;
+    }
+    this.monitorLinkTimeout = setTimeout(() => {
+      this.monitorBadLinks();
+    }, 5000);
+  }
+}
+
+function getBookmarks(): IContextMenuValue[] {
+  const bookmarks = BOOKMARKS_SERVICE.getCurrentBookmarks();
+  const bookmarkItems = bookmarks.map((n) => ({
+    content: `[${n.shortcutKey}] ${n.title}`,
+    className: "rgthree-contextmenu-item",
+    callback: () => {
+      n.canvasToBookmark();
+    },
+  }));
+
+  return !bookmarkItems.length
+    ? []
+    : [
+        {
+          content: "🔖 Bookmarks",
+          disabled: true,
+          className: "rgthree-contextmenu-item rgthree-contextmenu-label",
+        },
+        ...bookmarkItems,
+      ];
+}
+
+export const rgthree = new Rgthree();
+// Expose it on window because, why not.
+(window as any).rgthree = rgthree;
+
+app.registerExtension({
+  name: "Comfy.RgthreeComfy",
+
+  aboutPageBadges: [
+    {
+      label: `rgthree-comfy v${rgthree.version}`,
+      url: "https://github.com/rgthree/rgthree-comfy",
+      icon: "rgthree-comfy-about-badge-logo",
+    },
+  ],
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/seed.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/seed.ts
new file mode 100644
index 0000000000000000000000000000000000000000..638385bb45846cc270de003c923ff3c206afe48f
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/seed.ts
@@ -0,0 +1,334 @@
+import type {
+  IContextMenuOptions,
+  ContextMenu,
+  LGraphNode as TLGraphNode,
+  IWidget,
+  LGraphCanvas,
+  IContextMenuValue,
+  LGraphNodeConstructor,
+  ISerialisedNode,
+  IButtonWidget,
+} from "@comfyorg/frontend";
+import type {ComfyNodeDef, ComfyApiPrompt} from "typings/comfy.js";
+
+import {app} from "scripts/app.js";
+import {ComfyWidgets} from "scripts/widgets.js";
+import {RgthreeBaseServerNode} from "./base_node.js";
+import {rgthree} from "./rgthree.js";
+import {
+  addConnectionLayoutSupport,
+  getFullNodeIdFromApiPrompt,
+  getNodeByIdFromApiPrompt,
+} from "./utils.js";
+import {NodeTypesString} from "./constants.js";
+import {RgthreeBetterButtonWidget} from "./utils_widgets.js";
+
+const LAST_SEED_BUTTON_LABEL = "♻️ (Use Last Queued Seed)";
+
+const SPECIAL_SEED_RANDOM = -1;
+const SPECIAL_SEED_INCREMENT = -2;
+const SPECIAL_SEED_DECREMENT = -3;
+const SPECIAL_SEEDS = [SPECIAL_SEED_RANDOM, SPECIAL_SEED_INCREMENT, SPECIAL_SEED_DECREMENT];
+
+interface SeedSerializedCtx {
+  inputSeed?: number;
+  seedUsed?: number;
+}
+
+class RgthreeSeed extends RgthreeBaseServerNode {
+  static override title = NodeTypesString.SEED;
+  static override type = NodeTypesString.SEED;
+  static comfyClass = NodeTypesString.SEED;
+
+  override serialize_widgets = true;
+
+  private logger = rgthree.newLogSession(`[Seed]`);
+
+  static override exposedActions = ["Randomize Each Time", "Use Last Queued Seed"];
+
+  static "@randomMax" = {type: "number"};
+  static "@randomMin" = {type: "number"};
+
+  lastSeed?: number = undefined;
+  serializedCtx: SeedSerializedCtx = {};
+  seedWidget!: IWidget;
+
+  // lastSeedButton!: RgthreeBetterButtonWidget;
+  lastSeedButton!: IWidget;
+  lastSeedValue: IWidget | null = null;
+
+  private handleApiHijackingBound = this.handleApiHijacking.bind(this);
+
+  constructor(title = RgthreeSeed.title) {
+    super(title);
+
+    this.properties["randomMax"] = 1125899906842624;
+    // We can have a full range of seeds, including negative. But, for the randomRange we'll
+    // only generate positives, since that's what folks assume.
+    this.properties["randomMin"] = 0;
+
+    rgthree.addEventListener(
+      "comfy-api-queue-prompt-before",
+      this.handleApiHijackingBound as EventListener,
+    );
+
+    console.log("SEED NODE STARTED!");
+  }
+
+  override onPropertyChanged(prop: string, value: unknown, prevValue?: unknown): boolean {
+    if (prop === "randomMax") {
+      this.properties["randomMax"] = Math.min(1125899906842624, Number(value as number));
+    } else if (prop === "randomMin") {
+      this.properties["randomMin"] = Math.max(-1125899906842624, Number(value as number));
+    }
+    return true;
+  }
+
+  override onRemoved() {
+    console.log("SEED NODE onRemoved!");
+    rgthree.removeEventListener(
+      "comfy-api-queue-prompt-before",
+      this.handleApiHijackingBound as EventListener,
+    );
+  }
+
+  override onExecuted(output: any): void {
+    console.log(`SEED ON EXECUTED. #${this.id}.`, output);
+  }
+
+  override configure(info: ISerialisedNode): void {
+    super.configure(info);
+    if (this.properties?.["showLastSeed"]) {
+      this.addLastSeedValue();
+    }
+  }
+
+  override async handleAction(action: string) {
+    if (action === "Randomize Each Time") {
+      this.seedWidget.value = SPECIAL_SEED_RANDOM;
+    } else if (action === "Use Last Queued Seed") {
+      this.seedWidget.value = this.lastSeed != null ? this.lastSeed : this.seedWidget.value;
+      this.lastSeedButton.name = LAST_SEED_BUTTON_LABEL;
+      this.lastSeedButton.disabled = true;
+    }
+  }
+
+  override onNodeCreated() {
+    super.onNodeCreated?.();
+    // Grab the already available widgets, and remove the built-in control_after_generate
+    for (const [i, w] of this.widgets.entries()) {
+      if (w.name === "seed") {
+        this.seedWidget = w; // as ComfyWidget;
+        this.seedWidget.value = SPECIAL_SEED_RANDOM;
+      } else if (w.name === "control_after_generate") {
+        this.widgets.splice(i, 1);
+      }
+    }
+
+    this.addWidget(
+      "button",
+      "🎲 Randomize Each Time",
+      "",
+      () => {
+        this.seedWidget.value = SPECIAL_SEED_RANDOM;
+      },
+      {serialize: false},
+    );
+
+    this.addWidget(
+      "button",
+      "🎲 New Fixed Random",
+      "",
+      () => {
+        this.seedWidget.value = this.generateRandomSeed();
+      },
+      {serialize: false},
+    );
+
+    this.lastSeedButton = this.addWidget(
+      "button",
+      'USE_LAST_SEED',
+      "okay",
+      () => {
+        this.seedWidget.value = this.lastSeed != null ? this.lastSeed : this.seedWidget.value;
+        this.lastSeedButton.label = LAST_SEED_BUTTON_LABEL;
+        this.lastSeedButton.disabled = true;
+      },
+      {width: 50, serialize: false} as any,
+    ) as IButtonWidget;
+
+    // this.lastSeedButton = this.addCustomWidget(
+    //   new RgthreeBetterButtonWidget(
+    //     LAST_SEED_BUTTON_LABEL,
+    //     () => {
+    //       this.seedWidget.value = this.lastSeed != null ? this.lastSeed : this.seedWidget.value;
+    //       this.lastSeedButton.label = LAST_SEED_BUTTON_LABEL;
+    //       this.lastSeedButton.disabled = true;
+    //       return true;
+    //     },
+    //   ),
+    // ) as RgthreeBetterButtonWidget;
+    this.lastSeedButton.label = LAST_SEED_BUTTON_LABEL;
+    this.lastSeedButton.disabled = true;
+  }
+
+  generateRandomSeed() {
+    let step = this.seedWidget.options.step || 1;
+    const randomMin = Number(this.properties["randomMin"] || 0);
+    const randomMax = Number(this.properties["randomMax"] || 1125899906842624);
+    const randomRange = (randomMax - randomMin) / (step / 10);
+    let seed = Math.floor(Math.random() * randomRange) * (step / 10) + randomMin;
+    if (SPECIAL_SEEDS.includes(seed)) {
+      seed = 0;
+    }
+    return seed;
+  }
+
+  override getExtraMenuOptions(canvas: LGraphCanvas, options: IContextMenuValue[]) {
+    super.getExtraMenuOptions?.apply(this, [...arguments] as any);
+    options.splice(options.length - 1, 0, {
+      content: "Show/Hide Last Seed Value",
+      callback: (
+        _value: IContextMenuValue,
+        _options: IContextMenuOptions,
+        _event: MouseEvent,
+        _parentMenu: ContextMenu | undefined,
+        _node: TLGraphNode,
+      ) => {
+        this.properties["showLastSeed"] = !this.properties["showLastSeed"];
+        if (this.properties["showLastSeed"]) {
+          this.addLastSeedValue();
+        } else {
+          this.removeLastSeedValue();
+        }
+      },
+    });
+    return [];
+  }
+
+  addLastSeedValue() {
+    if (this.lastSeedValue) return;
+    this.lastSeedValue = ComfyWidgets["STRING"](
+      this,
+      "last_seed",
+      ["STRING", {multiline: true}],
+      app,
+    ).widget as unknown as IWidget;
+    this.lastSeedValue!.inputEl!.readOnly = true;
+    this.lastSeedValue!.inputEl!.style.fontSize = "0.75rem";
+    this.lastSeedValue!.inputEl!.style.textAlign = "center";
+    this.computeSize();
+  }
+
+  removeLastSeedValue() {
+    if (!this.lastSeedValue) return;
+    this.lastSeedValue!.inputEl!.remove();
+    this.widgets.splice(this.widgets.indexOf(this.lastSeedValue), 1);
+    this.lastSeedValue = null;
+    this.computeSize();
+  }
+
+  /**
+   * Intercepts the prompt right before ComfyUI sends it to the server (as fired from rgthree) so we
+   * can inspect the prompt and workflow data and change swap in the seeds.
+   *
+   * Note, the original implementation tried to change the widget value itself when the graph was
+   * queued (and the relied on ComfyUI serializing the data changed data) and then changing it back.
+   * This worked well until other extensions kept calling graphToPrompt during asynchronous
+   * operations within, causing the widget to get confused without a reliable state to reflect upon.
+   */
+  handleApiHijacking(e: CustomEvent<ComfyApiPrompt>) {
+    // Don't do any work if we're muted/bypassed.
+    if (this.mode === LiteGraph.NEVER || this.mode === 4) {
+      return;
+    }
+
+    const output = e.detail.output;
+    const fullId = getFullNodeIdFromApiPrompt(e.detail, this.id) ?? "";
+    let workflowNode = getNodeByIdFromApiPrompt(e.detail, fullId);
+    let outputInputs = output?.[fullId]?.inputs;
+
+    if (
+      !workflowNode ||
+      !outputInputs ||
+      outputInputs[this.seedWidget.name || "seed"] === undefined
+    ) {
+      const [n, v] = this.logger.warnParts(
+        `Node ${fullId} not found in prompt data sent to server. This may be fine if only ` +
+          `queuing part of the workflow. If not, then this could be a bug.`,
+      );
+      console[n]?.(...v);
+      return;
+    }
+
+    const seedToUse = this.getSeedToUse();
+    const seedWidgetndex = this.widgets.indexOf(this.seedWidget);
+
+    workflowNode.widgets_values![seedWidgetndex] = seedToUse;
+    outputInputs[this.seedWidget.name || "seed"] = seedToUse;
+
+    this.lastSeed = seedToUse;
+    if (seedToUse != this.seedWidget.value) {
+      this.lastSeedButton.label = `♻️ ${this.lastSeed}`;
+      this.lastSeedButton.disabled = false;
+    } else {
+      this.lastSeedButton.label = LAST_SEED_BUTTON_LABEL;
+      this.lastSeedButton.disabled = true;
+    }
+    if (this.lastSeedValue) {
+      this.lastSeedValue.value = `Last Seed: ${this.lastSeed}`;
+    }
+  }
+
+  /**
+   * Determines a seed to use depending on the seed widget's current value and the last used seed.
+   * There are no sideffects to calling this method.
+   */
+  private getSeedToUse() {
+    const inputSeed = Number(this.seedWidget.value);
+    let seedToUse: number | null = null;
+
+    // If our input seed was a special seed, then handle it.
+    if (SPECIAL_SEEDS.includes(inputSeed)) {
+      // If the last seed was not a special seed and we have increment/decrement, then do that on
+      // the last seed.
+      if (typeof this.lastSeed === "number" && !SPECIAL_SEEDS.includes(this.lastSeed)) {
+        if (inputSeed === SPECIAL_SEED_INCREMENT) {
+          seedToUse = this.lastSeed + 1;
+        } else if (inputSeed === SPECIAL_SEED_DECREMENT) {
+          seedToUse = this.lastSeed - 1;
+        }
+      }
+      // If we don't have a seed to use, or it's special seed (like we incremented into one), then
+      // we randomize.
+      if (seedToUse == null || SPECIAL_SEEDS.includes(seedToUse)) {
+        seedToUse = this.generateRandomSeed();
+      }
+    }
+
+    return seedToUse ?? inputSeed;
+  }
+
+  static override setUp(comfyClass: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, RgthreeSeed);
+  }
+
+  static override onRegisteredForOverride(comfyClass: any, ctxClass: any) {
+    addConnectionLayoutSupport(RgthreeSeed, app, [
+      ["Left", "Right"],
+      ["Right", "Left"],
+    ]);
+    setTimeout(() => {
+      RgthreeSeed.category = comfyClass.category;
+    });
+  }
+}
+
+app.registerExtension({
+  name: "rgthree.Seed",
+  async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) {
+    if (nodeData.name === RgthreeSeed.type) {
+      RgthreeSeed.setUp(nodeType, nodeData);
+    }
+  },
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/services/bookmarks_services.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/services/bookmarks_services.ts
new file mode 100644
index 0000000000000000000000000000000000000000..25a34c7aecf12483b1827c8ccb502a4d54741103
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/services/bookmarks_services.ts
@@ -0,0 +1,34 @@
+import type {Bookmark} from "../bookmark.js";
+
+import {app} from "scripts/app.js";
+import {NodeTypesString} from "../constants.js";
+import {reduceNodesDepthFirst} from "../utils.js";
+
+const SHORTCUT_DEFAULTS = "1234567890abcdefghijklmnopqrstuvwxyz".split("");
+
+class BookmarksService {
+  /**
+   * Gets a list of the current bookmarks within the current workflow.
+   */
+  getCurrentBookmarks(): Bookmark[] {
+    return reduceNodesDepthFirst<Bookmark[]>(app.graph.nodes, (n, acc) => {
+      if (n.type === NodeTypesString.BOOKMARK) {
+        acc.push(n as Bookmark);
+      }
+    }, []).sort((a, b) => a.title.localeCompare(b.title));
+  }
+
+  getExistingShortcuts() {
+    const bookmarkNodes = this.getCurrentBookmarks();
+    const usedShortcuts = new Set(bookmarkNodes.map((n) => n.shortcutKey));
+    return usedShortcuts;
+  }
+
+  getNextShortcut() {
+    const existingShortcuts = this.getExistingShortcuts();
+    return SHORTCUT_DEFAULTS.find((char) => !existingShortcuts.has(char)) ?? "1";
+  }
+}
+
+/** The BookmarksService singleton. */
+export const SERVICE = new BookmarksService();
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/services/config_service.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/services/config_service.ts
new file mode 100644
index 0000000000000000000000000000000000000000..4737ab11c9190055b7bfa5a62b3c1268a305733e
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/services/config_service.ts
@@ -0,0 +1,40 @@
+// @ts-ignore
+import {rgthreeConfig} from "rgthree/config.js";
+import {getObjectValue, setObjectValue} from "rgthree/common/shared_utils.js";
+import {rgthreeApi} from "rgthree/common/rgthree_api.js";
+
+/**
+ * A singleton service exported as `SERVICE` to handle configuration routines.
+ */
+class ConfigService extends EventTarget {
+  getConfigValue(key: string, def?: any) {
+    return getObjectValue(rgthreeConfig, key, def);
+  }
+
+  getFeatureValue(key: string, def?: any) {
+    key = "features." + key.replace(/^features\./, "");
+    return getObjectValue(rgthreeConfig, key, def);
+  }
+
+  /**
+   * Given an object of key:value changes it will send to the server and wait for a successful
+   * response before setting the values on the local rgthreeConfig.
+   */
+  async setConfigValues(changed: {[key: string]: any}) {
+    const body = new FormData();
+    body.append("json", JSON.stringify(changed));
+    const response = await rgthreeApi.fetchJson("/config", {method: "POST", body});
+    if (response.status === "ok") {
+      for (const [key, value] of Object.entries(changed)) {
+        setObjectValue(rgthreeConfig, key, value);
+        this.dispatchEvent(new CustomEvent("config-change", {detail: {key, value}}));
+      }
+    } else {
+      return false;
+    }
+    return true;
+  }
+}
+
+/** The ConfigService singleton. */
+export const SERVICE = new ConfigService();
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/services/context_service.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/services/context_service.ts
new file mode 100644
index 0000000000000000000000000000000000000000..3a3cfaff57d724f8bf9965518b149a28c9f89e1c
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/services/context_service.ts
@@ -0,0 +1,74 @@
+import type {DynamicContextNodeBase} from "../dynamic_context_base.js";
+
+import {NodeTypesString} from "../constants.js";
+import {getConnectedOutputNodesAndFilterPassThroughs} from "../utils.js";
+import {INodeInputSlot, INodeOutputSlot, INodeSlot, LGraphNode} from "@comfyorg/frontend";
+
+export let SERVICE: ContextService;
+
+const OWNED_PREFIX = "+";
+const REGEX_PREFIX = /^[\+⚠️]\s*/;
+const REGEX_EMPTY_INPUT = /^\+\s*$/;
+
+export function stripContextInputPrefixes(name: string) {
+  return name.replace(REGEX_PREFIX, "");
+}
+
+export function getContextOutputName(inputName: string) {
+  if (inputName === "base_ctx") return "CONTEXT";
+  return stripContextInputPrefixes(inputName).toUpperCase();
+}
+
+export enum InputMutationOperation {
+  "UNKNOWN",
+  "ADDED",
+  "REMOVED",
+  "RENAMED",
+}
+
+export type InputMutation = {
+  operation: InputMutationOperation;
+  node: DynamicContextNodeBase;
+  slotIndex: number;
+  slot: INodeSlot;
+};
+
+export class ContextService {
+  constructor() {
+    if (SERVICE) {
+      throw new Error("ContextService was already instantiated.");
+    }
+  }
+
+  onInputChanges(node: any, mutation: InputMutation) {
+    const childCtxs = getConnectedOutputNodesAndFilterPassThroughs(
+      node,
+      node,
+      0,
+    ) as DynamicContextNodeBase[];
+    for (const childCtx of childCtxs) {
+      childCtx.handleUpstreamMutation(mutation);
+    }
+  }
+
+  getDynamicContextInputsData(node: DynamicContextNodeBase) {
+    return node
+      .getContextInputsList()
+      .map((input: INodeInputSlot, index: number) => ({
+        name: stripContextInputPrefixes(input.name),
+        type: String(input.type),
+        index,
+      }))
+      .filter((i) => i.type !== "*");
+  }
+
+  getDynamicContextOutputsData(node: LGraphNode) {
+    return node.outputs.map((output: INodeOutputSlot, index: number) => ({
+      name: stripContextInputPrefixes(output.name),
+      type: String(output.type),
+      index,
+    }));
+  }
+}
+
+SERVICE = new ContextService();
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/services/fast_groups_service.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/services/fast_groups_service.ts
new file mode 100644
index 0000000000000000000000000000000000000000..dc765e8f45f1f8b1e5766146833ee6ad874a2581
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/services/fast_groups_service.ts
@@ -0,0 +1,231 @@
+import type {LGraphGroup as TLGraphGroup} from "@comfyorg/frontend";
+import type {BaseFastGroupsModeChanger} from "../fast_groups_muter.js";
+
+import {app} from "scripts/app.js";
+import {getGraphDependantNodeKey, getGroupNodes, reduceNodesDepthFirst} from "../utils.js";
+
+type Vector4 = [number, number, number, number];
+
+
+
+/**
+ * A service that keeps global state that can be shared by multiple FastGroupsMuter or
+ * FastGroupsBypasser nodes rather than calculate it on it's own.
+ */
+class FastGroupsService {
+  private msThreshold = 400;
+  private msLastUnsorted = 0;
+  private msLastAlpha = 0;
+  private msLastPosition = 0;
+
+  private groupsUnsorted: TLGraphGroup[] = [];
+  private groupsSortedAlpha: TLGraphGroup[] = [];
+  private groupsSortedPosition: TLGraphGroup[] = [];
+
+  private readonly fastGroupNodes: BaseFastGroupsModeChanger[] = [];
+
+  private runScheduledForMs: number | null = null;
+  private runScheduleTimeout: number | null = null;
+  private runScheduleAnimation: number | null = null;
+
+  private cachedNodeBoundings: {[key: string]: Vector4} | null = null;
+
+  constructor() {
+    // Don't need to do anything, wait until a signal.
+  }
+
+  addFastGroupNode(node: BaseFastGroupsModeChanger) {
+    this.fastGroupNodes.push(node);
+    // Schedule it because the node may not be ready to refreshWidgets (like, when added it may
+    // not have cloned properties to filter against, etc.).
+    this.scheduleRun(8);
+  }
+
+  removeFastGroupNode(node: BaseFastGroupsModeChanger) {
+    const index = this.fastGroupNodes.indexOf(node);
+    if (index > -1) {
+      this.fastGroupNodes.splice(index, 1);
+    }
+    // If we have no more group nodes, then clear out data; it could be because of a canvas clear.
+    if (!this.fastGroupNodes?.length) {
+      this.clearScheduledRun();
+      this.groupsUnsorted = [];
+      this.groupsSortedAlpha = [];
+      this.groupsSortedPosition = [];
+    }
+  }
+
+  private run() {
+    // We only run if we're scheduled, so if we're not, then bail.
+    if (!this.runScheduledForMs) {
+      return;
+    }
+    for (const node of this.fastGroupNodes) {
+      node.refreshWidgets();
+    }
+    this.clearScheduledRun();
+    this.scheduleRun();
+  }
+
+  private scheduleRun(ms = 500) {
+    // If we got a request for an immediate schedule and already have on scheduled for longer, then
+    // cancel the long one to expediate a fast one.
+    if (this.runScheduledForMs && ms < this.runScheduledForMs) {
+      this.clearScheduledRun();
+    }
+    if (!this.runScheduledForMs && this.fastGroupNodes.length) {
+      this.runScheduledForMs = ms;
+      this.runScheduleTimeout = setTimeout(() => {
+        this.runScheduleAnimation = requestAnimationFrame(() => this.run());
+      }, ms);
+    }
+  }
+
+  private clearScheduledRun() {
+    this.runScheduleTimeout && clearTimeout(this.runScheduleTimeout);
+    this.runScheduleAnimation && cancelAnimationFrame(this.runScheduleAnimation);
+    this.runScheduleTimeout = null;
+    this.runScheduleAnimation = null;
+    this.runScheduledForMs = null;
+  }
+
+  /**
+   * Returns the boundings for all nodes on the graph, then clears it after a short delay. This is
+   * to increase efficiency by caching the nodes' boundings when multiple groups are on the page.
+   */
+  getBoundingsForAllNodes() {
+    if (!this.cachedNodeBoundings) {
+      this.cachedNodeBoundings = reduceNodesDepthFirst(
+        app.graph._nodes,
+        (node, acc) => {
+          let bounds = node.getBounding();
+          // If the bounds are zero'ed out, then we could be a subgraph that hasn't rendered yet and
+          // need to update them.
+          if (bounds[0] === 0 && bounds[1] === 0 && bounds[2] === 0 && bounds[3] === 0) {
+            const ctx = node.graph?.primaryCanvas?.canvas.getContext("2d");
+            if (ctx) {
+              node.updateArea(ctx);
+              bounds = node.getBounding();
+            }
+          }
+          acc[getGraphDependantNodeKey(node)] = bounds as Vector4;
+        },
+        {} as {[key: string]: Vector4},
+      );
+      setTimeout(() => {
+        this.cachedNodeBoundings = null;
+      }, 50);
+    }
+    return this.cachedNodeBoundings;
+  }
+
+  /**
+   * This overrides `TLGraphGroup.prototype.recomputeInsideNodes` to be much more efficient when
+   * calculating for many groups at once (only compute all nodes once in `getBoundingsForAllNodes`).
+   */
+  recomputeInsideNodesForGroup(group: TLGraphGroup) {
+    // If the canvas is currently being dragged (includes if a group is being dragged around) then
+    // don't recompute anything.
+    if (app.canvas.isDragging) return;
+    const cachedBoundings = this.getBoundingsForAllNodes();
+    const nodes = group.graph!.nodes;
+    group._children.clear();
+    group.nodes.length = 0;
+
+    for (const node of nodes) {
+      const nodeBounding = cachedBoundings[getGraphDependantNodeKey(node)];
+      const nodeCenter =
+        nodeBounding &&
+        ([nodeBounding[0] + nodeBounding[2] * 0.5, nodeBounding[1] + nodeBounding[3] * 0.5] as [
+          number,
+          number,
+        ]);
+      if (nodeCenter) {
+        const grouBounds = group._bounding as unknown as [number, number, number, number];
+        if (
+          nodeCenter[0] >= grouBounds[0] &&
+          nodeCenter[0] < grouBounds[0] + grouBounds[2] &&
+          nodeCenter[1] >= grouBounds[1] &&
+          nodeCenter[1] < grouBounds[1] + grouBounds[3]
+        ) {
+          group._children.add(node);
+          group.nodes.push(node);
+        }
+      }
+    }
+  }
+
+  /**
+   * Everything goes through getGroupsUnsorted, so we only get groups once. However, LiteGraph's
+   * `recomputeInsideNodes` is inefficient when calling multiple groups (it iterates over all nodes
+   * each time). So, we'll do our own dang thing, once.
+   */
+  private getGroupsUnsorted(now: number) {
+    const canvas = app.canvas;
+    const graph = canvas.getCurrentGraph() ?? app.graph;
+
+    if (
+      // Don't recalculate nodes if we're moving a group (added by ComfyUI in app.js)
+      // TODO: This doesn't look available anymore... ?
+      !canvas.selected_group_moving &&
+      (!this.groupsUnsorted.length || now - this.msLastUnsorted > this.msThreshold)
+    ) {
+      this.groupsUnsorted = [...graph._groups];
+      const subgraphs = graph.subgraphs?.values();
+      if (subgraphs) {
+        let s;
+        while ((s = subgraphs.next().value)) this.groupsUnsorted.push(...(s.groups ?? []));
+      }
+      for (const group of this.groupsUnsorted) {
+        this.recomputeInsideNodesForGroup(group);
+        group.rgthree_hasAnyActiveNode = getGroupNodes(group).some(
+          (n) => n.mode === LiteGraph.ALWAYS,
+        );
+      }
+      this.msLastUnsorted = now;
+    }
+    return this.groupsUnsorted;
+  }
+
+  private getGroupsAlpha(now: number) {
+    if (!this.groupsSortedAlpha.length || now - this.msLastAlpha > this.msThreshold) {
+      this.groupsSortedAlpha = [...this.getGroupsUnsorted(now)].sort((a, b) => {
+        return a.title.localeCompare(b.title);
+      });
+      this.msLastAlpha = now;
+    }
+    return this.groupsSortedAlpha;
+  }
+
+  private getGroupsPosition(now: number) {
+    if (!this.groupsSortedPosition.length || now - this.msLastPosition > this.msThreshold) {
+      this.groupsSortedPosition = [...this.getGroupsUnsorted(now)].sort((a, b) => {
+        // Sort by y, then x, clamped to 30.
+        const aY = Math.floor(a._pos[1] / 30);
+        const bY = Math.floor(b._pos[1] / 30);
+        if (aY == bY) {
+          const aX = Math.floor(a._pos[0] / 30);
+          const bX = Math.floor(b._pos[0] / 30);
+          return aX - bX;
+        }
+        return aY - bY;
+      });
+      this.msLastPosition = now;
+    }
+    return this.groupsSortedPosition;
+  }
+
+  getGroups(sort?: string) {
+    const now = +new Date();
+    if (sort === "alphanumeric") {
+      return this.getGroupsAlpha(now);
+    }
+    if (sort === "position") {
+      return this.getGroupsPosition(now);
+    }
+    return this.getGroupsUnsorted(now);
+  }
+}
+
+/** The FastGroupsService singleton. */
+export const SERVICE = new FastGroupsService();
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/services/key_events_services.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/services/key_events_services.ts
new file mode 100644
index 0000000000000000000000000000000000000000..6d9a332147e1f10b033b5430f33e8f9b63f9bfcb
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/services/key_events_services.ts
@@ -0,0 +1,184 @@
+/**
+ * A service responsible for capturing keys within LiteGraph's canvas, and outside of it, allowing
+ * nodes and other services to confidently determine what's going on.
+ */
+class KeyEventService extends EventTarget {
+  readonly downKeys: { [key: string]: boolean } = {};
+  readonly shiftDownKeys: { [key: string]: boolean } = {};
+
+  ctrlKey = false;
+  altKey = false;
+  metaKey = false;
+  shiftKey = false;
+
+  private readonly isMac: boolean = !!(
+    navigator.platform?.toLocaleUpperCase().startsWith("MAC") ||
+    (navigator as any).userAgentData?.platform?.toLocaleUpperCase().startsWith("MAC")
+  );
+
+  constructor() {
+    super();
+    this.initialize();
+  }
+
+  initialize() {
+    const that = this;
+    // [🤮] Sometimes ComfyUI and/or LiteGraph stop propagation of key events which makes it hard
+    // to determine if keys are currently pressed. To attempt to get around this, we'll hijack
+    // LiteGraph's processKey to try to get better consistency.
+    const processKey = LGraphCanvas.prototype.processKey;
+    LGraphCanvas.prototype.processKey = function (e: KeyboardEvent) {
+      if (e.type === "keydown" || e.type === "keyup") {
+        that.handleKeyDownOrUp(e);
+      }
+      return processKey.apply(this, [...arguments] as any) as any;
+    };
+
+    // Now that ComfyUI has more non-canvas UI (like the top bar), we listen on window as well, and
+    // de-dupe when we get multiple events from both window and/or LiteGraph.
+    window.addEventListener("keydown", (e) => {
+      that.handleKeyDownOrUp(e);
+    });
+    window.addEventListener("keyup", (e) => {
+      that.handleKeyDownOrUp(e);
+    });
+
+    // If we get a visibilitychange, then clear the keys since we can't listen for keys up/down when
+    // not visible.
+    document.addEventListener("visibilitychange", (e) => {
+      this.clearKeydowns();
+    });
+
+    // If we get a blur, then also clear the keys since we can't listen for keys up/down when
+    // blurred. This can happen w/o a visibilitychange, like a browser alert.
+    window.addEventListener("blur", (e) => {
+      this.clearKeydowns();
+    });
+  }
+
+  /**
+   * Adds a new queue item, unless the last is the same.
+   */
+  handleKeyDownOrUp(e: KeyboardEvent) {
+    const key = e.key.toLocaleUpperCase();
+    // If we're already down, or already up, then ignore and don't fire.
+    if ((e.type === 'keydown' && this.downKeys[key] === true)
+        || (e.type === 'keyup' && this.downKeys[key] === undefined)) {
+      return;
+    }
+
+    this.ctrlKey = !!e.ctrlKey;
+    this.altKey = !!e.altKey;
+    this.metaKey = !!e.metaKey;
+    this.shiftKey = !!e.shiftKey;
+    if (e.type === "keydown") {
+      this.downKeys[key] = true;
+      this.dispatchCustomEvent("keydown", { originalEvent: e });
+
+      // If SHIFT is pressed down as well, then we need to keep track of this separetly to "release"
+      // it once SHIFT is also released.
+      if (this.shiftKey && key !== 'SHIFT') {
+        this.shiftDownKeys[key] = true;
+      }
+    } else if (e.type === "keyup") {
+      // See https://github.com/rgthree/rgthree-comfy/issues/238
+      // A little bit of a hack, but Mac reportedly does something odd with copy/paste. ComfyUI
+      // gobbles the copy event propagation, but it happens for paste too and reportedly 'Enter' which
+      // I can't find a reason for in LiteGraph/comfy. So, for Mac only, whenever we lift a Command
+      // (META) key, we'll also clear any other keys.
+      if (key === "META" && this.isMac) {
+        this.clearKeydowns();
+      } else {
+        delete this.downKeys[key];
+      }
+
+      // If we're releasing the SHIFT key, then we may also be releasing all other keys we pressed
+      // during the SHIFT key as well. We should get an additional keydown for them after.
+      if (key === 'SHIFT') {
+        for (const key in this.shiftDownKeys) {
+          delete this.downKeys[key];
+          delete this.shiftDownKeys[key];
+        }
+      }
+      this.dispatchCustomEvent("keyup", { originalEvent: e });
+    }
+
+  }
+
+  private clearKeydowns() {
+    this.ctrlKey = false;
+    this.altKey = false;
+    this.metaKey = false;
+    this.shiftKey = false;
+    for (const key in this.downKeys) delete this.downKeys[key];
+  }
+
+  /**
+   * Wraps `dispatchEvent` for easier CustomEvent dispatching.
+   */
+  private dispatchCustomEvent(event: string, detail?: any) {
+    if (detail != null) {
+      return this.dispatchEvent(new CustomEvent(event, { detail }));
+    }
+    return this.dispatchEvent(new CustomEvent(event));
+  }
+
+  /**
+   * Parses a shortcut string.
+   *
+   *   - 's' => ['S']
+   *   - 'shift + c' => ['SHIFT', 'C']
+   *   - 'shift + meta + @' => ['SHIFT', 'META', '@']
+   *   - 'shift + + + @' => ['SHIFT', '__PLUS__', '=']
+   *   - '+ + p' => ['__PLUS__', 'P']
+   */
+  private getKeysFromShortcut(shortcut: string | string[]) {
+    let keys;
+    if (typeof shortcut === "string") {
+      // Rip all spaces out. Note, Comfy swallows space, so we don't have to handle it. Otherwise,
+      // we would require space to be fed as "Space" or "Spacebar" instead of " ".
+      shortcut = shortcut.replace(/\s/g, "");
+      // Change a real "+" to something we can encode.
+      shortcut = shortcut.replace(/^\+/, "__PLUS__").replace(/\+\+/, "+__PLUS__");
+      keys = shortcut.split("+").map((i) => i.replace("__PLUS__", "+"));
+    } else {
+      keys = [...shortcut];
+    }
+    return keys.map((k) => k.toLocaleUpperCase());
+  }
+
+  /**
+   * Checks if all keys passed in are down.
+   */
+  areAllKeysDown(keys: string | string[]) {
+    keys = this.getKeysFromShortcut(keys);
+    return keys.every((k) => {
+      return this.downKeys[k];
+    });
+  }
+
+  /**
+   * Checks if only the keys passed in are down; optionally and additionally allowing "shift" key.
+   */
+  areOnlyKeysDown(keys: string | string[], alsoAllowShift = false) {
+    keys = this.getKeysFromShortcut(keys);
+    const allKeysDown = this.areAllKeysDown(keys);
+    const downKeysLength = Object.values(this.downKeys).length;
+    // All keys are down and they're the only ones.
+    if (allKeysDown && keys.length === downKeysLength) {
+      return true;
+    }
+    // Special case allowing the shift key in addition to the shortcut keys. This helps when a user
+    // may had originally defined "$" as a shortcut, but needs to press "shift + $" since it's an
+    // upper key character, etc.
+    if (alsoAllowShift && !keys.includes("SHIFT") && keys.length === downKeysLength - 1) {
+      // If we're holding down shift, have one extra key held down, and the original keys don't
+      // include shift, then we're good to go.
+      return allKeysDown && this.areAllKeysDown(["SHIFT"]);
+    }
+    return false;
+  }
+}
+
+/** The KeyEventService singleton. */
+export const SERVICE = new KeyEventService();
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/testing/comfyui_env.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/testing/comfyui_env.ts
new file mode 100644
index 0000000000000000000000000000000000000000..23d88331bba9f9d7b393b69476b4d85b17d3876d
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/testing/comfyui_env.ts
@@ -0,0 +1,71 @@
+import type {LGraphNode} from "@comfyorg/frontend";
+
+import {app} from "scripts/app.js";
+import {wait} from "rgthree/common/shared_utils.js";
+import {NodeTypesString} from "../constants.js";
+
+type addNodeOptions = {
+  placement?: string;
+};
+
+/**
+ * A testing environment to make setting up, clearing, and queuing more predictable in an
+ * integration test environment.
+ */
+export class ComfyUITestEnvironment {
+  private lastNode: LGraphNode | null = null;
+  private maxY = 0;
+
+  constructor() {}
+
+  wait = wait;
+
+  async addNode(nodeString: string, options: addNodeOptions = {}) {
+    const [canvas, graph] = [app.canvas, app.graph];
+    const node = LiteGraph.createNode(nodeString)!;
+    let x = 0;
+    let y = 30;
+    if (this.lastNode) {
+      const placement = options.placement || "right";
+      if (placement === "under") {
+        x = this.lastNode.pos[0];
+        y = this.lastNode.pos[1] + this.lastNode.size[1] + 30;
+      } else if (placement === "right") {
+        x = this.lastNode.pos[0] + this.lastNode.size[0] + 100;
+        y = this.lastNode.pos[1];
+      } else if (placement === "start") {
+        x = 0;
+        y = this.maxY + 50;
+      }
+    }
+    canvas.graph!.add(node);
+    node.pos = [x, y];
+    canvas.selectNode(node);
+    app.graph.setDirtyCanvas(true, true);
+    await wait();
+    this.lastNode = node;
+    this.maxY = Math.max(this.maxY, y + this.lastNode.size[1]);
+    return (this.lastNode = node);
+  }
+
+  async clear() {
+    app.clean();
+    app.graph.clear();
+    const nodeConfig = await this.addNode(NodeTypesString.KSAMPLER_CONFIG);
+    const displayAny = await this.addNode(NodeTypesString.DISPLAY_ANY);
+    nodeConfig.widgets = nodeConfig.widgets || [];
+    nodeConfig.widgets[0]!.value = Math.round(Math.random() * 100);
+    nodeConfig.connect(0, displayAny, 0);
+    await this.queuePrompt();
+    app.clean();
+    app.graph.clear();
+    this.lastNode = null;
+    this.maxY = 0;
+    await wait();
+  }
+
+  async queuePrompt() {
+    await app.queuePrompt(0);
+    await wait(150);
+  }
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/testing/runner.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/testing/runner.ts
new file mode 100644
index 0000000000000000000000000000000000000000..2cf36d48fc6cf69d37ca6d23b6ed757d67905f1c
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/testing/runner.ts
@@ -0,0 +1,167 @@
+/**
+ * @fileoverview A set of methods that mimic a bit of the Jasmine testing library, but simpler and
+ * more succinct for manipulating a comfy integration test.
+ *
+ * Tests are not bundled by default, to test build with "--with-tests" and then invoke from the
+ * dev console like `rgthree_tests.TestDescribeLabel()`. The output is in the test itself.
+ */
+import { wait } from "rgthree/common/shared_utils.js";
+
+declare global {
+  interface Window {
+    rgthree_tests: {
+      [key: string]: any;
+    };
+  }
+}
+
+
+window.rgthree_tests = window.rgthree_tests || {};
+
+type TestContext = {
+  label?: string;
+  beforeEach?: Function[];
+};
+
+let contexts: TestContext[] = [];
+
+export function describe(label: string, fn: Function) {
+  if (!label.startsWith('Test')) {
+    throw new Error('Test labels should start with "Test"');
+  }
+   window.rgthree_tests[label] = async () => {
+    await describeRun(label, fn);
+  };
+  return window.rgthree_tests[label];
+}
+
+export async function describeRun(label: string, fn: Function) {
+  await wait();
+  contexts.push({ label });
+  console.group(`[Start] ${contexts[contexts.length - 1]!.label}`);
+  await fn();
+  contexts.pop();
+  console.groupEnd();
+}
+
+export async function should(declaration: string, fn: Function) {
+  if (!contexts[contexts.length - 1]) {
+    throw Error("Called should outside of a describe.");
+  }
+  console.group(`...should ${declaration}`);
+  try {
+    for (const context of contexts) {
+      for (const beforeEachFn of context?.beforeEach || []) {
+        await beforeEachFn();
+      }
+    }
+    await fn();
+  } catch (e: any) {
+    fail(e);
+  }
+  console.groupEnd();
+}
+
+export async function beforeEach(fn: Function) {
+  if (!contexts[contexts.length - 1]) {
+    throw Error("Called beforeEach outside of a describe.");
+  }
+  const last = contexts[contexts.length - 1]!;
+  last.beforeEach = last?.beforeEach || [];
+  last.beforeEach.push(fn);
+}
+
+export function fail(e: Error) {
+  log(`X Failure: ${e}`, "color:#600; background:#fdd; padding: 2px 6px;");
+}
+
+function log(msg: string, styles: string) {
+  if (styles) {
+    console.log(`%c ${msg}`, styles);
+  } else {
+    console.log(msg);
+  }
+}
+
+class Expectation {
+  private propertyLabel: string | null = "";
+  private expectedLabel: string | null = "";
+  private verbLabel: string | null = "be";
+  private expectedFn!: (v: any) => boolean;
+  private value: any;
+
+  constructor(value: any) {
+    this.value = value;
+  }
+
+  toBe(labelOrExpected: any, maybeExpected?: any) {
+    const expected = maybeExpected !== undefined ? maybeExpected : labelOrExpected;
+    this.propertyLabel = maybeExpected !== undefined ? labelOrExpected : null;
+    this.expectedLabel = JSON.stringify(expected);
+    this.expectedFn = (v) => v == expected;
+    return this.toBeEval();
+  }
+  toMatchJson(labelOrExpected: any, maybeExpected?: any) {
+    const expected = maybeExpected !== undefined ? maybeExpected : labelOrExpected;
+    this.propertyLabel = maybeExpected !== undefined ? labelOrExpected : null;
+    this.expectedLabel = JSON.stringify(expected);
+    this.expectedFn = (v) => JSON.stringify(JSON.parse(v)) == JSON.stringify(JSON.parse(expected));
+    return this.toBeEval();
+  }
+  toBeUndefined(propertyLabel: string) {
+    this.expectedFn = (v) => v === undefined;
+    this.propertyLabel = propertyLabel || "";
+    this.expectedLabel = "undefined";
+    return this.toBeEval(true);
+  }
+  toBeNullOrUndefined(propertyLabel: string) {
+    this.expectedFn = (v) => v == null;
+    this.propertyLabel = propertyLabel || "";
+    this.expectedLabel = "null or undefined";
+    return this.toBeEval(true);
+  }
+  toBeTruthy(propertyLabel: string) {
+    this.expectedFn = (v) => !v;
+    this.propertyLabel = propertyLabel || "";
+    this.expectedLabel = "truthy";
+    return this.toBeEval(false);
+  }
+  toBeANumber(propertyLabel: string) {
+    this.expectedFn = (v) => typeof v === "number";
+    this.propertyLabel = propertyLabel || "";
+    this.expectedLabel = "a number";
+    return this.toBeEval();
+  }
+  toContain(labelOrExpected: any, maybeExpected?: any) {
+    const expected = maybeExpected !== undefined ? maybeExpected : labelOrExpected;
+    this.propertyLabel = maybeExpected !== undefined ? labelOrExpected : null;
+    this.verbLabel = 'contain';
+    this.expectedLabel = JSON.stringify(expected);
+    this.expectedFn = (v) => v.includes(expected);
+    return this.toBeEval();
+  }
+  toBeEval(strict = false) {
+    let evaluation = this.expectedFn(this.value);
+    let msg = `Expected ${this.propertyLabel ? this.propertyLabel + ` to ${this.verbLabel} ` : ""}${
+      this.expectedLabel
+    }`;
+    msg += evaluation ? "." : `, but was ${JSON.stringify(this.value)}`;
+    this.log(evaluation, msg);
+    return evaluation;
+  }
+  log(value: boolean, msg: string) {
+    if (value) {
+      log(`🗸 ${msg}`, "color:#060; background:#cec; padding: 2px 6px;");
+    } else {
+      log(`X ${msg}`, "color:#600; background:#fdd; padding: 2px 6px;");
+    }
+  }
+}
+
+export function expect(value: any, msg?: string) {
+  const expectation = new Expectation(value);
+  if (msg) {
+    expectation.log(value, msg);
+  }
+  return expectation;
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/testing/utils_test.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/testing/utils_test.ts
new file mode 100644
index 0000000000000000000000000000000000000000..9c805f2cf79283fcce377d7505a1ef578503c903
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/testing/utils_test.ts
@@ -0,0 +1,41 @@
+import type {LGraphNode} from "@comfyorg/frontend";
+import type {ComfyUITestEnvironment} from "./comfyui_env";
+
+export const PNG_1x1 =
+  "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVQIW2P4v5ThPwAG7wKklwQ/bwAAAABJRU5ErkJggg==";
+export const PNG_1x2 =
+  "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAACCAYAAACZgbYnAAAAEElEQVQIW2NgYGD4D8QM/wEHAwH/OMSHKAAAAABJRU5ErkJggg==";
+export const PNG_2x1 =
+  "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAIAAAABCAYAAAD0In+KAAAAD0lEQVQIW2NkYGD4D8QMAAUNAQFqjhCLAAAAAElFTkSuQmCC";
+
+export async function pasteImageToLoadImageNode(
+  env: ComfyUITestEnvironment,
+  dataUrl?: string,
+  node?: LGraphNode,
+) : Promise<LGraphNode> {
+  const dataArr = (dataUrl ?? PNG_1x1).split(",");
+  const mime = dataArr[0]!.match(/:(.*?);/)![1];
+  const bstr = atob(dataArr[1]!);
+  let n = bstr.length;
+  const u8arr = new Uint8Array(n);
+  while (n--) {
+    u8arr[n] = bstr.charCodeAt(n);
+  }
+  const filename = `test_image_${+new Date()}.png`;
+  const file = new File([u8arr], filename, {type: mime});
+  if (!node) {
+    node = await env.addNode("LoadImage");
+  }
+  await (node as any).pasteFiles([file]);
+  let i = 0;
+  let good = false;
+  while (i++ < 10 || good) {
+    good = node.widgets![0]!.value === filename;
+    if (good) break;
+    await env.wait(100);
+  }
+  if (!good) {
+    throw new Error("Expected file not loaded.");
+  }
+  return node;
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/tests/context_dynamic_tests.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/tests/context_dynamic_tests.ts
new file mode 100644
index 0000000000000000000000000000000000000000..45c2b2c82c85fa073208ee84687c6c3057d4a3a7
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/tests/context_dynamic_tests.ts
@@ -0,0 +1,182 @@
+import type {LGraphNode} from "@comfyorg/frontend";
+
+import {NodeTypesString} from "../constants.js";
+import {wait} from "rgthree/common/shared_utils.js";
+import {describe, should, beforeEach, expect, describeRun} from "../testing/runner.js";
+import {ComfyUITestEnvironment} from "../testing/comfyui_env.js";
+
+const env = new ComfyUITestEnvironment();
+
+function verifyInputAndOutputName(
+  node: LGraphNode,
+  index: number,
+  inputName: string | null,
+  isLinked?: boolean,
+) {
+  if (inputName != null) {
+    expect(node.inputs[index]!.name).toBe(`input ${index} name`, inputName);
+  }
+  if (isLinked) {
+    expect(node.inputs[index]!.link).toBeANumber(`input ${index} connection`);
+  } else if (isLinked === false) {
+    expect(node.inputs[index]!.link).toBeNullOrUndefined(`input ${index} connection`);
+  }
+  if (inputName != null) {
+    if (inputName === "+") {
+      expect(node.outputs[index]).toBeUndefined(`output ${index}`);
+    } else {
+      let outputName =
+        inputName === "base_ctx" ? "CONTEXT" : inputName.replace(/^\+\s/, "").toUpperCase();
+      expect(node.outputs[index]!.name).toBe(`output ${index} name`, outputName);
+    }
+  }
+}
+
+function vertifyInputsStructure(node: LGraphNode, expectedLength: number) {
+  expect(node.inputs.length).toBe("inputs length", expectedLength);
+  expect(node.outputs.length).toBe("outputs length", expectedLength - 1);
+  verifyInputAndOutputName(node, expectedLength - 1, "+", false);
+}
+
+describe("TestContextDynamic", async () => {
+  let nodeConfig!: LGraphNode;
+  let nodeCtx!: LGraphNode;
+
+  let lastNode: LGraphNode | null = null;
+
+  await beforeEach(async () => {
+    await env.clear();
+    lastNode = nodeConfig = await env.addNode(NodeTypesString.KSAMPLER_CONFIG);
+    lastNode = nodeCtx = await env.addNode(NodeTypesString.DYNAMIC_CONTEXT);
+    nodeConfig.connect(0, nodeCtx, 1); // steps
+    nodeConfig.connect(2, nodeCtx, 2); // cfg
+    nodeConfig.connect(4, nodeCtx, 3); // scheduler
+    nodeConfig.connect(0, nodeCtx, 4); // This is the step.1
+    nodeConfig.connect(0, nodeCtx, 5); // This is the step.2
+    nodeCtx.disconnectInput(2);
+    nodeCtx.disconnectInput(5);
+    nodeConfig.connect(0, nodeCtx, 6); // This is the step.3
+    nodeCtx.disconnectInput(6);
+    await wait();
+  });
+
+  await should("add correct inputs", async () => {
+    vertifyInputsStructure(nodeCtx, 8);
+    let i = 0;
+    verifyInputAndOutputName(nodeCtx, i++, "base_ctx", false);
+    verifyInputAndOutputName(nodeCtx, i++, "+ steps", true);
+    verifyInputAndOutputName(nodeCtx, i++, "+ cfg", false);
+    verifyInputAndOutputName(nodeCtx, i++, "+ scheduler", true);
+    verifyInputAndOutputName(nodeCtx, i++, "+ steps.1", true);
+    verifyInputAndOutputName(nodeCtx, i++, "+ steps.2", false);
+    verifyInputAndOutputName(nodeCtx, i++, "+ steps.3", false);
+  });
+
+  await should("add evaluate correct outputs", async () => {
+    const displayAny1 = await env.addNode(NodeTypesString.DISPLAY_ANY, {placement: "right"});
+    const displayAny2 = await env.addNode(NodeTypesString.DISPLAY_ANY, {placement: "under"});
+    const displayAny3 = await env.addNode(NodeTypesString.DISPLAY_ANY, {placement: "under"});
+    const displayAny4 = await env.addNode(NodeTypesString.DISPLAY_ANY, {placement: "under"});
+
+    nodeCtx.connect(1, displayAny1, 0); // steps
+    nodeCtx.connect(3, displayAny2, 0); // scheduler
+    nodeCtx.connect(4, displayAny3, 0); // steps.1
+    nodeCtx.connect(6, displayAny4, 0); // steps.3 (unlinked)
+
+    await env.queuePrompt();
+
+    expect(displayAny1.widgets![0]!.value).toBe("output 1", 30);
+    expect(displayAny2.widgets![0]!.value).toBe("output 3", '"normal"');
+    expect(displayAny3.widgets![0]!.value).toBe("output 4", 30);
+    expect(displayAny4.widgets![0]!.value).toBe("output 6", "None");
+  });
+
+  await describeRun("Nested", async () => {
+    let nodeConfig2!: LGraphNode;
+    let nodeCtx2!: LGraphNode;
+
+    await beforeEach(async () => {
+      nodeConfig2 = await env.addNode(NodeTypesString.KSAMPLER_CONFIG, {placement: "start"});
+      nodeConfig2.widgets = nodeConfig2.widgets || [];
+      nodeConfig2.widgets[0]!.value = 111;
+      nodeConfig2.widgets[2]!.value = 11.1;
+      nodeCtx2 = await env.addNode(NodeTypesString.DYNAMIC_CONTEXT, {placement: "right"});
+      nodeConfig2.connect(0, nodeCtx2, 1); // steps
+      nodeConfig2.connect(2, nodeCtx2, 2); // cfg
+      nodeConfig2.connect(3, nodeCtx2, 3); // sampler
+      nodeConfig2.connect(2, nodeCtx2, 4); // This is the cfg.1
+      nodeConfig2.connect(0, nodeCtx2, 5); // This is the steps.1
+      nodeCtx2.disconnectInput(2);
+      nodeCtx2.disconnectInput(5);
+      nodeConfig2.connect(2, nodeCtx2, 6); // This is the cfg.2
+      nodeCtx2.disconnectInput(6);
+
+      await wait();
+    });
+
+    await should("disallow context node to be connected to non-first spot.", async () => {
+      // Connect to first node.
+      let expectedInputs = 8;
+
+      nodeCtx2.connect(0, nodeCtx, expectedInputs - 1);
+      console.log(nodeCtx.inputs);
+
+      vertifyInputsStructure(nodeCtx, expectedInputs);
+      verifyInputAndOutputName(nodeCtx, 0, "base_ctx", false);
+      verifyInputAndOutputName(nodeCtx, nodeCtx.inputs.length - 1, null, false);
+
+      nodeCtx2.connect(0, nodeCtx, 0);
+      expectedInputs = 14;
+      vertifyInputsStructure(nodeCtx, expectedInputs);
+      verifyInputAndOutputName(nodeCtx, 0, "base_ctx", true);
+      verifyInputAndOutputName(nodeCtx, expectedInputs - 1, null, false);
+    });
+
+    await should("add inputs from connected above owned.", async () => {
+      // Connect to first node.
+      nodeCtx2.connect(0, nodeCtx, 0);
+
+      let expectedInputs = 14;
+      vertifyInputsStructure(nodeCtx, expectedInputs);
+      let i = 0;
+      verifyInputAndOutputName(nodeCtx, i++, "base_ctx", true);
+      verifyInputAndOutputName(nodeCtx, i++, "steps", false);
+      verifyInputAndOutputName(nodeCtx, i++, "cfg", false);
+      verifyInputAndOutputName(nodeCtx, i++, "sampler", false);
+      verifyInputAndOutputName(nodeCtx, i++, "cfg.1", false);
+      verifyInputAndOutputName(nodeCtx, i++, "steps.1", false);
+      verifyInputAndOutputName(nodeCtx, i++, "cfg.2", false);
+      verifyInputAndOutputName(nodeCtx, i++, "+ steps.2", true);
+      verifyInputAndOutputName(nodeCtx, i++, "+ cfg.3", false);
+      verifyInputAndOutputName(nodeCtx, i++, "+ scheduler", true);
+      verifyInputAndOutputName(nodeCtx, i++, "+ steps.3", true);
+      verifyInputAndOutputName(nodeCtx, i++, "+ steps.4", false);
+      verifyInputAndOutputName(nodeCtx, i++, "+ steps.5", false);
+      verifyInputAndOutputName(nodeCtx, i++, "+", false);
+    });
+
+    await should("add then remove inputs when disconnected.", async () => {
+      // Connect to first node.
+      nodeCtx2.connect(0, nodeCtx, 0);
+
+      let expectedInputs = 14;
+      expect(nodeCtx.inputs.length).toBe("inputs length", expectedInputs);
+      expect(nodeCtx.outputs.length).toBe("outputs length", expectedInputs - 1);
+
+      nodeCtx.disconnectInput(0);
+
+      expectedInputs = 8;
+      expect(nodeCtx.inputs.length).toBe("inputs length", expectedInputs);
+      expect(nodeCtx.outputs.length).toBe("outputs length", expectedInputs - 1);
+      let i = 0;
+      verifyInputAndOutputName(nodeCtx, i++, "base_ctx", false);
+      verifyInputAndOutputName(nodeCtx, i++, "+ steps", true);
+      verifyInputAndOutputName(nodeCtx, i++, "+ cfg", false);
+      verifyInputAndOutputName(nodeCtx, i++, "+ scheduler", true);
+      verifyInputAndOutputName(nodeCtx, i++, "+ steps.1", true);
+      verifyInputAndOutputName(nodeCtx, i++, "+ steps.2", false);
+      verifyInputAndOutputName(nodeCtx, i++, "+ steps.3", false);
+      verifyInputAndOutputName(nodeCtx, i++, "+", false);
+    });
+  });
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/tests/image_or_latent_size_tests.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/tests/image_or_latent_size_tests.ts
new file mode 100644
index 0000000000000000000000000000000000000000..0cb932bc66afcf23b9b8d8ed17fd4098c5c1f887
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/tests/image_or_latent_size_tests.ts
@@ -0,0 +1,103 @@
+import type {LGraphNode} from "@comfyorg/frontend";
+
+import {NodeTypesString} from "../constants";
+import {ComfyUITestEnvironment} from "../testing/comfyui_env";
+import {describe, should, beforeEach, expect, describeRun} from "../testing/runner.js";
+import {pasteImageToLoadImageNode, PNG_1x1, PNG_1x2, PNG_2x1} from "../testing/utils_test.js";
+
+const env = new ComfyUITestEnvironment();
+
+describe("TestImageOrLatentSize", async () => {
+  await beforeEach(async () => {
+    await env.clear();
+  });
+
+  await describeRun("LoadImage", async () => {
+    let imageNode: LGraphNode;
+    let displayAnyW: LGraphNode;
+    let displayAnyH: LGraphNode;
+
+    await beforeEach(async () => {
+      await env.clear();
+      imageNode = await env.addNode("LoadImage");
+      const sizeNode = await env.addNode(NodeTypesString.IMAGE_OR_LATENT_SIZE);
+      displayAnyW = await env.addNode(NodeTypesString.DISPLAY_ANY);
+      displayAnyH = await env.addNode(NodeTypesString.DISPLAY_ANY);
+      imageNode.connect(0, sizeNode, 0);
+      sizeNode.connect(0, displayAnyW, 0);
+      sizeNode.connect(1, displayAnyH, 0);
+      await env.wait();
+    });
+
+    await should("get correct size for a 1x1 image", async () => {
+      await pasteImageToLoadImageNode(env, PNG_1x1, imageNode);
+      await env.queuePrompt();
+      expect(displayAnyW.widgets![0]!.value).toBe("width", 1);
+      expect(displayAnyH.widgets![0]!.value).toBe("height", 1);
+    });
+
+    await should("get correct size for a 1x2 image", async () => {
+      await pasteImageToLoadImageNode(env, PNG_1x2, imageNode);
+      await env.queuePrompt();
+      expect(displayAnyW.widgets![0]!.value).toBe("width", 1);
+      expect(displayAnyH.widgets![0]!.value).toBe("height", 2);
+    });
+
+    await should("get correct size for a 2x1 image", async () => {
+      await pasteImageToLoadImageNode(env, PNG_2x1, imageNode);
+      await env.queuePrompt();
+      expect(displayAnyW.widgets![0]!.value).toBe("width", 2);
+      expect(displayAnyH.widgets![0]!.value).toBe("height", 1);
+    });
+  });
+
+  await describeRun("Latent", async () => {
+    let latentNode: LGraphNode;
+    let displayAnyW: LGraphNode;
+    let displayAnyH: LGraphNode;
+
+    await beforeEach(async () => {
+      await env.clear();
+      latentNode = await env.addNode("EmptyLatentImage");
+      const sizeNode = await env.addNode(NodeTypesString.IMAGE_OR_LATENT_SIZE);
+      displayAnyW = await env.addNode(NodeTypesString.DISPLAY_ANY);
+      displayAnyH = await env.addNode(NodeTypesString.DISPLAY_ANY);
+      latentNode.connect(0, sizeNode, 0);
+      sizeNode.connect(0, displayAnyW, 0);
+      sizeNode.connect(1, displayAnyH, 0);
+      await env.wait();
+      latentNode.widgets![0]!.value = 16; // Width
+      latentNode.widgets![1]!.value = 16; // Height
+      latentNode.widgets![2]!.value = 1; // Batch
+      await env.wait();
+    });
+
+    await should("get correct size for a 16x16 latent", async () => {
+      await env.queuePrompt();
+      expect(displayAnyW.widgets![0]!.value).toBe("width", 16);
+      expect(displayAnyH.widgets![0]!.value).toBe("height", 16);
+    });
+
+    await should("get correct size for a 16x32 latent", async () => {
+      latentNode.widgets![1]!.value = 32;
+      await env.queuePrompt();
+      expect(displayAnyW.widgets![0]!.value).toBe("width", 16);
+      expect(displayAnyH.widgets![0]!.value).toBe("height", 32);
+    });
+
+    await should("get correct size for a 32x16 image", async () => {
+      latentNode.widgets![0]!.value = 32;
+      await env.queuePrompt();
+      expect(displayAnyW.widgets![0]!.value).toBe("width", 32);
+      expect(displayAnyH.widgets![0]!.value).toBe("height", 16);
+    });
+
+    await should("get correct size with a batch", async () => {
+      latentNode.widgets![0]!.value = 32;
+      latentNode.widgets![2]!.value = 2;
+      await env.queuePrompt();
+      expect(displayAnyW.widgets![0]!.value).toBe("width", 32);
+      expect(displayAnyH.widgets![0]!.value).toBe("height", 16);
+    });
+  });
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/tests/power_puter.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/tests/power_puter.ts
new file mode 100644
index 0000000000000000000000000000000000000000..4ed575990f65282e44a63a3c10d8342d79781562
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/tests/power_puter.ts
@@ -0,0 +1,162 @@
+import type {LGraphNode} from "@comfyorg/frontend";
+
+import {NodeTypesString} from "../constants";
+import {ComfyUITestEnvironment} from "../testing/comfyui_env";
+import {describe, should, beforeEach, expect, describeRun} from "../testing/runner.js";
+import {pasteImageToLoadImageNode, PNG_1x1, PNG_1x2, PNG_2x1} from "../testing/utils_test.js";
+
+const env = new ComfyUITestEnvironment();
+
+function setPowerPuterValue(node: LGraphNode, outputType: string, value: string) {
+  // Strip as much whitespace on first non-empty line from all lines.
+  if (value.includes("\n")) {
+    value = value.replace(/^\n/gm, "");
+    const strip = value.match(/^(.*?)\S/)?.[1]?.length;
+    if (strip) {
+      value = value.replace(new RegExp(`^.{${strip}}`, "mg"), "");
+    }
+  }
+  node.widgets![1]!.value = value;
+  node.widgets![0]!.value = outputType;
+}
+
+describe("TestPowerPuter", async () => {
+  let powerPuter!: LGraphNode;
+  let displayAny!: LGraphNode;
+
+  await beforeEach(async () => {
+    await env.clear();
+    powerPuter = await env.addNode(NodeTypesString.POWER_PUTER);
+    displayAny = await env.addNode(NodeTypesString.DISPLAY_ANY);
+    powerPuter.connect(0, displayAny, 0);
+    await env.wait();
+  });
+
+  await should("output constants and concatenation", async () => {
+    const checks: Array<[string, string, string]> = [
+      ["1", "1", "STRING"],
+      ['"abc"', "abc", "STRING"],
+      ["1 + 2", "3", "STRING"],
+      ['"abc" + "xyz"', "abcxyz", "STRING"],
+      // INT
+      ["1", "1", "INT"],
+      ["1 + 2", "3", "INT"],
+      // FLOAT
+      ["1", "1.0", "FLOAT"],
+      ["1.3 + 2.8", "4.1", "FLOAT"],
+      // BOOLEAN
+      ["1", "True", "BOOLEAN"],
+      ["1 - 1", "False", "BOOLEAN"],
+    ];
+    for (const data of checks) {
+      setPowerPuterValue(powerPuter, data[2], data[0]);
+      await env.queuePrompt();
+      expect(displayAny.widgets![0]!.value).toBe(data[0], data[1]);
+    }
+  });
+
+  await should("handle inputs", async () => {
+    // TODO
+  });
+
+  await should("handle complex inputs", async () => {
+    // TODO
+  });
+
+  await should("handle a for loop", async () => {
+    setPowerPuterValue(
+      powerPuter,
+      "STRING",
+      `
+        a = 0
+        b = ''
+        for n in range(4):
+          a += n
+          for m in range(2):
+            b += f'{str(n)}-{str(m)}.'
+        f'a:{a} b:{b}'
+      `,
+    );
+    await env.queuePrompt();
+    expect(displayAny.widgets![0]!.value).toBe("a:6 b:0-0.0-1.1-0.1-1.2-0.2-1.3-0.3-1.");
+  });
+
+  await should("handle assigning with a subscript slice", async () => {
+    setPowerPuterValue(
+      powerPuter,
+      "STRING",
+      `
+        a = [1,2,0]
+        a[a[2]] = 3
+        tuple(a)
+      `,
+    );
+    await env.queuePrompt();
+    expect(displayAny.widgets![0]!.value).toBe("(3, 2, 0)");
+  });
+
+  await should("handle aug assigning with a subscript slice", async () => {
+    setPowerPuterValue(
+      powerPuter,
+      "STRING",
+      `
+        a = [1,2,0]
+        a[a[2]] += 3
+        tuple(a)
+      `,
+    );
+    await env.queuePrompt();
+    expect(displayAny.widgets![0]!.value).toBe("(4, 2, 0)");
+  });
+
+  await should("disallow calls to some methods", async () => {
+    const imageNode = await pasteImageToLoadImageNode(env);
+    imageNode.connect(0, powerPuter, 0);
+    setPowerPuterValue(
+      powerPuter,
+      "STRING",
+      `a.numpy().tofile('/tmp/test')
+      `,
+    );
+    await env.queuePrompt();
+
+    // Check to see if there's an error.
+    expect(document.querySelector(".p-dialog-mask .p-card-body")!.textContent).toContain(
+      "error message",
+      "Disallowed access to \"tofile\" for type <class 'numpy.ndarray'>",
+    );
+    (document.querySelector(".p-dialog-mask .p-dialog-close-button")! as HTMLButtonElement).click();
+  });
+
+  await should("handle boolean operators correctly", async () => {
+    const checks: Array<[string, string, string, ('toMatchJson'|'toBe')?]> = [
+      // And operator all success
+      ["1 and 42", "42", "STRING"],
+      ["True and [42]", "[42]", "STRING", "toMatchJson"],
+      ["a = 42\nTrue and [a]", "[42]", "STRING", "toMatchJson"],
+      ["1 and 3 and True and [1] and 42", "42", "STRING"],
+      // And operator w/ a failure
+      ["1 and 3 and True and [] and 42", "[]", "STRING", "toMatchJson"],
+      ["1 and 0 and True and [] and 42", "0", "STRING"],
+      ["1 and 2 and False and [] and 42", "False", "STRING"],
+      ["b = None\n1 and 2 and True and b and 42", "None", "STRING"],
+      // Or operator
+      ["1 or 42", "1", "STRING"],
+      ["0 or 42", "42", "STRING"],
+      ["0 or None or False or [] or 42", "42", "STRING"],
+      ["b=42\n0 or None or False or [] or b", "42", "STRING"],
+      ["b=42\n0 or None or False or [b] or b", "[42]", "STRING", "toMatchJson"],
+      ["b=42\n0 or None or True or [b] or b", "True", "STRING"],
+      // Mix
+      ["1 and 2 and 0 or 5", "5", "STRING"],
+      ["None and 1 or True", "True", "STRING"],
+      ["0 or False and True", "False", "STRING"],
+
+    ];
+    for (const data of checks) {
+      setPowerPuterValue(powerPuter, data[2], data[0]);
+      await env.queuePrompt();
+      expect(displayAny.widgets![0]!.value)[data[3] || 'toBe'](data[0], data[1]);
+    }
+  });
+});
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/utils.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/utils.ts
new file mode 100644
index 0000000000000000000000000000000000000000..72bd0a15c1e3819d4c7eb4e08f204c81115bda56
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/utils.ts
@@ -0,0 +1,1160 @@
+import type {
+  Vector2,
+  LGraphCanvas as TLGraphCanvas,
+  LLink,
+  LGraph,
+  IContextMenuOptions,
+  ContextMenu,
+  LGraphNode as TLGraphNode,
+  INodeSlot,
+  INodeInputSlot,
+  INodeOutputSlot,
+  IContextMenuValue,
+  ISerialisedNode,
+  ISlotType,
+  LGraphNodeConstructor,
+  LGraphEventMode,
+  NodeProperty,
+  LinkDirection,
+  Point,
+  ComfyApp,
+  LGraphGroup,
+  NodeId,
+  Subgraph,
+  SubgraphNode,
+} from "@comfyorg/frontend";
+import type {ComfyApiPrompt, ComfyNodeDef} from "typings/comfy";
+import type {Constructor} from "typings/rgthree";
+
+import {app} from "scripts/app.js";
+import {api} from "scripts/api.js";
+import {Resolver, getResolver, wait} from "rgthree/common/shared_utils.js";
+import {RgthreeHelpDialog} from "rgthree/common/dialog.js";
+
+/**
+ * Override the api.getNodeDefs call to add a hook for refreshing node defs.
+ * This is necessary for power prompt's custom combos. Since API implements
+ * add/removeEventListener already, this is rather trivial.
+ */
+const oldApiGetNodeDefs = api.getNodeDefs;
+api.getNodeDefs = async function () {
+  const defs = await oldApiGetNodeDefs.call(api);
+  this.dispatchEvent(new CustomEvent("fresh-node-defs", {detail: defs}));
+  return defs;
+};
+
+export enum IoDirection {
+  INPUT,
+  OUTPUT,
+}
+
+const PADDING = 0;
+
+type LiteGraphDir =
+  | typeof LiteGraph.LEFT
+  | typeof LiteGraph.RIGHT
+  | typeof LiteGraph.UP
+  | typeof LiteGraph.DOWN;
+export const LAYOUT_LABEL_TO_DATA: {[label: string]: [LiteGraphDir, Vector2, Vector2]} = {
+  Left: [LiteGraph.LEFT, [0, 0.5], [PADDING, 0]],
+  Right: [LiteGraph.RIGHT, [1, 0.5], [-PADDING, 0]],
+  Top: [LiteGraph.UP, [0.5, 0], [0, PADDING]],
+  Bottom: [LiteGraph.DOWN, [0.5, 1], [0, -PADDING]],
+};
+export const LAYOUT_LABEL_OPPOSITES: {[label: string]: string} = {
+  Left: "Right",
+  Right: "Left",
+  Top: "Bottom",
+  Bottom: "Top",
+};
+export const LAYOUT_CLOCKWISE = ["Top", "Right", "Bottom", "Left"];
+
+interface MenuConfig {
+  name: string | ((node: TLGraphNode) => string);
+  property?: string;
+  prepareValue?: (value: NodeProperty | undefined, node: TLGraphNode) => any;
+  callback?: (node: TLGraphNode, value?: string) => void;
+  subMenuOptions?: (string | null)[] | ((node: TLGraphNode) => (string | null)[]);
+}
+
+export function addMenuItem(
+  node: LGraphNodeConstructor,
+  _app: ComfyApp,
+  config: MenuConfig,
+  after = "Shape",
+) {
+  const oldGetExtraMenuOptions = node.prototype.getExtraMenuOptions;
+  node.prototype.getExtraMenuOptions = function (
+    canvas: TLGraphCanvas,
+    menuOptions: IContextMenuValue[],
+  ) {
+    oldGetExtraMenuOptions && oldGetExtraMenuOptions.apply(this, [canvas, menuOptions]);
+    addMenuItemOnExtraMenuOptions(this, config, menuOptions, after);
+  };
+}
+
+/**
+ * Waits for the canvas to be available on app using a single promise.
+ */
+let canvasResolver: Resolver<TLGraphCanvas> | null = null;
+export function waitForCanvas() {
+  if (canvasResolver === null) {
+    canvasResolver = getResolver<TLGraphCanvas>();
+    function _waitForCanvas() {
+      if (!canvasResolver!.completed) {
+        if (app?.canvas) {
+          canvasResolver!.resolve(app.canvas);
+        } else {
+          requestAnimationFrame(_waitForCanvas);
+        }
+      }
+    }
+    _waitForCanvas();
+  }
+  return canvasResolver.promise;
+}
+
+/**
+ * Waits for the graph to be available on app using a single promise.
+ */
+let graphResolver: Resolver<LGraph> | null = null;
+export function waitForGraph() {
+  if (graphResolver === null) {
+    graphResolver = getResolver<LGraph>();
+    function _wait() {
+      if (!graphResolver!.completed) {
+        if (app?.graph) {
+          graphResolver!.resolve(app.graph);
+        } else {
+          requestAnimationFrame(_wait);
+        }
+      }
+    }
+    _wait();
+  }
+  return graphResolver.promise;
+}
+
+export function addMenuItemOnExtraMenuOptions(
+  node: TLGraphNode,
+  config: MenuConfig,
+  menuOptions: (IContextMenuValue | null)[],
+  after = "Shape",
+) {
+  let idx = menuOptions
+    .slice()
+    .reverse()
+    .findIndex((option) => (option as any)?.isRgthree);
+  if (idx == -1) {
+    idx = menuOptions.findIndex((option) => option?.content?.includes(after)) + 1;
+    if (!idx) {
+      idx = menuOptions.length - 1;
+    }
+    // Add a separator, and move to the next one.
+    menuOptions.splice(idx, 0, null);
+    idx++;
+  } else {
+    idx = menuOptions.length - idx;
+  }
+
+  const subMenuOptions =
+    typeof config.subMenuOptions === "function"
+      ? config.subMenuOptions(node)
+      : config.subMenuOptions;
+
+  menuOptions.splice(idx, 0, {
+    content: typeof config.name == "function" ? config.name(node) : config.name,
+    has_submenu: !!subMenuOptions?.length,
+    isRgthree: true, // Mark it, so we can find it.
+    callback: (
+      value: IContextMenuValue,
+      _options: IContextMenuOptions,
+      event: MouseEvent,
+      parentMenu: ContextMenu | undefined,
+      _node: TLGraphNode,
+    ) => {
+      if (!!subMenuOptions?.length) {
+        new LiteGraph.ContextMenu(
+          subMenuOptions.map((option) => (option ? {content: option} : null)),
+          {
+            event,
+            parentMenu,
+            callback: (
+              subValue: IContextMenuValue,
+              _options: IContextMenuOptions,
+              _event: MouseEvent,
+              _parentMenu: ContextMenu | undefined,
+              _node: TLGraphNode,
+            ) => {
+              if (config.property) {
+                node.properties = node.properties || {};
+                node.properties[config.property] = config.prepareValue
+                  ? config.prepareValue(subValue!.content || "", node)
+                  : subValue!.content || "";
+              }
+              config.callback && config.callback(node, subValue?.content);
+            },
+          },
+        );
+        return;
+      }
+      if (config.property) {
+        node.properties = node.properties || {};
+        node.properties[config.property] = config.prepareValue
+          ? config.prepareValue(node.properties[config.property], node)
+          : !node.properties[config.property];
+      }
+      config.callback && config.callback(node, value?.content);
+    },
+  } as IContextMenuValue);
+}
+
+export function addConnectionLayoutSupport(
+  node: LGraphNodeConstructor,
+  app: ComfyApp,
+  options = [
+    ["Left", "Right"],
+    ["Right", "Left"],
+  ],
+  callback?: (node: TLGraphNode) => void,
+) {
+  addMenuItem(node, app, {
+    name: "Connections Layout",
+    property: "connections_layout",
+    subMenuOptions: options.map((option) => option[0] + (option[1] ? " -> " + option[1] : "")),
+    prepareValue: (value, node) => {
+      const values = String(value).split(" -> ");
+      if (!values[1] && !node.outputs?.length) {
+        values[1] = LAYOUT_LABEL_OPPOSITES[values[0]!]!;
+      }
+      if (!LAYOUT_LABEL_TO_DATA[values[0]!] || !LAYOUT_LABEL_TO_DATA[values[1]!]) {
+        throw new Error(`New Layout invalid: [${values[0]}, ${values[1]}]`);
+      }
+      return values;
+    },
+    callback: (node) => {
+      callback && callback(node);
+      node.graph?.setDirtyCanvas(true, true);
+    },
+  });
+
+  // [🤮] This is deprecated in a https://github.com/Comfy-Org/litegraph.js/pull/716 @v0.9.9 and
+  // replaced by getInputPos and getOutputPos conveniently added below.
+  node.prototype.getConnectionPos = function (isInput: boolean, slotNumber: number, out: Vector2) {
+    // Purposefully do not need to call the old one.
+    return getConnectionPosForLayout(this, isInput, slotNumber, out);
+  };
+  node.prototype.getInputPos = function (slotNumber: number): Vector2 {
+    // Purposefully do not need to call the existing one because we're overriding.
+    return getConnectionPosForLayout(this, true, slotNumber, [0, 0]);
+  };
+  node.prototype.getOutputPos = function (slotNumber: number): Vector2 {
+    // Purposefully do not need to call the existing one because we're overriding.
+    return getConnectionPosForLayout(this, false, slotNumber, [0, 0]);
+  };
+}
+
+export function setConnectionsLayout(node: TLGraphNode, newLayout: [string, string]) {
+  newLayout = newLayout || (node as any).defaultConnectionsLayout || ["Left", "Right"];
+  // If we didn't supply an output layout, and there's no outputs, then just choose the opposite of the
+  // input as a safety.
+  if (!newLayout[1] && !node.outputs?.length) {
+    newLayout[1] = LAYOUT_LABEL_OPPOSITES[newLayout[0]!]!;
+  }
+  if (!LAYOUT_LABEL_TO_DATA[newLayout[0]] || !LAYOUT_LABEL_TO_DATA[newLayout[1]]) {
+    throw new Error(`New Layout invalid: [${newLayout[0]}, ${newLayout[1]}]`);
+  }
+  node.properties = node.properties || {};
+  node.properties["connections_layout"] = newLayout;
+}
+
+/** Allows collapsing of connections into one. Pretty unusable, unless you're the muter. */
+export function setConnectionsCollapse(
+  node: TLGraphNode,
+  collapseConnections: boolean | null = null,
+) {
+  node.properties = node.properties || {};
+  collapseConnections =
+    collapseConnections !== null ? collapseConnections : !node.properties["collapse_connections"];
+  node.properties["collapse_connections"] = collapseConnections;
+}
+
+export function getConnectionPosForLayout(
+  node: TLGraphNode,
+  isInput: boolean,
+  slotNumber: number,
+  out: Vector2,
+) {
+  out = out || new Float32Array(2);
+  node.properties = node.properties || {};
+  const layout = node.properties["connections_layout"] ||
+    (node as any).defaultConnectionsLayout || ["Left", "Right"];
+  const collapseConnections = (node.properties["collapse_connections"] as boolean) || false;
+  const offset = (node.constructor as any).layout_slot_offset ?? LiteGraph.NODE_SLOT_HEIGHT * 0.5;
+  let side = isInput ? layout[0] : layout[1];
+  const otherSide = isInput ? layout[1] : layout[0];
+  let data = LAYOUT_LABEL_TO_DATA[side]!; // || LAYOUT_LABEL_TO_DATA[isInput ? 'Left' : 'Right'];
+  const slotList = node[isInput ? "inputs" : "outputs"];
+  const cxn = slotList[slotNumber];
+  if (!cxn) {
+    console.log("No connection found.. weird", isInput, slotNumber);
+    return out;
+  }
+  // Experimental; doesn't work without node.clip_area set (so it won't draw outside),
+  // but litegraph.core inexplicably clips the title off which we want... so, no go.
+  // if (cxn.hidden) {
+  //   out[0] = node.pos[0] - 100000
+  //   out[1] = node.pos[1] - 100000
+  //   return out
+  // }
+  if (cxn.disabled) {
+    // Let's store the original colors if have them and haven't yet overridden
+    if (cxn.color_on !== "#666665") {
+      (cxn as any)._color_on_org = (cxn as any)._color_on_org || cxn.color_on;
+      (cxn as any)._color_off_org = (cxn as any)._color_off_org || cxn.color_off;
+    }
+    cxn.color_on = "#666665";
+    cxn.color_off = "#666665";
+  } else if (cxn.color_on === "#666665") {
+    cxn.color_on = (cxn as any)._color_on_org || undefined;
+    cxn.color_off = (cxn as any)._color_off_org || undefined;
+  }
+  const displaySlot = collapseConnections
+    ? 0
+    : slotNumber -
+      slotList.reduce<number>((count, ioput, index) => {
+        count += index < slotNumber && ioput.hidden ? 1 : 0;
+        return count;
+      }, 0);
+  // Set the direction first. This is how the connection line will be drawn.
+  cxn.dir = data[0];
+
+  // If we are only 10px tall or wide, then look at connections_dir for the direction.
+  const connections_dir = node.properties["connections_dir"] as [LinkDirection, LinkDirection];
+  if ((node.size[0] == 10 || node.size[1] == 10) && connections_dir) {
+    cxn.dir = connections_dir[isInput ? 0 : 1]!;
+  }
+
+  if (side === "Left") {
+    if (node.flags.collapsed) {
+      var w = (node as any)._collapsed_width || LiteGraph.NODE_COLLAPSED_WIDTH;
+      out[0] = node.pos[0];
+      out[1] = node.pos[1] - LiteGraph.NODE_TITLE_HEIGHT * 0.5;
+    } else {
+      // If we're an output, then the litegraph.core hates us; we need to blank out the name
+      // because it's not flexible enough to put the text on the inside.
+      toggleConnectionLabel(cxn, !isInput || collapseConnections || !!(node as any).hideSlotLabels);
+      out[0] = node.pos[0] + offset;
+      if ((node.constructor as any)?.type.includes("Reroute")) {
+        out[1] = node.pos[1] + node.size[1] * 0.5;
+      } else {
+        out[1] =
+          node.pos[1] +
+          (displaySlot + 0.7) * LiteGraph.NODE_SLOT_HEIGHT +
+          ((node.constructor as any).slot_start_y || 0);
+      }
+    }
+  } else if (side === "Right") {
+    if (node.flags.collapsed) {
+      var w = (node as any)._collapsed_width || LiteGraph.NODE_COLLAPSED_WIDTH;
+      out[0] = node.pos[0] + w;
+      out[1] = node.pos[1] - LiteGraph.NODE_TITLE_HEIGHT * 0.5;
+    } else {
+      // If we're an input, then the litegraph.core hates us; we need to blank out the name
+      // because it's not flexible enough to put the text on the inside.
+      toggleConnectionLabel(cxn, isInput || collapseConnections || !!(node as any).hideSlotLabels);
+      out[0] = node.pos[0] + node.size[0] + 1 - offset;
+      if ((node.constructor as any)?.type.includes("Reroute")) {
+        out[1] = node.pos[1] + node.size[1] * 0.5;
+      } else {
+        out[1] =
+          node.pos[1] +
+          (displaySlot + 0.7) * LiteGraph.NODE_SLOT_HEIGHT +
+          ((node.constructor as any).slot_start_y || 0);
+      }
+    }
+
+    // Right now, only reroute uses top/bottom, so this may not work for other nodes
+    // (like, applying to nodes with titles, collapsed, multiple inputs/outputs, etc).
+  } else if (side === "Top") {
+    if (!(cxn as any).has_old_label) {
+      (cxn as any).has_old_label = true;
+      (cxn as any).old_label = cxn.label;
+      cxn.label = " ";
+    }
+    out[0] = node.pos[0] + node.size[0] * 0.5;
+    out[1] = node.pos[1] + offset;
+  } else if (side === "Bottom") {
+    if (!(cxn as any).has_old_label) {
+      (cxn as any).has_old_label = true;
+      (cxn as any).old_label = cxn.label;
+      cxn.label = " ";
+    }
+    out[0] = node.pos[0] + node.size[0] * 0.5;
+    out[1] = node.pos[1] + node.size[1] - offset;
+  }
+  return out;
+}
+
+function toggleConnectionLabel(cxn: any, hide = true) {
+  if (hide) {
+    if (!(cxn as any).has_old_label) {
+      (cxn as any).has_old_label = true;
+      (cxn as any).old_label = cxn.label;
+    }
+    cxn.label = " ";
+  } else if (!hide && (cxn as any).has_old_label) {
+    (cxn as any).has_old_label = false;
+    cxn.label = (cxn as any).old_label;
+    (cxn as any).old_label = undefined;
+  }
+  return cxn;
+}
+
+export function addHelpMenuItem(
+  node: TLGraphNode,
+  content: string,
+  menuOptions: (IContextMenuValue | null)[],
+) {
+  addMenuItemOnExtraMenuOptions(
+    node,
+    {
+      name: "🛟 Node Help",
+      callback: (node) => {
+        if ((node as any).showHelp) {
+          (node as any).showHelp();
+        } else {
+          new RgthreeHelpDialog(node, content).show();
+        }
+      },
+    },
+    menuOptions,
+    "Properties Panel",
+  );
+}
+
+export enum PassThroughFollowing {
+  ALL,
+  NONE,
+  REROUTE_ONLY,
+}
+
+/**
+ * Determines if, when doing a chain lookup for connected nodes, we want to pass through this node,
+ * like reroutes, etc.
+ */
+export function shouldPassThrough(
+  node?: TLGraphNode | null,
+  passThroughFollowing = PassThroughFollowing.ALL,
+) {
+  const type = (node?.constructor as typeof TLGraphNode)?.type;
+  if (!type || passThroughFollowing === PassThroughFollowing.NONE) {
+    return false;
+  }
+  if (passThroughFollowing === PassThroughFollowing.REROUTE_ONLY) {
+    return type.includes("Reroute");
+  }
+  return (
+    type.includes("Reroute") || type.includes("Node Combiner") || type.includes("Node Collector")
+  );
+}
+
+function filterOutPassthroughNodes(
+  infos: ConnectedNodeInfo[],
+  passThroughFollowing = PassThroughFollowing.ALL,
+) {
+  return infos.filter((i) => !shouldPassThrough(i.node, passThroughFollowing));
+}
+
+/**
+ * Looks through the immediate chain of a node to collect all connected nodes, passing through nodes
+ * like reroute, etc. Will also disconnect duplicate nodes from a provided node
+ */
+export function getConnectedInputNodes(
+  startNode: TLGraphNode,
+  currentNode?: TLGraphNode,
+  slot?: number,
+  passThroughFollowing = PassThroughFollowing.ALL,
+): TLGraphNode[] {
+  return getConnectedNodesInfo(
+    startNode,
+    IoDirection.INPUT,
+    currentNode,
+    slot,
+    passThroughFollowing,
+  ).map((n) => n.node);
+}
+export function getConnectedInputInfosAndFilterPassThroughs(
+  startNode: TLGraphNode,
+  currentNode?: TLGraphNode,
+  slot?: number,
+  passThroughFollowing = PassThroughFollowing.ALL,
+) {
+  return filterOutPassthroughNodes(
+    getConnectedNodesInfo(startNode, IoDirection.INPUT, currentNode, slot, passThroughFollowing),
+    passThroughFollowing,
+  );
+}
+export function getConnectedInputNodesAndFilterPassThroughs(
+  startNode: TLGraphNode,
+  currentNode?: TLGraphNode,
+  slot?: number,
+  passThroughFollowing = PassThroughFollowing.ALL,
+): TLGraphNode[] {
+  return getConnectedInputInfosAndFilterPassThroughs(
+    startNode,
+    currentNode,
+    slot,
+    passThroughFollowing,
+  ).map((n) => n.node);
+}
+
+export function getConnectedOutputNodes(
+  startNode: TLGraphNode,
+  currentNode?: TLGraphNode,
+  slot?: number,
+  passThroughFollowing = PassThroughFollowing.ALL,
+): TLGraphNode[] {
+  return getConnectedNodesInfo(
+    startNode,
+    IoDirection.OUTPUT,
+    currentNode,
+    slot,
+    passThroughFollowing,
+  ).map((n) => n.node);
+}
+
+export function getConnectedOutputNodesAndFilterPassThroughs(
+  startNode: TLGraphNode,
+  currentNode?: TLGraphNode,
+  slot?: number,
+  passThroughFollowing = PassThroughFollowing.ALL,
+): TLGraphNode[] {
+  return filterOutPassthroughNodes(
+    getConnectedNodesInfo(startNode, IoDirection.OUTPUT, currentNode, slot, passThroughFollowing),
+    passThroughFollowing,
+  ).map((n) => n.node);
+}
+
+export type ConnectedNodeInfo = {
+  node: TLGraphNode;
+  travelFromSlot: number;
+  travelToSlot: number;
+  originTravelFromSlot: number;
+};
+
+export function getConnectedNodesInfo(
+  startNode: TLGraphNode,
+  dir = IoDirection.INPUT,
+  currentNode?: TLGraphNode,
+  slot?: number,
+  passThroughFollowing = PassThroughFollowing.ALL,
+  originTravelFromSlot?: number,
+): ConnectedNodeInfo[] {
+  currentNode = currentNode || startNode;
+  let rootNodes: ConnectedNodeInfo[] = [];
+  if (startNode === currentNode || shouldPassThrough(currentNode, passThroughFollowing)) {
+    let linkIds: Array<number | undefined | null>;
+
+    slot = slot != null && slot > -1 ? slot : undefined;
+    if (dir == IoDirection.OUTPUT) {
+      if (slot != null) {
+        linkIds = [...(currentNode.outputs?.[slot]?.links || [])];
+      } else {
+        linkIds = currentNode.outputs?.flatMap((i) => i.links) || [];
+      }
+    } else {
+      if (slot != null) {
+        linkIds = [currentNode.inputs?.[slot]?.link];
+      } else {
+        linkIds = currentNode.inputs?.map((i) => i.link) || [];
+      }
+    }
+    const graph = currentNode.graph ?? app.graph;
+    for (const linkId of linkIds) {
+      let link: LLink | null = null;
+      if (typeof linkId == "number") {
+        link = graph.links[linkId] ?? null;
+      }
+      if (!link) {
+        continue;
+      }
+      const travelFromSlot = dir == IoDirection.OUTPUT ? link.origin_slot : link.target_slot;
+      const connectedId = dir == IoDirection.OUTPUT ? link.target_id : link.origin_id;
+      const travelToSlot = dir == IoDirection.OUTPUT ? link.target_slot : link.origin_slot;
+      originTravelFromSlot = originTravelFromSlot != null ? originTravelFromSlot : travelFromSlot;
+      const originNode: TLGraphNode = graph.getNodeById(connectedId)!;
+      if (!link) {
+        console.error("No connected node found... weird");
+        continue;
+      }
+      if (rootNodes.some((n) => n.node == originNode)) {
+        console.log(
+          `${startNode.title} (${startNode.id}) seems to have two links to ${originNode.title} (${
+            originNode.id
+          }). One may be stale: ${linkIds.join(", ")}`,
+        );
+      } else {
+        // Add the node and, if it's a pass through, let's collect all its nodes as well.
+        rootNodes.push({node: originNode, travelFromSlot, travelToSlot, originTravelFromSlot});
+        if (shouldPassThrough(originNode, passThroughFollowing)) {
+          for (const foundNode of getConnectedNodesInfo(
+            startNode,
+            dir,
+            originNode,
+            undefined,
+            undefined,
+            originTravelFromSlot,
+          )) {
+            if (!rootNodes.map((n) => n.node).includes(foundNode.node)) {
+              rootNodes.push(foundNode);
+            }
+          }
+        }
+      }
+    }
+  }
+  return rootNodes;
+}
+
+export type ConnectionType = {
+  type: string | string[];
+  name: string | undefined;
+  label: string | undefined;
+};
+
+/**
+ * Follows a connection until we find a type associated with a slot.
+ * `skipSelf` skips the current slot, useful when we may have a dynamic slot that we want to start
+ * from, but find a type _after_ it (in case it needs to change).
+ */
+export function followConnectionUntilType(
+  node: TLGraphNode,
+  dir: IoDirection,
+  slotNum?: number,
+  skipSelf = false,
+): ConnectionType | null {
+  const slots = dir === IoDirection.OUTPUT ? node.outputs : node.inputs;
+  if (!slots || !slots.length) {
+    return null;
+  }
+  let type: ConnectionType | null = null;
+  if (slotNum) {
+    if (!slots[slotNum]) {
+      return null;
+    }
+    type = getTypeFromSlot(slots[slotNum], dir, skipSelf);
+  } else {
+    for (const slot of slots) {
+      type = getTypeFromSlot(slot, dir, skipSelf);
+      if (type) {
+        break;
+      }
+    }
+  }
+  return type;
+}
+
+/**
+ * Gets the type from a slot. If the type is '*' then it will follow the node to find the next slot.
+ */
+function getTypeFromSlot(
+  slot: INodeInputSlot | INodeOutputSlot | undefined,
+  dir: IoDirection,
+  skipSelf = false,
+): ConnectionType | null {
+  let graph = app.canvas.getCurrentGraph()!;
+  let type = slot?.type;
+  if (!skipSelf && type != null && type != "*") {
+    return {type: type as string, label: slot?.label, name: slot?.name};
+  }
+  const links = getSlotLinks(slot);
+  for (const link of links) {
+    const connectedId = dir == IoDirection.OUTPUT ? link.link.target_id : link.link.origin_id;
+    const connectedSlotNum =
+      dir == IoDirection.OUTPUT ? link.link.target_slot : link.link.origin_slot;
+    const connectedNode: TLGraphNode = graph.getNodeById(connectedId)!;
+    // Reversed since if we're traveling down the output we want the connected node's input, etc.
+    const connectedSlots =
+      dir === IoDirection.OUTPUT ? connectedNode.inputs : connectedNode.outputs;
+    let connectedSlot = connectedSlots[connectedSlotNum];
+    if (connectedSlot?.type != null && connectedSlot?.type != "*") {
+      return {
+        type: connectedSlot.type as string,
+        label: connectedSlot?.label,
+        name: connectedSlot?.name,
+      };
+    } else if (connectedSlot?.type == "*") {
+      return followConnectionUntilType(connectedNode, dir);
+    }
+  }
+  return null;
+}
+
+export async function replaceNode(
+  existingNode: TLGraphNode,
+  typeOrNewNode: string | TLGraphNode,
+  inputNameMap?: Map<string, string>,
+) {
+  const existingCtor = existingNode.constructor as typeof TLGraphNode;
+
+  const newNode =
+    typeof typeOrNewNode === "string" ? LiteGraph.createNode(typeOrNewNode)! : typeOrNewNode;
+  // Port title (maybe) the position, size, and properties from the old node.
+  if (existingNode.title != existingCtor.title) {
+    newNode.title = existingNode.title;
+  }
+  newNode.pos = [...existingNode.pos] as Point;
+  newNode.properties = {...existingNode.properties};
+  const oldComputeSize = [...existingNode.computeSize()];
+  // oldSize to use. If we match the smallest size (computeSize) then don't record and we'll use
+  // the smalles side after conversion.
+  const oldSize = [
+    existingNode.size[0] === oldComputeSize[0] ? null : existingNode.size[0],
+    existingNode.size[1] === oldComputeSize[1] ? null : existingNode.size[1],
+  ];
+
+  let setSizeIters = 0;
+  const setSizeFn = () => {
+    // Size gets messed up when ComfyUI adds the text widget, so reset after a delay.
+    // Since we could be adding many more slots, let's take the larger of the two.
+    const newComputesize = newNode.computeSize();
+    newNode.size[0] = Math.max(oldSize[0] || 0, newComputesize[0]);
+    newNode.size[1] = Math.max(oldSize[1] || 0, newComputesize[1]);
+    setSizeIters++;
+    if (setSizeIters > 10) {
+      requestAnimationFrame(setSizeFn);
+    }
+  };
+  setSizeFn();
+
+  // We now collect the links data, inputs and outputs, of the old node since these will be
+  // lost when we remove it.
+  const links: {
+    node: TLGraphNode;
+    slot: number | string;
+    targetNode: TLGraphNode;
+    targetSlot: number | string;
+  }[] = [];
+  const graph = existingNode.graph || app.graph;
+  for (const [index, output] of existingNode.outputs.entries()) {
+    for (const linkId of output.links || []) {
+      const link: LLink = graph.links[linkId]!;
+      if (!link) continue;
+      const targetNode = graph.getNodeById(link.target_id)!;
+      links.push({node: newNode, slot: output.name, targetNode, targetSlot: link.target_slot});
+    }
+  }
+  for (const [index, input] of existingNode.inputs.entries()) {
+    const linkId = input.link;
+    if (linkId) {
+      const link: LLink = graph.links[linkId]!;
+      const originNode = graph.getNodeById(link.origin_id)!;
+      links.push({
+        node: originNode,
+        slot: link.origin_slot,
+        targetNode: newNode,
+        targetSlot: inputNameMap?.has(input.name)
+          ? inputNameMap.get(input.name)!
+          : input.name || index,
+      });
+    }
+  }
+  // Add the new node, remove the old node.
+  graph.add(newNode);
+  await wait();
+  // Now go through and connect the other nodes up as they were.
+  for (const link of links) {
+    link.node.connect(link.slot, link.targetNode, link.targetSlot);
+  }
+  await wait();
+  graph.remove(existingNode);
+  newNode.size = newNode.computeSize();
+  newNode.setDirtyCanvas(true, true);
+  return newNode;
+}
+
+export function getOriginNodeByLink(linkId?: number | null) {
+  let node: TLGraphNode | null = null;
+  if (linkId != null) {
+    const link = getLinkById(linkId);
+    node = (link != null && getNodeById(link.origin_id)) || null;
+  }
+  return node;
+}
+
+/**
+ * Gets a link by id across all graphs and subgraphs.
+ */
+export function getLinkById(linkId?: number | null) {
+  if (linkId == null) return null;
+  let link: LLink | null = app.graph.links[linkId] ?? null;
+  link = link ?? app.canvas.getCurrentGraph()?.links[linkId] ?? null;
+  return link || findSomethingInAllSubgraphs((subgraph) => subgraph?.links[linkId] ?? null);
+}
+
+/**
+ * Gets a node by id across all graphs and subgraphs.
+ */
+export function getNodeById(id: NodeId) {
+  if (id == null) return null;
+  let node = app.graph.getNodeById(id);
+  node = node ?? app.canvas.getCurrentGraph()?.getNodeById(id) ?? null;
+  return node || findSomethingInAllSubgraphs((subgraph) => subgraph?.getNodeById(id) ?? null);
+}
+
+/**
+ * Given a serialized workflow, get the mutable node data.
+ *
+ * Should be kept up to date with `py/utils_graph#get_worflow_node`
+ */
+export function getNodeByIdFromApiPrompt(apiPrompt: ComfyApiPrompt, id: string | number) {
+  const fullId = getFullNodeIdFromApiPrompt(apiPrompt, id);
+  const workflow = apiPrompt.workflow ?? {};
+  const nodeIds = String(fullId).split(":");
+  const workflowNodes = workflow?.["nodes"] ?? [];
+  const workflowSubgraphs = workflow?.["definitions"]?.["subgraphs"] ?? [];
+
+  let nodesList = workflowNodes;
+  let found: ISerialisedNode | null = null;
+
+  for (const nodeId of nodeIds) {
+    found = nodesList.find((n: ISerialisedNode) => String(n.id) === String(nodeId)) || null;
+    if (found?.["type"]) {
+      const subgraph =
+        workflowSubgraphs.find((n: ISerialisedNode) => n.id === found!["type"]) || null;
+      if (subgraph?.["nodes"]) {
+        nodesList = subgraph["nodes"];
+      }
+    }
+  }
+  return found;
+}
+
+/**
+ * Finds the correct "full id" of a node (that is, with subgraph prefixes like "5:12:5"). This is
+ * really only important when attempting to modify the prompt sent to the backend, like the seed
+ * node does to generate a new seed on the client.
+ *
+ * Because the prompt `output` is an object keyed by full id, we can look to see if it has either
+ * the id passed in (either a single id, or an already-full id), or ends with the id passed in.
+ *
+ * NOTE: This does NOT find subgraph nodes since they do not exist in `apiPrompt.output`.
+ * TODO: We could search for subgraph nodes in the `apiPrompt.workflow.definitions.subgraphs`.
+ */
+export function getFullNodeIdFromApiPrompt(apiPrompt: ComfyApiPrompt, id: string | number) {
+  const output = apiPrompt.output ?? {};
+  return output[id] ? id : Object.keys(output).find((i) => i.endsWith(`:${id}`));
+}
+
+/**
+ * [🤮] At some point ComfyUI added a second param for `openSubgraph` which is the node representing
+ * the subgraph in the UI that a user would double click (at least, from what I can tell).
+ * Unfortunately, we sometimes want to open a subgraph without knowing what that node is (like, from
+ * a bookmark).
+ *
+ * While that doesn't break right now, it does throw an error. As a best-attempt to fix their
+ * no longer allowing a subgraph to just open, we can search all nodes for subgraphs for the one
+ * that represents the subgraph itself.
+ */
+export function findFromNodeForSubgraph(subgraphId: string): SubgraphNode | null {
+  const node =
+    findSomethingInAllSubgraphs((subgraph) =>
+      subgraph.nodes
+        .filter((node) => node.isSubgraphNode())
+        .find((node) => node.subgraph.id === subgraphId),
+    ) ?? null;
+  return node;
+}
+
+/**
+ * Finds something across all the graphs and subgraphs.
+ */
+function findSomethingInAllSubgraphs<T>(fn: (subgraph: Subgraph) => T | null): T | null {
+  const rootGraph = app.rootGraph ?? app.graph.rootGraph;
+  const subgraphs = [rootGraph, ...rootGraph.subgraphs?.values()] as Subgraph[];
+  for (const subgraph of subgraphs) {
+    const thing = fn(subgraph);
+    if (thing) return thing;
+  }
+  return null;
+}
+
+export function applyMixins(original: Constructor<TLGraphNode>, constructors: any[]) {
+  constructors.forEach((baseCtor) => {
+    Object.getOwnPropertyNames(baseCtor.prototype).forEach((name) => {
+      Object.defineProperty(
+        original.prototype,
+        name,
+        Object.getOwnPropertyDescriptor(baseCtor.prototype, name) || Object.create(null),
+      );
+    });
+  });
+}
+
+/**
+ * Retruns a list of `{id: number, link: LLlink}` for a given input or output.
+ *
+ * Obviously, for an input, this will be a max of one.
+ */
+export function getSlotLinks(inputOrOutput?: INodeInputSlot | INodeOutputSlot | null) {
+  const links: {id: number; link: LLink}[] = [];
+  if (!inputOrOutput) {
+    return links;
+  }
+  if ((inputOrOutput as INodeOutputSlot).links?.length) {
+    const output = inputOrOutput as INodeOutputSlot;
+    for (const linkId of output.links || []) {
+      const link: LLink = (app.graph as LGraph).links[linkId]!;
+      if (link) {
+        links.push({id: linkId, link: link});
+      }
+    }
+  }
+  if ((inputOrOutput as INodeInputSlot).link) {
+    const input = inputOrOutput as INodeInputSlot;
+    const link: LLink = (app.graph as LGraph).links[input.link!]!;
+    if (link) {
+      links.push({id: input.link!, link: link});
+    }
+  }
+  return links;
+}
+
+/**
+ * Given a node, whether we're dealing with INPUTS or OUTPUTS, and the server data, re-arrange then
+ * slots to match the order.
+ */
+export async function matchLocalSlotsToServer(
+  node: TLGraphNode,
+  direction: IoDirection,
+  serverNodeData: ComfyNodeDef,
+) {
+  const serverSlotNames =
+    direction == IoDirection.INPUT
+      ? Object.keys(serverNodeData.input?.optional || {})
+      : serverNodeData.output_name;
+  const serverSlotTypes =
+    direction == IoDirection.INPUT
+      ? (Object.values(serverNodeData.input?.optional || {}).map((i) => i[0]) as string[])
+      : serverNodeData.output;
+  const slots = direction == IoDirection.INPUT ? node.inputs : node.outputs;
+
+  // Let's go through the node data names and make sure our current ones match, and update if not.
+  let firstIndex = slots.findIndex((o, i) => i !== serverSlotNames.indexOf(o.name));
+  if (firstIndex > -1) {
+    // Have mismatches. First, let's go through and save all our links by name.
+    const links: {[key: string]: {id: number; link: LLink}[]} = {};
+    slots.map((slot) => {
+      // There's a chance we have duplicate names on an upgrade, so we'll collect all links to one
+      // name so we don't ovewrite our list per name.
+      links[slot.name] = links[slot.name] || [];
+      links[slot.name]?.push(...getSlotLinks(slot));
+    });
+
+    // Now, go through and rearrange outputs by splicing
+    for (const [index, serverSlotName] of serverSlotNames.entries()) {
+      const currentNodeSlot = slots.map((s) => s.name).indexOf(serverSlotName);
+      if (currentNodeSlot > -1) {
+        if (currentNodeSlot != index) {
+          const splicedItem = slots.splice(currentNodeSlot, 1)[0]!;
+          slots.splice(index, 0, splicedItem as any);
+        }
+      } else if (currentNodeSlot === -1) {
+        const splicedItem = {
+          name: serverSlotName,
+          type: serverSlotTypes![index],
+          links: [],
+        };
+        slots.splice(index, 0, splicedItem as any);
+      }
+    }
+
+    if (slots.length > serverSlotNames.length) {
+      for (let i = slots.length - 1; i > serverSlotNames.length - 1; i--) {
+        if (direction == IoDirection.INPUT) {
+          node.disconnectInput(i);
+          node.removeInput(i);
+        } else {
+          node.disconnectOutput(i);
+          node.removeOutput(i);
+        }
+      }
+    }
+
+    // Now, go through the link data again and make sure the origin_slot is the correct slot.
+    for (const [name, slotLinks] of Object.entries(links)) {
+      let currentNodeSlot = slots.map((s) => s.name).indexOf(name);
+      if (currentNodeSlot > -1) {
+        for (const linkData of slotLinks) {
+          if (direction == IoDirection.INPUT) {
+            linkData.link.target_slot = currentNodeSlot;
+          } else {
+            linkData.link.origin_slot = currentNodeSlot;
+            // If our next node is a Reroute, then let's get it to update the type.
+            const nextNode = app.graph.getNodeById(linkData.link.target_id);
+            // (Check nextNode, as sometimes graphs seem to have very stale data and that node id
+            //  doesn't exist).
+            if (nextNode && (nextNode.constructor as any)?.type!.includes("Reroute")) {
+              (nextNode as any).stabilize && (nextNode as any).stabilize();
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+export function isValidConnection(ioA?: INodeSlot | null, ioB?: INodeSlot | null) {
+  if (!ioA || !ioB) {
+    return false;
+  }
+  const typeA = String(ioA.type);
+  const typeB = String(ioB.type);
+  // What does litegraph think, which includes looking at array values.
+  let isValid = LiteGraph.isValidConnection(typeA, typeB);
+
+  // This is here to fix the churn happening in list types in comfyui itself..
+  // https://github.com/comfyanonymous/ComfyUI/issues/1674
+  if (!isValid) {
+    let areCombos =
+      (typeA.includes(",") && typeB === "COMBO") || (typeA === "COMBO" && typeB.includes(","));
+    // We don't want to let any old combo connect to any old combo, so we'll look at the names too.
+    if (areCombos) {
+      // Some nodes use "_name" and some use "model" and "ckpt", so normalize
+      const nameA = ioA.name.toUpperCase().replace("_NAME", "").replace("CKPT", "MODEL");
+      const nameB = ioB.name.toUpperCase().replace("_NAME", "").replace("CKPT", "MODEL");
+      isValid = nameA.includes(nameB) || nameB.includes(nameA);
+    }
+  }
+  return isValid;
+}
+
+/**
+ * Patches the LiteGraph.isValidConnection so old nodes can connect to this new COMBO type for all
+ * lists (without users needing to go through and re-create all their nodes one by one).
+ */
+const oldIsValidConnection = LiteGraph.isValidConnection;
+LiteGraph.isValidConnection = function (typeA: ISlotType, typeB: ISlotType): boolean {
+  let isValid = oldIsValidConnection.call(LiteGraph, typeA, typeB);
+  if (!isValid) {
+    typeA = String(typeA);
+    typeB = String(typeB);
+    // This is waaaay too liberal and now any combos can connect to any combos. But we only have the
+    // types (not names like my util above), and connecting too liberally is better than old nodes
+    // with lists not being able to connect to this new COMBO type. And, anyway, it matches the
+    // current behavior today with new nodes anyway, where all lists are COMBO types.
+    // Refs: https://github.com/comfyanonymous/ComfyUI/issues/1674
+    //       https://github.com/comfyanonymous/ComfyUI/pull/1675
+    let areCombos =
+      (typeA.includes(",") && typeB === "COMBO") || (typeA === "COMBO" && typeB.includes(","));
+    isValid = areCombos;
+  }
+  return isValid;
+};
+
+/**
+ * Returns a list of output nodes given a list of nodes.
+ */
+export function getOutputNodes(nodes: TLGraphNode[]) {
+  return (
+    nodes?.filter((n) => {
+      return (
+        n.mode != LiteGraph.NEVER && ((n.constructor as any).nodeData as ComfyNodeDef)?.output_node
+      );
+    }) || []
+  );
+}
+
+/**
+ * Changes the mode of a node. We must go through this to change a node's mode after the
+ * introduction of subgraphs, as ComfyUI doesn't update the mode of a node in a subgraph on its own.
+ */
+export function changeModeOfNodes(nodeOrNodes: TLGraphNode | TLGraphNode[], mode: LGraphEventMode) {
+  reduceNodesDepthFirst(nodeOrNodes, (n) => {
+    n.mode = mode;
+  });
+}
+
+/**
+ * Performs depth-first traversal of nodes and their subgraphs.
+ * Adapted from ComfyUI Frontend's method.
+ */
+export function reduceNodesDepthFirst(
+  nodeOrNodes: TLGraphNode | TLGraphNode[],
+  reduceFn: (node: TLGraphNode) => void,
+): void;
+export function reduceNodesDepthFirst<T>(
+  nodeOrNodes: TLGraphNode | TLGraphNode[],
+  reduceFn: (node: TLGraphNode, reduceTo: T) => T | void,
+  reduceTo: T,
+): T;
+export function reduceNodesDepthFirst<T>(
+  nodeOrNodes: TLGraphNode | TLGraphNode[],
+  reduceFn: (node: TLGraphNode, reduceTo: T) => T | void,
+  reduceTo?: T,
+): T {
+  const nodes = Array.isArray(nodeOrNodes) ? nodeOrNodes : [nodeOrNodes];
+  const stack: Array<{node: TLGraphNode}> = nodes.map((node) => ({node}));
+
+  // Process stack iteratively (DFS)
+  while (stack.length > 0) {
+    const {node} = stack.pop()!;
+    const result = reduceFn(node, reduceTo as T);
+    if (result !== undefined && result !== reduceTo) {
+      reduceTo = result;
+    }
+
+    // If it's a subgraph and we should expand, add children to stack
+    if (node.isSubgraphNode?.() && node.subgraph) {
+      // Process children in reverse order to maintain left-to-right DFS processing
+      // when popping from stack (LIFO). Iterate backwards to avoid array reversal.
+      const children = node.subgraph.nodes;
+      for (let i = children.length - 1; i >= 0; i--) {
+        stack.push({node: children[i]!});
+      }
+    }
+  }
+  return reduceTo as T;
+}
+
+/**
+ * Found an issue where group._nodes had nodes that weren't in the actual group. group._nodes is
+ * marked deprecated, so we'll go ahead and use _children and filter.
+ */
+export function getGroupNodes(group: LGraphGroup): TLGraphNode[] {
+  return Array.from(group._children).filter((c) => c instanceof LGraphNode);
+}
+
+/**
+ * Gets a node identifier alongside the graph identifier.
+ *
+ * Perhaps a bug, but it appears the same node id _could_ be in different subgraphs (or, at least,
+ * was definitively reported to be in the main graph, and a subgraph). So, if we're trying
+ * identifying a node alongside other nodes (like, a cache map), we need to keep the graph id along
+ * side it as well.
+ */
+export function getGraphDependantNodeKey(node: TLGraphNode): string {
+  const graph = node.graph ?? app.graph;
+  return `${graph.id}:${node.id}`;
+}
+
+/**
+ * Gets a full color string, including parsing from the LGraphCanvas data.
+ */
+export function getFullColor(
+  color?: string,
+  liteGraphKey: "groupcolor" | "color" | "bgcolor" = "color",
+) {
+  if (!color) {
+    return "";
+  }
+  if (LGraphCanvas.node_colors[color]) {
+    color = LGraphCanvas.node_colors[color]![liteGraphKey];
+  }
+  color = color.replace("#", "").toLocaleLowerCase();
+  if (color.length === 3) {
+    color = color.replace(/(.)(.)(.)/, "$1$1$2$2$3$3");
+  }
+  return `#${color}`;
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/utils_canvas.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/utils_canvas.ts
new file mode 100644
index 0000000000000000000000000000000000000000..4d1c227d76fc7c81b3edacb34f29fe43cdccbcf6
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/utils_canvas.ts
@@ -0,0 +1,399 @@
+import type {LGraphCanvas as TLGraphCanvas, Vector2} from "@comfyorg/frontend";
+
+import {app} from "scripts/app.js";
+
+function binarySearch(max: number, getValue: (n: number) => number, match: number) {
+  let min = 0;
+
+  while (min <= max) {
+    let guess = Math.floor((min + max) / 2);
+    const compareVal = getValue(guess);
+
+    if (compareVal === match) return guess;
+    if (compareVal < match) min = guess + 1;
+    else max = guess - 1;
+  }
+
+  return max;
+}
+
+/**
+ * Fits a string against a max width for a ctx. Font should be defined on ctx beforehand.
+ */
+export function fitString(ctx: CanvasRenderingContext2D, str: string, maxWidth: number) {
+  let width = ctx.measureText(str).width;
+  const ellipsis = "…";
+  const ellipsisWidth = measureText(ctx, ellipsis);
+  if (width <= maxWidth || width <= ellipsisWidth) {
+    return str;
+  }
+
+  const index = binarySearch(
+    str.length,
+    (guess) => measureText(ctx, str.substring(0, guess)),
+    maxWidth - ellipsisWidth,
+  );
+
+  return str.substring(0, index) + ellipsis;
+}
+
+/** Measures the width of text for a canvas context. */
+export function measureText(ctx: CanvasRenderingContext2D, str: string) {
+  return ctx.measureText(str).width;
+}
+
+export type WidgetRenderingOptionsPart = {
+  type?: "toggle" | "custom";
+  margin?: number;
+  fillStyle?: string;
+  strokeStyle?: string;
+  lowQuality?: boolean;
+  draw?(ctx: CanvasRenderingContext2D, x: number, lowQuality: boolean): number;
+};
+
+type WidgetRenderingOptions = {
+  size: [number, number];
+  pos: [number, number];
+  borderRadius?: number;
+  colorStroke?: string;
+  colorBackground?: string;
+};
+
+export function isLowQuality() {
+  const canvas = app.canvas as TLGraphCanvas;
+  return (canvas.ds?.scale || 1) <= 0.5;
+}
+
+export function drawNodeWidget(ctx: CanvasRenderingContext2D, options: WidgetRenderingOptions) {
+  const lowQuality = isLowQuality();
+
+  const data = {
+    width: options.size[0],
+    height: options.size[1],
+    posY: options.pos[1],
+    lowQuality,
+    margin: 15,
+    colorOutline: LiteGraph.WIDGET_OUTLINE_COLOR,
+    colorBackground: LiteGraph.WIDGET_BGCOLOR,
+    colorText: LiteGraph.WIDGET_TEXT_COLOR,
+    colorTextSecondary: LiteGraph.WIDGET_SECONDARY_TEXT_COLOR,
+  };
+
+  // Draw background.
+  ctx.strokeStyle = options.colorStroke || data.colorOutline;
+  ctx.fillStyle = options.colorBackground || data.colorBackground;
+  ctx.beginPath();
+  ctx.roundRect(
+    data.margin,
+    data.posY,
+    data.width - data.margin * 2,
+    data.height,
+    lowQuality ? [0] : options.borderRadius ? [options.borderRadius] : [options.size[1] * 0.5],
+  );
+  ctx.fill();
+  if (!lowQuality) {
+    ctx.stroke();
+  }
+
+  return data;
+}
+
+/** Draws a rounded rectangle. */
+export function drawRoundedRectangle(
+  ctx: CanvasRenderingContext2D,
+  options: WidgetRenderingOptions,
+) {
+  const lowQuality = isLowQuality();
+  options = {...options};
+  ctx.save();
+  ctx.strokeStyle = options.colorStroke || LiteGraph.WIDGET_OUTLINE_COLOR;
+  ctx.fillStyle = options.colorBackground || LiteGraph.WIDGET_BGCOLOR;
+  ctx.beginPath();
+  ctx.roundRect(
+    ...options.pos,
+    ...options.size,
+    lowQuality ? [0] : options.borderRadius ? [options.borderRadius] : [options.size[1] * 0.5],
+  );
+  ctx.fill();
+  !lowQuality && ctx.stroke();
+  ctx.restore();
+}
+
+type DrawNumberWidgetPartOptions = {
+  posX: number;
+  posY: number;
+  height: number;
+  value: number;
+  direction?: 1 | -1;
+  textColor?: string;
+};
+
+/**
+ * Draws a number picker with arrows off to each side.
+ *
+ * This is for internal widgets that may have many hit areas (full-width, default number widgets put
+ * the arrows on either side of the full-width row).
+ */
+export function drawNumberWidgetPart(
+  ctx: CanvasRenderingContext2D,
+  options: DrawNumberWidgetPartOptions,
+): [Vector2, Vector2, Vector2] {
+  const arrowWidth = 9;
+  const arrowHeight = 10;
+  const innerMargin = 3;
+  const numberWidth = 32;
+
+  const xBoundsArrowLess: Vector2 = [0, 0];
+  const xBoundsNumber: Vector2 = [0, 0];
+  const xBoundsArrowMore: Vector2 = [0, 0];
+
+  ctx.save();
+
+  let posX = options.posX;
+  const {posY, height, value, textColor} = options;
+  const midY = posY + height / 2;
+
+  // If we're drawing parts from right to left (usually when something in the middle will be
+  // flexible), then we can simply move left the expected width of our widget and draw forwards.
+  if (options.direction === -1) {
+    posX = posX - arrowWidth - innerMargin - numberWidth - innerMargin - arrowWidth;
+  }
+
+  // Draw the strength left arrow.
+  ctx.fill(
+    new Path2D(
+      `M ${posX} ${midY} l ${arrowWidth} ${
+        arrowHeight / 2
+      } l 0 -${arrowHeight} L ${posX} ${midY} z`,
+    ),
+  );
+
+  xBoundsArrowLess[0] = posX;
+  xBoundsArrowLess[1] = arrowWidth;
+  posX += arrowWidth + innerMargin;
+
+  // Draw the strength text.
+  ctx.textAlign = "center";
+  ctx.textBaseline = "middle";
+  const oldTextcolor = ctx.fillStyle;
+  if (textColor) {
+    ctx.fillStyle = textColor;
+  }
+  ctx.fillText(fitString(ctx, value.toFixed(2), numberWidth), posX + numberWidth / 2, midY);
+  ctx.fillStyle = oldTextcolor;
+
+  xBoundsNumber[0] = posX;
+  xBoundsNumber[1] = numberWidth;
+  posX += numberWidth + innerMargin;
+
+  // Draw the strength right arrow.
+  ctx.fill(
+    new Path2D(
+      `M ${posX} ${midY - arrowHeight / 2} l ${arrowWidth} ${arrowHeight / 2} l -${arrowWidth} ${
+        arrowHeight / 2
+      } v -${arrowHeight} z`,
+    ),
+  );
+
+  xBoundsArrowMore[0] = posX;
+  xBoundsArrowMore[1] = arrowWidth;
+
+  ctx.restore();
+
+  return [xBoundsArrowLess, xBoundsNumber, xBoundsArrowMore];
+}
+drawNumberWidgetPart.WIDTH_TOTAL = 9 + 3 + 32 + 3 + 9;
+
+type DrawTogglePartOptions = {
+  posX: number;
+  posY: number;
+  height: number;
+  value: boolean | null;
+};
+
+/**
+ * Draws a toggle for a widget. The toggle is a three-way switch with left being false, right being
+ * true, and a middle state being null.
+ */
+export function drawTogglePart(
+  ctx: CanvasRenderingContext2D,
+  options: DrawTogglePartOptions,
+): Vector2 {
+  const lowQuality = isLowQuality();
+  ctx.save();
+
+  const {posX, posY, height, value} = options;
+
+  const toggleRadius = height * 0.36; // This is the standard toggle height calc.
+  const toggleBgWidth = height * 1.5; // We don't draw a separate bg, but this would be it.
+
+  // Toggle Track
+  if (!lowQuality) {
+    ctx.beginPath();
+    ctx.roundRect(posX + 4, posY + 4, toggleBgWidth - 8, height - 8, [height * 0.5]);
+    ctx.globalAlpha = app.canvas.editor_alpha * 0.25;
+    ctx.fillStyle = "rgba(255,255,255,0.45)";
+    ctx.fill();
+    ctx.globalAlpha = app.canvas.editor_alpha;
+  }
+
+  // Toggle itself
+  ctx.fillStyle = value === true ? "#89B" : "#888";
+  const toggleX =
+    lowQuality || value === false
+      ? posX + height * 0.5
+      : value === true
+        ? posX + height
+        : posX + height * 0.75;
+  ctx.beginPath();
+  ctx.arc(toggleX, posY + height * 0.5, toggleRadius, 0, Math.PI * 2);
+  ctx.fill();
+
+  ctx.restore();
+
+  return [posX, toggleBgWidth];
+}
+
+export function drawInfoIcon(
+  ctx: CanvasRenderingContext2D,
+  x: number,
+  y: number,
+  size: number = 12,
+  treatment: 'FILLED' | 'OUTLINED' | 'GRAYED' = 'GRAYED'
+) {
+  ctx.save();
+  ctx.beginPath();
+  ctx.roundRect(x, y, size, size, [size * 0.1]);
+  if (treatment === 'GRAYED') {
+    ctx.fillStyle = "#aaa";
+    ctx.strokeStyle = "#aaa";
+  } else {
+    ctx.fillStyle = "#2f82ec";
+    ctx.strokeStyle = "#2f82ec";
+    // ctx.strokeStyle = "#0f2a5e";
+  }
+  if (treatment === 'FILLED') {
+    ctx.fill();
+  } else {
+    ctx.stroke();
+  }
+  ctx.strokeStyle = "#FFF";
+  ctx.lineWidth = 2;
+  // ctx.lineCap = 'round';
+  const midX = x + size / 2;
+  const serifSize = size * 0.175;
+  ctx.stroke(
+    new Path2D(`
+    M ${midX} ${y + size * 0.15}
+    v 2
+    M ${midX - serifSize} ${y + size * 0.45}
+    h ${serifSize}
+    v ${size * 0.325}
+    h ${serifSize}
+    h -${serifSize * 2}
+  `),
+  );
+  ctx.restore();
+}
+
+export function drawPlusIcon(
+  ctx: CanvasRenderingContext2D,
+  x: number,
+  midY: number,
+  size: number = 12,
+) {
+  ctx.save();
+  const s = size / 3;
+  const plus = new Path2D(`
+    M ${x} ${midY + s / 2}
+    v-${s} h${s} v-${s} h${s}
+    v${s} h${s} v${s} h-${s}
+    v${s} h-${s} v-${s} h-${s}
+    z
+  `);
+  ctx.lineJoin = "round";
+  ctx.lineCap = "round";
+  ctx.fillStyle = "#3a3";
+  ctx.strokeStyle = "#383";
+  ctx.fill(plus);
+  ctx.stroke(plus);
+
+  ctx.restore();
+}
+
+/**
+ * Draws a better button.
+ */
+export function drawWidgetButton(
+  ctx: CanvasRenderingContext2D,
+  options: WidgetRenderingOptions,
+  text: string | null = null,
+  isMouseDownedAndOver: boolean = false,
+) {
+  const borderRadius = isLowQuality() ? 0 : (options.borderRadius ?? 4);
+  ctx.save();
+
+  if (!isLowQuality() && !isMouseDownedAndOver) {
+    drawRoundedRectangle(ctx, {
+      size: [options.size[0] - 2, options.size[1]],
+      pos: [options.pos[0] + 1, options.pos[1] + 1],
+      borderRadius,
+      colorBackground: "#000000aa",
+      colorStroke: "#000000aa",
+    });
+  }
+
+  // BG
+  drawRoundedRectangle(ctx, {
+    size: options.size,
+    pos: [options.pos[0], options.pos[1] + (isMouseDownedAndOver ? 1 : 0)],
+    borderRadius,
+    colorBackground: isMouseDownedAndOver ? "#444" : LiteGraph.WIDGET_BGCOLOR,
+    colorStroke: "transparent",
+  });
+
+  if (isLowQuality()) {
+    ctx.restore();
+    return;
+  }
+
+  if (!isMouseDownedAndOver) {
+    // Shadow
+    drawRoundedRectangle(ctx, {
+      size: [options.size[0] - 0.75, options.size[1] - 0.75],
+      pos: options.pos,
+      borderRadius: borderRadius - 0.5,
+      colorBackground: "transparent",
+      colorStroke: "#00000044",
+    });
+
+    // Highlight
+    drawRoundedRectangle(ctx, {
+      size: [options.size[0] - 0.75, options.size[1] - 0.75],
+      pos: [options.pos[0] + 0.75, options.pos[1] + 0.75],
+      borderRadius: borderRadius - 0.5,
+      colorBackground: "transparent",
+      colorStroke: "#ffffff11",
+    });
+  }
+
+  // Stroke
+  drawRoundedRectangle(ctx, {
+    size: options.size,
+    pos: [options.pos[0], options.pos[1] + (isMouseDownedAndOver ? 1 : 0)],
+    borderRadius,
+    colorBackground: "transparent",
+  });
+
+  if (!isLowQuality() && text) {
+    ctx.textBaseline = "middle";
+    ctx.textAlign = "center";
+    ctx.fillStyle = LiteGraph.WIDGET_TEXT_COLOR;
+    ctx.fillText(
+      text,
+      options.size[0] / 2,
+      options.pos[1] + options.size[1] / 2 + (isMouseDownedAndOver ? 1 : 0),
+    );
+  }
+  ctx.restore();
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/utils_deprecated_comfyui.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/utils_deprecated_comfyui.ts
new file mode 100644
index 0000000000000000000000000000000000000000..2077e6e92f8e459ca4c85688e8daf186efdad3ab
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/utils_deprecated_comfyui.ts
@@ -0,0 +1,294 @@
+/**
+ * [🤮] ComfyUI started deprecating the use of their legacy JavaScript files. These are ports/shims
+ * since we relied on them at one point.
+ *
+ * TODO: Should probably remove these all together at some point.
+ */
+
+import {app} from "scripts/app.js";
+
+import type {INodeInputSlot, INodeOutputSlot, InputSpec, LGraphNode} from "@comfyorg/frontend";
+
+/** Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/extensions/core/widgetInputs.ts#L462 */
+interface PrimitiveNode extends LGraphNode {
+  recreateWidget(): void;
+  onLastDisconnect(): void;
+}
+
+/** Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/renderer/utils/nodeTypeGuards.ts */
+function isPrimitiveNode(node: LGraphNode): node is PrimitiveNode {
+  return node.type === "PrimitiveNode";
+}
+
+/**
+ * CONFIG and GET_CONFIG in https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/services/litegraphService.ts
+ * are not accessible publicly, so we need to look at a slot.widget's symbols and check to see if it
+ * matches rather than access it directly. Cool...
+ */
+function getWidgetGetConfigSymbols(slot: INodeOutputSlot | INodeInputSlot): {
+  CONFIG?: symbol;
+  GET_CONFIG?: symbol;
+} {
+  const widget = slot?.widget;
+  if (!widget) return {};
+  const syms = Object.getOwnPropertySymbols(widget || {});
+  for (const sym of syms) {
+    const symVal = widget![sym];
+    const isGetConfig = typeof symVal === "function";
+    let maybeCfg = isGetConfig ? symVal() : symVal;
+    if (
+      Array.isArray(maybeCfg) &&
+      maybeCfg.length >= 2 &&
+      typeof maybeCfg[0] === "string" &&
+      (maybeCfg[0] === "*" || typeof maybeCfg[1]?.type === "string")
+    ) {
+      return isGetConfig ? {GET_CONFIG: sym} : {CONFIG: sym};
+    }
+  }
+  return {};
+}
+
+/**
+ * Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/extensions/core/widgetInputs.ts
+ */
+export function getWidgetConfig(slot: INodeOutputSlot | INodeInputSlot): InputSpec {
+  const configSyms = getWidgetGetConfigSymbols(slot);
+  const widget = slot.widget || ({} as any);
+  return (
+    (configSyms.CONFIG && widget[configSyms.CONFIG]) ??
+    (configSyms.GET_CONFIG && widget[configSyms.GET_CONFIG]?.()) ?? ["*", {}]
+  );
+}
+
+/**
+ * This is lossy, since we don't have access to GET_CONFIG Symbol, we cannot accurately set it. As a
+ * best-chance we can look for a function that seems to return a
+ *
+ * Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/extensions/core/widgetInputs.ts
+ */
+export function setWidgetConfig(
+  slot: INodeOutputSlot | INodeInputSlot | undefined,
+  config: InputSpec,
+) {
+  if (!slot?.widget) return;
+  if (config) {
+    const configSyms = getWidgetGetConfigSymbols(slot);
+    const widget = slot.widget || ({} as any);
+    if (configSyms.GET_CONFIG) {
+      widget[configSyms.GET_CONFIG] = () => config;
+    } else if (configSyms.CONFIG) {
+      widget[configSyms.CONFIG] = config;
+    } else {
+      console.error(
+        "Cannot set widget Config. This is due to ComfyUI removing the ability to call legacy " +
+          "JavaScript APIs that are now deprecated without new, supported APIs. It's possible " +
+          "some things in rgthree-comfy do not work correctly. If you see this, please file a bug.",
+      );
+    }
+  } else {
+    delete slot.widget;
+  }
+
+  if ("link" in slot) {
+    const link = app.graph.links[(slot as INodeInputSlot)?.link ?? -1];
+    if (link) {
+      const originNode = app.graph.getNodeById(link.origin_id);
+      if (originNode && isPrimitiveNode(originNode)) {
+        if (config) {
+          originNode.recreateWidget();
+        } else if (!app.configuringGraph) {
+          originNode.disconnectOutput(0);
+          originNode.onLastDisconnect();
+        }
+      }
+    }
+  }
+}
+
+/**
+ * A slimmed-down version of `mergeIfValid` for only what was needed in rgthree-comfy.
+ *
+ * Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/extensions/core/widgetInputs.ts
+ */
+export function mergeIfValid(
+  output: INodeOutputSlot | INodeInputSlot,
+  config2: InputSpec,
+): InputSpec[1] | null {
+  const config1 = getWidgetConfig(output);
+  const customSpec = mergeInputSpec(config1, config2);
+  if (customSpec) {
+    setWidgetConfig(output, customSpec);
+  }
+  return customSpec?.[1] ?? null;
+}
+
+/**
+ * Merges two input specs.
+ *
+ * Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/utils/nodeDefUtil.ts
+ */
+const mergeInputSpec = (spec1: InputSpec, spec2: InputSpec): InputSpec | null => {
+  const type1 = getInputSpecType(spec1);
+  const type2 = getInputSpecType(spec2);
+
+  if (type1 !== type2) {
+    return null;
+  }
+
+  if (isIntInputSpec(spec1) || isFloatInputSpec(spec1)) {
+    return mergeNumericInputSpec(spec1, spec2 as typeof spec1);
+  }
+
+  if (isComboInputSpec(spec1)) {
+    return mergeComboInputSpec(spec1, spec2 as typeof spec1);
+  }
+
+  return mergeCommonInputSpec(spec1, spec2);
+};
+
+/**
+ * Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/schemas/nodeDefSchema.ts
+ */
+function getInputSpecType(inputSpec: InputSpec): string {
+  return isComboInputSpec(inputSpec) ? "COMBO" : inputSpec[0];
+}
+
+/**
+ * Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/schemas/nodeDefSchema.ts
+ */
+function isComboInputSpecV1(inputSpec: InputSpec) {
+  return Array.isArray(inputSpec[0]);
+}
+
+/**
+ * Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/schemas/nodeDefSchema.ts
+ */
+function isIntInputSpec(inputSpec: InputSpec) {
+  return inputSpec[0] === "INT";
+}
+
+/**
+ * Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/schemas/nodeDefSchema.ts
+ */
+function isFloatInputSpec(inputSpec: InputSpec) {
+  return inputSpec[0] === "FLOAT";
+}
+
+/**
+ * Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/schemas/nodeDefSchema.ts
+ */
+function isComboInputSpecV2(inputSpec: InputSpec) {
+  return inputSpec[0] === "COMBO";
+}
+
+/**
+ * Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/schemas/nodeDefSchema.ts
+ */
+function isComboInputSpec(inputSpec: InputSpec) {
+  return isComboInputSpecV1(inputSpec) || isComboInputSpecV2(inputSpec);
+}
+
+/** Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/utils/nodeDefUtil.ts */
+const getRange = (options: any) => {
+  const min = options.min ?? -Infinity;
+  const max = options.max ?? Infinity;
+  return {min, max};
+};
+
+/** Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/utils/nodeDefUtil.ts */
+const mergeNumericInputSpec = <T extends any>(spec1: any, spec2: any): T | null => {
+  const type = spec1[0];
+  const options1 = spec1[1] ?? {};
+  const options2 = spec2[1] ?? {};
+
+  const range1 = getRange(options1);
+  const range2 = getRange(options2);
+
+  // If the ranges do not overlap, return null
+  if (range1.min > range2.max || range1.max < range2.min) {
+    return null;
+  }
+
+  const step1 = options1.step ?? 1;
+  const step2 = options2.step ?? 1;
+
+  const mergedOptions = {
+    // Take intersection of ranges
+    min: Math.max(range1.min, range2.min),
+    max: Math.min(range1.max, range2.max),
+    step: lcm(step1, step2),
+  };
+
+  return mergeCommonInputSpec(
+    [type, {...options1, ...mergedOptions}] as T,
+    [type, {...options2, ...mergedOptions}] as T,
+  );
+};
+
+/** Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/utils/nodeDefUtil.ts */
+const mergeComboInputSpec = <T extends any>(spec1: any, spec2: any): T | null => {
+  const options1 = spec1[1] ?? {};
+  const options2 = spec2[1] ?? {};
+
+  const comboOptions1 = getComboSpecComboOptions(spec1);
+  const comboOptions2 = getComboSpecComboOptions(spec2);
+
+  const intersection = comboOptions1.filter((value) => comboOptions2.includes(value));
+
+  // If the intersection is empty, return null
+  if (intersection.length === 0) {
+    return null;
+  }
+
+  return mergeCommonInputSpec(
+    ["COMBO", {...options1, options: intersection}] as T,
+    ["COMBO", {...options2, options: intersection}] as T,
+  );
+};
+
+/** Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/utils/nodeDefUtil.ts */
+const mergeCommonInputSpec = <T extends InputSpec>(spec1: T, spec2: T): T | null => {
+  const type = getInputSpecType(spec1);
+  const options1 = spec1[1] ?? {};
+  const options2 = spec2[1] ?? {};
+
+  const compareKeys = [...new Set([...Object.keys(options1), ...Object.keys(options2)])].filter(
+    (key: string) => !IGNORE_KEYS.has(key),
+  );
+
+  const mergeIsValid = compareKeys.every((key: string) => {
+    const value1 = options1[key];
+    const value2 = options2[key];
+    return value1 === value2 || (value1 == null && value2 == null);
+  });
+
+  return mergeIsValid ? ([type, {...options1, ...options2}] as T) : null;
+};
+
+/** Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/utils/nodeDefUtil.ts */
+const IGNORE_KEYS = new Set<string>([
+  "default",
+  "forceInput",
+  "defaultInput",
+  "control_after_generate",
+  "multiline",
+  "tooltip",
+  "dynamicPrompts",
+]);
+
+/**
+ * Derived from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/schemas/nodeDefSchema.ts
+ */
+function getComboSpecComboOptions(inputSpec: any): (number | string)[] {
+  return (isComboInputSpecV2(inputSpec) ? inputSpec[1]?.options : inputSpec[0]) ?? [];
+}
+
+/** Taken from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/utils/mathUtil.ts */
+const lcm = (a: number, b: number): number => {
+  return Math.abs(a * b) / gcd(a, b);
+};
+
+/** Taken from https://github.com/Comfy-Org/ComfyUI_frontend/blob/1f3fb90b1b79c4190b3faa7928b05a8ba3671307/src/utils/mathUtil.ts */
+const gcd = (a: number, b: number): number => {
+  return b === 0 ? a : gcd(b, a % b);
+};
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/utils_inputs_outputs.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/utils_inputs_outputs.ts
new file mode 100644
index 0000000000000000000000000000000000000000..69a215d6631fdfff01ec6c3bc31114b7868a73f1
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/utils_inputs_outputs.ts
@@ -0,0 +1,18 @@
+import type {LGraphNode} from "@comfyorg/frontend";
+import { RgthreeBaseNode } from "./base_node";
+
+/** Removes all inputs from the end. */
+export function removeUnusedInputsFromEnd(node: LGraphNode, minNumber = 1, nameMatch?: RegExp) {
+  // No need to remove inputs from nodes that have been removed. This can happen because we may
+  // have debounced cleanup tasks.
+  if ((node as RgthreeBaseNode).removed) return;
+  for (let i = node.inputs.length - 1; i >= minNumber; i--) {
+    if (!node.inputs[i]?.link) {
+      if (!nameMatch || nameMatch.test(node.inputs[i]!.name)) {
+        node.removeInput(i);
+      }
+      continue;
+    }
+    break;
+  }
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/utils_menu.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/utils_menu.ts
new file mode 100644
index 0000000000000000000000000000000000000000..fdf921a0f066716f3f55ed081bbcb4bd8ba2690d
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/utils_menu.ts
@@ -0,0 +1,98 @@
+import type {
+  LGraphCanvas as TLGraphCanvas,
+  LGraphNode,
+  ContextMenu,
+  IContextMenuValue,
+  IBaseWidget,
+  IContextMenuOptions,
+} from "@comfyorg/frontend";
+
+import {app} from "scripts/app.js";
+import {rgthreeApi} from "rgthree/common/rgthree_api.js";
+
+const PASS_THROUGH = function <T extends any, I extends any>(item: T) {
+  return item as T;
+};
+
+/**
+ * Shows a lora chooser context menu.
+ */
+export async function showLoraChooser(
+  event: PointerEvent | MouseEvent,
+  callback: IContextMenuOptions["callback"],
+  parentMenu?: ContextMenu | null,
+  loras?: string[],
+) {
+  const canvas = app.canvas as TLGraphCanvas;
+  if (!loras) {
+    loras = ["None", ...(await rgthreeApi.getLoras().then((loras) => loras.map((l) => l.file)))];
+  }
+  new LiteGraph.ContextMenu(loras, {
+    event: event,
+    parentMenu: parentMenu != null ? parentMenu : undefined,
+    title: "Choose a lora",
+    scale: Math.max(1, canvas.ds?.scale ?? 1),
+    className: "dark",
+    callback,
+  });
+}
+
+/**
+ * Shows a context menu chooser of nodes.
+ *
+ * @param mapFn The function used to map each node to the context menu item. If null is returned
+ *     it will be filtered out (rather than use a separate filter method).
+ */
+export function showNodesChooser<T extends IContextMenuValue>(
+  event: PointerEvent | MouseEvent,
+  mapFn: (n: LGraphNode) => T | null,
+  callback: IContextMenuOptions["callback"],
+  parentMenu?: ContextMenu,
+) {
+  const canvas = app.canvas as TLGraphCanvas;
+  const nodesOptions: T[] = (app.graph._nodes as LGraphNode[])
+    .map(mapFn)
+    .filter((e): e is NonNullable<any> => e != null);
+
+  nodesOptions.sort((a: any, b: any) => {
+    return a.value - b.value;
+  });
+
+  new LiteGraph.ContextMenu(nodesOptions, {
+    event: event,
+    parentMenu,
+    title: "Choose a node id",
+    scale: Math.max(1, canvas.ds?.scale ?? 1),
+    className: "dark",
+    callback,
+  });
+}
+
+/**
+ * Shows a conmtext menu chooser for a specific node.
+ *
+ * @param mapFn The function used to map each node to the context menu item. If null is returned
+ *     it will be filtered out (rather than use a separate filter method).
+ */
+export function showWidgetsChooser<T extends IContextMenuValue>(
+  event: PointerEvent | MouseEvent,
+  node: LGraphNode,
+  mapFn: (n: IBaseWidget) => T | null,
+  callback: IContextMenuOptions["callback"],
+  parentMenu?: ContextMenu,
+) {
+  const options: T[] = (node.widgets || [])
+    .map(mapFn)
+    .filter((e): e is NonNullable<any> => e != null);
+  if (options.length) {
+    const canvas = app.canvas as TLGraphCanvas;
+    new LiteGraph.ContextMenu(options, {
+      event,
+      parentMenu,
+      title: "Choose an input/widget",
+      scale: Math.max(1, canvas.ds?.scale ?? 1),
+      className: "dark",
+      callback,
+    });
+  }
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/comfyui/utils_widgets.ts b/custom_nodes/rgthree-comfy/src_web/comfyui/utils_widgets.ts
new file mode 100644
index 0000000000000000000000000000000000000000..b6c765b97cb0d1cb79380a5f4bf3faa1b18f67ac
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/comfyui/utils_widgets.ts
@@ -0,0 +1,506 @@
+import type {
+  LGraphNode,
+  LGraphCanvas as TLGraphCanvas,
+  Vector2,
+  ICustomWidget,
+  IWidgetOptions,
+  CanvasPointerEvent,
+} from "@comfyorg/frontend";
+
+import {app} from "scripts/app.js";
+import {drawNodeWidget, drawWidgetButton, fitString, isLowQuality} from "./utils_canvas.js";
+
+type Vector4 = [number, number, number, number];
+
+/**
+ * Draws a label on teft, and a value on the right, ellipsizing when out of space.
+ */
+export function drawLabelAndValue(
+  ctx: CanvasRenderingContext2D,
+  label: string,
+  value: string,
+  width: number,
+  posY: number,
+  height: number,
+  options?: {offsetLeft: number},
+) {
+  const outerMargin = 15;
+  const innerMargin = 10;
+  const midY = posY + height / 2;
+  ctx.save();
+  ctx.textAlign = "left";
+  ctx.textBaseline = "middle";
+  ctx.fillStyle = LiteGraph.WIDGET_SECONDARY_TEXT_COLOR;
+  const labelX = outerMargin + innerMargin + (options?.offsetLeft ?? 0);
+  ctx.fillText(label, labelX, midY);
+
+  const valueXLeft = labelX + ctx.measureText(label).width + 7;
+  const valueXRight = width - (outerMargin + innerMargin);
+
+  ctx.fillStyle = LiteGraph.WIDGET_TEXT_COLOR;
+  ctx.textAlign = "right";
+  ctx.fillText(fitString(ctx, value, valueXRight - valueXLeft), valueXRight, midY);
+  ctx.restore();
+}
+
+export type RgthreeBaseWidgetBounds = {
+  /** The bounds, either [x, width] assuming the full height, or [x, y, width, height] if height. */
+  bounds: Vector2 | Vector4;
+  onDown?(
+    event: CanvasPointerEvent,
+    pos: Vector2,
+    node: LGraphNode,
+    bounds: RgthreeBaseWidgetBounds,
+  ): boolean | void;
+  onUp?(
+    event: CanvasPointerEvent,
+    pos: Vector2,
+    node: LGraphNode,
+    bounds: RgthreeBaseWidgetBounds,
+  ): boolean | void;
+  onMove?(
+    event: CanvasPointerEvent,
+    pos: Vector2,
+    node: LGraphNode,
+    bounds: RgthreeBaseWidgetBounds,
+  ): boolean | void;
+  onClick?(
+    event: CanvasPointerEvent,
+    pos: Vector2,
+    node: LGraphNode,
+    bounds: RgthreeBaseWidgetBounds,
+  ): boolean | void;
+  data?: any;
+  wasMouseClickedAndIsOver?: boolean;
+};
+
+export type RgthreeBaseHitAreas<Keys extends string> = {
+  [K in Keys]: RgthreeBaseWidgetBounds;
+};
+
+type NotArray<T> = T extends Array<any> ? never : T;
+
+/**
+ * A base widget that handles mouse events more properly.
+ */
+export abstract class RgthreeBaseWidget<V extends ICustomWidget["value"]> implements ICustomWidget {
+  // Needed here b/c it was added to ComfyUI's types for IBaseWidget. No idea what they use it for,
+  // or why it's only boolean.
+  [symbol: symbol]: boolean;
+
+  // We don't want our value to be an array as a widget will be serialized as an "input" for the API
+  // which uses an array value to represent a link. To keep things simpler, we'll avoid using an
+  // array at all.
+  abstract value: NotArray<V>;
+
+  type: ICustomWidget["type"] = "custom";
+  name: string;
+  options: IWidgetOptions = {};
+  y: number = 0;
+  last_y: number = 0;
+  disabled: boolean = false;
+
+  protected mouseDowned: Vector2 | null = null;
+  protected isMouseDownedAndOver: boolean = false;
+
+  // protected hitAreas: {[key: string]: RgthreeBaseWidgetBounds} = {};
+  protected readonly hitAreas: RgthreeBaseHitAreas<any> = {};
+  private downedHitAreasForMove: RgthreeBaseWidgetBounds[] = [];
+  private downedHitAreasForClick: RgthreeBaseWidgetBounds[] = [];
+
+  constructor(name: string) {
+    this.name = name;
+  }
+
+  serializeValue(node: LGraphNode, index: number): Promise<V> | V {
+    return this.value;
+  }
+
+  private clickWasWithinBounds(pos: Vector2, bounds: Vector2 | Vector4) {
+    let xStart = bounds[0];
+    let xEnd = xStart + (bounds.length > 2 ? bounds[2]! : bounds[1]!);
+    const clickedX = pos[0] >= xStart && pos[0] <= xEnd;
+    if (bounds.length === 2) {
+      return clickedX;
+    }
+    return clickedX && pos[1] >= bounds[1] && pos[1] <= bounds[1] + bounds[3]!;
+  }
+
+  mouse(event: CanvasPointerEvent, pos: Vector2, node: LGraphNode) {
+    const canvas = app.canvas as TLGraphCanvas;
+
+    if (event.type == "pointerdown") {
+      this.mouseDowned = [...pos] as Vector2;
+      this.isMouseDownedAndOver = true;
+      this.downedHitAreasForMove.length = 0;
+      this.downedHitAreasForClick.length = 0;
+      // Loop over out bounds data and call any specifics.
+      let anyHandled = false;
+      for (const part of Object.values(this.hitAreas)) {
+        if (this.clickWasWithinBounds(pos, part.bounds)) {
+          if (part.onMove) {
+            this.downedHitAreasForMove.push(part);
+          }
+          if (part.onClick) {
+            this.downedHitAreasForClick.push(part);
+          }
+          if (part.onDown) {
+            const thisHandled = part.onDown.apply(this, [event, pos, node, part]);
+            anyHandled = anyHandled || thisHandled == true;
+          }
+          part.wasMouseClickedAndIsOver = true;
+        }
+      }
+      return this.onMouseDown(event, pos, node) ?? anyHandled;
+    }
+
+    // This only fires when LiteGraph has a node_widget (meaning it's pressed), but we may not be
+    // the original widget pressed, so we still need `mouseDowned`.
+    if (event.type == "pointerup") {
+      if (!this.mouseDowned) return true;
+      this.downedHitAreasForMove.length = 0;
+      const wasMouseDownedAndOver = this.isMouseDownedAndOver;
+      this.cancelMouseDown();
+      let anyHandled = false;
+      for (const part of Object.values(this.hitAreas)) {
+        if (part.onUp && this.clickWasWithinBounds(pos, part.bounds)) {
+          const thisHandled = part.onUp.apply(this, [event, pos, node, part]);
+          anyHandled = anyHandled || thisHandled == true;
+        }
+        part.wasMouseClickedAndIsOver = false;
+      }
+      for (const part of this.downedHitAreasForClick) {
+        if (this.clickWasWithinBounds(pos, part.bounds)) {
+          const thisHandled = part.onClick!.apply(this, [event, pos, node, part]);
+          anyHandled = anyHandled || thisHandled == true;
+        }
+      }
+      this.downedHitAreasForClick.length = 0;
+      if (wasMouseDownedAndOver) {
+        const thisHandled = this.onMouseClick(event, pos, node);
+        anyHandled = anyHandled || thisHandled == true;
+      }
+      return this.onMouseUp(event, pos, node) ?? anyHandled;
+    }
+
+    // This only fires when LiteGraph has a node_widget (meaning it's pressed).
+    if (event.type == "pointermove") {
+      this.isMouseDownedAndOver = !!this.mouseDowned;
+      // If we've moved off the button while pressing, then consider us no longer pressing.
+      if (
+        this.mouseDowned &&
+        (pos[0] < 15 ||
+          pos[0] > node.size[0] - 15 ||
+          pos[1] < this.last_y ||
+          pos[1] > this.last_y + LiteGraph.NODE_WIDGET_HEIGHT)
+      ) {
+        this.isMouseDownedAndOver = false;
+      }
+      for (const part of Object.values(this.hitAreas)) {
+        if (this.downedHitAreasForMove.includes(part)) {
+          part.onMove!.apply(this, [event, pos, node, part]);
+        }
+        if (this.downedHitAreasForClick.includes(part)) {
+          part.wasMouseClickedAndIsOver = this.clickWasWithinBounds(pos, part.bounds);
+        }
+      }
+      return this.onMouseMove(event, pos, node) ?? true;
+    }
+    return false;
+  }
+
+  /** Sometimes we want to cancel a mouse down, so that an up/move aren't fired. */
+  cancelMouseDown() {
+    this.mouseDowned = null;
+    this.isMouseDownedAndOver = false;
+    this.downedHitAreasForMove.length = 0;
+  }
+
+  /** An event that fires when the pointer is pressed down (once). */
+  onMouseDown(event: CanvasPointerEvent, pos: Vector2, node: LGraphNode): boolean | void {
+    return;
+  }
+
+  /**
+   * An event that fires when the pointer is let go. Only fires if this was the widget that was
+   * originally pressed down.
+   */
+  onMouseUp(event: CanvasPointerEvent, pos: Vector2, node: LGraphNode): boolean | void {
+    return;
+  }
+
+  /**
+   * An event that fires when the pointer is let go _over the widget_ and when the widget that was
+   * originally pressed down.
+   */
+  onMouseClick(event: CanvasPointerEvent, pos: Vector2, node: LGraphNode): boolean | void {
+    return;
+  }
+
+  /**
+   * An event that fires when the pointer is moving after pressing down. Will fire both on and off
+   * of the widget. Check `isMouseDownedAndOver` to determine if the mouse is currently over the
+   * widget or not.
+   */
+  onMouseMove(event: CanvasPointerEvent, pos: Vector2, node: LGraphNode): boolean | void {
+    return;
+  }
+}
+
+/**
+ * A better implementation of the LiteGraph button widget.
+ */
+export class RgthreeBetterButtonWidget extends RgthreeBaseWidget<string> {
+  override readonly type = "custom";
+
+  value: string = "";
+  label: string = "";
+  mouseClickCallback: (event: CanvasPointerEvent, pos: Vector2, node: LGraphNode) => boolean | void;
+
+  constructor(
+    name: string,
+    mouseClickCallback: (
+      event: CanvasPointerEvent,
+      pos: Vector2,
+      node: LGraphNode,
+    ) => boolean | void,
+    label?: string,
+  ) {
+    super(name);
+    this.mouseClickCallback = mouseClickCallback;
+    this.label = label || name;
+  }
+
+  draw(ctx: CanvasRenderingContext2D, node: LGraphNode, width: number, y: number, height: number) {
+    drawWidgetButton(
+      ctx,
+      {size: [width - 30, height], pos: [15, y]},
+      this.label,
+      this.isMouseDownedAndOver,
+    );
+  }
+
+  override onMouseClick(event: CanvasPointerEvent, pos: Vector2, node: LGraphNode) {
+    return this.mouseClickCallback(event, pos, node);
+  }
+}
+
+/**
+ * A better implementation of the LiteGraph text widget, including auto ellipsis.
+ */
+export class RgthreeBetterTextWidget extends RgthreeBaseWidget<string> {
+  value: string;
+
+  constructor(name: string, value: string) {
+    super(name);
+    this.name = name;
+    this.value = value;
+  }
+
+  draw(ctx: CanvasRenderingContext2D, node: LGraphNode, width: number, y: number, height: number) {
+    const widgetData = drawNodeWidget(ctx, {size: [width, height], pos: [15, y]});
+
+    if (!widgetData.lowQuality) {
+      drawLabelAndValue(ctx, this.name, this.value, width, y, height);
+    }
+  }
+
+  override mouse(event: CanvasPointerEvent, pos: Vector2, node: LGraphNode): boolean {
+    const canvas = app.canvas as TLGraphCanvas;
+    if (event.type == "pointerdown") {
+      canvas.prompt("Label", this.value, (v: string) => (this.value = v), event);
+      return true;
+    }
+    return false;
+  }
+}
+
+/**
+ * Options for the Divider Widget.
+ */
+type RgthreeDividerWidgetOptions = {
+  marginTop: number;
+  marginBottom: number;
+  marginLeft: number;
+  marginRight: number;
+  color: string;
+  thickness: number;
+};
+
+/**
+ * A divider widget; can also be used as a spacer if fed a 0 thickness.
+ */
+export class RgthreeDividerWidget extends RgthreeBaseWidget<{}> {
+  override value = {};
+  override options = {serialize: false};
+  override readonly type = "custom";
+
+  private readonly widgetOptions: RgthreeDividerWidgetOptions = {
+    marginTop: 7,
+    marginBottom: 7,
+    marginLeft: 15,
+    marginRight: 15,
+    color: LiteGraph.WIDGET_OUTLINE_COLOR,
+    thickness: 1,
+  };
+
+  constructor(widgetOptions?: Partial<RgthreeDividerWidgetOptions>) {
+    super("divider");
+    Object.assign(this.widgetOptions, widgetOptions || {});
+  }
+
+  draw(ctx: CanvasRenderingContext2D, node: LGraphNode, width: number, posY: number, h: number) {
+    if (this.widgetOptions.thickness) {
+      ctx.strokeStyle = this.widgetOptions.color;
+      const x = this.widgetOptions.marginLeft;
+      const y = posY + this.widgetOptions.marginTop;
+      const w = width - this.widgetOptions.marginLeft - this.widgetOptions.marginRight;
+      ctx.stroke(new Path2D(`M ${x} ${y} h ${w}`));
+    }
+  }
+
+  computeSize(width: number): [number, number] {
+    return [
+      width,
+      this.widgetOptions.marginTop + this.widgetOptions.marginBottom + this.widgetOptions.thickness,
+    ];
+  }
+}
+
+/**
+ * Options for the Label Widget.
+ */
+export type RgthreeLabelWidgetOptions = {
+  align?: "left" | "center" | "right";
+  color?: string;
+  italic?: boolean;
+  size?: number;
+  text?: string | (() => string); // Text, or fall back to the name.
+
+  /** A label to put on the right side. */
+  actionLabel?: "__PLUS_ICON__" | string;
+  actionCallback?: (event: PointerEvent | CanvasPointerEvent) => void;
+};
+
+/**
+ * A simple label widget, drawn with no background.
+ */
+export class RgthreeLabelWidget extends RgthreeBaseWidget<string> {
+  override readonly type = "custom";
+  override options = {serialize: false};
+  value = "";
+
+  private readonly widgetOptions: RgthreeLabelWidgetOptions = {};
+  private posY: number = 0;
+
+  constructor(name: string, widgetOptions?: RgthreeLabelWidgetOptions) {
+    super(name);
+    Object.assign(this.widgetOptions, widgetOptions);
+  }
+
+  update(widgetOptions: RgthreeLabelWidgetOptions) {
+    Object.assign(this.widgetOptions, widgetOptions);
+  }
+
+  draw(
+    ctx: CanvasRenderingContext2D,
+    node: LGraphNode,
+    width: number,
+    posY: number,
+    height: number,
+  ) {
+    this.posY = posY;
+    ctx.save();
+
+    let text = this.widgetOptions.text ?? this.name;
+    if (typeof text === "function") {
+      text = text();
+    }
+
+    ctx.textAlign = this.widgetOptions.align || "left";
+    ctx.fillStyle = this.widgetOptions.color || LiteGraph.WIDGET_TEXT_COLOR;
+    const oldFont = ctx.font;
+    if (this.widgetOptions.italic) {
+      ctx.font = "italic " + ctx.font;
+    }
+    if (this.widgetOptions.size) {
+      ctx.font = ctx.font.replace(/\d+px/, `${this.widgetOptions.size}px`);
+    }
+
+    const midY = posY + height / 2;
+    ctx.textBaseline = "middle";
+
+    if (this.widgetOptions.align === "center") {
+      ctx.fillText(text, node.size[0] / 2, midY);
+    } else {
+      ctx.fillText(text, 15, midY);
+    } // TODO(right);
+
+    ctx.font = oldFont;
+
+    if (this.widgetOptions.actionLabel === "__PLUS_ICON__") {
+      const plus = new Path2D(
+        `M${node.size[0] - 15 - 2} ${posY + 7} v4 h-4 v4 h-4 v-4 h-4 v-4 h4 v-4 h4 v4 h4 z`,
+      );
+      ctx.lineJoin = "round";
+      ctx.lineCap = "round";
+      ctx.fillStyle = "#3a3";
+      ctx.strokeStyle = "#383";
+      ctx.fill(plus);
+      ctx.stroke(plus);
+    }
+    ctx.restore();
+  }
+
+  override mouse(event: CanvasPointerEvent, nodePos: Vector2, node: LGraphNode): boolean {
+    if (
+      event.type !== "pointerdown" ||
+      isLowQuality() ||
+      !this.widgetOptions.actionLabel ||
+      !this.widgetOptions.actionCallback
+    ) {
+      return false;
+    }
+
+    const pos: Vector2 = [nodePos[0], nodePos[1] - this.posY];
+    const rightX = node.size[0] - 15;
+    if (pos[0] > rightX || pos[0] < rightX - 16) {
+      return false;
+    }
+    this.widgetOptions.actionCallback(event);
+    return true;
+  }
+}
+
+/** An invisible widget. */
+export class RgthreeInvisibleWidget<T extends ICustomWidget["value"]> extends RgthreeBaseWidget<T> {
+  override readonly type = "custom";
+
+  value: NotArray<T>;
+  private serializeValueFn?: (node: LGraphNode, index: number) => Promise<T> | T;
+
+  constructor(
+    name: string,
+    type: string,
+    value: NotArray<T>,
+    serializeValueFn?: (node: LGraphNode, index: number) => Promise<T> | T,
+  ) {
+    super(name);
+    // this.type = type;
+    this.value = value;
+    this.serializeValueFn = serializeValueFn;
+  }
+
+  draw() {
+    return;
+  }
+  computeSize(width: number): Vector2 {
+    return [0, 0];
+  }
+
+  override serializeValue(node: LGraphNode, index: number): T | Promise<T> {
+    return this.serializeValueFn != null
+      ? this.serializeValueFn(node, index)
+      : super.serializeValue(node, index);
+  }
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/common/comfyui_shim.ts b/custom_nodes/rgthree-comfy/src_web/common/comfyui_shim.ts
new file mode 100644
index 0000000000000000000000000000000000000000..45befcf386a0ac5c16237293ca7d8fc5bb633841
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/common/comfyui_shim.ts
@@ -0,0 +1,49 @@
+/**
+ * [🤮] At some point the new ComfyUI frontend stopped loading it's source as modules and started
+ * bundling them together. This removed the ability for to import the individual modules (like the
+ * api.js or pnginfo.js) in stand alone pages, like link_fixer.
+ *
+ * So, what do we have to do? Well, we have to fork, hardcode, or port what we would want to load
+ * from ComfyUI as our own, independant files again, which is unforunate for several reasons;
+ * duplicate code, risk of falling behind, etc...
+ *
+ * Anyway, this file is a shim that will either detect we're in the ComfyUI app and pass through the
+ * bundled module from the ComfyUI global or load from our own code when that's not available
+ * (because we're not in the actual ComfyUI UI).
+ */
+
+import type {getPngMetadata, getWebpMetadata} from "typings/comfy.js";
+
+const shimCache = new Map<string, any>();
+
+async function shimComfyUiModule(moduleName: string, prop?: string) {
+  let module = shimCache.get(moduleName);
+  if (!module) {
+    if (window.comfyAPI?.[moduleName]) {
+      module = window.comfyAPI?.[moduleName];
+    } else {
+      module = await import(`./comfyui_shim_${moduleName}.js`);
+    }
+    if (!module) {
+      throw new Error(`Module ${moduleName} could not be loaded.`);
+    }
+    shimCache.set(moduleName, module);
+  }
+  if (prop) {
+    if (!module[prop]) {
+      throw new Error(`Property ${prop} on module ${moduleName} could not be loaded.`);
+    }
+    return module[prop];
+  }
+  return module;
+}
+
+export async function getPngMetadata(file: File | Blob) {
+  const fn = (await shimComfyUiModule("pnginfo", "getPngMetadata")) as getPngMetadata;
+  return fn(file);
+}
+
+export async function getWebpMetadata(file: File | Blob) {
+  const fn = (await shimComfyUiModule("pnginfo", "getWebpMetadata")) as getWebpMetadata;
+  return fn(file);
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/common/comfyui_shim_pnginfo.ts b/custom_nodes/rgthree-comfy/src_web/common/comfyui_shim_pnginfo.ts
new file mode 100644
index 0000000000000000000000000000000000000000..e3d4639796c63f009be27d24f34e807579b36b70
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/common/comfyui_shim_pnginfo.ts
@@ -0,0 +1,541 @@
+
+/**
+ * [🤮] See `./comfyui_shim.ts`.
+ *
+ * This code has been forked from https://github.com/Comfy-Org/ComfyUI_frontend/blob/0937c1f2cd5026f390a6efa64f630e01ea414d1d/src/scripts/pnginfo.ts
+ * with some modifications made, such as removing unneeded exported functions, cleaning up trivial
+ * typing, etc.
+ */
+
+import { rgthreeApi } from "./rgthree_api.js";
+
+/**
+ * [🤮] A type to add to untypd portions of the code below where they were not yet typed in Comfy's
+ * code.
+ */
+type lazyComfyAny = any;
+
+
+// [🤮] A shim for ComfyAPI getEmbeddings.
+const api = {
+  async getEmbeddings(): Promise<any> {
+    const resp = await rgthreeApi.fetchComfyApi('/embeddings', { cache: 'no-store' })
+    return await resp.json();
+  }
+}
+
+
+function getFromPngBuffer(buffer: ArrayBuffer) {
+  // Get the PNG data as a Uint8Array
+  const pngData = new Uint8Array(buffer)
+  const dataView = new DataView(pngData.buffer)
+
+  // Check that the PNG signature is present
+  if (dataView.getUint32(0) !== 0x89504e47) {
+    console.error('Not a valid PNG file')
+    return
+  }
+
+  // Start searching for chunks after the PNG signature
+  let offset = 8
+  let txt_chunks: Record<string, string> = {}
+  // Loop through the chunks in the PNG file
+  while (offset < pngData.length) {
+    // Get the length of the chunk
+    const length = dataView.getUint32(offset)
+    // Get the chunk type
+    const type = String.fromCharCode(...pngData.slice(offset + 4, offset + 8))
+    if (type === 'tEXt' || type == 'comf' || type === 'iTXt') {
+      // Get the keyword
+      let keyword_end = offset + 8
+      while (pngData[keyword_end] !== 0) {
+        keyword_end++
+      }
+      const keyword = String.fromCharCode(
+        ...pngData.slice(offset + 8, keyword_end)
+      )
+      // Get the text
+      const contentArraySegment = pngData.slice(
+        keyword_end + 1,
+        offset + 8 + length
+      )
+      const contentJson = new TextDecoder('utf-8').decode(contentArraySegment)
+      txt_chunks[keyword] = contentJson
+    }
+
+    offset += 12 + length
+  }
+  return txt_chunks
+}
+
+function getFromPngFile(file: File) {
+  return new Promise<Record<string, string>>((r) => {
+    const reader = new FileReader()
+    reader.onload = (event) => {
+      r(getFromPngBuffer((event.target as lazyComfyAny).result as ArrayBuffer) as lazyComfyAny)
+    }
+
+    reader.readAsArrayBuffer(file)
+  })
+}
+
+function parseExifData(exifData: lazyComfyAny) {
+  // Check for the correct TIFF header (0x4949 for little-endian or 0x4D4D for big-endian)
+  const isLittleEndian = String.fromCharCode(...exifData.slice(0, 2)) === 'II'
+
+  // Function to read 16-bit and 32-bit integers from binary data
+  function readInt(offset: lazyComfyAny, isLittleEndian: lazyComfyAny, length: lazyComfyAny) {
+    let arr = exifData.slice(offset, offset + length)
+    if (length === 2) {
+      return new DataView(arr.buffer, arr.byteOffset, arr.byteLength).getUint16(
+        0,
+        isLittleEndian
+      )
+    } else if (length === 4) {
+      return new DataView(arr.buffer, arr.byteOffset, arr.byteLength).getUint32(
+        0,
+        isLittleEndian
+      )
+    }
+    // lazyComfyAny
+    throw new Error('Shouldn\'t get here.');
+  }
+
+  // Read the offset to the first IFD (Image File Directory)
+  const ifdOffset = readInt(4, isLittleEndian, 4)
+
+  function parseIFD(offset: lazyComfyAny) {
+    const numEntries = readInt(offset, isLittleEndian, 2) as lazyComfyAny;
+    const result = {} as lazyComfyAny
+
+    for (let i = 0; i < numEntries; i++) {
+      const entryOffset = offset + 2 + i * 12
+      const tag = readInt(entryOffset, isLittleEndian, 2) as lazyComfyAny
+      const type = readInt(entryOffset + 2, isLittleEndian, 2)
+      const numValues = readInt(entryOffset + 4, isLittleEndian, 4)
+      const valueOffset = readInt(entryOffset + 8, isLittleEndian, 4) as lazyComfyAny;
+
+      // Read the value(s) based on the data type
+      let value
+      if (type === 2) {
+        // ASCII string
+        value = new TextDecoder('utf-8').decode(
+          exifData.subarray(valueOffset, valueOffset + numValues - 1)
+        )
+      }
+
+      result[tag] = value
+    }
+
+    return result
+  }
+
+  // Parse the first IFD
+  const ifdData = parseIFD(ifdOffset)
+  return ifdData
+}
+
+function splitValues(input: lazyComfyAny) {
+  var output = {} as lazyComfyAny
+  for (var key in input) {
+    var value = input[key]
+    var splitValues = value.split(':', 2)
+    output[splitValues[0]] = splitValues[1]
+  }
+  return output
+}
+
+export function getPngMetadata(file: File): Promise<Record<string, string>> {
+  return getFromPngFile(file)
+}
+
+export function getWebpMetadata(file: lazyComfyAny) {
+  return new Promise<Record<string, string>>((r) => {
+    const reader = new FileReader()
+    reader.onload = (event) => {
+      const webp = new Uint8Array((event.target as lazyComfyAny).result as ArrayBuffer)
+      const dataView = new DataView(webp.buffer)
+
+      // Check that the WEBP signature is present
+      if (
+        dataView.getUint32(0) !== 0x52494646 ||
+        dataView.getUint32(8) !== 0x57454250
+      ) {
+        console.error('Not a valid WEBP file')
+        r({})
+        return
+      }
+
+      // Start searching for chunks after the WEBP signature
+      let offset = 12
+      let txt_chunks = {} as lazyComfyAny
+      // Loop through the chunks in the WEBP file
+      while (offset < webp.length) {
+        const chunk_length = dataView.getUint32(offset + 4, true)
+        const chunk_type = String.fromCharCode(
+          ...webp.slice(offset, offset + 4)
+        )
+        if (chunk_type === 'EXIF') {
+          if (
+            String.fromCharCode(...webp.slice(offset + 8, offset + 8 + 6)) ==
+            'Exif\0\0'
+          ) {
+            offset += 6
+          }
+          let data = parseExifData(
+            webp.slice(offset + 8, offset + 8 + chunk_length)
+          )
+          for (var key in data) {
+            const value = data[key] as string
+            if (typeof value === 'string') {
+              const index = value.indexOf(':')
+              txt_chunks[value.slice(0, index)] = value.slice(index + 1)
+            }
+          }
+          break
+        }
+
+        offset += 8 + chunk_length
+      }
+
+      r(txt_chunks)
+    }
+
+    reader.readAsArrayBuffer(file)
+  })
+}
+
+export function getLatentMetadata(file: lazyComfyAny) {
+  return new Promise((r) => {
+    const reader = new FileReader()
+    reader.onload = (event) => {
+      const safetensorsData = new Uint8Array((event.target as lazyComfyAny).result as ArrayBuffer)
+      const dataView = new DataView(safetensorsData.buffer)
+      let header_size = dataView.getUint32(0, true)
+      let offset = 8
+      let header = JSON.parse(
+        new TextDecoder().decode(
+          safetensorsData.slice(offset, offset + header_size)
+        )
+      )
+      r(header.__metadata__)
+    }
+
+    var slice = file.slice(0, 1024 * 1024 * 4)
+    reader.readAsArrayBuffer(slice)
+  })
+}
+
+
+export async function importA1111(graph: lazyComfyAny, parameters: lazyComfyAny) {
+  const p = parameters.lastIndexOf('\nSteps:')
+  if (p > -1) {
+    const embeddings = await api.getEmbeddings()
+    const opts = parameters
+      .substr(p)
+      .split('\n')[1]
+      .match(
+        new RegExp('\\s*([^:]+:\\s*([^"\\{].*?|".*?"|\\{.*?\\}))\\s*(,|$)', 'g')
+      )
+      .reduce((p: lazyComfyAny, n: lazyComfyAny) => {
+        const s = n.split(':')
+        if (s[1].endsWith(',')) {
+          s[1] = s[1].substr(0, s[1].length - 1)
+        }
+        p[s[0].trim().toLowerCase()] = s[1].trim()
+        return p
+      }, {})
+    const p2 = parameters.lastIndexOf('\nNegative prompt:', p)
+    if (p2 > -1) {
+      let positive = parameters.substr(0, p2).trim()
+      let negative = parameters.substring(p2 + 18, p).trim()
+
+      const ckptNode = LiteGraph.createNode('CheckpointLoaderSimple')!
+      const clipSkipNode = LiteGraph.createNode('CLIPSetLastLayer')!
+      const positiveNode = LiteGraph.createNode('CLIPTextEncode')!
+      const negativeNode = LiteGraph.createNode('CLIPTextEncode')!
+      const samplerNode = LiteGraph.createNode('KSampler')!
+      const imageNode = LiteGraph.createNode('EmptyLatentImage')!
+      const vaeNode = LiteGraph.createNode('VAEDecode')!
+      const vaeLoaderNode = LiteGraph.createNode('VAELoader')!
+      const saveNode = LiteGraph.createNode('SaveImage')!
+      let hrSamplerNode = null as lazyComfyAny
+      let hrSteps = null
+
+      const ceil64 = (v: lazyComfyAny) => Math.ceil(v / 64) * 64
+
+      const getWidget = (node: lazyComfyAny, name: lazyComfyAny) => {
+        return node.widgets.find((w: lazyComfyAny) => w.name === name)
+      }
+
+      const setWidgetValue = (node: lazyComfyAny, name: lazyComfyAny, value: lazyComfyAny, isOptionPrefix?: lazyComfyAny) => {
+        const w = getWidget(node, name)
+        if (isOptionPrefix) {
+          const o = w.options.values.find((w: lazyComfyAny) => w.startsWith(value))
+          if (o) {
+            w.value = o
+          } else {
+            console.warn(`Unknown value '${value}' for widget '${name}'`, node)
+            w.value = value
+          }
+        } else {
+          w.value = value
+        }
+      }
+
+      const createLoraNodes = (clipNode:lazyComfyAny, text: lazyComfyAny, prevClip: lazyComfyAny, prevModel: lazyComfyAny) => {
+        const loras = [] as lazyComfyAny
+        text = text.replace(/<lora:([^:]+:[^>]+)>/g, function (m: lazyComfyAny, c: lazyComfyAny) {
+          const s = c.split(':')
+          const weight = parseFloat(s[1])
+          if (isNaN(weight)) {
+            console.warn('Invalid LORA', m)
+          } else {
+            loras.push({ name: s[0], weight })
+          }
+          return ''
+        })
+
+        for (const l of loras) {
+          const loraNode = LiteGraph.createNode('LoraLoader')
+          graph.add(loraNode)
+          setWidgetValue(loraNode, 'lora_name', l.name, true)
+          setWidgetValue(loraNode, 'strength_model', l.weight)
+          setWidgetValue(loraNode, 'strength_clip', l.weight)
+          prevModel.node.connect(prevModel.index, loraNode, 0)
+          prevClip.node.connect(prevClip.index, loraNode, 1)
+          prevModel = { node: loraNode, index: 0 }
+          prevClip = { node: loraNode, index: 1 }
+        }
+
+        prevClip.node.connect(1, clipNode, 0)
+        prevModel.node.connect(0, samplerNode, 0)
+        if (hrSamplerNode) {
+          prevModel.node.connect(0, hrSamplerNode, 0)
+        }
+
+        return { text, prevModel, prevClip }
+      }
+
+      const replaceEmbeddings = (text: lazyComfyAny) => {
+        if (!embeddings.length) return text
+        return text.replaceAll(
+          new RegExp(
+            '\\b(' +
+              embeddings
+                .map((e: lazyComfyAny) => e.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'))
+                .join('\\b|\\b') +
+              ')\\b',
+            'ig'
+          ),
+          'embedding:$1'
+        )
+      }
+
+      const popOpt = (name: lazyComfyAny) => {
+        const v = opts[name]
+        delete opts[name]
+        return v
+      }
+
+      graph.clear()
+      graph.add(ckptNode)
+      graph.add(clipSkipNode)
+      graph.add(positiveNode)
+      graph.add(negativeNode)
+      graph.add(samplerNode)
+      graph.add(imageNode)
+      graph.add(vaeNode)
+      graph.add(vaeLoaderNode)
+      graph.add(saveNode)
+
+      ckptNode.connect(1, clipSkipNode, 0)
+      clipSkipNode.connect(0, positiveNode, 0)
+      clipSkipNode.connect(0, negativeNode, 0)
+      ckptNode.connect(0, samplerNode, 0)
+      positiveNode.connect(0, samplerNode, 1)
+      negativeNode.connect(0, samplerNode, 2)
+      imageNode.connect(0, samplerNode, 3)
+      vaeNode.connect(0, saveNode, 0)
+      samplerNode.connect(0, vaeNode, 0)
+      vaeLoaderNode.connect(0, vaeNode, 1)
+
+      const handlers = {
+        model(v: lazyComfyAny) {
+          setWidgetValue(ckptNode, 'ckpt_name', v, true)
+        },
+        vae(v: lazyComfyAny) {
+          setWidgetValue(vaeLoaderNode, 'vae_name', v, true)
+        },
+        'cfg scale'(v: lazyComfyAny) {
+          setWidgetValue(samplerNode, 'cfg', +v)
+        },
+        'clip skip'(v: lazyComfyAny) {
+          setWidgetValue(clipSkipNode, 'stop_at_clip_layer', -v)
+        },
+        sampler(v: lazyComfyAny) {
+          let name = v.toLowerCase().replace('++', 'pp').replaceAll(' ', '_')
+          if (name.includes('karras')) {
+            name = name.replace('karras', '').replace(/_+$/, '')
+            setWidgetValue(samplerNode, 'scheduler', 'karras')
+          } else {
+            setWidgetValue(samplerNode, 'scheduler', 'normal')
+          }
+          const w = getWidget(samplerNode, 'sampler_name')
+          const o = w.options.values.find(
+            (w: lazyComfyAny) => w === name || w === 'sample_' + name
+          )
+          if (o) {
+            setWidgetValue(samplerNode, 'sampler_name', o)
+          }
+        },
+        size(v: lazyComfyAny) {
+          const wxh = v.split('x')
+          const w = ceil64(+wxh[0])
+          const h = ceil64(+wxh[1])
+          const hrUp = popOpt('hires upscale')
+          const hrSz = popOpt('hires resize')
+          hrSteps = popOpt('hires steps')
+          let hrMethod = popOpt('hires upscaler')
+
+          setWidgetValue(imageNode, 'width', w)
+          setWidgetValue(imageNode, 'height', h)
+
+          if (hrUp || hrSz) {
+            let uw, uh
+            if (hrUp) {
+              uw = w * hrUp
+              uh = h * hrUp
+            } else {
+              const s = hrSz.split('x')
+              uw = +s[0]
+              uh = +s[1]
+            }
+
+            let upscaleNode
+            let latentNode
+
+            if (hrMethod.startsWith('Latent')) {
+              latentNode = upscaleNode = LiteGraph.createNode('LatentUpscale')!
+              graph.add(upscaleNode)
+              samplerNode.connect(0, upscaleNode, 0)
+
+              switch (hrMethod) {
+                case 'Latent (nearest-exact)':
+                  hrMethod = 'nearest-exact'
+                  break
+              }
+              setWidgetValue(upscaleNode, 'upscale_method', hrMethod, true)
+            } else {
+              const decode = LiteGraph.createNode('VAEDecodeTiled')!
+              graph.add(decode)
+              samplerNode.connect(0, decode, 0)
+              vaeLoaderNode.connect(0, decode, 1)
+
+              const upscaleLoaderNode = LiteGraph.createNode('UpscaleModelLoader')!
+              graph.add(upscaleLoaderNode)
+              setWidgetValue(upscaleLoaderNode, 'model_name', hrMethod, true)
+
+              const modelUpscaleNode = LiteGraph.createNode(
+                'ImageUpscaleWithModel'
+              )!
+              graph.add(modelUpscaleNode)
+              decode.connect(0, modelUpscaleNode, 1)
+              upscaleLoaderNode.connect(0, modelUpscaleNode, 0)
+
+              upscaleNode = LiteGraph.createNode('ImageScale')!
+              graph.add(upscaleNode)
+              modelUpscaleNode.connect(0, upscaleNode, 0)
+
+              const vaeEncodeNode = (latentNode =
+                LiteGraph.createNode('VAEEncodeTiled')!)!
+              graph.add(vaeEncodeNode)
+              upscaleNode.connect(0, vaeEncodeNode, 0)
+              vaeLoaderNode.connect(0, vaeEncodeNode, 1)
+            }
+
+            setWidgetValue(upscaleNode, 'width', ceil64(uw))
+            setWidgetValue(upscaleNode, 'height', ceil64(uh))
+
+            hrSamplerNode = LiteGraph.createNode('KSampler')!
+            graph.add(hrSamplerNode)
+            ckptNode.connect(0, hrSamplerNode, 0)
+            positiveNode.connect(0, hrSamplerNode, 1)
+            negativeNode.connect(0, hrSamplerNode, 2)
+            latentNode.connect(0, hrSamplerNode, 3)
+            hrSamplerNode.connect(0, vaeNode, 0)
+          }
+        },
+        steps(v: lazyComfyAny) {
+          setWidgetValue(samplerNode, 'steps', +v)
+        },
+        seed(v: lazyComfyAny) {
+          setWidgetValue(samplerNode, 'seed', +v)
+        }
+      }
+
+      for (const opt in opts) {
+        if (opt in handlers) {
+          ((handlers as lazyComfyAny)[opt] as lazyComfyAny)(popOpt(opt))
+        }
+      }
+
+      if (hrSamplerNode) {
+        setWidgetValue(
+          hrSamplerNode,
+          'steps',
+          hrSteps ? +hrSteps : getWidget(samplerNode, 'steps').value
+        )
+        setWidgetValue(
+          hrSamplerNode,
+          'cfg',
+          getWidget(samplerNode, 'cfg').value
+        )
+        setWidgetValue(
+          hrSamplerNode,
+          'scheduler',
+          getWidget(samplerNode, 'scheduler').value
+        )
+        setWidgetValue(
+          hrSamplerNode,
+          'sampler_name',
+          getWidget(samplerNode, 'sampler_name').value
+        )
+        setWidgetValue(
+          hrSamplerNode,
+          'denoise',
+          +(popOpt('denoising strength') || '1')
+        )
+      }
+
+      let n = createLoraNodes(
+        positiveNode,
+        positive,
+        { node: clipSkipNode, index: 0 },
+        { node: ckptNode, index: 0 }
+      )
+      positive = n.text
+      n = createLoraNodes(negativeNode, negative, n.prevClip, n.prevModel)
+      negative = n.text
+
+      setWidgetValue(positiveNode, 'text', replaceEmbeddings(positive))
+      setWidgetValue(negativeNode, 'text', replaceEmbeddings(negative))
+
+      graph.arrange()
+
+      for (const opt of [
+        'model hash',
+        'ensd',
+        'version',
+        'vae hash',
+        'ti hashes',
+        'lora hashes',
+        'hashes'
+      ]) {
+        delete opts[opt]
+      }
+
+      console.warn('Unhandled parameters:', opts)
+    }
+  }
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/common/components/base_custom_element.ts b/custom_nodes/rgthree-comfy/src_web/common/components/base_custom_element.ts
new file mode 100644
index 0000000000000000000000000000000000000000..659bee5918fb72320ef69047f57789987180b6b2
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/common/components/base_custom_element.ts
@@ -0,0 +1,252 @@
+import {$el, getActionEls} from "rgthree/common/utils_dom.js";
+import {bind, register} from "../utils_templates";
+
+const CSS_STYLE_SHEETS = new Map<string, string>();
+const CSS_STYLE_SHEETS_ADDED = new Map<string, HTMLLinkElement>();
+const HTML_TEMPLATE_FILES = new Map<string, string>();
+
+function getCommonPath(name: string, extension: string) {
+  return `rgthree/common/components/${name.replace("rgthree-", "").replace(/\-/g, "_")}.${extension}`;
+}
+
+/**
+ * Fetches the stylesheet for the component, matched by the element name (minus the "rgthree-"
+ * prefix).
+ */
+async function getStyleSheet(name: string, markupOrPath: string) {
+  if (markupOrPath.includes("{")) {
+    return markupOrPath;
+  }
+  if (!CSS_STYLE_SHEETS.has(name)) {
+    try {
+      const path = markupOrPath || getCommonPath(name, "css");
+      const text = await (await fetch(path)).text();
+      CSS_STYLE_SHEETS.set(name, text);
+    } catch (e) {
+      // alert("Error loading rgthree custom component css.");
+    }
+  }
+  return CSS_STYLE_SHEETS.get(name)!;
+}
+
+/**
+ * Adds the stylesheet to the page, once.
+ */
+async function addStyleSheet(name: string, markupOrPath: string) {
+  if (markupOrPath.includes("{")) {
+    throw new Error("Page-level stylesheets should be passed a path.");
+  }
+  if (!CSS_STYLE_SHEETS_ADDED.has(name)) {
+    const link = document.createElement("link");
+    link.rel = "stylesheet";
+    link.href = markupOrPath;
+    document.head.appendChild(link);
+    CSS_STYLE_SHEETS_ADDED.set(name, link);
+  }
+  return CSS_STYLE_SHEETS_ADDED.get(name)!;
+}
+
+/**
+ * Fetches the stylesheet for the component, matched by the element name (minus the "rgthree-"
+ * prefix).
+ */
+async function getTemplateMarkup(name: string, markupOrPath: string) {
+  if (markupOrPath.includes("<template")) {
+    return markupOrPath;
+  }
+  if (!HTML_TEMPLATE_FILES.has(name)) {
+    try {
+      const path = markupOrPath || getCommonPath(name, "html");
+      const text = await (await fetch(path)).text();
+      HTML_TEMPLATE_FILES.set(name, text);
+    } catch (e) {
+      // alert("Error loading rgthree custom component markup.");
+    }
+  }
+  return HTML_TEMPLATE_FILES.get(name)!;
+}
+
+/**
+ * A base custom element.
+ */
+export abstract class RgthreeCustomElement extends HTMLElement {
+  static readonly NAME: `rgthree-${string}` = "rgthree-override";
+  static readonly USE_SHADOW: boolean = true;
+  static readonly TEMPLATES: string = "";
+  static readonly CSS: string = "";
+
+  static create<T extends RgthreeCustomElement>(): T {
+    if (this.NAME === "rgthree-override") {
+      throw new Error("Must override component NAME");
+    }
+    if (!window.customElements.get(this.NAME)) {
+      window.customElements.define(this.NAME, this as unknown as CustomElementConstructor);
+    }
+    return document.createElement(this.NAME) as T;
+  }
+
+  protected ctor = this.constructor as typeof RgthreeCustomElement;
+  protected hasBeenConnected: boolean = false;
+  protected connected: boolean = false;
+  protected root!: ShadowRoot | HTMLElement;
+  protected readonly templates = new Map<string, HTMLTemplateElement>();
+  protected firstConnectedPromiseResolver!: Function;
+  protected firstConnectedPromise = new Promise(
+    (resolve) => (this.firstConnectedPromiseResolver = resolve),
+  );
+
+  onFirstConnected(): void {
+    // Optionally overridden.
+  }
+  onReconnected(): void {
+    // Optionally overridden.
+  }
+  onConnected(): void {
+    // Optionally overridden.
+  }
+  onDisconnected(): void {
+    // Optionally overridden.
+  }
+  onAction(action: string, e?: Event): void {
+    console.log("onAction", action, e);
+    // Optionally overridden.
+  }
+
+  getElement<E extends HTMLElement>(query: string) {
+    const el = this.querySelector(query);
+    if (!el) {
+      throw new Error("No element found for query: " + query);
+    }
+    return el as E;
+  }
+
+  private onActionInternal(action: string, e?: Event): void {
+    if (typeof (this as any)[action] === "function") {
+      (this as any)[action](e);
+    } else {
+      this.onAction(action, e);
+    }
+  }
+
+  private onConnectedInternal(): void {
+    this.connectActionElements();
+    this.onConnected();
+  }
+
+  private onDisconnectedInternal(): void {
+    this.disconnectActionElements();
+    this.onDisconnected();
+  }
+
+  async connectedCallback() {
+    const elementName = this.ctor.NAME;
+    const wasConnected = this.connected;
+    if (!wasConnected) {
+      this.connected = true;
+    }
+    if (!this.hasBeenConnected) {
+      const [stylesheet, markup] = await Promise.all([
+        this.ctor.USE_SHADOW
+          ? getStyleSheet(elementName, this.ctor.CSS)
+          : addStyleSheet(elementName, this.ctor.CSS),
+        getTemplateMarkup(elementName, this.ctor.TEMPLATES),
+      ]);
+
+      if (markup) {
+        const temp = $el("div");
+        const templatesMarkup = markup.match(/<template[^]*?<\/template>/gm) || [];
+        for (const markup of templatesMarkup) {
+          temp.innerHTML = markup;
+          const template = temp.children[0];
+          if (!(template instanceof HTMLTemplateElement)) {
+            throw new Error("Not a template element.");
+          }
+          let id = template.getAttribute("id");
+          if (!id) {
+            id = this.ctor.NAME;
+            // throw new Error("Not template id.");
+          }
+          this.templates.set(id, template);
+        }
+      }
+
+      // If we're using a shadow, then it's our root as a ShadowRoot. If we're not, then the root is
+      // the custom element itself. This allows easy binding on "this.root" but it also means if we
+      // want to set an atrtibute or otherwise access the actual custom element, we should use
+      // "this" to be compatible with both.
+      if (this.ctor.USE_SHADOW) {
+        this.root = this.attachShadow({mode: "open"});
+        if (typeof stylesheet === "string") {
+          const sheet = new CSSStyleSheet();
+          sheet.replaceSync(stylesheet);
+          this.root.adoptedStyleSheets = [sheet];
+        }
+      } else {
+        this.root = this;
+      }
+
+      let template: HTMLTemplateElement | undefined;
+      if (this.templates.has(elementName)) {
+        template = this.templates.get(elementName);
+      } else if (this.templates.has(elementName.replace("rgthree-", ""))) {
+        template = this.templates.get(elementName.replace("rgthree-", ""));
+      }
+      if (template) {
+        this.root.appendChild(template.content.cloneNode(true));
+        for (const name of template.getAttributeNames()) {
+          if (name != "id" && template.getAttribute(name)) {
+            this.setAttribute(name, template.getAttribute(name)!);
+          }
+        }
+      }
+
+      this.onFirstConnected();
+      this.hasBeenConnected = true;
+      this.firstConnectedPromiseResolver();
+    } else {
+      this.onReconnected();
+    }
+    this.onConnectedInternal();
+  }
+
+  disconnectedCallback() {
+    this.connected = false;
+    this.onDisconnected();
+  }
+
+  private readonly eventElements = new Map<Element, {[event: string]: EventListener}>();
+
+  private connectActionElements() {
+    const data = getActionEls(this);
+    for (const dataItem of Object.values(data)) {
+      const mapItem = this.eventElements.get(dataItem.el) || {};
+      for (const [event, action] of Object.entries(dataItem.actions)) {
+        if (mapItem[event]) {
+          console.warn(`Element already has an event for ${event}`);
+          continue;
+        }
+        mapItem[event] = (e: Event) => {
+          this.onActionInternal(action, e);
+        };
+        dataItem.el.addEventListener(event as keyof ElementEventMap, mapItem[event]);
+      }
+    }
+  }
+
+  private disconnectActionElements() {
+    for (const [el, eventData] of this.eventElements.entries()) {
+      for (const [event, fn] of Object.entries(eventData)) {
+        el.removeEventListener(event, fn);
+      }
+    }
+  }
+
+  async bindWhenConnected(data: any, el?: HTMLElement | ShadowRoot) {
+    await this.firstConnectedPromise;
+    this.bind(data, el);
+  }
+
+  bind(data: any, el?: HTMLElement | ShadowRoot) {
+    bind(el || this.root, data);
+  }
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/common/css/buttons.scss b/custom_nodes/rgthree-comfy/src_web/common/css/buttons.scss
new file mode 100644
index 0000000000000000000000000000000000000000..569d380aeda6b6846ed3051cc348f029eeaa87b5
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/common/css/buttons.scss
@@ -0,0 +1,156 @@
+:not(#fakeid) .rgthree-button-reset {
+  position: relative;
+  appearance: none;
+  cursor: pointer;
+  border: 0;
+  background: transparent;
+  color: inherit;
+  padding: 0;
+  margin: 0;
+}
+
+$borderRadius:  0.33rem;
+
+:not(#fakeid) .rgthree-button {
+  --padding-top: 7px;
+  --padding-bottom: 9px;
+  --padding-x: 16px;
+  position: relative;
+  cursor: pointer;
+  border: 0;
+  border-radius: $borderRadius;
+  background: rgba(0, 0, 0, 0.5);
+  color: white;
+  font-family: system-ui, sans-serif;
+  font-size: calc(16rem / 16);
+  line-height: 1;
+  white-space: nowrap;
+  text-decoration: none;
+  margin: 0.25rem;
+  box-shadow: 0px 0px 2px rgb(0, 0, 0);
+  background: #212121;
+  transition: all 0.1s ease-in-out;
+  padding: var(--padding-top) var(--padding-x) var(--padding-bottom);
+  display: inline-flex;
+  flex-direction: row;
+  align-items: center;
+  justify-content: center;
+
+  &::before,
+  &::after {
+    content: "";
+    display: block;
+    position: absolute;
+    border-radius: $borderRadius;
+    left: 0;
+    top: 0;
+    width: 100%;
+    height: 100%;
+    box-shadow:
+      inset 1px 1px 0px rgba(255, 255, 255, 0.12),
+      inset -1px -1px 0px rgba(0, 0, 0, 0.75);
+    background: linear-gradient(to bottom, rgba(255, 255, 255, 0.06), rgba(0, 0, 0, 0.15));
+    mix-blend-mode: screen;
+  }
+
+  &::after {
+    mix-blend-mode: multiply;
+  }
+
+  &:hover {
+    background: #303030;
+  }
+  &:active {
+    box-shadow: 0px 0px 0px rgba(0, 0, 0, 0);
+    background: #121212;
+    padding: calc(var(--padding-top) + 1px) calc(var(--padding-x) - 1px)
+      calc(var(--padding-bottom) - 1px) calc(var(--padding-x) + 1px);
+  }
+
+  &:active::before,
+  &:active::after {
+    box-shadow:
+      1px 1px 0px rgba(255, 255, 255, 0.15),
+      inset 1px 1px 0px rgba(0, 0, 0, 0.5),
+      inset 1px 3px 5px rgba(0, 0, 0, 0.33);
+  }
+
+  &.-blue {
+    background: #346599 !important;
+  }
+  &.-blue:hover {
+    background: #3b77b8 !important;
+  }
+  &.-blue:active {
+    background: #1d5086 !important;
+  }
+
+  &.-green {
+    background: linear-gradient(to bottom, rgba(255, 255, 255, 0.06), rgba(0, 0, 0, 0.15)), #14580b;
+  }
+  &.-green:hover {
+    background: linear-gradient(to bottom, rgba(255, 255, 255, 0.06), rgba(0, 0, 0, 0.15)), #1a6d0f;
+  }
+  &.-green:active {
+    background: linear-gradient(to bottom, rgba(0, 0, 0, 0.15), rgba(255, 255, 255, 0.06)), #0f3f09;
+  }
+
+  &[disabled] {
+    box-shadow: none;
+    background: #666 !important;
+    color: #aaa;
+    pointer-events: none;
+  }
+  &[disabled]::before,
+  &[disabled]::after {
+    display: none;
+  }
+}
+
+:not(#fakeid) .rgthree-comfybar-top-button-group {
+  font-size: 0;
+  flex: 1 1 auto;
+  display: flex;
+  align-items: stretch;
+
+  .rgthree-comfybar-top-button {
+    margin: 0;
+    flex: 1 1;
+    height: 36px;
+    padding: 0 12px;
+    border-radius: 0;
+    background: var(--p-button-secondary-background);
+    color: var(--p-button-secondary-color);
+
+    &.-primary {
+      background: var(--p-button-primary-background);
+      color: var(--p-button-primary-color);
+    }
+
+    &::before,
+    &::after {
+      border-radius: 0;
+    }
+
+
+    svg {
+      fill: currentColor;
+      width: 28px;
+      height: 28px;
+    }
+  }
+
+  .rgthree-comfybar-top-button:first-of-type,
+  .rgthree-comfybar-top-button:first-of-type::before,
+  .rgthree-comfybar-top-button:first-of-type::after {
+    border-top-left-radius: 0.33rem;
+    border-bottom-left-radius: 0.33rem;
+
+  }
+  .rgthree-comfybar-top-button:last-of-type,
+  .rgthree-comfybar-top-button:last-of-type::before,
+  .rgthree-comfybar-top-button:last-of-type::after {
+    border-top-right-radius: 0.33rem;
+    border-bottom-right-radius: 0.33rem;
+  }
+}
\ No newline at end of file
diff --git a/custom_nodes/rgthree-comfy/src_web/common/css/dialog.scss b/custom_nodes/rgthree-comfy/src_web/common/css/dialog.scss
new file mode 100644
index 0000000000000000000000000000000000000000..c47e2f9d651102c5546d0d0a974f9639f91d3828
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/common/css/dialog.scss
@@ -0,0 +1,129 @@
+
+.rgthree-dialog {
+  outline: 0;
+  border: 0;
+  border-radius: 6px;
+  background: #414141;
+  color: #fff;
+  box-shadow:
+    inset 1px 1px 0px rgba(255, 255, 255, 0.05),
+    inset -1px -1px 0px rgba(0, 0, 0, 0.5),
+    2px 2px 20px rgb(0, 0, 0);
+  max-width: 800px;
+  box-sizing: border-box;
+  font-family: "Segoe UI", Tahoma, Geneva, Verdana, sans-serif;
+  font-size: 1rem;
+  padding: 0;
+  max-height: calc(100% - 32px);
+
+  *, *::before, *::after {
+    box-sizing: inherit;
+  }
+}
+
+.rgthree-dialog-container {
+  // padding: 16px;
+  > * {
+    padding: 8px 16px;
+
+    &:first-child {
+      padding-top: 16px;
+    }
+    &:last-child {
+      padding-bottom: 16px;
+    }
+  }
+}
+
+.rgthree-dialog.-iconed::after {
+  content: "";
+  font-size: 276px;
+  position: absolute;
+  right: 0px;
+  bottom: 0px;
+  opacity: 0.15;
+  display: block;
+  width: 237px;
+  overflow: hidden;
+  height: 186px;
+  line-height: 1;
+  pointer-events: none;
+  z-index: -1;
+}
+.rgthree-dialog.-iconed.-help::after {
+  content: "🛟";
+}
+.rgthree-dialog.-iconed.-settings::after {
+  content: "⚙️";
+}
+
+@media (max-width: 832px) {
+  .rgthree-dialog {
+    max-width: calc(100% - 32px);
+  }
+}
+
+.rgthree-dialog-container-title {
+  display: flex;
+  flex-direction: row;
+  align-items: center;
+  justify-content: start;
+}
+.rgthree-dialog-container-title > svg:first-child {
+  width: 36px;
+  height: 36px;
+  margin-right: 16px;
+}
+.rgthree-dialog-container-title h2 {
+  font-size: calc(22rem / 16);
+  margin: 0;
+  font-weight: bold;
+}
+
+.rgthree-dialog-container-title h2 small {
+  font-size: calc(13rem / 16);
+  font-weight: normal;
+  opacity: 0.75;
+}
+
+.rgthree-dialog-container-content {
+  overflow: auto;
+  max-height: calc(100vh - 200px); /* Arbitrary height to copensate for margin, title, and footer.*/
+}
+.rgthree-dialog-container-content p {
+  font-size: calc(13rem / 16);
+  margin-top: 0;
+}
+
+.rgthree-dialog-container-content ul li p {
+  margin-bottom: 4px;
+}
+
+.rgthree-dialog-container-content ul li p + p {
+  margin-top: 0.5em;
+}
+
+.rgthree-dialog-container-content ul li ul {
+  margin-top: 0.5em;
+  margin-bottom: 1em;
+}
+
+.rgthree-dialog-container-content p code {
+  display: inline-block;
+  padding: 2px 4px;
+  margin: 0px 2px;
+  border: 1px solid rgba(255, 255, 255, 0.25);
+  border-radius: 3px;
+  background: rgba(255, 255, 255, 0.1);
+}
+
+.rgthree-dialog-container-footer {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+}
+
+body.rgthree-dialog-open > *:not(.rgthree-dialog):not(.rgthree-top-messages-container) {
+  filter: blur(5px);
+}
+
diff --git a/custom_nodes/rgthree-comfy/src_web/common/css/dialog_lora_chooser.scss b/custom_nodes/rgthree-comfy/src_web/common/css/dialog_lora_chooser.scss
new file mode 100644
index 0000000000000000000000000000000000000000..373152d1d51baf93c90303978181eec378acc3f9
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/common/css/dialog_lora_chooser.scss
@@ -0,0 +1,161 @@
+
+.rgthree-lora-chooser-dialog {
+  max-width: 100%;
+
+
+  .rgthree-dialog-container-title {
+    display: flex;
+    flex-direction: column;
+  }
+  .rgthree-dialog-container-title h2 {
+    display: flex;
+    width: 100%;
+  }
+  .rgthree-lora-chooser-search {
+    margin-left: auto;
+    border-radius: 50px;
+    width: 50%;
+    max-width: 170px;
+    padding: 2px 8px;
+  }
+
+  .rgthree-lora-chooser-header {
+    display: flex;
+    flex-direction: row;
+  }
+
+  .rgthree-lora-filters-container {
+    svg {width: 16px; height: 16px;}
+  }
+
+  .rgthree-dialog-container-content {
+    width: 80vw;
+    height: 80vh;
+  }
+
+  .rgthree-button-reset {
+    width: 32px;
+    height: 32px;
+    > svg {width: 100%; height: 100%;}
+
+  }
+
+  ul.rgthree-lora-chooser-list {
+    list-style: none;
+    margin: 0;
+    padding: 0;
+    position: relative;
+    display: flex;
+    flex-direction: row;
+    flex-wrap: wrap;
+    align-items: start;
+    justify-content: space-around;
+
+    > li {
+      position: relative;
+      flex: 0 0 auto;
+      width: 170px;
+      max-width: 100%;
+      margin: 8px 8px 16px;
+
+      label {
+        position: absolute;
+        display: block;
+        inset: 0;
+        z-index: 3;
+        cursor: pointer;
+      }
+      input[type="checkbox"] {
+        position: absolute;
+        right: 8px;
+        top: 8px;
+        margin: 0;
+        z-index: 2;
+        appearance: none;
+        background-color: #fff;
+        width: 48px;
+        height: 48px;
+        border-radius: 4px;
+        border: 1px solid rgba(120,120,120,1);
+        opacity: 0;
+        transition: opacity 0.15s ease-in-out;
+
+        &:checked {
+          opacity: 1;
+          background: #0060df;
+          &::before {
+            content: "";
+            display: block;
+            width: 100%;
+            height: 100%;
+            box-shadow: inset 100px 100px #fff;
+            clip-path: polygon(40.13% 68.39%, 23.05% 51.31%, 17.83% 48.26%, 12.61% 49.57%, 9.57% 53.04%, 8% 60%, 34.13% 85.87%, 39.82% 89.57%, 45.88% 86.73%, 90.66% 32.39%, 88.92% 26.1%, 83.03% 22.17%, 76.94% 22.62%)
+          }
+        }
+      }
+
+
+      figure {
+        position: relative;
+        display: block;
+        margin: 0 0 8px;
+        padding: 0;
+        border: 1px solid rgba(120, 120, 120, .8);
+        background: rgba(120, 120, 120, .5);
+        width: 100%;
+        padding-top: 120%;
+        transition: box-shadow 0.15s ease-in-out;
+        opacity: 0.75;
+        &::after {
+          content: '';
+          display: block;
+          position: absolute;
+          inset: 0;
+        }
+
+        &:empty {
+          &::before {
+            content: 'No image.';
+            color: rgba(200, 200, 200, .8);
+            position: absolute;
+            display: block;
+            inset: 0;
+            font-size: 1.2em;
+            text-align: center;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+          }
+        }
+
+        > img, > video {
+          position: absolute;
+          width: 100%;
+          height: 100%;
+          top: 0;
+          left: 0;
+          object-fit: cover;
+        }
+      }
+      div {
+        word-wrap: break-word;
+        font-size: 0.8rem;
+        opacity: 0.75;
+      }
+
+      &:hover figure::after{
+        box-shadow: 0px 2px 6px rgba(0,0,0,0.75);
+      }
+      :checked ~ figure::after {
+        box-shadow: 0 0 5px #fff, 0px 0px 15px rgba(49, 131, 255, 0.88), inset 0 0 3px #fff, inset 0px 0px 5px rgba(49, 131, 255, 0.88)
+      }
+
+      &:hover *,
+      &:hover input[type="checkbox"],
+      :checked ~ * {
+        opacity: 1
+      }
+
+    }
+  }
+}
\ No newline at end of file
diff --git a/custom_nodes/rgthree-comfy/src_web/common/css/dialog_model_info.scss b/custom_nodes/rgthree-comfy/src_web/common/css/dialog_model_info.scss
new file mode 100644
index 0000000000000000000000000000000000000000..247cdc08ad504bf0f7bffa5c10c7e16d42b277e6
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/common/css/dialog_model_info.scss
@@ -0,0 +1,400 @@
+
+.rgthree-info-dialog {
+
+  width: 90vw;
+  max-width: 960px;
+
+  .rgthree-info-area {
+    list-style: none;
+    padding: 0;
+    margin: 0;
+    display: flex;
+
+    > li {
+      display: inline-flex;
+      margin: 0;
+      vertical-align: top;
+
+      + li {
+        margin-left: 6px;
+      }
+      &:not(.-link) + li.-link {
+        margin-left: auto;
+      }
+
+      &.rgthree-info-tag > * {
+        min-height: 24px;
+        border-radius: 4px;
+        line-height: 1;
+        color: rgba(255,255,255,0.85);
+        background: rgb(69, 92, 85);;
+        font-size: 14px;
+        font-weight: bold;
+        text-decoration: none;
+        display: flex;
+        height: 1.6em;
+        padding-left: .5em;
+        padding-right: .5em;
+        padding-bottom: .1em;
+        align-content: center;
+        justify-content: center;
+        align-items: center;
+        box-shadow: inset 0px 0px 0 1px  rgba(0, 0, 0, 0.5);
+
+        > svg {
+          width: 16px;
+          height: 16px;
+
+          &:last-child {
+            margin-left: .5em;
+          }
+        }
+
+
+        &[href] {
+          box-shadow: inset 0px 1px 0px rgba(255,255,255,0.25), inset 0px -1px 0px rgba(0,0,0,0.66);
+        }
+
+        &:empty {
+          display: none;
+        }
+      }
+
+      // &.-civitai > * {
+      //   color: #ddd;
+      //   background: #1b65aa;
+      //   transition: all 0.15s ease-in-out;
+      //   &:hover {
+      //     color: #fff;
+      //     border-color: #1971c2;
+      //     background: #1971c2;
+      //   }
+      // }
+      &.-type > * {
+        background: rgb(73, 54, 94);
+        color: rgb(228, 209, 248);
+      }
+
+      &.rgthree-info-menu {
+        margin-left: auto;
+
+        :not(#fakeid) & .rgthree-button {
+          margin: 0;
+          min-height: 24px;
+          padding: 0 12px;
+        }
+
+        svg {
+          width: 16px;
+          height: 16px;
+        }
+      }
+    }
+  }
+
+  .rgthree-info-table {
+    border-collapse: collapse;
+    margin: 16px 0px;
+    width: 100%;
+    font-size: 12px;
+
+    tr.editable button {
+      display: flex;
+      width: 28px;
+      height: 28px;
+      align-items: center;
+      justify-content: center;
+
+      svg + svg {display: none;}
+    }
+    tr.editable.-rgthree-editing button {
+      svg {display: none;}
+      svg + svg {display: inline-block;}
+    }
+
+    td {
+      position: relative;
+      border: 1px solid rgba(255,255,255,0.25);
+      padding: 0;
+      vertical-align: top;
+
+      &:first-child {
+        background: rgba(255,255,255,0.075);
+        width: 10px; // Small, so it doesn't adjust.
+        > *:first-child {
+          white-space: nowrap;
+          padding-right: 32px;
+        }
+
+        small {
+          display: block;
+          margin-top: 2px;
+          opacity: 0.75;
+
+          > [data-action] {
+            text-decoration: underline;
+            cursor: pointer;
+            &:hover {
+              text-decoration: none;
+            }
+          }
+        }
+      }
+
+      a, a:hover, a:visited {
+        color: inherit;
+      }
+
+      svg {
+        width: 1.3333em;
+        height: 1.3333em;
+        vertical-align: -0.285em;
+
+        &.logo-civitai {
+          margin-right: 0.3333em;
+        }
+      }
+
+      > *:first-child {
+        display: block;
+        padding: 6px 10px;
+      }
+
+      > input, > textarea{
+        padding: 5px 10px;
+        border: 0;
+        box-shadow: inset 1px 1px 5px 0px rgba(0,0,0,0.5);
+        font: inherit;
+        appearance: none;
+        background: #fff;
+        color: #121212;
+        resize: vertical;
+
+        &:only-child {
+          width: 100%;
+        }
+      }
+
+      :not(#fakeid) & .rgthree-button[data-action="fetch-civitai"] {
+        font-size: inherit;
+        padding: 6px 16px;
+        margin: 2px;
+      }
+    }
+
+    tr[data-field-name="userNote"] td > span:first-child {
+      white-space: pre;
+    }
+
+    tr.rgthree-info-table-break-row td {
+      border: 0;
+      background: transparent;
+      padding: 12px 4px 4px;
+      font-size: 1.2em;
+
+      > small {
+        font-style: italic;
+        opacity: 0.66;
+      }
+
+      &:empty {
+        padding: 4px;
+      }
+    }
+
+    td .-help {
+      border: 1px solid currentColor;
+      position: absolute;
+      right: 5px;
+      top: 6px;
+      line-height: 1;
+      font-size: 11px;
+      width: 12px;
+      height: 12px;
+      border-radius: 8px;
+      display: flex;
+      align-content: center;
+      justify-content: center;
+      cursor: help;
+      &::before {
+        content: '?';
+      }
+
+    }
+
+    td > ul.rgthree-info-trained-words-list {
+      list-style: none;
+      padding: 2px 8px;
+      margin: 0;
+      display: flex;
+      flex-direction: row;
+      flex-wrap: wrap;
+      max-height: 15vh;
+      overflow: auto;
+
+      > li {
+        display: inline-flex;
+        margin: 2px;
+        vertical-align: top;
+        border-radius: 4px;
+        line-height: 1;
+        color: rgba(255,255,255,0.85);
+        background: rgb(73, 91, 106);
+        font-size: 1.2em;
+        font-weight: 600;
+        text-decoration: none;
+        display: flex;
+        height: 1.6em;
+        align-content: center;
+        justify-content: center;
+        align-items: center;
+        box-shadow: inset 0px 0px 0 1px  rgba(0, 0, 0, 0.5);
+        cursor: pointer;
+        white-space: nowrap;
+        max-width: 183px;
+
+        &:hover {
+          background: rgb(68, 109, 142);
+        }
+
+        > svg {
+          width: auto;
+          height: 1.2em;
+        }
+
+        > span {
+          padding-left: .5em;
+          padding-right: .5em;
+          padding-bottom: .1em;
+          text-overflow: ellipsis;
+          overflow: hidden;
+        }
+
+        > small {
+          align-self: stretch;
+          display: flex;
+          align-items: center;
+          justify-content: center;
+          padding: 0 0.5em;
+          background: rgba(0,0,0,0.2);
+        }
+
+        &.-rgthree-is-selected {
+          background: rgb(42, 126, 193);
+        }
+      }
+    }
+  }
+
+  .rgthree-info-images {
+    list-style:none;
+    padding:0;
+    margin:0;
+    scroll-snap-type: x mandatory;
+    display:flex;
+    flex-direction:row;
+    overflow: auto;
+
+    > li {
+      scroll-snap-align: start;
+      max-width: 90%;
+      flex: 0 0 auto;
+      display: flex;
+      align-items: center;
+      justify-content: center;
+      flex-direction: column;
+      overflow: hidden;
+      padding: 0;
+      margin: 6px;
+      font-size: 0;
+      position: relative;
+
+      figure {
+        margin: 0;
+        position: static;
+
+        video, img {
+          max-height: 45vh;
+        }
+
+        figcaption {
+          position: absolute;
+          left: 0;
+          width: 100%;
+          bottom: 0;
+          padding: 12px;
+          font-size: 12px;
+          background: rgba(0,0,0,0.85);
+          opacity: 0;
+          transform: translateY(50px);
+          transition: all 0.25s ease-in-out;
+
+          > span {
+            display: inline-block;
+            padding: 2px 4px;
+            margin: 2px;
+            border-radius: 2px;
+            border: 1px solid rgba(255,255,255,0.2);
+            word-break: break-word;
+
+            label {
+              display: inline;
+              padding: 0;
+              margin: 0;
+              opacity: 0.5;
+              pointer-events: none;
+              user-select: none;
+            }
+            a {
+              color: inherit;
+              text-decoration: underline;
+              &:hover {
+                text-decoration: none;
+              }
+
+              svg {
+                height: 10px;
+                margin-left: 4px;
+                fill: currentColor;
+              }
+            }
+          }
+          &:empty {
+            text-align: center;
+
+            &::before {
+              content: 'No data.';
+            }
+          }
+        }
+      }
+
+      &:hover figure figcaption {
+        opacity: 1;
+        transform: translateY(0px);
+      }
+
+      .rgthree-info-table {
+        width: calc(100% - 16px);
+      }
+    }
+  }
+
+  .rgthree-info-civitai-link {
+    margin: 8px;
+    color: #eee;
+
+    a, a:hover, a:visited {
+      color: inherit;
+      text-decoration: none;
+    }
+
+    > svg {
+      width: 16px;
+      height: 16px;
+      margin-right: 8px;
+    }
+  }
+}
+
+
diff --git a/custom_nodes/rgthree-comfy/src_web/common/css/menu.scss b/custom_nodes/rgthree-comfy/src_web/common/css/menu.scss
new file mode 100644
index 0000000000000000000000000000000000000000..31b661a1164964cecff95148f26cef67733a7137
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/common/css/menu.scss
@@ -0,0 +1,122 @@
+
+
+.rgthree-menu {
+  list-style: none;
+  padding: 0;
+  margin: 0;
+  position: fixed;
+  z-index: 999999;
+  pointer-events: none;
+  opacity: 0;
+  transition: opacity 0.08s ease-in-out;
+
+  color: #dde;
+  background-color: #111;
+  font-size: 12px;
+  box-shadow: 0 0 10px black !important;
+
+  > li {
+    position: relative;
+    padding: 4px 6px;
+    z-index: 9999;
+    white-space: nowrap;
+
+    &[role="button"] {
+      background-color: var(--comfy-menu-bg) !important;
+      color: var(--input-text);
+      cursor: pointer;
+      &:hover {
+        filter: brightness(155%);
+      }
+    }
+  }
+
+  &[state^="measuring"] {
+    display: block;
+    opacity: 0;
+  }
+  &[state="open"] {
+    display: block;
+    opacity: 1;
+    pointer-events: all;
+  }
+}
+
+
+.rgthree-top-menu {
+  box-sizing: border-box;
+  white-space: nowrap;
+  background: var(--content-bg);
+  color: var(--content-fg);
+  display: flex;
+  flex-direction: column;
+  list-style: none;
+  padding: 0;
+  margin: 0;
+  * {
+    box-sizing: inherit;
+  }
+
+
+  > li:not(#fakeid) {
+    list-style: none;
+    padding: 0;
+    margin: 0;
+    position: relative;
+    z-index: 2;
+
+    > button {
+      cursor: pointer;
+      padding: 8px 12px 8px 8px;
+      width: 100%;
+      text-align: start;
+      display: flex;
+      flex-direction: row;
+      align-items: center;
+      justify-content: start;
+
+      &:hover {
+        background-color: var(--comfy-input-bg);
+      }
+
+      svg {
+        height: 16px;
+        width: auto;
+        margin-inline-end: 0.6em;
+
+        &.github-star {
+          fill: rgb(227, 179, 65);
+        }
+      }
+    }
+
+    &.rgthree-message {
+      // ComfyUI's code has strange behavior that that always puts the popupat to if its less than
+      // 30px... we'll force our message to be at least 32px tall so it won't do that unless it's
+      // actually on the bottom.
+      min-height: 32px;
+      > span {
+        padding: 8px 12px;
+        display: block;
+        width: 100%;
+        text-align: center;
+        font-style: italic;
+        font-size: 12px;
+      }
+    }
+  }
+
+  &.-modal::after {
+    content: '';
+    display: block;
+    position: fixed;
+    z-index: 1;
+    inset: 0;
+    background: #0001;
+  }
+}
+
+body.rgthree-modal-menu-open > *:not(.rgthree-menu):not(.rgthree-top-messages-container) {
+  filter: blur(2px);
+}
+
diff --git a/custom_nodes/rgthree-comfy/src_web/common/css/pages_base.scss b/custom_nodes/rgthree-comfy/src_web/common/css/pages_base.scss
new file mode 100644
index 0000000000000000000000000000000000000000..09f292edce41bd58342c8d18cc3f9efc8b8e88d8
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/common/css/pages_base.scss
@@ -0,0 +1,69 @@
+
+html, body {
+
+}
+html {
+  font-size: 100%;
+  overflow-y: scroll;
+  -webkit-text-size-adjust: 100%;
+  -ms-text-size-adjust: 100%;
+  box-sizing: border-box;
+}
+*, *:before, *:after {
+  box-sizing: inherit
+}
+
+:root {
+  --header-height: 56px;
+  --progress-height: 12px;
+}
+
+button {
+  all: unset;
+}
+
+.-bevel {
+  position: relative;
+}
+.-bevel::before {
+  content: '';
+  position: absolute;
+  left: 0;
+  top: 0;
+  width: 100%;
+  height: 100%;
+  border: 1px solid red;
+  border-color: rgba(255,255,255,0.15) rgba(255,255,255,0.15) rgba(0,0,0,0.5) rgba(0,0,0,0.5);
+  z-index: 5;
+  pointer-events: none;
+}
+
+
+body {
+  background: #202020;
+  font-family: Arial, sans-serif;
+  font-size: calc(16 * 0.0625rem);
+  font-weight: 400;
+  margin: 0;
+  padding-top: calc(var(--header-height) + var(--progress-height));
+  color: #ffffff;
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: start;
+}
+
+.app-header {
+  height: var( --header-height);
+  padding: 0;
+  position: fixed;
+  z-index: 99;
+  top: 0;
+  left: 0;
+  width: 100%;
+  background: #353535;
+  display: flex;
+  flex-direction: row;
+  align-items: center;
+  justify-content: start;
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/common/dialog.ts b/custom_nodes/rgthree-comfy/src_web/common/dialog.ts
new file mode 100644
index 0000000000000000000000000000000000000000..2f107106a6956c57f1ff9f4fff37d4d8520fc29a
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/common/dialog.ts
@@ -0,0 +1,165 @@
+import type { LGraphNode, LGraphNodeConstructor } from "@comfyorg/frontend";
+import { createElement as $el, getClosestOrSelf, setAttributes } from "./utils_dom.js";
+
+type RgthreeDialogButton = {
+  label: string;
+  className?: string;
+  closes?: boolean;
+  disabled?: boolean;
+  callback?: (e: PointerEvent | MouseEvent) => void;
+};
+
+export type RgthreeDialogOptions = {
+  content: string | HTMLElement | HTMLElement[];
+  class?: string | string[];
+  title?: string | HTMLElement | HTMLElement[];
+  closeX?: boolean;
+  closeOnEsc?: boolean;
+  closeOnModalClick?: boolean;
+  closeButtonLabel?: string | boolean;
+  buttons?: RgthreeDialogButton[];
+  onBeforeClose?: () => Promise<boolean> | boolean;
+};
+
+/**
+ * A Dialog that shows content, and closes.
+ */
+export class RgthreeDialog extends EventTarget {
+  element: HTMLDialogElement;
+  contentElement: HTMLDivElement;
+  titleElement: HTMLDivElement;
+  options: RgthreeDialogOptions;
+
+  constructor(options: RgthreeDialogOptions) {
+    super();
+    this.options = options;
+    let container = $el("div.rgthree-dialog-container");
+    this.element = $el("dialog", {
+      classes: ["rgthree-dialog", options.class || ""],
+      child: container,
+      parent: document.body,
+      events: {
+        click: (event: MouseEvent) => {
+          // Close the dialog if we've clicked outside of our container. The dialog modal will
+          // report itself as the dialog itself, so we use the inner container div (and CSS to
+          // remove default padding from the dialog element).
+          if (
+            !this.element.open ||
+            event.target === container ||
+            getClosestOrSelf(event.target, `.rgthree-dialog-container`) === container
+          ) {
+            return;
+          }
+          return this.close();
+        },
+      },
+    });
+    this.element.addEventListener("close", (event) => {
+      this.onDialogElementClose();
+    });
+
+    this.titleElement = $el("div.rgthree-dialog-container-title", {
+      parent: container,
+      children: !options.title
+        ? null
+        : options.title instanceof Element || Array.isArray(options.title)
+        ? options.title
+        : typeof options.title === "string"
+        ? !options.title.includes("<h2")
+          ? $el("h2", { html: options.title })
+          : options.title
+        : options.title,
+    });
+
+    this.contentElement = $el("div.rgthree-dialog-container-content", {
+      parent: container,
+      child: options.content,
+    });
+
+    const footerEl = $el("footer.rgthree-dialog-container-footer", { parent: container });
+    for (const button of options.buttons || []) {
+      $el("button", {
+        text: button.label,
+        className: button.className,
+        disabled: !!button.disabled,
+        parent: footerEl,
+        events: {
+          click: (e: MouseEvent) => {
+            button.callback?.(e);
+          },
+        },
+      });
+    }
+
+    if (options.closeButtonLabel !== false) {
+      $el("button", {
+        text: options.closeButtonLabel || "Close",
+        className: "rgthree-button",
+        parent: footerEl,
+        events: {
+          click: (e: MouseEvent) => {
+            this.close(e);
+          },
+        },
+      });
+    }
+  }
+
+  setTitle(content: string | HTMLElement | HTMLElement[]) {
+    const title =
+      typeof content !== "string" || content.includes("<h2")
+        ? content
+        : $el("h2", { html: content });
+    setAttributes(this.titleElement, { children: title });
+  }
+
+  setContent(content: string | HTMLElement | HTMLElement[]) {
+    setAttributes(this.contentElement, { children: content });
+  }
+
+  show() {
+    document.body.classList.add("rgthree-dialog-open");
+    this.element.showModal();
+    this.dispatchEvent(new CustomEvent("show"));
+    return this;
+  }
+
+  async close(e?: MouseEvent | PointerEvent) {
+    if (this.options.onBeforeClose && !(await this.options.onBeforeClose())) {
+      return;
+    }
+    this.element.close();
+  }
+
+  onDialogElementClose() {
+    document.body.classList.remove("rgthree-dialog-open");
+    this.element.remove();
+    this.dispatchEvent(new CustomEvent("close", this.getCloseEventDetail()));
+  }
+
+  protected getCloseEventDetail(): { detail: any } {
+    return { detail: null };
+  }
+}
+
+/**
+ * A help extension for the dialog class that standardizes help content.
+ */
+export class RgthreeHelpDialog extends RgthreeDialog {
+  constructor(
+    node: LGraphNode | LGraphNodeConstructor,
+    content: string,
+    opts: Partial<RgthreeDialogOptions> = {},
+  ) {
+    const title = (node.type || node.title || "").replace(
+      /\s*\(rgthree\).*/,
+      " <small>by rgthree</small>",
+    );
+    const options = Object.assign({}, opts, {
+      class: "-iconed -help",
+      title,
+      content,
+    });
+    super(options);
+  }
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/common/link_fixer.ts b/custom_nodes/rgthree-comfy/src_web/common/link_fixer.ts
new file mode 100644
index 0000000000000000000000000000000000000000..1ec3cf714049a5ee1cd522187f046957bc0c6abc
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/common/link_fixer.ts
@@ -0,0 +1,529 @@
+import type {
+  LGraph,
+  LGraphNode,
+  LLink,
+  ISlotType,
+  INodeOutputSlot,
+  INodeInputSlot,
+  SerialisedLLinkArray,
+  LinkId,
+  ISerialisedNode,
+  ISerialisedGraph,
+  NodeId,
+} from "@comfyorg/frontend";
+
+/**
+ * The bad links data returned from either a fixer `check()`, or the results of a `fix()` call.
+ */
+export interface BadLinksData<T = ISerialisedGraph | LGraph> {
+  hasBadLinks: boolean;
+  graph: T;
+  patches: number;
+  deletes: number;
+}
+
+enum IoDirection {
+  INPUT,
+  OUTPUT,
+}
+
+/**
+ * Data interface that mimics a nodes `inputs` and `outputs` holding the _to be_ mutated node data
+ * during a check.
+ */
+interface PatchedNodeSlots {
+  [nodeId: string]: {
+    inputs?: {[slot: number]: number | null};
+    outputs?: {
+      [slots: number]: {
+        links: number[];
+        changes: {[linkId: number]: "ADD" | "REMOVE"};
+      };
+    };
+  };
+}
+
+/**
+ * Link data derived from either a ISerialisedGraph or LGraph `links` property.
+ */
+interface LinkData {
+  id: LinkId;
+  origin_id: NodeId;
+  origin_slot: number;
+  target_id: NodeId;
+  target_slot: number;
+  type: ISlotType;
+}
+
+/**
+ * Returns a list of links data for the given links type; either from an LGraph or SerializedGraph.
+ */
+function getLinksData(
+  links: ISerialisedGraph["links"] | LGraph["links"] | {[key: string]: LLink},
+): LinkData[] {
+  if (links instanceof Map) {
+    const data: LinkData[] = [];
+    for (const [key, llink] of links.entries()) {
+      if (!llink) continue;
+      data.push(llink);
+    }
+    return data;
+  }
+  // This is apparently marked deprecated in ComfyUI but who knows if we would get stale data in
+  // here that's not a map (handled above). Go ahead and handle it anyway.
+  if (!Array.isArray(links)) {
+    const data: LinkData[] = [];
+    for (const key in links) {
+      const llink = (links.hasOwnProperty(key) && links[key]) || null;
+      if (!llink) continue;
+      data.push(llink);
+    }
+    return data;
+  }
+  return links.map((link: SerialisedLLinkArray) => ({
+    id: link[0],
+    origin_id: link[1],
+    origin_slot: link[2],
+    target_id: link[3],
+    target_slot: link[4],
+    type: link[5],
+  }));
+}
+
+/** The instruction data for fixing a node's inputs or outputs. */
+interface WorkflowLinkFixerNodeInstruction {
+  node: ISerialisedNode | LGraphNode;
+  op: "REMOVE" | "ADD";
+  dir: IoDirection;
+  slot: number;
+  linkId: number;
+  linkIdToUse: number | null;
+}
+
+/** The instruction data for fixing a link from a workflow links. */
+interface WorkflowLinkFixerLinksInstruction {
+  op: "DELETE";
+  linkId: number;
+  reason: string;
+}
+
+type WorkflowLinkFixerInstruction =
+  | WorkflowLinkFixerNodeInstruction
+  | WorkflowLinkFixerLinksInstruction;
+
+/**
+ * The WorkflowLinkFixer for either ISerialisedGraph or a live LGraph.
+ *
+ * Use `WorkflowLinkFixer.create(graph: ISerialisedGraph | LGraph)` to create a new instance.
+ */
+export abstract class WorkflowLinkFixer<
+  G extends ISerialisedGraph | LGraph,
+  N extends ISerialisedNode | LGraphNode,
+> {
+  silent: boolean = false;
+  checkedData: BadLinksData<G> | null = null;
+
+  protected logger: {log: (...args: any[]) => void} = console;
+  protected graph: G;
+  protected patchedNodeSlots: PatchedNodeSlots = {};
+  protected instructions: WorkflowLinkFixerInstruction[] = [];
+
+  /**
+   * Creates the WorkflowLinkFixer for the given graph type.
+   */
+  static create(graph: ISerialisedGraph): WorkflowLinkFixerSerialized;
+  static create(graph: LGraph): WorkflowLinkFixerGraph;
+  static create(
+    graph: ISerialisedGraph | LGraph,
+  ): WorkflowLinkFixerSerialized | WorkflowLinkFixerGraph {
+    if (typeof (graph as LGraph).getNodeById === "function") {
+      return new WorkflowLinkFixerGraph(graph as LGraph);
+    }
+    return new WorkflowLinkFixerSerialized(graph as ISerialisedGraph);
+  }
+
+  protected constructor(graph: G) {
+    this.graph = graph;
+  }
+
+  abstract getNodeById(id: NodeId): N | null;
+  abstract deleteGraphLink(id: LinkId): true | string;
+
+  /**
+   * Checks the current graph data for any bad links.
+   */
+  check(force: boolean = false): BadLinksData<G> {
+    if (this.checkedData && !force) {
+      return {...this.checkedData};
+    }
+    this.instructions = [];
+    this.patchedNodeSlots = {};
+
+    const instructions: (WorkflowLinkFixerInstruction | null)[] = [];
+
+    const links: LinkData[] = getLinksData(this.graph.links);
+    links.reverse();
+    for (const link of links) {
+      if (!link) continue;
+
+      const originNode = this.getNodeById(link.origin_id);
+      const originHasLink = () =>
+        this.nodeHasLinkId(originNode!, IoDirection.OUTPUT, link.origin_slot, link.id);
+      const patchOrigin = (op: "ADD" | "REMOVE", id = link.id) =>
+        this.getNodePatchInstruction(originNode!, IoDirection.OUTPUT, link.origin_slot, id, op);
+
+      const targetNode = this.getNodeById(link.target_id);
+      const targetHasLink = () =>
+        this.nodeHasLinkId(targetNode!, IoDirection.INPUT, link.target_slot, link.id);
+      const targetHasAnyLink = () =>
+        this.nodeHasAnyLink(targetNode!, IoDirection.INPUT, link.target_slot);
+      const patchTarget = (op: "ADD" | "REMOVE", id = link.id) =>
+        this.getNodePatchInstruction(targetNode!, IoDirection.INPUT, link.target_slot, id, op);
+
+      const originLog = `origin(${link.origin_id}).outputs[${link.origin_slot}].links`;
+      const targetLog = `target(${link.target_id}).inputs[${link.target_slot}].link`;
+
+      if (!originNode || !targetNode) {
+        if (!originNode && !targetNode) {
+          // This can fall through and continue; we remove it after this loop.
+        } else if (!originNode && targetNode) {
+          this.log(
+            `Link ${link.id} is funky... ` +
+              `origin ${link.origin_id} does not exist, but target ${link.target_id} does.`,
+          );
+          if (targetHasLink()) {
+            this.log(` > [PATCH] ${targetLog} does have link, will remove the inputs' link first.`);
+            instructions.push(patchTarget("REMOVE", -1));
+          }
+        } else if (!targetNode && originNode) {
+          this.log(
+            `Link ${link.id} is funky... ` +
+              `target ${link.target_id} does not exist, but origin ${link.origin_id} does.`,
+          );
+          if (originHasLink()) {
+            this.log(` > [PATCH] Origin's links' has ${link.id}; will remove the link first.`);
+            instructions.push(patchOrigin("REMOVE"));
+          }
+        }
+        continue;
+      }
+
+      if (targetHasLink() || originHasLink()) {
+        if (!originHasLink()) {
+          this.log(
+            `${link.id} is funky... ${originLog} does NOT contain it, but ${targetLog} does.`,
+          );
+          this.log(` > [PATCH] Attempt a fix by adding this ${link.id} to ${originLog}.`);
+          instructions.push(patchOrigin("ADD"));
+        } else if (!targetHasLink()) {
+          this.log(
+            `${link.id} is funky... ${targetLog} is NOT correct (is ${
+              targetNode.inputs?.[link.target_slot]?.link
+            }), but ${originLog} contains it`,
+          );
+          if (!targetHasAnyLink()) {
+            this.log(` > [PATCH] ${targetLog} is not defined, will set to ${link.id}.`);
+            let instruction = patchTarget("ADD");
+            if (!instruction) {
+              this.log(
+                ` > [PATCH] Nvm, ${targetLog} already patched. Removing ${link.id} from ${originLog}.`,
+              );
+              instruction = patchOrigin("REMOVE");
+            }
+            instructions.push(instruction);
+          } else {
+            this.log(` > [PATCH] ${targetLog} is defined, removing ${link.id} from ${originLog}.`);
+            instructions.push(patchOrigin("REMOVE"));
+          }
+        }
+      }
+    }
+
+    // Now that we've cleaned up the inputs, outputs, run through it looking for dangling links.,
+    for (let link of links) {
+      if (!link) continue;
+      const originNode = this.getNodeById(link.origin_id);
+      const targetNode = this.getNodeById(link.target_id);
+      if (!originNode && !targetNode) {
+        instructions.push({
+          op: "DELETE",
+          linkId: link.id,
+          reason: `Both nodes #${link.origin_id} & #${link.target_id} are removed`,
+        });
+      }
+      // Now that we've manipulated the linking, check again if they both exist.
+      if (
+        (!originNode ||
+          !this.nodeHasLinkId(originNode, IoDirection.OUTPUT, link.origin_slot, link.id)) &&
+        (!targetNode ||
+          !this.nodeHasLinkId(targetNode, IoDirection.INPUT, link.target_slot, link.id))
+      ) {
+        instructions.push({
+          op: "DELETE",
+          linkId: link.id,
+          reason:
+            `both origin node #${link.origin_id} ` +
+            `${!originNode ? "is removed" : `is missing link id output slot ${link.origin_slot}`}` +
+            `and target node #${link.target_id} ` +
+            `${!targetNode ? "is removed" : `is missing link id input slot ${link.target_slot}`}.`,
+        });
+        continue;
+      }
+    }
+
+    this.instructions = instructions.filter((i) => !!i);
+    this.checkedData = {
+      hasBadLinks: !!this.instructions.length,
+      graph: this.graph,
+      patches: this.instructions.filter((i) => !!(i as WorkflowLinkFixerNodeInstruction).node)
+        .length,
+      deletes: this.instructions.filter((i) => i.op === "DELETE").length,
+    };
+    return {...this.checkedData};
+  }
+
+  /**
+   * Fixes a checked graph by running through the instructions generated during the check run. Also
+   * double-checks for inconsistencies after the fix, recursively calling itself up to five times
+   * before giving up.
+   */
+  fix(force: boolean = false, times?: number): BadLinksData<G> {
+    if (!this.checkedData || force) {
+      this.check(force);
+    }
+    let patches = 0;
+    let deletes = 0;
+    for (const instruction of this.instructions) {
+      if ((instruction as WorkflowLinkFixerNodeInstruction).node) {
+        let {node, slot, linkIdToUse, dir, op} = instruction as WorkflowLinkFixerNodeInstruction;
+        if (dir == IoDirection.INPUT) {
+          node.inputs = node.inputs || [];
+          const old = node.inputs[slot]?.link;
+          node.inputs[slot] = node.inputs[slot] || ({} as INodeInputSlot);
+          node.inputs[slot].link = linkIdToUse;
+          this.log(`Node #${node.id}: Set link ${linkIdToUse} to input slot ${slot} (was ${old})`);
+        } else if (op === "ADD" && linkIdToUse != null) {
+          node.outputs = node.outputs || [];
+          node.outputs[slot] = node.outputs[slot] || ({} as INodeOutputSlot);
+          node.outputs[slot].links = node.outputs[slot].links || [];
+          node.outputs[slot].links.push(linkIdToUse);
+          this.log(`Node #${node.id}: Add link ${linkIdToUse} to output slot #${slot}`);
+        } else if (op === "REMOVE" && linkIdToUse != null) {
+          // We should never not have this data since the check call would have found it to be
+          // removed, but we can be safe and appease TS compiler at the same time.
+          if (node.outputs?.[slot]?.links?.length === undefined) {
+            this.log(
+              `Node #${node.id}: Couldn't remove link ${linkIdToUse} from output slot #${slot}` +
+                ` because it didn't exist.`,
+            );
+          } else {
+            let linkIdIndex = node.outputs![slot].links.indexOf(linkIdToUse);
+            node.outputs[slot].links.splice(linkIdIndex, 1);
+            this.log(`Node #${node.id}: Remove link ${linkIdToUse} from output slot #${slot}`);
+          }
+        } else {
+          throw new Error("Unhandled Node Instruction");
+        }
+        patches++;
+      } else if (instruction.op === "DELETE") {
+        const wasDeleted = this.deleteGraphLink(instruction.linkId);
+        if (wasDeleted === true) {
+          this.log(`Link #${instruction.linkId}: Removed workflow link b/c ${instruction.reason}`);
+        } else {
+          this.log(`Error Link #${instruction.linkId} was not removed!`);
+        }
+        deletes += wasDeleted ? 1 : 0;
+      } else {
+        throw new Error("Unhandled Instruction");
+      }
+    }
+
+    const newCheck = this.check(force);
+    times = times == null ? 5 : times;
+    let newFix = null;
+    // If we still have bad links, then recurse (up to five times).
+    if (newCheck.hasBadLinks && times > 0) {
+      newFix = this.fix(true, times - 1);
+    }
+
+    return {
+      hasBadLinks: newFix?.hasBadLinks ?? newCheck.hasBadLinks,
+      graph: this.graph,
+      patches: patches + (newFix?.patches ?? 0),
+      deletes: deletes + (newFix?.deletes ?? 0),
+    };
+  }
+
+  /** Logs if not silent. */
+  protected log(...args: any[]) {
+    if (this.silent) return;
+    this.logger.log(...args);
+  }
+
+  /**
+   * Patches a node for a check run, returning the instruction that would be made.
+   */
+  private getNodePatchInstruction(
+    node: N,
+    ioDir: IoDirection,
+    slot: number,
+    linkId: number,
+    op: "ADD" | "REMOVE",
+  ): WorkflowLinkFixerNodeInstruction | null {
+    const nodeId = node.id;
+    this.patchedNodeSlots[nodeId] = this.patchedNodeSlots[nodeId] || {};
+    const patchedNode = this.patchedNodeSlots[nodeId];
+    if (ioDir == IoDirection.INPUT) {
+      patchedNode["inputs"] = patchedNode["inputs"] || {};
+      // We can set to null (delete), so undefined means we haven't set it at all.
+      if (patchedNode["inputs"][slot] !== undefined) {
+        this.log(
+          ` > Already set ${nodeId}.inputs[${slot}] to ${patchedNode["inputs"][slot]} Skipping.`,
+        );
+        return null;
+      }
+      let linkIdToUse = op === "REMOVE" ? null : linkId;
+      patchedNode["inputs"][slot] = linkIdToUse;
+      return {node, dir: ioDir, op, slot, linkId, linkIdToUse};
+    }
+
+    patchedNode["outputs"] = patchedNode["outputs"] || {};
+    patchedNode["outputs"][slot] = patchedNode["outputs"][slot] || {
+      links: [...(node.outputs?.[slot]?.links || [])],
+      changes: {},
+    };
+    if (patchedNode["outputs"][slot]["changes"][linkId] !== undefined) {
+      this.log(
+        ` > Already set ${nodeId}.outputs[${slot}] to ${patchedNode["outputs"][slot]}! Skipping.`,
+      );
+      return null;
+    }
+    patchedNode["outputs"][slot]["changes"][linkId] = op;
+    if (op === "ADD") {
+      let linkIdIndex = patchedNode["outputs"][slot]["links"].indexOf(linkId);
+      if (linkIdIndex !== -1) {
+        this.log(` > Hmmm.. asked to add ${linkId} but it is already in list...`);
+        return null;
+      }
+      patchedNode["outputs"][slot]["links"].push(linkId);
+      return {node, dir: ioDir, op, slot, linkId, linkIdToUse: linkId};
+    }
+
+    let linkIdIndex = patchedNode["outputs"][slot]["links"].indexOf(linkId);
+    if (linkIdIndex === -1) {
+      this.log(` > Hmmm.. asked to remove ${linkId} but it doesn't exist...`);
+      return null;
+    }
+    patchedNode["outputs"][slot]["links"].splice(linkIdIndex, 1);
+    return {node, dir: ioDir, op, slot, linkId, linkIdToUse: linkId};
+  }
+
+  /** Checks if a node (or patched data) has a linkId. */
+  private nodeHasLinkId(node: N, ioDir: IoDirection, slot: number, linkId: number) {
+    const nodeId = node.id;
+    let has = false;
+    if (ioDir === IoDirection.INPUT) {
+      let nodeHasIt = node.inputs?.[slot]?.link === linkId;
+      if (this.patchedNodeSlots[nodeId]?.["inputs"]) {
+        let patchedHasIt = this.patchedNodeSlots[nodeId]["inputs"][slot] === linkId;
+        has = patchedHasIt;
+      } else {
+        has = nodeHasIt;
+      }
+    } else {
+      let nodeHasIt = node.outputs?.[slot]?.links?.includes(linkId);
+      if (this.patchedNodeSlots[nodeId]?.["outputs"]?.[slot]?.["changes"][linkId]) {
+        let patchedHasIt = this.patchedNodeSlots[nodeId]["outputs"][slot].links.includes(linkId);
+        has = !!patchedHasIt;
+      } else {
+        has = !!nodeHasIt;
+      }
+    }
+    return has;
+  }
+
+  /** Checks if a node (or patched data) has a linkId. */
+  private nodeHasAnyLink(node: N, ioDir: IoDirection, slot: number) {
+    // Patched data should be canonical. We can double check if fixing too.
+    const nodeId = node.id;
+    let hasAny = false;
+    if (ioDir === IoDirection.INPUT) {
+      let nodeHasAny = node.inputs?.[slot]?.link != null;
+      if (this.patchedNodeSlots[nodeId]?.["inputs"]) {
+        let patchedHasAny = this.patchedNodeSlots[nodeId]["inputs"][slot] != null;
+        hasAny = patchedHasAny;
+      } else {
+        hasAny = !!nodeHasAny;
+      }
+    } else {
+      let nodeHasAny = node.outputs?.[slot]?.links?.length;
+      if (this.patchedNodeSlots[nodeId]?.["outputs"]?.[slot]?.["changes"]) {
+        let patchedHasAny = this.patchedNodeSlots[nodeId]["outputs"][slot].links?.length;
+        hasAny = !!patchedHasAny;
+      } else {
+        hasAny = !!nodeHasAny;
+      }
+    }
+    return hasAny;
+  }
+}
+
+/**
+ * A WorkflowLinkFixer for serialized data.
+ */
+class WorkflowLinkFixerSerialized extends WorkflowLinkFixer<ISerialisedGraph, ISerialisedNode> {
+  constructor(graph: ISerialisedGraph) {
+    super(graph);
+  }
+
+  getNodeById(id: NodeId) {
+    return this.graph.nodes.find((node) => Number(node.id) === id) ?? null;
+  }
+
+  override fix(force: boolean = false, times?: number) {
+    const ret = super.fix(force, times);
+    // If we're a serialized graph, we can filter out the links because it's just an array.
+    this.graph.links = this.graph.links.filter((l) => !!l);
+    return ret;
+  }
+
+  deleteGraphLink(id: LinkId) {
+    // Sometimes we got objects instead of serializzed array for links if passed after ComfyUI's
+    // loadGraphData modifies the data. Let's find the id handling the bastardized objects just in
+    // case.
+    const idx = this.graph.links.findIndex((l) => l && (l[0] === id || (l as any).id === id));
+    if (idx === -1) {
+      return `Link #${id} not found in workflow links.`;
+    }
+    this.graph.links.splice(idx, 1);
+    return true;
+  }
+}
+
+/**
+ * A WorkflowLinkFixer for live LGraph data.
+ */
+class WorkflowLinkFixerGraph extends WorkflowLinkFixer<LGraph, LGraphNode> {
+  constructor(graph: LGraph) {
+    super(graph);
+  }
+
+  getNodeById(id: NodeId) {
+    return this.graph.getNodeById(id) ?? null;
+  }
+
+  deleteGraphLink(id: LinkId) {
+    if (this.graph.links instanceof Map) {
+      if (!this.graph.links.has(id)) {
+        return `Link #${id} not found in workflow links.`;
+      }
+      this.graph.links.delete(id);
+      return true;
+    }
+    if (this.graph.links[id] == null) {
+      return `Link #${id} not found in workflow links.`;
+    }
+    delete this.graph.links[id];
+    return true;
+  }
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/common/media/rgthree.svg b/custom_nodes/rgthree-comfy/src_web/common/media/rgthree.svg
new file mode 100644
index 0000000000000000000000000000000000000000..766a76152eefcdf8d05bf320c329b82afc704eaa
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/common/media/rgthree.svg
@@ -0,0 +1,7 @@
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 256 256" fill="currentColor">
+  <path d="M88.503,158.997 L152.731,196.103 L152.738,196.092 L152.762,196.103 L152.769,196.106 L152.771,196.103 L183.922,142.084     L174.153,136.437 L148.611,180.676 L101.512,153.484 L132.193,30.415 L156.124,71.869 L165.896,66.225 L128.002,0.59 z"></path>
+  <path d="M55.586,148.581l13.44,47.521l0.014,0.051l0.168-0.051l10.689-3.022l-6.589-23.313l45.609,26.335l0.087,0.051l0.027-0.051     l5.617-9.718l-42.648-24.622l35.771-143.45L33.232,164.729l9.77,5.645L55.586,148.581z M87.394,93.484l-16.708,67.018l-5.018-17.747     l-8.028,2.27L87.394,93.484z"></path>
+  <path d="M189.85,107.717 L137.892,137.718 L143.532,147.49 L185.723,123.133 L231.109,201.746 L24.895,201.746 L37.363,180.146     L27.592,174.505 L5.347,213.03 L250.653,213.03 z"></path>
+  <path d="M5.347,247.299v8.111h245.307v-8.111l-41.94-0.003c-1.336,0-2.404-1.065-2.441-2.396v-12.14     c0.037-1.315,1.089-2.368,2.41-2.385h41.972v-8.11H5.347v8.11h41.951c1.338,0.017,2.427,1.104,2.427,2.449v12.01     c0,1.365-1.105,2.462-2.457,2.462L5.347,247.299z M139.438,247.296c-1.334,0-2.406-1.065-2.439-2.396v-12.14     c0.033-1.315,1.085-2.368,2.41-2.385h46.415c1.335,0.017,2.425,1.104,2.425,2.449v12.01c0,1.365-1.103,2.462-2.459,2.462H139.438z       M70.193,247.296c-1.339,0-2.408-1.065-2.441-2.396v-12.14c0.033-1.315,1.086-2.368,2.407-2.385h46.418     c1.336,0.017,2.425,1.104,2.425,2.449v12.01c0,1.365-1.103,2.462-2.458,2.462H70.193z"></path>
+</svg>
\ No newline at end of file
diff --git a/custom_nodes/rgthree-comfy/src_web/common/media/svgs.ts b/custom_nodes/rgthree-comfy/src_web/common/media/svgs.ts
new file mode 100644
index 0000000000000000000000000000000000000000..b468cd0e9070b9cac52412ea0a7fe9e0eab5518c
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/common/media/svgs.ts
@@ -0,0 +1,210 @@
+import {createElement as $el} from "../utils_dom.js";
+
+// Some svg repo : https://www.svgrepo.com/svg/326731/open-outline
+
+export let logoRgthree: string = "";
+
+export async function logoRgthreeAsync(): Promise<string> {
+  if (logoRgthree) return logoRgthree;
+  let baseUrl = null;
+  if (window.location.pathname.includes("/rgthree/")) {
+    // Try to find how many relatives paths we need to go back to hit ./rgthree/api
+    const parts = window.location.pathname.split("/rgthree/")[1]?.split("/");
+    if (parts && parts.length) {
+      baseUrl = parts.map(() => "../").join("") + "rgthree";
+    }
+  }
+  baseUrl = baseUrl || "./rgthree";
+  return fetch(`${baseUrl}/logo_markup.svg?fg=currentColor&cssClass=rgthree-logo&w=auto&h=auto`)
+    .then((r) => r.text())
+    .then((t) => {
+      if (t.length < 100) {
+        t = `<svg viewBox="0 0 256 256" fill="currentColor" class="rgthree-logo">
+              <path d="M88.503,158.997 L152.731,196.103 L152.738,196.092 L152.762,196.103 L152.769,196.106 L152.771,196.103 L183.922,142.084     L174.153,136.437 L148.611,180.676 L101.512,153.484 L132.193,30.415 L156.124,71.869 L165.896,66.225 L128.002,0.59 "></path>
+              <path d="M55.586,148.581l13.44,47.521l0.014,0.051l0.168-0.051l10.689-3.022l-6.589-23.313l45.609,26.335l0.087,0.051l0.027-0.051     l5.617-9.718l-42.648-24.622l35.771-143.45L33.232,164.729l9.77,5.645L55.586,148.581z M87.394,93.484l-16.708,67.018l-5.018-17.747     l-8.028,2.27L87.394,93.484z"></path>
+              <path d="M189.85,107.717 L137.892,137.718 L143.532,147.49 L185.723,123.133 L231.109,201.746 L24.895,201.746 L37.363,180.146     L27.592,174.505 L5.347,213.03 L250.653,213.03 "></path>
+              <path d="M5.347,247.299v8.111h245.307v-8.111l-41.94-0.003c-1.336,0-2.404-1.065-2.441-2.396v-12.14     c0.037-1.315,1.089-2.368,2.41-2.385h41.972v-8.11H5.347v8.11h41.951c1.338,0.017,2.427,1.104,2.427,2.449v12.01     c0,1.365-1.105,2.462-2.457,2.462L5.347,247.299z M139.438,247.296c-1.334,0-2.406-1.065-2.439-2.396v-12.14     c0.033-1.315,1.085-2.368,2.41-2.385h46.415c1.335,0.017,2.425,1.104,2.425,2.449v12.01c0,1.365-1.103,2.462-2.459,2.462H139.438z       M70.193,247.296c-1.339,0-2.408-1.065-2.441-2.396v-12.14c0.033-1.315,1.086-2.368,2.407-2.385h46.418     c1.336,0.017,2.425,1.104,2.425,2.449v12.01c0,1.365-1.103,2.462-2.458,2.462H70.193z"></path>
+            </svg>`;
+      }
+      logoRgthree = t;
+      return t;
+    });
+}
+// Kick it off to cache upfront.
+logoRgthreeAsync();
+
+export const github = `<svg viewBox="0 0 16 16" fill="currentColor" class="github-logo">
+    <path d="M8 0c4.42 0 8 3.58 8 8a8.013 8.013 0 0 1-5.45 7.59c-.4.08-.55-.17-.55-.38 0-.27.01-1.13.01-2.2 0-.75-.25-1.23-.54-1.48 1.78-.2 3.65-.88 3.65-3.95 0-.88-.31-1.59-.82-2.15.08-.2.36-1.02-.08-2.12 0 0-.67-.22-2.2.82-.64-.18-1.32-.27-2-.27-.68 0-1.36.09-2 .27-1.53-1.03-2.2-.82-2.2-.82-.44 1.1-.16 1.92-.08 2.12-.51.56-.82 1.28-.82 2.15 0 3.06 1.86 3.75 3.64 3.95-.23.2-.44.55-.51 1.07-.46.21-1.61.55-2.33-.66-.15-.24-.6-.83-1.23-.82-.67.01-.27.38.01.53.34.19.73.9.82 1.13.16.45.68 1.31 2.69.94 0 .67.01 1.3.01 1.49 0 .21-.15.45-.55.38A7.995 7.995 0 0 1 0 8c0-4.42 3.58-8 8-8Z"></path>
+  </svg>`;
+
+export const iconStarFilled = `<svg viewBox="0 0 16 16" fill="currentColor" class="github-star">
+    <path d="M8 .25a.75.75 0 0 1 .673.418l1.882 3.815 4.21.612a.75.75 0 0 1 .416 1.279l-3.046 2.97.719 4.192a.751.751 0 0 1-1.088.791L8 12.347l-3.766 1.98a.75.75 0 0 1-1.088-.79l.72-4.194L.818 6.374a.75.75 0 0 1 .416-1.28l4.21-.611L7.327.668A.75.75 0 0 1 8 .25Z"></path>
+  </svg>`;
+
+export const iconReplace = `<svg viewBox="0 0 52 52" fill="currentColor">
+    <path d="M20,37.5c0-0.8-0.7-1.5-1.5-1.5h-15C2.7,36,2,36.7,2,37.5v11C2,49.3,2.7,50,3.5,50h15c0.8,0,1.5-0.7,1.5-1.5 V37.5z"/>
+    <path d="M8.1,22H3.2c-1,0-1.5,0.9-0.9,1.4l8,8.3c0.4,0.3,1,0.3,1.4,0l8-8.3c0.6-0.6,0.1-1.4-0.9-1.4h-4.7 c0-5,4.9-10,9.9-10V6C15,6,8.1,13,8.1,22z"/>
+    <path d="M41.8,20.3c-0.4-0.3-1-0.3-1.4,0l-8,8.3c-0.6,0.6-0.1,1.4,0.9,1.4h4.8c0,6-4.1,10-10.1,10v6 c9,0,16.1-7,16.1-16H49c1,0,1.5-0.9,0.9-1.4L41.8,20.3z"/>
+    <path d="M50,3.5C50,2.7,49.3,2,48.5,2h-15C32.7,2,32,2.7,32,3.5v11c0,0.8,0.7,1.5,1.5,1.5h15c0.8,0,1.5-0.7,1.5-1.5 V3.5z"/>
+  </svg>`;
+
+export const iconNode = `<svg viewBox="0 -0.5 25 25" fill="none">
+    <path fill-rule="evenodd" clip-rule="evenodd" d="M15.5 19H9.5C7.29086 19 5.5 17.2091 5.5 15V9C5.5 6.79086 7.29086 5 9.5 5H15.5C17.7091 5 19.5 6.79086 19.5 9V15C19.5 17.2091 17.7091 19 15.5 19Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/>
+    <path d="M19.5 9.75C19.9142 9.75 20.25 9.41421 20.25 9C20.25 8.58579 19.9142 8.25 19.5 8.25V9.75ZM5.5 8.25C5.08579 8.25 4.75 8.58579 4.75 9C4.75 9.41421 5.08579 9.75 5.5 9.75V8.25ZM11.5 14.25C11.0858 14.25 10.75 14.5858 10.75 15C10.75 15.4142 11.0858 15.75 11.5 15.75V14.25ZM13.5 15.75C13.9142 15.75 14.25 15.4142 14.25 15C14.25 14.5858 13.9142 14.25 13.5 14.25V15.75ZM19.5 8.25H5.5V9.75H19.5V8.25ZM11.5 15.75H13.5V14.25H11.5V15.75Z" fill="currentColor" />
+  </svg>`;
+
+export const iconGear = `<svg viewBox="0 0 24 24" fill="currentColor">
+    <path fill-rule="evenodd" clip-rule="evenodd" d="M12.7848 0.449982C13.8239 0.449982 14.7167 1.16546 14.9122 2.15495L14.9991 2.59495C15.3408 4.32442 17.1859 5.35722 18.9016 4.7794L19.3383 4.63233C20.3199 4.30175 21.4054 4.69358 21.9249 5.56605L22.7097 6.88386C23.2293 7.75636 23.0365 8.86366 22.2504 9.52253L21.9008 9.81555C20.5267 10.9672 20.5267 13.0328 21.9008 14.1844L22.2504 14.4774C23.0365 15.1363 23.2293 16.2436 22.7097 17.1161L21.925 18.4339C21.4054 19.3064 20.3199 19.6982 19.3382 19.3676L18.9017 19.2205C17.1859 18.6426 15.3408 19.6754 14.9991 21.405L14.9122 21.845C14.7167 22.8345 13.8239 23.55 12.7848 23.55H11.2152C10.1761 23.55 9.28331 22.8345 9.08781 21.8451L9.00082 21.4048C8.65909 19.6754 6.81395 18.6426 5.09822 19.2205L4.66179 19.3675C3.68016 19.6982 2.59465 19.3063 2.07505 18.4338L1.2903 17.1161C0.770719 16.2436 0.963446 15.1363 1.74956 14.4774L2.09922 14.1844C3.47324 13.0327 3.47324 10.9672 2.09922 9.8156L1.74956 9.52254C0.963446 8.86366 0.77072 7.75638 1.2903 6.8839L2.07508 5.56608C2.59466 4.69359 3.68014 4.30176 4.66176 4.63236L5.09831 4.77939C6.81401 5.35722 8.65909 4.32449 9.00082 2.59506L9.0878 2.15487C9.28331 1.16542 10.176 0.449982 11.2152 0.449982H12.7848ZM12 15.3C13.8225 15.3 15.3 13.8225 15.3 12C15.3 10.1774 13.8225 8.69998 12 8.69998C10.1774 8.69998 8.69997 10.1774 8.69997 12C8.69997 13.8225 10.1774 15.3 12 15.3Z" />
+  </svg>`;
+
+export const checkmark = `<svg viewBox="0 0 32 32" fill="currentColor" class="icon-checkmark">
+    <g transform="translate(-518.000000, -1039.000000)">
+      <path d="M548.783,1040.2 C547.188,1038.57 544.603,1038.57 543.008,1040.2 L528.569,1054.92 L524.96,1051.24 C523.365,1049.62 520.779,1049.62 519.185,1051.24 C517.59,1052.87 517.59,1055.51 519.185,1057.13 L525.682,1063.76 C527.277,1065.39 529.862,1065.39 531.457,1063.76 L548.783,1046.09 C550.378,1044.46 550.378,1041.82 548.783,1040.2"></path>
+    </g>
+  </svg>`;
+
+export const logoCivitai = `<svg viewBox="0 0 178 178" class="logo-civitai">
+    <defs>
+      <linearGradient id="bgblue" gradientUnits="userSpaceOnUse" x1="89.3" y1="-665.5" x2="89.3" y2="-841.1" gradientTransform="matrix(1 0 0 -1 0 -664)">
+        <stop offset="0" style="stop-color:#1284F7"/>
+        <stop offset="1" style="stop-color:#0A20C9"/>
+      </linearGradient>
+    </defs>
+    <path fill="#000" d="M13.3,45.4v87.7l76,43.9l76-43.9V45.4l-76-43.9L13.3,45.4z"/>
+    <path style="fill:url(#bgblue);" d="M89.3,29.2l52,30v60l-52,30l-52-30v-60 L89.3,29.2 M89.3,1.5l-76,43.9v87.8l76,43.9l76-43.9V45.4L89.3,1.5z" />
+    <path fill="#FFF" d="M104.1,97.2l-14.9,8.5l-14.9-8.5v-17l14.9-8.5l14.9,8.5h18.2V69.7l-33-19l-33,19v38.1l33,19l33-19V97.2H104.1z" />
+  </svg>`;
+
+export const iconOutLink = `<svg viewBox="0 0 32 32">
+    <path d="M 18 5 L 18 7 L 23.5625 7 L 11.28125 19.28125 L 12.71875 20.71875 L 25 8.4375 L 25 14 L 27 14 L 27 5 Z M 5 9 L 5 27 L 23 27 L 23 14 L 21 16 L 21 25 L 7 25 L 7 11 L 16 11 L 18 9 Z"></path>
+  </svg>`;
+
+export const link = `<svg viewBox="0 0 640 512">
+    <path d="M598.6 41.41C570.1 13.8 534.8 0 498.6 0s-72.36 13.8-99.96 41.41l-43.36 43.36c15.11 8.012 29.47 17.58 41.91 30.02c3.146 3.146 5.898 6.518 8.742 9.838l37.96-37.96C458.5 72.05 477.1 64 498.6 64c20.67 0 40.1 8.047 54.71 22.66c14.61 14.61 22.66 34.04 22.66 54.71s-8.049 40.1-22.66 54.71l-133.3 133.3C405.5 343.1 386 352 365.4 352s-40.1-8.048-54.71-22.66C296 314.7 287.1 295.3 287.1 274.6s8.047-40.1 22.66-54.71L314.2 216.4C312.1 212.5 309.9 208.5 306.7 205.3C298.1 196.7 286.8 192 274.6 192c-11.93 0-23.1 4.664-31.61 12.97c-30.71 53.96-23.63 123.6 22.39 169.6C293 402.2 329.2 416 365.4 416c36.18 0 72.36-13.8 99.96-41.41L598.6 241.3c28.45-28.45 42.24-66.01 41.37-103.3C639.1 102.1 625.4 68.16 598.6 41.41zM234 387.4L196.1 425.3C181.5 439.1 162 448 141.4 448c-20.67 0-40.1-8.047-54.71-22.66c-14.61-14.61-22.66-34.04-22.66-54.71s8.049-40.1 22.66-54.71l133.3-133.3C234.5 168 253.1 160 274.6 160s40.1 8.048 54.71 22.66c14.62 14.61 22.66 34.04 22.66 54.71s-8.047 40.1-22.66 54.71L325.8 295.6c2.094 3.939 4.219 7.895 7.465 11.15C341.9 315.3 353.3 320 365.4 320c11.93 0 23.1-4.664 31.61-12.97c30.71-53.96 23.63-123.6-22.39-169.6C346.1 109.8 310.8 96 274.6 96C238.4 96 202.3 109.8 174.7 137.4L41.41 270.7c-27.6 27.6-41.41 63.78-41.41 99.96c-.0001 36.18 13.8 72.36 41.41 99.97C69.01 498.2 105.2 512 141.4 512c36.18 0 72.36-13.8 99.96-41.41l43.36-43.36c-15.11-8.012-29.47-17.58-41.91-30.02C239.6 394.1 236.9 390.7 234 387.4z"/>
+  </svg>`;
+
+export const pencil = `<svg viewBox="0 0 24 24">
+    <path d="M 16.9375 1.0625 L 3.875 14.125 L 1.0742188 22.925781 L 9.875 20.125 L 22.9375 7.0625 C 22.9375 7.0625 22.8375 4.9615 20.9375 3.0625 C 19.0375 1.1625 16.9375 1.0625 16.9375 1.0625 z M 17.3125 2.6875 C 18.3845 2.8915 19.237984 3.3456094 19.896484 4.0214844 C 20.554984 4.6973594 21.0185 5.595 21.3125 6.6875 L 19.5 8.5 L 15.5 4.5 L 16.9375 3.0625 L 17.3125 2.6875 z M 4.9785156 15.126953 C 4.990338 15.129931 6.1809555 15.430955 7.375 16.625 C 8.675 17.825 8.875 18.925781 8.875 18.925781 L 8.9179688 18.976562 L 5.3691406 20.119141 L 3.8730469 18.623047 L 4.9785156 15.126953 z"/>
+  </svg>`;
+
+export const dotdotdot = `<svg viewBox="0 0 24 24" fill="currentColor">
+  <circle cy="12" r="3" cx="3"></circle>
+  <circle cy="12" r="3" cx="12"></circle>
+  <circle cx="21" cy="12" r="3"></circle>
+</svg>`;
+
+export const models = `<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
+    <path d="M4 4h6v6h-6z"></path>
+<path d="M14 4h6v6h-6z"></path>
+<path d="M4 14h6v6h-6z"></path>
+<path d="M17 17m-3 0a3 3 0 1 0 6 0a3 3 0 1 0 -6 0"></path>
+</svg>`;
+
+/** https://www.svgrepo.com/svg/402308/pencil */
+export const pencilColored = `<svg viewBox="0 0 64 64">
+    <path fill="#ffce31" d="M7.934 41.132L39.828 9.246l14.918 14.922l-31.895 31.886z"></path>
+    <path d="M61.3 4.6l-1.9-1.9C55.8-.9 50-.9 46.3 2.7l-6.5 6.5l15 15l6.5-6.5c3.6-3.6 3.6-9.5 0-13.1" fill="#ed4c5c"></path>
+    <path fill="#93a2aa" d="M35.782 13.31l4.1-4.102l14.92 14.92l-4.1 4.101z"></path>
+    <path fill="#c7d3d8" d="M37.338 14.865l4.1-4.101l11.739 11.738l-4.102 4.1z"></path>
+    <path fill="#fed0ac" d="M7.9 41.1l-6.5 17l4.5 4.5l17-6.5z"/>
+    <path d="M.3 61.1c-.9 2.4.3 3.5 2.7 2.6l8.2-3.1l-7.7-7.7l-3.2 8.2" fill="#333"></path>
+    <path fill="#ffdf85" d="M7.89 41.175l27.86-27.86l4.95 4.95l-27.86 27.86z"/>
+    <path fill="#ff8736" d="M17.904 51.142l27.86-27.86l4.95 4.95l-27.86 27.86z"></path>
+  </svg>`;
+
+/** https://www.svgrepo.com/svg/395640/save */
+export const diskColored = `<svg viewBox="-0.01 -0.008 100.016 100.016">
+  <path fill="#26f" fill_="#23475F" d="M88.555-.008H83v.016a2 2 0 0 1-2 2H19a2 2 0 0 1-2-2v-.016H4a4 4 0 0 0-4 4v92.016a4 4 0 0 0 4 4h92a4 4 0 0 0 4-4V11.517c.049-.089-11.436-11.454-11.445-11.525z"/>
+  <path fill="#04d" fill_="#1C3C50" d="M81.04 53.008H18.96a2 2 0 0 0-2 2v45h66.08v-45c0-1.106-.895-2-2-2zm-61.957-10h61.834a2 2 0 0 0 2-2V.547A1.993 1.993 0 0 1 81 2.007H19c-.916 0-1.681-.62-1.917-1.46v40.46a2 2 0 0 0 2 2.001z"/>
+  <path fill="#EBF0F1" d="M22 55.977h56a2 2 0 0 1 2 2v37.031a2 2 0 0 1-2 2H22c-1.104 0-2-.396-2-1.5V57.977a2 2 0 0 1 2-2z"/>
+  <path fill="#BCC4C8" d="M25 77.008h50v1H25v-1zm0 10h50v1H25v-1z"/>
+  <path fill="#1C3C50" d="M7 84.008h3a2 2 0 0 1 2 2v3a2 2 0 0 1-2 2H7a2 2 0 0 1-2-2v-3a2 2 0 0 1 2-2zm83 0h3a2 2 0 0 1 2 2v3a2 2 0 0 1-2 2h-3a2 2 0 0 1-2-2v-3a2 2 0 0 1 2-2z"/>
+  <path fill="#BCC4C8" d="M37 1.981v36.026a2 2 0 0 0 2 2h39a2 2 0 0 0 2-2V1.981c0 .007-42.982.007-43 0zm37 29.027a2 2 0 0 1-2 2h-6a2 2 0 0 1-2-2V10.981a2 2 0 0 1 2-2h6a2 2 0 0 1 2 2v20.027z"/>
+  <path fill="#FF9D00" d="M78 55.977H22a2 2 0 0 0-2 2v10.031h60V57.977a2 2 0 0 0-2-2z"/>
+</svg>`;
+
+/** https://www.svgrepo.com/svg/229838/folder */
+export const folderColored = `<svg viewBox="0 0 501.379 501.379">
+  <path style="fill:#EF9F2C;" d="M406.423,93.889H205.889c-17.067,0-30.933-13.867-30.933-30.933s-13.867-30.933-30.933-30.933H30.956
+      c-17.067,0-30.933,13.867-30.933,30.933v375.467c0,17.067,13.867,30.933,30.933,30.933h375.467
+      c17.067,0,30.933-13.867,30.933-30.933v-313.6C436.289,107.756,422.423,93.889,406.423,93.889z"/>
+  <path style="fill:#FEC656;" d="M470.423,157.889H97.089c-13.867,0-26.667,9.6-29.867,22.4l-66.133,249.6
+    c-5.333,19.2,9.6,38.4,29.867,38.4h373.333c13.867,0,26.667-9.6,29.867-22.4l66.133-248.533
+    C505.623,177.089,490.689,157.889,470.423,157.889z"/>
+</svg>`;
+
+export const modelsColored = `<svg viewBox="0 0 24 24">
+  <path fill="#aa3366" d="M0 0h10v10h-10z"></path>
+  <path d="M14 0h10v10h-10z" fill="#3366aa"></path>
+  <path d="M0 14h10v10h-10z" fill="#66aa33"></path>
+  <path fill="#dd9922" d="M19 19m-5 0 a5 5 0 1 0 10 0 a5 5 0 1 0 -10 0"></path>
+</svg>`;
+
+export const legoBlocksColored = `<svg viewBox="0 0 512 512">
+  <g>
+    <rect x="57.67" style="fill:#00BAB9;" width="101.275" height="78.769"/>
+    <rect x="205.363" style="fill:#00BAB9;" width="101.275" height="78.769"/>
+    <rect x="353.055" style="fill:#00BAB9;" width="101.275" height="78.769"/>
+  </g>
+  <polygon style="fill:#B8DE6F;" points="478.242,289.758 478.242,512 33.758,512 33.758,289.758 256,267.253 "/>
+  <polygon style="fill:#41D4D3;" points="478.242,67.516 478.242,289.758 33.758,289.758 33.758,67.516 57.67,67.516 158.945,67.516
+    205.363,67.516 306.637,67.516 353.055,67.516 454.33,67.516 "/>
+  <g>
+    <circle style="fill:#00BAB9;" cx="402.286" cy="143.473" r="8.44"/>
+    <circle style="fill:#00BAB9;" cx="368.527" cy="177.231" r="8.44"/>
+  </g>
+  <circle style="fill:#7BD288;" cx="109.714" cy="436.044" r="8.44"/>
+</svg>`;
+
+export const legoBlockColored = `<svg viewBox="0 0 256 256">
+  <style>
+  .s0 { fill: #ff0000 }
+  .s1 { fill: #c30000 }
+  .s2 { fill: #800000 }
+  .s3 { fill: #cc0000 }
+  .s4 { fill: #e00000 }
+  </style>
+  <g id="Folder 2">
+  <path id="Shape 1 copy 2" class="s0" d="m128 61l116 45-116 139-116-139z"/>
+  <path id="Shape 1" class="s1" d="m12 106l116 45v95l-116-45z"/>
+  <path id="Shape 1 copy" class="s2" d="m244 106l-116 45v95l116-45z"/>
+  <g id="Folder 1">
+    <path id="Shape 2" class="s3" d="m102 111.2c0-6.1 11.4-9.9 25.5-9.9 14.1 0 25.5 3.8 25.5 9.9 0 3.3 0 13.3 0 16.6 0 6.1-11.4 10.9-25.5 10.9-14.1 0-25.5-4.8-25.5-10.9 0-3.3 0-13.3 0-16.6z"/>
+    <path id="Shape 2 copy 4" class="s1" d="m102 111.2c0-6.1 11.4-9.9 25.5-9.9 14.1 0 25.5 3.8 25.5 9.9 0 3.3 0 13.3 0 16.6 0 6.1-11.4 10.9-25.5 10.9-14.1 0-25.5-4.8-25.5-10.9 0-3.3 0-13.3 0-16.6z"/>
+    <path id="Shape 2 copy 2" class="s2" d="m127.5 101.3c14.1 0 25.5 3.8 25.5 9.9 0 3.3 0 13.3 0 16.6 0 6.1-11.4 10.9-25.5 10.9 0-13.1 0-25.7 0-37.4z"/>
+    <path id="Shape 2 copy" class="s0" d="m127.5 118.8c-12.2 0-22-3.4-22-7.6 0-4.2 9.8-7.7 22-7.7 12.2 0 22 3.5 22 7.7 0 4.2-9.8 7.6-22 7.6zm0 0c-12.2 0-22-3.4-22-7.6 0-4.2 9.8-7.7 22-7.7 12.2 0 22 3.5 22 7.7 0 4.2-9.8 7.6-22 7.6z"/>
+  </g>
+  <g id="Folder 1 copy">
+    <path id="Shape 2" class="s4" d="m103 67.5c0-5.8 11-9.5 24.5-9.5 13.5 0 24.5 3.7 24.5 9.5 0 3.2 0 12.8 0 16 0 5.8-11 10.5-24.5 10.5-13.5 0-24.5-4.7-24.5-10.5 0-3.2 0-12.8 0-16z"/>
+    <path id="Shape 2 copy 4" class="s1" d="m103 67.5c0-5.8 11-9.5 24.5-9.5 13.5 0 24.5 3.7 24.5 9.5 0 3.2 0 12.8 0 16 0 5.8-11 10.5-24.5 10.5-13.5 0-24.5-4.7-24.5-10.5 0-3.2 0-12.8 0-16z"/>
+    <path id="Shape 2 copy 2" class="s2" d="m127.5 58c13.5 0 24.5 3.7 24.5 9.5 0 3.2 0 12.8 0 16 0 5.8-11 10.5-24.5 10.5 0-12.6 0-24.8 0-36z"/>
+    <path id="Shape 2 copy" class="s0" d="m127.5 74.9c-11.7 0-21.2-3.3-21.2-7.4 0-4.1 9.5-7.4 21.2-7.4 11.7 0 21.2 3.3 21.2 7.4 0 4.1-9.5 7.4-21.2 7.4zm0 0c-11.7 0-21.2-3.3-21.2-7.4 0-4.1 9.5-7.4 21.2-7.4 11.7 0 21.2 3.3 21.2 7.4 0 4.1-9.5 7.4-21.2 7.4z"/>
+  </g>
+  <g id="Folder 1 copy 2">
+    <path id="Shape 2" class="s4" d="m161 89.5c0-5.8 11-9.5 24.5-9.5 13.5 0 24.5 3.7 24.5 9.5 0 3.2 0 12.8 0 16 0 5.8-11 10.5-24.5 10.5-13.5 0-24.5-4.7-24.5-10.5 0-3.2 0-12.8 0-16z"/>
+    <path id="Shape 2 copy 4" class="s1" d="m161 89.5c0-5.8 11-9.5 24.5-9.5 13.5 0 24.5 3.7 24.5 9.5 0 3.2 0 12.8 0 16 0 5.8-11 10.5-24.5 10.5-13.5 0-24.5-4.7-24.5-10.5 0-3.2 0-12.8 0-16z"/>
+    <path id="Shape 2 copy 2" class="s2" d="m185.5 80c13.5 0 24.5 3.7 24.5 9.5 0 3.2 0 12.8 0 16 0 5.8-11 10.5-24.5 10.5 0-12.6 0-24.8 0-36z"/>
+    <path id="Shape 2 copy" class="s0" d="m185.5 96.9c-11.7 0-21.2-3.3-21.2-7.4 0-4.1 9.5-7.4 21.2-7.4 11.7 0 21.2 3.3 21.2 7.4 0 4.1-9.5 7.4-21.2 7.4zm0 0c-11.7 0-21.2-3.3-21.2-7.4 0-4.1 9.5-7.4 21.2-7.4 11.7 0 21.2 3.3 21.2 7.4 0 4.1-9.5 7.4-21.2 7.4z"/>
+  </g>
+  <g id="Folder 1 copy 3">
+    <path id="Shape 2" class="s4" d="m45 89.5c0-5.8 11-9.5 24.5-9.5 13.5 0 24.5 3.7 24.5 9.5 0 3.2 0 12.8 0 16 0 5.8-11 10.5-24.5 10.5-13.5 0-24.5-4.7-24.5-10.5 0-3.2 0-12.8 0-16z"/>
+    <path id="Shape 2 copy 4" class="s1" d="m45 89.5c0-5.8 11-9.5 24.5-9.5 13.5 0 24.5 3.7 24.5 9.5 0 3.2 0 12.8 0 16 0 5.8-11 10.5-24.5 10.5-13.5 0-24.5-4.7-24.5-10.5 0-3.2 0-12.8 0-16z"/>
+    <path id="Shape 2 copy 2" class="s2" d="m69.5 80c13.5 0 24.5 3.7 24.5 9.5 0 3.2 0 12.8 0 16 0 5.8-11 10.5-24.5 10.5 0-12.6 0-24.8 0-36z"/>
+    <path id="Shape 2 copy" class="s0" d="m69.5 96.9c-11.7 0-21.2-3.3-21.2-7.4 0-4.1 9.5-7.4 21.2-7.4 11.7 0 21.2 3.3 21.2 7.4 0 4.1-9.5 7.4-21.2 7.4zm0 0c-11.7 0-21.2-3.3-21.2-7.4 0-4.1 9.5-7.4 21.2-7.4 11.7 0 21.2 3.3 21.2 7.4 0 4.1-9.5 7.4-21.2 7.4z"/>
+  </g>
+  </g>
+</svg>`;
+
+export const gearColored = `<svg viewBox="0 0 128 128" preserveAspectRatio="xMidYMid meet">
+  <path d="M124 71.85v-15.7c0-.59-.45-1.09-1.03-1.15l-17.83-1.89c-.47-.05-.85-.38-.98-.83c-.86-2.95-2.03-5.76-3.48-8.39c-.23-.41-.19-.92.11-1.28l11.28-13.94c.37-.46.34-1.13-.08-1.54l-11.1-11.1a1.15 1.15 0 0 0-1.54-.08L85.39 27.22c-.37.3-.87.33-1.28.11a41.796 41.796 0 0 0-8.39-3.48c-.45-.13-.78-.51-.83-.98L73 5.03C72.94 4.45 72.44 4 71.85 4h-15.7c-.59 0-1.09.45-1.15 1.03l-1.89 17.83c-.05.47-.38.85-.83.98c-2.95.86-5.76 2.03-8.39 3.48c-.41.23-.92.19-1.28-.11L28.67 15.94a1.15 1.15 0 0 0-1.54.08l-11.1 11.1a1.15 1.15 0 0 0-.08 1.54L27.23 42.6c.3.37.33.87.11 1.28a41.796 41.796 0 0 0-3.48 8.39c-.13.45-.51.78-.98.83L5.03 55c-.58.06-1.03.56-1.03 1.15v15.7c0 .59.45 1.09 1.03 1.15l17.83 1.89c.47.05.85.38.98.83c.86 2.95 2.03 5.76 3.48 8.39c.23.41.19.92-.11 1.28L15.94 99.33c-.37.46-.34 1.13.08 1.54l11.1 11.1c.42.42 1.08.45 1.54.08l13.94-11.28c.37-.3.87-.33 1.28-.11c2.64 1.45 5.45 2.62 8.39 3.48c.45.13.78.51.83.98l1.9 17.85c.06.59.56 1.03 1.15 1.03h15.7c.59 0 1.09-.45 1.15-1.03l1.89-17.83c.05-.47.38-.85.83-.98c2.95-.86 5.76-2.03 8.39-3.48c.41-.23.92-.19 1.28.11l13.94 11.28c.46.37 1.13.34 1.54-.08l11.1-11.1c.42-.42.45-1.08.08-1.54l-11.28-13.94c-.3-.37-.33-.87-.11-1.28c1.45-2.64 2.62-5.45 3.48-8.39c.13-.45.51-.78.98-.83L122.97 73c.58-.06 1.03-.56 1.03-1.15zm-60 3.43c-6.23 0-11.28-5.05-11.28-11.28S57.77 52.72 64 52.72S75.28 57.77 75.28 64S70.23 75.28 64 75.28z" fill="#82aec0"></path>
+  <path d="M80.56 49.48c3.67 4.18 5.78 9.77 5.43 15.85c-.65 11.16-9.83 20.19-21 20.68c-4.75.21-9.18-1.09-12.86-3.45c-.28-.18-.58.2-.34.44a22.412 22.412 0 0 0 17.85 6.67c10.78-.85 19.56-9.5 20.55-20.27c.77-8.36-3.06-15.87-9.23-20.33c-.29-.2-.62.15-.4.41z" fill="#2f7889"></path>
+  <path d="M43.87 65.32c-.67-13.15 7.83-22.79 20.01-22.79c.65 0 1.68 0 2.48.92c1.01 1.18 1.1 2.6 0 3.77c-.81.86-1.95.92-2.53 1c-12.3 1.59-15.18 9.35-15.83 16.77c-.03.33.06 2.35-1.71 2.56c-2.15.25-2.41-1.91-2.42-2.23z" fill="#b9e4ea"></path>
+  <path d="M25.24 65.87c-.01-22.03 15.9-40.19 38.13-41.05c.68-.03 2.45 0 3.55.99c1.01.91 1.38 2.51.79 3.82c-.95 2.11-2.85 2.07-3.36 2.09c-18.51.66-34.18 15.73-34.19 33.95c0 .29-.05.58-.15.84l-.1.25c-.76 1.98-3.52 2.09-4.43.18c-.15-.34-.24-.7-.24-1.07z" fill="#94d1e0"></path>
+</svg>`;
+
+export function $svg(markup: string, attrs: {[key: string]: string}) {
+  if (!markup.match(/^\s*<svg/)) {
+    throw new Error("Cannot call $svg with non-svg markup.");
+  }
+  return $el(markup, attrs || {});
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/common/menu.ts b/custom_nodes/rgthree-comfy/src_web/common/menu.ts
new file mode 100644
index 0000000000000000000000000000000000000000..623d28b88c169c43329ee8034a622a686640c3e2
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/common/menu.ts
@@ -0,0 +1,130 @@
+import { generateId, wait } from "./shared_utils.js";
+import { createElement as $el, getClosestOrSelf, setAttributes} from "./utils_dom.js";
+
+/**
+ * A menu. Mimics the comfy menu.
+ */
+class Menu {
+
+  private element: HTMLMenuElement = $el('menu.rgthree-menu');
+  private callbacks: Map<string, (e: PointerEvent) => Promise<boolean|void>> = new Map();
+
+  private handleWindowPointerDownBound = this.handleWindowPointerDown.bind(this);
+
+  constructor(options: MenuOption[]) {
+    this.setOptions(options);
+    this.element.addEventListener('pointerup', async (e) => {
+      const target = getClosestOrSelf(e.target as HTMLElement, "[data-callback],menu");
+      if (e.which !== 1) {
+        return;
+      }
+      const callback = target?.dataset?.['callback'];
+      if (callback) {
+        const halt = await this.callbacks.get(callback)?.(e);
+        if (halt !== false) {
+          this.close();
+        }
+      }
+      e.preventDefault();
+      e.stopPropagation();
+      e.stopImmediatePropagation();
+    });
+  }
+
+  setOptions(options: MenuOption[]) {
+    for (const option of options) {
+      if (option.type === 'title') {
+        this.element.appendChild($el(`li`, {
+          html: option.label
+        }));
+      } else {
+        const id = generateId(8);
+        this.callbacks.set(id, async (e: PointerEvent) => { return option?.callback?.(e); });
+        this.element.appendChild($el(`li[role="button"][data-callback="${id}"]`, {
+          html: option.label
+        }));
+      }
+    }
+  }
+
+  toElement() {
+    return this.element;
+  }
+
+  async open(e: PointerEvent) {
+    const parent = (e.target as HTMLElement).closest('div,dialog,body') as HTMLElement
+    parent.appendChild(this.element);
+    setAttributes(this.element, {
+      style: {
+        left: `${e.clientX + 16}px`,
+        top: `${e.clientY - 16}px`,
+      }
+    });
+    this.element.setAttribute('state', 'measuring-open');
+    await wait(16);
+    const rect = this.element.getBoundingClientRect();
+    if (rect.right > window.innerWidth) {
+      this.element.style.left = `${e.clientX - rect.width - 16}px`;
+      await wait(16);
+    }
+    this.element.setAttribute('state', 'open');
+    setTimeout(() => {
+      window.addEventListener('pointerdown', this.handleWindowPointerDownBound);
+    });
+  }
+
+  handleWindowPointerDown(e:PointerEvent) {
+    if (!this.element.contains(e.target as HTMLElement)) {
+      this.close();
+    }
+  }
+
+  async close() {
+    window.removeEventListener('pointerdown', this.handleWindowPointerDownBound);
+    this.element.setAttribute('state', 'measuring-closed');
+    await wait(16);
+    this.element.setAttribute('state', 'closed');
+    this.element.remove();
+  }
+
+  isOpen() {
+    return (this.element.getAttribute('state') || '').includes('open');
+  }
+
+}
+
+type MenuOption = {
+  label: string;
+  type?: 'title'|'item'|'separator';
+  callback?: (e: PointerEvent) => void;
+}
+
+type MenuButtonOptions = {
+  icon: string;
+  options: MenuOption[];
+}
+
+export class MenuButton {
+
+  private options: MenuButtonOptions;
+  private menu: Menu;
+
+  private element: HTMLButtonElement = $el('button.rgthree-button[data-action="open-menu"]')
+
+  constructor(options: MenuButtonOptions) {
+    this.options = options;
+    this.element.innerHTML = options.icon;
+    this.menu = new Menu(options.options);
+
+    this.element.addEventListener('pointerdown', (e) => {
+      if (!this.menu.isOpen())  {
+        this.menu.open(e);
+      }
+    });
+  }
+
+  toElement() {
+    return this.element;
+  }
+
+}
\ No newline at end of file
diff --git a/custom_nodes/rgthree-comfy/src_web/common/model_info_service.ts b/custom_nodes/rgthree-comfy/src_web/common/model_info_service.ts
new file mode 100644
index 0000000000000000000000000000000000000000..efa36866c5c4203e0ecf65e6c999a518dcfbe57a
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/common/model_info_service.ts
@@ -0,0 +1,99 @@
+import type {RgthreeModelInfo} from "typings/rgthree.js";
+import {ModelInfoType, rgthreeApi} from "./rgthree_api.js";
+import {api} from "scripts/api.js";
+
+/**
+ * Abstract class defining information syncing for different types.
+ */
+abstract class BaseModelInfoService extends EventTarget {
+  private readonly fileToInfo = new Map<string, RgthreeModelInfo | null>();
+  protected abstract readonly modelInfoType: ModelInfoType;
+
+  protected abstract readonly apiRefreshEventString: string;
+
+  constructor() {
+    super();
+    this.init();
+  }
+
+  private init() {
+    api.addEventListener(
+      this.apiRefreshEventString,
+      this.handleAsyncUpdate.bind(this) as EventListener,
+    );
+  }
+
+  async getInfo(file: string, refresh: boolean, light: boolean) {
+    if (this.fileToInfo.has(file) && !refresh) {
+      return this.fileToInfo.get(file)!;
+    }
+    return this.fetchInfo(file, refresh, light);
+  }
+
+  async refreshInfo(file: string) {
+    return this.fetchInfo(file, true);
+  }
+
+  async clearFetchedInfo(file: string) {
+    await rgthreeApi.clearModelsInfo({type: this.modelInfoType, files: [file]});
+    this.fileToInfo.delete(file);
+    return null;
+  }
+
+  async savePartialInfo(file: string, data: Partial<RgthreeModelInfo>) {
+    let info = await rgthreeApi.saveModelInfo(this.modelInfoType, file, data);
+    this.fileToInfo.set(file, info);
+    return info;
+  }
+
+  handleAsyncUpdate(event: CustomEvent<{data: RgthreeModelInfo}>) {
+    const info = event.detail?.data as RgthreeModelInfo;
+    if (info?.file) {
+      this.setFreshInfo(info.file, info);
+    }
+  }
+
+  private async fetchInfo(file: string, refresh = false, light = false) {
+    let info = null;
+    if (!refresh) {
+      info = await rgthreeApi.getModelsInfo({type: this.modelInfoType, files: [file], light});
+    } else {
+      info = await rgthreeApi.refreshModelsInfo({type: this.modelInfoType, files: [file]});
+    }
+    info = info?.[0] ?? null;
+    if (!light) {
+      this.fileToInfo.set(file, info);
+    }
+    return info;
+  }
+
+  /**
+   * Single point to set data into the info cache, and fire an event. Note, this doesn't determine
+   * if the data is actually different.
+   */
+  private setFreshInfo(file: string, info: RgthreeModelInfo) {
+    this.fileToInfo.set(file, info);
+    // this.dispatchEvent(
+    //   new CustomEvent("rgthree-model-service-lora-details", { detail: { lora: info } }),
+    // );
+  }
+}
+
+/**
+ * Lora type implementation of ModelInfoTypeService.
+ */
+class LoraInfoService extends BaseModelInfoService {
+  protected override readonly apiRefreshEventString = "rgthree-refreshed-loras-info";
+  protected override readonly modelInfoType = 'loras';
+}
+
+/**
+ * Checkpoint type implementation of ModelInfoTypeService.
+ */
+class CheckpointInfoService extends BaseModelInfoService {
+  protected override readonly apiRefreshEventString = "rgthree-refreshed-checkpoints-info";
+  protected override readonly modelInfoType = 'checkpoints';
+}
+
+export const LORA_INFO_SERVICE = new LoraInfoService();
+export const CHECKPOINT_INFO_SERVICE = new CheckpointInfoService();
diff --git a/custom_nodes/rgthree-comfy/src_web/common/progress_bar.ts b/custom_nodes/rgthree-comfy/src_web/common/progress_bar.ts
new file mode 100644
index 0000000000000000000000000000000000000000..af55edab93c02595c2eb61e19445220c0febf5a1
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/common/progress_bar.ts
@@ -0,0 +1,218 @@
+/**
+ * Progress bar web component.
+ */
+
+import { SERVICE as PROMPT_SERVICE, type PromptExecution } from "rgthree/common/prompt_service.js";
+import { createElement } from "./utils_dom.js";
+
+/**
+ * The progress bar web component.
+ */
+export class RgthreeProgressBar extends HTMLElement {
+  static NAME = "rgthree-progress-bar";
+
+  static create(): RgthreeProgressBar {
+    return document.createElement(RgthreeProgressBar.NAME) as RgthreeProgressBar;
+  }
+
+  private shadow: ShadowRoot | null = null;
+  private progressNodesEl!: HTMLDivElement;
+  private progressStepsEl!: HTMLDivElement;
+  private progressTextEl!: HTMLSpanElement;
+
+  private currentPromptExecution: PromptExecution | null = null;
+
+  private readonly onProgressUpdateBound = this.onProgressUpdate.bind(this);
+
+  private connected: boolean = false;
+
+  /** The currentNodeId so outside callers can see what we're currently executing against. */
+  get currentNodeId() {
+    const prompt = this.currentPromptExecution;
+    const nodeId = prompt?.errorDetails?.node_id || prompt?.currentlyExecuting?.nodeId;
+    return nodeId || null;
+  }
+
+  constructor() {
+    super();
+  }
+
+  private onProgressUpdate(e: CustomEvent<{ queue: number; prompt: PromptExecution }>) {
+    if (!this.connected) return;
+
+    const prompt = e.detail.prompt;
+    this.currentPromptExecution = prompt;
+
+    if (prompt?.errorDetails) {
+      let progressText = `${prompt.errorDetails?.exception_type} ${
+        prompt.errorDetails?.node_id || ""
+      } ${prompt.errorDetails?.node_type || ""}`;
+      this.progressTextEl.innerText = progressText;
+      this.progressNodesEl.classList.add("-error");
+      this.progressStepsEl.classList.add("-error");
+      return;
+    }
+    if (prompt?.currentlyExecuting) {
+      this.progressNodesEl.classList.remove("-error");
+      this.progressStepsEl.classList.remove("-error");
+
+      const current = prompt?.currentlyExecuting;
+
+      let progressText = `(${e.detail.queue}) `;
+
+      // Sometimes we may get status updates for a workflow that was already running. In that case
+      // we don't know totalNodes.
+      if (!prompt.totalNodes) {
+        progressText += `??%`;
+        this.progressNodesEl.style.width = `0%`;
+      } else {
+        const percent = (prompt.executedNodeIds.length / prompt.totalNodes) * 100;
+        this.progressNodesEl.style.width = `${Math.max(2, percent)}%`;
+        // progressText += `Node ${prompt.executedNodeIds.length + 1} of ${prompt.totalNodes || "?"}`;
+        progressText += `${Math.round(percent)}%`;
+      }
+
+      let nodeLabel = current.nodeLabel?.trim();
+      let stepsLabel = "";
+      if (current.step != null && current.maxSteps) {
+        const percent = (current.step / current.maxSteps) * 100;
+        this.progressStepsEl.style.width = `${percent}%`;
+        // stepsLabel += `Step ${current.step} of ${current.maxSteps}`;
+        if (current.pass > 1 || current.maxPasses != null) {
+          stepsLabel += `#${current.pass}`;
+          if (current.maxPasses && current.maxPasses > 0) {
+            stepsLabel += `/${current.maxPasses}`;
+          }
+          stepsLabel += ` - `;
+        }
+        stepsLabel += `${Math.round(percent)}%`;
+      }
+
+      if (nodeLabel || stepsLabel) {
+        progressText += ` - ${nodeLabel || "???"}${stepsLabel ? ` (${stepsLabel})` : ""}`;
+      }
+      if (!stepsLabel) {
+        this.progressStepsEl.style.width = `0%`;
+      }
+      this.progressTextEl.innerText = progressText;
+    } else {
+      if (e?.detail.queue) {
+        this.progressTextEl.innerText = `(${e.detail.queue}) Running... in another tab`;
+      } else {
+        this.progressTextEl.innerText = "Idle";
+      }
+      this.progressNodesEl.style.width = `0%`;
+      this.progressStepsEl.style.width = `0%`;
+    }
+  }
+
+  connectedCallback() {
+    if (!this.connected) {
+      PROMPT_SERVICE.addEventListener(
+        "progress-update",
+        this.onProgressUpdateBound as EventListener,
+      );
+      this.connected = true;
+    }
+    // We were already connected, so we just need to reset.
+    if (this.shadow) {
+      this.progressTextEl.innerText = "Idle";
+      this.progressNodesEl.style.width = `0%`;
+      this.progressStepsEl.style.width = `0%`;
+      return;
+    }
+
+    this.shadow = this.attachShadow({ mode: "open" });
+    const sheet = new CSSStyleSheet();
+    sheet.replaceSync(`
+
+      :host {
+        position: relative;
+        overflow: hidden;
+        box-sizing: border-box;
+        background: var(--rgthree-progress-bg-color);
+        --rgthree-progress-bg-color: rgba(23, 23, 23, 0.9);
+        --rgthree-progress-nodes-bg-color: rgb(0, 128, 0);
+        --rgthree-progress-steps-bg-color: rgb(0, 128, 0);
+        --rgthree-progress-error-bg-color: rgb(128, 0, 0);
+        --rgthree-progress-text-color: #fff;
+      }
+      :host * {
+        box-sizing: inherit;
+      }
+
+      :host > div.bar {
+        background: var(--rgthree-progress-nodes-bg-color);
+        position: absolute;
+        left: 0;
+        top: 0;
+        width: 0%;
+        height: 50%;
+        z-index: 1;
+        transition: width 50ms ease-in-out;
+      }
+      :host > div.bar + div.bar {
+        background: var(--rgthree-progress-steps-bg-color);
+        top: 50%;
+        height: 50%;
+        z-index: 2;
+      }
+      :host > div.bar.-error {
+        background: var(--rgthree-progress-error-bg-color);
+      }
+
+      :host > .overlay {
+        position: absolute;
+        left: 0;
+        top: 0;
+        width: 100%;
+        height: 100%;
+        z-index: 5;
+        background: linear-gradient(to bottom, rgba(255,255,255,0.25), rgba(0,0,0,0.25));
+        mix-blend-mode: overlay;
+      }
+
+      :host > span {
+        position: relative;
+        z-index: 4;
+        text-align: left;
+        font-size: inherit;
+        height: 100%;
+        font-family: sans-serif;
+        text-shadow: 1px 1px 0px #000;
+        display: flex;
+        flex-direction: row;
+        padding: 0 6px;
+        align-items: center;
+        justify-content: start;
+        color: var(--rgthree-progress-text-color);
+        text-shadow: black 0px 0px 2px;
+      }
+
+      :host > div.bar[style*="width: 0%"]:first-child,
+      :host > div.bar[style*="width:0%"]:first-child {
+        height: 0%;
+      }
+      :host > div.bar[style*="width: 0%"]:first-child + div,
+      :host > div.bar[style*="width:0%"]:first-child + div {
+        bottom: 0%;
+      }
+    `);
+    this.shadow.adoptedStyleSheets = [sheet];
+
+    const overlayEl = createElement(`div.overlay[part="overlay"]`, { parent: this.shadow });
+    this.progressNodesEl = createElement(`div.bar[part="progress-nodes"]`, { parent: this.shadow });
+    this.progressStepsEl = createElement(`div.bar[part="progress-steps"]`, { parent: this.shadow });
+    this.progressTextEl = createElement(`span[part="text"]`, { text: "Idle", parent: this.shadow });
+  }
+
+  disconnectedCallback() {
+    this.connected = false;
+    PROMPT_SERVICE.removeEventListener(
+      "progress-update",
+      this.onProgressUpdateBound as EventListener,
+    );
+  }
+}
+
+customElements.define(RgthreeProgressBar.NAME, RgthreeProgressBar);
diff --git a/custom_nodes/rgthree-comfy/src_web/common/prompt_service.ts b/custom_nodes/rgthree-comfy/src_web/common/prompt_service.ts
new file mode 100644
index 0000000000000000000000000000000000000000..4ed8b40f5290b77e64e64c7b3a0e622e005f23cf
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/common/prompt_service.ts
@@ -0,0 +1,274 @@
+import type {
+  ComfyApiEventDetailCached,
+  ComfyApiEventDetailError,
+  ComfyApiEventDetailExecuted,
+  ComfyApiEventDetailExecuting,
+  ComfyApiEventDetailExecutionStart,
+  ComfyApiEventDetailProgress,
+  ComfyApiEventDetailStatus,
+  ComfyApiFormat,
+  ComfyApiPrompt,
+} from "typings/comfy.js";
+import { api } from "scripts/api.js";
+import type { LGraph as TLGraph, LGraphCanvas as TLGraphCanvas } from "@comfyorg/frontend";
+import { Resolver, getResolver } from "./shared_utils.js";
+
+/**
+ * Wraps general data of a prompt's execution.
+ */
+export class PromptExecution {
+  id: string;
+  promptApi: ComfyApiFormat | null = null;
+  executedNodeIds: string[] = [];
+  totalNodes: number = 0;
+  currentlyExecuting: {
+    nodeId: string;
+    nodeLabel?: string;
+    step?: number;
+    maxSteps?: number;
+    /** The current pass, for nodes with multiple progress passes. */
+    pass: number;
+    /**
+     * The max num of passes. Can be calculated for some nodes, or set to -1 when known there will
+     * be multiple passes, but the number cannot be calculated.
+     */
+    maxPasses?: number;
+  } | null = null;
+  errorDetails: any | null = null;
+
+  apiPrompt: Resolver<null> = getResolver();
+
+  constructor(id: string) {
+    this.id = id;
+  }
+
+  /**
+   * Sets the prompt and prompt-related data. This can technically come in lazily, like if the web
+   * socket fires the 'execution-start' event before we actually get a response back from the
+   * initial prompt call.
+   */
+  setPrompt(prompt: ComfyApiPrompt) {
+    this.promptApi = prompt.output;
+    this.totalNodes = Object.keys(this.promptApi).length;
+    this.apiPrompt.resolve(null);
+  }
+
+  getApiNode(nodeId: string | number) {
+    return this.promptApi?.[String(nodeId)] || null;
+  }
+
+  private getNodeLabel(nodeId: string | number) {
+    const apiNode = this.getApiNode(nodeId);
+    let label = apiNode?._meta?.title || apiNode?.class_type || undefined;
+    if (!label) {
+      const graphNode = this.maybeGetComfyGraph()?.getNodeById(Number(nodeId));
+      label = graphNode?.title || graphNode?.type || undefined;
+    }
+    return label;
+  }
+
+  /**
+   * Updates the execution data depending on the passed data, fed from api events.
+   */
+  executing(nodeId: string | null, step?: number, maxSteps?: number) {
+    if (nodeId == null) {
+      // We're done, any left over nodes must be skipped...
+      this.currentlyExecuting = null;
+      return;
+    }
+    if (this.currentlyExecuting?.nodeId !== nodeId) {
+      if (this.currentlyExecuting != null) {
+        this.executedNodeIds.push(nodeId);
+      }
+      this.currentlyExecuting = { nodeId, nodeLabel: this.getNodeLabel(nodeId), pass: 0 };
+      // We'll see if we're known node for multiple passes, that will come in as generic 'progress'
+      // updates from the api. If we're known to have multiple passes, then we'll pre-set data to
+      // allow the progress bar to handle intial rendering. If we're not, that's OK, the data will
+      // be shown with the second pass.
+      this.apiPrompt.promise.then(() => {
+        // If we execute with a null node id and clear the currently executing, then we can just
+        // move on. This seems to only happen with a super-fast execution (like, just seed node
+        // and display any for testing).
+        if (this.currentlyExecuting == null) {
+          return;
+        }
+        const apiNode = this.getApiNode(nodeId);
+        if (!this.currentlyExecuting.nodeLabel) {
+          this.currentlyExecuting.nodeLabel = this.getNodeLabel(nodeId);
+        }
+        if (apiNode?.class_type === "UltimateSDUpscale") {
+          // From what I can tell, UltimateSDUpscale, does an initial pass that isn't actually a
+          // tile. It seems to always be 4 steps... We'll start our pass at -1, so this prepass is
+          // "0" and "1" will start with the first tile. This way, a user knows they have 4 tiles,
+          // know this pass counter will go to 4 (and not 5). Also, we cannot calculate maxPasses
+          // for 'UltimateSDUpscale' :(
+          this.currentlyExecuting.pass--;
+          this.currentlyExecuting.maxPasses = -1;
+        } else if (apiNode?.class_type === "IterativeImageUpscale") {
+          this.currentlyExecuting.maxPasses = (apiNode?.inputs["steps"] as number) ?? -1;
+        }
+      });
+    }
+    if (step != null) {
+      // If we haven't had any stpes before, or the passes step is lower than the previous, then
+      // increase the passes.
+      if (!this.currentlyExecuting!.step || step < this.currentlyExecuting!.step) {
+        this.currentlyExecuting!.pass!++;
+      }
+      this.currentlyExecuting!.step = step;
+      this.currentlyExecuting!.maxSteps = maxSteps;
+    }
+  }
+
+  /**
+   * If there's an error, we add the details.
+   */
+  error(details: any) {
+    this.errorDetails = details;
+  }
+
+  private maybeGetComfyGraph(): TLGraph | null {
+    return ((window as any)?.app?.graph as TLGraph) || null;
+  }
+}
+
+/**
+ * A singleton service that wraps the Comfy API and simplifies the event data being fired.
+ */
+class PromptService extends EventTarget {
+  promptsMap: Map<string, PromptExecution> = new Map();
+  currentExecution: PromptExecution | null = null;
+  lastQueueRemaining = 0;
+
+  constructor(api: any) {
+    super();
+    const that = this;
+
+    // Patch the queuePrompt method so we can capture new data going through.
+    const queuePrompt = api.queuePrompt;
+    api.queuePrompt = async function (num: number, prompt: ComfyApiPrompt, ...args: any[]) {
+      let response;
+      try {
+        response = await queuePrompt.apply(api, [...arguments]);
+      } catch (e) {
+        const promptExecution = that.getOrMakePrompt("error");
+        promptExecution.error({ exception_type: "Unknown." });
+        // console.log("ERROR QUEUE PROMPT", response, arguments);
+        throw e;
+      }
+      // console.log("QUEUE PROMPT", response, arguments);
+      const promptExecution = that.getOrMakePrompt(response.prompt_id);
+      promptExecution.setPrompt(prompt);
+      if (!that.currentExecution) {
+        that.currentExecution = promptExecution;
+      }
+      that.promptsMap.set(response.prompt_id, promptExecution);
+      that.dispatchEvent(
+        new CustomEvent("queue-prompt", {
+          detail: {
+            prompt: promptExecution,
+          },
+        }),
+      );
+      return response;
+    };
+
+    api.addEventListener("status", (e: CustomEvent<ComfyApiEventDetailStatus>) => {
+      // console.log("status", JSON.stringify(e.detail));
+      // Sometimes a status message is fired when the app loades w/o any details.
+      if (!e.detail?.exec_info) return;
+      this.lastQueueRemaining = e.detail.exec_info.queue_remaining;
+      this.dispatchProgressUpdate();
+    });
+
+    api.addEventListener("execution_start", (e: CustomEvent<ComfyApiEventDetailExecutionStart>) => {
+      // console.log("execution_start", JSON.stringify(e.detail));
+      if (!this.promptsMap.has(e.detail.prompt_id)) {
+        console.warn("'execution_start' fired before prompt was made.");
+      }
+      const prompt = this.getOrMakePrompt(e.detail.prompt_id);
+      this.currentExecution = prompt;
+      this.dispatchProgressUpdate();
+    });
+
+    api.addEventListener("executing", (e: CustomEvent<ComfyApiEventDetailExecuting>) => {
+      // console.log("executing", JSON.stringify(e.detail));
+      if (!this.currentExecution) {
+        this.currentExecution = this.getOrMakePrompt("unknown");
+        console.warn("'executing' fired before prompt was made.");
+      }
+      this.currentExecution.executing(e.detail);
+      this.dispatchProgressUpdate();
+      if (e.detail == null) {
+        this.currentExecution = null;
+      }
+    });
+
+    api.addEventListener("progress", (e: CustomEvent<ComfyApiEventDetailProgress>) => {
+      // console.log("progress", JSON.stringify(e.detail));
+      if (!this.currentExecution) {
+        this.currentExecution = this.getOrMakePrompt(e.detail.prompt_id);
+        console.warn("'progress' fired before prompt was made.");
+      }
+      this.currentExecution.executing(e.detail.node, e.detail.value, e.detail.max);
+      this.dispatchProgressUpdate();
+    });
+
+    api.addEventListener("execution_cached", (e: CustomEvent<ComfyApiEventDetailCached>) => {
+      // console.log("execution_cached", JSON.stringify(e.detail));
+      if (!this.currentExecution) {
+        this.currentExecution = this.getOrMakePrompt(e.detail.prompt_id);
+        console.warn("'execution_cached' fired before prompt was made.");
+      }
+      for (const cached of e.detail.nodes) {
+        this.currentExecution.executing(cached);
+      }
+      this.dispatchProgressUpdate();
+    });
+
+    api.addEventListener("executed", (e: CustomEvent<ComfyApiEventDetailExecuted>) => {
+      // console.log("executed", JSON.stringify(e.detail));
+      if (!this.currentExecution) {
+        this.currentExecution = this.getOrMakePrompt(e.detail.prompt_id);
+        console.warn("'executed' fired before prompt was made.");
+      }
+    });
+
+    api.addEventListener("execution_error", (e: CustomEvent<ComfyApiEventDetailError>) => {
+      // console.log("execution_error", e.detail);
+      if (!this.currentExecution) {
+        this.currentExecution = this.getOrMakePrompt(e.detail.prompt_id);
+        console.warn("'execution_error' fired before prompt was made.");
+      }
+      this.currentExecution?.error(e.detail);
+      this.dispatchProgressUpdate();
+    });
+  }
+
+  /** A helper method, since we extend/override api.queuePrompt above anyway. */
+  async queuePrompt(prompt: ComfyApiPrompt) {
+    return await api.queuePrompt(-1, prompt);
+  }
+
+  dispatchProgressUpdate() {
+    this.dispatchEvent(
+      new CustomEvent("progress-update", {
+        detail: {
+          queue: this.lastQueueRemaining,
+          prompt: this.currentExecution,
+        },
+      }),
+    );
+  }
+
+  getOrMakePrompt(id: string) {
+    let prompt = this.promptsMap.get(id);
+    if (!prompt) {
+      prompt = new PromptExecution(id);
+      this.promptsMap.set(id, prompt);
+    }
+    return prompt;
+  }
+}
+
+export const SERVICE = new PromptService(api);
diff --git a/custom_nodes/rgthree-comfy/src_web/common/py_parser.ts b/custom_nodes/rgthree-comfy/src_web/common/py_parser.ts
new file mode 100644
index 0000000000000000000000000000000000000000..22d69d281c460082e043fce28626fadbeb110baf
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/common/py_parser.ts
@@ -0,0 +1,983 @@
+/**
+ * @fileoverview An AST executor using the TreeSitter parser to parse python-like code and execute
+ * in JS. This parser is self-contained and isolated from other parts of the app (like Comfy-UI
+ * specific types, etc). Instead, additional handlers, builtins, and types can be passed into the
+ * pure functions below.
+ */
+import type {Parser, Node as TreeSitterNode, Tree} from "web-tree-sitter";
+
+import {check, deepFreeze} from "./shared_utils.js";
+
+// Hacky memoization because I don't feel like writing a decorator.
+const MEMOIZED = {parser: null as unknown as Parser};
+
+interface Dict extends Object {
+  [k: string]: unknown;
+}
+interface ExecutionContextData extends Object {
+  [k: string]: unknown;
+}
+class ExecuteContext implements ExecutionContextData {
+  [k: string]: unknown;
+
+  constructor(existing: Object = {}) {
+    Object.assign(this, !!window.structuredClone ? structuredClone(existing) : {...existing});
+  }
+}
+class InitialExecuteContext extends ExecuteContext {}
+
+type NodeHandlerArgs = [ExecutionContextData, BuiltInFns];
+type NodeHandler = (node: Node, ...args: NodeHandlerArgs) => Promise<any>;
+
+const TYPE_TO_HANDLER = new Map<string, NodeHandler>([
+  ["module", handleChildren],
+  ["expression_statement", handleChildren],
+  ["interpolation", handleInterpolation],
+  ["block", handleChildren], // Block of code, like in a for loop
+
+  ["comment", handleSwallow],
+  ["return_statement", handleReturn],
+
+  ["assignment", handleAssignment],
+  ["named_expression", handleNamedExpression],
+
+  ["identifier", handleIdentifier],
+  ["attribute", handleAttribute],
+  ["subscript", handleSubscript],
+
+  ["call", handleCall],
+  ["argument_list", handleArgumentsList],
+
+  ["for_statement", handleForStatement],
+  ["list_comprehension", handleListComprehension],
+
+  ["comparison_operator", handleComparisonOperator],
+  ["boolean_operator", handleBooleanOperator],
+  ["binary_operator", handleBinaryOperator],
+  ["not_operator", handleNotOperator],
+  ["unary_operator", handleUnaryOperator],
+
+  // Types
+  ["integer", handleNumber],
+  ["float", handleNumber],
+  ["string", handleString],
+  ["tuple", handleList],
+  ["list", handleList],
+  ["dictionary", handleDictionary],
+  ["pair", handleDictionaryPair],
+  ["true", async (...args: any[]) => true],
+  ["false", async (...args: any[]) => false],
+]);
+
+type BuiltInFn = {fn: Function};
+type BuiltInFns = {[key: string]: BuiltInFn};
+
+const DEFAULT_BUILT_INS: BuiltInFns = {
+  round: {fn: (n: any) => Math.round(Number(n))},
+  ceil: {fn: (n: any) => Math.ceil(Number(n))},
+  floor: {fn: (n: any) => Math.floor(Number(n))},
+  // Function(name="sqrt", call=math.sqrt, args=(1, 1)),
+  // Function(name="min", call=min, args=(2, None)),
+  // Function(name="max", call=max, args=(2, None)),
+  // Function(name=".random_int", call=random.randint, args=(2, 2)),
+  // Function(name=".random_choice", call=random.choice, args=(1, 1)),
+  // Function(name=".random_seed", call=random.seed, args=(1, 1)),
+  // Function(name="re", call=re.compile, args=(1, 1)),
+  len: {fn: (n: any) => n?.__len__?.() ?? n?.length},
+  // Function(name="enumerate", call=enumerate, args=(1, 1)),
+  // Function(name="range", call=range, args=(1, 3)),
+
+  // Types
+  int: {fn: (n: any) => Math.floor(Number(n))},
+  float: {fn: (n: any) => Number(n)},
+  str: {fn: (n: any) => String(n)},
+  bool: {fn: (n: any) => !!n},
+  list: {fn: (tupl: any[] = []) => new PyList(tupl)},
+  tuple: {fn: (list: any[] = []) => new PyTuple(list)},
+  dict: {fn: (dict: Dict = {}) => new PyDict(dict)},
+
+  // Special
+  dir: {fn: (...args: any[]) => console.dir(...__unwrap__(...args))},
+  print: {fn: (...args: any[]) => console.log(...__unwrap__(...args))},
+  log: {fn: (...args: any[]) => console.log(...__unwrap__(...args))},
+};
+
+/**
+ * The main entry point to parse code.
+ */
+export async function execute(
+  code: string,
+  ctx: ExecutionContextData,
+  additionalBuiltins?: BuiltInFns,
+) {
+  const builtIns = deepFreeze({...DEFAULT_BUILT_INS, ...(additionalBuiltins ?? {})});
+  // When we start the execution, we create an InitialExecuteContext as an instance so we can check
+  // if we're the initial, global context during execution (as we may pass in a new context in the
+  // like if evaluating a list comprehension, or setting on an object).
+  ctx = new InitialExecuteContext(ctx);
+
+  const root = (await parse(code)).rootNode;
+  const value = await handleNode(new Node(root), ctx, builtIns);
+
+  console.log("=====");
+  console.log(`value`, value?.__unwrap__?.() ?? value);
+  console.log("context", ctx);
+
+  return value;
+}
+
+/**
+ * Parses a code string to a `Tree`.
+ */
+async function parse(code: string): Promise<Tree> {
+  if (!MEMOIZED.parser) {
+    // @ts-ignore - Path is rewritten.
+    const TreeSitter = (await import("rgthree/lib/tree-sitter.js")) as TreeSitter;
+    await TreeSitter.Parser.init();
+    const lang = await TreeSitter.Language.load("rgthree/lib/tree-sitter-python.wasm");
+    MEMOIZED.parser = new TreeSitter.Parser() as Parser;
+    MEMOIZED.parser.setLanguage(lang);
+  }
+  return MEMOIZED.parser.parse(code)!;
+}
+
+/**
+ * The generic node handler, calls out to specific handlers based on the node type. This is
+ * recursively called from other handlers.
+ */
+async function handleNode(
+  node: Node,
+  ctx: ExecutionContextData,
+  builtIns: BuiltInFns,
+): Promise<any> {
+  const type = node.type as string;
+
+  // If we have a returned value, then just return it, which should recursively settle.
+  if (ctx.hasOwnProperty("__returned__")) return ctx["__returned__"];
+
+  // console.log(`-----`);
+  // console.log(`eval_node`);
+  // console.log(`type: ${type}`);
+  // console.log(`text: ${node.text}`);
+  // console.log(`children: ${node.children?.length ?? 0}`);
+  // console.log(ctx);
+  // console.log(node);
+
+  const handler = TYPE_TO_HANDLER.get(type);
+  check(handler, "Unhandled type: " + type, node);
+  return handler(node, ctx, builtIns);
+}
+
+/**
+ * Generic handler to loop over children of a node, and evaluate each.
+ */
+async function handleChildren(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) {
+  let lastValue = null;
+  for (const child of node.children) {
+    if (!child) continue;
+    lastValue = await handleNode(child, ctx, builtIns);
+  }
+  return lastValue;
+}
+
+/**
+ * Swallows the execution. Likely just to allow development.
+ */
+async function handleSwallow(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) {
+  // No op
+}
+
+/**
+ * Handles a return statement.
+ */
+async function handleReturn(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) {
+  const value = node.children.length > 1 ? handleNode(node.child(1), ctx, builtIns) : undefined;
+  // Mark that we have a return value, as we may be deeper in evaluation, like going through an
+  // if condition's body.
+  ctx["__returned__"] = value;
+  return value;
+}
+
+/**
+ * Handles the retrieval of a variable identifier, already be set in the context.
+ */
+async function handleIdentifier(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) {
+  let value = ctx[node.text];
+  if (value === undefined) {
+    value = builtIns[node.text]?.fn ?? undefined;
+  }
+  return maybeWrapValue(value);
+}
+
+async function handleAttribute(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) {
+  const children = node.children;
+  check(children.length === 3, "Expected 3 children for attribute.");
+  check(children[1]!.type === ".", "Expected middle child to be '.' for attribute.");
+  const inst = await handleNode(children[0]!, ctx, builtIns);
+  // const attr = await handleNode(node.child(2), inst);
+  // console.log('handleAttribute', inst, attr);
+  const attr = children[2]!.text;
+  checkAttributeAccessibility(inst, attr);
+  let attribute = maybeWrapValue(inst[attr]);
+  // check(attribute !== undefined, `"${attr}" not found on instance of type ${typeof inst}.`);
+  // If the attribute is a function, then bind it to the instance.
+  return typeof attribute === "function" ? attribute.bind(inst) : attribute;
+}
+
+async function handleSubscript(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) {
+  const children = node.children;
+  check(children.length === 4, "Expected 4 children for subscript.");
+  check(children[1]!.type === "[", "Expected 2nd child to be '[' for subscript.");
+  check(children[3]!.type === "]", "Expected 4thd child to be ']' for subscript.");
+  const inst = await handleNode(children[0]!, ctx, builtIns);
+  const attr = await handleNode(children[2]!, ctx, builtIns);
+  if (inst instanceof PyTuple && isInt(attr)) {
+    return maybeWrapValue(inst.__at__(attr));
+  }
+  if (inst instanceof PyDict && typeof attr === "string") {
+    return maybeWrapValue(inst.get(attr));
+  }
+  checkAttributeAccessibility(inst, attr);
+  let attribute = maybeWrapValue(inst[attr]);
+  return typeof attribute === "function" ? attribute.bind(inst) : attribute;
+}
+
+/**
+ * Handles the assignment.
+ */
+async function handleAssignment(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) {
+  check(
+    node.children.length === 3,
+    "Expected 3 children for assignment: identifier/attr, =, and value.",
+  );
+  check(node.children[1]!.type === "=", "Expected middle child to be an '='.");
+
+  let right = await handleNode(node.children[2]!, ctx, builtIns);
+  const leftNode = node.children[0]!;
+  let leftObj: any = ctx;
+  let leftProp: string | number = "";
+  if (leftNode.type === "identifier") {
+    leftProp = leftNode.text;
+  } else if (leftNode.type === "attribute") {
+    leftObj = await handleNode(leftNode.children[0]!, ctx, builtIns);
+    check(
+      leftNode.children[2]!.type === "identifier",
+      "Expected left hand assignment attribute to be an identifier.",
+      leftNode,
+    );
+    leftProp = leftNode.children[2]!.text;
+  } else if (leftNode.type === "subscript") {
+    leftObj = await handleNode(leftNode.children[0]!, ctx, builtIns);
+    check(leftNode.children[1]!.type === "[");
+    check(leftNode.children[3]!.type === "]");
+    leftProp = await handleNode(leftNode.children[2]!, ctx, builtIns);
+  } else {
+    throw new Error(`Unhandled left-hand assignement type: ${leftNode.type}`);
+  }
+
+  if (leftProp == null) {
+    throw new Error(`No property to assign value`);
+  }
+  // If we're a PyTuple or extended from, then try add like a list (PyTuple will fail, PyList will
+  // allow).
+  if (leftObj instanceof PyTuple) {
+    check(isInt(leftProp), "Expected an int for list assignment");
+    leftObj.__put__(leftProp, right);
+  } else if (leftObj instanceof PyDict) {
+    check(typeof leftProp === "string", "Expected a string for dict assignment");
+    leftObj.__put__(leftProp, right);
+  } else {
+    check(typeof leftProp === "string", "Expected a string for object assignment");
+    // InitialExecutionContext can have anything added, otherwise we're a specific context and
+    // should check for attribute accessibility.
+    if (!(leftObj instanceof InitialExecuteContext)) {
+      checkAttributeAccessibility(leftObj, leftProp);
+    }
+    leftObj[leftProp] = right;
+  }
+  return right;
+}
+
+/**
+ * Handles a named expression, like assigning a var in a list comprehension with:
+ * `[name for node in node_list if (name := node.name)]`
+ */
+async function handleNamedExpression(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) {
+  check(node.children.length === 3, "Expected three children for named expression.");
+  check(node.child(0).type === "identifier", "Expected identifier first in named expression.");
+  const varName = node.child(0).text;
+  ctx[varName] = await handleNode(node.child(2), ctx, builtIns);
+  return maybeWrapValue(ctx[varName]);
+}
+
+/**
+ * Handles a function call.
+ */
+async function handleCall(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) {
+  check(node.children.length === 2, "Expected 2 children for call, identifier and arguments.");
+  const fn = await handleNode(node.children[0]!, ctx, builtIns);
+  const args = await handleNode(node.children[1]!, ctx, builtIns);
+  console.log("handleCall", fn, args);
+  return fn(...args);
+}
+
+async function handleArgumentsList(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) {
+  const args = (await handleList(node, ctx, builtIns)).__unwrap__(false);
+  return [...args];
+}
+
+/**
+ * Handles a simple for...in loop.
+ */
+async function handleForStatement(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) {
+  const childs = node.children;
+  check(childs.length === 6);
+  check(childs[4]!.type === ":");
+  check(childs[5]!.type === "block");
+  await helperGetLoopForIn(node, ctx, builtIns, async (forCtx) => {
+    await handleNode(childs[5]!, forCtx, builtIns);
+  });
+}
+
+async function handleListComprehension(
+  node: Node,
+  ctx: ExecutionContextData,
+  builtIns: BuiltInFns,
+) {
+  // Create a new context that we don't want to pollute our outer one.
+  const finalList = new PyList();
+  const newCtx = {...ctx};
+
+  let finalEntryNode;
+  const loopNodes: {forIn: Node; if?: Node}[] = [];
+
+  for (const child of node.children) {
+    if (!child || ["[", "]"].includes(child.type)) continue;
+    if (child.type === "identifier" || child.type === "attribute") {
+      if (finalEntryNode) {
+        throw Error("Already have a list comprehension finalEntryNode.");
+      }
+      finalEntryNode = child;
+    } else if (child.type === "for_in_clause") {
+      loopNodes.push({forIn: child});
+    } else if (child.type === "if_clause") {
+      loopNodes[loopNodes.length - 1]!["if"] = child;
+    }
+  }
+  if (!finalEntryNode) {
+    throw Error("No list comprehension finalEntryNode.");
+  }
+
+  console.log(`handleListComprehension.loopNodes`, loopNodes);
+
+  const handleLoop = async (loopNodes: {forIn: Node; if?: Node}[]) => {
+    const loopNode = loopNodes.shift()!;
+    await helperGetLoopForIn(
+      loopNode.forIn,
+      newCtx,
+      builtIns,
+      async (forCtx) => {
+        if (loopNode.if) {
+          const ifNode = loopNode.if;
+          check(ifNode.children.length === 2, "Expected 2 children for if_clause.");
+          check(ifNode.child(0).text === "if", "Expected first child to be 'if'.");
+          const good = await handleNode(ifNode.child(1), forCtx, builtIns);
+          if (!good) return;
+        }
+        Object.assign(newCtx, forCtx);
+        if (loopNodes.length) {
+          await handleLoop(loopNodes);
+        } else {
+          finalList.append(await handleNode(finalEntryNode, newCtx, builtIns));
+        }
+      },
+      () => ({...newCtx}),
+    );
+    loopNodes.unshift(loopNode);
+  };
+
+  await handleLoop(loopNodes);
+  return finalList;
+}
+
+/**
+ * Handles the identifiers, iterable, and initial looping with context setting. Handles both simple
+ * identifiers (like `for item in items`) or a pattern list (like `for key, val in mydict.items()`).
+ *
+ * @param eachFn The function to call for each iteration. Will be passed the current context with
+ *     the identifiers assigned.
+ * @param provideForCtx An optional function that can provide an `ctx`. If not supplied the passed
+ *     `ctx` param will be used. This is useful for providing a new ctx to use for cases like an
+ *     if condition in a list comprhension where we don't want to add to the current context unless
+ *     the condition is met.
+ */
+async function helperGetLoopForIn(
+  node: Node,
+  ctx: ExecutionContextData,
+  builtIns: BuiltInFns,
+  eachFn: (forCtx: ExecutionContextData) => Promise<void>,
+  provideForCtx?: () => ExecutionContextData,
+) {
+  const childs = node.children;
+  check(childs.length >= 3);
+  check(childs[0]!.type === "for");
+  check(
+    ["identifier", "pattern_list"].includes(childs[1]!.type),
+    "Expected identifier for for loop.",
+  );
+  check(childs[2]!.type === "in");
+
+  let identifiers: string[];
+  if (childs[1]!.type === "identifier") {
+    // identifier: for k in my_list
+    identifiers = [childs[1]!.text];
+  } else {
+    // pattern_list: for k,v in my_dict.items()
+    identifiers = childs[1]!.children
+      .map((n) => {
+        if (n.type === ",") return null;
+        check(n.type === "identifier");
+        return node.text;
+      })
+      .filter((n) => n != null);
+  }
+  const iterable = await handleNode(childs[3]!, ctx, builtIns);
+  check(iterable instanceof PyTuple, "Expected for loop instance to be a list/tuple.");
+
+  for (const item of iterable.__unwrap__(false)) {
+    const forCtx = provideForCtx?.() ?? ctx;
+    if (identifiers.length === 1) {
+      forCtx[identifiers[0]!] = item;
+    } else {
+      check(
+        Array.isArray(item) && identifiers.length === item.length,
+        "Expected iterable to be a list, like using dict.items()",
+      );
+      for (let i = 0; i < identifiers.length; i++) {
+        forCtx[identifiers[i]!] = item[i];
+      }
+    }
+    await eachFn(forCtx);
+  }
+}
+
+async function handleNumber(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) {
+  return Number(node.text);
+}
+
+async function handleString(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) {
+  // check(node.children.length === 3, "Expected 3 children for str (quotes and value).");
+  let str = "";
+  for (const child of node.children) {
+    if (!child || ["string_start", "string_end"].includes(child.type)) continue;
+    if (child.type === "string_content") {
+      str += child.text;
+    } else if (child.type === "interpolation") {
+      check(child.children.length === 3, "Expected interpolation");
+      str += await handleNode(child, ctx, builtIns);
+    }
+  }
+  return str;
+}
+
+async function handleInterpolation(node: Node, ...args: NodeHandlerArgs) {
+  check(node.children.length === 3, "Expected interpolation to be three nodes length.");
+  check(
+    node.children[0]!.type === "{" && node.children[2]!.type === "}",
+    'Expected interpolation to be wrapped in "{" and "}".',
+  );
+  return await handleNode(node.children[1]!, ...args);
+}
+
+async function handleList(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) {
+  const list = [];
+  for (const child of node.children) {
+    if (!child || ["(", "[", ",", "]", ")"].includes(child.type)) continue;
+    list.push(await handleNode(child, ctx, builtIns));
+  }
+  if (node.type === "tuple") {
+    return new PyTuple(list);
+  }
+  return new PyList(list);
+}
+
+async function handleComparisonOperator(
+  node: Node,
+  ctx: ExecutionContextData,
+  builtIns: BuiltInFns,
+) {
+  const op = node.child(1).text;
+  const left = await handleNode(node.child(0), ctx, builtIns);
+  const right = await handleNode(node.child(2), ctx, builtIns);
+  if (op === "==") return left === right; // Python '==' is equiv to '===' in JS.
+  if (op === "!=") return left !== right;
+  if (op === ">") return left > right;
+  if (op === ">=") return left >= right;
+  if (op === "<") return left < right;
+  if (op === "<=") return left <= right;
+  if (op === "in") return (right.__unwrap__ ? right.__unwrap__(false) : right).includes(left);
+  throw new Error(`Comparison not handled: "${op}"`);
+}
+async function handleBooleanOperator(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) {
+  const op = node.child(1).text;
+  const left = await handleNode(node.child(0), ctx, builtIns);
+  // If we're an AND and already false, then don't even evaluate the right.
+  if (!left && op === "and") return left;
+  const right = await handleNode(node.child(2), ctx, builtIns);
+  if (op === "and") return left && right;
+  if (op === "or") return left || right;
+}
+
+async function handleBinaryOperator(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) {
+  const op = node.child(1).text;
+  const left = await handleNode(node.child(0), ctx, builtIns);
+  const right = await handleNode(node.child(2), ctx, builtIns);
+  if (left.constructor !== right.constructor) {
+    throw new Error(`Can only run ${op} operator on same type.`);
+  }
+  if (op === "+") return left.__add__ ? left.__add__(right) : left + right;
+  if (op === "-") return left - right;
+  if (op === "/") return left / right;
+  if (op === "//") return Math.floor(left / right);
+  if (op === "*") return left * right;
+  if (op === "%") return left % right;
+  if (op === "&") return left & right;
+  if (op === "|") return left | right;
+  if (op === "^") return left ^ right;
+  if (op === "<<") return left << right;
+  if (op === ">>") return left >> right;
+  throw new Error(`Comparison not handled: "${op}"`);
+}
+
+async function handleNotOperator(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) {
+  check(node.children.length === 2, "Expected 2 children for not operator.");
+  check(node.child(0).text === "not", "Expected first child to be 'not'.");
+  const value = await handleNode(node.child(1), ctx, builtIns);
+  return !value;
+}
+
+async function handleUnaryOperator(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) {
+  check(node.children.length === 2, "Expected 2 children for not operator.");
+  const value = await handleNode(node.child(1), ctx, builtIns);
+  const op = node.child(0).text;
+  if (op === "-") return value * -1;
+  console.warn(`Unhandled unary operator: ${op}`);
+  return value;
+}
+
+async function handleDictionary(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) {
+  const dict = new PyDict();
+  for (const child of node.children) {
+    if (!child || ["{", ",", "}"].includes(child.type)) continue;
+    check(child.type === "pair", "Expected a pair type for dict.");
+    const pair = await handleNode(child, ctx, builtIns);
+    dict.__put__(pair[0], pair[1]);
+  }
+  return dict;
+}
+
+async function handleDictionaryPair(node: Node, ctx: ExecutionContextData, builtIns: BuiltInFns) {
+  check(node.children.length === 3, "Expected 3 children for dict pair.");
+  let varName = await handleNode(node.child(0)!, ctx, builtIns);
+  let varValue = await handleNode(node.child(2)!, ctx, builtIns);
+  check(typeof varName === "string", "Expected varname to be string.");
+  return [varName, varValue];
+}
+
+/**
+ * Wraps some common functionality of a TreeSitterNode.
+ */
+class Node {
+  type: string;
+  text: string;
+  children: Node[];
+  private node: TreeSitterNode;
+
+  constructor(node: TreeSitterNode) {
+    this.type = node.type;
+    this.text = node.text;
+    if (this.type === "ERROR") {
+      throw new Error(`Error found in parsing near "${this.text}"`);
+    }
+    this.children = [];
+    for (const child of node.children) {
+      this.children.push(new Node(child!));
+    }
+    this.node = node;
+  }
+
+  child(index: number): Node {
+    const child = this.children[index];
+    if (!child) throw Error(`No child at index ${index}.`);
+    return child;
+  }
+
+  log(tab = "", showNode = false) {
+    console.log(`${tab}--- Node`);
+    console.log(`${tab} type: ${this.type}`);
+    console.log(`${tab} text: ${this.text}`);
+    console.log(`${tab} children:`, this.children);
+    if (showNode) {
+      console.log(`${tab} node:`, this.node);
+    }
+  }
+}
+
+/**
+ * A type that mimics a Python Tuple.
+ */
+export class PyTuple {
+  protected list: any[];
+  constructor(...args: any[]) {
+    if (args.length === 1 && args[0] instanceof PyTuple) {
+      args = args[0].__unwrap__(false);
+    }
+    if (args.length === 1 && Array.isArray(args[0])) {
+      args = [...args[0]];
+    }
+    this.list = [...args];
+  }
+
+  @Exposed count(v: any) {
+    // TODO
+  }
+
+  @Exposed index() {
+    // TODO
+  }
+
+  __at__(index: number) {
+    index = this.__get_relative_index__(index);
+    return this.list[index];
+  }
+
+  __len__() {
+    return this.list.length;
+  }
+
+  __add__(v: any) {
+    if (!(v instanceof PyTuple)) {
+      throw new Error("Can only concatenate tuple to tuple.");
+    }
+    return new PyTuple(this.__unwrap__(false).concat(v.__unwrap__(false)));
+  }
+
+  /** Puts the value to the current, existing index. Not available for Tuple. */
+  __put__(index: number, v: any) {
+    throw new Error("Tuple does not support item assignment");
+  }
+
+  /** Gets the index for the current list, with negative index support. Throws if out of range. */
+  protected __get_relative_index__(index: number) {
+    if (index >= 0) {
+      check(this.list.length > index, `Index ${index} out of range.`);
+      return index;
+    }
+    const relIndex = this.list.length + index;
+    check(relIndex >= 0, `Index ${index} out of range.`);
+    return relIndex;
+  }
+
+  /**
+   * Recursively unwraps the PyTuple returning an Array.
+   */
+  __unwrap__(deep = true) {
+    const l = [...this.list];
+    if (deep) {
+      for (let i = 0; i < l.length; i++) {
+        l[i] = l[i]?.__unwrap__ ? l[i].__unwrap__(deep) : l[i];
+      }
+    }
+    return l;
+  }
+
+  // a = [
+  //   "__add__",
+  //   "__class__",
+  //   "__class_getitem__",
+  //   "__contains__",
+  //   "__delattr__",
+  //   "__dir__",
+  //   "__doc__",
+  //   "__eq__",
+  //   "__format__",
+  //   "__ge__",
+  //   "__getattribute__",
+  //   "__getitem__",
+  //   "__getnewargs__",
+  //   "__gt__",
+  //   "__hash__",
+  //   "__init__",
+  //   "__init_subclass__",
+  //   "__iter__",
+  //   "__le__",
+  //   "__len__",
+  //   "__lt__",
+  //   "__mul__",
+  //   "__ne__",
+  //   "__new__",
+  //   "__reduce__",
+  //   "__reduce_ex__",
+  //   "__repr__",
+  //   "__rmul__",
+  //   "__setattr__",
+  //   "__sizeof__",
+  //   "__str__",
+  //   "__subclasshook__",
+  //   "count",
+  //   "index",
+  // ];
+}
+
+/**
+ * A type that mimics a Python List.
+ */
+export class PyList extends PyTuple {
+  @Exposed append(...args: any[]) {
+    this.list.push(...args);
+  }
+
+  @Exposed clear() {
+    this.list.length = 0;
+  }
+
+  @Exposed copy() {
+    // TODO
+  }
+
+  @Exposed override count() {
+    // TODO
+  }
+  @Exposed extend() {
+    // TODO
+  }
+  @Exposed override index() {
+    // TODO
+  }
+  @Exposed insert() {
+    // TODO
+  }
+  @Exposed pop() {
+    // TODO
+  }
+  @Exposed remove() {
+    // TODO
+  }
+  @Exposed reverse() {
+    // TODO
+  }
+  @Exposed sort() {
+    // TODO
+  }
+
+  override __add__(v: any) {
+    if (!(v instanceof PyList)) {
+      throw new Error("Can only concatenate list to list.");
+    }
+    return new PyList(this.__unwrap__(false).concat(v.__unwrap__(false)));
+  }
+
+  /** Assigns an element to the current, existing index. Overriden for support on lists. */
+  override __put__(index: number, v: any) {
+    index = this.__get_relative_index__(index);
+    this.list[index] = v;
+  }
+
+  // aa = [
+  //   "__add__",
+  //   "__class__",
+  //   "__class_getitem__",
+  //   "__contains__",
+  //   "__delattr__",
+  //   "__delitem__",
+  //   "__dir__",
+  //   "__doc__",
+  //   "__eq__",
+  //   "__format__",
+  //   "__ge__",
+  //   "__getattribute__",
+  //   "__getitem__",
+  //   "__gt__",
+  //   "__hash__",
+  //   "__iadd__",
+  //   "__imul__",
+  //   "__init__",
+  //   "__init_subclass__",
+  //   "__iter__",
+  //   "__le__",
+  //   "__len__",
+  //   "__lt__",
+  //   "__mul__",
+  //   "__ne__",
+  //   "__new__",
+  //   "__reduce__",
+  //   "__reduce_ex__",
+  //   "__repr__",
+  //   "__reversed__",
+  //   "__rmul__",
+  //   "__setattr__",
+  //   "__setitem__",
+  //   "__sizeof__",
+  //   "__str__",
+  //   "__subclasshook__",
+  // ];
+}
+
+class PyInt {}
+
+class PyDict {
+  #dict: {[key: string]: any};
+  constructor(dict?: {[key: string]: any}) {
+    this.#dict = {...(dict ?? {})};
+  }
+
+  @Exposed clear() {} // Removes all the elements from the dictionary
+  @Exposed copy() {} // Returns a copy of the dictionary
+  @Exposed fromkeys() {} // Returns a dictionary with the specified keys and value
+  /** Returns the value of the specified key. */
+  @Exposed get(key: string) {
+    return this.#dict[key];
+  }
+  /** Returns a list containing a tuple for each key value pair. */
+  @Exposed items() {
+    return new PyTuple(Object.entries(this.#dict).map((e) => new PyTuple(e)));
+  }
+  @Exposed keys() {} // Returns a list containing the dictionary's keys
+  @Exposed pop() {} // Removes the element with the specified key
+  @Exposed popitem() {} // Removes the last inserted key-value pair
+  @Exposed setdefault() {} // Returns the value of the specified key. If the key does not exist: insert the key, with the specified value
+  @Exposed update() {} // Updates the dictionary with the specified key-value pairs
+  @Exposed values() {} // Returns a list of all the values in the dictionary
+
+  __put__(key: string, v: any) {
+    this.#dict[key] = v;
+  }
+
+  __len__() {
+    return Object.keys(this.#dict).length;
+  }
+
+  // a = [
+  //   "__class__",
+  //   "__class_getitem__",
+  //   "__contains__",
+  //   "__delattr__",
+  //   "__delitem__",
+  //   "__dir__",
+  //   "__doc__",
+  //   "__eq__",
+  //   "__format__",
+  //   "__ge__",
+  //   "__getattribute__",
+  //   "__getitem__",
+  //   "__gt__",
+  //   "__hash__",
+  //   "__init__",
+  //   "__init_subclass__",
+  //   "__ior__",
+  //   "__iter__",
+  //   "__le__",
+  //   "__lt__",
+  //   "__ne__",
+  //   "__new__",
+  //   "__or__",
+  //   "__reduce__",
+  //   "__reduce_ex__",
+  //   "__repr__",
+  //   "__reversed__",
+  //   "__ror__",
+  //   "__setattr__",
+  //   "__setitem__",
+  //   "__sizeof__",
+  //   "__str__",
+  //   "__subclasshook__",
+  // ];
+
+  /**
+   * Recursively unwraps the PyDict returning an Object.
+   */
+  __unwrap__(deep = true) {
+    const d = {...this.#dict};
+    if (deep) {
+      for (let k of Object.keys(d)) {
+        d[k] = d[k]?.__unwrap__ ? d[k].__unwrap__(deep) : d[k];
+      }
+    }
+    return d;
+  }
+}
+
+/**
+ * Deeply unwraps a list of values.
+ */
+function __unwrap__(...args: any[]) {
+  for (let i = 0; i < args.length; i++) {
+    args[i] = args[i]?.__unwrap__ ? args[i].__unwrap__(true) : args[i];
+  }
+  return args;
+}
+
+/**
+ * Checks if access to the attribute/method is allowed.
+ */
+function checkAttributeAccessibility(inst: any, attr: string) {
+  const instType = typeof inst;
+  check(
+    instType === "object" || instType === "function",
+    `Instance of type ${instType} does not have attributes.`,
+  );
+
+  // If the attr starts and ends with a "__" then consider it unaccessible.
+  check(!attr.startsWith("__") && !attr.endsWith("__"), `"${attr}" is not accessible.`);
+
+  const attrType = typeof inst[attr];
+  if (attrType === "function") {
+    const allowedMethods = inst.constructor?.__ALLOWED_METHODS__ ?? inst.__ALLOWED_METHODS__ ?? [];
+    check(allowedMethods.includes(attr), `Method ${attr} is not accessible.`);
+  } else {
+    const allowedProps =
+      inst.constructor?.__ALLOWED_PROPERTIES__ ?? inst.__ALLOWED_PROPERTIES__ ?? [];
+    check(allowedProps.includes(attr), `Property ${attr} is not accessible.`);
+  }
+}
+
+function maybeWrapValue(value: any) {
+  if (Array.isArray(value)) {
+    return new PyList(value);
+  }
+  return value;
+}
+
+function isInt(value: any): value is number {
+  return typeof value === "number" && Math.round(value) === value;
+}
+
+function isIntLike(value: any): boolean {
+  let is = isInt(value);
+  if (!is) {
+    is = typeof value === "string" && !!/^\d+$/.exec(value);
+  }
+  return is;
+}
+
+/**
+ * An experimental decorator to add allowed properties and methods to an instance. Decorated
+ * properties and methods on a class, and they'll be added to a static __ALLOWED_PROPERTIES__ and
+ * __ALLOWED_METHODS__ lists, which can then be checked while parsing to ensure entered code
+ * cannot end up calling something more.
+ *
+ * Note: The decorator does no work on static members; only on instance properties, methods, and
+ *   getters (or setters). If you wish to allow access to only a getter and not setter, then you'll
+ *   need not define the setter (or vice-versa), as adding `@Exposed` to a getter/setter decorates
+ *   the property entirely, not just that individual getter/setter.
+ */
+export function Exposed(target: any, key: string) {
+  const descriptor = Object.getOwnPropertyDescriptor(target, key);
+  if (typeof descriptor?.value === "function") {
+    target.constructor.__ALLOWED_METHODS__ = target.constructor.__ALLOWED_METHODS__ || [];
+    target.constructor.__ALLOWED_METHODS__.push(key);
+  } else {
+    target.constructor.__ALLOWED_PROPERTIES__ = target.constructor.__ALLOWED_PROPERTIES__ || [];
+    target.constructor.__ALLOWED_PROPERTIES__.push(key);
+  }
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/common/rgthree_api.ts b/custom_nodes/rgthree-comfy/src_web/common/rgthree_api.ts
new file mode 100644
index 0000000000000000000000000000000000000000..c9e36779930e09cac8bd5624adc91faa8679ec4b
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/common/rgthree_api.ts
@@ -0,0 +1,209 @@
+import type {RgthreeModelInfo} from "typings/rgthree.js";
+
+export type ModelInfoType = "loras" | "checkpoints";
+
+type ModelsOptions = {
+  type: ModelInfoType;
+  files?: string[];
+};
+
+type GetModelsOptions = ModelsOptions & {
+  type: ModelInfoType;
+  files?: string[];
+  format?: null | "details";
+};
+
+type GetModelsInfoOptions = GetModelsOptions & {
+  light?: boolean;
+};
+
+type GetModelsResponseDetails = {
+  file: string;
+  modified: number;
+  has_info: boolean;
+  image?: string;
+};
+
+class RgthreeApi {
+  private baseUrl!: string;
+  private comfyBaseUrl!: string;
+  getCheckpointsPromise: Promise<string[]> | null = null;
+  getSamplersPromise: Promise<string[]> | null = null;
+  getSchedulersPromise: Promise<string[]> | null = null;
+  getLorasPromise: Promise<GetModelsResponseDetails[]> | null = null;
+  getWorkflowsPromise: Promise<string[]> | null = null;
+
+  constructor(baseUrl?: string) {
+    this.setBaseUrl(baseUrl);
+  }
+
+  setBaseUrl(baseUrlArg?: string) {
+    let baseUrl = null;
+    if (baseUrlArg) {
+      baseUrl = baseUrlArg;
+    } else if (window.location.pathname.includes("/rgthree/")) {
+      // Try to find how many relatives paths we need to go back to hit ./rgthree/api
+      const parts = window.location.pathname.split("/rgthree/")[1]?.split("/");
+      if (parts && parts.length) {
+        baseUrl = parts.map(() => "../").join("") + "rgthree/api";
+      }
+    }
+    this.baseUrl = baseUrl || "./rgthree/api";
+
+    // Calculate the comfyUI api base path by checkin gif we're on an rgthree independant page (as
+    // we'll always use '/rgthree/' prefix) and, if so, assume the path before `/rgthree/` is the
+    // base path. If we're not, then just use the same pathname logic as the ComfyUI api.js uses.
+    const comfyBasePathname = location.pathname.includes("/rgthree/")
+      ? location.pathname.split("rgthree/")[0]!
+      : location.pathname;
+    this.comfyBaseUrl = comfyBasePathname.split("/").slice(0, -1).join("/");
+  }
+
+  apiURL(route: string) {
+    return `${this.baseUrl}${route}`;
+  }
+
+  fetchApi(route: string, options?: RequestInit) {
+    return fetch(this.apiURL(route), options);
+  }
+
+  async fetchJson(route: string, options?: RequestInit) {
+    const r = await this.fetchApi(route, options);
+    return await r.json();
+  }
+
+  async postJson(route: string, json: any) {
+    const body = new FormData();
+    body.append("json", JSON.stringify(json));
+    return await rgthreeApi.fetchJson(route, {method: "POST", body});
+  }
+
+  getLoras(force = false) {
+    if (!this.getLorasPromise || force) {
+      this.getLorasPromise = this.fetchJson("/loras?format=details", {cache: "no-store"});
+    }
+    return this.getLorasPromise;
+  }
+
+  async fetchApiJsonOrNull<T>(route: string, options?: RequestInit) {
+    const response = await this.fetchJson(route, options);
+    if (response.status === 200 && response.data) {
+      return (response.data as T) || null;
+    }
+    return null;
+  }
+
+  /**
+   * Fetches the lora information.
+   *
+   * @param light Whether or not to generate a json file if there isn't one. This isn't necessary if
+   * we're just checking for values, but is more necessary when opening an info dialog.
+   */
+
+  async getModelsInfo(options: GetModelsInfoOptions): Promise<RgthreeModelInfo[]> {
+    const params = new URLSearchParams();
+    if (options.files?.length) {
+      params.set("files", options.files.join(","));
+    }
+    if (options.light) {
+      params.set("light", "1");
+    }
+    if (options.format) {
+      params.set("format", options.format);
+    }
+    const path = `/${options.type}/info?` + params.toString();
+    return (await this.fetchApiJsonOrNull<RgthreeModelInfo[]>(path)) || [];
+  }
+  async getLorasInfo(options: Omit<GetModelsInfoOptions, "type"> = {}) {
+    return this.getModelsInfo({type: "loras", ...options});
+  }
+  async getCheckpointsInfo(options: Omit<GetModelsInfoOptions, "type"> = {}) {
+    return this.getModelsInfo({type: "checkpoints", ...options});
+  }
+
+  async refreshModelsInfo(options: ModelsOptions) {
+    const params = new URLSearchParams();
+    if (options.files?.length) {
+      params.set("files", options.files.join(","));
+    }
+    const path = `/${options.type}/info/refresh?` + params.toString();
+    const infos = await this.fetchApiJsonOrNull<RgthreeModelInfo[]>(path);
+    return infos;
+  }
+  async refreshLorasInfo(options: Omit<ModelsOptions, "type"> = {}) {
+    return this.refreshModelsInfo({type: "loras", ...options});
+  }
+  async refreshCheckpointsInfo(options: Omit<ModelsOptions, "type"> = {}) {
+    return this.refreshModelsInfo({type: "checkpoints", ...options});
+  }
+
+  async clearModelsInfo(options: ModelsOptions) {
+    const params = new URLSearchParams();
+    if (options.files?.length) {
+      // encodeURIComponent ?
+      params.set("files", options.files.join(","));
+    }
+    const path = `/${options.type}/info/clear?` + params.toString();
+    await this.fetchApiJsonOrNull<RgthreeModelInfo[]>(path);
+    return;
+  }
+  async clearLorasInfo(options: Omit<ModelsOptions, "type"> = {}) {
+    return this.clearModelsInfo({type: "loras", ...options});
+  }
+  async clearCheckpointsInfo(options: Omit<ModelsOptions, "type"> = {}) {
+    return this.clearModelsInfo({type: "checkpoints", ...options});
+  }
+
+  /**
+   * Saves partial data sending it to the backend..
+   */
+  async saveModelInfo(
+    type: ModelInfoType,
+    file: string,
+    data: Partial<RgthreeModelInfo>,
+  ): Promise<RgthreeModelInfo | null> {
+    const body = new FormData();
+    body.append("json", JSON.stringify(data));
+    return await this.fetchApiJsonOrNull<RgthreeModelInfo>(
+      `/${type}/info?file=${encodeURIComponent(file)}`,
+      {cache: "no-store", method: "POST", body},
+    );
+  }
+
+  async saveLoraInfo(
+    file: string,
+    data: Partial<RgthreeModelInfo>,
+  ): Promise<RgthreeModelInfo | null> {
+    return this.saveModelInfo("loras", file, data);
+  }
+
+  async saveCheckpointsInfo(
+    file: string,
+    data: Partial<RgthreeModelInfo>,
+  ): Promise<RgthreeModelInfo | null> {
+    return this.saveModelInfo("checkpoints", file, data);
+  }
+
+  /**
+   * [🤮] Fetches from the ComfyUI given a similar functionality to the real ComfyUI API
+   * implementation, but can be available on independant pages outside of the ComfyUI UI. This is
+   * because ComfyUI frontend stopped serving its modules independantly and opted for a giant bundle
+   * instead which no longer allows us to load its `api.js` file separately.
+   */
+  fetchComfyApi(route: string, options?: any): Promise<any> {
+    const url = this.comfyBaseUrl + "/api" + route;
+    options = options || {};
+    options.headers = options.headers || {};
+    options.cache = options.cache || "no-cache";
+    return fetch(url, options);
+  }
+
+  /**
+   * A way to log to the terminal from JS.
+   */
+  print(messageType: string) {
+    this.fetchApi(`/print?type=${messageType}`, {})
+  }
+}
+
+export const rgthreeApi = new RgthreeApi();
diff --git a/custom_nodes/rgthree-comfy/src_web/common/shared_utils.ts b/custom_nodes/rgthree-comfy/src_web/common/shared_utils.ts
new file mode 100644
index 0000000000000000000000000000000000000000..4adea7ed1f191f1fc5dfe267b9b98e49323b39f9
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/common/shared_utils.ts
@@ -0,0 +1,581 @@
+/**
+ * @fileoverview
+ * A bunch of shared utils that can be used in ComfyUI, as well as in any single-HTML pages.
+ */
+
+export type Resolver<T> = {
+  id: string;
+  completed: boolean;
+  resolved: boolean;
+  rejected: boolean;
+  promise: Promise<T>;
+  resolve: (data: T) => void;
+  reject: (e?: Error) => void;
+  timeout: number | null;
+  deferment?: {data?: any; timeout?: number | null; signal?: string};
+};
+
+/**
+ * Returns a new `Resolver` type that allows creating a "disconnected" `Promise` that can be
+ * returned and resolved separately.
+ */
+export function getResolver<T>(timeout: number = 5000): Resolver<T> {
+  const resolver: Partial<Resolver<T>> = {};
+  resolver.id = generateId(8);
+  resolver.completed = false;
+  resolver.resolved = false;
+  resolver.rejected = false;
+  resolver.promise = new Promise((resolve, reject) => {
+    resolver.reject = (e?: Error) => {
+      resolver.completed = true;
+      resolver.rejected = true;
+      reject(e);
+    };
+    resolver.resolve = (data: T) => {
+      resolver.completed = true;
+      resolver.resolved = true;
+      resolve(data);
+    };
+  });
+  resolver.timeout = setTimeout(() => {
+    if (!resolver.completed) {
+      resolver.reject!();
+    }
+  }, timeout);
+  return resolver as Resolver<T>;
+}
+
+/** The WeakMap for debounced functions. */
+const DEBOUNCE_FN_TO_PROMISE: WeakMap<Function, Promise<void>> = new WeakMap();
+
+/**
+ * Debounces a function call so it is only called once in the initially provided ms even if asked
+ * to be called multiple times within that period.
+ */
+export function debounce(fn: Function, ms = 64) {
+  if (!DEBOUNCE_FN_TO_PROMISE.get(fn)) {
+    DEBOUNCE_FN_TO_PROMISE.set(
+      fn,
+      wait(ms).then(() => {
+        DEBOUNCE_FN_TO_PROMISE.delete(fn);
+        fn();
+      }),
+    );
+  }
+  return DEBOUNCE_FN_TO_PROMISE.get(fn);
+}
+
+/** Checks that a value is not falsy. */
+export function check(value: any, msg = "", ...args: any[]): asserts value {
+  if (!value) {
+    console.error(msg, ...(args || []));
+    throw new Error(msg || "Error");
+  }
+}
+
+/** Waits a certain number of ms, as a `Promise.` */
+export function wait(ms = 16): Promise<void> {
+  // Special logic, if we're waiting 16ms, then trigger on next frame.
+  if (ms === 16) {
+    return new Promise((resolve) => {
+      requestAnimationFrame(() => {
+        resolve();
+      });
+    });
+  }
+  return new Promise((resolve) => {
+    setTimeout(() => {
+      resolve();
+    }, ms);
+  });
+}
+
+/** Deeply freezes the passed in object. */
+export function deepFreeze<T extends Object>(obj: T): T {
+  // Retrieve the property names defined on object
+  const propNames = Reflect.ownKeys(obj);
+
+  // Freeze properties before freezing self
+  for (const name of propNames) {
+    const value = (obj as any)[name];
+    if ((value && typeof value === "object") || typeof value === "function") {
+      deepFreeze(value);
+    }
+  }
+  return Object.freeze(obj);
+}
+
+function dec2hex(dec: number) {
+  return dec.toString(16).padStart(2, "0");
+}
+
+/** Generates an unique id of a specific length. */
+export function generateId(length: number) {
+  const arr = new Uint8Array(length / 2);
+  crypto.getRandomValues(arr);
+  return Array.from(arr, dec2hex).join("");
+}
+
+/**
+ * Returns the deep value of an object given a dot-delimited key.
+ */
+export function getObjectValue(obj: {[key: string]: any}, objKey: string, def?: any) {
+  if (!obj || !objKey) return def;
+
+  const keys = objKey.split(".");
+  const key = keys.shift()!;
+  const found = obj[key];
+  if (keys.length) {
+    return getObjectValue(found, keys.join("."), def);
+  }
+  return found;
+}
+
+/**
+ * Sets the deep value of an object given a dot-delimited key.
+ *
+ * By default, missing objects will be created while settng the path.  If `createMissingObjects` is
+ * set to false, then the setting will be abandoned if the key path is missing an intermediate
+ * value. For example:
+ *
+ *   setObjectValue({a: {z: false}}, 'a.b.c', true); // {a: {z: false, b: {c: true } } }
+ *   setObjectValue({a: {z: false}}, 'a.b.c', true, false); // {a: {z: false}}
+ *
+ */
+export function setObjectValue(obj: any, objKey: string, value: any, createMissingObjects = true) {
+  if (!obj || !objKey) return obj;
+
+  const keys = objKey.split(".");
+  const key = keys.shift()!;
+  if (obj[key] === undefined) {
+    if (!createMissingObjects) {
+      return;
+    }
+    obj[key] = {};
+  }
+  if (!keys.length) {
+    obj[key] = value;
+  } else {
+    if (typeof obj[key] != "object") {
+      obj[key] = {};
+    }
+    setObjectValue(obj[key], keys.join("."), value, createMissingObjects);
+  }
+  return obj;
+}
+
+/**
+ * Moves an item in an array (by item or its index) to another index.
+ */
+export function moveArrayItem(arr: any[], itemOrFrom: any, to: number) {
+  const from = typeof itemOrFrom === "number" ? itemOrFrom : arr.indexOf(itemOrFrom);
+  arr.splice(to, 0, arr.splice(from, 1)[0]!);
+}
+
+/**
+ * Moves an item in an array (by item or its index) to another index.
+ */
+export function removeArrayItem<T>(arr: T[], itemOrIndex: T | number) {
+  const index = typeof itemOrIndex === "number" ? itemOrIndex : arr.indexOf(itemOrIndex);
+  arr.splice(index, 1);
+}
+
+/**
+ * Injects CSS into the page with a promise when complete.
+ */
+export function injectCss(href: string): Promise<void> {
+  if (document.querySelector(`link[href^="${href}"]`)) {
+    return Promise.resolve();
+  }
+  return new Promise((resolve) => {
+    const link = document.createElement("link");
+    link.setAttribute("rel", "stylesheet");
+    link.setAttribute("type", "text/css");
+    const timeout = setTimeout(resolve, 1000);
+    link.addEventListener("load", (e) => {
+      clearInterval(timeout);
+      resolve();
+    });
+    link.href = href;
+    document.head.appendChild(link);
+  });
+}
+
+/**
+ * Calls `Object.defineProperty` with special care around getters and setters to call out to a
+ * parent getter or setter (like a super.set call)   to ensure any side effects up the chain
+ * are still invoked.
+ */
+export function defineProperty(instance: any, property: string, desc: PropertyDescriptor) {
+  const existingDesc = Object.getOwnPropertyDescriptor(instance, property);
+  if (existingDesc?.configurable === false) {
+    throw new Error(`Error: rgthree-comfy cannot define un-configurable property "${property}"`);
+  }
+
+  if (existingDesc?.get && desc.get) {
+    const descGet = desc.get;
+    desc.get = () => {
+      existingDesc.get!.apply(instance, []);
+      return descGet!.apply(instance, []);
+    };
+  }
+  if (existingDesc?.set && desc.set) {
+    const descSet = desc.set;
+    desc.set = (v: any) => {
+      existingDesc.set!.apply(instance, [v]);
+      return descSet!.apply(instance, [v]);
+    };
+  }
+
+  desc.enumerable = desc.enumerable ?? existingDesc?.enumerable ?? true;
+  desc.configurable = desc.configurable ?? existingDesc?.configurable ?? true;
+  if (!desc.get && !desc.set) {
+    desc.writable = desc.writable ?? existingDesc?.writable ?? true;
+  }
+  return Object.defineProperty(instance, property, desc);
+}
+
+/**
+ * Determines if two DataViews are equal.
+ */
+export function areDataViewsEqual(a: DataView, b: DataView) {
+  if (a.byteLength !== b.byteLength) {
+    return false;
+  }
+  for (let i = 0; i < a.byteLength; i++) {
+    if (a.getUint8(i) !== b.getUint8(i)) {
+      return false;
+    }
+  }
+  return true;
+}
+
+/**
+ * A cheap check if the source looks like base64.
+ */
+function looksLikeBase64(source: string) {
+  return source.length > 500 || source.startsWith("data:") || source.includes(";base64,");
+}
+
+/**
+ * Determines if two ArrayBuffers are equal.
+ */
+export function areArrayBuffersEqual(a?: ArrayBuffer | null, b?: ArrayBuffer | null) {
+  if (a == b || !a || !b) {
+    return a == b;
+  }
+  return areDataViewsEqual(new DataView(a), new DataView(b));
+}
+
+export function newCanvas(
+  widthOrPtOrImage: number | {width: number; height: number} | HTMLImageElement,
+  height?: number,
+) {
+  let width: number;
+  if (typeof widthOrPtOrImage !== "number") {
+    width = widthOrPtOrImage.width;
+    height = widthOrPtOrImage.height;
+  } else {
+    width = widthOrPtOrImage;
+    height = height;
+  }
+  if (height == null) {
+    throw new Error("Invalid height supplied when creating new canvas object.");
+  }
+  const canvas = document.createElement("canvas");
+  canvas.width = width;
+  canvas.height = height;
+  if (widthOrPtOrImage instanceof HTMLImageElement) {
+    const ctx = canvas.getContext("2d")!;
+    ctx.drawImage(widthOrPtOrImage, 0, 0, width, height);
+  }
+  return canvas;
+}
+
+/**
+ * Returns canvas image data for an HTML Image.
+ */
+export function getCanvasImageData(
+  image: HTMLImageElement,
+): [HTMLCanvasElement, CanvasRenderingContext2D, ImageData] {
+  const canvas = newCanvas(image);
+  const ctx = canvas.getContext("2d")!;
+  const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
+  return [canvas, ctx, imageData];
+}
+
+/** Union of types for image conversion. */
+type ImageConverstionTypes = string | Blob | ArrayBuffer | HTMLImageElement | HTMLCanvasElement;
+
+/**
+ * Converts an ImageConverstionTypes to a base64 string.
+ */
+export async function convertToBase64(
+  source: ImageConverstionTypes | Promise<ImageConverstionTypes>,
+): Promise<string> {
+  if (source instanceof Promise) {
+    source = await source;
+  }
+  if (typeof source === "string" && looksLikeBase64(source)) {
+    return source;
+  }
+  if (typeof source === "string" || source instanceof Blob || source instanceof ArrayBuffer) {
+    return convertToBase64(await loadImage(source));
+  }
+  if (source instanceof HTMLImageElement) {
+    if (looksLikeBase64(source.src)) {
+      return source.src;
+    }
+    const [canvas, ctx, imageData] = getCanvasImageData(source);
+    return convertToBase64(canvas);
+  }
+  if (source instanceof HTMLCanvasElement) {
+    return source.toDataURL("image/png");
+  }
+  throw Error("Unknown source to convert to base64.");
+}
+
+/**
+ * Converts an ImageConverstionTypes to an image array buffer.
+ */
+export async function convertToArrayBuffer(
+  source: ImageConverstionTypes | Promise<ImageConverstionTypes>,
+): Promise<ArrayBuffer> {
+  if (source instanceof Promise) {
+    source = await source;
+  }
+  if (source instanceof ArrayBuffer) {
+    return source;
+  }
+  if (typeof source === "string") {
+    if (looksLikeBase64(source)) {
+      var binaryString = atob(source.replace(/^.*?;base64,/, ""));
+      var bytes = new Uint8Array(binaryString.length);
+      for (var i = 0; i < binaryString.length; i++) {
+        bytes[i] = binaryString.charCodeAt(i);
+      }
+      return bytes.buffer;
+    }
+    return convertToArrayBuffer(await loadImage(source));
+  }
+  if (source instanceof HTMLImageElement) {
+    const [canvas, ctx, imageData] = getCanvasImageData(source);
+    return convertToArrayBuffer(canvas);
+  }
+  if (source instanceof HTMLCanvasElement) {
+    return convertToArrayBuffer(source.toDataURL());
+  }
+  if (source instanceof Blob) {
+    return source.arrayBuffer();
+  }
+  throw Error("Unknown source to convert to arraybuffer.");
+}
+
+/**
+ * Loads an image into an HTMLImageElement.
+ */
+export async function loadImage(
+  source: ImageConverstionTypes | Promise<ImageConverstionTypes>,
+): Promise<HTMLImageElement> {
+  if (source instanceof Promise) {
+    source = await source;
+  }
+  if (source instanceof HTMLImageElement) {
+    return loadImage(source.src);
+  }
+  if (source instanceof Blob) {
+    return loadImage(source.arrayBuffer());
+  }
+  if (source instanceof HTMLCanvasElement) {
+    return loadImage(source.toDataURL());
+  }
+  if (source instanceof ArrayBuffer) {
+    var binary = "";
+    var bytes = new Uint8Array(source);
+    var len = bytes.byteLength;
+    for (var i = 0; i < len; i++) {
+      binary += String.fromCharCode(bytes[i]!);
+    }
+    return loadImage(`data:${getMimeTypeFromArrayBuffer(bytes)};base64,${btoa(binary)}`);
+  }
+  return new Promise((resolve, reject) => {
+    const img = new Image();
+    img.addEventListener("load", () => {
+      resolve(img);
+    });
+    img.addEventListener("error", () => {
+      reject(img);
+    });
+    img.src = source;
+  });
+}
+
+/**
+ * Determines the mime type from an array buffer.
+ */
+function getMimeTypeFromArrayBuffer(buffer: Uint8Array) {
+  const len = 4;
+  if (buffer.length >= len) {
+    let signatureArr = new Array(len);
+    for (let i = 0; i < len; i++) signatureArr[i] = buffer[i]!.toString(16);
+    const signature = signatureArr.join("").toUpperCase();
+    switch (signature) {
+      case "89504E47":
+        return "image/png";
+      case "47494638":
+        return "image/gif";
+      case "25504446":
+        return "application/pdf";
+      case "FFD8FFDB":
+      case "FFD8FFE0":
+        return "image/jpeg";
+      case "504B0304":
+        return "application/zip";
+      default:
+        return null;
+    }
+  }
+  return null;
+}
+
+type BroadcasterMessage<T extends {}> = {
+  id: string;
+  replyId?: string;
+  action: string;
+  window: Window;
+  port: MessagePort;
+  payload?: T;
+};
+
+type BroadcasterMessageOptions = {
+  timeout?: number;
+  listenForReply?: boolean;
+};
+
+/**
+ * A Broadcaster is a wrapper around a BroadcastChannel for communication with other windows.
+ */
+export class Broadcaster<OutPayload extends {}, InPayload extends {}> extends EventTarget {
+  private channel: BroadcastChannel;
+  private queue: {[key: string]: Resolver<InPayload[]>} = {};
+
+  constructor(channelName: string) {
+    super();
+    this.queue = {};
+    this.channel = new BroadcastChannel(channelName);
+    this.channel.addEventListener("message", (e) => {
+      this.onMessage(e);
+    });
+  }
+
+  /**
+   * Returns a unique id within the queue.
+   */
+  private getId() {
+    let id: string;
+    do {
+      id = generateId(6);
+    } while (this.queue[id]);
+    return id;
+  }
+
+  /**
+   * Broadcasts an action, and waits for a response, with a timeout before cancelling.
+   */
+  async broadcastAndWait(
+    action: string,
+    payload?: OutPayload,
+    options?: BroadcasterMessageOptions,
+  ): Promise<InPayload[]> {
+    const id = this.getId();
+    this.queue[id] = getResolver<InPayload[]>(options?.timeout);
+    this.channel.postMessage({
+      id,
+      action,
+      payload,
+    });
+    let response: InPayload[];
+    try {
+      response = await this.queue[id]!.promise;
+    } catch (e) {
+      console.log("CAUGHT", e);
+      response = [];
+    }
+    return response;
+  }
+
+  broadcast(action: string, payload?: OutPayload) {
+    this.channel.postMessage({
+      id: this.getId(),
+      action,
+      payload,
+    });
+  }
+
+  reply(replyId: string, action: string, payload?: OutPayload) {
+    this.channel.postMessage({
+      id: this.getId(),
+      replyId,
+      action,
+      payload,
+    });
+  }
+
+  openWindowAndWaitForMessage(rgthreePath: string, windowName?: string) {
+    const id = this.getId();
+    this.queue[id] = getResolver();
+    const win = window.open(`/rgthree/${rgthreePath}#broadcastLoadMsgId=${id}`, windowName);
+    return {window: win, promise: this.queue[id]!.promise};
+  }
+
+  onMessage(e: MessageEvent<BroadcasterMessage<InPayload>>) {
+    const msgId = e.data?.replyId || "";
+    const queueItem = this.queue[msgId];
+    if (queueItem) {
+      if (queueItem.completed) {
+        console.error(`${msgId} already completed..`);
+      }
+      queueItem.deferment = queueItem.deferment || {data: []};
+      queueItem.deferment.data.push(e.data.payload);
+      queueItem.deferment.timeout && clearTimeout(queueItem.deferment.timeout);
+      queueItem.deferment.timeout = setTimeout(() => {
+        queueItem.resolve(queueItem.deferment!.data);
+      }, 250);
+    } else {
+      this.dispatchEvent(
+        new CustomEvent("rgthree-broadcast-message", {
+          detail: Object.assign({replyTo: e.data?.id}, e.data),
+        }),
+      );
+    }
+  }
+
+  addMessageListener(callback: EventListener, options?: any) {
+    return super.addEventListener("rgthree-broadcast-message", callback, options);
+  }
+}
+
+const broadcastChannelMap: Map<BroadcastChannel, {[key: string]: Resolver<any>}> = new Map();
+
+export function broadcastOnChannel<T extends {}>(
+  channel: BroadcastChannel,
+  action: string,
+  payload?: T,
+) {
+  let queue = broadcastChannelMap.get(channel);
+  if (!queue) {
+    broadcastChannelMap.set(channel, {});
+    queue = broadcastChannelMap.get(channel)!;
+  }
+  let id: string;
+  do {
+    id = generateId(6);
+  } while (queue[id]);
+  queue[id] = getResolver();
+  channel.postMessage({
+    id,
+    action,
+    payload,
+  });
+  return queue[id]!.promise;
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/common/utils_dom.ts b/custom_nodes/rgthree-comfy/src_web/common/utils_dom.ts
new file mode 100644
index 0000000000000000000000000000000000000000..143b68c54d8ac7e655185de921c002c3762735e1
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/common/utils_dom.ts
@@ -0,0 +1,429 @@
+/**
+ * Various dom manipulation utils that have followed me around.
+ */
+const DIRECT_ATTRIBUTE_MAP: {[name: string]: string} = {
+  cellpadding: "cellPadding",
+  cellspacing: "cellSpacing",
+  colspan: "colSpan",
+  frameborder: "frameBorder",
+  height: "height",
+  maxlength: "maxLength",
+  nonce: "nonce",
+  role: "role",
+  rowspan: "rowSpan",
+  type: "type",
+  usemap: "useMap",
+  valign: "vAlign",
+  width: "width",
+};
+
+const RGX_NUMERIC_STYLE_UNIT = "px";
+const RGX_NUMERIC_STYLE =
+  /^((max|min)?(width|height)|margin|padding|(margin|padding)?(left|top|bottom|right)|fontsize|borderwidth)$/i;
+const RGX_DEFAULT_VALUE_PROP = /input|textarea|select/i;
+
+function localAssertNotFalsy<T>(input?: T | null, errorMsg = `Input is not of type.`): T {
+  if (input == null) {
+    throw new Error(errorMsg);
+  }
+  return input;
+}
+
+const RGX_STRING_VALID = "[a-z0-9_-]";
+const RGX_TAG = new RegExp(`^([a-z]${RGX_STRING_VALID}*)(\\.|\\[|\\#|$)`, "i");
+const RGX_ATTR_ID = new RegExp(`#(${RGX_STRING_VALID}+)`, "gi");
+const RGX_ATTR_CLASS = new RegExp(`(^|\\S)\\.([a-z0-9_\\-\\.]+)`, "gi");
+const RGX_STRING_CONTENT_TO_SQUARES = "(.*?)(\\[|\\])";
+const RGX_ATTRS_MAYBE_OPEN = new RegExp(`\\[${RGX_STRING_CONTENT_TO_SQUARES}`, "gi");
+const RGX_ATTRS_FOLLOW_OPEN = new RegExp(`^${RGX_STRING_CONTENT_TO_SQUARES}`, "gi");
+
+type QueryParent = HTMLElement | Document | DocumentFragment;
+
+export function queryAll<K extends keyof HTMLElementTagNameMap>(
+  selectors: K,
+  parent?: QueryParent,
+): Array<HTMLElementTagNameMap[K]>;
+export function queryAll<K extends keyof SVGElementTagNameMap>(
+  selectors: K,
+  parent?: QueryParent,
+): Array<SVGElementTagNameMap[K]>;
+export function queryAll<K extends keyof MathMLElementTagNameMap>(
+  selectors: K,
+  parent?: QueryParent,
+): Array<MathMLElementTagNameMap[K]>;
+export function queryAll<T extends HTMLElement>(selectors: string, parent?: QueryParent): Array<T>;
+export function queryAll(selectors: string, parent: QueryParent = document) {
+  return Array.from(parent.querySelectorAll(selectors)).filter((n) => !!n);
+}
+
+export function query<K extends keyof HTMLElementTagNameMap>(
+  selectors: K,
+  parent?: QueryParent,
+): HTMLElementTagNameMap[K] | null;
+export function query<K extends keyof SVGElementTagNameMap>(
+  selectors: K,
+  parent?: QueryParent,
+): SVGElementTagNameMap[K] | null;
+export function query<K extends keyof MathMLElementTagNameMap>(
+  selectors: K,
+  parent?: QueryParent,
+): MathMLElementTagNameMap[K] | null;
+export function query<T extends HTMLElement>(selectors: string, parent?: QueryParent): T | null;
+export function query(selectors: string, parent: QueryParent = document) {
+  return parent.querySelector(selectors) ?? null;
+}
+
+export function createText(text: string) {
+  return document.createTextNode(text);
+}
+
+export function getClosestOrSelf(
+  element: EventTarget | HTMLElement | null,
+  query: string,
+): HTMLElement | null {
+  const el = element as HTMLElement;
+  return (el?.closest && (((el.matches(query) && el) || el.closest(query)) as HTMLElement)) || null;
+}
+
+export function containsOrSelf(
+  parent: EventTarget | HTMLElement | null,
+  contained: EventTarget | HTMLElement | null,
+): boolean {
+  return (
+    parent === contained || (parent as HTMLElement)?.contains?.(contained as HTMLElement) || false
+  );
+}
+
+type Attrs = {
+  [name: string]: any;
+};
+
+export function createElement<T extends HTMLElement>(selectorOrMarkup: string, attrs?: Attrs) {
+  const frag = getHtmlFragment(selectorOrMarkup);
+  let element = frag?.firstElementChild as HTMLElement;
+  let selector = "";
+  if (!element) {
+    selector = selectorOrMarkup.replace(/[\r\n]\s*/g, "");
+    const tag = getSelectorTag(selector) || "div";
+    element = document.createElement(tag);
+    selector = selector.replace(RGX_TAG, "$2");
+    const brackets = selector.match(/(\[[^\]]+\])/g) || [];
+    for (const bracket of brackets) {
+      selector = selector.replace(bracket, "");
+    }
+    // Turn id and classname into [attr]s that can be nested
+    selector = selector.replace(RGX_ATTR_ID, '[id="$1"]');
+    selector = selector.replace(
+      RGX_ATTR_CLASS,
+      (match, p1, p2) => `${p1}[class="${p2.replace(/\./g, " ")}"]`,
+    );
+    selector += brackets.join("");
+  }
+
+  const selectorAttrs = getSelectorAttributes(selector);
+  if (selectorAttrs) {
+    for (const attr of selectorAttrs) {
+      let matches = attr.substring(1, attr.length - 1).split("=");
+      let key = localAssertNotFalsy(matches.shift());
+      let value: string = matches.join("=");
+      if (value === undefined) {
+        setAttribute(element, key, true);
+      } else {
+        value = value.replace(/^['"](.*)['"]$/, "$1");
+        setAttribute(element, key, value);
+      }
+    }
+  }
+  if (attrs) {
+    setAttributes(element, attrs);
+  }
+  return element as T;
+}
+export const $el = createElement;
+
+function getSelectorTag(str: string) {
+  return tryMatch(str, RGX_TAG);
+}
+
+function getSelectorAttributes(selector: string) {
+  RGX_ATTRS_MAYBE_OPEN.lastIndex = 0;
+  let attrs: string[] = [];
+  let result;
+  while ((result = RGX_ATTRS_MAYBE_OPEN.exec(selector))) {
+    let attr = result[0];
+    if (attr.endsWith("]")) {
+      attrs.push(attr);
+    } else {
+      attr =
+        result[0] + getOpenAttributesRecursive(selector.substr(RGX_ATTRS_MAYBE_OPEN.lastIndex), 2);
+      RGX_ATTRS_MAYBE_OPEN.lastIndex += attr.length - result[0].length;
+      attrs.push(attr);
+    }
+  }
+  return attrs;
+}
+
+function getOpenAttributesRecursive(selectorSubstring: string, openCount: number) {
+  let matches = selectorSubstring.match(RGX_ATTRS_FOLLOW_OPEN);
+  let result = "";
+  if (matches && matches.length) {
+    result = matches[0];
+    openCount += result.endsWith("]") ? -1 : 1;
+    if (openCount > 0) {
+      result += getOpenAttributesRecursive(selectorSubstring.substr(result.length), openCount);
+    }
+  }
+  return result;
+}
+
+function tryMatch(str: string, rgx: RegExp, index = 1) {
+  let found = "";
+  try {
+    found = str.match(rgx)?.[index] || "";
+  } catch (e) {
+    found = "";
+  }
+  return found;
+}
+
+export function setAttributes(element: HTMLElement, data: {[name: string]: any}) {
+  let attr;
+  for (attr in data) {
+    if (data.hasOwnProperty(attr)) {
+      setAttribute(element, attr, data[attr]);
+    }
+  }
+}
+
+function getHtmlFragment(value: string) {
+  if (value.match(/^\s*<.*?>[\s\S]*<\/[a-z0-9]+>\s*$/)) {
+    return document.createRange().createContextualFragment(value.trim());
+  }
+  return null;
+}
+
+function getChild(value: any): HTMLElement | DocumentFragment | Text | null {
+  if (value instanceof Node) {
+    return value as HTMLElement;
+  }
+  if (typeof value === "string") {
+    let child = getHtmlFragment(value);
+    if (child) {
+      return child;
+    }
+    if (getSelectorTag(value)) {
+      return createElement(value);
+    }
+    return createText(value);
+  }
+  if (value && typeof value.toElement === "function") {
+    return value.toElement() as HTMLElement;
+  }
+  return null;
+}
+
+export function setAttribute(element: HTMLElement, attribute: string, value: any) {
+  let isRemoving = value == null;
+
+  if (attribute === "default") {
+    attribute = RGX_DEFAULT_VALUE_PROP.test(element.nodeName) ? "value" : "text";
+  }
+
+  if (attribute === "text") {
+    empty(element).appendChild(createText(value != null ? String(value) : ""));
+  } else if (attribute === "html") {
+    empty(element).innerHTML += value != null ? String(value) : "";
+  } else if (attribute == "style") {
+    if (typeof value === "string") {
+      element.style.cssText = isRemoving ? "" : value != null ? String(value) : "";
+    } else {
+      for (const [styleKey, styleValue] of Object.entries(value as {[key: string]: any})) {
+        element.style[styleKey as "display"] = styleValue;
+      }
+    }
+  } else if (attribute == "events") {
+    for (const [key, fn] of Object.entries(value as {[key: string]: (e: Event) => void})) {
+      addEvent(element, key, fn);
+    }
+  } else if (attribute === "parent") {
+    value.appendChild(element);
+  } else if (attribute === "child" || attribute === "children") {
+    // Try to handle an array, like [li,li,li]. Not nested brackets, though
+    if (typeof value === "string" && /^\[[^\[\]]+\]$/.test(value)) {
+      const parseable = value.replace(/^\[([^\[\]]+)\]$/, '["$1"]').replace(/,/g, '","');
+      try {
+        const parsed = JSON.parse(parseable);
+        value = parsed;
+      } catch (e) {
+        console.error(e);
+      }
+    }
+
+    // "children" is a replace of the children, while "child" appends a new child if others exist.
+    if (attribute === "children") {
+      empty(element);
+    }
+
+    let children = value instanceof Array ? value : [value];
+    for (let child of children) {
+      child = getChild(child);
+      if (child instanceof Node) {
+        if (element instanceof HTMLTemplateElement) {
+          element.content.appendChild(child);
+        } else {
+          element.appendChild(child);
+        }
+      }
+    }
+  } else if (attribute == "for") {
+    (element as HTMLLabelElement).htmlFor = value != null ? String(value) : "";
+    if (isRemoving) {
+      // delete (element as HTMLLabelElement).htmlFor;
+      element.removeAttribute("for");
+    }
+  } else if (attribute === "class" || attribute === "className" || attribute === "classes") {
+    element.className = isRemoving ? "" : Array.isArray(value) ? value.join(" ") : String(value);
+  } else if (attribute === "dataset") {
+    if (typeof value !== "object") {
+      console.error("Expecting an object for dataset");
+      return;
+    }
+    for (const [key, val] of Object.entries(value)) {
+      element.dataset[key] = String(val);
+    }
+  } else if (attribute.startsWith("on") && typeof value === "function") {
+    element.addEventListener(attribute.substring(2), value);
+  } else if (["checked", "disabled", "readonly", "required", "selected"].includes(attribute)) {
+    // Could be input, button, etc. We are not discriminate.
+    (element as HTMLInputElement)[attribute as "checked"] = !!value;
+    if (!value) {
+      (element as HTMLInputElement).removeAttribute(attribute);
+    } else {
+      (element as HTMLInputElement).setAttribute(attribute, attribute);
+    }
+  } else if (DIRECT_ATTRIBUTE_MAP.hasOwnProperty(attribute)) {
+    if (isRemoving) {
+      element.removeAttribute(DIRECT_ATTRIBUTE_MAP[attribute]!);
+    } else {
+      element.setAttribute(DIRECT_ATTRIBUTE_MAP[attribute]!, String(value));
+    }
+  } else if (isRemoving) {
+    element.removeAttribute(attribute);
+  } else {
+    let oldVal = element.getAttribute(attribute);
+    if (oldVal !== value) {
+      element.setAttribute(attribute, String(value));
+    }
+  }
+}
+
+function addEvent(element: HTMLElement, key: string, fn: (e: Event) => void) {
+  element.addEventListener(key, fn);
+}
+
+function setStyles(element: HTMLElement, styles: {[name: string]: string | number} | null = null) {
+  if (styles) {
+    for (let name in styles) {
+      setStyle(element, name, styles[name]!);
+    }
+  }
+  return element;
+}
+
+export function setStyle(element: HTMLElement, name: string, value: string | number | null) {
+  // Note: Old IE uses 'styleFloat'
+  name = name.indexOf("float") > -1 ? "cssFloat" : name;
+  // Camelcase
+  if (name.indexOf("-") != -1) {
+    name = name.replace(/-\D/g, (match) => {
+      return match.charAt(1).toUpperCase();
+    });
+  }
+  if (value == String(Number(value)) && RGX_NUMERIC_STYLE.test(name)) {
+    value = value + RGX_NUMERIC_STYLE_UNIT;
+  }
+  if (name === "display" && typeof value !== "string") {
+    value = !!value ? null : "none";
+  }
+  (element.style as any)[name] = value === null ? null : String(value);
+  return element;
+}
+
+export function empty(element: HTMLElement) {
+  while (element.firstChild) {
+    element.removeChild(element.firstChild);
+  }
+  return element;
+}
+
+export function remove(element: HTMLElement) {
+  while (element.parentElement) {
+    element.parentElement.removeChild(element);
+  }
+  return element;
+}
+
+export function replaceChild(oldChildNode: Node, newNode: Node) {
+  oldChildNode.parentNode!.replaceChild(newNode, oldChildNode);
+  return newNode;
+}
+
+type ChildType = HTMLElement | DocumentFragment | Text | string | null;
+export function appendChildren(el: HTMLElement, children: ChildType | ChildType[]) {
+  children = !Array.isArray(children) ? [children] : children;
+  for (let child of children) {
+    child = getChild(child);
+    if (child instanceof Node) {
+      if (el instanceof HTMLTemplateElement) {
+        el.content.appendChild(child);
+      } else {
+        el.appendChild(child);
+      }
+    }
+  }
+}
+
+/**
+ * Returns elements and their actions.
+ *
+ * data-action="click:action-signal"
+ */
+export function getActionEls(parent: Element | Document = document) {
+  const els = Array.from(parent.querySelectorAll("[data-action],[on-action],[on]"));
+  if (parent instanceof Element) {
+    els.unshift(parent);
+  }
+  return els
+    .map((actionEl) => {
+      const actions: {[action: string]: string} = {};
+      const actionSegments = (
+        actionEl.getAttribute("data-action") ||
+        actionEl.getAttribute("on-action") ||
+        actionEl.getAttribute("on") ||
+        ""
+      ).split(";");
+      for (let segment of actionSegments) {
+        let actionsData = segment
+          .trim()
+          .split(/\s*:\s*/g)
+          .filter((i) => !!i.trim()) as [string, string?];
+        if (!actionsData.length) continue;
+        if (actionsData.length === 1) {
+          if (actionEl instanceof HTMLInputElement) {
+            actionsData.unshift("input");
+          } else {
+            actionsData.unshift("click");
+          }
+        }
+        if (actionsData[0] && actionsData[1]) {
+          actions[actionsData[0]] = actionsData[1];
+          // actionEl.addEventListener(actionsData[0], (e) => {this.handleAction(actionsData[1]!, actionEl, e);});
+        }
+      }
+      return {
+        el: actionEl,
+        actions,
+      };
+    })
+    .filter((el) => !!el);
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/common/utils_templates.ts b/custom_nodes/rgthree-comfy/src_web/common/utils_templates.ts
new file mode 100644
index 0000000000000000000000000000000000000000..897f4289947df703421d0a6fe90d7ad3b8aa3ed5
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/common/utils_templates.ts
@@ -0,0 +1,879 @@
+import * as dom from "./utils_dom";
+import {getObjectValue} from "./shared_utils";
+
+const CONFIG_DEFAULT = {
+  attrBind: "data-bind",
+  attrIf: "data-if",
+  attrIfIs: "data-if-is",
+};
+
+const CONFIG = Object.assign({}, CONFIG_DEFAULT, {
+  attrBind: "bind",
+  attrIf: "if",
+  attrIfIs: "if-is",
+});
+
+export interface BindOptions {
+  /**
+   * If true then only those data-bind keys in the data map will be bound,
+   * and no `data-bind` fields will be unbound.
+   */
+  onlyDefined?: boolean;
+
+  /** If true, then binding/init will not be called on nested templates. */
+  singleScoped?: boolean;
+
+  /** Context elemnt. */
+  contextElement?: HTMLElement;
+}
+
+export interface InflateOptions {
+  skipInit?: boolean;
+  bindOptions?: BindOptions;
+}
+
+export interface TemplateData {
+  fragment: DocumentFragment;
+  preProcessScript: (data: any) => void;
+}
+
+export interface BindingContext {
+  data: any;
+  contextElement: HTMLElement;
+  currentElement: HTMLElement;
+}
+
+// const RGX_COMPARISON = /^\(?([a-z0-9\.\-\[\]'"]+)((?:<|>|==)=?)([a-z0-9\.\-\[\]'"]+)\)?$/i;
+const RGX_COMPARISON = (() => {
+  // /^\(?([a-z0-9\.\-\[\]'"]+)((?:<|>|==)=?)([a-z0-9\.\-\[\]'"]+)\)?$/i;
+  let value = "((?:\\!*)[_a-z0-9\\.\\-\\[\\]'\"]+)";
+  let comparison = "((?:<|>|==|\\!=)=?)";
+  return new RegExp(`^(?:\\!*)\\(?${value}\\s*${comparison}\\s*${value}\\)?$`, "i");
+})();
+
+const RGXPART_BIND_FN_TEMPLATE_STRING = "template|tpl";
+const RGXPART_BIND_FN_ELEMENT_STRING = "element|el";
+const RGX_BIND_FN_TEMPLATE = new RegExp(
+  `^(?:${RGXPART_BIND_FN_TEMPLATE_STRING})\\(([^\\)]+)\\)`,
+  "i",
+);
+const RGX_BIND_FN_ELEMENT = new RegExp(
+  `^(?:${RGXPART_BIND_FN_ELEMENT_STRING})\\(([^\\)]+)\\)`,
+  "i",
+);
+const RGX_BIND_FN_TEMPLATE_OR_ELEMENT = new RegExp(
+  `^(?:${RGXPART_BIND_FN_TEMPLATE_STRING}|${RGXPART_BIND_FN_ELEMENT_STRING})\\(([^\\)]+)\\)`,
+  "i",
+);
+const RGX_BIND_FN_LENGTH = /^(?:length|len|size)\(([^\)]+)\)/i;
+const RGX_BIND_FN_FORMAT = /^(?:format|fmt)\(([^\,]+),([^\)]+)\)/i;
+const RGX_BIND_FN_CALL = /^([^\(]+)\(([^\)]*)\)/i;
+
+const EMPTY_PREPROCESS_FN = (data: any) => data;
+
+// This is used within exec, so we don't need to check the first part since it's
+// always the lastIndex start position
+// const RGX_BIND_DECLARATIONS = /\s*((?:[\$_a-z0-9-\.]|\?\?|\|\|)+(?:\([^\)]+\))?)(?::(.*?))?(\s|$)/ig;
+const RGX_BIND_DECLARATIONS =
+  /\s*(\!*(?:[\$_a-z0-9-\.\'\"]|\?\?|\|\||\&\&|(?:(?:<|>|==|\!=)=?))+(?:\`[^\`]+\`)?(?:\([^\)]*\))?)(?::(.*?))?(\s|$)/gi;
+
+/**
+ * Asserts that something is not null of undefined.
+ */
+function localAssertNotFalsy<T>(input?: T | null, errorMsg = `Input is not of type.`): T {
+  if (input == null) {
+    throw new Error(errorMsg);
+  }
+  return input;
+}
+
+/**
+ * Cleans a key.
+ */
+function cleanKey(key: string) {
+  return key.toLowerCase().trim().replace(/\s/g, "");
+}
+
+/**
+ * Ensures the value is an array, converting array-like items to an array.
+ */
+function toArray(value: any | any[]): any[] {
+  if (Array.isArray(value)) {
+    return value;
+  }
+  if (value instanceof Set) {
+    return Array.from(value);
+  }
+  // Array-like.
+  if (typeof value === "object" && typeof value.length === "number") {
+    return [].slice.call(value);
+  }
+  return [value];
+}
+
+/**
+ * Flattens an array.
+ */
+function flattenArray(arr: any | any[]): any[] {
+  return toArray(arr).reduce((acc, val) => {
+    return acc.concat(Array.isArray(val) ? flattenArray(val) : val);
+  }, []);
+}
+
+/**
+ * Gets an object value by a string lookup.
+ */
+function getObjValue(lookup: string, obj: any): any {
+  // If we want to cast as a boolean via a /!+/ prefix.
+  let booleanMatch: string[] = lookup.match(/^(\!+)(.+?)$/i) || [];
+  let booleanNots: string[] = [];
+  if (booleanMatch[1] && booleanMatch[2]) {
+    booleanNots = booleanMatch[1].split("");
+    lookup = booleanMatch[2];
+  }
+
+  let value = getObjectValue(obj, lookup);
+  while (booleanNots.length) {
+    value = !value;
+    booleanNots.shift();
+  }
+  return value;
+}
+
+/**
+ * Gets a primotove or object value.
+ */
+function getPrimitiveOrObjValue(stringValue: string | null | undefined, data: any) {
+  let value;
+  if (stringValue == null) {
+    return stringValue;
+  }
+  let negate = getNegates(stringValue);
+  if (negate != null) {
+    stringValue = stringValue.replace(/^\!+/, "");
+  }
+  try {
+    const cleanedStringValue = stringValue.replace(/^'(.*)'$/, '"$1"');
+    value = JSON.parse(cleanedStringValue);
+  } catch (e) {
+    value = getObjValue(stringValue, data);
+  }
+  value = negate !== null ? (negate === 1 ? !value : !!value) : value;
+  return value;
+}
+
+/**
+ * Get the negates for a string. A `null` value means there are no negates,
+ * otherwise a `1` means it should be negated, and a `0` means double-negate
+ * (e.g. cast as boolean).
+ *
+ *   'boolVar' => null
+ *   '!boolVar' => 1
+ *   '!!boolVar' => 0
+ *   '!!!boolVar' => 1
+ *   '!!!!boolVar' => 0
+ */
+function getNegates(stringValue: string): number | null {
+  let negate = null;
+  let negateMatches = stringValue.match(/^(\!+)(.*)/);
+  if (negateMatches && negateMatches.length >= 3) {
+    negate = negateMatches[1]!.length % 2;
+  }
+  return negate;
+}
+
+/**
+ * Returns the boolean for a comparison of object values. A `null` value would
+ * be if the experssion does not match a comparison, and undefined if there was
+ * but it's not a known comparison (===, ==, <=, >=, !==, !=, <, >).
+ */
+function getStringComparisonExpression(
+  bindingPropName: string,
+  data: any,
+): boolean | null | undefined {
+  let comparisonMatches = bindingPropName.match(RGX_COMPARISON);
+  if (!comparisonMatches?.length) {
+    return null;
+  }
+  let a = getPrimitiveOrObjValue(comparisonMatches[1]!, data);
+  let b = getPrimitiveOrObjValue(comparisonMatches[3]!, data);
+  let c = comparisonMatches[2];
+  let value = (() => {
+    switch (c) {
+      case "===":
+        return a === b;
+      case "==":
+        return a == b;
+      case "<=":
+        return a <= b;
+      case ">=":
+        return a >= b;
+      case "!==":
+        return a !== b;
+      case "!=":
+        return a != b;
+      case "<":
+        return a < b;
+      case ">":
+        return a > b;
+      default:
+        return undefined;
+    }
+  })();
+  return value;
+}
+
+/**
+ * Replaces a <tpl> element with children with special attribute copies for
+ * single children.
+ */
+function replaceTplElementWithChildren(
+  tplEl: HTMLElement,
+  fragOrElOrEls: DocumentFragment | HTMLElement | Array<DocumentFragment | HTMLElement>,
+) {
+  const els = Array.isArray(fragOrElOrEls) ? fragOrElOrEls : [fragOrElOrEls];
+  tplEl.replaceWith(...els);
+  // dom.replaceChild(tplEl, fragOrElOrEls);
+  const numOfChildren = Array.isArray(fragOrElOrEls)
+    ? fragOrElOrEls.length
+    : fragOrElOrEls.childElementCount;
+  if (numOfChildren === 1) {
+    const firstChild = Array.isArray(fragOrElOrEls)
+      ? fragOrElOrEls[0]
+      : fragOrElOrEls.firstElementChild;
+    if (firstChild instanceof Element) {
+      if (tplEl.className.length) {
+        firstChild.className += ` ${tplEl.className}`;
+      }
+      let attr = tplEl.getAttribute("data");
+      if (attr) {
+        firstChild.setAttribute("data", attr);
+      }
+      attr = tplEl.getAttribute(CONFIG.attrBind);
+      if (attr) {
+        firstChild.setAttribute(CONFIG.attrBind, attr);
+      }
+    }
+  }
+}
+
+/**
+ * Entry point to get data from a binding name. Checks for null coelescing and
+ * logical-or operators.
+ *
+ * Handles templates as well:
+ *
+ *   my.value`some string ${value} is cool.`
+ */
+function getValueForBinding(bindingPropName: string, context: BindingContext) {
+  console.log("getValueForBinding", bindingPropName, context);
+  const data = context.data;
+  let stringTemplate = null;
+  let stringTemplates = /^(.*?)\`([^\`]*)\`$/.exec(bindingPropName.trim());
+  if (stringTemplates?.length === 3) {
+    bindingPropName = stringTemplates[1]!;
+    stringTemplate = stringTemplates[2];
+  }
+  let value = null;
+
+  let hadALogicalOp = false;
+  const opsToValidation = new Map([
+    [/\s*\?\?\s*/, (v: any) => v != null],
+    [/\s*\|\|\s*/, (v: any) => !!v],
+    [/\s*\&\&\s*/, (v: any) => !v],
+  ]);
+  for (const [op, fn] of opsToValidation.entries()) {
+    if (bindingPropName.match(op)) {
+      hadALogicalOp = true;
+      const bindingPropNames = bindingPropName.split(op);
+      for (const propName of bindingPropNames) {
+        value = getValueForBindingPropName(propName, context);
+        if (fn(value)) {
+          break;
+        }
+      }
+      break;
+    }
+  }
+
+  if (!hadALogicalOp) {
+    value = getValueForBindingPropName(bindingPropName, context);
+  }
+
+  return stringTemplate && value != null
+    ? stringTemplate.replace(/\$\{value\}/g, String(value))
+    : value;
+}
+
+/**
+ * Gets the value for a binding prop name.
+ */
+function getValueForBindingPropName(bindingPropName: string, context: BindingContext) {
+  const data = context.data;
+  let negate = getNegates(bindingPropName);
+  if (negate != null) {
+    bindingPropName = bindingPropName.replace(/^\!+/, "");
+  }
+  let value;
+  RGX_COMPARISON.lastIndex = 0;
+  if (RGX_COMPARISON.test(bindingPropName)) {
+    value = getStringComparisonExpression(bindingPropName, data);
+  } else if (RGX_BIND_FN_LENGTH.test(bindingPropName)) {
+    bindingPropName = RGX_BIND_FN_LENGTH.exec(bindingPropName)![1]!;
+    value = getPrimitiveOrObjValue(bindingPropName, data);
+    value = (value && value.length) || 0;
+  } else if (RGX_BIND_FN_FORMAT.test(bindingPropName)) {
+    let matches = RGX_BIND_FN_FORMAT.exec(bindingPropName);
+    bindingPropName = matches![1]!;
+    value = getPrimitiveOrObjValue(bindingPropName, data);
+    value = matches![2]!.replace(/^['"]/, "").replace(/['"]$/, "").replace(/\$1/g, value);
+  } else if (RGX_BIND_FN_CALL.test(bindingPropName)) {
+    console.log("-----");
+    console.log(bindingPropName);
+    let matches = RGX_BIND_FN_CALL.exec(bindingPropName);
+    const functionName = matches![1]!;
+    const maybeDataName = matches![2] ?? null;
+    value = getPrimitiveOrObjValue(maybeDataName, data);
+    console.log(functionName, maybeDataName, value);
+    // First, see if the instance has this call
+    if (typeof value?.[functionName] === "function") {
+      value = value[functionName](value, data, context.currentElement, context.contextElement);
+    } else if (typeof data?.[functionName] === "function") {
+      value = data[functionName](value, data, context.currentElement, context.contextElement);
+    } else if (typeof (context.currentElement as any)?.[functionName] === "function") {
+      value = (context.currentElement as any)[functionName](
+        value,
+        data,
+        context.currentElement,
+        context.contextElement,
+      );
+    } else if (typeof (context.contextElement as any)?.[functionName] === "function") {
+      value = (context.contextElement as any)[functionName](
+        value,
+        data,
+        context.currentElement,
+        context.contextElement,
+      );
+    } else {
+      console.error(
+        `No method named ${functionName} on data or element instance. Just calling regular value.`,
+      );
+      value = getPrimitiveOrObjValue(bindingPropName, data);
+    }
+  } else {
+    value = getPrimitiveOrObjValue(bindingPropName, data);
+  }
+
+  if (value !== undefined) {
+    value = negate !== null ? (negate === 1 ? !value : !!value) : value;
+  }
+  return value;
+}
+
+/**
+ * Removes data-bind attributes, ostensibly "freezing" the current element.
+ *
+ * @param deep Will remove all data-bind attributes when true. default behavior
+ *    is only up to the next data-tpl.
+ */
+function removeBindingAttributes(
+  elOrEls: DocumentFragment | HTMLElement | HTMLElement[],
+  deep = false,
+) {
+  flattenArray(elOrEls || []).forEach((el) => {
+    el.removeAttribute(CONFIG.attrBind);
+    const innerBinds = dom.queryAll(`:scope [${CONFIG.attrBind}]`, el);
+    // If we're deep, then pretend there are no data-tpl.
+    const innerTplBinds = deep ? [] : dom.queryAll(`:scope [data-tpl] [${CONFIG.attrBind}]`);
+
+    innerBinds.forEach((el) => {
+      if (deep || !innerTplBinds.includes(el)) {
+        el.removeAttribute(CONFIG.attrBind);
+      }
+    });
+  });
+}
+
+const templateCache: {[key: string]: TemplateData} = {};
+
+/**
+ * Checks if a template exists.
+ */
+export function checkKey(key: string) {
+  return !!templateCache[cleanKey(key)];
+}
+
+/**
+ * Register a template to it's key and a DocumentFragment to store the markup.
+ * Uses `<template>` shadow DOM if possible. Overloaded to accept a register
+ * a script as second param.
+ */
+export function register(
+  key: string,
+  htmlOrElement: string | Element | null = null,
+  preProcessScript?: (data: any) => void,
+) {
+  key = cleanKey(key);
+  if (templateCache[key]) {
+    return templateCache[key];
+  }
+
+  let fragment: DocumentFragment | null = null;
+  if (typeof htmlOrElement === "string") {
+    const frag = document.createDocumentFragment();
+    if (htmlOrElement.includes("<")) {
+      const html = htmlOrElement.trim();
+      const htmlParentTag =
+        (html.startsWith("<tr") && "tbody") ||
+        (/^<t(body|head|foot)/i.test(html) && "table") ||
+        (/^<t(d|h)/i.test(html) && "tr") ||
+        "div";
+      const temp = document.createElement(htmlParentTag);
+      temp.innerHTML = html;
+      for (const child of temp.children) {
+        frag.appendChild(child);
+      }
+    } else {
+      frag.appendChild(dom.createElement(htmlOrElement));
+    }
+    fragment = frag;
+  } else if (htmlOrElement instanceof Element) {
+    const element = htmlOrElement as HTMLElement;
+    const tag = element.nodeName.toLowerCase();
+    if (tag === "template" && (element as HTMLTemplateElement).content) {
+      fragment = (element as HTMLTemplateElement).content;
+    } else {
+      throw Error("Non-template element not handled");
+    }
+  } else if (!htmlOrElement) {
+    let element = dom.query(`template[id="${key}"],template[data-id="${key}"]`);
+    if (element && (element as HTMLTemplateElement).content) {
+      fragment = (element as HTMLTemplateElement).content;
+    } else {
+      throw Error("Non-template element not handled");
+    }
+  }
+
+  if (fragment) {
+    templateCache[key] = {
+      fragment,
+      preProcessScript: preProcessScript || EMPTY_PREPROCESS_FN,
+    };
+  }
+  return templateCache[key] || null;
+}
+
+export function getPreProcessScript(keyOrEl: string | Element) {
+  if (typeof keyOrEl === "string") {
+    if (!templateCache[keyOrEl]) {
+      throw Error(`Template key does not exist ${keyOrEl}`);
+    }
+    return templateCache[keyOrEl].preProcessScript;
+  }
+  if (keyOrEl instanceof Element) {
+    const tpl = keyOrEl.getAttribute("data-tpl") || "";
+    return templateCache[tpl]?.preProcessScript || EMPTY_PREPROCESS_FN;
+  }
+  return EMPTY_PREPROCESS_FN;
+}
+
+/** Gets a template Node. */
+export function getTemplateFragment(key: string): DocumentFragment {
+  key = cleanKey(key);
+  if (!checkKey(key)) {
+    register(key);
+  }
+  let templateData = templateCache[key];
+  if (templateData && templateData.fragment) {
+    let imported: DocumentFragment;
+    if (document.importNode) {
+      imported = document.importNode(templateData.fragment, true);
+    } else {
+      imported = templateData.fragment.cloneNode(true) as DocumentFragment;
+    }
+    (imported as any).__templateid__ = key;
+    return imported;
+  } else {
+    throw new Error("Ain't no template called " + key + " (" + typeof templateCache[key] + ")");
+  }
+}
+
+// ### templates::inflate
+//
+// Inflate a template.
+//
+export function inflate(
+  nodeOrKey: string | HTMLElement | DocumentFragment,
+  templateData: any = null,
+  inflateOptions: InflateOptions = {},
+) {
+  let node = nodeOrKey as HTMLElement | DocumentFragment;
+  if (typeof node === "string") {
+    node = getTemplateFragment(node);
+  }
+  if (node) {
+    // Check for nested templates by way of a [data-template] attribute
+    // Commented out line below, as :scope doesn't seem to work when node is a document fragment..
+    // dom.queryAll([':scope [data-template]', ':scope [data-templateid]',':scope [template]'], node).forEach((child: HTMLElement) => {
+    // const els = dom.queryAll(['[data-template]', '[data-templateid]','[template]'], node);
+    const els = dom.queryAll("[data-template], [data-templateid], [template]", node);
+    for (const child of els) {
+      // If there's a class name specified on the template call, then we want to add it
+      let className = child.className || null;
+      let childTemplateId = localAssertNotFalsy(
+        child.getAttribute("data-template") ||
+          child.getAttribute("data-templateid") ||
+          child.getAttribute("template"),
+        "No child template id provided.",
+      );
+
+      const dataAttribute = child.getAttribute("data") || "";
+
+      const childData = (dataAttribute && getObjValue(dataAttribute, templateData)) || templateData;
+      const tplsInflateOptions = Object.assign({}, inflateOptions);
+      // If we passed in skipInit we'll use it, otherwise set to true assuming
+      // this pass is initializing final markup
+      if (tplsInflateOptions.skipInit != null) {
+        tplsInflateOptions.skipInit = true;
+      }
+      let tpls = localAssertNotFalsy(
+        inflate(childTemplateId, childData, tplsInflateOptions),
+        `No template inflated from ${childTemplateId}.`,
+      );
+      tpls = !Array.isArray(tpls) ? [tpls] : tpls;
+      if (className) {
+        for (const tpl of tpls) {
+          tpl.classList.add(className);
+        }
+      }
+      if (child.nodeName.toUpperCase() === "TPL") {
+        replaceTplElementWithChildren(child, tpls);
+      } else {
+        child.append(...tpls);
+      }
+      // Old.
+      // tpls.reverse().forEach((tplChild) => {
+      //   dom.insertAfter(tplChild, child);
+      //   if (className) {
+      //     tplChild.classList.add(className);
+      //   }
+      // });
+      child.remove();
+    }
+
+    let children: HTMLElement[] = [];
+    for (const child of node.children) {
+      let tplAttributes = (child.getAttribute("data-tpl") || "").split(" ");
+      if (!tplAttributes.includes((node as any).__templateid__)) {
+        tplAttributes.push((node as any).__templateid__);
+      }
+      child.setAttribute("data-tpl", tplAttributes.join(" ").trim());
+      children.push(child as HTMLElement);
+    }
+    let childOrChildren = children.length === 1 ? children[0]! : children;
+    if (!inflateOptions.skipInit) {
+      init(childOrChildren, templateData, inflateOptions.bindOptions);
+    }
+    return childOrChildren;
+  }
+  return null;
+}
+
+export function inflateSingle(
+  nodeOrKey: string | HTMLElement,
+  scriptData: any = null,
+  bindOptions: InflateOptions = {},
+): HTMLElement {
+  const inflated = localAssertNotFalsy(inflate(nodeOrKey, scriptData, bindOptions));
+  return Array.isArray(inflated) ? inflated[0]! : inflated;
+}
+
+/**
+ * Same as inflate, but removes bindings after inflating.
+ * Useful when an element only needs to be inflated once without a desire to rebind
+ * (or accidentally unbind elements)
+ */
+export function inflateOnce(
+  nodeOrKey: string | HTMLElement | DocumentFragment,
+  templateData: any = null,
+  inflateOptions: InflateOptions = {},
+) {
+  let children = inflate(nodeOrKey, templateData, inflateOptions);
+  children && removeBindingAttributes(children, false);
+  return children;
+}
+
+export function inflateSingleOnce(
+  nodeOrKey: string | HTMLElement,
+  scriptData: any = null,
+  bindOptions: InflateOptions = {},
+): HTMLElement {
+  const inflated = inflate(nodeOrKey, scriptData, bindOptions) || [];
+  removeBindingAttributes(inflated, false);
+  return Array.isArray(inflated) ? inflated[0]! : inflated;
+}
+
+/**
+ * Initialize a template and bind to it's data.
+ * Different than bind in that it will check for a registered script
+ * and call that (bind simply binds the data to data-bind fields)
+ */
+export function init(els: HTMLElement | HTMLElement[], data: any, bindOptions: BindOptions = {}) {
+  (!els ? [] : els instanceof Element ? [els] : els).forEach((el) => {
+    const dataTplAttr = el.getAttribute("data-tpl");
+    if (dataTplAttr) {
+      const tpls = dataTplAttr.split(" ");
+      tpls.forEach((tpl) => {
+        // if (templateCache[tpl].script)
+        //   templateCache[tpl].script(el, (data && (data[tpl] || data[tpl.replace('tpl:','')])) || data, options);
+        // else
+        const dataAttribute = el.getAttribute("data") || "";
+        const childData = (dataAttribute && getObjValue(dataAttribute, data)) || data;
+        bind(el, childData, bindOptions);
+      });
+    } else {
+      bind(el, data, bindOptions);
+    }
+  });
+}
+
+// ### templates::bind
+//
+// Binds all elements under a template to the passed `data` JSON object. This *does not* call a registered script.
+// It will stop binding elements once it reaches an element in the DOM with a `data-autobind` attribute set to false
+// (which **MooVeeStar.View**s do automatically -- so each view is in control of it's binding)
+//
+//  - If a single empty element is passed, and data is a string value, then it will be used
+//    as the value for that element
+//  - If `options.onlyDefined === true` then no `data-bind` fields will be unbound, only those
+//    `data-bind` keys in the data map will be bound
+//
+export function bind(
+  elOrEls: HTMLElement | ShadowRoot | HTMLElement[],
+  data: any = {},
+  bindOptions: BindOptions = {},
+) {
+  if (elOrEls instanceof HTMLElement) {
+    data = getPreProcessScript(elOrEls)({...data});
+  }
+
+  // If `els` is a single empty element w/ no `[data-bind]` set _and_
+  // `data` is a string, set it to be the value of the el
+  if (typeof data !== "object") {
+    data = {value: data};
+    if (
+      elOrEls instanceof HTMLElement &&
+      elOrEls.children.length === 0 &&
+      !elOrEls.getAttribute(CONFIG.attrBind)
+    ) {
+      dom.setAttributes(elOrEls, {[CONFIG.attrBind]: "value"});
+    }
+  }
+
+  // Get all children to be bind that are not inner binds
+  let passedEls = !Array.isArray(elOrEls) ? [elOrEls] : elOrEls;
+  for (const el of passedEls) {
+    // First, get any condition els, evaluate them, then we'll skip them and children from binding
+    // if they are false.
+    const conditionEls = toArray(dom.queryAll(`[${CONFIG.attrIf}]`, el));
+    const contextElement =
+      bindOptions.contextElement ?? (el instanceof ShadowRoot ? (el.host as HTMLElement) : el);
+
+    for (const conditionEl of conditionEls) {
+      getValueForBindingPropName;
+      // const isTrue = getStringComparisonExpression(conditionEl.getAttribute(CONFIG.attrIf), data);
+      let isTrue = getValueForBinding(conditionEl.getAttribute(CONFIG.attrIf), {
+        data,
+        contextElement: contextElement,
+        currentElement: conditionEl,
+      });
+      conditionEl.setAttribute(CONFIG.attrIfIs, String(!!isTrue));
+    }
+
+    let toBindEls = toArray(
+      dom.queryAll(
+        `:not([${CONFIG.attrIfIs}="false"]) [${CONFIG.attrBind}]:not([data-tpl]):not([${CONFIG.attrIfIs}="false"])`,
+        el,
+      ),
+    );
+    if (el instanceof HTMLElement && el.getAttribute(CONFIG.attrBind)) {
+      toBindEls.unshift(el);
+    }
+
+    if (toBindEls.length) {
+      // Exclude any els that are in their own data-tpl (which will follow)
+      // let innerBindsElements = dom.queryAll([':scope [data-tpl] [data-bind]', ':scope [data-autobind="false"] [data-bind]'], el);
+      let innerBindsElements = dom.queryAll(
+        `:scope [data-tpl] [${CONFIG.attrBind}], :scope [data-autobind="false"] [${CONFIG.attrBind}]`,
+        el,
+      );
+      toBindEls = toBindEls.filter((maybeBind) => !innerBindsElements.includes(maybeBind));
+      toBindEls.forEach((child) => {
+        // Get the bindings this elements wants
+        // let bindings = child.getAttribute('data-bind').replace(/\s+/,' ').trim().split(' ') || [];
+        RGX_BIND_DECLARATIONS.lastIndex = 0;
+        let bindings = [];
+        let bindingMatch;
+        while (
+          (bindingMatch = RGX_BIND_DECLARATIONS.exec(
+            child.getAttribute(CONFIG.attrBind).replace(/\s+/, " ").trim(),
+          )) !== null
+        ) {
+          bindings.push([bindingMatch[1], bindingMatch[2]]);
+        }
+
+        // let bindingStrings: string[] = child.getAttribute(CONFIG.attrBind).split(' ') || [];
+        // bindingStrings.forEach((bindingString) => {
+        bindings.forEach((bindings) => {
+          // let bindingStringsSplit = bindings.split(':');
+          let bindingDataProperty = localAssertNotFalsy(bindings.shift());
+          let bindingFields = ((bindings.length && bindings[0]) || "default")
+            .trim()
+            .replace(/^\[(.*?)\]$/i, "$1")
+            .split(",");
+
+          let value = getValueForBinding(bindingDataProperty, {
+            data,
+            contextElement: contextElement,
+            currentElement: child,
+          });
+          if (value === undefined) {
+            if (bindOptions.onlyDefined === true) {
+              return;
+            } else {
+              value = null;
+            }
+          }
+          bindingFields.forEach((field) => {
+            if (field.startsWith("style.")) {
+              let stringVal = String(value);
+              if (
+                value &&
+                !stringVal.includes("url(") &&
+                stringVal !== "none" &&
+                (field.includes("background-image") || stringVal.startsWith("http"))
+              ) {
+                value = `url(${value})`;
+              }
+              dom.setStyle(child, field.replace("style.", ""), value);
+
+              // special element methods.
+            } else if (field.startsWith("el.")) {
+              if (field === "el.remove") {
+                if (value === true) {
+                  child.remove();
+                }
+              } else if (field === "el.toggle") {
+                dom.setStyle(child, "display", value === true ? "" : "none");
+              } else if (field.startsWith("el.classList.toggle")) {
+                const cssClass = field.replace(/el.classList.toggle\(['"]?(.*?)['"]?\)/, "$1");
+                child.classList.toggle(cssClass, !!value);
+              }
+
+              // [array]:tpl(<templatename>) will inflate the specified template for each item
+            } else if (RGX_BIND_FN_TEMPLATE_OR_ELEMENT.test(field)) {
+              dom.empty(child);
+              let elementOrTemplateName = RGX_BIND_FN_TEMPLATE_OR_ELEMENT.exec(field)![1]!;
+              if (Array.isArray(value) || value instanceof Set) {
+                const arrayVals = toArray(value);
+                let isElement = RGX_BIND_FN_ELEMENT.test(field);
+                let frag = document.createDocumentFragment();
+                arrayVals.forEach((item, index) => {
+                  let itemData: {};
+                  if (typeof item === "object") {
+                    itemData = Object.assign({$index: index}, item);
+                  } else {
+                    itemData = {$index: index, value: item};
+                  }
+
+                  const els = bindToElOrTemplate(elementOrTemplateName, itemData);
+                  frag.append(...els);
+                });
+                // If we're a <tpl>
+                if (child.nodeName.toUpperCase() === "TPL") {
+                  replaceTplElementWithChildren(child, frag);
+                } else {
+                  dom.empty(child).appendChild(frag);
+                }
+              } else if (value) {
+                const els = bindToElOrTemplate(elementOrTemplateName, value);
+                // If we're a <tpl>
+                if (child.nodeName.toUpperCase() === "TPL") {
+                  replaceTplElementWithChildren(child, els);
+                } else {
+                  child.append(...els);
+                }
+              }
+            } else {
+              dom.setAttributes(child, {[field]: value});
+            }
+          });
+        });
+      });
+    }
+
+    // Now loop over children w/ "data-tpl" and init them, unless they have an "data-autobind" set to "false"
+    // (as in, they have a separate View Controller rendering their data)
+    if (bindOptions.singleScoped !== true) {
+      let toInitEls = toArray(el.querySelectorAll(":scope *[data-tpl]"));
+      if (toInitEls.length) {
+        // let innerInits = dom.queryAll([':scope *[data-tpl] *[data-tpl]', ':scope [data-autobind="false"] [data-tpl]'], el);
+        let innerInits = dom.queryAll(
+          ':scope *[data-tpl] *[data-tpl], :scope [data-autobind="false"] [data-tpl]',
+          el,
+        );
+        toInitEls = toInitEls.filter((maybeInitEl) => {
+          // If the el is inside another [data-tpl] don't init now (it will recursively next time)
+          if (innerInits.includes(maybeInitEl)) {
+            return false;
+          }
+
+          // If we passed in a specific map in data for this, then init
+          let tplKey = maybeInitEl.getAttribute("data-tpl");
+          if (data && (data[tplKey] || data[tplKey.replace("tpl:", "")])) {
+            return true;
+          }
+
+          // Only init cascadingly if autobind is not "false"
+          // (as in, a separate controller handles it's own rendering)
+          return maybeInitEl.getAttribute("data-autobind") !== "false";
+        });
+        toInitEls.forEach((toInitEl) => {
+          var tplKey = toInitEl.getAttribute("data-tpl");
+          init(toInitEl, (data && (data[tplKey] || data[tplKey.replace("tpl:", "")])) || data);
+        });
+      }
+    }
+  }
+}
+
+function bindToElOrTemplate(elementOrTemplateName: string, data: any) {
+  let el: DocumentFragment | HTMLElement | HTMLElement[] | null =
+    getTemplateFragment(elementOrTemplateName);
+  if (!el) {
+    el = dom.createElement(elementOrTemplateName, data);
+  } else {
+    // Inflate each template passing in item and have them init (force false skipInit)
+    el = inflateOnce(el, data, {skipInit: false});
+  }
+  // Then, remove data-tpl b/c we just inflated it (and, presumably, it's data is
+  // already set so we don't want to set it again below).
+  // const els = (Array.isArray(el) ? el : [el]).filter(el => !!el) as HTMLElement[];
+  // els.forEach(el => {
+  //   el.removeAttribute('data-tpl');
+  //   dom.queryAll('[data-tpl]', el).forEach(c => c.removeAttribute('data-tpl'));
+  // });
+  const els = (Array.isArray(el) ? el : [el]).filter((el) => !!el) as HTMLElement[];
+  els.forEach((el) => {
+    el.removeAttribute("data-tpl");
+    let toBindEls = dom.queryAll("[data-tpl]", el);
+    // let innerBindsElements = dom.queryAll([':scope [data-tpl] [data-bind]', ':scope [data-autobind="false"] [data-bind]'], el);
+    let innerBindsElements = dom.queryAll(
+      `:scope [data-tpl] [${CONFIG.attrBind}], :scope [data-autobind="false"] [${CONFIG.attrBind}]`,
+      el,
+    );
+    toBindEls = toBindEls.filter((maybeBind) => !innerBindsElements.includes(maybeBind));
+    toBindEls.forEach((c) => c.removeAttribute("data-tpl"));
+  });
+  return els;
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/common/utils_workflow.ts b/custom_nodes/rgthree-comfy/src_web/common/utils_workflow.ts
new file mode 100644
index 0000000000000000000000000000000000000000..4c3668ea371e74c5cf39e67feb1e98823784db7a
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/common/utils_workflow.ts
@@ -0,0 +1,72 @@
+import type {ISerialisedGraph} from "@comfyorg/frontend";
+import type {ComfyApiFormat} from "typings/comfy.js";
+
+import {getResolver} from "./shared_utils.js";
+import {getPngMetadata, getWebpMetadata} from "./comfyui_shim.js";
+
+/**
+ * Parses the workflow JSON and do any necessary cleanup.
+ */
+function parseWorkflowJson(stringJson?: string) {
+  stringJson = stringJson || "null";
+  // Starting around August 2024 the serialized JSON started to get messy and contained `NaN` (for
+  // an is_changed property, specifically). NaN is not parseable, so we'll get those on out of there
+  // and cleanup anything else we need.
+  stringJson = stringJson.replace(/:\s*NaN/g, ": null");
+  return JSON.parse(stringJson);
+}
+
+export async function tryToGetWorkflowDataFromEvent(
+  e: DragEvent,
+): Promise<{workflow: ISerialisedGraph | null; prompt: ComfyApiFormat | null}> {
+  let work;
+  for (const file of e.dataTransfer?.files || []) {
+    const data = await tryToGetWorkflowDataFromFile(file);
+    if (data.workflow || data.prompt) {
+      return data;
+    }
+  }
+  const validTypes = ["text/uri-list", "text/x-moz-url"];
+  const match = (e.dataTransfer?.types || []).find((t) => validTypes.find((v) => t === v));
+  if (match) {
+    const uri = e.dataTransfer!.getData(match)?.split("\n")?.[0];
+    if (uri) {
+      return tryToGetWorkflowDataFromFile(await (await fetch(uri)).blob());
+    }
+  }
+  return {workflow: null, prompt: null};
+}
+
+export async function tryToGetWorkflowDataFromFile(
+  file: File | Blob,
+): Promise<{workflow: ISerialisedGraph | null; prompt: ComfyApiFormat | null}> {
+  if (file.type === "image/png") {
+    const pngInfo = await getPngMetadata(file);
+    return {
+      workflow: parseWorkflowJson(pngInfo?.workflow),
+      prompt: parseWorkflowJson(pngInfo?.prompt),
+    };
+  }
+
+  if (file.type === "image/webp") {
+    const pngInfo = await getWebpMetadata(file);
+    // Support loading workflows from that webp custom node.
+    const workflow = parseWorkflowJson(pngInfo?.workflow || pngInfo?.Workflow || "null");
+    const prompt = parseWorkflowJson(pngInfo?.prompt || pngInfo?.Prompt || "null");
+    return {workflow, prompt};
+  }
+
+  if (file.type === "application/json" || (file as File).name?.endsWith(".json")) {
+    const resolver = getResolver<{workflow: any; prompt: any}>();
+    const reader = new FileReader();
+    reader.onload = async () => {
+      const json = parseWorkflowJson(reader.result as string);
+      const isApiJson = Object.values(json).every((v: any) => v.class_type);
+      const prompt = isApiJson ? json : null;
+      const workflow = !isApiJson && !json?.templates ? json : null;
+      return {workflow, prompt};
+    };
+    return resolver.promise;
+  }
+  return {workflow: null, prompt: null};
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/lib/tree-sitter.js b/custom_nodes/rgthree-comfy/src_web/lib/tree-sitter.js
new file mode 100644
index 0000000000000000000000000000000000000000..1ef49495cdd5971427d2b1b78f36cf2a1c41ab41
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/lib/tree-sitter.js
@@ -0,0 +1,3978 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+
+// src/constants.ts
+var SIZE_OF_SHORT = 2;
+var SIZE_OF_INT = 4;
+var SIZE_OF_CURSOR = 4 * SIZE_OF_INT;
+var SIZE_OF_NODE = 5 * SIZE_OF_INT;
+var SIZE_OF_POINT = 2 * SIZE_OF_INT;
+var SIZE_OF_RANGE = 2 * SIZE_OF_INT + 2 * SIZE_OF_POINT;
+var ZERO_POINT = { row: 0, column: 0 };
+var INTERNAL = Symbol("INTERNAL");
+function assertInternal(x) {
+  if (x !== INTERNAL) throw new Error("Illegal constructor");
+}
+__name(assertInternal, "assertInternal");
+function isPoint(point) {
+  return !!point && typeof point.row === "number" && typeof point.column === "number";
+}
+__name(isPoint, "isPoint");
+function setModule(module2) {
+  C = module2;
+}
+__name(setModule, "setModule");
+var C;
+
+// src/lookahead_iterator.ts
+var LookaheadIterator = class {
+  static {
+    __name(this, "LookaheadIterator");
+  }
+  /** @internal */
+  [0] = 0;
+  // Internal handle for WASM
+  /** @internal */
+  language;
+  /** @internal */
+  constructor(internal, address, language) {
+    assertInternal(internal);
+    this[0] = address;
+    this.language = language;
+  }
+  /** Get the current symbol of the lookahead iterator. */
+  get currentTypeId() {
+    return C._ts_lookahead_iterator_current_symbol(this[0]);
+  }
+  /** Get the current symbol name of the lookahead iterator. */
+  get currentType() {
+    return this.language.types[this.currentTypeId] || "ERROR";
+  }
+  /** Delete the lookahead iterator, freeing its resources. */
+  delete() {
+    C._ts_lookahead_iterator_delete(this[0]);
+    this[0] = 0;
+  }
+  /**
+   * Reset the lookahead iterator.
+   *
+   * This returns `true` if the language was set successfully and `false`
+   * otherwise.
+   */
+  reset(language, stateId) {
+    if (C._ts_lookahead_iterator_reset(this[0], language[0], stateId)) {
+      this.language = language;
+      return true;
+    }
+    return false;
+  }
+  /**
+   * Reset the lookahead iterator to another state.
+   *
+   * This returns `true` if the iterator was reset to the given state and
+   * `false` otherwise.
+   */
+  resetState(stateId) {
+    return Boolean(C._ts_lookahead_iterator_reset_state(this[0], stateId));
+  }
+  /**
+   * Returns an iterator that iterates over the symbols of the lookahead iterator.
+   *
+   * The iterator will yield the current symbol name as a string for each step
+   * until there are no more symbols to iterate over.
+   */
+  [Symbol.iterator]() {
+    return {
+      next: /* @__PURE__ */ __name(() => {
+        if (C._ts_lookahead_iterator_next(this[0])) {
+          return { done: false, value: this.currentType };
+        }
+        return { done: true, value: "" };
+      }, "next")
+    };
+  }
+};
+
+// src/tree.ts
+function getText(tree, startIndex, endIndex, startPosition) {
+  const length = endIndex - startIndex;
+  let result = tree.textCallback(startIndex, startPosition);
+  if (result) {
+    startIndex += result.length;
+    while (startIndex < endIndex) {
+      const string = tree.textCallback(startIndex, startPosition);
+      if (string && string.length > 0) {
+        startIndex += string.length;
+        result += string;
+      } else {
+        break;
+      }
+    }
+    if (startIndex > endIndex) {
+      result = result.slice(0, length);
+    }
+  }
+  return result ?? "";
+}
+__name(getText, "getText");
+var Tree = class _Tree {
+  static {
+    __name(this, "Tree");
+  }
+  /** @internal */
+  [0] = 0;
+  // Internal handle for WASM
+  /** @internal */
+  textCallback;
+  /** The language that was used to parse the syntax tree. */
+  language;
+  /** @internal */
+  constructor(internal, address, language, textCallback) {
+    assertInternal(internal);
+    this[0] = address;
+    this.language = language;
+    this.textCallback = textCallback;
+  }
+  /** Create a shallow copy of the syntax tree. This is very fast. */
+  copy() {
+    const address = C._ts_tree_copy(this[0]);
+    return new _Tree(INTERNAL, address, this.language, this.textCallback);
+  }
+  /** Delete the syntax tree, freeing its resources. */
+  delete() {
+    C._ts_tree_delete(this[0]);
+    this[0] = 0;
+  }
+  /** Get the root node of the syntax tree. */
+  get rootNode() {
+    C._ts_tree_root_node_wasm(this[0]);
+    return unmarshalNode(this);
+  }
+  /**
+   * Get the root node of the syntax tree, but with its position shifted
+   * forward by the given offset.
+   */
+  rootNodeWithOffset(offsetBytes, offsetExtent) {
+    const address = TRANSFER_BUFFER + SIZE_OF_NODE;
+    C.setValue(address, offsetBytes, "i32");
+    marshalPoint(address + SIZE_OF_INT, offsetExtent);
+    C._ts_tree_root_node_with_offset_wasm(this[0]);
+    return unmarshalNode(this);
+  }
+  /**
+   * Edit the syntax tree to keep it in sync with source code that has been
+   * edited.
+   *
+   * You must describe the edit both in terms of byte offsets and in terms of
+   * row/column coordinates.
+   */
+  edit(edit) {
+    marshalEdit(edit);
+    C._ts_tree_edit_wasm(this[0]);
+  }
+  /** Create a new {@link TreeCursor} starting from the root of the tree. */
+  walk() {
+    return this.rootNode.walk();
+  }
+  /**
+   * Compare this old edited syntax tree to a new syntax tree representing
+   * the same document, returning a sequence of ranges whose syntactic
+   * structure has changed.
+   *
+   * For this to work correctly, this syntax tree must have been edited such
+   * that its ranges match up to the new tree. Generally, you'll want to
+   * call this method right after calling one of the [`Parser::parse`]
+   * functions. Call it on the old tree that was passed to parse, and
+   * pass the new tree that was returned from `parse`.
+   */
+  getChangedRanges(other) {
+    if (!(other instanceof _Tree)) {
+      throw new TypeError("Argument must be a Tree");
+    }
+    C._ts_tree_get_changed_ranges_wasm(this[0], other[0]);
+    const count = C.getValue(TRANSFER_BUFFER, "i32");
+    const buffer = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+    const result = new Array(count);
+    if (count > 0) {
+      let address = buffer;
+      for (let i2 = 0; i2 < count; i2++) {
+        result[i2] = unmarshalRange(address);
+        address += SIZE_OF_RANGE;
+      }
+      C._free(buffer);
+    }
+    return result;
+  }
+  /** Get the included ranges that were used to parse the syntax tree. */
+  getIncludedRanges() {
+    C._ts_tree_included_ranges_wasm(this[0]);
+    const count = C.getValue(TRANSFER_BUFFER, "i32");
+    const buffer = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+    const result = new Array(count);
+    if (count > 0) {
+      let address = buffer;
+      for (let i2 = 0; i2 < count; i2++) {
+        result[i2] = unmarshalRange(address);
+        address += SIZE_OF_RANGE;
+      }
+      C._free(buffer);
+    }
+    return result;
+  }
+};
+
+// src/tree_cursor.ts
+var TreeCursor = class _TreeCursor {
+  static {
+    __name(this, "TreeCursor");
+  }
+  /** @internal */
+  [0] = 0;
+  // Internal handle for WASM
+  /** @internal */
+  [1] = 0;
+  // Internal handle for WASM
+  /** @internal */
+  [2] = 0;
+  // Internal handle for WASM
+  /** @internal */
+  [3] = 0;
+  // Internal handle for WASM
+  /** @internal */
+  tree;
+  /** @internal */
+  constructor(internal, tree) {
+    assertInternal(internal);
+    this.tree = tree;
+    unmarshalTreeCursor(this);
+  }
+  /** Creates a deep copy of the tree cursor. This allocates new memory. */
+  copy() {
+    const copy = new _TreeCursor(INTERNAL, this.tree);
+    C._ts_tree_cursor_copy_wasm(this.tree[0]);
+    unmarshalTreeCursor(copy);
+    return copy;
+  }
+  /** Delete the tree cursor, freeing its resources. */
+  delete() {
+    marshalTreeCursor(this);
+    C._ts_tree_cursor_delete_wasm(this.tree[0]);
+    this[0] = this[1] = this[2] = 0;
+  }
+  /** Get the tree cursor's current {@link Node}. */
+  get currentNode() {
+    marshalTreeCursor(this);
+    C._ts_tree_cursor_current_node_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /**
+   * Get the numerical field id of this tree cursor's current node.
+   *
+   * See also {@link TreeCursor#currentFieldName}.
+   */
+  get currentFieldId() {
+    marshalTreeCursor(this);
+    return C._ts_tree_cursor_current_field_id_wasm(this.tree[0]);
+  }
+  /** Get the field name of this tree cursor's current node. */
+  get currentFieldName() {
+    return this.tree.language.fields[this.currentFieldId];
+  }
+  /**
+   * Get the depth of the cursor's current node relative to the original
+   * node that the cursor was constructed with.
+   */
+  get currentDepth() {
+    marshalTreeCursor(this);
+    return C._ts_tree_cursor_current_depth_wasm(this.tree[0]);
+  }
+  /**
+   * Get the index of the cursor's current node out of all of the
+   * descendants of the original node that the cursor was constructed with.
+   */
+  get currentDescendantIndex() {
+    marshalTreeCursor(this);
+    return C._ts_tree_cursor_current_descendant_index_wasm(this.tree[0]);
+  }
+  /** Get the type of the cursor's current node. */
+  get nodeType() {
+    return this.tree.language.types[this.nodeTypeId] || "ERROR";
+  }
+  /** Get the type id of the cursor's current node. */
+  get nodeTypeId() {
+    marshalTreeCursor(this);
+    return C._ts_tree_cursor_current_node_type_id_wasm(this.tree[0]);
+  }
+  /** Get the state id of the cursor's current node. */
+  get nodeStateId() {
+    marshalTreeCursor(this);
+    return C._ts_tree_cursor_current_node_state_id_wasm(this.tree[0]);
+  }
+  /** Get the id of the cursor's current node. */
+  get nodeId() {
+    marshalTreeCursor(this);
+    return C._ts_tree_cursor_current_node_id_wasm(this.tree[0]);
+  }
+  /**
+   * Check if the cursor's current node is *named*.
+   *
+   * Named nodes correspond to named rules in the grammar, whereas
+   * *anonymous* nodes correspond to string literals in the grammar.
+   */
+  get nodeIsNamed() {
+    marshalTreeCursor(this);
+    return C._ts_tree_cursor_current_node_is_named_wasm(this.tree[0]) === 1;
+  }
+  /**
+   * Check if the cursor's current node is *missing*.
+   *
+   * Missing nodes are inserted by the parser in order to recover from
+   * certain kinds of syntax errors.
+   */
+  get nodeIsMissing() {
+    marshalTreeCursor(this);
+    return C._ts_tree_cursor_current_node_is_missing_wasm(this.tree[0]) === 1;
+  }
+  /** Get the string content of the cursor's current node. */
+  get nodeText() {
+    marshalTreeCursor(this);
+    const startIndex = C._ts_tree_cursor_start_index_wasm(this.tree[0]);
+    const endIndex = C._ts_tree_cursor_end_index_wasm(this.tree[0]);
+    C._ts_tree_cursor_start_position_wasm(this.tree[0]);
+    const startPosition = unmarshalPoint(TRANSFER_BUFFER);
+    return getText(this.tree, startIndex, endIndex, startPosition);
+  }
+  /** Get the start position of the cursor's current node. */
+  get startPosition() {
+    marshalTreeCursor(this);
+    C._ts_tree_cursor_start_position_wasm(this.tree[0]);
+    return unmarshalPoint(TRANSFER_BUFFER);
+  }
+  /** Get the end position of the cursor's current node. */
+  get endPosition() {
+    marshalTreeCursor(this);
+    C._ts_tree_cursor_end_position_wasm(this.tree[0]);
+    return unmarshalPoint(TRANSFER_BUFFER);
+  }
+  /** Get the start index of the cursor's current node. */
+  get startIndex() {
+    marshalTreeCursor(this);
+    return C._ts_tree_cursor_start_index_wasm(this.tree[0]);
+  }
+  /** Get the end index of the cursor's current node. */
+  get endIndex() {
+    marshalTreeCursor(this);
+    return C._ts_tree_cursor_end_index_wasm(this.tree[0]);
+  }
+  /**
+   * Move this cursor to the first child of its current node.
+   *
+   * This returns `true` if the cursor successfully moved, and returns
+   * `false` if there were no children.
+   */
+  gotoFirstChild() {
+    marshalTreeCursor(this);
+    const result = C._ts_tree_cursor_goto_first_child_wasm(this.tree[0]);
+    unmarshalTreeCursor(this);
+    return result === 1;
+  }
+  /**
+   * Move this cursor to the last child of its current node.
+   *
+   * This returns `true` if the cursor successfully moved, and returns
+   * `false` if there were no children.
+   *
+   * Note that this function may be slower than
+   * {@link TreeCursor#gotoFirstChild} because it needs to
+   * iterate through all the children to compute the child's position.
+   */
+  gotoLastChild() {
+    marshalTreeCursor(this);
+    const result = C._ts_tree_cursor_goto_last_child_wasm(this.tree[0]);
+    unmarshalTreeCursor(this);
+    return result === 1;
+  }
+  /**
+   * Move this cursor to the parent of its current node.
+   *
+   * This returns `true` if the cursor successfully moved, and returns
+   * `false` if there was no parent node (the cursor was already on the
+   * root node).
+   *
+   * Note that the node the cursor was constructed with is considered the root
+   * of the cursor, and the cursor cannot walk outside this node.
+   */
+  gotoParent() {
+    marshalTreeCursor(this);
+    const result = C._ts_tree_cursor_goto_parent_wasm(this.tree[0]);
+    unmarshalTreeCursor(this);
+    return result === 1;
+  }
+  /**
+   * Move this cursor to the next sibling of its current node.
+   *
+   * This returns `true` if the cursor successfully moved, and returns
+   * `false` if there was no next sibling node.
+   *
+   * Note that the node the cursor was constructed with is considered the root
+   * of the cursor, and the cursor cannot walk outside this node.
+   */
+  gotoNextSibling() {
+    marshalTreeCursor(this);
+    const result = C._ts_tree_cursor_goto_next_sibling_wasm(this.tree[0]);
+    unmarshalTreeCursor(this);
+    return result === 1;
+  }
+  /**
+   * Move this cursor to the previous sibling of its current node.
+   *
+   * This returns `true` if the cursor successfully moved, and returns
+   * `false` if there was no previous sibling node.
+   *
+   * Note that this function may be slower than
+   * {@link TreeCursor#gotoNextSibling} due to how node
+   * positions are stored. In the worst case, this will need to iterate
+   * through all the children up to the previous sibling node to recalculate
+   * its position. Also note that the node the cursor was constructed with is
+   * considered the root of the cursor, and the cursor cannot walk outside this node.
+   */
+  gotoPreviousSibling() {
+    marshalTreeCursor(this);
+    const result = C._ts_tree_cursor_goto_previous_sibling_wasm(this.tree[0]);
+    unmarshalTreeCursor(this);
+    return result === 1;
+  }
+  /**
+   * Move the cursor to the node that is the nth descendant of
+   * the original node that the cursor was constructed with, where
+   * zero represents the original node itself.
+   */
+  gotoDescendant(goalDescendantIndex) {
+    marshalTreeCursor(this);
+    C._ts_tree_cursor_goto_descendant_wasm(this.tree[0], goalDescendantIndex);
+    unmarshalTreeCursor(this);
+  }
+  /**
+   * Move this cursor to the first child of its current node that contains or
+   * starts after the given byte offset.
+   *
+   * This returns `true` if the cursor successfully moved to a child node, and returns
+   * `false` if no such child was found.
+   */
+  gotoFirstChildForIndex(goalIndex) {
+    marshalTreeCursor(this);
+    C.setValue(TRANSFER_BUFFER + SIZE_OF_CURSOR, goalIndex, "i32");
+    const result = C._ts_tree_cursor_goto_first_child_for_index_wasm(this.tree[0]);
+    unmarshalTreeCursor(this);
+    return result === 1;
+  }
+  /**
+   * Move this cursor to the first child of its current node that contains or
+   * starts after the given byte offset.
+   *
+   * This returns the index of the child node if one was found, and returns
+   * `null` if no such child was found.
+   */
+  gotoFirstChildForPosition(goalPosition) {
+    marshalTreeCursor(this);
+    marshalPoint(TRANSFER_BUFFER + SIZE_OF_CURSOR, goalPosition);
+    const result = C._ts_tree_cursor_goto_first_child_for_position_wasm(this.tree[0]);
+    unmarshalTreeCursor(this);
+    return result === 1;
+  }
+  /**
+   * Re-initialize this tree cursor to start at the original node that the
+   * cursor was constructed with.
+   */
+  reset(node) {
+    marshalNode(node);
+    marshalTreeCursor(this, TRANSFER_BUFFER + SIZE_OF_NODE);
+    C._ts_tree_cursor_reset_wasm(this.tree[0]);
+    unmarshalTreeCursor(this);
+  }
+  /**
+   * Re-initialize a tree cursor to the same position as another cursor.
+   *
+   * Unlike {@link TreeCursor#reset}, this will not lose parent
+   * information and allows reusing already created cursors.
+   */
+  resetTo(cursor) {
+    marshalTreeCursor(this, TRANSFER_BUFFER);
+    marshalTreeCursor(cursor, TRANSFER_BUFFER + SIZE_OF_CURSOR);
+    C._ts_tree_cursor_reset_to_wasm(this.tree[0], cursor.tree[0]);
+    unmarshalTreeCursor(this);
+  }
+};
+
+// src/node.ts
+var Node = class {
+  static {
+    __name(this, "Node");
+  }
+  /** @internal */
+  [0] = 0;
+  // Internal handle for WASM
+  /** @internal */
+  _children;
+  /** @internal */
+  _namedChildren;
+  /** @internal */
+  constructor(internal, {
+    id,
+    tree,
+    startIndex,
+    startPosition,
+    other
+  }) {
+    assertInternal(internal);
+    this[0] = other;
+    this.id = id;
+    this.tree = tree;
+    this.startIndex = startIndex;
+    this.startPosition = startPosition;
+  }
+  /**
+   * The numeric id for this node that is unique.
+   *
+   * Within a given syntax tree, no two nodes have the same id. However:
+   *
+   * * If a new tree is created based on an older tree, and a node from the old tree is reused in
+   *   the process, then that node will have the same id in both trees.
+   *
+   * * A node not marked as having changes does not guarantee it was reused.
+   *
+   * * If a node is marked as having changed in the old tree, it will not be reused.
+   */
+  id;
+  /** The byte index where this node starts. */
+  startIndex;
+  /** The position where this node starts. */
+  startPosition;
+  /** The tree that this node belongs to. */
+  tree;
+  /** Get this node's type as a numerical id. */
+  get typeId() {
+    marshalNode(this);
+    return C._ts_node_symbol_wasm(this.tree[0]);
+  }
+  /**
+   * Get the node's type as a numerical id as it appears in the grammar,
+   * ignoring aliases.
+   */
+  get grammarId() {
+    marshalNode(this);
+    return C._ts_node_grammar_symbol_wasm(this.tree[0]);
+  }
+  /** Get this node's type as a string. */
+  get type() {
+    return this.tree.language.types[this.typeId] || "ERROR";
+  }
+  /**
+   * Get this node's symbol name as it appears in the grammar, ignoring
+   * aliases as a string.
+   */
+  get grammarType() {
+    return this.tree.language.types[this.grammarId] || "ERROR";
+  }
+  /**
+   * Check if this node is *named*.
+   *
+   * Named nodes correspond to named rules in the grammar, whereas
+   * *anonymous* nodes correspond to string literals in the grammar.
+   */
+  get isNamed() {
+    marshalNode(this);
+    return C._ts_node_is_named_wasm(this.tree[0]) === 1;
+  }
+  /**
+   * Check if this node is *extra*.
+   *
+   * Extra nodes represent things like comments, which are not required
+   * by the grammar, but can appear anywhere.
+   */
+  get isExtra() {
+    marshalNode(this);
+    return C._ts_node_is_extra_wasm(this.tree[0]) === 1;
+  }
+  /**
+   * Check if this node represents a syntax error.
+   *
+   * Syntax errors represent parts of the code that could not be incorporated
+   * into a valid syntax tree.
+   */
+  get isError() {
+    marshalNode(this);
+    return C._ts_node_is_error_wasm(this.tree[0]) === 1;
+  }
+  /**
+   * Check if this node is *missing*.
+   *
+   * Missing nodes are inserted by the parser in order to recover from
+   * certain kinds of syntax errors.
+   */
+  get isMissing() {
+    marshalNode(this);
+    return C._ts_node_is_missing_wasm(this.tree[0]) === 1;
+  }
+  /** Check if this node has been edited. */
+  get hasChanges() {
+    marshalNode(this);
+    return C._ts_node_has_changes_wasm(this.tree[0]) === 1;
+  }
+  /**
+   * Check if this node represents a syntax error or contains any syntax
+   * errors anywhere within it.
+   */
+  get hasError() {
+    marshalNode(this);
+    return C._ts_node_has_error_wasm(this.tree[0]) === 1;
+  }
+  /** Get the byte index where this node ends. */
+  get endIndex() {
+    marshalNode(this);
+    return C._ts_node_end_index_wasm(this.tree[0]);
+  }
+  /** Get the position where this node ends. */
+  get endPosition() {
+    marshalNode(this);
+    C._ts_node_end_point_wasm(this.tree[0]);
+    return unmarshalPoint(TRANSFER_BUFFER);
+  }
+  /** Get the string content of this node. */
+  get text() {
+    return getText(this.tree, this.startIndex, this.endIndex, this.startPosition);
+  }
+  /** Get this node's parse state. */
+  get parseState() {
+    marshalNode(this);
+    return C._ts_node_parse_state_wasm(this.tree[0]);
+  }
+  /** Get the parse state after this node. */
+  get nextParseState() {
+    marshalNode(this);
+    return C._ts_node_next_parse_state_wasm(this.tree[0]);
+  }
+  /** Check if this node is equal to another node. */
+  equals(other) {
+    return this.tree === other.tree && this.id === other.id;
+  }
+  /**
+   * Get the node's child at the given index, where zero represents the first child.
+   *
+   * This method is fairly fast, but its cost is technically log(n), so if
+   * you might be iterating over a long list of children, you should use
+   * {@link Node#children} instead.
+   */
+  child(index) {
+    marshalNode(this);
+    C._ts_node_child_wasm(this.tree[0], index);
+    return unmarshalNode(this.tree);
+  }
+  /**
+   * Get this node's *named* child at the given index.
+   *
+   * See also {@link Node#isNamed}.
+   * This method is fairly fast, but its cost is technically log(n), so if
+   * you might be iterating over a long list of children, you should use
+   * {@link Node#namedChildren} instead.
+   */
+  namedChild(index) {
+    marshalNode(this);
+    C._ts_node_named_child_wasm(this.tree[0], index);
+    return unmarshalNode(this.tree);
+  }
+  /**
+   * Get this node's child with the given numerical field id.
+   *
+   * See also {@link Node#childForFieldName}. You can
+   * convert a field name to an id using {@link Language#fieldIdForName}.
+   */
+  childForFieldId(fieldId) {
+    marshalNode(this);
+    C._ts_node_child_by_field_id_wasm(this.tree[0], fieldId);
+    return unmarshalNode(this.tree);
+  }
+  /**
+   * Get the first child with the given field name.
+   *
+   * If multiple children may have the same field name, access them using
+   * {@link Node#childrenForFieldName}.
+   */
+  childForFieldName(fieldName) {
+    const fieldId = this.tree.language.fields.indexOf(fieldName);
+    if (fieldId !== -1) return this.childForFieldId(fieldId);
+    return null;
+  }
+  /** Get the field name of this node's child at the given index. */
+  fieldNameForChild(index) {
+    marshalNode(this);
+    const address = C._ts_node_field_name_for_child_wasm(this.tree[0], index);
+    if (!address) return null;
+    return C.AsciiToString(address);
+  }
+  /** Get the field name of this node's named child at the given index. */
+  fieldNameForNamedChild(index) {
+    marshalNode(this);
+    const address = C._ts_node_field_name_for_named_child_wasm(this.tree[0], index);
+    if (!address) return null;
+    return C.AsciiToString(address);
+  }
+  /**
+   * Get an array of this node's children with a given field name.
+   *
+   * See also {@link Node#children}.
+   */
+  childrenForFieldName(fieldName) {
+    const fieldId = this.tree.language.fields.indexOf(fieldName);
+    if (fieldId !== -1 && fieldId !== 0) return this.childrenForFieldId(fieldId);
+    return [];
+  }
+  /**
+    * Get an array of this node's children with a given field id.
+    *
+    * See also {@link Node#childrenForFieldName}.
+    */
+  childrenForFieldId(fieldId) {
+    marshalNode(this);
+    C._ts_node_children_by_field_id_wasm(this.tree[0], fieldId);
+    const count = C.getValue(TRANSFER_BUFFER, "i32");
+    const buffer = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+    const result = new Array(count);
+    if (count > 0) {
+      let address = buffer;
+      for (let i2 = 0; i2 < count; i2++) {
+        result[i2] = unmarshalNode(this.tree, address);
+        address += SIZE_OF_NODE;
+      }
+      C._free(buffer);
+    }
+    return result;
+  }
+  /** Get the node's first child that contains or starts after the given byte offset. */
+  firstChildForIndex(index) {
+    marshalNode(this);
+    const address = TRANSFER_BUFFER + SIZE_OF_NODE;
+    C.setValue(address, index, "i32");
+    C._ts_node_first_child_for_byte_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /** Get the node's first named child that contains or starts after the given byte offset. */
+  firstNamedChildForIndex(index) {
+    marshalNode(this);
+    const address = TRANSFER_BUFFER + SIZE_OF_NODE;
+    C.setValue(address, index, "i32");
+    C._ts_node_first_named_child_for_byte_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /** Get this node's number of children. */
+  get childCount() {
+    marshalNode(this);
+    return C._ts_node_child_count_wasm(this.tree[0]);
+  }
+  /**
+   * Get this node's number of *named* children.
+   *
+   * See also {@link Node#isNamed}.
+   */
+  get namedChildCount() {
+    marshalNode(this);
+    return C._ts_node_named_child_count_wasm(this.tree[0]);
+  }
+  /** Get this node's first child. */
+  get firstChild() {
+    return this.child(0);
+  }
+  /**
+   * Get this node's first named child.
+   *
+   * See also {@link Node#isNamed}.
+   */
+  get firstNamedChild() {
+    return this.namedChild(0);
+  }
+  /** Get this node's last child. */
+  get lastChild() {
+    return this.child(this.childCount - 1);
+  }
+  /**
+   * Get this node's last named child.
+   *
+   * See also {@link Node#isNamed}.
+   */
+  get lastNamedChild() {
+    return this.namedChild(this.namedChildCount - 1);
+  }
+  /**
+   * Iterate over this node's children.
+   *
+   * If you're walking the tree recursively, you may want to use the
+   * {@link TreeCursor} APIs directly instead.
+   */
+  get children() {
+    if (!this._children) {
+      marshalNode(this);
+      C._ts_node_children_wasm(this.tree[0]);
+      const count = C.getValue(TRANSFER_BUFFER, "i32");
+      const buffer = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+      this._children = new Array(count);
+      if (count > 0) {
+        let address = buffer;
+        for (let i2 = 0; i2 < count; i2++) {
+          this._children[i2] = unmarshalNode(this.tree, address);
+          address += SIZE_OF_NODE;
+        }
+        C._free(buffer);
+      }
+    }
+    return this._children;
+  }
+  /**
+   * Iterate over this node's named children.
+   *
+   * See also {@link Node#children}.
+   */
+  get namedChildren() {
+    if (!this._namedChildren) {
+      marshalNode(this);
+      C._ts_node_named_children_wasm(this.tree[0]);
+      const count = C.getValue(TRANSFER_BUFFER, "i32");
+      const buffer = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+      this._namedChildren = new Array(count);
+      if (count > 0) {
+        let address = buffer;
+        for (let i2 = 0; i2 < count; i2++) {
+          this._namedChildren[i2] = unmarshalNode(this.tree, address);
+          address += SIZE_OF_NODE;
+        }
+        C._free(buffer);
+      }
+    }
+    return this._namedChildren;
+  }
+  /**
+   * Get the descendants of this node that are the given type, or in the given types array.
+   *
+   * The types array should contain node type strings, which can be retrieved from {@link Language#types}.
+   *
+   * Additionally, a `startPosition` and `endPosition` can be passed in to restrict the search to a byte range.
+   */
+  descendantsOfType(types, startPosition = ZERO_POINT, endPosition = ZERO_POINT) {
+    if (!Array.isArray(types)) types = [types];
+    const symbols = [];
+    const typesBySymbol = this.tree.language.types;
+    for (const node_type of types) {
+      if (node_type == "ERROR") {
+        symbols.push(65535);
+      }
+    }
+    for (let i2 = 0, n = typesBySymbol.length; i2 < n; i2++) {
+      if (types.includes(typesBySymbol[i2])) {
+        symbols.push(i2);
+      }
+    }
+    const symbolsAddress = C._malloc(SIZE_OF_INT * symbols.length);
+    for (let i2 = 0, n = symbols.length; i2 < n; i2++) {
+      C.setValue(symbolsAddress + i2 * SIZE_OF_INT, symbols[i2], "i32");
+    }
+    marshalNode(this);
+    C._ts_node_descendants_of_type_wasm(
+      this.tree[0],
+      symbolsAddress,
+      symbols.length,
+      startPosition.row,
+      startPosition.column,
+      endPosition.row,
+      endPosition.column
+    );
+    const descendantCount = C.getValue(TRANSFER_BUFFER, "i32");
+    const descendantAddress = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+    const result = new Array(descendantCount);
+    if (descendantCount > 0) {
+      let address = descendantAddress;
+      for (let i2 = 0; i2 < descendantCount; i2++) {
+        result[i2] = unmarshalNode(this.tree, address);
+        address += SIZE_OF_NODE;
+      }
+    }
+    C._free(descendantAddress);
+    C._free(symbolsAddress);
+    return result;
+  }
+  /** Get this node's next sibling. */
+  get nextSibling() {
+    marshalNode(this);
+    C._ts_node_next_sibling_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /** Get this node's previous sibling. */
+  get previousSibling() {
+    marshalNode(this);
+    C._ts_node_prev_sibling_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /**
+   * Get this node's next *named* sibling.
+   *
+   * See also {@link Node#isNamed}.
+   */
+  get nextNamedSibling() {
+    marshalNode(this);
+    C._ts_node_next_named_sibling_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /**
+   * Get this node's previous *named* sibling.
+   *
+   * See also {@link Node#isNamed}.
+   */
+  get previousNamedSibling() {
+    marshalNode(this);
+    C._ts_node_prev_named_sibling_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /** Get the node's number of descendants, including one for the node itself. */
+  get descendantCount() {
+    marshalNode(this);
+    return C._ts_node_descendant_count_wasm(this.tree[0]);
+  }
+  /**
+   * Get this node's immediate parent.
+   * Prefer {@link Node#childWithDescendant} for iterating over this node's ancestors.
+   */
+  get parent() {
+    marshalNode(this);
+    C._ts_node_parent_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /**
+   * Get the node that contains `descendant`.
+   *
+   * Note that this can return `descendant` itself.
+   */
+  childWithDescendant(descendant) {
+    marshalNode(this);
+    marshalNode(descendant, 1);
+    C._ts_node_child_with_descendant_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /** Get the smallest node within this node that spans the given byte range. */
+  descendantForIndex(start2, end = start2) {
+    if (typeof start2 !== "number" || typeof end !== "number") {
+      throw new Error("Arguments must be numbers");
+    }
+    marshalNode(this);
+    const address = TRANSFER_BUFFER + SIZE_OF_NODE;
+    C.setValue(address, start2, "i32");
+    C.setValue(address + SIZE_OF_INT, end, "i32");
+    C._ts_node_descendant_for_index_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /** Get the smallest named node within this node that spans the given byte range. */
+  namedDescendantForIndex(start2, end = start2) {
+    if (typeof start2 !== "number" || typeof end !== "number") {
+      throw new Error("Arguments must be numbers");
+    }
+    marshalNode(this);
+    const address = TRANSFER_BUFFER + SIZE_OF_NODE;
+    C.setValue(address, start2, "i32");
+    C.setValue(address + SIZE_OF_INT, end, "i32");
+    C._ts_node_named_descendant_for_index_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /** Get the smallest node within this node that spans the given point range. */
+  descendantForPosition(start2, end = start2) {
+    if (!isPoint(start2) || !isPoint(end)) {
+      throw new Error("Arguments must be {row, column} objects");
+    }
+    marshalNode(this);
+    const address = TRANSFER_BUFFER + SIZE_OF_NODE;
+    marshalPoint(address, start2);
+    marshalPoint(address + SIZE_OF_POINT, end);
+    C._ts_node_descendant_for_position_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /** Get the smallest named node within this node that spans the given point range. */
+  namedDescendantForPosition(start2, end = start2) {
+    if (!isPoint(start2) || !isPoint(end)) {
+      throw new Error("Arguments must be {row, column} objects");
+    }
+    marshalNode(this);
+    const address = TRANSFER_BUFFER + SIZE_OF_NODE;
+    marshalPoint(address, start2);
+    marshalPoint(address + SIZE_OF_POINT, end);
+    C._ts_node_named_descendant_for_position_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /**
+   * Create a new {@link TreeCursor} starting from this node.
+   *
+   * Note that the given node is considered the root of the cursor,
+   * and the cursor cannot walk outside this node.
+   */
+  walk() {
+    marshalNode(this);
+    C._ts_tree_cursor_new_wasm(this.tree[0]);
+    return new TreeCursor(INTERNAL, this.tree);
+  }
+  /**
+   * Edit this node to keep it in-sync with source code that has been edited.
+   *
+   * This function is only rarely needed. When you edit a syntax tree with
+   * the {@link Tree#edit} method, all of the nodes that you retrieve from
+   * the tree afterward will already reflect the edit. You only need to
+   * use {@link Node#edit} when you have a specific {@link Node} instance that
+   * you want to keep and continue to use after an edit.
+   */
+  edit(edit) {
+    if (this.startIndex >= edit.oldEndIndex) {
+      this.startIndex = edit.newEndIndex + (this.startIndex - edit.oldEndIndex);
+      let subbedPointRow;
+      let subbedPointColumn;
+      if (this.startPosition.row > edit.oldEndPosition.row) {
+        subbedPointRow = this.startPosition.row - edit.oldEndPosition.row;
+        subbedPointColumn = this.startPosition.column;
+      } else {
+        subbedPointRow = 0;
+        subbedPointColumn = this.startPosition.column;
+        if (this.startPosition.column >= edit.oldEndPosition.column) {
+          subbedPointColumn = this.startPosition.column - edit.oldEndPosition.column;
+        }
+      }
+      if (subbedPointRow > 0) {
+        this.startPosition.row += subbedPointRow;
+        this.startPosition.column = subbedPointColumn;
+      } else {
+        this.startPosition.column += subbedPointColumn;
+      }
+    } else if (this.startIndex > edit.startIndex) {
+      this.startIndex = edit.newEndIndex;
+      this.startPosition.row = edit.newEndPosition.row;
+      this.startPosition.column = edit.newEndPosition.column;
+    }
+  }
+  /** Get the S-expression representation of this node. */
+  toString() {
+    marshalNode(this);
+    const address = C._ts_node_to_string_wasm(this.tree[0]);
+    const result = C.AsciiToString(address);
+    C._free(address);
+    return result;
+  }
+};
+
+// src/marshal.ts
+function unmarshalCaptures(query, tree, address, patternIndex, result) {
+  for (let i2 = 0, n = result.length; i2 < n; i2++) {
+    const captureIndex = C.getValue(address, "i32");
+    address += SIZE_OF_INT;
+    const node = unmarshalNode(tree, address);
+    address += SIZE_OF_NODE;
+    result[i2] = { patternIndex, name: query.captureNames[captureIndex], node };
+  }
+  return address;
+}
+__name(unmarshalCaptures, "unmarshalCaptures");
+function marshalNode(node, index = 0) {
+  let address = TRANSFER_BUFFER + index * SIZE_OF_NODE;
+  C.setValue(address, node.id, "i32");
+  address += SIZE_OF_INT;
+  C.setValue(address, node.startIndex, "i32");
+  address += SIZE_OF_INT;
+  C.setValue(address, node.startPosition.row, "i32");
+  address += SIZE_OF_INT;
+  C.setValue(address, node.startPosition.column, "i32");
+  address += SIZE_OF_INT;
+  C.setValue(address, node[0], "i32");
+}
+__name(marshalNode, "marshalNode");
+function unmarshalNode(tree, address = TRANSFER_BUFFER) {
+  const id = C.getValue(address, "i32");
+  address += SIZE_OF_INT;
+  if (id === 0) return null;
+  const index = C.getValue(address, "i32");
+  address += SIZE_OF_INT;
+  const row = C.getValue(address, "i32");
+  address += SIZE_OF_INT;
+  const column = C.getValue(address, "i32");
+  address += SIZE_OF_INT;
+  const other = C.getValue(address, "i32");
+  const result = new Node(INTERNAL, {
+    id,
+    tree,
+    startIndex: index,
+    startPosition: { row, column },
+    other
+  });
+  return result;
+}
+__name(unmarshalNode, "unmarshalNode");
+function marshalTreeCursor(cursor, address = TRANSFER_BUFFER) {
+  C.setValue(address + 0 * SIZE_OF_INT, cursor[0], "i32");
+  C.setValue(address + 1 * SIZE_OF_INT, cursor[1], "i32");
+  C.setValue(address + 2 * SIZE_OF_INT, cursor[2], "i32");
+  C.setValue(address + 3 * SIZE_OF_INT, cursor[3], "i32");
+}
+__name(marshalTreeCursor, "marshalTreeCursor");
+function unmarshalTreeCursor(cursor) {
+  cursor[0] = C.getValue(TRANSFER_BUFFER + 0 * SIZE_OF_INT, "i32");
+  cursor[1] = C.getValue(TRANSFER_BUFFER + 1 * SIZE_OF_INT, "i32");
+  cursor[2] = C.getValue(TRANSFER_BUFFER + 2 * SIZE_OF_INT, "i32");
+  cursor[3] = C.getValue(TRANSFER_BUFFER + 3 * SIZE_OF_INT, "i32");
+}
+__name(unmarshalTreeCursor, "unmarshalTreeCursor");
+function marshalPoint(address, point) {
+  C.setValue(address, point.row, "i32");
+  C.setValue(address + SIZE_OF_INT, point.column, "i32");
+}
+__name(marshalPoint, "marshalPoint");
+function unmarshalPoint(address) {
+  const result = {
+    row: C.getValue(address, "i32") >>> 0,
+    column: C.getValue(address + SIZE_OF_INT, "i32") >>> 0
+  };
+  return result;
+}
+__name(unmarshalPoint, "unmarshalPoint");
+function marshalRange(address, range) {
+  marshalPoint(address, range.startPosition);
+  address += SIZE_OF_POINT;
+  marshalPoint(address, range.endPosition);
+  address += SIZE_OF_POINT;
+  C.setValue(address, range.startIndex, "i32");
+  address += SIZE_OF_INT;
+  C.setValue(address, range.endIndex, "i32");
+  address += SIZE_OF_INT;
+}
+__name(marshalRange, "marshalRange");
+function unmarshalRange(address) {
+  const result = {};
+  result.startPosition = unmarshalPoint(address);
+  address += SIZE_OF_POINT;
+  result.endPosition = unmarshalPoint(address);
+  address += SIZE_OF_POINT;
+  result.startIndex = C.getValue(address, "i32") >>> 0;
+  address += SIZE_OF_INT;
+  result.endIndex = C.getValue(address, "i32") >>> 0;
+  return result;
+}
+__name(unmarshalRange, "unmarshalRange");
+function marshalEdit(edit, address = TRANSFER_BUFFER) {
+  marshalPoint(address, edit.startPosition);
+  address += SIZE_OF_POINT;
+  marshalPoint(address, edit.oldEndPosition);
+  address += SIZE_OF_POINT;
+  marshalPoint(address, edit.newEndPosition);
+  address += SIZE_OF_POINT;
+  C.setValue(address, edit.startIndex, "i32");
+  address += SIZE_OF_INT;
+  C.setValue(address, edit.oldEndIndex, "i32");
+  address += SIZE_OF_INT;
+  C.setValue(address, edit.newEndIndex, "i32");
+  address += SIZE_OF_INT;
+}
+__name(marshalEdit, "marshalEdit");
+function unmarshalLanguageMetadata(address) {
+  const result = {};
+  result.major_version = C.getValue(address, "i32");
+  address += SIZE_OF_INT;
+  result.minor_version = C.getValue(address, "i32");
+  address += SIZE_OF_INT;
+  result.field_count = C.getValue(address, "i32");
+  return result;
+}
+__name(unmarshalLanguageMetadata, "unmarshalLanguageMetadata");
+
+// src/query.ts
+var PREDICATE_STEP_TYPE_CAPTURE = 1;
+var PREDICATE_STEP_TYPE_STRING = 2;
+var QUERY_WORD_REGEX = /[\w-]+/g;
+var CaptureQuantifier = {
+  Zero: 0,
+  ZeroOrOne: 1,
+  ZeroOrMore: 2,
+  One: 3,
+  OneOrMore: 4
+};
+var isCaptureStep = /* @__PURE__ */ __name((step) => step.type === "capture", "isCaptureStep");
+var isStringStep = /* @__PURE__ */ __name((step) => step.type === "string", "isStringStep");
+var QueryErrorKind = {
+  Syntax: 1,
+  NodeName: 2,
+  FieldName: 3,
+  CaptureName: 4,
+  PatternStructure: 5
+};
+var QueryError = class _QueryError extends Error {
+  constructor(kind, info2, index, length) {
+    super(_QueryError.formatMessage(kind, info2));
+    this.kind = kind;
+    this.info = info2;
+    this.index = index;
+    this.length = length;
+    this.name = "QueryError";
+  }
+  static {
+    __name(this, "QueryError");
+  }
+  /** Formats an error message based on the error kind and info */
+  static formatMessage(kind, info2) {
+    switch (kind) {
+      case QueryErrorKind.NodeName:
+        return `Bad node name '${info2.word}'`;
+      case QueryErrorKind.FieldName:
+        return `Bad field name '${info2.word}'`;
+      case QueryErrorKind.CaptureName:
+        return `Bad capture name @${info2.word}`;
+      case QueryErrorKind.PatternStructure:
+        return `Bad pattern structure at offset ${info2.suffix}`;
+      case QueryErrorKind.Syntax:
+        return `Bad syntax at offset ${info2.suffix}`;
+    }
+  }
+};
+function parseAnyPredicate(steps, index, operator, textPredicates) {
+  if (steps.length !== 3) {
+    throw new Error(
+      `Wrong number of arguments to \`#${operator}\` predicate. Expected 2, got ${steps.length - 1}`
+    );
+  }
+  if (!isCaptureStep(steps[1])) {
+    throw new Error(
+      `First argument of \`#${operator}\` predicate must be a capture. Got "${steps[1].value}"`
+    );
+  }
+  const isPositive = operator === "eq?" || operator === "any-eq?";
+  const matchAll = !operator.startsWith("any-");
+  if (isCaptureStep(steps[2])) {
+    const captureName1 = steps[1].name;
+    const captureName2 = steps[2].name;
+    textPredicates[index].push((captures) => {
+      const nodes1 = [];
+      const nodes2 = [];
+      for (const c of captures) {
+        if (c.name === captureName1) nodes1.push(c.node);
+        if (c.name === captureName2) nodes2.push(c.node);
+      }
+      const compare = /* @__PURE__ */ __name((n1, n2, positive) => {
+        return positive ? n1.text === n2.text : n1.text !== n2.text;
+      }, "compare");
+      return matchAll ? nodes1.every((n1) => nodes2.some((n2) => compare(n1, n2, isPositive))) : nodes1.some((n1) => nodes2.some((n2) => compare(n1, n2, isPositive)));
+    });
+  } else {
+    const captureName = steps[1].name;
+    const stringValue = steps[2].value;
+    const matches = /* @__PURE__ */ __name((n) => n.text === stringValue, "matches");
+    const doesNotMatch = /* @__PURE__ */ __name((n) => n.text !== stringValue, "doesNotMatch");
+    textPredicates[index].push((captures) => {
+      const nodes = [];
+      for (const c of captures) {
+        if (c.name === captureName) nodes.push(c.node);
+      }
+      const test = isPositive ? matches : doesNotMatch;
+      return matchAll ? nodes.every(test) : nodes.some(test);
+    });
+  }
+}
+__name(parseAnyPredicate, "parseAnyPredicate");
+function parseMatchPredicate(steps, index, operator, textPredicates) {
+  if (steps.length !== 3) {
+    throw new Error(
+      `Wrong number of arguments to \`#${operator}\` predicate. Expected 2, got ${steps.length - 1}.`
+    );
+  }
+  if (steps[1].type !== "capture") {
+    throw new Error(
+      `First argument of \`#${operator}\` predicate must be a capture. Got "${steps[1].value}".`
+    );
+  }
+  if (steps[2].type !== "string") {
+    throw new Error(
+      `Second argument of \`#${operator}\` predicate must be a string. Got @${steps[2].name}.`
+    );
+  }
+  const isPositive = operator === "match?" || operator === "any-match?";
+  const matchAll = !operator.startsWith("any-");
+  const captureName = steps[1].name;
+  const regex = new RegExp(steps[2].value);
+  textPredicates[index].push((captures) => {
+    const nodes = [];
+    for (const c of captures) {
+      if (c.name === captureName) nodes.push(c.node.text);
+    }
+    const test = /* @__PURE__ */ __name((text, positive) => {
+      return positive ? regex.test(text) : !regex.test(text);
+    }, "test");
+    if (nodes.length === 0) return !isPositive;
+    return matchAll ? nodes.every((text) => test(text, isPositive)) : nodes.some((text) => test(text, isPositive));
+  });
+}
+__name(parseMatchPredicate, "parseMatchPredicate");
+function parseAnyOfPredicate(steps, index, operator, textPredicates) {
+  if (steps.length < 2) {
+    throw new Error(
+      `Wrong number of arguments to \`#${operator}\` predicate. Expected at least 1. Got ${steps.length - 1}.`
+    );
+  }
+  if (steps[1].type !== "capture") {
+    throw new Error(
+      `First argument of \`#${operator}\` predicate must be a capture. Got "${steps[1].value}".`
+    );
+  }
+  const isPositive = operator === "any-of?";
+  const captureName = steps[1].name;
+  const stringSteps = steps.slice(2);
+  if (!stringSteps.every(isStringStep)) {
+    throw new Error(
+      `Arguments to \`#${operator}\` predicate must be strings.".`
+    );
+  }
+  const values = stringSteps.map((s) => s.value);
+  textPredicates[index].push((captures) => {
+    const nodes = [];
+    for (const c of captures) {
+      if (c.name === captureName) nodes.push(c.node.text);
+    }
+    if (nodes.length === 0) return !isPositive;
+    return nodes.every((text) => values.includes(text)) === isPositive;
+  });
+}
+__name(parseAnyOfPredicate, "parseAnyOfPredicate");
+function parseIsPredicate(steps, index, operator, assertedProperties, refutedProperties) {
+  if (steps.length < 2 || steps.length > 3) {
+    throw new Error(
+      `Wrong number of arguments to \`#${operator}\` predicate. Expected 1 or 2. Got ${steps.length - 1}.`
+    );
+  }
+  if (!steps.every(isStringStep)) {
+    throw new Error(
+      `Arguments to \`#${operator}\` predicate must be strings.".`
+    );
+  }
+  const properties = operator === "is?" ? assertedProperties : refutedProperties;
+  if (!properties[index]) properties[index] = {};
+  properties[index][steps[1].value] = steps[2]?.value ?? null;
+}
+__name(parseIsPredicate, "parseIsPredicate");
+function parseSetDirective(steps, index, setProperties) {
+  if (steps.length < 2 || steps.length > 3) {
+    throw new Error(`Wrong number of arguments to \`#set!\` predicate. Expected 1 or 2. Got ${steps.length - 1}.`);
+  }
+  if (!steps.every(isStringStep)) {
+    throw new Error(`Arguments to \`#set!\` predicate must be strings.".`);
+  }
+  if (!setProperties[index]) setProperties[index] = {};
+  setProperties[index][steps[1].value] = steps[2]?.value ?? null;
+}
+__name(parseSetDirective, "parseSetDirective");
+function parsePattern(index, stepType, stepValueId, captureNames, stringValues, steps, textPredicates, predicates, setProperties, assertedProperties, refutedProperties) {
+  if (stepType === PREDICATE_STEP_TYPE_CAPTURE) {
+    const name2 = captureNames[stepValueId];
+    steps.push({ type: "capture", name: name2 });
+  } else if (stepType === PREDICATE_STEP_TYPE_STRING) {
+    steps.push({ type: "string", value: stringValues[stepValueId] });
+  } else if (steps.length > 0) {
+    if (steps[0].type !== "string") {
+      throw new Error("Predicates must begin with a literal value");
+    }
+    const operator = steps[0].value;
+    switch (operator) {
+      case "any-not-eq?":
+      case "not-eq?":
+      case "any-eq?":
+      case "eq?":
+        parseAnyPredicate(steps, index, operator, textPredicates);
+        break;
+      case "any-not-match?":
+      case "not-match?":
+      case "any-match?":
+      case "match?":
+        parseMatchPredicate(steps, index, operator, textPredicates);
+        break;
+      case "not-any-of?":
+      case "any-of?":
+        parseAnyOfPredicate(steps, index, operator, textPredicates);
+        break;
+      case "is?":
+      case "is-not?":
+        parseIsPredicate(steps, index, operator, assertedProperties, refutedProperties);
+        break;
+      case "set!":
+        parseSetDirective(steps, index, setProperties);
+        break;
+      default:
+        predicates[index].push({ operator, operands: steps.slice(1) });
+    }
+    steps.length = 0;
+  }
+}
+__name(parsePattern, "parsePattern");
+var Query = class {
+  static {
+    __name(this, "Query");
+  }
+  /** @internal */
+  [0] = 0;
+  // Internal handle for WASM
+  /** @internal */
+  exceededMatchLimit;
+  /** @internal */
+  textPredicates;
+  /** The names of the captures used in the query. */
+  captureNames;
+  /** The quantifiers of the captures used in the query. */
+  captureQuantifiers;
+  /**
+   * The other user-defined predicates associated with the given index.
+   *
+   * This includes predicates with operators other than:
+   * - `match?`
+   * - `eq?` and `not-eq?`
+   * - `any-of?` and `not-any-of?`
+   * - `is?` and `is-not?`
+   * - `set!`
+   */
+  predicates;
+  /** The properties for predicates with the operator `set!`. */
+  setProperties;
+  /** The properties for predicates with the operator `is?`. */
+  assertedProperties;
+  /** The properties for predicates with the operator `is-not?`. */
+  refutedProperties;
+  /** The maximum number of in-progress matches for this cursor. */
+  matchLimit;
+  /**
+   * Create a new query from a string containing one or more S-expression
+   * patterns.
+   *
+   * The query is associated with a particular language, and can only be run
+   * on syntax nodes parsed with that language. References to Queries can be
+   * shared between multiple threads.
+   *
+   * @link {@see https://tree-sitter.github.io/tree-sitter/using-parsers/queries}
+   */
+  constructor(language, source) {
+    const sourceLength = C.lengthBytesUTF8(source);
+    const sourceAddress = C._malloc(sourceLength + 1);
+    C.stringToUTF8(source, sourceAddress, sourceLength + 1);
+    const address = C._ts_query_new(
+      language[0],
+      sourceAddress,
+      sourceLength,
+      TRANSFER_BUFFER,
+      TRANSFER_BUFFER + SIZE_OF_INT
+    );
+    if (!address) {
+      const errorId = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+      const errorByte = C.getValue(TRANSFER_BUFFER, "i32");
+      const errorIndex = C.UTF8ToString(sourceAddress, errorByte).length;
+      const suffix = source.slice(errorIndex, errorIndex + 100).split("\n")[0];
+      const word = suffix.match(QUERY_WORD_REGEX)?.[0] ?? "";
+      C._free(sourceAddress);
+      switch (errorId) {
+        case QueryErrorKind.Syntax:
+          throw new QueryError(QueryErrorKind.Syntax, { suffix: `${errorIndex}: '${suffix}'...` }, errorIndex, 0);
+        case QueryErrorKind.NodeName:
+          throw new QueryError(errorId, { word }, errorIndex, word.length);
+        case QueryErrorKind.FieldName:
+          throw new QueryError(errorId, { word }, errorIndex, word.length);
+        case QueryErrorKind.CaptureName:
+          throw new QueryError(errorId, { word }, errorIndex, word.length);
+        case QueryErrorKind.PatternStructure:
+          throw new QueryError(errorId, { suffix: `${errorIndex}: '${suffix}'...` }, errorIndex, 0);
+      }
+    }
+    const stringCount = C._ts_query_string_count(address);
+    const captureCount = C._ts_query_capture_count(address);
+    const patternCount = C._ts_query_pattern_count(address);
+    const captureNames = new Array(captureCount);
+    const captureQuantifiers = new Array(patternCount);
+    const stringValues = new Array(stringCount);
+    for (let i2 = 0; i2 < captureCount; i2++) {
+      const nameAddress = C._ts_query_capture_name_for_id(
+        address,
+        i2,
+        TRANSFER_BUFFER
+      );
+      const nameLength = C.getValue(TRANSFER_BUFFER, "i32");
+      captureNames[i2] = C.UTF8ToString(nameAddress, nameLength);
+    }
+    for (let i2 = 0; i2 < patternCount; i2++) {
+      const captureQuantifiersArray = new Array(captureCount);
+      for (let j = 0; j < captureCount; j++) {
+        const quantifier = C._ts_query_capture_quantifier_for_id(address, i2, j);
+        captureQuantifiersArray[j] = quantifier;
+      }
+      captureQuantifiers[i2] = captureQuantifiersArray;
+    }
+    for (let i2 = 0; i2 < stringCount; i2++) {
+      const valueAddress = C._ts_query_string_value_for_id(
+        address,
+        i2,
+        TRANSFER_BUFFER
+      );
+      const nameLength = C.getValue(TRANSFER_BUFFER, "i32");
+      stringValues[i2] = C.UTF8ToString(valueAddress, nameLength);
+    }
+    const setProperties = new Array(patternCount);
+    const assertedProperties = new Array(patternCount);
+    const refutedProperties = new Array(patternCount);
+    const predicates = new Array(patternCount);
+    const textPredicates = new Array(patternCount);
+    for (let i2 = 0; i2 < patternCount; i2++) {
+      const predicatesAddress = C._ts_query_predicates_for_pattern(address, i2, TRANSFER_BUFFER);
+      const stepCount = C.getValue(TRANSFER_BUFFER, "i32");
+      predicates[i2] = [];
+      textPredicates[i2] = [];
+      const steps = new Array();
+      let stepAddress = predicatesAddress;
+      for (let j = 0; j < stepCount; j++) {
+        const stepType = C.getValue(stepAddress, "i32");
+        stepAddress += SIZE_OF_INT;
+        const stepValueId = C.getValue(stepAddress, "i32");
+        stepAddress += SIZE_OF_INT;
+        parsePattern(
+          i2,
+          stepType,
+          stepValueId,
+          captureNames,
+          stringValues,
+          steps,
+          textPredicates,
+          predicates,
+          setProperties,
+          assertedProperties,
+          refutedProperties
+        );
+      }
+      Object.freeze(textPredicates[i2]);
+      Object.freeze(predicates[i2]);
+      Object.freeze(setProperties[i2]);
+      Object.freeze(assertedProperties[i2]);
+      Object.freeze(refutedProperties[i2]);
+    }
+    C._free(sourceAddress);
+    this[0] = address;
+    this.captureNames = captureNames;
+    this.captureQuantifiers = captureQuantifiers;
+    this.textPredicates = textPredicates;
+    this.predicates = predicates;
+    this.setProperties = setProperties;
+    this.assertedProperties = assertedProperties;
+    this.refutedProperties = refutedProperties;
+    this.exceededMatchLimit = false;
+  }
+  /** Delete the query, freeing its resources. */
+  delete() {
+    C._ts_query_delete(this[0]);
+    this[0] = 0;
+  }
+  /**
+   * Iterate over all of the matches in the order that they were found.
+   *
+   * Each match contains the index of the pattern that matched, and a list of
+   * captures. Because multiple patterns can match the same set of nodes,
+   * one match may contain captures that appear *before* some of the
+   * captures from a previous match.
+   *
+   * @param {Node} node - The node to execute the query on.
+   *
+   * @param {QueryOptions} options - Options for query execution.
+   */
+  matches(node, options = {}) {
+    const startPosition = options.startPosition ?? ZERO_POINT;
+    const endPosition = options.endPosition ?? ZERO_POINT;
+    const startIndex = options.startIndex ?? 0;
+    const endIndex = options.endIndex ?? 0;
+    const matchLimit = options.matchLimit ?? 4294967295;
+    const maxStartDepth = options.maxStartDepth ?? 4294967295;
+    const timeoutMicros = options.timeoutMicros ?? 0;
+    const progressCallback = options.progressCallback;
+    if (typeof matchLimit !== "number") {
+      throw new Error("Arguments must be numbers");
+    }
+    this.matchLimit = matchLimit;
+    if (endIndex !== 0 && startIndex > endIndex) {
+      throw new Error("`startIndex` cannot be greater than `endIndex`");
+    }
+    if (endPosition !== ZERO_POINT && (startPosition.row > endPosition.row || startPosition.row === endPosition.row && startPosition.column > endPosition.column)) {
+      throw new Error("`startPosition` cannot be greater than `endPosition`");
+    }
+    if (progressCallback) {
+      C.currentQueryProgressCallback = progressCallback;
+    }
+    marshalNode(node);
+    C._ts_query_matches_wasm(
+      this[0],
+      node.tree[0],
+      startPosition.row,
+      startPosition.column,
+      endPosition.row,
+      endPosition.column,
+      startIndex,
+      endIndex,
+      matchLimit,
+      maxStartDepth,
+      timeoutMicros
+    );
+    const rawCount = C.getValue(TRANSFER_BUFFER, "i32");
+    const startAddress = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+    const didExceedMatchLimit = C.getValue(TRANSFER_BUFFER + 2 * SIZE_OF_INT, "i32");
+    const result = new Array(rawCount);
+    this.exceededMatchLimit = Boolean(didExceedMatchLimit);
+    let filteredCount = 0;
+    let address = startAddress;
+    for (let i2 = 0; i2 < rawCount; i2++) {
+      const patternIndex = C.getValue(address, "i32");
+      address += SIZE_OF_INT;
+      const captureCount = C.getValue(address, "i32");
+      address += SIZE_OF_INT;
+      const captures = new Array(captureCount);
+      address = unmarshalCaptures(this, node.tree, address, patternIndex, captures);
+      if (this.textPredicates[patternIndex].every((p) => p(captures))) {
+        result[filteredCount] = { pattern: patternIndex, patternIndex, captures };
+        const setProperties = this.setProperties[patternIndex];
+        result[filteredCount].setProperties = setProperties;
+        const assertedProperties = this.assertedProperties[patternIndex];
+        result[filteredCount].assertedProperties = assertedProperties;
+        const refutedProperties = this.refutedProperties[patternIndex];
+        result[filteredCount].refutedProperties = refutedProperties;
+        filteredCount++;
+      }
+    }
+    result.length = filteredCount;
+    C._free(startAddress);
+    C.currentQueryProgressCallback = null;
+    return result;
+  }
+  /**
+   * Iterate over all of the individual captures in the order that they
+   * appear.
+   *
+   * This is useful if you don't care about which pattern matched, and just
+   * want a single, ordered sequence of captures.
+   *
+   * @param {Node} node - The node to execute the query on.
+   *
+   * @param {QueryOptions} options - Options for query execution.
+   */
+  captures(node, options = {}) {
+    const startPosition = options.startPosition ?? ZERO_POINT;
+    const endPosition = options.endPosition ?? ZERO_POINT;
+    const startIndex = options.startIndex ?? 0;
+    const endIndex = options.endIndex ?? 0;
+    const matchLimit = options.matchLimit ?? 4294967295;
+    const maxStartDepth = options.maxStartDepth ?? 4294967295;
+    const timeoutMicros = options.timeoutMicros ?? 0;
+    const progressCallback = options.progressCallback;
+    if (typeof matchLimit !== "number") {
+      throw new Error("Arguments must be numbers");
+    }
+    this.matchLimit = matchLimit;
+    if (endIndex !== 0 && startIndex > endIndex) {
+      throw new Error("`startIndex` cannot be greater than `endIndex`");
+    }
+    if (endPosition !== ZERO_POINT && (startPosition.row > endPosition.row || startPosition.row === endPosition.row && startPosition.column > endPosition.column)) {
+      throw new Error("`startPosition` cannot be greater than `endPosition`");
+    }
+    if (progressCallback) {
+      C.currentQueryProgressCallback = progressCallback;
+    }
+    marshalNode(node);
+    C._ts_query_captures_wasm(
+      this[0],
+      node.tree[0],
+      startPosition.row,
+      startPosition.column,
+      endPosition.row,
+      endPosition.column,
+      startIndex,
+      endIndex,
+      matchLimit,
+      maxStartDepth,
+      timeoutMicros
+    );
+    const count = C.getValue(TRANSFER_BUFFER, "i32");
+    const startAddress = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+    const didExceedMatchLimit = C.getValue(TRANSFER_BUFFER + 2 * SIZE_OF_INT, "i32");
+    const result = new Array();
+    this.exceededMatchLimit = Boolean(didExceedMatchLimit);
+    const captures = new Array();
+    let address = startAddress;
+    for (let i2 = 0; i2 < count; i2++) {
+      const patternIndex = C.getValue(address, "i32");
+      address += SIZE_OF_INT;
+      const captureCount = C.getValue(address, "i32");
+      address += SIZE_OF_INT;
+      const captureIndex = C.getValue(address, "i32");
+      address += SIZE_OF_INT;
+      captures.length = captureCount;
+      address = unmarshalCaptures(this, node.tree, address, patternIndex, captures);
+      if (this.textPredicates[patternIndex].every((p) => p(captures))) {
+        const capture = captures[captureIndex];
+        const setProperties = this.setProperties[patternIndex];
+        capture.setProperties = setProperties;
+        const assertedProperties = this.assertedProperties[patternIndex];
+        capture.assertedProperties = assertedProperties;
+        const refutedProperties = this.refutedProperties[patternIndex];
+        capture.refutedProperties = refutedProperties;
+        result.push(capture);
+      }
+    }
+    C._free(startAddress);
+    C.currentQueryProgressCallback = null;
+    return result;
+  }
+  /** Get the predicates for a given pattern. */
+  predicatesForPattern(patternIndex) {
+    return this.predicates[patternIndex];
+  }
+  /**
+   * Disable a certain capture within a query.
+   *
+   * This prevents the capture from being returned in matches, and also
+   * avoids any resource usage associated with recording the capture.
+   */
+  disableCapture(captureName) {
+    const captureNameLength = C.lengthBytesUTF8(captureName);
+    const captureNameAddress = C._malloc(captureNameLength + 1);
+    C.stringToUTF8(captureName, captureNameAddress, captureNameLength + 1);
+    C._ts_query_disable_capture(this[0], captureNameAddress, captureNameLength);
+    C._free(captureNameAddress);
+  }
+  /**
+   * Disable a certain pattern within a query.
+   *
+   * This prevents the pattern from matching, and also avoids any resource
+   * usage associated with the pattern. This throws an error if the pattern
+   * index is out of bounds.
+   */
+  disablePattern(patternIndex) {
+    if (patternIndex >= this.predicates.length) {
+      throw new Error(
+        `Pattern index is ${patternIndex} but the pattern count is ${this.predicates.length}`
+      );
+    }
+    C._ts_query_disable_pattern(this[0], patternIndex);
+  }
+  /**
+   * Check if, on its last execution, this cursor exceeded its maximum number
+   * of in-progress matches.
+   */
+  didExceedMatchLimit() {
+    return this.exceededMatchLimit;
+  }
+  /** Get the byte offset where the given pattern starts in the query's source. */
+  startIndexForPattern(patternIndex) {
+    if (patternIndex >= this.predicates.length) {
+      throw new Error(
+        `Pattern index is ${patternIndex} but the pattern count is ${this.predicates.length}`
+      );
+    }
+    return C._ts_query_start_byte_for_pattern(this[0], patternIndex);
+  }
+  /** Get the byte offset where the given pattern ends in the query's source. */
+  endIndexForPattern(patternIndex) {
+    if (patternIndex >= this.predicates.length) {
+      throw new Error(
+        `Pattern index is ${patternIndex} but the pattern count is ${this.predicates.length}`
+      );
+    }
+    return C._ts_query_end_byte_for_pattern(this[0], patternIndex);
+  }
+  /** Get the number of patterns in the query. */
+  patternCount() {
+    return C._ts_query_pattern_count(this[0]);
+  }
+  /** Get the index for a given capture name. */
+  captureIndexForName(captureName) {
+    return this.captureNames.indexOf(captureName);
+  }
+  /** Check if a given pattern within a query has a single root node. */
+  isPatternRooted(patternIndex) {
+    return C._ts_query_is_pattern_rooted(this[0], patternIndex) === 1;
+  }
+  /** Check if a given pattern within a query has a single root node. */
+  isPatternNonLocal(patternIndex) {
+    return C._ts_query_is_pattern_non_local(this[0], patternIndex) === 1;
+  }
+  /**
+   * Check if a given step in a query is 'definite'.
+   *
+   * A query step is 'definite' if its parent pattern will be guaranteed to
+   * match successfully once it reaches the step.
+   */
+  isPatternGuaranteedAtStep(byteIndex) {
+    return C._ts_query_is_pattern_guaranteed_at_step(this[0], byteIndex) === 1;
+  }
+};
+
+// src/language.ts
+var LANGUAGE_FUNCTION_REGEX = /^tree_sitter_\w+$/;
+var Language = class _Language {
+  static {
+    __name(this, "Language");
+  }
+  /** @internal */
+  [0] = 0;
+  // Internal handle for WASM
+  /**
+   * A list of all node types in the language. The index of each type in this
+   * array is its node type id.
+   */
+  types;
+  /**
+   * A list of all field names in the language. The index of each field name in
+   * this array is its field id.
+   */
+  fields;
+  /** @internal */
+  constructor(internal, address) {
+    assertInternal(internal);
+    this[0] = address;
+    this.types = new Array(C._ts_language_symbol_count(this[0]));
+    for (let i2 = 0, n = this.types.length; i2 < n; i2++) {
+      if (C._ts_language_symbol_type(this[0], i2) < 2) {
+        this.types[i2] = C.UTF8ToString(C._ts_language_symbol_name(this[0], i2));
+      }
+    }
+    this.fields = new Array(C._ts_language_field_count(this[0]) + 1);
+    for (let i2 = 0, n = this.fields.length; i2 < n; i2++) {
+      const fieldName = C._ts_language_field_name_for_id(this[0], i2);
+      if (fieldName !== 0) {
+        this.fields[i2] = C.UTF8ToString(fieldName);
+      } else {
+        this.fields[i2] = null;
+      }
+    }
+  }
+  /**
+   * Gets the name of the language.
+   */
+  get name() {
+    const ptr = C._ts_language_name(this[0]);
+    if (ptr === 0) return null;
+    return C.UTF8ToString(ptr);
+  }
+  /**
+   * @deprecated since version 0.25.0, use {@link Language#abiVersion} instead
+   * Gets the version of the language.
+   */
+  get version() {
+    return C._ts_language_version(this[0]);
+  }
+  /**
+   * Gets the ABI version of the language.
+   */
+  get abiVersion() {
+    return C._ts_language_abi_version(this[0]);
+  }
+  /**
+  * Get the metadata for this language. This information is generated by the
+  * CLI, and relies on the language author providing the correct metadata in
+  * the language's `tree-sitter.json` file.
+  */
+  get metadata() {
+    C._ts_language_metadata(this[0]);
+    const length = C.getValue(TRANSFER_BUFFER, "i32");
+    const address = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+    if (length === 0) return null;
+    return unmarshalLanguageMetadata(address);
+  }
+  /**
+   * Gets the number of fields in the language.
+   */
+  get fieldCount() {
+    return this.fields.length - 1;
+  }
+  /**
+   * Gets the number of states in the language.
+   */
+  get stateCount() {
+    return C._ts_language_state_count(this[0]);
+  }
+  /**
+   * Get the field id for a field name.
+   */
+  fieldIdForName(fieldName) {
+    const result = this.fields.indexOf(fieldName);
+    return result !== -1 ? result : null;
+  }
+  /**
+   * Get the field name for a field id.
+   */
+  fieldNameForId(fieldId) {
+    return this.fields[fieldId] ?? null;
+  }
+  /**
+   * Get the node type id for a node type name.
+   */
+  idForNodeType(type, named) {
+    const typeLength = C.lengthBytesUTF8(type);
+    const typeAddress = C._malloc(typeLength + 1);
+    C.stringToUTF8(type, typeAddress, typeLength + 1);
+    const result = C._ts_language_symbol_for_name(this[0], typeAddress, typeLength, named ? 1 : 0);
+    C._free(typeAddress);
+    return result || null;
+  }
+  /**
+   * Gets the number of node types in the language.
+   */
+  get nodeTypeCount() {
+    return C._ts_language_symbol_count(this[0]);
+  }
+  /**
+   * Get the node type name for a node type id.
+   */
+  nodeTypeForId(typeId) {
+    const name2 = C._ts_language_symbol_name(this[0], typeId);
+    return name2 ? C.UTF8ToString(name2) : null;
+  }
+  /**
+   * Check if a node type is named.
+   *
+   * @see {@link https://tree-sitter.github.io/tree-sitter/using-parsers/2-basic-parsing.html#named-vs-anonymous-nodes}
+   */
+  nodeTypeIsNamed(typeId) {
+    return C._ts_language_type_is_named_wasm(this[0], typeId) ? true : false;
+  }
+  /**
+   * Check if a node type is visible.
+   */
+  nodeTypeIsVisible(typeId) {
+    return C._ts_language_type_is_visible_wasm(this[0], typeId) ? true : false;
+  }
+  /**
+   * Get the supertypes ids of this language.
+   *
+   * @see {@link https://tree-sitter.github.io/tree-sitter/using-parsers/6-static-node-types.html?highlight=supertype#supertype-nodes}
+   */
+  get supertypes() {
+    C._ts_language_supertypes_wasm(this[0]);
+    const count = C.getValue(TRANSFER_BUFFER, "i32");
+    const buffer = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+    const result = new Array(count);
+    if (count > 0) {
+      let address = buffer;
+      for (let i2 = 0; i2 < count; i2++) {
+        result[i2] = C.getValue(address, "i16");
+        address += SIZE_OF_SHORT;
+      }
+    }
+    return result;
+  }
+  /**
+   * Get the subtype ids for a given supertype node id.
+   */
+  subtypes(supertype) {
+    C._ts_language_subtypes_wasm(this[0], supertype);
+    const count = C.getValue(TRANSFER_BUFFER, "i32");
+    const buffer = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+    const result = new Array(count);
+    if (count > 0) {
+      let address = buffer;
+      for (let i2 = 0; i2 < count; i2++) {
+        result[i2] = C.getValue(address, "i16");
+        address += SIZE_OF_SHORT;
+      }
+    }
+    return result;
+  }
+  /**
+   * Get the next state id for a given state id and node type id.
+   */
+  nextState(stateId, typeId) {
+    return C._ts_language_next_state(this[0], stateId, typeId);
+  }
+  /**
+   * Create a new lookahead iterator for this language and parse state.
+   *
+   * This returns `null` if state is invalid for this language.
+   *
+   * Iterating {@link LookaheadIterator} will yield valid symbols in the given
+   * parse state. Newly created lookahead iterators will return the `ERROR`
+   * symbol from {@link LookaheadIterator#currentType}.
+   *
+   * Lookahead iterators can be useful for generating suggestions and improving
+   * syntax error diagnostics. To get symbols valid in an `ERROR` node, use the
+   * lookahead iterator on its first leaf node state. For `MISSING` nodes, a
+   * lookahead iterator created on the previous non-extra leaf node may be
+   * appropriate.
+   */
+  lookaheadIterator(stateId) {
+    const address = C._ts_lookahead_iterator_new(this[0], stateId);
+    if (address) return new LookaheadIterator(INTERNAL, address, this);
+    return null;
+  }
+  /**
+   * @deprecated since version 0.25.0, call `new` on a {@link Query} instead
+   *
+   * Create a new query from a string containing one or more S-expression
+   * patterns.
+   *
+   * The query is associated with a particular language, and can only be run
+   * on syntax nodes parsed with that language. References to Queries can be
+   * shared between multiple threads.
+   *
+   * @link {@see https://tree-sitter.github.io/tree-sitter/using-parsers/queries}
+   */
+  query(source) {
+    console.warn("Language.query is deprecated. Use new Query(language, source) instead.");
+    return new Query(this, source);
+  }
+  /**
+   * Load a language from a WebAssembly module.
+   * The module can be provided as a path to a file or as a buffer.
+   */
+  static async load(input) {
+    let bytes;
+    if (input instanceof Uint8Array) {
+      bytes = Promise.resolve(input);
+    } else {
+      if (globalThis.process?.versions.node) {
+        const fs2 = await import("fs/promises");
+        bytes = fs2.readFile(input);
+      } else {
+        bytes = fetch(input).then((response) => response.arrayBuffer().then((buffer) => {
+          if (response.ok) {
+            return new Uint8Array(buffer);
+          } else {
+            const body2 = new TextDecoder("utf-8").decode(buffer);
+            throw new Error(`Language.load failed with status ${response.status}.
+
+${body2}`);
+          }
+        }));
+      }
+    }
+    const mod = await C.loadWebAssemblyModule(await bytes, { loadAsync: true });
+    const symbolNames = Object.keys(mod);
+    const functionName = symbolNames.find((key) => LANGUAGE_FUNCTION_REGEX.test(key) && !key.includes("external_scanner_"));
+    if (!functionName) {
+      console.log(`Couldn't find language function in WASM file. Symbols:
+${JSON.stringify(symbolNames, null, 2)}`);
+      throw new Error("Language.load failed: no language function found in WASM file");
+    }
+    const languageAddress = mod[functionName]();
+    return new _Language(INTERNAL, languageAddress);
+  }
+};
+
+// lib/tree-sitter.mjs
+var Module2 = (() => {
+  var _scriptName = import.meta.url;
+  return async function(moduleArg = {}) {
+    var moduleRtn;
+    var Module = moduleArg;
+    var readyPromiseResolve, readyPromiseReject;
+    var readyPromise = new Promise((resolve, reject) => {
+      readyPromiseResolve = resolve;
+      readyPromiseReject = reject;
+    });
+    var ENVIRONMENT_IS_WEB = typeof window == "object";
+    var ENVIRONMENT_IS_WORKER = typeof WorkerGlobalScope != "undefined";
+    var ENVIRONMENT_IS_NODE = typeof process == "object" && typeof process.versions == "object" && typeof process.versions.node == "string" && process.type != "renderer";
+    var ENVIRONMENT_IS_SHELL = !ENVIRONMENT_IS_WEB && !ENVIRONMENT_IS_NODE && !ENVIRONMENT_IS_WORKER;
+    if (ENVIRONMENT_IS_NODE) {
+      const { createRequire } = await import("module");
+      var require = createRequire(import.meta.url);
+    }
+    Module.currentQueryProgressCallback = null;
+    Module.currentProgressCallback = null;
+    Module.currentLogCallback = null;
+    Module.currentParseCallback = null;
+    var moduleOverrides = Object.assign({}, Module);
+    var arguments_ = [];
+    var thisProgram = "./this.program";
+    var quit_ = /* @__PURE__ */ __name((status, toThrow) => {
+      throw toThrow;
+    }, "quit_");
+    var scriptDirectory = "";
+    function locateFile(path) {
+      if (Module["locateFile"]) {
+        return Module["locateFile"](path, scriptDirectory);
+      }
+      return scriptDirectory + path;
+    }
+    __name(locateFile, "locateFile");
+    var readAsync, readBinary;
+    if (ENVIRONMENT_IS_NODE) {
+      var fs = require("fs");
+      var nodePath = require("path");
+      if (!import.meta.url.startsWith("data:")) {
+        scriptDirectory = nodePath.dirname(require("url").fileURLToPath(import.meta.url)) + "/";
+      }
+      readBinary = /* @__PURE__ */ __name((filename) => {
+        filename = isFileURI(filename) ? new URL(filename) : filename;
+        var ret = fs.readFileSync(filename);
+        return ret;
+      }, "readBinary");
+      readAsync = /* @__PURE__ */ __name(async (filename, binary2 = true) => {
+        filename = isFileURI(filename) ? new URL(filename) : filename;
+        var ret = fs.readFileSync(filename, binary2 ? void 0 : "utf8");
+        return ret;
+      }, "readAsync");
+      if (!Module["thisProgram"] && process.argv.length > 1) {
+        thisProgram = process.argv[1].replace(/\\/g, "/");
+      }
+      arguments_ = process.argv.slice(2);
+      quit_ = /* @__PURE__ */ __name((status, toThrow) => {
+        process.exitCode = status;
+        throw toThrow;
+      }, "quit_");
+    } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) {
+      if (ENVIRONMENT_IS_WORKER) {
+        scriptDirectory = self.location.href;
+      } else if (typeof document != "undefined" && document.currentScript) {
+        scriptDirectory = document.currentScript.src;
+      }
+      if (_scriptName) {
+        scriptDirectory = _scriptName;
+      }
+      if (scriptDirectory.startsWith("blob:")) {
+        scriptDirectory = "";
+      } else {
+        scriptDirectory = scriptDirectory.slice(0, scriptDirectory.replace(/[?#].*/, "").lastIndexOf("/") + 1);
+      }
+      {
+        if (ENVIRONMENT_IS_WORKER) {
+          readBinary = /* @__PURE__ */ __name((url) => {
+            var xhr = new XMLHttpRequest();
+            xhr.open("GET", url, false);
+            xhr.responseType = "arraybuffer";
+            xhr.send(null);
+            return new Uint8Array(
+              /** @type{!ArrayBuffer} */
+              xhr.response
+            );
+          }, "readBinary");
+        }
+        readAsync = /* @__PURE__ */ __name(async (url) => {
+          if (isFileURI(url)) {
+            return new Promise((resolve, reject) => {
+              var xhr = new XMLHttpRequest();
+              xhr.open("GET", url, true);
+              xhr.responseType = "arraybuffer";
+              xhr.onload = () => {
+                if (xhr.status == 200 || xhr.status == 0 && xhr.response) {
+                  resolve(xhr.response);
+                  return;
+                }
+                reject(xhr.status);
+              };
+              xhr.onerror = reject;
+              xhr.send(null);
+            });
+          }
+          var response = await fetch(url, {
+            credentials: "same-origin"
+          });
+          if (response.ok) {
+            return response.arrayBuffer();
+          }
+          throw new Error(response.status + " : " + response.url);
+        }, "readAsync");
+      }
+    } else {
+    }
+    var out = Module["print"] || console.log.bind(console);
+    var err = Module["printErr"] || console.error.bind(console);
+    Object.assign(Module, moduleOverrides);
+    moduleOverrides = null;
+    if (Module["arguments"]) arguments_ = Module["arguments"];
+    if (Module["thisProgram"]) thisProgram = Module["thisProgram"];
+    var dynamicLibraries = Module["dynamicLibraries"] || [];
+    var wasmBinary = Module["wasmBinary"];
+    var wasmMemory;
+    var ABORT = false;
+    var EXITSTATUS;
+    function assert(condition, text) {
+      if (!condition) {
+        abort(text);
+      }
+    }
+    __name(assert, "assert");
+    var HEAP, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAP64, HEAPU64, HEAPF64;
+    var HEAP_DATA_VIEW;
+    var runtimeInitialized = false;
+    var isFileURI = /* @__PURE__ */ __name((filename) => filename.startsWith("file://"), "isFileURI");
+    function updateMemoryViews() {
+      var b = wasmMemory.buffer;
+      Module["HEAP_DATA_VIEW"] = HEAP_DATA_VIEW = new DataView(b);
+      Module["HEAP8"] = HEAP8 = new Int8Array(b);
+      Module["HEAP16"] = HEAP16 = new Int16Array(b);
+      Module["HEAPU8"] = HEAPU8 = new Uint8Array(b);
+      Module["HEAPU16"] = HEAPU16 = new Uint16Array(b);
+      Module["HEAP32"] = HEAP32 = new Int32Array(b);
+      Module["HEAPU32"] = HEAPU32 = new Uint32Array(b);
+      Module["HEAPF32"] = HEAPF32 = new Float32Array(b);
+      Module["HEAPF64"] = HEAPF64 = new Float64Array(b);
+      Module["HEAP64"] = HEAP64 = new BigInt64Array(b);
+      Module["HEAPU64"] = HEAPU64 = new BigUint64Array(b);
+    }
+    __name(updateMemoryViews, "updateMemoryViews");
+    if (Module["wasmMemory"]) {
+      wasmMemory = Module["wasmMemory"];
+    } else {
+      var INITIAL_MEMORY = Module["INITIAL_MEMORY"] || 33554432;
+      wasmMemory = new WebAssembly.Memory({
+        "initial": INITIAL_MEMORY / 65536,
+        // In theory we should not need to emit the maximum if we want "unlimited"
+        // or 4GB of memory, but VMs error on that atm, see
+        // https://github.com/emscripten-core/emscripten/issues/14130
+        // And in the pthreads case we definitely need to emit a maximum. So
+        // always emit one.
+        "maximum": 32768
+      });
+    }
+    updateMemoryViews();
+    var __RELOC_FUNCS__ = [];
+    function preRun() {
+      if (Module["preRun"]) {
+        if (typeof Module["preRun"] == "function") Module["preRun"] = [Module["preRun"]];
+        while (Module["preRun"].length) {
+          addOnPreRun(Module["preRun"].shift());
+        }
+      }
+      callRuntimeCallbacks(onPreRuns);
+    }
+    __name(preRun, "preRun");
+    function initRuntime() {
+      runtimeInitialized = true;
+      callRuntimeCallbacks(__RELOC_FUNCS__);
+      wasmExports["__wasm_call_ctors"]();
+      callRuntimeCallbacks(onPostCtors);
+    }
+    __name(initRuntime, "initRuntime");
+    function preMain() {
+    }
+    __name(preMain, "preMain");
+    function postRun() {
+      if (Module["postRun"]) {
+        if (typeof Module["postRun"] == "function") Module["postRun"] = [Module["postRun"]];
+        while (Module["postRun"].length) {
+          addOnPostRun(Module["postRun"].shift());
+        }
+      }
+      callRuntimeCallbacks(onPostRuns);
+    }
+    __name(postRun, "postRun");
+    var runDependencies = 0;
+    var dependenciesFulfilled = null;
+    function getUniqueRunDependency(id) {
+      return id;
+    }
+    __name(getUniqueRunDependency, "getUniqueRunDependency");
+    function addRunDependency(id) {
+      runDependencies++;
+      Module["monitorRunDependencies"]?.(runDependencies);
+    }
+    __name(addRunDependency, "addRunDependency");
+    function removeRunDependency(id) {
+      runDependencies--;
+      Module["monitorRunDependencies"]?.(runDependencies);
+      if (runDependencies == 0) {
+        if (dependenciesFulfilled) {
+          var callback = dependenciesFulfilled;
+          dependenciesFulfilled = null;
+          callback();
+        }
+      }
+    }
+    __name(removeRunDependency, "removeRunDependency");
+    function abort(what) {
+      Module["onAbort"]?.(what);
+      what = "Aborted(" + what + ")";
+      err(what);
+      ABORT = true;
+      what += ". Build with -sASSERTIONS for more info.";
+      var e = new WebAssembly.RuntimeError(what);
+      readyPromiseReject(e);
+      throw e;
+    }
+    __name(abort, "abort");
+    var wasmBinaryFile;
+    function findWasmBinary() {
+      if (Module["locateFile"]) {
+        return locateFile("tree-sitter.wasm");
+      }
+      return new URL("tree-sitter.wasm", import.meta.url).href;
+    }
+    __name(findWasmBinary, "findWasmBinary");
+    function getBinarySync(file) {
+      if (file == wasmBinaryFile && wasmBinary) {
+        return new Uint8Array(wasmBinary);
+      }
+      if (readBinary) {
+        return readBinary(file);
+      }
+      throw "both async and sync fetching of the wasm failed";
+    }
+    __name(getBinarySync, "getBinarySync");
+    async function getWasmBinary(binaryFile) {
+      if (!wasmBinary) {
+        try {
+          var response = await readAsync(binaryFile);
+          return new Uint8Array(response);
+        } catch {
+        }
+      }
+      return getBinarySync(binaryFile);
+    }
+    __name(getWasmBinary, "getWasmBinary");
+    async function instantiateArrayBuffer(binaryFile, imports) {
+      try {
+        var binary2 = await getWasmBinary(binaryFile);
+        var instance2 = await WebAssembly.instantiate(binary2, imports);
+        return instance2;
+      } catch (reason) {
+        err(`failed to asynchronously prepare wasm: ${reason}`);
+        abort(reason);
+      }
+    }
+    __name(instantiateArrayBuffer, "instantiateArrayBuffer");
+    async function instantiateAsync(binary2, binaryFile, imports) {
+      if (!binary2 && typeof WebAssembly.instantiateStreaming == "function" && !isFileURI(binaryFile) && !ENVIRONMENT_IS_NODE) {
+        try {
+          var response = fetch(binaryFile, {
+            credentials: "same-origin"
+          });
+          var instantiationResult = await WebAssembly.instantiateStreaming(response, imports);
+          return instantiationResult;
+        } catch (reason) {
+          err(`wasm streaming compile failed: ${reason}`);
+          err("falling back to ArrayBuffer instantiation");
+        }
+      }
+      return instantiateArrayBuffer(binaryFile, imports);
+    }
+    __name(instantiateAsync, "instantiateAsync");
+    function getWasmImports() {
+      return {
+        "env": wasmImports,
+        "wasi_snapshot_preview1": wasmImports,
+        "GOT.mem": new Proxy(wasmImports, GOTHandler),
+        "GOT.func": new Proxy(wasmImports, GOTHandler)
+      };
+    }
+    __name(getWasmImports, "getWasmImports");
+    async function createWasm() {
+      function receiveInstance(instance2, module2) {
+        wasmExports = instance2.exports;
+        wasmExports = relocateExports(wasmExports, 1024);
+        var metadata2 = getDylinkMetadata(module2);
+        if (metadata2.neededDynlibs) {
+          dynamicLibraries = metadata2.neededDynlibs.concat(dynamicLibraries);
+        }
+        mergeLibSymbols(wasmExports, "main");
+        LDSO.init();
+        loadDylibs();
+        __RELOC_FUNCS__.push(wasmExports["__wasm_apply_data_relocs"]);
+        removeRunDependency("wasm-instantiate");
+        return wasmExports;
+      }
+      __name(receiveInstance, "receiveInstance");
+      addRunDependency("wasm-instantiate");
+      function receiveInstantiationResult(result2) {
+        return receiveInstance(result2["instance"], result2["module"]);
+      }
+      __name(receiveInstantiationResult, "receiveInstantiationResult");
+      var info2 = getWasmImports();
+      if (Module["instantiateWasm"]) {
+        return new Promise((resolve, reject) => {
+          Module["instantiateWasm"](info2, (mod, inst) => {
+            receiveInstance(mod, inst);
+            resolve(mod.exports);
+          });
+        });
+      }
+      wasmBinaryFile ??= findWasmBinary();
+      try {
+        var result = await instantiateAsync(wasmBinary, wasmBinaryFile, info2);
+        var exports = receiveInstantiationResult(result);
+        return exports;
+      } catch (e) {
+        readyPromiseReject(e);
+        return Promise.reject(e);
+      }
+    }
+    __name(createWasm, "createWasm");
+    var ASM_CONSTS = {};
+    class ExitStatus {
+      static {
+        __name(this, "ExitStatus");
+      }
+      name = "ExitStatus";
+      constructor(status) {
+        this.message = `Program terminated with exit(${status})`;
+        this.status = status;
+      }
+    }
+    var GOT = {};
+    var currentModuleWeakSymbols = /* @__PURE__ */ new Set([]);
+    var GOTHandler = {
+      get(obj, symName) {
+        var rtn = GOT[symName];
+        if (!rtn) {
+          rtn = GOT[symName] = new WebAssembly.Global({
+            "value": "i32",
+            "mutable": true
+          });
+        }
+        if (!currentModuleWeakSymbols.has(symName)) {
+          rtn.required = true;
+        }
+        return rtn;
+      }
+    };
+    var LE_HEAP_LOAD_F32 = /* @__PURE__ */ __name((byteOffset) => HEAP_DATA_VIEW.getFloat32(byteOffset, true), "LE_HEAP_LOAD_F32");
+    var LE_HEAP_LOAD_F64 = /* @__PURE__ */ __name((byteOffset) => HEAP_DATA_VIEW.getFloat64(byteOffset, true), "LE_HEAP_LOAD_F64");
+    var LE_HEAP_LOAD_I16 = /* @__PURE__ */ __name((byteOffset) => HEAP_DATA_VIEW.getInt16(byteOffset, true), "LE_HEAP_LOAD_I16");
+    var LE_HEAP_LOAD_I32 = /* @__PURE__ */ __name((byteOffset) => HEAP_DATA_VIEW.getInt32(byteOffset, true), "LE_HEAP_LOAD_I32");
+    var LE_HEAP_LOAD_U16 = /* @__PURE__ */ __name((byteOffset) => HEAP_DATA_VIEW.getUint16(byteOffset, true), "LE_HEAP_LOAD_U16");
+    var LE_HEAP_LOAD_U32 = /* @__PURE__ */ __name((byteOffset) => HEAP_DATA_VIEW.getUint32(byteOffset, true), "LE_HEAP_LOAD_U32");
+    var LE_HEAP_STORE_F32 = /* @__PURE__ */ __name((byteOffset, value) => HEAP_DATA_VIEW.setFloat32(byteOffset, value, true), "LE_HEAP_STORE_F32");
+    var LE_HEAP_STORE_F64 = /* @__PURE__ */ __name((byteOffset, value) => HEAP_DATA_VIEW.setFloat64(byteOffset, value, true), "LE_HEAP_STORE_F64");
+    var LE_HEAP_STORE_I16 = /* @__PURE__ */ __name((byteOffset, value) => HEAP_DATA_VIEW.setInt16(byteOffset, value, true), "LE_HEAP_STORE_I16");
+    var LE_HEAP_STORE_I32 = /* @__PURE__ */ __name((byteOffset, value) => HEAP_DATA_VIEW.setInt32(byteOffset, value, true), "LE_HEAP_STORE_I32");
+    var LE_HEAP_STORE_U16 = /* @__PURE__ */ __name((byteOffset, value) => HEAP_DATA_VIEW.setUint16(byteOffset, value, true), "LE_HEAP_STORE_U16");
+    var LE_HEAP_STORE_U32 = /* @__PURE__ */ __name((byteOffset, value) => HEAP_DATA_VIEW.setUint32(byteOffset, value, true), "LE_HEAP_STORE_U32");
+    var callRuntimeCallbacks = /* @__PURE__ */ __name((callbacks) => {
+      while (callbacks.length > 0) {
+        callbacks.shift()(Module);
+      }
+    }, "callRuntimeCallbacks");
+    var onPostRuns = [];
+    var addOnPostRun = /* @__PURE__ */ __name((cb) => onPostRuns.unshift(cb), "addOnPostRun");
+    var onPreRuns = [];
+    var addOnPreRun = /* @__PURE__ */ __name((cb) => onPreRuns.unshift(cb), "addOnPreRun");
+    var UTF8Decoder = typeof TextDecoder != "undefined" ? new TextDecoder() : void 0;
+    var UTF8ArrayToString = /* @__PURE__ */ __name((heapOrArray, idx = 0, maxBytesToRead = NaN) => {
+      var endIdx = idx + maxBytesToRead;
+      var endPtr = idx;
+      while (heapOrArray[endPtr] && !(endPtr >= endIdx)) ++endPtr;
+      if (endPtr - idx > 16 && heapOrArray.buffer && UTF8Decoder) {
+        return UTF8Decoder.decode(heapOrArray.subarray(idx, endPtr));
+      }
+      var str = "";
+      while (idx < endPtr) {
+        var u0 = heapOrArray[idx++];
+        if (!(u0 & 128)) {
+          str += String.fromCharCode(u0);
+          continue;
+        }
+        var u1 = heapOrArray[idx++] & 63;
+        if ((u0 & 224) == 192) {
+          str += String.fromCharCode((u0 & 31) << 6 | u1);
+          continue;
+        }
+        var u2 = heapOrArray[idx++] & 63;
+        if ((u0 & 240) == 224) {
+          u0 = (u0 & 15) << 12 | u1 << 6 | u2;
+        } else {
+          u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heapOrArray[idx++] & 63;
+        }
+        if (u0 < 65536) {
+          str += String.fromCharCode(u0);
+        } else {
+          var ch = u0 - 65536;
+          str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023);
+        }
+      }
+      return str;
+    }, "UTF8ArrayToString");
+    var getDylinkMetadata = /* @__PURE__ */ __name((binary2) => {
+      var offset = 0;
+      var end = 0;
+      function getU8() {
+        return binary2[offset++];
+      }
+      __name(getU8, "getU8");
+      function getLEB() {
+        var ret = 0;
+        var mul = 1;
+        while (1) {
+          var byte = binary2[offset++];
+          ret += (byte & 127) * mul;
+          mul *= 128;
+          if (!(byte & 128)) break;
+        }
+        return ret;
+      }
+      __name(getLEB, "getLEB");
+      function getString() {
+        var len = getLEB();
+        offset += len;
+        return UTF8ArrayToString(binary2, offset - len, len);
+      }
+      __name(getString, "getString");
+      function failIf(condition, message) {
+        if (condition) throw new Error(message);
+      }
+      __name(failIf, "failIf");
+      var name2 = "dylink.0";
+      if (binary2 instanceof WebAssembly.Module) {
+        var dylinkSection = WebAssembly.Module.customSections(binary2, name2);
+        if (dylinkSection.length === 0) {
+          name2 = "dylink";
+          dylinkSection = WebAssembly.Module.customSections(binary2, name2);
+        }
+        failIf(dylinkSection.length === 0, "need dylink section");
+        binary2 = new Uint8Array(dylinkSection[0]);
+        end = binary2.length;
+      } else {
+        var int32View = new Uint32Array(new Uint8Array(binary2.subarray(0, 24)).buffer);
+        var magicNumberFound = int32View[0] == 1836278016 || int32View[0] == 6386541;
+        failIf(!magicNumberFound, "need to see wasm magic number");
+        failIf(binary2[8] !== 0, "need the dylink section to be first");
+        offset = 9;
+        var section_size = getLEB();
+        end = offset + section_size;
+        name2 = getString();
+      }
+      var customSection = {
+        neededDynlibs: [],
+        tlsExports: /* @__PURE__ */ new Set(),
+        weakImports: /* @__PURE__ */ new Set()
+      };
+      if (name2 == "dylink") {
+        customSection.memorySize = getLEB();
+        customSection.memoryAlign = getLEB();
+        customSection.tableSize = getLEB();
+        customSection.tableAlign = getLEB();
+        var neededDynlibsCount = getLEB();
+        for (var i2 = 0; i2 < neededDynlibsCount; ++i2) {
+          var libname = getString();
+          customSection.neededDynlibs.push(libname);
+        }
+      } else {
+        failIf(name2 !== "dylink.0");
+        var WASM_DYLINK_MEM_INFO = 1;
+        var WASM_DYLINK_NEEDED = 2;
+        var WASM_DYLINK_EXPORT_INFO = 3;
+        var WASM_DYLINK_IMPORT_INFO = 4;
+        var WASM_SYMBOL_TLS = 256;
+        var WASM_SYMBOL_BINDING_MASK = 3;
+        var WASM_SYMBOL_BINDING_WEAK = 1;
+        while (offset < end) {
+          var subsectionType = getU8();
+          var subsectionSize = getLEB();
+          if (subsectionType === WASM_DYLINK_MEM_INFO) {
+            customSection.memorySize = getLEB();
+            customSection.memoryAlign = getLEB();
+            customSection.tableSize = getLEB();
+            customSection.tableAlign = getLEB();
+          } else if (subsectionType === WASM_DYLINK_NEEDED) {
+            var neededDynlibsCount = getLEB();
+            for (var i2 = 0; i2 < neededDynlibsCount; ++i2) {
+              libname = getString();
+              customSection.neededDynlibs.push(libname);
+            }
+          } else if (subsectionType === WASM_DYLINK_EXPORT_INFO) {
+            var count = getLEB();
+            while (count--) {
+              var symname = getString();
+              var flags2 = getLEB();
+              if (flags2 & WASM_SYMBOL_TLS) {
+                customSection.tlsExports.add(symname);
+              }
+            }
+          } else if (subsectionType === WASM_DYLINK_IMPORT_INFO) {
+            var count = getLEB();
+            while (count--) {
+              var modname = getString();
+              var symname = getString();
+              var flags2 = getLEB();
+              if ((flags2 & WASM_SYMBOL_BINDING_MASK) == WASM_SYMBOL_BINDING_WEAK) {
+                customSection.weakImports.add(symname);
+              }
+            }
+          } else {
+            offset += subsectionSize;
+          }
+        }
+      }
+      return customSection;
+    }, "getDylinkMetadata");
+    function getValue(ptr, type = "i8") {
+      if (type.endsWith("*")) type = "*";
+      switch (type) {
+        case "i1":
+          return HEAP8[ptr];
+        case "i8":
+          return HEAP8[ptr];
+        case "i16":
+          return LE_HEAP_LOAD_I16((ptr >> 1) * 2);
+        case "i32":
+          return LE_HEAP_LOAD_I32((ptr >> 2) * 4);
+        case "i64":
+          return HEAP64[ptr >> 3];
+        case "float":
+          return LE_HEAP_LOAD_F32((ptr >> 2) * 4);
+        case "double":
+          return LE_HEAP_LOAD_F64((ptr >> 3) * 8);
+        case "*":
+          return LE_HEAP_LOAD_U32((ptr >> 2) * 4);
+        default:
+          abort(`invalid type for getValue: ${type}`);
+      }
+    }
+    __name(getValue, "getValue");
+    var newDSO = /* @__PURE__ */ __name((name2, handle2, syms) => {
+      var dso = {
+        refcount: Infinity,
+        name: name2,
+        exports: syms,
+        global: true
+      };
+      LDSO.loadedLibsByName[name2] = dso;
+      if (handle2 != void 0) {
+        LDSO.loadedLibsByHandle[handle2] = dso;
+      }
+      return dso;
+    }, "newDSO");
+    var LDSO = {
+      loadedLibsByName: {},
+      loadedLibsByHandle: {},
+      init() {
+        newDSO("__main__", 0, wasmImports);
+      }
+    };
+    var ___heap_base = 78224;
+    var alignMemory = /* @__PURE__ */ __name((size, alignment) => Math.ceil(size / alignment) * alignment, "alignMemory");
+    var getMemory = /* @__PURE__ */ __name((size) => {
+      if (runtimeInitialized) {
+        return _calloc(size, 1);
+      }
+      var ret = ___heap_base;
+      var end = ret + alignMemory(size, 16);
+      ___heap_base = end;
+      GOT["__heap_base"].value = end;
+      return ret;
+    }, "getMemory");
+    var isInternalSym = /* @__PURE__ */ __name((symName) => ["__cpp_exception", "__c_longjmp", "__wasm_apply_data_relocs", "__dso_handle", "__tls_size", "__tls_align", "__set_stack_limits", "_emscripten_tls_init", "__wasm_init_tls", "__wasm_call_ctors", "__start_em_asm", "__stop_em_asm", "__start_em_js", "__stop_em_js"].includes(symName) || symName.startsWith("__em_js__"), "isInternalSym");
+    var uleb128Encode = /* @__PURE__ */ __name((n, target) => {
+      if (n < 128) {
+        target.push(n);
+      } else {
+        target.push(n % 128 | 128, n >> 7);
+      }
+    }, "uleb128Encode");
+    var sigToWasmTypes = /* @__PURE__ */ __name((sig) => {
+      var typeNames = {
+        "i": "i32",
+        "j": "i64",
+        "f": "f32",
+        "d": "f64",
+        "e": "externref",
+        "p": "i32"
+      };
+      var type = {
+        parameters: [],
+        results: sig[0] == "v" ? [] : [typeNames[sig[0]]]
+      };
+      for (var i2 = 1; i2 < sig.length; ++i2) {
+        type.parameters.push(typeNames[sig[i2]]);
+      }
+      return type;
+    }, "sigToWasmTypes");
+    var generateFuncType = /* @__PURE__ */ __name((sig, target) => {
+      var sigRet = sig.slice(0, 1);
+      var sigParam = sig.slice(1);
+      var typeCodes = {
+        "i": 127,
+        // i32
+        "p": 127,
+        // i32
+        "j": 126,
+        // i64
+        "f": 125,
+        // f32
+        "d": 124,
+        // f64
+        "e": 111
+      };
+      target.push(96);
+      uleb128Encode(sigParam.length, target);
+      for (var i2 = 0; i2 < sigParam.length; ++i2) {
+        target.push(typeCodes[sigParam[i2]]);
+      }
+      if (sigRet == "v") {
+        target.push(0);
+      } else {
+        target.push(1, typeCodes[sigRet]);
+      }
+    }, "generateFuncType");
+    var convertJsFunctionToWasm = /* @__PURE__ */ __name((func2, sig) => {
+      if (typeof WebAssembly.Function == "function") {
+        return new WebAssembly.Function(sigToWasmTypes(sig), func2);
+      }
+      var typeSectionBody = [1];
+      generateFuncType(sig, typeSectionBody);
+      var bytes = [
+        0,
+        97,
+        115,
+        109,
+        // magic ("\0asm")
+        1,
+        0,
+        0,
+        0,
+        // version: 1
+        1
+      ];
+      uleb128Encode(typeSectionBody.length, bytes);
+      bytes.push(...typeSectionBody);
+      bytes.push(
+        2,
+        7,
+        // import section
+        // (import "e" "f" (func 0 (type 0)))
+        1,
+        1,
+        101,
+        1,
+        102,
+        0,
+        0,
+        7,
+        5,
+        // export section
+        // (export "f" (func 0 (type 0)))
+        1,
+        1,
+        102,
+        0,
+        0
+      );
+      var module2 = new WebAssembly.Module(new Uint8Array(bytes));
+      var instance2 = new WebAssembly.Instance(module2, {
+        "e": {
+          "f": func2
+        }
+      });
+      var wrappedFunc = instance2.exports["f"];
+      return wrappedFunc;
+    }, "convertJsFunctionToWasm");
+    var wasmTableMirror = [];
+    var wasmTable = new WebAssembly.Table({
+      "initial": 31,
+      "element": "anyfunc"
+    });
+    var getWasmTableEntry = /* @__PURE__ */ __name((funcPtr) => {
+      var func2 = wasmTableMirror[funcPtr];
+      if (!func2) {
+        if (funcPtr >= wasmTableMirror.length) wasmTableMirror.length = funcPtr + 1;
+        wasmTableMirror[funcPtr] = func2 = wasmTable.get(funcPtr);
+      }
+      return func2;
+    }, "getWasmTableEntry");
+    var updateTableMap = /* @__PURE__ */ __name((offset, count) => {
+      if (functionsInTableMap) {
+        for (var i2 = offset; i2 < offset + count; i2++) {
+          var item = getWasmTableEntry(i2);
+          if (item) {
+            functionsInTableMap.set(item, i2);
+          }
+        }
+      }
+    }, "updateTableMap");
+    var functionsInTableMap;
+    var getFunctionAddress = /* @__PURE__ */ __name((func2) => {
+      if (!functionsInTableMap) {
+        functionsInTableMap = /* @__PURE__ */ new WeakMap();
+        updateTableMap(0, wasmTable.length);
+      }
+      return functionsInTableMap.get(func2) || 0;
+    }, "getFunctionAddress");
+    var freeTableIndexes = [];
+    var getEmptyTableSlot = /* @__PURE__ */ __name(() => {
+      if (freeTableIndexes.length) {
+        return freeTableIndexes.pop();
+      }
+      try {
+        wasmTable.grow(1);
+      } catch (err2) {
+        if (!(err2 instanceof RangeError)) {
+          throw err2;
+        }
+        throw "Unable to grow wasm table. Set ALLOW_TABLE_GROWTH.";
+      }
+      return wasmTable.length - 1;
+    }, "getEmptyTableSlot");
+    var setWasmTableEntry = /* @__PURE__ */ __name((idx, func2) => {
+      wasmTable.set(idx, func2);
+      wasmTableMirror[idx] = wasmTable.get(idx);
+    }, "setWasmTableEntry");
+    var addFunction = /* @__PURE__ */ __name((func2, sig) => {
+      var rtn = getFunctionAddress(func2);
+      if (rtn) {
+        return rtn;
+      }
+      var ret = getEmptyTableSlot();
+      try {
+        setWasmTableEntry(ret, func2);
+      } catch (err2) {
+        if (!(err2 instanceof TypeError)) {
+          throw err2;
+        }
+        var wrapped = convertJsFunctionToWasm(func2, sig);
+        setWasmTableEntry(ret, wrapped);
+      }
+      functionsInTableMap.set(func2, ret);
+      return ret;
+    }, "addFunction");
+    var updateGOT = /* @__PURE__ */ __name((exports, replace) => {
+      for (var symName in exports) {
+        if (isInternalSym(symName)) {
+          continue;
+        }
+        var value = exports[symName];
+        GOT[symName] ||= new WebAssembly.Global({
+          "value": "i32",
+          "mutable": true
+        });
+        if (replace || GOT[symName].value == 0) {
+          if (typeof value == "function") {
+            GOT[symName].value = addFunction(value);
+          } else if (typeof value == "number") {
+            GOT[symName].value = value;
+          } else {
+            err(`unhandled export type for '${symName}': ${typeof value}`);
+          }
+        }
+      }
+    }, "updateGOT");
+    var relocateExports = /* @__PURE__ */ __name((exports, memoryBase2, replace) => {
+      var relocated = {};
+      for (var e in exports) {
+        var value = exports[e];
+        if (typeof value == "object") {
+          value = value.value;
+        }
+        if (typeof value == "number") {
+          value += memoryBase2;
+        }
+        relocated[e] = value;
+      }
+      updateGOT(relocated, replace);
+      return relocated;
+    }, "relocateExports");
+    var isSymbolDefined = /* @__PURE__ */ __name((symName) => {
+      var existing = wasmImports[symName];
+      if (!existing || existing.stub) {
+        return false;
+      }
+      return true;
+    }, "isSymbolDefined");
+    var dynCall = /* @__PURE__ */ __name((sig, ptr, args2 = []) => {
+      var rtn = getWasmTableEntry(ptr)(...args2);
+      return rtn;
+    }, "dynCall");
+    var stackSave = /* @__PURE__ */ __name(() => _emscripten_stack_get_current(), "stackSave");
+    var stackRestore = /* @__PURE__ */ __name((val) => __emscripten_stack_restore(val), "stackRestore");
+    var createInvokeFunction = /* @__PURE__ */ __name((sig) => (ptr, ...args2) => {
+      var sp = stackSave();
+      try {
+        return dynCall(sig, ptr, args2);
+      } catch (e) {
+        stackRestore(sp);
+        if (e !== e + 0) throw e;
+        _setThrew(1, 0);
+        if (sig[0] == "j") return 0n;
+      }
+    }, "createInvokeFunction");
+    var resolveGlobalSymbol = /* @__PURE__ */ __name((symName, direct = false) => {
+      var sym;
+      if (isSymbolDefined(symName)) {
+        sym = wasmImports[symName];
+      } else if (symName.startsWith("invoke_")) {
+        sym = wasmImports[symName] = createInvokeFunction(symName.split("_")[1]);
+      }
+      return {
+        sym,
+        name: symName
+      };
+    }, "resolveGlobalSymbol");
+    var onPostCtors = [];
+    var addOnPostCtor = /* @__PURE__ */ __name((cb) => onPostCtors.unshift(cb), "addOnPostCtor");
+    var UTF8ToString = /* @__PURE__ */ __name((ptr, maxBytesToRead) => ptr ? UTF8ArrayToString(HEAPU8, ptr, maxBytesToRead) : "", "UTF8ToString");
+    var loadWebAssemblyModule = /* @__PURE__ */ __name((binary, flags, libName, localScope, handle) => {
+      var metadata = getDylinkMetadata(binary);
+      currentModuleWeakSymbols = metadata.weakImports;
+      function loadModule() {
+        var memAlign = Math.pow(2, metadata.memoryAlign);
+        var memoryBase = metadata.memorySize ? alignMemory(getMemory(metadata.memorySize + memAlign), memAlign) : 0;
+        var tableBase = metadata.tableSize ? wasmTable.length : 0;
+        if (handle) {
+          HEAP8[handle + 8] = 1;
+          LE_HEAP_STORE_U32((handle + 12 >> 2) * 4, memoryBase);
+          LE_HEAP_STORE_I32((handle + 16 >> 2) * 4, metadata.memorySize);
+          LE_HEAP_STORE_U32((handle + 20 >> 2) * 4, tableBase);
+          LE_HEAP_STORE_I32((handle + 24 >> 2) * 4, metadata.tableSize);
+        }
+        if (metadata.tableSize) {
+          wasmTable.grow(metadata.tableSize);
+        }
+        var moduleExports;
+        function resolveSymbol(sym) {
+          var resolved = resolveGlobalSymbol(sym).sym;
+          if (!resolved && localScope) {
+            resolved = localScope[sym];
+          }
+          if (!resolved) {
+            resolved = moduleExports[sym];
+          }
+          return resolved;
+        }
+        __name(resolveSymbol, "resolveSymbol");
+        var proxyHandler = {
+          get(stubs, prop) {
+            switch (prop) {
+              case "__memory_base":
+                return memoryBase;
+              case "__table_base":
+                return tableBase;
+            }
+            if (prop in wasmImports && !wasmImports[prop].stub) {
+              var res = wasmImports[prop];
+              return res;
+            }
+            if (!(prop in stubs)) {
+              var resolved;
+              stubs[prop] = (...args2) => {
+                resolved ||= resolveSymbol(prop);
+                return resolved(...args2);
+              };
+            }
+            return stubs[prop];
+          }
+        };
+        var proxy = new Proxy({}, proxyHandler);
+        var info = {
+          "GOT.mem": new Proxy({}, GOTHandler),
+          "GOT.func": new Proxy({}, GOTHandler),
+          "env": proxy,
+          "wasi_snapshot_preview1": proxy
+        };
+        function postInstantiation(module, instance) {
+          updateTableMap(tableBase, metadata.tableSize);
+          moduleExports = relocateExports(instance.exports, memoryBase);
+          if (!flags.allowUndefined) {
+            reportUndefinedSymbols();
+          }
+          function addEmAsm(addr, body) {
+            var args = [];
+            var arity = 0;
+            for (; arity < 16; arity++) {
+              if (body.indexOf("$" + arity) != -1) {
+                args.push("$" + arity);
+              } else {
+                break;
+              }
+            }
+            args = args.join(",");
+            var func = `(${args}) => { ${body} };`;
+            ASM_CONSTS[start] = eval(func);
+          }
+          __name(addEmAsm, "addEmAsm");
+          if ("__start_em_asm" in moduleExports) {
+            var start = moduleExports["__start_em_asm"];
+            var stop = moduleExports["__stop_em_asm"];
+            while (start < stop) {
+              var jsString = UTF8ToString(start);
+              addEmAsm(start, jsString);
+              start = HEAPU8.indexOf(0, start) + 1;
+            }
+          }
+          function addEmJs(name, cSig, body) {
+            var jsArgs = [];
+            cSig = cSig.slice(1, -1);
+            if (cSig != "void") {
+              cSig = cSig.split(",");
+              for (var i in cSig) {
+                var jsArg = cSig[i].split(" ").pop();
+                jsArgs.push(jsArg.replace("*", ""));
+              }
+            }
+            var func = `(${jsArgs}) => ${body};`;
+            moduleExports[name] = eval(func);
+          }
+          __name(addEmJs, "addEmJs");
+          for (var name in moduleExports) {
+            if (name.startsWith("__em_js__")) {
+              var start = moduleExports[name];
+              var jsString = UTF8ToString(start);
+              var parts = jsString.split("<::>");
+              addEmJs(name.replace("__em_js__", ""), parts[0], parts[1]);
+              delete moduleExports[name];
+            }
+          }
+          var applyRelocs = moduleExports["__wasm_apply_data_relocs"];
+          if (applyRelocs) {
+            if (runtimeInitialized) {
+              applyRelocs();
+            } else {
+              __RELOC_FUNCS__.push(applyRelocs);
+            }
+          }
+          var init = moduleExports["__wasm_call_ctors"];
+          if (init) {
+            if (runtimeInitialized) {
+              init();
+            } else {
+              addOnPostCtor(init);
+            }
+          }
+          return moduleExports;
+        }
+        __name(postInstantiation, "postInstantiation");
+        if (flags.loadAsync) {
+          if (binary instanceof WebAssembly.Module) {
+            var instance = new WebAssembly.Instance(binary, info);
+            return Promise.resolve(postInstantiation(binary, instance));
+          }
+          return WebAssembly.instantiate(binary, info).then((result) => postInstantiation(result.module, result.instance));
+        }
+        var module = binary instanceof WebAssembly.Module ? binary : new WebAssembly.Module(binary);
+        var instance = new WebAssembly.Instance(module, info);
+        return postInstantiation(module, instance);
+      }
+      __name(loadModule, "loadModule");
+      if (flags.loadAsync) {
+        return metadata.neededDynlibs.reduce((chain, dynNeeded) => chain.then(() => loadDynamicLibrary(dynNeeded, flags, localScope)), Promise.resolve()).then(loadModule);
+      }
+      metadata.neededDynlibs.forEach((needed) => loadDynamicLibrary(needed, flags, localScope));
+      return loadModule();
+    }, "loadWebAssemblyModule");
+    var mergeLibSymbols = /* @__PURE__ */ __name((exports, libName2) => {
+      for (var [sym, exp] of Object.entries(exports)) {
+        const setImport = /* @__PURE__ */ __name((target) => {
+          if (!isSymbolDefined(target)) {
+            wasmImports[target] = exp;
+          }
+        }, "setImport");
+        setImport(sym);
+        const main_alias = "__main_argc_argv";
+        if (sym == "main") {
+          setImport(main_alias);
+        }
+        if (sym == main_alias) {
+          setImport("main");
+        }
+      }
+    }, "mergeLibSymbols");
+    var asyncLoad = /* @__PURE__ */ __name(async (url) => {
+      var arrayBuffer = await readAsync(url);
+      return new Uint8Array(arrayBuffer);
+    }, "asyncLoad");
+    function loadDynamicLibrary(libName2, flags2 = {
+      global: true,
+      nodelete: true
+    }, localScope2, handle2) {
+      var dso = LDSO.loadedLibsByName[libName2];
+      if (dso) {
+        if (!flags2.global) {
+          if (localScope2) {
+            Object.assign(localScope2, dso.exports);
+          }
+        } else if (!dso.global) {
+          dso.global = true;
+          mergeLibSymbols(dso.exports, libName2);
+        }
+        if (flags2.nodelete && dso.refcount !== Infinity) {
+          dso.refcount = Infinity;
+        }
+        dso.refcount++;
+        if (handle2) {
+          LDSO.loadedLibsByHandle[handle2] = dso;
+        }
+        return flags2.loadAsync ? Promise.resolve(true) : true;
+      }
+      dso = newDSO(libName2, handle2, "loading");
+      dso.refcount = flags2.nodelete ? Infinity : 1;
+      dso.global = flags2.global;
+      function loadLibData() {
+        if (handle2) {
+          var data = LE_HEAP_LOAD_U32((handle2 + 28 >> 2) * 4);
+          var dataSize = LE_HEAP_LOAD_U32((handle2 + 32 >> 2) * 4);
+          if (data && dataSize) {
+            var libData = HEAP8.slice(data, data + dataSize);
+            return flags2.loadAsync ? Promise.resolve(libData) : libData;
+          }
+        }
+        var libFile = locateFile(libName2);
+        if (flags2.loadAsync) {
+          return asyncLoad(libFile);
+        }
+        if (!readBinary) {
+          throw new Error(`${libFile}: file not found, and synchronous loading of external files is not available`);
+        }
+        return readBinary(libFile);
+      }
+      __name(loadLibData, "loadLibData");
+      function getExports() {
+        if (flags2.loadAsync) {
+          return loadLibData().then((libData) => loadWebAssemblyModule(libData, flags2, libName2, localScope2, handle2));
+        }
+        return loadWebAssemblyModule(loadLibData(), flags2, libName2, localScope2, handle2);
+      }
+      __name(getExports, "getExports");
+      function moduleLoaded(exports) {
+        if (dso.global) {
+          mergeLibSymbols(exports, libName2);
+        } else if (localScope2) {
+          Object.assign(localScope2, exports);
+        }
+        dso.exports = exports;
+      }
+      __name(moduleLoaded, "moduleLoaded");
+      if (flags2.loadAsync) {
+        return getExports().then((exports) => {
+          moduleLoaded(exports);
+          return true;
+        });
+      }
+      moduleLoaded(getExports());
+      return true;
+    }
+    __name(loadDynamicLibrary, "loadDynamicLibrary");
+    var reportUndefinedSymbols = /* @__PURE__ */ __name(() => {
+      for (var [symName, entry] of Object.entries(GOT)) {
+        if (entry.value == 0) {
+          var value = resolveGlobalSymbol(symName, true).sym;
+          if (!value && !entry.required) {
+            continue;
+          }
+          if (typeof value == "function") {
+            entry.value = addFunction(value, value.sig);
+          } else if (typeof value == "number") {
+            entry.value = value;
+          } else {
+            throw new Error(`bad export type for '${symName}': ${typeof value}`);
+          }
+        }
+      }
+    }, "reportUndefinedSymbols");
+    var loadDylibs = /* @__PURE__ */ __name(() => {
+      if (!dynamicLibraries.length) {
+        reportUndefinedSymbols();
+        return;
+      }
+      addRunDependency("loadDylibs");
+      dynamicLibraries.reduce((chain, lib) => chain.then(() => loadDynamicLibrary(lib, {
+        loadAsync: true,
+        global: true,
+        nodelete: true,
+        allowUndefined: true
+      })), Promise.resolve()).then(() => {
+        reportUndefinedSymbols();
+        removeRunDependency("loadDylibs");
+      });
+    }, "loadDylibs");
+    var noExitRuntime = Module["noExitRuntime"] || true;
+    function setValue(ptr, value, type = "i8") {
+      if (type.endsWith("*")) type = "*";
+      switch (type) {
+        case "i1":
+          HEAP8[ptr] = value;
+          break;
+        case "i8":
+          HEAP8[ptr] = value;
+          break;
+        case "i16":
+          LE_HEAP_STORE_I16((ptr >> 1) * 2, value);
+          break;
+        case "i32":
+          LE_HEAP_STORE_I32((ptr >> 2) * 4, value);
+          break;
+        case "i64":
+          HEAP64[ptr >> 3] = BigInt(value);
+          break;
+        case "float":
+          LE_HEAP_STORE_F32((ptr >> 2) * 4, value);
+          break;
+        case "double":
+          LE_HEAP_STORE_F64((ptr >> 3) * 8, value);
+          break;
+        case "*":
+          LE_HEAP_STORE_U32((ptr >> 2) * 4, value);
+          break;
+        default:
+          abort(`invalid type for setValue: ${type}`);
+      }
+    }
+    __name(setValue, "setValue");
+    var ___memory_base = new WebAssembly.Global({
+      "value": "i32",
+      "mutable": false
+    }, 1024);
+    var ___stack_pointer = new WebAssembly.Global({
+      "value": "i32",
+      "mutable": true
+    }, 78224);
+    var ___table_base = new WebAssembly.Global({
+      "value": "i32",
+      "mutable": false
+    }, 1);
+    var __abort_js = /* @__PURE__ */ __name(() => abort(""), "__abort_js");
+    __abort_js.sig = "v";
+    var _emscripten_get_now = /* @__PURE__ */ __name(() => performance.now(), "_emscripten_get_now");
+    _emscripten_get_now.sig = "d";
+    var _emscripten_date_now = /* @__PURE__ */ __name(() => Date.now(), "_emscripten_date_now");
+    _emscripten_date_now.sig = "d";
+    var nowIsMonotonic = 1;
+    var checkWasiClock = /* @__PURE__ */ __name((clock_id) => clock_id >= 0 && clock_id <= 3, "checkWasiClock");
+    var INT53_MAX = 9007199254740992;
+    var INT53_MIN = -9007199254740992;
+    var bigintToI53Checked = /* @__PURE__ */ __name((num) => num < INT53_MIN || num > INT53_MAX ? NaN : Number(num), "bigintToI53Checked");
+    function _clock_time_get(clk_id, ignored_precision, ptime) {
+      ignored_precision = bigintToI53Checked(ignored_precision);
+      if (!checkWasiClock(clk_id)) {
+        return 28;
+      }
+      var now;
+      if (clk_id === 0) {
+        now = _emscripten_date_now();
+      } else if (nowIsMonotonic) {
+        now = _emscripten_get_now();
+      } else {
+        return 52;
+      }
+      var nsec = Math.round(now * 1e3 * 1e3);
+      HEAP64[ptime >> 3] = BigInt(nsec);
+      return 0;
+    }
+    __name(_clock_time_get, "_clock_time_get");
+    _clock_time_get.sig = "iijp";
+    var getHeapMax = /* @__PURE__ */ __name(() => (
+      // Stay one Wasm page short of 4GB: while e.g. Chrome is able to allocate
+      // full 4GB Wasm memories, the size will wrap back to 0 bytes in Wasm side
+      // for any code that deals with heap sizes, which would require special
+      // casing all heap size related code to treat 0 specially.
+      2147483648
+    ), "getHeapMax");
+    var growMemory = /* @__PURE__ */ __name((size) => {
+      var b = wasmMemory.buffer;
+      var pages = (size - b.byteLength + 65535) / 65536 | 0;
+      try {
+        wasmMemory.grow(pages);
+        updateMemoryViews();
+        return 1;
+      } catch (e) {
+      }
+    }, "growMemory");
+    var _emscripten_resize_heap = /* @__PURE__ */ __name((requestedSize) => {
+      var oldSize = HEAPU8.length;
+      requestedSize >>>= 0;
+      var maxHeapSize = getHeapMax();
+      if (requestedSize > maxHeapSize) {
+        return false;
+      }
+      for (var cutDown = 1; cutDown <= 4; cutDown *= 2) {
+        var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown);
+        overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296);
+        var newSize = Math.min(maxHeapSize, alignMemory(Math.max(requestedSize, overGrownHeapSize), 65536));
+        var replacement = growMemory(newSize);
+        if (replacement) {
+          return true;
+        }
+      }
+      return false;
+    }, "_emscripten_resize_heap");
+    _emscripten_resize_heap.sig = "ip";
+    var _fd_close = /* @__PURE__ */ __name((fd) => 52, "_fd_close");
+    _fd_close.sig = "ii";
+    function _fd_seek(fd, offset, whence, newOffset) {
+      offset = bigintToI53Checked(offset);
+      return 70;
+    }
+    __name(_fd_seek, "_fd_seek");
+    _fd_seek.sig = "iijip";
+    var printCharBuffers = [null, [], []];
+    var printChar = /* @__PURE__ */ __name((stream, curr) => {
+      var buffer = printCharBuffers[stream];
+      if (curr === 0 || curr === 10) {
+        (stream === 1 ? out : err)(UTF8ArrayToString(buffer));
+        buffer.length = 0;
+      } else {
+        buffer.push(curr);
+      }
+    }, "printChar");
+    var flush_NO_FILESYSTEM = /* @__PURE__ */ __name(() => {
+      if (printCharBuffers[1].length) printChar(1, 10);
+      if (printCharBuffers[2].length) printChar(2, 10);
+    }, "flush_NO_FILESYSTEM");
+    var SYSCALLS = {
+      varargs: void 0,
+      getStr(ptr) {
+        var ret = UTF8ToString(ptr);
+        return ret;
+      }
+    };
+    var _fd_write = /* @__PURE__ */ __name((fd, iov, iovcnt, pnum) => {
+      var num = 0;
+      for (var i2 = 0; i2 < iovcnt; i2++) {
+        var ptr = LE_HEAP_LOAD_U32((iov >> 2) * 4);
+        var len = LE_HEAP_LOAD_U32((iov + 4 >> 2) * 4);
+        iov += 8;
+        for (var j = 0; j < len; j++) {
+          printChar(fd, HEAPU8[ptr + j]);
+        }
+        num += len;
+      }
+      LE_HEAP_STORE_U32((pnum >> 2) * 4, num);
+      return 0;
+    }, "_fd_write");
+    _fd_write.sig = "iippp";
+    function _tree_sitter_log_callback(isLexMessage, messageAddress) {
+      if (Module.currentLogCallback) {
+        const message = UTF8ToString(messageAddress);
+        Module.currentLogCallback(message, isLexMessage !== 0);
+      }
+    }
+    __name(_tree_sitter_log_callback, "_tree_sitter_log_callback");
+    function _tree_sitter_parse_callback(inputBufferAddress, index, row, column, lengthAddress) {
+      const INPUT_BUFFER_SIZE = 10 * 1024;
+      const string = Module.currentParseCallback(index, {
+        row,
+        column
+      });
+      if (typeof string === "string") {
+        setValue(lengthAddress, string.length, "i32");
+        stringToUTF16(string, inputBufferAddress, INPUT_BUFFER_SIZE);
+      } else {
+        setValue(lengthAddress, 0, "i32");
+      }
+    }
+    __name(_tree_sitter_parse_callback, "_tree_sitter_parse_callback");
+    function _tree_sitter_progress_callback(currentOffset, hasError) {
+      if (Module.currentProgressCallback) {
+        return Module.currentProgressCallback({
+          currentOffset,
+          hasError
+        });
+      }
+      return false;
+    }
+    __name(_tree_sitter_progress_callback, "_tree_sitter_progress_callback");
+    function _tree_sitter_query_progress_callback(currentOffset) {
+      if (Module.currentQueryProgressCallback) {
+        return Module.currentQueryProgressCallback({
+          currentOffset
+        });
+      }
+      return false;
+    }
+    __name(_tree_sitter_query_progress_callback, "_tree_sitter_query_progress_callback");
+    var runtimeKeepaliveCounter = 0;
+    var keepRuntimeAlive = /* @__PURE__ */ __name(() => noExitRuntime || runtimeKeepaliveCounter > 0, "keepRuntimeAlive");
+    var _proc_exit = /* @__PURE__ */ __name((code) => {
+      EXITSTATUS = code;
+      if (!keepRuntimeAlive()) {
+        Module["onExit"]?.(code);
+        ABORT = true;
+      }
+      quit_(code, new ExitStatus(code));
+    }, "_proc_exit");
+    _proc_exit.sig = "vi";
+    var exitJS = /* @__PURE__ */ __name((status, implicit) => {
+      EXITSTATUS = status;
+      _proc_exit(status);
+    }, "exitJS");
+    var handleException = /* @__PURE__ */ __name((e) => {
+      if (e instanceof ExitStatus || e == "unwind") {
+        return EXITSTATUS;
+      }
+      quit_(1, e);
+    }, "handleException");
+    var lengthBytesUTF8 = /* @__PURE__ */ __name((str) => {
+      var len = 0;
+      for (var i2 = 0; i2 < str.length; ++i2) {
+        var c = str.charCodeAt(i2);
+        if (c <= 127) {
+          len++;
+        } else if (c <= 2047) {
+          len += 2;
+        } else if (c >= 55296 && c <= 57343) {
+          len += 4;
+          ++i2;
+        } else {
+          len += 3;
+        }
+      }
+      return len;
+    }, "lengthBytesUTF8");
+    var stringToUTF8Array = /* @__PURE__ */ __name((str, heap, outIdx, maxBytesToWrite) => {
+      if (!(maxBytesToWrite > 0)) return 0;
+      var startIdx = outIdx;
+      var endIdx = outIdx + maxBytesToWrite - 1;
+      for (var i2 = 0; i2 < str.length; ++i2) {
+        var u = str.charCodeAt(i2);
+        if (u >= 55296 && u <= 57343) {
+          var u1 = str.charCodeAt(++i2);
+          u = 65536 + ((u & 1023) << 10) | u1 & 1023;
+        }
+        if (u <= 127) {
+          if (outIdx >= endIdx) break;
+          heap[outIdx++] = u;
+        } else if (u <= 2047) {
+          if (outIdx + 1 >= endIdx) break;
+          heap[outIdx++] = 192 | u >> 6;
+          heap[outIdx++] = 128 | u & 63;
+        } else if (u <= 65535) {
+          if (outIdx + 2 >= endIdx) break;
+          heap[outIdx++] = 224 | u >> 12;
+          heap[outIdx++] = 128 | u >> 6 & 63;
+          heap[outIdx++] = 128 | u & 63;
+        } else {
+          if (outIdx + 3 >= endIdx) break;
+          heap[outIdx++] = 240 | u >> 18;
+          heap[outIdx++] = 128 | u >> 12 & 63;
+          heap[outIdx++] = 128 | u >> 6 & 63;
+          heap[outIdx++] = 128 | u & 63;
+        }
+      }
+      heap[outIdx] = 0;
+      return outIdx - startIdx;
+    }, "stringToUTF8Array");
+    var stringToUTF8 = /* @__PURE__ */ __name((str, outPtr, maxBytesToWrite) => stringToUTF8Array(str, HEAPU8, outPtr, maxBytesToWrite), "stringToUTF8");
+    var stackAlloc = /* @__PURE__ */ __name((sz) => __emscripten_stack_alloc(sz), "stackAlloc");
+    var stringToUTF8OnStack = /* @__PURE__ */ __name((str) => {
+      var size = lengthBytesUTF8(str) + 1;
+      var ret = stackAlloc(size);
+      stringToUTF8(str, ret, size);
+      return ret;
+    }, "stringToUTF8OnStack");
+    var AsciiToString = /* @__PURE__ */ __name((ptr) => {
+      var str = "";
+      while (1) {
+        var ch = HEAPU8[ptr++];
+        if (!ch) return str;
+        str += String.fromCharCode(ch);
+      }
+    }, "AsciiToString");
+    var stringToUTF16 = /* @__PURE__ */ __name((str, outPtr, maxBytesToWrite) => {
+      maxBytesToWrite ??= 2147483647;
+      if (maxBytesToWrite < 2) return 0;
+      maxBytesToWrite -= 2;
+      var startPtr = outPtr;
+      var numCharsToWrite = maxBytesToWrite < str.length * 2 ? maxBytesToWrite / 2 : str.length;
+      for (var i2 = 0; i2 < numCharsToWrite; ++i2) {
+        var codeUnit = str.charCodeAt(i2);
+        LE_HEAP_STORE_I16((outPtr >> 1) * 2, codeUnit);
+        outPtr += 2;
+      }
+      LE_HEAP_STORE_I16((outPtr >> 1) * 2, 0);
+      return outPtr - startPtr;
+    }, "stringToUTF16");
+    var wasmImports = {
+      /** @export */
+      __heap_base: ___heap_base,
+      /** @export */
+      __indirect_function_table: wasmTable,
+      /** @export */
+      __memory_base: ___memory_base,
+      /** @export */
+      __stack_pointer: ___stack_pointer,
+      /** @export */
+      __table_base: ___table_base,
+      /** @export */
+      _abort_js: __abort_js,
+      /** @export */
+      clock_time_get: _clock_time_get,
+      /** @export */
+      emscripten_resize_heap: _emscripten_resize_heap,
+      /** @export */
+      fd_close: _fd_close,
+      /** @export */
+      fd_seek: _fd_seek,
+      /** @export */
+      fd_write: _fd_write,
+      /** @export */
+      memory: wasmMemory,
+      /** @export */
+      tree_sitter_log_callback: _tree_sitter_log_callback,
+      /** @export */
+      tree_sitter_parse_callback: _tree_sitter_parse_callback,
+      /** @export */
+      tree_sitter_progress_callback: _tree_sitter_progress_callback,
+      /** @export */
+      tree_sitter_query_progress_callback: _tree_sitter_query_progress_callback
+    };
+    var wasmExports = await createWasm();
+    var ___wasm_call_ctors = wasmExports["__wasm_call_ctors"];
+    var _malloc = Module["_malloc"] = wasmExports["malloc"];
+    var _calloc = Module["_calloc"] = wasmExports["calloc"];
+    var _realloc = Module["_realloc"] = wasmExports["realloc"];
+    var _free = Module["_free"] = wasmExports["free"];
+    var _memcmp = Module["_memcmp"] = wasmExports["memcmp"];
+    var _ts_language_symbol_count = Module["_ts_language_symbol_count"] = wasmExports["ts_language_symbol_count"];
+    var _ts_language_state_count = Module["_ts_language_state_count"] = wasmExports["ts_language_state_count"];
+    var _ts_language_version = Module["_ts_language_version"] = wasmExports["ts_language_version"];
+    var _ts_language_abi_version = Module["_ts_language_abi_version"] = wasmExports["ts_language_abi_version"];
+    var _ts_language_metadata = Module["_ts_language_metadata"] = wasmExports["ts_language_metadata"];
+    var _ts_language_name = Module["_ts_language_name"] = wasmExports["ts_language_name"];
+    var _ts_language_field_count = Module["_ts_language_field_count"] = wasmExports["ts_language_field_count"];
+    var _ts_language_next_state = Module["_ts_language_next_state"] = wasmExports["ts_language_next_state"];
+    var _ts_language_symbol_name = Module["_ts_language_symbol_name"] = wasmExports["ts_language_symbol_name"];
+    var _ts_language_symbol_for_name = Module["_ts_language_symbol_for_name"] = wasmExports["ts_language_symbol_for_name"];
+    var _strncmp = Module["_strncmp"] = wasmExports["strncmp"];
+    var _ts_language_symbol_type = Module["_ts_language_symbol_type"] = wasmExports["ts_language_symbol_type"];
+    var _ts_language_field_name_for_id = Module["_ts_language_field_name_for_id"] = wasmExports["ts_language_field_name_for_id"];
+    var _ts_lookahead_iterator_new = Module["_ts_lookahead_iterator_new"] = wasmExports["ts_lookahead_iterator_new"];
+    var _ts_lookahead_iterator_delete = Module["_ts_lookahead_iterator_delete"] = wasmExports["ts_lookahead_iterator_delete"];
+    var _ts_lookahead_iterator_reset_state = Module["_ts_lookahead_iterator_reset_state"] = wasmExports["ts_lookahead_iterator_reset_state"];
+    var _ts_lookahead_iterator_reset = Module["_ts_lookahead_iterator_reset"] = wasmExports["ts_lookahead_iterator_reset"];
+    var _ts_lookahead_iterator_next = Module["_ts_lookahead_iterator_next"] = wasmExports["ts_lookahead_iterator_next"];
+    var _ts_lookahead_iterator_current_symbol = Module["_ts_lookahead_iterator_current_symbol"] = wasmExports["ts_lookahead_iterator_current_symbol"];
+    var _ts_parser_delete = Module["_ts_parser_delete"] = wasmExports["ts_parser_delete"];
+    var _ts_parser_reset = Module["_ts_parser_reset"] = wasmExports["ts_parser_reset"];
+    var _ts_parser_set_language = Module["_ts_parser_set_language"] = wasmExports["ts_parser_set_language"];
+    var _ts_parser_timeout_micros = Module["_ts_parser_timeout_micros"] = wasmExports["ts_parser_timeout_micros"];
+    var _ts_parser_set_timeout_micros = Module["_ts_parser_set_timeout_micros"] = wasmExports["ts_parser_set_timeout_micros"];
+    var _ts_parser_set_included_ranges = Module["_ts_parser_set_included_ranges"] = wasmExports["ts_parser_set_included_ranges"];
+    var _ts_query_new = Module["_ts_query_new"] = wasmExports["ts_query_new"];
+    var _ts_query_delete = Module["_ts_query_delete"] = wasmExports["ts_query_delete"];
+    var _iswspace = Module["_iswspace"] = wasmExports["iswspace"];
+    var _iswalnum = Module["_iswalnum"] = wasmExports["iswalnum"];
+    var _ts_query_pattern_count = Module["_ts_query_pattern_count"] = wasmExports["ts_query_pattern_count"];
+    var _ts_query_capture_count = Module["_ts_query_capture_count"] = wasmExports["ts_query_capture_count"];
+    var _ts_query_string_count = Module["_ts_query_string_count"] = wasmExports["ts_query_string_count"];
+    var _ts_query_capture_name_for_id = Module["_ts_query_capture_name_for_id"] = wasmExports["ts_query_capture_name_for_id"];
+    var _ts_query_capture_quantifier_for_id = Module["_ts_query_capture_quantifier_for_id"] = wasmExports["ts_query_capture_quantifier_for_id"];
+    var _ts_query_string_value_for_id = Module["_ts_query_string_value_for_id"] = wasmExports["ts_query_string_value_for_id"];
+    var _ts_query_predicates_for_pattern = Module["_ts_query_predicates_for_pattern"] = wasmExports["ts_query_predicates_for_pattern"];
+    var _ts_query_start_byte_for_pattern = Module["_ts_query_start_byte_for_pattern"] = wasmExports["ts_query_start_byte_for_pattern"];
+    var _ts_query_end_byte_for_pattern = Module["_ts_query_end_byte_for_pattern"] = wasmExports["ts_query_end_byte_for_pattern"];
+    var _ts_query_is_pattern_rooted = Module["_ts_query_is_pattern_rooted"] = wasmExports["ts_query_is_pattern_rooted"];
+    var _ts_query_is_pattern_non_local = Module["_ts_query_is_pattern_non_local"] = wasmExports["ts_query_is_pattern_non_local"];
+    var _ts_query_is_pattern_guaranteed_at_step = Module["_ts_query_is_pattern_guaranteed_at_step"] = wasmExports["ts_query_is_pattern_guaranteed_at_step"];
+    var _ts_query_disable_capture = Module["_ts_query_disable_capture"] = wasmExports["ts_query_disable_capture"];
+    var _ts_query_disable_pattern = Module["_ts_query_disable_pattern"] = wasmExports["ts_query_disable_pattern"];
+    var _ts_tree_copy = Module["_ts_tree_copy"] = wasmExports["ts_tree_copy"];
+    var _ts_tree_delete = Module["_ts_tree_delete"] = wasmExports["ts_tree_delete"];
+    var _ts_init = Module["_ts_init"] = wasmExports["ts_init"];
+    var _ts_parser_new_wasm = Module["_ts_parser_new_wasm"] = wasmExports["ts_parser_new_wasm"];
+    var _ts_parser_enable_logger_wasm = Module["_ts_parser_enable_logger_wasm"] = wasmExports["ts_parser_enable_logger_wasm"];
+    var _ts_parser_parse_wasm = Module["_ts_parser_parse_wasm"] = wasmExports["ts_parser_parse_wasm"];
+    var _ts_parser_included_ranges_wasm = Module["_ts_parser_included_ranges_wasm"] = wasmExports["ts_parser_included_ranges_wasm"];
+    var _ts_language_type_is_named_wasm = Module["_ts_language_type_is_named_wasm"] = wasmExports["ts_language_type_is_named_wasm"];
+    var _ts_language_type_is_visible_wasm = Module["_ts_language_type_is_visible_wasm"] = wasmExports["ts_language_type_is_visible_wasm"];
+    var _ts_language_supertypes_wasm = Module["_ts_language_supertypes_wasm"] = wasmExports["ts_language_supertypes_wasm"];
+    var _ts_language_subtypes_wasm = Module["_ts_language_subtypes_wasm"] = wasmExports["ts_language_subtypes_wasm"];
+    var _ts_tree_root_node_wasm = Module["_ts_tree_root_node_wasm"] = wasmExports["ts_tree_root_node_wasm"];
+    var _ts_tree_root_node_with_offset_wasm = Module["_ts_tree_root_node_with_offset_wasm"] = wasmExports["ts_tree_root_node_with_offset_wasm"];
+    var _ts_tree_edit_wasm = Module["_ts_tree_edit_wasm"] = wasmExports["ts_tree_edit_wasm"];
+    var _ts_tree_included_ranges_wasm = Module["_ts_tree_included_ranges_wasm"] = wasmExports["ts_tree_included_ranges_wasm"];
+    var _ts_tree_get_changed_ranges_wasm = Module["_ts_tree_get_changed_ranges_wasm"] = wasmExports["ts_tree_get_changed_ranges_wasm"];
+    var _ts_tree_cursor_new_wasm = Module["_ts_tree_cursor_new_wasm"] = wasmExports["ts_tree_cursor_new_wasm"];
+    var _ts_tree_cursor_copy_wasm = Module["_ts_tree_cursor_copy_wasm"] = wasmExports["ts_tree_cursor_copy_wasm"];
+    var _ts_tree_cursor_delete_wasm = Module["_ts_tree_cursor_delete_wasm"] = wasmExports["ts_tree_cursor_delete_wasm"];
+    var _ts_tree_cursor_reset_wasm = Module["_ts_tree_cursor_reset_wasm"] = wasmExports["ts_tree_cursor_reset_wasm"];
+    var _ts_tree_cursor_reset_to_wasm = Module["_ts_tree_cursor_reset_to_wasm"] = wasmExports["ts_tree_cursor_reset_to_wasm"];
+    var _ts_tree_cursor_goto_first_child_wasm = Module["_ts_tree_cursor_goto_first_child_wasm"] = wasmExports["ts_tree_cursor_goto_first_child_wasm"];
+    var _ts_tree_cursor_goto_last_child_wasm = Module["_ts_tree_cursor_goto_last_child_wasm"] = wasmExports["ts_tree_cursor_goto_last_child_wasm"];
+    var _ts_tree_cursor_goto_first_child_for_index_wasm = Module["_ts_tree_cursor_goto_first_child_for_index_wasm"] = wasmExports["ts_tree_cursor_goto_first_child_for_index_wasm"];
+    var _ts_tree_cursor_goto_first_child_for_position_wasm = Module["_ts_tree_cursor_goto_first_child_for_position_wasm"] = wasmExports["ts_tree_cursor_goto_first_child_for_position_wasm"];
+    var _ts_tree_cursor_goto_next_sibling_wasm = Module["_ts_tree_cursor_goto_next_sibling_wasm"] = wasmExports["ts_tree_cursor_goto_next_sibling_wasm"];
+    var _ts_tree_cursor_goto_previous_sibling_wasm = Module["_ts_tree_cursor_goto_previous_sibling_wasm"] = wasmExports["ts_tree_cursor_goto_previous_sibling_wasm"];
+    var _ts_tree_cursor_goto_descendant_wasm = Module["_ts_tree_cursor_goto_descendant_wasm"] = wasmExports["ts_tree_cursor_goto_descendant_wasm"];
+    var _ts_tree_cursor_goto_parent_wasm = Module["_ts_tree_cursor_goto_parent_wasm"] = wasmExports["ts_tree_cursor_goto_parent_wasm"];
+    var _ts_tree_cursor_current_node_type_id_wasm = Module["_ts_tree_cursor_current_node_type_id_wasm"] = wasmExports["ts_tree_cursor_current_node_type_id_wasm"];
+    var _ts_tree_cursor_current_node_state_id_wasm = Module["_ts_tree_cursor_current_node_state_id_wasm"] = wasmExports["ts_tree_cursor_current_node_state_id_wasm"];
+    var _ts_tree_cursor_current_node_is_named_wasm = Module["_ts_tree_cursor_current_node_is_named_wasm"] = wasmExports["ts_tree_cursor_current_node_is_named_wasm"];
+    var _ts_tree_cursor_current_node_is_missing_wasm = Module["_ts_tree_cursor_current_node_is_missing_wasm"] = wasmExports["ts_tree_cursor_current_node_is_missing_wasm"];
+    var _ts_tree_cursor_current_node_id_wasm = Module["_ts_tree_cursor_current_node_id_wasm"] = wasmExports["ts_tree_cursor_current_node_id_wasm"];
+    var _ts_tree_cursor_start_position_wasm = Module["_ts_tree_cursor_start_position_wasm"] = wasmExports["ts_tree_cursor_start_position_wasm"];
+    var _ts_tree_cursor_end_position_wasm = Module["_ts_tree_cursor_end_position_wasm"] = wasmExports["ts_tree_cursor_end_position_wasm"];
+    var _ts_tree_cursor_start_index_wasm = Module["_ts_tree_cursor_start_index_wasm"] = wasmExports["ts_tree_cursor_start_index_wasm"];
+    var _ts_tree_cursor_end_index_wasm = Module["_ts_tree_cursor_end_index_wasm"] = wasmExports["ts_tree_cursor_end_index_wasm"];
+    var _ts_tree_cursor_current_field_id_wasm = Module["_ts_tree_cursor_current_field_id_wasm"] = wasmExports["ts_tree_cursor_current_field_id_wasm"];
+    var _ts_tree_cursor_current_depth_wasm = Module["_ts_tree_cursor_current_depth_wasm"] = wasmExports["ts_tree_cursor_current_depth_wasm"];
+    var _ts_tree_cursor_current_descendant_index_wasm = Module["_ts_tree_cursor_current_descendant_index_wasm"] = wasmExports["ts_tree_cursor_current_descendant_index_wasm"];
+    var _ts_tree_cursor_current_node_wasm = Module["_ts_tree_cursor_current_node_wasm"] = wasmExports["ts_tree_cursor_current_node_wasm"];
+    var _ts_node_symbol_wasm = Module["_ts_node_symbol_wasm"] = wasmExports["ts_node_symbol_wasm"];
+    var _ts_node_field_name_for_child_wasm = Module["_ts_node_field_name_for_child_wasm"] = wasmExports["ts_node_field_name_for_child_wasm"];
+    var _ts_node_field_name_for_named_child_wasm = Module["_ts_node_field_name_for_named_child_wasm"] = wasmExports["ts_node_field_name_for_named_child_wasm"];
+    var _ts_node_children_by_field_id_wasm = Module["_ts_node_children_by_field_id_wasm"] = wasmExports["ts_node_children_by_field_id_wasm"];
+    var _ts_node_first_child_for_byte_wasm = Module["_ts_node_first_child_for_byte_wasm"] = wasmExports["ts_node_first_child_for_byte_wasm"];
+    var _ts_node_first_named_child_for_byte_wasm = Module["_ts_node_first_named_child_for_byte_wasm"] = wasmExports["ts_node_first_named_child_for_byte_wasm"];
+    var _ts_node_grammar_symbol_wasm = Module["_ts_node_grammar_symbol_wasm"] = wasmExports["ts_node_grammar_symbol_wasm"];
+    var _ts_node_child_count_wasm = Module["_ts_node_child_count_wasm"] = wasmExports["ts_node_child_count_wasm"];
+    var _ts_node_named_child_count_wasm = Module["_ts_node_named_child_count_wasm"] = wasmExports["ts_node_named_child_count_wasm"];
+    var _ts_node_child_wasm = Module["_ts_node_child_wasm"] = wasmExports["ts_node_child_wasm"];
+    var _ts_node_named_child_wasm = Module["_ts_node_named_child_wasm"] = wasmExports["ts_node_named_child_wasm"];
+    var _ts_node_child_by_field_id_wasm = Module["_ts_node_child_by_field_id_wasm"] = wasmExports["ts_node_child_by_field_id_wasm"];
+    var _ts_node_next_sibling_wasm = Module["_ts_node_next_sibling_wasm"] = wasmExports["ts_node_next_sibling_wasm"];
+    var _ts_node_prev_sibling_wasm = Module["_ts_node_prev_sibling_wasm"] = wasmExports["ts_node_prev_sibling_wasm"];
+    var _ts_node_next_named_sibling_wasm = Module["_ts_node_next_named_sibling_wasm"] = wasmExports["ts_node_next_named_sibling_wasm"];
+    var _ts_node_prev_named_sibling_wasm = Module["_ts_node_prev_named_sibling_wasm"] = wasmExports["ts_node_prev_named_sibling_wasm"];
+    var _ts_node_descendant_count_wasm = Module["_ts_node_descendant_count_wasm"] = wasmExports["ts_node_descendant_count_wasm"];
+    var _ts_node_parent_wasm = Module["_ts_node_parent_wasm"] = wasmExports["ts_node_parent_wasm"];
+    var _ts_node_child_with_descendant_wasm = Module["_ts_node_child_with_descendant_wasm"] = wasmExports["ts_node_child_with_descendant_wasm"];
+    var _ts_node_descendant_for_index_wasm = Module["_ts_node_descendant_for_index_wasm"] = wasmExports["ts_node_descendant_for_index_wasm"];
+    var _ts_node_named_descendant_for_index_wasm = Module["_ts_node_named_descendant_for_index_wasm"] = wasmExports["ts_node_named_descendant_for_index_wasm"];
+    var _ts_node_descendant_for_position_wasm = Module["_ts_node_descendant_for_position_wasm"] = wasmExports["ts_node_descendant_for_position_wasm"];
+    var _ts_node_named_descendant_for_position_wasm = Module["_ts_node_named_descendant_for_position_wasm"] = wasmExports["ts_node_named_descendant_for_position_wasm"];
+    var _ts_node_start_point_wasm = Module["_ts_node_start_point_wasm"] = wasmExports["ts_node_start_point_wasm"];
+    var _ts_node_end_point_wasm = Module["_ts_node_end_point_wasm"] = wasmExports["ts_node_end_point_wasm"];
+    var _ts_node_start_index_wasm = Module["_ts_node_start_index_wasm"] = wasmExports["ts_node_start_index_wasm"];
+    var _ts_node_end_index_wasm = Module["_ts_node_end_index_wasm"] = wasmExports["ts_node_end_index_wasm"];
+    var _ts_node_to_string_wasm = Module["_ts_node_to_string_wasm"] = wasmExports["ts_node_to_string_wasm"];
+    var _ts_node_children_wasm = Module["_ts_node_children_wasm"] = wasmExports["ts_node_children_wasm"];
+    var _ts_node_named_children_wasm = Module["_ts_node_named_children_wasm"] = wasmExports["ts_node_named_children_wasm"];
+    var _ts_node_descendants_of_type_wasm = Module["_ts_node_descendants_of_type_wasm"] = wasmExports["ts_node_descendants_of_type_wasm"];
+    var _ts_node_is_named_wasm = Module["_ts_node_is_named_wasm"] = wasmExports["ts_node_is_named_wasm"];
+    var _ts_node_has_changes_wasm = Module["_ts_node_has_changes_wasm"] = wasmExports["ts_node_has_changes_wasm"];
+    var _ts_node_has_error_wasm = Module["_ts_node_has_error_wasm"] = wasmExports["ts_node_has_error_wasm"];
+    var _ts_node_is_error_wasm = Module["_ts_node_is_error_wasm"] = wasmExports["ts_node_is_error_wasm"];
+    var _ts_node_is_missing_wasm = Module["_ts_node_is_missing_wasm"] = wasmExports["ts_node_is_missing_wasm"];
+    var _ts_node_is_extra_wasm = Module["_ts_node_is_extra_wasm"] = wasmExports["ts_node_is_extra_wasm"];
+    var _ts_node_parse_state_wasm = Module["_ts_node_parse_state_wasm"] = wasmExports["ts_node_parse_state_wasm"];
+    var _ts_node_next_parse_state_wasm = Module["_ts_node_next_parse_state_wasm"] = wasmExports["ts_node_next_parse_state_wasm"];
+    var _ts_query_matches_wasm = Module["_ts_query_matches_wasm"] = wasmExports["ts_query_matches_wasm"];
+    var _ts_query_captures_wasm = Module["_ts_query_captures_wasm"] = wasmExports["ts_query_captures_wasm"];
+    var _memset = Module["_memset"] = wasmExports["memset"];
+    var _memcpy = Module["_memcpy"] = wasmExports["memcpy"];
+    var _memmove = Module["_memmove"] = wasmExports["memmove"];
+    var _iswalpha = Module["_iswalpha"] = wasmExports["iswalpha"];
+    var _iswblank = Module["_iswblank"] = wasmExports["iswblank"];
+    var _iswdigit = Module["_iswdigit"] = wasmExports["iswdigit"];
+    var _iswlower = Module["_iswlower"] = wasmExports["iswlower"];
+    var _iswupper = Module["_iswupper"] = wasmExports["iswupper"];
+    var _iswxdigit = Module["_iswxdigit"] = wasmExports["iswxdigit"];
+    var _memchr = Module["_memchr"] = wasmExports["memchr"];
+    var _strlen = Module["_strlen"] = wasmExports["strlen"];
+    var _strcmp = Module["_strcmp"] = wasmExports["strcmp"];
+    var _strncat = Module["_strncat"] = wasmExports["strncat"];
+    var _strncpy = Module["_strncpy"] = wasmExports["strncpy"];
+    var _towlower = Module["_towlower"] = wasmExports["towlower"];
+    var _towupper = Module["_towupper"] = wasmExports["towupper"];
+    var _setThrew = wasmExports["setThrew"];
+    var __emscripten_stack_restore = wasmExports["_emscripten_stack_restore"];
+    var __emscripten_stack_alloc = wasmExports["_emscripten_stack_alloc"];
+    var _emscripten_stack_get_current = wasmExports["emscripten_stack_get_current"];
+    var ___wasm_apply_data_relocs = wasmExports["__wasm_apply_data_relocs"];
+    Module["setValue"] = setValue;
+    Module["getValue"] = getValue;
+    Module["UTF8ToString"] = UTF8ToString;
+    Module["stringToUTF8"] = stringToUTF8;
+    Module["lengthBytesUTF8"] = lengthBytesUTF8;
+    Module["AsciiToString"] = AsciiToString;
+    Module["stringToUTF16"] = stringToUTF16;
+    Module["loadWebAssemblyModule"] = loadWebAssemblyModule;
+    function callMain(args2 = []) {
+      var entryFunction = resolveGlobalSymbol("main").sym;
+      if (!entryFunction) return;
+      args2.unshift(thisProgram);
+      var argc = args2.length;
+      var argv = stackAlloc((argc + 1) * 4);
+      var argv_ptr = argv;
+      args2.forEach((arg) => {
+        LE_HEAP_STORE_U32((argv_ptr >> 2) * 4, stringToUTF8OnStack(arg));
+        argv_ptr += 4;
+      });
+      LE_HEAP_STORE_U32((argv_ptr >> 2) * 4, 0);
+      try {
+        var ret = entryFunction(argc, argv);
+        exitJS(
+          ret,
+          /* implicit = */
+          true
+        );
+        return ret;
+      } catch (e) {
+        return handleException(e);
+      }
+    }
+    __name(callMain, "callMain");
+    function run(args2 = arguments_) {
+      if (runDependencies > 0) {
+        dependenciesFulfilled = run;
+        return;
+      }
+      preRun();
+      if (runDependencies > 0) {
+        dependenciesFulfilled = run;
+        return;
+      }
+      function doRun() {
+        Module["calledRun"] = true;
+        if (ABORT) return;
+        initRuntime();
+        preMain();
+        readyPromiseResolve(Module);
+        Module["onRuntimeInitialized"]?.();
+        var noInitialRun = Module["noInitialRun"];
+        if (!noInitialRun) callMain(args2);
+        postRun();
+      }
+      __name(doRun, "doRun");
+      if (Module["setStatus"]) {
+        Module["setStatus"]("Running...");
+        setTimeout(() => {
+          setTimeout(() => Module["setStatus"](""), 1);
+          doRun();
+        }, 1);
+      } else {
+        doRun();
+      }
+    }
+    __name(run, "run");
+    if (Module["preInit"]) {
+      if (typeof Module["preInit"] == "function") Module["preInit"] = [Module["preInit"]];
+      while (Module["preInit"].length > 0) {
+        Module["preInit"].pop()();
+      }
+    }
+    run();
+    moduleRtn = readyPromise;
+    return moduleRtn;
+  };
+})();
+var tree_sitter_default = Module2;
+
+// src/bindings.ts
+var Module3 = null;
+async function initializeBinding(moduleOptions) {
+  if (!Module3) {
+    Module3 = await tree_sitter_default(moduleOptions);
+  }
+  return Module3;
+}
+__name(initializeBinding, "initializeBinding");
+function checkModule() {
+  return !!Module3;
+}
+__name(checkModule, "checkModule");
+
+// src/parser.ts
+var TRANSFER_BUFFER;
+var LANGUAGE_VERSION;
+var MIN_COMPATIBLE_VERSION;
+var Parser = class {
+  static {
+    __name(this, "Parser");
+  }
+  /** @internal */
+  [0] = 0;
+  // Internal handle for WASM
+  /** @internal */
+  [1] = 0;
+  // Internal handle for WASM
+  /** @internal */
+  logCallback = null;
+  /** The parser's current language. */
+  language = null;
+  /**
+   * This must always be called before creating a Parser.
+   *
+   * You can optionally pass in options to configure the WASM module, the most common
+   * one being `locateFile` to help the module find the `.wasm` file.
+   */
+  static async init(moduleOptions) {
+    setModule(await initializeBinding(moduleOptions));
+    TRANSFER_BUFFER = C._ts_init();
+    LANGUAGE_VERSION = C.getValue(TRANSFER_BUFFER, "i32");
+    MIN_COMPATIBLE_VERSION = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+  }
+  /**
+   * Create a new parser.
+   */
+  constructor() {
+    this.initialize();
+  }
+  /** @internal */
+  initialize() {
+    if (!checkModule()) {
+      throw new Error("cannot construct a Parser before calling `init()`");
+    }
+    C._ts_parser_new_wasm();
+    this[0] = C.getValue(TRANSFER_BUFFER, "i32");
+    this[1] = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+  }
+  /** Delete the parser, freeing its resources. */
+  delete() {
+    C._ts_parser_delete(this[0]);
+    C._free(this[1]);
+    this[0] = 0;
+    this[1] = 0;
+  }
+  /**
+   * Set the language that the parser should use for parsing.
+   *
+   * If the language was not successfully assigned, an error will be thrown.
+   * This happens if the language was generated with an incompatible
+   * version of the Tree-sitter CLI. Check the language's version using
+   * {@link Language#version} and compare it to this library's
+   * {@link LANGUAGE_VERSION} and {@link MIN_COMPATIBLE_VERSION} constants.
+   */
+  setLanguage(language) {
+    let address;
+    if (!language) {
+      address = 0;
+      this.language = null;
+    } else if (language.constructor === Language) {
+      address = language[0];
+      const version = C._ts_language_version(address);
+      if (version < MIN_COMPATIBLE_VERSION || LANGUAGE_VERSION < version) {
+        throw new Error(
+          `Incompatible language version ${version}. Compatibility range ${MIN_COMPATIBLE_VERSION} through ${LANGUAGE_VERSION}.`
+        );
+      }
+      this.language = language;
+    } else {
+      throw new Error("Argument must be a Language");
+    }
+    C._ts_parser_set_language(this[0], address);
+    return this;
+  }
+  /**
+   * Parse a slice of UTF8 text.
+   *
+   * @param {string | ParseCallback} callback - The UTF8-encoded text to parse or a callback function.
+   *
+   * @param {Tree | null} [oldTree] - A previous syntax tree parsed from the same document. If the text of the
+   *   document has changed since `oldTree` was created, then you must edit `oldTree` to match
+   *   the new text using {@link Tree#edit}.
+   *
+   * @param {ParseOptions} [options] - Options for parsing the text.
+   *  This can be used to set the included ranges, or a progress callback.
+   *
+   * @returns {Tree | null} A {@link Tree} if parsing succeeded, or `null` if:
+   *  - The parser has not yet had a language assigned with {@link Parser#setLanguage}.
+   *  - The progress callback returned true.
+   */
+  parse(callback, oldTree, options) {
+    if (typeof callback === "string") {
+      C.currentParseCallback = (index) => callback.slice(index);
+    } else if (typeof callback === "function") {
+      C.currentParseCallback = callback;
+    } else {
+      throw new Error("Argument must be a string or a function");
+    }
+    if (options?.progressCallback) {
+      C.currentProgressCallback = options.progressCallback;
+    } else {
+      C.currentProgressCallback = null;
+    }
+    if (this.logCallback) {
+      C.currentLogCallback = this.logCallback;
+      C._ts_parser_enable_logger_wasm(this[0], 1);
+    } else {
+      C.currentLogCallback = null;
+      C._ts_parser_enable_logger_wasm(this[0], 0);
+    }
+    let rangeCount = 0;
+    let rangeAddress = 0;
+    if (options?.includedRanges) {
+      rangeCount = options.includedRanges.length;
+      rangeAddress = C._calloc(rangeCount, SIZE_OF_RANGE);
+      let address = rangeAddress;
+      for (let i2 = 0; i2 < rangeCount; i2++) {
+        marshalRange(address, options.includedRanges[i2]);
+        address += SIZE_OF_RANGE;
+      }
+    }
+    const treeAddress = C._ts_parser_parse_wasm(
+      this[0],
+      this[1],
+      oldTree ? oldTree[0] : 0,
+      rangeAddress,
+      rangeCount
+    );
+    if (!treeAddress) {
+      C.currentParseCallback = null;
+      C.currentLogCallback = null;
+      C.currentProgressCallback = null;
+      return null;
+    }
+    if (!this.language) {
+      throw new Error("Parser must have a language to parse");
+    }
+    const result = new Tree(INTERNAL, treeAddress, this.language, C.currentParseCallback);
+    C.currentParseCallback = null;
+    C.currentLogCallback = null;
+    C.currentProgressCallback = null;
+    return result;
+  }
+  /**
+   * Instruct the parser to start the next parse from the beginning.
+   *
+   * If the parser previously failed because of a timeout, cancellation,
+   * or callback, then by default, it will resume where it left off on the
+   * next call to {@link Parser#parse} or other parsing functions.
+   * If you don't want to resume, and instead intend to use this parser to
+   * parse some other document, you must call `reset` first.
+   */
+  reset() {
+    C._ts_parser_reset(this[0]);
+  }
+  /** Get the ranges of text that the parser will include when parsing. */
+  getIncludedRanges() {
+    C._ts_parser_included_ranges_wasm(this[0]);
+    const count = C.getValue(TRANSFER_BUFFER, "i32");
+    const buffer = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+    const result = new Array(count);
+    if (count > 0) {
+      let address = buffer;
+      for (let i2 = 0; i2 < count; i2++) {
+        result[i2] = unmarshalRange(address);
+        address += SIZE_OF_RANGE;
+      }
+      C._free(buffer);
+    }
+    return result;
+  }
+  /**
+   * @deprecated since version 0.25.0, prefer passing a progress callback to {@link Parser#parse}
+   *
+   * Get the duration in microseconds that parsing is allowed to take.
+   *
+   * This is set via {@link Parser#setTimeoutMicros}.
+   */
+  getTimeoutMicros() {
+    return C._ts_parser_timeout_micros(this[0]);
+  }
+  /**
+   * @deprecated since version 0.25.0, prefer passing a progress callback to {@link Parser#parse}
+   *
+   * Set the maximum duration in microseconds that parsing should be allowed
+   * to take before halting.
+   *
+   * If parsing takes longer than this, it will halt early, returning `null`.
+   * See {@link Parser#parse} for more information.
+   */
+  setTimeoutMicros(timeout) {
+    C._ts_parser_set_timeout_micros(this[0], 0, timeout);
+  }
+  /** Set the logging callback that a parser should use during parsing. */
+  setLogger(callback) {
+    if (!callback) {
+      this.logCallback = null;
+    } else if (typeof callback !== "function") {
+      throw new Error("Logger callback must be a function");
+    } else {
+      this.logCallback = callback;
+    }
+    return this;
+  }
+  /** Get the parser's current logger. */
+  getLogger() {
+    return this.logCallback;
+  }
+};
+export {
+  CaptureQuantifier,
+  LANGUAGE_VERSION,
+  Language,
+  LookaheadIterator,
+  MIN_COMPATIBLE_VERSION,
+  Node,
+  Parser,
+  Query,
+  Tree,
+  TreeCursor
+};
+//# sourceMappingURL=tree-sitter.js.map
diff --git a/custom_nodes/rgthree-comfy/src_web/link_fixer/icon_file_json.png b/custom_nodes/rgthree-comfy/src_web/link_fixer/icon_file_json.png
new file mode 100644
index 0000000000000000000000000000000000000000..ad3a1cb2b89a2051010d53d69b12cca8735af353
Binary files /dev/null and b/custom_nodes/rgthree-comfy/src_web/link_fixer/icon_file_json.png differ
diff --git a/custom_nodes/rgthree-comfy/src_web/link_fixer/index.html b/custom_nodes/rgthree-comfy/src_web/link_fixer/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..2e1b66ecd2f5e0c4cb1489b02eba9b226e86d819
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/link_fixer/index.html
@@ -0,0 +1,140 @@
+<!doctype html>
+<html>
+  <head>
+    <title>rgthree's comfy: Workflow Link Fixer</title>
+    <style>
+      html, body {
+      }
+      html {
+        font-size: 100%;
+        overflow-y: scroll;
+        -webkit-text-size-adjust: 100%;
+        -ms-text-size-adjust: 100%;
+        box-sizing: border-box
+      }
+
+      *, *:before, *:after {
+        box-sizing: inherit
+      }
+
+      body {
+        background: #222;
+        font-family: Arial, sans-serif;
+        font-size: calc(16 * 0.0625rem);
+        font-weight: 400;
+        margin: 0;
+        color: #ffffff;
+        padding-bottom: 64px;
+        display: flex;
+      }
+
+      .box, figure, picture {
+        display: flex;
+        flex-direction: column;
+        align-items: center;
+      }
+
+      .box {
+        margin: 32px auto;
+        max-width: 720px;
+        padding: 16px 16px 32px;
+        background: rgba(125,125,125,0.33);
+        box-shadow: 0px 8px 10px rgba(0,0,0,0.85);
+        border-radius: 8px;
+      }
+      h1 {
+        margin-top: 0;
+      }
+      .box > * {
+        text-align: center;
+      }
+      .box > p {
+        margin: 0 0 .6em;
+        text-align: left;
+        line-height: 1.25;
+      }
+      .box > small {
+        margin: 0 0 .6em;
+        text-align: left;
+        line-height: 1.25;
+        opacity: 0.75;
+      }
+
+      picture > img {
+        display: block;
+        height: 256px;
+        min-width: 256px;
+        border: 10px dashed rgba(125,125,125,0.66);
+        border-radius: 32px;
+        object-fit: contain;
+        pointer-events: none;
+
+      }
+      figcaption {
+        opacity: 0.66;
+        padding: 4px;
+        font-size: calc(11 * 0.0625rem);
+      }
+
+      .-has-file picture > img {
+        border-color: transparent;
+        border-radius: 32px;
+      }
+
+      .output {
+        margin: 8px 8px 32px;
+        border-radius: 4px;
+        border: 1px solid rgba(125,125,125,0.66);
+        padding: 4px 8px;
+        min-width: 80%;
+      }
+
+      .output:empty {
+        display: none;
+      }
+
+      .btn-fix {
+        display: none;
+        cursor: pointer;
+        font-size: calc(24 * 0.0625rem);
+      }
+      .-has-fixable-results .btn-fix {
+        display: inline-block;
+      }
+
+    </style>
+  </head>
+  <body>
+
+    <div class="box">
+      <h1>rgthree's Workflow Link Fixer</h1>
+      <p>
+        Drag and drop a comfy-generated image or workflow json into this window to check its
+        serialized links and attempt to fix.
+      </p>
+      <small>
+        Sometimes as you have complex workflows the internal data can become corrupt, and the
+        ComfyUI doesn't always understand or display correctly. Maybe links disappear, or reconnect
+        to another node when changing something. Load it here to detect and, if possible, attempt
+        to fix it (sometimes, however, fixing it just isn't feasible, so fingers crossed).
+      </small>
+
+      <figure>
+        <picture>
+          <img class="output-image" />
+        </picture>
+        <figcaption></figcaption>
+      </figure>
+
+      <div class="output"></div>
+
+      <button class="btn-fix">Fix & Save new workflow</button>
+
+    </div>
+
+    <script type="module">
+      import {LinkPage} from './link_page.js';
+      new LinkPage();
+    </script>
+  </body>
+</html>
\ No newline at end of file
diff --git a/custom_nodes/rgthree-comfy/src_web/link_fixer/link_page.ts b/custom_nodes/rgthree-comfy/src_web/link_fixer/link_page.ts
new file mode 100644
index 0000000000000000000000000000000000000000..024ff6bc9c1c8ae6c2b1e007b310fedafe03781f
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/link_fixer/link_page.ts
@@ -0,0 +1,225 @@
+import type {ISerialisedGraph} from "@comfyorg/frontend";
+import type {BadLinksData} from "../common/link_fixer.js";
+
+import {WorkflowLinkFixer} from "../common/link_fixer.js";
+import {getPngMetadata} from "../common/comfyui_shim.js";
+
+function wait(ms = 16, value?: any) {
+  return new Promise((resolve) => {
+    setTimeout(() => {
+      resolve(value);
+    }, ms);
+  });
+}
+
+const logger = {
+  logTo: console as Console | HTMLElement,
+  log: (...args: any[]) => {
+    logger.logTo === console
+      ? console.log(...args)
+      : ((logger.logTo as HTMLElement).innerText += args.join(",") + "\n");
+  },
+};
+
+export class LinkPage {
+  private containerEl: HTMLDivElement;
+  private figcaptionEl: HTMLElement;
+  private btnFix: HTMLButtonElement;
+  private outputeMessageEl: HTMLDivElement;
+  private outputImageEl: HTMLImageElement;
+
+  private file?: File | Blob;
+  private graph?: ISerialisedGraph;
+  private graphResults?: BadLinksData;
+  private graphFinalResults?: BadLinksData;
+
+  private fixer: WorkflowLinkFixer<any, any> | null = null;
+
+  constructor() {
+    // const consoleEl = document.getElementById("console")!;
+    this.containerEl = document.querySelector(".box")!;
+    this.figcaptionEl = document.querySelector("figcaption")!;
+    this.outputeMessageEl = document.querySelector(".output")!;
+    this.outputImageEl = document.querySelector(".output-image")!;
+    this.btnFix = document.querySelector(".btn-fix")!;
+
+    // Need to prevent on dragover to allow drop...
+    document.addEventListener(
+      "dragover",
+      (e) => {
+        e.preventDefault();
+      },
+      false,
+    );
+    document.addEventListener("drop", (e) => {
+      this.onDrop(e);
+    });
+    this.btnFix.addEventListener("click", (e) => {
+      this.onFixClick(e);
+    });
+  }
+
+  private async onFixClick(e: MouseEvent) {
+    if (!this.fixer?.checkedData || !this.graph) {
+      this.updateUi("⛔ Fix button click without results.");
+      return;
+    }
+    this.graphFinalResults = this.fixer.fix();
+
+    if (this.graphFinalResults.hasBadLinks) {
+      this.updateUi(
+        "⛔ Hmm... Still detecting bad links. Can you file an issue at https://github.com/rgthree/rgthree-comfy/issues with your image/workflow.",
+      );
+    } else {
+      this.updateUi(
+        "✅ Workflow fixed.<br><br><small>Please load new saved workflow json and double check linking and execution.</small>",
+      );
+    }
+    await wait(16);
+    await this.saveFixedWorkflow();
+  }
+
+  private async onDrop(event: DragEvent) {
+    if (!event.dataTransfer) {
+      return;
+    }
+    this.reset();
+
+    event.preventDefault();
+    event.stopPropagation();
+
+    // Dragging from Chrome->Firefox there is a file but its a bmp, so ignore that
+    if (event.dataTransfer.files.length && event.dataTransfer.files?.[0]?.type !== "image/bmp") {
+      await this.handleFile(event.dataTransfer.files[0]!);
+      return;
+    }
+
+    // Try loading the first URI in the transfer list
+    const validTypes = ["text/uri-list", "text/x-moz-url"];
+    const match = [...event.dataTransfer.types].find((t) => validTypes.find((v) => t === v));
+    if (match) {
+      const uri = event.dataTransfer.getData(match)?.split("\n")?.[0];
+      if (uri) {
+        await this.handleFile(await (await fetch(uri)).blob());
+      }
+    }
+  }
+
+  reset() {
+    this.file = undefined;
+    this.graph = undefined;
+    this.graphResults = undefined;
+    this.graphFinalResults = undefined;
+    this.updateUi();
+  }
+
+  private updateUi(msg?: string) {
+    this.outputeMessageEl.innerHTML = "";
+    if (this.file && !this.containerEl.classList.contains("-has-file")) {
+      this.containerEl.classList.add("-has-file");
+      this.figcaptionEl.innerHTML = (this.file as File).name || this.file.type;
+      if (this.file.type === "application/json") {
+        this.outputImageEl.src = "icon_file_json.png";
+      } else {
+        const reader = new FileReader();
+        reader.onload = () => (this.outputImageEl.src = reader.result as string);
+        reader.readAsDataURL(this.file);
+      }
+    } else if (!this.file && this.containerEl.classList.contains("-has-file")) {
+      this.containerEl.classList.remove("-has-file");
+      this.outputImageEl.src = "";
+      this.outputImageEl.removeAttribute("src");
+    }
+
+    if (this.graphResults) {
+      this.containerEl.classList.add("-has-results");
+      if (!this.graphResults.patches && !this.graphResults.deletes) {
+        this.outputeMessageEl.innerHTML = "✅ No bad links detected in the workflow.";
+      } else {
+        this.containerEl.classList.add("-has-fixable-results");
+        this.outputeMessageEl.innerHTML = `⚠️ Found ${this.graphResults.patches} links to fix, and ${this.graphResults.deletes} to be removed.`;
+      }
+    } else {
+      this.containerEl.classList.remove("-has-results");
+      this.containerEl.classList.remove("-has-fixable-results");
+    }
+
+    if (msg) {
+      this.outputeMessageEl.innerHTML = msg;
+    }
+  }
+
+  private async handleFile(file: File | Blob) {
+    this.file = file;
+    this.updateUi();
+
+    let workflow: string | undefined | null = null;
+    if (file.type.startsWith("image/")) {
+      const pngInfo = await getPngMetadata(file);
+      workflow = pngInfo?.workflow;
+    } else if (
+      file.type === "application/json" ||
+      (file instanceof File && file.name.endsWith(".json"))
+    ) {
+      workflow = await new Promise((resolve) => {
+        const reader = new FileReader();
+        reader.onload = () => {
+          resolve(reader.result as string);
+        };
+        reader.readAsText(file);
+      });
+    }
+    if (!workflow) {
+      this.updateUi("⛔ No workflow found in dropped item.");
+    } else {
+      try {
+        this.graph = JSON.parse(workflow);
+      } catch (e) {
+        this.graph = undefined;
+      }
+      if (!this.graph) {
+        this.updateUi("⛔ Invalid workflow found in dropped item.");
+      } else {
+        this.loadGraphData(this.graph);
+      }
+    }
+  }
+
+  private async loadGraphData(graphData: ISerialisedGraph) {
+    this.fixer = WorkflowLinkFixer.create(graphData);
+    this.graphResults = this.fixer.check();
+    this.updateUi();
+  }
+
+  private async saveFixedWorkflow() {
+    if (!this.graphFinalResults) {
+      this.updateUi("⛔ Save w/o final graph patched.");
+      return false;
+    }
+
+    let filename: string | null = (this.file as File).name || "workflow.json";
+    let filenames = filename.split(".");
+    filenames.pop();
+    filename = filenames.join(".");
+    filename += "_fixed.json";
+    filename = prompt("Save workflow as:", filename);
+    if (!filename) return false;
+    if (!filename.toLowerCase().endsWith(".json")) {
+      filename += ".json";
+    }
+    const json = JSON.stringify(this.graphFinalResults.graph, null, 2);
+    const blob = new Blob([json], {type: "application/json"});
+    const url = URL.createObjectURL(blob);
+    const anchor = document.createElement("a");
+    anchor.download = filename;
+    anchor.href = url;
+    anchor.style.display = "none";
+    document.body.appendChild(anchor);
+    await wait();
+    anchor.click();
+    await wait();
+    anchor.remove();
+    window.URL.revokeObjectURL(url);
+    return true;
+  }
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/models/components/model-info-card.html b/custom_nodes/rgthree-comfy/src_web/models/components/model-info-card.html
new file mode 100644
index 0000000000000000000000000000000000000000..3be75c9d3adab73695a711b1943d3edf92212486
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/models/components/model-info-card.html
@@ -0,0 +1,18 @@
+
+<template>
+  <div class="rgthree-model-info-card">
+    <figure class="rgthree-model-info-card-media-container">
+      <video if="images.0 && images.0.type == 'video'" bind="images.0.url:src" loop autoplay></video>
+      <img if="images.0 && images.0.type != 'video'" bind="images.0.url:src"  loading="lazy" />
+    </figure>
+    <div class="rgthree-model-info-card-data-container">
+      <div bind="name:text"></div>
+      <div bind="file:text"></div>
+      <div bind="getModified(modified):text"></div>
+      <div bind="sha256:text"></div>
+      <div bind="hasInfoFile"></div>
+      <a if="getCivitaiLink(links)" bind="getCivitaiLink(links):href">Civitai</a>
+    </div>
+  </div>
+</template>
+
diff --git a/custom_nodes/rgthree-comfy/src_web/models/components/model-info-card.scss b/custom_nodes/rgthree-comfy/src_web/models/components/model-info-card.scss
new file mode 100644
index 0000000000000000000000000000000000000000..82e085a5a468e320ab6cd4092a3c772718b98265
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/models/components/model-info-card.scss
@@ -0,0 +1,42 @@
+$space: 8px;
+
+.rgthree-model-info-card {
+  display: block;
+  padding: $space;
+}
+
+.-is-hidden {
+  display: none;
+}
+
+
+.rgthree-model-info-card {
+  display: flex;
+  flex-direction: row;
+
+  >.rgthree-model-info-card-media-container {
+    width: 100px;
+    height: auto;
+    display: block;
+    margin: 0 $space 0 0;
+    padding: 0;
+    flex: 0 0 auto;
+
+    >img,
+    >video {
+      width: 100%;
+      height: 100%;
+      object-fit: contain;
+    }
+  }
+
+  >.rgthree-model-info-card-data-container {
+
+    [bind*="name:"] {
+      font-size: 1.3em;
+      margin-bottom: calc($space / 2);
+      font-weight: bold;
+    }
+
+  }
+}
\ No newline at end of file
diff --git a/custom_nodes/rgthree-comfy/src_web/models/components/model-info-card.ts b/custom_nodes/rgthree-comfy/src_web/models/components/model-info-card.ts
new file mode 100644
index 0000000000000000000000000000000000000000..3d5cdea97df5b1e5197ee3ea829a32439b004793
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/models/components/model-info-card.ts
@@ -0,0 +1,53 @@
+import {RgthreeCustomElement} from "rgthree/common/components/base_custom_element";
+import {RgthreeModelInfo} from "typings/rgthree";
+
+export class RgthreeModelInfoCard extends RgthreeCustomElement {
+  static override readonly NAME = "rgthree-model-info-card";
+  static override readonly TEMPLATES = "components/model-info-card.html";
+  static override readonly CSS = "components/model-info-card.css";
+  static override readonly USE_SHADOW = false;
+
+  private data: RgthreeModelInfo = {};
+
+  getModified(
+    value: number,
+    data: any,
+    currentElement: HTMLElement,
+    contextElement: RgthreeModelInfoCard,
+  ) {
+    const date = new Date(value);
+    return String(`${date.toLocaleDateString()} ${date.toLocaleTimeString()}`);
+  }
+
+  getCivitaiLink(links: string[] | undefined) {
+    return links?.find((i) => i.includes("civitai.com/models")) || null;
+  }
+
+  setModelData(data: RgthreeModelInfo) {
+    this.data = data;
+  }
+
+  hasBaseModel(baseModel: string) {
+    return this.data.baseModel === baseModel;
+  }
+
+  hasData(field: string) {
+    // return !!this.data.hasInfoFile;
+    if (field === "civitai") {
+      return !!this.getCivitaiLink(this.data.links)?.length;
+    }
+    return !!(this.data as any)[field];
+  }
+
+  matchesQueryText(query: string) {
+    return (this.data.name || this.data.file)?.includes(query);
+  }
+
+  hide() {
+    this.classList.add("-is-hidden");
+  }
+
+  show() {
+    this.classList.remove("-is-hidden");
+  }
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/models/index.html b/custom_nodes/rgthree-comfy/src_web/models/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..137a1f72a9f72a76d585b6a006c4d6eebceb3fe6
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/models/index.html
@@ -0,0 +1,18 @@
+<!DOCTYPE html>
+<html>
+  <head>
+  <title>rgthree-comfy: Models Manager</title>
+  <link href="./models.css" rel="stylesheet">
+  <!-- <link rel="icon" href="./favicon.webp"> -->
+</head>
+<body>
+  <header>
+    <input type="text" placeholder="Search" id="searchbox" />
+  </header>
+  <ul id="models-list"></ul>
+  <script type="module">
+    import {ModelsInfoPage} from './models_info_page.js';
+    window.page = new ModelsInfoPage();
+  </script>
+</body>
+</html>
\ No newline at end of file
diff --git a/custom_nodes/rgthree-comfy/src_web/models/models.scss b/custom_nodes/rgthree-comfy/src_web/models/models.scss
new file mode 100644
index 0000000000000000000000000000000000000000..160ff1743023a8151abecc317606f60fa0094357
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/models/models.scss
@@ -0,0 +1,40 @@
+$space: 8px;
+
+:root {
+  --rgthree-bg-color: rgba(23, 23, 23, 0.9);
+  --rgthree-on-bg-color: rgba(48, 48, 48, 0.9);
+}
+
+html, body {
+  margin: 0;
+  padding: 0;
+  font-family: Arial, sans-serif;
+  box-sizing: border-body;
+  background: var(--rgthree-bg-color);
+}
+
+*, *::before, *::after {
+  box-sizing: inherit;
+}
+
+[if-is="false"] {
+  display: none;
+}
+
+.models-list {
+  list-style: none;
+  margin: 0;
+  padding: 0;
+}
+
+.model-item {
+  display: block;
+  margin: 8px;
+}
+
+rgthree-model-info-card {
+  background: var(--rgthree-on-bg-color);
+  margin: $space * 2;
+  display: block;
+  border-radius: 4px;
+}
\ No newline at end of file
diff --git a/custom_nodes/rgthree-comfy/src_web/models/models_info_page.ts b/custom_nodes/rgthree-comfy/src_web/models/models_info_page.ts
new file mode 100644
index 0000000000000000000000000000000000000000..df6e38e415f0701a2f65107c0d527d5d59efa19d
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/models/models_info_page.ts
@@ -0,0 +1,136 @@
+import {createElement, getActionEls, query, queryAll} from "../common/utils_dom.js";
+
+import {rgthreeApi} from "rgthree/common/rgthree_api.js";
+import {RgthreeModelInfoCard} from "./components/model-info-card.js";
+
+function parseQuery(query: string) {
+  // Split on spaces
+  const matches = query.match(/"[^\"]+"/g) || [];
+  for (const match of matches) {
+    let cleaned = match.substring(1, match.length - 1);
+    cleaned = cleaned.replace(/\s+/g, " ").trim().replace(/\s/g, "__SPACE__");
+    query = query.replace(match, ` ${cleaned} `);
+  }
+  const queryParts = query
+    .replace(/\s+/g, " ")
+    .trim()
+    .split(" ")
+    .map((p) => p.replace(/__SPACE__/g, " "));
+  return queryParts;
+}
+
+export class ModelsInfoPage {
+  private readonly selectBaseModel: HTMLSelectElement = createElement(
+    'select[name="baseModel"][on="change:filter"]',
+  );
+  private readonly searchbox: HTMLInputElement = query<HTMLInputElement>("#searchbox")!;
+  private readonly modelsList: HTMLUListElement = query("#models-list")!;
+  private queryLast = "";
+  private doSearchDebounce: number = 0;
+
+  constructor() {
+    console.log("hello model page");
+    // rgthreeApi.setBaseUrl('../api');
+    this.init();
+  }
+
+  async init() {
+    this.searchbox.addEventListener("input", (e) => {
+      if (this.doSearchDebounce) {
+        return;
+      }
+      this.doSearchDebounce = setTimeout(() => {
+        this.doSearch();
+        this.doSearchDebounce = 0;
+      }, 250);
+    });
+
+    const loras = await rgthreeApi.getLorasInfo({light: true});
+    console.log(loras);
+
+    const baseModels = new Set<string>();
+
+    for (const lora of loras) {
+      const el = RgthreeModelInfoCard.create<RgthreeModelInfoCard>();
+      el.setModelData(lora);
+      el.bindWhenConnected(lora);
+      console.log(el);
+      lora.baseModel && baseModels.add(lora.baseModel);
+      this.modelsList.appendChild(createElement("li.model-item", {child: el}));
+    }
+
+    if (baseModels.size > 1) {
+      createElement(`option[value="ALL"][text="Choose base model."]`, {
+        parent: this.selectBaseModel,
+      });
+      for (const baseModel of baseModels.values()) {
+        createElement(`option[value="${baseModel}"][text="${baseModel}"]`, {
+          parent: this.selectBaseModel,
+        });
+      }
+      this.searchbox.insertAdjacentElement("afterend", this.selectBaseModel);
+    }
+
+    const data = getActionEls(document.body);
+    for (const dataItem of Object.values(data)) {
+      for (const [event, action] of Object.entries(dataItem.actions)) {
+        dataItem.el.addEventListener(event as keyof ElementEventMap, (e) => {
+          if (typeof (this as any)[action] != "function") {
+            throw new Error(`"${action}" does not exist on instance.`);
+          }
+          (this as any)[action](e);
+        });
+      }
+    }
+  }
+
+  filter() {
+    const parts = parseQuery(this.queryLast);
+    const baseModel = this.selectBaseModel.value;
+    const els = queryAll<RgthreeModelInfoCard>(RgthreeModelInfoCard.NAME);
+    const shouldHide = (el: RgthreeModelInfoCard) => {
+      let hide = baseModel !== "ALL" && !el.hasBaseModel(baseModel);
+      if (!hide) {
+        for (let part of parts) {
+          let negate = false;
+          if (part.startsWith("-")) {
+            negate = true;
+            part = part.substring(1);
+          }
+          if (!part) continue;
+
+          if (part.startsWith("has:")) {
+            if (part === "has:civitai") {
+              hide = !el.hasData(part.replace("has:", ""));
+            }
+          } else {
+            hide = !el.matchesQueryText(part);
+          }
+          hide = negate ? !hide : hide;
+          if (hide) {
+            break;
+          }
+        }
+      }
+      return hide;
+    };
+    for (const el of els) {
+      const hide = shouldHide(el);
+      if (hide) {
+        el.hide();
+      } else {
+        el.show();
+      }
+    }
+
+    console.log("filter");
+  }
+
+  doSearch() {
+    const query = this.searchbox.value.trim();
+    if (this.queryLast != query) {
+      this.queryLast = query;
+      this.filter();
+    }
+  }
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/scripts_comfy/README.md b/custom_nodes/rgthree-comfy/src_web/scripts_comfy/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..9b5b422571a25740dd1732db363bd442c89c21dc
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/scripts_comfy/README.md
@@ -0,0 +1,6 @@
+Here lies dummy ts files that decalre/export ComfyUI's own scripts files as typed types w/o needing
+to symlink to the actual implementation.
+
+Actual code in the comfyui/ directory can import these like `import {app} from "/scripts/app.js"`
+and have access to `app` as the fully typed `ComfyApp`. The `__build__.py` script will rewrite these
+to the relative browser path.
\ No newline at end of file
diff --git a/custom_nodes/rgthree-comfy/src_web/scripts_comfy/api.ts b/custom_nodes/rgthree-comfy/src_web/scripts_comfy/api.ts
new file mode 100644
index 0000000000000000000000000000000000000000..dc4400852938db227c995d9be9c845c71476bdad
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/scripts_comfy/api.ts
@@ -0,0 +1,10 @@
+interface ComfyApi extends EventTarget {
+  api_base: string;
+  getNodeDefs(): any;
+  apiURL(url: string): string;
+  queuePrompt(num: number, data: { output: {}; workflow: {} }): Promise<{}>;
+  fetchApi(route: string, options?: RequestInit) : Promise<Response>;
+  interrupt(): void;
+}
+
+export declare const api: ComfyApi;
diff --git a/custom_nodes/rgthree-comfy/src_web/scripts_comfy/app.ts b/custom_nodes/rgthree-comfy/src_web/scripts_comfy/app.ts
new file mode 100644
index 0000000000000000000000000000000000000000..9bec3ee977a1781792aaf9e9dbcd12bf618d7c96
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/scripts_comfy/app.ts
@@ -0,0 +1,7 @@
+import {ComfyApp} from "@comfyorg/frontend";
+
+/**
+ * A dummy ComfyApp that we can import from our code, which we'll rewrite later to the comfyui
+ * hosted app.js
+ */
+export declare const app: ComfyApp;
diff --git a/custom_nodes/rgthree-comfy/src_web/scripts_comfy/widgets.ts b/custom_nodes/rgthree-comfy/src_web/scripts_comfy/widgets.ts
new file mode 100644
index 0000000000000000000000000000000000000000..7a6508d11c51f7ab3cfe136433a866e912fef0ef
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/scripts_comfy/widgets.ts
@@ -0,0 +1,18 @@
+import type {ComfyApp, IStringWidget, IComboWidget, IWidget, LGraphNode} from "@comfyorg/frontend";
+
+type ComfyWidgetFn<WidgetType extends IWidget> = (
+  node: LGraphNode,
+  inputName: string,
+  inputData: any,
+  app: ComfyApp,
+) => {widget: WidgetType};
+
+/**
+ * A dummy ComfyWidgets that we can import from our code, which we'll rewrite later to the comfyui
+ * hosted widgets.js
+ */
+export declare const ComfyWidgets: {
+  COMBO: ComfyWidgetFn<IComboWidget>;
+  STRING: ComfyWidgetFn<IStringWidget>;
+  [key: string]: ComfyWidgetFn<IWidget>;
+};
diff --git a/custom_nodes/rgthree-comfy/src_web/typings/comfy.d.ts b/custom_nodes/rgthree-comfy/src_web/typings/comfy.d.ts
new file mode 100644
index 0000000000000000000000000000000000000000..f6f2e1b5fcd2b21a7288cd287bb2041603428080
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/typings/comfy.d.ts
@@ -0,0 +1,153 @@
+/**
+ * These contain types from ComfyUI (or LiteGraph) that are either copied or manually determined
+ * added here mostly because @comfyorg/comfyui-frontend-types hasn't exported them oir they weren't
+ * available.
+ */
+import type {SerializedGraph} from './index.js';
+
+export type getPngMetadata = (file: File | Blob) => { workflow?: string; prompt?: string };
+export type getWebpMetadata = (file: File | Blob) => {
+  Workflow?: string;
+  workflow?: string;
+  Prompt?: string;
+  prompt?: string;
+};
+
+// Below are types derived from the Serialized version of a workflow.
+
+// export type SerializedLink = [
+//   number, // this.id,
+//   number, // this.origin_id,
+//   number, // this.origin_slot,
+//   number, // this.target_id,
+//   number, // this.target_slot,
+//   string, // this.type
+// ];
+
+// interface SerializedNodeInput {
+//   name: string;
+//   type: string;
+//   link: number;
+// }
+// interface SerializedNodeOutput extends SerializedNodeInput {
+//   slot_index: number;
+//   links: number[];
+// }
+
+// export interface SerializedNode {
+//   // id: number;
+//   // inputs: SerializedNodeInput[];
+//   // outputs: SerializedNodeOutput[];
+//   mode: number;
+//   order: number;
+//   pos: [number, number];
+//   properties: any;
+//   size: [number, number];
+//   type: string;
+//   widgets_values: Array<number | string>;
+// }
+
+// export interface SerializedGraph {
+  // config: any;
+  // extra: any;
+  // groups: any;
+  // last_link_id: number;
+  // last_node_id: number;
+  // links: SerializedLink[];
+  // nodes: SerializedNode[];
+// }
+
+
+/**
+ * ComfyUI-Frontend defines a ComfyNodeDef from Zod, but doesn't expose it. This is a shim.
+ */
+export type ComfyNodeDef = {
+	name: string;
+	display_name?: string;
+	description?: string;
+	category: string;
+	input?: {
+		required?: Record<string, [string | any[]] | [string | any[], any]>;
+		optional?: Record<string, [string | any[]] | [string | any[], any]>;
+		hidden?: Record<string, [string | any[]] | [string | any[], any]>;
+	};
+	output?: string[];
+	output_name: string[];
+	// @rgthree
+	output_node?: boolean;
+};
+
+
+
+// Below are types derived from the formats for the ComfyAPI.
+
+// @rgthree
+type ComfyApiInputLink = [
+  /** The id string of the connected node. */
+  string,
+  /** The output index. */
+  number,
+]
+
+
+type ComfyApiFormatNode = {
+  "inputs": {
+    [input_name: string]: string|number|boolean|ComfyApiInputLink,
+  },
+  "class_type": string,
+  "_meta": {
+    "title": string,
+  }
+}
+
+export type ComfyApiFormat = {
+  [node_id: string]: ComfyApiFormatNode
+}
+
+export type ComfyApiPrompt = {
+  workflow: SerializedGraph,
+  output: ComfyApiFormat,
+}
+
+export type ComfyApiEventDetailStatus = {
+  exec_info: {
+    queue_remaining: number;
+  };
+};
+
+export type ComfyApiEventDetailExecutionStart = {
+  prompt_id: string;
+};
+
+export type ComfyApiEventDetailExecuting = null | string;
+
+export type ComfyApiEventDetailProgress = {
+  node: string;
+  prompt_id: string;
+  max: number;
+  value: number;
+};
+
+export type ComfyApiEventDetailExecuted = {
+  node: string;
+  prompt_id: string;
+  output: any;
+};
+
+export type ComfyApiEventDetailCached = {
+  nodes: string[];
+  prompt_id: string;
+};
+
+export type ComfyApiEventDetailError = {
+  prompt_id: string;
+  exception_type: string;
+  exception_message: string;
+  node_id: string;
+  node_type: string;
+  node_id: string;
+  traceback: string;
+  executed: any[];
+  current_inputs:  {[key: string]: (number[]|string[])};
+  current_outputs: {[key: string]: (number[]|string[])};
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/typings/index.d.ts b/custom_nodes/rgthree-comfy/src_web/typings/index.d.ts
new file mode 100644
index 0000000000000000000000000000000000000000..ac6f9f01a55a244a321a52b71da51098f246621f
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/typings/index.d.ts
@@ -0,0 +1,30 @@
+/**
+ * Declare any global properties and import our other typings here.
+ */
+import "@comfyorg/frontend";
+import "./litegraph";
+import "./rgthree";
+import "./comfy";
+
+import type {
+  LGraphGroup as TLGraphGroup,
+  LGraphNode as TLGraphNode,
+  LGraph as TLGraph,
+  LGraphCanvas as TLGraphCanvas,
+  LiteGraph as TLiteGraph,
+} from "@comfyorg/frontend";
+
+declare global {
+  const LiteGraph: typeof TLiteGraph;
+  const LGraph: typeof TLGraph;
+  const LGraphNode: typeof TLGraphNode;
+  const LGraphCanvas: typeof TLGraphCanvas;
+  const LGraphGroup: typeof TLGraphGroup;
+  interface Window {
+    // Used in the common/comfyui_shim to determine if we're in the app or not.
+    comfyAPI: {
+      // So much more stuffed in here, add as needed.
+      [key: string]: any;
+    };
+  }
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/typings/litegraph.d.ts b/custom_nodes/rgthree-comfy/src_web/typings/litegraph.d.ts
new file mode 100644
index 0000000000000000000000000000000000000000..24b171a77879172e89a4c45de793f8065084d961
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/typings/litegraph.d.ts
@@ -0,0 +1,193 @@
+/**
+ * This used to augment the LiteGraph types, either to fix them for how they actually behave
+ * (e.g. marking args that are typed as required as optional because they actually are, etc.) or
+ * adding properties/methods that rgthree-comfy adds/uses. Mostly the latter are prefixed 'rgthree_'
+ * but not always.
+ */
+import "@comfyorg/frontend";
+
+declare module "@comfyorg/frontend" {
+  interface INodeSlot {
+    // @rgthree: Hides a slot for rgthree-comfy draw methods.
+    hidden?: boolean;
+
+    // @rgthree: Used to "disable" an input/output. Used in PowerPrompt to disallow connecting
+    // an output if there's no optional corresponding input (since, that would just break).
+    disabled?: boolean;
+
+    // @rgthree: A status we put on some nodes so we can draw things around it.
+    rgthree_status?: "WARN" | "ERROR";
+  }
+
+  interface LGraph {
+    // @rgthree (Fix): `result` arg is optional in impl.
+    findNodesByType(type: string, result?: LGraphNode[]): LGraphNode[];
+  }
+
+  interface LGraphNode {
+    // @rgthree: rgthree-comfy added this before comfyui did and it was a bit more flexible.
+    removeWidget(widget: IBaseWidget | IWidget | number | undefined): void;
+
+    // @rgthree (Fix): Implementation allows a falsy value to be returned and it will suppress the
+    // menu all together.
+    // NOTE: [🤮] We can't actually augment this because it's a return.. but keeping here because
+    // this is how it's actually implemented.
+    // getSlotMenuOptions?(this: LGraphNode, slot: IFoundSlot): IContextMenuValue[] | void;
+
+    // @rgthree (Fix): Implementation allows a falsy value to be returned and it will not add items.
+    // NOTE: [🤮] We can't actually augment this because it's a return.. but keeping here because
+    // this is how it's actually implemented.
+    // getExtraMenuOptions?(
+    //   canvas: LGraphCanvas,
+    //   options: (IContextMenuValue<unknown> | null)[],
+    // ): (IContextMenuValue<unknown> | null)[] | void;
+
+    /**
+     * Copied from https://github.com/Comfy-Org/ComfyUI_frontend/blob/ba355b543d0b753365c4e2b40ec376e186727c8c/src/types/litegraph-augmentation.d.ts
+     *
+     * Callback invoked when the node is dragged over from an external source, i.e.
+     * a file or another HTML element.
+     * @param e The drag event
+     * @returns {boolean} True if the drag event should be handled by this node, false otherwise
+     */
+    onDragOver?(e: DragEvent): boolean;
+
+    /**
+     * Copied from https://github.com/Comfy-Org/ComfyUI_frontend/blob/ba355b543d0b753365c4e2b40ec376e186727c8c/src/types/litegraph-augmentation.d.ts
+     *
+     * Callback invoked when the node is dropped from an external source, i.e.
+     * a file or another HTML element.
+     * @param e The drag event
+     * @returns {boolean} True if the drag event should be handled by this node, false otherwise
+     */
+    onDragDrop?(e: DragEvent): Promise<boolean> | boolean;
+
+    /** Copied from https://github.com/Comfy-Org/ComfyUI_frontend/blob/ba355b543d0b753365c4e2b40ec376e186727c8c/src/types/litegraph-augmentation.d.ts */
+    onExecuted?(output: any): void;
+
+    /**
+     * Copied from https://github.com/Comfy-Org/ComfyUI_frontend/blob/ba355b543d0b753365c4e2b40ec376e186727c8c/src/types/litegraph-augmentation.d.ts
+     *
+     * Index of the currently selected image on a multi-image node such as Preview Image
+     */
+    imageIndex?: number | null;
+
+    /** Copied from https://github.com/Comfy-Org/ComfyUI_frontend/blob/ba355b543d0b753365c4e2b40ec376e186727c8c/src/types/litegraph-augmentation.d.ts */
+    overIndex?: number | null;
+
+    /** Copied from https://github.com/Comfy-Org/ComfyUI_frontend/blob/ba355b543d0b753365c4e2b40ec376e186727c8c/src/types/litegraph-augmentation.d.ts */
+    imgs?: HTMLImageElement[];
+
+    /** Copied from https://github.com/Comfy-Org/ComfyUI_frontend/blob/ba355b543d0b753365c4e2b40ec376e186727c8c/src/types/litegraph-augmentation.d.ts */
+    refreshComboInNode?(defs: Record<string, ComfyNodeDef>);
+
+    /**
+     * Copied from https://github.com/Comfy-Org/ComfyUI_frontend/blob/ba355b543d0b753365c4e2b40ec376e186727c8c/src/types/litegraph-augmentation.d.ts
+     *
+     * widgets_values is set to LGraphNode by `LGraphNode.configure`, but it is not
+     * used by litegraph internally. We should remove the dependency on it later.
+     */
+    widgets_values?: unknown[];
+  }
+
+  /**
+   * Copied from https://github.com/Comfy-Org/ComfyUI_frontend/blob/ba355b543d0b753365c4e2b40ec376e186727c8c/src/types/litegraph-augmentation.d.ts
+   *
+   * Only used by the Primitive node. Primitive node is using the widget property
+   * to store/access the widget config.
+   * We should remove this hacky solution once we have a proper solution.
+   *
+   * @rgthree - Changed this to add `widget?: IWidgetLocator` to INodeOutputSlot (which matches
+   *     INodeInputSlot) and then `[key: symbol]: unknown` to IWidgetLocator as that's how it's
+   *     used for CONFIG and GET_CONFIG symbols.
+   */
+  interface INodeOutputSlot {
+    widget?: IWidgetLocator; //{name: string; [key: symbol]: unknown};
+  }
+  // @rgthree - See above.
+  interface IWidgetLocator {
+    [key: symbol]: unknown;
+  }
+
+  interface LGraphGroup {
+    // @rgthree: Track whether a group has any active node from the fast group mode changers.
+    rgthree_hasAnyActiveNode?: boolean;
+  }
+
+  interface LGraphCanvas {
+    // @rgthree (Fix): At one point this was in ComfyUI's app.js. I don't see it now... perhaps it's
+    // been removed? We were using it in rgthree-comfy.
+    selected_group_moving?: boolean;
+
+    // @rgthree (Fix): Allows LGraphGroup to be passed (it could be `{size: Point, pos: Point}`).
+    centerOnNode(node: LGraphNode | LGraphGroup);
+
+    // @rgthree (Fix): Makes item's fields optiona, and other params nullable, as well as adds
+    // LGraphGroup to the node, since the implementation accomodates all of these as typed below.
+    // NOTE: [🤮] We can't actually augment this because it's static.. but keeping here because
+    // this is how it's actually implemented.
+    // static onShowPropertyEditor(
+    //   item: {
+    //     property?: keyof LGraphNode | undefined;
+    //     type?: string;
+    //   },
+    //   options: IContextMenuOptions<string> | null,
+    //   e: MouseEvent | null,
+    //   menu: ContextMenu<string> | null,
+    //   node: LGraphNode | LGraphGroup,
+    // ): void;
+  }
+
+  interface LGraphNodeConstructor {
+    // @rgthree (Fix): Fixes ComfyUI-Frontend which marks this as required, even though elsewhere it
+    // defines it as optional (like for the actual for LGraphNode). Our virtual nodes do not have
+    // a comfyClass since there's nothing to tie it back to.
+    comfyClass?: string;
+
+    // @rgthree: reference the original nodeType data as sometimes extensions clobber it.
+    nodeType?: LGraphNodeConstructor | null;
+  }
+}
+
+declare module "@/lib/litegraph/src/types/widgets" {
+  interface IBaseWidget {
+    // @rgthree (Fix): Where is this in Comfy types?
+    inputEl?: HTMLInputElement;
+
+    // @rgthree: A status we put on some nodes so we can draw things around it.
+    rgthree_lastValue?: any;
+
+    /** Copied from https://github.com/Comfy-Org/ComfyUI_frontend/blob/ba355b543d0b753365c4e2b40ec376e186727c8c/src/types/litegraph-augmentation.d.ts */
+    onRemove?(): void;
+    /** Copied from https://github.com/Comfy-Org/ComfyUI_frontend/blob/ba355b543d0b753365c4e2b40ec376e186727c8c/src/types/litegraph-augmentation.d.ts */
+    serializeValue?(node: LGraphNode, index: number): Promise<unknown> | unknown;
+  }
+
+  interface IWidgetOptions {
+    /**
+     * Copied from https://github.com/Comfy-Org/ComfyUI_frontend/blob/ba355b543d0b753365c4e2b40ec376e186727c8c/src/types/litegraph-augmentation.d.ts
+     *
+     * Controls whether the widget's value is included in the API workflow/prompt.
+     * - If false, the value will be excluded from the API workflow but still serialized as part of the graph state
+     * - If true or undefined, the value will be included in both the API workflow and graph state     *
+     * @default true
+     * @use {@link IBaseWidget.serialize} if you don't want the widget value to be included in both
+     * the API workflow and graph state.
+     */
+    serialize?: boolean;
+  }
+}
+
+declare module "@/lib/litegraph/src/interfaces" {
+  // @rgthree (Fix): widget is (or was?) available when inputs were moved from a widget.
+  interface IFoundSlot {
+    widget?: IBaseWidget;
+  }
+}
+
+declare module "@comfyorg/litegraph/dist/LiteGraphGlobal" {
+  interface LiteGraphGlobal {
+    // @rgthree (Fix): Window is actually optional in the code.
+    closeAllContextMenus(ref_window?: Window): void;
+  }
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/typings/rgthree.d.ts b/custom_nodes/rgthree-comfy/src_web/typings/rgthree.d.ts
new file mode 100644
index 0000000000000000000000000000000000000000..e1573a0ca0061a6329cc33b9ed2083946ed117d1
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/typings/rgthree.d.ts
@@ -0,0 +1,75 @@
+/**
+ * Typings specific to rgthree-comfy.
+ */
+import type { LGraphNode, Vector2 } from "@comfyorg/frontend";
+import type { CanvasMouseEvent } from "@comfyorg/litegraph/dist/types/events.js";
+import type { RgthreeBaseNode, RgthreeBaseVirtualNode } from '../comfyui/base_node.js'
+
+export type AdjustedMouseCustomEvent = CustomEvent<{ originalEvent: CanvasMouseEvent }>;
+
+export type Constructor<T> = new(...args: any[]) => T;
+
+export interface RgthreeBaseNodeConstructor extends Constructor<RgthreeBaseNode> {
+	static type: string;
+	static category: string;
+  static comfyClass: string;
+	static exposedActions: string[];
+}
+
+export interface RgthreeBaseVirtualNodeConstructor extends Constructor<RgthreeBaseVirtualNode> {
+	static type: string;
+	static category: string;
+	static _category: string;
+}
+
+export interface RgthreeBaseServerNodeConstructor extends Constructor<RgthreeBaseServerNode> {
+	static nodeType: ComfyNodeConstructor;
+	static nodeData: ComfyObjectInfo;
+	static __registeredForOverride__: boolean;
+  onRegisteredForOverride(comfyClass: any, rgthreeClass: any) : void;
+}
+
+export type RgthreeModelInfoDetails = {
+  file?: string;
+  path?: string;
+  hasInfoFile?: boolean;
+  image?: string;
+}
+
+export type RgthreeModelInfo = RgthreeModelInfoDetails & {
+  name?: string;
+  type?: string;
+  baseModel?: string;
+  baseModelFile?: string;
+  links?: string[];
+  strengthMin?: number;
+  strengthMax?: number;
+  triggerWords?: string[];
+  trainedWords?: {
+    word: string;
+    count?: number;
+    civitai?: boolean
+    user?: boolean
+  }[];
+  description?: string;
+  sha256?: string;
+  path?: string;
+  images?: {
+    url: string;
+    civitaiUrl?: string;
+    steps?: string|number;
+    cfg?: string|number;
+    type?: 'image'|'video';
+    sampler?: string;
+    model?: string;
+    seed?: string;
+    negative?: string;
+    positive?: string;
+    resources?: {name?: string, type?: string, weight?: string|number}[];
+  }[]
+  userTags?: string[];
+  userNote?: string;
+  raw?: any;
+  // This one is just on the client.
+  filterDir?: string;
+}
diff --git a/custom_nodes/rgthree-comfy/src_web/typings/web-tree-sitter.d.ts b/custom_nodes/rgthree-comfy/src_web/typings/web-tree-sitter.d.ts
new file mode 100644
index 0000000000000000000000000000000000000000..87b3100f108993f4f60e17fd5b4e3a65b5fa48bd
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/src_web/typings/web-tree-sitter.d.ts
@@ -0,0 +1 @@
+import 'web-tree-sitter';
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/any_switch.js b/custom_nodes/rgthree-comfy/web/comfyui/any_switch.js
new file mode 100644
index 0000000000000000000000000000000000000000..50e0d796eb939bab92ef8084c1939d7cfdc66d02
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/any_switch.js
@@ -0,0 +1,69 @@
+import { app } from "../../scripts/app.js";
+import { IoDirection, addConnectionLayoutSupport, followConnectionUntilType } from "./utils.js";
+import { RgthreeBaseServerNode } from "./base_node.js";
+import { NodeTypesString } from "./constants.js";
+import { removeUnusedInputsFromEnd } from "./utils_inputs_outputs.js";
+import { debounce } from "../../rgthree/common/shared_utils.js";
+class RgthreeAnySwitch extends RgthreeBaseServerNode {
+    constructor(title = RgthreeAnySwitch.title) {
+        super(title);
+        this.stabilizeBound = this.stabilize.bind(this);
+        this.nodeType = null;
+        this.addAnyInput(5);
+    }
+    onConnectionsChange(type, slotIndex, isConnected, linkInfo, ioSlot) {
+        var _a;
+        (_a = super.onConnectionsChange) === null || _a === void 0 ? void 0 : _a.call(this, type, slotIndex, isConnected, linkInfo, ioSlot);
+        this.scheduleStabilize();
+    }
+    onConnectionsChainChange() {
+        this.scheduleStabilize();
+    }
+    scheduleStabilize(ms = 64) {
+        return debounce(this.stabilizeBound, ms);
+    }
+    addAnyInput(num = 1) {
+        for (let i = 0; i < num; i++) {
+            this.addInput(`any_${String(this.inputs.length + 1).padStart(2, "0")}`, (this.nodeType || "*"));
+        }
+    }
+    stabilize() {
+        removeUnusedInputsFromEnd(this, 4);
+        this.addAnyInput();
+        let connectedType = followConnectionUntilType(this, IoDirection.INPUT, undefined, true);
+        if (!connectedType) {
+            connectedType = followConnectionUntilType(this, IoDirection.OUTPUT, undefined, true);
+        }
+        this.nodeType = (connectedType === null || connectedType === void 0 ? void 0 : connectedType.type) || "*";
+        for (const input of this.inputs) {
+            input.type = this.nodeType;
+        }
+        for (const output of this.outputs) {
+            output.type = this.nodeType;
+            output.label =
+                output.type === "RGTHREE_CONTEXT"
+                    ? "CONTEXT"
+                    : Array.isArray(this.nodeType) || this.nodeType.includes(",")
+                        ? (connectedType === null || connectedType === void 0 ? void 0 : connectedType.label) || String(this.nodeType)
+                        : String(this.nodeType);
+        }
+    }
+    static setUp(comfyClass, nodeData) {
+        RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, RgthreeAnySwitch);
+        addConnectionLayoutSupport(RgthreeAnySwitch, app, [
+            ["Left", "Right"],
+            ["Right", "Left"],
+        ]);
+    }
+}
+RgthreeAnySwitch.title = NodeTypesString.ANY_SWITCH;
+RgthreeAnySwitch.type = NodeTypesString.ANY_SWITCH;
+RgthreeAnySwitch.comfyClass = NodeTypesString.ANY_SWITCH;
+app.registerExtension({
+    name: "rgthree.AnySwitch",
+    async beforeRegisterNodeDef(nodeType, nodeData, app) {
+        if (nodeData.name === "Any Switch (rgthree)") {
+            RgthreeAnySwitch.setUp(nodeType, nodeData);
+        }
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/base_any_input_connected_node.js b/custom_nodes/rgthree-comfy/web/comfyui/base_any_input_connected_node.js
new file mode 100644
index 0000000000000000000000000000000000000000..09e2950c76a3c503df1fa35102829af0aa0d00ec
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/base_any_input_connected_node.js
@@ -0,0 +1,208 @@
+import { app } from "../../scripts/app.js";
+import { RgthreeBaseVirtualNode } from "./base_node.js";
+import { rgthree } from "./rgthree.js";
+import { PassThroughFollowing, addConnectionLayoutSupport, addMenuItem, getConnectedInputNodes, getConnectedInputNodesAndFilterPassThroughs, getConnectedOutputNodes, getConnectedOutputNodesAndFilterPassThroughs, } from "./utils.js";
+export class BaseAnyInputConnectedNode extends RgthreeBaseVirtualNode {
+    constructor(title = BaseAnyInputConnectedNode.title) {
+        super(title);
+        this.isVirtualNode = true;
+        this.inputsPassThroughFollowing = PassThroughFollowing.NONE;
+        this.debouncerTempWidth = 0;
+        this.schedulePromise = null;
+    }
+    onConstructed() {
+        this.addInput("", "*");
+        return super.onConstructed();
+    }
+    clone() {
+        const cloned = super.clone();
+        if (!rgthree.canvasCurrentlyCopyingToClipboardWithMultipleNodes) {
+            while (cloned.inputs.length > 1) {
+                cloned.removeInput(cloned.inputs.length - 1);
+            }
+            if (cloned.inputs[0]) {
+                cloned.inputs[0].label = "";
+            }
+        }
+        return cloned;
+    }
+    scheduleStabilizeWidgets(ms = 100) {
+        if (!this.schedulePromise) {
+            this.schedulePromise = new Promise((resolve) => {
+                setTimeout(() => {
+                    this.schedulePromise = null;
+                    this.doStablization();
+                    resolve();
+                }, ms);
+            });
+        }
+        return this.schedulePromise;
+    }
+    stabilizeInputsOutputs() {
+        var _a;
+        let changed = false;
+        const hasEmptyInput = !((_a = this.inputs[this.inputs.length - 1]) === null || _a === void 0 ? void 0 : _a.link);
+        if (!hasEmptyInput) {
+            this.addInput("", "*");
+            changed = true;
+        }
+        for (let index = this.inputs.length - 2; index >= 0; index--) {
+            const input = this.inputs[index];
+            if (!input.link) {
+                this.removeInput(index);
+                changed = true;
+            }
+            else {
+                const node = getConnectedInputNodesAndFilterPassThroughs(this, this, index, this.inputsPassThroughFollowing)[0];
+                const newName = (node === null || node === void 0 ? void 0 : node.title) || "";
+                if (input.name !== newName) {
+                    input.name = (node === null || node === void 0 ? void 0 : node.title) || "";
+                    changed = true;
+                }
+            }
+        }
+        return changed;
+    }
+    doStablization() {
+        if (!this.graph) {
+            return;
+        }
+        let dirty = false;
+        this._tempWidth = this.size[0];
+        dirty = this.stabilizeInputsOutputs();
+        const linkedNodes = getConnectedInputNodesAndFilterPassThroughs(this);
+        dirty = this.handleLinkedNodesStabilization(linkedNodes) || dirty;
+        if (dirty) {
+            this.graph.setDirtyCanvas(true, true);
+        }
+        this.scheduleStabilizeWidgets(500);
+    }
+    handleLinkedNodesStabilization(linkedNodes) {
+        linkedNodes;
+        throw new Error("handleLinkedNodesStabilization should be overridden.");
+    }
+    onConnectionsChainChange() {
+        this.scheduleStabilizeWidgets();
+    }
+    onConnectionsChange(type, index, connected, linkInfo, ioSlot) {
+        super.onConnectionsChange &&
+            super.onConnectionsChange(type, index, connected, linkInfo, ioSlot);
+        if (!linkInfo)
+            return;
+        const connectedNodes = getConnectedOutputNodesAndFilterPassThroughs(this);
+        for (const node of connectedNodes) {
+            if (node.onConnectionsChainChange) {
+                node.onConnectionsChainChange();
+            }
+        }
+        this.scheduleStabilizeWidgets();
+    }
+    removeInput(slot) {
+        this._tempWidth = this.size[0];
+        return super.removeInput(slot);
+    }
+    addInput(name, type, extra_info) {
+        this._tempWidth = this.size[0];
+        return super.addInput(name, type, extra_info);
+    }
+    addWidget(type, name, value, callback, options) {
+        this._tempWidth = this.size[0];
+        return super.addWidget(type, name, value, callback, options);
+    }
+    removeWidget(widget) {
+        this._tempWidth = this.size[0];
+        super.removeWidget(widget);
+    }
+    computeSize(out) {
+        var _a, _b;
+        let size = super.computeSize(out);
+        if (this._tempWidth) {
+            size[0] = this._tempWidth;
+            this.debouncerTempWidth && clearTimeout(this.debouncerTempWidth);
+            this.debouncerTempWidth = setTimeout(() => {
+                this._tempWidth = null;
+            }, 32);
+        }
+        if (this.properties["collapse_connections"]) {
+            const rows = Math.max(((_a = this.inputs) === null || _a === void 0 ? void 0 : _a.length) || 0, ((_b = this.outputs) === null || _b === void 0 ? void 0 : _b.length) || 0, 1) - 1;
+            size[1] = size[1] - rows * LiteGraph.NODE_SLOT_HEIGHT;
+        }
+        setTimeout(() => {
+            var _a;
+            (_a = this.graph) === null || _a === void 0 ? void 0 : _a.setDirtyCanvas(true, true);
+        }, 16);
+        return size;
+    }
+    onConnectOutput(outputIndex, inputType, inputSlot, inputNode, inputIndex) {
+        let canConnect = true;
+        if (super.onConnectOutput) {
+            canConnect = super.onConnectOutput(outputIndex, inputType, inputSlot, inputNode, inputIndex);
+        }
+        if (canConnect) {
+            const nodes = getConnectedInputNodes(this);
+            if (nodes.includes(inputNode)) {
+                alert(`Whoa, whoa, whoa. You've just tried to create a connection that loops back on itself, ` +
+                    `a situation that could create a time paradox, the results of which could cause a ` +
+                    `chain reaction that would unravel the very fabric of the space time continuum, ` +
+                    `and destroy the entire universe!`);
+                canConnect = false;
+            }
+        }
+        return canConnect;
+    }
+    onConnectInput(inputIndex, outputType, outputSlot, outputNode, outputIndex) {
+        let canConnect = true;
+        if (super.onConnectInput) {
+            canConnect = super.onConnectInput(inputIndex, outputType, outputSlot, outputNode, outputIndex);
+        }
+        if (canConnect) {
+            const nodes = getConnectedOutputNodes(this);
+            if (nodes.includes(outputNode)) {
+                alert(`Whoa, whoa, whoa. You've just tried to create a connection that loops back on itself, ` +
+                    `a situation that could create a time paradox, the results of which could cause a ` +
+                    `chain reaction that would unravel the very fabric of the space time continuum, ` +
+                    `and destroy the entire universe!`);
+                canConnect = false;
+            }
+        }
+        return canConnect;
+    }
+    connectByTypeOutput(slot, sourceNode, sourceSlotType, optsIn) {
+        const lastInput = this.inputs[this.inputs.length - 1];
+        if (!(lastInput === null || lastInput === void 0 ? void 0 : lastInput.link) && (lastInput === null || lastInput === void 0 ? void 0 : lastInput.type) === "*") {
+            var sourceSlot = sourceNode.findOutputSlotByType(sourceSlotType, false, true);
+            return sourceNode.connect(sourceSlot, this, slot);
+        }
+        return super.connectByTypeOutput(slot, sourceNode, sourceSlotType, optsIn);
+    }
+    static setUp() {
+        super.setUp();
+        addConnectionLayoutSupport(this, app, [
+            ["Left", "Right"],
+            ["Right", "Left"],
+        ]);
+        addMenuItem(this, app, {
+            name: (node) => { var _a; return `${((_a = node.properties) === null || _a === void 0 ? void 0 : _a["collapse_connections"]) ? "Show" : "Collapse"} Connections`; },
+            property: "collapse_connections",
+            prepareValue: (_value, node) => { var _a; return !((_a = node.properties) === null || _a === void 0 ? void 0 : _a["collapse_connections"]); },
+            callback: (_node) => {
+                var _a;
+                (_a = app.canvas.getCurrentGraph()) === null || _a === void 0 ? void 0 : _a.setDirtyCanvas(true, true);
+            },
+        });
+    }
+}
+const oldLGraphNodeConnectByType = LGraphNode.prototype.connectByType;
+LGraphNode.prototype.connectByType = function connectByType(slot, targetNode, targetSlotType, optsIn) {
+    if (targetNode.inputs) {
+        for (const [index, input] of targetNode.inputs.entries()) {
+            if (!input.link && input.type === "*") {
+                this.connect(slot, targetNode, index);
+                return null;
+            }
+        }
+    }
+    return ((oldLGraphNodeConnectByType &&
+        oldLGraphNodeConnectByType.call(this, slot, targetNode, targetSlotType, optsIn)) ||
+        null);
+};
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/base_node.js b/custom_nodes/rgthree-comfy/web/comfyui/base_node.js
new file mode 100644
index 0000000000000000000000000000000000000000..a004ca9fa5138965b1ec544a18f0a78eab892a00
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/base_node.js
@@ -0,0 +1,316 @@
+import { app } from "../../scripts/app.js";
+import { ComfyWidgets } from "../../scripts/widgets.js";
+import { SERVICE as KEY_EVENT_SERVICE } from "./services/key_events_services.js";
+import { LogLevel, rgthree } from "./rgthree.js";
+import { addHelpMenuItem } from "./utils.js";
+import { RgthreeHelpDialog } from "../../rgthree/common/dialog.js";
+import { importIndividualNodesInnerOnDragDrop, importIndividualNodesInnerOnDragOver, } from "./feature_import_individual_nodes.js";
+import { defineProperty, moveArrayItem } from "../../rgthree/common/shared_utils.js";
+export class RgthreeBaseNode extends LGraphNode {
+    constructor(title = RgthreeBaseNode.title, skipOnConstructedCall = true) {
+        super(title);
+        this.comfyClass = "__NEED_COMFY_CLASS__";
+        this.nickname = "rgthree";
+        this.isVirtualNode = false;
+        this.isDropEnabled = false;
+        this.removed = false;
+        this.configuring = false;
+        this._tempWidth = 0;
+        this.__constructed__ = false;
+        this.helpDialog = null;
+        if (title == "__NEED_CLASS_TITLE__") {
+            throw new Error("RgthreeBaseNode needs overrides.");
+        }
+        this.widgets = this.widgets || [];
+        this.properties = this.properties || {};
+        setTimeout(() => {
+            if (this.comfyClass == "__NEED_COMFY_CLASS__") {
+                throw new Error("RgthreeBaseNode needs a comfy class override.");
+            }
+            if (this.constructor.type == "__NEED_CLASS_TYPE__") {
+                throw new Error("RgthreeBaseNode needs overrides.");
+            }
+            this.checkAndRunOnConstructed();
+        });
+        defineProperty(this, "mode", {
+            get: () => {
+                return this.rgthree_mode;
+            },
+            set: (mode) => {
+                if (this.rgthree_mode != mode) {
+                    const oldMode = this.rgthree_mode;
+                    this.rgthree_mode = mode;
+                    this.onModeChange(oldMode, mode);
+                }
+            },
+        });
+    }
+    checkAndRunOnConstructed() {
+        var _a;
+        if (!this.__constructed__) {
+            this.onConstructed();
+            const [n, v] = rgthree.logger.logParts(LogLevel.DEV, `[RgthreeBaseNode] Child class did not call onConstructed for "${this.type}.`);
+            (_a = console[n]) === null || _a === void 0 ? void 0 : _a.call(console, ...v);
+        }
+        return this.__constructed__;
+    }
+    onDragOver(e) {
+        if (!this.isDropEnabled)
+            return false;
+        return importIndividualNodesInnerOnDragOver(this, e);
+    }
+    async onDragDrop(e) {
+        if (!this.isDropEnabled)
+            return false;
+        return importIndividualNodesInnerOnDragDrop(this, e);
+    }
+    onConstructed() {
+        var _a;
+        if (this.__constructed__)
+            return false;
+        this.type = (_a = this.type) !== null && _a !== void 0 ? _a : undefined;
+        this.__constructed__ = true;
+        rgthree.invokeExtensionsAsync("nodeCreated", this);
+        return this.__constructed__;
+    }
+    configure(info) {
+        this.configuring = true;
+        super.configure(info);
+        for (const w of this.widgets || []) {
+            w.last_y = w.last_y || 0;
+        }
+        this.configuring = false;
+    }
+    clone() {
+        const cloned = super.clone();
+        if ((cloned === null || cloned === void 0 ? void 0 : cloned.properties) && !!window.structuredClone) {
+            cloned.properties = structuredClone(cloned.properties);
+        }
+        cloned.graph = this.graph;
+        return cloned;
+    }
+    onModeChange(from, to) {
+    }
+    async handleAction(action) {
+        action;
+    }
+    removeWidget(widget) {
+        var _a;
+        if (typeof widget === "number") {
+            widget = this.widgets[widget];
+        }
+        if (!widget)
+            return;
+        const canUseComfyUIRemoveWidget = false;
+        if (canUseComfyUIRemoveWidget && typeof super.removeWidget === 'function') {
+            super.removeWidget(widget);
+        }
+        else {
+            const index = this.widgets.indexOf(widget);
+            if (index > -1) {
+                this.widgets.splice(index, 1);
+            }
+            (_a = widget.onRemove) === null || _a === void 0 ? void 0 : _a.call(widget);
+        }
+    }
+    replaceWidget(widgetOrSlot, newWidget) {
+        let index = null;
+        if (widgetOrSlot) {
+            index = typeof widgetOrSlot === "number" ? widgetOrSlot : this.widgets.indexOf(widgetOrSlot);
+            this.removeWidget(this.widgets[index]);
+        }
+        index = index != null ? index : this.widgets.length - 1;
+        if (this.widgets.includes(newWidget)) {
+            moveArrayItem(this.widgets, newWidget, index);
+        }
+        else {
+            this.widgets.splice(index, 0, newWidget);
+        }
+    }
+    defaultGetSlotMenuOptions(slot) {
+        var _a, _b;
+        const menu_info = [];
+        if ((_b = (_a = slot === null || slot === void 0 ? void 0 : slot.output) === null || _a === void 0 ? void 0 : _a.links) === null || _b === void 0 ? void 0 : _b.length) {
+            menu_info.push({ content: "Disconnect Links", slot });
+        }
+        let inputOrOutput = slot.input || slot.output;
+        if (inputOrOutput) {
+            if (inputOrOutput.removable) {
+                menu_info.push(inputOrOutput.locked ? { content: "Cannot remove" } : { content: "Remove Slot", slot });
+            }
+            if (!inputOrOutput.nameLocked) {
+                menu_info.push({ content: "Rename Slot", slot });
+            }
+        }
+        return menu_info;
+    }
+    onRemoved() {
+        var _a;
+        (_a = super.onRemoved) === null || _a === void 0 ? void 0 : _a.call(this);
+        this.removed = true;
+    }
+    static setUp(...args) {
+    }
+    getHelp() {
+        return "";
+    }
+    showHelp() {
+        const help = this.getHelp() || this.constructor.help;
+        if (help) {
+            this.helpDialog = new RgthreeHelpDialog(this, help).show();
+            this.helpDialog.addEventListener("close", (e) => {
+                this.helpDialog = null;
+            });
+        }
+    }
+    onKeyDown(event) {
+        KEY_EVENT_SERVICE.handleKeyDownOrUp(event);
+        if (event.key == "?" && !this.helpDialog) {
+            this.showHelp();
+        }
+    }
+    onKeyUp(event) {
+        KEY_EVENT_SERVICE.handleKeyDownOrUp(event);
+    }
+    getExtraMenuOptions(canvas, options) {
+        var _a, _b, _c, _d, _e, _f;
+        if (super.getExtraMenuOptions) {
+            (_a = super.getExtraMenuOptions) === null || _a === void 0 ? void 0 : _a.apply(this, [canvas, options]);
+        }
+        else if ((_c = (_b = this.constructor.nodeType) === null || _b === void 0 ? void 0 : _b.prototype) === null || _c === void 0 ? void 0 : _c.getExtraMenuOptions) {
+            (_f = (_e = (_d = this.constructor.nodeType) === null || _d === void 0 ? void 0 : _d.prototype) === null || _e === void 0 ? void 0 : _e.getExtraMenuOptions) === null || _f === void 0 ? void 0 : _f.apply(this, [canvas, options]);
+        }
+        const help = this.getHelp() || this.constructor.help;
+        if (help) {
+            addHelpMenuItem(this, help, options);
+        }
+        return [];
+    }
+}
+RgthreeBaseNode.exposedActions = [];
+RgthreeBaseNode.title = "__NEED_CLASS_TITLE__";
+RgthreeBaseNode.type = "__NEED_CLASS_TYPE__";
+RgthreeBaseNode.category = "rgthree";
+RgthreeBaseNode._category = "rgthree";
+export class RgthreeBaseVirtualNode extends RgthreeBaseNode {
+    constructor(title = RgthreeBaseNode.title) {
+        super(title, false);
+        this.isVirtualNode = true;
+    }
+    static setUp() {
+        if (!this.type) {
+            throw new Error(`Missing type for RgthreeBaseVirtualNode: ${this.title}`);
+        }
+        LiteGraph.registerNodeType(this.type, this);
+        if (this._category) {
+            this.category = this._category;
+        }
+    }
+}
+export class RgthreeBaseServerNode extends RgthreeBaseNode {
+    constructor(title) {
+        super(title, true);
+        this.isDropEnabled = true;
+        this.serialize_widgets = true;
+        this.setupFromServerNodeData();
+        this.onConstructed();
+    }
+    getWidgets() {
+        return ComfyWidgets;
+    }
+    async setupFromServerNodeData() {
+        var _a, _b, _c, _d, _e;
+        const nodeData = this.constructor.nodeData;
+        if (!nodeData) {
+            throw Error("No node data");
+        }
+        this.comfyClass = nodeData.name;
+        let inputs = nodeData["input"]["required"];
+        if (nodeData["input"]["optional"] != undefined) {
+            inputs = Object.assign({}, inputs, nodeData["input"]["optional"]);
+        }
+        const WIDGETS = this.getWidgets();
+        const config = {
+            minWidth: 1,
+            minHeight: 1,
+            widget: null,
+        };
+        for (const inputName in inputs) {
+            const inputData = inputs[inputName];
+            const type = inputData[0];
+            if ((_a = inputData[1]) === null || _a === void 0 ? void 0 : _a.forceInput) {
+                this.addInput(inputName, type);
+            }
+            else {
+                let widgetCreated = true;
+                if (Array.isArray(type)) {
+                    Object.assign(config, WIDGETS.COMBO(this, inputName, inputData, app) || {});
+                }
+                else if (`${type}:${inputName}` in WIDGETS) {
+                    Object.assign(config, WIDGETS[`${type}:${inputName}`](this, inputName, inputData, app) || {});
+                }
+                else if (type in WIDGETS) {
+                    Object.assign(config, WIDGETS[type](this, inputName, inputData, app) || {});
+                }
+                else {
+                    this.addInput(inputName, type);
+                    widgetCreated = false;
+                }
+                if (widgetCreated && ((_b = inputData[1]) === null || _b === void 0 ? void 0 : _b.forceInput) && (config === null || config === void 0 ? void 0 : config.widget)) {
+                    if (!config.widget.options)
+                        config.widget.options = {};
+                    config.widget.options.forceInput = inputData[1].forceInput;
+                }
+                if (widgetCreated && ((_c = inputData[1]) === null || _c === void 0 ? void 0 : _c.defaultInput) && (config === null || config === void 0 ? void 0 : config.widget)) {
+                    if (!config.widget.options)
+                        config.widget.options = {};
+                    config.widget.options.defaultInput = inputData[1].defaultInput;
+                }
+            }
+        }
+        for (const o in nodeData["output"]) {
+            let output = nodeData["output"][o];
+            if (output instanceof Array)
+                output = "COMBO";
+            const outputName = nodeData["output_name"][o] || output;
+            const outputShape = nodeData["output_is_list"][o]
+                ? LiteGraph.GRID_SHAPE
+                : LiteGraph.CIRCLE_SHAPE;
+            this.addOutput(outputName, output, { shape: outputShape });
+        }
+        const s = this.computeSize();
+        s[0] = Math.max((_d = config.minWidth) !== null && _d !== void 0 ? _d : 1, s[0] * 1.5);
+        s[1] = Math.max((_e = config.minHeight) !== null && _e !== void 0 ? _e : 1, s[1]);
+        this.size = s;
+        this.serialize_widgets = true;
+    }
+    static registerForOverride(comfyClass, nodeData, rgthreeClass) {
+        if (OVERRIDDEN_SERVER_NODES.has(comfyClass)) {
+            throw Error(`Already have a class to override ${comfyClass.type || comfyClass.name || comfyClass.title}`);
+        }
+        OVERRIDDEN_SERVER_NODES.set(comfyClass, rgthreeClass);
+        if (!rgthreeClass.__registeredForOverride__) {
+            rgthreeClass.__registeredForOverride__ = true;
+            rgthreeClass.nodeType = comfyClass;
+            rgthreeClass.nodeData = nodeData;
+            rgthreeClass.onRegisteredForOverride(comfyClass, rgthreeClass);
+        }
+    }
+    static onRegisteredForOverride(comfyClass, rgthreeClass) {
+    }
+}
+RgthreeBaseServerNode.nodeType = null;
+RgthreeBaseServerNode.nodeData = null;
+RgthreeBaseServerNode.__registeredForOverride__ = false;
+const OVERRIDDEN_SERVER_NODES = new Map();
+const oldregisterNodeType = LiteGraph.registerNodeType;
+LiteGraph.registerNodeType = async function (nodeId, baseClass) {
+    var _a;
+    const clazz = OVERRIDDEN_SERVER_NODES.get(baseClass) || baseClass;
+    if (clazz !== baseClass) {
+        const classLabel = clazz.type || clazz.name || clazz.title;
+        const [n, v] = rgthree.logger.logParts(LogLevel.DEBUG, `${nodeId}: replacing default ComfyNode implementation with custom ${classLabel} class.`);
+        (_a = console[n]) === null || _a === void 0 ? void 0 : _a.call(console, ...v);
+    }
+    return oldregisterNodeType.call(LiteGraph, nodeId, clazz);
+};
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/base_node_collector.js b/custom_nodes/rgthree-comfy/web/comfyui/base_node_collector.js
new file mode 100644
index 0000000000000000000000000000000000000000..3dd924f744bda3fba05c090e272ea74873e8fc16
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/base_node_collector.js
@@ -0,0 +1,51 @@
+import { rgthree } from "./rgthree.js";
+import { BaseAnyInputConnectedNode } from "./base_any_input_connected_node.js";
+import { PassThroughFollowing, getConnectedInputNodes, getConnectedInputNodesAndFilterPassThroughs, shouldPassThrough, } from "./utils.js";
+export class BaseCollectorNode extends BaseAnyInputConnectedNode {
+    constructor(title) {
+        super(title);
+        this.inputsPassThroughFollowing = PassThroughFollowing.REROUTE_ONLY;
+        this.logger = rgthree.newLogSession("[BaseCollectorNode]");
+    }
+    clone() {
+        const cloned = super.clone();
+        return cloned;
+    }
+    handleLinkedNodesStabilization(linkedNodes) {
+        return false;
+    }
+    onConnectInput(inputIndex, outputType, outputSlot, outputNode, outputIndex) {
+        var _a, _b, _c, _d;
+        let canConnect = super.onConnectInput(inputIndex, outputType, outputSlot, outputNode, outputIndex);
+        if (canConnect) {
+            const allConnectedNodes = getConnectedInputNodes(this);
+            const nodesAlreadyInSlot = getConnectedInputNodes(this, undefined, inputIndex);
+            if (allConnectedNodes.includes(outputNode)) {
+                const [n, v] = this.logger.debugParts(`${outputNode.title} is already connected to ${this.title}.`);
+                (_a = console[n]) === null || _a === void 0 ? void 0 : _a.call(console, ...v);
+                if (nodesAlreadyInSlot.includes(outputNode)) {
+                    const [n, v] = this.logger.debugParts(`... but letting it slide since it's for the same slot.`);
+                    (_b = console[n]) === null || _b === void 0 ? void 0 : _b.call(console, ...v);
+                }
+                else {
+                    canConnect = false;
+                }
+            }
+            if (canConnect && shouldPassThrough(outputNode, PassThroughFollowing.REROUTE_ONLY)) {
+                const connectedNode = getConnectedInputNodesAndFilterPassThroughs(outputNode, undefined, undefined, PassThroughFollowing.REROUTE_ONLY)[0];
+                if (connectedNode && allConnectedNodes.includes(connectedNode)) {
+                    const [n, v] = this.logger.debugParts(`${connectedNode.title} is already connected to ${this.title}.`);
+                    (_c = console[n]) === null || _c === void 0 ? void 0 : _c.call(console, ...v);
+                    if (nodesAlreadyInSlot.includes(connectedNode)) {
+                        const [n, v] = this.logger.debugParts(`... but letting it slide since it's for the same slot.`);
+                        (_d = console[n]) === null || _d === void 0 ? void 0 : _d.call(console, ...v);
+                    }
+                    else {
+                        canConnect = false;
+                    }
+                }
+            }
+        }
+        return canConnect;
+    }
+}
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/base_node_mode_changer.js b/custom_nodes/rgthree-comfy/web/comfyui/base_node_mode_changer.js
new file mode 100644
index 0000000000000000000000000000000000000000..8d2b20783025517227b9acab8076a113b838d36d
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/base_node_mode_changer.js
@@ -0,0 +1,93 @@
+import { BaseAnyInputConnectedNode } from "./base_any_input_connected_node.js";
+import { changeModeOfNodes, PassThroughFollowing } from "./utils.js";
+import { wait } from "../../rgthree/common/shared_utils.js";
+export class BaseNodeModeChanger extends BaseAnyInputConnectedNode {
+    constructor(title) {
+        super(title);
+        this.inputsPassThroughFollowing = PassThroughFollowing.ALL;
+        this.isVirtualNode = true;
+        this.modeOn = -1;
+        this.modeOff = -1;
+        this.properties["toggleRestriction"] = "default";
+    }
+    onConstructed() {
+        wait(10).then(() => {
+            if (this.modeOn < 0 || this.modeOff < 0) {
+                throw new Error("modeOn and modeOff must be overridden.");
+            }
+        });
+        this.addOutput("OPT_CONNECTION", "*");
+        return super.onConstructed();
+    }
+    handleLinkedNodesStabilization(linkedNodes) {
+        let changed = false;
+        for (const [index, node] of linkedNodes.entries()) {
+            let widget = this.widgets && this.widgets[index];
+            if (!widget) {
+                this._tempWidth = this.size[0];
+                widget = this.addWidget("toggle", "", false, "", { on: "yes", off: "no" });
+                changed = true;
+            }
+            if (node) {
+                changed = this.setWidget(widget, node) || changed;
+            }
+        }
+        if (this.widgets && this.widgets.length > linkedNodes.length) {
+            this.widgets.length = linkedNodes.length;
+            changed = true;
+        }
+        return changed;
+    }
+    setWidget(widget, linkedNode, forceValue) {
+        let changed = false;
+        const value = forceValue == null ? linkedNode.mode === this.modeOn : forceValue;
+        let name = `Enable ${linkedNode.title}`;
+        if (widget.name !== name) {
+            widget.name = `Enable ${linkedNode.title}`;
+            widget.options = { on: "yes", off: "no" };
+            widget.value = value;
+            widget.doModeChange = (forceValue, skipOtherNodeCheck) => {
+                var _a, _b, _c;
+                let newValue = forceValue == null ? linkedNode.mode === this.modeOff : forceValue;
+                if (skipOtherNodeCheck !== true) {
+                    if (newValue && ((_b = (_a = this.properties) === null || _a === void 0 ? void 0 : _a["toggleRestriction"]) === null || _b === void 0 ? void 0 : _b.includes(" one"))) {
+                        for (const widget of this.widgets) {
+                            widget.doModeChange(false, true);
+                        }
+                    }
+                    else if (!newValue && ((_c = this.properties) === null || _c === void 0 ? void 0 : _c["toggleRestriction"]) === "always one") {
+                        newValue = this.widgets.every((w) => !w.value || w === widget);
+                    }
+                }
+                changeModeOfNodes(linkedNode, (newValue ? this.modeOn : this.modeOff));
+                widget.value = newValue;
+            };
+            widget.callback = () => {
+                widget.doModeChange();
+            };
+            changed = true;
+        }
+        if (forceValue != null) {
+            const newMode = (forceValue ? this.modeOn : this.modeOff);
+            if (linkedNode.mode !== newMode) {
+                changeModeOfNodes(linkedNode, newMode);
+                changed = true;
+            }
+        }
+        return changed;
+    }
+    forceWidgetOff(widget, skipOtherNodeCheck) {
+        widget.doModeChange(false, skipOtherNodeCheck);
+    }
+    forceWidgetOn(widget, skipOtherNodeCheck) {
+        widget.doModeChange(true, skipOtherNodeCheck);
+    }
+    forceWidgetToggle(widget, skipOtherNodeCheck) {
+        widget.doModeChange(!widget.value, skipOtherNodeCheck);
+    }
+}
+BaseNodeModeChanger.collapsible = false;
+BaseNodeModeChanger["@toggleRestriction"] = {
+    type: "combo",
+    values: ["default", "max one", "always one"],
+};
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/base_power_prompt.js b/custom_nodes/rgthree-comfy/web/comfyui/base_power_prompt.js
new file mode 100644
index 0000000000000000000000000000000000000000..c682f237fafb1d78113e1563c73309cdb42bc84d
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/base_power_prompt.js
@@ -0,0 +1,252 @@
+import { api } from "../../scripts/api.js";
+import { wait } from "../../rgthree/common/shared_utils.js";
+import { rgthree } from "./rgthree.js";
+export class PowerPrompt {
+    constructor(node, nodeData) {
+        this.combos = {};
+        this.combosValues = {};
+        this.configuring = false;
+        this.node = node;
+        this.node.properties = this.node.properties || {};
+        this.node.properties["combos_filter"] = "";
+        this.nodeData = nodeData;
+        this.isSimple = this.nodeData.name.includes("Simple");
+        this.promptEl = node.widgets[0].inputEl;
+        this.addAndHandleKeyboardLoraEditWeight();
+        this.patchNodeRefresh();
+        const oldConfigure = this.node.configure;
+        this.node.configure = (info) => {
+            this.configuring = true;
+            oldConfigure === null || oldConfigure === void 0 ? void 0 : oldConfigure.apply(this.node, [info]);
+            this.configuring = false;
+        };
+        const oldOnConnectionsChange = this.node.onConnectionsChange;
+        this.node.onConnectionsChange = (type, slotIndex, isConnected, link_info, _ioSlot) => {
+            oldOnConnectionsChange === null || oldOnConnectionsChange === void 0 ? void 0 : oldOnConnectionsChange.apply(this.node, [type, slotIndex, isConnected, link_info, _ioSlot]);
+            this.onNodeConnectionsChange(type, slotIndex, isConnected, link_info, _ioSlot);
+        };
+        const oldOnConnectInput = this.node.onConnectInput;
+        this.node.onConnectInput = (inputIndex, outputType, outputSlot, outputNode, outputIndex) => {
+            let canConnect = true;
+            if (oldOnConnectInput) {
+                canConnect = oldOnConnectInput.apply(this.node, [
+                    inputIndex,
+                    outputType,
+                    outputSlot,
+                    outputNode,
+                    outputIndex,
+                ]);
+            }
+            return (this.configuring ||
+                !!rgthree.loadingApiJson ||
+                (canConnect && !this.node.inputs[inputIndex].disabled));
+        };
+        const oldOnConnectOutput = this.node.onConnectOutput;
+        this.node.onConnectOutput = (outputIndex, inputType, inputSlot, inputNode, inputIndex) => {
+            let canConnect = true;
+            if (oldOnConnectOutput) {
+                canConnect = oldOnConnectOutput === null || oldOnConnectOutput === void 0 ? void 0 : oldOnConnectOutput.apply(this.node, [
+                    outputIndex,
+                    inputType,
+                    inputSlot,
+                    inputNode,
+                    inputIndex,
+                ]);
+            }
+            return (this.configuring ||
+                !!rgthree.loadingApiJson ||
+                (canConnect && !this.node.outputs[outputIndex].disabled));
+        };
+        const onPropertyChanged = this.node.onPropertyChanged;
+        this.node.onPropertyChanged = (property, value, prevValue) => {
+            const v = onPropertyChanged && onPropertyChanged.call(this.node, property, value, prevValue);
+            if (property === "combos_filter") {
+                this.refreshCombos(this.nodeData);
+            }
+            return v !== null && v !== void 0 ? v : true;
+        };
+        for (let i = this.node.widgets.length - 1; i >= 0; i--) {
+            if (this.shouldRemoveServerWidget(this.node.widgets[i])) {
+                this.node.widgets.splice(i, 1);
+            }
+        }
+        this.refreshCombos(nodeData);
+        setTimeout(() => {
+            this.stabilizeInputsOutputs();
+        }, 32);
+    }
+    onNodeConnectionsChange(_type, _slotIndex, _isConnected, _linkInfo, _ioSlot) {
+        this.stabilizeInputsOutputs();
+    }
+    stabilizeInputsOutputs() {
+        if (this.configuring || rgthree.loadingApiJson) {
+            return;
+        }
+        const clipLinked = this.node.inputs.some((i) => i.name.includes("clip") && !!i.link);
+        const modelLinked = this.node.inputs.some((i) => i.name.includes("model") && !!i.link);
+        for (const output of this.node.outputs) {
+            const type = output.type.toLowerCase();
+            if (type.includes("model")) {
+                output.disabled = !modelLinked;
+            }
+            else if (type.includes("conditioning")) {
+                output.disabled = !clipLinked;
+            }
+            else if (type.includes("clip")) {
+                output.disabled = !clipLinked;
+            }
+            else if (type.includes("string")) {
+                output.color_off = "#7F7";
+                output.color_on = "#7F7";
+            }
+            if (output.disabled) {
+            }
+        }
+    }
+    onFreshNodeDefs(event) {
+        this.refreshCombos(event.detail[this.nodeData.name]);
+    }
+    shouldRemoveServerWidget(widget) {
+        var _a, _b, _c, _d;
+        return (((_a = widget.name) === null || _a === void 0 ? void 0 : _a.startsWith("insert_")) ||
+            ((_b = widget.name) === null || _b === void 0 ? void 0 : _b.startsWith("target_")) ||
+            ((_c = widget.name) === null || _c === void 0 ? void 0 : _c.startsWith("crop_")) ||
+            ((_d = widget.name) === null || _d === void 0 ? void 0 : _d.startsWith("values_")));
+    }
+    refreshCombos(nodeData) {
+        var _a, _b, _c;
+        this.nodeData = nodeData;
+        let filter = null;
+        if ((_a = this.node.properties["combos_filter"]) === null || _a === void 0 ? void 0 : _a.trim()) {
+            try {
+                filter = new RegExp(this.node.properties["combos_filter"].trim(), "i");
+            }
+            catch (e) {
+                console.error(`Could not parse "${filter}" for Regular Expression`, e);
+                filter = null;
+            }
+        }
+        let data = Object.assign({}, ((_b = this.nodeData.input) === null || _b === void 0 ? void 0 : _b.optional) || {}, ((_c = this.nodeData.input) === null || _c === void 0 ? void 0 : _c.hidden) || {});
+        for (const [key, value] of Object.entries(data)) {
+            if (Array.isArray(value[0])) {
+                let values = value[0];
+                if (key.startsWith("insert")) {
+                    values = filter
+                        ? values.filter((v, i) => i < 1 || (i == 1 && v.match(/^disable\s[a-z]/i)) || (filter === null || filter === void 0 ? void 0 : filter.test(v)))
+                        : values;
+                    const shouldShow = values.length > 2 || (values.length > 1 && !values[1].match(/^disable\s[a-z]/i));
+                    if (shouldShow) {
+                        if (!this.combos[key]) {
+                            this.combos[key] = this.node.addWidget("combo", key, values[0], (selected) => {
+                                if (selected !== values[0] && !selected.match(/^disable\s[a-z]/i)) {
+                                    wait().then(() => {
+                                        if (key.includes("embedding")) {
+                                            this.insertSelectionText(`embedding:${selected}`);
+                                        }
+                                        else if (key.includes("saved")) {
+                                            this.insertSelectionText(this.combosValues[`values_${key}`][values.indexOf(selected)]);
+                                        }
+                                        else if (key.includes("lora")) {
+                                            this.insertSelectionText(`<lora:${selected}:1.0>`);
+                                        }
+                                        this.combos[key].value = values[0];
+                                    });
+                                }
+                            }, {
+                                values,
+                                serialize: true,
+                            });
+                            this.combos[key].oldComputeSize = this.combos[key].computeSize;
+                            let node = this.node;
+                            this.combos[key].computeSize = function (width) {
+                                var _a, _b;
+                                const size = ((_b = (_a = this).oldComputeSize) === null || _b === void 0 ? void 0 : _b.call(_a, width)) || [
+                                    width,
+                                    LiteGraph.NODE_WIDGET_HEIGHT,
+                                ];
+                                if (this === node.widgets[node.widgets.length - 1]) {
+                                    size[1] += 10;
+                                }
+                                return size;
+                            };
+                        }
+                        this.combos[key].options.values = values;
+                        this.combos[key].value = values[0];
+                    }
+                    else if (!shouldShow && this.combos[key]) {
+                        this.node.widgets.splice(this.node.widgets.indexOf(this.combos[key]), 1);
+                        delete this.combos[key];
+                    }
+                }
+                else if (key.startsWith("values")) {
+                    this.combosValues[key] = values;
+                }
+            }
+        }
+    }
+    insertSelectionText(text) {
+        if (!this.promptEl) {
+            console.error("Asked to insert text, but no textbox found.");
+            return;
+        }
+        let prompt = this.promptEl.value;
+        let first = prompt.substring(0, this.promptEl.selectionEnd).replace(/ +$/, "");
+        first = first + (["\n"].includes(first[first.length - 1]) ? "" : first.length ? " " : "");
+        let second = prompt.substring(this.promptEl.selectionEnd).replace(/^ +/, "");
+        second = (["\n"].includes(second[0]) ? "" : second.length ? " " : "") + second;
+        this.promptEl.value = first + text + second;
+        this.promptEl.focus();
+        this.promptEl.selectionStart = first.length;
+        this.promptEl.selectionEnd = first.length + text.length;
+    }
+    addAndHandleKeyboardLoraEditWeight() {
+        this.promptEl.addEventListener("keydown", (event) => {
+            var _a, _b;
+            if (!(event.key === "ArrowUp" || event.key === "ArrowDown"))
+                return;
+            if (!event.ctrlKey && !event.metaKey)
+                return;
+            const delta = event.shiftKey ? 0.01 : 0.1;
+            let start = this.promptEl.selectionStart;
+            let end = this.promptEl.selectionEnd;
+            let fullText = this.promptEl.value;
+            let selectedText = fullText.substring(start, end);
+            if (!selectedText) {
+                const stopOn = "<>()\r\n\t";
+                if (fullText[start] == ">") {
+                    start -= 2;
+                    end -= 2;
+                }
+                if (fullText[end - 1] == "<") {
+                    start += 2;
+                    end += 2;
+                }
+                while (!stopOn.includes(fullText[start]) && start > 0) {
+                    start--;
+                }
+                while (!stopOn.includes(fullText[end - 1]) && end < fullText.length) {
+                    end++;
+                }
+                selectedText = fullText.substring(start, end);
+            }
+            if (!selectedText.startsWith("<lora:") || !selectedText.endsWith(">")) {
+                return;
+            }
+            let weight = (_b = Number((_a = selectedText.match(/:(-?\d*(\.\d*)?)>$/)) === null || _a === void 0 ? void 0 : _a[1])) !== null && _b !== void 0 ? _b : 1;
+            weight += event.key === "ArrowUp" ? delta : -delta;
+            const updatedText = selectedText.replace(/(:-?\d*(\.\d*)?)?>$/, `:${weight.toFixed(2)}>`);
+            this.promptEl.setRangeText(updatedText, start, end, "select");
+            event.preventDefault();
+            event.stopPropagation();
+        });
+    }
+    patchNodeRefresh() {
+        this.boundOnFreshNodeDefs = this.onFreshNodeDefs.bind(this);
+        api.addEventListener("fresh-node-defs", this.boundOnFreshNodeDefs);
+        const oldNodeRemoved = this.node.onRemoved;
+        this.node.onRemoved = () => {
+            oldNodeRemoved === null || oldNodeRemoved === void 0 ? void 0 : oldNodeRemoved.call(this.node);
+            api.removeEventListener("fresh-node-defs", this.boundOnFreshNodeDefs);
+        };
+    }
+}
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/bookmark.js b/custom_nodes/rgthree-comfy/web/comfyui/bookmark.js
new file mode 100644
index 0000000000000000000000000000000000000000..c48877185179124eff4200ea6344cdd32603d995
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/bookmark.js
@@ -0,0 +1,105 @@
+import { app } from "../../scripts/app.js";
+import { RgthreeBaseVirtualNode } from "./base_node.js";
+import { SERVICE as KEY_EVENT_SERVICE } from "./services/key_events_services.js";
+import { SERVICE as BOOKMARKS_SERVICE } from "./services/bookmarks_services.js";
+import { NodeTypesString } from "./constants.js";
+import { getClosestOrSelf, query } from "../../rgthree/common/utils_dom.js";
+import { wait } from "../../rgthree/common/shared_utils.js";
+import { findFromNodeForSubgraph } from "./utils.js";
+export class Bookmark extends RgthreeBaseVirtualNode {
+    get _collapsed_width() {
+        return this.___collapsed_width;
+    }
+    set _collapsed_width(width) {
+        const canvas = app.canvas;
+        const ctx = canvas.canvas.getContext("2d");
+        const oldFont = ctx.font;
+        ctx.font = canvas.title_text_font;
+        this.___collapsed_width = 40 + ctx.measureText(this.title).width;
+        ctx.font = oldFont;
+    }
+    constructor(title = Bookmark.title) {
+        super(title);
+        this.comfyClass = NodeTypesString.BOOKMARK;
+        this.___collapsed_width = 0;
+        this.isVirtualNode = true;
+        this.serialize_widgets = true;
+        const nextShortcutChar = BOOKMARKS_SERVICE.getNextShortcut();
+        this.addWidget("text", "shortcut_key", nextShortcutChar, (value, ...args) => {
+            value = value.trim()[0] || "1";
+        }, {
+            y: 8,
+        });
+        this.addWidget("number", "zoom", 1, (value) => { }, {
+            y: 8 + LiteGraph.NODE_WIDGET_HEIGHT + 4,
+            max: 2,
+            min: 0.5,
+            precision: 2,
+        });
+        this.keypressBound = this.onKeypress.bind(this);
+        this.title = "🔖";
+        this.onConstructed();
+    }
+    get shortcutKey() {
+        var _a, _b, _c;
+        return (_c = (_b = (_a = this.widgets[0]) === null || _a === void 0 ? void 0 : _a.value) === null || _b === void 0 ? void 0 : _b.toLocaleLowerCase()) !== null && _c !== void 0 ? _c : "";
+    }
+    onAdded(graph) {
+        KEY_EVENT_SERVICE.addEventListener("keydown", this.keypressBound);
+    }
+    onRemoved() {
+        KEY_EVENT_SERVICE.removeEventListener("keydown", this.keypressBound);
+    }
+    onKeypress(event) {
+        const originalEvent = event.detail.originalEvent;
+        const target = originalEvent.target;
+        if (getClosestOrSelf(target, 'input,textarea,[contenteditable="true"]')) {
+            return;
+        }
+        if (KEY_EVENT_SERVICE.areOnlyKeysDown(this.widgets[0].value, true)) {
+            this.canvasToBookmark();
+            originalEvent.preventDefault();
+            originalEvent.stopPropagation();
+        }
+    }
+    onMouseDown(event, pos, graphCanvas) {
+        var _a;
+        const input = query(".graphdialog > input.value");
+        if (input && input.value === ((_a = this.widgets[0]) === null || _a === void 0 ? void 0 : _a.value)) {
+            input.addEventListener("keydown", (e) => {
+                KEY_EVENT_SERVICE.handleKeyDownOrUp(e);
+                e.preventDefault();
+                e.stopPropagation();
+                input.value = Object.keys(KEY_EVENT_SERVICE.downKeys).join(" + ");
+            });
+        }
+        return false;
+    }
+    async canvasToBookmark() {
+        var _a, _b;
+        const canvas = app.canvas;
+        if (this.graph !== app.canvas.getCurrentGraph()) {
+            const subgraph = this.graph;
+            const fromNode = findFromNodeForSubgraph(subgraph.id);
+            canvas.openSubgraph(subgraph, fromNode);
+            await wait(16);
+        }
+        if ((_a = canvas === null || canvas === void 0 ? void 0 : canvas.ds) === null || _a === void 0 ? void 0 : _a.offset) {
+            canvas.ds.offset[0] = -this.pos[0] + 16;
+            canvas.ds.offset[1] = -this.pos[1] + 40;
+        }
+        if (((_b = canvas === null || canvas === void 0 ? void 0 : canvas.ds) === null || _b === void 0 ? void 0 : _b.scale) != null) {
+            canvas.ds.scale = Number(this.widgets[1].value || 1);
+        }
+        canvas.setDirty(true, true);
+    }
+}
+Bookmark.type = NodeTypesString.BOOKMARK;
+Bookmark.title = NodeTypesString.BOOKMARK;
+Bookmark.slot_start_y = -20;
+app.registerExtension({
+    name: "rgthree.Bookmark",
+    registerCustomNodes() {
+        Bookmark.setUp();
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/bypasser.js b/custom_nodes/rgthree-comfy/web/comfyui/bypasser.js
new file mode 100644
index 0000000000000000000000000000000000000000..96e47e21d6b9f5e20d52c81d2105dc3c770a6ec6
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/bypasser.js
@@ -0,0 +1,45 @@
+import { app } from "../../scripts/app.js";
+import { BaseNodeModeChanger } from "./base_node_mode_changer.js";
+import { NodeTypesString } from "./constants.js";
+const MODE_BYPASS = 4;
+const MODE_ALWAYS = 0;
+class BypasserNode extends BaseNodeModeChanger {
+    constructor(title = BypasserNode.title) {
+        super(title);
+        this.comfyClass = NodeTypesString.FAST_BYPASSER;
+        this.modeOn = MODE_ALWAYS;
+        this.modeOff = MODE_BYPASS;
+        this.onConstructed();
+    }
+    async handleAction(action) {
+        if (action === "Bypass all") {
+            for (const widget of this.widgets || []) {
+                this.forceWidgetOff(widget, true);
+            }
+        }
+        else if (action === "Enable all") {
+            for (const widget of this.widgets || []) {
+                this.forceWidgetOn(widget, true);
+            }
+        }
+        else if (action === "Toggle all") {
+            for (const widget of this.widgets || []) {
+                this.forceWidgetToggle(widget, true);
+            }
+        }
+    }
+}
+BypasserNode.exposedActions = ["Bypass all", "Enable all", "Toggle all"];
+BypasserNode.type = NodeTypesString.FAST_BYPASSER;
+BypasserNode.title = NodeTypesString.FAST_BYPASSER;
+app.registerExtension({
+    name: "rgthree.Bypasser",
+    registerCustomNodes() {
+        BypasserNode.setUp();
+    },
+    loadedGraphNode(node) {
+        if (node.type == BypasserNode.title) {
+            node._tempWidth = node.size[0];
+        }
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/comfy_ui_bar.js b/custom_nodes/rgthree-comfy/web/comfyui/comfy_ui_bar.js
new file mode 100644
index 0000000000000000000000000000000000000000..a95fb0ff22b968a0df603a75d6645f8bc6a01684
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/comfy_ui_bar.js
@@ -0,0 +1,197 @@
+import { app } from "../../scripts/app.js";
+import { iconGear, iconStarFilled, logoRgthreeAsync } from "../../rgthree/common/media/svgs.js";
+import { $el, empty } from "../../rgthree/common/utils_dom.js";
+import { SERVICE as BOOKMARKS_SERVICE } from "./services/bookmarks_services.js";
+import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js";
+import { RgthreeConfigDialog } from "./config.js";
+import { wait } from "../../rgthree/common/shared_utils.js";
+let rgthreeButtonGroup = null;
+function addRgthreeTopBarButtons() {
+    var _a, _b, _c;
+    if (!CONFIG_SERVICE.getFeatureValue("comfy_top_bar_menu.enabled")) {
+        if ((_a = rgthreeButtonGroup === null || rgthreeButtonGroup === void 0 ? void 0 : rgthreeButtonGroup.element) === null || _a === void 0 ? void 0 : _a.parentElement) {
+            rgthreeButtonGroup.element.parentElement.removeChild(rgthreeButtonGroup.element);
+        }
+        return;
+    }
+    else if (rgthreeButtonGroup) {
+        (_b = app.menu) === null || _b === void 0 ? void 0 : _b.settingsGroup.element.before(rgthreeButtonGroup.element);
+        return;
+    }
+    const buttons = [];
+    const rgthreeButton = new RgthreeComfyButton({
+        icon: "<svg></svg>",
+        tooltip: "rgthree-comfy",
+        primary: true,
+        enabled: true,
+        classList: "comfyui-button comfyui-menu-mobile-collapse primary",
+    });
+    buttons.push(rgthreeButton);
+    logoRgthreeAsync().then((t) => {
+        rgthreeButton.setIcon(t);
+    });
+    rgthreeButton.withPopup(new RgthreeComfyPopup({ target: rgthreeButton.element }, $el("menu.rgthree-menu.rgthree-top-menu", {
+        children: [
+            $el("li", {
+                child: $el("button.rgthree-button-reset", {
+                    html: iconGear + "Settings (rgthree-comfy)",
+                    onclick: () => new RgthreeConfigDialog().show(),
+                }),
+            }),
+            $el("li", {
+                child: $el("button.rgthree-button-reset", {
+                    html: iconStarFilled + "Star on Github",
+                    onclick: () => window.open("https://github.com/rgthree/rgthree-comfy", "_blank"),
+                }),
+            }),
+        ],
+    })), "click");
+    if (CONFIG_SERVICE.getFeatureValue("comfy_top_bar_menu.button_bookmarks.enabled")) {
+        const bookmarksListEl = $el("menu.rgthree-menu.rgthree-top-menu");
+        bookmarksListEl.appendChild($el("li.rgthree-message", {
+            child: $el("span", { text: "No bookmarks in current workflow." }),
+        }));
+        const bookmarksButton = new RgthreeComfyButton({
+            icon: "bookmark",
+            tooltip: "Workflow Bookmarks (rgthree-comfy)",
+        });
+        const bookmarksPopup = new RgthreeComfyPopup({ target: bookmarksButton.element, modal: false }, bookmarksListEl);
+        bookmarksPopup.onOpen(() => {
+            const bookmarks = BOOKMARKS_SERVICE.getCurrentBookmarks();
+            empty(bookmarksListEl);
+            if (bookmarks.length) {
+                for (const b of bookmarks) {
+                    bookmarksListEl.appendChild($el("li", {
+                        child: $el("button.rgthree-button-reset", {
+                            text: `[${b.shortcutKey}] ${b.title}`,
+                            onclick: () => {
+                                b.canvasToBookmark();
+                            },
+                        }),
+                    }));
+                }
+            }
+            else {
+                bookmarksListEl.appendChild($el("li.rgthree-message", {
+                    child: $el("span", { text: "No bookmarks in current workflow." }),
+                }));
+            }
+        });
+        bookmarksButton.withPopup(bookmarksPopup, "hover");
+        buttons.push(bookmarksButton);
+    }
+    rgthreeButtonGroup = new RgthreeComfyButtonGroup(...buttons);
+    (_c = app.menu) === null || _c === void 0 ? void 0 : _c.settingsGroup.element.before(rgthreeButtonGroup.element);
+}
+app.registerExtension({
+    name: "rgthree.TopMenu",
+    async setup() {
+        addRgthreeTopBarButtons();
+        CONFIG_SERVICE.addEventListener("config-change", ((e) => {
+            var _a, _b;
+            if ((_b = (_a = e.detail) === null || _a === void 0 ? void 0 : _a.key) === null || _b === void 0 ? void 0 : _b.includes("features.comfy_top_bar_menu")) {
+                addRgthreeTopBarButtons();
+            }
+        }));
+    },
+});
+class RgthreeComfyButtonGroup {
+    constructor(...buttons) {
+        this.element = $el("div.rgthree-comfybar-top-button-group");
+        this.buttons = buttons;
+        this.update();
+    }
+    insert(button, index) {
+        this.buttons.splice(index, 0, button);
+        this.update();
+    }
+    append(button) {
+        this.buttons.push(button);
+        this.update();
+    }
+    remove(indexOrButton) {
+        if (typeof indexOrButton !== "number") {
+            indexOrButton = this.buttons.indexOf(indexOrButton);
+        }
+        if (indexOrButton > -1) {
+            const btn = this.buttons.splice(indexOrButton, 1);
+            this.update();
+            return btn;
+        }
+        return null;
+    }
+    update() {
+        this.element.replaceChildren(...this.buttons.map((b) => { var _a; return (_a = b["element"]) !== null && _a !== void 0 ? _a : b; }));
+    }
+}
+class RgthreeComfyButton {
+    constructor(opts) {
+        this.element = $el("button.rgthree-comfybar-top-button.rgthree-button-reset.rgthree-button");
+        this.iconElement = $el("span.rgthree-button-icon");
+        opts.icon && this.setIcon(opts.icon);
+        opts.tooltip && this.element.setAttribute("title", opts.tooltip);
+        opts.primary && this.element.classList.add("-primary");
+    }
+    setIcon(iconOrMarkup) {
+        const markup = iconOrMarkup.startsWith("<")
+            ? iconOrMarkup
+            : `<i class="mdi mdi-${iconOrMarkup}"></i>`;
+        this.iconElement.innerHTML = markup;
+        if (!this.iconElement.parentElement) {
+            this.element.appendChild(this.iconElement);
+        }
+    }
+    withPopup(popup, trigger) {
+        if (trigger === "click") {
+            this.element.addEventListener("click", () => {
+                popup.open();
+            });
+        }
+        if (trigger === "hover") {
+            this.element.addEventListener("pointerenter", () => {
+                popup.open();
+            });
+        }
+    }
+}
+class RgthreeComfyPopup {
+    constructor(opts, element) {
+        this.onOpenFn = null;
+        this.onWindowClickBound = this.onWindowClick.bind(this);
+        this.element = element;
+        this.opts = opts;
+        opts.target && (this.target = opts.target);
+        opts.modal && this.element.classList.add("-modal");
+    }
+    async open() {
+        if (!this.target) {
+            throw new Error("No target for RgthreeComfyPopup");
+        }
+        if (this.onOpenFn) {
+            await this.onOpenFn();
+        }
+        await wait(16);
+        const rect = this.target.getBoundingClientRect();
+        this.element.setAttribute("state", "measuring");
+        document.body.appendChild(this.element);
+        this.element.style.position = "fixed";
+        this.element.style.left = `${rect.left}px`;
+        this.element.style.top = `${rect.top + rect.height}px`;
+        this.element.setAttribute("state", "open");
+        if (this.opts.modal) {
+            document.body.classList.add("rgthree-modal-menu-open");
+        }
+        window.addEventListener("click", this.onWindowClickBound);
+    }
+    close() {
+        this.element.remove();
+        document.body.classList.remove("rgthree-modal-menu-open");
+        window.removeEventListener("click", this.onWindowClickBound);
+    }
+    onOpen(fn) {
+        this.onOpenFn = fn;
+    }
+    onWindowClick() {
+        this.close();
+    }
+}
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/config.js b/custom_nodes/rgthree-comfy/web/comfyui/config.js
new file mode 100644
index 0000000000000000000000000000000000000000..698d42551e72f79bf77c7c944c579091e44d02f8
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/config.js
@@ -0,0 +1,378 @@
+import { app } from "../../scripts/app.js";
+import { RgthreeDialog } from "../../rgthree/common/dialog.js";
+import { createElement as $el, queryAll as $$ } from "../../rgthree/common/utils_dom.js";
+import { checkmark, logoRgthree } from "../../rgthree/common/media/svgs.js";
+import { rgthree } from "./rgthree.js";
+import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js";
+var ConfigType;
+(function (ConfigType) {
+    ConfigType[ConfigType["UNKNOWN"] = 0] = "UNKNOWN";
+    ConfigType[ConfigType["BOOLEAN"] = 1] = "BOOLEAN";
+    ConfigType[ConfigType["STRING"] = 2] = "STRING";
+    ConfigType[ConfigType["NUMBER"] = 3] = "NUMBER";
+    ConfigType[ConfigType["ARRAY"] = 4] = "ARRAY";
+})(ConfigType || (ConfigType = {}));
+var ConfigInputType;
+(function (ConfigInputType) {
+    ConfigInputType[ConfigInputType["UNKNOWN"] = 0] = "UNKNOWN";
+    ConfigInputType[ConfigInputType["CHECKLIST"] = 1] = "CHECKLIST";
+})(ConfigInputType || (ConfigInputType = {}));
+const TYPE_TO_STRING = {
+    [ConfigType.UNKNOWN]: "unknown",
+    [ConfigType.BOOLEAN]: "boolean",
+    [ConfigType.STRING]: "string",
+    [ConfigType.NUMBER]: "number",
+    [ConfigType.ARRAY]: "array",
+};
+const CONFIGURABLE = {
+    features: [
+        {
+            key: "features.progress_bar.enabled",
+            type: ConfigType.BOOLEAN,
+            label: "Prompt Progress Bar",
+            description: `Shows a minimal progress bar for nodes and steps at the top of the app.`,
+            subconfig: [
+                {
+                    key: "features.progress_bar.height",
+                    type: ConfigType.NUMBER,
+                    label: "Height of the bar",
+                },
+                {
+                    key: "features.progress_bar.position",
+                    type: ConfigType.STRING,
+                    label: "Position at top or bottom of window",
+                    options: ["top", "bottom"],
+                },
+            ],
+        },
+        {
+            key: "features.import_individual_nodes.enabled",
+            type: ConfigType.BOOLEAN,
+            label: "Import Individual Nodes Widgets",
+            description: "Dragging & Dropping a similar image/JSON workflow onto (most) current workflow nodes" +
+                "will allow you to import that workflow's node's widgets when it has the same " +
+                "id and type. This is useful when you have several images and you'd like to import just " +
+                "one part of a previous iteration, like a seed, or prompt.",
+        },
+    ],
+    menus: [
+        {
+            key: "features.comfy_top_bar_menu.enabled",
+            type: ConfigType.BOOLEAN,
+            label: "Enable Top Bar Menu",
+            description: "Have quick access from ComfyUI's new top bar to rgthree-comfy bookmarks, settings " +
+                "(and more to come).",
+        },
+        {
+            key: "features.menu_queue_selected_nodes",
+            type: ConfigType.BOOLEAN,
+            label: "Show 'Queue Selected Output Nodes'",
+            description: "Will show a menu item in the right-click context menus to queue (only) the selected " +
+                "output nodes.",
+        },
+        {
+            key: "features.menu_auto_nest.subdirs",
+            type: ConfigType.BOOLEAN,
+            label: "Auto Nest Subdirectories in Menus",
+            description: "When a large, flat list of values contain sub-directories, auto nest them. (Like, for " +
+                "a large list of checkpoints).",
+            subconfig: [
+                {
+                    key: "features.menu_auto_nest.threshold",
+                    type: ConfigType.NUMBER,
+                    label: "Number of items needed to trigger nesting.",
+                },
+            ],
+        },
+        {
+            key: "features.menu_bookmarks.enabled",
+            type: ConfigType.BOOLEAN,
+            label: "Show Bookmarks in context menu",
+            description: "Will list bookmarks in the rgthree-comfy right-click context menu.",
+        },
+    ],
+    groups: [
+        {
+            key: "features.group_header_fast_toggle.enabled",
+            type: ConfigType.BOOLEAN,
+            label: "Show fast toggles in Group Headers",
+            description: "Show quick toggles in Groups' Headers to quickly mute, bypass or queue.",
+            subconfig: [
+                {
+                    key: "features.group_header_fast_toggle.toggles",
+                    type: ConfigType.ARRAY,
+                    label: "Which toggles to show.",
+                    inputType: ConfigInputType.CHECKLIST,
+                    options: [
+                        { value: "queue", label: "queue" },
+                        { value: "bypass", label: "bypass" },
+                        { value: "mute", label: "mute" },
+                    ],
+                },
+                {
+                    key: "features.group_header_fast_toggle.show",
+                    type: ConfigType.STRING,
+                    label: "When to show them.",
+                    options: [
+                        { value: "hover", label: "on hover" },
+                        { value: "always", label: "always" },
+                    ],
+                },
+            ],
+        },
+    ],
+    power_lora_loader: [
+        {
+            key: "nodes.power_lora_loader.show_info_badge",
+            type: ConfigType.BOOLEAN,
+            label: "Show info badge/button",
+            description: "Show an info badge/button on each lora row to signal and open lora details.",
+        },
+    ],
+    advanced: [
+        {
+            key: "features.show_alerts_for_corrupt_workflows",
+            type: ConfigType.BOOLEAN,
+            label: "Detect Corrupt Workflows",
+            description: "Will show a message at the top of the screen when loading a workflow that has " +
+                "corrupt linking data.",
+        },
+        {
+            key: "log_level",
+            type: ConfigType.STRING,
+            label: "Log level for browser dev console.",
+            description: "Further down the list, the more verbose logs to the console will be. For instance, " +
+                "selecting 'IMPORTANT' means only important message will be logged to the browser " +
+                "console, while selecting 'WARN' will log all messages at or higher than WARN, including " +
+                "'ERROR' and 'IMPORTANT' etc.",
+            options: ["IMPORTANT", "ERROR", "WARN", "INFO", "DEBUG", "DEV"],
+            isDevOnly: true,
+            onSave: function (value) {
+                rgthree.setLogLevel(value);
+            },
+        },
+        {
+            key: "features.invoke_extensions_async.node_created",
+            type: ConfigType.BOOLEAN,
+            label: "Allow other extensions to call nodeCreated on rgthree-nodes.",
+            isDevOnly: true,
+            description: "Do not disable unless you are having trouble (and then file an issue at rgthree-comfy)." +
+                "Prior to Apr 2024 it was not possible for other extensions to invoke their nodeCreated " +
+                "event on some rgthree-comfy nodes. Now it's possible and this option is only here in " +
+                "for easy if something is wrong.",
+        },
+    ],
+};
+function fieldrow(item) {
+    var _a;
+    const initialValue = CONFIG_SERVICE.getConfigValue(item.key);
+    const container = $el(`div.fieldrow.-type-${TYPE_TO_STRING[item.type]}`, {
+        dataset: {
+            name: item.key,
+            initial: initialValue,
+            type: item.type,
+        },
+    });
+    $el(`label[for="${item.key}"]`, {
+        children: [
+            $el(`span[text="${item.label}"]`),
+            item.description ? $el("small", { html: item.description }) : null,
+        ],
+        parent: container,
+    });
+    let input;
+    if ((_a = item.options) === null || _a === void 0 ? void 0 : _a.length) {
+        if (item.inputType === ConfigInputType.CHECKLIST) {
+            const initialValueList = initialValue || [];
+            input = $el(`fieldset.rgthree-checklist-group[id="${item.key}"]`, {
+                parent: container,
+                children: item.options.map((o) => {
+                    const label = o.label || String(o);
+                    const value = o.value || o;
+                    const id = `${item.key}_${value}`;
+                    return $el(`span.rgthree-checklist-item`, {
+                        children: [
+                            $el(`input[type="checkbox"][value="${value}"]`, {
+                                id,
+                                checked: initialValueList.includes(value),
+                            }),
+                            $el(`label`, {
+                                for: id,
+                                text: label,
+                            })
+                        ]
+                    });
+                }),
+            });
+        }
+        else {
+            input = $el(`select[id="${item.key}"]`, {
+                parent: container,
+                children: item.options.map((o) => {
+                    const label = o.label || String(o);
+                    const value = o.value || o;
+                    const valueSerialized = JSON.stringify({ value: value });
+                    return $el(`option[value="${valueSerialized}"]`, {
+                        text: label,
+                        selected: valueSerialized === JSON.stringify({ value: initialValue }),
+                    });
+                }),
+            });
+        }
+    }
+    else if (item.type === ConfigType.BOOLEAN) {
+        container.classList.toggle("-checked", !!initialValue);
+        input = $el(`input[type="checkbox"][id="${item.key}"]`, {
+            parent: container,
+            checked: initialValue,
+        });
+    }
+    else {
+        input = $el(`input[id="${item.key}"]`, {
+            parent: container,
+            value: initialValue,
+        });
+    }
+    $el("div.fieldrow-value", { children: [input], parent: container });
+    return container;
+}
+export class RgthreeConfigDialog extends RgthreeDialog {
+    constructor() {
+        const content = $el("div");
+        content.appendChild(RgthreeConfigDialog.buildFieldset(CONFIGURABLE["features"], "Features"));
+        content.appendChild(RgthreeConfigDialog.buildFieldset(CONFIGURABLE["menus"], "Menus"));
+        content.appendChild(RgthreeConfigDialog.buildFieldset(CONFIGURABLE["groups"], "Groups"));
+        content.appendChild(RgthreeConfigDialog.buildFieldset(CONFIGURABLE["power_lora_loader"], "Power Lora Loader"));
+        content.appendChild(RgthreeConfigDialog.buildFieldset(CONFIGURABLE["advanced"], "Advanced"));
+        content.addEventListener("input", (e) => {
+            const changed = this.getChangedFormData();
+            $$(".save-button", this.element)[0].disabled =
+                !Object.keys(changed).length;
+        });
+        content.addEventListener("change", (e) => {
+            const changed = this.getChangedFormData();
+            $$(".save-button", this.element)[0].disabled =
+                !Object.keys(changed).length;
+        });
+        const dialogOptions = {
+            class: "-iconed -settings",
+            title: logoRgthree + `<h2>Settings - rgthree-comfy</h2>`,
+            content,
+            onBeforeClose: () => {
+                const changed = this.getChangedFormData();
+                if (Object.keys(changed).length) {
+                    return confirm("Looks like there are unsaved changes. Are you sure you want close?");
+                }
+                return true;
+            },
+            buttons: [
+                {
+                    label: "Save",
+                    disabled: true,
+                    className: "rgthree-button save-button -blue",
+                    callback: async (e) => {
+                        var _a, _b;
+                        const changed = this.getChangedFormData();
+                        if (!Object.keys(changed).length) {
+                            this.close();
+                            return;
+                        }
+                        const success = await CONFIG_SERVICE.setConfigValues(changed);
+                        if (success) {
+                            for (const key of Object.keys(changed)) {
+                                (_b = (_a = Object.values(CONFIGURABLE)
+                                    .flat()
+                                    .find((f) => f.key === key)) === null || _a === void 0 ? void 0 : _a.onSave) === null || _b === void 0 ? void 0 : _b.call(_a, changed[key]);
+                            }
+                            this.close();
+                            rgthree.showMessage({
+                                id: "config-success",
+                                message: `${checkmark} Successfully saved rgthree-comfy settings!`,
+                                timeout: 4000,
+                            });
+                            $$(".save-button", this.element)[0].disabled = true;
+                        }
+                        else {
+                            alert("There was an error saving rgthree-comfy configuration.");
+                        }
+                    },
+                },
+            ],
+        };
+        super(dialogOptions);
+    }
+    static buildFieldset(datas, label) {
+        const fieldset = $el(`fieldset`, { children: [$el(`legend[text="${label}"]`)] });
+        for (const data of datas) {
+            if (data.isDevOnly && !rgthree.isDevMode()) {
+                continue;
+            }
+            const container = $el("div.formrow");
+            container.appendChild(fieldrow(data));
+            if (data.subconfig) {
+                for (const subfeature of data.subconfig) {
+                    container.appendChild(fieldrow(subfeature));
+                }
+            }
+            fieldset.appendChild(container);
+        }
+        return fieldset;
+    }
+    getChangedFormData() {
+        return $$("[data-name]", this.contentElement).reduce((acc, el) => {
+            const name = el.dataset["name"];
+            const type = el.dataset["type"];
+            const initialValue = CONFIG_SERVICE.getConfigValue(name);
+            let currentValueEl = $$("fieldset.rgthree-checklist-group, input, textarea, select", el)[0];
+            let currentValue = null;
+            if (type === String(ConfigType.BOOLEAN)) {
+                currentValue = currentValueEl.checked;
+                el.classList.toggle("-checked", currentValue);
+            }
+            else {
+                currentValue = currentValueEl === null || currentValueEl === void 0 ? void 0 : currentValueEl.value;
+                if (currentValueEl.nodeName === "SELECT") {
+                    currentValue = JSON.parse(currentValue).value;
+                }
+                else if (currentValueEl.classList.contains('rgthree-checklist-group')) {
+                    currentValue = [];
+                    for (const check of $$('input[type="checkbox"]', currentValueEl)) {
+                        if (check.checked) {
+                            currentValue.push(check.value);
+                        }
+                    }
+                }
+                else if (type === String(ConfigType.NUMBER)) {
+                    currentValue = Number(currentValue) || initialValue;
+                }
+            }
+            if (JSON.stringify(currentValue) !== JSON.stringify(initialValue)) {
+                acc[name] = currentValue;
+            }
+            return acc;
+        }, {});
+    }
+}
+app.ui.settings.addSetting({
+    id: "rgthree.config",
+    defaultValue: null,
+    name: "Open rgthree-comfy config",
+    type: () => {
+        return $el("tr.rgthree-comfyui-settings-row", {
+            children: [
+                $el("td", {
+                    child: `<div>${logoRgthree} [rgthree-comfy] configuration / settings</div>`,
+                }),
+                $el("td", {
+                    child: $el('button.rgthree-button.-blue[text="rgthree-comfy settings"]', {
+                        events: {
+                            click: (e) => {
+                                new RgthreeConfigDialog().show();
+                            },
+                        },
+                    }),
+                }),
+            ],
+        });
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/constants.js b/custom_nodes/rgthree-comfy/web/comfyui/constants.js
new file mode 100644
index 0000000000000000000000000000000000000000..b05bf4bda40f45921483f291808aa8809b0b6a9f
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/constants.js
@@ -0,0 +1,62 @@
+import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js";
+export function addRgthree(str) {
+    return str + " (rgthree)";
+}
+export function stripRgthree(str) {
+    return str.replace(/\s*\(rgthree\)$/, "");
+}
+export const NodeTypesString = {
+    ANY_SWITCH: addRgthree("Any Switch"),
+    CONTEXT: addRgthree("Context"),
+    CONTEXT_BIG: addRgthree("Context Big"),
+    CONTEXT_SWITCH: addRgthree("Context Switch"),
+    CONTEXT_SWITCH_BIG: addRgthree("Context Switch Big"),
+    CONTEXT_MERGE: addRgthree("Context Merge"),
+    CONTEXT_MERGE_BIG: addRgthree("Context Merge Big"),
+    DYNAMIC_CONTEXT: addRgthree("Dynamic Context"),
+    DYNAMIC_CONTEXT_SWITCH: addRgthree("Dynamic Context Switch"),
+    DISPLAY_ANY: addRgthree("Display Any"),
+    IMAGE_OR_LATENT_SIZE: addRgthree("Image or Latent Size"),
+    NODE_MODE_RELAY: addRgthree("Mute / Bypass Relay"),
+    NODE_MODE_REPEATER: addRgthree("Mute / Bypass Repeater"),
+    FAST_MUTER: addRgthree("Fast Muter"),
+    FAST_BYPASSER: addRgthree("Fast Bypasser"),
+    FAST_GROUPS_MUTER: addRgthree("Fast Groups Muter"),
+    FAST_GROUPS_BYPASSER: addRgthree("Fast Groups Bypasser"),
+    FAST_ACTIONS_BUTTON: addRgthree("Fast Actions Button"),
+    LABEL: addRgthree("Label"),
+    POWER_PRIMITIVE: addRgthree("Power Primitive"),
+    POWER_PROMPT: addRgthree("Power Prompt"),
+    POWER_PROMPT_SIMPLE: addRgthree("Power Prompt - Simple"),
+    POWER_PUTER: addRgthree("Power Puter"),
+    POWER_CONDUCTOR: addRgthree("Power Conductor"),
+    SDXL_EMPTY_LATENT_IMAGE: addRgthree("SDXL Empty Latent Image"),
+    SDXL_POWER_PROMPT_POSITIVE: addRgthree("SDXL Power Prompt - Positive"),
+    SDXL_POWER_PROMPT_NEGATIVE: addRgthree("SDXL Power Prompt - Simple / Negative"),
+    POWER_LORA_LOADER: addRgthree("Power Lora Loader"),
+    KSAMPLER_CONFIG: addRgthree("KSampler Config"),
+    NODE_COLLECTOR: addRgthree("Node Collector"),
+    REROUTE: addRgthree("Reroute"),
+    RANDOM_UNMUTER: addRgthree("Random Unmuter"),
+    SEED: addRgthree("Seed"),
+    BOOKMARK: addRgthree("Bookmark"),
+    IMAGE_COMPARER: addRgthree("Image Comparer"),
+    IMAGE_INSET_CROP: addRgthree("Image Inset Crop"),
+};
+const UNRELEASED_KEYS = {
+    [NodeTypesString.DYNAMIC_CONTEXT]: "dynamic_context",
+    [NodeTypesString.DYNAMIC_CONTEXT_SWITCH]: "dynamic_context",
+    [NodeTypesString.POWER_CONDUCTOR]: "power_conductor",
+};
+export function getNodeTypeStrings() {
+    const unreleasedKeys = Object.keys(UNRELEASED_KEYS);
+    return Object.values(NodeTypesString)
+        .map((i) => stripRgthree(i))
+        .filter((i) => {
+        if (unreleasedKeys.includes(i)) {
+            return !!CONFIG_SERVICE.getConfigValue(`unreleased.${UNRELEASED_KEYS[i]}.enabled`);
+        }
+        return true;
+    })
+        .sort();
+}
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/context.js b/custom_nodes/rgthree-comfy/web/comfyui/context.js
new file mode 100644
index 0000000000000000000000000000000000000000..e6185dada9d8e2d802990b5589104d8a6dc66390
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/context.js
@@ -0,0 +1,322 @@
+import { app } from "../../scripts/app.js";
+import { IoDirection, addConnectionLayoutSupport, addMenuItem, matchLocalSlotsToServer, replaceNode, } from "./utils.js";
+import { RgthreeBaseServerNode } from "./base_node.js";
+import { SERVICE as KEY_EVENT_SERVICE } from "./services/key_events_services.js";
+import { debounce, wait } from "../../rgthree/common/shared_utils.js";
+import { removeUnusedInputsFromEnd } from "./utils_inputs_outputs.js";
+import { NodeTypesString } from "./constants.js";
+function findMatchingIndexByTypeOrName(otherNode, otherSlot, ctxSlots) {
+    const otherNodeType = (otherNode.type || "").toUpperCase();
+    const otherNodeName = (otherNode.title || "").toUpperCase();
+    let otherSlotType = otherSlot.type;
+    if (Array.isArray(otherSlotType) || otherSlotType.includes(",")) {
+        otherSlotType = "COMBO";
+    }
+    const otherSlotName = otherSlot.name.toUpperCase().replace("OPT_", "").replace("_NAME", "");
+    let ctxSlotIndex = -1;
+    if (["CONDITIONING", "INT", "STRING", "FLOAT", "COMBO"].includes(otherSlotType)) {
+        ctxSlotIndex = ctxSlots.findIndex((ctxSlot) => {
+            const ctxSlotName = ctxSlot.name.toUpperCase().replace("OPT_", "").replace("_NAME", "");
+            let ctxSlotType = ctxSlot.type;
+            if (Array.isArray(ctxSlotType) || ctxSlotType.includes(",")) {
+                ctxSlotType = "COMBO";
+            }
+            if (ctxSlotType !== otherSlotType) {
+                return false;
+            }
+            if (ctxSlotName === otherSlotName ||
+                (ctxSlotName === "SEED" && otherSlotName.includes("SEED")) ||
+                (ctxSlotName === "STEP_REFINER" && otherSlotName.includes("AT_STEP")) ||
+                (ctxSlotName === "STEP_REFINER" && otherSlotName.includes("REFINER_STEP"))) {
+                return true;
+            }
+            if ((otherNodeType.includes("POSITIVE") || otherNodeName.includes("POSITIVE")) &&
+                ((ctxSlotName === "POSITIVE" && otherSlotType === "CONDITIONING") ||
+                    (ctxSlotName === "TEXT_POS_G" && otherSlotName.includes("TEXT_G")) ||
+                    (ctxSlotName === "TEXT_POS_L" && otherSlotName.includes("TEXT_L")))) {
+                return true;
+            }
+            if ((otherNodeType.includes("NEGATIVE") || otherNodeName.includes("NEGATIVE")) &&
+                ((ctxSlotName === "NEGATIVE" && otherSlotType === "CONDITIONING") ||
+                    (ctxSlotName === "TEXT_NEG_G" && otherSlotName.includes("TEXT_G")) ||
+                    (ctxSlotName === "TEXT_NEG_L" && otherSlotName.includes("TEXT_L")))) {
+                return true;
+            }
+            return false;
+        });
+    }
+    else {
+        ctxSlotIndex = ctxSlots.map((s) => s.type).indexOf(otherSlotType);
+    }
+    return ctxSlotIndex;
+}
+export class BaseContextNode extends RgthreeBaseServerNode {
+    constructor(title) {
+        super(title);
+        this.___collapsed_width = 0;
+    }
+    get _collapsed_width() {
+        return this.___collapsed_width;
+    }
+    set _collapsed_width(width) {
+        const canvas = app.canvas;
+        const ctx = canvas.canvas.getContext("2d");
+        const oldFont = ctx.font;
+        ctx.font = canvas.title_text_font;
+        let title = this.title.trim();
+        this.___collapsed_width = 30 + (title ? 10 + ctx.measureText(title).width : 0);
+        ctx.font = oldFont;
+    }
+    connectByType(slot, targetNode, targetSlotType, optsIn) {
+        var _a;
+        let canConnect = (_a = super.connectByType) === null || _a === void 0 ? void 0 : _a.call(this, slot, targetNode, targetSlotType, optsIn);
+        if (!super.connectByType) {
+            canConnect = LGraphNode.prototype.connectByType.call(this, slot, targetNode, targetSlotType, optsIn);
+        }
+        if (!canConnect && slot === 0) {
+            const ctrlKey = KEY_EVENT_SERVICE.ctrlKey;
+            for (const [index, input] of (targetNode.inputs || []).entries()) {
+                if (input.link && !ctrlKey) {
+                    continue;
+                }
+                const thisOutputSlot = findMatchingIndexByTypeOrName(targetNode, input, this.outputs);
+                if (thisOutputSlot > -1) {
+                    this.connect(thisOutputSlot, targetNode, index);
+                }
+            }
+        }
+        return null;
+    }
+    connectByTypeOutput(slot, sourceNode, sourceSlotType, optsIn) {
+        var _a, _b;
+        let canConnect = (_a = super.connectByTypeOutput) === null || _a === void 0 ? void 0 : _a.call(this, slot, sourceNode, sourceSlotType, optsIn);
+        if (!super.connectByType) {
+            canConnect = LGraphNode.prototype.connectByTypeOutput.call(this, slot, sourceNode, sourceSlotType, optsIn);
+        }
+        if (!canConnect && slot === 0) {
+            const ctrlKey = KEY_EVENT_SERVICE.ctrlKey;
+            for (const [index, output] of (sourceNode.outputs || []).entries()) {
+                if (((_b = output.links) === null || _b === void 0 ? void 0 : _b.length) && !ctrlKey) {
+                    continue;
+                }
+                const thisInputSlot = findMatchingIndexByTypeOrName(sourceNode, output, this.inputs);
+                if (thisInputSlot > -1) {
+                    sourceNode.connect(index, this, thisInputSlot);
+                }
+            }
+        }
+        return null;
+    }
+    static setUp(comfyClass, nodeData, ctxClass) {
+        RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, ctxClass);
+        wait(500).then(() => {
+            LiteGraph.slot_types_default_out["RGTHREE_CONTEXT"] =
+                LiteGraph.slot_types_default_out["RGTHREE_CONTEXT"] || [];
+            LiteGraph.slot_types_default_out["RGTHREE_CONTEXT"].push(comfyClass.comfyClass);
+        });
+    }
+    static onRegisteredForOverride(comfyClass, ctxClass) {
+        addConnectionLayoutSupport(ctxClass, app, [
+            ["Left", "Right"],
+            ["Right", "Left"],
+        ]);
+        setTimeout(() => {
+            ctxClass.category = comfyClass.category;
+        });
+    }
+}
+class ContextNode extends BaseContextNode {
+    constructor(title = ContextNode.title) {
+        super(title);
+    }
+    static setUp(comfyClass, nodeData) {
+        BaseContextNode.setUp(comfyClass, nodeData, ContextNode);
+    }
+    static onRegisteredForOverride(comfyClass, ctxClass) {
+        BaseContextNode.onRegisteredForOverride(comfyClass, ctxClass);
+        addMenuItem(ContextNode, app, {
+            name: "Convert To Context Big",
+            callback: (node) => {
+                replaceNode(node, ContextBigNode.type);
+            },
+        });
+    }
+}
+ContextNode.title = NodeTypesString.CONTEXT;
+ContextNode.type = NodeTypesString.CONTEXT;
+ContextNode.comfyClass = NodeTypesString.CONTEXT;
+class ContextBigNode extends BaseContextNode {
+    constructor(title = ContextBigNode.title) {
+        super(title);
+    }
+    static setUp(comfyClass, nodeData) {
+        BaseContextNode.setUp(comfyClass, nodeData, ContextBigNode);
+    }
+    static onRegisteredForOverride(comfyClass, ctxClass) {
+        BaseContextNode.onRegisteredForOverride(comfyClass, ctxClass);
+        addMenuItem(ContextBigNode, app, {
+            name: "Convert To Context (Original)",
+            callback: (node) => {
+                replaceNode(node, ContextNode.type);
+            },
+        });
+    }
+}
+ContextBigNode.title = NodeTypesString.CONTEXT_BIG;
+ContextBigNode.type = NodeTypesString.CONTEXT_BIG;
+ContextBigNode.comfyClass = NodeTypesString.CONTEXT_BIG;
+class BaseContextMultiCtxInputNode extends BaseContextNode {
+    constructor(title) {
+        super(title);
+        this.stabilizeBound = this.stabilize.bind(this);
+        this.addContextInput(5);
+    }
+    addContextInput(num = 1) {
+        for (let i = 0; i < num; i++) {
+            this.addInput(`ctx_${String(this.inputs.length + 1).padStart(2, "0")}`, "RGTHREE_CONTEXT");
+        }
+    }
+    onConnectionsChange(type, slotIndex, isConnected, link, ioSlot) {
+        var _a;
+        (_a = super.onConnectionsChange) === null || _a === void 0 ? void 0 : _a.apply(this, [...arguments]);
+        if (type === LiteGraph.INPUT) {
+            this.scheduleStabilize();
+        }
+    }
+    scheduleStabilize(ms = 64) {
+        return debounce(this.stabilizeBound, 64);
+    }
+    stabilize() {
+        removeUnusedInputsFromEnd(this, 4);
+        this.addContextInput();
+    }
+}
+class ContextSwitchNode extends BaseContextMultiCtxInputNode {
+    constructor(title = ContextSwitchNode.title) {
+        super(title);
+    }
+    static setUp(comfyClass, nodeData) {
+        BaseContextNode.setUp(comfyClass, nodeData, ContextSwitchNode);
+    }
+    static onRegisteredForOverride(comfyClass, ctxClass) {
+        BaseContextNode.onRegisteredForOverride(comfyClass, ctxClass);
+        addMenuItem(ContextSwitchNode, app, {
+            name: "Convert To Context Switch Big",
+            callback: (node) => {
+                replaceNode(node, ContextSwitchBigNode.type);
+            },
+        });
+    }
+}
+ContextSwitchNode.title = NodeTypesString.CONTEXT_SWITCH;
+ContextSwitchNode.type = NodeTypesString.CONTEXT_SWITCH;
+ContextSwitchNode.comfyClass = NodeTypesString.CONTEXT_SWITCH;
+class ContextSwitchBigNode extends BaseContextMultiCtxInputNode {
+    constructor(title = ContextSwitchBigNode.title) {
+        super(title);
+    }
+    static setUp(comfyClass, nodeData) {
+        BaseContextNode.setUp(comfyClass, nodeData, ContextSwitchBigNode);
+    }
+    static onRegisteredForOverride(comfyClass, ctxClass) {
+        BaseContextNode.onRegisteredForOverride(comfyClass, ctxClass);
+        addMenuItem(ContextSwitchBigNode, app, {
+            name: "Convert To Context Switch",
+            callback: (node) => {
+                replaceNode(node, ContextSwitchNode.type);
+            },
+        });
+    }
+}
+ContextSwitchBigNode.title = NodeTypesString.CONTEXT_SWITCH_BIG;
+ContextSwitchBigNode.type = NodeTypesString.CONTEXT_SWITCH_BIG;
+ContextSwitchBigNode.comfyClass = NodeTypesString.CONTEXT_SWITCH_BIG;
+class ContextMergeNode extends BaseContextMultiCtxInputNode {
+    constructor(title = ContextMergeNode.title) {
+        super(title);
+    }
+    static setUp(comfyClass, nodeData) {
+        BaseContextNode.setUp(comfyClass, nodeData, ContextMergeNode);
+    }
+    static onRegisteredForOverride(comfyClass, ctxClass) {
+        BaseContextNode.onRegisteredForOverride(comfyClass, ctxClass);
+        addMenuItem(ContextMergeNode, app, {
+            name: "Convert To Context Merge Big",
+            callback: (node) => {
+                replaceNode(node, ContextMergeBigNode.type);
+            },
+        });
+    }
+}
+ContextMergeNode.title = NodeTypesString.CONTEXT_MERGE;
+ContextMergeNode.type = NodeTypesString.CONTEXT_MERGE;
+ContextMergeNode.comfyClass = NodeTypesString.CONTEXT_MERGE;
+class ContextMergeBigNode extends BaseContextMultiCtxInputNode {
+    constructor(title = ContextMergeBigNode.title) {
+        super(title);
+    }
+    static setUp(comfyClass, nodeData) {
+        BaseContextNode.setUp(comfyClass, nodeData, ContextMergeBigNode);
+    }
+    static onRegisteredForOverride(comfyClass, ctxClass) {
+        BaseContextNode.onRegisteredForOverride(comfyClass, ctxClass);
+        addMenuItem(ContextMergeBigNode, app, {
+            name: "Convert To Context Switch",
+            callback: (node) => {
+                replaceNode(node, ContextMergeNode.type);
+            },
+        });
+    }
+}
+ContextMergeBigNode.title = NodeTypesString.CONTEXT_MERGE_BIG;
+ContextMergeBigNode.type = NodeTypesString.CONTEXT_MERGE_BIG;
+ContextMergeBigNode.comfyClass = NodeTypesString.CONTEXT_MERGE_BIG;
+const contextNodes = [
+    ContextNode,
+    ContextBigNode,
+    ContextSwitchNode,
+    ContextSwitchBigNode,
+    ContextMergeNode,
+    ContextMergeBigNode,
+];
+const contextTypeToServerDef = {};
+function fixBadConfigs(node) {
+    const wrongName = node.outputs.find((o, i) => o.name === "CLIP_HEIGTH");
+    if (wrongName) {
+        wrongName.name = "CLIP_HEIGHT";
+    }
+}
+app.registerExtension({
+    name: "rgthree.Context",
+    async beforeRegisterNodeDef(nodeType, nodeData) {
+        for (const ctxClass of contextNodes) {
+            if (nodeData.name === ctxClass.type) {
+                contextTypeToServerDef[ctxClass.type] = nodeData;
+                ctxClass.setUp(nodeType, nodeData);
+                break;
+            }
+        }
+    },
+    async nodeCreated(node) {
+        const type = node.type || node.constructor.type;
+        const serverDef = type && contextTypeToServerDef[type];
+        if (serverDef) {
+            fixBadConfigs(node);
+            matchLocalSlotsToServer(node, IoDirection.OUTPUT, serverDef);
+            if (!type.includes("Switch") && !type.includes("Merge")) {
+                matchLocalSlotsToServer(node, IoDirection.INPUT, serverDef);
+            }
+        }
+    },
+    async loadedGraphNode(node) {
+        const type = node.type || node.constructor.type;
+        const serverDef = type && contextTypeToServerDef[type];
+        if (serverDef) {
+            fixBadConfigs(node);
+            matchLocalSlotsToServer(node, IoDirection.OUTPUT, serverDef);
+            if (!type.includes("Switch") && !type.includes("Merge")) {
+                matchLocalSlotsToServer(node, IoDirection.INPUT, serverDef);
+            }
+        }
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/dialog_info.js b/custom_nodes/rgthree-comfy/web/comfyui/dialog_info.js
new file mode 100644
index 0000000000000000000000000000000000000000..f2679b6cfdf40a15e7c1235411a311675b09abe9
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/dialog_info.js
@@ -0,0 +1,292 @@
+import { RgthreeDialog } from "../../rgthree/common/dialog.js";
+import { createElement as $el, empty, appendChildren, getClosestOrSelf, query, queryAll, setAttributes, } from "../../rgthree/common/utils_dom.js";
+import { logoCivitai, link, pencilColored, diskColored, dotdotdot, } from "../../rgthree/common/media/svgs.js";
+import { CHECKPOINT_INFO_SERVICE, LORA_INFO_SERVICE } from "../../rgthree/common/model_info_service.js";
+import { rgthree } from "./rgthree.js";
+import { MenuButton } from "../../rgthree/common/menu.js";
+import { generateId, injectCss } from "../../rgthree/common/shared_utils.js";
+class RgthreeInfoDialog extends RgthreeDialog {
+    constructor(file) {
+        const dialogOptions = {
+            class: "rgthree-info-dialog",
+            title: `<h2>Loading...</h2>`,
+            content: "<center>Loading..</center>",
+            onBeforeClose: () => {
+                return true;
+            },
+        };
+        super(dialogOptions);
+        this.modifiedModelData = false;
+        this.modelInfo = null;
+        this.init(file);
+    }
+    async init(file) {
+        var _a, _b;
+        const cssPromise = injectCss("rgthree/common/css/dialog_model_info.css");
+        this.modelInfo = await this.getModelInfo(file);
+        await cssPromise;
+        this.setContent(this.getInfoContent());
+        this.setTitle(((_a = this.modelInfo) === null || _a === void 0 ? void 0 : _a["name"]) || ((_b = this.modelInfo) === null || _b === void 0 ? void 0 : _b["file"]) || "Unknown");
+        this.attachEvents();
+    }
+    getCloseEventDetail() {
+        const detail = {
+            dirty: this.modifiedModelData,
+        };
+        return { detail };
+    }
+    attachEvents() {
+        this.contentElement.addEventListener("click", async (e) => {
+            const target = getClosestOrSelf(e.target, "[data-action]");
+            const action = target === null || target === void 0 ? void 0 : target.getAttribute("data-action");
+            if (!target || !action) {
+                return;
+            }
+            await this.handleEventAction(action, target, e);
+        });
+    }
+    async handleEventAction(action, target, e) {
+        var _a, _b;
+        const info = this.modelInfo;
+        if (!(info === null || info === void 0 ? void 0 : info.file)) {
+            return;
+        }
+        if (action === "fetch-civitai") {
+            this.modelInfo = await this.refreshModelInfo(info.file);
+            this.setContent(this.getInfoContent());
+            this.setTitle(((_a = this.modelInfo) === null || _a === void 0 ? void 0 : _a["name"]) || ((_b = this.modelInfo) === null || _b === void 0 ? void 0 : _b["file"]) || "Unknown");
+        }
+        else if (action === "copy-trained-words") {
+            const selected = queryAll(".-rgthree-is-selected", target.closest("tr"));
+            const text = selected.map((el) => el.getAttribute("data-word")).join(", ");
+            await navigator.clipboard.writeText(text);
+            rgthree.showMessage({
+                id: "copy-trained-words-" + generateId(4),
+                type: "success",
+                message: `Successfully copied ${selected.length} key word${selected.length === 1 ? "" : "s"}.`,
+                timeout: 4000,
+            });
+        }
+        else if (action === "toggle-trained-word") {
+            target === null || target === void 0 ? void 0 : target.classList.toggle("-rgthree-is-selected");
+            const tr = target.closest("tr");
+            if (tr) {
+                const span = query("td:first-child > *", tr);
+                let small = query("small", span);
+                if (!small) {
+                    small = $el("small", { parent: span });
+                }
+                const num = queryAll(".-rgthree-is-selected", tr).length;
+                small.innerHTML = num
+                    ? `${num} selected | <span role="button" data-action="copy-trained-words">Copy</span>`
+                    : "";
+            }
+        }
+        else if (action === "edit-row") {
+            const tr = target.closest("tr");
+            const td = query("td:nth-child(2)", tr);
+            const input = td.querySelector("input,textarea");
+            if (!input) {
+                const fieldName = tr.dataset["fieldName"];
+                tr.classList.add("-rgthree-editing");
+                const isTextarea = fieldName === "userNote";
+                const input = $el(`${isTextarea ? "textarea" : 'input[type="text"]'}`, {
+                    value: td.textContent,
+                });
+                input.addEventListener("keydown", (e) => {
+                    if (!isTextarea && e.key === "Enter") {
+                        const modified = saveEditableRow(info, tr, true);
+                        this.modifiedModelData = this.modifiedModelData || modified;
+                        e.stopPropagation();
+                        e.preventDefault();
+                    }
+                    else if (e.key === "Escape") {
+                        const modified = saveEditableRow(info, tr, false);
+                        this.modifiedModelData = this.modifiedModelData || modified;
+                        e.stopPropagation();
+                        e.preventDefault();
+                    }
+                });
+                appendChildren(empty(td), [input]);
+                input.focus();
+            }
+            else if (target.nodeName.toLowerCase() === "button") {
+                const modified = saveEditableRow(info, tr, true);
+                this.modifiedModelData = this.modifiedModelData || modified;
+            }
+            e === null || e === void 0 ? void 0 : e.preventDefault();
+            e === null || e === void 0 ? void 0 : e.stopPropagation();
+        }
+    }
+    getInfoContent() {
+        var _a, _b, _c, _d, _e, _f, _g, _h, _j, _k, _l, _m, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y;
+        const info = this.modelInfo || {};
+        const civitaiLink = (_a = info.links) === null || _a === void 0 ? void 0 : _a.find((i) => i.includes("civitai.com/models"));
+        const html = `
+      <ul class="rgthree-info-area">
+        <li title="Type" class="rgthree-info-tag -type -type-${(info.type || "").toLowerCase()}"><span>${info.type || ""}</span></li>
+        <li title="Base Model" class="rgthree-info-tag -basemodel -basemodel-${(info.baseModel || "").toLowerCase()}"><span>${info.baseModel || ""}</span></li>
+        <li class="rgthree-info-menu" stub="menu"></li>
+        ${""}
+      </ul>
+
+      <table class="rgthree-info-table">
+        ${infoTableRow("File", info.file || "")}
+        ${infoTableRow("Hash (sha256)", info.sha256 || "")}
+        ${civitaiLink
+            ? infoTableRow("Civitai", `<a href="${civitaiLink}" target="_blank">${logoCivitai}View on Civitai</a>`)
+            : ((_c = (_b = info.raw) === null || _b === void 0 ? void 0 : _b.civitai) === null || _c === void 0 ? void 0 : _c.error) === "Model not found"
+                ? infoTableRow("Civitai", '<i>Model not found</i> <span class="-help" title="The model was not found on civitai with the sha256 hash. It\'s possible the model was removed, re-uploaded, or was never on civitai to begin with."></span>')
+                : ((_e = (_d = info.raw) === null || _d === void 0 ? void 0 : _d.civitai) === null || _e === void 0 ? void 0 : _e.error)
+                    ? infoTableRow("Civitai", (_g = (_f = info.raw) === null || _f === void 0 ? void 0 : _f.civitai) === null || _g === void 0 ? void 0 : _g.error)
+                    : !((_h = info.raw) === null || _h === void 0 ? void 0 : _h.civitai)
+                        ? infoTableRow("Civitai", `<button class="rgthree-button" data-action="fetch-civitai">Fetch info from civitai</button>`)
+                        : ""}
+
+        ${infoTableRow("Name", info.name || ((_k = (_j = info.raw) === null || _j === void 0 ? void 0 : _j.metadata) === null || _k === void 0 ? void 0 : _k.ss_output_name) || "", "The name for display.", "name")}
+
+        ${!info.baseModelFile && !info.baseModelFile
+            ? ""
+            : infoTableRow("Base Model", (info.baseModel || "") + (info.baseModelFile ? ` (${info.baseModelFile})` : ""))}
+
+
+        ${!((_l = info.trainedWords) === null || _l === void 0 ? void 0 : _l.length)
+            ? ""
+            : infoTableRow("Trained Words", (_m = getTrainedWordsMarkup(info.trainedWords)) !== null && _m !== void 0 ? _m : "", "Trained words from the metadata and/or civitai. Click to select for copy.")}
+
+        ${!((_p = (_o = info.raw) === null || _o === void 0 ? void 0 : _o.metadata) === null || _p === void 0 ? void 0 : _p.ss_clip_skip) || ((_r = (_q = info.raw) === null || _q === void 0 ? void 0 : _q.metadata) === null || _r === void 0 ? void 0 : _r.ss_clip_skip) == "None"
+            ? ""
+            : infoTableRow("Clip Skip", (_t = (_s = info.raw) === null || _s === void 0 ? void 0 : _s.metadata) === null || _t === void 0 ? void 0 : _t.ss_clip_skip)}
+        ${infoTableRow("Strength Min", (_u = info.strengthMin) !== null && _u !== void 0 ? _u : "", "The recommended minimum strength, In the Power Lora Loader node, strength will signal when it is below this threshold.", "strengthMin")}
+        ${infoTableRow("Strength Max", (_v = info.strengthMax) !== null && _v !== void 0 ? _v : "", "The recommended maximum strength. In the Power Lora Loader node, strength will signal when it is above this threshold.", "strengthMax")}
+        ${""}
+        ${infoTableRow("Additional Notes", (_w = info.userNote) !== null && _w !== void 0 ? _w : "", "Additional notes you'd like to keep and reference in the info dialog.", "userNote")}
+
+      </table>
+
+      <ul class="rgthree-info-images">${(_y = (_x = info.images) === null || _x === void 0 ? void 0 : _x.map((img) => `
+        <li>
+          <figure>${img.type === 'video'
+            ? `<video src="${img.url}" autoplay loop></video>`
+            : `<img src="${img.url}" />`}
+            <figcaption><!--
+              -->${imgInfoField("", img.civitaiUrl
+            ? `<a href="${img.civitaiUrl}" target="_blank">civitai${link}</a>`
+            : undefined)}<!--
+              -->${imgInfoField("seed", img.seed)}<!--
+              -->${imgInfoField("steps", img.steps)}<!--
+              -->${imgInfoField("cfg", img.cfg)}<!--
+              -->${imgInfoField("sampler", img.sampler)}<!--
+              -->${imgInfoField("model", img.model)}<!--
+              -->${imgInfoField("positive", img.positive)}<!--
+              -->${imgInfoField("negative", img.negative)}<!--
+            --><!--${""}--></figcaption>
+          </figure>
+        </li>`).join("")) !== null && _y !== void 0 ? _y : ""}</ul>
+    `;
+        const div = $el("div", { html });
+        if (rgthree.isDevMode()) {
+            setAttributes(query('[stub="menu"]', div), {
+                children: [
+                    new MenuButton({
+                        icon: dotdotdot,
+                        options: [
+                            { label: "More Actions", type: "title" },
+                            {
+                                label: "Open API JSON",
+                                callback: async (e) => {
+                                    var _a;
+                                    if ((_a = this.modelInfo) === null || _a === void 0 ? void 0 : _a.file) {
+                                        window.open(`rgthree/api/loras/info?file=${encodeURIComponent(this.modelInfo.file)}`);
+                                    }
+                                },
+                            },
+                            {
+                                label: "Clear all local info",
+                                callback: async (e) => {
+                                    var _a, _b, _c;
+                                    if ((_a = this.modelInfo) === null || _a === void 0 ? void 0 : _a.file) {
+                                        this.modelInfo = await LORA_INFO_SERVICE.clearFetchedInfo(this.modelInfo.file);
+                                        this.setContent(this.getInfoContent());
+                                        this.setTitle(((_b = this.modelInfo) === null || _b === void 0 ? void 0 : _b["name"]) || ((_c = this.modelInfo) === null || _c === void 0 ? void 0 : _c["file"]) || "Unknown");
+                                    }
+                                },
+                            },
+                        ],
+                    }),
+                ],
+            });
+        }
+        return div;
+    }
+}
+export class RgthreeLoraInfoDialog extends RgthreeInfoDialog {
+    async getModelInfo(file) {
+        return LORA_INFO_SERVICE.getInfo(file, false, false);
+    }
+    async refreshModelInfo(file) {
+        return LORA_INFO_SERVICE.refreshInfo(file);
+    }
+    async clearModelInfo(file) {
+        return LORA_INFO_SERVICE.clearFetchedInfo(file);
+    }
+}
+export class RgthreeCheckpointInfoDialog extends RgthreeInfoDialog {
+    async getModelInfo(file) {
+        return CHECKPOINT_INFO_SERVICE.getInfo(file, false, false);
+    }
+    async refreshModelInfo(file) {
+        return CHECKPOINT_INFO_SERVICE.refreshInfo(file);
+    }
+    async clearModelInfo(file) {
+        return CHECKPOINT_INFO_SERVICE.clearFetchedInfo(file);
+    }
+}
+function infoTableRow(name, value, help = "", editableFieldName = "") {
+    return `
+    <tr class="${editableFieldName ? "editable" : ""}" ${editableFieldName ? `data-field-name="${editableFieldName}"` : ""}>
+      <td><span>${name} ${help ? `<span class="-help" title="${help}"></span>` : ""}<span></td>
+      <td ${editableFieldName ? "" : 'colspan="2"'}>${String(value).startsWith("<") ? value : `<span>${value}<span>`}</td>
+      ${editableFieldName
+        ? `<td style="width: 24px;"><button class="rgthree-button-reset rgthree-button-edit" data-action="edit-row">${pencilColored}${diskColored}</button></td>`
+        : ""}
+    </tr>`;
+}
+function getTrainedWordsMarkup(words) {
+    let markup = `<ul class="rgthree-info-trained-words-list">`;
+    for (const wordData of words || []) {
+        markup += `<li title="${wordData.word}" data-word="${wordData.word}" class="rgthree-info-trained-words-list-item" data-action="toggle-trained-word">
+      <span>${wordData.word}</span>
+      ${wordData.civitai ? logoCivitai : ""}
+      ${wordData.count != null ? `<small>${wordData.count}</small>` : ""}
+    </li>`;
+    }
+    markup += `</ul>`;
+    return markup;
+}
+function saveEditableRow(info, tr, saving = true) {
+    var _a;
+    const fieldName = tr.dataset["fieldName"];
+    const input = query("input,textarea", tr);
+    let newValue = (_a = info[fieldName]) !== null && _a !== void 0 ? _a : "";
+    let modified = false;
+    if (saving) {
+        newValue = input.value;
+        if (fieldName.startsWith("strength")) {
+            if (Number.isNaN(Number(newValue))) {
+                alert(`You must enter a number into the ${fieldName} field.`);
+                return false;
+            }
+            newValue = (Math.round(Number(newValue) * 100) / 100).toFixed(2);
+        }
+        LORA_INFO_SERVICE.savePartialInfo(info.file, { [fieldName]: newValue });
+        modified = true;
+    }
+    tr.classList.remove("-rgthree-editing");
+    const td = query("td:nth-child(2)", tr);
+    appendChildren(empty(td), [$el("span", { text: newValue })]);
+    return modified;
+}
+function imgInfoField(label, value) {
+    return value != null ? `<span>${label ? `<label>${label} </label>` : ""}${value}</span>` : "";
+}
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/display_any.js b/custom_nodes/rgthree-comfy/web/comfyui/display_any.js
new file mode 100644
index 0000000000000000000000000000000000000000..cff00f9258cced5da324de9e18aa91de7d857038
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/display_any.js
@@ -0,0 +1,35 @@
+import { app } from "../../scripts/app.js";
+import { ComfyWidgets } from "../../scripts/widgets.js";
+import { addConnectionLayoutSupport } from "./utils.js";
+import { rgthree } from "./rgthree.js";
+let hasShownAlertForUpdatingInt = false;
+app.registerExtension({
+    name: "rgthree.DisplayAny",
+    async beforeRegisterNodeDef(nodeType, nodeData, app) {
+        if (nodeData.name === "Display Any (rgthree)" || nodeData.name === "Display Int (rgthree)") {
+            const onNodeCreated = nodeType.prototype.onNodeCreated;
+            nodeType.prototype.onNodeCreated = function () {
+                onNodeCreated ? onNodeCreated.apply(this, []) : undefined;
+                this.showValueWidget = ComfyWidgets["STRING"](this, "output", ["STRING", { multiline: true }], app).widget;
+                this.showValueWidget.inputEl.readOnly = true;
+                this.showValueWidget.serializeValue = async (node, index) => {
+                    const n = rgthree.getNodeFromInitialGraphToPromptSerializedWorkflowBecauseComfyUIBrokeStuff(node);
+                    if (n) {
+                        n.widgets_values[index] = "";
+                    }
+                    else {
+                        console.warn("No serialized node found in workflow. May be attributed to " +
+                            "https://github.com/comfyanonymous/ComfyUI/issues/2193");
+                    }
+                    return "";
+                };
+            };
+            addConnectionLayoutSupport(nodeType, app, [["Left"], ["Right"]]);
+            const onExecuted = nodeType.prototype.onExecuted;
+            nodeType.prototype.onExecuted = function (message) {
+                onExecuted === null || onExecuted === void 0 ? void 0 : onExecuted.apply(this, [message]);
+                this.showValueWidget.value = message.text[0];
+            };
+        }
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/dynamic_context.js b/custom_nodes/rgthree-comfy/web/comfyui/dynamic_context.js
new file mode 100644
index 0000000000000000000000000000000000000000..5a2bc16a8916e0761e36690c4b7a342d797640e9
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/dynamic_context.js
@@ -0,0 +1,253 @@
+import { app } from "../../scripts/app.js";
+import { IoDirection, followConnectionUntilType, getConnectedInputInfosAndFilterPassThroughs, } from "./utils.js";
+import { rgthree } from "./rgthree.js";
+import { SERVICE as CONTEXT_SERVICE, InputMutationOperation, } from "./services/context_service.js";
+import { NodeTypesString } from "./constants.js";
+import { removeUnusedInputsFromEnd } from "./utils_inputs_outputs.js";
+import { DynamicContextNodeBase } from "./dynamic_context_base.js";
+import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js";
+const OWNED_PREFIX = "+";
+const REGEX_OWNED_PREFIX = /^\+\s*/;
+const REGEX_EMPTY_INPUT = /^\+\s*$/;
+export class DynamicContextNode extends DynamicContextNodeBase {
+    constructor(title = DynamicContextNode.title) {
+        super(title);
+    }
+    onNodeCreated() {
+        this.addInput("base_ctx", "RGTHREE_DYNAMIC_CONTEXT");
+        this.ensureOneRemainingNewInputSlot();
+        super.onNodeCreated();
+    }
+    onConnectionsChange(type, slotIndex, isConnected, link, ioSlot) {
+        var _a;
+        (_a = super.onConnectionsChange) === null || _a === void 0 ? void 0 : _a.call(this, type, slotIndex, isConnected, link, ioSlot);
+        if (this.configuring) {
+            return;
+        }
+        if (type === LiteGraph.INPUT) {
+            if (isConnected) {
+                this.handleInputConnected(slotIndex);
+            }
+            else {
+                this.handleInputDisconnected(slotIndex);
+            }
+        }
+    }
+    onConnectInput(inputIndex, outputType, outputSlot, outputNode, outputIndex) {
+        var _a;
+        let canConnect = true;
+        if (super.onConnectInput) {
+            canConnect = super.onConnectInput.apply(this, [...arguments]);
+        }
+        if (canConnect &&
+            outputNode instanceof DynamicContextNode &&
+            outputIndex === 0 &&
+            inputIndex !== 0) {
+            const [n, v] = rgthree.logger.warnParts("Currently, you can only connect a context node in the first slot.");
+            (_a = console[n]) === null || _a === void 0 ? void 0 : _a.call(console, ...v);
+            canConnect = false;
+        }
+        return canConnect;
+    }
+    handleInputConnected(slotIndex) {
+        const ioSlot = this.inputs[slotIndex];
+        const connectedIndexes = [];
+        if (slotIndex === 0) {
+            let baseNodeInfos = getConnectedInputInfosAndFilterPassThroughs(this, this, 0);
+            const baseNodes = baseNodeInfos.map((n) => n.node);
+            const baseNodesDynamicCtx = baseNodes[0];
+            if (baseNodesDynamicCtx === null || baseNodesDynamicCtx === void 0 ? void 0 : baseNodesDynamicCtx.provideInputsData) {
+                const inputsData = CONTEXT_SERVICE.getDynamicContextInputsData(baseNodesDynamicCtx);
+                console.log("inputsData", inputsData);
+                for (const input of baseNodesDynamicCtx.provideInputsData()) {
+                    if (input.name === "base_ctx" || input.name === "+") {
+                        continue;
+                    }
+                    this.addContextInput(input.name, input.type, input.index);
+                    this.stabilizeNames();
+                }
+            }
+        }
+        else if (this.isInputSlotForNewInput(slotIndex)) {
+            this.handleNewInputConnected(slotIndex);
+        }
+    }
+    isInputSlotForNewInput(slotIndex) {
+        const ioSlot = this.inputs[slotIndex];
+        return ioSlot && ioSlot.name === "+" && ioSlot.type === "*";
+    }
+    handleNewInputConnected(slotIndex) {
+        if (!this.isInputSlotForNewInput(slotIndex)) {
+            throw new Error('Expected the incoming slot index to be the "new input" input.');
+        }
+        const ioSlot = this.inputs[slotIndex];
+        let cxn = null;
+        if (ioSlot.link != null) {
+            cxn = followConnectionUntilType(this, IoDirection.INPUT, slotIndex, true);
+        }
+        if ((cxn === null || cxn === void 0 ? void 0 : cxn.type) && (cxn === null || cxn === void 0 ? void 0 : cxn.name)) {
+            let name = this.addOwnedPrefix(this.getNextUniqueNameForThisNode(cxn.name));
+            if (name.match(/^\+\s*[A-Z_]+(\.\d+)?$/)) {
+                name = name.toLowerCase();
+            }
+            ioSlot.name = name;
+            ioSlot.type = cxn.type;
+            ioSlot.removable = true;
+            while (!this.outputs[slotIndex]) {
+                this.addOutput("*", "*");
+            }
+            this.outputs[slotIndex].type = cxn.type;
+            this.outputs[slotIndex].name = this.stripOwnedPrefix(name).toLocaleUpperCase();
+            if (cxn.type === "COMBO" || cxn.type.includes(",") || Array.isArray(cxn.type)) {
+                this.outputs[slotIndex].widget = true;
+            }
+            this.inputsMutated({
+                operation: InputMutationOperation.ADDED,
+                node: this,
+                slotIndex,
+                slot: ioSlot,
+            });
+            this.stabilizeNames();
+            this.ensureOneRemainingNewInputSlot();
+        }
+    }
+    handleInputDisconnected(slotIndex) {
+        var _a, _b;
+        const inputs = this.getContextInputsList();
+        if (slotIndex === 0) {
+            for (let index = inputs.length - 1; index > 0; index--) {
+                if (index === 0 || index === inputs.length - 1) {
+                    continue;
+                }
+                const input = inputs[index];
+                if (!this.isOwnedInput(input.name)) {
+                    if (input.link || ((_b = (_a = this.outputs[index]) === null || _a === void 0 ? void 0 : _a.links) === null || _b === void 0 ? void 0 : _b.length)) {
+                        this.renameContextInput(index, input.name, true);
+                    }
+                    else {
+                        this.removeContextInput(index);
+                    }
+                }
+            }
+            this.setSize(this.computeSize());
+            this.setDirtyCanvas(true, true);
+        }
+    }
+    ensureOneRemainingNewInputSlot() {
+        removeUnusedInputsFromEnd(this, 1, REGEX_EMPTY_INPUT);
+        this.addInput(OWNED_PREFIX, "*");
+    }
+    getNextUniqueNameForThisNode(desiredName) {
+        const inputs = this.getContextInputsList();
+        const allExistingKeys = inputs.map((i) => this.stripOwnedPrefix(i.name).toLocaleUpperCase());
+        desiredName = this.stripOwnedPrefix(desiredName);
+        let newName = desiredName;
+        let n = 0;
+        while (allExistingKeys.includes(newName.toLocaleUpperCase())) {
+            newName = `${desiredName}.${++n}`;
+        }
+        return newName;
+    }
+    removeInput(slotIndex) {
+        const slot = this.inputs[slotIndex];
+        super.removeInput(slotIndex);
+        if (this.outputs[slotIndex]) {
+            this.removeOutput(slotIndex);
+        }
+        this.inputsMutated({ operation: InputMutationOperation.REMOVED, node: this, slotIndex, slot });
+        this.stabilizeNames();
+    }
+    stabilizeNames() {
+        const inputs = this.getContextInputsList();
+        const names = [];
+        for (const [index, input] of inputs.entries()) {
+            if (index === 0 || index === inputs.length - 1) {
+                continue;
+            }
+            input.label = undefined;
+            this.outputs[index].label = undefined;
+            let origName = this.stripOwnedPrefix(input.name).replace(/\.\d+$/, "");
+            let name = input.name;
+            if (!this.isOwnedInput(name)) {
+                names.push(name.toLocaleUpperCase());
+            }
+            else {
+                let n = 0;
+                name = this.addOwnedPrefix(origName);
+                while (names.includes(this.stripOwnedPrefix(name).toLocaleUpperCase())) {
+                    name = `${this.addOwnedPrefix(origName)}.${++n}`;
+                }
+                names.push(this.stripOwnedPrefix(name).toLocaleUpperCase());
+                if (input.name !== name) {
+                    this.renameContextInput(index, name);
+                }
+            }
+        }
+    }
+    getSlotMenuOptions(slot) {
+        const editable = this.isOwnedInput(slot.input.name) && this.type !== "*";
+        return [
+            {
+                content: "✏️ Rename Input",
+                disabled: !editable,
+                callback: () => {
+                    var dialog = app.canvas.createDialog("<span class='name'>Name</span><input autofocus type='text'/><button>OK</button>", {});
+                    var dialogInput = dialog.querySelector("input");
+                    if (dialogInput) {
+                        dialogInput.value = this.stripOwnedPrefix(slot.input.name || "");
+                    }
+                    var inner = () => {
+                        this.handleContextMenuRenameInputDialog(slot.slot, dialogInput.value);
+                        dialog.close();
+                    };
+                    dialog.querySelector("button").addEventListener("click", inner);
+                    dialogInput.addEventListener("keydown", (e) => {
+                        var _a;
+                        dialog.is_modified = true;
+                        if (e.keyCode == 27) {
+                            dialog.close();
+                        }
+                        else if (e.keyCode == 13) {
+                            inner();
+                        }
+                        else if (e.keyCode != 13 && ((_a = e.target) === null || _a === void 0 ? void 0 : _a.localName) != "textarea") {
+                            return;
+                        }
+                        e.preventDefault();
+                        e.stopPropagation();
+                    });
+                    dialogInput.focus();
+                },
+            },
+            {
+                content: "🗑️ Delete Input",
+                disabled: !editable,
+                callback: () => {
+                    this.removeInput(slot.slot);
+                },
+            },
+        ];
+    }
+    handleContextMenuRenameInputDialog(slotIndex, value) {
+        app.graph.beforeChange();
+        this.renameContextInput(slotIndex, value);
+        this.stabilizeNames();
+        this.setDirtyCanvas(true, true);
+        app.graph.afterChange();
+    }
+}
+DynamicContextNode.title = NodeTypesString.DYNAMIC_CONTEXT;
+DynamicContextNode.type = NodeTypesString.DYNAMIC_CONTEXT;
+DynamicContextNode.comfyClass = NodeTypesString.DYNAMIC_CONTEXT;
+const contextDynamicNodes = [DynamicContextNode];
+app.registerExtension({
+    name: "rgthree.DynamicContext",
+    async beforeRegisterNodeDef(nodeType, nodeData) {
+        if (!CONFIG_SERVICE.getConfigValue("unreleased.dynamic_context.enabled")) {
+            return;
+        }
+        if (nodeData.name === DynamicContextNode.type) {
+            DynamicContextNode.setUp(nodeType, nodeData);
+        }
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/dynamic_context_base.js b/custom_nodes/rgthree-comfy/web/comfyui/dynamic_context_base.js
new file mode 100644
index 0000000000000000000000000000000000000000..4ead4a61d44c9e532ba1c8f021f9c69320705dd4
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/dynamic_context_base.js
@@ -0,0 +1,192 @@
+import { app } from "../../scripts/app.js";
+import { BaseContextNode } from "./context.js";
+import { RgthreeBaseServerNode } from "./base_node.js";
+import { moveArrayItem, wait } from "../../rgthree/common/shared_utils.js";
+import { RgthreeInvisibleWidget } from "./utils_widgets.js";
+import { getContextOutputName, InputMutationOperation, } from "./services/context_service.js";
+import { SERVICE as CONTEXT_SERVICE } from "./services/context_service.js";
+const OWNED_PREFIX = "+";
+const REGEX_OWNED_PREFIX = /^\+\s*/;
+const REGEX_EMPTY_INPUT = /^\+\s*$/;
+export class DynamicContextNodeBase extends BaseContextNode {
+    constructor() {
+        super(...arguments);
+        this.hasShadowInputs = false;
+    }
+    getContextInputsList() {
+        return this.inputs;
+    }
+    provideInputsData() {
+        const inputs = this.getContextInputsList();
+        return inputs
+            .map((input, index) => ({
+            name: this.stripOwnedPrefix(input.name),
+            type: String(input.type),
+            index,
+        }))
+            .filter((i) => i.type !== "*");
+    }
+    addOwnedPrefix(name) {
+        return `+ ${this.stripOwnedPrefix(name)}`;
+    }
+    isOwnedInput(inputOrName) {
+        const name = typeof inputOrName == "string" ? inputOrName : (inputOrName === null || inputOrName === void 0 ? void 0 : inputOrName.name) || "";
+        return REGEX_OWNED_PREFIX.test(name);
+    }
+    stripOwnedPrefix(name) {
+        return name.replace(REGEX_OWNED_PREFIX, "");
+    }
+    handleUpstreamMutation(mutation) {
+        console.log(`[node ${this.id}] handleUpstreamMutation`, mutation);
+        if (mutation.operation === InputMutationOperation.ADDED) {
+            const slot = mutation.slot;
+            if (!slot) {
+                throw new Error("Cannot have an ADDED mutation without a provided slot data.");
+            }
+            this.addContextInput(this.stripOwnedPrefix(slot.name), slot.type, mutation.slotIndex);
+            return;
+        }
+        if (mutation.operation === InputMutationOperation.REMOVED) {
+            const slot = mutation.slot;
+            if (!slot) {
+                throw new Error("Cannot have an REMOVED mutation without a provided slot data.");
+            }
+            this.removeContextInput(mutation.slotIndex);
+            return;
+        }
+        if (mutation.operation === InputMutationOperation.RENAMED) {
+            const slot = mutation.slot;
+            if (!slot) {
+                throw new Error("Cannot have an RENAMED mutation without a provided slot data.");
+            }
+            this.renameContextInput(mutation.slotIndex, slot.name);
+            return;
+        }
+    }
+    clone() {
+        const cloned = super.clone();
+        while (cloned.inputs.length > 1) {
+            cloned.removeInput(cloned.inputs.length - 1);
+        }
+        while (cloned.widgets.length > 1) {
+            cloned.removeWidget(cloned.widgets.length - 1);
+        }
+        while (cloned.outputs.length > 1) {
+            cloned.removeOutput(cloned.outputs.length - 1);
+        }
+        return cloned;
+    }
+    onNodeCreated() {
+        const node = this;
+        this.addCustomWidget(new RgthreeInvisibleWidget("output_keys", "RGTHREE_DYNAMIC_CONTEXT_OUTPUTS", "", () => {
+            return (node.outputs || [])
+                .map((o, i) => i > 0 && o.name)
+                .filter((n) => n !== false)
+                .join(",");
+        }));
+    }
+    addContextInput(name, type, slot = -1) {
+        const inputs = this.getContextInputsList();
+        if (this.hasShadowInputs) {
+            inputs.push({ name, type, link: null, boundingRect: null });
+        }
+        else {
+            this.addInput(name, type);
+        }
+        if (slot > -1) {
+            moveArrayItem(inputs, inputs.length - 1, slot);
+        }
+        else {
+            slot = inputs.length - 1;
+        }
+        if (type !== "*") {
+            const output = this.addOutput(getContextOutputName(name), type);
+            if (type === "COMBO" || String(type).includes(",") || Array.isArray(type)) {
+                output.widget = true;
+            }
+            if (slot > -1) {
+                moveArrayItem(this.outputs, this.outputs.length - 1, slot);
+            }
+        }
+        this.fixInputsOutputsLinkSlots();
+        this.inputsMutated({
+            operation: InputMutationOperation.ADDED,
+            node: this,
+            slotIndex: slot,
+            slot: inputs[slot],
+        });
+    }
+    removeContextInput(slotIndex) {
+        if (this.hasShadowInputs) {
+            const inputs = this.getContextInputsList();
+            const input = inputs.splice(slotIndex, 1)[0];
+            if (this.outputs[slotIndex]) {
+                this.removeOutput(slotIndex);
+            }
+        }
+        else {
+            this.removeInput(slotIndex);
+        }
+    }
+    renameContextInput(index, newName, forceOwnBool = null) {
+        const inputs = this.getContextInputsList();
+        const input = inputs[index];
+        const oldName = input.name;
+        newName = this.stripOwnedPrefix(newName.trim() || this.getSlotDefaultInputLabel(index));
+        if (forceOwnBool === true || (this.isOwnedInput(oldName) && forceOwnBool !== false)) {
+            newName = this.addOwnedPrefix(newName);
+        }
+        if (oldName !== newName) {
+            input.name = newName;
+            input.removable = this.isOwnedInput(newName);
+            this.outputs[index].name = getContextOutputName(inputs[index].name);
+            this.inputsMutated({
+                node: this,
+                operation: InputMutationOperation.RENAMED,
+                slotIndex: index,
+                slot: input,
+            });
+        }
+    }
+    getSlotDefaultInputLabel(slotIndex) {
+        const inputs = this.getContextInputsList();
+        const input = inputs[slotIndex];
+        let defaultLabel = this.stripOwnedPrefix(input.name).toLowerCase();
+        return defaultLabel.toLocaleLowerCase();
+    }
+    inputsMutated(mutation) {
+        CONTEXT_SERVICE.onInputChanges(this, mutation);
+    }
+    fixInputsOutputsLinkSlots() {
+        if (!this.hasShadowInputs) {
+            const inputs = this.getContextInputsList();
+            for (let index = inputs.length - 1; index > 0; index--) {
+                const input = inputs[index];
+                if ((input === null || input === void 0 ? void 0 : input.link) != null) {
+                    app.graph.links[input.link].target_slot = index;
+                }
+            }
+        }
+        const outputs = this.outputs;
+        for (let index = outputs.length - 1; index > 0; index--) {
+            const output = outputs[index];
+            if (output) {
+                output.nameLocked = true;
+                for (const link of output.links || []) {
+                    app.graph.links[link].origin_slot = index;
+                }
+            }
+        }
+    }
+    static setUp(comfyClass, nodeData) {
+        RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, this);
+        wait(500).then(() => {
+            LiteGraph.slot_types_default_out["RGTHREE_DYNAMIC_CONTEXT"] =
+                LiteGraph.slot_types_default_out["RGTHREE_DYNAMIC_CONTEXT"] || [];
+            const comfyClassStr = comfyClass.comfyClass;
+            if (comfyClassStr) {
+                LiteGraph.slot_types_default_out["RGTHREE_DYNAMIC_CONTEXT"].push(comfyClassStr);
+            }
+        });
+    }
+}
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/dynamic_context_switch.js b/custom_nodes/rgthree-comfy/web/comfyui/dynamic_context_switch.js
new file mode 100644
index 0000000000000000000000000000000000000000..a91197e5d0f9e2ab38c3b0b8858ee74fcfb6c3a9
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/dynamic_context_switch.js
@@ -0,0 +1,147 @@
+import { app } from "../../scripts/app.js";
+import { DynamicContextNodeBase } from "./dynamic_context_base.js";
+import { NodeTypesString } from "./constants.js";
+import { SERVICE as CONTEXT_SERVICE, getContextOutputName, } from "./services/context_service.js";
+import { getConnectedInputNodesAndFilterPassThroughs } from "./utils.js";
+import { debounce, moveArrayItem } from "../../rgthree/common/shared_utils.js";
+import { measureText } from "./utils_canvas.js";
+import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js";
+class DynamicContextSwitchNode extends DynamicContextNodeBase {
+    constructor(title = DynamicContextSwitchNode.title) {
+        super(title);
+        this.hasShadowInputs = true;
+        this.lastInputsList = [];
+        this.shadowInputs = [
+            { name: "base_ctx", type: "RGTHREE_DYNAMIC_CONTEXT", link: null, count: 0, boundingRect: null },
+        ];
+    }
+    getContextInputsList() {
+        return this.shadowInputs;
+    }
+    handleUpstreamMutation(mutation) {
+        this.scheduleHardRefresh();
+    }
+    onConnectionsChange(type, slotIndex, isConnected, link, inputOrOutput) {
+        var _a;
+        (_a = super.onConnectionsChange) === null || _a === void 0 ? void 0 : _a.call(this, type, slotIndex, isConnected, link, inputOrOutput);
+        if (this.configuring) {
+            return;
+        }
+        if (type === LiteGraph.INPUT) {
+            this.scheduleHardRefresh();
+        }
+    }
+    scheduleHardRefresh(ms = 64) {
+        return debounce(() => {
+            this.refreshInputsAndOutputs();
+        }, ms);
+    }
+    onNodeCreated() {
+        this.addInput("ctx_1", "RGTHREE_DYNAMIC_CONTEXT");
+        this.addInput("ctx_2", "RGTHREE_DYNAMIC_CONTEXT");
+        this.addInput("ctx_3", "RGTHREE_DYNAMIC_CONTEXT");
+        this.addInput("ctx_4", "RGTHREE_DYNAMIC_CONTEXT");
+        this.addInput("ctx_5", "RGTHREE_DYNAMIC_CONTEXT");
+        super.onNodeCreated();
+    }
+    addContextInput(name, type, slot) { }
+    refreshInputsAndOutputs() {
+        var _a;
+        const inputs = [
+            { name: "base_ctx", type: "RGTHREE_DYNAMIC_CONTEXT", link: null, count: 0, boundingRect: null },
+        ];
+        let numConnected = 0;
+        for (let i = 0; i < this.inputs.length; i++) {
+            const childCtxs = getConnectedInputNodesAndFilterPassThroughs(this, this, i);
+            if (childCtxs.length > 1) {
+                throw new Error("How is there more than one input?");
+            }
+            const ctx = childCtxs[0];
+            if (!ctx)
+                continue;
+            numConnected++;
+            const slotsData = CONTEXT_SERVICE.getDynamicContextInputsData(ctx);
+            console.log(slotsData);
+            for (const slotData of slotsData) {
+                const found = inputs.find((n) => getContextOutputName(slotData.name) === getContextOutputName(n.name));
+                if (found) {
+                    found.count += 1;
+                    continue;
+                }
+                inputs.push({
+                    name: slotData.name,
+                    type: slotData.type,
+                    link: null,
+                    count: 1,
+                    boundingRect: null,
+                });
+            }
+        }
+        this.shadowInputs = inputs;
+        let i = 0;
+        for (i; i < this.shadowInputs.length; i++) {
+            const data = this.shadowInputs[i];
+            let existing = this.outputs.find((o) => getContextOutputName(o.name) === getContextOutputName(data.name));
+            if (!existing) {
+                existing = this.addOutput(getContextOutputName(data.name), data.type);
+            }
+            moveArrayItem(this.outputs, existing, i);
+            delete existing.rgthree_status;
+            if (data.count !== numConnected) {
+                existing.rgthree_status = "WARN";
+            }
+        }
+        while (this.outputs[i]) {
+            const output = this.outputs[i];
+            if ((_a = output === null || output === void 0 ? void 0 : output.links) === null || _a === void 0 ? void 0 : _a.length) {
+                output.rgthree_status = "ERROR";
+                i++;
+            }
+            else {
+                this.removeOutput(i);
+            }
+        }
+        this.fixInputsOutputsLinkSlots();
+    }
+    onDrawForeground(ctx, canvas) {
+        var _a, _b;
+        const low_quality = ((_b = (_a = canvas === null || canvas === void 0 ? void 0 : canvas.ds) === null || _a === void 0 ? void 0 : _a.scale) !== null && _b !== void 0 ? _b : 1) < 0.6;
+        if (low_quality || this.size[0] <= 10) {
+            return;
+        }
+        let y = LiteGraph.NODE_SLOT_HEIGHT - 1;
+        const w = this.size[0];
+        ctx.save();
+        ctx.font = "normal " + LiteGraph.NODE_SUBTEXT_SIZE + "px Arial";
+        ctx.textAlign = "right";
+        for (const output of this.outputs) {
+            if (!output.rgthree_status) {
+                y += LiteGraph.NODE_SLOT_HEIGHT;
+                continue;
+            }
+            const x = w - 20 - measureText(ctx, output.name);
+            if (output.rgthree_status === "ERROR") {
+                ctx.fillText("🛑", x, y);
+            }
+            else if (output.rgthree_status === "WARN") {
+                ctx.fillText("⚠️", x, y);
+            }
+            y += LiteGraph.NODE_SLOT_HEIGHT;
+        }
+        ctx.restore();
+    }
+}
+DynamicContextSwitchNode.title = NodeTypesString.DYNAMIC_CONTEXT_SWITCH;
+DynamicContextSwitchNode.type = NodeTypesString.DYNAMIC_CONTEXT_SWITCH;
+DynamicContextSwitchNode.comfyClass = NodeTypesString.DYNAMIC_CONTEXT_SWITCH;
+app.registerExtension({
+    name: "rgthree.DynamicContextSwitch",
+    async beforeRegisterNodeDef(nodeType, nodeData) {
+        if (!CONFIG_SERVICE.getConfigValue("unreleased.dynamic_context.enabled")) {
+            return;
+        }
+        if (nodeData.name === DynamicContextSwitchNode.type) {
+            DynamicContextSwitchNode.setUp(nodeType, nodeData);
+        }
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/fast_actions_button.js b/custom_nodes/rgthree-comfy/web/comfyui/fast_actions_button.js
new file mode 100644
index 0000000000000000000000000000000000000000..62d3bac592ff8a5fc56dd079583278d282b3b77e
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/fast_actions_button.js
@@ -0,0 +1,276 @@
+import { app } from "../../scripts/app.js";
+import { BaseAnyInputConnectedNode } from "./base_any_input_connected_node.js";
+import { NodeTypesString } from "./constants.js";
+import { addMenuItem, changeModeOfNodes } from "./utils.js";
+import { rgthree } from "./rgthree.js";
+const MODE_ALWAYS = 0;
+const MODE_MUTE = 2;
+const MODE_BYPASS = 4;
+class FastActionsButton extends BaseAnyInputConnectedNode {
+    constructor(title) {
+        super(title);
+        this.comfyClass = NodeTypesString.FAST_ACTIONS_BUTTON;
+        this.logger = rgthree.newLogSession("[FastActionsButton]");
+        this.isVirtualNode = true;
+        this.serialize_widgets = true;
+        this.widgetToData = new Map();
+        this.nodeIdtoFunctionCache = new Map();
+        this.executingFromShortcut = false;
+        this.properties["buttonText"] = "🎬 Action!";
+        this.properties["shortcutModifier"] = "alt";
+        this.properties["shortcutKey"] = "";
+        this.buttonWidget = this.addWidget("button", this.properties["buttonText"], "", () => {
+            this.executeConnectedNodes();
+        }, { serialize: false });
+        this.keypressBound = this.onKeypress.bind(this);
+        this.keyupBound = this.onKeyup.bind(this);
+        this.onConstructed();
+    }
+    configure(info) {
+        super.configure(info);
+        setTimeout(() => {
+            if (info.widgets_values) {
+                for (let [index, value] of info.widgets_values.entries()) {
+                    if (index > 0) {
+                        if (typeof value === "string" && value.startsWith("comfy_action:")) {
+                            value = value.replace("comfy_action:", "");
+                            this.addComfyActionWidget(index, value);
+                        }
+                        if (this.widgets[index]) {
+                            this.widgets[index].value = value;
+                        }
+                    }
+                }
+            }
+        }, 100);
+    }
+    clone() {
+        const cloned = super.clone();
+        cloned.properties["buttonText"] = "🎬 Action!";
+        cloned.properties["shortcutKey"] = "";
+        return cloned;
+    }
+    onAdded(graph) {
+        window.addEventListener("keydown", this.keypressBound);
+        window.addEventListener("keyup", this.keyupBound);
+    }
+    onRemoved() {
+        window.removeEventListener("keydown", this.keypressBound);
+        window.removeEventListener("keyup", this.keyupBound);
+    }
+    async onKeypress(event) {
+        const target = event.target;
+        if (this.executingFromShortcut ||
+            target.localName == "input" ||
+            target.localName == "textarea") {
+            return;
+        }
+        if (this.properties["shortcutKey"].trim() &&
+            this.properties["shortcutKey"].toLowerCase() === event.key.toLowerCase()) {
+            const shortcutModifier = this.properties["shortcutModifier"];
+            let good = shortcutModifier === "ctrl" && event.ctrlKey;
+            good = good || (shortcutModifier === "alt" && event.altKey);
+            good = good || (shortcutModifier === "shift" && event.shiftKey);
+            good = good || (shortcutModifier === "meta" && event.metaKey);
+            if (good) {
+                setTimeout(() => {
+                    this.executeConnectedNodes();
+                }, 20);
+                this.executingFromShortcut = true;
+                event.preventDefault();
+                event.stopImmediatePropagation();
+                app.canvas.dirty_canvas = true;
+                return false;
+            }
+        }
+        return;
+    }
+    onKeyup(event) {
+        const target = event.target;
+        if (target.localName == "input" || target.localName == "textarea") {
+            return;
+        }
+        this.executingFromShortcut = false;
+    }
+    onPropertyChanged(property, value, prevValue) {
+        var _a, _b;
+        if (property == "buttonText" && typeof value === "string") {
+            this.buttonWidget.name = value;
+        }
+        if (property == "shortcutKey" && typeof value === "string") {
+            this.properties["shortcutKey"] = (_b = (_a = value.trim()[0]) === null || _a === void 0 ? void 0 : _a.toLowerCase()) !== null && _b !== void 0 ? _b : "";
+        }
+        return true;
+    }
+    handleLinkedNodesStabilization(linkedNodes) {
+        var _a, _b, _c, _d, _e, _f, _g, _h;
+        let changed = false;
+        for (const [widget, data] of this.widgetToData.entries()) {
+            if (!data.node) {
+                continue;
+            }
+            if (!linkedNodes.includes(data.node)) {
+                const index = this.widgets.indexOf(widget);
+                if (index > -1) {
+                    this.widgetToData.delete(widget);
+                    this.removeWidget(widget);
+                    changed = true;
+                }
+                else {
+                    const [m, a] = this.logger.debugParts("Connected widget is not in widgets... weird.");
+                    (_a = console[m]) === null || _a === void 0 ? void 0 : _a.call(console, ...a);
+                }
+            }
+        }
+        const badNodes = [];
+        let indexOffset = 1;
+        for (const [index, node] of linkedNodes.entries()) {
+            if (!node) {
+                const [m, a] = this.logger.debugParts("linkedNode provided that does not exist. ");
+                (_b = console[m]) === null || _b === void 0 ? void 0 : _b.call(console, ...a);
+                badNodes.push(node);
+                continue;
+            }
+            let widgetAtSlot = this.widgets[index + indexOffset];
+            if (widgetAtSlot && ((_c = this.widgetToData.get(widgetAtSlot)) === null || _c === void 0 ? void 0 : _c.comfy)) {
+                indexOffset++;
+                widgetAtSlot = this.widgets[index + indexOffset];
+            }
+            if (!widgetAtSlot || ((_e = (_d = this.widgetToData.get(widgetAtSlot)) === null || _d === void 0 ? void 0 : _d.node) === null || _e === void 0 ? void 0 : _e.id) !== node.id) {
+                let widget = null;
+                for (let i = index + indexOffset; i < this.widgets.length; i++) {
+                    if (((_g = (_f = this.widgetToData.get(this.widgets[i])) === null || _f === void 0 ? void 0 : _f.node) === null || _g === void 0 ? void 0 : _g.id) === node.id) {
+                        widget = this.widgets.splice(i, 1)[0];
+                        this.widgets.splice(index + indexOffset, 0, widget);
+                        changed = true;
+                        break;
+                    }
+                }
+                if (!widget) {
+                    const exposedActions = node.constructor.exposedActions || [];
+                    widget = this.addWidget("combo", node.title, "None", "", {
+                        values: ["None", "Mute", "Bypass", "Enable", ...exposedActions],
+                    });
+                    widget.serializeValue = async (_node, _index) => {
+                        return widget === null || widget === void 0 ? void 0 : widget.value;
+                    };
+                    this.widgetToData.set(widget, { node });
+                    changed = true;
+                }
+            }
+        }
+        for (let i = this.widgets.length - 1; i > linkedNodes.length + indexOffset - 1; i--) {
+            const widgetAtSlot = this.widgets[i];
+            if (widgetAtSlot && ((_h = this.widgetToData.get(widgetAtSlot)) === null || _h === void 0 ? void 0 : _h.comfy)) {
+                continue;
+            }
+            this.removeWidget(widgetAtSlot);
+            changed = true;
+        }
+        return changed;
+    }
+    removeWidget(widget) {
+        widget = typeof widget === "number" ? this.widgets[widget] : widget;
+        if (widget && this.widgetToData.has(widget)) {
+            this.widgetToData.delete(widget);
+        }
+        super.removeWidget(widget);
+    }
+    async executeConnectedNodes() {
+        var _a, _b;
+        for (const widget of this.widgets) {
+            if (widget == this.buttonWidget) {
+                continue;
+            }
+            const action = widget.value;
+            const { comfy, node } = (_a = this.widgetToData.get(widget)) !== null && _a !== void 0 ? _a : {};
+            if (comfy) {
+                if (action === "Queue Prompt") {
+                    await comfy.queuePrompt(0);
+                }
+                continue;
+            }
+            if (node) {
+                if (action === "Mute") {
+                    changeModeOfNodes(node, MODE_MUTE);
+                }
+                else if (action === "Bypass") {
+                    changeModeOfNodes(node, MODE_BYPASS);
+                }
+                else if (action === "Enable") {
+                    changeModeOfNodes(node, MODE_ALWAYS);
+                }
+                if (node.handleAction) {
+                    if (typeof action !== "string") {
+                        throw new Error("Fast Actions Button action should be a string: " + action);
+                    }
+                    await node.handleAction(action);
+                }
+                (_b = this.graph) === null || _b === void 0 ? void 0 : _b.change();
+                continue;
+            }
+            console.warn("Fast Actions Button has a widget without correct data.");
+        }
+    }
+    addComfyActionWidget(slot, value) {
+        let widget = this.addWidget("combo", "Comfy Action", "None", () => {
+            if (String(widget.value).startsWith("MOVE ")) {
+                this.widgets.push(this.widgets.splice(this.widgets.indexOf(widget), 1)[0]);
+                widget.value = String(widget.rgthree_lastValue);
+            }
+            else if (String(widget.value).startsWith("REMOVE ")) {
+                this.removeWidget(widget);
+            }
+            widget.rgthree_lastValue = widget.value;
+        }, {
+            values: ["None", "Queue Prompt", "REMOVE Comfy Action", "MOVE to end"],
+        });
+        widget.rgthree_lastValue = value;
+        widget.serializeValue = async (_node, _index) => {
+            return `comfy_app:${widget === null || widget === void 0 ? void 0 : widget.value}`;
+        };
+        this.widgetToData.set(widget, { comfy: app });
+        if (slot != null) {
+            this.widgets.splice(slot, 0, this.widgets.splice(this.widgets.indexOf(widget), 1)[0]);
+        }
+        return widget;
+    }
+    onSerialize(serialised) {
+        var _a, _b;
+        (_a = super.onSerialize) === null || _a === void 0 ? void 0 : _a.call(this, serialised);
+        for (let [index, value] of (serialised.widgets_values || []).entries()) {
+            if (((_b = this.widgets[index]) === null || _b === void 0 ? void 0 : _b.name) === "Comfy Action") {
+                serialised.widgets_values[index] = `comfy_action:${value}`;
+            }
+        }
+    }
+    static setUp() {
+        super.setUp();
+        addMenuItem(this, app, {
+            name: "➕ Append a Comfy Action",
+            callback: (nodeArg) => {
+                nodeArg.addComfyActionWidget();
+            },
+        });
+    }
+}
+FastActionsButton.type = NodeTypesString.FAST_ACTIONS_BUTTON;
+FastActionsButton.title = NodeTypesString.FAST_ACTIONS_BUTTON;
+FastActionsButton["@buttonText"] = { type: "string" };
+FastActionsButton["@shortcutModifier"] = {
+    type: "combo",
+    values: ["ctrl", "alt", "shift"],
+};
+FastActionsButton["@shortcutKey"] = { type: "string" };
+FastActionsButton.collapsible = false;
+app.registerExtension({
+    name: "rgthree.FastActionsButton",
+    registerCustomNodes() {
+        FastActionsButton.setUp();
+    },
+    loadedGraphNode(node) {
+        if (node.type == FastActionsButton.title) {
+            node._tempWidth = node.size[0];
+        }
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/fast_groups_bypasser.js b/custom_nodes/rgthree-comfy/web/comfyui/fast_groups_bypasser.js
new file mode 100644
index 0000000000000000000000000000000000000000..80ee99108156aac3d96e66cbe91db224b5877c99
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/fast_groups_bypasser.js
@@ -0,0 +1,27 @@
+import { app } from "../../scripts/app.js";
+import { NodeTypesString } from "./constants.js";
+import { BaseFastGroupsModeChanger } from "./fast_groups_muter.js";
+export class FastGroupsBypasser extends BaseFastGroupsModeChanger {
+    constructor(title = FastGroupsBypasser.title) {
+        super(title);
+        this.comfyClass = NodeTypesString.FAST_GROUPS_BYPASSER;
+        this.helpActions = "bypass and enable";
+        this.modeOn = LiteGraph.ALWAYS;
+        this.modeOff = 4;
+        this.onConstructed();
+    }
+}
+FastGroupsBypasser.type = NodeTypesString.FAST_GROUPS_BYPASSER;
+FastGroupsBypasser.title = NodeTypesString.FAST_GROUPS_BYPASSER;
+FastGroupsBypasser.exposedActions = ["Bypass all", "Enable all", "Toggle all"];
+app.registerExtension({
+    name: "rgthree.FastGroupsBypasser",
+    registerCustomNodes() {
+        FastGroupsBypasser.setUp();
+    },
+    loadedGraphNode(node) {
+        if (node.type == FastGroupsBypasser.title) {
+            node.tempSize = [...node.size];
+        }
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/fast_groups_muter.js b/custom_nodes/rgthree-comfy/web/comfyui/fast_groups_muter.js
new file mode 100644
index 0000000000000000000000000000000000000000..11ee58f03605f014060d74a3cd67f19fa8567163
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/fast_groups_muter.js
@@ -0,0 +1,437 @@
+import { app } from "../../scripts/app.js";
+import { RgthreeBaseVirtualNode } from "./base_node.js";
+import { NodeTypesString } from "./constants.js";
+import { SERVICE as FAST_GROUPS_SERVICE } from "./services/fast_groups_service.js";
+import { drawNodeWidget, fitString } from "./utils_canvas.js";
+import { RgthreeBaseWidget } from "./utils_widgets.js";
+import { changeModeOfNodes, getGroupNodes } from "./utils.js";
+const PROPERTY_SORT = "sort";
+const PROPERTY_SORT_CUSTOM_ALPHA = "customSortAlphabet";
+const PROPERTY_MATCH_COLORS = "matchColors";
+const PROPERTY_MATCH_TITLE = "matchTitle";
+const PROPERTY_SHOW_NAV = "showNav";
+const PROPERTY_SHOW_ALL_GRAPHS = "showAllGraphs";
+const PROPERTY_RESTRICTION = "toggleRestriction";
+export class BaseFastGroupsModeChanger extends RgthreeBaseVirtualNode {
+    constructor(title = FastGroupsMuter.title) {
+        super(title);
+        this.modeOn = LiteGraph.ALWAYS;
+        this.modeOff = LiteGraph.NEVER;
+        this.debouncerTempWidth = 0;
+        this.tempSize = null;
+        this.serialize_widgets = false;
+        this.helpActions = "mute and unmute";
+        this.properties[PROPERTY_MATCH_COLORS] = "";
+        this.properties[PROPERTY_MATCH_TITLE] = "";
+        this.properties[PROPERTY_SHOW_NAV] = true;
+        this.properties[PROPERTY_SHOW_ALL_GRAPHS] = true;
+        this.properties[PROPERTY_SORT] = "position";
+        this.properties[PROPERTY_SORT_CUSTOM_ALPHA] = "";
+        this.properties[PROPERTY_RESTRICTION] = "default";
+    }
+    onConstructed() {
+        this.addOutput("OPT_CONNECTION", "*");
+        return super.onConstructed();
+    }
+    onAdded(graph) {
+        FAST_GROUPS_SERVICE.addFastGroupNode(this);
+    }
+    onRemoved() {
+        FAST_GROUPS_SERVICE.removeFastGroupNode(this);
+    }
+    refreshWidgets() {
+        var _a, _b, _c, _d, _e, _f, _g, _h, _j;
+        const canvas = app.canvas;
+        let sort = ((_a = this.properties) === null || _a === void 0 ? void 0 : _a[PROPERTY_SORT]) || "position";
+        let customAlphabet = null;
+        if (sort === "custom alphabet") {
+            const customAlphaStr = (_c = (_b = this.properties) === null || _b === void 0 ? void 0 : _b[PROPERTY_SORT_CUSTOM_ALPHA]) === null || _c === void 0 ? void 0 : _c.replace(/\n/g, "");
+            if (customAlphaStr && customAlphaStr.trim()) {
+                customAlphabet = customAlphaStr.includes(",")
+                    ? customAlphaStr.toLocaleLowerCase().split(",")
+                    : customAlphaStr.toLocaleLowerCase().trim().split("");
+            }
+            if (!(customAlphabet === null || customAlphabet === void 0 ? void 0 : customAlphabet.length)) {
+                sort = "alphanumeric";
+                customAlphabet = null;
+            }
+        }
+        const groups = [...FAST_GROUPS_SERVICE.getGroups(sort)];
+        if (customAlphabet === null || customAlphabet === void 0 ? void 0 : customAlphabet.length) {
+            groups.sort((a, b) => {
+                let aIndex = -1;
+                let bIndex = -1;
+                for (const [index, alpha] of customAlphabet.entries()) {
+                    aIndex =
+                        aIndex < 0 ? (a.title.toLocaleLowerCase().startsWith(alpha) ? index : -1) : aIndex;
+                    bIndex =
+                        bIndex < 0 ? (b.title.toLocaleLowerCase().startsWith(alpha) ? index : -1) : bIndex;
+                    if (aIndex > -1 && bIndex > -1) {
+                        break;
+                    }
+                }
+                if (aIndex > -1 && bIndex > -1) {
+                    const ret = aIndex - bIndex;
+                    if (ret === 0) {
+                        return a.title.localeCompare(b.title);
+                    }
+                    return ret;
+                }
+                else if (aIndex > -1) {
+                    return -1;
+                }
+                else if (bIndex > -1) {
+                    return 1;
+                }
+                return a.title.localeCompare(b.title);
+            });
+        }
+        let filterColors = (((_e = (_d = this.properties) === null || _d === void 0 ? void 0 : _d[PROPERTY_MATCH_COLORS]) === null || _e === void 0 ? void 0 : _e.split(",")) || []).filter((c) => c.trim());
+        if (filterColors.length) {
+            filterColors = filterColors.map((color) => {
+                color = color.trim().toLocaleLowerCase();
+                if (LGraphCanvas.node_colors[color]) {
+                    color = LGraphCanvas.node_colors[color].groupcolor;
+                }
+                color = color.replace("#", "").toLocaleLowerCase();
+                if (color.length === 3) {
+                    color = color.replace(/(.)(.)(.)/, "$1$1$2$2$3$3");
+                }
+                return `#${color}`;
+            });
+        }
+        let index = 0;
+        for (const group of groups) {
+            if (filterColors.length) {
+                let groupColor = (_f = group.color) === null || _f === void 0 ? void 0 : _f.replace("#", "").trim().toLocaleLowerCase();
+                if (!groupColor) {
+                    continue;
+                }
+                if (groupColor.length === 3) {
+                    groupColor = groupColor.replace(/(.)(.)(.)/, "$1$1$2$2$3$3");
+                }
+                groupColor = `#${groupColor}`;
+                if (!filterColors.includes(groupColor)) {
+                    continue;
+                }
+            }
+            if ((_h = (_g = this.properties) === null || _g === void 0 ? void 0 : _g[PROPERTY_MATCH_TITLE]) === null || _h === void 0 ? void 0 : _h.trim()) {
+                try {
+                    if (!new RegExp(this.properties[PROPERTY_MATCH_TITLE], "i").exec(group.title)) {
+                        continue;
+                    }
+                }
+                catch (e) {
+                    console.error(e);
+                    continue;
+                }
+            }
+            const showAllGraphs = (_j = this.properties) === null || _j === void 0 ? void 0 : _j[PROPERTY_SHOW_ALL_GRAPHS];
+            if (!showAllGraphs && group.graph !== app.canvas.getCurrentGraph()) {
+                continue;
+            }
+            let isDirty = false;
+            const widgetLabel = `Enable ${group.title}`;
+            let widget = this.widgets.find((w) => w.label === widgetLabel);
+            if (!widget) {
+                this.tempSize = [...this.size];
+                widget = this.addCustomWidget(new FastGroupsToggleRowWidget(group, this));
+                this.setSize(this.computeSize());
+                isDirty = true;
+            }
+            if (widget.label != widgetLabel) {
+                widget.label = widgetLabel;
+                isDirty = true;
+            }
+            if (group.rgthree_hasAnyActiveNode != null &&
+                widget.toggled != group.rgthree_hasAnyActiveNode) {
+                widget.toggled = group.rgthree_hasAnyActiveNode;
+                isDirty = true;
+            }
+            if (this.widgets[index] !== widget) {
+                const oldIndex = this.widgets.findIndex((w) => w === widget);
+                this.widgets.splice(index, 0, this.widgets.splice(oldIndex, 1)[0]);
+                isDirty = true;
+            }
+            if (isDirty) {
+                this.setDirtyCanvas(true, false);
+            }
+            index++;
+        }
+        while ((this.widgets || [])[index]) {
+            this.removeWidget(index++);
+        }
+    }
+    computeSize(out) {
+        let size = super.computeSize(out);
+        if (this.tempSize) {
+            size[0] = Math.max(this.tempSize[0], size[0]);
+            size[1] = Math.max(this.tempSize[1], size[1]);
+            this.debouncerTempWidth && clearTimeout(this.debouncerTempWidth);
+            this.debouncerTempWidth = setTimeout(() => {
+                this.tempSize = null;
+            }, 32);
+        }
+        setTimeout(() => {
+            var _a;
+            (_a = this.graph) === null || _a === void 0 ? void 0 : _a.setDirtyCanvas(true, true);
+        }, 16);
+        return size;
+    }
+    async handleAction(action) {
+        var _a, _b, _c, _d, _e;
+        if (action === "Mute all" || action === "Bypass all") {
+            const alwaysOne = ((_a = this.properties) === null || _a === void 0 ? void 0 : _a[PROPERTY_RESTRICTION]) === "always one";
+            for (const [index, widget] of this.widgets.entries()) {
+                widget === null || widget === void 0 ? void 0 : widget.doModeChange(alwaysOne && !index ? true : false, true);
+            }
+        }
+        else if (action === "Enable all") {
+            const onlyOne = (_b = this.properties) === null || _b === void 0 ? void 0 : _b[PROPERTY_RESTRICTION].includes(" one");
+            for (const [index, widget] of this.widgets.entries()) {
+                widget === null || widget === void 0 ? void 0 : widget.doModeChange(onlyOne && index > 0 ? false : true, true);
+            }
+        }
+        else if (action === "Toggle all") {
+            const onlyOne = (_c = this.properties) === null || _c === void 0 ? void 0 : _c[PROPERTY_RESTRICTION].includes(" one");
+            let foundOne = false;
+            for (const [index, widget] of this.widgets.entries()) {
+                let newValue = onlyOne && foundOne ? false : !widget.value;
+                foundOne = foundOne || newValue;
+                widget === null || widget === void 0 ? void 0 : widget.doModeChange(newValue, true);
+            }
+            if (!foundOne && ((_d = this.properties) === null || _d === void 0 ? void 0 : _d[PROPERTY_RESTRICTION]) === "always one") {
+                (_e = this.widgets[this.widgets.length - 1]) === null || _e === void 0 ? void 0 : _e.doModeChange(true, true);
+            }
+        }
+    }
+    getHelp() {
+        return `
+      <p>The ${this.type.replace("(rgthree)", "")} is an input-less node that automatically collects all groups in your current
+      workflow and allows you to quickly ${this.helpActions} all nodes within the group.</p>
+      <ul>
+        <li>
+          <p>
+            <strong>Properties.</strong> You can change the following properties (by right-clicking
+            on the node, and select "Properties" or "Properties Panel" from the menu):
+          </p>
+          <ul>
+            <li><p>
+              <code>${PROPERTY_MATCH_COLORS}</code> - Only add groups that match the provided
+              colors. Can be ComfyUI colors (red, pale_blue) or hex codes (#a4d399). Multiple can be
+              added, comma delimited.
+            </p></li>
+            <li><p>
+              <code>${PROPERTY_MATCH_TITLE}</code> - Filter the list of toggles by title match
+              (string match, or regular expression).
+            </p></li>
+            <li><p>
+              <code>${PROPERTY_SHOW_NAV}</code> - Add / remove a quick navigation arrow to take you
+              to the group. <i>(default: true)</i>
+            </p></li>
+            <li><p>
+              <code>${PROPERTY_SHOW_ALL_GRAPHS}</code> - Show groups from all [sub]graphs in the
+              workflow. <i>(default: true)</i>
+            </p></li>
+            <li><p>
+              <code>${PROPERTY_SORT}</code> - Sort the toggles' order by "alphanumeric", graph
+              "position", or "custom alphabet". <i>(default: "position")</i>
+            </p></li>
+            <li>
+              <p>
+                <code>${PROPERTY_SORT_CUSTOM_ALPHA}</code> - When the
+                <code>${PROPERTY_SORT}</code> property is "custom alphabet" you can define the
+                alphabet to use here, which will match the <i>beginning</i> of each group name and
+                sort against it. If group titles do not match any custom alphabet entry, then they
+                will be put after groups that do, ordered alphanumerically.
+              </p>
+              <p>
+                This can be a list of single characters, like "zyxw..." or comma delimited strings
+                for more control, like "sdxl,pro,sd,n,p".
+              </p>
+              <p>
+                Note, when two group title match the same custom alphabet entry, the <i>normal
+                alphanumeric alphabet</i> breaks the tie. For instance, a custom alphabet of
+                "e,s,d" will order groups names like "SDXL, SEGS, Detailer" eventhough the custom
+                alphabet has an "e" before "d" (where one may expect "SE" to be before "SD").
+              </p>
+              <p>
+                To have "SEGS" appear before "SDXL" you can use longer strings. For instance, the
+                custom alphabet value of "se,s,f" would work here.
+              </p>
+            </li>
+            <li><p>
+              <code>${PROPERTY_RESTRICTION}</code> - Optionally, attempt to restrict the number of
+              widgets that can be enabled to a maximum of one, or always one.
+              </p>
+              <p><em><strong>Note:</strong> If using "max one" or "always one" then this is only
+              enforced when clicking a toggle on this node; if nodes within groups are changed
+              outside of the initial toggle click, then these restriction will not be enforced, and
+              could result in a state where more than one toggle is enabled. This could also happen
+              if nodes are overlapped with multiple groups.
+            </p></li>
+
+          </ul>
+        </li>
+      </ul>`;
+    }
+}
+BaseFastGroupsModeChanger.type = NodeTypesString.FAST_GROUPS_MUTER;
+BaseFastGroupsModeChanger.title = NodeTypesString.FAST_GROUPS_MUTER;
+BaseFastGroupsModeChanger.exposedActions = ["Mute all", "Enable all", "Toggle all"];
+BaseFastGroupsModeChanger["@matchColors"] = { type: "string" };
+BaseFastGroupsModeChanger["@matchTitle"] = { type: "string" };
+BaseFastGroupsModeChanger["@showNav"] = { type: "boolean" };
+BaseFastGroupsModeChanger["@showAllGraphs"] = { type: "boolean" };
+BaseFastGroupsModeChanger["@sort"] = {
+    type: "combo",
+    values: ["position", "alphanumeric", "custom alphabet"],
+};
+BaseFastGroupsModeChanger["@customSortAlphabet"] = { type: "string" };
+BaseFastGroupsModeChanger["@toggleRestriction"] = {
+    type: "combo",
+    values: ["default", "max one", "always one"],
+};
+export class FastGroupsMuter extends BaseFastGroupsModeChanger {
+    constructor(title = FastGroupsMuter.title) {
+        super(title);
+        this.comfyClass = NodeTypesString.FAST_GROUPS_MUTER;
+        this.helpActions = "mute and unmute";
+        this.modeOn = LiteGraph.ALWAYS;
+        this.modeOff = LiteGraph.NEVER;
+        this.onConstructed();
+    }
+}
+FastGroupsMuter.type = NodeTypesString.FAST_GROUPS_MUTER;
+FastGroupsMuter.title = NodeTypesString.FAST_GROUPS_MUTER;
+FastGroupsMuter.exposedActions = ["Bypass all", "Enable all", "Toggle all"];
+class FastGroupsToggleRowWidget extends RgthreeBaseWidget {
+    constructor(group, node) {
+        super("RGTHREE_TOGGLE_AND_NAV");
+        this.value = { toggled: false };
+        this.options = { on: "yes", off: "no" };
+        this.type = "custom";
+        this.label = "";
+        this.group = group;
+        this.node = node;
+    }
+    doModeChange(force, skipOtherNodeCheck) {
+        var _a, _b, _c, _d;
+        this.group.recomputeInsideNodes();
+        const hasAnyActiveNodes = getGroupNodes(this.group).some((n) => n.mode === LiteGraph.ALWAYS);
+        let newValue = force != null ? force : !hasAnyActiveNodes;
+        if (skipOtherNodeCheck !== true) {
+            if (newValue && ((_b = (_a = this.node.properties) === null || _a === void 0 ? void 0 : _a[PROPERTY_RESTRICTION]) === null || _b === void 0 ? void 0 : _b.includes(" one"))) {
+                for (const widget of this.node.widgets) {
+                    if (widget instanceof FastGroupsToggleRowWidget) {
+                        widget.doModeChange(false, true);
+                    }
+                }
+            }
+            else if (!newValue && ((_c = this.node.properties) === null || _c === void 0 ? void 0 : _c[PROPERTY_RESTRICTION]) === "always one") {
+                newValue = this.node.widgets.every((w) => !w.value || w === this);
+            }
+        }
+        changeModeOfNodes(getGroupNodes(this.group), (newValue ? this.node.modeOn : this.node.modeOff));
+        this.group.rgthree_hasAnyActiveNode = newValue;
+        this.toggled = newValue;
+        (_d = this.group.graph) === null || _d === void 0 ? void 0 : _d.setDirtyCanvas(true, false);
+    }
+    get toggled() {
+        return this.value.toggled;
+    }
+    set toggled(value) {
+        this.value.toggled = value;
+    }
+    toggle(value) {
+        value = value == null ? !this.toggled : value;
+        if (value !== this.toggled) {
+            this.value.toggled = value;
+            this.doModeChange();
+        }
+    }
+    draw(ctx, node, width, posY, height) {
+        var _a;
+        const widgetData = drawNodeWidget(ctx, { size: [width, height], pos: [15, posY] });
+        const showNav = ((_a = node.properties) === null || _a === void 0 ? void 0 : _a[PROPERTY_SHOW_NAV]) !== false;
+        let currentX = widgetData.width - widgetData.margin;
+        if (!widgetData.lowQuality && showNav) {
+            currentX -= 7;
+            const midY = widgetData.posY + widgetData.height * 0.5;
+            ctx.fillStyle = ctx.strokeStyle = "#89A";
+            ctx.lineJoin = "round";
+            ctx.lineCap = "round";
+            const arrow = new Path2D(`M${currentX} ${midY} l -7 6 v -3 h -7 v -6 h 7 v -3 z`);
+            ctx.fill(arrow);
+            ctx.stroke(arrow);
+            currentX -= 14;
+            currentX -= 7;
+            ctx.strokeStyle = widgetData.colorOutline;
+            ctx.stroke(new Path2D(`M ${currentX} ${widgetData.posY} v ${widgetData.height}`));
+        }
+        else if (widgetData.lowQuality && showNav) {
+            currentX -= 28;
+        }
+        currentX -= 7;
+        ctx.fillStyle = this.toggled ? "#89A" : "#333";
+        ctx.beginPath();
+        const toggleRadius = height * 0.36;
+        ctx.arc(currentX - toggleRadius, posY + height * 0.5, toggleRadius, 0, Math.PI * 2);
+        ctx.fill();
+        currentX -= toggleRadius * 2;
+        if (!widgetData.lowQuality) {
+            currentX -= 4;
+            ctx.textAlign = "right";
+            ctx.fillStyle = this.toggled ? widgetData.colorText : widgetData.colorTextSecondary;
+            const label = this.label;
+            const toggleLabelOn = this.options.on || "true";
+            const toggleLabelOff = this.options.off || "false";
+            ctx.fillText(this.toggled ? toggleLabelOn : toggleLabelOff, currentX, posY + height * 0.7);
+            currentX -= Math.max(ctx.measureText(toggleLabelOn).width, ctx.measureText(toggleLabelOff).width);
+            currentX -= 7;
+            ctx.textAlign = "left";
+            let maxLabelWidth = widgetData.width - widgetData.margin - 10 - (widgetData.width - currentX);
+            if (label != null) {
+                ctx.fillText(fitString(ctx, label, maxLabelWidth), widgetData.margin + 10, posY + height * 0.7);
+            }
+        }
+    }
+    serializeValue(node, index) {
+        return this.value;
+    }
+    mouse(event, pos, node) {
+        var _a, _b, _c;
+        if (event.type == "pointerdown") {
+            if (((_a = node.properties) === null || _a === void 0 ? void 0 : _a[PROPERTY_SHOW_NAV]) !== false && pos[0] >= node.size[0] - 15 - 28 - 1) {
+                const canvas = app.canvas;
+                const lowQuality = (((_b = canvas.ds) === null || _b === void 0 ? void 0 : _b.scale) || 1) <= 0.5;
+                if (!lowQuality) {
+                    canvas.centerOnNode(this.group);
+                    const zoomCurrent = ((_c = canvas.ds) === null || _c === void 0 ? void 0 : _c.scale) || 1;
+                    const zoomX = canvas.canvas.width / this.group._size[0] - 0.02;
+                    const zoomY = canvas.canvas.height / this.group._size[1] - 0.02;
+                    canvas.setZoom(Math.min(zoomCurrent, zoomX, zoomY), [
+                        canvas.canvas.width / 2,
+                        canvas.canvas.height / 2,
+                    ]);
+                    canvas.setDirty(true, true);
+                }
+            }
+            else {
+                this.toggle();
+            }
+        }
+        return true;
+    }
+}
+app.registerExtension({
+    name: "rgthree.FastGroupsMuter",
+    registerCustomNodes() {
+        FastGroupsMuter.setUp();
+    },
+    loadedGraphNode(node) {
+        if (node.type == FastGroupsMuter.title) {
+            node.tempSize = [...node.size];
+        }
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/feature_group_fast_toggle.js b/custom_nodes/rgthree-comfy/web/comfyui/feature_group_fast_toggle.js
new file mode 100644
index 0000000000000000000000000000000000000000..8058a87b76ade8f1e76b209bf3c80ff91a150dd4
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/feature_group_fast_toggle.js
@@ -0,0 +1,220 @@
+import { app } from "../../scripts/app.js";
+import { rgthree } from "./rgthree.js";
+import { changeModeOfNodes, getGroupNodes, getOutputNodes } from "./utils.js";
+import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js";
+const BTN_SIZE = 20;
+const BTN_MARGIN = [6, 6];
+const BTN_SPACING = 8;
+const BTN_GRID = BTN_SIZE / 8;
+const TOGGLE_TO_MODE = new Map([
+    ["MUTE", LiteGraph.NEVER],
+    ["BYPASS", 4],
+]);
+function getToggles() {
+    return [...CONFIG_SERVICE.getFeatureValue("group_header_fast_toggle.toggles", [])].reverse();
+}
+function clickedOnToggleButton(e, group) {
+    const toggles = getToggles();
+    const pos = group.pos;
+    const size = group.size;
+    for (let i = 0; i < toggles.length; i++) {
+        const toggle = toggles[i];
+        if (LiteGraph.isInsideRectangle(e.canvasX, e.canvasY, pos[0] + size[0] - (BTN_SIZE + BTN_MARGIN[0]) * (i + 1), pos[1] + BTN_MARGIN[1], BTN_SIZE, BTN_SIZE)) {
+            return toggle;
+        }
+    }
+    return null;
+}
+app.registerExtension({
+    name: "rgthree.GroupHeaderToggles",
+    async setup() {
+        setInterval(() => {
+            if (CONFIG_SERVICE.getFeatureValue("group_header_fast_toggle.enabled") &&
+                CONFIG_SERVICE.getFeatureValue("group_header_fast_toggle.show") !== "always") {
+                app.canvas.setDirty(true, true);
+            }
+        }, 250);
+        rgthree.addEventListener("on-process-mouse-down", ((e) => {
+            if (!CONFIG_SERVICE.getFeatureValue("group_header_fast_toggle.enabled"))
+                return;
+            const canvas = app.canvas;
+            if (canvas.selected_group) {
+                const originalEvent = e.detail.originalEvent;
+                const group = canvas.selected_group;
+                const clickedOnToggle = clickedOnToggleButton(originalEvent, group) || "";
+                const toggleAction = clickedOnToggle === null || clickedOnToggle === void 0 ? void 0 : clickedOnToggle.toLocaleUpperCase();
+                if (toggleAction) {
+                    console.log(toggleAction);
+                    const nodes = getGroupNodes(group);
+                    if (toggleAction === "QUEUE") {
+                        const outputNodes = getOutputNodes(nodes);
+                        if (!(outputNodes === null || outputNodes === void 0 ? void 0 : outputNodes.length)) {
+                            rgthree.showMessage({
+                                id: "no-output-in-group",
+                                type: "warn",
+                                timeout: 4000,
+                                message: "No output nodes for group!",
+                            });
+                        }
+                        else {
+                            rgthree.queueOutputNodes(outputNodes);
+                        }
+                    }
+                    else {
+                        const toggleMode = TOGGLE_TO_MODE.get(toggleAction);
+                        if (toggleMode) {
+                            group.recomputeInsideNodes();
+                            const hasAnyActiveNodes = nodes.some((n) => n.mode === LiteGraph.ALWAYS);
+                            const isAllMuted = !hasAnyActiveNodes && nodes.every((n) => n.mode === LiteGraph.NEVER);
+                            const isAllBypassed = !hasAnyActiveNodes && !isAllMuted && nodes.every((n) => n.mode === 4);
+                            let newMode = LiteGraph.ALWAYS;
+                            if (toggleMode === LiteGraph.NEVER) {
+                                newMode = isAllMuted ? LiteGraph.ALWAYS : LiteGraph.NEVER;
+                            }
+                            else {
+                                newMode = isAllBypassed ? LiteGraph.ALWAYS : 4;
+                            }
+                            changeModeOfNodes(nodes, newMode);
+                        }
+                    }
+                    canvas.selected_group = null;
+                    canvas.dragging_canvas = false;
+                }
+            }
+        }));
+        const drawGroups = LGraphCanvas.prototype.drawGroups;
+        LGraphCanvas.prototype.drawGroups = function (canvasEl, ctx) {
+            drawGroups.apply(this, [...arguments]);
+            if (!CONFIG_SERVICE.getFeatureValue("group_header_fast_toggle.enabled") ||
+                !rgthree.lastCanvasMouseEvent) {
+                return;
+            }
+            const graph = app.canvas.graph;
+            let groups;
+            if (CONFIG_SERVICE.getFeatureValue("group_header_fast_toggle.show") !== "always") {
+                const hoverGroup = graph.getGroupOnPos(rgthree.lastCanvasMouseEvent.canvasX, rgthree.lastCanvasMouseEvent.canvasY);
+                groups = hoverGroup ? [hoverGroup] : [];
+            }
+            else {
+                groups = graph._groups || [];
+            }
+            if (!groups.length) {
+                return;
+            }
+            const toggles = getToggles();
+            ctx.save();
+            for (const group of groups || []) {
+                const nodes = getGroupNodes(group);
+                let anyActive = false;
+                let allMuted = !!nodes.length;
+                let allBypassed = allMuted;
+                for (const node of nodes) {
+                    if (!(node instanceof LGraphNode))
+                        continue;
+                    anyActive = anyActive || node.mode === LiteGraph.ALWAYS;
+                    allMuted = allMuted && node.mode === LiteGraph.NEVER;
+                    allBypassed = allBypassed && node.mode === 4;
+                    if (anyActive || (!allMuted && !allBypassed)) {
+                        break;
+                    }
+                }
+                for (let i = 0; i < toggles.length; i++) {
+                    const toggle = toggles[i];
+                    const pos = group._pos;
+                    const size = group._size;
+                    ctx.fillStyle = ctx.strokeStyle = group.color || "#335";
+                    const x = pos[0] + size[0] - BTN_MARGIN[0] - BTN_SIZE - (BTN_SPACING + BTN_SIZE) * i;
+                    const y = pos[1] + BTN_MARGIN[1];
+                    const midX = x + BTN_SIZE / 2;
+                    const midY = y + BTN_SIZE / 2;
+                    if (toggle === "queue") {
+                        const outputNodes = getOutputNodes(nodes);
+                        const oldGlobalAlpha = ctx.globalAlpha;
+                        if (!(outputNodes === null || outputNodes === void 0 ? void 0 : outputNodes.length)) {
+                            ctx.globalAlpha = 0.5;
+                        }
+                        ctx.lineJoin = "round";
+                        ctx.lineCap = "round";
+                        const arrowSizeX = BTN_SIZE * 0.6;
+                        const arrowSizeY = BTN_SIZE * 0.7;
+                        const arrow = new Path2D(`M ${x + arrowSizeX / 2} ${midY} l 0 -${arrowSizeY / 2} l ${arrowSizeX} ${arrowSizeY / 2} l -${arrowSizeX} ${arrowSizeY / 2} z`);
+                        ctx.stroke(arrow);
+                        if (outputNodes === null || outputNodes === void 0 ? void 0 : outputNodes.length) {
+                            ctx.fill(arrow);
+                        }
+                        ctx.globalAlpha = oldGlobalAlpha;
+                    }
+                    else {
+                        const on = toggle === "bypass" ? allBypassed : allMuted;
+                        ctx.beginPath();
+                        ctx.lineJoin = "round";
+                        ctx.rect(x, y, BTN_SIZE, BTN_SIZE);
+                        ctx.lineWidth = 2;
+                        if (toggle === "mute") {
+                            ctx.lineJoin = "round";
+                            ctx.lineCap = "round";
+                            if (on) {
+                                ctx.stroke(new Path2D(`
+                    ${eyeFrame(midX, midY)}
+                    ${eyeLashes(midX, midY)}
+                `));
+                            }
+                            else {
+                                const radius = BTN_GRID * 1.5;
+                                ctx.fill(new Path2D(`
+                    ${eyeFrame(midX, midY)}
+                    ${eyeFrame(midX, midY, -1)}
+                    ${circlePath(midX, midY, radius)}
+                    ${circlePath(midX + BTN_GRID / 2, midY - BTN_GRID / 2, BTN_GRID * 0.375)}
+                  `), "evenodd");
+                                ctx.stroke(new Path2D(`${eyeFrame(midX, midY)} ${eyeFrame(midX, midY, -1)}`));
+                                ctx.globalAlpha = this.editor_alpha * 0.5;
+                                ctx.stroke(new Path2D(`${eyeLashes(midX, midY)} ${eyeLashes(midX, midY, -1)}`));
+                                ctx.globalAlpha = this.editor_alpha;
+                            }
+                        }
+                        else {
+                            const lineChanges = on
+                                ? `a ${BTN_GRID * 3}, ${BTN_GRID * 3} 0 1, 1 ${BTN_GRID * 3 * 2},0
+                  l ${BTN_GRID * 2.0} 0`
+                                : `l ${BTN_GRID * 8} 0`;
+                            ctx.stroke(new Path2D(`
+                  M ${x} ${midY}
+                  ${lineChanges}
+                  M ${x + BTN_SIZE} ${midY} l -2  2
+                  M ${x + BTN_SIZE} ${midY} l -2 -2
+                `));
+                            ctx.fill(new Path2D(`${circlePath(x + BTN_GRID * 3, midY, BTN_GRID * 1.8)}`));
+                        }
+                    }
+                }
+            }
+            ctx.restore();
+        };
+    },
+});
+function eyeFrame(midX, midY, yFlip = 1) {
+    return `
+      M ${midX - BTN_SIZE / 2} ${midY}
+      c ${BTN_GRID * 1.5} ${yFlip * BTN_GRID * 2.5}, ${BTN_GRID * (8 - 1.5)} ${yFlip * BTN_GRID * 2.5}, ${BTN_GRID * 8} 0
+  `;
+}
+function eyeLashes(midX, midY, yFlip = 1) {
+    return `
+    M ${midX - BTN_GRID * 3.46} ${midY + yFlip * BTN_GRID * 0.9} l -1.15  ${1.25 * yFlip}
+    M ${midX - BTN_GRID * 2.38} ${midY + yFlip * BTN_GRID * 1.6} l -0.90  ${1.5 * yFlip}
+    M ${midX - BTN_GRID * 1.15} ${midY + yFlip * BTN_GRID * 1.95} l -0.50  ${1.75 * yFlip}
+    M ${midX + BTN_GRID * 0.0} ${midY + yFlip * BTN_GRID * 2.0} l  0.00  ${2.0 * yFlip}
+    M ${midX + BTN_GRID * 1.15} ${midY + yFlip * BTN_GRID * 1.95} l  0.50  ${1.75 * yFlip}
+    M ${midX + BTN_GRID * 2.38} ${midY + yFlip * BTN_GRID * 1.6} l  0.90  ${1.5 * yFlip}
+    M ${midX + BTN_GRID * 3.46} ${midY + yFlip * BTN_GRID * 0.9} l  1.15  ${1.25 * yFlip}
+`;
+}
+function circlePath(cx, cy, radius) {
+    return `
+      M ${cx} ${cy}
+      m ${radius}, 0
+      a ${radius},${radius} 0 1, 1 -${radius * 2},0
+      a ${radius},${radius} 0 1, 1  ${radius * 2},0
+  `;
+}
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/feature_import_individual_nodes.js b/custom_nodes/rgthree-comfy/web/comfyui/feature_import_individual_nodes.js
new file mode 100644
index 0000000000000000000000000000000000000000..ca5058aa6e254e0f690ded2f03ff87ea2222b230
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/feature_import_individual_nodes.js
@@ -0,0 +1,66 @@
+import { app } from "../../scripts/app.js";
+import { tryToGetWorkflowDataFromEvent } from "../../rgthree/common/utils_workflow.js";
+import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js";
+import { NodeTypesString } from "./constants.js";
+app.registerExtension({
+    name: "rgthree.ImportIndividualNodes",
+    async beforeRegisterNodeDef(nodeType, nodeData) {
+        const onDragOver = nodeType.prototype.onDragOver;
+        nodeType.prototype.onDragOver = function (e) {
+            var _a;
+            let handled = (_a = onDragOver === null || onDragOver === void 0 ? void 0 : onDragOver.apply) === null || _a === void 0 ? void 0 : _a.call(onDragOver, this, [...arguments]);
+            if (handled != null) {
+                return handled;
+            }
+            return importIndividualNodesInnerOnDragOver(this, e);
+        };
+        const onDragDrop = nodeType.prototype.onDragDrop;
+        nodeType.prototype.onDragDrop = async function (e) {
+            var _a;
+            const alreadyHandled = await ((_a = onDragDrop === null || onDragDrop === void 0 ? void 0 : onDragDrop.apply) === null || _a === void 0 ? void 0 : _a.call(onDragDrop, this, [...arguments]));
+            if (alreadyHandled) {
+                return alreadyHandled;
+            }
+            return importIndividualNodesInnerOnDragDrop(this, e);
+        };
+    },
+});
+export function importIndividualNodesInnerOnDragOver(node, e) {
+    var _a;
+    return ((((_a = node.widgets) === null || _a === void 0 ? void 0 : _a.length) && !!CONFIG_SERVICE.getFeatureValue("import_individual_nodes.enabled")) ||
+        false);
+}
+export async function importIndividualNodesInnerOnDragDrop(node, e) {
+    var _a, _b;
+    if (!((_a = node.widgets) === null || _a === void 0 ? void 0 : _a.length) || !CONFIG_SERVICE.getFeatureValue("import_individual_nodes.enabled")) {
+        return false;
+    }
+    const dynamicWidgetLengthNodes = [NodeTypesString.POWER_LORA_LOADER];
+    let handled = false;
+    const { workflow, prompt } = await tryToGetWorkflowDataFromEvent(e);
+    const exact = ((workflow === null || workflow === void 0 ? void 0 : workflow.nodes) || []).find((n) => {
+        var _a, _b;
+        return n.id === node.id &&
+            n.type === node.type &&
+            (dynamicWidgetLengthNodes.includes(node.type) ||
+                ((_a = n.widgets_values) === null || _a === void 0 ? void 0 : _a.length) === ((_b = node.widgets_values) === null || _b === void 0 ? void 0 : _b.length));
+    });
+    if (!exact) {
+        handled = !confirm("[rgthree-comfy] Could not find a matching node (same id & type) in the dropped workflow." +
+            " Would you like to continue with the default drop behaviour instead?");
+    }
+    else if (!((_b = exact.widgets_values) === null || _b === void 0 ? void 0 : _b.length)) {
+        handled = !confirm("[rgthree-comfy] Matching node found (same id & type) but there's no widgets to set." +
+            " Would you like to continue with the default drop behaviour instead?");
+    }
+    else if (confirm("[rgthree-comfy] Found a matching node (same id & type) in the dropped workflow." +
+        " Would you like to set the widget values?")) {
+        node.configure({
+            title: node.title,
+            widgets_values: [...((exact === null || exact === void 0 ? void 0 : exact.widgets_values) || [])],
+            mode: exact.mode,
+        });
+        handled = true;
+    }
+    return handled;
+}
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/image_comparer.js b/custom_nodes/rgthree-comfy/web/comfyui/image_comparer.js
new file mode 100644
index 0000000000000000000000000000000000000000..4c5d6b8d8f33f53387d8e385ba6d9ffe4ed4caba
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/image_comparer.js
@@ -0,0 +1,365 @@
+import { app } from "../../scripts/app.js";
+import { api } from "../../scripts/api.js";
+import { RgthreeBaseServerNode } from "./base_node.js";
+import { NodeTypesString } from "./constants.js";
+import { addConnectionLayoutSupport } from "./utils.js";
+import { RgthreeBaseWidget } from "./utils_widgets.js";
+import { measureText } from "./utils_canvas.js";
+function imageDataToUrl(data) {
+    return api.apiURL(`/view?filename=${encodeURIComponent(data.filename)}&type=${data.type}&subfolder=${data.subfolder}${app.getPreviewFormatParam()}${app.getRandParam()}`);
+}
+export class RgthreeImageComparer extends RgthreeBaseServerNode {
+    constructor(title = RgthreeImageComparer.title) {
+        super(title);
+        this.imageIndex = 0;
+        this.imgs = [];
+        this.serialize_widgets = true;
+        this.isPointerDown = false;
+        this.isPointerOver = false;
+        this.pointerOverPos = [0, 0];
+        this.canvasWidget = null;
+        this.properties["comparer_mode"] = "Slide";
+    }
+    onExecuted(output) {
+        var _a;
+        (_a = super.onExecuted) === null || _a === void 0 ? void 0 : _a.call(this, output);
+        if ("images" in output) {
+            this.canvasWidget.value = {
+                images: (output.images || []).map((d, i) => {
+                    return {
+                        name: i === 0 ? "A" : "B",
+                        selected: true,
+                        url: imageDataToUrl(d),
+                    };
+                }),
+            };
+        }
+        else {
+            output.a_images = output.a_images || [];
+            output.b_images = output.b_images || [];
+            const imagesToChoose = [];
+            const multiple = output.a_images.length + output.b_images.length > 2;
+            for (const [i, d] of output.a_images.entries()) {
+                imagesToChoose.push({
+                    name: output.a_images.length > 1 || multiple ? `A${i + 1}` : "A",
+                    selected: i === 0,
+                    url: imageDataToUrl(d),
+                });
+            }
+            for (const [i, d] of output.b_images.entries()) {
+                imagesToChoose.push({
+                    name: output.b_images.length > 1 || multiple ? `B${i + 1}` : "B",
+                    selected: i === 0,
+                    url: imageDataToUrl(d),
+                });
+            }
+            this.canvasWidget.value = { images: imagesToChoose };
+        }
+    }
+    onSerialize(serialised) {
+        var _a;
+        super.onSerialize && super.onSerialize(serialised);
+        for (let [index, widget_value] of (serialised.widgets_values || []).entries()) {
+            if (((_a = this.widgets[index]) === null || _a === void 0 ? void 0 : _a.name) === "rgthree_comparer") {
+                serialised.widgets_values[index] = this.widgets[index].value.images.map((d) => {
+                    d = { ...d };
+                    delete d.img;
+                    return d;
+                });
+            }
+        }
+    }
+    onNodeCreated() {
+        this.canvasWidget = this.addCustomWidget(new RgthreeImageComparerWidget("rgthree_comparer", this));
+        this.setSize(this.computeSize());
+        this.setDirtyCanvas(true, true);
+    }
+    setIsPointerDown(down = this.isPointerDown) {
+        const newIsDown = down && !!app.canvas.pointer_is_down;
+        if (this.isPointerDown !== newIsDown) {
+            this.isPointerDown = newIsDown;
+            this.setDirtyCanvas(true, false);
+        }
+        this.imageIndex = this.isPointerDown ? 1 : 0;
+        if (this.isPointerDown) {
+            requestAnimationFrame(() => {
+                this.setIsPointerDown();
+            });
+        }
+    }
+    onMouseDown(event, pos, canvas) {
+        var _a;
+        (_a = super.onMouseDown) === null || _a === void 0 ? void 0 : _a.call(this, event, pos, canvas);
+        this.setIsPointerDown(true);
+        return false;
+    }
+    onMouseEnter(event) {
+        var _a;
+        (_a = super.onMouseEnter) === null || _a === void 0 ? void 0 : _a.call(this, event);
+        this.setIsPointerDown(!!app.canvas.pointer_is_down);
+        this.isPointerOver = true;
+    }
+    onMouseLeave(event) {
+        var _a;
+        (_a = super.onMouseLeave) === null || _a === void 0 ? void 0 : _a.call(this, event);
+        this.setIsPointerDown(false);
+        this.isPointerOver = false;
+    }
+    onMouseMove(event, pos, canvas) {
+        var _a;
+        (_a = super.onMouseMove) === null || _a === void 0 ? void 0 : _a.call(this, event, pos, canvas);
+        this.pointerOverPos = [...pos];
+        this.imageIndex = this.pointerOverPos[0] > this.size[0] / 2 ? 1 : 0;
+    }
+    getHelp() {
+        return `
+      <p>
+        The ${this.type.replace("(rgthree)", "")} node compares two images on top of each other.
+      </p>
+      <ul>
+        <li>
+          <p>
+            <strong>Notes</strong>
+          </p>
+          <ul>
+            <li><p>
+              The right-click menu may show image options (Open Image, Save Image, etc.) which will
+              correspond to the first image (image_a) if clicked on the left-half of the node, or
+              the second image if on the right half of the node.
+            </p></li>
+          </ul>
+        </li>
+        <li>
+          <p>
+            <strong>Inputs</strong>
+          </p>
+          <ul>
+            <li><p>
+              <code>image_a</code> <i>Optional.</i> The first image to use to compare.
+              image_a.
+            </p></li>
+            <li><p>
+              <code>image_b</code> <i>Optional.</i> The second image to use to compare.
+            </p></li>
+            <li><p>
+              <b>Note</b> <code>image_a</code> and <code>image_b</code> work best when a single
+              image is provided. However, if each/either are a batch, you can choose which item
+              from each batch are chosen to be compared. If either <code>image_a</code> or
+              <code>image_b</code> are not provided, the node will choose the first two from the
+              provided input if it's a batch, otherwise only show the single image (just as
+              Preview Image would).
+            </p></li>
+          </ul>
+        </li>
+        <li>
+          <p>
+            <strong>Properties.</strong> You can change the following properties (by right-clicking
+            on the node, and select "Properties" or "Properties Panel" from the menu):
+          </p>
+          <ul>
+            <li><p>
+              <code>comparer_mode</code> - Choose between "Slide" and "Click". Defaults to "Slide".
+            </p></li>
+          </ul>
+        </li>
+      </ul>`;
+    }
+    static setUp(comfyClass, nodeData) {
+        RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, RgthreeImageComparer);
+    }
+    static onRegisteredForOverride(comfyClass) {
+        addConnectionLayoutSupport(RgthreeImageComparer, app, [
+            ["Left", "Right"],
+            ["Right", "Left"],
+        ]);
+        setTimeout(() => {
+            RgthreeImageComparer.category = comfyClass.category;
+        });
+    }
+}
+RgthreeImageComparer.title = NodeTypesString.IMAGE_COMPARER;
+RgthreeImageComparer.type = NodeTypesString.IMAGE_COMPARER;
+RgthreeImageComparer.comfyClass = NodeTypesString.IMAGE_COMPARER;
+RgthreeImageComparer["@comparer_mode"] = {
+    type: "combo",
+    values: ["Slide", "Click"],
+};
+class RgthreeImageComparerWidget extends RgthreeBaseWidget {
+    constructor(name, node) {
+        super(name);
+        this.type = "custom";
+        this.hitAreas = {};
+        this.selected = [];
+        this._value = { images: [] };
+        this.node = node;
+    }
+    set value(v) {
+        let cleanedVal;
+        if (Array.isArray(v)) {
+            cleanedVal = v.map((d, i) => {
+                if (!d || typeof d === "string") {
+                    d = { url: d, name: i == 0 ? "A" : "B", selected: true };
+                }
+                return d;
+            });
+        }
+        else {
+            cleanedVal = v.images || [];
+        }
+        if (cleanedVal.length > 2) {
+            const hasAAndB = cleanedVal.some((i) => i.name.startsWith("A")) &&
+                cleanedVal.some((i) => i.name.startsWith("B"));
+            if (!hasAAndB) {
+                cleanedVal = [cleanedVal[0], cleanedVal[1]];
+            }
+        }
+        let selected = cleanedVal.filter((d) => d.selected);
+        if (!selected.length && cleanedVal.length) {
+            cleanedVal[0].selected = true;
+        }
+        selected = cleanedVal.filter((d) => d.selected);
+        if (selected.length === 1 && cleanedVal.length > 1) {
+            cleanedVal.find((d) => !d.selected).selected = true;
+        }
+        this._value.images = cleanedVal;
+        selected = cleanedVal.filter((d) => d.selected);
+        this.setSelected(selected);
+    }
+    get value() {
+        return this._value;
+    }
+    setSelected(selected) {
+        this._value.images.forEach((d) => (d.selected = false));
+        this.node.imgs.length = 0;
+        for (const sel of selected) {
+            if (!sel.img) {
+                sel.img = new Image();
+                sel.img.src = sel.url;
+                this.node.imgs.push(sel.img);
+            }
+            sel.selected = true;
+        }
+        this.selected = selected;
+    }
+    draw(ctx, node, width, y) {
+        var _a;
+        this.hitAreas = {};
+        if (this.value.images.length > 2) {
+            ctx.textAlign = "left";
+            ctx.textBaseline = "top";
+            ctx.font = `14px Arial`;
+            const drawData = [];
+            const spacing = 5;
+            let x = 0;
+            for (const img of this.value.images) {
+                const width = measureText(ctx, img.name);
+                drawData.push({
+                    img,
+                    text: img.name,
+                    x,
+                    width: measureText(ctx, img.name),
+                });
+                x += width + spacing;
+            }
+            x = (node.size[0] - (x - spacing)) / 2;
+            for (const d of drawData) {
+                ctx.fillStyle = d.img.selected ? "rgba(180, 180, 180, 1)" : "rgba(180, 180, 180, 0.5)";
+                ctx.fillText(d.text, x, y);
+                this.hitAreas[d.text] = {
+                    bounds: [x, y, d.width, 14],
+                    data: d.img,
+                    onDown: this.onSelectionDown,
+                };
+                x += d.width + spacing;
+            }
+            y += 20;
+        }
+        if (((_a = node.properties) === null || _a === void 0 ? void 0 : _a["comparer_mode"]) === "Click") {
+            this.drawImage(ctx, this.selected[this.node.isPointerDown ? 1 : 0], y);
+        }
+        else {
+            this.drawImage(ctx, this.selected[0], y);
+            if (node.isPointerOver) {
+                this.drawImage(ctx, this.selected[1], y, this.node.pointerOverPos[0]);
+            }
+        }
+    }
+    onSelectionDown(event, pos, node, bounds) {
+        const selected = [...this.selected];
+        if (bounds === null || bounds === void 0 ? void 0 : bounds.data.name.startsWith("A")) {
+            selected[0] = bounds.data;
+        }
+        else if (bounds === null || bounds === void 0 ? void 0 : bounds.data.name.startsWith("B")) {
+            selected[1] = bounds.data;
+        }
+        this.setSelected(selected);
+    }
+    drawImage(ctx, image, y, cropX) {
+        var _a, _b;
+        if (!((_a = image === null || image === void 0 ? void 0 : image.img) === null || _a === void 0 ? void 0 : _a.naturalWidth) || !((_b = image === null || image === void 0 ? void 0 : image.img) === null || _b === void 0 ? void 0 : _b.naturalHeight)) {
+            return;
+        }
+        let [nodeWidth, nodeHeight] = this.node.size;
+        const imageAspect = (image === null || image === void 0 ? void 0 : image.img.naturalWidth) / (image === null || image === void 0 ? void 0 : image.img.naturalHeight);
+        let height = nodeHeight - y;
+        const widgetAspect = nodeWidth / height;
+        let targetWidth, targetHeight;
+        let offsetX = 0;
+        if (imageAspect > widgetAspect) {
+            targetWidth = nodeWidth;
+            targetHeight = nodeWidth / imageAspect;
+        }
+        else {
+            targetHeight = height;
+            targetWidth = height * imageAspect;
+            offsetX = (nodeWidth - targetWidth) / 2;
+        }
+        const widthMultiplier = (image === null || image === void 0 ? void 0 : image.img.naturalWidth) / targetWidth;
+        const sourceX = 0;
+        const sourceY = 0;
+        const sourceWidth = cropX != null ? (cropX - offsetX) * widthMultiplier : image === null || image === void 0 ? void 0 : image.img.naturalWidth;
+        const sourceHeight = image === null || image === void 0 ? void 0 : image.img.naturalHeight;
+        const destX = (nodeWidth - targetWidth) / 2;
+        const destY = y + (height - targetHeight) / 2;
+        const destWidth = cropX != null ? cropX - offsetX : targetWidth;
+        const destHeight = targetHeight;
+        ctx.save();
+        ctx.beginPath();
+        let globalCompositeOperation = ctx.globalCompositeOperation;
+        if (cropX) {
+            ctx.rect(destX, destY, destWidth, destHeight);
+            ctx.clip();
+        }
+        ctx.drawImage(image === null || image === void 0 ? void 0 : image.img, sourceX, sourceY, sourceWidth, sourceHeight, destX, destY, destWidth, destHeight);
+        if (cropX != null && cropX >= (nodeWidth - targetWidth) / 2 && cropX <= targetWidth + offsetX) {
+            ctx.beginPath();
+            ctx.moveTo(cropX, destY);
+            ctx.lineTo(cropX, destY + destHeight);
+            ctx.globalCompositeOperation = "difference";
+            ctx.strokeStyle = "rgba(255,255,255, 1)";
+            ctx.stroke();
+        }
+        ctx.globalCompositeOperation = globalCompositeOperation;
+        ctx.restore();
+    }
+    computeSize(width) {
+        return [width, 20];
+    }
+    serializeValue(node, index) {
+        const v = [];
+        for (const data of this._value.images) {
+            const d = { ...data };
+            delete d.img;
+            v.push(d);
+        }
+        return { images: v };
+    }
+}
+app.registerExtension({
+    name: "rgthree.ImageComparer",
+    async beforeRegisterNodeDef(nodeType, nodeData) {
+        if (nodeData.name === RgthreeImageComparer.type) {
+            RgthreeImageComparer.setUp(nodeType, nodeData);
+        }
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/image_inset_crop.js b/custom_nodes/rgthree-comfy/web/comfyui/image_inset_crop.js
new file mode 100644
index 0000000000000000000000000000000000000000..186e5c14cdcfc784675493ad1ff781985548def0
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/image_inset_crop.js
@@ -0,0 +1,59 @@
+import { app } from "../../scripts/app.js";
+import { RgthreeBaseServerNode } from "./base_node.js";
+import { NodeTypesString } from "./constants.js";
+class ImageInsetCrop extends RgthreeBaseServerNode {
+    constructor(title = ImageInsetCrop.title) {
+        super(title);
+    }
+    onAdded(graph) {
+        const measurementWidget = this.widgets[0];
+        let callback = measurementWidget.callback;
+        measurementWidget.callback = (...args) => {
+            this.setWidgetStep();
+            callback && callback.apply(measurementWidget, [...args]);
+        };
+        this.setWidgetStep();
+    }
+    configure(info) {
+        super.configure(info);
+        this.setWidgetStep();
+    }
+    setWidgetStep() {
+        const measurementWidget = this.widgets[0];
+        for (let i = 1; i <= 4; i++) {
+            if (measurementWidget.value === "Pixels") {
+                this.widgets[i].options.step = 80;
+                this.widgets[i].options.max = ImageInsetCrop.maxResolution;
+            }
+            else {
+                this.widgets[i].options.step = 10;
+                this.widgets[i].options.max = 99;
+            }
+        }
+    }
+    async handleAction(action) {
+        if (action === "Reset Crop") {
+            for (const widget of this.widgets) {
+                if (["left", "right", "top", "bottom"].includes(widget.name)) {
+                    widget.value = 0;
+                }
+            }
+        }
+    }
+    static setUp(comfyClass, nodeData) {
+        RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, ImageInsetCrop);
+    }
+}
+ImageInsetCrop.title = NodeTypesString.IMAGE_INSET_CROP;
+ImageInsetCrop.type = NodeTypesString.IMAGE_INSET_CROP;
+ImageInsetCrop.comfyClass = NodeTypesString.IMAGE_INSET_CROP;
+ImageInsetCrop.exposedActions = ["Reset Crop"];
+ImageInsetCrop.maxResolution = 8192;
+app.registerExtension({
+    name: "rgthree.ImageInsetCrop",
+    async beforeRegisterNodeDef(nodeType, nodeData) {
+        if (nodeData.name === NodeTypesString.IMAGE_INSET_CROP) {
+            ImageInsetCrop.setUp(nodeType, nodeData);
+        }
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/image_or_latent_size.js b/custom_nodes/rgthree-comfy/web/comfyui/image_or_latent_size.js
new file mode 100644
index 0000000000000000000000000000000000000000..3e12fc620295e6394c9cd008c41fa2d2c1f5ebb5
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/image_or_latent_size.js
@@ -0,0 +1,35 @@
+import { app } from "../../scripts/app.js";
+import { RgthreeBaseServerNode } from "./base_node.js";
+import { NodeTypesString } from "./constants.js";
+class RgthreeImageOrLatentSize extends RgthreeBaseServerNode {
+    static setUp(comfyClass, nodeData) {
+        RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, NODE_CLASS);
+    }
+    constructor(title = NODE_CLASS.title) {
+        super(title);
+    }
+    onNodeCreated() {
+        var _a;
+        (_a = super.onNodeCreated) === null || _a === void 0 ? void 0 : _a.call(this);
+        this.addInput("input", ["IMAGE", "LATENT", "MASK"]);
+    }
+    configure(info) {
+        var _a;
+        super.configure(info);
+        if ((_a = this.inputs) === null || _a === void 0 ? void 0 : _a.length) {
+            this.inputs[0].type = ["IMAGE", "LATENT", "MASK"];
+        }
+    }
+}
+RgthreeImageOrLatentSize.title = NodeTypesString.IMAGE_OR_LATENT_SIZE;
+RgthreeImageOrLatentSize.type = NodeTypesString.IMAGE_OR_LATENT_SIZE;
+RgthreeImageOrLatentSize.comfyClass = NodeTypesString.IMAGE_OR_LATENT_SIZE;
+const NODE_CLASS = RgthreeImageOrLatentSize;
+app.registerExtension({
+    name: "rgthree.ImageOrLatentSize",
+    async beforeRegisterNodeDef(nodeType, nodeData) {
+        if (nodeData.name === NODE_CLASS.type) {
+            NODE_CLASS.setUp(nodeType, nodeData);
+        }
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/label.js b/custom_nodes/rgthree-comfy/web/comfyui/label.js
new file mode 100644
index 0000000000000000000000000000000000000000..b6dedad371197d26c219ea1b21342b0590f88ad6
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/label.js
@@ -0,0 +1,163 @@
+import { app } from "../../scripts/app.js";
+import { RgthreeBaseVirtualNode } from "./base_node.js";
+import { NodeTypesString } from "./constants.js";
+import { rgthree } from "./rgthree.js";
+export class Label extends RgthreeBaseVirtualNode {
+    constructor(title = Label.title) {
+        super(title);
+        this.comfyClass = NodeTypesString.LABEL;
+        this.resizable = false;
+        this.properties["fontSize"] = 12;
+        this.properties["fontFamily"] = "Arial";
+        this.properties["fontColor"] = "#ffffff";
+        this.properties["textAlign"] = "left";
+        this.properties["backgroundColor"] = "transparent";
+        this.properties["padding"] = 0;
+        this.properties["borderRadius"] = 0;
+        this.properties["angle"] = 0;
+        this.color = "#fff0";
+        this.bgcolor = "#fff0";
+        this.onConstructed();
+    }
+    draw(ctx) {
+        var _a, _b, _c, _d;
+        this.flags = this.flags || {};
+        this.flags.allow_interaction = !this.flags.pinned;
+        ctx.save();
+        this.color = "#fff0";
+        this.bgcolor = "#fff0";
+        const fontColor = this.properties["fontColor"] || "#ffffff";
+        const backgroundColor = this.properties["backgroundColor"] || "";
+        ctx.font = `${Math.max(this.properties["fontSize"] || 0, 1)}px ${(_a = this.properties["fontFamily"]) !== null && _a !== void 0 ? _a : "Arial"}`;
+        const padding = (_b = Number(this.properties["padding"])) !== null && _b !== void 0 ? _b : 0;
+        const processedTitle = ((_c = this.title) !== null && _c !== void 0 ? _c : "").replace(/\\n/g, "\n").replace(/\n*$/, "");
+        const lines = processedTitle.split("\n");
+        const maxWidth = Math.max(...lines.map((s) => ctx.measureText(s).width));
+        this.size[0] = maxWidth + padding * 2;
+        this.size[1] = this.properties["fontSize"] * lines.length + padding * 2;
+        const angleDeg = parseInt(String((_d = this.properties["angle"]) !== null && _d !== void 0 ? _d : 0)) || 0;
+        if (angleDeg) {
+            const cx = this.size[0] / 2;
+            const cy = this.size[1] / 2;
+            ctx.translate(cx, cy);
+            ctx.rotate((angleDeg * Math.PI) / 180);
+            ctx.translate(-cx, -cy);
+        }
+        if (backgroundColor) {
+            ctx.beginPath();
+            const borderRadius = Number(this.properties["borderRadius"]) || 0;
+            ctx.roundRect(0, 0, this.size[0], this.size[1], [borderRadius]);
+            ctx.fillStyle = backgroundColor;
+            ctx.fill();
+        }
+        ctx.textAlign = "left";
+        let textX = padding;
+        if (this.properties["textAlign"] === "center") {
+            ctx.textAlign = "center";
+            textX = this.size[0] / 2;
+        }
+        else if (this.properties["textAlign"] === "right") {
+            ctx.textAlign = "right";
+            textX = this.size[0] - padding;
+        }
+        ctx.textBaseline = "top";
+        ctx.fillStyle = fontColor;
+        let currentY = padding;
+        for (let i = 0; i < lines.length; i++) {
+            ctx.fillText(lines[i] || " ", textX, currentY);
+            currentY += this.properties["fontSize"];
+        }
+        ctx.restore();
+    }
+    onDblClick(event, pos, canvas) {
+        LGraphCanvas.active_canvas.showShowNodePanel(this);
+    }
+    onShowCustomPanelInfo(panel) {
+        var _a, _b;
+        (_a = panel.querySelector('div.property[data-property="Mode"]')) === null || _a === void 0 ? void 0 : _a.remove();
+        (_b = panel.querySelector('div.property[data-property="Color"]')) === null || _b === void 0 ? void 0 : _b.remove();
+    }
+    inResizeCorner(x, y) {
+        return this.resizable;
+    }
+    getHelp() {
+        return `
+      <p>
+        The rgthree-comfy ${this.type.replace("(rgthree)", "")} node allows you to add a floating
+        label to your workflow.
+      </p>
+      <p>
+        The text shown is the "Title" of the node and you can adjust the the font size, font family,
+        font color, text alignment as well as a background color, padding, and background border
+        radius from the node's properties. You can double-click the node to open the properties
+        panel.
+      <p>
+      <ul>
+        <li>
+          <p>
+            <strong>Pro tip #1:</strong> You can add multiline text from the properties panel
+            <i>(because ComfyUI let's you shift + enter there, only)</i>.
+          </p>
+        </li>
+        <li>
+          <p>
+            <strong>Pro tip #2:</strong> You can use ComfyUI's native "pin" option in the
+            right-click menu to make the label stick to the workflow and clicks to "go through".
+            You can right-click at any time to unpin.
+          </p>
+        </li>
+        <li>
+          <p>
+            <strong>Pro tip #3:</strong> Color values are hexidecimal strings, like "#FFFFFF" for
+            white, or "#660000" for dark red. You can supply a 7th & 8th value (or 5th if using
+            shorthand) to create a transluscent color. For instance, "#FFFFFF88" is semi-transparent
+            white.
+          </p>
+        </li>
+      </ul>`;
+    }
+}
+Label.type = NodeTypesString.LABEL;
+Label.title = NodeTypesString.LABEL;
+Label.title_mode = LiteGraph.NO_TITLE;
+Label.collapsable = false;
+Label["@fontSize"] = { type: "number" };
+Label["@fontFamily"] = { type: "string" };
+Label["@fontColor"] = { type: "string" };
+Label["@textAlign"] = { type: "combo", values: ["left", "center", "right"] };
+Label["@backgroundColor"] = { type: "string" };
+Label["@padding"] = { type: "number" };
+Label["@borderRadius"] = { type: "number" };
+Label["@angle"] = { type: "number" };
+const oldDrawNode = LGraphCanvas.prototype.drawNode;
+LGraphCanvas.prototype.drawNode = function (node, ctx) {
+    if (node.constructor === Label.prototype.constructor) {
+        node.bgcolor = "transparent";
+        node.color = "transparent";
+        const v = oldDrawNode.apply(this, arguments);
+        node.draw(ctx);
+        return v;
+    }
+    const v = oldDrawNode.apply(this, arguments);
+    return v;
+};
+const oldGetNodeOnPos = LGraph.prototype.getNodeOnPos;
+LGraph.prototype.getNodeOnPos = function (x, y, nodes_list) {
+    var _a, _b;
+    if (nodes_list &&
+        rgthree.processingMouseDown &&
+        ((_a = rgthree.lastCanvasMouseEvent) === null || _a === void 0 ? void 0 : _a.type.includes("down")) &&
+        ((_b = rgthree.lastCanvasMouseEvent) === null || _b === void 0 ? void 0 : _b.which) === 1) {
+        let isDoubleClick = LiteGraph.getTime() - LGraphCanvas.active_canvas.last_mouseclick < 300;
+        if (!isDoubleClick) {
+            nodes_list = [...nodes_list].filter((n) => { var _a; return !(n instanceof Label) || !((_a = n.flags) === null || _a === void 0 ? void 0 : _a.pinned); });
+        }
+    }
+    return oldGetNodeOnPos.apply(this, [x, y, nodes_list]);
+};
+app.registerExtension({
+    name: "rgthree.Label",
+    registerCustomNodes() {
+        Label.setUp();
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/menu_auto_nest.js b/custom_nodes/rgthree-comfy/web/comfyui/menu_auto_nest.js
new file mode 100644
index 0000000000000000000000000000000000000000..d720ee8fad5b86900c2123b95b2d31b0f5247534
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/menu_auto_nest.js
@@ -0,0 +1,113 @@
+import { app } from "../../scripts/app.js";
+import { rgthree } from "./rgthree.js";
+import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js";
+const SPECIAL_ENTRIES = [/^(CHOOSE|NONE|DISABLE|OPEN)(\s|$)/i, /^\p{Extended_Pictographic}/gu];
+app.registerExtension({
+    name: "rgthree.ContextMenuAutoNest",
+    async setup() {
+        const logger = rgthree.newLogSession("[ContextMenuAutoNest]");
+        const existingContextMenu = LiteGraph.ContextMenu;
+        LiteGraph.ContextMenu = function (values, options) {
+            var _a, _b, _c, _d, _e, _f, _g, _h;
+            const threshold = CONFIG_SERVICE.getConfigValue("features.menu_auto_nest.threshold", 20);
+            const enabled = CONFIG_SERVICE.getConfigValue("features.menu_auto_nest.subdirs", false);
+            let incompatible = !enabled || !!((_a = options === null || options === void 0 ? void 0 : options.extra) === null || _a === void 0 ? void 0 : _a.rgthree_doNotNest);
+            if (!incompatible) {
+                if (values.length <= threshold) {
+                    incompatible = `Skipping context menu auto nesting b/c threshold is not met (${threshold})`;
+                }
+                if (!((_c = (_b = options.parentMenu) === null || _b === void 0 ? void 0 : _b.options) === null || _c === void 0 ? void 0 : _c.rgthree_originalCallback)) {
+                    if (!(options === null || options === void 0 ? void 0 : options.callback)) {
+                        incompatible = `Skipping context menu auto nesting b/c a callback was expected.`;
+                    }
+                    else if (values.some((i) => typeof i !== "string")) {
+                        incompatible = `Skipping context menu auto nesting b/c not all values were strings.`;
+                    }
+                }
+            }
+            if (incompatible) {
+                if (enabled) {
+                    const [n, v] = logger.infoParts("Skipping context menu auto nesting for incompatible menu.");
+                    (_d = console[n]) === null || _d === void 0 ? void 0 : _d.call(console, ...v);
+                }
+                return existingContextMenu.apply(this, [...arguments]);
+            }
+            const folders = {};
+            const specialOps = [];
+            const folderless = [];
+            for (const value of values) {
+                if (!value) {
+                    folderless.push(value);
+                    continue;
+                }
+                const newValue = typeof value === "string" ? { content: value } : Object.assign({}, value);
+                newValue.rgthree_originalValue = value.rgthree_originalValue || value;
+                const valueContent = newValue.content || "";
+                const splitBy = valueContent.indexOf("/") > -1 ? "/" : "\\";
+                const valueSplit = valueContent.split(splitBy);
+                if (valueSplit.length > 1) {
+                    const key = valueSplit.shift();
+                    newValue.content = valueSplit.join(splitBy);
+                    folders[key] = folders[key] || [];
+                    folders[key].push(newValue);
+                }
+                else if (SPECIAL_ENTRIES.some((r) => r.test(valueContent))) {
+                    specialOps.push(newValue);
+                }
+                else {
+                    folderless.push(newValue);
+                }
+            }
+            const foldersCount = Object.values(folders).length;
+            if (foldersCount > 0) {
+                options.rgthree_originalCallback =
+                    options.rgthree_originalCallback ||
+                        ((_f = (_e = options.parentMenu) === null || _e === void 0 ? void 0 : _e.options) === null || _f === void 0 ? void 0 : _f.rgthree_originalCallback) ||
+                        options.callback;
+                const oldCallback = options === null || options === void 0 ? void 0 : options.rgthree_originalCallback;
+                options.callback = undefined;
+                const newCallback = (item, options, event, parentMenu, node) => {
+                    oldCallback === null || oldCallback === void 0 ? void 0 : oldCallback(item === null || item === void 0 ? void 0 : item.rgthree_originalValue, options, event, undefined, node);
+                };
+                const [n, v] = logger.infoParts(`Nested folders found (${foldersCount}).`);
+                (_g = console[n]) === null || _g === void 0 ? void 0 : _g.call(console, ...v);
+                const newValues = [];
+                for (const [folderName, folderValues] of Object.entries(folders)) {
+                    newValues.push({
+                        content: `📁 ${folderName}`,
+                        has_submenu: true,
+                        callback: () => {
+                        },
+                        submenu: {
+                            options: folderValues.map((value) => {
+                                value.callback = newCallback;
+                                return value;
+                            }),
+                        },
+                    });
+                }
+                values = [].concat(specialOps.map((f) => {
+                    if (typeof f === "string") {
+                        f = { content: f };
+                    }
+                    f.callback = newCallback;
+                    return f;
+                }), newValues, folderless.map((f) => {
+                    if (typeof f === "string") {
+                        f = { content: f };
+                    }
+                    f.callback = newCallback;
+                    return f;
+                }));
+            }
+            if (options.scale == null) {
+                options.scale = Math.max(((_h = app.canvas.ds) === null || _h === void 0 ? void 0 : _h.scale) || 1, 1);
+            }
+            const oldCtrResponse = existingContextMenu.call(this, values, options);
+            if (oldCtrResponse === null || oldCtrResponse === void 0 ? void 0 : oldCtrResponse.constructor) {
+                oldCtrResponse.constructor = LiteGraph.ContextMenu;
+            }
+            return this;
+        };
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/menu_copy_image.js b/custom_nodes/rgthree-comfy/web/comfyui/menu_copy_image.js
new file mode 100644
index 0000000000000000000000000000000000000000..30d034f00dccda849f7868e53d52cd61d48ce66f
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/menu_copy_image.js
@@ -0,0 +1,62 @@
+import { app } from "../../scripts/app.js";
+const clipboardSupportedPromise = new Promise(async (resolve) => {
+    try {
+        const result = await navigator.permissions.query({ name: "clipboard-write" });
+        resolve(result.state === "granted");
+        return;
+    }
+    catch (e) {
+        try {
+            if (!navigator.clipboard.write) {
+                throw new Error();
+            }
+            new ClipboardItem({ "image/png": new Blob([], { type: "image/png" }) });
+            resolve(true);
+            return;
+        }
+        catch (e) {
+            resolve(false);
+        }
+    }
+});
+app.registerExtension({
+    name: "rgthree.CopyImageToClipboard",
+    async beforeRegisterNodeDef(nodeType, nodeData) {
+        if (nodeData.name.toLowerCase().includes("image")) {
+            if (await clipboardSupportedPromise) {
+                const getExtraMenuOptions = nodeType.prototype.getExtraMenuOptions;
+                nodeType.prototype.getExtraMenuOptions = function (canvas, options) {
+                    var _a, _b;
+                    options = (_a = getExtraMenuOptions === null || getExtraMenuOptions === void 0 ? void 0 : getExtraMenuOptions.call(this, canvas, options)) !== null && _a !== void 0 ? _a : options;
+                    if ((_b = this.imgs) === null || _b === void 0 ? void 0 : _b.length) {
+                        let img = this.imgs[this.imageIndex || 0] || this.imgs[this.overIndex || 0] || this.imgs[0];
+                        const foundIdx = options.findIndex((option) => { var _a; return (_a = option === null || option === void 0 ? void 0 : option.content) === null || _a === void 0 ? void 0 : _a.includes("Copy Image"); });
+                        if (img && foundIdx === -1) {
+                            const menuItem = {
+                                content: "Copy Image (rgthree)",
+                                callback: () => {
+                                    const canvas = document.createElement("canvas");
+                                    const ctx = canvas.getContext("2d");
+                                    canvas.width = img.naturalWidth;
+                                    canvas.height = img.naturalHeight;
+                                    ctx.drawImage(img, 0, 0, img.naturalWidth, img.naturalHeight);
+                                    canvas.toBlob((blob) => {
+                                        navigator.clipboard.write([new ClipboardItem({ "image/png": blob })]);
+                                    });
+                                },
+                            };
+                            let idx = options.findIndex((option) => { var _a; return (_a = option === null || option === void 0 ? void 0 : option.content) === null || _a === void 0 ? void 0 : _a.includes("Open Image"); }) + 1;
+                            if (idx != null) {
+                                options.splice(idx, 0, menuItem);
+                            }
+                            else {
+                                options.unshift(menuItem);
+                            }
+                        }
+                    }
+                    return [];
+                };
+            }
+        }
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/menu_queue_node.js b/custom_nodes/rgthree-comfy/web/comfyui/menu_queue_node.js
new file mode 100644
index 0000000000000000000000000000000000000000..a53250a496f5a82505f463f389b1feeeb7f76dac
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/menu_queue_node.js
@@ -0,0 +1,65 @@
+import { app } from "../../scripts/app.js";
+import { rgthree } from "./rgthree.js";
+import { getGroupNodes, getOutputNodes } from "./utils.js";
+import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js";
+function showQueueNodesMenuIfOutputNodesAreSelected(existingOptions) {
+    if (CONFIG_SERVICE.getConfigValue("features.menu_queue_selected_nodes") === false) {
+        return;
+    }
+    const outputNodes = getOutputNodes(Object.values(app.canvas.selected_nodes));
+    const menuItem = {
+        content: `Queue Selected Output Nodes (rgthree) &nbsp;`,
+        className: "rgthree-contextmenu-item",
+        callback: () => {
+            rgthree.queueOutputNodes(outputNodes);
+        },
+        disabled: !outputNodes.length,
+    };
+    let idx = existingOptions.findIndex((o) => (o === null || o === void 0 ? void 0 : o.content) === "Outputs") + 1;
+    idx = idx || existingOptions.findIndex((o) => (o === null || o === void 0 ? void 0 : o.content) === "Align") + 1;
+    idx = idx || 3;
+    existingOptions.splice(idx, 0, menuItem);
+}
+function showQueueGroupNodesMenuIfGroupIsSelected(existingOptions) {
+    if (CONFIG_SERVICE.getConfigValue("features.menu_queue_selected_nodes") === false) {
+        return;
+    }
+    const group = rgthree.lastCanvasMouseEvent &&
+        (app.canvas.getCurrentGraph() || app.graph).getGroupOnPos(rgthree.lastCanvasMouseEvent.canvasX, rgthree.lastCanvasMouseEvent.canvasY);
+    const outputNodes = (group && getOutputNodes(getGroupNodes(group))) || null;
+    const menuItem = {
+        content: `Queue Group Output Nodes (rgthree) &nbsp;`,
+        className: "rgthree-contextmenu-item",
+        callback: () => {
+            outputNodes && rgthree.queueOutputNodes(outputNodes);
+        },
+        disabled: !(outputNodes === null || outputNodes === void 0 ? void 0 : outputNodes.length),
+    };
+    let idx = existingOptions.findIndex((o) => { var _a; return (_a = o === null || o === void 0 ? void 0 : o.content) === null || _a === void 0 ? void 0 : _a.startsWith("Queue Selected "); }) + 1;
+    idx = idx || existingOptions.findIndex((o) => (o === null || o === void 0 ? void 0 : o.content) === "Outputs") + 1;
+    idx = idx || existingOptions.findIndex((o) => (o === null || o === void 0 ? void 0 : o.content) === "Align") + 1;
+    idx = idx || 3;
+    existingOptions.splice(idx, 0, menuItem);
+}
+app.registerExtension({
+    name: "rgthree.QueueNode",
+    async beforeRegisterNodeDef(nodeType, nodeData) {
+        const getExtraMenuOptions = nodeType.prototype.getExtraMenuOptions;
+        nodeType.prototype.getExtraMenuOptions = function (canvas, options) {
+            var _a;
+            const extraOptions = (_a = getExtraMenuOptions === null || getExtraMenuOptions === void 0 ? void 0 : getExtraMenuOptions.call(this, canvas, options)) !== null && _a !== void 0 ? _a : [];
+            showQueueNodesMenuIfOutputNodesAreSelected(options);
+            showQueueGroupNodesMenuIfGroupIsSelected(options);
+            return extraOptions;
+        };
+    },
+    async setup() {
+        const getCanvasMenuOptions = LGraphCanvas.prototype.getCanvasMenuOptions;
+        LGraphCanvas.prototype.getCanvasMenuOptions = function (...args) {
+            const options = getCanvasMenuOptions.apply(this, [...args]);
+            showQueueNodesMenuIfOutputNodesAreSelected(options);
+            showQueueGroupNodesMenuIfGroupIsSelected(options);
+            return options;
+        };
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/muter.js b/custom_nodes/rgthree-comfy/web/comfyui/muter.js
new file mode 100644
index 0000000000000000000000000000000000000000..f18210f61bfb2c1ba30ae871c61411b1435744ef
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/muter.js
@@ -0,0 +1,45 @@
+import { app } from "../../scripts/app.js";
+import { BaseNodeModeChanger } from "./base_node_mode_changer.js";
+import { NodeTypesString } from "./constants.js";
+const MODE_MUTE = 2;
+const MODE_ALWAYS = 0;
+class MuterNode extends BaseNodeModeChanger {
+    constructor(title = MuterNode.title) {
+        super(title);
+        this.comfyClass = NodeTypesString.FAST_MUTER;
+        this.modeOn = MODE_ALWAYS;
+        this.modeOff = MODE_MUTE;
+        this.onConstructed();
+    }
+    async handleAction(action) {
+        if (action === "Mute all") {
+            for (const widget of this.widgets) {
+                this.forceWidgetOff(widget, true);
+            }
+        }
+        else if (action === "Enable all") {
+            for (const widget of this.widgets) {
+                this.forceWidgetOn(widget, true);
+            }
+        }
+        else if (action === "Toggle all") {
+            for (const widget of this.widgets) {
+                this.forceWidgetToggle(widget, true);
+            }
+        }
+    }
+}
+MuterNode.exposedActions = ["Mute all", "Enable all", "Toggle all"];
+MuterNode.type = NodeTypesString.FAST_MUTER;
+MuterNode.title = NodeTypesString.FAST_MUTER;
+app.registerExtension({
+    name: "rgthree.Muter",
+    registerCustomNodes() {
+        MuterNode.setUp();
+    },
+    loadedGraphNode(node) {
+        if (node.type == MuterNode.title) {
+            node._tempWidth = node.size[0];
+        }
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/node_collector.js b/custom_nodes/rgthree-comfy/web/comfyui/node_collector.js
new file mode 100644
index 0000000000000000000000000000000000000000..477bf8689a8c2961faaeeac5f7210a1e549b35c3
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/node_collector.js
@@ -0,0 +1,113 @@
+import { app } from "../../scripts/app.js";
+import { addConnectionLayoutSupport } from "./utils.js";
+import { wait } from "../../rgthree/common/shared_utils.js";
+import { ComfyWidgets } from "../../scripts/widgets.js";
+import { BaseCollectorNode } from "./base_node_collector.js";
+import { NodeTypesString } from "./constants.js";
+class CollectorNode extends BaseCollectorNode {
+    constructor(title = CollectorNode.title) {
+        super(title);
+        this.comfyClass = NodeTypesString.NODE_COLLECTOR;
+        this.onConstructed();
+    }
+    onConstructed() {
+        this.addOutput("Output", "*");
+        return super.onConstructed();
+    }
+}
+CollectorNode.type = NodeTypesString.NODE_COLLECTOR;
+CollectorNode.title = NodeTypesString.NODE_COLLECTOR;
+class CombinerNode extends CollectorNode {
+    constructor(title = CombinerNode.title) {
+        super(title);
+        const note = ComfyWidgets["STRING"](this, "last_seed", ["STRING", { multiline: true }], app).widget;
+        note.inputEl.value =
+            'The Node Combiner has been renamed to Node Collector. You can right-click and select "Update to Node Collector" to attempt to automatically update.';
+        note.inputEl.readOnly = true;
+        note.inputEl.style.backgroundColor = "#332222";
+        note.inputEl.style.fontWeight = "bold";
+        note.inputEl.style.fontStyle = "italic";
+        note.inputEl.style.opacity = "0.8";
+        this.getExtraMenuOptions = (canvas, options) => {
+            options.splice(options.length - 1, 0, {
+                content: "‼️ Update to Node Collector",
+                callback: (_value, _options, _event, _parentMenu, _node) => {
+                    updateCombinerToCollector(this);
+                },
+            });
+            return [];
+        };
+    }
+    configure(info) {
+        super.configure(info);
+        if (this.title != CombinerNode.title && !this.title.startsWith("‼️")) {
+            this.title = "‼️ " + this.title;
+        }
+    }
+}
+CombinerNode.legacyType = "Node Combiner (rgthree)";
+CombinerNode.title = "‼️ Node Combiner [DEPRECATED]";
+async function updateCombinerToCollector(node) {
+    if (node.type === CombinerNode.legacyType) {
+        const newNode = new CollectorNode();
+        if (node.title != CombinerNode.title) {
+            newNode.title = node.title.replace("‼️ ", "");
+        }
+        newNode.pos = [...node.pos];
+        newNode.size = [...node.size];
+        newNode.properties = { ...node.properties };
+        const links = [];
+        const graph = (node.graph || app.graph);
+        for (const [index, output] of node.outputs.entries()) {
+            for (const linkId of output.links || []) {
+                const link = graph.links[linkId];
+                if (!link)
+                    continue;
+                const targetNode = graph.getNodeById(link.target_id);
+                links.push({ node: newNode, slot: index, targetNode, targetSlot: link.target_slot });
+            }
+        }
+        for (const [index, input] of node.inputs.entries()) {
+            const linkId = input.link;
+            if (linkId) {
+                const link = graph.links[linkId];
+                const originNode = graph.getNodeById(link.origin_id);
+                links.push({
+                    node: originNode,
+                    slot: link.origin_slot,
+                    targetNode: newNode,
+                    targetSlot: index,
+                });
+            }
+        }
+        graph.add(newNode);
+        await wait();
+        for (const link of links) {
+            link.node.connect(link.slot, link.targetNode, link.targetSlot);
+        }
+        await wait();
+        graph.remove(node);
+    }
+}
+app.registerExtension({
+    name: "rgthree.NodeCollector",
+    registerCustomNodes() {
+        addConnectionLayoutSupport(CollectorNode, app, [
+            ["Left", "Right"],
+            ["Right", "Left"],
+        ]);
+        LiteGraph.registerNodeType(CollectorNode.title, CollectorNode);
+        CollectorNode.category = CollectorNode._category;
+    },
+});
+app.registerExtension({
+    name: "rgthree.NodeCombiner",
+    registerCustomNodes() {
+        addConnectionLayoutSupport(CombinerNode, app, [
+            ["Left", "Right"],
+            ["Right", "Left"],
+        ]);
+        LiteGraph.registerNodeType(CombinerNode.legacyType, CombinerNode);
+        CombinerNode.category = CombinerNode._category;
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/node_mode_relay.js b/custom_nodes/rgthree-comfy/web/comfyui/node_mode_relay.js
new file mode 100644
index 0000000000000000000000000000000000000000..5daeffbe0f1d1159c151719bb2a516c4facb3214
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/node_mode_relay.js
@@ -0,0 +1,205 @@
+import { app } from "../../scripts/app.js";
+import { PassThroughFollowing, addConnectionLayoutSupport, changeModeOfNodes, getConnectedInputNodesAndFilterPassThroughs, getConnectedOutputNodesAndFilterPassThroughs, } from "./utils.js";
+import { wait } from "../../rgthree/common/shared_utils.js";
+import { BaseCollectorNode } from "./base_node_collector.js";
+import { NodeTypesString, stripRgthree } from "./constants.js";
+import { fitString } from "./utils_canvas.js";
+import { rgthree } from "./rgthree.js";
+const MODE_ALWAYS = 0;
+const MODE_MUTE = 2;
+const MODE_BYPASS = 4;
+const MODE_REPEATS = [MODE_MUTE, MODE_BYPASS];
+const MODE_NOTHING = -99;
+const MODE_TO_OPTION = new Map([
+    [MODE_ALWAYS, "ACTIVE"],
+    [MODE_MUTE, "MUTE"],
+    [MODE_BYPASS, "BYPASS"],
+    [MODE_NOTHING, "NOTHING"],
+]);
+const OPTION_TO_MODE = new Map([
+    ["ACTIVE", MODE_ALWAYS],
+    ["MUTE", MODE_MUTE],
+    ["BYPASS", MODE_BYPASS],
+    ["NOTHING", MODE_NOTHING],
+]);
+const MODE_TO_PROPERTY = new Map([
+    [MODE_MUTE, "on_muted_inputs"],
+    [MODE_BYPASS, "on_bypassed_inputs"],
+    [MODE_ALWAYS, "on_any_active_inputs"],
+]);
+const logger = rgthree.newLogSession("[NodeModeRelay]");
+class NodeModeRelay extends BaseCollectorNode {
+    constructor(title) {
+        super(title);
+        this.inputsPassThroughFollowing = PassThroughFollowing.ALL;
+        this.comfyClass = NodeTypesString.NODE_MODE_RELAY;
+        this.properties["on_muted_inputs"] = "MUTE";
+        this.properties["on_bypassed_inputs"] = "BYPASS";
+        this.properties["on_any_active_inputs"] = "ACTIVE";
+        this.onConstructed();
+    }
+    onConstructed() {
+        this.addOutput("REPEATER", "_NODE_REPEATER_", {
+            color_on: "#Fc0",
+            color_off: "#a80",
+            shape: LiteGraph.ARROW_SHAPE,
+        });
+        setTimeout(() => {
+            this.stabilize();
+        }, 500);
+        return super.onConstructed();
+    }
+    onModeChange(from, to) {
+        var _a;
+        super.onModeChange(from, to);
+        if (this.inputs.length <= 1 && !this.isInputConnected(0) && this.isAnyOutputConnected()) {
+            const [n, v] = logger.infoParts(`Mode change without any inputs; relaying our mode.`);
+            (_a = console[n]) === null || _a === void 0 ? void 0 : _a.call(console, ...v);
+            this.dispatchModeToRepeater(to);
+        }
+    }
+    onDrawForeground(ctx, canvas) {
+        var _a;
+        if ((_a = this.flags) === null || _a === void 0 ? void 0 : _a.collapsed) {
+            return;
+        }
+        if (this.properties["on_muted_inputs"] !== "MUTE" ||
+            this.properties["on_bypassed_inputs"] !== "BYPASS" ||
+            this.properties["on_any_active_inputs"] != "ACTIVE") {
+            let margin = 15;
+            ctx.textAlign = "left";
+            let label = `*(MUTE > ${this.properties["on_muted_inputs"]},  `;
+            label += `BYPASS > ${this.properties["on_bypassed_inputs"]},  `;
+            label += `ACTIVE > ${this.properties["on_any_active_inputs"]})`;
+            ctx.fillStyle = LiteGraph.WIDGET_SECONDARY_TEXT_COLOR;
+            const oldFont = ctx.font;
+            ctx.font = "italic " + (LiteGraph.NODE_SUBTEXT_SIZE - 2) + "px Arial";
+            ctx.fillText(fitString(ctx, label, this.size[0] - 20), 15, this.size[1] - 6);
+            ctx.font = oldFont;
+        }
+    }
+    computeSize(out) {
+        let size = super.computeSize(out);
+        if (this.properties["on_muted_inputs"] !== "MUTE" ||
+            this.properties["on_bypassed_inputs"] !== "BYPASS" ||
+            this.properties["on_any_active_inputs"] != "ACTIVE") {
+            size[1] += 17;
+        }
+        return size;
+    }
+    onConnectOutput(outputIndex, inputType, inputSlot, inputNode, inputIndex) {
+        var _a, _b;
+        let canConnect = (_a = super.onConnectOutput) === null || _a === void 0 ? void 0 : _a.call(this, outputIndex, inputType, inputSlot, inputNode, inputIndex);
+        let nextNode = (_b = getConnectedOutputNodesAndFilterPassThroughs(this, inputNode)[0]) !== null && _b !== void 0 ? _b : inputNode;
+        return canConnect && nextNode.type === NodeTypesString.NODE_MODE_REPEATER;
+    }
+    onConnectionsChange(type, slotIndex, isConnected, link_info, ioSlot) {
+        super.onConnectionsChange(type, slotIndex, isConnected, link_info, ioSlot);
+        setTimeout(() => {
+            this.stabilize();
+        }, 500);
+    }
+    stabilize() {
+        if (!this.graph || !this.isAnyOutputConnected() || !this.isInputConnected(0)) {
+            return;
+        }
+        const inputNodes = getConnectedInputNodesAndFilterPassThroughs(this, this, -1, this.inputsPassThroughFollowing);
+        let mode = undefined;
+        for (const inputNode of inputNodes) {
+            if (mode === undefined) {
+                mode = inputNode.mode;
+            }
+            else if (mode === inputNode.mode && MODE_REPEATS.includes(mode)) {
+                continue;
+            }
+            else if (inputNode.mode === MODE_ALWAYS || mode === MODE_ALWAYS) {
+                mode = MODE_ALWAYS;
+            }
+            else {
+                mode = undefined;
+            }
+        }
+        this.dispatchModeToRepeater(mode);
+        setTimeout(() => {
+            this.stabilize();
+        }, 500);
+    }
+    dispatchModeToRepeater(mode) {
+        var _a, _b;
+        if (mode != null) {
+            const propertyVal = (_a = this.properties) === null || _a === void 0 ? void 0 : _a[MODE_TO_PROPERTY.get(mode) || ""];
+            const newMode = OPTION_TO_MODE.get(propertyVal);
+            mode = (newMode !== null ? newMode : mode);
+            if (mode !== null && mode !== MODE_NOTHING) {
+                if ((_b = this.outputs) === null || _b === void 0 ? void 0 : _b.length) {
+                    const outputNodes = getConnectedOutputNodesAndFilterPassThroughs(this);
+                    for (const outputNode of outputNodes) {
+                        changeModeOfNodes(outputNode, mode);
+                        wait(16).then(() => {
+                            outputNode.setDirtyCanvas(true, true);
+                        });
+                    }
+                }
+            }
+        }
+    }
+    getHelp() {
+        return `
+      <p>
+        This node will relay its input nodes' modes (Mute, Bypass, or Active) to a connected
+        ${stripRgthree(NodeTypesString.NODE_MODE_REPEATER)} (which would then repeat that mode
+        change to all of its inputs).
+      </p>
+      <ul>
+          <li><p>
+            When all connected input nodes are muted, the relay will set a connected repeater to
+            mute (by default).
+          </p></li>
+          <li><p>
+            When all connected input nodes are bypassed, the relay will set a connected repeater to
+            bypass (by default).
+          </p></li>
+          <li><p>
+            When any connected input nodes are active, the relay will set a connected repeater to
+            active (by default).
+          </p></li>
+          <li><p>
+            If no inputs are connected, the relay will set a connected repeater to its mode <i>when
+            its own mode is changed</i>. <b>Note</b>, if any inputs are connected, then the above
+            will occur and the Relay's mode does not matter.
+          </p></li>
+      </ul>
+      <p>
+        Note, you can change which signals get sent on the above in the <code>Properties</code>.
+        For instance, you could configure an inverse relay which will send a MUTE when any of its
+        inputs are active (instead of sending an ACTIVE signal), and send an ACTIVE signal when all
+        of its inputs are muted (instead of sending a MUTE signal), etc.
+      </p>
+    `;
+    }
+}
+NodeModeRelay.type = NodeTypesString.NODE_MODE_RELAY;
+NodeModeRelay.title = NodeTypesString.NODE_MODE_RELAY;
+NodeModeRelay["@on_muted_inputs"] = {
+    type: "combo",
+    values: ["MUTE", "ACTIVE", "BYPASS", "NOTHING"],
+};
+NodeModeRelay["@on_bypassed_inputs"] = {
+    type: "combo",
+    values: ["BYPASS", "ACTIVE", "MUTE", "NOTHING"],
+};
+NodeModeRelay["@on_any_active_inputs"] = {
+    type: "combo",
+    values: ["BYPASS", "ACTIVE", "MUTE", "NOTHING"],
+};
+app.registerExtension({
+    name: "rgthree.NodeModeRepeaterHelper",
+    registerCustomNodes() {
+        addConnectionLayoutSupport(NodeModeRelay, app, [
+            ["Left", "Right"],
+            ["Right", "Left"],
+        ]);
+        LiteGraph.registerNodeType(NodeModeRelay.type, NodeModeRelay);
+        NodeModeRelay.category = NodeModeRelay._category;
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/node_mode_repeater.js b/custom_nodes/rgthree-comfy/web/comfyui/node_mode_repeater.js
new file mode 100644
index 0000000000000000000000000000000000000000..22300d11fd1c290f5bc0a07356aa90956db1d6a3
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/node_mode_repeater.js
@@ -0,0 +1,148 @@
+import { app } from "../../scripts/app.js";
+import { BaseCollectorNode } from "./base_node_collector.js";
+import { NodeTypesString, stripRgthree } from "./constants.js";
+import { PassThroughFollowing, addConnectionLayoutSupport, changeModeOfNodes, getConnectedInputNodesAndFilterPassThroughs, getConnectedOutputNodesAndFilterPassThroughs, getGroupNodes, } from "./utils.js";
+class NodeModeRepeater extends BaseCollectorNode {
+    constructor(title) {
+        super(title);
+        this.inputsPassThroughFollowing = PassThroughFollowing.ALL;
+        this.comfyClass = NodeTypesString.NODE_MODE_REPEATER;
+        this.hasRelayInput = false;
+        this.hasTogglerOutput = false;
+        this.onConstructed();
+    }
+    onConstructed() {
+        this.addOutput("OPT_CONNECTION", "*", {
+            color_on: "#Fc0",
+            color_off: "#a80",
+        });
+        return super.onConstructed();
+    }
+    onConnectOutput(outputIndex, inputType, inputSlot, inputNode, inputIndex) {
+        let canConnect = !this.hasRelayInput;
+        canConnect =
+            canConnect && super.onConnectOutput(outputIndex, inputType, inputSlot, inputNode, inputIndex);
+        let nextNode = getConnectedOutputNodesAndFilterPassThroughs(this, inputNode)[0] || inputNode;
+        return (canConnect &&
+            [
+                NodeTypesString.FAST_MUTER,
+                NodeTypesString.FAST_BYPASSER,
+                NodeTypesString.NODE_COLLECTOR,
+                NodeTypesString.FAST_ACTIONS_BUTTON,
+                NodeTypesString.REROUTE,
+                NodeTypesString.RANDOM_UNMUTER,
+            ].includes(nextNode.type || ""));
+    }
+    onConnectInput(inputIndex, outputType, outputSlot, outputNode, outputIndex) {
+        var _a;
+        let canConnect = (_a = super.onConnectInput) === null || _a === void 0 ? void 0 : _a.call(this, inputIndex, outputType, outputSlot, outputNode, outputIndex);
+        let nextNode = getConnectedOutputNodesAndFilterPassThroughs(this, outputNode)[0] || outputNode;
+        const isNextNodeRelay = nextNode.type === NodeTypesString.NODE_MODE_RELAY;
+        return canConnect && (!isNextNodeRelay || !this.hasTogglerOutput);
+    }
+    onConnectionsChange(type, slotIndex, isConnected, linkInfo, ioSlot) {
+        super.onConnectionsChange(type, slotIndex, isConnected, linkInfo, ioSlot);
+        let hasTogglerOutput = false;
+        let hasRelayInput = false;
+        const outputNodes = getConnectedOutputNodesAndFilterPassThroughs(this);
+        for (const outputNode of outputNodes) {
+            if ((outputNode === null || outputNode === void 0 ? void 0 : outputNode.type) === NodeTypesString.FAST_MUTER ||
+                (outputNode === null || outputNode === void 0 ? void 0 : outputNode.type) === NodeTypesString.FAST_BYPASSER) {
+                hasTogglerOutput = true;
+                break;
+            }
+        }
+        const inputNodes = getConnectedInputNodesAndFilterPassThroughs(this);
+        for (const [index, inputNode] of inputNodes.entries()) {
+            if ((inputNode === null || inputNode === void 0 ? void 0 : inputNode.type) === NodeTypesString.NODE_MODE_RELAY) {
+                if (hasTogglerOutput) {
+                    console.log(`Can't be connected to a Relay if also output to a toggler.`);
+                    this.disconnectInput(index);
+                }
+                else {
+                    hasRelayInput = true;
+                    if (this.inputs[index]) {
+                        this.inputs[index].color_on = "#FC0";
+                        this.inputs[index].color_off = "#a80";
+                    }
+                }
+            }
+            else {
+                changeModeOfNodes(inputNode, this.mode);
+            }
+        }
+        this.hasTogglerOutput = hasTogglerOutput;
+        this.hasRelayInput = hasRelayInput;
+        if (this.hasRelayInput) {
+            if (this.outputs[0]) {
+                this.disconnectOutput(0);
+                this.removeOutput(0);
+            }
+        }
+        else if (!this.outputs[0]) {
+            this.addOutput("OPT_CONNECTION", "*", {
+                color_on: "#Fc0",
+                color_off: "#a80",
+            });
+        }
+    }
+    onModeChange(from, to) {
+        var _a, _b;
+        super.onModeChange(from, to);
+        const linkedNodes = getConnectedInputNodesAndFilterPassThroughs(this).filter((node) => node.type !== NodeTypesString.NODE_MODE_RELAY);
+        if (linkedNodes.length) {
+            for (const node of linkedNodes) {
+                if (node.type !== NodeTypesString.NODE_MODE_RELAY) {
+                    changeModeOfNodes(node, to);
+                }
+            }
+        }
+        else if ((_b = (_a = this.graph) === null || _a === void 0 ? void 0 : _a._groups) === null || _b === void 0 ? void 0 : _b.length) {
+            for (const group of this.graph._groups) {
+                group.recomputeInsideNodes();
+                const groupNodes = getGroupNodes(group);
+                if (groupNodes === null || groupNodes === void 0 ? void 0 : groupNodes.includes(this)) {
+                    for (const node of groupNodes) {
+                        if (node !== this) {
+                            changeModeOfNodes(node, to);
+                        }
+                    }
+                }
+            }
+        }
+    }
+    getHelp() {
+        return `
+      <p>
+        When this node's mode (Mute, Bypass, Active) changes, it will "repeat" that mode to all
+        connected input nodes, or, if there are no connected nodes AND it is overlapping a group,
+        "repeat" it's mode to all nodes in that group.
+      </p>
+      <ul>
+        <li><p>
+          Optionally, connect this mode's output to a ${stripRgthree(NodeTypesString.FAST_MUTER)}
+          or ${stripRgthree(NodeTypesString.FAST_BYPASSER)} for a single toggle to quickly
+          mute/bypass all its connected nodes.
+        </p></li>
+        <li><p>
+          Optionally, connect a ${stripRgthree(NodeTypesString.NODE_MODE_RELAY)} to this nodes
+          inputs to have it automatically toggle its mode. If connected, this will always take
+          precedence (and disconnect any connected fast togglers).
+        </p></li>
+      </ul>
+    `;
+    }
+}
+NodeModeRepeater.type = NodeTypesString.NODE_MODE_REPEATER;
+NodeModeRepeater.title = NodeTypesString.NODE_MODE_REPEATER;
+app.registerExtension({
+    name: "rgthree.NodeModeRepeater",
+    registerCustomNodes() {
+        addConnectionLayoutSupport(NodeModeRepeater, app, [
+            ["Left", "Right"],
+            ["Right", "Left"],
+        ]);
+        LiteGraph.registerNodeType(NodeModeRepeater.type, NodeModeRepeater);
+        NodeModeRepeater.category = NodeModeRepeater._category;
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/power_conductor.js b/custom_nodes/rgthree-comfy/web/comfyui/power_conductor.js
new file mode 100644
index 0000000000000000000000000000000000000000..9c51a2f7e1a2477004b909c3abf88a63c4b2176f
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/power_conductor.js
@@ -0,0 +1,149 @@
+var __decorate = (this && this.__decorate) || function (decorators, target, key, desc) {
+    var c = arguments.length, r = c < 3 ? target : desc === null ? desc = Object.getOwnPropertyDescriptor(target, key) : desc, d;
+    if (typeof Reflect === "object" && typeof Reflect.decorate === "function") r = Reflect.decorate(decorators, target, key, desc);
+    else for (var i = decorators.length - 1; i >= 0; i--) if (d = decorators[i]) r = (c < 3 ? d(r) : c > 3 ? d(target, key, r) : d(target, key)) || r;
+    return c > 3 && r && Object.defineProperty(target, key, r), r;
+};
+var __classPrivateFieldSet = (this && this.__classPrivateFieldSet) || function (receiver, state, value, kind, f) {
+    if (kind === "m") throw new TypeError("Private method is not writable");
+    if (kind === "a" && !f) throw new TypeError("Private accessor was defined without a setter");
+    if (typeof state === "function" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError("Cannot write private member to an object whose class did not declare it");
+    return (kind === "a" ? f.call(receiver, value) : f ? f.value = value : state.set(receiver, value)), value;
+};
+var __classPrivateFieldGet = (this && this.__classPrivateFieldGet) || function (receiver, state, kind, f) {
+    if (kind === "a" && !f) throw new TypeError("Private accessor was defined without a getter");
+    if (typeof state === "function" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError("Cannot read private member from an object whose class did not declare it");
+    return kind === "m" ? f : kind === "a" ? f.call(receiver) : f ? f.value : state.get(receiver);
+};
+var _ComfyNodeWrapper_id, _ComfyWidgetWrapper_widget;
+import { app } from "../../scripts/app.js";
+import { Exposed, execute, PyTuple } from "../../rgthree/common/py_parser.js";
+import { RgthreeBaseVirtualNode } from "./base_node.js";
+import { RgthreeBetterButtonWidget } from "./utils_widgets.js";
+import { NodeTypesString } from "./constants.js";
+import { ComfyWidgets } from "../../scripts/widgets.js";
+import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js";
+import { changeModeOfNodes, getNodeById } from "./utils.js";
+const BUILT_INS = {
+    node: {
+        fn: (query) => {
+            if (typeof query === "number" || /^\d+(\.\d+)?/.exec(query)) {
+                return new ComfyNodeWrapper(Number(query));
+            }
+            return null;
+        },
+    },
+};
+class RgthreePowerConductor extends RgthreeBaseVirtualNode {
+    constructor(title = RgthreePowerConductor.title) {
+        super(title);
+        this.comfyClass = NodeTypesString.POWER_CONDUCTOR;
+        this.serialize_widgets = true;
+        this.codeWidget = ComfyWidgets.STRING(this, "", ["STRING", { multiline: true }], app).widget;
+        this.addCustomWidget(this.codeWidget);
+        (this.buttonWidget = new RgthreeBetterButtonWidget("Run", (...args) => {
+            this.execute();
+        })),
+            this.addCustomWidget(this.buttonWidget);
+        this.onConstructed();
+    }
+    execute() {
+        execute(this.codeWidget.value, {}, BUILT_INS);
+    }
+}
+RgthreePowerConductor.title = NodeTypesString.POWER_CONDUCTOR;
+RgthreePowerConductor.type = NodeTypesString.POWER_CONDUCTOR;
+const NODE_CLASS = RgthreePowerConductor;
+class ComfyNodeWrapper {
+    constructor(id) {
+        _ComfyNodeWrapper_id.set(this, void 0);
+        __classPrivateFieldSet(this, _ComfyNodeWrapper_id, id, "f");
+    }
+    getNode() {
+        return getNodeById(__classPrivateFieldGet(this, _ComfyNodeWrapper_id, "f"));
+    }
+    get id() {
+        return this.getNode().id;
+    }
+    get title() {
+        return this.getNode().title;
+    }
+    set title(value) {
+        this.getNode().title = value;
+    }
+    get widgets() {
+        var _a;
+        return new PyTuple((_a = this.getNode().widgets) === null || _a === void 0 ? void 0 : _a.map((w) => new ComfyWidgetWrapper(w)));
+    }
+    get mode() {
+        return this.getNode().mode;
+    }
+    mute() {
+        changeModeOfNodes(this.getNode(), 2);
+    }
+    bypass() {
+        changeModeOfNodes(this.getNode(), 4);
+    }
+    enable() {
+        changeModeOfNodes(this.getNode(), 0);
+    }
+}
+_ComfyNodeWrapper_id = new WeakMap();
+__decorate([
+    Exposed
+], ComfyNodeWrapper.prototype, "id", null);
+__decorate([
+    Exposed
+], ComfyNodeWrapper.prototype, "title", null);
+__decorate([
+    Exposed
+], ComfyNodeWrapper.prototype, "widgets", null);
+__decorate([
+    Exposed
+], ComfyNodeWrapper.prototype, "mode", null);
+__decorate([
+    Exposed
+], ComfyNodeWrapper.prototype, "mute", null);
+__decorate([
+    Exposed
+], ComfyNodeWrapper.prototype, "bypass", null);
+__decorate([
+    Exposed
+], ComfyNodeWrapper.prototype, "enable", null);
+class ComfyWidgetWrapper {
+    constructor(widget) {
+        _ComfyWidgetWrapper_widget.set(this, void 0);
+        __classPrivateFieldSet(this, _ComfyWidgetWrapper_widget, widget, "f");
+    }
+    get value() {
+        return __classPrivateFieldGet(this, _ComfyWidgetWrapper_widget, "f").value;
+    }
+    get label() {
+        return __classPrivateFieldGet(this, _ComfyWidgetWrapper_widget, "f").label;
+    }
+    toggle(value) {
+        if (typeof __classPrivateFieldGet(this, _ComfyWidgetWrapper_widget, "f")["toggle"] === "function") {
+            __classPrivateFieldGet(this, _ComfyWidgetWrapper_widget, "f")["toggle"](value);
+        }
+        else {
+        }
+    }
+}
+_ComfyWidgetWrapper_widget = new WeakMap();
+__decorate([
+    Exposed
+], ComfyWidgetWrapper.prototype, "value", null);
+__decorate([
+    Exposed
+], ComfyWidgetWrapper.prototype, "label", null);
+__decorate([
+    Exposed
+], ComfyWidgetWrapper.prototype, "toggle", null);
+app.registerExtension({
+    name: "rgthree.PowerConductor",
+    registerCustomNodes() {
+        if (CONFIG_SERVICE.getConfigValue("unreleased.power_conductor.enabled")) {
+            NODE_CLASS.setUp();
+        }
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/power_lora_loader.js b/custom_nodes/rgthree-comfy/web/comfyui/power_lora_loader.js
new file mode 100644
index 0000000000000000000000000000000000000000..b2146d71455ad5fa57502df09e48a1a74ee9b951
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/power_lora_loader.js
@@ -0,0 +1,626 @@
+var _a, _b;
+import { app } from "../../scripts/app.js";
+import { RgthreeBaseServerNode } from "./base_node.js";
+import { rgthree } from "./rgthree.js";
+import { addConnectionLayoutSupport } from "./utils.js";
+import { NodeTypesString } from "./constants.js";
+import { drawInfoIcon, drawNumberWidgetPart, drawRoundedRectangle, drawTogglePart, fitString, isLowQuality, } from "./utils_canvas.js";
+import { RgthreeBaseWidget, RgthreeBetterButtonWidget, RgthreeDividerWidget, } from "./utils_widgets.js";
+import { rgthreeApi } from "../../rgthree/common/rgthree_api.js";
+import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js";
+import { showLoraChooser } from "./utils_menu.js";
+import { moveArrayItem, removeArrayItem } from "../../rgthree/common/shared_utils.js";
+import { RgthreeLoraInfoDialog } from "./dialog_info.js";
+import { LORA_INFO_SERVICE } from "../../rgthree/common/model_info_service.js";
+const PROP_LABEL_SHOW_STRENGTHS = "Show Strengths";
+const PROP_LABEL_SHOW_STRENGTHS_STATIC = `@${PROP_LABEL_SHOW_STRENGTHS}`;
+const PROP_LABEL_LORA_MATCH = "Match";
+const PROP_LABEL_LORA_MATCH_STATIC = `@${PROP_LABEL_LORA_MATCH}`;
+const PROP_VALUE_SHOW_STRENGTHS_SINGLE = "Single Strength";
+const PROP_VALUE_SHOW_STRENGTHS_SEPARATE = "Separate Model & Clip";
+class RgthreePowerLoraLoader extends RgthreeBaseServerNode {
+    constructor(title = NODE_CLASS.title) {
+        super(title);
+        this.serialize_widgets = true;
+        this.logger = rgthree.newLogSession(`[Power Lora Stack]`);
+        this.loraWidgetsCounter = 0;
+        this.widgetButtonSpacer = null;
+        this.properties[PROP_LABEL_SHOW_STRENGTHS] = PROP_VALUE_SHOW_STRENGTHS_SINGLE;
+        this.properties[PROP_LABEL_LORA_MATCH] = "";
+        rgthreeApi.getLoras();
+        if (rgthree.loadingApiJson) {
+            const fullApiJson = rgthree.loadingApiJson;
+            setTimeout(() => {
+                this.configureFromApiJson(fullApiJson);
+            }, 16);
+        }
+    }
+    configureFromApiJson(fullApiJson) {
+        var _c, _d;
+        if (this.id == null) {
+            const [n, v] = this.logger.errorParts("Cannot load from API JSON without node id.");
+            (_c = console[n]) === null || _c === void 0 ? void 0 : _c.call(console, ...v);
+            return;
+        }
+        const nodeData = fullApiJson[this.id] || fullApiJson[String(this.id)] || fullApiJson[Number(this.id)];
+        if (nodeData == null) {
+            const [n, v] = this.logger.errorParts(`No node found in API JSON for node id ${this.id}.`);
+            (_d = console[n]) === null || _d === void 0 ? void 0 : _d.call(console, ...v);
+            return;
+        }
+        this.configure({
+            widgets_values: Object.values(nodeData.inputs).filter((input) => typeof (input === null || input === void 0 ? void 0 : input["lora"]) === "string"),
+        });
+    }
+    configure(info) {
+        var _c;
+        while ((_c = this.widgets) === null || _c === void 0 ? void 0 : _c.length)
+            this.removeWidget(0);
+        this.widgetButtonSpacer = null;
+        if (info.id != null) {
+            super.configure(info);
+        }
+        this._tempWidth = this.size[0];
+        this._tempHeight = this.size[1];
+        for (const widgetValue of info.widgets_values || []) {
+            if ((widgetValue === null || widgetValue === void 0 ? void 0 : widgetValue.lora) !== undefined) {
+                const widget = this.addNewLoraWidget();
+                widget.value = { ...widgetValue };
+            }
+        }
+        this.addNonLoraWidgets();
+        this.size[0] = this._tempWidth;
+        this.size[1] = Math.max(this._tempHeight, this.computeSize()[1]);
+    }
+    onNodeCreated() {
+        var _c;
+        (_c = super.onNodeCreated) === null || _c === void 0 ? void 0 : _c.call(this);
+        this.addNonLoraWidgets();
+        const computed = this.computeSize();
+        this.size = this.size || [0, 0];
+        this.size[0] = Math.max(this.size[0], computed[0]);
+        this.size[1] = Math.max(this.size[1], computed[1]);
+        this.setDirtyCanvas(true, true);
+    }
+    addNewLoraWidget(lora) {
+        this.loraWidgetsCounter++;
+        const widget = this.addCustomWidget(new PowerLoraLoaderWidget("lora_" + this.loraWidgetsCounter));
+        if (lora)
+            widget.setLora(lora);
+        if (this.widgetButtonSpacer) {
+            moveArrayItem(this.widgets, widget, this.widgets.indexOf(this.widgetButtonSpacer));
+        }
+        return widget;
+    }
+    addNonLoraWidgets() {
+        moveArrayItem(this.widgets, this.addCustomWidget(new RgthreeDividerWidget({ marginTop: 4, marginBottom: 0, thickness: 0 })), 0);
+        moveArrayItem(this.widgets, this.addCustomWidget(new PowerLoraLoaderHeaderWidget()), 1);
+        this.widgetButtonSpacer = this.addCustomWidget(new RgthreeDividerWidget({ marginTop: 4, marginBottom: 0, thickness: 0 }));
+        this.addCustomWidget(new RgthreeBetterButtonWidget("➕ Add Lora", (event, pos, node) => {
+            this.showLoraChooser(event, (value) => {
+                var _c;
+                if (value.includes("Power Lora Chooser")) {
+                }
+                else if (value !== "NONE") {
+                    this.addNewLoraWidget(value);
+                    const computed = this.computeSize();
+                    const tempHeight = (_c = this._tempHeight) !== null && _c !== void 0 ? _c : 15;
+                    this.size[1] = Math.max(tempHeight, computed[1]);
+                    this.setDirtyCanvas(true, true);
+                }
+            });
+            return true;
+        }));
+    }
+    async showLoraChooser(event, onChoose) {
+        const lorasDetails = await rgthreeApi.getLoras();
+        let loras = lorasDetails.map((l) => l.file);
+        let prefix = "";
+        if (this.properties[PROP_LABEL_LORA_MATCH]) {
+            const rgx = new RegExp(this.properties[PROP_LABEL_LORA_MATCH]);
+            loras = loras.filter((l) => l.match(rgx));
+            if (loras[0]) {
+                prefix = loras[0];
+                for (const lora of loras) {
+                    let similar = "";
+                    let i = 0;
+                    while (prefix[i] && prefix[i] === lora[i]) {
+                        similar += prefix[i++];
+                    }
+                    prefix = similar;
+                    if (!prefix)
+                        break;
+                }
+                if (prefix) {
+                    loras = loras.map((l) => l.replace(prefix, ""));
+                }
+            }
+        }
+        showLoraChooser(event, (value) => {
+            if (typeof value === "string") {
+                onChoose(prefix + value);
+            }
+            this.setDirtyCanvas(true, true);
+        }, null, [...loras]);
+    }
+    getSlotInPosition(canvasX, canvasY) {
+        var _c;
+        const slot = super.getSlotInPosition(canvasX, canvasY);
+        if (!slot) {
+            let lastWidget = null;
+            for (const widget of this.widgets) {
+                if (!widget.last_y)
+                    return;
+                if (canvasY > this.pos[1] + widget.last_y) {
+                    lastWidget = widget;
+                    continue;
+                }
+                break;
+            }
+            if ((_c = lastWidget === null || lastWidget === void 0 ? void 0 : lastWidget.name) === null || _c === void 0 ? void 0 : _c.startsWith("lora_")) {
+                return { widget: lastWidget, output: { type: "LORA WIDGET" } };
+            }
+        }
+        return slot;
+    }
+    getSlotMenuOptions(slot) {
+        var _c, _d, _e, _f, _g, _h;
+        if ((_d = (_c = slot === null || slot === void 0 ? void 0 : slot.widget) === null || _c === void 0 ? void 0 : _c.name) === null || _d === void 0 ? void 0 : _d.startsWith("lora_")) {
+            const widget = slot.widget;
+            const index = this.widgets.indexOf(widget);
+            const canMoveUp = !!((_f = (_e = this.widgets[index - 1]) === null || _e === void 0 ? void 0 : _e.name) === null || _f === void 0 ? void 0 : _f.startsWith("lora_"));
+            const canMoveDown = !!((_h = (_g = this.widgets[index + 1]) === null || _g === void 0 ? void 0 : _g.name) === null || _h === void 0 ? void 0 : _h.startsWith("lora_"));
+            const menuItems = [
+                {
+                    content: `ℹ️ Show Info`,
+                    callback: () => {
+                        widget.showLoraInfoDialog();
+                    },
+                },
+                null,
+                {
+                    content: `${widget.value.on ? "⚫" : "🟢"} Toggle ${widget.value.on ? "Off" : "On"}`,
+                    callback: () => {
+                        widget.value.on = !widget.value.on;
+                    },
+                },
+                {
+                    content: `⬆️ Move Up`,
+                    disabled: !canMoveUp,
+                    callback: () => {
+                        moveArrayItem(this.widgets, widget, index - 1);
+                    },
+                },
+                {
+                    content: `⬇️ Move Down`,
+                    disabled: !canMoveDown,
+                    callback: () => {
+                        moveArrayItem(this.widgets, widget, index + 1);
+                    },
+                },
+                {
+                    content: `🗑️ Remove`,
+                    callback: () => {
+                        removeArrayItem(this.widgets, widget);
+                    },
+                },
+            ];
+            new LiteGraph.ContextMenu(menuItems, {
+                title: "LORA WIDGET",
+                event: rgthree.lastCanvasMouseEvent,
+            });
+            return undefined;
+        }
+        return this.defaultGetSlotMenuOptions(slot);
+    }
+    refreshComboInNode(defs) {
+        rgthreeApi.getLoras(true);
+    }
+    hasLoraWidgets() {
+        var _c;
+        return !!((_c = this.widgets) === null || _c === void 0 ? void 0 : _c.find((w) => { var _c; return (_c = w.name) === null || _c === void 0 ? void 0 : _c.startsWith("lora_"); }));
+    }
+    allLorasState() {
+        var _c, _d, _e;
+        let allOn = true;
+        let allOff = true;
+        for (const widget of this.widgets) {
+            if ((_c = widget.name) === null || _c === void 0 ? void 0 : _c.startsWith("lora_")) {
+                const on = (_d = widget.value) === null || _d === void 0 ? void 0 : _d.on;
+                allOn = allOn && on === true;
+                allOff = allOff && on === false;
+                if (!allOn && !allOff) {
+                    return null;
+                }
+            }
+        }
+        return allOn && ((_e = this.widgets) === null || _e === void 0 ? void 0 : _e.length) ? true : false;
+    }
+    toggleAllLoras() {
+        var _c, _d;
+        const allOn = this.allLorasState();
+        const toggledTo = !allOn ? true : false;
+        for (const widget of this.widgets) {
+            if (((_c = widget.name) === null || _c === void 0 ? void 0 : _c.startsWith("lora_")) && ((_d = widget.value) === null || _d === void 0 ? void 0 : _d.on) != null) {
+                widget.value.on = toggledTo;
+            }
+        }
+    }
+    static setUp(comfyClass, nodeData) {
+        RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, NODE_CLASS);
+    }
+    static onRegisteredForOverride(comfyClass, ctxClass) {
+        addConnectionLayoutSupport(NODE_CLASS, app, [
+            ["Left", "Right"],
+            ["Right", "Left"],
+        ]);
+        setTimeout(() => {
+            NODE_CLASS.category = comfyClass.category;
+        });
+    }
+    getHelp() {
+        return `
+      <p>
+        The ${this.type.replace("(rgthree)", "")} is a powerful node that condenses 100s of pixels
+        of functionality in a single, dynamic node that allows you to add loras, change strengths,
+        and quickly toggle on/off all without taking up half your screen.
+      </p>
+      <ul>
+        <li><p>
+          Add as many Lora's as you would like by clicking the "+ Add Lora" button.
+          There's no real limit!
+        </p></li>
+        <li><p>
+          Right-click on a Lora widget for special options to move the lora up or down
+          (no image affect, only presentational), toggle it on/off, or delete the row all together.
+        </p></li>
+        <li>
+          <p>
+            <strong>Properties.</strong> You can change the following properties (by right-clicking
+            on the node, and select "Properties" or "Properties Panel" from the menu):
+          </p>
+          <ul>
+            <li><p>
+              <code>${PROP_LABEL_SHOW_STRENGTHS}</code> - Change between showing a single, simple
+              strength (which will be used for both model and clip), or a more advanced view with
+              both model and clip strengths being modifiable.
+            </p></li>
+          </ul>
+        </li>
+      </ul>`;
+    }
+}
+_a = PROP_LABEL_SHOW_STRENGTHS_STATIC, _b = PROP_LABEL_LORA_MATCH_STATIC;
+RgthreePowerLoraLoader.title = NodeTypesString.POWER_LORA_LOADER;
+RgthreePowerLoraLoader.type = NodeTypesString.POWER_LORA_LOADER;
+RgthreePowerLoraLoader.comfyClass = NodeTypesString.POWER_LORA_LOADER;
+RgthreePowerLoraLoader[_a] = {
+    type: "combo",
+    values: [PROP_VALUE_SHOW_STRENGTHS_SINGLE, PROP_VALUE_SHOW_STRENGTHS_SEPARATE],
+};
+RgthreePowerLoraLoader[_b] = {
+    type: "string",
+};
+class PowerLoraLoaderHeaderWidget extends RgthreeBaseWidget {
+    constructor(name = "PowerLoraLoaderHeaderWidget") {
+        super(name);
+        this.value = { type: "PowerLoraLoaderHeaderWidget" };
+        this.type = "custom";
+        this.hitAreas = {
+            toggle: { bounds: [0, 0], onDown: this.onToggleDown },
+        };
+        this.showModelAndClip = null;
+    }
+    draw(ctx, node, w, posY, height) {
+        if (!node.hasLoraWidgets()) {
+            return;
+        }
+        this.showModelAndClip =
+            node.properties[PROP_LABEL_SHOW_STRENGTHS] === PROP_VALUE_SHOW_STRENGTHS_SEPARATE;
+        const margin = 10;
+        const innerMargin = margin * 0.33;
+        const lowQuality = isLowQuality();
+        const allLoraState = node.allLorasState();
+        posY += 2;
+        const midY = posY + height * 0.5;
+        let posX = 10;
+        ctx.save();
+        this.hitAreas.toggle.bounds = drawTogglePart(ctx, { posX, posY, height, value: allLoraState });
+        if (!lowQuality) {
+            posX += this.hitAreas.toggle.bounds[1] + innerMargin;
+            ctx.globalAlpha = app.canvas.editor_alpha * 0.55;
+            ctx.fillStyle = LiteGraph.WIDGET_TEXT_COLOR;
+            ctx.textAlign = "left";
+            ctx.textBaseline = "middle";
+            ctx.fillText("Toggle All", posX, midY);
+            let rposX = node.size[0] - margin - innerMargin - innerMargin;
+            ctx.textAlign = "center";
+            ctx.fillText(this.showModelAndClip ? "Clip" : "Strength", rposX - drawNumberWidgetPart.WIDTH_TOTAL / 2, midY);
+            if (this.showModelAndClip) {
+                rposX = rposX - drawNumberWidgetPart.WIDTH_TOTAL - innerMargin * 2;
+                ctx.fillText("Model", rposX - drawNumberWidgetPart.WIDTH_TOTAL / 2, midY);
+            }
+        }
+        ctx.restore();
+    }
+    onToggleDown(event, pos, node) {
+        node.toggleAllLoras();
+        this.cancelMouseDown();
+        return true;
+    }
+}
+const DEFAULT_LORA_WIDGET_DATA = {
+    on: true,
+    lora: null,
+    strength: 1,
+    strengthTwo: null,
+};
+class PowerLoraLoaderWidget extends RgthreeBaseWidget {
+    constructor(name) {
+        super(name);
+        this.type = "custom";
+        this.haveMouseMovedStrength = false;
+        this.loraInfoPromise = null;
+        this.loraInfo = null;
+        this.showModelAndClip = null;
+        this.hitAreas = {
+            toggle: { bounds: [0, 0], onDown: this.onToggleDown },
+            lora: { bounds: [0, 0], onClick: this.onLoraClick },
+            info: { bounds: [0, 0], onDown: this.onInfoDown },
+            strengthDec: { bounds: [0, 0], onClick: this.onStrengthDecDown },
+            strengthVal: { bounds: [0, 0], onClick: this.onStrengthValUp },
+            strengthInc: { bounds: [0, 0], onClick: this.onStrengthIncDown },
+            strengthAny: { bounds: [0, 0], onMove: this.onStrengthAnyMove },
+            strengthTwoDec: { bounds: [0, 0], onClick: this.onStrengthTwoDecDown },
+            strengthTwoVal: { bounds: [0, 0], onClick: this.onStrengthTwoValUp },
+            strengthTwoInc: { bounds: [0, 0], onClick: this.onStrengthTwoIncDown },
+            strengthTwoAny: { bounds: [0, 0], onMove: this.onStrengthTwoAnyMove },
+        };
+        this._value = {
+            on: true,
+            lora: null,
+            strength: 1,
+            strengthTwo: null,
+        };
+    }
+    set value(v) {
+        this._value = v;
+        if (typeof this._value !== "object") {
+            this._value = { ...DEFAULT_LORA_WIDGET_DATA };
+            if (this.showModelAndClip) {
+                this._value.strengthTwo = this._value.strength;
+            }
+        }
+        this.getLoraInfo();
+    }
+    get value() {
+        return this._value;
+    }
+    setLora(lora) {
+        this._value.lora = lora;
+        this.getLoraInfo();
+    }
+    draw(ctx, node, w, posY, height) {
+        var _c, _d, _e, _f, _g, _h, _j, _k, _l, _m, _o, _p, _q, _r, _s, _t;
+        let currentShowModelAndClip = node.properties[PROP_LABEL_SHOW_STRENGTHS] === PROP_VALUE_SHOW_STRENGTHS_SEPARATE;
+        if (this.showModelAndClip !== currentShowModelAndClip) {
+            let oldShowModelAndClip = this.showModelAndClip;
+            this.showModelAndClip = currentShowModelAndClip;
+            if (this.showModelAndClip) {
+                if (oldShowModelAndClip != null) {
+                    this.value.strengthTwo = (_c = this.value.strength) !== null && _c !== void 0 ? _c : 1;
+                }
+            }
+            else {
+                this.value.strengthTwo = null;
+                this.hitAreas.strengthTwoDec.bounds = [0, -1];
+                this.hitAreas.strengthTwoVal.bounds = [0, -1];
+                this.hitAreas.strengthTwoInc.bounds = [0, -1];
+                this.hitAreas.strengthTwoAny.bounds = [0, -1];
+            }
+        }
+        ctx.save();
+        const margin = 10;
+        const innerMargin = margin * 0.33;
+        const lowQuality = isLowQuality();
+        const midY = posY + height * 0.5;
+        let posX = margin;
+        drawRoundedRectangle(ctx, { pos: [posX, posY], size: [node.size[0] - margin * 2, height] });
+        this.hitAreas.toggle.bounds = drawTogglePart(ctx, { posX, posY, height, value: this.value.on });
+        posX += this.hitAreas.toggle.bounds[1] + innerMargin;
+        if (lowQuality) {
+            ctx.restore();
+            return;
+        }
+        if (!this.value.on) {
+            ctx.globalAlpha = app.canvas.editor_alpha * 0.4;
+        }
+        ctx.fillStyle = LiteGraph.WIDGET_TEXT_COLOR;
+        let rposX = node.size[0] - margin - innerMargin - innerMargin;
+        const strengthValue = this.showModelAndClip
+            ? ((_d = this.value.strengthTwo) !== null && _d !== void 0 ? _d : 1)
+            : ((_e = this.value.strength) !== null && _e !== void 0 ? _e : 1);
+        let textColor = undefined;
+        if (((_f = this.loraInfo) === null || _f === void 0 ? void 0 : _f.strengthMax) != null && strengthValue > ((_g = this.loraInfo) === null || _g === void 0 ? void 0 : _g.strengthMax)) {
+            textColor = "#c66";
+        }
+        else if (((_h = this.loraInfo) === null || _h === void 0 ? void 0 : _h.strengthMin) != null && strengthValue < ((_j = this.loraInfo) === null || _j === void 0 ? void 0 : _j.strengthMin)) {
+            textColor = "#c66";
+        }
+        const [leftArrow, text, rightArrow] = drawNumberWidgetPart(ctx, {
+            posX: node.size[0] - margin - innerMargin - innerMargin,
+            posY,
+            height,
+            value: strengthValue,
+            direction: -1,
+            textColor,
+        });
+        this.hitAreas.strengthDec.bounds = leftArrow;
+        this.hitAreas.strengthVal.bounds = text;
+        this.hitAreas.strengthInc.bounds = rightArrow;
+        this.hitAreas.strengthAny.bounds = [leftArrow[0], rightArrow[0] + rightArrow[1] - leftArrow[0]];
+        rposX = leftArrow[0] - innerMargin;
+        if (this.showModelAndClip) {
+            rposX -= innerMargin;
+            this.hitAreas.strengthTwoDec.bounds = this.hitAreas.strengthDec.bounds;
+            this.hitAreas.strengthTwoVal.bounds = this.hitAreas.strengthVal.bounds;
+            this.hitAreas.strengthTwoInc.bounds = this.hitAreas.strengthInc.bounds;
+            this.hitAreas.strengthTwoAny.bounds = this.hitAreas.strengthAny.bounds;
+            let textColor = undefined;
+            if (((_k = this.loraInfo) === null || _k === void 0 ? void 0 : _k.strengthMax) != null && this.value.strength > ((_l = this.loraInfo) === null || _l === void 0 ? void 0 : _l.strengthMax)) {
+                textColor = "#c66";
+            }
+            else if (((_m = this.loraInfo) === null || _m === void 0 ? void 0 : _m.strengthMin) != null &&
+                this.value.strength < ((_o = this.loraInfo) === null || _o === void 0 ? void 0 : _o.strengthMin)) {
+                textColor = "#c66";
+            }
+            const [leftArrow, text, rightArrow] = drawNumberWidgetPart(ctx, {
+                posX: rposX,
+                posY,
+                height,
+                value: (_p = this.value.strength) !== null && _p !== void 0 ? _p : 1,
+                direction: -1,
+                textColor,
+            });
+            this.hitAreas.strengthDec.bounds = leftArrow;
+            this.hitAreas.strengthVal.bounds = text;
+            this.hitAreas.strengthInc.bounds = rightArrow;
+            this.hitAreas.strengthAny.bounds = [
+                leftArrow[0],
+                rightArrow[0] + rightArrow[1] - leftArrow[0],
+            ];
+            rposX = leftArrow[0] - innerMargin;
+        }
+        const infoIconSize = height * 0.66;
+        const infoWidth = infoIconSize + innerMargin + innerMargin;
+        if (CONFIG_SERVICE.getConfigValue("nodes.power_lora_loader.show_info_badge")) {
+            rposX -= innerMargin;
+            drawInfoIcon(ctx, rposX - infoIconSize, posY + (height - infoIconSize) / 2, infoIconSize, ((_r = (_q = this.loraInfo) === null || _q === void 0 ? void 0 : _q.raw) === null || _r === void 0 ? void 0 : _r.civitai) ? "FILLED" : ((_s = this.loraInfo) === null || _s === void 0 ? void 0 : _s.hasInfoFile) ? "OUTLINED" : "GRAYED");
+            this.hitAreas.info.bounds = [rposX - infoIconSize, infoWidth];
+            rposX = rposX - infoIconSize - innerMargin;
+        }
+        else {
+            this.hitAreas.info.bounds = [0, 0];
+        }
+        const loraWidth = rposX - posX;
+        ctx.textAlign = "left";
+        ctx.textBaseline = "middle";
+        const loraLabel = String(((_t = this.value) === null || _t === void 0 ? void 0 : _t.lora) || "None");
+        ctx.fillText(fitString(ctx, loraLabel, loraWidth), posX, midY);
+        this.hitAreas.lora.bounds = [posX, loraWidth];
+        posX += loraWidth + innerMargin;
+        ctx.globalAlpha = app.canvas.editor_alpha;
+        ctx.restore();
+    }
+    serializeValue(node, index) {
+        var _c;
+        const v = { ...this.value };
+        if (!this.showModelAndClip) {
+            delete v.strengthTwo;
+        }
+        else {
+            this.value.strengthTwo = (_c = this.value.strengthTwo) !== null && _c !== void 0 ? _c : 1;
+            v.strengthTwo = this.value.strengthTwo;
+        }
+        return v;
+    }
+    onToggleDown(event, pos, node) {
+        this.value.on = !this.value.on;
+        this.cancelMouseDown();
+        return true;
+    }
+    onInfoDown(event, pos, node) {
+        this.showLoraInfoDialog();
+    }
+    onLoraClick(event, pos, node) {
+        node.showLoraChooser(event, (value) => {
+            this.value.lora = value;
+            this.loraInfo = null;
+            this.getLoraInfo();
+        });
+        this.cancelMouseDown();
+    }
+    onStrengthDecDown(event, pos, node) {
+        this.stepStrength(-1, false);
+    }
+    onStrengthIncDown(event, pos, node) {
+        this.stepStrength(1, false);
+    }
+    onStrengthTwoDecDown(event, pos, node) {
+        this.stepStrength(-1, true);
+    }
+    onStrengthTwoIncDown(event, pos, node) {
+        this.stepStrength(1, true);
+    }
+    onStrengthAnyMove(event, pos, node) {
+        this.doOnStrengthAnyMove(event, false);
+    }
+    onStrengthTwoAnyMove(event, pos, node) {
+        this.doOnStrengthAnyMove(event, true);
+    }
+    doOnStrengthAnyMove(event, isTwo = false) {
+        var _c;
+        if (event.deltaX) {
+            let prop = isTwo ? "strengthTwo" : "strength";
+            this.haveMouseMovedStrength = true;
+            this.value[prop] = ((_c = this.value[prop]) !== null && _c !== void 0 ? _c : 1) + event.deltaX * 0.05;
+        }
+    }
+    onStrengthValUp(event, pos, node) {
+        this.doOnStrengthValUp(event, false);
+    }
+    onStrengthTwoValUp(event, pos, node) {
+        this.doOnStrengthValUp(event, true);
+    }
+    doOnStrengthValUp(event, isTwo = false) {
+        if (this.haveMouseMovedStrength)
+            return;
+        let prop = isTwo ? "strengthTwo" : "strength";
+        const canvas = app.canvas;
+        canvas.prompt("Value", this.value[prop], (v) => (this.value[prop] = Number(v)), event);
+    }
+    onMouseUp(event, pos, node) {
+        super.onMouseUp(event, pos, node);
+        this.haveMouseMovedStrength = false;
+    }
+    showLoraInfoDialog() {
+        if (!this.value.lora || this.value.lora === "None") {
+            return;
+        }
+        const infoDialog = new RgthreeLoraInfoDialog(this.value.lora).show();
+        infoDialog.addEventListener("close", ((e) => {
+            if (e.detail.dirty) {
+                this.getLoraInfo(true);
+            }
+        }));
+    }
+    stepStrength(direction, isTwo = false) {
+        var _c;
+        let step = 0.05;
+        let prop = isTwo ? "strengthTwo" : "strength";
+        let strength = ((_c = this.value[prop]) !== null && _c !== void 0 ? _c : 1) + step * direction;
+        this.value[prop] = Math.round(strength * 100) / 100;
+    }
+    getLoraInfo(force = false) {
+        if (!this.loraInfoPromise || force == true) {
+            let promise;
+            if (this.value.lora && this.value.lora != "None") {
+                promise = LORA_INFO_SERVICE.getInfo(this.value.lora, force, true);
+            }
+            else {
+                promise = Promise.resolve(null);
+            }
+            this.loraInfoPromise = promise.then((v) => (this.loraInfo = v));
+        }
+        return this.loraInfoPromise;
+    }
+}
+const NODE_CLASS = RgthreePowerLoraLoader;
+app.registerExtension({
+    name: "rgthree.PowerLoraLoader",
+    async beforeRegisterNodeDef(nodeType, nodeData) {
+        if (nodeData.name === NODE_CLASS.type) {
+            NODE_CLASS.setUp(nodeType, nodeData);
+        }
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/power_primitive.js b/custom_nodes/rgthree-comfy/web/comfyui/power_primitive.js
new file mode 100644
index 0000000000000000000000000000000000000000..eb55e8e93e7678730be16b476b28a68b191288ae
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/power_primitive.js
@@ -0,0 +1,175 @@
+import { app } from "../../scripts/app.js";
+import { RgthreeBaseServerNode } from "./base_node.js";
+import { NodeTypesString } from "./constants.js";
+import { ComfyWidgets } from "../../scripts/widgets.js";
+import { moveArrayItem } from "../../rgthree/common/shared_utils.js";
+const PROPERTY_HIDE_TYPE_SELECTOR = "hideTypeSelector";
+const PRIMITIVES = {
+    STRING: "STRING",
+    INT: "INT",
+    FLOAT: "FLOAT",
+    BOOLEAN: "BOOLEAN",
+};
+class RgthreePowerPrimitive extends RgthreeBaseServerNode {
+    constructor(title = NODE_CLASS.title) {
+        super(title);
+        this.typeState = '';
+        this.properties[PROPERTY_HIDE_TYPE_SELECTOR] = false;
+    }
+    static setUp(comfyClass, nodeData) {
+        RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, NODE_CLASS);
+    }
+    onNodeCreated() {
+        var _a;
+        (_a = super.onNodeCreated) === null || _a === void 0 ? void 0 : _a.call(this);
+        this.addInitialWidgets();
+    }
+    configure(info) {
+        super.configure(info);
+        if (this.outputTypeWidget.value === 'BOOL') {
+            this.outputTypeWidget.value = 'BOOLEAN';
+        }
+        setTimeout(() => {
+            this.setTypedData();
+        });
+    }
+    getExtraMenuOptions(canvas, options) {
+        const that = this;
+        super.getExtraMenuOptions(canvas, options);
+        const isHidden = !!this.properties[PROPERTY_HIDE_TYPE_SELECTOR];
+        const menuItems = [
+            {
+                content: `${isHidden ? "Show" : "Hide"} Type Selector Widget`,
+                callback: (...args) => {
+                    this.setProperty(PROPERTY_HIDE_TYPE_SELECTOR, !this.properties[PROPERTY_HIDE_TYPE_SELECTOR]);
+                },
+            },
+            {
+                content: `Set type`,
+                submenu: {
+                    options: Object.keys(PRIMITIVES),
+                    callback(value, ...args) {
+                        that.outputTypeWidget.value = value;
+                        that.setTypedData();
+                    },
+                },
+            },
+        ];
+        options.splice(0, 0, ...menuItems, null);
+        return [];
+    }
+    addInitialWidgets() {
+        if (!this.outputTypeWidget) {
+            this.outputTypeWidget = this.addWidget("combo", "type", "STRING", (...args) => {
+                this.setTypedData();
+            }, {
+                values: Object.keys(PRIMITIVES),
+            });
+            this.outputTypeWidget.hidden = this.properties[PROPERTY_HIDE_TYPE_SELECTOR];
+        }
+        this.setTypedData();
+    }
+    setTypedData() {
+        var _a, _b, _c, _d, _e;
+        const name = "value";
+        const type = this.outputTypeWidget.value;
+        const linked = !!((_b = (_a = this.inputs) === null || _a === void 0 ? void 0 : _a[0]) === null || _b === void 0 ? void 0 : _b.link);
+        const newTypeState = `${type}|${linked}`;
+        if (this.typeState == newTypeState)
+            return;
+        this.typeState = newTypeState;
+        let value = (_d = (_c = this.valueWidget) === null || _c === void 0 ? void 0 : _c.value) !== null && _d !== void 0 ? _d : null;
+        let newWidget = null;
+        if (linked) {
+            newWidget = ComfyWidgets["STRING"](this, name, ["STRING"], app).widget;
+            newWidget.value = "";
+        }
+        else if (type == "STRING") {
+            newWidget = ComfyWidgets["STRING"](this, name, ["STRING", { multiline: true }], app).widget;
+            newWidget.value = value ? "" : String(value);
+        }
+        else if (type === "INT" || type === "FLOAT") {
+            const isFloat = type === "FLOAT";
+            newWidget = this.addWidget("number", name, value !== null && value !== void 0 ? value : 1, undefined, {
+                precision: isFloat ? 1 : 0,
+                step2: isFloat ? 0.1 : 0,
+            });
+            value = Number(value);
+            value = value == null || isNaN(value) ? 0 : value;
+            newWidget.value = value;
+        }
+        else if (type === "BOOLEAN") {
+            newWidget = this.addWidget("toggle", name, !!(value !== null && value !== void 0 ? value : true), undefined, {
+                on: "true",
+                off: "false",
+            });
+            if (typeof value === "string") {
+                value = !["false", "null", "None", "", "0"].includes(value.toLowerCase());
+            }
+            newWidget.value = !!value;
+        }
+        if (newWidget == null) {
+            throw new Error(`Unsupported type "${type}".`);
+        }
+        if (this.valueWidget) {
+            this.replaceWidget(this.valueWidget, newWidget);
+        }
+        else {
+            if (!this.widgets.includes(newWidget)) {
+                this.addCustomWidget(newWidget);
+            }
+            moveArrayItem(this.widgets, newWidget, 1);
+        }
+        this.valueWidget = newWidget;
+        if (!((_e = this.inputs) === null || _e === void 0 ? void 0 : _e.length)) {
+            this.addInput("value", "*", { widget: this.valueWidget });
+        }
+        else {
+            this.inputs[0].widget = this.valueWidget;
+        }
+        const output = this.outputs[0];
+        const outputLabel = output.label === "*" || output.label === output.type ? null : output.label;
+        output.type = type;
+        output.label = outputLabel || output.type;
+    }
+    onConnectionsChange(type, index, isConnected, link_info, inputOrOutput) {
+        var _a;
+        (_a = super.onConnectionsChange) === null || _a === void 0 ? void 0 : _a.apply(this, [...arguments]);
+        if (this.inputs.includes(inputOrOutput)) {
+            this.setTypedData();
+        }
+    }
+    onPropertyChanged(name, value, prev_value) {
+        if (name === PROPERTY_HIDE_TYPE_SELECTOR) {
+            if (!this.outputTypeWidget) {
+                return true;
+            }
+            this.outputTypeWidget.hidden = this.properties[PROPERTY_HIDE_TYPE_SELECTOR];
+            if (this.outputTypeWidget.hidden) {
+                this.outputTypeWidget.computeLayoutSize = () => ({
+                    minHeight: 0,
+                    minWidth: 0,
+                    maxHeight: 0,
+                    maxWidth: 0,
+                });
+            }
+            else {
+                this.outputTypeWidget.computeLayoutSize = undefined;
+            }
+        }
+        return true;
+    }
+}
+RgthreePowerPrimitive.title = NodeTypesString.POWER_PRIMITIVE;
+RgthreePowerPrimitive.type = NodeTypesString.POWER_PRIMITIVE;
+RgthreePowerPrimitive.comfyClass = NodeTypesString.POWER_PRIMITIVE;
+RgthreePowerPrimitive["@hideTypeSelector"] = { type: "boolean" };
+const NODE_CLASS = RgthreePowerPrimitive;
+app.registerExtension({
+    name: "rgthree.PowerPrimitive",
+    async beforeRegisterNodeDef(nodeType, nodeData) {
+        if (nodeData.name === NODE_CLASS.type) {
+            NODE_CLASS.setUp(nodeType, nodeData);
+        }
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/power_prompt.js b/custom_nodes/rgthree-comfy/web/comfyui/power_prompt.js
new file mode 100644
index 0000000000000000000000000000000000000000..c82ac944710996b26eac2c32c2a97347662f0120
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/power_prompt.js
@@ -0,0 +1,42 @@
+import { app } from "../../scripts/app.js";
+import { addConnectionLayoutSupport } from "./utils.js";
+import { PowerPrompt } from "./base_power_prompt.js";
+import { NodeTypesString } from "./constants.js";
+let nodeData = null;
+app.registerExtension({
+    name: "rgthree.PowerPrompt",
+    async beforeRegisterNodeDef(nodeType, passedNodeData) {
+        if (passedNodeData.name.includes("Power Prompt") && passedNodeData.name.includes("rgthree")) {
+            nodeData = passedNodeData;
+            const onNodeCreated = nodeType.prototype.onNodeCreated;
+            nodeType.prototype.onNodeCreated = function () {
+                onNodeCreated ? onNodeCreated.apply(this, []) : undefined;
+                this.powerPrompt = new PowerPrompt(this, passedNodeData);
+            };
+            addConnectionLayoutSupport(nodeType, app, [
+                ["Left", "Right"],
+                ["Right", "Left"],
+            ]);
+        }
+    },
+    async loadedGraphNode(node) {
+        if (node.type === NodeTypesString.POWER_PROMPT) {
+            setTimeout(() => {
+                if (node.outputs[0].type === "STRING") {
+                    if (node.outputs[0].links) {
+                        node.outputs[3].links = node.outputs[3].links || [];
+                        for (const link of node.outputs[0].links) {
+                            node.outputs[3].links.push(link);
+                            (node.graph || app.graph).links[link].origin_slot = 3;
+                        }
+                        node.outputs[0].links = null;
+                    }
+                    node.outputs[0].type = nodeData.output[0];
+                    node.outputs[0].name = nodeData.output_name[0] || node.outputs[0].type;
+                    node.outputs[0].color_on = undefined;
+                    node.outputs[0].color_off = undefined;
+                }
+            }, 50);
+        }
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/power_puter.js b/custom_nodes/rgthree-comfy/web/comfyui/power_puter.js
new file mode 100644
index 0000000000000000000000000000000000000000..52231b3af0bf5151a7604dfca0b82b821929b4ef
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/power_puter.js
@@ -0,0 +1,314 @@
+import { app } from "../../scripts/app.js";
+import { RgthreeBaseServerNode } from "./base_node.js";
+import { NodeTypesString } from "./constants.js";
+import { removeUnusedInputsFromEnd } from "./utils_inputs_outputs.js";
+import { debounce } from "../../rgthree/common/shared_utils.js";
+import { ComfyWidgets } from "../../scripts/widgets.js";
+import { RgthreeBaseWidget } from "./utils_widgets.js";
+import { drawPlusIcon, drawRoundedRectangle, drawWidgetButton, isLowQuality, measureText, } from "./utils_canvas.js";
+import { rgthree } from "./rgthree.js";
+const ALPHABET = "abcdefghijklmnopqrstuv".split("");
+const OUTPUT_TYPES = ["STRING", "INT", "FLOAT", "BOOLEAN", "*"];
+class RgthreePowerPuter extends RgthreeBaseServerNode {
+    constructor(title = NODE_CLASS.title) {
+        super(title);
+        this.stabilizeBound = this.stabilize.bind(this);
+        this.addAnyInput(2);
+        this.addInitialWidgets();
+    }
+    static setUp(comfyClass, nodeData) {
+        RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, NODE_CLASS);
+    }
+    onConnectionsChange(...args) {
+        var _a;
+        (_a = super.onConnectionsChange) === null || _a === void 0 ? void 0 : _a.apply(this, [...arguments]);
+        this.scheduleStabilize();
+    }
+    scheduleStabilize(ms = 64) {
+        return debounce(this.stabilizeBound, ms);
+    }
+    stabilize() {
+        removeUnusedInputsFromEnd(this, 1);
+        this.addAnyInput();
+        this.setOutputs();
+    }
+    addInitialWidgets() {
+        if (!this.outputTypeWidget) {
+            this.outputTypeWidget = this.addCustomWidget(new OutputsWidget("outputs", this));
+            this.expressionWidget = ComfyWidgets["STRING"](this, "code", ["STRING", { multiline: true }], app).widget;
+        }
+    }
+    addAnyInput(num = 1) {
+        for (let i = 0; i < num; i++) {
+            this.addInput(ALPHABET[this.inputs.length], "*");
+        }
+    }
+    setOutputs() {
+        const desiredOutputs = this.outputTypeWidget.value.outputs;
+        for (let i = 0; i < Math.max(this.outputs.length, desiredOutputs.length); i++) {
+            const desired = desiredOutputs[i];
+            let output = this.outputs[i];
+            if (!desired && output) {
+                this.disconnectOutput(i);
+                this.removeOutput(i);
+                continue;
+            }
+            output = output || this.addOutput("", "");
+            const outputLabel = output.label === "*" || output.label === output.type ? null : output.label;
+            output.type = String(desired);
+            output.label = outputLabel || output.type;
+        }
+    }
+    getHelp() {
+        return `
+      <p>
+        The ${this.type.replace("(rgthree)", "")} is a powerful and versatile node that opens the
+        door for a wide range of utility by offering mult-line code parsing for output. This node
+        can be used for simple string concatenation, or math operations; to an image dimension or a
+        node's widgets with advanced list comprehension.
+        If you want to output something in your workflow, this is the node to do it.
+      </p>
+
+      <ul>
+        <li><p>
+          Evaluate almost any kind of input and more, and choose your output from INT, FLOAT,
+          STRING, or BOOLEAN.
+        </p></li>
+        <li><p>
+          Connect some nodes and do simply math operations like <code>a + b</code> or
+          <code>ceil(1 / 2)</code>.
+        </p></li>
+        <li><p>
+          Or do more advanced things, like input an image, and get the width like
+          <code>a.shape[2]</code>.
+        </p></li>
+        <li><p>
+          Even more powerful, you can target nodes in the prompt that's sent to the backend. For
+          instance; if you have a Power Lora Loader node at id #5, and want to get a comma-delimited
+          list of the enabled loras, you could enter
+          <code>', '.join([v.lora for v in node(5).inputs.values() if 'lora' in v and v.on])</code>.
+        </p></li>
+        <li><p>
+          See more at the <a target="_blank"
+          href="https://github.com/rgthree/rgthree-comfy/wiki/Node:-Power-Puter">rgthree-comfy
+          wiki</a>.
+        </p></li>
+      </ul>`;
+    }
+}
+RgthreePowerPuter.title = NodeTypesString.POWER_PUTER;
+RgthreePowerPuter.type = NodeTypesString.POWER_PUTER;
+RgthreePowerPuter.comfyClass = NodeTypesString.POWER_PUTER;
+const NODE_CLASS = RgthreePowerPuter;
+const OUTPUTS_WIDGET_CHIP_HEIGHT = LiteGraph.NODE_WIDGET_HEIGHT - 4;
+const OUTPUTS_WIDGET_CHIP_SPACE = 4;
+const OUTPUTS_WIDGET_CHIP_ARROW_WIDTH = 5.5;
+const OUTPUTS_WIDGET_CHIP_ARROW_HEIGHT = 4;
+class OutputsWidget extends RgthreeBaseWidget {
+    constructor(name, node) {
+        super(name);
+        this.type = "custom";
+        this._value = { outputs: ["STRING"] };
+        this.rows = 1;
+        this.neededHeight = LiteGraph.NODE_WIDGET_HEIGHT + 8;
+        this.hitAreas = {
+            add: { bounds: [0, 0], onClick: this.onAddChipDown },
+            output0: { bounds: [0, 0], onClick: this.onOutputChipDown, data: { index: 0 } },
+            output1: { bounds: [0, 0], onClick: this.onOutputChipDown, data: { index: 1 } },
+            output2: { bounds: [0, 0], onClick: this.onOutputChipDown, data: { index: 2 } },
+            output3: { bounds: [0, 0], onClick: this.onOutputChipDown, data: { index: 3 } },
+            output4: { bounds: [0, 0], onClick: this.onOutputChipDown, data: { index: 4 } },
+            output5: { bounds: [0, 0], onClick: this.onOutputChipDown, data: { index: 5 } },
+            output6: { bounds: [0, 0], onClick: this.onOutputChipDown, data: { index: 6 } },
+            output7: { bounds: [0, 0], onClick: this.onOutputChipDown, data: { index: 7 } },
+            output8: { bounds: [0, 0], onClick: this.onOutputChipDown, data: { index: 8 } },
+            output9: { bounds: [0, 0], onClick: this.onOutputChipDown, data: { index: 9 } },
+        };
+        this.node = node;
+    }
+    set value(v) {
+        let outputs = typeof v === "string" ? [v] : [...v.outputs];
+        outputs = outputs.map((o) => (o === "BOOL" ? "BOOLEAN" : o));
+        this._value.outputs = outputs;
+    }
+    get value() {
+        return this._value;
+    }
+    onAddChipDown(event, pos, node, bounds) {
+        new LiteGraph.ContextMenu(OUTPUT_TYPES, {
+            event: event,
+            title: "Add an output",
+            className: "rgthree-dark",
+            callback: (value) => {
+                if (isLowQuality())
+                    return;
+                if (typeof value === "string" && OUTPUT_TYPES.includes(value)) {
+                    this._value.outputs.push(value);
+                    this.node.scheduleStabilize();
+                }
+            },
+        });
+        this.cancelMouseDown();
+        return true;
+    }
+    onOutputChipDown(event, pos, node, bounds) {
+        const options = [...OUTPUT_TYPES];
+        if (this.value.outputs.length > 1) {
+            options.push(null, "🗑️ Delete");
+        }
+        new LiteGraph.ContextMenu(options, {
+            event: event,
+            title: `Edit output #${bounds.data.index + 1}`,
+            className: "rgthree-dark",
+            callback: (value) => {
+                var _a, _b;
+                const index = bounds.data.index;
+                if (typeof value !== "string" || value === this._value.outputs[index] || isLowQuality()) {
+                    return;
+                }
+                const output = this.node.outputs[index];
+                if (value.toLocaleLowerCase().includes("delete")) {
+                    if ((_a = output.links) === null || _a === void 0 ? void 0 : _a.length) {
+                        rgthree.showMessage({
+                            id: "puter-remove-linked-output",
+                            type: "warn",
+                            message: "[Power Puter] Removed and disconnected output from that was connected!",
+                            timeout: 3000,
+                        });
+                        this.node.disconnectOutput(index);
+                    }
+                    this.node.removeOutput(index);
+                    this._value.outputs.splice(index, 1);
+                    this.node.scheduleStabilize();
+                    return;
+                }
+                if (((_b = output.links) === null || _b === void 0 ? void 0 : _b.length) && value !== "*") {
+                    rgthree.showMessage({
+                        id: "puter-remove-linked-output",
+                        type: "warn",
+                        message: "[Power Puter] Changing output type of linked output! You should check for" +
+                            " compatibility.",
+                        timeout: 3000,
+                    });
+                }
+                this._value.outputs[index] = value;
+                this.node.scheduleStabilize();
+            },
+        });
+        this.cancelMouseDown();
+        return true;
+    }
+    computeLayoutSize(node) {
+        this.neededHeight =
+            OUTPUTS_WIDGET_CHIP_SPACE +
+                (OUTPUTS_WIDGET_CHIP_HEIGHT + OUTPUTS_WIDGET_CHIP_SPACE) * this.rows;
+        return {
+            minHeight: this.neededHeight,
+            maxHeight: this.neededHeight,
+            minWidth: 0,
+        };
+    }
+    draw(ctx, node, w, posY, height) {
+        var _a, _b;
+        ctx.save();
+        height = this.neededHeight;
+        const margin = 10;
+        const innerMargin = margin * 0.33;
+        const width = node.size[0] - margin * 2;
+        let borderRadius = LiteGraph.NODE_WIDGET_HEIGHT * 0.5;
+        let midY = posY + height * 0.5;
+        let posX = margin;
+        let rposX = node.size[0] - margin;
+        drawRoundedRectangle(ctx, { pos: [posX, posY], size: [width, height], borderRadius });
+        posX += innerMargin * 2;
+        rposX -= innerMargin * 2;
+        if (isLowQuality()) {
+            ctx.restore();
+            return;
+        }
+        ctx.fillStyle = LiteGraph.WIDGET_SECONDARY_TEXT_COLOR;
+        ctx.textAlign = "left";
+        ctx.textBaseline = "middle";
+        ctx.fillText("outputs", posX, midY);
+        posX += measureText(ctx, "outputs") + innerMargin * 2;
+        ctx.stroke(new Path2D(`M ${posX} ${posY} v ${height}`));
+        posX += 1 + innerMargin * 2;
+        const inititalPosX = posX;
+        posY += OUTPUTS_WIDGET_CHIP_SPACE;
+        height = OUTPUTS_WIDGET_CHIP_HEIGHT;
+        borderRadius = height * 0.5;
+        midY = posY + height / 2;
+        ctx.textAlign = "center";
+        ctx.lineJoin = ctx.lineCap = "round";
+        ctx.fillStyle = ctx.strokeStyle = LiteGraph.WIDGET_TEXT_COLOR;
+        let rows = 1;
+        const values = (_b = (_a = this.value) === null || _a === void 0 ? void 0 : _a.outputs) !== null && _b !== void 0 ? _b : [];
+        const fontSize = ctx.font.match(/(\d+)px/);
+        if (fontSize === null || fontSize === void 0 ? void 0 : fontSize[1]) {
+            ctx.font = ctx.font.replace(fontSize[1], `${Number(fontSize[1]) - 2}`);
+        }
+        let i = 0;
+        for (i; i < values.length; i++) {
+            const hitArea = this.hitAreas[`output${i}`];
+            const isClicking = !!hitArea.wasMouseClickedAndIsOver;
+            hitArea.data.index = i;
+            const text = values[i];
+            const textWidth = measureText(ctx, text) + innerMargin * 2;
+            const width = textWidth + OUTPUTS_WIDGET_CHIP_ARROW_WIDTH + innerMargin * 5;
+            if (posX + width >= rposX) {
+                posX = inititalPosX;
+                posY = posY + height + 4;
+                midY = posY + height / 2;
+                rows++;
+            }
+            drawWidgetButton(ctx, { pos: [posX, posY], size: [width, height], borderRadius }, null, isClicking);
+            const startX = posX;
+            posX += innerMargin * 2;
+            const newMidY = midY + (isClicking ? 1 : 0);
+            ctx.fillText(text, posX + textWidth / 2, newMidY);
+            posX += textWidth + innerMargin;
+            const arrow = new Path2D(`M${posX} ${newMidY - OUTPUTS_WIDGET_CHIP_ARROW_HEIGHT / 2}
+         h${OUTPUTS_WIDGET_CHIP_ARROW_WIDTH}
+         l-${OUTPUTS_WIDGET_CHIP_ARROW_WIDTH / 2} ${OUTPUTS_WIDGET_CHIP_ARROW_HEIGHT} z`);
+            ctx.fill(arrow);
+            ctx.stroke(arrow);
+            posX += OUTPUTS_WIDGET_CHIP_ARROW_WIDTH + innerMargin * 2;
+            hitArea.bounds = [startX, posY, width, height];
+            posX += OUTPUTS_WIDGET_CHIP_SPACE;
+        }
+        for (i; i < 9; i++) {
+            const hitArea = this.hitAreas[`output${i}`];
+            if (hitArea.bounds[0] > 0) {
+                hitArea.bounds = [0, 0, 0, 0];
+            }
+        }
+        const addHitArea = this.hitAreas["add"];
+        if (this.value.outputs.length < 10) {
+            const isClicking = !!addHitArea.wasMouseClickedAndIsOver;
+            const plusSize = 10;
+            let plusWidth = innerMargin * 2 + plusSize + innerMargin * 2;
+            if (posX + plusWidth >= rposX) {
+                posX = inititalPosX;
+                posY = posY + height + 4;
+                midY = posY + height / 2;
+                rows++;
+            }
+            drawWidgetButton(ctx, { size: [plusWidth, height], pos: [posX, posY], borderRadius }, null, isClicking);
+            drawPlusIcon(ctx, posX + innerMargin * 2, midY + (isClicking ? 1 : 0), plusSize);
+            addHitArea.bounds = [posX, posY, plusWidth, height];
+        }
+        else {
+            addHitArea.bounds = [0, 0, 0, 0];
+        }
+        this.rows = rows;
+        ctx.restore();
+    }
+}
+app.registerExtension({
+    name: "rgthree.PowerPuter",
+    async beforeRegisterNodeDef(nodeType, nodeData) {
+        if (nodeData.name === NODE_CLASS.type) {
+            NODE_CLASS.setUp(nodeType, nodeData);
+        }
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/random_unmuter.js b/custom_nodes/rgthree-comfy/web/comfyui/random_unmuter.js
new file mode 100644
index 0000000000000000000000000000000000000000..213aa8c0ac50da67b21f81bf4a969b0c3a0efe62
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/random_unmuter.js
@@ -0,0 +1,102 @@
+import { app } from "../../scripts/app.js";
+import { BaseAnyInputConnectedNode } from "./base_any_input_connected_node.js";
+import { NodeTypesString } from "./constants.js";
+import { rgthree } from "./rgthree.js";
+import { changeModeOfNodes, getConnectedInputNodesAndFilterPassThroughs } from "./utils.js";
+const MODE_MUTE = 2;
+const MODE_ALWAYS = 0;
+class RandomUnmuterNode extends BaseAnyInputConnectedNode {
+    constructor(title = RandomUnmuterNode.title) {
+        super(title);
+        this.comfyClass = NodeTypesString.RANDOM_UNMUTER;
+        this.modeOn = MODE_ALWAYS;
+        this.modeOff = MODE_MUTE;
+        this.tempEnabledNode = null;
+        this.processingQueue = false;
+        this.onQueueBound = this.onQueue.bind(this);
+        this.onQueueEndBound = this.onQueueEnd.bind(this);
+        this.onGraphtoPromptBound = this.onGraphtoPrompt.bind(this);
+        this.onGraphtoPromptEndBound = this.onGraphtoPromptEnd.bind(this);
+        rgthree.addEventListener("queue", this.onQueueBound);
+        rgthree.addEventListener("queue-end", this.onQueueEndBound);
+        rgthree.addEventListener("graph-to-prompt", this.onGraphtoPromptBound);
+        rgthree.addEventListener("graph-to-prompt-end", this.onGraphtoPromptEndBound);
+        this.onConstructed();
+    }
+    onRemoved() {
+        rgthree.removeEventListener("queue", this.onQueueBound);
+        rgthree.removeEventListener("queue-end", this.onQueueEndBound);
+        rgthree.removeEventListener("graph-to-prompt", this.onGraphtoPromptBound);
+        rgthree.removeEventListener("graph-to-prompt-end", this.onGraphtoPromptEndBound);
+    }
+    onQueue(event) {
+        this.processingQueue = true;
+    }
+    onQueueEnd(event) {
+        this.processingQueue = false;
+    }
+    onGraphtoPrompt(event) {
+        if (!this.processingQueue) {
+            return;
+        }
+        this.tempEnabledNode = null;
+        const linkedNodes = getConnectedInputNodesAndFilterPassThroughs(this);
+        let allMuted = true;
+        if (linkedNodes.length) {
+            for (const node of linkedNodes) {
+                if (node.mode !== this.modeOff) {
+                    allMuted = false;
+                    break;
+                }
+            }
+            if (allMuted) {
+                this.tempEnabledNode = linkedNodes[Math.floor(Math.random() * linkedNodes.length)] || null;
+                if (this.tempEnabledNode) {
+                    changeModeOfNodes(this.tempEnabledNode, this.modeOn);
+                }
+            }
+        }
+    }
+    onGraphtoPromptEnd(event) {
+        if (this.tempEnabledNode) {
+            changeModeOfNodes(this.tempEnabledNode, this.modeOff);
+            this.tempEnabledNode = null;
+        }
+    }
+    handleLinkedNodesStabilization(linkedNodes) {
+        return false;
+    }
+    getHelp() {
+        return `
+      <p>
+        Use this node to unmute on of its inputs randomly when the graph is queued (and, immediately
+        mute it back).
+      </p>
+      <ul>
+        <li><p>
+          NOTE: All input nodes MUST be muted to start; if not this node will not randomly unmute
+          another. (This is powerful, as the generated image can be dragged in and the chosen input
+          will already by unmuted and work w/o any further action.)
+        </p></li>
+        <li><p>
+          TIP: Connect a Repeater's output to this nodes input and place that Repeater on a group
+          without any other inputs, and it will mute/unmute the entire group.
+        </p></li>
+      </ul>
+    `;
+    }
+}
+RandomUnmuterNode.exposedActions = ["Mute all", "Enable all"];
+RandomUnmuterNode.type = NodeTypesString.RANDOM_UNMUTER;
+RandomUnmuterNode.title = RandomUnmuterNode.type;
+app.registerExtension({
+    name: "rgthree.RandomUnmuter",
+    registerCustomNodes() {
+        RandomUnmuterNode.setUp();
+    },
+    loadedGraphNode(node) {
+        if (node.type == RandomUnmuterNode.title) {
+            node._tempWidth = node.size[0];
+        }
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/reroute.js b/custom_nodes/rgthree-comfy/web/comfyui/reroute.js
new file mode 100644
index 0000000000000000000000000000000000000000..7d741cee6ec7713534de73f42c28066d596460bb
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/reroute.js
@@ -0,0 +1,981 @@
+var _a, _b;
+import { app } from "../../scripts/app.js";
+import { rgthreeConfig } from "../../rgthree/config.js";
+import { rgthree } from "./rgthree.js";
+import { IoDirection, LAYOUT_CLOCKWISE, LAYOUT_LABEL_OPPOSITES, LAYOUT_LABEL_TO_DATA, addConnectionLayoutSupport, addMenuItem, getSlotLinks, isValidConnection, setConnectionsLayout, waitForCanvas, } from "./utils.js";
+import { SERVICE as KEY_EVENT_SERVICE } from "./services/key_events_services.js";
+import { wait } from "../../rgthree/common/shared_utils.js";
+import { RgthreeBaseVirtualNode } from "./base_node.js";
+import { NodeTypesString } from "./constants.js";
+import { rgthreeApi } from "../../rgthree/common/rgthree_api.js";
+import { getWidgetConfig, mergeIfValid, setWidgetConfig } from "./utils_deprecated_comfyui.js";
+const CONFIG_REROUTE = ((_a = rgthreeConfig === null || rgthreeConfig === void 0 ? void 0 : rgthreeConfig["nodes"]) === null || _a === void 0 ? void 0 : _a["reroute"]) || {};
+const CONFIG_FAST_REROUTE = CONFIG_REROUTE["fast_reroute"];
+const CONFIG_FAST_REROUTE_ENABLED = (_b = CONFIG_FAST_REROUTE["enabled"]) !== null && _b !== void 0 ? _b : false;
+const CONFIG_KEY_CREATE_WHILE_LINKING = CONFIG_FAST_REROUTE["key_create_while_dragging_link"];
+const CONFIG_KEY_ROTATE = CONFIG_FAST_REROUTE["key_rotate"];
+const CONFIG_KEY_RESIZE = CONFIG_FAST_REROUTE["key_resize"];
+const CONFIG_KEY_MOVE = CONFIG_FAST_REROUTE["key_move"];
+const CONFIG_KEY_CXN_INPUT = CONFIG_FAST_REROUTE["key_connections_input"];
+const CONFIG_KEY_CXN_OUTPUT = CONFIG_FAST_REROUTE["key_connections_output"];
+let configWidth = Math.max(Math.round((Number(CONFIG_REROUTE["default_width"]) || 40) / 10) * 10, 10);
+let configHeight = Math.max(Math.round((Number(CONFIG_REROUTE["default_height"]) || 30) / 10) * 10, 10);
+while (configWidth * configHeight < 400) {
+    configWidth += 10;
+    configHeight += 10;
+}
+const configDefaultSize = [configWidth, configHeight];
+const configResizable = !!CONFIG_REROUTE["default_resizable"];
+let configLayout = CONFIG_REROUTE["default_layout"];
+if (!Array.isArray(configLayout)) {
+    configLayout = ["Left", "Right"];
+}
+if (!LAYOUT_LABEL_TO_DATA[configLayout[0]]) {
+    configLayout[0] = "Left";
+}
+if (!LAYOUT_LABEL_TO_DATA[configLayout[1]] || configLayout[0] == configLayout[1]) {
+    configLayout[1] = LAYOUT_LABEL_OPPOSITES[configLayout[0]];
+}
+class RerouteService {
+    constructor() {
+        this.isFastLinking = false;
+        this.handledNewRerouteKeypress = false;
+        this.connectingData = null;
+        this.fastReroutesHistory = [];
+        this.handleLinkingKeydownBound = this.handleLinkingKeydown.bind(this);
+        this.handleLinkingKeyupBound = this.handleLinkingKeyup.bind(this);
+        if (CONFIG_FAST_REROUTE_ENABLED && (CONFIG_KEY_CREATE_WHILE_LINKING === null || CONFIG_KEY_CREATE_WHILE_LINKING === void 0 ? void 0 : CONFIG_KEY_CREATE_WHILE_LINKING.trim())) {
+            this.onCanvasSetUpListenerForLinking();
+        }
+    }
+    async onCanvasSetUpListenerForLinking() {
+        const canvas = await waitForCanvas();
+        const canvasProperty = true ? "connecting_links" : "connecting_node";
+        canvas[`_${canvasProperty}`];
+        const thisService = this;
+        Object.defineProperty(canvas, canvasProperty, {
+            get: function () {
+                return this[`_${canvasProperty}`];
+            },
+            set: function (value) {
+                var _a;
+                const isValNull = !value || !(value === null || value === void 0 ? void 0 : value.length);
+                const isPropNull = !this[`_${canvasProperty}`] || !((_a = this[`_${canvasProperty}`]) === null || _a === void 0 ? void 0 : _a.length);
+                const isStartingLinking = !isValNull && isPropNull;
+                const isStoppingLinking = !isPropNull && isValNull;
+                this[`_${canvasProperty}`] = value;
+                if (isStartingLinking) {
+                    thisService.startingLinking();
+                }
+                if (isStoppingLinking) {
+                    thisService.stoppingLinking();
+                    thisService.connectingData = null;
+                }
+            },
+        });
+    }
+    startingLinking() {
+        this.isFastLinking = true;
+        KEY_EVENT_SERVICE.addEventListener("keydown", this.handleLinkingKeydownBound);
+        KEY_EVENT_SERVICE.addEventListener("keyup", this.handleLinkingKeyupBound);
+    }
+    stoppingLinking() {
+        this.isFastLinking = false;
+        this.fastReroutesHistory = [];
+        KEY_EVENT_SERVICE.removeEventListener("keydown", this.handleLinkingKeydownBound);
+        KEY_EVENT_SERVICE.removeEventListener("keyup", this.handleLinkingKeyupBound);
+    }
+    handleLinkingKeydown(event) {
+        if (!this.handledNewRerouteKeypress &&
+            KEY_EVENT_SERVICE.areOnlyKeysDown(CONFIG_KEY_CREATE_WHILE_LINKING)) {
+            this.handledNewRerouteKeypress = true;
+            this.insertNewRerouteWhileLinking();
+        }
+    }
+    handleLinkingKeyup(event) {
+        if (this.handledNewRerouteKeypress &&
+            !KEY_EVENT_SERVICE.areOnlyKeysDown(CONFIG_KEY_CREATE_WHILE_LINKING)) {
+            this.handledNewRerouteKeypress = false;
+        }
+    }
+    getConnectingData() {
+        var _a, _b, _c, _d;
+        const oldCanvas = app.canvas;
+        if (oldCanvas.connecting_node &&
+            oldCanvas.connecting_slot != null &&
+            ((_a = oldCanvas.connecting_pos) === null || _a === void 0 ? void 0 : _a.length)) {
+            return {
+                node: oldCanvas.connecting_node,
+                input: oldCanvas.connecting_input,
+                output: oldCanvas.connecting_output,
+                slot: oldCanvas.connecting_slot,
+                pos: [...oldCanvas.connecting_pos],
+            };
+        }
+        const canvas = app.canvas;
+        if ((_b = canvas.connecting_links) === null || _b === void 0 ? void 0 : _b.length) {
+            const link = canvas.connecting_links[0];
+            return {
+                node: link.node,
+                input: (_c = link.input) !== null && _c !== void 0 ? _c : undefined,
+                output: (_d = link.output) !== null && _d !== void 0 ? _d : undefined,
+                slot: link.slot,
+                pos: [...link.pos],
+            };
+        }
+        throw new Error("Error, handling linking keydown, but there's no link.");
+    }
+    setCanvasConnectingData(ctx) {
+        var _a, _b;
+        const oldCanvas = app.canvas;
+        if (oldCanvas.connecting_node &&
+            oldCanvas.connecting_slot != null &&
+            ((_a = oldCanvas.connecting_pos) === null || _a === void 0 ? void 0 : _a.length)) {
+            oldCanvas.connecting_node = ctx.node;
+            oldCanvas.connecting_input = ctx.input;
+            oldCanvas.connecting_output = ctx.output;
+            oldCanvas.connecting_slot = ctx.slot;
+            oldCanvas.connecting_pos = ctx.pos;
+        }
+        const canvas = app.canvas;
+        if ((_b = canvas.connecting_links) === null || _b === void 0 ? void 0 : _b.length) {
+            const link = canvas.connecting_links[0];
+            link.node = ctx.node;
+            link.input = ctx.input;
+            link.output = ctx.output;
+            link.slot = ctx.slot;
+            link.pos = ctx.pos;
+        }
+    }
+    insertNewRerouteWhileLinking() {
+        var _a;
+        const canvas = app.canvas;
+        this.connectingData = this.getConnectingData();
+        if (!this.connectingData) {
+            throw new Error("Error, handling linking keydown, but there's no link.");
+        }
+        const data = this.connectingData;
+        const node = LiteGraph.createNode("Reroute (rgthree)");
+        const entry = {
+            node,
+            previous: { ...this.connectingData },
+            current: undefined,
+        };
+        this.fastReroutesHistory.push(entry);
+        let connectingDir = (_a = (data.input || data.output)) === null || _a === void 0 ? void 0 : _a.dir;
+        if (!connectingDir) {
+            connectingDir = data.input ? LiteGraph.LEFT : LiteGraph.RIGHT;
+        }
+        let newPos = canvas.convertEventToCanvasOffset({
+            clientX: Math.round(canvas.last_mouse_position[0] / 10) * 10,
+            clientY: Math.round(canvas.last_mouse_position[1] / 10) * 10,
+        });
+        entry.node.pos = newPos;
+        canvas.graph.add(entry.node);
+        canvas.selectNode(entry.node);
+        const distX = entry.node.pos[0] - data.pos[0];
+        const distY = entry.node.pos[1] - data.pos[1];
+        const layout = ["Left", "Right"];
+        if (distX > 0 && Math.abs(distX) > Math.abs(distY)) {
+            layout[0] = data.output ? "Left" : "Right";
+            layout[1] = LAYOUT_LABEL_OPPOSITES[layout[0]];
+            node.pos[0] -= node.size[0] + 10;
+            node.pos[1] -= Math.round(node.size[1] / 2 / 10) * 10;
+        }
+        else if (distX < 0 && Math.abs(distX) > Math.abs(distY)) {
+            layout[0] = data.output ? "Right" : "Left";
+            layout[1] = LAYOUT_LABEL_OPPOSITES[layout[0]];
+            node.pos[1] -= Math.round(node.size[1] / 2 / 10) * 10;
+        }
+        else if (distY < 0 && Math.abs(distY) > Math.abs(distX)) {
+            layout[0] = data.output ? "Bottom" : "Top";
+            layout[1] = LAYOUT_LABEL_OPPOSITES[layout[0]];
+            node.pos[0] -= Math.round(node.size[0] / 2 / 10) * 10;
+        }
+        else if (distY > 0 && Math.abs(distY) > Math.abs(distX)) {
+            layout[0] = data.output ? "Top" : "Bottom";
+            layout[1] = LAYOUT_LABEL_OPPOSITES[layout[0]];
+            node.pos[0] -= Math.round(node.size[0] / 2 / 10) * 10;
+            node.pos[1] -= node.size[1] + 10;
+        }
+        setConnectionsLayout(entry.node, layout);
+        if (data.output) {
+            data.node.connect(data.slot, entry.node, 0);
+            data.node = entry.node;
+            data.output = entry.node.outputs[0];
+            data.slot = 0;
+            data.pos = entry.node.getConnectionPos(false, 0);
+            data.direction =
+                layout[0] === "Top" ? 2 : layout[0] === "Bottom" ? 1 : layout[0] === "Left" ? 4 : 3;
+        }
+        else {
+            entry.node.connect(0, data.node, data.slot);
+            data.node = entry.node;
+            data.input = entry.node.inputs[0];
+            data.slot = 0;
+            data.pos = entry.node.getConnectionPos(true, 0);
+            data.direction =
+                layout[1] === "Top" ? 2 : layout[1] === "Bottom" ? 1 : layout[1] === "Left" ? 4 : 3;
+        }
+        this.setCanvasConnectingData(data);
+        entry.current = { ...this.connectingData };
+        app.graph.setDirtyCanvas(true, true);
+    }
+    handleMoveOrResizeNodeMaybeWhileDragging(node) {
+        const data = this.connectingData;
+        if (this.isFastLinking && node === (data === null || data === void 0 ? void 0 : data.node)) {
+            const entry = this.fastReroutesHistory[this.fastReroutesHistory.length - 1];
+            if (entry) {
+                data.pos = entry.node.getConnectionPos(!!data.input, 0);
+                this.setCanvasConnectingData(data);
+            }
+        }
+    }
+    handleRemovedNodeMaybeWhileDragging(node) {
+        const currentEntry = this.fastReroutesHistory[this.fastReroutesHistory.length - 1];
+        if ((currentEntry === null || currentEntry === void 0 ? void 0 : currentEntry.node) === node) {
+            this.setCanvasConnectingData(currentEntry.previous);
+            this.fastReroutesHistory.splice(this.fastReroutesHistory.length - 1, 1);
+            if (currentEntry.previous.node) {
+                app.canvas.selectNode(currentEntry.previous.node);
+            }
+        }
+    }
+}
+const SERVICE = new RerouteService();
+class RerouteNode extends RgthreeBaseVirtualNode {
+    constructor(title = RerouteNode.title) {
+        super(title);
+        this.comfyClass = NodeTypesString.REROUTE;
+        this.isVirtualNode = true;
+        this.hideSlotLabels = true;
+        this.schedulePromise = null;
+        this.defaultConnectionsLayout = Array.from(configLayout);
+        this.shortcuts = {
+            rotate: { keys: CONFIG_KEY_ROTATE, state: false },
+            connection_input: { keys: CONFIG_KEY_CXN_INPUT, state: false },
+            connection_output: { keys: CONFIG_KEY_CXN_OUTPUT, state: false },
+            resize: {
+                keys: CONFIG_KEY_RESIZE,
+                state: false,
+                initialMousePos: [-1, -1],
+                initialNodeSize: [-1, -1],
+                initialNodePos: [-1, -1],
+                resizeOnSide: [-1, -1],
+            },
+            move: {
+                keys: CONFIG_KEY_MOVE,
+                state: false,
+                initialMousePos: [-1, -1],
+                initialNodePos: [-1, -1],
+            },
+        };
+        this.onConstructed();
+    }
+    onConstructed() {
+        var _a;
+        this.setResizable(!!((_a = this.properties["resizable"]) !== null && _a !== void 0 ? _a : configResizable));
+        this.size = RerouteNode.size;
+        this.addInput("", "*");
+        this.addOutput("", "*");
+        setTimeout(() => this.applyNodeSize(), 20);
+        return super.onConstructed();
+    }
+    configure(info) {
+        var _a, _b;
+        if ((_a = info.inputs) === null || _a === void 0 ? void 0 : _a.length) {
+            info.inputs.length = 1;
+        }
+        super.configure(info);
+        this.configuring = true;
+        this.setResizable(!!((_b = this.properties["resizable"]) !== null && _b !== void 0 ? _b : configResizable));
+        this.applyNodeSize();
+        this.configuring = false;
+    }
+    setResizable(resizable) {
+        this.properties["resizable"] = !!resizable;
+        this.resizable = this.properties["resizable"];
+    }
+    clone() {
+        const cloned = super.clone();
+        cloned.inputs[0].type = "*";
+        cloned.outputs[0].type = "*";
+        return cloned;
+    }
+    onConnectionsChange(type, _slotIndex, connected, _link_info, _ioSlot) {
+        if (connected && type === LiteGraph.OUTPUT) {
+            const types = new Set(this.outputs[0].links.map((l) => { var _a; return (_a = app.graph.links[l]) === null || _a === void 0 ? void 0 : _a.type; }).filter((t) => t && t !== "*"));
+            if (types.size > 1) {
+                const linksToDisconnect = [];
+                for (let i = 0; i < this.outputs[0].links.length - 1; i++) {
+                    const linkId = this.outputs[0].links[i];
+                    const link = app.graph.links[linkId];
+                    linksToDisconnect.push(link);
+                }
+                for (const link of linksToDisconnect) {
+                    const node = app.graph.getNodeById(link.target_id);
+                    node.disconnectInput(link.target_slot);
+                }
+            }
+        }
+        this.scheduleStabilize();
+    }
+    onDrawForeground(ctx, canvas) {
+        var _a, _b, _c, _d;
+        if ((_a = this.properties) === null || _a === void 0 ? void 0 : _a["showLabel"]) {
+            const low_quality = ((_b = canvas === null || canvas === void 0 ? void 0 : canvas.ds) === null || _b === void 0 ? void 0 : _b.scale) && canvas.ds.scale < 0.6;
+            if (low_quality || this.size[0] <= 10) {
+                return;
+            }
+            const fontSize = Math.min(14, (this.size[1] * 0.65) | 0);
+            ctx.save();
+            ctx.fillStyle = "#888";
+            ctx.font = `${fontSize}px Arial`;
+            ctx.textAlign = "center";
+            ctx.textBaseline = "middle";
+            ctx.fillText(String(this.title && this.title !== RerouteNode.title
+                ? this.title
+                : ((_d = (_c = this.outputs) === null || _c === void 0 ? void 0 : _c[0]) === null || _d === void 0 ? void 0 : _d.type) || ""), this.size[0] / 2, this.size[1] / 2, this.size[0] - 30);
+            ctx.restore();
+        }
+    }
+    findInputSlot(name, returnObj = false) {
+        return returnObj ? this.inputs[0] : 0;
+    }
+    findOutputSlot(name, returnObj) {
+        return returnObj ? this.outputs[0] : 0;
+    }
+    disconnectOutput(slot, targetNode) {
+        return super.disconnectOutput(slot, targetNode);
+    }
+    disconnectInput(slot) {
+        var _a;
+        if (rgthree.replacingReroute != null && ((_a = this.inputs[0]) === null || _a === void 0 ? void 0 : _a.link)) {
+            const graph = app.graph;
+            const link = graph.links[this.inputs[0].link];
+            const node = (link === null || link === void 0 ? void 0 : link.origin_id) != null ? graph.getNodeById(link.origin_id) : null;
+            if (rgthree.replacingReroute !== (node === null || node === void 0 ? void 0 : node.id)) {
+                return false;
+            }
+        }
+        return super.disconnectInput(slot);
+    }
+    scheduleStabilize(ms = 64) {
+        if (!this.schedulePromise) {
+            this.schedulePromise = new Promise((resolve) => {
+                setTimeout(() => {
+                    this.schedulePromise = null;
+                    this.stabilize();
+                    resolve();
+                }, ms);
+            });
+        }
+        return this.schedulePromise;
+    }
+    stabilize() {
+        var _a, _b, _c, _d, _e, _f, _g, _h, _j, _k, _l;
+        if (this.configuring) {
+            return;
+        }
+        let currentNode = this;
+        let updateNodes = [];
+        let input = null;
+        let inputType = null;
+        let inputNode = null;
+        let inputNodeOutputSlot = null;
+        while (currentNode) {
+            updateNodes.unshift(currentNode);
+            const linkId = currentNode.inputs[0].link;
+            if (linkId !== null) {
+                const link = app.graph.links[linkId];
+                const node = app.graph.getNodeById(link.origin_id);
+                if (!node) {
+                    app.graph.removeLink(linkId);
+                    currentNode = null;
+                    break;
+                }
+                const type = node.constructor.type;
+                if (type === null || type === void 0 ? void 0 : type.includes("Reroute")) {
+                    if (node === this) {
+                        currentNode.disconnectInput(link.target_slot);
+                        currentNode = null;
+                    }
+                    else {
+                        currentNode = node;
+                    }
+                }
+                else {
+                    inputNode = node;
+                    inputNodeOutputSlot = link.origin_slot;
+                    input = (_a = node.outputs[inputNodeOutputSlot]) !== null && _a !== void 0 ? _a : null;
+                    inputType = (_b = input === null || input === void 0 ? void 0 : input.type) !== null && _b !== void 0 ? _b : null;
+                    break;
+                }
+            }
+            else {
+                currentNode = null;
+                break;
+            }
+        }
+        const nodes = [this];
+        let outputNode = null;
+        let outputType = null;
+        let outputWidgetConfig = null;
+        let outputWidget = null;
+        while (nodes.length) {
+            currentNode = nodes.pop();
+            const outputs = (currentNode.outputs ? currentNode.outputs[0].links : []) || [];
+            if (outputs.length) {
+                for (const linkId of outputs) {
+                    const link = app.graph.links[linkId];
+                    if (!link)
+                        continue;
+                    const node = app.graph.getNodeById(link.target_id);
+                    if (!node)
+                        continue;
+                    const type = node.constructor.type;
+                    if (type === null || type === void 0 ? void 0 : type.includes("Reroute")) {
+                        nodes.push(node);
+                        updateNodes.push(node);
+                    }
+                    else {
+                        const output = (_d = (_c = node.inputs) === null || _c === void 0 ? void 0 : _c[link.target_slot]) !== null && _d !== void 0 ? _d : null;
+                        const nodeOutType = output === null || output === void 0 ? void 0 : output.type;
+                        if (nodeOutType == null) {
+                            console.warn(`[rgthree] Reroute - Connected node ${node.id} does not have type information for ` +
+                                `slot ${link.target_slot}. Skipping connection enforcement, but something is odd ` +
+                                `with that node.`);
+                        }
+                        else if (inputType &&
+                            inputType !== "*" &&
+                            nodeOutType !== "*" &&
+                            !isValidConnection(input, output)) {
+                            console.warn(`[rgthree] Reroute - Disconnecting connected node's input (${node.id}.${link.target_slot}) (${node.type}) because its type (${String(nodeOutType)}) does not match the reroute type (${String(inputType)})`);
+                            node.disconnectInput(link.target_slot);
+                        }
+                        else {
+                            outputType = nodeOutType;
+                            outputNode = node;
+                            outputWidgetConfig = null;
+                            outputWidget = null;
+                            if (output === null || output === void 0 ? void 0 : output.widget) {
+                                rgthreeApi.print("PRIMITIVE_REROUTE");
+                                try {
+                                    const config = getWidgetConfig(output);
+                                    if (!outputWidgetConfig && config) {
+                                        outputWidgetConfig = (_e = config[1]) !== null && _e !== void 0 ? _e : {};
+                                        outputType = config[0];
+                                        if (!outputWidget) {
+                                            outputWidget = (_f = outputNode.widgets) === null || _f === void 0 ? void 0 : _f.find((w) => { var _a; return w.name === ((_a = output === null || output === void 0 ? void 0 : output.widget) === null || _a === void 0 ? void 0 : _a.name); });
+                                        }
+                                        const merged = mergeIfValid(output, [config[0], outputWidgetConfig]);
+                                        if (merged === null || merged === void 0 ? void 0 : merged.customConfig) {
+                                            outputWidgetConfig = merged.customConfig;
+                                        }
+                                    }
+                                }
+                                catch (e) {
+                                    console.error("[rgthree] Could not propagate widget infor for reroute; maybe ComfyUI updated?");
+                                    outputWidgetConfig = null;
+                                    outputWidget = null;
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+            else {
+            }
+        }
+        const displayType = inputType || outputType || "*";
+        const color = LGraphCanvas.link_type_colors[displayType];
+        for (const node of updateNodes) {
+            node.outputs[0].type = inputType || "*";
+            node.__outputType = displayType;
+            node.outputs[0].name = (input === null || input === void 0 ? void 0 : input.name) || "";
+            node.size = node.computeSize();
+            (_h = (_g = node).applyNodeSize) === null || _h === void 0 ? void 0 : _h.call(_g);
+            for (const l of node.outputs[0].links || []) {
+                const link = app.graph.links[l];
+                if (link && color) {
+                    link.color = color;
+                }
+            }
+            try {
+                if (outputWidgetConfig && outputWidget && outputType) {
+                    rgthreeApi.print("PRIMITIVE_REROUTE");
+                    node.inputs[0].widget = { name: "value" };
+                    setWidgetConfig(node.inputs[0], [outputType !== null && outputType !== void 0 ? outputType : displayType, outputWidgetConfig]);
+                }
+                else {
+                    setWidgetConfig(node.inputs[0], null);
+                }
+            }
+            catch (e) {
+                console.error("[rgthree] Could not set widget config for reroute; maybe ComfyUI updated?");
+                outputWidgetConfig = null;
+                outputWidget = null;
+                if ((_j = node.inputs[0]) === null || _j === void 0 ? void 0 : _j.widget) {
+                    delete node.inputs[0].widget;
+                }
+            }
+        }
+        if (inputNode && inputNodeOutputSlot != null) {
+            const links = inputNode.outputs[inputNodeOutputSlot].links;
+            for (const l of links || []) {
+                const link = app.graph.links[l];
+                if (link && color) {
+                    link.color = color;
+                }
+            }
+        }
+        (_k = inputNode === null || inputNode === void 0 ? void 0 : inputNode.onConnectionsChainChange) === null || _k === void 0 ? void 0 : _k.call(inputNode);
+        (_l = outputNode === null || outputNode === void 0 ? void 0 : outputNode.onConnectionsChainChange) === null || _l === void 0 ? void 0 : _l.call(outputNode);
+        app.graph.setDirtyCanvas(true, true);
+    }
+    setSize(size) {
+        const oldSize = [...this.size];
+        const newSize = [...size];
+        super.setSize(newSize);
+        this.properties["size"] = [...this.size];
+        this.stabilizeLayout(oldSize, newSize);
+    }
+    stabilizeLayout(oldSize, newSize) {
+        if (newSize[0] === 10 || newSize[1] === 10) {
+            const props = this.properties;
+            props["connections_layout"] = props["connections_layout"] || ["Left", "Right"];
+            props["connections_dir"] = props["connections_dir"] || [-1, -1];
+            const layout = props["connections_layout"];
+            const dir = props["connections_dir"];
+            if (oldSize[0] > 10 && newSize[0] === 10) {
+                dir[0] = LiteGraph.DOWN;
+                dir[1] = LiteGraph.UP;
+                if (layout[0] === "Bottom") {
+                    layout[1] = "Top";
+                }
+                else if (layout[1] === "Top") {
+                    layout[0] = "Bottom";
+                }
+                else {
+                    layout[0] = "Top";
+                    layout[1] = "Bottom";
+                    dir[0] = LiteGraph.UP;
+                    dir[1] = LiteGraph.DOWN;
+                }
+                this.setDirtyCanvas(true, true);
+            }
+            else if (oldSize[1] > 10 && newSize[1] === 10) {
+                dir[0] = LiteGraph.RIGHT;
+                dir[1] = LiteGraph.LEFT;
+                if (layout[0] === "Right") {
+                    layout[1] = "Left";
+                }
+                else if (layout[1] === "Left") {
+                    layout[0] = "Right";
+                }
+                else {
+                    layout[0] = "Left";
+                    layout[1] = "Right";
+                    dir[0] = LiteGraph.LEFT;
+                    dir[1] = LiteGraph.RIGHT;
+                }
+                this.setDirtyCanvas(true, true);
+            }
+        }
+        SERVICE.handleMoveOrResizeNodeMaybeWhileDragging(this);
+    }
+    applyNodeSize() {
+        this.properties["size"] = this.properties["size"] || RerouteNode.size;
+        this.properties["size"] = [
+            Number(this.properties["size"][0]),
+            Number(this.properties["size"][1]),
+        ];
+        this.size = this.properties["size"];
+        app.graph.setDirtyCanvas(true, true);
+    }
+    rotate(degrees) {
+        const w = this.size[0];
+        const h = this.size[1];
+        this.properties["connections_layout"] =
+            this.properties["connections_layout"] || this.defaultConnectionsLayout;
+        const connections_layout = this.properties["connections_layout"];
+        const inputDirIndex = LAYOUT_CLOCKWISE.indexOf(connections_layout[0]);
+        const outputDirIndex = LAYOUT_CLOCKWISE.indexOf(connections_layout[1]);
+        if (degrees == 90 || degrees === -90) {
+            if (degrees === -90) {
+                connections_layout[0] = LAYOUT_CLOCKWISE[(((inputDirIndex - 1) % 4) + 4) % 4];
+                connections_layout[1] = LAYOUT_CLOCKWISE[(((outputDirIndex - 1) % 4) + 4) % 4];
+            }
+            else {
+                connections_layout[0] = LAYOUT_CLOCKWISE[(((inputDirIndex + 1) % 4) + 4) % 4];
+                connections_layout[1] = LAYOUT_CLOCKWISE[(((outputDirIndex + 1) % 4) + 4) % 4];
+            }
+        }
+        else if (degrees === 180) {
+            connections_layout[0] = LAYOUT_CLOCKWISE[(((inputDirIndex + 2) % 4) + 4) % 4];
+            connections_layout[1] = LAYOUT_CLOCKWISE[(((outputDirIndex + 2) % 4) + 4) % 4];
+        }
+        this.setSize([h, w]);
+    }
+    manuallyHandleMove(event) {
+        const shortcut = this.shortcuts.move;
+        if (shortcut.state) {
+            const diffX = Math.round((event.clientX - shortcut.initialMousePos[0]) / 10) * 10;
+            const diffY = Math.round((event.clientY - shortcut.initialMousePos[1]) / 10) * 10;
+            this.pos[0] = shortcut.initialNodePos[0] + diffX;
+            this.pos[1] = shortcut.initialNodePos[1] + diffY;
+            this.setDirtyCanvas(true, true);
+            SERVICE.handleMoveOrResizeNodeMaybeWhileDragging(this);
+        }
+    }
+    manuallyHandleResize(event) {
+        const shortcut = this.shortcuts.resize;
+        if (shortcut.state) {
+            let diffX = Math.round((event.clientX - shortcut.initialMousePos[0]) / 10) * 10;
+            let diffY = Math.round((event.clientY - shortcut.initialMousePos[1]) / 10) * 10;
+            diffX *= shortcut.resizeOnSide[0] === LiteGraph.LEFT ? -1 : 1;
+            diffY *= shortcut.resizeOnSide[1] === LiteGraph.UP ? -1 : 1;
+            const oldSize = [...this.size];
+            this.setSize([
+                Math.max(10, shortcut.initialNodeSize[0] + diffX),
+                Math.max(10, shortcut.initialNodeSize[1] + diffY),
+            ]);
+            if (shortcut.resizeOnSide[0] === LiteGraph.LEFT && oldSize[0] > 10) {
+                this.pos[0] = shortcut.initialNodePos[0] - diffX;
+            }
+            if (shortcut.resizeOnSide[1] === LiteGraph.UP && oldSize[1] > 10) {
+                this.pos[1] = shortcut.initialNodePos[1] - diffY;
+            }
+            this.setDirtyCanvas(true, true);
+        }
+    }
+    cycleConnection(ioDir) {
+        var _a, _b;
+        const props = this.properties;
+        props["connections_layout"] = props["connections_layout"] || ["Left", "Right"];
+        const connections_layout = this.properties["connections_layout"];
+        const propIdx = ioDir == IoDirection.INPUT ? 0 : 1;
+        const oppositeIdx = propIdx ? 0 : 1;
+        let currentLayout = connections_layout[propIdx];
+        let oppositeLayout = connections_layout[oppositeIdx];
+        if (this.size[0] === 10 || this.size[1] === 10) {
+            props["connections_dir"] = props["connections_dir"] || [-1, -1];
+            const connections_dir = this.properties["connections_dir"];
+            let currentDir = connections_dir[propIdx];
+            const options = this.size[0] === 10
+                ? currentLayout === "Bottom"
+                    ? [LiteGraph.DOWN, LiteGraph.RIGHT, LiteGraph.LEFT]
+                    : [LiteGraph.UP, LiteGraph.LEFT, LiteGraph.RIGHT]
+                : currentLayout === "Right"
+                    ? [LiteGraph.RIGHT, LiteGraph.DOWN, LiteGraph.UP]
+                    : [LiteGraph.LEFT, LiteGraph.UP, LiteGraph.DOWN];
+            let idx = options.indexOf(currentDir);
+            let next = (_a = options[idx + 1]) !== null && _a !== void 0 ? _a : options[0];
+            connections_dir[propIdx] = next;
+            return;
+        }
+        let next = currentLayout;
+        do {
+            let idx = LAYOUT_CLOCKWISE.indexOf(next);
+            next = (_b = LAYOUT_CLOCKWISE[idx + 1]) !== null && _b !== void 0 ? _b : LAYOUT_CLOCKWISE[0];
+        } while (next === oppositeLayout);
+        connections_layout[propIdx] = next;
+        this.setDirtyCanvas(true, true);
+    }
+    onMouseMove(event) {
+        if (this.shortcuts.move.state) {
+            const shortcut = this.shortcuts.move;
+            if (shortcut.initialMousePos[0] === -1) {
+                shortcut.initialMousePos[0] = event.clientX;
+                shortcut.initialMousePos[1] = event.clientY;
+                shortcut.initialNodePos[0] = this.pos[0];
+                shortcut.initialNodePos[1] = this.pos[1];
+            }
+            this.manuallyHandleMove(event);
+        }
+        else if (this.shortcuts.resize.state) {
+            const shortcut = this.shortcuts.resize;
+            if (shortcut.initialMousePos[0] === -1) {
+                shortcut.initialMousePos[0] = event.clientX;
+                shortcut.initialMousePos[1] = event.clientY;
+                shortcut.initialNodeSize[0] = this.size[0];
+                shortcut.initialNodeSize[1] = this.size[1];
+                shortcut.initialNodePos[0] = this.pos[0];
+                shortcut.initialNodePos[1] = this.pos[1];
+                const canvas = app.canvas;
+                const offset = canvas.convertEventToCanvasOffset(event);
+                shortcut.resizeOnSide[0] = this.pos[0] > offset[0] ? LiteGraph.LEFT : LiteGraph.RIGHT;
+                shortcut.resizeOnSide[1] = this.pos[1] > offset[1] ? LiteGraph.UP : LiteGraph.DOWN;
+            }
+            this.manuallyHandleResize(event);
+        }
+    }
+    onKeyDown(event) {
+        super.onKeyDown(event);
+        const canvas = app.canvas;
+        if (CONFIG_FAST_REROUTE_ENABLED) {
+            for (const [key, shortcut] of Object.entries(this.shortcuts)) {
+                if (!shortcut.state) {
+                    const keys = KEY_EVENT_SERVICE.areOnlyKeysDown(shortcut.keys);
+                    if (keys) {
+                        shortcut.state = true;
+                        if (key === "rotate") {
+                            this.rotate(90);
+                        }
+                        else if (key.includes("connection")) {
+                            this.cycleConnection(key.includes("input") ? IoDirection.INPUT : IoDirection.OUTPUT);
+                        }
+                        if (shortcut.initialMousePos) {
+                            canvas.node_capturing_input = this;
+                        }
+                    }
+                }
+            }
+        }
+    }
+    onKeyUp(event) {
+        super.onKeyUp(event);
+        const canvas = app.canvas;
+        if (CONFIG_FAST_REROUTE_ENABLED) {
+            for (const [key, shortcut] of Object.entries(this.shortcuts)) {
+                if (shortcut.state) {
+                    const keys = KEY_EVENT_SERVICE.areOnlyKeysDown(shortcut.keys);
+                    if (!keys) {
+                        shortcut.state = false;
+                        if (shortcut.initialMousePos) {
+                            shortcut.initialMousePos = [-1, -1];
+                            if ((canvas.node_capturing_input = this)) {
+                                canvas.node_capturing_input = null;
+                            }
+                            this.setDirtyCanvas(true, true);
+                        }
+                    }
+                }
+            }
+        }
+    }
+    onDeselected() {
+        var _a;
+        (_a = super.onDeselected) === null || _a === void 0 ? void 0 : _a.call(this);
+        const canvas = app.canvas;
+        for (const [key, shortcut] of Object.entries(this.shortcuts)) {
+            shortcut.state = false;
+            if (shortcut.initialMousePos) {
+                shortcut.initialMousePos = [-1, -1];
+                if ((canvas.node_capturing_input = this)) {
+                    canvas.node_capturing_input = null;
+                }
+                this.setDirtyCanvas(true, true);
+            }
+        }
+    }
+    onRemoved() {
+        var _a;
+        (_a = super.onRemoved) === null || _a === void 0 ? void 0 : _a.call(this);
+        setTimeout(() => {
+            SERVICE.handleRemovedNodeMaybeWhileDragging(this);
+        }, 32);
+    }
+    getHelp() {
+        return `
+      <p>
+        Finally, a comfortable, powerful reroute node with true multi-direction and powerful
+        shortcuts to bring your workflow to the next level.
+      </p>
+
+      ${!CONFIG_FAST_REROUTE_ENABLED
+            ? `<p><i>Fast Shortcuts are currently disabled.</b>`
+            : `
+        <ul>
+          <li><p>
+            <code>${CONFIG_KEY_CREATE_WHILE_LINKING}</code> Create a new reroute node while dragging
+            a link, connecting it to the link in the place and continuing the link.
+          </p></li>
+          <li><p>
+            <code>${CONFIG_KEY_ROTATE}</code> Rotate the selected reroute node counter clockwise 90
+            degrees.
+          </p></li>
+          <li><p>
+            <code>${CONFIG_KEY_RESIZE}</code> Resize the selected reroute node from the nearest
+            corner by holding down and moving your mouse.
+          </p></li>
+          <li><p>
+            <code>${CONFIG_KEY_MOVE}</code> Move the selected reroute node by holding down and
+            moving your mouse.
+          </p></li>
+          <li><p>
+            <code>${CONFIG_KEY_CXN_INPUT}</code> Change the input layout/direction of the selected
+            reroute node.
+          </p></li>
+          <li><p>
+            <code>${CONFIG_KEY_CXN_OUTPUT}</code> Change the output layout/direction of the selected
+            reroute node.
+          </p></li>
+        </ul>
+      `}
+      <p><small>
+        To change, ${!CONFIG_FAST_REROUTE_ENABLED ? "enable" : "disable"} or configure sohrtcuts,
+        make a copy of
+        <code>/custom_nodes/rgthree-comfy/rgthree_config.json.default</code> to
+        <code>/custom_nodes/rgthree-comfy/rgthree_config.json</code> and configure under
+        <code>nodes > reroute > fast_reroute</code>.
+      </small></p>
+    `;
+    }
+}
+RerouteNode.title = NodeTypesString.REROUTE;
+RerouteNode.type = NodeTypesString.REROUTE;
+RerouteNode.title_mode = LiteGraph.NO_TITLE;
+RerouteNode.collapsable = false;
+RerouteNode.layout_slot_offset = 5;
+RerouteNode.size = configDefaultSize;
+addMenuItem(RerouteNode, app, {
+    name: (node) => { var _a; return `${((_a = node.properties) === null || _a === void 0 ? void 0 : _a["showLabel"]) ? "Hide" : "Show"} Label/Title`; },
+    property: "showLabel",
+    callback: async (node, value) => {
+        app.graph.setDirtyCanvas(true, true);
+    },
+});
+addMenuItem(RerouteNode, app, {
+    name: (node) => `${node.resizable ? "No" : "Allow"} Resizing`,
+    callback: (node) => {
+        node.setResizable(!node.resizable);
+        node.size[0] = Math.max(40, node.size[0]);
+        node.size[1] = Math.max(30, node.size[1]);
+        node.applyNodeSize();
+    },
+});
+addMenuItem(RerouteNode, app, {
+    name: "Static Width",
+    property: "size",
+    subMenuOptions: (() => {
+        const options = [];
+        for (let w = 8; w > 0; w--) {
+            options.push(`${w * 10}`);
+        }
+        return options;
+    })(),
+    prepareValue: (value, node) => [Number(value), node.size[1]],
+    callback: (node) => {
+        node.setResizable(false);
+        node.applyNodeSize();
+    },
+});
+addMenuItem(RerouteNode, app, {
+    name: "Static Height",
+    property: "size",
+    subMenuOptions: (() => {
+        const options = [];
+        for (let w = 8; w > 0; w--) {
+            options.push(`${w * 10}`);
+        }
+        return options;
+    })(),
+    prepareValue: (value, node) => [node.size[0], Number(value)],
+    callback: (node) => {
+        node.setResizable(false);
+        node.applyNodeSize();
+    },
+});
+addConnectionLayoutSupport(RerouteNode, app, [
+    ["Left", "Right"],
+    ["Left", "Top"],
+    ["Left", "Bottom"],
+    ["Right", "Left"],
+    ["Right", "Top"],
+    ["Right", "Bottom"],
+    ["Top", "Left"],
+    ["Top", "Right"],
+    ["Top", "Bottom"],
+    ["Bottom", "Left"],
+    ["Bottom", "Right"],
+    ["Bottom", "Top"],
+], (node) => {
+    node.applyNodeSize();
+});
+addMenuItem(RerouteNode, app, {
+    name: "Rotate",
+    subMenuOptions: [
+        "Rotate 90° Clockwise",
+        "Rotate 90° Counter-Clockwise",
+        "Rotate 180°",
+        null,
+        "Flip Horizontally",
+        "Flip Vertically",
+    ],
+    callback: (node_, value) => {
+        const node = node_;
+        if (value === null || value === void 0 ? void 0 : value.startsWith("Rotate 90° Clockwise")) {
+            node.rotate(90);
+        }
+        else if (value === null || value === void 0 ? void 0 : value.startsWith("Rotate 90° Counter-Clockwise")) {
+            node.rotate(-90);
+        }
+        else if (value === null || value === void 0 ? void 0 : value.startsWith("Rotate 180°")) {
+            node.rotate(180);
+        }
+        else {
+            const connections_layout = node.properties["connections_layout"];
+            const inputDirIndex = LAYOUT_CLOCKWISE.indexOf(connections_layout[0]);
+            const outputDirIndex = LAYOUT_CLOCKWISE.indexOf(connections_layout[1]);
+            if (value === null || value === void 0 ? void 0 : value.startsWith("Flip Horizontally")) {
+                if (["Left", "Right"].includes(connections_layout[0])) {
+                    connections_layout[0] = LAYOUT_CLOCKWISE[(((inputDirIndex + 2) % 4) + 4) % 4];
+                }
+                if (["Left", "Right"].includes(connections_layout[1])) {
+                    connections_layout[1] = LAYOUT_CLOCKWISE[(((outputDirIndex + 2) % 4) + 4) % 4];
+                }
+            }
+            else if (value === null || value === void 0 ? void 0 : value.startsWith("Flip Vertically")) {
+                if (["Top", "Bottom"].includes(connections_layout[0])) {
+                    connections_layout[0] = LAYOUT_CLOCKWISE[(((inputDirIndex + 2) % 4) + 4) % 4];
+                }
+                if (["Top", "Bottom"].includes(connections_layout[1])) {
+                    connections_layout[1] = LAYOUT_CLOCKWISE[(((outputDirIndex + 2) % 4) + 4) % 4];
+                }
+            }
+        }
+    },
+});
+addMenuItem(RerouteNode, app, {
+    name: "Clone New Reroute...",
+    subMenuOptions: ["Before", "After"],
+    callback: async (node, value) => {
+        const clone = node.clone();
+        const pos = [...node.pos];
+        if (value === "Before") {
+            clone.pos = [pos[0] - 20, pos[1] - 20];
+            app.graph.add(clone);
+            await wait();
+            const inputLinks = getSlotLinks(node.inputs[0]);
+            for (const inputLink of inputLinks) {
+                const link = inputLink.link;
+                const linkedNode = app.graph.getNodeById(link.origin_id);
+                if (linkedNode) {
+                    linkedNode.connect(0, clone, 0);
+                }
+            }
+            clone.connect(0, node, 0);
+        }
+        else {
+            clone.pos = [pos[0] + 20, pos[1] + 20];
+            app.graph.add(clone);
+            await wait();
+            const outputLinks = getSlotLinks(node.outputs[0]);
+            node.connect(0, clone, 0);
+            for (const outputLink of outputLinks) {
+                const link = outputLink.link;
+                const linkedNode = app.graph.getNodeById(link.target_id);
+                if (linkedNode) {
+                    clone.connect(0, linkedNode, link.target_slot);
+                }
+            }
+        }
+    },
+});
+app.registerExtension({
+    name: "rgthree.Reroute",
+    registerCustomNodes() {
+        RerouteNode.setUp();
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/rgthree.css b/custom_nodes/rgthree-comfy/web/comfyui/rgthree.css
new file mode 100644
index 0000000000000000000000000000000000000000..a0493ebf7d7db17ec3636851faca41fb3a26282c
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/rgthree.css
@@ -0,0 +1,696 @@
+@charset "UTF-8";
+.rgthree-top-messages-container {
+  position: fixed;
+  z-index: 9999;
+  top: 0;
+  left: 0;
+  width: 100%;
+  height: 0;
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: start;
+}
+
+.rgthree-top-messages-container > div {
+  position: relative;
+  height: fit-content;
+  padding: 4px;
+  margin-top: -100px; /* re-set by JS */
+  opacity: 0;
+  transition: all 0.33s ease-in-out;
+  z-index: 3;
+}
+
+.rgthree-top-messages-container > div:last-child {
+  z-index: 2;
+}
+
+.rgthree-top-messages-container > div:not(.-show) {
+  z-index: 1;
+}
+
+.rgthree-top-messages-container > div.-show {
+  opacity: 1;
+  margin-top: 0px !important;
+}
+
+.rgthree-top-messages-container > div.-show {
+  opacity: 1;
+  transform: translateY(0%);
+}
+
+.rgthree-top-messages-container > div > div {
+  position: relative;
+  background: #353535;
+  color: #fff;
+  display: flex;
+  flex-direction: row;
+  align-items: center;
+  justify-content: center;
+  height: fit-content;
+  box-shadow: 0 0 10px rgba(0, 0, 0, 0.88);
+  padding: 6px 12px;
+  border-radius: 4px;
+  font-family: Arial, sans-serif;
+  font-size: 14px;
+}
+
+.rgthree-top-messages-container > div > div > span {
+  display: flex;
+  flex-direction: row;
+  align-items: center;
+  justify-content: center;
+}
+
+.rgthree-top-messages-container > div > div > span svg {
+  width: 20px;
+  height: auto;
+  margin-right: 8px;
+}
+
+.rgthree-top-messages-container > div > div > span svg.icon-checkmark {
+  fill: #2e9720;
+}
+
+.rgthree-top-messages-container [type=warn]::before,
+.rgthree-top-messages-container [type=success]::before {
+  content: "⚠️";
+  display: inline-block;
+  flex: 0 0 auto;
+  font-size: 18px;
+  margin-right: 4px;
+  line-height: 1;
+}
+
+.rgthree-top-messages-container [type=success]::before {
+  content: "🎉";
+}
+
+.rgthree-top-messages-container a {
+  cursor: pointer;
+  text-decoration: underline;
+  color: #fc0;
+  margin-left: 4px;
+  display: inline-block;
+  line-height: 1;
+}
+
+.rgthree-top-messages-container a:hover {
+  color: #fc0;
+  text-decoration: none;
+}
+
+/* Fix node selector being crazy long b/c of array types. */
+.litegraph.litesearchbox input,
+.litegraph.litesearchbox select {
+  max-width: 250px;
+}
+
+/* There's no reason for this z-index to be so high. It layers on top of things it shouldn't,
+  (like pythongssss' image gallery, the properties panel, etc.) */
+.comfy-multiline-input {
+  z-index: 1 !important;
+}
+
+.comfy-multiline-input:focus {
+  z-index: 2 !important;
+}
+
+.litegraph .dialog {
+  z-index: 3 !important; /* This is set to 1, but goes under the multi-line inputs, so bump it. */
+}
+
+:not(#fakeid) .rgthree-button-reset {
+  position: relative;
+  appearance: none;
+  cursor: pointer;
+  border: 0;
+  background: transparent;
+  color: inherit;
+  padding: 0;
+  margin: 0;
+}
+
+:not(#fakeid) .rgthree-button {
+  --padding-top: 7px;
+  --padding-bottom: 9px;
+  --padding-x: 16px;
+  position: relative;
+  cursor: pointer;
+  border: 0;
+  border-radius: 0.33rem;
+  background: rgba(0, 0, 0, 0.5);
+  color: white;
+  font-family: system-ui, sans-serif;
+  font-size: 1rem;
+  line-height: 1;
+  white-space: nowrap;
+  text-decoration: none;
+  margin: 0.25rem;
+  box-shadow: 0px 0px 2px rgb(0, 0, 0);
+  background: #212121;
+  transition: all 0.1s ease-in-out;
+  padding: var(--padding-top) var(--padding-x) var(--padding-bottom);
+  display: inline-flex;
+  flex-direction: row;
+  align-items: center;
+  justify-content: center;
+}
+:not(#fakeid) .rgthree-button::before, :not(#fakeid) .rgthree-button::after {
+  content: "";
+  display: block;
+  position: absolute;
+  border-radius: 0.33rem;
+  left: 0;
+  top: 0;
+  width: 100%;
+  height: 100%;
+  box-shadow: inset 1px 1px 0px rgba(255, 255, 255, 0.12), inset -1px -1px 0px rgba(0, 0, 0, 0.75);
+  background: linear-gradient(to bottom, rgba(255, 255, 255, 0.06), rgba(0, 0, 0, 0.15));
+  mix-blend-mode: screen;
+}
+:not(#fakeid) .rgthree-button::after {
+  mix-blend-mode: multiply;
+}
+:not(#fakeid) .rgthree-button:hover {
+  background: #303030;
+}
+:not(#fakeid) .rgthree-button:active {
+  box-shadow: 0px 0px 0px rgba(0, 0, 0, 0);
+  background: #121212;
+  padding: calc(var(--padding-top) + 1px) calc(var(--padding-x) - 1px) calc(var(--padding-bottom) - 1px) calc(var(--padding-x) + 1px);
+}
+:not(#fakeid) .rgthree-button:active::before, :not(#fakeid) .rgthree-button:active::after {
+  box-shadow: 1px 1px 0px rgba(255, 255, 255, 0.15), inset 1px 1px 0px rgba(0, 0, 0, 0.5), inset 1px 3px 5px rgba(0, 0, 0, 0.33);
+}
+:not(#fakeid) .rgthree-button.-blue {
+  background: #346599 !important;
+}
+:not(#fakeid) .rgthree-button.-blue:hover {
+  background: #3b77b8 !important;
+}
+:not(#fakeid) .rgthree-button.-blue:active {
+  background: #1d5086 !important;
+}
+:not(#fakeid) .rgthree-button.-green {
+  background: linear-gradient(to bottom, rgba(255, 255, 255, 0.06), rgba(0, 0, 0, 0.15)), #14580b;
+}
+:not(#fakeid) .rgthree-button.-green:hover {
+  background: linear-gradient(to bottom, rgba(255, 255, 255, 0.06), rgba(0, 0, 0, 0.15)), #1a6d0f;
+}
+:not(#fakeid) .rgthree-button.-green:active {
+  background: linear-gradient(to bottom, rgba(0, 0, 0, 0.15), rgba(255, 255, 255, 0.06)), #0f3f09;
+}
+:not(#fakeid) .rgthree-button[disabled] {
+  box-shadow: none;
+  background: #666 !important;
+  color: #aaa;
+  pointer-events: none;
+}
+:not(#fakeid) .rgthree-button[disabled]::before, :not(#fakeid) .rgthree-button[disabled]::after {
+  display: none;
+}
+
+:not(#fakeid) .rgthree-comfybar-top-button-group {
+  font-size: 0;
+  flex: 1 1 auto;
+  display: flex;
+  align-items: stretch;
+}
+:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button {
+  margin: 0;
+  flex: 1 1;
+  height: 36px;
+  padding: 0 12px;
+  border-radius: 0;
+  background: var(--p-button-secondary-background);
+  color: var(--p-button-secondary-color);
+}
+:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button.-primary {
+  background: var(--p-button-primary-background);
+  color: var(--p-button-primary-color);
+}
+:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button::before, :not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button::after {
+  border-radius: 0;
+}
+:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button svg {
+  fill: currentColor;
+  width: 28px;
+  height: 28px;
+}
+:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button:first-of-type,
+:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button:first-of-type::before,
+:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button:first-of-type::after {
+  border-top-left-radius: 0.33rem;
+  border-bottom-left-radius: 0.33rem;
+}
+:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button:last-of-type,
+:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button:last-of-type::before,
+:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button:last-of-type::after {
+  border-top-right-radius: 0.33rem;
+  border-bottom-right-radius: 0.33rem;
+}
+
+.rgthree-dialog {
+  outline: 0;
+  border: 0;
+  border-radius: 6px;
+  background: #414141;
+  color: #fff;
+  box-shadow: inset 1px 1px 0px rgba(255, 255, 255, 0.05), inset -1px -1px 0px rgba(0, 0, 0, 0.5), 2px 2px 20px rgb(0, 0, 0);
+  max-width: 800px;
+  box-sizing: border-box;
+  font-family: "Segoe UI", Tahoma, Geneva, Verdana, sans-serif;
+  font-size: 1rem;
+  padding: 0;
+  max-height: calc(100% - 32px);
+}
+.rgthree-dialog *, .rgthree-dialog *::before, .rgthree-dialog *::after {
+  box-sizing: inherit;
+}
+
+.rgthree-dialog-container > * {
+  padding: 8px 16px;
+}
+.rgthree-dialog-container > *:first-child {
+  padding-top: 16px;
+}
+.rgthree-dialog-container > *:last-child {
+  padding-bottom: 16px;
+}
+
+.rgthree-dialog.-iconed::after {
+  content: "";
+  font-size: 276px;
+  position: absolute;
+  right: 0px;
+  bottom: 0px;
+  opacity: 0.15;
+  display: block;
+  width: 237px;
+  overflow: hidden;
+  height: 186px;
+  line-height: 1;
+  pointer-events: none;
+  z-index: -1;
+}
+
+.rgthree-dialog.-iconed.-help::after {
+  content: "🛟";
+}
+
+.rgthree-dialog.-iconed.-settings::after {
+  content: "⚙️";
+}
+
+@media (max-width: 832px) {
+  .rgthree-dialog {
+    max-width: calc(100% - 32px);
+  }
+}
+.rgthree-dialog-container-title {
+  display: flex;
+  flex-direction: row;
+  align-items: center;
+  justify-content: start;
+}
+
+.rgthree-dialog-container-title > svg:first-child {
+  width: 36px;
+  height: 36px;
+  margin-right: 16px;
+}
+
+.rgthree-dialog-container-title h2 {
+  font-size: 1.375rem;
+  margin: 0;
+  font-weight: bold;
+}
+
+.rgthree-dialog-container-title h2 small {
+  font-size: 0.8125rem;
+  font-weight: normal;
+  opacity: 0.75;
+}
+
+.rgthree-dialog-container-content {
+  overflow: auto;
+  max-height: calc(100vh - 200px); /* Arbitrary height to copensate for margin, title, and footer.*/
+}
+
+.rgthree-dialog-container-content p {
+  font-size: 0.8125rem;
+  margin-top: 0;
+}
+
+.rgthree-dialog-container-content ul li p {
+  margin-bottom: 4px;
+}
+
+.rgthree-dialog-container-content ul li p + p {
+  margin-top: 0.5em;
+}
+
+.rgthree-dialog-container-content ul li ul {
+  margin-top: 0.5em;
+  margin-bottom: 1em;
+}
+
+.rgthree-dialog-container-content p code {
+  display: inline-block;
+  padding: 2px 4px;
+  margin: 0px 2px;
+  border: 1px solid rgba(255, 255, 255, 0.25);
+  border-radius: 3px;
+  background: rgba(255, 255, 255, 0.1);
+}
+
+.rgthree-dialog-container-footer {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+}
+
+body.rgthree-dialog-open > *:not(.rgthree-dialog):not(.rgthree-top-messages-container) {
+  filter: blur(5px);
+}
+
+.rgthree-menu {
+  list-style: none;
+  padding: 0;
+  margin: 0;
+  position: fixed;
+  z-index: 999999;
+  pointer-events: none;
+  opacity: 0;
+  transition: opacity 0.08s ease-in-out;
+  color: #dde;
+  background-color: #111;
+  font-size: 12px;
+  box-shadow: 0 0 10px black !important;
+}
+.rgthree-menu > li {
+  position: relative;
+  padding: 4px 6px;
+  z-index: 9999;
+  white-space: nowrap;
+}
+.rgthree-menu > li[role=button] {
+  background-color: var(--comfy-menu-bg) !important;
+  color: var(--input-text);
+  cursor: pointer;
+}
+.rgthree-menu > li[role=button]:hover {
+  filter: brightness(155%);
+}
+.rgthree-menu[state^=measuring] {
+  display: block;
+  opacity: 0;
+}
+.rgthree-menu[state=open] {
+  display: block;
+  opacity: 1;
+  pointer-events: all;
+}
+
+.rgthree-top-menu {
+  box-sizing: border-box;
+  white-space: nowrap;
+  background: var(--content-bg);
+  color: var(--content-fg);
+  display: flex;
+  flex-direction: column;
+  list-style: none;
+  padding: 0;
+  margin: 0;
+}
+.rgthree-top-menu * {
+  box-sizing: inherit;
+}
+.rgthree-top-menu > li:not(#fakeid) {
+  list-style: none;
+  padding: 0;
+  margin: 0;
+  position: relative;
+  z-index: 2;
+}
+.rgthree-top-menu > li:not(#fakeid) > button {
+  cursor: pointer;
+  padding: 8px 12px 8px 8px;
+  width: 100%;
+  text-align: start;
+  display: flex;
+  flex-direction: row;
+  align-items: center;
+  justify-content: start;
+}
+.rgthree-top-menu > li:not(#fakeid) > button:hover {
+  background-color: var(--comfy-input-bg);
+}
+.rgthree-top-menu > li:not(#fakeid) > button svg {
+  height: 16px;
+  width: auto;
+  margin-inline-end: 0.6em;
+}
+.rgthree-top-menu > li:not(#fakeid) > button svg.github-star {
+  fill: rgb(227, 179, 65);
+}
+.rgthree-top-menu > li:not(#fakeid).rgthree-message {
+  min-height: 32px;
+}
+.rgthree-top-menu > li:not(#fakeid).rgthree-message > span {
+  padding: 8px 12px;
+  display: block;
+  width: 100%;
+  text-align: center;
+  font-style: italic;
+  font-size: 12px;
+}
+.rgthree-top-menu.-modal::after {
+  content: "";
+  display: block;
+  position: fixed;
+  z-index: 1;
+  inset: 0;
+  background: rgba(0, 0, 0, 0.0666666667);
+}
+
+body.rgthree-modal-menu-open > *:not(.rgthree-menu):not(.rgthree-top-messages-container) {
+  filter: blur(2px);
+}
+
+.rgthree-dialog.-settings {
+  width: 100%;
+}
+
+.rgthree-dialog.-settings fieldset {
+  border: 1px solid rgba(255, 255, 255, 0.25);
+  padding: 0 12px 8px;
+  margin-bottom: 16px;
+}
+
+.rgthree-dialog.-settings fieldset > legend {
+  margin-left: 8px;
+  padding: 0 8px;
+  opacity: 0.5;
+}
+
+.rgthree-dialog.-settings .formrow {
+  display: flex;
+  flex-direction: column;
+}
+
+.rgthree-dialog.-settings .formrow + .formrow {
+  border-top: 1px solid rgba(255, 255, 255, 0.25);
+}
+
+.rgthree-dialog.-settings .fieldrow {
+  display: flex;
+  flex-direction: row;
+}
+
+.rgthree-dialog.-settings .fieldrow > label {
+  flex: 1 1 auto;
+  user-select: none;
+  padding: 8px 12px 12px;
+}
+
+.rgthree-dialog.-settings .fieldrow > label span {
+  font-weight: bold;
+}
+
+.rgthree-dialog.-settings .fieldrow > label small {
+  display: block;
+  margin-top: 4px;
+  font-size: 0.6875rem;
+  opacity: 0.75;
+  padding-left: 16px;
+}
+
+.rgthree-dialog.-settings .fieldrow ~ .fieldrow {
+  font-size: 0.9rem;
+  border-top: 1px dotted rgba(255, 255, 255, 0.25);
+}
+
+.rgthree-dialog.-settings .fieldrow ~ .fieldrow label {
+  padding-left: 28px;
+}
+
+.rgthree-dialog.-settings .fieldrow:first-child:not(.-checked) ~ .fieldrow {
+  display: none;
+}
+
+.rgthree-dialog.-settings .fieldrow:hover {
+  background: rgba(255, 255, 255, 0.1);
+}
+
+.rgthree-dialog.-settings .fieldrow ~ .fieldrow span {
+  font-weight: normal;
+}
+
+.rgthree-dialog.-settings .fieldrow > .fieldrow-value {
+  display: flex;
+  align-items: center;
+  justify-content: end;
+  flex: 0 0 auto;
+  width: 50%;
+  max-width: 230px;
+}
+
+.rgthree-dialog.-settings .fieldrow.-type-boolean > .fieldrow-value {
+  max-width: 64px;
+}
+
+.rgthree-dialog.-settings .fieldrow.-type-number input {
+  width: 48px;
+  text-align: right;
+}
+
+.rgthree-dialog.-settings .fieldrow input[type=checkbox] {
+  width: 24px;
+  height: 24px;
+  cursor: pointer;
+}
+
+.rgthree-dialog.-settings .fieldrow fieldset.rgthree-checklist-group {
+  padding: 0;
+  border: 0;
+  margin: 0;
+}
+.rgthree-dialog.-settings .fieldrow fieldset.rgthree-checklist-group > span.rgthree-checklist-item {
+  display: inline-block;
+  white-space: nowrap;
+  padding-right: 6px;
+  vertical-align: middle;
+}
+.rgthree-dialog.-settings .fieldrow fieldset.rgthree-checklist-group > span.rgthree-checklist-item input[type=checkbox] {
+  width: 16px;
+  height: 16px;
+}
+.rgthree-dialog.-settings .fieldrow fieldset.rgthree-checklist-group > span.rgthree-checklist-item label {
+  padding-left: 4px;
+  text-align: left;
+  cursor: pointer;
+}
+
+.rgthree-comfyui-settings-row div {
+  display: flex;
+  flex-direction: row;
+  align-items: center;
+  justify-content: end;
+}
+
+.rgthree-comfyui-settings-row div svg {
+  width: 36px;
+  height: 36px;
+  margin-right: 16px;
+}
+
+.litegraph.litecontextmenu .litemenu-title .rgthree-contextmenu-title-rgthree-comfy,
+.litegraph.litecontextmenu .litemenu-entry.rgthree-contextmenu-item {
+  display: flex;
+  flex-direction: row;
+  align-items: center;
+  justify-content: start;
+}
+
+.litegraph.litecontextmenu .litemenu-title .rgthree-contextmenu-title-rgthree-comfy svg,
+.litegraph.litecontextmenu .litemenu-entry.rgthree-contextmenu-item svg {
+  fill: currentColor;
+  width: auto;
+  height: 16px;
+  margin-right: 6px;
+}
+
+.litegraph.litecontextmenu .litemenu-entry.rgthree-contextmenu-item svg.github-star {
+  fill: rgb(227, 179, 65);
+}
+
+.litegraph.litecontextmenu .litemenu-title .rgthree-contextmenu-title-rgthree-comfy,
+.litegraph.litecontextmenu .litemenu-entry.rgthree-contextmenu-label {
+  color: #dde;
+  background-color: #212121 !important;
+  margin: 0;
+  padding: 2px;
+  cursor: default;
+  opacity: 1;
+  padding: 4px;
+  font-weight: bold;
+}
+
+.litegraph.litecontextmenu .litemenu-title .rgthree-contextmenu-title-rgthree-comfy {
+  font-size: 1.1em;
+  color: #fff;
+  background-color: #090909 !important;
+  justify-content: center;
+  padding: 4px 8px;
+}
+
+rgthree-progress-bar {
+  display: block;
+  position: relative;
+  z-index: 999;
+  top: 0;
+  left: 0;
+  height: 14px;
+  font-size: 10px;
+  width: 100%;
+  overflow: hidden;
+  box-shadow: 0px 0px 3px rgba(0, 0, 0, 0.25);
+  box-shadow: inset 0px -1px 0px rgba(0, 0, 0, 0.25), 0px 1px 0px rgba(255, 255, 255, 0.125);
+}
+
+* ~ rgthree-progress-bar,
+.comfyui-body-bottom rgthree-progress-bar {
+  box-shadow: 0px -1px 0px rgb(0, 0, 0), inset 0px 1px 0px rgba(255, 255, 255, 0.15), inset 0px -1px 0px rgba(0, 0, 0, 0.25), 0px 1px 0px rgba(255, 255, 255, 0.125);
+}
+
+body:not([style*=grid]):not([class*=grid]) rgthree-progress-bar {
+  position: fixed;
+  top: 0px;
+  bottom: auto;
+}
+body:not([style*=grid]):not([class*=grid]) rgthree-progress-bar.rgthree-pos-bottom {
+  top: auto;
+  bottom: 0px;
+}
+
+.rgthree-debug-keydowns {
+  display: block;
+  position: fixed;
+  z-index: 1050;
+  top: 3px;
+  right: 8px;
+  font-size: 10px;
+  color: #fff;
+  font-family: sans-serif;
+  pointer-events: none;
+}
+
+.rgthree-comfy-about-badge-logo {
+  width: 20px;
+  height: 20px;
+  background: url(/rgthree/logo.svg?bg=transparent&fg=%2393c5fd);
+  background-size: 100% 100%;
+}
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/rgthree.js b/custom_nodes/rgthree-comfy/web/comfyui/rgthree.js
new file mode 100644
index 0000000000000000000000000000000000000000..ec4bfa7357a032e63087c8706a26eacbd7bc18f2
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/rgthree.js
@@ -0,0 +1,747 @@
+import { app } from "../../scripts/app.js";
+import { api } from "../../scripts/api.js";
+import { SERVICE as CONFIG_SERVICE } from "./services/config_service.js";
+import { SERVICE as BOOKMARKS_SERVICE } from "./services/bookmarks_services.js";
+import { SERVICE as KEY_EVENT_SERVICE } from "./services/key_events_services.js";
+import { WorkflowLinkFixer } from "../../rgthree/common/link_fixer.js";
+import { injectCss, wait } from "../../rgthree/common/shared_utils.js";
+import { replaceNode, waitForCanvas, waitForGraph } from "./utils.js";
+import { NodeTypesString, addRgthree, getNodeTypeStrings } from "./constants.js";
+import { RgthreeProgressBar } from "../../rgthree/common/progress_bar.js";
+import { RgthreeConfigDialog } from "./config.js";
+import { iconGear, iconNode, iconReplace, iconStarFilled, logoRgthree, } from "../../rgthree/common/media/svgs.js";
+import { createElement, queryAll, query } from "../../rgthree/common/utils_dom.js";
+export var LogLevel;
+(function (LogLevel) {
+    LogLevel[LogLevel["IMPORTANT"] = 1] = "IMPORTANT";
+    LogLevel[LogLevel["ERROR"] = 2] = "ERROR";
+    LogLevel[LogLevel["WARN"] = 3] = "WARN";
+    LogLevel[LogLevel["INFO"] = 4] = "INFO";
+    LogLevel[LogLevel["DEBUG"] = 5] = "DEBUG";
+    LogLevel[LogLevel["DEV"] = 6] = "DEV";
+})(LogLevel || (LogLevel = {}));
+const LogLevelKeyToLogLevel = {
+    IMPORTANT: LogLevel.IMPORTANT,
+    ERROR: LogLevel.ERROR,
+    WARN: LogLevel.WARN,
+    INFO: LogLevel.INFO,
+    DEBUG: LogLevel.DEBUG,
+    DEV: LogLevel.DEV,
+};
+const LogLevelToMethod = {
+    [LogLevel.IMPORTANT]: "log",
+    [LogLevel.ERROR]: "error",
+    [LogLevel.WARN]: "warn",
+    [LogLevel.INFO]: "info",
+    [LogLevel.DEBUG]: "log",
+    [LogLevel.DEV]: "log",
+};
+const LogLevelToCSS = {
+    [LogLevel.IMPORTANT]: "font-weight: bold; color: blue;",
+    [LogLevel.ERROR]: "",
+    [LogLevel.WARN]: "",
+    [LogLevel.INFO]: "font-style: italic; color: blue;",
+    [LogLevel.DEBUG]: "font-style: italic; color: #444;",
+    [LogLevel.DEV]: "color: #004b68;",
+};
+let GLOBAL_LOG_LEVEL = LogLevel.ERROR;
+const apiURL = api.apiURL;
+api.apiURL = function (route) {
+    if (route.includes("rgthree/")) {
+        return (this.api_base + "/" + route).replace(/\/\//g, "/");
+    }
+    return apiURL.apply(this, arguments);
+};
+const INVOKE_EXTENSIONS_BLOCKLIST = [
+    {
+        name: "Comfy.WidgetInputs",
+        reason: "Major conflict with rgthree-comfy nodes' inputs causing instability and " +
+            "repeated link disconnections.",
+    },
+    {
+        name: "efficiency.widgethider",
+        reason: "Overrides value getter before widget getter is prepared. Can be lifted if/when " +
+            "https://github.com/jags111/efficiency-nodes-comfyui/pull/203 is pulled.",
+    },
+];
+class Logger {
+    log(level, message, ...args) {
+        var _a;
+        const [n, v] = this.logParts(level, message, ...args);
+        (_a = console[n]) === null || _a === void 0 ? void 0 : _a.call(console, ...v);
+    }
+    logParts(level, message, ...args) {
+        if (level <= GLOBAL_LOG_LEVEL) {
+            const css = LogLevelToCSS[level] || "";
+            if (level === LogLevel.DEV) {
+                message = `🔧 ${message}`;
+            }
+            return [LogLevelToMethod[level], [`%c${message}`, css, ...args]];
+        }
+        return ["none", []];
+    }
+}
+class LogSession {
+    constructor(name) {
+        this.name = name;
+        this.logger = new Logger();
+        this.logsCache = {};
+    }
+    logParts(level, message, ...args) {
+        message = `${this.name || ""}${message ? " " + message : ""}`;
+        return this.logger.logParts(level, message, ...args);
+    }
+    logPartsOnceForTime(level, time, message, ...args) {
+        message = `${this.name || ""}${message ? " " + message : ""}`;
+        const cacheKey = `${level}:${message}`;
+        const cacheEntry = this.logsCache[cacheKey];
+        const now = +new Date();
+        if (cacheEntry && cacheEntry.lastShownTime + time > now) {
+            return ["none", []];
+        }
+        const parts = this.logger.logParts(level, message, ...args);
+        if (console[parts[0]]) {
+            this.logsCache[cacheKey] = this.logsCache[cacheKey] || {};
+            this.logsCache[cacheKey].lastShownTime = now;
+        }
+        return parts;
+    }
+    debugParts(message, ...args) {
+        return this.logParts(LogLevel.DEBUG, message, ...args);
+    }
+    infoParts(message, ...args) {
+        return this.logParts(LogLevel.INFO, message, ...args);
+    }
+    warnParts(message, ...args) {
+        return this.logParts(LogLevel.WARN, message, ...args);
+    }
+    errorParts(message, ...args) {
+        return this.logParts(LogLevel.ERROR, message, ...args);
+    }
+    newSession(name) {
+        return new LogSession(`${this.name}${name}`);
+    }
+}
+class Rgthree extends EventTarget {
+    constructor() {
+        var _a, _b, _c, _d;
+        super();
+        this.api = api;
+        this.settingsDialog = null;
+        this.progressBarEl = null;
+        this.queueNodeIds = null;
+        this.version = CONFIG_SERVICE.getConfigValue("version");
+        this.logger = new LogSession("[rgthree]");
+        this.monitorBadLinksAlerted = false;
+        this.monitorLinkTimeout = null;
+        this.processingQueue = false;
+        this.loadingApiJson = null;
+        this.replacingReroute = null;
+        this.processingMouseDown = false;
+        this.processingMouseUp = false;
+        this.processingMouseMove = false;
+        this.lastCanvasMouseEvent = null;
+        this.canvasCurrentlyCopyingToClipboard = false;
+        this.canvasCurrentlyCopyingToClipboardWithMultipleNodes = false;
+        this.canvasCurrentlyPastingFromClipboard = false;
+        this.canvasCurrentlyPastingFromClipboardWithMultipleNodes = false;
+        this.initialGraphToPromptSerializedWorkflowBecauseComfyUIBrokeStuff = null;
+        this.isMac = !!(((_a = navigator.platform) === null || _a === void 0 ? void 0 : _a.toLocaleUpperCase().startsWith("MAC")) ||
+            ((_c = (_b = navigator.userAgentData) === null || _b === void 0 ? void 0 : _b.platform) === null || _c === void 0 ? void 0 : _c.toLocaleUpperCase().startsWith("MAC")));
+        const logLevel = (_d = LogLevelKeyToLogLevel[CONFIG_SERVICE.getConfigValue("log_level")]) !== null && _d !== void 0 ? _d : GLOBAL_LOG_LEVEL;
+        this.setLogLevel(logLevel);
+        this.initializeGraphAndCanvasHooks();
+        this.initializeComfyUIHooks();
+        this.initializeContextMenu();
+        this.rgthreeCssPromise = injectCss("extensions/rgthree-comfy/rgthree.css");
+        this.initializeProgressBar();
+        CONFIG_SERVICE.addEventListener("config-change", ((e) => {
+            var _a, _b;
+            if ((_b = (_a = e.detail) === null || _a === void 0 ? void 0 : _a.key) === null || _b === void 0 ? void 0 : _b.includes("features.progress_bar")) {
+                this.initializeProgressBar();
+            }
+        }));
+        if (CONFIG_SERVICE.getConfigValue("debug.keys_down.enabled")) {
+            const elDebugKeydowns = createElement("div.rgthree-debug-keydowns", {
+                parent: document.body,
+            });
+            const updateDebugKeyDown = () => {
+                elDebugKeydowns.innerText = Object.keys(KEY_EVENT_SERVICE.downKeys).join(" ");
+            };
+            KEY_EVENT_SERVICE.addEventListener("keydown", updateDebugKeyDown);
+            KEY_EVENT_SERVICE.addEventListener("keyup", updateDebugKeyDown);
+        }
+    }
+    async initializeProgressBar() {
+        var _a;
+        if (CONFIG_SERVICE.getConfigValue("features.progress_bar.enabled")) {
+            await this.rgthreeCssPromise;
+            if (!this.progressBarEl) {
+                this.progressBarEl = RgthreeProgressBar.create();
+                this.progressBarEl.setAttribute("title", "Progress Bar by rgthree. right-click for rgthree menu.");
+                this.progressBarEl.addEventListener("contextmenu", async (e) => {
+                    e.stopPropagation();
+                    e.preventDefault();
+                });
+                this.progressBarEl.addEventListener("pointerdown", async (e) => {
+                    var _a;
+                    LiteGraph.closeAllContextMenus();
+                    if (e.button == 2) {
+                        const canvas = await waitForCanvas();
+                        new LiteGraph.ContextMenu(this.getRgthreeIContextMenuValues(), {
+                            title: `<div class="rgthree-contextmenu-item rgthree-contextmenu-title-rgthree-comfy">${logoRgthree} rgthree-comfy</div>`,
+                            left: e.clientX,
+                            top: 5,
+                        });
+                        return;
+                    }
+                    if (e.button == 0) {
+                        const nodeId = (_a = this.progressBarEl) === null || _a === void 0 ? void 0 : _a.currentNodeId;
+                        if (nodeId) {
+                            const [canvas, graph] = await Promise.all([waitForCanvas(), waitForGraph()]);
+                            const node = graph.getNodeById(Number(nodeId));
+                            if (node) {
+                                canvas.centerOnNode(node);
+                                e.stopPropagation();
+                                e.preventDefault();
+                            }
+                        }
+                        return;
+                    }
+                });
+            }
+            const isUpdatedComfyBodyClasses = !!query(".comfyui-body-top");
+            const position = CONFIG_SERVICE.getConfigValue("features.progress_bar.position");
+            this.progressBarEl.classList.toggle("rgthree-pos-bottom", position === "bottom");
+            if (isUpdatedComfyBodyClasses) {
+                if (position === "bottom") {
+                    query(".comfyui-body-bottom").appendChild(this.progressBarEl);
+                }
+                else {
+                    query(".comfyui-body-top").appendChild(this.progressBarEl);
+                }
+            }
+            else {
+                document.body.appendChild(this.progressBarEl);
+            }
+            const height = CONFIG_SERVICE.getConfigValue("features.progress_bar.height") || 14;
+            this.progressBarEl.style.height = `${height}px`;
+            const fontSize = Math.max(10, Number(height) - 10);
+            this.progressBarEl.style.fontSize = `${fontSize}px`;
+            this.progressBarEl.style.fontWeight = fontSize <= 12 ? "bold" : "normal";
+        }
+        else {
+            (_a = this.progressBarEl) === null || _a === void 0 ? void 0 : _a.remove();
+        }
+    }
+    async initializeGraphAndCanvasHooks() {
+        const rgthree = this;
+        const graphSerialize = LGraph.prototype.serialize;
+        LGraph.prototype.serialize = function () {
+            const response = graphSerialize.apply(this, [...arguments]);
+            rgthree.initialGraphToPromptSerializedWorkflowBecauseComfyUIBrokeStuff = response;
+            return response;
+        };
+        const processMouseDown = LGraphCanvas.prototype.processMouseDown;
+        LGraphCanvas.prototype.processMouseDown = function (e) {
+            rgthree.processingMouseDown = true;
+            const returnVal = processMouseDown.apply(this, [...arguments]);
+            rgthree.dispatchCustomEvent("on-process-mouse-down", { originalEvent: e });
+            rgthree.processingMouseDown = false;
+            return returnVal;
+        };
+        const adjustMouseEvent = LGraphCanvas.prototype.adjustMouseEvent;
+        LGraphCanvas.prototype.adjustMouseEvent = function (e) {
+            adjustMouseEvent.apply(this, [...arguments]);
+            rgthree.lastCanvasMouseEvent = e;
+        };
+        const copyToClipboard = LGraphCanvas.prototype.copyToClipboard;
+        LGraphCanvas.prototype.copyToClipboard = function (items) {
+            rgthree.canvasCurrentlyCopyingToClipboard = true;
+            rgthree.canvasCurrentlyCopyingToClipboardWithMultipleNodes =
+                Object.values(items || this.selected_nodes || []).length > 1;
+            const value = copyToClipboard.apply(this, [...arguments]);
+            rgthree.canvasCurrentlyCopyingToClipboard = false;
+            rgthree.canvasCurrentlyCopyingToClipboardWithMultipleNodes = false;
+            return value;
+        };
+        const pasteFromClipboard = LGraphCanvas.prototype.pasteFromClipboard;
+        LGraphCanvas.prototype.pasteFromClipboard = function (...args) {
+            rgthree.canvasCurrentlyPastingFromClipboard = true;
+            pasteFromClipboard.apply(this, [...arguments]);
+            rgthree.canvasCurrentlyPastingFromClipboard = false;
+        };
+        const onGroupAdd = LGraphCanvas.onGroupAdd;
+        LGraphCanvas.onGroupAdd = function (...args) {
+            const graph = app.canvas.getCurrentGraph();
+            onGroupAdd.apply(this, [...args]);
+            LGraphCanvas.onShowPropertyEditor({}, null, null, null, graph._groups[graph._groups.length - 1]);
+        };
+    }
+    async invokeExtensionsAsync(method, ...args) {
+        var _a;
+        const comfyapp = app;
+        if (CONFIG_SERVICE.getConfigValue("features.invoke_extensions_async.node_created") === false) {
+            const [m, a] = this.logParts(LogLevel.INFO, `Skipping invokeExtensionsAsync for applicable rgthree-comfy nodes`);
+            (_a = console[m]) === null || _a === void 0 ? void 0 : _a.call(console, ...a);
+            return Promise.resolve();
+        }
+        return await Promise.all(comfyapp.extensions.map(async (ext) => {
+            var _a, _b;
+            if (ext === null || ext === void 0 ? void 0 : ext[method]) {
+                try {
+                    const blocked = INVOKE_EXTENSIONS_BLOCKLIST.find((block) => ext.name.toLowerCase().startsWith(block.name.toLowerCase()));
+                    if (blocked) {
+                        const [n, v] = this.logger.logPartsOnceForTime(LogLevel.WARN, 5000, `Blocked extension '${ext.name}' method '${method}' for rgthree-nodes because: ${blocked.reason}`);
+                        (_a = console[n]) === null || _a === void 0 ? void 0 : _a.call(console, ...v);
+                        return Promise.resolve();
+                    }
+                    return await ext[method](...args, comfyapp);
+                }
+                catch (error) {
+                    const [n, v] = this.logParts(LogLevel.ERROR, `Error calling extension '${ext.name}' method '${method}' for rgthree-node.`, { error }, { extension: ext }, { args });
+                    (_b = console[n]) === null || _b === void 0 ? void 0 : _b.call(console, ...v);
+                }
+            }
+        }));
+    }
+    dispatchCustomEvent(event, detail) {
+        if (detail != null) {
+            return this.dispatchEvent(new CustomEvent(event, { detail }));
+        }
+        return this.dispatchEvent(new CustomEvent(event));
+    }
+    async initializeContextMenu() {
+        const that = this;
+        setTimeout(async () => {
+            const getCanvasMenuOptions = LGraphCanvas.prototype.getCanvasMenuOptions;
+            LGraphCanvas.prototype.getCanvasMenuOptions = function (...args) {
+                let existingOptions = getCanvasMenuOptions.apply(this, [...args]);
+                const options = [];
+                options.push(null);
+                options.push(null);
+                options.push(null);
+                options.push({
+                    content: logoRgthree + `rgthree-comfy`,
+                    className: "rgthree-contextmenu-item rgthree-contextmenu-main-item-rgthree-comfy",
+                    submenu: {
+                        options: that.getRgthreeIContextMenuValues(),
+                    },
+                });
+                options.push(null);
+                options.push(null);
+                let idx = null;
+                idx = idx || existingOptions.findIndex((o) => { var _a, _b; return (_b = (_a = o === null || o === void 0 ? void 0 : o.content) === null || _a === void 0 ? void 0 : _a.startsWith) === null || _b === void 0 ? void 0 : _b.call(_a, "Queue Group"); }) + 1;
+                idx =
+                    idx || existingOptions.findIndex((o) => { var _a, _b; return (_b = (_a = o === null || o === void 0 ? void 0 : o.content) === null || _a === void 0 ? void 0 : _a.startsWith) === null || _b === void 0 ? void 0 : _b.call(_a, "Queue Selected"); }) + 1;
+                idx = idx || existingOptions.findIndex((o) => { var _a, _b; return (_b = (_a = o === null || o === void 0 ? void 0 : o.content) === null || _a === void 0 ? void 0 : _a.startsWith) === null || _b === void 0 ? void 0 : _b.call(_a, "Convert to Group"); });
+                idx = idx || existingOptions.findIndex((o) => { var _a, _b; return (_b = (_a = o === null || o === void 0 ? void 0 : o.content) === null || _a === void 0 ? void 0 : _a.startsWith) === null || _b === void 0 ? void 0 : _b.call(_a, "Arrange ("); });
+                idx = idx || existingOptions.findIndex((o) => !o) + 1;
+                idx = idx || 3;
+                existingOptions.splice(idx, 0, ...options);
+                for (let i = existingOptions.length; i > 0; i--) {
+                    if (existingOptions[i] === null && existingOptions[i + 1] === null) {
+                        existingOptions.splice(i, 1);
+                    }
+                }
+                return existingOptions;
+            };
+        }, 1016);
+    }
+    getRgthreeIContextMenuValues() {
+        const [canvas, graph] = [app.canvas, app.canvas.getCurrentGraph()];
+        const selectedNodes = Object.values(canvas.selected_nodes || {});
+        let rerouteNodes = [];
+        if (selectedNodes.length) {
+            rerouteNodes = selectedNodes.filter((n) => n.type === "Reroute");
+        }
+        else {
+            rerouteNodes = graph._nodes.filter((n) => n.type == "Reroute");
+        }
+        const rerouteLabel = selectedNodes.length ? "selected" : "all";
+        const showBookmarks = CONFIG_SERVICE.getFeatureValue("menu_bookmarks.enabled");
+        const bookmarkMenuItems = showBookmarks ? getBookmarks() : [];
+        return [
+            {
+                content: "Nodes",
+                disabled: true,
+                className: "rgthree-contextmenu-item rgthree-contextmenu-label",
+            },
+            {
+                content: iconNode + "All",
+                className: "rgthree-contextmenu-item",
+                has_submenu: true,
+                submenu: {
+                    options: getNodeTypeStrings(),
+                    callback: (value, options, event) => {
+                        const node = LiteGraph.createNode(addRgthree(value));
+                        if (node) {
+                            node.pos = [
+                                rgthree.lastCanvasMouseEvent.canvasX,
+                                rgthree.lastCanvasMouseEvent.canvasY,
+                            ];
+                            canvas.graph.add(node);
+                            canvas.selectNode(node);
+                            graph.setDirtyCanvas(true, true);
+                        }
+                    },
+                    extra: { rgthree_doNotNest: true },
+                },
+            },
+            {
+                content: "Actions",
+                disabled: true,
+                className: "rgthree-contextmenu-item rgthree-contextmenu-label",
+            },
+            {
+                content: iconGear + "Settings (rgthree-comfy)",
+                disabled: !!this.settingsDialog,
+                className: "rgthree-contextmenu-item",
+                callback: (...args) => {
+                    this.settingsDialog = new RgthreeConfigDialog().show();
+                    this.settingsDialog.addEventListener("close", (e) => {
+                        this.settingsDialog = null;
+                    });
+                },
+            },
+            {
+                content: iconReplace + ` Convert ${rerouteLabel} Reroutes`,
+                disabled: !rerouteNodes.length,
+                className: "rgthree-contextmenu-item",
+                callback: (...args) => {
+                    const msg = `Convert ${rerouteLabel} ComfyUI Reroutes to Reroute (rgthree) nodes? \n` +
+                        `(First save a copy of your workflow & check reroute connections afterwards)`;
+                    if (!window.confirm(msg)) {
+                        return;
+                    }
+                    (async () => {
+                        for (const node of [...rerouteNodes]) {
+                            if (node.type == "Reroute") {
+                                this.replacingReroute = node.id;
+                                await replaceNode(node, NodeTypesString.REROUTE);
+                                this.replacingReroute = null;
+                            }
+                        }
+                    })();
+                },
+            },
+            ...bookmarkMenuItems,
+            {
+                content: "More...",
+                disabled: true,
+                className: "rgthree-contextmenu-item rgthree-contextmenu-label",
+            },
+            {
+                content: iconStarFilled + "Star on Github",
+                className: "rgthree-contextmenu-item rgthree-contextmenu-github",
+                callback: (...args) => {
+                    window.open("https://github.com/rgthree/rgthree-comfy", "_blank");
+                },
+            },
+        ];
+    }
+    async queueOutputNodes(nodes) {
+        var _a;
+        const nodeIds = nodes.map((n) => n.id);
+        try {
+            this.queueNodeIds = nodeIds;
+            await app.queuePrompt(0);
+        }
+        catch (e) {
+            const [n, v] = this.logParts(LogLevel.ERROR, `There was an error queuing nodes ${nodeIds}`, e);
+            (_a = console[n]) === null || _a === void 0 ? void 0 : _a.call(console, ...v);
+        }
+        finally {
+            this.queueNodeIds = null;
+        }
+    }
+    recursiveAddNodes(nodeId, oldOutput, newOutput) {
+        let currentId = nodeId;
+        let currentNode = oldOutput[currentId];
+        if (newOutput[currentId] == null) {
+            newOutput[currentId] = currentNode;
+            for (const inputValue of Object.values(currentNode.inputs || [])) {
+                if (Array.isArray(inputValue)) {
+                    this.recursiveAddNodes(inputValue[0], oldOutput, newOutput);
+                }
+            }
+        }
+        return newOutput;
+    }
+    initializeComfyUIHooks() {
+        const rgthree = this;
+        const queuePrompt = app.queuePrompt;
+        app.queuePrompt = async function (number, batchCount) {
+            rgthree.processingQueue = true;
+            rgthree.dispatchCustomEvent("queue");
+            try {
+                return await queuePrompt.apply(app, [...arguments]);
+            }
+            finally {
+                rgthree.processingQueue = false;
+                rgthree.dispatchCustomEvent("queue-end");
+            }
+        };
+        const loadApiJson = app.loadApiJson;
+        app.loadApiJson = async function (apiData, fileName) {
+            rgthree.loadingApiJson = apiData;
+            try {
+                loadApiJson.apply(app, [...arguments]);
+            }
+            finally {
+                rgthree.loadingApiJson = null;
+            }
+        };
+        const graphToPrompt = app.graphToPrompt;
+        app.graphToPrompt = async function () {
+            rgthree.dispatchCustomEvent("graph-to-prompt");
+            let promise = graphToPrompt.apply(app, [...arguments]);
+            await promise;
+            rgthree.dispatchCustomEvent("graph-to-prompt-end");
+            return promise;
+        };
+        const apiQueuePrompt = api.queuePrompt;
+        api.queuePrompt = async function (index, prompt, ...args) {
+            var _a;
+            if (((_a = rgthree.queueNodeIds) === null || _a === void 0 ? void 0 : _a.length) && prompt.output) {
+                const oldOutput = prompt.output;
+                let newOutput = {};
+                for (const queueNodeId of rgthree.queueNodeIds) {
+                    rgthree.recursiveAddNodes(String(queueNodeId), oldOutput, newOutput);
+                }
+                prompt.output = newOutput;
+            }
+            rgthree.dispatchCustomEvent("comfy-api-queue-prompt-before", {
+                workflow: prompt.workflow,
+                output: prompt.output,
+            });
+            const response = apiQueuePrompt.apply(app, [index, prompt, ...args]);
+            rgthree.dispatchCustomEvent("comfy-api-queue-prompt-end");
+            return response;
+        };
+        const clean = app.clean;
+        app.clean = function () {
+            rgthree.clearAllMessages();
+            clean && clean.apply(app, [...arguments]);
+        };
+        const loadGraphData = app.loadGraphData;
+        app.loadGraphData = function (graph) {
+            if (rgthree.monitorLinkTimeout) {
+                clearTimeout(rgthree.monitorLinkTimeout);
+                rgthree.monitorLinkTimeout = null;
+            }
+            rgthree.clearAllMessages();
+            let graphCopy;
+            try {
+                graphCopy = JSON.parse(JSON.stringify(graph));
+            }
+            catch (e) {
+                graphCopy = null;
+            }
+            setTimeout(() => {
+                var _a, _b, _c;
+                const wasLoadingAborted = (_b = (_a = document
+                    .querySelector(".comfy-modal-content")) === null || _a === void 0 ? void 0 : _a.textContent) === null || _b === void 0 ? void 0 : _b.includes("Loading aborted due");
+                const graphToUse = wasLoadingAborted ? graphCopy || graph : app.graph;
+                const fixer = WorkflowLinkFixer.create(graphToUse);
+                const fixBadLinksResult = fixer.check();
+                if (fixBadLinksResult.hasBadLinks) {
+                    const [n, v] = rgthree.logParts(LogLevel.WARN, `The workflow you've loaded has corrupt linking data. Open ${new URL(location.href).origin}/rgthree/link_fixer to try to fix.`);
+                    (_c = console[n]) === null || _c === void 0 ? void 0 : _c.call(console, ...v);
+                    if (CONFIG_SERVICE.getConfigValue("features.show_alerts_for_corrupt_workflows")) {
+                        rgthree.showMessage({
+                            id: "bad-links",
+                            type: "warn",
+                            message: "The workflow you've loaded has corrupt linking data that may be able to be fixed.",
+                            actions: [
+                                {
+                                    label: "Open fixer",
+                                    href: "/rgthree/link_fixer",
+                                },
+                                {
+                                    label: "Fix in place",
+                                    href: "/rgthree/link_fixer",
+                                    callback: (event) => {
+                                        event.stopPropagation();
+                                        event.preventDefault();
+                                        if (confirm("This will attempt to fix in place. Please make sure to have a saved copy of your workflow.")) {
+                                            try {
+                                                const fixBadLinksResult = fixer.fix();
+                                                if (!fixBadLinksResult.hasBadLinks) {
+                                                    rgthree.hideMessage("bad-links");
+                                                    alert("Success! It's possible some valid links may have been affected. Please check and verify your workflow.");
+                                                    wasLoadingAborted && app.loadGraphData(fixBadLinksResult.graph);
+                                                    if (CONFIG_SERVICE.getConfigValue("features.monitor_for_corrupt_links") ||
+                                                        CONFIG_SERVICE.getConfigValue("features.monitor_bad_links")) {
+                                                        rgthree.monitorLinkTimeout = setTimeout(() => {
+                                                            rgthree.monitorBadLinks();
+                                                        }, 5000);
+                                                    }
+                                                }
+                                            }
+                                            catch (e) {
+                                                console.error(e);
+                                                alert("Unsuccessful at fixing corrupt data. :(");
+                                                rgthree.hideMessage("bad-links");
+                                            }
+                                        }
+                                    },
+                                },
+                            ],
+                        });
+                    }
+                }
+                else if (CONFIG_SERVICE.getConfigValue("features.monitor_for_corrupt_links") ||
+                    CONFIG_SERVICE.getConfigValue("features.monitor_bad_links")) {
+                    rgthree.monitorLinkTimeout = setTimeout(() => {
+                        rgthree.monitorBadLinks();
+                    }, 5000);
+                }
+            }, 100);
+            return loadGraphData && loadGraphData.apply(app, [...arguments]);
+        };
+    }
+    getNodeFromInitialGraphToPromptSerializedWorkflowBecauseComfyUIBrokeStuff(node) {
+        var _a, _b, _c;
+        return ((_c = (_b = (_a = this.initialGraphToPromptSerializedWorkflowBecauseComfyUIBrokeStuff) === null || _a === void 0 ? void 0 : _a.nodes) === null || _b === void 0 ? void 0 : _b.find((n) => n.id === node.id)) !== null && _c !== void 0 ? _c : null);
+    }
+    async showMessage(data) {
+        let container = document.querySelector(".rgthree-top-messages-container");
+        if (!container) {
+            container = document.createElement("div");
+            container.classList.add("rgthree-top-messages-container");
+            document.body.appendChild(container);
+        }
+        const dialogs = queryAll("dialog[open]");
+        if (dialogs.length) {
+            let dialog = dialogs[dialogs.length - 1];
+            dialog.appendChild(container);
+            dialog.addEventListener("close", (e) => {
+                document.body.appendChild(container);
+            });
+        }
+        await this.hideMessage(data.id);
+        const messageContainer = document.createElement("div");
+        messageContainer.setAttribute("type", data.type || "info");
+        const message = document.createElement("span");
+        message.innerHTML = data.message;
+        messageContainer.appendChild(message);
+        for (let a = 0; a < (data.actions || []).length; a++) {
+            const action = data.actions[a];
+            if (a > 0) {
+                const sep = document.createElement("span");
+                sep.innerHTML = "&nbsp;|&nbsp;";
+                messageContainer.appendChild(sep);
+            }
+            const actionEl = document.createElement("a");
+            actionEl.innerText = action.label;
+            if (action.href) {
+                actionEl.target = "_blank";
+                actionEl.href = action.href;
+            }
+            if (action.callback) {
+                actionEl.onclick = (e) => {
+                    return action.callback(e);
+                };
+            }
+            messageContainer.appendChild(actionEl);
+        }
+        const messageAnimContainer = document.createElement("div");
+        messageAnimContainer.setAttribute("msg-id", data.id);
+        messageAnimContainer.appendChild(messageContainer);
+        container.appendChild(messageAnimContainer);
+        await wait(64);
+        messageAnimContainer.style.marginTop = `-${messageAnimContainer.offsetHeight}px`;
+        await wait(64);
+        messageAnimContainer.classList.add("-show");
+        if (data.timeout) {
+            await wait(data.timeout);
+            this.hideMessage(data.id);
+        }
+    }
+    async hideMessage(id) {
+        const msg = document.querySelector(`.rgthree-top-messages-container > [msg-id="${id}"]`);
+        if (msg === null || msg === void 0 ? void 0 : msg.classList.contains("-show")) {
+            msg.classList.remove("-show");
+            await wait(750);
+        }
+        msg && msg.remove();
+    }
+    async clearAllMessages() {
+        let container = document.querySelector(".rgthree-top-messages-container");
+        container && (container.innerHTML = "");
+    }
+    setLogLevel(level) {
+        if (typeof level === "string") {
+            level = LogLevelKeyToLogLevel[CONFIG_SERVICE.getConfigValue("log_level")];
+        }
+        if (level != null) {
+            GLOBAL_LOG_LEVEL = level;
+        }
+    }
+    logParts(level, message, ...args) {
+        return this.logger.logParts(level, message, ...args);
+    }
+    newLogSession(name) {
+        return this.logger.newSession(name);
+    }
+    isDebugMode() {
+        if (window.location.href.includes("rgthree-debug=false")) {
+            return false;
+        }
+        return GLOBAL_LOG_LEVEL >= LogLevel.DEBUG || window.location.href.includes("rgthree-debug");
+    }
+    isDevMode() {
+        if (window.location.href.includes("rgthree-dev=false")) {
+            return false;
+        }
+        return GLOBAL_LOG_LEVEL >= LogLevel.DEV || window.location.href.includes("rgthree-dev");
+    }
+    monitorBadLinks() {
+        const badLinksFound = WorkflowLinkFixer.create(app.graph).check();
+        if (badLinksFound.hasBadLinks && !this.monitorBadLinksAlerted) {
+            this.monitorBadLinksAlerted = true;
+            alert(`Problematic links just found in live data. Can you save your workflow and file a bug with ` +
+                `the last few steps you took to trigger this at ` +
+                `https://github.com/rgthree/rgthree-comfy/issues. Thank you!`);
+        }
+        else if (!badLinksFound.hasBadLinks) {
+            this.monitorBadLinksAlerted = false;
+        }
+        this.monitorLinkTimeout = setTimeout(() => {
+            this.monitorBadLinks();
+        }, 5000);
+    }
+}
+function getBookmarks() {
+    const bookmarks = BOOKMARKS_SERVICE.getCurrentBookmarks();
+    const bookmarkItems = bookmarks.map((n) => ({
+        content: `[${n.shortcutKey}] ${n.title}`,
+        className: "rgthree-contextmenu-item",
+        callback: () => {
+            n.canvasToBookmark();
+        },
+    }));
+    return !bookmarkItems.length
+        ? []
+        : [
+            {
+                content: "🔖 Bookmarks",
+                disabled: true,
+                className: "rgthree-contextmenu-item rgthree-contextmenu-label",
+            },
+            ...bookmarkItems,
+        ];
+}
+export const rgthree = new Rgthree();
+window.rgthree = rgthree;
+app.registerExtension({
+    name: "Comfy.RgthreeComfy",
+    aboutPageBadges: [
+        {
+            label: `rgthree-comfy v${rgthree.version}`,
+            url: "https://github.com/rgthree/rgthree-comfy",
+            icon: "rgthree-comfy-about-badge-logo",
+        },
+    ],
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/seed.js b/custom_nodes/rgthree-comfy/web/comfyui/seed.js
new file mode 100644
index 0000000000000000000000000000000000000000..46c81d6422dda1ef0cb49bd741bfd51416a7844e
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/seed.js
@@ -0,0 +1,208 @@
+import { app } from "../../scripts/app.js";
+import { ComfyWidgets } from "../../scripts/widgets.js";
+import { RgthreeBaseServerNode } from "./base_node.js";
+import { rgthree } from "./rgthree.js";
+import { addConnectionLayoutSupport, getFullNodeIdFromApiPrompt, getNodeByIdFromApiPrompt, } from "./utils.js";
+import { NodeTypesString } from "./constants.js";
+const LAST_SEED_BUTTON_LABEL = "♻️ (Use Last Queued Seed)";
+const SPECIAL_SEED_RANDOM = -1;
+const SPECIAL_SEED_INCREMENT = -2;
+const SPECIAL_SEED_DECREMENT = -3;
+const SPECIAL_SEEDS = [SPECIAL_SEED_RANDOM, SPECIAL_SEED_INCREMENT, SPECIAL_SEED_DECREMENT];
+class RgthreeSeed extends RgthreeBaseServerNode {
+    constructor(title = RgthreeSeed.title) {
+        super(title);
+        this.serialize_widgets = true;
+        this.logger = rgthree.newLogSession(`[Seed]`);
+        this.lastSeed = undefined;
+        this.serializedCtx = {};
+        this.lastSeedValue = null;
+        this.handleApiHijackingBound = this.handleApiHijacking.bind(this);
+        this.properties["randomMax"] = 1125899906842624;
+        this.properties["randomMin"] = 0;
+        rgthree.addEventListener("comfy-api-queue-prompt-before", this.handleApiHijackingBound);
+        console.log("SEED NODE STARTED!");
+    }
+    onPropertyChanged(prop, value, prevValue) {
+        if (prop === "randomMax") {
+            this.properties["randomMax"] = Math.min(1125899906842624, Number(value));
+        }
+        else if (prop === "randomMin") {
+            this.properties["randomMin"] = Math.max(-1125899906842624, Number(value));
+        }
+        return true;
+    }
+    onRemoved() {
+        console.log("SEED NODE onRemoved!");
+        rgthree.removeEventListener("comfy-api-queue-prompt-before", this.handleApiHijackingBound);
+    }
+    onExecuted(output) {
+        console.log(`SEED ON EXECUTED. #${this.id}.`, output);
+    }
+    configure(info) {
+        var _a;
+        super.configure(info);
+        if ((_a = this.properties) === null || _a === void 0 ? void 0 : _a["showLastSeed"]) {
+            this.addLastSeedValue();
+        }
+    }
+    async handleAction(action) {
+        if (action === "Randomize Each Time") {
+            this.seedWidget.value = SPECIAL_SEED_RANDOM;
+        }
+        else if (action === "Use Last Queued Seed") {
+            this.seedWidget.value = this.lastSeed != null ? this.lastSeed : this.seedWidget.value;
+            this.lastSeedButton.name = LAST_SEED_BUTTON_LABEL;
+            this.lastSeedButton.disabled = true;
+        }
+    }
+    onNodeCreated() {
+        var _a;
+        (_a = super.onNodeCreated) === null || _a === void 0 ? void 0 : _a.call(this);
+        for (const [i, w] of this.widgets.entries()) {
+            if (w.name === "seed") {
+                this.seedWidget = w;
+                this.seedWidget.value = SPECIAL_SEED_RANDOM;
+            }
+            else if (w.name === "control_after_generate") {
+                this.widgets.splice(i, 1);
+            }
+        }
+        this.addWidget("button", "🎲 Randomize Each Time", "", () => {
+            this.seedWidget.value = SPECIAL_SEED_RANDOM;
+        }, { serialize: false });
+        this.addWidget("button", "🎲 New Fixed Random", "", () => {
+            this.seedWidget.value = this.generateRandomSeed();
+        }, { serialize: false });
+        this.lastSeedButton = this.addWidget("button", 'USE_LAST_SEED', "okay", () => {
+            this.seedWidget.value = this.lastSeed != null ? this.lastSeed : this.seedWidget.value;
+            this.lastSeedButton.label = LAST_SEED_BUTTON_LABEL;
+            this.lastSeedButton.disabled = true;
+        }, { width: 50, serialize: false });
+        this.lastSeedButton.label = LAST_SEED_BUTTON_LABEL;
+        this.lastSeedButton.disabled = true;
+    }
+    generateRandomSeed() {
+        let step = this.seedWidget.options.step || 1;
+        const randomMin = Number(this.properties["randomMin"] || 0);
+        const randomMax = Number(this.properties["randomMax"] || 1125899906842624);
+        const randomRange = (randomMax - randomMin) / (step / 10);
+        let seed = Math.floor(Math.random() * randomRange) * (step / 10) + randomMin;
+        if (SPECIAL_SEEDS.includes(seed)) {
+            seed = 0;
+        }
+        return seed;
+    }
+    getExtraMenuOptions(canvas, options) {
+        var _a;
+        (_a = super.getExtraMenuOptions) === null || _a === void 0 ? void 0 : _a.apply(this, [...arguments]);
+        options.splice(options.length - 1, 0, {
+            content: "Show/Hide Last Seed Value",
+            callback: (_value, _options, _event, _parentMenu, _node) => {
+                this.properties["showLastSeed"] = !this.properties["showLastSeed"];
+                if (this.properties["showLastSeed"]) {
+                    this.addLastSeedValue();
+                }
+                else {
+                    this.removeLastSeedValue();
+                }
+            },
+        });
+        return [];
+    }
+    addLastSeedValue() {
+        if (this.lastSeedValue)
+            return;
+        this.lastSeedValue = ComfyWidgets["STRING"](this, "last_seed", ["STRING", { multiline: true }], app).widget;
+        this.lastSeedValue.inputEl.readOnly = true;
+        this.lastSeedValue.inputEl.style.fontSize = "0.75rem";
+        this.lastSeedValue.inputEl.style.textAlign = "center";
+        this.computeSize();
+    }
+    removeLastSeedValue() {
+        if (!this.lastSeedValue)
+            return;
+        this.lastSeedValue.inputEl.remove();
+        this.widgets.splice(this.widgets.indexOf(this.lastSeedValue), 1);
+        this.lastSeedValue = null;
+        this.computeSize();
+    }
+    handleApiHijacking(e) {
+        var _a, _b, _c;
+        if (this.mode === LiteGraph.NEVER || this.mode === 4) {
+            return;
+        }
+        const output = e.detail.output;
+        const fullId = (_a = getFullNodeIdFromApiPrompt(e.detail, this.id)) !== null && _a !== void 0 ? _a : "";
+        let workflowNode = getNodeByIdFromApiPrompt(e.detail, fullId);
+        let outputInputs = (_b = output === null || output === void 0 ? void 0 : output[fullId]) === null || _b === void 0 ? void 0 : _b.inputs;
+        if (!workflowNode ||
+            !outputInputs ||
+            outputInputs[this.seedWidget.name || "seed"] === undefined) {
+            const [n, v] = this.logger.warnParts(`Node ${fullId} not found in prompt data sent to server. This may be fine if only ` +
+                `queuing part of the workflow. If not, then this could be a bug.`);
+            (_c = console[n]) === null || _c === void 0 ? void 0 : _c.call(console, ...v);
+            return;
+        }
+        const seedToUse = this.getSeedToUse();
+        const seedWidgetndex = this.widgets.indexOf(this.seedWidget);
+        workflowNode.widgets_values[seedWidgetndex] = seedToUse;
+        outputInputs[this.seedWidget.name || "seed"] = seedToUse;
+        this.lastSeed = seedToUse;
+        if (seedToUse != this.seedWidget.value) {
+            this.lastSeedButton.label = `♻️ ${this.lastSeed}`;
+            this.lastSeedButton.disabled = false;
+        }
+        else {
+            this.lastSeedButton.label = LAST_SEED_BUTTON_LABEL;
+            this.lastSeedButton.disabled = true;
+        }
+        if (this.lastSeedValue) {
+            this.lastSeedValue.value = `Last Seed: ${this.lastSeed}`;
+        }
+    }
+    getSeedToUse() {
+        const inputSeed = Number(this.seedWidget.value);
+        let seedToUse = null;
+        if (SPECIAL_SEEDS.includes(inputSeed)) {
+            if (typeof this.lastSeed === "number" && !SPECIAL_SEEDS.includes(this.lastSeed)) {
+                if (inputSeed === SPECIAL_SEED_INCREMENT) {
+                    seedToUse = this.lastSeed + 1;
+                }
+                else if (inputSeed === SPECIAL_SEED_DECREMENT) {
+                    seedToUse = this.lastSeed - 1;
+                }
+            }
+            if (seedToUse == null || SPECIAL_SEEDS.includes(seedToUse)) {
+                seedToUse = this.generateRandomSeed();
+            }
+        }
+        return seedToUse !== null && seedToUse !== void 0 ? seedToUse : inputSeed;
+    }
+    static setUp(comfyClass, nodeData) {
+        RgthreeBaseServerNode.registerForOverride(comfyClass, nodeData, RgthreeSeed);
+    }
+    static onRegisteredForOverride(comfyClass, ctxClass) {
+        addConnectionLayoutSupport(RgthreeSeed, app, [
+            ["Left", "Right"],
+            ["Right", "Left"],
+        ]);
+        setTimeout(() => {
+            RgthreeSeed.category = comfyClass.category;
+        });
+    }
+}
+RgthreeSeed.title = NodeTypesString.SEED;
+RgthreeSeed.type = NodeTypesString.SEED;
+RgthreeSeed.comfyClass = NodeTypesString.SEED;
+RgthreeSeed.exposedActions = ["Randomize Each Time", "Use Last Queued Seed"];
+RgthreeSeed["@randomMax"] = { type: "number" };
+RgthreeSeed["@randomMin"] = { type: "number" };
+app.registerExtension({
+    name: "rgthree.Seed",
+    async beforeRegisterNodeDef(nodeType, nodeData) {
+        if (nodeData.name === RgthreeSeed.type) {
+            RgthreeSeed.setUp(nodeType, nodeData);
+        }
+    },
+});
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/services/bookmarks_services.js b/custom_nodes/rgthree-comfy/web/comfyui/services/bookmarks_services.js
new file mode 100644
index 0000000000000000000000000000000000000000..839df30e7299b0d00910a141cbd5abc7dff82734
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/services/bookmarks_services.js
@@ -0,0 +1,24 @@
+import { app } from "../../../scripts/app.js";
+import { NodeTypesString } from "../constants.js";
+import { reduceNodesDepthFirst } from "../utils.js";
+const SHORTCUT_DEFAULTS = "1234567890abcdefghijklmnopqrstuvwxyz".split("");
+class BookmarksService {
+    getCurrentBookmarks() {
+        return reduceNodesDepthFirst(app.graph.nodes, (n, acc) => {
+            if (n.type === NodeTypesString.BOOKMARK) {
+                acc.push(n);
+            }
+        }, []).sort((a, b) => a.title.localeCompare(b.title));
+    }
+    getExistingShortcuts() {
+        const bookmarkNodes = this.getCurrentBookmarks();
+        const usedShortcuts = new Set(bookmarkNodes.map((n) => n.shortcutKey));
+        return usedShortcuts;
+    }
+    getNextShortcut() {
+        var _a;
+        const existingShortcuts = this.getExistingShortcuts();
+        return (_a = SHORTCUT_DEFAULTS.find((char) => !existingShortcuts.has(char))) !== null && _a !== void 0 ? _a : "1";
+    }
+}
+export const SERVICE = new BookmarksService();
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/services/config_service.js b/custom_nodes/rgthree-comfy/web/comfyui/services/config_service.js
new file mode 100644
index 0000000000000000000000000000000000000000..34f1087173c01cda464a519f4d8ebdfb81fbf25a
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/services/config_service.js
@@ -0,0 +1,28 @@
+import { rgthreeConfig } from "../../../rgthree/config.js";
+import { getObjectValue, setObjectValue } from "../../../rgthree/common/shared_utils.js";
+import { rgthreeApi } from "../../../rgthree/common/rgthree_api.js";
+class ConfigService extends EventTarget {
+    getConfigValue(key, def) {
+        return getObjectValue(rgthreeConfig, key, def);
+    }
+    getFeatureValue(key, def) {
+        key = "features." + key.replace(/^features\./, "");
+        return getObjectValue(rgthreeConfig, key, def);
+    }
+    async setConfigValues(changed) {
+        const body = new FormData();
+        body.append("json", JSON.stringify(changed));
+        const response = await rgthreeApi.fetchJson("/config", { method: "POST", body });
+        if (response.status === "ok") {
+            for (const [key, value] of Object.entries(changed)) {
+                setObjectValue(rgthreeConfig, key, value);
+                this.dispatchEvent(new CustomEvent("config-change", { detail: { key, value } }));
+            }
+        }
+        else {
+            return false;
+        }
+        return true;
+    }
+}
+export const SERVICE = new ConfigService();
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/services/context_service.js b/custom_nodes/rgthree-comfy/web/comfyui/services/context_service.js
new file mode 100644
index 0000000000000000000000000000000000000000..27e19658cbdc20d6213cd673fa6f10f79ee9ee7a
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/services/context_service.js
@@ -0,0 +1,51 @@
+import { getConnectedOutputNodesAndFilterPassThroughs } from "../utils.js";
+export let SERVICE;
+const OWNED_PREFIX = "+";
+const REGEX_PREFIX = /^[\+⚠️]\s*/;
+const REGEX_EMPTY_INPUT = /^\+\s*$/;
+export function stripContextInputPrefixes(name) {
+    return name.replace(REGEX_PREFIX, "");
+}
+export function getContextOutputName(inputName) {
+    if (inputName === "base_ctx")
+        return "CONTEXT";
+    return stripContextInputPrefixes(inputName).toUpperCase();
+}
+export var InputMutationOperation;
+(function (InputMutationOperation) {
+    InputMutationOperation[InputMutationOperation["UNKNOWN"] = 0] = "UNKNOWN";
+    InputMutationOperation[InputMutationOperation["ADDED"] = 1] = "ADDED";
+    InputMutationOperation[InputMutationOperation["REMOVED"] = 2] = "REMOVED";
+    InputMutationOperation[InputMutationOperation["RENAMED"] = 3] = "RENAMED";
+})(InputMutationOperation || (InputMutationOperation = {}));
+export class ContextService {
+    constructor() {
+        if (SERVICE) {
+            throw new Error("ContextService was already instantiated.");
+        }
+    }
+    onInputChanges(node, mutation) {
+        const childCtxs = getConnectedOutputNodesAndFilterPassThroughs(node, node, 0);
+        for (const childCtx of childCtxs) {
+            childCtx.handleUpstreamMutation(mutation);
+        }
+    }
+    getDynamicContextInputsData(node) {
+        return node
+            .getContextInputsList()
+            .map((input, index) => ({
+            name: stripContextInputPrefixes(input.name),
+            type: String(input.type),
+            index,
+        }))
+            .filter((i) => i.type !== "*");
+    }
+    getDynamicContextOutputsData(node) {
+        return node.outputs.map((output, index) => ({
+            name: stripContextInputPrefixes(output.name),
+            type: String(output.type),
+            index,
+        }));
+    }
+}
+SERVICE = new ContextService();
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/services/fast_groups_service.js b/custom_nodes/rgthree-comfy/web/comfyui/services/fast_groups_service.js
new file mode 100644
index 0000000000000000000000000000000000000000..3ee8778f2c2558ffa2f5bdadcf1e4901ccb6a38a
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/services/fast_groups_service.js
@@ -0,0 +1,163 @@
+import { app } from "../../../scripts/app.js";
+import { getGraphDependantNodeKey, getGroupNodes, reduceNodesDepthFirst } from "../utils.js";
+class FastGroupsService {
+    constructor() {
+        this.msThreshold = 400;
+        this.msLastUnsorted = 0;
+        this.msLastAlpha = 0;
+        this.msLastPosition = 0;
+        this.groupsUnsorted = [];
+        this.groupsSortedAlpha = [];
+        this.groupsSortedPosition = [];
+        this.fastGroupNodes = [];
+        this.runScheduledForMs = null;
+        this.runScheduleTimeout = null;
+        this.runScheduleAnimation = null;
+        this.cachedNodeBoundings = null;
+    }
+    addFastGroupNode(node) {
+        this.fastGroupNodes.push(node);
+        this.scheduleRun(8);
+    }
+    removeFastGroupNode(node) {
+        var _a;
+        const index = this.fastGroupNodes.indexOf(node);
+        if (index > -1) {
+            this.fastGroupNodes.splice(index, 1);
+        }
+        if (!((_a = this.fastGroupNodes) === null || _a === void 0 ? void 0 : _a.length)) {
+            this.clearScheduledRun();
+            this.groupsUnsorted = [];
+            this.groupsSortedAlpha = [];
+            this.groupsSortedPosition = [];
+        }
+    }
+    run() {
+        if (!this.runScheduledForMs) {
+            return;
+        }
+        for (const node of this.fastGroupNodes) {
+            node.refreshWidgets();
+        }
+        this.clearScheduledRun();
+        this.scheduleRun();
+    }
+    scheduleRun(ms = 500) {
+        if (this.runScheduledForMs && ms < this.runScheduledForMs) {
+            this.clearScheduledRun();
+        }
+        if (!this.runScheduledForMs && this.fastGroupNodes.length) {
+            this.runScheduledForMs = ms;
+            this.runScheduleTimeout = setTimeout(() => {
+                this.runScheduleAnimation = requestAnimationFrame(() => this.run());
+            }, ms);
+        }
+    }
+    clearScheduledRun() {
+        this.runScheduleTimeout && clearTimeout(this.runScheduleTimeout);
+        this.runScheduleAnimation && cancelAnimationFrame(this.runScheduleAnimation);
+        this.runScheduleTimeout = null;
+        this.runScheduleAnimation = null;
+        this.runScheduledForMs = null;
+    }
+    getBoundingsForAllNodes() {
+        if (!this.cachedNodeBoundings) {
+            this.cachedNodeBoundings = reduceNodesDepthFirst(app.graph._nodes, (node, acc) => {
+                var _a, _b;
+                let bounds = node.getBounding();
+                if (bounds[0] === 0 && bounds[1] === 0 && bounds[2] === 0 && bounds[3] === 0) {
+                    const ctx = (_b = (_a = node.graph) === null || _a === void 0 ? void 0 : _a.primaryCanvas) === null || _b === void 0 ? void 0 : _b.canvas.getContext("2d");
+                    if (ctx) {
+                        node.updateArea(ctx);
+                        bounds = node.getBounding();
+                    }
+                }
+                acc[getGraphDependantNodeKey(node)] = bounds;
+            }, {});
+            setTimeout(() => {
+                this.cachedNodeBoundings = null;
+            }, 50);
+        }
+        return this.cachedNodeBoundings;
+    }
+    recomputeInsideNodesForGroup(group) {
+        if (app.canvas.isDragging)
+            return;
+        const cachedBoundings = this.getBoundingsForAllNodes();
+        const nodes = group.graph.nodes;
+        group._children.clear();
+        group.nodes.length = 0;
+        for (const node of nodes) {
+            const nodeBounding = cachedBoundings[getGraphDependantNodeKey(node)];
+            const nodeCenter = nodeBounding &&
+                [nodeBounding[0] + nodeBounding[2] * 0.5, nodeBounding[1] + nodeBounding[3] * 0.5];
+            if (nodeCenter) {
+                const grouBounds = group._bounding;
+                if (nodeCenter[0] >= grouBounds[0] &&
+                    nodeCenter[0] < grouBounds[0] + grouBounds[2] &&
+                    nodeCenter[1] >= grouBounds[1] &&
+                    nodeCenter[1] < grouBounds[1] + grouBounds[3]) {
+                    group._children.add(node);
+                    group.nodes.push(node);
+                }
+            }
+        }
+    }
+    getGroupsUnsorted(now) {
+        var _a, _b, _c;
+        const canvas = app.canvas;
+        const graph = (_a = canvas.getCurrentGraph()) !== null && _a !== void 0 ? _a : app.graph;
+        if (!canvas.selected_group_moving &&
+            (!this.groupsUnsorted.length || now - this.msLastUnsorted > this.msThreshold)) {
+            this.groupsUnsorted = [...graph._groups];
+            const subgraphs = (_b = graph.subgraphs) === null || _b === void 0 ? void 0 : _b.values();
+            if (subgraphs) {
+                let s;
+                while ((s = subgraphs.next().value))
+                    this.groupsUnsorted.push(...((_c = s.groups) !== null && _c !== void 0 ? _c : []));
+            }
+            for (const group of this.groupsUnsorted) {
+                this.recomputeInsideNodesForGroup(group);
+                group.rgthree_hasAnyActiveNode = getGroupNodes(group).some((n) => n.mode === LiteGraph.ALWAYS);
+            }
+            this.msLastUnsorted = now;
+        }
+        return this.groupsUnsorted;
+    }
+    getGroupsAlpha(now) {
+        if (!this.groupsSortedAlpha.length || now - this.msLastAlpha > this.msThreshold) {
+            this.groupsSortedAlpha = [...this.getGroupsUnsorted(now)].sort((a, b) => {
+                return a.title.localeCompare(b.title);
+            });
+            this.msLastAlpha = now;
+        }
+        return this.groupsSortedAlpha;
+    }
+    getGroupsPosition(now) {
+        if (!this.groupsSortedPosition.length || now - this.msLastPosition > this.msThreshold) {
+            this.groupsSortedPosition = [...this.getGroupsUnsorted(now)].sort((a, b) => {
+                const aY = Math.floor(a._pos[1] / 30);
+                const bY = Math.floor(b._pos[1] / 30);
+                if (aY == bY) {
+                    const aX = Math.floor(a._pos[0] / 30);
+                    const bX = Math.floor(b._pos[0] / 30);
+                    return aX - bX;
+                }
+                return aY - bY;
+            });
+            this.msLastPosition = now;
+        }
+        return this.groupsSortedPosition;
+    }
+    getGroups(sort) {
+        const now = +new Date();
+        if (sort === "alphanumeric") {
+            return this.getGroupsAlpha(now);
+        }
+        if (sort === "position") {
+            return this.getGroupsPosition(now);
+        }
+        return this.getGroupsUnsorted(now);
+    }
+}
+export const SERVICE = new FastGroupsService();
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/services/key_events_services.js b/custom_nodes/rgthree-comfy/web/comfyui/services/key_events_services.js
new file mode 100644
index 0000000000000000000000000000000000000000..af1550488246ac2b9e53c34a0374f49f226dbb31
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/services/key_events_services.js
@@ -0,0 +1,115 @@
+class KeyEventService extends EventTarget {
+    constructor() {
+        var _a, _b, _c;
+        super();
+        this.downKeys = {};
+        this.shiftDownKeys = {};
+        this.ctrlKey = false;
+        this.altKey = false;
+        this.metaKey = false;
+        this.shiftKey = false;
+        this.isMac = !!(((_a = navigator.platform) === null || _a === void 0 ? void 0 : _a.toLocaleUpperCase().startsWith("MAC")) ||
+            ((_c = (_b = navigator.userAgentData) === null || _b === void 0 ? void 0 : _b.platform) === null || _c === void 0 ? void 0 : _c.toLocaleUpperCase().startsWith("MAC")));
+        this.initialize();
+    }
+    initialize() {
+        const that = this;
+        const processKey = LGraphCanvas.prototype.processKey;
+        LGraphCanvas.prototype.processKey = function (e) {
+            if (e.type === "keydown" || e.type === "keyup") {
+                that.handleKeyDownOrUp(e);
+            }
+            return processKey.apply(this, [...arguments]);
+        };
+        window.addEventListener("keydown", (e) => {
+            that.handleKeyDownOrUp(e);
+        });
+        window.addEventListener("keyup", (e) => {
+            that.handleKeyDownOrUp(e);
+        });
+        document.addEventListener("visibilitychange", (e) => {
+            this.clearKeydowns();
+        });
+        window.addEventListener("blur", (e) => {
+            this.clearKeydowns();
+        });
+    }
+    handleKeyDownOrUp(e) {
+        const key = e.key.toLocaleUpperCase();
+        if ((e.type === 'keydown' && this.downKeys[key] === true)
+            || (e.type === 'keyup' && this.downKeys[key] === undefined)) {
+            return;
+        }
+        this.ctrlKey = !!e.ctrlKey;
+        this.altKey = !!e.altKey;
+        this.metaKey = !!e.metaKey;
+        this.shiftKey = !!e.shiftKey;
+        if (e.type === "keydown") {
+            this.downKeys[key] = true;
+            this.dispatchCustomEvent("keydown", { originalEvent: e });
+            if (this.shiftKey && key !== 'SHIFT') {
+                this.shiftDownKeys[key] = true;
+            }
+        }
+        else if (e.type === "keyup") {
+            if (key === "META" && this.isMac) {
+                this.clearKeydowns();
+            }
+            else {
+                delete this.downKeys[key];
+            }
+            if (key === 'SHIFT') {
+                for (const key in this.shiftDownKeys) {
+                    delete this.downKeys[key];
+                    delete this.shiftDownKeys[key];
+                }
+            }
+            this.dispatchCustomEvent("keyup", { originalEvent: e });
+        }
+    }
+    clearKeydowns() {
+        this.ctrlKey = false;
+        this.altKey = false;
+        this.metaKey = false;
+        this.shiftKey = false;
+        for (const key in this.downKeys)
+            delete this.downKeys[key];
+    }
+    dispatchCustomEvent(event, detail) {
+        if (detail != null) {
+            return this.dispatchEvent(new CustomEvent(event, { detail }));
+        }
+        return this.dispatchEvent(new CustomEvent(event));
+    }
+    getKeysFromShortcut(shortcut) {
+        let keys;
+        if (typeof shortcut === "string") {
+            shortcut = shortcut.replace(/\s/g, "");
+            shortcut = shortcut.replace(/^\+/, "__PLUS__").replace(/\+\+/, "+__PLUS__");
+            keys = shortcut.split("+").map((i) => i.replace("__PLUS__", "+"));
+        }
+        else {
+            keys = [...shortcut];
+        }
+        return keys.map((k) => k.toLocaleUpperCase());
+    }
+    areAllKeysDown(keys) {
+        keys = this.getKeysFromShortcut(keys);
+        return keys.every((k) => {
+            return this.downKeys[k];
+        });
+    }
+    areOnlyKeysDown(keys, alsoAllowShift = false) {
+        keys = this.getKeysFromShortcut(keys);
+        const allKeysDown = this.areAllKeysDown(keys);
+        const downKeysLength = Object.values(this.downKeys).length;
+        if (allKeysDown && keys.length === downKeysLength) {
+            return true;
+        }
+        if (alsoAllowShift && !keys.includes("SHIFT") && keys.length === downKeysLength - 1) {
+            return allKeysDown && this.areAllKeysDown(["SHIFT"]);
+        }
+        return false;
+    }
+}
+export const SERVICE = new KeyEventService();
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/utils.js b/custom_nodes/rgthree-comfy/web/comfyui/utils.js
new file mode 100644
index 0000000000000000000000000000000000000000..747e78fe6390afc363fe7730046841930b611c5a
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/utils.js
@@ -0,0 +1,748 @@
+import { app } from "../../scripts/app.js";
+import { api } from "../../scripts/api.js";
+import { getResolver, wait } from "../../rgthree/common/shared_utils.js";
+import { RgthreeHelpDialog } from "../../rgthree/common/dialog.js";
+const oldApiGetNodeDefs = api.getNodeDefs;
+api.getNodeDefs = async function () {
+    const defs = await oldApiGetNodeDefs.call(api);
+    this.dispatchEvent(new CustomEvent("fresh-node-defs", { detail: defs }));
+    return defs;
+};
+export var IoDirection;
+(function (IoDirection) {
+    IoDirection[IoDirection["INPUT"] = 0] = "INPUT";
+    IoDirection[IoDirection["OUTPUT"] = 1] = "OUTPUT";
+})(IoDirection || (IoDirection = {}));
+const PADDING = 0;
+export const LAYOUT_LABEL_TO_DATA = {
+    Left: [LiteGraph.LEFT, [0, 0.5], [PADDING, 0]],
+    Right: [LiteGraph.RIGHT, [1, 0.5], [-PADDING, 0]],
+    Top: [LiteGraph.UP, [0.5, 0], [0, PADDING]],
+    Bottom: [LiteGraph.DOWN, [0.5, 1], [0, -PADDING]],
+};
+export const LAYOUT_LABEL_OPPOSITES = {
+    Left: "Right",
+    Right: "Left",
+    Top: "Bottom",
+    Bottom: "Top",
+};
+export const LAYOUT_CLOCKWISE = ["Top", "Right", "Bottom", "Left"];
+export function addMenuItem(node, _app, config, after = "Shape") {
+    const oldGetExtraMenuOptions = node.prototype.getExtraMenuOptions;
+    node.prototype.getExtraMenuOptions = function (canvas, menuOptions) {
+        oldGetExtraMenuOptions && oldGetExtraMenuOptions.apply(this, [canvas, menuOptions]);
+        addMenuItemOnExtraMenuOptions(this, config, menuOptions, after);
+    };
+}
+let canvasResolver = null;
+export function waitForCanvas() {
+    if (canvasResolver === null) {
+        canvasResolver = getResolver();
+        function _waitForCanvas() {
+            if (!canvasResolver.completed) {
+                if (app === null || app === void 0 ? void 0 : app.canvas) {
+                    canvasResolver.resolve(app.canvas);
+                }
+                else {
+                    requestAnimationFrame(_waitForCanvas);
+                }
+            }
+        }
+        _waitForCanvas();
+    }
+    return canvasResolver.promise;
+}
+let graphResolver = null;
+export function waitForGraph() {
+    if (graphResolver === null) {
+        graphResolver = getResolver();
+        function _wait() {
+            if (!graphResolver.completed) {
+                if (app === null || app === void 0 ? void 0 : app.graph) {
+                    graphResolver.resolve(app.graph);
+                }
+                else {
+                    requestAnimationFrame(_wait);
+                }
+            }
+        }
+        _wait();
+    }
+    return graphResolver.promise;
+}
+export function addMenuItemOnExtraMenuOptions(node, config, menuOptions, after = "Shape") {
+    let idx = menuOptions
+        .slice()
+        .reverse()
+        .findIndex((option) => option === null || option === void 0 ? void 0 : option.isRgthree);
+    if (idx == -1) {
+        idx = menuOptions.findIndex((option) => { var _a; return (_a = option === null || option === void 0 ? void 0 : option.content) === null || _a === void 0 ? void 0 : _a.includes(after); }) + 1;
+        if (!idx) {
+            idx = menuOptions.length - 1;
+        }
+        menuOptions.splice(idx, 0, null);
+        idx++;
+    }
+    else {
+        idx = menuOptions.length - idx;
+    }
+    const subMenuOptions = typeof config.subMenuOptions === "function"
+        ? config.subMenuOptions(node)
+        : config.subMenuOptions;
+    menuOptions.splice(idx, 0, {
+        content: typeof config.name == "function" ? config.name(node) : config.name,
+        has_submenu: !!(subMenuOptions === null || subMenuOptions === void 0 ? void 0 : subMenuOptions.length),
+        isRgthree: true,
+        callback: (value, _options, event, parentMenu, _node) => {
+            if (!!(subMenuOptions === null || subMenuOptions === void 0 ? void 0 : subMenuOptions.length)) {
+                new LiteGraph.ContextMenu(subMenuOptions.map((option) => (option ? { content: option } : null)), {
+                    event,
+                    parentMenu,
+                    callback: (subValue, _options, _event, _parentMenu, _node) => {
+                        if (config.property) {
+                            node.properties = node.properties || {};
+                            node.properties[config.property] = config.prepareValue
+                                ? config.prepareValue(subValue.content || "", node)
+                                : subValue.content || "";
+                        }
+                        config.callback && config.callback(node, subValue === null || subValue === void 0 ? void 0 : subValue.content);
+                    },
+                });
+                return;
+            }
+            if (config.property) {
+                node.properties = node.properties || {};
+                node.properties[config.property] = config.prepareValue
+                    ? config.prepareValue(node.properties[config.property], node)
+                    : !node.properties[config.property];
+            }
+            config.callback && config.callback(node, value === null || value === void 0 ? void 0 : value.content);
+        },
+    });
+}
+export function addConnectionLayoutSupport(node, app, options = [
+    ["Left", "Right"],
+    ["Right", "Left"],
+], callback) {
+    addMenuItem(node, app, {
+        name: "Connections Layout",
+        property: "connections_layout",
+        subMenuOptions: options.map((option) => option[0] + (option[1] ? " -> " + option[1] : "")),
+        prepareValue: (value, node) => {
+            var _a;
+            const values = String(value).split(" -> ");
+            if (!values[1] && !((_a = node.outputs) === null || _a === void 0 ? void 0 : _a.length)) {
+                values[1] = LAYOUT_LABEL_OPPOSITES[values[0]];
+            }
+            if (!LAYOUT_LABEL_TO_DATA[values[0]] || !LAYOUT_LABEL_TO_DATA[values[1]]) {
+                throw new Error(`New Layout invalid: [${values[0]}, ${values[1]}]`);
+            }
+            return values;
+        },
+        callback: (node) => {
+            var _a;
+            callback && callback(node);
+            (_a = node.graph) === null || _a === void 0 ? void 0 : _a.setDirtyCanvas(true, true);
+        },
+    });
+    node.prototype.getConnectionPos = function (isInput, slotNumber, out) {
+        return getConnectionPosForLayout(this, isInput, slotNumber, out);
+    };
+    node.prototype.getInputPos = function (slotNumber) {
+        return getConnectionPosForLayout(this, true, slotNumber, [0, 0]);
+    };
+    node.prototype.getOutputPos = function (slotNumber) {
+        return getConnectionPosForLayout(this, false, slotNumber, [0, 0]);
+    };
+}
+export function setConnectionsLayout(node, newLayout) {
+    var _a;
+    newLayout = newLayout || node.defaultConnectionsLayout || ["Left", "Right"];
+    if (!newLayout[1] && !((_a = node.outputs) === null || _a === void 0 ? void 0 : _a.length)) {
+        newLayout[1] = LAYOUT_LABEL_OPPOSITES[newLayout[0]];
+    }
+    if (!LAYOUT_LABEL_TO_DATA[newLayout[0]] || !LAYOUT_LABEL_TO_DATA[newLayout[1]]) {
+        throw new Error(`New Layout invalid: [${newLayout[0]}, ${newLayout[1]}]`);
+    }
+    node.properties = node.properties || {};
+    node.properties["connections_layout"] = newLayout;
+}
+export function setConnectionsCollapse(node, collapseConnections = null) {
+    node.properties = node.properties || {};
+    collapseConnections =
+        collapseConnections !== null ? collapseConnections : !node.properties["collapse_connections"];
+    node.properties["collapse_connections"] = collapseConnections;
+}
+export function getConnectionPosForLayout(node, isInput, slotNumber, out) {
+    var _a, _b, _c;
+    out = out || new Float32Array(2);
+    node.properties = node.properties || {};
+    const layout = node.properties["connections_layout"] ||
+        node.defaultConnectionsLayout || ["Left", "Right"];
+    const collapseConnections = node.properties["collapse_connections"] || false;
+    const offset = (_a = node.constructor.layout_slot_offset) !== null && _a !== void 0 ? _a : LiteGraph.NODE_SLOT_HEIGHT * 0.5;
+    let side = isInput ? layout[0] : layout[1];
+    const otherSide = isInput ? layout[1] : layout[0];
+    let data = LAYOUT_LABEL_TO_DATA[side];
+    const slotList = node[isInput ? "inputs" : "outputs"];
+    const cxn = slotList[slotNumber];
+    if (!cxn) {
+        console.log("No connection found.. weird", isInput, slotNumber);
+        return out;
+    }
+    if (cxn.disabled) {
+        if (cxn.color_on !== "#666665") {
+            cxn._color_on_org = cxn._color_on_org || cxn.color_on;
+            cxn._color_off_org = cxn._color_off_org || cxn.color_off;
+        }
+        cxn.color_on = "#666665";
+        cxn.color_off = "#666665";
+    }
+    else if (cxn.color_on === "#666665") {
+        cxn.color_on = cxn._color_on_org || undefined;
+        cxn.color_off = cxn._color_off_org || undefined;
+    }
+    const displaySlot = collapseConnections
+        ? 0
+        : slotNumber -
+            slotList.reduce((count, ioput, index) => {
+                count += index < slotNumber && ioput.hidden ? 1 : 0;
+                return count;
+            }, 0);
+    cxn.dir = data[0];
+    const connections_dir = node.properties["connections_dir"];
+    if ((node.size[0] == 10 || node.size[1] == 10) && connections_dir) {
+        cxn.dir = connections_dir[isInput ? 0 : 1];
+    }
+    if (side === "Left") {
+        if (node.flags.collapsed) {
+            var w = node._collapsed_width || LiteGraph.NODE_COLLAPSED_WIDTH;
+            out[0] = node.pos[0];
+            out[1] = node.pos[1] - LiteGraph.NODE_TITLE_HEIGHT * 0.5;
+        }
+        else {
+            toggleConnectionLabel(cxn, !isInput || collapseConnections || !!node.hideSlotLabels);
+            out[0] = node.pos[0] + offset;
+            if ((_b = node.constructor) === null || _b === void 0 ? void 0 : _b.type.includes("Reroute")) {
+                out[1] = node.pos[1] + node.size[1] * 0.5;
+            }
+            else {
+                out[1] =
+                    node.pos[1] +
+                        (displaySlot + 0.7) * LiteGraph.NODE_SLOT_HEIGHT +
+                        (node.constructor.slot_start_y || 0);
+            }
+        }
+    }
+    else if (side === "Right") {
+        if (node.flags.collapsed) {
+            var w = node._collapsed_width || LiteGraph.NODE_COLLAPSED_WIDTH;
+            out[0] = node.pos[0] + w;
+            out[1] = node.pos[1] - LiteGraph.NODE_TITLE_HEIGHT * 0.5;
+        }
+        else {
+            toggleConnectionLabel(cxn, isInput || collapseConnections || !!node.hideSlotLabels);
+            out[0] = node.pos[0] + node.size[0] + 1 - offset;
+            if ((_c = node.constructor) === null || _c === void 0 ? void 0 : _c.type.includes("Reroute")) {
+                out[1] = node.pos[1] + node.size[1] * 0.5;
+            }
+            else {
+                out[1] =
+                    node.pos[1] +
+                        (displaySlot + 0.7) * LiteGraph.NODE_SLOT_HEIGHT +
+                        (node.constructor.slot_start_y || 0);
+            }
+        }
+    }
+    else if (side === "Top") {
+        if (!cxn.has_old_label) {
+            cxn.has_old_label = true;
+            cxn.old_label = cxn.label;
+            cxn.label = " ";
+        }
+        out[0] = node.pos[0] + node.size[0] * 0.5;
+        out[1] = node.pos[1] + offset;
+    }
+    else if (side === "Bottom") {
+        if (!cxn.has_old_label) {
+            cxn.has_old_label = true;
+            cxn.old_label = cxn.label;
+            cxn.label = " ";
+        }
+        out[0] = node.pos[0] + node.size[0] * 0.5;
+        out[1] = node.pos[1] + node.size[1] - offset;
+    }
+    return out;
+}
+function toggleConnectionLabel(cxn, hide = true) {
+    if (hide) {
+        if (!cxn.has_old_label) {
+            cxn.has_old_label = true;
+            cxn.old_label = cxn.label;
+        }
+        cxn.label = " ";
+    }
+    else if (!hide && cxn.has_old_label) {
+        cxn.has_old_label = false;
+        cxn.label = cxn.old_label;
+        cxn.old_label = undefined;
+    }
+    return cxn;
+}
+export function addHelpMenuItem(node, content, menuOptions) {
+    addMenuItemOnExtraMenuOptions(node, {
+        name: "🛟 Node Help",
+        callback: (node) => {
+            if (node.showHelp) {
+                node.showHelp();
+            }
+            else {
+                new RgthreeHelpDialog(node, content).show();
+            }
+        },
+    }, menuOptions, "Properties Panel");
+}
+export var PassThroughFollowing;
+(function (PassThroughFollowing) {
+    PassThroughFollowing[PassThroughFollowing["ALL"] = 0] = "ALL";
+    PassThroughFollowing[PassThroughFollowing["NONE"] = 1] = "NONE";
+    PassThroughFollowing[PassThroughFollowing["REROUTE_ONLY"] = 2] = "REROUTE_ONLY";
+})(PassThroughFollowing || (PassThroughFollowing = {}));
+export function shouldPassThrough(node, passThroughFollowing = PassThroughFollowing.ALL) {
+    var _a;
+    const type = (_a = node === null || node === void 0 ? void 0 : node.constructor) === null || _a === void 0 ? void 0 : _a.type;
+    if (!type || passThroughFollowing === PassThroughFollowing.NONE) {
+        return false;
+    }
+    if (passThroughFollowing === PassThroughFollowing.REROUTE_ONLY) {
+        return type.includes("Reroute");
+    }
+    return (type.includes("Reroute") || type.includes("Node Combiner") || type.includes("Node Collector"));
+}
+function filterOutPassthroughNodes(infos, passThroughFollowing = PassThroughFollowing.ALL) {
+    return infos.filter((i) => !shouldPassThrough(i.node, passThroughFollowing));
+}
+export function getConnectedInputNodes(startNode, currentNode, slot, passThroughFollowing = PassThroughFollowing.ALL) {
+    return getConnectedNodesInfo(startNode, IoDirection.INPUT, currentNode, slot, passThroughFollowing).map((n) => n.node);
+}
+export function getConnectedInputInfosAndFilterPassThroughs(startNode, currentNode, slot, passThroughFollowing = PassThroughFollowing.ALL) {
+    return filterOutPassthroughNodes(getConnectedNodesInfo(startNode, IoDirection.INPUT, currentNode, slot, passThroughFollowing), passThroughFollowing);
+}
+export function getConnectedInputNodesAndFilterPassThroughs(startNode, currentNode, slot, passThroughFollowing = PassThroughFollowing.ALL) {
+    return getConnectedInputInfosAndFilterPassThroughs(startNode, currentNode, slot, passThroughFollowing).map((n) => n.node);
+}
+export function getConnectedOutputNodes(startNode, currentNode, slot, passThroughFollowing = PassThroughFollowing.ALL) {
+    return getConnectedNodesInfo(startNode, IoDirection.OUTPUT, currentNode, slot, passThroughFollowing).map((n) => n.node);
+}
+export function getConnectedOutputNodesAndFilterPassThroughs(startNode, currentNode, slot, passThroughFollowing = PassThroughFollowing.ALL) {
+    return filterOutPassthroughNodes(getConnectedNodesInfo(startNode, IoDirection.OUTPUT, currentNode, slot, passThroughFollowing), passThroughFollowing).map((n) => n.node);
+}
+export function getConnectedNodesInfo(startNode, dir = IoDirection.INPUT, currentNode, slot, passThroughFollowing = PassThroughFollowing.ALL, originTravelFromSlot) {
+    var _a, _b, _c, _d, _e, _f, _g, _h;
+    currentNode = currentNode || startNode;
+    let rootNodes = [];
+    if (startNode === currentNode || shouldPassThrough(currentNode, passThroughFollowing)) {
+        let linkIds;
+        slot = slot != null && slot > -1 ? slot : undefined;
+        if (dir == IoDirection.OUTPUT) {
+            if (slot != null) {
+                linkIds = [...(((_b = (_a = currentNode.outputs) === null || _a === void 0 ? void 0 : _a[slot]) === null || _b === void 0 ? void 0 : _b.links) || [])];
+            }
+            else {
+                linkIds = ((_c = currentNode.outputs) === null || _c === void 0 ? void 0 : _c.flatMap((i) => i.links)) || [];
+            }
+        }
+        else {
+            if (slot != null) {
+                linkIds = [(_e = (_d = currentNode.inputs) === null || _d === void 0 ? void 0 : _d[slot]) === null || _e === void 0 ? void 0 : _e.link];
+            }
+            else {
+                linkIds = ((_f = currentNode.inputs) === null || _f === void 0 ? void 0 : _f.map((i) => i.link)) || [];
+            }
+        }
+        const graph = (_g = currentNode.graph) !== null && _g !== void 0 ? _g : app.graph;
+        for (const linkId of linkIds) {
+            let link = null;
+            if (typeof linkId == "number") {
+                link = (_h = graph.links[linkId]) !== null && _h !== void 0 ? _h : null;
+            }
+            if (!link) {
+                continue;
+            }
+            const travelFromSlot = dir == IoDirection.OUTPUT ? link.origin_slot : link.target_slot;
+            const connectedId = dir == IoDirection.OUTPUT ? link.target_id : link.origin_id;
+            const travelToSlot = dir == IoDirection.OUTPUT ? link.target_slot : link.origin_slot;
+            originTravelFromSlot = originTravelFromSlot != null ? originTravelFromSlot : travelFromSlot;
+            const originNode = graph.getNodeById(connectedId);
+            if (!link) {
+                console.error("No connected node found... weird");
+                continue;
+            }
+            if (rootNodes.some((n) => n.node == originNode)) {
+                console.log(`${startNode.title} (${startNode.id}) seems to have two links to ${originNode.title} (${originNode.id}). One may be stale: ${linkIds.join(", ")}`);
+            }
+            else {
+                rootNodes.push({ node: originNode, travelFromSlot, travelToSlot, originTravelFromSlot });
+                if (shouldPassThrough(originNode, passThroughFollowing)) {
+                    for (const foundNode of getConnectedNodesInfo(startNode, dir, originNode, undefined, undefined, originTravelFromSlot)) {
+                        if (!rootNodes.map((n) => n.node).includes(foundNode.node)) {
+                            rootNodes.push(foundNode);
+                        }
+                    }
+                }
+            }
+        }
+    }
+    return rootNodes;
+}
+export function followConnectionUntilType(node, dir, slotNum, skipSelf = false) {
+    const slots = dir === IoDirection.OUTPUT ? node.outputs : node.inputs;
+    if (!slots || !slots.length) {
+        return null;
+    }
+    let type = null;
+    if (slotNum) {
+        if (!slots[slotNum]) {
+            return null;
+        }
+        type = getTypeFromSlot(slots[slotNum], dir, skipSelf);
+    }
+    else {
+        for (const slot of slots) {
+            type = getTypeFromSlot(slot, dir, skipSelf);
+            if (type) {
+                break;
+            }
+        }
+    }
+    return type;
+}
+function getTypeFromSlot(slot, dir, skipSelf = false) {
+    let graph = app.canvas.getCurrentGraph();
+    let type = slot === null || slot === void 0 ? void 0 : slot.type;
+    if (!skipSelf && type != null && type != "*") {
+        return { type: type, label: slot === null || slot === void 0 ? void 0 : slot.label, name: slot === null || slot === void 0 ? void 0 : slot.name };
+    }
+    const links = getSlotLinks(slot);
+    for (const link of links) {
+        const connectedId = dir == IoDirection.OUTPUT ? link.link.target_id : link.link.origin_id;
+        const connectedSlotNum = dir == IoDirection.OUTPUT ? link.link.target_slot : link.link.origin_slot;
+        const connectedNode = graph.getNodeById(connectedId);
+        const connectedSlots = dir === IoDirection.OUTPUT ? connectedNode.inputs : connectedNode.outputs;
+        let connectedSlot = connectedSlots[connectedSlotNum];
+        if ((connectedSlot === null || connectedSlot === void 0 ? void 0 : connectedSlot.type) != null && (connectedSlot === null || connectedSlot === void 0 ? void 0 : connectedSlot.type) != "*") {
+            return {
+                type: connectedSlot.type,
+                label: connectedSlot === null || connectedSlot === void 0 ? void 0 : connectedSlot.label,
+                name: connectedSlot === null || connectedSlot === void 0 ? void 0 : connectedSlot.name,
+            };
+        }
+        else if ((connectedSlot === null || connectedSlot === void 0 ? void 0 : connectedSlot.type) == "*") {
+            return followConnectionUntilType(connectedNode, dir);
+        }
+    }
+    return null;
+}
+export async function replaceNode(existingNode, typeOrNewNode, inputNameMap) {
+    const existingCtor = existingNode.constructor;
+    const newNode = typeof typeOrNewNode === "string" ? LiteGraph.createNode(typeOrNewNode) : typeOrNewNode;
+    if (existingNode.title != existingCtor.title) {
+        newNode.title = existingNode.title;
+    }
+    newNode.pos = [...existingNode.pos];
+    newNode.properties = { ...existingNode.properties };
+    const oldComputeSize = [...existingNode.computeSize()];
+    const oldSize = [
+        existingNode.size[0] === oldComputeSize[0] ? null : existingNode.size[0],
+        existingNode.size[1] === oldComputeSize[1] ? null : existingNode.size[1],
+    ];
+    let setSizeIters = 0;
+    const setSizeFn = () => {
+        const newComputesize = newNode.computeSize();
+        newNode.size[0] = Math.max(oldSize[0] || 0, newComputesize[0]);
+        newNode.size[1] = Math.max(oldSize[1] || 0, newComputesize[1]);
+        setSizeIters++;
+        if (setSizeIters > 10) {
+            requestAnimationFrame(setSizeFn);
+        }
+    };
+    setSizeFn();
+    const links = [];
+    const graph = existingNode.graph || app.graph;
+    for (const [index, output] of existingNode.outputs.entries()) {
+        for (const linkId of output.links || []) {
+            const link = graph.links[linkId];
+            if (!link)
+                continue;
+            const targetNode = graph.getNodeById(link.target_id);
+            links.push({ node: newNode, slot: output.name, targetNode, targetSlot: link.target_slot });
+        }
+    }
+    for (const [index, input] of existingNode.inputs.entries()) {
+        const linkId = input.link;
+        if (linkId) {
+            const link = graph.links[linkId];
+            const originNode = graph.getNodeById(link.origin_id);
+            links.push({
+                node: originNode,
+                slot: link.origin_slot,
+                targetNode: newNode,
+                targetSlot: (inputNameMap === null || inputNameMap === void 0 ? void 0 : inputNameMap.has(input.name))
+                    ? inputNameMap.get(input.name)
+                    : input.name || index,
+            });
+        }
+    }
+    graph.add(newNode);
+    await wait();
+    for (const link of links) {
+        link.node.connect(link.slot, link.targetNode, link.targetSlot);
+    }
+    await wait();
+    graph.remove(existingNode);
+    newNode.size = newNode.computeSize();
+    newNode.setDirtyCanvas(true, true);
+    return newNode;
+}
+export function getOriginNodeByLink(linkId) {
+    let node = null;
+    if (linkId != null) {
+        const link = getLinkById(linkId);
+        node = (link != null && getNodeById(link.origin_id)) || null;
+    }
+    return node;
+}
+export function getLinkById(linkId) {
+    var _a, _b, _c;
+    if (linkId == null)
+        return null;
+    let link = (_a = app.graph.links[linkId]) !== null && _a !== void 0 ? _a : null;
+    link = (_c = link !== null && link !== void 0 ? link : (_b = app.canvas.getCurrentGraph()) === null || _b === void 0 ? void 0 : _b.links[linkId]) !== null && _c !== void 0 ? _c : null;
+    return link || findSomethingInAllSubgraphs((subgraph) => { var _a; return (_a = subgraph === null || subgraph === void 0 ? void 0 : subgraph.links[linkId]) !== null && _a !== void 0 ? _a : null; });
+}
+export function getNodeById(id) {
+    var _a, _b;
+    if (id == null)
+        return null;
+    let node = app.graph.getNodeById(id);
+    node = (_b = node !== null && node !== void 0 ? node : (_a = app.canvas.getCurrentGraph()) === null || _a === void 0 ? void 0 : _a.getNodeById(id)) !== null && _b !== void 0 ? _b : null;
+    return node || findSomethingInAllSubgraphs((subgraph) => { var _a; return (_a = subgraph === null || subgraph === void 0 ? void 0 : subgraph.getNodeById(id)) !== null && _a !== void 0 ? _a : null; });
+}
+export function getNodeByIdFromApiPrompt(apiPrompt, id) {
+    var _a, _b, _c, _d;
+    const fullId = getFullNodeIdFromApiPrompt(apiPrompt, id);
+    const workflow = (_a = apiPrompt.workflow) !== null && _a !== void 0 ? _a : {};
+    const nodeIds = String(fullId).split(":");
+    const workflowNodes = (_b = workflow === null || workflow === void 0 ? void 0 : workflow["nodes"]) !== null && _b !== void 0 ? _b : [];
+    const workflowSubgraphs = (_d = (_c = workflow === null || workflow === void 0 ? void 0 : workflow["definitions"]) === null || _c === void 0 ? void 0 : _c["subgraphs"]) !== null && _d !== void 0 ? _d : [];
+    let nodesList = workflowNodes;
+    let found = null;
+    for (const nodeId of nodeIds) {
+        found = nodesList.find((n) => String(n.id) === String(nodeId)) || null;
+        if (found === null || found === void 0 ? void 0 : found["type"]) {
+            const subgraph = workflowSubgraphs.find((n) => n.id === found["type"]) || null;
+            if (subgraph === null || subgraph === void 0 ? void 0 : subgraph["nodes"]) {
+                nodesList = subgraph["nodes"];
+            }
+        }
+    }
+    return found;
+}
+export function getFullNodeIdFromApiPrompt(apiPrompt, id) {
+    var _a;
+    const output = (_a = apiPrompt.output) !== null && _a !== void 0 ? _a : {};
+    return output[id] ? id : Object.keys(output).find((i) => i.endsWith(`:${id}`));
+}
+export function findFromNodeForSubgraph(subgraphId) {
+    var _a;
+    const node = (_a = findSomethingInAllSubgraphs((subgraph) => subgraph.nodes
+        .filter((node) => node.isSubgraphNode())
+        .find((node) => node.subgraph.id === subgraphId))) !== null && _a !== void 0 ? _a : null;
+    return node;
+}
+function findSomethingInAllSubgraphs(fn) {
+    var _a, _b;
+    const rootGraph = (_a = app.rootGraph) !== null && _a !== void 0 ? _a : app.graph.rootGraph;
+    const subgraphs = [rootGraph, ...(_b = rootGraph.subgraphs) === null || _b === void 0 ? void 0 : _b.values()];
+    for (const subgraph of subgraphs) {
+        const thing = fn(subgraph);
+        if (thing)
+            return thing;
+    }
+    return null;
+}
+export function applyMixins(original, constructors) {
+    constructors.forEach((baseCtor) => {
+        Object.getOwnPropertyNames(baseCtor.prototype).forEach((name) => {
+            Object.defineProperty(original.prototype, name, Object.getOwnPropertyDescriptor(baseCtor.prototype, name) || Object.create(null));
+        });
+    });
+}
+export function getSlotLinks(inputOrOutput) {
+    var _a;
+    const links = [];
+    if (!inputOrOutput) {
+        return links;
+    }
+    if ((_a = inputOrOutput.links) === null || _a === void 0 ? void 0 : _a.length) {
+        const output = inputOrOutput;
+        for (const linkId of output.links || []) {
+            const link = app.graph.links[linkId];
+            if (link) {
+                links.push({ id: linkId, link: link });
+            }
+        }
+    }
+    if (inputOrOutput.link) {
+        const input = inputOrOutput;
+        const link = app.graph.links[input.link];
+        if (link) {
+            links.push({ id: input.link, link: link });
+        }
+    }
+    return links;
+}
+export async function matchLocalSlotsToServer(node, direction, serverNodeData) {
+    var _a, _b, _c;
+    const serverSlotNames = direction == IoDirection.INPUT
+        ? Object.keys(((_a = serverNodeData.input) === null || _a === void 0 ? void 0 : _a.optional) || {})
+        : serverNodeData.output_name;
+    const serverSlotTypes = direction == IoDirection.INPUT
+        ? Object.values(((_b = serverNodeData.input) === null || _b === void 0 ? void 0 : _b.optional) || {}).map((i) => i[0])
+        : serverNodeData.output;
+    const slots = direction == IoDirection.INPUT ? node.inputs : node.outputs;
+    let firstIndex = slots.findIndex((o, i) => i !== serverSlotNames.indexOf(o.name));
+    if (firstIndex > -1) {
+        const links = {};
+        slots.map((slot) => {
+            var _a;
+            links[slot.name] = links[slot.name] || [];
+            (_a = links[slot.name]) === null || _a === void 0 ? void 0 : _a.push(...getSlotLinks(slot));
+        });
+        for (const [index, serverSlotName] of serverSlotNames.entries()) {
+            const currentNodeSlot = slots.map((s) => s.name).indexOf(serverSlotName);
+            if (currentNodeSlot > -1) {
+                if (currentNodeSlot != index) {
+                    const splicedItem = slots.splice(currentNodeSlot, 1)[0];
+                    slots.splice(index, 0, splicedItem);
+                }
+            }
+            else if (currentNodeSlot === -1) {
+                const splicedItem = {
+                    name: serverSlotName,
+                    type: serverSlotTypes[index],
+                    links: [],
+                };
+                slots.splice(index, 0, splicedItem);
+            }
+        }
+        if (slots.length > serverSlotNames.length) {
+            for (let i = slots.length - 1; i > serverSlotNames.length - 1; i--) {
+                if (direction == IoDirection.INPUT) {
+                    node.disconnectInput(i);
+                    node.removeInput(i);
+                }
+                else {
+                    node.disconnectOutput(i);
+                    node.removeOutput(i);
+                }
+            }
+        }
+        for (const [name, slotLinks] of Object.entries(links)) {
+            let currentNodeSlot = slots.map((s) => s.name).indexOf(name);
+            if (currentNodeSlot > -1) {
+                for (const linkData of slotLinks) {
+                    if (direction == IoDirection.INPUT) {
+                        linkData.link.target_slot = currentNodeSlot;
+                    }
+                    else {
+                        linkData.link.origin_slot = currentNodeSlot;
+                        const nextNode = app.graph.getNodeById(linkData.link.target_id);
+                        if (nextNode && ((_c = nextNode.constructor) === null || _c === void 0 ? void 0 : _c.type.includes("Reroute"))) {
+                            nextNode.stabilize && nextNode.stabilize();
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+export function isValidConnection(ioA, ioB) {
+    if (!ioA || !ioB) {
+        return false;
+    }
+    const typeA = String(ioA.type);
+    const typeB = String(ioB.type);
+    let isValid = LiteGraph.isValidConnection(typeA, typeB);
+    if (!isValid) {
+        let areCombos = (typeA.includes(",") && typeB === "COMBO") || (typeA === "COMBO" && typeB.includes(","));
+        if (areCombos) {
+            const nameA = ioA.name.toUpperCase().replace("_NAME", "").replace("CKPT", "MODEL");
+            const nameB = ioB.name.toUpperCase().replace("_NAME", "").replace("CKPT", "MODEL");
+            isValid = nameA.includes(nameB) || nameB.includes(nameA);
+        }
+    }
+    return isValid;
+}
+const oldIsValidConnection = LiteGraph.isValidConnection;
+LiteGraph.isValidConnection = function (typeA, typeB) {
+    let isValid = oldIsValidConnection.call(LiteGraph, typeA, typeB);
+    if (!isValid) {
+        typeA = String(typeA);
+        typeB = String(typeB);
+        let areCombos = (typeA.includes(",") && typeB === "COMBO") || (typeA === "COMBO" && typeB.includes(","));
+        isValid = areCombos;
+    }
+    return isValid;
+};
+export function getOutputNodes(nodes) {
+    return ((nodes === null || nodes === void 0 ? void 0 : nodes.filter((n) => {
+        var _a;
+        return (n.mode != LiteGraph.NEVER && ((_a = n.constructor.nodeData) === null || _a === void 0 ? void 0 : _a.output_node));
+    })) || []);
+}
+export function changeModeOfNodes(nodeOrNodes, mode) {
+    reduceNodesDepthFirst(nodeOrNodes, (n) => {
+        n.mode = mode;
+    });
+}
+export function reduceNodesDepthFirst(nodeOrNodes, reduceFn, reduceTo) {
+    var _a;
+    const nodes = Array.isArray(nodeOrNodes) ? nodeOrNodes : [nodeOrNodes];
+    const stack = nodes.map((node) => ({ node }));
+    while (stack.length > 0) {
+        const { node } = stack.pop();
+        const result = reduceFn(node, reduceTo);
+        if (result !== undefined && result !== reduceTo) {
+            reduceTo = result;
+        }
+        if (((_a = node.isSubgraphNode) === null || _a === void 0 ? void 0 : _a.call(node)) && node.subgraph) {
+            const children = node.subgraph.nodes;
+            for (let i = children.length - 1; i >= 0; i--) {
+                stack.push({ node: children[i] });
+            }
+        }
+    }
+    return reduceTo;
+}
+export function getGroupNodes(group) {
+    return Array.from(group._children).filter((c) => c instanceof LGraphNode);
+}
+export function getGraphDependantNodeKey(node) {
+    var _a;
+    const graph = (_a = node.graph) !== null && _a !== void 0 ? _a : app.graph;
+    return `${graph.id}:${node.id}`;
+}
+export function getFullColor(color, liteGraphKey = "color") {
+    if (!color) {
+        return "";
+    }
+    if (LGraphCanvas.node_colors[color]) {
+        color = LGraphCanvas.node_colors[color][liteGraphKey];
+    }
+    color = color.replace("#", "").toLocaleLowerCase();
+    if (color.length === 3) {
+        color = color.replace(/(.)(.)(.)/, "$1$1$2$2$3$3");
+    }
+    return `#${color}`;
+}
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/utils_canvas.js b/custom_nodes/rgthree-comfy/web/comfyui/utils_canvas.js
new file mode 100644
index 0000000000000000000000000000000000000000..7c29bfdb76210831869bf2d29c482f5a5d4ae716
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/utils_canvas.js
@@ -0,0 +1,236 @@
+import { app } from "../../scripts/app.js";
+function binarySearch(max, getValue, match) {
+    let min = 0;
+    while (min <= max) {
+        let guess = Math.floor((min + max) / 2);
+        const compareVal = getValue(guess);
+        if (compareVal === match)
+            return guess;
+        if (compareVal < match)
+            min = guess + 1;
+        else
+            max = guess - 1;
+    }
+    return max;
+}
+export function fitString(ctx, str, maxWidth) {
+    let width = ctx.measureText(str).width;
+    const ellipsis = "…";
+    const ellipsisWidth = measureText(ctx, ellipsis);
+    if (width <= maxWidth || width <= ellipsisWidth) {
+        return str;
+    }
+    const index = binarySearch(str.length, (guess) => measureText(ctx, str.substring(0, guess)), maxWidth - ellipsisWidth);
+    return str.substring(0, index) + ellipsis;
+}
+export function measureText(ctx, str) {
+    return ctx.measureText(str).width;
+}
+export function isLowQuality() {
+    var _a;
+    const canvas = app.canvas;
+    return (((_a = canvas.ds) === null || _a === void 0 ? void 0 : _a.scale) || 1) <= 0.5;
+}
+export function drawNodeWidget(ctx, options) {
+    const lowQuality = isLowQuality();
+    const data = {
+        width: options.size[0],
+        height: options.size[1],
+        posY: options.pos[1],
+        lowQuality,
+        margin: 15,
+        colorOutline: LiteGraph.WIDGET_OUTLINE_COLOR,
+        colorBackground: LiteGraph.WIDGET_BGCOLOR,
+        colorText: LiteGraph.WIDGET_TEXT_COLOR,
+        colorTextSecondary: LiteGraph.WIDGET_SECONDARY_TEXT_COLOR,
+    };
+    ctx.strokeStyle = options.colorStroke || data.colorOutline;
+    ctx.fillStyle = options.colorBackground || data.colorBackground;
+    ctx.beginPath();
+    ctx.roundRect(data.margin, data.posY, data.width - data.margin * 2, data.height, lowQuality ? [0] : options.borderRadius ? [options.borderRadius] : [options.size[1] * 0.5]);
+    ctx.fill();
+    if (!lowQuality) {
+        ctx.stroke();
+    }
+    return data;
+}
+export function drawRoundedRectangle(ctx, options) {
+    const lowQuality = isLowQuality();
+    options = { ...options };
+    ctx.save();
+    ctx.strokeStyle = options.colorStroke || LiteGraph.WIDGET_OUTLINE_COLOR;
+    ctx.fillStyle = options.colorBackground || LiteGraph.WIDGET_BGCOLOR;
+    ctx.beginPath();
+    ctx.roundRect(...options.pos, ...options.size, lowQuality ? [0] : options.borderRadius ? [options.borderRadius] : [options.size[1] * 0.5]);
+    ctx.fill();
+    !lowQuality && ctx.stroke();
+    ctx.restore();
+}
+export function drawNumberWidgetPart(ctx, options) {
+    const arrowWidth = 9;
+    const arrowHeight = 10;
+    const innerMargin = 3;
+    const numberWidth = 32;
+    const xBoundsArrowLess = [0, 0];
+    const xBoundsNumber = [0, 0];
+    const xBoundsArrowMore = [0, 0];
+    ctx.save();
+    let posX = options.posX;
+    const { posY, height, value, textColor } = options;
+    const midY = posY + height / 2;
+    if (options.direction === -1) {
+        posX = posX - arrowWidth - innerMargin - numberWidth - innerMargin - arrowWidth;
+    }
+    ctx.fill(new Path2D(`M ${posX} ${midY} l ${arrowWidth} ${arrowHeight / 2} l 0 -${arrowHeight} L ${posX} ${midY} z`));
+    xBoundsArrowLess[0] = posX;
+    xBoundsArrowLess[1] = arrowWidth;
+    posX += arrowWidth + innerMargin;
+    ctx.textAlign = "center";
+    ctx.textBaseline = "middle";
+    const oldTextcolor = ctx.fillStyle;
+    if (textColor) {
+        ctx.fillStyle = textColor;
+    }
+    ctx.fillText(fitString(ctx, value.toFixed(2), numberWidth), posX + numberWidth / 2, midY);
+    ctx.fillStyle = oldTextcolor;
+    xBoundsNumber[0] = posX;
+    xBoundsNumber[1] = numberWidth;
+    posX += numberWidth + innerMargin;
+    ctx.fill(new Path2D(`M ${posX} ${midY - arrowHeight / 2} l ${arrowWidth} ${arrowHeight / 2} l -${arrowWidth} ${arrowHeight / 2} v -${arrowHeight} z`));
+    xBoundsArrowMore[0] = posX;
+    xBoundsArrowMore[1] = arrowWidth;
+    ctx.restore();
+    return [xBoundsArrowLess, xBoundsNumber, xBoundsArrowMore];
+}
+drawNumberWidgetPart.WIDTH_TOTAL = 9 + 3 + 32 + 3 + 9;
+export function drawTogglePart(ctx, options) {
+    const lowQuality = isLowQuality();
+    ctx.save();
+    const { posX, posY, height, value } = options;
+    const toggleRadius = height * 0.36;
+    const toggleBgWidth = height * 1.5;
+    if (!lowQuality) {
+        ctx.beginPath();
+        ctx.roundRect(posX + 4, posY + 4, toggleBgWidth - 8, height - 8, [height * 0.5]);
+        ctx.globalAlpha = app.canvas.editor_alpha * 0.25;
+        ctx.fillStyle = "rgba(255,255,255,0.45)";
+        ctx.fill();
+        ctx.globalAlpha = app.canvas.editor_alpha;
+    }
+    ctx.fillStyle = value === true ? "#89B" : "#888";
+    const toggleX = lowQuality || value === false
+        ? posX + height * 0.5
+        : value === true
+            ? posX + height
+            : posX + height * 0.75;
+    ctx.beginPath();
+    ctx.arc(toggleX, posY + height * 0.5, toggleRadius, 0, Math.PI * 2);
+    ctx.fill();
+    ctx.restore();
+    return [posX, toggleBgWidth];
+}
+export function drawInfoIcon(ctx, x, y, size = 12, treatment = 'GRAYED') {
+    ctx.save();
+    ctx.beginPath();
+    ctx.roundRect(x, y, size, size, [size * 0.1]);
+    if (treatment === 'GRAYED') {
+        ctx.fillStyle = "#aaa";
+        ctx.strokeStyle = "#aaa";
+    }
+    else {
+        ctx.fillStyle = "#2f82ec";
+        ctx.strokeStyle = "#2f82ec";
+    }
+    if (treatment === 'FILLED') {
+        ctx.fill();
+    }
+    else {
+        ctx.stroke();
+    }
+    ctx.strokeStyle = "#FFF";
+    ctx.lineWidth = 2;
+    const midX = x + size / 2;
+    const serifSize = size * 0.175;
+    ctx.stroke(new Path2D(`
+    M ${midX} ${y + size * 0.15}
+    v 2
+    M ${midX - serifSize} ${y + size * 0.45}
+    h ${serifSize}
+    v ${size * 0.325}
+    h ${serifSize}
+    h -${serifSize * 2}
+  `));
+    ctx.restore();
+}
+export function drawPlusIcon(ctx, x, midY, size = 12) {
+    ctx.save();
+    const s = size / 3;
+    const plus = new Path2D(`
+    M ${x} ${midY + s / 2}
+    v-${s} h${s} v-${s} h${s}
+    v${s} h${s} v${s} h-${s}
+    v${s} h-${s} v-${s} h-${s}
+    z
+  `);
+    ctx.lineJoin = "round";
+    ctx.lineCap = "round";
+    ctx.fillStyle = "#3a3";
+    ctx.strokeStyle = "#383";
+    ctx.fill(plus);
+    ctx.stroke(plus);
+    ctx.restore();
+}
+export function drawWidgetButton(ctx, options, text = null, isMouseDownedAndOver = false) {
+    var _a;
+    const borderRadius = isLowQuality() ? 0 : ((_a = options.borderRadius) !== null && _a !== void 0 ? _a : 4);
+    ctx.save();
+    if (!isLowQuality() && !isMouseDownedAndOver) {
+        drawRoundedRectangle(ctx, {
+            size: [options.size[0] - 2, options.size[1]],
+            pos: [options.pos[0] + 1, options.pos[1] + 1],
+            borderRadius,
+            colorBackground: "#000000aa",
+            colorStroke: "#000000aa",
+        });
+    }
+    drawRoundedRectangle(ctx, {
+        size: options.size,
+        pos: [options.pos[0], options.pos[1] + (isMouseDownedAndOver ? 1 : 0)],
+        borderRadius,
+        colorBackground: isMouseDownedAndOver ? "#444" : LiteGraph.WIDGET_BGCOLOR,
+        colorStroke: "transparent",
+    });
+    if (isLowQuality()) {
+        ctx.restore();
+        return;
+    }
+    if (!isMouseDownedAndOver) {
+        drawRoundedRectangle(ctx, {
+            size: [options.size[0] - 0.75, options.size[1] - 0.75],
+            pos: options.pos,
+            borderRadius: borderRadius - 0.5,
+            colorBackground: "transparent",
+            colorStroke: "#00000044",
+        });
+        drawRoundedRectangle(ctx, {
+            size: [options.size[0] - 0.75, options.size[1] - 0.75],
+            pos: [options.pos[0] + 0.75, options.pos[1] + 0.75],
+            borderRadius: borderRadius - 0.5,
+            colorBackground: "transparent",
+            colorStroke: "#ffffff11",
+        });
+    }
+    drawRoundedRectangle(ctx, {
+        size: options.size,
+        pos: [options.pos[0], options.pos[1] + (isMouseDownedAndOver ? 1 : 0)],
+        borderRadius,
+        colorBackground: "transparent",
+    });
+    if (!isLowQuality() && text) {
+        ctx.textBaseline = "middle";
+        ctx.textAlign = "center";
+        ctx.fillStyle = LiteGraph.WIDGET_TEXT_COLOR;
+        ctx.fillText(text, options.size[0] / 2, options.pos[1] + options.size[1] / 2 + (isMouseDownedAndOver ? 1 : 0));
+    }
+    ctx.restore();
+}
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/utils_deprecated_comfyui.js b/custom_nodes/rgthree-comfy/web/comfyui/utils_deprecated_comfyui.js
new file mode 100644
index 0000000000000000000000000000000000000000..a15f692b539220b6fb7e260481535c72e18e275b
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/utils_deprecated_comfyui.js
@@ -0,0 +1,177 @@
+import { app } from "../../scripts/app.js";
+function isPrimitiveNode(node) {
+    return node.type === "PrimitiveNode";
+}
+function getWidgetGetConfigSymbols(slot) {
+    var _a;
+    const widget = slot === null || slot === void 0 ? void 0 : slot.widget;
+    if (!widget)
+        return {};
+    const syms = Object.getOwnPropertySymbols(widget || {});
+    for (const sym of syms) {
+        const symVal = widget[sym];
+        const isGetConfig = typeof symVal === "function";
+        let maybeCfg = isGetConfig ? symVal() : symVal;
+        if (Array.isArray(maybeCfg) &&
+            maybeCfg.length >= 2 &&
+            typeof maybeCfg[0] === "string" &&
+            (maybeCfg[0] === "*" || typeof ((_a = maybeCfg[1]) === null || _a === void 0 ? void 0 : _a.type) === "string")) {
+            return isGetConfig ? { GET_CONFIG: sym } : { CONFIG: sym };
+        }
+    }
+    return {};
+}
+export function getWidgetConfig(slot) {
+    var _a, _b, _c;
+    const configSyms = getWidgetGetConfigSymbols(slot);
+    const widget = slot.widget || {};
+    return ((_c = (_a = (configSyms.CONFIG && widget[configSyms.CONFIG])) !== null && _a !== void 0 ? _a : (configSyms.GET_CONFIG && ((_b = widget[configSyms.GET_CONFIG]) === null || _b === void 0 ? void 0 : _b.call(widget)))) !== null && _c !== void 0 ? _c : ["*", {}]);
+}
+export function setWidgetConfig(slot, config) {
+    var _a;
+    if (!(slot === null || slot === void 0 ? void 0 : slot.widget))
+        return;
+    if (config) {
+        const configSyms = getWidgetGetConfigSymbols(slot);
+        const widget = slot.widget || {};
+        if (configSyms.GET_CONFIG) {
+            widget[configSyms.GET_CONFIG] = () => config;
+        }
+        else if (configSyms.CONFIG) {
+            widget[configSyms.CONFIG] = config;
+        }
+        else {
+            console.error("Cannot set widget Config. This is due to ComfyUI removing the ability to call legacy " +
+                "JavaScript APIs that are now deprecated without new, supported APIs. It's possible " +
+                "some things in rgthree-comfy do not work correctly. If you see this, please file a bug.");
+        }
+    }
+    else {
+        delete slot.widget;
+    }
+    if ("link" in slot) {
+        const link = app.graph.links[(_a = slot === null || slot === void 0 ? void 0 : slot.link) !== null && _a !== void 0 ? _a : -1];
+        if (link) {
+            const originNode = app.graph.getNodeById(link.origin_id);
+            if (originNode && isPrimitiveNode(originNode)) {
+                if (config) {
+                    originNode.recreateWidget();
+                }
+                else if (!app.configuringGraph) {
+                    originNode.disconnectOutput(0);
+                    originNode.onLastDisconnect();
+                }
+            }
+        }
+    }
+}
+export function mergeIfValid(output, config2) {
+    var _a;
+    const config1 = getWidgetConfig(output);
+    const customSpec = mergeInputSpec(config1, config2);
+    if (customSpec) {
+        setWidgetConfig(output, customSpec);
+    }
+    return (_a = customSpec === null || customSpec === void 0 ? void 0 : customSpec[1]) !== null && _a !== void 0 ? _a : null;
+}
+const mergeInputSpec = (spec1, spec2) => {
+    const type1 = getInputSpecType(spec1);
+    const type2 = getInputSpecType(spec2);
+    if (type1 !== type2) {
+        return null;
+    }
+    if (isIntInputSpec(spec1) || isFloatInputSpec(spec1)) {
+        return mergeNumericInputSpec(spec1, spec2);
+    }
+    if (isComboInputSpec(spec1)) {
+        return mergeComboInputSpec(spec1, spec2);
+    }
+    return mergeCommonInputSpec(spec1, spec2);
+};
+function getInputSpecType(inputSpec) {
+    return isComboInputSpec(inputSpec) ? "COMBO" : inputSpec[0];
+}
+function isComboInputSpecV1(inputSpec) {
+    return Array.isArray(inputSpec[0]);
+}
+function isIntInputSpec(inputSpec) {
+    return inputSpec[0] === "INT";
+}
+function isFloatInputSpec(inputSpec) {
+    return inputSpec[0] === "FLOAT";
+}
+function isComboInputSpecV2(inputSpec) {
+    return inputSpec[0] === "COMBO";
+}
+function isComboInputSpec(inputSpec) {
+    return isComboInputSpecV1(inputSpec) || isComboInputSpecV2(inputSpec);
+}
+const getRange = (options) => {
+    var _a, _b;
+    const min = (_a = options.min) !== null && _a !== void 0 ? _a : -Infinity;
+    const max = (_b = options.max) !== null && _b !== void 0 ? _b : Infinity;
+    return { min, max };
+};
+const mergeNumericInputSpec = (spec1, spec2) => {
+    var _a, _b, _c, _d;
+    const type = spec1[0];
+    const options1 = (_a = spec1[1]) !== null && _a !== void 0 ? _a : {};
+    const options2 = (_b = spec2[1]) !== null && _b !== void 0 ? _b : {};
+    const range1 = getRange(options1);
+    const range2 = getRange(options2);
+    if (range1.min > range2.max || range1.max < range2.min) {
+        return null;
+    }
+    const step1 = (_c = options1.step) !== null && _c !== void 0 ? _c : 1;
+    const step2 = (_d = options2.step) !== null && _d !== void 0 ? _d : 1;
+    const mergedOptions = {
+        min: Math.max(range1.min, range2.min),
+        max: Math.min(range1.max, range2.max),
+        step: lcm(step1, step2),
+    };
+    return mergeCommonInputSpec([type, { ...options1, ...mergedOptions }], [type, { ...options2, ...mergedOptions }]);
+};
+const mergeComboInputSpec = (spec1, spec2) => {
+    var _a, _b;
+    const options1 = (_a = spec1[1]) !== null && _a !== void 0 ? _a : {};
+    const options2 = (_b = spec2[1]) !== null && _b !== void 0 ? _b : {};
+    const comboOptions1 = getComboSpecComboOptions(spec1);
+    const comboOptions2 = getComboSpecComboOptions(spec2);
+    const intersection = comboOptions1.filter((value) => comboOptions2.includes(value));
+    if (intersection.length === 0) {
+        return null;
+    }
+    return mergeCommonInputSpec(["COMBO", { ...options1, options: intersection }], ["COMBO", { ...options2, options: intersection }]);
+};
+const mergeCommonInputSpec = (spec1, spec2) => {
+    var _a, _b;
+    const type = getInputSpecType(spec1);
+    const options1 = (_a = spec1[1]) !== null && _a !== void 0 ? _a : {};
+    const options2 = (_b = spec2[1]) !== null && _b !== void 0 ? _b : {};
+    const compareKeys = [...new Set([...Object.keys(options1), ...Object.keys(options2)])].filter((key) => !IGNORE_KEYS.has(key));
+    const mergeIsValid = compareKeys.every((key) => {
+        const value1 = options1[key];
+        const value2 = options2[key];
+        return value1 === value2 || (value1 == null && value2 == null);
+    });
+    return mergeIsValid ? [type, { ...options1, ...options2 }] : null;
+};
+const IGNORE_KEYS = new Set([
+    "default",
+    "forceInput",
+    "defaultInput",
+    "control_after_generate",
+    "multiline",
+    "tooltip",
+    "dynamicPrompts",
+]);
+function getComboSpecComboOptions(inputSpec) {
+    var _a, _b;
+    return (_b = (isComboInputSpecV2(inputSpec) ? (_a = inputSpec[1]) === null || _a === void 0 ? void 0 : _a.options : inputSpec[0])) !== null && _b !== void 0 ? _b : [];
+}
+const lcm = (a, b) => {
+    return Math.abs(a * b) / gcd(a, b);
+};
+const gcd = (a, b) => {
+    return b === 0 ? a : gcd(b, a % b);
+};
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/utils_inputs_outputs.js b/custom_nodes/rgthree-comfy/web/comfyui/utils_inputs_outputs.js
new file mode 100644
index 0000000000000000000000000000000000000000..e58b8411994d81c70561c1ec7745ec9ec44d2dbb
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/utils_inputs_outputs.js
@@ -0,0 +1,14 @@
+export function removeUnusedInputsFromEnd(node, minNumber = 1, nameMatch) {
+    var _a;
+    if (node.removed)
+        return;
+    for (let i = node.inputs.length - 1; i >= minNumber; i--) {
+        if (!((_a = node.inputs[i]) === null || _a === void 0 ? void 0 : _a.link)) {
+            if (!nameMatch || nameMatch.test(node.inputs[i].name)) {
+                node.removeInput(i);
+            }
+            continue;
+        }
+        break;
+    }
+}
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/utils_menu.js b/custom_nodes/rgthree-comfy/web/comfyui/utils_menu.js
new file mode 100644
index 0000000000000000000000000000000000000000..c8dc405db84d6c400a2eb44aa015204c610f0aed
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/utils_menu.js
@@ -0,0 +1,55 @@
+import { app } from "../../scripts/app.js";
+import { rgthreeApi } from "../../rgthree/common/rgthree_api.js";
+const PASS_THROUGH = function (item) {
+    return item;
+};
+export async function showLoraChooser(event, callback, parentMenu, loras) {
+    var _a, _b;
+    const canvas = app.canvas;
+    if (!loras) {
+        loras = ["None", ...(await rgthreeApi.getLoras().then((loras) => loras.map((l) => l.file)))];
+    }
+    new LiteGraph.ContextMenu(loras, {
+        event: event,
+        parentMenu: parentMenu != null ? parentMenu : undefined,
+        title: "Choose a lora",
+        scale: Math.max(1, (_b = (_a = canvas.ds) === null || _a === void 0 ? void 0 : _a.scale) !== null && _b !== void 0 ? _b : 1),
+        className: "dark",
+        callback,
+    });
+}
+export function showNodesChooser(event, mapFn, callback, parentMenu) {
+    var _a, _b;
+    const canvas = app.canvas;
+    const nodesOptions = app.graph._nodes
+        .map(mapFn)
+        .filter((e) => e != null);
+    nodesOptions.sort((a, b) => {
+        return a.value - b.value;
+    });
+    new LiteGraph.ContextMenu(nodesOptions, {
+        event: event,
+        parentMenu,
+        title: "Choose a node id",
+        scale: Math.max(1, (_b = (_a = canvas.ds) === null || _a === void 0 ? void 0 : _a.scale) !== null && _b !== void 0 ? _b : 1),
+        className: "dark",
+        callback,
+    });
+}
+export function showWidgetsChooser(event, node, mapFn, callback, parentMenu) {
+    var _a, _b;
+    const options = (node.widgets || [])
+        .map(mapFn)
+        .filter((e) => e != null);
+    if (options.length) {
+        const canvas = app.canvas;
+        new LiteGraph.ContextMenu(options, {
+            event,
+            parentMenu,
+            title: "Choose an input/widget",
+            scale: Math.max(1, (_b = (_a = canvas.ds) === null || _a === void 0 ? void 0 : _a.scale) !== null && _b !== void 0 ? _b : 1),
+            className: "dark",
+            callback,
+        });
+    }
+}
diff --git a/custom_nodes/rgthree-comfy/web/comfyui/utils_widgets.js b/custom_nodes/rgthree-comfy/web/comfyui/utils_widgets.js
new file mode 100644
index 0000000000000000000000000000000000000000..13dae464293db7ed02b1141ea37ee50c188fe091
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/comfyui/utils_widgets.js
@@ -0,0 +1,292 @@
+import { app } from "../../scripts/app.js";
+import { drawNodeWidget, drawWidgetButton, fitString, isLowQuality } from "./utils_canvas.js";
+export function drawLabelAndValue(ctx, label, value, width, posY, height, options) {
+    var _a;
+    const outerMargin = 15;
+    const innerMargin = 10;
+    const midY = posY + height / 2;
+    ctx.save();
+    ctx.textAlign = "left";
+    ctx.textBaseline = "middle";
+    ctx.fillStyle = LiteGraph.WIDGET_SECONDARY_TEXT_COLOR;
+    const labelX = outerMargin + innerMargin + ((_a = options === null || options === void 0 ? void 0 : options.offsetLeft) !== null && _a !== void 0 ? _a : 0);
+    ctx.fillText(label, labelX, midY);
+    const valueXLeft = labelX + ctx.measureText(label).width + 7;
+    const valueXRight = width - (outerMargin + innerMargin);
+    ctx.fillStyle = LiteGraph.WIDGET_TEXT_COLOR;
+    ctx.textAlign = "right";
+    ctx.fillText(fitString(ctx, value, valueXRight - valueXLeft), valueXRight, midY);
+    ctx.restore();
+}
+export class RgthreeBaseWidget {
+    constructor(name) {
+        this.type = "custom";
+        this.options = {};
+        this.y = 0;
+        this.last_y = 0;
+        this.disabled = false;
+        this.mouseDowned = null;
+        this.isMouseDownedAndOver = false;
+        this.hitAreas = {};
+        this.downedHitAreasForMove = [];
+        this.downedHitAreasForClick = [];
+        this.name = name;
+    }
+    serializeValue(node, index) {
+        return this.value;
+    }
+    clickWasWithinBounds(pos, bounds) {
+        let xStart = bounds[0];
+        let xEnd = xStart + (bounds.length > 2 ? bounds[2] : bounds[1]);
+        const clickedX = pos[0] >= xStart && pos[0] <= xEnd;
+        if (bounds.length === 2) {
+            return clickedX;
+        }
+        return clickedX && pos[1] >= bounds[1] && pos[1] <= bounds[1] + bounds[3];
+    }
+    mouse(event, pos, node) {
+        var _a, _b, _c;
+        const canvas = app.canvas;
+        if (event.type == "pointerdown") {
+            this.mouseDowned = [...pos];
+            this.isMouseDownedAndOver = true;
+            this.downedHitAreasForMove.length = 0;
+            this.downedHitAreasForClick.length = 0;
+            let anyHandled = false;
+            for (const part of Object.values(this.hitAreas)) {
+                if (this.clickWasWithinBounds(pos, part.bounds)) {
+                    if (part.onMove) {
+                        this.downedHitAreasForMove.push(part);
+                    }
+                    if (part.onClick) {
+                        this.downedHitAreasForClick.push(part);
+                    }
+                    if (part.onDown) {
+                        const thisHandled = part.onDown.apply(this, [event, pos, node, part]);
+                        anyHandled = anyHandled || thisHandled == true;
+                    }
+                    part.wasMouseClickedAndIsOver = true;
+                }
+            }
+            return (_a = this.onMouseDown(event, pos, node)) !== null && _a !== void 0 ? _a : anyHandled;
+        }
+        if (event.type == "pointerup") {
+            if (!this.mouseDowned)
+                return true;
+            this.downedHitAreasForMove.length = 0;
+            const wasMouseDownedAndOver = this.isMouseDownedAndOver;
+            this.cancelMouseDown();
+            let anyHandled = false;
+            for (const part of Object.values(this.hitAreas)) {
+                if (part.onUp && this.clickWasWithinBounds(pos, part.bounds)) {
+                    const thisHandled = part.onUp.apply(this, [event, pos, node, part]);
+                    anyHandled = anyHandled || thisHandled == true;
+                }
+                part.wasMouseClickedAndIsOver = false;
+            }
+            for (const part of this.downedHitAreasForClick) {
+                if (this.clickWasWithinBounds(pos, part.bounds)) {
+                    const thisHandled = part.onClick.apply(this, [event, pos, node, part]);
+                    anyHandled = anyHandled || thisHandled == true;
+                }
+            }
+            this.downedHitAreasForClick.length = 0;
+            if (wasMouseDownedAndOver) {
+                const thisHandled = this.onMouseClick(event, pos, node);
+                anyHandled = anyHandled || thisHandled == true;
+            }
+            return (_b = this.onMouseUp(event, pos, node)) !== null && _b !== void 0 ? _b : anyHandled;
+        }
+        if (event.type == "pointermove") {
+            this.isMouseDownedAndOver = !!this.mouseDowned;
+            if (this.mouseDowned &&
+                (pos[0] < 15 ||
+                    pos[0] > node.size[0] - 15 ||
+                    pos[1] < this.last_y ||
+                    pos[1] > this.last_y + LiteGraph.NODE_WIDGET_HEIGHT)) {
+                this.isMouseDownedAndOver = false;
+            }
+            for (const part of Object.values(this.hitAreas)) {
+                if (this.downedHitAreasForMove.includes(part)) {
+                    part.onMove.apply(this, [event, pos, node, part]);
+                }
+                if (this.downedHitAreasForClick.includes(part)) {
+                    part.wasMouseClickedAndIsOver = this.clickWasWithinBounds(pos, part.bounds);
+                }
+            }
+            return (_c = this.onMouseMove(event, pos, node)) !== null && _c !== void 0 ? _c : true;
+        }
+        return false;
+    }
+    cancelMouseDown() {
+        this.mouseDowned = null;
+        this.isMouseDownedAndOver = false;
+        this.downedHitAreasForMove.length = 0;
+    }
+    onMouseDown(event, pos, node) {
+        return;
+    }
+    onMouseUp(event, pos, node) {
+        return;
+    }
+    onMouseClick(event, pos, node) {
+        return;
+    }
+    onMouseMove(event, pos, node) {
+        return;
+    }
+}
+export class RgthreeBetterButtonWidget extends RgthreeBaseWidget {
+    constructor(name, mouseClickCallback, label) {
+        super(name);
+        this.type = "custom";
+        this.value = "";
+        this.label = "";
+        this.mouseClickCallback = mouseClickCallback;
+        this.label = label || name;
+    }
+    draw(ctx, node, width, y, height) {
+        drawWidgetButton(ctx, { size: [width - 30, height], pos: [15, y] }, this.label, this.isMouseDownedAndOver);
+    }
+    onMouseClick(event, pos, node) {
+        return this.mouseClickCallback(event, pos, node);
+    }
+}
+export class RgthreeBetterTextWidget extends RgthreeBaseWidget {
+    constructor(name, value) {
+        super(name);
+        this.name = name;
+        this.value = value;
+    }
+    draw(ctx, node, width, y, height) {
+        const widgetData = drawNodeWidget(ctx, { size: [width, height], pos: [15, y] });
+        if (!widgetData.lowQuality) {
+            drawLabelAndValue(ctx, this.name, this.value, width, y, height);
+        }
+    }
+    mouse(event, pos, node) {
+        const canvas = app.canvas;
+        if (event.type == "pointerdown") {
+            canvas.prompt("Label", this.value, (v) => (this.value = v), event);
+            return true;
+        }
+        return false;
+    }
+}
+export class RgthreeDividerWidget extends RgthreeBaseWidget {
+    constructor(widgetOptions) {
+        super("divider");
+        this.value = {};
+        this.options = { serialize: false };
+        this.type = "custom";
+        this.widgetOptions = {
+            marginTop: 7,
+            marginBottom: 7,
+            marginLeft: 15,
+            marginRight: 15,
+            color: LiteGraph.WIDGET_OUTLINE_COLOR,
+            thickness: 1,
+        };
+        Object.assign(this.widgetOptions, widgetOptions || {});
+    }
+    draw(ctx, node, width, posY, h) {
+        if (this.widgetOptions.thickness) {
+            ctx.strokeStyle = this.widgetOptions.color;
+            const x = this.widgetOptions.marginLeft;
+            const y = posY + this.widgetOptions.marginTop;
+            const w = width - this.widgetOptions.marginLeft - this.widgetOptions.marginRight;
+            ctx.stroke(new Path2D(`M ${x} ${y} h ${w}`));
+        }
+    }
+    computeSize(width) {
+        return [
+            width,
+            this.widgetOptions.marginTop + this.widgetOptions.marginBottom + this.widgetOptions.thickness,
+        ];
+    }
+}
+export class RgthreeLabelWidget extends RgthreeBaseWidget {
+    constructor(name, widgetOptions) {
+        super(name);
+        this.type = "custom";
+        this.options = { serialize: false };
+        this.value = "";
+        this.widgetOptions = {};
+        this.posY = 0;
+        Object.assign(this.widgetOptions, widgetOptions);
+    }
+    update(widgetOptions) {
+        Object.assign(this.widgetOptions, widgetOptions);
+    }
+    draw(ctx, node, width, posY, height) {
+        var _a;
+        this.posY = posY;
+        ctx.save();
+        let text = (_a = this.widgetOptions.text) !== null && _a !== void 0 ? _a : this.name;
+        if (typeof text === "function") {
+            text = text();
+        }
+        ctx.textAlign = this.widgetOptions.align || "left";
+        ctx.fillStyle = this.widgetOptions.color || LiteGraph.WIDGET_TEXT_COLOR;
+        const oldFont = ctx.font;
+        if (this.widgetOptions.italic) {
+            ctx.font = "italic " + ctx.font;
+        }
+        if (this.widgetOptions.size) {
+            ctx.font = ctx.font.replace(/\d+px/, `${this.widgetOptions.size}px`);
+        }
+        const midY = posY + height / 2;
+        ctx.textBaseline = "middle";
+        if (this.widgetOptions.align === "center") {
+            ctx.fillText(text, node.size[0] / 2, midY);
+        }
+        else {
+            ctx.fillText(text, 15, midY);
+        }
+        ctx.font = oldFont;
+        if (this.widgetOptions.actionLabel === "__PLUS_ICON__") {
+            const plus = new Path2D(`M${node.size[0] - 15 - 2} ${posY + 7} v4 h-4 v4 h-4 v-4 h-4 v-4 h4 v-4 h4 v4 h4 z`);
+            ctx.lineJoin = "round";
+            ctx.lineCap = "round";
+            ctx.fillStyle = "#3a3";
+            ctx.strokeStyle = "#383";
+            ctx.fill(plus);
+            ctx.stroke(plus);
+        }
+        ctx.restore();
+    }
+    mouse(event, nodePos, node) {
+        if (event.type !== "pointerdown" ||
+            isLowQuality() ||
+            !this.widgetOptions.actionLabel ||
+            !this.widgetOptions.actionCallback) {
+            return false;
+        }
+        const pos = [nodePos[0], nodePos[1] - this.posY];
+        const rightX = node.size[0] - 15;
+        if (pos[0] > rightX || pos[0] < rightX - 16) {
+            return false;
+        }
+        this.widgetOptions.actionCallback(event);
+        return true;
+    }
+}
+export class RgthreeInvisibleWidget extends RgthreeBaseWidget {
+    constructor(name, type, value, serializeValueFn) {
+        super(name);
+        this.type = "custom";
+        this.value = value;
+        this.serializeValueFn = serializeValueFn;
+    }
+    draw() {
+        return;
+    }
+    computeSize(width) {
+        return [0, 0];
+    }
+    serializeValue(node, index) {
+        return this.serializeValueFn != null
+            ? this.serializeValueFn(node, index)
+            : super.serializeValue(node, index);
+    }
+}
diff --git a/custom_nodes/rgthree-comfy/web/common/comfyui_shim.js b/custom_nodes/rgthree-comfy/web/common/comfyui_shim.js
new file mode 100644
index 0000000000000000000000000000000000000000..54007bafceead4ce4f70defc2e67be68f37c2744
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/common/comfyui_shim.js
@@ -0,0 +1,32 @@
+const shimCache = new Map();
+async function shimComfyUiModule(moduleName, prop) {
+    var _a, _b;
+    let module = shimCache.get(moduleName);
+    if (!module) {
+        if ((_a = window.comfyAPI) === null || _a === void 0 ? void 0 : _a[moduleName]) {
+            module = (_b = window.comfyAPI) === null || _b === void 0 ? void 0 : _b[moduleName];
+        }
+        else {
+            module = await import(`./comfyui_shim_${moduleName}.js`);
+        }
+        if (!module) {
+            throw new Error(`Module ${moduleName} could not be loaded.`);
+        }
+        shimCache.set(moduleName, module);
+    }
+    if (prop) {
+        if (!module[prop]) {
+            throw new Error(`Property ${prop} on module ${moduleName} could not be loaded.`);
+        }
+        return module[prop];
+    }
+    return module;
+}
+export async function getPngMetadata(file) {
+    const fn = (await shimComfyUiModule("pnginfo", "getPngMetadata"));
+    return fn(file);
+}
+export async function getWebpMetadata(file) {
+    const fn = (await shimComfyUiModule("pnginfo", "getWebpMetadata"));
+    return fn(file);
+}
diff --git a/custom_nodes/rgthree-comfy/web/common/comfyui_shim_pnginfo.js b/custom_nodes/rgthree-comfy/web/common/comfyui_shim_pnginfo.js
new file mode 100644
index 0000000000000000000000000000000000000000..7ef8e6b005f46b0b6c367839bb2518accb6e7b19
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/common/comfyui_shim_pnginfo.js
@@ -0,0 +1,392 @@
+import { rgthreeApi } from "./rgthree_api.js";
+const api = {
+    async getEmbeddings() {
+        const resp = await rgthreeApi.fetchComfyApi('/embeddings', { cache: 'no-store' });
+        return await resp.json();
+    }
+};
+function getFromPngBuffer(buffer) {
+    const pngData = new Uint8Array(buffer);
+    const dataView = new DataView(pngData.buffer);
+    if (dataView.getUint32(0) !== 0x89504e47) {
+        console.error('Not a valid PNG file');
+        return;
+    }
+    let offset = 8;
+    let txt_chunks = {};
+    while (offset < pngData.length) {
+        const length = dataView.getUint32(offset);
+        const type = String.fromCharCode(...pngData.slice(offset + 4, offset + 8));
+        if (type === 'tEXt' || type == 'comf' || type === 'iTXt') {
+            let keyword_end = offset + 8;
+            while (pngData[keyword_end] !== 0) {
+                keyword_end++;
+            }
+            const keyword = String.fromCharCode(...pngData.slice(offset + 8, keyword_end));
+            const contentArraySegment = pngData.slice(keyword_end + 1, offset + 8 + length);
+            const contentJson = new TextDecoder('utf-8').decode(contentArraySegment);
+            txt_chunks[keyword] = contentJson;
+        }
+        offset += 12 + length;
+    }
+    return txt_chunks;
+}
+function getFromPngFile(file) {
+    return new Promise((r) => {
+        const reader = new FileReader();
+        reader.onload = (event) => {
+            r(getFromPngBuffer(event.target.result));
+        };
+        reader.readAsArrayBuffer(file);
+    });
+}
+function parseExifData(exifData) {
+    const isLittleEndian = String.fromCharCode(...exifData.slice(0, 2)) === 'II';
+    function readInt(offset, isLittleEndian, length) {
+        let arr = exifData.slice(offset, offset + length);
+        if (length === 2) {
+            return new DataView(arr.buffer, arr.byteOffset, arr.byteLength).getUint16(0, isLittleEndian);
+        }
+        else if (length === 4) {
+            return new DataView(arr.buffer, arr.byteOffset, arr.byteLength).getUint32(0, isLittleEndian);
+        }
+        throw new Error('Shouldn\'t get here.');
+    }
+    const ifdOffset = readInt(4, isLittleEndian, 4);
+    function parseIFD(offset) {
+        const numEntries = readInt(offset, isLittleEndian, 2);
+        const result = {};
+        for (let i = 0; i < numEntries; i++) {
+            const entryOffset = offset + 2 + i * 12;
+            const tag = readInt(entryOffset, isLittleEndian, 2);
+            const type = readInt(entryOffset + 2, isLittleEndian, 2);
+            const numValues = readInt(entryOffset + 4, isLittleEndian, 4);
+            const valueOffset = readInt(entryOffset + 8, isLittleEndian, 4);
+            let value;
+            if (type === 2) {
+                value = new TextDecoder('utf-8').decode(exifData.subarray(valueOffset, valueOffset + numValues - 1));
+            }
+            result[tag] = value;
+        }
+        return result;
+    }
+    const ifdData = parseIFD(ifdOffset);
+    return ifdData;
+}
+function splitValues(input) {
+    var output = {};
+    for (var key in input) {
+        var value = input[key];
+        var splitValues = value.split(':', 2);
+        output[splitValues[0]] = splitValues[1];
+    }
+    return output;
+}
+export function getPngMetadata(file) {
+    return getFromPngFile(file);
+}
+export function getWebpMetadata(file) {
+    return new Promise((r) => {
+        const reader = new FileReader();
+        reader.onload = (event) => {
+            const webp = new Uint8Array(event.target.result);
+            const dataView = new DataView(webp.buffer);
+            if (dataView.getUint32(0) !== 0x52494646 ||
+                dataView.getUint32(8) !== 0x57454250) {
+                console.error('Not a valid WEBP file');
+                r({});
+                return;
+            }
+            let offset = 12;
+            let txt_chunks = {};
+            while (offset < webp.length) {
+                const chunk_length = dataView.getUint32(offset + 4, true);
+                const chunk_type = String.fromCharCode(...webp.slice(offset, offset + 4));
+                if (chunk_type === 'EXIF') {
+                    if (String.fromCharCode(...webp.slice(offset + 8, offset + 8 + 6)) ==
+                        'Exif\0\0') {
+                        offset += 6;
+                    }
+                    let data = parseExifData(webp.slice(offset + 8, offset + 8 + chunk_length));
+                    for (var key in data) {
+                        const value = data[key];
+                        if (typeof value === 'string') {
+                            const index = value.indexOf(':');
+                            txt_chunks[value.slice(0, index)] = value.slice(index + 1);
+                        }
+                    }
+                    break;
+                }
+                offset += 8 + chunk_length;
+            }
+            r(txt_chunks);
+        };
+        reader.readAsArrayBuffer(file);
+    });
+}
+export function getLatentMetadata(file) {
+    return new Promise((r) => {
+        const reader = new FileReader();
+        reader.onload = (event) => {
+            const safetensorsData = new Uint8Array(event.target.result);
+            const dataView = new DataView(safetensorsData.buffer);
+            let header_size = dataView.getUint32(0, true);
+            let offset = 8;
+            let header = JSON.parse(new TextDecoder().decode(safetensorsData.slice(offset, offset + header_size)));
+            r(header.__metadata__);
+        };
+        var slice = file.slice(0, 1024 * 1024 * 4);
+        reader.readAsArrayBuffer(slice);
+    });
+}
+export async function importA1111(graph, parameters) {
+    const p = parameters.lastIndexOf('\nSteps:');
+    if (p > -1) {
+        const embeddings = await api.getEmbeddings();
+        const opts = parameters
+            .substr(p)
+            .split('\n')[1]
+            .match(new RegExp('\\s*([^:]+:\\s*([^"\\{].*?|".*?"|\\{.*?\\}))\\s*(,|$)', 'g'))
+            .reduce((p, n) => {
+            const s = n.split(':');
+            if (s[1].endsWith(',')) {
+                s[1] = s[1].substr(0, s[1].length - 1);
+            }
+            p[s[0].trim().toLowerCase()] = s[1].trim();
+            return p;
+        }, {});
+        const p2 = parameters.lastIndexOf('\nNegative prompt:', p);
+        if (p2 > -1) {
+            let positive = parameters.substr(0, p2).trim();
+            let negative = parameters.substring(p2 + 18, p).trim();
+            const ckptNode = LiteGraph.createNode('CheckpointLoaderSimple');
+            const clipSkipNode = LiteGraph.createNode('CLIPSetLastLayer');
+            const positiveNode = LiteGraph.createNode('CLIPTextEncode');
+            const negativeNode = LiteGraph.createNode('CLIPTextEncode');
+            const samplerNode = LiteGraph.createNode('KSampler');
+            const imageNode = LiteGraph.createNode('EmptyLatentImage');
+            const vaeNode = LiteGraph.createNode('VAEDecode');
+            const vaeLoaderNode = LiteGraph.createNode('VAELoader');
+            const saveNode = LiteGraph.createNode('SaveImage');
+            let hrSamplerNode = null;
+            let hrSteps = null;
+            const ceil64 = (v) => Math.ceil(v / 64) * 64;
+            const getWidget = (node, name) => {
+                return node.widgets.find((w) => w.name === name);
+            };
+            const setWidgetValue = (node, name, value, isOptionPrefix) => {
+                const w = getWidget(node, name);
+                if (isOptionPrefix) {
+                    const o = w.options.values.find((w) => w.startsWith(value));
+                    if (o) {
+                        w.value = o;
+                    }
+                    else {
+                        console.warn(`Unknown value '${value}' for widget '${name}'`, node);
+                        w.value = value;
+                    }
+                }
+                else {
+                    w.value = value;
+                }
+            };
+            const createLoraNodes = (clipNode, text, prevClip, prevModel) => {
+                const loras = [];
+                text = text.replace(/<lora:([^:]+:[^>]+)>/g, function (m, c) {
+                    const s = c.split(':');
+                    const weight = parseFloat(s[1]);
+                    if (isNaN(weight)) {
+                        console.warn('Invalid LORA', m);
+                    }
+                    else {
+                        loras.push({ name: s[0], weight });
+                    }
+                    return '';
+                });
+                for (const l of loras) {
+                    const loraNode = LiteGraph.createNode('LoraLoader');
+                    graph.add(loraNode);
+                    setWidgetValue(loraNode, 'lora_name', l.name, true);
+                    setWidgetValue(loraNode, 'strength_model', l.weight);
+                    setWidgetValue(loraNode, 'strength_clip', l.weight);
+                    prevModel.node.connect(prevModel.index, loraNode, 0);
+                    prevClip.node.connect(prevClip.index, loraNode, 1);
+                    prevModel = { node: loraNode, index: 0 };
+                    prevClip = { node: loraNode, index: 1 };
+                }
+                prevClip.node.connect(1, clipNode, 0);
+                prevModel.node.connect(0, samplerNode, 0);
+                if (hrSamplerNode) {
+                    prevModel.node.connect(0, hrSamplerNode, 0);
+                }
+                return { text, prevModel, prevClip };
+            };
+            const replaceEmbeddings = (text) => {
+                if (!embeddings.length)
+                    return text;
+                return text.replaceAll(new RegExp('\\b(' +
+                    embeddings
+                        .map((e) => e.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'))
+                        .join('\\b|\\b') +
+                    ')\\b', 'ig'), 'embedding:$1');
+            };
+            const popOpt = (name) => {
+                const v = opts[name];
+                delete opts[name];
+                return v;
+            };
+            graph.clear();
+            graph.add(ckptNode);
+            graph.add(clipSkipNode);
+            graph.add(positiveNode);
+            graph.add(negativeNode);
+            graph.add(samplerNode);
+            graph.add(imageNode);
+            graph.add(vaeNode);
+            graph.add(vaeLoaderNode);
+            graph.add(saveNode);
+            ckptNode.connect(1, clipSkipNode, 0);
+            clipSkipNode.connect(0, positiveNode, 0);
+            clipSkipNode.connect(0, negativeNode, 0);
+            ckptNode.connect(0, samplerNode, 0);
+            positiveNode.connect(0, samplerNode, 1);
+            negativeNode.connect(0, samplerNode, 2);
+            imageNode.connect(0, samplerNode, 3);
+            vaeNode.connect(0, saveNode, 0);
+            samplerNode.connect(0, vaeNode, 0);
+            vaeLoaderNode.connect(0, vaeNode, 1);
+            const handlers = {
+                model(v) {
+                    setWidgetValue(ckptNode, 'ckpt_name', v, true);
+                },
+                vae(v) {
+                    setWidgetValue(vaeLoaderNode, 'vae_name', v, true);
+                },
+                'cfg scale'(v) {
+                    setWidgetValue(samplerNode, 'cfg', +v);
+                },
+                'clip skip'(v) {
+                    setWidgetValue(clipSkipNode, 'stop_at_clip_layer', -v);
+                },
+                sampler(v) {
+                    let name = v.toLowerCase().replace('++', 'pp').replaceAll(' ', '_');
+                    if (name.includes('karras')) {
+                        name = name.replace('karras', '').replace(/_+$/, '');
+                        setWidgetValue(samplerNode, 'scheduler', 'karras');
+                    }
+                    else {
+                        setWidgetValue(samplerNode, 'scheduler', 'normal');
+                    }
+                    const w = getWidget(samplerNode, 'sampler_name');
+                    const o = w.options.values.find((w) => w === name || w === 'sample_' + name);
+                    if (o) {
+                        setWidgetValue(samplerNode, 'sampler_name', o);
+                    }
+                },
+                size(v) {
+                    const wxh = v.split('x');
+                    const w = ceil64(+wxh[0]);
+                    const h = ceil64(+wxh[1]);
+                    const hrUp = popOpt('hires upscale');
+                    const hrSz = popOpt('hires resize');
+                    hrSteps = popOpt('hires steps');
+                    let hrMethod = popOpt('hires upscaler');
+                    setWidgetValue(imageNode, 'width', w);
+                    setWidgetValue(imageNode, 'height', h);
+                    if (hrUp || hrSz) {
+                        let uw, uh;
+                        if (hrUp) {
+                            uw = w * hrUp;
+                            uh = h * hrUp;
+                        }
+                        else {
+                            const s = hrSz.split('x');
+                            uw = +s[0];
+                            uh = +s[1];
+                        }
+                        let upscaleNode;
+                        let latentNode;
+                        if (hrMethod.startsWith('Latent')) {
+                            latentNode = upscaleNode = LiteGraph.createNode('LatentUpscale');
+                            graph.add(upscaleNode);
+                            samplerNode.connect(0, upscaleNode, 0);
+                            switch (hrMethod) {
+                                case 'Latent (nearest-exact)':
+                                    hrMethod = 'nearest-exact';
+                                    break;
+                            }
+                            setWidgetValue(upscaleNode, 'upscale_method', hrMethod, true);
+                        }
+                        else {
+                            const decode = LiteGraph.createNode('VAEDecodeTiled');
+                            graph.add(decode);
+                            samplerNode.connect(0, decode, 0);
+                            vaeLoaderNode.connect(0, decode, 1);
+                            const upscaleLoaderNode = LiteGraph.createNode('UpscaleModelLoader');
+                            graph.add(upscaleLoaderNode);
+                            setWidgetValue(upscaleLoaderNode, 'model_name', hrMethod, true);
+                            const modelUpscaleNode = LiteGraph.createNode('ImageUpscaleWithModel');
+                            graph.add(modelUpscaleNode);
+                            decode.connect(0, modelUpscaleNode, 1);
+                            upscaleLoaderNode.connect(0, modelUpscaleNode, 0);
+                            upscaleNode = LiteGraph.createNode('ImageScale');
+                            graph.add(upscaleNode);
+                            modelUpscaleNode.connect(0, upscaleNode, 0);
+                            const vaeEncodeNode = (latentNode =
+                                LiteGraph.createNode('VAEEncodeTiled'));
+                            graph.add(vaeEncodeNode);
+                            upscaleNode.connect(0, vaeEncodeNode, 0);
+                            vaeLoaderNode.connect(0, vaeEncodeNode, 1);
+                        }
+                        setWidgetValue(upscaleNode, 'width', ceil64(uw));
+                        setWidgetValue(upscaleNode, 'height', ceil64(uh));
+                        hrSamplerNode = LiteGraph.createNode('KSampler');
+                        graph.add(hrSamplerNode);
+                        ckptNode.connect(0, hrSamplerNode, 0);
+                        positiveNode.connect(0, hrSamplerNode, 1);
+                        negativeNode.connect(0, hrSamplerNode, 2);
+                        latentNode.connect(0, hrSamplerNode, 3);
+                        hrSamplerNode.connect(0, vaeNode, 0);
+                    }
+                },
+                steps(v) {
+                    setWidgetValue(samplerNode, 'steps', +v);
+                },
+                seed(v) {
+                    setWidgetValue(samplerNode, 'seed', +v);
+                }
+            };
+            for (const opt in opts) {
+                if (opt in handlers) {
+                    handlers[opt](popOpt(opt));
+                }
+            }
+            if (hrSamplerNode) {
+                setWidgetValue(hrSamplerNode, 'steps', hrSteps ? +hrSteps : getWidget(samplerNode, 'steps').value);
+                setWidgetValue(hrSamplerNode, 'cfg', getWidget(samplerNode, 'cfg').value);
+                setWidgetValue(hrSamplerNode, 'scheduler', getWidget(samplerNode, 'scheduler').value);
+                setWidgetValue(hrSamplerNode, 'sampler_name', getWidget(samplerNode, 'sampler_name').value);
+                setWidgetValue(hrSamplerNode, 'denoise', +(popOpt('denoising strength') || '1'));
+            }
+            let n = createLoraNodes(positiveNode, positive, { node: clipSkipNode, index: 0 }, { node: ckptNode, index: 0 });
+            positive = n.text;
+            n = createLoraNodes(negativeNode, negative, n.prevClip, n.prevModel);
+            negative = n.text;
+            setWidgetValue(positiveNode, 'text', replaceEmbeddings(positive));
+            setWidgetValue(negativeNode, 'text', replaceEmbeddings(negative));
+            graph.arrange();
+            for (const opt of [
+                'model hash',
+                'ensd',
+                'version',
+                'vae hash',
+                'ti hashes',
+                'lora hashes',
+                'hashes'
+            ]) {
+                delete opts[opt];
+            }
+            console.warn('Unhandled parameters:', opts);
+        }
+    }
+}
diff --git a/custom_nodes/rgthree-comfy/web/common/components/base_custom_element.js b/custom_nodes/rgthree-comfy/web/common/components/base_custom_element.js
new file mode 100644
index 0000000000000000000000000000000000000000..d389ffc23932ed933232c60be3a3841afa3bd7a1
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/common/components/base_custom_element.js
@@ -0,0 +1,207 @@
+import { $el, getActionEls } from "../../common/utils_dom.js";
+import { bind } from "../utils_templates.js";
+const CSS_STYLE_SHEETS = new Map();
+const CSS_STYLE_SHEETS_ADDED = new Map();
+const HTML_TEMPLATE_FILES = new Map();
+function getCommonPath(name, extension) {
+    return `rgthree/common/components/${name.replace("rgthree-", "").replace(/\-/g, "_")}.${extension}`;
+}
+async function getStyleSheet(name, markupOrPath) {
+    if (markupOrPath.includes("{")) {
+        return markupOrPath;
+    }
+    if (!CSS_STYLE_SHEETS.has(name)) {
+        try {
+            const path = markupOrPath || getCommonPath(name, "css");
+            const text = await (await fetch(path)).text();
+            CSS_STYLE_SHEETS.set(name, text);
+        }
+        catch (e) {
+        }
+    }
+    return CSS_STYLE_SHEETS.get(name);
+}
+async function addStyleSheet(name, markupOrPath) {
+    if (markupOrPath.includes("{")) {
+        throw new Error("Page-level stylesheets should be passed a path.");
+    }
+    if (!CSS_STYLE_SHEETS_ADDED.has(name)) {
+        const link = document.createElement("link");
+        link.rel = "stylesheet";
+        link.href = markupOrPath;
+        document.head.appendChild(link);
+        CSS_STYLE_SHEETS_ADDED.set(name, link);
+    }
+    return CSS_STYLE_SHEETS_ADDED.get(name);
+}
+async function getTemplateMarkup(name, markupOrPath) {
+    if (markupOrPath.includes("<template")) {
+        return markupOrPath;
+    }
+    if (!HTML_TEMPLATE_FILES.has(name)) {
+        try {
+            const path = markupOrPath || getCommonPath(name, "html");
+            const text = await (await fetch(path)).text();
+            HTML_TEMPLATE_FILES.set(name, text);
+        }
+        catch (e) {
+        }
+    }
+    return HTML_TEMPLATE_FILES.get(name);
+}
+export class RgthreeCustomElement extends HTMLElement {
+    constructor() {
+        super(...arguments);
+        this.ctor = this.constructor;
+        this.hasBeenConnected = false;
+        this.connected = false;
+        this.templates = new Map();
+        this.firstConnectedPromise = new Promise((resolve) => (this.firstConnectedPromiseResolver = resolve));
+        this.eventElements = new Map();
+    }
+    static create() {
+        if (this.NAME === "rgthree-override") {
+            throw new Error("Must override component NAME");
+        }
+        if (!window.customElements.get(this.NAME)) {
+            window.customElements.define(this.NAME, this);
+        }
+        return document.createElement(this.NAME);
+    }
+    onFirstConnected() {
+    }
+    onReconnected() {
+    }
+    onConnected() {
+    }
+    onDisconnected() {
+    }
+    onAction(action, e) {
+        console.log("onAction", action, e);
+    }
+    getElement(query) {
+        const el = this.querySelector(query);
+        if (!el) {
+            throw new Error("No element found for query: " + query);
+        }
+        return el;
+    }
+    onActionInternal(action, e) {
+        if (typeof this[action] === "function") {
+            this[action](e);
+        }
+        else {
+            this.onAction(action, e);
+        }
+    }
+    onConnectedInternal() {
+        this.connectActionElements();
+        this.onConnected();
+    }
+    onDisconnectedInternal() {
+        this.disconnectActionElements();
+        this.onDisconnected();
+    }
+    async connectedCallback() {
+        const elementName = this.ctor.NAME;
+        const wasConnected = this.connected;
+        if (!wasConnected) {
+            this.connected = true;
+        }
+        if (!this.hasBeenConnected) {
+            const [stylesheet, markup] = await Promise.all([
+                this.ctor.USE_SHADOW
+                    ? getStyleSheet(elementName, this.ctor.CSS)
+                    : addStyleSheet(elementName, this.ctor.CSS),
+                getTemplateMarkup(elementName, this.ctor.TEMPLATES),
+            ]);
+            if (markup) {
+                const temp = $el("div");
+                const templatesMarkup = markup.match(/<template[^]*?<\/template>/gm) || [];
+                for (const markup of templatesMarkup) {
+                    temp.innerHTML = markup;
+                    const template = temp.children[0];
+                    if (!(template instanceof HTMLTemplateElement)) {
+                        throw new Error("Not a template element.");
+                    }
+                    let id = template.getAttribute("id");
+                    if (!id) {
+                        id = this.ctor.NAME;
+                    }
+                    this.templates.set(id, template);
+                }
+            }
+            if (this.ctor.USE_SHADOW) {
+                this.root = this.attachShadow({ mode: "open" });
+                if (typeof stylesheet === "string") {
+                    const sheet = new CSSStyleSheet();
+                    sheet.replaceSync(stylesheet);
+                    this.root.adoptedStyleSheets = [sheet];
+                }
+            }
+            else {
+                this.root = this;
+            }
+            let template;
+            if (this.templates.has(elementName)) {
+                template = this.templates.get(elementName);
+            }
+            else if (this.templates.has(elementName.replace("rgthree-", ""))) {
+                template = this.templates.get(elementName.replace("rgthree-", ""));
+            }
+            if (template) {
+                this.root.appendChild(template.content.cloneNode(true));
+                for (const name of template.getAttributeNames()) {
+                    if (name != "id" && template.getAttribute(name)) {
+                        this.setAttribute(name, template.getAttribute(name));
+                    }
+                }
+            }
+            this.onFirstConnected();
+            this.hasBeenConnected = true;
+            this.firstConnectedPromiseResolver();
+        }
+        else {
+            this.onReconnected();
+        }
+        this.onConnectedInternal();
+    }
+    disconnectedCallback() {
+        this.connected = false;
+        this.onDisconnected();
+    }
+    connectActionElements() {
+        const data = getActionEls(this);
+        for (const dataItem of Object.values(data)) {
+            const mapItem = this.eventElements.get(dataItem.el) || {};
+            for (const [event, action] of Object.entries(dataItem.actions)) {
+                if (mapItem[event]) {
+                    console.warn(`Element already has an event for ${event}`);
+                    continue;
+                }
+                mapItem[event] = (e) => {
+                    this.onActionInternal(action, e);
+                };
+                dataItem.el.addEventListener(event, mapItem[event]);
+            }
+        }
+    }
+    disconnectActionElements() {
+        for (const [el, eventData] of this.eventElements.entries()) {
+            for (const [event, fn] of Object.entries(eventData)) {
+                el.removeEventListener(event, fn);
+            }
+        }
+    }
+    async bindWhenConnected(data, el) {
+        await this.firstConnectedPromise;
+        this.bind(data, el);
+    }
+    bind(data, el) {
+        bind(el || this.root, data);
+    }
+}
+RgthreeCustomElement.NAME = "rgthree-override";
+RgthreeCustomElement.USE_SHADOW = true;
+RgthreeCustomElement.TEMPLATES = "";
+RgthreeCustomElement.CSS = "";
diff --git a/custom_nodes/rgthree-comfy/web/common/css/buttons.css b/custom_nodes/rgthree-comfy/web/common/css/buttons.css
new file mode 100644
index 0000000000000000000000000000000000000000..2c9f13c0acd4416847b2778048a4b79a03234a60
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/common/css/buttons.css
@@ -0,0 +1,130 @@
+:not(#fakeid) .rgthree-button-reset {
+  position: relative;
+  appearance: none;
+  cursor: pointer;
+  border: 0;
+  background: transparent;
+  color: inherit;
+  padding: 0;
+  margin: 0;
+}
+
+:not(#fakeid) .rgthree-button {
+  --padding-top: 7px;
+  --padding-bottom: 9px;
+  --padding-x: 16px;
+  position: relative;
+  cursor: pointer;
+  border: 0;
+  border-radius: 0.33rem;
+  background: rgba(0, 0, 0, 0.5);
+  color: white;
+  font-family: system-ui, sans-serif;
+  font-size: 1rem;
+  line-height: 1;
+  white-space: nowrap;
+  text-decoration: none;
+  margin: 0.25rem;
+  box-shadow: 0px 0px 2px rgb(0, 0, 0);
+  background: #212121;
+  transition: all 0.1s ease-in-out;
+  padding: var(--padding-top) var(--padding-x) var(--padding-bottom);
+  display: inline-flex;
+  flex-direction: row;
+  align-items: center;
+  justify-content: center;
+}
+:not(#fakeid) .rgthree-button::before, :not(#fakeid) .rgthree-button::after {
+  content: "";
+  display: block;
+  position: absolute;
+  border-radius: 0.33rem;
+  left: 0;
+  top: 0;
+  width: 100%;
+  height: 100%;
+  box-shadow: inset 1px 1px 0px rgba(255, 255, 255, 0.12), inset -1px -1px 0px rgba(0, 0, 0, 0.75);
+  background: linear-gradient(to bottom, rgba(255, 255, 255, 0.06), rgba(0, 0, 0, 0.15));
+  mix-blend-mode: screen;
+}
+:not(#fakeid) .rgthree-button::after {
+  mix-blend-mode: multiply;
+}
+:not(#fakeid) .rgthree-button:hover {
+  background: #303030;
+}
+:not(#fakeid) .rgthree-button:active {
+  box-shadow: 0px 0px 0px rgba(0, 0, 0, 0);
+  background: #121212;
+  padding: calc(var(--padding-top) + 1px) calc(var(--padding-x) - 1px) calc(var(--padding-bottom) - 1px) calc(var(--padding-x) + 1px);
+}
+:not(#fakeid) .rgthree-button:active::before, :not(#fakeid) .rgthree-button:active::after {
+  box-shadow: 1px 1px 0px rgba(255, 255, 255, 0.15), inset 1px 1px 0px rgba(0, 0, 0, 0.5), inset 1px 3px 5px rgba(0, 0, 0, 0.33);
+}
+:not(#fakeid) .rgthree-button.-blue {
+  background: #346599 !important;
+}
+:not(#fakeid) .rgthree-button.-blue:hover {
+  background: #3b77b8 !important;
+}
+:not(#fakeid) .rgthree-button.-blue:active {
+  background: #1d5086 !important;
+}
+:not(#fakeid) .rgthree-button.-green {
+  background: linear-gradient(to bottom, rgba(255, 255, 255, 0.06), rgba(0, 0, 0, 0.15)), #14580b;
+}
+:not(#fakeid) .rgthree-button.-green:hover {
+  background: linear-gradient(to bottom, rgba(255, 255, 255, 0.06), rgba(0, 0, 0, 0.15)), #1a6d0f;
+}
+:not(#fakeid) .rgthree-button.-green:active {
+  background: linear-gradient(to bottom, rgba(0, 0, 0, 0.15), rgba(255, 255, 255, 0.06)), #0f3f09;
+}
+:not(#fakeid) .rgthree-button[disabled] {
+  box-shadow: none;
+  background: #666 !important;
+  color: #aaa;
+  pointer-events: none;
+}
+:not(#fakeid) .rgthree-button[disabled]::before, :not(#fakeid) .rgthree-button[disabled]::after {
+  display: none;
+}
+
+:not(#fakeid) .rgthree-comfybar-top-button-group {
+  font-size: 0;
+  flex: 1 1 auto;
+  display: flex;
+  align-items: stretch;
+}
+:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button {
+  margin: 0;
+  flex: 1 1;
+  height: 36px;
+  padding: 0 12px;
+  border-radius: 0;
+  background: var(--p-button-secondary-background);
+  color: var(--p-button-secondary-color);
+}
+:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button.-primary {
+  background: var(--p-button-primary-background);
+  color: var(--p-button-primary-color);
+}
+:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button::before, :not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button::after {
+  border-radius: 0;
+}
+:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button svg {
+  fill: currentColor;
+  width: 28px;
+  height: 28px;
+}
+:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button:first-of-type,
+:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button:first-of-type::before,
+:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button:first-of-type::after {
+  border-top-left-radius: 0.33rem;
+  border-bottom-left-radius: 0.33rem;
+}
+:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button:last-of-type,
+:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button:last-of-type::before,
+:not(#fakeid) .rgthree-comfybar-top-button-group .rgthree-comfybar-top-button:last-of-type::after {
+  border-top-right-radius: 0.33rem;
+  border-bottom-right-radius: 0.33rem;
+}
diff --git a/custom_nodes/rgthree-comfy/web/common/css/dialog.css b/custom_nodes/rgthree-comfy/web/common/css/dialog.css
new file mode 100644
index 0000000000000000000000000000000000000000..c9efdf67c5848facf4589d308ed53e300d67cdd9
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/common/css/dialog.css
@@ -0,0 +1,124 @@
+@charset "UTF-8";
+.rgthree-dialog {
+  outline: 0;
+  border: 0;
+  border-radius: 6px;
+  background: #414141;
+  color: #fff;
+  box-shadow: inset 1px 1px 0px rgba(255, 255, 255, 0.05), inset -1px -1px 0px rgba(0, 0, 0, 0.5), 2px 2px 20px rgb(0, 0, 0);
+  max-width: 800px;
+  box-sizing: border-box;
+  font-family: "Segoe UI", Tahoma, Geneva, Verdana, sans-serif;
+  font-size: 1rem;
+  padding: 0;
+  max-height: calc(100% - 32px);
+}
+.rgthree-dialog *, .rgthree-dialog *::before, .rgthree-dialog *::after {
+  box-sizing: inherit;
+}
+
+.rgthree-dialog-container > * {
+  padding: 8px 16px;
+}
+.rgthree-dialog-container > *:first-child {
+  padding-top: 16px;
+}
+.rgthree-dialog-container > *:last-child {
+  padding-bottom: 16px;
+}
+
+.rgthree-dialog.-iconed::after {
+  content: "";
+  font-size: 276px;
+  position: absolute;
+  right: 0px;
+  bottom: 0px;
+  opacity: 0.15;
+  display: block;
+  width: 237px;
+  overflow: hidden;
+  height: 186px;
+  line-height: 1;
+  pointer-events: none;
+  z-index: -1;
+}
+
+.rgthree-dialog.-iconed.-help::after {
+  content: "🛟";
+}
+
+.rgthree-dialog.-iconed.-settings::after {
+  content: "⚙️";
+}
+
+@media (max-width: 832px) {
+  .rgthree-dialog {
+    max-width: calc(100% - 32px);
+  }
+}
+.rgthree-dialog-container-title {
+  display: flex;
+  flex-direction: row;
+  align-items: center;
+  justify-content: start;
+}
+
+.rgthree-dialog-container-title > svg:first-child {
+  width: 36px;
+  height: 36px;
+  margin-right: 16px;
+}
+
+.rgthree-dialog-container-title h2 {
+  font-size: 1.375rem;
+  margin: 0;
+  font-weight: bold;
+}
+
+.rgthree-dialog-container-title h2 small {
+  font-size: 0.8125rem;
+  font-weight: normal;
+  opacity: 0.75;
+}
+
+.rgthree-dialog-container-content {
+  overflow: auto;
+  max-height: calc(100vh - 200px); /* Arbitrary height to copensate for margin, title, and footer.*/
+}
+
+.rgthree-dialog-container-content p {
+  font-size: 0.8125rem;
+  margin-top: 0;
+}
+
+.rgthree-dialog-container-content ul li p {
+  margin-bottom: 4px;
+}
+
+.rgthree-dialog-container-content ul li p + p {
+  margin-top: 0.5em;
+}
+
+.rgthree-dialog-container-content ul li ul {
+  margin-top: 0.5em;
+  margin-bottom: 1em;
+}
+
+.rgthree-dialog-container-content p code {
+  display: inline-block;
+  padding: 2px 4px;
+  margin: 0px 2px;
+  border: 1px solid rgba(255, 255, 255, 0.25);
+  border-radius: 3px;
+  background: rgba(255, 255, 255, 0.1);
+}
+
+.rgthree-dialog-container-footer {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+}
+
+body.rgthree-dialog-open > *:not(.rgthree-dialog):not(.rgthree-top-messages-container) {
+  filter: blur(5px);
+}
diff --git a/custom_nodes/rgthree-comfy/web/common/css/dialog_lora_chooser.css b/custom_nodes/rgthree-comfy/web/common/css/dialog_lora_chooser.css
new file mode 100644
index 0000000000000000000000000000000000000000..0142ab10a522512a0cc8dbdb11ce5793baa41f11
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/common/css/dialog_lora_chooser.css
@@ -0,0 +1,143 @@
+.rgthree-lora-chooser-dialog {
+  max-width: 100%;
+}
+.rgthree-lora-chooser-dialog .rgthree-dialog-container-title {
+  display: flex;
+  flex-direction: column;
+}
+.rgthree-lora-chooser-dialog .rgthree-dialog-container-title h2 {
+  display: flex;
+  width: 100%;
+}
+.rgthree-lora-chooser-dialog .rgthree-lora-chooser-search {
+  margin-left: auto;
+  border-radius: 50px;
+  width: 50%;
+  max-width: 170px;
+  padding: 2px 8px;
+}
+.rgthree-lora-chooser-dialog .rgthree-lora-chooser-header {
+  display: flex;
+  flex-direction: row;
+}
+.rgthree-lora-chooser-dialog .rgthree-lora-filters-container svg {
+  width: 16px;
+  height: 16px;
+}
+.rgthree-lora-chooser-dialog .rgthree-dialog-container-content {
+  width: 80vw;
+  height: 80vh;
+}
+.rgthree-lora-chooser-dialog .rgthree-button-reset {
+  width: 32px;
+  height: 32px;
+}
+.rgthree-lora-chooser-dialog .rgthree-button-reset > svg {
+  width: 100%;
+  height: 100%;
+}
+.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list {
+  list-style: none;
+  margin: 0;
+  padding: 0;
+  position: relative;
+  display: flex;
+  flex-direction: row;
+  flex-wrap: wrap;
+  align-items: start;
+  justify-content: space-around;
+}
+.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li {
+  position: relative;
+  flex: 0 0 auto;
+  width: 170px;
+  max-width: 100%;
+  margin: 8px 8px 16px;
+}
+.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li label {
+  position: absolute;
+  display: block;
+  inset: 0;
+  z-index: 3;
+  cursor: pointer;
+}
+.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li input[type=checkbox] {
+  position: absolute;
+  right: 8px;
+  top: 8px;
+  margin: 0;
+  z-index: 2;
+  appearance: none;
+  background-color: #fff;
+  width: 48px;
+  height: 48px;
+  border-radius: 4px;
+  border: 1px solid rgb(120, 120, 120);
+  opacity: 0;
+  transition: opacity 0.15s ease-in-out;
+}
+.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li input[type=checkbox]:checked {
+  opacity: 1;
+  background: #0060df;
+}
+.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li input[type=checkbox]:checked::before {
+  content: "";
+  display: block;
+  width: 100%;
+  height: 100%;
+  box-shadow: inset 100px 100px #fff;
+  clip-path: polygon(40.13% 68.39%, 23.05% 51.31%, 17.83% 48.26%, 12.61% 49.57%, 9.57% 53.04%, 8% 60%, 34.13% 85.87%, 39.82% 89.57%, 45.88% 86.73%, 90.66% 32.39%, 88.92% 26.1%, 83.03% 22.17%, 76.94% 22.62%);
+}
+.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li figure {
+  position: relative;
+  display: block;
+  margin: 0 0 8px;
+  padding: 0;
+  border: 1px solid rgba(120, 120, 120, 0.8);
+  background: rgba(120, 120, 120, 0.5);
+  width: 100%;
+  padding-top: 120%;
+  transition: box-shadow 0.15s ease-in-out;
+  opacity: 0.75;
+}
+.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li figure::after {
+  content: "";
+  display: block;
+  position: absolute;
+  inset: 0;
+}
+.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li figure:empty::before {
+  content: "No image.";
+  color: rgba(200, 200, 200, 0.8);
+  position: absolute;
+  display: block;
+  inset: 0;
+  font-size: 1.2em;
+  text-align: center;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+}
+.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li figure > img, .rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li figure > video {
+  position: absolute;
+  width: 100%;
+  height: 100%;
+  top: 0;
+  left: 0;
+  object-fit: cover;
+}
+.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li div {
+  word-wrap: break-word;
+  font-size: 0.8rem;
+  opacity: 0.75;
+}
+.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li:hover figure::after {
+  box-shadow: 0px 2px 6px rgba(0, 0, 0, 0.75);
+}
+.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li :checked ~ figure::after {
+  box-shadow: 0 0 5px #fff, 0px 0px 15px rgba(49, 131, 255, 0.88), inset 0 0 3px #fff, inset 0px 0px 5px rgba(49, 131, 255, 0.88);
+}
+.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li:hover *, .rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li:hover input[type=checkbox],
+.rgthree-lora-chooser-dialog ul.rgthree-lora-chooser-list > li :checked ~ * {
+  opacity: 1;
+}
diff --git a/custom_nodes/rgthree-comfy/web/common/css/dialog_model_info.css b/custom_nodes/rgthree-comfy/web/common/css/dialog_model_info.css
new file mode 100644
index 0000000000000000000000000000000000000000..5edee06a5eb3c3502c966320e3da1c6b0f655346
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/common/css/dialog_model_info.css
@@ -0,0 +1,336 @@
+.rgthree-info-dialog {
+  width: 90vw;
+  max-width: 960px;
+}
+.rgthree-info-dialog .rgthree-info-area {
+  list-style: none;
+  padding: 0;
+  margin: 0;
+  display: flex;
+}
+.rgthree-info-dialog .rgthree-info-area > li {
+  display: inline-flex;
+  margin: 0;
+  vertical-align: top;
+}
+.rgthree-info-dialog .rgthree-info-area > li + li {
+  margin-left: 6px;
+}
+.rgthree-info-dialog .rgthree-info-area > li:not(.-link) + li.-link {
+  margin-left: auto;
+}
+.rgthree-info-dialog .rgthree-info-area > li.rgthree-info-tag > * {
+  min-height: 24px;
+  border-radius: 4px;
+  line-height: 1;
+  color: rgba(255, 255, 255, 0.85);
+  background: rgb(69, 92, 85);
+  font-size: 14px;
+  font-weight: bold;
+  text-decoration: none;
+  display: flex;
+  height: 1.6em;
+  padding-left: 0.5em;
+  padding-right: 0.5em;
+  padding-bottom: 0.1em;
+  align-content: center;
+  justify-content: center;
+  align-items: center;
+  box-shadow: inset 0px 0px 0 1px rgba(0, 0, 0, 0.5);
+}
+.rgthree-info-dialog .rgthree-info-area > li.rgthree-info-tag > * > svg {
+  width: 16px;
+  height: 16px;
+}
+.rgthree-info-dialog .rgthree-info-area > li.rgthree-info-tag > * > svg:last-child {
+  margin-left: 0.5em;
+}
+.rgthree-info-dialog .rgthree-info-area > li.rgthree-info-tag > *[href] {
+  box-shadow: inset 0px 1px 0px rgba(255, 255, 255, 0.25), inset 0px -1px 0px rgba(0, 0, 0, 0.66);
+}
+.rgthree-info-dialog .rgthree-info-area > li.rgthree-info-tag > *:empty {
+  display: none;
+}
+.rgthree-info-dialog .rgthree-info-area > li.-type > * {
+  background: rgb(73, 54, 94);
+  color: rgb(228, 209, 248);
+}
+.rgthree-info-dialog .rgthree-info-area > li.rgthree-info-menu {
+  margin-left: auto;
+}
+:not(#fakeid) .rgthree-info-dialog .rgthree-info-area > li.rgthree-info-menu .rgthree-button {
+  margin: 0;
+  min-height: 24px;
+  padding: 0 12px;
+}
+.rgthree-info-dialog .rgthree-info-area > li.rgthree-info-menu svg {
+  width: 16px;
+  height: 16px;
+}
+.rgthree-info-dialog .rgthree-info-table {
+  border-collapse: collapse;
+  margin: 16px 0px;
+  width: 100%;
+  font-size: 12px;
+}
+.rgthree-info-dialog .rgthree-info-table tr.editable button {
+  display: flex;
+  width: 28px;
+  height: 28px;
+  align-items: center;
+  justify-content: center;
+}
+.rgthree-info-dialog .rgthree-info-table tr.editable button svg + svg {
+  display: none;
+}
+.rgthree-info-dialog .rgthree-info-table tr.editable.-rgthree-editing button svg {
+  display: none;
+}
+.rgthree-info-dialog .rgthree-info-table tr.editable.-rgthree-editing button svg + svg {
+  display: inline-block;
+}
+.rgthree-info-dialog .rgthree-info-table td {
+  position: relative;
+  border: 1px solid rgba(255, 255, 255, 0.25);
+  padding: 0;
+  vertical-align: top;
+}
+.rgthree-info-dialog .rgthree-info-table td:first-child {
+  background: rgba(255, 255, 255, 0.075);
+  width: 10px;
+}
+.rgthree-info-dialog .rgthree-info-table td:first-child > *:first-child {
+  white-space: nowrap;
+  padding-right: 32px;
+}
+.rgthree-info-dialog .rgthree-info-table td:first-child small {
+  display: block;
+  margin-top: 2px;
+  opacity: 0.75;
+}
+.rgthree-info-dialog .rgthree-info-table td:first-child small > [data-action] {
+  text-decoration: underline;
+  cursor: pointer;
+}
+.rgthree-info-dialog .rgthree-info-table td:first-child small > [data-action]:hover {
+  text-decoration: none;
+}
+.rgthree-info-dialog .rgthree-info-table td a, .rgthree-info-dialog .rgthree-info-table td a:hover, .rgthree-info-dialog .rgthree-info-table td a:visited {
+  color: inherit;
+}
+.rgthree-info-dialog .rgthree-info-table td svg {
+  width: 1.3333em;
+  height: 1.3333em;
+  vertical-align: -0.285em;
+}
+.rgthree-info-dialog .rgthree-info-table td svg.logo-civitai {
+  margin-right: 0.3333em;
+}
+.rgthree-info-dialog .rgthree-info-table td > *:first-child {
+  display: block;
+  padding: 6px 10px;
+}
+.rgthree-info-dialog .rgthree-info-table td > input, .rgthree-info-dialog .rgthree-info-table td > textarea {
+  padding: 5px 10px;
+  border: 0;
+  box-shadow: inset 1px 1px 5px 0px rgba(0, 0, 0, 0.5);
+  font: inherit;
+  appearance: none;
+  background: #fff;
+  color: #121212;
+  resize: vertical;
+}
+.rgthree-info-dialog .rgthree-info-table td > input:only-child, .rgthree-info-dialog .rgthree-info-table td > textarea:only-child {
+  width: 100%;
+}
+:not(#fakeid) .rgthree-info-dialog .rgthree-info-table td .rgthree-button[data-action=fetch-civitai] {
+  font-size: inherit;
+  padding: 6px 16px;
+  margin: 2px;
+}
+.rgthree-info-dialog .rgthree-info-table tr[data-field-name=userNote] td > span:first-child {
+  white-space: pre;
+}
+.rgthree-info-dialog .rgthree-info-table tr.rgthree-info-table-break-row td {
+  border: 0;
+  background: transparent;
+  padding: 12px 4px 4px;
+  font-size: 1.2em;
+}
+.rgthree-info-dialog .rgthree-info-table tr.rgthree-info-table-break-row td > small {
+  font-style: italic;
+  opacity: 0.66;
+}
+.rgthree-info-dialog .rgthree-info-table tr.rgthree-info-table-break-row td:empty {
+  padding: 4px;
+}
+.rgthree-info-dialog .rgthree-info-table td .-help {
+  border: 1px solid currentColor;
+  position: absolute;
+  right: 5px;
+  top: 6px;
+  line-height: 1;
+  font-size: 11px;
+  width: 12px;
+  height: 12px;
+  border-radius: 8px;
+  display: flex;
+  align-content: center;
+  justify-content: center;
+  cursor: help;
+}
+.rgthree-info-dialog .rgthree-info-table td .-help::before {
+  content: "?";
+}
+.rgthree-info-dialog .rgthree-info-table td > ul.rgthree-info-trained-words-list {
+  list-style: none;
+  padding: 2px 8px;
+  margin: 0;
+  display: flex;
+  flex-direction: row;
+  flex-wrap: wrap;
+  max-height: 15vh;
+  overflow: auto;
+}
+.rgthree-info-dialog .rgthree-info-table td > ul.rgthree-info-trained-words-list > li {
+  display: inline-flex;
+  margin: 2px;
+  vertical-align: top;
+  border-radius: 4px;
+  line-height: 1;
+  color: rgba(255, 255, 255, 0.85);
+  background: rgb(73, 91, 106);
+  font-size: 1.2em;
+  font-weight: 600;
+  text-decoration: none;
+  display: flex;
+  height: 1.6em;
+  align-content: center;
+  justify-content: center;
+  align-items: center;
+  box-shadow: inset 0px 0px 0 1px rgba(0, 0, 0, 0.5);
+  cursor: pointer;
+  white-space: nowrap;
+  max-width: 183px;
+}
+.rgthree-info-dialog .rgthree-info-table td > ul.rgthree-info-trained-words-list > li:hover {
+  background: rgb(68, 109, 142);
+}
+.rgthree-info-dialog .rgthree-info-table td > ul.rgthree-info-trained-words-list > li > svg {
+  width: auto;
+  height: 1.2em;
+}
+.rgthree-info-dialog .rgthree-info-table td > ul.rgthree-info-trained-words-list > li > span {
+  padding-left: 0.5em;
+  padding-right: 0.5em;
+  padding-bottom: 0.1em;
+  text-overflow: ellipsis;
+  overflow: hidden;
+}
+.rgthree-info-dialog .rgthree-info-table td > ul.rgthree-info-trained-words-list > li > small {
+  align-self: stretch;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  padding: 0 0.5em;
+  background: rgba(0, 0, 0, 0.2);
+}
+.rgthree-info-dialog .rgthree-info-table td > ul.rgthree-info-trained-words-list > li.-rgthree-is-selected {
+  background: rgb(42, 126, 193);
+}
+.rgthree-info-dialog .rgthree-info-images {
+  list-style: none;
+  padding: 0;
+  margin: 0;
+  scroll-snap-type: x mandatory;
+  display: flex;
+  flex-direction: row;
+  overflow: auto;
+}
+.rgthree-info-dialog .rgthree-info-images > li {
+  scroll-snap-align: start;
+  max-width: 90%;
+  flex: 0 0 auto;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  flex-direction: column;
+  overflow: hidden;
+  padding: 0;
+  margin: 6px;
+  font-size: 0;
+  position: relative;
+}
+.rgthree-info-dialog .rgthree-info-images > li figure {
+  margin: 0;
+  position: static;
+}
+.rgthree-info-dialog .rgthree-info-images > li figure video, .rgthree-info-dialog .rgthree-info-images > li figure img {
+  max-height: 45vh;
+}
+.rgthree-info-dialog .rgthree-info-images > li figure figcaption {
+  position: absolute;
+  left: 0;
+  width: 100%;
+  bottom: 0;
+  padding: 12px;
+  font-size: 12px;
+  background: rgba(0, 0, 0, 0.85);
+  opacity: 0;
+  transform: translateY(50px);
+  transition: all 0.25s ease-in-out;
+}
+.rgthree-info-dialog .rgthree-info-images > li figure figcaption > span {
+  display: inline-block;
+  padding: 2px 4px;
+  margin: 2px;
+  border-radius: 2px;
+  border: 1px solid rgba(255, 255, 255, 0.2);
+  word-break: break-word;
+}
+.rgthree-info-dialog .rgthree-info-images > li figure figcaption > span label {
+  display: inline;
+  padding: 0;
+  margin: 0;
+  opacity: 0.5;
+  pointer-events: none;
+  user-select: none;
+}
+.rgthree-info-dialog .rgthree-info-images > li figure figcaption > span a {
+  color: inherit;
+  text-decoration: underline;
+}
+.rgthree-info-dialog .rgthree-info-images > li figure figcaption > span a:hover {
+  text-decoration: none;
+}
+.rgthree-info-dialog .rgthree-info-images > li figure figcaption > span a svg {
+  height: 10px;
+  margin-left: 4px;
+  fill: currentColor;
+}
+.rgthree-info-dialog .rgthree-info-images > li figure figcaption:empty {
+  text-align: center;
+}
+.rgthree-info-dialog .rgthree-info-images > li figure figcaption:empty::before {
+  content: "No data.";
+}
+.rgthree-info-dialog .rgthree-info-images > li:hover figure figcaption {
+  opacity: 1;
+  transform: translateY(0px);
+}
+.rgthree-info-dialog .rgthree-info-images > li .rgthree-info-table {
+  width: calc(100% - 16px);
+}
+.rgthree-info-dialog .rgthree-info-civitai-link {
+  margin: 8px;
+  color: #eee;
+}
+.rgthree-info-dialog .rgthree-info-civitai-link a, .rgthree-info-dialog .rgthree-info-civitai-link a:hover, .rgthree-info-dialog .rgthree-info-civitai-link a:visited {
+  color: inherit;
+  text-decoration: none;
+}
+.rgthree-info-dialog .rgthree-info-civitai-link > svg {
+  width: 16px;
+  height: 16px;
+  margin-right: 8px;
+}
diff --git a/custom_nodes/rgthree-comfy/web/common/css/menu.css b/custom_nodes/rgthree-comfy/web/common/css/menu.css
new file mode 100644
index 0000000000000000000000000000000000000000..426469e745b16056409b4318cf89f479cf0e11bd
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/common/css/menu.css
@@ -0,0 +1,103 @@
+.rgthree-menu {
+  list-style: none;
+  padding: 0;
+  margin: 0;
+  position: fixed;
+  z-index: 999999;
+  pointer-events: none;
+  opacity: 0;
+  transition: opacity 0.08s ease-in-out;
+  color: #dde;
+  background-color: #111;
+  font-size: 12px;
+  box-shadow: 0 0 10px black !important;
+}
+.rgthree-menu > li {
+  position: relative;
+  padding: 4px 6px;
+  z-index: 9999;
+  white-space: nowrap;
+}
+.rgthree-menu > li[role=button] {
+  background-color: var(--comfy-menu-bg) !important;
+  color: var(--input-text);
+  cursor: pointer;
+}
+.rgthree-menu > li[role=button]:hover {
+  filter: brightness(155%);
+}
+.rgthree-menu[state^=measuring] {
+  display: block;
+  opacity: 0;
+}
+.rgthree-menu[state=open] {
+  display: block;
+  opacity: 1;
+  pointer-events: all;
+}
+
+.rgthree-top-menu {
+  box-sizing: border-box;
+  white-space: nowrap;
+  background: var(--content-bg);
+  color: var(--content-fg);
+  display: flex;
+  flex-direction: column;
+  list-style: none;
+  padding: 0;
+  margin: 0;
+}
+.rgthree-top-menu * {
+  box-sizing: inherit;
+}
+.rgthree-top-menu > li:not(#fakeid) {
+  list-style: none;
+  padding: 0;
+  margin: 0;
+  position: relative;
+  z-index: 2;
+}
+.rgthree-top-menu > li:not(#fakeid) > button {
+  cursor: pointer;
+  padding: 8px 12px 8px 8px;
+  width: 100%;
+  text-align: start;
+  display: flex;
+  flex-direction: row;
+  align-items: center;
+  justify-content: start;
+}
+.rgthree-top-menu > li:not(#fakeid) > button:hover {
+  background-color: var(--comfy-input-bg);
+}
+.rgthree-top-menu > li:not(#fakeid) > button svg {
+  height: 16px;
+  width: auto;
+  margin-inline-end: 0.6em;
+}
+.rgthree-top-menu > li:not(#fakeid) > button svg.github-star {
+  fill: rgb(227, 179, 65);
+}
+.rgthree-top-menu > li:not(#fakeid).rgthree-message {
+  min-height: 32px;
+}
+.rgthree-top-menu > li:not(#fakeid).rgthree-message > span {
+  padding: 8px 12px;
+  display: block;
+  width: 100%;
+  text-align: center;
+  font-style: italic;
+  font-size: 12px;
+}
+.rgthree-top-menu.-modal::after {
+  content: "";
+  display: block;
+  position: fixed;
+  z-index: 1;
+  inset: 0;
+  background: rgba(0, 0, 0, 0.0666666667);
+}
+
+body.rgthree-modal-menu-open > *:not(.rgthree-menu):not(.rgthree-top-messages-container) {
+  filter: blur(2px);
+}
diff --git a/custom_nodes/rgthree-comfy/web/common/css/pages_base.css b/custom_nodes/rgthree-comfy/web/common/css/pages_base.css
new file mode 100644
index 0000000000000000000000000000000000000000..49233231999fa37182bb313afe5f9f61389387c6
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/common/css/pages_base.css
@@ -0,0 +1,66 @@
+html {
+  font-size: 100%;
+  overflow-y: scroll;
+  -webkit-text-size-adjust: 100%;
+  -ms-text-size-adjust: 100%;
+  box-sizing: border-box;
+}
+
+*, *:before, *:after {
+  box-sizing: inherit;
+}
+
+:root {
+  --header-height: 56px;
+  --progress-height: 12px;
+}
+
+button {
+  all: unset;
+}
+
+.-bevel {
+  position: relative;
+}
+
+.-bevel::before {
+  content: "";
+  position: absolute;
+  left: 0;
+  top: 0;
+  width: 100%;
+  height: 100%;
+  border: 1px solid red;
+  border-color: rgba(255, 255, 255, 0.15) rgba(255, 255, 255, 0.15) rgba(0, 0, 0, 0.5) rgba(0, 0, 0, 0.5);
+  z-index: 5;
+  pointer-events: none;
+}
+
+body {
+  background: #202020;
+  font-family: Arial, sans-serif;
+  font-size: 1rem;
+  font-weight: 400;
+  margin: 0;
+  padding-top: calc(var(--header-height) + var(--progress-height));
+  color: #ffffff;
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: start;
+}
+
+.app-header {
+  height: var(--header-height);
+  padding: 0;
+  position: fixed;
+  z-index: 99;
+  top: 0;
+  left: 0;
+  width: 100%;
+  background: #353535;
+  display: flex;
+  flex-direction: row;
+  align-items: center;
+  justify-content: start;
+}
diff --git a/custom_nodes/rgthree-comfy/web/common/dialog.js b/custom_nodes/rgthree-comfy/web/common/dialog.js
new file mode 100644
index 0000000000000000000000000000000000000000..6c1a6eadfb830acb6a76459ea908d93d55849f49
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/common/dialog.js
@@ -0,0 +1,109 @@
+import { createElement as $el, getClosestOrSelf, setAttributes } from "./utils_dom.js";
+export class RgthreeDialog extends EventTarget {
+    constructor(options) {
+        super();
+        this.options = options;
+        let container = $el("div.rgthree-dialog-container");
+        this.element = $el("dialog", {
+            classes: ["rgthree-dialog", options.class || ""],
+            child: container,
+            parent: document.body,
+            events: {
+                click: (event) => {
+                    if (!this.element.open ||
+                        event.target === container ||
+                        getClosestOrSelf(event.target, `.rgthree-dialog-container`) === container) {
+                        return;
+                    }
+                    return this.close();
+                },
+            },
+        });
+        this.element.addEventListener("close", (event) => {
+            this.onDialogElementClose();
+        });
+        this.titleElement = $el("div.rgthree-dialog-container-title", {
+            parent: container,
+            children: !options.title
+                ? null
+                : options.title instanceof Element || Array.isArray(options.title)
+                    ? options.title
+                    : typeof options.title === "string"
+                        ? !options.title.includes("<h2")
+                            ? $el("h2", { html: options.title })
+                            : options.title
+                        : options.title,
+        });
+        this.contentElement = $el("div.rgthree-dialog-container-content", {
+            parent: container,
+            child: options.content,
+        });
+        const footerEl = $el("footer.rgthree-dialog-container-footer", { parent: container });
+        for (const button of options.buttons || []) {
+            $el("button", {
+                text: button.label,
+                className: button.className,
+                disabled: !!button.disabled,
+                parent: footerEl,
+                events: {
+                    click: (e) => {
+                        var _a;
+                        (_a = button.callback) === null || _a === void 0 ? void 0 : _a.call(button, e);
+                    },
+                },
+            });
+        }
+        if (options.closeButtonLabel !== false) {
+            $el("button", {
+                text: options.closeButtonLabel || "Close",
+                className: "rgthree-button",
+                parent: footerEl,
+                events: {
+                    click: (e) => {
+                        this.close(e);
+                    },
+                },
+            });
+        }
+    }
+    setTitle(content) {
+        const title = typeof content !== "string" || content.includes("<h2")
+            ? content
+            : $el("h2", { html: content });
+        setAttributes(this.titleElement, { children: title });
+    }
+    setContent(content) {
+        setAttributes(this.contentElement, { children: content });
+    }
+    show() {
+        document.body.classList.add("rgthree-dialog-open");
+        this.element.showModal();
+        this.dispatchEvent(new CustomEvent("show"));
+        return this;
+    }
+    async close(e) {
+        if (this.options.onBeforeClose && !(await this.options.onBeforeClose())) {
+            return;
+        }
+        this.element.close();
+    }
+    onDialogElementClose() {
+        document.body.classList.remove("rgthree-dialog-open");
+        this.element.remove();
+        this.dispatchEvent(new CustomEvent("close", this.getCloseEventDetail()));
+    }
+    getCloseEventDetail() {
+        return { detail: null };
+    }
+}
+export class RgthreeHelpDialog extends RgthreeDialog {
+    constructor(node, content, opts = {}) {
+        const title = (node.type || node.title || "").replace(/\s*\(rgthree\).*/, " <small>by rgthree</small>");
+        const options = Object.assign({}, opts, {
+            class: "-iconed -help",
+            title,
+            content,
+        });
+        super(options);
+    }
+}
diff --git a/custom_nodes/rgthree-comfy/web/common/link_fixer.js b/custom_nodes/rgthree-comfy/web/common/link_fixer.js
new file mode 100644
index 0000000000000000000000000000000000000000..eed3eee06e68f98f9da069e4b8dc7bbc225484f4
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/common/link_fixer.js
@@ -0,0 +1,365 @@
+var IoDirection;
+(function (IoDirection) {
+    IoDirection[IoDirection["INPUT"] = 0] = "INPUT";
+    IoDirection[IoDirection["OUTPUT"] = 1] = "OUTPUT";
+})(IoDirection || (IoDirection = {}));
+function getLinksData(links) {
+    if (links instanceof Map) {
+        const data = [];
+        for (const [key, llink] of links.entries()) {
+            if (!llink)
+                continue;
+            data.push(llink);
+        }
+        return data;
+    }
+    if (!Array.isArray(links)) {
+        const data = [];
+        for (const key in links) {
+            const llink = (links.hasOwnProperty(key) && links[key]) || null;
+            if (!llink)
+                continue;
+            data.push(llink);
+        }
+        return data;
+    }
+    return links.map((link) => ({
+        id: link[0],
+        origin_id: link[1],
+        origin_slot: link[2],
+        target_id: link[3],
+        target_slot: link[4],
+        type: link[5],
+    }));
+}
+export class WorkflowLinkFixer {
+    static create(graph) {
+        if (typeof graph.getNodeById === "function") {
+            return new WorkflowLinkFixerGraph(graph);
+        }
+        return new WorkflowLinkFixerSerialized(graph);
+    }
+    constructor(graph) {
+        this.silent = false;
+        this.checkedData = null;
+        this.logger = console;
+        this.patchedNodeSlots = {};
+        this.instructions = [];
+        this.graph = graph;
+    }
+    check(force = false) {
+        var _a, _b;
+        if (this.checkedData && !force) {
+            return { ...this.checkedData };
+        }
+        this.instructions = [];
+        this.patchedNodeSlots = {};
+        const instructions = [];
+        const links = getLinksData(this.graph.links);
+        links.reverse();
+        for (const link of links) {
+            if (!link)
+                continue;
+            const originNode = this.getNodeById(link.origin_id);
+            const originHasLink = () => this.nodeHasLinkId(originNode, IoDirection.OUTPUT, link.origin_slot, link.id);
+            const patchOrigin = (op, id = link.id) => this.getNodePatchInstruction(originNode, IoDirection.OUTPUT, link.origin_slot, id, op);
+            const targetNode = this.getNodeById(link.target_id);
+            const targetHasLink = () => this.nodeHasLinkId(targetNode, IoDirection.INPUT, link.target_slot, link.id);
+            const targetHasAnyLink = () => this.nodeHasAnyLink(targetNode, IoDirection.INPUT, link.target_slot);
+            const patchTarget = (op, id = link.id) => this.getNodePatchInstruction(targetNode, IoDirection.INPUT, link.target_slot, id, op);
+            const originLog = `origin(${link.origin_id}).outputs[${link.origin_slot}].links`;
+            const targetLog = `target(${link.target_id}).inputs[${link.target_slot}].link`;
+            if (!originNode || !targetNode) {
+                if (!originNode && !targetNode) {
+                }
+                else if (!originNode && targetNode) {
+                    this.log(`Link ${link.id} is funky... ` +
+                        `origin ${link.origin_id} does not exist, but target ${link.target_id} does.`);
+                    if (targetHasLink()) {
+                        this.log(` > [PATCH] ${targetLog} does have link, will remove the inputs' link first.`);
+                        instructions.push(patchTarget("REMOVE", -1));
+                    }
+                }
+                else if (!targetNode && originNode) {
+                    this.log(`Link ${link.id} is funky... ` +
+                        `target ${link.target_id} does not exist, but origin ${link.origin_id} does.`);
+                    if (originHasLink()) {
+                        this.log(` > [PATCH] Origin's links' has ${link.id}; will remove the link first.`);
+                        instructions.push(patchOrigin("REMOVE"));
+                    }
+                }
+                continue;
+            }
+            if (targetHasLink() || originHasLink()) {
+                if (!originHasLink()) {
+                    this.log(`${link.id} is funky... ${originLog} does NOT contain it, but ${targetLog} does.`);
+                    this.log(` > [PATCH] Attempt a fix by adding this ${link.id} to ${originLog}.`);
+                    instructions.push(patchOrigin("ADD"));
+                }
+                else if (!targetHasLink()) {
+                    this.log(`${link.id} is funky... ${targetLog} is NOT correct (is ${(_b = (_a = targetNode.inputs) === null || _a === void 0 ? void 0 : _a[link.target_slot]) === null || _b === void 0 ? void 0 : _b.link}), but ${originLog} contains it`);
+                    if (!targetHasAnyLink()) {
+                        this.log(` > [PATCH] ${targetLog} is not defined, will set to ${link.id}.`);
+                        let instruction = patchTarget("ADD");
+                        if (!instruction) {
+                            this.log(` > [PATCH] Nvm, ${targetLog} already patched. Removing ${link.id} from ${originLog}.`);
+                            instruction = patchOrigin("REMOVE");
+                        }
+                        instructions.push(instruction);
+                    }
+                    else {
+                        this.log(` > [PATCH] ${targetLog} is defined, removing ${link.id} from ${originLog}.`);
+                        instructions.push(patchOrigin("REMOVE"));
+                    }
+                }
+            }
+        }
+        for (let link of links) {
+            if (!link)
+                continue;
+            const originNode = this.getNodeById(link.origin_id);
+            const targetNode = this.getNodeById(link.target_id);
+            if (!originNode && !targetNode) {
+                instructions.push({
+                    op: "DELETE",
+                    linkId: link.id,
+                    reason: `Both nodes #${link.origin_id} & #${link.target_id} are removed`,
+                });
+            }
+            if ((!originNode ||
+                !this.nodeHasLinkId(originNode, IoDirection.OUTPUT, link.origin_slot, link.id)) &&
+                (!targetNode ||
+                    !this.nodeHasLinkId(targetNode, IoDirection.INPUT, link.target_slot, link.id))) {
+                instructions.push({
+                    op: "DELETE",
+                    linkId: link.id,
+                    reason: `both origin node #${link.origin_id} ` +
+                        `${!originNode ? "is removed" : `is missing link id output slot ${link.origin_slot}`}` +
+                        `and target node #${link.target_id} ` +
+                        `${!targetNode ? "is removed" : `is missing link id input slot ${link.target_slot}`}.`,
+                });
+                continue;
+            }
+        }
+        this.instructions = instructions.filter((i) => !!i);
+        this.checkedData = {
+            hasBadLinks: !!this.instructions.length,
+            graph: this.graph,
+            patches: this.instructions.filter((i) => !!i.node)
+                .length,
+            deletes: this.instructions.filter((i) => i.op === "DELETE").length,
+        };
+        return { ...this.checkedData };
+    }
+    fix(force = false, times) {
+        var _a, _b, _c, _d, _e, _f, _g;
+        if (!this.checkedData || force) {
+            this.check(force);
+        }
+        let patches = 0;
+        let deletes = 0;
+        for (const instruction of this.instructions) {
+            if (instruction.node) {
+                let { node, slot, linkIdToUse, dir, op } = instruction;
+                if (dir == IoDirection.INPUT) {
+                    node.inputs = node.inputs || [];
+                    const old = (_a = node.inputs[slot]) === null || _a === void 0 ? void 0 : _a.link;
+                    node.inputs[slot] = node.inputs[slot] || {};
+                    node.inputs[slot].link = linkIdToUse;
+                    this.log(`Node #${node.id}: Set link ${linkIdToUse} to input slot ${slot} (was ${old})`);
+                }
+                else if (op === "ADD" && linkIdToUse != null) {
+                    node.outputs = node.outputs || [];
+                    node.outputs[slot] = node.outputs[slot] || {};
+                    node.outputs[slot].links = node.outputs[slot].links || [];
+                    node.outputs[slot].links.push(linkIdToUse);
+                    this.log(`Node #${node.id}: Add link ${linkIdToUse} to output slot #${slot}`);
+                }
+                else if (op === "REMOVE" && linkIdToUse != null) {
+                    if (((_d = (_c = (_b = node.outputs) === null || _b === void 0 ? void 0 : _b[slot]) === null || _c === void 0 ? void 0 : _c.links) === null || _d === void 0 ? void 0 : _d.length) === undefined) {
+                        this.log(`Node #${node.id}: Couldn't remove link ${linkIdToUse} from output slot #${slot}` +
+                            ` because it didn't exist.`);
+                    }
+                    else {
+                        let linkIdIndex = node.outputs[slot].links.indexOf(linkIdToUse);
+                        node.outputs[slot].links.splice(linkIdIndex, 1);
+                        this.log(`Node #${node.id}: Remove link ${linkIdToUse} from output slot #${slot}`);
+                    }
+                }
+                else {
+                    throw new Error("Unhandled Node Instruction");
+                }
+                patches++;
+            }
+            else if (instruction.op === "DELETE") {
+                const wasDeleted = this.deleteGraphLink(instruction.linkId);
+                if (wasDeleted === true) {
+                    this.log(`Link #${instruction.linkId}: Removed workflow link b/c ${instruction.reason}`);
+                }
+                else {
+                    this.log(`Error Link #${instruction.linkId} was not removed!`);
+                }
+                deletes += wasDeleted ? 1 : 0;
+            }
+            else {
+                throw new Error("Unhandled Instruction");
+            }
+        }
+        const newCheck = this.check(force);
+        times = times == null ? 5 : times;
+        let newFix = null;
+        if (newCheck.hasBadLinks && times > 0) {
+            newFix = this.fix(true, times - 1);
+        }
+        return {
+            hasBadLinks: (_e = newFix === null || newFix === void 0 ? void 0 : newFix.hasBadLinks) !== null && _e !== void 0 ? _e : newCheck.hasBadLinks,
+            graph: this.graph,
+            patches: patches + ((_f = newFix === null || newFix === void 0 ? void 0 : newFix.patches) !== null && _f !== void 0 ? _f : 0),
+            deletes: deletes + ((_g = newFix === null || newFix === void 0 ? void 0 : newFix.deletes) !== null && _g !== void 0 ? _g : 0),
+        };
+    }
+    log(...args) {
+        if (this.silent)
+            return;
+        this.logger.log(...args);
+    }
+    getNodePatchInstruction(node, ioDir, slot, linkId, op) {
+        var _a, _b;
+        const nodeId = node.id;
+        this.patchedNodeSlots[nodeId] = this.patchedNodeSlots[nodeId] || {};
+        const patchedNode = this.patchedNodeSlots[nodeId];
+        if (ioDir == IoDirection.INPUT) {
+            patchedNode["inputs"] = patchedNode["inputs"] || {};
+            if (patchedNode["inputs"][slot] !== undefined) {
+                this.log(` > Already set ${nodeId}.inputs[${slot}] to ${patchedNode["inputs"][slot]} Skipping.`);
+                return null;
+            }
+            let linkIdToUse = op === "REMOVE" ? null : linkId;
+            patchedNode["inputs"][slot] = linkIdToUse;
+            return { node, dir: ioDir, op, slot, linkId, linkIdToUse };
+        }
+        patchedNode["outputs"] = patchedNode["outputs"] || {};
+        patchedNode["outputs"][slot] = patchedNode["outputs"][slot] || {
+            links: [...(((_b = (_a = node.outputs) === null || _a === void 0 ? void 0 : _a[slot]) === null || _b === void 0 ? void 0 : _b.links) || [])],
+            changes: {},
+        };
+        if (patchedNode["outputs"][slot]["changes"][linkId] !== undefined) {
+            this.log(` > Already set ${nodeId}.outputs[${slot}] to ${patchedNode["outputs"][slot]}! Skipping.`);
+            return null;
+        }
+        patchedNode["outputs"][slot]["changes"][linkId] = op;
+        if (op === "ADD") {
+            let linkIdIndex = patchedNode["outputs"][slot]["links"].indexOf(linkId);
+            if (linkIdIndex !== -1) {
+                this.log(` > Hmmm.. asked to add ${linkId} but it is already in list...`);
+                return null;
+            }
+            patchedNode["outputs"][slot]["links"].push(linkId);
+            return { node, dir: ioDir, op, slot, linkId, linkIdToUse: linkId };
+        }
+        let linkIdIndex = patchedNode["outputs"][slot]["links"].indexOf(linkId);
+        if (linkIdIndex === -1) {
+            this.log(` > Hmmm.. asked to remove ${linkId} but it doesn't exist...`);
+            return null;
+        }
+        patchedNode["outputs"][slot]["links"].splice(linkIdIndex, 1);
+        return { node, dir: ioDir, op, slot, linkId, linkIdToUse: linkId };
+    }
+    nodeHasLinkId(node, ioDir, slot, linkId) {
+        var _a, _b, _c, _d, _e, _f, _g, _h, _j;
+        const nodeId = node.id;
+        let has = false;
+        if (ioDir === IoDirection.INPUT) {
+            let nodeHasIt = ((_b = (_a = node.inputs) === null || _a === void 0 ? void 0 : _a[slot]) === null || _b === void 0 ? void 0 : _b.link) === linkId;
+            if ((_c = this.patchedNodeSlots[nodeId]) === null || _c === void 0 ? void 0 : _c["inputs"]) {
+                let patchedHasIt = this.patchedNodeSlots[nodeId]["inputs"][slot] === linkId;
+                has = patchedHasIt;
+            }
+            else {
+                has = nodeHasIt;
+            }
+        }
+        else {
+            let nodeHasIt = (_f = (_e = (_d = node.outputs) === null || _d === void 0 ? void 0 : _d[slot]) === null || _e === void 0 ? void 0 : _e.links) === null || _f === void 0 ? void 0 : _f.includes(linkId);
+            if ((_j = (_h = (_g = this.patchedNodeSlots[nodeId]) === null || _g === void 0 ? void 0 : _g["outputs"]) === null || _h === void 0 ? void 0 : _h[slot]) === null || _j === void 0 ? void 0 : _j["changes"][linkId]) {
+                let patchedHasIt = this.patchedNodeSlots[nodeId]["outputs"][slot].links.includes(linkId);
+                has = !!patchedHasIt;
+            }
+            else {
+                has = !!nodeHasIt;
+            }
+        }
+        return has;
+    }
+    nodeHasAnyLink(node, ioDir, slot) {
+        var _a, _b, _c, _d, _e, _f, _g, _h, _j, _k;
+        const nodeId = node.id;
+        let hasAny = false;
+        if (ioDir === IoDirection.INPUT) {
+            let nodeHasAny = ((_b = (_a = node.inputs) === null || _a === void 0 ? void 0 : _a[slot]) === null || _b === void 0 ? void 0 : _b.link) != null;
+            if ((_c = this.patchedNodeSlots[nodeId]) === null || _c === void 0 ? void 0 : _c["inputs"]) {
+                let patchedHasAny = this.patchedNodeSlots[nodeId]["inputs"][slot] != null;
+                hasAny = patchedHasAny;
+            }
+            else {
+                hasAny = !!nodeHasAny;
+            }
+        }
+        else {
+            let nodeHasAny = (_f = (_e = (_d = node.outputs) === null || _d === void 0 ? void 0 : _d[slot]) === null || _e === void 0 ? void 0 : _e.links) === null || _f === void 0 ? void 0 : _f.length;
+            if ((_j = (_h = (_g = this.patchedNodeSlots[nodeId]) === null || _g === void 0 ? void 0 : _g["outputs"]) === null || _h === void 0 ? void 0 : _h[slot]) === null || _j === void 0 ? void 0 : _j["changes"]) {
+                let patchedHasAny = (_k = this.patchedNodeSlots[nodeId]["outputs"][slot].links) === null || _k === void 0 ? void 0 : _k.length;
+                hasAny = !!patchedHasAny;
+            }
+            else {
+                hasAny = !!nodeHasAny;
+            }
+        }
+        return hasAny;
+    }
+}
+class WorkflowLinkFixerSerialized extends WorkflowLinkFixer {
+    constructor(graph) {
+        super(graph);
+    }
+    getNodeById(id) {
+        var _a;
+        return (_a = this.graph.nodes.find((node) => Number(node.id) === id)) !== null && _a !== void 0 ? _a : null;
+    }
+    fix(force = false, times) {
+        const ret = super.fix(force, times);
+        this.graph.links = this.graph.links.filter((l) => !!l);
+        return ret;
+    }
+    deleteGraphLink(id) {
+        const idx = this.graph.links.findIndex((l) => l && (l[0] === id || l.id === id));
+        if (idx === -1) {
+            return `Link #${id} not found in workflow links.`;
+        }
+        this.graph.links.splice(idx, 1);
+        return true;
+    }
+}
+class WorkflowLinkFixerGraph extends WorkflowLinkFixer {
+    constructor(graph) {
+        super(graph);
+    }
+    getNodeById(id) {
+        var _a;
+        return (_a = this.graph.getNodeById(id)) !== null && _a !== void 0 ? _a : null;
+    }
+    deleteGraphLink(id) {
+        if (this.graph.links instanceof Map) {
+            if (!this.graph.links.has(id)) {
+                return `Link #${id} not found in workflow links.`;
+            }
+            this.graph.links.delete(id);
+            return true;
+        }
+        if (this.graph.links[id] == null) {
+            return `Link #${id} not found in workflow links.`;
+        }
+        delete this.graph.links[id];
+        return true;
+    }
+}
diff --git a/custom_nodes/rgthree-comfy/web/common/media/rgthree.svg b/custom_nodes/rgthree-comfy/web/common/media/rgthree.svg
new file mode 100644
index 0000000000000000000000000000000000000000..766a76152eefcdf8d05bf320c329b82afc704eaa
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/common/media/rgthree.svg
@@ -0,0 +1,7 @@
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 256 256" fill="currentColor">
+  <path d="M88.503,158.997 L152.731,196.103 L152.738,196.092 L152.762,196.103 L152.769,196.106 L152.771,196.103 L183.922,142.084     L174.153,136.437 L148.611,180.676 L101.512,153.484 L132.193,30.415 L156.124,71.869 L165.896,66.225 L128.002,0.59 z"></path>
+  <path d="M55.586,148.581l13.44,47.521l0.014,0.051l0.168-0.051l10.689-3.022l-6.589-23.313l45.609,26.335l0.087,0.051l0.027-0.051     l5.617-9.718l-42.648-24.622l35.771-143.45L33.232,164.729l9.77,5.645L55.586,148.581z M87.394,93.484l-16.708,67.018l-5.018-17.747     l-8.028,2.27L87.394,93.484z"></path>
+  <path d="M189.85,107.717 L137.892,137.718 L143.532,147.49 L185.723,123.133 L231.109,201.746 L24.895,201.746 L37.363,180.146     L27.592,174.505 L5.347,213.03 L250.653,213.03 z"></path>
+  <path d="M5.347,247.299v8.111h245.307v-8.111l-41.94-0.003c-1.336,0-2.404-1.065-2.441-2.396v-12.14     c0.037-1.315,1.089-2.368,2.41-2.385h41.972v-8.11H5.347v8.11h41.951c1.338,0.017,2.427,1.104,2.427,2.449v12.01     c0,1.365-1.105,2.462-2.457,2.462L5.347,247.299z M139.438,247.296c-1.334,0-2.406-1.065-2.439-2.396v-12.14     c0.033-1.315,1.085-2.368,2.41-2.385h46.415c1.335,0.017,2.425,1.104,2.425,2.449v12.01c0,1.365-1.103,2.462-2.459,2.462H139.438z       M70.193,247.296c-1.339,0-2.408-1.065-2.441-2.396v-12.14c0.033-1.315,1.086-2.368,2.407-2.385h46.418     c1.336,0.017,2.425,1.104,2.425,2.449v12.01c0,1.365-1.103,2.462-2.458,2.462H70.193z"></path>
+</svg>
\ No newline at end of file
diff --git a/custom_nodes/rgthree-comfy/web/common/media/svgs.js b/custom_nodes/rgthree-comfy/web/common/media/svgs.js
new file mode 100644
index 0000000000000000000000000000000000000000..2bd25227abc482dc6bfd006c211ab856a398fa80
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/common/media/svgs.js
@@ -0,0 +1,183 @@
+import { createElement as $el } from "../utils_dom.js";
+export let logoRgthree = "";
+export async function logoRgthreeAsync() {
+    var _a;
+    if (logoRgthree)
+        return logoRgthree;
+    let baseUrl = null;
+    if (window.location.pathname.includes("/rgthree/")) {
+        const parts = (_a = window.location.pathname.split("/rgthree/")[1]) === null || _a === void 0 ? void 0 : _a.split("/");
+        if (parts && parts.length) {
+            baseUrl = parts.map(() => "../").join("") + "rgthree";
+        }
+    }
+    baseUrl = baseUrl || "./rgthree";
+    return fetch(`${baseUrl}/logo_markup.svg?fg=currentColor&cssClass=rgthree-logo&w=auto&h=auto`)
+        .then((r) => r.text())
+        .then((t) => {
+        if (t.length < 100) {
+            t = `<svg viewBox="0 0 256 256" fill="currentColor" class="rgthree-logo">
+              <path d="M88.503,158.997 L152.731,196.103 L152.738,196.092 L152.762,196.103 L152.769,196.106 L152.771,196.103 L183.922,142.084     L174.153,136.437 L148.611,180.676 L101.512,153.484 L132.193,30.415 L156.124,71.869 L165.896,66.225 L128.002,0.59 "></path>
+              <path d="M55.586,148.581l13.44,47.521l0.014,0.051l0.168-0.051l10.689-3.022l-6.589-23.313l45.609,26.335l0.087,0.051l0.027-0.051     l5.617-9.718l-42.648-24.622l35.771-143.45L33.232,164.729l9.77,5.645L55.586,148.581z M87.394,93.484l-16.708,67.018l-5.018-17.747     l-8.028,2.27L87.394,93.484z"></path>
+              <path d="M189.85,107.717 L137.892,137.718 L143.532,147.49 L185.723,123.133 L231.109,201.746 L24.895,201.746 L37.363,180.146     L27.592,174.505 L5.347,213.03 L250.653,213.03 "></path>
+              <path d="M5.347,247.299v8.111h245.307v-8.111l-41.94-0.003c-1.336,0-2.404-1.065-2.441-2.396v-12.14     c0.037-1.315,1.089-2.368,2.41-2.385h41.972v-8.11H5.347v8.11h41.951c1.338,0.017,2.427,1.104,2.427,2.449v12.01     c0,1.365-1.105,2.462-2.457,2.462L5.347,247.299z M139.438,247.296c-1.334,0-2.406-1.065-2.439-2.396v-12.14     c0.033-1.315,1.085-2.368,2.41-2.385h46.415c1.335,0.017,2.425,1.104,2.425,2.449v12.01c0,1.365-1.103,2.462-2.459,2.462H139.438z       M70.193,247.296c-1.339,0-2.408-1.065-2.441-2.396v-12.14c0.033-1.315,1.086-2.368,2.407-2.385h46.418     c1.336,0.017,2.425,1.104,2.425,2.449v12.01c0,1.365-1.103,2.462-2.458,2.462H70.193z"></path>
+            </svg>`;
+        }
+        logoRgthree = t;
+        return t;
+    });
+}
+logoRgthreeAsync();
+export const github = `<svg viewBox="0 0 16 16" fill="currentColor" class="github-logo">
+    <path d="M8 0c4.42 0 8 3.58 8 8a8.013 8.013 0 0 1-5.45 7.59c-.4.08-.55-.17-.55-.38 0-.27.01-1.13.01-2.2 0-.75-.25-1.23-.54-1.48 1.78-.2 3.65-.88 3.65-3.95 0-.88-.31-1.59-.82-2.15.08-.2.36-1.02-.08-2.12 0 0-.67-.22-2.2.82-.64-.18-1.32-.27-2-.27-.68 0-1.36.09-2 .27-1.53-1.03-2.2-.82-2.2-.82-.44 1.1-.16 1.92-.08 2.12-.51.56-.82 1.28-.82 2.15 0 3.06 1.86 3.75 3.64 3.95-.23.2-.44.55-.51 1.07-.46.21-1.61.55-2.33-.66-.15-.24-.6-.83-1.23-.82-.67.01-.27.38.01.53.34.19.73.9.82 1.13.16.45.68 1.31 2.69.94 0 .67.01 1.3.01 1.49 0 .21-.15.45-.55.38A7.995 7.995 0 0 1 0 8c0-4.42 3.58-8 8-8Z"></path>
+  </svg>`;
+export const iconStarFilled = `<svg viewBox="0 0 16 16" fill="currentColor" class="github-star">
+    <path d="M8 .25a.75.75 0 0 1 .673.418l1.882 3.815 4.21.612a.75.75 0 0 1 .416 1.279l-3.046 2.97.719 4.192a.751.751 0 0 1-1.088.791L8 12.347l-3.766 1.98a.75.75 0 0 1-1.088-.79l.72-4.194L.818 6.374a.75.75 0 0 1 .416-1.28l4.21-.611L7.327.668A.75.75 0 0 1 8 .25Z"></path>
+  </svg>`;
+export const iconReplace = `<svg viewBox="0 0 52 52" fill="currentColor">
+    <path d="M20,37.5c0-0.8-0.7-1.5-1.5-1.5h-15C2.7,36,2,36.7,2,37.5v11C2,49.3,2.7,50,3.5,50h15c0.8,0,1.5-0.7,1.5-1.5 V37.5z"/>
+    <path d="M8.1,22H3.2c-1,0-1.5,0.9-0.9,1.4l8,8.3c0.4,0.3,1,0.3,1.4,0l8-8.3c0.6-0.6,0.1-1.4-0.9-1.4h-4.7 c0-5,4.9-10,9.9-10V6C15,6,8.1,13,8.1,22z"/>
+    <path d="M41.8,20.3c-0.4-0.3-1-0.3-1.4,0l-8,8.3c-0.6,0.6-0.1,1.4,0.9,1.4h4.8c0,6-4.1,10-10.1,10v6 c9,0,16.1-7,16.1-16H49c1,0,1.5-0.9,0.9-1.4L41.8,20.3z"/>
+    <path d="M50,3.5C50,2.7,49.3,2,48.5,2h-15C32.7,2,32,2.7,32,3.5v11c0,0.8,0.7,1.5,1.5,1.5h15c0.8,0,1.5-0.7,1.5-1.5 V3.5z"/>
+  </svg>`;
+export const iconNode = `<svg viewBox="0 -0.5 25 25" fill="none">
+    <path fill-rule="evenodd" clip-rule="evenodd" d="M15.5 19H9.5C7.29086 19 5.5 17.2091 5.5 15V9C5.5 6.79086 7.29086 5 9.5 5H15.5C17.7091 5 19.5 6.79086 19.5 9V15C19.5 17.2091 17.7091 19 15.5 19Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/>
+    <path d="M19.5 9.75C19.9142 9.75 20.25 9.41421 20.25 9C20.25 8.58579 19.9142 8.25 19.5 8.25V9.75ZM5.5 8.25C5.08579 8.25 4.75 8.58579 4.75 9C4.75 9.41421 5.08579 9.75 5.5 9.75V8.25ZM11.5 14.25C11.0858 14.25 10.75 14.5858 10.75 15C10.75 15.4142 11.0858 15.75 11.5 15.75V14.25ZM13.5 15.75C13.9142 15.75 14.25 15.4142 14.25 15C14.25 14.5858 13.9142 14.25 13.5 14.25V15.75ZM19.5 8.25H5.5V9.75H19.5V8.25ZM11.5 15.75H13.5V14.25H11.5V15.75Z" fill="currentColor" />
+  </svg>`;
+export const iconGear = `<svg viewBox="0 0 24 24" fill="currentColor">
+    <path fill-rule="evenodd" clip-rule="evenodd" d="M12.7848 0.449982C13.8239 0.449982 14.7167 1.16546 14.9122 2.15495L14.9991 2.59495C15.3408 4.32442 17.1859 5.35722 18.9016 4.7794L19.3383 4.63233C20.3199 4.30175 21.4054 4.69358 21.9249 5.56605L22.7097 6.88386C23.2293 7.75636 23.0365 8.86366 22.2504 9.52253L21.9008 9.81555C20.5267 10.9672 20.5267 13.0328 21.9008 14.1844L22.2504 14.4774C23.0365 15.1363 23.2293 16.2436 22.7097 17.1161L21.925 18.4339C21.4054 19.3064 20.3199 19.6982 19.3382 19.3676L18.9017 19.2205C17.1859 18.6426 15.3408 19.6754 14.9991 21.405L14.9122 21.845C14.7167 22.8345 13.8239 23.55 12.7848 23.55H11.2152C10.1761 23.55 9.28331 22.8345 9.08781 21.8451L9.00082 21.4048C8.65909 19.6754 6.81395 18.6426 5.09822 19.2205L4.66179 19.3675C3.68016 19.6982 2.59465 19.3063 2.07505 18.4338L1.2903 17.1161C0.770719 16.2436 0.963446 15.1363 1.74956 14.4774L2.09922 14.1844C3.47324 13.0327 3.47324 10.9672 2.09922 9.8156L1.74956 9.52254C0.963446 8.86366 0.77072 7.75638 1.2903 6.8839L2.07508 5.56608C2.59466 4.69359 3.68014 4.30176 4.66176 4.63236L5.09831 4.77939C6.81401 5.35722 8.65909 4.32449 9.00082 2.59506L9.0878 2.15487C9.28331 1.16542 10.176 0.449982 11.2152 0.449982H12.7848ZM12 15.3C13.8225 15.3 15.3 13.8225 15.3 12C15.3 10.1774 13.8225 8.69998 12 8.69998C10.1774 8.69998 8.69997 10.1774 8.69997 12C8.69997 13.8225 10.1774 15.3 12 15.3Z" />
+  </svg>`;
+export const checkmark = `<svg viewBox="0 0 32 32" fill="currentColor" class="icon-checkmark">
+    <g transform="translate(-518.000000, -1039.000000)">
+      <path d="M548.783,1040.2 C547.188,1038.57 544.603,1038.57 543.008,1040.2 L528.569,1054.92 L524.96,1051.24 C523.365,1049.62 520.779,1049.62 519.185,1051.24 C517.59,1052.87 517.59,1055.51 519.185,1057.13 L525.682,1063.76 C527.277,1065.39 529.862,1065.39 531.457,1063.76 L548.783,1046.09 C550.378,1044.46 550.378,1041.82 548.783,1040.2"></path>
+    </g>
+  </svg>`;
+export const logoCivitai = `<svg viewBox="0 0 178 178" class="logo-civitai">
+    <defs>
+      <linearGradient id="bgblue" gradientUnits="userSpaceOnUse" x1="89.3" y1="-665.5" x2="89.3" y2="-841.1" gradientTransform="matrix(1 0 0 -1 0 -664)">
+        <stop offset="0" style="stop-color:#1284F7"/>
+        <stop offset="1" style="stop-color:#0A20C9"/>
+      </linearGradient>
+    </defs>
+    <path fill="#000" d="M13.3,45.4v87.7l76,43.9l76-43.9V45.4l-76-43.9L13.3,45.4z"/>
+    <path style="fill:url(#bgblue);" d="M89.3,29.2l52,30v60l-52,30l-52-30v-60 L89.3,29.2 M89.3,1.5l-76,43.9v87.8l76,43.9l76-43.9V45.4L89.3,1.5z" />
+    <path fill="#FFF" d="M104.1,97.2l-14.9,8.5l-14.9-8.5v-17l14.9-8.5l14.9,8.5h18.2V69.7l-33-19l-33,19v38.1l33,19l33-19V97.2H104.1z" />
+  </svg>`;
+export const iconOutLink = `<svg viewBox="0 0 32 32">
+    <path d="M 18 5 L 18 7 L 23.5625 7 L 11.28125 19.28125 L 12.71875 20.71875 L 25 8.4375 L 25 14 L 27 14 L 27 5 Z M 5 9 L 5 27 L 23 27 L 23 14 L 21 16 L 21 25 L 7 25 L 7 11 L 16 11 L 18 9 Z"></path>
+  </svg>`;
+export const link = `<svg viewBox="0 0 640 512">
+    <path d="M598.6 41.41C570.1 13.8 534.8 0 498.6 0s-72.36 13.8-99.96 41.41l-43.36 43.36c15.11 8.012 29.47 17.58 41.91 30.02c3.146 3.146 5.898 6.518 8.742 9.838l37.96-37.96C458.5 72.05 477.1 64 498.6 64c20.67 0 40.1 8.047 54.71 22.66c14.61 14.61 22.66 34.04 22.66 54.71s-8.049 40.1-22.66 54.71l-133.3 133.3C405.5 343.1 386 352 365.4 352s-40.1-8.048-54.71-22.66C296 314.7 287.1 295.3 287.1 274.6s8.047-40.1 22.66-54.71L314.2 216.4C312.1 212.5 309.9 208.5 306.7 205.3C298.1 196.7 286.8 192 274.6 192c-11.93 0-23.1 4.664-31.61 12.97c-30.71 53.96-23.63 123.6 22.39 169.6C293 402.2 329.2 416 365.4 416c36.18 0 72.36-13.8 99.96-41.41L598.6 241.3c28.45-28.45 42.24-66.01 41.37-103.3C639.1 102.1 625.4 68.16 598.6 41.41zM234 387.4L196.1 425.3C181.5 439.1 162 448 141.4 448c-20.67 0-40.1-8.047-54.71-22.66c-14.61-14.61-22.66-34.04-22.66-54.71s8.049-40.1 22.66-54.71l133.3-133.3C234.5 168 253.1 160 274.6 160s40.1 8.048 54.71 22.66c14.62 14.61 22.66 34.04 22.66 54.71s-8.047 40.1-22.66 54.71L325.8 295.6c2.094 3.939 4.219 7.895 7.465 11.15C341.9 315.3 353.3 320 365.4 320c11.93 0 23.1-4.664 31.61-12.97c30.71-53.96 23.63-123.6-22.39-169.6C346.1 109.8 310.8 96 274.6 96C238.4 96 202.3 109.8 174.7 137.4L41.41 270.7c-27.6 27.6-41.41 63.78-41.41 99.96c-.0001 36.18 13.8 72.36 41.41 99.97C69.01 498.2 105.2 512 141.4 512c36.18 0 72.36-13.8 99.96-41.41l43.36-43.36c-15.11-8.012-29.47-17.58-41.91-30.02C239.6 394.1 236.9 390.7 234 387.4z"/>
+  </svg>`;
+export const pencil = `<svg viewBox="0 0 24 24">
+    <path d="M 16.9375 1.0625 L 3.875 14.125 L 1.0742188 22.925781 L 9.875 20.125 L 22.9375 7.0625 C 22.9375 7.0625 22.8375 4.9615 20.9375 3.0625 C 19.0375 1.1625 16.9375 1.0625 16.9375 1.0625 z M 17.3125 2.6875 C 18.3845 2.8915 19.237984 3.3456094 19.896484 4.0214844 C 20.554984 4.6973594 21.0185 5.595 21.3125 6.6875 L 19.5 8.5 L 15.5 4.5 L 16.9375 3.0625 L 17.3125 2.6875 z M 4.9785156 15.126953 C 4.990338 15.129931 6.1809555 15.430955 7.375 16.625 C 8.675 17.825 8.875 18.925781 8.875 18.925781 L 8.9179688 18.976562 L 5.3691406 20.119141 L 3.8730469 18.623047 L 4.9785156 15.126953 z"/>
+  </svg>`;
+export const dotdotdot = `<svg viewBox="0 0 24 24" fill="currentColor">
+  <circle cy="12" r="3" cx="3"></circle>
+  <circle cy="12" r="3" cx="12"></circle>
+  <circle cx="21" cy="12" r="3"></circle>
+</svg>`;
+export const models = `<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
+    <path d="M4 4h6v6h-6z"></path>
+<path d="M14 4h6v6h-6z"></path>
+<path d="M4 14h6v6h-6z"></path>
+<path d="M17 17m-3 0a3 3 0 1 0 6 0a3 3 0 1 0 -6 0"></path>
+</svg>`;
+export const pencilColored = `<svg viewBox="0 0 64 64">
+    <path fill="#ffce31" d="M7.934 41.132L39.828 9.246l14.918 14.922l-31.895 31.886z"></path>
+    <path d="M61.3 4.6l-1.9-1.9C55.8-.9 50-.9 46.3 2.7l-6.5 6.5l15 15l6.5-6.5c3.6-3.6 3.6-9.5 0-13.1" fill="#ed4c5c"></path>
+    <path fill="#93a2aa" d="M35.782 13.31l4.1-4.102l14.92 14.92l-4.1 4.101z"></path>
+    <path fill="#c7d3d8" d="M37.338 14.865l4.1-4.101l11.739 11.738l-4.102 4.1z"></path>
+    <path fill="#fed0ac" d="M7.9 41.1l-6.5 17l4.5 4.5l17-6.5z"/>
+    <path d="M.3 61.1c-.9 2.4.3 3.5 2.7 2.6l8.2-3.1l-7.7-7.7l-3.2 8.2" fill="#333"></path>
+    <path fill="#ffdf85" d="M7.89 41.175l27.86-27.86l4.95 4.95l-27.86 27.86z"/>
+    <path fill="#ff8736" d="M17.904 51.142l27.86-27.86l4.95 4.95l-27.86 27.86z"></path>
+  </svg>`;
+export const diskColored = `<svg viewBox="-0.01 -0.008 100.016 100.016">
+  <path fill="#26f" fill_="#23475F" d="M88.555-.008H83v.016a2 2 0 0 1-2 2H19a2 2 0 0 1-2-2v-.016H4a4 4 0 0 0-4 4v92.016a4 4 0 0 0 4 4h92a4 4 0 0 0 4-4V11.517c.049-.089-11.436-11.454-11.445-11.525z"/>
+  <path fill="#04d" fill_="#1C3C50" d="M81.04 53.008H18.96a2 2 0 0 0-2 2v45h66.08v-45c0-1.106-.895-2-2-2zm-61.957-10h61.834a2 2 0 0 0 2-2V.547A1.993 1.993 0 0 1 81 2.007H19c-.916 0-1.681-.62-1.917-1.46v40.46a2 2 0 0 0 2 2.001z"/>
+  <path fill="#EBF0F1" d="M22 55.977h56a2 2 0 0 1 2 2v37.031a2 2 0 0 1-2 2H22c-1.104 0-2-.396-2-1.5V57.977a2 2 0 0 1 2-2z"/>
+  <path fill="#BCC4C8" d="M25 77.008h50v1H25v-1zm0 10h50v1H25v-1z"/>
+  <path fill="#1C3C50" d="M7 84.008h3a2 2 0 0 1 2 2v3a2 2 0 0 1-2 2H7a2 2 0 0 1-2-2v-3a2 2 0 0 1 2-2zm83 0h3a2 2 0 0 1 2 2v3a2 2 0 0 1-2 2h-3a2 2 0 0 1-2-2v-3a2 2 0 0 1 2-2z"/>
+  <path fill="#BCC4C8" d="M37 1.981v36.026a2 2 0 0 0 2 2h39a2 2 0 0 0 2-2V1.981c0 .007-42.982.007-43 0zm37 29.027a2 2 0 0 1-2 2h-6a2 2 0 0 1-2-2V10.981a2 2 0 0 1 2-2h6a2 2 0 0 1 2 2v20.027z"/>
+  <path fill="#FF9D00" d="M78 55.977H22a2 2 0 0 0-2 2v10.031h60V57.977a2 2 0 0 0-2-2z"/>
+</svg>`;
+export const folderColored = `<svg viewBox="0 0 501.379 501.379">
+  <path style="fill:#EF9F2C;" d="M406.423,93.889H205.889c-17.067,0-30.933-13.867-30.933-30.933s-13.867-30.933-30.933-30.933H30.956
+      c-17.067,0-30.933,13.867-30.933,30.933v375.467c0,17.067,13.867,30.933,30.933,30.933h375.467
+      c17.067,0,30.933-13.867,30.933-30.933v-313.6C436.289,107.756,422.423,93.889,406.423,93.889z"/>
+  <path style="fill:#FEC656;" d="M470.423,157.889H97.089c-13.867,0-26.667,9.6-29.867,22.4l-66.133,249.6
+    c-5.333,19.2,9.6,38.4,29.867,38.4h373.333c13.867,0,26.667-9.6,29.867-22.4l66.133-248.533
+    C505.623,177.089,490.689,157.889,470.423,157.889z"/>
+</svg>`;
+export const modelsColored = `<svg viewBox="0 0 24 24">
+  <path fill="#aa3366" d="M0 0h10v10h-10z"></path>
+  <path d="M14 0h10v10h-10z" fill="#3366aa"></path>
+  <path d="M0 14h10v10h-10z" fill="#66aa33"></path>
+  <path fill="#dd9922" d="M19 19m-5 0 a5 5 0 1 0 10 0 a5 5 0 1 0 -10 0"></path>
+</svg>`;
+export const legoBlocksColored = `<svg viewBox="0 0 512 512">
+  <g>
+    <rect x="57.67" style="fill:#00BAB9;" width="101.275" height="78.769"/>
+    <rect x="205.363" style="fill:#00BAB9;" width="101.275" height="78.769"/>
+    <rect x="353.055" style="fill:#00BAB9;" width="101.275" height="78.769"/>
+  </g>
+  <polygon style="fill:#B8DE6F;" points="478.242,289.758 478.242,512 33.758,512 33.758,289.758 256,267.253 "/>
+  <polygon style="fill:#41D4D3;" points="478.242,67.516 478.242,289.758 33.758,289.758 33.758,67.516 57.67,67.516 158.945,67.516
+    205.363,67.516 306.637,67.516 353.055,67.516 454.33,67.516 "/>
+  <g>
+    <circle style="fill:#00BAB9;" cx="402.286" cy="143.473" r="8.44"/>
+    <circle style="fill:#00BAB9;" cx="368.527" cy="177.231" r="8.44"/>
+  </g>
+  <circle style="fill:#7BD288;" cx="109.714" cy="436.044" r="8.44"/>
+</svg>`;
+export const legoBlockColored = `<svg viewBox="0 0 256 256">
+  <style>
+  .s0 { fill: #ff0000 }
+  .s1 { fill: #c30000 }
+  .s2 { fill: #800000 }
+  .s3 { fill: #cc0000 }
+  .s4 { fill: #e00000 }
+  </style>
+  <g id="Folder 2">
+  <path id="Shape 1 copy 2" class="s0" d="m128 61l116 45-116 139-116-139z"/>
+  <path id="Shape 1" class="s1" d="m12 106l116 45v95l-116-45z"/>
+  <path id="Shape 1 copy" class="s2" d="m244 106l-116 45v95l116-45z"/>
+  <g id="Folder 1">
+    <path id="Shape 2" class="s3" d="m102 111.2c0-6.1 11.4-9.9 25.5-9.9 14.1 0 25.5 3.8 25.5 9.9 0 3.3 0 13.3 0 16.6 0 6.1-11.4 10.9-25.5 10.9-14.1 0-25.5-4.8-25.5-10.9 0-3.3 0-13.3 0-16.6z"/>
+    <path id="Shape 2 copy 4" class="s1" d="m102 111.2c0-6.1 11.4-9.9 25.5-9.9 14.1 0 25.5 3.8 25.5 9.9 0 3.3 0 13.3 0 16.6 0 6.1-11.4 10.9-25.5 10.9-14.1 0-25.5-4.8-25.5-10.9 0-3.3 0-13.3 0-16.6z"/>
+    <path id="Shape 2 copy 2" class="s2" d="m127.5 101.3c14.1 0 25.5 3.8 25.5 9.9 0 3.3 0 13.3 0 16.6 0 6.1-11.4 10.9-25.5 10.9 0-13.1 0-25.7 0-37.4z"/>
+    <path id="Shape 2 copy" class="s0" d="m127.5 118.8c-12.2 0-22-3.4-22-7.6 0-4.2 9.8-7.7 22-7.7 12.2 0 22 3.5 22 7.7 0 4.2-9.8 7.6-22 7.6zm0 0c-12.2 0-22-3.4-22-7.6 0-4.2 9.8-7.7 22-7.7 12.2 0 22 3.5 22 7.7 0 4.2-9.8 7.6-22 7.6z"/>
+  </g>
+  <g id="Folder 1 copy">
+    <path id="Shape 2" class="s4" d="m103 67.5c0-5.8 11-9.5 24.5-9.5 13.5 0 24.5 3.7 24.5 9.5 0 3.2 0 12.8 0 16 0 5.8-11 10.5-24.5 10.5-13.5 0-24.5-4.7-24.5-10.5 0-3.2 0-12.8 0-16z"/>
+    <path id="Shape 2 copy 4" class="s1" d="m103 67.5c0-5.8 11-9.5 24.5-9.5 13.5 0 24.5 3.7 24.5 9.5 0 3.2 0 12.8 0 16 0 5.8-11 10.5-24.5 10.5-13.5 0-24.5-4.7-24.5-10.5 0-3.2 0-12.8 0-16z"/>
+    <path id="Shape 2 copy 2" class="s2" d="m127.5 58c13.5 0 24.5 3.7 24.5 9.5 0 3.2 0 12.8 0 16 0 5.8-11 10.5-24.5 10.5 0-12.6 0-24.8 0-36z"/>
+    <path id="Shape 2 copy" class="s0" d="m127.5 74.9c-11.7 0-21.2-3.3-21.2-7.4 0-4.1 9.5-7.4 21.2-7.4 11.7 0 21.2 3.3 21.2 7.4 0 4.1-9.5 7.4-21.2 7.4zm0 0c-11.7 0-21.2-3.3-21.2-7.4 0-4.1 9.5-7.4 21.2-7.4 11.7 0 21.2 3.3 21.2 7.4 0 4.1-9.5 7.4-21.2 7.4z"/>
+  </g>
+  <g id="Folder 1 copy 2">
+    <path id="Shape 2" class="s4" d="m161 89.5c0-5.8 11-9.5 24.5-9.5 13.5 0 24.5 3.7 24.5 9.5 0 3.2 0 12.8 0 16 0 5.8-11 10.5-24.5 10.5-13.5 0-24.5-4.7-24.5-10.5 0-3.2 0-12.8 0-16z"/>
+    <path id="Shape 2 copy 4" class="s1" d="m161 89.5c0-5.8 11-9.5 24.5-9.5 13.5 0 24.5 3.7 24.5 9.5 0 3.2 0 12.8 0 16 0 5.8-11 10.5-24.5 10.5-13.5 0-24.5-4.7-24.5-10.5 0-3.2 0-12.8 0-16z"/>
+    <path id="Shape 2 copy 2" class="s2" d="m185.5 80c13.5 0 24.5 3.7 24.5 9.5 0 3.2 0 12.8 0 16 0 5.8-11 10.5-24.5 10.5 0-12.6 0-24.8 0-36z"/>
+    <path id="Shape 2 copy" class="s0" d="m185.5 96.9c-11.7 0-21.2-3.3-21.2-7.4 0-4.1 9.5-7.4 21.2-7.4 11.7 0 21.2 3.3 21.2 7.4 0 4.1-9.5 7.4-21.2 7.4zm0 0c-11.7 0-21.2-3.3-21.2-7.4 0-4.1 9.5-7.4 21.2-7.4 11.7 0 21.2 3.3 21.2 7.4 0 4.1-9.5 7.4-21.2 7.4z"/>
+  </g>
+  <g id="Folder 1 copy 3">
+    <path id="Shape 2" class="s4" d="m45 89.5c0-5.8 11-9.5 24.5-9.5 13.5 0 24.5 3.7 24.5 9.5 0 3.2 0 12.8 0 16 0 5.8-11 10.5-24.5 10.5-13.5 0-24.5-4.7-24.5-10.5 0-3.2 0-12.8 0-16z"/>
+    <path id="Shape 2 copy 4" class="s1" d="m45 89.5c0-5.8 11-9.5 24.5-9.5 13.5 0 24.5 3.7 24.5 9.5 0 3.2 0 12.8 0 16 0 5.8-11 10.5-24.5 10.5-13.5 0-24.5-4.7-24.5-10.5 0-3.2 0-12.8 0-16z"/>
+    <path id="Shape 2 copy 2" class="s2" d="m69.5 80c13.5 0 24.5 3.7 24.5 9.5 0 3.2 0 12.8 0 16 0 5.8-11 10.5-24.5 10.5 0-12.6 0-24.8 0-36z"/>
+    <path id="Shape 2 copy" class="s0" d="m69.5 96.9c-11.7 0-21.2-3.3-21.2-7.4 0-4.1 9.5-7.4 21.2-7.4 11.7 0 21.2 3.3 21.2 7.4 0 4.1-9.5 7.4-21.2 7.4zm0 0c-11.7 0-21.2-3.3-21.2-7.4 0-4.1 9.5-7.4 21.2-7.4 11.7 0 21.2 3.3 21.2 7.4 0 4.1-9.5 7.4-21.2 7.4z"/>
+  </g>
+  </g>
+</svg>`;
+export const gearColored = `<svg viewBox="0 0 128 128" preserveAspectRatio="xMidYMid meet">
+  <path d="M124 71.85v-15.7c0-.59-.45-1.09-1.03-1.15l-17.83-1.89c-.47-.05-.85-.38-.98-.83c-.86-2.95-2.03-5.76-3.48-8.39c-.23-.41-.19-.92.11-1.28l11.28-13.94c.37-.46.34-1.13-.08-1.54l-11.1-11.1a1.15 1.15 0 0 0-1.54-.08L85.39 27.22c-.37.3-.87.33-1.28.11a41.796 41.796 0 0 0-8.39-3.48c-.45-.13-.78-.51-.83-.98L73 5.03C72.94 4.45 72.44 4 71.85 4h-15.7c-.59 0-1.09.45-1.15 1.03l-1.89 17.83c-.05.47-.38.85-.83.98c-2.95.86-5.76 2.03-8.39 3.48c-.41.23-.92.19-1.28-.11L28.67 15.94a1.15 1.15 0 0 0-1.54.08l-11.1 11.1a1.15 1.15 0 0 0-.08 1.54L27.23 42.6c.3.37.33.87.11 1.28a41.796 41.796 0 0 0-3.48 8.39c-.13.45-.51.78-.98.83L5.03 55c-.58.06-1.03.56-1.03 1.15v15.7c0 .59.45 1.09 1.03 1.15l17.83 1.89c.47.05.85.38.98.83c.86 2.95 2.03 5.76 3.48 8.39c.23.41.19.92-.11 1.28L15.94 99.33c-.37.46-.34 1.13.08 1.54l11.1 11.1c.42.42 1.08.45 1.54.08l13.94-11.28c.37-.3.87-.33 1.28-.11c2.64 1.45 5.45 2.62 8.39 3.48c.45.13.78.51.83.98l1.9 17.85c.06.59.56 1.03 1.15 1.03h15.7c.59 0 1.09-.45 1.15-1.03l1.89-17.83c.05-.47.38-.85.83-.98c2.95-.86 5.76-2.03 8.39-3.48c.41-.23.92-.19 1.28.11l13.94 11.28c.46.37 1.13.34 1.54-.08l11.1-11.1c.42-.42.45-1.08.08-1.54l-11.28-13.94c-.3-.37-.33-.87-.11-1.28c1.45-2.64 2.62-5.45 3.48-8.39c.13-.45.51-.78.98-.83L122.97 73c.58-.06 1.03-.56 1.03-1.15zm-60 3.43c-6.23 0-11.28-5.05-11.28-11.28S57.77 52.72 64 52.72S75.28 57.77 75.28 64S70.23 75.28 64 75.28z" fill="#82aec0"></path>
+  <path d="M80.56 49.48c3.67 4.18 5.78 9.77 5.43 15.85c-.65 11.16-9.83 20.19-21 20.68c-4.75.21-9.18-1.09-12.86-3.45c-.28-.18-.58.2-.34.44a22.412 22.412 0 0 0 17.85 6.67c10.78-.85 19.56-9.5 20.55-20.27c.77-8.36-3.06-15.87-9.23-20.33c-.29-.2-.62.15-.4.41z" fill="#2f7889"></path>
+  <path d="M43.87 65.32c-.67-13.15 7.83-22.79 20.01-22.79c.65 0 1.68 0 2.48.92c1.01 1.18 1.1 2.6 0 3.77c-.81.86-1.95.92-2.53 1c-12.3 1.59-15.18 9.35-15.83 16.77c-.03.33.06 2.35-1.71 2.56c-2.15.25-2.41-1.91-2.42-2.23z" fill="#b9e4ea"></path>
+  <path d="M25.24 65.87c-.01-22.03 15.9-40.19 38.13-41.05c.68-.03 2.45 0 3.55.99c1.01.91 1.38 2.51.79 3.82c-.95 2.11-2.85 2.07-3.36 2.09c-18.51.66-34.18 15.73-34.19 33.95c0 .29-.05.58-.15.84l-.1.25c-.76 1.98-3.52 2.09-4.43.18c-.15-.34-.24-.7-.24-1.07z" fill="#94d1e0"></path>
+</svg>`;
+export function $svg(markup, attrs) {
+    if (!markup.match(/^\s*<svg/)) {
+        throw new Error("Cannot call $svg with non-svg markup.");
+    }
+    return $el(markup, attrs || {});
+}
diff --git a/custom_nodes/rgthree-comfy/web/common/menu.js b/custom_nodes/rgthree-comfy/web/common/menu.js
new file mode 100644
index 0000000000000000000000000000000000000000..3e0e81429eb6f513667d9a2555e48a1db2e48dd2
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/common/menu.js
@@ -0,0 +1,98 @@
+import { generateId, wait } from "./shared_utils.js";
+import { createElement as $el, getClosestOrSelf, setAttributes } from "./utils_dom.js";
+class Menu {
+    constructor(options) {
+        this.element = $el('menu.rgthree-menu');
+        this.callbacks = new Map();
+        this.handleWindowPointerDownBound = this.handleWindowPointerDown.bind(this);
+        this.setOptions(options);
+        this.element.addEventListener('pointerup', async (e) => {
+            var _a, _b;
+            const target = getClosestOrSelf(e.target, "[data-callback],menu");
+            if (e.which !== 1) {
+                return;
+            }
+            const callback = (_a = target === null || target === void 0 ? void 0 : target.dataset) === null || _a === void 0 ? void 0 : _a['callback'];
+            if (callback) {
+                const halt = await ((_b = this.callbacks.get(callback)) === null || _b === void 0 ? void 0 : _b(e));
+                if (halt !== false) {
+                    this.close();
+                }
+            }
+            e.preventDefault();
+            e.stopPropagation();
+            e.stopImmediatePropagation();
+        });
+    }
+    setOptions(options) {
+        for (const option of options) {
+            if (option.type === 'title') {
+                this.element.appendChild($el(`li`, {
+                    html: option.label
+                }));
+            }
+            else {
+                const id = generateId(8);
+                this.callbacks.set(id, async (e) => { var _a; return (_a = option === null || option === void 0 ? void 0 : option.callback) === null || _a === void 0 ? void 0 : _a.call(option, e); });
+                this.element.appendChild($el(`li[role="button"][data-callback="${id}"]`, {
+                    html: option.label
+                }));
+            }
+        }
+    }
+    toElement() {
+        return this.element;
+    }
+    async open(e) {
+        const parent = e.target.closest('div,dialog,body');
+        parent.appendChild(this.element);
+        setAttributes(this.element, {
+            style: {
+                left: `${e.clientX + 16}px`,
+                top: `${e.clientY - 16}px`,
+            }
+        });
+        this.element.setAttribute('state', 'measuring-open');
+        await wait(16);
+        const rect = this.element.getBoundingClientRect();
+        if (rect.right > window.innerWidth) {
+            this.element.style.left = `${e.clientX - rect.width - 16}px`;
+            await wait(16);
+        }
+        this.element.setAttribute('state', 'open');
+        setTimeout(() => {
+            window.addEventListener('pointerdown', this.handleWindowPointerDownBound);
+        });
+    }
+    handleWindowPointerDown(e) {
+        if (!this.element.contains(e.target)) {
+            this.close();
+        }
+    }
+    async close() {
+        window.removeEventListener('pointerdown', this.handleWindowPointerDownBound);
+        this.element.setAttribute('state', 'measuring-closed');
+        await wait(16);
+        this.element.setAttribute('state', 'closed');
+        this.element.remove();
+    }
+    isOpen() {
+        return (this.element.getAttribute('state') || '').includes('open');
+    }
+}
+export class MenuButton {
+    constructor(options) {
+        this.element = $el('button.rgthree-button[data-action="open-menu"]');
+        this.options = options;
+        this.element.innerHTML = options.icon;
+        this.menu = new Menu(options.options);
+        this.element.addEventListener('pointerdown', (e) => {
+            if (!this.menu.isOpen()) {
+                this.menu.open(e);
+            }
+        });
+    }
+    toElement() {
+        return this.element;
+    }
+}
diff --git a/custom_nodes/rgthree-comfy/web/common/model_info_service.js b/custom_nodes/rgthree-comfy/web/common/model_info_service.js
new file mode 100644
index 0000000000000000000000000000000000000000..e7ec80824062b5deae01df26fb75feb21dfe097b
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/common/model_info_service.js
@@ -0,0 +1,72 @@
+import { rgthreeApi } from "./rgthree_api.js";
+import { api } from "../../scripts/api.js";
+class BaseModelInfoService extends EventTarget {
+    constructor() {
+        super();
+        this.fileToInfo = new Map();
+        this.init();
+    }
+    init() {
+        api.addEventListener(this.apiRefreshEventString, this.handleAsyncUpdate.bind(this));
+    }
+    async getInfo(file, refresh, light) {
+        if (this.fileToInfo.has(file) && !refresh) {
+            return this.fileToInfo.get(file);
+        }
+        return this.fetchInfo(file, refresh, light);
+    }
+    async refreshInfo(file) {
+        return this.fetchInfo(file, true);
+    }
+    async clearFetchedInfo(file) {
+        await rgthreeApi.clearModelsInfo({ type: this.modelInfoType, files: [file] });
+        this.fileToInfo.delete(file);
+        return null;
+    }
+    async savePartialInfo(file, data) {
+        let info = await rgthreeApi.saveModelInfo(this.modelInfoType, file, data);
+        this.fileToInfo.set(file, info);
+        return info;
+    }
+    handleAsyncUpdate(event) {
+        var _a;
+        const info = (_a = event.detail) === null || _a === void 0 ? void 0 : _a.data;
+        if (info === null || info === void 0 ? void 0 : info.file) {
+            this.setFreshInfo(info.file, info);
+        }
+    }
+    async fetchInfo(file, refresh = false, light = false) {
+        var _a;
+        let info = null;
+        if (!refresh) {
+            info = await rgthreeApi.getModelsInfo({ type: this.modelInfoType, files: [file], light });
+        }
+        else {
+            info = await rgthreeApi.refreshModelsInfo({ type: this.modelInfoType, files: [file] });
+        }
+        info = (_a = info === null || info === void 0 ? void 0 : info[0]) !== null && _a !== void 0 ? _a : null;
+        if (!light) {
+            this.fileToInfo.set(file, info);
+        }
+        return info;
+    }
+    setFreshInfo(file, info) {
+        this.fileToInfo.set(file, info);
+    }
+}
+class LoraInfoService extends BaseModelInfoService {
+    constructor() {
+        super(...arguments);
+        this.apiRefreshEventString = "rgthree-refreshed-loras-info";
+        this.modelInfoType = 'loras';
+    }
+}
+class CheckpointInfoService extends BaseModelInfoService {
+    constructor() {
+        super(...arguments);
+        this.apiRefreshEventString = "rgthree-refreshed-checkpoints-info";
+        this.modelInfoType = 'checkpoints';
+    }
+}
+export const LORA_INFO_SERVICE = new LoraInfoService();
+export const CHECKPOINT_INFO_SERVICE = new CheckpointInfoService();
diff --git a/custom_nodes/rgthree-comfy/web/common/progress_bar.js b/custom_nodes/rgthree-comfy/web/common/progress_bar.js
new file mode 100644
index 0000000000000000000000000000000000000000..e342f0e926f46af0d8b817e01b522d0bc0161260
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/common/progress_bar.js
@@ -0,0 +1,179 @@
+import { SERVICE as PROMPT_SERVICE } from "../common/prompt_service.js";
+import { createElement } from "./utils_dom.js";
+export class RgthreeProgressBar extends HTMLElement {
+    static create() {
+        return document.createElement(RgthreeProgressBar.NAME);
+    }
+    get currentNodeId() {
+        var _a, _b;
+        const prompt = this.currentPromptExecution;
+        const nodeId = ((_a = prompt === null || prompt === void 0 ? void 0 : prompt.errorDetails) === null || _a === void 0 ? void 0 : _a.node_id) || ((_b = prompt === null || prompt === void 0 ? void 0 : prompt.currentlyExecuting) === null || _b === void 0 ? void 0 : _b.nodeId);
+        return nodeId || null;
+    }
+    constructor() {
+        super();
+        this.shadow = null;
+        this.currentPromptExecution = null;
+        this.onProgressUpdateBound = this.onProgressUpdate.bind(this);
+        this.connected = false;
+    }
+    onProgressUpdate(e) {
+        var _a, _b, _c, _d;
+        if (!this.connected)
+            return;
+        const prompt = e.detail.prompt;
+        this.currentPromptExecution = prompt;
+        if (prompt === null || prompt === void 0 ? void 0 : prompt.errorDetails) {
+            let progressText = `${(_a = prompt.errorDetails) === null || _a === void 0 ? void 0 : _a.exception_type} ${((_b = prompt.errorDetails) === null || _b === void 0 ? void 0 : _b.node_id) || ""} ${((_c = prompt.errorDetails) === null || _c === void 0 ? void 0 : _c.node_type) || ""}`;
+            this.progressTextEl.innerText = progressText;
+            this.progressNodesEl.classList.add("-error");
+            this.progressStepsEl.classList.add("-error");
+            return;
+        }
+        if (prompt === null || prompt === void 0 ? void 0 : prompt.currentlyExecuting) {
+            this.progressNodesEl.classList.remove("-error");
+            this.progressStepsEl.classList.remove("-error");
+            const current = prompt === null || prompt === void 0 ? void 0 : prompt.currentlyExecuting;
+            let progressText = `(${e.detail.queue}) `;
+            if (!prompt.totalNodes) {
+                progressText += `??%`;
+                this.progressNodesEl.style.width = `0%`;
+            }
+            else {
+                const percent = (prompt.executedNodeIds.length / prompt.totalNodes) * 100;
+                this.progressNodesEl.style.width = `${Math.max(2, percent)}%`;
+                progressText += `${Math.round(percent)}%`;
+            }
+            let nodeLabel = (_d = current.nodeLabel) === null || _d === void 0 ? void 0 : _d.trim();
+            let stepsLabel = "";
+            if (current.step != null && current.maxSteps) {
+                const percent = (current.step / current.maxSteps) * 100;
+                this.progressStepsEl.style.width = `${percent}%`;
+                if (current.pass > 1 || current.maxPasses != null) {
+                    stepsLabel += `#${current.pass}`;
+                    if (current.maxPasses && current.maxPasses > 0) {
+                        stepsLabel += `/${current.maxPasses}`;
+                    }
+                    stepsLabel += ` - `;
+                }
+                stepsLabel += `${Math.round(percent)}%`;
+            }
+            if (nodeLabel || stepsLabel) {
+                progressText += ` - ${nodeLabel || "???"}${stepsLabel ? ` (${stepsLabel})` : ""}`;
+            }
+            if (!stepsLabel) {
+                this.progressStepsEl.style.width = `0%`;
+            }
+            this.progressTextEl.innerText = progressText;
+        }
+        else {
+            if (e === null || e === void 0 ? void 0 : e.detail.queue) {
+                this.progressTextEl.innerText = `(${e.detail.queue}) Running... in another tab`;
+            }
+            else {
+                this.progressTextEl.innerText = "Idle";
+            }
+            this.progressNodesEl.style.width = `0%`;
+            this.progressStepsEl.style.width = `0%`;
+        }
+    }
+    connectedCallback() {
+        if (!this.connected) {
+            PROMPT_SERVICE.addEventListener("progress-update", this.onProgressUpdateBound);
+            this.connected = true;
+        }
+        if (this.shadow) {
+            this.progressTextEl.innerText = "Idle";
+            this.progressNodesEl.style.width = `0%`;
+            this.progressStepsEl.style.width = `0%`;
+            return;
+        }
+        this.shadow = this.attachShadow({ mode: "open" });
+        const sheet = new CSSStyleSheet();
+        sheet.replaceSync(`
+
+      :host {
+        position: relative;
+        overflow: hidden;
+        box-sizing: border-box;
+        background: var(--rgthree-progress-bg-color);
+        --rgthree-progress-bg-color: rgba(23, 23, 23, 0.9);
+        --rgthree-progress-nodes-bg-color: rgb(0, 128, 0);
+        --rgthree-progress-steps-bg-color: rgb(0, 128, 0);
+        --rgthree-progress-error-bg-color: rgb(128, 0, 0);
+        --rgthree-progress-text-color: #fff;
+      }
+      :host * {
+        box-sizing: inherit;
+      }
+
+      :host > div.bar {
+        background: var(--rgthree-progress-nodes-bg-color);
+        position: absolute;
+        left: 0;
+        top: 0;
+        width: 0%;
+        height: 50%;
+        z-index: 1;
+        transition: width 50ms ease-in-out;
+      }
+      :host > div.bar + div.bar {
+        background: var(--rgthree-progress-steps-bg-color);
+        top: 50%;
+        height: 50%;
+        z-index: 2;
+      }
+      :host > div.bar.-error {
+        background: var(--rgthree-progress-error-bg-color);
+      }
+
+      :host > .overlay {
+        position: absolute;
+        left: 0;
+        top: 0;
+        width: 100%;
+        height: 100%;
+        z-index: 5;
+        background: linear-gradient(to bottom, rgba(255,255,255,0.25), rgba(0,0,0,0.25));
+        mix-blend-mode: overlay;
+      }
+
+      :host > span {
+        position: relative;
+        z-index: 4;
+        text-align: left;
+        font-size: inherit;
+        height: 100%;
+        font-family: sans-serif;
+        text-shadow: 1px 1px 0px #000;
+        display: flex;
+        flex-direction: row;
+        padding: 0 6px;
+        align-items: center;
+        justify-content: start;
+        color: var(--rgthree-progress-text-color);
+        text-shadow: black 0px 0px 2px;
+      }
+
+      :host > div.bar[style*="width: 0%"]:first-child,
+      :host > div.bar[style*="width:0%"]:first-child {
+        height: 0%;
+      }
+      :host > div.bar[style*="width: 0%"]:first-child + div,
+      :host > div.bar[style*="width:0%"]:first-child + div {
+        bottom: 0%;
+      }
+    `);
+        this.shadow.adoptedStyleSheets = [sheet];
+        const overlayEl = createElement(`div.overlay[part="overlay"]`, { parent: this.shadow });
+        this.progressNodesEl = createElement(`div.bar[part="progress-nodes"]`, { parent: this.shadow });
+        this.progressStepsEl = createElement(`div.bar[part="progress-steps"]`, { parent: this.shadow });
+        this.progressTextEl = createElement(`span[part="text"]`, { text: "Idle", parent: this.shadow });
+    }
+    disconnectedCallback() {
+        this.connected = false;
+        PROMPT_SERVICE.removeEventListener("progress-update", this.onProgressUpdateBound);
+    }
+}
+RgthreeProgressBar.NAME = "rgthree-progress-bar";
+customElements.define(RgthreeProgressBar.NAME, RgthreeProgressBar);
diff --git a/custom_nodes/rgthree-comfy/web/common/prompt_service.js b/custom_nodes/rgthree-comfy/web/common/prompt_service.js
new file mode 100644
index 0000000000000000000000000000000000000000..1f238889da859785842190511a7918a7cfa6d55f
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/common/prompt_service.js
@@ -0,0 +1,188 @@
+import { api } from "../../scripts/api.js";
+import { getResolver } from "./shared_utils.js";
+export class PromptExecution {
+    constructor(id) {
+        this.promptApi = null;
+        this.executedNodeIds = [];
+        this.totalNodes = 0;
+        this.currentlyExecuting = null;
+        this.errorDetails = null;
+        this.apiPrompt = getResolver();
+        this.id = id;
+    }
+    setPrompt(prompt) {
+        this.promptApi = prompt.output;
+        this.totalNodes = Object.keys(this.promptApi).length;
+        this.apiPrompt.resolve(null);
+    }
+    getApiNode(nodeId) {
+        var _a;
+        return ((_a = this.promptApi) === null || _a === void 0 ? void 0 : _a[String(nodeId)]) || null;
+    }
+    getNodeLabel(nodeId) {
+        var _a, _b;
+        const apiNode = this.getApiNode(nodeId);
+        let label = ((_a = apiNode === null || apiNode === void 0 ? void 0 : apiNode._meta) === null || _a === void 0 ? void 0 : _a.title) || (apiNode === null || apiNode === void 0 ? void 0 : apiNode.class_type) || undefined;
+        if (!label) {
+            const graphNode = (_b = this.maybeGetComfyGraph()) === null || _b === void 0 ? void 0 : _b.getNodeById(Number(nodeId));
+            label = (graphNode === null || graphNode === void 0 ? void 0 : graphNode.title) || (graphNode === null || graphNode === void 0 ? void 0 : graphNode.type) || undefined;
+        }
+        return label;
+    }
+    executing(nodeId, step, maxSteps) {
+        var _a;
+        if (nodeId == null) {
+            this.currentlyExecuting = null;
+            return;
+        }
+        if (((_a = this.currentlyExecuting) === null || _a === void 0 ? void 0 : _a.nodeId) !== nodeId) {
+            if (this.currentlyExecuting != null) {
+                this.executedNodeIds.push(nodeId);
+            }
+            this.currentlyExecuting = { nodeId, nodeLabel: this.getNodeLabel(nodeId), pass: 0 };
+            this.apiPrompt.promise.then(() => {
+                var _a;
+                if (this.currentlyExecuting == null) {
+                    return;
+                }
+                const apiNode = this.getApiNode(nodeId);
+                if (!this.currentlyExecuting.nodeLabel) {
+                    this.currentlyExecuting.nodeLabel = this.getNodeLabel(nodeId);
+                }
+                if ((apiNode === null || apiNode === void 0 ? void 0 : apiNode.class_type) === "UltimateSDUpscale") {
+                    this.currentlyExecuting.pass--;
+                    this.currentlyExecuting.maxPasses = -1;
+                }
+                else if ((apiNode === null || apiNode === void 0 ? void 0 : apiNode.class_type) === "IterativeImageUpscale") {
+                    this.currentlyExecuting.maxPasses = (_a = apiNode === null || apiNode === void 0 ? void 0 : apiNode.inputs["steps"]) !== null && _a !== void 0 ? _a : -1;
+                }
+            });
+        }
+        if (step != null) {
+            if (!this.currentlyExecuting.step || step < this.currentlyExecuting.step) {
+                this.currentlyExecuting.pass++;
+            }
+            this.currentlyExecuting.step = step;
+            this.currentlyExecuting.maxSteps = maxSteps;
+        }
+    }
+    error(details) {
+        this.errorDetails = details;
+    }
+    maybeGetComfyGraph() {
+        var _a;
+        return ((_a = window === null || window === void 0 ? void 0 : window.app) === null || _a === void 0 ? void 0 : _a.graph) || null;
+    }
+}
+class PromptService extends EventTarget {
+    constructor(api) {
+        super();
+        this.promptsMap = new Map();
+        this.currentExecution = null;
+        this.lastQueueRemaining = 0;
+        const that = this;
+        const queuePrompt = api.queuePrompt;
+        api.queuePrompt = async function (num, prompt, ...args) {
+            let response;
+            try {
+                response = await queuePrompt.apply(api, [...arguments]);
+            }
+            catch (e) {
+                const promptExecution = that.getOrMakePrompt("error");
+                promptExecution.error({ exception_type: "Unknown." });
+                throw e;
+            }
+            const promptExecution = that.getOrMakePrompt(response.prompt_id);
+            promptExecution.setPrompt(prompt);
+            if (!that.currentExecution) {
+                that.currentExecution = promptExecution;
+            }
+            that.promptsMap.set(response.prompt_id, promptExecution);
+            that.dispatchEvent(new CustomEvent("queue-prompt", {
+                detail: {
+                    prompt: promptExecution,
+                },
+            }));
+            return response;
+        };
+        api.addEventListener("status", (e) => {
+            var _a;
+            if (!((_a = e.detail) === null || _a === void 0 ? void 0 : _a.exec_info))
+                return;
+            this.lastQueueRemaining = e.detail.exec_info.queue_remaining;
+            this.dispatchProgressUpdate();
+        });
+        api.addEventListener("execution_start", (e) => {
+            if (!this.promptsMap.has(e.detail.prompt_id)) {
+                console.warn("'execution_start' fired before prompt was made.");
+            }
+            const prompt = this.getOrMakePrompt(e.detail.prompt_id);
+            this.currentExecution = prompt;
+            this.dispatchProgressUpdate();
+        });
+        api.addEventListener("executing", (e) => {
+            if (!this.currentExecution) {
+                this.currentExecution = this.getOrMakePrompt("unknown");
+                console.warn("'executing' fired before prompt was made.");
+            }
+            this.currentExecution.executing(e.detail);
+            this.dispatchProgressUpdate();
+            if (e.detail == null) {
+                this.currentExecution = null;
+            }
+        });
+        api.addEventListener("progress", (e) => {
+            if (!this.currentExecution) {
+                this.currentExecution = this.getOrMakePrompt(e.detail.prompt_id);
+                console.warn("'progress' fired before prompt was made.");
+            }
+            this.currentExecution.executing(e.detail.node, e.detail.value, e.detail.max);
+            this.dispatchProgressUpdate();
+        });
+        api.addEventListener("execution_cached", (e) => {
+            if (!this.currentExecution) {
+                this.currentExecution = this.getOrMakePrompt(e.detail.prompt_id);
+                console.warn("'execution_cached' fired before prompt was made.");
+            }
+            for (const cached of e.detail.nodes) {
+                this.currentExecution.executing(cached);
+            }
+            this.dispatchProgressUpdate();
+        });
+        api.addEventListener("executed", (e) => {
+            if (!this.currentExecution) {
+                this.currentExecution = this.getOrMakePrompt(e.detail.prompt_id);
+                console.warn("'executed' fired before prompt was made.");
+            }
+        });
+        api.addEventListener("execution_error", (e) => {
+            var _a;
+            if (!this.currentExecution) {
+                this.currentExecution = this.getOrMakePrompt(e.detail.prompt_id);
+                console.warn("'execution_error' fired before prompt was made.");
+            }
+            (_a = this.currentExecution) === null || _a === void 0 ? void 0 : _a.error(e.detail);
+            this.dispatchProgressUpdate();
+        });
+    }
+    async queuePrompt(prompt) {
+        return await api.queuePrompt(-1, prompt);
+    }
+    dispatchProgressUpdate() {
+        this.dispatchEvent(new CustomEvent("progress-update", {
+            detail: {
+                queue: this.lastQueueRemaining,
+                prompt: this.currentExecution,
+            },
+        }));
+    }
+    getOrMakePrompt(id) {
+        let prompt = this.promptsMap.get(id);
+        if (!prompt) {
+            prompt = new PromptExecution(id);
+            this.promptsMap.set(id, prompt);
+        }
+        return prompt;
+    }
+}
+export const SERVICE = new PromptService(api);
diff --git a/custom_nodes/rgthree-comfy/web/common/py_parser.js b/custom_nodes/rgthree-comfy/web/common/py_parser.js
new file mode 100644
index 0000000000000000000000000000000000000000..0251c1683d3e418032f676c74920fcf60ed24879
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/common/py_parser.js
@@ -0,0 +1,717 @@
+var __decorate = (this && this.__decorate) || function (decorators, target, key, desc) {
+    var c = arguments.length, r = c < 3 ? target : desc === null ? desc = Object.getOwnPropertyDescriptor(target, key) : desc, d;
+    if (typeof Reflect === "object" && typeof Reflect.decorate === "function") r = Reflect.decorate(decorators, target, key, desc);
+    else for (var i = decorators.length - 1; i >= 0; i--) if (d = decorators[i]) r = (c < 3 ? d(r) : c > 3 ? d(target, key, r) : d(target, key)) || r;
+    return c > 3 && r && Object.defineProperty(target, key, r), r;
+};
+var __classPrivateFieldSet = (this && this.__classPrivateFieldSet) || function (receiver, state, value, kind, f) {
+    if (kind === "m") throw new TypeError("Private method is not writable");
+    if (kind === "a" && !f) throw new TypeError("Private accessor was defined without a setter");
+    if (typeof state === "function" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError("Cannot write private member to an object whose class did not declare it");
+    return (kind === "a" ? f.call(receiver, value) : f ? f.value = value : state.set(receiver, value)), value;
+};
+var __classPrivateFieldGet = (this && this.__classPrivateFieldGet) || function (receiver, state, kind, f) {
+    if (kind === "a" && !f) throw new TypeError("Private accessor was defined without a getter");
+    if (typeof state === "function" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError("Cannot read private member from an object whose class did not declare it");
+    return kind === "m" ? f : kind === "a" ? f.call(receiver) : f ? f.value : state.get(receiver);
+};
+var _PyDict_dict;
+import { check, deepFreeze } from "./shared_utils.js";
+const MEMOIZED = { parser: null };
+class ExecuteContext {
+    constructor(existing = {}) {
+        Object.assign(this, !!window.structuredClone ? structuredClone(existing) : { ...existing });
+    }
+}
+class InitialExecuteContext extends ExecuteContext {
+}
+const TYPE_TO_HANDLER = new Map([
+    ["module", handleChildren],
+    ["expression_statement", handleChildren],
+    ["interpolation", handleInterpolation],
+    ["block", handleChildren],
+    ["comment", handleSwallow],
+    ["return_statement", handleReturn],
+    ["assignment", handleAssignment],
+    ["named_expression", handleNamedExpression],
+    ["identifier", handleIdentifier],
+    ["attribute", handleAttribute],
+    ["subscript", handleSubscript],
+    ["call", handleCall],
+    ["argument_list", handleArgumentsList],
+    ["for_statement", handleForStatement],
+    ["list_comprehension", handleListComprehension],
+    ["comparison_operator", handleComparisonOperator],
+    ["boolean_operator", handleBooleanOperator],
+    ["binary_operator", handleBinaryOperator],
+    ["not_operator", handleNotOperator],
+    ["unary_operator", handleUnaryOperator],
+    ["integer", handleNumber],
+    ["float", handleNumber],
+    ["string", handleString],
+    ["tuple", handleList],
+    ["list", handleList],
+    ["dictionary", handleDictionary],
+    ["pair", handleDictionaryPair],
+    ["true", async (...args) => true],
+    ["false", async (...args) => false],
+]);
+const DEFAULT_BUILT_INS = {
+    round: { fn: (n) => Math.round(Number(n)) },
+    ceil: { fn: (n) => Math.ceil(Number(n)) },
+    floor: { fn: (n) => Math.floor(Number(n)) },
+    len: { fn: (n) => { var _a, _b; return (_b = (_a = n === null || n === void 0 ? void 0 : n.__len__) === null || _a === void 0 ? void 0 : _a.call(n)) !== null && _b !== void 0 ? _b : n === null || n === void 0 ? void 0 : n.length; } },
+    int: { fn: (n) => Math.floor(Number(n)) },
+    float: { fn: (n) => Number(n) },
+    str: { fn: (n) => String(n) },
+    bool: { fn: (n) => !!n },
+    list: { fn: (tupl = []) => new PyList(tupl) },
+    tuple: { fn: (list = []) => new PyTuple(list) },
+    dict: { fn: (dict = {}) => new PyDict(dict) },
+    dir: { fn: (...args) => console.dir(...__unwrap__(...args)) },
+    print: { fn: (...args) => console.log(...__unwrap__(...args)) },
+    log: { fn: (...args) => console.log(...__unwrap__(...args)) },
+};
+export async function execute(code, ctx, additionalBuiltins) {
+    var _a, _b;
+    const builtIns = deepFreeze({ ...DEFAULT_BUILT_INS, ...(additionalBuiltins !== null && additionalBuiltins !== void 0 ? additionalBuiltins : {}) });
+    ctx = new InitialExecuteContext(ctx);
+    const root = (await parse(code)).rootNode;
+    const value = await handleNode(new Node(root), ctx, builtIns);
+    console.log("=====");
+    console.log(`value`, (_b = (_a = value === null || value === void 0 ? void 0 : value.__unwrap__) === null || _a === void 0 ? void 0 : _a.call(value)) !== null && _b !== void 0 ? _b : value);
+    console.log("context", ctx);
+    return value;
+}
+async function parse(code) {
+    if (!MEMOIZED.parser) {
+        const TreeSitter = (await import("../lib/tree-sitter.js"));
+        await TreeSitter.Parser.init();
+        const lang = await TreeSitter.Language.load("rgthree/lib/tree-sitter-python.wasm");
+        MEMOIZED.parser = new TreeSitter.Parser();
+        MEMOIZED.parser.setLanguage(lang);
+    }
+    return MEMOIZED.parser.parse(code);
+}
+async function handleNode(node, ctx, builtIns) {
+    const type = node.type;
+    if (ctx.hasOwnProperty("__returned__"))
+        return ctx["__returned__"];
+    const handler = TYPE_TO_HANDLER.get(type);
+    check(handler, "Unhandled type: " + type, node);
+    return handler(node, ctx, builtIns);
+}
+async function handleChildren(node, ctx, builtIns) {
+    let lastValue = null;
+    for (const child of node.children) {
+        if (!child)
+            continue;
+        lastValue = await handleNode(child, ctx, builtIns);
+    }
+    return lastValue;
+}
+async function handleSwallow(node, ctx, builtIns) {
+}
+async function handleReturn(node, ctx, builtIns) {
+    const value = node.children.length > 1 ? handleNode(node.child(1), ctx, builtIns) : undefined;
+    ctx["__returned__"] = value;
+    return value;
+}
+async function handleIdentifier(node, ctx, builtIns) {
+    var _a, _b;
+    let value = ctx[node.text];
+    if (value === undefined) {
+        value = (_b = (_a = builtIns[node.text]) === null || _a === void 0 ? void 0 : _a.fn) !== null && _b !== void 0 ? _b : undefined;
+    }
+    return maybeWrapValue(value);
+}
+async function handleAttribute(node, ctx, builtIns) {
+    const children = node.children;
+    check(children.length === 3, "Expected 3 children for attribute.");
+    check(children[1].type === ".", "Expected middle child to be '.' for attribute.");
+    const inst = await handleNode(children[0], ctx, builtIns);
+    const attr = children[2].text;
+    checkAttributeAccessibility(inst, attr);
+    let attribute = maybeWrapValue(inst[attr]);
+    return typeof attribute === "function" ? attribute.bind(inst) : attribute;
+}
+async function handleSubscript(node, ctx, builtIns) {
+    const children = node.children;
+    check(children.length === 4, "Expected 4 children for subscript.");
+    check(children[1].type === "[", "Expected 2nd child to be '[' for subscript.");
+    check(children[3].type === "]", "Expected 4thd child to be ']' for subscript.");
+    const inst = await handleNode(children[0], ctx, builtIns);
+    const attr = await handleNode(children[2], ctx, builtIns);
+    if (inst instanceof PyTuple && isInt(attr)) {
+        return maybeWrapValue(inst.__at__(attr));
+    }
+    if (inst instanceof PyDict && typeof attr === "string") {
+        return maybeWrapValue(inst.get(attr));
+    }
+    checkAttributeAccessibility(inst, attr);
+    let attribute = maybeWrapValue(inst[attr]);
+    return typeof attribute === "function" ? attribute.bind(inst) : attribute;
+}
+async function handleAssignment(node, ctx, builtIns) {
+    check(node.children.length === 3, "Expected 3 children for assignment: identifier/attr, =, and value.");
+    check(node.children[1].type === "=", "Expected middle child to be an '='.");
+    let right = await handleNode(node.children[2], ctx, builtIns);
+    const leftNode = node.children[0];
+    let leftObj = ctx;
+    let leftProp = "";
+    if (leftNode.type === "identifier") {
+        leftProp = leftNode.text;
+    }
+    else if (leftNode.type === "attribute") {
+        leftObj = await handleNode(leftNode.children[0], ctx, builtIns);
+        check(leftNode.children[2].type === "identifier", "Expected left hand assignment attribute to be an identifier.", leftNode);
+        leftProp = leftNode.children[2].text;
+    }
+    else if (leftNode.type === "subscript") {
+        leftObj = await handleNode(leftNode.children[0], ctx, builtIns);
+        check(leftNode.children[1].type === "[");
+        check(leftNode.children[3].type === "]");
+        leftProp = await handleNode(leftNode.children[2], ctx, builtIns);
+    }
+    else {
+        throw new Error(`Unhandled left-hand assignement type: ${leftNode.type}`);
+    }
+    if (leftProp == null) {
+        throw new Error(`No property to assign value`);
+    }
+    if (leftObj instanceof PyTuple) {
+        check(isInt(leftProp), "Expected an int for list assignment");
+        leftObj.__put__(leftProp, right);
+    }
+    else if (leftObj instanceof PyDict) {
+        check(typeof leftProp === "string", "Expected a string for dict assignment");
+        leftObj.__put__(leftProp, right);
+    }
+    else {
+        check(typeof leftProp === "string", "Expected a string for object assignment");
+        if (!(leftObj instanceof InitialExecuteContext)) {
+            checkAttributeAccessibility(leftObj, leftProp);
+        }
+        leftObj[leftProp] = right;
+    }
+    return right;
+}
+async function handleNamedExpression(node, ctx, builtIns) {
+    check(node.children.length === 3, "Expected three children for named expression.");
+    check(node.child(0).type === "identifier", "Expected identifier first in named expression.");
+    const varName = node.child(0).text;
+    ctx[varName] = await handleNode(node.child(2), ctx, builtIns);
+    return maybeWrapValue(ctx[varName]);
+}
+async function handleCall(node, ctx, builtIns) {
+    check(node.children.length === 2, "Expected 2 children for call, identifier and arguments.");
+    const fn = await handleNode(node.children[0], ctx, builtIns);
+    const args = await handleNode(node.children[1], ctx, builtIns);
+    console.log("handleCall", fn, args);
+    return fn(...args);
+}
+async function handleArgumentsList(node, ctx, builtIns) {
+    const args = (await handleList(node, ctx, builtIns)).__unwrap__(false);
+    return [...args];
+}
+async function handleForStatement(node, ctx, builtIns) {
+    const childs = node.children;
+    check(childs.length === 6);
+    check(childs[4].type === ":");
+    check(childs[5].type === "block");
+    await helperGetLoopForIn(node, ctx, builtIns, async (forCtx) => {
+        await handleNode(childs[5], forCtx, builtIns);
+    });
+}
+async function handleListComprehension(node, ctx, builtIns) {
+    const finalList = new PyList();
+    const newCtx = { ...ctx };
+    let finalEntryNode;
+    const loopNodes = [];
+    for (const child of node.children) {
+        if (!child || ["[", "]"].includes(child.type))
+            continue;
+        if (child.type === "identifier" || child.type === "attribute") {
+            if (finalEntryNode) {
+                throw Error("Already have a list comprehension finalEntryNode.");
+            }
+            finalEntryNode = child;
+        }
+        else if (child.type === "for_in_clause") {
+            loopNodes.push({ forIn: child });
+        }
+        else if (child.type === "if_clause") {
+            loopNodes[loopNodes.length - 1]["if"] = child;
+        }
+    }
+    if (!finalEntryNode) {
+        throw Error("No list comprehension finalEntryNode.");
+    }
+    console.log(`handleListComprehension.loopNodes`, loopNodes);
+    const handleLoop = async (loopNodes) => {
+        const loopNode = loopNodes.shift();
+        await helperGetLoopForIn(loopNode.forIn, newCtx, builtIns, async (forCtx) => {
+            if (loopNode.if) {
+                const ifNode = loopNode.if;
+                check(ifNode.children.length === 2, "Expected 2 children for if_clause.");
+                check(ifNode.child(0).text === "if", "Expected first child to be 'if'.");
+                const good = await handleNode(ifNode.child(1), forCtx, builtIns);
+                if (!good)
+                    return;
+            }
+            Object.assign(newCtx, forCtx);
+            if (loopNodes.length) {
+                await handleLoop(loopNodes);
+            }
+            else {
+                finalList.append(await handleNode(finalEntryNode, newCtx, builtIns));
+            }
+        }, () => ({ ...newCtx }));
+        loopNodes.unshift(loopNode);
+    };
+    await handleLoop(loopNodes);
+    return finalList;
+}
+async function helperGetLoopForIn(node, ctx, builtIns, eachFn, provideForCtx) {
+    var _a;
+    const childs = node.children;
+    check(childs.length >= 3);
+    check(childs[0].type === "for");
+    check(["identifier", "pattern_list"].includes(childs[1].type), "Expected identifier for for loop.");
+    check(childs[2].type === "in");
+    let identifiers;
+    if (childs[1].type === "identifier") {
+        identifiers = [childs[1].text];
+    }
+    else {
+        identifiers = childs[1].children
+            .map((n) => {
+            if (n.type === ",")
+                return null;
+            check(n.type === "identifier");
+            return node.text;
+        })
+            .filter((n) => n != null);
+    }
+    const iterable = await handleNode(childs[3], ctx, builtIns);
+    check(iterable instanceof PyTuple, "Expected for loop instance to be a list/tuple.");
+    for (const item of iterable.__unwrap__(false)) {
+        const forCtx = (_a = provideForCtx === null || provideForCtx === void 0 ? void 0 : provideForCtx()) !== null && _a !== void 0 ? _a : ctx;
+        if (identifiers.length === 1) {
+            forCtx[identifiers[0]] = item;
+        }
+        else {
+            check(Array.isArray(item) && identifiers.length === item.length, "Expected iterable to be a list, like using dict.items()");
+            for (let i = 0; i < identifiers.length; i++) {
+                forCtx[identifiers[i]] = item[i];
+            }
+        }
+        await eachFn(forCtx);
+    }
+}
+async function handleNumber(node, ctx, builtIns) {
+    return Number(node.text);
+}
+async function handleString(node, ctx, builtIns) {
+    let str = "";
+    for (const child of node.children) {
+        if (!child || ["string_start", "string_end"].includes(child.type))
+            continue;
+        if (child.type === "string_content") {
+            str += child.text;
+        }
+        else if (child.type === "interpolation") {
+            check(child.children.length === 3, "Expected interpolation");
+            str += await handleNode(child, ctx, builtIns);
+        }
+    }
+    return str;
+}
+async function handleInterpolation(node, ...args) {
+    check(node.children.length === 3, "Expected interpolation to be three nodes length.");
+    check(node.children[0].type === "{" && node.children[2].type === "}", 'Expected interpolation to be wrapped in "{" and "}".');
+    return await handleNode(node.children[1], ...args);
+}
+async function handleList(node, ctx, builtIns) {
+    const list = [];
+    for (const child of node.children) {
+        if (!child || ["(", "[", ",", "]", ")"].includes(child.type))
+            continue;
+        list.push(await handleNode(child, ctx, builtIns));
+    }
+    if (node.type === "tuple") {
+        return new PyTuple(list);
+    }
+    return new PyList(list);
+}
+async function handleComparisonOperator(node, ctx, builtIns) {
+    const op = node.child(1).text;
+    const left = await handleNode(node.child(0), ctx, builtIns);
+    const right = await handleNode(node.child(2), ctx, builtIns);
+    if (op === "==")
+        return left === right;
+    if (op === "!=")
+        return left !== right;
+    if (op === ">")
+        return left > right;
+    if (op === ">=")
+        return left >= right;
+    if (op === "<")
+        return left < right;
+    if (op === "<=")
+        return left <= right;
+    if (op === "in")
+        return (right.__unwrap__ ? right.__unwrap__(false) : right).includes(left);
+    throw new Error(`Comparison not handled: "${op}"`);
+}
+async function handleBooleanOperator(node, ctx, builtIns) {
+    const op = node.child(1).text;
+    const left = await handleNode(node.child(0), ctx, builtIns);
+    if (!left && op === "and")
+        return left;
+    const right = await handleNode(node.child(2), ctx, builtIns);
+    if (op === "and")
+        return left && right;
+    if (op === "or")
+        return left || right;
+}
+async function handleBinaryOperator(node, ctx, builtIns) {
+    const op = node.child(1).text;
+    const left = await handleNode(node.child(0), ctx, builtIns);
+    const right = await handleNode(node.child(2), ctx, builtIns);
+    if (left.constructor !== right.constructor) {
+        throw new Error(`Can only run ${op} operator on same type.`);
+    }
+    if (op === "+")
+        return left.__add__ ? left.__add__(right) : left + right;
+    if (op === "-")
+        return left - right;
+    if (op === "/")
+        return left / right;
+    if (op === "//")
+        return Math.floor(left / right);
+    if (op === "*")
+        return left * right;
+    if (op === "%")
+        return left % right;
+    if (op === "&")
+        return left & right;
+    if (op === "|")
+        return left | right;
+    if (op === "^")
+        return left ^ right;
+    if (op === "<<")
+        return left << right;
+    if (op === ">>")
+        return left >> right;
+    throw new Error(`Comparison not handled: "${op}"`);
+}
+async function handleNotOperator(node, ctx, builtIns) {
+    check(node.children.length === 2, "Expected 2 children for not operator.");
+    check(node.child(0).text === "not", "Expected first child to be 'not'.");
+    const value = await handleNode(node.child(1), ctx, builtIns);
+    return !value;
+}
+async function handleUnaryOperator(node, ctx, builtIns) {
+    check(node.children.length === 2, "Expected 2 children for not operator.");
+    const value = await handleNode(node.child(1), ctx, builtIns);
+    const op = node.child(0).text;
+    if (op === "-")
+        return value * -1;
+    console.warn(`Unhandled unary operator: ${op}`);
+    return value;
+}
+async function handleDictionary(node, ctx, builtIns) {
+    const dict = new PyDict();
+    for (const child of node.children) {
+        if (!child || ["{", ",", "}"].includes(child.type))
+            continue;
+        check(child.type === "pair", "Expected a pair type for dict.");
+        const pair = await handleNode(child, ctx, builtIns);
+        dict.__put__(pair[0], pair[1]);
+    }
+    return dict;
+}
+async function handleDictionaryPair(node, ctx, builtIns) {
+    check(node.children.length === 3, "Expected 3 children for dict pair.");
+    let varName = await handleNode(node.child(0), ctx, builtIns);
+    let varValue = await handleNode(node.child(2), ctx, builtIns);
+    check(typeof varName === "string", "Expected varname to be string.");
+    return [varName, varValue];
+}
+class Node {
+    constructor(node) {
+        this.type = node.type;
+        this.text = node.text;
+        if (this.type === "ERROR") {
+            throw new Error(`Error found in parsing near "${this.text}"`);
+        }
+        this.children = [];
+        for (const child of node.children) {
+            this.children.push(new Node(child));
+        }
+        this.node = node;
+    }
+    child(index) {
+        const child = this.children[index];
+        if (!child)
+            throw Error(`No child at index ${index}.`);
+        return child;
+    }
+    log(tab = "", showNode = false) {
+        console.log(`${tab}--- Node`);
+        console.log(`${tab} type: ${this.type}`);
+        console.log(`${tab} text: ${this.text}`);
+        console.log(`${tab} children:`, this.children);
+        if (showNode) {
+            console.log(`${tab} node:`, this.node);
+        }
+    }
+}
+export class PyTuple {
+    constructor(...args) {
+        if (args.length === 1 && args[0] instanceof PyTuple) {
+            args = args[0].__unwrap__(false);
+        }
+        if (args.length === 1 && Array.isArray(args[0])) {
+            args = [...args[0]];
+        }
+        this.list = [...args];
+    }
+    count(v) {
+    }
+    index() {
+    }
+    __at__(index) {
+        index = this.__get_relative_index__(index);
+        return this.list[index];
+    }
+    __len__() {
+        return this.list.length;
+    }
+    __add__(v) {
+        if (!(v instanceof PyTuple)) {
+            throw new Error("Can only concatenate tuple to tuple.");
+        }
+        return new PyTuple(this.__unwrap__(false).concat(v.__unwrap__(false)));
+    }
+    __put__(index, v) {
+        throw new Error("Tuple does not support item assignment");
+    }
+    __get_relative_index__(index) {
+        if (index >= 0) {
+            check(this.list.length > index, `Index ${index} out of range.`);
+            return index;
+        }
+        const relIndex = this.list.length + index;
+        check(relIndex >= 0, `Index ${index} out of range.`);
+        return relIndex;
+    }
+    __unwrap__(deep = true) {
+        var _a;
+        const l = [...this.list];
+        if (deep) {
+            for (let i = 0; i < l.length; i++) {
+                l[i] = ((_a = l[i]) === null || _a === void 0 ? void 0 : _a.__unwrap__) ? l[i].__unwrap__(deep) : l[i];
+            }
+        }
+        return l;
+    }
+}
+__decorate([
+    Exposed
+], PyTuple.prototype, "count", null);
+__decorate([
+    Exposed
+], PyTuple.prototype, "index", null);
+export class PyList extends PyTuple {
+    append(...args) {
+        this.list.push(...args);
+    }
+    clear() {
+        this.list.length = 0;
+    }
+    copy() {
+    }
+    count() {
+    }
+    extend() {
+    }
+    index() {
+    }
+    insert() {
+    }
+    pop() {
+    }
+    remove() {
+    }
+    reverse() {
+    }
+    sort() {
+    }
+    __add__(v) {
+        if (!(v instanceof PyList)) {
+            throw new Error("Can only concatenate list to list.");
+        }
+        return new PyList(this.__unwrap__(false).concat(v.__unwrap__(false)));
+    }
+    __put__(index, v) {
+        index = this.__get_relative_index__(index);
+        this.list[index] = v;
+    }
+}
+__decorate([
+    Exposed
+], PyList.prototype, "append", null);
+__decorate([
+    Exposed
+], PyList.prototype, "clear", null);
+__decorate([
+    Exposed
+], PyList.prototype, "copy", null);
+__decorate([
+    Exposed
+], PyList.prototype, "count", null);
+__decorate([
+    Exposed
+], PyList.prototype, "extend", null);
+__decorate([
+    Exposed
+], PyList.prototype, "index", null);
+__decorate([
+    Exposed
+], PyList.prototype, "insert", null);
+__decorate([
+    Exposed
+], PyList.prototype, "pop", null);
+__decorate([
+    Exposed
+], PyList.prototype, "remove", null);
+__decorate([
+    Exposed
+], PyList.prototype, "reverse", null);
+__decorate([
+    Exposed
+], PyList.prototype, "sort", null);
+class PyInt {
+}
+class PyDict {
+    constructor(dict) {
+        _PyDict_dict.set(this, void 0);
+        __classPrivateFieldSet(this, _PyDict_dict, { ...(dict !== null && dict !== void 0 ? dict : {}) }, "f");
+    }
+    clear() { }
+    copy() { }
+    fromkeys() { }
+    get(key) {
+        return __classPrivateFieldGet(this, _PyDict_dict, "f")[key];
+    }
+    items() {
+        return new PyTuple(Object.entries(__classPrivateFieldGet(this, _PyDict_dict, "f")).map((e) => new PyTuple(e)));
+    }
+    keys() { }
+    pop() { }
+    popitem() { }
+    setdefault() { }
+    update() { }
+    values() { }
+    __put__(key, v) {
+        __classPrivateFieldGet(this, _PyDict_dict, "f")[key] = v;
+    }
+    __len__() {
+        return Object.keys(__classPrivateFieldGet(this, _PyDict_dict, "f")).length;
+    }
+    __unwrap__(deep = true) {
+        var _a;
+        const d = { ...__classPrivateFieldGet(this, _PyDict_dict, "f") };
+        if (deep) {
+            for (let k of Object.keys(d)) {
+                d[k] = ((_a = d[k]) === null || _a === void 0 ? void 0 : _a.__unwrap__) ? d[k].__unwrap__(deep) : d[k];
+            }
+        }
+        return d;
+    }
+}
+_PyDict_dict = new WeakMap();
+__decorate([
+    Exposed
+], PyDict.prototype, "clear", null);
+__decorate([
+    Exposed
+], PyDict.prototype, "copy", null);
+__decorate([
+    Exposed
+], PyDict.prototype, "fromkeys", null);
+__decorate([
+    Exposed
+], PyDict.prototype, "get", null);
+__decorate([
+    Exposed
+], PyDict.prototype, "items", null);
+__decorate([
+    Exposed
+], PyDict.prototype, "keys", null);
+__decorate([
+    Exposed
+], PyDict.prototype, "pop", null);
+__decorate([
+    Exposed
+], PyDict.prototype, "popitem", null);
+__decorate([
+    Exposed
+], PyDict.prototype, "setdefault", null);
+__decorate([
+    Exposed
+], PyDict.prototype, "update", null);
+__decorate([
+    Exposed
+], PyDict.prototype, "values", null);
+function __unwrap__(...args) {
+    var _a;
+    for (let i = 0; i < args.length; i++) {
+        args[i] = ((_a = args[i]) === null || _a === void 0 ? void 0 : _a.__unwrap__) ? args[i].__unwrap__(true) : args[i];
+    }
+    return args;
+}
+function checkAttributeAccessibility(inst, attr) {
+    var _a, _b, _c, _d, _e, _f;
+    const instType = typeof inst;
+    check(instType === "object" || instType === "function", `Instance of type ${instType} does not have attributes.`);
+    check(!attr.startsWith("__") && !attr.endsWith("__"), `"${attr}" is not accessible.`);
+    const attrType = typeof inst[attr];
+    if (attrType === "function") {
+        const allowedMethods = (_c = (_b = (_a = inst.constructor) === null || _a === void 0 ? void 0 : _a.__ALLOWED_METHODS__) !== null && _b !== void 0 ? _b : inst.__ALLOWED_METHODS__) !== null && _c !== void 0 ? _c : [];
+        check(allowedMethods.includes(attr), `Method ${attr} is not accessible.`);
+    }
+    else {
+        const allowedProps = (_f = (_e = (_d = inst.constructor) === null || _d === void 0 ? void 0 : _d.__ALLOWED_PROPERTIES__) !== null && _e !== void 0 ? _e : inst.__ALLOWED_PROPERTIES__) !== null && _f !== void 0 ? _f : [];
+        check(allowedProps.includes(attr), `Property ${attr} is not accessible.`);
+    }
+}
+function maybeWrapValue(value) {
+    if (Array.isArray(value)) {
+        return new PyList(value);
+    }
+    return value;
+}
+function isInt(value) {
+    return typeof value === "number" && Math.round(value) === value;
+}
+function isIntLike(value) {
+    let is = isInt(value);
+    if (!is) {
+        is = typeof value === "string" && !!/^\d+$/.exec(value);
+    }
+    return is;
+}
+export function Exposed(target, key) {
+    const descriptor = Object.getOwnPropertyDescriptor(target, key);
+    if (typeof (descriptor === null || descriptor === void 0 ? void 0 : descriptor.value) === "function") {
+        target.constructor.__ALLOWED_METHODS__ = target.constructor.__ALLOWED_METHODS__ || [];
+        target.constructor.__ALLOWED_METHODS__.push(key);
+    }
+    else {
+        target.constructor.__ALLOWED_PROPERTIES__ = target.constructor.__ALLOWED_PROPERTIES__ || [];
+        target.constructor.__ALLOWED_PROPERTIES__.push(key);
+    }
+}
diff --git a/custom_nodes/rgthree-comfy/web/common/rgthree_api.js b/custom_nodes/rgthree-comfy/web/common/rgthree_api.js
new file mode 100644
index 0000000000000000000000000000000000000000..eae650f44bd2ab88f3468ad071978d21ac0d4489
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/common/rgthree_api.js
@@ -0,0 +1,131 @@
+class RgthreeApi {
+    constructor(baseUrl) {
+        this.getCheckpointsPromise = null;
+        this.getSamplersPromise = null;
+        this.getSchedulersPromise = null;
+        this.getLorasPromise = null;
+        this.getWorkflowsPromise = null;
+        this.setBaseUrl(baseUrl);
+    }
+    setBaseUrl(baseUrlArg) {
+        var _a;
+        let baseUrl = null;
+        if (baseUrlArg) {
+            baseUrl = baseUrlArg;
+        }
+        else if (window.location.pathname.includes("/rgthree/")) {
+            const parts = (_a = window.location.pathname.split("/rgthree/")[1]) === null || _a === void 0 ? void 0 : _a.split("/");
+            if (parts && parts.length) {
+                baseUrl = parts.map(() => "../").join("") + "rgthree/api";
+            }
+        }
+        this.baseUrl = baseUrl || "./rgthree/api";
+        const comfyBasePathname = location.pathname.includes("/rgthree/")
+            ? location.pathname.split("rgthree/")[0]
+            : location.pathname;
+        this.comfyBaseUrl = comfyBasePathname.split("/").slice(0, -1).join("/");
+    }
+    apiURL(route) {
+        return `${this.baseUrl}${route}`;
+    }
+    fetchApi(route, options) {
+        return fetch(this.apiURL(route), options);
+    }
+    async fetchJson(route, options) {
+        const r = await this.fetchApi(route, options);
+        return await r.json();
+    }
+    async postJson(route, json) {
+        const body = new FormData();
+        body.append("json", JSON.stringify(json));
+        return await rgthreeApi.fetchJson(route, { method: "POST", body });
+    }
+    getLoras(force = false) {
+        if (!this.getLorasPromise || force) {
+            this.getLorasPromise = this.fetchJson("/loras?format=details", { cache: "no-store" });
+        }
+        return this.getLorasPromise;
+    }
+    async fetchApiJsonOrNull(route, options) {
+        const response = await this.fetchJson(route, options);
+        if (response.status === 200 && response.data) {
+            return response.data || null;
+        }
+        return null;
+    }
+    async getModelsInfo(options) {
+        var _a;
+        const params = new URLSearchParams();
+        if ((_a = options.files) === null || _a === void 0 ? void 0 : _a.length) {
+            params.set("files", options.files.join(","));
+        }
+        if (options.light) {
+            params.set("light", "1");
+        }
+        if (options.format) {
+            params.set("format", options.format);
+        }
+        const path = `/${options.type}/info?` + params.toString();
+        return (await this.fetchApiJsonOrNull(path)) || [];
+    }
+    async getLorasInfo(options = {}) {
+        return this.getModelsInfo({ type: "loras", ...options });
+    }
+    async getCheckpointsInfo(options = {}) {
+        return this.getModelsInfo({ type: "checkpoints", ...options });
+    }
+    async refreshModelsInfo(options) {
+        var _a;
+        const params = new URLSearchParams();
+        if ((_a = options.files) === null || _a === void 0 ? void 0 : _a.length) {
+            params.set("files", options.files.join(","));
+        }
+        const path = `/${options.type}/info/refresh?` + params.toString();
+        const infos = await this.fetchApiJsonOrNull(path);
+        return infos;
+    }
+    async refreshLorasInfo(options = {}) {
+        return this.refreshModelsInfo({ type: "loras", ...options });
+    }
+    async refreshCheckpointsInfo(options = {}) {
+        return this.refreshModelsInfo({ type: "checkpoints", ...options });
+    }
+    async clearModelsInfo(options) {
+        var _a;
+        const params = new URLSearchParams();
+        if ((_a = options.files) === null || _a === void 0 ? void 0 : _a.length) {
+            params.set("files", options.files.join(","));
+        }
+        const path = `/${options.type}/info/clear?` + params.toString();
+        await this.fetchApiJsonOrNull(path);
+        return;
+    }
+    async clearLorasInfo(options = {}) {
+        return this.clearModelsInfo({ type: "loras", ...options });
+    }
+    async clearCheckpointsInfo(options = {}) {
+        return this.clearModelsInfo({ type: "checkpoints", ...options });
+    }
+    async saveModelInfo(type, file, data) {
+        const body = new FormData();
+        body.append("json", JSON.stringify(data));
+        return await this.fetchApiJsonOrNull(`/${type}/info?file=${encodeURIComponent(file)}`, { cache: "no-store", method: "POST", body });
+    }
+    async saveLoraInfo(file, data) {
+        return this.saveModelInfo("loras", file, data);
+    }
+    async saveCheckpointsInfo(file, data) {
+        return this.saveModelInfo("checkpoints", file, data);
+    }
+    fetchComfyApi(route, options) {
+        const url = this.comfyBaseUrl + "/api" + route;
+        options = options || {};
+        options.headers = options.headers || {};
+        options.cache = options.cache || "no-cache";
+        return fetch(url, options);
+    }
+    print(messageType) {
+        this.fetchApi(`/print?type=${messageType}`, {});
+    }
+}
+export const rgthreeApi = new RgthreeApi();
diff --git a/custom_nodes/rgthree-comfy/web/common/shared_utils.js b/custom_nodes/rgthree-comfy/web/common/shared_utils.js
new file mode 100644
index 0000000000000000000000000000000000000000..61ba549c24140e53dde893a8a8a43fab3528b090
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/common/shared_utils.js
@@ -0,0 +1,416 @@
+export function getResolver(timeout = 5000) {
+    const resolver = {};
+    resolver.id = generateId(8);
+    resolver.completed = false;
+    resolver.resolved = false;
+    resolver.rejected = false;
+    resolver.promise = new Promise((resolve, reject) => {
+        resolver.reject = (e) => {
+            resolver.completed = true;
+            resolver.rejected = true;
+            reject(e);
+        };
+        resolver.resolve = (data) => {
+            resolver.completed = true;
+            resolver.resolved = true;
+            resolve(data);
+        };
+    });
+    resolver.timeout = setTimeout(() => {
+        if (!resolver.completed) {
+            resolver.reject();
+        }
+    }, timeout);
+    return resolver;
+}
+const DEBOUNCE_FN_TO_PROMISE = new WeakMap();
+export function debounce(fn, ms = 64) {
+    if (!DEBOUNCE_FN_TO_PROMISE.get(fn)) {
+        DEBOUNCE_FN_TO_PROMISE.set(fn, wait(ms).then(() => {
+            DEBOUNCE_FN_TO_PROMISE.delete(fn);
+            fn();
+        }));
+    }
+    return DEBOUNCE_FN_TO_PROMISE.get(fn);
+}
+export function check(value, msg = "", ...args) {
+    if (!value) {
+        console.error(msg, ...(args || []));
+        throw new Error(msg || "Error");
+    }
+}
+export function wait(ms = 16) {
+    if (ms === 16) {
+        return new Promise((resolve) => {
+            requestAnimationFrame(() => {
+                resolve();
+            });
+        });
+    }
+    return new Promise((resolve) => {
+        setTimeout(() => {
+            resolve();
+        }, ms);
+    });
+}
+export function deepFreeze(obj) {
+    const propNames = Reflect.ownKeys(obj);
+    for (const name of propNames) {
+        const value = obj[name];
+        if ((value && typeof value === "object") || typeof value === "function") {
+            deepFreeze(value);
+        }
+    }
+    return Object.freeze(obj);
+}
+function dec2hex(dec) {
+    return dec.toString(16).padStart(2, "0");
+}
+export function generateId(length) {
+    const arr = new Uint8Array(length / 2);
+    crypto.getRandomValues(arr);
+    return Array.from(arr, dec2hex).join("");
+}
+export function getObjectValue(obj, objKey, def) {
+    if (!obj || !objKey)
+        return def;
+    const keys = objKey.split(".");
+    const key = keys.shift();
+    const found = obj[key];
+    if (keys.length) {
+        return getObjectValue(found, keys.join("."), def);
+    }
+    return found;
+}
+export function setObjectValue(obj, objKey, value, createMissingObjects = true) {
+    if (!obj || !objKey)
+        return obj;
+    const keys = objKey.split(".");
+    const key = keys.shift();
+    if (obj[key] === undefined) {
+        if (!createMissingObjects) {
+            return;
+        }
+        obj[key] = {};
+    }
+    if (!keys.length) {
+        obj[key] = value;
+    }
+    else {
+        if (typeof obj[key] != "object") {
+            obj[key] = {};
+        }
+        setObjectValue(obj[key], keys.join("."), value, createMissingObjects);
+    }
+    return obj;
+}
+export function moveArrayItem(arr, itemOrFrom, to) {
+    const from = typeof itemOrFrom === "number" ? itemOrFrom : arr.indexOf(itemOrFrom);
+    arr.splice(to, 0, arr.splice(from, 1)[0]);
+}
+export function removeArrayItem(arr, itemOrIndex) {
+    const index = typeof itemOrIndex === "number" ? itemOrIndex : arr.indexOf(itemOrIndex);
+    arr.splice(index, 1);
+}
+export function injectCss(href) {
+    if (document.querySelector(`link[href^="${href}"]`)) {
+        return Promise.resolve();
+    }
+    return new Promise((resolve) => {
+        const link = document.createElement("link");
+        link.setAttribute("rel", "stylesheet");
+        link.setAttribute("type", "text/css");
+        const timeout = setTimeout(resolve, 1000);
+        link.addEventListener("load", (e) => {
+            clearInterval(timeout);
+            resolve();
+        });
+        link.href = href;
+        document.head.appendChild(link);
+    });
+}
+export function defineProperty(instance, property, desc) {
+    var _a, _b, _c, _d, _e, _f;
+    const existingDesc = Object.getOwnPropertyDescriptor(instance, property);
+    if ((existingDesc === null || existingDesc === void 0 ? void 0 : existingDesc.configurable) === false) {
+        throw new Error(`Error: rgthree-comfy cannot define un-configurable property "${property}"`);
+    }
+    if ((existingDesc === null || existingDesc === void 0 ? void 0 : existingDesc.get) && desc.get) {
+        const descGet = desc.get;
+        desc.get = () => {
+            existingDesc.get.apply(instance, []);
+            return descGet.apply(instance, []);
+        };
+    }
+    if ((existingDesc === null || existingDesc === void 0 ? void 0 : existingDesc.set) && desc.set) {
+        const descSet = desc.set;
+        desc.set = (v) => {
+            existingDesc.set.apply(instance, [v]);
+            return descSet.apply(instance, [v]);
+        };
+    }
+    desc.enumerable = (_b = (_a = desc.enumerable) !== null && _a !== void 0 ? _a : existingDesc === null || existingDesc === void 0 ? void 0 : existingDesc.enumerable) !== null && _b !== void 0 ? _b : true;
+    desc.configurable = (_d = (_c = desc.configurable) !== null && _c !== void 0 ? _c : existingDesc === null || existingDesc === void 0 ? void 0 : existingDesc.configurable) !== null && _d !== void 0 ? _d : true;
+    if (!desc.get && !desc.set) {
+        desc.writable = (_f = (_e = desc.writable) !== null && _e !== void 0 ? _e : existingDesc === null || existingDesc === void 0 ? void 0 : existingDesc.writable) !== null && _f !== void 0 ? _f : true;
+    }
+    return Object.defineProperty(instance, property, desc);
+}
+export function areDataViewsEqual(a, b) {
+    if (a.byteLength !== b.byteLength) {
+        return false;
+    }
+    for (let i = 0; i < a.byteLength; i++) {
+        if (a.getUint8(i) !== b.getUint8(i)) {
+            return false;
+        }
+    }
+    return true;
+}
+function looksLikeBase64(source) {
+    return source.length > 500 || source.startsWith("data:") || source.includes(";base64,");
+}
+export function areArrayBuffersEqual(a, b) {
+    if (a == b || !a || !b) {
+        return a == b;
+    }
+    return areDataViewsEqual(new DataView(a), new DataView(b));
+}
+export function newCanvas(widthOrPtOrImage, height) {
+    let width;
+    if (typeof widthOrPtOrImage !== "number") {
+        width = widthOrPtOrImage.width;
+        height = widthOrPtOrImage.height;
+    }
+    else {
+        width = widthOrPtOrImage;
+        height = height;
+    }
+    if (height == null) {
+        throw new Error("Invalid height supplied when creating new canvas object.");
+    }
+    const canvas = document.createElement("canvas");
+    canvas.width = width;
+    canvas.height = height;
+    if (widthOrPtOrImage instanceof HTMLImageElement) {
+        const ctx = canvas.getContext("2d");
+        ctx.drawImage(widthOrPtOrImage, 0, 0, width, height);
+    }
+    return canvas;
+}
+export function getCanvasImageData(image) {
+    const canvas = newCanvas(image);
+    const ctx = canvas.getContext("2d");
+    const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
+    return [canvas, ctx, imageData];
+}
+export async function convertToBase64(source) {
+    if (source instanceof Promise) {
+        source = await source;
+    }
+    if (typeof source === "string" && looksLikeBase64(source)) {
+        return source;
+    }
+    if (typeof source === "string" || source instanceof Blob || source instanceof ArrayBuffer) {
+        return convertToBase64(await loadImage(source));
+    }
+    if (source instanceof HTMLImageElement) {
+        if (looksLikeBase64(source.src)) {
+            return source.src;
+        }
+        const [canvas, ctx, imageData] = getCanvasImageData(source);
+        return convertToBase64(canvas);
+    }
+    if (source instanceof HTMLCanvasElement) {
+        return source.toDataURL("image/png");
+    }
+    throw Error("Unknown source to convert to base64.");
+}
+export async function convertToArrayBuffer(source) {
+    if (source instanceof Promise) {
+        source = await source;
+    }
+    if (source instanceof ArrayBuffer) {
+        return source;
+    }
+    if (typeof source === "string") {
+        if (looksLikeBase64(source)) {
+            var binaryString = atob(source.replace(/^.*?;base64,/, ""));
+            var bytes = new Uint8Array(binaryString.length);
+            for (var i = 0; i < binaryString.length; i++) {
+                bytes[i] = binaryString.charCodeAt(i);
+            }
+            return bytes.buffer;
+        }
+        return convertToArrayBuffer(await loadImage(source));
+    }
+    if (source instanceof HTMLImageElement) {
+        const [canvas, ctx, imageData] = getCanvasImageData(source);
+        return convertToArrayBuffer(canvas);
+    }
+    if (source instanceof HTMLCanvasElement) {
+        return convertToArrayBuffer(source.toDataURL());
+    }
+    if (source instanceof Blob) {
+        return source.arrayBuffer();
+    }
+    throw Error("Unknown source to convert to arraybuffer.");
+}
+export async function loadImage(source) {
+    if (source instanceof Promise) {
+        source = await source;
+    }
+    if (source instanceof HTMLImageElement) {
+        return loadImage(source.src);
+    }
+    if (source instanceof Blob) {
+        return loadImage(source.arrayBuffer());
+    }
+    if (source instanceof HTMLCanvasElement) {
+        return loadImage(source.toDataURL());
+    }
+    if (source instanceof ArrayBuffer) {
+        var binary = "";
+        var bytes = new Uint8Array(source);
+        var len = bytes.byteLength;
+        for (var i = 0; i < len; i++) {
+            binary += String.fromCharCode(bytes[i]);
+        }
+        return loadImage(`data:${getMimeTypeFromArrayBuffer(bytes)};base64,${btoa(binary)}`);
+    }
+    return new Promise((resolve, reject) => {
+        const img = new Image();
+        img.addEventListener("load", () => {
+            resolve(img);
+        });
+        img.addEventListener("error", () => {
+            reject(img);
+        });
+        img.src = source;
+    });
+}
+function getMimeTypeFromArrayBuffer(buffer) {
+    const len = 4;
+    if (buffer.length >= len) {
+        let signatureArr = new Array(len);
+        for (let i = 0; i < len; i++)
+            signatureArr[i] = buffer[i].toString(16);
+        const signature = signatureArr.join("").toUpperCase();
+        switch (signature) {
+            case "89504E47":
+                return "image/png";
+            case "47494638":
+                return "image/gif";
+            case "25504446":
+                return "application/pdf";
+            case "FFD8FFDB":
+            case "FFD8FFE0":
+                return "image/jpeg";
+            case "504B0304":
+                return "application/zip";
+            default:
+                return null;
+        }
+    }
+    return null;
+}
+export class Broadcaster extends EventTarget {
+    constructor(channelName) {
+        super();
+        this.queue = {};
+        this.queue = {};
+        this.channel = new BroadcastChannel(channelName);
+        this.channel.addEventListener("message", (e) => {
+            this.onMessage(e);
+        });
+    }
+    getId() {
+        let id;
+        do {
+            id = generateId(6);
+        } while (this.queue[id]);
+        return id;
+    }
+    async broadcastAndWait(action, payload, options) {
+        const id = this.getId();
+        this.queue[id] = getResolver(options === null || options === void 0 ? void 0 : options.timeout);
+        this.channel.postMessage({
+            id,
+            action,
+            payload,
+        });
+        let response;
+        try {
+            response = await this.queue[id].promise;
+        }
+        catch (e) {
+            console.log("CAUGHT", e);
+            response = [];
+        }
+        return response;
+    }
+    broadcast(action, payload) {
+        this.channel.postMessage({
+            id: this.getId(),
+            action,
+            payload,
+        });
+    }
+    reply(replyId, action, payload) {
+        this.channel.postMessage({
+            id: this.getId(),
+            replyId,
+            action,
+            payload,
+        });
+    }
+    openWindowAndWaitForMessage(rgthreePath, windowName) {
+        const id = this.getId();
+        this.queue[id] = getResolver();
+        const win = window.open(`/rgthree/${rgthreePath}#broadcastLoadMsgId=${id}`, windowName);
+        return { window: win, promise: this.queue[id].promise };
+    }
+    onMessage(e) {
+        var _a, _b;
+        const msgId = ((_a = e.data) === null || _a === void 0 ? void 0 : _a.replyId) || "";
+        const queueItem = this.queue[msgId];
+        if (queueItem) {
+            if (queueItem.completed) {
+                console.error(`${msgId} already completed..`);
+            }
+            queueItem.deferment = queueItem.deferment || { data: [] };
+            queueItem.deferment.data.push(e.data.payload);
+            queueItem.deferment.timeout && clearTimeout(queueItem.deferment.timeout);
+            queueItem.deferment.timeout = setTimeout(() => {
+                queueItem.resolve(queueItem.deferment.data);
+            }, 250);
+        }
+        else {
+            this.dispatchEvent(new CustomEvent("rgthree-broadcast-message", {
+                detail: Object.assign({ replyTo: (_b = e.data) === null || _b === void 0 ? void 0 : _b.id }, e.data),
+            }));
+        }
+    }
+    addMessageListener(callback, options) {
+        return super.addEventListener("rgthree-broadcast-message", callback, options);
+    }
+}
+const broadcastChannelMap = new Map();
+export function broadcastOnChannel(channel, action, payload) {
+    let queue = broadcastChannelMap.get(channel);
+    if (!queue) {
+        broadcastChannelMap.set(channel, {});
+        queue = broadcastChannelMap.get(channel);
+    }
+    let id;
+    do {
+        id = generateId(6);
+    } while (queue[id]);
+    queue[id] = getResolver();
+    channel.postMessage({
+        id,
+        action,
+        payload,
+    });
+    return queue[id].promise;
+}
diff --git a/custom_nodes/rgthree-comfy/web/common/utils_dom.js b/custom_nodes/rgthree-comfy/web/common/utils_dom.js
new file mode 100644
index 0000000000000000000000000000000000000000..c07e876f8ec801f3467d05d78ef7b9b8c7db6ba3
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/common/utils_dom.js
@@ -0,0 +1,363 @@
+const DIRECT_ATTRIBUTE_MAP = {
+    cellpadding: "cellPadding",
+    cellspacing: "cellSpacing",
+    colspan: "colSpan",
+    frameborder: "frameBorder",
+    height: "height",
+    maxlength: "maxLength",
+    nonce: "nonce",
+    role: "role",
+    rowspan: "rowSpan",
+    type: "type",
+    usemap: "useMap",
+    valign: "vAlign",
+    width: "width",
+};
+const RGX_NUMERIC_STYLE_UNIT = "px";
+const RGX_NUMERIC_STYLE = /^((max|min)?(width|height)|margin|padding|(margin|padding)?(left|top|bottom|right)|fontsize|borderwidth)$/i;
+const RGX_DEFAULT_VALUE_PROP = /input|textarea|select/i;
+function localAssertNotFalsy(input, errorMsg = `Input is not of type.`) {
+    if (input == null) {
+        throw new Error(errorMsg);
+    }
+    return input;
+}
+const RGX_STRING_VALID = "[a-z0-9_-]";
+const RGX_TAG = new RegExp(`^([a-z]${RGX_STRING_VALID}*)(\\.|\\[|\\#|$)`, "i");
+const RGX_ATTR_ID = new RegExp(`#(${RGX_STRING_VALID}+)`, "gi");
+const RGX_ATTR_CLASS = new RegExp(`(^|\\S)\\.([a-z0-9_\\-\\.]+)`, "gi");
+const RGX_STRING_CONTENT_TO_SQUARES = "(.*?)(\\[|\\])";
+const RGX_ATTRS_MAYBE_OPEN = new RegExp(`\\[${RGX_STRING_CONTENT_TO_SQUARES}`, "gi");
+const RGX_ATTRS_FOLLOW_OPEN = new RegExp(`^${RGX_STRING_CONTENT_TO_SQUARES}`, "gi");
+export function queryAll(selectors, parent = document) {
+    return Array.from(parent.querySelectorAll(selectors)).filter((n) => !!n);
+}
+export function query(selectors, parent = document) {
+    var _a;
+    return (_a = parent.querySelector(selectors)) !== null && _a !== void 0 ? _a : null;
+}
+export function createText(text) {
+    return document.createTextNode(text);
+}
+export function getClosestOrSelf(element, query) {
+    const el = element;
+    return ((el === null || el === void 0 ? void 0 : el.closest) && ((el.matches(query) && el) || el.closest(query))) || null;
+}
+export function containsOrSelf(parent, contained) {
+    var _a;
+    return (parent === contained || ((_a = parent === null || parent === void 0 ? void 0 : parent.contains) === null || _a === void 0 ? void 0 : _a.call(parent, contained)) || false);
+}
+export function createElement(selectorOrMarkup, attrs) {
+    const frag = getHtmlFragment(selectorOrMarkup);
+    let element = frag === null || frag === void 0 ? void 0 : frag.firstElementChild;
+    let selector = "";
+    if (!element) {
+        selector = selectorOrMarkup.replace(/[\r\n]\s*/g, "");
+        const tag = getSelectorTag(selector) || "div";
+        element = document.createElement(tag);
+        selector = selector.replace(RGX_TAG, "$2");
+        const brackets = selector.match(/(\[[^\]]+\])/g) || [];
+        for (const bracket of brackets) {
+            selector = selector.replace(bracket, "");
+        }
+        selector = selector.replace(RGX_ATTR_ID, '[id="$1"]');
+        selector = selector.replace(RGX_ATTR_CLASS, (match, p1, p2) => `${p1}[class="${p2.replace(/\./g, " ")}"]`);
+        selector += brackets.join("");
+    }
+    const selectorAttrs = getSelectorAttributes(selector);
+    if (selectorAttrs) {
+        for (const attr of selectorAttrs) {
+            let matches = attr.substring(1, attr.length - 1).split("=");
+            let key = localAssertNotFalsy(matches.shift());
+            let value = matches.join("=");
+            if (value === undefined) {
+                setAttribute(element, key, true);
+            }
+            else {
+                value = value.replace(/^['"](.*)['"]$/, "$1");
+                setAttribute(element, key, value);
+            }
+        }
+    }
+    if (attrs) {
+        setAttributes(element, attrs);
+    }
+    return element;
+}
+export const $el = createElement;
+function getSelectorTag(str) {
+    return tryMatch(str, RGX_TAG);
+}
+function getSelectorAttributes(selector) {
+    RGX_ATTRS_MAYBE_OPEN.lastIndex = 0;
+    let attrs = [];
+    let result;
+    while ((result = RGX_ATTRS_MAYBE_OPEN.exec(selector))) {
+        let attr = result[0];
+        if (attr.endsWith("]")) {
+            attrs.push(attr);
+        }
+        else {
+            attr =
+                result[0] + getOpenAttributesRecursive(selector.substr(RGX_ATTRS_MAYBE_OPEN.lastIndex), 2);
+            RGX_ATTRS_MAYBE_OPEN.lastIndex += attr.length - result[0].length;
+            attrs.push(attr);
+        }
+    }
+    return attrs;
+}
+function getOpenAttributesRecursive(selectorSubstring, openCount) {
+    let matches = selectorSubstring.match(RGX_ATTRS_FOLLOW_OPEN);
+    let result = "";
+    if (matches && matches.length) {
+        result = matches[0];
+        openCount += result.endsWith("]") ? -1 : 1;
+        if (openCount > 0) {
+            result += getOpenAttributesRecursive(selectorSubstring.substr(result.length), openCount);
+        }
+    }
+    return result;
+}
+function tryMatch(str, rgx, index = 1) {
+    var _a;
+    let found = "";
+    try {
+        found = ((_a = str.match(rgx)) === null || _a === void 0 ? void 0 : _a[index]) || "";
+    }
+    catch (e) {
+        found = "";
+    }
+    return found;
+}
+export function setAttributes(element, data) {
+    let attr;
+    for (attr in data) {
+        if (data.hasOwnProperty(attr)) {
+            setAttribute(element, attr, data[attr]);
+        }
+    }
+}
+function getHtmlFragment(value) {
+    if (value.match(/^\s*<.*?>[\s\S]*<\/[a-z0-9]+>\s*$/)) {
+        return document.createRange().createContextualFragment(value.trim());
+    }
+    return null;
+}
+function getChild(value) {
+    if (value instanceof Node) {
+        return value;
+    }
+    if (typeof value === "string") {
+        let child = getHtmlFragment(value);
+        if (child) {
+            return child;
+        }
+        if (getSelectorTag(value)) {
+            return createElement(value);
+        }
+        return createText(value);
+    }
+    if (value && typeof value.toElement === "function") {
+        return value.toElement();
+    }
+    return null;
+}
+export function setAttribute(element, attribute, value) {
+    let isRemoving = value == null;
+    if (attribute === "default") {
+        attribute = RGX_DEFAULT_VALUE_PROP.test(element.nodeName) ? "value" : "text";
+    }
+    if (attribute === "text") {
+        empty(element).appendChild(createText(value != null ? String(value) : ""));
+    }
+    else if (attribute === "html") {
+        empty(element).innerHTML += value != null ? String(value) : "";
+    }
+    else if (attribute == "style") {
+        if (typeof value === "string") {
+            element.style.cssText = isRemoving ? "" : value != null ? String(value) : "";
+        }
+        else {
+            for (const [styleKey, styleValue] of Object.entries(value)) {
+                element.style[styleKey] = styleValue;
+            }
+        }
+    }
+    else if (attribute == "events") {
+        for (const [key, fn] of Object.entries(value)) {
+            addEvent(element, key, fn);
+        }
+    }
+    else if (attribute === "parent") {
+        value.appendChild(element);
+    }
+    else if (attribute === "child" || attribute === "children") {
+        if (typeof value === "string" && /^\[[^\[\]]+\]$/.test(value)) {
+            const parseable = value.replace(/^\[([^\[\]]+)\]$/, '["$1"]').replace(/,/g, '","');
+            try {
+                const parsed = JSON.parse(parseable);
+                value = parsed;
+            }
+            catch (e) {
+                console.error(e);
+            }
+        }
+        if (attribute === "children") {
+            empty(element);
+        }
+        let children = value instanceof Array ? value : [value];
+        for (let child of children) {
+            child = getChild(child);
+            if (child instanceof Node) {
+                if (element instanceof HTMLTemplateElement) {
+                    element.content.appendChild(child);
+                }
+                else {
+                    element.appendChild(child);
+                }
+            }
+        }
+    }
+    else if (attribute == "for") {
+        element.htmlFor = value != null ? String(value) : "";
+        if (isRemoving) {
+            element.removeAttribute("for");
+        }
+    }
+    else if (attribute === "class" || attribute === "className" || attribute === "classes") {
+        element.className = isRemoving ? "" : Array.isArray(value) ? value.join(" ") : String(value);
+    }
+    else if (attribute === "dataset") {
+        if (typeof value !== "object") {
+            console.error("Expecting an object for dataset");
+            return;
+        }
+        for (const [key, val] of Object.entries(value)) {
+            element.dataset[key] = String(val);
+        }
+    }
+    else if (attribute.startsWith("on") && typeof value === "function") {
+        element.addEventListener(attribute.substring(2), value);
+    }
+    else if (["checked", "disabled", "readonly", "required", "selected"].includes(attribute)) {
+        element[attribute] = !!value;
+        if (!value) {
+            element.removeAttribute(attribute);
+        }
+        else {
+            element.setAttribute(attribute, attribute);
+        }
+    }
+    else if (DIRECT_ATTRIBUTE_MAP.hasOwnProperty(attribute)) {
+        if (isRemoving) {
+            element.removeAttribute(DIRECT_ATTRIBUTE_MAP[attribute]);
+        }
+        else {
+            element.setAttribute(DIRECT_ATTRIBUTE_MAP[attribute], String(value));
+        }
+    }
+    else if (isRemoving) {
+        element.removeAttribute(attribute);
+    }
+    else {
+        let oldVal = element.getAttribute(attribute);
+        if (oldVal !== value) {
+            element.setAttribute(attribute, String(value));
+        }
+    }
+}
+function addEvent(element, key, fn) {
+    element.addEventListener(key, fn);
+}
+function setStyles(element, styles = null) {
+    if (styles) {
+        for (let name in styles) {
+            setStyle(element, name, styles[name]);
+        }
+    }
+    return element;
+}
+export function setStyle(element, name, value) {
+    name = name.indexOf("float") > -1 ? "cssFloat" : name;
+    if (name.indexOf("-") != -1) {
+        name = name.replace(/-\D/g, (match) => {
+            return match.charAt(1).toUpperCase();
+        });
+    }
+    if (value == String(Number(value)) && RGX_NUMERIC_STYLE.test(name)) {
+        value = value + RGX_NUMERIC_STYLE_UNIT;
+    }
+    if (name === "display" && typeof value !== "string") {
+        value = !!value ? null : "none";
+    }
+    element.style[name] = value === null ? null : String(value);
+    return element;
+}
+export function empty(element) {
+    while (element.firstChild) {
+        element.removeChild(element.firstChild);
+    }
+    return element;
+}
+export function remove(element) {
+    while (element.parentElement) {
+        element.parentElement.removeChild(element);
+    }
+    return element;
+}
+export function replaceChild(oldChildNode, newNode) {
+    oldChildNode.parentNode.replaceChild(newNode, oldChildNode);
+    return newNode;
+}
+export function appendChildren(el, children) {
+    children = !Array.isArray(children) ? [children] : children;
+    for (let child of children) {
+        child = getChild(child);
+        if (child instanceof Node) {
+            if (el instanceof HTMLTemplateElement) {
+                el.content.appendChild(child);
+            }
+            else {
+                el.appendChild(child);
+            }
+        }
+    }
+}
+export function getActionEls(parent = document) {
+    const els = Array.from(parent.querySelectorAll("[data-action],[on-action],[on]"));
+    if (parent instanceof Element) {
+        els.unshift(parent);
+    }
+    return els
+        .map((actionEl) => {
+        const actions = {};
+        const actionSegments = (actionEl.getAttribute("data-action") ||
+            actionEl.getAttribute("on-action") ||
+            actionEl.getAttribute("on") ||
+            "").split(";");
+        for (let segment of actionSegments) {
+            let actionsData = segment
+                .trim()
+                .split(/\s*:\s*/g)
+                .filter((i) => !!i.trim());
+            if (!actionsData.length)
+                continue;
+            if (actionsData.length === 1) {
+                if (actionEl instanceof HTMLInputElement) {
+                    actionsData.unshift("input");
+                }
+                else {
+                    actionsData.unshift("click");
+                }
+            }
+            if (actionsData[0] && actionsData[1]) {
+                actions[actionsData[0]] = actionsData[1];
+            }
+        }
+        return {
+            el: actionEl,
+            actions,
+        };
+    })
+        .filter((el) => !!el);
+}
diff --git a/custom_nodes/rgthree-comfy/web/common/utils_templates.js b/custom_nodes/rgthree-comfy/web/common/utils_templates.js
new file mode 100644
index 0000000000000000000000000000000000000000..973ca1b05e7ec588f2f4247d430fe5ab38884b3a
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/common/utils_templates.js
@@ -0,0 +1,592 @@
+import * as dom from "./utils_dom.js";
+import { getObjectValue } from "./shared_utils.js";
+const CONFIG_DEFAULT = {
+    attrBind: "data-bind",
+    attrIf: "data-if",
+    attrIfIs: "data-if-is",
+};
+const CONFIG = Object.assign({}, CONFIG_DEFAULT, {
+    attrBind: "bind",
+    attrIf: "if",
+    attrIfIs: "if-is",
+});
+const RGX_COMPARISON = (() => {
+    let value = "((?:\\!*)[_a-z0-9\\.\\-\\[\\]'\"]+)";
+    let comparison = "((?:<|>|==|\\!=)=?)";
+    return new RegExp(`^(?:\\!*)\\(?${value}\\s*${comparison}\\s*${value}\\)?$`, "i");
+})();
+const RGXPART_BIND_FN_TEMPLATE_STRING = "template|tpl";
+const RGXPART_BIND_FN_ELEMENT_STRING = "element|el";
+const RGX_BIND_FN_TEMPLATE = new RegExp(`^(?:${RGXPART_BIND_FN_TEMPLATE_STRING})\\(([^\\)]+)\\)`, "i");
+const RGX_BIND_FN_ELEMENT = new RegExp(`^(?:${RGXPART_BIND_FN_ELEMENT_STRING})\\(([^\\)]+)\\)`, "i");
+const RGX_BIND_FN_TEMPLATE_OR_ELEMENT = new RegExp(`^(?:${RGXPART_BIND_FN_TEMPLATE_STRING}|${RGXPART_BIND_FN_ELEMENT_STRING})\\(([^\\)]+)\\)`, "i");
+const RGX_BIND_FN_LENGTH = /^(?:length|len|size)\(([^\)]+)\)/i;
+const RGX_BIND_FN_FORMAT = /^(?:format|fmt)\(([^\,]+),([^\)]+)\)/i;
+const RGX_BIND_FN_CALL = /^([^\(]+)\(([^\)]*)\)/i;
+const EMPTY_PREPROCESS_FN = (data) => data;
+const RGX_BIND_DECLARATIONS = /\s*(\!*(?:[\$_a-z0-9-\.\'\"]|\?\?|\|\||\&\&|(?:(?:<|>|==|\!=)=?))+(?:\`[^\`]+\`)?(?:\([^\)]*\))?)(?::(.*?))?(\s|$)/gi;
+function localAssertNotFalsy(input, errorMsg = `Input is not of type.`) {
+    if (input == null) {
+        throw new Error(errorMsg);
+    }
+    return input;
+}
+function cleanKey(key) {
+    return key.toLowerCase().trim().replace(/\s/g, "");
+}
+function toArray(value) {
+    if (Array.isArray(value)) {
+        return value;
+    }
+    if (value instanceof Set) {
+        return Array.from(value);
+    }
+    if (typeof value === "object" && typeof value.length === "number") {
+        return [].slice.call(value);
+    }
+    return [value];
+}
+function flattenArray(arr) {
+    return toArray(arr).reduce((acc, val) => {
+        return acc.concat(Array.isArray(val) ? flattenArray(val) : val);
+    }, []);
+}
+function getObjValue(lookup, obj) {
+    let booleanMatch = lookup.match(/^(\!+)(.+?)$/i) || [];
+    let booleanNots = [];
+    if (booleanMatch[1] && booleanMatch[2]) {
+        booleanNots = booleanMatch[1].split("");
+        lookup = booleanMatch[2];
+    }
+    let value = getObjectValue(obj, lookup);
+    while (booleanNots.length) {
+        value = !value;
+        booleanNots.shift();
+    }
+    return value;
+}
+function getPrimitiveOrObjValue(stringValue, data) {
+    let value;
+    if (stringValue == null) {
+        return stringValue;
+    }
+    let negate = getNegates(stringValue);
+    if (negate != null) {
+        stringValue = stringValue.replace(/^\!+/, "");
+    }
+    try {
+        const cleanedStringValue = stringValue.replace(/^'(.*)'$/, '"$1"');
+        value = JSON.parse(cleanedStringValue);
+    }
+    catch (e) {
+        value = getObjValue(stringValue, data);
+    }
+    value = negate !== null ? (negate === 1 ? !value : !!value) : value;
+    return value;
+}
+function getNegates(stringValue) {
+    let negate = null;
+    let negateMatches = stringValue.match(/^(\!+)(.*)/);
+    if (negateMatches && negateMatches.length >= 3) {
+        negate = negateMatches[1].length % 2;
+    }
+    return negate;
+}
+function getStringComparisonExpression(bindingPropName, data) {
+    let comparisonMatches = bindingPropName.match(RGX_COMPARISON);
+    if (!(comparisonMatches === null || comparisonMatches === void 0 ? void 0 : comparisonMatches.length)) {
+        return null;
+    }
+    let a = getPrimitiveOrObjValue(comparisonMatches[1], data);
+    let b = getPrimitiveOrObjValue(comparisonMatches[3], data);
+    let c = comparisonMatches[2];
+    let value = (() => {
+        switch (c) {
+            case "===":
+                return a === b;
+            case "==":
+                return a == b;
+            case "<=":
+                return a <= b;
+            case ">=":
+                return a >= b;
+            case "!==":
+                return a !== b;
+            case "!=":
+                return a != b;
+            case "<":
+                return a < b;
+            case ">":
+                return a > b;
+            default:
+                return undefined;
+        }
+    })();
+    return value;
+}
+function replaceTplElementWithChildren(tplEl, fragOrElOrEls) {
+    const els = Array.isArray(fragOrElOrEls) ? fragOrElOrEls : [fragOrElOrEls];
+    tplEl.replaceWith(...els);
+    const numOfChildren = Array.isArray(fragOrElOrEls)
+        ? fragOrElOrEls.length
+        : fragOrElOrEls.childElementCount;
+    if (numOfChildren === 1) {
+        const firstChild = Array.isArray(fragOrElOrEls)
+            ? fragOrElOrEls[0]
+            : fragOrElOrEls.firstElementChild;
+        if (firstChild instanceof Element) {
+            if (tplEl.className.length) {
+                firstChild.className += ` ${tplEl.className}`;
+            }
+            let attr = tplEl.getAttribute("data");
+            if (attr) {
+                firstChild.setAttribute("data", attr);
+            }
+            attr = tplEl.getAttribute(CONFIG.attrBind);
+            if (attr) {
+                firstChild.setAttribute(CONFIG.attrBind, attr);
+            }
+        }
+    }
+}
+function getValueForBinding(bindingPropName, context) {
+    console.log("getValueForBinding", bindingPropName, context);
+    const data = context.data;
+    let stringTemplate = null;
+    let stringTemplates = /^(.*?)\`([^\`]*)\`$/.exec(bindingPropName.trim());
+    if ((stringTemplates === null || stringTemplates === void 0 ? void 0 : stringTemplates.length) === 3) {
+        bindingPropName = stringTemplates[1];
+        stringTemplate = stringTemplates[2];
+    }
+    let value = null;
+    let hadALogicalOp = false;
+    const opsToValidation = new Map([
+        [/\s*\?\?\s*/, (v) => v != null],
+        [/\s*\|\|\s*/, (v) => !!v],
+        [/\s*\&\&\s*/, (v) => !v],
+    ]);
+    for (const [op, fn] of opsToValidation.entries()) {
+        if (bindingPropName.match(op)) {
+            hadALogicalOp = true;
+            const bindingPropNames = bindingPropName.split(op);
+            for (const propName of bindingPropNames) {
+                value = getValueForBindingPropName(propName, context);
+                if (fn(value)) {
+                    break;
+                }
+            }
+            break;
+        }
+    }
+    if (!hadALogicalOp) {
+        value = getValueForBindingPropName(bindingPropName, context);
+    }
+    return stringTemplate && value != null
+        ? stringTemplate.replace(/\$\{value\}/g, String(value))
+        : value;
+}
+function getValueForBindingPropName(bindingPropName, context) {
+    var _a, _b, _c;
+    const data = context.data;
+    let negate = getNegates(bindingPropName);
+    if (negate != null) {
+        bindingPropName = bindingPropName.replace(/^\!+/, "");
+    }
+    let value;
+    RGX_COMPARISON.lastIndex = 0;
+    if (RGX_COMPARISON.test(bindingPropName)) {
+        value = getStringComparisonExpression(bindingPropName, data);
+    }
+    else if (RGX_BIND_FN_LENGTH.test(bindingPropName)) {
+        bindingPropName = RGX_BIND_FN_LENGTH.exec(bindingPropName)[1];
+        value = getPrimitiveOrObjValue(bindingPropName, data);
+        value = (value && value.length) || 0;
+    }
+    else if (RGX_BIND_FN_FORMAT.test(bindingPropName)) {
+        let matches = RGX_BIND_FN_FORMAT.exec(bindingPropName);
+        bindingPropName = matches[1];
+        value = getPrimitiveOrObjValue(bindingPropName, data);
+        value = matches[2].replace(/^['"]/, "").replace(/['"]$/, "").replace(/\$1/g, value);
+    }
+    else if (RGX_BIND_FN_CALL.test(bindingPropName)) {
+        console.log("-----");
+        console.log(bindingPropName);
+        let matches = RGX_BIND_FN_CALL.exec(bindingPropName);
+        const functionName = matches[1];
+        const maybeDataName = (_a = matches[2]) !== null && _a !== void 0 ? _a : null;
+        value = getPrimitiveOrObjValue(maybeDataName, data);
+        console.log(functionName, maybeDataName, value);
+        if (typeof (value === null || value === void 0 ? void 0 : value[functionName]) === "function") {
+            value = value[functionName](value, data, context.currentElement, context.contextElement);
+        }
+        else if (typeof (data === null || data === void 0 ? void 0 : data[functionName]) === "function") {
+            value = data[functionName](value, data, context.currentElement, context.contextElement);
+        }
+        else if (typeof ((_b = context.currentElement) === null || _b === void 0 ? void 0 : _b[functionName]) === "function") {
+            value = context.currentElement[functionName](value, data, context.currentElement, context.contextElement);
+        }
+        else if (typeof ((_c = context.contextElement) === null || _c === void 0 ? void 0 : _c[functionName]) === "function") {
+            value = context.contextElement[functionName](value, data, context.currentElement, context.contextElement);
+        }
+        else {
+            console.error(`No method named ${functionName} on data or element instance. Just calling regular value.`);
+            value = getPrimitiveOrObjValue(bindingPropName, data);
+        }
+    }
+    else {
+        value = getPrimitiveOrObjValue(bindingPropName, data);
+    }
+    if (value !== undefined) {
+        value = negate !== null ? (negate === 1 ? !value : !!value) : value;
+    }
+    return value;
+}
+function removeBindingAttributes(elOrEls, deep = false) {
+    flattenArray(elOrEls || []).forEach((el) => {
+        el.removeAttribute(CONFIG.attrBind);
+        const innerBinds = dom.queryAll(`:scope [${CONFIG.attrBind}]`, el);
+        const innerTplBinds = deep ? [] : dom.queryAll(`:scope [data-tpl] [${CONFIG.attrBind}]`);
+        innerBinds.forEach((el) => {
+            if (deep || !innerTplBinds.includes(el)) {
+                el.removeAttribute(CONFIG.attrBind);
+            }
+        });
+    });
+}
+const templateCache = {};
+export function checkKey(key) {
+    return !!templateCache[cleanKey(key)];
+}
+export function register(key, htmlOrElement = null, preProcessScript) {
+    key = cleanKey(key);
+    if (templateCache[key]) {
+        return templateCache[key];
+    }
+    let fragment = null;
+    if (typeof htmlOrElement === "string") {
+        const frag = document.createDocumentFragment();
+        if (htmlOrElement.includes("<")) {
+            const html = htmlOrElement.trim();
+            const htmlParentTag = (html.startsWith("<tr") && "tbody") ||
+                (/^<t(body|head|foot)/i.test(html) && "table") ||
+                (/^<t(d|h)/i.test(html) && "tr") ||
+                "div";
+            const temp = document.createElement(htmlParentTag);
+            temp.innerHTML = html;
+            for (const child of temp.children) {
+                frag.appendChild(child);
+            }
+        }
+        else {
+            frag.appendChild(dom.createElement(htmlOrElement));
+        }
+        fragment = frag;
+    }
+    else if (htmlOrElement instanceof Element) {
+        const element = htmlOrElement;
+        const tag = element.nodeName.toLowerCase();
+        if (tag === "template" && element.content) {
+            fragment = element.content;
+        }
+        else {
+            throw Error("Non-template element not handled");
+        }
+    }
+    else if (!htmlOrElement) {
+        let element = dom.query(`template[id="${key}"],template[data-id="${key}"]`);
+        if (element && element.content) {
+            fragment = element.content;
+        }
+        else {
+            throw Error("Non-template element not handled");
+        }
+    }
+    if (fragment) {
+        templateCache[key] = {
+            fragment,
+            preProcessScript: preProcessScript || EMPTY_PREPROCESS_FN,
+        };
+    }
+    return templateCache[key] || null;
+}
+export function getPreProcessScript(keyOrEl) {
+    var _a;
+    if (typeof keyOrEl === "string") {
+        if (!templateCache[keyOrEl]) {
+            throw Error(`Template key does not exist ${keyOrEl}`);
+        }
+        return templateCache[keyOrEl].preProcessScript;
+    }
+    if (keyOrEl instanceof Element) {
+        const tpl = keyOrEl.getAttribute("data-tpl") || "";
+        return ((_a = templateCache[tpl]) === null || _a === void 0 ? void 0 : _a.preProcessScript) || EMPTY_PREPROCESS_FN;
+    }
+    return EMPTY_PREPROCESS_FN;
+}
+export function getTemplateFragment(key) {
+    key = cleanKey(key);
+    if (!checkKey(key)) {
+        register(key);
+    }
+    let templateData = templateCache[key];
+    if (templateData && templateData.fragment) {
+        let imported;
+        if (document.importNode) {
+            imported = document.importNode(templateData.fragment, true);
+        }
+        else {
+            imported = templateData.fragment.cloneNode(true);
+        }
+        imported.__templateid__ = key;
+        return imported;
+    }
+    else {
+        throw new Error("Ain't no template called " + key + " (" + typeof templateCache[key] + ")");
+    }
+}
+export function inflate(nodeOrKey, templateData = null, inflateOptions = {}) {
+    let node = nodeOrKey;
+    if (typeof node === "string") {
+        node = getTemplateFragment(node);
+    }
+    if (node) {
+        const els = dom.queryAll("[data-template], [data-templateid], [template]", node);
+        for (const child of els) {
+            let className = child.className || null;
+            let childTemplateId = localAssertNotFalsy(child.getAttribute("data-template") ||
+                child.getAttribute("data-templateid") ||
+                child.getAttribute("template"), "No child template id provided.");
+            const dataAttribute = child.getAttribute("data") || "";
+            const childData = (dataAttribute && getObjValue(dataAttribute, templateData)) || templateData;
+            const tplsInflateOptions = Object.assign({}, inflateOptions);
+            if (tplsInflateOptions.skipInit != null) {
+                tplsInflateOptions.skipInit = true;
+            }
+            let tpls = localAssertNotFalsy(inflate(childTemplateId, childData, tplsInflateOptions), `No template inflated from ${childTemplateId}.`);
+            tpls = !Array.isArray(tpls) ? [tpls] : tpls;
+            if (className) {
+                for (const tpl of tpls) {
+                    tpl.classList.add(className);
+                }
+            }
+            if (child.nodeName.toUpperCase() === "TPL") {
+                replaceTplElementWithChildren(child, tpls);
+            }
+            else {
+                child.append(...tpls);
+            }
+            child.remove();
+        }
+        let children = [];
+        for (const child of node.children) {
+            let tplAttributes = (child.getAttribute("data-tpl") || "").split(" ");
+            if (!tplAttributes.includes(node.__templateid__)) {
+                tplAttributes.push(node.__templateid__);
+            }
+            child.setAttribute("data-tpl", tplAttributes.join(" ").trim());
+            children.push(child);
+        }
+        let childOrChildren = children.length === 1 ? children[0] : children;
+        if (!inflateOptions.skipInit) {
+            init(childOrChildren, templateData, inflateOptions.bindOptions);
+        }
+        return childOrChildren;
+    }
+    return null;
+}
+export function inflateSingle(nodeOrKey, scriptData = null, bindOptions = {}) {
+    const inflated = localAssertNotFalsy(inflate(nodeOrKey, scriptData, bindOptions));
+    return Array.isArray(inflated) ? inflated[0] : inflated;
+}
+export function inflateOnce(nodeOrKey, templateData = null, inflateOptions = {}) {
+    let children = inflate(nodeOrKey, templateData, inflateOptions);
+    children && removeBindingAttributes(children, false);
+    return children;
+}
+export function inflateSingleOnce(nodeOrKey, scriptData = null, bindOptions = {}) {
+    const inflated = inflate(nodeOrKey, scriptData, bindOptions) || [];
+    removeBindingAttributes(inflated, false);
+    return Array.isArray(inflated) ? inflated[0] : inflated;
+}
+export function init(els, data, bindOptions = {}) {
+    (!els ? [] : els instanceof Element ? [els] : els).forEach((el) => {
+        const dataTplAttr = el.getAttribute("data-tpl");
+        if (dataTplAttr) {
+            const tpls = dataTplAttr.split(" ");
+            tpls.forEach((tpl) => {
+                const dataAttribute = el.getAttribute("data") || "";
+                const childData = (dataAttribute && getObjValue(dataAttribute, data)) || data;
+                bind(el, childData, bindOptions);
+            });
+        }
+        else {
+            bind(el, data, bindOptions);
+        }
+    });
+}
+export function bind(elOrEls, data = {}, bindOptions = {}) {
+    var _a;
+    if (elOrEls instanceof HTMLElement) {
+        data = getPreProcessScript(elOrEls)({ ...data });
+    }
+    if (typeof data !== "object") {
+        data = { value: data };
+        if (elOrEls instanceof HTMLElement &&
+            elOrEls.children.length === 0 &&
+            !elOrEls.getAttribute(CONFIG.attrBind)) {
+            dom.setAttributes(elOrEls, { [CONFIG.attrBind]: "value" });
+        }
+    }
+    let passedEls = !Array.isArray(elOrEls) ? [elOrEls] : elOrEls;
+    for (const el of passedEls) {
+        const conditionEls = toArray(dom.queryAll(`[${CONFIG.attrIf}]`, el));
+        const contextElement = (_a = bindOptions.contextElement) !== null && _a !== void 0 ? _a : (el instanceof ShadowRoot ? el.host : el);
+        for (const conditionEl of conditionEls) {
+            getValueForBindingPropName;
+            let isTrue = getValueForBinding(conditionEl.getAttribute(CONFIG.attrIf), {
+                data,
+                contextElement: contextElement,
+                currentElement: conditionEl,
+            });
+            conditionEl.setAttribute(CONFIG.attrIfIs, String(!!isTrue));
+        }
+        let toBindEls = toArray(dom.queryAll(`:not([${CONFIG.attrIfIs}="false"]) [${CONFIG.attrBind}]:not([data-tpl]):not([${CONFIG.attrIfIs}="false"])`, el));
+        if (el instanceof HTMLElement && el.getAttribute(CONFIG.attrBind)) {
+            toBindEls.unshift(el);
+        }
+        if (toBindEls.length) {
+            let innerBindsElements = dom.queryAll(`:scope [data-tpl] [${CONFIG.attrBind}], :scope [data-autobind="false"] [${CONFIG.attrBind}]`, el);
+            toBindEls = toBindEls.filter((maybeBind) => !innerBindsElements.includes(maybeBind));
+            toBindEls.forEach((child) => {
+                RGX_BIND_DECLARATIONS.lastIndex = 0;
+                let bindings = [];
+                let bindingMatch;
+                while ((bindingMatch = RGX_BIND_DECLARATIONS.exec(child.getAttribute(CONFIG.attrBind).replace(/\s+/, " ").trim())) !== null) {
+                    bindings.push([bindingMatch[1], bindingMatch[2]]);
+                }
+                bindings.forEach((bindings) => {
+                    let bindingDataProperty = localAssertNotFalsy(bindings.shift());
+                    let bindingFields = ((bindings.length && bindings[0]) || "default")
+                        .trim()
+                        .replace(/^\[(.*?)\]$/i, "$1")
+                        .split(",");
+                    let value = getValueForBinding(bindingDataProperty, {
+                        data,
+                        contextElement: contextElement,
+                        currentElement: child,
+                    });
+                    if (value === undefined) {
+                        if (bindOptions.onlyDefined === true) {
+                            return;
+                        }
+                        else {
+                            value = null;
+                        }
+                    }
+                    bindingFields.forEach((field) => {
+                        if (field.startsWith("style.")) {
+                            let stringVal = String(value);
+                            if (value &&
+                                !stringVal.includes("url(") &&
+                                stringVal !== "none" &&
+                                (field.includes("background-image") || stringVal.startsWith("http"))) {
+                                value = `url(${value})`;
+                            }
+                            dom.setStyle(child, field.replace("style.", ""), value);
+                        }
+                        else if (field.startsWith("el.")) {
+                            if (field === "el.remove") {
+                                if (value === true) {
+                                    child.remove();
+                                }
+                            }
+                            else if (field === "el.toggle") {
+                                dom.setStyle(child, "display", value === true ? "" : "none");
+                            }
+                            else if (field.startsWith("el.classList.toggle")) {
+                                const cssClass = field.replace(/el.classList.toggle\(['"]?(.*?)['"]?\)/, "$1");
+                                child.classList.toggle(cssClass, !!value);
+                            }
+                        }
+                        else if (RGX_BIND_FN_TEMPLATE_OR_ELEMENT.test(field)) {
+                            dom.empty(child);
+                            let elementOrTemplateName = RGX_BIND_FN_TEMPLATE_OR_ELEMENT.exec(field)[1];
+                            if (Array.isArray(value) || value instanceof Set) {
+                                const arrayVals = toArray(value);
+                                let isElement = RGX_BIND_FN_ELEMENT.test(field);
+                                let frag = document.createDocumentFragment();
+                                arrayVals.forEach((item, index) => {
+                                    let itemData;
+                                    if (typeof item === "object") {
+                                        itemData = Object.assign({ $index: index }, item);
+                                    }
+                                    else {
+                                        itemData = { $index: index, value: item };
+                                    }
+                                    const els = bindToElOrTemplate(elementOrTemplateName, itemData);
+                                    frag.append(...els);
+                                });
+                                if (child.nodeName.toUpperCase() === "TPL") {
+                                    replaceTplElementWithChildren(child, frag);
+                                }
+                                else {
+                                    dom.empty(child).appendChild(frag);
+                                }
+                            }
+                            else if (value) {
+                                const els = bindToElOrTemplate(elementOrTemplateName, value);
+                                if (child.nodeName.toUpperCase() === "TPL") {
+                                    replaceTplElementWithChildren(child, els);
+                                }
+                                else {
+                                    child.append(...els);
+                                }
+                            }
+                        }
+                        else {
+                            dom.setAttributes(child, { [field]: value });
+                        }
+                    });
+                });
+            });
+        }
+        if (bindOptions.singleScoped !== true) {
+            let toInitEls = toArray(el.querySelectorAll(":scope *[data-tpl]"));
+            if (toInitEls.length) {
+                let innerInits = dom.queryAll(':scope *[data-tpl] *[data-tpl], :scope [data-autobind="false"] [data-tpl]', el);
+                toInitEls = toInitEls.filter((maybeInitEl) => {
+                    if (innerInits.includes(maybeInitEl)) {
+                        return false;
+                    }
+                    let tplKey = maybeInitEl.getAttribute("data-tpl");
+                    if (data && (data[tplKey] || data[tplKey.replace("tpl:", "")])) {
+                        return true;
+                    }
+                    return maybeInitEl.getAttribute("data-autobind") !== "false";
+                });
+                toInitEls.forEach((toInitEl) => {
+                    var tplKey = toInitEl.getAttribute("data-tpl");
+                    init(toInitEl, (data && (data[tplKey] || data[tplKey.replace("tpl:", "")])) || data);
+                });
+            }
+        }
+    }
+}
+function bindToElOrTemplate(elementOrTemplateName, data) {
+    let el = getTemplateFragment(elementOrTemplateName);
+    if (!el) {
+        el = dom.createElement(elementOrTemplateName, data);
+    }
+    else {
+        el = inflateOnce(el, data, { skipInit: false });
+    }
+    const els = (Array.isArray(el) ? el : [el]).filter((el) => !!el);
+    els.forEach((el) => {
+        el.removeAttribute("data-tpl");
+        let toBindEls = dom.queryAll("[data-tpl]", el);
+        let innerBindsElements = dom.queryAll(`:scope [data-tpl] [${CONFIG.attrBind}], :scope [data-autobind="false"] [${CONFIG.attrBind}]`, el);
+        toBindEls = toBindEls.filter((maybeBind) => !innerBindsElements.includes(maybeBind));
+        toBindEls.forEach((c) => c.removeAttribute("data-tpl"));
+    });
+    return els;
+}
diff --git a/custom_nodes/rgthree-comfy/web/common/utils_workflow.js b/custom_nodes/rgthree-comfy/web/common/utils_workflow.js
new file mode 100644
index 0000000000000000000000000000000000000000..5dc2963987e64bcc13d4e9e1e6d847bd44592b3a
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/common/utils_workflow.js
@@ -0,0 +1,55 @@
+import { getResolver } from "./shared_utils.js";
+import { getPngMetadata, getWebpMetadata } from "./comfyui_shim.js";
+function parseWorkflowJson(stringJson) {
+    stringJson = stringJson || "null";
+    stringJson = stringJson.replace(/:\s*NaN/g, ": null");
+    return JSON.parse(stringJson);
+}
+export async function tryToGetWorkflowDataFromEvent(e) {
+    var _a, _b, _c, _d;
+    let work;
+    for (const file of ((_a = e.dataTransfer) === null || _a === void 0 ? void 0 : _a.files) || []) {
+        const data = await tryToGetWorkflowDataFromFile(file);
+        if (data.workflow || data.prompt) {
+            return data;
+        }
+    }
+    const validTypes = ["text/uri-list", "text/x-moz-url"];
+    const match = (((_b = e.dataTransfer) === null || _b === void 0 ? void 0 : _b.types) || []).find((t) => validTypes.find((v) => t === v));
+    if (match) {
+        const uri = (_d = (_c = e.dataTransfer.getData(match)) === null || _c === void 0 ? void 0 : _c.split("\n")) === null || _d === void 0 ? void 0 : _d[0];
+        if (uri) {
+            return tryToGetWorkflowDataFromFile(await (await fetch(uri)).blob());
+        }
+    }
+    return { workflow: null, prompt: null };
+}
+export async function tryToGetWorkflowDataFromFile(file) {
+    var _a;
+    if (file.type === "image/png") {
+        const pngInfo = await getPngMetadata(file);
+        return {
+            workflow: parseWorkflowJson(pngInfo === null || pngInfo === void 0 ? void 0 : pngInfo.workflow),
+            prompt: parseWorkflowJson(pngInfo === null || pngInfo === void 0 ? void 0 : pngInfo.prompt),
+        };
+    }
+    if (file.type === "image/webp") {
+        const pngInfo = await getWebpMetadata(file);
+        const workflow = parseWorkflowJson((pngInfo === null || pngInfo === void 0 ? void 0 : pngInfo.workflow) || (pngInfo === null || pngInfo === void 0 ? void 0 : pngInfo.Workflow) || "null");
+        const prompt = parseWorkflowJson((pngInfo === null || pngInfo === void 0 ? void 0 : pngInfo.prompt) || (pngInfo === null || pngInfo === void 0 ? void 0 : pngInfo.Prompt) || "null");
+        return { workflow, prompt };
+    }
+    if (file.type === "application/json" || ((_a = file.name) === null || _a === void 0 ? void 0 : _a.endsWith(".json"))) {
+        const resolver = getResolver();
+        const reader = new FileReader();
+        reader.onload = async () => {
+            const json = parseWorkflowJson(reader.result);
+            const isApiJson = Object.values(json).every((v) => v.class_type);
+            const prompt = isApiJson ? json : null;
+            const workflow = !isApiJson && !(json === null || json === void 0 ? void 0 : json.templates) ? json : null;
+            return { workflow, prompt };
+        };
+        return resolver.promise;
+    }
+    return { workflow: null, prompt: null };
+}
diff --git a/custom_nodes/rgthree-comfy/web/lib/tree-sitter.js b/custom_nodes/rgthree-comfy/web/lib/tree-sitter.js
new file mode 100644
index 0000000000000000000000000000000000000000..1ef49495cdd5971427d2b1b78f36cf2a1c41ab41
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/lib/tree-sitter.js
@@ -0,0 +1,3978 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+
+// src/constants.ts
+var SIZE_OF_SHORT = 2;
+var SIZE_OF_INT = 4;
+var SIZE_OF_CURSOR = 4 * SIZE_OF_INT;
+var SIZE_OF_NODE = 5 * SIZE_OF_INT;
+var SIZE_OF_POINT = 2 * SIZE_OF_INT;
+var SIZE_OF_RANGE = 2 * SIZE_OF_INT + 2 * SIZE_OF_POINT;
+var ZERO_POINT = { row: 0, column: 0 };
+var INTERNAL = Symbol("INTERNAL");
+function assertInternal(x) {
+  if (x !== INTERNAL) throw new Error("Illegal constructor");
+}
+__name(assertInternal, "assertInternal");
+function isPoint(point) {
+  return !!point && typeof point.row === "number" && typeof point.column === "number";
+}
+__name(isPoint, "isPoint");
+function setModule(module2) {
+  C = module2;
+}
+__name(setModule, "setModule");
+var C;
+
+// src/lookahead_iterator.ts
+var LookaheadIterator = class {
+  static {
+    __name(this, "LookaheadIterator");
+  }
+  /** @internal */
+  [0] = 0;
+  // Internal handle for WASM
+  /** @internal */
+  language;
+  /** @internal */
+  constructor(internal, address, language) {
+    assertInternal(internal);
+    this[0] = address;
+    this.language = language;
+  }
+  /** Get the current symbol of the lookahead iterator. */
+  get currentTypeId() {
+    return C._ts_lookahead_iterator_current_symbol(this[0]);
+  }
+  /** Get the current symbol name of the lookahead iterator. */
+  get currentType() {
+    return this.language.types[this.currentTypeId] || "ERROR";
+  }
+  /** Delete the lookahead iterator, freeing its resources. */
+  delete() {
+    C._ts_lookahead_iterator_delete(this[0]);
+    this[0] = 0;
+  }
+  /**
+   * Reset the lookahead iterator.
+   *
+   * This returns `true` if the language was set successfully and `false`
+   * otherwise.
+   */
+  reset(language, stateId) {
+    if (C._ts_lookahead_iterator_reset(this[0], language[0], stateId)) {
+      this.language = language;
+      return true;
+    }
+    return false;
+  }
+  /**
+   * Reset the lookahead iterator to another state.
+   *
+   * This returns `true` if the iterator was reset to the given state and
+   * `false` otherwise.
+   */
+  resetState(stateId) {
+    return Boolean(C._ts_lookahead_iterator_reset_state(this[0], stateId));
+  }
+  /**
+   * Returns an iterator that iterates over the symbols of the lookahead iterator.
+   *
+   * The iterator will yield the current symbol name as a string for each step
+   * until there are no more symbols to iterate over.
+   */
+  [Symbol.iterator]() {
+    return {
+      next: /* @__PURE__ */ __name(() => {
+        if (C._ts_lookahead_iterator_next(this[0])) {
+          return { done: false, value: this.currentType };
+        }
+        return { done: true, value: "" };
+      }, "next")
+    };
+  }
+};
+
+// src/tree.ts
+function getText(tree, startIndex, endIndex, startPosition) {
+  const length = endIndex - startIndex;
+  let result = tree.textCallback(startIndex, startPosition);
+  if (result) {
+    startIndex += result.length;
+    while (startIndex < endIndex) {
+      const string = tree.textCallback(startIndex, startPosition);
+      if (string && string.length > 0) {
+        startIndex += string.length;
+        result += string;
+      } else {
+        break;
+      }
+    }
+    if (startIndex > endIndex) {
+      result = result.slice(0, length);
+    }
+  }
+  return result ?? "";
+}
+__name(getText, "getText");
+var Tree = class _Tree {
+  static {
+    __name(this, "Tree");
+  }
+  /** @internal */
+  [0] = 0;
+  // Internal handle for WASM
+  /** @internal */
+  textCallback;
+  /** The language that was used to parse the syntax tree. */
+  language;
+  /** @internal */
+  constructor(internal, address, language, textCallback) {
+    assertInternal(internal);
+    this[0] = address;
+    this.language = language;
+    this.textCallback = textCallback;
+  }
+  /** Create a shallow copy of the syntax tree. This is very fast. */
+  copy() {
+    const address = C._ts_tree_copy(this[0]);
+    return new _Tree(INTERNAL, address, this.language, this.textCallback);
+  }
+  /** Delete the syntax tree, freeing its resources. */
+  delete() {
+    C._ts_tree_delete(this[0]);
+    this[0] = 0;
+  }
+  /** Get the root node of the syntax tree. */
+  get rootNode() {
+    C._ts_tree_root_node_wasm(this[0]);
+    return unmarshalNode(this);
+  }
+  /**
+   * Get the root node of the syntax tree, but with its position shifted
+   * forward by the given offset.
+   */
+  rootNodeWithOffset(offsetBytes, offsetExtent) {
+    const address = TRANSFER_BUFFER + SIZE_OF_NODE;
+    C.setValue(address, offsetBytes, "i32");
+    marshalPoint(address + SIZE_OF_INT, offsetExtent);
+    C._ts_tree_root_node_with_offset_wasm(this[0]);
+    return unmarshalNode(this);
+  }
+  /**
+   * Edit the syntax tree to keep it in sync with source code that has been
+   * edited.
+   *
+   * You must describe the edit both in terms of byte offsets and in terms of
+   * row/column coordinates.
+   */
+  edit(edit) {
+    marshalEdit(edit);
+    C._ts_tree_edit_wasm(this[0]);
+  }
+  /** Create a new {@link TreeCursor} starting from the root of the tree. */
+  walk() {
+    return this.rootNode.walk();
+  }
+  /**
+   * Compare this old edited syntax tree to a new syntax tree representing
+   * the same document, returning a sequence of ranges whose syntactic
+   * structure has changed.
+   *
+   * For this to work correctly, this syntax tree must have been edited such
+   * that its ranges match up to the new tree. Generally, you'll want to
+   * call this method right after calling one of the [`Parser::parse`]
+   * functions. Call it on the old tree that was passed to parse, and
+   * pass the new tree that was returned from `parse`.
+   */
+  getChangedRanges(other) {
+    if (!(other instanceof _Tree)) {
+      throw new TypeError("Argument must be a Tree");
+    }
+    C._ts_tree_get_changed_ranges_wasm(this[0], other[0]);
+    const count = C.getValue(TRANSFER_BUFFER, "i32");
+    const buffer = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+    const result = new Array(count);
+    if (count > 0) {
+      let address = buffer;
+      for (let i2 = 0; i2 < count; i2++) {
+        result[i2] = unmarshalRange(address);
+        address += SIZE_OF_RANGE;
+      }
+      C._free(buffer);
+    }
+    return result;
+  }
+  /** Get the included ranges that were used to parse the syntax tree. */
+  getIncludedRanges() {
+    C._ts_tree_included_ranges_wasm(this[0]);
+    const count = C.getValue(TRANSFER_BUFFER, "i32");
+    const buffer = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+    const result = new Array(count);
+    if (count > 0) {
+      let address = buffer;
+      for (let i2 = 0; i2 < count; i2++) {
+        result[i2] = unmarshalRange(address);
+        address += SIZE_OF_RANGE;
+      }
+      C._free(buffer);
+    }
+    return result;
+  }
+};
+
+// src/tree_cursor.ts
+var TreeCursor = class _TreeCursor {
+  static {
+    __name(this, "TreeCursor");
+  }
+  /** @internal */
+  [0] = 0;
+  // Internal handle for WASM
+  /** @internal */
+  [1] = 0;
+  // Internal handle for WASM
+  /** @internal */
+  [2] = 0;
+  // Internal handle for WASM
+  /** @internal */
+  [3] = 0;
+  // Internal handle for WASM
+  /** @internal */
+  tree;
+  /** @internal */
+  constructor(internal, tree) {
+    assertInternal(internal);
+    this.tree = tree;
+    unmarshalTreeCursor(this);
+  }
+  /** Creates a deep copy of the tree cursor. This allocates new memory. */
+  copy() {
+    const copy = new _TreeCursor(INTERNAL, this.tree);
+    C._ts_tree_cursor_copy_wasm(this.tree[0]);
+    unmarshalTreeCursor(copy);
+    return copy;
+  }
+  /** Delete the tree cursor, freeing its resources. */
+  delete() {
+    marshalTreeCursor(this);
+    C._ts_tree_cursor_delete_wasm(this.tree[0]);
+    this[0] = this[1] = this[2] = 0;
+  }
+  /** Get the tree cursor's current {@link Node}. */
+  get currentNode() {
+    marshalTreeCursor(this);
+    C._ts_tree_cursor_current_node_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /**
+   * Get the numerical field id of this tree cursor's current node.
+   *
+   * See also {@link TreeCursor#currentFieldName}.
+   */
+  get currentFieldId() {
+    marshalTreeCursor(this);
+    return C._ts_tree_cursor_current_field_id_wasm(this.tree[0]);
+  }
+  /** Get the field name of this tree cursor's current node. */
+  get currentFieldName() {
+    return this.tree.language.fields[this.currentFieldId];
+  }
+  /**
+   * Get the depth of the cursor's current node relative to the original
+   * node that the cursor was constructed with.
+   */
+  get currentDepth() {
+    marshalTreeCursor(this);
+    return C._ts_tree_cursor_current_depth_wasm(this.tree[0]);
+  }
+  /**
+   * Get the index of the cursor's current node out of all of the
+   * descendants of the original node that the cursor was constructed with.
+   */
+  get currentDescendantIndex() {
+    marshalTreeCursor(this);
+    return C._ts_tree_cursor_current_descendant_index_wasm(this.tree[0]);
+  }
+  /** Get the type of the cursor's current node. */
+  get nodeType() {
+    return this.tree.language.types[this.nodeTypeId] || "ERROR";
+  }
+  /** Get the type id of the cursor's current node. */
+  get nodeTypeId() {
+    marshalTreeCursor(this);
+    return C._ts_tree_cursor_current_node_type_id_wasm(this.tree[0]);
+  }
+  /** Get the state id of the cursor's current node. */
+  get nodeStateId() {
+    marshalTreeCursor(this);
+    return C._ts_tree_cursor_current_node_state_id_wasm(this.tree[0]);
+  }
+  /** Get the id of the cursor's current node. */
+  get nodeId() {
+    marshalTreeCursor(this);
+    return C._ts_tree_cursor_current_node_id_wasm(this.tree[0]);
+  }
+  /**
+   * Check if the cursor's current node is *named*.
+   *
+   * Named nodes correspond to named rules in the grammar, whereas
+   * *anonymous* nodes correspond to string literals in the grammar.
+   */
+  get nodeIsNamed() {
+    marshalTreeCursor(this);
+    return C._ts_tree_cursor_current_node_is_named_wasm(this.tree[0]) === 1;
+  }
+  /**
+   * Check if the cursor's current node is *missing*.
+   *
+   * Missing nodes are inserted by the parser in order to recover from
+   * certain kinds of syntax errors.
+   */
+  get nodeIsMissing() {
+    marshalTreeCursor(this);
+    return C._ts_tree_cursor_current_node_is_missing_wasm(this.tree[0]) === 1;
+  }
+  /** Get the string content of the cursor's current node. */
+  get nodeText() {
+    marshalTreeCursor(this);
+    const startIndex = C._ts_tree_cursor_start_index_wasm(this.tree[0]);
+    const endIndex = C._ts_tree_cursor_end_index_wasm(this.tree[0]);
+    C._ts_tree_cursor_start_position_wasm(this.tree[0]);
+    const startPosition = unmarshalPoint(TRANSFER_BUFFER);
+    return getText(this.tree, startIndex, endIndex, startPosition);
+  }
+  /** Get the start position of the cursor's current node. */
+  get startPosition() {
+    marshalTreeCursor(this);
+    C._ts_tree_cursor_start_position_wasm(this.tree[0]);
+    return unmarshalPoint(TRANSFER_BUFFER);
+  }
+  /** Get the end position of the cursor's current node. */
+  get endPosition() {
+    marshalTreeCursor(this);
+    C._ts_tree_cursor_end_position_wasm(this.tree[0]);
+    return unmarshalPoint(TRANSFER_BUFFER);
+  }
+  /** Get the start index of the cursor's current node. */
+  get startIndex() {
+    marshalTreeCursor(this);
+    return C._ts_tree_cursor_start_index_wasm(this.tree[0]);
+  }
+  /** Get the end index of the cursor's current node. */
+  get endIndex() {
+    marshalTreeCursor(this);
+    return C._ts_tree_cursor_end_index_wasm(this.tree[0]);
+  }
+  /**
+   * Move this cursor to the first child of its current node.
+   *
+   * This returns `true` if the cursor successfully moved, and returns
+   * `false` if there were no children.
+   */
+  gotoFirstChild() {
+    marshalTreeCursor(this);
+    const result = C._ts_tree_cursor_goto_first_child_wasm(this.tree[0]);
+    unmarshalTreeCursor(this);
+    return result === 1;
+  }
+  /**
+   * Move this cursor to the last child of its current node.
+   *
+   * This returns `true` if the cursor successfully moved, and returns
+   * `false` if there were no children.
+   *
+   * Note that this function may be slower than
+   * {@link TreeCursor#gotoFirstChild} because it needs to
+   * iterate through all the children to compute the child's position.
+   */
+  gotoLastChild() {
+    marshalTreeCursor(this);
+    const result = C._ts_tree_cursor_goto_last_child_wasm(this.tree[0]);
+    unmarshalTreeCursor(this);
+    return result === 1;
+  }
+  /**
+   * Move this cursor to the parent of its current node.
+   *
+   * This returns `true` if the cursor successfully moved, and returns
+   * `false` if there was no parent node (the cursor was already on the
+   * root node).
+   *
+   * Note that the node the cursor was constructed with is considered the root
+   * of the cursor, and the cursor cannot walk outside this node.
+   */
+  gotoParent() {
+    marshalTreeCursor(this);
+    const result = C._ts_tree_cursor_goto_parent_wasm(this.tree[0]);
+    unmarshalTreeCursor(this);
+    return result === 1;
+  }
+  /**
+   * Move this cursor to the next sibling of its current node.
+   *
+   * This returns `true` if the cursor successfully moved, and returns
+   * `false` if there was no next sibling node.
+   *
+   * Note that the node the cursor was constructed with is considered the root
+   * of the cursor, and the cursor cannot walk outside this node.
+   */
+  gotoNextSibling() {
+    marshalTreeCursor(this);
+    const result = C._ts_tree_cursor_goto_next_sibling_wasm(this.tree[0]);
+    unmarshalTreeCursor(this);
+    return result === 1;
+  }
+  /**
+   * Move this cursor to the previous sibling of its current node.
+   *
+   * This returns `true` if the cursor successfully moved, and returns
+   * `false` if there was no previous sibling node.
+   *
+   * Note that this function may be slower than
+   * {@link TreeCursor#gotoNextSibling} due to how node
+   * positions are stored. In the worst case, this will need to iterate
+   * through all the children up to the previous sibling node to recalculate
+   * its position. Also note that the node the cursor was constructed with is
+   * considered the root of the cursor, and the cursor cannot walk outside this node.
+   */
+  gotoPreviousSibling() {
+    marshalTreeCursor(this);
+    const result = C._ts_tree_cursor_goto_previous_sibling_wasm(this.tree[0]);
+    unmarshalTreeCursor(this);
+    return result === 1;
+  }
+  /**
+   * Move the cursor to the node that is the nth descendant of
+   * the original node that the cursor was constructed with, where
+   * zero represents the original node itself.
+   */
+  gotoDescendant(goalDescendantIndex) {
+    marshalTreeCursor(this);
+    C._ts_tree_cursor_goto_descendant_wasm(this.tree[0], goalDescendantIndex);
+    unmarshalTreeCursor(this);
+  }
+  /**
+   * Move this cursor to the first child of its current node that contains or
+   * starts after the given byte offset.
+   *
+   * This returns `true` if the cursor successfully moved to a child node, and returns
+   * `false` if no such child was found.
+   */
+  gotoFirstChildForIndex(goalIndex) {
+    marshalTreeCursor(this);
+    C.setValue(TRANSFER_BUFFER + SIZE_OF_CURSOR, goalIndex, "i32");
+    const result = C._ts_tree_cursor_goto_first_child_for_index_wasm(this.tree[0]);
+    unmarshalTreeCursor(this);
+    return result === 1;
+  }
+  /**
+   * Move this cursor to the first child of its current node that contains or
+   * starts after the given byte offset.
+   *
+   * This returns the index of the child node if one was found, and returns
+   * `null` if no such child was found.
+   */
+  gotoFirstChildForPosition(goalPosition) {
+    marshalTreeCursor(this);
+    marshalPoint(TRANSFER_BUFFER + SIZE_OF_CURSOR, goalPosition);
+    const result = C._ts_tree_cursor_goto_first_child_for_position_wasm(this.tree[0]);
+    unmarshalTreeCursor(this);
+    return result === 1;
+  }
+  /**
+   * Re-initialize this tree cursor to start at the original node that the
+   * cursor was constructed with.
+   */
+  reset(node) {
+    marshalNode(node);
+    marshalTreeCursor(this, TRANSFER_BUFFER + SIZE_OF_NODE);
+    C._ts_tree_cursor_reset_wasm(this.tree[0]);
+    unmarshalTreeCursor(this);
+  }
+  /**
+   * Re-initialize a tree cursor to the same position as another cursor.
+   *
+   * Unlike {@link TreeCursor#reset}, this will not lose parent
+   * information and allows reusing already created cursors.
+   */
+  resetTo(cursor) {
+    marshalTreeCursor(this, TRANSFER_BUFFER);
+    marshalTreeCursor(cursor, TRANSFER_BUFFER + SIZE_OF_CURSOR);
+    C._ts_tree_cursor_reset_to_wasm(this.tree[0], cursor.tree[0]);
+    unmarshalTreeCursor(this);
+  }
+};
+
+// src/node.ts
+var Node = class {
+  static {
+    __name(this, "Node");
+  }
+  /** @internal */
+  [0] = 0;
+  // Internal handle for WASM
+  /** @internal */
+  _children;
+  /** @internal */
+  _namedChildren;
+  /** @internal */
+  constructor(internal, {
+    id,
+    tree,
+    startIndex,
+    startPosition,
+    other
+  }) {
+    assertInternal(internal);
+    this[0] = other;
+    this.id = id;
+    this.tree = tree;
+    this.startIndex = startIndex;
+    this.startPosition = startPosition;
+  }
+  /**
+   * The numeric id for this node that is unique.
+   *
+   * Within a given syntax tree, no two nodes have the same id. However:
+   *
+   * * If a new tree is created based on an older tree, and a node from the old tree is reused in
+   *   the process, then that node will have the same id in both trees.
+   *
+   * * A node not marked as having changes does not guarantee it was reused.
+   *
+   * * If a node is marked as having changed in the old tree, it will not be reused.
+   */
+  id;
+  /** The byte index where this node starts. */
+  startIndex;
+  /** The position where this node starts. */
+  startPosition;
+  /** The tree that this node belongs to. */
+  tree;
+  /** Get this node's type as a numerical id. */
+  get typeId() {
+    marshalNode(this);
+    return C._ts_node_symbol_wasm(this.tree[0]);
+  }
+  /**
+   * Get the node's type as a numerical id as it appears in the grammar,
+   * ignoring aliases.
+   */
+  get grammarId() {
+    marshalNode(this);
+    return C._ts_node_grammar_symbol_wasm(this.tree[0]);
+  }
+  /** Get this node's type as a string. */
+  get type() {
+    return this.tree.language.types[this.typeId] || "ERROR";
+  }
+  /**
+   * Get this node's symbol name as it appears in the grammar, ignoring
+   * aliases as a string.
+   */
+  get grammarType() {
+    return this.tree.language.types[this.grammarId] || "ERROR";
+  }
+  /**
+   * Check if this node is *named*.
+   *
+   * Named nodes correspond to named rules in the grammar, whereas
+   * *anonymous* nodes correspond to string literals in the grammar.
+   */
+  get isNamed() {
+    marshalNode(this);
+    return C._ts_node_is_named_wasm(this.tree[0]) === 1;
+  }
+  /**
+   * Check if this node is *extra*.
+   *
+   * Extra nodes represent things like comments, which are not required
+   * by the grammar, but can appear anywhere.
+   */
+  get isExtra() {
+    marshalNode(this);
+    return C._ts_node_is_extra_wasm(this.tree[0]) === 1;
+  }
+  /**
+   * Check if this node represents a syntax error.
+   *
+   * Syntax errors represent parts of the code that could not be incorporated
+   * into a valid syntax tree.
+   */
+  get isError() {
+    marshalNode(this);
+    return C._ts_node_is_error_wasm(this.tree[0]) === 1;
+  }
+  /**
+   * Check if this node is *missing*.
+   *
+   * Missing nodes are inserted by the parser in order to recover from
+   * certain kinds of syntax errors.
+   */
+  get isMissing() {
+    marshalNode(this);
+    return C._ts_node_is_missing_wasm(this.tree[0]) === 1;
+  }
+  /** Check if this node has been edited. */
+  get hasChanges() {
+    marshalNode(this);
+    return C._ts_node_has_changes_wasm(this.tree[0]) === 1;
+  }
+  /**
+   * Check if this node represents a syntax error or contains any syntax
+   * errors anywhere within it.
+   */
+  get hasError() {
+    marshalNode(this);
+    return C._ts_node_has_error_wasm(this.tree[0]) === 1;
+  }
+  /** Get the byte index where this node ends. */
+  get endIndex() {
+    marshalNode(this);
+    return C._ts_node_end_index_wasm(this.tree[0]);
+  }
+  /** Get the position where this node ends. */
+  get endPosition() {
+    marshalNode(this);
+    C._ts_node_end_point_wasm(this.tree[0]);
+    return unmarshalPoint(TRANSFER_BUFFER);
+  }
+  /** Get the string content of this node. */
+  get text() {
+    return getText(this.tree, this.startIndex, this.endIndex, this.startPosition);
+  }
+  /** Get this node's parse state. */
+  get parseState() {
+    marshalNode(this);
+    return C._ts_node_parse_state_wasm(this.tree[0]);
+  }
+  /** Get the parse state after this node. */
+  get nextParseState() {
+    marshalNode(this);
+    return C._ts_node_next_parse_state_wasm(this.tree[0]);
+  }
+  /** Check if this node is equal to another node. */
+  equals(other) {
+    return this.tree === other.tree && this.id === other.id;
+  }
+  /**
+   * Get the node's child at the given index, where zero represents the first child.
+   *
+   * This method is fairly fast, but its cost is technically log(n), so if
+   * you might be iterating over a long list of children, you should use
+   * {@link Node#children} instead.
+   */
+  child(index) {
+    marshalNode(this);
+    C._ts_node_child_wasm(this.tree[0], index);
+    return unmarshalNode(this.tree);
+  }
+  /**
+   * Get this node's *named* child at the given index.
+   *
+   * See also {@link Node#isNamed}.
+   * This method is fairly fast, but its cost is technically log(n), so if
+   * you might be iterating over a long list of children, you should use
+   * {@link Node#namedChildren} instead.
+   */
+  namedChild(index) {
+    marshalNode(this);
+    C._ts_node_named_child_wasm(this.tree[0], index);
+    return unmarshalNode(this.tree);
+  }
+  /**
+   * Get this node's child with the given numerical field id.
+   *
+   * See also {@link Node#childForFieldName}. You can
+   * convert a field name to an id using {@link Language#fieldIdForName}.
+   */
+  childForFieldId(fieldId) {
+    marshalNode(this);
+    C._ts_node_child_by_field_id_wasm(this.tree[0], fieldId);
+    return unmarshalNode(this.tree);
+  }
+  /**
+   * Get the first child with the given field name.
+   *
+   * If multiple children may have the same field name, access them using
+   * {@link Node#childrenForFieldName}.
+   */
+  childForFieldName(fieldName) {
+    const fieldId = this.tree.language.fields.indexOf(fieldName);
+    if (fieldId !== -1) return this.childForFieldId(fieldId);
+    return null;
+  }
+  /** Get the field name of this node's child at the given index. */
+  fieldNameForChild(index) {
+    marshalNode(this);
+    const address = C._ts_node_field_name_for_child_wasm(this.tree[0], index);
+    if (!address) return null;
+    return C.AsciiToString(address);
+  }
+  /** Get the field name of this node's named child at the given index. */
+  fieldNameForNamedChild(index) {
+    marshalNode(this);
+    const address = C._ts_node_field_name_for_named_child_wasm(this.tree[0], index);
+    if (!address) return null;
+    return C.AsciiToString(address);
+  }
+  /**
+   * Get an array of this node's children with a given field name.
+   *
+   * See also {@link Node#children}.
+   */
+  childrenForFieldName(fieldName) {
+    const fieldId = this.tree.language.fields.indexOf(fieldName);
+    if (fieldId !== -1 && fieldId !== 0) return this.childrenForFieldId(fieldId);
+    return [];
+  }
+  /**
+    * Get an array of this node's children with a given field id.
+    *
+    * See also {@link Node#childrenForFieldName}.
+    */
+  childrenForFieldId(fieldId) {
+    marshalNode(this);
+    C._ts_node_children_by_field_id_wasm(this.tree[0], fieldId);
+    const count = C.getValue(TRANSFER_BUFFER, "i32");
+    const buffer = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+    const result = new Array(count);
+    if (count > 0) {
+      let address = buffer;
+      for (let i2 = 0; i2 < count; i2++) {
+        result[i2] = unmarshalNode(this.tree, address);
+        address += SIZE_OF_NODE;
+      }
+      C._free(buffer);
+    }
+    return result;
+  }
+  /** Get the node's first child that contains or starts after the given byte offset. */
+  firstChildForIndex(index) {
+    marshalNode(this);
+    const address = TRANSFER_BUFFER + SIZE_OF_NODE;
+    C.setValue(address, index, "i32");
+    C._ts_node_first_child_for_byte_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /** Get the node's first named child that contains or starts after the given byte offset. */
+  firstNamedChildForIndex(index) {
+    marshalNode(this);
+    const address = TRANSFER_BUFFER + SIZE_OF_NODE;
+    C.setValue(address, index, "i32");
+    C._ts_node_first_named_child_for_byte_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /** Get this node's number of children. */
+  get childCount() {
+    marshalNode(this);
+    return C._ts_node_child_count_wasm(this.tree[0]);
+  }
+  /**
+   * Get this node's number of *named* children.
+   *
+   * See also {@link Node#isNamed}.
+   */
+  get namedChildCount() {
+    marshalNode(this);
+    return C._ts_node_named_child_count_wasm(this.tree[0]);
+  }
+  /** Get this node's first child. */
+  get firstChild() {
+    return this.child(0);
+  }
+  /**
+   * Get this node's first named child.
+   *
+   * See also {@link Node#isNamed}.
+   */
+  get firstNamedChild() {
+    return this.namedChild(0);
+  }
+  /** Get this node's last child. */
+  get lastChild() {
+    return this.child(this.childCount - 1);
+  }
+  /**
+   * Get this node's last named child.
+   *
+   * See also {@link Node#isNamed}.
+   */
+  get lastNamedChild() {
+    return this.namedChild(this.namedChildCount - 1);
+  }
+  /**
+   * Iterate over this node's children.
+   *
+   * If you're walking the tree recursively, you may want to use the
+   * {@link TreeCursor} APIs directly instead.
+   */
+  get children() {
+    if (!this._children) {
+      marshalNode(this);
+      C._ts_node_children_wasm(this.tree[0]);
+      const count = C.getValue(TRANSFER_BUFFER, "i32");
+      const buffer = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+      this._children = new Array(count);
+      if (count > 0) {
+        let address = buffer;
+        for (let i2 = 0; i2 < count; i2++) {
+          this._children[i2] = unmarshalNode(this.tree, address);
+          address += SIZE_OF_NODE;
+        }
+        C._free(buffer);
+      }
+    }
+    return this._children;
+  }
+  /**
+   * Iterate over this node's named children.
+   *
+   * See also {@link Node#children}.
+   */
+  get namedChildren() {
+    if (!this._namedChildren) {
+      marshalNode(this);
+      C._ts_node_named_children_wasm(this.tree[0]);
+      const count = C.getValue(TRANSFER_BUFFER, "i32");
+      const buffer = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+      this._namedChildren = new Array(count);
+      if (count > 0) {
+        let address = buffer;
+        for (let i2 = 0; i2 < count; i2++) {
+          this._namedChildren[i2] = unmarshalNode(this.tree, address);
+          address += SIZE_OF_NODE;
+        }
+        C._free(buffer);
+      }
+    }
+    return this._namedChildren;
+  }
+  /**
+   * Get the descendants of this node that are the given type, or in the given types array.
+   *
+   * The types array should contain node type strings, which can be retrieved from {@link Language#types}.
+   *
+   * Additionally, a `startPosition` and `endPosition` can be passed in to restrict the search to a byte range.
+   */
+  descendantsOfType(types, startPosition = ZERO_POINT, endPosition = ZERO_POINT) {
+    if (!Array.isArray(types)) types = [types];
+    const symbols = [];
+    const typesBySymbol = this.tree.language.types;
+    for (const node_type of types) {
+      if (node_type == "ERROR") {
+        symbols.push(65535);
+      }
+    }
+    for (let i2 = 0, n = typesBySymbol.length; i2 < n; i2++) {
+      if (types.includes(typesBySymbol[i2])) {
+        symbols.push(i2);
+      }
+    }
+    const symbolsAddress = C._malloc(SIZE_OF_INT * symbols.length);
+    for (let i2 = 0, n = symbols.length; i2 < n; i2++) {
+      C.setValue(symbolsAddress + i2 * SIZE_OF_INT, symbols[i2], "i32");
+    }
+    marshalNode(this);
+    C._ts_node_descendants_of_type_wasm(
+      this.tree[0],
+      symbolsAddress,
+      symbols.length,
+      startPosition.row,
+      startPosition.column,
+      endPosition.row,
+      endPosition.column
+    );
+    const descendantCount = C.getValue(TRANSFER_BUFFER, "i32");
+    const descendantAddress = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+    const result = new Array(descendantCount);
+    if (descendantCount > 0) {
+      let address = descendantAddress;
+      for (let i2 = 0; i2 < descendantCount; i2++) {
+        result[i2] = unmarshalNode(this.tree, address);
+        address += SIZE_OF_NODE;
+      }
+    }
+    C._free(descendantAddress);
+    C._free(symbolsAddress);
+    return result;
+  }
+  /** Get this node's next sibling. */
+  get nextSibling() {
+    marshalNode(this);
+    C._ts_node_next_sibling_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /** Get this node's previous sibling. */
+  get previousSibling() {
+    marshalNode(this);
+    C._ts_node_prev_sibling_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /**
+   * Get this node's next *named* sibling.
+   *
+   * See also {@link Node#isNamed}.
+   */
+  get nextNamedSibling() {
+    marshalNode(this);
+    C._ts_node_next_named_sibling_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /**
+   * Get this node's previous *named* sibling.
+   *
+   * See also {@link Node#isNamed}.
+   */
+  get previousNamedSibling() {
+    marshalNode(this);
+    C._ts_node_prev_named_sibling_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /** Get the node's number of descendants, including one for the node itself. */
+  get descendantCount() {
+    marshalNode(this);
+    return C._ts_node_descendant_count_wasm(this.tree[0]);
+  }
+  /**
+   * Get this node's immediate parent.
+   * Prefer {@link Node#childWithDescendant} for iterating over this node's ancestors.
+   */
+  get parent() {
+    marshalNode(this);
+    C._ts_node_parent_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /**
+   * Get the node that contains `descendant`.
+   *
+   * Note that this can return `descendant` itself.
+   */
+  childWithDescendant(descendant) {
+    marshalNode(this);
+    marshalNode(descendant, 1);
+    C._ts_node_child_with_descendant_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /** Get the smallest node within this node that spans the given byte range. */
+  descendantForIndex(start2, end = start2) {
+    if (typeof start2 !== "number" || typeof end !== "number") {
+      throw new Error("Arguments must be numbers");
+    }
+    marshalNode(this);
+    const address = TRANSFER_BUFFER + SIZE_OF_NODE;
+    C.setValue(address, start2, "i32");
+    C.setValue(address + SIZE_OF_INT, end, "i32");
+    C._ts_node_descendant_for_index_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /** Get the smallest named node within this node that spans the given byte range. */
+  namedDescendantForIndex(start2, end = start2) {
+    if (typeof start2 !== "number" || typeof end !== "number") {
+      throw new Error("Arguments must be numbers");
+    }
+    marshalNode(this);
+    const address = TRANSFER_BUFFER + SIZE_OF_NODE;
+    C.setValue(address, start2, "i32");
+    C.setValue(address + SIZE_OF_INT, end, "i32");
+    C._ts_node_named_descendant_for_index_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /** Get the smallest node within this node that spans the given point range. */
+  descendantForPosition(start2, end = start2) {
+    if (!isPoint(start2) || !isPoint(end)) {
+      throw new Error("Arguments must be {row, column} objects");
+    }
+    marshalNode(this);
+    const address = TRANSFER_BUFFER + SIZE_OF_NODE;
+    marshalPoint(address, start2);
+    marshalPoint(address + SIZE_OF_POINT, end);
+    C._ts_node_descendant_for_position_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /** Get the smallest named node within this node that spans the given point range. */
+  namedDescendantForPosition(start2, end = start2) {
+    if (!isPoint(start2) || !isPoint(end)) {
+      throw new Error("Arguments must be {row, column} objects");
+    }
+    marshalNode(this);
+    const address = TRANSFER_BUFFER + SIZE_OF_NODE;
+    marshalPoint(address, start2);
+    marshalPoint(address + SIZE_OF_POINT, end);
+    C._ts_node_named_descendant_for_position_wasm(this.tree[0]);
+    return unmarshalNode(this.tree);
+  }
+  /**
+   * Create a new {@link TreeCursor} starting from this node.
+   *
+   * Note that the given node is considered the root of the cursor,
+   * and the cursor cannot walk outside this node.
+   */
+  walk() {
+    marshalNode(this);
+    C._ts_tree_cursor_new_wasm(this.tree[0]);
+    return new TreeCursor(INTERNAL, this.tree);
+  }
+  /**
+   * Edit this node to keep it in-sync with source code that has been edited.
+   *
+   * This function is only rarely needed. When you edit a syntax tree with
+   * the {@link Tree#edit} method, all of the nodes that you retrieve from
+   * the tree afterward will already reflect the edit. You only need to
+   * use {@link Node#edit} when you have a specific {@link Node} instance that
+   * you want to keep and continue to use after an edit.
+   */
+  edit(edit) {
+    if (this.startIndex >= edit.oldEndIndex) {
+      this.startIndex = edit.newEndIndex + (this.startIndex - edit.oldEndIndex);
+      let subbedPointRow;
+      let subbedPointColumn;
+      if (this.startPosition.row > edit.oldEndPosition.row) {
+        subbedPointRow = this.startPosition.row - edit.oldEndPosition.row;
+        subbedPointColumn = this.startPosition.column;
+      } else {
+        subbedPointRow = 0;
+        subbedPointColumn = this.startPosition.column;
+        if (this.startPosition.column >= edit.oldEndPosition.column) {
+          subbedPointColumn = this.startPosition.column - edit.oldEndPosition.column;
+        }
+      }
+      if (subbedPointRow > 0) {
+        this.startPosition.row += subbedPointRow;
+        this.startPosition.column = subbedPointColumn;
+      } else {
+        this.startPosition.column += subbedPointColumn;
+      }
+    } else if (this.startIndex > edit.startIndex) {
+      this.startIndex = edit.newEndIndex;
+      this.startPosition.row = edit.newEndPosition.row;
+      this.startPosition.column = edit.newEndPosition.column;
+    }
+  }
+  /** Get the S-expression representation of this node. */
+  toString() {
+    marshalNode(this);
+    const address = C._ts_node_to_string_wasm(this.tree[0]);
+    const result = C.AsciiToString(address);
+    C._free(address);
+    return result;
+  }
+};
+
+// src/marshal.ts
+function unmarshalCaptures(query, tree, address, patternIndex, result) {
+  for (let i2 = 0, n = result.length; i2 < n; i2++) {
+    const captureIndex = C.getValue(address, "i32");
+    address += SIZE_OF_INT;
+    const node = unmarshalNode(tree, address);
+    address += SIZE_OF_NODE;
+    result[i2] = { patternIndex, name: query.captureNames[captureIndex], node };
+  }
+  return address;
+}
+__name(unmarshalCaptures, "unmarshalCaptures");
+function marshalNode(node, index = 0) {
+  let address = TRANSFER_BUFFER + index * SIZE_OF_NODE;
+  C.setValue(address, node.id, "i32");
+  address += SIZE_OF_INT;
+  C.setValue(address, node.startIndex, "i32");
+  address += SIZE_OF_INT;
+  C.setValue(address, node.startPosition.row, "i32");
+  address += SIZE_OF_INT;
+  C.setValue(address, node.startPosition.column, "i32");
+  address += SIZE_OF_INT;
+  C.setValue(address, node[0], "i32");
+}
+__name(marshalNode, "marshalNode");
+function unmarshalNode(tree, address = TRANSFER_BUFFER) {
+  const id = C.getValue(address, "i32");
+  address += SIZE_OF_INT;
+  if (id === 0) return null;
+  const index = C.getValue(address, "i32");
+  address += SIZE_OF_INT;
+  const row = C.getValue(address, "i32");
+  address += SIZE_OF_INT;
+  const column = C.getValue(address, "i32");
+  address += SIZE_OF_INT;
+  const other = C.getValue(address, "i32");
+  const result = new Node(INTERNAL, {
+    id,
+    tree,
+    startIndex: index,
+    startPosition: { row, column },
+    other
+  });
+  return result;
+}
+__name(unmarshalNode, "unmarshalNode");
+function marshalTreeCursor(cursor, address = TRANSFER_BUFFER) {
+  C.setValue(address + 0 * SIZE_OF_INT, cursor[0], "i32");
+  C.setValue(address + 1 * SIZE_OF_INT, cursor[1], "i32");
+  C.setValue(address + 2 * SIZE_OF_INT, cursor[2], "i32");
+  C.setValue(address + 3 * SIZE_OF_INT, cursor[3], "i32");
+}
+__name(marshalTreeCursor, "marshalTreeCursor");
+function unmarshalTreeCursor(cursor) {
+  cursor[0] = C.getValue(TRANSFER_BUFFER + 0 * SIZE_OF_INT, "i32");
+  cursor[1] = C.getValue(TRANSFER_BUFFER + 1 * SIZE_OF_INT, "i32");
+  cursor[2] = C.getValue(TRANSFER_BUFFER + 2 * SIZE_OF_INT, "i32");
+  cursor[3] = C.getValue(TRANSFER_BUFFER + 3 * SIZE_OF_INT, "i32");
+}
+__name(unmarshalTreeCursor, "unmarshalTreeCursor");
+function marshalPoint(address, point) {
+  C.setValue(address, point.row, "i32");
+  C.setValue(address + SIZE_OF_INT, point.column, "i32");
+}
+__name(marshalPoint, "marshalPoint");
+function unmarshalPoint(address) {
+  const result = {
+    row: C.getValue(address, "i32") >>> 0,
+    column: C.getValue(address + SIZE_OF_INT, "i32") >>> 0
+  };
+  return result;
+}
+__name(unmarshalPoint, "unmarshalPoint");
+function marshalRange(address, range) {
+  marshalPoint(address, range.startPosition);
+  address += SIZE_OF_POINT;
+  marshalPoint(address, range.endPosition);
+  address += SIZE_OF_POINT;
+  C.setValue(address, range.startIndex, "i32");
+  address += SIZE_OF_INT;
+  C.setValue(address, range.endIndex, "i32");
+  address += SIZE_OF_INT;
+}
+__name(marshalRange, "marshalRange");
+function unmarshalRange(address) {
+  const result = {};
+  result.startPosition = unmarshalPoint(address);
+  address += SIZE_OF_POINT;
+  result.endPosition = unmarshalPoint(address);
+  address += SIZE_OF_POINT;
+  result.startIndex = C.getValue(address, "i32") >>> 0;
+  address += SIZE_OF_INT;
+  result.endIndex = C.getValue(address, "i32") >>> 0;
+  return result;
+}
+__name(unmarshalRange, "unmarshalRange");
+function marshalEdit(edit, address = TRANSFER_BUFFER) {
+  marshalPoint(address, edit.startPosition);
+  address += SIZE_OF_POINT;
+  marshalPoint(address, edit.oldEndPosition);
+  address += SIZE_OF_POINT;
+  marshalPoint(address, edit.newEndPosition);
+  address += SIZE_OF_POINT;
+  C.setValue(address, edit.startIndex, "i32");
+  address += SIZE_OF_INT;
+  C.setValue(address, edit.oldEndIndex, "i32");
+  address += SIZE_OF_INT;
+  C.setValue(address, edit.newEndIndex, "i32");
+  address += SIZE_OF_INT;
+}
+__name(marshalEdit, "marshalEdit");
+function unmarshalLanguageMetadata(address) {
+  const result = {};
+  result.major_version = C.getValue(address, "i32");
+  address += SIZE_OF_INT;
+  result.minor_version = C.getValue(address, "i32");
+  address += SIZE_OF_INT;
+  result.field_count = C.getValue(address, "i32");
+  return result;
+}
+__name(unmarshalLanguageMetadata, "unmarshalLanguageMetadata");
+
+// src/query.ts
+var PREDICATE_STEP_TYPE_CAPTURE = 1;
+var PREDICATE_STEP_TYPE_STRING = 2;
+var QUERY_WORD_REGEX = /[\w-]+/g;
+var CaptureQuantifier = {
+  Zero: 0,
+  ZeroOrOne: 1,
+  ZeroOrMore: 2,
+  One: 3,
+  OneOrMore: 4
+};
+var isCaptureStep = /* @__PURE__ */ __name((step) => step.type === "capture", "isCaptureStep");
+var isStringStep = /* @__PURE__ */ __name((step) => step.type === "string", "isStringStep");
+var QueryErrorKind = {
+  Syntax: 1,
+  NodeName: 2,
+  FieldName: 3,
+  CaptureName: 4,
+  PatternStructure: 5
+};
+var QueryError = class _QueryError extends Error {
+  constructor(kind, info2, index, length) {
+    super(_QueryError.formatMessage(kind, info2));
+    this.kind = kind;
+    this.info = info2;
+    this.index = index;
+    this.length = length;
+    this.name = "QueryError";
+  }
+  static {
+    __name(this, "QueryError");
+  }
+  /** Formats an error message based on the error kind and info */
+  static formatMessage(kind, info2) {
+    switch (kind) {
+      case QueryErrorKind.NodeName:
+        return `Bad node name '${info2.word}'`;
+      case QueryErrorKind.FieldName:
+        return `Bad field name '${info2.word}'`;
+      case QueryErrorKind.CaptureName:
+        return `Bad capture name @${info2.word}`;
+      case QueryErrorKind.PatternStructure:
+        return `Bad pattern structure at offset ${info2.suffix}`;
+      case QueryErrorKind.Syntax:
+        return `Bad syntax at offset ${info2.suffix}`;
+    }
+  }
+};
+function parseAnyPredicate(steps, index, operator, textPredicates) {
+  if (steps.length !== 3) {
+    throw new Error(
+      `Wrong number of arguments to \`#${operator}\` predicate. Expected 2, got ${steps.length - 1}`
+    );
+  }
+  if (!isCaptureStep(steps[1])) {
+    throw new Error(
+      `First argument of \`#${operator}\` predicate must be a capture. Got "${steps[1].value}"`
+    );
+  }
+  const isPositive = operator === "eq?" || operator === "any-eq?";
+  const matchAll = !operator.startsWith("any-");
+  if (isCaptureStep(steps[2])) {
+    const captureName1 = steps[1].name;
+    const captureName2 = steps[2].name;
+    textPredicates[index].push((captures) => {
+      const nodes1 = [];
+      const nodes2 = [];
+      for (const c of captures) {
+        if (c.name === captureName1) nodes1.push(c.node);
+        if (c.name === captureName2) nodes2.push(c.node);
+      }
+      const compare = /* @__PURE__ */ __name((n1, n2, positive) => {
+        return positive ? n1.text === n2.text : n1.text !== n2.text;
+      }, "compare");
+      return matchAll ? nodes1.every((n1) => nodes2.some((n2) => compare(n1, n2, isPositive))) : nodes1.some((n1) => nodes2.some((n2) => compare(n1, n2, isPositive)));
+    });
+  } else {
+    const captureName = steps[1].name;
+    const stringValue = steps[2].value;
+    const matches = /* @__PURE__ */ __name((n) => n.text === stringValue, "matches");
+    const doesNotMatch = /* @__PURE__ */ __name((n) => n.text !== stringValue, "doesNotMatch");
+    textPredicates[index].push((captures) => {
+      const nodes = [];
+      for (const c of captures) {
+        if (c.name === captureName) nodes.push(c.node);
+      }
+      const test = isPositive ? matches : doesNotMatch;
+      return matchAll ? nodes.every(test) : nodes.some(test);
+    });
+  }
+}
+__name(parseAnyPredicate, "parseAnyPredicate");
+function parseMatchPredicate(steps, index, operator, textPredicates) {
+  if (steps.length !== 3) {
+    throw new Error(
+      `Wrong number of arguments to \`#${operator}\` predicate. Expected 2, got ${steps.length - 1}.`
+    );
+  }
+  if (steps[1].type !== "capture") {
+    throw new Error(
+      `First argument of \`#${operator}\` predicate must be a capture. Got "${steps[1].value}".`
+    );
+  }
+  if (steps[2].type !== "string") {
+    throw new Error(
+      `Second argument of \`#${operator}\` predicate must be a string. Got @${steps[2].name}.`
+    );
+  }
+  const isPositive = operator === "match?" || operator === "any-match?";
+  const matchAll = !operator.startsWith("any-");
+  const captureName = steps[1].name;
+  const regex = new RegExp(steps[2].value);
+  textPredicates[index].push((captures) => {
+    const nodes = [];
+    for (const c of captures) {
+      if (c.name === captureName) nodes.push(c.node.text);
+    }
+    const test = /* @__PURE__ */ __name((text, positive) => {
+      return positive ? regex.test(text) : !regex.test(text);
+    }, "test");
+    if (nodes.length === 0) return !isPositive;
+    return matchAll ? nodes.every((text) => test(text, isPositive)) : nodes.some((text) => test(text, isPositive));
+  });
+}
+__name(parseMatchPredicate, "parseMatchPredicate");
+function parseAnyOfPredicate(steps, index, operator, textPredicates) {
+  if (steps.length < 2) {
+    throw new Error(
+      `Wrong number of arguments to \`#${operator}\` predicate. Expected at least 1. Got ${steps.length - 1}.`
+    );
+  }
+  if (steps[1].type !== "capture") {
+    throw new Error(
+      `First argument of \`#${operator}\` predicate must be a capture. Got "${steps[1].value}".`
+    );
+  }
+  const isPositive = operator === "any-of?";
+  const captureName = steps[1].name;
+  const stringSteps = steps.slice(2);
+  if (!stringSteps.every(isStringStep)) {
+    throw new Error(
+      `Arguments to \`#${operator}\` predicate must be strings.".`
+    );
+  }
+  const values = stringSteps.map((s) => s.value);
+  textPredicates[index].push((captures) => {
+    const nodes = [];
+    for (const c of captures) {
+      if (c.name === captureName) nodes.push(c.node.text);
+    }
+    if (nodes.length === 0) return !isPositive;
+    return nodes.every((text) => values.includes(text)) === isPositive;
+  });
+}
+__name(parseAnyOfPredicate, "parseAnyOfPredicate");
+function parseIsPredicate(steps, index, operator, assertedProperties, refutedProperties) {
+  if (steps.length < 2 || steps.length > 3) {
+    throw new Error(
+      `Wrong number of arguments to \`#${operator}\` predicate. Expected 1 or 2. Got ${steps.length - 1}.`
+    );
+  }
+  if (!steps.every(isStringStep)) {
+    throw new Error(
+      `Arguments to \`#${operator}\` predicate must be strings.".`
+    );
+  }
+  const properties = operator === "is?" ? assertedProperties : refutedProperties;
+  if (!properties[index]) properties[index] = {};
+  properties[index][steps[1].value] = steps[2]?.value ?? null;
+}
+__name(parseIsPredicate, "parseIsPredicate");
+function parseSetDirective(steps, index, setProperties) {
+  if (steps.length < 2 || steps.length > 3) {
+    throw new Error(`Wrong number of arguments to \`#set!\` predicate. Expected 1 or 2. Got ${steps.length - 1}.`);
+  }
+  if (!steps.every(isStringStep)) {
+    throw new Error(`Arguments to \`#set!\` predicate must be strings.".`);
+  }
+  if (!setProperties[index]) setProperties[index] = {};
+  setProperties[index][steps[1].value] = steps[2]?.value ?? null;
+}
+__name(parseSetDirective, "parseSetDirective");
+function parsePattern(index, stepType, stepValueId, captureNames, stringValues, steps, textPredicates, predicates, setProperties, assertedProperties, refutedProperties) {
+  if (stepType === PREDICATE_STEP_TYPE_CAPTURE) {
+    const name2 = captureNames[stepValueId];
+    steps.push({ type: "capture", name: name2 });
+  } else if (stepType === PREDICATE_STEP_TYPE_STRING) {
+    steps.push({ type: "string", value: stringValues[stepValueId] });
+  } else if (steps.length > 0) {
+    if (steps[0].type !== "string") {
+      throw new Error("Predicates must begin with a literal value");
+    }
+    const operator = steps[0].value;
+    switch (operator) {
+      case "any-not-eq?":
+      case "not-eq?":
+      case "any-eq?":
+      case "eq?":
+        parseAnyPredicate(steps, index, operator, textPredicates);
+        break;
+      case "any-not-match?":
+      case "not-match?":
+      case "any-match?":
+      case "match?":
+        parseMatchPredicate(steps, index, operator, textPredicates);
+        break;
+      case "not-any-of?":
+      case "any-of?":
+        parseAnyOfPredicate(steps, index, operator, textPredicates);
+        break;
+      case "is?":
+      case "is-not?":
+        parseIsPredicate(steps, index, operator, assertedProperties, refutedProperties);
+        break;
+      case "set!":
+        parseSetDirective(steps, index, setProperties);
+        break;
+      default:
+        predicates[index].push({ operator, operands: steps.slice(1) });
+    }
+    steps.length = 0;
+  }
+}
+__name(parsePattern, "parsePattern");
+var Query = class {
+  static {
+    __name(this, "Query");
+  }
+  /** @internal */
+  [0] = 0;
+  // Internal handle for WASM
+  /** @internal */
+  exceededMatchLimit;
+  /** @internal */
+  textPredicates;
+  /** The names of the captures used in the query. */
+  captureNames;
+  /** The quantifiers of the captures used in the query. */
+  captureQuantifiers;
+  /**
+   * The other user-defined predicates associated with the given index.
+   *
+   * This includes predicates with operators other than:
+   * - `match?`
+   * - `eq?` and `not-eq?`
+   * - `any-of?` and `not-any-of?`
+   * - `is?` and `is-not?`
+   * - `set!`
+   */
+  predicates;
+  /** The properties for predicates with the operator `set!`. */
+  setProperties;
+  /** The properties for predicates with the operator `is?`. */
+  assertedProperties;
+  /** The properties for predicates with the operator `is-not?`. */
+  refutedProperties;
+  /** The maximum number of in-progress matches for this cursor. */
+  matchLimit;
+  /**
+   * Create a new query from a string containing one or more S-expression
+   * patterns.
+   *
+   * The query is associated with a particular language, and can only be run
+   * on syntax nodes parsed with that language. References to Queries can be
+   * shared between multiple threads.
+   *
+   * @link {@see https://tree-sitter.github.io/tree-sitter/using-parsers/queries}
+   */
+  constructor(language, source) {
+    const sourceLength = C.lengthBytesUTF8(source);
+    const sourceAddress = C._malloc(sourceLength + 1);
+    C.stringToUTF8(source, sourceAddress, sourceLength + 1);
+    const address = C._ts_query_new(
+      language[0],
+      sourceAddress,
+      sourceLength,
+      TRANSFER_BUFFER,
+      TRANSFER_BUFFER + SIZE_OF_INT
+    );
+    if (!address) {
+      const errorId = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+      const errorByte = C.getValue(TRANSFER_BUFFER, "i32");
+      const errorIndex = C.UTF8ToString(sourceAddress, errorByte).length;
+      const suffix = source.slice(errorIndex, errorIndex + 100).split("\n")[0];
+      const word = suffix.match(QUERY_WORD_REGEX)?.[0] ?? "";
+      C._free(sourceAddress);
+      switch (errorId) {
+        case QueryErrorKind.Syntax:
+          throw new QueryError(QueryErrorKind.Syntax, { suffix: `${errorIndex}: '${suffix}'...` }, errorIndex, 0);
+        case QueryErrorKind.NodeName:
+          throw new QueryError(errorId, { word }, errorIndex, word.length);
+        case QueryErrorKind.FieldName:
+          throw new QueryError(errorId, { word }, errorIndex, word.length);
+        case QueryErrorKind.CaptureName:
+          throw new QueryError(errorId, { word }, errorIndex, word.length);
+        case QueryErrorKind.PatternStructure:
+          throw new QueryError(errorId, { suffix: `${errorIndex}: '${suffix}'...` }, errorIndex, 0);
+      }
+    }
+    const stringCount = C._ts_query_string_count(address);
+    const captureCount = C._ts_query_capture_count(address);
+    const patternCount = C._ts_query_pattern_count(address);
+    const captureNames = new Array(captureCount);
+    const captureQuantifiers = new Array(patternCount);
+    const stringValues = new Array(stringCount);
+    for (let i2 = 0; i2 < captureCount; i2++) {
+      const nameAddress = C._ts_query_capture_name_for_id(
+        address,
+        i2,
+        TRANSFER_BUFFER
+      );
+      const nameLength = C.getValue(TRANSFER_BUFFER, "i32");
+      captureNames[i2] = C.UTF8ToString(nameAddress, nameLength);
+    }
+    for (let i2 = 0; i2 < patternCount; i2++) {
+      const captureQuantifiersArray = new Array(captureCount);
+      for (let j = 0; j < captureCount; j++) {
+        const quantifier = C._ts_query_capture_quantifier_for_id(address, i2, j);
+        captureQuantifiersArray[j] = quantifier;
+      }
+      captureQuantifiers[i2] = captureQuantifiersArray;
+    }
+    for (let i2 = 0; i2 < stringCount; i2++) {
+      const valueAddress = C._ts_query_string_value_for_id(
+        address,
+        i2,
+        TRANSFER_BUFFER
+      );
+      const nameLength = C.getValue(TRANSFER_BUFFER, "i32");
+      stringValues[i2] = C.UTF8ToString(valueAddress, nameLength);
+    }
+    const setProperties = new Array(patternCount);
+    const assertedProperties = new Array(patternCount);
+    const refutedProperties = new Array(patternCount);
+    const predicates = new Array(patternCount);
+    const textPredicates = new Array(patternCount);
+    for (let i2 = 0; i2 < patternCount; i2++) {
+      const predicatesAddress = C._ts_query_predicates_for_pattern(address, i2, TRANSFER_BUFFER);
+      const stepCount = C.getValue(TRANSFER_BUFFER, "i32");
+      predicates[i2] = [];
+      textPredicates[i2] = [];
+      const steps = new Array();
+      let stepAddress = predicatesAddress;
+      for (let j = 0; j < stepCount; j++) {
+        const stepType = C.getValue(stepAddress, "i32");
+        stepAddress += SIZE_OF_INT;
+        const stepValueId = C.getValue(stepAddress, "i32");
+        stepAddress += SIZE_OF_INT;
+        parsePattern(
+          i2,
+          stepType,
+          stepValueId,
+          captureNames,
+          stringValues,
+          steps,
+          textPredicates,
+          predicates,
+          setProperties,
+          assertedProperties,
+          refutedProperties
+        );
+      }
+      Object.freeze(textPredicates[i2]);
+      Object.freeze(predicates[i2]);
+      Object.freeze(setProperties[i2]);
+      Object.freeze(assertedProperties[i2]);
+      Object.freeze(refutedProperties[i2]);
+    }
+    C._free(sourceAddress);
+    this[0] = address;
+    this.captureNames = captureNames;
+    this.captureQuantifiers = captureQuantifiers;
+    this.textPredicates = textPredicates;
+    this.predicates = predicates;
+    this.setProperties = setProperties;
+    this.assertedProperties = assertedProperties;
+    this.refutedProperties = refutedProperties;
+    this.exceededMatchLimit = false;
+  }
+  /** Delete the query, freeing its resources. */
+  delete() {
+    C._ts_query_delete(this[0]);
+    this[0] = 0;
+  }
+  /**
+   * Iterate over all of the matches in the order that they were found.
+   *
+   * Each match contains the index of the pattern that matched, and a list of
+   * captures. Because multiple patterns can match the same set of nodes,
+   * one match may contain captures that appear *before* some of the
+   * captures from a previous match.
+   *
+   * @param {Node} node - The node to execute the query on.
+   *
+   * @param {QueryOptions} options - Options for query execution.
+   */
+  matches(node, options = {}) {
+    const startPosition = options.startPosition ?? ZERO_POINT;
+    const endPosition = options.endPosition ?? ZERO_POINT;
+    const startIndex = options.startIndex ?? 0;
+    const endIndex = options.endIndex ?? 0;
+    const matchLimit = options.matchLimit ?? 4294967295;
+    const maxStartDepth = options.maxStartDepth ?? 4294967295;
+    const timeoutMicros = options.timeoutMicros ?? 0;
+    const progressCallback = options.progressCallback;
+    if (typeof matchLimit !== "number") {
+      throw new Error("Arguments must be numbers");
+    }
+    this.matchLimit = matchLimit;
+    if (endIndex !== 0 && startIndex > endIndex) {
+      throw new Error("`startIndex` cannot be greater than `endIndex`");
+    }
+    if (endPosition !== ZERO_POINT && (startPosition.row > endPosition.row || startPosition.row === endPosition.row && startPosition.column > endPosition.column)) {
+      throw new Error("`startPosition` cannot be greater than `endPosition`");
+    }
+    if (progressCallback) {
+      C.currentQueryProgressCallback = progressCallback;
+    }
+    marshalNode(node);
+    C._ts_query_matches_wasm(
+      this[0],
+      node.tree[0],
+      startPosition.row,
+      startPosition.column,
+      endPosition.row,
+      endPosition.column,
+      startIndex,
+      endIndex,
+      matchLimit,
+      maxStartDepth,
+      timeoutMicros
+    );
+    const rawCount = C.getValue(TRANSFER_BUFFER, "i32");
+    const startAddress = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+    const didExceedMatchLimit = C.getValue(TRANSFER_BUFFER + 2 * SIZE_OF_INT, "i32");
+    const result = new Array(rawCount);
+    this.exceededMatchLimit = Boolean(didExceedMatchLimit);
+    let filteredCount = 0;
+    let address = startAddress;
+    for (let i2 = 0; i2 < rawCount; i2++) {
+      const patternIndex = C.getValue(address, "i32");
+      address += SIZE_OF_INT;
+      const captureCount = C.getValue(address, "i32");
+      address += SIZE_OF_INT;
+      const captures = new Array(captureCount);
+      address = unmarshalCaptures(this, node.tree, address, patternIndex, captures);
+      if (this.textPredicates[patternIndex].every((p) => p(captures))) {
+        result[filteredCount] = { pattern: patternIndex, patternIndex, captures };
+        const setProperties = this.setProperties[patternIndex];
+        result[filteredCount].setProperties = setProperties;
+        const assertedProperties = this.assertedProperties[patternIndex];
+        result[filteredCount].assertedProperties = assertedProperties;
+        const refutedProperties = this.refutedProperties[patternIndex];
+        result[filteredCount].refutedProperties = refutedProperties;
+        filteredCount++;
+      }
+    }
+    result.length = filteredCount;
+    C._free(startAddress);
+    C.currentQueryProgressCallback = null;
+    return result;
+  }
+  /**
+   * Iterate over all of the individual captures in the order that they
+   * appear.
+   *
+   * This is useful if you don't care about which pattern matched, and just
+   * want a single, ordered sequence of captures.
+   *
+   * @param {Node} node - The node to execute the query on.
+   *
+   * @param {QueryOptions} options - Options for query execution.
+   */
+  captures(node, options = {}) {
+    const startPosition = options.startPosition ?? ZERO_POINT;
+    const endPosition = options.endPosition ?? ZERO_POINT;
+    const startIndex = options.startIndex ?? 0;
+    const endIndex = options.endIndex ?? 0;
+    const matchLimit = options.matchLimit ?? 4294967295;
+    const maxStartDepth = options.maxStartDepth ?? 4294967295;
+    const timeoutMicros = options.timeoutMicros ?? 0;
+    const progressCallback = options.progressCallback;
+    if (typeof matchLimit !== "number") {
+      throw new Error("Arguments must be numbers");
+    }
+    this.matchLimit = matchLimit;
+    if (endIndex !== 0 && startIndex > endIndex) {
+      throw new Error("`startIndex` cannot be greater than `endIndex`");
+    }
+    if (endPosition !== ZERO_POINT && (startPosition.row > endPosition.row || startPosition.row === endPosition.row && startPosition.column > endPosition.column)) {
+      throw new Error("`startPosition` cannot be greater than `endPosition`");
+    }
+    if (progressCallback) {
+      C.currentQueryProgressCallback = progressCallback;
+    }
+    marshalNode(node);
+    C._ts_query_captures_wasm(
+      this[0],
+      node.tree[0],
+      startPosition.row,
+      startPosition.column,
+      endPosition.row,
+      endPosition.column,
+      startIndex,
+      endIndex,
+      matchLimit,
+      maxStartDepth,
+      timeoutMicros
+    );
+    const count = C.getValue(TRANSFER_BUFFER, "i32");
+    const startAddress = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+    const didExceedMatchLimit = C.getValue(TRANSFER_BUFFER + 2 * SIZE_OF_INT, "i32");
+    const result = new Array();
+    this.exceededMatchLimit = Boolean(didExceedMatchLimit);
+    const captures = new Array();
+    let address = startAddress;
+    for (let i2 = 0; i2 < count; i2++) {
+      const patternIndex = C.getValue(address, "i32");
+      address += SIZE_OF_INT;
+      const captureCount = C.getValue(address, "i32");
+      address += SIZE_OF_INT;
+      const captureIndex = C.getValue(address, "i32");
+      address += SIZE_OF_INT;
+      captures.length = captureCount;
+      address = unmarshalCaptures(this, node.tree, address, patternIndex, captures);
+      if (this.textPredicates[patternIndex].every((p) => p(captures))) {
+        const capture = captures[captureIndex];
+        const setProperties = this.setProperties[patternIndex];
+        capture.setProperties = setProperties;
+        const assertedProperties = this.assertedProperties[patternIndex];
+        capture.assertedProperties = assertedProperties;
+        const refutedProperties = this.refutedProperties[patternIndex];
+        capture.refutedProperties = refutedProperties;
+        result.push(capture);
+      }
+    }
+    C._free(startAddress);
+    C.currentQueryProgressCallback = null;
+    return result;
+  }
+  /** Get the predicates for a given pattern. */
+  predicatesForPattern(patternIndex) {
+    return this.predicates[patternIndex];
+  }
+  /**
+   * Disable a certain capture within a query.
+   *
+   * This prevents the capture from being returned in matches, and also
+   * avoids any resource usage associated with recording the capture.
+   */
+  disableCapture(captureName) {
+    const captureNameLength = C.lengthBytesUTF8(captureName);
+    const captureNameAddress = C._malloc(captureNameLength + 1);
+    C.stringToUTF8(captureName, captureNameAddress, captureNameLength + 1);
+    C._ts_query_disable_capture(this[0], captureNameAddress, captureNameLength);
+    C._free(captureNameAddress);
+  }
+  /**
+   * Disable a certain pattern within a query.
+   *
+   * This prevents the pattern from matching, and also avoids any resource
+   * usage associated with the pattern. This throws an error if the pattern
+   * index is out of bounds.
+   */
+  disablePattern(patternIndex) {
+    if (patternIndex >= this.predicates.length) {
+      throw new Error(
+        `Pattern index is ${patternIndex} but the pattern count is ${this.predicates.length}`
+      );
+    }
+    C._ts_query_disable_pattern(this[0], patternIndex);
+  }
+  /**
+   * Check if, on its last execution, this cursor exceeded its maximum number
+   * of in-progress matches.
+   */
+  didExceedMatchLimit() {
+    return this.exceededMatchLimit;
+  }
+  /** Get the byte offset where the given pattern starts in the query's source. */
+  startIndexForPattern(patternIndex) {
+    if (patternIndex >= this.predicates.length) {
+      throw new Error(
+        `Pattern index is ${patternIndex} but the pattern count is ${this.predicates.length}`
+      );
+    }
+    return C._ts_query_start_byte_for_pattern(this[0], patternIndex);
+  }
+  /** Get the byte offset where the given pattern ends in the query's source. */
+  endIndexForPattern(patternIndex) {
+    if (patternIndex >= this.predicates.length) {
+      throw new Error(
+        `Pattern index is ${patternIndex} but the pattern count is ${this.predicates.length}`
+      );
+    }
+    return C._ts_query_end_byte_for_pattern(this[0], patternIndex);
+  }
+  /** Get the number of patterns in the query. */
+  patternCount() {
+    return C._ts_query_pattern_count(this[0]);
+  }
+  /** Get the index for a given capture name. */
+  captureIndexForName(captureName) {
+    return this.captureNames.indexOf(captureName);
+  }
+  /** Check if a given pattern within a query has a single root node. */
+  isPatternRooted(patternIndex) {
+    return C._ts_query_is_pattern_rooted(this[0], patternIndex) === 1;
+  }
+  /** Check if a given pattern within a query has a single root node. */
+  isPatternNonLocal(patternIndex) {
+    return C._ts_query_is_pattern_non_local(this[0], patternIndex) === 1;
+  }
+  /**
+   * Check if a given step in a query is 'definite'.
+   *
+   * A query step is 'definite' if its parent pattern will be guaranteed to
+   * match successfully once it reaches the step.
+   */
+  isPatternGuaranteedAtStep(byteIndex) {
+    return C._ts_query_is_pattern_guaranteed_at_step(this[0], byteIndex) === 1;
+  }
+};
+
+// src/language.ts
+var LANGUAGE_FUNCTION_REGEX = /^tree_sitter_\w+$/;
+var Language = class _Language {
+  static {
+    __name(this, "Language");
+  }
+  /** @internal */
+  [0] = 0;
+  // Internal handle for WASM
+  /**
+   * A list of all node types in the language. The index of each type in this
+   * array is its node type id.
+   */
+  types;
+  /**
+   * A list of all field names in the language. The index of each field name in
+   * this array is its field id.
+   */
+  fields;
+  /** @internal */
+  constructor(internal, address) {
+    assertInternal(internal);
+    this[0] = address;
+    this.types = new Array(C._ts_language_symbol_count(this[0]));
+    for (let i2 = 0, n = this.types.length; i2 < n; i2++) {
+      if (C._ts_language_symbol_type(this[0], i2) < 2) {
+        this.types[i2] = C.UTF8ToString(C._ts_language_symbol_name(this[0], i2));
+      }
+    }
+    this.fields = new Array(C._ts_language_field_count(this[0]) + 1);
+    for (let i2 = 0, n = this.fields.length; i2 < n; i2++) {
+      const fieldName = C._ts_language_field_name_for_id(this[0], i2);
+      if (fieldName !== 0) {
+        this.fields[i2] = C.UTF8ToString(fieldName);
+      } else {
+        this.fields[i2] = null;
+      }
+    }
+  }
+  /**
+   * Gets the name of the language.
+   */
+  get name() {
+    const ptr = C._ts_language_name(this[0]);
+    if (ptr === 0) return null;
+    return C.UTF8ToString(ptr);
+  }
+  /**
+   * @deprecated since version 0.25.0, use {@link Language#abiVersion} instead
+   * Gets the version of the language.
+   */
+  get version() {
+    return C._ts_language_version(this[0]);
+  }
+  /**
+   * Gets the ABI version of the language.
+   */
+  get abiVersion() {
+    return C._ts_language_abi_version(this[0]);
+  }
+  /**
+  * Get the metadata for this language. This information is generated by the
+  * CLI, and relies on the language author providing the correct metadata in
+  * the language's `tree-sitter.json` file.
+  */
+  get metadata() {
+    C._ts_language_metadata(this[0]);
+    const length = C.getValue(TRANSFER_BUFFER, "i32");
+    const address = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+    if (length === 0) return null;
+    return unmarshalLanguageMetadata(address);
+  }
+  /**
+   * Gets the number of fields in the language.
+   */
+  get fieldCount() {
+    return this.fields.length - 1;
+  }
+  /**
+   * Gets the number of states in the language.
+   */
+  get stateCount() {
+    return C._ts_language_state_count(this[0]);
+  }
+  /**
+   * Get the field id for a field name.
+   */
+  fieldIdForName(fieldName) {
+    const result = this.fields.indexOf(fieldName);
+    return result !== -1 ? result : null;
+  }
+  /**
+   * Get the field name for a field id.
+   */
+  fieldNameForId(fieldId) {
+    return this.fields[fieldId] ?? null;
+  }
+  /**
+   * Get the node type id for a node type name.
+   */
+  idForNodeType(type, named) {
+    const typeLength = C.lengthBytesUTF8(type);
+    const typeAddress = C._malloc(typeLength + 1);
+    C.stringToUTF8(type, typeAddress, typeLength + 1);
+    const result = C._ts_language_symbol_for_name(this[0], typeAddress, typeLength, named ? 1 : 0);
+    C._free(typeAddress);
+    return result || null;
+  }
+  /**
+   * Gets the number of node types in the language.
+   */
+  get nodeTypeCount() {
+    return C._ts_language_symbol_count(this[0]);
+  }
+  /**
+   * Get the node type name for a node type id.
+   */
+  nodeTypeForId(typeId) {
+    const name2 = C._ts_language_symbol_name(this[0], typeId);
+    return name2 ? C.UTF8ToString(name2) : null;
+  }
+  /**
+   * Check if a node type is named.
+   *
+   * @see {@link https://tree-sitter.github.io/tree-sitter/using-parsers/2-basic-parsing.html#named-vs-anonymous-nodes}
+   */
+  nodeTypeIsNamed(typeId) {
+    return C._ts_language_type_is_named_wasm(this[0], typeId) ? true : false;
+  }
+  /**
+   * Check if a node type is visible.
+   */
+  nodeTypeIsVisible(typeId) {
+    return C._ts_language_type_is_visible_wasm(this[0], typeId) ? true : false;
+  }
+  /**
+   * Get the supertypes ids of this language.
+   *
+   * @see {@link https://tree-sitter.github.io/tree-sitter/using-parsers/6-static-node-types.html?highlight=supertype#supertype-nodes}
+   */
+  get supertypes() {
+    C._ts_language_supertypes_wasm(this[0]);
+    const count = C.getValue(TRANSFER_BUFFER, "i32");
+    const buffer = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+    const result = new Array(count);
+    if (count > 0) {
+      let address = buffer;
+      for (let i2 = 0; i2 < count; i2++) {
+        result[i2] = C.getValue(address, "i16");
+        address += SIZE_OF_SHORT;
+      }
+    }
+    return result;
+  }
+  /**
+   * Get the subtype ids for a given supertype node id.
+   */
+  subtypes(supertype) {
+    C._ts_language_subtypes_wasm(this[0], supertype);
+    const count = C.getValue(TRANSFER_BUFFER, "i32");
+    const buffer = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+    const result = new Array(count);
+    if (count > 0) {
+      let address = buffer;
+      for (let i2 = 0; i2 < count; i2++) {
+        result[i2] = C.getValue(address, "i16");
+        address += SIZE_OF_SHORT;
+      }
+    }
+    return result;
+  }
+  /**
+   * Get the next state id for a given state id and node type id.
+   */
+  nextState(stateId, typeId) {
+    return C._ts_language_next_state(this[0], stateId, typeId);
+  }
+  /**
+   * Create a new lookahead iterator for this language and parse state.
+   *
+   * This returns `null` if state is invalid for this language.
+   *
+   * Iterating {@link LookaheadIterator} will yield valid symbols in the given
+   * parse state. Newly created lookahead iterators will return the `ERROR`
+   * symbol from {@link LookaheadIterator#currentType}.
+   *
+   * Lookahead iterators can be useful for generating suggestions and improving
+   * syntax error diagnostics. To get symbols valid in an `ERROR` node, use the
+   * lookahead iterator on its first leaf node state. For `MISSING` nodes, a
+   * lookahead iterator created on the previous non-extra leaf node may be
+   * appropriate.
+   */
+  lookaheadIterator(stateId) {
+    const address = C._ts_lookahead_iterator_new(this[0], stateId);
+    if (address) return new LookaheadIterator(INTERNAL, address, this);
+    return null;
+  }
+  /**
+   * @deprecated since version 0.25.0, call `new` on a {@link Query} instead
+   *
+   * Create a new query from a string containing one or more S-expression
+   * patterns.
+   *
+   * The query is associated with a particular language, and can only be run
+   * on syntax nodes parsed with that language. References to Queries can be
+   * shared between multiple threads.
+   *
+   * @link {@see https://tree-sitter.github.io/tree-sitter/using-parsers/queries}
+   */
+  query(source) {
+    console.warn("Language.query is deprecated. Use new Query(language, source) instead.");
+    return new Query(this, source);
+  }
+  /**
+   * Load a language from a WebAssembly module.
+   * The module can be provided as a path to a file or as a buffer.
+   */
+  static async load(input) {
+    let bytes;
+    if (input instanceof Uint8Array) {
+      bytes = Promise.resolve(input);
+    } else {
+      if (globalThis.process?.versions.node) {
+        const fs2 = await import("fs/promises");
+        bytes = fs2.readFile(input);
+      } else {
+        bytes = fetch(input).then((response) => response.arrayBuffer().then((buffer) => {
+          if (response.ok) {
+            return new Uint8Array(buffer);
+          } else {
+            const body2 = new TextDecoder("utf-8").decode(buffer);
+            throw new Error(`Language.load failed with status ${response.status}.
+
+${body2}`);
+          }
+        }));
+      }
+    }
+    const mod = await C.loadWebAssemblyModule(await bytes, { loadAsync: true });
+    const symbolNames = Object.keys(mod);
+    const functionName = symbolNames.find((key) => LANGUAGE_FUNCTION_REGEX.test(key) && !key.includes("external_scanner_"));
+    if (!functionName) {
+      console.log(`Couldn't find language function in WASM file. Symbols:
+${JSON.stringify(symbolNames, null, 2)}`);
+      throw new Error("Language.load failed: no language function found in WASM file");
+    }
+    const languageAddress = mod[functionName]();
+    return new _Language(INTERNAL, languageAddress);
+  }
+};
+
+// lib/tree-sitter.mjs
+var Module2 = (() => {
+  var _scriptName = import.meta.url;
+  return async function(moduleArg = {}) {
+    var moduleRtn;
+    var Module = moduleArg;
+    var readyPromiseResolve, readyPromiseReject;
+    var readyPromise = new Promise((resolve, reject) => {
+      readyPromiseResolve = resolve;
+      readyPromiseReject = reject;
+    });
+    var ENVIRONMENT_IS_WEB = typeof window == "object";
+    var ENVIRONMENT_IS_WORKER = typeof WorkerGlobalScope != "undefined";
+    var ENVIRONMENT_IS_NODE = typeof process == "object" && typeof process.versions == "object" && typeof process.versions.node == "string" && process.type != "renderer";
+    var ENVIRONMENT_IS_SHELL = !ENVIRONMENT_IS_WEB && !ENVIRONMENT_IS_NODE && !ENVIRONMENT_IS_WORKER;
+    if (ENVIRONMENT_IS_NODE) {
+      const { createRequire } = await import("module");
+      var require = createRequire(import.meta.url);
+    }
+    Module.currentQueryProgressCallback = null;
+    Module.currentProgressCallback = null;
+    Module.currentLogCallback = null;
+    Module.currentParseCallback = null;
+    var moduleOverrides = Object.assign({}, Module);
+    var arguments_ = [];
+    var thisProgram = "./this.program";
+    var quit_ = /* @__PURE__ */ __name((status, toThrow) => {
+      throw toThrow;
+    }, "quit_");
+    var scriptDirectory = "";
+    function locateFile(path) {
+      if (Module["locateFile"]) {
+        return Module["locateFile"](path, scriptDirectory);
+      }
+      return scriptDirectory + path;
+    }
+    __name(locateFile, "locateFile");
+    var readAsync, readBinary;
+    if (ENVIRONMENT_IS_NODE) {
+      var fs = require("fs");
+      var nodePath = require("path");
+      if (!import.meta.url.startsWith("data:")) {
+        scriptDirectory = nodePath.dirname(require("url").fileURLToPath(import.meta.url)) + "/";
+      }
+      readBinary = /* @__PURE__ */ __name((filename) => {
+        filename = isFileURI(filename) ? new URL(filename) : filename;
+        var ret = fs.readFileSync(filename);
+        return ret;
+      }, "readBinary");
+      readAsync = /* @__PURE__ */ __name(async (filename, binary2 = true) => {
+        filename = isFileURI(filename) ? new URL(filename) : filename;
+        var ret = fs.readFileSync(filename, binary2 ? void 0 : "utf8");
+        return ret;
+      }, "readAsync");
+      if (!Module["thisProgram"] && process.argv.length > 1) {
+        thisProgram = process.argv[1].replace(/\\/g, "/");
+      }
+      arguments_ = process.argv.slice(2);
+      quit_ = /* @__PURE__ */ __name((status, toThrow) => {
+        process.exitCode = status;
+        throw toThrow;
+      }, "quit_");
+    } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) {
+      if (ENVIRONMENT_IS_WORKER) {
+        scriptDirectory = self.location.href;
+      } else if (typeof document != "undefined" && document.currentScript) {
+        scriptDirectory = document.currentScript.src;
+      }
+      if (_scriptName) {
+        scriptDirectory = _scriptName;
+      }
+      if (scriptDirectory.startsWith("blob:")) {
+        scriptDirectory = "";
+      } else {
+        scriptDirectory = scriptDirectory.slice(0, scriptDirectory.replace(/[?#].*/, "").lastIndexOf("/") + 1);
+      }
+      {
+        if (ENVIRONMENT_IS_WORKER) {
+          readBinary = /* @__PURE__ */ __name((url) => {
+            var xhr = new XMLHttpRequest();
+            xhr.open("GET", url, false);
+            xhr.responseType = "arraybuffer";
+            xhr.send(null);
+            return new Uint8Array(
+              /** @type{!ArrayBuffer} */
+              xhr.response
+            );
+          }, "readBinary");
+        }
+        readAsync = /* @__PURE__ */ __name(async (url) => {
+          if (isFileURI(url)) {
+            return new Promise((resolve, reject) => {
+              var xhr = new XMLHttpRequest();
+              xhr.open("GET", url, true);
+              xhr.responseType = "arraybuffer";
+              xhr.onload = () => {
+                if (xhr.status == 200 || xhr.status == 0 && xhr.response) {
+                  resolve(xhr.response);
+                  return;
+                }
+                reject(xhr.status);
+              };
+              xhr.onerror = reject;
+              xhr.send(null);
+            });
+          }
+          var response = await fetch(url, {
+            credentials: "same-origin"
+          });
+          if (response.ok) {
+            return response.arrayBuffer();
+          }
+          throw new Error(response.status + " : " + response.url);
+        }, "readAsync");
+      }
+    } else {
+    }
+    var out = Module["print"] || console.log.bind(console);
+    var err = Module["printErr"] || console.error.bind(console);
+    Object.assign(Module, moduleOverrides);
+    moduleOverrides = null;
+    if (Module["arguments"]) arguments_ = Module["arguments"];
+    if (Module["thisProgram"]) thisProgram = Module["thisProgram"];
+    var dynamicLibraries = Module["dynamicLibraries"] || [];
+    var wasmBinary = Module["wasmBinary"];
+    var wasmMemory;
+    var ABORT = false;
+    var EXITSTATUS;
+    function assert(condition, text) {
+      if (!condition) {
+        abort(text);
+      }
+    }
+    __name(assert, "assert");
+    var HEAP, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAP64, HEAPU64, HEAPF64;
+    var HEAP_DATA_VIEW;
+    var runtimeInitialized = false;
+    var isFileURI = /* @__PURE__ */ __name((filename) => filename.startsWith("file://"), "isFileURI");
+    function updateMemoryViews() {
+      var b = wasmMemory.buffer;
+      Module["HEAP_DATA_VIEW"] = HEAP_DATA_VIEW = new DataView(b);
+      Module["HEAP8"] = HEAP8 = new Int8Array(b);
+      Module["HEAP16"] = HEAP16 = new Int16Array(b);
+      Module["HEAPU8"] = HEAPU8 = new Uint8Array(b);
+      Module["HEAPU16"] = HEAPU16 = new Uint16Array(b);
+      Module["HEAP32"] = HEAP32 = new Int32Array(b);
+      Module["HEAPU32"] = HEAPU32 = new Uint32Array(b);
+      Module["HEAPF32"] = HEAPF32 = new Float32Array(b);
+      Module["HEAPF64"] = HEAPF64 = new Float64Array(b);
+      Module["HEAP64"] = HEAP64 = new BigInt64Array(b);
+      Module["HEAPU64"] = HEAPU64 = new BigUint64Array(b);
+    }
+    __name(updateMemoryViews, "updateMemoryViews");
+    if (Module["wasmMemory"]) {
+      wasmMemory = Module["wasmMemory"];
+    } else {
+      var INITIAL_MEMORY = Module["INITIAL_MEMORY"] || 33554432;
+      wasmMemory = new WebAssembly.Memory({
+        "initial": INITIAL_MEMORY / 65536,
+        // In theory we should not need to emit the maximum if we want "unlimited"
+        // or 4GB of memory, but VMs error on that atm, see
+        // https://github.com/emscripten-core/emscripten/issues/14130
+        // And in the pthreads case we definitely need to emit a maximum. So
+        // always emit one.
+        "maximum": 32768
+      });
+    }
+    updateMemoryViews();
+    var __RELOC_FUNCS__ = [];
+    function preRun() {
+      if (Module["preRun"]) {
+        if (typeof Module["preRun"] == "function") Module["preRun"] = [Module["preRun"]];
+        while (Module["preRun"].length) {
+          addOnPreRun(Module["preRun"].shift());
+        }
+      }
+      callRuntimeCallbacks(onPreRuns);
+    }
+    __name(preRun, "preRun");
+    function initRuntime() {
+      runtimeInitialized = true;
+      callRuntimeCallbacks(__RELOC_FUNCS__);
+      wasmExports["__wasm_call_ctors"]();
+      callRuntimeCallbacks(onPostCtors);
+    }
+    __name(initRuntime, "initRuntime");
+    function preMain() {
+    }
+    __name(preMain, "preMain");
+    function postRun() {
+      if (Module["postRun"]) {
+        if (typeof Module["postRun"] == "function") Module["postRun"] = [Module["postRun"]];
+        while (Module["postRun"].length) {
+          addOnPostRun(Module["postRun"].shift());
+        }
+      }
+      callRuntimeCallbacks(onPostRuns);
+    }
+    __name(postRun, "postRun");
+    var runDependencies = 0;
+    var dependenciesFulfilled = null;
+    function getUniqueRunDependency(id) {
+      return id;
+    }
+    __name(getUniqueRunDependency, "getUniqueRunDependency");
+    function addRunDependency(id) {
+      runDependencies++;
+      Module["monitorRunDependencies"]?.(runDependencies);
+    }
+    __name(addRunDependency, "addRunDependency");
+    function removeRunDependency(id) {
+      runDependencies--;
+      Module["monitorRunDependencies"]?.(runDependencies);
+      if (runDependencies == 0) {
+        if (dependenciesFulfilled) {
+          var callback = dependenciesFulfilled;
+          dependenciesFulfilled = null;
+          callback();
+        }
+      }
+    }
+    __name(removeRunDependency, "removeRunDependency");
+    function abort(what) {
+      Module["onAbort"]?.(what);
+      what = "Aborted(" + what + ")";
+      err(what);
+      ABORT = true;
+      what += ". Build with -sASSERTIONS for more info.";
+      var e = new WebAssembly.RuntimeError(what);
+      readyPromiseReject(e);
+      throw e;
+    }
+    __name(abort, "abort");
+    var wasmBinaryFile;
+    function findWasmBinary() {
+      if (Module["locateFile"]) {
+        return locateFile("tree-sitter.wasm");
+      }
+      return new URL("tree-sitter.wasm", import.meta.url).href;
+    }
+    __name(findWasmBinary, "findWasmBinary");
+    function getBinarySync(file) {
+      if (file == wasmBinaryFile && wasmBinary) {
+        return new Uint8Array(wasmBinary);
+      }
+      if (readBinary) {
+        return readBinary(file);
+      }
+      throw "both async and sync fetching of the wasm failed";
+    }
+    __name(getBinarySync, "getBinarySync");
+    async function getWasmBinary(binaryFile) {
+      if (!wasmBinary) {
+        try {
+          var response = await readAsync(binaryFile);
+          return new Uint8Array(response);
+        } catch {
+        }
+      }
+      return getBinarySync(binaryFile);
+    }
+    __name(getWasmBinary, "getWasmBinary");
+    async function instantiateArrayBuffer(binaryFile, imports) {
+      try {
+        var binary2 = await getWasmBinary(binaryFile);
+        var instance2 = await WebAssembly.instantiate(binary2, imports);
+        return instance2;
+      } catch (reason) {
+        err(`failed to asynchronously prepare wasm: ${reason}`);
+        abort(reason);
+      }
+    }
+    __name(instantiateArrayBuffer, "instantiateArrayBuffer");
+    async function instantiateAsync(binary2, binaryFile, imports) {
+      if (!binary2 && typeof WebAssembly.instantiateStreaming == "function" && !isFileURI(binaryFile) && !ENVIRONMENT_IS_NODE) {
+        try {
+          var response = fetch(binaryFile, {
+            credentials: "same-origin"
+          });
+          var instantiationResult = await WebAssembly.instantiateStreaming(response, imports);
+          return instantiationResult;
+        } catch (reason) {
+          err(`wasm streaming compile failed: ${reason}`);
+          err("falling back to ArrayBuffer instantiation");
+        }
+      }
+      return instantiateArrayBuffer(binaryFile, imports);
+    }
+    __name(instantiateAsync, "instantiateAsync");
+    function getWasmImports() {
+      return {
+        "env": wasmImports,
+        "wasi_snapshot_preview1": wasmImports,
+        "GOT.mem": new Proxy(wasmImports, GOTHandler),
+        "GOT.func": new Proxy(wasmImports, GOTHandler)
+      };
+    }
+    __name(getWasmImports, "getWasmImports");
+    async function createWasm() {
+      function receiveInstance(instance2, module2) {
+        wasmExports = instance2.exports;
+        wasmExports = relocateExports(wasmExports, 1024);
+        var metadata2 = getDylinkMetadata(module2);
+        if (metadata2.neededDynlibs) {
+          dynamicLibraries = metadata2.neededDynlibs.concat(dynamicLibraries);
+        }
+        mergeLibSymbols(wasmExports, "main");
+        LDSO.init();
+        loadDylibs();
+        __RELOC_FUNCS__.push(wasmExports["__wasm_apply_data_relocs"]);
+        removeRunDependency("wasm-instantiate");
+        return wasmExports;
+      }
+      __name(receiveInstance, "receiveInstance");
+      addRunDependency("wasm-instantiate");
+      function receiveInstantiationResult(result2) {
+        return receiveInstance(result2["instance"], result2["module"]);
+      }
+      __name(receiveInstantiationResult, "receiveInstantiationResult");
+      var info2 = getWasmImports();
+      if (Module["instantiateWasm"]) {
+        return new Promise((resolve, reject) => {
+          Module["instantiateWasm"](info2, (mod, inst) => {
+            receiveInstance(mod, inst);
+            resolve(mod.exports);
+          });
+        });
+      }
+      wasmBinaryFile ??= findWasmBinary();
+      try {
+        var result = await instantiateAsync(wasmBinary, wasmBinaryFile, info2);
+        var exports = receiveInstantiationResult(result);
+        return exports;
+      } catch (e) {
+        readyPromiseReject(e);
+        return Promise.reject(e);
+      }
+    }
+    __name(createWasm, "createWasm");
+    var ASM_CONSTS = {};
+    class ExitStatus {
+      static {
+        __name(this, "ExitStatus");
+      }
+      name = "ExitStatus";
+      constructor(status) {
+        this.message = `Program terminated with exit(${status})`;
+        this.status = status;
+      }
+    }
+    var GOT = {};
+    var currentModuleWeakSymbols = /* @__PURE__ */ new Set([]);
+    var GOTHandler = {
+      get(obj, symName) {
+        var rtn = GOT[symName];
+        if (!rtn) {
+          rtn = GOT[symName] = new WebAssembly.Global({
+            "value": "i32",
+            "mutable": true
+          });
+        }
+        if (!currentModuleWeakSymbols.has(symName)) {
+          rtn.required = true;
+        }
+        return rtn;
+      }
+    };
+    var LE_HEAP_LOAD_F32 = /* @__PURE__ */ __name((byteOffset) => HEAP_DATA_VIEW.getFloat32(byteOffset, true), "LE_HEAP_LOAD_F32");
+    var LE_HEAP_LOAD_F64 = /* @__PURE__ */ __name((byteOffset) => HEAP_DATA_VIEW.getFloat64(byteOffset, true), "LE_HEAP_LOAD_F64");
+    var LE_HEAP_LOAD_I16 = /* @__PURE__ */ __name((byteOffset) => HEAP_DATA_VIEW.getInt16(byteOffset, true), "LE_HEAP_LOAD_I16");
+    var LE_HEAP_LOAD_I32 = /* @__PURE__ */ __name((byteOffset) => HEAP_DATA_VIEW.getInt32(byteOffset, true), "LE_HEAP_LOAD_I32");
+    var LE_HEAP_LOAD_U16 = /* @__PURE__ */ __name((byteOffset) => HEAP_DATA_VIEW.getUint16(byteOffset, true), "LE_HEAP_LOAD_U16");
+    var LE_HEAP_LOAD_U32 = /* @__PURE__ */ __name((byteOffset) => HEAP_DATA_VIEW.getUint32(byteOffset, true), "LE_HEAP_LOAD_U32");
+    var LE_HEAP_STORE_F32 = /* @__PURE__ */ __name((byteOffset, value) => HEAP_DATA_VIEW.setFloat32(byteOffset, value, true), "LE_HEAP_STORE_F32");
+    var LE_HEAP_STORE_F64 = /* @__PURE__ */ __name((byteOffset, value) => HEAP_DATA_VIEW.setFloat64(byteOffset, value, true), "LE_HEAP_STORE_F64");
+    var LE_HEAP_STORE_I16 = /* @__PURE__ */ __name((byteOffset, value) => HEAP_DATA_VIEW.setInt16(byteOffset, value, true), "LE_HEAP_STORE_I16");
+    var LE_HEAP_STORE_I32 = /* @__PURE__ */ __name((byteOffset, value) => HEAP_DATA_VIEW.setInt32(byteOffset, value, true), "LE_HEAP_STORE_I32");
+    var LE_HEAP_STORE_U16 = /* @__PURE__ */ __name((byteOffset, value) => HEAP_DATA_VIEW.setUint16(byteOffset, value, true), "LE_HEAP_STORE_U16");
+    var LE_HEAP_STORE_U32 = /* @__PURE__ */ __name((byteOffset, value) => HEAP_DATA_VIEW.setUint32(byteOffset, value, true), "LE_HEAP_STORE_U32");
+    var callRuntimeCallbacks = /* @__PURE__ */ __name((callbacks) => {
+      while (callbacks.length > 0) {
+        callbacks.shift()(Module);
+      }
+    }, "callRuntimeCallbacks");
+    var onPostRuns = [];
+    var addOnPostRun = /* @__PURE__ */ __name((cb) => onPostRuns.unshift(cb), "addOnPostRun");
+    var onPreRuns = [];
+    var addOnPreRun = /* @__PURE__ */ __name((cb) => onPreRuns.unshift(cb), "addOnPreRun");
+    var UTF8Decoder = typeof TextDecoder != "undefined" ? new TextDecoder() : void 0;
+    var UTF8ArrayToString = /* @__PURE__ */ __name((heapOrArray, idx = 0, maxBytesToRead = NaN) => {
+      var endIdx = idx + maxBytesToRead;
+      var endPtr = idx;
+      while (heapOrArray[endPtr] && !(endPtr >= endIdx)) ++endPtr;
+      if (endPtr - idx > 16 && heapOrArray.buffer && UTF8Decoder) {
+        return UTF8Decoder.decode(heapOrArray.subarray(idx, endPtr));
+      }
+      var str = "";
+      while (idx < endPtr) {
+        var u0 = heapOrArray[idx++];
+        if (!(u0 & 128)) {
+          str += String.fromCharCode(u0);
+          continue;
+        }
+        var u1 = heapOrArray[idx++] & 63;
+        if ((u0 & 224) == 192) {
+          str += String.fromCharCode((u0 & 31) << 6 | u1);
+          continue;
+        }
+        var u2 = heapOrArray[idx++] & 63;
+        if ((u0 & 240) == 224) {
+          u0 = (u0 & 15) << 12 | u1 << 6 | u2;
+        } else {
+          u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heapOrArray[idx++] & 63;
+        }
+        if (u0 < 65536) {
+          str += String.fromCharCode(u0);
+        } else {
+          var ch = u0 - 65536;
+          str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023);
+        }
+      }
+      return str;
+    }, "UTF8ArrayToString");
+    var getDylinkMetadata = /* @__PURE__ */ __name((binary2) => {
+      var offset = 0;
+      var end = 0;
+      function getU8() {
+        return binary2[offset++];
+      }
+      __name(getU8, "getU8");
+      function getLEB() {
+        var ret = 0;
+        var mul = 1;
+        while (1) {
+          var byte = binary2[offset++];
+          ret += (byte & 127) * mul;
+          mul *= 128;
+          if (!(byte & 128)) break;
+        }
+        return ret;
+      }
+      __name(getLEB, "getLEB");
+      function getString() {
+        var len = getLEB();
+        offset += len;
+        return UTF8ArrayToString(binary2, offset - len, len);
+      }
+      __name(getString, "getString");
+      function failIf(condition, message) {
+        if (condition) throw new Error(message);
+      }
+      __name(failIf, "failIf");
+      var name2 = "dylink.0";
+      if (binary2 instanceof WebAssembly.Module) {
+        var dylinkSection = WebAssembly.Module.customSections(binary2, name2);
+        if (dylinkSection.length === 0) {
+          name2 = "dylink";
+          dylinkSection = WebAssembly.Module.customSections(binary2, name2);
+        }
+        failIf(dylinkSection.length === 0, "need dylink section");
+        binary2 = new Uint8Array(dylinkSection[0]);
+        end = binary2.length;
+      } else {
+        var int32View = new Uint32Array(new Uint8Array(binary2.subarray(0, 24)).buffer);
+        var magicNumberFound = int32View[0] == 1836278016 || int32View[0] == 6386541;
+        failIf(!magicNumberFound, "need to see wasm magic number");
+        failIf(binary2[8] !== 0, "need the dylink section to be first");
+        offset = 9;
+        var section_size = getLEB();
+        end = offset + section_size;
+        name2 = getString();
+      }
+      var customSection = {
+        neededDynlibs: [],
+        tlsExports: /* @__PURE__ */ new Set(),
+        weakImports: /* @__PURE__ */ new Set()
+      };
+      if (name2 == "dylink") {
+        customSection.memorySize = getLEB();
+        customSection.memoryAlign = getLEB();
+        customSection.tableSize = getLEB();
+        customSection.tableAlign = getLEB();
+        var neededDynlibsCount = getLEB();
+        for (var i2 = 0; i2 < neededDynlibsCount; ++i2) {
+          var libname = getString();
+          customSection.neededDynlibs.push(libname);
+        }
+      } else {
+        failIf(name2 !== "dylink.0");
+        var WASM_DYLINK_MEM_INFO = 1;
+        var WASM_DYLINK_NEEDED = 2;
+        var WASM_DYLINK_EXPORT_INFO = 3;
+        var WASM_DYLINK_IMPORT_INFO = 4;
+        var WASM_SYMBOL_TLS = 256;
+        var WASM_SYMBOL_BINDING_MASK = 3;
+        var WASM_SYMBOL_BINDING_WEAK = 1;
+        while (offset < end) {
+          var subsectionType = getU8();
+          var subsectionSize = getLEB();
+          if (subsectionType === WASM_DYLINK_MEM_INFO) {
+            customSection.memorySize = getLEB();
+            customSection.memoryAlign = getLEB();
+            customSection.tableSize = getLEB();
+            customSection.tableAlign = getLEB();
+          } else if (subsectionType === WASM_DYLINK_NEEDED) {
+            var neededDynlibsCount = getLEB();
+            for (var i2 = 0; i2 < neededDynlibsCount; ++i2) {
+              libname = getString();
+              customSection.neededDynlibs.push(libname);
+            }
+          } else if (subsectionType === WASM_DYLINK_EXPORT_INFO) {
+            var count = getLEB();
+            while (count--) {
+              var symname = getString();
+              var flags2 = getLEB();
+              if (flags2 & WASM_SYMBOL_TLS) {
+                customSection.tlsExports.add(symname);
+              }
+            }
+          } else if (subsectionType === WASM_DYLINK_IMPORT_INFO) {
+            var count = getLEB();
+            while (count--) {
+              var modname = getString();
+              var symname = getString();
+              var flags2 = getLEB();
+              if ((flags2 & WASM_SYMBOL_BINDING_MASK) == WASM_SYMBOL_BINDING_WEAK) {
+                customSection.weakImports.add(symname);
+              }
+            }
+          } else {
+            offset += subsectionSize;
+          }
+        }
+      }
+      return customSection;
+    }, "getDylinkMetadata");
+    function getValue(ptr, type = "i8") {
+      if (type.endsWith("*")) type = "*";
+      switch (type) {
+        case "i1":
+          return HEAP8[ptr];
+        case "i8":
+          return HEAP8[ptr];
+        case "i16":
+          return LE_HEAP_LOAD_I16((ptr >> 1) * 2);
+        case "i32":
+          return LE_HEAP_LOAD_I32((ptr >> 2) * 4);
+        case "i64":
+          return HEAP64[ptr >> 3];
+        case "float":
+          return LE_HEAP_LOAD_F32((ptr >> 2) * 4);
+        case "double":
+          return LE_HEAP_LOAD_F64((ptr >> 3) * 8);
+        case "*":
+          return LE_HEAP_LOAD_U32((ptr >> 2) * 4);
+        default:
+          abort(`invalid type for getValue: ${type}`);
+      }
+    }
+    __name(getValue, "getValue");
+    var newDSO = /* @__PURE__ */ __name((name2, handle2, syms) => {
+      var dso = {
+        refcount: Infinity,
+        name: name2,
+        exports: syms,
+        global: true
+      };
+      LDSO.loadedLibsByName[name2] = dso;
+      if (handle2 != void 0) {
+        LDSO.loadedLibsByHandle[handle2] = dso;
+      }
+      return dso;
+    }, "newDSO");
+    var LDSO = {
+      loadedLibsByName: {},
+      loadedLibsByHandle: {},
+      init() {
+        newDSO("__main__", 0, wasmImports);
+      }
+    };
+    var ___heap_base = 78224;
+    var alignMemory = /* @__PURE__ */ __name((size, alignment) => Math.ceil(size / alignment) * alignment, "alignMemory");
+    var getMemory = /* @__PURE__ */ __name((size) => {
+      if (runtimeInitialized) {
+        return _calloc(size, 1);
+      }
+      var ret = ___heap_base;
+      var end = ret + alignMemory(size, 16);
+      ___heap_base = end;
+      GOT["__heap_base"].value = end;
+      return ret;
+    }, "getMemory");
+    var isInternalSym = /* @__PURE__ */ __name((symName) => ["__cpp_exception", "__c_longjmp", "__wasm_apply_data_relocs", "__dso_handle", "__tls_size", "__tls_align", "__set_stack_limits", "_emscripten_tls_init", "__wasm_init_tls", "__wasm_call_ctors", "__start_em_asm", "__stop_em_asm", "__start_em_js", "__stop_em_js"].includes(symName) || symName.startsWith("__em_js__"), "isInternalSym");
+    var uleb128Encode = /* @__PURE__ */ __name((n, target) => {
+      if (n < 128) {
+        target.push(n);
+      } else {
+        target.push(n % 128 | 128, n >> 7);
+      }
+    }, "uleb128Encode");
+    var sigToWasmTypes = /* @__PURE__ */ __name((sig) => {
+      var typeNames = {
+        "i": "i32",
+        "j": "i64",
+        "f": "f32",
+        "d": "f64",
+        "e": "externref",
+        "p": "i32"
+      };
+      var type = {
+        parameters: [],
+        results: sig[0] == "v" ? [] : [typeNames[sig[0]]]
+      };
+      for (var i2 = 1; i2 < sig.length; ++i2) {
+        type.parameters.push(typeNames[sig[i2]]);
+      }
+      return type;
+    }, "sigToWasmTypes");
+    var generateFuncType = /* @__PURE__ */ __name((sig, target) => {
+      var sigRet = sig.slice(0, 1);
+      var sigParam = sig.slice(1);
+      var typeCodes = {
+        "i": 127,
+        // i32
+        "p": 127,
+        // i32
+        "j": 126,
+        // i64
+        "f": 125,
+        // f32
+        "d": 124,
+        // f64
+        "e": 111
+      };
+      target.push(96);
+      uleb128Encode(sigParam.length, target);
+      for (var i2 = 0; i2 < sigParam.length; ++i2) {
+        target.push(typeCodes[sigParam[i2]]);
+      }
+      if (sigRet == "v") {
+        target.push(0);
+      } else {
+        target.push(1, typeCodes[sigRet]);
+      }
+    }, "generateFuncType");
+    var convertJsFunctionToWasm = /* @__PURE__ */ __name((func2, sig) => {
+      if (typeof WebAssembly.Function == "function") {
+        return new WebAssembly.Function(sigToWasmTypes(sig), func2);
+      }
+      var typeSectionBody = [1];
+      generateFuncType(sig, typeSectionBody);
+      var bytes = [
+        0,
+        97,
+        115,
+        109,
+        // magic ("\0asm")
+        1,
+        0,
+        0,
+        0,
+        // version: 1
+        1
+      ];
+      uleb128Encode(typeSectionBody.length, bytes);
+      bytes.push(...typeSectionBody);
+      bytes.push(
+        2,
+        7,
+        // import section
+        // (import "e" "f" (func 0 (type 0)))
+        1,
+        1,
+        101,
+        1,
+        102,
+        0,
+        0,
+        7,
+        5,
+        // export section
+        // (export "f" (func 0 (type 0)))
+        1,
+        1,
+        102,
+        0,
+        0
+      );
+      var module2 = new WebAssembly.Module(new Uint8Array(bytes));
+      var instance2 = new WebAssembly.Instance(module2, {
+        "e": {
+          "f": func2
+        }
+      });
+      var wrappedFunc = instance2.exports["f"];
+      return wrappedFunc;
+    }, "convertJsFunctionToWasm");
+    var wasmTableMirror = [];
+    var wasmTable = new WebAssembly.Table({
+      "initial": 31,
+      "element": "anyfunc"
+    });
+    var getWasmTableEntry = /* @__PURE__ */ __name((funcPtr) => {
+      var func2 = wasmTableMirror[funcPtr];
+      if (!func2) {
+        if (funcPtr >= wasmTableMirror.length) wasmTableMirror.length = funcPtr + 1;
+        wasmTableMirror[funcPtr] = func2 = wasmTable.get(funcPtr);
+      }
+      return func2;
+    }, "getWasmTableEntry");
+    var updateTableMap = /* @__PURE__ */ __name((offset, count) => {
+      if (functionsInTableMap) {
+        for (var i2 = offset; i2 < offset + count; i2++) {
+          var item = getWasmTableEntry(i2);
+          if (item) {
+            functionsInTableMap.set(item, i2);
+          }
+        }
+      }
+    }, "updateTableMap");
+    var functionsInTableMap;
+    var getFunctionAddress = /* @__PURE__ */ __name((func2) => {
+      if (!functionsInTableMap) {
+        functionsInTableMap = /* @__PURE__ */ new WeakMap();
+        updateTableMap(0, wasmTable.length);
+      }
+      return functionsInTableMap.get(func2) || 0;
+    }, "getFunctionAddress");
+    var freeTableIndexes = [];
+    var getEmptyTableSlot = /* @__PURE__ */ __name(() => {
+      if (freeTableIndexes.length) {
+        return freeTableIndexes.pop();
+      }
+      try {
+        wasmTable.grow(1);
+      } catch (err2) {
+        if (!(err2 instanceof RangeError)) {
+          throw err2;
+        }
+        throw "Unable to grow wasm table. Set ALLOW_TABLE_GROWTH.";
+      }
+      return wasmTable.length - 1;
+    }, "getEmptyTableSlot");
+    var setWasmTableEntry = /* @__PURE__ */ __name((idx, func2) => {
+      wasmTable.set(idx, func2);
+      wasmTableMirror[idx] = wasmTable.get(idx);
+    }, "setWasmTableEntry");
+    var addFunction = /* @__PURE__ */ __name((func2, sig) => {
+      var rtn = getFunctionAddress(func2);
+      if (rtn) {
+        return rtn;
+      }
+      var ret = getEmptyTableSlot();
+      try {
+        setWasmTableEntry(ret, func2);
+      } catch (err2) {
+        if (!(err2 instanceof TypeError)) {
+          throw err2;
+        }
+        var wrapped = convertJsFunctionToWasm(func2, sig);
+        setWasmTableEntry(ret, wrapped);
+      }
+      functionsInTableMap.set(func2, ret);
+      return ret;
+    }, "addFunction");
+    var updateGOT = /* @__PURE__ */ __name((exports, replace) => {
+      for (var symName in exports) {
+        if (isInternalSym(symName)) {
+          continue;
+        }
+        var value = exports[symName];
+        GOT[symName] ||= new WebAssembly.Global({
+          "value": "i32",
+          "mutable": true
+        });
+        if (replace || GOT[symName].value == 0) {
+          if (typeof value == "function") {
+            GOT[symName].value = addFunction(value);
+          } else if (typeof value == "number") {
+            GOT[symName].value = value;
+          } else {
+            err(`unhandled export type for '${symName}': ${typeof value}`);
+          }
+        }
+      }
+    }, "updateGOT");
+    var relocateExports = /* @__PURE__ */ __name((exports, memoryBase2, replace) => {
+      var relocated = {};
+      for (var e in exports) {
+        var value = exports[e];
+        if (typeof value == "object") {
+          value = value.value;
+        }
+        if (typeof value == "number") {
+          value += memoryBase2;
+        }
+        relocated[e] = value;
+      }
+      updateGOT(relocated, replace);
+      return relocated;
+    }, "relocateExports");
+    var isSymbolDefined = /* @__PURE__ */ __name((symName) => {
+      var existing = wasmImports[symName];
+      if (!existing || existing.stub) {
+        return false;
+      }
+      return true;
+    }, "isSymbolDefined");
+    var dynCall = /* @__PURE__ */ __name((sig, ptr, args2 = []) => {
+      var rtn = getWasmTableEntry(ptr)(...args2);
+      return rtn;
+    }, "dynCall");
+    var stackSave = /* @__PURE__ */ __name(() => _emscripten_stack_get_current(), "stackSave");
+    var stackRestore = /* @__PURE__ */ __name((val) => __emscripten_stack_restore(val), "stackRestore");
+    var createInvokeFunction = /* @__PURE__ */ __name((sig) => (ptr, ...args2) => {
+      var sp = stackSave();
+      try {
+        return dynCall(sig, ptr, args2);
+      } catch (e) {
+        stackRestore(sp);
+        if (e !== e + 0) throw e;
+        _setThrew(1, 0);
+        if (sig[0] == "j") return 0n;
+      }
+    }, "createInvokeFunction");
+    var resolveGlobalSymbol = /* @__PURE__ */ __name((symName, direct = false) => {
+      var sym;
+      if (isSymbolDefined(symName)) {
+        sym = wasmImports[symName];
+      } else if (symName.startsWith("invoke_")) {
+        sym = wasmImports[symName] = createInvokeFunction(symName.split("_")[1]);
+      }
+      return {
+        sym,
+        name: symName
+      };
+    }, "resolveGlobalSymbol");
+    var onPostCtors = [];
+    var addOnPostCtor = /* @__PURE__ */ __name((cb) => onPostCtors.unshift(cb), "addOnPostCtor");
+    var UTF8ToString = /* @__PURE__ */ __name((ptr, maxBytesToRead) => ptr ? UTF8ArrayToString(HEAPU8, ptr, maxBytesToRead) : "", "UTF8ToString");
+    var loadWebAssemblyModule = /* @__PURE__ */ __name((binary, flags, libName, localScope, handle) => {
+      var metadata = getDylinkMetadata(binary);
+      currentModuleWeakSymbols = metadata.weakImports;
+      function loadModule() {
+        var memAlign = Math.pow(2, metadata.memoryAlign);
+        var memoryBase = metadata.memorySize ? alignMemory(getMemory(metadata.memorySize + memAlign), memAlign) : 0;
+        var tableBase = metadata.tableSize ? wasmTable.length : 0;
+        if (handle) {
+          HEAP8[handle + 8] = 1;
+          LE_HEAP_STORE_U32((handle + 12 >> 2) * 4, memoryBase);
+          LE_HEAP_STORE_I32((handle + 16 >> 2) * 4, metadata.memorySize);
+          LE_HEAP_STORE_U32((handle + 20 >> 2) * 4, tableBase);
+          LE_HEAP_STORE_I32((handle + 24 >> 2) * 4, metadata.tableSize);
+        }
+        if (metadata.tableSize) {
+          wasmTable.grow(metadata.tableSize);
+        }
+        var moduleExports;
+        function resolveSymbol(sym) {
+          var resolved = resolveGlobalSymbol(sym).sym;
+          if (!resolved && localScope) {
+            resolved = localScope[sym];
+          }
+          if (!resolved) {
+            resolved = moduleExports[sym];
+          }
+          return resolved;
+        }
+        __name(resolveSymbol, "resolveSymbol");
+        var proxyHandler = {
+          get(stubs, prop) {
+            switch (prop) {
+              case "__memory_base":
+                return memoryBase;
+              case "__table_base":
+                return tableBase;
+            }
+            if (prop in wasmImports && !wasmImports[prop].stub) {
+              var res = wasmImports[prop];
+              return res;
+            }
+            if (!(prop in stubs)) {
+              var resolved;
+              stubs[prop] = (...args2) => {
+                resolved ||= resolveSymbol(prop);
+                return resolved(...args2);
+              };
+            }
+            return stubs[prop];
+          }
+        };
+        var proxy = new Proxy({}, proxyHandler);
+        var info = {
+          "GOT.mem": new Proxy({}, GOTHandler),
+          "GOT.func": new Proxy({}, GOTHandler),
+          "env": proxy,
+          "wasi_snapshot_preview1": proxy
+        };
+        function postInstantiation(module, instance) {
+          updateTableMap(tableBase, metadata.tableSize);
+          moduleExports = relocateExports(instance.exports, memoryBase);
+          if (!flags.allowUndefined) {
+            reportUndefinedSymbols();
+          }
+          function addEmAsm(addr, body) {
+            var args = [];
+            var arity = 0;
+            for (; arity < 16; arity++) {
+              if (body.indexOf("$" + arity) != -1) {
+                args.push("$" + arity);
+              } else {
+                break;
+              }
+            }
+            args = args.join(",");
+            var func = `(${args}) => { ${body} };`;
+            ASM_CONSTS[start] = eval(func);
+          }
+          __name(addEmAsm, "addEmAsm");
+          if ("__start_em_asm" in moduleExports) {
+            var start = moduleExports["__start_em_asm"];
+            var stop = moduleExports["__stop_em_asm"];
+            while (start < stop) {
+              var jsString = UTF8ToString(start);
+              addEmAsm(start, jsString);
+              start = HEAPU8.indexOf(0, start) + 1;
+            }
+          }
+          function addEmJs(name, cSig, body) {
+            var jsArgs = [];
+            cSig = cSig.slice(1, -1);
+            if (cSig != "void") {
+              cSig = cSig.split(",");
+              for (var i in cSig) {
+                var jsArg = cSig[i].split(" ").pop();
+                jsArgs.push(jsArg.replace("*", ""));
+              }
+            }
+            var func = `(${jsArgs}) => ${body};`;
+            moduleExports[name] = eval(func);
+          }
+          __name(addEmJs, "addEmJs");
+          for (var name in moduleExports) {
+            if (name.startsWith("__em_js__")) {
+              var start = moduleExports[name];
+              var jsString = UTF8ToString(start);
+              var parts = jsString.split("<::>");
+              addEmJs(name.replace("__em_js__", ""), parts[0], parts[1]);
+              delete moduleExports[name];
+            }
+          }
+          var applyRelocs = moduleExports["__wasm_apply_data_relocs"];
+          if (applyRelocs) {
+            if (runtimeInitialized) {
+              applyRelocs();
+            } else {
+              __RELOC_FUNCS__.push(applyRelocs);
+            }
+          }
+          var init = moduleExports["__wasm_call_ctors"];
+          if (init) {
+            if (runtimeInitialized) {
+              init();
+            } else {
+              addOnPostCtor(init);
+            }
+          }
+          return moduleExports;
+        }
+        __name(postInstantiation, "postInstantiation");
+        if (flags.loadAsync) {
+          if (binary instanceof WebAssembly.Module) {
+            var instance = new WebAssembly.Instance(binary, info);
+            return Promise.resolve(postInstantiation(binary, instance));
+          }
+          return WebAssembly.instantiate(binary, info).then((result) => postInstantiation(result.module, result.instance));
+        }
+        var module = binary instanceof WebAssembly.Module ? binary : new WebAssembly.Module(binary);
+        var instance = new WebAssembly.Instance(module, info);
+        return postInstantiation(module, instance);
+      }
+      __name(loadModule, "loadModule");
+      if (flags.loadAsync) {
+        return metadata.neededDynlibs.reduce((chain, dynNeeded) => chain.then(() => loadDynamicLibrary(dynNeeded, flags, localScope)), Promise.resolve()).then(loadModule);
+      }
+      metadata.neededDynlibs.forEach((needed) => loadDynamicLibrary(needed, flags, localScope));
+      return loadModule();
+    }, "loadWebAssemblyModule");
+    var mergeLibSymbols = /* @__PURE__ */ __name((exports, libName2) => {
+      for (var [sym, exp] of Object.entries(exports)) {
+        const setImport = /* @__PURE__ */ __name((target) => {
+          if (!isSymbolDefined(target)) {
+            wasmImports[target] = exp;
+          }
+        }, "setImport");
+        setImport(sym);
+        const main_alias = "__main_argc_argv";
+        if (sym == "main") {
+          setImport(main_alias);
+        }
+        if (sym == main_alias) {
+          setImport("main");
+        }
+      }
+    }, "mergeLibSymbols");
+    var asyncLoad = /* @__PURE__ */ __name(async (url) => {
+      var arrayBuffer = await readAsync(url);
+      return new Uint8Array(arrayBuffer);
+    }, "asyncLoad");
+    function loadDynamicLibrary(libName2, flags2 = {
+      global: true,
+      nodelete: true
+    }, localScope2, handle2) {
+      var dso = LDSO.loadedLibsByName[libName2];
+      if (dso) {
+        if (!flags2.global) {
+          if (localScope2) {
+            Object.assign(localScope2, dso.exports);
+          }
+        } else if (!dso.global) {
+          dso.global = true;
+          mergeLibSymbols(dso.exports, libName2);
+        }
+        if (flags2.nodelete && dso.refcount !== Infinity) {
+          dso.refcount = Infinity;
+        }
+        dso.refcount++;
+        if (handle2) {
+          LDSO.loadedLibsByHandle[handle2] = dso;
+        }
+        return flags2.loadAsync ? Promise.resolve(true) : true;
+      }
+      dso = newDSO(libName2, handle2, "loading");
+      dso.refcount = flags2.nodelete ? Infinity : 1;
+      dso.global = flags2.global;
+      function loadLibData() {
+        if (handle2) {
+          var data = LE_HEAP_LOAD_U32((handle2 + 28 >> 2) * 4);
+          var dataSize = LE_HEAP_LOAD_U32((handle2 + 32 >> 2) * 4);
+          if (data && dataSize) {
+            var libData = HEAP8.slice(data, data + dataSize);
+            return flags2.loadAsync ? Promise.resolve(libData) : libData;
+          }
+        }
+        var libFile = locateFile(libName2);
+        if (flags2.loadAsync) {
+          return asyncLoad(libFile);
+        }
+        if (!readBinary) {
+          throw new Error(`${libFile}: file not found, and synchronous loading of external files is not available`);
+        }
+        return readBinary(libFile);
+      }
+      __name(loadLibData, "loadLibData");
+      function getExports() {
+        if (flags2.loadAsync) {
+          return loadLibData().then((libData) => loadWebAssemblyModule(libData, flags2, libName2, localScope2, handle2));
+        }
+        return loadWebAssemblyModule(loadLibData(), flags2, libName2, localScope2, handle2);
+      }
+      __name(getExports, "getExports");
+      function moduleLoaded(exports) {
+        if (dso.global) {
+          mergeLibSymbols(exports, libName2);
+        } else if (localScope2) {
+          Object.assign(localScope2, exports);
+        }
+        dso.exports = exports;
+      }
+      __name(moduleLoaded, "moduleLoaded");
+      if (flags2.loadAsync) {
+        return getExports().then((exports) => {
+          moduleLoaded(exports);
+          return true;
+        });
+      }
+      moduleLoaded(getExports());
+      return true;
+    }
+    __name(loadDynamicLibrary, "loadDynamicLibrary");
+    var reportUndefinedSymbols = /* @__PURE__ */ __name(() => {
+      for (var [symName, entry] of Object.entries(GOT)) {
+        if (entry.value == 0) {
+          var value = resolveGlobalSymbol(symName, true).sym;
+          if (!value && !entry.required) {
+            continue;
+          }
+          if (typeof value == "function") {
+            entry.value = addFunction(value, value.sig);
+          } else if (typeof value == "number") {
+            entry.value = value;
+          } else {
+            throw new Error(`bad export type for '${symName}': ${typeof value}`);
+          }
+        }
+      }
+    }, "reportUndefinedSymbols");
+    var loadDylibs = /* @__PURE__ */ __name(() => {
+      if (!dynamicLibraries.length) {
+        reportUndefinedSymbols();
+        return;
+      }
+      addRunDependency("loadDylibs");
+      dynamicLibraries.reduce((chain, lib) => chain.then(() => loadDynamicLibrary(lib, {
+        loadAsync: true,
+        global: true,
+        nodelete: true,
+        allowUndefined: true
+      })), Promise.resolve()).then(() => {
+        reportUndefinedSymbols();
+        removeRunDependency("loadDylibs");
+      });
+    }, "loadDylibs");
+    var noExitRuntime = Module["noExitRuntime"] || true;
+    function setValue(ptr, value, type = "i8") {
+      if (type.endsWith("*")) type = "*";
+      switch (type) {
+        case "i1":
+          HEAP8[ptr] = value;
+          break;
+        case "i8":
+          HEAP8[ptr] = value;
+          break;
+        case "i16":
+          LE_HEAP_STORE_I16((ptr >> 1) * 2, value);
+          break;
+        case "i32":
+          LE_HEAP_STORE_I32((ptr >> 2) * 4, value);
+          break;
+        case "i64":
+          HEAP64[ptr >> 3] = BigInt(value);
+          break;
+        case "float":
+          LE_HEAP_STORE_F32((ptr >> 2) * 4, value);
+          break;
+        case "double":
+          LE_HEAP_STORE_F64((ptr >> 3) * 8, value);
+          break;
+        case "*":
+          LE_HEAP_STORE_U32((ptr >> 2) * 4, value);
+          break;
+        default:
+          abort(`invalid type for setValue: ${type}`);
+      }
+    }
+    __name(setValue, "setValue");
+    var ___memory_base = new WebAssembly.Global({
+      "value": "i32",
+      "mutable": false
+    }, 1024);
+    var ___stack_pointer = new WebAssembly.Global({
+      "value": "i32",
+      "mutable": true
+    }, 78224);
+    var ___table_base = new WebAssembly.Global({
+      "value": "i32",
+      "mutable": false
+    }, 1);
+    var __abort_js = /* @__PURE__ */ __name(() => abort(""), "__abort_js");
+    __abort_js.sig = "v";
+    var _emscripten_get_now = /* @__PURE__ */ __name(() => performance.now(), "_emscripten_get_now");
+    _emscripten_get_now.sig = "d";
+    var _emscripten_date_now = /* @__PURE__ */ __name(() => Date.now(), "_emscripten_date_now");
+    _emscripten_date_now.sig = "d";
+    var nowIsMonotonic = 1;
+    var checkWasiClock = /* @__PURE__ */ __name((clock_id) => clock_id >= 0 && clock_id <= 3, "checkWasiClock");
+    var INT53_MAX = 9007199254740992;
+    var INT53_MIN = -9007199254740992;
+    var bigintToI53Checked = /* @__PURE__ */ __name((num) => num < INT53_MIN || num > INT53_MAX ? NaN : Number(num), "bigintToI53Checked");
+    function _clock_time_get(clk_id, ignored_precision, ptime) {
+      ignored_precision = bigintToI53Checked(ignored_precision);
+      if (!checkWasiClock(clk_id)) {
+        return 28;
+      }
+      var now;
+      if (clk_id === 0) {
+        now = _emscripten_date_now();
+      } else if (nowIsMonotonic) {
+        now = _emscripten_get_now();
+      } else {
+        return 52;
+      }
+      var nsec = Math.round(now * 1e3 * 1e3);
+      HEAP64[ptime >> 3] = BigInt(nsec);
+      return 0;
+    }
+    __name(_clock_time_get, "_clock_time_get");
+    _clock_time_get.sig = "iijp";
+    var getHeapMax = /* @__PURE__ */ __name(() => (
+      // Stay one Wasm page short of 4GB: while e.g. Chrome is able to allocate
+      // full 4GB Wasm memories, the size will wrap back to 0 bytes in Wasm side
+      // for any code that deals with heap sizes, which would require special
+      // casing all heap size related code to treat 0 specially.
+      2147483648
+    ), "getHeapMax");
+    var growMemory = /* @__PURE__ */ __name((size) => {
+      var b = wasmMemory.buffer;
+      var pages = (size - b.byteLength + 65535) / 65536 | 0;
+      try {
+        wasmMemory.grow(pages);
+        updateMemoryViews();
+        return 1;
+      } catch (e) {
+      }
+    }, "growMemory");
+    var _emscripten_resize_heap = /* @__PURE__ */ __name((requestedSize) => {
+      var oldSize = HEAPU8.length;
+      requestedSize >>>= 0;
+      var maxHeapSize = getHeapMax();
+      if (requestedSize > maxHeapSize) {
+        return false;
+      }
+      for (var cutDown = 1; cutDown <= 4; cutDown *= 2) {
+        var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown);
+        overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296);
+        var newSize = Math.min(maxHeapSize, alignMemory(Math.max(requestedSize, overGrownHeapSize), 65536));
+        var replacement = growMemory(newSize);
+        if (replacement) {
+          return true;
+        }
+      }
+      return false;
+    }, "_emscripten_resize_heap");
+    _emscripten_resize_heap.sig = "ip";
+    var _fd_close = /* @__PURE__ */ __name((fd) => 52, "_fd_close");
+    _fd_close.sig = "ii";
+    function _fd_seek(fd, offset, whence, newOffset) {
+      offset = bigintToI53Checked(offset);
+      return 70;
+    }
+    __name(_fd_seek, "_fd_seek");
+    _fd_seek.sig = "iijip";
+    var printCharBuffers = [null, [], []];
+    var printChar = /* @__PURE__ */ __name((stream, curr) => {
+      var buffer = printCharBuffers[stream];
+      if (curr === 0 || curr === 10) {
+        (stream === 1 ? out : err)(UTF8ArrayToString(buffer));
+        buffer.length = 0;
+      } else {
+        buffer.push(curr);
+      }
+    }, "printChar");
+    var flush_NO_FILESYSTEM = /* @__PURE__ */ __name(() => {
+      if (printCharBuffers[1].length) printChar(1, 10);
+      if (printCharBuffers[2].length) printChar(2, 10);
+    }, "flush_NO_FILESYSTEM");
+    var SYSCALLS = {
+      varargs: void 0,
+      getStr(ptr) {
+        var ret = UTF8ToString(ptr);
+        return ret;
+      }
+    };
+    var _fd_write = /* @__PURE__ */ __name((fd, iov, iovcnt, pnum) => {
+      var num = 0;
+      for (var i2 = 0; i2 < iovcnt; i2++) {
+        var ptr = LE_HEAP_LOAD_U32((iov >> 2) * 4);
+        var len = LE_HEAP_LOAD_U32((iov + 4 >> 2) * 4);
+        iov += 8;
+        for (var j = 0; j < len; j++) {
+          printChar(fd, HEAPU8[ptr + j]);
+        }
+        num += len;
+      }
+      LE_HEAP_STORE_U32((pnum >> 2) * 4, num);
+      return 0;
+    }, "_fd_write");
+    _fd_write.sig = "iippp";
+    function _tree_sitter_log_callback(isLexMessage, messageAddress) {
+      if (Module.currentLogCallback) {
+        const message = UTF8ToString(messageAddress);
+        Module.currentLogCallback(message, isLexMessage !== 0);
+      }
+    }
+    __name(_tree_sitter_log_callback, "_tree_sitter_log_callback");
+    function _tree_sitter_parse_callback(inputBufferAddress, index, row, column, lengthAddress) {
+      const INPUT_BUFFER_SIZE = 10 * 1024;
+      const string = Module.currentParseCallback(index, {
+        row,
+        column
+      });
+      if (typeof string === "string") {
+        setValue(lengthAddress, string.length, "i32");
+        stringToUTF16(string, inputBufferAddress, INPUT_BUFFER_SIZE);
+      } else {
+        setValue(lengthAddress, 0, "i32");
+      }
+    }
+    __name(_tree_sitter_parse_callback, "_tree_sitter_parse_callback");
+    function _tree_sitter_progress_callback(currentOffset, hasError) {
+      if (Module.currentProgressCallback) {
+        return Module.currentProgressCallback({
+          currentOffset,
+          hasError
+        });
+      }
+      return false;
+    }
+    __name(_tree_sitter_progress_callback, "_tree_sitter_progress_callback");
+    function _tree_sitter_query_progress_callback(currentOffset) {
+      if (Module.currentQueryProgressCallback) {
+        return Module.currentQueryProgressCallback({
+          currentOffset
+        });
+      }
+      return false;
+    }
+    __name(_tree_sitter_query_progress_callback, "_tree_sitter_query_progress_callback");
+    var runtimeKeepaliveCounter = 0;
+    var keepRuntimeAlive = /* @__PURE__ */ __name(() => noExitRuntime || runtimeKeepaliveCounter > 0, "keepRuntimeAlive");
+    var _proc_exit = /* @__PURE__ */ __name((code) => {
+      EXITSTATUS = code;
+      if (!keepRuntimeAlive()) {
+        Module["onExit"]?.(code);
+        ABORT = true;
+      }
+      quit_(code, new ExitStatus(code));
+    }, "_proc_exit");
+    _proc_exit.sig = "vi";
+    var exitJS = /* @__PURE__ */ __name((status, implicit) => {
+      EXITSTATUS = status;
+      _proc_exit(status);
+    }, "exitJS");
+    var handleException = /* @__PURE__ */ __name((e) => {
+      if (e instanceof ExitStatus || e == "unwind") {
+        return EXITSTATUS;
+      }
+      quit_(1, e);
+    }, "handleException");
+    var lengthBytesUTF8 = /* @__PURE__ */ __name((str) => {
+      var len = 0;
+      for (var i2 = 0; i2 < str.length; ++i2) {
+        var c = str.charCodeAt(i2);
+        if (c <= 127) {
+          len++;
+        } else if (c <= 2047) {
+          len += 2;
+        } else if (c >= 55296 && c <= 57343) {
+          len += 4;
+          ++i2;
+        } else {
+          len += 3;
+        }
+      }
+      return len;
+    }, "lengthBytesUTF8");
+    var stringToUTF8Array = /* @__PURE__ */ __name((str, heap, outIdx, maxBytesToWrite) => {
+      if (!(maxBytesToWrite > 0)) return 0;
+      var startIdx = outIdx;
+      var endIdx = outIdx + maxBytesToWrite - 1;
+      for (var i2 = 0; i2 < str.length; ++i2) {
+        var u = str.charCodeAt(i2);
+        if (u >= 55296 && u <= 57343) {
+          var u1 = str.charCodeAt(++i2);
+          u = 65536 + ((u & 1023) << 10) | u1 & 1023;
+        }
+        if (u <= 127) {
+          if (outIdx >= endIdx) break;
+          heap[outIdx++] = u;
+        } else if (u <= 2047) {
+          if (outIdx + 1 >= endIdx) break;
+          heap[outIdx++] = 192 | u >> 6;
+          heap[outIdx++] = 128 | u & 63;
+        } else if (u <= 65535) {
+          if (outIdx + 2 >= endIdx) break;
+          heap[outIdx++] = 224 | u >> 12;
+          heap[outIdx++] = 128 | u >> 6 & 63;
+          heap[outIdx++] = 128 | u & 63;
+        } else {
+          if (outIdx + 3 >= endIdx) break;
+          heap[outIdx++] = 240 | u >> 18;
+          heap[outIdx++] = 128 | u >> 12 & 63;
+          heap[outIdx++] = 128 | u >> 6 & 63;
+          heap[outIdx++] = 128 | u & 63;
+        }
+      }
+      heap[outIdx] = 0;
+      return outIdx - startIdx;
+    }, "stringToUTF8Array");
+    var stringToUTF8 = /* @__PURE__ */ __name((str, outPtr, maxBytesToWrite) => stringToUTF8Array(str, HEAPU8, outPtr, maxBytesToWrite), "stringToUTF8");
+    var stackAlloc = /* @__PURE__ */ __name((sz) => __emscripten_stack_alloc(sz), "stackAlloc");
+    var stringToUTF8OnStack = /* @__PURE__ */ __name((str) => {
+      var size = lengthBytesUTF8(str) + 1;
+      var ret = stackAlloc(size);
+      stringToUTF8(str, ret, size);
+      return ret;
+    }, "stringToUTF8OnStack");
+    var AsciiToString = /* @__PURE__ */ __name((ptr) => {
+      var str = "";
+      while (1) {
+        var ch = HEAPU8[ptr++];
+        if (!ch) return str;
+        str += String.fromCharCode(ch);
+      }
+    }, "AsciiToString");
+    var stringToUTF16 = /* @__PURE__ */ __name((str, outPtr, maxBytesToWrite) => {
+      maxBytesToWrite ??= 2147483647;
+      if (maxBytesToWrite < 2) return 0;
+      maxBytesToWrite -= 2;
+      var startPtr = outPtr;
+      var numCharsToWrite = maxBytesToWrite < str.length * 2 ? maxBytesToWrite / 2 : str.length;
+      for (var i2 = 0; i2 < numCharsToWrite; ++i2) {
+        var codeUnit = str.charCodeAt(i2);
+        LE_HEAP_STORE_I16((outPtr >> 1) * 2, codeUnit);
+        outPtr += 2;
+      }
+      LE_HEAP_STORE_I16((outPtr >> 1) * 2, 0);
+      return outPtr - startPtr;
+    }, "stringToUTF16");
+    var wasmImports = {
+      /** @export */
+      __heap_base: ___heap_base,
+      /** @export */
+      __indirect_function_table: wasmTable,
+      /** @export */
+      __memory_base: ___memory_base,
+      /** @export */
+      __stack_pointer: ___stack_pointer,
+      /** @export */
+      __table_base: ___table_base,
+      /** @export */
+      _abort_js: __abort_js,
+      /** @export */
+      clock_time_get: _clock_time_get,
+      /** @export */
+      emscripten_resize_heap: _emscripten_resize_heap,
+      /** @export */
+      fd_close: _fd_close,
+      /** @export */
+      fd_seek: _fd_seek,
+      /** @export */
+      fd_write: _fd_write,
+      /** @export */
+      memory: wasmMemory,
+      /** @export */
+      tree_sitter_log_callback: _tree_sitter_log_callback,
+      /** @export */
+      tree_sitter_parse_callback: _tree_sitter_parse_callback,
+      /** @export */
+      tree_sitter_progress_callback: _tree_sitter_progress_callback,
+      /** @export */
+      tree_sitter_query_progress_callback: _tree_sitter_query_progress_callback
+    };
+    var wasmExports = await createWasm();
+    var ___wasm_call_ctors = wasmExports["__wasm_call_ctors"];
+    var _malloc = Module["_malloc"] = wasmExports["malloc"];
+    var _calloc = Module["_calloc"] = wasmExports["calloc"];
+    var _realloc = Module["_realloc"] = wasmExports["realloc"];
+    var _free = Module["_free"] = wasmExports["free"];
+    var _memcmp = Module["_memcmp"] = wasmExports["memcmp"];
+    var _ts_language_symbol_count = Module["_ts_language_symbol_count"] = wasmExports["ts_language_symbol_count"];
+    var _ts_language_state_count = Module["_ts_language_state_count"] = wasmExports["ts_language_state_count"];
+    var _ts_language_version = Module["_ts_language_version"] = wasmExports["ts_language_version"];
+    var _ts_language_abi_version = Module["_ts_language_abi_version"] = wasmExports["ts_language_abi_version"];
+    var _ts_language_metadata = Module["_ts_language_metadata"] = wasmExports["ts_language_metadata"];
+    var _ts_language_name = Module["_ts_language_name"] = wasmExports["ts_language_name"];
+    var _ts_language_field_count = Module["_ts_language_field_count"] = wasmExports["ts_language_field_count"];
+    var _ts_language_next_state = Module["_ts_language_next_state"] = wasmExports["ts_language_next_state"];
+    var _ts_language_symbol_name = Module["_ts_language_symbol_name"] = wasmExports["ts_language_symbol_name"];
+    var _ts_language_symbol_for_name = Module["_ts_language_symbol_for_name"] = wasmExports["ts_language_symbol_for_name"];
+    var _strncmp = Module["_strncmp"] = wasmExports["strncmp"];
+    var _ts_language_symbol_type = Module["_ts_language_symbol_type"] = wasmExports["ts_language_symbol_type"];
+    var _ts_language_field_name_for_id = Module["_ts_language_field_name_for_id"] = wasmExports["ts_language_field_name_for_id"];
+    var _ts_lookahead_iterator_new = Module["_ts_lookahead_iterator_new"] = wasmExports["ts_lookahead_iterator_new"];
+    var _ts_lookahead_iterator_delete = Module["_ts_lookahead_iterator_delete"] = wasmExports["ts_lookahead_iterator_delete"];
+    var _ts_lookahead_iterator_reset_state = Module["_ts_lookahead_iterator_reset_state"] = wasmExports["ts_lookahead_iterator_reset_state"];
+    var _ts_lookahead_iterator_reset = Module["_ts_lookahead_iterator_reset"] = wasmExports["ts_lookahead_iterator_reset"];
+    var _ts_lookahead_iterator_next = Module["_ts_lookahead_iterator_next"] = wasmExports["ts_lookahead_iterator_next"];
+    var _ts_lookahead_iterator_current_symbol = Module["_ts_lookahead_iterator_current_symbol"] = wasmExports["ts_lookahead_iterator_current_symbol"];
+    var _ts_parser_delete = Module["_ts_parser_delete"] = wasmExports["ts_parser_delete"];
+    var _ts_parser_reset = Module["_ts_parser_reset"] = wasmExports["ts_parser_reset"];
+    var _ts_parser_set_language = Module["_ts_parser_set_language"] = wasmExports["ts_parser_set_language"];
+    var _ts_parser_timeout_micros = Module["_ts_parser_timeout_micros"] = wasmExports["ts_parser_timeout_micros"];
+    var _ts_parser_set_timeout_micros = Module["_ts_parser_set_timeout_micros"] = wasmExports["ts_parser_set_timeout_micros"];
+    var _ts_parser_set_included_ranges = Module["_ts_parser_set_included_ranges"] = wasmExports["ts_parser_set_included_ranges"];
+    var _ts_query_new = Module["_ts_query_new"] = wasmExports["ts_query_new"];
+    var _ts_query_delete = Module["_ts_query_delete"] = wasmExports["ts_query_delete"];
+    var _iswspace = Module["_iswspace"] = wasmExports["iswspace"];
+    var _iswalnum = Module["_iswalnum"] = wasmExports["iswalnum"];
+    var _ts_query_pattern_count = Module["_ts_query_pattern_count"] = wasmExports["ts_query_pattern_count"];
+    var _ts_query_capture_count = Module["_ts_query_capture_count"] = wasmExports["ts_query_capture_count"];
+    var _ts_query_string_count = Module["_ts_query_string_count"] = wasmExports["ts_query_string_count"];
+    var _ts_query_capture_name_for_id = Module["_ts_query_capture_name_for_id"] = wasmExports["ts_query_capture_name_for_id"];
+    var _ts_query_capture_quantifier_for_id = Module["_ts_query_capture_quantifier_for_id"] = wasmExports["ts_query_capture_quantifier_for_id"];
+    var _ts_query_string_value_for_id = Module["_ts_query_string_value_for_id"] = wasmExports["ts_query_string_value_for_id"];
+    var _ts_query_predicates_for_pattern = Module["_ts_query_predicates_for_pattern"] = wasmExports["ts_query_predicates_for_pattern"];
+    var _ts_query_start_byte_for_pattern = Module["_ts_query_start_byte_for_pattern"] = wasmExports["ts_query_start_byte_for_pattern"];
+    var _ts_query_end_byte_for_pattern = Module["_ts_query_end_byte_for_pattern"] = wasmExports["ts_query_end_byte_for_pattern"];
+    var _ts_query_is_pattern_rooted = Module["_ts_query_is_pattern_rooted"] = wasmExports["ts_query_is_pattern_rooted"];
+    var _ts_query_is_pattern_non_local = Module["_ts_query_is_pattern_non_local"] = wasmExports["ts_query_is_pattern_non_local"];
+    var _ts_query_is_pattern_guaranteed_at_step = Module["_ts_query_is_pattern_guaranteed_at_step"] = wasmExports["ts_query_is_pattern_guaranteed_at_step"];
+    var _ts_query_disable_capture = Module["_ts_query_disable_capture"] = wasmExports["ts_query_disable_capture"];
+    var _ts_query_disable_pattern = Module["_ts_query_disable_pattern"] = wasmExports["ts_query_disable_pattern"];
+    var _ts_tree_copy = Module["_ts_tree_copy"] = wasmExports["ts_tree_copy"];
+    var _ts_tree_delete = Module["_ts_tree_delete"] = wasmExports["ts_tree_delete"];
+    var _ts_init = Module["_ts_init"] = wasmExports["ts_init"];
+    var _ts_parser_new_wasm = Module["_ts_parser_new_wasm"] = wasmExports["ts_parser_new_wasm"];
+    var _ts_parser_enable_logger_wasm = Module["_ts_parser_enable_logger_wasm"] = wasmExports["ts_parser_enable_logger_wasm"];
+    var _ts_parser_parse_wasm = Module["_ts_parser_parse_wasm"] = wasmExports["ts_parser_parse_wasm"];
+    var _ts_parser_included_ranges_wasm = Module["_ts_parser_included_ranges_wasm"] = wasmExports["ts_parser_included_ranges_wasm"];
+    var _ts_language_type_is_named_wasm = Module["_ts_language_type_is_named_wasm"] = wasmExports["ts_language_type_is_named_wasm"];
+    var _ts_language_type_is_visible_wasm = Module["_ts_language_type_is_visible_wasm"] = wasmExports["ts_language_type_is_visible_wasm"];
+    var _ts_language_supertypes_wasm = Module["_ts_language_supertypes_wasm"] = wasmExports["ts_language_supertypes_wasm"];
+    var _ts_language_subtypes_wasm = Module["_ts_language_subtypes_wasm"] = wasmExports["ts_language_subtypes_wasm"];
+    var _ts_tree_root_node_wasm = Module["_ts_tree_root_node_wasm"] = wasmExports["ts_tree_root_node_wasm"];
+    var _ts_tree_root_node_with_offset_wasm = Module["_ts_tree_root_node_with_offset_wasm"] = wasmExports["ts_tree_root_node_with_offset_wasm"];
+    var _ts_tree_edit_wasm = Module["_ts_tree_edit_wasm"] = wasmExports["ts_tree_edit_wasm"];
+    var _ts_tree_included_ranges_wasm = Module["_ts_tree_included_ranges_wasm"] = wasmExports["ts_tree_included_ranges_wasm"];
+    var _ts_tree_get_changed_ranges_wasm = Module["_ts_tree_get_changed_ranges_wasm"] = wasmExports["ts_tree_get_changed_ranges_wasm"];
+    var _ts_tree_cursor_new_wasm = Module["_ts_tree_cursor_new_wasm"] = wasmExports["ts_tree_cursor_new_wasm"];
+    var _ts_tree_cursor_copy_wasm = Module["_ts_tree_cursor_copy_wasm"] = wasmExports["ts_tree_cursor_copy_wasm"];
+    var _ts_tree_cursor_delete_wasm = Module["_ts_tree_cursor_delete_wasm"] = wasmExports["ts_tree_cursor_delete_wasm"];
+    var _ts_tree_cursor_reset_wasm = Module["_ts_tree_cursor_reset_wasm"] = wasmExports["ts_tree_cursor_reset_wasm"];
+    var _ts_tree_cursor_reset_to_wasm = Module["_ts_tree_cursor_reset_to_wasm"] = wasmExports["ts_tree_cursor_reset_to_wasm"];
+    var _ts_tree_cursor_goto_first_child_wasm = Module["_ts_tree_cursor_goto_first_child_wasm"] = wasmExports["ts_tree_cursor_goto_first_child_wasm"];
+    var _ts_tree_cursor_goto_last_child_wasm = Module["_ts_tree_cursor_goto_last_child_wasm"] = wasmExports["ts_tree_cursor_goto_last_child_wasm"];
+    var _ts_tree_cursor_goto_first_child_for_index_wasm = Module["_ts_tree_cursor_goto_first_child_for_index_wasm"] = wasmExports["ts_tree_cursor_goto_first_child_for_index_wasm"];
+    var _ts_tree_cursor_goto_first_child_for_position_wasm = Module["_ts_tree_cursor_goto_first_child_for_position_wasm"] = wasmExports["ts_tree_cursor_goto_first_child_for_position_wasm"];
+    var _ts_tree_cursor_goto_next_sibling_wasm = Module["_ts_tree_cursor_goto_next_sibling_wasm"] = wasmExports["ts_tree_cursor_goto_next_sibling_wasm"];
+    var _ts_tree_cursor_goto_previous_sibling_wasm = Module["_ts_tree_cursor_goto_previous_sibling_wasm"] = wasmExports["ts_tree_cursor_goto_previous_sibling_wasm"];
+    var _ts_tree_cursor_goto_descendant_wasm = Module["_ts_tree_cursor_goto_descendant_wasm"] = wasmExports["ts_tree_cursor_goto_descendant_wasm"];
+    var _ts_tree_cursor_goto_parent_wasm = Module["_ts_tree_cursor_goto_parent_wasm"] = wasmExports["ts_tree_cursor_goto_parent_wasm"];
+    var _ts_tree_cursor_current_node_type_id_wasm = Module["_ts_tree_cursor_current_node_type_id_wasm"] = wasmExports["ts_tree_cursor_current_node_type_id_wasm"];
+    var _ts_tree_cursor_current_node_state_id_wasm = Module["_ts_tree_cursor_current_node_state_id_wasm"] = wasmExports["ts_tree_cursor_current_node_state_id_wasm"];
+    var _ts_tree_cursor_current_node_is_named_wasm = Module["_ts_tree_cursor_current_node_is_named_wasm"] = wasmExports["ts_tree_cursor_current_node_is_named_wasm"];
+    var _ts_tree_cursor_current_node_is_missing_wasm = Module["_ts_tree_cursor_current_node_is_missing_wasm"] = wasmExports["ts_tree_cursor_current_node_is_missing_wasm"];
+    var _ts_tree_cursor_current_node_id_wasm = Module["_ts_tree_cursor_current_node_id_wasm"] = wasmExports["ts_tree_cursor_current_node_id_wasm"];
+    var _ts_tree_cursor_start_position_wasm = Module["_ts_tree_cursor_start_position_wasm"] = wasmExports["ts_tree_cursor_start_position_wasm"];
+    var _ts_tree_cursor_end_position_wasm = Module["_ts_tree_cursor_end_position_wasm"] = wasmExports["ts_tree_cursor_end_position_wasm"];
+    var _ts_tree_cursor_start_index_wasm = Module["_ts_tree_cursor_start_index_wasm"] = wasmExports["ts_tree_cursor_start_index_wasm"];
+    var _ts_tree_cursor_end_index_wasm = Module["_ts_tree_cursor_end_index_wasm"] = wasmExports["ts_tree_cursor_end_index_wasm"];
+    var _ts_tree_cursor_current_field_id_wasm = Module["_ts_tree_cursor_current_field_id_wasm"] = wasmExports["ts_tree_cursor_current_field_id_wasm"];
+    var _ts_tree_cursor_current_depth_wasm = Module["_ts_tree_cursor_current_depth_wasm"] = wasmExports["ts_tree_cursor_current_depth_wasm"];
+    var _ts_tree_cursor_current_descendant_index_wasm = Module["_ts_tree_cursor_current_descendant_index_wasm"] = wasmExports["ts_tree_cursor_current_descendant_index_wasm"];
+    var _ts_tree_cursor_current_node_wasm = Module["_ts_tree_cursor_current_node_wasm"] = wasmExports["ts_tree_cursor_current_node_wasm"];
+    var _ts_node_symbol_wasm = Module["_ts_node_symbol_wasm"] = wasmExports["ts_node_symbol_wasm"];
+    var _ts_node_field_name_for_child_wasm = Module["_ts_node_field_name_for_child_wasm"] = wasmExports["ts_node_field_name_for_child_wasm"];
+    var _ts_node_field_name_for_named_child_wasm = Module["_ts_node_field_name_for_named_child_wasm"] = wasmExports["ts_node_field_name_for_named_child_wasm"];
+    var _ts_node_children_by_field_id_wasm = Module["_ts_node_children_by_field_id_wasm"] = wasmExports["ts_node_children_by_field_id_wasm"];
+    var _ts_node_first_child_for_byte_wasm = Module["_ts_node_first_child_for_byte_wasm"] = wasmExports["ts_node_first_child_for_byte_wasm"];
+    var _ts_node_first_named_child_for_byte_wasm = Module["_ts_node_first_named_child_for_byte_wasm"] = wasmExports["ts_node_first_named_child_for_byte_wasm"];
+    var _ts_node_grammar_symbol_wasm = Module["_ts_node_grammar_symbol_wasm"] = wasmExports["ts_node_grammar_symbol_wasm"];
+    var _ts_node_child_count_wasm = Module["_ts_node_child_count_wasm"] = wasmExports["ts_node_child_count_wasm"];
+    var _ts_node_named_child_count_wasm = Module["_ts_node_named_child_count_wasm"] = wasmExports["ts_node_named_child_count_wasm"];
+    var _ts_node_child_wasm = Module["_ts_node_child_wasm"] = wasmExports["ts_node_child_wasm"];
+    var _ts_node_named_child_wasm = Module["_ts_node_named_child_wasm"] = wasmExports["ts_node_named_child_wasm"];
+    var _ts_node_child_by_field_id_wasm = Module["_ts_node_child_by_field_id_wasm"] = wasmExports["ts_node_child_by_field_id_wasm"];
+    var _ts_node_next_sibling_wasm = Module["_ts_node_next_sibling_wasm"] = wasmExports["ts_node_next_sibling_wasm"];
+    var _ts_node_prev_sibling_wasm = Module["_ts_node_prev_sibling_wasm"] = wasmExports["ts_node_prev_sibling_wasm"];
+    var _ts_node_next_named_sibling_wasm = Module["_ts_node_next_named_sibling_wasm"] = wasmExports["ts_node_next_named_sibling_wasm"];
+    var _ts_node_prev_named_sibling_wasm = Module["_ts_node_prev_named_sibling_wasm"] = wasmExports["ts_node_prev_named_sibling_wasm"];
+    var _ts_node_descendant_count_wasm = Module["_ts_node_descendant_count_wasm"] = wasmExports["ts_node_descendant_count_wasm"];
+    var _ts_node_parent_wasm = Module["_ts_node_parent_wasm"] = wasmExports["ts_node_parent_wasm"];
+    var _ts_node_child_with_descendant_wasm = Module["_ts_node_child_with_descendant_wasm"] = wasmExports["ts_node_child_with_descendant_wasm"];
+    var _ts_node_descendant_for_index_wasm = Module["_ts_node_descendant_for_index_wasm"] = wasmExports["ts_node_descendant_for_index_wasm"];
+    var _ts_node_named_descendant_for_index_wasm = Module["_ts_node_named_descendant_for_index_wasm"] = wasmExports["ts_node_named_descendant_for_index_wasm"];
+    var _ts_node_descendant_for_position_wasm = Module["_ts_node_descendant_for_position_wasm"] = wasmExports["ts_node_descendant_for_position_wasm"];
+    var _ts_node_named_descendant_for_position_wasm = Module["_ts_node_named_descendant_for_position_wasm"] = wasmExports["ts_node_named_descendant_for_position_wasm"];
+    var _ts_node_start_point_wasm = Module["_ts_node_start_point_wasm"] = wasmExports["ts_node_start_point_wasm"];
+    var _ts_node_end_point_wasm = Module["_ts_node_end_point_wasm"] = wasmExports["ts_node_end_point_wasm"];
+    var _ts_node_start_index_wasm = Module["_ts_node_start_index_wasm"] = wasmExports["ts_node_start_index_wasm"];
+    var _ts_node_end_index_wasm = Module["_ts_node_end_index_wasm"] = wasmExports["ts_node_end_index_wasm"];
+    var _ts_node_to_string_wasm = Module["_ts_node_to_string_wasm"] = wasmExports["ts_node_to_string_wasm"];
+    var _ts_node_children_wasm = Module["_ts_node_children_wasm"] = wasmExports["ts_node_children_wasm"];
+    var _ts_node_named_children_wasm = Module["_ts_node_named_children_wasm"] = wasmExports["ts_node_named_children_wasm"];
+    var _ts_node_descendants_of_type_wasm = Module["_ts_node_descendants_of_type_wasm"] = wasmExports["ts_node_descendants_of_type_wasm"];
+    var _ts_node_is_named_wasm = Module["_ts_node_is_named_wasm"] = wasmExports["ts_node_is_named_wasm"];
+    var _ts_node_has_changes_wasm = Module["_ts_node_has_changes_wasm"] = wasmExports["ts_node_has_changes_wasm"];
+    var _ts_node_has_error_wasm = Module["_ts_node_has_error_wasm"] = wasmExports["ts_node_has_error_wasm"];
+    var _ts_node_is_error_wasm = Module["_ts_node_is_error_wasm"] = wasmExports["ts_node_is_error_wasm"];
+    var _ts_node_is_missing_wasm = Module["_ts_node_is_missing_wasm"] = wasmExports["ts_node_is_missing_wasm"];
+    var _ts_node_is_extra_wasm = Module["_ts_node_is_extra_wasm"] = wasmExports["ts_node_is_extra_wasm"];
+    var _ts_node_parse_state_wasm = Module["_ts_node_parse_state_wasm"] = wasmExports["ts_node_parse_state_wasm"];
+    var _ts_node_next_parse_state_wasm = Module["_ts_node_next_parse_state_wasm"] = wasmExports["ts_node_next_parse_state_wasm"];
+    var _ts_query_matches_wasm = Module["_ts_query_matches_wasm"] = wasmExports["ts_query_matches_wasm"];
+    var _ts_query_captures_wasm = Module["_ts_query_captures_wasm"] = wasmExports["ts_query_captures_wasm"];
+    var _memset = Module["_memset"] = wasmExports["memset"];
+    var _memcpy = Module["_memcpy"] = wasmExports["memcpy"];
+    var _memmove = Module["_memmove"] = wasmExports["memmove"];
+    var _iswalpha = Module["_iswalpha"] = wasmExports["iswalpha"];
+    var _iswblank = Module["_iswblank"] = wasmExports["iswblank"];
+    var _iswdigit = Module["_iswdigit"] = wasmExports["iswdigit"];
+    var _iswlower = Module["_iswlower"] = wasmExports["iswlower"];
+    var _iswupper = Module["_iswupper"] = wasmExports["iswupper"];
+    var _iswxdigit = Module["_iswxdigit"] = wasmExports["iswxdigit"];
+    var _memchr = Module["_memchr"] = wasmExports["memchr"];
+    var _strlen = Module["_strlen"] = wasmExports["strlen"];
+    var _strcmp = Module["_strcmp"] = wasmExports["strcmp"];
+    var _strncat = Module["_strncat"] = wasmExports["strncat"];
+    var _strncpy = Module["_strncpy"] = wasmExports["strncpy"];
+    var _towlower = Module["_towlower"] = wasmExports["towlower"];
+    var _towupper = Module["_towupper"] = wasmExports["towupper"];
+    var _setThrew = wasmExports["setThrew"];
+    var __emscripten_stack_restore = wasmExports["_emscripten_stack_restore"];
+    var __emscripten_stack_alloc = wasmExports["_emscripten_stack_alloc"];
+    var _emscripten_stack_get_current = wasmExports["emscripten_stack_get_current"];
+    var ___wasm_apply_data_relocs = wasmExports["__wasm_apply_data_relocs"];
+    Module["setValue"] = setValue;
+    Module["getValue"] = getValue;
+    Module["UTF8ToString"] = UTF8ToString;
+    Module["stringToUTF8"] = stringToUTF8;
+    Module["lengthBytesUTF8"] = lengthBytesUTF8;
+    Module["AsciiToString"] = AsciiToString;
+    Module["stringToUTF16"] = stringToUTF16;
+    Module["loadWebAssemblyModule"] = loadWebAssemblyModule;
+    function callMain(args2 = []) {
+      var entryFunction = resolveGlobalSymbol("main").sym;
+      if (!entryFunction) return;
+      args2.unshift(thisProgram);
+      var argc = args2.length;
+      var argv = stackAlloc((argc + 1) * 4);
+      var argv_ptr = argv;
+      args2.forEach((arg) => {
+        LE_HEAP_STORE_U32((argv_ptr >> 2) * 4, stringToUTF8OnStack(arg));
+        argv_ptr += 4;
+      });
+      LE_HEAP_STORE_U32((argv_ptr >> 2) * 4, 0);
+      try {
+        var ret = entryFunction(argc, argv);
+        exitJS(
+          ret,
+          /* implicit = */
+          true
+        );
+        return ret;
+      } catch (e) {
+        return handleException(e);
+      }
+    }
+    __name(callMain, "callMain");
+    function run(args2 = arguments_) {
+      if (runDependencies > 0) {
+        dependenciesFulfilled = run;
+        return;
+      }
+      preRun();
+      if (runDependencies > 0) {
+        dependenciesFulfilled = run;
+        return;
+      }
+      function doRun() {
+        Module["calledRun"] = true;
+        if (ABORT) return;
+        initRuntime();
+        preMain();
+        readyPromiseResolve(Module);
+        Module["onRuntimeInitialized"]?.();
+        var noInitialRun = Module["noInitialRun"];
+        if (!noInitialRun) callMain(args2);
+        postRun();
+      }
+      __name(doRun, "doRun");
+      if (Module["setStatus"]) {
+        Module["setStatus"]("Running...");
+        setTimeout(() => {
+          setTimeout(() => Module["setStatus"](""), 1);
+          doRun();
+        }, 1);
+      } else {
+        doRun();
+      }
+    }
+    __name(run, "run");
+    if (Module["preInit"]) {
+      if (typeof Module["preInit"] == "function") Module["preInit"] = [Module["preInit"]];
+      while (Module["preInit"].length > 0) {
+        Module["preInit"].pop()();
+      }
+    }
+    run();
+    moduleRtn = readyPromise;
+    return moduleRtn;
+  };
+})();
+var tree_sitter_default = Module2;
+
+// src/bindings.ts
+var Module3 = null;
+async function initializeBinding(moduleOptions) {
+  if (!Module3) {
+    Module3 = await tree_sitter_default(moduleOptions);
+  }
+  return Module3;
+}
+__name(initializeBinding, "initializeBinding");
+function checkModule() {
+  return !!Module3;
+}
+__name(checkModule, "checkModule");
+
+// src/parser.ts
+var TRANSFER_BUFFER;
+var LANGUAGE_VERSION;
+var MIN_COMPATIBLE_VERSION;
+var Parser = class {
+  static {
+    __name(this, "Parser");
+  }
+  /** @internal */
+  [0] = 0;
+  // Internal handle for WASM
+  /** @internal */
+  [1] = 0;
+  // Internal handle for WASM
+  /** @internal */
+  logCallback = null;
+  /** The parser's current language. */
+  language = null;
+  /**
+   * This must always be called before creating a Parser.
+   *
+   * You can optionally pass in options to configure the WASM module, the most common
+   * one being `locateFile` to help the module find the `.wasm` file.
+   */
+  static async init(moduleOptions) {
+    setModule(await initializeBinding(moduleOptions));
+    TRANSFER_BUFFER = C._ts_init();
+    LANGUAGE_VERSION = C.getValue(TRANSFER_BUFFER, "i32");
+    MIN_COMPATIBLE_VERSION = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+  }
+  /**
+   * Create a new parser.
+   */
+  constructor() {
+    this.initialize();
+  }
+  /** @internal */
+  initialize() {
+    if (!checkModule()) {
+      throw new Error("cannot construct a Parser before calling `init()`");
+    }
+    C._ts_parser_new_wasm();
+    this[0] = C.getValue(TRANSFER_BUFFER, "i32");
+    this[1] = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+  }
+  /** Delete the parser, freeing its resources. */
+  delete() {
+    C._ts_parser_delete(this[0]);
+    C._free(this[1]);
+    this[0] = 0;
+    this[1] = 0;
+  }
+  /**
+   * Set the language that the parser should use for parsing.
+   *
+   * If the language was not successfully assigned, an error will be thrown.
+   * This happens if the language was generated with an incompatible
+   * version of the Tree-sitter CLI. Check the language's version using
+   * {@link Language#version} and compare it to this library's
+   * {@link LANGUAGE_VERSION} and {@link MIN_COMPATIBLE_VERSION} constants.
+   */
+  setLanguage(language) {
+    let address;
+    if (!language) {
+      address = 0;
+      this.language = null;
+    } else if (language.constructor === Language) {
+      address = language[0];
+      const version = C._ts_language_version(address);
+      if (version < MIN_COMPATIBLE_VERSION || LANGUAGE_VERSION < version) {
+        throw new Error(
+          `Incompatible language version ${version}. Compatibility range ${MIN_COMPATIBLE_VERSION} through ${LANGUAGE_VERSION}.`
+        );
+      }
+      this.language = language;
+    } else {
+      throw new Error("Argument must be a Language");
+    }
+    C._ts_parser_set_language(this[0], address);
+    return this;
+  }
+  /**
+   * Parse a slice of UTF8 text.
+   *
+   * @param {string | ParseCallback} callback - The UTF8-encoded text to parse or a callback function.
+   *
+   * @param {Tree | null} [oldTree] - A previous syntax tree parsed from the same document. If the text of the
+   *   document has changed since `oldTree` was created, then you must edit `oldTree` to match
+   *   the new text using {@link Tree#edit}.
+   *
+   * @param {ParseOptions} [options] - Options for parsing the text.
+   *  This can be used to set the included ranges, or a progress callback.
+   *
+   * @returns {Tree | null} A {@link Tree} if parsing succeeded, or `null` if:
+   *  - The parser has not yet had a language assigned with {@link Parser#setLanguage}.
+   *  - The progress callback returned true.
+   */
+  parse(callback, oldTree, options) {
+    if (typeof callback === "string") {
+      C.currentParseCallback = (index) => callback.slice(index);
+    } else if (typeof callback === "function") {
+      C.currentParseCallback = callback;
+    } else {
+      throw new Error("Argument must be a string or a function");
+    }
+    if (options?.progressCallback) {
+      C.currentProgressCallback = options.progressCallback;
+    } else {
+      C.currentProgressCallback = null;
+    }
+    if (this.logCallback) {
+      C.currentLogCallback = this.logCallback;
+      C._ts_parser_enable_logger_wasm(this[0], 1);
+    } else {
+      C.currentLogCallback = null;
+      C._ts_parser_enable_logger_wasm(this[0], 0);
+    }
+    let rangeCount = 0;
+    let rangeAddress = 0;
+    if (options?.includedRanges) {
+      rangeCount = options.includedRanges.length;
+      rangeAddress = C._calloc(rangeCount, SIZE_OF_RANGE);
+      let address = rangeAddress;
+      for (let i2 = 0; i2 < rangeCount; i2++) {
+        marshalRange(address, options.includedRanges[i2]);
+        address += SIZE_OF_RANGE;
+      }
+    }
+    const treeAddress = C._ts_parser_parse_wasm(
+      this[0],
+      this[1],
+      oldTree ? oldTree[0] : 0,
+      rangeAddress,
+      rangeCount
+    );
+    if (!treeAddress) {
+      C.currentParseCallback = null;
+      C.currentLogCallback = null;
+      C.currentProgressCallback = null;
+      return null;
+    }
+    if (!this.language) {
+      throw new Error("Parser must have a language to parse");
+    }
+    const result = new Tree(INTERNAL, treeAddress, this.language, C.currentParseCallback);
+    C.currentParseCallback = null;
+    C.currentLogCallback = null;
+    C.currentProgressCallback = null;
+    return result;
+  }
+  /**
+   * Instruct the parser to start the next parse from the beginning.
+   *
+   * If the parser previously failed because of a timeout, cancellation,
+   * or callback, then by default, it will resume where it left off on the
+   * next call to {@link Parser#parse} or other parsing functions.
+   * If you don't want to resume, and instead intend to use this parser to
+   * parse some other document, you must call `reset` first.
+   */
+  reset() {
+    C._ts_parser_reset(this[0]);
+  }
+  /** Get the ranges of text that the parser will include when parsing. */
+  getIncludedRanges() {
+    C._ts_parser_included_ranges_wasm(this[0]);
+    const count = C.getValue(TRANSFER_BUFFER, "i32");
+    const buffer = C.getValue(TRANSFER_BUFFER + SIZE_OF_INT, "i32");
+    const result = new Array(count);
+    if (count > 0) {
+      let address = buffer;
+      for (let i2 = 0; i2 < count; i2++) {
+        result[i2] = unmarshalRange(address);
+        address += SIZE_OF_RANGE;
+      }
+      C._free(buffer);
+    }
+    return result;
+  }
+  /**
+   * @deprecated since version 0.25.0, prefer passing a progress callback to {@link Parser#parse}
+   *
+   * Get the duration in microseconds that parsing is allowed to take.
+   *
+   * This is set via {@link Parser#setTimeoutMicros}.
+   */
+  getTimeoutMicros() {
+    return C._ts_parser_timeout_micros(this[0]);
+  }
+  /**
+   * @deprecated since version 0.25.0, prefer passing a progress callback to {@link Parser#parse}
+   *
+   * Set the maximum duration in microseconds that parsing should be allowed
+   * to take before halting.
+   *
+   * If parsing takes longer than this, it will halt early, returning `null`.
+   * See {@link Parser#parse} for more information.
+   */
+  setTimeoutMicros(timeout) {
+    C._ts_parser_set_timeout_micros(this[0], 0, timeout);
+  }
+  /** Set the logging callback that a parser should use during parsing. */
+  setLogger(callback) {
+    if (!callback) {
+      this.logCallback = null;
+    } else if (typeof callback !== "function") {
+      throw new Error("Logger callback must be a function");
+    } else {
+      this.logCallback = callback;
+    }
+    return this;
+  }
+  /** Get the parser's current logger. */
+  getLogger() {
+    return this.logCallback;
+  }
+};
+export {
+  CaptureQuantifier,
+  LANGUAGE_VERSION,
+  Language,
+  LookaheadIterator,
+  MIN_COMPATIBLE_VERSION,
+  Node,
+  Parser,
+  Query,
+  Tree,
+  TreeCursor
+};
+//# sourceMappingURL=tree-sitter.js.map
diff --git a/custom_nodes/rgthree-comfy/web/link_fixer/icon_file_json.png b/custom_nodes/rgthree-comfy/web/link_fixer/icon_file_json.png
new file mode 100644
index 0000000000000000000000000000000000000000..ad3a1cb2b89a2051010d53d69b12cca8735af353
Binary files /dev/null and b/custom_nodes/rgthree-comfy/web/link_fixer/icon_file_json.png differ
diff --git a/custom_nodes/rgthree-comfy/web/link_fixer/index.html b/custom_nodes/rgthree-comfy/web/link_fixer/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..2e1b66ecd2f5e0c4cb1489b02eba9b226e86d819
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/link_fixer/index.html
@@ -0,0 +1,140 @@
+<!doctype html>
+<html>
+  <head>
+    <title>rgthree's comfy: Workflow Link Fixer</title>
+    <style>
+      html, body {
+      }
+      html {
+        font-size: 100%;
+        overflow-y: scroll;
+        -webkit-text-size-adjust: 100%;
+        -ms-text-size-adjust: 100%;
+        box-sizing: border-box
+      }
+
+      *, *:before, *:after {
+        box-sizing: inherit
+      }
+
+      body {
+        background: #222;
+        font-family: Arial, sans-serif;
+        font-size: calc(16 * 0.0625rem);
+        font-weight: 400;
+        margin: 0;
+        color: #ffffff;
+        padding-bottom: 64px;
+        display: flex;
+      }
+
+      .box, figure, picture {
+        display: flex;
+        flex-direction: column;
+        align-items: center;
+      }
+
+      .box {
+        margin: 32px auto;
+        max-width: 720px;
+        padding: 16px 16px 32px;
+        background: rgba(125,125,125,0.33);
+        box-shadow: 0px 8px 10px rgba(0,0,0,0.85);
+        border-radius: 8px;
+      }
+      h1 {
+        margin-top: 0;
+      }
+      .box > * {
+        text-align: center;
+      }
+      .box > p {
+        margin: 0 0 .6em;
+        text-align: left;
+        line-height: 1.25;
+      }
+      .box > small {
+        margin: 0 0 .6em;
+        text-align: left;
+        line-height: 1.25;
+        opacity: 0.75;
+      }
+
+      picture > img {
+        display: block;
+        height: 256px;
+        min-width: 256px;
+        border: 10px dashed rgba(125,125,125,0.66);
+        border-radius: 32px;
+        object-fit: contain;
+        pointer-events: none;
+
+      }
+      figcaption {
+        opacity: 0.66;
+        padding: 4px;
+        font-size: calc(11 * 0.0625rem);
+      }
+
+      .-has-file picture > img {
+        border-color: transparent;
+        border-radius: 32px;
+      }
+
+      .output {
+        margin: 8px 8px 32px;
+        border-radius: 4px;
+        border: 1px solid rgba(125,125,125,0.66);
+        padding: 4px 8px;
+        min-width: 80%;
+      }
+
+      .output:empty {
+        display: none;
+      }
+
+      .btn-fix {
+        display: none;
+        cursor: pointer;
+        font-size: calc(24 * 0.0625rem);
+      }
+      .-has-fixable-results .btn-fix {
+        display: inline-block;
+      }
+
+    </style>
+  </head>
+  <body>
+
+    <div class="box">
+      <h1>rgthree's Workflow Link Fixer</h1>
+      <p>
+        Drag and drop a comfy-generated image or workflow json into this window to check its
+        serialized links and attempt to fix.
+      </p>
+      <small>
+        Sometimes as you have complex workflows the internal data can become corrupt, and the
+        ComfyUI doesn't always understand or display correctly. Maybe links disappear, or reconnect
+        to another node when changing something. Load it here to detect and, if possible, attempt
+        to fix it (sometimes, however, fixing it just isn't feasible, so fingers crossed).
+      </small>
+
+      <figure>
+        <picture>
+          <img class="output-image" />
+        </picture>
+        <figcaption></figcaption>
+      </figure>
+
+      <div class="output"></div>
+
+      <button class="btn-fix">Fix & Save new workflow</button>
+
+    </div>
+
+    <script type="module">
+      import {LinkPage} from './link_page.js';
+      new LinkPage();
+    </script>
+  </body>
+</html>
\ No newline at end of file
diff --git a/custom_nodes/rgthree-comfy/web/link_fixer/link_page.js b/custom_nodes/rgthree-comfy/web/link_fixer/link_page.js
new file mode 100644
index 0000000000000000000000000000000000000000..56609dc0e591bf7c2d0a1cd47dc9b31be864add1
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/link_fixer/link_page.js
@@ -0,0 +1,189 @@
+import { WorkflowLinkFixer } from "../common/link_fixer.js";
+import { getPngMetadata } from "../common/comfyui_shim.js";
+function wait(ms = 16, value) {
+    return new Promise((resolve) => {
+        setTimeout(() => {
+            resolve(value);
+        }, ms);
+    });
+}
+const logger = {
+    logTo: console,
+    log: (...args) => {
+        logger.logTo === console
+            ? console.log(...args)
+            : (logger.logTo.innerText += args.join(",") + "\n");
+    },
+};
+export class LinkPage {
+    constructor() {
+        this.fixer = null;
+        this.containerEl = document.querySelector(".box");
+        this.figcaptionEl = document.querySelector("figcaption");
+        this.outputeMessageEl = document.querySelector(".output");
+        this.outputImageEl = document.querySelector(".output-image");
+        this.btnFix = document.querySelector(".btn-fix");
+        document.addEventListener("dragover", (e) => {
+            e.preventDefault();
+        }, false);
+        document.addEventListener("drop", (e) => {
+            this.onDrop(e);
+        });
+        this.btnFix.addEventListener("click", (e) => {
+            this.onFixClick(e);
+        });
+    }
+    async onFixClick(e) {
+        var _a;
+        if (!((_a = this.fixer) === null || _a === void 0 ? void 0 : _a.checkedData) || !this.graph) {
+            this.updateUi("⛔ Fix button click without results.");
+            return;
+        }
+        this.graphFinalResults = this.fixer.fix();
+        if (this.graphFinalResults.hasBadLinks) {
+            this.updateUi("⛔ Hmm... Still detecting bad links. Can you file an issue at https://github.com/rgthree/rgthree-comfy/issues with your image/workflow.");
+        }
+        else {
+            this.updateUi("✅ Workflow fixed.<br><br><small>Please load new saved workflow json and double check linking and execution.</small>");
+        }
+        await wait(16);
+        await this.saveFixedWorkflow();
+    }
+    async onDrop(event) {
+        var _a, _b, _c, _d;
+        if (!event.dataTransfer) {
+            return;
+        }
+        this.reset();
+        event.preventDefault();
+        event.stopPropagation();
+        if (event.dataTransfer.files.length && ((_b = (_a = event.dataTransfer.files) === null || _a === void 0 ? void 0 : _a[0]) === null || _b === void 0 ? void 0 : _b.type) !== "image/bmp") {
+            await this.handleFile(event.dataTransfer.files[0]);
+            return;
+        }
+        const validTypes = ["text/uri-list", "text/x-moz-url"];
+        const match = [...event.dataTransfer.types].find((t) => validTypes.find((v) => t === v));
+        if (match) {
+            const uri = (_d = (_c = event.dataTransfer.getData(match)) === null || _c === void 0 ? void 0 : _c.split("\n")) === null || _d === void 0 ? void 0 : _d[0];
+            if (uri) {
+                await this.handleFile(await (await fetch(uri)).blob());
+            }
+        }
+    }
+    reset() {
+        this.file = undefined;
+        this.graph = undefined;
+        this.graphResults = undefined;
+        this.graphFinalResults = undefined;
+        this.updateUi();
+    }
+    updateUi(msg) {
+        this.outputeMessageEl.innerHTML = "";
+        if (this.file && !this.containerEl.classList.contains("-has-file")) {
+            this.containerEl.classList.add("-has-file");
+            this.figcaptionEl.innerHTML = this.file.name || this.file.type;
+            if (this.file.type === "application/json") {
+                this.outputImageEl.src = "icon_file_json.png";
+            }
+            else {
+                const reader = new FileReader();
+                reader.onload = () => (this.outputImageEl.src = reader.result);
+                reader.readAsDataURL(this.file);
+            }
+        }
+        else if (!this.file && this.containerEl.classList.contains("-has-file")) {
+            this.containerEl.classList.remove("-has-file");
+            this.outputImageEl.src = "";
+            this.outputImageEl.removeAttribute("src");
+        }
+        if (this.graphResults) {
+            this.containerEl.classList.add("-has-results");
+            if (!this.graphResults.patches && !this.graphResults.deletes) {
+                this.outputeMessageEl.innerHTML = "✅ No bad links detected in the workflow.";
+            }
+            else {
+                this.containerEl.classList.add("-has-fixable-results");
+                this.outputeMessageEl.innerHTML = `⚠️ Found ${this.graphResults.patches} links to fix, and ${this.graphResults.deletes} to be removed.`;
+            }
+        }
+        else {
+            this.containerEl.classList.remove("-has-results");
+            this.containerEl.classList.remove("-has-fixable-results");
+        }
+        if (msg) {
+            this.outputeMessageEl.innerHTML = msg;
+        }
+    }
+    async handleFile(file) {
+        this.file = file;
+        this.updateUi();
+        let workflow = null;
+        if (file.type.startsWith("image/")) {
+            const pngInfo = await getPngMetadata(file);
+            workflow = pngInfo === null || pngInfo === void 0 ? void 0 : pngInfo.workflow;
+        }
+        else if (file.type === "application/json" ||
+            (file instanceof File && file.name.endsWith(".json"))) {
+            workflow = await new Promise((resolve) => {
+                const reader = new FileReader();
+                reader.onload = () => {
+                    resolve(reader.result);
+                };
+                reader.readAsText(file);
+            });
+        }
+        if (!workflow) {
+            this.updateUi("⛔ No workflow found in dropped item.");
+        }
+        else {
+            try {
+                this.graph = JSON.parse(workflow);
+            }
+            catch (e) {
+                this.graph = undefined;
+            }
+            if (!this.graph) {
+                this.updateUi("⛔ Invalid workflow found in dropped item.");
+            }
+            else {
+                this.loadGraphData(this.graph);
+            }
+        }
+    }
+    async loadGraphData(graphData) {
+        this.fixer = WorkflowLinkFixer.create(graphData);
+        this.graphResults = this.fixer.check();
+        this.updateUi();
+    }
+    async saveFixedWorkflow() {
+        if (!this.graphFinalResults) {
+            this.updateUi("⛔ Save w/o final graph patched.");
+            return false;
+        }
+        let filename = this.file.name || "workflow.json";
+        let filenames = filename.split(".");
+        filenames.pop();
+        filename = filenames.join(".");
+        filename += "_fixed.json";
+        filename = prompt("Save workflow as:", filename);
+        if (!filename)
+            return false;
+        if (!filename.toLowerCase().endsWith(".json")) {
+            filename += ".json";
+        }
+        const json = JSON.stringify(this.graphFinalResults.graph, null, 2);
+        const blob = new Blob([json], { type: "application/json" });
+        const url = URL.createObjectURL(blob);
+        const anchor = document.createElement("a");
+        anchor.download = filename;
+        anchor.href = url;
+        anchor.style.display = "none";
+        document.body.appendChild(anchor);
+        await wait();
+        anchor.click();
+        await wait();
+        anchor.remove();
+        window.URL.revokeObjectURL(url);
+        return true;
+    }
+}
diff --git a/custom_nodes/rgthree-comfy/web/models/components/model-info-card.css b/custom_nodes/rgthree-comfy/web/models/components/model-info-card.css
new file mode 100644
index 0000000000000000000000000000000000000000..87845235fddbce3b221de911322456dea687104a
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/models/components/model-info-card.css
@@ -0,0 +1,32 @@
+.rgthree-model-info-card {
+  display: block;
+  padding: 8px;
+}
+
+.-is-hidden {
+  display: none;
+}
+
+.rgthree-model-info-card {
+  display: flex;
+  flex-direction: row;
+}
+.rgthree-model-info-card > .rgthree-model-info-card-media-container {
+  width: 100px;
+  height: auto;
+  display: block;
+  margin: 0 8px 0 0;
+  padding: 0;
+  flex: 0 0 auto;
+}
+.rgthree-model-info-card > .rgthree-model-info-card-media-container > img,
+.rgthree-model-info-card > .rgthree-model-info-card-media-container > video {
+  width: 100%;
+  height: 100%;
+  object-fit: contain;
+}
+.rgthree-model-info-card > .rgthree-model-info-card-data-container [bind*="name:"] {
+  font-size: 1.3em;
+  margin-bottom: 4px;
+  font-weight: bold;
+}
diff --git a/custom_nodes/rgthree-comfy/web/models/components/model-info-card.html b/custom_nodes/rgthree-comfy/web/models/components/model-info-card.html
new file mode 100644
index 0000000000000000000000000000000000000000..3be75c9d3adab73695a711b1943d3edf92212486
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/models/components/model-info-card.html
@@ -0,0 +1,18 @@
+
+<template>
+  <div class="rgthree-model-info-card">
+    <figure class="rgthree-model-info-card-media-container">
+      <video if="images.0 && images.0.type == 'video'" bind="images.0.url:src" loop autoplay></video>
+      <img if="images.0 && images.0.type != 'video'" bind="images.0.url:src"  loading="lazy" />
+    </figure>
+    <div class="rgthree-model-info-card-data-container">
+      <div bind="name:text"></div>
+      <div bind="file:text"></div>
+      <div bind="getModified(modified):text"></div>
+      <div bind="sha256:text"></div>
+      <div bind="hasInfoFile"></div>
+      <a if="getCivitaiLink(links)" bind="getCivitaiLink(links):href">Civitai</a>
+    </div>
+  </div>
+</template>
+
diff --git a/custom_nodes/rgthree-comfy/web/models/components/model-info-card.js b/custom_nodes/rgthree-comfy/web/models/components/model-info-card.js
new file mode 100644
index 0000000000000000000000000000000000000000..76cb7dc9465d5a9546b41f11f3bc28dea3a5597f
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/models/components/model-info-card.js
@@ -0,0 +1,41 @@
+import { RgthreeCustomElement } from "../../common/components/base_custom_element.js";
+export class RgthreeModelInfoCard extends RgthreeCustomElement {
+    constructor() {
+        super(...arguments);
+        this.data = {};
+    }
+    getModified(value, data, currentElement, contextElement) {
+        const date = new Date(value);
+        return String(`${date.toLocaleDateString()} ${date.toLocaleTimeString()}`);
+    }
+    getCivitaiLink(links) {
+        return (links === null || links === void 0 ? void 0 : links.find((i) => i.includes("civitai.com/models"))) || null;
+    }
+    setModelData(data) {
+        this.data = data;
+    }
+    hasBaseModel(baseModel) {
+        return this.data.baseModel === baseModel;
+    }
+    hasData(field) {
+        var _a;
+        if (field === "civitai") {
+            return !!((_a = this.getCivitaiLink(this.data.links)) === null || _a === void 0 ? void 0 : _a.length);
+        }
+        return !!this.data[field];
+    }
+    matchesQueryText(query) {
+        var _a;
+        return (_a = (this.data.name || this.data.file)) === null || _a === void 0 ? void 0 : _a.includes(query);
+    }
+    hide() {
+        this.classList.add("-is-hidden");
+    }
+    show() {
+        this.classList.remove("-is-hidden");
+    }
+}
+RgthreeModelInfoCard.NAME = "rgthree-model-info-card";
+RgthreeModelInfoCard.TEMPLATES = "components/model-info-card.html";
+RgthreeModelInfoCard.CSS = "components/model-info-card.css";
+RgthreeModelInfoCard.USE_SHADOW = false;
diff --git a/custom_nodes/rgthree-comfy/web/models/index.html b/custom_nodes/rgthree-comfy/web/models/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..137a1f72a9f72a76d585b6a006c4d6eebceb3fe6
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/models/index.html
@@ -0,0 +1,18 @@
+<!DOCTYPE html>
+<html>
+  <head>
+  <title>rgthree-comfy: Models Manager</title>
+  <link href="./models.css" rel="stylesheet">
+  <!-- <link rel="icon" href="./favicon.webp"> -->
+</head>
+<body>
+  <header>
+    <input type="text" placeholder="Search" id="searchbox" />
+  </header>
+  <ul id="models-list"></ul>
+  <script type="module">
+    import {ModelsInfoPage} from './models_info_page.js';
+    window.page = new ModelsInfoPage();
+  </script>
+</body>
+</html>
\ No newline at end of file
diff --git a/custom_nodes/rgthree-comfy/web/models/models.css b/custom_nodes/rgthree-comfy/web/models/models.css
new file mode 100644
index 0000000000000000000000000000000000000000..8fa8e91e5ea8289098885c7c75c83ec35b942dc6
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/models/models.css
@@ -0,0 +1,38 @@
+:root {
+  --rgthree-bg-color: rgba(23, 23, 23, 0.9);
+  --rgthree-on-bg-color: rgba(48, 48, 48, 0.9);
+}
+
+html, body {
+  margin: 0;
+  padding: 0;
+  font-family: Arial, sans-serif;
+  box-sizing: border-body;
+  background: var(--rgthree-bg-color);
+}
+
+*, *::before, *::after {
+  box-sizing: inherit;
+}
+
+[if-is=false] {
+  display: none;
+}
+
+.models-list {
+  list-style: none;
+  margin: 0;
+  padding: 0;
+}
+
+.model-item {
+  display: block;
+  margin: 8px;
+}
+
+rgthree-model-info-card {
+  background: var(--rgthree-on-bg-color);
+  margin: 16px;
+  display: block;
+  border-radius: 4px;
+}
diff --git a/custom_nodes/rgthree-comfy/web/models/models_info_page.js b/custom_nodes/rgthree-comfy/web/models/models_info_page.js
new file mode 100644
index 0000000000000000000000000000000000000000..55b08ed9ad7929e894509a53cca3a8a50d07fe2f
--- /dev/null
+++ b/custom_nodes/rgthree-comfy/web/models/models_info_page.js
@@ -0,0 +1,121 @@
+import { createElement, getActionEls, query, queryAll } from "../common/utils_dom.js";
+import { rgthreeApi } from "../common/rgthree_api.js";
+import { RgthreeModelInfoCard } from "./components/model-info-card.js";
+function parseQuery(query) {
+    const matches = query.match(/"[^\"]+"/g) || [];
+    for (const match of matches) {
+        let cleaned = match.substring(1, match.length - 1);
+        cleaned = cleaned.replace(/\s+/g, " ").trim().replace(/\s/g, "__SPACE__");
+        query = query.replace(match, ` ${cleaned} `);
+    }
+    const queryParts = query
+        .replace(/\s+/g, " ")
+        .trim()
+        .split(" ")
+        .map((p) => p.replace(/__SPACE__/g, " "));
+    return queryParts;
+}
+export class ModelsInfoPage {
+    constructor() {
+        this.selectBaseModel = createElement('select[name="baseModel"][on="change:filter"]');
+        this.searchbox = query("#searchbox");
+        this.modelsList = query("#models-list");
+        this.queryLast = "";
+        this.doSearchDebounce = 0;
+        console.log("hello model page");
+        this.init();
+    }
+    async init() {
+        this.searchbox.addEventListener("input", (e) => {
+            if (this.doSearchDebounce) {
+                return;
+            }
+            this.doSearchDebounce = setTimeout(() => {
+                this.doSearch();
+                this.doSearchDebounce = 0;
+            }, 250);
+        });
+        const loras = await rgthreeApi.getLorasInfo({ light: true });
+        console.log(loras);
+        const baseModels = new Set();
+        for (const lora of loras) {
+            const el = RgthreeModelInfoCard.create();
+            el.setModelData(lora);
+            el.bindWhenConnected(lora);
+            console.log(el);
+            lora.baseModel && baseModels.add(lora.baseModel);
+            this.modelsList.appendChild(createElement("li.model-item", { child: el }));
+        }
+        if (baseModels.size > 1) {
+            createElement(`option[value="ALL"][text="Choose base model."]`, {
+                parent: this.selectBaseModel,
+            });
+            for (const baseModel of baseModels.values()) {
+                createElement(`option[value="${baseModel}"][text="${baseModel}"]`, {
+                    parent: this.selectBaseModel,
+                });
+            }
+            this.searchbox.insertAdjacentElement("afterend", this.selectBaseModel);
+        }
+        const data = getActionEls(document.body);
+        for (const dataItem of Object.values(data)) {
+            for (const [event, action] of Object.entries(dataItem.actions)) {
+                dataItem.el.addEventListener(event, (e) => {
+                    if (typeof this[action] != "function") {
+                        throw new Error(`"${action}" does not exist on instance.`);
+                    }
+                    this[action](e);
+                });
+            }
+        }
+    }
+    filter() {
+        const parts = parseQuery(this.queryLast);
+        const baseModel = this.selectBaseModel.value;
+        const els = queryAll(RgthreeModelInfoCard.NAME);
+        const shouldHide = (el) => {
+            let hide = baseModel !== "ALL" && !el.hasBaseModel(baseModel);
+            if (!hide) {
+                for (let part of parts) {
+                    let negate = false;
+                    if (part.startsWith("-")) {
+                        negate = true;
+                        part = part.substring(1);
+                    }
+                    if (!part)
+                        continue;
+                    if (part.startsWith("has:")) {
+                        if (part === "has:civitai") {
+                            hide = !el.hasData(part.replace("has:", ""));
+                        }
+                    }
+                    else {
+                        hide = !el.matchesQueryText(part);
+                    }
+                    hide = negate ? !hide : hide;
+                    if (hide) {
+                        break;
+                    }
+                }
+            }
+            return hide;
+        };
+        for (const el of els) {
+            const hide = shouldHide(el);
+            if (hide) {
+                el.hide();
+            }
+            else {
+                el.show();
+            }
+        }
+        console.log("filter");
+    }
+    doSearch() {
+        const query = this.searchbox.value.trim();
+        if (this.queryLast != query) {
+            this.queryLast = query;
+            this.filter();
+        }
+    }
+}